[ { "path": ".circleci/config.yml", "content": "version: 2.1\n\n# this allows you to use CircleCI's dynamic configuration feature\nsetup: true\n\n# the path-filtering orb is required to continue a pipeline based on\n# the path of an updated fileset\norbs:\n path-filtering: circleci/path-filtering@0.1.2\n\nworkflows:\n # the always-run workflow is always triggered, regardless of the pipeline parameters.\n always-run:\n jobs:\n # the path-filtering/filter job determines which pipeline\n # parameters to update.\n - path-filtering/filter:\n name: check-updated-files\n # 3-column, whitespace-delimited mapping. One mapping per\n # line:\n # \n mapping: |\n configs/.* lint_only true\n docs/.* lint_only true\n .dev_scripts/.* lint_only true\n .github/.* lint_only true\n demo/.* lint_only true\n projects/.* lint_only true\n mmagic/.* lint_only false\n requirements/.* lint_only false\n tests/.* lint_only false\n .circleci/.* lint_only false\n tools/.* lint_only true\n base-revision: main\n # this is the path of the configuration we should trigger once\n # path filtering and pipeline parameter value updates are\n # complete. In this case, we are using the parent dynamic\n # configuration itself.\n config-path: .circleci/test.yml\n" }, { "path": ".circleci/docker/Dockerfile", "content": "ARG PYTORCH=\"1.8.1\"\nARG CUDA=\"10.2\"\nARG CUDNN=\"7\"\n\nFROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel\n\n# To fix GPG key error when running apt-get update\nRUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub\nRUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub\n\nRUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx git\n" }, { "path": ".circleci/test.yml", "content": "version: 2.1\n\n# the default pipeline parameters, which will be updated according to\n# the results of the path-filtering orb\nparameters:\n lint_only:\n type: boolean\n default: true\n\njobs:\n lint:\n docker:\n - image: cimg/python:3.7.4\n steps:\n - checkout\n - run:\n name: Install pre-commit hook\n command: |\n pip install pre-commit\n pre-commit install\n - run:\n name: Linting\n command: pre-commit run --all-files\n - run:\n name: Check docstring coverage\n command: |\n pip install interrogate\n interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex \"__repr__\" --fail-under 90 mmagic\n build_cpu:\n parameters:\n # The python version must match available image tags in\n # https://circleci.com/developer/images/image/cimg/python\n python:\n type: string\n torch:\n type: string\n torchvision:\n type: string\n docker:\n - image: cimg/python:<< parameters.python >>\n resource_class: large\n steps:\n - checkout\n - run:\n name: Install Libraries\n command: |\n sudo apt-get update\n sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5\n - run:\n name: Configure Python & pip\n command: |\n pip install --upgrade pip\n pip install wheel\n - run:\n name: Install PyTorch\n command: |\n python -V\n pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html\n - run:\n name: Install mmagic dependencies\n command: |\n pip install git+https://github.com/open-mmlab/mmengine.git@main\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n pip install -r requirements/tests.txt\n - run:\n name: Build and install\n command: |\n pip install -e .\n - run:\n name: Run unittests\n command: |\n coverage run --branch --source mmagic -m pytest tests/\n coverage xml\n coverage report -m\n build_cuda:\n parameters:\n torch:\n type: string\n cuda:\n type: enum\n enum: [\"10.1\", \"10.2\", \"11.1\"]\n cudnn:\n type: integer\n default: 7\n machine:\n image: ubuntu-2004-cuda-11.4:202110-01\n # docker_layer_caching: true\n resource_class: gpu.nvidia.small\n steps:\n - checkout\n - run:\n # Cloning repos in VM since Docker doesn't have access to the private key\n name: Clone Repos\n command: |\n git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine\n - run:\n name: Build Docker image\n command: |\n docker build .circleci/docker -t mmagic:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>\n docker run --gpus all -t -d -v /home/circleci/project:/mmagic -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmdetection:/mmdetection -w /mmagic --name mmagic mmagic:gpu\n - run:\n name: Install mmagic dependencies\n command: |\n docker exec mmagic pip install -e /mmengine\n docker exec mmagic pip install -U openmim\n docker exec mmagic mim install 'mmcv >= 2.0.0'\n docker exec mmagic pip install -r requirements/tests.txt\n - run:\n name: Build and install\n command: |\n docker exec mmagic pip install -e .\n - run:\n name: Run unittests\n command: |\n docker exec mmagic pytest tests/\n\nworkflows:\n pr_stage_lint:\n when: << pipeline.parameters.lint_only >>\n jobs:\n - lint:\n name: lint\n filters:\n branches:\n ignore:\n - dev-1.x\n - main\n pr_stage_test:\n when:\n not:\n << pipeline.parameters.lint_only >>\n jobs:\n - lint:\n name: lint\n filters:\n branches:\n ignore:\n - dev-1.x\n - main\n - build_cpu:\n name: minimum_version_cpu\n torch: 1.10.0\n torchvision: 0.11.0\n python: 3.8.15\n requires:\n - lint\n - build_cpu:\n name: maximum_version_cpu\n torch: 1.13.0\n torchvision: 0.14.0\n python: 3.8.15\n requires:\n - minimum_version_cpu\n # - hold:\n # type: approval\n # requires:\n # - maximum_version_cpu\n # - build_cuda:\n # name: mainstream_version_gpu\n # torch: 1.8.1\n # # Use double quotation mark to explicitly specify its type\n # # as string instead of number\n # cuda: \"10.2\"\n # requires:\n # - hold\n merge_stage_test:\n when:\n not:\n << pipeline.parameters.lint_only >>\n jobs:\n - build_cuda:\n name: minimum_version_gpu\n torch: 1.8.1\n # Use double quotation mark to explicitly specify its type\n # as string instead of number\n cuda: \"10.2\"\n filters:\n branches:\n only:\n - dev-1.x\n" }, { "path": ".dele.yml", "content": "dele_rules:\n issue:\n create:\n - name: \"english needs\"\n conditions:\n - \"issue.title ~= ANSI\"\n actions:\n - comment:\n message: \"@{{ issue.author.name }}, english-please.\"\n\n - name: \"add assignee\"\n conditions:\n - \"issue.assignees.count == 0\"\n actions:\n - assign_with_owners\n\n - name: \"set stale\"\n conditions:\n - \"issue.status.inactive_time >= 30d\"\n actions:\n - label:\n add: [\"stale\"]\n - comment:\n message: \"This issue will be closed in 5 days because it has been open 30 days with no activity, comment it or remove stale label to avoid it.\"\n\n pull_request:\n create:\n - name: \"add size label\"\n conditions:\n - \"pr.labels ~= size/*\"\n actions:\n - label:\n add: [\"size\"]\n\n - name: \"welcome new contributor\"\n conditions:\n - \"pr.author.login ~= first_pr\"\n actions:\n - comment:\n message: \"Welcome @{{ pr.author.login }}! Thanks for your contribution to {{ pr.org }}/{{ .pr.repo }} 🎉\"\n" }, { "path": ".dev_scripts/README.md", "content": "# Scripts for developing MMagic\n\n- [1. Check UT](#1-check-ut)\n- [2. Test all the models](#2-test-all-the-models)\n- [3. Train all the models](#3-train-all-the-models)\n - [3.1 Train for debugging](#31-train-for-debugging)\n - [3.2 Train for FP32](#32-train-for-fp32)\n - [3.3 Train for FP16](#33-train-for-fp16)\n- [4. Monitor your training](#4-monitor-your-training)\n- [5. Train with a list of models](#5-train-with-a-list-of-models)\n- [6. Train with skipping a list of models](#6-train-with-skipping-a-list-of-models)\n- [7. Train failed or canceled jobs](#7-train-failed-or-canceled-jobs)\n- [8. Deterministic training](#8-deterministic-training)\n- [9. Automatically check links](#9-automatically-check-links)\n- [10. Calculate flops](#10-calculate-flops)\n- [11. Update model idnex](#11-update-model-index)\n\n## 1. Check UT\n\nPlease check your UT by the following scripts:\n\n```python\ncd mmagic/\npython .dev_script/update_ut.py\n```\n\nThen, you will find some redundant UT, missing UT and blank UT.\nPlease create UTs according to your package code implementation.\n\n## 2. Test all the models\n\nPlease follow these steps to test all the models in MMagic:\n\nFirst, you will need download all the pre-trained checkpoints by:\n\n```shell\npython .dev_scripts/download_models.py\n```\n\nThen, you can start testing all the benchmarks by：\n\n```shell\npython .dev_scripts/test_benchmark.py\n```\n\n## 3. Train all the models\n\n### 3.1 Train for debugging\n\nIn order to test all the pipelines of training, visualization, etc., you may want to set the total iterations of all the models as less steps (e.g., 100 steps) for quick evaluation. You can use the following steps:\n\nFirst, since our datasets are stored in ceph, you need to create ceph configs.\n\n```shell\n# create configs\npython .dev_scripts/create_ceph_configs.py \\\n --target-dir configs_ceph_debug \\\n --gpus-per-job 2 \\\n --iters 100 \\\n --save-dir-prefix work_dirs/benchmark_debug \\\n --work-dir-prefix work_dirs/benchmark_debug\n```\n\nIf you only want to update a specific config file, you can specify it by `--test-file configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py`.\n\nHere, `--target-dir` denotes the path of new created configs, `--gpus-per-job` denotes the numbers of gpus used for each job, `--iters` denotes the total iterations of each model, `--save-dir-prefix` and `--work-dir-prefix` denote the working directory, where you can find the working logging.\n\nThen, you will need to submit all the jobs by running `train_benchmark.py`.\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol \\\n --config-dir configs_ceph_debug \\\n --run \\\n --gpus-per-job 2 \\\n --job-name debug \\\n --work-dir work_dirs/benchmark_debug \\\n --resume \\\n --quotatype=auto\n```\n\nHere, you will specify the configs files used for training by `--config-dir`, submit all the jobs to run by set `--run`. You can set the prefix name of the submitted jobs by `--job-name`, specify the working directory by `--work-dir`. We suggest using `--resume` to enable auto resume during training and `--quotatype=auto` to fully exploit all the computing resources.\n\n### 3.2 Train for FP32\n\nIf you want to train all the models with FP32 (i.e, regular settings as the same with `configs/`),\nyou can follow these steps:\n\n```shell\n# create configs for fp32\npython .dev_scripts/create_ceph_configs.py \\\n --target-dir configs_ceph_fp32 \\\n --gpus-per-job 4 \\\n --save-dir-prefix work_dirs/benchmark_fp32 \\\n --work-dir-prefix work_dirs/benchmark_fp32 \\\n```\n\nThen, submit the jobs to run by slurm:\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol \\\n --config-dir configs_ceph_fp32 \\\n --run \\\n --resume \\\n --gpus-per-job 4 \\\n --job-name fp32 \\\n --work-dir work_dirs/benchmark_fp32 \\\n --quotatype=auto\n```\n\n### 3.3 Train for FP16\n\nYou will also need to train the models with AMP (i.e., FP16), you can use the following steps to achieve this:\n\n```shell\npython .dev_scripts/create_ceph_configs.py \\\n --target-dir configs_ceph_amp \\\n --gpus-per-job 4 \\\n --save-dir-prefix work_dirs/benchmark_amp \\\n --work-dir-prefix work_dirs/benchmark_amp\n```\n\nThen, submit the jobs to run:\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol \\\n --config-dir configs_ceph_amp \\\n --run \\\n --resume \\\n --gpus-per-job 4 \\\n --amp \\\n --job-name amp \\\n --work-dir work_dirs/benchmark_amp \\\n --quotatype=auto\n```\n\n## 4. Monitor your training\n\nAfter you submitting jobs following [3-Train-all-the-models](#3-train-all-the-models), you will find a `xxx.log` file.\nThis log file list all the job name of job id you have submitted. With this log file, you can monitor your training by running `.dev_scripts/job_watcher.py`.\n\nFor example, you can run\n\n```shell\npython .dev_scripts/job_watcher.py --work-dir work_dirs/benchmark_fp32/ --log 20220923-140317.log\n```\n\nThen, you will find `20220923-140317.csv`, which reports the status and recent log of each job.\n\n## 5. Train with a list of models\n\nIf you only need to run some of the models, you can list all the models' name in a file, and specify the models when using `train_benchmark.py`.\n\nFor example,\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol \\\n --config-dir configs_ceph_fp32 \\\n --run \\\n --resume \\\n --gpus-per-job 4 \\\n --job-name fp32 \\\n --work-dir work_dirs/benchmark_fp32 \\\n --quotatype=auto \\\n --rerun \\\n --rerun-list 20220923-140317.log \\\n```\n\nSpecifically, you need to enable `--rerun`, and specify the list of models to rerun by `--rerun-list`\n\n## 6. Train with skipping a list of models\n\nIf you want to train all the models while skipping some models, you can also list all the models' name in a file, and specify the models when running `train_benchmark.py`.\n\nFor example,\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol \\\n --config-dir configs_ceph_fp32 \\\n --run \\\n --resume \\\n --gpus-per-job 4 \\\n --job-name fp32 \\\n --work-dir work_dirs/benchmark_fp32 \\\n --quotatype=auto \\\n --skip \\\n --skip-list 20220923-140317.log \\\n```\n\nSpecifically, you need to enable `--skip`, and specify the list of models to skip by `--skip-list`\n\n## 7. Train failed or canceled jobs\n\nIf you want to rerun failed or canceled jobs in the last run, you can combine `--rerun` flag with `--rerun-failure` and `--rerun-cancel` flags.\n\nFor example, the log file of the last run is `train-20221009-211904.log`, and now you want to rerun the failed jobs. You can use the following command:\n\n```bash\npython .dev_scripts/train_benchmark.py mm_lol \\\n --job-name RERUN \\\n --rerun train-20221009-211904.log \\\n --rerun-fail \\\n --run\n```\n\nWe can combine `--rerun-fail` and `--rerun-cancel` with flag `---models` to rerun a **subset** of failed or canceled model.\n\n```bash\npython .dev_scripts/train_benchmark.py mm_lol \\\n --job-name RERUN \\\n --rerun train-20221009-211904.log \\\n --rerun-fail \\\n --models sagan \\ # only rerun 'sagan' models in all failed tasks\n --run\n```\n\nSpecifically, `--rerun-fail` and `--rerun-cancel` can be used together to rerun both failed and cancaled jobs.\n\n## 8. `deterministic` training\n\nSet `torch.backends.cudnn.deterministic = True` and `torch.backends.cudnn.benchmark = False` can remove randomness operation in Pytorch training. You can add `--deterministic` flag when start your benchmark training to remove the influence of randomness operation.\n\n```shell\npython .dev_scripts/train_benchmark.py mm_lol --job-name xzn --models pix2pix --cpus-per-job 16 --run --deterministic\n```\n\n## 9. Automatically check links\n\nUse the following script to check whether the links in documentations are valid:\n\n```shell\npython .dev_scripts/doc_link_checker.py --target docs/zh_cn\npython .dev_scripts/doc_link_checker.py --target README_zh-CN.md\npython .dev_scripts/doc_link_checker.py --target docs/en\npython .dev_scripts/doc_link_checker.py --target README.md\n```\n\nYou can specify the `--target` by a file or a directory.\n\n**Notes:** DO NOT use it in CI, because requiring too many http requirements by CI will cause 503 and CI will propabaly fail.\n\n## 10. Calculate flops\n\nTo summarize the flops of different models, you can run the following commands:\n\n```bash\npython .dev_scripts/benchmark_valid_flop.py --flops --flops-str\n```\n\n## 11. Update model index\n\nTo update model-index according to `README.md`, please run the following commands,\n\n```bash\npython .dev_scripts/update_model_index.py\n```\n" }, { "path": ".dev_scripts/create_ceph_configs.py", "content": "import glob\nimport os.path as osp\nimport shutil\nfrom argparse import ArgumentParser\nfrom copy import deepcopy\n\nfrom mmengine import Config\nfrom tqdm import tqdm\n\n\ndef update_intervals(config, args):\n if args.iters is None:\n return config\n\n # 1. change max-iters and val-interval\n if 'train_cfg' in config and config['train_cfg']:\n config['train_cfg'] = dict(\n type='IterBasedTrainLoop',\n max_iters=args.iters,\n val_interval=args.iters // 5)\n\n # 2. change logging interval\n if 'default_hooks' in config and config['default_hooks']:\n config['default_hooks']['logger'] = dict(\n type='LoggerHook', interval=args.iters // 10)\n config['default_hooks']['checkpoint'] = dict(\n type='CheckpointHook', interval=args.iters // 15)\n\n return config\n\n\ndef convert_data_config(data_cfg):\n ceph_dataroot_prefix_temp = 'openmmlab:s3://openmmlab/datasets/{}/'\n local_dataroot_prefix = ['data', './data']\n # val_dataloader may None\n if data_cfg is None:\n return\n\n data_cfg_updated = deepcopy(data_cfg)\n dataset: dict = data_cfg['dataset']\n\n dataset_type: str = dataset['type']\n if dataset_type in ['ImageNet', 'CIFAR10']:\n repo_name = 'classification'\n else:\n repo_name = 'editing'\n ceph_dataroot_prefix = ceph_dataroot_prefix_temp.format(repo_name)\n\n if 'data_root' in dataset:\n data_root: str = dataset['data_root']\n\n for dataroot_prefix in local_dataroot_prefix:\n if data_root.startswith(dataroot_prefix):\n # avoid cvt './data/imagenet' ->\n # openmmlab:s3://openmmlab/datasets/classification//imagenet\n if data_root.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n data_root = data_root.replace(dataroot_prefix,\n ceph_dataroot_prefix)\n # add '/' at the end\n if not data_root.endswith('/'):\n data_root = data_root + '/'\n dataset['data_root'] = data_root\n\n elif 'data_roots' in dataset:\n # specific for pggan dataset, which need a dict of data_roots\n data_roots: dict = dataset['data_roots']\n for k, data_root in data_roots.items():\n for dataroot_prefix in local_dataroot_prefix:\n if data_root.startswith(dataroot_prefix):\n # avoid cvt './data/imagenet' ->\n # openmmlab:s3://openmmlab/datasets/classification//imagenet\n if data_root.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n data_root = data_root.replace(dataroot_prefix,\n ceph_dataroot_prefix)\n # add '/' at the end\n if not data_root.endswith('/'):\n data_root = data_root + '/'\n data_roots[k] = data_root\n dataset['data_roots'] = data_roots\n\n else:\n raise KeyError\n\n if hasattr(dataset, 'pipeline'):\n pipelines = dataset['pipeline']\n for pipeline in pipelines:\n type_ = pipeline['type']\n # only change mmcv(mmcls)'s loading config\n if type_ == 'mmcls.LoadImageFromFile':\n pipeline['file_client_args'] = dict(backend='petrel')\n elif type_ == 'LoadMask':\n if 'mask_list_file' in pipeline['mask_config']:\n local_mask_path = pipeline['mask_config']['mask_list_file']\n for dataroot_prefix in local_dataroot_prefix:\n if local_mask_path.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n local_mask_path = local_mask_path.replace(\n dataroot_prefix, ceph_dataroot_prefix)\n pipeline['mask_config']['mask_list_file'] = local_mask_path\n pipeline['mask_config']['prefix'] = osp.dirname(\n local_mask_path)\n pipeline['mask_config']['io_backend'] = 'petrel'\n pipeline['mask_config']['file_client_kwargs'] = dict(\n backend='petrel')\n elif type_ == 'RandomLoadResizeBg':\n bg_dir_path = pipeline['bg_dir']\n for dataroot_prefix in local_dataroot_prefix:\n if bg_dir_path.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n bg_dir_path = bg_dir_path.replace(dataroot_prefix,\n ceph_dataroot_prefix)\n bg_dir_path = bg_dir_path.replace(repo_name, 'detection')\n pipeline['bg_dir'] = bg_dir_path\n elif type_ == 'CompositeFg':\n fg_dir_path = pipeline['fg_dirs']\n for i, fg in enumerate(fg_dir_path):\n for dataroot_prefix in local_dataroot_prefix:\n if fg.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n fg = fg.replace(dataroot_prefix, ceph_dataroot_prefix)\n pipeline['fg_dirs'][i] = fg\n\n alpha_dir_path = pipeline['alpha_dirs']\n for i, alpha_dir in enumerate(alpha_dir_path):\n for dataroot_prefix in local_dataroot_prefix:\n if alpha_dir.startswith(dataroot_prefix + '/'):\n dataroot_prefix = dataroot_prefix + '/'\n alpha_dir = alpha_dir.replace(dataroot_prefix,\n ceph_dataroot_prefix)\n pipeline['alpha_dirs'][i] = alpha_dir\n\n data_cfg_updated['dataset'] = dataset\n return data_cfg_updated\n\n\ndef update_ceph_config(filename, args, dry_run=False):\n if filename.startswith(osp.join(args.target_dir, '_base_')):\n # Skip base configs\n return None\n\n if args.ceph_path is not None:\n if args.ceph_path.endswith('/'):\n args.ceph_path = args.ceph_path[:-1]\n work_dir = f'{args.ceph_path}/{args.work_dir_prefix}'\n save_dir = f'{args.ceph_path}/{args.save_dir_prefix}'\n if not work_dir.endswith('/'):\n work_dir = work_dir + '/'\n if not save_dir.endswith('/'):\n save_dir = save_dir + '/'\n else:\n # disable save local results to ceph\n work_dir = args.work_dir_prefix\n save_dir = args.save_dir_prefix\n\n try:\n config = Config.fromfile(filename)\n\n # 0. update intervals\n config = update_intervals(config, args)\n\n # 1. change dataloader\n dataloader_prefix = [\n f'{p}_dataloader' for p in ['train', 'val', 'test']\n ]\n\n for prefix in dataloader_prefix:\n if not hasattr(config, prefix):\n continue\n data_cfg = config[prefix]\n if not isinstance(data_cfg, list):\n data_cfg_list = [data_cfg]\n data_cfg_is_list = False\n else:\n data_cfg_list = data_cfg\n data_cfg_is_list = True\n\n data_cfg_updated_list = [\n convert_data_config(cfg) for cfg in data_cfg_list\n ]\n if data_cfg_is_list:\n config[prefix] = data_cfg_updated_list\n else:\n config[prefix] = data_cfg_updated_list[0]\n\n # 2. change visualizer\n if hasattr(config, 'vis_backends'):\n # TODO: support upload to ceph\n # for vis_cfg in config['vis_backends']:\n # if vis_cfg['type'] == 'VisBackend':\n # vis_cfg['ceph_path'] = work_dir\n\n # add pavi config\n if args.add_pavi:\n _, project, name = filename.split('/')\n name = name[:-2]\n # check if pavi config is inheritance from _base_\n find_inherit = False\n for vis_cfg in config['vis_backends']:\n if vis_cfg['type'] == 'PaviVisBackend':\n vis_cfg['exp_name'] = name\n vis_cfg['project'] = project\n find_inherit = True\n break\n\n if not find_inherit:\n pavi_cfg = dict(\n type='PaviVisBackend', exp_name=name, project=project)\n config['vis_backends'].append(pavi_cfg)\n\n # add wandb config\n if args.add_wandb:\n _, project, name = filename.split('/')\n name = name[:-2]\n # check if wandb config is inheritance from _base_\n find_inherit = False\n for vis_cfg in config['vis_backends']:\n if vis_cfg['type'] == 'WandbVisBackend':\n vis_cfg['name'] = name # name of config\n vis_cfg['project'] = project # name of model\n find_inherit = True\n break\n\n if not find_inherit:\n pavi_cfg = dict(\n type='WandbVisBackend',\n init_kwargs=dict(name=name, project=project))\n config['vis_backends'].append(pavi_cfg)\n\n # add tensorboard config\n if args.add_tensorboard:\n find_inherit = False\n for vis_cfg in config['vis_backends']:\n if vis_cfg['type'] == 'TensorboardVisBackend':\n find_inherit = True\n break\n\n if not find_inherit:\n tensorboard_cfg = dict(type='TensorboardVisBackend')\n config['vis_backends'].append(tensorboard_cfg)\n\n config['visualizer']['vis_backends'] = config['vis_backends']\n\n # 3. change logger hook and checkpoint hook\n if hasattr(config, 'default_hooks'):\n # file_client_args = dict(backend='petrel')\n\n for name, hooks in config['default_hooks'].items():\n if name == 'logger':\n hooks['out_dir'] = save_dir\n # hooks['file_client_args'] = file_client_args\n elif name == 'checkpoint':\n hooks['out_dir'] = save_dir\n # hooks['file_client_args'] = file_client_args\n\n # 4. save\n config.dump(config.filename)\n return True\n\n except Exception as e: # noqa\n print(e)\n return False\n\n\nif __name__ == '__main__':\n\n parser = ArgumentParser()\n parser.add_argument('--ceph-path', type=str, default=None)\n parser.add_argument('--gpus-per-job', type=int, default=None)\n parser.add_argument(\n '--save-dir-prefix',\n type=str,\n default='work_dirs',\n help='Default prefix of the work dirs in the bucket')\n parser.add_argument(\n '--work-dir-prefix',\n type=str,\n default='work_dirs',\n help='Default prefix of the work dirs in the bucket')\n parser.add_argument(\n '--target-dir', type=str, default='configs_ceph', help='configs path')\n parser.add_argument(\n '--iters', type=int, default=None, help='set intervals')\n parser.add_argument(\n '--test-file', type=str, default=None, help='Dry-run on a test file.')\n parser.add_argument(\n '--add-pavi', action='store_true', help='Add pavi config or not.')\n parser.add_argument(\n '--add-wandb', action='store_true', help='Add wandb config or not.')\n parser.add_argument(\n '--add-tensorboard',\n action='store_true',\n help='Add Tensorboard config or not.')\n\n args = parser.parse_args()\n\n if args.test_file is None:\n\n print('Copying config files to \"config_ceph\" ...')\n shutil.copytree('configs', args.target_dir, dirs_exist_ok=True)\n\n print('Updating ceph configuration ...')\n files = glob.glob(\n osp.join(args.target_dir, '**', '*.py'), recursive=True)\n pbars = tqdm(files)\n res = []\n for f in pbars:\n pbars.set_description(f'Processing {f}')\n res.append(update_ceph_config(f, args))\n\n count_skip = res.count(None)\n count_done = res.count(True)\n count_fail = res.count(False)\n fail_list = [fn for status, fn in zip(res, files) if status is False]\n skip_list = [fn for status, fn in zip(res, files) if status is None]\n\n print(f'Successfully update {count_done} configs.')\n print(f'Skip {count_skip} configs.')\n if count_skip > 0:\n print(skip_list)\n print(f'Fail {count_fail} configs.')\n if count_fail > 0:\n print(fail_list)\n\n else:\n shutil.copy(args.test_file,\n args.test_file.replace('configs', args.target_dir))\n update_ceph_config(\n args.test_file.replace('configs', args.target_dir),\n args,\n dry_run=True)\n" }, { "path": ".dev_scripts/doc_link_checker.py", "content": "# Copyright (c) MegFlow. All rights reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\n# /bin/python3\n\nimport argparse\nimport os\nimport re\n\nimport requests\nfrom tqdm import tqdm\n\n\ndef make_parser():\n parser = argparse.ArgumentParser('Doc link checker')\n parser.add_argument(\n '--target',\n default='./docs',\n type=str,\n help='the directory or file to check')\n parser.add_argument(\n '--ignore', type=str, nargs='+', default=[], help='input image size')\n return parser\n\n\npattern = re.compile(r'\\[.*?\\]\\(.*?\\)')\n\n\ndef analyze_doc(home, path):\n problem_list = []\n code_block = 0\n with open(path) as f:\n lines = f.readlines()\n for line in lines:\n line = line.strip()\n if line.startswith('```'):\n code_block = 1 - code_block\n\n if code_block > 0:\n continue\n\n if '[' in line and ']' in line and '(' in line and ')' in line:\n all = pattern.findall(line)\n for item in all:\n # skip ![]()\n if item.find('[') == item.find(']') - 1:\n continue\n\n # process the case [text()]()\n offset = item.find('](')\n if offset == -1:\n continue\n item = item[offset:]\n start = item.find('(')\n end = item.find(')')\n ref = item[start + 1:end]\n\n if ref.startswith('http'):\n if ref.startswith(\n 'https://download.openmmlab.com/'\n ) or ref.startswith('http://download.openmmlab.com/'):\n resp = requests.head(ref)\n if resp.status_code == 200:\n continue\n else:\n problem_list.append(ref)\n else:\n continue\n\n if ref.startswith('#'):\n continue\n\n if ref == '<>':\n continue\n\n if '.md#' in ref:\n ref = ref[:ref.find('#')]\n if ref.startswith('/'):\n fullpath = os.path.join(\n os.path.dirname(__file__), '../', ref[1:])\n else:\n fullpath = os.path.join(home, ref)\n if not os.path.exists(fullpath):\n problem_list.append(ref)\n else:\n continue\n if len(problem_list) > 0:\n print(f'{path}:')\n for item in problem_list:\n print(f'\\t {item}')\n print('\\n')\n raise Exception('found link error')\n\n\ndef traverse(args):\n target = args.target\n if os.path.isfile(target):\n analyze_doc(os.path.dirname(target), target)\n return\n target_files = list(os.walk(target))\n target_files.sort()\n for home, dirs, files in tqdm(target_files):\n if home in args.ignore:\n continue\n for filename in files:\n if filename.endswith('.md'):\n path = os.path.join(home, filename)\n if os.path.islink(path) is False:\n analyze_doc(home, path)\n\n\nif __name__ == '__main__':\n args = make_parser().parse_args()\n traverse(args)\n" }, { "path": ".dev_scripts/download_models.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\nimport argparse\nimport os\nimport platform\nimport posixpath as osp # Even on windows, use posixpath\nimport re\nimport subprocess\nfrom collections import OrderedDict\nfrom importlib.machinery import SourceFileLoader\nfrom pathlib import Path\n\nfrom modelindex import load\n\nMMagic_ROOT = Path(__file__).absolute().parent.parent\nDOWNLOAD_DIR = osp.join(MMagic_ROOT, 'work_dirs', 'download')\nIS_WINDOWS = (platform.system() == 'Windows')\n\n\ndef additional_download(args):\n \"\"\"Download additional weights file used in this repo, such as VGG.\"\"\"\n\n url_path = [\n 'https://www.adrianbulat.com/downloads/python-fan/2DFAN4-cd938726ad.zip', # noqa\n 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',\n 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'\n ]\n checkpoint_root = args.checkpoint_root\n if not checkpoint_root:\n checkpoint_root = DOWNLOAD_DIR\n for url in url_path:\n path = osp.join(checkpoint_root, 'hub', 'checkpoints',\n osp.basename(url))\n print()\n print(f'download {path} from {url}')\n\n if IS_WINDOWS:\n if args.dry_run:\n print(f'Call \\'wget.download({url}, {path})\\'')\n else:\n import wget\n wget.download(url, path)\n else:\n if args.dry_run:\n print(f'wget --no-check-certificate -N {url} {path}')\n else:\n os.system(f'wget --no-check-certificate -N {url} {path}')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Download the checkpoints')\n parser.add_argument('--checkpoint-root', help='Checkpoint file root path.')\n parser.add_argument(\n '--models', nargs='+', type=str, help='Specify model names to run.')\n parser.add_argument(\n '--force',\n action='store_true',\n help='Whether force re-download the checkpoints.')\n parser.add_argument(\n '--model-list', type=str, help='Path of algorithm list to download')\n parser.add_argument(\n '--dry-run',\n action='store_true',\n help='Only show download command but not run')\n\n args = parser.parse_args()\n return args\n\n\ndef download(args):\n model_index_file = MMagic_ROOT / 'model-index.yml'\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n models = OrderedDict({model.name: model for model in model_index.models})\n\n http_prefix_short = 'https://download.openmmlab.com/mmediting/'\n\n # load model list\n if args.model_list:\n file_list_path = args.model_list\n file_list = SourceFileLoader('model_list',\n file_list_path).load_module().model_list\n else:\n file_list = None\n\n if args.models:\n patterns = [re.compile(pattern) for pattern in args.models]\n filter_models = {}\n for k, v in models.items():\n if any([re.match(pattern, k) for pattern in patterns]):\n filter_models[k] = v\n if len(filter_models) == 0:\n print('No model found, please specify models in:')\n print('\\n'.join(models.keys()))\n return\n models = filter_models\n\n checkpoint_root = args.checkpoint_root\n if not checkpoint_root:\n checkpoint_root = DOWNLOAD_DIR\n\n for model_info in models.values():\n\n if file_list is not None and model_info.name not in file_list:\n continue\n\n model_weight_url = model_info.weights\n\n if model_weight_url.startswith(http_prefix_short):\n model_name = model_weight_url[len(http_prefix_short):]\n elif model_weight_url == '':\n print(f'{model_info.Name} weight is missing')\n return None\n else:\n raise ValueError(f'Unknown url prefix. \\'{model_weight_url}\\'')\n\n model_name_split = model_name.split('/')\n if len(model_name_split) == 3: # 'TASK/METHOD/MODEL.pth'\n # remove task name\n model_name = osp.join(*model_name_split[1:-1])\n else:\n model_name = osp.join(*model_name_split[:-1])\n ckpt_name = model_weight_url.split('/')[-1]\n\n download_path = osp.join(checkpoint_root, model_name, ckpt_name)\n download_root = osp.join(checkpoint_root, model_name)\n\n if osp.exists(download_path):\n print(f'Already exists {download_path}')\n # do not delete when dry-run is true\n if args.force and not args.dry_run:\n print(f'Delete {download_path} to force re-download.')\n os.system(f'rm -rf {download_path}')\n else:\n continue\n try:\n cmd_str_list = [\n 'wget', '-q', '--show-progress', '-p', download_root,\n model_weight_url\n ]\n\n if args.dry_run:\n print(' '.join(cmd_str_list))\n else:\n subprocess.run(cmd_str_list, check=True)\n except Exception:\n # for older version of wget\n cmd_str_list = ['wget', '-P', download_root, model_weight_url]\n if args.dry_run:\n print(' '.join(cmd_str_list))\n else:\n subprocess.run(cmd_str_list, check=True)\n\n\nif __name__ == '__main__':\n args = parse_args()\n download(args)\n additional_download(args)\n" }, { "path": ".dev_scripts/inference_benchmark.sh", "content": "python demo/download_inference_resources.py\n\n# Text-to-Image\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir demo_text2image_stable_diffusion_res.png\n\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 1 \\\n --text \"Room with blue walls and a yellow ceiling.\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \\\n --result-out-dir demo_text2image_controlnet_canny_res.png\n\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 2 \\\n --text \"masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png' \\\n --result-out-dir demo_text2image_controlnet_pose_res.png\n\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 3 \\\n --text \"black house, blue sky\" \\\n --control 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/243599897-553a4c46-c61d-46df-b820-59a49aaf6678.png' \\\n --result-out-dir demo_text2image_controlnet_seg_res.png\n\n# Conditional GANs\npython demo/mmagic_inference_demo.py \\\n --model-name biggan \\\n --model-setting 3 \\\n --label 1 \\\n --result-out-dir demo_conditional_biggan_res.jpg\n\n# Unconditional GANs\npython demo/mmagic_inference_demo.py \\\n --model-name styleganv1 \\\n --result-out-dir demo_unconditional_styleganv1_res.jpg\n\n# Image Translation\npython demo/mmagic_inference_demo.py \\\n --model-name pix2pix \\\n --img ./resources/input/translation/gt_mask_0.png \\\n --result-out-dir ./resources/output/translation/demo_translation_pix2pix_res.png\n\n# Inpainting\npython demo/mmagic_inference_demo.py \\\n --model-name deepfillv2 \\\n --img ./resources/input/inpainting/celeba_test.png \\\n --mask ./resources/input/inpainting/bbox_mask.png \\\n --result-out-dir ./resources/output/inpainting/demo_inpainting_deepfillv2_res.\n\n# Matting\npython demo/mmagic_inference_demo.py \\\n --model-name aot_gan \\\n --img ./resources/input/matting/GT05.jpg \\\n --trimap ./resources/input/matting/GT05_trimap.jpg \\\n --result-out-dir ./resources/output/matting/demo_matting_gca_res.png\n\n# Image Restoration\npython demo/mmagic_inference_demo.py \\\n --model-name nafnet \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_nafnet_res.png\n\n# Image Super-resolution\npython demo/mmagic_inference_demo.py \\\n --model-name esrgan \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_esrgan_res.png\n\npython demo/mmagic_inference_demo.py \\\n --model-name ttsr \\\n --img ./resources/input/restoration/000001.png \\\n --ref ./resources/input/restoration/000001.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_ttsr_res.png\n\n# Video Super-Resolution\npython demo/mmagic_inference_demo.py \\\n --model-name basicvsr \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4\n\npython demo/mmagic_inference_demo.py \\\n --model-name edvr \\\n --extra-parameters window_size=5 \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_edvr_res.mp4\n\npython demo/mmagic_inference_demo.py \\\n --model-name tdan \\\n --model-setting 2 \\\n --extra-parameters window_size=5 \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_tdan_res.mp4\n\n# Video interpolation\npython demo/mmagic_inference_demo.py \\\n --model-name flavr \\\n --video ./resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4 \\\n --result-out-dir ./resources/output/video_interpolation/demo_video_interpolation_flavr_res.mp4\n\n# Image Colorization\npython demo/mmagic_inference_demo.py \\\n --model-name inst_colorization \\\n --img https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg \\\n --result-out-dir demo_colorization_res.png\n\n# 3D-aware Generation\npython demo/mmagic_inference_demo.py \\\n --model-name eg3d \\\n --result-out-dir ./resources/output/eg3d-output\n" }, { "path": ".dev_scripts/job_watcher.py", "content": "import os\nimport os.path as osp\nimport time\nfrom argparse import ArgumentParser\nfrom functools import partial\n\nfrom prettytable import PrettyTable\nfrom pygments import formatters, highlight, lexers\nfrom pygments.util import ClassNotFound\nfrom simple_term_menu import TerminalMenu\n\nCACHE_DIR = osp.join(osp.abspath('~'), '.task_watcher')\n\n\ndef show_job_out(name, root, job_name_list):\n if name == 'Show Status':\n return show_job_status(root, job_name_list)\n\n job_id, job_name = name.split(' @ ')\n job_out_path = osp.join(root, job_name, f'job.{job_id.strip()}.out')\n try:\n with open(job_out_path, 'r') as file:\n out_content = file.read()\n except Exception:\n out_content = f'{job_out_path} not find.'\n\n out_content = out_content.split('\\n')[-20:]\n out_content = '\\n'.join(out_content)\n\n try:\n lexer = lexers.get_lexer_for_filename(\n job_out_path, stripnl=False, stripall=False)\n except ClassNotFound:\n lexer = lexers.get_lexer_by_name('text', stripnl=False, stripall=False)\n formatter = formatters.TerminalFormatter(bg='dark') # dark or light\n highlighted_file_content = highlight(out_content, lexer, formatter)\n return highlighted_file_content\n\n\ndef show_job_status(root, job_name_list, csv_path=None):\n \"\"\"Show job status and dump to csv.\n\n Args:\n root (_type_): _description_\n job_name_list (_type_): _description_\n Returns:\n _type_: _description_\n \"\"\"\n table = PrettyTable(title='Job Status')\n table.field_names = ['Name', 'ID', 'Status', 'Output']\n swatch_tmp = 'swatch examine {}'\n if csv_path is None:\n csv_path = 'status.cvs'\n\n for info in job_name_list:\n id_, name = info.split(' @ ')\n name = name.split('\\n')[0].strip()\n proc = os.popen(swatch_tmp.format(id_))\n stdout = proc.read().strip().split('\\n')\n job_info = [s for s in stdout[2].split(' ') if s]\n status = job_info[5]\n\n job_out_path = osp.join(root, name, f'job.{id_.strip()}.out')\n if osp.exists(job_out_path):\n with open(job_out_path, 'r') as file:\n out_content = file.read()\n out_content = out_content.split('\\n')\n if len(out_content) > 10:\n out_content = out_content[-7:-1]\n out_content = '\\n'.join(out_content)\n else:\n out_content = 'No output currently.'\n table.add_row([name, id_, status, out_content])\n with open(csv_path, 'w') as file:\n file.write(table.get_csv_string())\n print(f'save job status to {csv_path}')\n return table.get_string()\n\n\ndef save_for_resume(root, job_name_list):\n \"\"\"Save job name and job ID for resume.\n\n Args:\n root (_type_): _description_\n job_name_list (_type_): _description_\n \"\"\"\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))\n os.makedirs(CACHE_DIR, exist_ok=True)\n\n with open(osp.join(CACHE_DIR, timestamp), 'w') as file:\n file.write(f'{root}\\n')\n file.writelines([n + '\\n' for n in job_name_list])\n\n with open(osp.join(CACHE_DIR, 'latest'), 'w') as file:\n file.write(f'{root}\\n')\n file.writelines([n + '\\n' for n in job_name_list])\n\n\ndef resume_from_file(file_path):\n \"\"\"Resume TUI from file.\n\n Args:\n file_path (_type_): _description_\n \"\"\"\n with open((file_path), 'r') as file:\n resume_info = file.readlines()\n resume_info = [info.strip() for info in resume_info]\n root = resume_info[0]\n job_name_list = resume_info[1:]\n show_tui(root, job_name_list)\n\n\ndef start_from_proc(root, proc):\n \"\"\"Start TUI from proc.\n\n Args:\n root (_type_): _description_\n proc (_type_): _description_\n \"\"\"\n std_out = proc.read().strip()\n std_out_list = std_out.split('\\n')\n num_tasks = len(std_out_list) // 3\n job_name_list = []\n for idx in range(num_tasks):\n config = std_out_list[3 * idx].strip(' ')\n job_name = config.split('/')[-1].split('.')[0].strip(' ')\n job_id = std_out_list[3 * idx + 2].split(' ')[-1]\n job_name_list.append(f'{job_id} @ {job_name}')\n\n return job_name_list\n # save_for_resume(root, job_name_list)\n # show_tui(root, job_name_list)\n\n\ndef show_tui(root, job_name_list):\n \"\"\"Start a interactivate task watcher.\n Args:\n root (str): root for benchmark\n job_name_list (list[str]): List of 'JOBID @ JOBNAME'\n \"\"\"\n show_func = partial(show_job_out, root=root, job_name_list=job_name_list)\n terminal_menu = TerminalMenu(\n job_name_list + ['Show Status'],\n preview_command=show_func,\n preview_size=0.75)\n terminal_menu.show()\n\n\nif __name__ == '__main__':\n parser = ArgumentParser()\n parser.add_argument('--list', type=str, default=None)\n parser.add_argument('--resume', default='latest')\n parser.add_argument(\n '--work-dir', type=str, default='work_dirs/benchmark_amp')\n parser.add_argument(\n '--type',\n type=str,\n default='failed',\n choices=['running', 'success', 'queue', 'failed', 'all'],\n )\n args = parser.parse_args()\n\n if args.list is not None:\n f = open(args.list, 'r')\n job_name_list = f.readlines()\n csv_path = osp.basename(args.list).replace('.log', '.csv')\n plain_txt = show_job_status(args.work_dir, job_name_list, csv_path)\n with open('status.log', 'w') as f:\n f.write(plain_txt)\n print('save status to status.log')\n else:\n if args.resume.upper() == 'LATEST':\n resume_from_file(osp.join(CACHE_DIR, 'latest'))\n else:\n resume_from_file(args.resume)\n" }, { "path": ".dev_scripts/metric_mapping.py", "content": "# key-in-metafile: key-in-results.pkl\nMETRICS_MAPPING = {\n 'FID': {\n 'keys': ['FID-Full-50k/fid'],\n 'tolerance': 0.5,\n 'rule': 'less'\n },\n 'PSNR': {\n 'keys': ['PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'SSIM': {\n 'keys': ['MattingSSIM', 'SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'l1 error': {\n 'keys': ['MAE'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'CONN': {\n 'keys': ['ConnectivityError'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'GRAD': {\n 'keys': ['GradientError'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'MSE': {\n 'keys': ['MSE', 'MattingMSE'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'SAD': {\n 'keys': ['SAD'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'NIQE (Y)': {\n 'keys': ['NIQE'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n # verbose metric mapping for VSR\n 'REDS4 (BIx4) PSNR (RGB)': {\n 'keys': ['REDS4-BIx4-RGB/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vimeo-90K-T (BIx4) PSNR (Y)': {\n 'keys': ['Vimeo-90K-T-BIx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vimeo-90K-T (BDx4) PSNR (Y)': {\n 'keys': ['Vimeo-90K-T-BDx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'UDM10 (BDx4) PSNR (Y)': {\n 'keys': ['UDM10-BDx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vid4 (BDx4) PSNR (Y)': {\n 'keys': ['VID4-BDx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vid4 (BIx4) PSNR (Y)': {\n 'keys': ['VID4-BIx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'SPMCS-30 (BDx4) PSNR (Y)': {\n 'keys': ['SPMCS-BDx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'SPMCS-30 (BIx4) PSNR (Y)': {\n 'keys': ['SPMCS-BIx4-Y/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'REDS4 (BIx4) SSIM (RGB)': {\n 'keys': ['REDS4-BIx4-RGB/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vimeo-90K-T (BIx4) SSIM (Y)': {\n 'keys': ['Vimeo-90K-T-BIx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vimeo-90K-T (BDx4) SSIM (Y)': {\n 'keys': ['Vimeo-90K-T-BDx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'UDM10 (BDx4) SSIM (Y)': {\n 'keys': ['UDM10-BDx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vid4 (BDx4) SSIM (Y)': {\n 'keys': ['VID4-BDx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Vid4 (BIx4) SSIM (Y)': {\n 'keys': ['VID4-BIx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'SPMCS-30 (BDx4) SSIM (Y)': {\n 'keys': ['SPMCS-BDx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'SPMCS-30 (BIx4) SSIM (Y)': {\n 'keys': ['SPMCS-BIx4-Y/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Set5 SSIM': {\n 'keys': ['Set5/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Set5 PSNR': {\n 'keys': ['Set5/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Set14 SSIM': {\n 'keys': ['Set14/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Set14 PSNR': {\n 'keys': ['Set14/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'DIV2K SSIM': {\n 'keys': ['DIV2K/SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'DIV2K PSNR': {\n 'keys': ['DIV2K/PSNR'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n}\n\n# add LIIF metrics\nliif_metrics = {}\nfor dataset in ['Set5', 'Set14', 'DIV2K']:\n for metric, tolerance in zip(['PSNR', 'SSIM'], [0.1, 0.1]):\n for scale in [2, 3, 4, 6, 18, 30]:\n liif_metrics[f'{dataset}x{scale} {metric}'] = {\n 'keys': [f'{dataset}x{scale}/{metric}'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n }\nMETRICS_MAPPING.update(liif_metrics)\n\n\ndef filter_metric(metric_mapping, summary_data):\n\n used_metric = dict()\n for metric, metric_info in metric_mapping.items():\n for data_dict in summary_data.values():\n if metric in data_dict:\n used_metric[metric] = metric_info\n break\n\n return used_metric\n" }, { "path": ".dev_scripts/task_mapping.py", "content": "TASK_MAPPING = {\n 'Inpainting': [\n 'aot-gan',\n 'deepfillv1',\n 'deepfillv2',\n 'global_local',\n 'partial_conv',\n ],\n 'Matting': [\n 'dim',\n 'gca',\n 'indexnet',\n ],\n 'Restoreration': [\n 'srcnn',\n 'srresnet_srgan',\n 'edsr',\n 'esrgan',\n 'rdn',\n 'dic',\n 'ttsr',\n 'glean',\n ],\n 'LIIF': ['liif'],\n 'Video_restoration': [\n 'edvr',\n 'tof',\n 'tdan',\n 'basicvsr',\n 'basicvsr_plusplus',\n 'iconvsr',\n 'real_basicvsr',\n ],\n 'Video_interpolation': [\n 'cain',\n 'flavr',\n 'tof',\n ]\n}\n" }, { "path": ".dev_scripts/test_benchmark.py", "content": "import argparse\nimport os\nimport os.path as osp\nimport pickle\nimport re\nfrom collections import OrderedDict, defaultdict\nfrom datetime import datetime\nfrom importlib.machinery import SourceFileLoader\nfrom pathlib import Path\n\nfrom job_watcher import start_from_proc\nfrom metric_mapping import METRICS_MAPPING, filter_metric\nfrom modelindex.load_model_index import load\nfrom rich.console import Console\nfrom rich.syntax import Syntax\nfrom rich.table import Table\nfrom task_mapping import TASK_MAPPING\n\nconsole = Console()\nMMagic_ROOT = Path(__file__).absolute().parents[1]\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description=\"Test all models' accuracy in model-index.yml\")\n parser.add_argument(\n 'partition', type=str, help='Cluster partition to use.')\n parser.add_argument('checkpoint_root', help='Checkpoint file root path.')\n parser.add_argument(\n '--job-name', type=str, default=' ', help='Slurm job name prefix')\n parser.add_argument('--port', type=int, default=29666, help='dist port')\n parser.add_argument(\n '--use-ceph-config',\n action='store_true',\n default=False,\n help='Use ceph configs or not.')\n parser.add_argument(\n '--models', nargs='+', type=str, help='Specify model names to run.')\n parser.add_argument(\n '--work-dir',\n default='work_dirs/benchmark_test',\n help='the dir to save metric')\n parser.add_argument(\n '--run', action='store_true', help='run script directly')\n parser.add_argument(\n '--local',\n action='store_true',\n help='run at local instead of cluster.')\n parser.add_argument(\n '--mail', type=str, help='Mail address to watch test status.')\n parser.add_argument(\n '--mail-type',\n nargs='+',\n default=['BEGIN'],\n choices=['NONE', 'BEGIN', 'END', 'FAIL', 'REQUEUE', 'ALL'],\n help='Mail address to watch test status.')\n parser.add_argument(\n '--quotatype',\n default=None,\n choices=['reserved', 'auto', 'spot'],\n help='Quota type, only available for phoenix-slurm>=0.2')\n parser.add_argument(\n '--summary',\n action='store_true',\n help='Summarize benchmark test results.')\n parser.add_argument(\n '--by-task',\n action='store_true',\n help='Summairze benchmark results by task.')\n parser.add_argument('--save', action='store_true', help='Save the summary')\n\n group_parser = parser.add_mutually_exclusive_group()\n group_parser.add_argument(\n '--P0', action='store_true', help='Whether test model in P0 list')\n group_parser.add_argument(\n '--model-list',\n type=str,\n default='',\n help='Path of algorithm list to load')\n args = parser.parse_args()\n return args\n\n\ndef create_test_job_batch(commands, model_info, args, port, script_name):\n config_http_prefix_blob = ('https://github.com/open-mmlab/mmagic/'\n 'blob/main/')\n config_http_prefix_tree = ('https://github.com/open-mmlab/mmagic/'\n 'tree/main/')\n fname = model_info.name\n\n config = model_info.config\n if config.startswith('http'):\n config = config.replace(config_http_prefix_blob, './')\n config = config.replace(config_http_prefix_tree, './')\n if args.use_ceph_config:\n config = config.replace('configs', 'configs_ceph')\n\n config = Path(config)\n assert config.exists(), f'{fname}: {config} not found.'\n\n http_prefix_short = 'https://download.openmmlab.com/mmediting/'\n model_weight_url = model_info.weights\n\n if model_weight_url.startswith(http_prefix_short):\n model_name = model_weight_url[len(http_prefix_short):]\n elif model_weight_url == '':\n print(f'{fname} weight is missing')\n return None\n else:\n raise ValueError(f'Unknown url prefix. \\'{model_weight_url}\\'')\n\n model_name_split = model_name.split('/')\n if len(model_name_split) == 3: # 'TASK/METHOD/MODEL.pth'\n # remove task name\n model_name = osp.join(*model_name_split[1:])\n else:\n model_name = osp.join(*model_name_split)\n\n if 's3://' in args.checkpoint_root:\n from mmengine.fileio import FileClient\n from petrel_client.common.exception import AccessDeniedError\n file_client = FileClient.infer_client(uri=args.checkpoint_root)\n checkpoint = file_client.join_path(args.checkpoint_root, model_name)\n try:\n exists = file_client.exists(checkpoint)\n except AccessDeniedError:\n exists = False\n else:\n checkpoint_root = Path(args.checkpoint_root)\n checkpoint = checkpoint_root / model_name\n exists = checkpoint.exists()\n if not exists:\n print(f'WARNING: {fname}: {checkpoint} not found.')\n return None\n\n job_name = f'{args.job_name}_{fname}'.strip('_')\n work_dir = Path(args.work_dir) / fname\n work_dir.mkdir(parents=True, exist_ok=True)\n result_file = work_dir / 'result.pkl'\n\n if args.mail is not None and 'NONE' not in args.mail_type:\n mail_cfg = (f'#SBATCH --mail {args.mail}\\n'\n f'#SBATCH --mail-type {args.mail_type}\\n')\n else:\n mail_cfg = ''\n\n if args.quotatype is not None:\n quota_cfg = f'#SBATCH --quotatype {args.quotatype}\\n'\n else:\n quota_cfg = ''\n\n launcher = 'none' if args.local else 'slurm'\n runner = 'python' if args.local else 'srun python'\n\n # NOTE: set gpus as 2\n job_script = (f'#!/bin/bash\\n'\n f'#SBATCH --output {work_dir}/job.%j.out\\n'\n f'#SBATCH --partition={args.partition}\\n'\n f'#SBATCH --job-name {job_name}\\n'\n f'#SBATCH --gres=gpu:2\\n'\n f'{mail_cfg}{quota_cfg}'\n f'#SBATCH --ntasks-per-node=2\\n'\n f'#SBATCH --ntasks=2\\n'\n f'#SBATCH --cpus-per-task=16\\n\\n'\n f'export MASTER_PORT={port}\\n'\n f'export CUBLAS_WORKSPACE_CONFIG=:4096:8\\n'\n f'{runner} -u {script_name} {config} {checkpoint} '\n f'--work-dir={work_dir} '\n f'--out={result_file} '\n f'--launcher={launcher}\\n')\n\n with open(work_dir / 'job.sh', 'w') as f:\n f.write(job_script)\n\n commands.append(f'echo \"{config}\"')\n commands.append(f'echo \"{work_dir}\"')\n if args.local:\n commands.append(f'bash {work_dir}/job.sh')\n else:\n commands.append(f'sbatch {work_dir}/job.sh')\n\n return work_dir / 'job.sh'\n\n\ndef test(args):\n # parse model-index.yml\n model_index_file = MMagic_ROOT / 'model-index.yml'\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n models = OrderedDict({model.name: model for model in model_index.models})\n\n script_name = osp.join('tools', 'test.py')\n port = args.port\n\n commands = []\n if args.models:\n patterns = [re.compile(pattern) for pattern in args.models]\n filter_models = {}\n for k, v in models.items():\n if any([re.match(pattern, k) for pattern in patterns]):\n filter_models[k] = v\n if len(filter_models) == 0:\n print('No model found, please specify models in:')\n print('\\n'.join(models.keys()))\n return\n models = filter_models\n\n # load model list\n if args.P0:\n file_list = osp.join(osp.dirname(__file__), 'p0_test_list.py')\n elif args.model_list:\n file_list = args.model_list\n else:\n file_list = None\n\n if file_list:\n test_list = SourceFileLoader('model_list',\n file_list).load_module().model_list\n else:\n test_list = None\n\n preview_script = ''\n for model_info in models.values():\n\n if model_info.results is None:\n continue\n\n if test_list is not None and model_info.name not in test_list:\n continue\n\n script_path = create_test_job_batch(commands, model_info, args, port,\n script_name)\n preview_script = script_path or preview_script\n port += 1\n\n command_str = '\\n'.join(commands)\n\n preview = Table()\n preview.add_column(str(preview_script))\n preview.add_column('Shell command preview')\n preview.add_row(\n Syntax.from_path(\n preview_script,\n background_color='default',\n line_numbers=True,\n word_wrap=True),\n Syntax(\n command_str,\n 'bash',\n background_color='default',\n line_numbers=True,\n word_wrap=True))\n console.print(preview)\n\n if args.run:\n proc = os.popen(command_str)\n job_name_list = start_from_proc(args.work_dir, proc)\n history_log = datetime.now().strftime('test-%Y%m%d-%H%M%S') + '.log'\n with open(history_log, 'w') as fp:\n fp.write(args.work_dir + '\\n')\n for job in job_name_list:\n fp.write(job + '\\n')\n\n cache_path = osp.expanduser(osp.join('~', '.task_watcher'))\n # print(cache_path)\n os.makedirs(cache_path, exist_ok=True)\n with open(osp.join(cache_path, 'latest.log'), 'w') as fp:\n fp.write(args.work_dir + '\\n')\n for job in job_name_list:\n fp.write(job + '\\n')\n print(f'Have saved job submission history in {history_log}')\n else:\n console.print('Please set \"--run\" to start the job')\n\n\ndef show_summary(summary_data,\n models_map,\n work_dir,\n name='test_benchmark_summary',\n save=False):\n # table = Table(title='Test Benchmark Regression Summary')\n table_title = name.replace('_', ' ')\n table_title = table_title.capitalize()\n table = Table(title=table_title)\n table.add_column('Model')\n md_header = ['Model']\n\n used_metrics = filter_metric(METRICS_MAPPING, summary_data)\n for metric in used_metrics:\n table.add_column(f'{metric} (expect)')\n table.add_column(f'{metric}')\n md_header.append(f'{metric} (expect)')\n md_header.append(f'{metric}')\n table.add_column('Date')\n md_header.append('Config')\n\n def set_color(value, expect, tolerance, rule):\n if value > expect + tolerance:\n return 'green' if rule == 'larger' else 'red'\n elif value < expect - tolerance:\n return 'red' if rule == 'larger' else 'green'\n else:\n return 'white'\n\n md_rows = ['| ' + ' | '.join(md_header) + ' |\\n']\n md_rows.append('|:' + ':|:'.join(['---'] * len(md_header)) + ':|\\n')\n\n for model_name, summary in summary_data.items():\n row = [model_name]\n md_row = [model_name]\n for metric_key in used_metrics:\n if metric_key in summary:\n metric = summary[metric_key]\n expect = round(metric['expect'], 2)\n result = round(metric['result'], 2)\n tolerance = metric['tolerance']\n rule = metric['rule']\n color = set_color(result, expect, tolerance, rule)\n row.append(f'{expect:.2f}')\n row.append(f'[{color}]{result:.2f}[/{color}]')\n md_row.append(f'{expect:.4f}')\n md_row.append(f'{result:.4f}')\n else:\n row.extend([''] * 2)\n md_row.extend([''] * 2)\n if 'date' in summary:\n row.append(summary['date'])\n md_row.append(summary['date'])\n else:\n row.append('')\n md_row.append('')\n table.add_row(*row)\n\n # add config to row\n model_info = models_map[model_name]\n md_row.append(model_info.config)\n md_rows.append('| ' + ' | '.join(md_row) + ' |\\n')\n\n console.print(table)\n\n if save:\n summary_path = work_dir / f'{name}.md'\n with open(summary_path, 'w') as file:\n file.write('# Test Benchmark Regression Summary\\n')\n file.writelines(md_rows)\n\n\ndef summary(args):\n model_index_file = MMagic_ROOT / 'model-index.yml'\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n models = OrderedDict({model.name: model for model in model_index.models})\n\n work_dir = Path(args.work_dir)\n\n if args.models:\n patterns = [re.compile(pattern) for pattern in args.models]\n filter_models = {}\n for k, v in models.items():\n if any([re.match(pattern, k) for pattern in patterns]):\n filter_models[k] = v\n if len(filter_models) == 0:\n print('No model found, please specify models in:')\n print('\\n'.join(models.keys()))\n return\n models = filter_models\n\n summary_data = {}\n task_summary_data = defaultdict(dict)\n for model_name, model_info in models.items():\n\n if model_info.results is None:\n continue\n\n # Skip if not found result file.\n result_file = work_dir / model_name / 'result.pkl'\n if not result_file.exists():\n summary_data[model_name] = {}\n continue\n\n with open(result_file, 'rb') as file:\n results = pickle.load(file)\n date = datetime.fromtimestamp(result_file.lstat().st_mtime)\n\n expect_metrics = model_info.results[0].metrics\n\n # extract metrics\n summary = {'date': date.strftime('%Y-%m-%d')}\n for key_yml, value_yml in METRICS_MAPPING.items():\n key_results = value_yml['keys']\n tolerance = value_yml['tolerance']\n rule = value_yml['rule']\n\n for key_result in key_results:\n if key_yml in expect_metrics and key_result in results:\n expect_result = float(expect_metrics[key_yml])\n result = float(results[key_result])\n summary[key_yml] = dict(\n expect=expect_result,\n result=result,\n tolerance=tolerance,\n rule=rule)\n\n summary_data[model_name] = summary\n\n in_collection = model_info.data['In Collection']\n for task, collection_list in TASK_MAPPING.items():\n if in_collection.upper() in [c.upper() for c in collection_list]:\n task_summary_data[task][model_name] = summary\n break\n\n if args.by_task:\n for task_name, data in task_summary_data.items():\n show_summary(\n data, models, work_dir, f'{task_name}_summary', save=args.save)\n\n show_summary(summary_data, models, work_dir, save=args.save)\n\n\ndef main():\n args = parse_args()\n\n if args.summary:\n summary(args)\n else:\n test(args)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": ".dev_scripts/test_benchmark.yml", "content": "cases:\n- name: controlnet-1xb1-fill50k\n params:\n config: configs/controlnet/controlnet-1xb1-fill50k.py\n cpus_per_node: 16\n gpus: 1\n gpus_per_node: 1\n\n- name: dreambooth\n params:\n config: configs/dreambooth/dreambooth.py\n cpus_per_node: 16\n gpus: 1\n gpus_per_node: 1\n\n- name: basicvsr-pp_c64n7_8xb1-600k_reds4\n params:\n checkpoint: basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n config: configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n cpus_per_node: 16\n gpus: 4\n gpus_per_node: 4\n results:\n dataset: REDS4(BIx4)\n eval:\n - PSNR\n - SSIM\n metrics:\n PSNR (RGB): 32.3855\n SSIM (RGB): 0.9069\n\n- name: realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds\n params:\n checkpoint: realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n config: configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: REDS\n eval:\n - NIQE\n metrics:\n NIQE (Y): 3.7662\n\n- name: stylegan2_c2_8xb4-800kiters_ffhq-256x256\n params:\n checkpoint: stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth\n config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py\n cpus_per_node: 4\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: FFHQ256\n metrics:\n FID-50k: 3.992\n\n- name: deepfillv2_8xb2_celeba-256x256\n params:\n checkpoint: deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth\n config: configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: CelebA-val\n metrics:\n PSNR: 25.721\n SSIM: 0.871\n l1 error: 5.411\n\n- name: realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost\n params:\n checkpoint: realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth\n config: configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: df2k_ost\n metrics:\n PSNR: 28.0297\n SSIM: 0.8236\n\ncluster_num: '2'\ndefault_floating_range: 1.0\nmodel_floating_ranges: {}\npartition: mm_lol\nrepo: mmagic\nbranch: test\ntask_type: test\nthird_part_libs: []\n" }, { "path": ".dev_scripts/train_benchmark.py", "content": "import argparse\nimport os\nimport os.path as osp\nimport pickle\nimport re\nfrom collections import OrderedDict\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom job_watcher import start_from_proc\nfrom modelindex.load_model_index import load\nfrom rich.console import Console\nfrom rich.syntax import Syntax\nfrom rich.table import Table\nfrom tqdm import tqdm\nfrom utils import filter_jobs, parse_job_list_from_file\n\nconsole = Console()\nMMAGIC_ROOT = Path(__file__).absolute().parents[1]\n\n# key-in-metafile: key-in-results.pkl\nMETRICS_MAP = {\n 'SWD': {\n 'keys': ['SWD/avg'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'MS-SSIM': {\n 'keys': ['MS-SSIM'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'FID': {\n 'keys': ['FID-Full-50k/fid'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'FID50k': {\n 'keys': ['FID-Full-50k/fid'],\n 'tolerance': 0.1,\n 'rule': 'less'\n },\n 'IS': {\n 'keys': ['IS-50k/is'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'IS50k': {\n 'keys': ['IS-50k/is'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'Precision50k': {\n 'keys': ['PR-50K/precision'],\n 'tolerance': 0.1,\n 'rule': 'large'\n },\n 'Recall50k': {\n 'keys': ['PR-50K/recall'],\n 'tolerance': 0.1,\n 'rule': 'large'\n },\n 'Precision10k': {\n 'keys': ['PR-10K/precision'],\n 'tolerance': 0.1,\n 'rule': 'large'\n },\n 'Recall10k': {\n 'keys': ['PR-10K/recall'],\n 'tolerance': 0.1,\n 'rule': 'large'\n },\n # 'PPL': {}, # TODO: no ppl in metafiles?\n 'EQ-R': {\n 'keys': ['EQ/eqr'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n 'EQ-T': {\n 'keys': ['EQ/eqt_int'],\n 'tolerance': 0.1,\n 'rule': 'larger'\n },\n}\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description=\"Train models' accuracy in model-index.yml\")\n parser.add_argument(\n 'partition', type=str, help='Cluster partition to use.')\n parser.add_argument('--skip', type=str, default=None)\n parser.add_argument('--skip-list', default=None)\n parser.add_argument('--rerun', type=str, default=None)\n parser.add_argument(\n '--rerun-fail', action='store_true', help='only rerun failed tasks')\n parser.add_argument(\n '--rerun-cancel', action='store_true', help='only rerun cancel tasks')\n parser.add_argument('--rerun-list', default=None)\n parser.add_argument('--gpus-per-job', type=int, default=None)\n parser.add_argument('--cpus-per-job', type=int, default=16)\n parser.add_argument(\n '--amp', action='store_true', help='Whether to use amp.')\n parser.add_argument(\n '--resume', action='store_true', help='Whether to resume checkpoint.')\n parser.add_argument(\n '--job-name', type=str, default=' ', help='Slurm job name prefix')\n parser.add_argument('--port', type=int, default=29666, help='dist port')\n parser.add_argument(\n '--config-dir',\n type=str,\n default='configs_ceph',\n help='Use ceph configs or not.')\n parser.add_argument(\n '--models', nargs='+', type=str, help='Specify model names to run.')\n parser.add_argument(\n '--work-dir',\n default='work_dirs/benchmark_train',\n help='the dir to save metric')\n parser.add_argument(\n '--deterministic',\n action='store_true',\n help='Whether set `deterministic` during training.')\n parser.add_argument(\n '--run', action='store_true', help='run script directly')\n parser.add_argument(\n '--local',\n action='store_true',\n help='run at local instead of cluster.')\n parser.add_argument(\n '--mail', type=str, help='Mail address to watch train status.')\n parser.add_argument(\n '--mail-type',\n nargs='+',\n default=['BEGIN'],\n choices=['NONE', 'BEGIN', 'END', 'FAIL', 'REQUEUE', 'ALL'],\n help='Mail address to watch train status.')\n parser.add_argument(\n '--quotatype',\n default=None,\n choices=['reserved', 'auto', 'spot'],\n help='Quota type, only available for phoenix-slurm>=0.2')\n parser.add_argument(\n '--summary',\n action='store_true',\n help='Summarize benchmark train results.')\n parser.add_argument('--save', action='store_true', help='Save the summary')\n\n args = parser.parse_args()\n\n if args.skip is not None:\n with open(args.skip, 'r') as fp:\n skip_list = fp.readlines()\n skip_list = [j.split('\\n')[0] for j in skip_list]\n args.skip_list = skip_list\n print('skip_list: ', args.skip_list)\n elif args.rerun is not None:\n job_id_list_full, job_name_list_full = parse_job_list_from_file(\n args.rerun)\n filter_target = []\n\n if args.rerun_fail:\n filter_target += ['FAILED']\n if args.rerun_cancel:\n filter_target += ['CANCELLED']\n\n _, job_name_list = filter_jobs(\n job_id_list_full,\n job_name_list_full,\n filter_target,\n show_table=True,\n table_name='Rerun List')\n args.rerun_list = job_name_list\n\n return args\n\n\ndef create_train_job_batch(commands, model_info, args, port, script_name):\n config_http_prefix_blob = ('https://github.com/open-mmlab/mmagic/'\n 'blob/main/')\n config_http_prefix_tree = ('https://github.com/open-mmlab/mmagic/'\n 'tree/main/')\n fname = model_info.name\n\n config = model_info.config\n if config.startswith('http'):\n config = config.replace(config_http_prefix_blob, './')\n config = config.replace(config_http_prefix_tree, './')\n\n config = config.replace('configs', args.config_dir)\n\n config = Path(config)\n assert config.exists(), f'{fname}: {config} not found.'\n\n try:\n n_gpus = int(model_info.metadata.data['GPUs'].split()[0])\n except Exception:\n if 'official' in model_info.config:\n return None\n else:\n pattern = r'\\d+xb\\d+'\n parse_res = re.search(pattern, config.name)\n if not parse_res:\n # defaults to use 1 gpu\n n_gpus = 1\n else:\n n_gpus = int(parse_res.group().split('x')[0])\n\n if args.gpus_per_job is not None:\n n_gpus = min(args.gpus_per_job, n_gpus)\n\n job_name = f'{args.job_name}_{fname}'\n if (args.skip_list is not None) and model_info.name in args.skip_list:\n return None\n if (args.rerun_list is not None) and (model_info.name\n not in args.rerun_list):\n return None\n\n work_dir = Path(args.work_dir) / fname\n work_dir.mkdir(parents=True, exist_ok=True)\n\n if args.mail is not None and 'NONE' not in args.mail_type:\n mail_cfg = (f'#SBATCH --mail {args.mail}\\n'\n f'#SBATCH --mail-type {args.mail_type}\\n')\n else:\n mail_cfg = ''\n\n if args.quotatype is not None:\n quota_cfg = f'#SBATCH --quotatype {args.quotatype}\\n'\n else:\n quota_cfg = ''\n\n launcher = 'none' if args.local or n_gpus == 0 else 'slurm'\n runner = 'python' if args.local else 'srun python'\n\n job_script = (f'#!/bin/bash\\n'\n f'#SBATCH --output {work_dir}/job.%j.out\\n'\n f'#SBATCH --partition={args.partition}\\n'\n f'#SBATCH --job-name {job_name}\\n'\n f'{mail_cfg}{quota_cfg}')\n\n if n_gpus > 0:\n job_script += (f'#SBATCH --gres=gpu:{n_gpus}\\n'\n f'#SBATCH --ntasks-per-node={min(n_gpus, 8)}\\n'\n f'#SBATCH --ntasks={n_gpus}\\n'\n f'#SBATCH --cpus-per-task={args.cpus_per_job}\\n\\n')\n else:\n job_script += '\\n\\n' + 'export CUDA_VISIBLE_DEVICES=-1\\n'\n\n if args.deterministic:\n job_script += 'export CUBLAS_WORKSPACE_CONFIG=:4096:8\\n'\n\n job_script += (f'export MASTER_PORT={port}\\n'\n f'{runner} -u {script_name} {config} '\n f'--work-dir={work_dir} '\n f'--launcher={launcher}')\n\n if args.resume:\n job_script += ' --resume '\n\n if args.amp:\n job_script += ' --amp '\n\n if args.deterministic:\n job_script += ' --cfg-options randomness.deterministic=True'\n\n job_script += '\\n'\n\n with open(work_dir / 'job.sh', 'w') as f:\n f.write(job_script)\n\n commands.append(f'echo \"{config}\"')\n commands.append(f'echo \"{work_dir}\"')\n if args.local:\n commands.append(f'bash {work_dir}/job.sh')\n else:\n commands.append(f'sbatch {work_dir}/job.sh')\n\n return work_dir / 'job.sh'\n\n\ndef train(args):\n # parse model-index.yml\n model_index_file = MMAGIC_ROOT / 'model-index.yml'\n\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n models = OrderedDict({model.name: model for model in model_index.models})\n\n script_name = osp.join('tools', 'train.py')\n port = args.port\n\n commands = []\n if args.models:\n patterns = [re.compile(pattern) for pattern in args.models]\n filter_models = {}\n for k, v in models.items():\n if any([re.match(pattern, k) for pattern in patterns]):\n filter_models[k] = v\n if len(filter_models) == 0:\n print('No model found, please specify models in:')\n print('\\n'.join(models.keys()))\n return\n models = filter_models\n\n preview_script = ''\n pbar = tqdm(models.values())\n for model_info in pbar:\n\n if model_info.results is None:\n continue\n\n model_name = model_info.name\n pbar.set_description(model_name)\n if 'cvt' in model_name:\n print(f'Skip converted config: {model_name} ({model_info.config})')\n continue\n script_path = create_train_job_batch(commands, model_info, args, port,\n script_name)\n if script_path is not None:\n preview_script = script_path or preview_script\n port += 1\n\n command_str = '\\n'.join(commands)\n\n preview = Table()\n preview.add_column(str(preview_script))\n preview.add_column('Shell command preview')\n preview.add_row(\n Syntax.from_path(\n preview_script,\n background_color='default',\n line_numbers=True,\n word_wrap=True),\n Syntax(\n command_str,\n 'bash',\n background_color='default',\n line_numbers=True,\n word_wrap=True))\n console.print(preview)\n\n if args.run:\n proc = os.popen(command_str)\n job_name_list = start_from_proc(args.work_dir, proc)\n history_log = datetime.now().strftime('train-%Y%m%d-%H%M%S') + '.log'\n with open(history_log, 'w') as fp:\n for job in job_name_list:\n fp.write(job + '\\n')\n fp.close()\n print(f'Have saved job submission history in {history_log}')\n\n else:\n console.print('Please set \"--run\" to start the job')\n\n\ndef show_summary(summary_data, models_map, work_dir, save=False):\n table = Table(title='Train Benchmark Regression Summary')\n table.add_column('Model')\n md_header = ['Model']\n for metric in METRICS_MAP:\n table.add_column(f'{metric} (expect)')\n table.add_column(f'{metric}')\n md_header.append(f'{metric} (expect)')\n md_header.append(f'{metric}')\n table.add_column('Date')\n md_header.append('Config')\n\n def set_color(value, expect, tolerance, rule):\n if value > expect + tolerance:\n return 'green' if rule == 'larger' else 'red'\n elif value < expect - tolerance:\n return 'red' if rule == 'larger' else 'green'\n else:\n return 'white'\n\n md_rows = ['| ' + ' | '.join(md_header) + ' |\\n']\n md_rows.append('|:' + ':|:'.join(['---'] * len(md_header)) + ':|\\n')\n\n for model_name, summary in summary_data.items():\n row = [model_name]\n md_row = [model_name]\n for metric_key in METRICS_MAP:\n if metric_key in summary:\n metric = summary[metric_key]\n expect = round(metric['expect'], 2)\n result = round(metric['result'], 2)\n tolerance = metric['tolerance']\n rule = metric['rule']\n color = set_color(result, expect, tolerance, rule)\n row.append(f'{expect:.2f}')\n row.append(f'[{color}]{result:.2f}[/{color}]')\n md_row.append(f'{expect:.2f}')\n md_row.append(f'{result:.2f}')\n else:\n row.extend([''] * 2)\n md_row.extend([''] * 2)\n if 'date' in summary:\n row.append(summary['date'])\n md_row.append(summary['date'])\n else:\n row.append('')\n md_row.append('')\n table.add_row(*row)\n\n # add config to row\n model_info = models_map[model_name]\n md_row.append(model_info.config)\n md_rows.append('| ' + ' | '.join(md_row) + ' |\\n')\n\n console.print(table)\n\n if save:\n summary_path = work_dir / 'train_benchmark_summary.md'\n with open(summary_path, 'w') as file:\n file.write('# Train Benchmark Regression Summary\\n')\n file.writelines(md_rows)\n\n\ndef summary(args):\n model_index_file = MMAGIC_ROOT / 'model-index.yml'\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n models = OrderedDict({model.name: model for model in model_index.models})\n\n work_dir = Path(args.work_dir)\n\n if args.models:\n patterns = [re.compile(pattern) for pattern in args.models]\n filter_models = {}\n for k, v in models.items():\n if any([re.match(pattern, k) for pattern in patterns]):\n filter_models[k] = v\n if len(filter_models) == 0:\n print('No model found, please specify models in:')\n print('\\n'.join(models.keys()))\n return\n models = filter_models\n\n summary_data = {}\n for model_name, model_info in models.items():\n\n if model_info.results is None:\n continue\n\n # Skip if not found result file.\n result_file = work_dir / model_name / 'result.pkl'\n if not result_file.exists():\n summary_data[model_name] = {}\n continue\n\n with open(result_file, 'rb') as file:\n results = pickle.load(file)\n date = datetime.fromtimestamp(result_file.lstat().st_mtime)\n\n expect_metrics = model_info.results[0].metrics\n\n # extract metrics\n summary = {'date': date.strftime('%Y-%m-%d')}\n for key_yml, key_tolerance in METRICS_MAP.items():\n key_results = key_tolerance['keys']\n tolerance = key_tolerance['tolerance']\n rule = key_tolerance['rule']\n\n for key_result in key_results:\n if key_yml in expect_metrics and key_result in results:\n expect_result = float(expect_metrics[key_yml])\n result = float(results[key_result])\n summary[key_yml] = dict(\n expect=expect_result,\n result=result,\n tolerance=tolerance,\n rule=rule)\n\n summary_data[model_name] = summary\n\n show_summary(summary_data, models, work_dir, args.save)\n # if args.save:\n # save_summary(summary_data, models, work_dir)\n\n\ndef main():\n args = parse_args()\n\n if args.summary:\n summary(args)\n else:\n train(args)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": ".dev_scripts/train_benchmark.yml", "content": "cases:\n- name: controlnet-1xb1-fill50k\n params:\n config: configs/controlnet/controlnet-1xb1-fill50k.py\n cpus_per_node: 16\n gpus: 1\n gpus_per_node: 1\n\n- name: dreambooth\n params:\n config: configs/dreambooth/dreambooth.py\n cpus_per_node: 16\n gpus: 1\n gpus_per_node: 1\n\n- name: basicvsr-pp_c64n7_8xb1-600k_reds4\n params:\n checkpoint: basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n config: configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n cpus_per_node: 16\n gpus: 4\n gpus_per_node: 4\n results:\n dataset: REDS4(BIx4)\n eval:\n - PSNR\n - SSIM\n metrics:\n PSNR (RGB): 32.3855\n SSIM (RGB): 0.9069\n\n- name: realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds\n params:\n checkpoint: realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n config: configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: REDS\n eval:\n - NIQE\n metrics:\n NIQE (Y): 3.7662\n\n- name: stylegan2_c2_8xb4-800kiters_ffhq-256x256\n params:\n checkpoint: stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth\n config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py\n cpus_per_node: 4\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: FFHQ256\n metrics:\n FID-50k: 3.992\n\n- name: deepfillv2_8xb2_celeba-256x256\n params:\n checkpoint: deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth\n config: configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: CelebA-val\n metrics:\n PSNR: 25.721\n SSIM: 0.871\n l1 error: 5.411\n\n- name: realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost\n params:\n checkpoint: realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth\n checkpoint_url: https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth\n config: configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py\n cpus_per_node: 16\n gpus: 8\n gpus_per_node: 8\n results:\n dataset: df2k_ost\n metrics:\n PSNR: 28.0297\n SSIM: 0.8236\n\ncluster_num: '2'\ndefault_floating_range: 1.0\nmodel_floating_ranges: {}\npartition: mm_lol\nrepo: mmagic\nbranch: test\ntask_type: train\nthird_part_libs: []\n" }, { "path": ".dev_scripts/update_config_readme.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\n# This tool is used to update README.md and README_zh-CN.md in configs\n\nimport glob\nimport os\nimport posixpath as osp # Even on windows, use posixpath\nimport re\nimport sys\n\nMMagic_ROOT = osp.dirname(osp.dirname(osp.dirname(__file__)))\n\n\ndef update_md(md_file):\n \"\"\"Update README.md and README_zh-CN.md.\n\n Args:\n md_file (str): Path to .md file.\n Returns:\n Bool: If the target README.md file is different from the original.\n \"\"\"\n # See https://github.com/open-mmlab/mmagic/pull/798 for these comments\n # unique_dict = generate_unique_name(md_file)\n\n md_file = md_file.replace(os.sep, '/')\n config_dir, _ = osp.split(md_file)\n files = os.listdir(config_dir)\n config_files = [file for file in files if file.endswith('.py')]\n\n for readme in ['README.md', 'README_zh-CN.md']:\n\n readme = osp.join(config_dir, readme)\n changed = False\n\n with open(readme, 'r', encoding='utf-8') as f:\n data = f.read()\n\n for config in config_files:\n\n _, ext = osp.splitext(config)\n if ext != '.py':\n continue\n\n re_config = config.replace('.', '\\\\.')\n re_config = config.replace('-', '\\\\-')\n re_result = re.search(rf'\\]\\(/(.*?)/{re_config}', data)\n if re_result is None:\n print(f'Warning: No {config} in {readme}')\n continue\n old_dir = re_result.groups()[0]\n if old_dir != config_dir:\n data = data.replace(old_dir, config_dir)\n print(f'from {old_dir} to {config_dir}')\n changed = True\n\n if changed:\n with open(readme, 'w', encoding='utf-8') as f:\n f.write(data)\n\n return False\n\n\nif __name__ == '__main__':\n if len(sys.argv) <= 1:\n configs_root = osp.join(MMagic_ROOT, 'configs')\n file_list = glob.glob(\n osp.join(configs_root, '**', '*README.md'), recursive=True)\n file_list.sort()\n else:\n file_list = [\n fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md'\n ]\n\n if not file_list:\n sys.exit(0)\n\n file_modified = False\n for fn in file_list:\n print(f'process {fn}')\n file_modified |= update_md(fn)\n\n sys.exit(1 if file_modified else 0)\n" }, { "path": ".dev_scripts/update_model_index.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"This tool is used to update model-index.yml which is required by MIM, and\nwill be automatically called as a pre-commit hook. The updating will be\ntriggered if any change of model information (.md files in configs/) has been\ndetected before a commit.\n\nThe design of the metafile follows /wikcnz2ksDIyZiFM8iAMUoTmNHg.\nIt is forbidden to set value as `NULL`, `None` or empty list and str.\n\n`Collection`: indicates a algorithm (which may\n contains multiple configs), e.g. Mask R-CNN\n - `Name` (str): required\n - `README` (str): optional\n - `Paper` (dict): optional\n - `URL` (str): required\n - `Title` (str): required\n - `Task` (List[str]): optional\n - `Year` (int): optional\n - `Metadata` (dict): optional\n - `Architecture` (List[str]): optional\n - `Training Data` (Union[str, List[str]]): optional\n - `Epochs` (int): optional\n - `Batch Size` (int): optional\n - `Training Techniques` (List[str]): optional\n - `Training Resources` (str): optional\n - `FLOPs` (Union[int, float]): optional\n - `Parameters` (int): optional\n - `Training Time` (Union[int, float]): optional\n - `Train time (s/iter)` (Union[int, float]): optional\n - `Training Memory (GB)` (float): optional\n - `Weights` (Union[str, List[str]]): optional\n\n`Model`: indicates a specific config\n - `Name` (str): required, globally unique\n - `In Collection` (str): required\n - `Config` (str): required\n - `Results` (List[dict]): required\n - `Task` (str): required\n - `Dataset` (str): required\n - `Metrics` (dict): required\n - `Weights` (str): optional\n - `Metadata` (dict): optional\n - `Architecture` (List[str]): optional\n - `Training Resources` (str): optional\n - `Training Data` (Union[str, List[str]]): optional\n - `Epochs` (int): optional\n - `Batch Size` (int): optional\n - `Training Techniques` (List[str]): optional\n - `FLOPs` (Union[int, float]): optional\n - `Parameters` (int): optional\n - `Training Time` (Union[int, float]): optional\n - `Train time (s/iter)` (Union[int, float]): optional\n - `Training Memory (GB)` (float): optional\n - `inference time (ms/im)` (List[dict]): optional\n - `value` (float): required\n - `hardware` (str): required\n - `backend` (str): required\n - `batch size` (str): required\n - `mode` (str): required, e.g., FP32, FP16, INT8, etc.\n - `resolution` (Tuple(int, int)): required\n - `Training Log` (str): optional\n - `README` (str): optional\n - `Paper` (dict): optional\n - `URL` (str): required\n - `Title` (str): required\n - `Converted From` (dict): optional\n - `Weights` (str): required\n - `Code` (str): required\n - `Code` (dict): optional\n - `URL` (str): required\n - `Version` (str): required\n - `Image` (str): optional\n\nThe README.md file in MMagic follows following convention,\n - `Model`: [name_in_the_paper](path_to_config)\n - `Download`: [model](url_to_pre_trained_weights) | [log](url_to_log)\n\"\"\"\n\nimport glob\nimport posixpath as osp # Even on windows, use posixpath\nimport re\nimport sys\n\nimport tqdm\nfrom modelindex.models.Collection import Collection\nfrom modelindex.models.Metadata import Metadata\nfrom modelindex.models.Model import Model\nfrom modelindex.models.Result import Result\nfrom utils import (collate_metrics, dump_yaml_and_check_difference,\n found_table, modelindex_to_dict)\n\nMMagic_ROOT = osp.dirname(osp.dirname(__file__))\nKEYWORDS = [\n 'Model',\n 'Dataset',\n 'Download',\n]\n\n\ndef parse_md(md_file):\n \"\"\"Parse .md file and convert it to a .yml file which can be used for MIM.\n\n Args:\n md_file (str): Path to .md file.\n Returns:\n Bool: If the target YAML file is different from the original.\n \"\"\"\n readme = osp.relpath(md_file, MMagic_ROOT)\n readme = readme.replace('\\\\', '/') # for windows\n collection = Collection()\n collection_meta = Metadata()\n\n # force utf-8 instead of system defined\n with open(md_file, 'r', encoding='utf-8') as md:\n lines = md.readlines()\n\n # parse information for collection\n name = lines[0][2:]\n name, year = name.split('(', 1)\n year = int(re.sub('[^0-9]', '', year))\n collection_name = name.strip()\n task_line = lines[4]\n model_task = task_line.strip().split(':')[-1].strip()\n model_task_list = model_task.lower().split(', ')\n\n collection.name = collection_name\n collection.readme = readme\n collection.data['Year'] = year\n collection.data['Task'] = model_task_list\n collection_meta.architecture = [collection_name]\n\n i = 0\n model_list = {}\n while i < len(lines):\n\n # parse reference\n if lines[i].startswith('> ['):\n title, url = re.match(r'> \\[(.*)]\\((.*)\\)', lines[i]).groups()\n collection.paper = dict(URL=url, Title=title)\n i += 1\n\n # parse table\n elif found_table(lines, i):\n cols = [col.strip() for col in lines[i].split('|')][1:-1]\n\n # check required field for Model\n try:\n model_idx = cols.index('Model')\n dataset_idx = cols.index('Dataset')\n download_idx = cols.index('Download')\n used_metrics = collate_metrics(cols)\n if 'Task' in cols:\n task_idx = cols.index('Task')\n else:\n task_idx = None\n except Exception:\n raise ValueError(\n f'required fields: Model, Dataset, Download '\n f'are not included in line {i+1} of {md_file}')\n\n j = i + 2\n # parse table for valid fields\n while j < len(lines) and lines[j][0] == '|':\n line = lines[j].split('|')[1:-1]\n\n # name, in_collection, config of Model\n assert line[model_idx].find(\n '](') >= 0, f'invalid {model_idx} in {line} for {md_file}.'\n left = line[model_idx].index('](') + 2\n right = line[model_idx].index(')', left)\n config = line[model_idx][left:right].strip('./')\n model_name = osp.splitext(osp.basename(config))[0]\n if model_name in model_list:\n model = model_list[model_name]\n else:\n model = Model(\n name=model_name,\n in_collection=collection_name,\n config=osp.join(\n osp.dirname(md_file), model_name + '.py'))\n model_list[model_name] = model\n\n # find results in each row\n dataset = line[dataset_idx].replace(' ', '')\n\n metrics = {}\n for metric_name, idx in used_metrics.items():\n value = line[idx]\n value = value.replace('*', '')\n if '/' not in value:\n try:\n value = value.split('(')[0]\n metrics[metric_name] = float(value)\n except ValueError:\n value = value.replace(' ', '')\n else:\n try:\n PSNR, SSIM = [float(d) for d in value.split('/')]\n metrics[metric_name] = dict(PSNR=PSNR, SSIM=SSIM)\n except ValueError:\n pass\n\n task = model_task if task_idx is None else line[\n task_idx].strip()\n assert ',' not in task, (\n f'Find \",\" in \"task\" field of \"{md_file}\" (line {j}). '\n 'Please check your readme carefully.')\n assert task.lower() in model_task_list, (\n f'Task \"{task}\" not in \"{model_task_list}\" in \"{md_file}\" '\n f'(line {j}). Please check your readme carefully.')\n\n result = Result(task=task, dataset=dataset, metrics=metrics)\n if model.results is None:\n model.results = result\n else:\n model.results.data.append(result)\n\n # check weights\n if line[download_idx].find('](') >= 0:\n if line[download_idx].find('model](') >= 0:\n left = line[download_idx].index('model](') + 7\n else:\n left = line[download_idx].index('ckpt](') + 6\n right = line[download_idx].index(')', left)\n model.weights = line[download_idx][left:right]\n j += 1\n i = j\n\n else:\n i += 1\n\n collection = modelindex_to_dict(collection)\n models = [modelindex_to_dict(m) for n, m in model_list.items()]\n assert len(models) > 0, f\"'no model is found in {md_file}'\"\n result = {'Collections': [collection], 'Models': models}\n yml_file = md_file.replace('README.md', 'metafile.yml')\n is_different = dump_yaml_and_check_difference(result, yml_file)\n\n return is_different\n\n\ndef update_model_index():\n \"\"\"Update model-index.yml according to model .md files.\n\n Returns:\n Bool: If the updated model-index.yml is different from the original.\n \"\"\"\n configs_dir = osp.join(MMagic_ROOT, 'configs')\n yml_files = glob.glob(osp.join(configs_dir, '**', '*.yml'), recursive=True)\n yml_files.sort()\n\n model_index = {\n 'Import': [\n osp.relpath(yml_file, MMagic_ROOT).replace(\n '\\\\', '/') # force using / as path separators\n for yml_file in yml_files\n ]\n }\n model_index_file = osp.join(MMagic_ROOT, 'model-index.yml')\n is_different = dump_yaml_and_check_difference(model_index,\n model_index_file)\n\n return is_different\n\n\nif __name__ == '__main__':\n if len(sys.argv) <= 1:\n configs_root = osp.join(MMagic_ROOT, 'configs')\n file_list = glob.glob(\n osp.join(configs_root, '**', '*README.md'), recursive=True)\n file_list.sort()\n else:\n file_list = [\n fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md'\n ]\n\n if not file_list:\n sys.exit(0)\n\n file_modified = False\n file_list = file_list\n pbar = tqdm.tqdm(range(len(file_list)), initial=0, dynamic_ncols=True)\n for fn in file_list:\n file_modified |= parse_md(fn)\n pbar.update(1)\n pbar.set_description(f'processing {fn}')\n\n file_modified |= update_model_index()\n\n sys.exit(1 if file_modified else 0)\n" }, { "path": ".dev_scripts/update_ut.py", "content": "import os\nimport os.path as osp\nfrom argparse import ArgumentParser\nfrom fnmatch import fnmatch\nfrom glob import glob\n\nfrom tqdm import tqdm\n\nparser = ArgumentParser()\nparser.add_argument('--src', type=str, default='mmagic')\nparser.add_argument('--dst', type=str, default='tests')\nparser.add_argument(\n '--exclude',\n nargs='+',\n default=[\n 'mmagic/.mim', 'mmagic/registry.py', 'mmagic/version.py',\n '__pycache__', '__init__', '**/__init__.py', '**/stylegan3_ops/*',\n '**/conv2d_gradfix.py', '**/grid_sample_gradfix.py', '**/misc.py',\n '**/upfirdn2d.py', '**/all_gather_layer.py', '**/typing.py'\n ])\nargs = parser.parse_args()\n\n\ndef check_exclude(fn):\n for pattern in args.exclude:\n if fnmatch(fn, pattern):\n return True\n return False\n\n\ndef update_ut():\n\n target_ut = []\n missing_ut = []\n blank_ut = []\n\n file_list = glob('mmagic/**/*.py', recursive=True)\n\n for f in tqdm(file_list):\n if check_exclude(f):\n continue\n\n if osp.splitext(osp.basename(f))[0] != '__init__':\n\n dirname = osp.dirname(f)\n dirname = dirname.replace('__', '')\n dirname = dirname.replace('mmagic', 'tests')\n dirname = dirname.replace('/', '/test_')\n os.makedirs(dirname, exist_ok=True)\n\n basename = osp.basename(f)\n basename = 'test_' + basename\n\n dst_path = osp.join(dirname, basename)\n target_ut.append(dst_path)\n if not osp.exists(dst_path):\n missing_ut.append(dst_path)\n fp = open(dst_path, 'a')\n fp.close()\n else:\n text_lines = open(dst_path, 'r').readlines()\n if len(text_lines) <= 3:\n blank_ut.append(dst_path)\n\n existing_ut = glob('tests/test_*/**/*.py', recursive=True)\n additional_ut = list(set(existing_ut) - set(target_ut))\n\n if len(additional_ut) > 0:\n print('Additional UT:')\n for f in additional_ut:\n print(f)\n if len(missing_ut) > 0:\n print('Missing UT:')\n for f in missing_ut:\n print(f)\n if len(blank_ut) > 0:\n print('Blank UT:')\n for f in blank_ut:\n print(f)\n\n\nif __name__ == '__main__':\n update_ut()\n" }, { "path": ".dev_scripts/utils/__init__.py", "content": "from .job_util import (filter_jobs, get_info_from_id, parse_job_list,\n parse_job_list_from_file)\nfrom .modelindex import (collate_metrics, dump_yaml_and_check_difference,\n found_table, modelindex_to_dict)\n\n__all__ = [\n 'modelindex_to_dict', 'found_table', 'dump_yaml_and_check_difference',\n 'collate_metrics', 'parse_job_list', 'parse_job_list_from_file',\n 'get_info_from_id', 'filter_jobs'\n]\n" }, { "path": ".dev_scripts/utils/job_util.py", "content": "import os\nimport os.path as osp\nfrom typing import Tuple\n\nfrom rich import print as pprint\nfrom rich.table import Table\n\n\ndef parse_job_list(job_list) -> Tuple[list, list]:\n \"\"\"Parse task name and job id from list. All elements in `job_list` must.\n\n be formatted as `JOBID @ JOBNAME`.\n\n Args:\n job_list (list[str]): Job list.\n\n Returns:\n Tuple[list, list]: Job ID list and Job name list.\n \"\"\"\n assert all([\n ' @ ' in job for job in job_list\n ]), ('Each line of job list must be formatted like \\'JOBID @ JOBNAME\\'.')\n job_id_list, job_name_list = [], []\n for job_info in job_list:\n job_id, job_name = job_info.split(' @ ')\n job_id_list.append(job_id)\n job_name_list.append(job_name)\n return job_id_list, job_name_list\n\n\ndef parse_job_list_from_file(job_list_file: str) -> Tuple[list, list]:\n \"\"\"Parse job list from file and return a tuple contains list of job id and\n job name.\n\n Args:\n job_list_file (str): The path to the file list.\n\n Returns:\n Tuple[list, list]: A tuple contains list of job id and job name.\n \"\"\"\n if not osp.exists(job_list_file):\n return False\n with open(job_list_file, 'r') as file:\n job_list = [job.strip() for job in file.readlines()]\n return parse_job_list(job_list)\n\n\ndef get_info_from_id(job_id: str) -> dict:\n \"\"\"Get the basic information of a job id with `swatch examine` command.\n\n Args:\n job_id (str): The ID of the job.\n\n Returns:\n dict: A dict contains information of the corresponding job id.\n \"\"\"\n # NOTE: do not have exception handling here\n info_stream = os.popen(f'swatch examine {job_id}')\n info_str = [line.strip() for line in info_stream.readlines()]\n status_info = info_str[2].split()\n try:\n status_dict = {\n 'JobID': status_info[0],\n 'JobName': status_info[1],\n 'Partition': status_info[2],\n 'NNodes': status_info[3],\n 'AllocCPUS': status_info[4],\n 'State': status_info[5]\n }\n except Exception:\n print(job_id)\n print(info_str)\n return status_dict\n\n\ndef filter_jobs(job_id_list: list,\n job_name_list: list,\n select: list = ['FAILED'],\n show_table: bool = False,\n table_name: str = 'Filter Results') -> Tuple[list, list]:\n \"\"\"Filter the job which status not belong to :attr:`select`.\n\n Args:\n job_id_list (list): The list of job ids.\n job_name_list (list): The list of job names.\n select (list, optional): Which kind of jobs will be selected.\n Defaults to ['FAILED'].\n show_table (bool, optional): Whether display the filter result in a\n table. Defaults to False.\n table_name (str, optional): The name of the table. Defaults to\n 'Filter Results'.\n\n Returns:\n Tuple[list]: A tuple contains selected job ids and job names.\n \"\"\"\n # if ignore is not passed, return the original id list and name list\n if not select:\n return job_id_list, job_name_list\n filtered_id_list, filtered_name_list = [], []\n job_info_list = []\n for id_, name_ in zip(job_id_list, job_name_list):\n info = get_info_from_id(id_)\n job_info_list.append(info)\n if info['State'] in select:\n filtered_id_list.append(id_)\n filtered_name_list.append(name_)\n\n if show_table:\n filter_table = Table(title=table_name)\n for field in ['Name', 'ID', 'State', 'Is Selected']:\n filter_table.add_column(field)\n for id_, name_, info_ in zip(job_id_list, job_name_list,\n job_info_list):\n selected = '[green]True' \\\n if info_['State'] in select else '[red]False'\n filter_table.add_row(name_, id_, info_['State'], selected)\n pprint(filter_table)\n return filtered_id_list, filtered_name_list\n" }, { "path": ".dev_scripts/utils/modelindex.py", "content": "import os.path as osp\n\nimport mmengine\nfrom modelindex.models.Collection import Collection\nfrom modelindex.models.Model import Model\n\n\ndef dump_yaml_and_check_difference(obj, file):\n \"\"\"Dump object to a yaml file, and check if the file content is different\n from the original.\n\n Args:\n obj (any): The python object to be dumped.\n file (str): YAML filename to dump the object to.\n Returns:\n Bool: If the target YAML file is different from the original.\n \"\"\"\n\n str_dump = mmengine.dump(\n obj, None, file_format='yaml', sort_keys=True,\n line_break='\\n') # force use LF\n\n if osp.isfile(file):\n file_exists = True\n # print(f' exist {file}')\n with open(file, 'r', encoding='utf-8') as f:\n str_orig = f.read()\n else:\n file_exists = False\n str_orig = None\n\n if file_exists and str_orig == str_dump:\n is_different = False\n else:\n is_different = True\n print(f' update {file}')\n with open(file, 'w', encoding='utf-8') as f:\n f.write(str_dump)\n\n return is_different\n\n\ndef collate_metrics(keys):\n \"\"\"Collect metrics from the first row of the table.\n\n Args:\n keys (List): Elements in the first row of the table.\n\n Returns:\n dict: A dict of metrics.\n \"\"\"\n used_metrics = dict()\n for idx, key in enumerate(keys):\n if key in ['Model', 'Dataset', 'Training Resources', 'Download']:\n continue\n used_metrics[key] = idx\n return used_metrics\n\n\ndef found_table(lines, i):\n if i + 1 >= len(lines):\n return False\n if i - 2 < 0 or 'SKIP THIS TABLE' in lines[i - 2]:\n return False\n if lines[i][0] != '|':\n return False\n\n for c in ['| :', '|:', '|-']:\n if c in lines[i + 1]:\n return True\n return False\n\n\ndef modelindex_to_dict(model):\n if isinstance(model, Collection):\n result = model.data\n if model.metadata is not None:\n result['Metadata'] = model.metadata.data\n elif isinstance(model, Model):\n result = model.data\n if model.metadata is not None:\n result['Metadata'] = model.metadata.data\n if model.results is not None:\n results_list = []\n for r in model.results:\n results_list.append(r.data)\n result['Results'] = results_list\n return result\n" }, { "path": ".github/CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n- Using welcoming and inclusive language\n- Being respectful of differing viewpoints and experiences\n- Gracefully accepting constructive criticism\n- Focusing on what is best for the community\n- Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n- The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n- Trolling, insulting/derogatory comments, and personal or political attacks\n- Public or private harassment\n- Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n- Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at chenkaidev@gmail.com. All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n\n[homepage]: https://www.contributor-covenant.org\n" }, { "path": ".github/CONTRIBUTING.md", "content": "# Contributing to MMagic\n\nAll kinds of contributions are welcome, including but not limited to the following.\n\n- Fix typo or bugs\n- Add documentation or translate the documentation into other languages\n- Add new features and components\n\n## Workflow\n\n1. fork and pull the latest MMagic repository (MMagic)\n2. checkout a new branch (do not use master branch for PRs)\n3. commit your changes\n4. create a PR\n\n```{note}\nIf you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.\n```\n\n## Code style\n\n### Python\n\nWe adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.\n\nWe use the following tools for linting and formatting:\n\n- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.\n- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.\n- [yapf](https://github.com/google/yapf): A formatter for Python files.\n- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.\n- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.\n- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.\n\nStyle configurations can be found in [setup.cfg](/setup.cfg).\n\nWe use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,\nfixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.\nThe config for a pre-commit hook is stored in [.pre-commit-config](/.pre-commit-config.yaml).\n\nAfter you clone the repository, you will need to install initialize pre-commit hook.\n\n```shell\npip install -U pre-commit\n```\n\nFrom the repository folder\n\n```shell\npre-commit install\n```\n\nAfter this on every commit check code linters and formatter will be enforced.\n\n```{important}\nBefore you create a PR, make sure that your code lints and is formatted by yapf.\n```\n\n### C++ and CUDA\n\nWe follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).\n" }, { "path": ".github/ISSUE_TEMPLATE/1-bug-report.yml", "content": "name: \"🐞 Bug report\"\ndescription: \"Create a report to help us reproduce and fix the bug\"\nlabels: \"kind/bug,status/unconfirmed\"\ntitle: \"[Bug] \"\n\nbody:\n - type: markdown\n attributes:\n value: |\n If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [here](https://github.com/open-mmlab/mmagic/pulls)!\n If this issue is about installing MMCV, please file an issue at [MMCV](https://github.com/open-mmlab/mmcv/issues/new/choose).\n If you need our help, please fill in as much of the following form as you're able to.\n\n **The less clear the description, the longer it will take to solve it.**\n\n - type: checkboxes\n attributes:\n label: Prerequisite\n description: Please check the following items before creating a new issue.\n options:\n - label: I have searched [Issues](https://github.com/open-mmlab/mmagic/issues) and [Discussions](https://github.com/open-mmlab/mmagic/discussions) but cannot get the expected help.\n required: true\n - label: I have read the [FAQ documentation](https://mmagic.readthedocs.io/en/latest/faq.html) but cannot get the expected help.\n required: true\n - label: The bug has not been fixed in the [latest version (main)](https://github.com/open-mmlab/mmagic) or [latest version (0.x)](https://github.com/open-mmlab/mmagic/tree/0.x).\n required: true\n\n - type: dropdown\n id: task\n attributes:\n label: Task\n description: The problem arises when\n options:\n - I'm using the official example scripts/configs for the officially supported tasks/models/datasets.\n - I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.\n validations:\n required: true\n\n - type: dropdown\n id: branch\n attributes:\n label: Branch\n description: The problem arises when I'm working on\n options:\n - main branch https://github.com/open-mmlab/mmagic\n - 0.x branch https://github.com/open-mmlab/mmagic/tree/0.x\n validations:\n required: true\n\n\n - type: textarea\n attributes:\n label: Environment\n description: |\n Please run `python mmagic/utils/collect_env.py` to collect necessary environment information and copy-paste it here.\n You may add additional information that may be helpful for locating the problem, such as\n - How you installed PyTorch \\[e.g., pip, conda, source\\]\n - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Reproduces the problem - code sample\n description: |\n Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.\n placeholder: |\n ```python\n # Sample code to reproduce the problem\n ```\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Reproduces the problem - command or script\n description: |\n What command or script did you run?\n placeholder: |\n ```shell\n The command or script you run.\n ```\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Reproduces the problem - error message\n description: |\n Please provide the error message or logs you got, with the full traceback.\n placeholder: |\n ```\n The error message or logs you got, with the full traceback.\n ```\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Additional information\n description: Tell us anything else you think we should know.\n placeholder: |\n 1. What's your expected result?\n 2. What dataset did you use?\n 3. What do you think might be the reason?\n" }, { "path": ".github/ISSUE_TEMPLATE/2-feature-request.yml", "content": "name: 🚀 Feature request\ndescription: Suggest an idea for this project\nlabels: \"kind/enhancement,status/unconfirmed\"\ntitle: \"[Feature] \"\n\nbody:\n - type: markdown\n attributes:\n value: |\n We strongly appreciate you creating a PR to implement this feature [here](https://github.com/open-mmlab/mmagic/pulls)!\n If you need our help, please fill in as much of the following form as you're able to.\n\n **The less clear the description, the longer it will take to solve it.**\n\n - type: textarea\n attributes:\n label: What's the feature?\n description: |\n Tell us more about the feature and how this feature can help.\n placeholder: |\n E.g., It is inconvenient when \\[....\\].\n This feature can \\[....\\].\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Any other context?\n description: |\n Have you considered any alternative solutions or features? If so, what are they?\n Also, feel free to add any other context or screenshots about the feature request here.\n" }, { "path": ".github/ISSUE_TEMPLATE/3-new-model.yml", "content": "name: \"\\U0001F31F New model/dataset/scheduler addition\"\ndescription: Submit a proposal/request to implement a new model / dataset / scheduler\nlabels: \"kind/feature,status/unconfirmed\"\ntitle: \"[New Models] \"\n\n\nbody:\n - type: textarea\n id: description-request\n validations:\n required: true\n attributes:\n label: Model/Dataset/Scheduler description\n description: |\n Put any and all important information relative to the model/dataset/scheduler\n\n - type: checkboxes\n attributes:\n label: Open source status\n description: |\n Please provide the open-source status, which would be very helpful\n options:\n - label: \"The model implementation is available\"\n - label: \"The model weights are available.\"\n\n - type: textarea\n id: additional-info\n attributes:\n label: Provide useful links for the implementation\n description: |\n Please provide information regarding the implementation, the weights, and the authors.\n Please mention the authors by @gh-username if you're aware of their usernames.\n" }, { "path": ".github/ISSUE_TEMPLATE/4-documentation.yml", "content": "name: 📚 Documentation\ndescription: Report an issue related to the documentation.\nlabels: \"kind/doc,status/unconfirmed\"\ntitle: \"[Docs] \"\n\nbody:\n- type: dropdown\n id: branch\n attributes:\n label: Branch\n description: This issue is related to the\n options:\n - main branch https://mmagic.readthedocs.io/en/latest/\n - 0.x branch https://mmagic.readthedocs.io/en/0.x/\n validations:\n required: true\n\n- type: textarea\n attributes:\n label: 📚 The doc issue\n description: >\n A clear and concise description the issue.\n validations:\n required: true\n\n- type: textarea\n attributes:\n label: Suggest a potential alternative/fix\n description: >\n Tell us how we could improve the documentation in this regard.\n- type: markdown\n attributes:\n value: >\n Thanks for contributing 🎉!\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "blank_issues_enabled: false\n\ncontact_links:\n - name: 💬 Forum\n url: https://github.com/open-mmlab/mmagic/discussions\n about: Ask general usage questions and discuss with other MMagic community members\n - name: 🌐 Explore OpenMMLab\n url: https://openmmlab.com/\n about: Get to know more about OpenMMLab\n" }, { "path": ".github/pull_request_template.md", "content": "Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.\n\n## Motivation\n\nPlease describe the motivation of this PR and the goal you want to achieve through this PR.\n\n## Modification\n\nPlease briefly describe what modification is made in this PR.\n\n## BC-breaking (Optional)\n\nDoes the modification introduce changes that break the backward-compatibility of the downstream repositories?\nIf so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.\n\n## Use cases (Optional)\n\nIf this PR introduces a new feature, it is better to list some use cases here, and update the documentation.\n\n## Checklist\n\nSubmitting this pull request means that,\n\n**Before PR**:\n\n- [x] I have read and followed the workflow indicated in the [CONTRIBUTING.md](https://github.com/open-mmlab/mmagic/blob/main/.github/CONTRIBUTING.md) to create this PR.\n- [x] Pre-commit or linting tools indicated in [CONTRIBUTING.md](https://github.com/open-mmlab/mmagic/blob/main/.github/CONTRIBUTING.md) are used to fix the potential lint issues.\n- [x] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.\n- [x] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.\n- [x] The documentation has been modified accordingly, including docstring or example tutorials.\n\n**After PR**:\n\n- [x] If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.\n- [x] CLA has been signed and all committers have signed the CLA in this PR.\n" }, { "path": ".github/workflows/lint.yml", "content": "name: lint\n\non: [push, pull_request]\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n lint:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v2\n - name: Set up Python 3.7\n uses: actions/setup-python@v2\n with:\n python-version: 3.7\n - name: Install pre-commit hook\n run: |\n pip install pre-commit\n pre-commit install\n - name: Linting\n run: pre-commit run --all-files\n - name: Check docstring coverage\n run: |\n pip install interrogate\n interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex \"__repr__\" --fail-under 90 mmagic\n" }, { "path": ".github/workflows/merge_stage_test.yml", "content": "name: merge_stage_test\n\non:\n push:\n paths-ignore:\n - 'README.md'\n - 'README_zh-CN.md'\n - 'docs/**'\n - '.owners.yml'\n - '.github/ISSUE_TEMPLATE/**'\n - '.github/*.md'\n - '.dev_scripts/**'\n - '.circleci/**'\n - 'configs/**'\n - 'projects/**'\n\n branches:\n - dev-1.x\n - test-1.x\n - main\n - test-branch\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n build_cpu_py:\n runs-on: ubuntu-22.04\n strategy:\n matrix:\n python-version: [3.8, 3.9]\n torch: [1.8.1]\n include:\n - torch: 1.8.1\n torchvision: 0.9.1\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Install PyTorch\n run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html\n - name: Install MMEngine\n run: pip install git+https://github.com/open-mmlab/mmengine.git@main\n - name: Install MMCV\n run: |\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n - name: Install other dependencies\n run: |\n pip install -r requirements/tests.txt\n - name: Build and install\n run: rm -rf .eggs && pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n coverage run --branch --source mmagic -m pytest tests/\n coverage xml\n coverage report -m\n\n build_cpu_pt:\n runs-on: ubuntu-22.04\n strategy:\n matrix:\n python-version: [3.7]\n torch: [1.8.1, 1.9.1, 1.10.1, 1.11.0, 1.12.1, 1.13.0]\n include:\n - torch: 1.8.1\n torchvision: 0.9.1\n - torch: 1.9.1\n torchvision: 0.10.1\n - torch: 1.10.1\n torchvision: 0.11.2\n - torch: 1.11.0\n torchvision: 0.12.0\n - torch: 1.12.1\n torchvision: 0.13.1\n - torch: 1.13.0\n torchvision: 0.14.0\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Install PyTorch\n run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html\n - name: Install MMEngine\n run: pip install git+https://github.com/open-mmlab/mmengine.git@main\n - name: Install MMCV\n run: |\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n - name: Install other dependencies\n run: |\n pip install -r requirements/tests.txt\n - name: Build and install\n run: rm -rf .eggs && pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n coverage run --branch --source mmagic -m pytest tests/\n coverage xml --omit=\"**/stylegan3_ops/*,**/conv2d_gradfix.py,**/grid_sample_gradfix.py,**/misc.py,**/upfirdn2d.py,**all_gather_layer.py\"\n coverage report -m\n # Only upload coverage report for python3.7 && pytorch1.8.1 cpu\n - name: Upload coverage to Codecov\n if: ${{matrix.torch == '1.8.1' && matrix.python-version == '3.7'}}\n uses: codecov/codecov-action@v1.0.14\n with:\n file: ./coverage.xml\n flags: unittests\n env_vars: OS,PYTHON\n name: codecov-umbrella\n fail_ci_if_error: false\n\n build_cu102:\n runs-on: ubuntu-22.04\n container:\n image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel\n strategy:\n matrix:\n python-version: [3.7]\n include:\n - torch: 1.8.1\n cuda: 10.2\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Fetch GPG keys\n run: |\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2204/x86_64/7fa2af80.pub\n - name: Install system dependencies\n run: |\n apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6\n - name: Install PyTorch\n run: pip install torch==1.8.1+cpu torchvision==0.9.1+cpu -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n - name: Install mmagic dependencies\n run: |\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n pip install -r requirements/tests.txt\n - name: Build and install\n run: |\n pip install -e .\n\n build_cu116:\n runs-on: ubuntu-22.04\n container:\n image: pytorch/pytorch:1.13.0-cuda11.6-cudnn8-devel\n strategy:\n matrix:\n python-version: [3.7]\n include:\n - torch: 1.8.1\n cuda: 10.2\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Fetch GPG keys\n run: |\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2204/x86_64/7fa2af80.pub\n - name: Install system dependencies\n run: |\n apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6\n - name: Install PyTorch\n run: pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu\n - name: Install mmagic dependencies\n run: |\n pip install git+https://github.com/open-mmlab/mmengine.git@main\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n pip install -r requirements/tests.txt\n - name: Build and install\n run: |\n pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n coverage run --branch --source mmagic -m pytest tests/\n coverage xml --omit=\"**/stylegan3_ops/*,**/conv2d_gradfix.py,**/grid_sample_gradfix.py,**/misc.py,**/upfirdn2d.py,**all_gather_layer.py\"\n coverage report -m\n\n build_windows:\n runs-on: windows-2022\n strategy:\n matrix:\n python: [3.7]\n platform: [cpu, cu111]\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: python -m pip install pip --upgrade && pip install wheel\n - name: Install lmdb\n run: python -m pip install lmdb\n - name: Install PyTorch\n run: python -m pip install torch==1.8.1+${{matrix.platform}} torchvision==0.9.1+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n - name: Install mmagic dependencies\n run: |\n python -m pip install git+https://github.com/open-mmlab/mmengine.git@main\n python -m pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n python -m pip install -r requirements/tests.txt\n - name: Build and install\n run: |\n python -m pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n pytest tests/\n" }, { "path": ".github/workflows/pr_stage_test.yml", "content": "name: pr_stage_test\n\non:\n pull_request:\n paths-ignore:\n - 'README.md'\n - 'README_zh-CN.md'\n - '.owners.yml'\n - '.github/ISSUE_TEMPLATE/**'\n - '.github/*.md'\n - 'docs/**'\n - 'projects/**'\n - '.dev_scripts/**'\n - '.circleci/**'\n - 'configs/**'\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n build_cpu:\n runs-on: ubuntu-22.04\n strategy:\n matrix:\n python-version: [3.8]\n include:\n - torch: 2.0.1\n torchvision: 0.15.2\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Install PyTorch\n run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html\n - name: Install MMEngine\n run: pip install git+https://github.com/open-mmlab/mmengine.git@main\n - name: Install MMCV\n run: |\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n - name: Install other dependencies\n run: |\n pip install -r requirements/tests.txt\n - name: Build and install\n run: rm -rf .eggs && pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n coverage run --branch --source mmagic -m pytest tests/\n coverage xml --omit=\"**/stylegan3_ops/*,**/conv2d_gradfix.py,**/grid_sample_gradfix.py,**/misc.py,**/upfirdn2d.py,**all_gather_layer.py\"\n coverage report -m\n # Upload coverage report for python3.7 && pytorch1.8.1 cpu\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v1.0.14\n with:\n file: ./coverage.xml\n flags: unittests\n env_vars: OS,PYTHON\n name: codecov-umbrella\n fail_ci_if_error: false\n # - name: Setup tmate session\n # if: ${{ failure() }}\n # uses: mxschmitt/action-tmate@v3\n\n build_cu118:\n runs-on: ubuntu-22.04\n container:\n image: pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel\n strategy:\n matrix:\n python-version: [3.8]\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Fetch GPG keys\n run: |\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub\n apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2204/x86_64/7fa2af80.pub\n - name: Install system dependencies\n run: |\n apt-get update\n DEBIAN_FRONTEND=noninteractive apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libxrender-dev\n - name: Install PyTorch\n run: pip install torch==2.0.1+cpu torchvision==0.15.2+cpu -f https://download.pytorch.org/whl/torch_stable.html\n - name: Install mmagic dependencies\n run: |\n pip install git+https://github.com/open-mmlab/mmengine.git@main\n pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n pip install -r requirements/tests.txt\n - name: Build and install\n run: |\n pip install -e .\n # - name: Setup tmate session\n # if: ${{ failure() }}\n # uses: mxschmitt/action-tmate@v3\n\n build_windows:\n runs-on: windows-2022\n strategy:\n matrix:\n python-version: [3.8]\n platform: [cpu, cu118]\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: python -m pip install pip --upgrade && pip install wheel\n - name: Install lmdb\n run: python -m pip install lmdb\n - name: Install PyTorch\n run: python -m pip install torch==2.0.1+${{matrix.platform}} torchvision==0.15.2+${{matrix.platform}} -f https://download.pytorch.org/whl/torch_stable.html\n - name: Install mmagic dependencies\n run: |\n python -m pip install git+https://github.com/open-mmlab/mmengine.git@main\n python -m pip install -U openmim\n mim install 'mmcv >= 2.0.0'\n python -m pip install -r requirements/tests.txt\n - name: Build and install\n run: |\n python -m pip install -e .\n - name: Run unittests and generate coverage report\n run: |\n pytest tests/\n # - name: Setup tmate session\n # if: ${{ failure() }}\n # uses: mxschmitt/action-tmate@v3\n" }, { "path": ".github/workflows/publish-to-pypi.yml", "content": "name: deploy\n\non: push\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n build-n-publish:\n runs-on: ubuntu-latest\n if: startsWith(github.event.ref, 'refs/tags')\n steps:\n - uses: actions/checkout@v2\n - name: Set up Python 3.7\n uses: actions/setup-python@v1\n with:\n python-version: 3.7\n - name: Build MMagic\n run: |\n pip install torch==1.8.1+cpu torchvision==0.9.1+cpu -f https://download.pytorch.org/whl/torch_stable.html\n pip install wheel\n python setup.py sdist bdist_wheel\n - name: Publish distribution to PyPI\n run: |\n pip install twine\n twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}\n" }, { "path": ".github/workflows/test_mim.yml", "content": "name: test-mim\n\non:\n push:\n paths:\n - 'model-index.yml'\n - 'configs/**'\n\n pull_request:\n paths:\n - 'model-index.yml'\n - 'configs/**'\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n build_cpu:\n runs-on: ubuntu-22.04\n strategy:\n matrix:\n python-version: [3.7]\n torch: [1.8.0]\n include:\n - torch: 1.8.0\n torch_version: torch1.8\n torchvision: 0.9.0\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Upgrade pip\n run: pip install pip --upgrade && pip install wheel\n - name: Install PyTorch\n run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html\n - name: Install openmim\n run: pip install openmim\n - name: Build and install\n run: rm -rf .eggs && mim install -e .\n - name: test commands of mim\n run: mim search mmagic\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n**/*.pyc\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\ntests/data/out\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/en/_build/\ndocs/en/_tmp/\ndocs/zh_cn/_build/\ndocs/zh_cn/_tmp/\nrequirements/src/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# custom\n.vscode\n.idea\n*.pkl\n*.pkl.json\n*.log.json\nwork_dirs/\nwork_dirs\nconfigs_ceph/\nconfigs_ceph_*/\n/data/\n/data\nmmagic/.mim\ndemo/*.png\n*.csv\nout/*.png\ntests/data/out\nresources/\n\n# Pytorch\n*.pth\n\n# onnx and tensorrt\n*.onnx\n*.trt\n\n# local history\n.history/**\n\n# Pytorch Server\n*.mar\n\n# MacOS\n.DS_Store\n\n# Slurm\nbatchscript-*\n*.out\n\n*.png\n*.jpg\n*.zip\nwork_dir\nwork_dir/\n" }, { "path": ".owners.yml", "content": "# assign issues to owners automatically\nassign:\n issues: enabled # or disabled\n pull_requests: enabled # or disabled\n # assign strategy, both issues and pull requests follow the same strategy\n strategy:\n # random\n # round-robin\n daily-shift-based\n scedule:\n '*/1 * * * *'\n # assignees\n assignees:\n - zengyh1900\n - LeoXing1996\n - liuwenran\n - LeoXing1996\n - liuwenran\n - zengyh1900\n - zengyh1900\n" }, { "path": ".pre-commit-config.yaml", "content": "exclude: ^tests/data/\nrepos:\n - repo: https://github.com/PyCQA/flake8\n rev: 4.0.1\n hooks:\n - id: flake8\n - repo: https://github.com/zhouzaida/isort\n rev: 5.12.1\n hooks:\n - id: isort\n - repo: https://github.com/pre-commit/mirrors-yapf\n rev: v0.30.0\n hooks:\n - id: yapf\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v3.1.0\n hooks:\n - id: trailing-whitespace\n exclude: ^dicts/\n - id: check-yaml\n exclude: ^projects/animated_drawings/configs/retarget/\n - id: end-of-file-fixer\n exclude: ^dicts/\n - id: requirements-txt-fixer\n - id: double-quote-string-fixer\n - id: check-merge-conflict\n - id: fix-encoding-pragma\n args: [\"--remove\"]\n - id: mixed-line-ending\n args: [\"--fix=lf\"]\n - repo: https://github.com/codespell-project/codespell\n rev: v2.1.0\n hooks:\n - id: codespell\n args: [\"--skip\", \"*.ipynb\", \"-L\", \"formating,theis,te,nd,thre,Gool,gool,lod,patten,confectionary\"]\n - repo: https://github.com/executablebooks/mdformat\n rev: 0.7.9\n hooks:\n - id: mdformat\n args: [\"--number\", \"--table-width\", \"200\"]\n # language_version: python3.7\n additional_dependencies:\n - mdformat-openmmlab\n - mdformat_frontmatter\n - linkify-it-py\n - repo: local\n hooks:\n - id: update-model-index\n name: update-model-index\n description: Collect model information and update model-index.yml\n entry: .dev_scripts/update_model_index.py\n additional_dependencies: [mmengine, modelindex, tqdm, rich]\n language: python\n files: ^configs/.*\\.md$\n require_serial: true\n - repo: https://github.com/myint/docformatter\n rev: v1.3.1\n hooks:\n - id: docformatter\n args: [\"--in-place\", \"--wrap-descriptions\", \"79\"]\n - repo: https://github.com/open-mmlab/pre-commit-hooks\n rev: v0.4.0 # Use the ref you want to point at\n hooks:\n - id: check-algo-readme\n - id: check-copyright\n args: [\"demo\", \"mmagic\", \"tests\", \"tools\"]\n - id: remove-improper-eol-in-cn-docs\n - repo: local\n hooks:\n - id: update-model-zoo\n name: update-model-zoo\n description: update model-zoo.yml\n entry: docs/en/.dev_scripts/update_model_zoo.py\n additional_dependencies: [mmengine, modelindex, tqdm, rich]\n language: python\n files: ^configs/.*\\.md$\n require_serial: true\n" }, { "path": ".pylintrc", "content": "[MASTER]\n\n# A comma-separated list of package or module names from where C extensions may\n# be loaded. Extensions are loading into the active Python interpreter and may\n# run arbitrary code.\nextension-pkg-whitelist=\n\n# Specify a score threshold to be exceeded before program exits with error.\nfail-under=10.0\n\n# Add files or directories to the blacklist. They should be base names, not\n# paths.\nignore=CVS,configs\n\n# Add files or directories matching the regex patterns to the blacklist. The\n# regex matches against base names, not paths.\nignore-patterns=\n\n# Python code to execute, usually for sys.path manipulation such as\n# pygtk.require().\n#init-hook=\n\n# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the\n# number of processors available to use.\njobs=1\n\n# Control the amount of potential inferred values when inferring a single\n# object. This can help the performance when dealing with large functions or\n# complex, nested conditions.\nlimit-inference-results=100\n\n# List of plugins (as comma separated values of python module names) to load,\n# usually to register additional checkers.\nload-plugins=\n\n# Pickle collected data for later comparisons.\npersistent=yes\n\n# When enabled, pylint would attempt to guess common misconfiguration and emit\n# user-friendly hints instead of false-positive error messages.\nsuggestion-mode=yes\n\n# Allow loading of arbitrary C extensions. Extensions are imported into the\n# active Python interpreter and may run arbitrary code.\nunsafe-load-any-extension=no\n\n\n[MESSAGES CONTROL]\n\n# Only show warnings with the listed confidence levels. Leave empty to show\n# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.\nconfidence=\n\n# Disable the message, report, category or checker with the given id(s). You\n# can either give multiple identifiers separated by comma (,) or put this\n# option multiple times (only on the command line, not in the configuration\n# file where it should appear only once). You can also use \"--disable=all\" to\n# disable everything first and then reenable specific checks. For example, if\n# you want to run only the similarities checker, you can use \"--disable=all\n# --enable=similarities\". If you want to run only the classes checker, but have\n# no Warning level messages displayed, use \"--disable=all --enable=classes\n# --disable=W\".\ndisable=print-statement,\n parameter-unpacking,\n unpacking-in-except,\n old-raise-syntax,\n backtick,\n long-suffix,\n old-ne-operator,\n old-octal-literal,\n import-star-module-level,\n non-ascii-bytes-literal,\n raw-checker-failed,\n bad-inline-option,\n locally-disabled,\n file-ignored,\n suppressed-message,\n useless-suppression,\n deprecated-pragma,\n use-symbolic-message-instead,\n apply-builtin,\n basestring-builtin,\n buffer-builtin,\n cmp-builtin,\n coerce-builtin,\n execfile-builtin,\n file-builtin,\n long-builtin,\n raw_input-builtin,\n reduce-builtin,\n standarderror-builtin,\n unicode-builtin,\n xrange-builtin,\n coerce-method,\n delslice-method,\n getslice-method,\n setslice-method,\n no-absolute-import,\n old-division,\n dict-iter-method,\n dict-view-method,\n next-method-called,\n metaclass-assignment,\n indexing-exception,\n raising-string,\n reload-builtin,\n oct-method,\n hex-method,\n nonzero-method,\n cmp-method,\n input-builtin,\n round-builtin,\n intern-builtin,\n unichr-builtin,\n map-builtin-not-iterating,\n zip-builtin-not-iterating,\n range-builtin-not-iterating,\n filter-builtin-not-iterating,\n using-cmp-argument,\n eq-without-hash,\n div-method,\n idiv-method,\n rdiv-method,\n exception-message-attribute,\n invalid-str-codec,\n sys-max-int,\n bad-python3-import,\n deprecated-string-function,\n deprecated-str-translate-call,\n deprecated-itertools-function,\n deprecated-types-field,\n next-method-defined,\n dict-items-not-iterating,\n dict-keys-not-iterating,\n dict-values-not-iterating,\n deprecated-operator-function,\n deprecated-urllib-function,\n xreadlines-attribute,\n deprecated-sys-function,\n exception-escape,\n comprehension-escape,\n no-member,\n invalid-name,\n too-many-branches,\n wrong-import-order,\n too-many-arguments,\n missing-function-docstring,\n missing-module-docstring,\n too-many-locals,\n too-few-public-methods,\n abstract-method,\n broad-except,\n too-many-nested-blocks,\n too-many-instance-attributes,\n missing-class-docstring,\n duplicate-code,\n not-callable,\n protected-access,\n dangerous-default-value,\n no-name-in-module,\n logging-fstring-interpolation,\n super-init-not-called,\n redefined-builtin,\n attribute-defined-outside-init,\n arguments-differ,\n cyclic-import,\n bad-super-call,\n too-many-statements\n\n# Enable the message, report, category or checker with the given id(s). You can\n# either give multiple identifier separated by comma (,) or put this option\n# multiple time (only on the command line, not in the configuration file where\n# it should appear only once). See also the \"--disable\" option for examples.\nenable=c-extension-no-member\n\n\n[REPORTS]\n\n# Python expression which should return a score less than or equal to 10. You\n# have access to the variables 'error', 'warning', 'refactor', and 'convention'\n# which contain the number of messages in each category, as well as 'statement'\n# which is the total number of statements analyzed. This score is used by the\n# global evaluation report (RP0004).\nevaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)\n\n# Template used to display messages. This is a python new-style format string\n# used to format the message information. See doc for all details.\n#msg-template=\n\n# Set the output format. Available formats are text, parseable, colorized, json\n# and msvs (visual studio). You can also give a reporter class, e.g.\n# mypackage.mymodule.MyReporterClass.\noutput-format=text\n\n# Tells whether to display a full report or only the messages.\nreports=no\n\n# Activate the evaluation score.\nscore=yes\n\n\n[REFACTORING]\n\n# Maximum number of nested blocks for function / method body\nmax-nested-blocks=5\n\n# Complete name of functions that never returns. When checking for\n# inconsistent-return-statements if a never returning function is called then\n# it will be considered as an explicit return statement and no message will be\n# printed.\nnever-returning-functions=sys.exit\n\n\n[TYPECHECK]\n\n# List of decorators that produce context managers, such as\n# contextlib.contextmanager. Add to this list to register other decorators that\n# produce valid context managers.\ncontextmanager-decorators=contextlib.contextmanager\n\n# List of members which are set dynamically and missed by pylint inference\n# system, and so shouldn't trigger E1101 when accessed. Python regular\n# expressions are accepted.\ngenerated-members=\n\n# Tells whether missing members accessed in mixin class should be ignored. A\n# mixin class is detected if its name ends with \"mixin\" (case insensitive).\nignore-mixin-members=yes\n\n# Tells whether to warn about missing members when the owner of the attribute\n# is inferred to be None.\nignore-none=yes\n\n# This flag controls whether pylint should warn about no-member and similar\n# checks whenever an opaque object is returned when inferring. The inference\n# can return multiple potential results while evaluating a Python object, but\n# some branches might not be evaluated, which results in partial inference. In\n# that case, it might be useful to still emit no-member and other checks for\n# the rest of the inferred objects.\nignore-on-opaque-inference=yes\n\n# List of class names for which member attributes should not be checked (useful\n# for classes with dynamically set attributes). This supports the use of\n# qualified names.\nignored-classes=optparse.Values,thread._local,_thread._local\n\n# List of module names for which member attributes should not be checked\n# (useful for modules/projects where namespaces are manipulated during runtime\n# and thus existing member attributes cannot be deduced by static analysis). It\n# supports qualified module names, as well as Unix pattern matching.\nignored-modules=\n\n# Show a hint with possible names when a member name was not found. The aspect\n# of finding the hint is based on edit distance.\nmissing-member-hint=yes\n\n# The minimum edit distance a name should have in order to be considered a\n# similar match for a missing member name.\nmissing-member-hint-distance=1\n\n# The total number of similar names that should be taken in consideration when\n# showing a hint for a missing member.\nmissing-member-max-choices=1\n\n# List of decorators that change the signature of a decorated function.\nsignature-mutators=\n\n\n[SPELLING]\n\n# Limits count of emitted suggestions for spelling mistakes.\nmax-spelling-suggestions=4\n\n# Spelling dictionary name. Available dictionaries: none. To make it work,\n# install the python-enchant package.\nspelling-dict=\n\n# List of comma separated words that should not be checked.\nspelling-ignore-words=\n\n# A path to a file that contains the private dictionary; one word per line.\nspelling-private-dict-file=\n\n# Tells whether to store unknown words to the private dictionary (see the\n# --spelling-private-dict-file option) instead of raising a message.\nspelling-store-unknown-words=no\n\n\n[LOGGING]\n\n# The type of string formatting that logging methods do. `old` means using %\n# formatting, `new` is for `{}` formatting.\nlogging-format-style=old\n\n# Logging modules to check that the string format arguments are in logging\n# function parameter format.\nlogging-modules=logging\n\n\n[VARIABLES]\n\n# List of additional names supposed to be defined in builtins. Remember that\n# you should avoid defining new builtins when possible.\nadditional-builtins=\n\n# Tells whether unused global variables should be treated as a violation.\nallow-global-unused-variables=yes\n\n# List of strings which can identify a callback function by name. A callback\n# name must start or end with one of those strings.\ncallbacks=cb_,\n _cb\n\n# A regular expression matching the name of dummy variables (i.e. expected to\n# not be used).\ndummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_\n\n# Argument names that match this expression will be ignored. Default to name\n# with leading underscore.\nignored-argument-names=_.*|^ignored_|^unused_\n\n# Tells whether we should check for unused import in __init__ files.\ninit-import=no\n\n# List of qualified module names which can have objects that can redefine\n# builtins.\nredefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io\n\n\n[FORMAT]\n\n# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.\nexpected-line-ending-format=\n\n# Regexp for a line that is allowed to be longer than the limit.\nignore-long-lines=^\\s*(# )??$\n\n# Number of spaces of indent required inside a hanging or continued line.\nindent-after-paren=4\n\n# String used as indentation unit. This is usually \" \" (4 spaces) or \"\\t\" (1\n# tab).\nindent-string=' '\n\n# Maximum number of characters on a single line.\nmax-line-length=100\n\n# Maximum number of lines in a module.\nmax-module-lines=1000\n\n# Allow the body of a class to be on the same line as the declaration if body\n# contains single statement.\nsingle-line-class-stmt=no\n\n# Allow the body of an if to be on the same line as the test if there is no\n# else.\nsingle-line-if-stmt=no\n\n\n[STRING]\n\n# This flag controls whether inconsistent-quotes generates a warning when the\n# character used as a quote delimiter is used inconsistently within a module.\ncheck-quote-consistency=no\n\n# This flag controls whether the implicit-str-concat should generate a warning\n# on implicit string concatenation in sequences defined over several lines.\ncheck-str-concat-over-line-jumps=no\n\n\n[SIMILARITIES]\n\n# Ignore comments when computing similarities.\nignore-comments=yes\n\n# Ignore docstrings when computing similarities.\nignore-docstrings=yes\n\n# Ignore imports when computing similarities.\nignore-imports=no\n\n# Minimum lines number of a similarity.\nmin-similarity-lines=4\n\n\n[MISCELLANEOUS]\n\n# List of note tags to take in consideration, separated by a comma.\nnotes=FIXME,\n XXX,\n TODO\n\n# Regular expression of note tags to take in consideration.\n#notes-rgx=\n\n\n[BASIC]\n\n# Naming style matching correct argument names.\nargument-naming-style=snake_case\n\n# Regular expression matching correct argument names. Overrides argument-\n# naming-style.\n#argument-rgx=\n\n# Naming style matching correct attribute names.\nattr-naming-style=snake_case\n\n# Regular expression matching correct attribute names. Overrides attr-naming-\n# style.\n#attr-rgx=\n\n# Bad variable names which should always be refused, separated by a comma.\nbad-names=foo,\n bar,\n baz,\n toto,\n tutu,\n tata\n\n# Bad variable names regexes, separated by a comma. If names match any regex,\n# they will always be refused\nbad-names-rgxs=\n\n# Naming style matching correct class attribute names.\nclass-attribute-naming-style=any\n\n# Regular expression matching correct class attribute names. Overrides class-\n# attribute-naming-style.\n#class-attribute-rgx=\n\n# Naming style matching correct class names.\nclass-naming-style=PascalCase\n\n# Regular expression matching correct class names. Overrides class-naming-\n# style.\n#class-rgx=\n\n# Naming style matching correct constant names.\nconst-naming-style=UPPER_CASE\n\n# Regular expression matching correct constant names. Overrides const-naming-\n# style.\n#const-rgx=\n\n# Minimum line length for functions/classes that require docstrings, shorter\n# ones are exempt.\ndocstring-min-length=-1\n\n# Naming style matching correct function names.\nfunction-naming-style=snake_case\n\n# Regular expression matching correct function names. Overrides function-\n# naming-style.\n#function-rgx=\n\n# Good variable names which should always be accepted, separated by a comma.\ngood-names=i,\n j,\n k,\n ex,\n Run,\n _,\n x,\n y,\n w,\n h,\n a,\n b\n\n# Good variable names regexes, separated by a comma. If names match any regex,\n# they will always be accepted\ngood-names-rgxs=\n\n# Include a hint for the correct naming format with invalid-name.\ninclude-naming-hint=no\n\n# Naming style matching correct inline iteration names.\ninlinevar-naming-style=any\n\n# Regular expression matching correct inline iteration names. Overrides\n# inlinevar-naming-style.\n#inlinevar-rgx=\n\n# Naming style matching correct method names.\nmethod-naming-style=snake_case\n\n# Regular expression matching correct method names. Overrides method-naming-\n# style.\n#method-rgx=\n\n# Naming style matching correct module names.\nmodule-naming-style=snake_case\n\n# Regular expression matching correct module names. Overrides module-naming-\n# style.\n#module-rgx=\n\n# Colon-delimited sets of names that determine each other's naming style when\n# the name regexes allow several styles.\nname-group=\n\n# Regular expression which should only match function or class names that do\n# not require a docstring.\nno-docstring-rgx=^_\n\n# List of decorators that produce properties, such as abc.abstractproperty. Add\n# to this list to register other decorators that produce valid properties.\n# These decorators are taken in consideration only for invalid-name.\nproperty-classes=abc.abstractproperty\n\n# Naming style matching correct variable names.\nvariable-naming-style=snake_case\n\n# Regular expression matching correct variable names. Overrides variable-\n# naming-style.\n#variable-rgx=\n\n\n[DESIGN]\n\n# Maximum number of arguments for function / method.\nmax-args=5\n\n# Maximum number of attributes for a class (see R0902).\nmax-attributes=7\n\n# Maximum number of boolean expressions in an if statement (see R0916).\nmax-bool-expr=5\n\n# Maximum number of branch for function / method body.\nmax-branches=12\n\n# Maximum number of locals for function / method body.\nmax-locals=15\n\n# Maximum number of parents for a class (see R0901).\nmax-parents=7\n\n# Maximum number of public methods for a class (see R0904).\nmax-public-methods=20\n\n# Maximum number of return / yield for function / method body.\nmax-returns=6\n\n# Maximum number of statements in function / method body.\nmax-statements=50\n\n# Minimum number of public methods for a class (see R0903).\nmin-public-methods=2\n\n\n[IMPORTS]\n\n# List of modules that can be imported at any level, not just the top level\n# one.\nallow-any-import-level=\n\n# Allow wildcard imports from modules that define __all__.\nallow-wildcard-with-all=no\n\n# Analyse import fallback blocks. This can be used to support both Python 2 and\n# 3 compatible code, which means that the block might have code that exists\n# only in one or another interpreter, leading to false positives when analysed.\nanalyse-fallback-blocks=no\n\n# Deprecated modules which should not be used, separated by a comma.\ndeprecated-modules=optparse,tkinter.tix\n\n# Create a graph of external dependencies in the given file (report RP0402 must\n# not be disabled).\next-import-graph=\n\n# Create a graph of every (i.e. internal and external) dependencies in the\n# given file (report RP0402 must not be disabled).\nimport-graph=\n\n# Create a graph of internal dependencies in the given file (report RP0402 must\n# not be disabled).\nint-import-graph=\n\n# Force import order to recognize a module as part of the standard\n# compatibility libraries.\nknown-standard-library=\n\n# Force import order to recognize a module as part of a third party library.\nknown-third-party=enchant\n\n# Couples of modules and preferred modules, separated by a comma.\npreferred-modules=\n\n\n[CLASSES]\n\n# List of method names used to declare (i.e. assign) instance attributes.\ndefining-attr-methods=__init__,\n __new__,\n setUp,\n __post_init__\n\n# List of member names, which should be excluded from the protected access\n# warning.\nexclude-protected=_asdict,\n _fields,\n _replace,\n _source,\n _make\n\n# List of valid names for the first argument in a class method.\nvalid-classmethod-first-arg=cls\n\n# List of valid names for the first argument in a metaclass class method.\nvalid-metaclass-classmethod-first-arg=cls\n\n\n[EXCEPTIONS]\n\n# Exceptions that will emit a warning when being caught. Defaults to\n# \"BaseException, Exception\".\novergeneral-exceptions=BaseException,\n Exception\n" }, { "path": ".readthedocs.yml", "content": "version: 2\n\nformats: [pdf, epub]\n\nbuild:\n os: ubuntu-22.04\n tools:\n python: \"3.7\"\n\npython:\n install:\n - requirements: requirements/docs.txt\n - requirements: requirements/readthedocs.txt\n" }, { "path": "CITATION.cff", "content": "cff-version: 1.2.0\nmessage: \"If you use this software, please cite it as below.\"\nauthors:\n - family-names: MMagic\n given-names: Contributors\ntitle: \"MMagic: OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox\"\nversion: 1.0.0\ndate-released: 2023-04-25\nurl: \"https://github.com/open-mmlab/mmagic\"\nlicense: Apache-2.0\n" }, { "path": "LICENSE", "content": "Copyright (c) OpenMMLab. All rights reserved.\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2023 MMagic Authors. All rights reserved.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n Copyright 2020 MMEditing Authors. All rights reserved.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "MANIFEST.in", "content": "include requirements/*.txt\ninclude mmagic/.mim/VERSION\ninclude mmagic/.mim/model-index.yml\ninclude mmagic/evaluation/metrics/niqe_pris_params.npz\nrecursive-include mmagic/.mim/configs *.py *.yml\nrecursive-include mmagic/.mim/tools *.sh *.py\nrecursive-include mmagic/.mim/demo *.py\n" }, { "path": "README.md", "content": "
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\n Multimodal Advanced, Generative, and Intelligent Creation (MMagic [em'mædʒɪk])\n
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\n OpenMMLab website\n \n \n HOT\n \n \n     \n OpenMMLab platform\n \n \n TRY IT OUT\n \n \n
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\n\n[![PyPI](https://badge.fury.io/py/mmagic.svg)](https://pypi.org/project/mmagic/)\n[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmagic.readthedocs.io/en/latest/)\n[![badge](https://github.com/open-mmlab/mmagic/workflows/build/badge.svg)](https://github.com/open-mmlab/mmagic/actions)\n[![codecov](https://codecov.io/gh/open-mmlab/mmagic/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmagic)\n[![license](https://img.shields.io/github/license/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/blob/main/LICENSE)\n[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/issues)\n[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/issues)\n[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_demo.svg)](https://openxlab.org.cn/apps/detail/%E6%94%BF%E6%9D%B0/OpenMMLab-Projects)\n\n[📘Documentation](https://mmagic.readthedocs.io/en/latest/) |\n[🛠️Installation](https://mmagic.readthedocs.io/en/latest/get_started/install.html) |\n[📊Model Zoo](https://mmagic.readthedocs.io/en/latest/model_zoo/overview.html) |\n[🆕Update News](https://mmagic.readthedocs.io/en/latest/changelog.html) |\n[🚀Ongoing Projects](https://github.com/open-mmlab/mmagic/projects) |\n[🤔Reporting Issues](https://github.com/open-mmlab/mmagic/issues)\n\nEnglish | [简体中文](README_zh-CN.md)\n\n
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\n\n## 🚀 What's New \n\n### New release [**MMagic v1.2.0**](https://github.com/open-mmlab/mmagic/releases/tag/v1.2.0) \\[18/12/2023\\]:\n\n- An advanced and powerful inpainting algorithm named PowerPaint is released in our repository. [Click to View](https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint)\n\nWe are excited to announce the release of MMagic v1.0.0 that inherits from [MMEditing](https://github.com/open-mmlab/mmediting) and [MMGeneration](https://github.com/open-mmlab/mmgeneration).\n\nAfter iterative updates with OpenMMLab 2.0 framework and merged with MMGeneration, MMEditing has become a powerful tool that supports low-level algorithms based on both GAN and CNN. Today, MMEditing embraces Generative AI and transforms into a more advanced and comprehensive AIGC toolkit: **MMagic** (**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation). MMagic will provide more agile and flexible experimental support for researchers and AIGC enthusiasts, and help you on your AIGC exploration journey.\n\nWe highlight the following new features.\n\n**1. New Models**\n\nWe support 11 new models in 4 new tasks.\n\n- Text2Image / Diffusion\n - ControlNet\n - DreamBooth\n - Stable Diffusion\n - Disco Diffusion\n - GLIDE\n - Guided Diffusion\n- 3D-aware Generation\n - EG3D\n- Image Restoration\n - NAFNet\n - Restormer\n - SwinIR\n- Image Colorization\n - InstColorization\n\n**2. Magic Diffusion Model**\n\nFor the Diffusion Model, we provide the following \"magic\" :\n\n- Support image generation based on Stable Diffusion and Disco Diffusion.\n- Support Finetune methods such as Dreambooth and DreamBooth LoRA.\n- Support controllability in text-to-image generation using ControlNet.\n- Support acceleration and optimization strategies based on xFormers to improve training and inference efficiency.\n- Support video generation based on MultiFrame Render.\n- Support calling basic models and sampling strategies through DiffuserWrapper.\n\n**3. Upgraded Framework**\n\nBy using MMEngine and MMCV of OpenMMLab 2.0 framework, MMagic has upgraded in the following new features:\n\n- Refactor DataSample to support the combination and splitting of batch dimensions.\n- Refactor DataPreprocessor and unify the data format for various tasks during training and inference.\n- Refactor MultiValLoop and MultiTestLoop, supporting the evaluation of both generation-type metrics (e.g. FID) and reconstruction-type metrics (e.g. SSIM), and supporting the evaluation of multiple datasets at once.\n- Support visualization on local files or using tensorboard and wandb.\n- Support for 33+ algorithms accelerated by Pytorch 2.0.\n\n**MMagic** has supported all the tasks, models, metrics, and losses in [MMEditing](https://github.com/open-mmlab/mmediting) and [MMGeneration](https://github.com/open-mmlab/mmgeneration) and unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine) 😍.\n\nPlease refer to [changelog.md](docs/en/changelog.md) for details and release history.\n\nPlease refer to [migration documents](docs/en/migration/overview.md) to migrate from [old version](https://github.com/open-mmlab/mmagic/tree/0.x) MMEditing 0.x to new version MMagic 1.x .\n\n
\n\n## 📄 Table of Contents\n\n- [📖 Introduction](#-introduction)\n- [🙌 Contributing](#-contributing)\n- [🛠️ Installation](#️-installation)\n- [📊 Model Zoo](#-model-zoo)\n- [🤝 Acknowledgement](#-acknowledgement)\n- [🖊️ Citation](#️-citation)\n- [🎫 License](#-license)\n- [🏗️ ️OpenMMLab Family](#️-️openmmlab-family)\n\n## 📖 Introduction\n\nMMagic (**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation) is an advanced and comprehensive AIGC toolkit that inherits from [MMEditing](https://github.com/open-mmlab/mmediting) and [MMGeneration](https://github.com/open-mmlab/mmgeneration). It is an open-source image and video editing&generating toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.\n\nCurrently, MMagic support multiple image and video generation/editing tasks.\n\nhttps://user-images.githubusercontent.com/49083766/233564593-7d3d48ed-e843-4432-b610-35e3d257765c.mp4\n\n### ✨ Major features\n\n- **State of the Art Models**\n\n MMagic provides state-of-the-art generative models to process, edit and synthesize images and videos.\n\n- **Powerful and Popular Applications**\n\n MMagic supports popular and contemporary image restoration, text-to-image, 3D-aware generation, inpainting, matting, super-resolution and generation applications. Specifically, MMagic supports fine-tuning for stable diffusion and many exciting diffusion's application such as ControlNet Animation with SAM. MMagic also supports GAN interpolation, GAN projection, GAN manipulations and many other popular GAN’s applications. It’s time to begin your AIGC exploration journey!\n\n- **Efficient Framework**\n\n By using MMEngine and MMCV of OpenMMLab 2.0 framework, MMagic decompose the editing framework into different modules and one can easily construct a customized editor framework by combining different modules. We can define the training process just like playing with Legos and provide rich components and strategies. In MMagic, you can complete controls on the training process with different levels of APIs. With the support of [MMSeparateDistributedDataParallel](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/wrappers/seperate_distributed.py), distributed training for dynamic architectures can be easily implemented.\n\n### ✨ Best Practice\n\n- The best practice on our main branch works with **Python 3.9+** and **PyTorch 2.0+**.\n\n

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\n\n## 🙌 Contributing\n\nMore and more community contributors are joining us to make our repo better. Some recent projects are contributed by the community including:\n\n- [SDXL](configs/stable_diffusion_xl/README.md) is contributed by @okotaku.\n- [AnimateDiff](configs/animatediff/README.md) is contributed by @ElliotQi.\n- [ViCo](configs/vico/README.md) is contributed by @FerryHuang.\n- [DragGan](configs/draggan/README.md) is contributed by @qsun1.\n- [FastComposer](configs/fastcomposer/README.md) is contributed by @xiaomile.\n\n[Projects](projects/README.md) is opened to make it easier for everyone to add projects to MMagic.\n\nWe appreciate all contributions to improve MMagic. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/main/CONTRIBUTING.md) in MMCV and [CONTRIBUTING.md](https://github.com/open-mmlab/mmengine/blob/main/CONTRIBUTING.md) in MMEngine for more details about the contributing guideline.\n\n

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\n\n## 🛠️ Installation\n\nMMagic depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine) and [MMCV](https://github.com/open-mmlab/mmcv).\nBelow are quick steps for installation.\n\n**Step 1.**\nInstall PyTorch following [official instructions](https://pytorch.org/get-started/locally/).\n\n**Step 2.**\nInstall MMCV, MMEngine and MMagic with [MIM](https://github.com/open-mmlab/mim).\n\n```shell\npip3 install openmim\nmim install mmcv>=2.0.0\nmim install mmengine\nmim install mmagic\n```\n\n**Step 3.**\nVerify MMagic has been successfully installed.\n\n```shell\ncd ~\npython -c \"import mmagic; print(mmagic.__version__)\"\n# Example output: 1.0.0\n```\n\n**Getting Started**\n\nAfter installing MMagic successfully, now you are able to play with MMagic! To generate an image from text, you only need several lines of codes by MMagic!\n\n```python\nfrom mmagic.apis import MMagicInferencer\nsd_inferencer = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = 'output/sd_res.png'\nsd_inferencer.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\nPlease see [quick run](docs/en/get_started/quick_run.md) and [inference](docs/en/user_guides/inference.md) for the basic usage of MMagic.\n\n**Install MMagic from source**\n\nYou can also experiment on the latest developed version rather than the stable release by installing MMagic from source with the following commands:\n\n```shell\ngit clone https://github.com/open-mmlab/mmagic.git\ncd mmagic\npip3 install -e .\n```\n\nPlease refer to [installation](docs/en/get_started/install.md) for more detailed instruction.\n\n

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\n\n## 📊 Model Zoo\n\n
\n Supported algorithms\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n
\n Conditional GANs\n \n Unconditional GANs\n \n Image Restoration\n \n Image Super-Resolution\n
\n \n \n \n \n \n \n \n
\n Video Super-Resolution\n \n Video Interpolation\n \n Image Colorization\n \n Image Translation\n
\n \n \n \n \n \n \n \n
\n Inpainting\n \n Matting\n \n Text-to-Image(Video)\n \n 3D-aware Generation\n
\n \n \n \n \n \n \n \n
\n\nPlease refer to [model_zoo](https://mmagic.readthedocs.io/en/latest/model_zoo/overview.html) for more details.\n\n

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\n\n## 🤝 Acknowledgement\n\nMMagic is an open source project that is contributed by researchers and engineers from various colleges and companies. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.\n\nWe appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. Thank you all!\n\n\n \n\n\n

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\n\n## 🖊️ Citation\n\nIf MMagic is helpful to your research, please cite it as below.\n\n```bibtex\n@misc{mmagic2023,\n title = {{MMagic}: {OpenMMLab} Multimodal Advanced, Generative, and Intelligent Creation Toolbox},\n author = {{MMagic Contributors}},\n howpublished = {\\url{https://github.com/open-mmlab/mmagic}},\n year = {2023}\n}\n```\n\n```bibtex\n@misc{mmediting2022,\n title = {{MMEditing}: {OpenMMLab} Image and Video Editing Toolbox},\n author = {{MMEditing Contributors}},\n howpublished = {\\url{https://github.com/open-mmlab/mmediting}},\n year = {2022}\n}\n```\n\n

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\n\n## 🎫 License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\nPlease refer to [LICENSES](LICENSE) for the careful check, if you are using our code for commercial matters.\n\n

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\n\n## 🏗️ ️OpenMMLab Family\n\n- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\n- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab Pre-training Toolbox and Benchmark.\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.\n\n

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\n" }, { "path": "README_zh-CN.md", "content": "
\n \n
 
\n
\n Multimodal Advanced, Generative, and Intelligent Creation (MMagic [em'mædʒɪk])\n
\n
 
\n
\n OpenMMLab 官网\n \n \n HOT\n \n \n     \n OpenMMLab 开放平台\n \n \n TRY IT OUT\n \n \n
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\n\n[![PyPI](https://badge.fury.io/py/mmagic.svg)](https://pypi.org/project/mmagic/)\n[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmagic.readthedocs.io/zh_CN/latest/)\n[![badge](https://github.com/open-mmlab/mmagic/workflows/build/badge.svg)](https://github.com/open-mmlab/mmagic/actions)\n[![codecov](https://codecov.io/gh/open-mmlab/mmagic/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmagic)\n[![license](https://img.shields.io/github/license/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/blob/main/LICENSE)\n[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/issues)\n[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmagic.svg)](https://github.com/open-mmlab/mmagic/issues)\n[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_demo.svg)](https://openxlab.org.cn/apps/detail/%E6%94%BF%E6%9D%B0/OpenMMLab-Projects)\n\n[📘使用文档](https://mmagic.readthedocs.io/zh_CN/latest/) |\n[🛠️安装教程](https://mmagic.readthedocs.io/zh_CN/latest/get_started/install.html) |\n[📊模型库](https://mmagic.readthedocs.io/zh_CN/latest/model_zoo/overview.html) |\n[🆕更新记录](https://mmagic.readthedocs.io/zh_CN/latest/changelog.html) |\n[🚀进行中的项目](https://github.com/open-mmlab/mmagic/projects) |\n[🤔提出问题](https://github.com/open-mmlab/mmagic/issues)\n\n[English](README.md) | 简体中文\n\n
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\n\n## 🚀 最新进展 \n\n### 最新的 [**MMagic v1.2.0**](https://github.com/open-mmlab/mmagic/releases/tag/v1.2.0) 版本已经在 \\[18/12/2023\\] 发布:\n\n- 我们的代码仓库中发布了一个先进而强大的图像 inpainting 算法 PowerPaint。 [Click to View](https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint)\n\n我们正式发布 MMagic v1.0.0 版本，源自 [MMEditing](https://github.com/open-mmlab/mmediting) 和 [MMGeneration](https://github.com/open-mmlab/mmgeneration)。\n\n经过 OpenMMLab 2.0 框架的迭代更新以及与 MMGeneration 的合并，MMEditing 已经成为了一个支持基于 GAN 和 CNN 的底层视觉算法的强大工具。而今天，MMEditing 将更加拥抱生成式 AI（Generative AI），正式更名为 **MMagic**（**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation），致力于打造更先进、更全面的 AIGC 开源算法库。MMagic 将为广大研究者与 AIGC 爱好者们提供更加快捷灵活的实验支持，助力你的 AIGC 探索之旅。\n\n以下是此次版本发布的重点新功能:\n\n**1. 新算法**\n\n我们支持了4个新任务以及11个新算法。\n\n- Text2Image / Diffusion\n - ControlNet\n - DreamBooth\n - Stable Diffusion\n - Disco Diffusion\n - GLIDE\n - Guided Diffusion\n- 3D-aware Generation\n - EG3D\n- Image Restoration\n - NAFNet\n - Restormer\n - SwinIR\n- Image Colorization\n - InstColorization\n\n**2. Magic Diffusion Model**\n\n针对 Diffusion Model，我们提供了以下“魔法”\n\n- 支持基于 Stable Diffusion 与 Disco Diffusion 的图像生成.\n- 支持 Dreambooth 以及 DreamBooth LoRA 等 Finetune 方法.\n- 支持 ControlNet 进行可控性的文本到图像生成.\n- 支持 xFormers 加速和优化策略，提高训练与推理效率.\n- 支持基于 MultiFrame Render 的视频生成.\n- 支持通过 Wrapper 调用 Diffusers 的基础模型以及采样策略.\n\n**3. 框架升级**\n\n通过 OpenMMLab 2.0 框架的 MMEngine 和 MMCV， MMagic 在以下几方面完成升级：\n\n- 重构 DataSample，支持 batch 维度的组合与拆分.\n- 重构 DataPreprocessor，并统一各种任务在训练与推理时的数据格式.\n- 重构 MultiValLoop 与 MultiTestLoop，同时支持生成类型指标（e.g. FID）与重建类型指标（e.g. SSIM） 的评测，同时支持一次性评测多个数据集\n- 支持本地可视化以及使用 tensorboard 或 wandb的可视化.\n- 支持 33+ 算法 Pytorch 2.0 加速.\n\n**MMagic** 已经支持了[MMEditing](https://github.com/open-mmlab/mmediting)和[MMGeneration](https://github.com/open-mmlab/mmgeneration)中的全量任务、模型、优化函数和评价指标 ，并基于[MMEngine](https://github.com/open-mmlab/mmengine)统一了各组件接口 😍。\n\n如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/zh_cn/changelog.md)。如果想从[旧版本](https://github.com/open-mmlab/mmagic/tree/master) MMEditing 0.x 迁移到新版本 MMagic 1.x，请阅读[迁移文档](docs/zh_cn/migration/overview.md)。\n\n
\n\n## 📄 目录\n\n- [📖 介绍](#-介绍)\n- [🙌 参与贡献](#-参与贡献)\n- [🛠️ 安装](#%EF%B8%8F-安装)\n- [📊 模型库](#-模型库)\n- [🤝 致谢](#-致谢)\n- [🖊️ 引用](#%EF%B8%8F-引用)\n- [🎫 许可证](#-许可证)\n- [🏗️ ️OpenMMLab 的其他项目](#%EF%B8%8F-️openmmlab-的其他项目)\n\n## 📖 介绍\n\nMMagic 是基于 PyTorch 的图像&视频编辑和生成开源工具箱。是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一。\n\n目前 MMagic 支持多种图像和视频的生成/编辑任务。\n\nhttps://user-images.githubusercontent.com/49083766/233564593-7d3d48ed-e843-4432-b610-35e3d257765c.mp4\n\n### ✨ 主要特性\n\n- **SOTA 算法**\n\n MMagic 提供了处理、编辑、生成图像和视频的 SOTA 算法。\n\n- **强有力且流行的应用**\n\n MMagic 支持了流行的图像修复、图文生成、3D生成、图像修补、抠图、超分辨率和生成等任务的应用。特别是 MMagic 支持了 Stable Diffusion 的微调和许多激动人心的 diffusion 应用，例如 ControlNet 动画生成。MMagic 也支持了 GANs 的插值，投影，编辑和其他流行的应用。请立即开始你的 AIGC 探索之旅！\n\n- **高效的框架**\n\n 通过 OpenMMLab 2.0 框架的 MMEngine 和 MMCV， MMagic 将编辑框架分解为不同的组件，并且可以通过组合不同的模块轻松地构建自定义的编辑器模型。我们可以像搭建“乐高”一样定义训练流程，提供丰富的组件和策略。在 MMagic 中，你可以使用不同的 APIs 完全控制训练流程。得益于 [MMSeparateDistributedDataParallel](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/wrappers/seperate_distributed.py), 动态模型结构的分布式训练可以轻松实现。\n\n### ✨ 最佳实践\n\n- 主分支代码的最佳实践基于 **Python 3.9+** 和 **PyTorch 2.0+** 。\n\n

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\n\n## 🙌 参与贡献\n\n越来越多社区贡献者的加入使我们的算法库日益发展。最近由社区贡献的项目包括：\n\n- [SDXL](configs/stable_diffusion_xl/README.md) 来自 @okotaku.\n- [AnimateDiff](configs/animatediff/README.md) 来自 @ElliotQi.\n- [ViCo](configs/vico/README.md) 来自 @FerryHuang.\n- [DragGan](configs/draggan/README.md) 来自 @qsun1.\n- [FastComposer](configs/fastcomposer/README.md) 来自 @xiaomile.\n\n为使向 MMagic 中添加项目更加容易，我们开启了 [Projects](projects/README.md) 。\n\n感谢您为改善 MMagic 所做的所有贡献。请参阅 MMCV 中的 [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/main/CONTRIBUTING_zh-CN.md) 和 MMEngine 中的 [CONTRIBUTING.md](https://github.com/open-mmlab/mmengine/blob/main/CONTRIBUTING_zh-CN.md) 以获取贡献指南。\n\n

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\n\n## 🛠️ 安装\n\nMMagic 依赖 [PyTorch](https://pytorch.org/)，[MMEngine](https://github.com/open-mmlab/mmengine) 和 [MMCV](https://github.com/open-mmlab/mmcv)，以下是安装的简要步骤。\n\n**步骤 1.**\n依照[官方教程](https://pytorch.org/get-started/locally/)安装 PyTorch 。\n\n**步骤 2.**\n使用 [MIM](https://github.com/open-mmlab/mim) 安装 MMCV，MMEngine 和 MMagic 。\n\n```\npip3 install openmim\nmim install 'mmcv>=2.0.0'\nmim install 'mmengine'\nmim install 'mmagic'\n```\n\n**步骤 3.**\n验证 MMagic 安装成功。\n\n```shell\ncd ~\npython -c \"import mmagic; print(mmagic.__version__)\"\n# Example output: 1.0.0\n```\n\n**开始使用**\n\n成功安装 MMagic 后，你可以很容易地上手使用 MMagic！仅需几行代码，你就可以使用 MMagic 完成文本生成图像！\n\n```python\nfrom mmagic.apis import MMagicInferencer\nsd_inferencer = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = 'output/sd_res.png'\nsd_inferencer.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\n请参考[快速运行](docs/zh_cn/get_started/quick_run.md)和[推理演示](docs/zh_cn/user_guides/inference.md)获取 MMagic 的基本用法。\n\n**从源码安装 MMagic**\n\n使用以下命令从源码安装 MMagic，你可以选择不使用已发布的稳定版本，而在最新开发的版本上进行实验。\n\n```\ngit clone https://github.com/open-mmlab/mmagic.git\ncd mmagic\npip3 install -e .\n```\n\n更详细的安装指南请参考 [安装指南](docs/zh_cn/get_started/install.md) 。\n\n

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\n\n## 📊 模型库\n\n
\n 支持的算法\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n
\n Conditional GANs\n \n Unconditional GANs\n \n Image Restoration\n \n Image Super-Resolution\n
\n \n \n \n \n \n \n \n
\n Video Super-Resolution\n \n Video Interpolation\n \n Image Colorization\n \n Image Translation\n
\n \n \n \n \n \n \n \n
\n Inpainting\n \n Matting\n \n Text-to-Image(Video)\n \n 3D-aware Generation\n
\n \n \n \n \n \n \n \n
\n\n请参考[模型库](https://mmagic.readthedocs.io/zh_CN/latest/model_zoo/overview.html)了解详情。\n\n

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\n\n## 🤝 致谢\n\nMMagic 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。\n\n\n \n\n\n

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\n\n## 🖊️ 引用\n\n如果 MMagic 对您的研究有所帮助，请按照如下 bibtex 引用它。\n\n```bibtex\n@misc{mmagic2023,\n title = {{MMagic}: {OpenMMLab} Multimodal Advanced, Generative, and Intelligent Creation Toolbox},\n author = {{MMagic Contributors}},\n howpublished = {\\url{https://github.com/open-mmlab/mmagic}},\n year = {2023}\n}\n```\n\n```bibtex\n@misc{mmediting2022,\n title = {{MMEditing}: {OpenMMLab} Image and Video Editing Toolbox},\n author = {{MMEditing Contributors}},\n howpublished = {\\url{https://github.com/open-mmlab/mmediting}},\n year = {2022}\n}\n```\n\n

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\n\n## 🎫 许可证\n\n本项目开源自 [Apache 2.0 license](LICENSE)。\n\n

🔝返回目录

\n\n## 🏗️ ️OpenMMLab 的其他项目\n\n- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab MMEngine.\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库\n- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口\n- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 预训练工具箱\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准\n- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架\n\n

🔝返回目录

\n\n## 欢迎加入 OpenMMLab 社区\n\n扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMagic 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】\n\n
\n \n
\n\n我们会在 OpenMMLab 社区为大家\n\n- 📢 分享 AI 框架的前沿核心技术\n- 💻 解读 PyTorch 常用模块源码\n- 📰 发布 OpenMMLab 的相关新闻\n- 🚀 介绍 OpenMMLab 开发的前沿算法\n- 🏃 获取更高效的问题答疑和意见反馈\n- 🔥 提供与各行各业开发者充分交流的平台\n\n干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬\n" }, { "path": "configs/_base_/datasets/basicvsr_test_config.py", "content": "# configs for REDS4\nreds_data_root = 'data/REDS'\n\nreds_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\nreds_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=reds_data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_val.txt',\n depth=1,\n num_input_frames=100,\n fixed_seq_len=100,\n pipeline=reds_pipeline))\n\nreds_evaluator = [\n dict(type='PSNR', prefix='REDS4-BIx4-RGB'),\n dict(type='SSIM', prefix='REDS4-BIx4-RGB')\n]\n\n# configs for vimeo90k-bd and vimeo90k-bi\nvimeo_90k_data_root = 'data/vimeo90k'\nvimeo_90k_file_list = [\n 'im1.png', 'im2.png', 'im3.png', 'im4.png', 'im5.png', 'im6.png', 'im7.png'\n]\n\nvimeo_90k_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='MirrorSequence', keys=['img']),\n dict(type='PackInputs')\n]\n\nvimeo_90k_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),\n data_root=vimeo_90k_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_test_GT.txt',\n depth=2,\n num_input_frames=7,\n fixed_seq_len=7,\n load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),\n pipeline=vimeo_90k_pipeline))\n\nvimeo_90k_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),\n data_root=vimeo_90k_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_test_GT.txt',\n depth=2,\n num_input_frames=7,\n fixed_seq_len=7,\n load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),\n pipeline=vimeo_90k_pipeline))\n\nvimeo_90k_bd_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),\n]\n\nvimeo_90k_bi_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),\n]\n\n# config for UDM10 (BDx4)\nudm10_data_root = 'data/UDM10'\n\nudm10_pipeline = [\n dict(\n type='GenerateSegmentIndices',\n interval_list=[1],\n filename_tmpl='{:04d}.png'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\nudm10_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='udm10', task_name='vsr'),\n data_root=udm10_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n pipeline=udm10_pipeline))\n\nudm10_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='UDM10-BDx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='UDM10-BDx4-Y')\n]\n\n# config for vid4\nvid4_data_root = 'data/Vid4'\n\nvid4_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\nvid4_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=1,\n pipeline=vid4_pipeline))\n\nvid4_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=1,\n pipeline=vid4_pipeline))\n\nvid4_bd_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='VID4-BDx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='VID4-BDx4-Y'),\n]\nvid4_bi_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='VID4-BIx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='VID4-BIx4-Y'),\n]\n\n# config for test\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n reds_dataloader,\n vimeo_90k_bd_dataloader,\n vimeo_90k_bi_dataloader,\n udm10_dataloader,\n vid4_bd_dataloader,\n vid4_bi_dataloader,\n]\ntest_evaluator = [\n reds_evaluator,\n vimeo_90k_bd_evaluator,\n vimeo_90k_bi_evaluator,\n udm10_evaluator,\n vid4_bd_evaluator,\n vid4_bi_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/celeba.py", "content": "# Base config for CelebA-HQ dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/CelebA-HQ'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt=''),\n ann_file='train_celeba_img_list.txt',\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt=''),\n ann_file='val_celeba_img_list.txt',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE', mask_key='mask', scaling=100),\n # By default, compute with pixel value from 0-1\n # scale=2 to align with 1.0\n # scale=100 seems to align with readme\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "configs/_base_/datasets/cifar10_noaug.py", "content": "cifar_pipeline = [dict(type='PackInputs')]\ncifar_dataset = dict(\n type='CIFAR10',\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline)\n\ntrain_dataloader = dict(\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/cifar10_nopad.py", "content": "cifar_pipeline = [\n dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\ncifar_dataset = dict(\n type='CIFAR10',\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline)\n\n# test dataset do not use flip\ncifar_pipeline_test = [dict(type='PackInputs')]\ncifar_dataset_test = dict(\n type='CIFAR10',\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline_test)\n\ntrain_dataloader = dict(\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset_test,\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset_test,\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/comp1k.py", "content": "# Base config for Composition-1K dataset\n\n# dataset settings\ndataset_type = 'AdobeComp1kDataset'\ndata_root = 'data/adobe_composition-1k'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n ann_file='training_list.json',\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n ann_file='test_list.json',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\n# TODO: matting\nval_evaluator = [\n dict(type='SAD'),\n dict(type='MattingMSE'),\n dict(type='GradientError'),\n dict(type='ConnectivityError'),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "configs/_base_/datasets/deblurring-defocus_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='imgL',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='imgR',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\ndpdd_data_root = 'data/DPDD'\n\ndpdd_indoor_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='DPDD-Indoor', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n ann_file='indoor_labels.txt',\n pipeline=test_pipeline))\ndpdd_indoor_evaluator = [\n dict(type='MAE', prefix='DPDD-Indoor'),\n dict(type='PSNR', prefix='DPDD-Indoor'),\n dict(type='SSIM', prefix='DPDD-Indoor'),\n]\n\ndpdd_outdoor_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='DPDD-Outdoor', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n ann_file='outdoor_labels.txt',\n pipeline=test_pipeline))\ndpdd_outdoor_evaluator = [\n dict(type='MAE', prefix='DPDD-Outdoor'),\n dict(type='PSNR', prefix='DPDD-Outdoor'),\n dict(type='SSIM', prefix='DPDD-Outdoor'),\n]\n\ndpdd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='DPDD-Combined', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n pipeline=test_pipeline))\ndpdd_evaluator = [\n dict(type='MAE', prefix='DPDD-Combined'),\n dict(type='PSNR', prefix='DPDD-Combined'),\n dict(type='SSIM', prefix='DPDD-Combined'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n dpdd_indoor_dataloader,\n dpdd_outdoor_dataloader,\n dpdd_dataloader,\n]\ntest_evaluator = [\n dpdd_indoor_evaluator,\n dpdd_outdoor_evaluator,\n dpdd_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/deblurring-motion_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\ngopro_data_root = 'data/gopro/test'\ngopro_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='GoPro', task_name='deblurring'),\n data_root=gopro_data_root,\n data_prefix=dict(img='blur', gt='sharp'),\n pipeline=test_pipeline))\ngopro_evaluator = [\n dict(type='PSNR', prefix='GoPro'),\n dict(type='SSIM', prefix='GoPro'),\n]\n\nhide_data_root = 'data/HIDE'\nhide_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='HIDE', task_name='deblurring'),\n data_root=hide_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nhide_evaluator = [\n dict(type='PSNR', prefix='HIDE'),\n dict(type='SSIM', prefix='HIDE'),\n]\n\nrealblurj_data_root = 'data/RealBlur_J'\nrealblurj_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='RealBlur_J', task_name='deblurring'),\n data_root=realblurj_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrealblurj_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='RealBlurJ'),\n dict(type='SSIM', convert_to='Y', prefix='RealBlurJ'),\n]\n\nrealblurr_data_root = 'data/RealBlur_R'\nrealblurr_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='RealBlur_R', task_name='deblurring'),\n data_root=realblurr_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrealblurr_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='RealBlurR'),\n dict(type='SSIM', convert_to='Y', prefix='RealBlurR'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n gopro_dataloader,\n hide_dataloader,\n realblurj_dataloader,\n realblurr_dataloader,\n]\ntest_evaluator = [\n gopro_evaluator,\n hide_evaluator,\n realblurj_evaluator,\n realblurr_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/decompression_test_config.py", "content": "quality = 10\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[quality, quality]),\n bgr2rgb=True,\n keys=['img']),\n dict(type='PackInputs')\n]\n\nclassic5_data_root = 'data/Classic5'\nclassic5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='classic5', task_name='CAR'),\n data_root=classic5_data_root,\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\nclassic5_evaluator = [\n dict(type='PSNR', prefix='Classic5'),\n dict(type='SSIM', prefix='Classic5'),\n]\n\nlive1_data_root = 'data/LIVE1'\nlive1_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='live1', task_name='CAR'),\n data_root=live1_data_root,\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\nlive1_evaluator = [\n dict(type='PSNR', prefix='LIVE1'),\n dict(type='SSIM', prefix='LIVE1'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n classic5_dataloader,\n live1_dataloader,\n]\ntest_evaluator = [\n classic5_evaluator,\n live1_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/denoising-gaussian_color_test_config.py", "content": "sigma = 15\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/denoising_gaussian_test'\ncbsd68_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='CBSD68', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='CBSD68', gt='CBSD68'),\n pipeline=test_pipeline))\ncbsd68_evaluator = [\n dict(type='PSNR', prefix='CBSD68'),\n dict(type='SSIM', prefix='CBSD68'),\n]\n\nkodak24_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Kodak24', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Kodak24', gt='Kodak24'),\n pipeline=test_pipeline))\nkodak24_evaluator = [\n dict(type='PSNR', prefix='Kodak24'),\n dict(type='SSIM', prefix='Kodak24'),\n]\n\nmcmaster_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='McMaster', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='McMaster', gt='McMaster'),\n pipeline=test_pipeline))\nmcmaster_evaluator = [\n dict(type='PSNR', prefix='McMaster'),\n dict(type='SSIM', prefix='McMaster'),\n]\n\nurban100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Urban100', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Urban100', gt='Urban100'),\n pipeline=test_pipeline))\nurban100_evaluator = [\n dict(type='PSNR', prefix='Urban100'),\n dict(type='SSIM', prefix='Urban100'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n cbsd68_dataloader,\n kodak24_dataloader,\n mcmaster_dataloader,\n urban100_dataloader,\n]\ntest_evaluator = [\n cbsd68_evaluator,\n kodak24_evaluator,\n mcmaster_evaluator,\n urban100_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/denoising-gaussian_gray_test_config.py", "content": "sigma = 15\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n to_y_channel=True,\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n to_y_channel=True,\n imdecode_backend='cv2'),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=1),\n keys=['img']),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/denoising_gaussian_test'\nset12_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Set12', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Set12', gt='Set12'),\n pipeline=test_pipeline))\nset12_evaluator = [\n dict(type='PSNR', prefix='Set12'),\n dict(type='SSIM', prefix='Set12'),\n]\n\nbsd68_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='BSD68', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='BSD68', gt='BSD68'),\n pipeline=test_pipeline))\nbsd68_evaluator = [\n dict(type='PSNR', prefix='BSD68'),\n dict(type='SSIM', prefix='BSD68'),\n]\n\nurban100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Urban100', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Urban100', gt='Urban100'),\n pipeline=test_pipeline))\nurban100_evaluator = [\n dict(type='PSNR', prefix='Urban100'),\n dict(type='SSIM', prefix='Urban100'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n set12_dataloader,\n bsd68_dataloader,\n urban100_dataloader,\n]\ntest_evaluator = [\n set12_evaluator,\n bsd68_evaluator,\n urban100_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/denoising-real_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\nsidd_data_root = 'data/SIDD/val/'\nsidd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='SIDD', task_name='denoising'),\n data_root=sidd_data_root,\n data_prefix=dict(img='noisy', gt='gt'),\n filename_tmpl=dict(gt='{}_GT', img='{}_NOISY'),\n pipeline=test_pipeline))\nsidd_evaluator = [\n dict(type='PSNR', prefix='SIDD'),\n dict(type='SSIM', prefix='SIDD'),\n]\n\ndnd_data_root = 'data/DND'\ndnd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='DND', task_name='denoising'),\n data_root=dnd_data_root,\n data_prefix=dict(img='input', gt='groundtruth'),\n pipeline=test_pipeline))\ndnd_evaluator = [\n dict(type='PSNR', prefix='DND'),\n dict(type='SSIM', prefix='DND'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n sidd_dataloader,\n # dnd_dataloader,\n]\ntest_evaluator = [\n sidd_evaluator,\n # dnd_dataloader,\n]\n" }, { "path": "configs/_base_/datasets/deraining_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\nrain100h_data_root = 'data/Rain100H'\nrain100h_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Rain100H', task_name='deraining'),\n data_root=rain100h_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrain100h_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Rain100H'),\n dict(type='SSIM', convert_to='Y', prefix='Rain100H'),\n]\n\nrain100l_data_root = 'data/Rain100L'\nrain100l_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Rain100L', task_name='deraining'),\n data_root=rain100l_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrain100l_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Rain100L'),\n dict(type='SSIM', convert_to='Y', prefix='Rain100L'),\n]\n\ntest100_data_root = 'data/Test100'\ntest100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Test100', task_name='deraining'),\n data_root=test100_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest100_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Test100'),\n dict(type='SSIM', convert_to='Y', prefix='Test100'),\n]\n\ntest1200_data_root = 'data/Test1200'\ntest1200_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Test1200', task_name='deraining'),\n data_root=test1200_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest1200_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Test1200'),\n dict(type='SSIM', convert_to='Y', prefix='Test1200'),\n]\n\ntest2800_data_root = 'data/Test2800'\ntest2800_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='Test2800', task_name='deraining'),\n data_root=test2800_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest2800_evaluator = [\n dict(type='PSNR', convert_to='Y', prefix='Test2800'),\n dict(type='SSIM', convert_to='Y', prefix='Test2800'),\n]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n rain100h_dataloader,\n rain100l_dataloader,\n test100_dataloader,\n test1200_dataloader,\n test2800_dataloader,\n]\ntest_evaluator = [\n rain100h_evaluator,\n rain100l_evaluator,\n test100_evaluator,\n test1200_evaluator,\n test2800_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/ffhq_flip.py", "content": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Flip', keys=['gt'], direction='horizontal'),\n dict(type='PackInputs', keys='gt')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='PackInputs', keys=['gt'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py", "content": "dataset_type = 'GrowScaleImgDataset'\n\npipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Flip', keys='gt', direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type='GrowScaleImgDataset',\n data_roots={\n '1024': './data/ffhq/images',\n '256': './data/ffhq/ffhq_imgs/ffhq_256',\n },\n gpu_samples_base=4,\n # note that this should be changed with total gpu number\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4,\n '128': 4,\n '256': 4,\n '512': 4,\n '1024': 4\n },\n len_per_stage=300000,\n pipeline=pipeline),\n sampler=dict(type='InfiniteSampler', shuffle=True))\n\ntest_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type='BasicImageDataset',\n data_prefix=dict(gt=''),\n pipeline=pipeline,\n data_root='./data/ffhq/images'),\n sampler=dict(type='DefaultSampler', shuffle=False))\n\nval_dataloader = test_dataloader\n" }, { "path": "configs/_base_/datasets/imagenet_128.py", "content": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='RandomCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(128, 128), keys='gt', backend='pillow'),\n dict(type='Flip', keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='CenterCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(128, 128), keys='gt', backend='pillow'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/_base_/datasets/imagenet_256.py", "content": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='RandomCropLongEdge', keys=['img']),\n dict(type='Resize', scale=(256, 256), keys=['img'], backend='pillow'),\n dict(type='Flip', keys=['img'], flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='CenterCropLongEdge', keys=['img']),\n dict(type='Resize', scale=(256, 256), backend='pillow'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/_base_/datasets/imagenet_512.py", "content": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='RandomCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(512, 512), keys='gt', backend='pillow'),\n dict(type='Flip', keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='CenterCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(512, 512), keys='gt', backend='pillow'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/_base_/datasets/imagenet_64.py", "content": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='RandomCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(64, 64), keys='gt', backend='pillow'),\n dict(type='Flip', keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='CenterCropLongEdge', keys='gt'),\n dict(type='Resize', scale=(64, 64), keys='gt', backend='pillow'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/_base_/datasets/imagenet_noaug_128.py", "content": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# Remove `RandomFlip` augmentation and change `RandomCropLongEdge` to\n# `CenterCropLongEdge` to eliminate randomness.\n# dataset settings\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='CenterCropLongEdge'),\n dict(type='Resize', scale=(128, 128), backend='pillow'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='CenterCropLongEdge'),\n dict(type='Resize', scale=(128, 128), backend='pillow'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='data/imagenet',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/_base_/datasets/liif_test_config.py", "content": "scale_test_list = [2, 3, 4, 6, 18, 30]\n\ntest_pipelines = [[\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomDownSampling', scale_min=scale_test, scale_max=scale_test),\n dict(type='GenerateCoordinateAndCell', scale=scale_test, reshape_gt=False),\n dict(type='PackInputs')\n] for scale_test in scale_test_list]\n\n# test config for Set5\nset5_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root='data/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\nset5_evaluators = [[\n dict(type='PSNR', crop_border=scale, prefix=f'Set5x{scale}'),\n dict(type='SSIM', crop_border=scale, prefix=f'Set5x{scale}'),\n] for scale in scale_test_list]\n\n# test config for Set14\nset14_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root='data/Set14',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\nset14_evaluators = [[\n dict(type='PSNR', crop_border=scale, prefix=f'Set14x{scale}'),\n dict(type='SSIM', crop_border=scale, prefix=f'Set14x{scale}'),\n] for scale in scale_test_list]\n\n# test config for DIV2K\ndiv2k_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root='data/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\ndiv2k_evaluators = [[\n dict(type='PSNR', crop_border=scale, prefix=f'DIV2Kx{scale}'),\n dict(type='SSIM', crop_border=scale, prefix=f'DIV2Kx{scale}'),\n] for scale in scale_test_list]\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n *set5_dataloaders,\n *set14_dataloaders,\n *div2k_dataloaders,\n]\ntest_evaluator = [\n *set5_evaluators,\n *set14_evaluators,\n *div2k_evaluators,\n]\n" }, { "path": "configs/_base_/datasets/lsun_stylegan.py", "content": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/paired_imgs_256x256_crop.py", "content": "dataset_type = 'PairedImageDataset'\n# domain_a = None # set by user\n# domain_b = None # set by user\n\ntrain_pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='A',\n domain_b='B',\n color_type='color'),\n dict(\n type='Resize',\n keys=['img_A', 'img_B'],\n scale=(286, 286),\n interpolation='bicubic'),\n dict(type='FixedCrop', keys=['img_A', 'img_B'], crop_size=(256, 256)),\n dict(type='Flip', keys=['img_A', 'img_B'], direction='horizontal'),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type='PackInputs',\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\ntest_pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='A',\n domain_b='B',\n color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='Resize',\n scale=(256, 256),\n keys='img',\n interpolation='bicubic')\n ]),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type='PackInputs',\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/places.py", "content": "# Base config for places365 dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/Places'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt='data_large'),\n ann_file='meta/places365_train_challenge.txt',\n # Note that Places365-standard (1.8M images) and\n # Place365-challenge (8M images) use different image lists.\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt='val_large'),\n ann_file='meta/places365_val.txt',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE', mask_key='mask', scaling=100),\n # By default, compute with pixel value from 0-1\n # scale=2 to align with 1.0\n # scale=100 seems to align with readme\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "configs/_base_/datasets/sisr_x2_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=2, prefix='Set5'),\n dict(type='SSIM', crop_border=2, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=2, prefix='Set14'),\n dict(type='SSIM', crop_border=2, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n # ann_file='meta_info_DIV2K800sub_GT.txt',\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=2, prefix='DIV2K'),\n dict(type='SSIM', crop_border=2, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/sisr_x3_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=3, prefix='Set5'),\n dict(type='SSIM', crop_border=3, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=3, prefix='Set14'),\n dict(type='SSIM', crop_border=3, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X3_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=3, prefix='DIV2K'),\n dict(type='SSIM', crop_border=3, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/sisr_x4_test_config.py", "content": "test_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=4, prefix='Set5'),\n dict(type='SSIM', crop_border=4, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=4, prefix='Set14'),\n dict(type='SSIM', crop_border=4, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=4, prefix='DIV2K'),\n dict(type='SSIM', crop_border=4, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/tdan_test_config.py", "content": "# configs for SPMCS-30\nSPMC_data_root = 'data/SPMCS'\n\nSPMC_pipeline = [\n dict(type='GenerateFrameIndiceswithPadding', padding='reflection'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\nSPMC_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='spmcs', task_name='vsr'),\n data_root=SPMC_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_SPMCS_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=SPMC_pipeline))\n\nSPMC_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='spmcs', task_name='vsr'),\n data_root=SPMC_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_SPMCS_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=SPMC_pipeline))\n\nSPMC_bd_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(\n type='PSNR', crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),\n dict(\n type='SSIM', crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),\n ])\nSPMC_bi_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(\n type='PSNR', crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),\n dict(\n type='SSIM', crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),\n ])\n\n# config for vid4\nvid4_data_root = 'data/Vid4'\n\nvid4_pipeline = [\n # dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='GenerateFrameIndiceswithPadding', padding='reflection'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\nvid4_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=vid4_pipeline))\n\nvid4_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=vid4_pipeline))\n\nvid4_bd_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', convert_to='Y', prefix='VID4-BDx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='VID4-BDx4-Y'),\n ])\nvid4_bi_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', convert_to='Y', prefix='VID4-BIx4-Y'),\n dict(type='SSIM', convert_to='Y', prefix='VID4-BIx4-Y'),\n ])\n\n# config for test\ntest_cfg = dict(type='MultiTestLoop')\ntest_dataloader = [\n SPMC_bd_dataloader,\n SPMC_bi_dataloader,\n vid4_bd_dataloader,\n vid4_bi_dataloader,\n]\ntest_evaluator = [\n SPMC_bd_evaluator,\n SPMC_bi_evaluator,\n vid4_bd_evaluator,\n vid4_bi_evaluator,\n]\n" }, { "path": "configs/_base_/datasets/unconditional_imgs_128x128.py", "content": "# dataset_type = 'BasicImageDataset'\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(128, 128)),\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/unconditional_imgs_64x64.py", "content": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(64, 64)),\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/unconditional_imgs_flip_512x512.py", "content": "dataset_type = 'BasicImageDataset'\n\n# TODO:\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(512, 512)),\n dict(type='Flip', keys=['gt'], direction='horizontal'), # TODO:\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py", "content": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='Resize',\n keys='gt',\n scale=(256, 256),\n interpolation='lanczos',\n backend='pillow'),\n dict(type='Flip', keys=['gt'], direction='horizontal'),\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/datasets/unpaired_imgs_256x256.py", "content": "dataset_type = 'UnpairedImageDataset'\ndomain_a = None # set by user\ndomain_b = None # set by user\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img_A', color_type='color'),\n dict(type='LoadImageFromFile', key='img_B', color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(type='Resize', scale=(286, 286), interpolation='bicubic'),\n dict(\n type='Crop',\n keys=['img'],\n crop_size=(256, 256),\n random_crop=True),\n ]),\n dict(type='Flip', keys=['img_A'], direction='horizontal'),\n dict(type='Flip', keys=['img_B'], direction='horizontal'),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type='PackInputs',\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img_A', color_type='color'),\n dict(type='LoadImageFromFile', key='img_B', color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=dict(\n type='Resize', scale=(256, 256), interpolation='bicubic'),\n ),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type='PackInputs',\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n test_mode=True,\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n test_mode=True,\n pipeline=test_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "configs/_base_/default_runtime.py", "content": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n out_dir=save_dir,\n by_epoch=False,\n max_keep_ckpts=10,\n save_best='PSNR',\n rule='greater',\n ),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO'\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False)\n\nload_from = None\nresume = False\n\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n" }, { "path": "configs/_base_/gen_default_runtime.py", "content": "default_scope = 'mmagic'\n\nrandomness = dict(seed=2022, diff_rank_seed=True)\n# env settings\ndist_params = dict(backend='nccl')\n# disable opencv multithreading to avoid system being overloaded\nopencv_num_threads = 0\n# set multi-process start method as `fork` to speed up the training\nmp_start_method = 'fork'\n\n# configure for default hooks\ndefault_hooks = dict(\n # record time of every iteration.\n timer=dict(type='IterTimerHook'),\n # print log every 100 iterations.\n logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),\n # save checkpoint per 10000 iterations\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n max_keep_ckpts=20,\n less_keys=['FID-Full-50k/fid', 'FID-50k/fid', 'swd/avg'],\n greater_keys=['IS-50k/is', 'ms-ssim/avg'],\n save_optimizer=True))\n\n# config for environment\nenv_cfg = dict(\n # whether to enable cudnn benchmark.\n cudnn_benchmark=True,\n # set multi process parameters.\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n # set distributed parameters.\n dist_cfg=dict(backend='nccl'))\n\n# set log level\nlog_level = 'INFO'\nlog_processor = dict(type='LogProcessor', by_epoch=False)\n\n# load from which checkpoint\nload_from = None\n\n# whether to resume training from the loaded checkpoint\nresume = None\n\n# config for model wrapper\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=False)\n\n# set visualizer\nvis_backends = [dict(type='VisBackend')]\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n\n# config for training\ntrain_cfg = dict(by_epoch=False, val_begin=1, val_interval=10000)\n\n# config for val\nval_cfg = dict(type='MultiValLoop')\nval_evaluator = dict(type='Evaluator')\n\n# config for test\ntest_cfg = dict(type='MultiTestLoop')\ntest_evaluator = dict(type='Evaluator')\n\n# config for optim_wrapper_constructor\noptim_wrapper = dict(constructor='MultiOptimWrapperConstructor')\n" }, { "path": "configs/_base_/inpaint_default_runtime.py", "content": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict(\n type='CheckpointHook',\n interval=50000,\n by_epoch=False,\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n\nlog_level = 'INFO'\nlog_processor = dict(type='LogProcessor', by_epoch=False)\n\nload_from = None\nresume = False\n\n# TODO: support auto scaling lr\n" }, { "path": "configs/_base_/matting_default_runtime.py", "content": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='trimap_path',\n img_keys=['pred_alpha', 'trimap', 'gt_merged', 'gt_alpha'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=2000)]\n\nlog_level = 'INFO'\nlog_processor = dict(type='LogProcessor', by_epoch=False)\n\nload_from = None\nresume = False\n\n# TODO: support auto scaling lr\n" }, { "path": "configs/_base_/models/base_cyclegan.py", "content": "_domain_a = None # set by user\n_domain_b = None # set by user\nmodel = dict(\n type='CycleGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='ResnetGenerator',\n in_channels=3,\n out_channels=3,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02)),\n default_domain=None, # set by user\n reachable_domains=None, # set by user\n related_domains=None # set by user\n)\n" }, { "path": "configs/_base_/models/base_deepfillv1.py", "content": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n type='DeepFillv1Inpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='DeepFillEncoderDecoder',\n stage1=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='DeepFillEncoder', padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n in_channels=128,\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=128,\n act_cfg=dict(type='ELU'),\n padding_mode='reflect')),\n stage2=dict(\n type='DeepFillRefiner',\n encoder_attention=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_attention',\n padding_mode='reflect'),\n encoder_conv=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_conv',\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=128,\n act_cfg=dict(type='ELU'),\n padding_mode='reflect'),\n contextual_attention=dict(\n type='ContextualAttentionNeck',\n in_channels=128,\n padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n in_channels=256,\n padding_mode='reflect'))),\n disc=dict(\n type='DeepFillv1Discriminators',\n global_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=256,\n fc_in_channels=256 * 16 * 16,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n local_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 8 * 8,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2))),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type='GANLoss',\n gan_type='wgan',\n loss_weight=0.0001,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_gp=dict(type='GradientPenaltyLoss', loss_weight=10.),\n loss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001))\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0001)),\n disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0001)))\n\n# learning policy\n# Fixed\n" }, { "path": "configs/_base_/models/base_deepfillv2.py", "content": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n type='TwoStageInpaintor',\n disc_input_with_mask=True,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='DeepFillEncoderDecoder',\n stage1=dict(\n type='GLEncoderDecoder',\n encoder=dict(\n type='DeepFillEncoder',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n conv_type='gated_conv',\n in_channels=96,\n channel_factor=0.75,\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect')),\n stage2=dict(\n type='DeepFillRefiner',\n encoder_attention=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_attention',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n encoder_conv=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_conv',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect'),\n contextual_attention=dict(\n type='ContextualAttentionNeck',\n in_channels=96,\n conv_type='gated_conv',\n padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n in_channels=192,\n conv_type='gated_conv',\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'))),\n disc=dict(\n type='MultiLayerDiscriminator',\n in_channels=4,\n max_channels=256,\n fc_in_channels=None,\n num_convs=6,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=True,\n ),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type='GANLoss',\n gan_type='hinge',\n loss_weight=0.1,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n)\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0001)),\n disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0001)))\n\n# learning policy\n# Fixed\n" }, { "path": "configs/_base_/models/base_edvr.py", "content": "_base_ = '../default_runtime.py'\n\nscale = 4\n\ntrain_pipeline = [\n dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n dict(type='TemporalReverse', keys='img_path', reverse_ratio=0),\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/REDS'\nsave_dir = './work_dirs'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_train.txt',\n depth=2,\n num_input_frames=5,\n num_output_frames=1,\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_val.txt',\n depth=2,\n num_input_frames=5,\n num_output_frames=1,\n pipeline=val_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=600_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)),\n)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n out_dir=save_dir,\n by_epoch=False))\n" }, { "path": "configs/_base_/models/base_gl.py", "content": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n type='GLInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='GLEncoder', norm_cfg=dict(type='SyncBN')),\n decoder=dict(type='GLDecoder', norm_cfg=dict(type='SyncBN')),\n dilation_neck=dict(\n type='GLDilationNeck', norm_cfg=dict(type='SyncBN'))),\n disc=dict(\n type='GLDiscs',\n global_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='SyncBN'),\n ),\n local_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=5,\n norm_cfg=dict(type='SyncBN'),\n ),\n ),\n loss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ))\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)),\n disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)))\n\n# learning policy\n# Fixed\n" }, { "path": "configs/_base_/models/base_glean.py", "content": "_base_ = '../default_runtime.py'\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel', find_unused_parameters=True)\n\nsave_dir = './work_dirs'\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='CosineAnnealingLR', by_epoch=False, T_max=600_000, eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n save_best=['MAE', 'PSNR', 'SSIM'],\n rule=['less', 'greater', 'greater']),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/_base_/models/base_liif.py", "content": "_base_ = '../default_runtime.py'\nwork_dir = './work_dirs/liif'\nsave_dir = './work_dirs'\n\nscale_min, scale_max = 1, 4\nscale_test = 4\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomDownSampling',\n scale_min=scale_min,\n scale_max=scale_max,\n patch_size=48),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='GenerateCoordinateAndCell', sample_quantity=2304),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='RandomDownSampling', scale_min=scale_max, scale_max=scale_max),\n dict(type='GenerateCoordinateAndCell', reshape_gt=False),\n dict(type='PackInputs')\n]\n# test_pipeline = [\n# dict(\n# type='LoadImageFromFile',\n# key='gt',\n# color_type='color',\n# channel_order='rgb',\n# imdecode_backend='cv2'),\n# dict(\n# type='LoadImageFromFile',\n# key='img',\n# color_type='color',\n# channel_order='rgb',\n# imdecode_backend='cv2'),\n# dict(type='GenerateCoordinateAndCell', scale=scale_test,\n# reshape_gt=False),\n# dict(type='PackInputs')\n# ]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(gt='DIV2K_train_HR_sub'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale_max),\n dict(type='SSIM', crop_border=scale_max),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=3000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[200_000, 400_000, 600_000, 800_000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=3000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/_base_/models/base_pconv.py", "content": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n type='PConvInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='SyncBN', requires_grad=False),\n norm_eval=True),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='SyncBN'))),\n disc=None,\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg16',\n layer_weights={\n '4': 1.,\n '9': 1.,\n '16': 1.,\n },\n perceptual_weight=0.05,\n style_weight=120,\n pretrained=('torchvision://vgg16')),\n loss_out_percep=True,\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=6.,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.,\n ),\n loss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1,\n ))\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00005))\n\n# learning policy\n# Fixed\n" }, { "path": "configs/_base_/models/base_pix2pix.py", "content": "source_domain = None # set by user\ntarget_domain = None # set by user\n# model settings\nmodel = dict(\n type='Pix2Pix',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='UnetGenerator',\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02)),\n loss_config=dict(pixel_loss_weight=100.0),\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain])\n" }, { "path": "configs/_base_/models/base_styleganv1.py", "content": "model = dict(\n type='StyleGANv1',\n data_preprocessor=dict(type='DataPreprocessor'),\n style_channels=512,\n generator=dict(\n type='StyleGANv1Generator', out_size=None, style_channels=512),\n discriminator=dict(type='StyleGAN1Discriminator', in_size=None))\n" }, { "path": "configs/_base_/models/base_styleganv2.py", "content": "# define GAN model\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nloss_config = dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2)\n\nmodel = dict(\n type='StyleGAN2',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='StyleGANv2Generator',\n out_size=None, # Need to be set.\n style_channels=512,\n ),\n discriminator=dict(\n type='StyleGAN2Discriminator',\n in_size=None, # Need to be set.\n ),\n ema_config=dict(type='ExponentialMovingAverage'),\n loss_config=loss_config)\n" }, { "path": "configs/_base_/models/base_styleganv3.py", "content": "# define GAN model\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nmodel = dict(\n type='StyleGAN3',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='StyleGANv3Generator',\n noise_size=512,\n style_channels=512,\n out_size=None, # Need to be set.\n img_channels=3,\n ),\n discriminator=dict(\n type='StyleGAN2Discriminator',\n in_size=None, # Need to be set.\n ),\n ema_config=dict(type='ExponentialMovingAverage'),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n" }, { "path": "configs/_base_/models/base_tof.py", "content": "_base_ = '../default_runtime.py'\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ntrain_dataset_type = 'BasicFramesDataset'\nval_dataset_type = 'BasicFramesDataset'\ndata_root = 'data/vimeo_triplet'\nsave_dir = './work_dirs'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=train_dataset_type,\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=val_dataset_type,\n ann_file='tri_testlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\n# 5000 iters == 1 epoch\nepoch_length = 5000\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=epoch_length)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(\n type='Adam',\n lr=5e-5,\n betas=(0.9, 0.99),\n weight_decay=1e-4,\n ),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n gamma=0.5,\n milestones=[200000, 400000, 600000, 800000])\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=epoch_length,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/_base_/models/biggan/base_biggan_128x128.py", "content": "model = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')),\n generator_steps=1,\n discriminator_steps=1)\n" }, { "path": "configs/_base_/models/dcgan/base_dcgan_128x128.py", "content": "# define GAN model\nmodel = dict(\n type='DCGAN',\n noise_size=100,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='DCGANGenerator', output_scale=128, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=128,\n output_scale=4,\n out_channels=100))\n" }, { "path": "configs/_base_/models/dcgan/base_dcgan_64x64.py", "content": "# define GAN model\nmodel = dict(\n type='DCGAN',\n noise_size=100,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(type='DCGANGenerator', output_scale=64, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=64,\n output_scale=4,\n out_channels=1))\n" }, { "path": "configs/_base_/models/sagan/base_sagan_128x128.py", "content": "model = dict(\n type='SAGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='SAGANGenerator',\n output_scale=128,\n base_channels=64,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=4,\n with_spectral_norm=True),\n discriminator=dict(\n type='ProjDiscriminator',\n input_scale=128,\n base_channels=64,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=1,\n with_spectral_norm=True),\n generator_steps=1,\n discriminator_steps=1)\n" }, { "path": "configs/_base_/models/sagan/base_sagan_32x32.py", "content": "model = dict(\n type='SAGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n num_classes=10,\n generator=dict(\n type='SAGANGenerator',\n num_classes=10,\n output_scale=32,\n base_channels=256,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=2,\n with_spectral_norm=True),\n discriminator=dict(\n type='ProjDiscriminator',\n num_classes=10,\n input_scale=32,\n base_channels=128,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=1,\n with_spectral_norm=True),\n generator_steps=1,\n discriminator_steps=5)\n" }, { "path": "configs/_base_/models/sngan_proj/base_sngan_proj_128x128.py", "content": "# define GAN model\nmodel = dict(\n type='SNGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(type='SNGANGenerator', output_scale=128, base_channels=64),\n discriminator=dict(\n type='ProjDiscriminator', input_scale=128, base_channels=64),\n discriminator_steps=2)\n" }, { "path": "configs/_base_/models/sngan_proj/base_sngan_proj_32x32.py", "content": "# define GAN model\nmodel = dict(\n type='SNGAN',\n num_classes=10,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(type='SNGANGenerator', output_scale=32, base_channels=256),\n discriminator=dict(\n type='ProjDiscriminator', input_scale=32, base_channels=128),\n discriminator_steps=5)\n" }, { "path": "configs/_base_/schedules/.gitkeep", "content": "" }, { "path": "configs/animatediff/README.md", "content": "# AnimateDiff (2023)\n\n> [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)\n\n> **Task**: Text2Video\n\n\n\n## Abstract\n\n\n\nWith the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs.\n\n\n\n![512](https://github.com/ElliotQi/mmagic/assets/46469021/54d92aca-dfa9-4eeb-ba38-3f6c981e5399)\n\n## Pretrained models\n\nWe use Stable Diffusion's weights provided by HuggingFace Diffusers. You do not have to download the weights manually. If you use Diffusers wrapper, the weights will be downloaded automatically.\n\nThis model has several weights including vae, unet and clip. You should download the weights from [stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and change the 'pretrained_model_path' in config to the weights dir.\n\n| Model | Dataset | Download |\n| :------------------------------------------------------------: | :-----: | :-----------------------------------------------------------------------------------------------------------------: |\n| [ToonYou](./animatediff_ToonYou.py) | - | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/toonyou_beta3.safetensors) |\n| [Lyriel](./animatediff_Lyriel.py) | - | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/lyriel_v16.safetensors) |\n| [RcnzCartoon](./animatediff_RcnzCartoon.py) | - | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/rcnzCartoon3d_v10.safetensors) |\n| [MajicMix](./animatediff_MajicMix.py) | - | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/majicmixRealistic_v5Preview.safetensors) |\n| [RealisticVision](./animatediff_RealisticVision.py) | - | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/realisticVisionV51_v20Novae.safetensors) |\n| [MotionModel_v1-5_v2](./animatediff_RealisticVision_v2.py) | WebVid | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/mm_sd_v15_v2.ckpt) |\n| [MotionModel_v1-5_2Mval](./animatediff_RealisticVision_v1.py) | WebVid | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/mm_fromscratch_2.5Mval.ckpt) |\n| [MotionModel_v1-5_10Mval](./animatediff_RealisticVision_v1.py) | WebVid | [model](https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/mm_fromscratch_10Mval.ckpt) |\n\nLatest models could be looked up on [OpenXLab_AnimateDiff](https://openxlab.org.cn/models/detail/ElliotQi/AnimateDiff).\n\n## Quick Start\n\nRunning the following codes, you can get a text-generated image.\n\n### Reccomendation\n\nIt's highly recommended to install [xformers](https://github.com/facebookresearch/xformers). It would save about 20G memory for 512\\*512 resolution generation.\n\n### Steps\n\n1. Download [ToonYou](https://civitai.com/api/download/models/78775) and MotionModule checkpoint\n\n```bash\n#!/bin/bash\n\nmkdir models && cd models\nmkdir Motion_Module && mkdir DreamBooth_LoRA\ngdown 1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq -O Motion_Module/\ngdown 1ql0g_Ys4UCz2RnokYlBjyOYPbttbIpbu -O models/Motion_Module/\nwget https://civitai.com/api/download/models/78775 -P DreamBooth_LoRA/ --content-disposition --no-check-certificate\n```\n\n2. Modify the config file in `configs/animatediff/animatediff_ToonYou.py`\n\n```python\n models_path = {Your Checkpoints Path}\n motion_module_cfg=dict(\n path={Your MotionModule Path}\n ),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path={Your Dreambooth_Lora Path},\n steps=25,\n guidance_scale=7.5)\n```\n\n3. Enjoy Text2Video world\n\n```python\nfrom mmengine import Config\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nimport os\nimport torch\nfrom pathlib import Path\nimport datetime\nfrom mmagic.models.editors.animatediff import save_videos_grid\n\n\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/animatediff/animatediff_ToonYou.py')\nanimatediff = MODELS.build(cfg.model).cuda()\nprompts = [\n \"best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress\",\n\n \"masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes,\",\n\n \"best quality, masterpiece, 1boy, formal, abstract, looking at viewer, masculine, marble pattern\",\n\n \"best quality, masterpiece, 1girl, cloudy sky, dandelion, contrapposto, alternate hairstyle,\"\n]\n\nnegative_prompts = [\n \"\",\n \"badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth\",\n \"\",\n \"\",\n]\n\nsample_idx = 0\nrandom_seeds = cfg.randomness['seed']\nrandom_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)\nsamples = []\ntime_str = datetime.datetime.now().strftime(\"%Y-%m-%dT%H-%M-%S\")\nsavedir = f\"samples/{Path(cfg.model['dream_booth_lora_cfg']['type']).stem}-{time_str}\"\nos.makedirs(savedir)\nfor prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, negative_prompts, random_seeds)):\n output_dict = animatediff.infer(prompt,negative_prompt=n_prompt, video_length=16, height=256, width=256, seed=random_seed,num_inference_steps=cfg.model['dream_booth_lora_cfg']['steps'])\n sample = output_dict['samples']\n prompt = \"-\".join((prompt.replace(\"/\", \"\").split(\" \")[:10]))\n save_videos_grid(sample, f\"{savedir}/sample/{sample_idx}-{prompt}.gif\")\n print(f\"save to {savedir}/sample/{prompt}.gif\")\n samples.append(sample)\n sample_idx += 1\n\nsamples = torch.concat(samples)\nsave_videos_grid(samples, f\"{savedir}/sample.gif\", n_rows=4)\n\n\n```\n\n### Prompts for other config\n\n- Lyriel\n\n```yaml\n prompt:\n - \"dark shot, epic realistic, portrait of halo, sunglasses, blue eyes, tartan scarf, white hair by atey ghailan, by greg rutkowski, by greg tocchini, by james gilleard, by joe fenton, by kaethe butcher, gradient yellow, black, brown and magenta color scheme, grunge aesthetic!!! graffiti tag wall background, art by greg rutkowski and artgerm, soft cinematic light, adobe lightroom, photolab, hdr, intricate, highly detailed, depth of field, faded, neutral colors, hdr, muted colors, hyperdetailed, artstation, cinematic, warm lights, dramatic light, intricate details, complex background, rutkowski, teal and orange\"\n - \"A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal\"\n - \"dark theme, medieval portrait of a man sharp features, grim, cold stare, dark colors, Volumetric lighting, baroque oil painting by Greg Rutkowski, Artgerm, WLOP, Alphonse Mucha dynamic lighting hyperdetailed intricately detailed, hdr, muted colors, complex background, hyperrealism, hyperdetailed, amandine van ray\"\n - \"As I have gone alone in there and with my treasures bold, I can keep my secret where and hint of riches new and old. Begin it where warm waters halt and take it in a canyon down, not far but too far to walk, put in below the home of brown.\"\n\n n_prompt:\n - \"3d, cartoon, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, young, loli, elf, 3d, illustration\"\n - \"3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular\"\n - \"dof, grayscale, black and white, bw, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular,badhandsv5-neg, By bad artist -neg 1, monochrome\"\n - \"holding an item, cowboy, hat, cartoon, 3d, disfigured, bad art, deformed,extra limbs,close up,b&w, weird colors, blurry, duplicate, morbid, mutilated, [out of frame], extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, out of frame, ugly, extra limbs, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, Photoshop, video game, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, 3d render\"\n```\n\n- RcnzCartoon\n\n```yaml\nprompt:\n - \"Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded\"\n - \"close up Portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal [rust], elegant, sharp focus, photo by greg rutkowski, soft lighting, vibrant colors, masterpiece, streets, detailed face\"\n - \"absurdres, photorealistic, masterpiece, a 30 year old man with gold framed, aviator reading glasses and a black hooded jacket and a beard, professional photo, a character portrait, altermodern, detailed eyes, detailed lips, detailed face, grey eyes\"\n - \"a golden labrador, warm vibrant colours, natural lighting, dappled lighting, diffused lighting, absurdres, highres,k, uhd, hdr, rtx, unreal, octane render, RAW photo, photorealistic, global illumination, subsurface scattering\"\n\n n_prompt:\n - \"deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation\"\n - \"nude, cross eyed, tongue, open mouth, inside, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, red eyes, muscular\"\n - \"easynegative, cartoon, anime, sketches, necklace, earrings worst quality, low quality, normal quality, bad anatomy, bad hands, shiny skin, error, missing fingers, extra digit, fewer digits, jpeg artifacts, signature, watermark, username, blurry, chubby, anorectic, bad eyes, old, wrinkled skin, red skin, photograph By bad artist -neg, big eyes, muscular face,\"\n - \"beard, EasyNegative, lowres, chromatic aberration, depth of field, motion blur, blurry, bokeh, bad quality, worst quality, multiple arms, badhand\"\n\n```\n\n- MajicMix\n\n```yaml\nprompt:\n - \"1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic\"\n - \"best quality, masterpiece, photorealistic, 1boy, 50 years old beard, dramatic lighting\"\n - \"best quality, masterpiece, photorealistic, 1girl, light smile, shirt with collars, waist up, dramatic lighting, from below\"\n - \"male, man, beard, bodybuilder, skinhead,cold face, tough guy, cowboyshot, tattoo, french windows, luxury hotel masterpiece, best quality, photorealistic\"\n\n n_prompt:\n - \"ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles\"\n - \"nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome\"\n - \"nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome\"\n - \"nude, nsfw, ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, monochrome, grayscale watermark, moles, people\"\n```\n\n- Realistic & Realistic_v2 (same prompts with different random seed, find more details in their config files)\n\n```yaml\n prompt:\n - \"b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3\"\n - \"close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot\"\n - \"photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3\"\n - \"night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain\"\n\n n_prompt:\n - \"semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\"\n - \"semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\"\n - \"blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation\"\n - \"blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, art, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation\"\n\n```\n\n4. Start training motion module with the following command:\n\n```bash\n# 4 GPUS\nbash tools/dist_train.sh configs/animatediff/animatediff.py 4\n# 1 GPU\npython tools/train.py configs/animatediff/animatediff.py\n\n```\n\n## Citation\n\n```bibtex\n@article{guo2023animatediff,\n title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning},\n author={Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Wang, Yaohui and Qiao, Yu and Lin, Dahua and Dai, Bo},\n journal={arXiv preprint arXiv:2307.04725},\n year={2023}\n}\n```\n" }, { "path": "configs/animatediff/README_zh-CN.md", "content": "# AnimateDiff (2023)\n\n> [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)\n\n> **任务**: 视频生成, 扩散模型\n\n\n\n## 摘要\n\n\n\n年来，AIGC 宛如 AI 海洋中最不可或缺的波涛，逐渐凝成滔天的巨浪，突破壁垒、扑向海岸，并酝酿着下一波潮水高涨。以 Stable Diffusion 这股翻腾最为汹涌的波涛为代表的文生图模型飞速发展，使得更多非专业用户也能通过简单的文字提示生成高质量的图片内容。然而，文生图模型的训练成本往往十分高昂，为减轻微调模型的代价，相应的模型定制化方法如 DreamBooth, LoRA 应运而生，使得用户在开源权重的基础上，用少量数据和消费级显卡即可实现模型个性化和特定风格下的图像生成质量的提升。这极大推动了 HuggingFace, CivitAI 等开源模型社区的发展，众多艺术家和爱好者在其中贡献了许多高质量的微调模型。不觉间，平静的海洋洪水滔天，海滩上留下数不清的色彩斑斓的鹅卵石，便是爱好者们精心调制的 AI 画作。\n\n与动画相比，静态图像的表达能力是有限的。随着越来越多效果惊艳的微调模型的出现和视频生成技术的发展，人们期待着能够赋予这些定制化模型生成动画的能力。在最新开源的 AnimateDiff 中，作者提出了一种将任何定制化文生图模型拓展用于动画生成的框架，可以在保持原有定制化模型画面质量的基础上，生成相应的动画片段。为色彩斑斓的鹅卵石，增添一些动态的光泽。\n\n\n\n![512](https://github.com/ElliotQi/mmagic/assets/46469021/54d92aca-dfa9-4eeb-ba38-3f6c981e5399)\n\n## 模型与结果\n\n我们使用HuggingFace提供的Stable Diffusion权重。如果您使用Diffusers wrapper，您不必手动下载权重，其将自动下载。\n\n\n\n| 模型 | 下载 |\n| :------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: |\n| [ToonYou](./animatediff_ToonYou.py) | [model](https://civitai.com/api/download/models/78775) |\n| [Lyriel](./animatediff_Lyriel.py) | [model](https://civitai.com/api/download/models/72396) |\n| [RcnzCartoon](./animatediff_RcnzCartoon.py) | [model](https://civitai.com/api/download/models/71009) |\n| [MajicMix](./animatediff_MajicMix.py) | [model](https://civitai.com/api/download/models/79068) |\n| [RealisticVision](./animatediff_RealisticVision.py) | [model](https://civitai.com/api/download/models/29460) |\n| [RealisticVision_v2](./animatediff_RealisticVision_v2.py) | [model](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) |\n| [MotionModel_v1-5_v2](./animatediff_RealisticVision_v2.py) | [model](https://download.openxlab.org.cn/models/ElliotQi/AnimateDiff/weight/mm_sd_v15_v2) |\n| [MotionModel_v1-5_2Mval](./animatediff_RealisticVision_v1.py) | [model](https://download.openxlab.org.cn/models/ElliotQi/AnimateDiff/weight/mm_fromscratch_2.5Mval) |\n| [MotionModel_v1-5_10Mval](./animatediff_RealisticVision_v1.py) | WebVid |\n\n最新模型更新请在[浦源_AnimateDiff](https://openxlab.org.cn/models/detail/ElliotQi/AnimateDiff)中查看\n\n## 快速开始\n\n运行以下代码，你可以使用AnimateDiff通过文本生成视频。\n\n#### 小建议\n\n强烈推荐安装 [xformers](https://github.com/facebookresearch/xformers)，512\\*512分辨率可以节省约20G显存。\n\n1. 下载 [ToonYou](https://civitai.com/api/download/models/78775) 和 MotionModule 权重\n\n```bash\n#!/bin/bash\n\nmkdir models && cd models\nmkdir Motion_Module && mkdir DreamBooth_LoRA\ngdown 1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq -O Motion_Module/\ngdown 1ql0g_Ys4UCz2RnokYlBjyOYPbttbIpbu -O models/Motion_Module/\nwget https://civitai.com/api/download/models/78775 -P DreamBooth_LoRA/ --content-disposition --no-check-certificate\n```\n\n2. 修改 `configs/animatediff/animatediff_ToonYou.py` 配置文件中的权重路径\n\n```python\n models_path = {Your Checkpoints Path}\n motion_module_cfg=dict(\n path={Your MotionModule Path}\n ),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path={Your Dreambooth_Lora Path},\n steps=25,\n guidance_scale=7.5)\n```\n\n3. 享受AnimateDiff视频生成吧！\n\n```python\nfrom mmengine import Config\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nimport os\nimport torch\nfrom pathlib import Path\nimport datetime\nfrom mmagic.models.editors.animatediff import save_videos_grid\n\n\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/animatediff/animatediff_ToonYou.py')\nanimatediff = MODELS.build(cfg.model).cuda()\nprompts = [\n \"best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress\",\n\n \"masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes,\",\n\n \"best quality, masterpiece, 1boy, formal, abstract, looking at viewer, masculine, marble pattern\",\n\n \"best quality, masterpiece, 1girl, cloudy sky, dandelion, contrapposto, alternate hairstyle,\"\n]\n\nnegative_prompts = [\n \"\",\n \"badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth\",\n \"\",\n \"\",\n]\n\nsample_idx = 0\nrandom_seeds = cfg.randomness['seed']\nrandom_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)\nsamples = []\ntime_str = datetime.datetime.now().strftime(\"%Y-%m-%dT%H-%M-%S\")\nsavedir = f\"samples/{Path(cfg.model['dream_booth_lora_cfg']['type']).stem}-{time_str}\"\nos.makedirs(savedir)\nfor prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, negative_prompts, random_seeds)):\n output_dict = animatediff.infer(prompt,negative_prompt=n_prompt, video_length=16, height=256, width=256, seed=random_seed,num_inference_steps=cfg.model['dream_booth_lora_cfg']['steps'])\n sample = output_dict['samples']\n prompt = \"-\".join((prompt.replace(\"/\", \"\").split(\" \")[:10]))\n save_videos_grid(sample, f\"{savedir}/sample/{sample_idx}-{prompt}.gif\")\n print(f\"save to {savedir}/sample/{prompt}.gif\")\n samples.append(sample)\n sample_idx += 1\n\nsamples = torch.concat(samples)\nsave_videos_grid(samples, f\"{savedir}/sample.gif\", n_rows=4)\n\n\n```\n\n### 其他配置文件的Prompts\n\n- Lyriel\n\n```yaml\n prompt:\n - \"dark shot, epic realistic, portrait of halo, sunglasses, blue eyes, tartan scarf, white hair by atey ghailan, by greg rutkowski, by greg tocchini, by james gilleard, by joe fenton, by kaethe butcher, gradient yellow, black, brown and magenta color scheme, grunge aesthetic!!! graffiti tag wall background, art by greg rutkowski and artgerm, soft cinematic light, adobe lightroom, photolab, hdr, intricate, highly detailed, depth of field, faded, neutral colors, hdr, muted colors, hyperdetailed, artstation, cinematic, warm lights, dramatic light, intricate details, complex background, rutkowski, teal and orange\"\n - \"A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal\"\n - \"dark theme, medieval portrait of a man sharp features, grim, cold stare, dark colors, Volumetric lighting, baroque oil painting by Greg Rutkowski, Artgerm, WLOP, Alphonse Mucha dynamic lighting hyperdetailed intricately detailed, hdr, muted colors, complex background, hyperrealism, hyperdetailed, amandine van ray\"\n - \"As I have gone alone in there and with my treasures bold, I can keep my secret where and hint of riches new and old. Begin it where warm waters halt and take it in a canyon down, not far but too far to walk, put in below the home of brown.\"\n\n n_prompt:\n - \"3d, cartoon, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, young, loli, elf, 3d, illustration\"\n - \"3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular\"\n - \"dof, grayscale, black and white, bw, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular,badhandsv5-neg, By bad artist -neg 1, monochrome\"\n - \"holding an item, cowboy, hat, cartoon, 3d, disfigured, bad art, deformed,extra limbs,close up,b&w, weird colors, blurry, duplicate, morbid, mutilated, [out of frame], extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, out of frame, ugly, extra limbs, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, Photoshop, video game, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, 3d render\"\n```\n\n- RcnzCartoon\n\n```yaml\nprompt:\n - \"Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded\"\n - \"close up Portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal [rust], elegant, sharp focus, photo by greg rutkowski, soft lighting, vibrant colors, masterpiece, streets, detailed face\"\n - \"absurdres, photorealistic, masterpiece, a 30 year old man with gold framed, aviator reading glasses and a black hooded jacket and a beard, professional photo, a character portrait, altermodern, detailed eyes, detailed lips, detailed face, grey eyes\"\n - \"a golden labrador, warm vibrant colours, natural lighting, dappled lighting, diffused lighting, absurdres, highres,k, uhd, hdr, rtx, unreal, octane render, RAW photo, photorealistic, global illumination, subsurface scattering\"\n\n n_prompt:\n - \"deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation\"\n - \"nude, cross eyed, tongue, open mouth, inside, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, red eyes, muscular\"\n - \"easynegative, cartoon, anime, sketches, necklace, earrings worst quality, low quality, normal quality, bad anatomy, bad hands, shiny skin, error, missing fingers, extra digit, fewer digits, jpeg artifacts, signature, watermark, username, blurry, chubby, anorectic, bad eyes, old, wrinkled skin, red skin, photograph By bad artist -neg, big eyes, muscular face,\"\n - \"beard, EasyNegative, lowres, chromatic aberration, depth of field, motion blur, blurry, bokeh, bad quality, worst quality, multiple arms, badhand\"\n\n```\n\n- MajicMix\n\n```yaml\nprompt:\n - \"1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic\"\n - \"best quality, masterpiece, photorealistic, 1boy, 50 years old beard, dramatic lighting\"\n - \"best quality, masterpiece, photorealistic, 1girl, light smile, shirt with collars, waist up, dramatic lighting, from below\"\n - \"male, man, beard, bodybuilder, skinhead,cold face, tough guy, cowboyshot, tattoo, french windows, luxury hotel masterpiece, best quality, photorealistic\"\n\n n_prompt:\n - \"ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles\"\n - \"nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome\"\n - \"nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome\"\n - \"nude, nsfw, ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, monochrome, grayscale watermark, moles, people\"\n```\n\n- Realistic & Realistic_v2 (两者使用相同prompts和不同的随机种子)\n\n```yaml\n prompt:\n - \"b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3\"\n - \"close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot\"\n - \"photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3\"\n - \"night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain\"\n\n n_prompt:\n - \"semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\"\n - \"semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\"\n - \"blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation\"\n - \"blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, art, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation\"\n\n```\n\n4. 开始训练运动模块脚本:\n\n```bash\n# 4 GPUS\nbash tools/dist_train.sh configs/animatediff/animatediff.py 4\n# 1 GPU\npython tools/train.py configs/animatediff/animatediff.py\n\n```\n\n## 引用\n\n```bibtex\n@article{guo2023animatediff,\n title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning},\n author={Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Wang, Yaohui and Qiao, Yu and Lin, Dahua and Dai, Bo},\n journal={arXiv preprint arXiv:2307.04725},\n year={2023}\n}\n```\n" }, { "path": "configs/animatediff/animatediff_Lyriel.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 10917152860782582783, 6399018107401806238, 15875751942533906793,\n 6653196880059936551\n ],\n diff_rank_seed=True)\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path + 'Motion_Module/mm_sd_v14.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path + 'DreamBooth_LoRA/lyriel_v16.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/animatediff_MajicMix.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 1572448948722921032, 1099474677988590681, 6488833139725635347,\n 18339859844376517918\n ],\n diff_rank_seed=True)\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path + 'Motion_Module/mm_sd_v14.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path +\n 'DreamBooth_LoRA/majicmixRealistic_v5Preview.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/animatediff_RcnzCartoon.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 16931037867122267877, 2094308009433392066, 4292543217695451092,\n 15572665120852309890\n ],\n diff_rank_seed=True)\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path + 'Motion_Module/mm_sd_v14.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path + 'DreamBooth_LoRA/rcnzCartoon3d_v10.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/animatediff_RealisticVision.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 5658137986800322009, 12099779162349365895, 10499524853910852697,\n 16768009035333711932\n ],\n diff_rank_seed=True)\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path + 'Motion_Module/mm_sd_v14.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path +\n 'DreamBooth_LoRA/realisticVisionV20_v20.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/animatediff_RealisticVision_v2.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 13100322578370451493, 14752961627088720670, 9329399085567825781,\n 16987697414827649302\n ],\n diff_rank_seed=True)\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n use_inflated_groupnorm=True,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=True,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=32,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path +\n 'Motion_Module/mm_sd_v15_v2.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path +\n 'DreamBooth_LoRA/realisticVisionV20_v20.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/animatediff_ToonYou.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'\nrandomness = dict(\n seed=[\n 10788741199826055526, 6520604954829636163, 6519455744612555650,\n 16372571278361863751\n ],\n diff_rank_seed=True)\n\nval_prompts = [\n 'best quality, masterpiece, 1girl, looking at viewer,\\\n blurry background, upper body, contemporary, dress',\n 'masterpiece, best quality, 1girl, solo, cherry blossoms,\\\n hanami, pink flower, white flower, spring season, wisteria,\\\n petals, flower, plum blossoms, outdoors, falling petals,\\\n white hair, black eyes,',\n 'best quality, masterpiece, 1boy, formal, abstract,\\\n looking at viewer, masculine, marble pattern',\n 'best quality, masterpiece, 1girl, cloudy sky,\\\n dandelion, contrapposto, alternate hairstyle,'\n]\nval_neg_propmts = [\n '',\n 'badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3,\\\n bad-artist, bad_prompt_version2-neg, teeth',\n '',\n '',\n]\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg=dict(path=models_path + 'Motion_Module/mm_sd_v14.ckpt'),\n dream_booth_lora_cfg=dict(\n type='ToonYou',\n path=models_path + 'DreamBooth_LoRA/toonyou_beta3.safetensors',\n steps=25,\n guidance_scale=7.5))\n" }, { "path": "configs/animatediff/metafile.yml", "content": "Collections:\n- Name: AnimateDiff\n Paper:\n Title: 'AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models\n without Specific Tuning'\n URL: https://arxiv.org/abs/2307.04725\n README: configs/animatediff/README.md\n Task:\n - text2video\n Year: 2023\nModels:\n- Config: configs/animatediff/animatediff_ToonYou.py\n In Collection: AnimateDiff\n Name: animatediff_ToonYou\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/toonyou_beta3.safetensors\n- Config: configs/animatediff/animatediff_Lyriel.py\n In Collection: AnimateDiff\n Name: animatediff_Lyriel\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/lyriel_v16.safetensors\n- Config: configs/animatediff/animatediff_RcnzCartoon.py\n In Collection: AnimateDiff\n Name: animatediff_RcnzCartoon\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/rcnzCartoon3d_v10.safetensors\n- Config: configs/animatediff/animatediff_MajicMix.py\n In Collection: AnimateDiff\n Name: animatediff_MajicMix\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/majicmixRealistic_v5Preview.safetensors\n- Config: configs/animatediff/animatediff_RealisticVision.py\n In Collection: AnimateDiff\n Name: animatediff_RealisticVision\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/realisticVisionV51_v20Novae.safetensors\n- Config: configs/animatediff/animatediff_RealisticVision_v2.py\n In Collection: AnimateDiff\n Name: animatediff_RealisticVision_v2\n Results:\n - Dataset: WebVid\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/mm_sd_v15_v2.ckpt\n- Config: configs/animatediff/animatediff_RealisticVision_v1.py\n In Collection: AnimateDiff\n Name: animatediff_RealisticVision_v1\n Results:\n - Dataset: WebVid\n Metrics: {}\n Task: Text2Video\n - Dataset: WebVid\n Metrics: {}\n Task: Text2Video\n Weights: https://download.openxlab.org.cn/models/Masbfca/AnimateDiff/weight/mm_fromscratch_10Mval.ckpt\n" }, { "path": "configs/aot_gan/README.md", "content": "# AOT-GAN (TVCG'2021)\n\n> [AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://arxiv.org/abs/2104.01431.pdf)\n\n> **Task**: Inpainting\n\n\n\n## Abstract\n\n\n\nState-of-the-art image inpainting approaches can suffer from generating distorted structures and blurry textures in high-resolution images (e.g., 512x512). The challenges mainly drive from (1) image content reasoning from distant contexts, and (2) fine-grained texture synthesis for a large missing region. To overcome these two challenges, we propose an enhanced GAN-based model, named Aggregated COntextual-Transformation GAN (AOT-GAN), for high-resolution image inpainting. Specifically, to enhance context reasoning, we construct the generator of AOT-GAN by stacking multiple layers of a proposed AOT block. The AOT blocks aggregate contextual transformations from various receptive fields, allowing to capture both informative distant image contexts and rich patterns of interest for context reasoning. For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task. Such a training objective forces the discriminator to distinguish the detailed appearances of real and synthesized patches, and in turn, facilitates the generator to synthesize clear textures. Extensive comparisons on Places2, the most challenging benchmark with 1.8 million high-resolution images of 365 complex scenes, show that our model outperforms the state-of-the-art by a significant margin in terms of FID with 38.60% relative improvement. A user study including more than 30 subjects further validates the superiority of AOT-GAN. We further evaluate the proposed AOT-GAN in practical applications, e.g., logo removal, face editing, and object removal. Results show that our model achieves promising completions in the real world. We release code and models in [this https URL](https://github.com/researchmm/AOT-GAN-for-Inpainting).\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Training Resources | Download |\n| :----------------------------------------: | :-----------------: | :----------------: | :--------: | :---------: | :-----------: | :------: | :---: | :---: | :---------------------: | :--------------------------------------------: |\n| [AOT-GAN](./aot-gan_smpgan_4xb4_places-512x512.py) | Places365-Challenge | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [model](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) |\n\nMore results for different mask area:\n\n\n\n| Metric | Mask Area | Paper Results | Reimplemented Results |\n| :-------------- | :-------- | :------------ | :-------------------- |\n| L1 (10^-2) | 1 – 10% | 0.55 | 0.54 |\n| (lower better) | 10 – 20% | 1.19 | 1.47 |\n| | 20 – 30% | 2.11 | 2.79 |\n| | 30 – 40% | 3.20 | 4.38 |\n| | 40 – 50% | 4.51 | 6.28 |\n| | 50 – 60% | 7.07 | 10.16 |\n| PSNR | 1 – 10% | 34.79 | inf |\n| (higher better) | 10 – 20% | 29.49 | 31.22 |\n| | 20 – 30% | 26.03 | 27.65 |\n| | 30 – 40% | 23.58 | 25.06 |\n| | 40 – 50% | 21.65 | 23.01 |\n| | 50 – 60% | 19.01 | 20.05 |\n| SSIM | 1 – 10% | 0.976 | 0.982 |\n| (higher better) | 10 – 20% | 0.940 | 0.951 |\n| | 20 – 30% | 0.890 | 0.911 |\n| | 30 – 40% | 0.835 | 0.866 |\n| | 40 – 50% | 0.773 | 0.815 |\n| | 50 – 60% | 0.682 | 0.739 |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py\n\n# single-gpu train\npython tools/train.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth\n\n# single-gpu test\npython tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{yan2021agg,\n author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},\n title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},\n booktitle = {Arxiv},\n pages={-},\n year = {2020}\n}\n```\n" }, { "path": "configs/aot_gan/README_zh-CN.md", "content": "# AOT-GAN (TVCG'2021)\n\n> [AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://arxiv.org/abs/2104.01431.pdf)\n\n> **任务**: 图像修复\n\n\n\n## 摘要\n\n\n\n\n\n
\n \n
\n\n## 结果与模型\n\n**Places365-Challenge**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :------------------------------------------------: | :----------------: | :-----: | :--------: | :-----------: | :-----: | :---: | :---: | :---------------------: | :------------------------------------------------------------: |\n| [AOT-GAN](./aot-gan_smpgan_4xb4_places-512x512.py) | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [模型](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) |\n\n\n\n| 评估指标 | 掩膜缺损 | 论文结果 | 复现结果 |\n| :-------------- | :------- | :------- | :------- |\n| L1 (10^-2) | 1 – 10% | 0.55 | 0.54 |\n| (lower better) | 10 – 20% | 1.19 | 1.47 |\n| | 20 – 30% | 2.11 | 2.79 |\n| | 30 – 40% | 3.20 | 4.38 |\n| | 40 – 50% | 4.51 | 6.28 |\n| | 50 – 60% | 7.07 | 10.16 |\n| PSNR | 1 – 10% | 34.79 | inf |\n| (higher better) | 10 – 20% | 29.49 | 31.22 |\n| | 20 – 30% | 26.03 | 27.65 |\n| | 30 – 40% | 23.58 | 25.06 |\n| | 40 – 50% | 21.65 | 23.01 |\n| | 50 – 60% | 19.01 | 20.05 |\n| SSIM | 1 – 10% | 0.976 | 0.982 |\n| (higher better) | 10 – 20% | 0.940 | 0.951 |\n| | 20 – 30% | 0.890 | 0.911 |\n| | 30 – 40% | 0.835 | 0.866 |\n| | 40 – 50% | 0.773 | 0.815 |\n| | 50 – 60% | 0.682 | 0.739 |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py\n\n# 单个GPU上训练\npython tools/train.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth\n\n# 单个GPU上测试\npython tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n\n## 引用\n\n```bibtex\n@inproceedings{yan2021agg,\n author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},\n title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},\n booktitle = {Arxiv},\n pages={-},\n year = {2020}\n}\n```\n" }, { "path": "configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py", "content": "_base_ = [\n '../_base_/inpaint_default_runtime.py', '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'aot-gan_smpgan_4xb4_places-512x512'\nsave_dir = './work_dirs'\n\ninput_shape = (512, 512)\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n type='AOTInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='AOTEncoderDecoder',\n encoder=dict(type='AOTEncoder'),\n decoder=dict(type='AOTDecoder'),\n dilation_neck=dict(\n type='AOTBlockNeck', dilation_rates=(1, 2, 4, 8), num_aotblock=8)),\n disc=dict(\n type='SoftMaskPatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n with_spectral_norm=True,\n ),\n loss_gan=dict(\n type='GANLoss',\n gan_type='smgan',\n loss_weight=0.01,\n ),\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg19',\n layer_weights={\n '1': 1.,\n '6': 1.,\n '11': 1.,\n '20': 1.,\n '29': 1.,\n },\n layer_weights_style={\n '8': 1.,\n '17': 1.,\n '26': 1.,\n '31': 1.,\n },\n perceptual_weight=0.1,\n style_weight=250),\n loss_out_percep=True,\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.,\n ),\n train_cfg=dict(\n disc_step=1,\n start_iter=0,\n ))\n\nmask_root = 'data/pconv_mask'\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(\n type='LoadMask',\n mask_mode='set',\n mask_config=dict(\n mask_list_file=f'{mask_root}/train_mask_list.txt',\n prefix=mask_root,\n io_backend='local',\n flag='unchanged',\n color_type='unchanged',\n file_client_kwargs=dict())),\n dict(\n type='RandomResizedCrop',\n keys=['gt'],\n crop_size=input_shape,\n ),\n dict(type='Flip', keys=['gt', 'mask'], direction='horizontal'),\n dict(\n type='Resize',\n keys=['mask'],\n scale=input_shape,\n keep_ratio=False,\n interpolation='nearest'),\n dict(type='RandomRotation', keys=['mask'], degrees=(0.0, 45.0)),\n dict(\n type='ColorJitter',\n keys=['gt'],\n brightness=0.5,\n contrast=0.5,\n saturation=0.5,\n hue=0.5),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(\n type='LoadMask',\n mask_mode='set',\n mask_config=dict(\n mask_list_file=f'{mask_root}/mask_0.5-0.6_list.txt',\n prefix=mask_root,\n io_backend='local',\n color_type='unchanged',\n flag='unchanged',\n file_client_kwargs=dict())),\n dict(\n type='RandomResizedCrop',\n keys=['gt'],\n crop_size=(512, 512),\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(\n batch_size=4,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=500002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.9))),\n disc=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.9))))\n" }, { "path": "configs/aot_gan/metafile.yml", "content": "Collections:\n- Name: AOT-GAN\n Paper:\n Title: 'AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image\n Inpainting'\n URL: https://arxiv.org/abs/2104.01431.pdf\n README: configs/aot_gan/README.md\n Task:\n - inpainting\n Year: 2021\nModels:\n- Config: configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py\n In Collection: AOT-GAN\n Name: aot-gan_smpgan_4xb4_places-512x512\n Results:\n - Dataset: Places365-Challenge\n Metrics:\n PSNR: 19.01\n SSIM: 0.682\n l1 error: 7.07\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth\n" }, { "path": "configs/basicvsr/README.md", "content": "# BasicVSR (CVPR'2021)\n\n> [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://arxiv.org/abs/2012.02181)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nVideo super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels for REDS4 and Y channel for others. The metrics are `PSNR` / `SSIM` .\nThe pretrained weights of SPyNet can be found [here](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth).\n\n| Model | Dataset | PSNR (RGB) | SSIM (RGB) | PSNR (Y) | SSIM (Y) | Training Resources | Download |\n| :----------------------------------------------------: | :----------------: | :---------: | :--------: | :---------: | :--------: | :----------------------: | :------------------------------------------------------------: |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | REDS4 (BIx4) | **31.4170** | **0.8909** | - | - | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | UDM10 (BDx4) | - | - | 33.4478 | 0.9306 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | Vimeo-90K-T (BIx4) | - | - | 36.2848 | 0.9395 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | Vimeo-90K-T (BDx4) | - | - | 34.4700 | 0.9286 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | Vid4 (BIx4) | - | - | 27.2694 | 0.8318 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | Vid4 (BDx4) | - | - | 24.4541 | 0.7455 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | REDS4 (BIx4) | 30.3128 | 0.8660 | - | - | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | UDM10 (BDx4) | - | - | 34.5554 | **0.9451** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | Vimeo-90K-T (BIx4) | - | - | **37.2026** | 0.9434 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | Vimeo-90K-T (BDx4) | - | - | 34.8097 | 0.9316 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | Vid4 (BIx4) | - | - | **27.2755** | **0.8248** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | Vid4 (BDx4) | - | - | 25.0517 | 0.7636 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | REDS4 (BIx4) | 29.0376 | 0.8481 | - | - | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | UDM10 (BDx4) | - | - | **39.9953** | **0.9695** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | Vimeo-90K-T (BIx4) | - | - | 34.6427 | 0.9335 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | Vimeo-90K-T (BDx4) | - | - | **37.5501** | **0.9499** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | Vid4 (BIx4) | - | - | 26.2708 | 0.8022 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | Vid4 (BDx4) | - | - | **27.9791** | **0.8556** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/basicvsr/basicvsr_2xb4_reds4.py\n\n# single-gpu train\npython tools/train.py configs/basicvsr/basicvsr_2xb4_reds4.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/basicvsr/basicvsr_2xb4_reds4.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\n\n# single-gpu test\npython tools/test.py configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{chan2021basicvsr,\n author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n" }, { "path": "configs/basicvsr/README_zh-CN.md", "content": "# BasicVSR (CVPR'2021)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nBasicVSR (CVPR'2021)\n\n```bibtex\n@InProceedings{chan2021basicvsr,\n author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n\n
\n\n
\n\n对于 REDS4，我们对 RGB 通道进行评估。对于其他数据集，我们对 Y 通道进行评估。我们使用 `PSNR` 和 `SSIM` 作为指标。\nSPyNet 的 预训练权重在[这里](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth)。\n\n| 算法 | REDS4 (BIx4)
PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)
PSNR/SSIM (Y) | Vid4 (BIx4)
PSNR/SSIM (Y) | UDM10 (BDx4)
PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)
PSNR/SSIM (Y) | Vid4 (BDx4)
PSNR/SSIM (Y) | GPU 信息 | 下载 |\n| :-: | :-----------------------------: | :---------------------------------: | :--------------------------: | :---------------------------: | :---------------------------------: | :--------------------------: | :-----: | :--: |\n| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | **31.4170/0.8909** | 36.2848/0.9395 | 27.2694/0.8318 | 33.4478/0.9306 | 34.4700/0.9286 | 24.4541/0.7455 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |\n| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | 30.3128/0.8660 | **37.2026/0.9451** | **27.2755/0.8248** | 34.5554/0.9434 | 34.8097/0.9316 | 25.0517/0.7636 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) |\n| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | 29.0376/0.8481 | 34.6427/0.9335 | 26.2708/0.8022 | **39.9953/0.9695** | **37.5501/0.9499** | **27.9791/0.8556** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/basicvsr/basicvsr_2xb4_reds4.py\n\n# 单个GPU上训练\npython tools/train.py configs/basicvsr/basicvsr_2xb4_reds4.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/basicvsr/basicvsr_2xb4_reds4.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\n\n# 单个GPU上测试\npython tools/test.py configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/basicvsr/basicvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/basicvsr/basicvsr_2xb4_reds4.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/basicvsr_test_config.py'\n]\n\nexperiment_name = 'basicvsr_2xb4_reds4'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 4\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRNet',\n mid_channels=64,\n num_blocks=30,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/REDS'\n\ntrain_dataloader = dict(\n num_workers=6,\n batch_size=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_train.txt',\n depth=1,\n num_input_frames=15,\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_val.txt',\n depth=1,\n num_input_frames=100,\n fixed_seq_len=100,\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99)),\n paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)}))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[300000],\n restart_weights=[1],\n eta_min=1e-7)\n\nfind_unused_parameters = True\n" }, { "path": "configs/basicvsr/basicvsr_2xb4_vimeo90k-bd.py", "content": "_base_ = './basicvsr_2xb4_reds4.py'\n\nscale = 4\nexperiment_name = 'basicvsr_2xb4_vimeo90k-bd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='MirrorSequence', keys=['img', 'gt']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data'\nfile_list = [\n 'im1.png', 'im2.png', 'im3.png', 'im4.png', 'im5.png', 'im6.png', 'im7.png'\n]\n\ntrain_dataloader = dict(\n num_workers=6,\n batch_size=4,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),\n data_root=f'{data_root}/vimeo90k',\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_train_GT.txt',\n depth=2,\n num_input_frames=7,\n fixed_seq_len=7,\n load_frames_list=dict(img=file_list, gt=file_list),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n _delete_=True,\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=f'{data_root}/Vid4',\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=1,\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', convert_to='Y'),\n dict(type='SSIM', convert_to='Y'),\n ])\n\nfind_unused_parameters = True\n" }, { "path": "configs/basicvsr/basicvsr_2xb4_vimeo90k-bi.py", "content": "_base_ = './basicvsr_2xb4_vimeo90k-bd.py'\n\nexperiment_name = 'basicvsr_2xb4_vimeo90k-bi'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\ntrain_dataloader = dict(\n dataset=dict(\n type='BasicFramesDataset', data_prefix=dict(img='BIx4', gt='GT')))\n\nval_dataloader = dict(\n dataset=dict(\n type='BasicFramesDataset', data_prefix=dict(img='BIx4', gt='GT')))\n\nfind_unused_parameters = True\n" }, { "path": "configs/basicvsr/metafile.yml", "content": "Collections:\n- Name: BasicVSR\n Paper:\n Title: 'BasicVSR: The Search for Essential Components in Video Super-Resolution\n and Beyond'\n URL: https://arxiv.org/abs/2012.02181\n README: configs/basicvsr/README.md\n Task:\n - video super-resolution\n Year: 2021\nModels:\n- Config: configs/basicvsr/basicvsr_2xb4_reds4.py\n In Collection: BasicVSR\n Name: basicvsr_2xb4_reds4\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 31.417\n SSIM (RGB): 0.8909\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 33.4478\n SSIM (Y): 0.9306\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 36.2848\n SSIM (Y): 0.9395\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 34.47\n SSIM (Y): 0.9286\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.2694\n SSIM (Y): 0.8318\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 24.4541\n SSIM (Y): 0.7455\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\n- Config: configs/basicvsr/basicvsr_2xb4_vimeo90k-bi.py\n In Collection: BasicVSR\n Name: basicvsr_2xb4_vimeo90k-bi\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 30.3128\n SSIM (RGB): 0.866\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 34.5554\n SSIM (Y): 0.9451\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 37.2026\n SSIM (Y): 0.9434\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 34.8097\n SSIM (Y): 0.9316\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.2755\n SSIM (Y): 0.8248\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 25.0517\n SSIM (Y): 0.7636\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth\n- Config: configs/basicvsr/basicvsr_2xb4_vimeo90k-bd.py\n In Collection: BasicVSR\n Name: basicvsr_2xb4_vimeo90k-bd\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 29.0376\n SSIM (RGB): 0.8481\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 39.9953\n SSIM (Y): 0.9695\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 34.6427\n SSIM (Y): 0.9335\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 37.5501\n SSIM (Y): 0.9499\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 26.2708\n SSIM (Y): 0.8022\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 27.9791\n SSIM (Y): 0.8556\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth\n" }, { "path": "configs/basicvsr_pp/README.md", "content": "# BasicVSR++ (CVPR'2022)\n\n> [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://arxiv.org/abs/2104.13371)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nA recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVSR by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a similar computational constraint. In particular, our model BasicVSR++ surpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. In addition to video super-resolution, BasicVSR++ generalizes well to other video restoration tasks such as compressed video enhancement. In NTIRE 2021, BasicVSR++ obtains three champions and one runner-up in the Video Super-Resolution and Compressed Video Enhancement Challenges. Codes and models will be released to MMagic.\n\n\n\n
\n \n
\n\n## Results and models\n\nThe pretrained weights of SPyNet can be found [here](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth).\n\n| Model | Dataset | PSNR (RGB) | SSIM (RGB) | PSNR (Y) | SSIM (Y) | Training Resources | Download |\n| :------------------------------------------------------: | :----------------: | :---------: | :--------: | :---------: | :--------: | :----------------------: | :----------------------------------------------------------: |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | REDS4 (BIx4) | **32.3855** | **0.9069** | - | - | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | UDM10 (BDx4) | - | - | 34.6868 | 0.9417 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | Vid4 (BIx4) | - | - | 27.7674 | 0.8444 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | Vid4 (BDx4) | - | - | 24.6209 | 0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | Vimeo-90K-T (BIx4) | - | - | 36.4445 | 0.9411 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | Vimeo-90K-T (BDx4) | - | - | 34.0372 | 0.9244 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | REDS4 (BIx4) | 31.0126 | 0.8804 | - | - | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | UDM10 (BDx4) | - | - | 33.1211 | 0.9270 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | Vid4 (BIx4) | - | - | **27.7882** | **0.8401** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | Vid4 (BDx4) | - | - | 23.6086 | 0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | Vimeo-90K-T (BIx4) | - | - | **37.7864** | **0.9500** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | Vimeo-90K-T (BDx4) | - | - | 33.8972 | 0.9195 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | REDS4 (BIx4) | 29.2041 | 0.8528 | - | - | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | UDM10 (BDx4) | - | - | **40.7216** | **0.9722** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | Vid4 (BIx4) | - | - | 26.4377 | 0.8074 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | Vid4 (BDx4) | - | - | **29.0400** | **0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | Vimeo-90K-T (BIx4) | - | - | 34.7248 | 0.9351 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | Vimeo-90K-T (BDx4) | - | - | **38.2054** | **0.9550** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n\n**NTIRE 2021 checkpoints**\n\nNote that the following models are finetuned from smaller models. The training schemes of these models will be released when MMagic reaches 5k stars. We provide the pre-trained models here.\n\n| Model | Dataset | Download |\n| :------------------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------------------: |\n| [basicvsr-pp_c128n25_600k_ntire-vsr](./basicvsr-pp_c128n25_600k_ntire-vsr.py) | NTIRE 2021 Video Super-Resolution - Track 1 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_vsr_20210311-1ff35292.pth) |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track1](./basicvsr-pp_c128n25_600k_ntire-decompress-track1.py) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 1 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track1_20210223-7b2eba02.pth) |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track2](./basicvsr-pp_c128n25_600k_ntire-decompress-track2.py) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 2 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track2_20210314-eeae05e6.pth) |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track3](./basicvsr-pp_c128n25_600k_ntire-decompress-track3.py) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 3 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304-6daf4a40.pth) |\n\n\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n\n# single-gpu train\npython tools/train.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n\n# single-gpu test\npython tools/test.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{chan2022basicvsrplusplus,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2022}\n}\n```\n" }, { "path": "configs/basicvsr_pp/README_zh-CN.md", "content": "# BasicVSR++ (CVPR'2022)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nBasicVSR++ (CVPR'2022)\n\n```bibtex\n@InProceedings{chan2022basicvsrplusplus,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2022}\n}\n```\n\n
\n\nSPyNet 的 预训练权重在[这里](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth)。\n\n| 算法 | REDS4 (BIx4) PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4) PSNR/SSIM (Y) | Vid4 (BIx4) PSNR/SSIM (Y) | UDM10 (BDx4) PSNR/SSIM (Y) | Vimeo-90K-T (BDx4) PSNR/SSIM (Y) | Vid4 (BDx4) PSNR/SSIM (Y) | GPU 信息 | Download |\n| :-----: | :--------------------------: | :------------------------------: | :-----------------------: | :------------------------: | :------------------------------: | :-----------------------: | :---------: | :------------: |\n| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **32.3855/0.9069** | 36.4445/0.9411 | 27.7674/0.8444 | 34.6868/0.9417 | 34.0372/0.9244 | 24.6209/0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 31.0126/0.8804 | **37.7864/0.9500** | **27.7882/0.8401** | 33.1211/0.9270 | 33.8972/0.9195 | 23.6086/0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) |\n| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 29.2041/0.8528 | 34.7248/0.9351 | 26.4377/0.8074 | **40.7216/0.9722** | **38.2054/0.9550** | **29.0400/0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |\n\n
\nNTIRE 2021 模型权重文件\n\n请注意，以下模型是从较小的模型中微调而来的。 这些模型的训练方案将在 MMagic 达到 5k star 时发布。 我们在这里提供预训练的模型。\n\n| 算法 | 模型 | 赛道 |\n| ---------------------------------------------------------------------- | ---------------------------------------------------------------------- | ------------------------------------------------------------ |\n| [basicvsr-pp_c128n25_600k_ntire-vsr](./basicvsr-pp_c128n25_600k_ntire-vsr.py) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_vsr_20210311-1ff35292.pth) | NTIRE 2021 Video Super-Resolution |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track1](./basicvsr-pp_c128n25_600k_ntire-decompress-track1.py) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track1_20210223-7b2eba02.pth) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 1 |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track2](./basicvsr-pp_c128n25_600k_ntire-decompress-track2.py) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track2_20210314-eeae05e6.pth) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 2 |\n| [basicvsr-pp_c128n25_600k_ntire-decompress-track3](./basicvsr-pp_c128n25_600k_ntire-decompress-track3.py) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304-6daf4a40.pth) | NTIRE 2021 Quality Enhancement of Compressed Video - Track 3 |\n\n
\n```\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n\n# 单个GPU上训练\npython tools/train.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n\n# 单个GPU上测试\npython tools/test.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track1.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-decompress-track1'\nwork_dir = f'./work_dirs/{experiment_name}'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRPlusPlusNet',\n mid_channels=128,\n num_blocks=25,\n is_low_res_input=False,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n cpu_cache_length=100),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n ensemble=dict(type='SpatialTemporalEnsemble', is_temporal_ensemble=False),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntest_pipeline = [\n dict(\n type='GenerateSegmentIndices',\n interval_list=[1],\n start_idx=1,\n filename_tmpl='{:03d}.png'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ntest_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='ntire21_track1', task_name='vsr'),\n data_root='data/NTIRE21_decompression_track1',\n data_prefix=dict(img='LQ', gt='GT'),\n pipeline=test_pipeline))\n\ntest_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntest_cfg = dict(type='MultiTestLoop')\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track2.py", "content": "_base_ = './basicvsr-pp_c128n25_600k_ntire-decompress-track1.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-decompress-track2'\nwork_dir = f'./work_dirs/{experiment_name}'\n\ntest_dataloader = dict(\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='ntire21_track2', task_name='vsr'),\n data_root='data/NTIRE21_decompression_track2'))\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track3.py", "content": "_base_ = './basicvsr-pp_c128n25_600k_ntire-decompress-track1.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-decompress-track3'\nwork_dir = f'./work_dirs/{experiment_name}'\n\ntest_dataloader = dict(\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='ntire21_track3', task_name='vsr'),\n data_root='data/NTIRE21_decompression_track3'))\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-vsr.py", "content": "experiment_name = 'basicvsr-pp_c128n25_600k_ntire-vsr'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRPlusPlusNet',\n mid_channels=128,\n num_blocks=25,\n is_low_res_input=True,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n cpu_cache_length=100),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n ensemble=dict(type='SpatialTemporalEnsemble', is_temporal_ensemble=False),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntest_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='MirrorSequence', keys=['img', 'gt']),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='MirrorSequence', keys=['img']),\n dict(type='PackInputs')\n]\n\ntest_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds_official', task_name='vsr'),\n data_root='data/REDS',\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_official_val.txt',\n depth=1,\n num_input_frames=100,\n fixed_seq_len=100,\n pipeline=test_pipeline))\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bd.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth' # noqa\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRPlusPlusNet',\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=-1),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_dataloader = dict(num_workers=6, batch_size=2)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),\n paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.25)}))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\nfind_unused_parameters = True\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bi.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth' # noqa\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRPlusPlusNet',\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=-1),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_dataloader = dict(num_workers=6, batch_size=2)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),\n paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.25)}))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\nfind_unused_parameters = True\n" }, { "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_reds4.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_8xb1-600k_reds4'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='BasicVSRPlusPlusNet',\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_dataloader = dict(\n num_workers=6, batch_size=1, dataset=dict(num_input_frames=30))\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=600_000, val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),\n paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.25)}))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[600000],\n restart_weights=[1],\n eta_min=1e-7)\n" }, { "path": "configs/basicvsr_pp/metafile.yml", "content": "Collections:\n- Name: BasicVSR++\n Paper:\n Title: 'BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation\n and Alignment'\n URL: https://arxiv.org/abs/2104.13371\n README: configs/basicvsr_pp/README.md\n Task:\n - video super-resolution\n Year: 2022\nModels:\n- Config: configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c64n7_8xb1-600k_reds4\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 32.3855\n SSIM (RGB): 0.9069\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 34.6868\n SSIM (Y): 0.9417\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.7674\n SSIM (Y): 0.8444\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 24.6209\n SSIM (Y): 0.754\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 36.4445\n SSIM (Y): 0.9411\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 34.0372\n SSIM (Y): 0.9244\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 31.0126\n SSIM (RGB): 0.8804\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 33.1211\n SSIM (Y): 0.927\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.7882\n SSIM (Y): 0.8401\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 23.6086\n SSIM (Y): 0.7033\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 37.7864\n SSIM (Y): 0.95\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 33.8972\n SSIM (Y): 0.9195\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 29.2041\n SSIM (RGB): 0.8528\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 40.7216\n SSIM (Y): 0.9722\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 26.4377\n SSIM (Y): 0.8074\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 29.04\n SSIM (Y): 0.8753\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 34.7248\n SSIM (Y): 0.9351\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 38.2054\n SSIM (Y): 0.955\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-vsr.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c128n25_600k_ntire-vsr\n Results:\n - Dataset: NTIRE2021VideoSuper-Resolution-Track1\n Metrics: {}\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_vsr_20210311-1ff35292.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track1.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c128n25_600k_ntire-decompress-track1\n Results:\n - Dataset: NTIRE2021QualityEnhancementofCompressedVideo-Track1\n Metrics: {}\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track1_20210223-7b2eba02.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track2.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c128n25_600k_ntire-decompress-track2\n Results:\n - Dataset: NTIRE2021QualityEnhancementofCompressedVideo-Track2\n Metrics: {}\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track2_20210314-eeae05e6.pth\n- Config: configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track3.py\n In Collection: BasicVSR++\n Name: basicvsr-pp_c128n25_600k_ntire-decompress-track3\n Results:\n - Dataset: NTIRE2021QualityEnhancementofCompressedVideo-Track3\n Metrics: {}\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304-6daf4a40.pth\n" }, { "path": "configs/biggan/README.md", "content": "# BigGAN (ICLR'2019)\n\n> [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/forum?id=B1xsqj09Fm)\n\n> **Task**: Conditional GANs\n\n\n\n## Abstract\n\n\n\nDespite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.\n\n\n\n
\n\n
\n\n## Introduction\n\nThe `BigGAN/BigGAN-Deep` is a conditional generation model that can generate both high-resolution and high-quality images by scaling up the batch size and the number of model parameters.\n\nWe have finished training `BigGAN` in `Cifar10` (32x32) and are aligning training performance in `ImageNet1k` (128x128). Some sampled results are shown below for your reference.\n\n
\n Results from our BigGAN trained in CIFAR10\n
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\n Results from our BigGAN trained in ImageNet\n
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\n\nEvaluation of our trained BigGAN.\n\n| Model | Dataset | FID (Iter) | IS (Iter) | Download |\n| :-------------------------------------------------------------------------: | :--------: | :---------------: | :-----------------: | :-----------------------------------------------------------------------------: |\n| [BigGAN 32x32](./biggan_2xb25-500kiters_cifar10-32x32.py) | CIFAR10 | 9.78(390000) | 8.70(390000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32_b25x2_500k_20210706_171051.log.json) |\n| [BigGAN 128x128 Best FID](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | ImageNet1k | **8.69**(1232000) | 101.15(1232000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) |\n| [BigGAN 128x128 Best IS](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | ImageNet1k | 13.51(1328000) | **129.07**(1328000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) |\n\n### Note on reproducibility\n\n`BigGAN 128x128` model is trained with V100 GPUs and CUDA 10.1 and can hardly reproduce the result with A100 and CUDA 11.3. If you have any idea about the reproducibility, please feel free to contact with us.\n\n## Converted weights\n\nSince we haven't finished training our models, we provide you with several pre-trained weights which have been evaluated. Here, we refer to [BigGAN-PyTorch](https://github.com/ajbrock/BigGAN-PyTorch) and [pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN).\n\nEvaluation results and download links are provided below.\n\n| Model | Dataset | FID | IS | Download | Original Download link |\n| :--------------------------------------------------: | :--------: | :-----: | :-----: | :-----------------------------------------------------: | :-------------------------------------------------------------------: |\n| [BigGAN 128x128](./biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py) | ImageNet1k | 10.1414 | 96.728 | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth) | [link](https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW) |\n| [BigGAN-Deep 128x128](./biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py) | ImageNet1k | 5.9471 | 107.161 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin) |\n| [BigGAN-Deep 256x256](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py) | ImageNet1k | 11.3151 | 135.107 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin) |\n| [BigGAN-Deep 512x512](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py) | ImageNet1k | 16.8728 | 124.368 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin) |\n\nSampling results are shown below.\n\n
\n Results from our BigGAN-Deep with Pre-trained weights in ImageNet 128x128 with truncation factor 0.4\n
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\n Results from our BigGAN-Deep with Pre-trained weights in ImageNet 256x256 with truncation factor 0.4\n
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\n Results from our BigGAN-Deep with Pre-trained weights in ImageNet 512x512 truncation factor 0.4\n
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\nSampling with truncation trick above can be performed by command below.\n\n```bash\npython demo/conditional_demo.py CONFIG_PATH CKPT_PATH --sample-cfg truncation=0.4 # set truncation value as you want\n```\n\nFor converted weights, we provide model configs under `configs/_base_/models` listed as follows:\n\n```bash\n# biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py\n# biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py\n# biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py\n# biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py\n```\n\n## Interpolation\n\nTo perform image Interpolation on BigGAN(or other conditional models), run\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH\n```\n\n
\n Image interpolating Results of our BigGAN-Deep\n
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\n\nTo perform image Interpolation on BigGAN with fixed noise, run\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH --fix-z\n```\n\n
\n Image interpolating Results of our BigGAN-Deep with fixed noise\n
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\nTo perform image Interpolation on BigGAN with fixed label, run\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH --fix-y\n```\n\n
\n Image interpolating Results of our BigGAN-Deep with fixed label\n
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\n\n## Citation\n\n```latex\n@inproceedings{\n brock2018large,\n title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},\n author={Andrew Brock and Jeff Donahue and Karen Simonyan},\n booktitle={International Conference on Learning Representations},\n year={2019},\n url={https://openreview.net/forum?id=B1xsqj09Fm},\n}\n```\n" }, { "path": "configs/biggan/README_zh-CN.md", "content": "# BigGAN (ICLR'2019)\n\n> [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/forum?id=B1xsqj09Fm)\n\n> **任务**: 条件生成对抗网络\n\n\n\n## Abstract\n\n\n\nDespite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.\n\n\n\n
\n\n
\n\n## Introduction\n\n`BigGAN/BigGAN-Deep`是一个条件生成模型，通过扩大批次大小和模型参数的数量，可以生成高分辨率和高质量的图像。\n\n我们已经在`Cifar10`（32x32）中完成了`BigGAN`的训练，并在`ImageNet1k`（128x128）上对齐了训练性能。下面是一些抽样的结果，供你参考。\n\n
\n 我们在 CIFAR10 上训练的 BigGAN 的结果\n
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\n 我们在 ImageNet 上训练的 BigGAN 的结果\n
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\n\n对我们训练的 BigGAN 进行评估.\n\n| 算法 | 数据集 | FID (Iter) | IS (Iter) | 下载 |\n| :---------------------------------------------------------------------------: | :--------: | :---------------: | :-----------------: | :---------------------------------------------------------------------------: |\n| [BigGAN 32x32](./biggan_2xb25-500kiters_cifar10-32x32.py) | CIFAR10 | 9.78(390000) | 8.70(390000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32_b25x2_500k_20210706_171051.log.json) |\n| [BigGAN 128x128 Best FID](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | ImageNet1k | **8.69**(1232000) | 101.15(1232000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) |\n| [BigGAN 128x128 Best IS](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | ImageNet1k | 13.51(1328000) | **129.07**(1328000) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth)\\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) |\n\n### 关于可复现性的说明\n\n`BigGAN 128x128`模型是用 V100 GPU 和 CUDA 10.1 训练的，用 A100 和 CUDA 11.3 很难再现结果。如果你对复现有任何想法，请随时与我们联系。\n\n## 转换后的权重\n\n由于我们还没有完成对模型的训练，我们为您提供了几个已经评估过的预训练权重。这里，我们指的是[BigGAN-PyTorch](https://github.com/ajbrock/BigGAN-PyTorch)和[pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN)。\n\n下面提供了评估结果和下载链接\n\n| 模型 | 数据集 | FID | IS | 下载 | 原始权重下载链接 |\n| :------------------------------------------------------: | :--------: | :-----: | :-----: | :-------------------------------------------------------: | :-------------------------------------------------------------: |\n| [BigGAN 128x128](./biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py) | ImageNet1k | 10.1414 | 96.728 | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth) | [link](https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW) |\n| [BigGAN-Deep 128x128](./biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py) | ImageNet1k | 5.9471 | 107.161 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin) |\n| [BigGAN-Deep 256x256](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py) | ImageNet1k | 11.3151 | 135.107 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin) |\n| [BigGAN-Deep 512x512](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py) | ImageNet1k | 16.8728 | 124.368 | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin) |\n\n采样结果如下。\n\n
\n BigGAN-Deep 在 ImageNet 128x128 中使用预训练权重的结果，截断因子为 0.4 \n
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\n BigGAN-Deep 在 ImageNet 256x256 中使用预训练权重的结果，截断因子为 0.4 \n
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\n BigGAN-Deep 在 ImageNet 512x512 中使用预训练权重的结果，截断因子为 0.4 \n
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\n上面的截断取样技巧可以通过下面的命令进行。\n\n```bash\npython demo/conditional_demo.py CONFIG_PATH CKPT_PATH --sample-cfg truncation=0.4 # set truncation value as you want\n```\n\n对于转换后的权重，我们在`configs/_base_/models`下提供模型配置，列举如下。\n\n```bash\n# biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py\n# biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py\n# biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py\n# biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py\n```\n\n## Interpolation\n\n要在 BigGAN（或其他条件模型）上执行图像插值，请运行\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH\n```\n\n
\n 我们的 BigGAN-Deep 的图像插值结果\n
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\n\n要在 BigGAN 上进行具有固定噪声的图像插值，请运行\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH --fix-z\n```\n\n
\n 我们的 BigGAN-Deep 在固定噪音下的图像插值结果 \n
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\n要在 BigGAN 上执行具有固定标签的图像插值，请运行\n\n```bash\npython apps/conditional_interpolate.py CONFIG_PATH CKPT_PATH --samples-path SAMPLES_PATH --fix-y\n```\n\n
\n 我们的 BigGAN-Deep 带有固定标签的图像插值结果\n
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\n\n## Citation\n\n```latex\n@inproceedings{\n brock2018large,\n title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},\n author={Andrew Brock and Jeff Donahue and Karen Simonyan},\n booktitle={International Conference on Learning Representations},\n year={2019},\n url={https://openreview.net/forum?id=B1xsqj09Fm},\n}\n```\n" }, { "path": "configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_noaug_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n ema_config=ema_config,\n generator=dict(\n type='BigGANDeepGenerator',\n output_scale=128,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDeepDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_noaug_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# setting image size to 256x256\n_base_.train_dataloader.dataset.pipeline[2].scale = (256, 256)\n_base_.test_dataloader.dataset.pipeline[2].scale = (256, 256)\n_base_.val_dataloader.dataset.pipeline[2].scale = (256, 256)\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n ema_config=ema_config,\n generator=dict(\n type='BigGANDeepGenerator',\n output_scale=256,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDeepDiscriminator',\n input_scale=256,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_noaug_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# setting image size to 512x512\n_base_.train_dataloader.dataset.pipeline[2].scale = (512, 512)\n_base_.test_dataloader.dataset.pipeline[2].scale = (512, 512)\n_base_.val_dataloader.dataset.pipeline[2].scale = (512, 512)\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n ema_config=ema_config,\n generator=dict(\n type='BigGANDeepGenerator',\n output_scale=512,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDeepDiscriminator',\n input_scale=512,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py", "content": "_base_ = [\n '../_base_/datasets/cifar10_noaug.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# define model\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n start_iter=1000)\n\nmodel = dict(\n type='BigGAN',\n num_classes=10,\n data_preprocessor=dict(\n type='DataPreprocessor', output_channel_order='BGR'),\n generator=dict(\n type='BigGANGenerator',\n output_scale=32,\n noise_size=128,\n num_classes=10,\n base_channels=64,\n with_shared_embedding=False,\n sn_eps=1e-8,\n sn_style='torch',\n split_noise=False,\n auto_sync_bn=False,\n init_cfg=dict(type='N02')),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=32,\n num_classes=10,\n base_channels=64,\n sn_eps=1e-8,\n sn_style='torch',\n with_spectral_norm=True,\n init_cfg=dict(type='N02')),\n generator_steps=1,\n discriminator_steps=4,\n ema_config=ema_config)\n\n# define dataset\ntrain_dataloader = dict(batch_size=25, num_workers=8)\nval_dataloader = dict(batch_size=25, num_workers=8)\ntest_dataloader = dict(batch_size=25, num_workers=8)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n]\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999))))\ntrain_cfg = dict(max_iters=500000)\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/models/biggan/base_biggan_128x128.py',\n '../_base_/datasets/imagenet_noaug_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# define model\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(ema_config=ema_config)\ntrain_cfg = dict(max_iters=1500000)\n\n# define dataset\ntrain_dataloader = dict(\n batch_size=32, num_workers=8, dataset=dict(data_root='data/imagenet'))\n\n# define optimizer\noptim_wrapper = dict(\n generator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=10000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n]\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_noaug_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n ema_config=ema_config,\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/biggan/metafile.yml", "content": "Collections:\n- Name: BigGAN\n Paper:\n Title: Large Scale GAN Training for High Fidelity Natural Image Synthesis\n URL: https://openreview.net/forum?id=B1xsqj09Fm\n README: configs/biggan/README.md\n Task:\n - conditional gans\n Year: 2019\nModels:\n- Config: configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py\n In Collection: BigGAN\n Name: biggan_2xb25-500kiters_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID (Iter): 9.78\n IS (Iter): 8.7\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth\n- Config: configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py\n In Collection: BigGAN\n Name: biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128\n Results:\n - Dataset: ImageNet1k\n Metrics:\n FID (Iter): 8.69\n IS (Iter): 101.15\n Task: Conditional GANs\n - Dataset: ImageNet1k\n Metrics:\n FID (Iter): 13.51\n IS (Iter): 129.07\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth\n- Config: configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py\n In Collection: BigGAN\n Name: biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128\n Results:\n - Dataset: ImageNet1k\n Metrics:\n FID: 10.1414\n IS: 96.728\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth\n- Config: configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py\n In Collection: BigGAN\n Name: biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128\n Results:\n - Dataset: ImageNet1k\n Metrics:\n FID: 5.9471\n IS: 107.161\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth\n- Config: configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py\n In Collection: BigGAN\n Name: biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256\n Results:\n - Dataset: ImageNet1k\n Metrics:\n FID: 11.3151\n IS: 135.107\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth\n- Config: configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py\n In Collection: BigGAN\n Name: biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512\n Results:\n - Dataset: ImageNet1k\n Metrics:\n FID: 16.8728\n IS: 124.368\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth\n" }, { "path": "configs/cain/README.md", "content": "# CAIN (AAAI'2020)\n\n> [Channel Attention Is All You Need for Video Frame Interpolation](https://aaai.org/ojs/index.php/AAAI/article/view/6693/6547)\n\n> **Task**: Video Interpolation\n\n\n\n## Abstract\n\n\n\nPrevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuffle, with a channel attention, which replaces the optical flow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels.\nThe metrics are `PSNR / SSIM` .\nThe learning rate adjustment strategy is `Step LR scheduler with min_lr clipping`.\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :---------------------------------------------------------------------: | :--------: | :-----: | :----: | :----------------------: | :--------------------------------------------------------------------------------: |\n| [cain_b5_g1b32_vimeo90k_triplet](./cain_g1b32_1xb5_vimeo90k-triplet.py) | vimeo90k-T | 34.6010 | 0.9578 | 1 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[log](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py\n\n# single-gpu train\npython tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth\n\n# single-gpu test\npython tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{choi2020channel,\n title={Channel attention is all you need for video frame interpolation},\n author={Choi, Myungsub and Kim, Heewon and Han, Bohyung and Xu, Ning and Lee, Kyoung Mu},\n booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n volume={34},\n number={07},\n pages={10663--10671},\n year={2020}\n}\n```\n" }, { "path": "configs/cain/README_zh-CN.md", "content": "# CAIN (AAAI'2020)\n\n> **任务**: 视频插帧\n\n\n\n
\nCAIN (AAAI'2020)\n\n```bibtex\n@inproceedings{choi2020channel,\n title={Channel attention is all you need for video frame interpolation},\n author={Choi, Myungsub and Kim, Heewon and Han, Bohyung and Xu, Ning and Lee, Kyoung Mu},\n booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n volume={34},\n number={07},\n pages={10663--10671},\n year={2020}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n学习率调整策略是等间隔调整策略。\n\n| 算法 | vimeo-90k-triplet | GPU 信息 | 下载 |\n| :---------------------------------------------------------------------: | :---------------: | :----------------------: | :--------------------------------------------------------------------------------------: |\n| [cain_b5_g1b32_vimeo90k_triplet](./cain_g1b32_1xb5_vimeo90k-triplet.py) | 34.6010 / 0.9578 | 1 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[日志](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py\n\n# 单个GPU上训练\npython tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth\n\n# 单个GPU上测试\npython tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'cain_g1b32_1xb5_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n type='CAIN',\n generator=dict(type='CAINNet'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n pad_mode='reflect',\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='FixedCrop', keys=['img', 'gt'], crop_size=(256, 256)),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(\n type='ColorJitter',\n keys=['img', 'gt'],\n channel_order='rgb',\n brightness=0.05,\n contrast=0.05,\n saturation=0.05,\n hue=0.05),\n dict(type='TemporalReverse', keys=['img'], reverse_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ntrain_dataset_type = 'BasicFramesDataset'\nval_dataset_type = 'BasicFramesDataset'\ndata_root = 'data/vimeo_triplet'\n\ntrain_dataloader = dict(\n num_workers=32,\n batch_size=32, # 1 gpu\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=True),\n dataset=dict(\n type=train_dataset_type,\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=val_dataset_type,\n ann_file='tri_testlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=val_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='ReduceLR',\n by_epoch=True,\n mode='min',\n factor=0.5,\n patience=5,\n cooldown=0)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=1,\n save_optimizer=True,\n by_epoch=True,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n param_scheduler=dict(\n type='ReduceLRSchedulerHook',\n by_epoch=True,\n interval=1,\n val_metric='MAE'),\n)\n\nlog_processor = dict(type='LogProcessor', by_epoch=True)\n" }, { "path": "configs/cain/metafile.yml", "content": "Collections:\n- Name: CAIN\n Paper:\n Title: Channel Attention Is All You Need for Video Frame Interpolation\n URL: https://aaai.org/ojs/index.php/AAAI/article/view/6693/6547\n README: configs/cain/README.md\n Task:\n - video interpolation\n Year: 2020\nModels:\n- Config: configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py\n In Collection: CAIN\n Name: cain_g1b32_1xb5_vimeo90k-triplet\n Results:\n - Dataset: vimeo90k-T\n Metrics:\n PSNR: 34.601\n SSIM: 0.9578\n Task: Video Interpolation\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth\n" }, { "path": "configs/controlnet/README.md", "content": "# Control Net (2023)\n\n> [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)\n\n> **Task**: Text2Image\n\n\n\n## Abstract\n\n\n\nWe present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (\\< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.\n\n\n\n
\n\n
\n\n## Pretrained models\n\nWe use ControlNet's weights provided by HuggingFace Diffusers. You do not have to download the weights manually. If you use Diffusers wrapper, the weights will be downloaded automatically.\n\nThis model has several weights including vae, unet and clip. You should download the weights from [stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and change the 'pretrained_model_path' in config to the weights dir.\n\n| Model | Dataset | Download |\n| :---------------------------------------------: | :-----: | :----------------------------------------------------------------------------------------------: |\n| [ControlNet-Demo](./controlnet-1xb1-fill50k.py) | - | - |\n| [ControlNet-Canny](./controlnet-canny.py) | - | [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_canny.pth) |\n| [ControlNet-Pose](./controlnet-pose.py) | - | [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_openpose.pth) |\n| [ControlNet-Segmentation](./controlnet-seg.py) | - | [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth) |\n\nNoted that, [ControlNet-Demo](./controlnet-1xb1-demo_dataset.py) is a demo config to train ControlNet with toy dataset named Fill50K.\n\nBesides above configs, ControlNet have weight with other condition inputs, such as [depth](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_depth.pth), [hed](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_hed.pth), [mlsd](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_mlsd.pth), [normal](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_normal.pth), [scribble](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_scribble.pth). You can simple change the `from_pretrained` field of ControlNet to use these weights. For example:\n\n```python\n# Switch from canny....\ncontrolnet=dict(\n type='ControlNetModel',\n from_pretrained='lllyasviel/sd-controlnet-canny')\n\n# To normal....\ncontrolnet=dict(\n type='ControlNetModel',\n from_pretrained='lllyasviel/sd-controlnet-normal')\n```\n\n## Quick Start\n\nRunning the following codes, you can get a text-generated image.\n\n```python\nimport cv2\nimport numpy as np\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/controlnet/controlnet-canny.py')\ncontrolnet = MODELS.build(cfg.model).cuda()\n\nprompt = 'Room with blue walls and a yellow ceiling.'\ncontrol_url = 'https://user-images.githubusercontent.com/28132635/230288866-99603172-04cb-47b3-8adb-d1aa532d1d2c.jpg'\ncontrol_img = mmcv.imread(control_url)\ncontrol = cv2.Canny(control_img, 100, 200)\ncontrol = control[:, :, None]\ncontrol = np.concatenate([control] * 3, axis=2)\ncontrol = Image.fromarray(control)\n\noutput_dict = controlnet.infer(prompt, control=control)\nsamples = output_dict['samples']\nfor idx, sample in enumerate(samples):\n sample.save(f'sample_{idx}.png')\ncontrols = output_dict['controls']\nfor idx, control in enumerate(controls):\n control.save(f'control_{idx}.png')\n```\n\n\n\n \n \n \n \n
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\n\nIf you want to pretrained weights rather than original Stable-Diffusion v1.5, you can refers to the following codes.\n\n```python\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/controlnet/controlnet-pose.py')\n# convert ControlNet's weight from SD-v1.5 to Counterfeit-v2.5\ncfg.model.unet.from_pretrained = 'gsdf/Counterfeit-V2.5'\ncfg.model.vae.from_pretrained = 'gsdf/Counterfeit-V2.5'\ncfg.model.init_cfg['type'] = 'convert_from_unet'\n\ncontrolnet = MODELS.build(cfg.model).cuda()\n# call init_weights manually to convert weight\ncontrolnet.init_weights()\n\nprompt = 'masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth'\n\ncontrol_url = 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png'\ncontrol_img = mmcv.imread(control_url)\ncontrol = Image.fromarray(control_img)\ncontrol.save('control.png')\n\noutput_dict = controlnet.infer(prompt, control=control, width=512, height=512, guidance_scale=7.5)\nsamples = output_dict['samples']\nfor idx, sample in enumerate(samples):\n sample.save(f'sample_{idx}.png')\ncontrols = output_dict['controls']\nfor idx, control in enumerate(controls):\n control.save(f'control_{idx}.png')\n```\n\n\n\n \n \n \n \n
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\n 'sample_0.png'\n
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\n\n### Using MMInferencer\n\nYou can only use several lines of codes to play controlnet by MMagic!\n\n```python\nfrom mmagic.apis import MMagicInferencer\n\n# controlnet-canny\ncontrolnet_canny_inferencer = MMagicInferencer(model_name='controlnet', model_setting=1)\ntext_prompts = 'Room with blue walls and a yellow ceiling.'\ncontrol = 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png'\nresult_out_dir = 'controlnet_canny_res.png'\ncontrolnet_canny_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)\n\n# controlnet-pose\ncontrolnet_pose_inferencer = MMagicInferencer(model_name='controlnet', model_setting=2)\ntext_prompts = 'masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth'\ncontrol = 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png'\nresult_out_dir = 'controlnet_pose_res.png'\ncontrolnet_pose_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)\n\n# controlnet-seg\ncontrolnet_seg_inferencer = MMagicInferencer(model_name='controlnet', model_setting=3)\ntext_prompts = 'black house, blue sky'\ncontrol = 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/243599897-553a4c46-c61d-46df-b820-59a49aaf6678.png'\nresult_out_dir = 'controlnet_seg_res.png'\ncontrolnet_seg_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)\n```\n\n## Train your own ControlNet!\n\nYou can start training your own ControlNet with the toy dataset [Fill50K](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip) with the following command:\n\n```bash\nbash tools/dist_train.sh configs/controlnet/controlnet-1xb1-demo_dataset 1\n```\n\nIf you want use gradient accumulation, you can add `accumulative_counts` field to the optimizer's config as follow:\n\n```python\n# From...\noptim_wrapper = dict(controlnet=dict(optimizer=dict(type='AdamW', lr=1e-5)))\n# To...\noptim_wrapper = dict(\n controlnet=dict(accumulative_counts=4, optimizer=dict(type='AdamW', lr=1e-5)))\n```\n\n## Use ToMe to accelerate your training and inference\n\nWe support **[tomesd](https://github.com/dbolya/tomesd)** now! It is developed for stable-diffusion-based models referring to [ToMe](https://github.com/facebookresearch/ToMe), an efficient ViT speed-up tool based on token merging. To work on with **tomesd** in `mmagic`, you just need to add `tomesd_cfg` to `model` in [ControlNet-Canny](./controlnet-canny.py). The only requirement is `torch >= 1.12.1` in order to properly support `torch.Tensor.scatter_reduce()` functionality. Please do check it before running the demo.\n\n```python\nmodel = dict(\n type='ControlStableDiffusion',\n ...\n tomesd_cfg=dict(ratio=0.5),\n ...\n init_cfg=dict(type='init_from_unet'))\n```\n\nFor more details, you can refer to [Stable Diffusion Acceleration](../stable_diffusion/README.md#use-tome-to-accelerate-your-stable-diffusion-model).\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py) and [ControlNet](https://huggingface.co/lllyasviel/ControlNet/tree/main/models).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@misc{zhang2023adding,\n title={Adding Conditional Control to Text-to-Image Diffusion Models},\n author={Lvmin Zhang and Maneesh Agrawala},\n year={2023},\n eprint={2302.05543},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n" }, { "path": "configs/controlnet/controlnet-1xb1-fill50k.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-controlnet-canny'\n\nmodel = dict(\n type='ControlStableDiffusion',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n controlnet=dict(\n type='ControlNetModel',\n # from_pretrained=controlnet_canny_rul\n from_config=controlnet_canny_url # train from scratch\n ),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg=dict(type='init_from_unet'))\n\n# config for training\ntrain_cfg = dict(max_iters=10000)\noptim_wrapper = dict(controlnet=dict(optimizer=dict(type='AdamW', lr=1e-5)))\n\n# Config for data loader\npipeline = [\n dict(type='LoadImageFromFile', key='source', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='target', channel_order='rgb'),\n dict(\n type='PackInputs',\n keys=['source', 'target'],\n data_keys='prompt',\n meta_keys=[\n 'source_channel_order', 'source_color_type',\n 'target_channel_order', 'target_color_type'\n ])\n]\ndataset = dict(\n type='ControlNetDataset',\n data_root='./data/fill50k',\n ann_file='prompt.json',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=4)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=300,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=4,\n n_row=2)\n]\n" }, { "path": "configs/controlnet/controlnet-canny.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-controlnet-canny'\n\nmodel = dict(\n type='ControlStableDiffusion',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n controlnet=dict(\n type='ControlNetModel', from_pretrained=controlnet_canny_url),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg=dict(type='init_from_unet'))\n" }, { "path": "configs/controlnet/controlnet-pose.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-controlnet-openpose'\n\nmodel = dict(\n type='ControlStableDiffusion',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained='gsdf/Counterfeit-V2.5',\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained='gsdf/Counterfeit-V2.5'),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n controlnet=dict(\n type='ControlNetModel', from_pretrained=controlnet_canny_url),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg=dict(type='convert_from_unet'))\n" }, { "path": "configs/controlnet/controlnet-seg.py", "content": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-controlnet-seg'\n\nmodel = dict(\n type='ControlStableDiffusion',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n controlnet=dict(\n type='ControlNetModel', from_pretrained=controlnet_canny_url),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg=dict(type='init_from_unet'))\n" }, { "path": "configs/controlnet/metafile.yml", "content": "Collections:\n- Name: Control Net\n Paper:\n Title: Adding Conditional Control to Text-to-Image Diffusion Models\n URL: https://arxiv.org/abs/2302.05543\n README: configs/controlnet/README.md\n Task:\n - text2image\n Year: 2023\nModels:\n- Config: configs/controlnet/controlnet-1xb1-fill50k.py\n In Collection: Control Net\n Name: controlnet-1xb1-fill50k\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/controlnet/controlnet-canny.py\n In Collection: Control Net\n Name: controlnet-canny\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_canny.pth\n- Config: configs/controlnet/controlnet-pose.py\n In Collection: Control Net\n Name: controlnet-pose\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_openpose.pth\n- Config: configs/controlnet/controlnet-seg.py\n In Collection: Control Net\n Name: controlnet-seg\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth\n" }, { "path": "configs/controlnet_animation/README.md", "content": "# Controlnet Animation (2023)\n\n> [Controlnet](https://github.com/lllyasviel/ControlNet) Application\n\n> **Task**: controlnet_animation\n\n\n\n## Abstract\n\n\n\nIt is difficult to keep consistency and avoid video frame flickering when using stable diffusion to generate video frame by frame.\nHere we reproduce two methods that effectively avoid video flickering:\n\n**Controlnet with multi-frame rendering**. [ControlNet](https://github.com/lllyasviel/ControlNet) is a neural network structure to control diffusion models by adding extra conditions.\n[Multi-frame rendering](https://xanthius.itch.io/multi-frame-rendering-for-stablediffusion) is a community method to reduce flickering.\nWe use controlnet with hed condition and stable diffusion img2img for multi-frame rendering.\n\n**Controlnet with attention injection**. Attention injection is widely used to generate the current frame from a reference image. There is an implementation in [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet#reference-only-control) and we use some of their code to create the animation in this repo.\n\nYou may need 40G GPU memory to run controlnet with multi-frame rendering and 10G GPU memory for controlnet with attention injection. If the config file is not changed, it defaults to using controlnet with attention injection.\n\n## Demos\n\nprompt key words: a handsome man, silver hair, smiling, play basketball\n\n
\n
\n\nprompt key words: a handsome man\n\n
\n
\n\n \n\n**Change prompt to get different result**\n\nprompt key words: a girl, black hair, white pants, smiling, play basketball\n\n
\n
\n\n## Pretrained models\n\nWe use pretrained model from hugging face.\n\n| Model | Dataset | Download |\n| :-----------------------------------------: | :-----: | :-------------------------------------------------------------------------------: |\n| [anythingv3 config](./anythingv3_config.py) | - | [stable diffusion model](https://huggingface.co/Linaqruf/anything-v3.0/tree/main) |\n\n## Quick Start\n\nThere are two ways to try controlnet animation.\n\n### 1. Use MMagic inference API.\n\nRunning the following codes, you can get an generated animation video.\n\n```python\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMEdit instance and infer\neditor = MMagicInferencer(model_name='controlnet_animation')\n\nprompt = 'a girl, black hair, T-shirt, smoking, best quality, extremely detailed'\nnegative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, ' + \\\n 'extra digit, fewer digits, cropped, worst quality, low quality'\n\n# you can download the example video with this link\n# https://user-images.githubusercontent.com/12782558/227418400-80ad9123-7f8e-4c1a-8e19-0892ebad2a4f.mp4\nvideo = '/path/to/your/input/video.mp4'\nsave_path = '/path/to/your/output/video.mp4'\n\n# Do the inference to get result\neditor.infer(video=video, prompt=prompt, negative_prompt=negative_prompt, save_path=save_path)\n```\n\n### 2. Use controlnet animation gradio demo.\n\n```python\npython demo/gradio_controlnet_animation.py\n```\n\n### 3. Change config to use multi-frame rendering or attention injection.\n\nchange \"inference_method\" in [anythingv3 config](./anythingv3_config.py)\n\nTo use multi-frame rendering.\n\n```python\ninference_method = 'multi-frame rendering'\n```\n\nTo use attention injection.\n\n```python\ninference_method = 'attention_injection'\n```\n\n## Play animation with SAM\n\nWe also provide a demo to play controlnet animation with sam, for details, please see [OpenMMLab PlayGround](https://github.com/open-mmlab/playground/blob/main/mmediting_sam/README.md).\n\n## Citation\n\n```bibtex\n@misc{zhang2023adding,\n title={Adding Conditional Control to Text-to-Image Diffusion Models},\n author={Lvmin Zhang and Maneesh Agrawala},\n year={2023},\n eprint={2302.05543},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n" }, { "path": "configs/controlnet_animation/anythingv3_config.py", "content": "# config for model\nstable_diffusion_v15_url = 'Linaqruf/anything-v3.0'\ncontrolnet_hed_url = 'lllyasviel/sd-controlnet-hed'\ncontrol_detector = 'lllyasviel/ControlNet'\ncontrol_scheduler = 'UniPCMultistepScheduler'\n\n# method type : 'multi-frame rendering' or 'attention_injection'\ninference_method = 'attention_injection'\n\nmodel = dict(\n type='ControlStableDiffusionImg2Img',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n controlnet=dict(\n type='ControlNetModel', from_pretrained=controlnet_hed_url),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg=dict(type='init_from_unet'),\n enable_xformers=False,\n)\n" }, { "path": "configs/controlnet_animation/metafile.yml", "content": "Collections:\n- Name: Controlnet Animation\n Paper:\n Title: Controlnet\n URL: https://github.com/lllyasviel/ControlNet\n README: configs/controlnet_animation/README.md\n Task:\n - controlnet_animation\n Year: 2023\nModels:\n- Config: configs/controlnet_animation/anythingv3_config.py\n In Collection: Controlnet Animation\n Name: anythingv3_config\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: controlnet_animation\n Weights: https://huggingface.co/Linaqruf/anything-v3.0/tree/main\n" }, { "path": "configs/cyclegan/README.md", "content": "# CycleGAN (ICCV'2017)\n\n> [CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks](https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html)\n\n> **Task**: Image2Image\n\n\n\n## Abstract\n\n\n\nImage-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G: X \\\\rightarrow Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y \\\\rightarrow X and introduce a cycle consistency loss to push F(G(X)) \\\\approx X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n Results from CycleGAN trained by mmagic\n
\n \n
\n\nWe use `FID` and `IS` metrics to evaluate the generation performance of CycleGAN.1\nhttps://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth\nhttps://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_in_1x1_80k_facades_20210902_165905-5e2c0876.pth\n\n| Model | Dataset | FID | IS | Download |\n| :--------------------------------------------------------------------: | :---------------: | :------: | :---: | :--------------------------------------------------------------------------------------------------: |\n| [Ours](./cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py) | facades | 124.8033 | 1.792 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth) \\| [log](https://download.openmmlab.com/mmediting/cyclegan/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210317_160938.log.json) 2 |\n| [Ours](./cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py) | facades-id0 | 125.1694 | 1.905 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth) |\n| [Ours](./cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | summer2winter | 83.7177 | 2.771 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) |\n| [Ours](./cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | summer2winter-id0 | 83.1418 | 2.720 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) |\n| [Ours](./cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | winter2summer | 72.8025 | 3.129 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) |\n| [Ours](./cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | winter2summer-id0 | 73.5001 | 3.107 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) |\n| [Ours](./cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | horse2zebra | 64.5225 | 1.418 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) |\n| [Ours](./cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | horse2zebra-id0 | 74.7770 | 1.542 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) |\n| [Ours](./cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | zebra2horse | 141.1517 | 3.154 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) |\n| [Ours](./cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | zebra2horse-id0 | 134.3728 | 3.091 | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) |\n\n`FID` comparison with official:\n\n\n\n| Dataset | facades | facades-id0 | summer2winter | summer2winter-id0 | winter2summer | winter2summer-id0 | horse2zebra | horse2zebra-id0 | zebra2horse | zebra2horse-id0 | average |\n| :------: | :---------: | :---------: | :-----------: | :---------------: | :-----------: | :---------------: | :---------: | :-------------: | :---------: | :-------------: | :--------: |\n| official | **123.626** | **119.726** | **77.342** | **76.773** | **72.631** | 74.239 | **62.111** | 77.202 | **138.646** | **137.050** | **95.935** |\n| ours | 124.8033 | 125.1694 | 83.7177 | 83.1418 | 72.8025 | **73.5001** | 64.5225 | **74.7770** | 141.1571 | **134.3728** | 97.79 |\n\n`IS` comparison with evaluation:\n\n\n\n| Dataset | facades | facades-id0 | summer2winter | summer2winter-id0 | winter2summer | winter2summer-id0 | horse2zebra | horse2zebra-id0 | zebra2horse | zebra2horse-id0 | average |\n| :------: | :-------: | :---------: | :-----------: | :---------------: | :-----------: | :---------------: | :---------: | :-------------: | :---------: | :-------------: | :-------: |\n| official | 1.638 | 1.697 | 2.762 | **2.750** | **3.293** | **3.110** | 1.375 | **1.584** | **3.186** | 3.047 | 2.444 |\n| ours | **1.792** | **1.905** | **2.771** | 2.720 | 3.129 | 3.107 | **1.418** | 1.542 | 3.154 | **3.091** | **2.462** |\n\nNote:\n\n1. With a larger identity loss, the image-to-image translation becomes more conservative, which makes less changes. The original authors did not say what is the best weight for identity loss. Thus, in addition to the default setting, we also set the weight of identity loss to 0 (denoting `id0`) to make a more comprehensive comparison.\n2. This is the training log before refactoring. Updated logs will be released soon.\n\n## Citation\n\n```latex\n@inproceedings{zhu2017unpaired,\n title={Unpaired image-to-image translation using cycle-consistent adversarial networks},\n author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},\n booktitle={Proceedings of the IEEE international conference on computer vision},\n pages={2223--2232},\n year={2017},\n url={https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html},\n}\n```\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ndomain_a = 'summer'\ndomain_b = 'winter'\ntrain_cfg = dict(max_iters=250000)\n\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.),\n default_domain=domain_b,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\ndataroot = './data/cyclegan/summer2winter_yosemite'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# learning policy\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=1250,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=125000,\n end=250000)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\n# testA: 309, testB:238\nnum_images_a = 309\nnum_images_b = 238\nmetrics = [\n dict(\n type='TransIS',\n prefix=f'IS-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n fake_key=f'fake_{domain_b}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransIS',\n prefix=f'IS-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n inception_style='PyTorch',\n real_key=f'img_{domain_b}',\n fake_key=f'fake_{domain_b}'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ntrain_cfg = dict(max_iters=270000)\n\ndomain_a = 'horse'\ndomain_b = 'zebra'\n\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.),\n default_domain=domain_b,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\ndataroot = './data/cyclegan/horse2zebra'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=1350,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=135000,\n end=270000)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\nnum_images_a = 120\nnum_images_b = 140\nmetrics = [\n dict(\n type='TransIS',\n prefix=f'IS-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n fake_key=f'fake_{domain_b}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransIS',\n prefix=f'IS-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n inception_style='PyTorch',\n real_key=f'img_{domain_b}',\n fake_key=f'fake_{domain_b}'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ntrain_cfg = dict(max_iters=80000)\n\ndomain_a = 'photo'\ndomain_b = 'mask'\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.),\n default_domain=domain_a,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=400,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=40000,\n end=80000)\n\ndataroot = './data/cyclegan/facades'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\nnum_images = 106\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=num_images,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=num_images,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ntrain_cfg = dict(max_iters=250000)\n\ndomain_a = 'summer'\ndomain_b = 'winter'\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.5),\n default_domain=domain_b,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\ndataroot = './data/cyclegan/summer2winter_yosemite'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# learning policy\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=1250,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=125000,\n end=250000)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\nnum_images_a = 309\nnum_images_b = 238\nmetrics = [\n dict(\n type='TransIS',\n prefix=f'IS-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n fake_key=f'fake_{domain_b}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransIS',\n prefix=f'IS-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n inception_style='PyTorch',\n real_key=f'img_{domain_b}',\n fake_key=f'fake_{domain_b}'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ntrain_cfg = dict(max_iters=270000)\n\ndomain_a = 'horse'\ndomain_b = 'zebra'\n\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.5),\n default_domain=domain_b,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\ndataroot = './data/cyclegan/horse2zebra'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=1350,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=135000,\n end=270000)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\nnum_images_a = 120\nnum_images_b = 140\nmetrics = [\n dict(\n type='TransIS',\n prefix=f'IS-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n fake_key=f'fake_{domain_b}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransIS',\n prefix=f'IS-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_a}-to-{domain_b}',\n fake_nums=num_images_b,\n inception_style='PyTorch',\n real_key=f'img_{domain_b}',\n fake_key=f'fake_{domain_b}'),\n dict(\n type='TransFID',\n prefix=f'FID-{domain_b}-to-{domain_a}',\n fake_nums=num_images_a,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py", "content": "_base_ = [\n '../_base_/models/base_cyclegan.py',\n '../_base_/datasets/unpaired_imgs_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\ntrain_cfg = dict(max_iters=80000)\n\ndomain_a = 'photo'\ndomain_b = 'mask'\nmodel = dict(\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.5),\n default_domain=domain_a,\n reachable_domains=[domain_a, domain_b],\n related_domains=[domain_a, domain_b],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\nparam_scheduler = dict(\n type='LinearLrInterval',\n interval=400,\n by_epoch=False,\n start_factor=0.0002,\n end_factor=0,\n begin=40000,\n end=80000)\n\ndataroot = './data/cyclegan/facades'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\noptim_wrapper = dict(\n generators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminators=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\nnum_images = 106\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=num_images,\n fake_key=f'fake_{domain_a}',\n use_pillow_resize=False,\n resize_method='bilinear',\n inception_style='PyTorch'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=num_images,\n inception_style='PyTorch',\n real_key=f'img_{domain_a}',\n fake_key=f'fake_{domain_a}')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/cyclegan/metafile.yml", "content": "Collections:\n- Name: CycleGAN\n Paper:\n Title: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial\n Networks'\n URL: https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html\n README: configs/cyclegan/README.md\n Task:\n - image2image\n Year: 2017\nModels:\n- Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-resnet-in_1xb1-80kiters_facades\n Results:\n - Dataset: facades\n Metrics:\n FID: 124.8033\n IS: 1.792\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth\n- Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades\n Results:\n - Dataset: facades-id0\n Metrics:\n FID: 125.1694\n IS: 1.905\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth\n- Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter\n Results:\n - Dataset: summer2winter\n Metrics:\n FID: 83.7177\n IS: 2.771\n Task: Image2Image\n - Dataset: winter2summer\n Metrics:\n FID: 72.8025\n IS: 3.129\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth\n- Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter\n Results:\n - Dataset: summer2winter-id0\n Metrics:\n FID: 83.1418\n IS: 2.72\n Task: Image2Image\n - Dataset: winter2summer-id0\n Metrics:\n FID: 73.5001\n IS: 3.107\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth\n- Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra\n Results:\n - Dataset: horse2zebra\n Metrics:\n FID: 64.5225\n IS: 1.418\n Task: Image2Image\n - Dataset: zebra2horse\n Metrics:\n FID: 141.1517\n IS: 3.154\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth\n- Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py\n In Collection: CycleGAN\n Name: cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra\n Results:\n - Dataset: horse2zebra-id0\n Metrics:\n FID: 74.777\n IS: 1.542\n Task: Image2Image\n - Dataset: zebra2horse-id0\n Metrics:\n FID: 134.3728\n IS: 3.091\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth\n" }, { "path": "configs/dcgan/README.md", "content": "# Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (ICLR'2016)\n\n> [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nIn recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n DCGAN 64x64, CelebA-Cropped\n
\n \n
\n\n| Model | Dataset | SWD | MS-SSIM | Download |\n| :------------------------------------------------------------------: | :------------: | :----------------------: | :-----: | :-------------------------------------------------------------------------------------: |\n| [DCGAN 64x64](./dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py) | MNIST (64x64) | 21.16, 4.4, 8.41/11.32 | 0.1395 | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth) \\| [log](https://download.openmmlab.com//mmgen/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.json) |\n| [DCGAN 64x64](./dcgan_1xb128-300kiters_celeba-cropped-64.py) | CelebA-Cropped | 8.93,10.53,50.32/23.26 | 0.2899 | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth) \\| [log](https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.json) |\n| [DCGAN 64x64](./dcgan_1xb128-5epoches_lsun-bedroom-64x64.py) | LSUN-Bedroom | 42.79, 34.55, 98.46/58.6 | 0.2095 | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth) \\| [log](https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.json) |\n\n## Citation\n\n```latex\n@article{radford2015unsupervised,\n title={Unsupervised representation learning with deep convolutional generative adversarial networks},\n author={Radford, Alec and Metz, Luke and Chintala, Soumith},\n journal={arXiv preprint arXiv:1511.06434},\n year={2015},\n url={https://arxiv.org/abs/1511.06434},\n}\n```\n" }, { "path": "configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# define dataset\n# batch_size and data_root must be set\nbatch_size = 128\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=10000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\nmodel = dict(type='DCGAN')\ntrain_cfg = dict(max_iters=300002)\n\n# METRICS\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n# save best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='swd/avg', rule='less'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# define dataset\n# batch_size and data_root must be set\nbatch_size = 128\ndata_root = './data/lsun/images/bedroom_train'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\ntrain_cfg = dict(max_iters=1500002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=10000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n# save best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='swd/avg', rule='less'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/dcgan/dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# output single channel\nmodel = dict(\n data_preprocessor=dict(mean=[127.5], std=[127.5]),\n generator=dict(out_channels=1),\n discriminator=dict(in_channels=1))\n\n# define dataset\n# modify train_pipeline to load gray scale images\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt', color_type='grayscale'),\n dict(type='Resize', keys='gt', scale=(64, 64)),\n dict(type='PackInputs')\n]\n\n# set ``batch_size``` and ``data_root```\nbatch_size = 128\ndata_root = 'data/mnist_64/train'\ntrain_dataloader = dict(\n batch_size=batch_size,\n dataset=dict(data_root=data_root, pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=batch_size,\n dataset=dict(data_root=data_root, pipeline=train_pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size,\n dataset=dict(data_root=data_root, pipeline=train_pipeline))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=500,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\ntrain_cfg = dict(max_iters=5000, val_interval=500)\n\n# METRICS\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=-1,\n sample_model='orig',\n image_shape=(1, 64, 64))\n]\n# save best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(interval=500, save_best='swd/avg', rule='less'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0004, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999))))\n" }, { "path": "configs/dcgan/metafile.yml", "content": "Collections:\n- Name: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial\n Networks\n Paper:\n Title: Unsupervised Representation Learning with Deep Convolutional Generative\n Adversarial Networks\n URL: https://arxiv.org/abs/1511.06434\n README: configs/dcgan/README.md\n Task:\n - unconditional gans\n Year: 2016\nModels:\n- Config: configs/dcgan/dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py\n In Collection: Unsupervised Representation Learning with Deep Convolutional Generative\n Adversarial Networks\n Name: dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64\n Results:\n - Dataset: MNIST(64x64)\n Metrics:\n MS-SSIM: 0.1395\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth\n- Config: configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py\n In Collection: Unsupervised Representation Learning with Deep Convolutional Generative\n Adversarial Networks\n Name: dcgan_1xb128-300kiters_celeba-cropped-64\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n MS-SSIM: 0.2899\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth\n- Config: configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\n In Collection: Unsupervised Representation Learning with Deep Convolutional Generative\n Adversarial Networks\n Name: dcgan_1xb128-5epoches_lsun-bedroom-64x64\n Results:\n - Dataset: LSUN-Bedroom\n Metrics:\n MS-SSIM: 0.2095\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth\n" }, { "path": "configs/deblurganv2/README.md", "content": "# DeblurGAN-v2 (ICCV'2019)\n\n> [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1908.03826)\n\n> **Task**: Deblurring\n\n\n\n## Abstract\n\n\n\nWe present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a double-scale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plug-in of sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-of-the-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides, we show the architecture to be effective for general image restoration tasks too.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n DEBLURGANv2 256x256\n
\n \n \n
\n\n| Model | Dataset | G Model | D Model | PSNR/
SSIM | Download |\n| :--------------------------------------------------------: | :----------------: | :----------------: | :--------: | :------------: | :--------------------------------------------------------------------------------: |\n| [fpn_inception](./deblurganv2_fpn-inception_1xb1_gopro.py) | GoPro Test Dataset | InceptionResNet-v2 | double_gan | 29.55/ 0.934 | [model](https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth) \\\\ [log](<>) |\n| [fpn_mobilenet](./deblurganv2_fpn-mobilenet_1xb1_gopro.py) | GoPro Test Dataset | MobileNet | double_gan | 28.17/ 0.925 | [model](https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-mobilenet.pth) \\\\ [log](<>) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py\n\n# single-gpu train\npython tools/train.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth\n\n# single-gpu test\npython tools/test.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{Kupyn_2019_ICCV,\nauthor = {Orest Kupyn and Tetiana Martyniuk and Junru Wu and Zhangyang Wang},\ntitle = {DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better},\nbooktitle = {The IEEE International Conference on Computer Vision (ICCV)},\nmonth = {Oct},\nyear = {2019}\n}\n```\n" }, { "path": "configs/deblurganv2/README_zh-CN.md", "content": "# DeblurGAN-v2 (ICCV'2019)\n\n> [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1908.03826)\n\n> **任务**: 去模糊\n\n\n\n## 摘要\n\n\n\n我们提出了一种新的端到端的用于单图像运动去模糊生成对抗网络（GAN），名为DeblurGAN-v2，它显著提高了最高水平的去模糊效率、质量和灵活性。DeblurGAN-v2是基于具有双尺度鉴别器的相对论条件GAN。我们首次将特征金字塔网络引入去模糊来作为DeblurGAN-v2生成器的核心构建块。它可以灵活地与各种主干网络一起工作，以在性能和效率之间取得平衡。先进的主干网络的插件（例如，Inception-ResNet-v2）可以带来最先进的去模糊处理。同时，凭借轻量级主干网络（例如MobileNet及其变体），DeblurGAN-v2的速度比最接近的竞争对手快10-100倍，同时保持接近最先进的结果，这意味着可用于实时视频去模糊。我们证明了DeblurGAN-v2在几个流行的基准测试中获得了非常有竞争力的性能，包括去模糊质量（客观和主观）以及效率。此外，我们还展示了该架构对于一般图像恢复任务也依然有效。\n\n\n\n
\n\n
\n\n## 结果与模型\n\n
\n DEBLURGANv2 256x256\n
\n \n \n
\n\n| 算法 | 测试集 | 生成器模型 | 判别器模型 | PSNR/
SSIM | 下载 |\n| :--------------------------------------------------------: | :----------------: | :----------------: | :--------: | :------------: | :--------------------------------------------------------------------------------: |\n| [fpn_inception](./deblurganv2_fpn-inception_1xb1_gopro.py) | GoPro Test Dataset | InceptionResNet-v2 | double_gan | 29.55/ 0.934 | [模型](https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth) \\\\ [日志](<>) |\n| [fpn_mobilenet](./deblurganv2_fpn-mobilenet_1xb1_gopro.py) | GoPro Test Dataset | MobileNet | double_gan | 28.17/ 0.925 | [模型](https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-mobilenet.pth) \\\\ [日志](<>) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py\n\n# single-gpu train\npython tools/train.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth\n\n# single-gpu test\npython tools/test.py configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n\n## 引用\n\n```bibtex\n@InProceedings{Kupyn_2019_ICCV,\nauthor = {Orest Kupyn and Tetiana Martyniuk and Junru Wu and Zhangyang Wang},\ntitle = {DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better},\nbooktitle = {The IEEE International Conference on Computer Vision (ICCV)},\nmonth = {Oct},\nyear = {2019}\n}\n```\n" }, { "path": "configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n_base_ = ['../_base_/default_runtime.py']\n\nsave_dir = './work_dir/'\n\nmodel = dict(\n type='DeblurGanV2',\n generator=dict(\n type='DeblurGanV2Generator',\n backbone='FPNInception',\n norm_layer='instance',\n output_ch=3,\n num_filter=128,\n num_filter_fpn=256,\n ),\n discriminator=dict(\n type='DeblurGanV2Discriminator',\n backbone='DoubleGan',\n norm_layer='instance',\n d_layers=3,\n ),\n pixel_loss=dict(\n type='PerceptualLoss', layer_weights={'14': 1}, criterion='mse'),\n disc_loss=dict(type='AdvLoss', loss_type='ragan-ls'),\n adv_lambda=0.001,\n warmup_num=3,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5] * 3,\n std=[127.5] * 3,\n ))\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedAlbuTransForms', size=256, lq_key='img', gt_key='gt'),\n dict(\n type='AlbuCorruptFunction',\n keys=['img'],\n config=[{\n 'name': 'cutout',\n 'prob': 0.5,\n 'num_holes': 3,\n 'max_h_size': 25,\n 'max_w_size': 25\n }, {\n 'name': 'jpeg',\n 'quality_lower': 70,\n 'quality_upper': 90\n }, {\n 'name': 'motion_blur'\n }, {\n 'name': 'median_blur'\n }, {\n 'name': 'gamma'\n }, {\n 'name': 'rgb_shift'\n }, {\n 'name': 'hsv_shift'\n }, {\n 'name': 'sharpen'\n }]),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedAlbuTransForms', size=256, lq_key='img', gt_key='gt'),\n dict(\n type='AlbuCorruptFunction',\n keys=['img'],\n config=[{\n 'name': 'cutout',\n 'prob': 0.5,\n 'num_holes': 3,\n 'max_h_size': 25,\n 'max_w_size': 25\n }, {\n 'name': 'jpeg',\n 'quality_lower': 70,\n 'quality_upper': 90\n }, {\n 'name': 'motion_blur'\n }, {\n 'name': 'median_blur'\n }, {\n 'name': 'gamma'\n }, {\n 'name': 'rgb_shift'\n }, {\n 'name': 'hsv_shift'\n }, {\n 'name': 'sharpen'\n }]),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/gopro'\n\ntrain_dataloader = dict(\n batch_size=1,\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/train',\n data_prefix=dict(img='input', gt='target'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/test',\n data_prefix=dict(img='input', gt='target'),\n pipeline=val_pipeline))\n\ntest_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/test',\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\n\nval_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntrain_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\ntest_evaluator = val_evaluator\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999))),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='LinearLR',\n start_factor=0.0001,\n end_factor=0.0000001,\n begin=50,\n)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=1000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n save_best='auto',\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=1),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nload_from = 'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'\\\n 'weight/DeblurGANv2_fpn-inception.pth'\n" }, { "path": "configs/deblurganv2/deblurganv2_fpn-mobilenet_1xb1_gopro.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n_base_ = ['../_base_/default_runtime.py']\n\nsave_dir = './work_dir/'\n\nmodel = dict(\n type='DeblurGanV2',\n generator=dict(\n type='DeblurGanV2Generator',\n backbone='FPNMobileNet',\n norm_layer='instance',\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128,\n ),\n discriminator=dict(\n type='DeblurGanV2Discriminator',\n backbone='DoubleGan',\n norm_layer='instance',\n d_layers=3,\n ),\n pixel_loss=dict(\n type='PerceptualLoss', layer_weights={'14': 1}, criterion='mse'),\n disc_loss=dict(type='AdvLoss', loss_type='ragan-ls'),\n adv_lambda=0.001,\n warmup_num=3,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5] * 3,\n std=[127.5] * 3,\n ))\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedAlbuTransForms', size=256, lq_key='img', gt_key='gt'),\n dict(\n type='AlbuCorruptFunction',\n keys=['img'],\n config=[{\n 'name': 'cutout',\n 'prob': 0.5,\n 'num_holes': 3,\n 'max_h_size': 25,\n 'max_w_size': 25\n }, {\n 'name': 'jpeg',\n 'quality_lower': 70,\n 'quality_upper': 90\n }, {\n 'name': 'motion_blur'\n }, {\n 'name': 'median_blur'\n }, {\n 'name': 'gamma'\n }, {\n 'name': 'rgb_shift'\n }, {\n 'name': 'hsv_shift'\n }, {\n 'name': 'sharpen'\n }]),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedAlbuTransForms', size=256, lq_key='img', gt_key='gt'),\n dict(\n type='AlbuCorruptFunction',\n keys=['img'],\n config=[{\n 'name': 'cutout',\n 'prob': 0.5,\n 'num_holes': 3,\n 'max_h_size': 25,\n 'max_w_size': 25\n }, {\n 'name': 'jpeg',\n 'quality_lower': 70,\n 'quality_upper': 90\n }, {\n 'name': 'motion_blur'\n }, {\n 'name': 'median_blur'\n }, {\n 'name': 'gamma'\n }, {\n 'name': 'rgb_shift'\n }, {\n 'name': 'hsv_shift'\n }, {\n 'name': 'sharpen'\n }]),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/gopro'\n\ntrain_dataloader = dict(\n batch_size=1,\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/train',\n data_prefix=dict(img='input', gt='target'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/test',\n data_prefix=dict(img='input', gt='target'),\n pipeline=val_pipeline))\n\ntest_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root=data_root + '/test',\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\n\nval_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntrain_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\ntest_evaluator = val_evaluator\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999))),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='LinearLR',\n start_factor=0.0001,\n end_factor=0.0000001,\n begin=50,\n)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=1000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n save_best='auto',\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=1),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nload_from = 'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'\\\n 'weight/DeblurGANv2_fpn-mobilenet.pth'\n" }, { "path": "configs/deblurganv2/metafile.yml", "content": "Collections:\n- Name: DeblurGAN-v2\n Paper:\n Title: 'DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better'\n URL: https://arxiv.org/abs/1908.03826\n README: configs/deblurganv2/README.md\n Task:\n - deblurring\n Year: 2019\nModels:\n- Config: configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py\n In Collection: DeblurGAN-v2\n Name: deblurganv2_fpn-inception_1xb1_gopro\n Results:\n - Dataset: GoProTestDataset\n Metrics:\n PSNR/
SSIM:\n PSNR: 29.55\n SSIM: 0.934\n Task: Deblurring\n Weights: https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-inception.pth\n- Config: configs/deblurganv2/deblurganv2_fpn-mobilenet_1xb1_gopro.py\n In Collection: DeblurGAN-v2\n Name: deblurganv2_fpn-mobilenet_1xb1_gopro\n Results:\n - Dataset: GoProTestDataset\n Metrics:\n PSNR/
SSIM:\n PSNR: 28.17\n SSIM: 0.925\n Task: Deblurring\n Weights: https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/weight/DeblurGANv2_fpn-mobilenet.pth\n" }, { "path": "configs/deepfillv1/README.md", "content": "# DeepFillv1 (CVPR'2018)\n\n> [Generative Image Inpainting with Contextual Attention](https://arxiv.org/abs/1801.07892)\n\n> **Task**: Inpainting\n\n\n\n## Abstract\n\n\n\nRecent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feed-forward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones.\n\n\n\n
\n \n
\n\n## Results and models\n\n**CelebA-HQ**\n\n| Model | Mask Type | Resolution | Train Iters | Dataset | l1 error | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :----------------: | :----------------------------------------------------------------------: |\n| [DeepFillv1](./deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) |\n\n**Places365-Challenge**\n\n| Model | Mask Type | Resolution | Train Iters | Dataset | l1 error | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :----------------: | :-------------------------------------------------------------------: |\n| [DeepFillv1](./deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py\n\n# single-gpu train\npython tools/train.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/deepfillv1/deepfillv1_8xb2_places-256x256.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth\n\n# single-gpu test\npython tools/test.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{yu2018generative,\n title={Generative image inpainting with contextual attention},\n author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={5505--5514},\n year={2018}\n}\n```\n" }, { "path": "configs/deepfillv1/README_zh-CN.md", "content": "# DeepFillv1 (CVPR'2018)\n\n> **任务**: 图像修复\n\n\n\n
\nDeepFillv1 (CVPR'2018)\n\n```bibtex\n@inproceedings{yu2018generative,\n title={Generative image inpainting with contextual attention},\n author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={5505--5514},\n year={2018}\n}\n```\n\n
\n\n
\n\n**CelebA-HQ**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------------------------: |\n| [DeepFillv1](./deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) |\n\n**Places365-Challenge**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :----------------------------------------------------------------------------------: |\n| [DeepFillv1](./deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py\n\n# 单个GPU上训练\npython tools/train.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/deepfillv1/deepfillv1_8xb2_places-256x256.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth\n\n# 单个GPU上测试\npython tools/test.py configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/deepfillv1/deepfillv1_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/deepfillv1/deepfillv1_4xb4_celeba-256x256.py", "content": "_base_ = [\n '../_base_/models/base_deepfillv1.py',\n '../_base_/inpaint_default_runtime.py', '../_base_/datasets/celeba.py'\n]\n\nexperiment_name = 'deepfillv1_4xb4_celeba-256x256'\nsave_dir = './work_dirs'\nmodel = dict(\n train_cfg=dict(disc_step=2, start_iter=0, local_size=(128, 128)), )\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='bbox',\n mask_config=dict(\n max_bbox_shape=(128, 128),\n max_bbox_delta=40,\n min_margin=20,\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=4,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=1500003,\n val_interval=250000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\ncheckpoint = dict(\n type='CheckpointHook', interval=250000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/deepfillv1/deepfillv1_8xb2_places-256x256.py", "content": "_base_ = [\n '../_base_/models/base_deepfillv1.py',\n '../_base_/inpaint_default_runtime.py', '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'deepfillv1_8xb2_places-256x256'\nsave_dir = './work_dirs'\nmodel = dict(train_cfg=dict(disc_step=5, start_iter=0, local_size=(128, 128)))\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='bbox',\n mask_config=dict(\n max_bbox_shape=(128, 128),\n max_bbox_delta=40,\n min_margin=20,\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=2,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=5000003,\n val_interval=250000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\ncheckpoint = dict(\n type='CheckpointHook', interval=250000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/deepfillv1/metafile.yml", "content": "Collections:\n- Name: DeepFillv1\n Paper:\n Title: Generative Image Inpainting with Contextual Attention\n URL: https://arxiv.org/abs/1801.07892\n README: configs/deepfillv1/README.md\n Task:\n - inpainting\n Year: 2018\nModels:\n- Config: configs/deepfillv1/deepfillv1_4xb4_celeba-256x256.py\n In Collection: DeepFillv1\n Name: deepfillv1_4xb4_celeba-256x256\n Results:\n - Dataset: CelebA-val\n Metrics:\n PSNR: 26.878\n SSIM: 0.911\n l1 error: 6.677\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth\n- Config: configs/deepfillv1/deepfillv1_8xb2_places-256x256.py\n In Collection: DeepFillv1\n Name: deepfillv1_8xb2_places-256x256\n Results:\n - Dataset: Places365-val\n Metrics:\n PSNR: 23.429\n SSIM: 0.862\n l1 error: 11.019\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth\n" }, { "path": "configs/deepfillv2/README.md", "content": "# DeepFillv2 (CVPR'2019)\n\n> [Free-Form Image Inpainting with Gated Convolution](https://arxiv.org/abs/1806.03589)\n\n> **Task**: Inpainting\n\n\n\n## Abstract\n\n\n\nWe present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces.\n\n\n\n
\n \n
\n\n## Results and models\n\n**CelebA-HQ**\n\n| Model | Mask Type | Resolution | Train Iters | Dataset | l1 error | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :----------------: | :------------------------------------------------------------------------: |\n| [DeepFillv2](./deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) |\n\n**Places365-Challenge**\n\n| Model | Mask Type | Resolution | Train Iters | Dataset | l1 error | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :----------------: | :---------------------------------------------------------------------: |\n| [DeepFillv2](./deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py\n\n# single-gpu train\npython tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth\n\n# single-gpu test\npython tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{yu2019free,\n title={Free-form image inpainting with gated convolution},\n author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},\n booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n pages={4471--4480},\n year={2019}\n}\n```\n" }, { "path": "configs/deepfillv2/README_zh-CN.md", "content": "# DeepFillv2 (CVPR'2019)\n\n> **任务**: 图像修复\n\n\n\n
\nDeepFillv2 (CVPR'2019)\n\n```bibtex\n@inproceedings{yu2019free,\n title={Free-form image inpainting with gated convolution},\n author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},\n booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n pages={4471--4480},\n year={2019}\n}\n```\n\n
\n\n
\n\n**CelebA-HQ**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :---------------------------------------------------------------------------------------: |\n| [DeepFillv2](./deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) |\n\n**Places365-Challenge**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------------------------: |\n| [DeepFillv2](./deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py\n\n# 单个GPU上训练\npython tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth\n\n# 单个GPU上测试\npython tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py", "content": "_base_ = [\n '../_base_/models/base_deepfillv2.py',\n '../_base_/inpaint_default_runtime.py', '../_base_/datasets/celeba.py'\n]\n\nsave_dir = './work_dirs'\nexperiment_name = 'deepfillv2_8xb2_celeba-256x256'\nmodel = dict(train_cfg=dict(disc_step=1, start_iter=0))\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=2,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=500003,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\ncheckpoint = dict(\n type='CheckpointHook', interval=50000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/deepfillv2/deepfillv2_8xb2_places-256x256.py", "content": "_base_ = [\n '../_base_/models/base_deepfillv2.py',\n '../_base_/inpaint_default_runtime.py', '../_base_/datasets/places.py'\n]\n\nsave_dir = './work_dirs'\nexperiment_name = 'deepfillv2_8xb2_places-256x256'\nmodel = dict(train_cfg=dict(disc_step=1, start_iter=0))\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=2,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=1000003,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\ncheckpoint = dict(\n type='CheckpointHook', interval=50000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/deepfillv2/metafile.yml", "content": "Collections:\n- Name: DeepFillv2\n Paper:\n Title: Free-Form Image Inpainting with Gated Convolution\n URL: https://arxiv.org/abs/1806.03589\n README: configs/deepfillv2/README.md\n Task:\n - inpainting\n Year: 2019\nModels:\n- Config: configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py\n In Collection: DeepFillv2\n Name: deepfillv2_8xb2_celeba-256x256\n Results:\n - Dataset: CelebA-val\n Metrics:\n PSNR: 25.721\n SSIM: 0.871\n l1 error: 5.411\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth\n- Config: configs/deepfillv2/deepfillv2_8xb2_places-256x256.py\n In Collection: DeepFillv2\n Name: deepfillv2_8xb2_places-256x256\n Results:\n - Dataset: Places365-val\n Metrics:\n PSNR: 22.398\n SSIM: 0.815\n l1 error: 8.635\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth\n" }, { "path": "configs/dic/README.md", "content": "# DIC (CVPR'2020)\n\n> [Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation](https://arxiv.org/abs/2003.13063)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nRecent works based on deep learning and facial priors have succeeded in super-resolving severely degraded facial images. However, the prior knowledge is not fully exploited in existing methods, since facial priors such as landmark and component maps are always estimated by low-resolution or coarsely super-resolved images, which may be inaccurate and thus affect the recovery performance. In this paper, we propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively. In each recurrent step, the recovery branch utilizes the prior knowledge of landmarks to yield higher-quality images which facilitate more accurate landmark estimation in turn. Therefore, the iterative information interaction between two processes boosts the performance of each other progressively. Moreover, a new attentive fusion module is designed to strengthen the guidance of landmark maps, where facial components are generated individually and aggregated attentively for better restoration. Quantitative and qualitative experimental results show the proposed method significantly outperforms state-of-the-art FSR methods in recovering high-quality face images.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\nIn the log data of `dic_gan_x8c48b6_g4_150k_CelebAHQ`, DICGAN is verified on the first 9 pictures of the test set of CelebA-HQ, so `PSNR` and `SSIM` shown in the follow table is different from the log data.\n\n`Training Resources`: Training Resourcesrmation during training.\n\n| Model | Dataset | scale | PSNR | SSIM | Training Resources | Download |\n| :--------------------------------------------------------------------------: | :------: | :---: | :-----: | :----: | :-----------------: | :-----------------------------------------------------------------------------: |\n| [dic_x8c48b6_g4_150k_CelebAHQ](./dic_x8c48b6_4xb2-150k_celeba-hq.py) | CelebAHQ | x8 | 25.2319 | 0.7422 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) |\n| [dic_gan_x8c48b6_g4_500k_CelebAHQ](./dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | CelebAHQ | x8 | 23.6241 | 0.6721 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py\n\n# single-gpu train\npython tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth\n\n# single-gpu test\npython tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{ma2020deep,\n title={Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation},\n author={Ma, Cheng and Jiang, Zhenyu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},\n booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},\n pages={5569--5578},\n year={2020}\n}\n```\n" }, { "path": "configs/dic/README_zh-CN.md", "content": "# DIC (CVPR'2020)\n\n> **任务**: 图像超分辨率\n\n\n\n
\n\nDIC (CVPR'2020)\n\n```bibtex\n@inproceedings{ma2020deep,\n title={Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation},\n author={Ma, Cheng and Jiang, Zhenyu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},\n booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},\n pages={5569--5578},\n year={2020}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n在 `dic_gan_x8c48b6_g4_150k_CelebAHQ` 的日志中，DICGAN 在 CelebA-HQ 测试集的前9张图片上进行了验证，因此下表中的 `PSNR/SSIM` 与日志数据不同。\n\n`GPU 信息`: 训练过程中的 GPU 信息.\n\n| 算法 | scale | CelebA-HQ | GPU 信息 | 下载 |\n| :--------------------------------------------------------------------------: | :---: | :--------------: | :-----------------: | :----------------------------------------------------------------------------------: |\n| [dic_x8c48b6_g4_150k_CelebAHQ](./dic_x8c48b6_4xb2-150k_celeba-hq.py) | x8 | 25.2319 / 0.7422 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) |\n| [dic_gan_x8c48b6_g4_500k_CelebAHQ](./dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | x8 | 23.6241 / 0.6721 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py\n\n# 单个GPU上训练\npython tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth\n\n# 单个GPU上测试\npython tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py", "content": "_base_ = './dic_x8c48b6_4xb2-150k_celeba-hq.py'\n\nexperiment_name = 'dic_gan-x8c48b6_4xb2-500k_celeba-hq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 8\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\npretrained_light_cnn = 'https://download.openmmlab.com/mmediting/' + \\\n 'restorers/dic/light_cnn_feature.pth'\nmodel = dict(\n type='DIC',\n generator=dict(\n type='DICNet', in_channels=3, out_channels=3, mid_channels=48),\n discriminator=dict(type='LightCNN', in_channels=3),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n align_loss=dict(type='MSELoss', loss_weight=0.1, reduction='mean'),\n feature_loss=dict(\n type='LightCNNFeatureLoss',\n pretrained=pretrained_light_cnn,\n loss_weight=0.1,\n criterion='l1'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.005,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(pixel_init=10000, disc_repeat=2),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[129.795, 108.12, 96.39],\n std=[255, 255, 255],\n ))\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=500_000, val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-4)),\n discriminator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-5)))\n\n# learning policy\nparam_scheduler = dict(\n _delete_=True,\n type='MultiStepLR',\n by_epoch=False,\n milestones=[100000, 200000, 300000, 400000],\n gamma=0.5)\n" }, { "path": "configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'dic_x8c48b6_4xb2-150k_celeba-hq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 8\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\nmodel = dict(\n type='DIC',\n generator=dict(\n type='DICNet', in_channels=3, out_channels=3, mid_channels=48),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n align_loss=dict(type='MSELoss', loss_weight=0.1, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[129.795, 108.12, 96.39],\n std=[255, 255, 255],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Resize',\n scale=(128, 128),\n keys=['gt'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=1 / 8,\n keep_ratio=True,\n keys=['gt'],\n output_keys=['img'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='GenerateFacialHeatmap',\n image_key='gt',\n ori_size=128,\n target_size=32,\n sigma=1.0),\n dict(type='PackInputs')\n]\nvalid_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Resize',\n scale=(128, 128),\n keys=['gt'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=1 / 8,\n keep_ratio=True,\n keys=['gt'],\n output_keys=['img'],\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs')\n]\ntest_pipeline = valid_pipeline\n\ninference_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Resize',\n scale=(128, 128),\n keys=['img'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=1 / 8,\n keep_ratio=True,\n keys=['img'],\n output_keys=['img'],\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=2, # gpus 4\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='celeba', task_name='fsr'),\n data_root=data_root + '/CelebA-HQ',\n data_prefix=dict(gt='train_256/all_256'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='celeba', task_name='fsr'),\n data_root=data_root + '/CelebA-HQ',\n data_prefix=dict(gt='test_256/all_256'),\n pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=150_000, val_interval=2000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-4)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[10000, 20000, 40000, 80000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=2000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/dic/metafile.yml", "content": "Collections:\n- Name: DIC\n Paper:\n Title: Deep Face Super-Resolution with Iterative Collaboration between Attentive\n Recovery and Landmark Estimation\n URL: https://arxiv.org/abs/2003.13063\n README: configs/dic/README.md\n Task:\n - image super-resolution\n Year: 2020\nModels:\n- Config: configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py\n In Collection: DIC\n Name: dic_x8c48b6_4xb2-150k_celeba-hq\n Results:\n - Dataset: CelebAHQ\n Metrics:\n PSNR: 25.2319\n SSIM: 0.7422\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth\n- Config: configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py\n In Collection: DIC\n Name: dic_gan-x8c48b6_4xb2-500k_celeba-hq\n Results:\n - Dataset: CelebAHQ\n Metrics:\n PSNR: 23.6241\n SSIM: 0.6721\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth\n" }, { "path": "configs/diffusers_pipeline/README.md", "content": "# Diffusers Pipeline (2023)\n\n> [Diffusers Pipeline](https://github.com/huggingface/diffusers)\n\n> **Task**: Diffusers Pipeline\n\n\n\n## Abstract\n\n\n\nFor the convenience of our community users, this inferencer supports using the pipelines from diffusers for inference to compare the results with the algorithms supported within our algorithm library.\n\n## Configs\n\n| Model | Dataset | Download |\n| :---------------------------------------: | :-----: | :------: |\n| [diffusers pipeline](./sd_xl_pipeline.py) | - | - |\n\n## Quick Start\n\n### sd_xl_pipeline\n\nTo run stable diffusion XL with mmagic inference API, follow these codes:\n\n```python\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMEdit instance and infer\neditor = MMagicInferencer(model_name='diffusers_pipeline')\ntext_prompts = 'Japanese anime style, girl, beautiful, cute, colorful, best quality, extremely detailed'\nnegative_prompt = 'bad face, bad hands'\nresult_out_dir = 'sd_xl_japanese.png'\neditor.infer(text=text_prompts,\n negative_prompt=negative_prompt,\n result_out_dir=result_out_dir)\n```\n\nYou will get this picture:\n\n
\n \n
\n\n## Citation\n\n```bibtex\n@misc{von-platen-etal-2022-diffusers,\n author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},\n title = {Diffusers: State-of-the-art diffusion models},\n year = {2022},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/huggingface/diffusers}}\n}\n```\n" }, { "path": "configs/diffusers_pipeline/metafile.yml", "content": "Collections:\n- Name: Diffusers Pipeline\n Paper:\n Title: Diffusers Pipeline\n URL: https://github.com/huggingface/diffusers\n README: configs/diffusers_pipeline/README.md\n Task:\n - diffusers pipeline\n Year: 2023\nModels:\n- Config: configs/diffusers_pipeline/sd_xl_pipeline.py\n In Collection: Diffusers Pipeline\n Name: sd_xl_pipeline\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Diffusers Pipeline\n" }, { "path": "configs/diffusers_pipeline/sd_xl_pipeline.py", "content": "# config for model\n\nmodel = dict(\n type='DiffusionPipeline',\n from_pretrained='stabilityai/stable-diffusion-xl-base-1.0')\n" }, { "path": "configs/dim/README.md", "content": "# DIM (CVPR'2017)\n\n> [Deep Image Matting](https://arxiv.org/abs/1703.03872)\n\n> **Task**: Matting\n\n\n\n## Abstract\n\n\n\nImage matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems. Our deep model has two parts. The first part is a deep convolutional encoder-decoder network that takes an image and the corresponding trimap as inputs and predict the alpha matte of the image. The second part is a small convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values and sharper edges. In addition, we also create a large-scale image matting dataset including 49300 training images and 1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images. Experimental results clearly demonstrate the superiority of our algorithm over previous methods.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | SAD | MSE | GRAD | CONN | Training Resources | Download |\n| :--------------------------------------------------------------------: | :------------: | :--: | :---: | :------: | :------: | :----------------: | :-----------------------------------------------------------------------: |\n| [stage1 (our)](./dim_stage1-v16_1xb1-1000k_comp1k.py) | Composition-1k | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) |\n| [stage2 (our)](./dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | Composition-1k | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) |\n| [stage3 (our)](./dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | Composition-1k | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) |\n| [stage1 (online merge)](./dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py) | Composition-1k | - | - | - | - | - | - |\n\n\n\n**NOTE**\n\n- stage1: train the encoder-decoder part without the refinement part.\n- stage2: fix the encoder-decoder part and train the refinement part.\n- stage3: fine-tune the whole network.\n\n> The performance of the model is not stable during the training. Thus, the reported performance is not from the last checkpoint. Instead, it is the best performance of all validations during training.\n\n> The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance.\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\nDIM is trained with three stages.\n\n**Stage 1**: train the encoder-decoder part without the refinement part.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py\n\n# single-gpu train\npython tools/train.py configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py 8\n```\n\n**Stage 2**: fix the encoder-decoder part and train the refinement part.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py\n\n# single-gpu train\npython tools/train.py configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py 8\n```\n\n**Stage 3**: fine-tune the whole network.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py\n\n# single-gpu train\npython tools/train.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth\n\n# single-gpu test\npython tools/test.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{xu2017deep,\n title={Deep image matting},\n author={Xu, Ning and Price, Brian and Cohen, Scott and Huang, Thomas},\n booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n pages={2970--2979},\n year={2017}\n}\n```\n" }, { "path": "configs/dim/README_zh-CN.md", "content": "# DIM (CVPR'2017)\n\n> **任务**: 图像抠图\n\n\n\n
\nDIM (CVPR'2017)\n\n```bibtex\n@inproceedings{xu2017deep,\n title={Deep image matting},\n author={Xu, Ning and Price, Brian and Cohen, Scott and Huang, Thomas},\n booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n pages={2970--2979},\n year={2017}\n}\n```\n\n
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\n\n| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 |\n| :---------------------------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :--------------------------------------------------------------------------------: |\n| 第一阶段 (原文) | 54.6 | 0.017 | 36.7 | 55.3 | - | - |\n| 第三阶段 (原文) | **50.4** | **0.014** | 31.0 | 50.8 | - | - |\n| [第一阶段 (复现)](./dim_stage1-v16_1xb1-1000k_comp1k.py) | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) |\n| [第二阶段 (复现)](./dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) |\n| [第三阶段 (复现)](./dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) |\n| [第一阶段 (online merge)](./dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py) | - | - | - | - | - | - |\n\n**注**\n\n- 第一阶段：训练不带精炼器的编码器-解码器部分。 \\\\\n- 第二阶段：固定编码器-解码器部分，训练精炼器部分。 \\\\\n- 第三阶段：微调整个网络模型。\n\n> 模型在训练过程中的性能不稳定。因此，展示的性能并非来自最后一个模型权重文件，而是训练期间在验证集上取得的最佳性能。\n\n> 不同随机种子的训练性能（最佳性能）的发散程度很大，您可能需要为每个设置运行多个实验以获得上述性能。\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\nDIM 训练有三个阶段。\n\n**阶段 1**: 训练不带精炼器的编码器-解码器部分。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py\n\n# 单个GPU上训练\npython tools/train.py configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py 8\n```\n\n**阶段 2**: 固定编码器-解码器部分，训练精炼器部分。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py\n\n# 单个GPU上训练\npython tools/train.py configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py 8\n```\n\n**阶段 3**: 微调整个网络模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py\n\n# 单个GPU上训练\npython tools/train.py configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py onfigs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth\n\n# 单个GPU上测试\npython tools/test.py onfigs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh onfigs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py", "content": "_base_ = [\n '../_base_/datasets/comp1k.py', '../_base_/matting_default_runtime.py'\n]\n\nsave_dir = './work_dirs/'\nexperiment_name = 'dim_stage1-v16_1xb1-1000k_comp1k'\n\n# model settings\nmodel = dict(\n type='DIM',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='VGG16',\n in_channels=4,\n init_cfg=dict(\n type='Pretrained', checkpoint='open-mmlab://mmedit/vgg16')),\n decoder=dict(type='PlainDecoder')),\n loss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5),\n loss_comp=dict(type='CharbonnierCompLoss', loss_weight=0.5),\n train_cfg=dict(train_backbone=True, train_refiner=False),\n test_cfg=dict(\n refine=False,\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ),\n)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='LoadImageFromFile', key='bg'),\n dict(type='LoadImageFromFile', key='merged'),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged', 'fg', 'bg'],\n crop_sizes=[320, 480, 640]),\n dict(type='Flip', keys=['alpha', 'merged', 'fg', 'bg']),\n dict(\n type='Resize',\n keys=['alpha', 'merged', 'fg', 'bg'],\n scale=(320, 320),\n keep_ratio=False),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='PackInputs'),\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(batch_size=1, dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(batch_size=1, dataset=dict(pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=1_000_000,\n val_interval=40000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n)\n\n# checkpoint saving\ndefault_hooks = dict(\n checkpoint=dict(type='CheckpointHook', interval=40000, out_dir=save_dir))\n" }, { "path": "configs/dim/dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py", "content": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\nsave_dir = './work_dirs/'\nexperiment_name = 'dim_stage1-v16_1xb1-1000k_comp1k_online-merge'\n\n# dataset settings\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='LoadImageFromFile', key='bg'),\n dict(type='MergeFgAndBg'),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged', 'fg', 'bg'],\n crop_sizes=[320, 480, 640]),\n dict(type='Flip', keys=['alpha', 'merged', 'fg', 'bg']),\n dict(\n type='Resize',\n keys=['alpha', 'merged', 'fg', 'bg'],\n scale=(320, 320),\n keep_ratio=False),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(dataset=dict(pipeline=train_pipeline))\n" }, { "path": "configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py", "content": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\nsave_dir = './work_dirs/'\nexperiment_name = 'dim_stage2-v16-pln_1xb1-1000k_comp1k'\n\n# model settings\nmodel = dict(\n refiner=dict(type='PlainRefiner'),\n loss_refine=dict(type='CharbonnierLoss'),\n train_cfg=dict(train_backbone=False, train_refiner=True),\n test_cfg=dict(refine=True),\n)\n\n# load_from = \\\n# 'https://download.openmmlab.com/mmediting/mattors/dim/'\\\n# 'dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth'\n" }, { "path": "configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py", "content": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\n\nsave_dir = './work_dirs'\nexperiment_name = 'dim_stage3-v16-pln_1xb1-1000k_comp1k'\n\n# model settings\nmodel = dict(\n refiner=dict(type='PlainRefiner'),\n loss_refine=dict(type='CharbonnierLoss'),\n train_cfg=dict(train_backbone=True, train_refiner=True),\n test_cfg=dict(refine=True),\n)\n\n# load_from = \\\n# 'https://download.openmmlab.com/mmediting/mattors/dim/'\\\n# 'dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth'\n" }, { "path": "configs/dim/metafile.yml", "content": "Collections:\n- Name: DIM\n Paper:\n Title: Deep Image Matting\n URL: https://arxiv.org/abs/1703.03872\n README: configs/dim/README.md\n Task:\n - matting\n Year: 2017\nModels:\n- Config: configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py\n In Collection: DIM\n Name: dim_stage1-v16_1xb1-1000k_comp1k\n Results:\n - Dataset: Composition-1k\n Metrics:\n CONN: 54.5\n GRAD: 32.7\n MSE: 0.017\n SAD: 53.8\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth\n- Config: configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py\n In Collection: DIM\n Name: dim_stage2-v16-pln_1xb1-1000k_comp1k\n Results:\n - Dataset: Composition-1k\n Metrics:\n CONN: 52.4\n GRAD: 29.4\n MSE: 0.016\n SAD: 52.3\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth\n- Config: configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py\n In Collection: DIM\n Name: dim_stage3-v16-pln_1xb1-1000k_comp1k\n Results:\n - Dataset: Composition-1k\n Metrics:\n CONN: 50.7\n GRAD: 29.0\n MSE: 0.015\n SAD: 50.6\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth\n- Config: configs/dim/dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py\n In Collection: DIM\n Name: dim_stage1-v16_1xb1-1000k_comp1k_online-merge\n Results:\n - Dataset: Composition-1k\n Metrics: {}\n Task: Matting\n" }, { "path": "configs/disco_diffusion/README.md", "content": "# Disco Diffusion (2022)\n\n> [Disco Diffusion](https://github.com/alembics/disco-diffusion)\n\n> **Task**: Text2Image, Image2Image\n\n\n\n## Abstract\n\n\n\nDisco Diffusion (DD) is a Google Colab Notebook which leverages an AI Image generating technique called CLIP-Guided Diffusion to allow you to create compelling and beautiful images from text inputs.\n\nCreated by Somnai, augmented by Gandamu, and building on the work of RiversHaveWings, nshepperd, and many others. See more details in [Credits](#credits).\n\n\n\n\n\n \n \n \n \n \n\n
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\n\n## Results and models\n\nWe have converted several `unet` weights and offer related configs. See more details of different `unet` in [Tutorial](#tutorials).\n\n| Model | Dataset | Task | Download |\n| :---------------------------------------------------------------------------------------: | :------: | :--------: | :-------------------------------------------------------------------------------------------: |\n| [512x512_diffusion_uncond_finetune_008100](./disco-diffusion_adm-u-finetuned_imagenet-512x512.py) | ImageNet | Text2Image | [model](https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u_finetuned_imagenet-512x512-ab471d70.pth) |\n| [256x256_diffusion_uncond](./disco-diffusion_adm-u-finetuned_imagenet-256x256.py) | ImageNet | Text2Image | [model](<>) |\n| [portrait_generator_v001](./disco-diffusion_portrait-generator-v001.py) | unknown | Text2Image | [model](https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u-cvt-rgb_portrait-v001-f4a3f3bc.pth) |\n\n\n\n| Model | Download |\n| :--------------------------: | :----------: |\n| pixelartdiffusion_expanded | Coming soon! |\n| pixel_art_diffusion_hard_256 | Coming soon! |\n| pixel_art_diffusion_soft_256 | Coming soon! |\n| pixelartdiffusion4k | Coming soon! |\n| watercolordiffusion_2 | Coming soon! |\n| watercolordiffusion | Coming soon! |\n| PulpSciFiDiffusion | Coming soon! |\n\n## To-do List\n\n- [x] Text2Image\n- [x] Image2Image\n- [x] Imagenet, portrait diffusion models\n- [ ] pixelart, watercolor, sci-fiction diffusion models\n- [ ] image prompt\n- [ ] video generation\n- [ ] faster sampler(plms, dpm-solver etc.)\n\nWe really welcome community users supporting these items and any other interesting stuffs!\n\n## Quick Start\n\nRunning the following codes, you can get a text-generated image.\n\n```python\nfrom mmengine import Config, MODELS\nfrom mmengine.registry import init_default_scope\nfrom torchvision.utils import save_image\n\ninit_default_scope('mmagic')\n\ndisco = MODELS.build(\n Config.fromfile('configs/disco_diffusion/disco-baseline.py').model).cuda().eval()\ntext_prompts = {\n 0: [\n \"A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.\",\n \"yellow color scheme\"\n ]\n}\nimage = disco.infer(\n height=768,\n width=1280,\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=250,\n eta=0.8)['samples']\nsave_image(image, \"image.png\")\n\n```\n\n## Tutorials\n\nConsidering that `disco-diffusion` contains many adjustable parameters, we provide users with a [jupyter-notebook](./tutorials.ipynb) / [colab](https://githubtocolab.com/open-mmlab/mmagic/blob/main/configs/disco_diffusion/tutorials.ipynb) tutorial that exhibits the meaning of different parameters, and gives results corresponding to adjustment.\nRefer to [Disco Sheet](https://docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g/edit).\n\n## Credits\n\nSince our adaptation of disco-diffusion are heavily influenced by disco [colab](https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb#scrollTo=License), here we copy the credits below.\n\n
\nCredits\nOriginal notebook by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses either OpenAI's 256x256 unconditional ImageNet or Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images.\n\nModified by Daniel Russell (https://github.com/russelldc, https://twitter.com/danielrussruss) to include (hopefully) optimal params for quick generations in 15-100 timesteps rather than 1000, as well as more robust augmentations.\n\nFurther improvements from Dango233 and nshepperd helped improve the quality of diffusion in general, and especially so for shorter runs like this notebook aims to achieve.\n\nVark added code to load in multiple Clip models at once, which all prompts are evaluated against, which may greatly improve accuracy.\n\nThe latest zoom, pan, rotation, and keyframes features were taken from Chigozie Nri's VQGAN Zoom Notebook (https://github.com/chigozienri, https://twitter.com/chigozienri)\n\nAdvanced DangoCutn Cutout method is also from Dango223.\n\n\\--\n\nDisco:\n\nSomnai (https://twitter.com/Somnai_dreams) added Diffusion Animation techniques, QoL improvements and various implementations of tech and techniques, mostly listed in the changelog below.\n\n3D animation implementation added by Adam Letts (https://twitter.com/gandamu_ml) in collaboration with Somnai. Creation of disco.py and ongoing maintenance.\n\nTurbo feature by Chris Allen (https://twitter.com/zippy731)\n\nImprovements to ability to run on local systems, Windows support, and dependency installation by HostsServer (https://twitter.com/HostsServer)\n\nVR Mode by Tom Mason (https://twitter.com/nin_artificial)\n\nHorizontal and Vertical symmetry functionality by nshepperd. Symmetry transformation_steps by huemin (https://twitter.com/huemin_art). Symmetry integration into Disco Diffusion by Dmitrii Tochilkin (https://twitter.com/cut_pow).\n\nWarp and custom model support by Alex Spirin (https://twitter.com/devdef).\n\nPixel Art Diffusion, Watercolor Diffusion, and Pulp SciFi Diffusion models from KaliYuga (https://twitter.com/KaliYuga_ai). Follow KaliYuga's Twitter for the latest models and for notebooks with specialized settings.\n\nIntegration of OpenCLIP models and initiation of integration of KaliYuga models by Palmweaver / Chris Scalf (https://twitter.com/ChrisScalf11)\n\nIntegrated portrait_generator_v001 from Felipe3DArtist (https://twitter.com/Felipe3DArtist)\n\n
\n\n## Citation\n\n```bibtex\n@misc{github,\n author={alembics},\n title={disco-diffusion},\n year={2022},\n url={https://github.com/alembics/disco-diffusion},\n}\n```\n" }, { "path": "configs/disco_diffusion/README_zh-CN.md", "content": "# Disco Diffusion (2022)\n\n> [Disco Diffusion](https://github.com/alembics/disco-diffusion)\n\n> **任务**: 图文生成, 图像到图像的翻译, 扩散模型\n\n\n\n## 摘要\n\n\n\nDisco Diffusion(DD)是一个 Google Colab 笔记本，它利用一种叫做 CLIP-Guided Diffusion 的人工智能图像生成技术，让你从文本输入中创造出引人注目的精美图像。\n\n由 Somnai 创建，由 Gandamu 改进，并建立在 RiversHaveWings、nshepperd 和许多其他人的工作之上。更多细节见[Credits](#credits)。\n\n\n\n\n\n \n \n \n \n \n\n
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\n\n## 模型与结果\n\n我们已经转换了几个 `unet` 的权重，并提供相关的配置文件。在[Tutorial](#tutorials)中可以看到更多关于不同 `unet` 的细节。\n\n| 模型 | 数据集 | 下载 |\n| :----------------------------------------------------------------------------------------------: | :------: | :----------------------------------------------------------------------------------------------: |\n| [512x512_diffusion_uncond_finetune_008100](./disco-diffusion_adm-u-finetuned_imagenet-512x512.py) | ImageNet | [model](https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u_finetuned_imagenet-512x512-ab471d70.pth) |\n| [256x256_diffusion_uncond](./disco-diffusion_adm-u-finetuned_imagenet-256x256.py) | ImageNet | [model](<>) |\n| [portrait_generator_v001](./disco-diffusion_portrait-generator-v001.py) | unknown | [model](https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u-cvt-rgb_portrait-v001-f4a3f3bc.pth) |\n\n\n\n| 模型 | 下载 |\n| :--------------------------: | :----------: |\n| pixelartdiffusion_expanded | Coming soon! |\n| pixel_art_diffusion_hard_256 | Coming soon! |\n| pixel_art_diffusion_soft_256 | Coming soon! |\n| pixelartdiffusion4k | Coming soon! |\n| watercolordiffusion_2 | Coming soon! |\n| watercolordiffusion | Coming soon! |\n| PulpSciFiDiffusion | Coming soon! |\n\n## 待办列表\n\n- [x] 图文生成\n- [x] 图像到图像的翻译\n- [x] Imagenet, portrait 扩散模型\n- \\[\\] 像素艺术，水彩，科幻小说的扩散模型\n- \\[\\] 支持图像提示\n- \\[\\] 支持视频生成\n- \\[\\] 支持更快的采样器(plms，dpm-solver等)\n\n我们很欢迎社区用户支持这些项目和任何其他有趣的工作!\n\n## Quick Start\n\n运行以下代码，你可以使用文本生成图像。\n\n```python\nfrom mmengine import Config, MODELS\nfrom mmagic.utils import register_all_modules\nfrom torchvision.utils import save_image\n\nregister_all_modules()\n\ndisco = MODELS.build(\n Config.fromfile('configs/disco_diffusion/disco-baseline.py').model).cuda().eval()\ntext_prompts = {\n 0: [\n \"A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.\",\n \"yellow color scheme\"\n ]\n}\nimage = disco.infer(\n height=768,\n width=1280,\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=250,\n eta=0.8)['samples']\nsave_image(image, \"image.png\")\n\n```\n\n## 教程\n\n考虑到`disco-diffusion`包含许多可调整的参数，我们为用户提供了一个[jupyter-notebook](./tutorials.ipynb)/[colab](https://githubtocolab.com/open-mmlab/mmagic/blob/main/configs/disco_diffusion/tutorials.ipynb)的教程，展示了不同参数的含义，并给出相应的调整结果。\n请参考[Disco Sheet](https://docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g/edit)。\n\n## 鸣谢\n\nSince our adaptation of disco-diffusion are heavily influenced by disco [colab](https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb#scrollTo=License), here we copy the credits below.\n\n
\n鸣谢\nOriginal notebook by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses either OpenAI's 256x256 unconditional ImageNet or Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images.\n\nModified by Daniel Russell (https://github.com/russelldc, https://twitter.com/danielrussruss) to include (hopefully) optimal params for quick generations in 15-100 timesteps rather than 1000, as well as more robust augmentations.\n\nFurther improvements from Dango233 and nshepperd helped improve the quality of diffusion in general, and especially so for shorter runs like this notebook aims to achieve.\n\nVark added code to load in multiple Clip models at once, which all prompts are evaluated against, which may greatly improve accuracy.\n\nThe latest zoom, pan, rotation, and keyframes features were taken from Chigozie Nri's VQGAN Zoom Notebook (https://github.com/chigozienri, https://twitter.com/chigozienri)\n\nAdvanced DangoCutn Cutout method is also from Dango223.\n\n\\--\n\nDisco:\n\nSomnai (https://twitter.com/Somnai_dreams) added Diffusion Animation techniques, QoL improvements and various implementations of tech and techniques, mostly listed in the changelog below.\n\n3D animation implementation added by Adam Letts (https://twitter.com/gandamu_ml) in collaboration with Somnai. Creation of disco.py and ongoing maintenance.\n\nTurbo feature by Chris Allen (https://twitter.com/zippy731)\n\nImprovements to ability to run on local systems, Windows support, and dependency installation by HostsServer (https://twitter.com/HostsServer)\n\nVR Mode by Tom Mason (https://twitter.com/nin_artificial)\n\nHorizontal and Vertical symmetry functionality by nshepperd. Symmetry transformation_steps by huemin (https://twitter.com/huemin_art). Symmetry integration into Disco Diffusion by Dmitrii Tochilkin (https://twitter.com/cut_pow).\n\nWarp and custom model support by Alex Spirin (https://twitter.com/devdef).\n\nPixel Art Diffusion, Watercolor Diffusion, and Pulp SciFi Diffusion models from KaliYuga (https://twitter.com/KaliYuga_ai). Follow KaliYuga's Twitter for the latest models and for notebooks with specialized settings.\n\nIntegration of OpenCLIP models and initiation of integration of KaliYuga models by Palmweaver / Chris Scalf (https://twitter.com/ChrisScalf11)\n\nIntegrated portrait_generator_v001 from Felipe3DArtist (https://twitter.com/Felipe3DArtist)\n\n
\n\n## Citation\n\n```bibtex\n@misc{github,\n author={alembics},\n title={disco-diffusion},\n year={2022},\n url={https://github.com/alembics/disco-diffusion},\n}\n```\n" }, { "path": "configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-256x256.py", "content": "unet = dict(\n type='DenoisingUnet',\n image_size=256,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.0,\n num_classes=0,\n use_fp16=True,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True)\n\nunet_ckpt_path = 'https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u-cvt-rgb_finetuned_imagenet-256x256-f0f6e11b.pth' # noqa\nsecondary_model_ckpt_path = 'https://download.openmmlab.com/mmediting/synthesizers/disco/secondary_model_imagenet_2.pth' # noqa\npretrained_cfgs = dict(\n unet=dict(ckpt_path=unet_ckpt_path, prefix='unet'),\n secondary_model=dict(ckpt_path=secondary_model_ckpt_path, prefix=''))\n\nsecondary_model = dict(type='SecondaryDiffusionImageNet2')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='linear',\n clip_sample=False)\n\nclip_models = [\n dict(type='ClipWrapper', clip_type='clip', name='ViT-B/32', jit=False),\n dict(type='ClipWrapper', clip_type='clip', name='ViT-B/16', jit=False),\n dict(type='ClipWrapper', clip_type='clip', name='RN50', jit=False)\n]\n\nmodel = dict(\n type='DiscoDiffusion',\n unet=unet,\n diffusion_scheduler=diffusion_scheduler,\n secondary_model=secondary_model,\n clip_models=clip_models,\n use_fp16=True,\n pretrained_cfgs=pretrained_cfgs)\n" }, { "path": "configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py", "content": "unet = dict(\n type='DenoisingUnet',\n image_size=512,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.0,\n num_classes=0,\n use_fp16=True,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True)\n\nunet_ckpt_path = 'https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u_finetuned_imagenet-512x512-ab471d70.pth' # noqa\nsecondary_model_ckpt_path = 'https://download.openmmlab.com/mmediting/synthesizers/disco/secondary_model_imagenet_2.pth' # noqa\npretrained_cfgs = dict(\n unet=dict(ckpt_path=unet_ckpt_path, prefix='unet'),\n secondary_model=dict(ckpt_path=secondary_model_ckpt_path, prefix=''))\n\nsecondary_model = dict(type='SecondaryDiffusionImageNet2')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='linear',\n clip_sample=False)\n\nclip_models = [\n dict(type='ClipWrapper', clip_type='clip', name='ViT-B/32', jit=False),\n dict(type='ClipWrapper', clip_type='clip', name='ViT-B/16', jit=False),\n dict(type='ClipWrapper', clip_type='clip', name='RN50', jit=False)\n]\n\nmodel = dict(\n type='DiscoDiffusion',\n unet=unet,\n diffusion_scheduler=diffusion_scheduler,\n secondary_model=secondary_model,\n clip_models=clip_models,\n use_fp16=True,\n pretrained_cfgs=pretrained_cfgs)\n" }, { "path": "configs/disco_diffusion/disco-diffusion_portrait-generator-v001.py", "content": "_base_ = ['./disco-diffusion_adm-u-finetuned_imagenet-512x512.py']\nunet_ckpt_path = 'https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u-cvt-rgb_portrait-v001-f4a3f3bc.pth' # noqa\nmodel = dict(\n unet=dict(base_channels=128),\n secondary_model=None,\n pretrained_cfgs=dict(\n _delete_=True, unet=dict(ckpt_path=unet_ckpt_path, prefix='unet')))\n" }, { "path": "configs/disco_diffusion/metafile.yml", "content": "Collections:\n- Name: Disco Diffusion\n Paper:\n Title: Disco Diffusion\n URL: https://github.com/alembics/disco-diffusion\n README: configs/disco_diffusion/README.md\n Task:\n - text2image\n - image2image\n Year: 2022\nModels:\n- Config: configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py\n In Collection: Disco Diffusion\n Name: disco-diffusion_adm-u-finetuned_imagenet-512x512\n Results:\n - Dataset: ImageNet\n Metrics: {}\n Task: Text2Image\n Weights: https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u_finetuned_imagenet-512x512-ab471d70.pth\n- Config: configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-256x256.py\n In Collection: Disco Diffusion\n Name: disco-diffusion_adm-u-finetuned_imagenet-256x256\n Results:\n - Dataset: ImageNet\n Metrics: {}\n Task: Text2Image\n Weights: <>\n- Config: configs/disco_diffusion/disco-diffusion_portrait-generator-v001.py\n In Collection: Disco Diffusion\n Name: disco-diffusion_portrait-generator-v001\n Results:\n - Dataset: unknown\n Metrics: {}\n Task: Text2Image\n Weights: https://download.openmmlab.com/mmediting/synthesizers/disco/adm-u-cvt-rgb_portrait-v001-f4a3f3bc.pth\n" }, { "path": "configs/disco_diffusion/tutorials.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Disco Tutorials\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Disco Diffusion is a colab work cooperated by many talented people. From the algorithm aspect, this work is based on guided-diffusion and clip-guided diffusion. \\n\",\n \"\\n\",\n \"The generation process of disco-diffusion consists of following steps,\\n\",\n \"1. Disco-diffusion takes as inputs user-provided text prompts and image prompts.\\n\",\n \"2. A pretrained clip model calculates the embeddings of the text and image prompts.\\n\",\n \"3. The Unet will predict the original sample.\\n\",\n \"3. A list of image cutouts will be generated from original sample by a cutter.\\n\",\n \"4. The generation process is optimized by calculating the similarity between the embeddings of the image cutouts, text and image prompts, and other losses.\\n\",\n \"5. Finally, with the carefully-designed loss functions, the model enables classifier-guidance samping to generate desired images according to the text and image prompts.\\n\",\n \"\\n\",\n \"So corresponding to above processes, we will introduce what to set for generating and show your results by adjusting arguments/ configs. \\n\",\n \"\\n\",\n \"The contents of this tutorials are as follows:\\n\",\n \"\\n\",\n \"[1.Runtime Settings](#1-Runtime-Settings)\\n\",\n \"\\n\",\n \"[2.Unet Settings](#2-Unet-Settings)\\n\",\n \"\\n\",\n \"[3.CLIP Models Settings](#3-CLIP-Models-Settings)\\n\",\n \"\\n\",\n \"[4.Diffusion Scheduler Settings](#4-Diffusion-Scheduler-Settings)\\n\",\n \"\\n\",\n \"[5.Loss Settings](#5-Loss-Settings)\\n\",\n \"\\n\",\n \"[6.Cutter Settings](#6-Cutter-Settings)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Install MMagic\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check PyTorch version and CLIP version\\n\",\n \"!pip list | grep torch\\n\",\n \"!pip list | grep clip\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install mmcv dependency via openmim\\n\",\n \"!pip install openmim\\n\",\n \"!mim install 'mmcv>=2.0.0'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install mmagic from source\\n\",\n \"%cd /content/\\n\",\n \"!rm -rf mmagic\\n\",\n \"!git clone https://github.com/open-mmlab/mmagic.git \\n\",\n \"%cd mmagic\\n\",\n \"!pip install -r requirements.txt\\n\",\n \"!pip install -e .\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Runtime Settings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"from mmengine import Config, MODELS\\n\",\n \"from mmengine.registry import init_default_scope\\n\",\n \"from mmagic.registry import MODULES\\n\",\n \"from mmcv import tensor2imgs\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"\\n\",\n \"from torchvision.transforms import ToPILImage, Normalize, Compose\\n\",\n \"from IPython.display import Image\\n\",\n \"\\n\",\n \"init_default_scope('mmagic')\\n\",\n \"\\n\",\n \"\\n\",\n \"def show_tensor(image_tensor, index=0):\\n\",\n \" normalized_image = ((image_tensor + 1.) / 2.).clamp(0, 1)\\n\",\n \" out = tensor2imgs(normalized_image * 255, to_rgb=False)\\n\",\n \" plt.imshow(out[index])\\n\",\n \" plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"config = 'configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\\n\",\n \"text_prompts = {\\n\",\n \" 0: [\\\"clouds surround the mountains and Chinese palaces, sunshine, lake, overlook, overlook, unreal engine, light effect, Dream, Greg Rutkowski, James Gurney, artstation\\\"]\\n\",\n \"}\\n\",\n \"\\n\",\n \"seed = 2022\\n\",\n \"num_inference_steps = 250\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Image Resolution\\n\",\n \"Despite the limit of your device limitation, you can set height and width of image as you like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# image resolution\\n\",\n \"image = disco.infer(width=768, height=1280, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### image_prompts\\n\",\n \" Work in progress.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # image prompts\\n\",\n \"# image_prompts = ['path_of_image']\\n\",\n \"# image = disco.infer(width=768, height=1280, image_prompts=image_prompts, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, seed=seed)['samples']\\n\",\n \"# show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### clip_guidance_scale\\n\",\n \"clip_guidance_scale is one of the most important parameters you will use. It tells DD how strongly you want CLIP to move toward your prompt each timestep. Higher is generally better, but if CGS is too strong it will overshoot the goal and distort the image. So a happy medium is needed, and it takes experience to learn how to adjust CGS.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# clip guidance scale\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, clip_guidance_scale=8000, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, clip_guidance_scale=4000, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Unet Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Disco-Diffusion provides different unet, and we offer configs for the different unets and convert the weights. You only need to select these configs to use the different unets freely.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 256x256_diffusion_uncond\\n\",\n \"text_prompts = {\\n\",\n \" 0: [\\\"clouds surround the mountains and Chinese palaces,sunshine,lake,overlook,overlook,unreal engine,light effect,Dream，Greg Rutkowski,James Gurney,artstation\\\"]\\n\",\n \"}\\n\",\n \"config = 'configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-256x256.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\\n\",\n \"image = disco.infer(width=512, height=448, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# 512x512_diffusion_uncond_finetune_008100\\n\",\n \"config = 'configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# portrait_generator_v001\\n\",\n \"text_prompts = {\\n\",\n \" 0: [\\\"a portrait of supergirl, by artgerm, rosstran, trending on artstation.\\\"]\\n\",\n \"}\\n\",\n \"config = 'disco-diffusion_portrait-generator-v001.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\\n\",\n \"image = disco.infer(width=512, height=512, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, tv_scale=5000, range_scale = 5000, sat_scale = 15250, clip_guidance_scale=20000, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### The rest unets will come soon!\\n\",\n \"- pixelartdiffusion_expanded\\n\",\n \"- pixel_art_diffusion_hard_256\\n\",\n \"- pixelartdiffusion4k\\n\",\n \"- watercolordiffusion_2\\n\",\n \"- watercolordiffusion\\n\",\n \"- PulpSciFiDiffusion\\n\",\n \"- secondary\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.CLIP Models Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Disco-Diffusion uses different clip models to guide the image generation, and the images generated by different clip models have different characteristics. In practice, we combine multiple clip models to generate images.\\n\",\n \" In order to study the effect of different clip models, in the following example, we only use one clip model at a time, with other settings keeping the same, you can experience the characteristics of different clip models by observing the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from mmagic.models.editors.disco_diffusion.guider import ImageTextGuider\\n\",\n \"\\n\",\n \"\\n\",\n \"config = 'configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\\n\",\n \"text_prompts = {0: [\\\"A beautiful painting of a map of the city of Atlantis\\\"]}\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"### ViTB32\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='ViT-B/32', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB16\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='ViT-B/16', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTL14\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='ViT-L/14', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTL14_336px\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper', clip_type='clip', name='ViT-L/14@336px',\\n\",\n \" jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='RN50', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50x4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='RN50x4', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50x16\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='RN50x16', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50x64\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='RN50x64', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN101\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(type='ClipWrapper', clip_type='clip', name='RN101', jit=False),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB32_laion2b_e16\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-32',\\n\",\n \" pretrained='laion2b_e16',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB32_laion400m_e31\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-32',\\n\",\n \" pretrained='laion400m_e31',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB32_laion400m_32\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-32',\\n\",\n \" pretrained='laion400m_e32',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB32quickgelu_laion400m_e31\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-32-quickgelu',\\n\",\n \" pretrained='laion400m_e31',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB32quickgelu_laion400m_e32\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-32-quickgelu',\\n\",\n \" pretrained='laion400m_e32',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB16_laion400m_e31\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-16',\\n\",\n \" pretrained='laion400m_e31',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ViTB16_laion400m_e32\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='ViT-B-16',\\n\",\n \" pretrained='laion400m_e32',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50_yffcc15m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN50',\\n\",\n \" pretrained='yfcc15m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50_cc12m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN50',\\n\",\n \" pretrained='cc12m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50_quickgelu_yfcc15m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN50-quickgelu',\\n\",\n \" pretrained='yfcc15m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN50_quickgelu_cc12m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN50-quickgelu',\\n\",\n \" pretrained='cc12m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN101_yfcc15m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN101',\\n\",\n \" pretrained='yfcc15m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RN101_quickgelu_yfcc15m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clip_models = []\\n\",\n \"clip_models_cfg = [\\n\",\n \" dict(\\n\",\n \" type='ClipWrapper',\\n\",\n \" clip_type='open_clip',\\n\",\n \" model_name='RN101-quickgelu',\\n\",\n \" pretrained='yfcc15m',\\n\",\n \" device='cuda'),\\n\",\n \"]\\n\",\n \"for clip_cfg in clip_models_cfg:\\n\",\n \" clip_models.append(MODULES.build(clip_cfg))\\n\",\n \"disco.guider = ImageTextGuider(clip_models).cuda()\\n\",\n \"\\n\",\n \"image = disco.infer(\\n\",\n \" width=1280,\\n\",\n \" height=768,\\n\",\n \" text_prompts=text_prompts,\\n\",\n \" show_progress=True,\\n\",\n \" num_inference_steps=num_inference_steps,\\n\",\n \" eta=0.8,\\n\",\n \" seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.Diffusion Scheduler Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Typically, a diffusion model generates images by a reverse scheduler. Many researchers are working on designing different reverse schedulers to improve diffusion models. Now, we only support DDIM scheduler. More kinds of reverse schedulers are coming soon.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### skip_steps\\n\",\n \"First, When you set the value of `init_image`, you can adjust `skip_steps` for creative reasons. With low skip_steps you can get a result “inspired by” the init_image which will retain the colors and rough layout and shapes but look quite different. With high skip_steps you can preserve most of the init_image contents and only fine-tune the texture.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!wget https://user-images.githubusercontent.com/22982797/205579254-30c3b446-63bb-4172-bbfe-8d1d05e151cf.png -O init.png\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_path = 'init.png'\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, init_image=init_path, skip_steps=50, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, init_image=init_path, skip_steps=200, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that, you can still use `skip_steps` to reduce rendering time even you don't set the value of `init_image`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### steps\\n\",\n \"Increasing steps will provide more opportunities for the AI to adjust the image, and each adjustment will be smaller, and thus will yield a more precise, detailed image. Using a larger `steps` will generally increase image quality but also increasing the render time. However, some intricate images can take 1000, 2000, or more steps. It is really up to the user. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"steps = 100\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"steps = 1000\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=steps, eta=0.8, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.Loss Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We define `loss_cfg` in config like this. The loss function defines the distance between the generated image and the expected target, so you can adjust loss settings for you purpose.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"loss_cfg = dict(tv_scale=0, range_scale=150, sat_scale=0, init_scale=1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"config = 'configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py'\\n\",\n \"disco = MODELS.build(Config.fromfile(config).model).cuda().eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### range_scale\\n\",\n \"Optional, set to zero to turn off. It is used to adjust color contrast. Lower range_scale will increase contrast. Very low numbers create a reduced color palette, resulting in more vibrant or poster-like images. Higher range_scale will reduce contrast, for more muted images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# range_scale\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, range_scale=50, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, range_scale=200, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### tv_scale\\n\",\n \"Total variance denoising. Optional, set to zero to turn off. Controls ‘smoothness’ of final output. If used, tv_scale will try to smooth out your final image to reduce overall noise. If your image is too 'crunchy', increase tv_scale. TV denoising is good at preserving edges while smoothing away noise in flat regions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# tv_scale\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, tv_scale=0.1, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, tv_scale=0.9, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### sat_scale\\n\",\n \"Saturation scale. Optional, set to zero to turn off. If used, sat_scale will help mitigate oversaturation. If your image is too saturated, increase sat_scale to reduce saturation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# sat_scale\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, sat_scale=0.1, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, sat_scale=0.9, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### init_scale\\n\",\n \" This controls how strongly CLIP will try to match the init_image provided. This is balanced against the clip_guidance_scale (CGS) above. An extreamly large value of `init_scale` won't change the results dramatcally, while an exteamly large value of CGS will destroy the `init_image`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# init_scale\\n\",\n \"init_path = 'init.png'\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, init_image=init_path, skip_steps=125, init_scale=1000, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, init_image=init_path, skip_steps=125, init_scale=100, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.Cutter Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Cutter Settings\\n\",\n \"This section determines the schedule of CLIP cuts, or snapshots that CLIP uses to evaluate your image while processing. In DD, there are two types of cuts: overview cuts, which take a snapshot of the entire image and evaluate that against the prompt, and inner cuts, which are smaller cropped images from the interior of the image, helpful in tuning fine details. The size of the inner cuts can be adjusted using the cut_ic_pow parameter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### cut_overview\\n\",\n \"The schedule of overview cuts\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# cut_overview\\n\",\n \"cut_overview = [12] * 100 + [4] * 900\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_overview=cut_overview, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"cut_overview = [12] * 900 + [4] * 100\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_overview=cut_overview, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### cut_innercut\\n\",\n \"The schedule of inner cuts\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# cut_innercut\\n\",\n \"cut_innercut = [4] * 100 + [12] * 900\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_innercut=cut_innercut, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"cut_innercut = [4] * 900 + [12] * 100\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_innercut=cut_innercut, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### cut_ic_pow\\n\",\n \"This sets the size of the border used for inner cuts. High cut_ic_pow values have larger borders, and therefore the cuts themselves will be smaller and provide finer details. If you have too many or too-small inner cuts, you may lose overall image coherency and/or it may cause an undesirable ‘mosaic’ effect. Low cut_ic_pow values will allow the inner cuts to be larger, helping image coherency while still helping with some details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# cut_ic_pow\\n\",\n \"cut_ic_pow = [1] * 200 + [0] * 800\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_ic_pow=cut_ic_pow, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"cut_ic_pow = [1] * 800 + [0] * 200\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_ic_pow=cut_ic_pow, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### cut_icgray_p\\n\",\n \"In addition to the overall cut schedule, a portion of the cuts can be set to be grayscale instead of color. This may help with improved definition of shapes and edges, especially in the early diffusion steps where the image structure is being defined. cut_icgray_p affects overview and inner cuts\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# cut_icgray_p\\n\",\n \"cut_icgray_p=[0.2] * 200 + [0] * 800\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_icgray_p=cut_icgray_p, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"cut_icgray_p=[0.2] * 800 + [0] * 200\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cut_icgray_p=cut_icgray_p, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### cutn_batches\\n\",\n \"Each iteration, the AI cuts the image into smaller pieces known as cuts, and compares each cut to the prompt to decide how to guide the next diffusion step. More cuts can generally lead to better images, since DD has more chances to fine-tune the image precision at each timesteps. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# cutn_batches\\n\",\n \"cutn_batches = 2\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cutn_batches=cutn_batches, seed=seed)['samples']\\n\",\n \"show_tensor(image)\\n\",\n \"\\n\",\n \"cutn_batches = 8\\n\",\n \"image = disco.infer(width=1280, height=768, text_prompts=text_prompts, show_progress=True, num_inference_steps=num_inference_steps, eta=0.8, cutn_batches=cutn_batches, seed=seed)['samples']\\n\",\n \"show_tensor(image)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.16 ('mmedit': conda)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.16\"\n },\n \"orig_nbformat\": 4,\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"35fd9fbe4d1b297d4be2b8092ffedf61ac9a34aef1ae0231052d90ec0a3e8f9f\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "configs/draggan/README.md", "content": "# DragGAN (SIGGRAPH'2023)\n\n> [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://arxiv.org/pdf/2305.10973.pdf)\n\n> **Task**: DragGAN\n\n\n\n## Abstract\n\n\n\nSynthesizing visual content that meets users’ needs often requires flexible\nand precise controllability of the pose, shape, expression, and layout of the\ngenerated objects. Existing approaches gain controllability of generative\nadversarial networks (GANs) via manually annotated training data or a\nprior 3D model, which often lack flexibility, precision, and generality. In\nthis work, we study a powerful yet much less explored way of controlling\nGANs, that is, to \"drag\" any points of the image to precisely reach target\npoints in a user-interactive manner, as shown in Fig.1. To achieve this, we\npropose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towards\nthe target position, and 2) a new point tracking approach that leverages\nthe discriminative generator features to keep localizing the position of the\nhandle points. Through DragGAN, anyone can deform an image with precise\ncontrol over where pixels go, thus manipulating the pose, shape, expression,and layout of diverse categories such as animals, cars, humans, landscapes,\netc. As these manipulations are performed on the learned generative image\nmanifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming\nshapes that consistently follow the object’s rigidity. Both qualitative and\nquantitative comparisons demonstrate the advantage of DragGAN over prior\napproaches in the tasks of image manipulation and point tracking. We also\nshowcase the manipulation of real images through GAN inversion.\n\n\n\n
\n\n
\n\n## Results and Models\n\n\n Gradio Demo of DragGAN StyleGAN2-elephants-512 by MMagic \n\n\n
\n\n| Model | Dataset | Comment | FID50k | Precision50k | Recall50k | Download |\n| :--------------------------------------------------: | :----------------: | :-----------------------------: | :----: | :----------: | :-------: | :----------------------------------------------------------------------: |\n| [stylegan2_lion_512x512](./stylegan2_512x512.py) | Internet Lions | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-Lions-internet) |\n| [stylegan2_elphants_512x512](./stylegan2_512x512.py) | Internet Elephants | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-elephants-internet) |\n| [stylegan2_cats_512x512](./stylegan2_512x512.py) | Cat AFHQ | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-cat-AFHQ) |\n| [stylegan2_face_512x512](./stylegan2_512x512.py) | FFHQ | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-FFHQ) |\n| [stylegan2_horse_256x256](./stylegan2_256x256.py) | LSUN-Horse | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-lsun-horses) |\n| [stylegan2_dogs_1024x1024](./stylegan2_1024x1024.py) | Internet Dogs | self-distilled StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-dogs-internet) |\n| [stylegan2_car_512x512](./stylegan2_512x512.py) | Car | transfer from official training | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-car-official) |\n| [stylegan2_cat_256x256](./stylegan2_256x256.py) | Cat | transfer from official training | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-cat-official) |\n\n## Demo\n\nTo run DragGAN demo, please follow these two steps:\n\nFirst, put your checkpoint path in `./checkpoints`, *e.g.* `./checkpoints/stylegan2_lions_512_pytorch_mmagic.pth`. To be specific,\n\n```shell\nmkdir checkpoints\ncd checkpoints\nwget -O stylegan2_lions_512_pytorch_mmagic.pth https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-Lions-internet\n```\n\nThen, try on the script:\n\n```shell\npython demo/gradio_draggan.py\n```\n\n## Citation\n\n```latex\n@inproceedings{pan2023drag,\n title={Drag your gan: Interactive point-based manipulation on the generative image manifold},\n author={Pan, Xingang and Tewari, Ayush and Leimk{\\\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian},\n booktitle={ACM SIGGRAPH 2023 Conference Proceedings},\n pages={1--11},\n year={2023}\n}\n```\n" }, { "path": "configs/draggan/README_zh-CN.md", "content": "# DragGAN (SIGGRAPH'2023)\n\n> [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://arxiv.org/pdf/2305.10973.pdf)\n\n> **Task**: DragGAN\n\n\n\n## 摘要\n\n\n\n要合成满足用户需求的视觉内容，通常需要灵活、精确地控制人物的姿势、形状、表情和布局。\n和精确地控制生成对象的姿势、形状、表情和布局。\n生成对象。现有的方法是通过人工标注训练数据来获得生成对抗网络（GANs）的可控性。\n生成式对抗网络 (GAN) 的可控性是通过人工标注的训练数据或预先的 3D 模型来实现的。\n生成式对抗网络（GAN）的可控性是通过人工标注的训练数据或事先建立的三维模型来实现的，而这些方法往往缺乏灵活性、精确性和通用性。在中，我们研究了一种功能强大但探索较少的生成式对抗网络（GAN）控制方法。\n即 \"拖动 \"图像中的任意点，以用户互动的方式精确到达目标点。\n如图 1 所示。为此，我们\nDragGAN 由两个主要部分组成： 1) 基于特征的运动监督，驱动手柄点向目标位置移动；以及目标位置，以及 2) 一种新的点跟踪方法，利用的新点跟踪方法。\n手柄点的位置。通过 DragGAN，任何人都可以对图像进行变形，并精确控制像素的移动位置。\n通过 DragGAN，任何人都可以精确控制像素的位置，从而改变图像的姿态、形状、表情和布局，如动物、汽车、人类、风景等、\n等等。由于这些操作是在 GAN 的学习生成图像流形上进行的流形上进行，因此即使是在具有挑战性的情况下，它们也能产生逼真的输出，例如幻觉遮挡内容和变形形状始终遵循物体的刚性。定性和定量比较都表明，在图像处理和点跟踪任务中，DragGAN\n在图像处理和点跟踪任务中的优势。我们还展示了通过 GAN 反演对真实图像的操作。\n\n\n\n
\n\n
\n\n## 结果和模型\n\n\n MMagic下实现的DragGAN（StyleGAN2-elephants-512） Gradio示例 \n\n\n
\n\n| 模型 | 数据集 | 评论 | FID50k | Precision50k | Recall50k | 权重下载链接 |\n| :--------------------------------------------------: | :----------------: | :------------: | :----: | :----------: | :-------: | :---------------------------------------------------------------------------------------: |\n| [stylegan2_lion_512x512](./stylegan2_512x512.py) | Internet Lions | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-Lions-internet) |\n| [stylegan2_elphants_512x512](./stylegan2_512x512.py) | Internet Elephants | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-elephants-internet) |\n| [stylegan2_cats_512x512](./stylegan2_512x512.py) | Cat AFHQ | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-cat-AFHQ) |\n| [stylegan2_face_512x512](./stylegan2_512x512.py) | FFHQ | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-FFHQ) |\n| [stylegan2_horse_256x256](./stylegan2_256x256.py) | LSUN-Horse | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-lsun-horses) |\n| [stylegan2_dogs_1024x1024](./stylegan2_1024x1024.py) | Internet Dogs | 自蒸馏StyleGAN | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-dogs-internet) |\n| [stylegan2_car_512x512](./stylegan2_512x512.py) | Car | 官方训练 | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-car-official) |\n| [stylegan2_cat_256x256](./stylegan2_256x256.py) | Cat | 官方训练 | 0.0 | 0.0 | 0.0 | [model](https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-cat-official) |\n\n## 演示\n\n为了使用DragGAN演示, 请执行以下两步:\n\n首先，把模型文件放在 `./checkpoints`下, 比如 `./checkpoints/stylegan2_lions_512_pytorch_mmagic.pth`. 具体来说,\n\n```shell\nmkdir checkpoints\ncd checkpoints\nwget -O stylegan2_lions_512_pytorch_mmagic.pth https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-Lions-internet\n```\n\n然后，执行下面的脚本:\n\n```shell\npython demo/gradio_draggan.py\n```\n\n## 引用\n\n```latex\n@inproceedings{pan2023drag,\n title={Drag your gan: Interactive point-based manipulation on the generative image manifold},\n author={Pan, Xingang and Tewari, Ayush and Leimk{\\\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian},\n booktitle={ACM SIGGRAPH 2023 Conference Proceedings},\n pages={1--11},\n year={2023}\n}\n```\n" }, { "path": "configs/draggan/metafile.yml", "content": "Collections:\n- Name: DragGAN\n Paper:\n Title: 'Drag Your GAN: Interactive Point-based Manipulation on the Generative\n Image Manifold'\n URL: https://arxiv.org/pdf/2305.10973.pdf\n README: configs/draggan/README.md\n Task:\n - draggan\n Year: 2023\nModels:\n- Config: configs/draggan/stylegan2_512x512.py\n In Collection: DragGAN\n Name: stylegan2_512x512\n Results:\n - Dataset: InternetLions\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n - Dataset: InternetElephants\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n - Dataset: CatAFHQ\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n - Dataset: FFHQ\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n - Dataset: Car\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n Weights: https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-car-official\n- Config: configs/draggan/stylegan2_256x256.py\n In Collection: DragGAN\n Name: stylegan2_256x256\n Results:\n - Dataset: LSUN-Horse\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n - Dataset: Cat\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n Weights: https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-cat-official\n- Config: configs/draggan/stylegan2_1024x1024.py\n In Collection: DragGAN\n Name: stylegan2_1024x1024\n Results:\n - Dataset: InternetDogs\n Metrics:\n FID50k: 0.0\n Precision50k: 0.0\n Recall50k: 0.0\n Task: DragGAN\n Weights: https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-dogs-internet\n" }, { "path": "configs/draggan/stylegan2_1024x1024.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(\n out_size=1024,\n update_mean_latent_with_ema=True,\n bgr2rgb=False,\n fixed_noise=True),\n discriminator=dict(in_size=1024), # useless\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 1\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/draggan/stylegan2_256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(\n out_size=256,\n update_mean_latent_with_ema=True,\n bgr2rgb=False,\n fixed_noise=True),\n discriminator=dict(in_size=256), # useless\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 1\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/draggan/stylegan2_512x512.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(\n out_size=512,\n update_mean_latent_with_ema=True,\n bgr2rgb=False,\n fixed_noise=True),\n discriminator=dict(in_size=512), # useless\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 1\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/dreambooth/README.md", "content": "# DreamBooth (2022)\n\n> [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242)\n\n> **Task**: Text2Image\n\n\n\n## Abstract\n\n\n\nLarge text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for \"personalization\" of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering, all while preserving the subject's key features. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation.\n\n\n\n
\n\n
\n\n## Configs\n\n| Model | Dataset | Download |\n| :----------------------------------------------------------------------------: | :-----: | :------: |\n| [DreamBooth](./dreambooth.py) | - | - |\n| [DreamBooth (Finetune Text Encoder)](./dreambooth-finetune_text_encoder.py) | - | - |\n| [DreamBooth with Prior-Preservation Loss](./dreambooth-prior_pre.py) | - | - |\n| [DreamBooth LoRA](./dreambooth-lora.py) | - | - |\n| [DreamBooth LoRA with Prior-Preservation Loss](./dreambooth-lora-prior_pre.py) | - | - |\n\n## Quick Start\n\n1. Download [data](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save to `data/dreambooth/`\n\nThe file structure will be like this:\n\n```text\ndata\n└── dreambooth\n └──imgs\n ├── alvan-nee-Id1DBHv4fbg-unsplash.jpeg\n ├── alvan-nee-bQaAJCbNq3g-unsplash.jpeg\n ├── alvan-nee-brFsZ7qszSY-unsplash.jpeg\n └── alvan-nee-eoqnr8ikwFE-unsplash.jpeg\n```\n\n2. Start training with the following command:\n\n```bash\nbash tools/dist_train.sh configs/dreambooth/dreambooth.py 1\n# or\nbash tools/dist_train.sh configs/dreambooth/dreambooth-lora.py 1\n```\n\n\n\n \n \n \n \n
\n
\n \n
\n 'dreambooth'\n
\n
\n \n
\n 'dreambooth-lora'\n
\n
\n\n## Use ToMe to accelerate your training and inference\n\nWe support **[tomesd](https://github.com/dbolya/tomesd)** now! It is developed for stable-diffusion-based models referring to [ToMe](https://github.com/facebookresearch/ToMe), an efficient ViT speed-up tool based on token merging. To work on with **tomesd** in `mmagic`, you just need to add `tomesd_cfg` to `model` in [DreamBooth](./dreambooth.py). The only requirement is `torch >= 1.12.1` in order to properly support `torch.Tensor.scatter_reduce()` functionality. Please do check it before running the demo.\n\n```python\nmodel = dict(\n type='DreamBooth',\n ...\n tomesd_cfg=dict(ratio=0.5),\n ...\n val_prompts=val_prompts)\n```\n\nFor more details, you can refer to [Stable Diffusion Acceleration](../stable_diffusion/README.md#use-tome-to-accelerate-your-stable-diffusion-model).\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@article{ruiz2022dreambooth,\n title={Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation},\n author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},\n journal={arXiv preprint arXiv:2208.12242},\n year={2022}\n}\n```\n" }, { "path": "configs/dreambooth/dreambooth-finetune_text_encoder.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\n\nmodel = dict(\n type='DreamBooth',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n finetune_text_encoder=True,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor', data_keys=None),\n val_prompts=val_prompts)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper = dict(\n modules='.*unet',\n optimizer=dict(type='AdamW', lr=5e-6),\n accumulative_counts=4) # batch size = 4 * 1 = 4\n\npipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='Resize', scale=(512, 512)),\n dict(type='PackInputs')\n]\ndataset = dict(\n type='DreamBoothDataset',\n data_root='./data/dreambooth',\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks = dict(logger=dict(interval=10))\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=50,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "configs/dreambooth/dreambooth-lora-prior_pre.py", "content": "_base_ = 'dreambooth-lora.py'\n\n# config for model\nmodel = dict(prior_loss_weight=1, class_prior_prompt='a dog')\n" }, { "path": "configs/dreambooth/dreambooth-lora.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\n\nlora_config = dict(target_modules=['to_q', 'to_k', 'to_v'])\nmodel = dict(\n type='DreamBooth',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor', data_keys=None),\n prior_loss_weight=0,\n val_prompts=val_prompts,\n lora_config=lora_config)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper = dict(\n # Only optimize LoRA mappings\n modules='.*.lora_mapping',\n # NOTE: lr should be larger than dreambooth finetuning\n optimizer=dict(type='AdamW', lr=5e-4),\n accumulative_counts=1)\n\npipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='Resize', scale=(512, 512)),\n dict(type='PackInputs')\n]\ndataset = dict(\n type='DreamBoothDataset',\n data_root='./data/dreambooth',\n # TODO: rename to instance\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks = dict(logger=dict(interval=10))\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=50,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "configs/dreambooth/dreambooth-prior_pre.py", "content": "_base_ = './dreambooth.py'\n\n# config for model\nmodel = dict(prior_loss_weight=1, class_prior_prompt='a dog')\n" }, { "path": "configs/dreambooth/dreambooth.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\n\nmodel = dict(\n type='DreamBooth',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor', data_keys=None),\n val_prompts=val_prompts)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper = dict(\n modules='.*unet',\n optimizer=dict(type='AdamW', lr=5e-6),\n accumulative_counts=4) # batch size = 4 * 1 = 4\n\npipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='Resize', scale=(512, 512)),\n dict(type='PackInputs')\n]\ndataset = dict(\n type='DreamBoothDataset',\n data_root='./data/dreambooth',\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks = dict(logger=dict(interval=10))\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=50,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "configs/dreambooth/metafile.yml", "content": "Collections:\n- Name: DreamBooth\n Paper:\n Title: 'DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven\n Generation'\n URL: https://arxiv.org/abs/2208.12242\n README: configs/dreambooth/README.md\n Task:\n - text2image\n Year: 2022\nModels:\n- Config: configs/dreambooth/dreambooth.py\n In Collection: DreamBooth\n Name: dreambooth\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/dreambooth/dreambooth-finetune_text_encoder.py\n In Collection: DreamBooth\n Name: dreambooth-finetune_text_encoder\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/dreambooth/dreambooth-prior_pre.py\n In Collection: DreamBooth\n Name: dreambooth-prior_pre\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/dreambooth/dreambooth-lora.py\n In Collection: DreamBooth\n Name: dreambooth-lora\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/dreambooth/dreambooth-lora-prior_pre.py\n In Collection: DreamBooth\n Name: dreambooth-lora-prior_pre\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n" }, { "path": "configs/edsr/README.md", "content": "# EDSR (CVPR'2017)\n\n> [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nRecent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------------------------------: | :-----: | :-----: | :----: | :----------------: | :--------------------------------------------------------------------------------------------: |\n| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | Set5 | 35.7592 | 0.9372 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) |\n| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | Set14 | 31.4290 | 0.8874 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) |\n| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | DIV2K | 34.5896 | 0.9352 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) |\n| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | Set5 | 32.3301 | 0.8912 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) |\n| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | Set14 | 28.4125 | 0.8022 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) |\n| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | DIV2K | 30.9154 | 0.8711 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) |\n| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | Set5 | 30.2223 | 0.8500 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) |\n| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | Set14 | 26.7870 | 0.7366 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) |\n| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | DIV2K | 28.9675 | 0.8172 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth\n\n# single-gpu test\npython tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{lim2017enhanced,\n title={Enhanced deep residual networks for single image super-resolution},\n author={Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},\n pages={136--144},\n year={2017}\n}\n```\n" }, { "path": "configs/edsr/README_zh-CN.md", "content": "# EDSR (CVPR'2017)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nEDSR (CVPR'2017)\n\n```bibtex\n@inproceedings{lim2017enhanced,\n title={Enhanced deep residual networks for single image super-resolution},\n author={Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},\n pages={136--144},\n year={2017}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |\n| :------------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :------: | :--------------------------------------------------------------------------: |\n| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | 35.7592 / 0.9372 | 31.4290 / 0.8874 | 34.5896 / 0.9352 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) |\n| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | 32.3301 / 0.8912 | 28.4125 / 0.8022 | 30.9154 / 0.8711 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) |\n| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | 30.2223 / 0.8500 | 26.7870 / 0.7366 | 28.9675 / 0.8172 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth\n\n# 单个GPU上测试\npython tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x2_test_config.py'\n]\n\nexperiment_name = 'edsr_x2c64b16_1xb16-300k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale,\n res_scale=1,\n rgb_mean=[0.4488, 0.4371, 0.4040],\n rgb_std=[1.0, 1.0, 1.0]),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=96),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/edsr/edsr_x3c64b16_1xb16-300k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x3_test_config.py'\n]\n\nexperiment_name = 'edsr_x3c64b16_1xb16-300k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nload_from = None # based on pre-trained x2 model\n\nscale = 3\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale,\n res_scale=1,\n rgb_mean=[0.4488, 0.4371, 0.4040],\n rgb_std=[1.0, 1.0, 1.0]),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=144),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X3_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'edsr_x4c64b16_1xb16-300k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nload_from = None # based on pre-trained x2 model\n\nscale = 4\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale,\n res_scale=1,\n rgb_mean=[0.4488, 0.4371, 0.4040],\n rgb_std=[1.0, 1.0, 1.0]),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=196),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/edsr/metafile.yml", "content": "Collections:\n- Name: EDSR\n Paper:\n Title: Enhanced Deep Residual Networks for Single Image Super-Resolution\n URL: https://arxiv.org/abs/1707.02921\n README: configs/edsr/README.md\n Task:\n - image super-resolution\n Year: 2017\nModels:\n- Config: configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py\n In Collection: EDSR\n Name: edsr_x2c64b16_1xb16-300k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 35.7592\n SSIM: 0.9372\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 31.429\n SSIM: 0.8874\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.5896\n SSIM: 0.9352\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth\n- Config: configs/edsr/edsr_x3c64b16_1xb16-300k_div2k.py\n In Collection: EDSR\n Name: edsr_x3c64b16_1xb16-300k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 32.3301\n SSIM: 0.8912\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 28.4125\n SSIM: 0.8022\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 30.9154\n SSIM: 0.8711\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth\n- Config: configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py\n In Collection: EDSR\n Name: edsr_x4c64b16_1xb16-300k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 30.2223\n SSIM: 0.85\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 26.787\n SSIM: 0.7366\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 28.9675\n SSIM: 0.8172\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth\n" }, { "path": "configs/edvr/README.md", "content": "# EDVR (CVPRW'2019)\n\n> [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://arxiv.org/abs/1905.02716?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%253A+arxiv%252FQSXk+%2528ExcitingAds%2521+cs+updates+on+arXiv.org%2529)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nVideo restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels.\nThe metrics are `PSNR and SSIM` .\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :--------------------------------------------------------------------------: | :-----: | :-----: | :----: | :----------------------: | :------------------------------------------------------------------------------: |\n| [edvrm_wotsa_x4_8x4_600k_reds](./edvrm_wotsa_8xb4-600k_reds.py) | REDS | 30.3430 | 0.8664 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) |\n| [edvrm_x4_8x4_600k_reds](./edvrm_8xb4-600k_reds.py) | REDS | 30.4194 | 0.8684 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) |\n| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | REDS | 31.0010 | 0.8784 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) |\n| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | REDS | 31.0467 | 0.8793 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edvr/edvrm_8xb4-600k_reds.py\n\n# single-gpu train\npython tools/train.py configs/edvr/edvrm_8xb4-600k_reds.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/edvr/edvrm_8xb4-600k_reds.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py python tools/test.py configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth\n\n# single-gpu test\npython tools/test.py configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{wang2019edvr,\n author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {EDVR: Video restoration with enhanced deformable convolutional networks},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},\n month = {June},\n year = {2019},\n}\n```\n" }, { "path": "configs/edvr/README_zh-CN.md", "content": "# EDVR (CVPRW'2019)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nEDVR (CVPRW'2019)\n\n```bibtex\n@InProceedings{wang2019edvr,\n author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {EDVR: Video restoration with enhanced deformable convolutional networks},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},\n month = {June},\n year = {2019},\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | REDS4 | GPU 信息 | 下载 |\n| :------------------------------------------------------------------------------: | :--------------: | :----------------------: | :------------------------------------------------------------------------------: |\n| [edvrm_wotsa_x4_8x4_600k_reds](./edvrm_wotsa_8xb4-600k_reds.py) | 30.3430 / 0.8664 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) |\n| [edvrm_x4_8x4_600k_reds](./edvrm_8xb4-600k_reds.py) | 30.4194 / 0.8684 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) |\n| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0010 / 0.8784 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) |\n| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0467 / 0.8793 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edvr/edvrm_8xb4-600k_reds.py\n\n# 单个GPU上训练\npython tools/train.py configs/edvr/edvrm_8xb4-600k_reds.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/edvr/edvrm_8xb4-600k_reds.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth\n\n# 单个GPU上测试\npython tools/test.py configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/edvr/edvrm_8xb4-600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/edvr/edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py", "content": "_base_ = '../_base_/models/base_edvr.py'\n\nexperiment_name = 'edvrl_c128b40_8xb8-lr2e-4-600k_reds4'\nsave_dir = './work_dirs'\nwork_dir = f'./work_dirs/{experiment_name}'\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth' # noqa: E501\n\n# model settings\nmodel = dict(\n type='EDVR',\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=128,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=40,\n center_frame_idx=2,\n with_tsa=True),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict(tsa_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[50000, 100000, 150000, 150000, 150000],\n restart_weights=[1, 0.5, 0.5, 0.5, 0.5],\n eta_min=1e-7)\n\nfind_unused_parameters = True\n" }, { "path": "configs/edvr/edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py", "content": "_base_ = '../_base_/models/base_edvr.py'\n\nexperiment_name = 'edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4'\nsave_dir = './work_dirs'\nwork_dir = f'./work_dirs/{experiment_name}'\n\n# model settings\nmodel = dict(\n type='EDVR',\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=128,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=40,\n center_frame_idx=2,\n with_tsa=False),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict(tsa_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_dataloader = dict(num_workers=6, batch_size=8)\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[150000, 150000, 150000, 150000],\n restart_weights=[1, 0.5, 0.5, 0.5],\n eta_min=1e-7)\n" }, { "path": "configs/edvr/edvrm_8xb4-600k_reds.py", "content": "_base_ = '../_base_/models/base_edvr.py'\n\nexperiment_name = 'edvrm_8xb4-600k_reds'\nsave_dir = './work_dirs'\nwork_dir = f'./work_dirs/{experiment_name}'\n\n# model settings\npretrain_generator_url = (\n 'https://download.openmmlab.com/mmediting/restorers/edvr/'\n 'edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth')\nmodel = dict(\n type='EDVR',\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=10,\n center_frame_idx=2,\n with_tsa=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint=pretrain_generator_url,\n prefix='generator.')),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict(tsa_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_dataloader = dict(num_workers=6, batch_size=4)\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[50000, 100000, 150000, 150000, 150000],\n restart_weights=[1, 1, 1, 1, 1],\n eta_min=1e-7)\n\nfind_unused_parameters = True\n" }, { "path": "configs/edvr/edvrm_wotsa_8xb4-600k_reds.py", "content": "_base_ = '../_base_/models/base_edvr.py'\n\nexperiment_name = 'edvrm_wotsa_8xb4-600k_reds'\nsave_dir = './work_dirs'\nwork_dir = f'./work_dirs/{experiment_name}'\n\n# model settings\nmodel = dict(\n type='EDVR',\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=10,\n center_frame_idx=2,\n with_tsa=False),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict(tsa_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[150000, 150000, 150000, 150000],\n restart_weights=[1, 0.5, 0.5, 0.5],\n eta_min=1e-7)\n" }, { "path": "configs/edvr/metafile.yml", "content": "Collections:\n- Name: EDVR\n Paper:\n Title: 'EDVR: Video Restoration with Enhanced Deformable Convolutional Networks'\n URL: https://arxiv.org/abs/1905.02716?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%253A+arxiv%252FQSXk+%2528ExcitingAds%2521+cs+updates+on+arXiv.org%2529\n README: configs/edvr/README.md\n Task:\n - video super-resolution\n Year: 2019\nModels:\n- Config: configs/edvr/edvrm_wotsa_8xb4-600k_reds.py\n In Collection: EDVR\n Name: edvrm_wotsa_8xb4-600k_reds\n Results:\n - Dataset: REDS\n Metrics:\n PSNR: 30.343\n SSIM: 0.8664\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n- Config: configs/edvr/edvrm_8xb4-600k_reds.py\n In Collection: EDVR\n Name: edvrm_8xb4-600k_reds\n Results:\n - Dataset: REDS\n Metrics:\n PSNR: 30.4194\n SSIM: 0.8684\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth\n- Config: configs/edvr/edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py\n In Collection: EDVR\n Name: edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4\n Results:\n - Dataset: REDS\n Metrics:\n PSNR: 31.001\n SSIM: 0.8784\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth\n- Config: configs/edvr/edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py\n In Collection: EDVR\n Name: edvrl_c128b40_8xb8-lr2e-4-600k_reds4\n Results:\n - Dataset: REDS\n Metrics:\n PSNR: 31.0467\n SSIM: 0.8793\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth\n" }, { "path": "configs/eg3d/README.md", "content": "# EG3D (CVPR'2022)\n\n> [Efficient geometry-aware 3D generative adversarial networks](https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html)\n\n> **Task**: 3D-aware Generation\n\n\n\n## Abstract\n\n\n\nUnsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the generated images and the latter adversely affects multi-view consistency and shape quality. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, our framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN2, and inherit their efficiency and expressiveness. We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats, among other experiments.\n\n\n\n
\n\n
\n\n## Results and Models\n\n| Model | Dataset | Comment | FID50k | FID50k-Camera | Download |\n| :---------------------------------------------------------: | :-----------: | :-------------: | :----: | :-----------: | :--------------------------------------------------------------------------------------------: |\n| [ShapeNet-Car](./eg3d_cvt-official-rgb_shapenet-128x128.py) | ShaperNet-Car | official weight | 5.6573 | 5.2325 | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_shapenet-128x128-85757f4d.pth) |\n| [AFHQ](./eg3d_cvt-official-rgb_afhq-512x512.py) | AFHQ | official weight | 2.9134 | 6.4213 | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_afhq-512x512-ca1dd7c9.pth) |\n| [FFHQ](./eg3d_cvt-official-rgb_ffhq-512x512.py) | FFHQ | official weight | 4.3076 | 6.4453 | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_ffhq-512x512-5a0ddcb6.pth) |\n\n- `FID50k-Camera` denotes image generated with random sampled camera position.\n- `FID50k` denotes image generated with camera position randomly sampled from the original dataset.\n\n### Influence of FP16\n\nAll metrics are evaluated under FP32, and it's hard to determine how they will change if we use FP16.\nFor example, if we use FP16 at the super resolution module in [FFHQ model](./eg3d_cvt-official-rgb_ffhq-512x512.py), the output images will be slightly blurrier than the ones generated under FP32, but FID (**4.03**) will be better than FP32 ones.\n\n## About generate images and videos with High-Level API\n\nYou can use the following command to generate sequence images with continuous changed camera position as input.\n\n```shell\npython demo/mmagic_inference_demo.py --model-name eg3d \\\n --model-config configs/eg3d/eg3d_cvt-official-rgb_afhq-512x512.py \\\n --model-ckpt https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_afhq-512x512-ca1dd7c9.pth \\\n --result-out-dir eg3d_output \\ # save images and videos to `eg3d_output`\n --interpolation camera \\ # interpolation camera position only\n --num-images 100 # generate 100 images during interpolation\n```\n\nThe the following video will be saved to `eg3d_output`.\n\n
\n
\n\nTo interpolate the camera position and style code at the same time, you can use the following command.\n\n```shell\npython demo/mmagic_inference_demo.py --model-name eg3d \\\n --model-config configs/eg3d/eg3d_cvt-official-rgb_ffhq-512x512.py \\\n --model-ckpt https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_ffhq-512x512-5a0ddcb6.pth \\\n --result-out-dir eg3d_output \\ # save images and videos to `eg3d_output`\n --interpolation both \\ # interpolation camera and conditioning both\n --num-images 100 # generate 100 images during interpolation\n --seed 233 # set random seed as 233\n```\n\n
\n
\n\nIf you only want to save video of depth map, you can use the following command:\n\n```shell\npython demo/mmagic_inference_demo.py --model-name eg3d \\\n --model-config configs/eg3d/eg3d_cvt-official-rgb_shapenet-128x128.py \\\n --model-ckpt https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_shapenet-128x128-85757f4d.pth \\\n --result-out-dir eg3d_output \\ # save images and videos to `eg3d_output`\n --interpolation camera \\ # interpolation camera position only\n --num-images 100 \\ # generate 100 images during interpolation\n --vis-mode depth # only visualize depth image\n```\n\n
\n
\n\n## How to prepare dataset\n\nYou should prepare your dataset follow the official [repo](https://github.com/NVlabs/eg3d/tree/main/dataset_preprocessing). Then preprocess the `dataset.json` with the following script:\n\n```python\nimport json\nfrom argparse import ArgumentParser\n\nfrom mmengine.fileio.io import load\n\n\ndef main():\n\n parser = ArgumentParser()\n parser.add_argument(\n 'in-anno', type=str, help='Path to the official annotation file.')\n parser.add_argument(\n 'out-anno', type=str, help='Path to MMagicing\\'s annotation file.')\n args = parser.parse_args()\n\n anno = load(args.in_anno)\n label = anno['labels']\n\n anno_dict = {}\n for line in label:\n name, label = line\n anno_dict[name] = label\n\n with open(args.out_anno, 'w') as file:\n json.dump(anno_dict, file)\n\n\nif __name__ == '__main__':\n main()\n```\n\n## Citation\n\n```latex\n@InProceedings{Chan_2022_CVPR,\n author = {Chan, Eric R. and Lin, Connor Z. and Chan, Matthew A. and Nagano, Koki and Pan, Boxiao and De Mello, Shalini and Gallo, Orazio and Guibas, Leonidas J. and Tremblay, Jonathan and Khamis, Sameh and Karras, Tero and Wetzstein, Gordon},\n title = {Efficient Geometry-Aware 3D Generative Adversarial Networks},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2022},\n pages = {16123-16133}\n}\n```\n" }, { "path": "configs/eg3d/eg3d_cvt-official-rgb_afhq-512x512.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\nmodel = dict(\n type='EG3D',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='TriplaneGenerator',\n out_size=512,\n triplane_channels=32,\n triplane_size=256,\n num_mlps=2,\n sr_add_noise=False,\n sr_in_size=128,\n neural_rendering_resolution=128,\n renderer_cfg=dict(\n ray_start=2.25,\n ray_end=3.3,\n box_warp=1,\n depth_resolution=48,\n depth_resolution_importance=48,\n white_back=False,\n ),\n rgb2bgr=True),\n camera=dict(\n type='GaussianCamera',\n horizontal_mean=3.14 / 2,\n horizontal_std=0.35,\n vertical_mean=3.14 / 2 - 0.05,\n vertical_std=0.25,\n radius=2.7,\n fov=18.837,\n look_at=[0, 0, 0.2]))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_afhq.pkl'\nmetrics = [\n dict(\n type='FID-Full',\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type='FID-Full',\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', color_type='color'),\n dict(type='PackInputs')\n]\ntest_dataset = dict(\n type='BasicConditionalDataset',\n data_root='./data/eg3d/afhq',\n ann_file='afhq.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 4` cost nearly **9.5GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "configs/eg3d/eg3d_cvt-official-rgb_ffhq-512x512.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\nmodel = dict(\n type='EG3D',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='TriplaneGenerator',\n out_size=512,\n triplane_channels=32,\n triplane_size=256,\n num_mlps=2,\n neural_rendering_resolution=128,\n sr_add_noise=False,\n sr_in_size=128,\n # NOTE: double hidden channels and out channels for FFHQ-512\n sr_hidden_channels=256,\n sr_out_channels=128,\n renderer_cfg=dict(\n ray_start=2.25,\n ray_end=3.3,\n box_warp=1,\n depth_resolution=48,\n depth_resolution_importance=48,\n white_back=False,\n ),\n rgb2bgr=True),\n camera=dict(\n type='GaussianCamera',\n horizontal_mean=3.14 / 2,\n horizontal_std=0.35,\n vertical_mean=3.14 / 2 - 0.05,\n vertical_std=0.25,\n radius=2.7,\n fov=18.837,\n look_at=[0, 0, 0.2]))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_ffhq_512.pkl'\nmetrics = [\n dict(\n type='FID-Full',\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type='FID-Full',\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', color_type='color'),\n dict(type='PackInputs')\n]\ntest_dataset = dict(\n type='BasicConditionalDataset',\n data_root='./data/eg3d/ffhq_512',\n ann_file='ffhq_512.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 4` cost nearly **9.5GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "configs/eg3d/eg3d_cvt-official-rgb_shapenet-128x128.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\nmodel = dict(\n type='EG3D',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='TriplaneGenerator',\n out_size=128,\n zero_cond_input=True,\n cond_scale=0,\n sr_in_size=64,\n renderer_cfg=dict(\n # Official implementation set ray_start, ray_end and box_warp as\n # 0.1, 2.6 and 1.6 respectively, and FID is 7.2441\n # ray_start=0.1,\n # ray_end=2.6,\n # box_warp=1.6,\n ray_start=0.4,\n ray_end=2.0,\n box_warp=1.7,\n depth_resolution=64,\n depth_resolution_importance=64,\n white_back=True,\n ),\n rgb2bgr=True),\n camera=dict(\n type='UniformCamera',\n horizontal_mean=3.141,\n horizontal_std=3.141,\n vertical_mean=3.141 / 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n up=[0, 0, 1],\n radius=1.2),\n)\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_shapenet.pkl'\nmetrics = [\n dict(\n type='FID-Full',\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type='FID-Full',\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig'),\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', color_type='color'),\n dict(type='PackInputs')\n]\ntest_dataset = dict(\n type='BasicConditionalDataset',\n data_root='./data/eg3d/shapenet-car',\n ann_file='shapenet.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 16` cost nearly **12GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=16,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # save_at_test=False,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "configs/eg3d/metafile.yml", "content": "Collections:\n- Name: EG3D\n Paper:\n Title: Efficient geometry-aware 3D generative adversarial networks\n URL: https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html\n README: configs/eg3d/README.md\n Task:\n - 3d-aware generation\n Year: 2022\nModels:\n- Config: configs/eg3d/eg3d_cvt-official-rgb_shapenet-128x128.py\n In Collection: EG3D\n Name: eg3d_cvt-official-rgb_shapenet-128x128\n Results:\n - Dataset: ShaperNet-Car\n Metrics:\n FID50k: 5.6573\n FID50k-Camera: 5.2325\n Task: 3D-aware Generation\n Weights: https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_shapenet-128x128-85757f4d.pth\n- Config: configs/eg3d/eg3d_cvt-official-rgb_afhq-512x512.py\n In Collection: EG3D\n Name: eg3d_cvt-official-rgb_afhq-512x512\n Results:\n - Dataset: AFHQ\n Metrics:\n FID50k: 2.9134\n FID50k-Camera: 6.4213\n Task: 3D-aware Generation\n Weights: https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_afhq-512x512-ca1dd7c9.pth\n- Config: configs/eg3d/eg3d_cvt-official-rgb_ffhq-512x512.py\n In Collection: EG3D\n Name: eg3d_cvt-official-rgb_ffhq-512x512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.3076\n FID50k-Camera: 6.4453\n Task: 3D-aware Generation\n Weights: https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_ffhq-512x512-5a0ddcb6.pth\n" }, { "path": "configs/esrgan/README.md", "content": "# ESRGAN (ECCVW'2018)\n\n> [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nThe Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------------------------------------: | :-----: | :-----: | :----: | :----------------: | :---------------------------------------------------------------------------------: |\n| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | Set5 | 30.6428 | 0.8559 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) |\n| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | Set14 | 27.0543 | 0.7447 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) |\n| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | DIV2K | 29.3354 | 0.8263 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) |\n| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | Set5 | 28.2700 | 0.7778 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) |\n| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | Set14 | 24.6328 | 0.6491 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) |\n| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | DIV2K | 26.6531 | 0.7340 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n\n# single-gpu test\npython tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{wang2018esrgan,\n title={Esrgan: Enhanced super-resolution generative adversarial networks},\n author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},\n booktitle={Proceedings of the European Conference on Computer Vision Workshops(ECCVW)},\n pages={0--0},\n year={2018}\n}\n```\n" }, { "path": "configs/esrgan/README_zh-CN.md", "content": "# ESRGAN (ECCVW'2018)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nESRGAN (ECCVW'2018)\n\n```bibtex\n@inproceedings{wang2018esrgan,\n title={Esrgan: Enhanced super-resolution generative adversarial networks},\n author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},\n booktitle={Proceedings of the European Conference on Computer Vision Workshops(ECCVW)},\n pages={0--0},\n year={2018}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |\n| :---------------------------------------------------------------------: | :---------------: | :--------------: | :--------------: | :------: | :----------------------------------------------------------------------: |\n| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | 30.6428 / 0.8559 | 27.0543 / 0.7447 | 29.3354 / 0.8263 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420_112550.log.json) |\n| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | 28.2700 / 0.7778 | 24.6328 / 0.6491 | 26.6531 / 0.7340 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508_191042.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n\n# 单个GPU上测试\npython tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=data_root + '/Set14',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[250000, 250000, 250000, 250000],\n restart_weights=[1, 1, 1, 1],\n eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py", "content": "_base_ = './esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py'\n\nexperiment_name = 'esrgan_x4c64b23g32_1xb16-400k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\n\nscale = 4\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\npretrain_generator_url = (\n 'https://download.openmmlab.com/mmediting/restorers/esrgan'\n '/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth')\nmodel = dict(\n type='ESRGAN',\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32,\n upscale_factor=scale,\n init_cfg=dict(\n type='Pretrained',\n checkpoint=pretrain_generator_url,\n prefix='generator.')),\n discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=64),\n pixel_loss=dict(type='L1Loss', loss_weight=1e-2, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'34': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-3,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_cfg = dict(\n _delete_=True,\n type='IterBasedTrainLoop',\n max_iters=400_000,\n val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n _delete_=True,\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n)\n\n# learning policy\nparam_scheduler = dict(\n _delete_=True,\n type='MultiStepLR',\n by_epoch=False,\n milestones=[50000, 100000, 200000, 300000],\n gamma=0.5)\n" }, { "path": "configs/esrgan/metafile.yml", "content": "Collections:\n- Name: ESRGAN\n Paper:\n Title: 'ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks'\n URL: https://arxiv.org/abs/1809.00219\n README: configs/esrgan/README.md\n Task:\n - image super-resolution\n Year: 2018\nModels:\n- Config: configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py\n In Collection: ESRGAN\n Name: esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 30.6428\n SSIM: 0.8559\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 27.0543\n SSIM: 0.7447\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 29.3354\n SSIM: 0.8263\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth\n- Config: configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py\n In Collection: ESRGAN\n Name: esrgan_x4c64b23g32_1xb16-400k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 28.27\n SSIM: 0.7778\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 24.6328\n SSIM: 0.6491\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 26.6531\n SSIM: 0.734\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n" }, { "path": "configs/fastcomposer/README.md", "content": "# FastComposer (2023)\n\n> [FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention](https://arxiv.org/abs/2305.10431)\n\n> **Task**: Text2Image\n\n\n\n## Abstract\n\n\n\nDiffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend features among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300x-2500x speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation.\n\n\n\n
\n\n
\n\n## Pretrained models\n\nThis model has several weights including vae, unet and clip. You should download the weights from [stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and [clipModel](https://huggingface.co/openai/clip-vit-large-patch14),and then change the 'stable_diffusion_v15_url' and 'clip_vit_url' in config to the corresponding weights path and \"finetuned_model_path\" to the weight path of fastcomposer.\n\n| Model | Dataset | Download |\n| :------------------------------------------: | :-----: | :---------------------------------------------------------------------------------------------: |\n| [FastComposer](./fastcomposer_8xb16_FFHQ.py) | - | [model](https://download.openxlab.org.cn/models/xiaomile/fastcomposer/weight/pytorch_model.bin) |\n\n## Quick Start\n\nYou can run the demo locally by\n\n```bash\npython demo/gradio_fastcomposer.py\n```\n\nOr running the following codes, you can get a text-generated image.\n\n```python\nimport numpy as np\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\nimport torch, gc\n\ngc.collect()\ntorch.cuda.empty_cache()\n\nregister_all_modules()\n\ncfg_file = Config.fromfile('configs/fastcomposer/fastcomposer_8xb16_FFHQ.py')\n\nfastcomposer = MODELS.build(cfg_file.model).cuda()\n\nprompt = \"A man img and a man img sitting in a park\"\nnegative_prompt = \"((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))\"\nalpha_ = 0.75\nguidance_scale = 5\nnum_steps = 50\nnum_images = 1\nimage = []\nseed = -1\n\nimage1 = mmcv.imread('https://user-images.githubusercontent.com/14927720/265911400-91635451-54b6-4dc6-92a7-c1d02f88b62e.jpeg')\nimage2 = mmcv.imread('https://user-images.githubusercontent.com/14927720/265911502-66b67f53-dff0-4d25-a9af-3330e446aa48.jpeg')\n\nimage.append(Image.fromarray(image1))\nimage.append(Image.fromarray(image2))\n\nif len(image) == 0:\n raise Exception(\"You need to upload at least one image.\")\n\nnum_subject_in_text = (\n np.array(fastcomposer.special_tokenizer.encode(prompt))\n == fastcomposer.image_token_id\n).sum()\nif num_subject_in_text != len(image):\n raise Exception(f\"Number of subjects in the text description doesn't match the number of reference images, #text subjects: {num_subject_in_text} #reference image: {len(image)}\",\n )\n\nif seed == -1:\n seed = np.random.randint(0, 1000000)\n\ndevice = torch.device('cuda' if torch.cuda.is_available(\n ) else 'cpu')\ngenerator = torch.Generator(device=device)\ngenerator.manual_seed(seed)\n\noutput_dict = fastcomposer.infer(prompt,\n negative_prompt=negative_prompt,\n height=512,\n width=512,\n num_inference_steps=num_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images,\n generator=generator,\n alpha_=alpha_,\n reference_subject_images=image)\n\nsamples = output_dict['samples']\nfor idx, sample in enumerate(samples):\n sample.save(f'sample_{idx}.png')\n```\n\n## Citation\n\n```bibtex\n@article{xiao2023fastcomposer,\n title={FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention},\n author={Xiao, Guangxuan and Yin, Tianwei and Freeman, William T. and Durand, Frédo and Han, Song},\n journal={arXiv},\n year={2023}\n }\n```\n" }, { "path": "configs/fastcomposer/README_zh-CN.md", "content": "# FastComposer (2023)\n\n> [FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention](https://arxiv.org/abs/2305.10431)\n\n> **任务**: 文本转图像\n\n\n\n## 摘要\n\n\n\n扩散模型在文本到图像生成方面表现出色，尤其在以主题驱动的个性化图像生成方面。然而，现有方法由于主题特定的微调而效率低下，因为需要大量的计算资源，而这限制了扩散模型高效部署的可能性。此外，现有方法在多主题生成方面存在困难，因为它们经常在不同主题之间混合特征。因此我们提出了FastComposer，它可以实现高效、个性化、多主题的文本到图像生成，而无需进行微调。FastComposer利用图像编码器提取的主题嵌入来增强扩散模型中的通用文本条件，只需进行前向传递即可基于主题图像和文本指令进行个性化图像生成。为了解决多主题生成中的身份混合问题，FastComposer在训练过程中提出了交叉注意力定位监督，强制参考主题的注意力定位于目标图像中的正确区域。简单地基于主题嵌入进行条件设定会导致主题过度拟合的问题。为了在以主题驱动的图像生成中同时保持身份和可编辑性，FastComposer在去噪步骤中提出了延迟主题条件设定的方法。FastComposer可以生成具有不同风格、动作和背景的多个未知个体的图像。与基于微调的方法相比，它实现了300倍到2500倍的加速，并且对于新主题不需要额外的存储空间。正因如此FastComposer为高效、个性化和高质量的多主题图像创作铺平了道路。\n\n\n\n
\n\n
\n\n## 预训练模型\n\n该模型有几个权重，包括vae，unet和clip。您应该先从[stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) 和 [clipModel](https://huggingface.co/openai/clip-vit-large-patch14) 下载权重，然后将配置中的“stable_diffusion_v15_url”和”clip_vit_url“更改为对应的权重路径，将”finetuned_model_path“更改为fastcomposer的权重路径。\n\n| Model | Dataset | Download |\n| :------------------------------------------: | :-----: | :---------------------------------------------------------------------------------------------: |\n| [FastComposer](./fastcomposer_8xb16_FFHQ.py) | - | [model](https://download.openxlab.org.cn/models/xiaomile/fastcomposer/weight/pytorch_model.bin) |\n\n## 快速开始\n\n您可以通过以下方式在本地运行来演示\n\n```bash\npython demo/gradio_fastcomposer.py\n```\n\n或者运行一下代码，您就能获得依照文本生成的特定图像。\n\n```python\nimport numpy as np\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\nimport torch, gc\n\ngc.collect()\ntorch.cuda.empty_cache()\n\nregister_all_modules()\n\ncfg_file = Config.fromfile('configs/fastcomposer/fastcomposer_8xb16_FFHQ.py')\n\nfastcomposer = MODELS.build(cfg_file.model).cuda()\n\nprompt = \"A man img and a man img sitting in a park\"\nnegative_prompt = \"((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))\"\nalpha_ = 0.75\nguidance_scale = 5\nnum_steps = 50\nnum_images = 1\nimage = []\nseed = -1\n\nimage1 = mmcv.imread('https://user-images.githubusercontent.com/14927720/265911400-91635451-54b6-4dc6-92a7-c1d02f88b62e.jpeg')\nimage2 = mmcv.imread('https://user-images.githubusercontent.com/14927720/265911502-66b67f53-dff0-4d25-a9af-3330e446aa48.jpeg')\n\nimage.append(Image.fromarray(image1))\n\nimage.append(Image.fromarray(image2))\n\nif len(image) == 0:\n raise Exception(\"You need to upload at least one image.\")\n\nnum_subject_in_text = (\n np.array(fastcomposer.special_tokenizer.encode(prompt))\n == fastcomposer.image_token_id\n).sum()\nif num_subject_in_text != len(image):\n raise Exception(f\"Number of subjects in the text description doesn't match the number of reference images, #text subjects: {num_subject_in_text} #reference image: {len(image)}\",\n )\n\nif seed == -1:\n seed = np.random.randint(0, 1000000)\n\ndevice = torch.device('cuda' if torch.cuda.is_available(\n ) else 'cpu')\ngenerator = torch.Generator(device=device)\ngenerator.manual_seed(seed)\n\noutput_dict = fastcomposer.infer(prompt,\n negative_prompt=negative_prompt,\n height=512,\n width=512,\n num_inference_steps=num_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images,\n generator=generator,\n alpha_=alpha_,\n reference_subject_images=image)\n\nsamples = output_dict['samples']\nfor idx, sample in enumerate(samples):\n sample.save(f'sample_{idx}.png')\n```\n\n## 引用\n\n```bibtex\n@article{xiao2023fastcomposer,\n title={FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention},\n author={Xiao, Guangxuan and Yin, Tianwei and Freeman, William T. and Durand, Frédo and Han, Song},\n journal={arXiv},\n year={2023}\n }\n```\n" }, { "path": "configs/fastcomposer/fastcomposer_8xb16_FFHQ.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nclip_vit_url = 'openai/clip-vit-large-patch14'\nfinetuned_model_path = 'https://download.openxlab.org.cn/models/xiaomile/'\\\n 'fastcomposer/weight/pytorch_model.bin'\n\nmodel = dict(\n type='FastComposer',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n pretrained_cfg=dict(\n finetuned_model_path=finetuned_model_path,\n enable_xformers_memory_efficient_attention=None,\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n image_encoder=clip_vit_url,\n revision=None,\n non_ema_revision=None,\n object_localization=None,\n object_localization_weight=0.01,\n localization_layers=5,\n mask_loss=None,\n mask_loss_prob=0.5,\n object_localization_threshold=1.0,\n object_localization_normalize=None,\n no_object_augmentation=True,\n object_resolution=256),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n dtype='fp32',\n data_preprocessor=dict(type='DataPreprocessor'))\n" }, { "path": "configs/fastcomposer/metafile.yml", "content": "Collections:\n- Name: FastComposer\n Paper:\n Title: 'FastComposer: Tuning-Free Multi-Subject Image Generation with Localized\n Attention'\n URL: https://arxiv.org/abs/2305.10431\n README: configs/fastcomposer/README.md\n Task:\n - text2image\n Year: 2023\nModels:\n- Config: configs/fastcomposer/fastcomposer_8xb16_FFHQ.py\n In Collection: FastComposer\n Name: fastcomposer_8xb16_FFHQ\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n Weights: https://download.openxlab.org.cn/models/xiaomile/fastcomposer/weight/pytorch_model.bin\n" }, { "path": "configs/flavr/README.md", "content": "# FLAVR (arXiv'2020)\n\n> [FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation](https://arxiv.org/pdf/2012.08512.pdf)\n\n> **Task**: Video Interpolation\n\n\n\n## Abstract\n\n\n\nMost modern frame interpolation approaches rely on explicit bidirectional optical flows between adjacent frames, thus are sensitive to the accuracy of underlying flow estimation in handling occlusions while additionally introducing computational bottlenecks unsuitable for efficient deployment. In this work, we propose a flow-free approach that is completely end-to-end trainable for multi-frame video interpolation. Our method, FLAVR, is designed to reason about non-linear motion trajectories and complex occlusions implicitly from unlabeled videos and greatly simplifies the process of training, testing and deploying frame interpolation models. Furthermore, FLAVR delivers up to 6× speed up compared to the current state-of-the-art methods for multi-frame interpolation while consistently demonstrating superior qualitative and quantitative results compared with prior methods on popular benchmarks including Vimeo-90K, Adobe-240FPS, and GoPro. Finally, we show that frame interpolation is a competitive self-supervised pre-training task for videos via demonstrating various novel applications of FLAVR including action recognition, optical flow estimation, motion magnification, and video object tracking. Code and trained models are provided in the supplementary material.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | scale | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------------------------------------: | :--------: | :---: | :-----: | :-----: | :-----------------: | :----------------------------------------------------------------------------: |\n| [flavr_in4out1_g8b4_vimeo90k_septuplet](./flavr_in4out1_8xb4_vimeo90k-septuplet.py) | vimeo90k-T | x2 | 36.3340 | 0.96015 | 8 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) |\n\nNote: FLAVR for x8 VFI task will supported in the future.\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py\n\n# single-gpu train\npython tools/train.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\n\n# single-gpu test\npython tools/test.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@article{kalluri2020flavr,\n title={Flavr: Flow-agnostic video representations for fast frame interpolation},\n author={Kalluri, Tarun and Pathak, Deepak and Chandraker, Manmohan and Tran, Du},\n journal={arXiv preprint arXiv:2012.08512},\n year={2020}\n}\n```\n" }, { "path": "configs/flavr/README_zh-CN.md", "content": "# FLAVR (arXiv'2020)\n\n> [FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation](https://arxiv.org/abs/2012.08512.pdf)\n\n> **任务**: 视频插帧\n\n\n\n## 预训练模型测试结果\n\n在 RGB 通道上评估。\n评估指标 `PSNR / SSIM`。\n\n| 算法 | scale | Vimeo90k-triplet | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------------: | :---: | :---------------: | :-----------------: | :------------------------------------------------------------------------------: |\n| [flavr_in4out1_g8b4_vimeo90k_septuplet](./flavr_in4out1_8xb4_vimeo90k-septuplet.py) | x2 | 36.3340 / 0.96015 | 8 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) |\n\n注：FLAVR 中的 8 倍视频插帧算法将会在未来版本中支持。\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py\n\n# 单个GPU上训练\npython tools/train.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\n\n# 单个GPU上测试\npython tools/test.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n\n## Citation\n\n```bibtex\n@article{kalluri2020flavr,\n title={Flavr: Flow-agnostic video representations for fast frame interpolation},\n author={Kalluri, Tarun and Pathak, Deepak and Chandraker, Manmohan and Tran, Du},\n journal={arXiv preprint arXiv:2012.08512},\n year={2020}\n}\n```\n" }, { "path": "configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'flavr_in4out1_8xb4_vimeo90k-septuplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='FLAVRNet',\n num_input_frames=4,\n num_output_frames=1,\n mid_channels_list=[512, 256, 128, 64],\n encoder_layers_list=[2, 2, 2, 2],\n bias=False,\n norm_cfg=None,\n join_type='concat',\n up_mode='transpose'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=4,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(type='DataPreprocessor', ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='FixedCrop', keys=['img', 'gt'], crop_size=(256, 256)),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(\n type='ColorJitter',\n keys=['img', 'gt'],\n channel_order='rgb',\n brightness=0.05,\n contrast=0.05,\n saturation=0.05,\n hue=0.05),\n dict(type='TemporalReverse', keys=['img'], reverse_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ntrain_dataset_type = 'BasicFramesDataset'\nval_dataset_type = 'BasicFramesDataset'\ndata_root = 'data/vimeo90k'\n\ntrain_dataloader = dict(\n num_workers=16,\n batch_size=4, # 8 gpu\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=True),\n dataset=dict(\n type=train_dataset_type,\n ann_file='txt/sep_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k_septenary', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='GT', gt='GT'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(\n img=['im1.png', 'im3.png', 'im5.png', 'im7.png'], gt=['im4.png'])))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=val_dataset_type,\n ann_file='txt/sep_testlist.txt',\n metainfo=dict(dataset_type='vimeo90k_septenary', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='GT', gt='GT'),\n pipeline=val_pipeline,\n depth=2,\n load_frames_list=dict(\n img=['im1.png', 'im3.png', 'im5.png', 'im7.png'], gt=['im4.png'])))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),\n)\n\n# learning policy\nparam_scheduler = dict(\n type='ReduceLR',\n by_epoch=True,\n mode='min',\n factor=0.5,\n patience=10,\n cooldown=20)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=1,\n save_optimizer=True,\n by_epoch=True,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n param_scheduler=dict(\n type='ReduceLRSchedulerHook',\n by_epoch=True,\n interval=1,\n val_metric='MAE'),\n)\n\nlog_processor = dict(type='LogProcessor', by_epoch=True)\n" }, { "path": "configs/flavr/metafile.yml", "content": "Collections:\n- Name: FLAVR\n Paper:\n Title: 'FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation'\n URL: https://arxiv.org/pdf/2012.08512.pdf\n README: configs/flavr/README.md\n Task:\n - video interpolation\n Year: 2020\nModels:\n- Config: configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py\n In Collection: FLAVR\n Name: flavr_in4out1_8xb4_vimeo90k-septuplet\n Results:\n - Dataset: vimeo90k-T\n Metrics:\n PSNR: 36.334\n SSIM: 0.96015\n Task: Video Interpolation\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\n" }, { "path": "configs/gca/README.md", "content": "# GCA (AAAI'2020)\n\n> [Natural Image Matting via Guided Contextual Attention](https://arxiv.org/abs/2001.04069)\n\n> **Task**: Matting\n\n\n\n## Abstract\n\n\n\nOver the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area. This is due to the local ambiguity of transparent objects. One possible solution is to leverage the far-surrounding information to estimate the local opacity. Traditional affinity-based methods often suffer from the high computational complexity, which are not suitable for high resolution alpha estimation. Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting. Guided contextual attention module directly propagates high-level opacity information globally based on the learned low-level affinity. The proposed method can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously. Experiment results on Composition-1k testing set and this http URL benchmark dataset demonstrate that our method outperforms state-of-the-art approaches in natural image matting.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | SAD | MSE | GRAD | CONN | Training Resources | Download |\n| :--------------------------------------------------------------: | :------------: | :-------: | :--------: | :-------: | :-------: | :----------------: | :-----------------------------------------------------------------: |\n| [baseline (our)](./baseline_r34_4xb10-200k_comp1k.py) | Composition-1K | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) |\n| [GCA (our)](./gca_r34_4xb10-200k_comp1k.py) | Composition-1K | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) |\n| [baseline (with DIM pipeline)](./baseline_r34_4xb10-dimaug-200k_comp1k.py) | Composition-1K | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) |\n| [GCA (with DIM pipeline)](./gca_r34_4xb10-dimaug-200k_comp1k.py) | Composition-1K | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) |\n\n\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/gca/gca_r34_4xb10-200k_comp1k.py\n\n# single-gpu train\npython tools/train.py configs/gca/gca_r34_4xb10-200k_comp1k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/gca/gca_r34_4xb10-200k_comp1k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth\n\n# single-gpu test\npython tools/test.py configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{li2020natural,\n title={Natural Image Matting via Guided Contextual Attention},\n author={Li, Yaoyi and Lu, Hongtao},\n booktitle={Association for the Advancement of Artificial Intelligence (AAAI)},\n year={2020}\n}\n```\n" }, { "path": "configs/gca/README_zh-CN.md", "content": "# GCA (AAAI'2020)\n\n> **任务**: 图像抠图\n\n\n\n
\nGCA (AAAI'2020)\n\n```bibtex\n@inproceedings{li2020natural,\n title={Natural Image Matting via Guided Contextual Attention},\n author={Li, Yaoyi and Lu, Hongtao},\n booktitle={Association for the Advancement of Artificial Intelligence (AAAI)},\n year={2020}\n}\n```\n\n
\n\n
\n\n| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 |\n| :------------------------------------------------: | :-------: | :--------: | :-------: | :-------: | :------: | :-------------------------------------------------------------------------------------------------------: |\n| 基线 (原文) | 40.62 | 0.0106 | 21.53 | 38.43 | - | - |\n| GCA (原文) | 35.28 | 0.0091 | 16.92 | 32.53 | - | - |\n| [基线 (复现)](./baseline_r34_4xb10-200k_comp1k.py) | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) |\n| [GCA (复现)](./gca_r34_4xb10-200k_comp1k.py) | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) |\n\n**其他结果**\n\n| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 |\n| :------------------------------------------------------------------: | :---: | :----: | :---: | :---: | :------: | :-----------------------------------------------------------------------------------------------------: |\n| [基线 (使用 DIM 流水线)](./baseline_r34_4xb10-dimaug-200k_comp1k.py) | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) |\n| [GCA (使用 DIM 流水线)](./gca_r34_4xb10-dimaug-200k_comp1k.py) | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/gca/gca_r34_4xb10-200k_comp1k.py\n\n# 单个GPU上训练\npython tools/train.py configs/gca/gca_r34_4xb10-200k_comp1k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/gca/gca_r34_4xb10-200k_comp1k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth\n\n# 单个GPU上测试\npython tools/test.py configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/gca/gca_r34_4xb10-200k_comp1k.py https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/gca/baseline_r34_4xb10-200k_comp1k.py", "content": "_base_ = [\n '../_base_/datasets/comp1k.py', '../_base_/matting_default_runtime.py'\n]\n\nexperiment_name = 'baseline_r34_4xb10-200k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='GCA',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='as_is', # proc by pipeline FormatTrimap\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='ResShortcutEnc',\n block='BasicBlock',\n layers=[3, 4, 4, 2],\n in_channels=6,\n with_spectral_norm=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='open-mmlab://mmedit/res34_en_nomixup')),\n decoder=dict(\n type='ResShortcutDec',\n block='BasicBlockDec',\n layers=[2, 3, 3, 2],\n with_spectral_norm=True)),\n loss_alpha=dict(type='L1Loss'),\n test_cfg=dict(\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ))\n\n# dataset settings\ndata_root = 'data/adobe_composition-1k'\nbg_dir = 'data/coco/train2017'\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='RandomLoadResizeBg', bg_dir=bg_dir),\n dict(\n type='CompositeFg',\n fg_dirs=[\n f'{data_root}/Training_set/Adobe-licensed images/fg',\n f'{data_root}/Training_set/Other/fg'\n ],\n alpha_dirs=[\n f'{data_root}/Training_set/Adobe-licensed images/alpha',\n f'{data_root}/Training_set/Other/alpha'\n ]),\n dict(\n type='RandomAffine',\n keys=['alpha', 'fg'],\n degrees=30,\n scale=(0.8, 1.25),\n shear=10,\n flip_ratio=0.5),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='CropAroundCenter', crop_size=512),\n dict(type='RandomJitter'),\n dict(type='MergeFgAndBg'),\n dict(type='FormatTrimap', to_onehot=True),\n dict(type='PackInputs'),\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='FormatTrimap', to_onehot=True),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(\n batch_size=10,\n num_workers=8,\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=200_000,\n val_interval=10_000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=4e-4, betas=[0.5, 0.999]))\n# learning policy\nparam_scheduler = [\n dict(\n type='LinearLR',\n start_factor=0.001,\n begin=0,\n end=5000,\n by_epoch=False,\n ),\n dict(\n type='CosineAnnealingLR',\n T_max=200_000, # TODO, need more check\n eta_min=0,\n begin=0,\n end=200_000,\n by_epoch=False,\n )\n]\n\n# checkpoint saving\n# inheritate from default_runtime.py\n\n# runtime settings\n# inheritate from default_runtime.py\n" }, { "path": "configs/gca/baseline_r34_4xb10-dimaug-200k_comp1k.py", "content": "_base_ = ['./baseline_r34_4xb10-200k_comp1k.py']\n\nexperiment_name = 'baseline_r34_4xb10-dimaug-200k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(backbone=dict(encoder=dict(in_channels=4)))\n\n# dataset settings\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='merged'),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged'],\n crop_sizes=[320, 480, 640]),\n dict(type='Flip', keys=['alpha', 'merged']),\n dict(\n type='Resize',\n keys=['alpha', 'merged'],\n scale=(320, 320),\n keep_ratio=False),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='FormatTrimap', to_onehot=False),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='FormatTrimap', to_onehot=False),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(dataset=dict(pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/gca/gca_r34_4xb10-200k_comp1k.py", "content": "_base_ = [\n '../_base_/datasets/comp1k.py', '../_base_/matting_default_runtime.py'\n]\n\nexperiment_name = 'gca_r34_4xb10-200k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='GCA',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='as_is',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='ResGCAEncoder',\n block='BasicBlock',\n layers=[3, 4, 4, 2],\n in_channels=6,\n with_spectral_norm=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='open-mmlab://mmedit/res34_en_nomixup')),\n decoder=dict(\n type='ResGCADecoder',\n block='BasicBlockDec',\n layers=[2, 3, 3, 2],\n with_spectral_norm=True)),\n loss_alpha=dict(type='L1Loss'),\n test_cfg=dict(\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ))\n\n# dataset settings\ndata_root = 'data/adobe_composition-1k'\nbg_dir = 'data/coco/train2017'\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='RandomLoadResizeBg', bg_dir=bg_dir),\n dict(\n type='CompositeFg',\n fg_dirs=[\n f'{data_root}/Training_set/Adobe-licensed images/fg',\n f'{data_root}/Training_set/Other/fg'\n ],\n alpha_dirs=[\n f'{data_root}/Training_set/Adobe-licensed images/alpha',\n f'{data_root}/Training_set/Other/alpha'\n ]),\n dict(\n type='RandomAffine',\n keys=['alpha', 'fg'],\n degrees=30,\n scale=(0.8, 1.25),\n shear=10,\n flip_ratio=0.5),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='CropAroundCenter', crop_size=512),\n dict(type='RandomJitter'),\n dict(type='MergeFgAndBg'),\n dict(type='FormatTrimap', to_onehot=True),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='FormatTrimap', to_onehot=True),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(\n batch_size=10,\n num_workers=8,\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=200_000,\n val_interval=10_000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=4e-4, betas=[0.5, 0.999]))\n# learning policy\nparam_scheduler = [\n dict(\n type='LinearLR',\n start_factor=0.001,\n begin=0,\n end=5000,\n by_epoch=False,\n ),\n dict(\n type='CosineAnnealingLR',\n T_max=200_000, # TODO, need more check\n eta_min=0,\n begin=0,\n end=200_000,\n by_epoch=False,\n )\n]\n\n# checkpoint saving\n# inheritate from _base_\n\n# runtime settings\n# inheritate from _base_\n" }, { "path": "configs/gca/gca_r34_4xb10-dimaug-200k_comp1k.py", "content": "_base_ = ['./gca_r34_4xb10-200k_comp1k.py']\n\nexperiment_name = 'gca_r34_4xb10-dimaug-200k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(backbone=dict(encoder=dict(in_channels=4)))\n\n# dataset settings\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='merged'),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged'],\n crop_sizes=[320, 480, 640]),\n dict(type='Flip', keys=['alpha', 'merged']),\n dict(\n type='Resize',\n keys=['alpha', 'merged'],\n scale=(320, 320),\n keep_ratio=False),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='FormatTrimap', to_onehot=False),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='FormatTrimap', to_onehot=False),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(dataset=dict(pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/gca/metafile.yml", "content": "Collections:\n- Name: GCA\n Paper:\n Title: Natural Image Matting via Guided Contextual Attention\n URL: https://arxiv.org/abs/2001.04069\n README: configs/gca/README.md\n Task:\n - matting\n Year: 2020\nModels:\n- Config: configs/gca/baseline_r34_4xb10-200k_comp1k.py\n In Collection: GCA\n Name: baseline_r34_4xb10-200k_comp1k\n Results:\n - Dataset: Composition-1K\n Metrics:\n CONN: 32.12\n GRAD: 16.21\n MSE: 0.0083\n SAD: 34.61\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth\n- Config: configs/gca/gca_r34_4xb10-200k_comp1k.py\n In Collection: GCA\n Name: gca_r34_4xb10-200k_comp1k\n Results:\n - Dataset: Composition-1K\n Metrics:\n CONN: 30.59\n GRAD: 14.96\n MSE: 0.0081\n SAD: 33.38\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth\n- Config: configs/gca/baseline_r34_4xb10-dimaug-200k_comp1k.py\n In Collection: GCA\n Name: baseline_r34_4xb10-dimaug-200k_comp1k\n Results:\n - Dataset: Composition-1K\n Metrics:\n CONN: 49.67\n GRAD: 30.21\n MSE: 0.0144\n SAD: 49.95\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth\n- Config: configs/gca/gca_r34_4xb10-dimaug-200k_comp1k.py\n In Collection: GCA\n Name: gca_r34_4xb10-dimaug-200k_comp1k\n Results:\n - Dataset: Composition-1K\n Metrics:\n CONN: 49.47\n GRAD: 28.07\n MSE: 0.0129\n SAD: 49.42\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth\n" }, { "path": "configs/ggan/README.md", "content": "# GGAN (ArXiv'2017)\n\n> [Geometric GAN](https://arxiv.org/abs/1705.02894)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nGenerative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n GGAN 64x64, CelebA-Cropped\n
\n \n
\n\n| Model | Dataset | SWD | MS-SSIM | FID | Download |\n| :-------------------------------------------------------------------: | :------------: | :-----------------------------: | :-----: | :-----: | :----------------------------------------------------------------------: |\n| [GGAN 64x64](./ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | CelebA-Cropped | 11.18, 12.21, 39.16/20.85 | 0.3318 | 20.1797 | [model](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth) \\| [log](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210430_113839.log.json) |\n| [GGAN 128x128](./ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | CelebA-Cropped | 9.81, 11.29, 19.22, 47.79/22.03 | 0.3149 | 18.7647 | [model](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth) \\| [log](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_154258.log.json) |\n| [GGAN 64x64](./ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py) | LSUN-Bedroom | 9.1, 6.2, 12.27/9.19 | 0.0649 | 39.9261 | [model](https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth) \\| [log](https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210428_202027.log.json) |\n\nNote: In the original implementation of [GGAN](https://github.com/lim0606/pytorch-geometric-gan), they set `G_iters` to 10. However our framework does not support `G_iters` currently, so we dropped the settings in the original implementation and conducted several experiments with our own settings. We have shown above the experiment results with the lowest `fid` score. \\\nOriginal settings and our settings:\n\n\n\n| Model | Dataset | Architecture | optimizer | lr_G | lr_D | G_iters | D_iters |\n| :----------------: | :------------: | :----------: | :-------: | :----: | :----: | :-----: | :-----: |\n| GGAN(origin) 64x64 | CelebA-Cropped | dcgan-archi | RMSprop | 0.0002 | 0.0002 | 10 | 1 |\n| GGAN(ours) 64x64 | CelebA-Cropped | dcgan-archi | Adam | 0.001 | 0.001 | 1 | 1 |\n| GGAN(origin) 64x64 | LSUN-Bedroom | dcgan-archi | RMSprop | 0.0002 | 0.0002 | 10 | 1 |\n| GGAN(ours) 64x64 | LSUN-Bedroom | lsgan-archi | Adam | 0.0001 | 0.0001 | 1 | 1 |\n\n## Citation\n\n```latex\n@article{lim2017geometric,\n title={Geometric gan},\n author={Lim, Jae Hyun and Ye, Jong Chul},\n journal={arXiv preprint arXiv:1705.02894},\n year={2017},\n url={https://arxiv.org/abs/1705.02894},\n}\n```\n" }, { "path": "configs/ggan/ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\nmodel = dict(discriminator=dict(output_scale=4, out_channels=1))\n# define dataset\nbatch_size = 128\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0.5, 0.99))))\n\ntrain_cfg = dict(max_iters=100000)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(\n checkpoint=dict(\n max_keep_ckpts=20, save_best='FID-Full-50k/fid', rule='less'))\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/ggan/ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_128x128.py',\n '../_base_/datasets/unconditional_imgs_128x128.py',\n '../_base_/gen_default_runtime.py'\n]\n\nmodel = dict(discriminator=dict(output_scale=4, out_channels=1))\n\n# define dataset\nbatch_size = 64\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))))\n\ntrain_cfg = dict(max_iters=160000)\n\ndefault_hooks = dict(\n checkpoint=dict(\n max_keep_ckpts=20, save_best='FID-Full-50k/fid', rule='less'))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 128, 128))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/ggan/ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py", "content": "_base_ = [\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\n\nmodel = dict(\n type='GGAN',\n noise_size=1024,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(type='LSGANGenerator', output_scale=64),\n discriminator=dict(type='LSGANDiscriminator', input_scale=64))\n\n# define dataset\nbatch_size = 128\ndata_root = 'data/lsun/images/bedroom_train'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))))\n\ndefault_hooks = dict(\n checkpoint=dict(\n max_keep_ckpts=20,\n save_best=['FID-Full-50k/fid', 'swd/avg', 'ms-ssim/avg'],\n rule=['less', 'less', 'greater']))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\ntrain_cfg = dict(max_iters=160000)\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n\nval_metrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n]\n\nval_evaluator = dict(metrics=val_metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/ggan/metafile.yml", "content": "Collections:\n- Name: GGAN\n Paper:\n Title: Geometric GAN\n URL: https://arxiv.org/abs/1705.02894\n README: configs/ggan/README.md\n Task:\n - unconditional gans\n Year: 2017\nModels:\n- Config: configs/ggan/ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py\n In Collection: GGAN\n Name: ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n FID: 20.1797\n MS-SSIM: 0.3318\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth\n- Config: configs/ggan/ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py\n In Collection: GGAN\n Name: ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n FID: 18.7647\n MS-SSIM: 0.3149\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth\n- Config: configs/ggan/ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py\n In Collection: GGAN\n Name: ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64\n Results:\n - Dataset: LSUN-Bedroom\n Metrics:\n FID: 39.9261\n MS-SSIM: 0.0649\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth\n" }, { "path": "configs/glean/README.md", "content": "# GLEAN (CVPR'2021)\n\n> [GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution](https://arxiv.org/abs/2012.00739)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nWe show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). While most existing SR approaches attempt to generate realistic textures through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single forward pass to generate the upscaled image. GLEAN can be easily incorporated in a simple encoder-bank-decoder architecture with multi-resolution skip connections. Switching the bank allows the method to deal with images from diverse categories, e.g., cat, building, human face, and car. Images upscaled by GLEAN show clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.\n\n\n\n
\n \n
\n\n## Results and models\n\nFor the meta info used in training and test, please refer to [here](https://github.com/ckkelvinchan/GLEAN). The results are evaluated on RGB channels.\n\n| Model | Dataset | PSNR | Training Resources | Download |\n| :------------------------------------------------------------------------------: | :------: | :---: | :----------------------: | :---------------------------------------------------------------------------------: |\n| [glean_cat_8x](./glean_x8_2xb8_cat.py) | LSUN-CAT | 23.98 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) |\n| [glean_ffhq_16x](./glean_x16_2xb8_ffhq.py) | FFHQ | 26.91 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) |\n| [glean_cat_16x](./glean_x16_2xb8_cat.py) | LSUN-CAT | 20.88 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) |\n| [glean_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | FFHQ | 27.94 | 4 (Tesla V100-SXM3-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) |\n| [glean_fp16_cat_8x](./glean_x8-fp16_2xb8_cat.py) | LSUN-CAT | - | - | - |\n| [glean_fp16_ffhq_16x](./glean_x16-fp16_2xb8_ffhq.py) | FFHQ | - | - | - |\n| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | FFHQ | - | - | - |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/glean/glean_x8_2xb8_cat.py\n\n# single-gpu train\npython tools/train.py configs/glean/glean_x8_2xb8_cat.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/glean/glean_x8_2xb8_cat.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth\n\n# single-gpu test\npython tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{chan2021glean,\n author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},\n title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n" }, { "path": "configs/glean/README_zh-CN.md", "content": "# GLEAN (CVPR'2021)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nGLEAN (CVPR'2021)\n\n```bibtex\n@InProceedings{chan2021glean,\n author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},\n title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n\n
\n\n
\n\n有关训练和测试中使用的元信息，请参阅[此处](https://github.com/ckkelvinchan/GLEAN)。 结果在 RGB 通道上进行评估。\n\n| 算法 | PSNR | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------------------: | :---: | :----------------------: | :------------------------------------------------------------------------------------: |\n| [glean_cat_8x](./glean_x8_2xb8_cat.py) | 23.98 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) |\n| [glean_ffhq_16x](./glean_x16_2xb8_ffhq.py) | 26.91 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) |\n| [glean_cat_16x](./glean_x16_2xb8_cat.py) | 20.88 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) |\n| [glean_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | 27.94 | 4 (Tesla V100-SXM3-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) |\n| [glean_fp16_cat_8x](./glean_x8-fp16_2xb8_cat.py) | - | - | - |\n| [glean_fp16_ffhq_16x](./glean_x16-fp16_2xb8_ffhq.py) | - | - | - |\n| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | - | - | - |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/glean/glean_x8_2xb8_cat.py\n\n# 单个GPU上训练\npython tools/train.py configs/glean/glean_x8_2xb8_cat.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/glean/glean_x8_2xb8_cat.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth\n\n# 单个GPU上测试\npython tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/glean/glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py", "content": "_base_ = './glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py'\n\nexperiment_name = 'glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n generator=dict(fp16_enabled=True), discriminator=dict(fp16_enabled=True))\n" }, { "path": "configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py", "content": "_base_ = '../_base_/models/base_glean.py'\n\nexperiment_name = 'glean_in128out1024_4xb2-300k_ffhq-celeba-hq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 8\n# model settings\nmodel = dict(\n type='SRGAN',\n generator=dict(\n type='GLEANStyleGANv2',\n in_size=128,\n out_size=1024,\n style_channels=512,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-ffhq-config-f-official_20210327'\n '_171224-bce9310c.pth',\n prefix='generator_ema')),\n discriminator=dict(\n type='StyleGANv2Discriminator',\n in_size=1024,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-ffhq-config-f-official_20210327'\n '_171224-bce9310c.pth',\n prefix='discriminator')),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'21': 1.0},\n vgg_type='vgg16',\n perceptual_weight=1e-2,\n style_weight=0,\n norm_img=True,\n criterion='mse',\n pretrained='torchvision://vgg16'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-2,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ),\n)\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='CopyValues', src_keys=['gt'], dst_keys=['img']),\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[41],\n kernel_list=['iso', 'aniso'],\n kernel_prob=[0.5, 0.5],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n ),\n keys=['img'],\n ),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0, 1, 0], # up, down, keep\n resize_scale=[0.03125, 1],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n keys=['img'],\n ),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[0, 50],\n gaussian_gray_noise_prob=0),\n keys=['img'],\n ),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[5, 50]),\n keys=['img']),\n dict(\n type='RandomResize',\n params=dict(\n target_size=(1024, 1024),\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n keys=['img'],\n ),\n dict(type='Clip', keys=['img']),\n dict(\n type='RandomResize',\n params=dict(\n target_size=(128, 128), resize_opt=['area'], resize_prob=[1]),\n keys=['img'],\n ),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='RandomResize',\n params=dict(\n target_size=(128, 128), resize_opt=['area'], resize_prob=[1]),\n keys=['img'],\n ),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=6,\n batch_size=2,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='ffhq_celebahq', task_name='sisr'),\n data_root='data/FFHQ_CelebAHQ',\n data_prefix=dict(gt='GT'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='ffhq', task_name='sisr'),\n data_root='data/CelebA-HQ',\n data_prefix=dict(img='BIx8_down', gt='GT'),\n ann_file='meta_info_CelebAHQ_val100_GT.txt',\n pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/glean/glean_x16-fp16_2xb8_ffhq.py", "content": "_base_ = './glean_x16_2xb8_ffhq.py'\n\nexperiment_name = 'glean_x16-fp16_2xb8_ffhq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n generator=dict(fp16_enabled=True), discriminator=dict(fp16_enabled=True))\n" }, { "path": "configs/glean/glean_x16_2xb8_cat.py", "content": "_base_ = '../_base_/models/base_glean.py'\n\nexperiment_name = 'glean_x16_2xb8_cat'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 16\n# model settings\nmodel = dict(\n type='SRGAN',\n generator=dict(\n type='GLEANStyleGANv2',\n in_size=16,\n out_size=256,\n style_channels=512,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-cat-config-f-official_20210327'\n '_172444-15bc485b.pth',\n prefix='generator_ema')),\n discriminator=dict(\n type='StyleGANv2Discriminator',\n in_size=256,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-cat-config-f-official_20210327'\n '_172444-15bc485b.pth',\n prefix='discriminator')),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'21': 1.0},\n vgg_type='vgg16',\n perceptual_weight=1e-2,\n style_weight=0,\n norm_img=False,\n criterion='mse',\n pretrained='torchvision://vgg16'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-2,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ),\n)\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(type='PackInputs')\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cat', task_name='sisr'),\n data_root='data/cat_train',\n data_prefix=dict(img='BIx16_down', gt='GT'),\n ann_file='meta_info_LSUNcat_GT.txt',\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cat', task_name='sisr'),\n data_root='data/cat_test',\n data_prefix=dict(img='BIx16_down', gt='GT'),\n ann_file='meta_info_Cat100_GT.txt',\n pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/glean/glean_x16_2xb8_ffhq.py", "content": "_base_ = '../_base_/models/base_glean.py'\n\nexperiment_name = 'glean_x16_2xb8_ffhq'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 16\n# model settings\nmodel = dict(\n type='SRGAN',\n generator=dict(\n type='GLEANStyleGANv2',\n in_size=64,\n out_size=1024,\n style_channels=512,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-ffhq-config-f-official_20210327'\n '_171224-bce9310c.pth',\n prefix='generator_ema')),\n discriminator=dict(\n type='StyleGANv2Discriminator',\n in_size=1024,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-ffhq-config-f-official_20210327'\n '_171224-bce9310c.pth',\n prefix='discriminator')),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'21': 1.0},\n vgg_type='vgg16',\n perceptual_weight=1e-2,\n style_weight=0,\n norm_img=False,\n criterion='mse',\n pretrained='torchvision://vgg16'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-2,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ),\n)\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(type='PackInputs')\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='ffhq', task_name='sisr'),\n data_root='data/ffhq',\n data_prefix=dict(img='BIx16_down', gt='images'),\n ann_file='meta_info_FFHQ_GT.txt',\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='ffhq', task_name='sisr'),\n data_root='data/CelebA-HQ',\n data_prefix=dict(img='BIx16_down', gt='GT'),\n ann_file='meta_info_CelebAHQ_val100_GT.txt',\n pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/glean/glean_x8-fp16_2xb8_cat.py", "content": "_base_ = './glean_x8_2xb8_cat.py'\n\nexperiment_name = 'glean_x8-fp16_2xb8_cat'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n generator=dict(fp16_enabled=True), discriminator=dict(fp16_enabled=True))\n" }, { "path": "configs/glean/glean_x8_2xb8_cat.py", "content": "_base_ = '../_base_/models/base_glean.py'\n\nexperiment_name = 'glean_x8_2xb8_cat'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\nscale = 8\n# model settings\nmodel = dict(\n type='SRGAN',\n generator=dict(\n type='GLEANStyleGANv2',\n in_size=32,\n out_size=256,\n style_channels=512,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-cat-config-f-official_20210327'\n '_172444-15bc485b.pth',\n prefix='generator_ema')),\n discriminator=dict(\n type='StyleGANv2Discriminator',\n in_size=256,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='http://download.openmmlab.com/mmediting/stylegan2/'\n 'official_weights/stylegan2-cat-config-f-official_20210327'\n '_172444-15bc485b.pth',\n prefix='discriminator')),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'21': 1.0},\n vgg_type='vgg16',\n perceptual_weight=1e-2,\n style_weight=0,\n norm_img=False,\n criterion='mse',\n pretrained='torchvision://vgg16'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-2,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ),\n)\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(type='PackInputs')\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ninference_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='Resize',\n scale=(32, 32),\n keys=['img'],\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cat', task_name='sisr'),\n data_root='data/cat_train',\n data_prefix=dict(img='BIx8_down', gt='GT'),\n ann_file='meta_info_LSUNcat_GT.txt',\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cat', task_name='sisr'),\n data_root='data/cat_test',\n data_prefix=dict(img='BIx8_down', gt='GT'),\n ann_file='meta_info_Cat100_GT.txt',\n pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/glean/metafile.yml", "content": "Collections:\n- Name: GLEAN\n Paper:\n Title: 'GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution'\n URL: https://arxiv.org/abs/2012.00739\n README: configs/glean/README.md\n Task:\n - image super-resolution\n Year: 2021\nModels:\n- Config: configs/glean/glean_x8_2xb8_cat.py\n In Collection: GLEAN\n Name: glean_x8_2xb8_cat\n Results:\n - Dataset: LSUN-CAT\n Metrics:\n PSNR: 23.98\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth\n- Config: configs/glean/glean_x16_2xb8_ffhq.py\n In Collection: GLEAN\n Name: glean_x16_2xb8_ffhq\n Results:\n - Dataset: FFHQ\n Metrics:\n PSNR: 26.91\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth\n- Config: configs/glean/glean_x16_2xb8_cat.py\n In Collection: GLEAN\n Name: glean_x16_2xb8_cat\n Results:\n - Dataset: LSUN-CAT\n Metrics:\n PSNR: 20.88\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth\n- Config: configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py\n In Collection: GLEAN\n Name: glean_in128out1024_4xb2-300k_ffhq-celeba-hq\n Results:\n - Dataset: FFHQ\n Metrics:\n PSNR: 27.94\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth\n- Config: configs/glean/glean_x8-fp16_2xb8_cat.py\n In Collection: GLEAN\n Name: glean_x8-fp16_2xb8_cat\n Results:\n - Dataset: LSUN-CAT\n Metrics: {}\n Task: Image Super-Resolution\n- Config: configs/glean/glean_x16-fp16_2xb8_ffhq.py\n In Collection: GLEAN\n Name: glean_x16-fp16_2xb8_ffhq\n Results:\n - Dataset: FFHQ\n Metrics: {}\n Task: Image Super-Resolution\n- Config: configs/glean/glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py\n In Collection: GLEAN\n Name: glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq\n Results:\n - Dataset: FFHQ\n Metrics: {}\n Task: Image Super-Resolution\n" }, { "path": "configs/global_local/README.md", "content": "# Global&Local (ToG'2017)\n\n> [Globally and Locally Consistent Image Completion](http://iizuka.cs.tsukuba.ac.jp/projects/completion/data/completion_sig2017.pdf)\n\n> **Task**: Inpainting\n\n\n\n## Abstract\n\n\n\nWe present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by flling in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the image.\n\n\n\n
\n \n
\n\n## Results and models\n\n*Note that we do not apply the post-processing module in Global&Local for a fair comparison with current deep inpainting methods.*\n\n| Model | Dataset | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------: | :-----------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :----------------: | :-----------------------------------------------------: |\n| [Global&Local](./gl_8xb12_places-256x256.py) | Places365-Challenge | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) |\n| [Global&Local](./gl_8xb12_celeba-256x256.py) | CelebA-HQ | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/global_local/gl_8xb12_places-256x256.py\n\n# single-gpu train\npython tools/train.py configs/global_local/gl_8xb12_places-256x256.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/global_local/gl_8xb12_places-256x256.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth\n\n# single-gpu test\npython tools/test.py configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@article{iizuka2017globally,\n title={Globally and locally consistent image completion},\n author={Iizuka, Satoshi and Simo-Serra, Edgar and Ishikawa, Hiroshi},\n journal={ACM Transactions on Graphics (ToG)},\n volume={36},\n number={4},\n pages={1--14},\n year={2017},\n publisher={ACM New York, NY, USA}\n}\n```\n" }, { "path": "configs/global_local/README_zh-CN.md", "content": "# Global&Local (ToG'2017)\n\n> **任务**: 图像修复\n\n\n\n
\nGlobal&Local (ToG'2017)\n\n```bibtex\n@article{iizuka2017globally,\n title={Globally and locally consistent image completion},\n author={Iizuka, Satoshi and Simo-Serra, Edgar and Ishikawa, Hiroshi},\n journal={ACM Transactions on Graphics (ToG)},\n volume={36},\n number={4},\n pages={1--14},\n year={2017},\n publisher={ACM New York, NY, USA}\n}\n```\n\n
\n\n
\n\n*请注意，为了与当前的深度图像修复方法进行公平比较，我们没有在 Global&Local 中使用后处理模块。*\n\n**Places365-Challenge**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :---------------------------------------------------------------------------------------: |\n| [Global&Local](./gl_8xb12_places-256x256.py) | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) |\n\n**CelebA-HQ**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------------------------------: |\n| [Global&Local](./gl_8xb12_celeba-256x256.py) | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/global_local/gl_8xb12_places-256x256.py\n\n# 单个GPU上训练\npython tools/train.py configs/global_local/gl_8xb12_places-256x256.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/global_local/gl_8xb12_places-256x256.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth\n\n# 单个GPU上测试\npython tools/test.py configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/global_local/gl_8xb12_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/global_local/gl_8xb12_celeba-256x256.py", "content": "_base_ = [\n '../_base_/models/base_gl.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/celeba.py'\n]\n\nexperiment_name = 'gl_8xb12_celeba-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=1,\n iter_tc=40000,\n iter_td=50000,\n start_iter=0,\n local_size=(128, 128)), )\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='bbox',\n mask_config=dict(\n max_bbox_shape=(128, 128),\n max_bbox_delta=40,\n min_margin=20,\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=12,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=300002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# runtime settings\n# inheritate from _base_\n" }, { "path": "configs/global_local/gl_8xb12_places-256x256.py", "content": "_base_ = [\n '../_base_/models/base_gl.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'gl_8xb12_places-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=1,\n iter_tc=90000,\n iter_td=100000,\n start_iter=350000,\n local_size=(128, 128)), )\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='bbox',\n mask_config=dict(\n max_bbox_shape=(128, 128),\n max_bbox_delta=40,\n min_margin=20,\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=12,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=500002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# runtime settings\n# inheritate from _base_\n" }, { "path": "configs/global_local/metafile.yml", "content": "Collections:\n- Name: Global&Local\n Paper:\n Title: Globally and Locally Consistent Image Completion\n URL: http://iizuka.cs.tsukuba.ac.jp/projects/completion/data/completion_sig2017.pdf\n README: configs/global_local/README.md\n Task:\n - inpainting\n Year: 2017\nModels:\n- Config: configs/global_local/gl_8xb12_places-256x256.py\n In Collection: Global&Local\n Name: gl_8xb12_places-256x256\n Results:\n - Dataset: Places365-Challenge\n Metrics:\n PSNR: 23.152\n SSIM: 0.862\n l1 error: 11.164\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth\n- Config: configs/global_local/gl_8xb12_celeba-256x256.py\n In Collection: Global&Local\n Name: gl_8xb12_celeba-256x256\n Results:\n - Dataset: CelebA-HQ\n Metrics:\n PSNR: 26.78\n SSIM: 0.904\n l1 error: 6.678\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth\n" }, { "path": "configs/guided_diffusion/README.md", "content": "# Guided Diffusion (NeurIPS'2021)\n\n> [Diffusion Models Beat GANs on Image Synthesis](https://papers.nips.cc/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf)\n\n> **Task**: Image Generation\n\n\n\n## Abstract\n\n\n\nWe show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128x128, 4.59 on ImageNet 256x256, and 7.72 on ImageNet 512x512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256x256 and 3.85 on ImageNet 512x512.\n\n\n\n
\n \n
\n\n## Results and models\n\n
\n hamster, classifier-guidance samplings with CGS=1.0\n
\n \n
\n\n**ImageNet**\n\n| Model | Dataset | Scheduler | Steps | CGS | Time Consuming(A100) | FID-Full-50K | Download |\n| :----------------------------------------------------------------: | :--------------: | :-------: | :---: | :-: | :------------------: | :----------: | :-------------------------------------------------------------------: |\n| [adm_ddim250_8xb32_imagenet-64x64](./adm_ddim250_8xb32_imagenet-64x64.py) | ImageNet 64x64 | DDIM | 250 | - | 1h | 3.2284 | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth) |\n| [adm-g_ddim25_8xb32_imagenet-64x64](configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-64x64.py) | ImageNet 64x64 | DDIM | 25 | 1.0 | 2h | 3.7566 | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-64x64-2c0fbeda.pth) |\n| [adm_ddim250_8xb32_imagenet-256x256](configs/guided_diffusion/adm_ddim250_8xb32_imagenet-256x256.py) | ImageNet 256x256 | DDIM | 250 | - | - | - | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm_8xb32_imagenet-256x256-f94735fe.pth) |\n| [adm-g_ddim25_8xb32_imagenet-256x256](configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-256x256.py) | ImageNet 256x256 | DDIM | 25 | 1.0 | - | - | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-256x256-aec3fc9f.pth) |\n| [adm_ddim250_8xb32_imagenet-512x512](configs/guided_diffusion/adm_ddim250_8xb32_imagenet-512x512.py) | ImageNet 512x512 | DDIM | 250 | - | - | - | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm-u_8xb32_imagenet-512x512-60b381cb.pth) |\n| [adm-g_ddim25_8xb32_imagenet-512x512](configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-512x512.py) | ImageNet 512x512 | DDIM | 25 | 1.0 | - | - | [ckpt](https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-512x512-23cf0b58.pth) |\n\n## Quick Start\n\n**infer**\n\n
\nInfer Instructions\n\nYou can run adm as follows:\n\n```python\nfrom mmengine import Config, MODELS\nfrom mmengine.registry import init_default_scope\nfrom torchvision.utils import save_image\n\ninit_default_scope('mmagic')\n\n# sampling without classifier guidance, CGS=1.0\nconfig = 'configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-64x64.py'\nckpt_path = 'https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-64x64-2c0fbeda.pth' # noqa\n\nmodel_cfg = Config.fromfile(config).model\nmodel_cfg.pretrained_cfgs = dict(unet=dict(ckpt_path=ckpt_path, prefix='unet'),\n classifier=dict(ckpt_path=ckpt_path, prefix='classifier'))\nmodel = MODELS.build(model_cfg).cuda().eval()\n\nsamples = model.infer(\n init_image=None,\n batch_size=4,\n num_inference_steps=25,\n labels=333,\n classifier_scale=1.0,\n show_progress=True)['samples']\n\n# sampling without classifier guidance\nconfig = 'configs/guided_diffusion/adm_ddim250_8xb32_imagenet-64x64.py'\nckpt_path = 'https://download.openmmlab.com/mmediting/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth' # noqa\n\nmodel_cfg = Config.fromfile(config).model\nmodel_cfg.pretrained_cfgs = dict(unet=dict(ckpt_path=ckpt_path, prefix='unet'))\nmodel = MODELS.build(model_cfg).cuda().eval()\n\nsamples = model.infer(\n init_image=None,\n batch_size=4,\n num_inference_steps=250,\n labels=None,\n classifier_scale=0.0,\n show_progress=True)['samples']\n```\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/guided_diffusion/adm-u_ddim250_8xb32_imagenet-64x64.py https://download.openmmlab.com/mmgen/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth\n\n# single-gpu test\npython tools/test.py configs/guided_diffusion/adm-u_ddim250_8xb32_imagenet-64x64.py https://download.openmmlab.com/mmgen/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/guided_diffusion/adm-u_ddim250_8xb32_imagenet-64x64.py https://download.openmmlab.com/mmgen/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@article{PrafullaDhariwal2021DiffusionMB,\n title={Diffusion Models Beat GANs on Image Synthesis},\n author={Prafulla Dhariwal and Alex Nichol},\n journal={arXiv: Learning},\n year={2021}\n}\n```\n" }, { "path": "configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-256x256.py", "content": "_base_ = ['./adm_ddim250_8xb32_imagenet-256x256.py']\n\nmodel = dict(\n classifier=dict(\n type='EncoderUNetModel',\n image_size=256,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=2,\n attention_resolutions=(8, 16, 32),\n channel_mult=(1, 1, 2, 2, 4, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention'))\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-512x512.py", "content": "_base_ = ['./adm_ddim250_8xb32_imagenet-512x512.py']\n\nmodel = dict(\n classifier=dict(\n type='EncoderUNetModel',\n image_size=512,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=2,\n attention_resolutions=(16, 32, 64),\n channel_mult=(0.5, 1, 1, 2, 2, 4, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention'))\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-64x64.py", "content": "_base_ = ['./adm_ddim250_8xb32_imagenet-64x64.py']\n\nmodel = dict(\n classifier=dict(\n type='EncoderUNetModel',\n image_size=64,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=4,\n attention_resolutions=(2, 4, 8),\n channel_mult=(1, 2, 3, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention'))\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/guided_diffusion/adm_ddim250_8xb32_imagenet-256x256.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_64.py',\n '../_base_/gen_default_runtime.py',\n]\n\nmodel = dict(\n type='AblatedDiffusionModel',\n data_preprocessor=dict(type='DataPreprocessor'),\n unet=dict(\n type='DenoisingUnet',\n image_size=256,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='linear'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader = dict(batch_size=32, num_workers=8)\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(type='VisualizationHook', interval=5000, fixed_input=True)\n]\n" }, { "path": "configs/guided_diffusion/adm_ddim250_8xb32_imagenet-512x512.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_512.py',\n '../_base_/gen_default_runtime.py',\n]\n\nmodel = dict(\n type='AblatedDiffusionModel',\n data_preprocessor=dict(type='DataPreprocessor'),\n unet=dict(\n type='DenoisingUnet',\n image_size=512,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='linear'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader = dict(batch_size=32, num_workers=8)\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/guided_diffusion/adm_ddim250_8xb32_imagenet-64x64.py", "content": "_base_ = [\n '../_base_/datasets/imagenet_64.py',\n '../_base_/gen_default_runtime.py',\n]\n\nmodel = dict(\n type='AblatedDiffusionModel',\n data_preprocessor=dict(type='DataPreprocessor'),\n unet=dict(\n type='DenoisingUnet',\n image_size=64,\n in_channels=3,\n base_channels=192,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=True),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='squaredcos_cap_v2'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader = dict(batch_size=32, num_workers=8)\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/guided_diffusion/metafile.yml", "content": "Collections:\n- Name: Guided Diffusion\n Paper:\n Title: Diffusion Models Beat GANs on Image Synthesis\n URL: https://papers.nips.cc/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf\n README: configs/guided_diffusion/README.md\n Task:\n - image generation\n Year: 2021\nModels:\n- Config: configs/guided_diffusion/adm_ddim250_8xb32_imagenet-64x64.py\n In Collection: Guided Diffusion\n Name: adm_ddim250_8xb32_imagenet-64x64\n Results:\n - Dataset: ImageNet64x64\n Metrics:\n FID-Full-50K: 3.2284\n Steps: 250.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth\n- Config: configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-64x64.py\n In Collection: Guided Diffusion\n Name: adm-g_ddim25_8xb32_imagenet-64x64\n Results:\n - Dataset: ImageNet64x64\n Metrics:\n CGS: 1.0\n FID-Full-50K: 3.7566\n Steps: 25.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-64x64-2c0fbeda.pth\n- Config: configs/guided_diffusion/adm_ddim250_8xb32_imagenet-256x256.py\n In Collection: Guided Diffusion\n Name: adm_ddim250_8xb32_imagenet-256x256\n Results:\n - Dataset: ImageNet256x256\n Metrics:\n Steps: 250.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm_8xb32_imagenet-256x256-f94735fe.pth\n- Config: configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-256x256.py\n In Collection: Guided Diffusion\n Name: adm-g_ddim25_8xb32_imagenet-256x256\n Results:\n - Dataset: ImageNet256x256\n Metrics:\n CGS: 1.0\n Steps: 25.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-256x256-aec3fc9f.pth\n- Config: configs/guided_diffusion/adm_ddim250_8xb32_imagenet-512x512.py\n In Collection: Guided Diffusion\n Name: adm_ddim250_8xb32_imagenet-512x512\n Results:\n - Dataset: ImageNet512x512\n Metrics:\n Steps: 250.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm-u_8xb32_imagenet-512x512-60b381cb.pth\n- Config: configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet-512x512.py\n In Collection: Guided Diffusion\n Name: adm-g_ddim25_8xb32_imagenet-512x512\n Results:\n - Dataset: ImageNet512x512\n Metrics:\n CGS: 1.0\n Steps: 25.0\n Task: Image Generation\n Weights: https://download.openmmlab.com/mmediting/guided_diffusion/adm-g_8xb32_imagenet-512x512-23cf0b58.pth\n" }, { "path": "configs/iconvsr/README.md", "content": "# IconVSR (CVPR'2021)\n\n> [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://arxiv.org/abs/2012.02181)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nVideo super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels for REDS4 and Y channel for others. The metrics are `PSNR` / `SSIM` .\nThe pretrained weights of the IconVSR components can be found here: [SPyNet](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth), [EDVR-M for REDS](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_reds_20210413-3867262f.pth), and [EDVR-M for Vimeo-90K](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth).\n\n| Model | Dataset | PSNR (RGB) | SSIM (RGB) | PSNR (Y) | SSIM (Y) | Training Resources | Download |\n| :---------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :----------------------: | :-------------------: |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | REDS4 (BIx4) | **31.6926** | **0.8951** |-|-| 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | UDM10 (BDx4) | -|-|35.3377| 0.9471| 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | Vid4 (BIx4) | -|-|**27.4809**| **0.8354** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | Vid4 (BDx4) | -|-| 25.2110 | 0.7732 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | Vimeo-90K-T (BIx4) | -|-|36.4983 | 0.9416 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | Vimeo-90K-T (BDx4) | -|-| 34.4299 | 0.9287 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | REDS4 (BIx4) | 30.3452 |0.8659 |-|-| 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | UDM10 (BDx4) |-|-|34.2595 |0.9398 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | Vid4 (BIx4) |-|-|27.4238 |0.8297 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | Vid4 (BDx4) |-|-|24.6666|0.7491 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | Vimeo-90K-T (BIx4) |-|-|**37.3729**| **0.9467** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | Vimeo-90K-T (BDx4) |-|-|34.5548 | 0.9295 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | REDS4 (BIx4) |29.0150 | 0.8465 |-|-| 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | UDM10 (BDx4) |-|-| **40.0640** | **0.9697** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | Vid4 (BIx4) |-|-|26.3109| 0.8028 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | Vid4 (BDx4) | -|-| **28.2464** | **0.8612** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | Vimeo-90K-T (BIx4) | -|-|34.6780| 0.9339| 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | Vimeo-90K-T (BDx4) |-|-|**37.7573** | **0.9517** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) | [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/iconvsr/iconvsr_2xb4_reds4.py\n\n# single-gpu train\npython tools/train.py configs/iconvsr/iconvsr_2xb4_reds4.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/iconvsr/iconvsr_2xb4_reds4.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth\n\n# single-gpu test\npython tools/test.py configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{chan2021basicvsr,\n author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n" }, { "path": "configs/iconvsr/README_zh-CN.md", "content": "# IconVSR (CVPR'2021)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nIconVSR (CVPR'2021)\n\n```bibtex\n@InProceedings{chan2021basicvsr,\n author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},\n title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n\n
\n\n
\n\n对于 REDS4，我们对 RGB 通道进行评估。对于其他数据集，我们对 Y 通道进行评估。我们使用 `PSNR` 和 `SSIM` 作为指标。\nIconVSR 组件的预训练权重可以在这里找到：[SPyNet](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth)，[用于 REDS 的 EDVR-M](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_reds_20210413-3867262f.pth)，以及 [用于 Vimeo-90K 的 EDVR-M](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth)。\n\n| 算法 | REDS4 (BIx4)
PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)
PSNR/SSIM (Y) | Vid4 (BIx4)
PSNR/SSIM (Y) | UDM10 (BDx4)
PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)
PSNR/SSIM (Y) | Vid4 (BDx4)
PSNR/SSIM (Y) | GPU 信息 | 下载 |\n| :-: | :-----------------------------: | :---------------------------------: | :--------------------------: | :---------------------------: | :---------------------------------: | :--------------------------: | :-----: | :--: |\n| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | **31.6926/0.8951** | 36.4983/0.9416 | **27.4809/0.8354** | 35.3377/0.9471 | 34.4299/0.9287 | 25.2110/0.7732 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |\n| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | 30.3452/0.8659 | **37.3729/0.9467** | 27.4238/0.8297 | 34.2595/0.9398 | 34.5548/0.9295 | 24.6666/0.7491 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |\n| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | 29.0150/0.8465 | 34.6780/0.9339 | 26.3109/0.8028 | **40.0640/0.9697** | **37.7573/0.9517** | **28.2464/0.8612** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/iconvsr/iconvsr_2xb4_reds4.py\n\n# 单个GPU上训练\npython tools/train.py configs/iconvsr/iconvsr_2xb4_reds4.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/iconvsr/iconvsr_2xb4_reds4.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth\n\n# 单个GPU上测试\npython tools/test.py configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/iconvsr/iconvsr_2xb4_reds4.py https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/iconvsr/iconvsr_2xb4_reds4.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_reds4.py'\n\nexperiment_name = 'iconvsr_2xb4_reds4'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='IconVSRNet',\n mid_channels=64,\n num_blocks=30,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n edvr_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'iconvsr/edvrm_reds_20210413-3867262f.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\n" }, { "path": "configs/iconvsr/iconvsr_2xb4_vimeo90k-bd.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bd.py'\n\nexperiment_name = 'iconvsr_2xb4_vimeo90k-bd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='IconVSRNet',\n mid_channels=64,\n num_blocks=30,\n keyframe_stride=5,\n padding=3,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n edvr_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\n\nfind_unused_parameters = True\n" }, { "path": "configs/iconvsr/iconvsr_2xb4_vimeo90k-bi.py", "content": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bi.py'\n\nscale = 4\nexperiment_name = 'iconvsr_2xb4_vimeo90k-bi'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BasicVSR',\n generator=dict(\n type='IconVSRNet',\n mid_channels=64,\n num_blocks=30,\n keyframe_stride=5,\n padding=3,\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n edvr_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth'),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=5000),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ndefault_hooks = dict(checkpoint=dict(out_dir=save_dir))\nfind_unused_parameters = True\n" }, { "path": "configs/iconvsr/metafile.yml", "content": "Collections:\n- Name: IconVSR\n Paper:\n Title: 'BasicVSR: The Search for Essential Components in Video Super-Resolution\n and Beyond'\n URL: https://arxiv.org/abs/2012.02181\n README: configs/iconvsr/README.md\n Task:\n - video super-resolution\n Year: 2021\nModels:\n- Config: configs/iconvsr/iconvsr_2xb4_reds4.py\n In Collection: IconVSR\n Name: iconvsr_2xb4_reds4\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 31.6926\n SSIM (RGB): 0.8951\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 35.3377\n SSIM (Y): 0.9471\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.4809\n SSIM (Y): 0.8354\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 25.211\n SSIM (Y): 0.7732\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 36.4983\n SSIM (Y): 0.9416\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 34.4299\n SSIM (Y): 0.9287\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth\n- Config: configs/iconvsr/iconvsr_2xb4_vimeo90k-bi.py\n In Collection: IconVSR\n Name: iconvsr_2xb4_vimeo90k-bi\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 30.3452\n SSIM (RGB): 0.8659\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 34.2595\n SSIM (Y): 0.9398\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 27.4238\n SSIM (Y): 0.8297\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 24.6666\n SSIM (Y): 0.7491\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 37.3729\n SSIM (Y): 0.9467\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 34.5548\n SSIM (Y): 0.9295\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth\n- Config: configs/iconvsr/iconvsr_2xb4_vimeo90k-bd.py\n In Collection: IconVSR\n Name: iconvsr_2xb4_vimeo90k-bd\n Results:\n - Dataset: REDS4(BIx4)\n Metrics:\n PSNR (RGB): 29.015\n SSIM (RGB): 0.8465\n Task: Video Super-Resolution\n - Dataset: UDM10(BDx4)\n Metrics:\n PSNR (Y): 40.064\n SSIM (Y): 0.9697\n Task: Video Super-Resolution\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 26.3109\n SSIM (Y): 0.8028\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 28.2464\n SSIM (Y): 0.8612\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BIx4)\n Metrics:\n PSNR (Y): 34.678\n SSIM (Y): 0.9339\n Task: Video Super-Resolution\n - Dataset: Vimeo-90K-T(BDx4)\n Metrics:\n PSNR (Y): 37.7573\n SSIM (Y): 0.9517\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth\n" }, { "path": "configs/indexnet/README.md", "content": "# IndexNet (ICCV'2019)\n\n> [Indices Matter: Learning to Index for Deep Image Matting](https://arxiv.org/abs/1908.00672)\n\n> **Task**: Matting\n\n\n\n## Abstract\n\n\n\nWe show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation. By looking at the indices as a function of the feature map, we introduce the concept of learning to index, and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without the need of supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically predicts indices given an input. Due to its flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf convolutional networks that have coupled downsampling and upsampling stages.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | SAD | MSE | GRAD | CONN | Training Resources | Download |\n| :------------------------------------------------------------------: | :------------: | :------: | :-------: | :------: | :--: | :----------------: | :---------------------------------------------------------------------: |\n| [M2O DINs (our)](./indexnet_mobv2_1xb16-78k_comp1k.py) | Composition-1k | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) |\n| [M2O DINs (with DIM pipeline)](./indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | Composition-1k | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \\| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) |\n\n\n\n> The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance.\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py\n\n# single-gpu train\npython tools/train.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth\n\n# single-gpu test\npython tools/test.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{hao2019indexnet,\n title={Indices Matter: Learning to Index for Deep Image Matting},\n author={Lu, Hao and Dai, Yutong and Shen, Chunhua and Xu, Songcen},\n booktitle={Proc. IEEE/CVF International Conference on Computer Vision (ICCV)},\n year={2019}\n}\n```\n" }, { "path": "configs/indexnet/README_zh-CN.md", "content": "# IndexNet (ICCV'2019)\n\n> **任务**: 图像抠图\n\n\n\n
\nIndexNet (ICCV'2019)\n\n```bibtex\n@inproceedings{hao2019indexnet,\n title={Indices Matter: Learning to Index for Deep Image Matting},\n author={Lu, Hao and Dai, Yutong and Shen, Chunhua and Xu, Songcen},\n booktitle={Proc. IEEE/CVF International Conference on Computer Vision (ICCV)},\n year={2019}\n}\n```\n\n
\n\n
\n\n| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 |\n| :-----------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :------------------------------------------------------------------------------------------------------: |\n| M2O DINs (原文) | 45.8 | 0.013 | 25.9 | **43.7** | - | - |\n| [M2O DINs (复现)](./indexnet_mobv2_1xb16-78k_comp1k.py) | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) |\n\n> The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance.\n\n**其他结果**\n\n| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------: | :--: | :---: | :--: | :--: | :------: | :----------------------------------------------------------------------------------------------------: |\n| [M2O DINs (使用 DIM 流水线)](./indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \\| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py\n\n# 单个GPU上训练\npython tools/train.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth\n\n# 单个GPU上测试\npython tools/test.py configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/indexnet/indexnet_mobv2-dimaug_1xb16-78k_comp1k.py", "content": "_base_ = ['./indexnet_mobv2_1xb16-78k_comp1k.py']\n\nexperiment_name = 'indexnet_mobv2-dimaug_1xb16-78k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n test_cfg=dict(\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ))\n\n# dataset settings\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='LoadImageFromFile', key='bg'),\n dict(type='LoadImageFromFile', key='merged'),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged', 'fg', 'bg'],\n crop_sizes=[320, 480, 640]),\n dict(type='Flip', keys=['alpha', 'merged', 'fg', 'bg']),\n dict(\n type='Resize',\n keys=['alpha', 'merged', 'fg', 'bg'],\n scale=(320, 320),\n keep_ratio=False),\n dict(type='GenerateTrimap', kernel_size=(1, 30)),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(dataset=dict(pipeline=train_pipeline))\n\nval_dataloader = dict(dataset=dict(pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n" }, { "path": "configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py", "content": "_base_ = [\n '../_base_/datasets/comp1k.py', '../_base_/matting_default_runtime.py'\n]\n\nexperiment_name = 'indexnet_mobv2_1xb16-78k_comp1k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='IndexNet',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='IndexNetEncoder',\n in_channels=4,\n freeze_bn=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='open-mmlab://mmedit/mobilenet_v2')),\n decoder=dict(type='IndexNetDecoder')),\n loss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5, sample_wise=True),\n loss_comp=dict(\n type='CharbonnierCompLoss', loss_weight=1.5, sample_wise=True),\n test_cfg=dict(\n resize_method='interp',\n resize_mode='bicubic',\n size_divisor=32,\n ),\n)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='alpha', color_type='grayscale'),\n dict(type='LoadImageFromFile', key='fg'),\n dict(type='LoadImageFromFile', key='bg'),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='GenerateTrimapWithDistTransform', dist_thr=20),\n dict(\n type='CropAroundUnknown',\n keys=['alpha', 'merged', 'fg', 'bg', 'trimap'],\n crop_sizes=[320, 480, 640],\n interpolations=['bicubic', 'bicubic', 'bicubic', 'bicubic',\n 'nearest']),\n dict(\n type='Resize',\n keys=['trimap'],\n scale=(320, 320),\n keep_ratio=False,\n interpolation='nearest'),\n dict(\n type='Resize',\n keys=['alpha', 'merged', 'fg', 'bg'],\n scale=(320, 320),\n keep_ratio=False,\n interpolation='bicubic'),\n dict(type='Flip', keys=['alpha', 'merged', 'fg', 'bg', 'trimap']),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='alpha',\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile',\n key='trimap',\n color_type='grayscale',\n save_original_img=True),\n dict(type='LoadImageFromFile', key='merged'),\n dict(type='PackInputs'),\n]\n\ntrain_dataloader = dict(\n batch_size=16,\n num_workers=8,\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=78000,\n val_interval=2600,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-2),\n paramwise_cfg=dict(custom_keys={'encoder.layers': dict(lr_mult=0.01)}),\n)\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n milestones=[52000, 67600],\n gamma=0.1,\n by_epoch=False,\n)\n\n# checkpoint saving\ndefault_hooks = dict(checkpoint=dict(interval=2600, out_dir=save_dir))\n\n# runtime settings\n# inheritate from _base_\n" }, { "path": "configs/indexnet/metafile.yml", "content": "Collections:\n- Name: IndexNet\n Paper:\n Title: 'Indices Matter: Learning to Index for Deep Image Matting'\n URL: https://arxiv.org/abs/1908.00672\n README: configs/indexnet/README.md\n Task:\n - matting\n Year: 2019\nModels:\n- Config: configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py\n In Collection: IndexNet\n Name: indexnet_mobv2_1xb16-78k_comp1k\n Results:\n - Dataset: Composition-1k\n Metrics:\n CONN: 44.8\n GRAD: 25.5\n MSE: 0.012\n SAD: 45.6\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth\n- Config: configs/indexnet/indexnet_mobv2-dimaug_1xb16-78k_comp1k.py\n In Collection: IndexNet\n Name: indexnet_mobv2-dimaug_1xb16-78k_comp1k\n Results:\n - Dataset: Composition-1k\n Metrics:\n CONN: 49.5\n GRAD: 30.8\n MSE: 0.016\n SAD: 50.1\n Task: Matting\n Weights: https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth\n" }, { "path": "configs/inst_colorization/README.md", "content": "# Instance-aware Image Colorization (CVPR'2020)\n\n> [Instance-Aware Image Colorization](https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html)\n\n> **Task**: Colorization\n\n\n\n## Abstract\n\n\n\nImage colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | Download |\n| :---------------------------------------------------------------------------------------------: | :-----: | :------------------------------------------------------------------------------------------------: |\n| [instance_aware_colorization_officiial](./inst-colorizatioon_full_official_cocostuff-256x256.py) | MS-COCO | [model](https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) |\n\n## Quick Start\n\n
\nColorization demo\n\nYou can use the following commands to colorize an image.\n\n```shell\n\npython demo/mmagic_inference_demo.py --model-name inst_colorization --img input.jpg --result-out-dir output.png\n```\n\nFor more demos, you can refer to [Tutorial 3: inference with pre-trained models](https://mmagic.readthedocs.io/en/latest/user_guides/3_inference.html).\n\n
\n\n
\nInstance-aware Image Colorization (CVPR'2020)\n\n```bibtex\n@inproceedings{Su-CVPR-2020,\n author = {Su, Jheng-Wei and Chu, Hung-Kuo and Huang, Jia-Bin},\n title = {Instance-aware Image Colorization},\n booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n year = {2020}\n}\n```\n\n
\n" }, { "path": "configs/inst_colorization/README_zh-CN.md", "content": "# Instance-aware Image Colorization (CVPR'2020)\n\n> [Instance-Aware Image Colorization](https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html)\n\n> **任务**: 图像上色\n\n\n\n## 摘要\n\n\n\nImage colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization.\n\n\n\n
\n \n
\n\n## 结果和模型\n\n| Model | Download |\n| :----------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: |\n| [instance_aware_colorization_officiial](./inst-colorizatioon_full_official_cocostuff-256x256.py) | [model](https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) |\n\n## 快速开始\n\n
\n图像上色模型\n\n您可以使用以下命令来对一张图像进行上色。\n\n```shell\npython demo/mmagic_inference_demo.py --model-name inst_colorization --img input.jpg --result-out-dir output.png\n```\n\n更多细节可以参考 [Tutorial 3: inference with pre-trained models](https://mmagic.readthedocs.io/en/latest/user_guides/3_inference.html)。\n\n
\n\n
\nInstance-aware Image Colorization (CVPR'2020)\n\n```bibtex\n@inproceedings{Su-CVPR-2020,\n author = {Su, Jheng-Wei and Chu, Hung-Kuo and Huang, Jia-Bin},\n title = {Instance-aware Image Colorization},\n booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n year = {2020}\n}\n```\n\n
\n" }, { "path": "configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py", "content": "_base_ = ['../_base_/default_runtime.py']\n\nexperiment_name = 'inst-colorization_full_official_cocostuff_256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nstage = 'full'\n\nmodel = dict(\n type='InstColorization',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n image_model=dict(\n type='ColorizationNet', input_nc=4, output_nc=2, norm_type='batch'),\n instance_model=dict(\n type='ColorizationNet', input_nc=4, output_nc=2, norm_type='batch'),\n fusion_model=dict(\n type='FusionNet', input_nc=4, output_nc=2, norm_type='batch'),\n color_data_opt=dict(\n ab_thresh=0,\n p=1.0,\n sample_PS=[\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n ],\n ab_norm=110,\n ab_max=110.,\n ab_quant=10.,\n l_norm=100.,\n l_cent=50.,\n mask_cent=0.5),\n which_direction='AtoB',\n loss=dict(type='HuberLoss', delta=.01))\n\n# yapf: disable\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(\n type='InstanceCrop',\n config_file='mmdet::mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py', # noqa\n from_pretrained=None,\n finesize=256,\n box_num_upbound=5),\n dict(\n type='Resize',\n keys=['img', 'cropped_img'],\n scale=(256, 256),\n keep_ratio=False),\n dict(\n type='PackInputs',\n data_keys=['box_info', 'box_info_2x', 'box_info_4x', 'box_info_8x']),\n]\n" }, { "path": "configs/inst_colorization/metafile.yml", "content": "Collections:\n- Name: Instance-aware Image Colorization\n Paper:\n Title: Instance-Aware Image Colorization\n URL: https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html\n README: configs/inst_colorization/README.md\n Task:\n - colorization\n Year: 2020\nModels:\n- Config: configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py\n In Collection: Instance-aware Image Colorization\n Name: inst-colorizatioon_full_official_cocostuff-256x256\n Results:\n - Dataset: MS-COCO\n Metrics: {}\n Task: Colorization\n Weights: https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth\n" }, { "path": "configs/liif/README.md", "content": "# LIIF (CVPR'2021)\n\n> [Learning Continuous Image Representation with Local Implicit Image Function](https://arxiv.org/abs/2012.09161)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nHow to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | scale | PSNR | SSIM | Training Resources | Download |\n| :--------------------------------------------------------------------------: | :-----: | :---: | :-----: | :-----: | :----------------: | :------------------------------------------------------------------------------: |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x2 | 35.7131 | 0.9366 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x2 | 31.5579 | 0.8889 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x2 | 34.6647 | 0.9355 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x3 | 32.3805 | 0.8915 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x3 | 0.8039 | 30.9808 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x3 | 28.4605 | 0.8724 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x4 | 30.2748 | 0.8509 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x4 | 26.8415 | 0.7381 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x4 | 29.0245 | 0.8187 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x6 | 27.1187 | 0.7774 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x6 | 24.7461 | 0.6444 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x6 | 26.7770 | 0.7425 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x18 | 20.8516 | 0.5406 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x18 | 20.0096 | 0.4525 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x18 | 22.1987 | 0.5955 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x30 | 18.8467 | 0.5010 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x30 | 18.1321 | 0.3963 | △ | △ |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x30 | 20.5050 | 0.5577 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x2 | 35.7874 | 0.9366 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x2 | 31.6866 | 0.8896 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x2 | 34.7548 | 0.9356 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x3 | 32.4992 | 0.8923 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x3 | 28.4905 | 0.8037 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x3 | 31.0744 | 0.8731 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x4 | 30.3835 | 0.8513 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x4 | 26.8734 | 0.7373 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x4 | 29.1101 | 0.8197 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x6 | 27.1914 | 0.7751 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x6 | 24.7824 | 0.6434 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x6 | 26.8693 | 0.7437 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x18 | 20.8913 | 0.5329 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x18 | 20.1077 | 0.4537 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x18 | 22.2972 | 0.5950 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set5 | x30 | 18.9354 | 0.4864 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | Set14 | x30 | 18.1448 | 0.3942 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | DIV2K | x30 | 20.5663 | 0.5560 | △ | △ |\n\nNote:\n\n- △ refers to ditto.\n- Evaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth\n\n# single-gpu test\npython tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{chen2021learning,\n title={Learning continuous image representation with local implicit image function},\n author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={8628--8638},\n year={2021}\n}\n```\n" }, { "path": "configs/liif/README_zh-CN.md", "content": "# LIIF (CVPR'2021)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nLIIF (CVPR'2021)\n\n```bibtex\n@inproceedings{chen2021learning,\n title={Learning continuous image representation with local implicit image function},\n author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={8628--8638},\n year={2021}\n}\n```\n\n
\n\n
\n\n| 算法 | scale | Set5
PSNR / SSIM | Set14
PSNR / SSIM | DIV2K
PSNR / SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------------------: | :---: | :-----------------: | :------------------: | :-------------------: | :----------: | :------------------------------------------------------------: |\n| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7131 / 0.9366 | 31.5579 / 0.8889 | 34.6647 / 0.9355 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) |\n| △ | x3 | 32.3805 / 0.8915 | 28.4605 / 0.8039 | 30.9808 / 0.8724 | △ | △ |\n| △ | x4 | 30.2748 / 0.8509 | 26.8415 / 0.7381 | 29.0245 / 0.8187 | △ | △ |\n| △ | x6 | 27.1187 / 0.7774 | 24.7461 / 0.6444 | 26.7770 / 0.7425 | △ | △ |\n| △ | x18 | 20.8516 / 0.5406 | 20.0096 / 0.4525 | 22.1987 / 0.5955 | △ | △ |\n| △ | x30 | 18.8467 / 0.5010 | 18.1321 / 0.3963 | 20.5050 / 0.5577 | △ | △ |\n| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7874 / 0.9366 | 31.6866 / 0.8896 | 34.7548 / 0.9356 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) |\n| △ | x3 | 32.4992 / 0.8923 | 28.4905 / 0.8037 | 31.0744 / 0.8731 | △ | △ |\n| △ | x4 | 30.3835 / 0.8513 | 26.8734 / 0.7373 | 29.1101 / 0.8197 | △ | △ |\n| △ | x6 | 27.1914 / 0.7751 | 24.7824 / 0.6434 | 26.8693 / 0.7437 | △ | △ |\n| △ | x18 | 20.8913 / 0.5329 | 20.1077 / 0.4537 | 22.2972 / 0.5950 | △ | △ |\n| △ | x30 | 18.9354 / 0.4864 | 18.1448 / 0.3942 | 20.5663 / 0.5560 | △ | △ |\n\n注：\n\n- △ 指同上。\n- 这两个配置仅在 _testing pipeline_ 上有所不同。 所以他们使用相同的检查点。\n- 数据根据 [EDSR](../edsr/README.md) 进行正则化。\n- 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth\n\n# 单个GPU上测试\npython tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/models/base_liif.py', '../_base_/datasets/liif_test_config.py'\n]\n\nexperiment_name = 'liif-edsr-norm_c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale_min, scale_max = 1, 4\n\n# model settings\nmodel = dict(\n type='LIIF',\n generator=dict(\n type='LIIFEDSRNet',\n encoder=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16),\n imnet=dict(\n type='MLPRefiner',\n in_dim=64,\n out_dim=3,\n hidden_list=[256, 256, 256, 256]),\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=30000),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.4488 * 255, 0.4371 * 255, 0.4040 * 255],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/liif/liif-rdn-norm_c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/models/base_liif.py', '../_base_/datasets/liif_test_config.py'\n]\n\nexperiment_name = 'liif-rdn-norm_c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale_min, scale_max = 1, 4\n\n# model settings\nmodel = dict(\n type='LIIF',\n generator=dict(\n type='LIIFRDNNet',\n encoder=dict(\n type='RDNNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4,\n num_layers=8,\n channel_growth=64),\n imnet=dict(\n type='MLPRefiner',\n in_dim=64,\n out_dim=3,\n hidden_list=[256, 256, 256, 256]),\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=30000),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.5 * 255, 0.5 * 255, 0.5 * 255],\n std=[0.5 * 255, 0.5 * 255, 0.5 * 255],\n ))\n" }, { "path": "configs/liif/metafile.yml", "content": "Collections:\n- Name: LIIF\n Paper:\n Title: Learning Continuous Image Representation with Local Implicit Image Function\n URL: https://arxiv.org/abs/2012.09161\n README: configs/liif/README.md\n Task:\n - image super-resolution\n Year: 2021\nModels:\n- Config: configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py\n In Collection: LIIF\n Name: liif-edsr-norm_c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 35.7131\n SSIM: 0.9366\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 31.5579\n SSIM: 0.8889\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.6647\n SSIM: 0.9355\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 32.3805\n SSIM: 0.8915\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 0.8039\n SSIM: 30.9808\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 28.4605\n SSIM: 0.8724\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 30.2748\n SSIM: 0.8509\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 26.8415\n SSIM: 0.7381\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 29.0245\n SSIM: 0.8187\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 27.1187\n SSIM: 0.7774\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 24.7461\n SSIM: 0.6444\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 26.777\n SSIM: 0.7425\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 20.8516\n SSIM: 0.5406\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 20.0096\n SSIM: 0.4525\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 22.1987\n SSIM: 0.5955\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 18.8467\n SSIM: 0.501\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 18.1321\n SSIM: 0.3963\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 20.505\n SSIM: 0.5577\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth\n- Config: configs/liif/liif-rdn-norm_c64b16_1xb16-1000k_div2k.py\n In Collection: LIIF\n Name: liif-rdn-norm_c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 35.7874\n SSIM: 0.9366\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 31.6866\n SSIM: 0.8896\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.7548\n SSIM: 0.9356\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 32.4992\n SSIM: 0.8923\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 28.4905\n SSIM: 0.8037\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 31.0744\n SSIM: 0.8731\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 30.3835\n SSIM: 0.8513\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 26.8734\n SSIM: 0.7373\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 29.1101\n SSIM: 0.8197\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 27.1914\n SSIM: 0.7751\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 24.7824\n SSIM: 0.6434\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 26.8693\n SSIM: 0.7437\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 20.8913\n SSIM: 0.5329\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 20.1077\n SSIM: 0.4537\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 22.2972\n SSIM: 0.595\n Task: Image Super-Resolution\n - Dataset: Set5\n Metrics:\n PSNR: 18.9354\n SSIM: 0.4864\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 18.1448\n SSIM: 0.3942\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 20.5663\n SSIM: 0.556\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth\n" }, { "path": "configs/lsgan/README.md", "content": "# LSGAN (ICCV'2017)\n\n> [Least Squares Generative Adversarial Networks](https://openaccess.thecvf.com/content_iccv_2017/html/Mao_Least_Squares_Generative_ICCV_2017_paper.html)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nUnsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson χ2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on five scene datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n LSGAN 64x64, CelebA-Cropped\n
\n \n
\n\n| Model | Dataset | SWD | MS-SSIM | FID | Download |\n| :-------------------------------------------------------------------: | :------------: | :-----------------------------: | :-----: | :-----: | :----------------------------------------------------------------------: |\n| [LSGAN 64x64](./lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | CelebA-Cropped | 6.16, 6.83, 37.64/16.87 | 0.3216 | 11.9258 | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth)\\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210422_131925.log.json) |\n| [LSGAN 64x64](./lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py) | LSUN-Bedroom | 5.66, 9.0, 18.6/11.09 | 0.0671 | 30.7390 | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth)\\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210423_005020.log.json) |\n| [LSGAN 128x128](./lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | CelebA-Cropped | 21.66, 9.83, 16.06, 70.76/29.58 | 0.3691 | 38.3752 | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth)\\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_132126.log.json) |\n| [LSGAN 128x128](./lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py) | LSUN-Bedroom | 19.52, 9.99, 7.48, 14.3/12.82 | 0.0612 | 51.5500 | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth)\\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_142302.log.json) |\n\n## Citation\n\n```latex\n@inproceedings{mao2017least,\n title={Least squares generative adversarial networks},\n author={Mao, Xudong and Li, Qing and Xie, Haoran and Lau, Raymond YK and Wang, Zhen and Paul Smolley, Stephen},\n booktitle={Proceedings of the IEEE international conference on computer vision},\n pages={2794--2802},\n year={2017},\n url={https://openaccess.thecvf.com/content_iccv_2017/html/Mao_Least_Squares_Generative_ICCV_2017_paper.html},\n}\n```\n" }, { "path": "configs/lsgan/lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\nmodel = dict(type='LSGAN')\ntotal_iters = 100000\ndisc_step = 1\ntrain_cfg = dict(max_iters=total_iters * disc_step)\n# define dataset\n# batch_size and data_root must be set\nbatch_size = 128\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0.5, 0.99))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# METRICS\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n real_nums=50000,\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_64x64.py',\n '../_base_/datasets/unconditional_imgs_64x64.py',\n '../_base_/gen_default_runtime.py'\n]\n\nmodel = dict(type='LSGAN', discriminator=dict(output_scale=4, out_channels=1))\ntotal_iters = 100000\ndisc_step = 1\ntrain_cfg = dict(max_iters=total_iters * disc_step)\n\n# define dataset\n# `batch_size` and `data_root` need to be set.\nbatch_size = 128\ndata_root = './data/lsun/images/bedroom_train'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_bs = 32\nval_dataloader = dict(batch_size=test_bs, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(batch_size=test_bs, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))))\ndefault_hooks = dict(\n checkpoint=dict(save_best=['FID-Full-50k/fid'], rule=['less']))\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py", "content": "_base_ = [\n '../_base_/models/dcgan/base_dcgan_128x128.py',\n '../_base_/datasets/unconditional_imgs_128x128.py',\n '../_base_/gen_default_runtime.py'\n]\nmodel = dict(type='LSGAN', discriminator=dict(output_scale=4, out_channels=1))\n\ntotal_iters = 160000\ndisc_step = 1\ntrain_cfg = dict(max_iters=total_iters * disc_step)\n\n# define dataset\n# you must set `samples_per_gpu` and `imgs_root`\n# `batch_size` and `data_root` need to be set.\nbatch_size = 64\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))))\n\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# METRICS\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n\n# TODO\n# metrics = dict(\n# ms_ssim10k=dict(type='MS_SSIM', num_images=10000),\n# swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 128, 128)),\n# fid50k=dict(type='FID', num_images=50000, inception_pkl=None))\n" }, { "path": "configs/lsgan/lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py", "content": "_base_ = [\n '../_base_/datasets/unconditional_imgs_128x128.py',\n '../_base_/gen_default_runtime.py'\n]\n# define model\nmodel = dict(\n type='LSGAN',\n noise_size=1024,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='LSGANGenerator',\n output_scale=128,\n base_channels=256,\n noise_size=1024),\n discriminator=dict(\n type='LSGANDiscriminator', input_scale=128, base_channels=64))\n\ntotal_iters = 160000\ndisc_step = 1\ntrain_cfg = dict(max_iters=total_iters * disc_step)\n\n# define dataset\nbatch_size = 64\ndata_root = './data/lsun/images/bedroom_train'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_bs = 32\nval_dataloader = dict(batch_size=test_bs, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(batch_size=test_bs, dataset=dict(data_root=data_root))\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99))))\ndefault_hooks = dict(\n checkpoint=dict(save_best=['FID-Full-50k/fid'], rule=['less']))\n# adjust running config\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='PyTorch',\n sample_model='orig')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/lsgan/metafile.yml", "content": "Collections:\n- Name: LSGAN\n Paper:\n Title: Least Squares Generative Adversarial Networks\n URL: https://openaccess.thecvf.com/content_iccv_2017/html/Mao_Least_Squares_Generative_ICCV_2017_paper.html\n README: configs/lsgan/README.md\n Task:\n - unconditional gans\n Year: 2017\nModels:\n- Config: configs/lsgan/lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py\n In Collection: LSGAN\n Name: lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n FID: 11.9258\n MS-SSIM: 0.3216\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth\n- Config: configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py\n In Collection: LSGAN\n Name: lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64\n Results:\n - Dataset: LSUN-Bedroom\n Metrics:\n FID: 30.739\n MS-SSIM: 0.0671\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth\n- Config: configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py\n In Collection: LSGAN\n Name: lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n FID: 38.3752\n MS-SSIM: 0.3691\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth\n- Config: configs/lsgan/lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py\n In Collection: LSGAN\n Name: lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128\n Results:\n - Dataset: LSUN-Bedroom\n Metrics:\n FID: 51.55\n MS-SSIM: 0.0612\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth\n" }, { "path": "configs/nafnet/README.md", "content": "# NAFNet (ECCV'2022)\n\n> [Simple Baselines for Image Restoration](https://arxiv.org/abs/2204.04676)\n\n> **Task**: Image Restoration\n\n\n\n## Abstract\n\n\n\nAlthough there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods. In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. To further simplify the baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid, ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by multiplication or removed. Thus, we derive a Nonlinear Activation Free Network, namely NAFNet, from the baseline. SOTA results are achieved on various challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring), exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs; 40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28 dB with less than half of its computational costs.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Dataset | image size | PSNR | SSIM | GPU Info | Download |\n| :---------------------------------------------------------------------: | :-----: | :--------: | :--------------: | :------------: | :------: | :------------------------------------------------------------------------: |\n| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](./nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | SIDD | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \\| log(coming soon) |\n| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](./nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | GoPro | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \\| log(coming soon) |\n\nNote:\n\n- a(b) where a denotes the value run by MMagic, b denotes the value copied from the original paper.\n- PSNR is evaluated on RGB channels.\n- SSIM is evaluated by averaging SSIMs on RGB channels, however the original paper uses the 3D SSIM kernel.\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py\n\n# single-gpu train\npython tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](../../docs/en/user_guides/train_test.md).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint\n\n# single-gpu test\npython tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint\n\n# multi-gpu test\n./tools/dist_test.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint 8\n```\n\nPretrained checkpoints will come soon.\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](../../docs/en/user_guides/train_test.md).\n\n
\n\n## Citation\n\n```bibtex\n@article{chen2022simple,\n title={Simple Baselines for Image Restoration},\n author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},\n journal={arXiv preprint arXiv:2204.04676},\n year={2022}\n}\n```\n" }, { "path": "configs/nafnet/README_zh-CN.md", "content": "# NAFNET (ECCV'2022)\n\n> **任务**: 图像恢复\n\n\n\n
\nNAFNET (ECCV'2022)\n\n```bibtex\n@article{chen2022simple,\n title={Simple Baselines for Image Restoration},\n author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},\n journal={arXiv preprint arXiv:2204.04676},\n year={2022}\n}\n```\n\n
\n\n| 方法 | 图片尺寸 | PSNR | SSIM | GPU信息 | 下载 |\n| :---------------------------------------------------------------------------: | :------: | :--------------: | :------------: | :------: | :---------------------------------------------------------------------------: |\n| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](./nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \\| 日志(即将到来) |\n| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](./nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \\| 日志(即将到来) |\n\nNote:\n\n- 评估结果a(b)中，a代表由MMagic测量，b代表由原论文提供。\n- PSNR是在RGB通道评估。\n- SSIM是平均的分别在RGB通道评估的SSIM, 而原论文使用了3D的SSIM卷积核做统一评估。\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py\n\n# 单个GPU上训练\npython tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py 8\n```\n\n更多细节可以参考 [train_test.md](../../docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint\n\n# 单个GPU上测试\npython tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py /path/to/checkpoint 8\n```\n\n预训练模型未来将会上传，敬请等待。\n更多细节可以参考 [train_test.md](../../docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/nafnet/metafile.yml", "content": "Collections:\n- Name: NAFNet\n Paper:\n Title: Simple Baselines for Image Restoration\n URL: https://arxiv.org/abs/2204.04676\n README: configs/nafnet/README.md\n Task:\n - image restoration\n Year: 2022\nModels:\n- Config: configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py\n In Collection: NAFNet\n Name: nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd\n Results:\n - Dataset: SIDD\n Metrics:\n GPU Info: 1.0\n PSNR: 40.3045\n SSIM: 0.9253\n Task: Image Restoration\n Weights: https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth\n- Config: configs/nafnet/nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py\n In Collection: NAFNet\n Name: nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro\n Results:\n - Dataset: GoPro\n Metrics:\n GPU Info: 1.0\n PSNR: 33.7246\n SSIM: 0.9479\n Task: Image Restoration\n Weights: https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth\n" }, { "path": "configs/nafnet/nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'nafnet_c64eb11128mb1db1111_lr1e-3_400k_gopro'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='NAFNetLocal',\n img_channels=3,\n mid_channels=64,\n enc_blk_nums=[1, 1, 1, 28],\n middle_blk_num=1,\n dec_blk_nums=[1, 1, 1, 1],\n ),\n pixel_loss=dict(type='PSNRLoss'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8, # gpus 4\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root='./data/gopro/train',\n data_prefix=dict(gt='sharp', img='blur'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='gopro', task_name='deblur'),\n data_root='./data/gopro/test',\n data_prefix=dict(gt='sharp', img='blur'),\n pipeline=val_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=400_000, val_interval=20000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='AdamW', lr=1e-3, weight_decay=1e-3, betas=(0.9, 0.9)))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineAnnealingLR', by_epoch=False, T_max=400_000, eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nrandomness = dict(seed=10, diff_rank_seed=True)\n" }, { "path": "configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'nafnet_c64eb2248mb12db2222_lr1e-3_400k_sidd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='NAFNet',\n img_channels=3,\n mid_channels=64,\n enc_blk_nums=[2, 2, 4, 8],\n middle_blk_num=12,\n dec_blk_nums=[2, 2, 2, 2],\n ),\n pixel_loss=dict(type='PSNRLoss'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.0, 0.0, 0.0],\n std=[255.0, 255.0, 255.0],\n ))\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8, # gpus 4\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='sidd', task_name='denoising'),\n data_root='./data/SIDD/train',\n data_prefix=dict(gt='gt', img='noisy'),\n filename_tmpl=dict(img='{}_NOISY', gt='{}_GT'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='sidd', task_name='denoising'),\n data_root='./data/SIDD/val/',\n data_prefix=dict(gt='gt', img='noisy'),\n filename_tmpl=dict(gt='{}_GT', img='{}_NOISY'),\n pipeline=val_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=400_000, val_interval=20000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='AdamW', lr=1e-3, weight_decay=1e-3, betas=(0.9, 0.9)))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineAnnealingLR', by_epoch=False, T_max=400_000, eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nvisualizer = dict(bgr2rgb=False)\n" }, { "path": "configs/partial_conv/README.md", "content": "# PConv (ECCV'2018)\n\n> [Image Inpainting for Irregular Holes Using Partial Convolutions](https://arxiv.org/abs/1804.07723)\n\n> **Task**: Inpainting\n\n\n\n## Abstract\n\n\n\nExisting deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach.\n\n\n\n
\n \n
\n\n## Results and models\n\n| Model | Mask Type | Resolution | Train Iters | Dataset | l1 error | PSNR | SSIM | Training Resources | Download |\n| :----------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :----------------: | :----------------------------------------------------------------: |\n| [PConv_Stage1](./pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - |\n| [PConv_Stage2](./pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) |\n| [PConv_Stage1](./pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - |\n| [PConv_Stage2](./pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \\| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py\n\n# single-gpu train\npython tools/train.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/partial_conv/pconv_stage2_4xb2_places-256x256.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth\n\n# single-gpu test\npython tools/test.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{liu2018image,\n title={Image inpainting for irregular holes using partial convolutions},\n author={Liu, Guilin and Reda, Fitsum A and Shih, Kevin J and Wang, Ting-Chun and Tao, Andrew and Catanzaro, Bryan},\n booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},\n pages={85--100},\n year={2018}\n}\n```\n" }, { "path": "configs/partial_conv/README_zh-CN.md", "content": "# PConv (ECCV'2018)\n\n> **任务**: 图像修复\n\n\n\n
\nPConv (ECCV'2018)\n\n```bibtex\n@inproceedings{liu2018image,\n title={Image inpainting for irregular holes using partial convolutions},\n author={Liu, Guilin and Reda, Fitsum A and Shih, Kevin J and Wang, Ting-Chun and Tao, Andrew and Catanzaro, Bryan},\n booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},\n pages={85--100},\n year={2018}\n}\n```\n\n
\n\n
\n\n**Places365-Challenge**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :----------------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------------------: |\n| [PConv_Stage1](./pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - |\n| [PConv_Stage2](./pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) |\n\n**CelebA-HQ**\n\n| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 |\n| :---------------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-----------------------------------------------------------------------------------: |\n| [PConv_Stage1](./pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - |\n| [PConv_Stage2](./pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \\| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py\n\n# 单个GPU上训练\npython tools/train.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/partial_conv/pconv_stage2_4xb2_places-256x256.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth\n\n# 单个GPU上测试\npython tools/test.py configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/partial_conv/pconv_stage2_4xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/partial_conv/metafile.yml", "content": "Collections:\n- Name: PConv\n Paper:\n Title: Image Inpainting for Irregular Holes Using Partial Convolutions\n URL: https://arxiv.org/abs/1804.07723\n README: configs/partial_conv/README.md\n Task:\n - inpainting\n Year: 2018\nModels:\n- Config: configs/partial_conv/pconv_stage1_8xb12_places-256x256.py\n In Collection: PConv\n Name: pconv_stage1_8xb12_places-256x256\n Results:\n - Dataset: Places365-val\n Metrics: {}\n Task: Inpainting\n- Config: configs/partial_conv/pconv_stage2_4xb2_places-256x256.py\n In Collection: PConv\n Name: pconv_stage2_4xb2_places-256x256\n Results:\n - Dataset: Places365-val\n Metrics:\n PSNR: 22.762\n SSIM: 0.801\n l1 error: 8.776\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth\n- Config: configs/partial_conv/pconv_stage1_8xb1_celeba-256x256.py\n In Collection: PConv\n Name: pconv_stage1_8xb1_celeba-256x256\n Results:\n - Dataset: CelebA-val\n Metrics: {}\n Task: Inpainting\n- Config: configs/partial_conv/pconv_stage2_4xb2_celeba-256x256.py\n In Collection: PConv\n Name: pconv_stage2_4xb2_celeba-256x256\n Results:\n - Dataset: CelebA-val\n Metrics:\n PSNR: 25.404\n SSIM: 0.853\n l1 error: 5.99\n Task: Inpainting\n Weights: https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth\n" }, { "path": "configs/partial_conv/pconv_stage1_8xb12_places-256x256.py", "content": "_base_ = [\n '../_base_/models/base_pconv.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'pconv_stage1_8xb1_places-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=0,\n start_iter=0,\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='SyncBN'))),\n)\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=12,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=800002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0002))\n\ncheckpoint = dict(\n type='CheckpointHook', interval=50000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/partial_conv/pconv_stage1_8xb1_celeba-256x256.py", "content": "_base_ = [\n '../_base_/models/base_pconv.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/celeba.py'\n]\n\nexperiment_name = 'pconv_stage1_8xb1_celeba-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=0,\n start_iter=0,\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='SyncBN'))),\n)\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=1,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=800002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.0002))\n\ncheckpoint = dict(\n type='CheckpointHook', interval=50000, by_epoch=False, out_dir=save_dir)\n" }, { "path": "configs/partial_conv/pconv_stage2_4xb2_celeba-256x256.py", "content": "_base_ = [\n '../_base_/models/base_pconv.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/celeba.py'\n]\n\nexperiment_name = 'pconv_stage2_4xb2_celeba-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=0,\n start_iter=0,\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='SyncBN', requires_grad=False),\n norm_eval=True),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='SyncBN'))),\n)\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=2,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(\n batch_size=1,\n dataset=dict(pipeline=test_pipeline),\n)\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=300002,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00005))\n\ncheckpoint = dict(\n type='CheckpointHook', interval=50000, by_epoch=False, out_dir=save_dir)\n\n# load_from = 'pconv_stage1_8xb1_celeba-256x256/iter_800002.pth'\n" }, { "path": "configs/partial_conv/pconv_stage2_4xb2_places-256x256.py", "content": "_base_ = [\n '../_base_/models/base_pconv.py', '../_base_/inpaint_default_runtime.py',\n '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'pconv_stage2_4xb2_places-256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nmodel = dict(\n train_cfg=dict(\n disc_step=0,\n start_iter=0,\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='SyncBN', requires_grad=False),\n norm_eval=True),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='SyncBN'))),\n)\n\ninput_shape = (256, 256)\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='irregular',\n mask_config=dict(\n num_vertices=(4, 10),\n max_angle=6.0,\n length_range=(20, 128),\n brush_width=(10, 45),\n area_ratio_range=(0.15, 0.65),\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\ntest_pipeline = train_pipeline\n\ntrain_dataloader = dict(\n batch_size=2,\n sampler=dict(shuffle=False),\n dataset=dict(pipeline=train_pipeline),\n)\n\nval_dataloader = dict(batch_size=1, dataset=dict(pipeline=test_pipeline))\n\ntest_dataloader = val_dataloader\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',\n max_iters=500000,\n val_interval=50000,\n)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00005))\n\ncheckpoint_config = dict(\n type='CheckpointHook', by_epoch=False, interval=50000, out_dir=save_dir)\n\n# load_from = 'pconv_stage1_8xb1_places-256x256/iter_800002.pth'\n" }, { "path": "configs/pggan/README.md", "content": "# PGGAN (ICLR'2018)\n\n> [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nWe describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n Results (compressed) from our PGGAN trained in CelebA-HQ@1024\n
\n \n
\n\n| Model | Dataset | MS-SSIM | SWD(xx,xx,xx,xx/avg) | Download |\n| :-------------------------------------------------------------: | :------------: | :-----: | :--------------------------: | :--------------------------------------------------------------------------------------: |\n| [pggan_128x128](./pggan_8xb4-12Mimgs_celeba-cropped-128x128.py) | celeba-cropped | 0.3023 | 3.42, 4.04, 4.78, 20.38/8.15 | [model](https://download.openmmlab.com/mmediting/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth) |\n| [pggan_128x128](./pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py) | lsun-bedroom | 0.0602 | 3.5, 2.96, 2.76, 9.65/4.72 | [model](https://download.openmmlab.com/mmediting/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth) |\n| [pggan_1024x1024](./pggan_8xb4-12Mimg_celeba-hq-1024x1024.py) | celeba-hq | 0.3379 | 8.93, 3.98, 3.07, 2.64/4.655 | [model](https://download.openmmlab.com/mmediting/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth) |\n\n## Citation\n\nPGGAN (arXiv'2017)\n\n```latex\n@article{karras2017progressive,\n title={Progressive growing of gans for improved quality, stability, and variation},\n author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},\n journal={arXiv preprint arXiv:1710.10196},\n year={2017},\n url={https://arxiv.org/abs/1710.10196},\n}\n```\n" }, { "path": "configs/pggan/metafile.yml", "content": "Collections:\n- Name: PGGAN\n Paper:\n Title: Progressive Growing of GANs for Improved Quality, Stability, and Variation\n URL: https://arxiv.org/abs/1710.10196\n README: configs/pggan/README.md\n Task:\n - unconditional gans\n Year: 2018\nModels:\n- Config: configs/pggan/pggan_8xb4-12Mimgs_celeba-cropped-128x128.py\n In Collection: PGGAN\n Name: pggan_8xb4-12Mimgs_celeba-cropped-128x128\n Results:\n - Dataset: celeba-cropped\n Metrics:\n MS-SSIM: 0.3023\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth\n- Config: configs/pggan/pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py\n In Collection: PGGAN\n Name: pggan_8xb4-12Mimgs_lsun-bedroom-128x128\n Results:\n - Dataset: lsun-bedroom\n Metrics:\n MS-SSIM: 0.0602\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth\n- Config: configs/pggan/pggan_8xb4-12Mimg_celeba-hq-1024x1024.py\n In Collection: PGGAN\n Name: pggan_8xb4-12Mimg_celeba-hq-1024x1024\n Results:\n - Dataset: celeba-hq\n Metrics:\n MS-SSIM: 0.3379\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth\n" }, { "path": "configs/pggan/pggan_8xb4-12Mimg_celeba-hq-1024x1024.py", "content": "_base_ = ['../_base_/gen_default_runtime.py']\n\n# define GAN model\nmodel = dict(\n type='ProgressiveGrowingGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n noise_size=512,\n generator=dict(type='PGGANGenerator', out_scale=1024, noise_size=512),\n discriminator=dict(type='PGGANDiscriminator', in_scale=1024),\n nkimgs_per_scale={\n '4': 600,\n '8': 1200,\n '16': 1200,\n '32': 1200,\n '64': 1200,\n '128': 1200,\n '256': 1200,\n '512': 1200,\n '1024': 12000,\n },\n transition_kimgs=600,\n ema_config=dict(interval=1))\n\n# MODEL\nmodel_wrapper_cfg = dict(find_unused_parameters=True)\n\n# TRAIN\ntrain_cfg = dict(max_iters=280000)\n\noptim_wrapper = dict(\n constructor='PGGANOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=dict(\n generator={\n '128': 0.0015,\n '256': 0.002,\n '512': 0.003,\n '1024': 0.003\n },\n discriminator={\n '128': 0.0015,\n '256': 0.002,\n '512': 0.003,\n '1024': 0.003\n }))\n\n# DATA\ndataset_type = 'GrowScaleImgDataset'\n\npipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Flip', keys='gt', direction='horizontal'),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type=dataset_type,\n data_roots={\n '64': './data/celebahq/imgs_64',\n '256': './data/celebahq/imgs_256',\n '512': './data/celebahq/imgs_512',\n '1024': './data/celebahq/imgs_1024'\n },\n gpu_samples_base=4,\n # note that this should be changed with total gpu number\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4\n },\n len_per_stage=300000,\n pipeline=pipeline),\n sampler=dict(type='InfiniteSampler', shuffle=True))\n\ntest_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type='BasicImageDataset',\n pipeline=pipeline,\n data_prefix=dict(gt=''),\n data_root='./data/celebahq/imgs_1024'),\n sampler=dict(type='DefaultSampler', shuffle=False))\n\n# VIS_HOOK + DATAFETCH\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n dict(type='PGGANFetchDataHook')\n]\ndefault_hooks = dict(\n checkpoint=dict(\n max_keep_ckpts=20,\n save_best=['swd/avg', 'ms-ssim/avg'],\n rule=['less', 'greater']))\n\n# METRICS\nmetrics = [\n dict(\n type='SWD', fake_nums=16384, image_shape=(3, 1024, 1024),\n prefix='SWD'),\n dict(type='MS_SSIM', fake_nums=10000, prefix='MS-SSIM')\n]\n\n# do not evaluate in training\nval_cfg = val_evaluator = val_dataloader = None\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/pggan/pggan_8xb4-12Mimgs_celeba-cropped-128x128.py", "content": "_base_ = ['../_base_/gen_default_runtime.py']\n\n# define GAN model\nmodel = dict(\n type='ProgressiveGrowingGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n noise_size=512,\n generator=dict(type='PGGANGenerator', out_scale=128),\n discriminator=dict(type='PGGANDiscriminator', in_scale=128),\n nkimgs_per_scale={\n '4': 600,\n '8': 1200,\n '16': 1200,\n '32': 1200,\n '64': 1200,\n '128': 12000\n },\n transition_kimgs=600,\n ema_config=dict(interval=1))\n\n# MODEL\nmodel_wrapper_cfg = dict(find_unused_parameters=True)\n\n# TRAIN\ntrain_cfg = dict(max_iters=280000)\n\noptim_wrapper = dict(\n constructor='PGGANOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=dict(generator={'128': 0.0015}, discriminator={'128': 0.0015}))\n\n# DATA\ndataset_type = 'GrowScaleImgDataset'\ndata_roots = {'128': './data/celeba-cropped/cropped_images_aligned_png'}\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(128, 128)),\n dict(type='Flip', keys='gt', direction='horizontal'),\n dict(type='PackInputs')\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(128, 128)),\n dict(type='PackInputs')\n]\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=64, # initialize batch size\n dataset=dict(\n type=dataset_type,\n pipeline=train_pipeline,\n data_roots=data_roots,\n gpu_samples_base=4,\n # note that this should be changed with total gpu number\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4\n },\n len_per_stage=-1),\n sampler=dict(type='InfiniteSampler', shuffle=True))\n\ntest_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type='BasicImageDataset',\n pipeline=test_pipeline,\n data_prefix=dict(gt=''),\n data_root=data_roots['128']),\n sampler=dict(type='DefaultSampler', shuffle=False))\n\n# VIS_HOOK + DATAFETCH\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n dict(type='PGGANFetchDataHook')\n]\ndefault_hooks = dict(\n checkpoint=dict(\n max_keep_ckpts=20,\n save_best=['swd/avg', 'ms-ssim/avg'],\n rule=['less', 'greater']))\n\n# METRICS\nmetrics = [\n dict(type='SWD', fake_nums=16384, image_shape=(3, 128, 128), prefix='SWD'),\n dict(type='MS_SSIM', fake_nums=10000, prefix='MS-SSIM')\n]\n\n# do not evaluate in training\nval_cfg = val_evaluator = val_dataloader = None\n\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/pggan/pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py", "content": "_base_ = ['./pggan_8xb4-12Mimgs_celeba-cropped-128x128.py']\n\n# Overwrite data configs\ndata_roots = {'128': './data/lsun/images/bedroom_train'}\ntrain_dataloader = dict(batch_size=64, dataset=dict(data_roots=data_roots))\ntest_dataloader = dict(dataset=dict(data_root=data_roots['128']))\n" }, { "path": "configs/pix2pix/README.md", "content": "# Pix2Pix (CVPR'2017)\n\n> [Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks](https://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html)\n\n> **Task**: Image2Image\n\n\n\n## Abstract\n\n\n\nWe investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n Results from Pix2Pix trained by mmagic\n
\n \n
\nWe use `FID` and `IS` metrics to evaluate the generation performance of pix2pix.1\n\n| Model | Dataset | FID | IS | Download |\n| :----------------------------------------------------------------------------: | :---------: | :------: | :---: | :------------------------------------------------------------------------------------------------: |\n| [Ours](./pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py) | facades | 124.9773 | 1.620 | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth) \\| [log](https://download.openmmlab.com/mmediting/pix2pix/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210317_172625.log.json)2 |\n| [Ours](./pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py) | aerial2maps | 122.5856 | 3.137 | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth) |\n| [Ours](./pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py) | maps2aerial | 88.4635 | 3.310 | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth) |\n| [Ours](./pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py) | edges2shoes | 84.3750 | 2.815 | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth) |\n\n`FID` comparison with official:\n\n\n\n| Dataset | facades | aerial2maps | maps2aerial | edges2shoes | average |\n| :------: | :---------: | :----------: | :---------: | :---------: | :----------: |\n| official | **119.135** | 149.731 | 102.072 | **75.774** | 111.678 |\n| ours | 124.9773 | **122.5856** | **88.4635** | 84.3750 | **105.1003** |\n\n`IS` comparison with official:\n\n\n\n| Dataset | facades | aerial2maps | maps2aerial | edges2shoes | average |\n| :------: | :-------: | :---------: | :---------: | :---------: | :--------: |\n| official | **1.650** | 2.529 | **3.552** | 2.766 | 2.624 |\n| ours | 1.620 | **3.137** | 3.310 | **2.815** | **2.7205** |\n\nNote:\n\n1. we strictly follow the [paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf) setting in Section 3.3: \"*At inference time, we run the generator net in exactly\n the same manner as during the training phase. This differs\n from the usual protocol in that we apply dropout at test time,\n and we apply batch normalization using the statistics of\n the test batch, rather than aggregated statistics of the training batch.*\" (i.e., use model.train() mode), thus may lead to slightly different inference results every time.\n2. This is the training log before refactoring. Updated logs will be released soon.\n\n## Citation\n\n```latex\n@inproceedings{isola2017image,\n title={Image-to-image translation with conditional adversarial networks},\n author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={1125--1134},\n year={2017},\n url={https://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html},\n}\n```\n" }, { "path": "configs/pix2pix/metafile.yml", "content": "Collections:\n- Name: Pix2Pix\n Paper:\n Title: 'Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks'\n URL: https://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html\n README: configs/pix2pix/README.md\n Task:\n - image2image\n Year: 2017\nModels:\n- Config: configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py\n In Collection: Pix2Pix\n Name: pix2pix_vanilla-unet-bn_1xb1-80kiters_facades\n Results:\n - Dataset: facades\n Metrics:\n FID: 124.9773\n IS: 1.62\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\n- Config: configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py\n In Collection: Pix2Pix\n Name: pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps\n Results:\n - Dataset: aerial2maps\n Metrics:\n FID: 122.5856\n IS: 3.137\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth\n- Config: configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py\n In Collection: Pix2Pix\n Name: pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial\n Results:\n - Dataset: maps2aerial\n Metrics:\n FID: 88.4635\n IS: 3.31\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth\n- Config: configs/pix2pix/pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py\n In Collection: Pix2Pix\n Name: pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes\n Results:\n - Dataset: edges2shoes\n Metrics:\n FID: 84.375\n IS: 2.815\n Task: Image2Image\n Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth\n" }, { "path": "configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py", "content": "_base_ = [\n '../_base_/models/base_pix2pix.py',\n '../_base_/datasets/paired_imgs_256x256_crop.py',\n '../_base_/gen_default_runtime.py'\n]\n# deterministic training can improve the performance of Pix2Pix\nrandomness = dict(deterministic=True)\n\nsource_domain = domain_a = 'aerial'\ntarget_domain = domain_b = 'map'\n# model settings\nmodel = dict(\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain],\n data_preprocessor=dict(\n data_keys=[f'img_{source_domain}', f'img_{target_domain}']))\n\ntrain_cfg = dict(max_iters=220000)\n\n# dataset settings\ndataroot = 'data/pix2pix/maps'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot, test_dir='val'))\nval_dataloader = dict(\n dataset=dict(data_root=dataroot, test_dir='val', test_mode=True))\ntest_dataloader = val_dataloader\n\n# optimizer\noptim_wrapper = dict(\n generators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))),\n discriminators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full/fid', rule='less'))\n\nfake_nums = 1098\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=fake_nums,\n fake_key=f'fake_{target_domain}',\n inception_style='PyTorch',\n sample_model='orig'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=fake_nums,\n inception_style='PyTorch',\n real_key=f'img_{target_domain}',\n fake_key=f'fake_{target_domain}',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py", "content": "_base_ = [\n '../_base_/models/base_pix2pix.py',\n '../_base_/datasets/paired_imgs_256x256_crop.py',\n '../_base_/gen_default_runtime.py'\n]\n# deterministic training can improve the performance of Pix2Pix\nrandomness = dict(deterministic=True)\n\nsource_domain = domain_b = 'map'\ntarget_domain = domain_a = 'aerial'\n# model settings\nmodel = dict(\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain],\n data_preprocessor=dict(\n data_keys=[f'img_{source_domain}', f'img_{target_domain}']))\n\ntrain_cfg = dict(max_iters=220000)\n\n# dataset settings\ndataroot = 'data/pix2pix/maps'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot, test_dir='val'))\nval_dataloader = dict(\n dataset=dict(data_root=dataroot, test_dir='val', test_mode=True))\ntest_dataloader = val_dataloader\n\n# optimizer\noptim_wrapper = dict(\n generators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))),\n discriminators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full/fid', rule='less'))\n\nfake_nums = 1098\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=fake_nums,\n fake_key=f'fake_{target_domain}',\n inception_style='PyTorch',\n sample_model='orig'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=fake_nums,\n inception_style='PyTorch',\n real_key=f'img_{target_domain}',\n fake_key=f'fake_{target_domain}',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py", "content": "_base_ = [\n '../_base_/models/base_pix2pix.py',\n '../_base_/datasets/paired_imgs_256x256_crop.py',\n '../_base_/gen_default_runtime.py'\n]\n# deterministic training can improve the performance of Pix2Pix\nrandomness = dict(deterministic=True)\n\nsource_domain = domain_b = 'mask'\ntarget_domain = domain_a = 'photo'\n# model settings\nmodel = dict(\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain],\n data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))\n\ntrain_cfg = dict(max_iters=80000)\n\n# dataset settings\ndataroot = 'data/pix2pix/facades'\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(dataset=dict(data_root=dataroot))\nval_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))\ntest_dataloader = val_dataloader\n\n# optimizer\noptim_wrapper = dict(\n generators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))),\n discriminators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full/fid', rule='less'))\n\nfake_nums = 106\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=fake_nums,\n inception_style='PyTorch',\n fake_key=f'fake_{target_domain}',\n sample_model='orig'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=fake_nums,\n inception_style='PyTorch',\n real_key=f'img_{target_domain}',\n fake_key=f'fake_{target_domain}',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/pix2pix/pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py", "content": "_base_ = [\n '../_base_/models/base_pix2pix.py',\n '../_base_/datasets/paired_imgs_256x256_crop.py',\n '../_base_/gen_default_runtime.py',\n]\n# deterministic training can improve the performance of Pix2Pix\nrandomness = dict(deterministic=True)\n\nsource_domain = domain_a = 'edges'\ntarget_domain = domain_b = 'photo'\n# model settings\nmodel = dict(\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain],\n data_preprocessor=dict(\n data_keys=[f'img_{source_domain}', f'img_{target_domain}']))\n\ntrain_cfg = dict(max_iters=190000)\n\n# dataset settings\ndataroot = './data/pix2pix/edges2shoes'\n# overwrite train pipeline since we do not use flip and crop\n_base_.train_dataloader.dataset.pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='A',\n domain_b='B',\n color_type='color'),\n dict(\n type='Resize',\n keys=['img_A', 'img_B'],\n scale=(256, 256),\n interpolation='bicubic'),\n]\ntrain_pipeline = _base_.train_dataloader.dataset.pipeline\nval_pipeline = _base_.val_dataloader.dataset.pipeline\ntest_pipeline = _base_.test_dataloader.dataset.pipeline\n\nkey_mapping = dict(\n type='KeyMapper',\n mapping={\n f'img_{domain_a}': 'img_A',\n f'img_{domain_b}': 'img_B'\n },\n remapping={\n f'img_{domain_a}': f'img_{domain_a}',\n f'img_{domain_b}': f'img_{domain_b}'\n })\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n\ntrain_pipeline += [key_mapping, pack_input]\nval_pipeline += [key_mapping, pack_input]\ntest_pipeline += [key_mapping, pack_input]\n\ntrain_dataloader = dict(\n batch_size=4, dataset=dict(data_root=dataroot, test_dir='val'))\nval_dataloader = dict(\n dataset=dict(data_root=dataroot, test_dir='val', test_mode=True))\ntest_dataloader = val_dataloader\n\n# optimizer\noptim_wrapper = dict(\n generators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))),\n discriminators=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))))\n\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(type='Translation', name='trans'),\n dict(type='TranslationVal', name='trans_val')\n ])\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full/fid', rule='less'))\n\nfake_nums = 200\nmetrics = [\n dict(\n type='TransIS',\n prefix='IS-Full',\n fake_nums=fake_nums,\n fake_key=f'fake_{target_domain}',\n inception_style='PyTorch',\n sample_model='orig'),\n dict(\n type='TransFID',\n prefix='FID-Full',\n fake_nums=fake_nums,\n inception_style='PyTorch',\n real_key=f'img_{target_domain}',\n fake_key=f'fake_{target_domain}',\n sample_model='orig')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/README.md", "content": "# Positional Encoding in GANs (CVPR'2021)\n\n> [Positional Encoding as Spatial Inductive Bias in GANs](https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nSinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improve SinGAN for more versatile image manipulation.\n\n\n\n
\n\n
\n\n## Results and models for MS-PIE\n\n
\n 896x896 results generated from a 256 generator using MS-PIE\n
\n \n
\n\n| Model | Dataset | Reference in Paper | Scales | FID50k | P&R10k | Download |\n| :--------------------------------------------------------------------: | :-----: | :----------------: | :------------: | :----: | :---------: | :------------------------------------------------------------------------: |\n| [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](./stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | FFHQ | Tab.5 config-a | 256 | 5.56 | 75.92/51.24 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) |\n| [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](./stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | FFHQ | Tab.5 config-b | 512 | 4.91 | 75.65/54.58 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) |\n| [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84/55.77 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) |\n| [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28/56.16 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) |\n| [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13/56.88 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) |\n| [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51/57.32 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) |\n| [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05/56.45 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) |\n| [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81/54.35 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) |\n| [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | 2 Tab.5 config-i | 56, 384, 512 | 3.76 | 73.26/54.71 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) |\n| [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11/54.63 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) |\n| [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05/51.07 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) |\n| [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | FFHQ | higher-resolution | 256, 512, 896 | 4.10 | 72.21/50.29 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) |\n| [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](./mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | FFHQ | higher-resolution | 256, 512, 1024 | 6.24 | 71.79/49.92 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) |\n\n| Model | Dataset | Reference in Paper | Scales | FID50k | Precision10k | Recall10k | Download |\n| :---------------------------------------------------------------: | :-----: | :----------------: | :------------: | :----: | :----------: | :-------: | :-------------------------------------------------------------------: |\n| [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](./stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | FFHQ | Tab.5 config-a | 256 | 5.56 | 75.92 | 51.24 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) |\n| [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](./stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | FFHQ | Tab.5 config-b | 512 | 4.91 | 75.65 | 54.58 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) |\n| [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84 | 55.77 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) |\n| [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28 | 56.16 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) |\n| [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13 | 56.88 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) |\n| [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51 | 57.32 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) |\n| [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05 | 56.45 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) |\n| [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81 | 54.35 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) |\n| [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-i | 256, 384, 512 | 3.76 | 73.26 | 54.71 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) |\n| [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11 | 54.63 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) |\n| [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | FFHQ | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05 | 51.07 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) |\n| [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | FFHQ | higher-resolution | 256, 512, 896 | 4.10 | 72.21 | 50.29 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) |\n| [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](./mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | FFHQ | higher-resolution | 256, 512, 1024 | 6.24 | 71.79 | 49.92 | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) |\n\nNote that we report the FID and P&R metric (FFHQ dataset) in the largest scale.\n\n## Results and Models for SinGAN\n\n
\n Positional Encoding in SinGAN\n
\n \n
\n\n| Model | Dataset | Num Scales | Download |\n| :-----------------------------------------------------------: | :--------------------------------------------------------------: | :--------: | :---------------------------------------------------------------: |\n| [singan_interp-pad_balloons](./singan_interp-pad_balloons.py) | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pkl) |\n| [singan_interp-pad_disc-nobn_balloons](./singan_interp-pad_disc-nobn_balloons.py) | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pkl) |\n| [singan_interp-pad_disc-nobn_fish](./singan_interp-pad_disc-nobn_fish.py) | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pkl) |\n| [singan-csg_fish](./singan-csg_fish.py) | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pkl) |\n| [singan-csg_bohemian](./singan-csg_bohemian.py) | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pkl) |\n| [singan_spe-dim4_fish](./singan_spe-dim4_fish.py) | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pkl) |\n| [singan_spe-dim4_bohemian](./singan_spe-dim4_bohemian.py) | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pkl) |\n| [singan_spe-dim8_bohemian](./singan_spe-dim8_bohemian.py) | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth) \\| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pkl) |\n\n## Citation\n\n```latex\n@article{xu2020positional,\n title={Positional Encoding as Spatial Inductive Bias in GANs},\n author={Xu, Rui and Wang, Xintao and Chen, Kai and Zhou, Bolei and Loy, Chen Change},\n journal={arXiv preprint arXiv:2012.05217},\n year={2020},\n url={https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html},\n}\n```\n" }, { "path": "configs/positional_encoding_in_gans/metafile.yml", "content": "Collections:\n- Name: Positional Encoding in GANs\n Paper:\n Title: Positional Encoding as Spatial Inductive Bias in GANs\n URL: https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html\n README: configs/positional_encoding_in_gans/README.md\n Task:\n - unconditional gans\n Year: 2021\nModels:\n- Config: configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py\n In Collection: Positional Encoding in GANs\n Name: stylegan2_c2_8xb3-1100kiters_ffhq-256x256\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 5.56\n P&R10k:\n PSNR: 75.92\n SSIM: 51.24\n Scales: 256.0\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 5.56\n Precision10k: 75.92\n Recall10k: 51.24\n Scales: 256.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth\n- Config: configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py\n In Collection: Positional Encoding in GANs\n Name: stylegan2_c2_8xb3-1100kiters_ffhq-512x512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.91\n P&R10k:\n PSNR: 75.65\n SSIM: 54.58\n Scales: 512.0\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.91\n Precision10k: 75.65\n Recall10k: 54.58\n Scales: 512.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.35\n P&R10k:\n PSNR: 73.84\n SSIM: 55.77\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.35\n Precision10k: 73.84\n Recall10k: 55.77\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.5\n P&R10k:\n PSNR: 73.28\n SSIM: 56.16\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.5\n Precision10k: 73.28\n Recall10k: 56.16\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.15\n P&R10k:\n PSNR: 74.13\n SSIM: 56.88\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.15\n Precision10k: 74.13\n Recall10k: 56.88\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 2.93\n P&R10k:\n PSNR: 73.51\n SSIM: 57.32\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 2.93\n Precision10k: 73.51\n Recall10k: 57.32\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.4\n P&R10k:\n PSNR: 73.05\n SSIM: 56.45\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.4\n Precision10k: 73.05\n Recall10k: 56.45\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.01\n P&R10k:\n PSNR: 72.81\n SSIM: 54.35\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.01\n Precision10k: 72.81\n Recall10k: 54.35\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.76\n P&R10k:\n PSNR: 73.26\n SSIM: 54.71\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.76\n Precision10k: 73.26\n Recall10k: 54.71\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.23\n P&R10k:\n PSNR: 73.11\n SSIM: 54.63\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.23\n Precision10k: 73.11\n Recall10k: 54.63\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.17\n P&R10k:\n PSNR: 73.05\n SSIM: 51.07\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.17\n Precision10k: 73.05\n Recall10k: 51.07\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.1\n P&R10k:\n PSNR: 72.21\n SSIM: 50.29\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.1\n Precision10k: 72.21\n Recall10k: 50.29\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth\n- Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py\n In Collection: Positional Encoding in GANs\n Name: mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 6.24\n P&R10k:\n PSNR: 71.79\n SSIM: 49.92\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 6.24\n Precision10k: 71.79\n Recall10k: 49.92\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth\n- Config: configs/positional_encoding_in_gans/singan_interp-pad_balloons.py\n In Collection: Positional Encoding in GANs\n Name: singan_interp-pad_balloons\n Results:\n - Dataset: '[balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png)'\n Metrics:\n Num Scales: 8.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth\n- Config: configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_balloons.py\n In Collection: Positional Encoding in GANs\n Name: singan_interp-pad_disc-nobn_balloons\n Results:\n - Dataset: '[balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png)'\n Metrics:\n Num Scales: 8.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth\n- Config: configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_fish.py\n In Collection: Positional Encoding in GANs\n Name: singan_interp-pad_disc-nobn_fish\n Results:\n - Dataset: '[fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth\n- Config: configs/positional_encoding_in_gans/singan-csg_fish.py\n In Collection: Positional Encoding in GANs\n Name: singan-csg_fish\n Results:\n - Dataset: '[fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth\n- Config: configs/positional_encoding_in_gans/singan-csg_bohemian.py\n In Collection: Positional Encoding in GANs\n Name: singan-csg_bohemian\n Results:\n - Dataset: '[bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth\n- Config: configs/positional_encoding_in_gans/singan_spe-dim4_fish.py\n In Collection: Positional Encoding in GANs\n Name: singan_spe-dim4_fish\n Results:\n - Dataset: '[fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth\n- Config: configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py\n In Collection: Positional Encoding in GANs\n Name: singan_spe-dim4_bohemian\n Results:\n - Dataset: '[bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth\n- Config: configs/positional_encoding_in_gans/singan_spe-dim8_bohemian.py\n In Collection: Positional Encoding in GANs\n Name: singan_spe-dim8_bohemian\n Results:\n - Dataset: '[bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png)'\n Metrics:\n Num Scales: 10.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=None,\n deconv2conv=True,\n up_after_conv=True,\n head_pos_size=(4, 4),\n interp_head=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]),\n ema_config=ema_config)\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(type='CSG'),\n deconv2conv=True,\n up_after_conv=True,\n head_pos_size=(4, 4),\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]),\n ema_config=ema_config)\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=True,\n head_pos_size=(4, 4),\n interp_head=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\ntrain_cfg = dict(max_iters=1500002)\n# `batch_size` and `imgs_root` need to be set.\nbatch_size = 2\ndata_root = './data/ffhq/images'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256,\n channel_multiplier=1),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 4, 12],\n multi_scale_probability=[0.5, 0.25, 0.25]),\n ema_config=ema_config)\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 4, 10],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/images'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256,\n channel_multiplier=1),\n discriminator=dict(\n type='MSStyleGAN2Discriminator',\n in_size=256,\n with_adaptive_pool=True,\n channel_multiplier=1),\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]),\n ema_config=ema_config)\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=None,\n deconv2conv=True,\n up_after_conv=False,\n interp_pad=4,\n no_pad=True,\n head_pos_size=(6, 6),\n interp_head=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(type='CSG'),\n deconv2conv=True,\n up_after_conv=False,\n interp_pad=4,\n no_pad=True,\n head_pos_size=(6, 6),\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=False,\n interp_pad=4,\n no_pad=True,\n head_pos_size=(6, 6),\n interp_head=True,\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py", "content": "_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\nema_half_life = 10.\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\nmodel = dict(\n type='MSPIEStyleGAN2',\n generator=dict(\n type='MSStyleGANv2Generator',\n head_pos_encoding=dict(\n type='SPE',\n embedding_dim=256,\n padding_idx=0,\n init_size=256,\n center_shift=100),\n deconv2conv=True,\n up_after_conv=False,\n interp_pad=4,\n no_pad=True,\n head_pos_size=(6, 6),\n up_config=dict(scale_factor=2, mode='bilinear', align_corners=True),\n out_size=256),\n discriminator=dict(\n type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True),\n ema_config=ema_config,\n train_settings=dict(\n num_upblocks=6,\n multi_input_scales=[0, 2, 4],\n multi_scale_probability=[0.5, 0.25, 0.25]))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\ntrain_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root))\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(256, 256)),\n dict(type='PackInputs', keys=['img'])\n]\n\nval_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\ntest_dataloader = dict(\n batch_size=batch_size, # set by user\n dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# define optimizer\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\ndefault_hooks = dict(checkpoint=dict(save_best=['FID-50k/fid'], rule=['less']))\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='pytorch',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/singan-csg_bohemian.py", "content": "_base_ = ['../singan/singan_bohemian.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_csg_bohemian_20210407_195455-5ed56db2.pkl' # noqa\ntest_pkl_data = None\n\nnum_scales = 10 # start from zero\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE',\n num_scales=num_scales,\n padding=1,\n pad_at_head=False,\n first_stage_in_channels=2,\n positional_encoding=dict(type='CSG')),\n discriminator=dict(num_scales=num_scales),\n first_fixed_noises_ch=2,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan-csg_fish.py", "content": "_base_ = ['../singan/singan_fish.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_csg_fis_20210406_175532-f0ec7b61.pkl' # noqa\ntest_pkl_data = None\n\nnum_scales = 10 # start from zero\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE',\n num_scales=num_scales,\n padding=1,\n pad_at_head=False,\n first_stage_in_channels=2,\n positional_encoding=dict(type='CSG')),\n discriminator=dict(num_scales=num_scales),\n first_fixed_noises_ch=2,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan_interp-pad_balloons.py", "content": "_base_ = ['../singan/singan_balloons.py']\n\n# TODO: bugs here\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_interp-pad_balloons_20210406_180014-96f51555.pkl' # noqa\ntest_pkl_data = None\n\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE', interp_pad=True, noise_with_pad=True),\n fixed_noise_with_pad=True,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_balloons.py", "content": "_base_ = ['../singan/singan_balloons.py']\n\n# TODO: have bugs\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pkl' # noqa\ntest_pkl_data = None\n\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE', interp_pad=True, noise_with_pad=True),\n discriminator=dict(norm_cfg=None),\n fixed_noise_with_pad=True)\n" }, { "path": "configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_fish.py", "content": "_base_ = ['../singan/singan_fish.py']\n\n# TODO: have bugs\n# MODEL\n# test_pkl_data = './work_dirs/singan_pkl/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pkl' # noqa\ntest_pkl_data = None\n\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE', interp_pad=True, noise_with_pad=True),\n discriminator=dict(norm_cfg=None),\n fixed_noise_with_pad=True,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py", "content": "_base_ = ['../singan/singan_bohemian.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pkl' # noqa\ntest_pkl_data = None\n\nembedding_dim = 4\nnum_scales = 10 # start from zero\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE',\n num_scales=num_scales,\n padding=1,\n pad_at_head=False,\n first_stage_in_channels=embedding_dim * 2,\n positional_encoding=dict(\n type='SPE',\n embedding_dim=embedding_dim,\n padding_idx=0,\n init_size=512,\n div_half_dim=False,\n center_shift=200)),\n discriminator=dict(num_scales=num_scales),\n first_fixed_noises_ch=embedding_dim * 2,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan_spe-dim4_fish.py", "content": "_base_ = ['../singan/singan_fish.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_spe-dim4_fish_20210406_175933-f483a7e3.pkl' # noqa\ntest_pkl_data = None\n\nembedding_dim = 4\nnum_scales = 10 # start from zero\nmodel = dict(\n type='PESinGAN',\n num_scales=num_scales,\n generator=dict(\n type='SinGANMSGeneratorPE',\n num_scales=num_scales,\n padding=1,\n pad_at_head=False,\n first_stage_in_channels=embedding_dim * 2,\n positional_encoding=dict(\n type='SPE',\n embedding_dim=embedding_dim,\n padding_idx=0,\n init_size=512,\n div_half_dim=False,\n center_shift=200)),\n discriminator=dict(num_scales=num_scales),\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/singan_spe-dim8_bohemian.py", "content": "_base_ = ['../singan/singan_bohemian.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pkl' # noqa\ntest_pkl_data = None\n\nembedding_dim = 8\nnum_scales = 10 # start from zero\nmodel = dict(\n type='PESinGAN',\n generator=dict(\n type='SinGANMSGeneratorPE',\n num_scales=num_scales,\n padding=1,\n pad_at_head=False,\n first_stage_in_channels=embedding_dim * 2,\n positional_encoding=dict(\n type='SPE',\n embedding_dim=embedding_dim,\n padding_idx=0,\n init_size=512,\n div_half_dim=False,\n center_shift=200)),\n discriminator=dict(num_scales=num_scales),\n first_fixed_noises_ch=embedding_dim * 2,\n test_pkl_data=test_pkl_data)\n" }, { "path": "configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))))\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_256'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=512),\n discriminator=dict(in_size=512),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))))\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 3\ndata_root = './data/ffhq/ffhq_imgs/ffhq_512'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=1100002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=10000, prefix='PR-10K')\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/rdn/README.md", "content": "# RDN (CVPR'2018)\n\n> [Residual Dense Network for Image Super-Resolution](https://arxiv.org/abs/1802.08797)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nA very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Extensive experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR and SSIM` .\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :----------------------------------------------------------------: | :-----: | :-----: | :----: | :----------------: | :----------------------------------------------------------------------------------------------: |\n| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | Set5 | 30.4922 | 0.8548 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) |\n| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | Set14 | 26.9570 | 0.7423 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) |\n| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | DIV2K | 29.1925 | 0.8233 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) |\n| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | Set5 | 32.6051 | 0.8943 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) |\n| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | Set14 | 28.6338 | 0.8077 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) |\n| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | DIV2K | 31.2153 | 0.8763 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) |\n| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | Set5 | 35.9883 | 0.9385 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) |\n| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | Set14 | 31.8366 | 0.8920 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) |\n| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | DIV2K | 34.9392 | 0.9380 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth\n\n# single-gpu test\npython tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{zhang2018residual,\n title={Residual dense network for image super-resolution},\n author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2472--2481},\n year={2018}\n}\n```\n" }, { "path": "configs/rdn/README_zh-CN.md", "content": "# RDN (CVPR'2018)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nRDN (CVPR'2018)\n\n```bibtex\n@inproceedings{zhang2018residual,\n title={Residual dense network for image super-resolution},\n author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2472--2481},\n year={2018}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |\n| :----------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :----------: | :------------------------------------------------------------------------: |\n| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) |\n| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) |\n| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth\n\n# 单个GPU上测试\npython tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/rdn/metafile.yml", "content": "Collections:\n- Name: RDN\n Paper:\n Title: Residual Dense Network for Image Super-Resolution\n URL: https://arxiv.org/abs/1802.08797\n README: configs/rdn/README.md\n Task:\n - image super-resolution\n Year: 2018\nModels:\n- Config: configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py\n In Collection: RDN\n Name: rdn_x4c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 30.4922\n SSIM: 0.8548\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 26.957\n SSIM: 0.7423\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 29.1925\n SSIM: 0.8233\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth\n- Config: configs/rdn/rdn_x3c64b16_1xb16-1000k_div2k.py\n In Collection: RDN\n Name: rdn_x3c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 32.6051\n SSIM: 0.8943\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 28.6338\n SSIM: 0.8077\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 31.2153\n SSIM: 0.8763\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth\n- Config: configs/rdn/rdn_x2c64b16_1xb16-1000k_div2k.py\n In Collection: RDN\n Name: rdn_x2c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 35.9883\n SSIM: 0.9385\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 31.8366\n SSIM: 0.892\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.9392\n SSIM: 0.938\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth\n" }, { "path": "configs/rdn/rdn_x2c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x2_test_config.py'\n]\n\nexperiment_name = 'rdn_x2c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='RDNNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=64),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[200000, 400000, 600000, 800000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/rdn/rdn_x3c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x3_test_config.py'\n]\n\nexperiment_name = 'rdn_x3c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 3\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='RDNNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=96),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X3_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[200000, 400000, 600000, 800000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'rdn_x4c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='RDNNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n # filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[200000, 400000, 600000, 800000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/real_basicvsr/README.md", "content": "# RealBasicVSR (CVPR'2022)\n\n> [RealBasicVSR: Investigating Tradeoffs in Real-World Video Super-Resolution](https://arxiv.org/abs/2111.12704)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nThe diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage indispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency. Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length tradeoff. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset will be made publicly available.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on Y channel. The code for computing NRQM, NIQE, and PI can be found [here](https://github.com/roimehrez/PIRM2018). MATLAB official code is used to compute BRISQUE.\n\n| Model | Dataset | NRQM (Y) | NIQE (Y) | PI (Y) | BRISQUE (Y) | Training Resources | Download |\n| :----------------------------------------------------------------: | :-----: | :------: | :------: | :----: | :---------: | :----------------------: | :--------------------------------------------------------------------: |\n| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](./realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | REDS | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) |\n| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](./realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | REDS | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n\n# single-gpu train\npython tools/train.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n\n# single-gpu test\npython tools/test.py python tools/test.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{chan2022investigating,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {RealBasicVSR: Investigating Tradeoffs in Real-World Video Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2022}\n}\n```\n" }, { "path": "configs/real_basicvsr/README_zh-CN.md", "content": "# RealBasicVSR (CVPR'2022)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nRealBasicVSR (CVPR'2022)\n\n```bibtex\n@InProceedings{chan2022investigating,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {RealBasicVSR: Investigating Tradeoffs in Real-World Video Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2022}\n}\n```\n\n
\n\n
\n\n在 Y 通道上评估。 计算 NRQM、NIQE 和 PI 的代码可以在[这里](https://github.com/roimehrez/PIRM2018)找到。我们使用 MATLAB 官方代码计算 BRISQUE。\n\n| 算法 | NRQM (Y) | NIQE (Y) | PI (Y) | BRISQUE (Y) | GPU 信息 | Download |\n| :------------------------------------------------------------------: | :------: | :------: | :----: | :---------: | :----------------------: | :-------------------------------------------------------------------------: |\n| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](./realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) |\n| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](./realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) |\n\n## 训练\n\n训练分为两个阶段：\n\n1. 使用 [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py](realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) 训练一个没有感知损失和对抗性损失的模型。\n2. 使用感知损失和对抗性损失 [realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py ](realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) 微调模型。\n\n**注:**\n\n1. 您可能希望将图像裁剪为子图像以加快 IO。请参阅[此处](../../tools/dataset_converters/reds/preprocess_reds_dataset.py)了解更多详情。\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n\n# 单个GPU上训练\npython tools/train.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n\n# 单个GPU上测试\npython tools/test.py configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/real_basicvsr/metafile.yml", "content": "Collections:\n- Name: RealBasicVSR\n Paper:\n Title: 'RealBasicVSR: Investigating Tradeoffs in Real-World Video Super-Resolution'\n URL: https://arxiv.org/abs/2111.12704\n README: configs/real_basicvsr/README.md\n Task:\n - video super-resolution\n Year: 2022\nModels:\n- Config: configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py\n In Collection: RealBasicVSR\n Name: realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds\n Results:\n - Dataset: REDS\n Metrics:\n BRISQUE (Y): 29.03\n NIQE (Y): 3.7662\n NRQM (Y): 6.0477\n PI (Y): 3.8593\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth\n- Config: configs/real_basicvsr/realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py\n In Collection: RealBasicVSR\n Name: realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds\n Results:\n - Dataset: REDS\n Metrics: {}\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth\n" }, { "path": "configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py", "content": "_base_ = './realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py'\n\nexperiment_name = 'realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth' # noqa\n\nscale = 4\n\n# model settings\nmodel = dict(\n type='RealBasicVSR',\n generator=dict(\n type='RealBasicVSRNet',\n mid_channels=64,\n num_propagation_blocks=20,\n num_cleaning_blocks=20,\n dynamic_refine_thres=255, # change to 5 for test\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n is_fix_cleaning=False,\n is_sequential_cleaning=False),\n discriminator=dict(\n type='UNetDiscriminatorWithSpectralNorm',\n in_channels=3,\n mid_channels=64,\n skip_connection=True),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n cleaning_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-2,\n real_label_val=1.0,\n fake_label_val=0),\n is_use_sharpened_gt_in_pixel=True,\n is_use_sharpened_gt_in_percep=True,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=True,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\n# optimizer\noptim_wrapper = dict(\n _delete_=True,\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=5e-5, betas=(0.9, 0.99))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n)\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=150_000, val_interval=5000)\n" }, { "path": "configs/real_basicvsr/realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n\n# model settings\nmodel = dict(\n type='RealBasicVSR',\n generator=dict(\n type='RealBasicVSRNet',\n mid_channels=64,\n num_propagation_blocks=20,\n num_cleaning_blocks=20,\n dynamic_refine_thres=255, # change to 1.5 for test\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n is_fix_cleaning=False,\n is_sequential_cleaning=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n cleaning_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n is_use_sharpened_gt_in_pixel=True,\n is_use_ema=True,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='FixedCrop', keys=['gt'], crop_size=(256, 256)),\n dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['gt'], transpose_ratio=0.5),\n dict(type='MirrorSequence', keys=['gt']),\n dict(\n type='UnsharpMasking',\n keys=['gt'],\n kernel_size=51,\n sigma=0,\n weight=0.5,\n threshold=10),\n dict(type='CopyValues', src_keys=['gt_unsharp'], dst_keys=['img']),\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 3],\n sigma_y=[0.2, 3],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2],\n sigma_x_step=0.02,\n sigma_y_step=0.02,\n rotate_angle_step=0.31416,\n beta_gaussian_step=0.05,\n beta_plateau_step=0.1,\n omega_step=0.0628),\n keys=['img'],\n ),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0.2, 0.7, 0.1], # up, down, keep\n resize_scale=[0.15, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3.0, 1 / 3.0, 1 / 3.0],\n resize_step=0.015,\n is_size_even=True),\n keys=['img'],\n ),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 30],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 3],\n poisson_gray_noise_prob=0.4,\n gaussian_sigma_step=0.1,\n poisson_scale_step=0.005),\n keys=['img'],\n ),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[30, 95], quality_step=3),\n keys=['img'],\n ),\n dict(\n type='RandomVideoCompression',\n params=dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5]),\n keys=['img'],\n ),\n dict(\n type='RandomBlur',\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 1.5],\n sigma_y=[0.2, 1.5],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2],\n sigma_x_step=0.02,\n sigma_y_step=0.02,\n rotate_angle_step=0.31416,\n beta_gaussian_step=0.05,\n beta_plateau_step=0.1,\n omega_step=0.0628),\n keys=['img'],\n ),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0.3, 0.4, 0.3], # up, down, keep\n resize_scale=[0.3, 1.2],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n resize_step=0.03,\n is_size_even=True),\n keys=['img'],\n ),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 25],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 2.5],\n poisson_gray_noise_prob=0.4,\n gaussian_sigma_step=0.1,\n poisson_scale_step=0.005),\n keys=['img'],\n ),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[30, 95], quality_step=3),\n keys=['img'],\n ),\n dict(\n type='DegradationsWithShuffle',\n degradations=[\n dict(\n type='RandomVideoCompression',\n params=dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5]),\n keys=['img'],\n ),\n [\n dict(\n type='RandomResize',\n params=dict(\n target_size=(64, 64),\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n ),\n dict(\n type='RandomBlur',\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=['sinc'],\n kernel_prob=[1],\n omega=[3.1416 / 3, 3.1416],\n omega_step=0.0628),\n ),\n ]\n ],\n keys=['img'],\n ),\n dict(type='Clip', keys=['img']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='GenerateSegmentIndices',\n interval_list=[1],\n filename_tmpl='{:04d}.png'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(\n type='GenerateSegmentIndices',\n interval_list=[1],\n filename_tmpl='{:08d}.png'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=10,\n batch_size=2,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='reds', task_name='vsr'),\n data_root=f'{data_root}/REDS',\n data_prefix=dict(img='train_sharp_sub', gt='train_sharp_sub'),\n depth=1,\n num_input_frames=15,\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='udm10', task_name='vsr'),\n data_root=f'{data_root}/UDM10',\n data_prefix=dict(img='BIx4', gt='GT'),\n pipeline=val_pipeline))\n\ntest_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='video_lq', task_name='vsr'),\n data_root=f'{data_root}/VideoLQ',\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\n\nval_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntest_evaluator = dict(\n type='Evaluator',\n metrics=[dict(type='NIQE', input_order='CHW', convert_to='Y')])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))))\n\n# NO learning policy\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n out_dir=save_dir,\n max_keep_ckpts=10,\n save_best='PSNR',\n rule='greater',\n by_epoch=False),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\ncustom_hooks = [\n dict(type='BasicVisualizationHook', interval=5),\n dict(\n type='ExponentialMovingAverageHook',\n module_keys=('generator_ema'),\n interval=1,\n interp_cfg=dict(momentum=0.001),\n )\n]\n\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=False)\n" }, { "path": "configs/real_esrgan/README.md", "content": "# Real-ESRGAN (ICCVW'2021)\n\n> [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://arxiv.org/abs/2107.10833)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nThough many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations. We also consider the common ringing and overshoot artifacts in the synthesis process. In addition, we employ a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics. Extensive comparisons have shown its superior visual performance than prior works on various real datasets. We also provide efficient implementations to synthesize training pairs on the fly.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on Set5 dataset with RGB channels. The metrics are `PSNR` and `SSIM`.\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :--------------------------------------------------------------------------: | :------: | :-----: | :----: | :----------------------: | :-----------------------------------------------------------------------------: |\n| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](./realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | df2k_ost | 28.0297 | 0.8236 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/log |\n| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](./realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | df2k_ost | 26.2204 | 0.7655 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[log](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py\n\n# single-gpu train\npython tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth\n\n# single-gpu test\npython tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{wang2021real,\n title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic data},\n author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},\n pages={1905--1914},\n year={2021}\n}\n```\n" }, { "path": "configs/real_esrgan/README_zh-CN.md", "content": "# Real-ESRGAN (ICCVW'2021)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nReal-ESRGAN (ICCVW'2021)\n\n```bibtex\n@inproceedings{wang2021real,\n title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic data},\n author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},\n pages={1905--1914},\n year={2021}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，指标为 `PSNR/SSIM`。\n\n| 算法 | Set5 | GPU 信息 | 下载 |\n| :-------------------------------------------------------------------------------: | :------------: | :----------------------: | :-------------------------------------------------------------------------------: |\n| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](./realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | 28.0297/0.8236 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/日志 |\n| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](./realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | 26.2204/0.7655 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[日志](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py\n\n# 单个GPU上训练\npython tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth\n\n# 单个GPU上测试\npython tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/real_esrgan/metafile.yml", "content": "Collections:\n- Name: Real-ESRGAN\n Paper:\n Title: 'Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic\n Data'\n URL: https://arxiv.org/abs/2107.10833\n README: configs/real_esrgan/README.md\n Task:\n - image super-resolution\n Year: 2021\nModels:\n- Config: configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py\n In Collection: Real-ESRGAN\n Name: realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost\n Results:\n - Dataset: df2k_ost\n Metrics:\n PSNR: 28.0297\n SSIM: 0.8236\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth\n- Config: configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py\n In Collection: Real-ESRGAN\n Name: realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost\n Results:\n - Dataset: df2k_ost\n Metrics:\n PSNR: 26.2204\n SSIM: 0.7655\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth\n" }, { "path": "configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py", "content": "_base_ = './realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py'\n\nexperiment_name = 'realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# load_from = # path of pre-trained real-esrnet\n\nscale = 4\n\n# model settings\nmodel = dict(\n type='RealESRGAN',\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32,\n upscale_factor=scale),\n discriminator=dict(\n type='UNetDiscriminatorWithSpectralNorm',\n in_channels=3,\n mid_channels=64,\n skip_connection=True),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-1,\n real_label_val=1.0,\n fake_label_val=0),\n is_use_sharpened_gt_in_pixel=True,\n is_use_sharpened_gt_in_percep=True,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=True,\n train_cfg=dict(start_iter=1000000),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_cfg = dict(\n _delete_=True,\n type='IterBasedTrainLoop',\n max_iters=400_000,\n val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n _delete_=True,\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n)\n\n# learning policy\nparam_scheduler = None\n" }, { "path": "configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\ngt_crop_size = 400\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\nmodel = dict(\n type='RealESRGAN',\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n is_use_sharpened_gt_in_pixel=True,\n is_use_ema=True,\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(gt_crop_size, gt_crop_size),\n random_crop=True),\n dict(\n type='UnsharpMasking',\n keys=['gt'],\n kernel_size=51,\n sigma=0,\n weight=0.5,\n threshold=10),\n dict(type='CopyValues', src_keys=['gt_unsharp'], dst_keys=['img']),\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 3],\n sigma_y=[0.2, 3],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2]),\n keys=['img'],\n ),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0.2, 0.7, 0.1], # up, down, keep\n resize_scale=[0.15, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3.0, 1 / 3.0, 1 / 3.0]),\n keys=['img'],\n ),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 30],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 3],\n poisson_gray_noise_prob=0.4),\n keys=['img'],\n ),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[30, 95]),\n keys=['img']),\n dict(\n type='RandomBlur',\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 1.5],\n sigma_y=[0.2, 1.5],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2]),\n keys=['img'],\n ),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0.3, 0.4, 0.3], # up, down, keep\n resize_scale=[0.3, 1.2],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3.0, 1 / 3.0, 1 / 3.0]),\n keys=['img'],\n ),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 25],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 2.5],\n poisson_gray_noise_prob=0.4),\n keys=['img'],\n ),\n dict(\n type='DegradationsWithShuffle',\n degradations=[\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[5, 50]),\n ),\n [\n dict(\n type='RandomResize',\n params=dict(\n target_size=(gt_crop_size // scale,\n gt_crop_size // scale),\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n ),\n dict(\n type='RandomBlur',\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=['sinc'],\n kernel_prob=[1],\n omega=[3.1416 / 3, 3.1416]),\n ),\n ]\n ],\n keys=['img'],\n ),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(type='Clip', keys=['img']),\n dict(\n type='UnsharpMasking',\n keys=['gt'],\n kernel_size=51,\n sigma=0,\n weight=0.5,\n threshold=10),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\n\ntrain_dataloader = dict(\n num_workers=12,\n batch_size=12, # gpus 4\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='df2k_ost', task_name='real_sr'),\n data_root='data/df2k_ost',\n data_prefix=dict(gt='GT_sub', img='GT_sub'),\n ann_file='meta_info_df2k_ost.txt',\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='real_sr'),\n data_root='data/Set5',\n data_prefix=dict(gt='GTmod12', img='LRbicx4'),\n pipeline=val_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = dict(\n type='Evaluator', metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=2000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99))))\n\n# NO learning policy\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\n# custom hook\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=False)\ncustom_hooks = [\n dict(type='BasicVisualizationHook', interval=1),\n dict(\n type='ExponentialMovingAverageHook',\n module_keys=('generator_ema'),\n interval=1,\n interp_cfg=dict(momentum=0.001),\n )\n]\n" }, { "path": "configs/restormer/README.md", "content": "# Restormer (CVPR'2022)\n\n> [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://arxiv.org/abs/2111.09881)\n\n> **Task**: Denoising, Deblurring, Deraining\n\n\n\n## Abstract\n\n\n\nSince convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and inadaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images. In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks, including image deraining, single-image motion deblurring, defocus deblurring (single-image and dual-pixel data), and image denoising (Gaussian grayscale/color denoising, and real image denoising).\n\n\n\n
\n \n
\n\n## Results and models\n\n### **Deraining**\n\nEvaluated on Y channels. The metrics are `PSNR` / `SSIM` .\n\n| Model | Dataset | Task | PSNR (Y) | SSIM (Y) | Training Resources | Download |\n| :-----------------------------------------------------------: | :------: | :-------: | :------: | :------: | :----------------: | :--------------------------------------------------------------------------------------: |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | Rain100H | Deraining | 31.4804 | 0.9056 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | Rain100L | Deraining | 39.1023 | 0.9787 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | Test100 | Deraining | 32.0287 | 0.9239 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | Test1200 | Deraining | 33.2251 | /0.9272 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | Test2800 | Deraining | 34.2170 | 0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n\n### **Motion Deblurring**\n\nEvaluated on RGB channels for GoPro and HIDE, and Y channel for ReakBlur-J and ReakBlur-R. The metrics are `PSNR` / `SSIM` .\n\n| Model | Dataset | Task | PSNR/SSIM (RGB) |
PSNR/SSIM (Y) | Training Resources | Download |\n| :-------------------------------------------------------: | :--------: | :--------: | :-------------: | :---------------: | :----------------: | :-----------------------------------------------------------------------: |\n| [restormer_official_gopro](./restormer_official_gopro.py) | GoPro | Deblurring | 32.9295/0.9496 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \\| log |\n| [restormer_official_gopro](./restormer_official_gopro.py) | HIDE | Deblurring | 31.2289/0.9345 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \\| log |\n| [restormer_official_gopro](./restormer_official_gopro.py) | RealBlur-J | Deblurring | - | 28.4356/0.8681 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \\| log |\n| [restormer_official_gopro](./restormer_official_gopro.py) | RealBlur-R | Deblurring | - | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \\| log |\n\n### **Defocus Deblurring**\n\nEvaluated on RGB channels. The metrics are `PSNR` / `SSIM` / `MAE` / `LPIPS`.\n\n| Model | Dataset | Task | PSNR | SSIM | MAE | Training Resources | Download |\n| :------------------------------------------------------------------: | :------------: | :--------: | :-----: | :----: | :----: | :----------------: | :---------------------------------------------------------------------: |\n| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | Indoor Scenes | Deblurring | 28.8681 | 0.8859 | 0.0251 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \\| log |\n| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | Outdoor Scenes | Deblurring | 23.2410 | 0.7509 | 0.0499 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \\| log |\n| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | Combined | Deblurring | 25.9805 | 0.8166 | 0.0378 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \\| log |\n| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | Indoor Scenes | Deblurring | 26.6160 | 0.8346 | 0.0354 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \\| log |\n| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | Outdoor Scenes | Deblurring | 26.6160 | 0.8346 | 0.0354 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \\| log |\n| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | Combined | Deblurring | 26.6160 | 0.8346 | 0.0354 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \\| log |\n\n### **Gaussian Denoising**\n\n**Test Grayscale Gaussian Noise**\n\nEvaluated on grayscale images. The metrics are `PSNR` / `SSIM` .\n\n**training a separate model for each noise level**\n\n| Model | Dataset | Task | $\\\\sigma$ | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------------------------------------: | :------:|:-:| | :-------: | :-----: | :----: | :----------------: | :----------------------------------------------------------------------------: |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | Set12 |Denoising| 15 | 34.0182 | 0.9160 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) | log |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | BSD68 |Denoising| 15 | 32.4987 | 0.8940 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) | log |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | Urban100 |Denoising| 15 | 34.4336 | 0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) | log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | Set12 |Denoising| 25 | 31.7289 | 0.8811 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) | log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | BSD68 |Denoising| 25 | 30.1613 | 0.8370 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) | log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | Urban100 |Denoising| 25 | 32.1162 | 0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) | log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | Set12 |Denoising| 50 | 28.6269 | 0.8188 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) | log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | BSD68 |Denoising| 50 | 27.3266 | 0.7434 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) | log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | Urban100 |Denoising| 50 | 28.9636 | 0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) | log |\n\n**learning a single model to handle various noise levels**\n\n| Model | Dataset | Task | $\\\\sigma$ | PSNR | SSIM | Training Resources | Download |\n| :--------------------------------------------------------------------: | :------: | :-------: | :-------: | :-----: | :----: | :----------------: | :-----------------------------------------------------------------------: |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | Set12 | Denoising | 15 | 33.9642 | 0.9153 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | BSD68 | Denoising | 15 | 32.4994 | 0.8928 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | Urban100 | Denoising | 15 | 34.3152 | 0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | Set12 | Denoising | 25 | 31.7106 | 0.8810 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | BSD68 | Denoising | 25 | 30.1486 | 0.8360 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | Urban100 | Denoising | 25 | 32.0457 | 0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | Set12 | Denoising | 50 | 28.6614 | 0.8197 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | BSD68 | Denoising | 50 | 27.3537 | 0.7422 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | Urban100 | Denoising | 50 | 28.9848 | 0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n\n**Test Color Gaussian Noise**\n\nEvaluated on RGB channels. The metrics are `PSNR` / `SSIM` .\n**training a separate model for each noise level**\n\n| Model | Dataset | Task | $\\\\sigma$ | PSNR (RGB) | SSIM (RGB) | Training Resources | Download |\n| :----------------------------------------------------------------: | :------: | :-------: | :-------: | :--------: | :--------: | :----------------: | :--------------------------------------------------------------------: |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | CBSD68 | Denoising | 15 | 34.3506 | 0.9352 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \\| log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | Kodak24 | Denoising | 15 | 35.4900 | 0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \\| log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | McMaster | Denoising | 15 | 35.6072 | 0.9352 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \\| log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | Urban100 | Denoising | 15 | 35.1522 | 0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | CBSD68 | Denoising | 25 | 31.7457 | 0.8942 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | Kodak24 | Denoising | 25 | 33.0489 | 0.8943 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | McMaster | Denoising | 25 | 33.3260 | 0.9066 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | Urban100 | Denoising | 25 | 32.9670 | 0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | CBSD68 | Denoising | 50 | 28.5569 | 0.8127 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | Kodak24 | Denoising | 50 | 30.0122 | 0.8238 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | McMaster | Denoising | 50 | 30.2608 | 0.8515 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | Urban100 | Denoising | 50 | 30.0230 | 0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \\| log |\n\n**learning a single model to handle various noise levels**\n\n| Model | Dataset |Task| $\\\\sigma$ | PSNR (RGB) | SSIM (RGB) | Training Resources | Download |\n| :---------------------------------------------------------------------: | :-----: | :-------: | :--------: | :--------: | :-----------------------------------------------------------------------------------: | :------: |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py)| CBSD68 |Denoising| 15 | 34.3422 | 0.9356 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py)| Kodak24 |Denoising| 15 | 35.4544 | 0.9308 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py)| McMaster |Denoising| 15 | 35.5473 | 0.9344 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py)| Urban100 |Denoising| 15 | 35.0754 | 0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py)| CBSD68 |Denoising| 25 | 31.7391 | 0.8945 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py)| Kodak24 |Denoising| 25 | 33.0380 | 0.8941 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py)| McMaster |Denoising| 25 | 33.3040 | 0.9063 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py)| Urban100 |Denoising| 25 | 32.9165 | 0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py)| CBSD68 |Denoising| 50 | 28.5582 | 0.8126 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py)| Kodak24 |Denoising| 50 | 30.0074 | 0.8233 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py)| McMaster |Denoising| 50 | 30.2671 | 0.8520 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py)| Urban100 |Denoising| 50 | 30.0172 | 0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) | log |\n\n### **Real Image Denoising**\n\nEvaluated on RGB channels. The metrics are `PSNR` / `SSIM` .\n\n| Model | Dataset | Task | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------------: | :-----: | :-------: | :-----: | :----: | :----------------: | :------------------------------------------------------------------------------------------------: |\n| [restormer_official_sidd](./restormer_official_sidd.py) | SIDD | Denoising | 40.0156 | 0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \\| log |\n\n## Quick Start\n\n**Train**\n\nYou can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\n# Deraining\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# single-gpu test\n# Deraining\npython tools/test.py configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\npython tools/test.py configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\npython tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\npython tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n\n# multi-gpu test\n# Deraining\n./tools/dist_test.sh configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\n./tools/dist_test.sh configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\n./tools/dist_test.sh configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\n./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat\n and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n" }, { "path": "configs/restormer/README_zh-CN.md", "content": "# Restormer (CVPR'2022)\n\n> [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://arxiv.org/abs/2111.09881)\n\n> **任务**: 图像去噪，图像去模糊，图像去雨\n\n\n\n
\n \n
\n\n## **各个任务下模型的测试结果**\n\n### **图像去雨**\n\n所有数据集均在Y通道上进行测试，测试指标为PSNR和SSIM。\n\n| 方法 | Rain100H
PSNR/SSIM (Y) | Rain100L
PSNR/SSIM (Y) | Test100
PSNR/SSIM (Y) | Test1200
PSNR/SSIM (Y) | Test2800
PSNR/SSIM (Y) | GPU信息 | 下载 |\n| :--------------------------------: | :-----------------------: | :-----------------------: | :----------------------: | :-----------------------: | :-----------------------: | :-----: | :---------------------------------: |\n| [restormer_official_rain13k](./restormer_official_rain13k.py) | 31.4804/0.9056 | 39.1023/0.9787 | 32.0287/0.9239 | 33.2251/0.9272 | 34.2170/0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \\| log |\n\n### **图像去模糊**\n\nGopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据集使用Y通道测试。测试指标为PSNR和SSIM。\n\n| 方法 | GoPro
PSNR/SSIM (RGB) | HIDE
PSNR/SSIM (RGB) | RealBlur-J
PSNR/SSIM (Y) | RealBlur-R
PSNR/SSIM (Y) | GPU信息 | 下载 |\n| :--------------------------------------------: | :----------------------: | :---------------------: | :-------------------------: | :-------------------------: | :-----: | :--------------------------------------------: |\n| [restormer_official_gopro](./restormer_official_gopro.py) | 32.9295/0.9496 | 31.2289/0.9345 | 28.4356/0.8681 | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \\| log |\n\n### **图像去失焦模糊**\n\n所有指标均在RGB通道上进行测试。测试指标为PSNR、SSIM、MAE和LPIPS.\n\n| 方法 | 室内场景图像的PSNR | 室内场景图像的SSIM | 室内场景图像的MAE | 室内场景图像的LPIPS | 室外场景图像的PSNR | 室外场景图像的SSIM | 室外场景图像的MAE | 室外场景图像的LPIPS | 所有图像平均PSNR | 所有图像平均SSIM | 所有图像平均MAE | 所有图像平均LPIPS | GPU 信息 | 下载 |\n| :----: | :-------------: | :-------------: | :------------: | :--------------: | :-------------: | :-------------: | :------------: | :--------------: | :------------: | :-------------: | :------------: | :--------------: | :------: | :-----: |\n| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | 28.8681 | 0.8859 | 0.0251 | - | 23.2410 | 0.7509 | 0.0499 | - | 25.9805 | 0.8166 | 0.0378 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \\| log |\n| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \\| log |\n\n### **图像高斯噪声去除**\n\n**灰度图的高斯噪声**\n\n使用PSNR和SSIM指标对数据集上的灰度图进行测试。\n\n| 方法 | $\\\\sigma$ | Set12
PSNR/SSIM | BSD68
PSNR/SSIM | Urban100
PSNR/SSIM | GPU信息 | 下载 |\n| :-------------------------------------------------------------: | :-------: | :----------------: | :----------------: | :-------------------: | :-----: | :--------------------------------------------------------------: |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 34.0182/0.9160 | 32.4987/0.8940 | 34.4336/0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7289/0.8811 | 30.1613/0.8370 | 32.1162/0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6269/0.8188 | 27.3266/0.7434 | 28.9636/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) \\| log |\n| | | | | | | |\n| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 33.9642/0.9153 | 32.4994/0.8928 | 34.3152/0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7106/0.8810 | 30.1486/0.8360 | 32.0457/0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6614/0.8197 | 27.3537/0.7422 | 28.9848/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \\| log |\n\n> 上面三行代表每个噪声等级训练一个单独的模型，下面三行代表学习一个单一的模型来处理各种噪音水平。\n\n**彩色图像的高斯噪声**\n\n所有指标均在RGB通道上进行测试，测试指标为PSNR和SSIM。\n\n| 方法 | $\\\\sigma$ | CBSD68
PSNR/SSIM (RGB) | Kodak24
PSNR/SSIM (RGB) | McMaster
PSNR/SSIM (RGB) | Urban100
PSNR/SSIM (RGB) | GPU信息 | 下载 |\n| :-------------------------------------: | :-------: | :-----------------------: | :------------------------: | :-------------------------: | :-------------------------: | :-----: | :--------------------------------------: |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3506/0.9352 | 35.4900/0.9312 | 35.6072/0.9352 | 35.1522/0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7457/0.8942 | 33.0489/0.8943 | 33.3260/0.9066 | 32.9670/0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5569/0.8127 | 30.0122/0.8238 | 30.2608/0.8515 | 30.0230/0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \\| log |\n| | | | | | | | |\n| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3422/0.9356 | 35.4544/0.9308 | 35.5473/0.9344 | 35.0754/0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \\| log |\n| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7391/0.8945 | 33.0380/0.8941 | 33.3040/0.9063 | 32.9165/0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \\| log |\n| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5582/0.8126 | 30.0074/0.8233 | 30.2671/0.8520 | 30.0172/0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \\| log |\n\n> 上面三行代表每个噪声等级训练一个单独的模型，下面三行代表学习一个单一的模型来处理各种噪音水平。\n\n### **真实场景图像去噪**\n\n所有指标均在RGB通道上进行测试，测试指标为PSNR和SSIM。\n\n| 方法 | SIDD
PSNR/SSIM | GPU信息 | 下载 |\n| :-----------------------------------------------------: | :---------------: | :-----: | :-----------------------------------------------------------------------------------------------------: |\n| [restormer_official_sidd](./restormer_official_sidd.py) | 40.0156/0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \\| log |\n\n## 使用方法\n\n**训练**\n\n可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# cpu test\n# Deraining\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# single-gpu test\n# Deraining\npython tools/test.py configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\npython tools/test.py configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\npython tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\npython tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\npython tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n\n# multi-gpu test\n# Deraining\n./tools/dist_test.sh configs/restormer/restormer_official_rain13k.py https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n\n# Motion Deblurring\n./tools/dist_test.sh configs/restormer/restormer_official_gopro.py https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n\n# Defocus Deblurring\n# Single\n./tools/dist_test.sh configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n# Dual\n./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n\n# Gaussian Denoising\n# Test Grayscale Gaussian Noise\n# sigma15\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma25\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# sigma50\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-gray-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n\n# Test Color Gaussian Noise\n# sigma15\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma15.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma25\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma25.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n# sigma50\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth\n\n./tools/dist_test.sh configs/restormer/restormer_official_dfwb-color-sigma50.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/restormer/metafile.yml", "content": "Collections:\n- Name: Restormer\n Paper:\n Title: 'Restormer: Efficient Transformer for High-Resolution Image Restoration'\n URL: https://arxiv.org/abs/2111.09881\n README: configs/restormer/README.md\n Task:\n - denoising\n - deblurring\n - deraining\n Year: 2022\nModels:\n- Config: configs/restormer/restormer_official_rain13k.py\n In Collection: Restormer\n Name: restormer_official_rain13k\n Results:\n - Dataset: Rain100H\n Metrics:\n PSNR (Y): 31.4804\n SSIM (Y): 0.9056\n Task: Deraining\n - Dataset: Rain100L\n Metrics:\n PSNR (Y): 39.1023\n SSIM (Y): 0.9787\n Task: Deraining\n - Dataset: Test100\n Metrics:\n PSNR (Y): 32.0287\n SSIM (Y): 0.9239\n Task: Deraining\n - Dataset: Test1200\n Metrics:\n PSNR (Y): 33.2251\n Task: Deraining\n - Dataset: Test2800\n Metrics:\n PSNR (Y): 34.217\n SSIM (Y): 0.9451\n Task: Deraining\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth\n- Config: configs/restormer/restormer_official_gopro.py\n In Collection: Restormer\n Name: restormer_official_gopro\n Results:\n - Dataset: GoPro\n Metrics:\n PSNR/SSIM (RGB):\n PSNR: 32.9295\n SSIM: 0.9496\n Task: Deblurring\n - Dataset: HIDE\n Metrics:\n PSNR/SSIM (RGB):\n PSNR: 31.2289\n SSIM: 0.9345\n Task: Deblurring\n - Dataset: RealBlur-J\n Metrics:\n
PSNR/SSIM (Y):\n PSNR: 28.4356\n SSIM: 0.8681\n Task: Deblurring\n - Dataset: RealBlur-R\n Metrics:\n
PSNR/SSIM (Y):\n PSNR: 35.9141\n SSIM: 0.9707\n Task: Deblurring\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth\n- Config: configs/restormer/restormer_official_dpdd-single.py\n In Collection: Restormer\n Name: restormer_official_dpdd-single\n Results:\n - Dataset: IndoorScenes\n Metrics:\n MAE: 0.0251\n PSNR: 28.8681\n SSIM: 0.8859\n Task: Deblurring\n - Dataset: OutdoorScenes\n Metrics:\n MAE: 0.0499\n PSNR: 23.241\n SSIM: 0.7509\n Task: Deblurring\n - Dataset: Combined\n Metrics:\n MAE: 0.0378\n PSNR: 25.9805\n SSIM: 0.8166\n Task: Deblurring\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth\n- Config: configs/restormer/restormer_official_dpdd-dual.py\n In Collection: Restormer\n Name: restormer_official_dpdd-dual\n Results:\n - Dataset: IndoorScenes\n Metrics:\n MAE: 0.0354\n PSNR: 26.616\n SSIM: 0.8346\n Task: Deblurring\n - Dataset: OutdoorScenes\n Metrics:\n MAE: 0.0354\n PSNR: 26.616\n SSIM: 0.8346\n Task: Deblurring\n - Dataset: Combined\n Metrics:\n MAE: 0.0354\n PSNR: 26.616\n SSIM: 0.8346\n Task: Deblurring\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth\n- Config: configs/restormer/restormer_official_dfwb-gray-sigma15.py\n In Collection: Restormer\n Name: restormer_official_dfwb-gray-sigma15\n Results:\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 34.0182\n SSIM: 0.916\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 32.4987\n SSIM: 0.894\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 34.4336\n SSIM: 0.9419\n Task: Denoising\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 33.9642\n SSIM: 0.9153\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 32.4994\n SSIM: 0.8928\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 15.0\n PSNR: 34.3152\n SSIM: 0.9409\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n- Config: configs/restormer/restormer_official_dfwb-gray-sigma25.py\n In Collection: Restormer\n Name: restormer_official_dfwb-gray-sigma25\n Results:\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 31.7289\n SSIM: 0.8811\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 30.1613\n SSIM: 0.837\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 32.1162\n SSIM: 0.914\n Task: Denoising\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 31.7106\n SSIM: 0.881\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 30.1486\n SSIM: 0.836\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 25.0\n PSNR: 32.0457\n SSIM: 0.9131\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n- Config: configs/restormer/restormer_official_dfwb-gray-sigma50.py\n In Collection: Restormer\n Name: restormer_official_dfwb-gray-sigma50\n Results:\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 28.6269\n SSIM: 0.8188\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 27.3266\n SSIM: 0.7434\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 28.9636\n SSIM: 0.8571\n Task: Denoising\n - Dataset: Set12\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 28.6614\n SSIM: 0.8197\n Task: Denoising\n - Dataset: BSD68\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 27.3537\n SSIM: 0.7422\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 50.0\n PSNR: 28.9848\n SSIM: 0.8571\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth\n- Config: configs/restormer/restormer_official_dfwb-color-sigma15.py\n In Collection: Restormer\n Name: restormer_official_dfwb-color-sigma15\n Results:\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 34.3506\n SSIM (RGB): 0.9352\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.49\n SSIM (RGB): 0.9312\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.6072\n SSIM (RGB): 0.9352\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.1522\n SSIM (RGB): 0.953\n Task: Denoising\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 34.3422\n SSIM (RGB): 0.9356\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.4544\n SSIM (RGB): 0.9308\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.5473\n SSIM (RGB): 0.9344\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 15.0\n PSNR (RGB): 35.0754\n SSIM (RGB): 0.9524\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n- Config: configs/restormer/restormer_official_dfwb-color-sigma25.py\n In Collection: Restormer\n Name: restormer_official_dfwb-color-sigma25\n Results:\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 31.7457\n SSIM (RGB): 0.8942\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 33.0489\n SSIM (RGB): 0.8943\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 33.326\n SSIM (RGB): 0.9066\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 32.967\n SSIM (RGB): 0.9317\n Task: Denoising\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 31.7391\n SSIM (RGB): 0.8945\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 33.038\n SSIM (RGB): 0.8941\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 33.304\n SSIM (RGB): 0.9063\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 25.0\n PSNR (RGB): 32.9165\n SSIM (RGB): 0.9312\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n- Config: configs/restormer/restormer_official_dfwb-color-sigma50.py\n In Collection: Restormer\n Name: restormer_official_dfwb-color-sigma50\n Results:\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 28.5569\n SSIM (RGB): 0.8127\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.0122\n SSIM (RGB): 0.8238\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.2608\n SSIM (RGB): 0.8515\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.023\n SSIM (RGB): 0.8902\n Task: Denoising\n - Dataset: CBSD68\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 28.5582\n SSIM (RGB): 0.8126\n Task: Denoising\n - Dataset: Kodak24\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.0074\n SSIM (RGB): 0.8233\n Task: Denoising\n - Dataset: McMaster\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.2671\n SSIM (RGB): 0.852\n Task: Denoising\n - Dataset: Urban100\n Metrics:\n $\\\\sigma$: 50.0\n PSNR (RGB): 30.0172\n SSIM (RGB): 0.8898\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth\n- Config: configs/restormer/restormer_official_sidd.py\n In Collection: Restormer\n Name: restormer_official_sidd\n Results:\n - Dataset: SIDD\n Metrics:\n PSNR: 40.0156\n SSIM: 0.9225\n Task: Denoising\n Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth\n" }, { "path": "configs/restormer/restormer_official_dfwb-color-sigma15.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_color_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_color_sigma15'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 15\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dfwb-color-sigma25.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_color_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_color_sigma25'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 25\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dfwb-color-sigma50.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_color_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_color_sigma50'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 50\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dfwb-gray-sigma15.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_gray_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_gray_sigma15'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 15\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=1,\n out_channels=1,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.],\n std=[255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dfwb-gray-sigma25.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_gray_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_gray_sigma25'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 25\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=1,\n out_channels=1,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.],\n std=[255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dfwb-gray-sigma50.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_gray_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dfwb_gray_sigma50'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 50\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma * 255, sigma * 255]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=1,\n out_channels=1,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.],\n std=[255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_dpdd-dual.py", "content": "_base_ = [\n 'restormer_official_dpdd-single.py',\n]\n\nexperiment_name = 'restormer_official_dpdd-dual'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify PackInputs\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[4] = dict(type='PackInputs', keys=['imgL', 'imgR'])\n\n# model settings\nmodel = dict(\n generator=dict(inp_channels=6, dual_pixel_task=True),\n data_preprocessor=dict(type='DataPreprocessor'))\n" }, { "path": "configs/restormer/restormer_official_dpdd-single.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/deblurring-defocus_test_config.py'\n]\n\nexperiment_name = 'restormer_official_dpdd-single'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_gopro.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/deblurring-motion_test_config.py'\n]\n\nexperiment_name = 'restormer_official_gopro'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_rain13k.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/deraining_test_config.py'\n]\n\nexperiment_name = 'restormer_official_rain13k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/restormer/restormer_official_sidd.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-real_test_config.py'\n]\n\nexperiment_name = 'restormer_official_sidd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='Restormer',\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n" }, { "path": "configs/sagan/README.md", "content": "# SAGAN (ICML'2019)\n\n> [Self-attention generative adversarial networks](https://proceedings.mlr.press/v97/zhang19d.html)\n\n> **Task**: Conditional GANs\n\n\n\n## Abstract\n\n\n\nIn this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN performs better than prior work, boosting the best published Inception score from 36.8 to 52.52 and reducing Fréchet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n Results from our SAGAN trained in CIFAR10\n
\n \n
\n\n| Model | Dataset | Inplace ReLU | dist_step | Total Batchsize (BZ_PER_GPU * NGPU) | Total Iters\\* | Iter | IS | FID | Download |\n| :------------------------------------------------: | :------: | :----------: | :-------: | :---------------------------------: | :-----------: | :----: | :-----: | :-----: | :---------------------------------------------------: |\n| [SAGAN-32x32-woInplaceReLU Best IS](./sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w/o | 5 | 64x1 | 500000 | 400000 | 9.3217 | 10.5030 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) |\n| [SAGAN-32x32-woInplaceReLU Best FID](./sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w/o | 5 | 64x1 | 500000 | 480000 | 9.3174 | 9.4252 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) |\n| [SAGAN-32x32-wInplaceReLU Best IS](./sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w | 5 | 64x1 | 500000 | 380000 | 9.2286 | 11.7760 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) |\n| [SAGAN-32x32-wInplaceReLU Best FID](./sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w | 5 | 64x1 | 500000 | 460000 | 9.2061 | 10.7781 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) |\n| [SAGAN-128x128-woInplaceReLU Best IS](./sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | ImageNet | w/o | 1 | 64x4 | 1000000 | 980000 | 31.5938 | 36.7712 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) |\n| [SAGAN-128x128-woInplaceReLU Best FID](./sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | ImageNet | w/o | 1 | 64x4 | 1000000 | 950000 | 28.4936 | 34.7838 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) |\n| [SAGAN-128x128-BigGAN Schedule Best IS](./sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) |\n| [SAGAN-128x128-BigGAN Schedule Best FID](./sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) \\| [Log](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) |\n\n'\\*' Iteration counting rule in our implementation is different from others. If you want to align with other codebases, you can use the following conversion formula:\n\n```\ntotal_iters (biggan/pytorch studio gan) = our_total_iters / dist_step\n```\n\nWe also provide converted pre-train models from [Pytorch-StudioGAN](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN).\nTo be noted that, in Pytorch Studio GAN, **inplace ReLU** is used in generator and discriminator.\n\n| Model | Dataset | Inplace ReLU | n_disc | Total Iters | IS (Our Pipeline) | FID (Our Pipeline) | IS (StudioGAN) | FID (StudioGAN) | Download | Original Download link |\n| :------------------------: | :------: | :----------: | :----: | :---------: | :---------------: | :----------------: | :------------: | :-------------: | :---------------------------: | :------------------------------------------: |\n| [SAGAN-32x32 StudioGAN](./sagan_cvt-studioGAN_cifar10-32x32.py) | CIFAR10 | w | 5 | 100000 | 9.116 | 10.2011 | 8.680 | 14.009 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth) | [model](https://drive.google.com/drive/folders/1FA8hcz4MB8-hgTwLuDA0ZUfr8slud5P_) |\n| [SAGAN0-128x128 StudioGAN](./sagan_128_cvt_studioGAN.py) | ImageNet | w | 1 | 1000000 | 27.367 | 40.1162 | 29.848 | 34.726 | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth) | [model](https://drive.google.com/drive/folders/1ZYaqeeumDgxOPDhRR5QLeLFIpgBJ9S6B) |\n\n- `Our Pipeline` denote results evaluated with our pipeline.\n- `StudioGAN` denote results released by Pytorch-StudioGAN.\n\nFor IS metric, our implementation is different from PyTorch-Studio GAN in the following aspects:\n\n1. We use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction.\n2. We use bicubic interpolation with PIL backend to resize image before feed them to Inception.\n\nFor FID evaluation, we follow the pipeline of [BigGAN](https://github.com/ajbrock/BigGAN-PyTorch/blob/98459431a5d618d644d54cd1e9fceb1e5045648d/calculate_inception_moments.py#L52), where the whole training set is adopted to extract inception statistics, and Pytorch Studio GAN uses 50000 randomly selected samples. Besides, we also use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction.\n\nYou can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/imagenet.pkl).\n\nYou can use following commands to extract those inception states by yourself.\n\n```\n# For CIFAR10\npython tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/cifar10_inception_stat.py --pklname cifar10.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train\n\n# For ImageNet1k\npython tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/imagenet_128x128_inception_stat.py --pklname imagenet.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train\n```\n\n## Citation\n\n```latex\n@inproceedings{zhang2019self,\n title={Self-attention generative adversarial networks},\n author={Zhang, Han and Goodfellow, Ian and Metaxas, Dimitris and Odena, Augustus},\n booktitle={International conference on machine learning},\n pages={7354--7363},\n year={2019},\n organization={PMLR},\n url={https://proceedings.mlr.press/v97/zhang19d.html},\n}\n```\n" }, { "path": "configs/sagan/metafile.yml", "content": "Collections:\n- Name: SAGAN\n Paper:\n Title: Self-attention generative adversarial networks\n URL: https://proceedings.mlr.press/v97/zhang19d.html\n README: configs/sagan/README.md\n Task:\n - conditional gans\n Year: 2019\nModels:\n- Config: configs/sagan/sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py\n In Collection: SAGAN\n Name: sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID: 10.503\n IS: 9.3217\n Iter: 400000.0\n Total Iters\\*: 500000.0\n dist_step: 5.0\n Task: Conditional GANs\n - Dataset: CIFAR10\n Metrics:\n FID: 9.4252\n IS: 9.3174\n Iter: 480000.0\n Total Iters\\*: 500000.0\n dist_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth\n- Config: configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py\n In Collection: SAGAN\n Name: sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID: 11.776\n IS: 9.2286\n Iter: 380000.0\n Total Iters\\*: 500000.0\n dist_step: 5.0\n Task: Conditional GANs\n - Dataset: CIFAR10\n Metrics:\n FID: 10.7781\n IS: 9.2061\n Iter: 460000.0\n Total Iters\\*: 500000.0\n dist_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth\n- Config: configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py\n In Collection: SAGAN\n Name: sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128\n Results:\n - Dataset: ImageNet\n Metrics:\n FID: 36.7712\n IS: 31.5938\n Iter: 980000.0\n Total Iters\\*: 1000000.0\n dist_step: 1.0\n Task: Conditional GANs\n - Dataset: ImageNet\n Metrics:\n FID: 34.7838\n IS: 28.4936\n Iter: 950000.0\n Total Iters\\*: 1000000.0\n dist_step: 1.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth\n- Config: configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py\n In Collection: SAGAN\n Name: sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128\n Results:\n - Dataset: ImageNet\n Metrics:\n FID: 12.8295\n IS: 69.535\n Iter: 826000.0\n Total Iters\\*: 1000000.0\n dist_step: 1.0\n Task: Conditional GANs\n - Dataset: ImageNet\n Metrics:\n FID: 12.8295\n IS: 69.535\n Iter: 826000.0\n Total Iters\\*: 1000000.0\n dist_step: 1.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth\n- Config: configs/sagan/sagan_cvt-studioGAN_cifar10-32x32.py\n In Collection: SAGAN\n Name: sagan_cvt-studioGAN_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID (Our Pipeline): 10.2011\n FID (StudioGAN): 14.009\n IS (Our Pipeline): 9.116\n IS (StudioGAN): 8.68\n Total Iters: 100000.0\n n_disc: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth\n- Config: configs/sagan/sagan_128_cvt_studioGAN.py\n In Collection: SAGAN\n Name: sagan_128_cvt_studioGAN\n Results:\n - Dataset: ImageNet\n Metrics:\n FID (Our Pipeline): 40.1162\n FID (StudioGAN): 34.726\n IS (Our Pipeline): 27.367\n IS (StudioGAN): 29.848\n Total Iters: 1000000.0\n n_disc: 1.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth\n" }, { "path": "configs/sagan/sagan_128_cvt_studioGAN.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/models/sagan/base_sagan_128x128.py',\n '../_base_/datasets/imagenet_noaug_128.py',\n]\n\n# NOTE:\n# * studio GAN train their model in 'RGB' order\nmodel = dict(generator=dict(rgb_to_bgr=True))\n\n# NOTE: do not support training for converted configs\ntrain_cfg = train_dataloader = optim_wrapper = None\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\n# EVALUATION\nval_dataloader = val_evaluator = val_cfg = None\ntest_dataloader = dict(batch_size=128)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sagan/sagan_cvt-studioGAN_cifar10-32x32.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/datasets/cifar10_noaug.py',\n '../_base_/models/sagan/base_sagan_32x32.py',\n]\n\n# NOTE:\n# * CIFAR is loaded in 'RGB'\n# * studio GAN train their model in 'RGB' order\nmodel = dict(\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(rgb_to_bgr=True))\n\n# NOTE: do not support training for converted configs\ntrain_cfg = train_dataloader = optim_wrapper = None\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\n# EVALUATION\nval_dataloader = val_evaluator = val_cfg = None\ntest_dataloader = dict(batch_size=128)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/datasets/cifar10_nopad.py',\n '../_base_/models/sagan/base_sagan_32x32.py',\n]\n\n# MODEL\ndisc_step = 5\ninit_cfg = dict(type='studio')\nmodel = dict(\n # CIFAR images are RGB, convert to BGR\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(act_cfg=dict(type='ReLU', inplace=True), init_cfg=init_cfg),\n discriminator=dict(\n act_cfg=dict(type='ReLU', inplace=True), init_cfg=init_cfg))\n\n# TRAIN\ntrain_cfg = dict(max_iters=100000 * disc_step)\ntrain_dataloader = dict(batch_size=64)\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py", "content": "# In this config, we follow the setting `launch_SAGAN_bz128x2_ema.sh` from\n# BigGAN's repo. Please refer to https://github.com/ajbrock/BigGAN-PyTorch/blob/master/scripts/launch_SAGAN_bs128x2_ema.sh # noqa\n# In summary, in this config:\n# 1. use eps=1e-8 for Spectral Norm\n# 2. not use syncBN\n# 3. not use Spectral Norm for embedding layers in cBN\n# 4. start EMA at iterations\n# 5. use xavier_uniform for weight initialization\n# 6. no data augmentation\n\n_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/models/sagan/base_sagan_128x128.py',\n '../_base_/datasets/imagenet_noaug_128.py',\n]\n\n# MODEL\ninit_cfg = dict(type='BigGAN')\nmodel = dict(\n num_classes=1000,\n generator=dict(\n num_classes=1000,\n init_cfg=init_cfg,\n norm_eps=1e-5,\n sn_eps=1e-8,\n auto_sync_bn=False,\n with_embedding_spectral_norm=False),\n discriminator=dict(num_classes=1000, init_cfg=init_cfg, sn_eps=1e-8),\n discriminator_steps=1,\n ema_config=dict(interval=1, momentum=0.999, start_iter=2000))\n\n# TRAINING\ntrain_cfg = dict(\n max_iters=1000000, val_interval=10000, dynamic_intervals=[(800000, 4000)])\ntrain_dataloader = dict(batch_size=32) # train on 8 gpus\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema.fake_img', 'orig.fake_img']))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='ema')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_dataloader = test_dataloader = dict(batch_size=64)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/models/sagan/base_sagan_128x128.py',\n '../_base_/datasets/imagenet_128.py',\n]\n\n# MODEL\ninit_cfg = dict(type='studio')\nmodel = dict(\n num_classes=1000,\n generator=dict(num_classes=1000, init_cfg=init_cfg),\n discriminator=dict(num_classes=1000, init_cfg=init_cfg))\n\n# TRAIN\ntrain_cfg = dict(\n max_iters=1000000, val_interval=10000, dynamic_intervals=[(800000, 4000)])\ntrain_dataloader = dict(batch_size=64) # train on 4 gpus\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_dataloader = test_dataloader = dict(batch_size=64)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sagan/sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/datasets/cifar10_nopad.py',\n '../_base_/models/sagan/base_sagan_32x32.py',\n]\n\n# MODEL\ndisc_step = 5\ninit_cfg = dict(type='studio')\nmodel = dict(\n # CIFAR images are RGB, convert to BGR\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(init_cfg=init_cfg),\n discriminator=dict(init_cfg=init_cfg))\n\n# TRAIN\ntrain_cfg = dict(max_iters=100000 * disc_step)\ntrain_dataloader = dict(batch_size=64)\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/singan/README.md", "content": "# SinGAN (ICCV'2019)\n\n> [Singan: Learning a Generative Model from a Single Natural Image](https://openaccess.thecvf.com/content_ICCV_2019/html/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.html)\n\n> **Task**: Internal Learning\n\n\n\n## Abstract\n\n\n\nWe introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n SinGAN balloons\n
\n \n
\n\n| Model | Dataset | Num Scales | Download |\n| :----------------------------: | :-----------------------------------------------------------------------------: | :--------: | :-------------------------------------------------------------------------------: |\n| [SinGAN](./singan_balloons.py) | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pth) \\| [pkl](https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pkl) |\n| [SinGAN](./singan_fish.py) | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pth) \\| [pkl](https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pkl) |\n| [SinGAN](./singan_bohemian.py) | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pth) \\| [pkl](https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pkl) |\n\n## Notes for using SinGAN\n\nWhen training SinGAN models, users may obtain the number of scales (stages) in advance via the following commands. This number is important for constructing config file, which is related to the generator, discriminator, the training iterations and so on.\n\n```shell\n>>> from mmgen.datasets.singan_dataset import create_real_pyramid\n>>> import mmcv\n>>> real = mmcv.imread('real_img_path')\n>>> _, _, num_scales = create_real_pyramid(real, min_size=25, max_size=300, scale_factor_init=0.75)\n```\n\nWhen testing SinGAN models, users have to modify the config file to add the `test_cfg`. As shown in `configs/singan/singan_balloons.py`, the only thing you need to do is add the path for `pkl` data. There are some important data containing in the pickle files which you can download from our website.\n\n```python\ntest_cfg = dict(\n _delete_ = True\n pkl_data = 'path to pkl data'\n)\n```\n\n## Citation\n\n```latex\n@inproceedings{shaham2019singan,\n title={Singan: Learning a generative model from a single natural image},\n author={Shaham, Tamar Rott and Dekel, Tali and Michaeli, Tomer},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={4570--4580},\n year={2019},\n url={https://openaccess.thecvf.com/content_ICCV_2019/html/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.html},\n}\n```\n" }, { "path": "configs/singan/metafile.yml", "content": "Collections:\n- Name: SinGAN\n Paper:\n Title: 'Singan: Learning a Generative Model from a Single Natural Image'\n URL: https://openaccess.thecvf.com/content_ICCV_2019/html/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.html\n README: configs/singan/README.md\n Task:\n - internal learning\n Year: 2019\nModels:\n- Config: configs/singan/singan_balloons.py\n In Collection: SinGAN\n Name: singan_balloons\n Results:\n - Dataset: '[balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png)'\n Metrics:\n Num Scales: 8.0\n Task: Internal Learning\n Weights: https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pth\n- Config: configs/singan/singan_fish.py\n In Collection: SinGAN\n Name: singan_fish\n Results:\n - Dataset: '[fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg)'\n Metrics:\n Num Scales: 10.0\n Task: Internal Learning\n Weights: https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pth\n- Config: configs/singan/singan_bohemian.py\n In Collection: SinGAN\n Name: singan_bohemian\n Results:\n - Dataset: '[bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png)'\n Metrics:\n Num Scales: 10.0\n Task: Internal Learning\n Weights: https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pth\n" }, { "path": "configs/singan/singan_balloons.py", "content": "_base_ = ['./singan_fish.py']\n\n# MODEL\nnum_scales = 8 # start from zero\ngenerator_steps = 3\ndiscriminator_steps = 3\niters_per_scale = 2000\n\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_balloons_20210406_191047-8fcd94cf.pkl' # noqa\ntest_pkl_data = None\n\nmodel = dict(\n num_scales=num_scales,\n generator=dict(num_scales=num_scales),\n discriminator=dict(num_scales=num_scales),\n test_pkl_data=test_pkl_data)\n\n# DATA\npipeline = [\n dict(\n type='PackInputs',\n keys=[f'real_scale{i}' for i in range(num_scales)] + ['input_sample'])\n]\ndata_root = './data/singan/balloons.png'\ntrain_dataloader = dict(dataset=dict(data_root=data_root, pipeline=pipeline))\n\n# HOOKS\ncustom_hooks = [\n dict(\n type='PickleDataHook',\n output_dir='pickle',\n interval=-1,\n after_run=True,\n data_name_list=['noise_weights', 'fixed_noises', 'curr_stage']),\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='SinGAN', name='balloons'))\n]\n\n# TRAINING\ntotal_iters = (num_scales + 1) * iters_per_scale * discriminator_steps\ntrain_cfg = dict(max_iters=total_iters)\n" }, { "path": "configs/singan/singan_bohemian.py", "content": "_base_ = ['./singan_fish.py']\n\n# MODEL\n# NOTE: add by user, e.g.:\n# test_pkl_data = './work_dirs/singan_pkl/singan_bohemian_20210406_175439-f964ee38.pkl' # noqa\ntest_pkl_data = None\nmodel = dict(test_pkl_data=test_pkl_data)\n\n# HOOKS\ncustom_hooks = [\n dict(\n type='PickleDataHook',\n output_dir='pickle',\n interval=-1,\n after_run=True,\n data_name_list=['noise_weights', 'fixed_noises', 'curr_stage']),\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='SinGAN', name='bohemian'))\n]\n\n# DATA\nmin_size = 25\nmax_size = 500\ndata_root = './data/singan/bohemian.png'\ntrain_dataloader = dict(\n dataset=dict(data_root=data_root, min_size=min_size, max_size=max_size))\n" }, { "path": "configs/singan/singan_fish.py", "content": "_base_ = ['../_base_/gen_default_runtime.py']\n\n# MODEL WRAPPER\nmodel_wrapper_cfg = dict(find_unused_parameters=True)\n\n# MODEL\nnum_scales = 10 # start from zero\ngenerator_steps = 3\ndiscriminator_steps = 3\niters_per_scale = 2000\n# NOTE: add by user, e.g.:\n# test_pkl_data = ('./work_dirs/singan_fish/pickle/iter_66001.pkl')\ntest_pkl_data = None\n\nmodel = dict(\n type='SinGAN',\n data_preprocessor=dict(\n type='DataPreprocessor', non_image_keys=['input_sample']),\n generator=dict(\n type='SinGANMultiScaleGenerator',\n in_channels=3,\n out_channels=3,\n num_scales=num_scales,\n ),\n discriminator=dict(\n type='SinGANMultiScaleDiscriminator',\n in_channels=3,\n num_scales=num_scales,\n ),\n noise_weight_init=0.1,\n test_pkl_data=test_pkl_data,\n lr_scheduler_args=dict(milestones=[1600], gamma=0.1),\n generator_steps=generator_steps,\n discriminator_steps=discriminator_steps,\n iters_per_scale=iters_per_scale,\n num_scales=num_scales)\n\n# DATA\nmin_size = 25\nmax_size = 300\ndataset_type = 'SinGANDataset'\ndata_root = './data/singan/fish-crop.jpg'\n\npipeline = [\n dict(\n type='PackInputs',\n keys=[f'real_scale{i}' for i in range(num_scales)] + ['input_sample'])\n]\ndataset = dict(\n type=dataset_type,\n data_root=data_root,\n min_size=min_size,\n max_size=max_size,\n scale_factor_init=0.75,\n pipeline=pipeline)\n\ntrain_dataloader = dict(\n batch_size=1,\n num_workers=0,\n dataset=dataset,\n sampler=None,\n persistent_workers=False)\n\n# TRAINING\noptim_wrapper = dict(\n constructor='SinGANOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))))\n\ntotal_iters = (num_scales + 1) * iters_per_scale * discriminator_steps\ntrain_cfg = dict(max_iters=total_iters)\n\n# HOOK\ncustom_hooks = [\n dict(\n type='PickleDataHook',\n output_dir='pickle',\n interval=-1,\n after_run=True,\n data_name_list=['noise_weights', 'fixed_noises', 'curr_stage']),\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='SinGAN', name='fish'))\n]\n\n# NOTE: SinGAN do not support val_loop and test_loop, please use\n# 'tools/utils/inference_singan.py' to evaluate and generate images.\nval_cfg = test_cfg = None\nval_evaluator = test_evaluator = None\n" }, { "path": "configs/sngan_proj/README.md", "content": "# SNGAN (ICLR'2018)\n\n> [Spectral Normalization for Generative Adversarial Networks](https://openreview.net/forum?id=B1QRgziT-)\n\n> **Task**: Conditional GANs\n\n\n\n## Abstract\n\n\n\nOne of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n Results from our SNGAN-PROJ trained in CIFAR10 and ImageNet\n
\n   \n \n
\n\n| Model | Dataset | Inplace ReLU | disc_step | Total Iters\\* | Iter | IS | FID | Download |\n| :-----------------------------------------------------------------: | :------: | :----------: | :-------: | :-----------: | :----: | :-----: | :-----: | :---------------------------------------------------------------------: |\n| [SNGAN_Proj-32x32-woInplaceReLU Best IS](./sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w/o | 5 | 500000 | 400000 | 9.6919 | 9.8203 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) |\n| [SNGAN_Proj-32x32-woInplaceReLU Best FID](./sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w/o | 5 | 500000 | 490000 | 9.5659 | 8.1158 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) |\n| [SNGAN_Proj-32x32-wInplaceReLU Best IS](./sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_063454_is-cd863c74_fid-191b2648.json) |\n| [SNGAN_Proj-32x32-wInplaceReLU Best FID](./sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_063454_is-cd863c74_fid-191b2648.json) |\n| [SNGAN_Proj-128x128-woInplaceReLU Best IS](./sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | ImageNet | w/o | 5 | 1000000 | 952000 | 30.0651 | 33.4682 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) |\n| [SNGAN_Proj-128x128-woInplaceReLU Best FID](./sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | ImageNet | w/o | 5 | 1000000 | 989000 | 29.5779 | 32.6193 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) |\n| [SNGAN_Proj-128x128-wInplaceReLU Best IS](./sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | ImageNet | w | 5 | 1000000 | 944000 | 28.1799 | 34.3383 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) |\n| [SNGAN_Proj-128x128-wInplaceReLU Best FID](./sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | ImageNet | w | 5 | 1000000 | 988000 | 27.7948 | 33.4821 | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth) \\| [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) |\n\n'\\*' Iteration counting rule in our implementation is different from others. If you want to align with other codebases, you can use the following conversion formula:\n\n```\ntotal_iters (biggan/pytorch studio gan) = our_total_iters / disc_step\n```\n\nWe also provide converted pre-train models from [Pytorch-StudioGAN](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN).\nTo be noted that, in Pytorch Studio GAN, **inplace ReLU** is used in generator and discriminator.\n\n| Model | Dataset | Inplace ReLU | disc_step | Total Iters | IS (Our Pipeline) | FID (Our Pipeline) | IS (StudioGAN) | FID (StudioGAN) | Download | Original Download link |\n| :-----------------------: | :------: | :----------: | :-------: | :---------: | :---------------: | :----------------: | :------------: | :-------------: | :--------------------------: | :-----------------------------------------: |\n| [SAGAN_Proj-32x32 StudioGAN](./sngan-proj-cvt-studioGAN_cifar10-32x32.py) | CIFAR10 | w | 5 | 100000 | 9.372 | 10.2011 | 8.677 | 13.248 | [model](https://download.openmmlab.com/mmediting/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth) | [model](https://drive.google.com/drive/folders/16s5Cr-V-NlfLyy_uyXEkoNxLBt-8wYSM) |\n| [SAGAN_Proj-128x128 StudioGAN](./sngan-proj-cvt-studioGAN_imagenet1k-128x128.py) | ImageNet | w | 2 | 1000000 | 30.218 | 29.8199 | 32.247 | 26.792 | [model](https://download.openmmlab.com/mmediting/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth) | [model](https://drive.google.com/drive/folders/1Ek2wAMlxpajL_M8aub4DKQ9B313K8XhS) |\n\n- `Our Pipeline` denote results evaluated with our pipeline.\n- `StudioGAN` denote results released by Pytorch-StudioGAN.\n\nFor IS metric, our implementation is different from PyTorch-Studio GAN in the following aspects:\n\n1. We use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction.\n2. We use bicubic interpolation with PIL backend to resize image before feed them to Inception.\n\nFor FID evaluation, we follow the pipeline of [BigGAN](https://github.com/ajbrock/BigGAN-PyTorch/blob/98459431a5d618d644d54cd1e9fceb1e5045648d/calculate_inception_moments.py#L52), where the whole training set is adopted to extract inception statistics, and Pytorch Studio GAN uses 50000 randomly selected samples. Besides, we also use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction.\n\nYou can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/imagenet.pkl).\n\nYou can use following commands to extract those inception states by yourself.\n\n```\n# For CIFAR10\npython tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/cifar10_inception_stat.py --pklname cifar10.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train\n\n# For ImageNet1k\npython tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/imagenet_128x128_inception_stat.py --pklname imagenet.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train\n```\n\n## Citation\n\n```latex\n@inproceedings{miyato2018spectral,\n title={Spectral Normalization for Generative Adversarial Networks},\n author={Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},\n booktitle={International Conference on Learning Representations},\n year={2018},\n url={https://openreview.net/forum?id=B1QRgziT-},\n}\n```\n" }, { "path": "configs/sngan_proj/metafile.yml", "content": "Collections:\n- Name: SNGAN\n Paper:\n Title: Spectral Normalization for Generative Adversarial Networks\n URL: https://openreview.net/forum?id=B1QRgziT-\n README: configs/sngan_proj/README.md\n Task:\n - conditional gans\n Year: 2018\nModels:\n- Config: configs/sngan_proj/sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py\n In Collection: SNGAN\n Name: sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID: 9.8203\n IS: 9.6919\n Iter: 400000.0\n Total Iters\\*: 500000.0\n disc_step: 5.0\n Task: Conditional GANs\n - Dataset: CIFAR10\n Metrics:\n FID: 8.1158\n IS: 9.5659\n Iter: 490000.0\n Total Iters\\*: 500000.0\n disc_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth\n- Config: configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py\n In Collection: SNGAN\n Name: sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID: 8.3462\n IS: 9.5564\n Iter: 490000.0\n Total Iters\\*: 500000.0\n disc_step: 5.0\n Task: Conditional GANs\n - Dataset: CIFAR10\n Metrics:\n FID: 8.3462\n IS: 9.5564\n Iter: 490000.0\n Total Iters\\*: 500000.0\n disc_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth\n- Config: configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py\n In Collection: SNGAN\n Name: sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128\n Results:\n - Dataset: ImageNet\n Metrics:\n FID: 33.4682\n IS: 30.0651\n Iter: 952000.0\n Total Iters\\*: 1000000.0\n disc_step: 5.0\n Task: Conditional GANs\n - Dataset: ImageNet\n Metrics:\n FID: 32.6193\n IS: 29.5779\n Iter: 989000.0\n Total Iters\\*: 1000000.0\n disc_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth\n- Config: configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py\n In Collection: SNGAN\n Name: sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128\n Results:\n - Dataset: ImageNet\n Metrics:\n FID: 34.3383\n IS: 28.1799\n Iter: 944000.0\n Total Iters\\*: 1000000.0\n disc_step: 5.0\n Task: Conditional GANs\n - Dataset: ImageNet\n Metrics:\n FID: 33.4821\n IS: 27.7948\n Iter: 988000.0\n Total Iters\\*: 1000000.0\n disc_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth\n- Config: configs/sngan_proj/sngan-proj-cvt-studioGAN_cifar10-32x32.py\n In Collection: SNGAN\n Name: sngan-proj-cvt-studioGAN_cifar10-32x32\n Results:\n - Dataset: CIFAR10\n Metrics:\n FID (Our Pipeline): 10.2011\n FID (StudioGAN): 13.248\n IS (Our Pipeline): 9.372\n IS (StudioGAN): 8.677\n Total Iters: 100000.0\n disc_step: 5.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth\n- Config: configs/sngan_proj/sngan-proj-cvt-studioGAN_imagenet1k-128x128.py\n In Collection: SNGAN\n Name: sngan-proj-cvt-studioGAN_imagenet1k-128x128\n Results:\n - Dataset: ImageNet\n Metrics:\n FID (Our Pipeline): 29.8199\n FID (StudioGAN): 26.792\n IS (Our Pipeline): 30.218\n IS (StudioGAN): 32.247\n Total Iters: 1000000.0\n disc_step: 2.0\n Task: Conditional GANs\n Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth\n" }, { "path": "configs/sngan_proj/sngan-proj-cvt-studioGAN_cifar10-32x32.py", "content": "# _base_ = ['../_base_/models/base_sngan_proj_32x32.py']\n_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_32x32.py',\n '../_base_/datasets/cifar10_nopad.py', '../_base_/default_runtime.py'\n]\n\n# NOTE:\n# * CIFAR is loaded in 'RGB'\n# * studio GAN train their model in 'RGB' order\nmodel = dict(\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(rgb_to_bgr=True))\n\n# NOTE: do not support training for converted configs\ntrain_cfg = train_dataloader = optim_wrapper = None\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=128)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sngan_proj/sngan-proj-cvt-studioGAN_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_128x128.py',\n '../_base_/datasets/imagenet_noaug_128.py', '../_base_/default_runtime.py'\n]\n\n# NOTE: studio GAN train their model in 'RGB' order\nmodel = dict(generator=dict(rgb_to_bgr=True))\n\n# NOTE: do not support training for converted configs\ntrain_cfg = train_dataloader = optim_wrapper = None\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=128)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_128x128.py',\n '../_base_/datasets/imagenet_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\ndiscriminator_steps = 5\nnum_classes = 1000\ninit_cfg = dict(type='studio')\nmodel = dict(\n num_classes=num_classes,\n generator=dict(\n num_classes=num_classes,\n act_cfg=dict(type='ReLU', inplace=True),\n init_cfg=init_cfg),\n discriminator=dict(\n num_classes=num_classes,\n act_cfg=dict(type='ReLU', inplace=True),\n init_cfg=init_cfg),\n discriminator_steps=discriminator_steps)\n\n# TRAINING\ntrain_cfg = dict(\n max_iters=500000 * discriminator_steps,\n val_interval=10000,\n dynamic_intervals=[(800000, 4000)])\ntrain_dataloader = dict(batch_size=128) # train on 2 gpus\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=128)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py", "content": "# follow pytorch GAN-Studio, random flip is used in the dataset\n_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_32x32.py',\n '../_base_/datasets/cifar10_nopad.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\ndiscriminator_steps = 5\nnum_classes = 10\ninit_cfg = dict(type='studio')\nmodel = dict(\n num_classes=num_classes,\n # CIFAR images are RGB, convert to BGR\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(\n act_cfg=dict(type='ReLU', inplace=True),\n num_classes=num_classes,\n init_cfg=init_cfg),\n discriminator=dict(\n act_cfg=dict(type='ReLU', inplace=True),\n num_classes=num_classes,\n init_cfg=init_cfg),\n discriminator_steps=discriminator_steps)\n\n# TRAINING\ntrain_cfg = dict(max_iters=100000 * discriminator_steps)\ntrain_dataloader = dict(batch_size=64) # train on 1 gpu\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=64)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py", "content": "_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_128x128.py',\n '../_base_/datasets/imagenet_128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\ndiscriminator_steps = 5\nnum_classes = 1000\ninit_cfg = dict(type='studio')\nmodel = dict(\n num_classes=num_classes,\n generator=dict(num_classes=num_classes, init_cfg=init_cfg),\n discriminator=dict(num_classes=num_classes, init_cfg=init_cfg),\n discriminator_steps=discriminator_steps)\n\n# TRAINING\ntrain_cfg = dict(\n max_iters=500000 * discriminator_steps,\n val_interval=10000,\n dynamic_intervals=[(800000, 4000)])\ntrain_dataloader = dict(batch_size=128) # train on 2 gpus\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.00005, betas=(0.0, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/imagenet-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=128)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/sngan_proj/sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py", "content": "# follow pytorch GAN-Studio, random flip is used in the dataset\n_base_ = [\n '../_base_/models/sngan_proj/base_sngan_proj_32x32.py',\n '../_base_/datasets/cifar10_nopad.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\ndiscriminator_steps = 5\nnum_classes = 10\ninit_cfg = dict(type='studio')\nmodel = dict(\n num_classes=num_classes,\n # CIFAR images are RGB, convert to BGR\n data_preprocessor=dict(output_channel_order='BGR'),\n generator=dict(num_classes=num_classes, init_cfg=init_cfg),\n discriminator=dict(num_classes=num_classes, init_cfg=init_cfg),\n discriminator_steps=discriminator_steps)\n\n# TRAINING\ntrain_cfg = dict(max_iters=100000 * discriminator_steps)\ntrain_dataloader = dict(batch_size=64) # train on 1 gpu\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\ninception_pkl = './work_dirs/inception_pkl/cifar10-full.pkl'\nmetrics = [\n dict(\n type='InceptionScore',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\n# EVALUATION\nval_dataloader = test_dataloader = dict(batch_size=64)\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/srcnn/README.md", "content": "# SRCNN (TPAMI'2015)\n\n> [Image Super-Resolution Using Deep Convolutional Networks](https://arxiv.org/abs/1501.00092)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nWe propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------------------------------: | :-----: | :-----: | :----: | :----------------: | :--------------------------------------------------------------------------------------------: |\n| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | Set5 | 28.4316 | 0.8099 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) |\n| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | Set14 | 25.6486 | 0.7014 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) |\n| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | DIV2K | 27.7460 | 0.7854 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n\n# single-gpu test\npython tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@article{dong2015image,\n title={Image super-resolution using deep convolutional networks},\n author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},\n journal={IEEE transactions on pattern analysis and machine intelligence},\n volume={38},\n number={2},\n pages={295--307},\n year={2015},\n publisher={IEEE}\n}\n```\n" }, { "path": "configs/srcnn/README_zh-CN.md", "content": "# SRCNN (TPAMI'2015)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nSRCNN (TPAMI'2015)\n\n```bibtex\n@article{dong2015image,\n title={Image super-resolution using deep convolutional networks},\n author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},\n journal={IEEE transactions on pattern analysis and machine intelligence},\n volume={38},\n number={2},\n pages={295--307},\n year={2015},\n publisher={IEEE}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |\n| :------------------------------------------------------------------: | :--------------: | :---------------: | :--------------: | :------: | :-------------------------------------------------------------------------: |\n| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n\n# 单个GPU上测试\npython tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/srcnn/metafile.yml", "content": "Collections:\n- Name: SRCNN\n Paper:\n Title: Image Super-Resolution Using Deep Convolutional Networks\n URL: https://arxiv.org/abs/1501.00092\n README: configs/srcnn/README.md\n Task:\n - image super-resolution\n Year: 2015\nModels:\n- Config: configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py\n In Collection: SRCNN\n Name: srcnn_x4k915_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 28.4316\n SSIM: 0.8099\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 25.6486\n SSIM: 0.7014\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 27.746\n SSIM: 0.7854\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n" }, { "path": "configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'srcnn_x4k915_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SRCNNNet',\n channels=(3, 64, 32, 3),\n kernel_sizes=(9, 1, 5),\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(metrics=['PSNR'], crop_border=scale),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[250000, 250000, 250000, 250000],\n restart_weights=[1, 1, 1, 1],\n eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/srgan_resnet/README.md", "content": "# SRGAN (CVPR'2016)\n\n> [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nDespite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.\n\nThe metrics are `PSNR / SSIM` .\n\n| Model |Dataset| PSNR | SSIM | Training Resources | Download |\n| :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :----------------: |\n| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | Set5|30.2252 | 0.8491 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) |\n| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | Set14|26.7762 | 0.7369 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) |\n| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | DIV2K|28.9748 | 0.8178 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) |\n| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) |Set5|27.9499 | 0.7846 |1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) |\n| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) |Set14|24.7383 |0.6491 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) |\n| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) |DIV2K|26.5697 |0.7365 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) | [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py\n\n# single-gpu train\npython tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth\n\n# single-gpu test\npython tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{ledig2016photo,\n title={Photo-realistic single image super-resolution using a generative adversarial network},\n author={Ledig, Christian and Theis, Lucas and Husz{\\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},\n year={2016}\n}\n```\n" }, { "path": "configs/srgan_resnet/README_zh-CN.md", "content": "# SRGAN (CVPR'2016)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nSRGAN (CVPR'2016)\n\n```bibtex\n@inproceedings{ledig2016photo,\n title={Photo-realistic single image super-resolution using a generative adversarial network},\n author={Ledig, Christian and Theis, Lucas and Husz{\\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},\n year={2016}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |\n| :---------------------------------------------------------------------: | :---------------: | :--------------: | :--------------: | :------: | :----------------------------------------------------------------------: |\n| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | 30.2252 / 0.8491 | 26.7762 / 0.7369 | 28.9748 / 0.8178 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) |\n| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) | 27.9499 / 0.7846 | 24.7383 / 0.6491 | 26.5697 / 0.7365 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py\n\n# 单个GPU上训练\npython tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth\n\n# 单个GPU上测试\npython tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/srgan_resnet/metafile.yml", "content": "Collections:\n- Name: SRGAN\n Paper:\n Title: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial\n Network\n URL: https://arxiv.org/abs/1609.04802\n README: configs/srgan_resnet/README.md\n Task:\n - image super-resolution\n Year: 2016\nModels:\n- Config: configs/srgan_resnet/msrresnet_x4c64b16_1xb16-1000k_div2k.py\n In Collection: SRGAN\n Name: msrresnet_x4c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 30.2252\n SSIM: 0.8491\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 26.7762\n SSIM: 0.7369\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 28.9748\n SSIM: 0.8178\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth\n- Config: configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py\n In Collection: SRGAN\n Name: srgan_x4c64b16_1xb16-1000k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 27.9499\n SSIM: 0.7846\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 24.7383\n SSIM: 0.6491\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 26.5697\n SSIM: 0.7365\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth\n" }, { "path": "configs/srgan_resnet/msrresnet_x4c64b16_1xb16-1000k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'msrresnet_x4c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='MSRResNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=data_root + '/Set14',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='CosineRestartLR',\n by_epoch=False,\n periods=[250000, 250000, 250000, 250000],\n restart_weights=[1, 1, 1, 1],\n eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py", "content": "_base_ = './msrresnet_x4c64b16_1xb16-1000k_div2k.py'\n\nexperiment_name = 'srgan_x4c64b16_1xb16-1000k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\n\nscale = 4\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth' # noqa\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\nmodel = dict(\n type='SRGAN',\n generator=dict(\n type='MSRResNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=scale),\n discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=64),\n pixel_loss=dict(type='L1Loss', loss_weight=1e-2, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'34': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-3,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\n# optimizer\noptim_wrapper = dict(\n _delete_=True,\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n)\n\n# learning policy\nparam_scheduler = dict(\n _delete_=True,\n type='MultiStepLR',\n by_epoch=False,\n milestones=[50000, 100000, 200000, 300000],\n gamma=0.5)\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=400_000, val_interval=5000)\n" }, { "path": "configs/stable_diffusion/README.md", "content": "# Stable Diffusion (2022)\n\n> [Stable Diffusion](https://github.com/CompVis/stable-diffusion)\n\n> **Task**: Text2Image, Inpainting\n\n\n\n## Abstract\n\n\n\nStable Diffusion is a latent diffusion model conditioned on the text embeddings of a CLIP text encoder, which allows you to create images from text inputs. This model builds upon the CVPR'22 work [High-Resolution Image Synthesis with Latent Diffusion Models](https://ommer-lab.com/research/latent-diffusion-models/). The official code was released at [stable-diffusion](https://github.com/CompVis/stable-diffusion) and also implemented at [diffusers](https://github.com/huggingface/diffusers). We support this algorithm here to facilitate the community to learn together and compare it with other text2image methods.\n\n\n\n\n\n \n \n \n \n \n\n
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\n \n
\n A mecha robot in a favela in expressionist style\n
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\n A Chinese palace is beside a beautiful lake\n
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\n A panda is having dinner at KFC\n
\n\n## Pretrained models\n\n| Model | Task | Dataset | Download |\n| :----------------------------------------------------------------------------------: | :--------: | :-----: | :------: |\n| [stable_diffusion_v1.5](./stable-diffusion_ddim_denoisingunet.py) | Text2Image | - | - |\n| [stable_diffusion_v1.5_tomesd](./stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py) | Text2Image | - | - |\n| [stable_diffusion_v1.5_inpaint](./stable-diffusion_ddim_denoisingunet-inpaint.py) | Inpainting | - | - |\n\nWe use stable diffusion v1.5 weights. This model has several weights including vae, unet and clip.\n\nYou may download the weights from [stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and change the 'from_pretrained' in config to the weights dir.\n\nDownload with git:\n\n```shell\ngit lfs install\ngit clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\n## Quick Start\n\nRunning the following codes, you can get a text-generated image.\n\n```python\nfrom mmengine import MODELS, Config\nfrom torchvision import utils\n\nfrom mmengine.registry import init_default_scope\n\ninit_default_scope('mmagic')\n\nconfig = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py'\nconfig = Config.fromfile(config).copy()\n# change the 'pretrained_model_path' if you have downloaded the weights manually\n# config.model.unet.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\n# config.model.vae.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\n\nStableDiffuser = MODELS.build(config.model)\nprompt = 'A mecha robot in a favela in expressionist style'\nStableDiffuser = StableDiffuser.to('cuda')\n\nimage = StableDiffuser.infer(prompt)['samples'][0]\nimage.save('robot.png')\n```\n\nTo inpaint an image, you could run the following codes.\n\n```python\nimport mmcv\nfrom mmengine import MODELS, Config\nfrom mmengine.registry import init_default_scope\nfrom PIL import Image\n\ninit_default_scope('mmagic')\n\nconfig = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py'\nconfig = Config.fromfile(config).copy()\n# change the 'pretrained_model_path' if you have downloaded the weights manually\n# config.model.unet.from_pretrained = '/path/to/your/stable-diffusion-inpainting'\n# config.model.vae.from_pretrained = '/path/to/your/stable-diffusion-inpainting'\n\nStableDiffuser = MODELS.build(config.model)\nprompt = 'a mecha robot sitting on a bench'\n\nimg_url = 'https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png' # noqa\nmask_url = 'https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png' # noqa\n\nimage = Image.fromarray(mmcv.imread(img_url, channel_order='rgb'))\nmask = Image.fromarray(mmcv.imread(mask_url)).convert('L')\nStableDiffuser = StableDiffuser.to('cuda')\n\nimage = StableDiffuser.infer(\n prompt,\n image,\n mask\n)['samples'][0]\nimage.save('inpaint.png')\n```\n\n## Use ToMe to accelerate your stable diffusion model\n\nWe support **[tomesd](https://github.com/dbolya/tomesd)** now! It is developed based on [ToMe](https://github.com/facebookresearch/ToMe), an efficient ViT speed-up tool based on token merging. To work on with **tomesd** in `mmagic`, you just need to add `tomesd_cfg` to `model` as shown in [stable_diffusion_v1.5_tomesd](stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py). The only requirement is `torch >= 1.12.1` in order to properly support `torch.Tensor.scatter_reduce()` functionality. Please do check it before running the demo.\n\n```python\n...\nmodel = dict(\n type='StableDiffusion',\n unet=unet,\n vae=vae,\n enable_xformers=False,\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n tomesd_cfg=dict(\n ratio=0.5))\n```\n\nThe detailed settings for `tomesd_cfg` are as follows:\n\n- `ratio (float)`: The ratio of tokens to merge. For example, 0.4 would reduce the total number of tokens by 40%.The maximum value for this is 1-(1/(`sx` * `sy`)). **By default, the max ratio is 0.75, usually \\<= 0.5 is recommended.** Higher values result in more speed-up, but with more visual quality loss.\n- `max_downsample (int)`: Apply ToMe to layers with at most this amount of downsampling. E.g., 1 only applies to layers with no downsampling, while 8 applies to all layers. Should be chosen from 1, 2, 4, 8. **1, 2 are recommended.**\n- `sx, sy (int, int)`: The stride for computing dst sets. A higher stride means you can merge more tokens, **default setting of (2, 2) works well in most cases**. `sx` and `sy` do not need to divide image size.\n- `use_rand (bool)`: Whether or not to allow random perturbations when computing dst sets. By default: True, but if you're having weird artifacts you can try turning this off.\n- `merge_attn (bool)`: Whether or not to merge tokens for attention **(recommended)**.\n- `merge_crossattn (bool)`: Whether or not to merge tokens for cross attention **(not recommended)**.\n- `merge_mlp (bool)`: Whether or not to merge tokens for the mlp layers **(especially not recommended)**.\n\nFor more details about the **tomesd** setting, please refer to [Token Merging for Stable Diffusion](https://arxiv.org/abs/2303.17604).\n\nThen following the code below, you can evaluate the speed-up performance on stable diffusion models or stable-diffusion-based models ([DreamBooth](../dreambooth/README.md), [ControlNet](../controlnet/README.md)).\n\n```python\nimport time\nimport numpy as np\n\nfrom mmengine import MODELS, Config\nfrom mmengine.registry import init_default_scope\n\ninit_default_scope('mmagic')\n\n_device = 0\nwork_dir = '/path/to/your/work_dir'\nconfig = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py'\nconfig = Config.fromfile(config).copy()\n# # change the 'pretrained_model_path' if you have downloaded the weights manually\n# config.model.unet.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\n# config.model.vae.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\n\n# w/o tomesd\nconfig.model.tomesd_cfg = None\nStableDiffuser = MODELS.build(config.model).to(f'cuda:{_device}')\nprompt = 'A mecha robot in a favela in expressionist style'\n\n# inference time evaluation params\nsize = 512\nratios = [0.5, 0.75]\nsamples_perprompt = 5\n\nt = time.time()\nfor i in range(100//samples_perprompt):\n image = StableDiffuser.infer(prompt, height=size, width=size, num_images_per_prompt=samples_perprompt)['samples'][0]\n if i == 0:\n image.save(f\"{work_dir}/wo_tomesd.png\")\nprint(f\"Generating 100 images with {samples_perprompt} images per prompt, without ToMe speed-up, time used : {time.time() - t}s\")\n\nfor ratio in ratios:\n # w/ tomesd\n config.model.tomesd_cfg = dict(ratio=ratio)\n sd_model = MODELS.build(config.model).to(f'cuda:{_device}')\n\n t = time.time()\n for i in range(100//samples_perprompt):\n image = sd_model.infer(prompt, height=size, width=size, num_images_per_prompt=samples_perprompt)['samples'][0]\n if i == 0:\n image.save(f\"{work_dir}/w_tomesd_ratio_{ratio}.png\")\n\n print(f\"Generating 100 images with {samples_perprompt} images per prompt, merging ratio {ratio}, time used : {time.time() - t}s\")\n```\n\nHere are some inference performance comparisons running on **single RTX 3090** with `torch 2.0.0+cu118` as backends. The results are reasonable, when enabling `xformers`, the speed-up ratio is a little bit lower. But `tomesd` still effectively reduces the inference time. It is especially recommended that enable `tomesd` when the `image_size` and `num_images_per_prompt` are large, since the number of similar tokens are larger and `tomesd` can achieve better performance.\n\n| Model | Task | Dataset | Download | xformer | Ratio | Size / Num images per prompt | Time (s) |\n| :--------------------------------------------------------------: | :--------: | :-----: | :------: | :-----: | :-------------------------: | :--------------------------: | :-----------------------------------------------: |\n| [stable_diffusion_v1.5-tomesd](./stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py) | Text2Image | - | - | w/o | w/o tome
0.5
0.75 | 512 / 5 | 542.20
427.65 (↓21.1%)
393.05 (↓27.5%) |\n| [stable_diffusion_v1.5-tomesd](./stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py) | Text2Image | - | - | w/ | w/o tome
0.5
0.75 | 512 / 5 | 541.64
428.53 (↓20.9%)
396.38 (↓26.8%) |\n\n\n\n \n \n \n \n \n\n
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\n w/o ToMe \n
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\n w/ ToMe Speed-up (token merge ratio=0.5) \n
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\n w/ ToMe Speed-up (token merge ratio=0.75) \n
\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@misc{rombach2021highresolution,\n title={High-Resolution Image Synthesis with Latent Diffusion Models},\n author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},\n year={2021},\n eprint={2112.10752},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\n@article{bolya2023tomesd,\n title={Token Merging for Fast Stable Diffusion},\n author={Bolya, Daniel and Hoffman, Judy},\n journal={arXiv},\n year={2023}\n}\n\n@inproceedings{bolya2023tome,\n title={Token Merging: Your {ViT} but Faster},\n author={Bolya, Daniel and Fu, Cheng-Yang and Dai, Xiaoliang and Zhang, Peizhao and Feichtenhofer, Christoph and Hoffman, Judy},\n booktitle={International Conference on Learning Representations},\n year={2023}\n}\n```\n" }, { "path": "configs/stable_diffusion/metafile.yml", "content": "Collections:\n- Name: Stable Diffusion\n Paper:\n Title: Stable Diffusion\n URL: https://github.com/CompVis/stable-diffusion\n README: configs/stable_diffusion/README.md\n Task:\n - text2image\n - inpainting\n Year: 2022\nModels:\n- Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py\n In Collection: Stable Diffusion\n Name: stable-diffusion_ddim_denoisingunet\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n- Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py\n In Collection: Stable Diffusion\n Name: stable-diffusion_ddim_denoisingunet-tomesd_5e-1\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n - Dataset: '-'\n Metrics:\n Size / Num images per prompt:\n PSNR: 512.0\n SSIM: 5.0\n Task: Text2Image\n - Dataset: '-'\n Metrics:\n Size / Num images per prompt:\n PSNR: 512.0\n SSIM: 5.0\n Task: Text2Image\n- Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py\n In Collection: Stable Diffusion\n Name: stable-diffusion_ddim_denoisingunet-inpaint\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Inpainting\n" }, { "path": "configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py", "content": "# Use DiffuserWrapper!\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-inpainting'\nunet = dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url)\nvae = dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\nmodel = dict(\n type='StableDiffusionInpaint',\n unet=unet,\n vae=vae,\n enable_xformers=False,\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler)\n" }, { "path": "configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py", "content": "# MMagic's implementation of Stable Diffusion\n# unet = dict(\n# type='DenoisingUnet',\n# image_size=512,\n# base_channels=320,\n# channels_cfg=[1, 2, 4, 4],\n# unet_type='stable',\n# act_cfg=dict(type='silu'),\n# cross_attention_dim=768,\n# num_heads=8,\n# in_channels=4,\n# layers_per_block=2,\n# down_block_types=[\n# 'CrossAttnDownBlock2D',\n# 'CrossAttnDownBlock2D',\n# 'CrossAttnDownBlock2D',\n# 'DownBlock2D',\n# ],\n# up_block_types=[\n# 'UpBlock2D',\n# 'CrossAttnUpBlock2D',\n# 'CrossAttnUpBlock2D',\n# 'CrossAttnUpBlock2D',\n# ],\n# output_cfg=dict(var='fixed'))\n\n# vae = dict(\n# type='EditAutoencoderKL',\n# act_fn='silu',\n# block_out_channels=[128, 256, 512, 512],\n# down_block_types=[\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# ],\n# in_channels=3,\n# latent_channels=4,\n# layers_per_block=2,\n# norm_num_groups=32,\n# out_channels=3,\n# sample_size=512,\n# up_block_types=[\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# ])\n\n# Use DiffuserWrapper!\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nunet = dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url)\nvae = dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\nmodel = dict(\n type='StableDiffusion',\n unet=unet,\n vae=vae,\n enable_xformers=False,\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n tomesd_cfg=dict(ratio=0.5))\n" }, { "path": "configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py", "content": "# MMagic's implementation of Stable Diffusion\n# unet = dict(\n# type='DenoisingUnet',\n# image_size=512,\n# base_channels=320,\n# channels_cfg=[1, 2, 4, 4],\n# unet_type='stable',\n# act_cfg=dict(type='silu'),\n# cross_attention_dim=768,\n# num_heads=8,\n# in_channels=4,\n# layers_per_block=2,\n# down_block_types=[\n# 'CrossAttnDownBlock2D',\n# 'CrossAttnDownBlock2D',\n# 'CrossAttnDownBlock2D',\n# 'DownBlock2D',\n# ],\n# up_block_types=[\n# 'UpBlock2D',\n# 'CrossAttnUpBlock2D',\n# 'CrossAttnUpBlock2D',\n# 'CrossAttnUpBlock2D',\n# ],\n# output_cfg=dict(var='fixed'))\n\n# vae = dict(\n# type='EditAutoencoderKL',\n# act_fn='silu',\n# block_out_channels=[128, 256, 512, 512],\n# down_block_types=[\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# 'DownEncoderBlock2D',\n# ],\n# in_channels=3,\n# latent_channels=4,\n# layers_per_block=2,\n# norm_num_groups=32,\n# out_channels=3,\n# sample_size=512,\n# up_block_types=[\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# 'UpDecoderBlock2D',\n# ])\n\n# Use DiffuserWrapper!\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nunet = dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url)\nvae = dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\nmodel = dict(\n type='StableDiffusion',\n unet=unet,\n vae=vae,\n enable_xformers=False,\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler)\n" }, { "path": "configs/stable_diffusion_xl/README.md", "content": "# Stable Diffusion XL (2023)\n\n> [Stable Diffusion XL](https://arxiv.org/abs/2307.01952)\n\n> **Task**: Text2Image, Inpainting\n\n\n\n## Abstract\n\n\n\nWe present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.\n\n\n\n
\n\n
\n\n## Pretrained models\n\n| Model | Task | Dataset | Download |\n| :----------------------------------------------------------------: | :--------: | :-----: | :------: |\n| [stable_diffusion_xl](./stable-diffusion_xl_ddim_denoisingunet.py) | Text2Image | - | - |\n\nWe use stable diffusion xl weights. This model has several weights including vae, unet and clip.\n\nYou may download the weights from [stable-diffusion-xl](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and change the 'from_pretrained' in config to the weights dir.\n\n## Quick Start\n\nRunning the following codes, you can get a text-generated image.\n\n```python\nfrom mmengine import MODELS, Config\n\nfrom mmengine.registry import init_default_scope\n\ninit_default_scope('mmagic')\n\nconfig = 'configs/stable_diffusion_xl/stable-diffusion_xl_ddim_denoisingunet.py'\nconfig = Config.fromfile(config).copy()\n\nStableDiffuser = MODELS.build(config.model)\nprompt = 'A mecha robot in a favela in expressionist style'\nStableDiffuser = StableDiffuser.to('cuda')\n\nimage = StableDiffuser.infer(prompt)['samples'][0]\nimage.save('robot.png')\n```\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-xl](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).\n\nThanks for the efforts of the community!\n" }, { "path": "configs/stable_diffusion_xl/metafile.yml", "content": "Collections:\n- Name: Stable Diffusion XL\n Paper:\n Title: Stable Diffusion XL\n URL: https://arxiv.org/abs/2307.01952\n README: configs/stable_diffusion_xl/README.md\n Task:\n - text2image\n - inpainting\n Year: 2023\nModels:\n- Config: configs/stable_diffusion_xl/stable-diffusion_xl_ddim_denoisingunet.py\n In Collection: Stable Diffusion XL\n Name: stable-diffusion_xl_ddim_denoisingunet\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n" }, { "path": "configs/stable_diffusion_xl/stable-diffusion_xl_ddim_denoisingunet.py", "content": "# Use DiffuserWrapper!\nstable_diffusion_xl_url = 'stabilityai/stable-diffusion-xl-base-1.0'\nunet = dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_xl_url)\nvae = dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_xl_url,\n subfolder='vae')\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\nmodel = dict(\n type='StableDiffusionXL',\n unet=unet,\n vae=vae,\n enable_xformers=False,\n text_encoder_one=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_xl_url,\n subfolder='text_encoder'),\n tokenizer_one=stable_diffusion_xl_url,\n text_encoder_two=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_xl_url,\n subfolder='text_encoder_2'),\n tokenizer_two=stable_diffusion_xl_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler)\n" }, { "path": "configs/styleganv1/README.md", "content": "# StyleGANv1 (CVPR'2019)\n\n> [A Style-Based Generator Architecture for Generative Adversarial Networks](https://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nWe propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n Results (compressed) from StyleGANv1 trained by mmagic\n
\n \n
\n\n| Model | Dataset | FID50k | P&R50k_full | Download |\n| :-----------------------------------------------------------------: | :-----: | :----: | :-----------: | :---------------------------------------------------------------------------------------------------------: |\n| [styleganv1_ffhq_256](./styleganv1_ffhq-256x256_8xb4-25Mimgs.py) | FFHQ | 6.090 | 70.228/27.050 | [model](https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth) |\n| [styleganv1_ffhq_1024](./styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py) | FFHQ | 4.056 | 70.302/36.869 | [model](https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth) |\n\n## Citation\n\n```latex\n@inproceedings{karras2019style,\n title={A style-based generator architecture for generative adversarial networks},\n author={Karras, Tero and Laine, Samuli and Aila, Timo},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={4401--4410},\n year={2019},\n url={https://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html},\n}\n```\n" }, { "path": "configs/styleganv1/metafile.yml", "content": "Collections:\n- Name: StyleGANv1\n Paper:\n Title: A Style-Based Generator Architecture for Generative Adversarial Networks\n URL: https://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html\n README: configs/styleganv1/README.md\n Task:\n - unconditional gans\n Year: 2019\nModels:\n- Config: configs/styleganv1/styleganv1_ffhq-256x256_8xb4-25Mimgs.py\n In Collection: StyleGANv1\n Name: styleganv1_ffhq-256x256_8xb4-25Mimgs\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 6.09\n P&R50k_full:\n PSNR: 70.228\n SSIM: 27.05\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth\n- Config: configs/styleganv1/styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py\n In Collection: StyleGANv1\n Name: styleganv1_ffhq-1024x1024_8xb4-25Mimgs\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 4.056\n P&R50k_full:\n PSNR: 70.302\n SSIM: 36.869\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth\n" }, { "path": "configs/styleganv1/styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py", "content": "_base_ = [\n '../_base_/models/base_styleganv1.py',\n '../_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\nmodel_wrapper_cfg = dict(find_unused_parameters=True)\nema_half_life = 10. # G_smoothing_kimg\nema_config = dict(\n interval=1, momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\nmodel = dict(\n generator=dict(out_size=1024),\n discriminator=dict(in_size=1024),\n nkimgs_per_scale={\n '8': 1200,\n '16': 1200,\n '32': 1200,\n '64': 1200,\n '128': 1200,\n '256': 1200,\n '512': 1200,\n '1024': 166000\n },\n ema_config=ema_config)\n\n# TRAIN\ntrain_cfg = dict(max_iters=670000)\n\noptim_wrapper = dict(\n constructor='PGGANOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=dict(\n generator={\n '128': 0.0015,\n '256': 0.002,\n '512': 0.003,\n '1024': 0.003\n },\n discriminator={\n '128': 0.0015,\n '256': 0.002,\n '512': 0.003,\n '1024': 0.003\n }))\n\n# VIS_HOOK + DATAFETCH\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img')),\n dict(type='PGGANFetchDataHook')\n]\n\n# METRICS\ninception_pkl = './work_dirs/ffhq1024-full.pkl'\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, k=3, prefix='PR-50K'),\n]\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv1/styleganv1_ffhq-256x256_8xb4-25Mimgs.py", "content": "_base_ = [\n '../_base_/models/base_styleganv1.py',\n '../_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\nmodel_wrapper_cfg = dict(find_unused_parameters=True)\nema_half_life = 10. # G_smoothing_kimg\nema_config = dict(\n interval=1, momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n nkimgs_per_scale={\n '8': 1200,\n '16': 1200,\n '32': 1200,\n '64': 1200,\n '128': 1200,\n '256': 190000\n },\n ema_config=ema_config)\n\n# TRAIN\ntrain_cfg = dict(max_iters=670000)\n\noptim_wrapper = dict(\n constructor='PGGANOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=dict(\n generator={\n '128': 0.0015,\n '256': 0.002\n },\n discriminator={\n '128': 0.0015,\n '256': 0.002\n }))\n\n# VIS_HOOK + DATAFETCH\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img')),\n dict(type='PGGANFetchDataHook')\n]\n\n# METRICS\ninception_pkl = './work_dirs/ffhq256-full.pkl'\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=inception_pkl,\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, k=3, prefix='PR-50K'),\n]\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\n# use low resolution image to evaluate\n# NOTE: use cherry-picked file list?\n# val_dataloader = test_dataloader = dict(\n# dataset=dict(\n# data_root='./data/ffhq/ffhq_imgs/ffhq_256',\n# file_list='./data/ffhq256.txt'))\nval_dataloader = test_dataloader = dict(\n dataset=dict(data_root='./data/ffhq/ffhq_imgs/ffhq_256'))\nval_evaluator = test_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/README.md", "content": "# StyleGANv2 (CVPR'2020)\n\n> [Analyzing and Improving the Image Quality of Stylegan](https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nThe style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n Results (compressed) from StyleGAN2 config-f trained by mmagic\n
\n \n
\n\n| Model | Dataset | Comment | FID50k | Precision50k | Recall50k | Download |\n| :----------------------------------------------------------------------: | :---------: | :-------------: | :----: | :----------: | :-------: | :-------------------------------------------------------------------------: |\n| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | FFHQ | official weight | 2.8134 | 62.856 | 49.400 | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) |\n| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) | LSUN_CAR | official weight | 5.4316 | 65.986 | 48.190 | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth) |\n| [stylegan2_c2_8xb4-800kiters_lsun-horse-256x256](./stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py) | LSUN_HORSE | official weight | - | - | - | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth) |\n| [stylegan2_c2_8xb4-800kiters_lsun-church-256x256](./stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py) | LSUN_CHURCH | official weight | - | - | - | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth) |\n| [stylegan2_c2_8xb4-800kiters_lsun-cat-256x256](./stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py) | LSUN_CAT | official weight | - | - | - | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth) |\n| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | FFHQ | our training | 3.992 | 69.012 | 40.417 | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |\n| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | FFHQ | our training | 2.8185 | 68.236 | 49.583 | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) |\n| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) | LSUN_CAR | our training | 2.4116 | 66.760 | 50.576 | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth) |\n\n## FP16 Support and Experiments\n\nCurrently, we have supported FP16 training for StyleGAN2, and here are the results for the mixed-precision training. (Experiments for FFHQ1024 will come soon.)\n\n
\n Evaluation FID for FP32 and FP16 training \n
\n \n
\n\nAs shown in the figure, we provide **3** ways to do mixed-precision training for `StyleGAN2`:\n\n- [stylegan2_c2_fp16_PL-no-scaler](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py): In this setting, we try our best to follow the official FP16 implementation in [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada). Similar to the official version, we only adopt FP16 training for the higher-resolution feature maps (the last 4 stages in G and the first 4 stages). Note that we do not adopt the `clamp` way to avoid gradient overflow used in the official implementation. We use the `autocast` function from `torch.cuda.amp` package.\n- [stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py): In this config, we try to adopt mixed-precision training for the whole generator, but in partial discriminator (the first 4 higher-resolution stages). Note that we do not apply the loss scaler in the path length loss and gradient penalty loss. Because we always meet divergence after adopting the loss scaler to scale the gradient in these two losses.\n- [stylegan2_c2_apex_fp16_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py): In this setting, we adopt the [APEX](https://github.com/NVIDIA/apex) toolkit to implement mixed-precision training with multiple loss/gradient scalers. In APEX, you can assign different loss scalers for the generator and the discriminator respectively. Note that we still ignore the gradient scaler in the path length loss and gradient penalty loss.\n\n| Model | Comment | Dataset | FID50k | Download |\n| :----------------------------------------------------------------------: | :-------------------------------------: | :-----: | :----: | :--------------------------------------------------------------------------: |\n| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | baseline | FFHQ256 | 3.992 | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |\n| [stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py) | partial layers in fp16 | FFHQ256 | 4.331 | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth) |\n| [stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py) | the whole G in fp16 | FFHQ256 | 4.362 | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth) |\n| [stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py) | the whole G&D in fp16 + two loss scaler | FFHQ256 | 4.614 | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth) |\n\nAs shown in this table, `P&R50k_full` is the metric used in StyleGANv1 and StyleGANv2. `full` indicates that we use the whole dataset for extracting the real distribution, e.g., 70000 images in FFHQ dataset. However, adopting the VGG16 provided from [Tero](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt) requires that your PyTorch version must fulfill `>=1.6.0`. Be careful about using the PyTorch's VGG16 to extract features, which will cause higher precision and recall.\n\n## Citation\n\n```latex\n@inproceedings{karras2020analyzing,\n title={Analyzing and improving the image quality of stylegan},\n author={Karras, Tero and Laine, Samuli and Aittala, Miika and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={8110--8119},\n year={2020},\n url={https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html},\n}\n```\n" }, { "path": "configs/styleganv2/metafile.yml", "content": "Collections:\n- Name: StyleGANv2\n Paper:\n Title: Analyzing and Improving the Image Quality of Stylegan\n URL: https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html\n README: configs/styleganv2/README.md\n Task:\n - unconditional gans\n Year: 2020\nModels:\n- Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4_ffhq-1024x1024\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 2.8134\n Precision50k: 62.856\n Recall50k: 49.4\n Task: Unconditional GANs\n - Dataset: FFHQ\n Metrics:\n FID50k: 2.8185\n Precision50k: 68.236\n Recall50k: 49.583\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth\n- Config: configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4_lsun-car-384x512\n Results:\n - Dataset: LSUN_CAR\n Metrics:\n FID50k: 5.4316\n Precision50k: 65.986\n Recall50k: 48.19\n Task: Unconditional GANs\n - Dataset: LSUN_CAR\n Metrics:\n FID50k: 2.4116\n Precision50k: 66.76\n Recall50k: 50.576\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth\n- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4-800kiters_lsun-horse-256x256\n Results:\n - Dataset: LSUN_HORSE\n Metrics: {}\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth\n- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4-800kiters_lsun-church-256x256\n Results:\n - Dataset: LSUN_CHURCH\n Metrics: {}\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth\n- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4-800kiters_lsun-cat-256x256\n Results:\n - Dataset: LSUN_CAT\n Metrics: {}\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth\n- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2_8xb4-800kiters_ffhq-256x256\n Results:\n - Dataset: FFHQ\n Metrics:\n FID50k: 3.992\n Precision50k: 69.012\n Recall50k: 40.417\n Task: Unconditional GANs\n - Dataset: FFHQ256\n Metrics:\n FID50k: 3.992\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth\n- Config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256\n Results:\n - Dataset: FFHQ256\n Metrics:\n FID50k: 4.331\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth\n- Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256\n Results:\n - Dataset: FFHQ256\n Metrics:\n FID50k: 4.362\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth\n- Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py\n In Collection: StyleGANv2\n Name: stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256\n Results:\n - Dataset: FFHQ256\n Metrics:\n FID50k: 4.614\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth\n" }, { "path": "configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']\n\nmodel = dict(loss_config=dict(r1_use_apex_amp=False, g_reg_use_apex_amp=False))\n\ntrain_cfg = dict(max_iters=800002)\n\n# remain to be refactored\napex_amp = dict(mode='gan', init_args=dict(opt_level='O1', num_losses=2))\nresume_from = None\n" }, { "path": "configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']\n\nmodel = dict(\n generator=dict(out_size=256, fp16_enabled=True),\n discriminator=dict(in_size=256, fp16_enabled=False, num_fp16_scales=4),\n)\ntrain_cfg = dict(max_iters=800000)\noptim_wrapper = dict(\n generator=dict(type='AmpOptimWrapper', loss_scale=512),\n discriminator=dict(type='AmpOptimWrapper', loss_scale=512))\n" }, { "path": "configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']\n\nmodel = dict(\n generator=dict(out_size=256, num_fp16_scales=4),\n discriminator=dict(in_size=256, num_fp16_scales=4),\n loss_config=dict(scale_r1_loss=True))\n\noptim_wrapper = dict(\n generator=dict(type='AmpOptimWrapper', loss_scale=512),\n discriminator=dict(type='AmpOptimWrapper', loss_scale=512))\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/ffhq/ffhq_imgs/ffhq_256'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py", "content": "\"\"\"Note that this config is just for testing.\"\"\"\n\n_base_ = [\n '../_base_/datasets/lsun_stylegan.py',\n '../_base_/models/base_styleganv2.py', '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-cat'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py", "content": "\"\"\"Note that this config is just for testing.\"\"\"\n\n_base_ = [\n '../_base_/datasets/lsun_stylegan.py',\n '../_base_/models/base_styleganv2.py', '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-church'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py", "content": "\"\"\"Note that this config is just for testing.\"\"\"\n\n_base_ = [\n '../_base_/datasets/lsun_stylegan.py',\n '../_base_/models/base_styleganv2.py', '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-horse'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py", "content": "\"\"\"Config for the `config-f` setting in StyleGAN2.\"\"\"\n\n_base_ = [\n '../_base_/datasets/ffhq_flip.py', '../_base_/models/base_styleganv2.py',\n '../_base_/gen_default_runtime.py'\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=1024),\n discriminator=dict(in_size=1024),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\nextra_parameters = dict(num_batches=1, sample_model='orig')\n\ntrain_cfg = dict(max_iters=800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py", "content": "_base_ = [\n '../_base_/gen_default_runtime.py',\n '../_base_/models/base_styleganv2.py',\n]\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel = dict(\n generator=dict(out_size=512),\n discriminator=dict(in_size=512),\n ema_config=dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg = dict(max_iters=1800002)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n# DATA\nbatch_size = 4\ndata_root = './data/lsun/images/car'\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='NumpyPad',\n keys='img',\n padding=((64, 64), (0, 0), (0, 0)),\n ),\n dict(type='Flip', keys=['gt'], direction='horizontal'),\n dict(type='PackInputs')\n]\n\nval_pipeline = train_pipeline\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type, data_root=data_root, pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_root=data_root, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_root=data_root, # set by user\n pipeline=val_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/README.md", "content": "# StyleGANv3 (NeurIPS'2021)\n\n> [Alias-Free Generative Adversarial Networks](https://nvlabs-fi-cdn.nvidia.com/stylegan3/stylegan3-paper.pdf)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\nWe observe that despite their hierarchical convolutional nature, the synthesis\nprocess of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to\nbe glued to image coordinates instead of the surfaces of depicted objects. We trace\nthe root cause to careless signal processing that causes aliasing in the generator\nnetwork. Interpreting all signals in the network as continuous, we derive generally\napplicable, small architectural changes that guarantee that unwanted information\ncannot leak into the hierarchical synthesis process. The resulting networks match\nthe FID of StyleGAN2 but differ dramatically in their internal representations, and\nthey are fully equivariant to translation and rotation even at subpixel scales. Our\nresults pave the way for generative models better suited for video and animation.\n\n\n\n
\n\n
\n\n## Results and Models\n\n
\n Results (compressed) from StyleGAN3 config-T converted by mmagic\n
\n \n
\n\nWe perform experiments on StyleGANv3 paper settings and also experimental settings.\nFor user convenience, we also offer the converted version of official weights.\n\n### Paper Settings\n\n| Model | Dataset | Iter | FID50k | Download |\n| :---------------------------------------------------------------------------: | :---------------: | :----: | :---------------: | :---------------------------------------------------------------------------------: |\n| [stylegan3-t](./stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py) | ffhq 1024x1024 | 490000 | 3.37\\* | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth) \\| [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_20220322_090417.log.json) |\n| [stylegan3-t-ada](./stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py) | metface 1024x1024 | 130000 | 15.09 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth) \\| [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_20220328_142211.log.json) |\n\n### Experimental Settings\n\n| Model | Dataset | Iter | FID50k | Download |\n| :---------------------------------------------------------------------------: | :------------: | :----: | :----: | :-----------------------------------------------------------------------------------------------: |\n| [stylegan3-t](./stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py) | ffhq 256x256 | 740000 | 4.51 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth) \\| [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_20220323_144815.log.json) |\n| [stylegan3-r-ada](./stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py) | ffhq 1024x1024 | - | - | [ckpt](<>) |\n\n### Converted Weights\n\n| Model | Dataset | Comment | FID50k | EQ-T | EQ-R | Download |\n| :--------------------------------------------------------------------: | :------------: | :-------------: | :----: | :---: | :---: | :-----------------------------------------------------------------------------------: |\n| [stylegan3-t](./stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py) | ffhqu 256x256 | official weight | 4.62 | 63.01 | 13.12 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth) |\n| [stylegan3-t](./stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py) | afhqv2 512x512 | official weight | 4.04 | 60.15 | 13.51 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth) |\n| [stylegan3-t](./stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | ffhq 1024x1024 | official weight | 2.79 | 61.21 | 13.82 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth) |\n| [stylegan3-r](./stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py) | ffhqu 256x256 | official weight | 4.50 | 66.65 | 40.48 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth) |\n| [stylegan3-r](./stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py) | afhqv2 512x512 | official weight | 4.40 | 64.89 | 40.34 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth) |\n| [stylegan3-r](./stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | ffhq 1024x1024 | official weight | 3.07 | 64.76 | 46.62 | [ckpt](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth) |\n\n## Interpolation\n\nWe provide a tool to generate video by walking through GAN's latent space.\nRun this command to get the following video.\n\n```bash\npython apps/interpolate_sample.py configs/styleganv3/stylegan3_t_afhqv2_512_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official.pkl --export-video --samples-path work_dirs/demos/ --endpoint 6 --interval 60 --space z --seed 2022 --sample-cfg truncation=0.8\n```\n\nhttps://user-images.githubusercontent.com/22982797/151506918-83da9ee3-0d63-4c5b-ad53-a41562b92075.mp4\n\n## Equivarience Visualization && Evaluation\n\nWe also provide a tool to visualize the equivarience properties for StyleGAN3.\nRun these commands to get the results below.\n\n```bash\npython tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.5 --transform rotate --seed 5432\n\npython tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform x_t --seed 5432\n\npython tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform y_t --seed 5432\n```\n\nhttps://user-images.githubusercontent.com/22982797/151504902-f3cbfef5-9014-4607-bbe1-deaf48ec6d55.mp4\n\nhttps://user-images.githubusercontent.com/22982797/151504973-b96e1639-861d-434b-9d7c-411ebd4a653f.mp4\n\nhttps://user-images.githubusercontent.com/22982797/151505099-cde4999e-aab1-42d4-a458-3bb069db3d32.mp4\n\nIf you want to get EQ-Metric for StyleGAN3, just add following codes into config.\n\n```python\nmetrics = dict(\n eqv=dict(\n type='Equivariance',\n num_images=50000,\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True)))\n```\n\nAnd we highly recommend you to use [slurm_test.sh](../../tools/slurm_test.sh) script to accelerate evaluation time.\n\n## Citation\n\n```latex\n@inproceedings{Karras2021,\n author = {Tero Karras and Miika Aittala and Samuli Laine and Erik H\\\"ark\\\"onen and Janne Hellsten and Jaakko Lehtinen and Timo Aila},\n title = {Alias-Free Generative Adversarial Networks},\n booktitle = {Proc. NeurIPS},\n year = {2021}\n}\n```\n" }, { "path": "configs/styleganv3/metafile.yml", "content": "Collections:\n- Name: StyleGANv3\n Paper:\n Title: Alias-Free Generative Adversarial Networks\n URL: https://nvlabs-fi-cdn.nvidia.com/stylegan3/stylegan3-paper.pdf\n README: configs/styleganv3/README.md\n Task:\n - unconditional gans\n Year: 2021\nModels:\n- Config: configs/styleganv3/stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py\n In Collection: StyleGANv3\n Name: stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024\n Results:\n - Dataset: ffhq1024x1024\n Metrics:\n Iter: 490000.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth\n- Config: configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py\n In Collection: StyleGANv3\n Name: stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024\n Results:\n - Dataset: metface1024x1024\n Metrics:\n FID50k: 15.09\n Iter: 130000.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth\n- Config: configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py\n In Collection: StyleGANv3\n Name: stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256\n Results:\n - Dataset: ffhq256x256\n Metrics:\n FID50k: 4.51\n Iter: 740000.0\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth\n- Config: configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py\n In Collection: StyleGANv3\n Name: stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024\n Results:\n - Dataset: ffhq1024x1024\n Metrics: {}\n Task: Unconditional GANs\n Weights: <>\n- Config: configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py\n In Collection: StyleGANv3\n Name: stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256\n Results:\n - Dataset: ffhqu256x256\n Metrics:\n EQ-R: 13.12\n EQ-T: 63.01\n FID50k: 4.62\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth\n- Config: configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py\n In Collection: StyleGANv3\n Name: stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512\n Results:\n - Dataset: afhqv2512x512\n Metrics:\n EQ-R: 13.51\n EQ-T: 60.15\n FID50k: 4.04\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth\n- Config: configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py\n In Collection: StyleGANv3\n Name: stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024\n Results:\n - Dataset: ffhq1024x1024\n Metrics:\n EQ-R: 13.82\n EQ-T: 61.21\n FID50k: 2.79\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth\n- Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py\n In Collection: StyleGANv3\n Name: stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256\n Results:\n - Dataset: ffhqu256x256\n Metrics:\n EQ-R: 40.48\n EQ-T: 66.65\n FID50k: 4.5\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth\n- Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py\n In Collection: StyleGANv3\n Name: stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512\n Results:\n - Dataset: afhqv2512x512\n Metrics:\n EQ-R: 40.34\n EQ-T: 64.89\n FID50k: 4.4\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth\n- Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py\n In Collection: StyleGANv3\n Name: stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024\n Results:\n - Dataset: ffhq1024x1024\n Metrics:\n EQ-R: 46.62\n EQ-T: 64.76\n FID50k: 3.07\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth\n" }, { "path": "configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/ffhq_flip.py',\n '../_base_/gen_default_runtime.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\nr1_gamma = 3.3 # set by user\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nload_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth' # noqa\n\n# ada settings\naug_kwargs = {\n 'xflip': 1,\n 'rotate90': 1,\n 'xint': 1,\n 'scale': 1,\n 'rotate': 1,\n 'aniso': 1,\n 'xfrac': 1,\n 'brightness': 1,\n 'contrast': 1,\n 'lumaflip': 1,\n 'hue': 1,\n 'saturation': 1\n}\n\nema_half_life = 10. # G_smoothing_kimg\nema_kimg = 10\nema_nimg = ema_kimg * 1000\nema_beta = 0.5**(32 / max(ema_nimg, 1e-8))\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=ema_beta,\n start_iter=0)\n\nmodel = dict(\n generator=dict(\n out_size=1024,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(\n type='ADAStyleGAN2Discriminator',\n in_size=1024,\n input_bgr2rgb=True,\n data_aug=dict(type='ADAAug', aug_pipeline=aug_kwargs, ada_kimg=100)),\n loss_config=dict(\n r1_loss_weight=r1_gamma / 2.0 * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC'),\n ema_config=ema_config)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.0025 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/metfaces/images/'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=160000)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/ffhq_flip.py',\n '../_base_/gen_default_runtime.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\n\nr1_gamma = 32.8\nd_reg_interval = 16\n\nmodel = dict(\n generator=dict(\n out_size=1024,\n img_channels=3,\n synthesis_cfg=synthesis_cfg,\n rgb2bgr=True),\n discriminator=dict(type='StyleGAN2Discriminator', in_size=1024))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py',\n '../_base_/gen_default_runtime.py'\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 32768,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\nmodel = dict(\n generator=dict(\n out_size=256,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1))\n\nbatch_size = 4\ndata_root = './data/ffhqu/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py', '../_base_/gen_default_runtime.py',\n '../_base_/datasets/unconditional_imgs_flip_512x512.py'\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\nmodel = dict(\n generator=dict(\n type='StyleGANv3Generator',\n noise_size=512,\n style_channels=512,\n out_size=512,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(type='StyleGAN2Discriminator', in_size=512))\n\nbatch_size = 4\ndata_root = 'data/afhqv2/'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/ffhq_flip.py',\n '../_base_/gen_default_runtime.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 6.6 # set by user\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nload_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth' # noqa\n# ada settings\naug_kwargs = {\n 'xflip': 1,\n 'rotate90': 1,\n 'xint': 1,\n 'scale': 1,\n 'rotate': 1,\n 'aniso': 1,\n 'xfrac': 1,\n 'brightness': 1,\n 'contrast': 1,\n 'lumaflip': 1,\n 'hue': 1,\n 'saturation': 1\n}\n\nema_half_life = 10. # G_smoothing_kimg\n\nema_kimg = 10\nema_nimg = ema_kimg * 1000\nema_beta = 0.5**(32 / max(ema_nimg, 1e-8))\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=ema_beta,\n start_iter=0)\n\nmodel = dict(\n generator=dict(\n out_size=1024,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(\n type='ADAStyleGAN2Discriminator',\n in_size=1024,\n input_bgr2rgb=True,\n data_aug=dict(type='ADAAug', aug_pipeline=aug_kwargs, ada_kimg=100)),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.0025 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/metfaces/images/'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=160000)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py', '../_base_/gen_default_runtime.py',\n '../_base_/datasets/unconditional_imgs_flip_512x512.py'\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nmodel = dict(\n generator=dict(\n out_size=512,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=512))\n\nbatch_size = 4\ndata_root = 'data/afhqv2/'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/gen_default_runtime.py',\n '../_base_/datasets/ffhq_flip.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\n\nmodel = dict(\n generator=dict(\n out_size=1024,\n img_channels=3,\n synthesis_cfg=synthesis_cfg,\n rgb2bgr=True),\n discriminator=dict(in_size=1024))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/gen_default_runtime.py',\n '../_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 16384,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nmodel = dict(\n generator=dict(\n out_size=256,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1))\n\nbatch_size = 4\ndata_root = './data/ffhqu/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py',\n '../_base_/gen_default_runtime.py',\n]\n\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 16384,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 2. # set by user\nd_reg_interval = 16\n\nema_config = dict(\n type='RampUpEMA',\n interval=1,\n ema_kimg=10,\n ema_rampup=0.05,\n batch_size=32,\n eps=1e-8,\n start_iter=0)\n\nmodel = dict(\n generator=dict(out_size=256, img_channels=3, synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.0025 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=800002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='Equivariance',\n fake_nums=50000,\n sample_mode='ema',\n prefix='EQ',\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/styleganv3/stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py", "content": "_base_ = [\n '../_base_/models/base_styleganv3.py',\n '../_base_/datasets/ffhq_flip.py',\n '../_base_/gen_default_runtime.py',\n]\n\nbatch_size = 32\nmagnitude_ema_beta = 0.5**(batch_size / (20 * 1e3))\nsynthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 32.8\nd_reg_interval = 16\n\nema_config = dict(\n type='RampUpEMA',\n interval=1,\n ema_kimg=10,\n ema_rampup=0.05,\n batch_size=batch_size,\n eps=1e-8,\n start_iter=0)\n\nmodel = dict(\n generator=dict(out_size=1024, img_channels=3, synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=1024),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper = dict(\n generator=dict(\n optimizer=dict(\n type='Adam', lr=0.0025 * g_reg_ratio, betas=(0,\n 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type='Adam', lr=0.002 * d_reg_ratio, betas=(0,\n 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/ffhq/images'\n\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=800002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "configs/swinir/README.md", "content": "# SwinIR (ICCVW'2021)\n\n> [SwinIR: Image Restoration Using Swin Transformer](https://arxiv.org/abs/2108.10257)\n\n> **Task**: Image Super-Resolution, Image denoising, JPEG compression artifact reduction\n\n\n\n## Abstract\n\n\n\nImage restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.\n\n\n\n
\n \n
\n\n## Results and models\n\n### **Classical Image Super-Resolution**\n\nEvaluated on Y channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | Task | Scale | PSNR | SSIM | Training Resources | Download |\n| :----------------------------------------------------------------: | :-----: | :--------------------: | :---: | :-----: | :----: | :----------------: | :-------------------------------------------------------------------: |\n| [swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x2 | 38.3240 | 0.9626 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth) \\| log |\n| [swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x2 | 34.1174 | 0.9230 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth) \\| log |\n| [swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x2 | 37.8921 | 0.9481 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth) \\| log |\n| [swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x3 | 34.8640 | 0.9317 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth) \\| log |\n| [swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x3 | 30.7669 | 0.8508 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth) \\| log |\n| [swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x3 | 34.1397 | 0.8917 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth) \\| log |\n| [swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x4 | 32.7315 | 0.9029 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth) \\| log |\n| [swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x4 | 28.9065 | 0.7915 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth) \\| log |\n| [swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x4 | 32.0953 | 0.8418 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth) \\| log |\n| [swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set5 | Image Super-Resolution | x2 | 38.3971 | 0.9629 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth) \\| log |\n| [swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set14 | Image Super-Resolution | x2 | 34.4149 | 0.9252 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth) \\| log |\n| [swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | DIV2K | Image Super-Resolution | x2 | 37.9473 | 0.9488 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth) \\| log |\n| [swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set5 | Image Super-Resolution | x3 | 34.9335 | 0.9323 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth) \\| log |\n| [swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set14 | Image Super-Resolution | x3 | 30.9258 | 0.8540 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth) \\| log |\n| [swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | DIV2K | Image Super-Resolution | x3 | 34.2830 | 0.8939 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth) \\| log |\n| [swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set5 | Image Super-Resolution | x4 | 32.9214 | 0.9053 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth) \\| log |\n| [swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | Set14 | Image Super-Resolution | x4 | 29.0792 | 0.7953 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth) \\| log |\n| [swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k](/configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | DIV2K | Image Super-Resolution | x4 | 32.3021 | 0.8451 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth) \\| log |\n\n### **Lightweight Image Super-Resolution**\n\nEvaluated on Y channels, `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | Task | Scale | PSNR | SSIM | Training Resources | Download |\n| :----------------------------------------------------------------: | :-----: | :--------------------: | :---: | :-----: | :----: | :----------------: | :-------------------------------------------------------------------: |\n| [swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x2 | 38.1289 | 0.9617 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth) \\| log |\n| [swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x2 | 33.8404 | 0.9207 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth) \\| log |\n| [swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x2 | 37.5844 | 0.9459 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth) \\| log |\n| [swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x3 | 34.6037 | 0.9293 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth) \\| log |\n| [swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x3 | 30.5340 | 0.8468 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth) \\| log |\n| [swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x3 | 33.8394 | 0.8867 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth) \\| log |\n| [swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set5 | Image Super-Resolution | x4 | 32.4343 | 0.8984 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth) \\| log |\n| [swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | Set14 | Image Super-Resolution | x4 | 28.7441 | 0.7861 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth) \\| log |\n| [swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k](/configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | DIV2K | Image Super-Resolution | x4 | 31.8636 | 0.8353 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth) \\| log |\n\n### **Real-World Image Super-Resolution**\n\nEvaluated on Y channels.\nThe metrics are `NIQE` .\n\n| Model | Dataset | Task | NIQE | Training Resources | Download |\n| :-----------------------------------------------------------------: | :---------------: | :--------------------: | :----: | :----------------: | :--------------------------------------------------------------------: |\n| [swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 5.7975 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth) \\| log |\n| [swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 7.2738 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth) \\| log |\n| [swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 5.2329 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth) \\| log |\n| [swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 7.7460 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth) \\| log |\n| [swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 5.1464 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth) \\| log |\n| [swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost](/configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py) | RealSRSet+5images | Image Super-Resolution | 7.6378 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth) \\| log |\n\n### **Grayscale Image Deoising**\n\nEvaluated on grayscale images.\nThe metrics are `PSNR` .\n\n| Model | Dataset | Task | PSNR | Training Resources | Download |\n| :------------------------------------------------------------------------: | :------: | :-------------: | :-----: | :----------------: | :----------------------------------------------------------------------------: |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py) | Set12 | Image denoising | 33.9731 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py) | BSD68 | Image denoising | 32.5203 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py) | Urban100 | Image denoising | 34.3424 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py) | Set12 | Image denoising | 31.6434 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py) | BSD68 | Image denoising | 30.1377 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py) | Urban100 | Image denoising | 31.9493 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py) | Set12 | Image denoising | 28.5651 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py) | BSD68 | Image denoising | 27.3157 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py) | Urban100 | Image denoising | 28.6626 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth) \\| log |\n\n### **Color Image Deoising**\n\nEvaluated on RGB channels.\nThe metrics are `PSNR` .\n\n| Model | Dataset | Task | PSNR | Training Resources | Download |\n| :------------------------------------------------------------------------: | :------: | :-------------: | :-----: | :----------------: | :----------------------------------------------------------------------------: |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py) | CBSD68 | Image denoising | 34.4136 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py) | Kodak24 | Image denoising | 35.3555 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py) | McMaster | Image denoising | 35.6205 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py) | Urban100 | Image denoising | 35.1836 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py) | CBSD68 | Image denoising | 31.7626 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py) | Kodak24 | Image denoising | 32.9003 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py) | McMaster | Image denoising | 33.3198 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py) | Urban100 | Image denoising | 32.9458 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py) | CBSD68 | Image denoising | 28.5346 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py) | Kodak24 | Image denoising | 29.8058 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py) | McMaster | Image denoising | 30.2027 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50](/configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py) | Urban100 | Image denoising | 29.8832 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth) \\| log |\n\n### **JPEG Compression Artifact Reduction (grayscale)**\n\nEvaluated on grayscale images.\nThe metrics are \\`PSNR / SSIM\n\n| Model | Dataset | Task | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------------------: | :------: | :---------------------------------: | :-----: | :----: | :----------------: | :---------------------------------------------------------------: |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py) | Classic5 | JPEG compression artifact reduction | 30.2746 | 0.8254 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py) | LIVE1 | JPEG compression artifact reduction | 29.8611 | 0.8292 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py) | Classic5 | JPEG compression artifact reduction | 32.5331 | 0.8753 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py) | LIVE1 | JPEG compression artifact reduction | 32.2667 | 0.8914 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py) | Classic5 | JPEG compression artifact reduction | 33.7504 | 0.8966 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py) | LIVE1 | JPEG compression artifact reduction | 33.7001 | 0.9179 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py) | Classic5 | JPEG compression artifact reduction | 34.5377 | 0.9087 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py) | LIVE1 | JPEG compression artifact reduction | 34.6846 | 0.9322 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth) \\| log |\n\n### **JPEG Compression Artifact Reduction (color)**\n\nEvaluated on RGB channels.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | Task | PSNR | SSIM | Training Resources | Download |\n| :-----------------------------------------------------------: | :------: | :---------------------------------: | :-----: | :----: | :----------------: | :---------------------------------------------------------------: |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py) | Classic5 | JPEG compression artifact reduction | 30.1019 | 0.8217 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py) | LIVE1 | JPEG compression artifact reduction | 28.0676 | 0.8094 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py) | Classic5 | JPEG compression artifact reduction | 32.3489 | 0.8727 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py) | LIVE1 | JPEG compression artifact reduction | 30.4514 | 0.8745 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py) | Classic5 | JPEG compression artifact reduction | 33.6028 | 0.8949 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py) | LIVE1 | JPEG compression artifact reduction | 31.8235 | 0.9023 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py) | Classic5 | JPEG compression artifact reduction | 34.4344 | 0.9076 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40](/configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py) | LIVE1 | JPEG compression artifact reduction | 32.7610 | 0.9179 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth) \\| log |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n\n# 002 Lightweight Image Super-Resolution (small size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n\n# 003 Real-World Image Super-Resolution\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n\n# 004 Grayscale Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py\n\n# 005 Color Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py\n\n# color\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py\n\n\n# single-gpu train\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\npython tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\npython tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\npython tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\npython tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n\n# 002 Lightweight Image Super-Resolution (small size)\npython tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n\n# 003 Real-World Image Super-Resolution\npython tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n\n# 004 Grayscale Image Deoising (middle size)\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py\n\n# 005 Color Image Deoising (middle size)\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py\n\n# color\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py\n\n\n# multi-gpu train\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\n./tools/dist_train.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\n./tools/dist_train.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n\n# 002 Lightweight Image Super-Resolution (small size)\n./tools/dist_train.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n\n# 003 Real-World Image Super-Resolution\n./tools/dist_train.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8\n\n# 004 Grayscale Image Deoising (middle size)\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py 8\n\n# 005 Color Image Deoising (middle size)\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py 8\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py 8\n\n# color\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n\n# 002 Lightweight Image Super-Resolution (small size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n# 003 Real-World Image Super-Resolution\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n# 004 Grayscale Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n# 005 Color Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)\n# grayscale\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n\n# color\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth\n\n\n\n# single-gpu test\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\npython tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\npython tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\npython tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\npython tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\npython tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\npython tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n\n# 002 Lightweight Image Super-Resolution (small size)\npython tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\npython tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\npython tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n\n# 003 Real-World Image Super-Resolution\npython tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\npython tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\npython tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n\n# 004 Grayscale Image Deoising (middle size)\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n\n# 005 Color Image Deoising (middle size)\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)\n# grayscale\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n\n# color\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth\n\n\n\n# multi-gpu test\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\n./tools/dist_test.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\n./tools/dist_test.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n# 002 Lightweight Image Super-Resolution (small size)\n./tools/dist_test.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n# 003 Real-World Image Super-Resolution\n./tools/dist_test.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\n./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\n./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n# 004 Grayscale Image Deoising (middle size)\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n# 005 Color Image Deoising (middle size)\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n# color\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/\n\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{liang2021swinir,\n title={Swinir: Image restoration using swin transformer},\n author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={1833--1844},\n year={2021}\n}\n```\n" }, { "path": "configs/swinir/README_zh-CN.md", "content": "# SwinIR (ICCVW'2021)\n\n> **任务**: 图像超分辨率, 图像去噪, JPEG压缩伪影移除\n\n\n\n
\nSwinIR (ICCVW'2021)\n\n```bibtex\n@inproceedings{liang2021swinir,\n title={Swinir: Image restoration using swin transformer},\n author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={1833--1844},\n year={2021}\n}\n```\n\n
\n\n
\n\n### **Classical Image Super-Resolution**\n\n在 Y 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :-----------------------------------------------------------------: |\n| [swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k](./swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | 38.3240 | 34.1174 | 37.8921 | 0.9626 | 0.9230 | 0.9481 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth) \\| log |\n| [swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k](./swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | 34.8640 | 30.7669 | 34.1397 | 0.9317 | 0.8508 | 0.8917 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth) \\| log |\n| [swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k](./swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py) | 32.7315 | 28.9065 | 32.0953 | 0.9029 | 0.7915 | 0.8418 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth) \\| log |\n| [swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k](./swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | 38.3971 | 34.4149 | 37.9473 | 0.9629 | 0.9252 | 0.9488 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth) \\| log |\n| [swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k](./swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | 34.9335 | 30.9258 | 34.2830 | 0.9323 | 0.8540 | 0.8939 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth) \\| log |\n| [swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k](./swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py) | 32.9214 | 29.0792 | 32.3021 | 0.9053 | 0.7953 | 0.8451 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth) \\| log |\n\n### **Lightweight Image Super-Resolution**\n\n在 Y 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :-----------------------------------------------------------------: |\n| [swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k](./swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | 38.1289 | 33.8404 | 37.5844 | 0.9617 | 0.9207 | 0.9459 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth) \\| log |\n| [swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k](./swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | 34.6037 | 30.5340 | 33.8394 | 0.9293 | 0.8468 | 0.8867 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth) \\| log |\n| [swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k](./swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py) | 32.4343 | 28.7441 | 31.8636 | 0.8984 | 0.7861 | 0.8353 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth) \\| log |\n\n### **Real-World Image Super-Resolution**\n\n在 Y 通道上进行评估。\n我们使用 NIQE 作为指标。\n\n| 算法 | RealSRSet+5images NIQE | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------------------: | :--------------------: | :------: | :-----------------------------------------------------------------------------------: |\n| [swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](./swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | 5.7975 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth) \\| log |\n| [swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](./swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | 7.2738 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth) \\| log |\n| [swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](./swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | 5.2329 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth) \\| log |\n| [swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost](./swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py) | 7.7460 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth) \\| log |\n| [swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost](./swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py) | 5.1464 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth) \\| log |\n| [swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost](./swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py) | 7.6378 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth) \\| log |\n\n### **Grayscale Image Deoising**\n\n在灰度图上进行评估。\n我们使用 PSNR 作为指标。\n\n| 算法 | Set12 PSNR | BSD68 PSNR | Urban100 PSNR | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------------: | :--------: | :--------: | :-----------: | :------: | :------------------------------------------------------------------------------: |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py) | 33.9731 | 32.5203 | 34.3424 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py) | 31.6434 | 30.1377 | 31.9493 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py) | 28.5651 | 27.3157 | 28.6626 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth) \\| log |\n\n### **Color Image Deoising**\n\n在 RGB 通道上进行评估。\n我们使用 PSNR 作为指标。\n\n| 算法 | CBSD68 PSNR | Kodak24 PSNR | McMaster PSNR | Urban100 PSNR | GPU 信息 | 下载 |\n| :---------------------------------------------------------------------: | :---------: | :----------: | :-----------: | :-----------: | :------: | :----------------------------------------------------------------------: |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py) | 34.4136 | 35.3555 | 35.6205 | 35.1836 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py) | 31.7626 | 32.9003 | 33.3198 | 32.9458 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth) \\| log |\n| [swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50](./swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py) | 28.5346 | 29.8058 | 30.2027 | 29.8832 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth) \\| log |\n\n### **JPEG Compression Artifact Reduction (grayscale)**\n\n在灰度图上进行评估。\n我们使用 PSNR 和 SSIM 作为指标。\n\n| 算法 | Classic5 PSNR | Classic5 SSIM | LIVE1 PSNR | LIVE1 SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------: | :-----------: | :-----------: | :--------: | :--------: | :------: | :-----------------------------------------------------------------------: |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py) | 30.2746 | 0.8254 | 29.8611 | 0.8292 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py) | 32.5331 | 0.8753 | 32.2667 | 0.8914 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py) | 33.7504 | 0.8966 | 33.7001 | 0.9179 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py) | 34.5377 | 0.9087 | 34.6846 | 0.9322 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth) \\| log |\n\n### **JPEG Compression Artifact Reduction (color)**\n\n在 RGB 通道上进行评估。\n我们使用 PSNR 和 SSIM 作为指标。\n\n| 算法 | Classic5 PSNR | Classic5 SSIM | LIVE1 PSNR | LIVE1 SSIM | GPU 信息 | 下载 |\n| :-----------------------------------------------------------------------: | :-----------: | :-----------: | :--------: | :--------: | :------: | :-----------------------------------------------------------------------: |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py) | 30.1019 | 0.8217 | 28.0676 | 0.8094 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py) | 32.3489 | 0.8727 | 30.3489 | 0.8745 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py) | 33.6028 | 0.8949 | 31.8235 | 0.9023 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth) \\| log |\n| [swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40](./swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py) | 34.4344 | 0.9076 | 32.7610 | 0.9179 | 8 | [model](https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth) \\| log |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n\n# 002 Lightweight Image Super-Resolution (small size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n\n# 003 Real-World Image Super-Resolution\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n\n# 004 Grayscale Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py\n\n# 005 Color Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py\n\n# color\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py\n\n\n\n# 单个GPU上训练\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\npython tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\npython tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\npython tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\npython tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n\n# 002 Lightweight Image Super-Resolution (small size)\npython tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\npython tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n\n# 003 Real-World Image Super-Resolution\npython tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\npython tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n\n# 004 Grayscale Image Deoising (middle size)\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py\n\n# 005 Color Image Deoising (middle size)\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py\npython tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py\n\n# color\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py\npython tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py\n\n\n\n# 多个GPU上训练\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\n./tools/dist_train.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\n./tools/dist_train.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8\n\n# 002 Lightweight Image Super-Resolution (small size)\n./tools/dist_train.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n./tools/dist_train.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8\n\n# 003 Real-World Image Super-Resolution\n./tools/dist_train.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8\n./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8\n\n# 004 Grayscale Image Deoising (middle size)\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py 8\n\n# 005 Color Image Deoising (middle size)\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py 8\n./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py 8\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py 8\n\n# color\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py 8\n./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n\n# 002 Lightweight Image Super-Resolution (small size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n# 003 Real-World Image Super-Resolution\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n# 004 Grayscale Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n# 005 Color Image Deoising (middle size)\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)\n# grayscale\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n\n# color\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth\n\n\n\n# 单个GPU上测试\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\npython tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\npython tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\npython tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\npython tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\npython tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\npython tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n\n# 002 Lightweight Image Super-Resolution (small size)\npython tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\npython tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\npython tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n\n# 003 Real-World Image Super-Resolution\npython tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\npython tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\npython tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\npython tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n\n# 004 Grayscale Image Deoising (middle size)\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n\n# 005 Color Image Deoising (middle size)\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\npython tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)\n# grayscale\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n\n# color\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\npython tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth\n\n\n\n# 多GPU测试\n# 001 Classical Image Super-Resolution (middle size)\n# (setting1: when model is trained on DIV2K and with training_patch_size=48)\n./tools/dist_test.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n\n# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)\n./tools/dist_test.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n\n# 002 Lightweight Image Super-Resolution (small size)\n./tools/dist_test.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n\n./tools/dist_test.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n\n# 003 Real-World Image Super-Resolution\n./tools/dist_test.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n\n./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n\n./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n\n./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth\n\n# 004 Grayscale Image Deoising (middle size)\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n\n# 005 Color Image Deoising (middle size)\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n\n# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)\n# grayscale\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n\n# color\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\n./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/swinir/metafile.yml", "content": "Collections:\n- Name: SwinIR\n Paper:\n Title: 'SwinIR: Image Restoration Using Swin Transformer'\n URL: https://arxiv.org/abs/2108.10257\n README: configs/swinir/README.md\n Task:\n - image super-resolution\n - image denoising\n - jpeg compression artifact reduction\n Year: 2021\nModels:\n- Config: configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 38.324\n SSIM: 0.9626\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 34.1174\n SSIM: 0.923\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 37.8921\n SSIM: 0.9481\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth\n- Config: configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 34.864\n SSIM: 0.9317\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 30.7669\n SSIM: 0.8508\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.1397\n SSIM: 0.8917\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth\n- Config: configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 32.7315\n SSIM: 0.9029\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 28.9065\n SSIM: 0.7915\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 32.0953\n SSIM: 0.8418\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth\n- Config: configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n In Collection: SwinIR\n Name: swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 38.3971\n SSIM: 0.9629\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 34.4149\n SSIM: 0.9252\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 37.9473\n SSIM: 0.9488\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth\n- Config: configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n In Collection: SwinIR\n Name: swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 34.9335\n SSIM: 0.9323\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 30.9258\n SSIM: 0.854\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 34.283\n SSIM: 0.8939\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth\n- Config: configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py\n In Collection: SwinIR\n Name: swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 32.9214\n SSIM: 0.9053\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 29.0792\n SSIM: 0.7953\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 32.3021\n SSIM: 0.8451\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth\n- Config: configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 38.1289\n SSIM: 0.9617\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 33.8404\n SSIM: 0.9207\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 37.5844\n SSIM: 0.9459\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth\n- Config: configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 34.6037\n SSIM: 0.9293\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 30.534\n SSIM: 0.8468\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 33.8394\n SSIM: 0.8867\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth\n- Config: configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py\n In Collection: SwinIR\n Name: swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k\n Results:\n - Dataset: Set5\n Metrics:\n PSNR: 32.4343\n SSIM: 0.8984\n Task: Image Super-Resolution\n - Dataset: Set14\n Metrics:\n PSNR: 28.7441\n SSIM: 0.7861\n Task: Image Super-Resolution\n - Dataset: DIV2K\n Metrics:\n PSNR: 31.8636\n SSIM: 0.8353\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth\n- Config: configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 5.7975\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth\n- Config: configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 7.2738\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth\n- Config: configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 5.2329\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth\n- Config: configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 7.746\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth\n- Config: configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 5.1464\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n- Config: configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py\n In Collection: SwinIR\n Name: swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost\n Results:\n - Dataset: RealSRSet+5images\n Metrics:\n NIQE: 7.6378\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15\n Results:\n - Dataset: Set12\n Metrics:\n PSNR: 33.9731\n Task: Image denoising\n - Dataset: BSD68\n Metrics:\n PSNR: 32.5203\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 34.3424\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25\n Results:\n - Dataset: Set12\n Metrics:\n PSNR: 31.6434\n Task: Image denoising\n - Dataset: BSD68\n Metrics:\n PSNR: 30.1377\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 31.9493\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50\n Results:\n - Dataset: Set12\n Metrics:\n PSNR: 28.5651\n Task: Image denoising\n - Dataset: BSD68\n Metrics:\n PSNR: 27.3157\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 28.6626\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15\n Results:\n - Dataset: CBSD68\n Metrics:\n PSNR: 34.4136\n Task: Image denoising\n - Dataset: Kodak24\n Metrics:\n PSNR: 35.3555\n Task: Image denoising\n - Dataset: McMaster\n Metrics:\n PSNR: 35.6205\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 35.1836\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25\n Results:\n - Dataset: CBSD68\n Metrics:\n PSNR: 31.7626\n Task: Image denoising\n - Dataset: Kodak24\n Metrics:\n PSNR: 32.9003\n Task: Image denoising\n - Dataset: McMaster\n Metrics:\n PSNR: 33.3198\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 32.9458\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth\n- Config: configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py\n In Collection: SwinIR\n Name: swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50\n Results:\n - Dataset: CBSD68\n Metrics:\n PSNR: 28.5346\n Task: Image denoising\n - Dataset: Kodak24\n Metrics:\n PSNR: 29.8058\n Task: Image denoising\n - Dataset: McMaster\n Metrics:\n PSNR: 30.2027\n Task: Image denoising\n - Dataset: Urban100\n Metrics:\n PSNR: 29.8832\n Task: Image denoising\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 30.2746\n SSIM: 0.8254\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 29.8611\n SSIM: 0.8292\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 32.5331\n SSIM: 0.8753\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 32.2667\n SSIM: 0.8914\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 33.7504\n SSIM: 0.8966\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 33.7001\n SSIM: 0.9179\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 34.5377\n SSIM: 0.9087\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 34.6846\n SSIM: 0.9322\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 30.1019\n SSIM: 0.8217\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 28.0676\n SSIM: 0.8094\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 32.3489\n SSIM: 0.8727\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 30.4514\n SSIM: 0.8745\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 33.6028\n SSIM: 0.8949\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 31.8235\n SSIM: 0.9023\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth\n- Config: configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py\n In Collection: SwinIR\n Name: swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40\n Results:\n - Dataset: Classic5\n Metrics:\n PSNR: 34.4344\n SSIM: 0.9076\n Task: JPEG compression artifact reduction\n - Dataset: LIVE1\n Metrics:\n PSNR: 32.761\n SSIM: 0.9179\n Task: JPEG compression artifact reduction\n Weights: https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth\n" }, { "path": "configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py']\n\nexperiment_name = 'swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n" }, { "path": "configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py']\n\nexperiment_name = 'swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n" }, { "path": "configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py']\n\nexperiment_name = 'swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n" }, { "path": "configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['../_base_/default_runtime.py']\n\nexperiment_name = 'swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\nimg_size = 64\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=scale,\n in_chans=3,\n img_size=img_size,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='nearest+conv',\n resi_connection='1conv'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntest_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\ntest_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicImageDataset',\n metainfo=dict(dataset_type='realsrset', task_name='realsr'),\n data_root='data/RealSRSet+5images',\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\n\ntest_evaluator = [dict(type='NIQE', input_order='CHW', convert_to='Y')]\n\ntest_cfg = dict(type='TestLoop')\n" }, { "path": "configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py']\n\nexperiment_name = 'swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n\n# model settings\nmodel = dict(generator=dict(upscale=scale))\n" }, { "path": "configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py", "content": "_base_ = ['swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py']\n\nexperiment_name = 'swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# model settings\nmodel = dict(\n generator=dict(\n depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],\n embed_dim=240,\n num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],\n resi_connection='3conv'))\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/decompression_test_config.py'\n]\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nquality = 10\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=1,\n in_chans=3,\n img_size=126,\n window_size=7,\n img_range=255.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='',\n resi_connection='1conv'),\n pixel_loss=dict(type='CharbonnierLoss', eps=1e-9),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedRandomCrop', gt_patch_size=126),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[quality, quality], color_type='color'),\n keys=['img']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[quality, quality], color_type='color'),\n keys=['img']),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=2,\n batch_size=1,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DFWB8550sub_GT.txt',\n metainfo=dict(dataset_type='dfwb', task_name='CAR'),\n data_root=data_root + '/DFWB',\n data_prefix=dict(img='', gt=''),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=2,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='live1', task_name='CAR'),\n data_root=data_root + '/LIVE1',\n data_prefix=dict(img='', gt=''),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', prefix='LIVE1'),\n dict(type='SSIM', prefix='LIVE1'),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_600_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[800000, 1200000, 1400000, 1500000, 1600000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 20\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 30\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 40\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/decompression_test_config.py'\n]\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nquality = 10\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=1,\n in_chans=1,\n img_size=126,\n window_size=7,\n img_range=255.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='',\n resi_connection='1conv'),\n pixel_loss=dict(type='CharbonnierLoss', eps=1e-9),\n data_preprocessor=dict(type='DataPreprocessor', mean=[0.], std=[255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedRandomCrop', gt_patch_size=126),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[quality, quality], color_type='grayscale'),\n keys=['img']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[quality, quality], color_type='grayscale'),\n keys=['img']),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=1,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DFWB8550sub_GT.txt',\n metainfo=dict(dataset_type='dfwb', task_name='CAR'),\n data_root=data_root + '/DFWB',\n data_prefix=dict(img='', gt=''),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='classic5', task_name='CAR'),\n data_root=data_root + '/Classic5',\n data_prefix=dict(img='', gt=''),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', prefix='Classic5'),\n dict(type='SSIM', prefix='Classic5'),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_600_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\ntest_dataloader = _base_.test_dataloader\nfor idx in range(len(test_dataloader)):\n test_pipeline = test_dataloader[idx]['dataset']['pipeline']\n if idx > 0:\n test_pipeline[0]['to_y_channel'] = True\n test_pipeline[1]['to_y_channel'] = True\n else:\n test_pipeline[0]['color_type'] = 'grayscale'\n test_pipeline[1]['color_type'] = 'grayscale'\n test_pipeline[2]['color_type'] = 'grayscale'\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[800000, 1200000, 1400000, 1500000, 1600000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 20\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 30\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py", "content": "_base_ = ['swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py']\n\nexperiment_name = 'swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify JPEG quality factor of RandomJPEGCompression\nquality = 40\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['quality'] = [quality, quality]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['quality'] = [quality, quality]\n\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['quality'] = [quality, quality]\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_color_test_config.py'\n]\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 15\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=1,\n in_chans=3,\n img_size=128,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='',\n resi_connection='1conv'),\n pixel_loss=dict(type='CharbonnierLoss', eps=1e-9),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma * 255, sigma * 255],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma * 255, sigma * 255],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=1,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DFWB8550sub_GT.txt',\n metainfo=dict(dataset_type='dfwb', task_name='denoising'),\n data_root=data_root + '/DFWB',\n data_prefix=dict(img='', gt=''),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='mcmaster', task_name='denoising'),\n data_root=data_root + '/McMaster',\n data_prefix=dict(img='', gt=''),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', prefix='McMaster'),\n dict(type='SSIM', prefix='McMaster'),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_600_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[800000, 1200000, 1400000, 1500000, 1600000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py", "content": "_base_ = ['swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py']\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 25\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['gaussian_sigma'] = [sigma, sigma]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py", "content": "_base_ = ['swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py']\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 50\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['gaussian_sigma'] = [sigma, sigma]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py", "content": "_base_ = [\n '../_base_/default_runtime.py',\n '../_base_/datasets/denoising-gaussian_gray_test_config.py'\n]\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 15\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=1,\n in_chans=1,\n img_size=128,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='',\n resi_connection='1conv'),\n pixel_loss=dict(type='CharbonnierLoss', eps=1e-9),\n data_preprocessor=dict(type='DataPreprocessor', mean=[0.], std=[255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=1)),\n dict(type='PairedRandomCrop', gt_patch_size=128),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='grayscale',\n imdecode_backend='cv2'),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=1,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DFWB8550sub_GT.txt',\n metainfo=dict(dataset_type='dfwb', task_name='denoising'),\n data_root=data_root + '/DFWB',\n data_prefix=dict(img='', gt=''),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set12', task_name='denoising'),\n data_root=data_root + '/Set12',\n data_prefix=dict(img='', gt=''),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', prefix='Set12'),\n dict(type='SSIM', prefix='Set12'),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=1_600_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[800000, 1200000, 1400000, 1500000, 1600000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py", "content": "_base_ = ['swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py']\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 25\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['gaussian_sigma'] = [sigma, sigma]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n" }, { "path": "configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py", "content": "_base_ = ['swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py']\n\nexperiment_name = 'swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# modify sigma of RandomNoise\nsigma = 50\ntest_dataloader = _base_.test_dataloader\nfor dataloader in test_dataloader:\n test_pipeline = dataloader['dataset']['pipeline']\n test_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[-2]['params']['gaussian_sigma'] = [sigma, sigma]\n\nval_dataloader = _base_.val_dataloader\nval_pipeline = val_dataloader['dataset']['pipeline']\nval_pipeline[2]['params']['gaussian_sigma'] = [sigma, sigma]\n" }, { "path": "configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x2_test_config.py'\n]\n\nexperiment_name = 'swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\nimg_size = 48\n\n# evaluated on Y channels\ntest_evaluator = _base_.test_evaluator\nfor evaluator in test_evaluator:\n for metric in evaluator['metrics']:\n metric['convert_to'] = 'Y'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=scale,\n in_chans=3,\n img_size=img_size,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='pixelshuffle',\n resi_connection='1conv'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=img_size * scale),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=500_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[250000, 400000, 450000, 475000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = ['swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\nimg_size = 64\n\n# model settings\nmodel = dict(\n generator=dict(\n img_size=img_size,\n depths=[6, 6, 6, 6],\n embed_dim=60,\n num_heads=[6, 6, 6, 6],\n upsampler='pixelshuffledirect'))\n\n# modify patch size of train_dataloader\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n" }, { "path": "configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py", "content": "_base_ = ['swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 2\nimg_size = 64\n\n# model settings\nmodel = dict(generator=dict(img_size=img_size))\n\n# modify patch size of train_pipeline\ntrain_pipeline = _base_.train_pipeline\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DF2K3450sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DF2K',\n data_prefix=dict(\n img='DF2K_train_LR_bicubic/X2_sub', gt='DF2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n" }, { "path": "configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x3_test_config.py'\n]\n\nexperiment_name = 'swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 3\nimg_size = 48\n\n# evaluated on Y channels\ntest_evaluator = _base_.test_evaluator\nfor evaluator in test_evaluator:\n for metric in evaluator['metrics']:\n metric['convert_to'] = 'Y'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=scale,\n in_chans=3,\n img_size=img_size,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='pixelshuffle',\n resi_connection='1conv'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=img_size * scale),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X3_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=500_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[250000, 400000, 450000, 475000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = ['swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 3\nimg_size = 64\n\n# model settings\nmodel = dict(\n generator=dict(\n img_size=img_size,\n depths=[6, 6, 6, 6],\n embed_dim=60,\n num_heads=[6, 6, 6, 6],\n upsampler='pixelshuffledirect'))\n\n# modify patch size of train_dataloader\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n" }, { "path": "configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py", "content": "_base_ = ['swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 3\nimg_size = 64\n\n# model settings\nmodel = dict(generator=dict(img_size=img_size))\n\n# modify patch size of train_pipeline\ntrain_pipeline = _base_.train_pipeline\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DF2K3450sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DF2K',\n data_prefix=dict(\n img='DF2K_train_LR_bicubic/X3_sub', gt='DF2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n" }, { "path": "configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/sisr_x4_test_config.py'\n]\n\nexperiment_name = 'swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\nimg_size = 48\n\n# evaluated on Y channels\ntest_evaluator = _base_.test_evaluator\nfor evaluator in test_evaluator:\n for metric in evaluator['metrics']:\n metric['convert_to'] = 'Y'\n\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SwinIRNet',\n upscale=scale,\n in_chans=3,\n img_size=img_size,\n window_size=8,\n img_range=1.0,\n depths=[6, 6, 6, 6, 6, 6],\n embed_dim=180,\n num_heads=[6, 6, 6, 6, 6, 6],\n mlp_ratio=2,\n upsampler='pixelshuffle',\n resi_connection='1conv'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=img_size * scale),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = [\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n]\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=500_000, val_interval=5000)\nval_cfg = dict(type='ValLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR',\n by_epoch=False,\n milestones=[250000, 400000, 450000, 475000],\n gamma=0.5)\n" }, { "path": "configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py", "content": "_base_ = ['swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\nimg_size = 64\n\n# model settings\nmodel = dict(\n generator=dict(\n img_size=img_size,\n depths=[6, 6, 6, 6],\n embed_dim=60,\n num_heads=[6, 6, 6, 6],\n upsampler='pixelshuffledirect'))\n\n# modify patch size of train_dataloader\ntrain_dataloader = _base_.train_dataloader\ntrain_pipeline = train_dataloader['dataset']['pipeline']\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n" }, { "path": "configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py", "content": "_base_ = ['swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py']\n\nexperiment_name = 'swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\nimg_size = 64\n\n# model settings\nmodel = dict(generator=dict(img_size=img_size))\n\n# modify patch size of train_pipeline\ntrain_pipeline = _base_.train_pipeline\ntrain_pipeline[3]['gt_patch_size'] = img_size * scale\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=4,\n drop_last=True,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DF2K3450sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DF2K',\n data_prefix=dict(\n img='DF2K_train_LR_bicubic/X4_sub', gt='DF2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=train_pipeline))\n" }, { "path": "configs/tdan/README.md", "content": "# TDAN (CVPR'2020)\n\n> [TDAN: Temporally Deformable Alignment Network for Video Super-Resolution](https://arxiv.org/abs/1812.02898)\n\n> **Task**: Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nVideo super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not aligned. Therefore, temporal alignment is a challenging yet important problem for VSR. Previous VSR methods usually utilize optical flow between the reference frame and each supporting frame to wrap the supporting frame for temporal alignment. Therefore, the performance of these image-level wrapping-based models will highly depend on the prediction accuracy of optical flow, and inaccurate optical flow will lead to artifacts in the wrapped supporting frames, which also will be propagated into the reconstructed HR video frame. To overcome the limitation, in this paper, we propose a temporal deformable alignment network (TDAN) to adaptively align the reference frame and each supporting frame at the feature level without computing optical flow. The TDAN uses features from both the reference frame and each supporting frame to dynamically predict offsets of sampling convolution kernels. By using the corresponding kernels, TDAN transforms supporting frames to align with the reference frame. To predict the HR video frame, a reconstruction network taking aligned frames and the reference frame is utilized. Experimental results demonstrate the effectiveness of the proposed TDAN-based VSR model.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on Y-channel. 8 pixels in each border are cropped before evaluation.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | PSNR (Y) | SSIM (Y) | Training Resources | Download |\n| :--------------------------------------------------------------------: | :-------------: | :-------: | :-------: | :----------------------: | :-----------------------------------------------------------------------: |\n| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | 8 (Tesla V100-SXM2-32GB) | - |\n| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | 8 (Tesla V100-SXM2-32GB) | - |\n| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | Vid4 (BIx4) | **26.49** | **0.792** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | SPMCS-30 (BIx4) | **30.42** | **0.856** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | Vid4 (BDx4) | 25.93 | 0.772 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | SPMCS-30 (BDx4) | 29.69 | 0.842 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | Vid4 (BIx4) | 25.80 | 0.784 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | SPMCS-30 (BIx4) | 29.56 | 0.851 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | Vid4 (BDx4) | **26.87** | **0.815** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | SPMCS-30 (BDx4) | **30.77** | **0.868** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\nTDAN is trained with two stages.\n\n**Stage 1**: Train with a larger learning rate (1e-4)\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py\n\n# single-gpu train\npython tools/train.py configs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py\n\n# multi-gpu train\n./tools/dist_train.sh cconfigs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py 8\n```\n\n**Stage 2**: Fine-tune with a smaller learning rate (5e-5)\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py\n\n# single-gpu train\npython tools/train.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth\n\n# single-gpu test\npython tools/test.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@InProceedings{tian2020tdan,\n title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},\n author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},\n booktitle = {Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},\n year = {2020}\n}\n```\n" }, { "path": "configs/tdan/README_zh-CN.md", "content": "# TDAN (CVPR'2020)\n\n> **任务**: 视频超分辨率\n\n\n\n
\nTDAN (CVPR'2020)\n\n```bibtex\n@InProceedings{tian2020tdan,\n title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},\n author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},\n booktitle = {Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},\n year = {2020}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的8像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | Vid4 (BIx4) | SPMCS-30 (BIx4) | Vid4 (BDx4) | SPMCS-30 (BDx4) | GPU 信息 | 下载 |\n| :--------------------------------------------------------: | :-------------: | :-------------: | :-------------: | :-------------: | :----------------------: | :--------------------------------------------------------: |\n| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - |\n| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - |\n| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **26.49/0.792** | **30.42/0.856** | 25.93/0.772 | 29.69/0.842 | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |\n| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 25.80/0.784 | 29.56/0.851 | **26.87/0.815** | **30.77/0.868** | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\nTDAN 训练有两个阶段。\n\n**阶段 1**: 以更大的学习率训练 (1e-4)\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py\n\n# 单个GPU上训练\npython tools/train.py configs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/tdan/tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi.py 8\n```\n\n**阶段 2**: 以较小的学习率进行微调 (5e-5)\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py\n\n# 单个GPU上训练\npython tools/train.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth\n\n# 单个GPU上测试\npython tools/test.py configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/tdan/tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi.py https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/tdan/metafile.yml", "content": "Collections:\n- Name: TDAN\n Paper:\n Title: 'TDAN: Temporally Deformable Alignment Network for Video Super-Resolution'\n URL: https://arxiv.org/abs/1812.02898\n README: configs/tdan/README.md\n Task:\n - video super-resolution\n Year: 2020\nModels:\n- Config: configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py\n In Collection: TDAN\n Name: tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Video Super-Resolution\n- Config: configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py\n In Collection: TDAN\n Name: tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Video Super-Resolution\n- Config: configs/tdan/tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py\n In Collection: TDAN\n Name: tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi\n Results:\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 26.49\n SSIM (Y): 0.792\n Task: Video Super-Resolution\n - Dataset: SPMCS-30(BIx4)\n Metrics:\n PSNR (Y): 30.42\n SSIM (Y): 0.856\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 25.93\n SSIM (Y): 0.772\n Task: Video Super-Resolution\n - Dataset: SPMCS-30(BDx4)\n Metrics:\n PSNR (Y): 29.69\n SSIM (Y): 0.842\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth\n- Config: configs/tdan/tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py\n In Collection: TDAN\n Name: tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd\n Results:\n - Dataset: Vid4(BIx4)\n Metrics:\n PSNR (Y): 25.8\n SSIM (Y): 0.784\n Task: Video Super-Resolution\n - Dataset: SPMCS-30(BIx4)\n Metrics:\n PSNR (Y): 29.56\n SSIM (Y): 0.851\n Task: Video Super-Resolution\n - Dataset: Vid4(BDx4)\n Metrics:\n PSNR (Y): 26.87\n SSIM (Y): 0.815\n Task: Video Super-Resolution\n - Dataset: SPMCS-30(BDx4)\n Metrics:\n PSNR (Y): 30.77\n SSIM (Y): 0.868\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth\n" }, { "path": "configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py", "content": "_base_ = [\n '../_base_/default_runtime.py', '../_base_/datasets/tdan_test_config.py'\n]\n\nexperiment_name = 'tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\nscale = 4\n\n# model settings\nmodel = dict(\n type='TDAN',\n generator=dict(\n type='TDANNet',\n in_channels=3,\n mid_channels=64,\n out_channels=3,\n num_blocks_before_align=5,\n num_blocks_after_align=10),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n lq_pixel_loss=dict(type='MSELoss', loss_weight=0.01, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.5 * 255, 0.5 * 255, 0.5 * 255],\n std=[255, 255, 255],\n ))\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='PSNR', crop_border=8, convert_to='Y'),\n dict(type='SSIM', crop_border=8, convert_to='Y'),\n ])\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=192),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateFrameIndiceswithPadding', padding='reflection'),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vimeo_seq', task_name='vsr'),\n data_root=f'{data_root}/vimeo90k',\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_train_GT.txt',\n depth=2,\n num_input_frames=5,\n fixed_seq_len=7,\n load_frames_list=dict(\n img=['im2.png', 'im3.png', 'im4.png', 'im5.png', 'im6.png'],\n gt=['im4.png']),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=f'{data_root}/Vid4',\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=val_pipeline))\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, weight_decay=1e-6),\n)\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=400_000, val_interval=50000)\nval_cfg = dict(type='MultiValLoop')\n\n# No learning policy\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=50000,\n save_optimizer=True,\n out_dir=save_dir,\n by_epoch=False))\n" }, { "path": "configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py", "content": "_base_ = './tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py'\n\nexperiment_name = 'tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\ntrain_dataloader = dict(dataset=dict(data_prefix=dict(img='BIx4', gt='GT')))\n\nval_dataloader = dict(dataset=dict(data_prefix=dict(img='BIx4', gt='GT')))\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=400_000, val_interval=50000)\nval_cfg = dict(type='MultiValLoop')\n\n# No learning policy\n" }, { "path": "configs/tdan/tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py", "content": "_base_ = './tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py'\n\nexperiment_name = 'tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(_delete_=True, type='Adam', lr=5e-5),\n)\n\n# load_from = 'tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi/iter_400000.pth'\n" }, { "path": "configs/tdan/tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py", "content": "_base_ = './tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py'\n\nexperiment_name = 'tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type='OptimWrapper',\n optimizer=dict(_delete_=True, type='Adam', lr=5e-5),\n)\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=800_000, val_interval=50000)\n\n# load_from = 'tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd/iter_400000.pth'\n" }, { "path": "configs/textual_inversion/README.md", "content": "# Textual Inversion (2022)\n\n> [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://arxiv.org/abs/2208.01618)\n\n> **Task**: Text2Image\n\n\n\n## Abstract\n\n\n\nText-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new \"words\" in the embedding space of a frozen text-to-image model. These \"words\" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks.\n\n\n\n
\n\n
\n\n## Configs\n\n| Model | Dataset | Download |\n| :-----------------------------------------: | :-----: | :------: |\n| [Textual Inversion](./textual_inversion.py) | - | - |\n\n## Quick Start\n\n1. Download [data](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and [template](https://openxlab.org.cn/datasets/ferry/ViCo/tree/main)(two txt files)\n and save to `data`\n\nThe file structure will be like this:\n\n```text\ndata\n└── cat_toy\n ├── 1.jpeg\n ├── 2.jpeg\n ├── 3.jpeg\n ├── 3.jpeg\n ├── 4.jpeg\n ├── 6.jpeg\n └── 7.jpeg\n└── imagenet_templates_small.txt\n└── imagenet_style_templates_small.txt\n```\n\n2. Start training with the following command:\n\n```bash\nbash tools/dist_train.sh configs/textual_inversion/textual_inversion.py 1\n```\n\n
\n \n
\n
\n\n3. Inference with trained textual embedding:\n\n```python\nimport torch\nfrom mmengine import Config\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef process_state_dict(state_dict):\n new_state_dict = dict()\n for k, v in state_dict.items():\n new_k = k.replace('module.', '')\n new_state_dict[new_k] = v\n\n return new_state_dict\n\n\ncfg = Config.fromfile('configs/textual_inversion/textual_inversion.py')\ncheckpoint = torch.load('work_dirs/textual_inversion/iter_3000.pth')\nstate_dict = process_state_dict(checkpoint['state_dict'])\nmodel = MODELS.build(cfg.model)\nmodel.load_state_dict(state_dict)\n\nmodel = model.cuda()\nwith torch.no_grad():\n sample = model.infer('a bag')['samples'][0]\n\nsample.save('cat-toy-bag.png')\n```\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@misc{gal2022textual,\n doi = {10.48550/ARXIV.2208.01618},\n url = {https://arxiv.org/abs/2208.01618},\n author = {Gal, Rinon and Alaluf, Yuval and Atzmon, Yuval and Patashnik, Or and Bermano, Amit H. and Chechik, Gal and Cohen-Or, Daniel},\n title = {An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion},\n publisher = {arXiv},\n year = {2022},\n primaryClass={cs.CV}\n}\n\n```\n" }, { "path": "configs/textual_inversion/metafile.yml", "content": "Collections:\n- Name: Textual Inversion\n Paper:\n Title: 'An Image is Worth One Word: Personalizing Text-to-Image Generation using\n Textual Inversion'\n URL: https://arxiv.org/abs/2208.01618\n README: configs/textual_inversion/README.md\n Task:\n - text2image\n Year: 2022\nModels:\n- Config: configs/textual_inversion/textual_inversion.py\n In Collection: Textual Inversion\n Name: textual_inversion\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n" }, { "path": "configs/textual_inversion/textual_inversion.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\ndtype = 'fp16'\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nplaceholder_token = ''\ninitialize_token = 'toy'\nnum_vectors_per_token = 1\nval_prompts = [\n 'a on packbag', 'a on sofa',\n 'a in swimming pool', 'a '\n]\n\nmodel = dict(\n type='TextualInversion',\n placeholder_token=placeholder_token,\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='unet'),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n initialize_token=initialize_token,\n num_vectors_per_token=num_vectors_per_token,\n val_prompts=val_prompts,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor', data_keys=None))\n\ntrain_cfg = dict(max_iters=3000)\n\noptim_wrapper = dict(\n modules='.*trainable_embeddings',\n optimizer=dict(type='AdamW', lr=5e-4),\n accumulative_counts=1)\n\npipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='Resize', scale=(512, 512)),\n dict(type='PackInputs')\n]\n\ndataset = dict(\n type='TextualInversionDataset',\n data_root='./data/',\n concept_dir='cat_toy',\n placeholder=placeholder_token,\n template='data/imagenet_templates_small.txt',\n pipeline=pipeline)\n\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\ndefault_hooks = dict(\n logger=dict(interval=10),\n checkpoint=dict(type='CheckpointHook', interval=10))\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=50,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "configs/tof/README.md", "content": "# TOFlow (IJCV'2019)\n\n> [Video Enhancement with Task-Oriented Flow](https://arxiv.org/abs/1711.09078)\n\n> **Task**: Video Interpolation, Video Super-Resolution\n\n\n\n## Abstract\n\n\n\nMany video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on Vimeo90k-triplet (RGB channels).\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | Task | Pretrained SPyNet | PSNR | Training Resources | Download |\n| :---------------------------------------: | :--------------: | :-----------------: | :---------------------------------------------------: | :-----: | :-----------------: | :-------------------------------------------: |\n| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Interpolation | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) |\n| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Interpolation | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) |\n| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Interpolation | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) |\n| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Interpolation | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) |\n| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Interpolation | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) |\n\n| Model | Dataset | Task | Pretrained SPyNet | SSIM | Training Resources | Download |\n| :--------------------------------------: | :--------------: | :--------------------: | :---------------------------------------------------: | :----: | :-----------------: | :------------------------------------------: |\n| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Super-Resolution | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) |\n| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Super-Resolution | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 0.9466 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) |\n| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Super-Resolution | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 0.9464 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) |\n| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Super-Resolution | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) |\n| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | Vimeo90k-triplet | Video Super-Resolution | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 0.9467 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \\| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) |\n\nNote: These pretrained SPyNets don't contain BN layer since `batch_size=1`, which is consistent with `https://github.com/Coldog2333/pytoflow`.\n\nEvaluated on RGB channels.\nThe metrics are `PSNR / SSIM` .\n\n| Model | Dataset | Task | Vid4 | Training Resources | Download |\n| :-------------------------------------------------------: | :------: | :--------------------: | :--------------: | :----------------: | :-----------------------------------------------------------------------------: |\n| [tof_x4_vimeo90k_official](./tof_x4_official_vimeo90k.py) | vimeo90k | Video Super-Resolution | 24.4377 / 0.7433 | - | [model](https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\nTOF only supports video interpolation task for training now.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py\n\n# single-gpu train\npython tools/train.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\nTOF supports two tasks for testing.\n\n**Task 1**: Video Interpolation\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth\n\n# single-gpu test\npython tools/test.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth 8\n```\n\n**Task 2**: Video Super-Resolution\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth\n\n# single-gpu test\npython tools/test.py configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@article{xue2019video,\n title={Video enhancement with task-oriented flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n" }, { "path": "configs/tof/README_zh-CN.md", "content": "# TOFlow (IJCV'2019)\n\n> [Video Enhancement with Task-Oriented Flow](https://arxiv.org/abs/1711.09078)\n\n> **任务**: 视频插帧, 视频超分辨率\n\n\n\n## 预训练模型测试结果\n\n在 RGB 通道上评估。\n评估指标 `PSNR / SSIM`。\n\n| 算法 | 预训练 SPyNet | Vimeo90k-triplet | GPU 信息 | 下载 |\n| :--------------------------------------------------: | :----------------------------------------------------------: | :--------------: | :-----------------: | :---------------------------------------------------: |\n| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) |\n| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 / 0.9466 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) |\n| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 / 0.9464 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) |\n| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) |\n| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 / 0.9467 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \\| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) |\n\n注: 由于 `batch_size=1` 预训练的 SPyNet 不包含 BN 层，这与 `https://github.com/Coldog2333/pytoflow` 一致.\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\nTOF 的训练仅支持视频插帧任务。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py\n\n# 单个GPU上训练\npython tools/train.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\nTOF 的测试支持视频插帧和视频超分辨率两种任务。\n\n**任务 1**: 视频插帧\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth\n\n# 单个GPU上测试\npython tools/test.py configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth 8\n```\n\n**任务 2**: 视频超分辨率\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth\n\n# 单个GPU上测试\npython tools/test.py configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/tof/tof_x4_official_vimeo90k.py https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n\n## Citation\n\n```bibtex\n@article{xue2019video,\n title={Video enhancement with task-oriented flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n" }, { "path": "configs/tof/metafile.yml", "content": "Collections:\n- Name: TOFlow\n Paper:\n Title: Video Enhancement with Task-Oriented Flow\n URL: https://arxiv.org/abs/1711.09078\n README: configs/tof/README.md\n Task:\n - video interpolation\n - video super-resolution\n Year: 2019\nModels:\n- Config: configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py\n In Collection: TOFlow\n Name: tof_spynet-chair-wobn_1xb1_vimeo90k-triplet\n Results:\n - Dataset: Vimeo90k-triplet\n Metrics:\n PSNR: 33.3294\n Task: Video Interpolation\n - Dataset: Vimeo90k-triplet\n Metrics:\n SSIM: 0.9465\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth\n- Config: configs/tof/tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py\n In Collection: TOFlow\n Name: tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet\n Results:\n - Dataset: Vimeo90k-triplet\n Metrics:\n PSNR: 33.3339\n Task: Video Interpolation\n - Dataset: Vimeo90k-triplet\n Metrics:\n SSIM: 0.9466\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth\n- Config: configs/tof/tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py\n In Collection: TOFlow\n Name: tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet\n Results:\n - Dataset: Vimeo90k-triplet\n Metrics:\n PSNR: 33.317\n Task: Video Interpolation\n - Dataset: Vimeo90k-triplet\n Metrics:\n SSIM: 0.9464\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth\n- Config: configs/tof/tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py\n In Collection: TOFlow\n Name: tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet\n Results:\n - Dataset: Vimeo90k-triplet\n Metrics:\n PSNR: 33.3237\n Task: Video Interpolation\n - Dataset: Vimeo90k-triplet\n Metrics:\n SSIM: 0.9465\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth\n- Config: configs/tof/tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py\n In Collection: TOFlow\n Name: tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet\n Results:\n - Dataset: Vimeo90k-triplet\n Metrics:\n PSNR: 33.3426\n Task: Video Interpolation\n - Dataset: Vimeo90k-triplet\n Metrics:\n SSIM: 0.9467\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth\n- Config: configs/tof/tof_x4_official_vimeo90k.py\n In Collection: TOFlow\n Name: tof_x4_official_vimeo90k\n Results:\n - Dataset: vimeo90k\n Metrics:\n Vid4:\n PSNR: 24.4377\n SSIM: 0.7433\n Task: Video Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth\n" }, { "path": "configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py", "content": "_base_ = '../_base_/models/base_tof.py'\n\nexperiment_name = 'tof_spynet-chair-wobn_1xb1_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# pretrained SPyNet\nload_pretrained_spynet = 'https://download.openmmlab.com/mmediting/' +\\\n 'video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='TOFlowVFINet',\n flow_cfg=dict(norm_cfg=None, pretrained=load_pretrained_spynet)),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n pad_size_divisor=16,\n pad_mode='reflect',\n ))\n" }, { "path": "configs/tof/tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py", "content": "_base_ = '../_base_/models/base_tof.py'\n\nexperiment_name = 'tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# pretrained SPyNet\nload_pretrained_spynet = 'https://download.openmmlab.com/mmediting/' +\\\n 'video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='TOFlowVFINet',\n flow_cfg=dict(norm_cfg=None, pretrained=load_pretrained_spynet)),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n pad_size_divisor=16,\n pad_mode='reflect',\n ))\n" }, { "path": "configs/tof/tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py", "content": "_base_ = '../_base_/models/base_tof.py'\n\nexperiment_name = 'tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# pretrained SPyNet\nload_pretrained_spynet = 'https://download.openmmlab.com/mmediting/video_' +\\\n 'interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='TOFlowVFINet',\n flow_cfg=dict(norm_cfg=None, pretrained=load_pretrained_spynet)),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n pad_size_divisor=16,\n pad_mode='reflect',\n ))\n" }, { "path": "configs/tof/tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py", "content": "_base_ = '../_base_/models/base_tof.py'\n\nexperiment_name = 'tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# pretrained SPyNet\nload_pretrained_spynet = 'https://download.openmmlab.com/mmediting/video_' +\\\n 'interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='TOFlowVFINet',\n flow_cfg=dict(norm_cfg=None, pretrained=load_pretrained_spynet)),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n pad_size_divisor=16,\n pad_mode='reflect',\n ))\n" }, { "path": "configs/tof/tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py", "content": "_base_ = '../_base_/models/base_tof.py'\n\nexperiment_name = 'tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# pretrained SPyNet\nload_pretrained_spynet = 'https://download.openmmlab.com/mmediting/video_' +\\\n 'interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth'\n\n# model settings\nmodel = dict(\n type='BasicInterpolator',\n generator=dict(\n type='TOFlowVFINet',\n flow_cfg=dict(norm_cfg=None, pretrained=load_pretrained_spynet)),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n required_frames=2,\n step_frames=1,\n init_cfg=None,\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n pad_size_divisor=16,\n pad_mode='reflect',\n ))\n" }, { "path": "configs/tof/tof_x4_official_vimeo90k.py", "content": "# only testing the official model is supported\n_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'tof_x4_official_vimeo90k'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs'\n\n# model settings\nmodel = dict(\n type='EDVR', # use the shared model with EDVR\n generator=dict(type='TOFlowVSRNet', adapt_official_weights=True),\n pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255],\n ))\n\nval_pipeline = [\n dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndemo_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ndata_root = 'data/Vid4'\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type='BasicFramesDataset',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='BIx4up_direct', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=7,\n pipeline=val_pipeline))\n\n# TODO: data is not uploaded yet\n# test_dataloader = val_dataloader\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n# test_evaluator = val_evaluator\n\nval_cfg = dict(type='MultiValLoop')\n# test_cfg = dict(type='MultiTestLoop')\n" }, { "path": "configs/ttsr/README.md", "content": "# TTSR (CVPR'2020)\n\n> [Learning Texture Transformer Network for Image Super-Resolution](https://arxiv.org/abs/2006.04139)\n\n> **Task**: Image Super-Resolution\n\n\n\n## Abstract\n\n\n\nWe study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively. TTSR consists of four closely-related modules optimized for image generation tasks, including a learnable texture extractor by DNN, a relevance embedding module, a hard-attention module for texture transfer, and a soft-attention module for texture synthesis. Such a design encourages joint feature learning across LR and Ref images, in which deep feature correspondences can be discovered by attention, and thus accurate texture features can be transferred. The proposed texture transformer can be further stacked in a cross-scale way, which enables texture recovery from different levels (e.g., from 1x to 4x magnification). Extensive experiments show that TTSR achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.\n\n\n\n
\n \n
\n\n## Results and models\n\nEvaluated on CUFED dataset (RGB channels), `scale` pixels in each border are cropped before evaluation.\nThe metrics are `PSNR and SSIM` .\n\n| Model | Dataset | scale | PSNR | SSIM | Training Resources | Download |\n| :------------------------------------------------------------------------: | :-----: | :---: | :-----: | :----: | :----------------: | :---------------------------------------------------------------------------------: |\n| [ttsr-rec_x4_c64b16_g1_200k_CUFED](./ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | CUFED | x4 | 25.2433 | 0.7491 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) |\n| [ttsr-gan_x4_c64b16_g1_500k_CUFED](./ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | CUFED | x4 | 24.6075 | 0.7234 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \\| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) |\n\n## Quick Start\n\n**Train**\n\n
\nTrain Instructions\n\nYou can use the following commands to train a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu train\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py\n\n# single-gpu train\npython tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py\n\n# multi-gpu train\n./tools/dist_train.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py 8\n```\n\nFor more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMagic).\n\n
\n\n**Test**\n\n
\nTest Instructions\n\nYou can use the following commands to test a model with cpu or single/multiple GPUs.\n\n```shell\n# cpu test\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth\n\n# single-gpu test\npython tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth\n\n# multi-gpu test\n./tools/dist_test.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth 8\n```\n\nFor more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMagic).\n\n
\n\n## Citation\n\n```bibtex\n@inproceedings{yang2020learning,\n title={Learning texture transformer network for image super-resolution},\n author={Yang, Fuzhi and Yang, Huan and Fu, Jianlong and Lu, Hongtao and Guo, Baining},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={5791--5800},\n year={2020}\n}\n```\n" }, { "path": "configs/ttsr/README_zh-CN.md", "content": "# TTSR (CVPR'2020)\n\n> **任务**: 图像超分辨率\n\n\n\n
\nTTSR (CVPR'2020)\n\n```bibtex\n@inproceedings{yang2020learning,\n title={Learning texture transformer network for image super-resolution},\n author={Yang, Fuzhi and Yang, Huan and Fu, Jianlong and Lu, Hongtao and Guo, Baining},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n pages={5791--5800},\n year={2020}\n}\n```\n\n
\n\n
\n\n在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。\n我们使用 `PSNR` 和 `SSIM` 作为指标。\n\n| 算法 | scale | CUFED | GPU 信息 | 下载 |\n| :------------------------------------------------------------------------: | :---: | :--------------: | :----------: | :-------------------------------------------------------------------------------------------: |\n| [ttsr-rec_x4_c64b16_g1_200k_CUFED](./ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | x4 | 25.2433 / 0.7491 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) |\n| [ttsr-gan_x4_c64b16_g1_500k_CUFED](./ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | x4 | 24.6075 / 0.7234 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \\| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) |\n\n## 快速开始\n\n**训练**\n\n
\n训练说明\n\n您可以使用以下命令来训练模型。\n\n```shell\n# CPU上训练\nCUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py\n\n# 单个GPU上训练\npython tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py\n\n# 多个GPU上训练\n./tools/dist_train.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。\n\n
\n\n**测试**\n\n
\n测试说明\n\n您可以使用以下命令来测试模型。\n\n```shell\n# CPU上测试\nCUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth\n\n# 单个GPU上测试\npython tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth\n\n# 多个GPU上测试\n./tools/dist_test.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth 8\n```\n\n更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。\n\n
\n" }, { "path": "configs/ttsr/metafile.yml", "content": "Collections:\n- Name: TTSR\n Paper:\n Title: Learning Texture Transformer Network for Image Super-Resolution\n URL: https://arxiv.org/abs/2006.04139\n README: configs/ttsr/README.md\n Task:\n - image super-resolution\n Year: 2020\nModels:\n- Config: configs/ttsr/ttsr-rec_x4c64b16_1xb9-200k_CUFED.py\n In Collection: TTSR\n Name: ttsr-rec_x4c64b16_1xb9-200k_CUFED\n Results:\n - Dataset: CUFED\n Metrics:\n PSNR: 25.2433\n SSIM: 0.7491\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth\n- Config: configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py\n In Collection: TTSR\n Name: ttsr-gan_x4c64b16_1xb9-500k_CUFED\n Results:\n - Dataset: CUFED\n Metrics:\n PSNR: 24.6075\n SSIM: 0.7234\n Task: Image Super-Resolution\n Weights: https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth\n" }, { "path": "configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py", "content": "_base_ = './ttsr-rec_x4c64b16_1xb9-200k_CUFED.py'\n\nexperiment_name = 'ttsr-gan_x4c64b16_1xb9-500k_CUFED'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\nscale = 4\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\nmodel = dict(\n type='TTSR',\n generator=dict(\n type='TTSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=(16, 16, 8, 4)),\n extractor=dict(type='LTE'),\n transformer=dict(type='SearchTransformer'),\n discriminator=dict(type='TTSRDiscriminator', in_size=160),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'29': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1e-2,\n style_weight=0,\n criterion='mse'),\n transferal_perceptual_loss=dict(\n type='TransferalPerceptualLoss',\n loss_weight=1e-2,\n use_attention=False,\n criterion='mse'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-3,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(pixel_init=25000, disc_repeat=2),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ))\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=500_000, val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n extractor=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-5, betas=(0.9, 0.999))),\n discriminator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-5, betas=(0.9, 0.999))))\n\n# learning policy\nparam_scheduler = dict(\n _delete_=True,\n type='MultiStepLR',\n by_epoch=False,\n milestones=[100000, 200000, 300000, 400000],\n gamma=0.5)\n" }, { "path": "configs/ttsr/ttsr-rec_x4c64b16_1xb9-200k_CUFED.py", "content": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'ttsr-rec_x4c64b16_1xb9-200k_CUFED'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\n# model settings\nmodel = dict(\n type='TTSR',\n generator=dict(\n type='TTSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=(16, 16, 8, 4)),\n extractor=dict(type='LTE'),\n transformer=dict(type='SearchTransformer'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ))\n\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='ref',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='ModCrop', key='gt'),\n dict(type='CropLike', target_key='ref', reference_key='gt'),\n dict(\n type='Resize',\n scale=1 / scale,\n keep_ratio=True,\n keys=['gt', 'ref'],\n output_keys=['img', 'ref_down'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=float(scale),\n keep_ratio=True,\n keys=['img', 'ref_down'],\n output_keys=['img_lq', 'ref_lq'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Flip',\n keys=['img', 'gt', 'img_lq'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip',\n keys=['img', 'gt', 'img_lq'],\n flip_ratio=0.5,\n direction='vertical'),\n dict(\n type='RandomTransposeHW',\n keys=['img', 'gt', 'img_lq'],\n transpose_ratio=0.5),\n dict(\n type='Flip',\n keys=['ref', 'ref_lq'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip',\n keys=['ref', 'ref_lq'],\n flip_ratio=0.5,\n direction='vertical'),\n dict(\n type='RandomTransposeHW', keys=['ref', 'ref_lq'], transpose_ratio=0.5),\n dict(type='PackInputs')\n]\nvalid_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='ref',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='ModCrop', key='gt'),\n dict(type='CropLike', target_key='ref', reference_key='gt'),\n dict(\n type='Resize',\n scale=1 / scale,\n keep_ratio=True,\n keys=['gt', 'ref'],\n output_keys=['img', 'ref_down'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=float(scale),\n keep_ratio=True,\n keys=['img', 'ref_down'],\n output_keys=['img_lq', 'ref_lq'],\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs')\n]\ndemo_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type='LoadImageFromFile',\n key='ref',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='ModCrop', key='img'),\n dict(\n type='Resize',\n scale=1 / scale,\n keep_ratio=True,\n keys=['ref'],\n output_keys=['ref_down'],\n interpolation='bicubic',\n backend='pillow'),\n dict(\n type='Resize',\n scale=float(scale),\n keep_ratio=True,\n keys=['img', 'ref_down'],\n output_keys=['img_lq', 'ref_lq'],\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs')\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=9,\n batch_size=9,\n persistent_workers=False,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cufed', task_name='refsr'),\n data_root=data_root + '/CUFED',\n data_prefix=dict(ref='ref', gt='input'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=8,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='cufed', task_name='refsr'),\n data_root=data_root + '/CUFED',\n data_prefix=dict(ref='CUFED5', gt='CUFED5'),\n filename_tmpl=dict(ref='{}_1', gt='{}_0'),\n pipeline=valid_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(type='MAE'),\n dict(type='PSNR', crop_border=scale),\n dict(type='SSIM', crop_border=scale),\n ])\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=200_000, val_interval=5000)\nval_cfg = dict(type='MultiValLoop')\ntest_cfg = dict(type='MultiTestLoop')\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))),\n extractor=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-5, betas=(0.9, 0.999))))\n\n# learning policy\nparam_scheduler = dict(\n type='MultiStepLR', by_epoch=False, milestones=[100000], gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100),\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n" }, { "path": "configs/vico/README.md", "content": "# ViCo (2023)\n\n> [ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation](https://arxiv.org/abs/2306.00971)\n\n> **Task**: Text2Image\n\n\n\n## Abstract\n\n\n\nPersonalized text-to-image generation using diffusion models has recently been proposed and attracted lots of attention. Given a handful of images containing a novel concept (e.g., a unique toy), we aim to tune the generative model to capture fine visual details of the novel concept and generate photorealistic images following a text condition. We present a plug-in method, named ViCo, for fast and lightweight personalized generation. Specifically, we propose an image attention module to condition the diffusion process on the patch-wise visual semantics. We introduce an attention-based object mask that comes almost at no cost from the attention module. In addition, we design a simple regularization based on the intrinsic properties of text-image attention maps to alleviate the common overfitting degradation. Unlike many existing models, our method does not finetune any parameters of the original diffusion model. This allows more flexible and transferable model deployment. With only light parameter training (~6% of the diffusion U-Net), our method achieves comparable or even better performance than all state-of-the-art models both qualitatively and quantitatively.\n\n\n\n
\n\n
\n\n## Configs\n\n| Model | Dataset | Download |\n| :---------------: | :-----: | :------: |\n| [ViCo](./vico.py) | - | - |\n\n## Quick Start\n\n1. Download concept data and imagenet_templates_small.txt from [here](https://openxlab.org.cn/datasets/ferry/ViCo/tree/main).\n and save to `data/vico/`\n\nThe file structure will be like this:\n\n```text\ndata\n└── vico\n └──batman\n ├── 1.jpg\n ├── 2.jpg\n ├── 3.jpg\n └── 4.jpg\n └──clock\n ├── 1.jpg\n ├── 2.jpg\n ├── 3.jpg\n └── 4.jpg\n ...\n └──imagenet_templates_small.txt\n```\n\n2. Customize your config\n\n```\n# Only need to care about these\n\n# which concept you want to customize\nconcept_dir = 'dog7'\n\n# the new token to denote the concept\nplaceholder: str = 'S*'\n\n# better to be the superclass of concept\ninitialize_token: str = 'dog'\n```\n\n3. Start training with the following command:\n\n```bash\n# 4 GPUS\nbash tools/dist_train.sh configs/vico/vico.py 4\n# 1 GPU\npython tools/train.py configs/vico/vico.py\n```\n\n4. Use the [pretrained checkpoins](https://openxlab.org.cn/models/detail/ferry/ViCo) to inference\n\n```python\nimport torch\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n# say you have downloaded the pretrained weights\ncfg = Config.fromfile('configs/vico/dog.py')\nstate_dict = torch.load(\"./dog.pth\")\nvico = MODELS.build(cfg.model)\nvico.load_state_dict(state_dict, strict=False)\nvico = vico.cuda()\n\nprompt = [\"A photo of S*\", \"A photo of S* on the beach\"]\nreference = \"data/vico/dog7/01.jpg\"\nimage_ref = Image.open(reference)\nwith torch.no_grad():\n output = vico.infer(prompt=prompt, image_reference=image_ref, seed=123, num_images_per_prompt=2)['samples'][0]\noutput.save(\"infer.png\")\n```\n\n5. (Optional) If you want to use the weight trained by the\n commands at step3, here are codes to extract the trained parameters, then you can infer with it like step4\n\n```python\nimport torch\ndef extract_vico_parameters(state_dict):\n new_state_dict = dict()\n for k, v in state_dict.items():\n if 'image_cross_attention' in k or 'trainable_embeddings' in k:\n new_k = k.replace('module.', '')\n new_state_dict[new_k] = v\n return new_state_dict\n\ncheckpoint = torch.load(\"work_dirs/vico/iter_400.pth\")\nnew_checkpoint = extract_vico_parameters(checkpoint['state_dict'])\ntorch.save(new_checkpoint, \"work_dirs/vico/dog.pth\")\n```\n\n\n\n \n \n
\n
\n \n
\n 'vico'\n
\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@inproceedings{Hao2023ViCo,\n title={ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation},\n author={Shaozhe Hao and Kai Han and Shihao Zhao and Kwan-Yee K. Wong},\n year={2023}\n}\n```\n" }, { "path": "configs/vico/README_zh-CN.md", "content": "# ViCo (2023)\n\n> [ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation](https://arxiv.org/abs/2306.00971)\n\n> **Task**: 文本图像生成\n\n\n\n## 摘要\n\n\n\n最近，个性化文本到图像生成使用扩散模型的方法被提出，并引起了广泛关注。给定包含新概念（例如独特的玩具）的少量图像，我们旨在调整生成模型，以捕捉新概念的精细视觉细节，并根据文本条件生成逼真的图像。我们提出了一种名为ViCo的插件方法，用于快速轻量级个性化生成。具体而言，我们提出了一个图像注意力模块，以对基于补丁的视觉语义进行扩散过程的条件建模。我们引入了一种基于注意力模块的对象蒙版，几乎没有额外计算成本。此外，我们设计了一个简单的正则化方法，基于文本-图像注意力图的内在属性，以减轻常见的过拟合退化问题。与许多现有模型不同，我们的方法不对原始扩散模型的任何参数进行微调。这使得模型的部署更加灵活和可转移。通过仅进行轻量级参数训练（约为扩散U-Net的6%），我们的方法在质量和数量上都达到了与所有最先进模型相当甚至更好的性能。\n\n\n\n
\n\n
\n\n## 模型结构\n\n| 模型 | 数据集 | 下载 |\n| :---------------: | :-----------------------------------------------------------------------: | :--: |\n| [ViCo](./vico.py) | [textual_inversion_dataset](mmagic/datasets/textual_inversion_dataset.py) | - |\n\n## Quick Start\n\n1. 下载 [数据集](https://drive.google.com/drive/folders/1m8TCsY-C1tIOflHtWnFzTbw2C6dq67mC) 和 [模板](https://drive.google.com/drive/folders/1SpByLKECISmj5fhkaicT4yrsyqqpWL_T)\n and save to `data/vico/`\n\n文件夹结构应如下:\n\n```text\ndata\n└── vico\n └──batman\n ├── 1.jpg\n ├── 2.jpg\n ├── 3.jpg\n └── 4.jpg\n └──clock\n ├── 1.jpg\n ├── 2.jpg\n ├── 3.jpg\n └── 4.jpg\n ...\n └──imagenet_templates_small.txt\n```\n\n2. 自定义你自己的config文件\n\n```\n# 请关注以下需自定义的内容\n\n# 设置concept文件夹名\nconcept_dir = 'dog7'\n\n# 设置代表这个concept的新字符\nplaceholder: str = 'S*'\n\n# 初始化字符，最好是设置这个concept所属的类别\ninitialize_token: str = 'dog'\n```\n\n3. 使用以下命令进行**训练**:\n\n```bash\n# 4 GPUS\nbash tools/dist_train.sh configs/vico/vico.py 4\n# 1 GPU\npython tools/train.py configs/vico/vico.py\n```\n\n4. 使用 [预训练的权重](https://drive.google.com/drive/folders/1GQGVzzOP2IgEfsQ-6ii6o2DqElnFThHM) 进行**推理**\n\n```python\nimport torch\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n# say you have downloaded the pretrained weights\ncfg = Config.fromfile('configs/vico/dog.py')\nstate_dict = torch.load(\"./dog.pth\")\nvico = MODELS.build(cfg.model)\nvico.load_state_dict(state_dict, strict=False)\nvico = vico.cuda()\n\nprompt = [\"A photo of S*\", \"A photo of S* on the beach\"]\nreference = \"data/vico/dog7/01.jpg\"\nimage_ref = Image.open(reference)\nwith torch.no_grad():\n output = vico.infer(prompt=prompt, image_reference=image_ref, seed=123, num_images_per_prompt=2)['samples'][0]\noutput.save(\"infer.png\")\n```\n\n5. (可选) 如果你想使用第3步训练得到的checkpoint进行推理，可以先使用以下脚本将训练过的参数提取出来（文件大小会轻量很多），再使用第4步进行推理\n\n```python\nimport torch\ndef extract_vico_parameters(state_dict):\n new_state_dict = dict()\n for k, v in state_dict.items():\n if 'image_cross_attention' in k or 'trainable_embeddings' in k:\n new_k = k.replace('module.', '')\n new_state_dict[new_k] = v\n return new_state_dict\n\ncheckpoint = torch.load(\"work_dirs/vico/iter_400.pth\")\nnew_checkpoint = extract_vico_parameters(checkpoint['state_dict'])\ntorch.save(new_checkpoint, \"work_dirs/vico/dog.pth\")\n```\n\n\n\n \n \n
\n
\n \n
\n 'vico'\n
\n\n## Comments\n\nOur codebase for the stable diffusion models builds heavily on [diffusers codebase](https://github.com/huggingface/diffusers) and the model weights are from [stable-diffusion-1.5](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py).\n\nThanks for the efforts of the community!\n\n## Citation\n\n```bibtex\n@inproceedings{Hao2023ViCo,\n title={ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation},\n author={Shaozhe Hao and Kai Han and Shihao Zhao and Kwan-Yee K. Wong},\n year={2023}\n}\n```\n" }, { "path": "configs/vico/metafile.yml", "content": "Collections:\n- Name: ViCo\n Paper:\n Title: 'ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image\n Generation'\n URL: https://arxiv.org/abs/2306.00971\n README: configs/vico/README.md\n Task:\n - text2image\n Year: 2023\nModels:\n- Config: configs/vico/vico.py\n In Collection: ViCo\n Name: vico\n Results:\n - Dataset: '-'\n Metrics: {}\n Task: Text2Image\n" }, { "path": "configs/vico/vico.py", "content": "_base_ = '../_base_/gen_default_runtime.py'\n\nrandomness = dict(seed=2023, diff_rank_seed=True)\n# dtype=\"fp32\"\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\ndata_root = './data/vico'\nconcept_dir = 'dog7'\n\n# 1 for using image cross\nimage_cross_layers = [\n # down blocks (2x transformer block) * (3x down blocks) = 6\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n # mid block (1x transformer block) * (1x mid block)= 1\n 0,\n # up blocks (3x transformer block) * (3x up blocks) = 9\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n]\nreg_loss_weight: float = 5e-4\nplaceholder: str = 'S*'\nval_prompts = ['a photo of a S*']\ninitialize_token: str = 'dog'\nnum_vectors_per_token: int = 1\n\nmodel = dict(\n type='ViCo',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_v15_url),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n # dtype=dtype,\n data_preprocessor=dict(type='DataPreprocessor', data_keys=None),\n image_cross_layers=image_cross_layers,\n reg_loss_weight=reg_loss_weight,\n placeholder=placeholder,\n initialize_token=initialize_token,\n num_vectors_per_token=num_vectors_per_token,\n val_prompts=val_prompts)\n\ntrain_cfg = dict(max_iters=500)\n\nparamwise_cfg = dict(\n custom_keys={\n 'image_cross_attention': dict(lr_mult=2e-3),\n 'trainable_embeddings': dict(lr_mult=1.0)\n })\noptim_wrapper = dict(\n optimizer=dict(type='AdamW', lr=0.005, weight_decay=0.01),\n constructor='DefaultOptimWrapperConstructor',\n paramwise_cfg=paramwise_cfg,\n accumulative_counts=1)\n\npipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='img_ref', channel_order='rgb'),\n dict(type='Resize', keys=['img', 'img_ref'], scale=(512, 512)),\n dict(\n type='PackInputs',\n keys=['img', 'img_ref'],\n data_keys='prompt',\n meta_keys=[\n 'img_channel_order', 'img_color_type', 'img_ref_channel_order',\n 'img_ref_color_type'\n ])\n]\ndataset = dict(\n type='TextualInversionDataset',\n data_root=data_root,\n concept_dir=concept_dir,\n placeholder=placeholder,\n template='data/vico/imagenet_templates_small.txt',\n with_image_reference=True,\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks = dict(logger=dict(interval=10))\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=50,\n fixed_input=True,\n # visualize train dataset\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "configs/wgan-gp/README.md", "content": "# WGAN-GP (NeurIPS'2017)\n\n> [Improved Training of Wasserstein GANs](https://arxiv.org/abs/1704.00028)\n\n> **Task**: Unconditional GANs\n\n\n\n## Abstract\n\n\n\nGenerative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.\n\n\n\n
\n\n
\n\n## Results and models\n\n
\n WGAN-GP 128, CelebA-Cropped\n
\n \n
\n\n| Model | Dataset | Details | SWD | MS-SSIM | Download |\n| :--------------------------------------------------------------: | :------------: | :----------------: | :---------------------------: | :-----: | :------------------------------------------------------------------: |\n| [WGAN-GP 128](./wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py) | CelebA-Cropped | GN | 5.87, 9.76, 9.43, 18.84/10.97 | 0.2601 | [model](https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth) |\n| [WGAN-GP 128](./wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py) | LSUN-Bedroom | GN, GP-lambda = 50 | 11.7, 7.87, 9.82, 25.36/13.69 | 0.059 | [model](https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth) |\n\n## Citation\n\n```latex\n@article{gulrajani2017improved,\n title={Improved Training of Wasserstein GANs},\n author={Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron},\n journal={arXiv preprint arXiv:1704.00028},\n year={2017},\n url={https://arxiv.org/abs/1704.00028},\n}\n```\n" }, { "path": "configs/wgan-gp/metafile.yml", "content": "Collections:\n- Name: WGAN-GP\n Paper:\n Title: Improved Training of Wasserstein GANs\n URL: https://arxiv.org/abs/1704.00028\n README: configs/wgan-gp/README.md\n Task:\n - unconditional gans\n Year: 2017\nModels:\n- Config: configs/wgan-gp/wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py\n In Collection: WGAN-GP\n Name: wgangp_GN_1xb64-160kiters_celeba-cropped-128x128\n Results:\n - Dataset: CelebA-Cropped\n Metrics:\n MS-SSIM: 0.2601\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth\n- Config: configs/wgan-gp/wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py\n In Collection: WGAN-GP\n Name: wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128\n Results:\n - Dataset: LSUN-Bedroom\n Metrics:\n MS-SSIM: 0.059\n Task: Unconditional GANs\n Weights: https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth\n" }, { "path": "configs/wgan-gp/wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py", "content": "_base_ = ['./wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py']\n\nloss_config = dict(gp_norm_mode='HWC', gp_loss_weight=50)\nmodel = dict(loss_config=loss_config)\n\nbatch_size = 64\ndata_root = './data/lsun/images/bedroom_train'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n" }, { "path": "configs/wgan-gp/wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py", "content": "_base_ = [\n '../_base_/datasets/unconditional_imgs_128x128.py',\n '../_base_/gen_default_runtime.py',\n]\n\n# MODEL\nloss_config = dict(gp_norm_mode='HWC', gp_loss_weight=10)\nmodel = dict(\n type='WGANGP',\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(type='WGANGPGenerator', noise_size=128, out_scale=128),\n discriminator=dict(\n type='WGANGPDiscriminator',\n in_channel=3,\n in_scale=128,\n conv_module_cfg=dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='GN'),\n order=('conv', 'norm', 'act'))),\n discriminator_steps=5,\n loss_config=loss_config)\n\n# `batch_size` and `data_root` need to be set.\nbatch_size = 64\ndata_root = './data/celeba-cropped/cropped_images_aligned_png/'\ntrain_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader = dict(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = dict(max_iters=160000)\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.9))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.5, 0.9))))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig'),\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 128, 128))\n]\n\n# save multi best checkpoints\ndefault_hooks = dict(checkpoint=dict(save_best='swd/avg'))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "demo/README.md", "content": "# MMagic Demo\n\nThere are some mmagic demos in this folder. We provide python command line usage here to run these demos and more guidance could also be found in the [documentation](https://mmagic.readthedocs.io/en/latest/user_guides/inference.html)\n\nTable of contents:\n\n[1. Download sample images or videos](#1-download-sample-images-or-videos)\n\n[2. MMagic inference demo](#2-mmagic-inference-demo)\n\n    [2.1. Check supported tasks and models](#21-check-supported-tasks-and-models)\n\n    [2.2. Perform inference with command line](#22-perform-inference-with-command-line)\n\n      [2.2.1. Text-to-Image example](#221-text-to-image)\n\n      [2.2.2. Conditional GANs example](#222-conditional-gans)\n\n      [2.2.3. Unconditional GANs example](#223-unconditional-gans)\n\n      [2.2.4. Image Translation (Image2Image) example](#224-image-translation)\n\n      [2.2.5. Inpainting example](#225-inpainting)\n\n      [2.2.6. Matting example](#226-matting)\n\n      [2.2.7. Image Restoration example](#227-image-restoration)\n\n      [2.2.8. Image Super-Resolution example](#228-image-super-resolution)\n\n      [2.2.9. Video Super-Resolution example](#229-video-super-resolution)\n\n      [2.2.10. Video Interpolation example](#2210-video-interpolation)\n\n      [2.2.11. Image Colorization example](#2211-image-colorization)\n\n      [2.2.12. 3D-aware Generation example](#2212-3d-aware-generation)\n\n[3. Other demos](#3-other-demos)\n\n    [3.1. Gradio demo](#31-gradio-demo)\n\n      [3.1.1. DragGAN](#311-draggan)\n\n      [3.1.2. ViCo](#312-vico)\n\n      [3.1.3. FastComposer](#313-fastcomposer)\n\n      [3.1.4. AnimateDiff](#314-animatediff)\n\n## 1. Download sample images or videos\n\nWe prepared some images and videos for you to run demo with. After MMagic is well installed, you could use demos in this folder to infer these data.\nDownload with python script [download_inference_resources.py](./download_inference_resources.py).\n\n```shell\n# see all resources\npython demo/download_inference_resources.py --print-all\n\n# see all task types\npython demo/download_inference_resources.py --print-task-type\n\n# see resources of one specific task\npython demo/download_inference_resources.py --print-task 'Inpainting'\n\n# download all resources to default dir './resources'\npython demo/download_inference_resources.py\n\n# download resources of one task\npython demo/download_inference_resources.py --task 'Inpainting'\n\n# download to the directory you want\npython demo/download_inference_resources.py --root-dir './resources'\n```\n\n## 2. MMagic inference demo\n\n### 2.1 Check supported tasks and models\n\nprint all supported models for inference.\n\n```shell\npython demo/mmagic_inference_demo.py --print-supported-models\n```\n\nprint all supported tasks for inference.\n\n```shell\npython demo/mmagic_inference_demo.py --print-supported-tasks\n```\n\nprint all supported models for one task, take 'Image2Image' for example.\n\n```shell\npython demo/mmagic_inference_demo.py --print-task-supported-models 'Text2Image'\n```\n\n### 2.2 Perform inference with command line\n\nYou can use the following commands to perform inference with a MMagic model.\n\nUsage of python API can also be found in this [tutotial](./mmagic_inference_tutorial.ipynb).\n\n```shell\npython demo/mmagic_inference_demo.py \\\n [--img] \\\n [--video] \\\n [--label] \\\n [--trimap] \\\n [--mask] \\\n [--result-out-dir] \\\n [--model-name] \\\n [--model-setting] \\\n [--model-config] \\\n [--model-ckpt] \\\n [--device ] \\\n [--extra-parameters]\n```\n\nExamples for each kind of task:\n\n#### 2.2.1 Text-to-Image\n\nstable diffusion\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir demo_text2image_stable_diffusion_res.png\n```\n\ncontrolnet-canny\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 1 \\\n --text \"Room with blue walls and a yellow ceiling.\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \\\n --result-out-dir demo_text2image_controlnet_canny_res.png\n```\n\ncontrolnet-pose\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 2 \\\n --text \"masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png' \\\n --result-out-dir demo_text2image_controlnet_pose_res.png\n```\n\ncontrolnet-seg\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 3 \\\n --text \"black house, blue sky\" \\\n --control 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/243599897-553a4c46-c61d-46df-b820-59a49aaf6678.png' \\\n --result-out-dir demo_text2image_controlnet_seg_res.png\n```\n\n#### 2.2.2 Conditional GANs\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name biggan \\\n --model-setting 3 \\\n --label 1 \\\n --result-out-dir demo_conditional_biggan_res.jpg\n```\n\n#### 2.2.3 Unconditional GANs\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name styleganv1 \\\n --result-out-dir demo_unconditional_styleganv1_res.jpg\n```\n\n#### 2.2.4 Image Translation\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name pix2pix \\\n --img ./resources/input/translation/gt_mask_0.png \\\n --result-out-dir ./resources/output/translation/demo_translation_pix2pix_res.png\n```\n\n#### 2.2.5 Inpainting\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name deepfillv2 \\\n --img ./resources/input/inpainting/celeba_test.png \\\n --mask ./resources/input/inpainting/bbox_mask.png \\\n --result-out-dir ./resources/output/inpainting/demo_inpainting_deepfillv2_res.jpg\n```\n\n#### 2.2.6 Matting\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name aot_gan \\\n --img ./resources/input/matting/GT05.jpg \\\n --trimap ./resources/input/matting/GT05_trimap.jpg \\\n --result-out-dir ./resources/output/matting/demo_matting_gca_res.png\n```\n\n#### 2.2.7 Image Restoration\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name nafnet \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_nafnet_res.png\n```\n\n#### 2.2.8 Image Super-resolution\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name esrgan \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_esrgan_res.png\n```\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name ttsr \\\n --img ./resources/input/restoration/000001.png \\\n --ref ./resources/input/restoration/000001.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_ttsr_res.png\n```\n\n#### 2.2.9 Video Super-Resolution\n\nBasicVSR / BasicVSR++ / IconVSR / RealBasicVSR\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name basicvsr \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4\n```\n\nEDVR\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name edvr \\\n --extra-parameters window_size=5 \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_edvr_res.mp4\n```\n\nTDAN\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name tdan \\\n --model-setting 2 \\\n --extra-parameters window_size=5 \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_tdan_res.mp4\n```\n\n#### 2.2.10 Video interpolation\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name flavr \\\n --video ./resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4 \\\n --result-out-dir ./resources/output/video_interpolation/demo_video_interpolation_flavr_res.mp4\n```\n\n#### 2.2.11 Image Colorization\n\n```\npython demo/mmagic_inference_demo.py \\\n --model-name inst_colorization \\\n --img https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg \\\n --result-out-dir demo_colorization_res.png\n```\n\n#### 2.2.12 3D-aware Generation\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name eg3d \\\n --result-out-dir ./resources/output/eg3d-output\n```\n\n## 3. Other demos\n\n## 3.1 gradio demo\n\n#### 3.1.1 DragGAN\n\nFirst, put your checkpoint path in `./checkpoints`, *e.g.* `./checkpoints/stylegan2_lions_512_pytorch_mmagic.pth`. For example,\n\n```shell\nmkdir checkpoints\ncd checkpoints\nwget -O stylegan2_lions_512_pytorch_mmagic.pth https://download.openxlab.org.cn/models/qsun1/DragGAN-StyleGAN2-checkpoint/weight//StyleGAN2-Lions-internet\n```\n\nThen, try on the script:\n\n```shell\npython demo/gradio_draggan.py\n```\n\n#### 3.1.2 ViCo\n\nLaunch the UI.\n\n```shell\npython demo/gradio_vico.py\n```\n\n*Training*\n\n1. Submit your concept sample images to the interface and fill in the *init_token* and *placeholder*.\n\n2. Click the *Start Training* button.\n\n3. Your training results will be under the folder `./work_dirs/vico_gradio`.\n\n*Inference*\n\nFollow the [instructions](../configs/vico/README.md#quick-start#4) to download the pretrained weights (or [use your own weights](../configs/vico/README.md#quick-start#5)) and put them under the folder `./ckpts`\n\n```\nmkdir ckpts\n```\n\nyour folder structure should be like:\n\n```\nckpts\n└── barn.pth\n└── batman.pth\n└── clock.pth\n...\n```\n\nThen launch the UI and you can use the pretrained weights to generate images.\n\n1. Upload reference image.\n\n2. (Optional) Customize advanced settings.\n\n3. Click inference button.\n\n#### 3.1.3 FastComposer\n\nFirst, run the script:\n\n```shell\npython demo/gradio_fastcomposer.py\n```\n\nSecond, upload reference subject images.For example,\n\n\n\n \n \n \n \n
\n
\n \n
\n 'reference_0.png'\n
\n
\n \n
\n 'reference_1.png'\n
\n
\n\nThen, add prompt like `A man img and a man img sitting together` and press `run` button.\n\nFinally, you can get text-generated images.\n\n
\n\n
\n\n#### 3.1.4 AnimateDiff\n\n1. Download [ToonYou](https://civitai.com/api/download/models/78775) and MotionModule checkpoint\n\n```bash\n#!/bin/bash\n\nmkdir models && cd models\nmkdir Motion_Module && mkdir DreamBooth_LoRA\ngdown 1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq -O models/Motion_Module/\ngdown 1ql0g_Ys4UCz2RnokYlBjyOYPbttbIpbu -O models/Motion_Module/\nwget https://civitai.com/api/download/models/78775 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate\n```\n\n2. Modify the config file in `configs/animatediff/animatediff_ToonYou.py`\n\n```python\n\nmodels_path = '/home/AnimateDiff/models/'\n```\n\n3. Then, try on the script:\n\n```shell\n# may need to install imageio[ffmpeg]:\n# pip install imageio-ffmpeg\npython demo/gradio_animatediff.py\n```\n\n4. Select SD, MotionModule and DreamBooth checkpoints. Adjust inference parameters. Then input a selected prompt and its relative negative_prompt:\n\n```python\n\nprompts = [\n \"best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress\",\n\n \"masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes,\",\n\n \"best quality, masterpiece, 1boy, formal, abstract, looking at viewer, masculine, marble pattern\",\n\n \"best quality, masterpiece, 1girl, cloudy sky, dandelion, contrapposto, alternate hairstyle,\"\n]\nnegative_prompts = [\n \"\",\n \"badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth\",\n \"\",\n \"\",\n]\n# More test samples could be generated with other config files. Please check 'configs/animatediff/README.md'\n```\n\n5. Click the 'Generate' button.\n" }, { "path": "demo/download_inference_resources.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os.path as osp\n\nimport mmengine\nimport requests\n\nRESOURCES = {\n 'Matting': [\n 'https://download.openmmlab.com/mmediting/resources/input/matting/GT05.jpg', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/matting/GT05_trimap.jpg', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/matting/readme.md' # noqa\n ],\n 'Inpainting': [\n 'https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md' # noqa\n ],\n 'Image Super-Resolution': [\n 'https://download.openmmlab.com/mmediting/resources/input/restoration/000001.png', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/restoration/0901x2.png', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/restoration/readme.md' # noqa\n ],\n 'Image2Image Translation': [\n 'https://download.openmmlab.com/mmediting/resources/input/translation/gt_mask_0.png', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/translation/readme.md' # noqa\n ],\n 'Video Interpolation': [\n 'https://download.openmmlab.com/mmediting/resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/video_interpolation/readme.md' # noqa\n ],\n 'Video Super-Resolution': [\n 'https://download.openmmlab.com/mmediting/resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/video_restoration/readme.md', # noqa\n 'https://download.openmmlab.com/mmediting/resources/input/video_restoration/v_Basketball_g01_c01.avi' # noqa\n ]\n}\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Download resources')\n parser.add_argument(\n '--root-dir',\n type=str,\n help='resource root dir',\n default='../resources')\n parser.add_argument(\n '--task',\n type=str,\n help='one specific task, if None : download all resources',\n default=None)\n parser.add_argument(\n '--print-all', action='store_true', help='print all resources')\n parser.add_argument(\n '--print-task-type', action='store_true', help='print all task types')\n parser.add_argument(\n '--print-task',\n type=str,\n help='print all tasks that need input resources',\n default=None)\n\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.print_all:\n print('all inference resources:')\n for key in RESOURCES.keys():\n print(key)\n for value in RESOURCES[key]:\n print(value)\n return\n\n if args.print_task_type:\n print('all task type:')\n for key in RESOURCES.keys():\n print(key)\n return\n\n if args.print_task:\n print('RESOURCES of task ' + args.print_task + ':')\n for value in RESOURCES[args.print_task]:\n print(value)\n return\n\n to_be_download = []\n if args.task and args.task in RESOURCES.keys():\n to_be_download.extend(RESOURCES[args.task])\n else:\n for key in RESOURCES.keys():\n to_be_download.extend(RESOURCES[key])\n\n put_root_path = osp.join(osp.dirname(__file__), args.root_dir)\n for item in to_be_download:\n item_relative_path = item[item.find('input'):]\n put_path = osp.join(put_root_path, item_relative_path)\n mmengine.mkdir_or_exist(osp.dirname(put_path))\n response = requests.get(item)\n open(put_path, 'wb').write(response.content)\n print('Download finished: ' + item + ' to ' + put_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "demo/gradio_animatediff.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport json\nimport os\nimport random\nfrom datetime import datetime\nfrom glob import glob\n\nimport gradio as gr\nimport torch\nfrom diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler\nfrom mmengine import Config\nfrom safetensors import safe_open\n\nfrom mmagic.models.editors.animatediff import save_videos_grid\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nsample_idx = 0\nscheduler_dict = {\n 'Euler': EulerDiscreteScheduler,\n 'PNDM': PNDMScheduler,\n 'DDIM': DDIMScheduler,\n}\ncfg = Config.fromfile('configs/animatediff/animatediff_ToonYou.py')\ncss = \"\"\"\n.toolbutton {\n margin-buttom: 0em 0em 0em 0em;\n max-width: 2.5em;\n min-width: 2.5em !important;\n height: 2.5em;\n}\n\"\"\"\n\n\nclass AnimateController:\n\n def __init__(self):\n\n # config dirs\n self.basedir = cfg.models_path\n self.stable_diffusion_dir = os.path.join(self.basedir,\n 'StableDiffusion')\n self.motion_module_dir = os.path.join(self.basedir, 'Motion_Module')\n self.personalized_model_dir = os.path.join(self.basedir,\n 'DreamBooth_LoRA')\n self.savedir = os.path.join(\n os.getcwd(), 'samples',\n datetime.now().strftime('Gradio-%Y-%m-%dT%H-%M-%S'))\n self.savedir_sample = os.path.join(self.savedir, 'sample')\n os.makedirs(self.savedir, exist_ok=True)\n\n self.stable_diffusion_list = []\n self.motion_module_list = []\n self.personalized_model_list = []\n\n self.refresh_stable_diffusion()\n self.refresh_motion_module()\n self.refresh_personalized_model()\n\n # config models\n self.config = cfg\n self.animatediff = None\n self.lora_model_state_dict = {}\n\n def refresh_stable_diffusion(self):\n self.stable_diffusion_list = ['runwayml/stable-diffusion-v1-5']\n\n def refresh_motion_module(self):\n motion_module_list = glob(\n os.path.join(self.motion_module_dir, '*.ckpt'))\n self.motion_module_list = [\n os.path.basename(p) for p in motion_module_list\n ]\n\n def refresh_personalized_model(self):\n personalized_model_list = glob(\n os.path.join(self.personalized_model_dir, '*.safetensors'))\n self.personalized_model_list = [\n os.path.basename(p) for p in personalized_model_list\n ]\n\n def update_stable_diffusion(self, stable_diffusion_dropdown):\n self.config['stable_diffusion_v15_url'] = stable_diffusion_dropdown\n self.animatediff = MODELS.build(self.config.model).cuda()\n return gr.Dropdown.update()\n\n def update_motion_module(self, motion_module_dropdown):\n if self.animatediff.unet is None:\n gr.Info('Please select a pretrained model path.')\n return gr.Dropdown.update(value=None)\n else:\n motion_module_dropdown = os.path.join(self.motion_module_dir,\n motion_module_dropdown)\n self.config.model['motion_module_cfg']['path'] = \\\n motion_module_dropdown\n self.animatediff.init_motion_module(\n self.config.model['motion_module_cfg'])\n return gr.Dropdown.update()\n\n def update_base_model(self, base_model_dropdown):\n\n if self.animatediff.unet is None:\n gr.Info('Please select a pretrained model path.')\n return gr.Dropdown.update(value=None)\n else:\n base_model_dropdown = os.path.join(self.personalized_model_dir,\n base_model_dropdown)\n self.config.model['dream_booth_lora_cfg'][\n 'path'] = base_model_dropdown\n self.animatediff.init_dreambooth_lora(\n self.config.model['dream_booth_lora_cfg'])\n return gr.Dropdown.update()\n\n def update_lora_model(self, lora_model_dropdown):\n lora_model_dropdown = os.path.join(self.personalized_model_dir,\n lora_model_dropdown)\n self.lora_model_state_dict = {}\n if lora_model_dropdown == 'none':\n pass\n else:\n with safe_open(\n lora_model_dropdown, framework='pt', device='cpu') as f:\n for key in f.keys():\n self.lora_model_state_dict[key] = f.get_tensor(key)\n return gr.Dropdown.update()\n\n def animate(\n self,\n stable_diffusion_dropdown,\n motion_module_dropdown,\n base_model_dropdown,\n lora_alpha_slider,\n prompt_textbox,\n negative_prompt_textbox,\n # sampler_dropdown,\n sample_step_slider,\n width_slider,\n length_slider,\n height_slider,\n cfg_scale_slider,\n seed_textbox):\n if self.animatediff.unet is None:\n raise gr.Error('Please select a pretrained model path.')\n if motion_module_dropdown == '':\n raise gr.Error('Please select a motion module.')\n if base_model_dropdown == '':\n raise gr.Error('Please select a base DreamBooth model.')\n self.animatediff.cuda()\n self.animatediff.unet.set_use_memory_efficient_attention_xformers(True)\n\n # TODO: update lora\n # if self.lora_model_state_dict != {}:\n # pipeline = convert_lora(\n # pipeline,\n # self.lora_model_state_dict,\n # alpha=lora_alpha_slider)\n\n sample = self.animatediff.infer(\n prompt_textbox,\n negative_prompt=negative_prompt_textbox,\n num_inference_steps=sample_step_slider,\n guidance_scale=cfg_scale_slider,\n width=width_slider,\n height=height_slider,\n video_length=length_slider,\n seed=int(seed_textbox))['samples']\n\n save_sample_path = os.path.join(self.savedir_sample,\n f'{sample_idx}.mp4')\n save_videos_grid(sample, save_sample_path)\n seed = torch.initial_seed()\n sample_config = {\n 'prompt': prompt_textbox,\n 'n_prompt': negative_prompt_textbox,\n # \"sampler\": sampler_dropdown, # TODO: More samplers\n 'num_inference_steps': sample_step_slider,\n 'guidance_scale': cfg_scale_slider,\n 'width': width_slider,\n 'height': height_slider,\n 'video_length': length_slider,\n 'seed': seed\n }\n json_str = json.dumps(sample_config, indent=4)\n with open(os.path.join(self.savedir, 'logs.json'), 'a') as f:\n f.write(json_str)\n f.write('\\n\\n')\n\n return gr.Video.update(value=save_sample_path)\n\n\ncontroller = AnimateController()\n\n\ndef ui():\n with gr.Blocks(css=css) as demo:\n gr.Markdown(\"\"\"\n # [AnimateDiff: Animate Your Personalized\n # Text-to-Image Diffusion Models without Specific Tuning]\n # (https://arxiv.org/abs/2307.04725)\n Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang,\n Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)
\n [Arxiv Report](https://arxiv.org/abs/2307.04725) |\n [Project Page](https://animatediff.github.io/) |\n [Github](https://github.com/guoyww/animatediff/)\n \"\"\")\n with gr.Column(variant='panel'):\n gr.Markdown(\"\"\"\n ### 1. Model checkpoints (select pretrained model path first).\n \"\"\")\n with gr.Row():\n stable_diffusion_dropdown = gr.Dropdown(\n label='Pretrained Model Path',\n choices=controller.stable_diffusion_list,\n interactive=True,\n )\n stable_diffusion_dropdown.change(\n fn=controller.update_stable_diffusion,\n inputs=[stable_diffusion_dropdown],\n outputs=[stable_diffusion_dropdown])\n\n stable_diffusion_refresh_button = gr.Button(\n value='\\U0001F503', elem_classes='toolbutton')\n\n def update_stable_diffusion():\n controller.refresh_stable_diffusion()\n return gr.Dropdown.update(\n choices=controller.stable_diffusion_list)\n\n stable_diffusion_refresh_button.click(\n fn=update_stable_diffusion,\n inputs=[],\n outputs=[stable_diffusion_dropdown])\n\n with gr.Row():\n motion_module_dropdown = gr.Dropdown(\n label='Select motion module',\n choices=controller.motion_module_list,\n interactive=True,\n )\n motion_module_dropdown.change(\n fn=controller.update_motion_module,\n inputs=[motion_module_dropdown],\n outputs=[motion_module_dropdown])\n\n motion_module_refresh_button = gr.Button(\n value='\\U0001F503', elem_classes='toolbutton')\n\n def update_motion_module():\n controller.refresh_motion_module()\n return gr.Dropdown.update(\n choices=controller.motion_module_list)\n\n motion_module_refresh_button.click(\n fn=update_motion_module,\n inputs=[],\n outputs=[motion_module_dropdown])\n\n base_model_dropdown = gr.Dropdown(\n label='Select base Dreambooth model (required)',\n choices=controller.personalized_model_list,\n interactive=True,\n )\n base_model_dropdown.change(\n fn=controller.update_base_model,\n inputs=[base_model_dropdown],\n outputs=[base_model_dropdown])\n\n lora_model_dropdown = gr.Dropdown(\n label='Select LoRA model (optional)',\n choices=['none'] + controller.personalized_model_list,\n value='none',\n interactive=True,\n )\n lora_model_dropdown.change(\n fn=controller.update_lora_model,\n inputs=[lora_model_dropdown],\n outputs=[lora_model_dropdown])\n\n lora_alpha_slider = gr.Slider(\n label='LoRA alpha',\n value=0.8,\n minimum=0,\n maximum=2,\n interactive=True)\n\n personalized_refresh_button = gr.Button(\n value='\\U0001F503', elem_classes='toolbutton')\n\n def update_personalized_model():\n controller.refresh_personalized_model()\n return [\n gr.Dropdown.update(\n choices=controller.personalized_model_list),\n gr.Dropdown.update(choices=['none'] +\n controller.personalized_model_list)\n ]\n\n personalized_refresh_button.click(\n fn=update_personalized_model,\n inputs=[],\n outputs=[base_model_dropdown, lora_model_dropdown])\n\n with gr.Column(variant='panel'):\n gr.Markdown(\"\"\"\n ### 2. Configs for AnimateDiff.\n \"\"\")\n\n prompt_textbox = gr.Textbox(label='Prompt', lines=2)\n negative_prompt_textbox = gr.Textbox(\n label='Negative prompt', lines=2)\n\n with gr.Row().style(equal_height=False):\n with gr.Column():\n with gr.Row():\n sample_step_slider = gr.Slider(\n label='Sampling steps',\n value=25,\n minimum=10,\n maximum=100,\n step=1)\n\n width_slider = gr.Slider(\n label='Width',\n value=512,\n minimum=256,\n maximum=1024,\n step=64)\n height_slider = gr.Slider(\n label='Height',\n value=512,\n minimum=256,\n maximum=1024,\n step=64)\n length_slider = gr.Slider(\n label='Animation length',\n value=16,\n minimum=8,\n maximum=24,\n step=1)\n cfg_scale_slider = gr.Slider(\n label='CFG Scale', value=7.5, minimum=0, maximum=20)\n\n with gr.Row():\n seed_textbox = gr.Textbox(label='Seed', value=-1)\n seed_button = gr.Button(\n value='\\U0001F3B2', elem_classes='toolbutton')\n seed_button.click(\n fn=lambda: gr.Textbox.update(\n value=random.randint(1, 1e8)),\n inputs=[],\n outputs=[seed_textbox])\n\n generate_button = gr.Button(\n value='Generate', variant='primary')\n\n result_video = gr.Video(\n label='Generated Animation', interactive=False)\n\n generate_button.click(\n fn=controller.animate,\n inputs=[\n stable_diffusion_dropdown,\n motion_module_dropdown,\n base_model_dropdown,\n lora_alpha_slider,\n prompt_textbox,\n negative_prompt_textbox,\n # sampler_dropdown,\n sample_step_slider,\n width_slider,\n length_slider,\n height_slider,\n cfg_scale_slider,\n seed_textbox,\n ],\n outputs=[result_video])\n\n return demo\n\n\nif __name__ == '__main__':\n demo = ui()\n demo.launch(share=True)\n" }, { "path": "demo/gradio_controlnet_animation.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport gradio as gr\n\nfrom mmagic.apis import MMagicInferencer\n\neditor = MMagicInferencer(model_name='controlnet_animation')\n\n\ndef process(video, prompt, a_prompt, negative_prompt,\n controlnet_conditioning_scale, width, height):\n prompt = prompt + a_prompt\n save_path = editor.infer(\n video=video,\n prompt=prompt,\n negative_prompt=negative_prompt,\n width=width,\n height=height,\n controlnet_conditioning_scale=controlnet_conditioning_scale)\n\n return save_path\n\n\nblock = gr.Blocks().queue()\nwith block:\n with gr.Row():\n gr.Markdown('## Controlnet Animation')\n with gr.Row():\n with gr.Column():\n video_inp = gr.Video(\n label='Video Source',\n source='upload',\n type='filepath',\n elem_id='input-vid')\n prompt = gr.Textbox(label='Prompt')\n run_button = gr.Button(label='Run')\n with gr.Accordion('Advanced options', open=False):\n a_prompt = gr.Textbox(\n label='Added Prompt',\n value='best quality, extremely detailed')\n n_prompt = gr.Textbox(\n label='Negative Prompt',\n value='longbody, lowres, bad anatomy, bad hands, ' +\n 'missing fingers, extra digit, fewer digits, '\n 'cropped, worst quality, low quality')\n controlnet_conditioning_scale = gr.Slider(\n label='Control Weight',\n minimum=0.0,\n maximum=2.0,\n value=0.7,\n step=0.01)\n width = gr.Slider(\n label='Image Width',\n minimum=256,\n maximum=768,\n value=512,\n step=64)\n height = gr.Slider(\n label='Image Width',\n minimum=256,\n maximum=768,\n value=512,\n step=64)\n\n with gr.Column():\n video_out = gr.Video(label='Video Result', elem_id='video-output')\n ips = [\n video_inp, prompt, a_prompt, n_prompt, controlnet_conditioning_scale,\n width, height\n ]\n run_button.click(fn=process, inputs=ips, outputs=video_out)\n\nblock.launch(server_name='0.0.0.0', share=True)\n" }, { "path": "demo/gradio_draggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom argparse import ArgumentParser\nfrom functools import partial\n\nimport gradio as gr\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom demo.utils.gradio_utils import (EasyDict, ImageMask, draw_mask_on_image,\n draw_points_on_image,\n get_latest_points_pair, get_valid_mask,\n on_change_single_global_state)\nfrom demo.utils.renderer import Renderer, add_watermark_np\n\nparser = ArgumentParser()\nparser.add_argument('--share', action='store_true', default='True')\nparser.add_argument('--cache-dir', type=str, default='./checkpoints')\nparser.add_argument(\n '--listen',\n action='store_true',\n help=''\n 'launch gradio with 0.0.0.0 as server name, \\\n allowing to respond to network requests',\n)\nargs = parser.parse_args()\ncache_dir = args.cache_dir\n\n\ndef get_default_gr_state(device='cuda',\n init_pkl='stylegan2_lions_512_pytorch_mmagic.pth'):\n global_state = gr.State({\n 'images': {\n # image_orig: the original image, change with seed/model is changed\n # image_raw: image with mask and points, change during optimization\n # image_show: image showed on screen\n },\n 'temporal_params': {\n # stop\n },\n 'mask':\n None, # mask for visualization, 1 for editing and 0 for unchange\n 'last_mask': None, # last edited mask\n 'show_mask': True, # add button\n 'generator_params': EasyDict(),\n 'params': {\n 'seed': 0,\n 'motion_lambda': 20,\n 'r1_in_pixels': 3,\n 'r2_in_pixels': 12,\n 'magnitude_direction_in_pixels': 1.0,\n 'latent_space': 'w+',\n 'trunc_psi': 0.7,\n 'trunc_cutoff': None,\n 'lr': 0.001,\n },\n 'device': device,\n 'draw_interval': 1,\n 'renderer': Renderer(disable_timing=True),\n 'points': {},\n 'curr_point': None,\n 'curr_type_point': 'start',\n 'editing_state': 'add_points',\n 'pretrained_weight': init_pkl\n })\n return global_state\n\n\ndef reverse_point_pairs(points):\n new_points = []\n for p in points:\n new_points.append([p[1], p[0]])\n return new_points\n\n\ndef clear_state(global_state, target=None):\n \"\"\"Clear target history state from global_state If target is not defined,\n points and mask will be both removed.\n\n 1. set global_state['points'] as empty dict\n 2. set global_state['mask'] as full-one mask.\n \"\"\"\n if target is None:\n target = ['point', 'mask']\n if not isinstance(target, list):\n target = [target]\n if 'point' in target:\n global_state['points'] = dict()\n print('Clear Points State!')\n if 'mask' in target:\n image_raw = global_state['images']['image_raw']\n global_state['mask'] = np.ones((image_raw.size[1], image_raw.size[0]),\n dtype=np.uint8)\n print('Clear mask State!')\n\n return global_state\n\n\ndef init_images(global_state, cache_dir='./checkpoints'):\n \"\"\"This function is called only ones with Gradio App is started.\n\n 0. pre-process global_state, unpack value from global_state of need\n 1. Re-init renderer\n 2. run `renderer._render_drag_impl` with `is_drag=False` to generate\n new image\n 3. Assign images to global state and re-generate mask\n \"\"\"\n\n if isinstance(global_state, gr.State):\n state = global_state.value\n else:\n state = global_state\n\n state['renderer'].init_network(\n state['generator_params'], # res\n os.path.join(cache_dir, state['pretrained_weight']\n ), # StyleGAN2 checkpoint path from Official DragGAN\n state['params']['seed'], # w0_seed,\n None, # w_load\n state['params']['latent_space'] == 'w+', # w_plus\n 'const',\n state['params']['trunc_psi'], # trunc_psi,\n state['params']['trunc_cutoff'], # trunc_cutoff,\n None, # input_transform\n state['params']['lr'] # lr,\n )\n state['renderer']._render_drag_impl(\n state['generator_params'], is_drag=False, to_pil=True)\n\n init_image = state['generator_params'].image\n state['images']['image_orig'] = init_image\n state['images']['image_raw'] = init_image\n state['images']['image_show'] = Image.fromarray(\n add_watermark_np(np.array(init_image)))\n state['mask'] = np.ones((init_image.size[1], init_image.size[0]),\n dtype=np.uint8)\n return global_state\n\n\ndef update_image_draw(image, points, mask, show_mask, global_state=None):\n image_draw = draw_points_on_image(image, points)\n if show_mask and mask is not None and not (mask == 0).all() and not (\n mask == 1).all():\n image_draw = draw_mask_on_image(image_draw, mask)\n\n image_draw = Image.fromarray(add_watermark_np(np.array(image_draw)))\n if global_state is not None:\n global_state['images']['image_show'] = image_draw\n return image_draw\n\n\ndef preprocess_mask_info(global_state, image):\n \"\"\"Function to handle mask information.\n\n 1. last_mask is None: Do not need to change mask, return mask\n 2. last_mask is not None:\n 2.1 global_state is remove_mask:\n 2.2 global_state is add_mask:\n \"\"\"\n\n if isinstance(image, dict):\n last_mask = get_valid_mask(image['mask'])\n else:\n last_mask = None\n mask = global_state['mask']\n # mask in global state is a placeholder with all 1.\n if (mask == 1).all():\n mask = last_mask\n\n # last_mask = global_state['last_mask']\n editing_mode = global_state['editing_state']\n\n if last_mask is None:\n return global_state\n\n if editing_mode == 'remove_mask':\n updated_mask = np.clip(mask - last_mask, 0, 1)\n print(f'Last editing_state is {editing_mode}, do remove.')\n elif editing_mode == 'add_mask':\n updated_mask = np.clip(mask + last_mask, 0, 1)\n print(f'Last editing_state is {editing_mode}, do add.')\n else:\n updated_mask = mask\n print(f'Last editing_state is {editing_mode}, ' 'do nothing to mask.')\n\n global_state['mask'] = updated_mask\n # global_state['last_mask'] = None # clear buffer\n return global_state\n\n\nvalid_checkpoints_dict = {\n f.split('/')[-1]: osp.join(cache_dir, f)\n for f in os.listdir(cache_dir)\n if (f.endswith('pth') and osp.exists(osp.join(cache_dir, f)))\n}\nprint(f'File under cache_dir ({cache_dir}):')\nprint(os.listdir(cache_dir))\nprint('Valid checkpoint file:')\nprint(valid_checkpoints_dict)\n\nif __name__ == '__main__':\n init_pkl = 'stylegan2_lions_512_pytorch_mmagic.pth'\n device = 'cuda'\n\n with gr.Blocks() as app:\n\n # renderer = Renderer()\n global_state = get_default_gr_state(device, init_pkl)\n\n # init image\n global_state = init_images(global_state)\n\n with gr.Row():\n\n with gr.Row():\n\n # Left --> tools\n with gr.Column(scale=3):\n\n # Pickle\n with gr.Row():\n\n with gr.Column(scale=1, min_width=10):\n gr.Markdown(value='Pickle', show_label=False)\n\n with gr.Column(scale=4, min_width=10):\n form_pretrained_dropdown = gr.Dropdown(\n choices=list(valid_checkpoints_dict.keys()),\n label='Pretrained Model',\n value=init_pkl,\n )\n\n # Latent\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n gr.Markdown(value='Latent', show_label=False)\n\n with gr.Column(scale=4, min_width=10):\n form_seed_number = gr.Number(\n value=global_state.value['params']['seed'],\n interactive=True,\n label='Seed',\n )\n form_lr_number = gr.Number(\n value=global_state.value['params']['lr'],\n interactive=True,\n label='Step Size')\n\n with gr.Row():\n with gr.Column(scale=2, min_width=10):\n form_reset_image = gr.Button('Reset Image')\n with gr.Column(scale=3, min_width=10):\n form_latent_space = gr.Radio(\n ['w', 'w+'],\n value=global_state.value['params']\n ['latent_space'],\n interactive=True,\n label='Latent space to optimize',\n show_label=False,\n )\n\n # Drag\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n gr.Markdown(value='Drag', show_label=False)\n with gr.Column(scale=4, min_width=10):\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n enable_add_points = gr.Button('Add Points')\n with gr.Column(scale=1, min_width=10):\n undo_points = gr.Button('Reset Points')\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n form_start_btn = gr.Button('Start')\n with gr.Column(scale=1, min_width=10):\n form_stop_btn = gr.Button('Stop')\n\n form_steps_number = gr.Number(\n value=0, label='Steps', interactive=False)\n\n # Mask\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n gr.Markdown(value='Mask', show_label=False)\n with gr.Column(scale=4, min_width=10):\n enable_add_mask = gr.Button('Edit Flexible Area')\n with gr.Row():\n with gr.Column(scale=1, min_width=10):\n form_reset_mask_btn = gr.Button(\n 'Reset mask')\n with gr.Column(scale=1, min_width=10):\n show_mask = gr.Checkbox(\n label='Show Mask',\n value=global_state.value['show_mask'],\n show_label=False)\n\n with gr.Row():\n form_lambda_number = gr.Number(\n value=global_state.value['params']\n ['motion_lambda'],\n interactive=True,\n label='Lambda',\n )\n\n form_draw_interval_number = gr.Number(\n value=global_state.value['draw_interval'],\n label='Draw Interval (steps)',\n interactive=True,\n visible=False)\n\n # Right --> Image\n with gr.Column(scale=8):\n form_image = ImageMask(\n value=global_state.value['images']['image_show'],\n brush_radius=20).style(\n width=768,\n height=768) # NOTE: hard image size code here.\n gr.Markdown(\"\"\"\n ## Quick Start\n\n 1. Select desired `Pretrained Model` and adjust `Seed` to generate\n an initial image.\n 2. Click on image to add control points.\n 3. Click `Start` and enjoy it!\n\n ## Advance Usage\n\n 1. Change `Step Size` to adjust learning rate in drag optimization.\n 2. Select `w` or `w+` to change latent space to optimize:\n * Optimize on `w` space may cause greater influence to the image.\n * Optimize on `w+` space may work slower than `w`, but usually\n achieve better results.\n * Note that changing the latent space will reset the image, points\n and mask (this has the same effect as `Reset Image` button).\n 3. Click `Edit Flexible Area` to create a mask and constrain the\n unmasked region to remain unchanged.\n \"\"\")\n gr.HTML(\"\"\"\n \n
\n Gradio demo supported by\n \n OpenMMLab MMagic\n
\n \"\"\")\n\n # Network & latents tab listeners\n def on_change_pretrained_dropdown(pretrained_value, global_state):\n \"\"\"Function to handle model change.\n\n 1. Set pretrained value to global_state\n 2. Re-init images and clear all states\n \"\"\"\n\n global_state['pretrained_weight'] = pretrained_value\n init_images(global_state)\n clear_state(global_state)\n\n return global_state, global_state['images']['image_show']\n\n form_pretrained_dropdown.change(\n on_change_pretrained_dropdown,\n inputs=[form_pretrained_dropdown, global_state],\n outputs=[global_state, form_image],\n )\n\n def on_click_reset_image(global_state):\n \"\"\"Reset image to the original one and clear all states\n 1. Re-init images\n 2. Clear all states\n \"\"\"\n\n init_images(global_state)\n clear_state(global_state)\n\n return global_state, global_state['images']['image_show']\n\n form_reset_image.click(\n on_click_reset_image,\n inputs=[global_state],\n outputs=[global_state, form_image],\n )\n\n # Update parameters\n def on_change_update_image_seed(seed, global_state):\n \"\"\"Function to handle generation seed change.\n\n 1. Set seed to global_state\n 2. Re-init images and clear all states\n \"\"\"\n\n global_state['params']['seed'] = int(seed)\n init_images(global_state)\n clear_state(global_state)\n\n return global_state, global_state['images']['image_show']\n\n form_seed_number.change(\n on_change_update_image_seed,\n inputs=[form_seed_number, global_state],\n outputs=[global_state, form_image],\n )\n\n def on_click_latent_space(latent_space, global_state):\n \"\"\"Function to reset latent space to optimize.\n\n NOTE: this function we reset the image and all controls\n 1. Set latent-space to global_state\n 2. Re-init images and clear all state\n \"\"\"\n\n global_state['params']['latent_space'] = latent_space\n init_images(global_state)\n clear_state(global_state)\n\n return global_state, global_state['images']['image_show']\n\n form_latent_space.change(\n on_click_latent_space,\n inputs=[form_latent_space, global_state],\n outputs=[global_state, form_image])\n\n # ==== Params\n form_lambda_number.change(\n partial(on_change_single_global_state,\n ['params', 'motion_lambda']),\n inputs=[form_lambda_number, global_state],\n outputs=[global_state],\n )\n\n def on_change_lr(lr, global_state):\n if lr == 0:\n print('lr is 0, do nothing.')\n return global_state\n else:\n global_state['params']['lr'] = lr\n renderer = global_state['renderer']\n renderer.update_lr(lr)\n print('New optimizer: ')\n print(renderer.w_optim)\n return global_state\n\n form_lr_number.change(\n on_change_lr,\n inputs=[form_lr_number, global_state],\n outputs=[global_state],\n )\n\n def on_click_start(global_state, image):\n p_in_pixels = []\n t_in_pixels = []\n valid_points = []\n\n # handle of start drag in mask editing mode\n global_state = preprocess_mask_info(global_state, image)\n\n # Prepare the points for the inference\n if len(global_state['points']) == 0:\n # yield on_click_start_wo_points(global_state, image)\n image_raw = global_state['images']['image_raw']\n update_image_draw(\n image_raw,\n global_state['points'],\n global_state['mask'],\n global_state['show_mask'],\n global_state,\n )\n\n yield (\n global_state,\n 0,\n global_state['images']['image_show'],\n # gr.File.update(visible=False),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n # latent space\n gr.Radio.update(interactive=True),\n gr.Button.update(interactive=True),\n # NOTE: disable stop button\n gr.Button.update(interactive=False),\n\n # update other comps\n gr.Dropdown.update(interactive=True),\n gr.Number.update(interactive=True),\n gr.Number.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Checkbox.update(interactive=True),\n # gr.Number.update(interactive=True),\n gr.Number.update(interactive=True),\n )\n else:\n\n # Transform the points into torch tensors\n for key_point, point in global_state['points'].items():\n try:\n p_start = point.get('start_temp', point['start'])\n p_end = point['target']\n\n if p_start is None or p_end is None:\n continue\n\n except KeyError:\n continue\n\n p_in_pixels.append(p_start)\n t_in_pixels.append(p_end)\n valid_points.append(key_point)\n\n mask = torch.tensor(global_state['mask']).float()\n drag_mask = 1 - mask\n\n renderer: Renderer = global_state['renderer']\n global_state['temporal_params']['stop'] = False\n global_state['editing_state'] = 'running'\n\n # reverse points order\n p_to_opt = reverse_point_pairs(p_in_pixels)\n t_to_opt = reverse_point_pairs(t_in_pixels)\n print(p_to_opt)\n print('Running with:')\n print(f' Source: {p_in_pixels}')\n print(f' Target: {t_in_pixels}')\n step_idx = 0\n while True:\n if global_state['temporal_params']['stop']:\n break\n\n # do drage here!\n renderer._render_drag_impl(\n global_state['generator_params'],\n p_to_opt, # point\n t_to_opt, # target\n drag_mask, # mask,\n global_state['params']['motion_lambda'], # lambda_mask\n reg=0,\n feature_idx=5, # NOTE: do not support change for now\n r1=global_state['params']['r1_in_pixels'], # r1\n r2=global_state['params']['r2_in_pixels'], # r2\n # random_seed = 0,\n # noise_mode = 'const',\n trunc_psi=global_state['params']['trunc_psi'],\n # force_fp32 = False,\n # layer_name = None,\n # sel_channels = 3,\n # base_channel = 0,\n # img_scale_db = 0,\n # img_normalize = False,\n # untransform = False,\n is_drag=True,\n to_pil=True)\n\n if step_idx % global_state['draw_interval'] == 0:\n print('Current Source:')\n for key_point, p_i, t_i in zip(valid_points, p_to_opt,\n t_to_opt):\n global_state['points'][key_point]['start_temp'] = [\n p_i[1],\n p_i[0],\n ]\n global_state['points'][key_point]['target'] = [\n t_i[1],\n t_i[0],\n ]\n start_temp = global_state['points'][key_point][\n 'start_temp']\n print(f' {start_temp}')\n\n image_result = global_state['generator_params'][\n 'image']\n image_draw = update_image_draw( # noqa\n image_result,\n global_state['points'],\n global_state['mask'],\n global_state['show_mask'],\n global_state,\n )\n global_state['images']['image_raw'] = image_result\n\n yield (\n global_state,\n step_idx,\n global_state['images']['image_show'],\n # gr.File.update(visible=False),\n gr.Button.update(interactive=False),\n gr.Button.update(interactive=False),\n gr.Button.update(interactive=False),\n gr.Button.update(interactive=False),\n gr.Button.update(interactive=False),\n # latent space\n gr.Radio.update(interactive=False),\n gr.Button.update(interactive=False),\n # enable stop button in loop\n gr.Button.update(interactive=True),\n\n # update other comps\n gr.Dropdown.update(interactive=False),\n gr.Number.update(interactive=False),\n gr.Number.update(interactive=False),\n gr.Button.update(interactive=False),\n gr.Button.update(interactive=False),\n gr.Checkbox.update(interactive=False),\n # gr.Number.update(interactive=False),\n gr.Number.update(interactive=False),\n )\n\n # increate step\n step_idx += 1\n\n image_result = global_state['generator_params']['image']\n global_state['images']['image_raw'] = image_result\n # image_draw = update_image_draw(image_result,\n # global_state['points'],\n # global_state['mask'],\n # global_state['show_mask'],\n # global_state)\n\n # fp = NamedTemporaryFile(suffix=\".png\", delete=False)\n # image_result.save(fp, \"PNG\")\n\n global_state['editing_state'] = 'add_points'\n\n yield (\n global_state,\n 0, # reset step to 0 after stop.\n global_state['images']['image_show'],\n # gr.File.update(visible=True, value=fp.name),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n gr.Button.update(interactive=True),\n # latent space\n gr.Radio.update(interactive=True),\n gr.Button.update(interactive=True),\n # NOTE: disable stop button with loop finish\n gr.Button.update(interactive=False),\n\n # update other comps\n gr.Dropdown.update(interactive=True),\n gr.Number.update(interactive=True),\n gr.Number.update(interactive=True),\n gr.Checkbox.update(interactive=True),\n gr.Number.update(interactive=True),\n )\n\n form_start_btn.click(\n on_click_start,\n inputs=[global_state, form_image],\n outputs=[\n global_state,\n form_steps_number,\n form_image,\n # form_download_result_file,\n # >>> buttons\n form_reset_image,\n enable_add_points,\n enable_add_mask,\n undo_points,\n form_reset_mask_btn,\n form_latent_space,\n form_start_btn,\n form_stop_btn,\n # <<< buttonm\n # >>> inputs comps\n form_pretrained_dropdown,\n form_seed_number,\n form_lr_number,\n show_mask,\n form_lambda_number,\n ],\n )\n\n def on_click_stop(global_state):\n \"\"\"Function to handle stop button is clicked.\n\n 1. send a stop signal by\n set global_state[\"temporal_params\"][\"stop\"] as True\n 2. Disable Stop button\n \"\"\"\n global_state['temporal_params']['stop'] = True\n\n return global_state, gr.Button.update(interactive=False)\n\n form_stop_btn.click(\n on_click_stop,\n inputs=[global_state],\n outputs=[global_state, form_stop_btn])\n\n form_draw_interval_number.change(\n partial(\n on_change_single_global_state,\n 'draw_interval',\n map_transform=lambda x: int(x),\n ),\n inputs=[form_draw_interval_number, global_state],\n outputs=[global_state],\n )\n\n def on_click_remove_point(global_state):\n choice = global_state['curr_point']\n del global_state['points'][choice]\n\n choices = list(global_state['points'].keys())\n\n if len(choices) > 0:\n global_state['curr_point'] = choices[0]\n\n return (\n gr.Dropdown.update(choices=choices, value=choices[0]),\n global_state,\n )\n\n # Mask\n def on_click_reset_mask(global_state):\n global_state['mask'] = np.ones(\n (\n global_state['images']['image_raw'].size[1],\n global_state['images']['image_raw'].size[0],\n ),\n dtype=np.uint8,\n )\n image_draw = update_image_draw(global_state['images']['image_raw'],\n global_state['points'],\n global_state['mask'],\n global_state['show_mask'],\n global_state)\n return global_state, image_draw\n\n form_reset_mask_btn.click(\n on_click_reset_mask,\n inputs=[global_state],\n outputs=[global_state, form_image],\n )\n\n # Image\n def on_click_enable_draw(global_state, image):\n \"\"\"Function to start add mask mode.\n\n 1. Preprocess mask info from last state\n 2. Change editing state to add_mask\n 3. Set curr image with points and mask\n \"\"\"\n global_state = preprocess_mask_info(global_state, image)\n global_state['editing_state'] = 'add_mask'\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(image_raw, global_state['points'],\n global_state['mask'], True,\n global_state)\n return (global_state,\n gr.Image.update(value=image_draw, interactive=True))\n\n def on_click_remove_draw(global_state, image):\n \"\"\"Function to start remove mask mode.\n\n 1. Preprocess mask info from last state\n 2. Change editing state to remove_mask\n 3. Set curr image with points and mask\n \"\"\"\n global_state = preprocess_mask_info(global_state, image)\n global_state['edinting_state'] = 'remove_mask'\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(image_raw, global_state['points'],\n global_state['mask'], True,\n global_state)\n return (global_state,\n gr.Image.update(value=image_draw, interactive=True))\n\n enable_add_mask.click(\n on_click_enable_draw,\n inputs=[global_state, form_image],\n outputs=[\n global_state,\n form_image,\n ])\n\n def on_click_add_point(global_state, image: dict):\n \"\"\"Function switch from add mask mode to add points mode.\n\n 1. Updaste mask buffer if need\n 2. Change global_state['editing_state'] to 'add_points'\n 3. Set current image with mask\n \"\"\"\n\n global_state = preprocess_mask_info(global_state, image)\n global_state['editing_state'] = 'add_points'\n mask = global_state['mask']\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(image_raw, global_state['points'],\n mask, global_state['show_mask'],\n global_state)\n\n return (global_state,\n gr.Image.update(value=image_draw, interactive=False))\n\n enable_add_points.click(\n on_click_add_point,\n inputs=[global_state, form_image],\n outputs=[global_state, form_image])\n\n def on_click_image(global_state, evt: gr.SelectData):\n \"\"\"This function only support click for point selection.\"\"\"\n xy = evt.index\n if global_state['editing_state'] != 'add_points':\n print(f'In {global_state[\"editing_state\"]} state. '\n 'Do not add points.')\n\n return global_state, global_state['images']['image_show']\n\n points = global_state['points']\n\n point_idx = get_latest_points_pair(points)\n if point_idx is None:\n points[0] = {'start': xy, 'target': None}\n print(f'Click Image - Start - {xy}')\n elif points[point_idx].get('target', None) is None:\n points[point_idx]['target'] = xy\n print(f'Click Image - Target - {xy}')\n else:\n points[point_idx + 1] = {'start': xy, 'target': None}\n print(f'Click Image - Start - {xy}')\n\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(\n image_raw,\n global_state['points'],\n global_state['mask'],\n global_state['show_mask'],\n global_state,\n )\n\n return global_state, image_draw\n\n form_image.select(\n on_click_image,\n inputs=[global_state],\n outputs=[global_state, form_image],\n )\n\n def on_click_clear_points(global_state):\n \"\"\"Function to handle clear all control points\n 1. clear global_state['points'] (clear_state)\n 2. re-init network\n 2. re-draw image\n \"\"\"\n clear_state(global_state, target='point')\n\n renderer: Renderer = global_state['renderer']\n renderer.feat_refs = None\n\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(image_raw, {}, global_state['mask'],\n global_state['show_mask'],\n global_state)\n return global_state, image_draw\n\n undo_points.click(\n on_click_clear_points,\n inputs=[global_state],\n outputs=[global_state, form_image])\n\n def on_click_show_mask(global_state, show_mask):\n \"\"\"Function to control whether show mask on image.\"\"\"\n global_state['show_mask'] = show_mask\n\n image_raw = global_state['images']['image_raw']\n image_draw = update_image_draw(\n image_raw,\n global_state['points'],\n global_state['mask'],\n global_state['show_mask'],\n global_state,\n )\n return global_state, image_draw\n\n show_mask.change(\n on_click_show_mask,\n inputs=[global_state, show_mask],\n outputs=[global_state, form_image],\n )\n\n gr.close_all()\n app.queue(concurrency_count=3, max_size=20)\n app.launch(\n share=args.share,\n server_name='0.0.0.0' if args.listen else '127.0.0.1')\n" }, { "path": "demo/gradio_fastcomposer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport gc\n\nimport gradio as gr\nimport numpy as np\nimport PIL\nimport torch\nfrom mmengine import Config\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\ngc.collect()\ntorch.cuda.empty_cache()\nregister_all_modules()\n\n\nclass ModelWrapper:\n\n def __init__(self, model):\n super().__init__()\n self.model = model\n self.device = torch.device(\n 'cuda' if torch.cuda.is_available() else 'cpu')\n self.model = self.model.to(self.device)\n\n def inference(\n self,\n image1: PIL.Image.Image,\n image2: PIL.Image.Image,\n prompt: str,\n negative_prompt: str,\n seed: int,\n guidance_scale: float,\n alpha_: float,\n num_steps: int,\n num_images: int,\n ):\n print('Running model inference...')\n image = []\n if image1 is not None:\n image.append(image1)\n\n if image2 is not None:\n image.append(image2)\n\n if len(image) == 0:\n return [], 'You need to upload at least one image.'\n\n num_subject_in_text = (np.array(\n self.model.special_tokenizer.encode(prompt)) ==\n self.model.image_token_id).sum()\n if num_subject_in_text != len(image):\n return (\n [],\n \"Number of subjects in the text description doesn't \"\n 'match the number of reference images, #text subjects: '\n f'{num_subject_in_text} #reference image: {len(image)}',\n )\n\n if seed == -1:\n seed = np.random.randint(0, 1000000)\n\n generator = torch.Generator(device=self.device)\n generator.manual_seed(seed)\n\n return (\n self.model.infer(\n prompt=prompt,\n negative_prompt=negative_prompt,\n height=512,\n width=512,\n num_inference_steps=num_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images,\n generator=generator,\n alpha_=alpha_,\n reference_subject_images=image,\n )['samples'],\n 'run successfully',\n )\n\n\ndef create_demo():\n TITLE = 'FastComposer Demo'\n\n DESCRIPTION = \"\"\"To run the demo, you should:\n 1. Upload your images. The order of image1 and image2 needs to match the\n order of the subects in the prompt. You only need 1 image for\n single subject generation.\n 2. Input proper text prompts, such as \"A woman img and a man img in\n the snow\" or \"A painting of a man img in the style of Van Gogh\",\n where \"img\" specifies the token you want to augment and comes\n after the word.\n 3. Click the Run button. You can also adjust the hyperparameters to\n improve the results. Look at the job status to see\n if there are any errors with your input.\n \"\"\"\n\n cfg_file = Config.fromfile(\n 'configs/fastcomposer/fastcomposer_8xb16_FFHQ.py')\n fastcomposer = MODELS.build(cfg_file.model)\n model = ModelWrapper(fastcomposer)\n # model = ModelWrapper(convert_model_to_pipeline(args, \"cpu\"))\n with gr.Blocks() as demo:\n gr.Markdown(TITLE)\n gr.Markdown(DESCRIPTION)\n with gr.Row():\n with gr.Column():\n with gr.Box():\n image1 = gr.Image(label='Image 1', type='pil')\n\n image2 = gr.Image(label='Image 2', type='pil')\n\n gr.Markdown('Upload the image for your subject')\n\n prompt = gr.Text(\n value='A man img and a man img sitting in a park',\n label='Prompt',\n placeholder=\n 'e.g. \"A woman img and a man img in the snow\", \"A painting'\n ' of a man img in the style of Van Gogh\"',\n info='Use \"img\" to specify the word you want to augment.',\n )\n negative_prompt = gr.Text(\n value='((((ugly)))), (((duplicate))), ((morbid)), '\n '((mutilated)), [out of frame], extra fingers, '\n 'mutated hands, ((poorly drawn hands)), '\n '((poorly drawn face)), (((mutation))), '\n '(((deformed))), ((ugly)), blurry, ((bad anatomy)), '\n '(((bad proportions))), ((extra limbs)), cloned face, '\n '(((disfigured))). out of frame, ugly, extra limbs, '\n '(bad anatomy), gross proportions, (malformed limbs), '\n '((missing arms)), ((missing legs)), (((extra arms))), '\n '(((extra legs))), mutated hands, (fused fingers), '\n '(too many fingers), (((long neck)))',\n label='Negative Prompt',\n info='Features that you want to avoid.',\n )\n alpha_ = gr.Slider(\n label='alpha',\n minimum=0,\n maximum=1,\n step=0.05,\n value=0.75,\n info=\n 'A smaller alpha aligns images with text better, but may '\n 'deviate from the subject image. Increase alpha to improve'\n ' identity preservation, decrease it for '\n 'prompt consistency.',\n )\n num_images = gr.Slider(\n label='Number of generated images',\n minimum=1,\n maximum=8,\n step=1,\n value=4,\n )\n run_button = gr.Button('Run')\n with gr.Accordion(label='Advanced options', open=False):\n seed = gr.Slider(\n label='Seed',\n minimum=-1,\n maximum=1000000,\n step=1,\n value=-1,\n info='If set to -1, a different seed will be '\n 'used each time.',\n )\n guidance_scale = gr.Slider(\n label='Guidance scale',\n minimum=1,\n maximum=10,\n step=1,\n value=5,\n )\n num_steps = gr.Slider(\n label='Steps',\n minimum=1,\n maximum=300,\n step=1,\n value=50,\n )\n with gr.Column():\n result = gr.Gallery(label='Generated Images').style(\n grid=[2], height='auto')\n error_message = gr.Text(label='Job Status')\n\n inputs = [\n image1,\n image2,\n prompt,\n negative_prompt,\n seed,\n guidance_scale,\n alpha_,\n num_steps,\n num_images,\n ]\n run_button.click(\n fn=model.inference, inputs=inputs, outputs=[result, error_message])\n return demo\n\n\nif __name__ == '__main__':\n demo = create_demo()\n demo.queue(api_open=False)\n demo.launch(\n show_error=True,\n server_name='0.0.0.0',\n server_port=8080,\n )\n" }, { "path": "demo/gradio_inpainting.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport json\nimport os\nimport os.path as osp\nimport subprocess\nimport traceback\nimport warnings\nfrom typing import Dict, List, Optional, Union\n\nimport cv2\nimport gradio as gr\nimport numpy as np\nimport torch\nimport yaml\nfrom mmengine.registry import init_default_scope\n\nfrom mmagic.apis.inferencers.inpainting_inferencer import InpaintingInferencer\n\n\nclass InpaintingGradio:\n inpainting_supported_models = [\n # inpainting models\n 'aot_gan',\n 'deepfillv1',\n 'deepfillv2',\n 'global_local',\n 'partial_conv',\n ]\n inpainting_supported_models_cfg = {}\n inpainting_supported_models_cfg_inited = False\n pkg_path = ''\n error_color = '#FF0000'\n success_color = '#00FF00'\n warning_color = '#FFFF00'\n notice_message = ('', None, '')\n\n def __init__(self,\n model_name: str = None,\n model_setting: int = None,\n model_config: str = None,\n model_ckpt: str = None,\n device: torch.device = None,\n extra_parameters: Dict = None,\n seed: int = 2022,\n **kwargs) -> None:\n init_default_scope('mmagic')\n InpaintingGradio.init_inference_supported_models_cfg()\n self.model_name = model_name\n self.model_setting = model_setting\n self.model_config = model_config\n self.model_ckpt = model_ckpt\n self.device = device\n self.extra_parameters = extra_parameters\n self.seed = seed\n if model_name or (model_config and model_ckpt):\n inpainting_kwargs = {}\n inpainting_kwargs.update(\n self._get_inpainting_kwargs(model_name, model_setting,\n model_config, model_ckpt,\n extra_parameters))\n self.inference = InpaintingInferencer(\n device=device, seed=seed, **inpainting_kwargs)\n\n def model_reconfig(self,\n model_name: str = None,\n model_setting: int = None,\n model_config: str = None,\n model_ckpt: str = None,\n device: torch.device = None,\n extra_parameters: Dict = None,\n seed: int = 2022,\n **kwargs) -> None:\n inpainting_kwargs = {}\n # if model_config:\n # model_config = model_config.name\n # if model_ckpt:\n # model_ckpt = model_ckpt.name\n if not model_name and model_setting:\n self.send_notification(\n 'model_name should not be None when model_setting was used',\n self.error_color, 'error')\n return\n elif (not model_config and not model_name) and model_ckpt:\n self.send_notification(\n 'model_name and model_config should not be None when '\n 'model_ckpt was used', self.error_color, 'error')\n return\n elif (not model_ckpt and not model_name) and model_config:\n self.send_notification(\n 'model_name and model_ckpt should not be None when '\n 'model_config was used', self.error_color, 'error')\n return\n inpainting_kwargs.update(\n self._get_inpainting_kwargs(model_name, model_setting,\n model_config, model_ckpt,\n extra_parameters))\n try:\n self.inference = InpaintingInferencer(\n device=device, seed=seed, **inpainting_kwargs)\n except Exception as e:\n self.send_notification('inference Exception:' + str(e),\n self.error_color, 'error')\n traceback.print_exc()\n return\n self.send_notification('Model config Finished!', self.success_color,\n 'success')\n\n @staticmethod\n def change_text2dict(input_text: str) -> Union[Dict, None]:\n return_dict = None\n try:\n return_dict = json.loads(input_text)\n except Exception as e:\n InpaintingGradio.send_notification(\n 'Convert string to dict Exception:' + str(e),\n InpaintingGradio.error_color, 'error')\n return return_dict\n\n @staticmethod\n def get_package_path() -> str:\n p = subprocess.Popen(\n 'pip show mmagic', shell=True, stdout=subprocess.PIPE)\n out, err = p.communicate()\n out = out.decode()\n if 'Location' not in out:\n InpaintingGradio.send_notification('module mmagic not found',\n InpaintingGradio.error_color,\n 'error')\n raise Exception('module mmagic not found')\n package_path = out[out.find('Location') +\n len('Location: '):].split('\\r\\n')[0] + os.sep\n return package_path\n\n def get_model_config(self, model_name: str) -> Dict:\n \"\"\"Get the model configuration including model config and checkpoint\n url.\n\n Args:\n model_name (str): Name of the model.\n Returns:\n dict: Model configuration.\n \"\"\"\n if model_name not in self.inpainting_supported_models:\n self.send_notification(f'Model {model_name} is not supported.',\n self.error_color, 'error')\n raise ValueError(f'Model {model_name} is not supported.')\n else:\n return self.inpainting_supported_models_cfg[model_name]\n\n @staticmethod\n def init_inference_supported_models_cfg() -> None:\n if not InpaintingGradio.inpainting_supported_models_cfg_inited:\n InpaintingGradio.pkg_path = InpaintingGradio.get_package_path()\n # all_cfgs_dir = osp.join(osp.dirname(__file__), '..', 'configs')\n all_cfgs_dir = osp.join(InpaintingGradio.pkg_path, 'configs')\n for model_name in InpaintingGradio.inpainting_supported_models:\n meta_file_dir = osp.join(all_cfgs_dir, model_name,\n 'metafile.yml')\n with open(meta_file_dir, 'r') as stream:\n parsed_yaml = yaml.safe_load(stream)\n InpaintingGradio.inpainting_supported_models_cfg[\n model_name] = {}\n InpaintingGradio.inpainting_supported_models_cfg[model_name][\n 'settings'] = parsed_yaml['Models'] # noqa\n InpaintingGradio.inpainting_supported_models_cfg_inited = True\n\n def _get_inpainting_kwargs(self, model_name: Optional[str],\n model_setting: Optional[int],\n model_config: Optional[str],\n model_ckpt: Optional[str],\n extra_parameters: Optional[Dict]) -> Dict:\n \"\"\"Get the kwargs for the inpainting inferencer.\"\"\"\n kwargs = {}\n\n if model_name:\n cfgs = self.get_model_config(model_name)\n # kwargs['task'] = cfgs['task']\n setting_to_use = 0\n if model_setting:\n if isinstance(model_setting, str):\n model_setting = int(\n model_setting[0:model_setting.find(' - ')])\n setting_to_use = model_setting\n else:\n model_setting = 0\n if model_setting > len(\n cfgs['settings']) - 1 or model_setting < -len(\n cfgs['settings']):\n self.send_notification(\n f\"model_setting out of range of {model_name}'s \"\n 'cfgs settings', self.error_color, 'error')\n config_dir = cfgs['settings'][setting_to_use]['Config']\n config_dir = config_dir[config_dir.find('configs'):]\n # kwargs['config'] = os.path.join(\n # osp.dirname(__file__), '..', config_dir)\n kwargs['config'] = os.path.join(self.pkg_path, config_dir)\n kwargs['ckpt'] = cfgs['settings'][setting_to_use]['Weights']\n\n if model_config:\n if kwargs.get('config', None) is not None:\n warnings.warn(\n f'{model_name}\\'s default config '\n f'is overridden by {model_config}', UserWarning)\n kwargs['config'] = model_config\n\n if model_ckpt:\n if kwargs.get('ckpt', None) is not None:\n warnings.warn(\n f'{model_name}\\'s default checkpoint '\n f'is overridden by {model_ckpt}', UserWarning)\n kwargs['ckpt'] = model_ckpt\n\n if extra_parameters:\n kwargs['extra_parameters'] = extra_parameters\n\n return kwargs\n\n @staticmethod\n def send_notification(msg: str, color: str, label: str) -> None:\n InpaintingGradio.notice_message = (msg, color, label)\n\n @staticmethod\n def get_inpainting_supported_models() -> List:\n \"\"\"static function for getting inpainting inference supported modes.\"\"\"\n return InpaintingGradio.inpainting_supported_models\n\n def infer(self, input_img_arg: Dict) -> np.ndarray:\n result = self.inference(\n img=input_img_arg['image'], mask=input_img_arg['mask'])\n result = cv2.cvtColor(result[1], cv2.COLOR_RGB2BGR)\n return result\n\n def run(self) -> None:\n with gr.Blocks() as demo:\n with gr.Row():\n with gr.Column():\n input_model_dropdown = gr.Dropdown(\n choices=self.inpainting_supported_models,\n value=self.model_name,\n label='choose model')\n input_setting = gr.Dropdown(\n value=self.model_setting, label='choose setting')\n input_config = gr.Textbox(\n value=self.model_config, label='model_config_path')\n input_ckpt = gr.Textbox(\n value=self.model_ckpt, label='model_ckpt_path')\n input_device_dropdown = gr.Dropdown(\n choices=['cuda', 'cpu'],\n label='choose device',\n value='cuda')\n input_extra_parameters_input = gr.Textbox(\n value=json.dumps(self.extra_parameters),\n label='extra_parameters')\n input_extra_parameters = gr.JSON(\n value=self.extra_parameters, label='extra_parameters')\n input_seed = gr.Number(\n value=self.seed, precision=0, label='seed')\n config_button = gr.Button('CONFIG')\n input_extra_parameters_input.blur(\n self.change_text2dict, input_extra_parameters_input,\n input_extra_parameters)\n\n with gr.Column(visible=False) as output_col:\n input_image = gr.Image(\n image_mode='RGB',\n tool='sketch',\n type='filepath',\n label='Input image')\n infer_button = gr.Button('INFER')\n infer_button.style(full_width=False)\n output_image = gr.Image(\n label='Output image', interactive=False)\n output_image.style(height=500)\n infer_button.click(\n self.infer, inputs=input_image, outputs=output_image)\n\n with gr.Row():\n label = gr.Label(\n value=self.notice_message[0],\n color=self.notice_message[1],\n label=self.notice_message[2])\n\n def show_infer(*args) -> Dict:\n self.model_reconfig(*args)\n return {\n output_col:\n gr.update(visible=True),\n label:\n gr.update(\n value=self.notice_message[0],\n color=self.notice_message[1],\n label=self.notice_message[2])\n }\n\n def change_setting(model_name: str) -> Dict:\n settings = InpaintingGradio.inpainting_supported_models_cfg[\n model_name]['settings']\n return {\n input_setting:\n gr.update(choices=[\n str(i) + ' - ' + settings[i]['Config']\n for i in range(len(settings))\n ])\n }\n\n input_model_dropdown.change(\n fn=change_setting,\n inputs=input_model_dropdown,\n outputs=input_setting)\n\n config_button.click(show_infer, [\n input_model_dropdown,\n input_setting,\n input_config,\n input_ckpt,\n input_device_dropdown,\n input_extra_parameters,\n input_seed,\n ], [output_col, label])\n demo.launch()\n\n\nif __name__ == '__main__':\n inpaintingGradio = InpaintingGradio()\n inpaintingGradio.run()\n" }, { "path": "demo/gradio_vico.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\n\nimport gradio as gr\nimport torch\nfrom mmengine import Config\nfrom mmengine.runner import Runner\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\ncheckpoint_dir = 'ckpts'\nckpts = os.listdir(checkpoint_dir)\n\n\ndef load_base_model():\n global cfg, model\n cfg = Config.fromfile('configs/vico/vico.py')\n model = MODELS.build(cfg.model)\n\n\ndef train(train_data, init_token, placeholder):\n data_root = 'tmp'\n concept_dir = init_token\n image_dir = os.path.join(data_root, concept_dir)\n os.makedirs(image_dir, exist_ok=True)\n for i, data in enumerate(train_data):\n image = Image.open(data.name)\n image.save(os.path.join(image_dir, f'{i}.png'))\n global train_cfg\n train_cfg = Config.fromfile('configs/vico/vico.py')\n train_cfg.work_dir = os.path.join('./work_dirs', 'vico_gradio')\n train_cfg.dataset['data_root'] = data_root\n train_cfg.dataset['concept_dir'] = init_token\n train_cfg.dataset['placeholder'] = placeholder\n train_cfg.train_dataloader['dataset'] = train_cfg.dataset\n train_cfg.model['placeholder'] = placeholder\n train_cfg.model['initialize_token'] = init_token\n train_cfg.custom_hooks = None\n runner = Runner.from_cfg(train_cfg)\n runner.train()\n\n\nclass DummyModel:\n\n def __init__(self, model):\n self.model = model\n\n\ndef infer_fn(checkpoint, img_ref, prompt, negative_prompt, guidance_scale,\n width, height, seed, inference_steps, bs):\n state_dict = torch.load(os.path.join(checkpoint_dir, checkpoint))\n dummy_agent = DummyModel(model)\n dummy_agent.model.load_state_dict(state_dict, strict=False)\n dummy_agent.model = dummy_agent.model.cuda()\n img_ref = Image.open(img_ref)\n with torch.no_grad():\n output = dummy_agent.model.infer(\n prompt=prompt,\n image_reference=img_ref,\n width=width,\n height=height,\n negative_prompt=negative_prompt,\n guidance_scale=guidance_scale,\n num_inference_steps=inference_steps,\n num_images_per_prompt=bs,\n seed=int(seed))['samples']\n\n return output\n\n\nif __name__ == '__main__':\n load_base_model()\n block = gr.Blocks().queue()\n with block:\n with gr.Tab('Train'):\n with gr.Row():\n gr.Markdown('## ViCo')\n with gr.Row():\n with gr.Column():\n train_data = gr.File(\n label='Training Samples',\n file_count='directory',\n file_types=['image'],\n interactive=True,\n )\n with gr.Column():\n init_token = gr.Textbox(label='Init token')\n placeholder = gr.Textbox(label='Placeholder')\n train_button = gr.Button(value='Start Training')\n train_button.click(\n fn=train, inputs=[train_data, init_token, placeholder])\n with gr.Tab('Inference'):\n with gr.Row():\n gr.Markdown('## ViCo')\n with gr.Row():\n with gr.Column():\n checkpoint = gr.Dropdown(ckpts)\n img_ref = gr.Image(\n label='Image Reference',\n source='upload',\n type='filepath',\n elem_id='input-vid')\n prompt = gr.Textbox(label='Prompt')\n with gr.Accordion('Advanced options', open=False):\n n_prompt = gr.Textbox(\n label='Negative Prompt', value='')\n guidance_scale = gr.Slider(\n label='CFG',\n minimum=0.0,\n maximum=13.0,\n value=7.5,\n step=0.5)\n width = gr.Slider(\n label='Image Width',\n minimum=256,\n maximum=768,\n value=512,\n step=64)\n height = gr.Slider(\n label='Image Width',\n minimum=256,\n maximum=768,\n value=512,\n step=64)\n seed = gr.Number(\n label='Seed',\n value=0,\n )\n num_inference_steps = gr.Slider(\n label='Inference Steps',\n minimum=20,\n maximum=80,\n value=50,\n step=5)\n bs = gr.Slider(\n label='Batch Size',\n minimum=1,\n maximum=4,\n value=1,\n step=1)\n\n with gr.Column():\n output = gr.Gallery(\n label='Output Image', elem_id='image-output')\n run_button = gr.Button(label='Run')\n\n ips = [\n checkpoint, img_ref, prompt, n_prompt, guidance_scale, width,\n height, seed, num_inference_steps, bs\n ]\n run_button.click(fn=infer_fn, inputs=ips, outputs=output)\n block.launch(server_name='0.0.0.0', share=True)\n" }, { "path": "demo/mmagic_inference_demo.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# isort: off\nfrom argparse import ArgumentParser\nfrom mmengine import DictAction\nfrom mmagic.apis import MMagicInferencer\n\n\ndef parse_args():\n parser = ArgumentParser()\n parser.add_argument(\n '--img', type=str, default=None, help='Input image file.')\n parser.add_argument(\n '--video', type=str, default=None, help='Input video file.')\n parser.add_argument(\n '--label',\n type=int,\n default=None,\n help='Input label for conditional models.')\n parser.add_argument(\n '--trimap', type=str, default=None, help='Input for matting models.')\n parser.add_argument(\n '--mask',\n type=str,\n default=None,\n help='path to input mask file for inpainting models')\n parser.add_argument(\n '--text',\n type=str,\n default='',\n help='text input for text2image models')\n parser.add_argument(\n '--result-out-dir',\n type=str,\n default=None,\n help='Output img or video path.')\n parser.add_argument(\n '--model-name',\n type=str,\n default=None,\n help='Pretrained mmagic algorithm')\n parser.add_argument(\n '--model-setting',\n type=int,\n default=None,\n help='Pretrained mmagic algorithm setting')\n parser.add_argument(\n '--config-name',\n type=str,\n default=None,\n help='Pretrained mmagic algorithm config name')\n parser.add_argument(\n '--model-config',\n type=str,\n default=None,\n help='Path to the custom config file of the selected mmagic model.')\n parser.add_argument(\n '--model-ckpt',\n type=str,\n default=None,\n help='Path to the custom checkpoint file of the selected det model.')\n parser.add_argument(\n '--device',\n type=str,\n default='cuda',\n help='Device used for inference.')\n parser.add_argument(\n '--extra-parameters',\n nargs='+',\n action=DictAction,\n help='Other customized kwargs for different model')\n parser.add_argument(\n '--seed',\n type=int,\n default=2022,\n help='The random seed used in inference.')\n\n # print supported tasks and models\n parser.add_argument(\n '--print-supported-models',\n action='store_true',\n help='print all supported models for inference.')\n parser.add_argument(\n '--print-supported-tasks',\n action='store_true',\n help='print all supported tasks for inference.')\n parser.add_argument(\n '--print-task-supported-models',\n type=str,\n default=None,\n help='print all supported models for one task')\n\n args, unknown = parser.parse_known_args()\n\n return args, unknown\n\n\ndef main():\n args, unknown = parse_args()\n assert len(unknown) % 2 == 0, (\n 'User defined arguments must be passed in pair, but receive '\n f'{len(unknown)} arguments.')\n\n user_defined = {}\n for idx in range(len(unknown) // 2):\n key, val = unknown[idx * 2], unknown[idx * 2 + 1]\n assert key.startswith('--'), (\n 'Key of user define arguments must be start with \\'--\\', but '\n f'receive \\'{key}\\'.')\n\n key = key.replace('-', '_')\n val = int(val) if val.isdigit() else val\n user_defined[key[2:]] = val\n\n user_defined.update(vars(args))\n\n if args.print_supported_models:\n inference_supported_models = \\\n MMagicInferencer.get_inference_supported_models()\n print('all supported models:')\n print(inference_supported_models)\n return\n\n if args.print_supported_tasks:\n supported_tasks = MMagicInferencer.get_inference_supported_tasks()\n print('all supported tasks:')\n print(supported_tasks)\n return\n\n if args.print_task_supported_models:\n task_supported_models = \\\n MMagicInferencer.get_task_supported_models(\n args.print_task_supported_models)\n print('translation models:')\n print(task_supported_models)\n return\n\n editor = MMagicInferencer(**vars(args))\n editor.infer(**user_defined)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "demo/mmagic_inference_tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MMagic Inference Tutorial\\n\",\n \"\\n\",\n \"Welcome to MMagic! This is the official tutorial for using MMagic inference api to predict your own image or video.\\n\",\n \"\\n\",\n \"In this tutorial, you will learn how to\\n\",\n \"\\n\",\n \"[1. Install MMagic](#1-install-mmagic)\\n\",\n \"\\n\",\n \"[2. Check inference supported tasks and models](#2-check-inference-supported-tasks-and-models)\\n\",\n \"\\n\",\n \"[3. Perform inference using MMagic\\n\",\n \" inference API](#3-perform-inference-with-mmagic-api)\\n\",\n \"\\n\",\n \"  [3.1 Prepare some images or videos for inference](#31-prepare-some-images-or-videos-for-inference)\\n\",\n \"\\n\",\n \"  [3.2 Perform inference with two lines of python code](#32-perform-inference-with-two-lines-of-python-code)\\n\",\n \"\\n\",\n \"  [3.3 Infer with different settings of a specific model](#33-infer-with-different-settings-of-a-specific-model)\\n\",\n \"\\n\",\n \"  [3.4 Infer with extra parameters](#34-inference-with-extra-parameters)\\n\",\n \"\\n\",\n \"[4. Perform inference with models of different tasks including](#4-perform-inference-with-models-of-different-tasks):\\n\",\n \"\\n\",\n \"  [4.1 Inference of conditional GANs models](#41-inference-of-conditional-gan-models)\\n\",\n \"\\n\",\n \"  [4.2 Inference of inpanting models](#42-inference-of-inpainting-models)\\n\",\n \"\\n\",\n \"  [4.3 Inference of matting models](#43-inference-of-matting-models)\\n\",\n \"\\n\",\n \"  [4.4 Inference of super resolution models](#44-inference-of-image-super-resolution-models)\\n\",\n \"\\n\",\n \"  [4.5 Inference of image2image models](#45-inference-of-image-translation-models)\\n\",\n \"\\n\",\n \"  [4.6 Inference of unconditional GANs models](#46-inference-of-unconditional-gan-models)\\n\",\n \"\\n\",\n \"  [4.7 Inference of video interpolation models](#47-inference-of-video-interpolation-models)\\n\",\n \"\\n\",\n \"  [4.8 Inference of video super resolution models](#48-inference-of-video-restoration-models)\\n\",\n \"\\n\",\n \"  [4.9 Inference of text-to-image models](#49-inference-of-text-to-image-models)\\n\",\n \"\\n\",\n \"Let's start!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Install MMagic\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"shellscript\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"torch 1.12.1+cu113\\n\",\n \"torchvision 0.13.1+cu113\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Check PyTorch version\\n\",\n \"!pip3 list | grep torch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"shellscript\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Install mmcv dependency via openmim\\n\",\n \"!pip3 install openmim\\n\",\n \"!mim install 'mmcv>=2.0.0'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"shellscript\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Install mmagic from source\\n\",\n \"%cd ..\\n\",\n \"!pip3 install -e .\\n\",\n \"%cd demo\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.0.0rc7\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Check MMagic installation\\n\",\n \"import mmagic\\n\",\n \"print(mmagic.__version__)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Check inference supported tasks and models\\n\",\n \"\\n\",\n \"There are multiple task types in MMagic: Matting, Inpainting, Video Super-Resolution, Image Super-Resolution, Image2Image, Unconditional GANs, Conditional GANs, Video Interpolation. \\n\",\n \"\\n\",\n \"We provide some models for each task. All available models and tasks could be printed out like this.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"all supported models:\\n\",\n \"['inst_colorization', 'biggan', 'sngan_proj', 'sagan', 'dcgan', 'wgan-gp', 'lsgan', 'ggan', 'pggan', 'styleganv1', 'styleganv2', 'styleganv3', 'gca', 'global_local', 'aot_gan', 'pix2pix', 'cyclegan', 'srcnn', 'srgan_resnet', 'edsr', 'esrgan', 'rdn', 'dic', 'ttsr', 'glean', 'real_esrgan', 'flavr', 'cain', 'edvr', 'tdan', 'basicvsr', 'iconvsr', 'basicvsr_pp', 'real_basicvsr', 'disco_diffusion', 'eg3d', 'controlnet_animation']\\n\",\n \"all supported tasks:\\n\",\n \"['Unconditional GANs', 'controlnet_animation', 'Video Interpolation', '3D-aware Generation', 'Conditional GANs', 'Image Super-Resolution', 'Text2Image, Image2Image', 'Colorization', 'Inpainting', 'Matting', 'Image2Image', 'Video Super-Resolution']\\n\",\n \"translation models:\\n\",\n \"['pix2pix', 'cyclegan']\\n\"\n ]\n }\n ],\n \"source\": [\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# print all supported models for inference.\\n\",\n \"inference_supported_models = MMagicInferencer.get_inference_supported_models()\\n\",\n \"print('all supported models:')\\n\",\n \"print(inference_supported_models)\\n\",\n \"\\n\",\n \"# print all supported tasks for inference.\\n\",\n \"supported_tasks = MMagicInferencer.get_inference_supported_tasks()\\n\",\n \"print('all supported tasks:')\\n\",\n \"print(supported_tasks)\\n\",\n \"\\n\",\n \"# print all supported models for one task, take image translation for example.\\n\",\n \"task_supported_models = MMagicInferencer.get_task_supported_models('Image2Image')\\n\",\n \"print('translation models:')\\n\",\n \"print(task_supported_models)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Perform inference with MMagic API\\n\",\n \"\\n\",\n \"Next we describe how to perform inference with python code snippets.\\n\",\n \"\\n\",\n \"(We also provide command line interface for you to do inference by running mmagic_inference_demo.py. The usage of this interface could be found in [README.md](./README.md) and more guidance could be found in the [documentation](https://mmagic.readthedocs.io/en/latest/user_guides/3_inference.html#).)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.1 Prepare some images or videos for inference\\n\",\n \"\\n\",\n \"Before we start to perform inference with a pretrained model, some input images or videos should be prepared. \\n\",\n \"\\n\",\n \"Take image translation for example. We need a input image to be translated.\\n\",\n \"\\n\",\n \"Put your image to some directory and make a directory to save processed image.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"shellscript\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# make a dir for input image and output image\\n\",\n \"!mkdir -p ./../resources/input/translation\\n\",\n \"!mkdir -p ./../resources/output/translation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We also have prepared some images and videos for you. You can download by running [download_inference_resouces.py](./download_inference_resources.py).\\n\",\n \"\\n\",\n \"This script allows you to see what resources are available and makes download easier.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"shellscript\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"all inference resources:\\n\",\n \"Matting\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/matting/GT05.jpg\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/matting/GT05_trimap.jpg\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/matting/readme.md\\n\",\n \"Inpainting\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md\\n\",\n \"Image Super-Resolution\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/restoration/000001.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/restoration/0901x2.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/restoration/readme.md\\n\",\n \"Image2Image Translation\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/translation/gt_mask_0.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/translation/readme.md\\n\",\n \"Video Interpolation\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/video_interpolation/readme.md\\n\",\n \"Video Super-Resolution\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/video_restoration/readme.md\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/video_restoration/v_Basketball_g01_c01.avi\\n\",\n \"all task type:\\n\",\n \"Matting\\n\",\n \"Inpainting\\n\",\n \"Image Super-Resolution\\n\",\n \"Image2Image Translation\\n\",\n \"Video Interpolation\\n\",\n \"Video Super-Resolution\\n\",\n \"RESOURCES of task Inpainting:\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png\\n\",\n \"https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/GT05.jpg to ../resources/input/matting/GT05.jpg\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/GT05_trimap.jpg to ../resources/input/matting/GT05_trimap.jpg\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/readme.md to ../resources/input/matting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png to ../resources/input/inpainting/bbox_mask.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png to ../resources/input/inpainting/celeba_test.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md to ../resources/input/inpainting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/000001.png to ../resources/input/restoration/000001.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/0901x2.png to ../resources/input/restoration/0901x2.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/readme.md to ../resources/input/restoration/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/translation/gt_mask_0.png to ../resources/input/translation/gt_mask_0.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/translation/readme.md to ../resources/input/translation/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4 to ../resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_interpolation/readme.md to ../resources/input/video_interpolation/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 to ../resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/readme.md to ../resources/input/video_restoration/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/v_Basketball_g01_c01.avi to ../resources/input/video_restoration/v_Basketball_g01_c01.avi\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png to ../resources/input/inpainting/bbox_mask.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png to ../resources/input/inpainting/celeba_test.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md to ../resources/input/inpainting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/GT05.jpg to ../your_dir/input/matting/GT05.jpg\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/GT05_trimap.jpg to ../your_dir/input/matting/GT05_trimap.jpg\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/matting/readme.md to ../your_dir/input/matting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/bbox_mask.png to ../your_dir/input/inpainting/bbox_mask.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/celeba_test.png to ../your_dir/input/inpainting/celeba_test.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/inpainting/readme.md to ../your_dir/input/inpainting/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/000001.png to ../your_dir/input/restoration/000001.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/0901x2.png to ../your_dir/input/restoration/0901x2.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/restoration/readme.md to ../your_dir/input/restoration/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/translation/gt_mask_0.png to ../your_dir/input/translation/gt_mask_0.png\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/translation/readme.md to ../your_dir/input/translation/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4 to ../your_dir/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_interpolation/readme.md to ../your_dir/input/video_interpolation/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 to ../your_dir/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/readme.md to ../your_dir/input/video_restoration/readme.md\\n\",\n \"Download finished: https://download.openmmlab.com/mmediting/resources/input/video_restoration/v_Basketball_g01_c01.avi to ../your_dir/input/video_restoration/v_Basketball_g01_c01.avi\\n\"\n ]\n }\n ],\n \"source\": [\n \"# see all resources\\n\",\n \"!python download_inference_resources.py --print-all\\n\",\n \"# see all task types\\n\",\n \"!python download_inference_resources.py --print-task-type\\n\",\n \"# see resources of one specific task\\n\",\n \"!python download_inference_resources.py --print-task 'Inpainting'\\n\",\n \"# download all resouces to default dir '../resources'\\n\",\n \"!python download_inference_resources.py\\n\",\n \"# download resouces of one task\\n\",\n \"!python download_inference_resources.py --task 'Inpainting'\\n\",\n \"# download to the directory you want\\n\",\n \"!python download_inference_resources.py --root-dir '../resources'\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Perform inference with two lines of python code. \\n\",\n \"\\n\",\n \"There are two steps:\\n\",\n \"\\n\",\n \"First, create a MMagicInferencer instance by a pretrained model name.\\n\",\n \"\\n\",\n \"Second, infer your own image with this MMagicInferencer instance. The translated image will be saved to result_out_dir.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\\n\"\n ]\n }\n ],\n \"source\": [\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance\\n\",\n \"editor = MMagicInferencer('pix2pix')\\n\",\n \"# Infer a image. Input image path and output image path is needed.\\n\",\n \"results = editor.infer(img='../resources/input/translation/gt_mask_0.png', result_out_dir='../resources/output/translation/tutorial_translation_pix2pix_res.jpg')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You could see your result image by plotting it out.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the result image\\n\",\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"img = mmcv.imread('../resources/output/translation/tutorial_translation_pix2pix_res.jpg')\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 Infer with different settings of a specific model\\n\",\n \"\\n\",\n \"There are some different configs and checkpoints for one model.\\n\",\n \"\\n\",\n \"You could configure different settings by passing 'model_setting' to 'MMagicInferencer'. Every model's default setting is 0.\\n\",\n \"\\n\",\n \"Take conditional GAN model 'biggan' as an example. We have pretrained model for Cifar and Imagenet, and all pretrained models of 'biggan' are listed in its [metafile.yaml](../configs/biggan/metafile.yml)\\n\",\n \"\\n\",\n \"There are six settings in this metafile. If you choose setting 1, then the config 'configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py' will be used. If 'model_setting' is not passed to 'MMagicInferencer', the config ‘configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py’ will be used by default.\\n\",\n \"\\n\",\n \"And you could also use 'config_name' to replace 'model_setting'. For example, you can init a MMagicInferencer with 'MMagicInferencer('biggan', config_name='biggan_2xb25-500kiters_cifar10-32x32')', which is the same with 'MMagicInferencer('biggan', model_setting=0)'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"result_out_dir = '../resources/output/conditional/tutorial_conditinal_biggan_res_setting1.jpg'\\n\",\n \"# configure setting to 1\\n\",\n \"editor = MMagicInferencer('biggan', model_setting=1) \\n\",\n \"results = editor.infer(label=1, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.4 Infer with extra parameters\\n\",\n \"\\n\",\n \"Some models may have extra parameters that could be configured to perform inference.\\n\",\n \"\\n\",\n \"Take 'biggan' for example. You could configure 'num_batches' in a dict and pass it to 'MMagicInferencer'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"result_out_dir = '../resources/output/conditional/tutorial_conditinal_biggan_res_sample6.jpg'\\n\",\n \"# use a dict to pass the parameters, num_batches means images output num for one inference\\n\",\n \"editor = MMagicInferencer('biggan', model_setting=1, extra_parameters={'num_batches':6}) \\n\",\n \"results = editor.infer(label=1, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image and we could see 6 images in a inference batch\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To know what extra parameters that a model have, do like this.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\\n\",\n \"['num_batches', 'sample_model']\\n\"\n ]\n }\n ],\n \"source\": [\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"editor = MMagicInferencer('biggan', model_setting=1) \\n\",\n \"editor.print_extra_parameters()\\n\",\n \"# 'num_batches' and 'sample_model' are extra parameters in 'biggan' model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Perform inference with models of different tasks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Inference of conditional GAN models\\n\",\n \"\\n\",\n \"Conditional GAN models take a label as input and output a image. We take 'biggan' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/mnt/petrelfs/liuwenran/miniconda3/envs/py38pt19cu111/lib/python3.8/site-packages/mmcv/cnn/bricks/conv_module.py:153: UserWarning: Unnecessary conv bias before batch/instance norm\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/mnt/petrelfs/liuwenran/miniconda3/envs/py38pt19cu111/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\\n\",\n \" return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"result_out_dir = '../resources/output/conditional/tutorial_conditinal_biggan_res.jpg'\\n\",\n \"editor = MMagicInferencer('biggan', model_setting=1)\\n\",\n \"results = editor.infer(label=1, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Inference of inpainting models\\n\",\n \"\\n\",\n \"Inpaiting models take a masked image and mask pair as input, and output a inpainted image. We take 'global_local' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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cNV5tO38TkjgmWBw3zMTrqJ+Pzpu3tBq7YfHzpr2qHQS1TCU62p7PG+0bQl2NVYFLyaxyl0Lej+GExT7dF8WsEqFlfI6Hju8WvyVA8vXD0IgtdP9jVfVXnq55kOgRj7tGPjtuX+XwaRpaIyKuNpIAAWN7ZGKzeNiRJXV9dwgwDjEnqvJOTEwxmQH/dY7PdYMfef/7lS2hF6iIbVY4+AkjgtKPDAruyh3ELUzA6RICkM+p24l4PqZXCT4WgzkK8biAgMt/G95EAlJIotEZZFpjNZ1F68qCTpEiAZK1Fz6CtVPLUC8AnBpaK4hwgAmUtOalY62C9Y9WmhlYKxtr4DEpp/iTX9QiGLr313O7lPOAioRQo2RGjLksUWvF7M9gNA3a7HQDgydOn7FWrURQF3nvvPZzdu4eHDx/i/v0HKMuKnEa8j5IYvYfwktiphSVYIQSE9wRiYNwCYF2am+SliSg5hpUihYhqWhEWzU3U+0AToDCDMOZisq4z/cP+ghsLWKP7TpniI2x9Ol5kak+f5m4AslE70JWDzaf1Hq51TLt52/ZWA1cQnt8Muw9ILeFvNmdEkOYiTiRVw/i4w3fwo73Tt/UaPT8Cyjmkjvp2AGzTOa/2F/qhtsxGkgNGdgC8d3CG7D9d16FtG3Rdi6urK2w2a2w2azx7+hSbzQZts2XbjIUxBt/69rfQtC3atkU/GCaiDsMwZLFggGXVYDKCByGfXetzFeBEXRKkkdR3MVrk49eZ3pYJ+yKzEkBr7AmWOHtAGKDre4gG2O62KAvyxCvLEkrJ7BQGQQHyrjQ+2pg0g1Cwg0VJj+1HVVWirmdk9+t7bDYbdF0fuedgb/Kuw3qzJemRwzSC5GWsHTGY0U1akGuE9+S2v2sbNG2LtRSoi2Ic2pFJGoPpEWxc680aWmtUVYkHDx7i/r37ODu7h3v37mG+WKGuaxRFBa11dGV3EvTdS3bdTvFRwrFUaJN90Xlyu5cQcI7O9Z4Ah1zWw7u9BQMtRkPB54oIrPs2Lo+cOUov9ca7ABCj/16rjTt3sB3eczNVDmv6s7S3G7h8RhREALHbvZxcMOEt/G/YkdnFwp/8+Fzs5gmWg8VRQes4nhxpPpf2jz7fWA2ARPhG/fQIKun8+LFXVCKv3mc+QqPnGa+c45Mwf9h9bi3df39HIPj5Jz8wxDpZa9B1DXa7LZ48fgzDgbtXV5e4vLxkAtvAsa1FQKBpWgKurmNpga4/sOu6YyBymU0qqVOJuLpRv7In5uOcCIx9OM/B8YMF1Us+TuGn5BsSMRdjqXcyF4Pw4CEAjtPq4WCtg1b0LEXBHoJKQWoN50QMCwBLd57tXIbHSIa4RQ84ZyA4xqwsaygpUVcVubizBNsPA4ZhiC/TBZUXAM3u+V4A1roIdJJ8+EfMVmjBcQTeoxt6ZhyClCSiihQQrPq0EUjaTsE5i2a3xdXVBS4vz7BcnmCxXOLBg3ewWCwoBk9ISOUhPDtmIDF+EhIQLkpAKQzBA8LBCUEByI7UnuGYsOY9v/eR9iKb8mMpJbzUYDNLqsJsb0brEsjBBzlsXwOCvTk2biM+K+uQDxsOEK+R9ibSiOz+BzRDYTWndZBpG97UxoC3HLjSg/s0NCM59LiUEv+K/Chx5JTEuYyvMUGhjEHy+fSMs+/wa72pjfTKezPx2MwMs37KpafnG4FVtkpG9pjxChtd6zPMuXSNV+yIqjkOKKbVm7wIh2GAswOapsF2s8a3v/0tLJZEmK6urnBxcYHNZoNhIPtU8FQbjIGxlI0iEFwPHwPBg7NFGh+MFpt1efDzuOu5ZBSILIGDAzkK5gCcSxHUXJBAWJwPNMwG9WA4U6Z9QWrx8DAGGGAgIDAMPbmSlyXK+Zzsd46cCAi4ACEkhmHAYAzc0APwKIuCbVUChiVSAjkKGq7KEqUu4viZ62v0g4F1NgE8KGNMjyEMYCRaQdUWgEsqmTJisLdmGOu+ayMjAXjyTmRJTWkCDWstqfQsYIyANQPW11dQSmE+X2C5XOH09IycXdw7mM1mUEpDFwVLWuyBKSlw3/F31k1H4CIPRQEBCeEc2xTJIEZhGmnp5PZigfHST1q3tMBy2iJFkkSn6yIn/MnTcSzjTIT//SbE/lE5GWMasB/DmdGEQCuQ2f74MJ91PAfeeGve8CfaOSPpY3nbK6WZHGwyMJpg0OiMDJDG6rhX3I5n8fFj0iKPXw6+y7wDgYjmHoPh2+R5bugXcXc3TBwhRo4lNxx4rNNv2HgRhUWBtDgC16uUwjD06LoGL1+8wHa7wXq9RtPucH7xkhwpPLDZbbFrdmjbNkoUlrNTWOvYfX1MAIKLd1iKU++noGYdc7/ZSGTA5QXgXZYhgD8u3i1QACQuij3tLIkXgeYzU4bIlHnLIKlkJpFz3xhYB0Ng0nUdzDDg5OQEs9kMpycnuL66wq5psGsaKKXZIxMww0BSn9bQWqOua5LI+h7XV1dk2ypKLJdLVEWJeV2jrkpstjt0HDRt2AbpRLD/kdp1MCaOFwVfk+0pZHwIdi8AMY6MsnqE9EYOgyF72m7XUBwcpzsLWULo3Zq4Trdti5cXF9CPP8Unnz7GFz78kFSIZ6RKnC0WWCyW5IbuHawQkF6NiHf4L7nV+wh2ZA90pDLM5kLqD9vCMqZxNNsnwke0AYXJIoLEl4VXxPkznn8TpUQ86pUk8UgbA2FQi2O0JoKnLR0lI7YdRF2+6mE58fXaWw1cPiNoU3F1Txd8U5uIuNEwekCNkR01OUZM9oFfYop6/9wbU6ygfpnenv6m7ckfI6lVg6GfDuBnD0R5Mi5j6S9sC0tj+oQ3SYfTc8bHBhUbcZ1J8iA1HfXfmAFbVgOen7/E02dP0bQNPICiLOG8R13PMBiDzXYbPeuavkuA5ELuvsTIkAqSgEtm3HN6igRccSFnT8Cx0fHpnHCQMYYoeM3lYBgcA7KL8XbvRMwpOI7DoZcRnB6CJC0ERuuBHAtYcjIG2+2GAq+bivtKKrfBmhjHZWFhWPpRSkXXf5JWqX/OWnRtC2ctlNIw1kDCo5ASQmsCZ+8QgoO9p/RbzlpKZwawgw04d51EGg2WsjlWLFgGw/a40ryHYamnsOSar7VCpUsoRQ4dg7EwdiAnFGPg3Dm8tzg/P8fp6UvcO7uP1eoEp2dnOD09w2w+R1XX8N7R+/CCCTPNRSttlkIq6zGnWHMukya5EYAFV4/xciA6lbiS8KyCmdO0+vxkHubeoRkTle0TB5Zbmlp+tOTyEJiDpoi4VCMbOWHewp0dpE8ZTEbXzqTE6PjxJ1XiykXVEBUfNDFBCBsJrRkXc4gPOSzHZL+mdqS98/OzInIdZy1e+eJygjnuaC71xf5MhK3xYyZ7lQ+AKnwCLwRuL2XVyF18p8+cuh4e8BDA3wD6IyI9uTbA9hvBnGpiS0MKprbZYbNZo2kaXFxe4MmTJ9BFgbquoYsCzgwUlColE0B2EGDVl+frCWCUz85ZFyUuTIAreMgdchiJ39wEkgUrk6SEkgC5ZoMXbnjGPJ1QPkoeZHHhQGV+oQLk0QiAMl+E6TntS+ir45RNbYu+6zEUHZaLJZTg3IQ8PkJQ0t+Y1sk6OLDkIATKoqCEvYbsiN47KBVc4cltvBAUQO0dzSWlFDtm0HlSsns+e2Q66+ChkLvaW5fySkYay3NeZsyUdzRu1ipe+iWKwkOzus84C2PJ9d8KSlzb9x3K4hJXV5e4vrqiwObrexje/QBn9+9DCgFdlklqYAbHCcBaAe9TMmsIChimODCBUJfX58shMNWBHATGerIURD4BwmKeoE+aiwkEoo2VxS0fGaCcYWVQ9OH39J75QuSPCyCVdXI6Q7P+hfUUTQ3xzAmAheP2VJGv195u4HIhBVA0Fe6TysNMfdqWU/4pGh1Ap1wdNwWTo3T6pnZIvqcdWTcmoJl3d9rvQ0i215izw3HvwvHiEkf6OMKUg9c41G4/X9PSIZtWh/X1Fb71zW9E0Lq8vMTjx4/x7PlzlFWJ+WIBpTSeP3uG9WbDuf8GCsZlt+0oAXB2ikC0KbaI1Vw+qPSy4c1tCodAC+OFHmgI0QFLc1UIFIUit/vAhYaUSwLRC5Cy2wNKiizzfyDeMqazssayZJxGW0W7GoMWyPZUlSWUVNBSQfBxVVmiqir2MiSPQWtJOupcB1R8PQBlUcJLDyUlnPcoC3JbN4YcNLy3EAKY11VSqQtyyjDGoirIrqSUQtP12DY7yvphLSBcBLh+GAAhYhbxQBThPTpjo+s5QOpSJcle13Y9trsdexeSV2WtNdq2wzAYKEnj1rQNrq6v8Oz5M5S6QFWWuH/vId5/7328/8GH+NKXfwTzxQJlWUFqTcBnuJqDs9CaGSSO+TPWsmemTNK7pxkQ7JtAcHjxk7WcCLsUDCoWGRMqImCFLDEjOPMpJCOCZDYnRejLVAq7TTugYhHZb+/cHgHwJApyrsv9Glu0dhyES319k/Z2A5c/IK4KH/XT+20qMYn9PWKMBTlJmEowkSnKuMDpbTM+A3svKsOWV73C2J8MkIL6KBCJPP4jYlomhsVgxxyusmNyg3K8V6S+GXeV/Xwdrikt0v3yC9lBED4F3wbVRN8TaH383e/gxYvnkFLg5PQE3/z2t/Di5UtcXFygms1weX0N7z26tqOcf8OAtuvic7lgf2HwCs8nkCUwBRIxQFq/oznj0xjm0lWUqCCi/t97QGsZn94YA6E1tJSoqpJLoVAZlrIoIsF3TBCDSi+8bykklJNw1sFqFR1MQtb0YNh3zlFmDf5YYyE0oIoSStPSl2wPEyFzhVTRFjgMPQEDO0QEwhkYCmsMek+2H60456CiDB6BOajKEkppCClxfX0NCFLN3jtdYTar0LYdLi4vYT0lMC6LijLmW4eB00sF9Vtd11Aq9G3g+C+PQD+FoGwc1hGYtF0PLXVkRnhiARDwLoFk1/fo+gHrzRpPnj7Bxfk5Hj56hNPTMyxWJzg5PUFVz1DWFTabLYaOEjErThYshMDAWfG10pAqLWrnwQHM2ZoZrYXMRs8aBiFSTscwF3LgyuMxc0ks2ONoLmbXn2pFIgPm42aRSXikjUEEp9ypJniMwo0dk47JBymMIWP5mYYFrcGbtD8WwOV9BlZBtxAX2HE2I76mjJvI1X0ibUzSTbZt7zrZr8P3yvp+6HkwtoYFsX8MWnnHjt9v/8aZqjDbLjypOoLqYOy1hLEKNlxJ7HvT3Xzvye9cFBkxdZmkEneRqma32+Hy8gKffvIJrq4vUVVEfNfrNQUQDwO8EOQd5+yYAJsh3oemhh+7swNx/uzXbBo/60ilF4GVCadPwCIlXSsG3nrEcfbOw0sHKC6ToSUo9ADkvs5g5eHiy5KhJITg3HxmgGUCF1VFAtBSxqwWxphYQkRxdoicDSPnDlLNSS2hIaC1w2AGDAONIwJYSgnHEmsYxwDMEkgAWxBJcd4BxkBIinEqSsrDGDJoFFoBglRyG60gLL1/rRWsJdua8Z49FdM7UVJCArG0i/MeNhJWD3JsJ2JrrYURKTga/O4FA5fzHhISRlr0Qx+TIzvnsNmscXp2hpOTe3j3/fdwcnKCU3EPpL4lBmgYemivo01NSQUnZJQSo33OJxst87vZWsotzsS1jezq2XxL9C6jBfEePl80+R2YmT+QuJvPodAAytQveLuxlA80WOeCpFcUBYZhgJAChSpI3et8TGodnkRx4mQh6P5BowlPIRI5DXqT9lYD15T4gLmRY+hP28Zqlb1jxKE9OVId+37sjkCu8hpt9TeozabC2QFV4aEeHO8zRsA+yleIffDKD8+vuN/dA1Lc5M45hxmfhVUpYSGPVCzcR8pIQF5iT58+xsff/Q5+5//+HYQSHd57XK/XAIDFconNdksLTErMZjUtJpOumbhSvsdE3We9h/KkzlNCwGGfq8xkMnhkHoKBYxYCRVFgNqNcgCFbfN/38CIl1Q3BuFIAZVlQ5otCsyqxQFmSqssMpMIbzECZKrRGWVbYrDeAG+CFw+r0FMMwoOvI8WQxm6GuazRNg7Zt4IyBLktU8zkRpYEArSwK6KIArEFZljFUoG07KD1AF5rqgQ2OQamgLCXGxDniPUleLitxUlUVIAQGM2CzXqOTHYq+4CBdsp81ux1PMIvlfEbqSedgrEFB4i8ggc5x3Jx12KyvMed6ZnVVoTcmqinBcyhk9Ah96zlXI+0fSCoUIWM9eLtEqTSM8+jNgPU3/wDf/u63UegCZVXjyx99Ce+9+z6+8pWv4Itf+jIWdQ2hFDbbDdXFKyt4AAMDrOJ6Z3GhjkX2oyso2JbTxnzypdRZQiiiYjLLiu/G2g8/uUzmAzKicVJJlGWJxZxUoxTYb7Ber3lK05iGjCWnp2e4uDiHUhqr01N6944C+89fvqSrKoGKValCCBhn0bZtLC+0qBfw3qPtu4PjcZv2VgNXEp8lc09JAA9SAriIMZATHf41ou3iyMQ6ABiCAfAmDIsbMykndnzyHIc2jvYd6FdksCY+i37CsWVyIHE5Adn9GLB4PEZxXNNHCdfJDL+hxpH3fM4BriHKURNkE8iN7bTTBXWHp6DYYejRNFu8eP4cT548wYsXL7j4JpXXaHhBBNWf5bx5VCKEssOngoh0F6rhdWAwwVIEA4z0pDIJlWXD/BJCcDCxZ/BJcyEE9/Z9T8GuSqEqS1xfX8NaAziPotAoCvK+6/sOdhhQ1xXmdcWJfoO3YDoWLch2U5a4d+8eZrMZ2l1DAOaB+WyG09NTdG2LgQnPYj6HVoqlPlK7SUFZMqSUNF5dB+dsrIu1XC5J/cbVdQ1nEzHGYDmbj2LTQqNM8QMGMxDwlCWBhwB0WcIaw9lN2qi2q6s6XqOqSpQFlUbp+wHbZoeuH6CkgEfP8XVUYLVtW3R9H4VMDxoXStHl0XU9nKWM9YXWWC7mMdVV0/VoexO1CjFWzjlY68n7kKVaazx6a7HtWrS/3+K7H38Xv//7v4ePPvoS3n/vfXzpoy/h7N49QLAzCTyENZCuAnQBH+LUpEBOKARyiT6TinyKHRx59vmUzNc50j54KeO6DetLxMPHFCOqIo+0oihRVTXNp7YFeJ7P5ws0bQNjDObzBYqyhLUUWlEwk1OUBbabDZSSKMsCp2fEQHkPrFaryIDOZKpEUBYl6rqCcx51Pxzt16vaWw9cI25ajN2MD7YDACPEeNPYXpRhWqZSDP8IpO1pJ5jFOUAgp5uyXUdbumF2+PSEqLQ6eo08P1haMunhc1dV7KkNx/cTo7WVgaOYHhMvPulONrYIHn2WPdYGODaiN80O5+cv8eL5c1xeXka71WAMuqHHZreLYBuSxwZvM2MMea1lUnmUtrJBGD1LJodGtoHVUJRVQVB8lfXxFWdDDHJRp+ewrKrTSqGua9hhYJAIyWgBz+7DPmazoPuMyoAAVGyRpVICIIGiJCcBcj4hO9R8Po9lVjRLHoExEBBRcirYscDFQNrgwk7OF0rK6DQSjhuGIfap5Ezz8ES4rbNw1qF3VJdMa40ySEZSYuh7hBgl50gdmdtxnLIIcVjUN+q5sUHt56IKC1mcXeAmk0RNUltosXwLyF1+MDTW1nEGDL6GEx7OS9jw7N5DcjaL9W6DXbOjcIKhx/XVJbabNT744EMslgvM5gvUdQ1XGg4RKFCUJaTWkJwmSoT+xr8eQQ2bqwBpTYz9k0ezczSX88knkrbEjwwOaU1PJ7sI3q4EsNZaeidKRcYlxD3qghx4LrfbLKiebM8h3VdZVVE97gWVrgGAsqSYPSHAtdto4aQwlNdvbzdw5S8y++QuoyPF8l4T2Wc8uQ4fN92Go2dkdO/AuekJ0uf4IdO7R2gOLGfQet2AW5MOjbiwo+7wB/bR96wvMbs1a+p93sOc+O8LccGGLdkO0g4D2maH9XoNYwZsNmtcX1/hm9/8Bj7++GOcn7+EdQ49J8z13mPXNDBsQDbWQEuZlaJPfQmAlUpk5Wlo0nHBEmitiwNPRRknkoYHvA3puHyUwEnl6OD6HgJAVVao6xqrxYJSE1mLvu8427uArhRCSOZut0PJasa6rmPwb+DGrSUwPD8/p7x7SmG1WmG33ZJHYNPinXfeYQcP4PziAmagyslVWUFKCuqt6gpFock2kWV2N8aiaRq2NWl2X09ZLdquY3Wgxnw+ixkuDFc3ds6j63vsdjsopbBaLSGVQte1aJhIyZ6qO3ddj5yLa0NBTxkKS1IFZeepJH3I9GGtywK4x+s6TC/qS49+AHZtGxPxrhZzFBwf1rQdTJaHUEoHE+KwIFAxuJdakxOKHdBvO+x2W3zve9/D//yd/4kvfuFDfOHDL+DDDz/EF7/wEcqqRlFWsMZieXKKqp4BhYqM4EHKwuBlrWdvSZHlZkxy01gjkTNfYwlLAEkbAF6XEy1MBE4PqkYAkKOPMVBSAVKiCYmn2fu2qirAezRNQyDNhU+bdgcBgWEYcP/+fWilSBXctmiaBvAeq9WK7KxcgmfgMjifpb3dwMUqDQmVOJZ8fza592BHRLoUtmD/KGDv5BtVim/OQeQtqf/29W4ZLd67+/G+TQ+eoMgBVImQcwhxjlwuGpoTsh58isBRIqj3jEEPh2/8/u/j4uIC6/U1qqqkYo7W4k/96I9it9tis6USJevtlgjRaoWf/HN/Dm3b4mqzxpMnT2IRRIAIWJTADoxbAK+cP01ZvpM0FdiaQODjFkppF48HyHkALFX0fcfS14CqLDGfz1DNZ4BfYMtVk62zVIpDa5Sljqq9vuugpUI1owrA89mcx2aNi/Nzsk15IjhCCLLHlCW22y1MRZnRl1yjauBcgpTTLzwHKNWSIgVo9Bj0iHaP5XKJzXYD7z1lqWAgtdZivdmg0Jq8GIUgYsYEerfboOtbNG2Dk5MTSKWxWCxRVTW6rkXXUR20EEArpYxhC845zOczqjWmFWZ1DaU06rrCYB3avkc/GLT9gCRzjNdH4OeEILUXpYSyMEOPWV1DK4WT5RJN18IYSx/r4nWkEOitZVd7yYwJEXktFRQcBuvwBx9/jO88/hTV79T4kY8+wkdf+AIe3n+IqppjdUpBze998AUslkvoojzsHh49NQEVVIsARObgE9nbCXNO9trx/B2lmgNo7qsxA5qvgCBFe+9RVRW9X/ZwndV1lFiDjer09JQYGqUwm80wm82p4Od8ASEQ7YbRfi0VVFlShXD28DXWoq4qFFW1Nx63bW83cLmkKuQt2UsMs5d/vgpXsuNyw6qYbM8vl66XwPGmBE9+9N0f3Xesf/ntbn9wvjmTQnMObKIa9HzMcbVhfslskbB0OFIXZqgVJNogKTvnOE1Qj7Zt8PTpU1xfX6FpGty7dwYPKphY16SDL8sUb+S8g9wprLouc/uW0Z4hBHmO+Tg/UohA3v+D7yE8iEjy9NhGl+A4DKMQSe1CBCYnIB7gbAwSYlROxHvHJUlKzGZVlJYEE1xnLJwmoAlOCXVdk93HGHRdDykl2YCcI1UlMwSSCxIWRUESFKdCCtKRVCI6VJjBwAwDds0ORVmQOrAsoBoZA3c1lyspyhLeOfTGwA8DESFWHZKrOr3svutwdXUFXWgUuohxdGVZwrN0Ft6V1hoelBGl63pKCRVtVzYWcQweaUHKgxcxu0jufJNklaSGM86j7fuUdFhIFJpi58gblexIjt+1E+kqAoCXlBVeeofBOQgMQC+g2gbyE4Fts8Pp6glOlid4+PARHtx/CCklHr37HmbzBap6FoPiBdeRC5oSAS56KRMTGMNdovYorLMgdWUbD81oH+Z74MDG9mSApPi+76FY0g7rMjBw3nt0XQvnfHpPPnhmOiyXy1hHre3amBGlmtfsVUgSe0ilprRGXVDsXJCi36S93cCVcx6JGblly0SuA2g03hw8EcXoMLF/4OH7ZDLHGLwm025vA591BIemp45uGfu2f6aYUO9YB4ivM0qyycceUxUGghHTuAiMPBbTOKUR9CE7gzFomwbbzQYXF+d4/vwZSQxmwHK1jGopIYCyKlGUBSwT5cEM6IceRUGedhAhL1xIyptAmnAogDUzI/GVHJLUs2EUOSlI4xbVtEFNiKROzPcFD0U6hyRAIVKOPe8JdMuywLyeoSwLgIlHsPcJIdCzm/xsNoPzHtfX1xgGy7n7LLrBoGFu1nBOP7KDleyxWKAf+lhnzDobk9vWdY1e9nDOom0bDrLVXO9Koe977JodlC4wm81QzWbwzqFlhwtdFKgVOX1UzrJLO0maFxfn7GVJThJlWca6YR7geCwXgavve3R9B2UVtHUYrMFg6DkFu/YXArBWox8MZeGXqTClGdhOw04RoTQNPGDhYVqK6SuUQl2nMicA2UQ91weLy0em0iaU/ipJ9DoUqrTAp8+e4unz5yi0xjsPHuIL73+Aq0eX8N6j0BqO7UdFXUELDQE9UvkF7z3BRStDhv6RajrTw+QxW2nOjtWHYbGO1mduRgHZoZxrYa1BXc8AIBYS9RYQlgL/nfNQUmG5XEa3eWMMFoslaUxYRWwMMSKrs1O2a5F3add1ANvBCOxEtIG9SftjAVzwQbmWiEqMzZoQfdKm7YthY4HsJvFsilSvFH9ueACwxDj6Odp9/D6HfufP84q+ZdQ7+pDkBH167AS8ju0PCwxIizKAYZDmAkFudg3W6yucn7/EkyeP8fDRI0AAT58+we/+7u/g7OwM9azGH3zj9/H48RM8f/E8Dox1Hs5YPHn6FAA4F6Eb5WPMBzEUtU1lQ5L0GA3kk6GN3LDcfwMR1Fiic3BQXkBJklZkKdG2LbyjZLdFUcCYAW3j4KsqSmjOGlxcnKPd1RDe491Hj0h1A4+rq0ts1tew1uH8XGGxWGA2m+Pdd97F/Xv3sd3u8OL8Ai9evkTXdej7AedXVwxUJZaLGTt5kDdiXVcs6Qmst1tKoltVePDgIU5PTyGlwOnJCZ4+fYrtdouBpZOqqjBYg4uLS6w3a1xdX+HBg4cwXJTy008+wYMH9zGfzXg6yOhNGd8G2zqur6/x/Plz9H2Pk5MTVGWF3hDRE0Lg7OwehBBomgZX19cAZ+lw8Bj6bvSOpBJUudhZ+OAUwMc6O37HQCr4GFTUA0ugUkgoBvGqUhgGw6ouGTOukKclWB1GKauKAAIC0M5DK49hcPjW97+HT589xcliie9899vouwbvv/8h3n3vfZyc3cd8ucRssYQTAorNHMb0UZKRXPwzfA6t4xE2jb5NNAiZ5ATwWvc+aZXAmU1sR/XVwnlI9NVEmzKwa7Y4O70H7z2urq4p3IID+b1IsX1PHz+OasbBphI6V8bg4vwCUgh07MX4Ju2tBq4xn+EPbEstk5XihlyiCvahkTQhpuQq/zm9ohjvf5XkdxvJMNPo5aC2d09g3O/J3lziOXQPivEIgZK0GEcehnz+1Mgb+hVjlI/sDylgALI7BTWXtQa73Q5dRxzdMNAEV0rj5cuXePL0CS8Eh92uwa6hWBIpBRR740kmJPCBUPnRs+caO+89nKCg0zRm+XON1SxhJjkHCJXAOaZUAmW3iEZ1mQhgVNM4TxKmJeIKhVi6QwBAPYt5AMNiLityN57VMwgh0fcd+q4jr72+JyCqKlR1hbOzUwxmoPHZNRh2O/SDQT9Y9GaA1gVJdIXG0ixQVSXmrHYFyMbx/MVzvPPOO7h/7x4W8wWkELi4uMD19XVMyVSVJepZnQpJbskNX5cl2q7D5dU1mrbDyXLJjh9g1SCDB0tbp6enqKoKz549Q8+u69Wsxnw+j3GZSinoQqOsSlbhOQ4md3EuClbLQoDtmtk8DZWWfTYXBNlfwvuVIlWodjAQQlMQLnvUhaBxx6Vugmt3qPpM+UnYRuuoKrS1JHWHbBPOOfjvO8z+z/+BL754jh/bbfHRl34EQ99j6HvMVyu2YXn0bUuSoNTQmsutcMkVqWSUKnPVNJkqxQ2kJMxgwVqUtEbz8QnhJ5F5ZTVgeB9psZAqtW3bKInmlcJHffQ+gliyIQDeWkrm7P2fZFVh4CR8ouxj/EIuGIu9f18tLxF2iTGI7WHZIQnsNsh0m3ZE0rqx4/vQtgfcYWQESUP8BWA1334WjYy4T7k9ESSXlG0+uipnxALMkVlD9pRh6LHdbilwVggqR8Igt95s8OzZU2w2a1RVRRnEmVumGCsyWkteJICLWpx85OPuiSybZ0tJ0qeIKr7JKI2aFJRHD0LAApyRIgXAElHke4QyJJlqhzJWKHbHRkyXROVaBiitUJYV5vN5ZB66tkPbNBgGAqPT0zNoXWAxn6Ndrdg+otBxAHE/DOjNQIHEUlLpDw8svENZVVjUM3j21ru8vMTJagVx/z7u37sHwwb7tm2x2+3gnWN134wS9TYNtrsdqpIkOAiJza5B03YoOGWVlI7VdDR6we1+uVxisVhis9mi2e3Q9R3q+QxlVZHH5TDAezq2rCqy2zFxdNYCMnMv53kggns8/3beHjQZBDvlyKMWPmbRCHOX1L0EVjlhDu832CfJ6cPFigJWkEeiYoCwxqBrqIjper0GvEdV1TjrOpJmNdkLIcgrTxWaHWNEVidMQAo1kbymupnjyhKefJGzzZMO7Kl3RJrtuce2Rya9Oo+2azMVZgpDgBOQSoxohh/dK/xONu43bW83cIX//KFXSe2V9H3vgEz6euU1Dl7gle3VkBauy5MsdimIicnRIIHq6Otrtoxw572b2MLGxx65DKa7fTREh9xy2+0Wu+0Wm80aH3/8MQAKWAwVi7ebHaqqhpCCuN2u5e7QAxZFCSVdzCgBgGOPxvcPtgvinn0Ej7EkGp5IJCnJA8HjkOxWSOmWRFKjSCGgCw3pHOe+M9BmgCoK1HVF0iAvTs0qOgJNQAoFeKAderSNQV1VmNczLBYLVFVFXn2rJTp2Ky7LAutrSiz8/Y8/xvbeGlU9Q1XPMK9qLOZzeACzWYWr6zU22y02ux3Fu7EE0nQDFm0LB2B1usLpyQkW8zkef/8TPH/2HNdX1/jJn/z/YLVakv1HKXzHe2y2W1jvsVgsKOmsEJHhUEpjuVrh8uoKu7bD9z55gof3zzBjb0hregYyhfPzl1itTrFYLPAjX/4SXrx4iaurKzz+9BMsFktyIlEKV5dXEOwEMpvP4ZsG1nmy5/F8THkgUyoiyWmGNtstBmNh7f48Da7iYbYLAJBsC+P6ZUGCSyTesxqxpIwsnitGG8Meq8SwGTaOaV6EUgCFAJ5dvMTl9RW++e1v4ZNPPsFP/NhP4Cs/+mNYX13i0XvvYXV6QuVlug7OWnYc4XplSnLR0BCnxVN8wjweWL0Hma6DLWP2PQJdJeaLckEyMPFxQZLOLS4HS7eEsc40HuF9fVbG/q0GrnxQcgnrdk2kz0SNNoajnDNP0pUAsHfSwXvcske5HnAqMox+7t90X5oKvyfAKqb7g5AVdX28K+zzsazJ2Dnj8HLIJbRU9j4FWTrr0DYNqQ2FwPV6jU8//RTDMKCua8wXM3YgMPDOQqsCZVFSBD/nuAteTdF+xt9tZk+LT2jD/TNg95TKKYwZuSin2JikShHjcWUws3Rg5BiVkiO3eWsNjBQoPRVhDE4KxlAmCCkpo0RZlnwdi91mw/WytpjVFQSoHH1RFFBSYbVcYb5Y4GR1it1ui2fPn+P6ag1/tUZRlnj48CE5TdQ13n/0DpbzBUmsL15i2zTohiHadDZNA/vCUTyNsSjLCu+8+y7Oz1/ier3G7//BN/Dg/j2URQEPSqUFIbDebNBwXE9dVWiaBtZYGNtDqpZd1oGu7XG13qAbBpysPM8dQAiLwRq0bYvLS3Kf1lrh5GSFrm8RSpzoqoZSlGdyt93Csrt4WWjAk73QODoWkTFxHChL2TIKTTFq1tqYp8L7pCoM7zqq3UAByNaP14EQIX2TjM4S5EHn4aREqJ0twppgQHTeQwWHD+dBxUENrNvhf37997BpGlyv1/jyR19G13W4/+gh3vngPQzGwTtHORCLkgtl6r11nAlQ2WQ/siYxWfbJqBt/plHAHrk6FOsYwNznNASI61w4ESq88PlurB3jS4nJvV6nvdXAFUhVGpCpDmvSmLtOksv40CjFHL1IIGUZ2MU5JSaHvwZo3dDyaPsIWhlqxr7vIemoy3v78s2jjBqC9eH59yP2rXhOBmhj542xzcyzXjsEtHZdx8GglMtPKsqyHdymybOtxLbZEXd7aHyE4IwPLkre4+fMAzrH/R0fxUs8H+IDzwiAbRjBiB6ujzhOnglpWVZs8xJos2wOZMdhO5cAqcKshbWUecFyVo0Q7FlXFZazJcqCbF/b7Q6b9QZdT4lhF7MZ1dZSCnVZAn4OBYGupVxwUlJwt+GEw3Zr8PL8gstzzPDg7BQeVNLl5cuXEAAWc7K9zWYzsmt0HdqhZ883yWmWCEC6rouqTqkkejPAtZ4dQ2S0/QkLdD44C1CaKq01ZtUMbdciqHDrukbX93B9D28dZQVREgIyOkY4Z+NbgydblFesshUTP2Beqy5/h/Gf8E5oQx4ZJgUXBs3eO8AhDVKNmMhwn3hhT5DmvYeFj9k6nr58QUG+1qOUmm1ZwNn9MwhdAKA1oHVQueYqwox5DP8E0Mgnba7ey1FugmJ+BF77qsdIwSbCQTwgMouZzMeMsPAibc3XZfie28/eoL3VwMUwPt2IpCvKt+9TpH3J6oiE8kod3A37RjPgFu2QZi7vuwjAiaQqPCCFTR/l5pYmnkAmZQEHJa5bXTEen01qZFnEOUbny1/+MnbbLa6vr7HZbGJ+QsXuysbMsN1tMZghAkIAUcku2MIKGG+iamOsLvHReQOYDm+SsJJUNn3XgWPOiJvjwqVeAByXQ34XKg5717WYc7LbQmtcx0zxFAgrxByzusY7Dx9gd3qCtmmx227J6UCR5HBx/hI1qw/v3bsHgCSx5XKBrr+HzWaDy8srvHzxAs1uh2a5pOSmRYGq1HjvnUdYrpbYNg1eXl1ivd1RVWDv8Pz8AtfrDb7/yaf44ocf4HS5xGJ5gq5t8OzZM1RViS98+CGWS1LhOe8gtUbL7uqLxRJSNmiaFk3botAUK1bPZ5yWy+DpywvM6opUbHWFQqsINtfra7Rti0LrmJXBOYeh7/HOO+/GwpcXF5cEPILUxkS8Wy5ECYigzuKMJN57eOsgvI/Slg7v1LsomYeE8x6Ay22jGRh4ALBUhdk6xAwSIbzADAZd72EsJ/llhth79l4E5U91AFejBkoJPHnxDBeXFxi6njUJ5A375a98BfV8js12m81BirUj9/5MahkvK4Su51M3d8QYHcq2vNEcP9QyMip8AKhDZCXXfggKh5kwusKncU3H/om1cd0GD3Ju+wAwHdp1E1CN9mVSUPx9iz69JqMxvYMQ47iyo+B15CKHn4ykozing5QVviMwWUc6ny8kn2/yo0mqCw14skmc3buHtmlQliVWJytcr6/QdS2Gvkdd11iv17DWYjGfw8Gh74doLAfAqjpyuVWSUs1Iyul+0Dgf+nRogRO3L+MjZMwrxfX4YJsSmZFeUob1UDpEkaRkDanEmqaBUgqzWYV333kHu2aLjl3kQzqcQhOgUfLbIo6vVJQFPr/OYrGAAHldzmczaKUwn83Qdx2stbi4uIAQQD2bo6wo60HftYB3uHd6guVigbbtcLVeYzAGvbHoNzsMH38Pq/kcy/kMqwVJWm3fof32t/HOw4coywInZ6fohp6rCivUZUEelZa9RK3FYAy00YCQ8JAwHlg3PXpLcUHL+SyuGCkowfDAAeRxXkuBq8srFCWpygiwSTEVCoIWRQGxXpPK0HvO4k/vvG+7yM1LBIFDxOS5AyfjRSg1AgDCY0RC87kx2WQGA3gwmEhIraC8hzcmnzXpPBkqXpPEZsHu9MOAb3zvOzCweP7yBX7k4hLVrMY7772H+WKBtmkAZs5EqLicdUoAFPMVKmEzDcgTYIf5S3M9WG3TdiD4B0ylLZFkz4wRvBXW+VDcNwDmDcTuT6zEdVCGPdLEPgCkH4csR3zUa3g70PwUkWCnrftwdrTHk0MPeyxOT3hVr27aNulJpv+O3kFAtHeliT/q5F5dr6AymKoXpJTwzkNy4tnVakXctCHO1tmQzJXUiVppWG05pkVQZuwg/hySuIXInKPG9w/qVCXTWObOGkmd4SNwB1fhNG7771KIFHxMKZ+I27Ts9m+MwayqUVc1lBCUwdxRDFTTtBAAOQV4CgwmcCB1qfPknXZ1fQ3JpUhSYl3yFhRAvM8QyqgM5CTSDwOMo9pfEJRodjarIbsegzEYBotd01CS34Gyu1eaMku0bctBz5Q3EQG4WTVKOSYp84ZzPSfPNWyXIcJqDAVDN92AQmloraA5c3rIdm4GQ8G8QgCWcjYWxqCs62yC0beQ9UIrDQcKGA51xvJ3JTJGjDaTM02I4wtpeuMrH80R7G2ndwH0xsA6D61cLHuTJHZ2nmDJKNdjgDUZFrSWHDzWzQ5Pnj+HtY5sje+/A6UVvrRaYRh6QACFKJE9RNYfZqAFksou0yrE7BaC3Sw4LmSsMmc6lYFXAq183ud/E004tPbo+cOqmu6nZw+X/gy49ZYDVzaeN43BRKLOGk22IADn8tMxYWx87vTb67UoMe6/X/o7kuwOSF4HttN5h3p9JI5rem66APUxUxmOMmpMrpzzcz4IpZ6K+gkAVoTsEhIWllMKVfjgwy/gxQvK/K40SU99T+VIJGc7H4Y+cpZlUWAQ5NnkXYo1c55jeXwiFZGV4MeJ4MLZrJOKg57TRg6eNsZRCBKtCEQB8KzisZZLYUjAO0GVhRWlOIKnoNntBqiKAnVVYzmfY71ek2Q59Fiv1yiLktJfdWST8hBcn8qSi7fWuF5foSip7MhqtaIsF12LrgOnQ6Jg481mQ16FV9dQhWY1lUNnDDTX+DpdrdCWHZq2xdY3VK6Cs+5fXF/jZDHDvC5RVyWu11dU2v7+fRRFQVIAu4LDE2c9qytK4eQ9eo7FE1Ki1BrODTHzBZzDYlZDzepRYC9A0rMHuC8GUmtorgElORA4pIASnuKpCOw9l5CXXJSTvDWdI1sYBNk/nZvaOVldhSwXhhgT02Sy4uOch7EpQTJVkeaZJiXHVY3dx0P+Ri9E7KsAoKRA7xyeXZ5j02whvEdRF+j7Hl/68pcw9A3VFhSeEg0zYxMdgSaq7cBkjWyxCB6PaUlPM26E46Lklu3L8yjG5xqdlQ0mAyhlzpExRVdORMd87GdALbztwAXcVtgafd/TrN32CuP3cMNd8t8HBe0bTj3M0SOfqAcvJ7LPD6AdYKgOHQJQTSspZVxEKSjXcabxBd555x1cXV9CSonFYoHHTz5F3/cQUnCRQw3nLSAQpRrjgnefz7yWspb9lggq1lSiTXDl1+AhSGDoWH3EfQ+cqAgcKl1X8DNIDNBSwQIw/YC+7bg+UQklSWJUUqFtOxS6RFVWuH//PvquI4cT77FcLGGtwWat0LYUqwVQYtOyoBIZ9axG13fAJgUwKylhjQEEYOyArmtRlgXOzk4xn83w/Pw82vwEgKHrMPQDuraDLjS0lDhZztH2PUsxEsILDH2H6/XAZS7IMePi6hr37p2hKktopSnvopTQSnLWeIGy0JyqitJQaaUwKwsGEYuuo0rUxlicLOc0PlpDFCXgHKw1GKxhVVqPPkqV5F0XA9edg2Y1bYw3VJLUuvz+IlOIEKCewmZctj8eA/oR6cOk5VPL8e+QxiksuTxPaX5ty67kItvuPOAEx3+1Dh8/+RSbdosnz55AaYkf/9M/gaIssV1fQyodVZ0RoHJG8fDyi/vGR4kRJz/10B0/8ziWa+/CQLJTeWZgnYeTpPoO2/iu6RqfA5l6y4GLpoGP31M7CCO3EjmOAUDati9r7cPZFLaO9+xYHw7c/ZXg9Rr3uG0L98wnrwAEl1RNkz2fmpM3kqlrRjyXp3o+w9DDe4eqpPglIYCm3eHFi+dUHqFtyOPOeVJDBKkqk/6iepJF2OkIBMBxoBQ3IcI/9zSjsgup9DzZtxxnbJgyE7xQ2fZFRR7rND5CwAwGgx+il+SsruF9Da0KiIpKnkglsVgsY/zOZqtj1ePtdovGUQYMsJRJ3pVUM8t5UrmyMz89s/dQgjJxLBcLDkQ2sGw4Dyl8HAf5SilRcIyZlAKlLlBoypwwGBPdup13WK83GKoKiznF2EkXYnl89IAjV3QTmQGlZPSsNM6wRAV0XY+6qqAKibIo4Z2FMALaB69BDuylFBzwSAyLAGWwr2QRcyp6AQyDQdt1MYkwmbJElJjCixMY20DzNxs0IJlAfkBRluYvqdQCNIzlibgKIpEfz8VATYT36K3B1WYD9eQJ/s//6//C/QcPcHJ6AgdQ5epDyreMWfNZL/304URQFYZ3lbrlo+rutl5++bX9eGwRxi1L2B2kscmpRzbcur3lwLVHIkftIPm+NYK9+rAIY3sSU96nAHiTae8nn2P3eJWN7RXi46E9U5XJ606gaE/ga5EqIhGDtD9xbKHII1UYRgS93W6HHQezVlWJqq5QVhW6vsPLly+w3ayxZm9Dy7FcIuh0MtXOwVRTiBoM7gv9dXDRI1MgeToWWlO5EAFYQ0X0Bk41lFLfJCYiqEKEEORscnYGiODh1qPveipz72iBD7M5rDEQVYmSJZe6rinI1jvU/Oxt26Fpdri6viJCzCq1qizRaYXdbheTnWqtabSlBJRH07SAp4zn98/OsN7tINsWllU31of0SRZCSM5FWPJzeVSlRlXM4JzD8xfnUU1XFAU26y3MMEAKj9lsHk0sYZyVFECpYyVqy1Kp1hKq0ABLY8YOaBQVtCylRFEW8F5zcDNlajfWwg1DjMfy3kNqDcBzPJyEYG87KRV6Y7BrWh4vN0rvhIzRIy8/AcFVrUPf9ySCyTzK51rcF+eVz87L19aNrgkIeUU8AAug7To8f/kS//23///4sR/7Mbxv30M9o6wiyXZH819M7pMQd/8+uaqQ+ivSeaGfB0DL733Jf2bnxBvkohyDKTO5cSQyLuDm0bm5vfXAFRuxD3ubxzLQmMgLMZFexOTQ8GV0Wvbjs4i88b5iIuUdeJmjBcFTV4QpnF8qc+seTe7PUQI70IIabZQeKhsjn4GMh2diQ9zz5cU5Li8vcHF+DucdqrrCfD6j0t8AmralJJ8ueBOGGlqhZe7sdgxYtHc8quE7dYnh1XJWAA3Ak6GfAk4VSzqGUwLR+RLJu1AIoOO6W/AOy+UcVVXh3tk9PLh3H7sdFcZsdjtst1vKtsHxTT06XF1dxgKPRVlQ7saealO998EHGAbKrH15eQHfe8iBpKGr62uquTWfo6qozAiBJGc1IETFalZjOZ/hIe5hvdth17ZYbzaRqHpn0DYmekruvIOYz1HXNX78K1/G9XqNruvQtR2WqxnAzEao4QSQ04OFYzsjUJYazkkMg4GzBoO3sJa866SiysrbpoPnsZ3NZpjN59Hu9eLFS8pRyFnjwYS173s4joer6hqKVYSQEhIeSgKllgAUjHUwlgPGMZ6PYW6EXA+ev9NSTMHGYS5bTvkU+cxMczCVgA7PuP1VHfrgAAweuG4a1FpDmQHX2zV+67//Fj764hfxpY8+QlXXGPqOqw3r0cVCOqp4k6NYMJUXb9eml/PTv/mXKGGlOnc5kzsNVPks7e0Grlc+ew4K0z2HkOo1XmwEiOxetz/54DlCgLM3jzcmPfpYuhPZsyUX+cOgdcw1QwBRnXIUOEf3Q1QDhLbnrDGRflLSWepXsO30Q0+lN7RCoRUeP36Ki4uXUFrhxYvnePniOSwTsL3rZtzuKF4EicAceuZITkbjGMbec2FGUIiWJGeR4AgQVEgh8FgIgeBA7xzFHVlrKNXR3GCxWKDQmtIxWQK2rm2x2ZBDhlTkYdlZytWolELHcVJ930O1TbTbhNpGEAJKSzTNjkqAtC2WiwW0okziNiNkSro0/6WElhKV1jBlCQ9Wg7JKLqgK4Ty6toE1A7y1mC9mmNc1mqrBbruLRnxrTRpED7INOod8XSglYbOcf0GdSypaoOsHeA/I8wucnVKMVFkU0EWBgktrkCMEOdNYY+G5NldwDvGeAqKloADsqiyhtGOHEIO2t5FhiqSb1bu5RCGyOcVZp/cmypRoH18ttyXKIl7fATCRORI4v7jEcrHEvbMzXF5cUnLqfgAKwBYWyunUtVdIdvGZApSMmPAbzhztCu869zhkrUo8nl9uYFCzQU5jR9JhsHW/aXurgSu4Rd8Gv27ElRv2iVzyehU4iSyOK9ceHGr5KrgBM/Y8c6agN+ofIqcdv48kzFwOSX1OE27KlWWu4pnSP+ei4nMGI3kmDeXgoDgOxUsVuURKGKs447bE+fkLbLYbtF2Di4tLKjQY1INx3QSg9oh2YQavdO88YuXQmObjCTAe8LXItiUYnCAlBc4aH58z7CNi56KtbOh7doDoAU5MqyQ5EdRVTZnGhwHNbgdTGmh2D++6Lj5DKBXS9R0Tbsn2IxFfs+SCmUPfozFEyKuyRFWWVOKDVZteuSSZSwUpKKFvVRTw4JpfHBSdZwEx/cAZzAecrJbROcYOQ8zQHqrgCkGSVAgxEJKkHcfXcoJz3rHuToTxF4AxDtb2sPaanHKsg1xReXftNKV7sjaqcomxkOydZxmkaZ9Uimx7WkMDUNKQ84rrYK2PAcvBUXs6O3LCGlVvmQ11ClrHtsU5NjlqxItmUpgIE9JTyikPoBAS15sNLq6ucHl1jdXLc8qJGcIIuLimlCrRv0wiHBmxjrYsXCDv6xERa1xnOWkt8mNyvE+X8+Nz4o/b2tQOt7cauIAxSBxr+7BzSALLSf/noGhLlPDmqxzFQzH6PrWpTP87eP8AYG/0ADe3fTvXkZfAYBtUiWQvYRUQgKqqsb6+wvnFS1xcMHC1DXZNG9+tALISC0HqS7fw7LUmBJVXDzErQL5QkMYuAjsb+mPJdA/NrvIU98N59rQC2DXYk8cGuSp7smcBLIVpKhBJRfIMXj5/ToHFSmG5XECIEvBA37fYbrcAgBmnPZKK0LMoNHslUgwWhQaY6KwQg6UBaK5sfL1eE0CWBU7ZVd4ag77zUNwnXQh4MwDGQPiUGqkoNLSqqOIwO1H0/YB+GNC0DT795PtYLBZ459EjvP/OI+yaBtfrDdbX1yiKElVV4eRkCa0LSKnghaDM702DzWbLUqmAUGDnGnoHlZIxgWvTDXj24hx1tcFJs0NZUB2vQmt0IZGy9zHLPVUiprRPoTqyg+ASIxY1F7uEEJhVJfrBYBgMun4g25fnml2evfuyeeI9ewHGxPahYvebrJLjjQh/0hh4CDhrKTGzkHj64gW9d2OxXm/w/gfv47333yPAZicdKVniicz7cfnPi8N0INeivMlTTHGPJOopAzxZjX4/w83rtrceuFK7xTCM9EMHvx4FKXHwgDEkEuk7INXcpsXDx6LXFNcOR25lkpYYA5m4BXgd73EuZWVHMtEOKsxpMPJevxg1aLG6qOJqmgbnL57h8ePv4+OPv4P19VUiUlqxd1mKTzlkRA4g41j9E+xToRpysFEE6YoeNCRGJeJIcVhMYAVldRCKJS2uIlloRa7LzsFxAUIhBGRVZqyDR0jPG57ae0ccvyGpRngQoeXchENPCVXrusbJyQnKqoQZBrRdBy0tVF2TdNr3aLsOw2AwDH28P0mAEg5A31NdLhk8Aa1BcEGoa4GymMNYA9k0GHrD6hygYFd4yXnzRKmhlABYzWf6DleX51jMF6iKAo/u34dzFl3bk8end1gultC1RlnVEKBxFQA22ybW0vIBDISHVwJaSXhFY0Q5D3tcX68ZzKlPRVHAWw7M5grFlOtRcR6/sWcoPDD0YXwklBCoiwKl1iiUImcbR6pE67JM5ZmwESWDTGLIpYXbrOxXHTMi254Dolnp4b3Dtmshrq7oeayB1Aqz+RzL1SqtvUxtFzKH5AAcLp6OPaR1yZ7smMYnXC9XE2aOGYk1n0iSB+6UJC2PP8Epn8IA+tHEuklGCm7QR+HqIDBNdu8R5/3fn51BS9JMuGoeE7HX/amYvnc5gZRN93V6eAzWDm0fQ3cOdIBnychxEUkqLX91fYmLi3NcXlxQrjkkycx5h+T+fpzrHW0W6fyo9pke71L6Js3xUKT6SyCtBKcJ4oTAEoCVZFsx8FFlqLUmwAMQAjaVFFBCgYzU1EFnbQwhUFzYcRBA23Yw7J5vjEFRULnzQpPkBYALVSYCbczAjiqh3LuMSVy7vqe4JxnsXTRzjDGoKnrWQilYDHH9kJ+dpKSyoMBaLSWcIZB0xqDZ7VAozY4UNc5OTnDpr9FvdujaDmVRQClKWxWkv9lsRoHH/QDr6H75igoZR5SUXIXXwXcdpFRwTgMFgY0IBSPZ3umdJzUZghrSQciUJ9Jm6ZeU1lGNqLkSgADghYcXefkSTu01nU+YEN5pyxajyI4aSfpIRD0/bap6C33woBIuu7ZlyVpjsVwSaAERtLUuEPUeucpwwuBF1jGzSULk9PNwO2j6zo5PAJ+DYqYVwXGV/WeVYN9q4Mq5pNFmD+xT8kMDOFpG+8ccHHM67kaQeJOWT44kGtBPIfYeRSDjuPeh4g379/qQG5ivo5rCbGF7+JSOSBf44MMP8fXf+100u4aJdhmlAOsdhE0LMJjhcg7QMWGm+4jRX+tstNlITu6axoQJthCRoMV4JWeTRIYsWJnVaBRULGO/JBAzvRe6IGmN343i45y1JHk5qst1slqhqioYa/H8xUucX15hvV6j73sUZYHVaoX79+7h7OwM6/Ua2+0Gvigwn8/hvcdmvcH5xQWMsUw3CaSdBwbOou8UB2mbHn3fYbfbYFbXKHSBQmvAk+TovIO3BqKqoMoSQmtUZQUIwJoB1gxsU/LYbK7hnIHWEl/88ANUZQmBF3h5foUrc4HN9TWKskRV1SjKEqvlAlVZYbPb4fLqCsNgx440PH+11hDCsurQoW1bDINCryknYl1V0XFju91g4MwqAFUGMMaiKMtoCzOhlAyXmw9SqQCSI4eWwOCpEGiYWwjENgUZ31bKOrreblhSh87J72e9RzcMeHF+jsEYnF+c44P3P8CHH36IR48e4d79e6irKjF5sUqCG9ES5xGDl8GSXRQpgbT9xpaOidqPUBMtHOHJ1Z7Sw93SzvyG7e0GrkPNjzmfwy0QudccwFyA+Pyh68gNb3GfQNBFfvx02rx6PI4L+cB+Pw6yDAwk8SUwx0/BsyFN02p1AiW3+PST7+Pp409xfXmBQkkUhYpFH81gRh5ycgRMzGcKWrBxjTJIBoALhExpCSWpMF9RaFZjEVWS8NAhQS4EjE3qKCnE6Lm0lBBFAQAjm5PSKhWazOo26ULH4pNmGLjYpeCMIBJVWeIrf+pPYbPdoe1abLc7NG2D7WaDZrfDMAyYzWa4x271gxngrMPp6QmqqkLXddg1LdcvcwRUA6laHUu0KsR5saTrnIUxih04uP4TZwzp+57shFsdHUUA6r+zFn03oGt7bNZbSEGqvAcP7lHMG8fpWWux220hmgZqt0NZ1QA8x625lMqKUzQhjhk57VgBiuFipxEfSr1Ycj6pqopSTzlQ6RMLeDjOsqGISWEJC1JF+6A3hiRpTaVElFRwigBZCQ6B4PlqPcgRRLBTyZHVcusmMOHsjtils4sHL0NhLXzfw11dxYrUz188x8nqBO998B5+7Cs/hrPT05gBJtwnd5EXUmRSUQrrCB5+r8OseiTgAkJWEu66DPuikiXKD1PBjdapxJu2PxbAdUAZ9PoXEfmXKWAcBpB94X//1yt74rPP9PoiEeoEmiP0PNjDffnrUDsGUK97zKFnHU9REoDpqGEYOODW48XzZ1hv1uj6LgaSem9jpol0NRGdIeg3LQMqVSRHRu6918jqQucdpLfwjgoBhppNSnhOd0T1pLRVEbiC2sUFZwb2XgMQ1Z3OOa5uTE4ZLmRtEJRnEDJl9y6KAlorDH3PxJgkkOVijrquUFc1jUdHcVxt0zCnHKooa0BRBolCF+T6rTSpWBlIndaR606Cu4eDgHcWztJzhRRYMWdgLKMebII8ymxHCgTJOYthANbraw7WJgcPaS2slxDOR+AxbYdQ4kMKgbKuMAwGxoioro3vU9BzCUGJlsnu5eEESZEA2c0KTvMEkFRtHVcDcOxEYX0EQsp9KUgKDXkRnYNkjYzzwX46ZmBFmK4+/c3bTezcsZV/8zmHm/eeNQ90BecsAI+27bDebFBUBT76whdhV6sYF5lAxY+uk0tF6ZGyXh3mQ/f3+fF1k4oyaEWiHpL/jG2+4flpjN+c+X+rgesQYO1jwC2llnDoa7ax9HX4ArfuQex4rk4JaozMqzDeNUyJ8fGHxcgczt7Eo2cMYqM4LhxbwOkuSmk4B+yaBvPFAtvNBv/r61/HZrOBMTYmwA1ZzpVSnPCWs1wEEHEOEsm2oyJY7Bt6aeF7lqJ8dNuuyoIr5RYoNbmJhzgnzcRTCLDnGkkRijOhK0XlUzq6AQTA9h2SUuh5BrYpUTJZJQTqitJZ1XWNQWtyTtntqALuvXtYLhZ47913sdlusNs12G63OD+/QMOJZherJU5Xp6iqiqUhAs7lYomm2WEYKEluoXV0/Og7Uqk5a+GEgAURbu88oGQECrCkiuDoEQNuU00reHbvZ5vgixfPURQliqIE4CGVjPYxax363mDXNNhsNggZOpanZzGguuu6yL0juP0riUIqit1jVz8hBIZhgBkG1FWJ2WyGuq6glCLvR5aopKKKzeRtp1CUJQqt4csSgkMUhmHAYBwAx0DrswwbqQxHpCGeVMEum9xTAeUYPInJ/N8/bsxekmfhZFUy/TeOVM3OkQpwu2uwbXZ459FDmtveRa9b+iRVYUhCHfVQLA7ljhQ3EoNDNuKpHY2vP8ptGO4TsmYckAM+i87qrQYuaj77c/uhuPHIQwLXobNuiUgegD94nH8tXM3vG2jN0QnwRrPiAHv5qjNEWgA5r0l2cMHcPl1Ta4333nsPL168wCeffIr1eoPTkzPK0be+wNAbDP1AWRNAOQXJk8+n5KkMRoF71iwBSaUiuNGTiOjaLmVeOdZHb7SQNbzvepi+R9+1UJJy/w3DwPYrKtuhdEWjw9m9a46bAohYemuhtMZqsYAxBl3XYcb1p7xzaHZbwDv0fYe6qjBfLADvMQwdrq8u0ey26JoGJycnqE5WOFkscP/slL0IB8rAcXWJa+9RVhWWqyXqsoKeKSzqGoOh2CtrBmitoTU5bAxdH0Gu7Voi3n0fqwpLKTkXJEkkWlOiXOcpVyHZkqgp7ykFVEFq167vsWtaCKmiVF3oArPZHKtVhdOzU6w3WzRti7btsNlcc5YQBYEyFoAcWN2rBDEVEjMMZuB7kypXCIHdbov5rIZABa0V5vMZKB6QSo14UC2Cfhgo7lBQ1n6yoUl4IdB1Azu7kA1SegrEDm7yQfN1SEJ6Az3O0SbAsY188RDqEUNMklzCeTNJbSmEhGLD6/LkBLP5DGVVZkRGwNn9e6Xey70tr24sTsVPOjmMWWBmPdtDvfQxpGCcICAxCp+lvd3AxeOYe9Tcvh1EptsdPt14GxS8Td8O4MaeN9LUUeOGS41ATRx/4rEL/23Ug4elrz2Vi6ANIjIVpHa7vLzErtninXffhZIey+Ucjx7dx/e+913Kz9f3cbHGYWPOMXCmQT9vraV75KodkO7dGLBrOKfxmfQ9FPYD28Io2zmleQp2nVBjKwomTNyDfSZc0XEuQKWSh2JdVVHV560hKY6lIQEfvRqtB+AcumYXc/gppUgaBMemOQsBlv6Eh+17wDp4BmcFAEpBSxEzvUsBGKWiqtPYBYahJ3tZP6TUWyCp1FkHyeo1eA/rDAZDsVKWbWhUwXmI3H94NfA0nlRLrYVHiUoqzOqa3hkHTJPUqqPjjOe0WY49K6UAqR7DO3AuvgPvXaw5VtcVqrKEtQ5t22HXdsz4i5ihXoiB3yXXdtMFBkNBzc4lG2asL+YFVUhm4pyUp5PZL6bb/d6BKcZxf/XkFxLZsaMLTKU8kMt8cEgyzIi0XYuePTGjihtAytouWN07ueBteNRJt6IkFVSRmU0tqgpBNcBicl2fgEsAnKUn0YY3bW81cI3H9U34oVfJrDcMbIYIr+LI9oWqsa7B3zCRRjauEQrlCHG4awReU3LNt73VcL0axKYZ2oNXFm9Ans/COYe2aWLW96/82FcghIWWAvfOVmjbLfqhx3q9GSVKDQtCyKTuCJ+QySGoP0IBSsosbti1XJGbeXB7ZxAkvLCctFWgKoqY7UJIga5tAQSvNx5H71EWRXSTD3XAgsSiWDWoZIW6KiOQKYEYh2SGAXYgIj6ra5SVIueHtsGVoerPs9mcpBsAhRLQ8xkKJWLC2b5vMXgPCYmKwUEJUCCwAIQUKKREqSkRbckJhM0w4Hp9jYvLSyYuVI4kJLYNKtigLh0GKjjZdZRJw7HUGJgFHdWnnuOjBux2FsUwQEBgsVixd6XD+fklnJRw2qKqqhisXWiNvu+ZEDoqj6LIkYXi2UjV6BzFaHVKYbVaUo5G79HNauy6LmpdnPMUr8Wq5YrVhkVRYBg4cz3b4ZJLPs1S6T2EIyk/gFesgTVZDXtr6tACecU6i6A0WZBBQxO2h73WkSdpPwy4urrC9fUa8xnlxyQSka0Rl9aiUuwM4V+HWiZwyr179y6QqwpDnkJHOUmZ20MiYEEy+5MsceWcjp+MZbb7dm0fXj7bcbdtU8UEb82ZryA+5V3gv0ESSK7xk2N+yM0D6NqO0hTxAthuNmi2G6wWj1BVJbaba3z72y/w8uU5drsGhktqGFYT5m7/pAIUEahC2ZHcoUMKAVUU0V1XShGJX6wazESS7FTsycjeeVorLOZz1CcnKLjisLEWfdehp8qNcJbsCpaJfak1hBQxt6KAhRs6aFGSU8ZiTuBmHQQ8mmYH0/VozIDlYk5elXVJmTKaHXZDh5OTU45hEhgGi1KSHagsy1T2w1j2hqM8izChkKOAKiuSvJSEgkchgcVihnfunWD78D7ZoHY7DCy1AhQjNnAuyaIklRzgYYzF9XqNpmmxayiomCSZUF6FnCu0LDGYAXYYsNls0LZtzJAxn9XRBtN3DYOeRlmWKLWCtRZ932G5mNN1nYPpewiQDWy5XKLrOqyvr9F3LU7PziClIvthodEPFsaRs41nhoqS9dLfWVVTnTSlSG1qiOlQAigKAuBCSmjvMBhKh9VbN1pKtxFU8mOPnefgOSHy/rFx4YgxdRAABrarYjB48uwZPvn0Uwgh8N6778J7nwqlBpWd9yik4pRbDkIG16ZbevUFaZqvF79nQBagMGTICZqXFEsmDiP9Z2hvOXABY+llDAKfRXEY4Wmin71x8AUmVT5vav7Gn+maSWIKmSoO9flof47e+XUl1GN3O3Adz+oPDnoO6Z2sM+j6DmVVoCg11utrNM0Ol1eXePHsKa7W12i4npIHcZuhCGAAZVocIjpzxDx+4Ngqz+olIEpXQiKquqAVlCCQEWyDC5kzBMBBuKwmBEhCsJbtLyUKTZncQ+mOvu9z5SOUVhBCsRrKk+OHM1BCUjYK7nNdrMjGZgZIbwEmkFrwHLYefbODLgpIpXg7OVbYrom5/ySourLkODN4D8sej7ADICQEFHShUQoBLQDpHU4Xc6zmMzy8d4btboee454GYwg8uQaWZA65qEpU5X3ydux6rNmpxjkHXZQcN0VBxFrJ+A4tF36kcjbhfckIYNYOGAae28xEKCVRlRWU1hj6jt3ygbIs2AuS+tjsdijKErO6wqyuAfTwvQEKHWdmkswtSW9KARKoqgpAHx1RqHQNIISLqmLNNc+MpTpuVM87rYTbraB09P55e0rI0Z5RDOPkSGsd1psNnjx9Cmst1utrsssykD988IDq23H6rEC7xmEet+t9qIzg+J0lqWssMVAMpudvrIXBuALz59XeauDKXT9japbbAseBgTwmS01B7RggkA43dC5tjHD6qo4dUhVOOiem20cn+ySCHezcK+5/y3Z4DrIaAOS6DhfeiYiEHoY4+npWY7fTuDh/gev1Nc4vLvD4yRNstlvKJ5dVsQXYYB6dKwDnifhpUMLZoO7xbEsJdk/BgATBgcVCRNdiGeKIwFkyRAAu+uushQW5wFszoCgKFFpBSspebq2BNQYKdO2QhUArspMVhWZbEJVc1yLEK5EKrOLUSO1ui65tyelDAkVQhwKwfUd5BUMORC4d4hgwSDWqUSqqaVUWlGap7/voci+8g3SAhkYhPCQcYBzmqyXKqkZZlRTk3OzQth06M6DXVN+q73si2FJAa4VqVgMQsNbhvCywaxr0/YCqqtH1fVQ3DobSYw3Woh8ooNtZCyGp8KUUAkIqjj+zMIOnch3wcfoWhcZ8NkfHUhYFqBcoi4LTPw1o2wbOe8znc8xmNZyn6sqJSgYp0rCnYw+UBbRQqIqCbWEGhtM/Ub5LUhuGgHPtJb3fsLT8cTXh4XVxfNHlkpSfbBmrDvMjqTnvsGtavHj5AgOrf9umgZQSdVXBWovTk1OcrFYoioKTKIdLJX/ksL5iC/ZiZLiUqeb3JS2+BjI1JR9w0FMyJ0SfAcz+GAAXIqft046JMjZ/8UcI+43tcHaKN2qvAR7T+xwHrT8qbX9SCgEY9sSjooVUb+vFc4uvf/338Mn3v4/1Zo3NZk2eYSz2eBccB0QsKZI35xwFaMbikskIHwBIipABQ7DTAQDv0Gw25JpeVagKzZw+KNgVHhIK8BJaldCSMgHYoYcLthAzkOu2klicrXiRUuZ14anqb1GwW7ohV244chlXImTfoIBRbTUqWUdvxUBkICSpJvsBpunglaIg3lKj0HWkHJRiykH5Aco6KqWynEcV3263Q89lUmzn4Dnbve9a6EJjWczx4IN30XYddm2Ly8srNJwT0ToLoSgTxa5p0O02qOsZTk9O8Oj+GS4vr3B5dYXtdouiLgCUcEJQHkBL3p+7tqMSI5YS3TqWfLUSKIuK7XVDVJtJIbBZX1P2cwHUVcFj46O0CxBDcX5+gd12A+8s7j94iKoo4azDZrel9E1ColQKhSLVatt1sMMAOLKj1aVGDw9niEkJDI+Hh/CeM6VIeq/OwXp2GAFlowizPYDWUaUJ/z22/xgQHjsran4E0HPwdz2fQRUFNus1vv3xx/j2d76LRw8f4gsffIg/97/9WaxWK/I25QB6IpHBwWaKxqnHIc7Nc1HQ/R5GXWLUkkjv92nU5yxyvdXARS3y5VH6ilt9iskYvZfJTBkNaXCGOCaSHf95pHt5Dw7FmY2fY+8Gh164GP05eK1poPJtBC5x0wq78WEFqwj4zkw0wkBTVgq6PnHHs2icD+/NAzFfneFktoH5kFw6g/ooomdTSrsUVIeU/0AKkB2L1XUeKci4UAreWf4uoQQ4NolcuTVLBN47eNPDegUFj2pWcboklexh1kJ6Lk0vACU8qqqIpVq8NYDX8L6Gt2RUlwDZdBgwu7okjt8YDs4dICRJOavTVYwlCymOpCRniqIoUhA1S3laUd0zpQAlKfN+rSQGU2EYehSaiJa1BnAGpt1hJzyEp7phZ8s5FlVJxSy7Hm3fUSolpSCUxIuX5+gHA9s1WNSnePfhfTw4O8Hz5y/Q9T3FGwmypzgPGAdUVYt+MKSKZEmRbJKGXNRBYBWcTgCyEza7HZy10BwfV1UlFlxs0jmHYeihlcTgLANzBymA1WKOrm/ZuYckzFCDqi41nGVVmaWyJ6WWQKnQG3Y594jMsPWebaGcuUVQGibp02KJK9u/anWlNXhoLb5qyaXVRfeWQlIeRimJQWgaXF5e4Xq9xvnFFRSvp67t4YzFe+++h/v37+Pho0codEG206CCF0HNJ+PNfPTqzenXpHNuIo2BnFuC0094WHLAzZzYbjFWr2pvNXCNRdiwLQ3x8eFJg3pje0Mm4WaQGAvYrwKxlMpp0qn8z5F+7nM9/MfHJXC4p6/13ARaIy+hkKcsCF8BxARxylVVYjarUc9qNhbTgcGIH4h1kjBTGichgJTkwWfbBd8LrO4IKg8RVXiFVqiKEnCGVI2ccLbQKqr3VADJYP8SBAKlUqhKjbIo4L1F31OG90LSftJIOlLbaXJlF1pABZVkdARB9HITAPpSwxoLY7gOV9eSWkoKVJyHDqDM7+SAQglmy0JRGivJGTkYmM3QkYQpBeZVAVcoOFexmzqlyBr6AcMwQHgH23cYOgUlapS6xGw+Q6Uk+mpA0xZUG6soUNY1lAfW2y022y00PKq6hlRz2KEnteFgKEMHzy8nKBC47TqyXVoXgavtOn6FFOQ8iMCnEIiZgYLGy7JEWRbQqhgxI0KQ1Jpqu/VQqkBdlSi1xiBstHVGpbWUMaBdwhPjIkGODH6ApZzOseSGBzh2ClnIh4AToXqyiMvFHVlKectX+6uksLBvCl4+THJBUrl1lO7qer3B+SVJv7umgWY7oh0M6rKEkuSYtFgsIeakvveetAB5/5JX4kRxday3GdAH8CLHmPScEbQEeRinfW8uhf0xAK6gKpxEcsfvyMb7oPz1Q2k+/nPTEWFh7CsN8wDFP9KNiXZvqLbUbrfDfD7Dw0cP8IUvfIhvf+ubABvPKYUPfbwLYctEqJSQ8UklFy0MeQBT1mvHakUPaymRqlKSClWyyqdQAlIVJGEpheWsRlWW0FrBDQOsMZBCYhGqCnOsU1lolCWBV6ErOFclT8aQ2cA7CNNBSZK85vUcJZfUCPYnETkrnrOuTlk/mCB1XYfdboe2aSi5LCeYtSxpXF9dwZoOQiqqPVVw/4oCvtJc4kVgVhUxe8cw9Oi6jmpNzWfw8LGu09BuYfsGvS5wenqGs8UC+kSh7ztcX1/DWgM9dPjKFz4gIrnZ4NNPH1MaraLEo5NT9PMF+mHAZtvAAdBFidlihW3bU1HES6ri6/ldtV3HtcYGdB2pWaMmiiszU9CthbWUUaXZ7WLKLXiPWU3vzgMYuh6+8KiqGvNZxXYrYi4Me3qGMiZaKnboqGJGfe8tFcBkbUFgomxOKjxCIeHopBGoiRKhbE+m8cH+Er+N5uOmM4J7vgV5Sl5vttjsGlhr0DQNOZkAGJyD6CkkwRqDrmux3WxxeXnJGVBCqi854YtFZJb2ueKsL/Swe7Q3JN/1XJ0BnHslT1SA6WXfoL3VwJWjUlQ5ZRLYSMzNgeyYKPJaTUQx+M36fcOW7Mfhy/vRzqCGO9yXHACzs2/D8r1xY1uQzyQpSwldy7JAVSn0/Qnu3TtDWRWA8DCGQMMHpwPmrgNEKy5tAY+R63tkPvlRKEsGZe4QUsSijKTO8/DOULYBrVEqjUJJVJqlsbrkKsEK81mNuuKM4wLsPCFiSRIpSuLcWUUZAmpDZo5CaVRVCcX7S6VQlyWlh5ISW04NpQqyOwjmUo01mFclTjkDR8gjKJVEXS0gpcSjs1N0XR/HSrLNrGAnjlg8Mn6X0GpFYOUsur6LWeS9p8S8fd/DDAM211fodxtopVCVJWZawlrAdB12Fy9hnYe0Bid1BesBbx2cAEpwuY16hsE68p40Bo/OTvHg5AQfvvMI15s1uq5HNwwxz6CxFh17KXZDj2EwFMfFZVqMsRQqoCQES0lSklrUzeZMsCnRsDMGvW+xmNUwhjwk27aBKnVUXYWku327w6zUqAuNeb2ElqCCk8bRHALZsSyrGq116IYBsCyBeZGAFsHF3B9YhImZvtVSm54/Wqzg9eTgLcd48bwjJsQmL1zvqfq0J+9DKSlF1nw+x3K5JEm9LKJ6MOb7TI90lLtO0pWHR0g3xcdGMOOfCNlI0rWJgTt87du2txq4pmAFZFJXDlbImFxxBAxeF4AOaO9e3d/jv2iLj//m1x47Ck7OG820MUAl0JqgtSCHg8O9eJOWOLGgEog98J4Cb1kKKMsSQlhOOKvj35AdgU5PsVtB1SfYXT0UjATYdT0GFZMaJ8wDKcFZ3xVnZUdU+9VlgaooUCoFJYCqUKgKjXlVxiq7c3ax1prsXy6LuSk4G7ySMtqXQvwMgiMKq/AE97MsihQIqzUEB+s6S3kZBat+dKa6cZa93rhwZV1VlBNxPoPp2WORM+6H/ugijGnB1yUVmVYKkrNoFBxw7JnQOmPRcYxa0+xIdWYtXMioLjzrwywU6XsxryqyRzpyF6d3L+CkgDPsku885HyOqqywXMwwr0vsGiog2vRdVIM7ABeXZFvb7HYQSkfQ2jUNBw8TkxEy+ZdSA0KSF6H3VIKFS9pXVSgv42EUJzkGe+p5AQvAGgd4Cy1JbS3h0XR99GqNdc6UgHVcLsY7eOFYEhNk4+GZT5LahBuMoONHK/HwyokLJmkQ4oIaH8TCDrx16I2h5M5s0wsAWYT+A9i1LeqqxjBQ3kozUC5NpTWEcMluPLrhqFOj9bzvZZj+ivy4jD4naTXzO/gMxOftBi4OsHMHBnPktukPku/9dkwS+ywaudF5N6kvX3UdgdyWc+wm4wqnh7XIr6+uuLFjAMaLLTpFeM8cNWV/10WBelZg6BuyVVmDuiYvw5A4NSS1jewHqx7C99yupdhDLkg8iUQEBwXJKkECrUILVIXGal4n4ILHrCoxK0ss6iqqEOezmrJalAXquiS6zaXi4RGLTBZlAa1YwtHBQ5GLUAanFFZ1BhtXoSSWD+7DO4uLiwu0bQchJOq6gK6q0YKPxnPvI0CFLAneuRizFR1VuFxKURTQZRGzxhsuc6I12YGs4zHm92aZoO22G/RtC9MPlCnDUuXmuiwgNUku3gNlUVKQrrHYNi3kQDkHpXNwQw8zGPScnPjk9BSny/t49wEHPW83ePr8GcqqQlXXOLt3D8+fP8fl9TWevXgBVZawjvJFnp9fcGkaDa1LDnGQbF+jhL4B0Bquqk25DWncrdVRquj7ngLRAZJGrIFWwMlyhtPVHJttg812i812h6oqUVYV6tkMvTFou46k6a7HYB2M8dH/wHsqhRLtQtnSIMCcrpibpa8IIizZ7e3PZjqlthJxjYRXWpQ0l70QuN5scHZ6irKqKHuKMWjbFqFOmcw+aUVnD8R3hUiehSF3YqS/SNvHoHVAmvsc2tsNXAHpw2C5hP4J1W8pouftswDVfidv2ODD//unsYh4CKhSRu+w4bN28vNr0/5KIVFVNTynRvLWYrNe4+L8JZ4+eYz11SVM36EuKVHoMPSwnvLaHTMMU/onkmTCe/cCFCfEOnoqQEhZ4KtCoeZM8HVR4mReoS5K1GWBVV1jXleoywKLuoZWIqr3yP2a7FqabVR0f1CSWhlqbklOb0MZ0sM4BK2+AKBF0Pl7qhXFXm3vPHgIz9k/DKvvAoNiDNWi8mBHCCY0WkjOP8egzoTLWQcf1JdCouJqxABJDDHejDoYvwt2UhFVheViTpWGjYEZDEzfw3LGfg+ujlwUKOsZJEs9Dh7Weso0MRhKrNs0uN5sAKmgdYHCOzTXFxBCYFmVEA/u033LAmcnC1Ra4HQ1R6kEPASMsxiGASU8lC5QViWWqxNcXl+jaRq4ocOsKqGqEsu6xHCywma7xcXFBS7X61hHaz6bAeD6acZAsFOMd45c8B2psE9PzzCra6yWc1yvNyirMnrfdUOPsiCp+nKzRdtREDYMl7ChNzHKbRiD3wVJbNZlgPQ5thBqktYGrQ8vBIz3GKwBrMfj588xGIsPP/gQDx8+xGKxQMmqcO8B521MwRWqD+yx1FG6GvchZ4RzoSEczz/weULX2w1cUykL40HLbVz5pDksed1mUA8fE2Wcg6KMzz5j+Sp+358hsZdv5HlzCHjz3595/dx0cfqdK0fIE9DD2QG77Rbf+/g7+OT7H+Py4hxd22HoBybSqYjj6GoCEEIi5CsMqjhynmCVoRDQCjFRblVq1EWBslCYszRVaY26LDAvy/j3bLnAvKpQlQXqUkOL4MGI5GbONjDFgEHqPwomlkqm+DGZARcEkTKeFLEQJgiYJMhsraWAlAW89rBajwpjailgLbl/FyGVj5Sj4GkvmfP3gNdp3KRIaa5CgLaxKdO4DM8iKRg4SfMeXmtSPRUGtixjEVDH6bekUigKdpOXpIL0LFkYa1GVBdpuhsV8llRoQqA3A3HoAKSoOVWUgoLDvC4hXI32ZIFd02EwHtJJLGcVqqrGbD7DydkZYA2EpZIpSnhS81ZzDNZBKwEzDBwMzbkjuRI0fMpJGILkXZBdnIVzpBbVUqIqNNkHJcUQOgY3ITizh2BnEudhBakghRPkDAKqm5ZyWXp4b+GFH9nEbl1aKKcpmQIoXyI5PSN64jnbiAe8Q6E0emOw3m3x7PlzzGY1+qFHwVW1tS5QFDoy+dNq4hBJ05OEgtR/ms8hbyficaFD0asznBvp9Zu3txu4HHGcLos5mLrH04F7X7hNKfzNICEOnXLbvk5uP/092h7vJ47eK/crPNp+gJLYWB6iv2HCeu/hLHk2PXnyKX7v//5dfPLJ9/DixQu0bUtu4G1LGcpdUImpeNWgFoyZ2gEISZ5cQoDtVwJlqSmhriJ38UqT7WpZlzhdzlEXRfbRmFUlHpysMOP6XFoRoJCNDZxGiT0TlWSVoM7KoIMWNQJRlInV8JThIyxP6V1UcYYijoIJiwSrwiqFIVb/pYwZ5G1JAbiKHROkoPyFYZIHMAT3I7x3JWQkoJozJ5DTA6mXpCZJMji0REO6VPDaw+sCuUogODZQOqf0lpWUkFw6hAprCixmNc5WK/bOo3N6Qym/+mFApSUG5uqFGVCXBdSshjlZwfU9BJUhxsm8wnwxx3K5wr0HZ7DdDm5oMbQ7aDjUhcS90xUGQ8mazUDSYdv3VN4k1LESnKyYJS7nBAOogLOWspeAGCwlJbnFW4uuJ2eSwRjKhGINAEpr5T2gOGbNSo9+oFGh5Mrk/TgMlLWD7oUIXsHx/Fbk+8A6zoFE8DwMRMUD6Ic+Mlh1pWG9x6Zp8O2PvwPnLU5XJ9Ba49Gjd7BarXB2dkoMJhdmBVIIivOItrBxy4qBCoGYkD6jwVHliEwDlgHYm7a3G7iQARYTvKhTHelYgTB+ntbDrYl6oAOvgwFT2Wpf4+X3jg9EYyR9RQ7rGHoJJpzx6+37eVA6fLM2BlswYch0oN7j4uIlzs9f4Onj7+F73/sYjx9/gvPzc1xcko1nMCZ6DkrOQBHgmUpSaCKQ7CkF7ziQWKJUGlVZYDUnyanUGpWS0WtsOZvhdEHANSsLVIUmcFMKi1Kj1BJaC4rpQVDVkRNGKB5ZFGWscpwnzIk2CAYkyQPiERyBs9fpuRqSAEtcnOXDU0ZyIQVqpWBAxQOtMdCc4R0crCt8kjABkVzpPeWJk6DEt2FeeGPgBXlDVkXBxJnSUOXSI4WtCXKX504Hr84wqSi/H62hUF6DSJADzEBT1zlIZ6GFR6EJaIMDhlCSi0gSGPSmJ0kOHGcHBT+foa1LVErA1SW0piS8RVlAmg4LreAWc5QCULpArRXmCtj2FjMpcW8xB5zDtm3R9T15VbLkWZYFlvMFvPe4vrrCer2GYcZgt93GJMih/puxFptmRwHxnr30jAkO3vDSw7CDjBIa81LTHNEy5tmUoDReAgrCOlj4Ucb3kaT0ho2uM6Y11nHmD07K7J2Dtw7ff/wYSimsV2soIdA2Dc7OztAPPR7cv08lZTirCyB4LqdClXSPXN5Ceop0Sjwuglb21x3Qqrxue7uBy+17toSI72j3CvsmCrr0sg8rDRNYib19+1unHZt8DcCZb7zhxR0SEMWBb4fPDbxcdtwE0Dj3bZpvt55Dt5BI854wuFCF3gGm7/Hy5Qs8fvwYjx8/xvX6mmKL2L09xWMl2x65e7MCjhmS6E0IicWswqwmVeDJYhadLupCoi40Kq2wYFVhyfaukuO6CvYqVAJQ8LHEXnCuGH1YvRf6EXqXg4jIXtzUazNXv6T6XgnsJAi8IED5BcN7wnjOCSCqGSFAAdxJ+IseluGdh9kg4SEFSQFOIisZ42PMnCdUQ3KAkdOZlJ5Pipghw1myf4XJTmPKBn9WI3qQG7cSgJaAqAoUBQXQWgbtAbRvXhbwJdckq8oIxEpr2MUchSQJEiCHn0orGC3hCgVTFthKiZ01MG0DWdeoFgvUFWW6F5zxpORrOOswWBOJqfAeWghYScmctVQkWQkBVRZc582iHwwKL2GkhNUKxmHEMJBLvaTCpAKAIaB3nEEljeTxdjsmNONAs6/h3YZg70LpWFh1Pp/j9OwMp8sl7t+/h/mMxoe5FSipWPtBHqtBanYcq0g3yM0vqYgkzUMxAqYgTACIpoDc6epN2tsNXBP1YAAv2pcN1s1Xwa1hQYyP2Ts2k6b86GcOmmlfFJf3kGOKfK/uXbjeYZjF3tZ9me/Yka++9yHoD0xEUMP0bYum2eHZsyd49uwpnj9/jl2zi6qTuOh90oJF1ZoASVh87VCmpNAaq/kMy/kMq/kMp4sZSk2S1KxUqPl7zSrDQsoYE0Q2MsGA5SHgIL2I9icivqwCRKobK+Pr8kygZEzQG22qU8YkMA5sK1C8MTxb+NC5jvLk8fEBD0SmvqG+8D0zDjeOVWBdcgcMIF5TcJ7zmO3bZcGzPr3P4KkpJvORpElPtZc8OdyEByXVLRe3VFSGBSCgHAYXQVkXGh4ULG3YjgRORlwzaFU1xdIZS8HBUkqIeY1SScA5WOuhtEYpJYyScEpiUBJaAMIauKGHKAvMCo3FrIaWArvNluLn4KEFYr5L4Rw7v5CtUjl6flNqiIG2lXUFIST6YYBACw8GbufRGQsKx0CULskBRsJwRvVQOy4Q9sBYhH+zIb59myy+XJsX7tm2HdRMQnLoyWwxx+npCd5/912ccukeGeycIGYIlh1abGBq8hRQ+220feJPkPsejAKW/8SqClkcJztXEsEdc06KDuIPJgP12UTV4526ecdI8vN+ujtrN01lMZLkkH/PwPVWHNvnoasYXY49qYzlDOoDAIdvfev38f/8P/8Tv/mb/z9cr9ekHipLDINBUEsFSUuy+3KoQ+cAvo5PNZy0xKIucW+1wIqBa1mVKKRAoSRWdYVZoVBIKrBIZU4od52CJFWWABQcpAekI0cFOo6kOQkP6UhecMYQQZbJ8ypkEpeCgmOtdZxFY/IO2J6VJPmQ8YO+a5kydATHCUgB75PKKRAQEsMIcMnNm7PxM+IQYKUyMBASmTYTUfsQckF6n8CPQS93/oh4Gt8wgWuQKgPIjubbXvJWerdm6GHZxb4syWPPCwFlBKywgNOoyxItS6TKs2s9p6hSRQktFWRZYqhKrtUmoeGhnIUwPXzXQDuDVV2i0qc4WZ3gvffewdnpKZbzGb75zW/i+uoavbVYFRpzJeHmNbphYMIsIJWEsQ6DdVDCo0FLWTXaBtVsBl0qVGpGdcxI7IWDRNcP6AeDzlhACjgvyOuPa2GB323uxHDT8ttnRN+sGfZMFZLmbNN2uN5sMJ/NYK3FcrnEvXv3qIoyQPFeQTJi0Al219i3SYejA0bW+Ryb8pCWPHzpTdvnDlz//J//c/zyL//yaNtP/MRP4Otf/zoAoG1b/ON//I/xa7/2a+i6Dj/7sz+Lf/2v/zXefffd17+ZD4sIEcUTkidPwzSsfChumBAikZcjB7xuJzPpaiwJHj7q0KS4GVdu2nc47ivjzJiqHTcSHzr/gPLIB+82uo5SKqm2hMB2u8HFxTkGM8BYQ5kOmMsNBNN7T6ozGSQKupaEg+J4rbLQOJnPMK8rrBYz3FstMCtKkqqURKVVzIZRSi5X4l1U9WkhMjAjIJOeVKfCMcftKcO19OAM86T+kuGcoLpkSRBBPe0c298ygg26bjoh6PBIBhIkqsUJIuJ8DdcmaUvmUqgP1aFCGZB8RmdMmkw1ygByQohSGE9GwcfTdpbsvGWDvIhralwWPpfjECVOiJBLkueSc6DkRLTN2xScHTwxPXK1mkwB2jxv6rKClgqD0mQX68lRYuBy9VILlFrB1xW8c+jbFpUEVFXCVwXKQqJwFtL0EFbjwckKy4qyyDdtS56FUnKZFco+EZwx+sFg05RouhrGWRhPEl6IfYKUXMbFYtf0JKlLCY8BA3siWsOxdsbCeRvnQFp/gaE9tMpeTWsy9iK9//FOOHjOFWlQSIXzi3MoIbCaL+L9q7KKFcbhkVJrYTIPPHnKjvrLKpxoZ8/RC0wT2HmESLZnteMfIeACgD/7Z/8s/ut//a/pJjrd5h/9o3+E//yf/zP+03/6Tzg9PcXP//zP4+/8nb+D//bf/ttr3yeAQA5YI7f4yaSYCCWvbsfRbdyPI9/j70PIc0DYSiTrcE9zae21n+EPScBM12a9tgiEUEIqD88Etm0p8JRcq00MMg5u7hCUwSEY3xLrQERbcfbzqixwspxjOZvhZDnHqq5JPSgJtEqlUGrJ4CQo64MnwFEQ0EKwTYtBiIm38JQ+SXhJkgQ824V8LC5JRRWDGo8fPNpUkXMmPPdE0nsGIJPseCKC16TgeQt4zzE53lM9syC5+CBJ0T0DWOVSXLhW/pojCEbpJ2UWSSCJqJIUIDuUc1n/+VmCZiPNQpHCAFhyDMCVV1MlbQipnQiAkzo2NLLncQkR/ngACgKF0nRtD8pqYclpJdiKhFcQ8KgKDVtozAqNWaEAoaOaUsNB2AHCGpwsZvCzCvDArmko7k1plHUFayz6YcCuaVl6Im/Hdhiotpg1BOhSQCoNVWgMDHQ+q9Q9cH0vESg10phHKp+L5WGskDZ9Hi3MDQ/ELC1GKlyv1yi0xoPtPbJPek+VDwryDBVCUFUAOS78CQR6m99EIE2jCYCGNYH0N6fX7o+ajUtrjffee29v+9XVFf7Nv/k3+Pf//t/jr//1vw4A+Lf/9t/iz/yZP4Pf/M3fxF/5K3/lte4TBtFlBr+p/jR+zxbsaxPxMRtxvD/THzfcJ07nkTx9+Jwx5+VHE33/8M9r2r9JYxIaJV2SUqw1aHZbbNbXaNo2ulVLpaEkT0Hv4SPHR+cLBq1CK1QllRSZlQXeuX+Gk8UCJ/MZaq1QCDK010pCs2u8AiIoSQFoQXYrLZIjhgJJL8IhBvQKxt4ojSGoD8M2DzgTASlk0iBgkdlcwxi8wvjwtqQqZBDySIZrFxZ1LgcnoJBZKEBOLDwDSLAX0v1IAvQujCn3i/cHiTd4ilEwtIGzqayMyAIUY3+EJBsX6J5SpudUUvE7BNfmcjHfItm/CFCstTHfnhICXiaGJUhmAIMfZ+Goy5JK03DyXDv0GFqB2WyG5axCIU6oVAmDSNvsUEpAewtpB5zNZ9CaKim3LQUnS61Qz+fJbZ9Bqx8GXG93aLse7dBj27bYNg0GS8AEAVR1ieW8Rl1WuLhaQ2wb9ErBeUTnFAgJiB6DcRPWIl83f3icZRDqjfewzuD8+grGGszKErOqxHw2w9XlJR4+fIjZjMoOAcx88juQ7IwTGCzaL7I5nt8t6Q38ZN6EOTZVPb5u+0MBrj/4gz/ABx98gLqu8dM//dP4lV/5FXz00Uf47d/+bQzDgJ/5mZ+Jx/7pP/2n8dFHH+E3fuM3Xhu4ctKdGw8PucNHyQejL7ElzvXza378uqI0FbdnrMsIu0J/JyJ36mlORqbPEjjvQHTG5332duA6IoPWjGgHr6bLy0usN2u0TQNrLBFgTeqZSBwZoAgE2D1ckLqo1AqLusK8rnC2XOLde/co20WhyS0aRMirUF9LeCjQRyDkKGRnDIkYiBo8CoNzRPAglEpBs50nZKCXgeiz5x+ZeVgKIT3ZSL0ZeOv4YboerwkwCAWHCh/VjUCo/5RUcLSNgUupxMz4VEyeArTpmHBD7yWBUZx7SKqd0FOf1g+shXA+zpsQ9C2FYlUSXzebDSFBcCzdEvZHjps+yTZIzI3wDmCQdqCclrYfAOvoHSgGQEvAWxUFwLFigEffDaSq6zpUmuLralYbKq0gtYKZlez96NFs1oA1qKsK8/kCy3md4sx6LgcjAA0PqRUFo3sP6SyUoDCKRV2hZ6/Cq+2WCowCmFUV/MkSRaHRDpfojSU7u+AgdqkgAaSMl2m57K/gw2v11it4n1yMWjcMuN5s8fGnn+L9hw8pO4t1ODs5gVosUNdVdqFcQprYpnxghXya5DyfRrcNi+FzbJ87cH31q1/Fv/t3/w4/8RM/gcePH+OXf/mX8df+2l/D7/7u7+LJkycoyxJnZ2ejc9599108efLk6DUp9qOLv6+vrwEE5jYDqLD4s9+5eLInEYU2HdSIYuKNBnzEY+Ry9YgZ95NtUyvcfktJhCbXfWVfXv0YkXma3O3QIkrkeTonGZ5dehbrLC4uzrHZbChnIYgT9XzTwHWlKH3PxM8zwFAtrHlVUjwWZ7qotUYhRCwGqRG+s3ovqqYSUI2ASGCU1V2xqkqFUiYZwATvupHqOapMPEs5ad3GtZwR6pC9InwPHoAje0AAcX4fQib3ahojGc+NHYkqJxGnbM6FkSYnO4ffEkldALxg9aCLnLAP6tMAlMG1nfsDIaKBPXDclCXEjyZRGh9Ex4DgyBGCfyG4/pW1FHNmLAQ47ZaQHLdGLtpaK3ieM1VZQUmFYRjQWkrdFPMYFgV0QcHVVmu2QxkMfYeuaeCtRaE16lnNQcEO3nqAnWQoYwnNZcmSv4KPCZvLwqHTBrtmy05IFlqXKLWCqUqKXxM+vlOJ6fo7xGymYz4v2Wu0NrMp47xHbwZcb7dYzmZUOsZYrDcbVLMZ5oslANJMeJ8k5OnVRwx2xuiENRzIb96PYzb3122fO3D9zb/5N+P3n/zJn8RXv/pVfOlLX8J//I//MYqgr9t+5Vd+Zc/hAwAwlbICR0A78wPx+U2Hm9ph6NnbmktXo76l7/nrnfZ8790n9has99lfBa+cL2L/uhMl5bEOeYS8gSCDtLUcX+Rg7IDvffxdnL98gabZoSgoit9x9m/LDgMiOhKQOkUCkEpAa4mqUFjN5zhdLXH/5AQVJ8eFpTIpmoGpUhIhash7A8pK4Um6UixlRUmLvfkEuW0rKaF1SpGkJtJRIsgZB+STF2Q4NmrVQuLfDGhym5BkQp7HfoV68CHfogifXNUY+3KA0cnByWfzyo+zdsfzHTLVoE2Bod7HjPKUQZw9PTl62rPKk1Ih8WpzPnqOKZFNPkGgpRDAmsBKAdBSwyuKAzPWwg0G3hiyT3IyXCUEoDnDf8gkAoFZXUMpRfXJMi5fCQFZligYuDzIlDBw/sXLy0u0ux1LQ9SXGEeoNYRQ0FLCGcqO3+92sF0HOA9VCpRFASfIZX+91ui7DkPbQUoNwfkwy0JTailPzhlBOs/nziFqdCOFekOp5dg1rffYdh2eX5xjsy0xLytorWGdh+bckKHKQFlWAILjkiCQj9f2SejPVNBR68XHjJjuuC4OAeLt2h+6O/zZ2Rl+/Md/HN/4xjfwN/7G30Df97i8vBxJXU+fPj1oEwvtF3/xF/G1r30t/r6+vsYXv/jFKHElcTYwoX7yyQZ5tOCzlnGpn7n5CUkZCX6HVYXxmDHTOtk3nvzjA9687Utbr3Eu/5MkJlKICAC73Q7n5y9xdXUJrQusVqd4/uJZHOdg9/CecqtZYyK4kL2GvPnqssBqVmNV12TTAklYSpEtSwsRP5LHyYHTNolg20oglKsItUS8p5bB0QCgBZlnUPeJexSZao6vF/L8iWwc9zjtoMEDRkDos3eqOHeg5Iq1EYxCF3KdMvctVw3uTeGJRCbAdZzCfPIOCBKL99F+kduEY/aMcJrwcVyECnI59ZVyJJJHmnUOEiZeKkqcIEk8eJZ6M0A4CwkK/lVlCaV1dBCIU19JWCMoD9/gKAhZUVXr7XYLgHINWmcBVNHuZgYqAbNarSAANE2Dly9foCwLlGWZUngFhyElOAkvOcvUoYinMdg2DSAFhNY4Wy7g2MPRm54cRzxQFVQ41FiPwRmWWFOs3z5regvQ+hxbbllatx3aYUDTdWi+3eJqs8Fmu8WP/uiPoiwttDaUe1HJCD5CClAMRYitDGsjE71u6PzxKhe3b28Oebdsm80G3/zmN/H+++/jp37qp1AUBX7913897v9f/+t/4eOPP8ZP//RPH71GVVU4OTkZfajRwkrcZaa+8BmA5WhwA8czba+NZRGl9reRGjN2c0K4xvxzrvieYttrTeJjx2Y6JcHfY+2rqa5pdBJ/Ep2caFNFUnEBMGZA2zbRKylkJw+lS0J8EqXZsfE2UgrKyl6VVPaEizpWhWYunAi8Vio6TgRJKiRCDU4ZqdBjqsdFMVN58ccEKFR6ymdxTi59n3AWYvLOgjfidPSm2wJw5W7miZwlSW36LqeeswJIUlm0bYUXn2sfmFjwzXMsDPcLUqOW5EARckKCwczlnyCZxSkhDn5yFWkcZx5TF0qyGHIECdJSKIhZcJLbqMqVgp1tkkQaxt1z2iV4ivOjzTS2BRfSFKxipPIolFC2bVuKD1PpWvCeAIil0FBQsypLlFpDCcAZg263o/yYnLhZ8Hw3fc8ZWihvZi51xz7ftmUM7+0bp+/id3NMsgskxjiHwVq0xmDdtnhxeYGPH3+CTx5/ihcvX2K9XqPrOk4yHF53mkv7Xt3pHvF+2Y9ce/BZwOtzl7j+yT/5J/hbf+tv4Utf+hI+/fRT/NIv/RKUUvi5n/s5nJ6e4h/+w3+Ir33ta7h//z5OTk7wC7/wC/jpn/7pN3DMQFzAyQkjcYxjIEjHHxRpXjE5XjW8B19SBNQEOGPlTg5m+/Jg7tiRd+DWc9nf3PFoJon5n8YA9KprR4I4uU/ImOAc1Xfq2hZlWZJnIScyNZlayhoXoTvYmCi/msZ8XmM5m2G14AzuRRFjZQpJ8VpBggpJZMOckOy2rmQgxgRWoUZX+CSiypa7zAXdi+AdJVKMS5C22LYD7zgULshfYDDPuc8kaYh0CYr7yt5VyDgfs2LkAz1ixhypWYK6MXjfxdcTmDePEYFg1eR0aiipIIWH95JtYtkk8Q4OIqoEIQQ855SMLvAQEHDx4cL1A8jEScN2MAIVE+1DIZ5PSAkUOtqapAA8HILzvMvUyJoldWctjBkYZKjateFK2s45lFUZwbbdNRBCxGKbu90WUgqslotUVdtTlWg7EHBVZQEIwbn/JIwxGIYBu80Gy9MzlFpjVtUYmhZ2GDAYi3J+gqqkemXbdmA7XUgQbUdMTJohcdA/NwErvAeeBaN7hrllAbZJW1hn0V9e4nK9gXMe7z16Bw8fPMCsnlEOQ5nV6+ILO84KEi3iQuyR1/hkrNohIPfZ3Hj99rkD1/e//3383M/9HF6+fIlHjx7hr/7Vv4rf/M3fxKNHjwAA/+Jf/AtIKfF3/+7fHQUgv0mLNJ+5uENpRfL/Dl/hVYN3+8HNoGZCRDJikANaLo2NrnMcWMV0w7QP/nANrz/Ulg0jcWMSxvXY7ra4uHhJXoXrNZqm4QXE3H2pMMghTmaZSURFoTGfzXC6WuLB2QlWixlmukAZypkwEAQvwVzFF4LSlUwfndm4CiVHnDyp57BPUNh7DUIALgFAkFLje3A+Eu0oiSIHKnY0CEAT7pFjG+jdhaKQMtTZytXOmf0pMmDexxiivPPpNM+mB5HmXjZBc+CjaydR2gMxUDSf2/HpMmcRxVIMOJN96IAUIjGXLmW+p2Bkz96elLEhuKOrXIr3nt6NlHCDZekXKNnpQiuFWT2DhGBJkaRFaywa22C+mJMUVlWw1qDtekghcHp6gvOXL7HxHoVWODk5pVx+UsFYi74jRqYsCgipKGFuGCsh0DQtttsNBmPhnUVVaBhXAsJgvd2Q80ddo9gNaIce3jvWEBi4G9bv590iEyEmG/MmkvrQOgfjenzju9/By4sLPDg7gxkMPvzgA6yWSxQF2T2nFwvg6I/R1Ix5IlvwZ6NRnztw/dqv/dqN++u6xq/+6q/iV3/1Vz/7zXxagdHJIbNrJZVbWki5zBPbLcbwdsMcRbvJptC3BEp+eqjP+ugnEy1SGj/ZFi876UUyhh7tZpxIPstwfujgDCp9OidDYvrXISZ8TQ4ADtYO+OST7+Pq+hp9PwCC3WoBqq4HINhplBJRnacV1UWqywKzqkTJAchaKfY4CwHBLJuwJOI55x1x5slbMKjD1MjNPXn6Je9BesZgIwtbkoJjzL2mbYiOF7mENT0GEQR8BDqEc5FUaqMJkKtjAHjrIBVzrz6UlhgTjDzbAZ1j+VIMdEj9TGy5iO8Co2fktFfhuiNV4Hhs4mQgJOLpkhhKweq+UFssrFVSFXuWjGXWJTH6pOuTHUoKoCoLTrpLvVAyuMzTfJBKRtVzkMY8wBIEVVoWURvjSKIvNITgvItSxlyGs7qG9x6DGXCxviaPvGGAF+S4Aamw6TY8ZoiSI2WEYaD3uSQURjhvN6HM67c4j3mMoobKhVfOcXjeI3gEDtZivd3CWYtCfwuAx8P793F2ekKZ+pUaqT6j+tpJQCZHudHTiLEe4rO0tztXIbIF7ZEt1CgMI6fsewR+DyBwFKH8gd1+70v2I5f4InOc8a0ZUOX9S/0PgOGTZJk91fS+4XJTaesI/5M9j4AXRwZopMBIBxyATyQFkWf1gYX3VNX308efUAmJmBmDmgWt4ED8KAs7OUyUHHQ8q0rURQGtJHsCpoBgIbJs6N5xMLAFvGN3d+yBVMzcICZu70KkjBgeyG0T4b+wW7L0EsdGpPuEgyJQRQnL56fEa00Jc35fn4/uaK5M+Vr+lRGK0TuKuQPD/BKxj/n3kTQVvwVPwHSepwem/Z7ffZjOYQ1mhCsAn+D3LUGZJxznGLVsn4L3seZaZEYQbxWl7MAACE9gUhUFBBe87IcBSgmqt8UxbEJQiqmqqjD0A3u+mkh4rTHZ2DgopVGVJZTScU15fjdlVZFDETw2ux1a9JThHkBRlZCFhLzewvoAghlzJPYGOhvlVxP0QzToNk1kJ43og/CjOTcybQBouw790KPvO1RFATv00EqintUoyhJlVSZmNpzHUrcT5HHqJ2a96Ej02k8xbm81cEUpJoJXkljG8VzZoPqxF1cwcuctEZ036M7B2ZekpQhi4b8R2B6/7uHNuZLRZx/xxpP8s7XAMFh0fYvr9RVenr/A+flLOGNo8rMaL6iFgq8ScbcSpSL397PlCg9PT/Hw5ATzsoSkvEGAYw42y/QeUjZ5TwlNCQQpXVChJNu4ZLJp8T1jclif3rcKSArEuKA8fkkg8+IDzbNwDKnFWLJAkKIScxTcxRMHCkDp6CEVvfcywIRIkkeebkcqiq8hDy9EKSLecPLiR/ay2IlMVopSG0mbAXzi4Uqw+zhzBNnz019yYvE2OEq4dGlBfQxl4imImlzbYQ2sDxWGSWqRLDENhkDG2ODdSWpiz84Usigic6h1QUUs+x5910JpiRCmraSELgoU5QJSSux2O/QXPcWOecp2UpV0LWMtVKmwrMgFfNe01E8B8kIsqCK21grX2w1zRwovLi9RzeYoywqnqxXOr3fw1pGDhpQQIIekwHS5yZr+PKSQQ02A6Y2j2Mj8nQVmxwExnCGHFA/KMblrW3zrO9/GxflLtG2DL330RSwWCwCeqmdLCSFUxqAlWhQoUS4xJ83Em7e3G7h8/AeBC93LnjECh/y88OWzk/ZMtot//eR3OvKwvS3nVvftVAwIGdc9Asjsh8+IcP7jpieNMsFryPEjVUeY7YLVdd6iabZ48eIZPvnedxGyiXtP2bGtpTo/8ElaIdDSmFcFFrMKD05PsJrNUGsN6X10YVcIDhcpJosWHdk/JIOPDjW0RhLV2EkCqdv03XkgYlSSgKJtA/kCDNkhfJbTTcTy9kCQCjOgi+8tVa2VWT/Cu4jfRbgnSTn5OxdKR7uWG0ycVYILN4bz9l5aGINRDA29yaAtAnyKIQ1SnAMQ8q7m0mZ2BZJXcqEigWWoWZbmWJgredAzXTu4xMD7WPkB8JA6A0+IeJ6D57g4ibKgatbee6rzFd4j96uua4Sqx9vNBqYf0Ox2sM5yteuUXBYCUEpGNSZAKjCpJOq6wmqxIDd5a1k9aSE4M4dWLau7SzizwTD0GIZhxFDm3wXSGB5tGbM9eq2TFz197YGVzZU8NBV4T6a1outhpH1wHth1PcR6jU+fPMGjRw9R1TUqIeiZPcclcgHKxJjRk+Uey6lv+8zV67S3GrhyUBqpKaJ0E97UAaAYbRuDWib9jgl0drQ48otvnH1NfRkb2oPkdQTgJmrE/HqHHU0m/Qt6UH6e+PuGdkjKPCRBCiAWORzfFSAy4tAPHdbXl3jx4vk4yNaLkf4boIWnhORqvxqzssJqPse8LFCy52DwHiS39hB3RaVHgtcY5RIc265IskqEK0p4GEtaUeDg15kTO5n1MycSAXyCdMU/MdJB5+MkxIiFyLNqjLnRHLxE7E9ilEnqIY1lxgoJIt7h5DjGPB4MQRG4AvB6n5wyhA8v3QMyBRgDGBfHzK7NV0WUFIWgQNV4R0QJ0kWNx3idRmbCO3gqxxw5+Gjyz5kAvl9wio3vQCsoLhgJ9gSM4QXeQ2sFgPLzVWUJawxMP8AaA8nSXs4ZhvNdeAcgJw1dFFjMZ+iGAU3XQytF+R2FQaHLZFeVIgblU2Lpse4sG70onfgDR9wC1vbaGKTCP+NrRFqSH5tN33D0YA12LfDy8gJX19eo6xqz+ZzuIRy8EAiV5kZ3oBeTcYuJbfwMuPV2A1dizCbG64xTjFxfftxrTIB9WAqT65D7Qza5RqrLHBjzRevHv6eSYXa/wz1jLjD7JbL+IfT9FqD12Rr123oq3eAFZczYbje4vDxP+n1QWZvoiaY4YFWQtKUkBREXAljVNWZFiUppSGu46i2rBzX/lYC3HGcVpK3MzV1AZMP3/7L3LyG3bd1ZMPq03vsYY873XZd9+e45ySEF+fEgKCgEwYJiQCOIipVAChbEgJCCWBAEFQyCIBYkIqQogpZTsBAQLaQSggrCOf7+kHCSXw74Xfe6ve+cc4zRe2+n0C69jzHnWnvtvUVc+TI2715zjjku/dru7WlGVNvW6f/aVZByJlAmZ4RctbE2jk1e9jHmdt5XhwsQjYgDHXKGA88SiJq035I5jUjDsQIZaOH5Gn0YSDAWaUj6WkbN66ZMvBcLtPB5VjBfa7eZQ5VxmEbI3H6DjmmtDXXPEUEgz45m1qwSYu1mTx0PY1RRE5VTjGBHIGdFLJeeDzGJKatfv8z+XNOGzQRNIAzDgHVdUWD4k9GRXCxAZBgSntzdgUvB47piXWYEIgyDlLmvKABJ9OqQEkIgrFS0v4QhDnj+9BmWXHC+XHB3POJhXjCvBXfPj+KX44q8Vl3jNgZtPwe0fRq6X74Ye3q/wwSLJsx0P27ldjCAwtu9wQCWkvGjl6/wf/5f/xdev3mD/9c44smTJ7IkWFITbK0w2IUye10Txr46OfqwGddOXxEaw7u/PXm/8QgoXdlrEddc6+pm9npWTcLhXVvcBNif85c2ZtueuZ3YW+ZFe5+ZduykSKbXXegvo27/XGtO7ziuBTYY06qsScRBMN1Op0e8eXiDhzdvkLNA+lgFWDOFEsQPlVIUHDhIQufdJExrIFIzIW1gm8RvJaatXMWfIqHVUZ37ffHIrYZ1q8NNYne2sGVolUFRMQl7rUMHc1ujyOz32kc1d/VjKCjy7dkMC+9qkybMKQJBowbJKup2AhNDBYAkOV2hN3MpEwI5o3DBrtaWk0MkRKpK3x3lnQhUq/hwlItY4UqAQVX7DQmZN0YctFxLI2Cb0QJD1gqxaIYCtZUcK1FMr9F3qwTcsNf4Iup1VtPetlTYwZJV47E0BNN6apV/hyHhME0omgfGVQKKYkj6XDF8GvxWSgPmZQHXinWZkYYBd8cjnj55iqenCy7rKyzr6ikNlSvyusp6TBExZlBhrT5geI2+DHxmN8tztyZuH59LqNqj9NKrO3bMi7AdVrEUE0IMeP3wgO9+//s4HCb8H3/o/8AwTeI/q+xCVJPJ+YpJOd36CtzrA2dcRpCbl2kT6MDt93c+wxjFe8z/ZtMoEeyZSnv99Tv7qB1nZNDt12mC11rXjWfy7gMzms26VwxY1HiGl6EQ4tc0hrcfzYnqryf7hXebSYibmejm+YJ5vmg11dIIE3oprEVbWTDFYRhxfzhgiFE1HsUaVEne865Ui7EaUfacTTSgm+KaccIY2D5asP1q0uGWiLhWtZNu2scmIjDvnc/7edtKJi1wgltSMtTsx5tBbtYWtnVHgrLVmQhtEVqKAKquga4NxlgIhuhggSedKavL75JuB393iASu4WqtNn8Ga7Tqfn21CDZrUVDmWNUBZqZSIZxBAk/c3myBLxJE0vfFxjRoqL1NvM+/BoHIuxkxBqQhYhxH39O2hqsyHhtXGxtDgck5I00ThjTgeDiI6fHxjFmTqi06krn4kqHNKHR/xow7rXy7O9lNorcoGe+HeXfRXpAFsIEm25zvGrifOdbzy7ri8XTCixcvcD6fASKkNIBJhA7ErYC+XSM9rXw3XX7X8UEzLla1Y6vVmBOyI+iw63rG8t5vwbuI+1bi2zJN7M9yY3F7zdCaup/L6/b2J/r7LKhj128S8UqYVtsgZnohbf4NWW/fjY5ptc3iCz4EDEQgYlTOeHgUNPjKAqZbS0EtLVu+R0a38u/3xwM+evoEHz9/hlGBdC1JtSFeBCWuBvIqjMtQ3z1vq4d1QmNim+44YyNnCHbOy4p099HVvV0n3rZGqBcSbFDbHJIOJgMeYOLPs7pWDMl5M4ZthqWrdSHPMvDbmrOHmocoQS79IJjZMOwCEvp2GnrI5h5mICpDqbt2uFnU1iO6BaYaELfACsBqPbVbm0YOrY4sfS5sKCvCT7hUN3emNOgrCFFzjBrDahtL3i+MMsWAcUjgg4S4UxDTYIxB8t6sH1UFwiC1BgVpfkEcBkmAPh7x9OkzPM4rMhMelhXjMOAwVRCd2vtVCDHGZOICdGfa0PdmQ5vZtzGtq/l/y3Frme5lXzsCibeKOqGcIekrVCoCpGLHy5ev8OLlC9RacXf/RAOGQqNpvXVp8ypd/1+eb33YjKsNqzKq3Uw0DWen3fS/0xeoJvzOlnQ80t/Hm0m0d96WNPjG3+6KDU/qo4R4cw3R9m43rryDB7+bPd86VDq2YWeg1AyJLCz43ne/i9evXiHnooUjm81bAGRJk4kVmzAGPHtyj/vjEYdhgOViWe6WobXHQAgQ/0nNKyRSUVHBHdap83OZ1N1rVp125ph8kIhHK+HhviAluD2jotgQI2yjAvA+miZD/myY0tIxRzWthQ6Kl0UAuNrqNsC9mbNLuN1yZPbFyO6/2jNZfZdpWNE0O1ZzGbeFtNEujYMLsQ1Rkk2rYzlCx0GQMYRpwufdxqEXLE1giiGgAigWuGPjxCpEBEYptqv6FSj/1VrUd6e+U036sshF0YLJcQpjDJhTxFATCJoQre1LQ5K5K4oqb6j9G8GXsCwSyk9EmKYJh8MRhyXjYXlQBArxiVUuqBCsv0Rak8vJVZvfXvy9qe18lYN3y8ROm3y7u9jwHfsUD1kX4hKYV0HG+cEPfyD9VyuJBNdI/lzt0Iy273wbDXz/4wNnXEBjE/LZN6D9pwztWpO5PXC3CbhKvWbC8J0jxGRvKpRXdsyRbzCw/gZs27dtGm/+bcyx+313vZuq7FozM5Ayms5sxOhLDnwO4sY7DgZ0g0s4/JvXr3HWwpFW+M/MIr0Zz/ADU5DQYQEyJUWEIKXP0q6g15sJjGvVIofkiAvN17JNOO61qcZATYOxmlrXfwA6zUtu7uGb9AJ5Rq+d9Vpcr88aEd+/o1t4LoXvp2IjNVP/wHZNxzwlGEMoVkgiDW9sQfYPmcx/a090C+m2ONTGqDd17XOGeKfhuma2e3TP1KnV/WrEbrt/qHtAH51o7zGTPEiCSPr2y/oTH1uKoWlpJEw5AlgL3KDisrEKNaLtMSpLoMkwDBjGETFGMC/OgKvu+xA65geDWdL2dWNhI77di1fizOb6t1zm2ppHjeL9jl45d+Yl3wBIv+d1xZs3b3B//wTP5hkxDQBJzl3OAgMWY9lawHbv+LLHh824bCXpn5vM7De5qOlb7xypt/ymr+gZQY8sy+jO95pS9zrbahtmZZsRbVJ7U+Ft5nW7zSaHNsbTpFZpLm1Nhf0LevVsp5S917rqTUO1QsA6M16+fIXT40kgdoygkaGQt+Tf6JGCCcdpxJCiB1YYtBMqI0RDgJfcEQupj55gvCsWGbYEsBE08mZ7bhaaWcqvNW1px2TIw6uDSvSsQ0DuErLb0L2z8RbTBuTvmgBD39eCJwxiCdhjvJEzHpswYx0UgDgkOPlLabNYbetsCZlpkA0wt1+QVf9tzF4Taff7Su8lABUCwhqqMnvIOBEkydhtE2ZGU2bifi7VHKvlUnXtIYjvx8qSCNBv8H7EEF1YtDk103LVZNtW9Tq10HnANeEQgkTK9oKFMrs1V2FcRYJdhkHKpKQ0ALgArG2TQZKoWYZgDUPM47Uj6lsxSObKTW/UIDHfZ4/S1b9tDW9TUdpD+qUkOXCtVI27FiD10yozlmXFy9evcXd3j+fPnuNwvBNmThUEQkxZmXgF0CwTbcl8ec71YTMuoBHNG1rPdkM15vV5nL7XZq5lFFs2221vr+tZyZZ5tk83tbHGeTums3la+79rlTv5s2eyHUn8KprUex1qGosp4HQ640ef/QD//b//d7x48VI3AAA201jTRABhTkNKuDsecBwHqWysgRiCiCHXxhg9OZQVmSEE8VH0lYr7EhoOB2VjovRfIhSVSO0Wwy1NC7Dw9dB8QsrkuYt2a+aw2wusBT5o5FWg/kd/TqOQzm7ttvZ8Np9XaGuGxa9lb/fij2aONC2RoGC4+uyN+E7SP+72FKOtT2mAakxoa1YukyylypLMjbZTWFHE5XHdZPR7hAV5I5esY98InKFNMCSc3fvYjZ1cKswrpYhRy43Y2nEfeK3gWkRzjtQSjyk4o2jCimjxAAQlgki1JkbmBWVdMc8LcpD7YxTk+SdPnqCGiDeXBWmISFxRiYSRg4EKZN4yi/6zCB/StsLKSDZU7t2HrbQNlfqcm6/0O5d91dpBYmpdVwgqf2WcT2c8Pjzg4c0bfOub39AEeAl6GbQAKNRC4mu705y/7PFBMy6++pd9odv/jYHtr7XPvrH03NvYVHtDZ2ZjALRbTLqo2x7njo81v1QvfbSHNd8YNvf3DPf6Pr+ElE3xdtNb4qRItPLDNjjDiAx5H68GuRucTY7RLjzpfD7hu//jf+DFZ59JxBFLeLw8x4IrpB1DikgpYRwGAUrVGkxRQ92tFb0k3MKm4ZGGPRacKSd9orCGEbbfTKIXkd41rt7vFDRnipzL9jKu9t0k1bfa9PZHW026dJR+G3loUq3/zxhiMA1Nx75nYoAXPqwalUgAOJn5S5ikrT8iC3Dp5SP2V/aBTb15zjUg/W3bNVuXNihtHtp+sXuvfR8Uglea5m4vNM2VQIbAokzU5kY0v+qCGwiSRrFLJqbNZzgzkzWhgglF1caoK7ioe6wW1ZBMaKlY1yyYfgxcLgvWnBVAWOaplOxaZGRGDPosMEJtKyI0GeGKFnm6BrY0q7/mtqx0Y+zfcdBmvLvnMvveiCEAKaGAsNZVzIUPD/jRZz/CT87/D4zTASnF5i9WlY5NXUQbwB/bqEKgSQZtxm+xs+2ZtvhlDLdSN3eroi2VfqE0s4Ewif2T/dPOHNhPVAt/7+/dtvL6405bvKVRGvPxkGwlABa6b9Jk1173cXVmoNbb9zzU7HU6n/Hd7/4PvH71CvNFzCUxpobTR60uVlLpVBjXiKS+LtHKDJjVwt+ltVXhogyv0DS4W8xrezTm3DSxNmZGYPugC+1WG4cNs9i+oNeUiDrirvld3VKChWPLtWF7/15QsM50+ICbd+uat+g6Szh2hkfhOqzdzJSuqckjabMWjchbO73zHS7iTo934qj3KLKJ7wi7V01nfr+ZBUPQKswaio6G7ciqVRfmNn6t6doubbOUwUaw4ewtGTo+IQTEFD2wxAQWCkHyzGpQ35a1B4qbKDl7IIH3yjljnhecc8FlWbFkCRKxYqm5ZPfBMqSuV+WKgICgWmjPoBv5acIRocmHPfNqI/02gbtdafPZC8y0f4rtgd3zWNex7Y8hDSAQSslY14w3j4+IMeJ8OiHGhJSGjbn+ivZZi36cGZccG87VzvQDs/15x/He5/nAZrnsElldi3JpojGxXtppzGyrfXWK1YZgAPsWtgvbPmTvivmyNm2+prN4y8kvcdg4yPse3rzB//17v4dlnlG0hEQMSkC17yFExCia1jgMGMcB0zAgaTSYgKNqoibBK8+iVtS8yluNGNQWFNGUFGMgndSKxpQESV4IiSgyfIvTNf2nZ4TOeOpmhpgtcMTu9O3Zik5272CjVhQ2ROtmI/qm3Wina0XKzGGM1xAyPG0ArfYWIPldnUBxZbo0c6A1rCP810d3npoggCCsq9YKzhmuKXXRm2BuhRwh8xFgmQHmu5U/7dF27CvLs3XA7D0WVi5FK9sdQd+fYkINVYOHtutFIxqcoXGtyLlinmcABAoRxfcuK5J6wVoqMhNevnyFVw+PAAj3d0cMueC8rHKPDD4EOr2C2Fi0RFZ6uR9mMOomuOnGaL/XwZVRTfbwPsLkBxBI0ll2F1RltBwqwBGIFaGrCv3mzRtcLhe8KAXf//73EWPENE1glnw2UkSXHkGlckso/7LH7xPGtWVdfWh822dvYwTdSVKhtNeTiUxQbUTL1F/VtvbPawyrSTpuCtxJH1sTi/2uv23Fnk5a6rW1/fMaT92ypvZQtovALhEB6D53fqgbhFIvBmALUIBHayl4fHzA97/3Pd/wRbUAe0dUM1VwW2Abm83jAamjpOVMuFQt0V61kKRsAtIegaHmnjZXRsy3+qVIz8RWGDCCdqPENi/mj4NpL87KfLw2Cmo/XCbI2DyQMcD2LLIFtxMg2pnr8/55w+0IQJDwdH+XRBW6lu2NIw2Tb0yIeortJmRb+/b8rX1A/D3WBBYivFMf/C5lIpVIoZcUmLhnlGCArUJydSGQufi7DazZeLnvpVoFfFfnqWgYOgEeUMKldAnRpgmotgXy4bA+1VI1sMR2s5nKooS654ys5q9xGhEuMyqvWNYFLx5OuCwzKhjTYcJ5XhEjMI2EZS0ozh00cR6SNC2l5LZIExYpaaFVttzqZp1c07Qm77Bfa/Lt/qaGCgnbMu33bo5Nq5cozIRhSLicz1IRoFZ89uIFnj1/jqdPnyEZcr9ryG15WJj8j7HGdU3w2teOUXUEhHeX0v4W2j2Re8HTCD2cMLISnz3jbI7t5rfamgq73zoNyp56q+39931/jZkZMbSQ9xboYVwZajaUTnC/W/Xzpo5pE0FxfRgTlPeu64LT4wkvX7zowFl76aoL0HDiZvld/VuknZZMTEDTGrgLed4x+D4EfdPkPgq0I1A+XtRt3L0gsFWU9tLAbv0wdlTHL1dyKW0MHVkx5nE1rvru/kX2rp0wwSpgOJs1s2M/qNTGfMu00Bj0huEA3amuTe0d7XHmR921Gb0Q1PsLtY1KKV1j9H/lvPhre+bBPv96pzBY5oaXSITK5IKJzwMzKrGPDXMXKr8/GG52ZTRQaCJJbpZIQslPBGsOGBFqqViXFW8eH+U3SMJyWDMCCInSJmKS2jZURA6tHg1GH8gDiM+udgLErd34tqOnTdsfeHdNG6/2/M7zapqSju2gSPxZhY3Xr9/gfL4g54yhbv2YToc29OrLHx8042rbf/t5e+iSd5GDnaBsr+oJSE8Rby0TRm8q7AlX79huC6N7G3dt6P7adXx1+55ZmUbZohHRnmWBFr2Uvfncd+eLboHrI0bZcKVkvHrxCi8/e4HXr96IJNYBsTLgWlgIESFGzX0RyU0CNYIWf9T5VA2qqrbVCvNBqh+jwqprNQ3synLfvnVzZo5mcgeyjM9bR6RbEvvgBWPSMaX27s48Z5zZosT2nLD5HfuW69rruXoPN2JX3tj91Qg/VyHoQhkb4+r700Ejtd+6dnQMxuCOpL2d4LNriAl53EvWXEExgiyIpGRsDm5BEggRXDXtgYVJVG6hEj6W1lSdS3u34YBIEHYn4VvbiNSEac9rEak2H8K4AISIUjKKomcMwyDFLy8XzJcLCghZ9+q8Lng8n3A6ncCKm2klT2yAhyEpQwTKvKofTlMkcoNFizH4+FZlsqSft2EjW9q3X783KNBbj8LVwaVtDgOkGrkMs4yxBUgRM+7ujricCefzGT968QLfeP0anzyecDgcnTn7IGMrlH+V44NmXLfZ1VsG5i30Wbff1ZnbzySXvGSBNxGime96Ccf8UB2T6mWNjjv5AmP9375rvLn86pn75vfnqOuhy2xGnDQpGYBiGrLS995M1Lewe1UPwUMB8+WMklcMKW1GsvmcLCm2aUYpStjsoCZB93uAFaUbQFSkdgqa39X8Quaacfrea0NskENtPsDsycO9htJyvqLnWTkjYUDqdDUzn2nJDR2CADZSacEDWp3Knr8Zlf1YaguFsoJJa0kxJDUpRnPO2A3OUOQIqjm1NpNd52bJ7n3GMHP15NTrAaT2j+V31SrQ4fbmQLKW2Hx5vZAUHMKKQcK0fB10eT3sxqwmiAbpk+UQCUKL+X5IBZat0OjNVt2BdK7BglbB/fvJzIVt7EvJqAzkwuBg4fECT5VzQSmMlBilVBG+0oDz+YzH8wVrLohpkJymXHEpBXOuyMuKcRilInK2ytyWMA+wBj1Yiob0mtzUVnLZWF0IxljYa5/1q+C9xND3kFedzTCjVmwQZphlDPK64u54AHPFsswoecX59IiHhzd4/vwZcs4ioKam6UpSuYZT3pK63vP4wBkXvoLC4NRse5b7xdBMIGREgnpDmtnedw/Yq8j2Op0sZ2ab2zpT4Y37N34tvmp268sNJbF91Za7qRCAJicDxrSMSOwjDLF56EaC1V8eTydcLpfNPT0yRK9FgYTAeEi7mas685oTFY2QDCCHiBJae6uzt8ycu6MzpfUoFj0iRtNOmhZG9k7VYt1/2TOTVoHRx2VDXJRI7ke2a3773DXVO0T9DyaAiAYi/rr+Ib2AdN1/W8H9Gm3v6vq1H8c2NFstbtN+XT8eKQgPgpAm6IrkDjbMd5YJQ1uhon82ex6aXgcz/Hbjrvebj8Ua6Gu2awMDjoJhkdsyzbpfNYKw2j4NCrhbWQpEMjAOAyoFHJYV62UG5QWlFKQxInJASn3UnmrflTfry/IFo0Um+sRs51RYdxs34PZ6ettBuxX4tim28TGrRrB9oudjjBg0pSWvK5Z1xWU+azUIqQhRaxUIKRtzbuP+ZY8Pn3FdHW+hVnsmxd3JPfPqnmST5gqKE4tmXGwaVLe8OhOg0Y3eVt8T/nbd1tRI3bO23dg2uNfkzJZMu1IPXY8A6ggMQRmDbnyV0O2uHVnafu6eUWvFD37wA3z22WdYl0VOUzdOhBumMt6EzQpCQYvMM7MhIMm0kazelknNXf8ro0gpVj+/Z0CNQW2Zk2mC/fmgkvp2pMkDCpiEmPXlQQhoILncpEw3t7XF4Sau/gWbWTbzHWEbiMG9faD5y0gJKbwNO4ba98IYgf3mZjgG+jVI+zXEfj+I2vv0nVKJul0jCdt6r5oIufd9bE0I/ud7hasrgqYNm5nKItY8dcF4ugtIUoYDqrFV3U+9lmJgzU2sk3mNQZEiGBINB1nFa16R5xkhJgzDKOVkiFCqaNp393c4UMCby4yHy4xSCpZlUa1jwHGY4HmUXKX4JFU0nVT2SIoRIRpxb+bWXv4QRtKCNG4zgc9LWd6IVFdnTX9nFtOlBGUImr6bZitjTAOe3t/j9evXyOuC00kQc3LOiHFFrSMo8DtSVb748fuCcRG++ki0yLpbGkZHofcCvt9/zYBa2PuOifUErJd29YNbXDZ8gvc3t+d2wliDdtqQN5dH7VX7ERN+xa4LfP6Y9hKbNDYvM+bLGY8Pj1r5VRzbpWQxESojCmBEyAY9TCMO04hxSM0cwYwQTbqTNwVnfHDt7LqF5lchQCv8BkDR4oM78EP3Pca0CRbZPHgjlCr50ihKl+I3zLEbHfeZdDuVGxN/66q1nR2s/hYEiSJ0v1G3DplhiB6mCXoNLcZmvbR+wNskj7COquZmV4YAi0mTeSz+vEjRPwtuZL824cTZmCFUc7Y21dKFoKvgAAfEtcAM7q5pTKmyaD8yjwnjGF142prObb1zawcaoW9FNNucEbOWS1E/VKlaZkrWyvlyAueCXARxP40T7u6f4HHJKKVi5YIQI0JKSMOAcZowLytSBaZDxGGafH+vpaCGBAYkQlHnLCSSOmFF8rzO5+VqP5PuB+r2/1aH+nKHLf++UnigNl6sEFnjMGAYE2qWMPdxHBEjYV1XsbpQG/ltu9XMv6+r8gWPD5tx0e7fL3j43sdthPg+WspMhWw7CPDPpgU1SbEtpj5iirt/+yltm8021va3m23fSOrKxIgFl9CkfWvDhpk1NgbrlwOiiuYlDLCLSvS79Og1HScIjPsnTzAdDs2GrddKNFZwJAMZHpHAYjQm0mlC6BKJ0aQ0Mw/6edcA4BK3MT73z20YlcqQ+i91kWj9eug72fMz0wIapbDoML3foietnU442/Nt4q5MbPZWM6+5ZtqYlSFgOIky7bhr//Xi4I6J0PZl3VgJL9W59/52SBW4Zd68Ic9To0mbaEFfR+3/V4e+wFeqLWFbF9xWsJFVN6XuxhG2X7tHExE6vBDvK3w9yTlBmu8LjchFpmGWXJHLjGGSc+M44ZwrllKwaCkZs3yILy978UqzpBiSBlcWbVDXteyJ6GbDGKNiMZpA2TA1xeLfOy62I9uff/9j22+jbW3Ja0HOGDBiUK04aP7WAYFI/HJWVNKjKKmTmWzeviThxgfOuOjGl88fi2vGsfnIzVJuNm4xFXK7di9B97ull3B9A9vkt+tdQ+vNgL0GtmnyjlzcvEaf2zEvH5P9cu6DSgjNrOiEq/3eiHo/SMYyTOsTSflrX/8GPvr4EwQFNzUGmlJCSklDiTXVkgz9grxarUcMUvu3Uz78s3YKZqcXem6bQVpWq+R7EQn2XkDTgoyINYT3jpz69LEwkG7cJDahajsahp0fNmzOM42pdP4ZvSYYmyV9F+mPxrRC3EadWEKxCRRVNfNGDzYMYq8NWbQaiBA4bNYm1HRJ0BBxQ8YwXmdCQbfuwbURON7tOx8HY5ryr4+Hjn0zLcjLbB1thjSQVFs25mWCiIagi4nZjG32enJhro9GDM705SrWhgfXMZRM2ziSmoKLDLSYkyNKWXBZFhwogkLEOE6gy4x8mXGeZyzrqtofnNnVyshLRsnGvKRy9VoW1FIQhsEjCYch+dhZbTnLPQTUgkBBcQ+lt2SyTDcOX+bYCyaA+f1kDFJKWJYFAGPUABLbD0+e3IMgwSy1Vt3TEj3cUkB6wfPHlHEBewn58wZiP7PsA+8bnjb6SLvvpixz4/H9X6eNtGsacdmw0M6U2AhZe1v/Zxfeilb0lKW+P53WdHuI9CblaVv5/W1jas+U9+ec8cmnn+Cb3/wGvv71r+H1yxfOuKZpcuYk5pnqdZYk/JYdMd5ptb7ZrpF6XK1FRueFeO1lxNbuRlC7rcLYjB1tL2zP4MauBZ3cqLg0sAkQ1GSZ/m1agn6jPjCAsJV/qrU1BFBKcO0qRmVgweexxyQkCFOm2JKBNyH4dSdzW8SmQ0Bt57GNVC+tc9d+CbGX9dZjHYrm4Ncyd5ag9twQDDOxAiu7v4u5KpSXhllvJQhfB6xEnJk3ycvcmR5lbmSPVasJZU8hqy/VBdxYL0gYVwhyba0MVrR50gCTumpIPxFyznj18hUOd3cYj0cMKUkZj5wRowhpKVWEXFTrEqEnUkClgBqEsI9DlBIqwwCTAIYUsazZTZnjIObE4kIfNOF/F435DubVrYLPFe6Vkrim5DXNwAiK7whlUNPhIJiNeZXxi9Kfy+WCJ6Uz3W6Em/eh1e8+PnDG9dUHoD827Mg2tZsKTbxtGktHupsGtWMxfJPBoDEW/dzRkM3Cc+mv+9v6zJxL6nOaqbDrgj5XtTF7uptfdFmb9uU5Ol2j0G9ylUytjxDiIyjcSdHFocSsSeu9GVAQuRVrMLQcGmKz24vE20LQ0Qhap4l5u4g7SU7NKaG9c7tpPmcV9OZWG2M3L5KC9Lb5aUEYVlCxhVmzc+HuWd4O8lHsTZegTsV02YnbfPBOYrVCieBt7wiiqQQCODia/MafxwSUoi3QxwXVPqiZjgHrZ9CxvsH8bA8oE2vrh7ZjyTfMjr62RVPZVvLW56uSZhJ+M8t3FpRun9n7GsHuk6SbUMo6hrW2vVU0jyvEJNGGVX4PQWp1hRAxLxJFh5hAIfr6N0uNJd7HEDUvrCKl6AJXZkagikgSqFGi1PxuZXgIgVgYYanw+mTa7kBAVYGOocwRvKEtFgxi07099uytjUe7o81bVcQQrhUZwGWZNWgjYEwj8rr4PFsOHowedYL01vzw5Y4PnHF9xUOpaiMeSohUMzEGI/vbxBkj8m1CefdI+7cPcd+up1467e+9JSt1v/cPR08E7V1CaPxz30l/vhwe3eSMqDEt1t291URN9uVdm9thJkfTNvv+2xI1c12MYevfCn17bR6MSDUzoJ0jan3on21bbR+QsRmnK5HTmIi932TObqyU+LbClFuorL0QxWjmFUMeN3RDq3O096EIwzKmpZ9tVDpsN2OgMubQ0HAdub5vak1gGOPvAj46hs61IyZk86AUESJACBRR8DloBEhMqAaOC9USNmZwfZH5fnuZDd3Iq7Sjn/b7YLs3+m5WD5hp/e4ZXv9/a47tb2NUlduccWWUnFGYtf5bAzC2Ol8xJZSLmgUpgNKgALMawG7jQZakD3Ax5HhhfLxmBMqoZlKLsTFmIq1NIjXoYlGTYTHGz7DoV1lm+m+1dedLZ6ttoXV+s3e7UbbrLUTLRtIYF+n8zEuV0iXpgHEYUPKq88BdHb5uXrrnfVV94/cl42q+hf7YsYUr/tCmtulStP25f+SOAF5pVhutqGNo3P9112njZK75qn02+dtNb0S2fxa5xGdjsQ2waLBVrsG4fZB9sfaE7NbhfetCoOd1weUy43K5CJFWppNi8mcSxPRxGEccp4MgZhge4YbRVQSKnqgZqC37ToHxXdYkVPiGNrTv/XHFZq662O34ttWVn7Q7N8KjaRZxO79B86ugkrRz3S4twGpK9X4ANxf6dZ3vxxeDfq/cMcVtZ3qfkQdoWMOdkVhCqPBKh9YK1OyzzFuUeWVSMv+lwz/cMS1tk8l91a7thr2JCreY1f5zV9fL95i8TxhQt3uNAXSjJ/MhQkHWIIpSGKUWZVxCoJdlwVoKUpF7rYqx+EwTxnHEMCyY1xWny4zheAcaBhwOB0yHCcO8IFeWxGMOYF6xrgIwPcQIigFpXpBLxpozSs7N11wZ45gg1hPCuhbUHJE1t8v8wqOC11YIQy21Ys0Feak+ti24zEaljWa/hfZHL8pB+297+ng8AgDWdcHlcvG9tK4CgVVyxYuXL/H0+UeYDkcc7u4RYi9AfkWuhd9njMsnpeNQV/ypY18yoe2UCi7djeySomw8lTVcZGsmvn7jbaIKjRvdYGh9+7j79O7DzDAdU1TJzt7tfhv9vmFeXX8ak2D9PzXkDGrPdune29C+i+1bUB4kcioj5xW1FH9uQ42Qw8wtKSV13nawSzoRHjdG2Gpj3YOMGG52IJlmFPwZ/lgdJ3YNqTvIAjyCv9c1GyJnANUqw+rgu3/FeF1oyBuV2fOrGlyjMS9rgmpDRipsUioDoUOj2Es/u4P8mS2CS9a3rtkmqXQct8nivSnVfGlUGRS7MHtq6xh95Ki936UIE0K6hGNAGJydZzMLNgxKgK/kJN59Y96edWFCzXy11oZYBYJoi3Z3hQNC2X5BJ7SxoERUrgqkW1DqRXyVDKwaGs8s5j8rQFkgkEeUixSLrOwFR0MpIFJTrO1DElPnNE0YLxesOWNdV18bXAnTeABCQGVCrYtH5QYipBgEbWYcUGpwjSsrygZR2xbqpfJkZZNV9yNLkPAU0/HtXUQASgv+KKXgfLn4bxUszHddMU2jyjcRJVvyMStazg5y7Csyrw+acb2963t15dYVHcfC3lQI3/iyDzuC0jMwe1bPc/ov+r29oWsI9/ezM7/W3I5g9d+xeYT/bozTIsN6Uyaw7cNWC4OH9VvXGpJBO0xbM2Jgz+6lZFaTSjOTGAdEN1nyJagGYua85o+SPu/D442+bOG5qI2z0+TOnLcbp579OvPqrutRGgjGDKg9w94V2rxYkIQ9OiqklfXbnmNhw32koREzfXnTVBhwf5kxGBd+bBkYE+/HqEvuJmMmKsj0jGs/t3vBxLQpACDNIdPzPrc9cgX3E2RMDkBt0ZSNYRnyu3y2xOQWotL6Zo/p/VdtZapZvBdO0HxkRml9dzDACNulCBNO4PPlz2eAaxV4Jw3yK7nouhEkfl+/DCyXWcZHcQnF9Cdzzmxms269sjCvYRgwrBnL+ewRpUTAkMRvVhnI0XLWLG0kOGIFFYuqJHBkhGK5kHv5aCvE7T9S/9fvA2rrgyF+u3VdEUOQ6Ec0E+Ld8aDrKaAUqeQgVodeiKTtBHzJ44NmXLZf/Dtd/dppHnCiwLuJo+76njT6+W4j7Ed9z07M59WYFfZXwNhMI0ztp9va2FuORhUaYcNOw3KGc82wADjEk692r4nSGLV/7ImBnguBUDsfSYzRUQUIIn3lUpCC4b4xci7ImtOSgmheQxoQIcCdBEZKETEKxBOrxOheKOYWxdY1XSrFmimoMe6mVzitV0k9bGZT2ttMi20ojOjqeW27mTZ9eVALGGCG5vMoHwAQtCKxJEfvhRA9Uwo8kg1ozN+1pG5VeKe6a6QTWwZm8+Vmv27NVCV6XH0Oyf+3/Ze7e7hYpWXa3kOhWRQDSzkRu6cW1FIc2UMc+KZxOQZE6992hACwE1IzAde6reXVCxT2tJ3e548jBJDmOMYUYHhPCUBKEblIgm3JRdKwmWFZ9JHEV0WVQaqJrurvGocRBwAcCOd5xbKs6vOpCGFArQV5Fi1sGAY8fRKw5IxlXUEAhmHAYZqk5lepWHNGWhQRptNMh2EQfMUs5sY4jM7cSjeXkfqgju2I0u4P2s+s4MAEKEKMjZeY4dOQME0ToHO4LIszKACYlxk5r+BapAI6mk/X1/VXOD5oxnV1XM1Mr+1cMxkzf/SEeGex6hWsbt3b8/p8po7pdJKpnW+fO37TtecWc9pIwH0ftQ2MG31SCbudEwT1W8wLpBF8Lgg2KVbev2XU3lbT8jZ1kwQhflkWXM5nD7iIiuKQS9EnSQTVmAYMmrsilh7R1igGpBQxDgPMSmWuFiJB4tiMi/XVBYvOjCrJLzICnYblcEQgWCHHPuqt+WS2pl3TloyZkTE0f67BLskY11oAIgTHKycwBQmpvponab6hozuEk5mPbL5UM/Cwdx0HN7cRCdKG39eY/YaD9zW59DP7mFZ5lydCtzkX3hQ8mdY0SRfYuLgGY6HrPVMiiK9E/GKNcXF3TW+doNBAi2unNYZgyCjWBos0NQim3dogAsUmqAjqhAg7sJw4NHMjSMzZ0PIlDrLBGs8ZRUhbLzMeTyfRjvKKgorpeEQqFSlKZe9xmlRYyTpFui+6eb07HDEMo64rWbspCRNkkqTeeZ5FfqkFNRcMKWLu1m0gQgoSTs9LVrQZmd6Ga7gjZTanEMBrEzIMj7CtMXJtaxwGt64cxtHlpXmeMQwDQpBqyA8PD7i7f8Dz0wmH450mIwevl/ZVmNeHz7huUfz+J37LBdzMg26TQCehEzTyXQgF9WHwLl5yE3yNWKJjYGhMszE8IyTXre0X0o3mbvkX9z+0312pJ2q/U2dCbC13yacRZiER8DEwbaxvbMfZ/fXyeV0zlnlWyJeGWNH3UAIogoYOqyTGDa5HiE9QUM4uT2VnMjRhAWw+uI7J9Aytr1RN7jnbjbw8p0dFQffZtNiecTWpoWVEG4CwTXV/T1PzhCHsE229j30Ccj+H/ejr87ibg7aOWYgk2bw1hrTZD8aslAHT7j1qM9pKcoauDjiBAwDaB4Bo31kiPRrz6j7z5s9Cp7eCh2v+3J9XdtU2QPvrbuufs5m3NuM7KdUEy+pReSbQCDNTbHrW/LkgARG1VqzrChrFD2ZFU62pBNKcLhm5kquvi2EckUtBzcIEnTqYEKb7Iep+cA1IBYZwy2JAhCFGlFD8PWy2f5/ejvbZfZ02xIStz9mWLrZAySYwRE09KaV4bbKSM87ns+MWSlCJ5f51pvgveXz4jMuP7aK/ZlfOYVymA3RPEDZ5Bi6c6sYRGqaLqVPBunXQHtZpXeg+bhvVbbb+dyPGn9PLt/XdWINzMmw1rLd9di0CXX+hdG+/uEgWnlNnfUaMEZfzCW/evMGrV6+1TIMyJzapTloXUxSHLUgL8gGZGIkAQhSQUU36bONIGzGxJ7SbCEeSP/E9iTYVaac7KnGpYKAWj/arbGm1aJqd/mblWMzcZuHfPU+3cHNSJpbCKMEaWuaFNBTaxtzaYvdSNB9H6PAGO2bc3ddQLFqfmsTVLQubo66wpx97aaiXdXywbWyrm0gBAJoELQyxwSFR/7wOWLfWArBUyjXToGlbtViV410eGqA5ZvJrNZMgiblRErzFVCj12vr918+L+RI7k69ppdytH2VapRTUKjXASKUUsSgwCheEODQAXxY8Q+TizO1yWbCsYr7LJSOQYPuNw4jXrx+UiEc8ffoUD4+PYmJULUQgpEYQICH5paCYRo1O44aE2dv3oGVnAknUbim5DQURKt2mLb0YV0tpFubQlo4jligjD5omUUrBmjNoSAgqTBi+IgA8PLzB4XBEsaKbgCDka2TnHzCu3bGN6jMJRbdGL3l0X+yUmc4aoaNGFLY/bO7bP9Y1kR2z3NAKO7Ft/Y1PPSVqbLf3p3l3YBtSrzFzoD+K/Ekm0BK4XUetjzYypq15K3Tcqkn4Pa82gk4NLSA44QeoVq/BlZJBwYikSprXFdXxDRtDZVpbUtoE0/34baVxu5a6ulNShtKEFu40i77PPvLKVK6PxiTIxFL9NziTUnNQi+kXIrwp8WG/Sdsa9dj3j9of3bzgWtDoB+wtYk/XURhVZLONqUa75YXsxJ+tUrBh0oV2Jatfy4Iy9n+S/N40LiJsYLb2rU2qlUg3CU2D84bpjewjZe0VGKs2r4wiCBa1s3QEQtBEbl/HQWCOGAVrWVFZEOPzmsEAxumA+6eMH71+A46iJV/mCzJLlN88L2AWX29KA6ZpRFF8wjdv3gAgjOOEwZKZTRiojMLC+BAiADEdiiVCepFi1B5WNQmKiTPGKH4tnaqoZmwKLSnaogd75tGqLrNHTAYKWJdZx0/GYhwn+S2w557VWjGNo49vSgmX8wUPD2+wzBdM0wEcIkoREOKvenzgjKvblW8jYFebti1u50FGgI3I4Jqo+8bYmJBsE2x9WX27mt8L3W97fnWLoJiOcrvbTUrsNllXY6vjMo0oc28C1G2skpid7yX3phRsAz6MIbtJRNmLVZpt7TQ/TtNmyAjkDWErUIucAsw8YZFNNioEZyy0Hbt+rFxSdM5CzRTnEri9hDbalM+Aaj+9dGj9BWjHI9Qsgz4ii1w26S9135YSRmN45EyrD2nnpuHaQBDJ/HBvEvZmtP7Y/HD7qW+vreWeT3r7NDrPcsx8DH0M0Myg3V9blwCh+UBds9T/tXHcHk5Me+GjG78+F8g0NDLmyqR1LaWdVmqStU+VGSFUWOHLzX/G5ch2r7WZFGMTYF6w5AJGBSIDWtYkKEyX7AFGqYK8YbWoWlCD+KzWnLHmgmVZ1UQogl0MErCRYhKzpGP+hQ6QmpwM9dGvgagrKdPPY5s3uZbdkrKJ2t0sITkjkZEBtURkGy+3EJCaCaNoo7mAB8DcEpYes8wLHh4e8OTJs9Yuzf/7KscHzrjgi9K/X/Ex3l1thEOJqkYEOfo7mqRmhEe+6IQZL3TTw3V79u1y7csv4HZNf6/v7z1V76nSjdP9w0jfuyFobbFto+yMAO/8XN3tV3JBp5G6cADAaiSVWpt2xCrhkaKME22e20KECYSAFKMjXgCa7Kk1nZyBdcxFZOO6IWw+Ska8lVlZMrL825gYqEFQoWOUgUhzsoImEFuj974pUh6g2hJUoOmEBemn+Sf2a9aYZvD7lep28+2USvsVmhn71tLoOGpbIr1kbZ+DvqeD5GEAHjCxJVKeqxUqiAOqz0drJxfWOemDLCyMibzdrW0tUrBv28ZqcNXuremXYEgTAaxmOKiGBbZAe93vFo1KHVq+rn+v572RBaR8SVBB6HJZUABQHBAnIHMFgjCvXCvWWqVIdK4KqAtlHPLfNB3AmJFzxbpkcJIxjCGA4+D5XfMi7zEBLITgJnRDSmkmPPl95QzdWJKPZpBrveDSLSUD8LXONrgoOWfo9MMgkZC1GPSVhrmTIoisiyRRWyQiQXLQSsGyzHjx2Wf4xje+iaCCmZhid0LuFzw+eMZlRy8N9D4BIxJN8Nuyi2YdM4mw+bh8lsHYaDH9jV0LTDrcvWL7PnTC5DuOvcWnf37fZ++Y9YF3EitR976mOfVRdHKZ3UON+dn5z2mnba7Hx0fMmklfahXCVgWfrayCrZbGAaaJWVFAkJhABi1U5/SRAsQ4pHA7rHWCmkgJKwZoxItoxwDapW8Z9y3oqvB5Yzz71Ajr8PUzuhUjZ0zT9GcYEzXJuGMkodO2Nurlbn2ZVvPuGbnxvesFdYxWCbkLN7pwKSiSyU4q9iAVEDgGRZlnZaYQgkkF4m6yyD4dT2sH921q7TJYL2VbbT7226wbZcsT6v/s6U3oNF9lACClRYjFJ0tRyB/X6qU4SskoNQMExEgoRSwJBGCcJhwOBedlxWm+gGpFDQEFQAGLX6sIEO9aNOVDtalagXleMAyjr2F53gHDIIjr8zIDRDgcDuI7sjGo4ntKGoHbI+VL1yIOxyP4fJbEX2UuzQy6pRvGyHIpCFVRZkgYTV/1YFkWZM3ZmsYJzIySVzcBAxEpJqlgToIcHShiGASzca4zuBS8fvUSl9MJ4zhinI6u/db3IYRvOT5oxrXpt89Nz5T6CdsyK2cuhO0mUQKxlQbEpAYDqN0QfXLTTzN/9Ixq18ibn7s3dZvUdJENBd69osm0ylztszMiZVbe2dtIGv7yDSPr6cx1exszEKJxenyUiELoZmdtP0eYOdU0HgPBFTOIlTXpamZ1o0c61oRefmjytptN3AzYSe5XhhD9jdpn1yQAkLbFo/u6drTh31JSUubXMzUGPOqrmVhN06NNMJAA19J2XTEEEt9bvu01+AZRohs95m3bvf/diHg4uq0XW3mhi1wEC0Cz40Vun2F9MSR1eS5AVH3u92u+sdRm0txDdL1NcOrN1pto0tpMg0YgxYwoIyNYhIIK4h4zvbfWKsxwo9GoFsni9xmnCYUICwNLFaiozBKgYfPEFjIPEUgMQcKYSVZNLKaAcUySj1Urxm6d2fsNhSVA1xNplCAEsUUSfQv6VSnFL7kb96sFsBFn7IXtndHXrSDFEEaNFkQtbb4gWh1BYd10X9Uq4MGsYz1fzjifTjgcj5imww0x74sfHzTjAgDjGNx97f9tkiTgdhr9bKHA/tnoDnfE3nmATnWvmUA3zq45fl4343bP9hzn5qp6jy53/fHumI+rQ2Aggqa+wgIsbh1b5qXdtJVsHegl/c48aEyLa8Xj4wMu5/OGkGydv61wJJnzN1g4rYb7qobrU9WF7QnxbIKGPBOec+JF63qJ9EoBMQbVm+jIiYRXSVYHMm+EmF4Q6ucDTqR8DIkkSpABqlWeY+epD5jptCyiTX6WY9fZW2i35U1jancIoe3vuqJQPYnT9VJtvrRsRmgMFp3f0pkXjD32668RPgk2ge8TYXjX7bCnCKGT/glc0raPNmfm++T+hbo27bwzrQ4Cyuu6ESF78nNFYPF3ucam0Y+mEbufkrNXQR7GAQWEoQLzZUYpjNVM5CCE2Ps5AwIxlpwl6V5hkOxIUTSWlBJCWCWPC+jC8UmDH1gqD+sYsO7LUopCK2WFWJMBsOKUTqYY2xm38VCaR/rcqpqT7cNSiof3W2oLp9SexJJkn2JEGgY5XYWhVhWSa62YLxecHh9xd3+Pp8+e+Ts/n9K9/fjAGZe5VuVzI6j2uRFA92v1Epp+twkHgOsvPaX0ndL9a01h/3NtrmdSu3Z//nGlet3oefdemElwG1XYa4+EljDtzv+NhqWhAPvzN45+DCWXZcEPfvBDvHz1Uisgt9wghkQfpSDI2gbr1LQR9npbBBYkIAI4wAt49kNgcxk6Qm4mjh4R3kLYCVpM0nxcVoRwH3yhYboSHt9L/k3C9NEIDbnd71eCYKVMikn//UR6yHxoD3OkC9pcSxYSb2u7w0i8FpmsnSZsNQ1SBLC60erks4xDAAQBQf2TLfEZIIgGZQ513vbGhR7mDkmDyBlcY6yqWdq+IUGtYGXOVigxBNFwbNQ21oBeyNC6T1UDIVTUB3ND5jCBtJba7oGiveeMVTXlAqh/triftlXHhqLFFyyZsRSgMGtx1IK1rii1gELANAxAiChswkEGg3A6nbFaErPtsUC4jwmlVvAiiBlpGMAAlnVBTGKlQIHWulJ/U5R0EQpS8yorOgsApDSAmVBKVX2vwdfFoJWUwVd7WiIek+Ivkid3E4KbJSUXLeE4TTifT14TrpaCOI64u7sD14LL+Yy8ZoxDksKYzEApeP36JQ7HAz792tcEqorKTQDs9z0+cMZ149hoIUC/gOVctyn8Zz1HgEXTbRQQ51PsoKWAqexG8Dfy53Wzbp692YFGXKwBt7/cuLXrlzHXqv3RFgihaT/f0rbA1GgkWh83zdANbaY9ZsblfMIyz107mlQWqSUjs465O5ZVyrMKyL3gQJCaRGQv5i2yuPAYUvOj4R5uS5qYczsoIoUzLOoQ5Mn8LH3JkSaIiIBvomJnXnTNTjmvaRHUr4tW8FGIb+yQ4rdTDxUcYLlcgEYP1u3s24K6tSSIhODEKJpdFWFgy7Q64YsADgLF6kZl6yfrPBLB1V07160H2ULdytM1Z2Hovmxs3NhSy5ppSoazWTq4X8+d2crTF8zSQt08aO0xYtHiAjdTZUBAVJzJonlpbAE1rnEJfqKYaTvNnYE1Zyy5IrMwO+0GKksZjzEOEi4eE3KWvK9QKlJKijqfUSsjpohhEKZFRRKFp2lSMGahVTEuqmyK4BFiAgXR+CykfV1XQLWhaRhQWfZScZQaW43wqEQKhLwWFNOubf932he68bZilaUMGNOAu7s75LyiZEHJSVrV3AQEo6WlFN0OAbVmnB4fcD49Iq8rEiljfItQ/D7H7x/G5ZqVf23aj13Cm8vdCuHWiH5D2j7VRXvt49LvO9PMvkGbX29/dAbas5z9lCodeevvbFqU90uZa09M7FnOrOTsxue1aUUbNG8vGTtRwkLNMb8sC9Z1dbNrm5Nt5JjJoyLty190oF3RO9xX1V7qYc/9RG5Ng40p7RmXRQi61mXo8RvzYh8qv/ddbmYCxrRcg7IJ0hwxgyhiPdfgk6IzSmd4ZqpSlP3+N1+fRiU3jWkLgtAEBR0YbZuYkGs3q17c0hyGJqUEraVlEZY9c0Obvz1juTpE7QQRKdpEa6qtHkNZZzYNTy8RFQdtdbYNbXva1uWmrhrBsUp7ghhCUMKq3zVRt7DiLZI8xxKl3VSr8wFEn+6qiPGZgUzaB21fzQV1EEtDSkmT72U9pTRIpGER1PkYA9KQBPooS3sPh4No+1WvCQEcZb1HTkjD4BF+OReFSJOk5UgBKQ1OIOISVVht1hMK0Bp4EXnVatOAQ2qJObCtRxdQmB1jkpkxpAFDEsindW0m0JwFM9E07Kr9NPPuuiwe7BHS0ISjL3l82IyrH1yVjgF0psJ2mf7gRKKZEtknFzCibpsI2H3BtYi8HX3u/r25rd+y19910P6LEbW3PNqZCto6RLeI26rZMgBjtPvxuOK4+rs5v1mp0LouIo2pc9vrBo0jYpLNVDUBeRwSxhAQSQpNBAAR8j0EVnzCbY5Ro30aPqCBHVaEL6gp0piTIbUTifPYmVaIrpkBzTxIXjtLX1OBhqHXxh1uhNE5MNNaCI6S4UEAxjTTIH6z2EU9+pjaeqRWpXhjRqF+IuXQIIgr5kHwRGdrdy90WI5TIxwEpKS46exajNwuEZ9gFi3EJuMtDKv5+DTloFbEYJF/rUAkQyICi5Y5cUxLvYIo+voiRAC9nw3otx13f/5TMCZPCGxJt4q8oRYCYkFVr8zI+mf+sBQjsgZSmL/JwtGBjJwzzlkSmC2Qo9SKx9MjzsuMe7VEpDQgxYJpGn1s5nVFTGJ2e/PwAEAiHMdxxOFwEB/SukpeFwNEFQMNmKYJwziKqY5nLBp1mEJQDW5wNJp1XTHE4AEjBPGnDUPCOIxYlhUGgrJX+k2gtL8YAvIqVpNlmTHPFxyPR6SU8PjwiJIzTqdH5HXBkKJXdSi1esK0mBmjCrcz0mH6alwLHzjjus0ceoa19WnZpvF9x9tlz4CjR/S2dTKThKphLc+X3osRfd4lWy2HN1J3i3i7NdHd7jUmC5M8G6FrjIi6y5vZ0DS0XlK1/teNqdEf1sZHseaymg6yhtR6wIUGY7BJiIG03HcU8xkkGD2gJSgHAFvS3pDZyZmWbaxOk4qNIUnWv9nsO9McTPuKDY9NfwsUu7EWpADTXIKZAW0+dIxDbNqUFSgECayxm/tCQEhRCLqasyxF42pG3V8Gl3xtVhoDMvOVMhotguh90fFxJQ0QhqlrxnKp7LcegLW9yxBTtLuqvZmU7qbBXsBRM3qtRZz0irZAej+b7wgADwOoivZDO/bTWz3kREdd3TxivpiGDt+0NHSfSLUuNTUqg4whaAQhazh8RuHqsE3Cq1kShQsjFxt3nZ9agRQRQ8QwCFr6YkjytYJCQkqENAyYlxUgRogRPM/IWnDx8eFRNTBhXKRMCASkIYEBlFW0r5QixnHA8XDwIA8KETlkWce1arv7cjht/8cQHK0mxYAag3QhyD6MUUqs2DCP46TBHxlg1ohfsVKYsDgMA87ns0c2xrujlnsJSDH4orLqD0TAPF9wT89kHd7A63zf44NmXHLcZgsbLesWe1PC4UTdJFG93mztvbnQfFzcURJ2E2J37vNa+05ORk6c4Ua5Jh1f61ktSdoYTEtK3QZsXGthcv/tBrHf253ZtxTM4hcoJWNdFqnkSqRRg0FLlVMDaNVNJBvBeIYxBN70r7HZ/fjIPc30Fz2knszPZVK/MjILGhAmpIXtAnk4r4Skd4DA7N4epZNmRuxYqgsV6Ahq185AXmPJRFtX4Nugds/qfjOmdPNiNEvffr2p5te0Q3Rzv2MA3u7uqbZf3JdbnSP0pjm16F3NCyvV5000YpszOQJCTOIHq+TCz3693T5uS+p89bvNnf1Zu7gJhYAjsVeNoJNKv1bhRLS1kqvCNPXPo2aIUc2EOaNUKfExTtHXqCTcsge9SDCTaG4WFHKZZ9zlLIILLLpV4KWGIWlwkzALA9t1Hy1ZIExLDm599ibCI2ctapS6qEIYhJTcM6Suajm1oK4QJN8yqgn2fDpBCsiSzjl1QMMVtera02kpmttmVpsve/w+YFzbw82GO/MW+5/85vtWf9wiEDjJ2jAF28+b+lVoTOv2xrth0vMNTP6uq6OjMb1i8I6eK2+lhgaNxqR6utozbIu6Y2XCtuBrNb9UpxnY2HTPtnpB8yzlHeZ5dp0lBKm1ZSAQBEO6jmoaavAwIYiExpXdrXA9JrZR4abAmNImlD50/iSLKgyKQu8hynqeVbNSlUIZa+//6wdeNbUYdV3o5lQHNoWAWBtCiHBoCwZpyOEgRnAmaQPZCJFNUCtT0hN8+D2SKySzblFgxrRItTy7l0GI1MSAPkDJ9bBewKud1K611riWBvzr09FFMWq7KVggCNxy4NJciDreGgai/qVaWnThTRZEaKkBZMIfawFFNNO/CWb2f1/PQaIVXcvcjacC/tZaUSo6xqWsW82IWSsBR8UdXAAHkCUl5MuSUeoDnoXkZuPLPKNWYBxHpBilunIRc2Bl2QPzPCPnrBUT5DAmOE2jI8SbL0uAeqtHAYIZJWdFYi/qX9OqC7WtZoIixZCgfjQLcHBkd3ALfyeQBGgwo2ob7u/uQYFwPB7x8OZ1AyWuVUyuBOSVpfwJi++7lBW1ZluBbT98yeMDZ1y8+cS7L7qnXFpwKdYlQeFEzZQG17yajaR/T6ed7JE03quVG3LoYrMRwrdctf3u2gne/n5uv/HNy0zbtAAEC3qwcZCNDjTmtuNdYJbky3VdcD6d8Nlnn+GHP/whXr95I4gBVBEroZYg4KPKLVMaxDxBQRDYkxDV2GlcvYS4kRZpV3lWmZDnhXXRhKaFNa2raVl9KLvVvvKINMA3L2kdLR9DY24xgljQA/K6atKtQNc1BWybI1i5FTYMKXVEHYJULjfZzaK6VJUwHOSxXyO2PkmXqX5OCV6IqVsvbAEh+nzzzcg51lBy+Zdc8jbqLRBPTfrTNWQReH1RSSJQInkeFbm3rTqomqUFCJUpdMnPtw4POvLhZY3S62CDGLhKFmPLUWvg+JbjVbj5t2OMSDUBKACEmFcWLatY+KAS8LVUZLUeVGbM84yH0xmzBivUUpBLxbwsGCCgtJJ7JQ0QiKRVwWYJiRKYGZfLBefLxQWQdV2xLitqrRjSIAm+QaoeW9DRkBLAlseoPq0i2IZDSqLNESFzRs4r1pVwd3fE8TghBgKfzxIKr/6vwzTJWqkNuZ9ZQt5LraBAWJcVd3dHDMOANWeM44hlWWCBRSlFBALWZUatAetacbmcEVPCMB6wrCuKAwd/+eMDZ1xvPxqz0u/cn+9+o04D6S7cmtbM1t+bCrdmrXbsCcyt3/t/7St1H3szRDvXa2/NvGZP6y/unsvqrXhLWYPr1pFvbvPF2DNdY9V/xURYsCwL3rx+jcfTI+Z5djNsqRWhSNkQsvpBas6zCr+mafQRh64dgf03K0zp5kF0UXk2NsbcOtOcnN+GwAMaLo7ebKIk0fjZZmy74YQQYGl391z3b6GtOf2f92xvTiRq2nuvNXAzUTdEe2tcp+5a23wczKfWMa6gfaWooeJoC94eU6vcZ//aeQbEVEgwYFrvl4rqptEZ1mfXNHhCXjeOplldWUZ8A0LXrK1G/cmGwLXhnkF187RL2nZFbHO5jjHg5jOvh0WESkDRvlcNp69oOH1F/0hhjkqtmOcFpbRil6UWJGYPnECWMimWb0hBCoySluCZV4m6C1oKyIKkYoj+zlir5lsFDf5IIvzZ2tWkawt9tzGOVoFBLQIpRtQhIS5hM+ZDSgjBqpRnAditIhgW1ajWdQUA1wxTSsglI2vYfIwSxFLK6guh1oplXT3quJog9BWO37eMqx0d87Iz3DYOqfnGiFHvr+qtHL6R97zqBjPYX3KbBH7O4YSlEVWluW7e2/zuzK694yo/a9Om3vas+IV6rTGtwLt0AkAlMbFfF2Vc83zBZ5/9CA8PD5jnGSkJ4nUpFeAsDIfZQXYFoYLaJqam5dgYmQmRnA6TB2BE8zd5o/Q2kuRizxdz5ibmOmda9nwL7DDFp4P68Wu6KWsBK+zI2mkYZexDEOw7NnDXbuxdCkEj0KbhtoeLtmNUVp/ZGLDMFV1p+vbcIAEYKhSwgd+6Nhm7d0GLP2o7A+TdvRbYPRqqhTtjkkEQ+B9NJG4TASBrF7nDgTSBB73Fw+4IQJentvF9OIP2XQkwCzYemubTrBa9BtZ1GC1IS4a3BfkEFk1ImDcDFSi8IteMykW1O0au8rmUimXNSNMk65IIl8ss1a01wrUW0UiHYcDh7g58vuA8z6KB1YQYq6cflFKQTwKXRiHgcDhIgEMSbWxdV8SUEKOF2cvfqMEUojnKniQAIRpor/Sb1GTJak4MQSInU4jIWULjS2m+NDDjogwwUFBIKokANZOmFYYchwHrumiUoqQCTNOIWrLTzJQS8rpiXmYsy6zRm+8hRb/j+MAZ1zsYwY2Fe0v8agyrf+Tmy43v79eym1d39GtzIXcEUxnT1rdhlzetqmlfn9M89l52D2ob3BkWdBPAgi70jZ1JsdbqzlcKUKmv4vvf/77D9TBY82VEcjseJrA6oqsiMBCROJpJiRCz+KwCIUWJ1pMwaWFcpq1ZnpR316RN/WwanSfcqhbCILdyuU8tBJCG6TIzChhk9YrUl9ZMlx0jUaZHgRDNrIem9ZBqrHKNJB4HPS/ajmd3y1+pQCmoWlBQ/HIBYUhtcveT3TMZ0/aCMKimdZFqUGYafYuGA2r39+uJAQ+H79UaImFa1Qh9bX4x08T0/Vv0jwr1IMFrcgGgSCDoPNRWXQCAROKxhtOraQ+6HkVqZwBlq1H5Vrf3tuZDTd+evyecT/2lAwYKKExAuIAx4zyvWNeMeV1xWWbUMCCrsHbJGasmEHOtWIpAQ40HQq1nQbVwnyNhzSuGaURZCuZ1xjQddckQ4jDgfDmjMuNwOGCcJgDAOFWczvIsi9x1pBDo/tN9O02jlxaSfMoKsEQkAh1skwottoQkuAJY1xnMCZJmAsRhQBoSDheNMCwF87Lg8fERMQQc7+9w/+QeIQXM84xxiE4rhnGQNoCRxgHzMuNyOWNdV+Q1I9GPu6lwt6/353dWAv9uk+3ahsHPeJl7vZo6ZoHbStf+nUpF36F6NcKrZNBbaUTSrnPNw9Qt/10Jw4YR3ggE8Q9tFFy58WsYZmIhAAg2NhXMrUy6G3DsvSTS7rquePXqFaZpwjRNWJbZF3A1Dc3fvYvM6lEvXAMz8F1y3tO0gRYw40EQPRF3xkItQCPExuDYgH5VQ1FhQSw8Ww2wD5bYmgStDAqJhsV7JkCgiE2IvOdlufq+WxdKkJvpsJtAB2hsgo2bJZ05678xNGK5Y/RwQcRms2e623a5MHMl7MmeYPUTaoqxPKuaFKTPMPOj3ls7U6iUyRDmFZMEvIBMA7w2I5n/zIaHvAhJ0+TsYPbeNea1f55Db7F33+Y1pVHGjAIuS8aioLhCuDNmTbjNpWAtBWsuAj+Vxb9Fa9ZqySzJthRUS1txTy1waBgHibJTE2CpEmFo+wpAQwsxzUwZZdJcMEs1WZYZ4zi4CV+YleREMqqbCWMMyFkDZEJACLJ+ieBlSUIKGIaEEJO2Q8dI18fpfMYwDDje3yENIyZmDVqqLqymGEUzU7OvoIsUnM+XzdL+sseHz7iAz2VedmwVsN1CZ6ChY1zTlne+3uhWT7x6swoabdo2ruN0uxc2Pxdtz/Vmw81zyFVzeSJvpVB7Dd62YJpWKbB2beP0DLKZ4WRBr1V8XA8PDzgeD7hcDliWS4dYIEEcpATJ7PNmhgjUCtYZE2v+KMB8ED1dNSLlBJmar4UhDC1YrpaaFwE4A97kXYUALirla/CH+dTsLbYgLEKwz7ViJlBHnL09Ftln18a4kfp7BQYgsPrmake0m+VUZ8366Wgd5AyLTbvSNtpndu3OmJQNZNOOmvjUhK1miWDXSPs+itmX4a4Kw0K05ysTaMKhNUPDpC1HCGJecj+NBrpYG/x+E2J0SmRYJILRBatuXJvGZYKqDXjXNu8R+2oKJLlXUNPvZV5xWVZcaEUtQniXUrFWxsqMpUrAhueU1QKsKxAKcmVQuGCcDii6TxiCDRhS1FwtBor4q8pSYJGPVU1zpRSVj6riHUokoWAlSv6XIHqsSGoSLMoINwyPBR4qhoCiYx1jQClqvqcgyBcx6hiIadKii22xUox4PJ8xjCM+quw5aGlIKHlFXheUXDCORxdcDLkkl4LT6aQP+nH2cfmm/vxjT6z5rb/05+hzVKxdU249rdcG9he/hYvsmdY2eKERma029n5Hz8Bu9csST6s6n00DaZI6YFpos3mvuL+/w/Pnz7DMZzw+vO66zQIaCokcpEAYBkHOGFLEGCHSY4gO+OBScGj4hkK0uG+Aj63kjJn0SRJjEIOEykfVuKy7DMQ0yLOjMEhh1oIMXrPC6AQSdHcdAyaprUXMyqhMoek0PlsrboZUU18IHr0IIvBammnNbqIAxOS+m9ZP6taJBkIIfk/Tsuz9IQAh6W+G5qHaZjETXad5Kz6hx0sDnYZEbjiQhdE2ggsOpG0BACSASmOQpsEHUn7J8LDzUnC5XFDyAiKIdO9RZmImNaihnAsspN0CekDQ6AmHkW3mQbQt5fJpv8eo7S/J47N1oVogM2iZEWPCYRzw9P5egoxCQK0Frx4esFwWXHIGpySMlkgCFNYVy7wiJCDEhFIqcqn42t2d+I6I8Hg6qdlNAjqMCZ1qxTCMAum0Si5kZQH3tarKoRRM04RxHH09D2oqPJ/FzEiBMIyDANzqIp2mCaVk90sLUxMtMOeLwjMZ5qDW2qKENEgayd39HZZ1BTPwta9/DefTGed5xotXr5Cz1OsaYsK6zCilqCmyzUvOGQAhl4IHRQsxy8qXPT5sxoUmsfoO+7yx6FQjk+aIzGwi5gryDdqInW3i/eNd2/L2GJ6hv2arOVHfTnkf1HZnElIfbNFMhNfaVj/vV2vgilPvT7yLKZuUbDA4imdmo62mmJxXzPMMgPDp176G+Du/Lf2AAIC6pJWz5jJxy+MylGsUhy5yIuNzZJJwxzy1hRYc0WCcWnVjc+l4gBlt57NFqMkGcsLvpiOTyrsKyOo7kNBtI34kxRRd0JB3uSnPfU/KaNS8R7HpjEZxpV2k06Jav+BeCbPj1i7rNwcJRtmYCjXIgHvGBe00myZubwigIP/6eHdMp18LfaAIQTQCI/jWdQBAND8Vg6ElQjQkHR0WpGiXUZ/FsJyr7UI2E6uNQbdsu7HgbkP0gR0yx2Y0tG41Rm/rIQZCZXuOCDEUFCcwEKYhoR4mgJ/iNM9ID4+ob97gcV1QQMgIG9/kmg1IlpDr6prW4XDAMosZ3RiFwVvlvGIYJ1RmPD4+4kl4AkCAfU14q4DjFW60TD0vUYnBy6VIORYJYx/i4GvUohElJP8EIkHV6MsCxY7+SJCIVDuotWLS4pe5SMK11QOzUbfcLZDs9aJ0QJBIMs6XC+IwbnLWvujxQTOuDV0yovEuztWLX72pEFDfQi9V9vLlO5+6bZEhmd8qQGSLAT0J7ojl5jloRNDv6S4zQu40t/PLvcfhphNumpQTdiNGBGVeSqgN8kcJxLpmlw4/+ugjABZyC+SSdbhZIqzIEodb7a0YA6DgnZJ1j+Yv6vwhG26D1l9jWCb1976zPbPq52A/So0IWnSb+sc0gTiEqLEEorFsSGNPbK8+98nA1PnkDOUcjSFt7tfvYft89naSR7BxiBqQodepdte0MSX8oQueYBVCSNch6Yi4VtXtja4agplIXR9ji0RVhhwAcJT8Ldh8sgt+veYUVEuG3m9r19axbc+Gf9hAj+27mUqpki1W3S+M/VYw9mo7sP/dipi6wbcPHGFGCoTDkBDoiE+ePwMTcF5mPM4LSmVkrellZu48XxDioAII4zLPIAoYxwmvX79GGgaMHdHuUz4AYF4W3Ok+KqW0mlw6PlZipNZW0iQQKeiunBChy9Dd4Un/lSvGcVTmsmq/qycs94KF7RQDQWYWc+Xd8ShgvaV49ehaMgYPJiIsOQsogDNDVlpQscwLyl1BiF+e/XzQjMskwPc6uP3TmcKxWd12hp0vfI6lkLa/+p4XZtTC1nfX7fTELRGF39c0rEbAxPezRzRv9/qGvME3bx/bHl4zL0niNJw6G/NSM06nE3LJiCnh008+dQRoBtTEI88qqqFY0TwD33SJuGqyoyV7kkSrVWi0WmqbqnFqIRRA04IYio1oJdm7IWgEr/W0+W10sxOBOXTjGjywg2KSpOzeT9YzGXdidwykbwTbO6uHXN+cnxB1CAK8SBmU1FKnRaUk/6oJyv9Y5yikjnHZxFpDbjBaa2SARAz24fI2bn3QRK+VWT4BkyRThwgOtZnq7AFmDg6E4cBIim5ukalNo+3Q3GMD3HVtFy2wJ0D9gnaDhfVXQX7v96T1mXScXEcIAUMgFGaxqIIlCrYCZZnB64rAwN2Y8M1PPsI0DggUsBbG69MJy7wgUEKKAaWyJNnyBSkNGMYJP/rRD3E4HPHs+UdY1hVBQWdTGnTpVHz88cdY11VSSKLATtdakdcVd/f3al7MmtxPYgavDasxK6yU7E1D1CBQAUrJGqEbEWPCOE4AA8u4NqaovmhhhlWCTdRUKVMskFYPjw+i8QHgdZVoynnGsiz4+tc/QaxR/HFrRtFgK/MxG2LNPM/I64rxx5ZxATDyf0uq/tzDqRq/B5O69V57nUlwHcfoGQfdvs+kRJcWzYDT2X9v+bH6qMLrtqi8+XnMayONmtlBnxHkmYaaATJm38KVmRnDMILoHgDj4fVLCeGO5hfTZ4LAtSAEIKWgvq2EpMCeoYq0G4mQYmjA5lVMVhQIQUPUmSQAIFgxSlR1Hos0mijBgjla+H/HvnaCgF/hGl91gsYs+IukiAFNIe8iCjtmwf2ryDSFLsDDBphZ6kApnlCrhIwmiKkm4TlZOqeSRKzmwWCmx9DmjlmTjFVg0oCQDYQVhWYZtHcRac2vjlExCQPzRbRbc4TGfK1fPk/yHooDBFy3+B84yHs0nYBAoKhaWimm7Miq5F2AYdc0XyI31reXTLF2Yjvv9jBvrwkcph2ydo6lSgGxCFA1V4wh4fndURlXQXwRsJbXuFwW5AqUwkgxKQ5hRWUgFwEbjsOjmEhZoJyOxzvMy4yiCBQSgZdRc8GyLgCAGBNKLj6HPaOR1BTJwVqXRTSxwYTDJOj3pWJZF4zDiBDEjJk1iXhdVxggNQD1uQUMo0BMLWsWRhuCRCzqyF0uM8ZRNLeszLbWgnleALALFDmvIJKSLeCKEIWJmY/vxxxkt+kvskZNx+m5xYZ83DzexrTclLJX7rpz7V5qmomZWHrhFrS5136gzvHtmgVM6+pe2TGwFsBhe3HbEqBpTVd93Z/v6KrtX3tHg3xS6Jxa9RrWBS/1eUDkjmMrF2JaDUOk7KialoW5A3DcNBsSYVo6hprYz4WFaUHpbmcaEuLUUBCcEGvH7L+twND63+PcuZapDEjqNcFx8phIaT5p76zF3J6Fdv3VO3SQt9h69mybTLQ+hLCrnaVmUYqwcO7NAuk1C9jnbd/k+tgm3W4lkn7HKO9U0+jmNtdSbqwrbn1zkx4JoZLAlhahCgRQDI0DuSAmptomStmbdC25dusv9Hf1Q9onGruwsmlq84V5W/1fdEysStUCkQVkzRNAgwQ3fO3jj7DWijlnPMyLaHlVzG5rEfgnXhYwCDlnXC4Xf2cpotVAGZDtMwtRLxr2LgEk9cpESIAGQrBXbZ6myWuBGZZniRV1rmoEUCtHVTQQDTpxwU3nW96VgFXqa4UgqPWhNPQMy8PsNeq8ZqQUPJDF2k0w06MIscZwf4yxCrsFeYu5bNjXbWL9vu9ofMaYR8eE+gvZWE6DYqHuCXT7lvaDRWP1r3B6fP2MzbM7RuPMqOtoz7A2zIu7f1glZrBbvGyhN+SI6sRXFrVIeB9//DHevH6Fx4c3WNdFTBd6j2w6KYZXwSgsEngzuTFqLiiKWF2YHG2oFhsPVppSwSlhVMJoG8uQ6Ddkz6OkNO/IMBJ12EQT4AZqqwMsUZOKjA4zxzZG0bumgA50VzVn0RKVUHemLE+mRZvTNu5tUfj5zfx0ZNzWofn3gGay886hYQnm7BocXIW2oCTrgQV5sDw3C2q7aW2E4nN1tbeMYZVWkJEBiaw0XMTSyq+EOPiYS0K7CgIU1VcmGqDze93c5gsTvsKO4C6E0Gp8wZPkG9PrD88AcwLO3MaUAM0/lJIrQ4qIte2zQIQwJvzkt76F490d7u7v8OrxgmU9gUuRGlkhKOr7BYfDHXIuePPmDYZpAmmQg9WLq7XidD5pAn5AGEevpQUS39U4jhoUMQgeImVPSDa/8jhNUpokiCnR+riuGQwgpogRgvRSs2hJUdcmVwnCGIcB4zCKNqVFIomhAVrAmguYZxABeU36LhFgc84YhgkxRpzOj7oUBUIqKND0+XwRJH5NqP6yxwfNuDY+DzlzdU3b6m/77dYv/Uve8rl7btvKJoW/gysqATQToDvnu03m/is0M+GVv0u+XDXNzI3O+Lo8l6uOd59NkjbpU/4hMJvpTYkFGzQQVNLLjkE2DAPGcRRTYEqadyIJlTEljOOANI0iaZoUzlUkco5SlVYJEGJAARA0Os3qeqUhumRdGUgxCRyOFqr03hjhIisTLuPL1AgCLKxcTUFcqueM2Zg2EkdbYaIfeQIIwU1tzFImgyBBEqTCjDGkYJKqSdlkCgu3ZdDNrWsxzEAFAqt2FAJQWCMLu3VXStOAatM+XINEFyUI0lwbhjoZG8IHAAoJytk6pqVrRJkHijjnuRSQRpmx/iu3cGPgbh1qTEw0eWHoUvuuX29XmC/X4wItP1Kq4gnuNLZ+o2tbTA6xd1j6hwTSMGrNKDnjcp41QV6LIULWbWC59NOnTzEdDlhqwG//3/8//I8ffoYXj2fJhQoRJVjidVFU+Irj4YAwTQqNJpGDa17x9MlTqZbMFaXMsGrMNIwYxhGHg4S19/mRa85gZozT5FG1FrVrY2TXhxAw3d1jvlxU24qe71W0jh5D9vXpfMayrk0wqLKWCHAU+ZyzLQ0FE7YctJYvyCwAwk+fPsMwjkiDIILUr2AmBLCr1/cex2/8xm/gL/7Fv4jvfOc7ICL82q/92uZ3ZsY/+Af/AN/+9rdxPB7xsz/7s/jt3/7tzTWfffYZfuEXfgHPnj3DRx99hL/+1/+6x/d/+WNHzGl3/os+im6doM2pxi+bFtafk3+29xiha+eMULZ2E11/Vja2vY/au/eMDt33dotqC93re01h/7cxx/CWgFo4rcE4BRJbtlR77XpEFklI7gOz343IlMoOXGr4h2vOyCVjVamvZCWuaIET8P528r8Sdw/DrkbMNEeFG1hqrVrCvPb9NiuRfCZ7pv3rTL37rn+0mW/VrLywX23OF2szwTUTMw1SFPOQR8d1bevNccKUrE+1Pd/gl6r4jASaqajWw+6n2/y1xaBjZv3sglRMg2M0RqztKF62vfqatT/u3ud5bOhLhpgQIsQxl6ol7nkDaFu5mQoNvsuQ5Vt7+4Vt67Ru5xZwZtfPZ394rAkLA5OCk8UmXYg3Se2AMQXcHyZ845NP8I1PP8GnHz3HZKV80CwEplVKIEXWQIvGhAQirFkOTBt0xqPrQITERZ/RTIzGTJyakOUgakv0/pSSkxoB7G0wTaalSnkWqa3HxawDDeg6xhZdaogc5iuzSEIpJilWFmOoKSUcj0fpl87vlz2+MON6fHzEH/2jfxT/4l/8i5u//5N/8k/wK7/yK/jVX/1V/NZv/Rbu7+/x5/7cn3P7LgD8wi/8Av7rf/2v+Hf/7t/h3/7bf4vf+I3fwC/+4i9+qQ70fGLDJPofv+Sxv53ai7oFcn3DrXZsiOvnvaNnQMZ8dgRhwxzsXmphtS3E+zq4Y/9S1yqMV9l539s9M4MmLhb/sw3x9NlTHI936DUeEbRDY1qam8QAcpUSEQadsxiETilY14xlzVizwOfkoiXYyUqZGJgueRtNMjSGaFJiVelf/uqGmfUMrWk9u03VU1hW5qCmpGq+jNo2uGvSzgTsOtFK+ufUvMq57n43R3bMdTt1vZ9MNEUu1RmU+Vq4KPMqBVwziGvbJ51ZjAzmCk3bMsYtZ4Nr23AGoEypFJRldsZFRAip04LVgUmB2vxDEQt3glKpAl672Noq1aGVLBeoJRyreVDnud87viZsjg342E3HreCijepmb4WOySrz8oi7WkAQzTwSIxEwxYBvfPoRfuo738L/8ye+jWf394gEn2tbZ4BoGmvOuCwLzueL+7LMZxyTJaGTWnklsnBZFszzgvPjCZfzBcs8o2TxdUmF5NTyvZidaRkDs4KYXiySgIP6xIjIKzfXImM6X2aNcixS8VgT+lNsYL8Svi/9CiQ+r6SBVxLQMeJ4OODu7oigEY3CuICiydBf9vjCpsKf+7mfw8/93M/d/I2Z8c/+2T/D3/t7fw9/6S/9JQDAv/pX/wrf/OY38Wu/9mv4+Z//efy3//bf8Ou//uv4j//xP+JP/Ik/AQD45//8n+Mv/IW/gH/6T/8pvvOd73yB1tgG3J2lxiI+j3fd+r1pN02Lcuaxuef6G3f/b8yi13qaJrJ9o0ln1v6OOfafe4LT62q7QXBbPSCSfE+MXXq9lja3R0+87NbmTzAzkOSwBHz9a9/AyxcvcLi7w8vXr92zkDScvSpCQ6kVa8mIXS20uoqTO3BFBGNKEWOS+kOHcRTfVQwoCOAwgEMCKCJXRoBK3sQIqA7wCzO7coDlyrVhUiavshtpP0uV+6T2V5tfkYohxN/CvgmOw0ikYb/Urg9j0sg5IWKs4x6HBPMb3Rhy9IzJ1krLB1OMQiuAyQTULP4xm1LzGxKpCU4ZeJTw+RBTC6MHlNmZVljks60Nv0ZMgqxMiHNGni8o64IUJPqNrAwOaxSc7UMzV0IjQhOcIVftq2hae78HI6+rVxI4HCZfiZXZNQ5pZ9A8bFn1wnDEBOlz7/lQ25UfdFytXltUMze7yi1tKbW4xkIEjEPEECIKRfBa8J1PP8H98Q6lEv7P3/n/4vsvXuHVeQZXgqUyBCIPIZ/nWUzrw4Dz+eJguikqbqH6BEsuqtEEt3BUW6cxev00QeooyOo/M233cDgoY5Pij4+PjyhBUDjmywxUaGCF+KCnafIxBgjjOOEwTagsAisBSDHgMB1Q6+iaWG+dMaoVYsQ4ThiGSf2B93j+0UeYDgfNO/tyx/9UH9fv/u7v4rvf/S5+9md/1s89f/4cP/MzP4Pf/M3fxM///M/jN3/zN/HRRx850wKAn/3Zn0UIAb/1W7+Fv/JX/sr7v7AnQvtTN/Wbt9y++9yfpA2Xod1vjZkAynSMXBsX6tnLpr3NN7Z/uTFJe0yvbe0u3EjPm596wsGNuBoB8eZ1W5jt/7f4GXVmli4KyrQZycIPKnElN20R4JFLVhpBJGXCytLOoiG9tC4IXJEIYIxaG6mCV/VxMVCogGlFGhhjrUjDgBgl2guFEZhBVDf9Ie7MHQ4hpcQ95yamEPmEdOWn/Ek2j+J30Q3ahYCzObpVewgpgjTggXsia3A8vNVyzDbZ+5i264B0HrhvlpojjecxqFo0onTC/Eao1D3HBDHraOffZGsr68QrU+OqEZekuV7y7Kq1mEJVhBXTYM1n5Rpc8aUeQkQNoh2KaVALN/LWp1WrBGlUliADFwQsYV3H301uuz9b+ywXubnQ1jO0570Psjf32rpxTVqtCcQGjyT3RQCHFFCmEd/45GP88NNPURi4rD/CXOEM2hhOrQJObVG4BlZNRBKlq5NOEI1qSAlDGpCXRSL2SvG9JZraoIYBRbcYJ0FizwXVcwaDj4u9J6rlInapEx7UwrqENPowhYRAi4vrUh5lgDGsvGZAfdHCLAePcjQGbb8Nw4D5f5fgjO9+97sAgG9+85ub89/85jf9t+9+97v4xje+sW1ESvjkk0/8mv0xq3Rix+vXrwHs2clbDHGfx702T+ifeusq8k3fa0CbnCFb9GyL/l2v3V57dQPZW6n7jq0215sNdy9jlpo6tWesnUbQmFd3j/+vnem/NgJR/XsuRRMVSXEDLYCCYbW3rOZPcyzrxi8FoRZQKcByQWAxv4QYUSkgM7BUVhNFwFqBtQJDLpjKgAlBKs0GgKGag85PI15WZysgRZN8dWyKJYoLgbCE7iY59vK5at+2sc1PYiNV25jUUlCzScPBmT0IYtYLuznrmVWvbXVH+17hVn5CQ8Nw5kfaVurWYejuL415+XwLlqGYA4trSc0kyR5EIlpUlRprgPhCqqyzEEJniqvK4M1kq7BIgTSxu6CC1BSovk5u/gsT7CT/vWpOUbM4mDYJZWAiEFX/3s9hC+TY7hvzIwGEaCgjvr8tr9JAmU3w05I/Re+ngICARITDEPHpR8/w7W98DYUZP3r1Buu8qlJbHSHfGBWzaE0M9ryqEGLTmCBI8BL4NOB8QtNMyRhX0oKOxWQQpJTE7LqsGjYv50upHmQxqHkxxohhHMEMD3IxpkmAmjODh9n3SzalCObqZVcYEYkIx+OdX2+5YcMwIsSIaRwR04B8y+LwnscHEVX4j//xP8Y//If/8OZv1H94J5d429Fzg+353o91KxT96knkQvPVs6/O9c2GhT7LJ/NR0Y229ZGUgd7eFrtWzHikkdkdwQV8Y9xWsdDOd5JY78SVRa2I1VHMcTUXzPMMCyMekjhsZdcIo8oanluWCyhnZ1apFkQCUgiYEDAvGcwrUBnT4SBhuoUw1YCxVqwELMiIa0WIGQRoNdYiREHDsFOKGKIgG6QYcX88YhwGscnHpMoHgUpFAPlGgyZQAlCIqm78u+GRUWzGV8OpL+sCcEJCUlNKd2/nM/M58nlhG3Q3baFkBAUHRiDNPVbimlsIOmo1QzWgQgXXCqaA8XiHkHSB5tpMjx7UIf6wqmYyYsYyzz6OwU2BLMhSZFBJhLKKNm1h4MwSWdn6IME8si7lr6h/cGWgclCthLAo9p2MlGlxjHktPs6Sn918c9V8cp3y/z6V8QABAABJREFUanExFrEo/IkcxkvqgBEA8bXWNTe/C0EDJToSyejMW7JeZKYDME5IYQCGiI+eRPzEN78BChGfvX7A/L0fosyra9hc1XenjCvGgMM0YVXh7+7+HiDCuqxYFqnRlnPBMi+4XM7CSACJQAQkZ+x0xjBNYBByEeQKuf6C8/niVpxnz56q/1PMlQSI1hcjHk9nrDnj8fGEGAOmcUJIyUPuiQjTOAigNQWUnLGq1aXkLAE1S0YtjLu7O6m3FwXBJY0jhmnEME1Y1hXp/TSKtx7/UxnXt771LQDA9773PXz729/289/73vfwx/7YH/Nrvv/972/uyznjs88+8/v3x9/9u38Xf/tv/23//vr1a/zkT/4knBBstBS65i5b7vaW1hsLMdOAPe5aA7LPTdORU8zCeIw90K4NtxiRfze1nFu32ju2jIw2/Wnt3PLdBiJqQriVGrGjaRTSgKZt8eZ37r43h7qYayxXK8XobY8xSrY8REpPMWBKA8aUAGZcLrOYWtYFkSsiERCkKiurhnhas49ZCgnEgmY9lwVLZYyFsTIhzLYENBpqldD8xaRZSMj8kCJSDBhiwNP7exymEdM4YRpHQfHQIpYGi4MQUNUBHWDmTqBCvvdMR0aJBGWjmy8LCa/qX2mmyEZdqya5MpOa9Lox57aORNAvap7SmdcBr1kDREp1v1ut4h9SGzFCSoJsz+wIFQbOW4sEiZQ1Y50XYTDKhOaL5N0wiyBie8OgqypXlGXxdZRCdB9orVXYjvZzWRaUkjWNopkNWQUesDDDdc2ucY6jmaJEW2nBI0Wj4kSDqy5c2TP94W6iq5pMy+jBnzUQQ5mSoKg307ohVVjQhoEV+4YgAhk+KUEFL8L9NOCjp/f41tc/xfdfvMS8rqhrbSboypg16MJyEcHZtalN8AiLtirA1eyMJufSrUERFJZlxcPjI1JMmJcV61o2mqlBboGARRE0QgiIsATl5k4IGla/5oySi2p0zRpgIfEEsZDEXaQuhaiwV5KDdjgecX9/36Da/lcGZ7zr+Omf/ml861vfwr//9//eGdXr16/xW7/1W/ibf/NvAgD+5J/8k3j58iX+83/+z/jjf/yPAwD+w3/4D6i14md+5mduPtcKFL7toN3/b+k0V/zqJmO79bk90X1P7zQVdvfy/oFvb/+1snibiXYWwu6ctqZjXgw0Pxc7/boyFTZrUi/xw5/iEj+M1naagIm4aiKykF8p6T2i1iyal5YZH9MAroy1LCrhZ5GyA4EQwEMSiRGMsq6wyKgxSNBE0Gi+lYGhSD0kcBUTIUtNonldseqfmURM00oxYEgBl3XFcZpwnA64Ox4892yMESkOSNoHhmhaUKbFKgwYOBdvB6sROIiekFmK9Jn2sjnMj6Th8KYdN9CNJsHY3Nq1FQCppkREEviRJWIyRqkvVkvBOs8IpEQxRNF4qQMKDuKjqVkY1rosOGvZDdGqIlaVto1xmTXAgyssMlKXflsHMjdG+GsVjWFdF6x5xbIuXdQbkBcJwjhME5bVSmNAGK7uB4sf4VpRtVRGjQGRaUNUgzKijeZV1cwNSHCNCjoULIk9OoMtuQiyB8gFFtiYo262tImHqvsigMXfNSY8vTvi659+jONhxONlBtYMwSCU8VkVUil67SxpU85ZeaJWLK7tNwDuL3rz8LjZn6VIza/z6Yy74x1yXrtx6emKdCDn7PRDauMZfFiLSpRyM82XLUtWfF6BJP+LNOG6RoH3kjIsAlYdU8I0TRjSgGEQBuaA0F/h+MKM6+HhAb/zO7/j33/3d38X/+W//Bd88skn+Kmf+in8rb/1t/CP/tE/wh/6Q38IP/3TP42///f/Pr7zne/gL//lvwwA+MN/+A/jz//5P4+/8Tf+Bn71V38V67ril37pl/DzP//zXzCisClX/tkHw4i+feunq38AbvCV/hmNOb01n2p/K7dlDLCboXpNrbW/MZH+XM8gNw3dMFJzGH8+a+xt0m4+JKu31CL7Gutq1/dRWO5PkC+d6UeSMi3HpNaCklclIoSnd/e4OxwxDaOgyaskHiiohAsAjLNJkAYYCgIoYEhZNhIF1Z4k4384BYCLS8rrOqtPRKTreb6glIpIUvMpkjCUJ3ePuDtMuDse8fGzp7g7HHCYRtwfDhiGilQLCjMOlQWJICawQSH5PO5G3efQRgtidoQSt1ptkdpgtnsslyvs1wh5crFpzmAGSkXJ5ocgZyzCOCKIpAZZXrKvFVY8u1AKKEYrhgwAyOuK8+mE8/mMVy9fYVH8vHXNHuI8DIOOsxSADCGIcz+Kj+N8Psv8z4uvr6AmMF9fpJI2My5rQSmi3S3zjPPphBQTPvn4Y0kd0ACE82XGYRLNmEBY1oyySsHCZS0eRbiuq2r3EcfDUQByTaDXSMxSRKskIvHpAI6LmGJAHJIkx4Ma8dWcpGZdKd1+beBfqFkFgoAAwt2UQPEOJXwd3/nG11Eq43H+kYg8QZ+vjMaEJDOzMFccj0dAtcLz+YQhRqljNyY8eXKPaRzx8tUrFNVsKCRcLhfM8+Km8qL7SbR/Acw1v1ovjBPgwoqt0ekwKeh09YRm2Vcr1kXrqI33iEW08BCC+9KsX2yM63DQ5Gut3jyIybvshbkvcHxhxvWf/tN/wp/5M3/Gv5sJ76/9tb+Gf/kv/yX+zt/5O3h8fMQv/uIv4uXLl/hTf+pP4dd//dfVdCTHv/7X/xq/9Eu/hD/7Z/8sQgj4q3/1r+JXfuVXvnDj+2CJdrIRdqCjAf0X15gaS+sZ0i3N5uYzqEnEQnt3mlfPObt/dzGFaExuyxztETe7eNWmLTs1Kd022SbyUSVHYvGncSBlFL0ZkZ0Rt5IjLcfJwD0lMVTaviwLlnlBXjICZAyTJq86MLlpdwL9AJBGVOXiG836aGCn81pbqC5FJz6RAM02Fr9BAXJeYSgIS1apdllE6wqieZ3nFWOKmMYBbx4f8eTuDvfHIz79+CPcHY+YBolwC3EAMZBAWEsr6JeiBdFztxyadGCRlw2zioHQzIq9Zmy1zQCANLDETVVBS9o7Kq7Mcgg2w7IwYuwCIqpoKSERhs45HwDAIsw0f04SfIWhPT6ecHp8xKuXLyXht1YJkR6ymowC5kW0svkyS0FCJWiiPSzIOStRk0TawzRhvMwi5OSMUrNrbLnktl6ZAYpYS8EPfvSZ+K+0X+fLjGm4YJomfPT8KUIaAKgfp1ou3eqRb8zA+vi43QvKMENMKCzl5JdlRRqSlxIpVSJTjRVZdJ4APfcVGAIsn81kSQIANsQOma8YAsYoJsNvfPoxzvOCF2/e4LLI2Nq8L+sKaLFSCWYgXC4XLYwa3XxHoWl/VcPel2VBYQJFRlhXh3aSwpHCYK3GVi5ZAH21TIox46gmRzpfcJkF2NeCjCwlgLWtRIRhGMF1ltwuCqiKCGI5X0ZfGqOXKMJ5WUEkfs2BJmHg9X8h4/rTf/pPbzSE/UFE+OVf/mX88i//8luv+eSTT/Bv/s2/+aKvvvU2NM2oMSpq/7t9y9W5xmCaktNpOE4jttc1NmWESE1t5P+7eu9NbW2v6rylrYQtQyXv47u1gF7LkkvUvNFdb4zC6a/6WJpysAs13iVxEkFKd1t5cRUOzKHvYbgwqVs0karmslIlB4u0XTGarkKK2yabIobmuCe7glr2XC6sJpAguUGVseaKNauWlirWQpjXgMuSkas4/ec1S0CGMtIKYCwZoSYkZsfFC1QBRAcHDjq+sib6sZF2MgVwaH6BfsA7MUeudUvz9WIwq6zIRK1GlUvp1ISKAGV6QYoZiomNwWsWIocoQSw5Y11XzMuMy3wRxrRm5Fo0yTxjVq2amfF4OmG+zFK9WEFipQ7UNmJUNLSEORcMMXqiNtcGHMvG+DR5NqYkBDmvYmIMAcxJUDmKMNrj8YCUojJmKRWvfFgCK3WAclnVGkVO9CWfT8ZR0K2KBmgwiA05omrKALUo0M1mbHBsrv3aJoFG1eoekrSOgGmIePbkDk+fHHF3mJB5QV0tF1IKK0ILOcbYUknELEguGPbroJQCrKKNVdXwShFoNUtHqVx9v5HPkTxTKknLM4dhcFoQQsv7M8bnCPpqdg+xJZUbUxNGauZNYVYGOUWQHL9hgPctEAEdc/syxwcRVfiuo5d4+4gv//+GgW0Nfa7RdP/23Ms1sF4b6xnlTpMC4BpF7/m6bu3n9MlNk7T5bmbHTS/ewZ83286YVyCgyg0hRDTIFtPINP9G0nq9U435dESq28gAsOYVpYjJJKlvIhBQ1ow6Nsd5NUmZxcluZqFRAyjE6Z5UugWYRaLOzvyMKCn8TghIKWIcRvGjLAXrepFNRgHDMOF8OQtMDgMTDSgMrFyxPJxxWTIeLwssofn+eMTdkUEhIVcGggR1AAAxY0wJQxSpOhEhhRZCX4rAHxkiAgBl4oxWG0s/2zpT4tSMjLfnlDXUm0Ba70MnnyWXquQCEIMSIwbW6vZCJNZchKCnhMPxgHXNmBeJOrtczoKAAsJ4PGI5PWIpGfOy4jIvmOcZD6cTXnz2GS7zLAUIreYZV8zz7NL9/f097u/vxDT78ADTFwMRxmFw7ZirmMMO04gQE+6O9+IzyVLnjWvFWkxzrljyBYwX+PTjj8UveSCsS5AcQgqoyyL1tHJRtBXxwVnirZnESMe/1gpaV1SWUHPUhphuDB+wvL/mIxKTNVBhqBimLQtDiBRQawZzABAxxYBnT454/vQeHz17ikIn4HxBmReUUrEskmuVhwGffHwn+U8xaZCGJhvnjEJAJtk7l8sFRFqGJA6w/KzDdHB0i5cvXyB7mxs1SCkhrwUF4rMK4QAG4zJfdE7lnZfzWWGkggujRORIG1AGalq7MVpSLXyaDmpqFBrx9OlTNcNqNGgIyjS/3PFBM64tQQfexhhMO+n0i46hbVQYf0pvNkR/dsOzyCVnc9GKovU5KrC/0uo87ZA2Oo3uSirp7YbdT29liUJR/bMFbMjBnhxcFSvNECHElNg/iN2vZaZC/4UlB+VymbHMc8vXISXaoWGb5az2b4U5ItaihaZuiD0BAGEcE9Iw4vmTe1geEkNCtKu2IwbDRIsYh4T05B7306gEQcKbc60YooZTUCNGzCzEcS1grHjx+gG1Mk7HGc+eZeRShXAvK54/eYKUouaSSY5SJAKShNOL/6tqKHlVSZPUbaUmQ1Y/V2VUsrYIYxETo/okDMkhhLZWoBGdKhEHJJWcVfoHgZUxLvMCnsV0VJix1orH00nMnMMAGhLOlxnzIgUAcymah7Pi4fEBjyf1VxXGZy9e4OHxEa9fv8HlMkuycKkodXaNJJD4jGKKmNeMhx/8UMYnBAyjloznimkYMY0jRoUFAsnz1lwwz6trahQTGOInXZZFoj1jxGVe8cPPXmAaRzx7+hQhjYgJGMYRuShmIosWfZlXrOuCw+GgEj5cyyNA/GjMSOrvGdUsSwopVjxfsW0C6tayBCbkTjNrVhcuBXUFaoiIccBxCLifBtwfJxSSc6ATTpdFIiWh+VNV4Z3mWdEmBhzujqjjKNiGWfxLh0nyoKZpkkrYyhAOhwlJUzxevLAUANaAnQIrKVKyonIoYLYIfgOowyWUWnvSo3Ecsa4r3FxNJNBVq8xZiGOLXIQE6KQk67NWxjIvGKdJ1igDl3lW68bbiNbnHx8247J/O2nKVONOcepuoO29VxpTp7XdUMc2/KL73Axgb7f3XSlGyu0I2KAc9Bdsg0K27bdGbLXB6/4aozIGeT0obWiI1cKpEhW698vRRRJ2DmrW0GfDOzOzWNBooxijdpRbXhGLSSsZICcRDuPQikwGSVQcxxEHQ77WeVjn0RNck0LhpCjwUKVo6K6awdZcsOZVkOrVHNSQGsSvVrVd87Lg8RSUiZMz5FyyQE9NE0YaQCzQR1U3vcF1kZVQ0f635F0xQbFwOEnuNZMTAhDZtUfTurxwOjPI4J0IMN2sMoMqIwQ9QxIdV9WkaUjkRT8vCh+EKom8l2XWIoDS76wpBOfzBZfLRfyVa3HzoeQOkSMtBEMCUXNRNMGQWZFUKjIyrCovARodJ2anElk1amEW4FWwCUtxSZ1JfFmseFaWVJsVqWUaRxcmqq33EFF5BQNexNHWbS3FGRdq9jVVNE3Eg6JchiK3oLDNp+3lQAg1qPGiot0qM0gsKC4BjMOQcDeNuD9MWDhiXgvSMqCcLhJBQoQUxeRWQlHBb0JMEdM0ooSCZWEvzBhiRNJKDGvZWkWs3b3PNVBAIdHWrU6WRW32KQRB+8Qq0NphgTk2Rha1mXN2cN8YzTrSAr+aWVVN2FxBFDuq9b/Qx/W/07GZIGcuzr78mp6kbwIv/LY+GKK/trvOn9I9/y0SQ88e7fnXFxuZutGnXgvsmKm3d8N1b774yuZkeG37G8y/EoKZCNHe1xdENOnT/F16pxNJQwfnlkCdtPBdUmewRVIRJEdoCAGHQTAJpxRxGBKGGJEUOWAcpZzD8XDAoNAxMYl2YmMQlXnEYAXsNAdGIXXWdcX5csasBG/NGafzGfMiofOzQfBwwbwuAEtAx5pXzPMFh2nCk/s7DGkQf1wgVBLzVyRCooBKiodXNaeFoD4aYZRURTjxeVWhgH2c2eejN0tJ4rCtWS1hSQYrWIFgwMMEaOkNVKjmUTygZFX8uhCldMxlnnE+n12AOJ9OagKc8fD4iMv5ImbCy4yyZAQmjGnEoAnY4rck93MuiySyEmsu0DAhBwn1rrk6nl6tQM4McEYOVf1vEmVW1yyFC7m2SMYUsdYqlYWZcbjTEO95xg9+8APcH8W0dn9/p8JDEObNF4CCoDOUqonO4j+LwfLwzIwYvCKB7T8ZT9EkhfUAlngPtPXtyOteEFOpgzMQYWR304ind2IuPNczzhoYkktRlHzGNI6oJSMTQCSAu+MgWtVKq+S9LQFpHDCMgqJxOBxQzmePzis5o6gFIpqVQ024XKSNlkNnZlRxMUsCsYXl2zgYEzocJhSt1iBGhqia8ipMNCYp+RIask5VASQqsHIpIijItQI19b8UZPd/p8OVI+oZRDvnLKO7Ru+8YnA9b9n7tPwhOy3oSo8i8pwpV2GuGu1PvdGfzvTZ8cUbnrmbrPCLHM3/IhvPcjNQpT4W9w1o4QPu9PVf2LAKs5a3Ep9BJMIwCNOqquFQIVBKuL+7x9004vndEVMAIhgRjGf3dziME46T5FUJTIwwM2Faspktqk6YBDmslBDuqn44TYZkVge+hAKf5xkvXr/B4+MJD48nvHj1BkuW8uNWfoFRsS4LTpDEWGLGZzGpFrLgMAgKxxil8F6ASJMBEBRt0hpbZvuXss2ARViGuNVmVRN2gcFNhC0ohIL4GygGFTJkViqaMNEWsDxnzSuYSAWLFVBCA2iUn6KelCr1tMq6oqwLuBYEMEaNDCyl4nJOqEV8l5OCHpt1Y0jJy1mAoD4ixWs0xPCUcDqdpETNmvH48IBlWXA6X2RMD0cxm9aCh/NZTL9KzJkZIQPl1UuM+q7j3VPkkvF4viDnqsE8op0KBBIhBGGqMQ4IVD1dA5DcvqUUhFIxDBIEAdUUSU2cspZHT6bmnOExGQQBMwapwFZ9rpjhYMyBGccU8dH9Ed/52sd4dVpwGsSsPU0jigqB8zzjMA2IKSjo7smjY4mAvC4euZlzdgxAQHKw7o93WLJYB0Yecbw7OgORJPhm0l+WFQRJZAaAWgrmZcagfiuxWgiOYIwB4zBgGQfwLIFPFrizrivGYQKHFpRkkYhZk5stlN41dTWB+zh+yeODZlyqYrmW0gh+r23pdf5v29ymjDVT45bx2cbcakBo94O8MB8DzU8D7CIjdv/e6sfV99aWpvLdZHddy/uD288A+ihHi4oiO2WM3hm8658+hl0aMqyrjLoxyxUtcWJ1eQDSRb6CaEBK4ouaxhHHaZIgiERIJMzryWHCmBLGJInC4yDRVsOQvDjlOKYGiaVaXdRQYSJSxmVRWdI5Jnhi57KuuD9OeDyd8PBwwjhEnM4z5mVFrsXnPBCp8136dzmfPQiE6oiaIpBYUO5JE09JwFaF+ciYu+eDFZiVgkvkMj1Nw0LXLyjxN023liyMWZN2TQuwir9mpmGSyLDCtSMObd6D9uFwmLAuhHm+CAgxN215GgdMwwArIVNKxXEcEChgGIRxAeRa7jRKUUTD84ujQlMBgNWJooCHhwddLwWnx0fM8+wEUhAyCLVK2LYUDU1AHdX0yqot6fwk0jUmh5TXEVNtGlgjCNXoSs0EHhTiK6r/xUzGwOBm8JoVczJVJBhArieFibaM3j2gIF++odT8VhkURCg7pIBnd3e4P044rRnHtUjZF7NioGr9uaAaLRQdQ4B4QWbWa9GjWbUgGTcpSYIk+09AeeUvA8hZ/FLzvGC5zAiB8OSJAAWswwoCHNXEEpP7wAsrTcS5AfvW0gMbG/D2TkkwS42ugRCjnyvlx1bjaszFuFav6pKrW3Ci35hSO6dP8/u6F/hvvc7T32JM6xa/6p/7bqbVgidumfPap07T2vSnZ12mHXXP9df0zKv7wezgqE60K22vEQVg27uqEpZJcWte4QmLuvDl90V9C6KFTcOAaRxwGAc8OQ4YiDAAOA5Rk4QZI0H+AjCkgGnUjThEzfIXTWscBtFy3GwjUESspiNjyLVWRaGveP7kDqfLBY+PjxhTwJvHEx7PMx5OZwcqDSS+K1YziuTWiOkzMKPWBFbg10QK+B4IlZXhkOIAoieiulqIuqFn17iMKSEEgYhiK0svkjMnuSzF5AxazD7av2rYec0f00irOs2jhl4fDzhzxXJpfjgCIwVhTqIlDUrAGHk9YlINWKIDJWAiaRmMYRgEgSIGDAfBuAsxCAyi1l56fLgXTbhIgI0UKxTTlSW7rnnF4+kEhkj7st61jbWiKAZlJJJxUKa+aI5S1dpdlo6xNYgYUKxo6oVZQv9rq3YAbqHgoi0rkK5unxa5TNC4WvcxNhrCmvzMCJURuGCkgKfHCU/vDjjnglMuGAbx8TGArIUbVydXhJwTSlkRwiTCJpp/U/bWKrifql0bNqSZzpMKfQBjWUiT8mfM8wVDkqjdYRoxKIqJAeW2GmEyV0WDrSzEPWnNMK91VwuqRSpraZigvl+zyEgsEjUU+tJqlH2Z44NmXDDJGE1T6LWEXnug/nu/mHfmFTvb/r/9qfc33WqPSIe912x3SXfWwlRp97tfp1Fim2f5+2nTv/4J5gvp+Rbvfu+bDJgtXAivMTKQOv2pT002k6Es8vlykXDoagtRnOhgxjrPmwKikgQsgfZUC+o6g8aANEQcYsRxEL9XiqIRTF1hukFRDYgBVJEQBa4HAmtEKsm5ny0ghIZWwRGgkBSlYMK6HrA8u8enHz3FZVlxmVf86OVLvH7zgNP5goeHR1xUI1i4SJKr/kv8DGMeUPKgSc2EFAhDDFgWYAgB4xBxGDUkWEedAjmsTr8IzUcReJteYJPYSoNU12oCRZ8TYwyCmJD1GpGQl3lW+KsCCoSaV5zeSOTa6fSIhzevsSyLhvlHHJ89EdNfskjNURlUcl9j0hwxC6KRyr1aXHBIQBSNUOqvKbI6BawfrS1WRTUET2jX9IhSCi7zRdao5jeZ7CnAsBes84w3r1+jdsUP12URc9qy4HI+t5SNWjzVA2j4hMMQMSlGoCVcxzRIUrL6u4xBldqKUUq0nRZsLD2KhuladivB0x8qg2pFrBV3Y8L9YcTTteDJ/R2gKR+XcxA/3JrFNJ4GcC14PD1q0E1Rv98AC34QwUKCOpZ5kX1aipgU1wXrMmNdZjBDfcSSPF1ZtLvz+YzpdFLU/YAEYF2rCgErchYUlGwm0o5opGFAGgZczmdkTYNJScyf4zji7u4ofi9mmZtJNGGwYGNSuElB3/v4oBkXoWlZ/l9vbsFG2drqLi6R2eeeOTXN65YG1jQe2mhdsIiaK+Mt7f61z7y7qjHYxqeaNnnFBzcd27KvDawToCXbLYKoMW9rqveX1Snt4xjaePY3qRZhlU2josCbyW5ZFv8bh8E1JELnF1M/CxvQaVXsOBKTG0HMa4Qg1K4wPIuUABQhxKGL5vNItxhQuZtQzV/jEAES880YCXScMI0Jx2lACBVPjhMu84KHhzu8eXjEvCyYLwvmywmcV9S84jAM6sOoWFIED1KgMZI0L7MkRQ+xKAK7mLiYq8QHcusrd+uVLDfLqHt3hG5sRcIVDcTG0zAbai1glZhJES9GJBBPavoUDTIvM1AKhhQxpePmPZP6dySnbhDBISbNMYoebBOiYtnZ56imoCRRpMXWia4bDmGzpsQ62TD4LJhlXQ/NMG3zyRU1FzXJmikXWi5HiO26LFguFwxDknQIJd4tSw4NtSKqyVH9LvZuY3hiGhM/G2UJjsi1tG1vDJktRQHaTjhNaQKm7M4AYEwJ05AwDgl3h0mhqRg5RkUzKThfLjgeZT1EDS6hECRQQ7WjECLunzzB6XTGAglVJ8VbXJZFTXfCkFNKACIqB0XOECaWS8blchaYKGVeUa0Y5jcu3MrRmDZaqvgRJ/OR1WYmLCWglCilbkyLBRxwmouW1NHE6S97fNiMq1OrjdnsP9tK24Z1dzfLl0b8e2bQVqkuxutHAIQ9m3pLa7efr/hWL2W3a661w8Ywu4Z6W8yvIwTNLpMPe4SPzZOpWaw2/q7Q+m9Hb4qyUPQcopdrjzGqk1dMN2Ea1ZynEEadTdyQUy0cnEgQth2VwkZYI+x6ubaCUZYqEn5oRRwJANcGjgpCp6mEzRBKIjGQIgG4w91BCvDdHwZMY8Tj4xmvasVyOSOv0s55PiNoJOEyRwADpIyLQEFVZuTMyCkDiECIML+iUlvlv51QYMENbYCboEQWfCJMsGpouxELsGhlpEzdihXaOFIIoJSQ1ZTLJYNzRgBjSkn8SzpWMRDGScPMNaHWChlOY9KIz9AYqeactVIr7GtGTK2s2hd7KZKo0WXml6vMMCyHyhVlHP18zgKWzLUig8BF8sIGL3Ev/q35csG6zJgVNmlYFqzLgDkQsgafVGPoGjTF3Z4XSyQDLBF3Hryk/hwmMWEbM7LfuFaVNZpptrf+2J4zujSmKH7cIeI4TVjU57fEKInGynis6GKuAsk0DBIwYsDEVjwyDUlBeVsKRg+MLKZ78fNJ9GT2gAnDFMyKL2plapAiIoCshtDiaQTNN2UgvICMW60F0SoNKHaoe0DUomOmxcBw396XPT5sxhVMiu80A3Sayw0tbKuN9VpKzzCMYKC7rhER0LUR0J7IYGx+vCVUXJ1rz95EQ6K1g5SqN+Wr79vN0QGRyZnUqVa4YpiNaTXmGSzMm1rgg/HxNkxiFrTIufv7J7g8OeHy+IjH16+RAoEmSRZ9cidMYYhBpc4Bx2nCYRxwUL/XYUqYNJF4GKJGExpOXAEXybeSSDPH+YHlUYGrmggVrb7LVzHt02Rv0xCkTpBoGGMAhjGipoApEY5jxOn+iKfHCS+PE+ZZcpouDw+o64p1Fq2l1gPAI8YUEQfVFZmxrAuAUaCFQoCVSBRzTUfQov4eGlP1eVD0AjvXiJGVwhDiIkxBiOGaJfQfKh2XUlBWkaqrJn4PMWCIA4jEb2UBNeOYMI6j+5dQNJmaAsYg54L6EVs9OG6wSyUj1CpJsSFuoJMkdcH2rKU0MIJbA9QcWot/Nq2SiDCEAKTkeV7mLwlhlb5FKYiYhuSaTl4XnE+PuFzOinyfJX+vyD3EEvQBGt0/BkjBxZCLlJ1JEs4/jpMKgE3LEtlLGWFPiw0ZxZg1RAg4ThMOueCwJDx7+gSn8wXnecYQA2gYsAJYV8H8dJixkPBU/au1skfullKdKeRlQQpBI0WLM9OgEZKGJZizaFxEEjhlZvVxGFByBjEjBRIGXqQ223I+Id0/QdJcxpwLoprxG9xXQU3RKwOIebV02rQyuFLByXy3P6Y+ri1Tat9dVcdesepVdzvVMYObjGr7xo6dwJmcr1bzZvD2ge/oQbuve0NPvHotq2udfeqfI8TbOiHGI88RMtNK30Y7bRiFbL/2DBI+nr1JtZlZDXOMcP/kKebzCQ8vR/FNQCTnlMImysmdxyEiMAEsZqOgm92cz5kEi80ggmopkjTr9ZpayH1MVp01NkDSmsEQu74hHoi5Q0KKC1fU148gzTUBCQqDMZpIhLtxQHr2FHeTMK7LvOB8Fr9BXRecHhicM+o6YSBCinfCNKEAqqLuKWCq+j+5CSktavOWPEMO8EqB3Kdi5SWYBeIoz7PMmibXkvrFJMFYizUuixaE1Og89ZV4ZKaij6QUBe2jFsGnMy0RIp1bukGwAJIYQerTYiJwCPLsGB0v0f1cYK8bVrtlTyBP+ObObxQIQAiuvQukFXlwANC0TuIj8jggr6tqaRJVOg5JTM8a3biuM3JesSyat6hmQhF44OVMbAOI1UKjGQO5MCRaIKvg1PIhW590/hA0qENMwymKxnUYJDhpWVeEhfSZMj+Cui9BPuuyIA+rM2oKhHGacDgc8fr1a7eQSHkRqZYcokRMDuPgiyqvEq1pfr9aJO0j5yzmOzV9B9UYI1ccUkSJ5GbxwAGDJrmjil9Nh8BNpFHTWKbp0BhkNeFRxkhAiuG5c1/m+LAZV6fqN2JLm/NyIZzo9tIs0eZBMO0Dft2Gg7TnOdG2DPHOKWs+JNd1NgY+f9dblbLOPARjEK11GybsjBiNE1sTRYht5iZWqZaYOuZ1fWzG6NbvO0HBUT9AOEwHrb2TVGKXKCTzb8UozmULa3YR1YIP1HJYKqvfCiAWx3vNGTVrpdUsZojgkXtCHKx2FpiAQsg1qP8nAtDzZmok8ZuVUjQ/RyTAvFb110X1GxGmlCTaThlvgBQ7zFkgnvK6Yg0By7LgWA4eFl1rRQ4FsRT1bXULYS8tvW28AbAg+bY1zPCoQrYwaEA1F4JVNGbVulCKFLmEjFmMASk16CIzh0VNLWArBqgSOWnwQplX/y3GCFJYnzAkZVoEiglUqjKtqEJBbFo7d2Kf+0816Iab3873l8t2Mm+EZl61QByCRas1fxgHHYcQFPmhaskd0UgMaV2e09XmskRua4OZQzWYoCqDEV9Zs/b04Uu9AA0X9uTaQJI0nzTC0/x1QkukCoPNBdDMcLlI9C4RYRgGjNO42Y8MuOmWiFDZkoWNeUgwlZn3yMZUhULi6sUkYyCMIQAxgiF15RJEIEoErGDUwljrovtWDf1qIYhpQEqDAvJGlwP8UKHrfyk6/P9OR7BJx1aC3TKxnrjvtJcd8Xd9asf4NubF3SSYfbzpTn2ODrpP7yZSWzq21ag20ZBdm/cN2rSLjU43EdAJZ9eybRxJdx69IqlEgyS3JLgfRSV/EChETIcDpnFCDIQhEippEbpSQCR5OXfHI2KRyrx5KcixohChhIqcNcrMCmdVCZU+vXmD5bKIeQoBVkwqUADCiLVWxFIQg+EDakE73RgxJUSKPkYpTYJrmJKEUitqxOnxAeeHWc2LrKjlEllHgTDFiOFwxJQEC3FdM86XRfqYC+bLguWwgNAk5sJVfCweeCJEJuqScGQMHX6X3EHCdKitLCiRbEQanuwsQReqQeSMuq7gvHr15jQJ9lxQqd/WlaGom0mWi2AE1qzBMxqmXeYZy+kMVIXqGgYNeY8Io2hYiBFhGKX0SAhgCl700gIKLJVhSBKVJppQdTMuSANZTOMhCxYKoGiQUZKGYAJMA3/Obg7dCIZKjAlw7U36Ln6bygXLMksF7hgxTckZFukaAJFo6Kr5lVK8HlkIEVy1NJCZppVxIwSv3CzMovqfmXgjAUOMWLQfQSsHm/WBATw+PGK+zEjDqBrXQawMUeYgMnukXikFj4+PGNKAcRj1nDC+4+Hgkbqyn1SALAXTMGiwjiDaxyRm6nlZgBBRGGBUnLMCA1dbk9C2Ch2IyrRiGiX6MGkehy5yd/Xiyx8fNOMyqUzQnzvthm4wHeoF3d7c1p7VtKHOCHfFa6i/62abdtzgxuXGyPQ61dKMsVpfbknk+77159thBr9GCNvnG03umgBIs641uV6DbW0NFBBDwjgewMsFQxoxDhPGNLRSBy7RazVUllIhCVYvyOBpXGiWekMa3vzw5hGogt83hoBSTMomZI7gGlEpgsMAZjE/1lK9T2smAa5QCXpAQAGhVCCGERQDxnHEOD5BySvWdcbDw2usS8YyZ5xwlgrO4yQ+lHFApIQUVoCDmkIY62XB45tHrMsggKcpQrMLtC3kRJyiocOr1BN6B78tkbCd/916INJq0ykpQbbS6eLHAloyOKmEbwIQO8SRrhg1KS7zLCHny4Lz4wnz+QzOorGNQSIK4zB4PaYQE4ZxQlBGFocRcRjdVAhFCQER8io4kjVnqQisXU8xIkTV/FJspsYYEIJChoUg5W1qRSWSKtXQSE1mcAECDY5FaME/ZRWQ3kHzyqbDoGjnxceh5gxUlkAU8zfqnpAAF9W4GKhapqaaZoim5dk+aZGiEGaS2fsa1QQdwEAtCOpjm4aEPIwIlMV0j1YTrNaKucy4zDNSmr2Qp+wZ1YaLmC8Li0lwPl/Ao+SzsSTTIelaGUPEECKYKqYQwCkhHA4YAjn02vODCKDMFbRkjzQeRkLghMtKeJxXPC4rau1yBaMEaXm6tgkgYMcKje+w+Lzv8UEzrj2jutKyei3JrrN7jTmYxNAe2jicnXCFaWtedIPgniu4Zrd77ls70hOn1uDWjN7gaLfstbFOMtfS5M1S2HDHPLKte52Av3bnnUHZGO264ZcFyaEK7IXvLCk1hIBQJUlz2z4ghogIVtBNqKZhwRNC6NdcsCyrBkQU0bSIUADUqhF2YQBT0r/Y/SsTYj4BKVipwxoCQpTBlSRrKbbOIWCMgtwdQsQyz8hZMNZyqVgui4PYj5qfFZWQi1lQk0JXKZiXYlK8QmPGBobL3fjqHIE75gZAzWGwKEPqkFnsu4Vg2xpQybnlscELZwZN0A7R0OSVqXN1LaEqAb88PuJyPks+1Oks/jMlejxEtS6ZqUzvtciMWgVhnIQRUOSWjBqDQCBBgjYiaQi/alDrmmWOomAMkvot4zht0gQsPL4h/JOYMp2ByHWCVt7QLmKUcHIiBZpVkx+XplkSNTQQC7iw4CRjXEG11ZawDDXx+eptc2GT73NnZXiomQk1GdrAqM1c7tpwEBJtgRWsiBkW5m45ZbD2gLu9zq6VpiDBH4Ou8xQjwIQhRXABECPGGOQvBByi1BILREgsNcMKA0NmBE4YNMBmXTMWfycUyd8YP3uwRv9nJOZaKXj/44NmXGbmAOCMqg8gaL9vtYWeJ5n62vhVYxDb6MOtpOv3m2az0Z7QXfD22bFntti/7Xv8qp5x3rimZ9Bswoz/K1/cpGkSNvvjATu/Yb7te1tgjTkSNBwYsohqLE7IBU9OMM0ZcGw+gJCZgSi5M6ECoCD+EUDLSIqKsqwZ58uq5UkgEYpEWJlBiKAwgIYDOAr8EsUIjiM4BFQiUBIEh6SIDlY+QtaFYbhJYmVRojeE5BL34W5FzYIPd5nPeHh4jXnNSMuKp08V4YIkOpGqEVEhwEQBcchCgAsjVqkthVDVBKaVdZVZReVaFeYbgj6/QRrJ6KtBrSMCmxwoDfuWSLmwC1oRpg31LbCFiOcsibxqZnt8+RLn0wnrsorvrNqzBoSYwAQp65KLag5AoRmhVIRYQFFK0kgBzYDk5qJBxiWIyZjSKHWdmZGXCx7OJ6lebebCFJHGEdPxzs2StbAGR0BzkSRviZUIWgBP7fIEc86+kFMSsxXrGBatll1zxoWkarL7mwzVQRmXjJ0AzFZqPtqekfch8VXzCptJjD34IkWBPZuGhFMwFJjgzKTqRiXVXpZ1daEIDMyKOiJgxNGZeAyyxwS2S/L2ypoRAEzDgDCOonVFwf8EGNMwSH5dyTikiIGAIQBTAJ4fJxynActx8AoLj5eKYwqYS8TdmFCWFQ9LxTlXjWwNGglLCsBdulJIWwITduv7ixwfNONy5zKRbvatFgI0hoZeEzPxe8fQ4Pc0En0tFew0H8DZjhx867Lbj7hhu+vbeJNxbrS/Wy9oXEvlr5svu/n6vRBgGqkuMiZ2LUsQXtrmDCAxtUBCMgx0FhRwd3+HT376D+H5t38CgQgXABdRCvGiEzT25k5+2oI26EYb0c9zJ034p5mBedn1Vl/cd57V52NBTjyAw8fgEcDA4CODn7fSIv2G48OWOJmgQUSgQsCFQBeA3gAkqHEgWm6O++ed+3M/8QQ//WQQRsNVhXj2SDEP1PDlbe2UdlmBy5JXCUjRhNDL46MjUsyPJ3CpiAwMIbWISxDmZXGT50gRMTAiMSgLrBFlBqiIlZIITBEIK2IcMAwDCJLcOsQovjcTbWpBgvhHahWIsHXOuLwpeEyDpCyME6bjnZgio2QZ1Vx9fTSsRTheIoYE5knNp6JdhijBArUUXOoZXFf1jwFEDA5aosXNW4qCrnmAbtkNJEKhadMseXStgoL2jVnqpGnlBQM2JiJHwhiSQCati6LYF0ZImj83DM64Si2INeL169eSvjGOIohpRGYMgrBRlrWlKhCQiDCYBaAIIDQXALWAphEEKcGSKGAIjAFALBnHADw/DJieHpCL1Lc7nWdkEAoFLBzw5DDi+68e8P3XD8hcUPOCvCwYpqkJWLb7fKNb/t+XV7k+eMa1YUhG/DYaylvuNU0GQHcLfNdv1arrz2Yq3HEAC7l+R6tvgsZvr9hqUe+8tmO2PdH2cHgAblJx0xQJE4I1dct0e//W/v1XI2q2PbBGTMEBZ6sm/B7GI4bpgDhO3Vtu8u3/uQdffUDHZbqDustsUcTNT1+5DeXWybd9v30siu/mIdlGNB3eyBKwW6MFpUTWeilNszLGVXPGcrlgvpyxzjNqLk6c2x4SUx1DtGOEgBqSmLFiksKIMSnqt2AdWnDCmgtAAblA/GhZUCKSAhMHMAKx+2GgeVxUisCCVYEnquuCsq4Yj3eSsqAaGJtp3BLae3PUjeG1MSqFAYoIERgo4O5edH7RjEwwkBtryeCqkYsW2o3G3GTtq3jAlikom8eEC3uWgU4vyyq4mIAGYwhKSa1SXdlgsja5TvqskjPWRYQfouDBNkWBhglaVicXcMk43t9jGqSUSF6zYDwCyFXgpUCEEqOkjzBAkTClgPvDAc+ePMH9cfTgl8vlgsxAQUBGwFqk+sK8rniVzXTcsB5LqerbMibWJuOrbK0PnHH1f6YmGxMiZ2L2XW6CMh370v+7JdR7bcytiJvbRILfWgrfxS7f1Z+tFtUHlqDro3fqxrNbCS2STaR2eGFa6pi3zX71hO2HxvZ2B0MDOABzTAvwqSJeUEUiyenhEKWkuIUY/8Hx5Y/ayt73psL+byt8SNACKxxsNfTvRaCrOGeUNWM+n7FcLsjLih4dE/oZIAlKIICDmGVLTOCQEMOAkEYgDoIQgoDxcKeVcRNOpzNyEZ9TXjMKZazIGLScTSCW6tS1gjXplYoELfTRgusiBS6PpaAeDximg9sVCOQMqarmZSHigJlRRWARsxUjVwZRUj8RMB0mNZ9KHTZy4UBRINAzQtOsRWplxmbfCpxXaP5LF5ZkvpZ1xflywboq4kUIgowxjOKjo4xq5nNLSfCZgAM/m+9qSIImkrVKcSCpE5d5Rc0ZTw6T7MEYMZ/PqtlWlCzRjIgBZU3g+aypFxGHYcTT+zt8/PwZnj+9ExzTWnG5nLDmKmkrFLAyIdeK0+WC0+uT9JeL1+kjXauNebUxvIbGe//jg2ZcQQmlLd4t4C5gi2sTuLHfkp9rKtyGRTTz2a1jp37Zqf3V7/hKXTva763VbQFvTYnvPJR59U4rCwjY6lqtCzea4aZZ6gaEAiGwAueiIhFjIGBKgmpQSBAa+oqqf3B8ycNtLtjlcZkpTLQuUrws0aiLoxuIWU78LGUR5I88z8jzogEYUefXcuOCMiz9Nw3IIPlbCFQAyoyhAikxUgRiilhqxIQRh3REeHLECPEdLeczUFYgZ2BdsOYFqAXrkoG6gmtGrRnEgvwQImnCq4AIXy5nPOYV5zcSeTgdjxjGAdM4KcK7MKlgSc9aBFL8msJ4h6TaoJY3EQJaMM8n5FqUobVimQK43lUZ7nKPLCjDzZX9BOk8EAFJhbYSJbKzKpBwRVRtK6EWQgwrYpB0kEDAklecz2f3GYUgCffEjJozxmHA0yf3GIaE0+Oja1tDSAIgjAUZjE+e3ePj58+QYsLv/d7vYUgS0AROoMBiSrw/4LSeECDI+5989Bzf/va38RM/8W187eufIsaAUjNObx7w+vVr1xbT4Q4hJcy54MWSEQcpBOvRhD0N6fyBt90c73980IzLSbhpXWhaSvPQoBFZ/7ilyltT4fbK6/ftGQ0A7jLnlfLT1TP2z6PuejuzUff23XxLi7rObZ4g2pYhZ/R1w4h3ni9RGrtreMe1brxZI8oM5DmgotaMiIIxCWJ6LgQgKGjnHzCu/ykHK2xOtcKF1cOmTZylbq0YMQea0BUpuLZsaBz7GZYgDMZcMpbKuJSKh3mVkhxrwSlXFC3BEoZJ8pkowqrcTtOE50+e4u5OqhTfHY94Mo0Yg1TwBchD93tzmkQoql21ApGD+4wsGITAEjhzrqhrAq8rXJoihYViMQtUQLQYkMJ8mRkzCBAs1DcWhGH30bkOpdVVaWjGLm7nKYCoOt2pqMrDKszTbN9DFL/VNE54eJyxrlICJQRBbImJgRARhgjMhMt88YCHGBPGSVIyYoqKUSjmT0uMD0SIKWBKEXEcUKliIMZxTDiMI45jwjglIEQMkUAs4x+54m5KSFxxiIRxiBhHqcA8Ho4S7ckVFAZkhgbvFDx/lvDJacGnHz9g+v6PNPioF95J0DUYG63LtLAve3zQjMu1DzP9UfvcNIZtoIPxLOqubZxtx9CMW5ipcPNyX5LoH+H+gc9tOYzrbX1kV8xr932vCjlT6zkbaTvYQ8MNMcPZJXXMltsz9j6S/kXNRt0s1er5UEiYggTGmCysmNXnMXylCKI/OOxg17IM7UF8XJ1/i0yYk5nmzVzJ+Qrxs5L6hEyDt2lnCLL7nCvezCtOa8bDkvHZ4xkP84rXlwWvl4wMeRaH5KZnruxl5T9+/hxP7u7x9Mk9Pnr2DN/85GM8Ox5wP40YQ0TVJOWW62NwXyIUcWEwt3UjYpCu61pRlgLOq+RDBUXxCAKIWxFBxKh2V4D8ngzNgVCyhvAbLTBEdDShzpiBvV862flpiDRrIaDWAFC1mVKGq33R+wKRFkQdUd9cFDtRw+JDEJikwEjToHWwxNdlmtk0TRKVGQKWecE4yl63encSiCFMKdUBTIwhSK27u8OIu2lAGmQMUgyY54sklRPjeBgwKOOahohRIcBiSo6iMcURx2VFjBKE8TRN+Oj5jE/efIRpSFjCdXWMFhKPjYb6Y2sqdNMVmqYl59vvvTbVJx7LBZuL9VTT4K41NewYx/uou2+/xnld97hNSL+fbvqjn+8a+O43bHSr1od+821/3VznZlcyZte0NYJtzAKqK4ZQcRhINocmfaZ4wPFwBKU/8HF95cOgnNTHIUEGit+oTCwmCadnCEMrWYsLxqglOOAgrEFhrEYKEqhRCpY1I1fgsmZ87+VLfO/VA94sGQ+FUQ53WGPC5TjgTCtqTKgU8Or1GY+PJ1wuFzw8vMEf+SN/BOnZR3g1DPhP/+//DwKAp3dHfO3ZU/zkN7+Bn/j61/BTX/sYd4lwHAYchhFYZ/z/2fuTWFuWLM8L/i0zc/e9z7nNey8iIyOjqqhWQgyAAUgI6RsAWYJMJAZUTUqqAZ1gVCMGSDArgcSEEQxg8oEYwJghCAmVmJQQhT6EPgkVZJGCyiIiI15772m2u5vZYrCWmfve55zbvgRe5LWI8+5ufHtrttr/+q+aF+q8JfVLKQbu8HCeajXGE+enrM5AoYuiQ0XUmiaW4kTA3nIF96Zo5QHRPs85Gk0VStXgBdCBsophMGrzYHdxjEcWiogVw6sG0LCVKGD5qNpyzbv1K3jhdVCq4sS5gBSWbEXI6h5t8TzYdDjw7PkLggi5ZL797jt+8pOfWFfwIExpZBpHrg8Hyv0NIgNpCjw/jLw4Tjy7nvjxy2tOS6ZoYRgS9VQQKocU+M0XL7kaEtdD4jc+e87VlIhU759lyivEwNXzzxjHmeV04hiEHKw7xGf/6//GN3PmVLITJafNw8LnpBpTiTySY3+f8YNWXNDCIpuC6k79g3wXO6DDudC/VBR7VXWp6M69rvaf97cc3vSrbYq3U90py3ONdnFu2173nhbua7VUVwsV2v5b+LBtdRHAbN5mNww8pBKctduaixA1cxwCOlq/oRgjSSIyTMQUe63Wp/FxQ7B8URXp+YLW1gKvdWuht0ZiGhxWnYL3j5om1jSwpBOLRMtzVfUcxi2v55lTLhSJ/MbPfsZPDke4fs6cRl7NK1/fnZhuZ2pIFBXqr77m9fpzcl758Z/8k/x//uJf5Ge/9VvUWvjVd99x8+oVJ+Cr+3vWP/w5X736hm+++4LPjxMvDgO/8eyKl8eRIUTi4QpiRJaVuszkdekepiEHjeVcptF5BS/6t7ErZfdGlq1bgCH/qoGUgpCGaGFEraCBWANaDfZu6ybQOg60MGYjiFWsHrDJlIZotCJm8072oATrwG2NLEv2+jlHdda60dc15ntT3AbhN52bvAYNY/9fV6oqKRp/5jgYB+iYItfHA/fLrbH9B+H6MHAcoyEFx8hyf08tlWmI3NeFFOD5dOQnL695cTzw4njkJ5+/5NnxYKULTj5secFASMZ8Y+hkeKbweS589vI5d9+85nRaO4iIuvew6PflrDj7A8YPWnGdCXc2Ib95WTufZe9RsSm8B9L/7OVeQ1x6OOe/a8DYB1+9/0X1vwfq9NJZvDzvnTZs59j8o44q3DZxx8uVlbCVN8nZXrE4S+0Kf/ufNy539NU0BHIKXpgszlJh/ZHqJ8X18UMrVOkhPhyYUYp7UGysDo3E1KISBpkevCVIUIwyqSq6LJR5drThwnw6GWsIwvT8GS9/8zeZXnzG+NnnfFMq8fUt8zevqIdKkUgu8Oq0EsavYJ6J44gGQWMgjQPH5y9YSqGuK0WU2zVTyg1jEuZ54nYcmO9uWF4+52oaOQyJSKDGRBish5TrYROG2QRiCtaCpXMAokAr7HbWDBcEInhJWwvbOZgrCFptFofgzPZOCKzV6xbd4MNzNPvI/tYP7Tw8Zp3E+0Y9f9db3fsza4CaWt1odAULbFDy2or9QYKQs3EO5nXtv2+hwgCkFDhMAyUZf+EUlSlFUlCSVKYUGCNEhOeHiTkFhig8P4x89uzIZ1dXvLg68vx4sGcRgt+rRjZsXISNYBiUgyrX11e8eP6Mr25PyGndhQQ9nM250tqzaHzI+GErLtnc+PAIIOMcuLJJ47NIX3u/m+R900u99fAMHnm9+Stv0l+PPbJ90e8jF/DoyXQvcvugb3XWgVQfP6pZTRdchhebNU9L3duyOL4fpxakZkQXno0jJRnB7ZIrGo3QVaQ+3Omn8d6j5kJZrZWJOutFyRmtXvcDtJYbDWFoTP2RMVkRcIyRFAKxVuq6cBKYT/fcvnrF7c0Np9NMGiaO19d88Sf+Pn7zz/8Fps8+I1w/42///Od8e3+iauXl55+xVuG0FK6urri+vuJ0uudXv/ol//F//P/l5YsX/Ok/+2c5Ho58/qMfscwzz6cRKRmpKzPK13f3fP3tN/ztb37FTz//jM+ePeM3vviCz1885+owcTxeM44jZZkp80w+bb2fAkoMICQkFErGiXFNSAZnRW8NAwxfWICIUVzVTak0AuQYSERqNnh5KaDF1nMLc+EGXjeLHQSy9R1rXq8zeJwBnSo4vD6vq3dxtnPJeSWlyTzERZi9WFxVybk42a7l/+aTFYsHEW5vXlMn63MXRTgMA8+ujuj9RKyBQ6gMAUJZYYVjCnx+fSSmgZ/85m8y1pkoyhcvnvFbX3zG9TRxTIlDiowxWp8wv1oJEfG2PxpX6mKM9geB5/WKn/7Gb/Dl6zu+ubknd+DNuTFlBgBW7vARMuEHrrj2Iayd0tmHAtv38ohi4Fzm9/12FQgPlEdP+Jz9YOfGbK8/6LE8cri9qnpSh77rvnfrSBsoo+Ez2n3UTVnts2uXAJeAQlkIZXGqGKsVur25o6q1XzheHRmSoI3U/dP44FGLMcDXUo2uyZGEMcZObopYZ2hDbFnOaxgNyScIdc2c1pVXX3/tXZ0zQxqYpgO1KMerZ0iakGEkVSXf3Fp4qCrXIfJ8GHiWErd3d0gcOITIT7/4nFgyP/nsBXc3dyiV6TDx8urAZ5+9NLqieeHF1ZHjkJiiIHlmub3h7tV3fPflL/l7X33DL7/5lv/jF3/Ii+fPePnsOT/67DN+/PIFYxBSGtG0glZjzEJYVrsHsdi1G8nvRmslwRghqI4exEPWVSgN6oavJ93YR5oMMSALD7yDzWDeGiK2TgQdMedF4aVWwpAMVCLitE8Y4bFmUvToBHA4TBRVXt/edgb7q6sr7k53xtQB5JJZlpl1XXg2TdYsdF0gF66eXXFIgaiZQxIGIocYOQ6mhA7DwM9++lPWXIkx8eLlS56NEWpmDJaHPMTIIMHa4PhfaDcpYF0AWhpite7UIUTGYeA3f/Qj/u4vv+TwzXfcecO1s7BgDxVWlI8Da/2wFdfZ3yZo+787pXMp7LdQIWyvdkpJHn6+F9xnO3rg8n6YJXFWgNyPIeev37aPi/31BSdY3N5Pt9+a3ZuNALNd7/Z6u1Wb6g8CWguRynGwNgk1F9Y1O83PyDgOHTL/aXzkUEUaQMPRaoJ6yxBvpS54Gw0T4GkwTyvESF3N0l/u77i5uaEsC1qKw7AHhtHwdCGNSBqgVvL9neVLa4VSSTlzQJmz8eZpUCatvJgGRr3m2WBtQGJKBqcO1u4ixsCL44HPn13z/OqArDOvvx34TitfTwfm0z3LunJ3OnF7mnl1c8erm1vu7+95dpi4GhMThdQLia3gvYqQkq93955KyQSHERm5r9MLeWNDJHQPyZc52oiGnfVhq7A/n7ym06RHNKo2JpP9Y3IhrReeGJjh6P82ct2iSl4XJ/+F1SmeBG+AuVuLjUqqhehryVRRKMUKuqlIyUwxkFCGCEMMHA8Tz58/ZzpckbMVSF9fX3E1jWhZkTwzjSMJCyVTtVOCodpvh4gYwnDXcidgivj66sBxsk7g97PdA+kxQrvojwkP7sevheKCnWci23x7qNjo3sK2g+0Hj8zTh8fZf948lzec30eNM+W1ofveaxddQW1ZuDPCXZFtAbOFRfZh2I1Sy+poWqAwYgi1QZTnh4m63HmH4sLhcOBwPHAYjQvtk+76+BEa8EBbY0h7TqnlEi121p4yIsI0HRinyfIjJXOaT9y+fs13330LtRJD5JAG0jCiVVnX0tugaK0sNzfoaaa+esWSBuK88KwW1uLsCGROd3cctTCNiXAcuH72HEE4zTN1OUGpDKXw2Zj42Ref8ZMvviDVzFfTyJcBXv/qC778+kvu7iuneeG7V3egXxMFfv7ZZ/z45XN+9OIZP335nOshMAaAQgrmRQ5p6F2YS1nRIs6xmA3B1joxB2t5EkKkM/J7yEp1C0M2hn27jXtP61wK9FKEBnf3m2/vt99pNQnelq86t2SMgVQjuRZu7u4YpiOlwt3dHeI9rBpYAyzsWXIlBiAKtayUPFM0EWsllEwoK1IWjmMiVhgoDDHw8sVLfuM3fsKzFy85LUYuPY0jQQu6LuT7G4aSCaUQcrWmrWuhrEYNFvZr2CH6fhMQscaYzw4HrqaJaRiQee5gL5uzrXj7TMN/8Fr4QSuuPeLN3u8+vtgG2QTwuSpi++1T0vWJzd9wYsDjCu2RiOKD3z6Ew++Pt13Px2iDvaMoXXnZWbVbtvc0z8h/EfeijGT1EAIvrg/88vU3zKeZXE04WKGrkpfZINifxkeNWorzzxVnQfcQTLiYTbqbHrt5koaRq2eR4/HI1fHo+TFFsgEz1mVlOS2IGFy8qlpDSKddIq+mhKLw8jhwqpVTySRZOWmmqBIYuNLMMA6k43Nev35FRQkx8FIz6eYVWTMhCFcl85PDkfSn/xxfPnvOq5sbvv7uW7599Zr704nTfM93X33N6btv+WpI3P/kC/7kb/yIz59fM4wR1erEr/edHzE4KbAx4BszfEheAB8KdTXWjJASQQullRZsqsfC4L7Gyv7OypbXOgsfXiSHt1suTv3UWGpMwAdVNBeGNGJgdCElXx+ipGTKrFRrRDqf7hnGgShQlhmplSQwApMYk/sYIyHPxDJwFY25XaoQifzst36LP/Wn/zQ/+enPiIcr5tV6t4lWpKyQV3S+hvtb8v09+e6ePK+EdSUuG0JQWnjVOrZaR4blRBDz6p5dHXl2PHA1jeRvbggMaCh+Z0zB99rDj/S8ftiKy0cn2+UhuOFNumjzxc6Dhv1/T7hgwvnne7Fxjttj9/rdHtRm1F0e5fIc9mf9Jh3WXa6dxajsf7Hv39M12h6scnGEfZgxCqQo1swwF9ZlZV0WarH7EEVA80dZV5+GDW0tIkr2RpmOGe3dKpsX0Xj2DIUWQ0QG66gbvG3LdDway3ytsGYHNkS85BYwRF9pSfRaSKocUEKAIQSOJFaEq3FkzoW1KgUYp4lxmri+fsZ1svmUYuCLZ884DgOTViRXhmpw9ZeHA+HlZzwbJ67HkWNKvL654fWNUvNKQokoy/09d3e3HFLgODzrlv8GHjIYuWsJi1A1TaTWpVjCglZrYiqyITRVazfQrNxg52H5PrZpvyv+phl+tS/xLlXEOgyXHaoueS+sFAL3pYAaYm+cRuuaXZVpGin3J8+7mXdGMWZ38fBwDMIxDVxNg9XCxcRVChxTIFGRoiSBwzjw/Nkzrq6fMV0/Q6YryMXypesCuTU1xaD5y9qNFi0bnViDtfccebvnVS33pcb8fzxMHKfR1/uOCLrB4qt7Xp9Qhf56/yFd7rLZP/tJaH+XTstDNfe4z/Pw+21/jz8L7f+Vs0/O313uuefpdubzY+csfnDb5OI8z/JW5x+bjtod+yJf96halP1dMcWUvJC15MyyLMynmSoGVU5R0NJqaz6Njxm1FKpYHVNr6a79eXjuq+U+HFW4zDNCy5XQtiQOIyEUQyBKCwQZR2EHN5RsQs6F8ghuqBjvHsMIaUSHiaUqa63cLyuEyOF45IsvPufVlAgCU0pcjaM1mszGTxiwxoMyDByev2A9XvH59RVXKfDtOPBVUEpejN3CwRPz6cTdEPns+TUi1ro+tq67asSunU9QBEo1VKuCilBWb3IpGMEu5rDWqptM8P+oSO807M3D/P5tfdDsOBhgsMMs8LtpxltpBgXGBzmmgTEl6u0MYlRZ0+QNPwWmaeL+NNuT0up5ImfNNz3BEALPDxPPD0euppFnw8AUlENKxFrQupKmkavpwLPrZxyOR9I0weGIVEOmSghoEMimwGUYKI3lfwfdV69fky4bNilkuThBgjIOieM0cTwcoCvyLc/XlVczrD4CrvXDVlw7r+Mx5oy913AW+rrUXrttHznIIx887t9cRHDPttc3btNOQc4VzJm3I5cbP7GX9x1PKc+ejfVD7dFB/oexewe/v7lU1rWwrivT8ysOh4FxiMxr5QmN/mm8x9CSqboa+g16i5H9M+ltTkqh5MptrczzTHWUmpZKXszrkAZPXrMzchRXfMVBAJUYIWJWeYgDuSpLzpyqC6RSIBQGsTxbOiYkJa6urvjRy5ccYqSWFS2F4zgRVK1j8TiQXliS/9W333E63VNy5ngcuToM3P/oM27vf0xeZ/cws6HfUmQaEsM0Ml0dGceRcRyZDpbHU1oNVKF4W5SYNuUWYuzKSHCPLAiWsd0ZmOIk3njBdzMUdp2nt15g29ro9Vi+bmrBFCUmeVIITGng+nDgJit3a2GtmaIBDdnDg5b/UhmIKTGNVlwcgCSBFAKHFPnxi5f8+MUzrsaBKQYOFCiZOt9yPE786NkVP/nxF7y8vmIQgXWBySiuWp4MtcaatWTrLi6Ww65ayNnChdVDyjYEUrJr9UayYMp1SInrqyMvnl336xdXvk35tbquRgP1oeMHrbj2uav+UfuvPOKdPOpQXWx1kT86//YJ5fZgPBYqfLdxHgB85PzfdPxHldljikl2m/piPTtNufh7bJzBhSwx3goixVqTW+rF+/N88rg+fuxgxd1nvzAkWh2Xeuv6ku31nGbGYfRQT0Cx9h6tdb1zQXWl1wiUWwPFkIzkVWpFBUpRNJqS0pTIHmAM3iQwACXn7sQXP05TIJTS81HTcUKlUkpk1MrxauR5uWbNL8jrQs6LdSvOmSEFxpS4Ph44TAeG5Bx+x0MvkrUeXluRr3hfOAlbvtZIdZ2CSAQoXaiy6+21FwfbTFfPo0GHPOnFc/J1EzCvrVZ/VsXg5SlYF+RMJufKnAvLurKWwrwsFHUFI4FpGC2PFAIpKFMUrqeR3/jsJb/x8jlTCpTTiYMIBCWr8My7Fw8xoHmlLifK6Z44jHaK1eDs1gAsA3WTL17sDNXyhK2bM0AwBg1jvw9YLc321zzK6DB/CwTU3b310HMHa3zY+EErrk2sXigeLpTNI1vuARD7PM5DMb37nZx99MT4oxbQwpu8rctvOiXYZcfLy9/JBp1/3MncafKLY6l7Z609t+AhoB4mKN8bDPaP/TjHXfufURadNVL07RpUe1lWUhxcgEfzerAQWacycq+rVocMhEiMTpcUjcePaiG0IhZakjSiaSSoGtBAAhoCMQi1lM4laqGnAu79aAy9aHo8TFQqtSagklIEKhWrUcp5Ia+mvJK3uJ+GgZQGYgjEGKxRabLQYVUlZVdc2ho/NoDEjlTXFZeIOKxdzgTsgzzMPowucr4MpcXem5LTvtyCBKOH8maZovbZmAaWKiSyAWRKZlkzy7pY/ReCRONljCIMQRhRrobEs8OBL54/5/Pn1yRRbpcTUzD8bhwCz64OHKeBFLEmnPOJOt8Th8F79KlXWGOdkLWyYd59uatu1E3NYJIAMfWeY5vBZPMwBjFi3hAp4uRbZ2CWXWTgj63HBZxpk3cMFT6ugS5fv1lBvOl8HoYDn3hAjztDO9dqU5qPqeN3dP/ePNo16p5lo5HiXFTH7Vo/tHoYo3XC2kR4jsXCSo26JpvA+qS4Pnq0yGDveA/WqbdWVMoZrqbVdRUPbZVcyKVYw8JpQmMkh5XFvvSGk+1A/h8BgtKKcRQraI7DSNRAHA/E8UA4HCkKFWvpvnqNTwrC4XBFGUfmGNFaSENick+psX5MVwdCCqgWRJRhCHY8NSVaq4UK18XyXR0EEWP3ADQmaoioBNKQiGOb1573qxYipRbEASZBrObtHAZv9/dSsJ4LX5v/MbIJ9OjbtKhD2cLj0UOYxb2u4HnhGAy0MmpiGJRTPpFz5nSaWRdj1hiGwXLFQRhj5DoKL44Tn10f+eLZgedjRPPCzXwLY2KIwmGc+OJHn3lNVWJdTix3r0miRKp3VxYa26ioGsM+G8+jUj1nGNDcvC5McTX5pFYUL1KJYqHnGMwjHmKiqvTbo06IfBnW/tDxw1ZcDU3YlNP2xeNK57Ho1/cg+/eBhAdjn9x6izd2hhTc69a3xggfHm5LJG8fPpY/k/12/Ts5U/4PzlqaJ6fbjXerFQzOa+0QgoWGollfn8ZHDnUSWdmMY5OIxeXtVpMXRMyzkl0oqztiYkSpbRKoOvGrh3SNLgIVNT0RHKyB9a2KaSCERBhGgrO1qwRUhBoiuVbrBeeMHhmFOkCN7k15k8Xoxo8oYxiAaPOu5u4FGkOI5aqCK6qObosOzhgG4jCYBwBet+VGFs37qcQYqQ3sQfUSDY8yePiwVnmwnFsd4lag77EZV2INIKNqbVT2YUXjTQzdywpBCWLKsyxLZ9w4TBP3ayaGlRCs9UkQ651mNWvWYytRGQPWM0sqkmekrFyNgWkQxnHgeHXgRz/5MWMyxg6tlZpX1tMdESUmA2BITCBxZ6MUglRCUESq6ZlS0Go5LsWNBbHmMlakbGHG6qCeIEbzFmOE0pT5rmbOPeD96w8ZP2zF1cdOIz2W77rQVvL0i7N3Z18/jJT5bH1MaW1bmbWx/+4Nymt3jKavHiiyy+37uT3c4FJXbrUqO2gvnHld9rbdy913cvaLTR8LNE7u/SMIMXhPLiGFyPpJcX0Pw/MucKa8qqp5IpSdMdcQcft5IB0tFkNAYiQykoqVlAtCFUWr+P62GaN+nEb/E2MgRGdnt+QWjRgwErp7qMHh9TV1i1wC4Aztvns7Vxf5Vbdr3VMGbWUvprgMdGE1WY2KqCFxW2TASGsVNHivLa/vKtBIyLbQoXU96BX2ujf29Gx9iP9Q0daCq4cJt7yj/8wZ6WHHAg/dwxOEcUiWr4vehTpFAsY/WP0JBKxucoyBKQWSOC2TFq6mgRiFaRq4uj7y/OUL63Rcq3WeFqglk+cTmhtF2NgVkZUGtKJ2u5BuOLhnjO569fmc01JQCkr0usLar7Nttn+GT4Zh33P8oBXXJZDhcY1zucHjLlcPJz7IkfHIb76/0R7q+7eyfkqTvcNkuLhp3tikX3sjE91Cr+36g3+/We6lClmFokKMA0EitRTvNWTPZxwGlhDI73mFn8bF2Ieq8DnbvCN12qFWhOsC0uJZ25yujjIcnXC3NfkMcXHG8YFastfuFP+lzalSK5qzheNisryIFrQsiEbA+ktFJ7g1FlyhhMhwNue0RTvZda7CY0/0WGhT0kDH5TWlnCLDMHpnYGsj0pouqivrplx6uYhxlKHFabGq99DC+PZqta7GLd/bOyec5Q19tUhbaxdrrnmDu/etJ5XVbJkhZwrKVlSUwDCOHA8Ta87EGIghmXyryoJS8sqqleeHxPWYeHGcGIMSykoS5eVnz8i1MB4OPH/xnJeff04aDYhxen2DritaMiUvaPauzkNxpW+Kvymu0OeKFSovy8ywrsTs9Zi52l8p1GWhaoEgzKos80Jesz0+53EM7vHWhiisxbgXPyJ78INWXDu35MIj2j7Y2NMvUYZPKbV3Oehjv9+NbklcPJl3eVDde9ouaH9ml6/fvKM3HbDdnw3e2xa5JbAbQ4acWfG2cFuiO5BVWLNymjMSEoqwLItNfjPTvafPpw7I38/YhbaAXnvcBHrL+0ig5ymtJ4YVLTvx66qKDgMyGN1TCIGUBta8klfrFUUtXupgAryU7Fx6GQ2LG90VymrHi4E0joQyQDAQRxU5M3S6/9I9xC35rx5aCxKQuLFXRLVwlkUvzAuMjiZs8PZ6piw89yfmM27mao+Z95Y77UtV9f1scmOvmh6aujb/RbBom5rfUT3E0rYJIoTWuqRWRGLvgowa52IWJZbS28+IKkMaLIRbVq4OV5TTHfP9HS9+/DN+9PlLvnhxjZRMWWbSEHlxfc23NzcdUbmWSkojw2EiDCN1mdFlpdzfo+tqRMVV3UHy+18N1m6dmIXs90XdkHkAVGktV/BnnbO1Xclr70G2kRR7KHXHnPExSOMftuJ6IM4vPKq95/Xo7Lvcbv/lU99dHvdyPKG0Lr9+dK97xo6H+3/cKXtqWen5W/Zi45F9iDWd3CaTnKXWugPW7ptg0GPECk/nZaevtVv8QYQhxB46+DQ+Zrg1v3+mLmBDEM9bOI+cGBuC+8i0Jxv2RogLKyKEmAgObxdZyN7sMIRmG+5yZR2c455IrahURAMlePgsBPOmQujdf3eqg+ZhdfPKdufhvWgaOWzCUkSQaFD3LvJacfAufNjCm+YpYSGuPombZ0f33MRzbBtrRnD4uvfzUt1t615f93h3/zavTCrNT93Sv2YMNuRdwHJXoT8D9RIE9zJVGZKRVmvIJG/eGCg8uzpyGEfGGJGaOxhimiaG02x0VpLI2QyUJIE4HXorElGo4iS5Kn0ydUaLdr5hq98sXiKhtTiQw7xxzeZZhRAIw0D03xurCzul1WOmZyCXP7ahQhsXAvEsJ/OIsHyjIvoexxYSf8fxBqWyf71DougbfmVbv6vi0wdvN8/Vl6ov8tbZSAGJkYqw5MKr23ty2eLbxrDQWsPLJ8X1PQyLVO1Mi35fxXn6ogMywpmCMHndvKdAaLU8Ih34kIaBmAZGCZzSibCs1vOJ5nEpjVhVghj0PETvcNhg99YXy6Wjdb8aBqTlvNitCZHOX9tadoj5MF5K0byrBr4S0jShqpRaWNcFsBYusU9h7TeqeVrOb3sZmIGe/7JcjoRgoTK1ZpIihpaT3f+ie3bt3gt4HrdFExQ0EkIruDVV3VVdU/BYuUBsCs8VV2mtaoBpGDwsZwwj0/HAVTjw8vlzxmR1VBHheDxyfZw4TBNXV1cQE+N0ZJlX0mkhDBPT8YDEAcZKCImSTtRsrCjiHqKhKpth5AwqDgxZ15XVQ4VjKUgp6Jop8wyqpGFiur7i3j3ZoropLVrYdZvH3ev641rH9WD0nMxeoF96I81Ca++eUG7/dym4tuczZ3FDSvYv31fwX+ijdx66/eNz2nMoO1qcrsSgqlAL3K6ztboQ4frqyGEcOqFoSzh/Gh83DKJsreTbXAzgIS/7zDoCW6jX9NwGY04hEALEKBAiVlbkHlOpaKikceTq+hl6VBNY84m8rqzLjFYHY0hAwmB5kQbI8Llr3IDeVKR6cfMeNLFvwRKlOTW99c1Gxm6WX3CXr3lcAJHUgScWzobCSscGNLTaHlhR3U97UjbQz681lVS18CqNGLfi7VJwT2NfMoIjKw1Zp+61idK9FKESCWiAAeEwDsQ1Q1mhgdPVQorLYkohl8wkwmEaeH6cOIxjr417+fIlL69trYUwcHWVGI5Hnn/+BRIG5jmz6i0aLA8YgsA0QUiEbITNoTh0v6yoGqpSghLiALJSKSyL5T/rssBiBoPOJ/LpZCHWGAjjQL6DuRRO62oF4NpKNnAveQsR1o/wtuAHr7hc9TwQ7Gda4L30jTx49/Yfnz+Cx7fX3d/Tx97DTTYl/Kbt3/jdE1+3yvXLE9QzraU90dxyC3twAFh7cvUTzKVSik3i4/HI6An6VrfypnP9NN5t1FopUlwP7cNXHoRrYZwePtuGBAgqu9YoGzoRlKoFzYLIYsl6CaRhoLXsWLMT+/phIoEYk7FgxBays30h4goxeNlEyxdZmKB2b0S6IdQbYLj22VgWdqvZWTBAemsSG9pzsgT1+qENXIHuVt/e8m9t5XtU/eJ/+7CqhxRV3YPty0u7nmxcfP2et3NSK2GIIsaoLoFYca/LjI/qDRtDMM+uPZ/mfQYx70783zQMXF8/4+rqQIqRvBbGwxXH6+dcP/+cjJJRSlXWomiAiBCGCUkgRdE1w7JCznZB0RjgpVZj1I/RuCob80Wt6LravVtm8mo92fCWMWvOzOvKvK6stQFwdgprHyLsz+TDxg9ace311bnt0zZob88/72EwYTcBz7/90PFxdsTleDfF+eRP927X/sR2r7eF1pTU4xNtm3Bt/0KxRAOKmOLy/k7XV0cG5zOrTnHzSW19/Ki1UjDFZaz70nMinl3oOaj9OOurpgZ7VtkEtHqoD11RrcRhcqU0MIwTihCyWdFtDtQQIA3EafS8WluMJpBUlehhvd5YsYWNaqWqC0uMAFh3iraW3BWKuHY1MIr/z70+u97qRK9brqqDPfbKa2c2No+pFQvvmeG31kfQjOJ2bSEI1Sb9Du7t9UhKB7/0XNH+3iNe05jMENBK9JB6EENsChY+TCl2r0piRJxnMoh4DzXjZ7y+fsbhMIEqp/mOZ4drrp59xvXLL5jLymmZmdeFUgEVVCLTdDRvVYElUzmhrFAFSQ7OUCUMIxIHJOTOuC9a0WxF3GW2LswxQCtKPi0L97P9rbVS1QyX2jkK65kM+Rin6wetuN443klSPualvfvuH9z3HlvbfaSPbPfUHs+drQ88sw8Z+ujL/kG3fre6Hpq9GxJVIms2xnIJBu3VWg2YkRLpwvr/ND5s1FopWlv2xoUMZiU7crCF3ja4dpuSEXVZL61ANEQUMc5CMAQglvMpWkADaRw4jIl0GFnmmdb6Jg0DaTQS2FqLszFs57oV9G7JeZtGDRK993aacvHwZ9yaFCrNc6FTRIEJw9oonZzXr0HZY0yolN7RuIWltrIxvy8dKdeO7vt3D6vXeWHvg5yXrWhngtjeXwIOzFsytyqKQIpGdIxwSFZUPJTC/bqwqlBrZhoHptHKw3WFvJyYZOT5NCIUpjFydTwwHQ7OWFPIwOHF50wvPyc8e06qlWldCHlhOE6kw0QcR9LxaCHdojCvaA4oC1p9/gQLKQ5lZVhXSq6s9zNlWSjLTJ1PiATqMlM1Mx2OhCGRgW/v7vju7o5Xp5klF+OANCebombYXnq5Hzp+TRTXpZf1mIf15tDaBx7t0aGPvXvLkzpb9A+O8Paw4fuON57O5Xfdmj63XZUWQrEci7WYSKSYPNQhD3ocfhofM+z+11oRMeyaKoTQ8kGewwn7idLmX+08g7UUJLY80KYMxJWIOkgg50IdC8GBH+PhYII7BGdfkJa/t9ox7IRqLV2eB+fPaxOuFeJu4eedAuB8qohqh/n3ol82RShE8yTqNsksROn0TWiH1G/Hrz38GBx6XhvTvYccY0w9klVycf5G86pCCN1l6xRRbijsCXz9VnTvTtrrunkvKQSGGBlCgHXzQFMKPT+YTydiWRlT4NlxItRCFCtYTmnoodbheEW6viJeHZFpItRKiELKARlarVayuj4RO6Fc0TSgBRiFUB2VGoWynnq4sJRMXmYrXl5nK3vJ1qUgDoOHCQvzUrhfMvfzarktmoHvtWz92fUZ8I7z/uH4NVFcPh4I9k3Y7kOK32848PGbf/bp2cN6erxZsT6uzP4fG20huiCzpLuFQmKMNOnQcy+fxscP2WaQFdDWDdwQ1BB2QTDXij5FjNbLiXirUqUgJSMaHTXva0RBpTqc2ULBqsVChmEiDanXP4UYe1hZ3NMWMOJWO0M7j52XBFvorIUsTIFJVwJniqv9Gxq4Q52RHW9hEkwRivQcTO2ksK4gZLcn3UAB0oAYpk3Iu/5aNo83YoB9Lymn/TBvsG7b9Hu4yzvu65XUj78nrTXFFUg7607EWEm0WFuRZTlxBUwxcDUNhDyTgtMqpWhUXDEwHhPxMBGmCcYR0UqMYmTGIuZdt1yihIbqMcLcZN5m0EZ+jCuthETvgL3OlPkEy0yNFS2b4qoSWHLltJrSul9Mcamcz9lNDGzh5A8dP3DFtVNC8iaF5KEtYdd4b//dE+G499EN7/wM3u2BnfXm+qOPFb7TENl7W5vH5TPdW6Jb2wotxdBqKhA+9eP6XkYw4QK4AAWbT4VarZVIxWptei1O+23TcN7LK1eznEMDWNCg2eYtVT/GmheyCOspMR4OXWnFcdyQaupt3f184n4x1NI+BjYmeuvJ5KfG2SaUujH+9iUgcmZ8dsUDBr3PzfvxuVYVmne1W/NRwrb+lC3n5i1Q9gWyghUKl+w5t7Jd1z4FoGqEtJfgjLadPyVUrdVLqQZkigFnu08kh5FHEcY0UMhoMLb94yBcjYmrcSCEypgiQ4qIWKdmQmA6HIhTQoYAKRLjiK6RIIFS1o4clZwJabD5kxLhcEAHY8IImtElUEWRYbJc52AoRs0ZXWZ0nin1xLqulJKJ48CqcHt/4uvXd3z9+o7vbu4dI7m7D7v3HZvxEeOHrbiaQH+AKNy/3Wat7DeRR7Z5RDm8m77QC8dXd59un+wf1sNo3AON+ujYPf53PLszsfB+o6+6baHvj97TX1woMRHrxRQs7PKu1/ZpvHmEEIky0IuHPcxlo3XlFVpuJkjrO2UsG0YqKFtYzCdt9KLbltupPnM747+qCa55pojR/M0ixlfozQRDjD1MtiEebZ/9DGtTsi00B+yVkHtHrZj3rEjVQ4WhdS3FPX1f/8ELngOmmKq4UiqFjl5k+9NqSr6FBINIJ4LWvfLy0CHAqt5ws535rolny+X1gmnYvK0zJdcooOxsbY3YPcjLypILuSH7VBmCweanITImA6gMQ7T6yHHktK4WNA2h91LTmpFxNERvLax5sfMshZqz1/wJGiNhDDZdBoV1RrVAXojjRJomxnkyBKeqKb5SyMtKrcUMnmAUb0suvLq55+60spS6Kap+T7TLwO/DhP1hK65Hxt5/eoz/Tx559fZ9fojL83h48FFL40kdpG856huU19v02pP6bIvP77Wt9H/PD9FDPcgWigBvHhkgeIfkT5rro4d4G5mmuBoRrv3fHkwrYBXB6Ys859LCh21o64BsQlpaTnI72q77r+93Xf15W2ites0TIp1xPHhLm3aecjYn9p1wtSsgxeqmcGV31hxTtzCbhQR9hnbwic8sB09UCSC1Kydtyrwv4S121WmHLOa9LYnuPfm9CYJU5zDs53xeK6YOPmiNK4VdLyoBQ/WdC++GeBRn0SjF84q68QWm2BSXkfCKWpPWmAwSL85eoWoM/2Vd0Zx7OFX8vjalWmsldnCEGJ9kw6FUU2oSIyEZ435MxmWpbmyY52asozFZvqyUyrJmbk4z85ppzq8fwR/jPjx4aei///g1UFyy+1cefOwqbKd8LsKLl/L04S7ef+zjae+y7du+6Pt6nzN687Zt2T89Ni/rwZ7a+bg1beJT+v1UVXIpjia08Mf3CSz54zu2uds4+/rownirdeqevxoKL6gxfwdpCEQTmFIb1a1zz7nREzEknIpgxakGUii1ED0MZ9NSKMk5A4PVKpmnErtgrw4T7wLN2440pWYtvDY2C8RbeoTd+hZX3vuwoZ+rFShb6NLQa3RAxU4jdcV+dh+bgvQ11nt09QKtbTFLCFB8W90My1wyeTVOvxCtnY+1ObkQB0FMiVXvGO4GYUzJn5O9bzVdEiPPjgeeXR24Po4sdbH6rxgYp4G7+Z66GnhiubuDGEmHA+Px2O+JhVR9LlT3nGgtXTwf3ZKH3u7EctWmuCRE62C9roDXpAXjpaylcpoXXt3c8t3NDffL2guPOyClXf8jkZsPHb8GimsbLWxwLmkfl5gXKuyJb988Huqmh0+kb3P5lTaro0F+nz7jDup79Ns3nN1TE8Qt9keV17sqcvFjCI3iGrCFlx2GbKEQzlBjn8ZHjLM54LGFs1C4e1dyMYM9j1PyioZIiEbHJS1WWA1W3qiZOsGyHcD2EcTbyQsSIhAMkt48Jw9FWa7JPssO5NjPs17U61yEPWx3ptQ8BCmBmGIPIwbZeY1N2LLbeVtRatoqhHh2L7QJZ/Baq82rq067lHPm5vVrVi+0PV5d7yD9to8W5Kyqvd6qfy8eqG3eVIrgMPBaK7mqKay9TyJGjnw4TCCBeZlNMQiMEX7zx1/w+ctnHKaEzoGcV+7ubrm9uWG+P5FzQcLCMAzUFCnHIzU/Q4bR2qMM0b1zMxhUq+XrqjjIwnKboVYkWz6sGRpVzVioRcnZkI5FrTt2moyU+fb+nm9f3/D1t6+5PS1e8CzOtajsG0hqC0R/ZMzw10RxXQaiHi9GfrvV3/yzj/C2Lp/Gk5rr8si7wsfdwR//lfBgw77128/8jd7WJmG6JWtvt/0+vI8m7KrSwx1tP/KGq/g0PmD0nJF7HbLLDbHn/YNNkGO5Ja2E4J6X7vNQTt0kAKELY3BhD733V4eXe8hNesjPjbCdV9PzVP0nrrQa56HKtjTK1uCkhwoDaPX12Ky33fHa9tsr7QbS1oeLDkHv96yd/kVeyk+a7Czn50eQbeFceBLdsgyC1N25tfvSIhPqSqs6unHXTViwbsdVDYIfsALlMUZePr/meBiJAcZpIARj6n/16jvyanm6GAVdZ+o6UtcTmlcDXwQDS1VvSSL7+1tAs/fU8po4LYY4bcrLauFq91arF5RbOjSQa2VeM3enmbvT7PWc+8fUQwFb6LR/+uFy4QetuJ5SMG8S3U9HB9+8p0f3qRf/Pjm2h/d9jEbc+f4/bObm5Xl0MXX2yb7qv/3+PG8oZ3+5FNacWZaF1DXXFkz8NL6H0W5rezZePxX2YbYuwF2JaLFQT21gg4EaSvdsUhysIWOMvckjmBVem/KplZAiBCvTVWc5388JxZodGsmqW9qlnm1j3pPVMeGtVzbl0nSAWFE1FbJCjGgIlh9rHuFZGEL78WHjNwwhGMuDZqO0UmtkCaChksuGBAzBUMeth5ywKZ4Ox28dff1IewUaQrBQn4QHytUu0ZSW/TlAI4SdEIdxGBGJpkyWmSjKlCJffPaC4xBAM8+uj6Q4gFb+8A//kON0YEwD45jQ+R4dIvV0oC4n4jggYWSYJjIYMAMsNKxALrBmA96shnaUWuzPm0jmdWVZVlIIfg2t3MEsmbUWTsvKzf3MzWlhzmXnTe4ej251b48aDO85ftCKqw3p/3niy8sw25Na6AOUwT5MsfOAH4YRH/3R+fudh/O0Wr783Yf7hk/uUx7WpVjoaFcMerEwQwiUnFmXhXVdGOKF1f4pVPjxQ9naxLP955Krc3s+7nFJA6jvDSgLBeVSqKUSYyEGT8pHAyRQxVtaWF7rcDz245SSN6BIC885ciKGQAQT2F3xWb8p4xtsABNXNmp5tTabo5+rkdNC1YKIhaeMHd9g4K31hrjXGdqasTegpkQlCKKWu1qdJNacgeoCuvbysxgCL19+Rtkp7XVdttYedoNNUe3qxjZF5kqpeZBiBl3x3lT2pz2k3lCbKOQ1s67GCVmXhcMUeXZ14NlxImqmzJnD9RXHwxUhJL761VcspxNhKByOL4h5JS4n4nyP3r9Co0AMhGkkZpBSkc58a2Ab1hVxwl1qcQPCG4bmTC3WXyuOY+Nx9nC0si4z3y73/Orrr/n5l19zN6/k4pN0s50ADwXX5rXV7sl/6Pi1UFw29ta/f/KId3X25fc43iaW3+Yen3Ok7T9/837fRb297fePnpGfT8uhbN6X/Xdv9brosgVcai+KBFNaWvd9vj6NDx5nD3qnhFwJ7O2yHj70gtkYBXbw77M9aUPQeZNQB5ab8NkYDxp4ww65ee9Va88Zdeh5P77sOgAb0zrattvQdxK0X0qjbrKw6IWxtAvLtQsRF/zKtl66+SfOayiKirCUbEpF1VBxaqTDpdR+42L0MGM19GQPFrbiYwQJ2pVO793laISm8NpTqqrbZ34vjJm/esNF29eyLCzLyrqsBK0MceT51ZEpRVP40RpNDkMihIHD8UieF/MkEQZVYs7o6Z5yZ6zwGoSgV5ALwZKJvXuxLoshRYu9x3uCWcjQIO+1FOeOjD4vCiLJo7uVm9t7vnt9w7evXrOW6kzw4ha89DnE7vr7tH3/FdDHr5HisnHmsGyf7j545PUbXTYbu0j3W7fZPngorD9afL/xNC+/3HtFFxLrybDheWjQ/pFNGu4RXXtVq+pxcm1RBA8PqHPKfVJc38c4f8It5LJ5LmcLoHklbZ4H8WLbc8492c2NvaHUPGVhy521bVqeqoURe8gNOkTeLHRnAFS2fJns81attsy9QzWvp+XCtG3fPgsWrjQF6IrD55fg+bSm2FqkIMQeIjutmXVd0Fo5OtefYMS+7BVkE7YtNCbBap/aPTfUAup5q7C7762+zhTyprga7VH1v6Im/ItWSqnM88x8WqhL5hCVw5j4/PkzxiRICYQhGQFvNLqn62fPeF2+s/WmygBIydT7O+rtawJKpVp35RAJBFNO62oUU+uCLq04ueyKxZWaV/e4CrVmqg6WJ60FScaakmvl9e0t37664dtXN+TaigQ2j79FBvZKawNqfPj49VBcl8pKHn79PTtYbx57y+J78DXOVM6ZcPrw0e3VtkjfuG3zvs5/DyZkqshWw1KrM5fTF9RjVv6n8f7Dcih+71v0tVpLEokBs7ubVwIEIYbts9Dg7Lm64jEFEiV6j6zoNVjix1PUC/caAW2vzWqJ/mBceFocNbcuVnyOQbzPFWTox7W50QScUGWbJ5b+2oVLdqHQvK7mYSTLyQFQqx9Tu9JRzOBPQzIPKgilZJZ55v7ulrzMpM8/J3n9mSEUmyHb4ON27UECEg141I0FqntK5q3J4Fldv1+6Xyt+/hVlyZlVLXVXNVjbkVxZ12KciyqglYRyHBJfPL9iNHin9yEzsEUaR8YqyKvXlLwwn+64ujoS1Z4B84ni67IsmWGcrIdaLrCszoSxdM5BXPmra5qaV9blxDrfk5cFOUymCOtKIFJVWDO8ur23v7t7clW7dzuA0MO6z+1/H+Nz/Voorof66hFUIQ/fbp/JGzd5p/GUYNZHNrgM1bDzamATPH5GD8+pPfTzbx7f7unzk0c+bkJp//7x4dDr6gWOLdxQK1RtctN6CD2xh0/jfcf+me8Cz27hS7CaOfVwjQmNsBUGa3Shz5Zj0tahd2POaLVSIcTuyRkIrn0eNuQg9JCyCSr3gKqh82KIiNc1Sa/r2ilX95CktnnnoUpld064tyXWWqNYsW33/BWkNnFY+/1RsLqyFuKsagTCeSWvK3nN1q0bT4n5vaR7hpt3+FgN0mMtf7Q/I21ARlovL5FAVlN21lbS+tjlXMhetduAL2MMTFEYg3mKJqYaH+hATCMh2mdVYV6zoQjHgSFGNA6oRGqFUCqyFqtBL9X6b+VMXVc0L0bztUOCNo+rrCu5kelGa7fS4PS5FE6l8uruntf3J+7n1Tqgx5YL3+7f7o5d2Mgfbs3+WiguOBfacvbiofKSJ776oxu6PaNHlNZj4/L83vVUNz35SAjwIoFvm23BZmETHo+fULt3u7yDs3+3ya/OEScOqzbWhk/u1vcxWh5UoCPeWmhqH4npdUQVF24WamudkxsyrxWjtrqnLacpnTTZBBVb3VObAXsD5yKOrtVCX7VWaqwkBlJKG61Uu5ZH/raQtl5sd/7alGM4F4ztHKUHqQxJpzswSFc0DeodOy1Wu5RtTXiIstT9KfVxpriqbp+JNNatLR8ozsyv6gANU1zFATKlbKjKAByGyCFFhiDOPYmBZ5xfMoSEhNoV11IKkgbiOKHJyHMN4SjW5Zhi3nNVpBRHEi7UdUG1NMuEluMqOVPyasqtGCN9jAaCMVh/YV6U17f33N6fOC2rM37I7hk7ybFs97Pdk67UP3CEt29yPv7b//a/5Z/75/45fvaznyEi/Bf/xX9x9v2/+C/+iw8m4+/8zu+cbfP111/zV//qX+XFixd89tln/Cv/yr/Czc3NB1/EW4X/TjC/k8J6xFl7n+M/mONvUlZ9400oNUf7nSDvH6l8nwKFnN23livo1ng4+53WSlmswZxoscXRBIFT53yKFX78aMzsEsMmvGKEEFwobhb8uq7My8qy5l5bQxyI04Hp6prp6prxeCROE2Fwep9hQFKy/bVZ6C1NGiVT85CCN0UMIdJymSiEmBjGiWEY7bsQfR+Dh5H8GpwuquWjGjlzZ3roeS47j1oK2dvZxxgZhsG9qNpbmNi5uVcSjGDWQl6LowmVaZo4HI6M42RC1REhdk7ngrWHueQxu/NcSDSo++aJbt+XYh5KLpYfiuNAmoxVPVcLE+bV8kmiyhiFn37xGb/5xUteXh+IokQJpDQwDgeESHH2jiDJ7m0ckOMVHK/QYSJrIBelrIX1tJDvZ+ppQdeVOs+U+Z58umO5v2W9v2edT5RsHta6LuR1Ji8n8jIb92FHYBZyqSy5crcUfvHVt3z9+o5TUdIw9oLzdrM6d6MbuR3EIh8nD97b47q9veUf/of/Yf7lf/lf5i/9pb/06Da/8zu/w3/yn/wn/f00TWff/9W/+lf5+c9/zn/9X//XrOvKv/Qv/Uv8a//av8Z//p//5+97Ot2D2nsEPHj/mGB+J7XwlnFuHV56Fg+zW08/rM2Alkc+P3e3z97v3vZQx1Nnq48egnaEls3YLPsWy+GBMrfojfV2qnml5sUsQwXV2g3nukNSfRofN5oS2brI1l43pRjkGDFmgtrQbv65cdBVQlVUDeQQUvDeaUAzQ+pW22RzK3gEMaCtKFcs9KgVE2a4YFLvyaYjqspUDYSw8RXuQpLOjIHINscD3SvEw03qcPXq6D5jpI89OrCR4W60Ri222XM2alRXhGgdndXr3lLsSjrG6MXOjU9xQwLa8bWzXzRm+4aWVDaPtD8a6CCN3Kid/D7CZmQUz5PlbHRRUQsDlWOKXI0Dx3FijFYkTkjOG+i5os79CBoiTEfC8YhIy8e1qIizlxQDpuS7O+oyU5bZkIb4eQXnkXR6p7JYuLBRfwX3sqsqS668vl/49ube2TKqscp3RdWyWNLn1yZA3tMzeGS8t+L63d/9XX73d3/3jdtM08RPf/rTR7/7n//n/5n/8r/8L/nv//v/nn/0H/1HAfgP/oP/gH/2n/1n+ff+vX+Pn/3sZ+98Lo9e/qUCu/zinT69HBexkDdt+YSEfiIA9+TJyIPPz4/d9NV+n/KB2sEERNv/2/fRFZ8jyhpstosctY1a2KV+JILo07AhLvCtvkn9HhtDqgnMLVxVPQRltTNqIaZUQAIaCoN7QjEKrUWKuJBvOSqqOqM8binviGUlGIS9Bv98C+e1BIeoWsdmnycNDdhY46EZW+3g7JTcNlpoDwWVgIrVZm0KVrfwaTeULGTdQCRtLjZvrbU8oTW6bCAWTFkaz2CrO9rqj7riakW0NIOwPjDO+vx35WJK2gqVi24hwqYgA1bDNgiMUZhiYEyR5IpLG8NJ3/lutYZgjPDjZMpqXV0etEJsu4d5mVnme8o8o+tK2oXzNUZaX7MeKsyZiCmthg6tCmup3J0WbueF05rNeFJXXGd2drOW6d769zH+SHJcf+Nv/A1+8pOf8Pnnn/NP/VP/FP/Ov/Pv8KMf/QiAv/k3/yafffZZV1oAf/Ev/kVCCPx3/91/xz//z//zD/Y3zzPzPPf3r169shct0duG7D7ef3Dx8vxDuXjF++ip9x4PBbjNwMdCdk+p2u5keZjjvcbl5rbizSJtZ3QZy/TQSSMA3b5Xas3GFl0LyetMgrR8ilv7bQefxkeNphg2A3ajf2rPqXlatSp5WVndGylZKUVJw8AwFfQqMIyDddF1Tj8AqhK8g7U27p6meHb5ryawteVGhLPv24jQBXw/VR9NSG7Xd2E8+X6Ct0+hqudTdUe+y/ab5mW5wumAA99izdnWTgxEJ4hFMF49p7wSweiOaN5Qde7NakXIrrAa2XBrRU+/RotQqHsnuaH1RBBJ1Lp6eNDIade8UoutnSkEJklcqTIFSAJDCAwpoQSqRIaYCCkZ8KIEQ/WGQBgGmA5wOIArI3Hv2WSFGZh5ObHMJ8oyG2uGg21CSnYfVK3o+HRiPc2UZSGlxBATMVghe65wv2S+eX3Hzf2J2e+rGQMXcqzNiV1+do9M/dDxvSuu3/md3+Ev/aW/xJ/9s3+Wv/N3/g7/1r/1b/G7v/u7/M2/+TeJMfKLX/yCn/zkJ+cnkRJffPEFv/jFLx7d57/77/67/PW//tefPOYW1HrMB3vHkOB7eK/7eLc++HT3jVvB+tgml4dv6KmL14+eZ9uXXO70oVd29s2Z0PDQBRfFwd0S3p25C0xxn+pMwGgBLQiFIQVKisRkOZdikSuivPHSP413HX3Bu9CnCYHgXoLltzaGAkw4U1E9UavlIXKp1iAwREJUgij0EJ4fB1eHeaXmQqnFhG8IaFNMAeuiDBuNTzPJwYR5CwX6b7e5Le4pNe/xscvdwCKAew0egAqR0MJ3TbFi81aC9D5heV9DqMZ8oRKQJAZu8PtqxoCz0UtxL3QrMm772Dea3ASAbdevTaV7bW3/DZxRqrKuxVCAfllaC8v9HcOYiGPk5bMrXlxfcTWNbvSZhxs8f9c91modsNMwcHV97Yz0xmqyOlM9akhKzZmyLNzdvuZ0e0NZVkJVBgfNhFJJ7qWWkjnd3bGeTmjOHA5TLxtYc2UW5XbJfH1zy/1abJ17nnXnR/PYQ93m7P/NocK3jb/yV/5Kf/0P/oP/IP/QP/QP8ef//J/nb/yNv8Fv//Zvf9A+/81/89/kX//X//X+/tWrV/ypP/Wntg0ehNIeaqGPuU1dRzz2xf7fy8/PXr+b6Jb9K7+up879yfNqR9tbNCJnyeb9DjriSi48o0fim33CtSSrWoFiySujGNlnCMELLsUZtD8pru9jCK0OqtUcNSFu+R1V2ZQH7Vl5kWyp1nbDty9rpsRCHCpSoykh6IW7XQiFAFGhWiitASd60r3NT2ejUCdh7VOt7WvXu6u3XekoiHaFuv3Tp/9GImydtPc35Hxt6OXnO9Lbfk47oSm77Y3kwT5ppQPix2vKah/daMqr5b1qo7Laf99ChEiPWJTqXI6q1jokrOYdaSWIMsTAs+PE1XFiHFJfjyLBwpnSmnduCGARq5mbTyfmdWFeZpZ5wdC9ajVaHvo73d2RVys6bhRfwQ2K6oXIxZWcNeGEcRisZk6ErMpSK/dL5uZ0YqmV0rzu/WjCSba3l+NjlNcfORz+z/25P8ePf/xjfu/3fo/f/u3f5qc//Sm//OUvz7bJOfP1118/mRebpukBwGM/nvI5ngq1PWUJPP2bN4yzGIg+8vmDN0+Mx9B9Z2rsPc/tPVVFO/0W/tnJkHbkXfqCnl0pmbrO5PmeSazNQ4zRWh+oCdvUs1+fxseMEAIxJLRmSmt2JGbpB1XP/QQLEQUnm1V6l+TWDHIFltNMCNFh6tHynLJ7Tk3QO8ovxUhTkrQwz26eSGhNBqHVYVlTqdjryPwiNvJajzduesuVbotDwhYBUGhtcnYfeFgs9hKANowxJBLRznCuSKci604f9FYk7dcxpZ6fAvNaq1ZDV+5q2Eo1IZ9z7s0fGyt9bShDdfh7VXItvVcYEjhME6elMC+ZIQYOKXA1JT57bh7XYRpBjQDYDAiHwkfr3WWsNNuZf/Pll8x54eb2hmWevY6SblhoreiyMsbEmAYOV9ccDscOslrmE6UU8rKQ5xm0kmLkMB0IMVIR1gp3OfP6tPDN7b11bFbthfEPjOmdcd8Mh/6M/t+suP7gD/6Ar776it/6rd8C4B//x/9xvv32W/6H/+F/4B/5R/4RAP6b/+a/odbKP/aP/WMfdIwHDtf+/btK/Ec115t8msufN0u3/ffC2XpUj+h2lHae31Py8v3GpqUuYRTmjG3nuV2Ke1vrTFlPUFdjM3ChVnIr4gxICh81ST8NGxYZNgnQgDjdsBWxRoqe/FdV64YbQcRBAP6X14LwyuDXCCm5GHCFse2ZzXsRIBq8neZZtLq96h6gQ9273yF0AlpDF279sUDQ0EKf5nFsCqspsIpIwlsZ06NmmxTsHlwgeMiw9GNezjmjaWqkxBc9C8QppPxt8s6/KSXuyh1rznZtHVln0PBSirXx6cfbvFx2z6higAwD+wVitNzUNCSeHQ9Erbw4JD6/nvjs2RXHw8A4GERfQkRSQlJimCbSMKIK45DI68r9/R3LOhOGwbzZAIdowJsYhLwuLOtMXlekCsPxmsPxmusXn3E4HAGl5JVlmSmlsq4rpVbGYWBIiatn1xAiWZVV4du7ha9v7vj65palK+L+OBDYup43I6Xnueg58I+BbL234rq5ueH3fu/3+vvf//3f53/8H/9HvvjiC7744gv++l//6/zlv/yX+elPf8rf+Tt/h3/j3/g3+At/4S/wz/wz/wwA/8A/8A/wO7/zO/yr/+q/yn/0H/1HrOvKX/trf42/8lf+ynshCvt4kzyUN3/9IePJW/2ez2BvVLax17HvfN4X7uaTkcl30sH6UHnt0V8X56bVwoRmmQWGFMnOh1e0IlWIWml835/Gx43WPt26HBu6UMAQd7KFuTqqrqY+x86QcLUwn06EGBnSwOHqaD6xqpNGbIQ8ElrB8oYeBGgcg+J5MVM0mwRreS2tVpuk/pse9mu/7W5P2EKH1VWdNmXgJLmtHQetNpDzyMAuD9UDCLJB5mut1r4EOjhAMQi71d7aPcLD28iWK7OiXIXUOgbvGTN0Zz23+lXzDqVz91aQiIh5PrlWAzapEoIpoSEFhhgYUnDYf+jdqg3FudXUtVtt57Vyui9MHEmDeVOjU13FIAxBiAoLwjKvHYJfEfeyLdy35kx2sEgQYYiRcRyJMVHFIfxVuZ1nXp9m7pbV+ih3pdWQpbvX7bb0f/Zv/m/0uP7W3/pb/JP/5D/Z37fc07/wL/wL/If/4X/I//Q//U/8p//pf8q3337Lz372M/7pf/qf5t/+t//ts1Dff/af/Wf8tb/21/jt3/5tQgj85b/8l/n3//1//71P/lFn6uwmfdx46IT12XLmGe2VxduBMufuVz/GJULy8tjv4I3tF+/j37fA31vOfX/hT3qKak3lagYq45AYU2IJweh+arH8cQyP7eDT+IDRkXJaiTHZU4yRvC5G56Nea9NADGmzdtW9lpaPWeYTIkKKkevnzyxk56zoZ488urcc3PhQBY1ooAMnlLD1JWlWdoO+F+c1VDo442yuXzCIWwQ0b2m8bpkrjeHBlNaGRtzCllttk9VN2TrtHpJWqA4waehA/64pV1VjE8HzeCG0ejKllNK9uK2GbL+Gt3tiYVrjJ3R1RlADhFQvEF+r9cACZUiBFIQYIAYhenhVYlNarrhSghDNs8QUV62FOq8cppEhDBzGgWkarL1MEERG5hhJMbEur1nXTIwrSy4MDpNf1pV5Nq+MnEkhMIwj4zQZqa4IBVhq5dX9idenE/fOltEMlU1hnYMv9jWhbwSevcd4b8X1T/wT/8QbYYz/1X/1X711H1988cWHFRs/GHL215Ouj2mzHVLqXSOHj2+3U15v21NbDA8/Pt+dL/jubfnDfWrvH/LcDUj8xC+feJyXXB62rW4WZ81oWQlaTHGFyBgjQ4zcZ7Ps1lrI3tvo0/i4obX0Rn6xKRRVYOjCuxXqqjokOgihxi35Xi1cOM8n8rpwe/ua480Vx6srhsPk66RZMRauAyAUyEaoDAJpQEO08F/jIAzChszzORxGp+fZSfg+FTwBo81C8nWag7GV52zHsAvujSG1Fuvwi+5kUTtmoNbVYeqm4AFkBx5ov8nZPB4rMTAPr8Hei29nzO9WgKtihctdwV0+H8XBSHtXwwuY1WieCJGiMOfCUmpfYUkMfWvFvsbGH0IgOhM8aSCkgTSOpoyzGY6tp5jWShQYgnCIkaG1FXL6LVFrGBokuNd1YjjOhGGklsz97T3zslg7lSExTSNTSqRhMEWfIrnC63Xlm9sT393PzBUHjJjsDSL90QMbM4q/3qi1pN+bDx0/bK7CXVxNHnz4cLP3zh+9RTdd6p+37UrfsOEbzvqt337UeEyhtIWnD7dpzNjqHVLxbqk2aQNRzLJrVq7W4Errk+L66OGPpbVUt/qm6Iwlu2fkTOsNNi2loEkJnpOqobJmY1nPy8oyzwzDQBpS76hsO1LLY4kJQHHF0ENXKl1JqSMFxVwGRxPitVe6rSWL0/nlePGvyAaJFwdviHht1S7A1NjgxXdUO0W+h0wDEiu5rGiuDlwoHTV3qbysHUoTqokt67WFVWutHvYzZVoaatI9pfY/aYhO72sGWwuU1vW4KOS664DMJsKDKEnEva7QBYYqqLgCG0ZCGnrBf8lGEpy8vmqMkTFFDzsOqF/HWlf3hgIxjhQ1Q3KeF+L9Ca3mAUoIREkkKsbfa9eQS2FW4b4ot/PCTSs69no6oc2D3VQVN3vd+xTOzWDpz/HDxg9bcfVxeQPk4u8i5Pbob2w8DA++aZwnsh+Pqp2rt4fb+Ax9yhXcn+8jB9DHNn3rd2/WyNK32lu0+58r1EItq4UKtXrVvy261Crw1fZRPKTyaXzcaCS0LTwkzaNq+SE48/IbvZJ5W1ZTZVE9JS6RnLPxGi6LIeNqIe77bvVcWQMeNY/KWDfASF6p/q9gx2sKRwAt9Bl1FqVoprm9b/B7xR2wIHg8khYvwJGCUlv1YWFrRwIiiqopc8hOgeX3qYvN7V5utWdCqDtNQTtdZ4Pw+9zChZvigu71CV1peWCz78u8OCXX9lcpTSn1p6Xmcbnn0m6XBWPEenANIxIT2gqim+KKEQkWbhxSZEixA25a2xXzLGEYBkoVigrzPPuzMjb4EIRIICGEHVt8zplZlPus3MwLt/PKvbNltGfYCRR2oUIRA83s6/Gejoq93/iBK669X3oh+eUNN+d7c1fgMW3S499n27yD5L6M/+7W+FmYpV/qO4QrP3D05STnmMIQhEKlamZd7il5IZRM9GTuECMxBOZ5ZmBkGAdL+H5kq+5PowlGnwhNOHhbepOdVoqwnzi1VEpwxofuoRhqrpRKqSvLurJ6mw9wC7npmlJ63sgcIuM8RJzaQZpCgc1FaAItQPKGIQ56eBh6xtNVjgTs8l7O2TH8vK0WV/zF7ithE5bOZ4iY8pJSO4FusxERGFJiLU5IPM/W58r/9gpqazW/8T9qC71qU1Pe6bmUHZjFnpNWI+Fdc+V+WTktds+re7RRlRiVeDgyROuqIN2zjUgYCNFChe2CtRRqzgwhIOOAECy3NQwM40gcRlPIgISBZS0QMuNRKMzUZeW7V69YciZFIZBJJZuxKhZVqW60LMBtLnw7Z3713R1fvrrh1e1MNpLK7YbCmWcVHKBxNl/3n33E+GErLnnk38eE//dwo7onppw1iXu4xTvs5I3jiZO/WMMb4oqzL97lEHvvR7oCfOpc5ez7bkhrJecFLcYcHYNbeiE4QGBH3vlHo1v/+I2urEyoVcWZMmq3aK3tvFn9NXu+puRuSDVIMmwedS7ZapJqJezDjjvk3OZhuIUdqzM3eP1Wa2tfS0cmtg7I/WdtPreQY19Um9ek4BG7vQdZd+6HfW60UuZ99bCdh+okROKQGDAWCPOGHITRwoVqhL0K1FpY84qUQmnejisrdWOt1byV4rVYtdDYSwgN3t0il9U9Y7FoJhuIYyPc9XXloBlRsdYhzXNx5V2bl9ueWynUdaUsCzWvBuIYR4ZBGA8TaXSG9rgxbFQCoYJUdVb8iISZm9t7Tnd3BIEUlEMoxGgt9jwhgKqyaOU+G1vGNzd3vL5fuFsyuRkM7Tqc6LiFCDtiA8+btmcnH18e88NWXH3IxatHQAUfO94UQ3wnbfHmr9/tQT61E5tBD7690FA7Y/3dxs5R7Jfv4Sq0WudUNUs8Bk/OuuKy3zxS0/ZpfPjwvI80rwNjAe/slU05uEysamUJLV8DTfbv8KVhEzJ7p5623R6xB71mS6s3DhXxZqLFjyvWprnvJ27em4cUL4l4DcGHe2RsOS52LUu0nbH/x4WkeWkN1u7nG0y5x5TMj9utgz0qsAEKLKeku4JePBxbt+2CQdRLNWTj5m3RPd+msKvzOm6KazN2978JTSGp12r7cdqzsD+/Ty54qrNa5GVBSyUNiSCJ6TAwHo4kb1MTYqLlC61tV7EcdGoNQoVhSKzLYvcoKDW5J9keoZoNsVblVCp3a+HmtHBaMmuxVjmNyLf5DNtDOp+2/bH1ANkfY8W173mzDxU+vCUfocTepLAuNtlE9cU37yi1n0T9wbkSevTk3hw27EbeO479HWvCbrOC2zbGzpBiIHlRahRhdDYGxXjTaoifNNf3MIzuJ3VBCXhRbVMExs3X+AxbeLDRFbXuums2DyvEwBSPXF1fM04TaRisTokt3N3RhW5Vq9MI6Sre7dr/hsEU6E5JWa1t3DzEnDvQIQw79CFb/RlAqC64C3CheC5uiCkp3fJVDb0qIRBIpJaD0qb3jddxXZfO4NH6d5nurJTsEPNWYI10lB+4QVCyE/RLZxVRLV0BlwpFK6W252Zr1EKZgei1jYISqzCAEVQ7DL6KUBAi4p2jA1or690d96+NbzCgHK6vGaeRw7Mj49UVYbCeZlocYFKt8Nu8rGroxTgwjBMgfPvN1yzLjJa1R20rduyiprRuK7xeCq/mzLf3K3Op5m35PDGF1AHvmxy7COFu+bAPXAC78YNWXHtXdPehh0S292/e/iPG21wJfQdvQ4SHT/JpFbbB2t3L6koEzpqzPXS/zvb/+Kk84U723MhW+GkWozEphFo8vGHQ4XFIpBQpqiw5c0gfVyX/adhQD8SgQskb2WujGQK23IwL69oS89DbczQYeEiRNAwcnz3jcDwyjIOlovxIrbC0kebWWlz5efCnhRCbp6RbA1EDaVi4SmJEYvKIoHlDEuNmDJ2FIpugC6hsTBbazVTxMFwLS9bOjwimmEpeHQZuwJDgwArjJRIEU57FqZxaQXfzpCwcmC0/WLKhOFWdDX5X4iLna0ahhwEbOKPWSkgTou23gVqNyURLJvk5j0MkpbgL9VqYsLbQMErNmeV0MjqmUri+vubq+TXj8cBwfSQcDkbRJUJdM1IqoVSyLoRYCKVS1tLpwK6urliXmSEFlhkCxeu1LDy5KMzA6wLfzIWvT5nXS2atG9gqtjyrRwHO6rdkF/vSJp50U2QfIRJ+2IoLgL0y2l73ifXR+z+vgHryXu91xgNgxgc8ozee+Ka0ehiGc7316K9225/rqPOrO1NgZyirXagDPF/ggsDj8iKQYvAUhi92PmqOfho+PJcP7Fts1F5bhG61Sc14a2wTgnlIrR2HxEAaBqbDgWGcrKmiSFdy6otnHzasndrJ9tUjQzVAjdB6eEn1vmF+UjvrTS4vxhXXBudv62WbZwY3b+Z7299O6e0UdTteC4+KJ6W7V9CUijiDu7O82Ck5fN2pnIp7XU1x9bY+/rcvpNazC5T+SbufXSb1HbTzE4e0W4uSFipsBPv9nKtSamZdjEMwxsA0jYyHI8NhIk4HGEd6PkyFIBWlWJgwhAdKJcbAMCa0Dmhdodg5Z4QqkRWYUW5L5dWSeXVauFtWct3uxfZMt3jgvgC5Ka9tEm8v/1/NVfhHO3a9qextX3APwoPfk5P11GhraTMRHylQvPjkPBTz+PjY0367n7X7/rGJ9ITXKK6wqiPaUBA1qpyUBBoqSfnUSPJ7GtYc0shb13XtXHmN76IJXQvHyIXiYlMSYq0wDscjx+trBi9qzXXzpm0uRFSy14XZsfZNHgPRVqC2f9XyXrYXUzJeS9VAC+o5HZXiDTEvzBqtm1Lb57faJGsCXbcMqmjdclNsIA3V4piBBtXm7FjqIb/eb6u2vltGnFuqd5N2j67W83nc+o71HKJgIT2/jCrqhcy1d4JutrW08KOI54eNv7DnuFTd0GxgJ+NEXE8nUhCGw4Hj9RXxcLBeXL2vmhuTUYCCsamsNFb56HyL9pwtPKnDgOrEOlvjz1wViZGTCHeqfLvO/Opm5lev73h1d2IpBufv8mJnEPd7I8JWP3chQ9qLjxBuP2zFtQ/9nYUAd5aQyAdo9u/hzj7Y35uU074wb1O8jx39j1j/np2TvXjM2xSQSEgjGkfjXvNeR2AhIg82mSzTT3Vc38d4dXPHL5dbbm5esyyrKyFDEqboYb+jkaa2brdNUcVgobkQjFT3cDxyuLricDwSUzTBDCDW0j6IoCVAC3HJFjKrtZJipYaKxEgUE5hS1VkckrXSiIok88IkCjUX89JDa27YLB4Pdaq6xK87RaVbPdmupEJ3whcxxoYevooB1ELVRS2MLQIUL/6tluOa56V7V3Y0dSHseTiMAV4koGzIRTMMQm/f05eK80dWb/XRgBm1NaT0EGF2TsAQnPFfoueJo9VktRym5wDrLr98uDoyREfvOvV7j7Z4KBQPe1IFCgjGmiEDLHXdFGg09GXRgpSIhsSaM2upaIrcFOW7pfD3Xt/zh6/v+OrmnrsleyhR3PmWvj97MJwLqT8igfWDVlw9mCC7+/VHcqMeV2Sb/fUGxfS+AvuR8Oa7XtL3rRtcJtBUa7OcGiQ7xEQJ3u6gKCWwQ4S1hoeNJeCT5vrYcX8/8/p0z7pWyy06MGCaRoZhYBxGrq5McdVauLvBQAjFOvoOg9UojePIOB0YhsHqnTrSLdCaMIrXZmmMBK1oiUgUS/prIRclYK08kOxoRyuG7k0dpUANXVnZuAj1tc/a3y4EKC0UiG/vjSNp73fK7rJQvk3D3qjF82iKIQc7aIWLSIOAqCDqJMauOLRFDvy4IlvobwMe2LEsj1i8Tk5BA7kU68DsnIm1Woh3fx96HVrYFfTSQooBiWakxB0QJ9ZKqGo6vbHfW5ij/9XS7vkWzuuKJ4YNPIOwKpyKkrPyei28WgqvTis388L9msnOcGKXvOMh1b7j83vpGz8qwz5CJPygFVf3qvy1yj4ksH3/8PUbxqXF8Ngmu2TRJYrwjQJan3zzyCm++am+w2k+3M17KfWdf3UGammzNhDiiEqiaGQpC2WwmhEVb3SHR4/qJ4/r+xj388LdaWEcBw6Hg3HKDQPXV0fGcWQaJ6Zp8JChdcDVWyXrgoTAOE2M08TxcGA6HIxdwQVzI8ANjcy1UTlVg5SHWghroupKLZUlLyQGIqAZCM5TFyMEpz1yga8eRn5y+u3D5f31hdJyYawezjsLMbbJ5cJYdQOMtDYkjW3E5mTZCqrFiratdMwUm7hSqYGNXcM9qR729i4I2sOym2dUi1pBd6nGwK7CkgvrmrvSNuaTBlBpBd6N7y/0IvAWMgzB0IhDiN5OxgqnSQmiKa/WctzCsljOqqoZLqX6texuu3tnjekkK5wK3GZl1sy3S+HbeeXb+9kU12LtTkIMtMz2ZtLL7hHI+Wf+nwfi7Y9tqPBMWT0eKvwjG2+UxNvia8J7H+Ddc1Fs1sjDc97bo0+bLH9E1+lWWSsW7Oco1gpB4sB49Zz19jXrsnJb7nmOKaxTtdi+KTFhXnNnHPg0Pnz8yb/v7+PPTIHDOHY2C63FClejodJKzizziXmejb1kMCV3fX3N1dWxM2tYmGkXX6uOrLsw9uKQDJUnVkSal4V1npnXFUoGfKZnIQJhHBCih7qCM8b6Pl1IGtu8mAdw5jk1BcS5Amvfy/a6umK23UpXAqqVsq69QFjQDrAo/XztOLF3jgbU8jYWyitWlF2Kh0a1h/tK2XpvraUQvdBXwVGIphwtvGpQ+9JjiVYAXMW81jEEkoMzLoV6lwYKaUgM04E0DOZB+rMPazK0pgSHvme8M5ndNsPku+e1eZlqHVYsvBsTGgpZAq/nzHf3C9+eFm4k8839wtd3J/7gV1/yzasbTsuCAlGS1aC1WeKhzSBNPshOEu9k8uaYfrTY+kErrs5g8VgO68zrelyhPR4A5C3fP64sLvXYpqv0wednP9ov1rPxiHv9Vn97t8EDxfrGKjH/yVao6tJj9/N9rtA8rphG0uGaumbm+3tmNbDGglAk+PMRStks4E/jw8c4TRwOlgfJXjhanGKo1kJe1VpTLAs5r8ZmMiSGlBiG1CHILZezf8Qbw3dDnwVvg2XbSqzWGdhZI6IzwletxuBgOyHk7LVmxcACtboX4BEKrztrXAriSmMDKm3gjKacW56OGDA2PWBP3twAAB5ObB5M6xXX+m71bZytogE5qocgW+1Wzplc8haWbBNXdUPuihC9IWKQ0MOIzUCLDvdXCb0wGdSQfClSh4ExBEYRxiC9f5bQWPwbMlSIw0AcBissFow5vxREXXF5DdpmJT8c56b05ikVICssxZgxXs8rr04r35SVr27u+Pr2nle3d8zrQnGGnD0X4ZmZ0z/fhV+3fMN23EddsPcbP2jFtY3LkKAvkqdiqx98x96stN7mg33oeKvC6dvpGw50Dut/8/7Od3JWVLgLFUocSNMVZc0w3nDCCi5PFQqd/KWF3j8pro8cMRoqrAnI4n2dYgtblcL9/S3VIfHTODKmwXNZXqfkTCd4vyhFN5RZC3t169gUWAhqua6YiMkVV4rGNOHej8fliKVAybaPUAwqj1yE183j63miWjfl5aE63f11MEEQAi6kG+VSn1em9FqYUJ1BXb0NDO7kNRqjPZXVHnRSayHnleLnE+I+Byj9nA0V2BSX58F2TBsxJlAj1d0nEIxeKaApMYTAEKwVyTAkVwqtyNkNBmcACa642n1GjHVEYtqiTRdBjb6KXdtuhqmizmafq7LUyilXbubM6znz6rTy9Wnly9c3fHNzx839vfcN2+7BBoxrummvtHho6Pbx1Ov3Gz9oxaUeKvTpfiGWzz2rZmfobus/ghPiTHfoxRdPjN0cuNjZ2VYff358YHCx2QJtgah1Tc0IMl0xiHAQuP32S+7XW757fcecHcEWIoFggvLT+KixLAv3rEbkWkoPh+XVuuCu60KeF7PIURgG9pOyMYTbM1T3hoSqgjjLAkBQY7vQGLrHBdZyXUMipco4TazzzOph4FKrcf2tizFmxIKUCrH0zr3sFCTO8GBKUy3s6N5VrRuxrx049DCm7OZiY8FQVTS3wut65iHldW0nTxqShxP3QfiNcaP9Gb/hrqSg/0aIKdEOEELsyt4irwHB+BBjCtRSyH4/EHGuw2yK3gEaSCBghcdpGKz3FuL1eoFhmEiHA3EcPZ8VkBwh2Dlag0/pdI+yuzSt6sXOhbWYF7lWg+ZrreQC393PvL478dXtiT+8m/n65sRXdyd+/t1rvr294/Z0Mng87GrBNtl0Jtna3NqZEpx9t71pQK8PHT9oxXWe19rG95H5udzH28KKT+5FL95fDOkKYf+eDWIqT/x2f4LvOQO0xxXOj70v5NyjtC7y5X5Is8ZVAhoHZLoiXD1H54VFIjJOCGptxsv6R2Mo/DEbd7e3vLrPCMoQLWy3zjPrMtM6I0ttfdAqyzz3hpNDGqil0uDznbtQd/yAar23olodT2zWeQ8tOpo0RIZhoJZiSf9SDThRLGwYMKkkbPlRc962uh5RRRwsQSm07saAMWFU3y8KGq1Bowtc82wsi+rmq3PLN8/I8l7qNEtWwCxYbeGG6uuRNdWuAJtiEy8GlhBQiocam7KkN2gMrXmmNq/DqM9KtvqwXAqF4MdrPcD8vHdCJfjxQjQvLnrNVUzJjD7nX8SJgTfFasfrqEK154B7m8bdaKhBlYCKUhCyGv/gV7f3fP3qll98/Yr//ctv+Pr2nm9vZ749nTgtC0uu/XgtdLkfjxvd9PXeddnZb0ywPfa7dx0/bMXVRwsNspFZPrLVu/lZuu1EHlGBjyqMbdE8KDLeffK4ftmH4Nqryyf91pN+99ETC+1GXZ7vTjmdKar22QY4UYQqAZUIcSBMR2Q6ItOBNB6Qmmlhibexenwabx/zfOJOZ5IIOEBjmU/Mp3tQIzpOYVNKVi+UrcgUE2ibIjG0W7efW96pVKpYgr/nI6QJWS87l0iMyUhnQ2NN8bBbziDRkvexIBoRLaY4FDC1ZvVe2cEUOdPtNJGzMGFXRvj6dsG/tWjZVpfu1mE3CMNm2rcQa2N+32IwF+FD2YAG26LsJ2i1Wu4ZwhYqhD3LhXmhVZXqxfg9n9YLlncLW2QnClwphWCear8Dzar1/GOk5y3tVP3aCj30WtU4I6sYfVSRwKrKXCqvl4UvX9/xq+9e8/NvX/GH393w7d2JV/cz9zl3SqytIH13jtuJ91f9ki69q0fm8qXB/r7j10Rx8R434Xu2/B/zqPYS/x3G9+GNfB9X1S3OLgK0J7b3Pcaqw2qbvVsJFBUYjxxefMYXNbPO9yy3N+T51Pf5yef6uLEsM6f1jhSNj6/Wwnx/T14WbyuTkBR7x9mSs1n+0ZpG1myoOgmBcRy6xR6kheKAuInI3pqmPzjpQjXFRI0DNRZyWK1eqGZUYRALNSYwL9DST14ga5Z/yZm8WgPLZVmZDhMpGUBhy0/Vdkp2EkXNtaieu6qb0mqMG15GbWpX6IASY8BwzkE9w/XanNatK3F0UltEHIoefDtbA6UU8pq9QaOdhimp2ve1BzqVUr0Y2rZrirMGC9NWNXBHyYUcsxkfbAAIGlmuKqweIlS8meeFMtl1bi7VPL61KKvCooG7UrlZMq9PJ/7Pr7/hf/k//h6//OYVv/j6O3756o7TmplLofGQCAYMNeDONg3Oo0KXCq2ZQ+fpgS5ZPlJpwQ9ccXXS0T52Vknf5u1CXR/ZSN7px2cR3ncbun/ZrMOdW/0B40N+92jx5dkGnOlh4MFi78l5dXGhJlsyYrQwYCGOuCWdP40PH8MwMMpgUHCUGALH45EcQ/dAGsmuyTtl8RxPTFt+pxHQxhiJMTKOk3kgVaxOuOVH1GmZfKVZ5E17GK/WDYig3gHSvK6F7GZNiIP3hzKAQaOGah18q+fjaq3UUizEV7W7/uIL0bwsUC2OoFwdo2DKt4f/6r43V0Mjit8br+Wq2hGvzUjbPDEDZNjhPd/mLo0EIa9rp4Zq9U9NmbWlXdSZMwBCYJlPrFWNNLeHB82jslTjHm5fyaW4QrTt1zUTCQTfLqToBMbdX7Zr3inFrEpRIRO4WzOntXK/Zr68uecPv/2WL797xe///Of83V99zau7e17dzdws2ZSr7zGINaI84znsbrhfBue8P90Ml10+kt1vPA3xwON8z/GDVlz78S4Kyjb8wBvWhPf34to8/GhvzHSLhjdd08Mr3u/2fU5TH7zW8799GOXSA+sTcfcvBpqpblg07vJPHtfHjZQSQxjJ6xZWs35MeL6pONzctldVa3MSxL7DQ1Uldw9LJOyemxJK6T3VmjAXr4ciGiiH6tyASg8dGpO7exJ+LFNclZCS5c2Ch7jEjiViRk3a5ZPspKApH7uO2jsjdzBGrZ7DApE2F+v2vR+/keqagtgQhMYpuF134xOsfk+aWbknk/WT8cm8q1ui1W3te5q1z+k0UxvS1i6yrbCGdGzs/QbPh6zKnAvDmkneDFJrJSrEiIcR1f/vOUYPUeairEUN5r4Wbu4XXp8W/uBX3/D3vvyKX333Hf/HH37Fl69urCvzWoz1XaQrnX34dm9a79RTl6VNWW0fS/+35TXbnDwvu/mw8WuhuPTi36c3/ACl1S2kx7966pjNYnu4Lw/C+QJ44GU/Mt7qMT5ysg9OWx4PSJ6BMB7sdYP4NqGwj9F3RgE1FviSC0XVrMEU0SA9LPNJaX38GMeRowhlSCzzycI4KTGOg3lRubDMczd4Wj2SiFDEuAIbgSzeGj4m/PltgAWw4txa3COq1q5GxrjzuMwrkBCJUa35YMU69NaMFsjrQoir9fkaDxaWTAopmaKaRhKG1NtDtUWr7a8R7sq20tSLae1gXuAM3dsotYEf2MJZbm2uJTvM3e5PbTmglotyBW7tOYKjLulKGprOEi88lv4Z7HRaiKhYo8XWcLEqWADOnk4QU2gxuoKDrnBitdAeayHfzZTDwphhSHaNY1GGYSAlP7qHSFsB9LoW5qzMa+F+znx1u/Dldzf86rtX/P//19/n7335Jd/c3PD1zS2nWtw79MJhMUqpEMzg3IeJmxLDP9+3sNk23F4/9v3e6H2fdMrl+LVQXJfKqAnK/0cEpj6izN74kC4LezfH+/Hda/tV3/VDXfzwyt/ZG/XVpx5XN+u29HoXC5N4uwfvgtwYyDsyS1qXVchaGd7l2J/GW0etFYnw/Plz5iFR1tVyRPPaQ22l5DOhGmL0liUgXsBLLRCCd7ctaM7du7J6JLXaMAdCGN1QxHIWTSnYZ6phI6n1OW6ghU04VauaRWumZlc8aSAMwbnyjANx8z8aKMMFctNd0D9HoGqBXCgiRnGlm/JqykeCKZ7s+R5rbOmeUt1AE23dqbB5o0F2Qnb7XMWUTGjuYzvrlsdS+34tlSVnc4/E7mf2UC4KQwAhkYKh/JZSCaESVThV4e/+4pf8r3/w/2O4fsFPf/On/Inf+i1+84sfcTXBkJXAvIVe1T2tquSifHtz4ttXt3z16oaff/kdf/CHv+TnX37F7/3dv8d9ziy1smilNoUiFhsJuouytIuNcq6cdjHARrJ7VsfVlddOkvUQ4+6e/vENFT4t4rtz82D77Xs5e/H0uNzkEjn4tt9u/77pd+//ELe6nDcdeX/Nm9l41gBv73U9Fg7k3MPac7d1S72hmDx/sqwray6UWi25fv+a8C1ENnLW6GsiBmufPsZoPYk8DNMWQdxTBZnZ18lge5imOvO3toR5a6/erGCDLFdV5nmxVhYNZee3Iufi8HFDvpkAFg+nbY0bLTznTBM7mqRaihWLdti3NwD0/EojuY0hdsGZs3H/2Vrf0ehgIIHo+8p5ZVxnllqZppFWBFqKwciDCCGlzRJWg7SHaECJWmtnuyi1UAVjK193s1lB/JyKmAsVQ4tbSxe4VenUUaCsJRuggh1IwZWWqnl3IXrT0ehZ6RqoRfrcqh0l2OZSxQhjN8OuVg9ZakXEyXJbULCFCdF+bxSl1K2/Vr+xbX89rLh9Jtut2GxNn3c9nOmJqlZK0A01dQBFUfeyXAX7MRXZ5mU1pGZN3im81s7QlFX45vaen3/1Lf/L3/05N/UP+eIXX/HT//NX/Lk/8TN+9PIFL66veHF1RXLdWWtlzZUlF+6XlZ9/+S3fvLrlq+9u+NW3r/n5l1/xq2++5ZvbO7I4ViYABL90oRP09iiJfbYp7hZG3K3D/n5XvL4bPhXbHe1y6VOosI9zt/1pHbG7sXuN9GRI8H39tocHfpdHdOZtv+doc+DJn7/Dfms9n1RbGLDBance2E4xlNKaGlZnoi5WKHt/Yl0XaqkMUQivviLdfM1EYaAyCIwBjhGmGDkOiRfHA1NKjCmZMhOj1RnjwJAGJBqNVEiRmFpLhi2hPTuxaa7GN7e20CYQQzIPo1S+fv0tp3lmnhdXDpYruL+7Zxqn3rNoWWYEIaWB1YEEqpWr6yPJFVRTRKrKsqwcj+276Ig2C8Od5pnrZ8+4mq44TIeuGG/XW9a8gNDZFlpOYRoOTONAjJG7mzvm9Z55hWkYTGVUpTjcPQYDWiiDIfZyMW8rRghCLoU0jpa/qoWKkktGswn7FA3C3jxqc2oUGQaiK67SkXuWb+teXV4hmxFRPFfTJqXW7N6IMdpbx18xqZnVvL8u9OyYG1z9HBhQS95waqo7z19bG6otPCg2m9d1pTjv4LbANlBRV9u775r32HJhEk2BhpgIpVg7l7KxgYhYcTG1KeyGLrTFp0r3IU2ROtdgU2TuoTWltSj88pvv+N//8Gv+l7/3K37/q1ekYeD59ZG//0//Kf7Mb/2MP/EbP+bP/OynHKfBAS2VeS3czTPfvL7j9//gF3zz+pZXt/d8d3fim1c3vLq7465kKxHwHmUi7RouzPou9i4V0RYy3BhX2PgWZftdh9BfmPr7GsIPHT9oxbWJ2keGPNyup0NVHhHmT2qudzuXLcp2Hhp848PZtbF+cPJPnMfHnebD3e1OdWvbcK68Nut5l/jW8x0YUi0b6zXVa3wihGChphCtyL9anL8FhHKFFCxHkrWS1Mo9jYPAnlnRStBK0J31rSb0TuvquYHKvK69lYQBREADaLVEd6nmHeRamZeF+9M9L1684DBNpGQcgMFDGbVUpnEkiheCAjWaB3U1je4BgmUHbBGPKVqRrOeR1MNCVZXjYeI4jhyGgdH7Z8HAFAP3d3fc3d3y6psvubp+xuFwZJoOBriolRgCLz/7jGU+mvB2+qYmGBr5qy7Ks2fPSGlgrJWbmxtyydZPEKj3JnBqAzO0kJuAU8MSkd5FOYRA1OjgjIo4OEC1UrOFIUUExtEAFuOIpIH5/s7YIdyLWJeFejqx5Mzh6prpeEUaQR0eH1Jy5Ck+l0o/jpVbuRFVsk18n/sN3WfLYeetBlPAOa+sXgLQJHOrrcol9/VafV/N+xIv+A0SoAaie+At3BUkQpLuxalHMCRYT7IQDGkZU4XcQDHVPb+8rTHMYMylkhWytOhC4uffveYP7+64i4m7EMhL5uv1hl+8+tv8rb/9v3E9DvzWFy/5/MVzJjdI7k8nTkvmbs7cz9Y3SyVwWlZO68pSs4f9eFR+ODnX418+JW92ntfbLW+vFfwUKvShT7w+8/u5uPnvo+332sIV3+7jllT+kPHGX8lbFJjs3/DotibCvSX7oxPlEQ+x5blcQW0orb4BDdmFB2tKaYzaeatVcYSURjy8F4gBolYiFa15Mz485NZyBUZwasIoqno3WtCiRFFEA1RhXmYaaUCDIBsyytFyCJICEiNaKpqLCSVvvGihQbOSY2DrDE9liIP3TKoELGwWY2SIoaO/UCWMjopThw97KLMxVFSF4zQxpkjEwAcY25LtaxyoeeQ0DEblVDKqhWH0flmyhfpa+HCZTzYFYiQFE6IlZ5bFvMgQAuM0eg8oV0LJ8ztqnlo3ssRCbU6A0T3L6IwVVeuWm2lh46rEYCFKlQAxee2WGCF5McVd5hnzOuzc8ALpgxh5rN03Bz80tnijgaDBFrQR56o67J6uuEOQ3bRseZnzflt9afizbAbjFh7Urty3fWhvU9IiuC3igLjHGUIXwrW27+kGQV8rbT0U92SlGXMb8rCqoXDXCvOy8uXNLd/dn1jt1Ci+v/tshtf9srKWwlev7xiSedxrKQarN9PR6ryCstRC9jCqWwc0AdJ6jsnuNr197LyrR757p6H9Px80fviK61K2718/qrDa+zcphKcUweXO9OLev/lBXH77hG1zPt62wc5yeWp/Tyut/ZnsPMSdl7V9vh1PmzLT7TuziDcKoA49rgVVm2YmaBKRSqgFLdlYBbwNhkprI1SJGvpBTTGZJV5V0SwQLC+0rKt7V4HS5Z/R/VjEy3I/IUZKw3WFQBoMyZZi9LBb9ZCHW8JqAAUL6RkkfHAEXwpCdh44UaXBFkQ8vNks7pi6F3ucPASJdoLYliObUkLHkeM09WaDqsowjm50bPk3C0FGdJ7t2mL0vloruVaWZWEYB5IkhnGk5aZCjKTRYDJaDNDAziAxD7Z6D6e6u/ceUitly10B6sCMXhwcAkbxZIXH1fkUs0OsBcjrDPMJrZU0jnY/os+jumeIbdWZ21y0c6pbAKN5OQ5r36ZuRaucnSv9++16m4dkwI/NA1L/roUhz1G39r8gFiIOuuXP9oHGM0DHxZpqYTabC3alFrIMZAKlKvf3M9/c3PHqNLO44dNg6k1B5looN3e8up8NBeis/iEm4jCQBudNxKIN20o+N0BbEmoTD+9Gh6A+L98oVnay+aHPoOhH1Bb98BXXU0NbwPviY//Pm3TXex7o0U/cNn2nPeyVyv8TnH7SEu+7sa/Nqq3QuEHjG5IQ6Lxrcn7uxck9SymIjD38psEVSRXWdWathVSDMQtQCYqFWxBi0B7iEefck9raUdC9mcYmVPAcQjWanZwNCj6mgZwLy7JaH6kQmA4Hrg4HxpQ6aW1MiSjBYMWsaLZCX2plCMIhJY7jCGLFvwNuPWPCL4XAYZoYxpFxHN0TtRBRMu2IUfJZDg4RpmlCgHFMvHzxnIqxgcdhZJgmtBrzwv3phKym6IZkCjEOA6M3lFyXGbm/p+QVFQsfxhS5fnbdARolF4qzpacUWRZjlx/S0EEgpeYubwwlV0EFqcKyLj00NwWxui6nJNqDDmIaiaOdZ5yOlHUhrzPL3S3runA/n5DbG1SVsU6kYUBroznaPIJWhtHC1M2IeHrNXYS3wYEL+3yWe8PSQtE237StQQ975VJZ19WUjbPAg7gxYVyCZVl7nZaKK0MVltNs9FqYQTMlA/NQNyZ+WzuQc0XUPS0PZf/i1Q3fzQu3S+Z2XroSRLxDQIjeUkV6bgy1Z5pCJMaBKl4/qdUQlkqvhesGtwgbhYO8oyV9eePf9Qfnkk0/5Fi78YNWXJf9YDYVf/753prYj6f8qse+7693N3znjzy5gz5P3rihPHj5MEioZ97VmxzGh19fvNsryieUlu1rs1RbQqCFUlrcvhVNqrYcmZ1DDAZQqIgpgiAUUUpRikCt2QAPWkhRWEumxkARJaiRphqZVLt30sNdpRZfZyZ81lxYy8ppzWZjihDTYOGXqqx39yhtkWt3JCvKumbUmxIGIESrsRlTtGJPEauZiS6IRa3XFRgoIiVTntU+r8OAaCJFI6JVlNaDyurfrJh2dTRgjdEg4WqzNsVISoN7JAOkQPSw1bIsVAehaFULW06TUUBVA2PEFH3uWPGxihDV6n6aIaG1GAv5wJknEEJg3XHTxRgtHCrVX3ueplYjew3WKDQOg3u5wbzQYJ5XUWBIhGAhUQkQl6HnnizMq0xoR2NS23y8/LMZ2RBwFrLbef+7yd/mIWd7aMpsXyS8zXUT7lvkogGPaq3Oim/PJ0jshLgiuRt2uYWlg3R7WXzdhBBIIcIoxFL7Gs5ehlAVMjAX5VQK393fM9fKXAqneQHoSNvBw8ChkfnWtla2hplVK1HiLly5IWL3nqBKRSV2YEbYoQIfDVI9+g0PRNP+c/vJ48LqXQ37x8YPWnFt401i2j/r8exeAcVbTYxHnoZe/Hv5+mE88A2P5wnN9/hzfiJsebbxLqyhbV9PRK8f0aQ7W/cs/PJgcz23bFuX2I1he48Gs/yLptDPrrVer97Qr1Pe6Ka0mld1xi3nwrSoJ9LFwixrLsxr5rRk88xCREKj0fFEvOz5O6SfN17/pLWQJKBSu4DIasCIaRwZHH4fgVxKJ5lNMZlCpFKKdoMjBjHuQK/9aTmoUqULtQafDghIcACLEdgGDzXigJGhbIK0gQJSCMRhsEhdjIRkNUEmSMyrq7W6cUIXarUJ05QARwG6EdieqZ2LceiJrwPFCspzKYRSiA6EMSFoz6LQ2p9YnZiGgIgrJVET7Itwurvv+ymleE1YC2nvAUE75eVeffOcmgViMPiNiWKfc7Y51IjTdWd0thAd3qm49Svb9tGUneW6TBls9UqthsuOW7xGDB6CrQTz8DT4KvOwX2n1jxhZ9VyMluluXlkdtLGuuSP4gpgCDDuqKW3rcffs8LC330laqLJblZvvuTtDN/gfBqnOrqN/19a/bPLt3EF4Wil9HzGlXwPF9fRteLcb9Ijyeosr9uCR6DZpzibK2S/0/NVTz/XRY7/Zy/rocanEHijfh2/PIK0eyunJa7CmeAS0Kqdl5mqyGqaUACdjFQnEMFj7cAmGJuzKy85LSyWEzKgJJYBsymzfTPF0WilAigkJyRCEuZiwByRKT4qLOL1P8RzFaj2uiNFaqUcT6klgHAaur47O1mBCIUlkGkaGYdjx1cGSCtPhwDBOpJBMgYnloWIwVu7W3yq5Qktp4HA4oAhDKRRAQ0QVwjB23ry8rqRxsLDougJqVEqufNIwMpmrYP2kqjJOE0oDIuAhVfcwxMJe4zRxur8HVXKtrB5eTWIhy+LMDwQDuOAErCrGNm4lBo6edIt/iEblBKBrAQlIshaQVa1vV8s7tsaM+ym4n1MP5mcPSTeOzNbS5Pz3e5uwFefu6ZsMjOIKOwRCNDh7UwIhRGJoTa5k+9dDhsaa7m1IamC9X5FossT6dJlH3dk1VKneIkRC4yn0YGKMkBK3i9Ey3a0rp5xZSvVcqymsFBMxbNi/vfG4ZwwEet5ub7ZKU/vdqD2PUV0azA+jPrt73L7rwmybV80rvtzB/im9WbW9ffzAFdfZo3r41SNj0wv7O7p5Vu2BnKmzyzv+lrGzEZ/+sr0ROsLprfmttziIb54Jj//4/HT2Ctct9933bY5u3Yybnbv7XgSRaIvX+wAFNlDBOEXIYuuVyVqfiLCWwlrMsoyAOtlq977aWYmdVy2FJRdyNuqhGC00STDC2ft5sTwSllNb19nqvXJhHKxWbBgn8jxTVsv9aDRWbnV0m4hgkAM6O4gAYxoY08g4DCgWKpMQuQpCGsatcWIzaFzpCe61eQPCJnCqmqAf0kCS2JVECLEXL1uuUKxmLHqzQ0A98mQe1MDtzWtqtZ5dx+Ox5yVLKSzLTBDheDwSY+oeVkqJZZ5Zl4U1F1JKuzm8eTISDLqegJCs827BlHFnVimVNVu7j2EYkDQaahCFqKgEKsG7KQ/EIe3Y1+mKRFv4uZTmoPgU9Rqplm/1OerTAlWDvDep3K7BoB3bWqN7bpsi7D8A9ziVomZkVUCqEkN39HY1hIa6bJ5zr3/EclJm71TWdUGiecjq5RvqbVHmXLg9zdzNCwWsSD6v/dnKLoJhy3Srn9RqBMhdkbmSDmeaSHpaYANV9RvUVnxbZDTd0/TPpczYe1zsftd+uM9oPJXd+JhWRz9wxeXjDNyw//xDduax6u3tw7KvfQD9/MVbdn1hGe5eXwYXHrd3tvdN1+6iH2868CP7engS+tjnb7q0nYa2+erKV8SEdwt9uaywUJV11RWskFiKgpFaGLBCTdDYisWV5/a/dhuahW/HFqJAbSExVcqafT+ACvO8sCyrtbp//hxNhsAS7yTbhFM/drdPcfqrVrcZIOIFu97gUGJnQE9pMKRbF7TbY7eQj+Ww0FaiEP0ZCiEO1tHWFT7Ol9fzME3xOblqz096Eem+I7C4p2cIudprwoIrSGmGQa1dwFXdPBC8tYay8ehp806Sd0hGN0YKNiW3rLmznZuR4+zyaN+HXSNd8WzP1u+7e4fV/Gx/JufAi81mla65VBoxrr2pbIrGBGq7NsyDlm2bvu+wY4Tw+xJU0UgPN1pEzhnd3XjqS0Y9dF633hUt5NiqzZqBpAJFhXk1Q2wtdp9XzyVuLCz7EOQWGmzW5P78m4HZ5sF5iO8N6/nBeMTQbRP6ibzVdhB54u1uNf+xVlw7h0vOX+w2eOwmb3fzUS/rke3s3f69PpwI+/dvUFSbX7ObkM37esdh88cU7X6vb/PcmqXff7PTwxtmazvb83DDw4vZvt8prWSw3GEcOIQBVbV+UrqSqCSBIUUn87S7WtSrd8TzVB5aM2Fk+Zni4AGQLnRjsLYWpRTLZbkWSt49VhFuX99yfzqxrJnj8ZohGadbUawZZoiUouDeItBzb2suBMU705q3FMRCoaUUQlWkbtZmF75nLquHKpMwDBMhJGo1frisoBq64mrPoZUs2fECBBNEaRgoee0eE3Vn2UogRvEi2EhtAq8/c3HWC8g1d9qrECy3Nox232JK3nzQFM/aFFQQYjBvK+dC0QwhkIbRcy+R+9PcmVSG2Lr5Bg5TsvNLibws3Vu2CFzYSF3FORFLm2htdZ5Pxp6XYbtGtJBr2RkgVlxdfc4Ezzu2+9saQqo/a9v39tumHKpUoqQzhVFr9X5ii807z3sZ17AVG/f55edfxUAQhIik5N6gcHs6MedMVqPqWrLVQw4pYc03d6tv5yW9i4XZLmevRPd3di89hJ0n1N2u9n1TmE94S01j9mexM0q0b/C2032n8QNXXHL+3/cQ+pfjDT4JezVz+ekexLAzgDZLUre/J3f7Uef22PabYnqbcfRgDXT54EJBLv72S+hMeYlbh5GigsSBKoFcKzIm0iCkoKx5oWqmBiFFyxOF7joKKokqybrwyhaoaon0ECKHgwlZCYG1APPCus5kFyAOF7D+USIUFabxgBKQsLIuK/cIpShDjMQ0oVW4u7sjhJUyKGMIiPMfqrGqMqZETVYfU3QlSHVEI8RYrBh4qYYSdIRf8lxOGkaaZVwRE64V0jCYFR4ipbgYTuaVTccDBl4pzPcDyzJTiwFGdLCarBAC63KyJoQ5O92UPat5WcjL4sXLofd/urm5ZRyGM08MhWEYjRbK4fMSo4EKfCJlLxgHo+JacmZxmq1pOjAMA8MwEONAkErFW9eXSixCjEIKhvhM40jNK7mshjYchj6T8PkrDWXYldQGprBNneephZRVt3on93pEZANcYAYBTrtUaqsLa0jGnWLADLA0DG5gmHIvtbGwW/4rxEQaR+bTvBXJ+zmqbB6axEgcBtaK5+QqYRiIIVFUef3qhlWMbHdeV2YP20Krj9uZh8HKQvZLdr/Ez83xN49tyZsRdfbbndg7l1/br/ZHOC/4FlqXacS7CjQ5+JFKC37wimsbcvZiMy8+XJWdq6tuo7h7/9h22/tL3+ojntZuZl4EMe1I2gJaT5xDX/TbxGo9itqQBxOT3S3c/3f3tcfBu0ILoee1pFmUzQplFyBQg1YPyVFtEry42M4jVqOBKkoneFUt/VaIW/yKkEqlaDavSwzwYOzcHpJxpGPOhrJKMcEYKEVZlmy1Q5MVE2eFtSj3a6FUyDFAXolBKEmpeWUIkTllYgwMqRJDpORKqurXHolD7Yi8YRypk4Ak4hDds/F7bcVfSEjtwhBJfr9MwWhtnlBgmo5QlYzx9bW6oRCEGiJVavekg7Oi53XLg+yfXy2FVaH1zirVmiL2VvE7ZbXlO8QaOVZ7juYdBUJIaFkNXViqoTlz2dgiqjFg1GItPEjRm4sGpAra4e9+jC6fZctHNXeyxcWbJ9rz0t4k0gWjhOiCfTN4GgVYdcuy7tZCCBGta/emamktTtqD2iDsLYe2rquHYoMZH8t6tsq3dWFrQ9RCgMVzUKqwViUk7UAX9bDuaVnIrVFnbGbiPu6xDwHK2Vq/eLF7L5vn1Z20DVB2KbH23lkrFG7hyEeHnv/S9rt7vfv7PsYPWnG1QMEbN3jXsb+fcg4XVS7c54ea6pE3jzygneW3bfM0q8XZdNL2+3Pv7yxUeDkpRCzUtn3Qd7bvq/P4XNo82H1IBtkERghCqMZSEVwA9ERyiMY/FyKKIwAd/j5ORw5jYhoG8poN3ZcLGgIhVEJWcjFFZPDu2gWpiBhzBMKQKutajGk+BKZxYF4zdfX6r2Kgi2VerO5JAsM0cnc6OVCjggTyaiCPU1HKvDJLYQhCXWdSEOZhoMwnoljL+mkcmdJIitH2uyS/9kgIq9f5RMZDZcqgmkij1aDhRoO1GBGIVgAtGEcdEpyYtxDCSkqmuMZhouZs4a1SutCKEhliojonX5PxLXTW4fM9T2Z/67I4oMJYCoMTCzfofGsRYtRGNhekBCqm6BJidWYhWajTmUtYC+s8nzGR4O1voKJDQpMZLr1NSFNIezdeMJCKSEs52oVVWwNV6SHmqub5dhsvRaSIM91vRo+yQciLlwlELz+otVKzUSa1/FLrO9eURFNspVSWdSUpzlwyGAKzVlOY2hSie4QhejgY74RsqNbTsjJOIO6V43mwe5+fLfd5tiYf+Vd27ztz++5WPljTXeH7f7sA2EzjFt7bAzpaiPSBGf6ULvP91lqpwVumfE8K7AetuDYTYvewti/P/nl0nOmAvV98sZsnvO59CHB7uJw5WY/5Qn3KPNRLF7bPw1M9/+QtmrmF33bW5ZtGFxC7bZu1/Zjdt706X1whRA8bjazjSKknFyyZkUII3s13GKjZl7F7PGB5jzEZVD4GIdFYtC1E4+2lulBLKTAxcIwD47KyLCt3p8WKkY16gnxaWIv1KzotiyGfEX5VvvawTCavxrQ+pGgMGTUbG8aQiFpIMZDCQry/ZwjGDxcQpsHg7zEkr7Ox7+5e3Xueb+T44pZxMpaIlBIhNSWfmZ0dQYIwDKM/OuUubl7GmAaz2rVS8urUVN6mvWYjlS2ZqtnyZZ2Bwo0T1FnvjeC3rislG3P6NI2EFAgpuEjd0Hodai0WFmuAEXFUZkiJQ60sy0rOhbysnOaZkhsDh3mrJa+MY2IaBw5j4vo4QS2GOAxCrZlaBbVKXxrYREIwleFr1BSVk+JKaxWC82Ta843RcpsSIjkvnUQnxmgQeL8z0Q0rkI44XVcLf+4Bd60jtBlNieAeZas/M8BLQnNxgucd+7w0aIrlvubF5mWVwP08s6oS0gDujZ3WzHc3tx2q3w1GzKDoHudjhvTes+GhdOgyRLYftXPrXmzb8OLHjymafQnQpUHdn5YqW6hwVyRd6/s5Fhfjh624zsaWg7F323/fZ5zffmWDFNrTbI9bd1tfKpuexNyZJ/v8F+wmwkcZHk+HCvsWbf6fHexhKGGHD+mhxAa2sC8eHmVvt8E5A0eIzaIW7ucZDRVCZUwb+0CzSkOMNL6HltzO1dqTlGre3JnAAuMbdBBBVG9dsi9ilQBESrH2EVECp3nh7n4m388sS2bxjs3Fi59LL4SGED38NyTC1ZFpCBymgeM0MIqwzjN5Xjjd3VNXK05NIZHE0IYpRESFuETKPFBL5pS8aDmlDQauGD+hU1m1nFiIwVrT+9yLDl4A9T5hpriqVrIaA7mKIrKr29JqxdceChyHERXrsltrBi2Ilo393QltOxJPQDV4jke9pssE8mlerIV8VQji9WdCGBJhSAYHz9nybU4ou64LaEFLcqJk66s1xGD0XCEQovdEazNL9vO1eYOtaNr5VXb5HtQ7CwvOtJI683vPQVUrogYhRtDgz77loxozvDl45kn7Ojjja3RBLNU8dxELKa5rPiPcrWq1hIRIpw8WsWaeipE/x0AuVnS8rBv59D711hb0echup4TexZvZ39gu1/belN8DbXNo29dWp+qfb1Z7N9pFLk94PzY05FZs/mHj10JxnbnTjzlf7zg21XTuSsvuv/Z/Of/Vo37z9m//+jFl9Yg3s58Tl2e22xm0AMZHTIC+j51l1+/nA/22d8m2f5qy24+mmFThNC+EpMQkhMGEcgyxC9jGBSdiPUiqek+tUslBSHGzYBtXnTg6bRiGzkMXveg1hECKiRAGSlFiiCSJJAmUeSWsmXI6sZwWCoJ4W40YooE7JFBDQMZEvDoyff6C60Pi2fWRF9dHjkPg5rvvuP3uFaf1RNFCY4XQWhANlBoJxRgqypoo60zL28QYSUPy67WwYKuDCk4APIwDNVl7DZt6jZVjE0yKhV8zzkeXDL3XBNFmCUvvNBw0ILVQ82IhSq3UuiJVOtNIKzAWjEpKMHSneXQ28+ZlJdZKKJXozwA14yEk61u1riuDA3aEyjov5NU6IZvXXIkCDIlcMqEGVJMbiz43GuOJag/dqTZO3s272d0do+/y+ZWGhK6udB2Q0drAtPmy91KaURSj19O1/B8YCKSYl7tnI6FUNiYN/D5tpu2WTzMYvnlzoSsynBg418paCrm0kuottHke/dO++PafP8xWPTbElcs2miJs3nVTXiZazl28Nq9Ez+/bdh93nv4DYbwZwR87fi0UF1zen4+/Me802kxnmzRnCkf3E+381SUCZ/fmDQfb//uGGOb7jr6rDZp/iSbcn9lZyPCR34TQPCnzHvJqbOQ1BUIy8tg4JGrJzPNs+YoQSUOkFiOVnVGGIERRBonWYr4aM/c0VYbBUHuHcERSJKyJhmw06iUHcVRlDMLzw4H5+pqX48R1TNzc3HN7d+JUlPHqiuF4JB6PZKAEgSExHAeef/aCn/7sNxnHwPVx4MX1xG98/oLl/sTp9pavfvFL1rsTWgopJOb7mfV+Zr694/7b11BW1joTTq09B93LDNI47zDFlbPB/V0JldFrpqKgpW4WjXsYBIEoDvcGFe1eXAsxxjRACMa4H6MJrVpIpRLVPBMEU94pEqdjL6COw2R5iWpVWNXNf3VEYS0VlpU0Tt4KRYkqlOXkObdKEGvFEobIidak0kojarWQZwhX7i36uTRCZYVhSNRiubiN28/WgOWh8H5s5onj159rgbWaR149xOjQdes3BuMwmsIJMEwjuq4UZ4CRaPO3ris5G9luRMhlNc94MICQKlaaIFY0XVuPLpoyDYQwmHEYMqriHr71kWt96TS03Jr23Ja0EN6FHXnmcbVUyT5M2JXw25TYeUSmVmvhEuQh8/teZnX0JltXgbb27Xfh/Id+nsLWcPJjvC34gSuunoT+gHHm9Pgk2wMwfN2fv/b3mxLae1PbTpt82dzq9v4N59OMmrdtdHG9T20uF/8+8fMNnCHbGtgU2CNw+P63VfLbfvZKzv5ijIzTxHg4IOKtChVubu9ZTydCtRYopRRO+Z7rZy+s5mcYQa2OZc2m8Ho+Awsjihr1UppG4/NzARcHtX2uVvyqIkzjABUGEePYq5VjTEwifP3qFikZ1kw8Qg0BYkBTIk4T8XAgHA8QBTlMxKsr5HjNeDiSnr9guH7Oq2++pZbCyxcvESAvK/PNLd/88ksDVFQDj6B2bnlZnR/RoNWNcmmMkel4MK90SIwvj+bFUcmnGXI24Xt/Moj7kJiujoQx+f0xoVfWTFkz67oyXV8zHA5MV9fmoeWV5f6O9fVr1tM9y3zicHVFmibCOFIJ5Oz8kfOJrb2geDjNQnGneXXkaET1xppFlkxQIK+I9zDLxwPjYHnDdV2ss4BWa3ysleQ9y4w+KSJxA2yotjiHh3BzcYHp3nxVVF2hNcPLPdTGTViWtZNAh5hIcQC8Bq3WzhU5jKPdQ93q9xRnEane50qtfUkIARkE1Jpurt71oC2oECJ1tb5ZVZQQBr9vWJ1eKeYpZ+sO7drC0KxFWVdj5Pfs3q5o+cLYvYQ3N9CFmtepIfr6foga3F5vyk7cs9eKz7sm0M7DkU1YPWU2b3lRl887oXN+Jn/MQ4Xvev1nN3r35kxJ+Qf9XrdYb/tBszp3ez2PKV+44W/QRG0SPHn6uunDber0H4NPDn1kH/7tRYpuM98U7bmsy9H44/ZK7syrYmc17RTd9meHazmGYRihzpRqzAD39zMlwBgghbhZqgrgDfZWY5LPUqkqnu9pyfiNDHUYBkMvxkBeja1bi0JdrLVEO0ZRpEbqOCDHA5ILuqzc3dyZ4FkW6rKgznRu9148bBipEWqIlJjQYSIN1qpiPF6zeCHy8x//mHEy9N98e4ceJuNaFIPGg3lW6/09eV2prmD0629QVQ6HiWcvX5DGgTAlrr54gTGgZ5bXt7CulGXh9Oq11WyNI9efvSAdJxoIp2YLyS3zwul+5vjiBYdn1zz/4keUmlnnE3evXnE/jdy9/o71tXL80eeMV89I08RpzshpZl0y6zxDSDRDZF2zs7qvrLnlxTJ3N7ec7u+p60oCIkoKxrA/UKEOiA7Gwq+muIqaw1h9Poq5Xsb+4WujluIK35VQ1V3OLFDVCo07CbPj3XtuUK0QuDsnDfJfAyFsAruFZ2utZA+pNuE/DAMlWCNPLbrN74beYKcoduuwX4N/VtXKNJCAOhjJCuaBIFbKsePXZEfo6xfXVuK5tfmU6HArWPUyN70pIGlCr0uBTbY1YuX9b7pX1z65VIi6Ka39qe1FafMEt+N92Pj1UFzw5gjbmzRD//H+9Zu22U2EMy9ri7/vk5WPbNi/v/jmYtu2GOkKrFlEdjpy9sVjl9kBFnsl7UdrseyH5QSPhwobkKIvpvZ5U2PSGKx32wWDgMdhYD0ZJ+ArvUemBGNknAbQyjgOXF9fE4eRnDPzaYGSkRQIJKOBChFiIgwDNXjdSwiEYbCIWR3gdDIrvFS3avE+WZE1L6AZ0UwKcBgTen1gma/57v7E7f0dr+/vCNfXyOGAlCtOUpFp5Pm8MEqCJaN3M5//xoHp+hnDOLLc3qE3M5oz9fgcPR5BlXC4Ji6FaZy4fnbN8cWLXtiLNsJeRUvh//yDv0tZV6Zx4vnnL4nTQJgGxpfPzcfMK+urb5A1U5eFu6+/Zj7NrrheEqZpe+a5sJxmltPM/f3M4dkzDtfPefaTn1DWhXU+cXr9HadXr/ju269J337Dn/n7/36uP/8Rw/GaV7/6mvvXN9zf3fHt/8Xev8RMsmV3ofhv7VdEZn71OHW6+zS+Bl9GgAUS/BGSGySEELIHzGAMRmJktZHADBCIAQ8JCyaMDCNkRhYSEgjJIMRTIMAIXesOAF985fugsd0Pd59TVd+XmRGxH+s/WGvv2JGZX52qc5p7VfSNUtaXGRkZz73X87d+65OX2O/3sE64GF++fIWXL19i+s7H8KMFMiOeZ/zyf/1lnF69Ql4W3I0D7nYB+92A4ckdKHuFpht4S2BYFDbgkhu7RzVICFoXxgU5pdYrrFqWpXpmGspkIi3mVYb2okLfGOFBtBbOmRZezJp3EzTgWrdmnaBgU0oyQ7jmGy3GcQRqeDNnWFPbu6RWtiBtdmq4zCDGJCFSJ0q/eqkpS4jXKDS8taJR5pHp1RGn84KUGc6pZ/mI8NoY4NwphyqLoBRTNygurgAXzVNbRUs1fC+OdvV7qvKphYs69v7e2+rDjqzNWc2jgvlTl/9xFNcjy22ewdUiA9AJ9WZvtM/t6xt67VrprOvbsVbPemutYB0AdOu3Ny8GnVeol0DYdHCtA2Ud2KyC4ZaS2h6QNzdlXdZCz7pi+x2pwkJTaKY1JmSWpnyC7pXvBXhBUuhpGIMNuDscRLikiJQSLIt3WENUMReQKQiMRkVUwMr6LYIkpSjw+pyR4qKhX7l2Y0kFlNQ8OW8wDB7Pn97BOIvgF5g5YYoJSzpiOp1g5zsMwyDV0GwBtijFISbCNAmT+uk4Y44A2CLBYgh76ZzsRxTzCYrxgA1gcmAbhDW9JvuZgZTg909hUoQLAf7uKUzwoGCRoUXcloDxDnAZsAtol5HzEWbcw37wfUhpWZ9BTABmEGZYnmHCHmx3iJFQskVmj2JGnPMDij9g/CDAP/kQxe6xZAeEO9i9gzcjQrH44EsfwViL8+mMp26HRB7npeDJ4Q4mF8TThG//1/+GPQBTCj589gQvnj/BYT/i7m4HH5yO2aKghfV5EaCKQ1hQRBG1gSUAEWulozYXRGVvF7JhAxQBOFjvhRW/sPYb46aUmhnIgiRMWu/mnIfzHtZK3VzJWdhHtNdZk8MKxLBG+rERgNKaTCrKlQvmKAjHVFiPu47dxIxYMmLKOM+zdDbglU/SWIsYM6bzjGWJsEo5trVYuzmn8qSeZJUqdCG3Lqd5kz2X07wqHrPO7XVPK2Tr5kJbedA9vu02m2PVhrS3d/k2y//wiqsuIr+5Cecq3quaauqqUw4V2dl7zVVF9K7uVaiwt2g+q1PceWsrdLTLxVF937nrRBsl1iOECCsbOdY96fcXEwOX4YUbJ3YxWG/mwUyfB1Met6IcgRryseqVeeewxNQ6tTavTUNBa2djLQSlyqu4Wsgyl5WxAVogrQAIAxE6KAU5RmRnYV3GOHopjCaDpUxIMWFJWtPDjPPdActpwv5wAAphmTPmpYBMhomM43HCsoj3UNgI0ztZaUhpvRYVW0Db2ksISGuVCoOpgJwXyL4LMM7DOC8eJpGwbxNE6YFBxahSjGA3wu6fIp2PzcMvRsAG5AgwjAKPUgxyAkomlGxQ2CFmApPXHOEIhpPv4cAmgCzDuBHh8ATGWEyRYXyBsR4M2zxblIxdCDg8uYM3hA+fPcHzZ3fY7Qbs9yN8EDh6TBGUKuckN7vcquJqKFRI6I6shVFEn9RnCRrQ6gisbO9Q5SZef2mKj7nyEK51Q81QNKTUWNqipFT6pyJ+H0ldV+bUFF0dy0X7h5Us7VrqknOWgoIaAWEFbjA3byvmjCVK+506iax6j1GLqFPOjS0fXA3TOt+uFVMVNZ242G7xFk7N1Vzvcyc3lltKbEsCfL27GlZcI1EXQLZ3XN5rxfWpHo9+uGG0bByojUDXcJx4M6LBWpgZ0Adw7SP1ocLVXa9bqGXUJzYfeWo1Gsj1WHVgVu1ZB5UmTLm7sC1LRr9DfdvntCo7uaGLePb14NsUVrdr5utogNLbGC3+rKwE1nuUvKAUwrxEGJMxkEcIAaOziu4SoUAszOvBWTgN5+TMyEZ7cIFXImCzpd+yxgKGAQtYx9Jo0Uh9lYHs3zqLkhJKyciJMJL01HI+IBVgyUfEJYGmBQ+nI6wxePmFF/jyl38d5phx//oeLz6YkaMoxpffuccyTwiDtJ9f5gWueHjn4MMIp511iQEqKusUIcjK1iDC0sCQBdgArAFY63QcZYC80P84C9gZGWcUM8DunsEmkjBgVqQfBaEx4oS4iGJ3AXIfE6NEQk4EsPAK5mxF4DIjRSAniEdcLKwZNaRjEZeM6bzgfH/C9PIVMM/AMuHgDZ49+0DDhCO8F882eI/9YY+YI3BmFA2nMYCzKgnnHMIwCnOEUTCCE0YOskJqm5YFc4pIheGARp2UWGqijHXIJbWi40o1VrReqxW8AoIytBZhHJoySynBLhFg4Y4EgGmeEWOC9zJOa6PJeZpaSNFoyxLxJrMAL0i8wBgFtDHngsgkjU7nBad5kWu0tdWPh3Ues0L2syquRviL1cjEZs12frZ5eikBPkVANtlI6356FPHtRIIor0s08dUxunhmk2dNXl5exbst77Xi6perXOLGjXr0V/r3Rtzwckc3tF/VQ9f71FfTYuvubynb+lB7RXXzbDvdVaOd/WVcDoRbl18pXLb3ipqy7kcrd5fQjnDrHCtIQ3/clGljwDZYorBXAAn7QazsSp9kiDCdz5jnBUQGu3HUPZWW4E4lw2RCjBFLtGK5lywM8yQCcI5nzPOEHCNCGOB8gCVqgitnZQ4nBjkDP3iADaxnuJCVkTvD1zqw0wnmdMZ3/s+v4b/EjPtpxsN5xgd3z/H8+TMYA/yf/9v/jo+/823s9jvchR1efOkLoFBQaMFyOoOdgzMGPiRRUNyZPcyKapM2KIZIlFoqGt7NKKz1RzGDYMGREc8Z95+csMyEZx+8xHk6ye0mAyoGeclYTjN+9Wtfh/cBu8MBwe+leDYTbLZ4+M497u9fY5rOyFPBk2fPsdsfgJiQjhMeXr3CL/3iL+I7v/yrGMcB3jm8+s638fI738bxm99AmWccvMUheLz40od4erfHbhwwDkFBF4Kam+dJvCjrEAyJIlEUYFBWEWG6kPBazUtKqE8Kk9laGO8RDgdY58BGyJvP04TCBc755oEx0Jp7GiOgmJQSkBJyzgghwAVp3nk+n5uwlianDqEU3N/fgzM38EwxUthecpacl3oL53kBWcCwRRgCYqmteRhLiphTQSwMdoO0vAlBwpWa69rvd3DOYRhGLEjtGNeG8S0hxt3/63haPZtbW3bRklvi7lNlZVVU67aXSOKeBGJTJF1lZTPw+Uqevsvyfiuu5o1crhMfe/WNuK1b43+4Um7ytvtf/2OmFSqqQp67A3M3YOrq9f32u7f2j7kbcO0hd+dSz3cTluz3fxnIXtdV5QX0iqyGIh87vzU/10+MfpK1olfmRnhaFRnrtsY6hCHA+0Eb/snvcoxKRyQWcVwWZUeAhIMgoZekHIOGCrJVS7rljNbrNFq4yqgINEWEWWroMlgDzjosDDAMHrvFN+UcU0AhgjlPeP2rX8cnD0e8PJ7x3/73X8LxxQdw1uAb//fX8PLj7yAMAf/H3RN8/OELvT6PeZpw9/QOwRjkIcjdr0lp7u5ZJaTNRQQlqkKDNqEsQJQ8YZ4S5ocJ0/0ZnA3m+6Pki4wQ6+aUUJaINC04vXwtYIHzjC+8+AJc8Mip4Hx/wsdf/xZefvIxTqcHBHL44kdfxgcvPoT3Hsv9Pc4ff4KPf+WXMX38bQTvMYaA4/0rlGXGrhQMQ8BhDLjbDXjx9A673SiUTkOQ56l1Xc578TDAiDmBITmkmmNy3rcwYX2EkrgHalEvWQHhOJDmwgAu6DzMmpxRc6esrW9MB1MHkXpcro37fh6I11uL4eVnazNGCTuOwwAyEhJcYkIqVV6obNFHW8mec50sRK2ukZUxvy8r2UZsVuOwIvRqeO1Su9TI0CaC0ymHR+fzRSjwWtGt64h46xi0ffRe1+qpyT3Y5twYvJ5Xk2zfyx7XheC+EOMbV2zrcldFhqaQapiujsFavvDGUCGvAn3jMTG6B4T20HD5/urcm2myvu2Oe1X1vo7qyxtzcXuq8tX70JBEqyJrOr+7pjfl6DaKmqtCq4qrKq86OS2MdfCBsN8fMAQn/IVFBHdKCcOwg/cezjos0yTErAYg0kJRQOpvsigtx7WZ5Hp2xljAisARpVZ5/iCUaaVoA0sLpIysRb8FjHE3IMa0Pj8uSMwoKeHV17+J1y9f4eNX9/jF/f+CD168QPAeX/+v/zeOx3sYArKi8IZxxO6wx/MPP8BH3/99OIwBw84rks62Alt5LCQItVyQAJjJgJKF8Rm+DsBSQIuEFeNxwumTB5xfHlEWxunjVyDvJdRmC+I0AcuMfDxj+uQVlnnGvD9g+ejXYXj+HHlacP/Nb+Mbv/R/4eUn38H5fER8dY/lf75H+b4TPnzxAuePv4OHb34Dr/7b1/C6JBgCvLVATnj65A5fevECH754jv1uwDgOOOxHWCsekhTmynMqAIbdTq6RC3iemlc0ECmfpZPaLVo99awEvaRhcWOd8FXqM66M7wIdlxCd96HVcOVSOwAXIWTW/ZBRxeXcZqwSpCaMHDVeQkHHai6MpMYq59wouQqz5OZyZaEvqNDzCvsurF6gyherXt0cozCS1OJzhvYmWw2/Fr2oFmxTNM1iXOP03ap+nvKlnKmhfXQC7dLOpfUeS1sh2qQr6r3vl+aAqZFZZc1mOwaYOhmDCzn6jst7rbiuFFX1gIBePW3WVculxtpRt13NC1V26L7dbHnz+Ixu0F1YT4+f8/UFNQXQnf3to7/rUtXQo/igrbKt9Dqd9bexmPrcWn+zO2VrlJZp2O0R5zM4R8QSG9ltMVJwKaG0ASEMkGhZ1vEvzyIqg7wzBoSgHoq0n5imWXj8iIAiiX3jxBLMKSMjA2W9amMtTAhwWFFfrDfdGIIPFgzJUQVLmKYZx+MJXwoBH37pS4hf/BJefefX8Po73wYBeG4MvugDLBj8ax/D0ktkAu4J4C+8wO54xHMiDCDYMcA4B9Y+T1bzO5SBEjNyYjA5UAYQGWkq6z2NCfk8Y3p1j5df+1W8+pVfBXPBwy//KsK4k5yatZiO9yjThDyfkV++gikFyVj84q/9Gp5+8ALLsuBb3/wGyre+hWdc8OEworx+jU/+t1/Awy/9En7t2TMs0wlxmvARAcNuhHcWw+Dx9Nkz7HYjdvsRhycHuODhvDCeEJGQ5aKCK6SotrDAvpcY8fDw0JSIhAhNC+tVVCRRbcSY1POSGrmUheXfKKjCOIcwDCt4whpY8ggAYkwKsolAjPDBN0JdKUmQsRCC5rlKaYwOtTecrVyYxsA5C2OAEDwYjJSiEPHqkM+5CFGyleabiVkAGJZAkP501QMLYURMRT0uI4jbZVFG+Hxl0F7P4dWLkQtf50lvbLIq0tVwl3IDrPCVbq/yr3ZoJtVCXCQvabWOreb5uBSJRBABWs4AoqbAVm+1FxNy7k1+9EL6MyzvteLq3KDOseLmQa25nCpNa6gQqJg6sQSoMzgu76gKe1V416HCTtt0v2gnKKbdqozaTzY/uHyzPT7Xor768Ndv34T/u9oTA3RjH+v3vFFeTXHeDDvU+1lPZA0TyDg2agFLEpohsPi0THhtCZQD/D5oQt1IXqNTgEZDQpWbrxRGRtnmqyJJ2wynubSK9mKGcQ4CQROvzLbQkVFKKmoC1mQLtqIkrHPwDBgqYM3XGA35FCYEJpghICdRiIEIO+fgjYHxDOrYx8fEcN95jeMv/Vfw/Ql2GATq7j3cMMB6DxcGfPPrX8cyT0Bh7A4HaejoPYz1EvJkIM0zluMJ0/0DXn/9G7D391LMfDyiWItI0qQxz5OiKxLCMgvyj4B8fsDp4V6QeccjnmTBZ1ptB0JlgY0JFoBPETYnjCFgtxvgBw8/BNw9uUMYBwy7EdZ7UTK85qMA0t5e8vzCMAicnQ0sC4FwqXMG3AATHr4xwTchrM8ta24qKyLPQVndieCDtFzJmTvkoIasOou/MOv4Es5Ga20bB1JSxy28WAEb1lp4dOhWkt+llAQlmDIqfsJYKZpOOSMxhA5LIw6S75KQYqzNKwHtsyaw95yLGGed4Y3NO7knlyhgbnJnOy8v89DrtLrec/u+M9ZbWLKP0GANR761xKHNnysj/nak6O2W91txAejDc6oj2vq2tmv+SOi/XwVvU1f6hqGhimrh0Pq7bV6neyCb57BVWi2ednX+l+fbrmRzPWJFrfHkjYdIdGMf3d66TW+7b5cDqlph6yC+HS2/WNPt85IOillCOPN5xmsqoJKw95obMGbl39P9GGs2k7IwgwpUaRUYyjqZCwiC+iqa+AfWVhRcilAF1eunroi6NvmzBcRSV2ZdraFJCIMXi5sMztOMnApMKRiGAcVmcM6whXHnHAbnEKwFFW5pVGcc6PUR87Rg+fYnMEqrZMZBKJacBzmHX/vVX8H5fEaOCfsnT4UmaxhhvaADCUCcJpzv7xHPZ8SHB4zWADkjR+mKXC1mKhlOLg1eG0UWZrAhxId7MBF2ZOBIBV8ucNZKCUIu8POMol093TBgd9jDDwF2HDDsd/DjAL8bwSThMao1U1p7lCpTBQmNUtEByBAvqza25JJWVndGQ6OyPrOquGrBsBQPLyvy0FDrKlA46XFrKEuFu6neiZyrVZZ3q8dqI5vl+4pTZw0DCl+hVXJiyYEtMQolVD0WUVO4KSVBEaaiho6M+SUlRGV+F1Z6hrXaSBUSYYi630vVspbvrIjgXiY0OdMZfWsxMLZf3Ji9VO9Be1e/pBb+q15yNz3X8ODVQu1/Bq5+V3+4ieR8huW9Vlx9eI7Vqqrrm8KpQrsK4E7418e0ifleKoU3Hn/9uz2XVaGt9uX2nDfX0HtkTeF157ExvPScu2T2p3ncvW67fRXrBN96i1XZru+5e9VJUvR7yVdBrNgsVumyRCG9NRZkPTITHk6LFAGD8cHTp+DihSbIOZnQxiInmcj1HtT+SykL1Lgoa3lOFjkn5OQlEJIlPxJjVMtc6H2Kgh9KkX5LWSHxrHVFRTkYjK25tKThJ4JxUnc1nSekZYIzHtaSQP0LY28NBmswOifbE8GCGsyfiMDnGTxFMAiFpJatsCjtw/mMfZHQkT3LdRZjMAMqZAnggiElDKXAEcNlbUNi0HpWCT+g06HCkpcxyr2HorkkUQ6JxRMChJrJaE3dbi+kxeQt3H6EHQfAOWRnQIMHe4ei8aCiAdioDRgljJYQFdLtwqDGAcEbyS2lnBDjIs+PpLYa0Gsw4vGktCqrUvRVuwakiFKkbxaRwNGztijJaqQA4pWNIQBgodUqDLIyb2oHY+FOFKGcc0ZWFKuxBoMJory9E+NHW8SkrKFJssJ2z9AC+YzzvGCKGUWZRkDSgiaqMkuFlUTZYHAOu2FAKtKUcloW6TLwyByVWUoXay48p+pp1vkJ/jQRpgY6cCUCsU0rXMuZrdahzfpVeV0efl33ObQW3nvFdcPJqO6zPsAaKlwfw2rJtCiiPuB1V/2W2CL3LkOF9XiddmqKiHGx3Srsb8ayN/u4sXJjI+l5vpPZwhdul+yD+/PulJJ+fW3hYavAqsXK+qqUOFmBFK3tRG0ZQkBmwpwyTtMMYsZgDVxMUixrCMuyNMVidBLaKtiysGxX2t2ShVdvv9uhRtpP5zOCly7FTj2hwrJt0fCTFC1vTAYYL+AJx0rqqoW2YTfAWIMQPBAzkMTb8EYYzJ110q2YSJWXNGasY0yjpvL4jEU2EkoqpYCcATNpXY+VvIE12hFa+5ZBoNIo4ukZ5drhYjXkvRpv9WoctPatFEQFA2gcDzYIHZExhBC8htAMht0A8sIUT4OHHQbASk1dcWvuyhgDS9pFTSePtGwRwt8admsdnTkrO0UUnsaSNWQnxkTJWaOE69yQwnHl7CvSrLK2v2FmxLRIcXJOCEFh6nXgariSwXDBg0AIgxfgRkrI2ivMOru22EE1DEPbjzEGqQghr+TTaizctL5lNVfa6gyLdPxlGBQwYiqYFsmLTeez5E/HAGeNEhcXLMvS2OJvweGrcYyNDNPRS50Yan+3cmG7bvuXH0041Il/S/tdK6errTq5Icen5klz7X/2GZf3WnFt/JgqRKsgqh5CtVWYhYWgelx1EDTFtqIKqxLb5LW6Q/a3uykmXAyOy79Yh8mnXVG7qk4vrufWm0dystfW0O3btCpoGelr0SA3xwqd4Lixi6v99b9pg/JCmdXcQ6XBYbXWpRtxFEEfHHxTSEBMwuoNRZhZowXGqFBrgoUAPDgzMkFrwoTfLWoHXrCE+zZKt/67nNzaUwvGSCsMgnhpRLAAjLPwwSOdJhSTQSnDogo/08JdEo7SEJIe0xA1clgyBpYl/JkiA048H1FcRtq0ON/CVETSCJNLBpeMHKM+RwEWgEsnEMp6bURaU8XgFDV8JkW6UgIgXIHDGKQg1hr4McB4VZ7ewQStsbLidVbEnLCeCIP5Omplf2v5g4wNoXjSZpd1TvH6HGrOUsZyDfWJ+biS7sozqgAOAELRlIX4VzpH98oH6z6slU7azmHSAuKSsihsJYJuxigEut+H21pONWcNieo45vU6BRVvQIZbiIOZUJTyLKaMaY5YlkXuG8m1gSTEWVu29GG52/Vcm5hI+9vmYi+q2lzeWOWdurq9MFZGemCN7ly5bzfcuTfts4+OSR3m96ji2ngInbVVJ8f6QQU88xacQM1vWWdG3U7fv/H43d+KJry1RW9F3npUbdD1SnejwroxWVfRdv2nKa+qmqtirue12YZXpcPryvWcbpx8G4wtOV7UU1o3zkk44lLOyAASAwmEYhwephngAoMBFuoxaGNFjUgBgAAZvIVxFjFFmfzOIoTQLPmHh+OKMNT5z6W0SUiQEJIPfn3OSRRAYQkV5qrQrQHBCtO4EVg9sRGhTgDHBKQMVxikLe0LgGI1f2Kseh7QvlkKQlFYPwEC5ODcLCW20sLFeQsbHHxVxCTekNV6tZSStJ0qRerdWNqjlJxRstAUCUzc6LUCJiXtwit1UdD8jfUW424U79BqWNSocUEEdoIaK8TIOYkyNAaV4QN9eUb1lDTcmvVCqchDHIZRioK9KJAK4kkpY15m2JwE1s86Jw3BFOEoTCkilwQmQSNaKyzr1XpfxzU39onaE84pGKOi/0pOsNaq955VwZu1YSTQmnsCwqIxT9I12w/aKqRo92WVOs577KyDY8bCwFQASqxjawVgpKTsMGSgVQBSE1YV182ZfL1ulSedgsUa3KvKvhq7W0OtDwBe77NnxED3/pZ8uRU9ui2HVvlRZcT3rMclD2ZVDuD1RrbCWmZIEXFdBwDcwhvyqfOyukGyRpbrTVfuOFR5fktZYbUmL2Q999/3v6tKq9NVm702pVrPcB2AVwXI7YLqFujb6cjv+Tr23RLbTflsvadmJfHaiVheGg6sn3NBySy1Vim3vkdyj0UIV9qmAkJmQiyMJSYMyidriDDshlb8mVVREdDqtponagiOBLacc5JaIusw+NAE/bIsG97CvtklqadBmk/jah1YgMhKx2AHWNaQsRZN0+BgCgTmzpK8J+UjrDkqo4l7pyzl9aSNNShJvCNL2qxRQ1A2BFgvKD43jK1o1VjT7ofRcVdKBmISRGJK4JhAJYNihNHGihSUB3AcWgsNaQMjit84K14VaTNKrYWSbeuYq92Hi+YMBU9TB9VKEaSQckCUNgubO6zsM5cMwMMYwrzMolxLBhlCKAWssGtuA1fypDFGzMvSlHMG1LChdk/isjRjpDaKZACjdeLVsSh5YwhEtTZLw9jM8N7DMAR+X4SrspQMQ6aN+8wFSAlEQu20aAeCxIALijTNBfM0IxZCzNy6LZecQSgYg8MYPHZDaN6k7JsVqH4Rtut0zWZ9DcFsNryl8Pjix70BXZXSdrf9e9JIAakV2XhINyd3vWzlSydTWcaSodIMg8+yvNeKq4XouKqYzvtqoQjofator9VSqUgdketrzqs+5yvmjHqUS53T/b+GCi/Osb6/fNZX+7r4eyPUuIJJuE3WDbLwxnhaUUlcEccXt5K3Hld7rQSlrbarsTtXi1eUU22Nviq0rAKg1JNYJ2qlx0Et1GSR3Uas6RCCKJJSRFCpssyQrr017AZoCI4ItaV6M0z0fsm5GhQqdc0mFCSCUq6v2q1NeJLyBtbxUVg6B7MIaAcDU1j5CAlVATGk7Qop+7expjWPJCfltKYUgezrU61IPhs8bBgEheh9q12qoS/xJDMoZ1BIwgYSE8hHoZTShpOlFlsr/B/1ZdW7stph2WmPKHALBepd0iwNaVqHpNC4QsTVgGiksN04rc/LKGCAyICwdsqtY64boOurf945NwZ2AK2GqGyEXudlWavKSadHtw1DUaPVwM0XYAgNcbYcbc6w1rXeb6QcghXKE6Mo0awhWPEAZX1kQipQwBK3KI63FsE7eC+iV+jMSusp1iuUx9RCv17m8m1v7OYP+ZYxW3cGHe+rt9Xe1zHwiOe1/rxTbBfKizf/86PX9zbLe624+pgpNsIWq4JSj2tVUquA564yvNo5FXxB7f3qjdXfybG3uulmqPCGO//YcLzUd/UnN7dmNM9Pzm/VRFvFqVfcDHpqAmBdViFSJ5goni2zdq7IrpzXsJQqp5yFgSJp0jun2Drwpri0HBeYWtiEGJi1vqUQgYyF8wGDMsWPuwHLPEsNTEqAgjasAXbjAOuE546MKhWSHEZRVFuOGUMQFg5rbKNR4tzVgeXUUGu55A0ivzKP1zo0aK7JgGCDgxX/Co4MKBWgCL9vtW8KC+MDKdMCg8Ha+RjOSZE0RPHlCoMmghtH2EHg8hgGUAgwXtp+VNYCgrShrzkvMwQgZ5ic4QkgzXVlvfeiXVDdWfW6SPN56mFBugEXzZtxA7MAZKRflTVSV2ZVMbTohp47A8hcYEotPC7aPLEeQ9B7gjQVz9UYMVKM3ieJnZVWt1Vh7sZIHRUzt7HGWYAqXAqKdiseh1Fh5vq8ajwO0sqkKtllWYRWS3NcVPuCMWOJEfMSlSlDPAQyFj7sEFNESgVLKjjNEwoZwFpkEJacMS8RS0yYC5CYkLPs0wCwIAzeYvAOwVsULcyeo4BM3hQ5e5OQ75XXVh0QrtVMNdPXUmRaH9G6Q6yKCGjDZLNNnXc9wa5s07mJ9fybB7E1jD/r8k6K6yd/8ifx9/7e38N/+S//BbvdDr/7d/9u/NW/+lfxm37Tb2rbTNOEP/2n/zT+zt/5O5jnGT/yIz+Cv/E3/gY++uijts3XvvY1/NiP/Rj+5b/8l7i7u8OP/uiP4id/8idbXcPbLtX6B2wLFQKdN9UEiSoVWpOV1Lva1N/q1a297X5vTuBKkW28FayO0Xpu3YTv2iJcH6bztOppMtZ6tLq/+hmPWVrV62SgqDfQLrv3ONENJga0U219oUhzxlISSkna/yptFVZOgtorGbmklm+Z5wXLEhFTarciM+O8RJzmBd4AO2cQS4EtBJMTcrI4HY+YziekmDAOA5wzwlEXBXhBDD0nDcW0vBhJ12NmeFcQtF9TvWarXpCFKpicQVlYBQRQAg1ZqZAn8VysNrMcQpAchSoycQlFcVUJzSzhI2OthMBiRCJCyQkwTs/BwfmgSEDx0uxuhA0eZggozsF4UVzGezhnW3uNtCxSRyb9SpBjEr5HFgVEYLiSwZ1yJg3vMeeWmwEApXWU606SxyJLsCxhszqApVxpnRMtvV/btGD1lARxVxCX2HqmMYDpfMY0TyBDGMYRYQjS84zqPVfFzLXx49KUlhhya2FySpIzylH6q1ljMI4DYrIa2uwmnT73GuZuUQoSsEphRlEk7DRPrfZPjArxqJw3KDBIHJGWCdOSpI0MMzJZTCljSgmpANMcJVSYCZykHYt1Fk/3O9ztRoxDwJSFvX6JqfWsuy1vHpdBPU8hd69bcqA3ofnim4LLtpW0hgYv3eHO67r4ZitfHzuN9sv/hxTXv/pX/wpf/epX8bt+1+9CSgl/7s/9OfzwD/8wfuEXfgGHwwEA8Kf+1J/CP/yH/xB/9+/+XTx79gw//uM/jj/0h/4Q/u2//bcAxOL6g3/wD+LLX/4y/t2/+3f4+te/jj/6R/8ovPf4K3/lr7zTybdxeRkq7DV6F0HbuDPUsWXUQdwZCs1p6zwbYPXC6qqmFB51zy+GSPeRNiYMtttdfVYls23Qs1GcNz9319sfv1071sEvHuxFXqvlvPo8Vm59iVq9TVtXNuvllZrHVi86lyKWaVwwO4NUgvbcKigkFrGADIqGpupZk3DOKnMFWBFhnOEUOGCNtDOBCr9E69RrYa1NyEoFYuWbq9cOSCsS2wFWSFtjGANLRkKEtUlUlQCktVzDKEWvRkAXxhkgORGEWnxshkEfixbWjgpHD168AGfAzoCtgCokP2VhUJCihFxBJKg/1r5fOraIC0o2MDlLR2mjz7eIwq/Pmrpn3Qhq1YuX+SShX2IVbcoLZDTv2JB/qLx9ykaRV882piTQcg3BGZj1PnNp43kN4aMZlFyfEVcvsPJTcnvGxkrYTxgpAGANUdexTSwFwSudGdr3RZVrM1wAqf2ilTC61t0xhMIpltzQhJkEORhzUaUpv89JiuKJGd7ZprScteAkSl36zAHYqIPLZRv0rKuoGzvV278ErG29rnrfeimxPV4bPxeHXjufA49rpkfOnKr3RpfffKblnRTXP/7H/3jz+W//7b+NL33pS/j5n/95/N7f+3vx6tUr/K2/9bfwMz/zM/j9v//3AwB++qd/Gr/lt/wW/Pt//+/xQz/0Q/gn/+Sf4Bd+4Rfwz/7ZP8NHH32E3/7bfzv+8l/+y/gzf+bP4C/8hb8guY23XngTpuvzFr33hY7dve8OyVfKS63ILvxRn3GXMnncTrhSXnzj4+1f336EN5SeaJo2eHum90fPRZUW8xomqH3H2r1Q77WGX3sAhuQZUqu3qQopRanJaV6XKqmUhCsuNqu41k3J+Vbi0WmJOM0Gg7OIVWkVQgajRLF6CYTgfWu1bo30jioxS58nhhQDa8gnqGdiIcXIhVmEplrcttIKdUirmq8TaHmnzJgFYce8ihQNSTprlY6JxNNiNIFFei523AkfH5G0sFcYNopQUhnnYIYRRgVPIYC8BzkrNFaGAEtgW1CQmgS3xoCVF3BOESS8RSIcNKzZpFgiwCYgAZLnLaAC8boya7hRlBOrEljDP5Udg5CSUhsZgFnzY7VJpwIYoOjBftxIN+qEJc5SzMtFo4EZKcVmJPrgWwSgjtyeNb62sVmZNwoqDVFconi2GvpztBofOvKbssrK7EGdYq6hcNbZYYwBkqLeNMxd87LynFRx5YJiCCgFSRtBxlSQGCCoJ5Yy0hLhLWH0A57d3cEPDrAGZWZlkO+9lG2gbfu+m9NNcVc5sVVC/Zo++rPKivX+XOatbqpN3aYOLVPXXSiwavPUk7jSb7T+7pIw/F2Wz5XjevXqFQDgxYsXAICf//mfR4wRf+AP/IG2zW/+zb8Zv+E3/Ab83M/9HH7oh34IP/dzP4ff9tt+2yZ0+CM/8iP4sR/7Mfzn//yf8Tt+x++4Os48z5jnuX1+/fq1vOlDddwxZ7DC3pvXVadDj4bZtvb4bizNxmmKoFv3Lm7xLX2HVRb1I+vaorq1P93q0vCCTEiNMzaBIKAKCcOkJEwH8jliWeamjGJcpHAyJcQUlY1AXiknBSNsPa+UcltPLHQ4U0o4LQumJCG9YgiHELDfWUBrgOZl0dCf1/sgocth3MFwRWdJIShSxpJiQ+IRhMyUSYqDK9edYRZYuHNwBUhxquaA1D+pd5lyxqjoPqeKRaDQyipkrQi3JSFD8hnGSMEuFMHHGYBTTka10pkMFtLwG0nOKZF6EzkDDEE1FgF2VFogY62E1VnO0RpB7hELICSbSlgLYeTOAEi9K9TcHFBSRCJGibGhSdchU1YDDqJoliWCSFqohBIa5Lx5Lxq5EPJVA0PA6KUD8nI/N6/LeQ8YgZDX9AAXlmswVlGe0v4l5tSEJpfqzQi0fJ5nLMuCaTrDeS8GVFwkdKlzu7a06Y27CjQpqSAVYcMozMpW77E4hzzNrT8c9EVGclmpMJacAStlFQWE0zzjHCOWLCPAWAfHBtZkgAssWYzO4bAbQZaQdOwvMSkTTG2N8inCoK3jNv83a6sR30VI2jPi9Tc9L+IWMLF6bxvltzmT9TybD9X2R219v/QQp4ou/n9FcZVS8Cf/5J/E7/k9vwe/9bf+VgDAN77xDYQQ8Pz58822H330Eb7xjW+0bXqlVb+v391afvInfxJ/8S/+xav111YEVoXBVah3G1LdVj2PKsl1wsmfXjOs73nzOKuho4ME/UN9fOH+DXcP/da2fPlmux+6+QHdANouK6T/yo9rg73k1aptdDv54nX5vQIbNmHBnLrvq/W9KkTOUjRrIFbrvEScpglHr0WvXhLlok9J2RE0SU9a7AslSUX/gvzOWjhDah1Wr1q8yZRTe4w1lCy8d0I5xEnuRSVhzUXuS0y1w66BXZYWKrSmhirF4RA0IVAMkIkVyUhgaC0YJEkPVXzEWZ+hMGdwyVhHhigfw6KAMgQ8UQqAIlfGJMwddUyJThRIvkp7ufpMYiyARdkqEs4YI3kaQLzHOirqOSl57iUQqtjKdCGAnyoIpQu1EcSl0oNbK4wgZG0znpoC9sLcnlVRVmUIBhKnJnhl2yBFq7mGBSX35Z1vxLkCyLArytX0HiQ0PArt1yZjIKvXWX+fKzhJw6QSIpMwcmKs+SgjtXtJyznEIzOwVqimiESBEDMskYAynEUhpclqodQu53ZpYLZ5ente31rWOV1F1A3lhtXoXeXXZciQ+h9p6HY1/cVbvnVGW6RA32iyAT8I2xz/Oy6fWXF99atfxX/6T/8J/+bf/JvPfPC3Xf7sn/2z+Imf+In2+fXr1/j1v/7Xo7rL1Z+p9xd1bbvhdeUt37UqoRZRa3dcdFxVfuqs97mwtoNuxVsaEWuc+vFE5s0IINDR+tSQx8U+9cfrNkAVPpfn2e5guchj1VxWUz6r0rrMcQnKcGUXaKFEhcfnDTw+QVwAOYGYM6Yl4XiecfQODozBiYVYVFhK+3V5yqmUljdqTf5IrDiZTKJcnLFrjVthKMm61visXipRZYyvbeWLGhVS91VQw13CPMEkrVGcFraStw34UdRzYkPSskX9MmFQEEFvQCAjEGuq41eJaq2GSZvSMlZYHHRwruwV2kxRY76m91oYyvSioRjN9TU0JxjOUgMCkCqFSm5bNGQot5LgFG1XWEhiWaVh4QJRT2uYuU4cad4oirUwa58u3yiY6tiqiD7vPfI8SY8rQJ9lHUelUVWJcRFbrsVayReGAm1BItyUzjl9zjIu+1YmNWdLJC1KKqtHybltIzRl9T5UpQ0wMpbM0geMWUA3uSCVoszvogqc8yicAZa5Y8ASKhwCvDOIGpLPpQjxrtaToRlaLcCJZmV1ccO39VOaVOwFY53zTT50xv2NpeUb24+rd92fD3fbXxvjTaTSikTsvbbPsnwmxfXjP/7j+Nmf/Vn863/9r/H93//9bf2Xv/xlLMuCly9fbryub37zm/jyl7/ctvkP/+E/bPb3zW9+s313axmGQZBHN5aGJtyEAxgtV1E/qtLqQ4WrZG9b6vpVqWy3aXtvf1cvim9rmttnve6/Wx71wK7O892eeA2Ht8P119KDMVoBcW7tJCQ/FTfcg0kT7k1BNTh5bqE1ScgL1HeJUcKOmriu8xCGMc8JyAWGC57tBuxDQIbBwzQ3rwnOYfBekt0xqTehrd2zsLWPhz0sFcR5wjRNcJbgnaD3vDUwxun1CTKyWfZkYKQHo+TPqKBoaHltlSEoNoYSujLgfYB3HpUcp1r6lctPCI6ytIBnYZ0wJNRN8CTM8XoOWfMphQuWuLTw5TAOgBMPBix5MYI2YHQOXBhpibKNDvTKEZgVJo4i/IzTNMGol8sFQEmosWxWpVZSQoqzOmOq/KGWtyHNHcmhKgCDwFILVpSHjgsIq2d1fDgiF2l5b53TyAbjPJ1RmBBThnVelFUpWOYF0/msoejYjBLoMWMqiJkB4+C9hWMGOS8weO9F+VkrLVWMUaopUVgxJhyPJzAY+z217swcI87nCUsU6qiUMgADY3xT8IUZS8p4OE+YUhIE4RIx54w5STuTWcmGnZdxnnNGWhYMzuLJfsSHz55gDF7KFNRLTbXYmRmGaTNJq/p649zuxIMOxHUfdV73kaX+7+XvHzHs0X9/8fteN75xWd0vrJLubeXl9fJOvhoz48d//Mfx9//+38e/+Bf/Ar/xN/7Gzfe/83f+Tnjv8c//+T9v637xF38RX/va1/CVr3wFAPCVr3wF//E//kd861vfatv803/6T/H06VP84A/+4Ge+kP5RrPf1hoqpk3X10XDrBn72W3pjT7cPAWCrM7ebcff/p53UhWXF3Czj/nP7131ue+BKhqvoOqxglxpuEKTY6oXVRHnRvxX2XFiVWxJlV3JXxNwuQ7jcchELdkkJc8o4zjPujyfEXCQ055yEYlLCrDUvlT4qlYyH4xEvX7/GJy9fYl4SyDr4YYR1AQyDnBlLzJp3U6RZQ0dRh5TSQma12mu+r547IMqpgTIAvc7UPM1U5LqXlDAti7KJZ8SUlEFh/T4p5Y2xFi4EWB9A1iDmjJgyUmagy6/AOFgnfbz8sFMuQye8gypYU6moTvV0U1e2oHVTjNLylPX5CZhG2NKF4UG59KAAl5yUM5LQ57WKsqNwx94ux4vyinH1XIhWYAURwm7XaJiq0oiqrOZ50RKKBRWAIYW90rzRWItht5N8oxYF21ovx4x5njGdzzifTuu407GcG8CD23NszyamlksFjBoBjFSEzX+OCUuWliVC6WWRYbQHlwAzYiyY54Tz6SwKeJlwtxvw5LDDfhckLxyjeG2F1cjLnTCoYv3dJNDl1i0KVcOCl0Y19/Lhxvdvczz+NLXaW8q3FOJnd7neyeP66le/ip/5mZ/BP/gH/wBPnjxpOalnz55ht9vh2bNn+ON//I/jJ37iJ/DixQs8ffoUf+JP/Al85StfwQ/90A8BAH74h38YP/iDP4g/8kf+CP7aX/tr+MY3voE//+f/PL761a8+6lU9unSx2ia7ua5ZH0gLp7U4LfBIKqu553T5xX+H5RoeerncGBY3TulS2dW3/a+p80LXUOgl2qgHlqxKrq5vFpyyAaweSQ8n7hkPVmXWfsdo9Wj1kdQw1KJWrAPwZByE3JaUgicLIi+nCG+d6J4MTNPcBGLwHkNw8M7o+Qnkm6BsGwoc6IlauxukBLiCouMGa96eN1XQQ3edtdEgSlX+WTrEZg8mZYAnNJAApbSynAOioGkNi4IBcpoo16JtUawWZFRAl0q/JedVsiggS1a86HbuufW3Eq9GuQ4Jei/Qwr4VZt68LEB+r5EKsl0pQRsnknPbIFF1m3Ucrc/aqPIL3mNZZgX2pM5JEAVVAUA+sOahJM8oITUDH9Qzyh2EX3+fYsKyLJjnWZk0VnTiKifWZ1ufcy4FnBKMH3R7o2UcjFgENSgsF0pXpsCMwoQlFY0oSJ3hMs9Y5gmcI/bjMxx2A8Yh4DSftTxEirOrkbHmfjQz34Xy3tY5qfN4zfPfUlo3DOJ3XS7mztspr3VMXa7/LMs7Ka6/+Tf/JgDg9/2+37dZ/9M//dP4Y3/sjwEA/vpf/+swxuAP/+E/vClArou1Fj/7sz+LH/uxH8NXvvIVHA4H/OiP/ij+0l/6S+988nUy1DDJpeDevKert5uIYNv+M9zPrV93PUjWHd94xFRPYK01+u+yXCjqdp5diLOfzK20gNY7u31tf1O4QyRq36JcqaDUE8mK3gKzUBMZwJIDSNqcTHOCJ0JwDnMpMDGJwABLv6KUQDljcB5gCZPlUpRpnfD6/gHeWVhrRHllbdpHQLDCGRiCchiqAYNOuN6Ozss3eVlkFVWvTKioSs6NT9Aag7l6VwDs4EFWW2sYh2oALDEhxQxrFwktDQMA7RNVGIaEUipp+xSQsHC4AhTD4MytDCEvEd5K/7K4LPBOywGUh5FBgLsq7M4AAKB4SURBVCV472AMWnhXFOYaVqq8inqJkpvL0BorABo2FEUgUP4u176p16p5RtLcWd2p9U7AGM4jjKN2xF4wTZM8F81FpUZIKwhV6apsGl8hEWEYd2IgqMI8n8+ST1KYfh3XJedmqFSWjpQEon8q52Z4WedUiQDBWDCEBUSiAVlysakgFmApBXMW9ozEQCaD87ygJh6XJWKZJ5S4IBjGB093ePHsDs+fHnD8tRMYAiCqfbxizrCWmsKRcXLBcPOWgqGAgVJgzXVMqUmfBnzayqPrOMzFQr0S6j+/veSSnBl93kjhuymut3Enx3HET/3UT+GnfuqnHt3mB37gB/CP/tE/epdDv+msPuXz+tBos4Zu/oK2X33K4fhyxePrqXeBqP1/M/Tbf9uUMnD7xLjb7s3L5vlRb51tX3W32+e9CiC6eNWhXmttNq/Wn0vBGgoQEEAII3OSZoKFmkDwS8Sr4wkxZwRnkZOE3SwR9oeDFJLmBC4F3gcQGQkzJcY4Dhitgw2jKEcwLCQ5bpXfMOeiwpmagCOuNH4WMKIgDAu03XFt755bWYarFEVR69O0dcWSxUMx3jUvDURajKolAlnAIdKmxcAlbZehOTdSJIl0NpZH68lKWI5qnzBlMtEQZclZ0WsAciWIVWSlIakRA2CsdCYWN6kngGUpKNbmTiXX8CiAiv7qhnUFWlTPs3pIqWSsNd5as6eKzDgHskKvxNAiX6iS0x/V3FAtVs8lIysSMGl3a4LBNJ2xLAu4FBwOewHsWKOekwAzTGegVI+qhoQrMEIMEQdrGZUwOjMr4EKaQ1Yi3VjE85pTxmmJeFii9JfTwF7KGSUVnKYIxBmeCp4cdvjg+ZMWJjTaWJNZwotJPb4ayqWed5BumVK9td3P7fqAVHldyqAuz7XqjNWSbdGV6g2QQQWO9VgK6te1z3ShyC6XuoMLmfs5rPT3mqvw05fP5RB/9xYCahF0P8BW4f8pv33TwkDfWuLNtsV2uxr+q15H/74hqbieKzZKC1jDbdV6a+HCCicuQsBbLsKKciZ6rFKEM5IJ85LgCNINWbvHBu+0XRV1HgKrADKtTidH8XSMdbCuIOv97qPBRduIcA0bGiMoPyKAeH0exoAor6FFY1bWfEWK1UleuCjBLyuqj4UHUEESjS+Pu5xQllBeNrbRDYnX5roQZGm0QwAhUxZoPTUxIDVyKbZCWXABsWshPBhSMIYoOdZ2w9YKd6Mg8VejqClzlHU8GEEnUn1qen6NO7EZJhry0sGyQvJrmxfT+AArjyH03jZwBkSAN25LKNMFs46hClmH5uUWGCKE3R5grN0E8vqMemOseZqaN4spKalzzRNCQ5JiZAnidQGsFzJdZu1iLEwXS4xCmGwsiu4/x4Q4T3CcYL3F3X6H/TjAO4ucY1OkuUgdl3RO2JJCX2msNzgoVaY89v31um7LbfR0+4uNN6Xj4iovvG56Tb57Q+sCHVDk02TVm5f/ARTXY5L9tuf1WZT8Z/2dLE3MbD+2b1eI6MoL9hYn1O++OkmXTt5jP++UZ1+bs4b96qjqrDBgbYTYCgg7aKsK7gpAWOu6OhBHEyZio2YugrqDsL6/ejgiLQ5xcIiLxXAmDMHhww+eYQwehhnH8wlPxxEheATnhQMxSnfZqPmVzCy9qCzBQRShsMyJR+KcgXcO3jkY6yX/BKgi1HxWyzkItVFmRdSVop6MFDeXokqDDMhCCqSdoAvTssAoXyE0hFNUcc3zok9IPEDjCqyTlvKAQUpSs1aFftY2GwxhznBWcmpxmYGSVWkxRu8kL5UTBBsnzzAuM9jZltvKHWM5GSOMGgyxtOtzKtLGhNTzRJFnRUAD8tQxcwk2gTGS51MABSssP5UidXxRCovDMMB619hXZu0EzABgTCuCF8JbJ8z5RrzPFJPWcdn1mWn7ktqYkZRJg8go0EYNMkguigEYI2IwZgkLLqcJuTDmJeFhmuB3wje5MDDljAKCG0ak+weYwrBWwDwpRsR5QY4zvAFG7/HFD57iMAZYS5iXRbzfInRn07JoBII0VCjDZKNEeqv2RgBExmiz59qY7Tfn7l0NMrzzUpUTrYXE1djZns8mjrRZ352FvL5XG0n2buyVRnhnVfOmcNwbfvI5rAZh50azYDp//LPv9F2WTlFV65lrwWpL8K5hhVaDoeGO3v1naHv2hjhMK7Q+r7muknMLVxgNQ3ENHipRaUoRcQae7EeQNThrnsMCsDFhogUEg+ADhnFEIcKSReg/nM44LwuOZ0FzjcFj5y2QFhHwJeNw2LU8l3UW6MAmWQEm0ryRhE4JWiys9E9WmSu81g7VeqJ5iVII64QUd14WpLIASfYblyidd31AcALvT1GqvSyjgScARoFQBdU2HQWMZZ4Fyk+E3TjI/csJOS6tuHg529ZhtwpyKWoWjsKiXmS1oxvTgamdmyHFyrVYHGhKTDoukxLO+hZOAsR7KpBwmSh3A7IOLgzK+m5w/3Avx1Lo+zRPWtfH8M6h5KK5qtza28iYyzpcGc7IvW9doEvGPE3wleFdOKmEANnaFgYWcKawXwiBtJYpFAaXBGs9MqQcYkkJKUte9WGaEYwDrENig4fzrAo5oDCQlgVlnnH/6hUsE4IljHd77B3hC88O+P5f90WMwa2eOYS4d0lJKLeYN2Kkg2TIvWJ+ozyooHm5r9f0b5fKYk3s40p20SPy7FKyXn/bfXp0QzH/axTnXVGMl8t7rbjqQn385EJ5vfmmv83y+fytNy9bD6u53peHvzqXz3JOvSnHW+uOV+RcH0K8PbjWM+4LCfuwTO9Z1TqoTXGr7rfUCcfi8UTN4TAVjN6qICx4OJ7giOCNwc5aDJrUnpZFhFgpGv4zTTC0zrbGwHsvaZqSgZJaR9zqOYIYxrB+roW03F+yAAd4FRD1/rQQSZcrI4k5Ic5L68NVijKl5wJnrBIECxt7JXG11q0eOKMDFki3XGGBj1hyhCMR9s4QFlVcRgU5Wdc4EsXIZhRTPWS5JGOk0xa45qG6UKy+jK1F3Rr61BAZ1LMlvRfCnm7bfahMJgQN2zEAknolgdPLM5qnGfMyw5ABD9wQhj547XJsMZ3PKIVbaFfGrnREFhaQro6ufxYa9jREki/T5qatlELZL1jLDbgq3gKkzI34NjFL3zMYFGtxnmcUEGyB1uAlMUhygrcOwVocQsDzfcCLZ3d4ctjBOSBn1sJsaNlALQ+hNd6H66G3nXq9u3Qp52gTcmxeDTP6fl23Z/S1NPmscrPp2c2P+5gktzDh92wH5P7uXHmsj26pSw0Mv9NR3mJ5y2exeba0qtjrUOHlDq+V1u3z286A24HTPkTY1bs0D6xijKqltJ45af5izfl26EKujPK5QdKr8loVV4deYiF/nZaCYAFy0gmYjXSZPb88wnDB4Dzo6VPsBxKrOEYE7yXnwCwFpTpzyEih7jiMOOxHWBQloxXwgLOuAQJqTsZpUS8bIGJlTwcUTYe2uSAoUxIudqsWQMkSZksisOfjSeinvNPwUARnhjfigQCiv3KU8KFzrikvAMgpwVkjNFdWeA2XnDEdjwgEuN2IcNjjfj6LErXCtOGMgfdORgAbhbOvzxgshLlENVxbB6GS/Yo+04aXaJKoPj9GI8EHkSghsnKOit3X/RTEeW7COaWkPbGg3tWE0/kk950lp5lSwuHuDs5LycMnn3zcaJ2cFah/ihIOBNA6WTfKJwAVkVeBEOLxM5YlYppnrRfLIOsFIWotCioHoaAFRZERmAgxFxAxrDM4TRNSYbiU4YMHx4iYIogZjgiDs3i6D/jyFz7AF54/wd1hB84LFiinJFfDSpg25PbqXCVo94eLSdtN+csyjq0aW+d8A1tc7AaEbi6/aekNN7r9HptZvFmaM4FVkbb/q2z5Xg0VbuAt2y9ub/7Wn97um3fZvPeTVo9FLf4bP3r00r5LSxvinzKKO/8KvQJrjes24US1pnowRi3kLV2rbqrH7oQMCAWSF2EiTDGj5ARngMFZEEuuJ3FBJlYhQFhYWrXHlOFDUKBCRooF9/f3SMuEEkfstHnf6D3GYeiACFVoEGzwCihImBcpdq1Fx7V/FRmCr0wVQkEhDRaNhbUWy5KU8bsgWPEaS5Tmg9YY2GBBICzTDAaw3++R1TPIS4QbLax6ODlFIIlnND2csSwzKGc82e2wHwZ4K32+QvAYfMD+sIezYkyUlBT5SNLsMHUhGgZSjqJkaw0UKaJPjQ0ww1paCWpriLh5qaR1ekK+a5yDdRZ+t8M0L0DOcMaAGVjijGlZMI6j5MNSwjTP8GHAngjLsuB0PoMICMPYvGXnLJ4+faZsJat3SwTsdzukKKHUEDRPWbIiLSXEWYqFIYvj+YwlJRgbpMYqSi7LkTwLZsK0RJynCefzjIfzCcNuD+c9nj5/gVcPJ8RlEXJiPyLHKLk4LfEQMBGQ5zOWNGN4OuLDZ3f48PkdggXmKHm9rJyXcg6Sly2di7IK+87g1Lfrmq1W6z9tTFV9bplLU4ZNyTVjc7tP3kgnqC2zzv/2vt/kovvypUV+pWirMfy9Hirsua8q8gXohT5tbnT3wxt7etvl3W56P6DeFAW4+eGW9vqcCu1mNKK68fq+Rw9dn8b1CbSw4oXCXS2t/g7UcMEabqsoJWYgloJpLmALsCOMwSN4i8HVOiAH673WL0kn3MwFlCVZ72zAfhgQnOSpUkqIpNB4Y4BpWttxWBYlxjKGJDejaMUiATF5n1qoTc6T9TuCdYqcS7ovsPTqMhXyzXpPxctKy4KsrOcVAAFmIGfkZZHckBPvKccFJUKomJjhrRXIfwgSGswZQwgYgkdQL2W9z2t7wFYkjdKouZj5KociABWpOSMiQLcrpcCGoGARi5iSUnlJeM8VhgPgh1pykHE+n8VjidJJwFiLIZi17s0Jc3yMUb0m4U2MUdB3IQQNozLABSkp7J/WQmZnrfApApv2OjUXK1RPEcuSEHaueVHiZQuaMOWCaVmEvSNLF2PjYyNVln511HgT63OLpTQvGyXBGcIYHJ7sd9gPHqN3sJoDrK1TWvNLRWGuqN3OZ+IKL5fneKkb+hm4VWjbWd1ChZdRmsvJv7VNN9vVwvVPM6L7tALV69nIDmrbVUP3DRVjn7q894oLQBN63UdsFdal+/KYt/Uuyuvdl5YbqSd5YcBcx4bfdKY39n/x+TMNC74eTle37oYSq//3ttvmLDaWlqwrfesM7aKbUcBJuhnDEUhoV+F8QAhBugp7D+s9nHet8WRmlo7AStq6P+xhwbAsVnFUD8uahKhhOe+8kLC2vlLSy0rYyI32sBIvixpTlCpYDe1Ya5riYtYuwVxbYHQTlOWuMAPzMqsXIzkSY1270WlZYHwByIOYUVJsNFtOOQr3wyBhqlIQc8IQAoL3CjjBJsRZrfgmR9TVLUXOrHmeGjqXejKL1hmrIj9ZQpBk5ftlmjDNC+ZllpYgDCGdBbRliBQFQ5VYTBnGJng/wGv3Z1I0ZkoJwxBayG+ezg08UxQdCBbqKWZhtw9+3d4qk0jmtVauITmZkZJ0GbZDzSkZKcHQ3m4pR0zzghhTKwp2KcO4srawYS2n0ElaCiNrng1cUFJE2I047AY8u9tjP3jhyKxjX2vmBGovocui7CfVUKxTpXlE3VRrs1Lr2y6jN5vp1nbF/ao2X1uR8ZtkDV+uX5MZ2822IclNny6qv+sRhRf1op9xea8V16aR2Sa21v29ELJ9Funmg3+nE9C/b/MMVGj0x2zJ8l7ptgHyuR2rtzuht9iqnQ11cNhaoFgRhhtFfDG4myd3EeNmQatJp+GMTAC4gJCRuCA5YInSev58PuFuv8P3felLyAw8nCecPz5hPp9hjYVzHsFYpFJwnmcJ4XCBI8I+OGEMLwXH0wRvDLwfMI47zPPccnzkxAswxmJQ9gau4UBv9RrQwBYiwCTXBSs1Rrvd2K57yZUCCg06X0qBGTxylkLb+5cfY9ztMYwjhuEgSMScwMsCgpLdWoNh3MP7AONEkaMIYW4IXlgxCBuBsDKoiwBfZjSIuCGCC0HQhlp8DLWEYWoDRynyNsaCmVqhbEoL5iViUsaLeZlFoYYBIBJlRYSYJNzqg3a2roTMKcModdJ5mhDnGeN+j904whrxZF6/eoXpfJI6Ne3tVlk0Kov73eGA/e4gKME2tFYuRTDAGTDOgIwDmaznHbFEoRYb93fgXDAtEa/vj60QWoAqBiALGIfzNOE0LSCnHqAqrnmaGlI1nifsnt7hwyd3+ODpAUE9ZoAVVCK5tjku0mA1d92SH5mS9WOreeyMwGtZ14Xsm1a6lH9YxwbLfldZ0x/tOjCpP2xz4FHhdEMOX0qZz6u0gPdccTVhWj+pRSzv38J7+sya4V001qft6pGT+C4nubYRgHWGXN2lFrbrarSuTm0NPVUBWVkK6Oq8uXtVi0snWZfsr61aSENUiQHKsuEcCw77ALIep1kEJnfhLmcJhQoyC7s554xd8HiyG3EYByVkDchxwfl4xJP9flUoigzLGrKr1+G9g9eOvaZSJzVBZFuuiMx6BxU01pgk5nlWGWLgfJB8kzEoOYGQYcAYgsPglcEeDCpZHQVp2zF4Dx+CgC1IwAcotRklgzMjIwkzBgkgwdbuxIDUdLEqQC+9w4hIi6GlELYoEwdQpMdZBpggXIC0ysGo5MGn04QwDOLxGFGkdd7VGqykJRAO2gaGCG4ICLsdxt0eqTCiKpDDbsQ0TYBCxp2xkvNMCbvdDtN0lnwf0NoKpaS1cEwIfpAaM80jGWMV/CBQe6Me3hwT5iU1kmMhbU44nk6IOcGb0IqRU2ZQygCnFUihBLvLEnE6nTCdTvBGIfDeYucddsFhFwKCNbCGmrFWZ4LkESuvI7evxRsmPBpAI+qUyzqn+22r0rr+/W3K3tWIv1aCjy0iEt5GLt1QXhce5ecxzd9rxVWN/FVJVYHabbD+uZ2nubx31D/KW+YKviv6ag3TXCiPTvnK6bzDIKGbo7bb5tKSona8pnAuwq6Xh6jGArrfrMXIpnlgZOp+tobFOo/XGpYanqyhBkAQXbmIuoi5oMAggzDHiDRPAGutjrGQzl0ZhgrOpwkpLii7EfthABkrzOtGqHmEqb32QsqaWspa4CociJVNwXkLLtpgEjUEp9B4Pc/aabi1syQJxZQs3gKL1hBlYow2tyzKtl7gtYDWCdutdEjWR0kE4VfUZotb5Kacg6A11QgmudtcNSjLeVQD2SiK0kAoqKphUtTzbc+kJvarx6UPL6WEZYmY57kpLmkNI/VDuUjotzArA3pCIEDg/AbGeul6HII0hdQwHpHBPKmXXFGCihAcQkBOCQvNHTOHQSmQAmSlwiKsZLlkTANDmKJdBqxDWlJjxcilYIoLpllAGbkUOLlJQqqbs3SGzoxYe2aVKIS6y4LzWbxF6y3IeOyHgF1wGL3DGJQvkqjNyVavpUZbbQtT/RvuJXubIaLL3iaBwRd/23reNnXsZvH6vjf4qzrr9c5bGtDX4rX3uLjX4Y+c7dsv77niWi29rYe1qp1LeqLtg+g8itvOxVsujz+A5rTUTTaHXI+9CXvi4to+z6k9emJyDI3CN3qieqyei7CpV7p4mZUSyWiS3FoJy0mjRQEZWGthOzaGjVV5cesKlKYIBlm/XzLw8v6IGCPcB88whJ1EciAJ+Skm5GnBGAJiYYAs/LDD4ekz7PY7xCwNHY1zePrBCyAXnOYZKWex7KNY87tx18IYzjkBB+h9STEK3U/O8Na33AoBonhzBqx4O2DTEvU1PDY93AsdlcLqOS0CePAezgCGijAuqIIj9WT0AHJvlIUkxlkEvFI4VRg4IAXNJRcUBTpUpVILwQGIEs+151pqaE8y1HJDFVwRxlHyXiBM84Kkhb+Vqd1YQXuezieUYwFZJzV5MeLheMST589AZFrPqdqTbX+4w7jbY5kXnM6SL4vzhGU+4+6wx24YsBtGBOdQhgE5J3zjW9/E7nCHYZA+WmQcGAbzvMCSdjCvZMZKwDvHhELSHgfGohAJdRMDL1/dS5Hx+SzKkMVjnGPCORUwzShkcZwXTJMUtRMZzPOE8+kEUxJgCQ6ML754hg+fPcXzuwMOY8DgPawhNTJUURUot6F0Ta71bo+4SdvpupEPa9nKJpvce3d6TKph8MeiJ/qq3cLXdduIy5siWJ8uq74rlv5mea8VV5vYG+VEFw+ZttvfWPv51cJnd8MuPao1qblZ+ZZWz+V5XHzunUyu4YNVCVGpCDqp8blSVJXqSS3tqrBsNrAqlF1VXE5CbSn7ViRc+eZABOJa61PzK9xi7pkA0maOTEBkg8wGuRDO04KJM5y1GAeBN7f+SlasaWl1b/HJ63vM84wP7g4o3gI5I8UZwVjsQoCz0kq9skY4Z1vBdC0WBaB8i0psW/kI9T5aIs13SS0awcJZAgURXLO27igpCszdSN6pOIucgZIlh2e1UJrVEi85AajIRrQOwrYICbAfgtZDkdZsyW+OD6fG7BGGEUnBHbnkhhAz1qp3iObJ1bBrZVDJWUOdhAZiqGHTYAYscWmKbtjtMM3S4wvGYhwGVVTQGi2AOeF0Emb0VBjOOVTmklcvPxFaLrsSDNdxX/tjWefhfIC1TqmwrCpUxjTNWu8mYzMmAYywsrZnmFY7VWmd5hhhtZGk8w4xMWalDQvjiPMsfbOWrP3BNKcH1twfAc4QvAGCJXzx+TN89OELfPH5M4whwFXLiqXQvjAL3yGEsDfl2gZl5WWUdiYX03eNJa3jsf+rx7gsPM4smrJSljGvkRSqx8LWSDVkmsdrdE5I89NtK6Ctt7auX9vHrMeqIfUWXSH8f+CMNRRI7SFfhgOvLQDa/Ll631bc2O7zLhs9cl3jcPN03nieb3G4dQRtd6DEqRK+23pXFYDRzrG3xnQQF4WT10FuO4SX7Tyt6oVZk1FMQaYKR9Yz7IEaUKUG9RL0VHMRCzVm6UJr1IolEvhzRWYVkECt9RjzkmAApP1eamZSwjJPMMPYuAmd90IlVQl7VYCz1r4QKZpM715FiVF9EaHG6piU6JYgQAOrE56F66/yBBKgdEYKWFfhImwXVjsmZ+RcmUh4he8r4758ZoVqi7jaCgN9doxGtcW1eZdeL5Eq0Vpv1z8Lrve+chOqYFIvDjE1BKZzDlafQ9YQpHXSQgaq2KXRpoQQaZ5bAbG14pVaJ4qoKmiQ0iNVtn0jPbiMgoKktYyM0cKagzJShlCKCGYmkueu3nvWeymNI7MS5ApbfubYUImVoqpwUii/vJJ65igZlgBvCIO32A8BT+8OeLLfY78bu7Y5GiRjCWZXL6gy01djjjsNtBHna/y3fXxsordQ/y0AlIYne3HT3nZz3pgbUZUbryoHejnZvlcZTOoB68mgepXfLd/rvVZca21Kp7za3+5BdR7LbVf29jd09eaxhbvXuy3rgNiM0U8JD94agXr4x/359fzaRJG/ctxKgbR6WlUBVFokIoI1BlwMLDGK9qAqG4VVva3Qckfe+0a0a4uAxGsBM/VWJdFaJwMReAzGkhnnRRLtwUsbdHDBg/ZgIiIRnn5A2Dl4a6U1kgHYWDAZHM9npLggLwt2w047JQ/Y7ffwxmi334xK2EaExjwPCNNGQUEhEeRcWHNraKS0pWQtjhahKh6aEOAG7+G9h7EWORdphumNwPubEAYOT+4ACALteBRGjJRSK3BunapLQSFBKZ6XCRJcZex3ewlHqtdVvcOShdkeyjdIyjRhjJFSAi4oSZsyGqF78sPQFAgZYBhGgcqTwfNh0DyW1DtVr/o8ncWDtRbDOCKljHlecJ4muBBQcsIyTThPE4YhIARhNwEzyDJC8NL001hR3AaKWjXY7w5ClVRYmTVklnhfc5MybpkgqEAQ5uWEoq1HMjMKodE7MQDnvPR3owmnecYSI4wb1NgqWOIZS5QQae3vZkgAQXvv8Pxujy89f4YvPHuKJ/sRo9+K1MqEX5RVPtf2N9rtObGMdbMhIrhCMWxquaoeoI2Qe/NCnfFZ1WNTPVXhQOZ6JZCu46O1nblQYMJa0m3Xol2m7R8gFJQLxCM25/FZlv8BFNfWU4C8W13rmx5Lp9luPfg3a41bZ/IWW9D1g7pyrTq19cg5fDcdQADtnsm+V2+qBwnUAcpMKAWKsmOYImgtUwqck5BgKR5DHrAMS+uIvGgLdwBakyOWoNG6Hy7lRla5e2+kjiYWxnmJePrkDuCCzDOWKLU9NEfEmEVBOIvROzxV63fScKIxBsN+Dyg0/jxN2CkhrnT3FcFJRgQOmYyiIcO8RBHuCugoFkLzY+XJVrgyVe+LCc4YjMMAZxxiSsou4bDfK3t6YUyzgA6cCxiGAX4YNXdG2N89bYJalL3cQ6vnylDqKlSPKksorYNfV08FBKQYG/w6K3gBECVI1aPRZ1v93Vy0PYqxghhUAZz0XBjAovlCECEMATFGWACHu53spZPJy7Ig5xmv7l/j6d0dgvMIw4h5OqEUbkoXZIVqKTMAAZgcDk+U+Je1Bk9QjzLucgtdk7HIUeD4p/MZxgeQk7xk5TZMuYByASh3ioyQC3D/+rUiR4WNo6Skodsivd0ABAN8eHfA933hBb7/S1/Ei2dPsRuCMvdLQ8zCRRlLqsdTBf4aZjeZkfkRIc4tmF8n6K0Z3M3eVTE1hVSVS+vzJmUc6/HWUP1mr72C0ijBZUhw9bIuT00saBm23Nasl6UUcLcu5y2X91tx1b/Ux2/rm6qUqNMDtxXCBkVz6wAX7zUC87n93seOu/FCbpzC5U94s35rtV3YcNc7aMbdRUig3rE+fLixtmqI0KKY1eOyNrfi3uQSnEvNC8s5awsIA1NKU4bi2ZR2T3vIfD3FarnOCqE2GvIyKuTJiEU9K5sDSmldj1PhltcBCVpxyRkuRglhVUXNBeRF8HNOzYE3hhQo0uXjtNlhjmuifJvA5tUTtA4m+cbEYJxC2w3DZeHFc14Qd9VirffWJAnxVYsXQPO8Ki9HFVDgrkU9gEYCTNCcUPUo5Xm3nmlcYMlojknvOK3PhYyBrX2nmIEiUPlav2eMkeaVLPtbUoRlRkjSaLF2B0CKAMT4ySkhpyw9yUhChsxyLyq3YUUHpqz1Z9ai5mJqGQM0rFp7XRYFIhQGUimtfYspFSTRo9uUOFjZP2o7nmmaYH3YzKjWEgeAt8DoLZ7sRjzd7fBkN2JwriEh2/ypuZ02pldfp14HKaPLOtKvBcJ27RuEThV9vM7fy7kLkI6L7a62MmRVUu16br1qTqsfcxIz7z/pXqpxt4YxPwdV4futuIBr5UVV2qBTYhuXeg1RNR1G69/brveq7S6ed7fJm932Lf5n3ZQuB+pmH73SvTidm+fQn9wt9XZLS3ZhCSKAJHeSqayDtxv0FVINoOW0SnHqcXnUbsdSeCqCIPgg/aRyQXJROvsaRjarF6YkGk0JADWhy+BCyIaBzOCy4NX9AwbvMHqLYC28huFOJ4EoxyLoOUuSw8kAnAswENDBogKPSsGSBIJejEGw0ljSkAg97pk9dOLVnJF4KgDn1HJUVbCSnjtUcRnr4UHKlIBW4GpAGHdW2dwdfAji67BgKwmQtisgYfVQ4WedA1g6L1fwBpEB2VUEEut+9JHLOSrDPaHRMhVFFXILB6El9V0IosA1F0hG2s1zpUViUXY+BIWPC5PGPC8wJoGs1K9FNSaWZYFzsh+5R6Xl7MIQlEF/aV2TjXVSm7dI52ljDIZhUGqrSlgrY2wcBwlJZRnLBdIhQHKA4mEuaZEuxVUxW4vMjGWRouolZsSUJKyZSztPa4BiABgRmIMzOAwez+/2eLofcRgCvDbtbCKba7sg7UfM2iFZ1wPyDAyvY7/O0tsz9jJBdNMkbVO5biFKd+2fJ4oMLYd7a4/X8x5tXFdvq4UHO2NNZJyOwTXB1fa9pry0MejnAGi814qrxVQvfJFeh2ys4NsO16ce5b/b0p9j1Rv1q0td+7an8ahm/ZSTqOdhCFAvqFrsRsNahqSRoNgBcu/7+iLvNZxWCrxfGiBghWFr+AQEGJLeVy1MSADn7tRXzwBAyw/MOWIIFtZZuGGA0CPIvpYUkaHIOeeRIWS9Lx+OOAzCbRisxZwjpmnC6xSBFPFstxPhc9gjpwU5A/M0aQ8qqPCSfF9tfqnyEcapsmKAc8bp+IBq5TofwAw4MiArXX5JH64Pg7BaNAYS8WqcC03xLfPcclvkLBZtn0H1/rd7ZMBFwlnnOapQsaKs7OqFFfUSmRXVBwmzpYSNQWKcgyEHO3g8HI9YlgXT/T1Ye2EJus9K7mdKqnAB2xCEQCriudwNg5YBJMzzghAGDMOAh/t7CePNs4TWQkDKBcfjCbvdDmIkWTF2MiMr5VY1jAiADx4lG5zPZ9zfL3Bewo4P5zPOc0RMRT1s4Ql89XBE0rDouNshQ2D752nC6/ujensG47jDeZqxxKmhMokLHBg7CzwZPb7w7A4fPX+KJ7tR6r+YN4K+KlRmCYXXsohKPp1S0e+pUyrcFF6bBbyWvkvu9/EJL7ml6v3r+Gzeneb/oDmoi1Bd28uVV3UJyEA7XwLWSEDz5qCRjU53bRYFqVS6q8+4vN+Ka6Ov6MJt7S2KqrwuvJnt3noR3r2uNrutGB59BpKf2KBsLvf3tssjbtfVO7o+Ibo48ctzaeCWDmFoyEi7eHSDuKih0JRXB403NVyYFKjhW+da5xxc0jomK7BuQ+ba48R67r31WkMTYCOUPfOCs7fYDQGlMGJacJrOAENgvcsCCh6ulJYbiyZhMYS7IcAGasWwRiHxuRQsywKQAC1Qah6H1GNUhoma/9sEZOS0Wz6K1mQ3dUpBwmK2teFomSJei35XdJYoHmPl95UtxJgahGGsKEYDS05basl55ZiQ02oA5BbOy0pEu95l6a4sYJjM0tKltgSpTCBMpTE/RFWCVTGkLMW9DGDYjU0ZG2MhXVzW49cQau2PlRV8AsaaW+pfigQkfRYgLXhWDkcQkGIGyMB6UX5zKlhaXy1BNJIxgmYkI6HAGlLMWa9HQENZPbSkAArDAnzxzuDJLuD5YY8XT+7wdL/DPggnYQ8ZrxBwAM3DqmTBrGOEa9X4Zk7ehiv0ymudGFuPq41DlYEyBrpcFNDGVeu9Vsex/rgquHU+3w4R1pPoFVg/YfuQ/zqjr+OSnwcS/14rLgDdDdXPWAXKxZbN2t24Nre22a7arNsMofYMb/j5b3HOt7/EI+f23Vhkx9z9350UoGEEQ0ZatN8YtOJdyE1ZB/aquIxxm5yWyx7OCijBOteEdgUOVO+hnt8VhIUFDECKvDPWYUkJNEvOIXgvfZ+SWM7C0mABEhBDKIxUAC4JBiKADvsdgh+wcxZhEJYD4wyYpWfWmhRfI/85RRW8WUNCqynUC4fSR3SMaUXaLZ5PUi/WIN+V2YIA5NK6B8u9NA2mD0UocskqBLMquyw1TFY8NqtgEiHgzYgpasiw9qqS1iVZ6ZWMtUptxShZPIUlzsglSxNM1LBXUUUiYb85xcbUn/W+Re3mvNvtYKzUfhltwFkNgwoO8d6jlIicE3KKCAp1d96jkdqq0K8w9sgZg95ghih5QZVq52VjYHLBw/GMyECGMLpHBZQYazVUmOFyFgouFpRfhainrMS/WQA9OUko2FrC3kt48MWTO7x49kRRhF5QqXRrXuu5duCHXpHJgKlzsmfQqGOlF/zVwyaoQ7OZxUTU9YuTuWy6aFRVSiBpNbP29V7Pu5JL2xYGNG1uN7HZR4d6hVgV2IXR3F9PKw+gz6e0gPdccV16WBvN33lh1D5f7aD7fb++38cjv736DW8G2uXOLm2pHgRB62b/7yyqi+ukk6JT0jf9QIUW5kqCXRSJwnmL1O4IYsjDu9CS/z4EBA25LM4hVxSY1i3JvkvnlW6tMwYQoxR9WoPWoDJrQn0YArz3cGHEPM9Y0oIwjlhyxpwzCgGWJPmfOQPWAs4BTqDgqRSkDMCuSLIcE6q4MJUBoctTcskooFYSQAoHrmGzUhhZUXwxJlgfYOxao5TjgtoaZZ5nkDEYdiNCGLTH1YJhGCGtqKSGKKVF27ik1ueMc8TDMivTh3QObklzFJS8IC5STGuMbXRWMS7NKyrs5bmSKKhlOiGlBBs87p49E2U7O8xRmM1TkjCgLwWeJbQIY0EOKEsEWWHvt8agZGmtYl0Aw2CJCeY8gRl4cncAEeF0OmGJ4mk6a4XAdp5xPp/gnTBhWGvx6tUrpBixGwcc9rumuFIumHOBQULChCllsJFzmuKEaYlIWfJ4WRkrlpjAKeN4nnB/PCIVtE7QDw/HVtvmrcFgJaf10fMn+J++8BzPDzs834/YBVFarjLIkOQS65yqEmkdzSSgPpAq6QAq29CZ5DgfmafbpBcIBEsWoH5kqqJSsmRrbFNE7dwMgYqUehA0QqHNSm0DWtXfrDD4dd/rGKvjvvfeb513r6dqNOGRWOJbLe+14qrLKvwvFNnlfXyTYrhQVrc8r8+9NKl3O1L91jugizU3n//ltteuevueCbV+a2POXSni7YE3Ia1NQbLFWoxs1xCiemLGrh6FMYRcuuem51Pj/fVkG88fYUWeaT+lJQrg4jwJewMz43Q+41wK4hzxwdNn2D29AzEjLZNY5NOM2RqY0cMOAY4EIl9SkkaB06SOuZYCFKAkpU3KwpNotY9WWmILEwn6ThWHkuEKX6Hks4hI2r3HBaWwKDqJH8l6DeVIbkHY2EsuSHFRziDZNsVZvMBlQYoLojGIsxT91rxWVYBZQ5yiQliNfA21JamfMpaEiaMUGEvwRopz4yIdjCtVV/XShmEU5eS80CmVgpwyYhaaJTYOQwiYlxOmacb5PGF3OEgXYxDO5wmAeF7jOCIdj0hxwTJzCw1bYxDjIshMZ5VouOaslAkjRZzPCxIzmDIwJ5ANAAm3ZczCVcgMvH44IiYBm7C1mBdtGMras02V8qKoVGHft9g5g31w2HsnxM1DkN5weo62htWANQTIN6YlabF0Vp7MIoYRaYNOo54tctGWP7gpGGqovk4XgvSQa96eKlFSGLyzVmoLDWEltlQOR6wG6zaysobxW/ixV1htjK4yoA9GcbsBiuKsMm8jj76HFdfqrdTQzW3Xhbr/cbHFo2G7Tz32Spv09svjD+u2g3fTVbz98Y3j4CKE0X4jo426UMTb3I963/uBu4YNBbRRJ3VtENgsvwoc0FBaCxd2A7+/nAbA0efM2ruLYVCYWqFzbL2bgGmO4JRgyGBJCZmlnKgwcJpmZEMolnCwwOgsUjYSRkqlFc0K87ueXz1OjOAkDR2dlesAp5Yv8MMg9Tm1TocIbIyCVsSLK1ywaDsVo9+BCSVn8RyshXEOJUuTxRQjUlz0eCIcSxYFm1MUSikwShJFalTD18aFLXdSiiLqVgb7JS2AKXDean1YkXAjCTgnxUWZTiw08y4hWCUwJmOVmBatzMAsCbAZAULvtCRBIR72e+1cLfVr0BDqfjfKfYDUeSXt3jwOAUnLJgzZrugYCgApWJK0JWFjkbkglQTjhoYqzIVRSIJi52mWejrrYHzC8XSW8GBVgtqZOKckbCfGYHAOe2+xD17Z3z0G5+CNUeNFlFetDwOz5LA2YZ6qCIyGPitjRpH7Wo0jZhkHzMINdTl3N95cfSlVkzVtjlBVXKpcxIsyLaJRPX0ufahPf9eUUZ3fFwrtMu/Vzf16mpswYJfrkjpHUoOYPo/eer8VVx9T7p2tPga7KrWLL7FxsPq9vvXx+RGr4U22BANrmcOFl/f4Xh7d6M3LO/5EwA8FlKl5G1f7q68usdsiEKYO/K7A0nahB631aoWi3QSQ/beRjzrZdYUk55lgvIExHsxChsqaqyECXBgabyEDODx5gsN+D2bC17/xLVgDjN7h2X5EJosMA7IBKQt4o7YVkXwKA5mB0uUnojCGozBoGNR7gBKZKhpsWVbmiN0I46SdSOGCeVqkiy6JkAZRg80bDZEdTyeBnzuPYRhUaQlac78b4RUNaAzBeSd/nTRSLCVjWSYFGUh8plXSVMLdzMjzLIwb0xkPpwf4wePJ0yc4HPZwVpGiECVinANpQW1asoQavYcNXjoGa2nBaVpwPJ/xcJ4wF8KUCxKEw9IPO4xs8Or+Hnf7PYYQ8HA84zxNCN5jubvD3d0dxnHEg33Ar/zKr8B7D+ueAmDEoyj5w34P79Vr9w5xmhAzAD8IijQlpDnCksXpfMZxOiNreHKOCakA5zkClFCMxcPprF2PxViYY8IyR6kBdNKm5Mkw4MO7HZ6OAc/2A3bWIBiCM1JgXjswt5yljmFjKoEuGreida4pKsk1MkBSW1fZVoQazKDQ9vldT0PZdzUOpbFmP5fqlKJGxybRjQKCtAECZT2/fhvTPPbWcw8VBi/KdwVvmO0x0ck23lqhG6DGRejwsyzvueLChXCuWn/94jZQo9+2ffpcC2+01WphvL1LfOkTrmv7HNN264vfXt4LADXcdz1QVjeNAO1ztHpcvQXVjt/8y+sJdWk0yPsaQlSLk1ZOvrUOpMbKFfTBEGsfZfPkWJVYzozqFXgXYJ1t5+acBceEkhKmeYYxEcCEj/09RmsxBAcyDjEzgiMFFojSSsQI7gALQiEDWAcwUFC0bQaERsh5GIVUM0kRr7MWzlg5Pq+QbbFqJSRX23xUiPS438NbUTwrF5/HWAbJGRorzO1cJF+hbQmTen1EYmhYYxAV3WetAS+spMBFp0L1ZLkpypwSYpFWHTCmKaCUs3ixSvcwLxGWAcsifFNKKDCgDOwPQQwRItgwIL18jfN5whIz7p4N8CFgSQkPn7wSZg6SHldzTNKSJGUYG5AZePX6HmEYEELA/u4Oh7s7AJXH0cB4r80vPeYl4rxEjAzAOthgQUuUMC8Z2BBwfzrqtVmcjkfxxFQ5Za2l8zkjA1hyxvE0YRxHgKU8AClh9B6HMeCDuz2+8PQOT3YDnu8CdsEjOKEVq+O4htI2Yrp6XqzF81lynSmtXiCpJwjmVo/XgBxN//RCrkezSqWf0zrGw26vHrN45NztyxBaFICUtgoEaAvONfrRe1GbkOA6dyvgY/Ndkyd9cXevqNa4KRPrNd1Wym+7vNeKqy69xn9UAdE6h6nf8JYy6I2Wph23G27TP3zjPV9804K/TSBfq9RLV+z2Bd1WcZ+27rYCrUqrn3a90XYZNby0owDeKKpe8RFWr7jFzQ0pTPo65LCe7vqZa/hSj1gT18SkVD2KpqrhLCoQnkMgpgJrMqZpQdjvQFStXuVCZGkKmEpCBiPuitRplYzIACt7d2GGqyJD27737mfN7Vn1murJOivoxpxTg5UXLprbQFcjsxoJ1dsBJDTJqEn2FVbP4EYmbAjIOSoXoQqsKjhRm1uaqwdZao2aEUYRZoGAC/DF6jYMTgmZWRS6yh9m1rYm+sBUaDOtOT6GdFw+ns4AM5waAjkXMMtvK2pNuhsnDSdbYW0vXZ1RFZDWIvOMGDPYLCCyQpekSrEAyDCYYpTQJQNzVLYOsHaDlmtfloiUMmLMWJYoBfTqRbExAspwFqOXXluHccBhHBCcFaVltJzjwqKsHnQtb+jZ0Utmvcfctqst9GpOaI2x9IZ1nQurtyWNQsU79k7yhLXbddFOALVmDOAW4WBmUOv+gKaEGq0TLubljVcVDP0cb2atKt2+NWA97rrUUOH3rOJaBVx7d8vreMPvL720NgZ7qU0XP+H+w2dbeiVKVwd4i9PuP26snE93I6kfM0TCptDuw4Un9Q5ndhUL75K/pr1qiLDztGroVv/WGLxoeVovDVWRCTpsXjIiCY2Ucwas9TqFCbAOiYElCzsGGwvjA8K4g2fJL0znGXwuCMQYrMHxPAmThCbp4zIDED7AwXlYJhiViNmIsLdWSGfBjBwTGNImY7y7wzAOmGPEaRagh3MWlgWSXZttGqts5lpDtsLqSwMpGGMxjENDhRkn4SkBgIiymc5nQSfWW0VAThkgC0NSlF1UWLoQMKUkXppzGPc7WGsQk/T68tq40liL07yg8AKyEcP+ALIOTAav7x+kUNl5AID1AYe7p4CS7cZpFsMgRgF8UMJ+HBsDeyEpbXA+wHmP8zxjSQnee6m50pmRwaCiXgQZMAl28PQgLVLIynNdmJEyIxXGrPmqmBLO8wzr/KbOMKYZ968fMMWIeZbw4OAT/BAQnEUpCd4ATluWjN5j5z12g3Q2lhCh2Y7XajQQVe3enqFsI0X0KWUtPu4A7tVYq7VVneLrF2aZQzU0aUAILmAcBuz3e/EWwdr1WsiZY4ooJWMtckaj+eI272oYcDvDt/NUjZ86r4naeTfZUfNXq43eJFINIcrtqYbV96riavesuyv656YCqp/fsK8bH96wvEUY8C0jhS0yd8Pp+n9uWQdhu4cX769+sbHkWQYuoeWdQNeX34wNqsaGTtra4PamMVZFsvyVOh+p4amQdmtKu93S8ygBkHqeJ0+f4cXzZ/jw+VP8yv/1f4DjDMcFTweHbAnZWoTTGd4pEasneOcUcl4wLQuCsRisw6C1O+ItCLURWQUPaI6AOePV61cSgisZd0+eAhpajElQiyklYXBQKzyWDDIWzofGMFHzVbmwTP5ujBYWgIX1ATsNNc7ns6IPHcxoVqaGKOS0MAam1pHlGrKz8EHg67mUxuqxJCkkBzMyCA+nM6z38GEUyPo0I/OEbBzuHx6wpAw3jgjjDjkmfOfjj5GStGBxVhjapyUiLhExM6aUASu5u9P5CMSEgQGQRSoZcV5wOOwaYnUpwFKAOTGO0wQYBzIZyAXnacF5mnGv0HqGeOdTTALZVwCJCwEDGTBmQHNDh/0ezhkM3mPwDsN+wGHwuBs8nux22A8BQxASaW+lZ5U1Rp/H6g21N4TG21jJpWUO1T7ZaB4x12nTeT/VmKyCvQE/gIYQtBqC340jduMOB80dyjFY851CwTVN586zV69Lj7vls1zzVtZ2dVxWw/2mVYpdiYzL5TJNQjp9efP9Z1/eb8VVl41yWu/iahFUYXnDg7q94vYxuuVNt/2y6K4Ozsd9mYtVnXCiW58vf10/8I11jyzNkeHuQxeoeGwHGz1F/QD89IG4CelSN0npYr/9+fN2pUzLau2ZVTmykOxKWEIKT1EKEmmdT0yY5gUPxzOO5wkeBd5bWOdRiiiXwoSo/bhiSgqEECHiyMI4D++Fl86SJtIvrrvm9CrNT2OK8E7HhaLkrKItjdAMgRheiYLB2rGYamrqMkRjOwuXBJ0G8cwAau3ejVmJdJN6WAThfhTBvhbDtghnZRHXxLz1XpplGotpkfzUvCyACk8uBafzJL83JIowJSxxwTzPCGFUYQpk7ZsVsyABrfIfkl2QWcZ2zBk2BHCMWJYZIKfEyTNGFoOEjUEsjJKjcj4CsRTM6mHFlJulJblF6XRdJ5OE2iRMJw1E5TqICwwYg/fYBw0P7ncYNK9VlUUbt+jDfIxa5N2UVlnDgnW7es9rALyO+xp2bPmyDj5ujW3bSmdx28bZMAwYxwHjOGIcBpCiE8XLkrER49IpLoCLbR5h6zXXR0ouoiErQ8YaHQFW8MmtpZck4oiRhNh72fg5dNd7rbi2icu67tGNuzddTuXTNsdq6wM332Advmty8hKsUb+rA7JO5rdZql5eI5ePnDtdfrzRSuXm8gaVevHzzbl0lZKXEPo6mdcJSM2zuoLTNg9Mf9uN6CuV2rw0A7N5SJupsiouSA7jk1evME8TXn/yCU4PJzw/7DDuDtgdBsTzETlFkLOY5gnzPOPh4YjDfo/gBP48hAGDCgdbhLfOEuABoKQaHWrXUAWCsxakCfSUNV/mHIZxVEZ4B1NE6FnncT5PwvYQI2KMcIqiq/kZUC0ClxCatRZxjhoe6nNowjaRUkKKCTkndWVFqYqiZq0ryoiJUFiRh1QBI1J7Z5wXXsjjGadpwvl8xt2TZ0JuDODbn7yGGwVlWb+fNYd092QAM0tOKYviysy4P50krAjCaZ5xOAhUfkkFT57cAWbCeZkBY/Fweo1Xr1/j+fPnCMMAOA8mwnmawUQIpIXkLFRU07LAWvF+z/OMnOSeWEMYh7F5GDkpvZYjpBiRCSiG4MaA3RBwt9/h2ZM77MeAwQkQpY79GhHcROdVMTaC3cJCKK3GQ87ciHYBDRXrsN22ChHOQdJtrFm7dHvv1esysGSw2+2w2+2x3x+wG0c1hBgpxaa45nluhg8VasaqbJfXeWXWfG3ty1Xboqz0UReCoeqibi5uJSPhSojc3PLdlvdacdUwVvt4oZy271ANjnWcrUbIuy2dk8F8uWp1kW/xc7UPtK7m7lfXV3D96W2WKweG3s7CuT7Kp4VC1/Dgei8v3MsGnZdJWcMRK3y+WnVS07Qq3O0duFTC6xbcPrhmGUOYzZmxxBmffPwJjk7CfN/3xS9gGDwyWcyJMYx7BAOkAsxLxLxIcn/ROrDgg/bSEm5DihHOGngj0GhOEdC29mRqSxJCIY1+KgyZUUCWsNvvJXRVMl69PMI4B+c9RmOwxEVbcci1tA7DzmBZFhTtfVbh7zEuyEng8ihF+BqVhHY3DACgoToH1vNY5hknZWGHEXb6msMYxl0TXH7wmveKmHJGZqlTc8MOIINZWdWts82DePL0KV4/HAFDGHY7TPOMaZqFPHe/B7Qe7hwTcJqw5IJxHPDwnU9gjcH+sIPxUpxN1uP18YwlM8g6/Mo3v4X9/oAwjgjjHksBpmXBxy9fYooJc4yYYgQMtTDebn/AMi/KUC9s8c45BOeQotS5xXnGGAK8s4K0yxmDdziMO81pkRpJkj8Vz2NtxcNVgxUlUuqMNUEOiiG1xIg5Ji2JIMmTNcSea97cZb8rr4AXYwyCVwo1bdmz3+2w3+/x7NlT7MYdiNDChHIpCSEECcVD8546XUu2KGYt+G4C9SKsUr33Oh1X6inGSjJP6zx8K1fq/wsVXlgCzfBZP1C3frPRLUH9aaL7hqelWmdNxHYBNL6EiPL27eXubi69N4HtBT42SJr7sln55gN1inhV7pee1I1Dbfym6225bkVYJ4hO/tWD6xBKGsbpFfz2rLarq+AgkMa8GM4aMEtfrRQTUrVYnUeBwZILeMpwCIA3YAvEZQJA8D7AmlpgbKSWynlEG7FYC8cFmbSgWe9PrU0TjwWo/H6tg3QXWpFmhwaksPqqNHIpWg8kV7hBwcXYUIZrXkJe1nmloBJkXq3hqmFKUZLC1iC8jwZhCA2R57xvBapGPURAaLZSKSgQK31ekrAtWKd9twQA4UIQJF8uwijhPQoIqcg+Mksd3sPxLJ4QEcK4A4xBKtzyXgQxFkg78BZmcFqQGSDrJWe1RKTCiEH2y5Ai55iE5Fd6ahVYx7AWCj9XBKVab6xlANZIbzQ4B6chR2JG8E4UWJG2LwTXcrY9DByZ0f51ArtwpWASF5y59pPj5u0YY2GxMshQl7dq0HV9dl4NGGOMelxyvs45CRFqnmu3E28y5yRh2ywMLd77Ng+ZGTYbaRZbQ5MX81yG37XgpAsrv5/fN/0qnZe9iKJLIfEZl/dacd28B9Ulru8/z766pSklqIdU49Yq7Juq6mKFfRFo+01DzLU9b/70a99kkzQV1GIWt67j062a7YDb7ujT7x5dhSO3iKFV4a7Ka7UoqIvrN4+sxv831of+4moGdH4ur7UzMELJVEjboaSI7B2YhFcvMcBLxlwSvBEFFw2D4oSDtpO3KuRKFut1sU5qliBMG8QMWIZhSKt5EtqdfmzkwoBRaqgaptNzrwwjzrvm8eRSYL2DrRyR+hByKchqQdfi5VboXZRlPmckAM4HUTQlrwz1loFCyKkISs9aBBsgfPCA16Q+iKUvlyq/eVlQyDSKrfM0KwhFrk1qv4SL8mESVKAJA6z18DCIqQh0ncWeOE9nhDAg+IBxt1d0G2OaxZMsOSOdTo3411rpGVbJYY1xiOoRm3mSgnPmljdLOUs+c57hCsM5qJcqngAZ0+YkWNhPjNKQcU5AybBE2A+DtLEpwlxPer9bqFBICYGyhgV7o1W6Z9t1TjC0/1RVoAbWMphco0QztLLLWFtp0US51lCxNQZeOzk7azvFtdOQoSiulJKy8Qu7vfe+nSNzQTa5eXe94mrzcCMDVuXVbMmN8lIVXZVUW//Im94G/xxJrvdacVXBWd8/Lmo/Paf11kvvNFXPGCtzQv9q3lafoG0KsP5DE/Qb5VaP81057XcZIN3Q63JS63e3/M910oIhyqO7hssn008Y0ErSWYuQSZXx2yOPViXGalEbEmudc4IlYD8G7MYRwzggMoPJwPkByXl8fHwApxlfvNuLoNazLSkhLgvOpzMOw4iUM+ZlAbEHq0fng5L35oI8C+N4ZQsh7wEyYGNh/SChpCwdnL3XkKkfNvcyDGPLceUs8HDnpIEiID21zqcT9oe9hC59EI9Qhbb1AcY4gIV3kY0AWLwPQAgKzz8j5QQfBgz7PdxuRIoRMS5I04zDQclvF+lgzCAUpIYqHI1FGEelu5Ii3jCOsCy1U69fvhbv0Tkt5rbCZ6jeV8yCtHv+/Dmcc/jmN78hiixn3N+/xsuHB9QSiqd3dzidT5jOJ+yGEcfzJF7EsMNyf8IcIx5OZ8Siva+IABJm+pgSllxE4RgDThnOOhz2O3zw7Bnm8xkEzUNyhifC4Cz+py99iGAYjiSPubKl1/B2rd+SMVpzVq2zbykw6qXWOVD1pYR+LTxZeBtah2x5WVVUvhU3W+2BVtlnvPZC6xXXbrfD3ZMn2O9GANILzU/iZaWcMTwMrY6QmWGtdqSGSkZVyFKu0pEC9HO7cHPCuJOmrKieCnzsw4Zr5OnGPP7sOgvAe6+4tgtt4q2XqupttYAOwv43G29Lt2qWW9l4V+BV+dTkd6/IKiMzd9tdFir259KOyQB1HF9cW7A/4hp1onxzf9Zv9bKIr7qyCFy2D410kcduEl7ckfa3hQQvLbrN2WHdb81xoayJ71vW2MaD5ouvpJaKtGYpWEnYU0kgFOQUcZ5OmFPCaVoURhxQ7iM4zrAsHkthApOFcwbTPIsSVPRcTgmRGYNz7coTs/DaEeCMWPDGGA1F+bZdAYHUsrYQz4+IIO3OVFFWq5xVGGj4kGMESCHY6mGkXMAQ9NyyJORUUGAEnWcsCEJmyx1vIVe6qJKFbNcQ5hRx+uQTFZQSJkwQgRQLBNpPpGwP0nV4SRkcE5ackVjyPqlAeAlLwnkRFg7MAggpTDDOw9okqEzvxYuNC3LJGIYB8ywEyZkZOeYWqYgxi6API+4fHuCDhw8Bp0lY3yf1wGCphRgZ0ALmGjYTZpMwDPBenw8zgjJyWEOwMPCGEKwB5whjJQ82BvH8aqiwFfICTRFUzsnt7JO/DRShUl/g5YAHgbzmTe3aQbwqpNq3Tr5bFZdsJ8hCa6W+b9xJuHAYRXFZpeFaFqnJM53356xFthZW2woZw+o1red6uVwzeXRTv/PErufrI+vbfh//7tOW91pxNaMdFzKt32bzhh51zKpa2wS3WMNgVAV9p8D40kPqw1Wl87huKCjCxe+w2f+6Lz2jTn8SRGkRdxffX+0jsv1WnJmqTu610tXd+3Rl30KlFw+ihf/qhKiTt1u/Wnx9Dkx/358OPT4HqIYNVeEaIgTnsDQFKEWvuTDStMCGDAYL4CFF2JIQDBALI+mNcc5DSHWETQFQKieQwpyFTy7nAgKDLInS8r4JNueC0v3UXJfs0QItjwQu2lqkIvzKGk7U3FTNbXnn230U1KJsm3ORomtjkTkKy0YdsyTggKUoK7qzoOJhWNB9S4w4zzPG3Q7DIMIxasg1sdACMYtSkhAkwDEjYWlhusyMOWUt9i7S/iQlzTUJY4Yxruvy7ATYMc+NGy+miLgsoqh5pc0684SnT+6w2+0wLQuG/R7eezxMk+a1JFzpnG/Iu6Is+gwWL5fE6xwUkWcApLg0ZUOAdMd2BoMjDRk6OGdayJIUnCBNPakprfr3kh1iYzTS+qooQUcE4z2s1fowH+CrUqpdFDRM6J1rXnz9XDsvDIPU+4UgL0CJm1HgJ1GKlci55xik9rdoVKSb503vEi4FK6s47E3WPuZVcC1Lt5P1kUn8jst7rbjUlLm1dmPdr17Y2y691wWdBLL0IcBKB1RK0VYLRbnQVHl1tRwVaVQUUWSNbR6YUUHB1HllVbFhDZnVfNK7hj3fZNlsJlw32vpBua2/ujGeO2tso5SIGhVS7fOz1qBoSKLRP61eWs3vvH2osN4byTcNzuDpYQdaBsycsSwzLKBMChEms+QQnMV+8ID1YDBenyaMzmMMFn63wy5n5CUgL7MIC1WyKUkvsJkYOQSM6gX43QHOC3PEEiOePn2GUoTFwTjXnoUBQMorZ4w2O0wJ5/PUeouFELAsC+KieYqcUcIKpQckf8eFMe73KMzShn6WNiRWvb+oDPDH8xnDKHU+UM9rmSYcTydRtj7ADoOEKJOM2YWBMiVhwkhJWUky8rxgyQnDuIMLA17eP+DhNGFOCZklR5ZSwfF8AoEw+IBxHAWwkdf6ouNpAhfpkE0EwBBOxzOc84hR7kcIAec5g2zGePcUhUSxjoc7vDpPSGAYbzEOyizCysyhobHBWXij3pQBHAE5RXxyegCY4YwwwH/hw+fYjTs8O+zgOWMcxOMy+oysW7sbyHxfQR/Vs6rfAeKx1k7KAofnRnPlHIHIwQyDelmiuMZh3Ciupqi8b/NIQsc1NyahwwrQGIYdAIa1C0BACJPe25pPxkqk2ynt7Ztu4cuPldXjenu6+MtX315GSOiW6H7r5f1WXNXBINqueJOjcGEm9B/7mqNr66mGyMSSy4Wb0ip1YKqiagpM223UUCFU2RFRp9QKCpW2noz05Wl5s8IwZlVgLf+jZJUrlPX2ZT52D5pFXj2lGqq8cY/llnR5KTW3Vg9x+wNBylGzUsnQxtqrL2stLBnkWjNCq+KrDmV/jP46H7s+LiLMB+/gnjzB7Aw+/njGMs9YMiOmjOADchYL3+xHEKQVyJOndxj3I4yzePVwxHx/DyoZgQhjEKShAaRvUkXoDQTrB7gwwvoBbCBhPQhCjtUjccZIc8ki/Z5M14NsmWfMy4LTWYAJ1jnAGLgQMBoDn4OEmIzVvl6CuCu5YIkJU0zNe15SgfOm3aMlJek7FTPcADhtD3KeJ5E/1mF/dwcG4XgWVOU8T9ovCqg5OnKEYKxAumehc5pjwpKB43nGOSZMc8TrhxPG3U4Z5R2OD0fMNiLmjOAEjThNZ6SUBGJtrDKcaJ8tfe+cx/MPdnh4eMD9wz2O5yOC960NTtZx65wDZekMXU06Z4woGpK6J2lBI/3BZA5mGGVkd4bgrQFKQkkLcrJwVAA2MAbwwUu9nXo9pbZKUeh7CySoEVGLi7NuJ/ROpaUCjDHw5MTTGgKcC7DOIfgRwzDAq6LqFZfrPKz6XaWw8l66CNQQbPUKGSxhVe+FVkzLTND3vusF5YUyugzkdDNMQ4O8kT39z5tMrZGQ6520FMFnXd5vxbVZLnX+5bIJAq42AANEK0x1dU/Wm94rrj4E2HtTG0XUv+p3dUCzJG7rei4MNvK+QqRZcxnVA2shQp2aUoEua1aPiSQHxtfX/6jb/g53tV/ThwlqKK4fn2tksAsDmq0XVpmmr1ipe+XVeZyXXmb1sFCf0OoegwAhQz3sMRvG8f4VlilJN16WK6gIK9TzNATrgwhSZsR5RloiHAFD8EKJBG37UhhgIac1VgQajOTUOMv9IBKvg9u5aRNBFvSbqSAUoo23Lj2kpOmj8xr+yspQbwQoEVNu+a+cM2IWaibnfWtfUiHsmcUzKxBkX4EwMUgTxoyYMqwLErqLCww5nKcZOWf4MKDmUEmvkxV2XkiiByVHTPOMJQk8fppnwDo4x9ppOQlrBzPC04Pk6JJ0sJYeUrLUjsPV4zZW2sEYRRZmzfO1B23Ue1ESESnIFeSfd2IQWWMQ1OMwakwwycta+c5ZajROxghY0HsL74Smyjq7li80QcsXjBNr6H+NxnBTcJoRAGsYz6px4lxVih5DCAghdIpL0YPONu5I+ezhnCIRTVVsAdY45SqUc4pxgdHmrWsXBl7nF9bIyVVEhtfr2hislxtpeL5+Al3Lmd7YfZcIyqct77Xiovbf41tc6vumtJqwr3ke7u4+o0+wqLEEBhqFi4QKVuaBXHJraLh59eFCiKXOxsDa3EIOVAimaF0PUStQlLORAVcAgXhzTaRWy6e3bB65GXTjmx6RcSH01zum59ApoE+LUq75M2ooJaPIwRoe7N3iCvttyo1pc7xrkEY9wIV7SCzAByMCZz8MeDbeIe0HTA+vcZ5eIgHYBbFAvRV0F6CChglzSnjgDMcFJs4wLKzmYRRruKSMtCR4YzGOQ8s3wAhB7f3DEQBgnQABsrajrzxvrK1DxAvv2mFoPZEPAxYtoCVrsf/gA/GqlojzdI9grITYHh4wjjvx3AFM84z9wWF3OKAY2o47iQ/BOCGvrR01z9OC1w8nPJyO+OBLH+F4nvD6/l66SjOkzmiwzaAz1kkbkJRxOk9ww6AMHAX3x5PUbBUGjCirXFb0Wk4J8zRhPwbpeUYQsAuLp+ucw+k8ITNjGHfKsi/9wg53hxa5mJdFuP8IIqitBawFnEXwXsEUHns9N84ZUXNpUxTeSq85SACwxAjWYgwedzthyrjbj/jgbgfvrHhuNxjg62IUsQlUozY31v3C4v3XnE/9LGhTBxMC3CCclN4HDGFECBI6DCFchArdGmp3qxKTHl+2eYS1Ro6KlC8I7F5ptUja7EiI8Ib3c3Mua+FxtxFV7aSrqydnyHQKsPPEWiSHL5TXLY359st7rbiulqsH0CufGlPd3riqqzaNzwjA2oEbFdbZe1s5i9KphZ1VICXtxFuFR21sWGqIsaF5DGJK7Wx6Ik1DBjmrYKeCUkT4CQDNoI6c25bQZ1sux1Dzjvpaqzp4zda0WmmsgAqO6D2tlSFDtjFd6HD1wDSxjs9auqDtTQCgMEpKOOyewI4e04sPMJ1nvJ4mHGNEWQjZOuQQJKdDhJSAX/vkJZ6OATtnsbMGu2FEGDzCbqfceOI9hMMgHH7WIeUEJjE68hLhnMXoHLxSC7ECBQpEGTkEKbxNeQ21qEGypCi5T5D0yVoi5mXBsixgQIuL1XiCjMeUkoQPrShQJoPzLNRMKUW5HVosnCV2iJSPMNZj3B8AJx7WouAV5oL94Q5kHF6+fkDMGcZYjOMIY60UblsLFwYt+E2wziMu4lWN49iMuxQTnDXISRBuBPVenQHRvnUCjqVgBLBE4RpkMiBLcDonpChaws/TJHD4eZowDkIM7IxBms7IAGYAaTdi8AHBe+zGQb4P0ofKh0FDvkKL5Z3B4AzuxhHBGqBkpBhhuICstFhpgoBk/lUiZRRu3H6pCMKzGpONbLrLJRG0F51zMn6ch/fSh2wcd6qwLhWXelXWNcVlFAFqtXhZ9iXeFSBpBMkrOy1mts3jAnHXWqj271rnb71Y6uXLpZzpwvg1Fy2pDoPbyxo5aWs+h9IC3nvF9VhQ9jqyKhqfWsitfcmXv1st+d7TWvnHinpcRVm9tYan87KaVa3kplLWVH+fV8WniWpDhGw0/JNz64ZKRF3DOW3LfuVp4CL/U6/tht/e/WgdOJd/130RobU+6L+r51yubv7WW2pelzFX65sn8obX5mE2p3L1FLfeoU4OzZMsywJDgiR7/uQOL+92YjjwjFPOSCr8hnGEswJfF+EuBbyFSMJ6LBx4sAbEDkYZvzOz9gFbmS+KNmIEWfFQUmrI0aLw7OZlQRQPaqhZoy6VZSKlgnOFiCsqMWfJqzgtKK3nAWBlxlAvKKWMJaZm6IgyFEBQZlFGPowwPkiTReMQtE5q3O3hfMAnr+6xxAxjGRQTLAus3zoPJoNYFixK3lv0uDUkXItni7MAF+Q4g3MGkYNzFiEEqelKGfPxCJB6nSwNF+v4zEkZQ4qUShgCvLMAZyU/lq7WhrwYojlrF+E1HFjLCIgIzgcBcRRBgwZnsPMO+3FAcNrdWHNiro8GaGytH9M68bZhcZD2dKslMOhChWh5PaNwd2MNrKk5rBWYIa8VSdjqvDpovCGrzURX5STlLcLEURVcj9rFY3OsW7Zi47bSAjqF1yJba2++R3e68Ru+Vz2uW7FCHSS0XaVyvOaLLjSWmkl91EyU1eptVWXV2kzkLHQ4RfIPUZsFplyhwCpEUtrUcJWywlJ9kiJBmeiV0RpNidXvQJUrrV5yDbVxy0H0wBIAHcvE9Uhq4dH+Qq+2IYXjb0OFrV6FJV5PJasnVWPn+n8fJuy9qprfuvC62j76kMTFc726lEtdDUFxRS44nc7IucCPAR9+8Byn04PQQAGYXp8RpwkpS2iKxkHapQPCYuEcChdMWa5t8DNePHsihLfeYTmfQdkBRkJUtScVctFWGw7GOkzns7ZeIdicWw5AvHVFpuWiyXQAZJC0W7KdZ1G+ajFzKZhjhDEGw36PZZ6Q0urNp5QxzYuE6RRSnwtARoQrk2vFztbI9bnBYRcCPnn1EsMwYhh3eHV/xN2Tp9jt9/jlX/2mNGcsDI4JXlnj3SB9teYlCjM8CFGLpkHCpBGCx91+j5ITnCEgRaS0oGQHQ+JhMBHmZcG3P/lEhLlx2O0CpvkTMeogzBfz+YxlmbFXtnY/DMjOgHNGsAZPD3vc7Q/SAHRZUHLWXJETnj4N1TrnxaMtUtdXm0Xug8Ozu700kiRgPw6SJ+vGbBP4hsAseao1B80b1vhmzJaClHsZAm2IWTsDqNK5UlpeYfh98bF4T7WOq6IDATTaKKNyoVBp/dRaKN4YINNGca3NI3t5V6dUzfuvhnKd4zL9L9QbqUvA2+xzEzFVzKwJ++9hxdUvvSZHtQTWGimRShebc/X/+wdWSc9r7qM0y6kiBktVXHkNFdbwYNLakjcpLqtFjNYuax0I1sr72kG2bgcAbNrjV2Ug1lvRvJQhWhX2W0Xabrib/bctrN+1CycDotLCCNXaA8TTqSFEmRi3vSx5WX3RWgBqDFJTYOsZvuEUN+dqSKzrMk8AF3gvCDYC8PzpHah8GXe7PZxxuD9FHGPGkics8wwiaeZInPEJ3ePsLEZDCKYgFwfvLI4pYec9/BDAKeMUE6aU8SIMePVwFGHpPZ483Ul+Kxec5kUAAiGgkDCQLzHidDqt1rshpFQac/zxeBJm9Vzw4sULeB9gjMHxeARx0fYhLAXMzFimWbyumIBpAayF9QGOAVOA03RGzhlOz9uo8jnPs3hR84LztGDcWYQh4NkHL3CeFyyZcXj2DOfvfCKchJxBgQRyfzrDWovjtOA8R0RlfZcO1EaLj2cQM54/vYOlPbxhBGukr5W1OJ1P8MMI4z2ev/gCPn75UsOJMo65MFJOCN5j8BaEO4zOyVghghsH7EPAoOFAydw4YDcKdZbmgay1iEmYRaaznLchI8z+pIprCHAEWLDyWa45JO9dm2/iPQva0NGKDM45b/I7WeVByhlzLIiptrgpMJZUcQmYxuurgjSc8w2g0cAYjVhX12s/NblXle+SWr8sYtOMRqMoVFVF3XyWiUNajL0xXnurdgP24quwIde0xS3LcrPfzi2okazPES18vxVX0+C0hvgUJi4f+aZ8rrXiLSBN27BgRQwxd8wXtTXBheK6zGsJsWVqYZuc05rj4hV9aIyFs7GdXF/dbq1tHke1otaHTAAVHT9SQGi0K1T1pKovdmtpSkE1HPeD6U0jifoxy6uC6gEVXajw8je9R2XoFjP81seqE+E2QEN3vlnP6gVKV9gvffkjhCG0e3jYDUhxh+NhxJMxIGNGigXzJL2kUkoI3mFeEogBP3jAOLCxSCBMWTziZAwyicFgWEh2i4ZUyVrASIv72sywamKGEMRO07n1/CKSAlcYSWznViwrYriyZgif34zmsp1O2B8O2gV6EUZ3KwztQwjKI5jhQ0A5nzGnjIiIJbNY59biHJOE04ooG0EdMu6ePBW6pCWCQdp1WMLCmQlzTLg/nTGEIEz6td+V9Y0lfvWk5fq99zD7PSwV+OBB1uD4cEQ6nlBAyDCtwJkgAArmghQFtVgRg+DSxpojKeK1JOUJriuxaKUUkLBi4YKSBSSSUxLl5z2ccfCaIyOgAXvWWqfO2EJnEBuCYfHCWvFxKauXokcXwv7S1q3REzlXZyVPWnNVK+Fufb96Y6ZTxHX7FnOhrk9YPQ76CMbqA/Xn8hgI6spfajqpr23VAHTztKDzla+O1ajvunt460jvsrzfiqtFVsWjbSEzXpVR9UCYuw25E+xN+6+hQkl+r3VUtQ6rR2vdUlwpi8KKS+ygzflKcVlNeDu7otqa4iKCTemG4qpepPZKUrPJFCNwekB4HogvBqf+6soYuj1oHqdaWgOBtZatnk/1vqSdBrWfrMHDPnxIIIUdrzDdFZCxVV5o3mQzMrh+w9VZbveQC8Nbh/1hh9/wAz+AcnwFLgsAxi4ElN2A836HDw4jEhcsZcY8nZFKQUwBxhywxCTe624ErAFbi2wMppRRGHBGYdVY2eHJygVZ75QeSQwYdYuVGUPCYg+nM5yzSAppJ20FzySKq4dfx5hQWHI80zQBLFb+Ms9CMKsNGQ9Pngrs2RoEP2BZEkDi6VX4fE4ZMEJl5LzHvEgdlWelAVLKpqfP7nB/PEvYMWUUEgqswoxUGHNMOJ7PKMyYtK3JkjL2ewEU5FIUBSfCmbnAWYPB70BFuBfJEI6nE14fT1hywbC/E+CIzkrnHUpOWMAoKYsh54RIGLAwZGHBQMkS/lJovbOmMd2nnJFKgnNW5qyVnFhK0nySvHQz9ta0pqDWmFYn1eZPnTtNfsg8gzFAKUKDhdIiJ0AlDqjUUxUARhvPyFqrDBgrgMJ0CqvW7RljN6HCWrxvqDYhXTsANCBIWcOWzXrsDVP97tq07byiW7IBfeRwNe7rvapOxOY3F47cRrF/r4YKdZiAgMYBKN1nuTaHlZvTCd7+VrWQIFaYe/++1LyWelmrIpLkd+ryWos266s5ijXGndZ91xyXFRJQBuCTg/dC6ZPVuyMSlJkrHduCMepVKqWL0QmhOWJjDQoqNHW1glbl9cabeGOpiuR6IF5GFqraMc2iJBh05Lm0KqHVwxKvolm1Gvqo+2p8fUaeZztX2rzR/ZrmxX3xoy/hB77/+/Db/3+/A7/0H/9XHD/+NaQYsRs83N0ezgBLKTi8vMfu1QN+9dURKUUkEJaY9LkWjOMoz7N4GOvxakrwRgh7BytFr5aAh2VG0P5OYRixpIiICiMWj6WoEIrak8l5jyXOKCUjlazHkuOmyktYHKZ51tYmQvFU+3GRMXj98AAig2Ecm4VurUNMEWdt5jgcDiBjUUA4an1VyjPO04T9fo9xHEBk8PEnr7Db73DgA3ws+Pj1A17f3+M4zbBhQGTgeDwB1qGAEIYRD6czpmnGskSweqzVwNqNo9QleYvT8YjgLQ67Ec5aLDkhL1mYSciiTDO+9Z3vYJoq6jDg6UG6Dn/xwxcg5VUkEvopb0zztCwBzhoctFyhjhtnCCGM6gFKPyvejfjyF7+IeZrAOcOCcTcM8FZYHb01CN4heN/KUUQ3ZZS8jvVa2mCMhRCEMbLKgZ67sNU3NuNK/gYfYEOA92ELY19niMwonSPWWKw1aisQo+WGa1QJFZnMsC0nJ+APUWIKgyc0Y/FKFNR53RnJYihzO/+btViqFC/3qeb/utHm/edb3m/FpcqFOihmqUWMkBBWi59VpcXrLWR1d2v+ilFpnABWqG6r19p4UFK8mVJCLuJt1fBg0lYCG8XFUlVfQ4+rdwUk7zVEVNusy7mGwmBXC4zFGgQAsr3rrUJejD4NGdTi+Op9fYriql92A68vmq3/b8KEm99vw4Cb95tux2v9VvXSjFq0Vy0WLk8RpKx83XlVFxvr8bj8/9v78lhLjur8r6p6v8tbZnsz421sCMjxEkLAmqA4SLZsE4RI4A8CKDFRBMEZorAEIRB7lDghUhQlQvAfjhQgCRIEBREUs9iIMDjBwTK24/lhx2ZYZrzMzJt33126u6rO749TVd393vN4xZOH77Hu+L57+97urltVZ/vOdwjLy8s459xz0esPoGImua3rGkg5dzDoFVju5yHkuzYtsV5bVFWFmZqhVzCiLooTGF1Bk4AREtPaYEoGsAb9LEMs2PsaTadI4wipjZkNXTOLReQUCSxBWgIc4lBIBnUI6Yh7Lbg+yc2/sq5Dj6w4TjpWs3aefiyl8+iYjWNWVlDGQmqNLM+DAaSthSYbPCVYQlVrrE9niPMCESRqKExqA9QW0liIyQzrsxKTssakrDDMe4gkAUJiVlYOKel+CgeFTOKEjUdnuM2mCmQNpMhY2RoJAUK/l6OsWLGujSfM+0iELMtR1dqVG9TQdYRYAlACeZpAoCEylpafgyxv6kI4BhaeC8bF/MkaGO2K/msdiphjpRAnMbJIIVESipj7Pk7ikFP2pLkyTPrGU2kzw6OlpBrDlJHD3tgNnhd5EEe7s3ADVmoXOLc9qMbr25AzhggebViIfj17b8r9E3q8CR8haU+rLaIxbj/w3txWvllnK9jwtkB3l/AOgf/Q08ltedn2iitYNu2woXtCbnKxiJayaoUGqVFcbZYLz9TtARjc18kXFRvHOtDKa2mu8dHGBMWlbZfyiaxXXDK4yQyh7yquJm7sWKcJIMeI0EwKtuotAcLCKQALK11DPOECLy0l58UrFz+Gwr3YDUdS6yNN+O+xZDMAY6NC2/jehufuNKJ9zsYEdNfq32ozaYgmDAPC0tIyVvbudZBuZp7Qhmt4IqWQxgkGRRrCZw9nCUo9Q6lrRq657sSc95HQBNQEQBtYXcMaDaViGCUQk8S4qmDIcnfgpIapqtCwMhYcThLGcPG5AETEBaGQDMG3IFTaAG4D0tpAKhN6efk5TKIhnlUuJEdg2LyKakhrIKRCnKbMmiElKy7LCqyqNWC5BUllOMRlIFATMNMEqQ1EpaExxaw2qKxFqZlGicDRidmM2fK9de3XWRLHzfVpjbKcAbCII4WqqqCFgLUGcRxhWlYYz0qsjdYh48T192KWdkOuQN8akBYwSiDK2ZMSxES4ZAwEWQjiBovMfiFDqEq4iyKnQAgAGQMyFnVdISt6SCOFPE14zAAIEqFZI4COMmnF3Ts5L7/XeGnyW83e4UPqfhpbooD0666HtoJqHtI9sIVh1+SmvBHXWeKt49BqTeSUVMsY8kduUl4tZex5DjtrcMO9NzvTFp6c+6flzAUT9qnKtlZcTQM3G8JiYYT9jwNsUFiN0mpcezhWagohwQ5xbquYWGuGwWujW32QuNlgICN1KCbvdQXry3uIQmAmS8zKsiFUrWtH+5KiNhpZwoWJxhJTwSgViHoVEUjxhs6JZLexSwlpwcl+28S+jX/emqheQhuItsdFXeTRZtOq5fa3laILSQjBHrD3pqQPdzgYsAobw2bKpyZcQhvOs3nF+GXM55FYXBji3HPPw4UXPg+D4RC9/gDrWY5qPII2BknEm10vS2CNgSSL9cU+tNYoqwrjyQTrjk9PKIksS2EtQa9PMSgyLko3wNQQZrWGAoGKHCQsrKghplPoqoSSEmlCiA0hNgaJ4TYdxhLz06UJZFVyjsQNIoHbrMuIyXGFFM5j5/kzLUuuDxICZV1DKFZe4+kUUZrCd1a3SkKmCSLiQFapNaZVhUlVIU4lICNkRQ8yjqEBJvEVEquTEqenJbM4ZBniYgCalDi5ehpGG0ynU1RlCSWZeT9WEWqlYKRGmia8Noi4UFsbaFFhAjAjhuGQ2uraCFHCKMn18QwqNsGwmK6PIMgizVIUcQQlCPV0gpGuEEcKSRSh1+8jyTPEUYQsSRx/pKtTI/YKY+G6N5vaFfsLFBkzYWit0XN1ewIW1hpESiBNufDX7+V5L2c6K2sBsq7WihWOD4tZV8rAARKe4xylaUh228QFnt4tL3KIJOWways/5MN6bRLckBNz0QoBsSHg0bH04BWQ8MacCzf6kpSOIdry7tohvrbnyD13uB7uiUrYMzrJrQ3/bxbvU5btrbisnygtiyFYQ7wBeifVWzxbelne07LUEOR6ReUmaKjRcoCMRkEx5L3SNYxrHV7XdUvhdcEZ7WRm6XIYURRhOpshSzOkaQatDepMI3WeW+bqUlKHUIqUQkzN9q5cSEKAw4RA0x/I140RMeWUn6SeqaNRSS16LNG0mffvdjw2+EgJdV9z68ErJSE3W4i+eLNJQresyc169QzS3DOILb49u/eg3x8wg4CQSLMced6DVhF7w0oAscKwyJGqCEWSorbAeFZjNq0wqytM19eZIqgqMVwYhjYYxg4hiQAyUFEMGA1B/HuUSYQ0UihrA9IVIqVQW0BX6yjyDL2i4DF3sP/aMhUTSe56zGMvQEoh7/cDPFr7UgsHiKiqmg0fKWHcnNZkMatrSBduFqMR8xAaCyMEcwzGMUgKGD+2UsIQh1DLqsZkNgtGTlkbVE7RjqdTnjcOkVfNSqa6ymJUVQkILkZeXV1F7pg1pATSNIGUgluV6BqAgIoiTMqaPbuwEfNN2LpEL0uQxhGGvQK9xLGyk8Kw30OepsjTFFmaIFZcmBtHMecRqamN9MbPZDoBLHfBjjyBbm0QKwmrK2jjSjAEIxjzokCUxI5RnsFVbXutQfdJ7oNG5FhsXKF1pBwLiG325k06htfDyu49sFGC0ghMyqZGxyssZuZQXk11loNfbt4Id1+LYLyFoujGm/HX2DHo/VO0lVZbeTnMQOhY0Vzn1tHBMy/YMJTiCS7tJyDbXHF5ywAh1NJ2f30OC2gpLmKWgo0sGF3FZTo5qo7i0toxE9QhVFhrVlZNjku77/QMGt1Gknx1gJB12MTZa6uRujbmWmvUadoUPCdJCPlZh3qCaH5AIS0EOawGyY5SIRdC8aFDIbaquuDp1VhMCCEKtI71SmjjJ9E6blMYL7zXADGaXMHGhdT5VrSmPbDBZGvQjawQF5eWkBc5M4ZLhThJEacphGQUn1Ya1sRI4igkupf7FZaLDGtZgrVZhYmuUVsDC0b4+bYQeZ5zjoUsylrD6hqwJvR/0q7eh4yBsgSDEtV0xhyTMW+Kng1Bk0XtvXjHpOJjKVzQHIGkRF3XqOuaqZ/qmgveiRt/SsPccxYc/hPEIa7JrGJoOwiVIUY9RhGgFOcJiVhhEgOMyroOhc2+0Lmqa1jL89E4JSYAV1gfgYgLnn2HhKqquB4pFOs649C2DEXXukUYvwE6miAQYDTyjBVTHkcMrlASaZxh2CuQpSnSOEGsWv2k2HIKa0q2Zog1FkRcPK6Eo3cSTKbrw+gCaJHdqnaAhpG/YX42KE9/LyHS48NwjneS33eweHes8WPgwmlLi0vQMsJ6pTGrJx2Drp3Hcotq0yqlrZYCvGHYiay3vqsVlOvYo40yayukjXm7rdB/YRfzl8ohry0uNHxp891whvJWcccnKNtacXnIOUvXbW5bJc1kAzbC2wN9k1dc3svaAHn3CkoHZcWtGoyziGtHTbOROaMdKvT1YQieT+NtTKeTUDU/Gq2h1+sjzwsMh0NUvRppmiLPOJeWxAnSxCCzjk06auUcvNHllJwQCs1UaYJvbSvIHenqwTj0yoWqAAnbTH6ndOCKjQUkJAHkQ7VoPLVOjN6fR6C1SJtaroZdQ4SkuBQM+288VIQ4uf+/EIzEFILJenfu3InhYIAsSxEnCdI8R5KyJ6CrEhUsqsgR5MYCkRBY7mXYv9QHdIVa1zg505hYwqyuMBqtIYpjF761zCAO4qLbqgQZBirU2iC3hCTLQCRAlcZ4VqGuSlgpodIEs+k09CLj++H7YuOGAR1JHCNVBYfwdI3JdOIK2g1qIohIhZo9cmhDElxj5o2Wqq4dkhA4PVpnLyuKoJKEOxcT1/1VLlrg+4B5z19rjfW1EYfVihxkuJg3ThKMzRhlWWMczWANk97WdY04irk2DYQ4jjGdTiGEQJKy5zidlZiMx4jj2IXMeM1aXUOQhSKNftpDGknYcgqKFfJiiD27diGLYriCKFhjUZcNkW1ToN/MHSGEQ/nVAFkUSYzEMVFkqQNguOkYR8oRLbNS9g0o2XtpKJM8GXNVVY7pHmEtCCJI4tC83yfI5bmMsSgrDQPlkMXArp17UAkJtT7B2rgM3pCQ3ohziNSWQUfW5dBEd6P3hlsDTnKK2xnznXwctXSJO09jQre/09mtXmm56EAnMhKO2+T6NV/SOmnjMbYxhm3E4ZOX7a24DDf02/gjWDZ3Qnx5qzyW5xn0kPcmj+W8LPdaYMEwTOvElibT2zTgDB8ebGq6rGmazbE3B4RYPOCsIA9O6Papmk6nGK2vI4ljnDrVR38wQJ4XGPT7GA6GyNIMeZahrFKkSYw0iWGIY/+Ryz94BUGgDZ2ERaO5/JixyQR4j4e4uFGCQOSuiyzINtYhhAQJAw9j7Ib8fBIY8MrZK6Nu4tnXpHAIzbPId+Pxjy0Eahi4LWH37t3IsgLcDVgiTjOkeQ6VpFC2ghAWdVWB0gSRlIgShYVejvP27MSwlyPLUzz4yCpOjKd4ZFox60RdwxqL0WiELEmQJBG3imd+KFSaECcKWihMa4KuuMjVao1Icj4sKpks14dtoijitvWGFYkAQ5gTY6HSCgLcDHE8ngS4dJRxoTAR2JgSHLaaTGdIs5y1liFQ3bQ/Gc2mriNxjbXx2PXIYq+jnvH8ZiLfkqME2kBrLviNFbOvZ0XBdFjGoChyR3lEqB2gKHKEsFKy8ZAXBaqKORYt/NznEJw2wnEIMoFuliTMFRgp7FhaQJFlKNIEwlhESkAYg9rTN7l29Tbm655MJxiPpwyYspbZ4ZMEeZ6xwqUEsBZZmnJRsWSWCWtca5EoQpwkUD6cLRig4TsU+31YKRdmt0zt1lh7TSSgiey08zp+37EgwUaGkBLDhSEoShFnPRx/dBWMNG3ns8Kq3GrCt06xMWjH5wreoENUe2/RJ0H9V3gOUgrX311XYX2dYSFudLI2Sidn3o7cUHu3fmqyrRWXdbUvXvzgdymb2sW/1FJWBsY0bBjaQViNy1l5b05vUFy1NsHj0toXILdonoxl78spLO1ChcGCaV1/2Mi9lxFi3OzBVVWEytEEZdkEZVmirnkDqeoeK0mTsNcpWqjExIcN3ASRTXJVwE/ghudQEKPX2gFEIRDazXvrLYQcRLPYGX3YKKy2bERJPeYjLNpuojj8rvQYEFpiSi4QAz56RR9RFLnfHUjSDGlWOCRXBJAOvwmHCgWSOEK/YCb3yhhoAEkaoT65hnpcoTYGdVlhvL4Ok6YwOoF1KDgPiIiNhdQWs9rAGe2hH1ZlCKWxqEk4r4jnwazmOWQNFzxHkQIJye03XKh4UlYolOvbJAS0n6/EiMhaG5Sa4fHcAbmCcZuQIcJkVqJyXlVtGEEHYwFoWLCXUVc8v2ZT5kYUIORpChEBZG3wUHRtIaNGcVZ1HeaF3wyZMaQOnoBxKFvrUJ1G15ARF94nUiBPImRJhF4SI3H1VJKIFZur1wIs5xKtBVwPNyguFCZrYdy9J0nMyiuOEMeKN26ykK1p6RVSaGQawoHUCWEHQ7gFtmjnrzoexIZQWqMIPKLQIQzBrOxxHENlOYyIkCYptOXebT7S0M45+f86341G0XQ8GOoaxe31F8AZboVvDBFuWlpBET851RKUUwhztr6yraxa+8dTlW2tuLSDoHvxHhaARlFR62F948cWzN12eQeDx2VsyFl5xVXVTT8lVlw68BHWG3JhgR3ehSPb+S0v7Zi2krKT+5FCohICk8kE4/GYW3TnpzFaH6Pf72NhYQEL9RB5liFPUxABaWKQJLG7/5hJZYm4VbtXjKFxSDO5PbzcRaGbySVESEJ7z6mJLXKoUPi4ZNtUJDfpW5uBV04NYkp0NhEpXbHkJshxa9ycIeluMOQzIDnJXhQ9Zi53IeGi18dsMAiKSzgvuK5qRFIGtBpSQqQksiJFXmRYOr3mQA+nMSoNprMSp43FLImRpSm3ynD0OyZLIaOYQ4SQiCMJKWMACrWpURpCVBtA8KZpXUhnZgx3EC5LSBcmJBUhmlUgYjDEeFYiKQrIJIGUEapZCW0sKksoq9LNT4t+nEBXNdanM9TGOhom7RpIurkP5kr0hpm1FHpVaW0wmU5RliWKNAvjW1UVfJ1QVVeA6/VVVdw80lMsQQrE4B5as9kk1FQxWrNk8ASAupwhkSliqZBGAkUSoUgT9LMUtmbPr1oHBr0CUZYhzRKAJK/LugTIkc5KhX5RMLuJm9exMyY4ZGYA4qafRB5xLB3hLpr15iMDYT1yngnwBhEBvoElNesALYXVMGYE/yvkuXyERVvDvbgcC0aWphBRil6vwKzk7hFRG00YIg6t9dbe5NyDl1k7stRSQi1DM7BtSNF5v9OGhFrrN5yqDdLqXkt7uXeg+iHK0hpXNPsyhAv/t0OXT0G2teKq6jpwtAGNsiJsUFytkGCAuZsN6MEAXzcdj6sTKvQWpPWowjblU90UKbd6dfm23Q10v5EmbNZs6gEaG1BSnKQXcoLR+ginVk8hSVLkWY7l5Z1YGA4xHA6xY3kZRZYjS1P0igJFliGJeaONLRwMHWzZc6wAgXK+FfYIf7essWbONdcLAVg4xKIFbOiu2vWmAi9h+Jxo7rWlrJXvMuuUGlrnbteXwY8leDHUVYUdi0vYu3cfQBQ8V4LAcLgIKmdIix6kqVlpW87NRJFCmiRMxus2qShW2L97GQvDHpJYAdbiodUxHh1VmNYVt62fzrAmBNI4RpKwsqxrG3KQw0GfiVkjhclkhlk1xWhaQkq4cKgAJKBdZ+CyqmGNQZoYIEogpHHtNBIYmWC9NNCjKZSMAjx+VhqMpzOuCRQSZW0xLTVGkxLTqsKsLFFrzU0YAdTG4PRoFHKG1jJDRznj1i5k2aNJoogNH5f/jSOFsiwBUIgwzKoKk1kJIQSSJIGMFK+NqoK1BsJymE8J9ppEJEN4LRssol/kKLIUaaSQxgppHGFQMDM7g18EEu+VxVEo1xAA5y1TbuBpjUFVV/BFvabVpUDFUVBKptaupxWjAje6F43h2BBaA3BwdguhWnlagmtdg47S8hRPHPb2eUwR2HG01iBIniN5jqLfR0oSvV4PlibM9egK1j0XoRKqq1jc+duenGi/7qIPG6MZTBScQEUcakbrGDcAaCskH2X0u8Lj+kRig6G5wYZ1S7ZBNvtygcf73seRba24fCgvuM3k2dybibWJILejuDiMp13uqtMU0jaAjLbiagqQdVBS2jUYDGFH7SvnPSt0K6yAZjJ0J5jc4vnG/wuHWixRlTPUusb6eITTa6cxnU4x6A/QK3pYHGpURY0sSaG1RZYmDvjBZ/elAsoRqIbos/Cz9rHDBD4iyPsvQ7q9peknp0dZdcKHnfBA81w4RRZAG+48jfXqF2lDPdMuYzDGIM8L7Nq1K1wje28KSZSjNxhgx67dWJuuw2pm5baWN+G61kii1G1oXO+WxZyv2b9zGafXJ8iSGBJrODmuUFtCTdyqQoNck0hAW4ukLF2HXg5bRUqhLBlWrWqGZUcOUu2TjlZIkFCIkghCRai0Re14MqXgMCMqDWNLWCrZWLCEqvaM7OxRrK6to6wqTCvmF5yV3HxSRgzSqHSN0fo48BlGoUWGgomiTo4xcX9767wsS85pad1pbOl7QhkjYA3B1NopLgMRceNHKwXSiNGbkRLoFxmylOuvelmKNFaII4U0agAT0s1QKQAlBbKs8QCFYHAMWQOy2vVDEy4P5drHKBnmIUCIE2ZWjyJnFLmNtd29eNMc9/9Q07EBRKH4ukte0GzB7bnslWg7WiFVhCwvkOU5agvkeY610RhlWSJLs/CZrfrXPaYKcUs1eDE+rN9aXw0naOP5NCE79u5CGDScpqvMtlY1HKfxIA7hxp1a63jj54T3uJ56lBDAdldcuoZWslFU7VyWe26C8mrCg9ZZrh6goa0JSV6voJhHzjpouwnFx15xBW/MAzv8o6W4QpggKNUmorwp/+MnPDZOXM9S7UIIhgl4SzHDeDLG6ir3UhqN1rG0uIThYIiqrjGY9ZFnOapKo9crXPw/BiARUxMnl63zNp5X44m1gyBtaZBMFuTqRzwFTQB0tCHv4XNtXdZ4Y11F11hvQKtOx73eRoFaIvR6PezZteLuoLE0k1QBgyFW9u3H+PiPYcspQ80r7RBfFXp57rjiAKoqKCkQRwmSHUvstWQphAUkncak0phpwthaLrJ1YbdZVTH6MM1Q1hpJkiCJE0gibkEiBRKlEMWEyCpAEOIoAmQESIM0502r0hq1trDO0C5rA20BKV1TSLfZGtdGxFhCXdeY6VWekzXTQFVVjcmsBEkHl69rrK+PkaQJkjRlvjyHiIN1+aGIlS1cXSQRc/BNpzN4GrWqqplA2q0jpZjPTxDB1DXIaMAaxIhBxMTBedZDmkRIY4XhoHDEtgrDXsYtTiRTX1nNHZQDUM0Vqhd5Bm/A1FUNsiZ0om7nqqxogC9CCl6DxiLJUgdwadjepWCPULipzZutR+e5uU0iLIPQxdq2w/4NgYFbKiE0ziwu/JKSChBeqSoURQ95lkMai16vB2MfwngyRr/X5+7Goh3GbMJ5W+qulgLw6zmQaLj782tsK+XXAUGdQZGEchrCBpPWgTcc8nDjd7cc1bAuw3npDE0nn4Bsa8XFYYwN9VluQ/NJUWO2UlzU1Ge1Q4UbIfC2BbQIua9GOTV5MhPyBp3zhPCkj3e3Ll50J1cgmRUNQWt70gm4Y8JE5uR/OZtgvC6xeuoU0jRFluZYXtqBXTt2YWE4xI7lHVhYWECeZciyFIN+H7FLZOd5jiSJEUds+TbKy3f54lAlV8nYTffA16Tgga1SWjAValMcvimHtcGrbGppIgjXEdYjoYTw4fymXgtAKFcgArIkxdJwEXt27kIaM9NFWTHfn4gi5P0+zjlwAD+69y7o2RTC1K5RJKEq2fNibyiGlQYwBAnCMEtxyUUX4MA5+/D888/Fvfc/iJNrI6yuTXDs1DpOTzTGFQMyytpgJmYQkg2JKEqQ5Rl27dzJiDu4MI6tWPFojV6vD6Uk6tpguFiArMX6+DRG40e5wDyOMFobhV5M5PMGAiASoWVOVdUOxcpzNs0ylO612eqay3FZl1tAsEEEuAhdRdwMU8CjdBmqTsRKcW1tDUSENOUmlBzmZV7CurSArtHPM/SyBFIkgK45DBgpFBnTa2VJhDyJMRj2ONRGFpny3piEqS17sGDl0esVzCgTR6jKWRPOU9KtbWa0iCMOgfF7KRcmxzGDlSyhdvNPOUaKyHVDhlPKDG3nnm2MjFQOYs9jZGGCNxEiON7rQouY23uDLSOM4fQECEYjGld8nqQp5wU1YceOnbjnyP/DI48+Al1rnLP/HCSJa8UjG5YZIZvIS9uo3SQhTtcyAoNR3D7Qve4NRGx+z7/uFbMx5HVz63gRAFxCCBdy97OLwj7F4LMm2vJ0QBlenpTiuvHGG/H5z38e9957L/I8x6/+6q/iL//yL/GCF7wgHPPyl78ct956a+dzf/AHf4BPfvKT4e+jR4/ihhtuwDe+8Q30+31cf/31uPHGG0M7gScqtbO6TGsCtbsUe88qeD+moXJqAyg8EKNRXN6T6kLjO15V67k1DSqRn3uofVuJUneGCDRKSwgI26rHoMZCaj/nbsNdxdVMBgNjapSzKWazCdbWVlEUPSwuLGJ5eRn9Xh+D/gDLS8vIixxFnmOgDdI04U0i4nblyjNeuPbjIbDgmKfZ8+MdsM1CT0KE9/jRhfgrKWFcLqvdWbZ5uNi+iqCUboU3eJLz+DljwjHuCwIGgyX0e30URcHzh+BKEQgkJEQcIy4KLO7aDWE0ypOPIo0TwDToPSU4hBbHMedKQLBaI01iDPIUcbQIhXOxuraOk6dHWHjoJB5dm2B1vcSjoylKS8yUActIVSKUpHHqhECUcKPAxLFJEIDZbILKGPaAiXDi1CmQJZSzKeqyQhxzw8BJWUFUdVj8vm5JCNHMO93iyaw1Sq2Dh2+MhYoVYpWgP+AiaOk8nLKqXU2VdS1T4EAHvqbJBEMMRDB1BYBDeXmkICMFAQslgIU8CSzuWZqjSGLEkUQSSSwP+kjTGFkSYzjscSuPSLXoxwAkCYz2pQFA6rxCnptRBwgBwHmDcWBV7/CMOvRk5JotRq6IXEXcRsQrIRB7wgKqUTRStoxGGzyDdp68QRKicUHay1qIAMJQhpwxx+fyABKvELI0Q6/oIYpi3HvkXvR6fe5EHSebDDy0vUERNo+W+9OYdo3OagzjZtvxn3U/pn/fH9u6F79vtX+qdhDQR4o8cCWU3bSIvf01NNffDks+dQX2pDTFrbfeikOHDuElL3kJtNZ43/veh2uuuQb33HMPer1eOO7Nb34zPvrRj4a/C0d5A7BV98pXvhIrKyv49re/jWPHjuF3f/d3Eccx/vzP//xJXbzWDCXeSnGF7qQ+pOQ9IadM6lZOamM34zY34Sauwg3Kzysr41jgvSL0JJvWeXxt6wVolJa3fITcrLg6SixMQl68QXF560kwmwcgMJmMMVpfQxwnOHmyj1Ond2DQH2BxuIjxZILBYIjBYIDaGOR57jgSY6RxwgtdNaXICNfOuTGuA2onhwUcAU7rv66F2FZOmxWWsyJDTyIVutQKyWS37C14HknXmt5aSAjO6/V6rBhcE0H2dB3gX0WI0gyLO3fBTCeYnTzJABUARAxHt67ORynFChtM9iosIXWFq2mssDzsY3nYQ57EWDi5hhOnRxCkMaktKmMxNUBpLDRpmEpj3VhmeU9TZIYLoQHCeDwOtUdxFKFyAAiyFnVVI64jJAmzWlgH7pFKBq9AuHskV+Tq0bW11qC64lyW92glb9h5lqGazXhsPBGuizTVTjkBHDouZyWM6xcGYg8UhhU613gp5GkMYS2UICwVGWxdQZBFP8vQLzLEjg9woZcjSxnIMiwK5HmGNE1c0b4OnbPJ+oaLxATFcYws43CiD9WHaxKAtTEbKS1FBGrCypFracIevQqoPePu1SsrAXSZXML69OuPug9sXssexRT0gY8cBE+Hw+hJEjdKQDARc9HrIUlTPHj0h7jgggMYDoYY9PqNQSub9YQWlVrb6wqFyf4C/GfDqfy9NciJjtHsv8vd0yaIf2ffasJ9PvcsIFyotRmPsH+BIKnbTsrf/7OmuL7yla90/r7pppuwe/du3H777bjyyivD60VRYGVlZcvv+Pd//3fcc889+OpXv4o9e/bgl37pl/Cnf/qneM973oMPf/jDwVV+IjIrZ2wZUith71qR+FyXaSmaLg9hg/zTTvE07UvaXlYLIeiVUlBcbWXVWKhttoyAdKRuDdfGBCpsk2+yQnYmlv+RZesz3nRprBnh1y4AoDYVptN1rJ0+iYce/inD6dMcg+EilhaXsbS0jH3792NpcQn9/gDD4QB5krliT4aKh1oXNOEZtkZdOIXYmmRAR2PGeY/LN8PjJngGynJdkFQRpDQh9Cm8QlOeXkd1lJaQCtb9JpU2rCid5bZ7z24sLCww0gwefCK5CSIEICOorI/9B54PaQgnfvwTGNROQQOGDGpdAaSQpQlTIzlQTVnOEFOMTGZY6PUx7PewsnsHztu/F6dOr+Hk6mkc+d8HMZpwG5C1SYUTa2NMKoNJTRhVNbSuUU0nKMcKMuINRBuDejZmFJryCkYijmJACJRSQpUzVlDGh5qb+eHZNwgAWUJVV0Hh65pZNCIp0S9yVOUM1WQdk7VVboUCuFo3N2eNZc/JNzaVElSWsJo7JGexQqwU0sgZLMZAaI1zdi+hn6cosgTDXg/CzQ9rNHbtXIYSAuPRCP08QZokXEYQR8yrWdeIheDuzz40JZp5zQz9ztN2CotzajLkI+u65tCilJCJDF6QEMLluTxa1SlxALpir1FIidQ1tIQbQ3Jz3FoXQiO7eSP3HtaGyIkXcmEy31kOzhsk4s4ERa8HAue90lRBTioMB0MsDIc4uXoS37/7+yhnM5yzdx/avo1n8GAuSLFp0+e8IDV5JGc0ek3C3qdTUN6Dcu95QJQP7jWhQdMoO7+6hQgcp34f4rGzndc6e1vHh3vm5GnluE6fPg0AWF5e7rz+6U9/Gv/wD/+AlZUVvOpVr8IHPvCB4HUdPnwYl156Kfbs2ROOv/baa3HDDTfg7rvvxote9KJN5ynLMuSzAGBtbQ0At0cHmrxWUCKd0IFD9wVl1aD9ukqoDdzoKqpGwXneQRtCNVwQ2vKyQpEzPzdhUTSwWS9NAnOjp0VhorS9LSs2WymdhKePWbuF5Rcb5+BqVOUM65MRHj3xMNI0w9Ef/xCLDtCxc+cuLC0toZcX6OU58jxjyzeKGNYseJInceQIQduTUkJIQMpQ1gwhLaTie/bKy1oL6SG/kVdoEfeBajFgCyGCElNGwSiDWiMYE0IxOkwKgcXFITNut0IqBIYi1waISCBRCbLBIgY7dmN5736sHz8KguXcmrAgrVHZGnHsvD0wbNpo7uisleZcigMWFEUOKQXyPEWacKuOsuKC4eOPnMDq+gQnTo/x6KjEtDaY1QaGTFD8iAAi44AGCHkIMjWMdals6ZF9IgAI2iGigAlwlrJwORwpLHQ5xXRKsFMFshqARQQC1RxGRM0ks9JtwsN+BunmXBormDgLCq1IEiRxhCyJkcUxBDGqb9hLMBz00C8K5CnnTKUUIGNQ9ApIAL0kcgXBEdKEPeIkSRDFEeq6DqG/KImhXK2hJQ5BW4cYtuQpmJqOvwRwk0hHbeRRhETNxuu7v1mjYTQFMEAcxQ4RqULbEhIWsC6C4Ly+TnFx69EUw3vPCyGM2YHI24YxBGB0Y3/Q5/uIFCLi/GJR9LCwsITBYIhjDx8HhMDzLnwezj/vAjYw4C7K/fTeaOm4N+CIiFf+bPQ1hsDGsLtorbNwHBiE4qMssrUvob1rhTxa+9zNJYUwYHuP8v+I5oWNYcknK09ZcVlr8fa3vx0ve9nLcMkll4TX3/CGN+D888/Hvn37cOedd+I973kPjhw5gs9//vMAgOPHj3eUFoDw9/Hjx7c814033oiPfOQjm143RqNGo7DIUmBp9ooseFyt5/71juJq5ai6sPk2bVP7uxs4vfXM0OT78NimrUE7xNAOLwDw+SGeHDJsTh3QRhMs7npcLekma73yalmL1sIAqAHYmdsYhcJoNMLJkyfQ6/Vx8tQJ7FjeiX6vh+FgyJx/aYos5XqwSLFFnucZkhZKy+e5OKTI05FC+FKCWQG6DyE3hAV9qEZubLDX1HoFoAtZRCJyipRrYdI0DYqLiyYZqMPIOwCxQpQVyIeLWNy5B5MTxwBjQcJCKoFaW5DLbfowpVIKda2DN01E4H6KEnEkIPIMccwbc1XVqI1mNGeRYHVtHYv90+idXMdoUmJ9VmFWmSaMo4Tz8gm19hYr5zkrzwTTotHq/J5gpeVpL6WU3A0YFkpaxADgUXeQkIL4IQXSiDct6RRlpJhlfXmhCB51nmZBUcIapFEUugP3ix7nQMG9sQb9HnpF4Tz02IVaKfD/JZE/B9eHEZF7nkAKEUpZYmfIQPC4wIVBQ/86F4HoGn0SQrYVV2uMbFOrRJZ5Dgk+79pt0kgu2uFbeAi/ZjamrzrhNGod4xDDLUO2E2ZzCjGKIuR53niC4JxulmXo9fro9/o4/shDeOiRh/HgDx/Enj0riOPYXWO4hGb9t5RHk9tqXm8rpe4+0nyXEO3j/Nd19yhWcOiMf+tXQAj7tKKV4fxbqaenER5sy1NWXIcOHcJdd92Fb33rW53X3/KWt4Tnl156Kfbu3YurrroK999/Py666KKndK73vve9eOc73xn+Xltbw7nnnouqLGEdQszzEPrGjfy8AUmwh9Rsfl4pkaUAzPCfbyuqdtivS5hrw2esaUKT7ffbtWVtRQI468QpLX7JbHazw1wTzSLzr7R+/7Yl1Za269+OW/sq+3J1ilOrj0AIiSiKUeRM7Lu4sIjdu3ZjMBhgYbiAQW+ALMuQpSkWBgPOiXlexCR2vbZEWPQcGuWQnZCAVDGkspCWIKXuKC2lIkSRhYp0QBSqKEIcJyHfCN30pjJEiHzoVEoMhwOHQosglAI7Sz5MzIrLQkElOYrFHVg58DycOvZD1KMTqKsxUlcDZKxGWU6hpEQUJYGBA8LThvESZNY5IIk4J9jLC2jrPBkB7Nuzizv8rq3h2EOP4tTpEU6ujnBqbQQhFCA5v1ZWNeraYFrVqGod4O2V9YXfbL5a4vlswA4bK2TAgf8gBJAoICIgIYlYCeQKgATSxHJreofq6hU5kjgKReiDfh87lheRJ1HIpeVZHsJ0ZC10Vbl2HxbDhUVmaslzAAxmiR0isa655UrsioOttbCCuADes+I7QEqWpCiKIhBTG+ONQRcWFCIwTPh0gCYgkUlorNiZ586YDDkhJUNISygJKBWODfVbflMHQMJx+bWbP8J7aS6vSx5J2KQamrVEoaDcG7bCoQgBrkdL4wiDfi+ELgkCURSjV/SxOFzE8tIOPPTIw3j0xKP49m3fxvkXXACpFHq9XlAuQaWI9voXmxSHzy9zbgxBSTd7S6NSGuOz60j5/GhT77bBWA77jldsjWLsvN762/t6fIpu6uTJylNSXG9729vwpS99Cd/85jdxzjnnnPHYK664AgBw33334aKLLsLKygr+8z//s3PMQw89BACPmRdL0zTAcdvClfOMIPOgC7uFcgm5rxY4Y6NyajPF+8lnqAk1BmJeskEpbhkicLFyb4nRFkqLn/PPyBueazVC5MKFAHxC04UOQY0CC5ZPmFDUmVxbGzWtayBq/c3XWNcGI11hPF7FyVMP46c/PYrE58UGC+j3+uj3B9i1YycG/QHyPEcvL5BmKQM7ksSRlPrzu965BBirYUkAQnJ/KJfzkhE/V5FFHHMzP7+JyYg7+kLIpi4OroOslIgkF8sWRQ9xmjh2bT/mzR0TCVihQEpAZQWKpR0Y7t6LETRmp2bcfDNWIFiUVQkpFVIC0jR3ZLUUWs4oS5CKEDk+PKW4eaACs4MDhEhI5HEMNRggSxLs281MFpPJjOt0VAStuZyiNgbTsmK6parGdFYyea37EaezEmVdQWtHnOt+NuOL7N1vOOxl6OUZer2cPQw3E5JYBa+w3++hyJmqSgggjpNQBqGkgDfrpVTQjgVGAK35TciLvgvvciDOGN60i6LXeDDguWy1xmw6BVnLQIs05QiFIUxnMwBw+Wk2SLRDihIIeZ6zcakN2oaeFwGOtvjcnxB8nUIISFcEZ8HoQhXFUKrxWkM/uFauRkrp5pcIeSwGPXkErevubDfML2rQjp59xCOUrfEoSAdoiWOkWcpz1O0NcAo6SVMsL+9AHMdYG53GTx76KW6/478xnZX4pctfFEJ/FtTwGbY8JeEMbr+qgyKSnB8OCl0KBkooCWlVR6G1gSg8phtKVzYYyyEk6JR/o0xbyrF1rb627ZmSJ6W4iAh/9Ed/hC984Qu45ZZbcODAgcf9zB133AEA2Lt3LwDg4MGD+LM/+zM8/PDD2L17NwDg5ptvxnA4xMUXX/ykLp5zEOh4XF4psRLygIl2TdcGxeVfb+fF2s+p+bszUamp79jqgRBG2OzxdKRlLXXGOmSL3Pui+24wwQQ/Z++teb7Vl7bj801IoFmMFgYcU6xQlTMoGSFWMcaTMfKsQF4UWB+tod8foMgL9Ht95EXuYL0Fsixr0ev4mhYE6zUgx4R0m0KX9smHBpu4vGiMCdbiIbcmBCfe4yThEJVoPEyyjOZsfh8AUkLGCeK8h97SDlTj05itr8JQDSEjqIi4o7XWkKJGkmaOnNjPHeMWoGiNY+tHdHyPSkrmhgTzz2VJgiJL0EvToLjY6OFW9ZXWmM1KlHWN2axEpXWw8mdecRmmBGrPQ7baeWx7WcKGRC8PG42SjJQUAoijCIPBAFnGHgtZgzhJmGLJjVkIAhFcPo43dK11+M3yogcI6UoTuFEk132RAxCw9woQE+G65oxRHEHFESQpLmB2rUHChij59/Prig0CwFgTngMIipFsA3jqhKQIHHZtdtVueM2Nm5INUnXTZipE8xpP3s4G7EOGfm56bdGsfes8zmZcub4scsCzZh/ghquuli5NWYESYVaWePDoDzEYLOCCCw5gcWmJ1xIvckYXCt+/iDcQEraJ/4V7d3PB56QlIHwIOhQlo+UltYdBhD0neHBhbKj1myCcM4AxOp/rKrfH2JqetDwpxXXo0CF85jOfwRe/+EUMBoOQk1pYWECe57j//vvxmc98Br/xG7+BHTt24M4778Q73vEOXHnllbjssssAANdccw0uvvhi/M7v/A4+9rGP4fjx43j/+9+PQ4cObelVnUm0awnert1qGBW6ua5O6G+rkGLLk+qS8m5ECLZzWbwhW7chE8FZ3l1F9VhKi+AD6a1FJPyk2OxpeWvW+Wqd0MHWcqYZ4pRJx8ryrjwvQK0NtK4wq6Y47cJ7x+IMecbKqt8boNfvoSh6jrVjwfGxpdwO3dECyZYSA1k27qVXXs4aU6pltSNszFwEzmzmXMvENFWQkqHmsVNcaBSXsUxSaxx7AlkCqQgyShH3Bhju3otyMsJ47RTK8UnkDoRSl8xHaAnITI4oZvJcH2IWrd8c1sIaAcCjz/j+oiiCtExzRNYgkhIKgCT3nooAcFsNIRnFZ4lRXHVdoyxLCBcu9XWD1m3mZTWDMQz3TtMUQogQ+o7jmAvQ8yzw+ZG1KGczAEw4nMT8WlWViH07duKaNfcUBEbAqShCFMeYTCach4pT9PpDLtyumb2lqipUVYnVU6uw1rgQsEDtar64Jo/bkUiXOwQc96DzNjxYoaqqAHmPk4Rh64ArIXC/q2kYaYwxLh/J32OCx8A5Zim64bGgtFxJQXtdsoHje3G11lPLCAWa7t3eK2MYug3H+ToyozUjWp2Hl8Qx0jRDluYhdwflxsejdwPgCTAg3Pe/9yOKY+zbvx+X9C9zIVmB2M19SG5aGua98xABBiaxTpPuHDH/NkLCiCYl0WXNafaSTmBPeE/chx/90LT2llY4MIRyfTiyrdhaO1Lbc30q8qQU1yc+8QkAwMtf/vLO65/61Kfwpje9CUmS4Ktf/Sr+5m/+BuPxGOeeey5e+9rX4v3vf384VimFL33pS7jhhhtw8OBB9Ho9XH/99Z26rycqta5BZFuKy/Xc6gAoup5Yo+CMS/423heRB3Q0ntQmpWWbYuI2ce4mRYUNHtiTFgfccCqKnLXF04O6yupxvr8TogzK0v8VzFNsCmUL992CQOCwzqysUZZjCCHx6EkFJWNEUYw0ydAfDFEUBfqDARYXF1AUucuJZKzAhGcX4PCGJeIiYQfgiNPMNTc0mM5mmJYzjKcTzMoyKG2porBJZlmBNMugHPpKEIefhAZ0XaOuKugoAqUpKCKQFEAUY3FlP4yuYIzGo/87RU0aAhZFf4jJ+jpTHc2m6EW84caKlQgZCys0rBSoyUIZGQqMeahsp4WMtUxmy4g52VJcbpMRDCaJkxgEIDWuCSExoi9NE1c+oBAnccihEFFAXUZxwsz3LU/Ab4JVWUJGrrxERCAox5cXQ9cVdF2hKlmJRYoBN7WuYUjAaovZbIzZbAYVxcjzARsCRIAlSEdSHCkFImJv0t17Wc6YDV1FwSsSUqKuKjZCiJAmCcgyIIZ0HYyVOE9cIbh13pYrUdGaf0/NuTSvXD3UXbQmLaFhjE+T1IUhrQsx8mYf6rb8+rROAcER6Xpv3TShfiGYnxNCNrVTzlgla/lYw4rJKwQlJVSSIMtSZFkaUMckKCisOInR6/eQJikiFcHoCpUp8b9HH8D6l/8V995/P4bDBfR6AywvL2NxcRGDfh+LwwWkmfPUACR+XrWaUobtofV3aPDqwFC+ELm9i3iAVec7guGMrhZq5bdIdLemreXpu1xPOlR4Jjn33HM3sWZsJeeffz6+/OUvP5lTbynWoeUCkS61EH4bmOHDhPHeUssbe6xw35bv+QBNWxmQD9q04tcbhmrj0InOE6fwmqetN7f4osefGRs+0lZc7YvpWk2bJmc4jkNb3jME+R5FElpoVLpEWc1Q6hKjcYLTo1WcXjuJLGNYfd8tytAzydWVgIjhys5KJaeQCcBkOsV0NkNZVeztyIZd3m9ySWhi2OJz22D5+n5q1kaAdCi8NEM+XMJw5wrWHzkGO1lDXc/Qi1PESQ2ja9Q112DFAlBRBHYArPPANADX20k43jvBlm5AN3qjQxGssogiE6xechdLW1i7Ko6Z41Cg4b0Tzdg3iityrSraxbOuWaJlvkilYlewzMWuohXiURFb8AIcOhSu42/sW9ULtthlzUX+HpDgFaNSEaxgJeBzXACY3FdGrk8bFwIHrxpc5mCd12gFuJu2kEwX5BROOw9rbYuxxugQmfB5KR62Vqds+FY5KhgRwXh0iso6LysoLv97eO8rpAIaAzQsl8faL3z+C/46hEN0soLwrO/hNxSG2fqd1o3jmL0vJSEM/y5lOcMjJx4BfnAEWZYjTTMURQ99V3S/tLgUol1FUWD3zp0o8hxZlnLUgQASkg0bqUK4j1ohz86+cAYJRrkf5FZIMGwgW30OAi0dj2aEttgkn4RsS65CP4hlWTFbQmtBWzTPN4b9/MT0pLzGJfzbhJnGNlB2D8poc5JZy+eg1mc2578aJCF5i+yp3ita6itYfq1WC23Z+NJjKE9qTZomt0G82ZALUba+Llhs1JxAgEECggxgBTQqlPUUni0jiiLEMYcM2VrsocgzFHmBLOOutMJ/ubUg4k2w0gwtP70+wvp4HdPZjK1v4dku/O/BQIK61piVPjSlXENMgdpoEFlubAigLBNGPgoCdIVaKIhiCMr67GVNNWQRo4ZCTRr1dAotBAODsjRw+AkpIBUjrqIogoEreBVcg8SbpQibmTYaRltUmiCEAVyZtFdc7qZdWNTAtDZg0hoA59ZkpVGVMxjLYb00sZBxjEgbSBmHECvTJvGmlCZpYHAwtXYoWp7fzOIgQDLGbFZBSnIKKXaKGDCkUBkCGY16fYJIxS6EGEFZwUX7xjCoxnLofjyeBZAFEZBmOUeFrQWEhJURCBZVVQcC6khJV/xvAF+b6Ruyah04QsvSdZFWCgYa1tYAmnBiO5cSWUBZYKYtEwATQUURlGm84mA2uDE32sA6Ng+vtFxoJaxvTzTQMOwYx0Ti+/MxGTcEQVuLUhsgMphVGpNZibXRCLKsYWWCiiTWx+uYTCfQ7ndth+601hiNRlgbjUAt9ok4zpAkKYYLi9i9aw+WlhaxsmcPDlxwAZYWF7C4sOiY+zW0rlAZ5q6sXJ82T2rs+VbbD8B3O3IGoveYBc+rjpHYki4augkNdnKGaNQWiML5nkpEStBTi2OdVfnxj3+Mc88992xfxlzmMpe5zOVpyo9+9KPHRadvlG2puKy1OHLkCC6++GL86Ec/wnA4PNuX9H9OfK3bfHy2lvn4nFnm4/P4Mh+jM8vjjQ8RYTQaYd++fQ0jyBOUbRkqlFJi//79AICh6wA8l61lPj5nlvn4nFnm4/P4Mh+jM8uZxmdhYeEpfeeTU3Nzmctc5jKXuZxlmSuuucxlLnOZy7aSbau40jTFhz70oSddtPxckfn4nFnm43NmmY/P48t8jM4sP8vx2ZbgjLnMZS5zmctzV7atxzWXucxlLnN5bspccc1lLnOZy1y2lcwV11zmMpe5zGVbyVxxzWUuc5nLXLaVbEvF9fGPfxwXXHABsizDFVdcsakx5XNFPvzhD3dYwYUQeOELXxjen81mOHToEHbs2IF+v4/Xvva1oWnnz6t885vfxKte9Srs27cPQgj8y7/8S+d9IsIHP/hB7N27F3me4+qrr8YPfvCDzjEnT57EG9/4RgyHQywuLuL3f//3sb6+/izexc9OHm983vSmN22aU9ddd13nmJ/X8bnxxhvxkpe8BIPBALt378Zv/uZv4siRI51jnsiaOnr0KF75ylcy8e3u3Xj3u98N7VrHbHd5ImP08pe/fNMceutb39o55umO0bZTXP/0T/+Ed77znfjQhz6E//7v/8bll1+Oa6+9Fg8//PDZvrSzIr/4i7+IY8eOhce3vvWt8N473vEO/Ou//is+97nP4dZbb8VPf/pTvOY1rzmLV/uzl/F4jMsvvxwf//jHt3z/Yx/7GP72b/8Wn/zkJ3Hbbbeh1+vh2muvxcx15QWAN77xjbj77rtx8803h07fb3nLW56tW/iZyuONDwBcd911nTn12c9+tvP+z+v43HrrrTh06BC+853v4Oabb0Zd17jmmmswHo/DMY+3powxeOUrX4mqqvDtb38bf//3f4+bbroJH/zgB8/GLT3j8kTGCADe/OY3d+bQxz72sfDeMzJGtM3kpS99KR06dCj8bYyhffv20Y033ngWr+rsyIc+9CG6/PLLt3xvdXWV4jimz33uc+G1//mf/yEAdPjw4WfpCs+uAKAvfOEL4W9rLa2srNBf/dVfhddWV1cpTVP67Gc/S0RE99xzDwGg//qv/wrH/Nu//RsJIegnP/nJs3btz4ZsHB8iouuvv55e/epXP+Znnkvj8/DDDxMAuvXWW4noia2pL3/5yySlpOPHj4djPvGJT9BwOKSyLJ/dG3gWZOMYERH9+q//Ov3xH//xY37mmRijbeVxVVWF22+/HVdffXV4TUqJq6++GocPHz6LV3b25Ac/+AH27duHCy+8EG984xtx9OhRAMDtt9+Ouq47Y/XCF74Q55133nN2rB544AEcP368MyYLCwu44oorwpgcPnwYi4uL+JVf+ZVwzNVXXw0pJW677bZn/ZrPhtxyyy3YvXs3XvCCF+CGG27AiRMnwnvPpfE5ffo0AGB5eRnAE1tThw8fxqWXXoo9e/aEY6699lqsra3h7rvvfhav/tmRjWPk5dOf/jR27tyJSy65BO9973sxmUzCe8/EGG0rkt1HH30UxpjODQPAnj17cO+9956lqzp7csUVV+Cmm27CC17wAhw7dgwf+chH8Gu/9mu46667cPz4cSRJgsXFxc5n9uzZg+PHj5+dCz7L4u97q/nj3zt+/Dh2797deT+KIiwvLz8nxu26667Da17zGhw4cAD3338/3ve+9+EVr3gFDh8+DKXUc2Z8rLV4+9vfjpe97GW45JJLAOAJranjx49vOb/8ez9PstUYAcAb3vAGnH/++di3bx/uvPNOvOc978GRI0fw+c9/HsAzM0bbSnHNpSuveMUrwvPLLrsMV1xxBc4//3z88z//M/I8P4tXNpftKr/9278dnl966aW47LLLcNFFF+GWW27BVVdddRav7NmVQ4cO4a677urkjOfSlccao3a+89JLL8XevXtx1VVX4f7778dFF130jJx7W4UKd+7cCaXUJhTPQw89hJWVlbN0Vf93ZHFxEb/wC7+A++67DysrK6iqCqurq51jnstj5e/7TPNnZWVlE9BHa42TJ08+J8ftwgsvxM6dO3HfffcBeG6Mz9ve9jZ86Utfwje+8Y1Og8MnsqZWVla2nF/+vZ8Xeawx2kquuOIKAOjMoac7RttKcSVJghe/+MX42te+Fl6z1uJrX/saDh48eBav7P+GrK+v4/7778fevXvx4he/GHEcd8bqyJEjOHr06HN2rA4cOICVlZXOmKytreG2224LY3Lw4EGsrq7i9ttvD8d8/etfh7U2LMDnkvz4xz/GiRMnsHfvXgA/3+NDRHjb296GL3zhC/j617+OAwcOdN5/Imvq4MGD+P73v99R7jfffDOGwyEuvvjiZ+dGfobyeGO0ldxxxx0A0JlDT3uMniKY5KzJP/7jP1KapnTTTTfRPffcQ295y1tocXGxg1B5rsi73vUuuuWWW+iBBx6g//iP/6Crr76adu7cSQ8//DAREb31rW+l8847j77+9a/Td7/7XTp48CAdPHjwLF/1z1ZGoxF973vfo+9973sEgP76r/+avve979EPf/hDIiL6i7/4C1pcXKQvfvGLdOedd9KrX/1qOnDgAE2n0/Ad1113Hb3oRS+i2267jb71rW/R85//fHr9619/tm7pGZUzjc9oNKI/+ZM/ocOHD9MDDzxAX/3qV+mXf/mX6fnPfz7NZrPwHT+v43PDDTfQwsIC3XLLLXTs2LHwmEwm4ZjHW1Naa7rkkkvommuuoTvuuIO+8pWv0K5du+i9733v2bilZ1web4zuu+8++uhHP0rf/e536YEHHqAvfvGLdOGFF9KVV14ZvuOZGKNtp7iIiP7u7/6OzjvvPEqShF760pfSd77znbN9SWdFXve619HevXspSRLav38/ve51r6P77rsvvD+dTukP//APaWlpiYqioN/6rd+iY8eOncUr/tnLN77xDQKw6XH99dcTEUPiP/CBD9CePXsoTVO66qqr6MiRI53vOHHiBL3+9a+nfr9Pw+GQfu/3fo9Go9FZuJtnXs40PpPJhK655hratWsXxXFM559/Pr35zW/eZBT+vI7PVuMCgD71qU+FY57ImnrwwQfpFa94BeV5Tjt37qR3vetdVNf1s3w3Pxt5vDE6evQoXXnllbS8vExpmtLznvc8eve7302nT5/ufM/THaN5W5O5zGUuc5nLtpJtleOay1zmMpe5zGWuuOYyl7nMZS7bSuaKay5zmctc5rKtZK645jKXucxlLttK5oprLnOZy1zmsq1krrjmMpe5zGUu20rmimsuc5nLXOayrWSuuOYyl7nMZS7bSuaKay5zmctc5rKtZK645jKXucxlLttK5oprLnOZy1zmsq1krrjmMpe5zGUu20r+PwKBWqaN2WKvAAAAAElFTkSuQmCC\",\n 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loads checkpoint by http backend from path: https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"img = '../resources/input/inpainting/celeba_test.png'\\n\",\n \"mask = '../resources/input/inpainting/bbox_mask.png'\\n\",\n \"\\n\",\n \"# show input image and mask\\n\",\n \"input_img = mmcv.imread(img)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(input_img))\\n\",\n \"plt.show()\\n\",\n \"input_mask = mmcv.imread(mask)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(input_mask))\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"result_out_dir = '../resources/output/inpainting/tutorial_inpainting_global_local_res.jpg'\\n\",\n \"editor = MMagicInferencer('global_local', model_setting=1)\\n\",\n \"results = editor.infer(img=img, mask=mask, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Inference of matting models\\n\",\n \"\\n\",\n \"Inpaiting models take a image and trimap pair as input, and output a alpha image. We take 'gca' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth\\n\",\n \"The model and loaded state dict do not match exactly\\n\",\n \"\\n\",\n \"unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"img = '../resources/input/matting/GT05.jpg'\\n\",\n \"trimap = '../resources/input/matting/GT05_trimap.jpg'\\n\",\n \"\\n\",\n \"# show input image and mask\\n\",\n \"input_img = mmcv.imread(img)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(input_img))\\n\",\n \"plt.show()\\n\",\n \"input_trimap = mmcv.imread(trimap)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(input_trimap))\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"result_out_dir = '../resources/output/matting/tutorial_matting_gca_res.png'\\n\",\n \"editor = MMagicInferencer('gca')\\n\",\n \"results = editor.infer(img=img, trimap=trimap, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.4 Inference of image super resolution models\\n\",\n \"\\n\",\n \"Image super resolution models take a image as input, and output a high resolution image. We take 'esrgan' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"img = '../resources/input/restoration/0901x2.png'\\n\",\n \"result_out_dir = '../resources/output/restoration/tutorial_restoration_esrgan_res.png'\\n\",\n \"editor = MMagicInferencer('esrgan')\\n\",\n \"results = editor.infer(img=img, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.5 Inference of image translation models\\n\",\n \"\\n\",\n \"Image translation models take a image as input, and output a translated image. We take 'pix2pix' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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w08LzrGKQnSkcy721TnglkxlFKW03y8U3O8pzMZMaVkX0iQ0WKTwQbORnXLNJIbwO9bNAUUTM6EzRLX0mSmziaIV82h5rfN12yJoeOd6SQ5hnTe03IpYyqDPQM2rPpj3jWHbHVBYMuMAskc9pXlEYhv/ZM6t09fl1m1TCaS++9eyyX3GM6LqV2xEa6NLBMW97XC5bjhi5uIW3d/CQ401InOLLTnsJkzEwfZ1z0mQGU8RhJUzaxTl3HW7D8v49p/tl8HxgiRspMK0lgE5Y8kyM22rMJrnnVnnppZGKSfRWOKuOS6IzLCuMKmebfRoltZzJBjF4hxAHGiMTEaW/oeIHGDX0Y6HLH4K5TkkVS1mqTdM243OriVo24VyScl9Cg18ElSJCKvtdhuDAdtWMT3mItD1nbMSNLEh2TebbsHT+uhXptd5++OtxpxoWsSOGETXif5+GIL8IpvcFa324Yl28cSWSmBRhEZfKLobngru1JIZJNAKW0v2Xt4/DxlARdtDcTm5iMGZ069UvRQBbUsv/ZlxIlsGHBWb/ihS74TZacdcdsZcFGlt4HB5oMdWuku2Kquc05T6RZ801FcrO5LkX6NHIcN3yYzjkd15zEDY906xqYJUJsNcSIlI7UWQubSngdnsP94931sbpuBdlTLa/iz1WoOYCYuj9LhLUuOA/HnOmSj2XFb+gRz2XJc1lBvwRcsCBlAQb82ZXgtYzmVY5f93uv/pyiEXdEjuKah+MZf1Ce8yi+4Dies0znqIwgRqLLnNrQ5IKfZM3B5zX/kDUuU0cfjZCJ3tzg0Ej0kAsF4EJG2bs2MTDJVgTJGlcUIWpg1I7n/SnfZsUTWfCsO2Edlm6ZECWkhKqiooypaFMyu28t8WYg1adcCPXNgpKQFCFuOQkDR93AcRp4bzFwNJ6ximsepAEdkgt3opjFPG+7FSaa+bFmrGJ79OjVcUnqM1v8ca0uPzfhQpy6Of1FeI9n8i5f2PsMy2PGzTtEO0FYEqzDKu1TxDqwsqfm7/KqcKcZ10DHha34dHvCr78w1oPxWXqLpQnCgBEJla5nNR0/TiLZAT9JpJMhozlX6iAePM7EIG+IS4+tmFtcHgHDkmGyzDqKq+WFcV1oz3l/xPniiM9PH/MsHLHRBVtdMUpgqg9WiGb2FtnU1flGoTUZVGTKsnL2vwlGn0aWaeSELU82n3O0OWO1OeexDSxSpLNElwRJhXGNFVGoFoyr57AgmOVxXbk+lNYKRftKeP00N3NqDuW1rHWvQ8+L7ojz7ogvVg/4+PhtXnRLnoUlMSxcqjdFUqg7JOntELvbAsmC3ILE0XDBaXoBLz7i4fYZx8MZKztHxYlmIpAsawz5R61lXD5vzri0EkFK77vWxAyZOZH301Rg1YnqLuNyTazOrfnzRlGG0PFiecp3jx/yfHHEs/6UdVgRRYmiaDQ0m/6ThLpHq6xRtM6C81UDmVj9TYISQSOiI6e25TgOHMcNHz3/guPhmFXc8CAOdNEIyRlS0UaLUNbebX5cxbMqwF6XHr0UlyhMTRDRiflZsVQFzvUtzvUhzzjhs80DzpKbDpNMIfFSpNxiraHQkx9gxnVuSz4ZTlm+gKdrOBnfomeBMGJMavEkKaZqHio9Yxz2Mw+gUWakOeaAUUYuubY5dsaV8uI748o6oWsy4pvhInScL4/YHh2zfvwhT7sVm9Cz1RVDWXjJUm1lJkWqkSpB3hy0Vfan580Yl0GnkVUcObEtT8Yl3cUz+vMXPByccfVmdBaQ5LNvxIoo5b6v7H674riKIprLzEgp/Kle5zITy5I4ue46XnQr1osjzvUdnj38gLPFEc+7niE4IiYTdOyqGS1mX85dYV2FcS0xVlxwPLxge5Y4vVhytD1iaWs0R4omAlYYF0Yw5owLdcYliqbGFya5tUXd+IVxNOQm79+JoBWm5WtVq4fXdRWiCCPCEAJnRyc8XbzLxfKEF91p1rhyIJOUKD7B6CbG1cxBZVyN5F/I6U0bLYSIZDfFcdpyLFuO7QKeC8fbY1bjwMmwZWnZ8hOzCZ0idU+OVJvhdqn7B8Wa8Cr06OXHU6S2qkcLiEFKRrFUreUBG33EhZ7yyeYRL9IpWzl2gaHY1IHalSMLp2VmXhfuNOM6W5zwgsfE7chvbh+wSBt0eJtOstNSPBzDOX6RAMv2TC5BA+C2+EOBtW0YtGY8M6Zj8EVOeU3qsfnf82Nz6QvfYkldlXYGWOzZwhBgOO4Rjnm4+gZnC2GrykDIZq/J5Vl1IbMclXgoL+VNoc2/SI0EGJwwZEkqGPQxsdJIsA/ZXjxh+/kT+ouRLgldUhbao8l9itb6TSReMW/U3mnleLYmV6yP4ThTTIi1bbiVgp9OIAxjCMKZJNJqwbJ/Hw1fYVwuuOiUgZFouAaii+r7mdIr7grr8r2zIrAgsogbfu2ZEJ6f061d6hcp7xYy3cnBHOaEXitRyw0Fd81wUlpftBqXZsZVmMiheSufFbEyecBPFsYSkiNt4WIVWL71NQgnbPqeoc/+NhEY/bYuWC0aA8S0j8XKqIu218zQzSNQpUfLOLDcjnTjhk+/OGJxEem3xnKbWGqPIqSxMC4HnbA8R+4VF0eos6bmGldhxlfh0i5eXYZLKeNNNEOUyrimCRJGW5L0iBROeZIecB4esulOGGQF2YUQkpsurdKQN4c7zbhGWZD0lDMRNnFLkBENA6qu2pIzuJ0xTItfEAOmaVRXgyraFJDmmjc5hkw8q2lPcMLvZFOJbvaTxKhC6hXtl6Tufc67xKAwNBqib12pxNmdp21k1mXY9zobJxP8HI02gWLSQY5yVBM6gQFDuhXrcMomPKJfKCEqIQU6cgSY5dHWAU8M8ao5vOrzg8eS17T40uo1bYCJI/2gxhAituhYdo/o9BGjBC7MSCFrmpIJXZFOmzseen6VgG/g+Kr3vP79nGiPZixUWHbHPNMPCCESeuhCB8VMlHJQkXjKhRN7q9J4jdWV1vyTaCNNJ2FQwfo655Npa5fByXSfqslK9rkoqBHVeCEDD/qvIIsV2w4GGTCXPgldoKSVjEWbEsMrN+RxWXmiuObdEO1bkPwoWv3CEstg9H3kyWJDPwh9EnpTAtnc2hcaJJmJTAS/4nvWeKg4X1JPrk+Prvq84IwJRKyqY5KmIk6CkCxg0pNkybY7YtAlo/Qg07hK0kmLKw6vn3Z0pxlXoiPKko0og0RUfbO7PVYxCU3b6Ea6z7JiC6VMlMDe9N4UWJZgCoKIaUaYhOYeNkkSKQgWhBB6ej1hrSODZkGyYVzM1O6GB8Ckou8N4g0YF4bILpEpRML1vZTrlqHKhSob7RnDEiWg4lqZlckQrYRxf1PfDBRL/WTepEqtRXIsEu0YIilECErSU5a6YhRh46jrc6oCppN/uRnzbTCr9vgmGJdHDBqWIiIBDcqFPkQDaFCCLiCVfSlojbgteXnT+5ZozJRTPOa4NZFX8iyTFkz7ptXi29I/DRNs8woLkVYjauI8XNCFh2i3ZBsig2zcZ6PQWVdpwGiFaRlWGFfjk6zh8DVIoR7eKBhSA5AQiJrYhMfEEBg00IfO0yWlCAQlAEoqbcoDLgcz50YxAd8oZJxJ6uk74OPTyiwVj+73Mk+WffBzc+YUedxmzr4plb3TjIsYMBYMLFBJiDoZNcDMuXwx4c2m0naIfP0AakLqDW/eiXA2IyljEzevJDwElkz4TTtGlNEC0Twhub5AJkVVfZdrboPXyrD3GZyk1OaTPFmVmJphJmzHnhiXmEEMp07OxHO3EtHZiXbTuG8a6ZrxOVmV7EOcCNSUPFs07YHEgCAE6TGPOfZkrVJJw1pCu7uzJnSUA8ftNdc9PnTu0P2u/xxx4SM4cYwjpHBE1CK7d0jJFRRFayZkQyDz+0eZIgvnZiBPjveZGfM5BVkwBV/ELASVCD/za7L5azb4GmBh3l03ROiUIbgpc9TAKAuqdo0SklYC6zlF1giODUsXb4lU5JjbYFr+GC8wQCpanQBLRhae6qIdxTak0rmQalLfG6yay9tOgkWvrSduGEq6ikewW51jKRw4iAeaWYlEdEGU3E5KWonR7wiS7WD2g9oBWQOIc/2IImaT9FdClKW4YqlqtE/8fJ3bTVt/3/iA2weSpVonhqrBJcIioRqO5AKe38X0gzHlpkxExXnIbWguU94YTN4JcAUk95NDs6CQAOkCBE8Cj6nHiwF4Rx40h6BrwKKnEdxGyZodhXSalZnU0tTOaDZFTFqNWMkADdPcl41U7mjT/O89qzl+Xea1e5xecs2V98jSR0K84sQIRg+U8kglV9CrNcQimFTaaGgOCihReC5s5f0oWSNFfN6k9R9NicJTlQ1t1iL7wWrYfAs5irec73qGJEj0MB+TznFeDIu+nyrjYjLzekAWtJsgaS7pVZjaLTCA0smiKk8qrtlKh+mSGBe0ApGhLjg1kLLJto4+a8VtrcBbgTYdcUfFL/vCaZhmETEz1la5bSKTq/WmWDJeA+4249qJFpqHLDAnUGbOxBqTgDazVgWDukC3uBHK2Jxr+bitYUaVKDaSVJXSyoDzr8y4pD3fit47G+21jvMAbPfU9ArZjOQhy9XykisdlO/6de43qXXm6jvffMkaO3A0G3hRVQsrEG0kGHWJ16jvUOelrM/tKIm3Cv4KxXhTDHnZ32eFcWR/kkxrPfkffa8KhWjm7882XJ4vyVrUbA/6H/Opk+Z7OrtuflmLk1px2Z813d+y8OS+uYlgTsKpsCv4mcz3dTNZb45Lsy+35wVK7T8reGIZUdo5nG7U/iVV2JDbo1eH9njzKpMwJLNLW7lOqtRTOFl+xzdAoDvNuMyKWY2dOTDm7J4JGdoQzQypfFSvm25zK1AEzcwpzcw1kiBIKMmtvrjFl1LobEW8RgMqVXWkIK7Nq+K3dFbyOXaOFao/cPeY5rrZlEg+n6XZIvUFNcaUNZmWCYtA9jtmUgIhU5wpxe7lc/cq61JsVtUUOX/WxPZbWdHQ0OVKGU7kUpE6K1Ft9M5blnFuFDKFtkzsNYAHm0h2/jUMoYj1jQBoljBLGb2c2dVSPlUYax+o+7gJeKBEmq9LS4AL7qaU90075/5ZjsXAtKYT+2ehyPX+f9FSJqZViEbzXu0YmsdX/GmPG1w6hFeH8EcsOwms+dHMdJLke5c6kbtQ/IFpNscmjVn1dWjVdXHp0P5uz1U/NTVaU/OeMMjCavMu1tpsXg/uNOOaqwDXBJk2YNWA53zgdqFslmESrDSASCBZdNOZ+gemMvlV9rhqhoahCC3qTY/bPb7s893Uxt1x79KY2fvsDKnrBVMj2ujEp9QwU2cRB2nZdeB19nuyUkHW6aVOrxAH0F5QVYa4hV4zQZlDfc2ZBH2XONYczKgCRJXuZ8a1y2C++LWwVfXdXOvpzb2mwItJemwuyZYSwEs5Zf8JMeYQBtycORM4swYgJdG29Ssf0OybPXwd/Lns+DJcKudTygTXmEyGln18uaxTef7e/O9KjHXu3mAP3oJgXpOWkZJ4ciuPutuMq2x029m3InPcqL93tJTp69M1t6VllUcURtNKeknyChdVrI0i2rnBnpBf9LN04OKbAydwzWS2EnaRVwuDLY4ur2XVSMyyP73F+XhbYy+3ziKw6iQJ+olGOtdChj1fy/eV1DRPJ/ZygEDf8qa5QajDluw8L2ujmWEXLZPM2YLsIBeuZRnVQe/MwdffZhfKdLyLW1KkxVJ89QDJruOjMUkWH3COchTfVUrxVZYRlSc3LGSG6Icgj/+mA4VMJsVV8viKqSwHZqGJHM2eR1dMmXm8tah2+b/1a92iALVLW3c/3320NPiVX3NWkUSma95k3HeacbXWwOKvyuSJvTI8uzMuu/tzZxJveO9Wkp2JoWSCacmo7ZBEc/VM8F1sFXlbOVho3rfmhsU5kt40ZHOA+0Had8kzV+vYeZRWHC1bBPIVhRamBoNhsqncVnAGOds/Rg/l7Y1xyMMXn/JE8nH03mrCkpGy71EwQt4rhUebNdqFlLW9O8yrjFlUXQFpfqxwaN+cWT2VxryqpctNe7tKcYs5ez4bE6Gu36tJv60ENwmTVHlHm0tCM7aYPy8ChmtgBoj01ZUSqyY3IxDMRrgjhEytQm4GDEUlJ43MVJCJcYmO5MI+jsc7PjhH8zAbakWvvXe4Gdhd44M7vKqJMvu7bCOEyWUwSU2Zeb0+vbrTjOsqqP6gVvO6jLbsVCi/1v2vuN2V32ueU4UONSQIHlU4gA35fABdYDpWidOZQ5baJOYkzZQ3x24OxQ3BrKBvRqA0YWAh3IoTGtGevvOk3TEkGAamShXlpf1PUak8+rZAIEdtwjg0G0Kypiqj/9gAnSGLgPQR6ToICWPwRMtyM/GeY/79128//j0DA4iYef864lkWizvgCLpsghqZfEw7vqrXW66ZyuVniuKRhQhVPNoxJS+LViLrzCCASvQWIGxBUi7SPGDFjAiZ+GdcmNWdnsoi7cm1UvWtGwfNdtkxGb3lSh7BQGPOALBsbw2gHSLhSnz4vhOTrmMwKZExBgckm1eGO8243DfsEtdBGalxXLVR4oXpWPtHTVKy+r2XrcWrCzlpX5rLGpMLWAPGALrNBLIDWWIyMPU+SpNEWt6i5q+EXDHkZsUvK470Um0kt1eRKjXaJPmKV4uP0XOi0MGv1QWYeKukoq5kp/SuOeKGB0+yhGRR1yMap3mDrY+x/MiAJ9Umki6wYEQdMC0p68ZURLZ5l7sCVSI2RPO66YVzjNB5iDY5JF2NSdigqlZWeFhL/W0nsrVo2TTWhjLnTd6J1PviQUo1byXPaxAXKBiRkMC2JNmCrEG2OYR9RNTXRDBUBcxzBeNkEmAW3FE1tYbutqbwm4Jy8xJcGY3Sygg2+Fw3jS9zQFYdRqPI7NYT3H/QbezExtbTRgVOJ/1nJ4eoTLngOygmIaWiZZWxHnAdXBPuNuMqEzlZIji0dK1JcfdT2/tr2jVXTeqralw1Xs3ifAOIRwyJurZlsnXGpYD2mKxANngl9Qi5MG25xbSBBSmdn2+acZXac8X3lss8+dZrNDEBF4tHEhtMBrfdN2loMTUh56lseDmAiG8Ovka2I9azQ8jyfHdbCBuwNahgIWJh5QF3YcSj5/w+3nmg+Fr6Q1Tk+xdk+iWMaIhId451AWIHMUCM1BYuBWz+/boBMwMg7YSn70GLW4cwp0TQNmYlEd8/jMAGdIvZBmMLusbkAmTEZES03D+hFl1Issy4aPCNUnBtKkjbiLdULnNT0DzE/auCaciVry5wst7n8WWrhKU8940qk+n9pVvNZr9udOizR5RE85xgXMp+TVe35mApBiVyN6cbE1TvNOOap8LSzIbsnd6dqD3G84qxDa++QbJO1AVMBpKdQ3cGuoGwJtpzkC2EAUKEpcBiAf1TpBtQ9coaWMoVuqk1w1zgCTl4oIysIF+J7SmIe+h4d0bmx1q0vcz9VbOp0ITQVEwQEdQCIj3d6Ui8OIduA/1jUjoFO6FbvkUcAjYqRA//f735vB6ICNIFb4de3s1ypQaNoOd0Dy6QowsGPoV+ix7B4u0lywcrbAkaIsG6Knsm2XoqhoFYKWN0x8CU3jr6saN761NiUOxiQbAPiGc9NiwJ4RHxYgBTCIvpu63QBUy2t+uYgA5rqFWbxXKKhIGNkJ7C6hz6F9j2Y+iS/ywH0uIFor0zLZ1qJGoT0q+NMlAsBqXe4r5btT2xiz+XHV8Pl8ozQ9+j0jnuHv9WydaH/i0Y3oJ4AuMJkPu/XXdvvaok/VowCQeXP2+aDylKO7ur3tb+eD2404xL07QtHKSZi2lSahfPKsG009h8Zzeh8QY3QlmoZAmzAfQF735DePerD/jw6+9h3RckzjHZkDTCAuQooO/3DEGmkjo2VWh280bWhHbHW3NxbOc4I1g9Lt9tohnLcdGGqqSamWXrMG7MRSkzrpACPOvh7AQ77wibx/yPX3nOx98+Y/0JmD0EWRJCqfnX2EBuEOodzdDyrOIxztorqzU/+n855Ru/7x3Cg7eJ8hTrN3QPQE4DSRODjQR6SumjFFbUhNydyuJ3BwSNSjcG4o99lbA5hvURTz9a8f/7D5/wybfPsa3SySkQiImZdg9ZY95TxeaH0G7LXdzLn1bGl38sgoygWxYPL/jhHznih37XQ6+WIVtGGYn9wOK9I+QoYH1CSisVpnw8w4jaVFBP+/Uldt0tZcdfjj+vjkttJYkQlWCKRuH8d7+DDgt0XBG27/HRfzc+/yjy8f/4DHgMtoBc1eSwqnXZ+ZuB/dmS+Y8V7bRde3eHlCLgVFPMjtWjfvf14E4zrhJ+XW2lh8LgoRLeSa3ySWsaG8yY1iyo48bAF83pZgS2PHxnxdd+54of+d8ek5aQWJBYEyW66bsX0kkgasDbaWe0yqUpDENyjykkzctYXYlgzXFrNL/suEyH4c0Xd98r/5/Uq1urBXQt6LBCh2MW2wc8P7vgyRcvuPjuBdhJ3r/aOKFvCwFtqtgBHulYNYYIYcsHP/SYH/1DD1m8fezxCHqOLAds4RrsMAqdLihIGsPU4FBukXDcHuSxJ0FHZfG1U/rxFNYnfPfX4H/98nf4RM6x+ACVHF1ppaPBIWK2+6cduK582NyjotykzfvJbB7ULd3xlg+/ccrv/8MPsaPIhg2DDGzDQH/aoT2YRkrSgpH72hWs1ylJt2uiIWuQZOG/5NU1XHOr0bDpjXGpvrUBMWfLJWH88CEhHRHSMf3mLSSticM5H//Pc+DE3ynl8lt79rVy11YY/zIgMxyDWWToRHgouOUJ0jLtid2CEPdRhS+DVuMqkJNiOYBml+HdTUCNYBx58LDnvQ86fvh3wtA/9GAA3YDg5XYlMWrKkVXiRTrzxkk5p8UDJ1x7ULPbUFx82LUK/C74Rs1N2LFcoLWzDoYlMi45MuFX/g+j7xMEq0ic3PJ5u/ENAoiQxsZmUfaDjWBbjo4SDx8bHCXoBBRGRo/0NO97tFTvO5Y9ke5cl8LK7Esw09wglDlwezPHS4WtkgSOT6HrRoSBLgBjBBsR6Zg65GbYfecr11Au+V1uVNanKXUkIyEMvPtu4nf+HmWtp6TFCUkTa7ZuUne9Kq9M9rvoVPapCFyucZVHy6SVNbKuMjXJvOn1VLQ2tbUSgGEhM64lRyN89utLnn7k0ZHeRiZle4cL3PMhfdkMq8AhrXn/Ch9daWgSPLgMm1/wBnCnGZeJFwJ1ODSRjRTXZswb3KgD9hWgmCGMnrMvlCcfCR/9Dzg3Q0JAwgLRRIyjI14O4/YvtwVC3Y5fmuyp9r7BXyGuXPaN/JfCdNtaxAanCB5taDTVqw03oUWvxXOkxtnnPXG7gtRhldxbzmtpSgbdNBT1uYRVZ0ZWaeW44MnHym/9qsBRQMISU8MIaM1NUYRAsuJF8e7HIuLMr6zQVVqruE9EZlJ8OT4sxVu66n67x3LJvQ8/JxEpvaiepJ6QFLbw0f+Ei+dHWEpE6TxdwchtLQqVn3BuSt/ZYd5yaEEPcL1iJpglxnWuaZiQtoGnnwq/9d9hGyBKJEkkBiPl5HaVztu0MCVVG16xPO0MoQzRpNRib4YCtyT4CTUJWdyMaJbc1zUKIRkLgy++I5w96SAeAwuwrjLYMlXtPQ/lbN/G0Gcwm9ADCDsrqdcyuKY6yswC9npwtxlXtZ/uf7K/mNJ+unf6SxGYK6YIsOD55wO/9evnaBi5GNdIiGjwEjDJSsh1KcBLQzcEajKmFxJVFWxPq7wart0rWbKUWP5AJ2Lc5HhpEWFRhCUWe0g9Cwl89p2B7ZniNvvJRHjr814IbaFQBlMljwBpwSe/taVfPkGWWwjnIFuMrfvzRLJJ08PhU6YWWhJjW8Y1oyKHjguhb4n6zrFNY60VLK5177yebZCEXP6cxJhZXYC4JXAB45Ivvrvg/IWCrbKkfAnl3/nzoCI2SVzzL7Uh1bTXFGTMayMd4ybw3d9c8ytHnzPqOZHo0nuIOXfd1yKlnABPLuJsJQ9szmtbxlVO7Fqwbh6EEkkruZWCSUJN0RiQ1NHZMR/9xoLnXxhwBLQCHpOcSOlOfXujveo9djIgylEzkl3hZv79Obw+9ovZK4jo3yfw7NkzHj16RP+//SqD/u4DV1zzlV627lfe5jVEHQHYggyZQH4B/QaWW4ifU3xVpBGCt2xxp0y1yFPtHpWBcIB6XEEYr0EA9473sjVL2H0CHalGmWp7CZ4TZM64GI9gXEE8AnsX4gqsR6z3AD+BWan+Q/N2aGgvhUazqtVISsG4bGINLyB8AeFp/tmCjP55RcCmkgEyaS6l1MFdgso06sTjzrwlpBXEd2E8hrRE7BiLYWIkNcrtEmFx/2FXjKHd0zCLoDN8X3Ub0E+hy2vD500JqtZfLTTlZ6a/sZ3YAd0f/qElrPhVLrhKyLiGRlyeTQCLU3x4ir4MKQBvwfA+Eh/A+DBrW6WkyfTItjfaJMSWP66BGK+KS3sSiey/2s5NptP+BkuE4Ysz4tkF6dmZF5c0gOfAH+bp06c8fPjw5WNv4E5rXDcCB0XF5rNL961da2/vCxlO9JXemxrGDZxvITyaiErNmpRs1cpSvWYsq+XdQw6GyAVTy7MEbjIcvhIBwaVGC9TWFzoy5XaU6yUXtu28+sfY4eG9fTaDdBMjaAnLVfMmlxxfNfftNNQvKozmY5EeNfX+TR4DD2NhxGkqslaqIRdNrZwrt/yyBd83hfpO+X1GA+mB3pmWLZ1wptybqxJH2Z/f14Y5cpXAsyrIxAC2gu4tiD3IChbvTriRmvHPZG+blGvLpV6lnC3jlwZZ5p2Dp9Pl+mJihYNm3YKzVwU61fdVr0QS8GoZkgtSmZCGDsZHmB0BK9hDimbKODT916BH7X0OHR/63u61s+jSw9Cs6lxWLnN6XSZ7Bfw2ZVxy5Z8T7EzeHpPh8kV+1eMK3vDSRqBbZgLeNcgnSOoac4tvcCQzrtIKwgqyZAaXZIdxXbaLr8msZoxrEkvFBLEpAdm0NL+0+nndl0lAFKHDUnCmncLEtKyUE7rOvF0CV859s75F0NApnQADGzskHCPSkbbrieBgHjFolk21ssO4mufdJcbVVjgovdFiRKQDAhaXOFnQ5idDu6/23nnnxFV0qWpGLbEtFSOkFs0wUxgXiJ6AdtiQuVoRjEIx15YFodm6WZAqvrk69jk1dn+sZXrajkemC64Kh3+JxiW7jMvM/YUxF9YtJvjYgR2BtV2i26mV+kozt+BlDObQ8ctg93u79z3EBK96SCpzsPvBG0s9d5tx+QIelD12TlwysS19vg68imZ1yRjcoZ+ccckCL+u0cAQzJxTCshJMr7SRJsYV8GuTZWuJkEQzkxAO+/xe8R0OaS5VMtWcu5RLIJWx7V4u4tKlGdotSNFcmiZUWmjJqFXxrzOHL4Or1qQQsBJUUZSqUQh6hLAkbYTcY6a8RPNGhTBKQ29zhe5Xmc+beJ832ofkfTYFmIiNFN+kpc5v8spFj+2auJRnrvCALLBZ8xKh9NNKYENA+hM0nDBu1xVHHF9ySZaUFzNr722hYKvPbNa0oQdC2b5p96MbgfJ8Kb5fAdKYczkTlkoSdw+s8HqRDewEuUhz10vX+yZwaf4Ch/8++IzDm6CJ42yueX0GdrcZV9vqvoK+Mj+6Ei5dOGFmothd0EPKC2T8EDR0NeDBsl9IUEfGMX9VcHMbEcPDkncT9wx1ZC6Mryb4XTEW2zk+9A6z904zhLFCsIFJg2urCuDvYQaWiFsnHCKCdpkgAdpJrmgh1O5/L5vDq+b20DuIu0UiPhZiJKFI8vDk5UJJyUhbEHETjeXEWqvSv01RibVuzXXm7ZLPXuXdrrNWr/ScQsQ7306WkLDworbeOZOQ/Uil8tObwIyn7oxZLGtMOZQdDFQZc41pb0jdedqEgXJStY3qITOvVQhk8/SuCiI7P602M62l0TRwvQUo6biWn4X2rjGSBdCouFk2D9MautZqyTa9wcHyzod4wqvi0u7e2xNirwcTlZAs4GbfsYx5L4yvPdt3mnEdhlcQN66ctevc45JrriIw+XsiHs47r++WQ5pxOilqRDUvJFyYQ+2fJLmDXvPT9pt4nY166eel01H+v0iowJQ8WohAQ+DFHdLSFNZNsd4lWwx3seMlc/jSud0B8xYlRYOYu0Qkl8nKWWihI+ExW07jI209zNaM2Movdwsy8S6mUzOPcOsENSWNnrxLdIIq6vvt4FRfIX3vTstuKHe9Re4RU7U/oanSIZNlNuGpEzbxX2+6miYrhKSJcdUErV3GZbOxO+PLO7Jd69fFHw4c54sMyeMsw0kgmgtjh3wN+3t8T07yk7XewsuqULwqLs2eZ7O/D0cVXvLY2bzMOeqbigd3m3EVGjnbJM1sHjJ37CBQ2yfLJYMDl90SzJqpSXNSMpKqYZqgywiVsjlE1aW1sYw05fYTHNZC3hRqY8uK7c24bdrN9bxkviYTpyjxGzF/Z8a0bhEqoZsII3loztTqDBIkONOqxXhzBJgUoSAfl/qn5T53CsoGL/4igxQRDR5WboLFIoQwUR+5BmI0eLh32WXzlHFglppRH+XCjaR5Q/uyzUrBaunz52I5yzjvwYpfuk9AG8ZVmXitqHLFO742ZFyIsRE8ARMsaWZgWWgqr1BfeH6nyrjL691qYPh83V+ufEnzuZUsAKbgmJuZ3DvNuJLIPM/kFpoR3gYYXpCAoC5pBneQW0oVGVORHhcJfRCgE5KELFVSo9FnN221r1sZdYFM9GTnvGVzoQVEl9gmwcWADYaoO//d9ORYF0cDacKgbwOEqu05jcpBJgKWC6FLp6gaMW2doHQKK0VWAYJgMubCmJmoaVED7M5Fw8+Ej1Kl4mJL3CbiYBCOQDrElL5Tb6X2qvLFqwhPAogndrtwk7IJbfq8WNDiMDiBDyDBIGwIK2X1zhFjMJKmjDdSl0VqJ4MypCnadlKiU/28BGjcJAa1vnhJgRA6ggS2zxPpScTO3V8307aKGfUA09prByI3POBL4NWTs6U2Qk8S3BeP4N0ljB/gkk87JiZh+vvak3yJieqA9eqNYEeALWM1M4jFSWsTXqlBb3QPe47fCYSVkLrOrcIimAopueW87QQ7f4lbGv8lUBKQxYSAMrxQts+MYTNgQw4vD1pdGqKtr+yWoAbFZKJUay/m57apcMGrGkiX6B4Ejh4ruhBSp0Tp8d5PeLJ3sZR+Kbr5TYOvkRkQhUVUzj9fs3mypdhyzYK3oPHLr3tbB4OpsOou2M4mkolAV19iq12VWpPkEly5p50CXSSs4PgxpGVHVCOKN5+sa9qUd5qP0RmDM5XGdMnLTWCvCmJNlccEIXdDDr2y3iTGTRaAclkyaefxwA6zls7Vh9zsmC+D2eq8VKApuFaNmy4sVT/xDzrjKhN4bRpy+ML5LW5JipmZmNxUY9URW8wy4F2RDV0qi1OlO1JSH1oFglgQumjht0lDr8O4Gmtal3N9x41NJpgSwFHnwEPTZ5LmLYLPbkOkqkkwP1zzJCroUlg+EHTpaUQjudpNQ2dbC9pdgTpco+ZgryywPd+yCe1aGXGm0V9jgS67ZGbOumyjtpPbIkmqj/du2S7gWcGRzuhWQjqSmnKWdNptNXq9DHHnb6DWECyvcPOMa5KDs0LoOBI9RiO3NK8DEC1zINNcFEGgCGCVeb2qoP4q8LKbXm+ifJSl4seO0PKacLcZV2sVe1Vzxh60WPpl1DGUubkGqC/kceNuUsRInad5rCXnZTb7teTI1rveFiHd3cM7zymuoNwaCRKMCqMWCtkQpVL/7ssyswnXM3dlB7+JMAqMnqPMVj3oa6dfXiPV397QbwNKrI+qW0XHwatNTkSyhJy332qR7QB+XAv/2nvMRjQdllp49V6B4iC1lB+igVxm3YNwOxjw3OTYVZ485Y+3Iyj8sTym8OovASSB5GwQNeg7psVo8ALLxX6zVlKzJIvKNt3xyxk4+bmCR4JmqALnFcMQnB5ocl+eVWnizcZ+txnXLswo+CXXzMJiZsJ/sULfxsgu8bU2dkNr/i4bVBIpZGYVHDG99qjV2AdDqgmrvMil1We4fg2NtqDObNAtMRByBeuyQcV/Mo+K+vKanO0sfCkgewc7D8+Rm2rexb7UCtaMbyrzLAiZz9tlczg7vmx9/LHXWp/XfU5102X6Vyuu5Hc7mHi8N4WthaDRVi4jYjPubs3P7nmZzrVqeC231WruTMKD5DUSNxUmLWl6TVHq2T6cjFezgKKd97lJkKydFwtDceVZKSrQzom1szFVvnFNq537Lx/mYVpXE1zBqjI5N9jurv2rwx1nXJeRu2uSwRntkh0Eu1mYGEsztqruszPkRuqUEpnjla6TTtkmNb1DXGIu1d5t97YNcZ1qXFx+XAjjQTpUp2kacL1OcMqbxKXdPPb63jvv2JKsLwV2bUCzP/MbV7uOx1+3c1+Egl0J/jKG0s7h3rE017frc421etPn5FeZ0+m9it7l4iJMTU+tz275T7nHoX1cv9Xs6/ksHvhu8/1WtBf2JPYauZZ/ZpqWzG9V13jGPBvufkvQ3jpnADR7Z87Ii5DRpm/N4JCkdyvI1Eg+vKyDw/zBmXRNDLsKCu21rz/YO824ZDYJu3DZebn8+MsQYq4QNqb92NqkIl5dQyAYpnGOcjI1xJwhx4FHvunxbKCZ+JRgE8OlRyvVPNSZ7DzPq+gDJZ78kkm4SbgKN6w9sFxGqMiUnmODGimkzAS0vXoi9gcIzKsc38ZaXfUcsazE5BNSpA7T6XcxCRXmJcZU8qT56JDmuruGLQdpE+nqdddZ9DLJNj8lky8riRBzzlkSdm7bzEJbIqr56Pb0rUmIQHIlpJKrVhlD1qzy9EyWiiYicjZfO4x+j0HfFMxvPJdLLttxkv8vQSkyldbK1009ql8P7jTjunyRLnOefBmc6QagDWYQpRTHSJlRlN5HpadQ2Qpf3vgOnzZxxzh46plDSeAqF5X/ppscEiC/XBBm6mtjpJkVZy1S8pWS592AZP4KSffp+JcDb7DqB+bemiXc7cHVXMVh2uCta743UMZkjTbp++6wErWrKn8/wcSUfPQppysJllu5lJxIu1LpeDncacY15/4vvYIJWXZE5F2LxpcBLfGzfbnGit27+K9mpoViu5+0Lf/al7WZixbFnuJUJcvZiTxq25GxDjGAm57/HZ/mYapH7uFUtA1g5oRuuuW23vxaDfb7kYhcBZb/NeMuSdZVFGqR4xAesXfddPaKOa9r3mhelzxnSjsu5ZIa7Z1prLPtdmgprsTziQlcuz/d64I1JrRDH87GtPdm+bxOc/pl0KprTclcpy8jdSHPqjZco3hvQFS904zLd8EhJKIxR+TNXfdFyyIa4rsLN05Ayy9rHjuNQcozBVy3yiVsaDqgFksOksVKbYU05pv/FmD2nPKgNvrMDvxuTQO7YLc75Dq90xP2XG00cz9zlhQTTWZo00XMHOQWmF7iNRDytXD4zZ9TPKX+bi4dU/dc06trb013GY5L121l9Rq8UbXXnbGWNZGdv9vnyKz3b5XgJ2Eu1mtF5ut76H391tMzfHWVVsO+TeSRAz/TI6353QrWNr/BjhAgzTzeSvGMdg7L2hbaNcOtslapoUVF2CumW6na1yS4/EDnce1CRDyeKNOa1FzVxly19nqZ7YkJD1qVoMoRzbOvOt75nvmNtbHLJNO8AYRg6ghviciA2RZLEaxDWFWZ0/mwV453AWYyF04tFA6N+83ex+fSPOAi/663sSKrptwjT/HiSYlkIzFtCZZQvMCt4f28pPryWgn6dd7hkvexIqFHJr/h7k9CzREvWiSmEZLQ2ZKQdFadqtQxNov1GUVW3yd+19gTeU5ffX1e/zlVlreEWE6ERRAi2JaQegKGELHcAcC3XDacNnXxxCbmMi+8PDGVqUWKoKTCLv24aOKzSr6t1lcYYI5ylOSChyUGBswGxIxg0E8jnY3HNZzC8HKsYeWZxZc31Qmslo5bgmBTykgXA0MaIG4IRp0nyfgxrWOqQ2qjQyvZN2qAlsMV++BVjut9p7t2+fMSYDI3vbrjziS/Q9kvomjpSP1as7YPd5txZSJeiKaHXyakMC8zVGJzTZEkaX7DPPRqdnNmi7nXOK5BrKwdFQZ1GfHQilh4CZQs4WsVYQxNA6SBZAlJRpciYkIXR4+YykTUE0MmN6fYRJgu35BXBcSXTViOJ+JeFQ4xUv4BqZUHPAoqM66U6K2DFEm2gbSmS4GQQpbASp5QmMZr6fJ5u+5xqxmVNTGYQop35d0831Z+J7xkeqC3js5WJBN6S4wmuQh/CUt2UiepJRZXz+H+8Zuuz+s+x48CRkBQM0IaUNvmdRpRU0ih+lTNSoD5FLjReliLL9CfkphUgK4+V6rA4w0ereyh1Jb9ylpfFfDKvGZnnOBJyHFgSFvfa2kkpeQJySl5RZasSU34kDPimdI3rHYltjz2HYZ502DOIj2vCfpkbNNAsC2LkqOG5FypVqiziZdUpmuEsvcKTZC8DlfRoFfCpWaF83M9dsn/SDb/nmEewGTO4lxuFDqU0SIuyGacfEPz+p1mXILVOmBCQkkEG1EbECKB6Mfmn8ssqHgH9k5nsWa342mtqt1wOstEopodmmOZH5cO9UkgNaYKbbSlZCNiIzEZaksW6RiJiajRpX6bfnyvC0nSNRnXZRL9ZYRR6jyXr02MSzOxz/qguPYSDBbJUFsjdkFIL+iTEpJmMTH75grjssnUcNm8zY931mRvfebHlt9JkAZhChFNlXFFvOeYpcDShIWpa8UWXV9IZB9XpHTNbbvnXs5QXnVNXrY+7flDxy+7txPHYEKfjEWCPq3p4wXLlAhJCNH7rhWfXsIFC2nMqHXe8z3NSij3xNCKNuNz5ZvXJHcmtjzm3N/NYVfjKoxSmzDx5MJd3NDFFct0CskIMTGQCGV/SWkzn+/V+PB8popmUxq3vpxxXUI9WjH40i8KWdvKP8sUGNI5pAtWFvJYCw2ZzGhSMvVt2r5iEHbOG1SGfTUu5Xu/FJeolnNpxl+eWXOJG6HFSmd06UnmwmmHEIgki0x9By+byevB3WZchueKEulsQ2cbFrxglV6wsAuW9oKFnaE2EizimlhjjwWmhBZopZt2I9b9dBVYke+KpNt8r9xPmvNAlCKNeuKuP9pIGrmIxigKm0c82H6IdCvWKoyjZg+YG1wmh+clyCY2bbY61J1UwAYZCrTWZ7FJm0s5p6mYOMW00WFcawkkjqISty8Yt8+w7acsxkRX+yW1xM4J6qySwWxO98dZ/m4Ryj9u5sCymastxGzTe1nDuMp7xwTnKqSw5Gh4h254nzH0rIOxpa9zbQwuDwvV3DVnNM3L2M0cF8HGx82VMHf+NwIRVCla6QnRWMTEw+2W0+13ON98yvFG6MdJmq8BCwefmf1iQKJtwHnZWHYSbcuNra9jnElIVTCcEqITvgeHGFlj9JsT3t08c9zohDHB2KSITIwra3kTMjKF/UtOdTCKOXHOSKfjyyIWd3Gsvlt5e3Mmqrkqhpqx2ibOtx8zbJ+z2tos1KoI5FcR+JDfbWLFV9EqOfDBVZrlAf/TAXx0slUYVyARMDqiHLPmlJFj0MekXEB8KHP8/aZx/f2///f52Z/92dm5H/3RH+WXf/mXAViv1/ytv/W3+Bf/4l+w2Wz4yZ/8Sf7xP/7HfPDBB6/+sJwUqSQ6W7Ow5zyI3+FH3jPe7s94nD7mlC8IaYOmDZ1MWpf3wyoL4JWZa+m65FvB/UhuQqikVoqjuGm1YI2vw4TJeW9zCVSMqR+PMFqHZem3azbeKImLPrDuO54/fMgYP2dYL7mQBUPoiFKMoVmaLcmhZbM3lTRm2lIZVWbWU7JreQ8fWhDNdgBrEMjHV5hWK+MX4iAYwVzrPUojS1mzPDnnra885WgYWKQRxAWIMijL0XwJQSUHeiRDpXRcrlPM9JTCQKh+lvxGzUuWufHvpsbHIw0jS0UrEtiqsO4WDP2C9fHnPBs+5YIl56Fj0L5K8Vo1xBm5YGJc7fHLtJ/9Y9+TO4wrTfeeQr9t9m2Q6gOV/LZ1r0ryyiv4XATp6FNkOY68s3lBv3pG//4LHq03LMYBTe7H03qP0vg0a0xqiPguTCnhnXtzM9DK4OdC07Q+O4yr+LjKsjT4UhmnTSscBbZBOVsExtUS237GGT3bThlCIGqo99Zqyi1aluWxZUJuBZdb6S01c5eYDPyp2YdkzTHPcOlkjBBE81xlP5sVX5ugVixDkZNhw+r0GQs553g7ZF9jwVlnoO5PLS1PFHLvOGeEgoY8Q2ZEc/Or7x+d1iE34gTotKv+SqdPLfOamJtUc7DjS7LGJ6la91xksmgkCZgFEgvW+i5f8CFfbBO/9vE5azthKN3P0xt2J+WWNK4/8Af+AP/u3/276SHd9Ji/+Tf/Jv/m3/wb/vW//tc8evSIn/qpn+Iv/sW/yL//9//+tZ4l4lJ+x5aFnXFkn/H108DXjl7woX3MY/suXbrIjGucCI5Y7cSLdtP0izjjMsNImXFlSddAVXLjN6lN8KpcOlOxyrcayUlK2xJf/DF1VeLraxSbMSqcdz0X3YJPVs95Zp+zHhdcyBHbtGAUJWpmXI002rbHnoifNczFwXLF7KSzq6t03IlSJycHgeTZnqoSVEF8kmbdBJLobOAobTiRDQ+WG7726Dkn44ZFHFEGkBEkIeI28YQQkxC0c4ZpRhDwwJqJCbujuJgiCpmfgivm0WzFROVqWSxIV/py5ZeIlryMkwobUbbdkouw4Em34qP0lLNxxQuWjClrXCh9sfM3gg1Fip8xrpf5nsrxvl/LmmPB0JTPZ8JULD9W/Y95HzZKSwmJANdSomSN2QwVZZEiR+OWd+MzHi4ueBDWvL18wSJuCBane5gi1mdBLxO7ECvjshQR6xHzQoFFM01yGeNqwBGwJkH7lsvXSa4GY1nCFK/bFxG2oed5t+Ci63iRPuX5uGBrHUPqiZqJdtZwyvJImc1G8GqJ9UzYgxyIktykL1AYV5n/wqMVcrtopwSdBJJZNp02VgnzyoNulYic2hlvr855ENacjGsCWgVFZQSLPheiIMXUnQMgECwJIajTLIvZj2R57l0DFvH+akUW6LSHonliWGmWatOOwcQFlrKPMuNKhQaGKTgnFgon7pwx64gsOQuJjzjhN8/gt9LIC1MCSxwvv08ZV9d1fPjhh3vnnz59yj/5J/+Ef/7P/zl/+k//aQD+6T/9p/zYj/0Yv/RLv8Sf+BN/4pWeIzagRISRXi5Y6QsexE95X0d+ePGU37v4dd7e/k8WrFmwRW3jDKkqGCWht2hVvoBdI5F76w03zWETggF1MyiTdJK/Rsu8prOGV8IA1FVqy2E7ITMJA6IGLrqe87DgYz3lhZywYcmGY7a2yBpXcTN3lMgtNT9bNK7y5LCbL5P57ZhyPpgUopeyOcO9UJXuMyFryveophhx5lKYjRIJjCzCBcey5lTWfG3xlBPdsrQRaRiX5Y7NrgUoITMbMWeemp38MTN7E4iZcZXna5FrM5Or62A5/qmO2WqLjABVc074GqPKlp4zlAvpeKanfCxvcaYrznSFETAUb9kyIfXkFL95qITUGlQ3Z0Dt59M7lwSJaS8WgTqJkYITGFMn6L0MrHTD291zHsg5p3LBI3nO0rYEG3HMyIQ3BlTc55UkEtlQVkOAIAuEDkxI2QxfjWzWrMtsZ5ZXkobxN29WwvRLFK1LfJgoa11w3h2x1p4XYcmLsGSrPYP22ZstWWZJjdbozyhdx1sBrJ1z4YDpz3IGiqW6pwozKj6o0r5E6TErEZQhMxaoAp66leike8Fb9ozTcM5JXLsWheSmziOS8WMSR/N+zwJUis7U3TKRg1MUEGUs80rR1MqRALFqT6ZjsyL5CivfqZhf2wKVjhR+OhECVfgcTUm2YJQjvmDNKnVY9zaPCbywY9YmkFbtznhtuBXG9d/+23/jq1/9KqvVip/4iZ/g537u5/jGN77Bf/yP/5FhGPgzf+bP1Gt/3+/7fXzjG9/gF3/xF1+ZcXUyAltIa7A1ypqlnHOsW074guPxO5yOv8nCLjLjGsjb0rexUjUr90VmBEnZrGAFqfLSlvD18hnu+1GZFmHfLLcjZVaczL6XjD2hwaCUhFXqOAkdR90R63HBKB2JnoiS1PwHQayElrdtea2xxdvUxJcGWQVGpJoTRKRqO5pcs7T6j+pPzLGb1ACLJkpsYiAJZcMyDazSwNtxwyqNdJa8ZFXh+K3JwjzCrdjBKj/F3KmbpTyX+mV6icpKm8kt80K5Xayky/0LeSbyGhaJdkR4KB2DBi7ignfiKZvQs9Wu3NVRO/qzDWoLDdtd+DeEXfLelRPiOTGHv5SFK6AEJpQUCZNE1CKoGJoiwSJdGjiJW1Zpy8JGVnZBx4iHx2eTmQmSPMWhaKejbdzagXn0q8cnegflgiPS4sOuWbWAZA1ifq4whRphaFDCwg3h2HpObcEgHRsJrGXBqIGkHbHBx2Al6SL3IGueUn21GV9af5yazHC5Ylb9Th5p1vYVqRFzQYOb7fDeYEXC1Rrd7M9ZpC1H6Zxl2rBMW4r1xE2KMeOAeI8x8wAZ0VwLOQ+maNg+TVY5apuQIBKqdKUFmSveFfOis0XKes8S8KPTB5GpD50Vf3bRtoRoHVFWDBwzyAnH9h6rFFimBV26IMSeYD1Yl4VP3dsN14UbZ1zf/OY3+Wf/7J/xoz/6o3znO9/hZ3/2Z/lTf+pP8V/+y3/ho48+YrFY8Pjx49l3PvjgAz766KNL77nZbNhsNvXvZ8+eARBsQ7IzgqzpOKNLL+jsBQs2LOwZi/SEJU9ZcEHPFmHIBL5RuyviST524i1SpHWrGkoxGwKTei1MvhkKsYVKSncZVzYHtIqYRwhNsnIyz+vqLdDJBUN1embirYkk2ddgWpmXq+z1tnkYzogKma9bVjwcv4Z2V8aViXtwV3a0qcaC5E1aN10teOcSn9TfCWygs+jmKBtZZCNfLEExM/OeS40BN0tNja/8LRKxErdk02haxjWHkkdCRvjoGpyVPBopUsnEuArrFW+guJKOpZ0xxJAJ4eT3kJTnGog6BUuU+d09fh1oi+2KFUd8ObHPuOp2qoOZ/CoCJDViCeXH6Cy5NmKRZRrps9/F8SSbALNOrwhBNRNH11jUSoCK408JWa9lyKZh+OgK0d5RqspFZUn8z8n0XnX74h8uMg8dC+uIEhhQBuncx6K6z7jKfU1nj58KQU81NyctqtXS9ue5SrZkIq6afX2GBCVlvdOCVUFNi9aYf4JFFmlNZ1u6nDeXxSs07zdFiJlpJVKdz6pL5blSqaEnmFllbo4tob7LZCtxwpOqaFreSfLemd69uEyK6IaLKIh5BGrB+khP1C2ByIIX9HZBZ+uMc2O2jiXeBC8K3Djj+nN/7s/V4z/0h/4Q3/zmN/nhH/5h/tW/+lccHR291j1/7ud+bi/gA0DT5wRbcNxvOE6fcGQfs4jfoU8jHZ/Syecs+6cs7AJNW8QiJbnPMLzbq2+2rrTKhinYSIVocSKNu5SpgX2BuzCu+Qeh5LWkrKEky6Y5RfMGS3gjxmUSVnTEutGFYmIzyYQliefcZMY2k9QFyL6ndhhJcdu7BGdMVliiX6eSIHgK6mAx80Sr2pnHDeoOAc0MKBO8RHSJzIwFeLCF42QWQCd5y9/fN2MgmwzNX8CAkREzD6MYs7CA1Et8nmkk4UbTyFySTjz8OyToik+phhNnzRPJwr3rjCexI4n79UpEqj+kr7dOMkWa5cL4fsx+8HpLNF92vMe46rsIe5sPIPt1Eu53KAppMTEaEInZyR5ZSshEOhNVPOCizXdK4qkNnQoLDQybkWQQTQiVyPveMc1zGCQLF9OGmyL7LoGyHOayU8D3WWEiWpkfSLG0J/fhGIJZZlZFK5BJgGO0Khz6/bIvWdLEvLK1xQWo6bo9nN4VCuuBEkRJFp1d5TZEKZtVUxYAJEvDlpPvBUO10CWaTSyE1ITWiAseSRIopCxgdsHxRdCashgxYop02cVbkzNSayzNwxervKp9JWFuKq3R8waCh7dLcnNnfa4ZfVgw2AYY6eUFHed0uO9Oiaj4PMTKLK+Karwabj0c/vHjx/zIj/wIv/qrv8qf/bN/lu12y5MnT2Za13e/+92DPrECP/MzP8O3vvWt+vezZ8/4+te/zgP9DUTPWMVPObWPOEkf84Bf5UgDi+4F2j/nYnjGyEDoLOfrui7tx75RNUKKOaNelKFL2XUkRKt7iaKUwJxmG4cYl5/Y3RQDoyNSgiXACHGAtI2eECoCopnQew5VnwlxV6KKJJEkVVOMb6ji7Zkjlm/CaYNYVjRNQLTLYap5HkTyO/oFI745+wcLYkjEMLrZIkuFbU0SIEtUVafzcwahhvr7RJWwiUkDcmJl5waDweAJ1+UFjYSsIOTGjmmKYp5BE3jna+Tx33TW0SWBdSK+GHOSOjvYmblqZjNKYkFqCluUiDpxbRfAjN6m91XLqJgZcWt+OnRcHtse135c4qMoMNO4diCJkWJyR3kPlnuJJc1dspmIj+J+vnl343aPT/os+H4fMQYbiTlwUDpxyhwTRKPrAjEkfy5xsuKWdTgw5sqUy7WFcaWJoGa5CxWfz7j13yFmYadGviY6K3NGNef50mmjZdB2Z6ljsJTXpGIQM81oPuZpIYJUDyuKMhLrjy4E6SB11kTe5Tsnz9sMYow54GN6mtMpy8zJeV0TkBPyHOWo/fWQSDGhwGLZuR9YHE+Mhl5lHN9V1luBrwlinbkW2nMhGtuNB0R1FBHO18gkMzMSSPRuERlXLQhRxfva0QzsNeHWGdeLFy/4tV/7Nf7qX/2r/PiP/zh93/MLv/AL/KW/9JcA+JVf+RV+/dd/nZ/4iZ+49B7L5ZLlcrl3/mvvjLz9WDklcZISxwneHgJffX/k3eORBwGOht6Ng014tWRxXXDiJgPY89GjbzC64x6WiiyVkMW1IpSXsiWNNWIuTO56exsxZyapJXNiuk1wEbGYIKXqUysSmGaMVnPkqAmcM8ZVNJ2y9acHTVL19Oj6V8Idy3lT1lcy33Aes2D0x4FuGYiLQNKp3JHtYIHk77Zn3V6vlXF11SeWn2lSG1DGtIZk2JDfqTE7haUixwE96UghTZjU6CvT1E8UTEzBOsLaSDKQzsfcSXeXoFqOZpOpBZUVTgMl4KVoQoXZBJuiGds5rMdFeGi+0x7bFcfVv8hleULkufQoWcToVoG0CFgnpFD2fJYOJK9bnZd8B4MpF2S+i2o6RRbw0IBoIA5GXLupaXXUoyvBOjDNa3NIw7rMD5j3kZkT7GL+KkJ5aXWv54l0FrEc8TqNbfJJI1SXWBGUKkOoz2tmzybhYYY9VsYg9TvFr10o+ozJSUAkB170RnfSO/1YJDqZgsAkMy4vt5Wqz3H6FEh+TZ1CTdNuCAFLOaIwwvrFFgbXHsODJdIJQSPSZd992cPFH5bHUTdWEcJm69X69/JBUiQpOirxyRq2cUZMBBd47cC/hEwRqe0avAHvunHG9bf/9t/mz//5P88P//AP81u/9Vv8vb/39wgh8Ff+yl/h0aNH/I2/8Tf41re+xdtvv83Dhw/56Z/+aX7iJ37ilQMzAH7sG/D7f88DHodnnKZjVumYB8NDvnb8lEcBHqEs4jGC21dnpqFi04nAhbHenDkimnHycImc9shx14gfGfnLParTlUaMkcOLITu/iyg0BjgfsKdrxout9x/Pvpdy28JYPNS88dtIm7/kN1WYMROpak4DLWHMUYRFQ5A8LCVL5AEkQHfaow8CHAuE7c57tYmaaZoTZBLjrdlmOgVk+Dq4T8sGQdYbbDBs3aBSvo2uAvpwgbx7DDpmiaMVIW2a3zrH2fdnHTwbGQ2GJ5tp2XaWpDjRS56Y2iTsNFOXGZy/d6Ctfcd8vt/guMYjZAyf8vHy3DSIX5ehg8VpQE4WcNRBF6GJNJ2okeKycrNfZ4nsZX1sOk7loT1Gz+b51s3oCRanS+ThAlYKvUvbleu0cGkAS5EKcXWilkIrEyH+Gs+3bMYL4jbVr5W5qX/uzZ+1T5mvN41cwlzYaFZ7uufuRznXjSzQ0oF0hi5g8WhFOO2wo+h1ksSTnD3wIdOfmtNpzCpamOS9m5+jA7VkTljgdsgAA4z6FFu7/3/x3indMrhKGvLaW8zt7wqzKnjZvLS/zHxJanWLQjA6GDtsHVhvRko0o9VkWEgpW4KK2GVOT1PKvunKQ9uNzmvBjTOub3/72/yVv/JX+Oyzz3jvvff4k3/yT/JLv/RLvPfeewD8g3/wD1BV/tJf+kuzBOTXgd/73v/im7/7iIfDf+EofoeVPeFUnnFkTwjpjDA+RcIANvomy2a4KfJCXa8+V8ZPLjxwQMAWA3Js8MAmcY+CkIVxad4YcFitaqDFGMw3RQowBBiBkBitmMym24Pv1UpTZuLK7MQMjDkSXwXlOaWMY3mOFVNTAI4WsEjQDdBtmUrnZCJTIRMsw+e15JgV9U3IYnt+BxNInc9FUDbBKYcILLK2Z+J3jV2ExUA4GkG2vhYS2bN9tETXAtCBHcHFQNSRtfnXQh6ewtwZdRnkSdW8JSybEa87zzcNZduJ+TqNwJBg0Rs8UHjYw7IDG/AUjDbODKbamgcEsJZmt8cybcpeR9YbY9waHEU4jXAE9CMugTW4Mj109w2m49L6ICaXlijE3fx3NLhYEzUxCtXze8BiXM/MBLcDC9W+3sFhHQDZ+aNoMNEsv7GxFVislHCscBSxMIKmqYp9Ku9WVL2yZ8sThKnGo00CczKfm/L9rTJoYo37fx8cjXBszii1rMHoIakFZQuFaeelEpc0/6zOiUFc5lrVgUFdSK2pEkw0SjUQtCPIAtUelQ5Fs88wB8rkCEj7fvJx/Yt/8S+u/Hy1WvHzP//z/PzP//wbP2vJZxzxHU70NznhI5b2lCM5o7czRNeIrEFHpqKfDRMydekOhS4TR3FCnnREVJCuENysTUjR2szDs9MhineYlE12f0dG54EB0+yzYqIbwkRXyp6ahBSpG33GoGQXEV8uypTv131b6NeUh0gkD0ZTptqxmY8SlVduWHK6CgNvNqZkSUvTNDorGTZSl8RkLhMU+cJqkbcIMnq0pk6h8JP8UDQFqo/L0lDHL+q4X967NFSUTDtnM7sjdO+urHD4/M2Bj2bPbMN83eunknl1cMmfrlReyISz3MN8Lqv2kHHDTaS7zKtsjFYgEL9/yPfQ0RctJAiZcdXSSS00g21toi3jqiXBZNLyTFz+KSZi5kuzG8rOrqlrZ4HKvtqfzctwxnfY7DZ1+LkgQZmnotAEcaW28xQQKZV1DUd0baXZooo0z5OSNJwgqGv+BtiYv2KgXfUjIWSGheNIGCfhTnPkZAIJlv3Hc5wx2fGQt9Nm5HspdJZx0d8hjZajS7MYYWDJ03VaRlzTKihpCW+GNXe6VmEXv6CPH7O0j1nyMUt5TuAMlS1IRiAtWkCRz4qkWRDFmVOpwSeK5xqpQigUriHWhXFpKpSRl5KwGXFx6QMBxohprJFpmjfvjFlJu4Gs4W4TAr8M7Q6Op+zPiQ9S8gKlSJJZ0K2DUfN3lthQitzl1NzP0pIVKcxLGslS42TYSUZdkxIJlplXseQaUzCGleeXua/I0c5zfo6RCUVB3ubddEoIrVa0dg7L/MyIyfxY6n+3CcXvsb+ylUe3J8oWL30zNGZC54yr7hWzGu1RQrUFyfzetSqZRVjkzZFK7SBBuib/RyImoxNwnRLML2VcLW+sBN/xUfKemiI984YQX/9iZpLdNZKMHhN7Zn7hfBizMxMFn+7fnjv8FtP38jMkS0AGNbLQmUjEpNG4KtNqNpCWtSjgwralhITg72bia5DIeKjUkNzqOE1UDTs/G3LyveKRozt9sKzgMXZwr/k4o79QSf8Qn9sp0VrqHkq4CdGtmw3jmjZo3tWvj0B3m3GJ0WlE4wZsTWINDJhswQYSI0GLGN0ikoD0rjUZkDaE3rAuK1GBKTS4ULaCvDmb3ZlXvl3dcIcoyS6MHopO50Q8JGJxxTBnRDAJplUytYlplOvaAJ1X2QrFtF7v3zLY8qO7X5LZq7WS61TNohlNZVpzp70jSCupOtEqBq2Qrbi1vh5T4nO1v9sOkhVptjiAGgGyzI3jqFTbfNfhONkw7peCtQVld0xwNwjXXcsaWCBgOVem7tdqGbJGzpLJWlD28p5mItPil4mLmSiW8Ecp34yIhlySJEv6u5NfYZ+J+NkScJEfVtX+NG2y3ZzIVgDbHX77xJ0P5iOYmEgRunZ3cRW0duNos6Zlw0jf92wtYrHsOw8iSds14aR+vXlm8O/X+c+ll5gHkbhM6HZ8EaXmA4iBbVE1r14BSCrMpREUS1WPMqV1b0wiUSk1cJhp5eEWjTHnYZl52H2f8VYoxMRoG3uWGTUCJoFkSvTYUN6k9NOdZlxiCU2eRBk00eESpjF6Vr8KKeXM9qJsVcgIuLOrp9JolXRnrJgQfZfBtPEBc3HM6tEuJKYkZyvEuZUEpdnTFSbs3Hud14Aaigyz0n7FDzzx5sZhf2lkWPPLpDiC8slUkbIKssZEYEsycJ4/x2dnTgJTSF1J9Cn4nijY2Lhn8hhnVsqJte6NvqXfFN7aqgSNhIBLkJ4ouvfqNwrTPQ/M9yUPrOXIcgqBpWIusul7TnXqWs+IVdV2yn1o5IMEWkoBeYLtpJTtCHaX7pMdfJDmd93aeRO0kkTZl83lRcHYFw+tboYca9gqVPtzltd7uqY1mLVfOvA+JRw/+2+LBTA0sp160c3pTqmMqKl8K7j2ms23IkzycZ6KWtYJsi3dH2jW8HZtfMkwrbM1y7QzV2VLtEuxM5sUIbBEqDb1e7OyWczOCRUvIkzTiaMmeVNQa5eCvjrcacblbg9DLBIkJ7iVni/qDeVSbHwqdU2LJNLYxhrcnmFI/fYugr/B1GehrJZRkYY1yHTfNvqtPSifzZ7dRgo1o750fDvXVWbA/PX3o/5tb5fvS2ot8WgYeGbSe4SmTAgtjZ2kwfnllzDOAzC9/2UyNhONnc21NTfYYVEyv9su4t8YXPaaL3nQbLiWTdt7Zcdszo9bXrG75+tUmGtViDMua/frjjnz0ujB6467/X4zqNlpqdftP87z7Vo2We6yR8ALQS40YLa3X/IeZjUAsqURTtRz0011SbBkP85exIrbwqopd29/CbM1QGwSONW10lSEi7YeVMO0dqameb8Dr3T5R/WLVT4q75pfvTCqUhFn+slIlhmYa4I7NPUV4U4zroDSI/Q2Emwk2IAwUiJwTLKmdWixKuPKu6CYhg8+qUXKmXj4WqMGwaKCdPnxAbNYt/5uo/k69p11LgJyvbLB4JfsT6okXTSOQpOmU6hAV3ep280tm05bJJyl7dQPYnPFHuv3KUxCpYCihOQWLBvBUqyJoeW22oq1xRFXUG1XZCwanzFpaeLJz0E9YVNiGeb0NgcZ5YyYOwEpsuouc/9eQUneraZtNV9TLfy34cx5rwPzwcvOuSJlmDlRLCaeFErucQ74K/XtymwUMWwXdjZxGdKMWLcSVbOojQm48Ak9ZHKo4z2sae2lmOWgBzHbWchdXDrwPtVKYRheVaQwEYujO2fBJ6ogtOZBpfmeLbherbkt3cpTIQI1+9gMpCNZZlwtsSh++Cp47Qibr8wvsnk+KTNrhDnv9OCqUmYq5dZGCWFsfgaQARgzsTFqoMlrwJ1mXCmVPLhc7kg0Szl5XktcwKV3KLshTLEHghfYLElMJUOxbmy/m7QEzXbuuXOu7hUhRyBNkXTZm5npsXhxW/NqBUCtLO38o1KU+gC/n58vzvJ2OFEmSahlhU5jJnFJJTtV8yWW/x7N8v37jG+Z2s+c2PtzbFhTctAyn8kBMEjVNidb98RoRKBXzW0xPMw41OlWpkgWJqpXzBhStIApOdVz5qJXl2jMoJNQUF4615KTiTlZmStJddZTMRWKR3sVnb6UfMq0bKJNNvGAelyk6APH0o5pn+UD+d7546jGqEZUGJLSmRcQc7wAy6WeNO87zXM/Mensm7AdTpC8Z51fK2DqzvaUi+pK8Z+UDsaZMrcvO9sZ/k5TbpzNqfXOdf6TfUEIUQIm0esuasjSfmFkVr/SetisWY/9kfg6zv20+5DkwKdCNRgngUgiqpECXh1Ce1Dv2pDUo2BNcv1Bsx0emRczlQAn26NbFV+KSd3wuVGq6a5Koym5xKnqQSKpFJmeY2nJV6xIV27RLE1Jrp6im4Kby8t1VfXemVwOz3sVamYpAK8Od5pxRRGiqBd3pM91ABWVQInKqUm6jUZQFsaRqIbk+OdlPveYkcz+qhF+lWjuX9fibSVO5bl5IJKlvRLFVBhw5Zf1Fg3TyYOshFwKAk6Mqx2+zihDgzJVHc1VvovfIihREiOlj1Vf75ucjVTqX91PO5wrNfkpIddiq+9irfhZAi5kNmgRR/AZ4ak+ybKqRQgoIueBKLbynWZSSgzG3Lo6jdAJnnjTjmzKbSsBCJVS7D5tds/d4xIlmmRahfZ434xa/HzT4LO4MLv3KIkxN4ocpUNzxcck3uLDRL2qgRRS3diFxTIDygEAtLmJOz/mxLVI1/N92cy1KFPl/112UV62TEwjAM3QZ//5hmScB9VQovonfKSY3SfDXI23yeMtwqnvqWnCi+GivnoDqfXPzkaXxyVeuilJIooxYpgusNy+PonmyGEP7giWw893hD5pooRaeiVWa26Aea3DijOzwR7iAjL/xGSfFiJ1/uq32j2QZArtrVrB9MSWtpVef37PEmAj89/li6/JtOCuM66wZAxLthzTcwy2RRlYiJdTUTHc7gRFVp5F9pQFiV45vETnJfOS/W57yZO9y6RMp3vthd41YDvHOQJDC+NKeJv0CJaUSIfJgpSTimJjzqzBG2KTFGi7spTviMQuYXbQdqyipREYghHTiAmE0DF4aVuvUaonQCLZQEzqOVSFKe09Ib+ieti8WiSwdSm9EnvFTJFU2i00mzxLYZbNvUJ2dle/v2QJ0O8zvbLVv2caphSTrOWAHV/WmBlJVbaahTKUQQKD9C4Ylcge84g9pYRtS+63mclYw3t3jxMTwUwyxZtcdjy9XyZk+bcT5rkhOUoiaiSGSB+OCLpEpWcrg/dbynvF2+DkAAvNEyfuI1ZG/7FNE6DSIVkoIlc+L+a6voy15IDXKCMt9qMJWpNfFRCKtF0+K5J7S9gmAcdyNNqWyCgQQyCY5hY1/jsbL5ju6vFyNVKcSVFvLmpaAE3DnfvN0ixFY7pQ6/XeNiYRJbFl5KGckGTJNm2IaSR1KTf+Hby5fbRag1GQHNAxUNrJlKocXq90nh7gfc88lLDkJU9CgGRLilIDZTKtMXDzfB1//qh5WWmPSmKlSQ7vdeZVC1jnRxbtPyZIBJJ0GB2u8fvvnBuQx73DxF4D7jTjOpevctH9Hp6nc8Z0ypInrPgC4nN6W9OlC7xXV17A1jdigSyiepoL2TofoUsdOvawXfgOLh9SdklZ6F3RpSzGVfF+WXKxADHkTg1HbINxwRHJjtn0D0i69E0Qp3bhJdne+V+RVKfFL9KnZZPcroTo10zXJ8NzmkS8PEtyE6B2ymBjthAGtPsK2gnWJcawpSYdS5pMNUWSL7qZbuhsS8+Gh/E5Xdqg5vbuKm2ZuF0lKoxGh9Z2LURnxgGjB+8APCps8/VF5J8tx6TlVTUnZaTbBDR6+sQMZRrciRqI0jFIz7kecdY9ZKtLBu29eWPeR8Em8mhvENL7cigvCMIUmZmmxB0wIWliYGDUgceLtzhaHbFYdWzDmiRe4lVVoHQoLmbV7Bs+wvvVLWzDUXwOaaDm2LXOz/I7GgwbNAqdgcYOSb2viyhomIjmDC+oxLL64io0InzR2gh4ZV9fc9EFF8E4CwHTB5jkqr90JMsFb2XajZ7sGiemZRP+1KfuMVjJ+7o9nRm/zL9glk3e6lx8YHABYmnY8qusliuGMDD2IykkRnW/T1Cj6xJdil6s1hJLW3MUX9DFNRIvsrCV56HNxif4OiataapdTjOVQaHvQHrIwiISkVoKVwrS1/Xwoso79KwVJCvxyXg3ZI2RXSFAvI4ivg6lU3nrDa9iS/WF/oAyricXb/HRs3d4bl/hOBkrO+KUjsdhyZGdcTQKISXEcsBG8I1Q7LYWEzYYsqWW8kFARkU2CueZOJb4VqAtaVGLtdbQ70NShM1+TeYuhQhpGxjSkouu54JTNnbK8+5dhnDMKH2ulj35WuZhpX4g+f5FsqyMS+ZDEHYYVxl7+TszLlFhbBjX2fgOrMFSJGpuStDkibSJhW6ejaics0wXHNkZH2wiR9HL2HU2ohZzQ0cmbhzBNsmrw6fyXi76SkrY2khnEbqNF/rN+7462wvPykTGGqamSbHziK3TLJ3MoLgWMIExBLayZC0rnoUHfNa9x4UecxGOGHMzSYo2XiEcNC/dLEzmwqIfFwFIcDPUKCOjDjwPj1jJip6OrV14E05KtYQu+yeMIIkubenTmofxGSd2zgnnLMaIjhdoGhsGUofhwkE0uEiEwVgkkAFkna/pi6Sv1ETwlgC2sB/OmYUNKM09GfO7psA49Jyz5GlYseYtRlmQ6EjSM9K5STQbQ8uPN2S0Ksu0ie37qFoIwHR9mfP6CvkPg6pqFFftyECUiPUw8D6LtGIMichISpGYIiKRkJlVFwc6Ios08CA+460BjkZYjFskmxUn+7IxM+clg81IN3otQKFdAypXmbTr9qWLnmfT/dtFqNpuc3o0GBOsB89rT/Plc5pA3Z/WLHmlinVOd573GnCnGdd3nr3DL3/nbY75KqcoR3bMWyZ84+SIxzylGwY0bXMU1egOyxIdFdQrso8JNq45BN+HsFbvLzRCjdrQIq2Qf8Qbx0GWZi8bpc0Pi8Ymfhy3yjoueNE/5gt5yNP0iI/7H+Kie8CgKzcdZkbpWpRNAjC4jy4zL7WSN0HWXKjVBCpzazubeiSKH1upHeabOaYxK4aBo/URaRtJRG9imbWqKQwYRAKWOtSSm2vTU07Scx6lJZv1BY/jyEkcOLJInzb0FultksfMIJ5TyxCaufQs2dmcziKMEdZbhvz+otSavTW4Bl+fKNM0IwpbsK1NZfTyBBbrYxJhCD3nesyZnPBpeJtv91/lWXjIs/CIQY+w7JjWRopH540TbxamvaONESxlM47kzteJRNJEDJFjOWKRFnRDYCsXYEXQkKxx+aZYhsQiXrAan/P+9hPesmeM9oyjcc1iGJExm9U1E7YS5FJC7F8k+k0WMs4Nkwhbc6LZRr8cUKryazTn02ReNCaDRcFBM8yEzfaYZ+kdPg2P+ELeZy1LBunZ6oJBj0gSiBYaQ6oRGPNaNYT1Ej7anmz0qp2jTADqTfxdNYg3NJEEvfHd9Jh+WHg/N0aSOePyiFYjkOjjmqVtOEpr3hs+xTYDj8eRbjhDU4l6TpN0VaJGy2CGkX4b0TFP93Nz4a+j0WbtQJHd8pLGXOttIOXvFsRK4szrPKFZuKwV8KQsW6rCdX12E8RVBGuftUNC/vXhTjOu/+f/N/IffuU7LOUpD3jGSfqCd7a/yf/+ezp+x4Mtv6Pf8Gh8Rmdbr+Kd5340PGAwSw0rQNbeKXihcPHp2plc8MKlRfiLcdKyVWGIhWhLdRn4CWYSG81pKQ3dAmwibOSIC3nAZ/KIj+wdvsPb/Jp8nafyFms5IbJ0c1RhWkzRbc6SSlO6LAe2m6h5cmFebUyBIa514i1EJO8qFSEGv0MyY/x8RFUQpTKteveqsQnYAk2JYFtO7ClvpS94L31Gtz5jjCMpDXRJCcm78ZYky2KZCIY3vMuBGSWSSiLYBmyEeEaNMi4KADClhuShVPcjsLVEn4epQMglnwp+RbPcc6rjha34Qk75TXuHX7av8kl6h0/1XTacuBChHV2yurazFukvOa5M+iXH8/CaScea1q0Eh+TyYTixSBjD5xvSs0gKkUGWhFBMwZDyxAWgS1tWUTkdtvyu0fgaA1/hgs34nNPhGcu4QS2nQ2QanUYnGL2YV1IbveHp5vMzhidZWOiYjb6szS6Jmt6TWjvWw73dwuURtjCMAB1RFnxXTvi2PeA35UO+3X2dp5ywZsmGI7ayytqXBw4VP6dCTQWYawVzzJyP7yqyWrQyatfjZInOhCi5T/EQsY88wjB1MKQtiJtrA8781UYWw3NOkvLQEr9rhN8/XvBhfMFX4jOWNnpls+hM0bLWGMrmLnOaLRdisL54Vi3jUmSUQhMiYEIXJl9+edfCzFv/auuCSOaVbMSArffkCinHAodpLqXUrKvSx5z2lTHr3qevDneacb0Y38LS2yzDkk0MXIyBbnjCxaAMQyDxBYwBMc3NA6cmhmQppTYzLM3pzIspk2zSlrMmpjuxGsUSshe5eAmUjQa40hcFVcXCgrg44SI84BmP+Kh/ly8W73ERHhDtCHdyCqX+20TKSlSk1HtLi5xN+NChsHXDo78K46L8zhu+sMlkMb+z1PN1g1ZOKJAWqCSCDZykJ4x2QrAFa/kuo52T0jlqHSGpm3AzAyhh3S6RlXNWBknIAqfl3KNZAIPNkWI21801pfIXdV6mcfu/QCIwSs9GVpx1j/hU3+Xj7n0+XnyFcz0iSgfS0VlAsp0/5r5oU2bX9RnUlcc7Idhzn4tzkhJlVhUANUZGkrp2HBnRJl/Isik3YATbcGzPubAj3olnvCWRLSPjtocxoEnoYsp5fC5wh+S4Esp4ivCfCZkIlKDEXVQoa1SgRHVaPi45lFJSlHKuYLKsYRIY+mMuuoc807f4bniXp/1bXOgxZ3rMoEuP3kOzEOn1+bS0EvG3qHhRfMCWo3N8T00s7VJdoNXKRN1SYclrbhehTlNuNuqCX2zEG++vF9EUOeYFj3hOSs94EddshhNSfIaMnQuAybuXe9vv/EybaM1uj7bJ99ysTRluTr2pcTMzyaLZe5VOTBcoTFWkxolOtkJwEZwVQ3MQVGvGd2bp9C7WfniXFpl6KdxpxnUhb5P4ChbewWIH1rPhaW5Rrlj6BE0LQnK7stqYO3X694tpqTCtsnB9DuLJhZknydk4bGLI34NGupbpdEODG7FasBgYrENlQZJjNnrKC3nEp4t3+Gz5PufhEWM6wSNzYIoSamXCSTZ0W/40kEObYsZcRXek+0NfmEuq7bzNigxnH4qY0dnIJj5EWXGchK0+ZuQJxoJAIFhwc1tBhpoO0Eh6AgX7ikXT5qdn837F8KsAWNxpNUS6XCGaw8c7Blmw1SMuuoc80Xf4rHufTxZf4bkuSeItmHvrAc+niZKm0P9X4U5lT0izJg3nm17L6ke7Z8q71WhjyD5QzxmyPUnKTa9KIsiGDU8hrTiTz9lIzL6YFSH19GlLH5OHnkgJTKB2tJY8cBOraUNlkJctjzQ9njIK5H09fSmk+fdEAqN7gkicsA4Ped495lN9myeLDznvTnkuJwzaYwhJhC6XekspoSw8CrQgdB5zlGJ6LxXLQYpp7spNtU8ApBmxAWiaNP68P6zYyiwhklCJPJQzwviEpX3B2p6T7CGkZ4S0oBsTwSLBxmY0mcmStck0bZmSM6lQCwvs0qpW2GihroEdejuqNiZGfWbdd+0cNPu8NoGlWXERRK9Bc64Bd5tx8Q6DfMhgIw9E6cOC7fiUkTVjMjbbE4a4yrK05z9Uc1pWYGoeEo1EblMelcQiLQnkRo7T6rbfajnczmdNsdKUcr6MdqgsUXmAySljeMyFvMVzeYtny/f4bPEeL/QR0R5gLPLT9vOxdqOk5p9Obooq3R44hjlT3j1OWcQrupZaqZRgleqWArhqicDAkE5YqPLQtpyNj9joKUM8ZkwLxthX6S9okVotEw7HwDh715bVpGk+d1jQIbBMkErYbsyzk0RqKHXC0xAGVmz1lHV4wAt9wNP+HZ4sPuDJ8is8CysGApGOYB3F9Bp3yyndGPj+ufb6HPz+PuRsKDrdEvUBPUueX/wWFwxsZUtKD4hxTYoGtsZS3v8KoFhSUmloWiQCxqm2HqBMEX7SPNVRL+OFjI126d+aO58sM98FkRWjHrGWE17oY54GFyg+6z7gvHvAi/CApMFbE5mhwXINP0Ey4/JO21TGNZS2LnnEZZTgbelvDjx1pVa2t+T1DNWw8RmdHdHHnjWfMtpjUnqKDUeQBLERKRV1pL1jFlDbIKEqBBQJoo32bUxFu7hETaNmSr+w+bGk7LZ0GhjreFJzvZDo2aaODYGNBpJ1HvmpAe0CNipjSlhlO68/z3eacSVZYXLEOF4QrSfZwn8QopwQ5ZTEQ6IFhnhBYMgE2NX66ELAjomvJAGX5bOqgkuShlHsEkyrmfy1egKlYlneGmJIKBUZAkmWrOWYNSesWbGxJVtZMFqPscBkSaLHrEiT0yaZZK1dMSmjXyPRJz9dycyh4yIB0xxPhMVmjMvNMZKvy4xL/F2FSCCyNmMtsFFlkCWjHhPDKTGdMOoAST33g8m/ILP/WqFgH9n2jy+BjGBl3WK+v5ZQZjx0d5SOjS7Z6oKt9mylZ6QnSuc/umCUzrOdrOo7SHHq3ArYleuze3wdEAy16JUebEFkSWRFkmNMTknykCRr7wNqveeBkXIlFw+PlzLn2XTtay5VW64eJqlvUXFpEubK3inimCNjan12koi6YGDJIMds5ZhBjxn1mFGPGGXBQE/SvEZ43mMNRlHBQ+Ul+zb9weXeqe61xpRuoamUcVNgk3CWE8H9ZyDqiqgryPNvckK0o5wO7pFEo+aKf1XzcXOc1cT+WhGwvs9cim5V/uvgkl9fNKMakyZCtFB1SyPu3HnBqAuinjDKCVFXJOmJ5NqW5hXlc9hj881XhzvNuJCFS2RpIFqfK2gsGegY5IRBH7GVtyD1RFugeNt5jyJLezbiadEz8lCWPSu71RHpPpH51kgFXXNjO8mMqyRJOuMqAYjJhCgrLuQBF/qQNads5IgtKyI9Jj1Ih5k7nN3A0zKuPMI2JjWP32BuqnyF4zI/9SkClk2UU68iz9Go5QClzGnWbhC2CoMoW+3YyoqtnrDVh2z0BaJGZIuoZRONMPOVVcmwwCGJsZUMXwaTA7oQJU8iLrKmMmjHWk9Z6wkbPWHLilEWeU8FogRS/qnONgQ1fQP0ux4cWp/22CXt692rFJRV6zyYwRZEjolywigXbOURG9nSSUDFE/pNjKHMe8lBzJK4z15y82Fl5x4DOWmCpVLErsVgMtv52ATVSZtLmkjaM7B04U5O2cgxWz0i6YpRXLDwpNeQyxAJpfOYqY91TrpT0auy4Oabt/qsb2sxc6SlR+8KppL31oKYBbtRH7CVh2z1EV3qiAwEiQwiJNEcZZsZlyV6LdGtkmkE1We3jzOXHV+iZTU5mROZnNYHzHsJlruZkGTBqEu2euzvYccM4vQsIZV5zTrVvibcacbldlPFZMmYVmw54aJ7h6cifC5HnISEsWKRLgi6RhipeVBMy7cPjdmPCQG9mQM4wQqkiqalkkVCJEshOVPcGnSeQtmNMRkmR2y6U170b/Fp+AZP5S3O9TEbechgK2LKeSozaaqFiVHNGJowi7BrLR8tr77suD3nhDEwbWgouT2pvaYyQa9wMIQFm+6YtT3miX7IZ6r04SHCI47kjM689UzKRc+05oG1r9cy5evA/PstXkxE1EctuMnTgKjKIMqL7pRP+3f4PLzFs/BV1v1jNuGYDYGtuf/EKVxpqy5TDo3N5dybhuvc94BYc/DYY98WjLZiY8ds+/d5Zj2f2TGfdgNDeocjO2fJQMiVVcYqSGij7k0sQE09ItSKNlFSDVK9rlaAcP2n2V9jvbcWM2TRuCQwsGAjKz7tf5gn+iHPeZtBHxIzURzpMCuMSJv7+noVPI+40FWaxu7ufxF9aYDVq0IVx/LWKZrXmIxN6thwzFojz7sP+Wz4Bieh57g/ZpHOUfOmnDEzrZK/54zLWDQd2Kd+dbcJUpm75U3vjMvXPREYtWfQFV/wNb6wr/FMHrEOia0uGUvoaG01/vpm9jvNuIojyggMcsSaR3whH/LfziLP4pLfkpEH1tOlgWDelbXUbispGJXoCBN7me3o1mwYK0JoRpQqu0jWuCxljSsnhyIzTcyKbiaCWccQVmz6B3y7f4cvjt/li+O3udC32XDCQH9gM86ZV1XnBcrW3rvsJqTIxqxStM56fi+RVEmyZBMe8HRM/Nr5V3l2tuR/bh7xaHiLFaOTLRViLqtVSmAdfu41Yef7JfqOarZyLUFy0nS5dcRr4J13K570D3jen/LxyQd8vnqHs+6UIXQ5z6h8ow1RhJIeXMJ8ryvzliG3x1XmfYM1u1I4EfM9zYJIZG2nPJUf4n+eL/j8rOPTdeJ0fJ9F3Hhlh6xtp2oWnIt7xahU/J40jMvfY0q0k5z047mHff6+4WX6nQBqcpLk/q1IDMpIYCsLvtu/zbcfvcenx49ZL08Y5IgoC5KFmvdXQ8BnY3TkThnP92IGi9BlRWNhFlD10rV6yXF5H5HoWr4JZkLUBZvulOdR+V8XZ/D0Q3593fPB8IDOtgjRfVy1LllmXLilIjQLnA7UzrwRsCn/yv/USUgtndttYlwmQpSOM32XT9L7fHJxytOgrOUhUTu3NlvMNPYHtDp8rZQsSpQFW444l0d8dLHlYjC+4BHHZmga3eEqyc09efKLTV4MVKbcjzQrmjuVLZHMuPw70pwvZrTJUFFybApiF+nEK7ALooEUYQw92+6Ij/sTtvqAzdFjBk7dhEBXn3Dw9aX5vaNZ3S7I/pBk/keSnoEV5xb5aP2QsxeRo/OOk2HJQsyDZXL0197Xb3KcSBU+yESyhhTnqzxoQ1h3Pc/CERfLY866R7ywEza6ImnHbISHOEvDd19Fq9397FV1zFeFKQMtkOgZZcWFPGK7Puez5xecXbzFcTymj5FQk2DzvFVTsdR7lbEqxU8kM9+pVaGm4JcTYEkl4MhAnIAJiqYSAFUYF0QCWwl81p/wbHnK+fKYQZZE6Ss+5y/t/J7+nMosXm+n2YEtPs3h9Pvlx82RJULWnjAn8IPCRTI+G4+xs4d8dhb5ZOg875TcXxCq66EITXPxgWr9uWkQyC1KfMyFdrbNITXXL3S/pmGibPQRT+2Up8MJ5ywYZZWDvCLeXUCwN9jpd5txYbnumksvW4MXFvkfL56xtMQyPWJhSy8ZlKl6Qt0eLJolCV/wQMySOHg4cdEusgkLqCUXWrGr/eOAv6kda5LsoBUlhJ5hGBiBrQaeLVcsu1OWDx4xyGk1S01mQDiESqmqijYjnldJjO3drnPsX959n50LrD3hkXobgxdR+F/PHxO+AH2xpN+e0KMeLqsBqy3kXRcpJreZ21im6M/ZMZN0e/gdyvpNxJAsPmhbtQPvNLBV5VyV8eiIsHjM+N4Dt9Hnck+uUWT/p5X/mPhjMy0vO34l5rZ3UvZPXpduZfOoKUTtGPSI5wjPz59y/tmS3zh/xCoZXXKCZGMuGRWKV2jaB8Yk6XuY9LQHD2uNidK3S2xZzxlDvqsCfZ7XCBq9gLAIowhP+57u6AQ9OiKxcG2rZi5TcrIvncfLeVZrLZhOvcpazY+n9Zn+9wS1KfBDSapsrcMkcLE+4eMvHtA9NU6H4ym9wKymizhuhAkXNc+03A7j8rQX966XZ+YEierThmxdMrc3aA4vHVix0WMGjtnKAwYRoozAOpfKezPx7G4zrkbA8hwbZRR4bj3ndkonj+gLszGvE117dzE51dUsSziuMZWqE070Jn/GJH3mjQg7FH5ajEmPkOZzMIuoGCEoo3pq4qDC2UJI/XtI94gUMnGxgpOHGdc8DHr+2asj28uPr4Y2ydmZvRGI0vOMD0AeIGFL13sIvOOkNPaO1Nxpnxm9MvMtEnbWuKZIT7+LkHLNwRJxKAxB2OAJ4b0+JsiJayYx5SaNhkh0B7Vk5LU3kRtfBXaZ1ctm6IrjMvZOGaTnRVRe9A8577ZsOmURA50GOhaYF5T07uLYJFnXpHi/t4aYda2YfVvlqSVBvhmnqfuAjYxLOcEdN8HHrOGBF6ctjOs8JHp9QM8JfTgiqu6lAihtC9Mrpm8Pdj98hfnc25Xt/cqcCyqt0OPV5hTc1KmPoPsKoXubZFKZsWHei66aVabgCG/uWgTy29C4ChUsJr3CuLR50+KbDhOTI3kktxxhskD0iCGsibIBNkwi6euP+e4zriJhmUfeeCQYqPQMtszRUE7F3ME5Ma6iPAmlY2epPNEwLmsYVzY3Zhn9ynmv0prMLxOLtSJzEm/+lwLELjHqgoEuI35+1kyLm5ChSnKXMa+b2sd7yF64084HjdlV6uce7bSVldd+1CWhC7k5puTSPiV6yd91MkO9GemYkxFpfJpG8beUhNNy7SjKoID2iKy8/iJzr44A0tR1lOnVbw8KA345u37J8bQZXVvyKMutqIcx98fQe7Shpo6OrBXlpGUfxpxxWdaU1T2FkEOkycLApKk3Myjz1hhS1p4iHsbsGxtJaiTxHlyjjiAr0J6QG43uiw07Jihpdkh7ejq7d7Q/568sNu0dF8dCKtnBIjVJN4l3JjA9ImrnjTl1ChfX5P5VMU9HKAzFZLx9xiWW15X8nEDbzqUUesZyM15cgPE1WwAdSk9koBaH1t2cvVeHu824oLbkKIU4R+mR2vfaJ10ysjiiZaZVGZdfF3KvpbLlTMhtC0ofmYL0hZm0VeocJgm/UO8cBkzRv1KtVZZs9PEU3tgNROnYWu46i2W1ezdz3n+XYPvUnubNNsPhCW4PSmbafpgsJVS3fCU7kg1hlCVRFQuJEAKSFEt+p6A7yaw1nPemwO87hf4XZlWeN6VFlJZDhIBK7344DJFECGVcBozemBFj6sR8g0P+EiDl/8RgsEAKPSyOiWNPTN4uRFkhKKXog0NpWTHlOxapvGhcMBG1aT2bvdtIc2JT5XsX1CJG7hiszsBq/zHZEHWBaEcMgucR5ZJlmVtWM119XKYFJVleqNcW2K8wcrNQUz4Ar5yB1/VTzRUujKSQQk8MOeVCOkrYuNLnuSxm7yLMRqYKKfMgrtcV/ObHNgXXZNjtBefvF5DU5Qo30fUxMe8piBLMiKbTd0PnHQau6mP4ErjTjMv5Vdn0+aQUTclJe93YFFNhM+mNVDgKlCxyKR82Ny1boziCtQ2OtxKcIVlizOclE2R1aVRkxKJraqrCUDLqcbNICAv6bsVgXschevGqpqBzM6bK3PLmKv1zbpzwF5iYrzbTXfRUmBJ6QVyMNJceTdegA2jy7q0ScARcIrmcVapIcnPMt1ZCyKYuk4jpQExbr2EXlBQjYymW3CmmEVHJdGKJagLdejWVsjPKNANfYkTMjYICQYRehU57VDZgFzjjccaVgE6XiIVcBTwb1I1aYqjsuUTvybUWGhybJ5m2q1r0tvJXYut7K4wga4yRSERD5742FJMR0y1jMLYoMSdHY4nQhSnJtyQfZ+HTOfT0q47h4Da7edyRrC0JSlDB2DpdskSngnYGcoGx9jEtjrJg664Kk6VXLElKCJJb04AlQ0Pn75msmc2X6t1wyfFhi0V7YYmdbS1BJdgNTEL2hFrj6gBp/MvOtMr+eD2404xrIuSHTGZT3kOZ4D3i3zCw+p0Dq+UCmjDJHiXduJi5/HPHjZIAnMVUGTHOMNlgbJCuVPfNS6/O2OgiSQe3A2dNSzSixOyfmedpFN+XkXJV90LwdyXcNwVrfk8mj1b3o0jXhXGaokFR6Z0ZdE+wbuPvaIqMC2ABcprFitwllZb5vmQ4V0GjXfnSJqKsMd1g3QXYOUld6rWuIFHBpYgthNQfYWGJqSG6pc1fapsVIo00fWNwy8zQvMp4KDlOoYfwBXTPoetgsYDYA6ekcYukHpElhRmZaLMBnOR5Ol6rYTGtY2P+bb9TcMfNtxELW1J3RuIZJgNIwoJAysywH31sXY+Fc4wB16YTom6qSimhtTJF0excIyuFX9vE+7J988DgxteSipdmIQs9ri2VWjOiBt0F9OfYwkijV7r3NjRLUuqBJSWxu2qVGqaG7A3OtLvn0PHLPt89nkNLUws1zO+X59K16Gnu6zeLKavCDyzjos6L0MzJHgFvzFqtmaBMrHOa3Vu6JNOcnS9svrcVU9QUUFGTwtRABzRcIHqGhnNMM0KSCCVdXxOsInq8RZYXhGVCuoip1/j29RZ2u+0WVjpVtGgl3ZuESWsFdjQucnBDdsJbNg8Q6OkInRJOn6DjGnRErINxiaUjbFTSVrC0AFbUflHNG74S7ImN2YyiCdMLdHFBd3IB+sy1u2IjLDlaY/S1WAr6YEl/vISVIWEkIll6bxnVLRE6YO5DvEFGZj76zgKaQEdYdT3x9Alp/RwWAcYVklaoBbbPB9J2gQ3imtfs3W2H2l1XaKrGQURc+NIwIv2WbnUB3QsIAwQ3GZK8wJitDB4tkAcdy+MF0bakbLLvddK4fE9K40ZxRO5sProqiMo0rlsBU7c0JPUWSSVE0BIdgkSDkwvSozMsJGRcgqywtIQxsnlxQvEpVBG8RB2b7b7EzUKrkrUni6+uzGgRBjL9K+PUKojOhZg3xZ27zbhaDjNTcq85Ia9EG2znoqIdlMfOn2k2YnEL+pQf+r097371bR69/4CNPmOULYkRCzIV4NSB5amxPFnDMuUuv8V+nf0JO+HI07gmOeyWtq+zLSt9xzLpFiPK2NjBJ+aVktIRCFG5+OEN6cJg6OjDMTI+YLw44sXHx/z3//enbJ4vYXwH7PS6g3Fol739TNo/RqKc8/Cr8I0ffZs/8n99m7j4lKhrIhu8D0fpawYR79sQjgV9IKQuMchA9YkahKSNpHvb1Qp2992bQq7TaJ23lUnGUnqG847t+jE2KL08oueElT3iF/8fv8l3/vuGz//XFxA1h7G7djPRS9shSNcdimEScd/VJ7z1rvBjf+IDfucf+B2E1YaR5wz2AkVQEQZNpFWCJXSnHWPa4BGPlv3aZDNmLgqLMbWDyOY6KxrepC3MEyNuXtu10rxWO6J5WLxgkBIdnbee2TwgPd+iA/QcAQ+Im2PWz4/5v//ffpX1061bcccV3kG11Aq9lSFPcFlxC6n/7Z2fVe2BZqOU2AKdLDSvCXeacc0l30PSX3PlNSTYGR888LRLd4hIzk3IXywSvBjIlg+/9oDf/fuPeffrxiacMurIKFsv8Z99VEkGwkIIvbjJJGtlopNxroYgNzLjZIQpprz5K+wpIa913CRbZyLvNzaSDj5WiVOSpHkVcW9hothXHsIWJ3ws6eIJ2xcLPvmfHd/+P7/L5kzcXJp2Y/gOwCsIG5OWnViuOh691fG1ryvbxSlJFz72tKHTDhX1iuhFS+sScZk7C+cAn9q5K02VM9xEc9Us7q7EqxwXPd/npej88kbPcUKudF5KIiaW2pEerkjRSFHRdIqmJf0oPHjc8cXxQG1gV4c2FTKbJvzwur3cZenz3h91vPvhER/8UEe3ggHDgifhqwijRVKXsM6QpZKsp+CA98bK85XzJbHMuPKedOZVrC1Tvcn5HFVd8Jrz2R4f3gc1KEyVmCavMMlDYJSARoHHAxqFhayI8Rgbe8YzIXQjosmbQzb5lF+me3VvCa3s/ebDPRJZdkgRbKb994OtcTF/9R2dh/nGmn7bJd9wDcrmf8/ud8kIBKa2olLVeIeBh4963v/whA+/DpvuiJgZl2bG5XljLumLJqIM1QQibZ0yKxGR5VEpDzc/uwmdP5iwm4+LANQWFb3qOM1Mj5LryQmQiGHAJJIkZhOib0xLHQElmNI9Dnjcv2Kpp7cVm6cBzowQSiRaNnteOtcNklzGJy6DBEE7Fn3H8TFoWGBBMe1ISehCRxAlDoao5eTKyCBN9CBOHBUhoHV502y8Vw3uUHFTLjme1rF2JjCZHb86syz39r2p9FloiiwkID04LwtYXCGxp1PoeyV4WfXKqqSRqqdEd9lZijmWzenZjnhVkphFWCx7VisIq4CyRLpaL4JkkaiTabzGADemKMNjWae415ZIKpOw27QrupJxXbZW1xcJi+hhph5sBdUv7TtKPU2EnpCEThaMaQmjEkuwCeSk/cyQS02t16f914O9xWvB5kxr9qXdSzLDqhVA3kxRvNOM6xL57pJPjVxmc/o5hN9XwgGzUN3nUnAjL3ZeqGHEojfbI3lWg1gP5pUJyIwLem8DjtHJEaXm2iRVTRU8rHl4DRnZKW39Zqi2f2z1r4lxJQAdSBpRiV6NIr+X6iJL9JZNPT1iHYbSJ4jRAw0ZhKY8OB6+9hLz2+XSyv6F1gHHbJ8KLz4KfPxr8GIcsRCRzhDtsTFBighKLeQRvIyYt8eAWKY5M66AL3k0D8n2cbwJQzl0Ps99FjhK6oNeeyEPSSO+SRN9nSUdB4IKqsJo4oxejNMgPP0Yzp8FSMfAAqHz8ZY9fmABrk9PFczTDuKwZPNM+O6vj5ycusY1ihBTzMKCT0lS9ZQFSbndT75NCtU8GHLSdB1MQcpZ8u5uzZU629eYw6vmvDnemRNPsXE/l2rmP6aQhGButvWYrhEJiXEjvHgijMMJNvbEbcSTjmWiM6mRRG8LpHmldl5qHUqYJxVL+9XmaApwamf8deBOMy6AmXFM8hmrZ/I1jQS7u76Hca98sZnZZqr3Rcjp+kjmMQraAyf8t//6BZ+ffcHx/+cFMQwkiZiV5EGrY/A+QlDyNwxqqaqJ4LSPLdUMirw7STI1kbck9eb71861+Zo2sOOq41KXDPOWGJJzMGJxoFNqq5XX6fLeLhKj27VjDPTpBFuv2H5+wvqsg7SkJla2VUn2pGG5fKfXNbSdk4qw4MUX5/zX//ycj757TpSnIIObJ4n+bCncKc+kyhRoozkcHGesMq3OtLcKIW+dP9dVaS87Lm/U3rZ91V24NkMTTBd5f3uemsQIlrBuCSkgKRBSzxefLdm8WIKdZsLfavxMvr52VHvKQINIMl9DQbCoiJ7y4sWa//z/+g1+7ZefId3g4e82eM4TQmkMVfllE29tJSfI3GpRn1nj9oVa+gtzBtBqwS1lfl3p7pBc0tAPK2NWy+Hh5uMu5veii2QfZEpL4njM9uwEbAm6QKTzvWhNAQSZPeiW4BCrKaHxZCZWFme2fevXPWrXXGrFe/f9gDKuhmg0O8nmf7bzefgWu5sQu4RplZvt3qC5h0EtQy0BOOLzj9ecXawJq2ckGTEiXli0LSFVoqBKgqbuMC5/iDXHU/D9vN61M6WS3Z4oLdOdiWlmXO3xYWZVkMg01e9g4uHRuWRPCnHGuPz7INksM5kyPcAkpkAXnVDKRce4XQLLRhq+Al5rlzvBGrbC9vPE0+cbj1iTLTCCJQgt48rahKtTfj5YNgWXn6mMWGVQea5qqLfxhoxr5xUOEsMDc3NdQtu2/pYEcfT3XagTxaQwRGAJsUNsUYmrNQzrqtSFlzHaavI0AVkwbkeefPyCJ59tXLCQEWwEzbUtY5xuOItUg8NMiR3GVTRjr8oxTeoOgbglxuXPSR65WjQWK8UNikUl7x0r4+tgeAuvpl8EB5sWoNK+W4ZGcvJtWvZ4s4YcZloT5thER4BLinNdC+4442pgVzhvJNTpg52sb2P+u0JsrtfZ5zNffLOSBY9SSpByUUpV0Ec8/9h4/kmEcDoNTgHbMjHecvMsWe6ZYWaclEnaOSDazH5fhVGNxGSapaadY9Q1k5pAqBA7r1OVArmQWr7f2DxamkcVRi64I6WHuIQYEDtFbIHNuqLOfWrtVF8KlxLSnIhuKyQFbLsiLN/HbCClrb/TtlybfXdlHYbRf3fddG8xyPlDbutpUGh33maa41XrsHu8Q0QPXfYmIDCtlTffKXkNZisgm922gPSIlTyuYqpuhMUWF2TnIQ1vvGzgXrg4AIsceKAgDzNRjxBHgiqqyrjd1DFL8fsiroGEth38FpnCPvHuABmX6z5uGFcJ3GBKMn8jaNZqqtIheTtYTs/INKAQquQCljMyMnPzeWE8cZwp3YfrPd0vCLdZq3B+2HbUcLkjzK7bY1qZnKUqG3gaigfPDK89sjvOuA5h8C7Bb89zyWdvOAZLGeks598q1DDpFdjbwEMYx2kDBMFiJoDVtpkRclY5tPV/HED8/FF16Rd6calEn6+sx1kKtZbQZgQvzFPHGePS2CEpeE+yWDTFRhOhNU+CR0d6pKUlw8OjAowBkwW1I+qN55+VdyVHfQaQFXFdxlrMLZm51UAz7/SaYvSAjBSIY35/MdcGKlVuizDfJhy4f2XWr6HZ5SregqEpVaI9bnTSQC1BcgZRfFuXlhR+jdcv9Nfdw70nGg/imq8WLcSI20SKJcK2KbVlObfOgK5UiTcYB7co1DGV9Qn5vXNAUMtUanj2bayjj9XbHDm98Or3ZT+Rn7/IjEAgxCwYgScf+7iCuLZTu13kfna19NhtwjUEppl8ddmQBGql4deEO8249iamnGgkEoddifcS0XVvo7P3eUW2hg5MET4lv6n9QpaaLID00w0iYAsfQzY5TYjWSjptvkNebGkvkUbKsZ13oN778mNr5sum77a+tVSQ3bU0q92ds5mjlt0qTDgzxTpB0YmfkN0KoXmvnHQ8n7ibBSuECSduKWWi2OUp00k0rJUgfGyGYKmIBVnLQqd3tv72xl2h7MdmTS5bK2v9NuWYZr3z9XXT2Pw7eX08LyozifKqMDGHqlK8ZNizP/YJ1bQLi9DSTxYLyWklKWLmZrXqU0xNoWmM2g7FbFrr/Nn0VKXW+pvNk/+2K8b5ujBZ9NRpQE3c9coZZmVPBYSFv6MJ1edb36dZL6CY+Kf/2r1xU7A/F9Uavnfd/Jp8BPisx/qdbIp+QyHhbjOuatZhf6+9ZHL91GUTV/o3Hfio0PqW0F5SbsVPFCTSaRfX6/p6JHS+iUlZoi8f7Ur0xRmaP89SZ5a/GjPOdeAKYrhLGMvYNYfnm7eynyLwS1RKgb7Jq3HktDIfJSy2JI1eZ8CvtcddoKhmlD1BJ/9O3k9I8DB4KA1Kc/RkzdFzn6CgmGWJWLoDN74NuOZa0Qo6u0JPI7jVKB8npFOEfQlX/v+z92+x1m3bQR74tT7GnHOt/7Yv57bPCbbKxk6VgwKkoOQi5AFiK/bhJWC/HImSCEhYimSkiAckokSRCRJS8EMwDyDlIXEkkOrJKfHiCMVFUapyLHMqDqrgpGLjAD6cfW778u//suaco/dWD6213vsYc67rv9Z/9jp7973Xv/qac4x+bb3dersE8c71KVuX6COa09YMMmfoZkyUdu/N16sZHAl2JorDdnd2UvNig6DDzlBJhiEZdsyTt+Hj7e+Ra7fJmSc/V9VgpCcAt0S8xOehvRoyuXvZALonAguE4b/xRN2dXX2rtHEGSqgO1ndVZF49tr96/PFQ5gx4eEL1sx4W15/okE/3ovQbtNjlGf7xy9d6b0SzgZ611SQvVUGKE4AajO08d/djRa5W7whXP6UqbCkzJqJaeQGNy122+zpLx4XPTKEtxUbV1qpbfx1Itv6ZhuXncLdS4l2Xun9CUZlNU3UBb1fct7aGr4pLlwhtzp0LvoV0lrfFrXRHCMKnbsHXzLY7BuRQXdN+rurZe2UP4PAFbIQn+KLKMJRi6uzKTHHOki+pROoI78ettBzxEeXKSpy/T6jEdWG58Zock8wu+vuKHXWHGhqzV7+zE0i7R4lsy4nqfyazhwk1h8YdUYWOu4DiIFwyh7kwuy9xV2afeUIQnzSwJIR3NMrD0ve7PCyNW62OqbGGSaHkxdgr23iH6/w6itj84p5kJrSNB3s7q8/AXecwfFmfl350wVmqgpDtjznGT6hkGAo1gKaA3c35/I4SLqHeJQUMzNJ33DbhcqlOUnfOLdRb7Xef0OIRWmRoI1gyEf3ZnxH018RA3aSbo8s0Z0auW753CdfHtBz6mPlZBNCIoOEms26B016ubzAD3ubY0rV6TEV0lTrnfO+/h076ioj0dRLqBlvJCFmvqjqQXl5TWTIZs7seHJ/Z4GwZ1dZ+EOoFfo0yTiNcoYJKx9bu4160Ea6ikIYWvCSs8+peXYAYpT0YSYTsk7tYj+D4FMtppeBnRdaYlqI65eKEK86Pdkxd7H0Hn+ov+TN3AqbVPYZKWGsGCcWuCbLdBSfprQdj+kf2Qbu/9fDr7+XyKeH6LpQlOQhLcU2CjAOnn9ugY+TqavcJJsxE4N0OUh2oU38IZ730nNpl9Rhhd3mNJ2IUpSTLhxq0a1DzO0vhIpNBd8r225MboFzBP+t1lwVj3Wx6CjoW2IA8SO0O2XM7VcQXxgPIPGDAvSpOgHVgYKC8zOi2WIT8Ko1coy245js3K2acahJjWsH4YOTxZ0+YNuZ6pglkameqmWEvRniEOsVz5Q6mETyCDDhhhTG5AiMrZx9k9t8p6Euadeuyje63HvviE1Q+JVyvuYSbhqB+QMxwoQjooMgGNm8CJ4KO1aW33rtYBmddMFqu7qoAfMQP6kCtcF79WBupctdFpGoLE6bpSCoMYXi4V/RM2H3oEbrDAXRm8fkay0F/S9UhWMQSRYc98jAxPhJOPuNGKM5QVFwiytDd393pvfgdFVEsoroKKUPaCS8+VLZPnZj1yb0vnODrV5eWcHSXydw01gPpsZBOjd5qsuuuJECq7mnATEA8WpSmQLiLktS0hSHcJWAUYILdNiHDZLBY3HikM2IxY075JNKoo+VTwvW6SsXb0qkjXF0j4ZtiVwzDQ5AHgo6OQyret0bq4TpyyGYc5U21hbO6NNylZtIehkyNcLl2LQM7QQf1eweZ47YOCR6Sjzss53TQ8hz6IFNGNkJ6mNi8ldDBuPgSgQIFkMR15ZGPVXGeZxDfognSS9htM9sX0O7u5ozGd51Ad4BdLVWHgo4JOQE9BR19vyZqpvuZ8KIdO1aFxAYcKnflyNvYQwVzji7tG5lAVtL8ukLZqkcaWHz8SaVknxKu112kr2j3t/l2KDCtNnYIR/Pvn6k7KrItLil0KUdwdeFVy1WJGHP6E+NJ4oSrwEpN9YFHsTEzZSPS7R7s6kN7vcURoShF9kwpM61GppUFtZ/c0jru2OPKpL7K/SJk9RoP89tdD9jdaj4D3VQ1qIhYMIeP1d45xzQIDIoOyrSC7WgBXXIXfAKhxQFgRqNa3QmFgejd7mQNlFHa32s19adFdBJkgGFMlPwxWvKPYfmUcL3uUs3bXW9QL20dK0qxoBJiPzk1nbtUblgdyRaqL5t75tfbratAvVy93oLKuoQioQVUu5vDk/TV+6C4/A5Kdljm1+B3iPqD1V320l8yAqbDMcSogzENGduHav8iCrkL7ewBYO8P4bKAzLaH7ku3ohk3RC457QhWz8gAc5+oaPV1FWmUqWOIKm0VNWJLp9WI49bzir1qQoq/v7znFa5/XxzvzutCnBdpBDVT74sziqqZxJdSaMk/ap7z89fjE1g+JVyvq+jiN9KdIAf8uJV1opCx33FNNAPRUDd2kpjCQqF/u0ViAmGh5aEwanIVSQvxo8co8zI7iHd59qp+pg2ndbkQK+MjE13jDv2omiwyUx9V33zMi6CQHJmixlckfE/TfFku4i36kPXdA3eiVqyMhzFquiBa9og6XxhU1+u+T0vr9UrYKnGbQ8fxXdUL6ud/1x/7fvzh2957Xqo22W9+TpbjWRLIT075lHC97hK4TnFmTpvDazU9asegBremV2X0Fn8L9cYsFMw1B3Yux9j3EAP3uqdJt7jAjXAddH/szL2WspAOLr1n0/n3svhchYgpaQiIQ4nkY15CMhGgqFqOgYiOssDZ7R7QPzy+aPXPu1uGgEOXBsscyAIqy4xD7CbRhRWLfNJVDLsgwv211BIXfh/w42uYhFJcBe0LpyKeq2tovsoHZQFwwb3eI/i7jfIp4XrdZQmNigGzRwKwQLQO22EV5Y82HCqIDnbP5Yj0QAcmHQaqh/ZYXetjh/WGHOwjwWKqWbDTGqhXzGkSz9Ri10XiuYb6sEPfpbLsfinszabqgXfU7vAGp9ElqJi4i10wF/cSYSSXk5fgKIZJXZ09n9rVOKHKNN3RulTFXQ3MfJ6adsHQgZ2bqNZnOuevIHC3Huw5+uo6b/TLYMnhTFWctkl79MhUaqaG7/bZ+i6VTwnXx6a4+g1tPrIz5NIH07VPxYmWzkQcXfymw87H6lxQ7zjsIl3LfrhrFIPmfnogz1TG99gBu0gPdRvlyDzjGz32cRvzwTVOaJx6s35Z/P44lzrGYFYaYmwPUNVlh4FU+6KvX8rs+bDZB+eMo5eiZlq7TjqavbfUXtzy5I6s53yNo2+d7cm5KvW7tNu/B+VTwvW6Sm92e/BRVQwScQbDV0urkYNZEB4C+6zFczrXy+szNdGivlAUzi+iY7yOCIOD9Icv0Cx1n94hBrygaeXIlfdseVpqDDn2LDLLuvWxLgfbHguTjj/jpafNwIyp+u6UYEb0iFDVAR6Lz5GDx/VgHY6KNrcyXGCWu/OAzzznNZXlGacjvule8Ex3UT4lXK+7dAFwtf5bsGSNlrDRAqgXStxXJQvhoGVoDLO/e8Axe72Pibis94x3T/L6pKqzBKvdeOtZr1+kGQmLO/764MdRl3ZA70tTfSZQUYqYU3hOdgdZPH+VSbdNRbMUVPWCet/tVesXtXfTfuLpCMkoVR91Fyqyuyi6+GlKv2Vs2qsj9buDU6GdwZp+iwpunWvFUuo70pIugfeTWT4lXN+NEhSjcq5XB8K5bLXkt/SQUPSahp4YHSFMx+pHkUClfPMO+gN6qHo5r9wxYbtAoNPzJo/MUKJK30inYjqixblsPa9artPedfqRg3/Pecf9745CpsIiFTgXGzjcRukx//liSj+ntmv980f1irczxHNK9L4MO3rYa7CycuRLWRxAac9+AsunhOu7Uao6wzko6Y/bEa53cTtrb3akq+YWOS4FvPJQaURJfDjVclhbyoIDfDLLP3aMgtz1oXP0dQzPLcXN2TuxL92j/R51+2Gzuh73e1vE7Sb99LBzPs8U0SmWMlysqHQwvOzprkvA+sVrHiRg/tThfI69davlqIDUzukMknqv9gvXVO5kqPepfEq4XntZOiqC66L8TBYnAmKZNQSzzjsHjiMP1kLuutWy5MyrtjPRLCBxS7yOiNk4PkbWT5cujEC1hDTrQSXSfix91Ppm7xcWkQUhMoZEaSahkaLmEtXhZfj1TkrVbTYe4/wnFwxTY+7O37I72st6GXp8wL2M/11a2HtVPiVcr63Med5DDsw5eY1LZKnSjIaYc267Hg2hkxJurZynK1pqio4k5psfRr57Z/E8XHROTi1hnq1ELltX6St6jfp5jV2njWv2c641mtJyUnVquZ65ise6PkJr/N2k3cKyf6Ea3hx+2ak6l0XvDEbVmdDZkh7VBGD7cBDjjTsb230snxKu110qsPbQK7OfkLguUkEdHsrls7cF5Z26T0JtZgc8nEHju0B5ofrQIKgHbQkHhPiuDuVFxiH1q+Dez1vLK2Dlcx+5RDE4Sxd8E0J33X7k6FMzYtU/dlQ6kPlXdwV6Vywz14VIbFrnoN05EZrFbK8ujblzpwS4nVn7Q7vhzM+I/cji9BxTN35Sy6eE62NaGh49wmrNmObeRbl/+/aK4mGBDiSUILTutBsaTwni5jHxWuTdWx3XrZUYIzCTOOr3tA3pp6D9l3A4v0uICXC+hcix+m31c0npdb3XReavY4sbbp8HOz4Yp8DMYcFVhUe2l7vSWCyKLuoV2uqB7yZWk11+WpblU8J1UTlXxXTdBnT201x1m2xi5vAT7QKpIP2z9fEyb9qf6yU2nQ1SF/VrJpL0eITNJLEnlMn9kBNKoqREiigeKTI5Y0EX+4uxIHj9/K8s5Vxl8ft5xL9p9n3jZD31rxTMHcGRm6i7JTgSkTynUbX0SU5el77sBv1UAtTDpAVHrpmfJeOb5e+0+754X2s7LQVHJQjHF+icwSyf1eM/UubPxB3cMIFssQjB6sFjQsIvPtw4V9oGrxFEuOVRCJV20LQ4P9FGD6fzPM8X1aOV5i5SQbAK+AkRca3lHmTvF8UWt1RjTbWdmVr/mPKAr6t8SrgOykKiOCbMLHUkS81O/UqdHgRyNM4+DoDUXFdxwPa0Q2iR1wKPamUp5wfZDkAQnv7OIRBOSDvBxR2r49xdN4mZw1ffZ4SehTiJEonEBDtsSZ0I+/epi+nXES6NgzvnPW1msz7rh8glJ7atkz9fL+PnUb7bHX1uyDAIVyTOlGJZqOt3+GYspeAg8B/zMhtitx4iTTKWTM1aUHNryOx9g98F4Yo10NamP2z/aPcZghF87ba8h2/3acRz5vR5VTz9DLIH2YEoItlgSPzMhKQvAYf94Uw2RkmV3vaZD/DzKAfnp9UFh09N1Ej7mmZwN69H9z31t3MjDK6t2KOyd1iDGtKq+tcN/tNRvwqG9wD2brl8SrheoUhX0Smbriw5YKkfuEGg7EH3lmnRg5IlvxSyTMjFki+mHaDI8KYdOAqW+F4pZIoTNtCWMd6Rf/CbdVxONJRiqStwhCOVF0Wk4wz7enCM0vhOe19AMyKlHvZSEkJikBUwupqwWHZCEZAVQ0rkyVI2SJI6nlIEhrGtV0d0KkKYrfflHP3svSXfoQlZpKUXshEvJmAHDKgIWRRNGU2ZkjwlcARkDcIV0uh9LFqcQBWQFTb/PeDwJOrrMjLj8F3qN1hZqqmlrVFPuIramklCZEBIlJKYqy+tPZE9yhlVAhnUCWjxlCsuGaa9S/UnMDxAUkGkMVXiGU51psUQ0MHhz5kqtciNgDn8ByEL15KOcM3q9Rk/ddJkrjhjrV6fanNVzHKYFYnEIFuyvPBEaWd1fJYeYg2sQTZQhkbUctfeJ4x4fUq4bquMQ6tH0FwB9DnIh4bIx5eEaipToHTIegRWBU6EfdqRB7VDSyKJcaCFvXOiFv4pleDYnHB1uOIiMF4KkZeXILJBCF2N6bbwFvhoBWm0XEiDwvpbMCkUYdKNZZkcEgwjJSdE1gynb5CzSzkpm/qnjq5w5Kz7zM6ZXUivy3ptQ4zk9/dt4iralGF1Bg83lNMNL8ZMHieby4gjemdMJq1rMvMqvSelAJTMviSmkuz3yYfw8DnIiOrKsphmwXLFeakEIIjWsX0QZ9pCtSWYNL6Cs4TqGi1rTMVnZRgh5z2wReUZ48MdabVDVlv25WmF+TQksmZjjEZFNoX0ZMPq8RZZJ9aDMg3ZIp/Ef4MzHcCQjWhWOCDcODzjlW9puWUaENnL1ZUXcWqHLAye+E22W/Jbz5jWE+yMOCVWjJzC7hF5uyNvt6CPgQ3IaEJjdp+NYbh4EN9j5dqE6x/+w3/IX//rf52vfvWrfP3rX+eXfumX+JN/8k/W71WV/+Q/+U/4L/6L/4IPPviAP/pH/yh/62/9LX74h3+4PvPee+/xF/7CX+Dv/b2/R0qJn/7pn+Zv/I2/waNHj25lUjcvOv9dGSevnAfQqi6xdE0IjtTOePhGYfMAVg9BJaNMdhgjp4ETLh0LnAqn77ygrBQdTKIqqaCSyWlCJbfmy2ASlyNidRVJf6XbIh06nr2kHm33iVO044yVQhIliRGu4gmFkkygI7oX9KWyO5uQM0X2iaR7Jxam6ii7xG478eyjD0gPTxhOB1aPV+Qx7p6Mm72QJBwxv6dDEG32/T52v3vynkxC0DEhTwR5BOPnCimSK47Ue3IRDKFrqJbuH+GyDZbG9b9QhnGARyNyNiAuDUmWHggarT9orF9eccnWGBrzRxwRXfPy6xP6ssDORQW/MNMSKr0tjM/4vh98xIM316wfr9mpqy+TSVPV+CcVWGVWD0Yefxb2Y6EkJSetPEVR0LGNdsiuaNZeO6E1a7gCehnc3aAYQXTY1ka4UjaWTzJM7yTO3llRzkZSHoGRpGuG8pD9hyd8+1/u+dbXnkM5wdKjJ1J3NVCqZeQnQ/K6NuF6/vw5f+AP/AH+3J/7c/zUT/3Uwff/2X/2n/ELv/AL/OIv/iI/8AM/wH/8H//H/MRP/AT/5J/8E05OTgD403/6T/P1r3+dv//3/z77/Z4/+2f/LD/zMz/D3/27f/fVZ/TKJRCnV2WB9UzB7X97eg81dZwg81x0qQDP+Pz3rfnc73nIk88PTOkFRfZomvzaxNrMqaBDQVeweWeiDMV9XkcDeVHKoN2BE6Q0i74YdzwrbcRXJlz9zVVPuEpMSKFoMY2oqFkRluKjmUwqygl2wovPrhlyYqUDIwlKwtIKb9i93PDet3b8z//D11m/8/2cfvEhT77/ES83LymRUalmpT0s0sSvw92L+I79XnZvziU2lxzEGYNhTdoMjCcjJ0/W7MUQJaNQcvaL9EQqiqr/3EOJy8ywzTAj6UjaKbvnwvRibfYBZSA54ZIFImz3kT2EdO2SUAwuBxJjSaymNePZhn+ZP2T6tqLT1FL/Ksb8rPYwvITVU/6P/+b386UfPOWNLyq74Rma9qhM7POWNK4QSWSdmGQHgzJuElvdm6ovWeIdDdXz4FoBhaGEpJ3QjrXrzSrmjM/tlaLZ9CwFkq0MFPGzJKQi6NkTUhY26RQpK8gn6PYhT38Xvvr/+pd86xvfhvIY9iYNCwPjMKDA7v6B4SuVaxOuL3/5y3z5y18++p2q8p//5/85/9F/9B/x7/67/y4A//V//V/zhS98gf/mv/lv+MpXvsJv/uZv8su//Mv8+q//On/4D/9hAP7m3/yb/Ik/8Sf4+Z//eb70pS+9wnRuq/T3FnEn0iPBQPVQOU4NZOpQXwqmA3jBD/zet/iRf+MJ7/ygsk1qEtQwkbIbaQhkzeSUKakwnSrZEXcqfsslSkmZSrr80lZq+loQMlKNP9pIe9J7k3qkHFcSWuzy2/E56ORCR6LIYAQqJ4btyMjAKANashMuYSUDzz8Sfuc3v83//Ov/E4++8IN87l/b8Ht+dM2zkxU5xTI2AjRL6iwtddmynHd2r3SmXVKdMEvI1WZkH7YJyZErLpxhkmcLi3TFPj5GJavdvwwpuTHhIySbdnoWwst5tTQH93nxyWsH+gKMKqwmGM9g+Eh4+vU1z7c7ds+mlvrXxBwzpKOg+zMePlZOH8HqBIZ1QocBkiDjiuJMQ3EJWD0o9doNakTC3MEGX8pUxz1IGDAZgSqi0N2LUdXIt10KDAMyKFoE0RFhpOTm8D2MQEokTYyS0DIiOZHWMLwFDzYFpgkZBjTb/ZYCOWebT7qLcX98y63ecf3O7/wO7777Lj/+4z9eP3vjjTf40R/9UX71V3+Vr3zlK/zqr/4qb775ZiVaAD/+4z9OSolf+7Vf40/9qT91m0N69XIB96X9Q7p8Vu0ED0KSREqDc1e48cBYCYDxvqaLFwp5PzE44k5l9NaUJKUzWJBKvBrhKv5zu2jUCIaYGqW0PMxJBNG93XXhySRduhplxRAXfZKR5GojhFKEqYwwrpB1Qk4EHkF54FZ8UOepne9YXJuUc7S2V591Y0B67roaPguUQSFr/Rvt6lXQCE79vgV9csIkBpAldznVtGkFk9rcegPTRSv2S+dSiroKMisMU4J1Mul+tUPTDpNy9+DwIgnPaDzA+IBvfg0mnTh9f4L1DpWJwmTGFr7SpWSSS1PF2YgwyTchOHakMaHJiaS6VGWq9SBcZkko9VzdYol+JBuRthG4tsKNtRA3mFTXhIgxFPuBs2+t+OA7a9CHoCtbJ0n1PN4/6Hv1cquE69133wXgC1/4wuzzL3zhC/W7d999l89//vPzQYwjb7/9dn1mWbbbLdvttv799OnT2xz2LZQmo1Sjszg8MnB2Vnj2dM8H7ys7UbKYAJKKS1zgaidj+HY114QDrxcN4tH32x0yIxO3r+fw827MaNx/IAwoqXKo7XJbFVbixieKHVhMJz+q8N63hacfFlidogPkEfYr2G32ZOeCLaVLY/vDPF7ErRFjBW5yZrvQRnPDNrd6cymqkF0iFkoYoghoancLqja2+4Y6khMuBSYySRKSBiNSTqTDohWlZuiNEgYHwTw59Nu/lfAlShqBgXQyMMmZGRihmCn72qpJKJPY3zzhn/2W8p3vbDl5vEVWZ5SyJ+e9jTPcsDQzjFL3oNk3SrO2Exg6SUSc81HE78qccDg8JHXV+y0TLrt3zmSd7G5L9iRGgx3/L5H8btF/0gRlgkk5e/8R337X1kbzCDoa0R8cLuETc7cV5V5YFf61v/bX+Lmf+7nv8ijknDrup2QUS4tTrYSZS+kJv/7f/3P+h//xGdP4HmUsrgbTLs2IIK6iQiBXAwxTK0R/ZRm5IcbShQySrt3bKiXY76o3jIEqg06ET0+R1ClURyx+YgH2iNiFs5QNJW/QXYJtYsqZPRPTCLvVS7Kbnmdylbb630phkLCk7FbhYM6pWVlq/1zca3WSXJVaTfYVFVP9SuR6ViNW3kkle53UdX+KkjRcdAFV8lDIKRkX7yKXmYkHMyYkJ3JVHWjfAC3VvMzFV08jtyLpCePq1F2RilvLtlbKhCPjU9CB/+V//DakM+AF8NKVCkIjMmVOSWdh06D6oRmXU8fTnHfjOTeVD5GycmivusaLUqX0TqdasLHFHakMJk1VWFRswdYwPUHzCeSHwElVCxZVs9aFe+uVcdNyq4TrnXfeAeAb3/gGX/ziF+vn3/jGN/iDf/AP1me++c1vzt6bpon33nuvvr8sf/kv/2X+4l/8i/Xvp0+f8n3f9323OfTDUnUjPSAfv1jRSatlWpW4cGMEecT+RWG/GyjD6AYbcUqktiHVAi4cGgPIG+E6CLlUdVcxlqYCu/VS8UL435jOLlfxchHUNLmDaTiUhvNkGSGtvI1M0qGz6lqoB30tdHYPYdxrE3LdGWAmObXWDBeFJKDNB62zwtJu/GEsgxREjVCamqe5qYZSF1zt0xnDfOyLUhFoXMtKSg7m2iKfdFoDm3O1i2sJToFwsE3u7G33q26olJJFVZFkFn5ZICdD2KU4ByCGtJNbneYw118DJ8CuuZaUkNai7x7O+zseJ04Sz4iDU3umMh3hfB5rs4jneNtF+vAcms24JyLv6tiIazV+SSR9YGqJMhLnwohfNjN4+WTdb8EtE64f+IEf4J133uG/++/+u0qonj59yq/92q/x7//7/z4Af+SP/BE++OADvvrVr/KH/tAfAuBXfuVXKKXwoz/6o0fb3Ww2bDab2xzqJaXp7md/n1e0PSUizXxcAU4pu2K6sPEBdIi+Apya6iPUMuZ0GVzguBhCWfx9bDB3RLjAOcN2+DS67PsXNcfrqurzQ6iuIx3FrQB2Fckd7a/iECdi3v5cp38E0QigheJ3L5VFEI/44HsgNLPoGfn1PpMTTHWVYTDKFhQpTL7NLeDeEK4jJUmqbIHEv1Uga0TdVF463wEn2ur7a+tnTIr6HRoSkVM6GKi6PXEJyRkbVXeyDdhxQw5RyLtu0MkNPKgO+DEDDZ+7SuBcGtTmTi++xzojXNHO7RGCGY01LsHpqWJ3Xr4Qqk6YgnA1n0bhxFIbBVGLQ1cNwjom+BNSrk24nj17xm/91m/Vv3/nd36H3/iN3+Dtt9/m+7//+/kP/oP/gL/6V/8qP/zDP1zN4b/0pS9VX68f+ZEf4Sd/8if583/+z/O3//bfZr/f87M/+7N85Stf+ZhYFMLSYvCyR9MolH3BLn17YjfAfm2OuWJWQTB4s523lSporoCYVqkG3hCOcIkVbZQLhngdT64Y73n10g5I6g6PCkkb6bHAO24iPkXcP0z6KqHeESg7SFtYXXbggugtx9Q+mf8tsy+Ty7FtJh77UTzkUxhcOIfeoWj/3AmXmiQSjt5mZ5pcUuxJ3/0o4ipR3KQ/4MDjs8xiCVcSJfGeuYCIfxfPheSpLkmHFBHELit+j2ZO6pQErj4mh0YhWSSM4vESwxWlZDsfqfPzlMFMI4MK1Wgm0Clz64z16N2VdgTSJ9l9VT+6rH5pCS1FnGdfd5lcBe8wHoQrDUTkHEomFzdikQElVeukdLKi7IutQ/rUAfnC8o/+0T/ij//xP17/DhXen/kzf4b/6r/6r/hLf+kv8fz5c37mZ36GDz74gH/r3/q3+OVf/uXqwwXwd/7O3+Fnf/Zn+bEf+7HqgPwLv/ALtzCd2yjX4VwMcs00VzoJzdtRxUJJBIFwtV9Ragib2s5Q45TqFB/rjHOUhXiTVOvbxlDOOc/W9i14cqlLK5mZNkVJjqwExXxKzB46NTYzVBzBIQ7JJlOeuj/U8dWtl+mO/BoH6n1LI1Zxz9KXytB38tTsS4HWciOixnMLSX3vijKk8E/CDTjsSSn3VdqyvU+oba8HxTRin404mQrBHl8aBRGETO3qKvC/tPszSVD2O2BkHDcUzWiZ7J40Dfa7QLcZ/nfc2zipVKqTfWgXmaTSJ8WvwHDeKO5jjxTtGcE6Iycex9SEeoX6BaVJpnGdVRzeA96C4I4Q8CaC3XfZmYlzMGsMvxt0N4BPWrk24fpjf+yPOZd2vIgIf+Wv/BX+yl/5K+c+8/bbb39MnI2XRa4IkI0Lba8eg544CNq1G2oBQAbjUivhiEeaBV1vAizdvzGK2oNgbcwkvnjqqhKXnFPv5hxSXje1onbH0Shs60MALQvVSxD0GaFthCecqqvlGvj9uczXXBfjXHDUM9/xrpeYT0f2idVMiEd7SHZvU6xuBht4+MnBiKY0Zvl6pZc0z6vfRjnedkPXcYeYzfhCwkfP1jWkqtm5qBvTIkcmtGa+NtVq976a1CozSQhMCjlnyB0c2O/UBKlE22dl/ruey16SUudw1NRtB6Gq/LlEY6zgdrch+nH4qRJeckpdz1NYFXYD6FDNwXJ9wgwy+nIvrAo/HqUDm+sCdcUZStXzE1JKQwqVA0zSDt7g78Udz0xoULeSC1jvEXsP5vkK9cvm0CH6hqUMMWmy6FVV9Qmo+69JYtpi8w2iWjwg8bAhDaMFPQ38oWaqnDRR/GQOOprJeQixXVlas/XWhUbsgvCL/98QeaSDiUgPpkBbGaHKI2kaGXVkLZuqyY0QV+bLKjekQTcgVq9M69qDxUUbTR7gmR1lyKhkdrxkGBMpGbHY573B2eCO+CqEUiqp3xIqJL/nU01OrGz/A+xLDqkOqmqvZ7K0kUqgU4vTngF/r2Fz8X2t+5yKqaBnwXWjqf6OqNN21Ognd0gJipoVYA1CHN1PTWTUMYDWv4uxHgD9svKJK58SrmuXxp1fGXBmVnHz6OT2/bL9YjszAOtMyykU37fffcYHu9C9+rCuV0Ka6kzJk1KDx5Gs/+qMVihZLAq4DO2w1qs3scfLCnQ05Ff89iiBilQHWJVMOSeiQeXGl2P1OHi4CX0fJ24ZRHVUQTSRyoohr5nO4MNvPeejf/Ed8jaROGXQjd1qSSIXT5UhoEVoKViOqFgP6nAxFbqOWveifi7oUwqkyUKPsUNPCl/4Vz7D2194wiqNlLJHkwV2XssKEDSbdaAGoacuAeH0HqYuKSQuD/c1ysiYmgSFKuRSDZDkIE2MjdfCaQVT1LZ2ts0xN5lgpciJsHpzTRkGihSP7B8vGmxKJ6m3an8vdkwUvI3iC1b3QaEMFh6rbMgfChpeAPHYzFJYTSrrmdO7iE91D8qnhOtapeP2jpWeS5w9duSdWb4if7c3d08gK1i/OVBcAjOpan6w+rA8d1Xa2QhfJkfaUjr9e1z4C1IS5ZlQzpSyLXaf0eOE5BPWRCojQ0mME6z2a4onn9TOAVklL3jhNtu0vFc80PW4dBEO3doIVzxZ1YN5ZMwr5KygH45sv1GYXhZgIpXR4jNiYYTqtJ0jbhLCKxCU2SJd9vMqhMtzv6UJlQk2e/RRYfXmSBo3aBrQQPpBoMNKUJslJrWnkF61rqe6ZmFTEuv9yCrjwXa7sfTScb8CQpsC2iQ0us/CqKSq3TKMICcDqzeEsrJzU+JuruYKmzsZVwVIhR09GM9N61EiSkfU48GIUZ9UWO2F7STkopSXrhOtgY374NO+p+KNfTLp1qeE63rlCAHqEWdPVI7defWZTKO5ntgRfwsMynACT764Yj9IFxW7Q7ja7qFv2+l4NuyOHlRLZh+PdP2Ljydl2H8Htu9v2W0nSBsiGrmblxnnyMhQ1qz3IydnwpROa6xCu0Oy9Wxm2G2R4tJ+XnrCFZ/MCb26Y3L/qRliWJSSVYHheWH88CHy/kfwvEAZKZosYkdIB27mPe8v9revL39Y1K8jcfWS3WX9LPv0fipYTp5mZgXDxPBZWL0trNcnLu1rF12CucS75PLF1zlgxJep7OEUOJmE9UtllU/Yqod8ivvYyucdY+5i3Mui1DQz4Kq+CRkT6XTk5G3QdSKPQmGooFdXX6VqB9XH24h616R/d1F9uVPRRF8vcW6lsRwojAorO+qsdsBO2U5K+WjCQjvNk6w2prHb2yWf9gkpnxKuOylLaBraRxcRmJqR2KNojNjJX9lXOTFTc2UOCdddOP4fDDMQf8cEDzEG00IxvoT9M7VRzpxa47QnKGt235o4+9+Esycjed0IYpuHcc2z/p3hPkd52AifGomb3btLo6Exl97gflLY7wS+NSIfvYFsiwU8Tev6vt1H+CDKvO9D9HUbvHvUz0ONN2hPHkKOdCFnnH3tAR/tYL1xzwW1yAyW+FMqjVjeIV5UYmT7CfJzSO+tkRcJpgLZs18Pbv1+9O0Yd9dRrPvB/Iu7iCllBdMKpgFyKTVBJKVGz6y/g5DMgn50PEmRZrFYHVg6QqjdulxU18UWxAyKGuHyk+KMws65wORBebvxfcJCO51XPiVcNyo9J3uFsrR267/S7pMuhh6i6CDoYP6YOcG0IFzxZthKLI2t7qr0VxUVvWg71MVjxxViYH4rIs5ZF39QBs7e3/He72zJ20weM5FTrE6OLp5gqHfaV8wR3HyMQbhm73aOzMh8a4y+ClkHXpa1G2ckiiaU0Z5XRSI0Enqwt/eliLp6rxQkJ1788y3f/NoLRjHHbWjhywTmTr51e86Ze+dTNZZMygPsR158e6S8TIiu3KHW4WHWzJKCRHv4sYjdW2ycUxNF3cpemTBCptKrcpmp141wySHh8sdVqp9zqwfhWtQvJmJahaWwjrUo+toIYMIUEQOeq89IZVNlRqONs5O2NAfL971cPiVcF5YlCVia8MqRZ67a6nnvhdgCJKmBBnJScnJfzf7MFqkEA1gg/dsojvBnw9XG+AVtogWeFYGpuIdUwi7iI19ZjQsnUEbO3ttx9vSM9/7FC3NKrsQbDpxDg00911+t/92Nf2Y2r+3HfZdmU00JVit4600efPZt0rhiQsjasjZV3WklnPcNW2iV0pMqwz7x/Jvf4ul777vjuK91hGYCX6fOatQ+PKd5x+hJYL/HQhmdwos3kXxKkjVZU/NnSixEEweiGq5JnPnpJc/UnqtjEQ9c667jqsb4BQwNSlHxUF5N1VfkMAeX3nJ9JqnG8g6xBzSiFSkkInJOnXPPOFj9Lq8HPu7lU8J143IesroEkR183ROaTNU96TyUUFNr99AaUsycy5vpJF5J/dTNU/pPeuIyP1Rh7TsMwjSIKfLzhJbBUldEv7oCNvDyDEs69AAzYe7b7VnjfgD9fUtc0HfEbllmWCnQWvd+TDMVG8NmgJPHjHmFjok9LbNudcTNJkne15TpAkhWZFJOdQW7E/KLh7Ad3blXbG1qpJRgInzvQwqo4km3J9kZgnGEvLdXcgJ9iOaRXBxeBhyL9xZ9vck6HQj4Oz2Mx3AETB3vloxOYzUJe+yzqiXw/UtqWRqacUbX3y2XJTaoGuYmOKECWZWSsxF7TqA5HnQN+ToEuAe6uJ9geONy/wnXdQGtR2wzGtAQ9PzLLs7e/IHuz76hPhrGsdIj2I6r6kWYTvy3g+UR4qR7tlObzYZ1MP5Xq0dkiYY0FhhFPVqGFFcPtnmIh6zpRCUnOn7wJkA8v1AZIYLnQke4mNPRuIA42A+d7+1sn6X9PfcfmEtjqcA4WZzF/dj6EKE5iqoh9MGtvipXfJ+KjTcJDCKwxS6FyglsE0yjuRnoYNaBgSt7VXbP2MyYl25vthkL6+REUEePlqHNkXhmLThnqyoztPg8LEQP7xftKRMUPVFkb3nYdRJBqw4av4DnvFEJEJHmpdbCaEUMSKnJWu3nmL/ZkuPtzv9tj/misuCbDz/sv4jYlUcYhCUDec1yzwmXA3aPPGQO9MfLkYWexTq7yTiWHvlX6H+G9BbSTjwidlBLqNiENlbaM3delkDXT6GnuaLG1YqnNCl9tASYWcSVgvl4JZo1Sk/Ejw2iss7LQVGJCzRWtEoFTZLtpwHUcELmu+Rmk2WYGXTM9kehpkLO/Xf3pRj8CMIgnvOtJGMe9gnJa0RHkg5mqOGmo1IlW0e/QUBCjRiAmEbb2/0Oxj5CSrhF9MBDt43HU6AesFRHlzvgQmucTz1oH5ol43lM2y0fpjasSkDbv47UVdHwzxKhhsOaEaslvAfw3nba2AvKsqOli0L7o1X1vPEtGMhrlntOuKhzr36tl3JNQelbaJuLk1Ict127+XPdMEJ6CHWJQFMP2NdZYEpOuHCnXDkCQ7O/7oKSHZdSerJbRy4mqZkC3ychqflz1ReDBxVuBxQ7fUnoThSgMNelLKSz6hSuVROWhmSX9kpd+zrv8Cd/nZzuLZYU7G8xsFslU50xAWmNlhWqA8ow06TOolbglCG2sMbkxMPnC4ynsERoIvOjIvHPBbquI/AewhKAZkxaViw0V0ROEj/dHRM4b1YqszUb4x0V66WT3AkDJlCP0N/7TM7dHo4UWfx+TeVwPY9pMVo1q/FFLWKKP/8KFPf+Ey7pIrbQnaNaueRllfk7hy35nxdJAZd0dPBqSHjE6Vo81Nrs76tr5SLi/FrMZQ8wwKEkc1QimnOMzQd78eyrsJBHWfahfRFqv9n31r+lrhdHivPEicvxiX92ngb5Y1tm29QzH7EHqf70DF19ps59qSlYxqPsFuZAwSH1q4Nnjq3jUiCaPVvFZj/O2pq7EOdXsndI0e6inDOvg1OyXNaj58IlXpJLwbymc8/RK2T7wn/r4YfzYMbGHFYH8BuO454Trp7SG0SfvxDHoH8pinN04S8v1wAa6ftetHHkY11+jxzSyvkSXHtIVyrnmV0dEK2wdpLFITwc0Gyp7xR5LBe2EyO0+12JbVOBHNDgY+vQOdLeh1K344BRk/mf80p9uRE6X7xjnN+V1+MV970SryaCLcHv6GuVb7yEaN5WWaCfyLItEtHvu+77CcwI2AUzuiv4mw+Kg0US5qCjHEhkArMQjMEmws3VnPeacIUaoPSfHAHkWmZYyBOTv26HvkB0yy2rn/cf4NMYZlLA0n+kPn2RJPaKZUbil3eqnR9N8FF2b+Q6m2OD6jm0O0f6Sw4/OqYKv7b0i8gm/oDG8wFb0kz/72NpFnamwqkX5/1PD0sXcdk933cgLdxFmTMVVm93myqdg/kV9qc/S3dWqlAowDAjUqIR9glSsQguRdumHPOVm0Xfv2sg7OFgRuMXlPiIE+ksqo7/nqsxrnm90pWbv/kxKdWcFBZqG2Vu9vy6R3aNcjC249Y2BxqXc+p3Xa4ulHaYTRc/taED1v/1lnPhoo3pUD3ycQamq5U4HcqhU/uNG3ytZaFnEz0XlGa4wcvSZOjOyrKThaYk/LhkeTZu0PR3rVzAsFReqFrLNMHhVcjPvZa4Du8XFqR9Ft3g0GHvgCu7RBq/9dJrWnT5hQ1IFi8c06jVZIp3MMSu577DOafXrdvRaSwOa69aeq1L3usqDhZyPgrRToWj3R4t/ejuZTk2hwVHMYtKfoFI0pvuvfaN9Losfp/77EK9eeTp1yJ61bHYb+0+smPRNEY6M428TAS+y3IOrPfnWdu56d+aaRO103Z9UgmXAeBCDSSKpemNFS1IZ6obqgE4R3K4a9hYYuqm+cDIw3lsl3aGGu17IS2Iyl3pCrtQTDWj8+yBI0S1MQ/ituXandLUTf4ul3s2HP89o7uB80JtqC2AMeouR+HO5w0dl3ZfFxl+lX4agJtiNALOBtx1vlpH0o3M11OZ6aoX1mSvr5TuTHSSl3Z/x3OdX9dxouV4ovvoqvUZX3SkHupYxWFL/MQ7b21LqNTUQTWmmix6PEesvKtyCAazUfRh60R7cqQzniLqWlXyN/eavt+qwqOnQ7ufI1/R7cEFcPBaSsfNxwcSttZd6KPZM710iVbn5NeDMKOf7D/ztZ7r7hdDXTRzH2SV/hAutVA17cq5PzCf+E3ry/Zuo59jM+ofnXEYVyuvVQi4GHpC9RYkaD7Ti/dsuSrXqcs59UtLMNK9tkh6HPBaF/f88krD6PCavLqi9l5LXFUzcJTFCQ6gyydQP9X2YmW1z+vglkuMoxvPMshDO0SNY5lFf+hVIt27ps66O4nL4sVFt8tFb3MRle6TY211w14u/W0OX4+0fYyf6RnaThpX6LTNQsvg241T6VDecgK3MZnrotHrtrkcf/fI+bzfkRabWrscW4rbKjVOpFQbJ8si3jYm1LtJ+0gu3T4vZ9GfxdseblRm4sn552K+rT6RI4YPF753F0UPKou6Y1YxX1rBmLsWR0ibNFnD2n1CI2eImgpqdufXeSBLH2dkCbSRkK3DVOKQfdQF6Q6LddfJTZ3aw0Lk2bhEtTt8PmjHuDbcAbQmqq9PnFdfZng6rz4bqxbz8icSPTbQDKKV0GZe0qc06UUtWR7qO+Aq+8kuP1t83zMMSwdvmQ3UkUllga4KKDfl469bLmtP2zb4RHX5TFiJnofozx2jnv/VbZZeSFpGrfc7FlP1xskqHTxGGzqHbV0Cy1VO0FGOeVavgaU76VhCSds1N3skYEwVk1LSxftwAS185XJ0S3scCjXpJQsaKxYwLvK6qRRzFC/qROtoPpsrlXtNuJIWUvBUaiqzGglawUOT0yJJg5IRMTWXgZflERi6FPD27V2J6I03bTE7FAtbEAQs1/qgMDp87BMMJdI0aLNIqpjXxnsdK7HhCvWkxe6oqpRXLL7ajPooQxEGhaEESBYsrMEEntC96bf9+9dU2pIkH22bj9RP7DDZDhWSRzA3c2VtqcTi3m4mZb1GTucWyqC2V0ktH1TbX0eWXpqiLdatLxEUWkmWjvMAnd92mRuIO6NZ1dYGq6sCkbw5iz1XBFJqt8EHO9YN2JQberM6fh67hp2eehg0JTnRHLSNY1AYNaMlO5mNOSXQOD9NquSwh2ut49WKReTvCXzpvuuJcZAS4bh6cx5lA9+6m4/5XhMuZCIxkcpESra8++zx5hCn8r701XfK7mekxuxJJM0MDNWbWyLO3i0CQ9vKHgE0xCkYwKoqMpg0IzowFsvK60oQB37ae9oih7Rstbc2bO8nPEdi1NKQuDMKosJaLRj86NhrrwXKvqZsFx2AwXM7gWqp2/JaIq5ph4AlCFXPQ2eKTICQyp6RNahSsiJivy1uZLwX4YTuE9Ey1J/KYIxGhlWQHM0Ik88mQZlINRxWJ2GKSTFI8XMEiUTyuHTNdOcO9jT8mwSPsq6ITKhOJE2MMjA4Yy949EtxdjCkS7HUJsE+1naZV0NL1zvU9pq769QDTARBSpMKIwLWsIcxK0Uz+7L3dxPCYASrWkZXk6YF1N0VE6hdtmiPLhMTQymVkZiqACFYVJyCJyGtuddo0uMy2PU1y70mXCITiT1D2TImc+zTUpBkaQxUlSwN7UIhVaJVKvefNDFoI1xBtC6Kw3GTYrHIkvO0WoPqGDotiEZ8dSMMQxlYlYJml7w0lB6dtKBdPHq5Szmxl5N6pUGQ1MSqSCW0qRR2umOrO0ZNiE526GT0g6iUTl3zekKFhkM0rj5yAhQHUzNZ94gKYxHWao7fWiZywRgLlDJoPXi9uueoJqmv9xjnqvWrtn3FfozZSSQdGXJizMJGE2PZk8qWpGp75TuTZogzeHBjCgNegfn5waXSc+MD3byoE64kQlYlUShaKLpn0MSqwLAfDMmmZrEHkKV3JwkolgoTtQ/RA2J13foxIgaFJEa4COLlsXXTFlbThOYd+7K18WkiaequPBpzgCyX9+60F72qXF16NUIUWofAozYO8c8zA+hQYak9yysjqXtNuAbdI/qM0/IR691EQnmgwJAsM3wgGoSS7OZmYEI0V6BVEqKJ0b3WAYsu3Zt/31JRQCM5pMb9Vejii3HzHuxUd8p4NvL288J+mMipWDZXcsNFHW4Q54LV27kNfNdzdAF06heKGlKTf5vwNSyJIQtnHz1Dzl6yymesdG0PFmvDuDabSaxBpH24/RKkPFwiIgZh7G+pxiSZwvbsjFTg8fSEt8rbUOClnpGngvpgi3PGxl1ekQrVmDdXYdt9UfSKbV+jH7MvSQxlNMYoJx5MI/nsW5SX7zPsTxh0RWJgxVAjrfdOk6YmbhKXYFEfKoxQ7s4cvjjiVkGL9zMqk+7hTEkfCuPXH5BTQfFz7ockSTB37Vyb39ERwhXd0QXw7urX5UUiveUgqUpcxvT42LJw+hFsXiqnOTthszUVHSrszsc51xjcRTF4Kc6siLPbVfCNgYBftQy+dnuEPQ9Q2SBYsOU9Qp5wlZBd0dy03GvCNeqWxDM+N77Hav8UKVu0TAzO9mTPJaX1AldJZFJIVCGNqdgdV0enVO6ChykejkYrwWroACeyhiB1UmQvpP/f/8IwFFNfyiw7VuOgA31WPbseHLYOfV+b8QetqdtNamxyXYxGXHK10DWJx1vh9OXEdDYx6irETSNQbrBRgLBUtLW/be+MKh9QZUaN3EdhXKJNUsjKXnakIjzeP+GNs29DEbbs6jsmtClFXa3brcLhyvUGQBfVj+3QefVX68cQkTDoiqEkxpxY74Vx/y1Opw9I0wopltJkIFGyS1RihD/OzMxCTI0diajzfTSb2ypztK1IFoqbr0mBvezIUyFvM8MuUaRQOitYJOh5f8hN+lwSriIOidrVYwUdlKIec416dDGvRz694sZLsVZAsu+kwLBdkfYDshdE2p1W6sZX11VsRNp9/jrOT4V46QhnSIW1CHs54Wx4mz2P2E4PIa/QUjAl6Fz9fJNyrwnXmj2btOWLpy9Y7z5gyM8R3TE61NWUAXXBC4PrYOm4CFRmRCSA4/a5RkN4gOm2Z4TLpBDVQhFFByNU08tMTs0go/gAq4otuDJoqdC5uZR1rF5/+1krncNn0yIYh5j8Z8XGDuVKGRhnjseSjICpgHpq+HSEo3z10h88CKkrjFeCAMc9lYqSZY9shAfDcx7lj9As7NhTrSOLGuFCiYs+OXflLiMocLgrd0G4+n5snwZGhiKkLKxK4nR4nzc2T0nJCJepqaQS51STdzrxqneEbf/E276r8xN3QnXWfpkrgzDJnonMxIRm17RocaMRX4HY91i5cwjXq6oEjxIuXy+72wqiBCrZKK+fmWEYGWSozN2ScDXDK63w61DM3cheIWPZWNrEGuGik1rV39jKE54NG14U4f0dFD1lCmI9SwR7s3KvCdeT8YzPnp7xf/rSxOP8ghP9iI1sSZgax4obagCg1RIR7P6LCmB9htLLuUW5wjPHXgqLtJQ6rouGauLCuYgjSJlaivEwH9XOMrGzhrwLsG2ll7K0nuBig6gzaJLXc0MaEWS3I6qVtIqpTIGGXm6JTW+ctmIqPRC/VWxca9Ra0Kw9e0oqyAmM45oiyl7MqssYi0zlHrouos/blDIumttN+5H4tyRSsdOxWa3YvbVjf5IZ2ZB0rExVEtdEZIM9rWuoaApnUpDSSMR1zZrOY5oOx93OiWAqW8QIl10N2B1l6dSBiWFGvEqFXYfXkjrCcPvXA7N2XY0rrga0bZjQpCSEIRcGNQvnqRqHHRKkdv60k7nS9WHiEkCa9erUeEHi/VfblSIDRVZ8JKd8cyp880Xmn7x8ylSUXRmd8/CEsZ9Uq8InfJMvjGf8vsfv8nb+pzzI32EtLxjyFtGClPCZsMNmlkRuCSVKkS7n0ozr0rprSRv3aA6N/jmH9XqgLuDSULO4SZFOQe2zhFR2sqhWXbxIF+062vNKqjoD464rR3kXh6+TDnto7zy2sNH4eIrNUSShxflBMQ6+pS6XWSy2m16ER73uFb3tZvCjUjlDYwIaxyodeyyDrd+OHWWrlGQqZ5EBdYMSpdQ9TCXFq5dKsOfJWBfVryIR36SfqAmwjtVaC0k3hJ+RTpkhGdKXodsnxN1OGkJu6iInanL5Xl3nLNWxOxOSUjLCFTMbOynQJRVR8ehJh2ekLUEjXKbVuF1r4lpiPVxlLiWhUiiD+TfZnEzSlSJoKt0o5ve/NUmr1w2vDBUAEhfjoOuepVCnz+4DexsA/67IyCQrpnTCB8Pv4cke1tMT/lmGD8tE0lNgAzIyT+x6/XKvCddGn/NQC2+lb/KZ/C95KN9iwzNWbBEKSZSEWRhF6gqpnIxW7tukhjloQCcFBV7r6xepBjgfYdT7rcrjByponFV/gdyrXuxLb1HjjWhHF//eFO0dq8fc5jPp16oVn4eG8ayYykbClLZ/T+qCVYWeXL6GFyL3bh8a0QpSJtQIiXWNe38s9fUu7MrOHBT8gAqD8xhBuGw+g6dY7yX281YzeinSXBhmdW5++X/1fjpfR8zMf6iq8gHVFSJB9rOpfrtxBbQG4a8MFqneg8YeH92rG5wl+nak3bmFa4jSXtJqhBJm/9K10M5VdeNw/zP7sDNucmA8tDD0dvo6cQ7P3xW7S7fnbGzJrSHdb0twl5yAT2arKDTC0sOu3Z9J3bP+/NzGWap/S6iCZ7Ew6tMiQmbFnhU7Tkmy5ml6i0cirEti4DGJFSIrI7zOSNy03GvCtS7POMkvebT/Gk92/5xH+V1OecqaHYMqA8bFKKYPFzCE6lx+zUXEcfSb9CrbfFWQsHpVjUVOJ6Ui+bhHqQChHbDWFqI9b7PjJu0KR7tnrj6unkAd/WwpcS0uvjvyBkpNd68qqBZSSiTB48GI8wlaGYZ5FOybjPv8ulSSEJxuIC4nXCp1nYtz7SoTU25IvvrTSIcNFCdcbjqu7Q6ilxxuOoOZJNK3fV79yn2WJimJoqU5EGdNJHG0UKbax1jfboxUSYE4nQAcijPXmPUVz48j+ZGEFpPYSymUWCixf2x6wqDjrKVqTVrvZzqJyy0lr3p+qk/Tsq6yIMQNxSf/T0oy1WbJFMkgQipjRwzdTn5mVNMYsoDfgM+kbX+uv/bH64eBDIQhm6RkaKctujkmjGxZcZZPyax5VD7DAx04YcNKJsaUja0pe2pUkBuWe024EhOjTpyW5zziOY95yUa3DOWlR3uIQ+qqOWhqB5WqYlWhqj5mz3gv1+d7j1mNxbdNUlLteJbOvLyQade3walRv1/Qk+6PeGCJvm5itdbm0HPJfUfS/dkfZ+PARjfOt4Mb668l47YZHbGKuVxmecc59fPmoN2PYCa7qXLp2h16QSpSCJVh7vYn5X6sjkCVFo3Hew/n715dI13d5nm1+lL1OWt7Ua8rdUnbFVJjz0TdAMMkqUGHyliYB4khm1T6dRWygBQnXIJx4hpSba/LeFWb1jkcaJ2LMFSFsNZIb+KrYjKNILqve1at+2g+fIgR3IVdYWMIJeo4FfI5uHpUl3XUYSwYHmPc4rvkDrmJhBns5zqWxFglm1yUULmmkuoK9JqEkD7nOCGevAo+uvwsxRhs+kJyFZ8qzRHf1y8zIumENBSe6zNW5QVDeWnLWbKbHQioqzrNS/lG5V4TLhEPSaJGwEYmBp0YNBO+Wsfue6oawP9WQrry72dPl+6pm3GJ/e5IxxlJIEZpvQaxbYA6H80BE0QHotp/suw/vrxKfQ74hyuo9anlIBqxdYs+tX2Ko1c0u9qQephbGw7M587hOvU2h9ZDQdx3xI9kHXMzd1Ebe3GVl/QIoxU31aDGj/QyD5XVZtDX9Rr1HsrODcnF1fvptIRdkNq21xJEQCENQvhCzS0n6y2YW1dSOehQCIuvY90TObY/fX0Jez13Fp+XJgWokNxKr/pdaTBEcR8j9CRJnJWaw12ofbt1oamPg6i3elnU1de20OCpr8sM1ozOGTEUsDl4G4mJagIf72iYFMXYYkFaH/NyHXx0cT1OT2ODxQ3bbE5S50h3enbmaqF7cz0iHJIxhiglj2a1OPvXLPeacEVJJZPIDGSSWl08nXcc/hl+9A+CXsS2ysEzixeP1i/7fln3ozlD9Nah81GGFKQzeV/MVxd/3Lb3xkHpO5Tj4NYvl2IcVnzeCIOhOhFXG/bRoa+83terz46HYD58YfziG1+RsnTHW9v9UOrxaj9YMeRcXuEAfjdK5Bk7oA+hOnTpUpYCkj1FEK7eUbdazHm1NXvJXul5zyw7tgHXs6Mt6mW/v1I6OgruD9VKSDfV1qTrWxoYtM8urEsdU5AojtRBKz9mGWOcoUudDKo2utBCz9xdQiKuAyi1zcBtbRa3d34Uw4PBJAlqZ8GfDCai33PVxFD2DMnxsRuekMwNJu5B62LcsNxrwlUwSxqRES2JUiAnKG7mOwZULw+qI9/ekFD6QMXd5tx2kdlJ6/sKHsYvizU+OS5LH+CT11mkHbYYx9JGSBSPVEC7z1KtPna3HlDxvFKRUaA5Ac3G8XZnyB+avzP7rnT75rN1E0+7zrtfxOs8wBHfo3ox778zIDLYa8V5/V6k69u8taXoDisYx16c0Visd2/UEuMmxrhssSw+WAxZZy2ep1pbUP1L6tqL0ENHWH0q/X2S+OLbnWqarXOViwSG/hKUW1z26xbBOX27KR2ApJlSJorfk2YtTKUwTZkkp0Cy++8bdnmvCVctGrKKcysh+pt24ZDtRmcH0xCPdGJWp9rrXn2V+my4/Rcd/Dd32URwckmgNzlvz7hSQaBF9u4lgYvq2gHboh4jX5omdSOoCgVfZMEvcos2IsX50u4Bzuw/uO3TZ+weESHF1H8tfmUSM0tWVSTpufRU+pqODTlKn1BTmO3DndZfoQ3pnbKpawFUc3DtXrE7Qan/2f8hMc0O12KtunLjfdVZtdckh0SwLEUSFi0nVSLQD6zFf2htaD0Dy1m8yv4wqy+bN1V65aw6+pyqH52oScEh1tjdakz+cO7zHs/HQVd+rw1vfpbPYXzMDxWLKRkIajCrRzEfEpPoe7eYG5R7TbhsLy3YZwVGNXE29QAzf6HtQhAMR8DNNqjbQj8s/UHmCnVZ1olDIo1D720oYkCSyKGbF+PUes4/CFu9NfJgptLP9dj5q/WOWM2IUlef3eQvCVe7ILcStx2CSCHsiyLiQj/3tiRzuy2UQwR0W1wCVEKlOJ6VwfdTgBEz4IkoEHIUGUr9V4DB24x1vJJH0mI1rlJfXpbfXj/nEa4i0Uabd40wg5s8SJ/ApGCpa87FZbNysEXnvbTEmsdeWMB2wKU5wQ6oDFC6uJoB1gsT9wrPcpUZvFrp529dLvzGBESFMQmpKFIyQ/hqOYxXPFUWDV8RB51XP4br4vtm1NZe6nni+CqY6iJGvOq6VpwbvpuvxqHea8I16MRAYUyFsSgjmaFMDMl2tBT7EYHU6bKOc2mNrkWZXWQvPptxH+e9t6wLRnTqZfDU6GMd5GjX4RIOhe7gijqgNMKl0WggkGoZeRXLoZjFVer+u0puxYmlzccmoMhguYZGCkPZ03zm5gxiVR8u4DeIbnxV7wWuUa+j1ua4ncmUwdZ10sQwrG0Q2ay7Wppjf0d0proxg4fOfHCGMXNnAHTRGl62J329J1bX3avLLchmfEhF3OGTZaspfoeUNNVzEXJKSm6Fq0a0TLltDORl962zUV9EJ84laNqWJjGTwIrCROIlj5hkTZE1OgyVMC0h0T5r8pcZer0aQr1oQknTLDu0OnRKl08MYNDCuuwZMKK1Icd0LWakxDoUiPvIYdbEhTio8gN9PR5Y4rp+H7T7EWZ3oHbSk613GskyMIkwKeScKTnX8G6Gq/qIOtcv95pwKZBLYpI1O12xKytWeQTxC/UhQfL0gB1Sr2YQVWWULLQNQiYsZ26yohdxvXZblYsdlpQSKQ1VtJ7SCTmtyWnFCx0paSBSoCyPF5WrSTPc355Z3otdFemdV++IVp2Tgtga+o0zgxZGChudON0/Z8TypcV4YvVTXfV2aspshs2JNeq9o/WyvnwWQMS4vizCJIlp2LCTkRe6QtcPKWWgZFMVSmlWZRFeq8h83XvrzjlkKLNMsB97iWuOLIJZmJk9gxEupaqsEtJy1IndYUjZM+ieE3Zs3DVlLJOvR+XIujEsCddVOO8OW1awbJf8ESJNVZhkxS6d8sH4BV6mh2zTCdu0xoK8uQajV7tXwpUItd08tuXtlYg6EX1CMAt2PgzzmLZinSce5BeclB0P9Yy8/5BRd8aoS+fThWKprmJvL2JggsPT4/ULYC9yIEgvkYYfWfTnDsiTrtjpiiltQE6QtDZDJzEcm3y2FfBuWO434dKBgjDJCXv/ETlDnEiIDBRpBrDQ1IqB9gKFlprgsCNc117XJaDM60Vw8PSoC2LxEicVdukhU9qwG9Y8kxP2aWUqD1K13ponvxPPNdYJQtpbM91mCR+nHrC1k0xMpbHSwlonTssOcmJdtqx0DzKBm/6X8BGiD96Jh9+6XfvIIkKWxD6NnKWHvJANT+WU3eoNpjKSh2QGBxqEycOAhXN4dUtYkKoDQwztDmKHPJb3hEfvHQt0YXSufAd5YT/H+uz7oc6qSSFzYBcsk7XgEqeIxWl0hmUse8ayZVW2POYFD8uWTdlR5Axh6saXDtpu3/n9zUHPfWmX1HYO/FxJOLeb9FdkYJdOeTE85lvD53maHvNResjz8SGTjGbI5Wscc7ZWTZZpN8d+h3SLJVwtUnEVWuAcKWjKKMVN4QujZk7TGU/0Qx6nMz4jL0EL6/yCVdmycvZMfE96ULjYD/I8xuZyv84izfdU6ntzHy9IRrhYM7Ehy4YiK1RGZwwsRF0lXEfh4urlXhOuzJopbXi5+gxn+lnGlNGcmMpzhNzxTrHk6ryXfZo1LnHF1HRKp+ZWT39ynRLAccjJhKpCViMq4jFLTSbZkng2vsHLdMqLdMr76zfYrh6yT2v2Kq4oUPNPix4kCEHj4CJ6gSy46lctRjTprsOcaAHZnToHzZzqxGnZ8nh6yTvl6zzKLzgtZwzsiOSdZhEV470Jc3D1kmVkkpEz2fC+vMUHwxPeHd/m6eZzbNmwE2MORNXVmmAWg6kSikBmVZVWOU3b19QT9UP8f4Vyg5deoZ9gdHrwsP1tsQbB9jpCRClqMQITSLI75NPygtPpBQ/ycz5f3uet3Qc8yM840aeMMtk5UwWZMyNzOqWLaZw3qVLXXYaG8MpkuekKA9NwyvPhMe+Nb/P/5Yf4Gm/yTd7g/dUX2KYTJln5uUg+iBY0uLcUbOT89g5QqCADnyiDM6VY3jApCOaHui47nuyf8rnd13gnPeMHNs/5Ph14PH3Ig+kjTimQJ9ACaaxjj71q67isX1fadyJbmThTlZva3An8TLJOZNbs0yk7eciexxR5gLIBHUnFDE5wvAcr/7lZudeE6+XwJh8Oj/mXBXaiPBo+x4PhKVKeA7lyZVQLIwegEIFFOk7fuQkHgLvApyFxFDWEvxo3TMOKXVrxHqd8yJoPZc27+QEvxKSvKa39wtx03xErIJyXg3gZ4mw80VVA8zJFlHTPJ6pNGYHMMjh7UBgonLDnQdnyJD/nf7/5HE/ySx7kM9Y6gXPrObWVTbMguzGqGP11DtvxA2t69pHtcMI35THfGh7zu+NbvJuf8JIVOxmRcWWIRRSRRFGpFlGJMpvv7D9H/pb7LVWm59XRxRVmeQv92D2q/VFJQY1HZ9Ja82/z38kIGDnzoJzxmDMeyxlflKd8fnjKG/KCt8aXJN3VAAC9irk/U2FUxOKZgOP6tDSJa3bBrJgzK0JJAy/Tho+GR3x7fIvfWf9evp7e4pvpDd5dvcnZcMJeRkSH2q5o8T2cEy7cwOhCGnDthfeEmxrZzQdgcPwThGvPqNkI1/iUZ+nzvOQZJX1Ezm/yJH3E4+E5DymQ92gplJRcOBTm3mq3U6rhSsUE4oTL/choBk2iQmZDHk7Ypwd8hy/xrelLvC9vsF3BPp0ysfJUNDHWT6jE9YJHvJff5J8+m/hOfsGpnnDKm4iegVviSCilQlevbvXmSLQ3LQeqyuguinG75lefc2EcV+RhZD+s+Y6seL465cXqlG+Mp7wcN+yGFVNaVVF9KMbFGgiFGO+id4/NuOAM+QcKB4nwlvHvQtKyzzwQqwgRDzGjlGRjGVRZlz2nuuXJ9ILpxXd4PJ3xMG9ZoeBhb3IIJuKHreMUm7ArvXjH4X50nx2La+T1KSUmGdgOG769esQHmyd8c/NZ3pWH7GTFlAZkGN0y1aw3szaeuxGuNoI+sKytS5MA6hqGSsSHI+fUjw1/mY2gr4f/zqv3I/Wm11i6XsJoDFwg8BZXEiNcqXBaJh6VHQ/ljKe7D/jW9kOe7F/wlm5JZU8z9e4YlfqnwkWEq7Jn0W/Ax5yoCINpL9LAC1nxbDjl/dUb/LP0Rd7bvM0Hqzf5YDzlLK2Z0gjdXqF6SLg8ukdbmRjFFdiJC+AwEm5KsINqKe1xwmXfWcSfdZqYeMhm2pCnj9i9fI8XzzNP8nMe5jNOS0E8+3YwwbFrt066KuHqegi46GwFbOlMVVjSmpw2fMBnebe8ybvbxzxLwjadMskKnRllXFej1cq9Jlzvlzc4236W//e7ymZSVuUNxuJqKVelpWGFqlBy6GkNUFTUnJW9LUPSkSrkKuV6xK1y6G7lNOUJEaUMA9M48P44IG++ibz5Jk9PHrPbbNgNA1t/PykMKEMpzTQV53skTLgNkC48Q4v6VRhL6z8iiEcwGr/f8jsrQRn3mY3seDC95J+8+7/ycLflQbYLZQBVCyAU7geps3JroYqCsl0ReVTHsMP6lGBKwllKPH/yJrs332L35It8uH6ADiMMFjhX8+SRPgpZaBHhNfhM6O3l6lqrxaWLOSzX7SKCsozafh6xakT0/P2Z3WBc0g/0WoZow+6ktJt3tN2ibDh753u+IXEyTax3W9L732T9/nfYvHjOk/2eITQbiyPStlRZIq2AiSBiTUXV1nvQYvn0nCBmGSiSyJJ4gXA2rnmxecDTd75IHj7DdPomz5KwGxKTNKKl0u+ngEf+D4nrGqJUV7/MeCZiCwqiA6m4M3clAJmEsi2FaXgDSU/49tkH/K/fXvH2BytO9ztO8p4hK0MSkmDSWjABAQPXQ0tXKj2uaR/2eyioxq217ctZesJH6S2eT4/4lqx5LidsU3JNrUGt6nTjMd1rwvVMn/BRfpvdR4mVPmHQPWix4KAouRSPdJ2I/C9hnFGSksW5LvGLaDW+4kIz3VpuBiGRldnOYianwj4Vng6Fh0++wMPN53j58AEvB2GXYPJDKyhD8aTXlQlN1eS7SG8Of/slMsWK+/koSlYlJftsEIHk17O7LWfbzGabOS1CkpF2WyQ1/tugKRj51g9zZAzXQx09ot8LTKJsJVMePkHSW4wn7/DhKG6Sr2ZD7RkVhzGxl0y7OekIPSMgLq3YJzajRMRlRJOrfWwWnT3WOXU4tJJMXjtex/uhu4i/Xj/dnUWdh6lxLQP0NNsHI16+V7jcoMI6DawSpDxxlk+RszcYX2x5nC3Viyz21XrsYbNZmi59gVrPTbKMESedSFooCiorNA2UlDijsJuEXRko0xdY8SbD+IjnaSInqqSv3Z2bzohXrNIRXmjJ+C14q6gftb2Jehxa9bMklsgzSjDVKSlZC8P6CVs95cOnE0/O3uIkwyYLuisMg2k+Jp3qnfztmjX1xbQpvhzUi5Z63wvLmJAK7GTDXh4zccJ+dcJ+LOSUga0Z+XySJa4sazKnPEUZ5IEdQFFHroqm7H46BiiokMQ4lEJTWyk4WmpIEy4iActvrk4s1P8xKSVbKBQyz1MhyVuIPGHHmp0oe4kDZyqrktxtQ4LrDrP4ZjRwdyW1jn3Vilq0a/PjEdIAechMacVzeZutKGckBlkbovHxBrEauuzNoXzr9+BVvZ6yKJMoO5ko5YRRH3CSNuQhelMn+BaVe5+ESXL7Tqmm+yXyuoV0G+tQEiRXKQZBcaaiRRYXI2hVmtAr1n1G4sQKN/N7pX5ixf23FixnnUkEJQ307gVBuEaSJ9K0e1rSaFMfRl7wEENpe4oONffaXOXLOT5SPr4u+k3LLdWZSih2D4mnuheTlCChOrAjs1dlpyByisqKUYYKA+H60tvl+gIQqVmcTNc+6xL1hHXBZelV6t2ZsZppG2ZhnvxfRZkksQW26ZRteswzVXY6sGagpMIgjXD1+3TbpbE4bd9KLwnXVYtcFo53ETIjmVMKK0gbJs6caV/A3w3LvSZchdH8nxjpTdzjW3HLJgu7b4QrfDVKMnPyMHwYlRptPkOvtbikXI9gzLLtlkJJhTJkplVhl95g1IdmSaiRgsEIlx2kdqjaxanXK8K6fRBWDFfqzGvYGICEqxCLMI6JMik5jZwNb7IfEjtGI1zg5stCcnXBICHhKh7M5ooSl9T6Rca8Odn67pnIKqx1wyjJ9V8W3SOnBDIgSZgk7gsdxWXfJwzZFDDfuVQ5B6vHiKQn7p51O6I2zOp6xXo3o07VtezH6iFNXdZPgw9xq7+W5sVvfyNdRVisOUIvmivcTSQkJTQV9nJKSYKkDGlVz1pdpJAKZsekPROm9g0RYhHbXbotSo0go2EYIGYQEDCfgT2ZPRND2pCSOfDHGTciYcYZJvwo5jxeaI7XvQR2+TnSK9U7/DCbf5iSN1gOxigp7IC9rNHVY14KTDIypZE8KknM8zp75H6k7fntFrv77b1Jy7F+VBDC8MUnKYnMiDIwsmLSiawTc9nw5mO+14Qrs2YyA9FuPToUp7mFd1GLSB7Mj+liGy8xhJUsnjpBFnB2Ybm6yCtdAqeU1AhXypDOzI8rheNsqtKWigFFVqlSIlDH6TOYTf18pN/ql2nla30GX30rmeImDMmZ6yJCkjW6+gw5Dyij6b+zokVBBsbR9Pt5XzyhppGtqzEKXPmQ1jBOwwQrkHFEEuxLF9F9NViEFQWbhKvntGdJCpP0zMJSMTN0sDKc8/k5yE2YuQ0dPtPP9bzEJlfoc9aPtyzGQJSYszMQJsXFU4ms5jhvEUcUVWXrrlqjCCWdggyoFHbpxJhHbY69dRAVUffE1j8ZbVw5F4+w4DEwS/jSiR/SQPSZwYVRihEoTROkHSWNlJQoiUq0jtsJxz7mGc69u9J30p+j9knY6ZUBW5T0AB1X7HRgnz3qTnIJZ+zkxjuJ+KGVicOlpeqD1eEYqUxIMB22X9n/NugcCcbhNvzk7jXhMg4x+TnoTkdnGVh9u8UzySjOybnawFffLLZMiL+eMXyQgKs+3ffpkScA3EKqHjQJs+s+E2kHqPHncqh+Hq7CDZYr1oPYS3Gn7Xo9Em4EDrTqRhaqhriyG8XISEru+1XsjgTB4smlGNc11lAuxjNtuQI5BvlJ5OKWmOJpFpKrPmaHSWo/luwzVa68EtcqOfvivArWuwtm+Qr9aLeQodqTUAUFXu10ZtoBndCBX0hXktwfsjn26wE8BoLzwZjgbhluRIx5qFZ+zg15gNYcG+DSk2rxqOvaVLi49Ojt1ouonmPrxhIGBfZwb3V4teXUq9Rnkm7cRYeFYXKVMJX5E2g2IjXDtl95JFPZ2tx6A5u7KAbX6lSqSaVNSozea04z5/him+pPMBk1R5tJvDct95tw0TiwA3G8Yhn7acTI/g6LODpgh/nhvKjnVm4CMuI9BogHp985iDYLjMZ5X6Wr24bgkA79vNX7P6FDIt2B82FLkQhlhwxOJARK6SKwN0bt2gO/yGuhtlnjugUBM+RnArgjyUCOi/nG71I5/C5KwLLfuzDleh3F8bUtZTAf0mjVEsw7bBxG5Amdczoidb11ljdo3vVsxYoTrmS+fWGpGu2JSxnkWUc+9mAalizZxczEDB/MqEyctnb2+m1fgsBFDNSs/crgCGbV7NbBHSBLddmJM0SnVnGGQexcWVMxduEwmsttlBhb33Yf9PX4G8e+T7hWoxKuwieWcNU1XUJTXTChLbQDolyk2HtdrG/XXf0JTswZz+BEpSpc7l6TcYWhJmipC2j8VxCtOHSkAUbjJsuUgYQkgUGIaOIMQ+XQ7mTtNQ5HMAdmQDKuU7U0K1N2ApdY5H+vpuXN36cHtmPlMh78PBR4Uf1KPP0N+umWaUG7Dx73posv1SAwDmaJ2/zJTcGcp8nX08ciQ8+dzEsAVASIVd+CUkxKEZNIVCBXCtu1JWacIVhyUkTRlHyrz7/36ZmeY2eq9+O6Tl26fZDZnhwZQ52/Vg0FdIxfPFCNUKTOSQYn2j6PxmzfFYaQxe+rPa6LD+t+yKuH1brXhCuQ5SxK/oxoxUcBCf1pdHRbAcbZT65iDi/dr2tuQHAc9d0InRLe6PZx2EAFxxbEoeH4uI/o0dHdEF5FyEk6YtoOf/FeB5WK/Ozee485WZoxRNGCZJAxuf+GApMPPmZ3zXLZK+Jrm4ylV4WiI1PeU1RdTekHSgQNr2tCygrwCLgJRLVcoAWGv9X6ZZ9drz2Dn46ACVXdFneU52AeS4CIOZ6TlWTBN/FovEbFBpwxESp707cpHcz36n2cedABijvZJ6GQzOm5FCQNM4MGG6LUhIQagFnMMbdAp4rvutJuywIvdJT7cvvWwCWX1eOdY3sRZuUtIk3Lq5ealltw/JDsDBWl5n4ueOBjcB+gOyhytNoX8yXzWmW+G+nOft1RtZ43F7Rqud+EqydWHcNhVVk8GBygdgey41A1Io13X115IFd5qB9sB9TSi832nShdXWh4pMaV9hZKPV726Q0JwEXFEYWq+iW3I6JKYY2a6RI5pT1IceKQiEgFppbP1AMd6g69g4MX4t8QlF8gQSG3w5Ysm4CF4uH48kndgPinh5TXLglL3f+bvHsgbwX4tzU6WIjuYGFhn8zm1WN9pgIpmwgWollwWBFmaUYsC6SpEQ1NULrLe6d7RryIaGE++Dg/8/XXYAK1UFXEAo1JnJ+NxmsE0Qp47FeqI77Q1Zc/XFAP2OlYz55oq3RWnLHOdtcu8WzMoTqOO4MdMHwHx75OY1aWREzrc4fuOM1GNPgJrS+++mDvNeGqnD9wPjfafRbmxeIvHxCxuy5xQHqpK2OSx4SJJNoBhdJOsZn+1mCj/v7hqJfroEfq/aG6rN5YpMqt1y7Ca1sd/4xoUnQs6IOdzWevMGwccRWUnTOwcbHhgNzdR75y6blpwSSBEyFtHjCsH5BQjxEHpIQWacjxPOI1Kx2iY+4bdNFqXrQj59WPtcc5n1+1n1krobapiKVwoN5TbK9GnIpkZHRJYQA2WxifwTjBemPPhLGDRlvOmKTscN7S3cAA+xWUNehD2KzRLORJq6WnDHYWRDKRpblmA677bVaFDGvSODAMA+i2m7zDV0cAtUqA2sHg7RdrNflyLglbBOEuJjUyUHRF0R2UF06MVzCuqWcljs5wd67HBzNYAlT9ypnX/muZv9akX5ufc403Hs29JlwzHB0ItczXFOi4P6mwaz+lArHWi87+nSv2fy32Nw5KELAJxFN/yISmPSUJJU3kVMzPSNuBE29DO9VnnyuqEb1ucAehAKLuzqtweb02F+7GfgA7xFdSpgyZPG7hwbfggVuZlY/cV1RBz+ySJDj7Eifwlg9gj6hU4NEp0yPhTNdu7e1MS97VOaV1ImtYUelsCVN1llZUcvP90bjPmUtsx+pVrX9Z/YI2rlJXH/O5/RDKLINFldzh65EqtRTXQbiFayKi5GckJfZ5zz5nePwMeAbbnXUsU9fpBncC9I9cuyA7+7sI5BWij2CX0WcZ9g+BFegIjLaLWijlBYwvjTiJ91WLutQ3AadoOaHkDSI7CzpgO9TkJGvUx2SwcCeuUNCdOe9OYt2ZdRoxRzMjZXWCjltIz0ySLWsoE8gD3Asc0uqGOOiq4wbDL+0DDaazzSYm5U/0Ew3JEFpMyGBkCnM3juuVe024wkxzxiQd28hzL4edR5X2WDR3JTgIgnjel5W4hK4/pCvnOnEVS9qDvDQk7ybBGqqUuOiecTjLCS44/mXMGZb1mLO0w3ulevTiZvDq92wSAUsxwjucIU+2PHrjAY/fekiesuGhpKiO6BAEBUOSwK0TrjpWEBV2wwl7Ejt9gUp3V6J7UDeJTv09RYfJFCju86eKpqlJnx3hurZYdazcVDS7aj++lRrUTYrDoz8fTqJVr9vgV7uwYhaty5JGPvzims0XHzKwBi1O2H19y8oYg3oGTfUlsqlWdZJHhnzK/qORD/4Z7D6YXKLw9XVGb1xlxgeZtJ5QeTmfnwf/LeNEepTYPEisHgij7Gdait5xv/eDlC7e39VLjykuqC+RSaxhd449siqiiVXOnK4Gxu3E9NjWPr+cmF4q6JrqE9U7Wd4F4Yqh9jPS7oP2VKsew4kHixo45BMqcb3yXvX2zb4zx28PrgDOFc+Fj0Uc+FADuhpDtl7fA9nuB4YJxjMY9jDszJc4FUTUHXSNos5wUqcjr4evjrRnH69TD6CMurWt/dlTD0XkqlapgGwHsaQdZfWc9KWJ7/99T/g//L7v4yzbFUYZLH5g6Ri2uFMud3XwMAHvvffgW9/8iH/xT79hAxn8Zx+GBMJEaQOqRN4m38bs+1oXe7gZ4boI390m4TrWjzKH0Wp/jUtHcfmf0I7p0hIETsgUGDPjmHjnh7+fz39h4OHDSNlj4FGcV6uuEt1Pwq4ek8JYhPUO3v/dzD/+lT3vP3tK3qkxctmZo5R59Kbw+HMD68eJPIQLuVY4LBSmVFh/obB5o7B+XNiRKUG41MM7ifn0WainMI7wuI4uDZwfS7Jb4Jrb61h9rrFo988N+CvhcsfvCAU1ZOV0X9idCOsywgcjz76debbfU/auLkyDaXRD43pz4eVKpR3PI2x9T6x6ePMSAQ0Oj/j12IS+3GvCVct1kd7sUMurrN8cUWBMqu4UhmwX0OkZDC+QccvqwZb98CE67EirCbLHVhwKebNlfOMRJ48ekh6N7JOBsoF540wsLuH8cnemKjyAnJuy/l2LcdD9YEUakFQUkaE6H+a8ZVpvmU5eICcTfOaU6TNP2A1b8mogDwMTG4qmavUlw92Z+kdIoIGB6dHE8AY8fHLCIGu0WASIUQZE7NRP075Kvb1lWaAtoS17lHLAUVxSX5QLd+G2pKz+Kx//4ITLJI9SGRQrJulIMZQjUkii7rRv1n85W8zAcUykL07s3khwIuQyGVEJOq4rt9gNOd00DYk9QkLKwJBXnL4Qzp6BPiiY6jyDDL7gW8bVxL/6B7/I7/8/P+IL/ztlP77TycZmgWsZudW0aoOYgSK5+kSWWAmJ3WxnxdKwxG6fv8DNgCvq2rXg9XoX3eqWeNWkqrrYEu0o9epAYSgD4/6UYUqMe+Wjf5H46v/z6/z6/+PbMJwibKAkZNfxGueA0KuW48oqOafe/ek0WrBryoSCmllUi7N581F/DxCuc07qkttcFhNbrDKLgnDOs+d2rd1jflrdstd2raCy5/RB4V/7N95CHp4gq8n01rllld2PE5vHG9aP1/BwYB8HbSF6Fz+Eccdiif+OjfU22HNH2X7Qi3gwXTXuVbLF0xOxkE9TmZjKnr3uyadbPvt7HjM+zqRxz0527ETYaUZlpCBkzSbhSNhH3m4xQSKRdKAMMK7g0eqU1bABVbQ4OZbkhHf08UhFR201IwJ8NV72lVs4Jl+BoFyZ7rwi4Tr6lUs/kTIFZ4RKrVMlLinJZ5oR2XvU/AGRkVLciTZBeqMwne4pKzFXgyG3tkquMGo5z4oRL5lsb0oiZWUoK/YbpYwTLQV9SLYKqbAvOyZdO+Ozw2PLmKwSkU0olD2UyTUAVaJZqOb87Fd/KXR2Hxjr1Pu4hQtIvBLPL/ft8Di6Kr1K8U2qjdxWVYUKqA6UKSFlRSlrck4UTVV0cZ7DxlI1HpfgsFspS6Lef35YtaLOQGr9u63QJ5pwXVCutIlXeOhAOtbDagC1moMgUsw/KIFK5uGjxL/5R/8VHnxOSBtTp6RSjabYArIBWZuB1YSrWqKPjpNRqBaVMyngrtgu7y/GUHMteTRiESO+WS0Nyz4p280LyoOJfDIxDoWct2xLZlsyDGuyCPuyr4TiLk6cES4BBsbNmtVp4smjEzbrB6Y20kwpuUYz1/DqdAmzH1PqnE+G0jLptsj896eEo3jAEZTZPEKtJTp4YNwMjBQtICNJVqgIRTNFC2mV2Q97VDL7vIM0uZQBlH2VWS0JqiFoTZlUxIxesrLaD+xXSp6ZyRuhRBJFhA+fvuAb70IZEnn8kOI6CTMuceJT52BlFgbqyD71NjgH33F4pGafqS6P//F6rK1gEqsmhMF/ZyLhT1MjJoayRcoJTI/46N01Hz0bIG1sPTQ1ovnaQe8659QhaaZmv50Bf28TrtdZYj/DyjuZYyT7BDIwnSW+/U14MJkxUFY3tHO8vRM8KChMQ/PqSrTDNeP8+q7vGngX/dXoGBpmrjbQnCAPe/bjlv1bLxjeMkns7CRzphNbCmUoFNmbo+tKUSaq6uSWS65N7ilMaBrQNLKL6PBkyKXmFLNUEbkF8+gcTyNdOQhDacemWXfeJ+IlRHy8RrhKh/TD+MbmKQJDKkx5X5GuDMljBRYy+yp/MmREpqpQI7X7HSN8tlYqhSymKpS8Y0qFnEAHRQe3RmWEvAKEnAf+2W/u+Be/9T4ynIG8QAPZV8LlY6il5/xm02dmGXeRZuaich11V8+BasIsJo+RxijhjL0CfYOyHWH7WUTeRAl1NsjKh1LOaeZ7tHxKuA7K9TiKqk8IIO4danUAWSFyytnLif/Pr3+T8fQlMkyo5no3JCg5qVndiVLGVIOECunAub9qN3y8c8J1Bwi0GprYQJp6JQxHXEqRCR13lPVL+KGBz/zw27z94C3ORot2ryKWU6yqOrtkJjVg6E11Y8dUGBA3aFPcYCdlj2cN8IU1w6yI0afdnUFrW3p3/6rK0upTZL3bPYfUnvWckR6/8J/XIe5bjtVv3o+/5YlVWzDnNo8IkNssygpZJzQVTFXqEmdy1wFaDjOkJ/LQHI6ojrb4votYOKfB+ZYSqj2NO5AEjB7OaSS/TOSdgJwAD2iHItw1lkq6C8SpiwjXNVHAlR+sdMqJV92PXp3Sq/x8UdgYASvrFh0kzM9vSnTvebnnhOsOdusYIM40WUd0tUI1362BB8C407RGUma7PeN//p/eh/QUZAu4IUDxg5rUdYcKo8fNU6E6cB4w9dpUbLNLljvg/ruQVObjE5PuL/PUrCbHLWxeMOhDeOuER9//WXZTIo/unOxRvANNBuFKXaTriznRg8Exxzwxf7sQUB9zZDVOCYqaJDiIc/guYRVt9HMeWigIlxHtPLPg6vNIzS/ri1unHasnl+Za3e5qEom4u4m69Z/O7Ud9DY/349JO16dRt4DdMBowk/NKuHRoseW0oLq3uIQS/dGdi8bY2D1gtLKM8aHd2vowBIvQLxCsDMV9AxEQu1NDFN0LlI2PPXdtKrNkX310jfOcs46JKDchXFcplZsIUT4Y3Z54QfNxwuejnbntCKxMjSB0l5S0hfwElWsTrn/4D/8hf/2v/3W++tWv8vWvf51f+qVf4k/+yT9Zv//3/r1/j1/8xV+cvfMTP/ET/PIv/3L9+7333uMv/IW/wN/7e3+PlBI//dM/zd/4G3+DR48e3Xwmr7XMiYMRK+csw0y1GNCl8cQQ4z7Bfg+bB9U1a5CBokrJ2VSLYS04JJgi+sBIvdACmk8YNKLVEa6IZHFnUw+kJbQUFI5IZAfjC+ApeTsyTW+yL0+YyK4etDsu8TBLyg4YkNl9Usyn/6xwGC/O51uf1yNtOJIV098mUQYGsgRyd3IRwVrF2j9cPUU9aZ/LaF2fzfbTiMu83iS3uVyUO6xzXr2lEqWO9bx+6J6a9zPvU2ZrGmvYS03RDjQb61K3O8jg1Eng9kSpPScdEE1OPMdOCxxm4kpkL7aUJb7XFXYDxt3XRzFJL53aXFXpLk5sxJ0/hXZrOC9y/OP24gVfvmq54FzGGa7qQ+wuoYZ7ckYsDDR6PvVgzz4Z5dqE6/nz5/yBP/AH+HN/7s/xUz/1U0ef+cmf/En+y//yv6x/bzab2fd/+k//ab7+9a/z9//+32e/3/Nn/+yf5Wd+5mf4u3/37153OK+xXALQXfQE6bj1MoHKgMiatHrLzLCzISSR0ZnD8B/yA5s6sU07VWE3liZkhSoougyA76G7d6yNerxwlbrz8wKldNw2QbzAfNK2ICurDgPIxq0HByadmBSKBIEI43pm/czX+rKxcUm9tNHX5XCGwKW+on2m4eRENEhO31eTcOb9tHxkIWUc1ufzO++p+Rtt3Y9/e81+pFMp9356ixZ10XY0KdUbpzR1Xr0Tq3JalfDU05o0ybXbvy4ekKgZxVRWIyLgVJ5IKfXCV4yYLcMz1e1o4z16XpdhrPoyw/v92lxWv8ZZOrCk6P8OS0N/K9m7WoAhdsTmrzU9QyF5cs278oP8uJZrE64vf/nLfPnLX77wmc1mwzvvvHP0u9/8zd/kl3/5l/n1X/91/vAf/sMA/M2/+Tf5E3/iT/DzP//zfOlLX7rukF5DuRgqtBfVteORxSUvSZBGJK3QXDzibgYzAgAAlzxJREFUhGLZgXGT1rB11fkBc894M9AIRNGNSSpIdxzYTQ5bIKZjdQKreHTqbj2qP4pLQqVAObO6DBWlxc0HIoRCauidnu+E020SQVsBrevYPxZXk/FcaJ561Gy8SWuzofjrcbvHycyrtHLFfg7w9mLtddFqFyWh3bGp8Vb+RUByPCh4dgWHi0ObmwVpVo78re2Pmc259E/a75kg0zNlNPCe8RpyHNSWxO+1lJ4J7O94G9NqDv+NOTUQ7FXFXpZm8d/j5U7uuP7BP/gHfP7zn+ett97i3/63/23+6l/9q3zmM58B4Fd/9Vd58803K9EC+PEf/3FSSvzar/0af+pP/amD9rbbLdvttv799OnTuxj2NcoRzq2GZTLCY+pAoWRIaUQE8jaThrHCpqp092FdW5a+qjGUlSD16pGLQiXdlEtM59SZ4ZBGIKkxgEkKeYQ0gg6UaYdq8YS4BU0KgzCQ2LEHCgOymNNtF1NfhfSUHLGal4LVDdWliiTmmLD/W7stsrBIEbPxUAr8OJdAgr2qsCy+heq0REhqgUDF3AdkJAhZIZO7tQ6HWvXUHXAATcTWe4sWRaMomrOpyXLxS0eFZLnchhXkrM13SXvpsJvhgvdbzv3y9Tl2Pi77/uJ66sBKCX40HR+Sgk6+J2kgJU/KGv4xgrtwDJSSbT3SPTdXuGa59dn+5E/+JD/1Uz/FD/zAD/Dbv/3b/If/4X/Il7/8ZX71V3+VYRh49913+fznPz8fxDjy9ttv8+677x5t86/9tb/Gz/3cz932UF+xLNQp8ZELTwp+HSQeLkhBhirSH0V1fZOlh+jgjx3p9PEH+xfPOci3USqHHqqa5SFVD0+l9pPGNZK6+fo7E1MYW9/RSPsxC7EiQbYCGQcpinBaMZO+VO5W04zZDx8vu5Lp1DvaCaCX1QOJHauH9HdB/eb9mEWnhDSkjfzanBpkVhXgTFxzV+xo110/Qt1a3SO6VoI9SfGpSxESElPY+yAG9+EjEqa1fqeY964i72N3LqREjf6X+vVq7XtV4nWsftn3h/WesWnHuwO42KwFbykpVek253mboZgxWqXHh/g9Xm6dcH3lK1+p9X/9X//X+f2///fze3/v7+Uf/IN/wI/92I/dqM2//Jf/Mn/xL/7F+vfTp0/5vu/7vlce69FyXSA4lqgO6NUscwOKUpEKgTRrfJyunZBqxN5RoHlL9lxyZynGZXlXr190WZsRrXMedgJqHvP+t7qjciDDeeN34cZFU2iBuKFBDQ+0QM6G0OP7NqiKeBb3MpUgdgPv796P1S/7vtYDeWkDi2X91foJ6786o24vzKTCHGZrkKRYDt83qa1Y6e9QdfbOYVbg2P8esWutiySqY2AXYLqZ2zdGo07BYauqLHsjj/qsdOM87HsG0T1XeZ36gvhAQwNIMEpQ03tUnuc48alWmd3etLWw92rwn8PXv6fLncuXP/iDP8hnP/tZfuu3fosf+7Ef45133uGb3/zm7JlpmnjvvffOvRfbbDYHBh53V46BgFzySEekZhzUEUhG28GqeY86trh2GW1E0kVtRl6qNOs+7DAX6HX8tw/I2o2pL9FnMZWF25aLhbIzFYkHWh0ULDmGfZCCe68Tue0SG5K6sx/ow03lHaGE/V9C6zSTDEaAC36vAFVU8Wgb0l24J5r1ctQbEbxhfdHeRfUl835RPUm4CkCKu9bYjSQg2faJCJ1kPSSSm8k7Aa/+ROHAbGvbyHz0W2gRIvzvXrpTQJOpIEus8WhnpGSD99XQNRoEjs4gCo9+AjXqfS1ClfsOwLg0Ia4fOLdXbxCnWDSQwda5uLFQghquXrEIPASxbSVgUETJxW1GPyH3Wn25c8L1u7/7u3znO9/hi1/8IgB/5I/8ET744AO++tWv8of+0B8C4Fd+5VcopfCjP/qjdz2c11S0+33sDiqKH6Rl2pK+ncExvyP7sLSyrxUi8kSCejF221TLpb7j5rxxMi2KggUXVoq6pZMKm9UJWSf2k0doGOrsCMlFuYvDNw/bdF7pZxWKMynCMK0ZdGRkTcpjbU+A7POTo2vy8S+KhboaJFGyNPVaykxlS5YdrKDIHu3uwLQSn2U4cjPoOTB8mZW5CX1LW6Luz2XWqDKM5qJQcKQeCDysZWfUwNuKD7KdmcEzLxC5xpwYVmInbSzSjSfaegUp66BeGbt2b4V4apLijKkMdpQzHvJlxUWwa11EQsZPXrk24Xr27Bm/9Vu/Vf/+nd/5HX7jN36Dt99+m7fffpuf+7mf46d/+qd55513+O3f/m3+0l/6S/zQD/0QP/ETPwHAj/zIj/CTP/mT/Pk//+f523/7b7Pf7/nZn/1ZvvKVr3xMLQq5Amx0eoEDWOuhmAVAw/ImuX2txjUOBVbien8XYap6cHmKejP42yydlDj7uEcibtwwDHAyoqMh9/3kUpYKRQVJQbXiIt/GP49wf9ulraosPrbdEefCTaJIJSE5cfb+RHk5oS+3pLxBGBAdEPHo9gothNEdDv8WS5O6zDk5iZjBg0duIW3hpJAeKCefWXsYeROfIwVISCy9RIhoVWceOwO9mhBpilpJWu+EzT2hdLjeG/SYn9V4xo+UxED6s5QK6cFAWgtpVShDIoJE90xds6j3/fPVEWv4DghXL+Wbxa1FxwfRRCKRXyhlq+iZtphv5xVf6OPr/b1frk24/tE/+kf88T/+x+vfcff0Z/7Mn+Fv/a2/xT/+x/+YX/zFX+SDDz7gS1/6Ev/Ov/Pv8J/+p//pTNX3d/7O3+Fnf/Zn+bEf+7HqgPwLv/ALtzCdVy13wb30xKzXRywOCEEXelVHgbEwPBxhTBTJhlxqUFqd91M52tsu1fyrK8G9dlKeCqQ1bE7QVWavwsutIsWCpKoKKQ0+0kJhqsqQdBfDrqU13owVOjWMGMmyFCgjqQwM+zXvvfseL759xtm3dqTykKQrpIxuUWf3D8Xvgu4L31s1YaWZruSc0dEC36o8Y/XZFQ8+d8LjN95EpJCTQOcaXaXSjpc5L4itzmqNSAThUjGJKgtkVaYyzZ618+DiWGkSW9JUjUqqSb4UGJTx0cDqkTCeWsDqkto4+ohKIYD1gWdmxPgKa3kV+tZWwCODOOwF3KcCqwxn72emZ5m832PZC+ZM7bL3ILafxHJtwvXH/tgfa+aoR8p/+9/+t5e28fbbb3/MnY1fpSxFqr6EtKTtT1cTzs98AZmQFazePOHJOzA+AB1XnJWOCe20Nb27y6VWazHMy7jH+p4e0q3lgVHP1DKcsl894oV8jQ8+eM7+axP/ymffYL0+YVgNnOkLu08SYWTt9yj9+lw0oPPql6EM/6RnEg6+ddXLJEge2OgD8tOnTN8W+HqhyJpSVh4B2RdfhDCNv/tIx7dUKgYPbO4wuSo11FgWyJvEpjwi7ycYFFkJLawTiPYxKwONHl97+7TdhVaU61RDs7aAGmCRY1ZDU59FWLRqwSgzAqOqlOI5vFImna5ZvSGsnxjRkuTXZli9jjKIloPNkhhfpcgV6v06iKYKtSIm0I4F1nvYnyXKWSGXPbC+vON7AnJ3UT5Zxv/XLecCxvILab9rjLXzGxCWNwFS/w1OFI/WnWWPrkbKOpFHc3Gp9oSDv6NNkLsq4VrWkzYL/KiHkYLFuGh/96tQDSLV8EtJyrRSdLSLdVVLw0ARyB5/TyJ6hVqMwmr23K/ldcp5xMtGaYR3Hqw2+hRfQMUY9sTAgBEnySNS1tbctIZptECF2lm8xT3DfWF8l6IH2owfdIK0QcqKlEc0J1IaSRRKsXQj1cCi1xyAM7O+DqW/t4zIiephoBrjJjoiOpJ0YEAYUPPt02JjKtnX2qVj1zYoFm8yVJMa8aiSgAiTKJMoKQl5wAJYV8lK6l7ZuyY1G9HqLGFve9kl1qOVou6vL0ZgTQQTI9i9JiMo6lHG4L4A3u2WTwnXtctCn3AgysepOA+ZXQXQnO2TbNzvMMAAu1HZa1MIDnE3G0THD8cxoqTn1f1sRB2cCHWEC/++d6I0gtA42XhGkzKlYoOTkaQjSc0RG4TBqa2ikFzppPjF9XWkrOvUm/l9YxF6EmdqwlSEUQcGHZAspDKSdG0Lk9dGtKYB96puLVzApHz8SmyYNISY97TkcBvfsxUUGHTlEo3fgQFCPoLcGxGT0giWdmtthMtpG4Ll+xpIunLC5Y7hpaClNNVgWC9GeLHgO2rXjuyTQvIzs/gprpLsYaMye6L10zaLW1xvqJaZ3RSQ0vz3zZBSGjM002HGG2HeL21NXoXfu8flnhOu7xay6Lkf5dDCCo5zRdeBLqcUG0tJVAbYCuwHA94kiQmqpZKInGvTla9ZPzaT5EaFyU94+DyVRR4ki5JRYAdpP7Ca1pyURJ425DJSZKoB5VWEyb0rk1iK8xre5lXqhJ+QSVsl5ZpUMs0uNCKjk3H0SRNDWTNMA6s9bPYnbHfCdguUNegaGQZTN1ncXsP/dxn84w6K9uCYgNUK2FsW0LJi3G1Y7zest5B0wzisWI0nqEwYlJmxhmhCNFVJvSJnbS7NPfINqR3Acn659DolxrPEuCuspuTWdUaZ0jA2IjVpQ9x9EYxRCuI7iDU9usGHKCWirc+yEMSA5qdGZ31ch1EKSVLqfZ/VG5Hs4wpI6qzgS1u/OX6po+q4xlCF3HP0/Qrlkzvzo6UB3+UlsJZc8EoHpd0BvqiHPkpg/7yiTNoiB7QDIA7HdxuJol5kK3h8BONUlwn84tCXNau85mRasXoO45jQIZGGoWatsMPcHXAX+8Qlt3AA1t7yclFv/lWKlGbWLcXa0/p+EGBXVkl8F1KjqYlGEkMR0hY2+zWrCcgF8azHMZ6qGmM4jkw/pmUGexHTWV1SV4upuc4nnE4bNi9hXRIliedTGxHUMxlru6NaSN5VcqnqRCuzTBy+5ogwIIwvYXiZWOcNwhp0MOIFVC/bRXp68YYU9fASBVJGSyGSYWrErj1C6wAzi5ca06PCc4y/dMGJVaTTUhyrGyymrp1Y5lppC2Nr4cTLrkxNy4JObRHPtSeQ4/TtfoDhK5fvIcK1kJkvog5X3txXgYKeaC2/WnwQ5/Kgv4VKA1wtMlfB9A/dFdwGrQj3F+05QKkD8IcSsEK3A/lp4cXX93YLnZRhdPPrYlx36qLZal0HrnVPVzVfzrXWujPXvatP6rUtdATZkUQWd/+ZEtNHCT1Lri5suZLMsrPF+tNrMTwfozJDfGILVlawTeSPlJfvbi1LdXIaIg3vVvzr62+We7Y+4e59AOZtq9uai2fx+Sjx8juQX6qpF3y96xBrxIzW6ELW77ghPdyO6qE8Hwtu7XpsaY4pSqr0dMX6YpDzeoXByFPnZ2r2czA0qsrju1IuIKTnlSV+uIVyzwnXscXScz6/TptXWeRXBJz+9YMDnjzTKcbtu+NxSpZBuDL7swN1keT36mXWtEt9561VIiF6yrNvnPH0a0/557/xAbDH/IGSc8eCjBsLqnqsI7lm/aB0VLC23SOvDjlAqxfjmSVt0PKINJyyWj8GGZhUyTl7KilHpDmi8N0zVjf8oopSEIaUkGR54D969yUf/sszfvd/+AjouH+gimlBfeh+qpQRTkg9M0MTfWbtKVAY8yP4aMX0vwFnaygjMB7CXW1rIYzEHdgwkJJF9zgUVo4wtRegC138ftX6IZgIpRQmhV1xHzYpnuE0YLOnnnJeQx/r0uTL23PXudeEy9TWPQtVBXXOlXj6ve/LbE1lDheVPeuVeMfutRabcoQrqlyqHI6hRquRhNlYKQzO0TshWx7GCBKruKrB1Yn9GQ/4V44bZET9wKqQUJf4mlQdTavLkoo6MluhTC8GykcDfEsMEenYxCpczahhQdXHDxfmURJ6x+p+j69Yr4uwQKYx3p4Iq0Uo0UHhcyPyeIWMA5O4z9HY9lHreDls92NeUhCdZGlmIpb7qCPlxQAfJfgGFvGfHlhjDfvo8ksC1lta+nMqoGsaDPmBEwUyOSvsFJ6dwHRqsDJDT9p1r34WpGlp67BMD11ULeDzjEhS/3jt15Kp57O6AQWxD5P96mTdpCrBz31x8dRDviWJbNks5nib5VjDfQSURI8PqkQr7QlDX35+WkRljmahvmK514RrXjoCVnVI5+ymLN45tjkV2cUjC+4x3lvgq/NkwPM/lSb5K7SAp4cSTQgKc6pER830+AC6YV6k3mBRnx2Imd5nLi4uFZTVrHga4OUIH6zCtIuZH9ts/SOiRiC/Ttd3ad0RoHb+VNqtnfqzcXD6rjWIZsxhsmdWGZ6s4XRF3KM0JCh1qfu7jHtRelCRltfJ6IEBokwjnK3gwxWyNwMKyxJdn2SOvIJR8P0NJqEnXAiUDW2dSyNeTGgZTE05bUBX1AzJszFXzu6Qg+sevJSF+K5sWc+x0jB7JeTx1BKndMSLw88P3Ehue26zY3/emp+DP2fVI0zjK5TvIcIFVZdqt6PHgXuJ9IHqe8VxvHdUJXXeHhz7vNvwo9bzHX6t6R/q5Ut800NCT2WCh5uld7z9ejXvjwHHGHopx6cU9GE/wG4DZ4+srgMWg81b7lJWNAn26AZdox6L2SLDW3Frx9RUXRKEVBNaI8nvYcio7kn5AalYzLiWnMP6Eec21NtuYz8GNMs1u8F8rlW/qD1pstMM56SWL7qsSBnKWSbtNlAGcur3J/YtN0aACHqbmJtZ9t+PjekggCSMEczhWNjQbsm6okBRZHBGL4mDUOy3zsAw6NtBGweIVg9kydstvl6qZuIPmN6/35fu2d4x0uG0MY/9mQtmw33qBjls7lbG3pW6fhc/dmHpeZ1XKN9bhKuW4Pjcblk62qE9MnYW2hn2YKhLNDHbkKU4fLhb5zsuegc1OKlU5rQ27UxrPX/d5WykM0/zFuur8ZfMardZorcWQ73v2YYuXezBtp5mcjYguoIyooyz9+fJGxtyPVQPnle/SFVIN974PNdX7RzOJS7jeDMUoehgJvT1bSewuojtOlvv265f9tkN2u4YJQNlmSGUUgZKUWAN6numkQDVoSzguap7HK5R/8yf6e/BdGC+d0FlomNnBLRb3dSNW1KHO6Pt/m/fJV2ulH0nR+6Glucn/pL6jV5YV/+rpiDxd+cZin1+EbXDO7IwjWpXc4j/Tc0UTgKyIkk8/BMegJdK40x1yJ2Wc5UtHHe/iR2fO1wPiHpg4Qudb65WvkcJV5Q4PA2SBYh8ShVV6hKc43UJ7Dv/bFHm23fs+yV3e+S5+qe2jMf95WwQVu2G5V/MebfzkNvNSj+3o+fDkZmeA8IiLs24z49WBNekA6lz7olVj2QuIlzztg7rPsiamLCRd/u1RGbdHanDSdsGuWARXl85RArXKzr7fcGcVNC6PvFcwGOsb6xXPKfd71DlCv16GjDH39BCYHQT7Edb982/7K1Z+63uzpB9rZ0hUxC2efs1lcuFRc+pQw/557MYjTnTSPtTE2n53yUSk3aMm+LfD22u0Wg9AncHe4H6ZrckMvu24bbL2lKXyM/TIl2zfG8SrrrBHRsZwFwXzg7UPKHdcaXPZYhpDrg9ol8Qkd6qqrG7By3p8hAuOUjVakhhTUWf7rvSvX6s3o/3sno7JeXowY+/6/PxiATXGuscq+1qN+242jjERw1eXrU0wh9EdjZQldlj/eSWRof941GWLMtF63lVMjwb7pH2OOfzK/dzDjjLrBbQPAPGQ5iVvtcgUA7c1QKpH6XOftUBHUHKRniKSRkqcw/mOEcxlEofg6yrEYdA/gSR8tpsDUpHCwMnzNnA43U9qB+ZHA37RwJPRULFF+dbzWCmEq6SW3R89ezdIXEKFxD72yNkS/YPlkYt7Q5y1qvMV8DG6bBw8MXNyvcg4VpunFk4BdCWCpjz5wrzA32TeGW9j9HxcbWMwJXpiKFcxICE/tBxQu/sOX+loaeLjtFN6rZmlxGWZd9SF/XV+KtbKnV5Sh1QoLlWgkBH4sOGLy9jbi9bw9vek1fpZ1k6skNSkww0DCj0gr0/MNtetnrwAj1UVcu5njZq91w9I0oahYrvl+2VApKhTBaiSrFbgtEbr06/B1ygzdUZLal932YpfXetczUVYSjPLd69TVjSWJ+pxGxJEJi6BlfcRalBB2KrpFu8ay3TEue+Gka434RreS70vC/ss8bFysFzR40m+naXrPcR4tcO8RIZLkdhnFfNE1ubVJrllF98awP6MF+vacqrGNABQGR9nalhFvU4QQd28+fU56TogOmezaHKVj5Xz0SrkhDJaISpWd6c35V1nnZLoLifltSRxu+qqgxutr7vESJS11j97hCOPralF3o4AsthWr58pyL6ajY0/7o2pzOYPze/2uL8BHiFRXWpFAcQsWtqNWbCCEw39p7AyQRpgmGPpDUSQXctvDwHcblq3EKt8zzmiHw7xVemElC7Ea5ZdVQgrVDJIHvQnYdyG5A0oHlFNX5RHBF4sLKEr+Ph/tzS0HvLfF+rwFvSP3akzJAIl6tkr17uNeE6ikCBdqF0+KW9syAYUWT+2XzZo6bzh2d6vQVim/dqb3m4HJVySASC8IkfRHfkDM5fukNmH5Q5kakIqWu054aP1uOdLtjUrN6eWdLy46VLOyHFEUoGEVcJ+gFTOoJ6YYO3UhrxakSrj+RwsIbx3mWNvo7BH3Z8sz5nk1m8f4wBPm9NjpyReeMXrZp053PxcfRVg0DGYxmY0MgCjrqzoT8vFuqJIXeEKyFJENkh4uGgYibSn4GOAN4h/xFJOC1Tgd37VjSkYoYpskNlB/LSw8Wv0LRG82Bv98xXNdzytatudbcPizOidSxszTGms3GD/mfHJBwi1muXe024jhcXtY8xxstybUDtrWEu4m60+ynGRYUKKkndUHEOrN5RzaI4eH4hQOWkOe91Ut3xw+YSnQOTHtQTWp9JRFp20WRe+0fqMIc3g8nDxIGCZdQdNCFyAuxReQHpBQwrYKKGFYogq3TMM9xgTy4uS1e85RXX4mlqaKFjEuXRsfUN9QTlWP0q4u2yfqw9zvn8Ov1cVKT+HMvEdvzZq5b2fD+KWXDZoe2BsgXOQLYwTsDOz0Xu7GqKJYNLEwyZzEuynjLqhpR2IKXaw4bQopWA9iM4j/F81WIDLZroI9ErmclhMsmG3bgjr3aweQn7EeQUhseQBWWDuQwwPywzhuNcIL2lEp11fRzjoeTYd73fX3x587Hec8K1QBq9wNF/Ex70x94/+PyoCEcjQlD1TkcanUlnUuzAyUv/vTNNlRMf4/6OHZZih3QsltwvubJeCpLUceuCcIh2zTTgkiN16Z6pdemyhNW69NcDBzy1ztQv7nSMgCTjFMeXMLyA4ZlFV5cNlD1wArqBcrqQWG+3yOL3oat0X4KLLYv9cGsvx6VzB7/7WnoEJFWAqalr6nPBVTvsV2gJeDq+mrPVkYNPmO238wqNqw/mwe/Y2DKc7FidZB5/RgzmZI+mqR5FpaCSKTKR08TmnZGTN2H9pLCVnaU0OUK4msl6d7ZnMziPEbiIaThWj3VO5jISkpJmBGVQYaPKg2HL/tGe3emOsSSkFJgyH/zuFt0LWiwIstGA5Iay3T6+jnIUkZ5HMF0qDOlsli2Uc965WrnXhGvJ7/efB7+oB9/rEQCdv3/Y9rFH+j+0warSCWMmOaX1ljSekcaXqLraIpC+LAkACAUdMmwKPBgYTwbSyco4x8HTNABFShuFiy2HvOyrl3PdBaTEBKiESy11yEpWlN1LysMX6MNnMJ0geQ95xXRW0CmjeL4WTV0Ht3cAl2gGjqMWrU58HYOiTdao2lvtM0wtmN1zy6vO56qE8ppST8foxPxSaYYoQiBF7YjX4ZCWI+uFgYOhafRNh+sWwDULpltAzhhPJh68Vfj+f/UUhmzxOlNoEpQiCUVMoShw8hlYPy6MDye2ZGfw5me9eCxDIczhi6mya9/9gT6Y3ZH6RYTLDFwquzBbAnM42Ghm91CZngnbz46sygaZ1nA28tG3zpiy2FmRCMEF5gXW93PH5UKT2stgdCFxvSLDeq8J10UlcOFBOKwZgmq/LjQZm62xcKAmnNEwfzg49+GMh5/d8+CNzMnjCU1nqEyWsl4MaZYOAuIg5TShK0WewOPPFVYnaxgSewrq0laOTFKdjrml27idUhnhc9tteSMa0kuMKbF7a8v2rZecPdoyTEKaBnQ78Z1//oLt85UlZtyftjiGd1DOW4nZdh8QrQI6kVBLra4wZjNcY8Btv6qStbPyPIatj/19k/pVnru8PSHiFKbmqGD3/KCmvB38z1ImOjtzmoGGYd6Dtb0Mhx4TRMANEfzAirgBTYG0h/SUh28qv+eHVvzU/+Vz6ImSx4k8PKOdQxu8SqGkwjSY5JVTNqLmSLXh1ta50al2n9vYkr7tY/VrSl/1nDcJLAiXAKkI6/yAMQ8MeQ37xwz7FbxM/Pw/+cc82z5woD3xNQItYkYoLPfiqszODcp5zS73vGB3jAqaC6VkZ9q1MTCvYExyvwlX5QSXnEAG9UCxEf6pfud3SotIDaYisefMc73fozjZmea7MLTviln3GBAlVCaUCcoO5AW/70fe5gf/tVM+//2FnEqX2M5RZTc8QyyWTjwnZbeaWD3YIIOQSyGrGTrI6JEqKuFdSpJyQf26qg5DJL1VkB14QSJb68ynQ1Ayku3aQZ4JY04MeYDtyP/t//pP+Re/9QLyR/Z+3ce78eOqyGgGJgEDXlcFyX7ZLSgDYxFWE6x2NMuqHC63zkEL6Kzhi4jKq8/lNvoxX6LOy06pwS5GgL1AhkkGM7RR0LjfDSfacDyeXdbPOLjujMXPUrKuoqz7LYlJFHHHhULZYyGnBzOm0+KELhg4ey45KyGqSIFBE6XQEkgCQwmC0RMudabM93FGvC5bx+6UaEMzh3UbQxuvE//uuaQwFmVQZUAtQDywn3BGKruafQesIA0tRU9lorq1v9VimhUJy1OXVOO6Q4PXAOapqZOvUTee5AdJ48zfrNxvwuUHoqqyfFW1iqWh2umokNB8VOIjTR3tl5rxoVn9OOGSIGDJnqrhb0K1lBBRlEwpTryYePxoxec+85B3vgB7jwJdAs8vOFfjvlwSE+VsmEhrezjnTC7FOJnB8/dUvOADDsjqidWBCXyPbK5ab/rpeuzdv0eqc2E7rYWJgcSgA6sHAykLaQLO4GQNQoaSGcjA0BDjbRcBPcrZ9QesSVtStBGps4yOMCXIlOZD2W1YJA/0rq5VP2e4N27vKv2E4TQ9n0MjXCIJfQFsswk9CqLa3aVEu75msyCvcWCi1U6Ui3PYSTTqDIvUcyugyftyKYqBvC2cPS187Z++RDdb8rCjDC9mZydVqUspyYwxakRy2rlq0k4YHVlizBhhMCIXsX1H6xcQrt6HTKslqgFZ7U9h1EJSIemOlBX2G/bPV5S9hUuSGhAqV7xWbxnuxKgk5ldcLSvURNJ1Rk0OVZgdDnEmtlo+OmPQgPMTS7gAjEMRcM7JCIzzN25+Lp3ePgh+HyOjGNGZIaR+D/wA6r5yTOISggikBPtpstaSkCSjZYfqlrQulL0yvVT2L2AnHij9POFCLediHEIZFT0zp0o8FZRdSGidQ4unt4wI4A2eW78aCpw5bQb/pPZdcKvzOxCtKlChUKbEtDWGUV+CTpb1FlXLuKShRrh9P5TmTLAsfhBj7J3YoVrY7zP5gy3lhXD2AezKnlD/BqcrgWjP5bQP6zOMd3zA9furtHezfrT7gRbUVhjTCnagO0W2mcHdYoOQFGkBiedttDUVQthyp+BKFvwZ9T4doSWonHjJmWkys/eUlMJDXr5/xru7Hf/3X3qXnJ6S00tTuc9mEMXUhb2TfmhTUlVtiCnZHYdW/s8tfIvYO8Cl9avsyax01xSFhEqTvoY4Zgolj0h5gOQnTE9PGadTkm6woMQmnUoZqpZzSBy5y3v1EsYvlQhpswTueeKjvRbDq4MM7GGGQw7uTa9Z7jXhSkysZMdJfsmgO5LuQfeMg61qcSSUEIYFRu/VgSoRbqV93xCAUC2cZPIPBXRspuICpeRaH1ZCZk+WLWX3Hv/0v//f+Pr/tGM4/YhpmPxglerTCs3Qwg5SkKHCJHtEjPgmRk9FUey/op7PMCHmqdkBUI/BLgpEu6zHu2FFZpZcsU4qxblwG2dwyanjphWlpMlInMJY1qSyQcqGYf+E518vvHG2YVRBpjPi4vpijH6z4i4y9Y/mGNvCfYUhBkBy5DWVCX0q5ARTygwl2z6I3UeEr5oWnS/1RfXLeYTD+rVZ/yu27WoFDRVtwM4wMOhAKkKahFUaOiIERcQRbs8ohvOxkyYdHI7PI1wV0gGYNJOSr6djwmp5J1v05Rl6tuVb738NSTtIE0X29Yza+e0IQgdG4g+E0VAsk0qaLYc9tiTot1sM+Tez+2AC7EdI2sVNlBHKiE7f5q3pM2jeQvmIrLkSkVFOYuRo6dxqbnHAdpa1GfBoQhb7p7K8XhGmksiyRmXNODwkJ2UvlieNGiX/5uVeE65B96Ryxmn5kA0vGHVLKi8Y1SyJLJlc8bAqDZoDNI0rM3F9qLrZAJx2COudWBAuBbCkiL1SwRC169vdPHfKH7B67xn60Ut2w0fokCmpGHenDZ+oeFZgGuECNUMOlxrNoihIWjGkI4mqgrkx4Yrnj9dN5VJHhFYp1p2K1UNpBWJwdU2oMjUnkm5IukHLEx6+WPNwf8JKM2TPd3UX3CJBuMKMQjyVic+rkxDQSqYhWIO9kEWZKM64+O1EFg9aXhVv9660MH6KyOBXDoomIUkiqTCWgaG4ul2Lq6/dIo9YQSNegXyhES57KAiX33goNMppzxdHxjijU8G5FGAPfmecy0tEJovAIsXN3HvCZeV8wgWxwyV1jiIV1O+KaDWNUCVcUK0hK+HqiaZYGiDRNUPeu6pwNKMuQFQYiMSccTJvedRuvp61VMnU4CGuBTwDQ73rCogQ9mzY8ZDMKVqUHcXUubO7eG683PeccO0QfcYj/TZP0jNO5AVDfp9RzeE3Oxc26PLavz+AQbgWPgYO5FJWlfPoDyFq3uzx3DgOFC3ksoddYRiBQdlOH5FenqHbHbm8qNas3fn0TZ/qJ9LnLErF7rUUEqPfz5kqLqVQM0jLS1TL7bH+jWhZ6X19ekE2FZcURU0V6myz7iaSviTpiiRnMK1JZc1KnlcsU1is/20UV1lmd+Q2JNVyyfSEq96T1kDC4vYCTppqahlxA41AmnfDnb+2oiBprAxJiTRACIOOJPc1Sm4MUYBcndI7E+cwavAsABWGOrhMFdk1bh2wzN0lkK8ht0IhF7sjTkMipYRqJmmGIKKpSRmzbZA5JNXEpkDcK2Wtjy7A7vb301amdIQreu5wkNuq1gxvKqQ0MAxr9rtvk2RkGEY0tfHp5HahMqcFtzdwW+dcgvEzdWbdR5e8QsVqox8oMrJLj3khb7LlES9z5qysmRSaL+yrDe1eE661nnGSPuIHHr/PD7255zOblzzSZ6x5ZmoKhCElE01znnE0pvKQehEv2oKqWgmVhlvN1XBFLn1p/xwkSVS9bTIJSjWzL1vS4DHXOGltuNgdB7+kRlqlxvMDGYRSsnOMI1qC84VhcMBRMbWhiw3HSNVF9cuKVs4qxtd92SGJVG/9PXq9ACKIbjBLWGFIhZK3iO4Z2ftSzA1qrj7yi+txOa/SzHDDlm4WEaIjxEHKkghZQv5SSJaOJSkwVZtCtEeeXI8lOFauz1Zcr5+6QuoIHSGl0bllJbMnsjoPmpBihEIorjJOLikY4VoiYiVUSXUXut7DArXfJ4OuyBCQUJek7F7ZwjaZKjzn4jAGSLHUVC4lHVXzQ2MCNcJt209OTRk+c7s8smavXkqdWwywt2C2JSmIptr/VEAkMQw71BU9IkImV2jTIiQx6dauRG63qI8tLDNFlUFLk7iq64/tV2agyIpJNrxIK97Ne759tuW3v/E+e31E1pFtDffGKxGve024VmXLCR/x2fF9vu90yxcfPOON/DU2+iGiEyiMkowoaTZ1QuUOmqgOcQgP/elrgrzZJjXOKRBd8eCt4q+oZopmM6LACJ+dzeLvq0sogCg55bqPMku4VyhqnP7IiuLcpopWU/+wLLW7sBZSBm5D3mJGzuMzXNKsBErVJTFbn9xZ340yUkpTMabNaCuVcXUnzrHX1s8Z+XXrIQ3YAkm/j0hF1nMzfx+FCO12Bkwla00NY2vbuH4qaFx0WX9VA86rtndu3Zf0vH5sRx3uVUxV6M9MTN6/MGoyoqWm5rG7DL+TEdqdTAw4WD8HEJUgKLEfTT3YYy4pxujEWTI9rJu8u+m9kGBs2RWQQo61r2yIjSUIr+1XU4cPPtQiWjOlCM3g4lUQ6UVFpY0RmqM+mBrb4hdmpCf6VX1bWK2NYS3qjFI1dw6Ni8H47RNdlxU7pmJwV6N2lqg4NctAlg17OeXpsGO9hyGf8bVpYFWEsZwYE6sLF5oblHtOuM44IfFW+oB31s/5PZunvD39czblPVLZQ1EGEQaUJIVUwqxd/RAONLPYUonXXDXW63NH/7SQ6HT3IuSpGTPIYMYahcwwrizFggqpxil0rXaXVGtOuLw/xdQlYpzVSsYaI7BIQUQpoTYWqcRU0Rl66OsX3XYFMxv1QPF93cZng1YnXCGxNhViIXvWVi2wHtYUsfvGUpT1aoUI5MlCYAmWxqEEtkVo6TSiHkQ+6kGQzq/3qhdxzNyCXBnhMqOYjnjVaBFCVjdKQdDULFNXaXAGwaSDgJekNAQcdX/nWB0OiUvSxuke1G+hnzr7CIQMaGlEvJr9I4wMVRNhhCs5y5bqmoYfWCNcOGPlCFl7hBpaBe2gzQ0LvA0hIUOqz+Tsz2liTKumEpdClrm5u93FmTYl1qa/Lx6Uul+VcGlHuO6oxF26lTQ3FPH7usTUqVgFSStKKUzTxOlqTcmFnCcYExHhQxgoxVS46fYNco0pJlFk8L1WBt13Z6VhFpVClhV7OWGXHvLeCDtZ83LcsZkS6/yQUQeSrigMeGs3Htr9JlxkNnrGafmAx/k9nkzf5vH+t3mYv82IOy4qfvgyCfsdBCdX095E8gOKEwXqU51OXleE2C/VIVmhwDDYZqDClO3QiSglt8vMOd+pM2OGXB0lG0cmGC4uxTjksbvjKuJWXqWgBcZVmucxuvUSBDcIa6Azgir4GnoRW21VSF268UKGrYIWtOwZx5W1kxfqu1sozuiSkGoVFa2HmrjI3GC+3tskmJxwkaRzRxJWnS5K28f3pMQ+NqOSHKJHBGN2IjLIiJRKuru5JmcCGvEMtaMh6Vy1AnT99FEn0ox4uUTlpyPvcwtZKFCKwdEwDNXTNSzZesIVxK6/dxRNdd+DqTKpi/recuuaLiXavGo9+p2zgUHX60g15guQiaDB1VqXRNEBkUQSobx0PCNKmdp9dkpjjZRzJ3etzkArKwwvFQYmw6Hq86y+rYV9WrHjAWfyiAnhDT3lsU6sdcVa3mGFab1KREp5hbN+rwlXA5TG9XaoBNF2ZTjXtQcIxbO5cmy9pZ8snmemPOpNVwOYih/oRjz6ezU75KU7LO0eRmrHQioDNcSS+MUuyfMFebtuyjw4kqXYHG4R7y9KFxbHdZOC2h0ioNpJrDBHbL4GS7VcksGkTqcw8z26jWLINHYg9XeT1WrUBlk1lirVIdbolRkKJAyLClDUrtItOtEczd2P4upyhyEdGtpPJDd5BrSYm4XEO1ZEzQLWPpUGy8R6xoovLS5Ddj+0gKtaArVA0jGeOIgWUHZqwfvdLLJGawJDomIuJIHIUwm1+0HcF59LB5/C4hmhudfS1StEHanTfRYr1NT34imDWu6vuDc3WBJgEEGKGXOQElLdUYIh9hGW3mn/bsTGdma975kRVah7Gr4lGME+RJweW3177qY2ufeacAVJKjKSWZFlQ5ENWU4QHVydUkhi5u5SJa5OX2+Z2BhK327pYLvnyXr01B1M6eT0oxhM6udSXCpx1Uxw/f3rY90Ws4iLJHFJ+u0yc3/jSl3d0nHNt18cOFO7vBIRP4i2ZsLUntaRcJm3mdpaFae9InbUIwbAXUgscd8SkQlEnXjZ6DtEFeriMNuwue4lU6SQKYZM3EhHFQZJiIjdY97HInZnUrSY+slTC4+6IklEhkmuFtRqORYX8R0rduADSTWPvgyZ9srpeLeXXGYDJpCjRWMQU8GHWtBhTCWzH9xKVZQk7m+p/RluTEsSn1LghF4yAi6Wsq6ueK+9JzOmmiWtDO7JQ46NMoImR/hSDYDMHkLb2MPQgbuR+JupvmmaRBRVU5PHuap40FWFmRMmOSHLhsyKIiMqg6V06RmGuoY3K/eacO0YOZMTXshbPB82PB9O2ehL9ukNRPeoFk+LLR6NIiPd4ZsbZwT3dVe8i5UOb7qefXbiERU3KDF14D7tXar2uICucwg/rnCKDTODuxp8qGVEnE5rcLmup9ZMqtHqk13AEj5mEY6nHW1Ry9sVa8GlSO4GY66Hy0elkJxFV2mfx32iETdTe2TJ7FceVzLuwRRDKCsbdKz7vSwuuqgUMkouGYpyohs3gx8ZyoopWzBoGWhES+aEy4hX28K78MmLkktGVS2wdFohhr5NLpaJnPbs1i+ZmChaSKwQjbBk3Zg1ILXBRAlr2PocXEy4rkEtpFkVBuGSwa8Q1DQWSS2izMDAOIzOKPlYFgwuCGOBuPtdZpi4jWKMfX9OlLHk+RL43Czo98gkJ+zkIR+N38dH5XM84zF7Efa6YlK7mkm3cGruNeHaDw95kd7i3ekFv/1iy7PyJk90xVo/MnWGmjm8aIHsDnCOoQqHVoVAS6x6B8WPeN3wvAjTIoAUYSgWn1tTIY97S2WiOnc4d6625fYJNdjts17VwRCOZDxvRhP1ol5B9AQJwtWF6I+L//6CuqquuGWEVwllxKycX8T3ypwoYwIobMuOSQtlUMqgaJ7qC0IKWteFDrtnpYTYK+5DZ/txqhvSPpF0YCUb82XT7r7KN3gmlWi7oaL79k5IV6q6QsKqTotSdG+pgNYF3RSmNJHJiI7tTnYmTTUJLqTsgBX6uV1KuDpO9MK6q9FELGauCsMwVGZTc2HFiGTIZ5k0Can0PnFWmlGOEy6axuO217uulzQV4DALOdSrkZUiA5k1k5zwbHiHr/3/2/v3mMuu+r4ff33W2uc888zVHsx47HCpISnUsSEpJWaKSvgWy4aiKilIvzRBCVQRCHdA5VKEQDRcosQtldqqVUr/qaB/hKRFgqLSFIWbjQiGNA6IghsLKKnTxIMB47k/zzl7rc/vj3Xd+5zz3OZ5ZuZ49nt05lln733WWntdPrf1WZ/VHuPU5AAXVgzro8NMpys4I+CTt+h0x3VbasbVygrrsp/HJwf5i3MNZ9cN+/0aI/aR9nGZ6A6PdyUsjATm5CRJFIFxJam6O4B3B1lu0WIWcTHmU63+i4L1Nki1xuFGLRqCS+CcVgMpZJg9Y70PJpQ8UbYzqTZO5wgFkKOFZ/fyrPRHH6FE39qVuDBexTPstYaJnn9pfdDvgdYFRLdnLc3W4/E1u7QGFMdE19EDFhlH86BvMzkWMVlOWE7GFfskEnRP0GCMCuPJCHfBQQtj2Ye6GGmieKd0ZSOtLBb1ZfaGcUm0PoS84wqyKs5PkRXFYhhhccbhKs/UNJ997vPIuOg5FW2bcdXpxXMpsUVjQtR6zQ5dkn8+1gb1ytrkInrBQ5uE2EIzkqeoUUIYu45L+m63edoHGUaAEOMpZjd8QbNpVwmqwQgnYy6aI/zQH+DxyX4uElzkXXXETJiEO6/tUjOuiaww1f385cUDnFnzrGhLM92PZUQgPyBxY67teB/FQLfYzLhMx/FC8oDfbaRTkDxlHaDPuAxhf4c3nnbflNH+BgxMowdBGq9iyZqW9yG0lYhQXMljplt0H+/6TleTXqpVDWOI/v2U+ITBYaMRE/ZmTRV/3iBOEF9H3k9IXpNRqKiY9+4juKwXt+1uOdl8mQWAFk9La8fsP3aQ8aExK81KtdFYg1t4Cq9l/B4QjL1D8v5rmqAyJqZkCWGe3AXHmcfOMDk/ZaQr2LinMBD84kgAUO/F2WuX8lD5uJeJNB6bbEJTaRkfaFi9foXD1x9EbTwaKM+zMrsLvY8al6T9ZmXO11sKZgLrxjxyms2Dp6VN9habTZImbjg2gDUNI9fQti0/Pv04F35wESZBiBUtbuOJRQbmlWKEFta4u1DCfrmwly+tq6XIGWXLcyo9uLh7RkzMChfNiHXGrHOEdRqcKDAJWnNq7x1WeqkZV2tGqBzkNMe5oEewTLDNBBu313sVwtpLtWudSKdFOowruHh2Vrz2pM71ERs148rSfEx542AMoxsaDj31EM3qCLUGbwKhaXHRbhfzys4n8zSAS9W+smyaKy6ZcZX/rRNoFb/mefTbP0AmgnUmnKxb7aHKa0pULb0HjCszFEnabrXOkD5ZEIiL+9rixdE2U44cfQr7jqyy77p9Yc1E4nEZRMcGwFib81sGJKLXRC+ENPIaabDe4poJP7zwI9bcGiO/j0ZG4bk50nHaShLyjY4TFV/bkxmUGJAqiM3OTRO9wKixrI5WuG71EG4UzIVek+BFODmZSlCRFPU8ecGRx2jNuGp5bsN9dhQm1t8TmUadNTYwX69I3h8VNnuP/YjWTfmh/JB25BA1NL7B+KYSIjX3YWJ8hQfsrvavsSHSlhE0BCvPwbUjtUkyvgfSHtSphj1dKisYe5DWTvAyAaa7shaz1IzLY/EyZl0O0MoYo21wEDDBTBiUEU/aNpl3UmkgxD4dKUA0FWbGVUeOljzokn9cud5PJ0PdbJqYB5SFySLtV+pzZlwt2nhGoxGy/wij1TF+FKKVO8KBkkEQS6pC2lu2WeikrbjzCv1txzXj6hKosI5lEWwrMA2bEc+PFGJIGuslHgXSYVU9Ile35uZtmNKz/VPSxSyUCHYRDGpNqwgQDiUwLt9MMKMbmIz3sT5eYRoZV9K8ksbc8SjtYaNe2E56M2w3PwEaV4+bsEfQeoNfaTk/NlwcrdG4FayOQrvONfdWnmKa1hCrVSKdN3/q9PbmUtB2YyoGmFYBNcrEX8Aaw9SOMfYwzjqmpg2iXOzjFOUkM674LfnOhYwrA3ig1V1DRCEhhCDTsd4bpuN7aLRYKDFoQNxjpop1hn0yxjUt50bQWodYg2WEZVSt06V8NYaMqxlXep/t0aOUnumfmGf9LsYbiudyCUCQ2lTiF68WJxZkxEhWcBqECNL2imyq3hmWmnGpWFoa1OynyACQNxmqxDBIMe5ZRYy1+kAKsptQBkR10tVMWqm86PCZMHfS4qk3GteToLCp/EZEdQZnJ6hxiLFIc5hmNKKtDjV0+MC44n6W5NKLbJdx9WXDeeku46rXBNJmaYvFxgGpxrNmW9TGs2vFBnNMaOnwu1SdmHuq0Wy7zWvDOr24fzpFkESGckXTW4oSznDzqGlRaVE7Qe31jOwK42bEhDYwrbJPFlCc77L7Rel+JJL+zqA6vUiU2K1yAEY2CXBR4vcG6ww0cKFR1kfriKxgXJH2oR5JiYBJzjvt3anL2bh/tjeX8pjUJOxFJxmrTGUVa4WpGWHtYdrIuJxJOylLeK56v1aiDbnOOvum2xP85s2r8uvSU2H9HVXwIVzafsZo47mwYmjtFIzB+nFwj49CdmBSGgWFKu+occ2lQRvSo03mUiZYxGgrvXDlQod5FcNJbEURUEurklbJCuOas4iwVSw148JbMA2uGvZAPJQQwt4nyBvlZjxvQmemyZ8mThZGiRMv7mERNdntdH5aooTVGzRSBr6Ip1eJqiZp4EfvLWOx4wZGBmdhXZUWzTbysCeEsJdFIDCF4JG4vU2T88hkP9BTvKPFZdgKeJ95J9oATkLAYDGARf0I500QIqo1kYrbBqaYA7fOtpuiG6SryaZp8TdNwtK6RVzpwqf/NWpc2hKOr7F4sw9nG6YGptIEM61RnLZxw3LQKEvbFCFyYbr0cGqFmXTqBb+V/HZYjrMao1tpOOhPw5YRAzg7AqtoO8bF8DzJIKxVHvWoLVuSyWdZK3T76hLnEtlqUo3TJAFZh7dCa0a0Vmgby9QYWu1qBGXqFSOeUJ2/Lf3EdoTAjRlXaock+Zi4TCCE/YUNBjXgzH7UTMAYvBnj/CiX68RnOSIcNVPmaG6V2IYpnmBpw+3NpWQBSlRSYnvXQmeuD3XbhvYzJookXvP6fXlw59oWLDvjSgM6bgotCNJJ2qRYprGZbS8NUyGtF2ZjUu6M1FOR7OUNtxun8wbDOh1lmboO9SAIgycN7EAQ01qKSJxmRrLUnzdLRrNJqKNCRyqydUkbpPuyu3TvRxNQ8NAzIZwVYX1BNHhIhqoHL0RNTFSSV1SlE0t621JENgGRRLe6DXWTtESC2OurBM1G4tnuB+qjTsprezyC0+BFGbetE2L6FW0ukthIIDpFzk/3/m6Y3kp+Wy1HyCHBNLVVIqNGwtTI8QuD0JE+gfxUbZTyjv09r/zyTNVXlzqXMKHGeY6Q80bKgkAIoBTC1nZMWakd8g+7O4rmyJNbwKK5JLP3Y/unPXQujl0RBSvB2xAizUpzpnaCgBTgOPTjvLldtVu6vtO5FPMOlLNLFwt9jH01h4yopD1qZY4Ud9z0jjvDcjOuuiU7fWfLtbTvoHO/GqLxemZw0ntG+mWkdJ/79J6R6mad7g+yarbHWBqxg4PU5jVuuSG68hqJe1nCKkuOOUrX9LkQGwmQ2be+nyYSPi0n/0ogezliExqCCJs4iCUt3EfNqyJIVFmX7/0+2aANN0z3+qF6R51HWQkvk6MCaOoDG7VEqpXDuMZQS7lJlN+k2a8KJKYhldCg0QFBUr+lB4VArkofpuczbarbW/N/VVls3j9bSdd5zKOeqZOlMK42fjS/M513LknpdN0lLLv0sCij6t0ygQ9Tw2u6VLSyEOQ2ze8k4GnHMWtG8tkOPdpsLsW8/cJ26d2QwPiK6SBZg6RMc9XZ320Ty824Nnv37YhQ1UDaGnYmn22ebZiA0YAQL0mmlV6CV6HPHCvcmDHs1cpTv6pbEve76UDUpMesYvkieFUchalldz0fNBSZk+3VgrhcFePhhUmngPjIeiWaRQEXXKeoN4Ndje+0OSIHMoL3nkYFm9R6r127Yo8fXTV9mSuThInwPVuko/WuDqg7L4t+eq/eqzMVe4UkzaS6Ej7z9jpdTiEpVXpemfMaqqI7SbkKoeEaYviVqiF23tLLzbig16D9hpC8/rL4R3OkxD3HRtKYbtCfM2IsSdIEMi3q4BJnYaFbUZKqSkh7mRQQLYv08zHnnWe47G6jb1LqEo6aUCXmVSTuShLv1DOJydUMXQZta0OUdqp3JaSNptJ7wR4vy1c7hP/Sher52EIszlq76msKGzGvPWfENc3OBQfmFORCyWbAbWMvKz+P7PTLrlVVWdSi1XVRuITN+8vPuOYhSsPdZivEnXnX+51yOQbClsqoR41WBKG+XjLU/s92FT3zRDYpljrMs5ot3KJ1OUT2eYQgtmG9ay9YL7pqZudXmRimPrji+sYuotdjmTMF9aXfDumRPmau7XUTLQjIm6pfaGmXGGg1f0T1sgzDUmKuUZUqG6DnyeFZqSRZMzYoZC/Vxfqv1n8rSaGvoeWm79GxBeLPVvHkZFxZnE4tOFftmvnJZZWcY79tucjMJHQ+d2CG1O4y5tU0nXVUTbgs6pIFq34uu2Di3j42sAkl55dUYZXyN5nq00bLJwe76go9NRkJS8ICks6RKqfyQu/9L6edrY+F65ULv/QeK4KW6ZnjpPp/hgpvmO5T7366X7d6C08IVNy3DNZ7HWd+vpVru4X6dfrXtHctGmd0ZrCYXavj8jOuukEX9XBuwVnPqLl5bfTMHmKRtpTJjFKIyDxCcVmrHCZkf1NxBzNEZNF60GWqeH/SVdLhvKUE7b1WEi5nM6uvbUa85oik2/rdpaTncZqKONYDsBKOtPf8hgwrF3sZB2PyLtkIff5R1dtnt+9aBOtT491Kl+8Cebl0M3SqP09w3QOmNcOj4vrvvD6e3/qlLWccxy6xvkvNuNIu7VnbVPzb00ZnyGbV+JeNZ/VU7m6Nkm1AZ3hBV0ae+WH8uWxtFlwiJNkrsnljowZLM7NERwgi2eLH9wyXmHfQFvtMYN67zGcQGw+s7TKjrZazaHLQGW7UQ67PsbeEK62Ozit/kbbTeyTiEmK+bgvzzH2ZlvUYQmeqQdmrdxnqqvG/ejT0rTpzWZcSfQvmVHKX6r7kjCsmNP83XxPJ6naM/p6dDEpjV9tiL4Mpq1dX6d/ThTaCdDk7QVTP+CQp7wX6WqBCNhUuUj5M2nkf3Tay55d263k5mdWMGEk1PuYoifUzKtEL2VS3+21+udKXkkeF+H7hrDJiW2zEuHqMU+dc3ktsSvhSnfoSYny5eQpuel+ptIKtyA1bQj2IhE6bVeVI/WjPJh1M2fXeqoLLxMM6gYZTW3a39Zd3nBGsOy9XX9w5rVpqxgWLOq7H/WPSbLAQO5dwXXZEIi/l3KOOTKP9K11c9nfYsKDapqDhnbos9/KiP2cW1H07hOByEY29RJQvuqiPXS8BncoPrhbMYwCLBKlwNz9TTxad4+SRb+uCNNvRkWO5c2ZvNhXOHUiJHhRBb6/k0s1Q1oH7VzdZfolQE84X3K3xs9SMK2tRpLD6cWCkPUTMU1zCCCnWkPi98szcS4JUW/KyYJhnRDWCe9L+PI4133V2L0f2AjNBJTXKnOuROpR3rCVK7Ty4JzCdJt2oLO19WKAVlzGynMwr1Fjo91mUlrOW7PNzW3nHOqL6nqLaQ1f6Kh3Dk8ILhb9hK1d4Q1PRAp9faFPR9xLThdX14/il1Oy8iRXN+wXrUatVlSXnsWdjMEa/mLetKFLOnpdmnBe5UmmWxPEkFfO6hEovNeMqskxLbkYVwGbTTu5/YqQzI2gWMavhEBu6Pwi2JFUVgWjDZyDGUs9qd4l8obWWpTpHMdEO0yu1L0RWYt67TTd8TaWJwQkR0BTrTAtRqCTIQDw08qr4tzeRSxvp5m1YVWMrfZKuZcYVqkyI7d4jHimWZU20papnmriaJOf0uxKxe7lQmJLkf6lVPMTASTlenZb3L02XxkEhvPVRH+mJTefMovTc39aCRZokISBXepdg9tRAIyttqjCybsaamICEvu3LWDWdTdravPTm75NC02lus0QPjHYjHHZijHbGV4l1muJx5vptcf7U6Y3vF6LYpYl5gqO5H8KLptiLpcVS/TyI6zKufsbbwFIzrhAbfh1hAlpFRVcTPqRjuwO8KB6PajltNxFPq/QIaY3ZzoMyuPs3q9CEsY9SWdI9lyrGIQxMMxwHoiac+eRpUfWMfMM+t4J1ghGHd+CMx4nP3lAzddplqJa4iGHyR+lVw+QRlTgBw9Hj6hwTfwGjhkZjgFAfY6+l889iW0AY/J0wRLuEJLDm9cv8HiVuXiIa+ZxfneLUMdUp+9wKI7fC2DU03sVjTRwmRc+gMOLi2FxP/X6s9jp9qUGQt1rOgjIlCBUCGCeMfINtDXjHml6gZUKDQzrh8Aujqlq58owLket8kr9mqtyrt24hXWtXSdDTclBHKNfh/ATjDaN2zIHpCq0oEyln1gmEIzlSC0hnVRuPi6+3+Qwq877un9mH5o7nGDlZ1SBiQghjDfR8xUPbetb8hKmfoCoYWqxOoD++lDh/4tf6Vv0KNQ1KjC2FwataYB7hCHNEYqDlMEdMVhJSu4XzuSTHekvNKKiGoMhWLG2IHDlTwk6x1IwrdOoUqxcx4pBsm49BJTUFCE2do6h6FBePXJdMdE20HYhXNJ0ZQyBys+p6QOomScHGYjlefYzpJ4gGRpkIfungwtJCf0em5T1OHM63qFfG7Yh9bkTjR4yMo3UO5z3OhMkmRsJR8n53D5GroSSmD1ZMkA6T1i8hcrh4sIzBK9451vw5jA+MK7yz6YqvJEKSHDiK9rmbSIwJQvEeivNNnMvhbCMAj2OKw2G9Z9WNWWk9q24fUwnndDnnsElqluTmm9+IrTORjhxfpbdzbMbOykkSc3j3IEBY5zCt4FzLqD2Ha9cZ+wmCBZUSRWOOI0rWbhUEH5mCRg6WpPEe8609YBelkSiQSk5r6j/i0RsCqKfVKba1jNuW0fkGs+4xxqGVgFqfX6VS2lOS0JgCPUvKv6tn1BQ+OSflPul6ipVfV4xQ4sBL4Z2MNFFoEqw3NB7kYsvK2gVo1xEHxlmsNrEGhRumvsslJsGvYpipnUzs+tBU6Ryv8KBEuhUjn+bnsidwDNmWuLWhTaJmsFuYJhxo6fuCW6CdIoLxo3CeXXaQSX+v0cgZlinCOmM9R8MUg48nAcdpqUHKT1pNkDBdPPYkiYThLCnRcLSFqKJqqkEcNLmkFNdIXRUkjnjEOdD6FmNMCDqrHnyLxOMjClONv84Cpg/aoHim0tJKOCl0xY3Z31pGbYM2nomfxGNNFKcOYy3WGtrWVTXbRQ4QJ0IwnimNtRgniA+mGGMig3YwNitoq7i25YL7McZbRr5BsuYrpKNlwjwoB4/vpS9y9oiiMsV0CICGTaiitG6KV0fr4MAE9q2vs7q+n9a0OFqcVowLzcup2btwQ2KciNiik7Iqjrowjy2mcz7dMhNzgcK0DAbxBmmhnToutE9Ae5EV2YeoLVp17MMu8ypIgmPxzNuE+Wqd7jKoRYw4BECO5aVN0ihTN8VOLePJCiunHb5xOAlachhf0mFcPpm4I+OCYlUI5q406vPbxbolgZSKKcXn8ll4OefcFklYMGJQDUd9GNMEC4wXDE04cHW9ZfXiOew0MC7rbTzpOFlqUh8WpPX9ogdRDv6Mz2Zd03sMoU/VC9aGNnTxWJ/AzOJRlIm/QDG5SqCdTsOnkXEYO07yJv5MK42gavG6j4kKDhebJ5k7dz7nl5pxrbgfMVLhRvsYY3caq2tYJulQE0Qs6fhIH9cwxDtEXaSTQcW1YisH5+RWHlI+EtUQL7ZIb3k9SSV0XHSlDQxFk4CC4DHxHCHRNJDjAEzEjtjdPpgyW+NwtMgU9k1WOHjmEHbdROIZpEgVxfkQkV1Ewpk3aTTv8ppLWGwPdRcJ2lVydjJEIcALI22QVtA15fr2CfAGqw0WE5l7atHYjpgYI3DvmBbVJFclH9+e+seQBJogAaoJY2V9fYL5wRg5Y5EfGNZ1HY0nv5l6wpn5RPyqhy8al6jFYsJhoG3DkXMXcK1jbFcQV5iWiVpzd5NuWZuMGUfBb2+qncybIibLOyowdRP0IugU9IIy9VOmfoptiiAg4cjwyLhCfkYVq2Xeh6N6ojkhx03JIirGF5Jp8vqZlmej5pNPV1eb74v6eApBzMtY1Ic+sDQ0dgQejl6cRppkIi2LVo1Yh3lNGyxJitdidk+MLht5fdT0vIkWWEGcoFKv04ZGNT48nNvKRwN74t+Rslk/AqfQhjVGYwSsYYrQ+hVa9jHhKKfaQ5xzY1qiBaZitDvBUjOuo+PTHN3XcMdTf8xBd4p9eoaRXsD4Nk7IBhWbCX0emlrkIDQdklcMAzkl0YRAkcTSE+nkXyGYIIK5IQTJbCMBDBYdR5OOAvEOzVJiIea1wKxoWL8yLpy4O/aM7BgBHA6fpDuNcl5agE5uUntAMBKbCYzLZGYQGGUwHWDAegmmy7HBHfeIM5hori2kTjtOhcWpY/crnshNrcDktbqq/BLyR4OUKJ6JtvgVgzbgLUyZAoHwmErqr0pZIlSyuRIlfhv+aYPb78CBlQbxyXkgmQohv29anK8EpdC/WzheZ4cw6rNWkJgWxuA1CnRG8WNP66c432Jt0togkTsFfDyAzyg0PmlJEq0zmi0t4figinHl9fPIGPI6c2JyyWSXKHzRrEV8VxMyyQwb6yLhu6bzTZKATFVeD2X0FTagAk59YZBiMT46rUjR3uKZ3/mXkhxxUCQdxivpkNdqjohBNZwn1pjAuMR5TD5t2uDsKmtykPP+ID9YNzx4TuHiPtbcfvANYQnHUtuJtoOlZlwHzVmOrRh++ik/4qj7K1b94+zTcxg3jRPOotJEFakMLCAPhWSyS10W7qVRU6ng0t0AmJiOSY4JPhJkA1N88cj3jsYE5uh9Gw+2SyYXQ/F8rIafeHz8TJiEk44J5oV8UiwSz+mSNL8q1KR5O+nCmLv3U9sI9ZlaBvDSkswDhmAKMdYyOjzGqMkELw97mWVcVO+/m8jHlVRvOY9x1cKDmDChpzhcE02yokEDFo3HDfnCbLU2B+285beS7vfOzsvUcBJ3SjuJUn1gX7IStSyxJJ8FkwlwQYrpmGsjaYbt3VEvptpjlk//NibOTw3zpmlx2uLVYQ0UxmVz3bwJAphVaJI1EbL2URhXOl099rFaaktJTSe6psXYIJnSBMblq7Voicd8iApegzt/OALExu+p2MV7H03+G7cFRdrS+rQeKFhpME4wXrDGZm6norTekX0Da8blHenIIpW+Bm3CyTceRmYUlVOlERvPDxTc6DrOyxFOu3X+/NwK/8dazqhi/AiNp2rLJbCfpWZc+/2jHHE/4On2EW5wD3NAf8B+zoe1LwFoYjd4gt3cRfttdpMAwkmpXcYVzV8ZyW5bjEQmDjijQqOSVXUFHBp0o2jjH2EwIuGalIFipThrSFbogwNJmkg04NqWdA5WcX0MZs7gXTprvtkaKasdcDdKUyZ2aLLCmNN6oQFpPRbBqqWJdvQoEocsRHFxQbaeCIsNIJeGet9OfIWKtAj97RICaBsmvBPCWoTEY91Fw/mdAho1eqpRNA8LWz4J6hulF/x2t8pR2vCchDVe74OpWogmqthvkkyKpvuetawesq2Idl7P3M3N5kmWL9pRIswpDJfTlomfBq9h48OajZtSj+VwGKjgIiO2PnyS6BG0RR+dNZLZrZyPFxyKeqNK0vtXoytbZJKgF9Q776ckAVA0HG9PPKnde48YYTQaE5fMSwvX64Gkk9JtKFMVXHAIi50VahLLb2jQNcArIx2x7iZ447FjQystLnpZB5YcfQSMy0sPbWK2IqSKCUFgGJtxds4KZlQBRkz0KGd5Kj/WG1gDVqfQtILh0I61rBpLzbhW9AesKozdXzFyjzJyP6KRizS0UVKw8dTecC6qqAefFlZt9pSyQvYkFAHvo8tnGizRJTctuEJ0a1fJ+y80Kd6qiAndq4QJL9HrphFomUZzn+Y8wt4Tm98rD1ZR8ILzbbhhBTTZyKP1Opkaau1IPKgpE0pNkQo3TROpXFwU1ziYq/0ZjrBw6/OJyIJVwTqwGBqxiCv7zlSjDhulvK40Ht92C27I20XHRTu3TSonvRuZgYWrYfKrBlnHo8G8I2E5K/DqKACJdFa8FmlFG97c8Efb+/nWfhTGlWaPLqFpGryPDgNpw5oSFvFDq6EurWWFmz6lk7etlD17Jo/J3UUypYfxYuKacbghBlpxNKalOTjCrhrMStBksvYUHYNUgrAKYe6GrR31/AkCSxmr1ejQaN7rCYpaWXSKMJS8MaNXbjy1PKy/B+1Lchg0ibTDsLLPYEcmnkqtOd8iemmn7PDzWpqUbJIUAVlX1n50kenpCX7qOXhIsPssq0dWccaHJYmsbQUnC1+ND2PSep3i1eUyRJXGt+hFhzvbsn52GjXzEeI9Uyz73IgVfw6jU4wojW3wPmpmMxrq1rHUjOvI+DSHVjz7midY8ecYm/OMWM+WUy8WIuMycXEUBcmn8powYACSrCGKtcmT0BdiThySycznTdyTFeQ41UpKs2UIGzH4qYAXmpGATKNElyS95ORhOxQor6cJWHWoEGzImtzG44m19WBN0qhoUWk2TS8gh5FZSyWFmbzwHEhAIHQ2tIMDG7XPRmyg+oknZo2WOeqDr6bf3sCo5sV4tPR3zbjys1WIAI0EQDODjq3sfNBARDpkZKY14wUhtNlWnALrDbwpXXXHlsvZuMzcMQA0vglStyqWMMbCOrCJBDE5PyfEkRY9MXM61icwrq30zPbhiabm7CxCDg4veDCelRVoDoww+5tomo8VKzMkehWWtirCnxZNKzPKci+5fc8yrsRUUn7hXvJYVMDFzbdCFJBdFA7D5EK9x1hltK+lsZLr3F3m6G+OS8maFiSPzbgmfVHxZyd4uwatYzQyjFYb9h0Zo01YSw98SbPW5r0rVTNBgAnCnC/CAgbjBG8cbrKOP7ce27Gh8WNGcpiRrtHoBNGWSnzM/3aKpWZcT3/qj3jGwSnXH36cI+159rl1RupA26C4m7Dz30hUcAWMI/ZAiAyQNCjvPN5B62G8GiS4TsOmnoM4bmzeuGqEYJ4geuCYPJyxAuvnQt7jVYu1xXHDeIqXlLYdeu6rcn2cO9Z4orMPxHLz3g7lEofCYsRXCvWTxLhCPRoFWsWdbzFteKd630imrPlLYQzFE2sPGNcMgyyXu2/WfS7QitTPpiPcFoVDQU3Q0r3LJL3W7OaJBDWpq/johubBeczqksuJ2mJiyqY1iA/WgiQclQYLuVhT8qludtoz3Vp4AO6lIOaZukQVrJqs+ScHIRXYtzrCHBI43IBNazXFlBdoe+hDqGTT+r0Tr0rbGJIE4dOL9QZYxRypH8nMBjSZWyUydl++I+CdgjjMaB00Wn16a/MBPbOtEgTFoD9SzNcGxATvInsB0QkCWAvNSoscbJCRlh3QPtVFsPVGYw9ItXUlvbBacIp1LebChCnr8bZhzAoTs85IJhhdB7eObye0borDoIxm+3gb2Bbjuvfee/n4xz/On/3Zn7G6usrf/tt/m3/xL/4Fz3nOc/Iza2trvP3tb+f3f//3WV9f5+677+bf//t/z4033pifeeSRR7jnnnv4whe+wMGDB3nta1/LvffeS9Nsj4/+4v+n3Dy+yNNGa9iJQdxq4PhxwIXQTmnHdpBupNJQkpSiTuHCFLfuYH3K6PhBpBH6dusiugWpIozpKGmJx8bVrTBzI3fyI9ofXkDWHaNDhxjt92E1WByZw9E3rWieJ8QJ2WGcHaR3qt2S5+0TWiivbyktOTKJwcYFW5xBpiP8hZb21OkQFGAaPt2e1JlJnCdFaub8vmxMtTdLz6XaVZqaiPdduXu8LgvaRTsp7+PAB83eVOXUadksncqr+WfN9DfI49LKCS9TnnXYemhL9aPUbxUDryG9v3shOAHZg1CL0B6IqYD1YQ6G2Z5ctgMTwLogrUp3Lod+7zMuoGdl6XZIKLPcivMqO39Rvuf8Ujl5MMX8TCQRhV6Ji7SEVF+dk18avHVHp6/p9wQX9WTul2I1aRJZMgSpM0mbWTKLZTAt+Ropl+sytI2yZ5wLnrwOPNVCFwWlsZ5x4xi3DueL4LtTbItT3H///Zw8eZIXvvCFtG3Lu9/9bu666y4eeughDhw4AMBb3/pW/vt//+987GMf48iRI7zpTW/iVa96FX/0R38EgHOOV77ylRw/fpwvf/nLPProo/zar/0ao9GI3/7t395W5Y8deoIbVs4w1rNIMwluLj6u7RSv1YgkslUx/5K5oJWwn4KQhawKMi6DtTuwKV/SpI4Lr2kRNTveqQFn8WPBOQneWqsCjUSJr6YSlXQf/ZjKwImsLDG56nUqL4meRNSffIuuzbwYXYmykvaqdhMRcBYmFnWKM2HQmvqVFmIO9esLlTtNb/RaVHOvSi9cjpE6Rl9gcvUSWacfdpKeW+YepOchdm9iBoHYBwGojK0+Ad4g27pNpXNpV9GtWjxaI9P3NLcrDqwbNIpSft+vsS76TV8i6r+lbPzi9b2OYBDnPNJlWvXA7ghf3fkpUnUoVMwtrF3PrZIkATqZSSpHEh8HRd54nEz6vhJ8Yl37AhJUWntgYMGL1QUmp4nZs+NBsi3G9elPf7rz/SMf+QjHjh3jwQcf5CUveQmnT5/mP/7H/8hHP/pR/u7f/bsAfPjDH+Zv/I2/wVe+8hVe9KIX8Yd/+Ic89NBDfPazn+XGG2/kZ37mZ/jN3/xN3vnOd/K+972P8Xi85frsM3/FqjkN7jzapI2wNhJWsiQRmxlIC8jhiprgBSMWuOjx1tMa0LFDxqAmOHWorx2eY+SL7BsTJQurKA6vDmPicefqwUFrgks11gem1Sj58KM8wWrNznel37Qm4dP1OKDSJNX4m0tY7NwciYhpHN+RcntBrdBqGExJiOz8ap44Xjl71I/sDcpkrslOp1yZJwHOEu45XuF7XPc9QBxOCkUBjuNM/Wx31cxoRhnpIzGtLQkw20OuR6aJcR4He2GYnYYizHkptvvKLT454HT2ZeSXNV1BKpfXf5mqdUTC7zahwpLWnjqNUxiUxskjsqAs6X5PwcIlmUprRp6eF819nOUQjVRRlBxI2VDRFF8YSyc6SFnrTM5JRZmMziR50bfMHVUfohV5F70qfdccvQNc0hrX6dOnATh69CgADz74INPplDvvvDM/89znPpdnPOMZPPDAA7zoRS/igQce4Pbbb++YDu+++27uuecevvWtb/GzP/uzM+Wsr6+zvr6ev585cwaAsa4xknVo2tAw6hEmwfsOSCFnxBtIXnups5JKTozFpcllHogREjwOg6tC9AMUtSKNZxGyx1BgYz5sgvYKU8PI+8Kn8kIYXSrY6cck5VRfBbpaWfU7JU6c6Bkoe8DA0uKCT/U2MAGcRbzSYKF1zPN17QqvtUP6lUI5CCfOOQCci3SrX7FagKcrxM+cZbUsqN6nM6wWDLH0k03Rlwx2Gb7siyXXzJBd4q0A56bhgakHsx4Xk+ODuS8ldLiH3BAikfN56o3DOT3jcaKk9an0tapcVxJKG5LT/Mi/iUyiCR7HWBjtt8GmF7Y7gU7JkoapCyrT36SGSE4aeQO0pFWS8JAjhw0N9ahohkuqtym/R+NqQ+BQhq6bTtZAUxSQ/Ho28r8YUi96yZgYoyVsPt75QNkx4/Le85a3vIUXv/jF3HbbbQCcOnWK8XjMdddd13n2xhtv5NSpU/mZmmml++nePNx77728//3vn1OJFvVTxAYtSolmuuzpBEFN7YhpVesWSaY7JJOOVpsVu/fqX/gowcSlpqo7DIjFIEFbWnNh8DSVtDPHfNGpGxDsxMw+C3RMF1K5LO+2tNtxn9Mw4aYKrkXWXIhfGGTKOLfK+tEi2p7m015BZtqyKq0Xj02olhvSM5ofzV/7ZsVtEfWrBXUz9JXKRS+SNDTp0+95P9g7bm4SY9WgaQV+U7ayiFfaC21Yt744jWs4UebyhO0QEnST4GlMCFZAUtICrai9Bsv6c7V5Ps8zyfWp374iLfV/mXHV4dlUFG+gNQ4ZBxd6e7CJAnNa/tAwQBXSoJXKAza2TtQiqzW1yIgkeno6H+eyl7BZMTGupJ1ma44tLxMjeYSwdibQRTHByyM/pyEggpC3yKT6BZqYKEN287ok7JhxnTx5km9+85t86UtfuuRKbIZ3vetdvO1tb8vfz5w5w9Of/nRQj/cOa6FIX3FmaRGictDMdN5CRBlPJrqyk9u0Jl6dZpaoa0V+kdbt8ydp2LmTgrs4XtELbVDq7CZa0UJmuYUOF9Bd1rjC/JFeNRSmgvgJrCnSBnf4sD0xPJitIukNekRvHo/ulLEb6TrrZFqSsk6Y1pChMK6FdLcMreVGTVTTpXltJrPpzhIPpa83Kmc3kTRi76rsJTAvjZtxpxccuu5QAybSVU8wCKRh7ICxjQ51bWCInrjxXLqadJ1OsS4VivdwlOOygFNb2kyhM9q/F5UbLzCVsGTcrMLKGMz+cSQRk1Bo7Y2Tli7EUE7EgHKck0mNEisZthYneVNc+NCaZFuNL1czLl8mQ+JIBLoWsrXgm/hRkid3iLhSzJ1K2gdpEGkowYfhUgbIjhjXm970Jj71qU/xxS9+kac97Wn5+vHjx5lMJjzxxBMdrev73/8+x48fz8/88R//cSe/73//+/nePKysrLCysjJz3ZsxakbgpyHQLSAuxSYMpr60v0MSB8rikgA2jsQYwiTzNhMHR5JganWDMnE1rKXZOBLTEBKkSDQt+HWYnlUmFy9my8JG+1zq7VZbRj25dln8N5ACj2Snl0ToRqyFpcR1Lft3EnGTPN575re0z6dHQat2nfGcWpTOrvZa/Y4uJdbqcQix4KKncHo3VcJa5yZzSaIJpbziXq4r7iXqxbq49tsbN30mVZ4O6JrQZ7HLwzCUnbYH5qGTJI9g2QhzT3HxuZEE2ixE+mpCj617ZTwOG+V14rFxXGfFIwughXHV8ypZ4HzUKIwJIbIEE8aXJ9OHsMdTURuDR2nQcAIDMHHYOlwDtrGM3QGY7I8miTHgIvPQLsduRuDj/iiTpGipKhs/a4JtDWM1WPGMseGcvHYMNMSAndEAlJikyZpdbAWSBStw48j0JiZIBOoxIwmnQzgFa7LmFRS8Mc6McDLCaXI/m7NgvEVsi3GpKm9+85v5xCc+wX333cctt9zSuf+CF7yA0WjE5z73OV796lcD8PDDD/PII49w4sQJAE6cOMFv/dZv8dhjj3Hs2DEAPvOZz3D48GFuvfXWbVV+ao7RygrN9AmQNrhshzEcNh0bXyQSpduZSaVWA60NA8m3GCbgx3GjYVtG5hwxtWhsxZkiLI5GacSNwK8guo7Q4lpPoynSdlHt5vn8bH/Sl02fplSMDYn+3DQz12qTqUAmCmkjtPp0QGaQ3tJRmMl8m3iIUsIshSJSWmcl/u0IYwt+l9sDyfVQkRhBOwqXSZLVYARBqpaIET/ScEnZ1646G68nbrftt9M/Oy8nEM8u46oFpXw+Wr+UyqGmlhdq1LXc7U3IyQwezHVpLocboiZ61hm8qaK8uCBtCRLWx2xkFN7jW4vx4aijtNE6BeCux6l2StPynibQEI+EaBAS1m5CdJ7wi6Chh/ngvUdsZIpxLyBIkHFxTJxD1g1yRqBdB6uIOIy2BGcmj+TwSwQ3f41zjXQ6RKJvuRdopg3uvKKtwXjL9KKjlZaRXceZtkQJ0VowK/tKTZrnmVbFPYxiMVOBCwoXPD4pD9LgJbSHNxY1NjP5oEJcurlwW4zr5MmTfPSjH+WTn/wkhw4dymtSR44cYXV1lSNHjvDrv/7rvO1tb+Po0aMcPnyYN7/5zZw4cYIXvehFANx1113ceuut/Oqv/iof/OAHOXXqFO95z3s4efLkXK1qI5ybHuWwHdNMFSNrYXe2c+RjRU08il214j2x0VTIcbdaA1NFnYTgktNxkAhM2GBXdCnmtrdQmRlFQBtwFvUNMh0jOkI0aYaWEsdMKCFd5ue9CH2akCNw5CvV4nJn71TNIbaezqFk0/yGsCCu8a/kMJ+oujg45+2PL5HipVOvXaZyVe1DhIsQViqE+wkRv0O1TQja5MmRUMqPC+Oqtw912ke6d652pDc0HXfC+YwLum+lKfJDbRHojdl6DF4aaeqiCBCJcAeJPWiFUrpB6kpoFFaDJuQ0MOwgw/rgEasaw7OZvJFZw8tmAcVU7gi1kUt9IOAeg6PBaTCFISH6eWbsEoP/SoqVSmayyWzmxDPRKd4p7QUDU4eIYozD+rAXVVBs9CwWYh/EfaQa3dcltUsS1oGxF1gTaBsMlnYyiVKniwekhvolIhZ4uMvNbBPjiky2ntXGC2YCMgGNJ0EgDR6Lk/DBhBYMnoypk7ikKbMtxvWhD30IgJe+9KWd6x/+8Id53eteB8C//tf/GmMMr371qzsbkBOstXzqU5/innvu4cSJExw4cIDXvva1fOADH9h25R9+9GbOrU74yfEKq/5xrL8Ifp0SiKdEwxKTj8/Lsy4FyrXO0F60+HYF2lWmT+wDSzimPc/haiLnGSnFJJyIeZTCxAk4YeQMbm0/zjcgU3wVkzDlV02FOeRiFvPIvMT3KXL07jKudB4PvpJ2TYxqTQih4+MheRo3PobJVTMu6bSd7dg098DkJqVVWwhmmcZGCRdcPM7CWhPMhD3m5WeiYtdIvZA2rC4H4wKJzggmC1BA7z3L+3TNY0XfnHnbqg3CMT97UXcfjSZxbGlyjEhOWUkrKKe+jceRmKqEOHvWFgeudopBaWREPp0472XqmVLRTtBmBVoVnGlwZsQFGqZYvDRgxkHoDbyBEvswMEBjkoVCSJHnxcKUdRwtTFrMtMXiaXxL49ax6mlUGUvQ1lR9PI/PxjW/aXRSiQwGsvOKkwY7teFQSjOCdoJ3jkkLE28DWzba3VoVT3E3SHS2DlHlvQnHmSSmHoJqh1BvltAHXg3rOuKiH7GmDVNjadXhtcXQRhPzpZ0gIHqpDvVXAGfOnOHIkSO8/s7/H087ZPn5p0/Z3/6QkZ6nMS0mRixXBRNj1YRdVglBMwgmO7Bqg8blQ/Ry2WdC4EnjMVpC4PSPazB5EMX9DVGiFzHgA2FYYYy/2KITj0yFcXTWkLgeoySi4bsa/gbQ6m8t3Za4a0COlqHbTKfcks+f5LrlsqOmZawJQUyJtkOTwgj5eNpz+U0hjKHWKbJ+MKXo7tuVIDJTwTeWdfW0Ak4E14To2cHWHk6mTg40yVwIXU/pNKNL+6Y6+86ez62k+/EEZ+ITbjO/7ZQT7wbTWj3gNhl7HYGq11dhpJTZ0Q/6vHtIYkgqP8ztZKQmatFefByXSRAJzgnex/UuIbCM6YRGPSso48x3Q909YS5DWgLS+EnvbJg2lqlpmJiGM9KwhmUqltau4KM2qF6TVBm0ffV57FsbovJ7VYwVoAVpMTqlceuM8ayKckBbGuewbcsYHw2fytR7JEUPie2dxpFLLaVKQzARmmjKS4PYi6PNZnOpcwiMK/9LNLSsKCYbjEEw6rEa74pBxXJBDnDWPJXT/ga+e/EWPv6nR/k/j1/PoxePs65H8IxALwC3cfr0aQ4fPrytkbDUsQr/zw8PsbZ+gGdfJ6y2Y8Z6nsY6xLcxUCjYGEbKa9x/kAiwRkurhmVd48LMVcC3PktJJq1zEAZrjXzsd49x5b0UCCsyhqkirWLVMKIchZ6JXiSEG8ntHbqfkj2C1WVctWzYT29P48qEos4GwXqT17M0H1wX3sTO1SzTG3YZF8Ilb0ich1aEVgwTsUwawxSY4APjkkCcPBr2loiEoyGiFg3Bt6Z63cy4OgdPUlykTWIWVIxjg3TKoSM+KJ38FqW3U05HDMljpds/NfosZ7ZneowrCnAS22LvGBckR6kw1CrGlYa2SNG4YqNK8sRBYj09VjwNLWPvWHGOVaLGoWRNKBHq5LsQTkuO74xhnYZ1Gta04Qk74kJkYmtmFOPxSZ5yKgJGcD664GuIyu9cCG5sjGDjvtFGJ6z4NVbUsV89R/CMtKXxLSPfYk3w3JtWh2o2lL2JSvQ500LnjMaAyU5jtPew/pc1WAm/l/h2Gtd8S2jp0vNpjVEx8XQMjQd8+sy4LsoBLshRzuj1nPZHmZqDeLOC1xS5/tLm+1Izrsf0Z1D3FL69DqvtD2n8BUQc3q3H3fSCtemMmDCcC8XwWVG3KrmTRAztepvdZo2GDkodVcMWRR+RMGwCgyyEyIrF+mgLJnndxZLnaBn9K1mjyiasxBRrIhWYbHxw40br8LGqDhukg7kvHskQj5MQDwaLmMC4nHokbvxWBesbMldOi78VaxaIZxuRpf3d9oacWsOaCI+3Bjl8iKkVLtDSCnhrUGNxBO3QiKCua65MkcWTtpEIf9r/E65ufqxJh3HQ1X7mpTMT2yC/Sy1nMWTbm6q3nvelo65bjoGsIN5TipYcwsoBRLMctoF2inhPg3LEKqP1KeP1ixx0jpFXrFesB2+SYBM3tmi4HtbDgln8grdcEMs50/DjZpULzQprzYgzYpliQSyNbSKxr+hCtC4b05TTB9DAnNSxqo7DrLEynbCyvsZR79nXOsZti3VTGiNYa4IHpHfh945cjkLk6Wl0aDz7D/xUsSZoXs5pOIVZ0snPPe/pJMlHTxyVGFoqCg0eg419L9GTMmhkhomsMrFHuOAP8v2L13NeGqZmhFNbs9cdj4OlZlw/bJ/OmbM3sPbQD9in12F1FXWT7NFpxOBTaHVAk/eQhIMf06qXUfLeCBGTbdFBFQ4hnmY8mYjmEVXEB4kped109uqKxPN+uppQIoh5mHQ2OJUyTP4u/ZI7P6vP7fGbMa8dIB3zYMSEfSA+SHMSd/I77xBbdu5bb/O7EqOKdNaCtPtGyb14NzGxwmRkOb9/H9cfegYyXmHSwLp4vBHUCK13WLGIGHzrguaV1krSOBAgbVaF0p+ESVp169JAayGug83Wp/o6XMitTofjf2avb6BnUno/peeXk465T22upDkW8+rX3RicC6SyacbgHKKOxrcc9oI7f5YLjz2G/fFZxh7GSGBcQvfoEw01s5oYl3DBWi42hrWVEfq0m5HV69H9+zlrDRMEHxnXxLXxUNlgAdJ0aKfYrOFbFNNOaLxj1bcclAnu3Gku/tWjrJ6+yH4V9osg0/WsdZY9WMF6EXXRyGACjUvnajXWYIzgpw4T+zhsAQmM1aviNG4fiozN+3gYrti4H02zdSSSzLhb1WA1SnliUAytWlxzgCn7OD9teGxtPxd0hBqLeMPG9qXNsdSMa8IhJv56TrWeMfsxOkX9NNuxjZhwVABF50obqZKUk5XgzLjCk+lXommBvjrHKSMyLg1H1medQkqXiETzRFaIpKNhaJVXzr1iXFnLQnINio+f9OiDj/d9ncVCLCRQc36b3MlF4uK+hvODJG/CdGFdL2qEHXfriqlWqkqnIJWtD+O6JeYi+PUyVZiK4aKOac1TMTYyLuPjXjShVRdMhQgqwVSYoyHETemhlCLRG9+Jq4DugaCw11hcZ9km45plLl3GtdX11c0YV0gnM6SmTWaS7oa8utvRAiH1UQuxpkEknMtnfcvEWyayyjkvyPQAYy+MEIynElSrt9aaccGaF9YUJqahkRuw5nqwq1ywMInrq0YMU3Fxi5VgTdz17Mt5Z0I0t9FivWPde6bOMZURZ92UfdM1VlXYh0HbKSk0ncmbglMLJPqTZodmE7x1UQt1UYfMSk8wGauGU9uBHIfQp/X7uI4WiGhs55jMq2C9aB1eLd7vwzGm9fu5yJipSOyLWoDZGZaacbVyECdHeUJWg7cKIShuR5cxRTom3UmKQM244s0uzZZoKmQhI5CZ38yaSzouwpqYqOCiZF/VLBP+mXIASV6SUu2FyCbEijmk/zdhXotp16zuUz8qYmbu599str6RmFjvua0w2pxFfuP+C5SJKgJto7QjmIzA2mOIHbHeKFNbvLw8iREFz66SE2SHma6skiXSUO/aMLw82Gh87JxxhfTOGdfmeZc0ca9lqnAU2iTVPz6nphxHruFcsbDzqmW9HTNtDnChWYXRlMYLVk00R85rhJoeeKbiWbeeiRVWm+NYewDMiDVRnAljzANqkzUmzBuj5CgtaY8UohgJMU2nDlwLk2bMuZEwGXsuttB4wTfxlGJiAJ4oINbWgflzKahYpoFFG3+TM1L1C0Bwea9joEGpLfJp4skUmzqAsOTivQUabLOPdTthKi3KpOrDa5VxscqEI0zkcCXV10Q3O8BvRsM72EsB2sbR5SV5QBVWIx3GVQZX8eVJmpTg4x60ctqs5jaoq5/XTXoaTZIg50JKu5UfFfPkXmHGI65Kd9dqwk2Pdr3wsswZftvaFm89alrO28NgG9YNTJq05hbaFBMZV2dTbiKENXGN5fg0bRTE9Z7ZDgGOeXTSe0HoL1c5Kb0Tj9at5Z3GPwhiytj3psz7DuMimF9EQb0nHCwcHDRaRkybfZyzK9CA9QajNhw2mzOrNohp1X7iaGUaxpcFba6nMWHP0iRHl9F4CKxSVJYQwtuZ4Ojhomk9yXFGoQV8A61V1qxj2lis2nA6dXx/QbCu0AkvYeFsXjCDHSPSjCxga2Ce2Rs6jadOWJzEuCIzVsOYEVM5i5M1wjqDXrKct9SMyzPCy4jcCp39NF0peB7JlYVq1N7Iz0KMqiLF3NBf/A/P1TvLA9PKphBJLC5GA6DrMtJnUGntaJ6EvZANVeY8kbp2l4lxpXSc7/X617x0FlqpN4qCE4NKiDoQPD6j2SibLNKT4WPoT8AgBpSzj8L1QDCzaN9r29pbr07Xfdrv383S83C1lrOVMrdTTr2tIjnzVJJ+lK60c6yIVJ/4fBxUGtdqRA1ODE4a1IxBwMWo5SnQbk9yq+rvQXzY54eLjhc2mNRymaXWhemVtugYu7PlpRx5q0LYEyoWJ2FtSL2N2mwYfz56SiYhbrdjlBYGFeaBQCwzbeCfS1Xj/6XtNTpyzI6NnWPJGVfcmR0huZmhz7jmwSwgxhur3DtD4gUdmX5eGQpp82N5n+5DWklvJh3Kln8h3U2jdOZ5tz6bvV9HENh7xlXK7dV7q+mee0d2kxGXSYWKVJOxeDWiaTG+nliRuFRSfocOIXSJ7IC9xIzwlYS0NE5jPyILxqqREBCdEB4pzD8BGcU+tTi18ffznVfCpcxe4jhQkudx4nll2spMxcNm5qTWxdiq86TKtLVGwsZmlXTOSdy8bBIj0Djnt7a2vXXM0lOfmc9mFpgk+Enn022P+SbLrWC5GVeP4896/RFV567UV8lNc7G7nV9KhrI3aCHSwCe8T3jcLtQdgznCx5+ZimmVd9be+9f12XrNLxPTmlPy1tKKJiYisZdTqANp8xQsJCc+uGGbSCFi1bW90MYH7ADVXNnS+KyEHEhmeqmk1L7mOIswWko4s5BvrZnMKTIJO/G7bGVKJek5+DICDZrJtSygUTtnBBuj0g6BRI82ngeF+mquW6UBX8mDJK8OLO79LtMqzxbb7I6y3RnmaVbpb/9e5jNasZ1ERPuZpUGVTFr9/FIBPe1ty+93+RnWztDVlLpTraeC5mt1e/SomlT5de4tS3s8mVGRTamuRWpeLwGULqu0MgiewIQIKvXelBRNROt1mKxdl1w7ZmYtm9BVqY4+mR0rpT6VyS0JqArGB3nLevCeyF0NM05Pna9SjevdRpdA+d6djdHdFF0Ui8zCd1yr5Wdci979UonMXg2EeVWaK4FplxajzDs3PtnIswQ41+7c11GezMR3k46rBYbFtqDF+WSCNse0M2DvMdMtWhhTOvFcq0drplWR3c7llI0Ghpa+a38+zlhzundTCTnbjlYhlcaXiHb3EMq8X8yXaB2FYXWj1Zc8U317gtduYcaqNT89+7v0dj3tsDO/dl7Z5WZcTzZiMcNTugbC+d1cmzbmNcjcUfMkRiXJ5Ulvyrlg0Fnf23jqzLv7ZGf8Vzfmt3wVvVC7priQTAbimZ/FDc21dr3FbR29fLZX6bTeU4Jf12cuAxv5mfW+XJmxuBnTSkJDn3fvFmddbsa1Ef/fUBObh0W2vF1G31oXezhLh704ePW+iRI1sC8FxUk2a0HscrxF6aUnxIsmcjLlxC0BWiTZfOy70dx+fdqQDMs5/wXETKvnt9PstdFkJ7+7msthD8qkk05WhjT+07VKK4oGiNoqUf8+7gMmm+3iI+H4liqvsvu2e3xKeklK5PiOTSTuL8tnNmg1xnI1kxOIxccwTU6judEIMZQFRX+p20UXcIjdwqxVQed1SKrLwmuJNvW58TUa8qmjZ8/eWPh1PqGeY5Heg7HQj1gg1YAsd7IPXHwmXp0zNjS5+i6q7yLefinvtuvmiB2WIzOJ2d+nMVK1XbL2SZ74Zf73FaqukFgzsPJcv5qLdNyNdN8tyRcbYDfL2ajMrZaz3TI3K0fm3J1dww73alNhCCqcOqoQ0rIOFmuRmNaiSvQv1+OmV8e0tyyNsTriTTcXpaiHHtR2LH6d2d/nIZ26lnbYzT2oMya+fnPntM65NpMb9Vtd6l6u5WZcMyOtug7UU0PmtvyGOW/xya1D6xEfa9X9Vpfuq2+zU7fzo40YVyfPRRksEeZSu3nvVjeKdiZharKu8Kx5s/PiMCnMlWy3y6h2mr5c5VyJMje7P18mi2Fxendnh0iVo3Y3m5fnal2pCl+UHqrHhGpn6ac/JIpWVJhXZiwUsbRUQEFCZPUkQXWznH3HMF5rG8z2vV3rWNobPLXNTOPfjoZl6b7DpTEtWHLG1d0wWqNP6LcqG3aROraO6FDSJUbd4mfm0cCaPUlnlkn1jM7ZHzR3Etccdqvi+aVikQxQX9tuejvlbPYbiNJ12CiaGVf8mHjkQyEaYSzlw67n9tnihr0cTT5gDmT2S4rbUV8WYigwY8CFIzti1Nhqy0T6penQ146EUz1OFXOUGMFDUtR3IQS6DuE6Kk2rqplCiDEWB12KeaqBtqQjl7rzRfPFsBfNlANs0yMb0qxZetTf21k/O7+dtwhNP6yJU9qE3Le3bh9LzbgCZObbPGmro3Zvk3ktTssWnllQXakeqn9T36x/3zF50LtR/UT6Dywgq1q11dzmWNRGWuo8M9K3mO7IERtwtV7bzFZR4xlg3RmeuqZsQPZlFHjNkb4hRMBOJwYY4oqihryNav6ddqTERGzCt84JEul9tpPO77md9FVcztw+3Ep6kzYEultZtHziGSdpWKYV4RCVPYwDE8dKCArrCefiKSHIksahWbSexFBSWvIZVaFMryHSvHofDpmkIss5MnsIWRWq6+K5V4n1xHeS8EQm7YU/hUW4xARTkPA05Wu7YqUCboseMf/+7LNdCrpZ+Ld8Nw+HmnZtnw73sdSMax5J7jbvMmIO0+o/UdmHNf4vWs6LyhKNhHS53ssn8Z0593wVV6QTLU4ioScc2JcC7pZ0IPIGQzqDq59OgZkCo4i5qxCk3iqdD+Sc1wgay6oZV3jp9AsfosKFNtDEtBw4sCI0QKsxqoYI1pgYXEtBfZF6s5NzRRxmGpKK0PfSCCGit1TptGAf07mcSh1UMz+/3S4np7dYTt1Xi8pho3K2827VmABU4jEyCiopoHYYjaJh7cpGhqQCrWtRExwgGm8iDwibpKwb4ZwDvx7zGIVRLzaMF63fJ7Aam1miA6aoTsE7jDpG8UBSrx5cPPsneynG0700HfcTonh4k5hWYlxhXAbtLTLOGA1ejI1vGtrGJyaw1+RO838RGxRYM6bqJ6Hd03jp57d9LDXjupYRIoC1WD/F0gIu2u1TGKNu0NFuOKyUx+Lh4yPTMJCPgAdQSUdLgIpukI5MLAba9JHRhHQ8liGn57LIKj0PkXHVGpd0XYqnrONMi2unrHAEK4aRDecOAVH6dXgf5XBpcPmkbI/1s+2WWG54T0I9a1erRem5gWhZkJ7DUDbKezfKYZvl6KWUs513644D8aXty+GkPpzwq+lk7SaUoYq3U9RHBuCDwBL2RymrarAyZWov0lqPeIuoxXgb5wxBtEobm0nH3oR3GNmWqUwRWlb8PlZ0FesaRNdxxqFGwSYtj7zpmThPWxGmCC4KTI0PZ4EZhbHEKDvGg5+Ct+EML7tC8VKseMCey+pF691RYXkT9Q5/38NSM66O1jn35g5Q53dpQsF81DRmzs3MTOaaybR6zmN1wljXGek6ja6FePGZSZVYa/mQvUXFMvuqGk0SQooMHesm6WwxWcC4yvHtmswv89KkvEuD1FFCuml66aRt1e1ZdsKkZ1qmuLbFTR3XXRxjZYSzSitTijOm4iIBENPEYKUQiGFpz+7+zv4gqTt1O+l52G4e11I59V+yMBSuSmRcFMZFYFxBwFHEm3wSsHgYuQusr61jJ+eYTFrEWaxvkHi0SWBcSjLrdd0fPOiU1ji8eA5MPCtr+8PBkbqOMz6MbZOYb1q/CiGcvFomZsRFu8K6GdNKg9XAuKQNSqBOKQFNNdUmDdyukLb36JdWfV/Y/WnS1IKIzN7fAZaacQUsaLXLIYHsmLNp6cCKKZCZTiToC8xk6ViGERNW/AX2+Qus6hlG6rA4rEabfWVGybrCgirPP3K91nhmNbZdR1XU7FJL8QKrI+UvXl5RnG/x6vFrnqNnp9hpg7PgZAqUM5HiqeNgbCUMJUITwqeW42dqArrH7TFgAUIf1Cd9B8YVBQ2NTg+ieNOSTOfWN+HwxTiUbWtYPzth5eI51tdajFoamsIAIWszsz3t8X6KaxwK7L9wgZXxKo1rcDLBSdLcJa6FBTMjGLxaHCPWzEGkORSosLFIXH/FwXQN3JpAWzS0rtuJhsv15uldR3/+06NbO81zQ+l9S1huxnV5RY4edjZQ0obieYubac/HxrqRBxzClBV/nkPucQ67xzk6+UsOuMdZ8efY5y+S1raAYNPPOfq5OXffxiy4vkfoSc5po2Y9ZTrTp+N/7GcelEicvPF4UdzjDvMD8EZp8ZhRERTC4reAGLwRnKbDKJUmMq4Qi04jAZMYIXvBq0RpOFRzeRhbXe+goVzByixETUijsBX7xUStRLXWuz1qgylXFKw2BCtx6P/GNEgryMRgnMmiWWJakteX6kjoqQ7h4EcVjzeeyRnFW0WNoupR46MzVRMP4Yu/1YaWFaas8uPxTZxdeRrt6BjnxjcC+8LzvsHIGJ0KXFwBVkIH9YfdHL5yubCBXL3gB8XDtzqYaMflLzfjWiLInFTnkqYzt6A4+peOrTWB4DjgaHTCPn+eg+4Jrm+/z5Hp91n1p9nvz0A2cMQJmKden2XOk34W7QmZZ8bps5edmIJqybmUP8u4kuNEr+6J8VXm1WCGjBPECH7icOJx4hHro0AeJVwxhONOwgGfSQtuooty0O6iyUggnT6dmKjkEntNmYTUmilfJdBOm1dSdLjZe+ZqqXlRfZL1TCPzkrSGEjqF1Gde2siIDCPGqItM2UTvUrUYZ4Plr9LgrCaHp2QqTNFtiru8UZcVoXUXTIaKBuuggKqE9bEcOEPxOmbKPiZyADNuuLh6Hesr13G+DedreRrQJgbYJZwoaQqzzJOh0yE6N3npqDOT6v8tFJOEijyWEg3T6vo1yrhy320knm+WrrHo+i5glgTMFhTo7gZbCrOaoIh6Gp2y4i+y353lcPsjrmsf44B7nAP+x/knRot0U+dXlpYS0e+Kc/P3nwnzHShSvTZYiO+kU51KevONkIvK6b3DTBqMWJy2ePE4o6i4zLSMAumQSZUYZieU1vjUZj6aUuOp1VVcSI35CDI3vrF2Juj8Z64E6nrJ3Ir3GNdVUu9k004MKzMvrYLrxv1O4Tj7NvQNhrGuoC68l1pFfXAWsjR477P2BmFd2MS+VUmzJ4yTUAiIdxgDYoR11+LiWlYjgZsp4DzRCSjUp2UfU1ZZl4NM3PX8WC5yFkcjQivhMEpVU529k0LNaz2Mch22MHF2CfPKke79LJN2Bc8Ooa6sK9cs4yoSFlUbCB07R902i9I7ttduH51ipL5ejjmon51HL4QwGRpdZ78/x5H2cY5Ov891k7/kQPvDzLiSthB+E6deIljSb7jFlEnz/XnMalF664xrSzRx0Y7KmXfovY+COkVskHzVGryugw9rWCa6V6sKLnGsSKUb76nNt0krcxXxSmMt8LX5nKtTo6uEA9RmzM0Y19VQ7zT6jA8EvDYTlmgnRZggmvEkBan0QtOOSFKJ2jRiBSsW713aOIIANgktcYwp8Qh7JDsuGa+IKGKUKQ6NHqYWG9bJ4llayRNRjWfKPqZmP2tyhDU5xg/GF1lxLY0zUeOKB1La8NbqJZiz0/SRaBaNZC5vM9tpo+6U7vXLnCuPJ0FAKKZCDxIP0cxC9fax1Iyrq7yWa5ttjpvBXLVVdj4gNi1r0S2Z6fe6OkmaSV+MOqyfMHJrwUHDnWfVnWO/P0vWsKr1LTPDubcyaiXXrfxuN9L1W/dftlt2uL2IcW30Dml3TJj8qor3itcWg2DTWpgG85GILXqpaCZeyaswffMlcmul0ZKJ+4w5JTGJq4RpAV3hbrN6V89cSQQrUzUSpYqIosWEp/h4zwehJf6z2mDjIYht2+KjJ6oxBu/T2AyOGZZkJo4zRaLlToplIrjVe/DKOO9vBONTLESJexijlueUKVOmxmOsZZ+/wNhPaaKbfL2bK27SCqXn8S7BahjXYcPyW2yAzDi22U+bPt6dXx3vWpn/TPo+f3bujoaw1Ixr75rlCiBKjTpzka45QDMpxqCEHSAtlimWKY1OaZggac1MS9SIWaFo/oCry8/H2+9hy24cZa03s2a05s1+G4iBagj1k06MtjFSRmJcHrC4vGcHSRG/A7tKmz3TOlcov2fO1S7dmHFwuFoHZ7/e+T96F68spPoAYaOvRo0xzx3N/4h78kQCAzH4wLgUoEU1Mhtviv+ESp5buZyQLQqklWgheeiWoxLTzCwWx6h1adptFrbgq46wOsFqi8Xn9bSinZQFg9LuxVw7vysuj2Cx5WFQ5Os9wVIzLg+72F9XQKLsKyAyey+ZM4KgVcLtmiRlZa0jTA3EgToMLukHmfh2UY+syiRUVSqS7HhlXh7LgMS84ntmAlXSNn5StIxwv5tLZwVwo2ZYxiaCpap3mib9vupbK5QwTwIjCWaqNJ6bZK6uIlNsVo7SlSFrJjpPvRCKN2KasyLJVNai6hAcRkIkj7AulzIxdUaEGRjZWdKycqyxvYTMTXagi56pjsxUUznQ1GG1doalZlzXJkpop8xoOq6myZySmFYixlr9unyfZVr139p0vUSUrYOtmkQHPNnQYSwZO19XmZ/fRuiPPaEcahl3Gj+Zx+bcVxuC7D65sVDNTmaJzVAzpJJdNwbAXMNkvppcgWdNjAMGXHnsdDxu93eXMu5n1g0v5VfLNAFnTPo1Lp2iLN5JOWDAgAEDBlyFWHLGtUwiyIABAwYM2A0sOeMaMGDAgAHXGgbGNWDAgAEDlgoD4xowYMCAAUuFgXENGDBgwIClwsC4BgwYMGDAUmFgXAMGDBgwYKkwMK4BAwYMGLBUGBjXgAEDBgxYKgyMa8CAAQMGLBUGxjVgwIABA5YKS864hpBPAwYMGHCtYckZ14ABAwYMuNYwMK4BAwYMGLBUGBjXgAEDBgxYKgyMa8CAAQMGLBUGxjVgwIABA5YKA+NaWigSP/1r1dcBAwZcNdDykTBby7UtTFfdykPXBporXYFrDvM8+Lc9GMMIFjTMgTIXSNMhfa+Lk+rXG20kqJ8bMGDAbkBBBERRKTO43IvXFkxM0TBrNfG6OdmHB3e52lcpBsZ1lWPDcZiYk4KpGJUQVOntjuH6+f7cuEbmw4ABewjFx48T8AacASegSZxUs+FkE+3NzWt0Yg6M60qgIzVp5/Ki57X7aOc3WbNSWSyy7aRqyFyNbcCAAVuD1rNJwcjsPFo8r/rqlcarcs1bQwbGdSWwhVG3+JHApvpSV/qezA/Jfr5Ii+oyJ537TP19kYViwIABm0Oqv52Pdq93UWZ1sSpK93vO/NoSKwfGdaWxFW6QFqWk65JRbOVF2VIBj0RdSTpZdNOzK1k+X52V6DZbFxswYMBipNloPVgJpn3j49+4Tp2e6QqllUOHEtbJUoadnK+tRa7Bq/AqxAwvmzsWBcWgIhUzm2VGfX+ljdZ1+98HDWvAgN1BX8uyFaPK69PJ0Woh5rkVKkHkvLYwaFxXAtodfBs5Rcz9OcXxXZHMvNIdFUW1y8ZmreW1sVB7z2jvTro3sLIBA7aLNK/myZ81M8uLABsqT/Fi55lrQ8uqMTCuyw3tGuo6t/oXtuCzPmsq1FCGaGA0Oo/ZBNfcPJkW8qPyHGwmDQ4YMGAeaqEwWUb8zBPM2ZdJNTeLzqYLWeC1gyVnXNdWZ20GQRA1c1a4Fj2/1XwHDBhwKUhMyyN4MaiYjtCJeBZLkINvbx9LzriWExsPveruXJNBfwUqTom0CVlBVCLzCrKe6Zj7wgSqtafy7Jy6ZPd6hSrP+tri9DyfxO2kr9VyLrXMJ1s5OynzaiqnzDsnBi/RvC9bcWsvk1/nXLtWMTCuK47tDsIot2kyOCS5LdnJu//CRmTpTKV+mcH4UDOvcN8jFP8d32Na/Xzmpbfz7KWkn2zlXIkyh3L2opyyPQW8GLxYPAaHCTNK5jtV9bF4geHaxOBVuGRIdnBTsRWhKwcWVrZxTsNE2G1crvZ8spVzLaDM2mQq9CJ4IXzYypwdkLAtxnXvvffywhe+kEOHDnHs2DF+8Rd/kYcffrjzzEtf+lJEpPN54xvf2HnmkUce4ZWvfCX79+/n2LFjvOMd76Bt20t/myXEDKOZx080OlkoGNUQt0x9vFa0rmAr187+Lr/gr+Z/3WfDJ4SlCf/SNzr63fCZ99GhnOHT+ZRWDCgbV8L8TS7wku/SpQFSfy+5Xuty57ZMhffffz8nT57khS98IW3b8u53v5u77rqLhx56iAMHDuTnXv/61/OBD3wgf9+/f39OO+d45StfyfHjx/nyl7/Mo48+yq/92q8xGo347d/+7V14pasdc+zaiwagVrfiopSkHFQR9SXHOAMUAuPSMMSFtLG4OLZrvxDKs/07Wn3rmhsHDBiwHYgqRjQLn/lDWRXrzq/ejMsTdN4svLa42LYY16c//enO94985CMcO3aMBx98kJe85CX5+v79+zl+/PjcPP7wD/+Qhx56iM9+9rPceOON/MzP/Ay/+Zu/yTvf+U7e9773MR6Pd/Aa1wYkmgfLBYkb6asF4Cyphb1c81Bf3Xi4B3Y2j6kNGDBgO/AYFKM+fxo0mvoHcXC7uKQ1rtOnTwNw9OjRzvXf/d3f5YYbbuC2227jXe96FxcuXMj3HnjgAW6//XZuvPHGfO3uu+/mzJkzfOtb35pbzvr6OmfOnOl8nrSoxa7oKji7vyPKaGKoo2eARMUr6lYLuE1/XWwzXONWiQEDLgFlDhs8hhbDFEOLSIuI66xXmxkToXQncrZDKmGZoG/AvzawY69C7z1vectbePGLX8xtt92Wr//Kr/wKz3zmM7n55pv5xje+wTvf+U4efvhhPv7xjwNw6tSpDtMC8vdTp07NLevee+/l/e9//06renVhcwei2edru1/+eWRnEkM/YWatCtfOOB4w4CpFMPeVqetB4icJpFlA3SSftLyVLCnRkqhy7dlEdsy4Tp48yTe/+U2+9KUvda6/4Q1vyOnbb7+dm266iZe97GV897vf5dnPfvaOynrXu97F2972tvz9zJkzPP3pT99Zxa8WbGGc9R1u8yqXBvksyXGKzcwrMzPt6kk6k04mwOLPtGjqlNW1AQMGbA+JqaQ5luZt7xFNkW46jy9A3N6iEhigsti88iTFjhjXm970Jj71qU/xxS9+kac97WkbPnvHHXcA8J3vfIdnP/vZHD9+nD/+4z/uPPP9738fYOG62MrKCisrKzup6pMCmv8aHAoYjBgcFkeDk4ZWRjixOAyq4DXs4irMqZtXgYksyeeNyuG5buDejsQ4YMCALaKY8RFhakZMZUwrY5w0cQ6nSBqRoS3ylKr+lpl6be5o2hbjUlXe/OY384lPfIL77ruPW265ZdPffP3rXwfgpptuAuDEiRP81m/9Fo899hjHjh0D4DOf+QyHDx/m1ltv3Wb1rxEIFRMJGpVXwWOqTY02TAQZ4WSMhh0j8RPcblW00qpqX6bgZl+cn2tTZJoY9VbKAQMGbBV5LhlhKiu0skIrY9rIuDx2RlDckHlBvnmtzsZtMa6TJ0/y0Y9+lE9+8pMcOnQor0kdOXKE1dVVvvvd7/LRj36Uv/f3/h5PecpT+MY3vsFb3/pWXvKSl/C85z0PgLvuuotbb72VX/3VX+WDH/wgp06d4j3veQ8nT568RrSq/lDr2wVmVX5NUlVkYIFphfAxQWKztGZEa1Zo7SoTDmDxiHoEh9GSk5cy4EUNdKZL31E+mSJBRGfuDxgwYHMk86AXYSL7mJhVpuYALftoZYyXJvkcktey0tp2jbI35pqfidtiXB/60IcAeOlLX9q5/uEPf5jXve51jMdjPvvZz/Jv/s2/4fz58zz96U/n1a9+Ne95z3vys9ZaPvWpT3HPPfdw4sQJDhw4wGtf+9rOvq8Bs+huYTQoDV6aqGU1TGUfa/ZIYFjSRA+myLh8zEE0M0GVwrjKKlatcQF5MnXvDRgwYOtQKULghBFTWWFd9rNmDjGRA0xZxbGC0pBNf9eev8W2sG1T4UZ4+tOfzv33379pPs985jP5gz/4g+0UfQ0h+8JHrSh/rRLSOUAysCiLkzGt7GMqky7jMinfHuNi64wre0ANGDBgW9C41zIE2W1wshI+jPGMCDu6LGlud+fZrIVmjsXwmsNSBtlNDFTdWWDZ9nRtYirU+nplLaicBBWH1wnOnaf1F5j4Nda0pYmOG04arLYIgtU6mEzaUCxzlafkxDGr3QVT4TU6RwYMuGSE4ABCG3dyTTCsecfUT2jdGmrOorQgDbUnRg4XlclENCdeTZNx0UqH99FhUlB/HtU10PMVUTsXfr6JQjQPS8m4zp49C0D70M9c2YpcQXjgsfgZMGDAkuLHV7oCewsPXNjkmbNnz3LkyJFt5Su6E3Z3heG95+GHH+bWW2/lL/7iLzh8+PCVrtJVh7TXbWif+RjaZ2MM7bM5hjbaGJu1j6py9uxZbr75ZozZnlv/Umpcxhh+4id+AoDDhw8Pg2YDDO2zMYb22RhD+2yOoY02xkbts11NK+Ha3L02YMCAAQOWFgPjGjBgwIABS4WlZVwrKyu8973vvUY2LW8fQ/tsjKF9NsbQPptjaKONsZfts5TOGQMGDBgw4NrF0mpcAwYMGDDg2sTAuAYMGDBgwFJhYFwDBgwYMGCpMDCuAQMGDBiwVFhKxvU7v/M7/LW/9tfYt28fd9xxx8zBlNcK3ve+9yEinc9zn/vcfH9tbY2TJ0/ylKc8hYMHD/LqV786H9r5ZMUXv/hF/v7f//vcfPPNiAj/9b/+1859VeU3fuM3uOmmm1hdXeXOO+/k29/+dueZxx9/nNe85jUcPnyY6667jl//9V/n3Llzl/Et9g6btc/rXve6mTH18pe/vPPMk7V97r33Xl74whdy6NAhjh07xi/+4i/y8MMPd57Zypx65JFHeOUrX8n+/fs5duwY73jHO2jb9nK+yp5hK2300pe+dGYMvfGNb+w8c6lttHSM6z//5//M2972Nt773vfyp3/6pzz/+c/n7rvv5rHHrs2ofT/90z/No48+mj9f+tKX8r23vvWt/Lf/9t/42Mc+xv33389f/dVf8apXveoK1nbvcf78eZ7//OfzO7/zO3Pvf/CDH+Tf/tt/y3/4D/+Br371qxw4cIC7776btbW1/MxrXvMavvWtb/GZz3wmn/T9hje84XK9wp5is/YBePnLX94ZU7/3e7/Xuf9kbZ/777+fkydP8pWvfIXPfOYzTKdT7rrrLs6fP5+f2WxOOed45StfyWQy4ctf/jL/6T/9Jz7ykY/wG7/xG1filXYdW2kjgNe//vWdMfTBD34w39uVNtIlw8/93M/pyZMn83fnnN5888167733XsFaXRm8973v1ec///lz7z3xxBM6Go30Yx/7WL72v//3/1ZAH3jggctUwysLQD/xiU/k7957PX78uP7Lf/kv87UnnnhCV1ZW9Pd+7/dUVfWhhx5SQP/n//yf+Zn/8T/+h4qI/uVf/uVlq/vlQL99VFVf+9rX6i/8wi8s/M211D6PPfaYAnr//fer6tbm1B/8wR+oMUZPnTqVn/nQhz6khw8f1vX19cv7ApcB/TZSVf35n/95/Sf/5J8s/M1utNFSaVyTyYQHH3yQO++8M18zxnDnnXfywAMPXMGaXTl8+9vf5uabb+ZZz3oWr3nNa3jkkUcAePDBB5lOp522eu5zn8sznvGMa7atvve973Hq1KlOmxw5coQ77rgjt8kDDzzAddddx9/6W38rP3PnnXdijOGrX/3qZa/zlcB9993HsWPHeM5znsM999zDj370o3zvWmqf06dPA3D06FFga3PqgQce4Pbbb+fGG2/Mz9x9992cOXOGb33rW5ex9pcH/TZK+N3f/V1uuOEGbrvtNt71rndx4UKJEb8bbbRUQXZ/+MMf4pzrvDDAjTfeyJ/92Z9doVpdOdxxxx185CMf4TnPeQ6PPvoo73//+/k7f+fv8M1vfpNTp04xHo+57rrrOr+58cYbOXXq1JWp8BVGeu954yfdO3XqFMeOHevcb5qGo0ePXhPt9vKXv5xXvepV3HLLLXz3u9/l3e9+N694xSt44IEHsNZeM+3jvectb3kLL37xi7ntttsAtjSnTp06NXd8pXtPJsxrI4Bf+ZVf4ZnPfCY333wz3/jGN3jnO9/Jww8/zMc//nFgd9poqRjXgC5e8YpX5PTznvc87rjjDp75zGfyX/7Lf2F1dfUK1mzAsuIf/sN/mNO33347z3ve83j2s5/Nfffdx8te9rIrWLPLi5MnT/LNb36zs2Y8oItFbVSvd95+++3cdNNNvOxlL+O73/0uz372s3el7KUyFd5www1Ya2e8eL7//e9z/PjxK1SrqwfXXXcdf/2v/3W+853vcPz4cSaTCU888UTnmWu5rdJ7bzR+jh8/PuPo07Ytjz/++DXZbs961rO44YYb+M53vgNcG+3zpje9iU996lN84Qtf4GlPe1q+vpU5dfz48bnjK917smBRG83DHXfcAdAZQ5faRkvFuMbjMS94wQv43Oc+l6957/nc5z7HiRMnrmDNrg6cO3eO7373u9x000284AUvYDQaddrq4Ycf5pFHHrlm2+qWW27h+PHjnTY5c+YMX/3qV3ObnDhxgieeeIIHH3wwP/P5z38e732egNcS/t//+3/86Ec/4qabbgKe3O2jqrzpTW/iE5/4BJ///Oe55ZZbOve3MqdOnDjB//pf/6vD3D/zmc9w+PBhbr311svzInuIzdpoHr7+9a8DdMbQJbfRDp1Jrhh+//d/X1dWVvQjH/mIPvTQQ/qGN7xBr7vuuo6HyrWCt7/97Xrffffp9773Pf2jP/ojvfPOO/WGG27Qxx57TFVV3/jGN+oznvEM/fznP69/8id/oidOnNATJ05c4VrvLc6ePatf+9rX9Gtf+5oC+q/+1b/Sr33ta/p//+//VVXVf/7P/7led911+slPflK/8Y1v6C/8wi/oLbfcohcvXsx5vPzlL9ef/dmf1a9+9av6pS99SX/qp35Kf/mXf/lKvdKuYqP2OXv2rP7Tf/pP9YEHHtDvfe97+tnPflb/5t/8m/pTP/VTura2lvN4srbPPffco0eOHNH77rtPH3300fy5cOFCfmazOdW2rd52221611136de//nX99Kc/rU996lP1Xe9615V4pV3HZm30ne98Rz/wgQ/on/zJn+j3vvc9/eQnP6nPetaz9CUveUnOYzfaaOkYl6rqv/t3/06f8Yxn6Hg81p/7uZ/Tr3zlK1e6SlcEv/RLv6Q33XSTjsdj/Ymf+An9pV/6Jf3Od76T71+8eFH/8T/+x3r99dfr/v379R/8g3+gjz766BWs8d7jC1/4ggIzn9e+9rWqGlzi/9k/+2d644036srKir7sZS/Thx9+uJPHj370I/3lX/5lPXjwoB4+fFj/0T/6R3r27Nkr8Da7j43a58KFC3rXXXfpU5/6VB2NRvrMZz5TX//6188IhU/W9pnXLoB++MMfzs9sZU79+Z//ub7iFa/Q1dVVveGGG/Ttb3+7TqfTy/w2e4PN2uiRRx7Rl7zkJXr06FFdWVnRn/zJn9R3vOMdevr06U4+l9pGw7EmAwYMGDBgqbBUa1wDBgwYMGDAwLgGDBgwYMBSYWBcAwYMGDBgqTAwrgEDBgwYsFQYGNeAAQMGDFgqDIxrwIABAwYsFQbGNWDAgAEDlgoD4xowYMCAAUuFgXENGDBgwIClwsC4BgwYMGDAUmFgXAMGDBgwYKkwMK4BAwYMGLBU+P8DZdM8KdgmFDkAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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vZLTPUmYmWcFSJbMz6oWvupQxihCjYReDGUas60PgJa/tLx0UEp+nVSq5jiFo0kjVPP8I4cGfvN2oplFp5enjg4XRijMH25pGnc+OH/80v85f+u7/E//qf/hM//tHv8eWXP2Jsxruvv+a2bTw8POC2UMSxbWcQAS4YfYRDwkCroaVSaqNUpczygQjmAd9161GqEAeN+uWwcd+fB4xvEfAlSmIW9tA9MjD1kU5wOnEwH9i+QavICLsiKKhHwCRQ8TARZqg4D48nvvjiu3QtnC87N4vA53q7/izm/lvHnxk54zd/8zd/Zmjwjx7/1//bv2KRDqJh9GthXXIDFGWtBZOI0BBBexR3VQVtDTQWow0omuQKF0wlov5hVL1HXNIU77FQliaHjTaBscdDKkoUqwNt5LaPwPCJ6HbHD+PaIqyJjKoW+jCsJ9SRsKGpoi6UIqxLoZSK1obWhYfzA+vSWFplqcrNIisooqxro7bGujasnVCtFK0sS6WVQtHC5hoLTYVlKbSqoIUh+TkakM6yFIYr3WFcL0dth7GzowwD23dqyUK2DW5WwkiOTi1BnECcjXYYiCJC357Z+uCyG88//RK3Dj7otysdpbvgtw3VOO+4Xnm/Gb13fN+DpJIG82ZgPeBA8c7YBmMMrmMEpIJTxOJ6AxhmdL8bNIQ9M8HqwtLCmA3I3CuyxSqRaTpwHZ3iQaJZyjz3XBOZvhEkjeFBhjmtjTfnxlJjbXagm9C7YLaFIc0aYs0MrbJjWVQfCO/f72y3ndvtEr9vlbYsPKwL56WyLpW6VuqyBizmC8NuAX8OZ7OsxTpU6XEv3LkN53od9H3Q+y2yFgnHU1SpKtQilFrwzLiMyAhmFfI2PAgR5iwJeEhRvCzs3QNaMqNmQDW0RKZYNa59rUipuFZaW6gamUwtyjYcQ1hKBKetVdbzCSuNWheWth7nUlVu3eKaVVhapS0VROgu1NYo6Txb08yogd5jjadn2j0B3CQazUzs0gOMi2srnB9WlnWlLg8s6xokm1p588lbTuczT2/f8smnn/DNr/yQr37yJb/7//4P/O6Pv+T3/tMfsl2vRyal7gzPGrJUTucla6iGSWFdT5zOD/zSD34QmV/WM4OPJQkKRU1yOAGHq0bmVCJT9YmuZN101ml1VkbuyHwSYgLBUlVqrbRaeDwtdLlhVnGUsiw8vXmk98Hp9IjJwi4n2sPnPH36Bdoap+F8/f7CSPTo5z0+Clbhf+l4/81XFPZwLBowzMPSshbkFBWMyF5qqdAjFBRxpMaiFxH2EREdgLjSllgoPgb4fmD2p3Wh99iQtRh9xBIuJdh+kpj4yIhCVLnsO5ORZy5IKcFYHIZKD9jQnbos7D2KuzDAI2IstSXkAksVXAQtjdpW1mUNp9WidrEnlCUiYbhqoS4L0s4BU2nlvNRkjxW6FwpOVViXQlsCnjGp4eiKULP+ZijDhP36gSJJk7DBoMQG6TttkhWGsVnBx47bTlNQjaJ3l5bcv9gQfbuw9SggX74JxyU+6NvOSJ7k2LZwFjbYrzcuPQgKvu+I3OtQJiWysNFh7NiM/KXkPY3sZ+9+sAL3PeFhN8wmVJSGUif+D1KiDhW50MDzXpsIRQOyu8mgh6VAi9J7wDrhXO9EhTEq9EIpkrBaFsqlcVrLkU1ety3ZdBLOxYN55wLXW8BWirF1o+/KfrvSbwv7WlhriRs8jVM5cVoreNT7nq/XCLCAJoNuRjdjG4Nti8CqFplFrDTkSlGhlaijSLI/N4v9FqxboVs4wWGDpWTCooLUxrY7e4+iv+a99iS6tFI4L4VlbeFstVJLwIpVg/m5JfuwiHJaImtdziekLGhttLqwthZEDhFue0CPVYXzWmmnFdESAV6r4YyTNBOEHPC+JUsTXIRu8TskjPZkCV93p6gfTvF2CcclZeX88BBOty3gme3gnM4L8JZahef37/n6+cLz84Xbxdi3W9Zhjb1nPZPC+qFG/RDHRVnWE6fLhVrrrBTTzWgtMtWAwRs2HZdEPVdFKVnT8oRe020dhB4s7JtmpuVEMO4HE7SgIhQtcc8mH8AjOWjLwul8opSGu+KulLaitVFqY1FnPUVgb3xkUOEv6pgYKqJ0ArtfS2HzHvWKHrfmJEotsKlC7/gYGEqrAfvtBcZto4/B3uGTZNt0Im3fMzo9lRob0o1+23PhFh6XJcBBi2h/Mzi1xlKFgeO9Y8PY3HlYVkSUjjNuWxgpgzcEjX63gfc9DKALD6uzZyH66sbuwWA6LQtfC1RJB+CxmARhc6NKEju0cmqxWa0oa4lozFGaVjzp7GtxypKbvywMEQJRjZqKoIgr2/6Mekfc0dJQiQh2YBQfuQEEpWI+cN+pPqLQroVaGtexM6yjPtjGwE0QV/Z+iSzNOsPKEZHvNtDRwyHuA00WW7cOvee5lbWtURewwdg2hsWaOLfK0BJkjm704SyiLFqwpSDdGd3ZzVhroapCSQoxQYZ4qC3qUC4wjN2CQbi2ilQJwsKIulxR5dQqewtorfjg/bWzZEbipdDHoHfj2o0mQmmN9rDyg+9+xvW289U3z7x//z62tsBTiwBrklRKXXk6nfji6YGvv7mybztjdLQKtSqtFS5b57bdGOawbPzy+XNECh8unevlwj6ipeJUnT6ipYOEpuvS+O7bJ26bhSFzY8+1tihYmZm7MsZO9TBstyE0lXTkhhDMzGHCGWcoWBGaSBjn4TScrRtDHOnKdauUUllq44NbnE+FPfkY7s42Bqcq1FKp65mlLlFz08K5aRhTkySIGKrwdKqU9RywJAWvgWQsVRimzFJPt42qSUCRFumzCN4C3ShaUKn07hHcVWVtwrIulFIZLpwfHlhOJ06PbwJyU82As1FK4emTN3z6S99nN+N8Wvjyq0cuX3/F7Xrlct3pz8/s286H7YK8jzVOKdRa0OuV8v497z+8i6xMI+M+nU+0Vmm1UdfHZMDGAiqTaamSZNtweC/K3Pn9o05ZJRmebpSlsG/BfK2lZvvIvTThPpm0TmmN9eGMSMFNwZRaKj3Jc8OhnZYM7MfPb/t/7nf+/8Hxyeff4dwiOpmFXdz5ZG1oMga0LhF5mPGk9SAmuAaDpuMspSLLE5KwyN6Dsn1SQc9LQgZRvF6TJFE48UmpAd0MY9E7GwmJ4qjhvNWF2RclGud24CyC8sgsOg93HrKgHchkbKStD1oSLEQFUctoCVprR+ZQa6Unw27SjDXrXMOCSt1y05UaGde2GzMwV4lNDFHIX8+nLMYL7VSj3WA41R+SqUfAkz2YjaVEP5CmkbluseiVgFwls5d9GI96hiw+a4mMY++OjC1YdMnknC0Gc0NNp9R7bC6xqHWRz3TrzmPW0a0/xebJe6hi4CvwSN96nttpBNEBOL6juyfZoKbjikxCNWBYykrL8v8+DJGkwpTGqcUFWGZtQcRQPv3kdBT26yLEy5KR6lBKZV3PrHXldHrik0+/w5u3nyDqtAqfP51Z1gVRze+z0ErloTW2rlyuN54/PONstDRSo8DlemPfB8Mk1rkon336yLqcggknxlo1yB0lexMtAqCmwpCCjah5uSdiQTifWcf6VE9o/r0juQ6NR5I3NgskKI8SSEKVyRck1odYEj+UsmQ7S+DJCVcKXkt2ByS71oP+3pYkNWmNTNAj41WN5181CCWYUVpAZg4sj2tCf5F9zT61pbw5vqeUFkSqzLjEOHr+RvoT1azljRGw8xC+evf7mA3U4fzJI2050dqZ01poNSDQH3zxhl/67A1mxu16ZWw3+rZxu9748O6ZDx+e+ebrb/iDH/0+1+uF6+3Kvhm3rbNtFz68+yrWnxYeH95QPv0EWoNaKU8RKCpKaQVNB44GjCsSjlaPx+MJZce+K/SDPIMHkUe1cH7zlsc3bzid1oCozw+4RdC+rJGF6z6CVVsUXyrnNytv3j5Ql0ancN2T2fmxkTN+Ucdt33NBCi1TYXywtBL1FtFwXGb0EZh6mY2pquwWUYSoULUkdFG47jsiTi2BXc+AZO/jMNqtCCo1GFtjJEkiNhkEdXUkSy1ounE9t3RcpUiyiAj4JiMd3CMyIqj3t70nbBFwhuq9N0yKJu4cmcw2gl1XS6HVktBAiUg6+3eWrG+pFpxBzY1XVajZB4RNBzR/TviZjEKVYJc1kTscofFvFaGIsCftXTITnpRzA2Z1vBJGZJjjxYImWzVrTkHPHpMCT9SiLBtUcUNnP4pHaFC2+RxmIb5gBnXfs27oqID1IFYMi6ZLTSfinnXGNOgvW6yj7y6hNxz3gE/3PtAS567i9zYgCQilSLJbpQbrFKe2hNs0GqXRCVEqffR4PrXx9PSIyKCosSyVZWloKbQVxhbfsfcdiB6x9dTYk7bso4NUlhbU9qhbRT2YqrTWiGpZQGnakiBRhL6HM2GMg8SgyW5NygM918qdkRf3sFCYPUIiJHwsB9ln9lQ1lcz8wcq9Plx1sn3zLTUIJCoS5JSE3IoWama3pUTWLxJEj0L2vM3GYsk/pmjCp8G/CHhzrheBRAwmCVZIel58S5u1HwEPONWzni1iBKnP6ebcbjfMBlWcdi34MMa241uht0pvCw8ilGWJLKm9AX+ArH9vW+d227hcLnz/h7/E9Xpl225cLzfeffOB9+8+8Ic/+ZIPl50+oNQl4bskbmmyMrWirWQQK0cpY0LIk/VJ1upMAuouWiJgJPf8ZMRqCRuXpZTZS1ersi4NBPa2czqdKMuJZVmSaBPXZgg1g/Naf36N94/acb3/8B5MOOtKKQtNY7MsbeLWBS0Vs2jeqyXS1qIFKcEsMw9jstaa9PJKa2FoS3FOS0sMN+CJ3ncw41QL7lHfKRbGsqjSkn7bzehu7N5pWaeAQqmxKUtV1hpptyDsNuhJ7W4lmFdmQlt2IJzfUlNpA8eI85d0uiLBTDQPev3SClULhRb0bQCJOllg9FG/mpFuK1DmVpbDxQSxxIQZjnoaiyrJopQ7Pq4liCQZRgd9HYvsb0ITAp2ITIsKmCT8FXBOlaCnM4Su6bxwWtYV3CSMjw0EZfIkzZRSokdFE0I1K5ElVqWUOH9TwTvRGGzGdduPjBPWCDayLqViWcvSg8zhDioW53alDwumlxqLwnbNWmmJesmsr4wRqg2OUZrQquR9qZQWQcq2eTAjEYpUHs4L7h28Yx7UfxzKqbI/b/S9c7EdkYeEKOFmg75v2BjUdqKtQVS4+s7eb1mjU1oVQJn9e5oG/LQ2rrazDWOM7fjO2dQY5WRVNGtts+dOMnNRStREIIK/GgGlUnB1xgjHEETaVGlQQTXW8lI1aOYCqCNVc70Jro1hPZx/rawZhAkVF8Ml1m/LYE6kEh1s4YBbRmAugms2RLumU84m30Q/WpJifES/liGYCct0xC4HhC84FGeM6CvsPqJuLtGbNW4b1gcbF3rRcFRLxc1o5wfqurKswRYupVLLitRAAcxg7LdQV+mdy/M7fvLjn/LTP/yK3/vd/8BPf3Lhw2Xj+bZlHanSlghWag0iFyXq1bOdsyTaMCADsXDI6BQzkGwRIbJyFWqr2dwcVgLLSrVKKr0UTmuoYvS9cX54oi4PnNZTNoQnA9EJdEaEWv6ckjPUjAcqnz+th7zPfNC2BzXmYX2gamHRgno0iprAwkITAQX3Tt9vgCBSeKwVckOOy/XIXB4yS3E6fXumSEQ259rAB2bRyKlaw4GpMMag2y3gp1I4tSUiPh9YvyISGclDbbgP3Du32zMilUrhVCpOZFJjHxRJtYES1zKSiVjrypL9WviObVtAG1o5lxVN52A3Q0pDy8pTadlw0/E+IHtszqUh+x734GZoWzhJASlIvwVWHZVaFoklJGPQvWNEFnGWuG63TrdgxIkIa1lYRzg1Y36fwqkUfLLe3BCMJbMRs554e2RfD6UGY986w4MogQT7MRzmoNtOQam1cFprQpDxp2iQZEQKp3pXDSjS8dIQqdE7dahlKGg9+necoN27Wbw+acNFDa8JAdaK7UZRpxSCyKMriFB8JOsts5jMGOzktBb1R3PHNfrO1AZNBO/PSBfolcWMtUY2Z7alDJBwpqCPZ6bEWbD+4EELpT4SDffBHDXreDeaBoXc/Yb3gcrOQzPKKQKYaM9tsM3mfeCgnRS2MVDpSSAI5psTWVurlhmTInXBh+ceSehKSlxj7/kMYt+KFLQ09v0WZCqNOmjJ3sVx2RklIFl3KHUlI7fI9iWer5SGjxv4TtfkjkpB6opLUuzVwRWkJhx4o08mMfFaULzHuj/6HMuK247ZTpeeELeg7RyN/0Tt9P2Hr+KZSgW7UUoQX2wfIdOUJJTzeWVZgnily0NA9Vp4eDwlyzhaBL54Wvjs9B3+wg8/5Q/+45f86Pd+zP/jf/qf+PoP33MBrktD2inaF0qjLAulZuuNRNnEcNDOp2/f0lowiB8fzgyCWSveo0YohSaF5dxYzye+9/3PeTo9JGEjgvjWhNOp8fTwGPt6KI9vHtH1gfXNmba0YCK2SoUj47PbR9aA/Is6lhIRWqJbAEnhLllnGuy9YxqwX9QTgq1ztei7iCgkeiWCsTVY3bNnOaAYIXq+SjbW4YaNEfqEUqg1oDVzC1058WTuJfU0Yal9GG1SalWCxg0JLyoypYOGRaQtFml1IbKdzOJ8SlrNyMeM3bbA+FWjlcXj59seC0uJDKnbyCZpo1SjyEAkKOXdezDHiqEaBHB3o4yBpJpC0aCYuwXxpeiSwbExxj6BI1RbwoOW0kDxgLQEQBR1vc5gJFOpohoKI5ZyPyJT+y+uzyyJAprQEkmLt1S0kBrGMFmJwQQr+T2zOJyKGmEUKup+OMarD1R7qEYI9LEn4y9qi5q1zjG2YBQSNY9WYq34sJnbIuZHG0Q0msc/VJSlarLwiGwjjaSmvBBZQA/ppRbQLJ3ZiB4GKJyNYFBSc08KeopnH9lxSDxFMN2YOnxdolZXpYZahO+pQBICJhxNuyPh7sgySlgr3O2An0nIzRKm3r0jWpOBmGs5zkaZ5AozrHcspclEKu4dYTB8HM+nlnHUXcCTHdfCCboz+oSKQWbLrBHBTPZ7tQbR1D3ofdDzumuL+0PiFyoFLYOiIVJgxLnHgNIcQbHusS9zv7ZGBGe+M8YtJcKEugTiYv6imRhFpCHaMTU6Rt87IhU0hA1uz0k1X9ZgSWql1Mrt+RxZrijt9IBaQSm005m6FM6PK9/7/DNEhcvtyvV2S3ZyrosaslmqypqyUCh46XxlnWVpnNaVtVZcS8KiASlaMgi1xrXV0mgt1tJgpOEVzGDroYNo5lyvG1U6o78QEsh10LPccsiB/RzHR+241hZGQIg+GwgnMGV1ukf6Tsr0lLypU/fLs5ajJRzOMDCJXopagqwQxf+guUfRMiATM49G1aQ6L62EU8xoxn1Sri0ZN5bsRM3+Dg366cSRdWRW5OmIU2qIoALP+rZnf4xZRqcetZ+5acDTwBCLr2c0PCHGMdujjTYEqiPqh/hp1NYksgcSHiRtmTql+mG84vp7GN5UTZiySAFDekaznr/zeFKzScSi0VFsGvDJxIjnNutV7hYqGRkAFJGg5BcLlYwQFUhnbkA41zG/swE1zjPGyA0WUGBTTTmuwW0EDFi1QFV63xkevEvVrPuJRx2JgI9EHF3SEGSGFvdjUCRNoA2GFtTSCdSSmVtkRPhdGmmMAQhSs/6SsKwNOGS3JKSz3DWIDeVeViulYqOnnBgHFFRKvNbdcQk6+oT9bE+412dtMA4zo3tmKUQXm79Yy0FmShPWB8MGV4s+xKIKreCeQYtBLXlu98jE6bEfJMkz2bKwj4FKwUrCf1PeSQvVOOjzs4E2lKJGBjlx3Z61Gh2dMmuVuU+AYLRJ7D13gyoROGIZEyScNgyRCOii/zIdlygi+7He3EYKJ0dtdU/HhfWE30r0fDVPHcYIusKmBzw5epBKln3HiGyr1Yr1a7YjCOvtlkhPtgwUWB8Xvvf9L9Cl8s2H9/z066/xno7HBtZH1gYDvl1aECvA2W5X3HqQZV58N7dgfA6yPSR70lpbjvqoec9IZ6oWdWoS1rZ9g9qzTSgPAUGOdWb3pfYzHx+14/rOw5mzXFi0chVJeZtoHi5KNCRTIzo22LctC4yaqu/GGCA2aLVSUx/sugUuXjHqkoVNM249YBvF8zzBRLvtHesbWkOPrknQQ/veQx6lRUe99s42PLK7IbTTmmQHpxt4D3mg88MDUzx278Q5tHA6nVCb5QaHvlN0gRbpPZ4OawtRV5rgahRp2OiMfU9BWgVxahWKGeogbc2orLCUhvdbwDO1QrYNqChqN8o5Ns3eB+oSdYD9gp7OmERtR6mI7yid9vZNRKDmeAd6qJRLa/TMaDBB/HIIiF6vt4DihtH3K0VaMKJGx1xxOj467XTKGiTsW7INgfPjY/YmOftujFvUv0pbksIdDMfRe9RnSgUf3Lpx3Qe320BrTXaacOsD8vlIBh3dBpdtp12C4NBmE7cL3fagv0tm+hL9Y8PgoYCU6INZlsqePmYSE2Ts0C9cd6diFDxUEtJ4dVcqHI5H7MawgCMr01DGn33EOtIkO5g7mw0uG0G+8GiQj4Xj7ENRswMSDCUG2DqUkfBxRtDRR+yHdI9j3Hrn2kk2n1BbXHcRYXhJbT1jWRoWNX76EBaFVpV1qSypRL+PQfGoZ5XWcIXenbHv8fpTZA/RSB6IQlVYnx5DqBdhuw2UQS2F9fwUbGITxh5oQMnGbZfQp7R9UNg5P5zR2tiNUGIZBqOznN7iEmSd7dYR2yjuLE9vgiVpTt+dJSINSg3lnj4GfRsInVpjjTdVfETGeNJCy6ZlrcL1siPWw6ljRz3w8uE94oEWbKOjtfDZ95745V//QRjF6DLm/ftgmT5/+MD1svPlH/6E9x8+cF5PrOsDrTVOrdDHlvtscD49sLty7bDtN3rfg4Qjb3k8LXzyySPf/+F3qVKw0dmuBd+jBi8SdRpZlLKsnE4rWqCIRRN8sp+LFpr5nyjbgo/ccb1588Bnj08s7YGeFGQXOLWgWYsKxaJ7xl1QNrQGMUOZMpwOvoeBJppK9xEYr9KpSwutsIywGbOHKSRWJqtOPQUuW0WpB63ULT9TY/l1iyxAMNrpRNVC1Ro1oN4RG6nqUbPWIUhPnL8U1FLOSWDcrolaaJzXIyNT65R1CSzfC7W1qO/sN6Q2ugexQEUR2xCMej4jEsSVtjR8v4QjWxbQJWAaAcaF0sJx9U5Kzgxsv1LWU0BRQ+K2jhuMG2VdAi/3gL9suwIWdSBKZgWCj+dgL9XC6FO5wOm3SygvoDl6QcA2sAu6tjj3iLYEH1tAQ7UeaubDgLEhGnVGGynbA/i2I2W2DZR4rRk6dqRqRNYIPUL5ICJUwS1Ed7cBxfdgFeYIkFRrjXOLReRf2qHMULMNIIrghUMMlVhXWoKe3k0Qy/pZSUhZou7AbHgv4bzcc61YRPGBxQjDo8YVbFWit0eErWdB2Dql5I0CjIona1PEjjU4XJC+R9QkFvc8Terw0AeFcMz9xd5qS6EgoS1YSjhLM9qioC20DlHUo/bYWuqBEiQK23uMXKkVsqmW7Curp1jjuM58EHGnndajXjW6Ib5Fdn06RZaaz8H6HgHo0gDN7N1Qu1GXaJoduW4xw7aNukad0jzq1/Qb2EY7LQlZR4a6bzu4o61k5pPZm91CbLlVphKLO4x9UJcWz16Ilo++Q7+ynpcjOEMKPgRcWZ8eo45VG8v5keV0pq4r9fTAZz1g2jEiYLzdNnofCBIOunfG7cqH919xvVy4vI++sdEHfevcbhdufcNL4eHhxNPbT3h88wmtrUi2oEQrftb7VFgyKCxqnM9P9D2YhxPOD3Qpg61Jzvo5j4/ccb3hs08XTssjpgVPCad1iYwLVarHAgnQLZyIThbeC8cVNKdgkIVxTMfVWqb5kxIbjkuK4F5T2wsY1xTjDTbThACtX/NnGteY1G33TmkrtbRQ9VANp2hGqSF3ExiQ4tPQl4KMms2M4QCmAe6uCW8YYp26rsTsrVAISeVY2nqie6EPiS79fgUxlvMDIvVgI/m4BY11WTEJsVPB8f5MXRqild6z98sN7zfq6Yy70HvCe/slzrNEE6dnncT2LeDRUjHC0KsK1i9oSXURSzgIZ+zx3ECwMSn7N6w/o61iJll/MHzsYANPNfdJ9mXsiEYLgWdD6eFccq24t4OAIX0LCmQajHhmMVJDSqwT98guxYJuX+rLfrvCuN3iuYmjZQmtSA9SiU4msEkYdI97Vkr0CGkJpACPETaq+aBFIqjJniFRj9YGiTUuox/1BB9yOEXc0UKsJy3RcGsDvPNi0gou4bg8a4yh1heOwfstHZdjIyElwHxnOi6nMGR+psXUAzQ0MEtFsv+uNsWlwaStjz3YoK2GiI1En8bY96jvlBJaipNHOgZlbQEPWvQseRKQypIObfJpbUcYlGXBTdMBSGbboQIyZ1eBI32jLBGcxfcMuNJu8XPPmg543JNxoy6NyR9ClX3bQ8C4ajiurMUxbuEsa41pALkQtutGbdEHZRb1Rh87tl1oaz1QFiklxnWZsjycI6DUSmk5w6012rrQTskCLAVta8LogRTcrjf265Xr+3d8/ZPCh3fvEIRvfvpV1k0DylRCy/Tp4cTDwzkVMQo+OoJHq5Bl7TbZkloKdTfWdeV2+8C2z/rprEW+rHf9/MdH7bh+8Cvf54efrpwfPoPzCS8FG4PTOhsDS274MEBFtlSXTjHOCByi2U4qBnTAe/CoVA2RFkYtAHGK56gDSkAdeBTY7Dk2q8wIVzPcv8XfRQiZw2iQxMMoePZXqFQKKZ1TWmjjiVDrCfotEquypOp2OMfS5KhN7ZajSjyK6uvDGcmsTSg5OkVZHx5Dosmcfrkx9gt4OK4wziAu0ftRajZwz1DSYL9Ql1OMThizAJxN2bVi2Vvk3bB+g7FRWsvs04EwQnPm1CCK14pG5pv9OMOzNnn056RUVo9I1sYWWWGJiHbkoEiZXaTAzqwTCoV+BBZTvkck6PSHon7ShKPCf2XSxc1nhBlZEQTB+iAEeY+MmFQTEMnXxjOJ7IEkOYCwxZoAujlTrTv6vzU1NCU/MwkY3GV14ucTaglDMAkUwn689n4rkt48VU4kv0Uap2D/6LxtzP6mVJjkmAtg1+Mze0JcxzXm947JVZoN4juiQeuOAKLkGgfVyvCBeSqw2CAmJdQcnKrBpIMQ81VleI8gMmGpEOHNmsl0aAJaG5MmU0rNYDMCv1lXPgLXzI6Hdaa8FtZzjUYztWQd0i1GfJhFa4KWCn2PoKhokJbcA1WIhkVEld63MO4SwUIENyWkxI42D4tAwI29b1EjtI73yQRNSSYhhbAFtDByMoL1K5dv3nPN3lU9nWjtxLKeePr8e7T1HPJTUjk9nhj7me1poeiVU3Oa71y/+pKhA1+EVRqPFMr5xA+/8xnrw8LSoNHZxpUiwvoYML2QvaR1ASmYK+u68P7y+9g7CZvKXag3rtnpua9+nuOjdlxffPc7fPrZmdEbXiuUqMm0NtejcDqdmKM0lC1/kRppeR6xHrdVlFVLOoeMPKQcxjPwuWRskRtqVtX9nCc7QuOAF/t2bHAPtdHMMCa8kfiwlCiMe0QvYQCDRWXbLZxmbTHCQmMESl1Wxr7T+46ulaIhYIt3tMSmji+Z+HKqdDPhLBc8dfpUc3OmoZPssYgIkqPfzPZbstqCpk6OdojdNyG4YFsyOlg/IlbP7lxJaSr3yewinfkcuaCMVGmYxe64Fj/O42PH9phD5bMIPUNei2uoU5fNPDJiyWdWSj4nDa7IfIT53HALOGZeYxb0gwgTLDmd2TCRcUVmlQFOBpW+zy/GixEyDhZZmSP3UU/u+HBEjegteulI4j9Bw84BE2M/HLTMRnYh4D+Zc73mZxLEDNkJTc+KHb+Zyaek0fFQtI8IhjlJO5qPo00k9lMoieOO2DzJnJQcgYzKKddqhCZSgoU5x7YMt+OeByQq0Ys00Q2IoCuv2Y9+VUfGQFtkVhOW9Nw/ZVmPhxq8oSi6lVKP7PlY2BPWTWsgELByqozMnqdgQo5AQ3CshaP17AvUWqmzPOBBbIj9WxOKDBjTR7BWpVRqMkPxweiW9gSarcDA+s4gHPu85iBNzBlxUYKYJYL4OtG8LsDoN662U76ujIcn6nqmnc5xP9eV1kLnceQIoB/+xf+WbY8Zbh++/JJRoZ5XfvUv/HdoQtK1reynM71v9H1j7MbEpg2j6JxccWJ05/q88e7Dle9+no3aCg/LguOMZeHnPT5qx/Xw9Jbl4cR2tZDZz0J6a3L0mtR1PZyOSj2cGHB3XP5i2Wr00CSgl6l8GG0UDvlvpjGaCE7UyMJ4+T2SGsvxORHpZwahNTv7c9O4IzkqQbTc/YEWLM2M1jWjzIiQ67pktA3tdAoqryYEdPR0hbLEnPsVRISSXfMVvGU0JIDd62Rzkw87hvTFTKW4phieWVJVOgyfjR53ViNyxgpYsIwmk8rzO+H5M5HDuDOjWxE0YU8SXrCUgQpeQAQAQgMEk5FhAuGMj6h0DpmMOlNaU9CgjovExj+gtUm9doB63D9GGgZ35kRnNLPlSQuf0XuciDmfKR6iHMY15BfmOgmlAj8ynIOkla+OlgQjo/W5KvO+xEun+/GAMafBl6hLHWvrxaoXONa4xOPK9ea51g6A9X5vsPw6mutAmVObpwDvoX/kYaTmv+dakxK1rslG9LGFvuccszHvx3EfOda3kbO4yOdT5Phd+M95Q+Tu5DXWgPiL1x4nfvF8YpHluf24FzDl1UauoVhLGdvF39WPEoXPd704hx8uMf8mxDw4iKDNJ9Mz/y2Ka5zMRfAyKerTgfoxJUCRvF/x8whe8hkmi9AHbNf3mA9Kv2G2hehtabS2sJYHOMf71/UUwyu78X5dsQLl1Hj65NNAmSQyKxXQvWSMt4VtSchXa6OYs65Rlxt9yoX5ESCVvO/twKh/9uOjdlynTz6nniroDZaG1hChDC2+7J5omjf2DjvB7CFIGElzno87u3UKEU1JMumcZOsRVPkJykTKG6wlpCVzpuAyY8bYNJpGarORzaoCZaG1U8CTNiKTyUZn0YZ51BnA0RaRtiYpwg1G75RswJ3qH1GDmZRcy32YkbwIboUxog9IaqXUu4GSkjg/0aOmM+NKoxoGM34fRigWcL9do18FjQwss4KYiJubynOTM5uII5OxsUUPEpI+ImouQDTHWjq6ZEV60pxrLeHslGBL5h8bezq1MKoxn2tgY4/pshItC8XDCGpRap0ZubPvN7AdN0mF/OhbGuKR4eQ3mwKj5g7SD5ejWg64CO+BAGSwMo7MbRAUcEl2muEWDj/Gzmf06pb3Ir5PmU29bviEVPNaIvOZ9y4maUNhyGT8OUVCCR3VkPWJdPBY42H8X5xboq9sMAOInplm/G4c9P/ogSoZ/Q8R8Jil1j0k1koJLUDNWp9qZkrXb4LpOm65XoOhGgzPzOakAjuYBKIwfQweJIGM9t0GQszp8u7pQBNAnAFJqm6Ek3ekLMceMYmwQGaNM5PsQbAJZ+avRK/i8NnTmf1y7tlnGK0BPiF0H9kTFmtfZzbsIx6ZjTuEnj1zRpzb3bEMbueE95BZ4giMd+ZMrRDGxUeICcwhqJF7obd3SCm084n1/MSyPqJPn1NLBNDSgkEbtT5hWTS+oxbWc6BJIrOGn9MByt19aCkISl1PmDqPjye0FEaWDubU7PiT7/nzSs64XjfOp4Xl/CksAUW0UpA6NfMU1xkHheqzJK66j5i5oyhDopHOcYZGVShqGxtF6h0vR44aUK0tGTUhc7SPYJApzm7XKJynrFBJ5tyNoNO7RTre6imMrBC9GoSBGnsoMk9po1qX6KavRr9tXD+85/3XX0V95xrCnLSgc8/G46W2dK6w71tGicqpJlRWQlGaNFDrGqMIkJDCqes5WEnXDV1q6CQWxUantcj8tFSev/lpyGCVBj3HcJQYH2FmOWLkjseJWBAGjCjIt8ahobbdOLTjajsyoGGK953Z+7SczkgSN/rtesA/Yw9auCcz0Ktkph2K8JF1FrSsqHZENrY2IoM2ZxDEFvdoEF0SbjVqQrDR17RI6K+FmsOIpu7c5CFUHH1So6cslQjuQbxxM3bb8YTQUA/ma9Ywam2ZFYLYKYOSqNv4bAweA8s+qqrhoMAQM3az6HPK+lCxNeEo0FRQR+WYyhyal0tmgQHZWQYXWmJ6QBB+nH1sTGWUIwPyHClPRVCiw61hNtj6Ru0O3DB/l+NwBHGhXzvdb3FfPAaE4lASCo/gxrhed5RwmEeztjtb36kJuWtpKQAQpJklSwZSK1UlRAhsRIuyZn1VnL1npmZOWU9oiVrg7balc3E8blf2bXr0hGmB2qhV6fvOvu+xr+XuKm5bTIQo4khbUhhA2LeeyX8GkJkt3jYLXEcFz+sOAYGeItWafYRE1u9R+dSlIanCctu3yPY9FFgmlNi3D5mNgbwT6unEcjrTr8+xj3O9dLOEXgvffP2HXC8fMDO+M34t1nEJJ3Z9fh+B5Mj2kwPZyf3u8Pj0hu989/tQCp+8fUPJidvBwo6Aecw0++c4PmrH9Qd/+BOGDx6WFa+hbt1cqGsLhk2tETFZpMK7XaMIas5QCbxdNIq5exhGiiMj0u3h0ac062JqgAZbZ2k7PfCuMBieUTOhOxiQYqjPT07iZhs6crhh75yXh4SqhCJ7Kror/bqnirXjtxsPbx5Zzw+UUrAd+vN7rl/9hN07tw8Xtss1R1IkG6hC1WACmkPfolepLJVLaTP34bSuSd4StCzBoMxM9fz2k5gJ9f4ZV2gtBFuRyLhUopv++euvwlGez4xtY9YKT+cHYgS6MVKNHYICKzWNtoUKiEo27TIiKygFSacXWZYekk3dZl9T0OY163Lmjt1uHI205owRmYuPDWklxUcfAGW7dfp2xfU5qN0GHY++FDe8Kq0E9GpakW5J7lBGc1xHQDl9MJLyLjm+xc1SWuwusttNjpFgu2QzhhDDGPest6jihJyVi+P7CPkkgW2PScIg2BbGU1WpWjANmJAxiJwzGtZNBdsB12w9aNlTBtIDCBvilD1YsiLRZeAadcY2NOL27DHc87wF2CO4h+GYCq2GAnuXgSSt/OYxAiWccjQCRxOvsX+4IBqN4FUj+xaIvRpikjEKaNsyy3K2vR9N+sag1YZKwTUo9wGzZdZXAhWoIoFe4FStqagB6qFkfyAS2xY1YJXMEGauErxKj6+asJ7CPqBWxtijjm2eZC2Ja8x6q5tjhIy8miZkTgSIJDQcqTTgwQT1Qc+AgWGHUoikwIAmsUk8qeVoKMAkciAS5JcQw01nnTYzwADH9kG/bdi2B/uwtlT9Cce1vf+GfbsF1OsEwjERViSRDTnUerSE1uusny6Pb/jku99FSuXx8TF0E2fgxT1j/HmPj9px/aff/T2u12ce1wUrwciou7HmCIjzusbE4WH4blztivU9nNey4j2hvNOJcbsEZKMGu4eoKQN6FIFVK745up4ptXFaKnuqRTAGQwMaDFkl8D6w4ewqVEL6Ze8XtIdS/fPe+eTxKUQrVXl6bLRs1tzeX9AKgtE/vKP4W4q/xZYT1oVxfaZ/8zUftne8f/eBD+8+cLncoDtFnNNTAS9MKrdtRlsbp6cTNwp77/RhoTkW7S4BIa4B+SzaUO9s2843P/2azTbO64mH04lSI4HCHa1w/fpDLMLP37A976mKD3Z+olZFCtwue8gnmbPWmDulNepcI+HairCcC1KXhDX3YGzZQFvUTYYbvm+RMcgS96smq3AYsgUkUZLQabvTe2d7/ob1zZmqj9Ezp43teuPdT7+m25UxJIZkijCeb+BOOcfkXbTi7QSbo61R18a5DUzC8NvW8RpZczT0KdaNft2Q00rVmBpsDGyLeW2jSfZhQWmN/pyG7bRyqrN/yvBrx3J46Ng32rKCC/vzDV3XUIpXQ2o4Ft87vRCQoRO09+uOu1Afa/Q3ztrMrWMS87Fk3KLXUJT+vOOtobWyaMj6+DB8G/SSGIOnKs0WWpm+Fk5TVss3ZI85X5tKDLT0kP2qNmJYau/Y8zPLEsK30ko2SKdT6jvePfaQbYEimnH55gP0aGAuiyBrMGeHgRpB/V4aG2SwGVCVlBHoxXKmp5NQA28Bv9pwxt6pqpgoXkaQUtCAWi1rMy2IQLhnbU4zcE2UZpKtLJwqWQMdHlMjimta3HBCriUdl7BmwJh8Zmy7IkQdqLX1qJOq9yBfZGBKqVkmHlnvCw6tHi0wFqhG1uk76eysYHsQmlQLskbNMSYnC9v7dxF4LafD4YcDVKosDA39HW2atPsaDFItmDj18S1vv/dL1BqOq7V2oBSzMjs1H3+e46N2XP/3//F/ZFkXGsJ1QB8DH5eMNrKLo51jPIkal245ldfwembfrjE80hR6LBQtlaYhwdLNYd+CXqoLZjuDsPQnNaSe0SK0EnJBWA+oSU+4h+aea6NmS6aJxLTZPnh3MX7psyc+efuWz77zHf4P//v/HZ9+8V1Oj2+wa/Ru1BzpcK+5Vfq1o58XTg8PbLcLX/3hl3zz058wthGjzJfG6aHRezQeiivWd9qysD4+sF231HiDt599kpwCQXQ5itk+Og+ffs4YzptPv8B9sJwWlnXJukZG5MvKdnmHiHB6+oTb8wf63hl9sJ4W5lbc956ElEJbKnU5hYG3jvWohakS83kk+3qkRAaJU5eVaDNwRu+hfl3CybrL0dy5toWkPwVl2bNG5xF8lLYEBu+Nr3/yFb/3v/57/v3/6z/w02/e8XzrnD/9LsU2bHQue0dGZ6BsLKjvdIug5CQD04qrshbnMizu93bFy4qb0/cbrksMkhTDZaGlxuItcM2A78qZvt9CjghllawTSmEpg92Fbo7vV+rygEihbxe6x0SwJgMpa4j5qrOFBEQYrfKAjR1zo3th1ZHjLRpLGQxiLL2OjVLWFKTd2T2YegsdyoKqsBSjU1CiRmjljHlkwVYqqxpNgVJYq4EW9rLydl3Y987z5cZSOqWurOsD//2v/xI//OUf8tnnn/H5d77IfqiB206/Rp1TJVRCbHRG3+jPGyOHjZoFfBa9hwvrqVGXNe9RJjzmjP0DnoMcz09v8GymD7nHECcwd8atR4vF2DEfOTG8QGkstWaGvwaC0Af7rTP259jjxGw8KUvCpz3q3BLybnbU66IeLYmsICH9FalMfEaQdir79X1kc6NzOiclHpIhCSA5tiTyQR83Rs/saTo6C3UZSVamTB3CRKEYFioeyQtwE7bblctzlB/KKcaSBHM3arE2NBT6Zy9ICgtPSFmJloOvf/rMN1/dOOXovZnNzsLNnwAlBD5yx+Wjc/uw8+62cenG7o77Tu/BwFE3pJ2yMU+59qkQbgwuUdQ3Y98tBhx5sLfWtcaU45Hd6wcDDXoyhasYWldqLayLcttT1dwH5s8EA84ZJlSZgwk9YBF3rpuxFKedTny3FT77/g/45LMvOK2PjNsHisbwxRj9MXLhDIZ2anNEW/ZxvaFWxwcsrVJbTL+Nkd2VUk+M/RI1v1K5rTemmvrTmxhkiQj19AQlFOr77QNtDZWCkKtaqEuoO9se4xOkNurywHo9Rx2urSlKPBgjMfZ8Ts1BtGUNKPrUEMG9YT0JKOr4KN8iuMSAo0EGvwFZmJAaJkRbw9TdM7QOZI6rn7qLCZ2100pZYkaQWSz76+XCv/9Pv8+XX7/j+bpRv3yXoy+c6zZC88+FTgkYLJUIqlgYJlVOp8Z1i5ET1neyChIGLqU7VKIfcFlC5unWx0HRds/x6xbrrXgEKYhyXkMSq5th+zXuYfb69HluLBhiNZTwtxHrxN1AFsJ1O71bDDtMBti6hupLd0NGTyZf9D3uPafZEpMEakpTjWQiqjjD68E4NHeWdJwiUdhHlU7l7XnFxmC77Tw+VD5/+wmP6wOff+c7fPL2E54eH1mWCJoCItRgx2bt1VgywNnpy4aPUziuHrPFpFRqO7GsUXctbT3qhTYc75kFqbKcH6CkCoyG45rX3+uIjGNsUSLIGq62E0WTOFOiFjn6QMuGbbnAJSZYi2YfjgeBYwZrIRRvCSXDlD9y5hpP4kfNNg0v0JOQUyQgek3H1WeLCKAJN5PbuJbcY5PiX5JMNfeVYGTWSHZWidwJLnFSpsqPSu5D4YCpZ58aElkm6nlvNDUkleLC0+OJ7373U07nldPa7uQ4JjHuT+a8PmrHVVTwHmPIr+5Ro0C4bXsWrx0xobnTZOXWpwwToRcIuElEyyNwafGBlYZ5TLe1cRf5LK3S9+ijgEEtTm2NXVb2PQv0BLlBVWMmUx/J3A0pplpigezD2FLc8+HxxNvPv+DNm89Y2spo4OMKRGOrD8XEMDoiHSmWIsAN90dqA/UY5qaaQ/+yB6yd3jC256C1u1FaeAEphdNpjUKFFtrjE9JOuBnbM0d/SKmFenqK6clVsRvocqYsK2V5ojfBxx4Q+IjNPpxQ4ye3h9YX0egOTKp+NMOKEEriXQJTT4UEH1OKwAkl3mRqMhAPqZ+gvI+k1ke2EsK+PVhmIggjdBlboS4L5pVSCsOMP/zmPT/+6TveX67o+wvLGt3rW7dkCEYbg2Zz+9j7wYZSKawPD2xb6LqFCn4aAlX21FuM2l5lPa3U1rhtW1yzW3QulJqajKEbF85OeHh8CjjSBvt2xf0SdYVS6ft+GKBaCktrtGXhNkks7rjf0BoqLj2JL0IEZ6eHc9RtLAR5YzpzNJH3vafgMoeSyrKuWLJcRWCYHAbY+k5TUA1C0rIG2aMP4flxDUJUH4g+8sUnhTePD3zxne/y5s0bzuspZm0VhRd9YpIztZBTMkODvo2fwDpjju0plZrae1oqqikybDPrSpBMC2VdIZ2LSgR/QFK17WjfGDYobQmkpZ1yLUn0U7kjOgKGV7uzj2vN+pBAqnPM3riRzfvqAnUq+YcTCZa9ARNpCHi/SLLuZM7wm2okd2m3oOhb1qDI1oNs1yAJPuTUgQnLOdn+ky079x/HXsvzGFErTlJ9ZrcJZk4ij4B4kpQy4JQSs9oeH6OH73ReWWrqvabdjqGtLxzwz3F81I7r00/fsjTl3Fb83KBFv0gp2SRonbY8UtqJ0k5cfEsV63jSIxs0C8a7r76KbMKN8+NnMWjOO/vlGkSsokg9RVF/j/ld66Is68r58ROGR0Oo5KILHbYSMIWPcFrjghNTcy/XK9/7zif86g++w1/8te+Fcn3f6Nbpl6/x/gG3DWw7rldrDQq5T5J6pZ3OtPWEis1uM+ZIhuiJMuoS/U5IQcrlcLDue2aJSTKRUMZuyxI0/a0HY5FvEFvBKv36nmKWYywc356z4Cz07RrRlE7Zq1iYkvOznA79dig6hBJBSew7Ke4R/JE7LCAvSDFSB0bIJAnJDojC/tivMPuCskF2zCx1v2FjZxlvM1tcePrkLT/41V/hf/u/+THn/+V3+fIn33B+e6bUU8BcxNDDORYCiYGkbhGI+KS9lzNbvzJ6Z0/5qKnt12970LRzKveyPlKXlW5bOoeoMbi2mFO171wvH6IuIcLTJ19Ql3iiX/3kpxzZcVvi1qZBXJvGaPj1zO5bXmcEHUg0r9q+M+XKijjt/Cb2iO9st9vRj4e2YyJCU81soyBlZfiWjk+oyxJMUlHEdxYVmjpNd6SudIPr1oP1K84ig+/94Af893/hL/KXfv0v8J3v/IClRUVnf/4AJadXlxgqeSwED03GUpRaFlp7EzTrMaHCbCLud2KQ9+x7LEJp52imP/rgouEXD/V0z5qXSIjDlvJAHxs62Y2q2H47WjNCdy962uoaNPHo2ZrEg8wmfArJ5rWQBtscV+Po6zuMd5KRiHPFKfUgb3H0Zvlh9D1ba6ZqiWamHvZh5L3jqDGCxzPzrMYlM8Rtp9+uWecPR79fo4wScwN3Ss4RnN9xZnnuFg31iSh5KpOsp5XbFjW7PmIvzh7NeVf+3GZc+7Vwrie+890H3lPZHHy7QXug1YBm6uOnKagZM6XKEpR1Lwt17AjOaTnTlq8ZfYtFb87QAlop68bsZLo+P+MtYLOntbCuLSViHG3nZMdF1Oo5WiIgrw1lcCrCfrvGqIWHznISkJXb1bg+v6Mw8FIYt3e4XcC36A+Zs4b2GKNwyBJNfqknXU3iAqQGpISD92tMQU32UT2d8L7howd1u6xBm60ejtId6NjesX3D9gvP7z7QlkZbVlwdHRd0K8hzzWmmATMlTyuMtc4m65pMu9hIQfOeDmnA2HAq1DUK6AIwGD3UsWfTMpOpZRukBiNSsO2arK6NmI2UEfScFeQW2ZJt9P2Z7fo17aRU7TydCp9+9n0+/Xpj90p780W2ExVqOQMjmj/riu23o7ZQUtjILWYq+fIGdaPsQRcfY2DbjXU5sbTKukQR/hgdo5W2BvxrFpGzSKHoiTf9EhlDjzpPOZ2Q1nijb44J2WDU5YlWG6e1hkJIUu2lBjwoEDWZY5xFo3jQvJW4R52YqNDah1xOMbaj6gktlfOSVH4Lw1fqelCyl9Pj8UxVCmsxanXWVhgjyD+yOGs1mgxWNc7rKSLw80JbFfGQBOvZCjLFkiVrm44gVZmVkXA9cU3Ssnnco2RgyaeEaEkIRuqIrKnEQFInAg5mXckn4zidNg10wloG7Pg+wLZk0kXPZtT2drRG7ZVszbAR5JvsWE8oOGs6GpmPzw7zyWac5jsdjiQDU9XScYHmkMpwVOPIkvCRWXQILjgxhxCBsW9E71ScW3O9wpZ1ztifEQhOjc+OUCMwSUZwQMNzhqAerMpIzPKJZNYpJTQzC8JpXRhvJNiEL7OtuPP3+PTnPD5qxwWVomceHle2UQPu8wtlecOyrjycF3j8lL7v3K4XpDwkmwWsLtCvFHHW81taDfmkfd/ZLs8gDeqJkp0pZk7pBakP1HbizWPAPj4G++UDrJ+kMjJJr7+FUKs2sBvKoCyN7u/QsbOoo3XDTLneOn3f6HtFvWRWtYOnooCUzFBGikxE1OgTxrQRzb8K4hoZT4mirfU9mgXn5i8FM80GzBK4eImCsmWjpY+dsW/hEMbGfnmH94bbhq41mWMCRkjIlIrS7s40PysgBEmHkpHqEWsF7OdmoGGM4+Hk++3egD1FAeO9+Ufi/24pKzUzM7JhVuZnAEWzp2xn9Bvat4BNS2E9PXF+fMvD7pTHL3LAoVLqG0TDcfVyYlzeQdYtmmRzqBvGM1VbOINlwyih1agX2vKWh/OJh1OQWm6X51ARqKeAh9wim7ZOKY1lfcuiUc/Zbxu3y3v04RFdTtT6wNa3HOK5s54+Yz2deHpYYEQf1H67IutjxC+A1BVLEkitjzTZ0Cyi77cLkDUMbfOR0PcrWh+pdeG85nyr0eF2oZzeBAW/wLI+RU2o75Sy0sqgVWdZG9v1guugVaHqTqVTtLMsK3VZKEvAztLBMcx3xGpG4zOoCYsv2vEZzxwBW7QdzIt22xMqjgxDNNh8ERAEtJneGo5mkBn0TQdmcWqTDIxGOsUUEM6sJ5KbCLpELAJFmaD4i/XJi/WHw2xIzp4nvrVO49rkyAg7U/hGsmEZwmmR9d0w/n7/k9cXTsUTTr///HjAHplPNlakf82aK9kE6YSjk6mYk+Z2pkgTijwgQz2gxKmuU4uyLi0JJzN7zrfNEt2fV1bhw5tPOD+9BYR2PqO1ci5COz3S6sLSGpfa8FtEdE3Px83dNXD1IrCsJ06PZ0bfeffhA9oWhlRc14hQs2H37WefI7pSysLj2thHZ9uudHN0WXNCaAiTqiraFkARr1E7k0FP+ZZWA0e+bMZPv3qH5eSlkIVqaZg1Ib25STPxGDBGOKV75pVQnwC9U9Zz/HjbkjKbtaG2BFxhUaPBgoQwZjHbY5Jx3/aMFkNFwpNFZ2NkvxjQHSkVdXIwXQ7b8zl7awe2gBVnverYjBnxSUCLEU9n78t8wAcA7xnZBktr4hw+h3xqyR4XIKcQy5yuOI0bYK7YgNs2uGzOh17oFE5Pn/BWT4zl03BwrnRdQ+FLFENTzioiz6VwDJ8crkdGqzjU0Puz4ZzWtzysK+e1cb09c31+Ztt29rIcznY2Yba68Hh+y+dvH+m9c71eeP/1V7AsUEPMdbc9v4lwWp9Y28rjaWHbPkSd9/KMrw8BuYkkLB2Z21LPPK4F8cHtdom2A0LIlnU9IKsxOiJr1OSaxlroOyaKnh9pNeZlaW2MvTBKjR7A4hQ1TGBIxZIQUEtFfcNdWB7eUNcHtC7U9oBnr6RJsOwOVQWPxmbDGTkgUjVdgnmwD5M0hVvsg1wvQkLTkkoQk7k37vsnSAZ2Z8ZNiNFj/hg2NU0TqpyGOpttVSteplDxHQ4MkeVYc6o+8974hIRjDkUZF0pVpspNbHdN59XggPEH00lMhpKbZ10rmImRwE2HHPehpJ0BTwZrymzl9XgKIYAeFH93Oeq4uIQk1OkMoi+GTHJ8J88gwjUyYZ/Bownd8o7M2tx0tf5H/N/PeXzUjut7P/ge5/UUwpDygOhCbYIuJ7ooNzNuo2Ks0KC4xrwgg241O+/j8d66RcOqLpwfY1yJEYVVlwpaWRYYhGLGT6+dboLZgixvwhkOYxvGsJhaW7LZMwbuKejCqEahs1Tn/WWjuyMFrvvgNBy0ImXF9oQ6SoFUlDfbM4JWVBfuGnwe0WY6LqkxXyhCp5pstDnnKBoMY5xIObTecGMKvFZRsMzyBNYnoS2Vti5oa9jYsN7xapR2DkHfGG0YDtd6sjBznIvdUk4mGGlu80MV9yAClNT8m9p1NvweqRG9KRCGNeSPorZh3kJ6azo9n4VfUgon97zEXKneO/iN7XZlu1zYLWaxtVNhp+Uk3hwQOqC7sNEQzlR1UOdqA5FgVi5agxo/I+m6Rm1IQltyd2e7DmChPlSWRzCtx5ykkGBMxfBSeHfZwvGsD3zx/VMECxKTuPuICDlIY8FefO4GstLOjeUU430mEjukUlvAt6rCEEeolPWRN8tK9A8LfTzgx9ypmEocjt6QstCa0dZ+bxxHMSuoVLRGLak7DC9UaVAFJUaxOIM+YvLwu9vO84cr128u3D69IR7qCzG+PCAoP8ZmRCQftjVhQ0/mX7akuO1M4WUtoRTjGcBMYdzCfQ29RAPuoFVCYOaE/mUKIGcwGKNEpqGGw/ia4a7H2gq9xRcZFNMJBd1ezClEA/tdkm1ChZI1Xo0s0QdCPUgP88MnrChJ4IDpOGd2NrMgDj1EILKw7AO7i6Bm6uNxfyecGLCe084L9XSiLWs4wfl+zfE5MuWbMutKcgYSfYtvHsJ5lVqSuCMHgXHiJ39u+7jW04l2OtHZUFpUHzI6dxF2TzF35IjAzWLYnUssonkju99pmuEsMjrxXOSp4WUSD+Q24jziZIOe5bljw6jGIjBNZNwt0ayMcsXpnuzC7uzbxugnvNQ00rnZJCM5iE2ZmyeOLJZKKm9LMn5qvdfBdKbxmplbrp5gnBDwwZ0aO8uvU85HgLIsMSCztJAe8qDqC3II9uKpqj+lv4/ruy/OubH+uN8dDKODbZQbem6YKbyamd38f4z6mMYiM7rDOMX1yPxs4vmLxGTloLt79rlkkgtMGevuMf3XRVFpiBpaY8Ktzs0754eJJNs0s8lcg8OCRl80omApGqM2NIyZDg9mloCoxmvRaKGoJVQskm4sOYJdp5H36Dcs+bxK0q8ZnqoMmgbdo0l5RG1OUGpt80qh59o0EC+YaI6n6Wl6FfWp7BGlRnVPIw9a47ubeyAGU6lBUkEeQRyut53bbWPfog1FPVsZXkTixzLIDObuZF4ul/ta4YXhns4ifn3k7cd7/IXjkvubjrUZDNQXuNi3Xj/p4C9O7fc1PJnH0xG5vHzdy8vw49wzJ4vPjkAEm58/3U5kffdzRxj3Ek2Yt+ae2QREen/tvO6MRF86DP8j92OyBQ+GYr5/PokXj+O4jG9hifGCUkJzcdb9j5e9uPQ/txkXtSLLitxCAcPNuVyvR99DNw+82O14KCbGwKlid8y5aNQ23FAbmBVsRrqAmIFvbB02jUbRMaZoqoMmAu3O6CFJFIslJGgMDekh20IORgJc6GZU3xl753b5wH5aGFXDiGpsXC0FN8UnHVwC+Ap/KkxWkTY5RrtTKmPfAjJIiny8MSVhVCNaLiVrWrNgzVyJh6F3PBQJVNLx3wVD4b4YQ6ht9mSlwTmIFfZt+5MML+BOsz2K2WlgjkBZXowEIUWI4+ee54no777RDwHlFJg9/N78/LQqTgQ5yWmJ+o/FnDSpNZh4TNmmSmlQWsJXmZeoKl4LliQdxh5KHBPvt1BhKSOyG/d7nwwiVAWzqAWWUtjNMOuM4Vw7SMhLUEpFiSx86zvUYIrGrRr5CDSrIVHJqdhRfyhLod9uYCN7xaLB1kVj6jaexi5FV4lHIlMAuMSU7gjOgqQwVcFLLTgdbBwTmKPKElCSeEetc3m+crluXLc9Rp/kMw+VivzAY12/tPfp/MpU878bU2amMa2j3d95t6e5X2zWn+5r3NPRiczV7kHYyBrkzMpIxxh1Z39BO5/7cc5++yPnjgV3XIxn7c2P/XbPVnyM41pfnvvuoF9kXPN7HU7nnplZsg8PiFMkiUDzu2sGwfM6/LgW8l92kFam/mmC1Fru95xC8CMtyCE2CBjUMYtWoHskEmjInyDJ+tbxUTuu56/e49fBbbsyziVkn3anptEcY+PhYQ09tb3zWCuX3tlk0E6PFDGKOudS2IczqmJ1pW9XinVWUTbrUayVgvVOU2VQuNiN0mpsqbGxqtCtc/WoQ1QKVQplGKPvEeELx5TjWiSGUjqIFaChUqmlUjScp4sRhqTlILyRdalkp5WexiMMTEkGlc2UXsi6bD+iqDB2cxHWDJ8FlakqLwfkEcMRp8J4Tpgd8fkz+rPeY1yF1tg7SRn/Firjxy7M/6Uhgiwgv/j1EYnpCyXwu1J9vC+UNeZ03sMWzexAZgG/MeFUkYqURqknPDMiQdiuN0aPz6tKzEtKjQj3G10q1gq+bzRXFgrDblQNRqWPnZZXee0XdEmKtHVqjbEOfQzW05rZhbNU2LbQVazLio+A8trS2LwjGioKfbtSi6E6sP191PhaOMy6hprBZp3W5iw347SEUsXoOap+xLNflpLqLoKWFesbIrAqVNmhZrl+7wlnCRffg0QhgoxBbZXejVsOUdRktTUCKjMzJDwxKkZJiBNz1IS+DWzbkX1HPYWtvVBa5RjJUssxA2vWREM1n6jVlJwMTv7Mo+VBD2Akm2SRNJTTKRKbYTq2bxnQ6Rzua/QwuITepfhU7s/eQfPAbBHcNM+b9Rxx/EWv4mT+5QbJAHJeR/521mRnpqQlnbGAR3/gpNC/PHeEKQIpqnw4uns+h0vDJWtUEW4xrypCnDiGWzCw3eg9nqtqENBCoWP+keNc0XoWsK7WlQlHtgUolsLF9xrmCzPwJzo+asfVuzG6YRaLQkvBShTwRQSvLbrIccQqrVSs5iIqQpFCVaga2LqY0Sfchgdxo+hRZxDLTJsYUsiMNtxCiV5i/+/ulCK0omyeFQOPSUeMmKxslvWf4dy2VOfIBsRY0JFZiDamJlpEdRm1jXuE585BxgvHdI8qZ1QexZSRkKEfEVQgCJnpzI76qcHI/XJm3Wju7iPim6xGen6O3187N5Ddl+qxsSThyHmdIswZQgfRIt/r3u/1Lo1nHUXmhAvxhABnvDthxZdbJHqxpDRiBlnWO3vHLIxzqTWiUs9nnJlqlzvCUsSpGSzMO5zJKCWbv8PY5dwhFVxDxRwPiK2K4KlmXqqG5ptqaOXVMFhS5IBqFI7houZCsSmfo5QJBafhLRrnlhqDQ02Cil9SkNdFknIoTDPc8tyOgiUF/lvfx/O7S67xvL4cGzLhoHt8krSEEk3i1iWmJMjsu8qsO+Fe0RqsWPy+Nv1OU88riHXICzhvGu8Xji5QEDn2wgzUSKjecy2HwZ6w80sv9nK9ZniWRUPzcfdxswfM8lx+ENszMJxBFi8+4/6aI7vx3BM5YeBA9I7l+6Juy7z2zMqOJziv1V/YgPkkgl6P3Yd2zurHyww3zp3jU7L+GmWNF/U9eXlf5LjWmdlNJHJaRtKhRdR8fPFjb/5Jsq+P2nGZwXBNwxONrLScyaUl5mylcRR3aqksDUDZJUQ5S0mKczLvhu9xwz3es9TKkMD9u43YdGkgxsHkCY9WBJqGtlwpSmv1Xidwz/6PqZIdm3gM40MPfcXZeR9GOLIKLUsqMngqHCQJIWQBMIk6hXkg/LNwPLf7AX0EOBlGYsQMn3sj45wlNCGP/BySzu4v1uyLephZ9ICID7CdA9ifm3EaiGMDEvfKyEWdmZfk54z5Xrl/lhveI+KT1H7yF6PmnZJQxjjOc9QA7iNzs05VoFTcWhJviD4siZaAVhvbGAEN4xRVXJSR9kEl15HeN6PlR4kIrZQYKohEhA7p7cqL+l4UtGuJAZvR+BdjJY6fC8dQQeIO0Wqhp3N3r+FElJynJIcRinPkPLqs4YZTVWqt0fQsBlmwF6DVSUYCtxLOz4nvI/ksNZxUFQloNEcCKVEuLem57IXhKhrB5E7Mj1tao86xIqrgUWOV0qCHY/CR+pWHMwLKnWFbXJmKKbGq7k7EzWINHQHRDMyC7o5z7B+wnKfGHarMNTondd+XoeM+YtK11BcsPM/f21z2x7q3DB7ntPDjfEdrxzT+lghKSFhNx/oy8Jvfw47Pzes71vofCfwy64zL8oN1OffFDDrn6+f+iOwxxAVCwCCCy3uQ6kh5GQwmO9Ozjyxtg3uMLjkysz+Bg/ovHR+143pcK2wX3n/5h9wuz3BaWYvjvtHWM5+eHkE3tn7ldvvA9o3hA4o7VgqlxTDBXQpvnz7BiuLXztc//XF0i4vzw+//MpQgV9xkDzUGF9biUEILzMqg7Ds2nGpOGTtP2nhYjLp3Gju7DG5u7NpRM/QysA8fsO1C9c5aK2s90/SMbWlYFFRPeNkjYWKhb++iJpPDIGfZ1McGusbPRsmF2QnF+8lOmgbdckYWWT9ynPEybcsFOhf2BpITgSmZ/XWghxGRpOmmKnnsvvVwTi5zlEOu4GMvO8gcF1Iio7XZOAzR+1KQ9u2VH2zFErCX7ow9VCfKEhTvePWcBhz3cbeCeKPKKeS+SkFOJ2y7gLSE4zb2999w2/bQmCwLtaxIfcAZlD3aAuoI0ovh9O3C9arH/Lc1afsuRhkpZieWGVo4RO0jCBVSKBBjSXDUPbM0ImsSQy1EbZeiLET2NjQboHGaxjOM/h/QKcIqQtNwpEiY/VpqTi7u3EpE4cWclr022VmX7Qwh2xU4dNQ2ywgWX5MpFAuzT2rXrLQJ7JJG8TlaJ+TWKX3w6dMDD+cHip7oN6ektp1IgK3eN/ptw/s1a34NXdMxI9i4Yb6gVCYbEMuak4TEms0pwple6XS+KtkwfV/XMdV3JAF37rlk/WUWMVMkObKGTsBzHdElt1RkO/eERHMUisfrJRRigqZ/P2d8jB5+Lepn89yTUDW35Mu9OfdxBKTHgNxZ5ztaRSKYEIk6VboVNDUvbWZT83XaqC0nuOu9R6xoPWqyKfLIlOWaGX8YqxQWFmElIN5S9GAeHsnWf9Gi/9cfH7XjevvJW6oLfdvYHx/xtdHEefNwYmkthGqjGyTmDp2We9tTVdZ1odSguq+tso9OU+NxrXjLnp2l4khg6KUySrDnxrZzTCgmh8IlbFZlBOHc73CLikdRXCMbXFWxvuA0mt0SKoiyumWNQNLJHGynVGj2NBixUFLqJaNrEtZ5yaC6rxSP91sqbVjPSDWU7CWjxbkRI4ycTY9RnxHlgHKiSz/+yJSsyUjNZw0DwWcPCDB12mAqBDjueo8k53Wn8ZV8z+g5/ps0VplRxXXmZqfdmy7diYnABqZJzsgsMMkFVaOfLrLXUCRYiiJLzK5y8xjr0beYplwaVix6xTITGb3jJaDkRFc4VLjnxF6PPp0pNFrKfTyGWD/mHc35ZSRzUN1TezKfa0oViYdhcQ8mrHoGHZI6leTzsTlXKmoRLo5YkCVKrotJf55qFfP+qcS5g54UWYkIAVH3+MyDCl0K2sexZqreg5lhPfZA1ej1syC3DPOsRcZ9iGGbRBvI6JmRSYr2zWyl430P4ssxuXmSG6Y+IHfSFDCDhTsRIf5MyJwJkcsLYkVCf5DORYQZCFnvx3lCYzAZv9iRgUlCquKE0jyBocl8LseRWYkkhT2by2M/HYDbC/TC705OhGE9xbsnGYojWJw2YBJXYszSQIuD1/teYF5Coh5SKFYP1q1PqSiP2lqsj3Rqfv8a36pTw6GPeBDgfsHHR+24nh4fObUWOluPD/jSKO48PT2goqE87hYRSquU5eH+rJbCup6ptSElak26GVsT3jycgWDTnU9LzHsyo7BgErL9tx7zjOIIVe2BHH0VNQJnaknmmAQHp0iw/9ZWwFZcbtSeJImEHl82JUrqok1pKZ90bkmfUVP5mfLCcemxgCIivC/SaaQhxpcEBGGJgduLzTshllQIeBHxvTg1s8YXBiNHnE+Jp5kX2LifVQmj5mS0LGlUktmYxoQJVcweFMtJvBqfc4c87HDCB/1fZjQ7zwtao4dsBheSGoJrFUZPh6EFlsZijVFW9tvGPgi1ih5K8YLGJGNJRpd59oAGfGt9RyQG8gUZI0xbFTuYnK3VHC0T6u4iGYAo0Wsl4YybRMRaslH+W+KoqjFPa0TdLephAc2NDH6CppP1uxJKFaaxhloaNJUcfEiQKKLZV48sRTIjVok+PZORpJlBSQciReO1Fs6xJPTm5ohF3U6oqIezGDaSZZtBj/u9XJXDQyck7SPHyWsEABF4JQSsk9CQdew0kJYOHplQ1ngBF87f5SJOODJOMS9iBnySaz7IDBFHzuCMb++JeQ/dE9KWgHsDKgk7dLwuN89kfU5kwjJDzaDiXjvKoHFOkBbJvsHJTEz6+8xo8rpnkhPlh9zvx3ci9+cMKEk4OKZIzGkUlnv3gBfnv46/y/3/LxzUoQb/Imj+RWKGH7XjWsT5/HHh8Ze/S/vsc+rpTCkrhYgUxr5xM0/kSVhOD4dRrOsaFOOEPLbbldu28e6ysF/foirUqjycH9iHs3djpAK82aA/Ni7DoyHfDdtuibU3Rq/Rt0PnpDfers5eha+vyu1yTVahcnpo6PrEwimanAmjUVqj2EBloHSU6DPiVPERkajWSNVrmZT5jIo1/j4NAekYJDe4TgcG2H4hGFq5vA+/ZJnZSEAOSmLfneJLeGETGAFJSDFkUaZ/EwTbbhy0d5vdPJEFqYdgp9uL2sB0ZhN7t7x2DWikqGbBON4nEhuKseHbFbtd8OUJ1ax1jpk3hmZkrTH2RGVHpdOv77l99SVvGJSmQZI4nbGuMRlAGrZvbN34sDluD9mnBE2VLpUhilpIG5WS0Nwx+qKznhutFGop9Nao+feWUmER6ceY+um4fA1qvaVDqSXYc2PbcY3GW/EMWHC8d3bkgG2mir2bE5Oq0zkV8FNlmLGlUKwSQzetjyOn1iQSuDt2XthzvZSSk53NjtrmTIaKGHup9CFsNhl9jtlO14otj7ifUG2xluHeTuKGLEapgg6hK6Hhp1BqBGlRs1OMFqPmS5gtsT0Crv2W8FY02evomf2Bb5cDVsM6vl1jrdeCyl0dnoTtwO9Nv3A41YDbc7yReDjrAwJLx5bBk8xzeJIzRI5zas1GfcjWgfidasP7RHGizjhhObVoxJaRAgMvYP/JcYnMN/4hWlLWLJyv5PrKHDLbFPPcB5EJ0GBORgk67qCJIqUdDjBIcPUIcLTc6fyT7YkE0efbzusXm3V91I5rev1aJQRU+5V921lrDTLE6Oi6BA26NPDBfrvS9w2u5QgaikjCQZ3bdmPrEQWqeAx0y5LQ3i1HxRt9u3G13OwqNI86wOgdxahSw2i5cxk36J2msDwuFFWWqrz/8J69Xxi+sd0ubLcLe5UYqscG6ugoDNuzeTSiapdkMfVOd6eUMCRaQirKxmQ/RYbAoSYx7mwost/Jg7ouM9JM3PzOYkx6sWUFZITOX7xkVloGvm/5dkkUIlmPfrgsIDJI834YCWZR1wbW7bg2ZgCL4dvtrpztySDUiEDrGjJeWhc8Zw0ZEbmaGJPfdWdFxscWEZZSePt0Zl1X2nqiPX3K7RYyTl7O9O3K3o1Ll8OROiGU7LUhtXJa1mDUjc52fQ4ijzljRFDhHrUz851RKpRCsZEDHoMOTjqXKsroW8YFd7VvEWe/XUFrDijMvrVs+diT3q9FWcqSEl4Ds4SpVTm1GlqN2bRs6Xgc6GPHk/RSVKMn0mMYZs/Mw4eGY85IXNyyrgcLYL5lf1dc2xx8+tw3tHgSSDZGv9JvV/brMypXqNBOMbNr9C2y/oTXBfAR06ZFyEkCLcaLzOz+gJcl13bitdwzEJ9SIqlOMl9PrrXpfDiajO1wRvefOdk3ke/VDLpeMBkPhp8fQdks6IrEgNOoLc/nOmFziR6ugxBVj3qRH9d4Z2ROMsiE0Y/6lv0RVCRTrrHvd8LFbNKOG8PEHufPxuiM25bKPX6cLvpcodSWX2mmcy+zL457nuhrXMKRdskL//UngxA/asfl2QwYpKSN3QaXywWvNeutnSKnUIi2Be8b2/WZfbtFVaRHBFkElOjmv43BPoiU3Qd7qzGmwoQ+BhrUK8bYuFmk7DOydRshSaQC3sAralfEnhHrVCm0dqKq0IpwkUFMEets+5Xt9sxWHGFHpKfMUwm82ezIPmaEHtqCMUtJctOKx6b5FmU4F9idJj9Tf9JBHTf0qI8c+H+mRH7QfvcJmiTUQ7CiZpQr9xqL5MaeKhwvmYZ3aCfjQH9hAI6icSz+w0ilsZlF5lBtWJhK1/sezE0ZIRhssjNJC/QOOcvMcUotnM4n3rw1Tuczy+nM6c1nXG5b2JZ6pm+VvRvXQTDd8vOfnwdSG9oaDw8PMAZjF94NZ7OOyYTpBsM6ve/0oYwSY87Rhnuodpjp4bg6Eg6NcFyimkJaHvO45gh1mX1bcf49lT5UlV4W5vTtPsJoFFVsKREckBKzHmTwCnTbc2BgYWhkdJjTx0b3yNyHaEp7BXmjHNk8gCHjhlrIcc05aAWhakDNIdIS0wb2W+V2/QbRDYay9Ae0R/3K8VAJKZlB2JHGZwaSpAWEo9Fv/sn1jM5M58Vql8ioIhN6UePK7MPDwt4hwHkCzc8hmYXHidOb5kY60IU0xuJERpa9AscIljmyJXvCjtqQ24sMSo+samZsQZ7QuXPT56ZDOxi36fgm9JivmcQMZzqceZ3zvrz8Gbmv53n0/rMDQZX7/ck9L+T3vd+SP/aQ//Kvfqbjo3ZcV7/gZaUtwkhVgSHA7WvMdtw733zjmAvmmsKcId553Zx936Mm0S+0JVLzTmPbAlZQ76ynBXMN1QMjJyWHhM+1j6h7SGFLdo8WZaDYXtlVkP1DOEeHxkKxE4VCU+F7n0AfC30UbpeveS83uDZOj6coklVFfNYoAIXRA/OPLKMcBXIfM8TxGA9CGLwxfVUaOoSjBwybdSRHdKS2nwMj92bUg8izRdYxyQ+GE2Kcsan7tzZ1wD2ajLBJzkgAR6bBmEYhMkSp8xU+92D8p3BsqhAxyL6iqpRlwesSjcW3G9vla/bbe6zvmG+xYaVSdyjrRkUYDsvTmU9/+Zd4+/kHal1intXTd3m43PA+Yjy9KVvvvL9u3G4D6zdGv1FlwzTGqxR2rH8ISGo8s91uIQUmim+XZOgZH24bU2e4Mqg57bYjWd406FsyxnI6LiWbfOMh7hNBpTD2K5JGfnuRqS4CtcY03TENrDuFHkrdGr1d7hLIACOZdyVZZgI9JhPUWtn6iOtz4apx7tIWWqlBknBjjAsiEn1vXhn7zOY7T8uSbLrCPja2y4Vv+CmPf7ixtSBIeXVOkorlFZZlDeelEkSaknU0CDWUKGUeELmhiPqxPuZ6PUgVM0OAuLeTRDGN7lxT83UHhR0mlD0DpiNhg5wKfH/plIWLX97p5UjOBqu5iafpPshFL/aNRYYcgynvZt6HQc/nOWHH4lHT1RIkMzgc6Bh6OCxdCkqJbV3j3Foa2l54/fwSNYOC5emR+vBIWU84OapJov6lWWKJ2md+ZmaIUyhYJ3w5HfmLZOvnz7Pux0ftuJa14jrYbjfaeWFZG09PD+zPztiu9B3aGhtyIGzXENUVgT5K6gcOGGeWFqym3o33lx33gsrC+RQK757OS9KQqEC2H8fGsZh6XDRo3bNAqmNCJ0r3yp7NrmsV1odH8OjhePu0cF4rS9MYe+9bFLdZOJp6h8WoEaKe5TZCy64UKHcc26aidAoASTbpzuxoFnLlgB0m0yp7TyRoxZNtFSPCk1VmkzwQmdPBNgsKFRMGucs++bdWqhzXFffn5UgEn7p16bwONQETQlA1OVxScev0zbCxHT7ZR8BqpRbEd6rMPqSCa7A2fdwYt2DWLUul9wYSqid6ex/q8DYY+8ZILcOlQDkXtl3YbkbNZlS3nbHv9O2ZMTaWNngrKaNUKm6aE7edzyV7Dh1Gv6Y8VzRSD5Psq+uRyUhAvEbAezb2gL550SjsZ0SCvaXhwvLne5A5Di1Kzah6RL0so+jhBbOO2xb1K58DZ0oSBKK2dEBZXkA6RcuhczhH7FRrIQsmBdHGnnp7RY22nCk1jN3eB0U6rcLbt480iYngrXjqFgZMbcOBqL+E8kdDtDHnXpFr1jVbM6wjlk73WGjTIBcCDs+syHsGAnOisEdWp5OA8bKBP35/ZCRofJZnlpwwN5ktxZqdPWARMLjt4WjmbBZLuDGHpB4NuggRwMXenZlf1LwgWK+hQDmzKWfO48qPS6HfcF45KuiA65z5hCX3rs/RBNO5lHoElBEMx0w+Zs9nXFE4xEnWnAIPCa+SjtqOVXnkjS/2f96rP96s/1cdH7XjenrzwOPbM+yFdl7QpaL1xL44Yz+FkRdJDTdlu92OgqG55gRUQ9mzHmHs287pusXPBc6nNdEtwbQhx2TRo30wsrG+hW5bNlOmtUBtjQ1NTALd85Y3MU4P51hEPlgXobUYL1+aop10MPdNMTv6M6HnHim9WA5ZyznMvwEzGoV7RJiF29hccdZJvr0bgJfOZ8I08fsp6HuHambn/p1WPK8nov44Y+yRjEa/deUvXpOXx2Rv5XUG3Dh19BzGwLwftTUsnGgpijKnPgMUhhDwEyH82WoFTsGuI4x8KRVtIRjrGwwLsWLDMWm0XqibMub0YndMCuOk4QQ8xHujobnhzDoMqBgHgLhf7tGoC4OSBiEp5jMgl8YYe6qlz9sTY2ZIrcCA5MLJuyhuW0okzZrIzB5G3peoLg5KfAfbqKlZGSWNyqE5KRqMRZFw/p7BWam5LDNK91j7Qa0XBjXfP6gt1MW1FLoRP1Pn/HCi+EaRkLoqRrBM+8jm63z2U5bjZcg+12Su9akQ/3JdzYBJyn0tIXMIq2fD7MwIZmaa2YzbsRbn8p3SKe5zv8mxP2ZsdlxD1rQCnsva2QulkLnHAvLO80hk2H7IRb3QbySDr9mbmCr2sHN8yWOMT+yvAwrFjs+9q83M7/xyr98z0uirbMnalOPXcnzv/9Lxi8il/uuOj9px/fBXvssXX7yJpaCBbbssYE/MaH5YMOdEF7xfgFBDQIQtWYJLq2jKzuy3d9z26P8p4rTlMQrmY7CcPsVsw62nXucF86iJ+XbJMQ8rYwuFihglcgMPOmrfnpF2jigoJxMriYQ1p61CWwt1jd4yiZkXSK2xgHdLRlU6xmPkvSRZAsADpkuKsI3cHE5IY5FMwSxiR/A6XWHShiOUSoMgGbXdYY6ZTXpOY/W5IefrkfxZHjI3q6ca+70RcxJD/jNcfCZ16WDjr2msjsDbYppyfMiRCVNr/LEtXqiVKiU2YimIVNoirC7UIkhZQArGiKZSB9uegVBH2caO0xg+6BYqBDZiXhJlRSyjUoQx9vz+FXzPYECx8QE0jUG/4D5HhziUJb+z531M2nY5JVQ4MsvM6FvbUd/FFex6MNPEtwMuisipEdDjpN07yqB7ZCLindrO0WLgA5c1amFu4ILYJT+zwtgRSQUSk4SHCoVJYzXYL1DPUYPrlzTNElxCXSg1GptFQEewBU9vHtHuWN8gx7pILTHJm5ps2ZS14l4rnSxWG3LY4yMYSnKRUtNxxe9GBmmx5mq2kmhsKW3x+hfmOdCLdDqe1PwsD+ixR6aKix/ruJQ1rmcq5ZNTuyNNixeJEatGwz7Jlg4qX4twn+hQCRVWR2QJx5gTIaLYux7ZWsCfBr7FOqXghMwZuuBzzJHM7CzzMAekQlFKe6C0M9JCf/DQI9R7Q/F9eOQkmsS9PdzxUSe7O78/ur1/3uOjdlytVGppuO3Mzv8wvtmomd3fnqQCZjOvEAVNdsAzQk2YoBT2HhFuSbq57QPbb1i7MJl5+L1pkrHFaPcSI+t9RPQtomAJZU0DOzXJRhqhhPdUd6wXrMY8orm4rfdg8iUbMBogo340+kBLNDXrnHEl2SSZDZmWxv2gMc+uejO0aY44D0dn/oJGPNmGwNyNStCvmfOJzDCf9/yeucnhFCPC1doOY6PiMG5MNQ9PmErNsD6YxebZWxYklBtjD1bhvW8yMtCSckex6SQNer7Ps7dLegp/eMI8LZh3vcMQSjW0tixwZz+ZZV1uFjR8HPTt4w8er7MJtR60lTAiB+Q0R3dYDDS0hHFImG9mv+Q1CiiFY2LL7GZNKSZjTswCF4taSzb8qs9zZNQv83ri5ypC0YYPPzKR0ACJqwz1Ez9gu5KQ78BCRV5npp1SaglHp17UgQbcJ11zUPJdQlZs4NQquVw1194WhJo+kCpRjxGCsFGSAu8Eq1eCUet1OfaSzWfvCTEnRFsMfOwR5JTKsFjjonY4c01Fi9jb8fqpQh+qSLO/cgZ54fjQfsD4IqGUnssQn9lQ3iXP6RVz34vIMUR0snWjRSByKcv7GXsiZ6zNpuieQgXDshcvllpMLYggM2a9hbZlv13oe+znmsGqEVJQM4icaMswY9+N7XpjOT3GIMhJ1prIT9ommT+Hg4k6t0w3p6JRm39xJ35Rx0ftuJZaqRI9L5ZwgjiRrZCb4ngkHL0GMX+pxoA0N2pdUa2xaTFqjq2oRVjamk2R0VMTkEk8HJUOfUP3PaYplxKivq3lFQpkBI87w0pGbg7ejuhF0WAmZuEzqLPjUFRgyjHJ3OxhyOQFhDKxZzLafBnOTNp6WK/4M2PXfEVAFDb/dVx9/iDp8JIG+RC0lQOquRtnAjKbuL/fDWeeipcELMnfxfq3/Hy5O6fcHEZCMuk0JE92RNPmTAFeQdL411CLwI++FQiYpZQYyYGHQRNtUSuciKXOfptQsAgRfcU9+qfUX0ycna0EImjxo641t1d8n5LLwAIVOCCkFL4lI9oZWOHZ2zZZcOW4f6FoMYvxgki/PzN/GQFXZkVjMupmv1iVnr5XaG3NhuDUrCyZVRDQHmTDNtmgXGp8N49VolIPh4XXw3GITpZboeS1zHpLqZKOMGBELxrqLyrf/lNKEFYUpuam5Agdt36P3Gdrxlxg32rnyCGPL8RqjyhoombzZ3Nz53c7/g8vzv0CAZhvFWFqY6a7PrKQCe/NZx6PO87zolEkbFPCssdFHTWwuV7u+WCgj5P5d6e5x2UVZi0r+Ov5eI4ZYpIZeKq8HFZy2plYLyV76OZ3/zYrUdLO/pGM6sVLvl3f+sUdH7XjOp1WqkQusItgCjocL0vezIA1QiCzB26bdOJSG63u4ZDaOfS4fIAKrW+IOK0UluXELOzWukQvDdnDIR1U2bcL0hqqjVpaKGrP7n+p3DXHekjreFDEZ89L1CMKtS73Bksx8FCICHmfRKhbTmvFD9LHPdMAkByHct8Sx5LRF/9MfbwI0SGw8PhRGP50cvOzCNkrZ2RGFRvmPuPnJYSY74WsK3Fvhs6fzT6d43Xz7zI3W0KJEEQRSbdm9nJ/HI7r2CIyFUQ4jH3Q0KKPxtNghGJKZYwNLUti+uH0DYG6MCPrSHZm0Twzc8nMzqYEUEA+kM5pjKSYJ8uLqFXFPLPKNETRk5TMM21JdvHj+U8ZrKL1cOg2dpB2rB2Tnhnei16dJEocqiVlifWioeggesUt6iZLO8d9dsf7hlFTlLiC7OA7pW94if1USguHm5Cz6BJ1XMk9kX1eYtkQLMFiw2YDbSrn+x59ZqVAq0FwKpJTlTVEglMEG5HjPkcYMxUdwqnNIMJTLskxXFLRHQOJ9/gRyk7neCy+A+qSw8JKLlzN1wxmpnk4Pzho+i8jspeDGBXyuRakTGKScG90juxF5xie+dmSwdWLdTf3GBDCzEo6yCDH+AzwsrlaPNmSo0dwOmfCMefa1el+wD0Ek4sh9WUgnc42ewJ5YXfmvXux6RBS6u4lTPgLPj5qx4U2pJ6QknUAQlFhWBhxFQ21d0/NQkuWnQ58jjh32LaNWgPusRTR9THYRuL/M9rQxshCsAB93Oh7QhwjFnUm0cxhfHbUfOaCKsiEHkinkQs42Gw7ShgiISSi5oiR2BDEe0ywvuMvFrN7iWh0avnJ3HBERDo25oiUcFphqCW/XzlIHHm+MeGx+NkUIA3m1KTppzp73w8oIzbByynO+iICIwzsmKmSZOZYjuu6N5FmMdidvt1Cw7FWvMzMdUascnxPn1OND+DG77ChBM+pW6etTyxtZduy98slRYsz4n1hIMI4t2MTqkg2D9vxzGc2EvFwQbUxZmOwCtYDki7u7P0WdaIkT5Q6SRQ1FdkH7h31FuxFLCDxnMkkfgqGqyi1VLpdmVqSvYfOn4riVEoJB2wSGpmIM3yP2UnWQy+y1iCEmiNVjzXLiwbYwzBr1gopUYdyMI9saa7LyGRjTRepUe/VJXKAzITMbgHhCYz9GlCxjZgTlsHUZLYdkf6LdRF4l97XFBm8jT3U7481DlNcV4oezwkcRo81pSMgs1mKzZ7J+Lw08BEx5AtmHhGOOTpzI/iYtR9w5igRF8kRXOVoMo5QEGY25d6Z0lSxzCZqcz8k7wlz7XnPWmNc58vXhfi+AjVtWk78HgK+5mfCQd7ILGuiGH3f6duNfrsGzDqV6HM/JCiahMwwpHLsibl+OF73iz4+asf1/utvKD2owr0JltH9rY8cAQCUe4RuI1UgMLRtaTzD0BclDfJg3zUaf10wj6zMPEaQdEvlhiQJjK70sTCs0vdKt6zZJCwz/j/k/TusbGmW3wf+1voee++IOI97b2ZlVrG7uinMkE1yIGOImWYDoiNIEGTIES0asuRKhig5siRaNGVRniBZAiHZMgYjDTCGhjRGHoEhR+xRd7OrKjMr7+OcExH78b1krG/HuS2qhWF3tZFQNKoz89x74sSJ2Ptba/3X/9HcfolS8fvJTZWKtkJtipYeOSGRKt4cCPqBUd1Oc7b9BbV9tufpnmhVuEF1O6ywkyp6U9fohIHbY4fZOlPqFm7XHxosh6za9NXUGf24xj2ZgcaOf/cb8zN67U0kuj/3DlX0o731X2LnVu03zv497RYeab9Ta33S3AN/Pv8dPv9euMFCvR29wUm3cEJcJ0pExFVSKpQto5EOewm1+k4vvq1vKDRya+RWbKoVkCaUquw9yA0WbfQUZOsPcpO+9mpsxd/eaTM97fsxJ+TPc/rKHuwnOCm3D7MVYzOKNLzmV4isVZNb9IlZtfaBVmlq2XN2Rgm1QKlKrRYOeXt/aiVXS1J23aeQztgsdUR4vbb31qJJwPccLzo832qlFCMVGbLQeoaYPZ8UcDicgsuKE0elGnmgOVpz1H593673Wrgdn3vBYO+rOsOQbnnWR6Ja6u2aacXg11cidmfbFds1shvFN71ppur+87F7udX+CkRoe94NYn/f9WmrNYM32YuaWJHthJC9EfyjeyOltn0NIabf7NedXdu9IQZeIUlnm9Le0DRxt2t8f2da34jWJpTS0LwTqD6bEPf3AW7wau3nXG07zK5/5B679Yv98tibh5tuS29z3J/J4wdduD5+90vq4YAXoRwGWvR4dcwp9eRVkNC7FIGcG7uBp7p8u9HK7ZozCm/O2SjRrbHO862TUumehb1wibMpLmdHLrYT2fUMzpmzd2ndZEVsCst74SqgzYquVBgnj9SANI9U6ygVaM6zk00sM2u/FDo1fL8ped1L9Fan/7UdMlSMmbRf/vtz9cOkcusUrWvynX1Jv7aN6WgHRD8kxHUopu9jZJe82s0gvbNsTbuTALRWeiHfj/nOTGSP5uiL7NvNan9W+59L7xRv70JrHQLd6Qq9c6z1NtXsANNutLoLYlGH6EhKF5Z1Q5OFfFrnaYnX9tYZRT63SqqVlhOyo32tUorcDlY7L21fWNSaCZVGEyODlFJYq7KnITtni3TEIa4RVKzgaKHmRGnmOu/avtOEmitFbAL0Aup6vHoppBbYaUreu77fFFBzbHHacK52WyjrNaSl/m4KVCt+rZmg2HUkopVC2fOzJFuqLdyua6902M8mglKFLdvEJLWgLXeRrL220BKjt5wxV2ySazRjx0EvXn2vszccu1OLyOtktBMLZBdh640kZNf+ZyhCFYs32ffCot2SrJsKC33Xuz+H/ax95VX3ooiAOJtebhR3QVD2aJFdGtBq+6z481p0bhd3vV23OzvRJhn7/tr0VhlanzRlv5+b3cdVPm/2dhd86UXLNFW1GqPSlf48uNvP3e/X1nqjVK3gl+7z+kdg0L0Zlf5L/C/3W/09k/3a+AwqbHz++GyC+xM8ftCF6//5f/u/83A48XA6UO5P6GHi7u5AxhbeKtXsm3rnUSpIZ3vVIv36bz3SfOsjthqLrzOnjoPYLqBHd+f9IJGCc4ZJ52w01j0aobXQzVGVXEz0bJFCwX5ms2gHz0LLlbrBmy/ecn9/4nQ6cDrYJLM3bbZ+6j1UeQ2ArNkOWRNWsh/rZm/U025bs8nF4IJih0HrFOBXkQu72e6th71NJv2w2qnc/Uazya6b+rYKrYfsNZtCEG/dZmk3NtxeiWoyZw51au+tiB0aud46x9d2rlFyMgZgrYagtH06eK1gt8zIzyJKaq23wmdWOwbd1G4JlXOiNWXdVp6fn/j09MS2rJRSaRqh5n4jC0Ilt0ZqDakLudkE1loh94nLa2bbOgGgVdYs/dgoVBehw36pORxW8GsxDViujTU1Avm2dC9l6xZPyiEKN4o0lTXZ5+SlmH9c3w1mokXqSAOMNl9qZUuCk9wDTgMi+77HMYQKzRvUrNVIRCiDN8cExAxza7VpymnFyWQQEZCz4NUYiGGYgEoqhcuccKy0VCmrfVbeCdEr7+6EL9684/7+AX+4g1JN/qHeRK+dYWdTeT8yd1YdFXLuWXK9OWKfEHqwIT2ZrRcHwFIg6uvut2JTrO4xNPQaWQq7dPZ1wGhdlG+HvKpC/QyK30ePag1Zza/wWu1FS0q7wf6tH+j7vnonNr1CoZ8JpHfozb7R2MF9SjfouE+F3Y/w9WHFrawLeTVmLn7sX6d7L1rCu9XEQkmZvKyky0I9pBubmVa7hVVlX5aLQC21773sutyfu3aTAtPN3ga0X9njB124/v4/+j2GEBmHQAkBDZ7jOJDEqM3eKUvq9F4aqZttSh/Nh2C//nXN5LT0RXY16xq14LfDaOGEoHjXSNkKl3fS+xm7bVLb9zh2oDvX/eeKxVKIQGkFxejOTh1fPEy2pAa+WhM/SQUV5TieXuGBpuw/ibavboWbMPPWX79C9AZTvN6e7IyyZrYvtw62P17ZSHZ5GczXXqetAlSHVk/bQ/+akDssZLlKQs5G26e17unYO9jWafM7I0r2vZba6+t43K4/EoT+TvVuGqgWPdODYvuv/now7JT+Ha83FP4VuxcnvRluXcCrlK1AGLnMlV/+8oV/9Lv/hE8fn7hcF56v6SYhKKUyTSO5NrZcjZ6/U/3VmKaqSvDCtu75VcK87TR5+xX362BJIOTbZOa9ieGXtZil2O5h4XbNoTLGHp4oxojcumJCu5hJ+8SzZvvMja0ptzTjZS1dNmLJzjF2raB6hmHfSYkZVheDyYNlqbDPvLsjhkozYkDvLba0L+NBnDmb1wZrqgyDN6H4llBpeIXBCz/9+g3lNyecf+ChjVAXtCoq0aJTpEPY+7XZZwdjTe4Xe78DWyff3KYSDKpkb6L6bFF6vlonM+wc1lY7UxGD4/fm7gZk7wNeh1it4ajGIN7fBF6hP7s0bZpTMdu2emsYb5WQfdbaPUZrVVsF8ArHGgN414vx+n5I7YiRfa0233fWnZ5Pw0JaQYPxAAzu6cQMcTjfJ7rbfgvM+d5svbSTcOz84HXq2s+5jkIp3AopvG619r3266v+1T1+0IXrn77/xJ64WQBUGLwjY4UreH8rXCpWuPZboWGFSwQu1428LTf3g+i9LeqdZxoN1xcBr/QDw/RdpRZEGtEpW3sV4+3aCgVStf2VCKSy4LCDI3jP9asvGKInBscwTTzcnUibuXo39zpKvGLbvTBJP+T1Fb/v3Hlb7Qj/C/zf/l9XiNyeaM8Oe+3s6Nfl67u0f+eth5UuwuwX7/53RQzyvEWTfzZiyU5L7r/ELWDus58k++u8EQNe/yku0Lfst8ltN/TcO1RrTPdiuE+Rr0VS9JUggliiaykVFx0pVS7Xje/ff+Lb777n0/OZX34836C9nDPH4/G1cOVXt23v/a1rD96TUoJmRWrZyuvgSCN4j3NqBW2HWKsJ4GttzGumpIVdJxbjYIVLpTt9dBf54Pt12AHWlnHOrslle026bbWaJyIwz9nYiJjl1ziOqAtWuMbh9jkHr6RsRcM5mwb3/dTW4TPtk/B+ZqdEP2gBqr0nCKkph+MRqYWWV8ska5XoYHDKV19urKlRm0c6PHnbvXz2WX4ODd+u0c/c0O2eaLcr9bbhuTVn/d5orU/mr03bjaDQIei9ONy+s71KP7A+hP2n6n47vdai1xO7Y4+7RZd0yJzbbPf6c/s8xw3Wb5/fFftUuf8WfT9MvwfY74HPCUX9+uqo0f45m3Ztf6IdsN+h09f3XsT8DG/xKf1n2FT42R6L/azoB9MNvfmjhUr+F//8VTx+0IVLBHLNrFtmK8YmLCWTmx3sTrXb51nnicgtg6vkcvOl27qFz35RBe97hILjct6Xqa3DT59N79gNOwRvnoHquh7MMrtarXZpOttH1LpZfpEath8UHu5OvH28Q7u/nJP+vfp6MQntVmR0P+EFdm+/Xfiq0m633G3665h76zuy2zXa0TjzHbN9oO6hlNpAOz2a0q2smhEJctd8IbcU3VtX1QW/2vc0e/gj1E6X3ndmrx2hxQ7tvm/2PtO7651NphJwzn/23HojXljX1+HSfdcgPf9r177p/nP7+1b7BNvZa4p5Gh+nyGEamNcVpJHyZuyqXEjJpqxc6t7Y3orlfj465wzStGGnGzD3+HIFYqQ6b9PhfhG3wrZlSqmsy2ruEf0AuAUGCuTtsyPEOVLusTEdIvTO4Zzr134/TrprfW2NdU3UnmYrGGNUnQOn1DLdjslNhVRMt+ZUAEMBvPe3bC7tN8J+fJV+v9lep5Kds4MyRFROJpBuguJo20ZKiZfzmdRF5a2zQ2+/X/9srUky2Ff7RXa7/uGVANCvg9YaLfed0n4pseu77Dp0Erl1lTtZtw9OuqsX6l4O7bm169paafvt1OtqL2Gt9ffJG31f90nMSs7NtepWbO0eUOT2WltVqBut7rvCXrBuchQ+u5dfr7n9ft5XEPvnu0/dog3vA94HS+vOtg9tzRKR276Xk8/OBtTgZ+RGiLHa9Hq97XDhH0l93vd1TUyW0JEP9ib8V/j4QReu3/4//3WmYWJbr3xKwpIq2/VCrjsdthCO9zYc10KIkdYyjUJqA2m+UnNGXeA4OeIQiYc7ynxB1FvGU1uoGql48vWK02AptrHhx4NdJOtKnE6mLikZN55Iy0xeF6ruwXMC0dGWBSkbUjaoK8Nh4PR4zzANqFeKNEpZyUgXZFo8Bq1AydxyhZvBaVJtorHurxcx525R7Du0aCJtoKRe4DqM122QmrXNn7GuFPrFrWJL9gqID/3gx/YR+/5AIkVm0IyokJv9uTiTFjQXTIJAb6hpSP8sTGBcMSujPnDcOuBGcwazlj2qo5rxKuK7w0Mv8L042Xaj3goVVErdmXbCVuzntVqY6iPxcODhR1/xfxzv+emysSwbH56uXC9PpNLI1VHWZEVOlcPR0SSQM3z89htjmYowDI4W7+11p4QLA84b/OaGg10PKRGnkRA6bdxFnt6/J6dMa4p3qZNHPE/ff4v6gAuBGJTqDpQmrOezXZ/SNTdDpGwbeb7ih6FL1oSqI/PLCzltqAZiMDd5dYH15QVRc9c/HCIaDiCR+fmTieHVMQyCnyYjIM0zIUZ2jZm6kbQs5G3Dx4Ppr1Rxw0i+Xu32c8H4PM6B95TrC3V+gXTh3WPk/vGE85Xl/B1ES4m2dOvPc+BAWkFbRV0XIN8OatPWtU4sMKNggZLt+hI7vncChTlO9KO90TV91aysvEHO0kD9YNT3Toi5aQhFqSlZsVSlNsvAUzXyA05oTvs13ospDdc1f61Va8D6o8nu+2+Hf91NhLVS6Zi4094IOepOnqqvxR6xwlil3ZpmIyHRkwkEiR43RHwr6E71F0H8TmG1RqHWCs6hUQjDQBgCPgRuhWovRMZE6nlv1qXVWm0f2qny8sfVqv3r7Y/58/8/Hz/owvUX/sJf5vH+gbQtPLfAWhp5vvb9Q0VaRg/3NialTBwC1ESthVUntvMLNWdCiNwfI3EcCIc7lucnELXCVReqGyjiKZcLwUecdwxRkHGCWinzhWG6I+fMtq3o4Z68LJRt6V5l1bqOMcB8peWVlmZePn6LeCVMAy6Y9cq+O6nVlsG1mk/47gRQb7sfOrSywyJ9Xrx9yXrofU90w8elf5/sf/+1hWwdKqifg3gd4751eTfSR+v0W/txihU9wRkZo4tgXxlc7vb37bE7we/Ejdfd2r6fskW7TX275m6Hjuy32SGUfdoCuW0O4PYXW9+Z24+lFAtTbFItY815hsMdXxy+IKjt0i5LYrmeSbWRqmc9z4hYlMrp5Gk6Uqrw6dtf3DgiwQt1fLD3NyVciLieH8hwYLtcKOvKeJqIzoS3LYw8ff89NdvrGH1DQ6A5z4df/BwNHh+M7FP8kdyEdH4xh3YVnFQYB9KysF3OxGm4TbElHLh+eiJvGzFGxkH7lBU4v39vCETwtssaTqADy9Mn69CdMkTBHQ7UUtnOZ4Yx2rTWCi1MbNcreV0ZxpPtuJyi04Ht5WyIBo6SF6pztBgp12dYX9B05W5sBoW2Rk4z1Xmq4/V63iHCShe+N27GMA07MPsb3+joQOvX9u7awmcQcj8xd9JFa/167VPRfh/s4PgfvS/6dfb6NP067FMvvKKD2Muy2vAq0DBEct+v9a/uU8oNo5Q+ae5TXLs1Y61Dp6brk371f/bcrd1+/v7n9fUtsufdCx87BPr5afoKj5q7SnfNcLtrRttRwFtjcZsIP9/x9d+j1rbLUz97z/js/t+1YH+yxw+6cP3V3/m/8uVXX0GGepjAeTRlW0Y1IFdWp2zzxnaZ8YOgXd81q2c9X2nF4i1OMaLek4Pj/P0nmjTUK1qg+kD19twhOLxXIkoKjpwz6XrFecc6J67nhXqccNW83dh3PwJl9OZAkDdyuvCL3/1druuFpSXUR3KDLWXAvPoKxuDqfaMVj1649nw9up7HLG/USA4t32pXK6+sK8BIJ7seCtdbydaLjLcbur9eEWP+WSaY3fD1MyeBVvXVU40CGjBFvmJ88X5zOWMw3rRZted9dR1YRdCmN/+82rF+y1GrJlTtuLxh+Xtcx+te8UbEAHbn9FfrpdaDGYUqkHM2qi+K2xKoZ5yO3D98wZuHB4YYbXpOlVQbS66cP74AZlV08B5iBOeR9Fc67FTJa2YLJvjVfnq10qA0Fq9s15m6bsTR4bDXXWKg/vTPocAwBI4+QHA071iezqg3YklLjc15qgixNlx0Vq4LpKjkdSMvq13jzYgBKXjSdaWVwjB4hi7c3lS4fny2lGQq1+crNY7gIy4XglfTV9VGGUzAWpaVGIWWKyUVFnWULdFyIg4DXhyqjjpEyryQt8R121ifr2w0Nif400hojwQ2xrISAqTcSKlQoxo83unvN+/LWxNmOj7b65innvY/Kh2BuDm/79DWTnWv0qE916ezfi12xxqnHrPQMgJFqftWWaDpDrrZbKeuw2b22lo1TeZtR1Y7m1U7DcLtjZn9MqWzk0WNiGESl517DzdxdPsM4txdSeirih0+1P01WiEqpby+MKxImCyoUIp5rGqv/DcYX8yotwmdlbg/P92ZxVjFxhy2e6jtkdQNKrU7suzyEXuvcjO/R3V/GtL7H//4QRcuNw00VbJWW5rXzkXrB3MT4boJuXlKGJm8fSilNq5VCKc7pFWWeeH8sljsSAVqQpzgunt1zQ0qHA8TuTWbskoizbZ4CD4yBc+6wRMb+VKJQYlOKekK1Xzglpcz3nWjWZTND+RWaThK8OgQiKOjsaK14GrDSYe92n7RGS6tqInmoRO/Sk+f7a+/T1YGI8Yb5dyQnoyUjDT3SiPHUaTrkLoQ0+m+ki39+xxlp/MCteab3dvNeqZWNK8WF3LzRbSDoolNZtKNWh2K5ZthhatnFWkTsmzsLCwb9NwNblFnxJkdNmr73KWBm4asZYTcoS3TD+2a9NwquVZqq/icEScE9QSF3epKULJUUqlsawbvyTmzzIm5bWis+BCYordI+07ceb6sgDBGT9sMai3FUpSFhjpPw3FNiVwKZal4haCCrJXlcrYD1Xn7OoriWGqy1AIR4mS/Z2uNrRXKaq93GEdCcF2DU6EKcRjY3VHWrVBrsmtctO+eGi0kc03PlXEyhwtaZc0baTYD6xAiMXrmuvKSNpYtEYMjxJGUM6VTy3M2Kn71u+7PWaFLhZw2TgoilZf5I5Irvjo4vqVVB0VuELeZD8t+cWOHp6UumC9g94rsI445m1jx23WQXT4MuyAdsbNBKort9/Yit09oVuhe991oQ00Mepu6pFvIwW7RVGl400thh7gTk7NoE/RmTwVgWX+GDXQ7Lu3sz/111/57YnCfaKA6R93t5tR0gIYRGuHmFfLrv4ekXmgUkYoPRhDxLePdnpXZd3ci/SzpzZBrxDHgg3bE4FVTab9IL5AqUJKhIz3Kx3Bque08/8i49St8/KAL1/N1oeoLy7yy+ZGizrKJnL8tzVOLFnFfM7lASpktFeYCxzHiBJZlY5sXSi7k0m7Qnngl+GCRKa52xqjZQtVkGh8VaMG6sHnLrKlSte+cGqxbRntXtZViIXlSqS1T1fzY9LZsbRj3PBuuT0Oa62LVVxjhtrxvpd+Q+6DfO7dm4ZH2La1DFq2n0u7Rl72dfW32EGeH9k5t1tt8x20BfkNV+j93I2Olww9dQ6T6OZTSoyzlFbCRtheIDmpK31/sv4+8wgiv5Qt2evnrf+9QT3t9XfTf+TOocYdS7dDrgt0KpWZoZrzbWiGljdIaqcDLdWVdM/OScKLkUsg5QaloaqjPLF4J0a63lAuX62rTXjEhcKuNWgq5C1edCskZOzHlQqoZr0J2SguVvGzsvEDvlDAGnPdsqbBmuw5cqzaJ0WildvsnuxZTrZScyaX2n2nvtyXomsWRkS/K7cBat0LBrksr3taVl5T7QSxoE1YnbLmw5ULKZecfsKbNrItFKA6cWMlYRcgIWYSM0eODJLxsaCfy7J+9tHZzu5B9pOkN2W4K/TmYZ3hhB5tv5+N+b+xFq99PN1eYTjzary/5/Fr8zEB2F/nu909v7mwQ2e8fg5rpn7Fo/x20p03sz7vfl4Ldk+0G3tvv3sdGFbHi38rtvm59R+Vur5lO8Njfi32Xv6Mp9lw3qnqFpg3ZJ05pVhQ7s9TOlNf3fCdIaev3TrU9sN1W7fXd39XZVToWaYWzld3ZRHqS9w6CvBav/d/+5CChPX7Qhev7j594um48fXgmxZHqPA7QOHDTUcSTkcda5TrAPK/M88a1VN7eHxm8Mi8L6XKl5tI/h0pRoQbhEAbwHnEJqY1EpbSKJAvV806gFBZV5mVj2zJucpQClMZl3XCl3whOSFuh1MzWFqqYkNFXDC4oFrNhHVnpN95tE8R+gde9ePXR3Apaf1M6niBup632rrVhjuY3l+/22UVp36ydyYfvBpl7oZOu41CLHwGDDFzfSwkNqZUqfYkumFDR1Lt2E0m7LYXtBNhFlDvW3vV1Ygeniuk7abvA0p5L3euNv9f6/TlupOHbbstu7NpFlNYx22TXikXS189gyFozy7pS18x5zbx/OrPMG8uSOE0HGo3SGpISTZPt7GojjB7nHV6U62wTV94SLnpb9rfWxe9djOCtAKSUWXNBm9HQaw3kZSXnQi4Gd4Yp4mKgFkjFyAglZTT0/Ux7pYsLIF7JOZNTvsk/aBgaUaxgl1LZtmyRMt7RUu60+26c2w8dl4uxa6uwFZtUty2RU6HWQk4GL53XlYBpE3UwqK2IsjgHHeGozSbXpa14uTJJxSndhko6Yahr7Rq3SWv3GXw9BPv+p1ZEXd/H2EneOjT82WaXRv9+xNiDNNrNWHq/XnZ2bv857IWiv54dlaiN0j1E7dZqt8nvxp5F8Q4rUK12hrB9faejCp0gKK8FUxFKNq1dK+avWptQi6J+MBNe7ButHjY+N1a2Im736c3AWve3bm94KxbX04v6Z0uu/TW1Zp4BrWTzH93dY3jdLd8Ke4co+5tDq9lQIRTxr7T5/7XHn3YW+0EXrpKhlI3n50/o/RdI9NRSeDiMOO/IJRNCsP1Pzgw+UryQoqBFyXi0CsJI0QTRM3rP08dP+BA5xROPjwckBAsgrA0thq9XEU7TaAdszgQXCS5yGEea84Q+zi8vZztiFZx6nuczaVupaePN/YAbDHMv+UqQQGRAi3kaFjIlz4SqZrGjlSqK+IgOA2xWZM1qqLPrRNh33PtEbwLNbnSqBcnmTJDzZgtY73BhslRcdRAMLtojRGpJRnnoMeKu5z+VjE06KrgwGB2+bEjdaL7HbmDEefv5/fnbYDd/KzcrKIulf12uWVqwo6mi+M6AKoh4Y0Jqz2fyPQyz9Imv9c5WKsLQBdB9ESyCU4drWHcLiB9J2RxRRgkEF1H1DIwcDoJoAp/ITXCiRFV0NI2VU+X8cuX+cGAYAtEr68mo7SXXHptjJr5TpyPXUhnDgJZGcJ4oQqsQnOMwDKw+3libJRfGcSCGwLYlihjcWUrFSbffIjNE+55aCkOMJNn1TEraErRGjCO1bjiFGCLvv/+Ic444jjA0qpoWsqwFrz3hwGVOk1Hlt3XjOE3UoXKaMmsqBKdoa2w//46KTXBjGFjmKwLch4lwEuZtw18KVQp+XSHPHB8PHI+R6RBtN1qU3W2LakbBONtJya55a+1GCpAGlL4rbVC7Ccze6tkep/Qsr96IqXQYUMCHmy+trYhXm/jFImt2AXDbC4SYwN0IW5mWN0rJ9hlIo6aNsin4wDi+tf1VzuT5QqmzxSgNR2KIOKf4EMgld9jbwe6Dmgu5ZNw4dFp+JafVcvOSZzgeoV/ftZijSauFnDfGeMBpwAffG5C9ByjEOOGdpy5muFwwf0hr8I3cgnTrq87ULaWaVKjdThcrRreYJINhS7FViPiDnQ/Si+crv/5X/vhBF67LZQFxpLRQrhdkKwwKl8uVBmzbgviMNvA0thS5Xq9clpVz85BHRm9d5rostFbJzrPMs+0+FLxWZBjAB7xY51xro9WNC71IlMIm6bY/C5MtJZ02Sk2UbDeFxpFtW1muV5bLM1oizhXEbTzeDfgY0Djg4ojUK61UalFImSpCiIOB070IGV7ex3TEIJEm1GoL4IZYcfGmlN+zvqTnGpV0vXWzLhqhQsQDjlJ27wp6IJ1pP1T357GgSKQztJrQLamxRfceJd54jRY3V/Habbe0V1YDU2wZvzPYazMkwgbILvitBkHWTigxWOP1xthJTdKMItwwgkYp5nqwC5SbdBPR3LvaWsidadjHE3Iq5HVlWxPXOUG2kFDnHEohxoxqIK0bW/Q2UVYTHaeUWJfVCnUp1JpxcYJm2V65Vubr1aaufnA4dcx+Y5vPNwmD9oV7HiJ5SySwnduyosGgSGrCh2iHSq34xbOtK9uW2NRTsu0hQxioaUZFCDFyfrngvGcolvSdRUlNSPOCD4M1J3UjbTaV53Vj60WwlmIyj+BxNOZ1pVRLLsYHtutsU9d1Zl1X5nXlvCz4ulLTgraCc68+kuxM2Y4ctJuhsDUrdm2b+LyDizftPXQyz55CbBcFuyLmRt9WsfzTncjTtIc0dhi9W6Lh1Kb7Tolrpe+w9lGwX5+tQV7XTvlXfBysYXKdEt6ZjT4G6mYJ6unyRAkDMSdqsR2neiOFCbY2cM3ilfK2dXKSEAfTh1bptP9ajEhREtt8IW0rS8rUu0eGcUIUyjaTSyLnZIbG3nR+Zttmn6FBmzthqjeQnTAit0nNine97R49u99rwxCLWopB1skRBwEczVULaL1RC3+1jx904Xr+9EwTZb68sK4CfuQUlHktlNbYlpkiV4Iog4M4BebrlXlZueiA5iM5BlptpPnaD2JlOV9QJ2zblbKeYBiRMDD6PmQ3OzAWt6LNNGJtF+qJchc8EgWvRmBI63Jbli/LyvVy5vnje+oSCbERxsZX7+4I44QbD4TxBJt1mDVXWko0BR/M1Vx0QN1AExPK7lZNewtZmy1LrYgJNI8SzK7JWwNZtZqreTXWYFOzc7JY7x6FIH1pLM4iJzr19ZXqDiZD7gdQD/A0ym/oE0F7ZRmqFbR2y5mCnWZfu4O89vfQou3N1brmZB0hr3+f7hl585Sj/3uzEa5vFuzv1/7W7OynZqzMUqyDr6WbiuZm+1Aq67qyLivLvHK9brRts58jimsZF8x5QktCWybFQPCOy1pY1pX5/GK2VaVQu7YvaDfFjZ7lemXLmVU6wxBBxFGWS/9dlaDCMk+M04DUylJhTZnrywuEiAi4VsAHHNac4ZW0LqSUSH6AvndwIVJXm4RciEYU8o44Dgxe2cSzNWF7ecHFEXUObQnnr6YJ2laGwaA5aUI8HGEMeIXLfKWk2fZ8BNJ8Yds2ztvG04cn5m1lKYmv3h6pbcVpwfXMvNb6tXFTAXcFsCio74nBdt2ZY5tVDts07QOXxd3bZ97tmHeB7Y5k9Wur9QaqoZSa2AXCe9hqk/bqdnErFHArWsrtz1PKRjbyzfRz7Hlv7UY393GgtMy2zmzrtUcFGYSYcjatX4g4b5OYhEAr1jRVBNS0VE397eypfbqmFLZlYV2uXJaFGKJZj4XAus6kbSPnhIoy6gEX+nQHr+YIeyWmU/OryR32fdpr4bJd2r673u+zkjM1mYF06ueCaqC6Qqj+MyjxV/v4QReu/8//+//FMi9QM6s/0TQQxC4K5xxDHFkJCBVtmVYyzg+4MNIO96RpIHi7iF032swFXj59Q7o+k+czb99+RQ0D1TsGbwesoIQ4kKuYz1ldcKUyjRP3j488HH6LcfKM4gilsiyVZUtcL4WP37/n/Ol7nn7x+3ynwuPbO37yaz/i4eFr3rx7x+PjI49vHtiev2M7f2L59AEZV5pT8njARYcMIzqe8BIpaaGl1Tzu/EBDaTKRnWmmsmZcvKeFSAuBTKOElcxAejhaZo/3uMOdYf/OI/EI22rLYaCVzZhSnUrfnOsWWxckTr1rM2im1gx5RYaJ3Zmtyu4qDs4NSDe5rSVT09V+hjhjPzqDFJkvIHakU16QYNClCOAnNES8j4RoN3rN2Ypp3iyTqUDpzujiHE7AuUCIE08fCyUbISO6ezYSay58+uRIm4kpP/zyyvU6M28b27IyX86s1wvr9UIIgSUZQSFogmUhqPL4+Jb3y8bz+YkPv/wZeSuEODKMB/DRjGgp1LQyTUfURzZVaLaT2pbEOE5G5kgZyoKXRnSOH331Y86lcV0XPnz3MyqOGAfu7u7NuQJzYE/LlSGOhGGgDUfTLZbCtplAtFZIW+H68j0tb2irfP31r7GJZymVpw/f0JpBYse7B7bUSNvKNj+jOXGYTty/ecdXv/bnuT+OBAff/P4fknKhNEj6ifPTL7k8vefDt7/PPF/AKX4c8L/1l3l3PHA8HRmOP2IchENUhjChUTqVtaFtsUPTBXKK1LxZ80Lqk7sdpqnfiyIO8UeDuWpha4XclFwBHEEdvnlwJ/u7LkA4oN3izQ7iFfUD6geceNO7Aa1ewUXb/3UUo0omV6EcHtDgcXHAjVM3xRbER4tQwiYODYI/HhnaF9QtUXNm3RLX80fccMDFifFwIOgBFU9RR+puOyEENAZU9+LuyApJGrkmJAS8HLg7PnC6eyBE+x3W+TvmdWVLhYe7Axojfhxt79alJN4Z25Y+Uak40EZTwR8iLrgux3DE7rOpzlNzb/SKNZ9pXljPF2SstBjpmhz2//uzePzKC9d//B//x/ztv/23/8jX/uJf/Iv8o3/0jwBYloV//9//9/l7f+/vsa4r/9q/9q/xn/6n/ylfffXVP/fPen7+hNPAGDyFLt6tq+3+ayOzGATRMq1t0PcbAkhp5GUhS2NZkmHa6hCJtFwIPhJPj6gzrda2JrLLlCKIeg4nqNlBK0jt3dGy0j498we/+3uc3z1ymEa+/e4Dy7KQayW5gBPHGCfK6YF1fianhfn8xPP7TxxVGamMQU0Lk3I/7KPpqdSSZJ0f8GFAmlB9pMaNmtc+0ShNi7GPOuIhYTAIQzytJQwEcEZ06NOQAOIC4kJ3dAjsyyPnBrPwcR7KK5mCcUL2zhmhldUKp7N0XjtULO3YNDPaJ7ruyO2U5gzSQx2t+++VtJFzMfdvFZwf+lK7os7h43jrVFVtN6adet3UW+HKK3KzdgqgioZAGEbicKCxGQknFz59+MR371/47pMwnUYAXl5mpCVSSlyvC8t2Zp1ntmXpRrRmruA0EUQoznOdZ5bzQlpWpEe+lJxJ24IUEN/ML66alozaWHOjNLOWWubEts4ds2lISzTn0Khsa7L/bYtNH9no5et8NUusHoXiOvRTc6XUmVI2cinMs6ULm2TB2QDq7XpsDUrKlGSJBynboZRToiYxFiWmE1xT5unTEyn9Hqe7I8MQ+P79B8TZtakxElzgcDjB2x/z9PKeUjeaNObLhUvNDGXj4/v3uGNADpFhGqG96n3EBdR7nI+20/QDrSTb9ez7q/3viyIacN4kH9IaraZOABJw1nCputv9Q086MH5E6ffJhPpg13N3+280ZC9aNmphYZW1w9pCK428JVZezDLNeVSUmjbbPbWEd4I6j/cDKVVKTeTN7kODGzo0WHL/7HJvQIW8wXI5G1Nazfy22nIfWmWcjogqfpgIalKI+eXF9meqDKMzx5NWKdtGSQl86KQqXmHNjow4FUQt+d25btO274gbNNl1Z6ZzddmR08rLx+/J8sIyL0wPC+O7r3DeW9DsZ2SX2+NPWc/+TCauv/JX/gr/7X/7377+EP/6Y/69f+/f47/5b/4b/uv/+r/m4eGBf+ff+Xf4N//Nf5P//r//7/+5f46QmYaR++ORoUZyNeZc7jlT6hTfsVob6SPOe9Tra5BjtdyjJg11geCF0xTwGu2Da5W2GiZNM+W7ijkWuE7HNWqydqLGxodffkPeZqbDxOVyptZsOgmFw+gZ9cDY3nB+KQRfIM2cP33kSTMuX6h5ge1CywutrHgEp2bqGxnxQGlqF3jNUI3mTHe3rynfPN0anTVWKmiGulFztqiQRocdKqoNqQPiK1KhZkvhlWaLcnGd7ltM1wU71bxHIwAUez17dL2xF/uiqnX37poNjuhUW1Pl2+dVir2unDaDYXyzzneHRnZ7qVxoJGP4wQ16EfXmxVYylGSNjGFEpsvrN+f+M9aUqM9PPH98z6dffuS7XxaGKYII85oZgtBKZl1WSl2oabWJjoZUhzbBS2UIkRg8TitRK0RH8EcW74wx5z2KI0bBe9ubOGcJ3LUklIJopbqKI9+IAdE7oo/EGAkOghZG19DjSE7GOoyh4aV7XwZHKdY0gBhjUQoihSTG+FJxeN+Lvzq8C4yDB8m01gh+YEv2vsWgFFUIEcYDOUMtRtY5P31PKwvbNFDLarZWzhMDjH6kTY6H48jwUVnXCynNeCpSEjU1nj++x6dATQNxDJCd6YukEqLgY8Q3uw6lw8o7VLazB2+s1Obsc++ic2ruzijSGwiMhZhWpGBTRcF2WrVAy7ie5iwFzK+sw2GIhaj2fWkr5rxTcrFom9bIDfKKxd37gEvJWHk1UfNi10YY0FhZ54V1vrDNVysCYvvjkrfXe6o08F3IDqR1tsLl7HlMLlApaSXEgOsGz2W1/fn500e2fL25vKd1tn0cjbou+OGA8+ab+SoNqWjYd4vS45OgSqZsW9dY7gLsrV/XoZ+RjZIW1nUxz80tcYwnIxZFf6Nx/CoffyaFy3vP119//c98/enpif/sP/vP+C//y/+Sf/lf/pcB+M//8/+cv/SX/hL/4B/8A/7aX/tr/1w/57d+/R339wfevv0JpfM5o/dcX66kVEilsdZCCJ5xiLRiQZBrrjxvZ/xuU/NmJDdQ75mmiR9/+edw0mgp8e3375nXRMqVcRhRF9kjETQoPgwM8YFQK6UUtpz57uc/4/3P/xBRx8OXXxDHEQ2OppW70z2DPhLKW9btx1yf3nP58As+/ez3OP9i4/dkI5fK4CtjdJzuH5G04gRCiBzv73AuICitbqbOV8GNnqbRdjbzbLIA9YR4ojKY5ELM79CMqwVxI1ITWrOJJF20qc0NoLmzBDMuetABNGI+7bYXcMFTe0YUWg3KUxAvIKNNSLXcWEvmuhis8LVqBUZz369Fs+vqXaE6Qf1osGHbSMtiN48WmoyUWik5UVMh12T7Ch3ZdWpBPbXbP5WqNDIhRqbDgY8fXji/XLlcZiRMXK8z67JxLI70cc9tE4a7E84rx6lwGAeogZrFxM/d/eDhNDH1w6MAW444J4yDZ1sSOTdSsWSC4zRYBE9KrFtm2TaeLk8M4QCtkVPp8CiINB4fTgS1RikVWFKlNs80PNiesgmlCqgwDGYQXFNm2zJrSjxfnnE6GLkiVZusex7Xuzf3RG8Q6stl4bzMrEmYhgMQqE0tIiVEYgycDiPrvPL8/Mz7Dx94fvrA4DcGP/Kjf+ENrpMM3KDcHb60uKHgef/ha16e3/Py6XuCGxm0Esh8+O73ePpmJnrhu59/yTSAlwq1crp3jIcD0/Ee22B1Vu1oMSw0gTTfGKSCIxWlYLEluxxEncOHA+SFljfTXkowhxiJoGuXRmRzyfEDohHZ1u4C0QjjBDra/qxPxjaiCU1GEwCXzWjjzhmyoQMhWEHM60IIzkTEEtmun1ivM9u6cbi/ww9H1AeURFqTNaAqFLECJc20n2aYraAjw+AJwXa3wzQQ4oCLJ84ffsHzhw989803xCkQpxNxPOLKzDqv5GTT3/H+S4ZxYujEtNp3WBom222q7ceD94QQyfNGWp6oNeGdx0+ROI6Mp3vicOJ4OlDfPvL0/Xs+fvtP+eU//X3G4hinwDAGBuIPo3D9j//j/8hPfvITxnHkd37nd/g7f+fv8NOf/pT/4X/4H0gp8a/8K//K7e/+1m/9Fj/96U/5+3//7/9zF66f/PgrTsexq9mNxTM4wd+PlNrIuSExEINnCpGqwrqaG/xbsU5VFapznR+oRoJgRUXQEb5+d0fRk7k+tJ1dJLQsyDiiTglinXlaE8s1c/dwYJlnckpou+JyRfFsDp4/XvDSGLQRG5S8MpfMd9/+PtQVbYlBG2NQYvAcP54pZUVaI6iS6b6BteEwEagTwR8Gg2rEoTkTxObMQkTdQKGRWocYnBlsDsMRyTaxuZaYs8FBzg2UurxOcyr23C4SKlbwBZp3N8cCdcZ/cMHhx4DzI3vsqmuFVFsnoEdy2TqlOCPRdlBopKXcPTYEDQ6Lr1ZEMtI1SJnMlhrrunG9XKnJBLtVGs4NtK4xGZ1nrZlcGjlDbisimMsFHuci3g/Eo3RYMTGEyGm091HiQFBHiIHxOHAahr6Yr0ZmcWZtNagwHqzzrEXQYHEgwSmlGeEjFyteQXuKuzTSlk3QzI/MZWTfB/QAQRGI2jO8VEhbpXb9URSh0LoOTWwnoUpw5o5SUiHnwta+tORkmiU31+5o4BxjsLw69crpmkyf2CkOuQmlwrY1Wu+qx+Ao9yNvHie++vKR3H6KNPushrujwcku2DXRzI5JRQjugbePI9tP3lK3glYzl357vbItZ3JauV6fybkRnDCo48OHlfr+A61Kd8Ox5iwcBrwfDPZLG0HsEltSZdsaqRYSRlpwweGDZxyPtGzWVOREquaZqW4g18WmopSoDrwfzBV9Xtk91ZoPeD+aXVjLBrWpoMET4tGatZKpeWOrlYzg3YDKRquJtK3d2NiuubKtlJTIOeOnkRBGnDcIP6dkbEjvCH7qlNpsjMRq15D3Eed6UGgBNwS0w7TX54+sy8x8ndHgOuEj0Mpi6EwxTebx+AtiiESjDJNrZSvgXTD2obeonykOTMPE2x99jZOMasPFwOHxkeFwNHbpuFHSho8j8e4t12+e+PnPvuUP/uD/wWmauDscOR4Pn5kP2KPzd//Ej1954frt3/5t/ov/4r/gL/7Fv8gvfvEL/vbf/tv89b/+1/mH//Af8s033xBj5PHx8Y98z1dffcU333zzxz7nuq6s63r77+fnZwAeJs8UG5f5ikq1Riw5fIgWDSCVMDq8VtRtPXE329ed7aeMoFYJzujVuWbSkvoobO4FTq2glS2bHkRsqjDLPKPPlpZpJFRWvNtwLlHrBvWFlldqVbI0y2uqplIaxTFfXnh+/sTTyye0JbxUqhdKElan5mpQe6ZYgy2Xfk/Zja7dmcJNHtVgWV/NikhrQq6KOvPey+Ywi3hFvCP4ESkZqlnYLLmab6D2XVgttJqxhHJLO/alM+PMmtz2Bqo4b+4KLjjCZEWOWmnFpq7STFItEsglGYmjlF647MZrKduB12Feus7E+bYnkFCksKXCsmxcr1fytnU3H0E03BhPQZWtFHJt5CIUrPhrgyGODOORcTyylcIyz6Z38pEaJ0IcGKPBSWSPlGKCYm8HvVQjeqj0jjXnTq9uuJ4dljPmmVUt2deL/XuhIQ6UgpeG92aTBZ3eXQ0CFBVrMjrj0Intr0xHs4u8TdxsdGoT2joV0IK4Hoja/R9VDRY2h4RqOqS2h3B2eFLFILfuzmqOL57WhDUrGpTgIRxtssjJDtXDaAbD6qC6Ss65Q3mNabDrNGZh69ZT0gTRgdUl1qVyyQu5drux1khpMYeb1eKGBONt+DHgw2BQaNqMLJ8r85rYssF2BYOp1auZYY9TL1wGIafaZzgNFIymXkumSMW5gHeBtq5GQmrQ1M4TUbNeK9n80CQ4vJ+6SL9ATixdUmGISKbdCh09GiiY7+auewq2x1NVhGIGCEBzDu9sUt5ZiKnY/2zt0kXVFTQ4xNl+fruebzE8llZs05Pt/HbbOGUYL0QfGAxn70Hldt87Z1ZgKRVGHxniwOV8YRwdIXjiGEkpMxxm1uvCOB6NWDSvlKrM88LT8xO//81Hnt7/NbZl+VPVkz/u8SsvXP/6v/6v3/79X/wX/0V++7d/m9/4jd/gv/qv/iumafoTPeff+Tt/558hfACc5MqYNq4vM0TICucM98cjXgVHJXAgpcb5UvDOtj7Q0Bq5rNkuBsRYTa1S0sbzZbPDOih3R+t8Smm8zIlp9EQvDF5o2VFFSFVIqTtB1ErNT0BFXKXmF9sZ1Mo1Zebc2LbMcl3xwHqZOX98pjQYB4dG8yRbSqalRllSXzj3CbIvcFHhMAxdhNjQdaV2Bf7klbTVm1lmbvTiYqa6PYyVlBuuZSu8PXfODDJtv2EizgpqN2SpDVe7GQYwhEDDNGPBO1q1acMPndHVdwgl05e+aovxzjgqBdQb4cL5QF0Xti2TtoK7acYcw2gxG64XyEwnJ2wrc08clma2QrWzxHKyyPqK0MQTQmN3EDnFmThf8eGF1oTz+cKyrpjP7MQ0Tnz57h2tWQrx8CGw5MoQA+MQ8doYxhEfIktuhAAilZo2DuNEbcKyVUKMnTZcidNgVkutEYN0j0Yr5POabrlxlA3vleAd69bwwWJRpGU7+JowrwXXw0lphcPxQGmQcyW63W2iouItnLL0bKSyIdLwDpat9cwkE69a2q3nsnT/yWbAbsF2OGuG6e7IYZo4HkZcEa6XhXlNiN7RuhxA1LSDVrcqwQVIZ+rLR7Yl4Z0SgyNOE048wUUyhbQVtlpZ00ZdEuuauCwJoB/0FZ7PiA92na4z21bIW2FdN2PDqpF+Spc0IMaQo1pzlpLd+7U3Ue6za7y00n39FNknHwSPCaFVlOBc/7pNv6lf49KnWYPDbRfn9DMPdyndud28GHXfundrLBDCTQoiNIVcjaikzWQ0+3TSxHbddTe9dd0AwAdrkIs5xZRs+3hE2YOsBUvbRh1eHXfRzgOnjsEHSwYXbhIA6Ttkfu8PeXw8cTxMHA8HHh6eCUPEx0Ac71iWlXmeefvuKz59fM/z8wd+8Xv/mI/f/Zzr878AfM2v2mr3z5wO//j4yF/4C3+Bf/JP/gn/6r/6r7JtG58+ffojU9e33377v7oT2x//4X/4H/K3/tbfuv338/Mzv/7rv86HD99zGBplK2xNSSKklHlKK04hNHh6fmLdMtd1w0c1aawIbjxxnheWLbEuiUFNkFdr5bwkmjO46jhFWrNAystqKLpKY3INjSYK3KoyTAPRe6JXnp+fmdeNLRVCUCbXcGoFZF0bpQoxOKYoSFFmbcxLJkbFB8/9IUC1VNytCsEH+9hLYZDOGgPUu5snWnf6Y3fOqENGquAlGJVCrFjdKOVYLpVTY4yZINgqWhGlaO1f2+NUjDm4q6Z2091dG+WD2V+ZQ4GSm3mfuf4zW+miT3VIcIiCo7GmzfLNhgHyxLZsRoZYs9nYOLW4GWeTbik2VVTVniZdd2cntmo5W7U1omauS6KWStPe4drbw/cfzqz5ia12J4b2up9DnnDe8bNvvmVwHu8c3nvmLXc6sHLobiNNlGVL+K7Z8xWmw0RpMK+bMdoQgz6jIxULL/VBiP3w8HHgMm+9yDYGpLPQjMHnvOCdwYM+DiDCdUloCDgRoggu+teQS62v13gcmJfNQicB3+24nIM1FZo086fM2XLW1HGZzXBXaAwKqZlD/pob092B42Hi/nRA1PP0cuFymRnGyCE6YnCMMVBdwIfI4XDAe8fl6Yn3337D+/dPDDEwjQPHw8C2JlIqLBbnzG7PNYhJEkquxgQW8KrkglmOibH0pkHRGHF3JzNIU6WpQ1u9NTAV2ANOa+6WWVXItdFit1PrBCVxVvi07j6MAtncW2zH1XMWWutGzXYnmtuG3detFxmDjCEo1lxie2Uo9tkALe/O86BqKIEAqTRKV1c7NbNemloBE6Wo2VYFgVzss1UXoBnE2WpB6v7cavem2GeattI1akKQhtdOLKvejHK1T/tU5uvGtmQkJLZ5RjujMm2VGDwxCgvfc35ZOJ9n/g+/pVyvF7MbWxPPHz7y/OHDDRb8VZauP/PCdT6f+d3f/V3+rX/r3+Kv/tW/SgiB/+6/++/4G3/jbwDwj//xP+YP/uAP+J3f+Z0/9jmGYWAYhn/m62U1htegsScQg7Roab3SDN9vhaqN0StSXxlKhcVEpSmjOd1YVWAHaq0NSa3b7Ai5CjU3RMz+pdbGMEQ0REaNBiOJ0exrSpQ13zKWWvckG8YJ8ebIrED0UJatW//vZqGN0skPdKfrtkOaLZMxCKNUoOSbGa4de1ZWlL5sbUJR+7utP7fTVxbf7svYxN4vs3apmAWVFWjEvBn3wlUaN185adWEwy2b2LZPX9LUprzaKGp/v1WhVaWWHjOhEJyw5Q1tHpyZx+aW2WomlQ2KQFESZi8knT0sarEvadvMbqZJh7NqzyuyQpSrMSdbhdQPx0ozT8lS2IrBJE66TY0zeIXS2JZGlZ5K7RzrfkAoZqmkRldOtRFWwYnZgS3rSsUMZdVpP6QEDd3WuEHw5uVnz22uB2UXwMItlDE38A46O5vSPQW3VHHJdo8NcMnIQqWCdralAi4ktmTG0Z1+aYbebr8mGiSzKxJXaOpIqzE9hUaS1oNLzaGkzgupNebO1ryeZ5Z5xbdMKg6tETdGhjjigsfTmM9nnj594vv3H/nw4YVpjKxTZFkie3i8yQM2ctc3jc6atlSqUb1FbDfY9YQ2PVqgo4pZF+XWuxJV8z/s76Y1JqVbPdn1WpoVh7pZE+eo5FyR5lCtuFp72oHdl7WY2Lm2RuiGv6W1rp9rr/du7+paE0o/ZzxQtXV3jz4JdqZrE4PoWgPfqeZ0f8RSdnd6g4T3ZtEKoHl6qr46xJhVVp/MGuDMCi13iNB+T2i6vwL7PZq2m4UofWIU6fDuYA45GfMvSbXiW+EYIz4EXBB0K6Rt4fLyxHp+Imrl4TRyfzjgWjWYdmcO/wqr16+8cP0H/8F/wL/xb/wb/MZv/AY///nP+Y/+o/8I5xx/82/+TR4eHvi3/+1/m7/1t/4Wb9++5f7+nn/33/13+Z3f+Z1/bmIGgCuVkCrDYcAFYVChRG9+fN05ubTMhFCaGmstJ0qxKHZXGkMTK2o2jtiB4swPrtaKK/0GKuCqY4hmoukUHk/3jIcjw3AgbTNbyizrRkuFlgqUxtSTUNV5Tnf3QOi0Wztg18ti00en1tdWWUrBd4eCWi1VuTYLPZwRaoGcTTTotO8vWkHE4hJkt60XS9mtRToTsBJC92CTbuCJLXpdME2KOVIoJWczQfXCQk+XbWYE2pMLkGp6JpoRNmKUjpE7StFOlKg4Z8WjNaFU1w1kGyEqGUuFHWoiIlyXlcuysK6ruSQ0ENcJIU4sol6DdX7rSqqtEziUdauUmmn9d7K9iQCBvC5GWNjtaW6ed/V28ATnccEgSd/MB65UgarmuIBR79e2d61qhIRsHf6Wc7fGUQqWVIDQXUVcX6I7BumJ1rkTCZrrZ54VXDNaVoKPBAHfO3rt3nxRnKVH7a+9gFebDqXkTvM2TFirEJod/DczYlH8zd+w733MK4so3ij0fYzddT04e8/dajKDbcvkOdNyZToFAsqgyv3pgdP9WzvwljMfnl749P0nvv/lR55fVtJho+TI9eo4HQ4MQySGwLpemNeF82Vm9p5SGyl19wlnuVaUzfR6CHlbTSrRx+hS5KYZjKHniSm0qrSS2TPgXoknlVwzujdQxajgKvbZG8omeHWkvDPjG9FhcB5QSxflSsP1fXcTIWcxSUpuFG/No+0SjcSl2r38UHKxwh0d1ij15in3LkdL6/exFfHayRTWqEo3vu8LkLrH6BTECVuprFvF5cbgLDon95Jlfp7NnEtUcK5RcufhVjopKTIchJelUrSRaVSF48M94zha43C58LFVlvmF9fkjd6cj07t7vnr3luPgjSkKvWi1GyPjT1u/fuWF6w//8A/5m3/zb/L+/Xu+/PJL/qV/6V/iH/yDf8CXX34JwH/yn/wnqCp/42/8jT8iQP6TPKbjgYfHI6onwiBUZweMMeIStWy0Fs3FWzw5DqR1JqXNDGpVrNMtmZovlNoXnuzqfLguM7kouZoX2hiMACGSeHo683ReaXrBqxn+bmnm6WVmWQ0nP92PtJSoa+Ljyy9Yix14Y7Ad3Pn5zHWtiDNIIjcs/C/v3n3OdGC1mqlnE+sy1QpA65EppRWitxjx0nFua3DUDjS1mG1duhsGhos7seLSrpXovIk1vYJXarO9WusQilMhd6+9neXovbfJUJtNZsXGQe+97bEabGvpr9nMRaszx5GUjKXpvMeFSAyOtRTqVdgSRozxRoxJtcJWWWtlGG03sXfOtLrPKgbhobbXaAoixCEyTIFaMsuSGNSbjZU6fGi4bhMUgscPIypm1VS6XY73AXEDSKVJoeSVXKzA+zgQ1YpOSgun4YjrQlbVZvE1reCd2fp47wmaKSWZh6BE0Njp20Krq00JOFwc8dos5bglYphQF2kSb8v/WhNIQNTYZV4KtaxGfqmB2gzO9Q5qNT/O2sSkD0BrhbSeu5VYpDRHKxu1buR8IRUFFwjDxHpZuC4Xns7PrEmYl0Kp8KV7IDeYt41vv/2OX35cQJRWV/6nP/hD3r//xPfvz5RS0eAITQgNzteV6zWRq3JNdl/mVNiSGdqaryCkVGG1uI9hcKgKS7VJ0UAS6YSsDrXNZuXkpBFjtODQnMm59AnHGoPSU5S3zRpG5wUfrNHIyeyioqPbRNlhu2UDvhrgnaNJM2AgVYPYVEzb5PrOvNp7ZM2ro4iZdOdU8d4IT+oUP5phsYEhjmEI9nN6EyJik/8wBFIzFMSWx/a8IQ6k1Sjv27IZWaUHPlY18ILefIYYiN4xeUURPA5PQIOSSibXTFNntlzNQbt2s+ZKDJGHx3tCGFguM5+ezpxfLizzxjffvTBNb3j77h2/9X8a+Mv/l9/mz/+lv4yTf7ZM/WkYhfBnULj+3t/7e/+bfz6OI3/37/5d/u7f/bt/6p9Vcma+nAljJDvDc1HXTVm7S3cPDGxkyrre0m9LLeRkF1ErRk+tHXIrteBUmAYljhPP55V5mbnMGWWklQAqzOdPpAq5Kl99cYf3Due8FbB1NYim3iOYCPJyvnJdK8F7Dg/H3rXZcnjotP0QAik7St5sAUslOMV7pREouaJihpmhGaRSaiOXDd+V6qMIuWJap1JwztPNtu1Qbtb5qPMoe/aQBV+q2nuosudeCZWK94EQAkc1XL3kQs3WAXvniIOnlO6jlnNX3psSP1co2QxafRiZ+p6g1EauG94H4jjw7u0jbpjIEljL98QQGELgcJpYUyalTJoXWww7xeuR3MwzLiULzjO4xXQvadsAMzq9H+2g3kolVGN+qfcmNei3kYgxHMEgyhpMImFBeuYdUZt0LZFNkClnnNebpVRze5Jv7YenOdL35L5eOMzcVBHQLsyW1qfmncSitq/DaOXqzei5uT5VN0FxqDcnfXo8RoMbUaE1Z1AzUFrpPoBdYNrvoYaAG16FsKUgdYNq5tT2ixZa3Xj79sijHtnKW37+829Yt5VlMXp7rsqa4OW6IH4mhsDxYNeM9w56zI2qZVal2li2mZIquUAclWmIxLvjzWC2oYxjtIM+F7b50mnlyl04sG5mu7VtRm4J3jFNA6kzCKUW1DlOQ7hp4eZ1NXuq0jpD2GgDOefuTqOcxkC2mmYunJ3ditqkYte4uet4Z1NSrQYP1oY1OrL7+cGSV1SVGCLTaPeJve7N0grUNE9ODRMu2QycvXPE6Ni2YuL8WohD5BjN2LfkwlYSCAxx4HCcqNVIOpfr1e6LbCSxIXi8KrXAMI5E75mcQ8Sj2FTeUHK1SB0fBzwWSzOmQimOGAPT6Z54OOHEwXUzSRFAq4S2MrrMaVJ+8ydveXxzYjiMf9Qx41f0+EF7FaaUuZYrox4pvXA5ddS69ZG6kVLujseNsq12YHZ35Jxzdx1P5NLj7p0FBnoNTEPADRPLspLTzMvzhdEd0Wp+YJ/OZ+bN9ghfPDhiOOBcpJVESitb7otS9bSmzPPKdU4cxsigd+RmVGdqJXhliCaUrlJJW6a0jAAx2EWMs3gLweA4j+0gcm3IZjdM8I7TGFkypJJZ1hnvPE6NnaY+GFuommbLujzzV4vekJeCEqV2xwxHWhf8MDCOE4+nkWUrrNvG9fIJ7wzquTuOzLmRtkRaZsZp4jAOjENkq8o8X8h5w4eRaKZurEWY52fLJIueN+/e0FxgzpVP5w+E4BingR/96A2frhvzvFBrJkTPNA3cHQ9kAvO8cL1ezAS4s8nEOfxqDvbee+7uDzjvKeoJKZkbRYxcL892fbTuItI6yiaYtqzvYG5fb9aBNzVD3pQSvgt7pTt4A0graO1edaLd2duKtbRqDE+1FGs6rF1Lts95N3Kt3RZIlKCRfLPpKfjWCQTqDO4RqGIkg1czYWPAGSTY5SCymxffMm8o4m7uIyVtSNmQnvPU+k61lo03775gOp5wceB6/cSnpydymmllZSvKmisv84wLF06HifvjGw7TyDhGcwyp7ZbBNafG5boYEac0vh7uOY4jDw8PvJxfyKVSq+PN/YmtNpYtUZJNcs457g8Tz/NGWzbWZLvuEDx3pwMvS6KshlqoOu6Oh9t1+PH5iWVd2TZLk5Zu9VaW1t9q4e44UsQE5TmvqHQZggqot+SAeSUOkTEEhuCs2dpWcsn4EAliTM6Csp6zkYyGyJfvHkmlcV0Tn54+4NUTvGc6DKiP1Fq5ns+IcwzjwP3pyPN147rMpHXmEDyn+xPjNDJvhZfLE7UWfAw83B1RF8jN4T99b83zlmlamMbRQnFLYxxHgvcMaqbaroG/Xft9v+8HKI2aMtOyUmsgDAPD8Q4XJ0P9VInO9YJbiW1lcJnj2JhOjxyPAy64W5P0gyJn/Fk+rusKo4NtQ+OIettDGH5sO6OmkSqNKgUXe3BiA5pHNVNLouTC0CKl035rs5yqabQCodIXnSVDK6hUVF51M6dj4Ne+OiB4ns/2fDnZP+s208SRciWvmeDg/hj5c1+f+Pi0sF6kO3tX7g+B+4cDet7YrhdqLjjfOI4HhhjsZtpsj1IpPc7e9GrrnHAHiKPj6y8e+PAyc54bywxItsMMi9/QHmDZxBphFWO9BWfdp7bGKUZ7P53y3ftP0BqH0fPFwxs+vlytU14T8SBErxwGz3V+YT7PvDzPjD+O3N098MW7dzyfF2iJec4E1xid5VSt28qHX35CFe4eJsr6hjQ/c335xMf3LzzcDYyhcRy/stDQTy98980nfvyjO+7vJr7+6h0va+3waCPn1XaYtTIvM+fzTGtwf29OG4P33B2PnOLANE3EYeTTx+97/ENhPidK69BrMaPm1vb9iWn8cskG2W520HuthOAtCLJZfIhTRaURKK+HoxtwTjrTspp+S4ys4pwRcEq2g7Oi3b2j9cnOpvLaRfbaYekKaFFw4y1zqqn0yHRDknYacq3lBhPmqjjX7bJaIy9b9+PrUSN9qV5qRcJE8IHxNPLlV2853b/FD/f8/u/9PjF+orZPXF9eWDJct8KH54Uvv3rkdDjya1/fIQrXcyCqUiUzeJiicJkXXl4uXC4rISiOI4OYGXZKxr5UV3Bk0rLw/DLz/fdPTGPg/jhw/25iWRtLM0r84VQ4ROF+ijydz7y8XLieZ949Tnz99sTDaeT9y2pQ5JrY1oUwhBsp4enjFR+EwxSYhkfcEKkoTy/5lvQsQMLu7ctlY4iB4zTxeHdiqcrLBZYVRCt3hwHvHLk1vvtuw3m4PwR+8vUD1znx/tOZpw8N5yrRVUavVK0sKfHyPHN/1wgucn8c+fB85vxy5ny+8HAcuL+buH+455cfnvnZH55Z1oV37478+d/8cxwOR5rY2TZfLqzLzJYz4xiYhoGgA3GMOFFLoFCHNLEw21YxqpdDQmA9G/QYvaEQLhhqMV8Xc+tRGKcJ7wI1N67XK7UYOlPjaMPBtuHiP0us+9M+ftCF6/HtW97dRab7H1EmU+2bkDJ3MMQ0SbVHYOTlBTpMdVg2crYDqpYZ6Uv0LTcu68wYPKdx4HrNhGlmPK08NGF6PDJMA7jAXYggjWEI+DgxL4XnZSarR4cR7wpL7ewzBDdGpiEwHQ80HNclkRqEw8AwjfghojHggh14pTbalklpM+KGODSIsZ9oBjs0WAv4KExj4O448PAwklsFqaQcbH/jHcPgwXmDGnFGpOiHY1WHCwZbaamMR/PsW3MXzao5efgQbvDGMAwdFnHUnFjmjXXL5FY5nQ7cnQ4cjxPzZoGIO7LtnOne1nVjXjeCV1qxTnOZF0oq3B0nhmispvV6ZZtX0prItVgcR1RigKOa0Fla5eU5kasxMT2eyZvVzNGP+AJDc9yP9zw+mIu2854uoAKE9LXe9Ec1bQa99X1IFaWUTM4btRbWZESPUu2QCs6CS2OczNlBlCilP7Vaxpq6vvAv5ojRpzmbyM0cVnYHckwX5Pblv/eY36M1UlIW2GFePyK7IWpZ2ZGZtBnhwHRtyz5gUTCRqTHVGvP1YnsvK2XmbtEyIo2KIt4Tx4EqkafzzPL+woeXM1WV6XSixYOtE7UQRXn48i13bx/xw4Ems6UPTAOtVjYRlgJ4R9tZTmLaNLlsXPOVVME5c/bIJTPPKy8vM+tWGAd7r5Z5ZrkupDUZxR8x7V9ZkJRpufRATzMjzmk1F5egaHa01SC50mnjl2XDbRae+PRy4U49Pno0RANXO10/LYWUCrkZC7d270TtjiTinV0n1SDh65a5rolBLFKkiVKawZapAqkBBa8La2nMa+Y8bzgnxOA5Xc9Qm60GYkBUSWljvl759OmFTy9X0rZxOkRKXhFGQrRGd5vVfEFLoiZHdQ4/RqKasQKt2sRfmsXH1NezNa/V4nG21fxKnScGx+kwMsROJvLK6XQkjoFGJfjA4f6Rhy9/jL77iuFwMvuoP4PHD7pw3T/c8eZhYrh7Q5oCxZtvmXOlU0iVIsKe65Tmkb1wxXWhFOksnNmYeRiDcFhWgiqD82S5Mp027lKlucjhbiIOlo9zHCwpdhojTSOZlVTBDwOxdZLEnqOjih8b42SRE7kqWzb663CYiNOAi9Gi0r0ZoTrvqMmWytqFierMy84JxMGbHiXbv49jZBojw+AZR0+ugTUPtFKI3jFOviefepx4Bt/DCJvZ/OjgjBWVCnEaLEOsJHON9h7vA/Sdjfee6WCC8lbN3WRZNrZO2Z6miTBEczDoMEkDcko0CqVa4Sq14sw8kbk74qsobx5OWMx849OnZ66XhbRlnFOmaWQcBpyDQR0cBpxASSsrDa2NjdbJJkIQd2N5Duo5Hu+64BYO08l2fT7ANBg02Bp1m23awQg/FddNgDdas6KVqvkanqaId0JKiRAni4cQT5BudiyCDhEL6lS8ZGOPSc8K04h1D9kKl/Ti1TrkKPUGCZrjvkfr8kpKcabBQhQtm7G3wLLc+m7OXncP/8P2dCLGTPTXobuxGCzpsZ/pvO0zEYOYqzjWdeP5fCU3iNPEHUo4HsmpEUvlOAaOjw+MJ4sZcj4Qp4Hj3ZG1WCROaqA+EGIgxIITWFKhXDfaXImDJ3qLtLnMK+fLwuWymA7OuBFs20YphnpEb1G/OWWWeaZsxhxuDVKuzMtKDJ7WPCE4YvYsol0rltmK6dq8E0JW25lVa9a897TSuhFtYVmz7Zy6IcCWzHNSQzD5g3ds28aWjYp4uSZSLUQ86ux+zdV2paqOUivrWqDMLFtl3kzXFldlmFdeLhcEZYjBzjRxLMtKSpkPH565XBYLnkxWaEo+EKfuqiP0FIHMimm8grg+QdGd8TtMnDN7IGxtwpIKy7yQ1g1HwYk5h0Rn4nhpRkrB+74DtgYsHk5MD29xD+/wceyOI/9rjz1H8E/2+EEXrtPbNxweT6Tk8fFIGG1KcPROVw3Tr/0QGLqTgQjcG2HeFr/rhUannDc4bdmU+qVyuH/D6eGRL58v/OzbT4TQUGe+YZEJcYIPyozQwsD9G4OFrmthLZWxW/bVCiHaclWC55JMqX86Hnjj73DDQBiU5oRhHHj79oFpjHz6+NI1UTBEC2NUlW4K2u2dvDHK7k4T0zSwrJkQHHcnK7KXy4o6S1Ld1kLwnhgG7qbpJqhMtaGxF65cmabAum60unA6HRmnIxpGE+IG5XQ38ebhwHfff+JymfkwX3n/0Zbnx9OROJ7IBZ5fzjQKx8PIOEQ+fHxmWRYu15nn57MRM4YBjRNPFyNTvHlz4qsvHvj//f4v+Nk37/n//u43lAYxBt5+8cBPf/M3uT8OlJJxUXl7vCP+6B2n48jTh088fXzm+fyBGKxQzMtqTiTieHw8GwQo3IqshoEwjITjEe0ZRXWdjJkn3UdRnB1epYBZuXYbKwtwFCqlJEI8oGpJ09o29hTZBBawieDYcGpaLdUA6k2f1qxwIa/C1JqNKGH6oJ0s4vAo0gvUluvOA+gTmRFN4hShO/cbHdz+fvft7/BXI4TInk4NELTiHfjg+1RpC/uybWhUjifhp7/+a7z9ItkhO8L5klhToUjj7vENfojMufLw9oEwBR4ejvyT3/uOVhMqBS+ed2/hOE3M68aybTxfVp4vWxe3BoYhUEriMq/MS2L0auLhpiwZhsHbZwx8fJ65zhvfvX9hLd0JRpXznPnZd888nVe+/NE7Ho6RwcN6nbkuKy/XlU8vFoU0DpGHu8nQj+BwXpha4HxdWbfEPM98eLqSc0Ob49PLwrolXs4zX/84EL2iEnj6+MK2rpRSuF5XfPAM04SPE0/nmZwrIQjv7o98+HTm5TLz7dyZyA3UR2bvaW1hWRM/+fHXfPXmjmEIPD+f+fk3H3h+PvOzbz9SgGkcqM3x6eMLIoLzitcKNbEuM+d5ZVmeqDlzOkz85EdfcppGXIWMNZ5azGllS41lLTxfZtKy0XLl7jgislJqYZ6vvAseaiWljfOnF66X1XLeakTCA/7wjuaG7mP6x0xcf0pa4Q+6cN19+ROGAdLzzJKv1MUC6KLvOqMCVXy3K6pIzTcbE/WBVGuP7M5GnadSVdjK2uEnGMeBU7gjHEee0wo10Wg4HHhzdkCEgzdo8oASTgvDbBd79BYtUhukbMy/wSknLxyObw0Sib6nCxeQyp0cqW+PrPPGMHnK9UJQ5XQ3cni8xwdvlPyauhDWsZVEcObcMfnG/Xgyhpw63r//YILnIYAEgnhzbfAR78zhommgKuRaWOaNGJWQBiQOxPtjj6wIiFZO9ydCjMToOKeNIgU3NH7z7T3DOHF3f+L+3R1BLRt5Op0IMVhjQGZbhXCM6GGyac55vPNIujJOBw7HE1988cDH68zT9cJ1uXB/f+Tx8Z6f/uav8Rt//tcYvCJ5QcLAdDgxHg4cHu5YvvqC6/nKm28/cnBKWhPffPuebXnheBo4vrnj/s09zkdKE+brs9nktMxlPkPfjXqqaZ+00DC/w9o7EMUgFqPjV3Y3FRWMrIB1rV6MuaaqJNlj6htBTFCtTXFAzmZtJALaTXObCFvO/Zot5pfZbIqorRv29oTaqiZdKM1eW8P2lqhNiXtnu4tyC5B6XIjSEAoiHpFKyvkmY8ilmgYJc4pQX8yrcBxx45dM14VlMcbc6cHE6aVuRvtXiFIYRwuOfHz3rpNxrmxpw/vIm7ePtNrYto38fDbPu1xJrXT5R6E1xxfhztJ7UyI4R/TCFIXpdNc1gw2JJkr3Tnj75dtOj3dcLzOhZaJX3nzxlsPDkZQTx7uBbStclsTz2Wj1p8PI/Wnii4eB4+kePwzMeUXfP7GtGw9vj/zozzm88wxhME1jNX/Rd1++JUzBvBJdd+3oPpnNCUOMHKcJ1cbx7sDju4H85Yr7xff4D08cNt9DR0dOD/dMcUBqhrTwxY++4nh3ZJgi77/7niEK0+QYjgem48g0jby9v+PdY+Tu4Y6Ht1+gIbBsGy/nF+LkKfWINuFhGnn75i3TODI46fFIlbIl1qWgy0YuK8/PL2hrDD7w9s0bQnAcTid+/OOf8Hh/T0mJejlQ5MwgjqgDDyEY8Wwa0eOEDx51/1uUjP+dTlw4T9n91EoiI52YYbsBAQq5G61aQqxxqsQEqtl2FEYby+YkgB0QsHuAdZqxCsMUzHi1GTxT1A6JBngP4jxePVvOZCriIDqDZSomehR1RCfmYt8xa5wJe01t2wghULPipBGC3hbyMSqHg6nWvRNqMvacOkcstt/zKgSxZawLEXxgWc0MNIYeQikB34P1vOsuAc7SkXOHWLxr7AGR4q04q3Tj4mDUWB9MXT9MkTjA4XjPOE4cj0fi4NFWDIoKjhAdtSnD5FG1dFUXB6Pf7xPAWpkOI4eTXfTH08SbN3fUvPL45p7Hx3u+ePfANEUzkNXcHVMaSDX2mhgLMxUh1MpymfH+kzluB0cYAj54wjCCeEpNbKvFWOzwkshnFjxqOpg+Etk10psfEdt7VIz0Y/upLtaujao7RYg+3RvkpFpw4m5mAq3VvpGV7g7S2aIldW+7nk5rV6Wx/wSo0g2gjcpeq90NotK1YPVGv2/s8xQgzYyba0UpBKXrpowkUlu/qls1YlO3QlOxfDR1iviRgjHxVIRBAxUhZYPlXbOmJUZrnhxwOh1QbfhNzbgWI534xcJepVbERdaWbMLLGecaYRzxIbD04qatMkQ4HCPqPNuaOd2NKDBEx09+8iVxnHDOc3k507YFoTIdR053B9NWbSspVe62zPEuGdtuMKj9ODmmw2ihjakxTUO/5h1hODDEgXEY2ZaVmlbIG9NhIE6BKo3HN8fuGYhJFdQ+J+8ciqEh0zhQR8fDfESkUfLIdDwxTgfuHx+IIdDSRr6eOR5HpikyTAOPj3eoVFsHHCvTaWQcB47jyPHkOuloYMgTx7s73rxben2wSf0+BI53DwwxMmjfpeZGWTNLTISYUL9wOj6htTHGyJu3b4kxcDgeOd0/MowjRR2H6cBFFLVb4jVxQHdruD+tWuuPf/ygC9fL8zM5QtkKCVOnV8xk1GvDI2xVrItLGSETlO7UbVqK2i2PvLSb9ZKq7VycGp6bq7EIhyGiTVEnuGHkcn0hpWSC3J4ZZOdLtlE9mCuHC2bGuxuAeoUYhGH0pJKZl5VGw7tmOHuAlBI1zazz1fKpMEcQL62LhumuGeb27TTgnDF9ItK9/RqlVMbBIFJFGYIZm3odLN6k5RtwtOd0xeCgJoIDHZzBYwKilo0krVDzyrxmonfcnyaiGzjcvyHGkeAHkycVu6JrXtmaWb/E4AgaqQ2OOPJmhqGVxjDd45zH0bg8v3B/NxF/42t+8m4kTAeGYWLykbRdqQKSE1uZuV6MUj8dj2gzd4rT3YH56cycMtdtI3RhqFeDQv1gWWoSJ6Ott4Zr8mrjow0XbC/R+j6s1EypGddMeOpawxWzEjI7LZMs1KZop+ars8iRrWI7qdodzJ2BhqIObTYxoeYxWOg7lVxtD4G5fauPOLQzDa3RUvQmQm+lktiQpmhzVNc61b8bgu1mjWLXd2smXA83Ox6LPalmV2zNQf1M5C71tjt2YhOfD657MZrhsixbp/xbg+ickptprXxwHA6RcQCcZ50Xtm1lnRcOrjEdBu4f3lCiTa9ehdPB9smpwM/+p5/x8nKmZjO7HoKlNy858/AwcRgjD6eJH/25r4nDAXWRvC1s84W0rqxr7lZYgS++fGfJ5qnymCrb1SbN7jBsDQgFqY3DYUB15HQYONw9EsNIdAOlWk6c7ZaMIOQEvnj3aInStVGbkpMpRC2h1ezBhIID3r195M2joRNhOhHiSAwHfFBK2ljPo51RxdKov/zRl9zfHVmXlefrZubZTgnRcxgdcRjMBcZHvvzyR7x985ZWrWFWcYRqYm2niusNTSvQihks5FRIa2IaB8piUo9f/83fJPT08MPjG8JwxLsNHh744Dy1Vra0Mo0Br1Bzpl5n6p3pacX96gkaP+jC9U//8A95+3BgDCPNxb6cbtCU5hSTwRdcA2VgLovdsKpIFbQHnK3ZwuG0gmvCSSdySaRt48P5W3OTrsKYsRC6BrVUDu5oTuu5IG2DbiQ7uZH7u2hOFi3Z/sB5/BcDLVdcq4xq25F12Yh55jSODJMnDAF1B75dvuOyCuXiOXDgYTjx48efMp7uTdgrjdwSohFxgeK8FWWFgYz6gSrK0ir3dw/GigJCGE0NjzHzarF9XqlC04pIZXT9oKoNyRU/bjg1eMocZizqYmsr03G0PYkT1A2AkUKGYnZK0GjFmY8gFS+B1sl8pQrZZbQ1gjYOMQKOUoWPzx8ZwiMPD5747jdI2ai6IQwEGXvA3pW8VuZ1Ycsbb3NjHC3m4WW58vRy4eV8NleRcSJp4MNLQr79yMO9cHev1DUhJeI14gcHqbuF14WaHDS74QccBY+5MoJpkxtoIxXD/8km6vZqxILUmgmAq+DbhhalFQG3kz0cTiq+dAKRmisK0p1JqiM3c17IayJOHucDAUeqym7P1UpnzKVKdY7c3Y0HBwET52VMfL57841xIJeNrcwW7dEsrkMrbLVbA7ldVqKEppSaegRGozQztZpcZ2IW+50P4Z6WoZZMTjN5sd8tVM+PDm9uoZ8lN87bC1fUYMutUmbbm8hhYJiMCj5IBIkkEY7DwqR3OBrvTgMaD6y5suVnHo4T0zhwOB5o+Y6lmIOK1oAXC2x0gnmk1IaTQjxFhgJxK8whWTGRxhhMmNtE0Slzf2h4Fbx3aBtJm7I0xeGROlkWmShZ7BoPoZluDaFVb3EuNPyemi7WsCwtdQs5ZYiRSqA24ZqEo3qcjBzGkZQsby4VEO6YDo/ECZq7kkrCqXB/nDgc46vLiz8zDo7Be0IYEOlF/jITYzAf0rRQq2FQiMe7YPtZHKe3PyZfXmgpcffuS6QKLgwcDm+IISKxEv3Al7/5zJfPH/jRx2/48ic/YjodzE2kVLZ5NhZzHHqkjiFiv4rHD7pwfXj/ASkrQxipQzSj1r7Idk4IwbOmbvDa4LKteKX7gVnnWGtj3hJBmk0s3vYOqSTWtLLOZ3KFWsXoyN0cUNUCEFuzDqORzLrFBy7LxjQWiBU7MqQLQgVK7Xqg1Nl4iXneOARzhJdq1j9U64SkmTB5iiNjmHBiruBOrHN0YcSFgSyuT2ONQV4Llzab8MCgUx9GaA5piveWxVRrofROsHWIyOxqzHEcny07qkc10DcjkgNOzB0iBCtmDQtqjNpuPoBVzHG79jwmsAY/dyGZdapmJ2OHDUQ/MAyRMUYO48g8rz2ew0Ppx6kGQrCbpJbGupg4W5yyrLN5TDrH3f2dTZEifHx6oWXPNifW68xlOZvvYzPGG8VE46kYs1ScQ7zHNe2MMHPMt5RnoBRyt6L1omgc7DU04Zq6iat009U+iZQ+rbj+Z5RqO6UGXrr4WSptXamyIwJCnGzXqK2xZHOcC05I1aI1pDZyN171PelYuhRkLR0+N+4HQR2lFVLdzPXb8GK0CUuncg/eXBdFrGFJZes7SXNYuKHspta2K0wcLbfuB7rgvev7qkrL5iNZxeI6ttWSGeZlheuMNpjnjIyRwxRZjxPHu5EwHOwwbkL0keiEcZxw8YCvUHDcHw0yG6eJEA9UdsjS9+u2EaNQnLPpp2yE6UBtwlAaw1QN2qbYJIfR1n0rBDW9pnOW41WbkjIEcmfmmedfaoVKNY9Kc8mm4onVyDue0mUIRhrTmgnOIoHGYaI0a9p8hmlwZkUWbSqsza6D6LtQGSFlweeMU2UaR8bBWzabBMZaLULGO4bxAOLtti4GmSu1u6XY5y5idnFOPSqesm2sKZln5LpBhSieOB6IMVpOWKnE44n7xzd8+e5L7r74ini8R/1AEd8NkbsgH7kpkE1m8qeDEX/Qhev7774jL2Y/kjpTTDElvogNXNu2e+kJ52XDSTWH9B7JWVrluhQGrd3I1SNUtpJZcyavL6QEuVh8hIsR5z1DUFts1kbOG6ix/sYxcJ0Lx8PE4dDjt+nea+4CzaLC27qa1dTWSAmmydGk0FohoOS0UUvuMfCRYYj47oq+m2tGPxqVfjyQiuuJyIUgBR8GUJsUnHZHaoQ4jtC8wUnSLIm42ZamdICkVYOoajNDz2GzfYX2Xc7uITflYjsLp4QYu8WWULJRvm+Fq9N+W62dIbfvfYTgBYtPsRvQ2HNwdzgQo7HLjofJDsI1mzdtW3HOvOIOh26oKsLlejarHycs8wtNIB4O3D/cIXnjcl34/v0H5g8zz4ePTIeRTy9n1m0llULKYu9Cs89/9H33g0cFS6Gt4MndgQJa3cB7vHecDgFk6rqwysvV9kLG1o5muaWWg6ZiO526FUreSKWypGKM2O7m0PLVimYIPBwjPhx7R5y4rACW7VVrwDlz/l/r3tmDNEfrjMDzUvFqsgpxnjHYhFcVJt96nL35Uc7ZXMKn8OocrtLYSkLEGqHVXGABKDV0+EtIxZvZa62kUojOHEa2VFGyRfF4zxTg+XnlfF54uryQni60XG+u+/a5D7x5PHD/8IbD6Y6ccxd7d2uzcWBUzzAeOBxiZyKOuGEyancVHMlszaRZSfHBCul2JR6ONPVW5IplZUnNBC9UMdg4VWuqVKX7eAZKEXICzwY9+6wivZjbvaPOGztUnQnBW//9O5mriTBVg/q9CuN0uhXbnJUpKl4aUneHH4OZY7DXgChlKpRq3prj6BmioQ2o4ygH6GbT3sd+zwpjNc9S6TrPKg4zJojUZrtXFUXUdqbblpjXTyjCEWE4HgjjZDvCZSEcTjy8+YKffP3rPHz9U8aHL3DDiQq4aHtCs1KzqrXbPwmGAPxJHz/owvUPf+9bgn5LztkummbY8daD7KBSNVpyZww8X1bWzcw8nSgPdydijKQsXM7PrNvGsiZSsvE7Bs/gtWPVRjtVZ7j9GAOpZPNCrCZCPkwjx26J9PbtPY/3J2iVddtIOdtiWStaG24tIJlxGLk7nRjVcXIjkxzYLqBzxa0ZvyVKScwp877CZV0tF0iVxy/fMd3dMR6OxGk07UmrjJqJ04QGM3vNGI0aUciCug2nSrYNSd+9lM5as6h7Rc2ho2SUbPh3FQue7FYCUjIVNSuiDUreOnW6sjXTCCm20KeLNVNqtgvsFjpabRewlYYnWVfrHJFMXRLXeWb+8MT55cyyLMyzec3FGHg4HXj88h3runA+n/nl+yfu7w8cjiN3hweGt2a8mrPgS2U8bBBe+MUf/Jzf/dnP+P7jM0/LStqSabQ6lRiEXDohvS+ep8NEa7Yfm+drN2ztXoRd+K5dxK6qhODZUjHCjLegzDgEnFPm68q6zObfWCuHaQSxvCkTA7fOBenGv97dgjidswl8zV3MreBCZBgCQwwsayKntTc+dl2qOpZM9+Q0nVAr5bYbiTv5Rh2PDyeL8AFUzJNPMPdz0Z7+LUY3v7s/9RBLm9Byyrx8PJOy2Ul5dUxTMOg1m3h4CJEhRI6jZ+s6IVJmlIAfjLTQXCes5ML8dGWdCxJeGAfHJsJFhPn7wng6EYeROB5JZ4fznugDcdr1Q7bz9lici8YRugn19TIzDEsn1ABebnvCoDap3yzPOm9K6fvq1gyJqK0TkLJJb/oOMyWzmhLtRsGumoau9f20mCcimJwiNyW9LJ2Nak3TSxO0JlxZ8SF0265K3jKiJl+w085kPN+XwvHg8THixwmNoQvmM+N4NG1isya6Nksb8FJxYeiWVqb3i3EixgN+ABcNbSlbJUx3xPs7pnfvzOB78ZR1YdiOhLsJdxyQ4GlOqZ0Zp8EE2XAbtn5ljx904fp0Plv2TukuzK2SSybXPp5KQ4Mtp6Uz84J3qIQuaTFIY1szl3lmWTe21ULn3ODxTnrR6lSzrpAXoR/o9ENccN1ANafcTVRtce1dYFkXttWKl0qFVKjXjUZhG/cO2wgBtMLThydenp+4Xi6ktHFNph9SdSzNpienwscP75nnK8MwIYOZkVILQRLTOKI+0FzohqUG6UzBRMHavR1bsYlLCmSxwzgn8y3bDfqKmNDVoIaE6wzDKnQGmh16Usy1Onf2HLUgtRBcuBnAbqmya5u8OjJ26JVakWZ0Z+88a9uJBa3nY22klFm2zJYS3ivn54HrsrBsK5d55uWyoGK2UnePbzmd7mkifPr0RCp2AB+m0QyBa+HlOnNd15uYVb0jELq+yoST5mhhnoQWtVG4zLNR40UYQiA6u40u82IwtSopmwTC7KiUgIU21uYQbfhgLK+WO3miVNYtc55N+Oyc4ziOHA4jMXheXi4gmVyEcougt8IZW7NrXcGCDc32rPWIi1wq85JJ20ouhZyrMQHFQ1Mu1w1j0RrRo/aJwCsmC2H3522392OfmnMxVm/pQtfzee5p4A2HMM/mdFFrZRw8Y4gMITCfxViN0piGgHfW0ETv0EMwT0NA1BCR5XzmejVBrbTKKJm744U4DITx2AuqIStTHC38sd+TUu1ejXG0HWCtzNflZuemoia+b1a4aBteLXVboidnCysFITrz3ks7TlpMsuB6enBtjS2ZobGIw4ujhl64enPoxJlJrTN3FCPGmJtNpbEUSw6nJCSvTMHITKVUtm2jiekEo4sUNYJN2jZiNEZtjBPhNJKzmU/HOKDdLDqOwaJ3WsW32s2wA94HmjTGYeIwHvAh8PzLj1yfL6gfUA2UbSMtK805Skr23jpPbZZDd335xN31TFlnUhPSYUV9sAZ6t3P5DCL8360AeV42ghcCYvuYWli3RMOsdcxV3RkNVR3H04DIaNBBscTgUqwbs6Ik3dAW7g4j93cHEwViN+qgHhed0cdrRYPFbzuE1l2rBcF5IUbPOA3E4Hl+fjF4olmGVk2ZtGyUkqitmRNHB89KSzx/fM/LyxPX+ULOiblmgw1VacEOVmnC88sT63wluMDmXNcZZYSNYxxQ5ynuMwf52ji6YPT2PrqXvFFLwVdHUtPrpK306BJ6GKXRo60wb3hxOFGq166DswvQV6PXJpqx0Uqm1cyokZ7MZzqhRrdE8hT3ygSl5VvhSvrZczezcSq1sVVYtxUVePHKfJlZcuK6mXffcRTuDgblTtPBCnF5T91WWjV4cRgC6h21GQQknU0XYmSIAe/N2sqrYfG5NnPZTplamlGbvWnPjtPA8XQ07VO1pmI/RF0vgKrmHGLpAc5cP2SCHs2xpsK2ZVIv6s47xnHky3dveHw04ekf/uE31D4Rl1J7kbKJKwSH98ZqDWPA2C+RkhK52Gu3dGFTkvlgBtKHMTJNI58+PXcIunGdF+iUZvF68z20s852VblUnFeWzoatNEoq5uCeCiltPdutsW72/TbJKzlmthC4lIIPjil6Hu8n0xM2a4iGMTAEz+iUwsa6XVjnhXN6lS4MWpmvKzFGNF4QbA9UW+XQ97sF6dB86pqkkRq8Fa5lodJw4vDicVNgD5wsZSXgcOphCAbV9ZigQbwVLjAJTUm0kokEmpMue0m0bq4cxFNCnyCLsVK9OLx6CP7VQxIlYs99LSYebiVB2jj62GHLZu+tGMHj4EeKV0qrrMuMcwXvPGMYiPcTuRTSlvBBCRoILnC4O1B7IrQrhSGOhBCJ/zN7/9YkSbZcZ4Kf7puZucclM+tyLgBBguxmN4U9/ThPIzL//2FERigjQwqFbLJBEMCpU6cyIyPC3cz2RedB1TyiQAhlCBw+lLD9IFFVkREW7mZ7b1VdunStskBQlmnmvJyYlzOff/+F1+cr948fKDmzXy9sr6+MlH1vD9NLVdj3jfXlmf3ySluvVA20fSdNjeTP/71K/P/QPa5vH7/jbi6UDE87XPZGePnKNM+clpmPj2dO3/wJ2pWx73z47W/49LjweJ5ZJPNv/vW/4ceffiL8euIfXb8QYuJ0/4lZKh+/+ZbvfvVbXj7/nuet87JVvv74B669mWfN/kqZzYrknAua72mj0uoV6sY3d9/z7f23VFFivqJxQBzUdUVjJjyc2F6+UOaFbx4fGRWu15WNK3u/olHNDChCykZzb6FzN90RYwYCU2rU1lj3K2hieBBelmAQjHTzYVot0xMaT91VFhREhSgdkcHeOw0xW4RqBpzq1dAxqxMRUlTzQgIkFnOFVnNQNmdcHIoI1huQweu4GKmCwGhGghGUF1XD5LH3Mk82L1clIHkieBslBzELkhgYtaPRpKqyKPv6TPWK+P58pswJDcq6fYYvQh+B7bKybQY7Pr08MzHz6eER+ROI528Yo6IInH9NVEyt++GBbx4mQsq0Ufjhb/6K1py6v33m9PAN83JPGivf/ObPkJT48a/+T57XyuvlwvPnz4w8Mc+F0zIRpo9cnp+o28qnbz/y2+8+cLfMBE38q3/1/+b55WI9jv0rH7/9Nb/+R/+U//XPfs3jp29IZeI//Nt/y+fnZ748PfEX/8e/R5PJF53mSFo+sq8r6+sz9x8/8HBXuFsyicJf/MVf8PnLE/ePnfMcmE9nzh9/zcdT4vHxkccPH/nL//Dv+fL8ypeXV/76L//SdC6DciqB6XS2SmLb0VzsANZGCck8rqolOSEKc4mcHhfWlxe6e2upGmNXgDkFTqc7pmVhvX5lBGENwh/WSlIxp4Ix+HSJ7LLzPBpdKpfrTlVjI5qVWOLDQyFG8yGbY2ZdmyVDCa7NjTqHIMN93cJgrTu0jg4hVEjBKO+Nnd5Nfy8CpwRt7FTdCb0wqkGAKspr9wRUlaRCkk5ksI4rQ6xf1uogZdCgtjY37+moGWIOaWyyE0axAD8Mwtvk6BoKQSGqkXL6qKCBpBYI1Ucc9nZBsrE5J4UlTxCgaWe9GvmLYXutyU6TylpXZFjeepeEy7ojIRHDhQRcY+IpJK618rufXni9Vn77q+9ptSOS+fyXf8mHb78lT5m8LJCFu28e+PTrT8RP3zGmQtUG+WxEkf8OVHj4hQeuf/kv/298/+1Hs/q+f4BcyK2TlomUInOKcPeRupmP04fvP3J/KpymQomZ88dveXl54f7xEdpuViGnE4/Lwvn+jvPDA/v1wnXvNg/09Mw2GnvbWV9ejBGHEhW0LNR9Y7u+sl9f+fbjBx7v73i6vvKy7pAKNQZCr2TB4JKvn/l4d+ZPvv+G893ZewqVmIRvPj3wXf6Gf/HP/5ywv7BfV56eXshhQdzo0o4Ej0JdIEVEXKLVq0hpw5qxHgTM6y4YdBDl1sPxlqAxvlxax5rFxk464MHgRnhHa14PRXnwYWLrBRzDygHT87O5gcC42Y0bzp5j8ooHYtKbTh94T0WNwzhaRyUw5cxSjJCRopEGRoxojNw/PPDw4cTdvdljhGz9mg9D+Pz1hUbkTiPfffMb/uzPC5Bo5URvzVDR0/fEodb7WSbuJmNGVTK/+u2fGtScI0scpPlkVi9BOD8+QhCeP//PfN1Wtm2nXq60lO2TKPS0sF8ujL1y/7hwP5sFjWqkx0KrjTxNnJJwfnzg4Ztv+bNffUsuxfpNMbMP83D63/63/x3N0cVlBy3N7NeV7fXC6WFmKeat1ofw4dtf83q5oCnz4TxT5oV8d893jw+cTgvzaeGf/NN/xqs7T3/53Y83WnfsO2Ga7DO0Srm/M8S8d/Z14+uXL7x8/crLbkaNKkI5PVA4NOkVbTutVra6U6Rzvr9nmmc+/+F3bPvV/ND6ilQlAXOKfP/9d0wlmf1N3Hn6+srXr6+s++YzYnCOgyEB0UgZxp50iADpzfzKNLhSv83ahQHBB4I1BtrYXXFESSHgWgBEMY1HBtbHEl++qs4aNDJQjkLQY41yq2iN9DQc6Rg35X7AYFiMmNRdz9ASP99PYuzFGG7TFreXACkFWhuud2hD8jEEk55LBo+CJV8i9t5M/cUukFK2cY4gnIuzGyURpVCSEGwnowjn5URMg28+PZKTB8ze36ipArU1ejctob0Lg4zKxL420688DooDKfzjHP2/7MD1Z//kH/Mnv/k1p1hI331PPp+5i4kw24EhfbDlhbpV6nXj4dOJKVszOqaCxkLdNj59+mg26kGgZL7/9I2J4S4m67L7n7pu1N7YW+Xy9ZnaN0ZvhrvnwrauXF9eWC/PPN7fW9P9Dz/w8PiTlfkpU6QxxcjdPHE9FT7c3/P9999RxGzce905351Z7s6czme+/+47xuWJ169P/PV//oG6RfeFsv5TTIFRMuy+JEQR6SYvpIFEcQil04fZkkeH+mIK5hQ9OrUPqjsnD1Vr0Du8VBwyEj8bulrVNjCYQLDNNxwOiBJ8XsXIIkMVk58It4WrWN+j5HK7toZ3gSuUd4dfN73GEJjmhVKs/2ji4orkTCgTdw/3nO8XV0i4Y8hM69aqe907tUNrcP/xAx8ev+X+4SMXTdS90YbQlm/IeHVZAkWEIYFdEqe7D5QcWKbEOSeDbFPi4/lk8Jwo12+/5/N1NZWWPuhiJpf7trNJQvcGvXNaIjKsD9Ql8qtuB8yyLDwsM+U0M92d+PX339B1sNfKx+87IxkUHdpAspEJRq1cCbSt0q4r08k6JaKDvStluqPtlTxP3C2TNfqnmX/0q++Z54lYEh8/fsu1NbbaWP/xC51ukPZ6RX2WIurg9PERQemt8fL8wpcff+Tp82e+vF7Z9g1VyOcHHuaFkpK5Ue8727ZxWa8UKuf7O6Z54m+WwsvrE9v2yn59Ylx3spgk0Xfff8/deWGZMyFVvnz5ytOXJy6vz3ZI9k5oO13FKoqWCdGk2CSY/UxQl9SKgrlFWwJlwy4CKFvDjWbxwGUjCmgjuICyEhhioUb1CMhG704BZOitbyM+wH4TScbQCkv6jt96/LSZyIoLgR0wWgAfd9Eb886uZtBjiuJiyXaNKZk/WSAgyXqTAeu9HgmhDehxM7O0cQnhlJON9kgihem2z9FAzI0mmTqEb7/7ljlNpJsIuCvd0127U4jJRKSRDJLodUNvGfHf/fqHBLFfdOD69Z/8hu+/+4acMnV5QKYTcTJnWe1K1UbdQELh9GC2EmvrXFs3Lb67T5TzoMwTn3/6zLpv7LrycPdAnCbCEDQkmgRaSCwPE3M3osQynXm5rPTefEYjsuYriYmH+QMfPzywnApPz8+clg+sm7DGQEpKiZEpT2ir3D1+4sO3f0rSKyUmchB+++vvSFMil8z9/T3037CvF775/nf89V//gdfXK9fLlVky5/PCaZ7Zn63xfpgiBoEQIjFNdG/S99aMAef6eTGIO6s2Wt/pA5+5MTjU1NA3p6irQ4zBae4gKVsAG0rfmzssc6vOwPe0NmIuLmBrJBFVJWgjF6OPD1VIs6k3hIDkxcgFtXK9vAJKLIXlwwfrF0WDhaYMcT4R55PBVcuZPBWgMTDFBR2Z8+nMuneeticeX75yfrgnnRL9NXIdsFZYVXlYCiUGWoPnNqga2IgELZRNeV07f3n9ypBEmWb+5T+byWJ6hpeq/Ocn0x38sGSosG6N16tyqZW5mCL/VpXL1QxIO7DWRBjKtg+eXi7EvFPmyvcfP7LuO6/XK1+3yuvV2GifzhNp2PzgusM+lBgnlseFMBr7vlHrbp89W9acifzhxy/U1mkkvn+wLJoQ6UTrHWpk/jgRmrEOa17pGNFjKollnp0sdGHK93z/q4VvPn3PDz/8zvQGh7KHiQ/3J+ZSmPKCjsG67VzXlftz5rwUcoC+X7m/O7FtF16eC5fPX5li4NffPvI//S//nMdPH7l/uCfGwHZ5Yn3+zN/8xX/g8urs0pednE0xv25wuVztYC7F6gWJCJHeNpu7YzBGc1cAYfTBum7ucWb9qwNDv5nM6gCJLgVmxKzed2fbWv/VfddBO12Ejhir8pCew1yth5qa/JDETb1/WI9dnSWcff+A9Xdt+D0ZczRaz00HiFRSH66+M92ck5uP/YQYKPPJdS0HIZnlUZky+XSGg6wSMrnYgHIpZ6fOZ1Iq1Lax7qCh8Kd//k9tjmuaefjVB8q9uRq32og5cH488+1vvufx8YNJZUUhR6sOY4p/dEYh/MID19PzKykl2rbzKp+pREKwhmOM5loa4tkgwBSZ7s/0Vqmt0usfiAGmFKkPZ/7zX//Aly9f+OmnP/DjD3/Nh2+/4fHbbwl54rp39jq4OyW0NnrrbC5VgxpDTBqO+e8sEU5tIfVEU9i72YbEVGw+aghrG2ZySaCNxjwvTDkxRUGT0vuFvr7w5fKTrfNhvaHvf/OdzUoNC0xJjHZ+nV/M7bnbTFbOgRQTuSy8fv1Ka0bdT8Ga+70PAtaoF8kEMe25MQZtr2yjos18fAiH/YuRi6PYQGaZZ/ZqLLXehlWNjj222l1HMTDFCaIpEfQ63ApBKAHyZIFLB+AWIzEllruP1HVl31Yur8+kFMyB9cMnG8INQpRBTiAlI9mcAXKZTP19QCdBUPYWKXMy88beePrDD+7CrHzlA1+fK5dN0Rm2F6Ofa070NmhEmkyEXilRKQFePr/welkBSNItKYrCy+vKf/rhC613phTcjcCe3V7hNRgbNOdoXlC9Uxu8PD/bjE7MPJwWQh3stfPTl1e+Xi88vb7y8nph9TGPL3+AEj0TH8M8yER81sj9l5xRdr1eGX0wp4nr8zPX64XXlwvffrzj4dMj0/2Zl9eVr9fKdaukAEltyHq0nd6HQ0yRMhVqrazrlb5fraqKgdetet/FBvc/f3kixUyZdhvmruYpN5V7SnGYu9uwexD1+USDncu8kLIhAevrMzkLjEaZJj599yuW08K+ruyPyjxPNizbE9vlatV+tpkyVXeu3ja0bz5ob2r/qlY1GlHISBNbrbehWonBnNFVCTnTqrtFBKGuV3dNN0sUCVZNMZqJHWNec+qSa9HnE3WYxUqLiUPrMsZMb93uXRCyK2vklO0aw8QT5iKUMpHzZOiRIyMlYkmaCHVr9GRO4tNciGWxkY3aSUvidJqZpsxICXpzOnxyJfyZlJebJqZIhKsxtRGYcmIMEzwIMXIQtRh2j4YnmLn42IbCqB0rWcfPSBnH6x8KGf6iA9e6VS6Xjf164YXKrpYRnZYzKSViCkyT012DoClSa2Xfd65b4+5UiGGi9s7ruvH8euHr8zOfP39BY2LkiZB3tjZoTak1QLPyeK87bauWVW1C37pnYIPT3eRT6WpQ1TBX0ZxnRt1tOHMokQxi7DbcqiSlgGpEh9JGY9SV0Q0yyKWwLCeXeSrEGKA3Mz3sxgbs3YYhSzbmW86Tze9UM7fLQam79R1kANmUJlKwADp6pwmmddciGk1TfPhm0SMDDELOhRRNGqq3QZZu9PneCVoJORFzYi6mRKBADwPJNs805XiruEA8cJmH03J3x54TpSRiDkzzRJ4m8vke3NJDRMkJiBFNJsUVYnqj+mPKISEIyYNl92d33Vau1yt7uuOyrbxeO4kzVPNBkzIZGSMkRopQd0a0gd3Xbef1uqFj8PX5wkggUXh6fuVyuVBr4yrKMs9EMfhljGASUAK5FT9IB23AVptpGGpgHsP6Fmr0+OvVLGCul43doah1NKYUfTpvoPo2yBmSmPXxaLeZLTscI6/bzrpurNuVy/VKuk6MnHi9rFxX2xPQyQhBhw1XjzcIOu2W9G3rlUAzv6oQTAHFn68gdt9GY9BQ7fRu6ixtWG9GBiDBzTGLD8hGJCameSblhIg5Wss79GA+LfRm912icL67J4bM6IF5mqxaT4Ek4oe+onWi183cH8YRDIY5lDPBsMAV3b1XxyDk5NR1NWZucXZrgBqwz9M7vcUbBV/U9CQHQksdjQBKxCTAVKF3pQazzRFVI1k5ktFRkvi+itH2sdshLbMhNDnbmqzNZlazGLlJFfbY0JLIU2E5u4B0G7TeyUu2sYpS6GLD0KYlGZhOJ6JLPUWf50ICtQVya6iM2wxfcA85I9t4tRmt5ZCKsUFTDu/Eo/9hnlv/tdcvOnDt10rNG73uhCkREfrWkK4+PFopc4fR6HuH68Jo1cQrW+eb+Z67+zO5TIyYoEwsD4/M00KOidAHe71CMBuR6+ur6R6qItooOTHaoF43ri+bVRElc3d3z+QbqW4wRiKEhdPykXW80LeNuq2kMhkbUJU+dvNrSsGsLPZBjIGUT6yXbqoG04JMZ9J0osyuvdY3+r5C20nNgghiAqVRhESA+xPV53lKCtTXV+rFCBwjJ3AK+r6uNJSAq8/3TNsjw+QiXHzDBittZGiQT8WEWfsgYuSSum4kSebmPBXmZTFZrN5hgpCNcj7PMyGYkG3IiZYmC1yxoAKpJCQthPPM3eMHcp6AQK3VSNjB5+h8lm4MG3Gww85GIAIKIZDzDCEbZHde6CFRNSAkWmts20pIm6mb10jsZitCVkKaqL0hw2DMtXdIRqNWwQ7i0dkvV1JQRhg257KblcmQQUyzVcNDTf1j7NaTKQXNgo5Al8SKVTfLMplS+17p1439usI0E0Jgq6a/aRzORo7lZgMSWmaMZmrrKdJTNPhKIi+904Mw3d9R5kKO0bQ9q42o5xzYtso4tL2dYXlYjww6tdqA/sPDHfN5MW28F4PMAaZcnHRgc0zHsHwIkS7mPiAayNNMJKF9QkcnlmfSVDh/eKAsMzHBaCu1NnJMNwfgoYPaKkohZfv9re6u1qCuh2kB3WgQmbolRpugD8Z2ZbRGikAqmMD/QE3DyWDqFDxoKKghB4j1iWopxpjcdx8hsH1hRIjDcNJYf3AYPB/DzIE2DvjcyRbFRKF7HU4ksT4XURyGh2U21ZwUEpKzzRR2c7rYriu9D/I0wWJO6ue7O/Z1NeRTAqFElnkmp4xqJaZsrgejMc1nA0laIySD8yUEpiqM3cZZQqhAJiRMQCFaxTgSTLFw/jDR6sTdxzv3RATcakeHOhXeb5S/5Gf/9d/++kUHrnW9sM+ZJCbHoiFCsXmqGAMxxJtCQzxKd7FGbExmgzFPxTYXbpMezd1XVKnryiAgkw3YZre6FwVpwSzQgzDPhf26k2PkNE88fnjgtMz2wGIi5kjqgZASZZoYLoJ6N0dKSYwOMRZCLEjK5Phgcy+7qbTH4pJRKTOG0JpC7cSxY7P3kKYTUg6Y3jQLRRXakYYVVgABAABJREFUgGCYdgqB88dPjPMdfb3S2n6zdgkCWiZSLgYNovReaTnSNsuuQ4zoOFrMg147y/0jIsL2asPguOzOATPgKhIaI6pCLsmDk9lEdL8vqRQIGYmJEBJ9KDGbmHAYSikzqUz0YTYKdjAMFwSJLhVkEjoSjsZ2tIorZnNiTjbLklMhxUwMiU3xzxbYamWKJoUlvk6aKnvd0WbKGi0YUcHcqE2wdd82jCeGw3Xh1uO4QaeH5JWTWSKH1cm4DcNH6USUHMQa5WDySXVH+87okaHRIC3AT1269HfjDQa/DYWGEmNC1CqMqBBjYpkzU54IIvTaGL3emKVHHhDUiAmIDcenlGh1R3pnipH7hzPn8x0pJr58eabu1i9KZTKNRKMvMFqkdYMG52khF0uK7u4+sK9faaMxTQujdpps9NZI5UROgSER4u66jgE0MU0nehv0ZghFzAmJmVhmq+raavsYU/y3akhd734nzSfrY6k5OviJyv56IWarWAjmvTb6oG07aZqspzMac4mMNjOqV5HdiCzRA83tJtoDsTGWauoxMWfqwKssZdRqQTpn+u4yXQKqQpmLVWJtuFmmiR2fPnw0FuwwD7j15RVVpdyd7ezLmVwmrl+fbAg6meRdTpkQhb5Hyulka7c3I1woaFcfFDYSlYlY45AlbOsrsTb6h+/wSA0SQbszlGcjsjhx7OuXK6cPlenUKfxfyhk/e+VkWmx9dFrc6TG5oZ8FoRzjLbNBBvRKaI3YO0sS5iSUKLS9U2KixMIu1abmMWsKcUp3kG70d8WyiGCzFkQM9krWuzgthamYbtoYgylG5pgZcTClQszdRVTNSXmoTcNLOJsBX54IXYlpcuZgIA0cDoqYbp75aoUh6KjQQWJCuvWYgssQidrnnkLwLND6bFHMvDHWlVjdkwyQZJCVBHxwt3mTu5nHTggMjaDm8yTSKdPJKrfNSCoSQEogdp+NkkBy+wkQymSuqCEYCyqEYH2tnE0RwN2ctTaHJryiwRT5jal1VNRvemeqR+H1xvsaaqBGSoVYhTAiowmjQiQzlxOdzJInWlLWagOoRhTZiHr8flNxjKpIs/kaEVNy763TN1snSQfl0MULWGPe3p1BZi4JxBCkNx98x8gdCmHYmpxSsLXrH0yGQmuE7M7ebmtzrO1jgGAwMG+aw1bEVPu7wr52QxEkUZLPQGGQdHDTy+FrMr7LkIOnxhIsyCYMjp1zZs6ZnDKnMrFjxB6JCdFux6wKPfjzCzDF4D+T0PmM9IqMAfnd+AZeKccAIyJSPBjjA6+WlMIx3G3D4ylFRzWzBy3rAYYgaAYhURGzm/F7Iz5MrKqkMpNKoswTIWfbL72zx/XWRwq1oiNb4pAbvVXSsF5QiOGNXeiBD3zuPprqTcqZ4LCuAm3fKLMxPbv734nD5tNcfM0rKcYbQ69MMz0NV1sfeKHLfL4jlImQkukp7s2hU9NMNJ9C6yPGOJFygqzWD1ZQ6Z4AGgAtIUM47HzsM4q6LBzcMpwgmVQWpuWO4Pqoo9ss2RDQP5Ia/N9+/aID12lZCMMqo02FkTJzFoJ2orgGm5o5nowG+xWqUV0floVTGkzBmoinOFFjY5dGlEI00wJErVmdtEJojBH8gdj8h4jNE7UpUKbM+Tyb51Wy6uQuZ05xhhQ4l5nGQBOQoLWV2iqXtSLhW9Jkgrn9Ugl5clV38zky9vQghEJKM1O5Bzp9v9DHBUlu8T6aMZBSMiptEuacUfVr9IrkCGVi7InU39oYda9WFaDUMQi9EcJEjLsLrYJiShxjDGJWynwGhD7ZVH+IgTwlM9iTCCGa7huWpJVilddxQcPILRuFSMhmgKkyXAMwmivwGLSx0cUU6A6oIQaD49RVMIwCaZulD7OxyCVTrkLombpCe+0kLdwvD8iYaZPCnmiXq1F83QU4x4xQ0BzpwRr39EFmgCQCQt028hCyQOmNPkzkeEQbbbXKT4BOVft7rQZfGy05I2G3INs65/KBU4mUaEwxm0cCtkYq3QKIjNvsUPTnMnSgNCvA/RkmTGaqq7K1RimTqWuETAyZIJExTNRZGMQwaKF7AHGGGi5lFjrJG+05ZaYQWVJkKon7aWIX68uYF4J6VTnMBkY6UQZzhPNUmMoJ6QK9E7CKyLQehSiuM+gVfhCzfgnYbFlwaSsJ8WfSXCliclpaDN5TmxOM0RT7ezRiEW2zvwckTN57GqTpTC6RPE+U+WxfH52QL6SSQCHu1TUKOzIqbd9uupJEqz5wwknvRrNXEVJsN83KJBkN1vON+UKZDDYf2Waq8HGSUpKNiWDq7cMVOlKZiEN8RqwTJwt2eTqTlwdL+kRoUwVHkJBk1jWjE0ImxsXcz3MG8H6ksWFxyxVbPZGBMSxVLFkfohzb17QYJ8rSOQ1I02JjAKMxoprHUvy5kO4RxlTkVu3+fV6/6MD1/a9/zZIz16ef+LyuvG4rT1+f6HffcDrfMZWI7hdardS68aLKaA1QHvKZy9eItJ2gM9pXRrvQtxfaNaP5bAdoG2jfGLspqdcR6MNK+KHDzAnnmfuHO6OVpszr1ye0L2ZhEGyR1drp9UrfzdvqdJ5Yr5W+vbBePtOun9D7E8jMtr4w2tUs21NiWhZQY8pJMOuC0Xck4FJJg7Kcmc4PHC66fd9AB0ES0/1HtEPddurr11uln6YTdLPD0AE5JGMLoWhrSI2GxsW3w6i1aOrXIkgJhGTyM+V0Ql+tWhMRa7yHiIrNzaTshnc5GlBmqS6STVQ3JGOWHYdWmW1jSYiECnXfLeMWuX1GxGeuvDGgEm1n6GA02zeCzWbnHMkRYt9pl0bsG+cpoZJ5fX2FsTLq1YLACGwBLvUCKRG3KzG6qnitvDx9NkFjCbyezjyeCyXBqDvXejF1h5RJ8+TyXIHRrvTaqM2UF+q2oqNBMEV9kUgKM6/PM9vLV74oyPgt2/WKjs6+rTSpdkI7UUfcPVrqyuiD2jrrWGn7Ztbqsrskj7C/VrZ1pe2Vdt35cDdx/3jPvBTXPlxpavpzRhkPBATGdmON2dzOoNWNv/qLJ77/1fd8/PSJqQR6h702vj49Mfp+k09re2XfX9nrC99/c0/OgdN54uvXJ9brM5eXz+yXr4aSpGgZfW9ISogEd3OIroXZSFEoMThJyGDZ6FU6A4ZFcR8mDja3RiC0Qd0aoeCzUwMVU65QbZRlNm3RUgiTfV1GRALE7BVPiIzr5gFBCPEm52ks3WhDzAF1KyTTLo2TzSrG6PaRajDytEzkkoghmt2KoxIh2PsO0Rh+ow83ubXZwd4dkxyd5C7iqUzEyXzBdChpmrxSNrKXHQpixo6HAPDxEoe2JSAh3UwnDfbGh5gtCTW90ob6DKcO1ywMEdWdVgf7dWe7rNYHPErCP/LrFx24Hh4eebw/Ue9nzns1sdWXF3KayTk7QcLKfO+t2mGhg3IqjNq5PF+4O088PtzZz5SZ83lhOU3M08TaXkEPKSRr/ooKSYAQDXKLiUSklMIyuUbcbjNTMQSiGMtu1OYUWogE5phYNXDdOvvaTdy2gY6IjuB0YcsqGVY5qrMSGZ08z85GjK5an10VG8eojeU36urNZsvYPZUHOAbgEbHNcIioBsEcdgOgAY80SDRbbg3WzzHKoxKSELIYjMhwewVnXB2/Jyj4XIuB+Va5INyEjE0sVq1S9sYu6gKlw6wYDkjlYIsewe7ox1jWZxsxSCBiCt2nIny6m5mKWUAEbZymxCnBNQ4uWSnZAmaeC3Xf6QR6DLTmiiclUB5PzhY1WOg0Z+YSoAfialYwICQZ3n9VRjCHAhM8Huyr0Gplb43LZSUG4TxHPt6f0DbYrxsvT88o1j+5OxcoGXXYN/mhkqMQQiJhsj+gtC1R68beAruLod6dJh7vbBj/5cuzVZWjEyLkLMgwcpP27vfNkhV1sdkQIkffXYDr5St1q1xfr1bxBFOSSEFo3aooeqOuL2zXV7btle31Qr/f0LnR1pW2buzXjetlNbebkpBQCGEixEJMRic/CAttNGKYCKEbZH20OSWZGPBwzQe3WDkCga0LS8AYh+ccfiL72ozx9gcJSDQR5aNPfhAMbn1LUSM5gEG5Ug1OF8DpLYrcNCWtdeSuCI5wGPzZb4PKNszv8HQ0KxAR4diygM+R+dwj3XDYIEgMb3ub7m023zccCW53iN1Ff/1ZHm7V3PaaclD2Bbv34ozD0Suj270xdMYq41SK9cd1JyUhl+CJ+88rrj/W6xcduJZl5uHxAe5m7hRaH2yr2WhrH4g2tJxs/meazExQD8q28vvf/8BWXzktj9zfn1iWE/N0ZzTrEskpstHRYRylfdfbkG0KgkSzjLgxhCRQcmJ/3enN6doxkBPkOJAxjFnn+H9KmSaR0QK1Qq/mXWTT59kObzFoTnulbhtah+HlJVOKmUqqd3VF3qmaJ5u3GL3StguqYpP6gRutSXHEziu34CrwJvlpkEAQ9VoLQDgsdIIescd4iBIjMVmAt8DlWV3wai2YncMhJmzGcr4BcDUAwWnu73qJmC/QcPq4+Zr5jF44oHb77ATfvDJuw5jDfbO6mL/Wx/NMnAolBbTtTEFs7i7D3RxYTrZW5tPMvpmMzbUnLvVihn9zptwX9gZ7G2x75e5UOM2JqMa8qrvZhySBFMyPq8dk/cUYCUHZVwsqr9dG3yspBB5Phe8+nNnWxlPtvH59Ic+RlOHuPKG5MMR0KGMQUrRgQXY6c0wGW9dM3TOXrfDy/MLog8fzifu7O+rW+EO0IV2GHXA5QkyJRGJU71X6ATeCWVXkDCUKKZpQ8O/rZkP+60Ysk/mqDbNcCaKIdpIo17GjbUdrp60bfa/G4KvNq31M71LME8oMDxNQDEbW/RZE0YRQCFJpbRj0HcV6db1578pg5IOFrerBCSPujIMmr3oj4VjgctPQ4NXJAZVKtATQ94xLQhsHIyWDvsYAbbzPxw5UI7hU1HG9G53c+5A6TN39pvn07nfLbV9zk5/qbbfWAcAxN+m0eI7kk4bIkeyp0bdG9+Blv5PxNkB93K9jnOf4EAZHuwiUb/zequ3DEex6njjmnI1gpYNUTdg3xMCbeoc/j5/929t//be+ftGB68cffsfl+QthXBmlEErhPJ/IU0Zbp287Eoya25uQlrM1qBFe95VBZV1f+eF3/5kUErUpr9fKellJJTCfMndF0OSme7WSo8GBcyqsvfH68pWffvyRujc+ffyW3/7mTylz5JxnltPE48d7flO/5+FyT0+J9UUJdOYJkiRyemA5B07398QUzS6lZNL0gIjJJT391V/z9ac/8Pu/+SuoifPdHR++/cQ3OsglktzattfdsqnRDT6RQIjG6bktViyr+9lL1YSID6ou5u4sXjm8HQK3/WvLXa1SkiNT801n328wluftoMF6W15ZIcEyOP9+5wK4zFQnxMkwdjWFiOHDopIOCr3N/RCgK+4bNZB4VHZilhs+z/Tysrva/o6Gznr5yvPTH+grtL1yXgonV5NIOVNOJ2h31AFXjVyfMjknUy6PkXUYmeP58xNTtmFoRiNKQ5NRsqelIDqsyhfx3lMyWPnemGpVAtvLtwbvpMh5DgZTpcHl6QupWvY6pUgo3veJahktmIr4ANXGGJEyn7ibT6RwpsVAva7oGO4kvVBr53x/x37ZUK2sl2fiaISSSNm856IAQ9muF8YNNjQK+1RssFp+8w0lGTlD/eQbY3A6T9Ctqs450K7fMjC33RwiHz594P7xgdN5Yd9/Q10vvHz5zL//t/9f1vXCl6fP/NVf/gdDPObCKSgpmQeeaGBfN67Przw/vfLpN0KeC2k+mcJLb/RugrQH6001vMkySaT3inZLzIaa7p6qQbsGORZGe6tOzE/KK5R+BD1T2zD2qw3ZWi/dnYoPmrzvNtWb+uat2tOjsXxUfz+jjJvZp4To+9a/zX9k9OHyahDLRCoTqSxAcKjfGLUagN4N9sdg9pCL9ZNDRFtjeFDHA9dojTGqw5PRXL3zYu4K207bt1u8GTqgWV9cXZdxqNA7/PTjE59+s3Hf//7B6b/2+kUHrv70O0ItoJU6FciF0E6UlI3eTuPy+tV6UgpPP6oRJ3KCvDDlgZwD16ff83K9cll3vjxXeu+UKXFeMuFxJpYFSTMpJagXelWu2nm6rLxeV75+/cqPv/uB5y+PaP3Mn/zpP2VfbIL/609/zfXLD2zrlVps6l+wTPPx/kxeEqfTB5ZlprfO5eXVmsHBejlK4OnpwpefvvL1dSOFgbRK3nfmLz8xn2bKUm7WLUdGZ8KewZqqzhSU5Ien22MArirqe6dbhma9o2h1UYfQ38EKBIMxh2XlwZmBNrMTGdEyvZgdJggHZIc14qNDn54GB+dS2DljYqEHxGjxcJh4cTP1gsiV1ppvNrX5r9qMWIIguoNWRDAV/6H0Hvjp8zNPT6/sUmnbRnj5AjlwPq1WWaig/cwmkQ14/dxsTk4SO5NBP22wXztNlJ1II1DCoF2+GIuqV16uV4L4QOwabonE69ZNSEDh+iVymosNVM8PTClZsG1XXj+/UJtSZPDNp0zdL7Rm6vbUYqMDMaFXq2yh8/qspuHn115md8w+3fssH7Be2OoTbVjL/f6cGGOnr09ctwsjBDRGhEAc5uLb+8q1QlNAha8/2UzR6bRAzOh8QkuhbVe0V/robNX6HTEEdDc9SYOhAtoar1//QNteLRC5nmWZjcq+byuDzl/9p5UYraIufeN8t3A+nyjlxOc//IHXp2e2pxfOn77jpIZM3BA8wCODHdbBxGZVgtHR14vdtxjfXHmxoWKJEUmKdI9FGBtUb0wYdcNJDLmJB9TuXztgSCeEimIVRzO2bUiCqhFiDGRzNqWrnhxVmkr3BAyOYGT70pI++9ZgyEYYEAYhYYnhsKTQ6kLx3lT3TaYo3eFR8fbDMSTsCWWw3p1u3GrLkBPSqmElvd+Co2DP0O6TVWgxJFKZWE4L2S2Y/q6XvgvEf5/XLzpwSX0htYJqJ6SOxsHowtDNeyWDsTcTye2d675SSqRMmfn0DWWaiBK5/nRhu/yBy2Xj8rwbQ2hE4oisaSa3e1JpxOVErRu172i98PK6cd0rddt5/vp7RC98PsG3H7/hkiN1u/L593/N5elHtn2jz3furNuhNk6LUlIhxUJrK20dXIcyn06kGDzwmIng63VDQ7JG71QgWVO99+6mhsHwO8eqQ7BFZa8jC/NFZi6R9r3HujJk0eOJmCeTilHWD1hejJKvrqIhQMzh1kQO3RrAGpy08a7/dGhZxOSSMjiEEo5qS7xHILfMUzwVPLQWx+gg3YasdVhmPwrNn8EgQLvA2AnJoDhjFiZeX7+ytZ0wBca1sdeNy/riMlVWQWprqDPt2vpi6vkhM+TEtMyMVmn7StfGiAWNmTIvtO2V3jZar7R1N5WOWTC3R8ve63WndatK+qWh55l5ORFUmO/vnIm2sq1XhkRyzMz3d7w+X7m+Vq71GdEMPSPLhNZu/Vbp1Eujdct423UwlolxWlhESZOROPq+0WgY4TyxnO6pe2fdruzrFzqCunpF7M1s7KWxrZ3abS6MXtnnQm8LZXowSxwmtpcneruaKkkPxDKZDqbCsiQ7ZJvNxG3XZ15FKFOy0Y+YaF3Z1gvbekXDoF5fGMMUN3K98Pjhgf3xgdOy8fmnL7x8faF/vWDoYHxXjbzBT4LPzAX3gVL5Wb9FDsr9sT+C90+D98c4gtdwnPGA75xWPhSfAr+BC9YK9orpWFVqSiHiw/JCMIRCHP1w364QowOQDv0FSzYPZOPoyUkIDrkb6QtXkCHYfKkJATu0783lcDD1nc5+RHiD6C3A+00h+Dykylsv7HbfvKo+kl5xVRgVq6iDgjpZZppn3+v/jYf6/5+vX3TgOn+45/wwoXXn8eEDYVkgJbbXF3rd6HVlyYV5ntEYaWsG6YSgFBpTmRklMd8vnPpMyoFTOSElG/TSNl6//kROV3JeuEwT6+sLdbtAfYEwEdLE3Tzzm19/opRICJUvf/Xv+fGv/iNrH/z+b/5PRI1llec70mlhaOP58oXXlx+IoyOtMVIihUwWY/QsWXwANvLl6YkyFf78n/05d4+PLKd7TnePlDhuFPi37aJv/SgJNnsWbF5Hm1N3b/Minvngi/Cw4QCDWYYSYyVET40kAJlRQbsgCWO3ReP31/Xiwcrkto6OuM2AiUsvRSCDmomluurFQdgwpXnvuqsg6qaJvRrLMmQQG4iWlJEYKPmE3D1Y8OMDgvlAWb9NkJQpd/dcriuvl43164VYImnK1HVjb9VmlGIllhlFqXWjbzsQCbKDzqZvtxrEnPJMLjNxSTbe4JDQw5yJOVOKMDbPREfn8Tzf6M6MSspCykKmIdjv39sKMsgpMU2F8+OZUoQyJ/jDRoqmMiIZejVVAxicSkJ9qF17JyUB6cS2IZOgKmz1CjQ3GoyczplaABovLz9yTBcN7ex1hVbJEbIaa7MHGwMJoaNjZ1yf2aTRWub1y4+0ejWIl0xYTnYwbhvXF2Hsg7p2ajClh7puhIjR9UXQXPhPf/mXSAj89uE3lDLRqslTLadCztZL6usL7frCfn0FGuIzlGAkpgFGFjqWsDiBKASH20zCSUR83il5z6e9I/pga+zoZAVxcgKMlIitEWxAkhCMJKEHecPX+rGHzFlcCc6ctYN8oosZEoUQvLKMhDQz6sHqfGPN+lZ28oVVUOLwbV0vHNJnImqJBwq9wyGLhiA50Ju5LFg09j9gfSqORNcJKiKIims3tlt/7DZgrcfnT7d9qurHg90y15GU2+/5Y79+0YHrV7/+jk8f7xASsiyQM4TC+HDxTNc0wlUShIRq9d6KUEy/hDGUx7s79v1P6G1QG65MXuntyn5db03NUE70ekFHNbuOINQxuO6d34Y/5Xz/wOOHb5CnJ56eXnh+ufDpn/8Zp/tHyjSjQcjzPYjS24vNXLSNvr3SNJElkEKEULh8+WvW12e2Hc73hbv7O77/R782Ash8Yrm/M4mlujHaZsOc0XSZQserIG/uciAICj5bdLwchTfa7NBb5npkbkECSL5VQSoRGdGIGzERghEDJBZiShbQAjeWH35u2NVsgBqx7FJclumAEw0GeQuq4MaY54UQLQiUKfl17H2KOfbZ+40J0YpoQ0Khd1ACpIlv48S67VyuF/h2EHMhlcIkg66mC8eIxHICCfR6tTkcFeuTxGxkn2HNcUKEmJA8o04jH0OR1ggxkacJaRtHBVA1oMEOHKHeZJBKMIFgy+Lru/sWKad77sYDrVe++e4bn2ESJBVCXS3Ai0F5KtEPru5M10gWLBkR0Lp55WvXn86PKEr/ZuP+w4PvF0VDRNpqsmMIDVfhGALazSV6OhF7hWDEF/0w+33ClNTj5BXxZs+3GylphGSSa+7hhF/7sg3KVCjzxJ/+k/+J0NfbypT9hVFN1q21jV//4z/h+w5Sd779za9Z7u8NousmMK39WG/+R2+NWwvOIrdAY8iDWoIExm3vVkndQESLhj44fpiJOOFnHy5rBDqsmlINXs3YdW0RBUy80FRhVByCs91qzNdoyZwvRLTful3+894fNjcw+7mQXXAhQleD+MQQDzkS0oAhHEFx7QAL0gep6xaN5I2gIlY1pTK/DZW7mlCIgcMpQqLboAQIGonThA7r3Z8ePqBijue3PsO71z+0EPtFB667x0fuHu+BjJaMRs/mi2fuEti18eYT043WGSI5YMyk0ZmzZaWukYHExWifdeXy8kJrG2M0crlDxwJqlOEm5mEUX1fuz2ceHr/h4eN3rAG3Y6/cP2Qev/mO6XRHp5PKvZN27iBm+n6hXr9y3c3upKRMLHcEvYIM2jrQWJmXzDQVaEZhDlHQZgeXQR5yW3TWMwq+6LFVoscGtbRInQ5/bGq/YwjG7juCmM12+DKzgu6Nch8sCHEESIcrDZt/ly4ef9RNIHxA+PYeCe+C1RvceWDvIYhZcKD2Twn+9903kjDUWUzeyA7B4BTF58KKkVSGdnJIxDyRy8wc280KBo3EcgYJaF8Y6uobQxkaEBpBGyoJDdHUwCWZegkDJCKtIiGS8kzoyW+Z0CQwjsAl/XZ4RGs++t23ikpxg78y+70c5DwhnnRILKQxeeASlzMKDBJBhgWuEIk0D5Qgww5NxMSH03QCUVQnQoj2GXSgEgndAhcIXcxnrXZLcHKZmeYzUi82ZNs7sXQkFqsCWqWPaIkeBSW6qKvPTQ2TmOrNYKXWBzyv9HbPPM88fvhA6FevQjJ6ndmvL2zrhX2PLGmxWaPtwnRabE6rWgXDMQzsawrU+07d4oGjEOIV0xG4xNe5Vc1wa0YeryO+dQ9S6maQR6Ln1zZ1mzckA098DEMMHoDAdByDJ3P2d0ZQsr37t8kY7/eSKq6JaLvVBrffoPeDRfn2U+LsQGPYmhKO/Q8BeV/aie+7wyMsJh/2fqPxH4zKtzdna/BQMOGAU0OitWGSUeqVNT9//UOC1y86cJ3vPzHf3dPqzojBZlz8gDxYOjGYnYbJD1kmGqMrW8gO1R5TKsXoymVBKYze6PtEr7sNxWtmOt0hOnFMyG9tI0Qbxnt8/Mj9w0fu7z7QTz+RpxdSMYp4LoUyTaakkJxuqzMxZSqdtkeu+1e0LMQyc3/3iLbvycvEfN1Zf1JyjNTrldGquaSWhX75SsrJhE5T8UZ0gGiHvI6D4eTq7tVZgkcWB6DNDyzfuE7u6L15Q9iHesWw7dE9sPj0P4d9hHbvYr1nUMnb5nUGodwODMsEbxtxADq8D2C792j6GltSb4QNwSSntDeGCBzmgKPZwOOoEE01Xb25Pdqw4fPeicWUsnMp5iw5XC07FWK2HpwGJ+6PYaMNikk1IUbXz9kSDxXqZjNmuQRT2w9m+Ed/O4VMwcBp1CH5WIUQ8Zkc/2OK21Y9EoM37SPzZAQM1GbpRI/7ZDqCneBfiybOHAORaM9gOLQjltzEPN1GClQjpVTvDVuwEiLiGUCMCXvkxkorxYgfSKNuK63ZgGlKwZX5A9veQIUcJ47Wk6AghVYtOYoZYjKjz9fLagnTaIz9YvNWx2xSmSgipDIxjwaSGarsjnqI6tv8VbfgK5QbNd2EAvZbkJFgfn0Sgq2VgxIekiViBBu2vvVxwAKLuXCP7rOOvAVJ+2e8JWSquJ+Wlz0IOuzPOOYR8TfogeMQRjhguFui97eDl0KvHUNO1JJyl2nSQ3NyOENRPZiNdiNTHVAg3vOSd9c+vtfMcZsr71iFxwDtpk9qwQ10dLru9n0hec/NioDrVu3vQjGFIf4LLvM/6PWLDlxlWciTuXL2AENMEZ00c8BfQY7MW2y40WmmOUZEVlraDbv2RnFMxTN4G6q7i5nedoZ2Usk3vLf1TmkN9p3KhWn5QMwnc2WNkdOHM3GBaQqU+3tCWQxqODTouiAhk3MknDN7i8zTidNyT4qFHGcapgai3aZH9qt5ICWJ5FiI053Zt+Rkenq3eZGMNWJNYsgWcHBZmgEdD0qCSDZ8XLHGrWIb4QaxCHZqB2ex661qG8OZSxgEJZJ4Gzo5vmabZHiP25I68X9Xw3X8sDxo894dxrJUv1+je2ZrzX9EGdIZdXiFIqYx5yCMjncDqEFIJMyHCcJmG8p8LQspc5uxSckq8uaHiqoSkgmuHnCPBFOzlxgZQ4nh5BmwSyfFaDN6NPv1IbAf1RXW04gh2oaXo1IwhCDEyXzMQrTeiOvHjVzoLTO0m2Zks+QsSGCEYHdPLVE7Kq7gzr/mnRQ9sJiWXTzGJMCU2/vO6F65jHzrFUk0hffUdlKcSHkye56gFsyCqbOkcjKx2zGI2cYy4jEUbg/QmKdjYQwI2dTde1dqVUowibIkJxIDaQqtkcLksGxGRbk8v1LXjbaDNnseMQpNN4JkNB+TxscMoCdvXoWHEBiODohT21X7DRJ+kz/26sNnlQBrLWj3a3ZiMjQi+jXVE7BwECjUZJ/kWM7ie8BVKOToLTlpCl+uDnXYHFmIjKZot+doyalV8SGY1UgIR0sg+Nu0E8+mAqwvpt7XCsJbf8+DzBEw5ejZBUGmieiBXlIklWSknrt7Ui6WUKkjB8FIPTZLlgjAcr+gZMbfknz6Y71+0YFreMMwxmyDiMc9ulW+8lZtAQSXEDoOhpg8JxVTEA/Ry3U7hDQIuSy+eRsxBrQHNETTXyMSB4SwO1QSb1JAZZqcKq4+k/H2AC0rwlhxQMwT03ym5IUYM3Vb6fvuChy7mz4Kow1DPX2x4FRfxnBpFXHGnjhkeIztOyUdo2ePm0juEZTel/1230KIXmENh1be7ukNuBDeelI+l0V4N49yHBA3wP1vP8G3LBLPbI9Mk3dwhgWww7TOIU//fer2ICrGItPhQfKAO1GTXOqWMWobaHoLyirmCxWja94l90jD7s8YgyEmGnsE1MP64RiujDE6FMVN6SSEYM9dxK3Vj+Ta4JoYDy264/B02MrHBcS1+8R7YTbqYMmFyOEJYLJGTd7goxCyfZ5grNChDR32LH72vrm9IZvrucFG1l+xg9XgvKCDqN7rkPj2vL2Zb9isK04E/z23Q9kJAx64ZFi1GFJ2u3elTAt69j5UyITg1QRqfdPdepCHQWHfKzLeJTjKz9eaV6Jv6/ptuR2fXeRgGardnyNuCA4bvilsCFhidDBkb3Nifh0RV1sfnggYdA1YVSguyhwcTnzXw70J6x5vGcGG9Y/ncZxj77eMvF3itoH+NhBnX5JbtvjzLcftJ/xnnWovfnZIEE/W/Nl5n9qGii3lGWMYMuGCxm/4KIaUqAmg/52eXLdq8+/3+kUHrrZttGpW7TH5wJ0e/QiHlo4toGp+V0df5TgU1NWQj8rALSMGVnVJjASysRv07elHAaIQYr9t5DGUXm2GKJWZqBO974DcoAoLWoPWKnRTZ84pMc93xFCAwOX1D+zXZ/b1lf16oe07kpM1vr1BLBI84zdTO90vhOzWKMBNsEU8axLTCNTekWgOt/ZR/HBHf7ZRYoomRaOeed+SgTcoMLjigPWTvOGLwVG2fuV2uHP0vcRZ+9hhe3vp+wMHP4jktq/UKfDj2CBipA7V6gKz40aQGMOYif2YN9FIr+bK3PvgFrc4KOA+fBoCMc8gwYRsAaTbQRvaW58kWBU4jua+mKZd10HyQGNfM/WHkCLR5RQs+CRPnsQb/m8DnDc5rOOGuytAdBUC9UpYNSKiNi/lQd1uoUs0RYMOg1O6zWbG4eAxnO6M7YkQkOg7RYefVcNbicf7Tt7zM0cDu+cCklzZ5M1ORUK6BcbDMRe1wPV2AtvwugJ5mq0q6EffxpOiYDYsre70YbBea7upsqdsn0X9efsePIaFDds61FreH/RH/5tb4LLve7vlIUaXVQMV+7uA7aMYDXJT6W9rW4QYEwPv5wSDgDWIDfGHcesBHfqSdpM9ibgxEX3nHj2l2547qja9/b1d43juDmncZlvegq5j8fa1Y7959XWcR6DuVOBdN0+yTS7qTXHjlkceGaL2W+tAfLYTsa+1baczbPb1eBtvH+nvCrP/Ta9fdOD6m//z37F++UCKE+HhZOVtyLTDdhsM0vFFve4/Ik43L8Vu9OiddVtpdbz1XoKpLuy1cb10M3dMgRRNpdmkcgxTNk+eRt+aSeBslevlB3vYIiz3d1ZJhchoHcX6R62ulGlhXhbOy0JdL+YonDIhQM6ZkQuMytOXF3qt/PTDj/zqu++5f+20XZiWCdEKfYftQigTMU/IBBKNzjr2ikSrxlAxB+Y+HAJLtLbR60a7vlo2HwJpmoh5vilsd+9j+Bo3soYIDCH0dlvE2qofrBbE1bNDUz04+kTdDnXwA2Py/ebZp75t4UPKI3Y1UzsdFsBb9/dgKhP4PMtog0C0hKWbeoX/IlTMTmaEQEwTYyjr9YJKobVGSjCfZmIwyEv76vI2mF4hmeHWH3GYZ1dwncCIBbG+N+K8ONkBhvRbJRCjOnyEHb71gDcFiPQw2EczWbFgg7utD8bYQDdyyjYwK4J22BVQ41eaAKsdZrVVhnSvDoariA+2Xs2VGiFHCNFm3Hp3uNGD/+hK8wowIYTQ/eCM1LYR2AkSkWB9oKGR1gayNYTmWoHR+4o7JRduMkex0JwIk6KQs1VnaVogTqZgXjc6ie3lCy9ffuDDwyMpRWIxJZg8Z7oLTMMAt6HptdJaNSsedTRmtBtsh1e+R9+X3n0NOyXpsDgZJsl2FCodfL94IOSogI7vP6rJcKssj7GotyonYT0wDzxerY7ebtJPhCPxMLhtKEgft4B29Mj0mNs6kkz3wT4CEAiiASF6oJNbBWnehNHV6e3nDa62nxX/ugw1/cfkGYTqDbJOZSKIMXtNgOBABAohTz5T2iFGYyZO+XYv/5ivX3Tg+vLjj4xttwHe58JIiSCR7qlEFGW4EnIIwrrXW7WSo/og7WDv3TXMDpjJsrjeB9s+TFHAh+m0WQM48DZFPrQTw0/0Zt5a+/50g+KWu4WcJyOJdFOIPrTXTncHi6nz8vzVJIVKYS6J1ge1m8p03RvbdeU6KkucGN3UK8qUzHxQOqHtlPMZndUyfX3bpIrQ9516eWV7/uqZciTPC11tHkiGuQrLEHoQUA/szSwKjsU9uh62rjCCQzh6+17b4PFWAdjmdgNOfK7Gg9Qhnip6TPAfWaxw8HmtKV792tYjvFVfnm2OYc8xBlc58MPjULoniDXWXTqqj2EHVh+s24Xr5UJtjfsPj3z4BHle2Lux31prrHtDxOaYutWtSDDfs5QnAPpQ9m1l3XdQsdEKz2SjCM17PiKBWnfyMWwdA0ESHahqZoLBDyGRQGvDAjXHvBCMrqzeNCyOMhwGlvvebhbwOMFEFXbMoNIGYu3+tz5ozSxAFDGii5oXm2LahIFhlWiK7Hu9fR6CQ7c6zIj0OOyHV1ljMNpGydmb94GQiq9pZcqR4J5gvXeuroJfonB3XgAoizFvh5pDcd93c+munTxnCxNebRz6g+pEFAsaFswPWbIbdO73xDQIPbdxCRcdWEDx4t++x/dR67aO9B2zTg9qOLf9Jkd149VgCAeaczBhrZrUPuiuCylivVqCuj7icR1jMGvvN+juIHfYNo2234b6XJ/DG/pWZY2x+/syJAXXANWjGvMPq9j7Hc1cEA7rmBviwfB5rjfGr1Xv4fY1VVPK//p8oSxCzuNdkH1Xad2u8fd7/aID10+//5H9shFj4hKhcsiPCCJKkME+xAf9AtvucicoMtrtIQyyCcSq0HYQMYkU1UAbnRGN/qyjQ29elncOt0/ojKa02thrZajN70iAabYBSsvCE8uUyCkby1AD27ZziYGfPn9mypFlnvj+00fqdWNbN6oLua7rRt9XvoYnerPFlYKagG9SisCN/p1moubboS4Ibb1y/fwHvv7+b6x/kSam8/2NvjoVh3QEYzjpZlbze4U5eUCxYK4OwYDL4wi3DX+ImIIHHbUK5ZhbUccNgwRws8vjEBjjrUIJvLtGPzyy7FC/ie96ZBzDqsIeIzc+PHKzVJAQXM7q6O8dcGRgW6/88Lu/4fnpifuPn9iuMJ3u2QNo300I97KTc6aLkWRS9KwdpZTF5wEH23ohhczoyr5VqpgyfAywVhv+jSHQaqOkgFlumannCIEeInMROyCrVYj7VqnVFDuaJ1atmcJKCDBn6K2bZUyM7FsjipICBlVZI5IeMssUkDFo1x0VC1p1b2ZTEZJR4YNVmIgwJzvXQhByidTd/LBSxPQH/fA7KqiAoFWI2c1G951yEExiIueJrUMbymlOB2JFbYOvT58RUR7vz/zpn/yGZZ45P34y2a19pW1Xri9fWa8row3y6TCBxA5iD1r6DjY8YDztByV7/AyjugUuOeBfS6B6a7cq8X3g6rWagDfcUDkblTgS2XFDCd7Lqh2H+vBEGcWIen5tb0w4bJtMNKCLQbKuGahjGLqgoKbBZX51w1iW0odpLIr1uQ+qv2KD/sd6D34uMA7iSbLxGYBha6nVSqs7qUwQHJL1fvHwfXTMwuGBi/B2/3sffP36wh2ZZfm7B5D/h4YK//X/59/xeP9IrZ1VzKq8D5ftUaW3yufrxfoqAdbNzNdG71wvr36oRFK55/HhnoBQ150+qgeuyMvrV9c1C6Rguul2UK6IWkCcSuRaB1ttbPtOyEJMRkmeYrY+kprIxTQbczGlhbu7BZVBb5WX1xceTye+fXjgX/6L/4X2/Hv6dqFL4oc/fGbfdkrJ5O3CJoPL2DlpJyY7NO6niRETIyTCpLTqw6/aATOXkxQY0af4izBopOlEnmbyebGBWZ8NMiXqSq1XUj4hMXmtYdUFoiQ6Gg/WmH29j2Z70WExBVpv5ikn5tMVggewd3/0eEjHJvf5EAmBfD7R8cAWfDs6YSTkbP3Gm2abQ4U6CGoKCIh5KpWpsJzPxMePhHIi5Jn5ofP0deWH3z3xf/y//hUq/5q9C7+/dgueashPyjbELiFxKoFtu7LXnQGUMlkg7pXelHWvPL9eIU0EbYhWxqhmgxEibQg5uJitmMBrSBOpnHk8Zca+sa2vtKFctsZ123l++kzTQ7ddIM/EECjS6duzD31HtqpEEes30Ux+KmZiOnO/FOiV9fUrdQy6BqfzXy2ZOUw900yMiTlCqysiSk6RHizw5igoZpETgiBxIQb/na3dKgxtw++hPatUImG6I01nPtwV6vWVuq+s287z1y+clpn/+c//Mb/5019x+vjI48dviarU7cL2+sTz19+jaSA5stzfm2s2g1qvtLrZ+lDv5x1rRARjy/rBfgSrIBZ4wThJbdzYg0fC5eXDWwGHDWmMoT6+dMz5mUswvTs9/2DUemX2bi8MtQAnquawDhyzVHpAft1ZjWB9xe6/T83FPNxU5uUG39s//L7r4GCqCbxBgHL8PQ6f+nzZ7eX9tjjofRDVmMIiiRiL9fjSGW7zm0LILnYdC2Z2NpA4KCmxzBPzVP7Os/sfihz+ogPXv/mP/4l5+oG2D2oKdLGD+hiQDao818rAcOGtHuX74HK93ggJIX7h8XRyDBz2uqHObNvXlRGs0WourNbcTcewZjBJnHWYpt7oA5LczuQkkeiHbBtCTpZVSczcLRMDpbZG6w2++cBdSdTtFXV4bOuNbd1ofbAsHmSmiVgKcVRS8GzXA8ibh1X3SmXzQNTJ5zPnx4/G/EoJSYU4zcRcOKi0eNPa4HZrEoe/1SwGhwe9OX1M598GG33eCoe2OP4cWPptqPkYpHyfCb8BCkczO5WJuO5v2bFXERDcg0wIPZi24OEzBhyDlG/wv11vmhbSyQ7QdlIeP33g4ff3/Lvnr/zF77/w5XXluQ7i7XdFSj6Gd4Us6gK+llXHaJ99tMZQqK1z3XafGTITihjF2YuJAWSxddRVIQRCSKQ88zBntDdaM6+utXa2Wrm+vjKc9m9VdSKKUIIy6srRSKj9zRW5HdBriMRYeCoZURMtbn2YRK9Cq67Y4ciEhEwMgTmamy0Y3DUkEqOQQ2B4ZSsiSCrkYKMo9pgPjFgpRkdDgZQiml6QVHgqibp7H5FB2ysgXLb9RrBpvTP6xtBGiJHlfGdzdV1vlh+35SLHQe/WQfJuLb9h1m/QoFf2qm9wurURjrf+FnB9Z1my5SaQt0HcI+CMAzZUoh4ECn2rwG60BrlVN/Y2DmhcDN52+I1wsKGPYGMRUoj+3g9iitklBX2DhW/7St4FOFestwrOQvYh3HuD8OVQpAmGtrRKqAmm+ZZg3ogj/hrDqnANB5Q43qre/woc+D90xfWXP/5IDIG+KyMFNArBtb6iCFmEK46D62DrThBQ5bJvPpipKK/sl1eDclJm3VerGhCDB8RmxIxYYAt5CqBORxeU/Vhc2BzEsfBFzHRSBLpGc7J2qvqlZFcl6KQU+ebOVDkYO0du3ZuRRIZj9zHZHE6I6QZDxeh9BTnmQXxAdzTavqI+WxRLoSyz7fMYCWkyJmI65l54W1G+4H9G1/WvH7ne7etH9LL08rawVd9+zH727XsPqPFt5ssrCccOxa8TQnB9uYNJ9fYm1aEd+zmnMB+793gWx9eOHkwQUi6UMpGXmTQCp/OZeZ6o284PP/7I77++sA1IfuhLypSU/VBRM+m0u8xxSOhQszhH6GNQa7v18aJALmYBEqM1OJIfuPXwRQqBlApbNuLK0MFWK1vr1NZNgPYmGW7MzShCCw75+PNrKm+BazipAJCQ2FO8GQz23ulqgcvMTZ3eLpaEhCC0KLc7jQ4jtoRACuIk22O4OlNiIPkIgI7mHo1CT+lGpKkVGjtdAq9iUK4yiMkEWmvr1NZuh2bdrqbi4eukzLMRavRQyugwbMTjGEa+9YAOry2/zz87KOXn/3qbodJjlfytlxzr+vj+I0E6AtKRhHE7X+Top737NvnZ7357R4aeBw9yx8+42sQRSeG2tsWhXLr39n427Px+/R/7zYPIeCeSewQVPapSvKdmb3L0Tm+NGOvtDPjbQevt9bbfjtdhwPkPD1F/9+sXHbi+frWqqemwodAQTJ/N3UBFIEzFFv4YhOGlPlBSMkZSH7SmvF5WYgpM04Rqpw2lNmXb99ta03FI9cAOxNhMqwurvmwb2+yJUYTtUF7VZlqmKdP90KitcrkaNKeq3N8tlBh5PM/cnRONwEYkDmEfnX1vTOuF+zYhfRCGwDx5tS+MaMHVDtRqAvd9p25P1KGkMjGlO7Z6taoQYb53p+VmP8XRm2p2GA4d4LT44EEnHAHF/513GfWQgzrrShb4P9xUUpw2TziyRlc+CEAUU3rwDXibswniWXajt91QlGCHR+umZtCaqeTnbDNZ9t7G7UwRsQolRCElbL4ugoiS4sShjJA1s4TCXV44xYUYBhoSI9qzUTWGno4LMSdiiuQUuFwq67bR2zNBEiEMY6JKtgREhvU13fNJw3SjVcdhrraoEKRQe3dL+kzJga6D1juESgzZgsFoRLLPeSnTNFtgGYPoNPkgSsYYjGMoRpgBgpmdooM2YO8CXIkxE0Nm9N2gIe8R5WxCqr01TFGDW+9MezNh3ZBvz/W0LOzbFRmQQybnQC6FnCfavvK6d9atc+1XSonkHEjF+11RaH2nriuXpy/Uy1fmFEjeD8x+v7Up/fKV6sO3bauoD0+HozrszYgN6uaQTtQ4kkodh2rE0Wv0NYcRW25Vmojv0SMBsBGD8Y7Wb2fAMY5jSYSodZwHWLDxNZ1uddrbPNRRBYZbAMGCmBN4bB1H+wjen7olYg5bokK8CQnwFkiGjVTfYksfxho84Ho9cBLx++djCnU3WFMiKSWf/U8+3hBu1Vc49A1dLNgSkcj9/R252J777/H6RQeu+9MJgJf1Cq4WMKfAvExWYQTYAgSsfG/haFqCxkDUjii0HsAtrkcIFBHD/rsa2wkAIYxGTMmRt37M15sjSMmeyQ7CVOwAHwOJ5TYoHVyoUhVCsVmq1iyI3Z3vON+fWe7OTNOZsa6IVMoUmUq+Ufd7beg0bpYM3ZlMg0CrHdl36vViNu9qUjZBrDG8fv3Kvm4Oj7rDcjdFcCvxK6httlGbExo6Ieth80P3HoY6hCauSoDaPJkxn7g1uzlQPYdABOj7bkKgUSDZ5rHc4hirdRgHfObtenNeDZ6EjKG0vZtoa++0Ztl3r+aYexzqVrUl+nq1YdZ5Mep03S0oaaT2QZdIunvk48eVfKrI6dGqgAEvLTDa6oEv8e39P0JKtBGJEHh+3blerrw+/fQu01bSckcOgSJw6atJk42B5IXTVIyoMQbr9avxfSSzpKMGsJ6eFEh12BrAerf7vpKmMyUX7qZCCJ0DpmmSSNEqutor63qht8YgUY7ZVzF4O0ukEGHcccxYreszMc3kVLhfZjQo46jc80ROBptqEOpuzyXP95ymQo7G5ojRVNdzKCxTJudMzBNtn+hbpedG3iOnc2FeEqdlZr1eWeaJZZrM/NTp5q1bz7SrUq+v5qDspCHtDdVD6uioOgyMiy5JFuMxQ2lEBAsijki08XbI++whcBvmhxsCaXiAwqGSfgCGFjTeUAMw5qa2Zn///tp6EJcC1gt6WysmFOwnSkwcAr3mXyz+vq3ytHm/Ay1/qzIPEgoIN6kalFarm2NCytmu29wmiKPKPn7M7m1bN2R24e2D+BGg10oqpj6P2pkUHEYd7rg8eufl9cqikVIO9+ifv96C5d/v9YsOXL/6za8IEvj6+kLFehBLFKZTIcZAFLiqZ7ZgIpP+s5oSSYfBKsECTe2NS91JahlVG0KZCwNzUo2j21xYMObdPowWFcZAUvTDZiDFZlIYg1AWxjDdvxEi4odzRBBt7PsOmzCdTpzv7rl7eCCmYlWCl+iHPJB2p9sKpBjsYHbrDxyiCu5dFdUllKKgDQ96Ru82iZjoqJveqOO3hQe39380qt+1et+xpXyjHRvnHfxwQH03JOW47x54FEseeHfdN0zlPTRp79Gyu+gCDcEzazsUFHyeKSK89V5u6atYALQquDOGSWHJGMaOExO0vfv2W34VIo9NkbtvEBXWLiw10PcLKQrTFPn+8QEOaHoop8vG9Xrl5f6eFH2QWWC6M5PIKUS+bK9crxf2bWNI4DwVYoy0oWzro0GQIXPK1nfpauMFWxvstdP2K12T0+4vxLIw5cL9PBsxBWO0NYcho9gs4rZdHMKMFKfH96HkaBXSkEjQjmLivuv1TEwTKRcelsWcv0en1xVKJsdAiRFSMlua0SjzmaUUs+HpjX29tz4T0ZybY0bSRKobY67E1hj1yvlUmOfEPBd0WHWX8sTRS7RhZ1s1Q00x4zaEfwzhHsffDXp2mM57N/avzjQ9ILN3q+0ACm008ZjfOiBSbj/3vl90W1+IxSB1+FBthuqGTb77XTe47bau3yESx+85ftexB251kKEDb2/Jg9W7T/GzazhmeUPqDvrmz7ei//tRvb2D2dG3CvN4m7fohvel9e33Hb/zHVyoN5r9/1Vx/Rev//v/8/9BjpHL9ZXPr8JajeZObohrqe0Vq3w8I4tRCCmgTAQ1ttTHj4/Azuv1lb/58QeulwutC0MTpykzdGGMYllMTmgUlMa6VdMx3K/EtiNuAEc4GYW5D/LpAdXOYV/e64YAOZkD7Xa9sF5eWR4f+OZXv+K3v/0TYnh38A4b6DTb8Y5IJyc4LZGCDQgHgh/gbp8t4Sb1RzTvptqbmWC23dQ6AoxjcXtm1odlcwN5pyogb1mc2CGEw1/W4rX+2BiDN+NKvUEJh4yQfdX6WDEIGjxZxTdFSE6EEo5j44AXo2M5qsP9l+xaabYLdK/ARAQZ1eibt81n/SBcuFWj0rQaPzLaMHo8nZg+feLP/vd/wZ/0xggFTt/TJLHpxOtYiHSKNErslCw2lDyUtl2p64V9X7lcLszTA1POnKbA+XRmmibyVHhaldeXFy4vzzw//d6kwCQgYSaJ9SpTDExFnJgwjAK+rmzbRt13Ggt9BOr2SkBJMTCXbDYigDHqrph4qonNHrRoqwcaQwetD5KYjNDgIAMsqEyMtoLLopWc0bz4YTpI7OBzf3l5ZC6ZKUdK9ORJhyvGN2prXK4763WlkWhhIjJ4kE6QTh8rU4qkKAidba3kAKmcGWLEEm2DnOKtUul7c6FoyMtCKpMFmUNvi+OoPyzsbR2FMG5BZPhhfuQ0+rYIbwK0KYZ3vaBmRAmf+ZA2PDHjZwHm7dB3DOadcvwBe4d0DN7fIop9XY4VbeQbiW89pYMtYrJKw67N8Nk735ZirQgNyZPNfqv+wIxDjdl8KIZYT1xy9K35FvwkRhPxztEZpgmJyfuXkZiLVXliaiCHIsihSSDYte/vZ/JcSEt+i368hTGVd//x93j9ogPXb//xn/Phm4/0tXHtmb1Du1xgavSxs29XlHQLAAdDChFCnIkhklPk4X4hJuXl5Qunv8z88MMfIE5M8wMP5zsId8Bii3BKjsU3trpb32W9MK5PBFFiDHQK21apbTA9fOQQOu3S6NXUl7VVar2wrxe2ywuPjw8sp4UUM328ghgODRBRsghTKpQpk6dETIkcbPAzOFsqZTNHnE5nSono2Bl9NesI7KNTdxN5jdm+NIbPcejNX0dVyTEbLOKB9pa1iQ1zm3ixBysViIGUC9IdInG7c5H3oIDcFvbBKnyDd36eOxpb1+i5QRIiziq8/e5jQ5vsUcCUEUYLaBODj97tjOAGkaMr9boS02rCsMXe45QL/+Sf/CMeP3xPmu/Y0sJl62wjspKhNrTvnmysJqqKyY6tlxfqvnF398ivf/Udd8vCeZ4YWNVTW2d6zMh3H9HeeX75NS+vz4zeOS1nHu/uXNxXiXpIV3W2y8p1r6x15+XrM/PpkWlaOJfC3ivX65WvT19IqZh6gQT29kptG9o75+WOu9MdOdtYwKjm0rzVSt+7wTtReHl5YZ7vWOY7Hs5nKsreOi9fX0jLQkrRqix2Qw96Z1nOLNPEVDIinbpu9PamKjMUtqH0vdOJ9JiJOuhjp/eNtl+obaPtG68vz8zLiZIjpw8fOBZ+EB+YFuuDxhxJ1SjjMWc72I8kX2x+U9Up+mIJlQQf/L3VPr6+vDfrtsG2lryvNFyY+va9xzo9fu5W1R8/qrfvvxVU0VCa25nzX9RHYntcjtgkt71yrH/+VhV0/LQlPLhSTUf1naO4vxf92QcwnP+miekfZzSDYQV1Vnw4CkWUeIP9DmaqRJvpohtcOgQOxRaVaJwB70e3bQMyLZfbvftjvn7RgQvJqBTiVEgyo0OQkJHSUCp5Wey/b4FrYJixktKMiLGk8jIRpJLbyjIvTDkT08RyuuPDx09oeEDlBKqMFBkBeq9Mo9Hqzn4p9BwQ7YQArSdiUrODWO4Q1wMcuHpDb4x9h9WgyjkGljmbO3C02YqQEiFnpDeXFhLbvAFyFKYiJExkNRBMXSEeOm+uCycHrPE25BgOEVcXJR70I+G8bWT1+yRyzLb40LEeMM1hjaAe0Dx4eTYHDreIBzDeNrQTzDgIM8YKHDe4Uw5IZ6iTN/Cs0vtqx8WO5vl7NpX30DiEUFX8bQXvJ1imaL/dVQyCWXKkKVGmiWVZSMuChJmqjT6ENHygViI6IiMnpytDGD747GoRyzyxLBMlZ++tDBPBFZxqLpxOix3arZFLsj/JnaLVrdR1EEbnGKTX88x8XpimE6dSWBhMUyaIEZMOmKv0wLZFWtuZTzPLeWYq5c20tNuQ/H69ek/fyAllvmOazyzzzOS9txCEUIrNOoY3SF1HZ5oXlpJtwBilxkBvidYqUYyEkyVSt2bD+7FQIrS20epGrYl929jXK23bSCGQRMhRiJiobwyWtB2Ackj2OdUp7+oBpvfGGz0cwm0IXX2m7633o6PdDvjh/loMZTjkF9SuL+8So4P4Mlp3Id0D1tMbV8KWua8vfde/elf73GA5eV+t3b58gx/fE/du9irH93mV9haD38PsPzscjyvcvufAMtSxD/XrvQ/pR+x7PwJjyInv7XAgQdwEOt7erle4aghCOEbF/ju8ftGB6/l1R9OVPGWuSWhERsg2OxUzaZpJyViFRpUNjGFwRy7zrZ9TpTBaZeuAZLIIOSSWOPHw8JEaH2lyIuhgU1ymZSeLUOvmlOSI9p2uG71FOwhT5Lm7QKo/wJCa9aGkwX5hnoTTMiOymXpHMOvwWGZS68SxEnMkVNAwSKJMCU5zgFFJwVQ59lWROEAao6+efZpem3luNYZWpDgrKIBENYq/GnsODyJjVGu6iiDRqNlBbcOP1iDOSEhoPUjhwjhEiI9ege80PXTMDzgBn9sCJ6242oB0Zyl6EHQqt5hwo13i2P46DLoZfmr4njWpHmdA+lySxTbrHxnaOQjZTA+HdnIU8pyYlkxX2NtuA8BErntj7cK1N2TfidpINGJQnxkz3lgSk/2q3YghymDdryTFmuC9c63DDuH32acOattZt1d6S6SYSceIgyiiG/Qr0naWbM+i07nur5ymiWVOxHjP3rFDvHeiLkBDpCNJUXH/Nh99YERKC1zGakoODMrdjOYCKbC1K2VaKDly/7jQD6Zn28lpxnQIOzEnt0Ezq4xcIiEqVZtD0EIOE63bXGUoibspUmug1miQZypsIdEuLySEqJ1MJY9KUSNJJW65D5onkBfTB903ryShN5tDM2GAfuvZqsOph+mAqFiPzNGDgRzCFTZMLfpOhd0hQA/UR494NCdl+FrWA7IzPh7HDKWZg+Jx4+jHeWFzq6yik8KGX+9g24a3qunAA38WiHwv6JEFHjozR113BEQf5j+gUAm3Kyj4HOYbNG8/Zy2A5C2V4OILt4TwGKp+y0nf3S5xR2bISUhTIBdDaH7+7t/e29/39YsOXOe7M3d3dzbcHk3ZIIfA3QIpKMGVHQ63WVqFLujI9Gy4dSSQy8z+WrmMwPPlyv3jN9zff+TTp1/x8Ok7XnXm0i2gLWKyQfse0aS0HsihMzSgLTNG4TJh1OeYiD3QWmNoZ0+QZUZbo7ZOIlFK5HSKBJ1pbfDly1e+/XhPU2iqRIlMpdCrZX3b3mgdQpzprdL9sM/z3c0SvV4rKRRUG3vdaW3QaqNu1dTQsV5E2avZDmCSMapO4tiNXm5Ds2bBAdz6DD027w+oHxACJJqLCJvwprxliEfI8cOk7+22MXWoWyMMG5M7muDq6tUijKou6OGb8oaavB9GPvos7QZNGNvSvrdt6y17DL0Se6O3Tq1OF8eqjL3udCKbCDkW64NpJEYlaiARLUgh9KGEOTP2RBiJKBM5xxvrFBFyMrFRNND3HUbntBS+efwNQYSmak4Bw1hrMfhQhSo5Z+q20cQUXqp2WttpvZPEemJzStazdeWG5TSR4wMpCBoDvVqVHEIihUjrjddhIryqShiDEAs9mJwVOmh14zaceyRcYr2ooFYpv76+MKZMmDJTSSg2N7juO0vOZpTJIIdAQ9n3naetE/1gSxLogTfvqL6Zeet2RV26KKQCboJo1ZEf9mojHAdJI0RXxA+3pcCxSIJ48PEEK/q8lA6jhav3W83Wxt/LoaR/VEbYWssabvRur99uA7e8U5rHzSJv1ZLrpQo22mFIhsOc46jesEjwHqFQ8Wu/q6gUk2F7Rzg5ApJJSDkkebCNxzCzT7Weqlkb4eulvQt61krpvdH2nVYrsSz2DAjG4ByVHvc3YV4V6yenAip0EVSFWit/87sfePwmkvLyd57d/1Dw8BcduFIxx1qlmTqBZ1gxQoo+k3DANII1IxOgh/ClNURjCFz6oDalNWt4l2nmdD5RcqIOYQQTKUWCSQAFoaoiQ2kCPUS6JJoqdTSCVxHrZgryXU03UWNEBgxJbF2JSdDoMz65YOr1AR2R3iJdhZhP5CLUumOOpwVJC2EMm/mJnjn6/EnXRj+myrxJrGI+YuKVFbg8E4I1sw9IxQKzXdDfCz7EzUCDNZCtwHpTEFAdzmpXs3w4DDM13AKXOJx4VEGjm96gwE3h4DgQ3m9UGHa94zA5UL8DRzz2vohlrYj/Xr1lhcfQZogJQkYlgWREEqnMlLmTdJg6f4xkLPmJKNI7fTSEjtCJvAn8inRK6MSozDFySjYrNgB69XNJGN0cd43e7wK2MTKFSMSYoP0meGuHn/mYNUbbUBoq2Q2pB6rtNkukR1XkmXtyqxyirdHeOq2bK0DvjdarQcXDek5abZxCkzhM6d5uPi5yiFGOfsBcSm8bPXRa7FBmgzN1cLm8sEsweDHOlsNLZITIdV+tmgxCjB3RTgqDebKRDRmD0A0qFV+jB0QsTi6wPC0YqUTeJS7hYATa075BhccX1Qfcu/33AX9xfL6fIQVHNQ1It0oDGz9UPdbpYUETMTFjq1bEWVFyrHGfszRo3vuz79mJR6T196n+e4/3IseCP2B5xPvKwdl9t7fLwYZ8r/px+IAduwAZEN5+73uFC5ccfJtzdBKGBJD+PtQcyeiBE/raG+/maWO0szi8VZt/zNcvOnDFYj5cvVdTsVYTGz3YLtEZbQdydJjzgT0gDsdf9an9OtAeQBIxF6bTbPYLoowwKA4PDcfiL5vncWobvxLZdVB7RTATtZfLDhgEsYkwcib6Yty6ErurHUzZm+wZwQwre4+0HohpIRUYQ4kxE2IhpBkcS05BaW07CEj00UySR9+gDNMCNKkpRkfd5E1CugHR6rT2oR0c7tEQPGhZ8zukxPBejb7739C3wwMZEI+BSt+YbiBoabFXb6PRfcvHEDiGsQfYJngHOYYDNA9vME3wQWQOuEcEuc3TGETi4lXeew6ElA0WDdkCV8jkcmJeBB2VkguEiOmZWuYcGtS6glrfL+DMMlVkVIo0NJn30DlDShbi1l4t2++DtkdaP8R5u1HQS2aeig2OMqh9t4MO67+JKDqqWX0MgWgsuigwtBoixaDjA7yqtJap4e2QHKPTe2XbdvBn3lo1odxm12+9+gHsvROvjI9qRz3zV+8FBVH6fqVJoofGWA5yQLdKrDWEQAoTc8lIykhMrJdnG1NJkRwHcwxkscAVgyDd9PssNewWvA9mnXA8ZDQI3ZEymycMHMKPFuyNOARerdyqA47I5iMVhlJb4NJjrtaqkFvuZA7Ih3KLHPFDsa8HcWjyQBjsViJyE9c4ekNWGR/BBWQcbgjR8y8nOhzv2wOLXfsIEm/qFXqb1Rq3wHvsY3sjDkimI9iKGa7ePqsHFYtP/vOBSCQk8d54P7gdxynyM2hR5HZLvRCw5HCZZ6acDar1PfjHfP2iA9eIkZEybd3ouC+PRuo4MiiF4Ermkm6Lw461IwNV9u2Ful0Q3TnNJsc08OzH+xhjKBIjjWOuKdghMFyfjgoCU8oWtFrlet346fOPZkseI3tO6FgsoPZG3Tde1xVe/0D57Xe0UkAXtnWl7TujVnQIbdvRvTOFyOm0UKZiAbSuRq04MHzk0Ak1ZQDtNuRKxXaXzY8Rjg0C79agvxz+GyCtmRqHs60kBMQN/MCSA6M/K611o8ymbB5TngoKrgbgmwIcOtFhTfHWGCEwRjKh0hsAPyzYiQW64W7REoMdDH6YCq7I4Vk5Pn9meMgx0ByIZTIVaw20dTVW5XwyYkY0dmlKiX3vXK47P76sXNbO69b5em0krQQ1Z67QGuU0kefMJMEq/ZBIKZl3VBA6sG5GZd9r5WkTdASGmmeW6CAEZc5CcWuU0TvLnDlN9seGkiMabGZqVFPYGCHyuq1WGY3GuntwUfu9x/B7zpCjWVMMTFlhzpn780KORlpqvdPaRt02eh20GNn7zhgVeuO6D1MrGUKX4IOyHZErj/dnxv0d0xSIIdm8WO+8vj6zbTvb2tF90Bg0USYxz7N5mSlR+HS/MOfAaJuJ9HqVqEcVcCRRw+10RjfIyhU7XNLP2acuLuD2Gwf8ZjHrbfbwrTyxdXGQBOU2GnIsQA8QMSMyvM/VPEbY7zvIGceVDwd1wYV5FWj9rVWBV4eHWkUMRq7hEAHwreXB9iicxnjjx4rvH1C0VpfrEnDV95uc0/H9Yq4X3Z0bkrtcqOA+Y54EWoRFXKHejDA6o9ZbQnm8Cx3dg1XwFrPpsUo6DhRlXharQfuxB/+4r1904MoSyQTW3m2exQ38zL1gULUSo9IUVFdGSCYd1BspwmU1644pLEjYmWbh08cHugrzbLDd3nYTJO3Ka73QJNrD6httDEav7G1FJCLer55you4d6YrsK2GevNmpSF9tYbVuBpAYoaD1nRiF5ZSJujHPQhiJl7WSYkeKVXzznCjFjAljSXaQ6qD3zSxHgFDiO98wYX15oTZlaKakwjHcSW2EqEgYxGMzYJ8piEFtOixwGXkwMJWZ3htopISJba3UvbKvnWUxw0zhnV0JrghwNEs820MV7Q3JZkgnQw/wAf+x2z8OsFGGQhsuWWVJiPZxM6y0/28ZrElJxdtmVrUDK4i4LFO+HTQpBpMTypH1Wul7ZVQTL51S5OF+Jmonaidoo9fNxhJyMgh6NNMjTIkUbQ5IJBuFPGcgsEhmr2rGjdF6N0FgWrw/I9YTuj8VlpJZSqLWxsvLlaFXrtcLiEGVXaBk02/UEZhDoHWlNTvgezeR2rzMhFIoOTHlSI42t3gqhSCBPgJ9NLa20nyuro9hBoAxQagsMdA67LUjGkzWi8qyfGA537Gc7zid7l0NvPGb7yo/hsS2Vvopsm2bBY4UCaOTS6TkSAyNqUzkGGjSQGzeJ8SCxAKxMMRmEw1FU0JUQigWdzTQa7OghjrRR6G7fY6X4SMYu1XerSmPBvS+W++sD7++97CGcFDr0ebQ7DAn5urrKehhZuC9YcFMNO0PYhVR3xsjRVJOSMlv8lOeNd4YioybKrwQPGl7hyAcYyNvPkCmz+i9uiNpNfKky4E7/CjdwUu1/t0BVN4Grt/99/EOzGNPzZVaoldfPlvmAU85CBxmtBtjghCJQJkWGoPaKrec4Y/4+kUHLqPQWskfg0AK9DYc7TALkyBYg3J0Osq2r7S+M6XE6/XiEk0R6OQklNNEV6EUO3BModqYRVutdIx91tpKCta3aG1Hme1QDLCEaE38ngjaCJJdEFcZx2ZpHUZF3GFWRycmYZoysa7kHKAE4q5EKxbJSYg3REfNkE4xu4N2+It5n+JoELfO+nqldwjZQTTP4sboRFVi1Nssl6pl1ET/92H9rT6MXZj7oNFQMWr99XXlermyXiralDwX8jBViEMv8vBLsteROXe0NUTLTZgU5Z2ppH8veuMb643GZI12Q3vlBhkZJOxV2i1Ftc3au1HrJbrCRngHk/jPRWtimLTUUHKAGBKlLDborZ0wGrUFD1LRqNfNZ+2SQV7RySk2yBoZGihhsqwaU5OnN0IwDUkQYhiUMDgtM3NOpjgREilNiGRaV+s/qLj/ldnDMAIpFGobSLAKx1S/xaSb5oWpZO7mRI7DrXYsudj2gcrK0OA3JjGGUlImR4GRyP672ZolMb2hPTBNhWk+My13zNOZKZs+58fHynat5NgQmbiuFyO4FGPuHvsgyH4L/CEkVK2CibEYnB3MA+949hKUQDYoXYyLbWt+uEWOvgnc4jDvoQb/Di48hHjHGOYG3obPKuH+cPE2sqguuRQchRit0veGE5Q54I2BMhocNPMgx7objH1HRyKQHc7EL+7rW47QNaDbFQ7oUBxWN9Ti6FsdVaVfx6vLcPTGOIA5tc8rB6TtzOrxfoDavvMGtog67Km36tXQknd9quNtiAOHYnDpAbHiM2YSE3XbkVrfMS//eK9fdOAqJVOmTMmZ5j2XlALqD9v8nyCosb4u3WZToga6dpNSIlB7JXRhNKXuV+7vvrFeB0oKBpF0hTS4Cbr2ZrTfWht972z1SooJKTalfjqfmeaFefoLJwUdiwzU+2BDLaiOPgjBLC9CTKR4Yrtu7GqbMaVMQ2mj8fT0SprO3H/o6OXipoEGaTRv8Kc2iAlGbTx//sL1yyukQo4LKvZ5971aKyoFQupkb+73vVLXSgsQYySkaD9TB60NiBMxCminfr3wF//xP/H0+ZmnryuP9wunu5n7D3d8+vSJ5XxiOi/My2JN8W6YuWncmXXH6LNpoXW3o7D6y838rE82dDgt16b5DzbiGxPeIpKCH3RCiOL31yqQ9fqMhGiWMPcLrZtBp1yvrkxRLfFI0XzP9kEJmREzNSZDY1WQAVM53zLNEgTtOwFYciG6ArxkMchZ7SAY3i8kWC2Qo4nh7r2TY0IVam92IjrhoJTM6Xzift1p+xU9LUiZSBKZl+JuyYqGQhtmMLnuDe0zaKcFmOeZuWSQRk6F5FXFlGdqh9PaiAy0OCV+CFMJpChElCGZPmDbO3v1vtNoPF+/olhg7wAhmA9YSoSSyARymk0UNwY0J3tfTtUvJILYuIaIMLrZwJa5OAvPWLmiroLiT9qEi02zs4nDZmJKH1ZxDUekveIaWMWEYgC/lyW9W6A4YGXeBoD7ttkMHkcf1fYszefHBKvaxVRO6G8k9OD08lFNXuz4mrEek2ks+roNfujjEPfo3UcXnIk39Mb8e8MOrYrT0Z2r5LDlTV3e5yodotOh9G2j14qOQYxngz0bLkwcb4FOxdZQ2ypt20yqyyFO7d6KqdXRAjtvez2IXMJgR5LZN319ufD8dGU+dVo37sHfrSz/93v9ogPXKQmzdi5tYx+VFoMNDw+TRwnaGdUXvCqnmBklWk/Gpm+ptVFrJfSNsV/ZXy8w3RO1M0e1jeNwU55Mrn9vsPWrDSS2Su4rowlpCIVgsIBWtHdKGCRWQt8Zo1DrQJw51dcnRBqdQNId6AzBM8GdVq88vzyhdRB7R/rKtQVey1dez39gmSOoKWOP1tDd3GH310ROJ3qD18vO02VHkjKnjew9l33bCCLkEUkjEA5mW/X+QYfeO6kJuwqtWZ8vZA9co3O5fqWHgcyB3BPlbiYvGQmD2htZB0UEYry5vRpByg4s7Q16NUJMi4ScLYsbypDh5BAhBRvItka9Q49y2HA4iVkBH6Y2CvwbHBOkE723GUZH6k7MM1mCiQk3hSGMHqFHgg5KSNgJGMgBdDSbUYkBGWYCGQOUqBAssKUYSARySKQSSZJo2q1i6R1qR/qwaonhKvbBJJ9QooKMThiWXOUcuJ8L3J+RfWWfJkZyiSXpnv2q/8wgjs45R0gZ1YRm4VQCOVmmHdQq7ExiKQHmhJ5n9ggtRXoIMJr3W5ze4Dc0jsYcDimxwLkUlhQpcZAxwV5Q8hjMqkTtpLETR3XDSlOYv3HWQ7wNoh/iy0mEZCqe3msaaCzWY3GdSY2eQTpL0MgSDZwwJMmehRGvbK7oYA8csk0H0SFosFGqEW6kkRijsRZvKIEjIjqcie+Q8zHI79fuikF3vgJHCzdKesyZVCaTQJrKDb420oPVSKPuSHdzxuSzU+8qnVsVhCBRffzSiBbisGAsPos5FHVoXgTCiL7n3vg3ZhhpQttWOxnxJQZhMMwgMtkfgphG69B3laA9wxjN4fqo3IIIUQJLtp5+juEd2vL2+hmp4+/x+kUHriQQdBBVjQGFwYVRLP7LsUDwBxideGE+GvaQgrh0iWUzWk11OuggRQgD9mHq8iGGG147oh3mXQ1KKEGIMojDrSa7KZcLw5QARG9Q0QgGcwZTvCOJqWEYy8m03oLY4dhHJROMLoxy3Rt13anrxvl0RxAnOhyZj5rVh53edrh3H9atrbn9eKcPg97iAYkMa6QOF8A9LMaHCn1gJIzhtuaumxaSsNyfkJzJ087D/QNTSUwlELJVa2YxE0C6K2kYjKa+6W/D4bgigHpPwlliemzug4ASvPdGwIZhfTF4n8vaC0ffwO6p+oa0brHBN0yNQ8rn2GxRoqtEKCX5kHMIrquoLv1o7ysFIXm1GyV6E9xngDRiISwYk3JYJRB8tsd62NaviJgXlT1v8XVrf5LAlCNtyqxTgWj2NSXJrZLTPlzyyyqMGJP3ZxSiOS0nGc4LsCZ8ZJCCMOXIacoE7VSB7gy52odxXDwoGoN6uAqFMAYuyWvsSJeBPJo9JlslSr7NJQ37P+0IduCnIORogTqKMGVTry/JFGRSNNKMkDxwCWgjpsgY9hlD8CHfg4EarJo8VC9Q640ddbkgb4xAQOIbVes2aBsCIck7eNuD6BBkWAL1Fri8SpNj3b1VPBIssIw2rApNth8QV1xXh0Cdk6/aiY6ti++bG9buyIzTIv06xgq9fVDfP+/HIjgQCcH7yvzs+62Ic9D9CPABZ/CKw4FHpSu3NvXbDbxhhu+uba/oKNONMHJs0eMK+rNv/29+/aIDVxsGwYQYiFrtsY5ODOm2CJLTZSUkQo7mvxUGsdlijzEwnSJaK3u/UrXR+o5iA7iERKyVRIeQyck3yJi57jtDDMqb5uJzKDuhN9pQ9lqNqp8yOUWDGLJBITlHPs+FIsL9kjidTgQJtG1HtDJNhaBn8pdnlgZRIyRhfXpxSE0ppdjckRY6g9580aplUDknPnz8wOcvz9Rm2b623bL8lOxgksNjKxCDosGbsONtBiYFE5Q9oDiJpuT9cP89n34V6QPa2ihpImWrNi7XK9NUKMU0ITVhemZAnAMxNow6m83U0unuB8uphEJXt1bHlNJjTMQyE5gsR3R4zKjxAiH7mIPtisMVdvRIyj431hqtPZNygdMdcXqgJCEFZVoKouoQc6DWbhBf/P+R9y8h163bfhf6a8+l9z7Ge/m++c251lyXvZJsNceTc1NQCKIFxYDGirfKBguiYECIIBYEQRGDIKgFjQUFKypoVcFKQLSQSgjJPiclwyGerGRn773WmmvO+d3ed4ze+3Npp9Da08c7d7biWmtzDhMHfHN+33sZo48+nudprf3bv/3/0JsFzCh2i1NQg9OCMM8TinK9Xii7IlSD8aSbN5cokWoZbQxMOZgNizaDgrD3nycxaSXp3hudyFGYciAEJYonSQhkszjp3s/sgqnSJ2d69ubNe2O7pWwJXZROxACiKQb6ktg3kx4TTxgEoYkJ/Uq3wB+CIMno9UUbvT7T90DbM/HR1lvXStuvFnSDME3JA7klhUST1spzZg6whI3QBKmRTx/PTEF4vF+4O5+YTyfyYLD6oHuPiuiZlJIzSq0n2brPDXryAuIwnCc5Wq1iIrqfVHeGoAWZ4HBb8DKg9cKhUi3+gYv1n0JIHtzGKT6YeXIQL9Rl2lS6MzAjw81bwWE9+GYQCQdFXWJyc1d8ON7e1/ib9WijIT7O8mQM7Ht/uPcX4zA+JN0VxJNTWjusTkwXVRkIAw5xt2YSdWO2s3frT2rz+yNqw0ZRCGLrTXuld6FVlxVT3Mj3D7bL9e0OXNeVvQfgxHk5Y/1dJXTxLHpGgy/aWohi1UWsiq4r4mKRSWam0yMtzFxaZt122tPKlj6SgzXlMx3tK0okIX50mvJGoKHrEwEliXCSwPO2Up5XPlsm7peJnAJrbVSt7NuVy9fPtPcfaUnROPODV4+8ub/nYTmTeiSGQukrr6cPfHz6aKoXvRHnE4TE5Xknnr5DzplAo29/k2nJxNzZSqeUQMwTy+vv8Z0fCOu6spVKjwvRoSnF1LdjCPQeIFRCjkwhEdNCEAPYyn4ld6U5ZdOM4zLz+RHJE3tpvFvfU7adUwicpgfe3L22GaS2U9ZnkHTbwK50kLBZvJQzaZpsNtRJJSFFtHafQSrsW0FC4u7Va2JKeIHsaiDWExJt9lwpWT/B6dTaOjHZMKRGQest041aaX2D3onhhGglijJPC9qu1N6opdP3YrNGwXoDmo08YBYg2dsqzXpXwVQycjBWYVLFWHMGZ0VRegYhHoEmReE0JbIEpjAx5RkhkbNwXuDxfGJVpflBlLopgofldFQg2jGiTe+gCUmmuxdUObknVIyJZToRJRujdBJqXql0mgcvjW4p0pSWMr0bFBvF7U9yZpPKNE2cY+DVabJRkQLnuwej5ddqlXQ21EIa9NVmybRGehR6dgZoS+S8EIKytkZx2FOaJVSD+2azloVaC4JBcb11ym5ED7TTmiUEHfMoSzE4aUnxMhWwc6I4Q7XXbrJqAR/WFYZHnCmt2/9r3emlm5Fmjm626ZiXB1e8LyZidPNyXS1xnSfTVEzJftarruDzUbUWtFnVIzkRs7FeEYP/uvfARASyV3v+QiOJY2gKCoQ4e/+u+/sVUz3JE93Zf0g80ChxNERVoClpnknZZMJUbaTIEMIAaX5R4ZpjRIiZlBezjuqdkDIPbx6ZlpNVzn+A/S34JQLXn//zf57/4D/4D/jN3/xNfvKTn/Df/rf/Lf/UP/VPHd9XVf7tf/vf5j//z/9z3r17xz/4D/6D/Kf/6X/KH/2jf/T4ma+//pp/5V/5V/jv//v/nhAC/+w/+8/yH//H/zH39/e/0LX0WmnJFkttJm8S/JAa0+pgjEDtJtopatnlnM0bqzUl9Io0LAsphevTE9KVOSXiMtG0sLdCr0qT6HCj0GuBXgg+JApqEjItQ6tIK9zlyBzdwn0MCkZgTry6v2POyuNdppfC+vxsPRAtpNpp28Z+3VivG7Vag3NKE10D18vO04eLVZRtZ/vwAdQUHfYKvWfCVJEU+fjhmeu2sdfGWcXug+Pa2pId6Coo1t/orblRpc1K1bIezCCJiiXCYq6/687lsvHTn35F74X7uzPalfPdPUIDrS4Eaw6uhmRYk7+5Dpy24M3fdrCn1MVOddi47ztKcajRtdNUKMWybUnJSAa90auBerV70tKVPFn/oHdoGqm12nPKM/tmZJGyF54+XthLp8lMLcWsS0Ro62oq6b5uQjappes18nBng+pl32HKLrAqtLLTqyUctfSDKZnEoCHE+iu9VONktMAmZla55smq4taopXD5+JGrH8cBIedkh4gItcajqJkyHNTpHJz+rPTdlRYkck0XpnlGtdNK5fnDe3ZtVB1q5Xb9XWEvyYZmVVmSBzaga6VujbUXnj8sSIhmirleTJFFm5uMFi9YDEoPipMilNI7RRvr5XKoqF+uF959/XOmKftQslUd2hWtG9v1mVYLU55vDgfNVHOQ23sHq0a7N+puRpN8Q2MPxhCts+vG935PhXCgbR4ZrA/nvDx9QWt3bG70tw4HBbHgb6MhI7ngYOgNWA0fNlYnh/hsz4u+mA+DO8woTlpizNjhkKhfilVfQ2SY3zPDqf5Z6/F2B1nMoEyH+YHDgFdvajS367pVd2DIxuVypan16cKLoPXyrv4qNdgvHLien5/5e/6ev4d/8V/8F/ln/pl/5m/7/r//7//7/Nk/+2f5L//L/5Jf//Vf59/6t/4t/rF/7B/jf/6f/2eWZQHgn/vn/jl+8pOf8D/8D/8DpRT+hX/hX+BP/ak/xX/z3/w3v9C1tFYtY+qFy2Wl9EJ0he7oApExDRuRak36YLDM+Wx25/u20bYL697Znp959/ZLvv7qPfvDPTnCKb9i3a88b6tp7Ilh3M0Xp1HbK71slpUJxDzT2o5o524yQkHThvZCCpFpTrx6OHOaJubUeFjg/du3PL//SAqRJI2ZgJbGuy/fcrnshBC4e1gIeaI14enjys/+5t+i7StlfUbLe1deDzRJPLzeCDlTOvz4r/0N1lLQFPj009ekZEOpiU5OyfF0pTQzWOx7sRmf2tmreTiNkYPl7sRyquRa2NcrHz9eePvuiR//1s8gNj55uOfy4QOfvH7NvEwsS+bubvH+glU5tWz0YvNSCSxo9U4f8E4we/ahAK4HxFdZny7e08AhDSHNM3lZyCma4aEqZa9WIWIbfF5sTqhpQNMCeYV0QS8rbbtSto0PlwsfP1xY98amE5MkiJGWE/XyzDxlU493pYaOsq8rn755YJ4StXXu7xev+DLb5Uqr5nH28cNG2c0lekpOSAgQ5oi0jnloVdOyw4LHnCzbbbXx9PTEKia5dZpO5kKsNvi97fiArnJ3MgsTRZE5Og26g1aGE7dqZJmMJNBq43q5cHWCUyQYOzZYgrZuFuxDh1d3k5mQBmG6y0izGcKyPlnvCXi6bA7VC1mg7avtFSI5wmD7aatctyulrDy/+8C+X5AA7969o+7vbKAVpV12tq1Qa+P+vLCtV1SVh4fX3D0+MM0zc8q0YGMGKdl8W9dAx216BgU8eisHO9ypHY12r5I7m4OipXGUInBAnTEEWsL6P8mJQa0b5ChO/EAJwdahACkl8rKQ8kyaZmP3qd4qEBlB0fYgYSAS3tR1lix4cBVL8mxtYyzWbOr/dd+8TSBHlamqppFpWSfpxVyY9maEk9EXw9Za3Y2klScIMpKi5tftGpciLlXoOogOkYpYUvjVl18zT/dM+fzN5pY/flXg8BcOXH/yT/5J/uSf/JO/7/dUlf/oP/qP+Df/zX+Tf/Kf/CcB+K/+q/+Kzz//nP/uv/vv+I3f+A3+6l/9q/y5P/fn+Et/6S/x9//9fz8A/8l/8p/wT/wT/wT/4X/4H/KDH/zgf/e11K9/SngoUCtTskoiRiWWK1JMz+2pbKDVdNeme1NKyJHT/QN3VGYa23rh+f1bysdntq+e0KcnWzzXgr6/MPUdWkF6MY221qjXCyVOKIFJO1pxkdtizsq9svRC2VfW52dqbYTFLNuDWAb85vFMr4VyXXn7/ku21RaN9GJYuri4ZYhM08TcE4/zgmpnvVz4y3/p/8XHj09cLld6VE5TZp4zd4+PfPpkWdNXb7/md373Z1z3SlX47PVbltPMvEw8zpmxZBGX0dGOtsLXXz4ZRFcKISfmlDhNme9+5zM+eTMxzfDlz3/Gz774kufnK2vZOC0zNTVYd67vL2QJ5Lszd4+f2FBsMxy9uZSQhGqDaNF00vI8mwRVCNTr1YagU6YvMIdIKIX9cqFVYd93tnUlTBNsz/ABp92vaNup3tOQEAlpYt0Dacqk5UQnIrrbMPj+nu36zPXpiZ/81k9ozWbWSgOmhKQTTA/klAlXaNd3TIuw1s5aOk/XlfVtYpkj57tMvSw2p6WBp3fm0RaCcP1ypTRDBj5uF7L7663dDjrRDq0yTQP6CabojhoxJ3TvowRoC+XJSRw06nOn+nWvrZAyxCgUVxVBO2wb02w+ZnsVPgzNyt7IGbTabF1FqE0dLg2E3YZhFeHtWxuSz1Pg2S1GAL74rSvLySDaysQyn8kjMcpq/WaSe7xZmZFTRetOq5UpN/LUab2y7l9hnl4WXt7+zs/ZSqF2ZYqZ2pUYE5+3xsNnr1nu75jTNMwADKaLwwzF6dxqRC0B3NXIerut2Txj86CGx4g4tDtxNqkc7FXpThoas0tJB/fkoKCbAaMFj1YrcV5IeTLjyxRup7b3kS2dS6bsIsFYqjEyvK76iz6Vmao6HNqVOE2EKSM5mYyYU9NDz0f12TnfKqhksJ4liDaIfuDuCCF2YqyU9UpMJhiN4rClEKeFwz5IhJgEicn61F6pBxHus/Dw5oHHTx8PZODl41cFDv9Ae1w//vGP+elPf8qf+BN/4vjaq1ev+ON//I/zF/7CX+A3fuM3+At/4S/w+vXrI2gB/Ik/8ScIIfAX/+Jf5J/+p//p/92v9+7dV2ixrPMpNHYaoduQozh7qqGWzWrjGp/NfykGrueTUbJLpV52rh/fc3m+sj5dWJ8vxCBMOdKeM1vd2Jx00TAIS8pGEaNv28R6tMXVGtt+pe6Fuu+UutPXldY79XJlTfYht5BsQNXVmC/rSi2dWoxifxi0dTVzyFaZYiIm2PaNd+8/8tMv37KtNoMkyZTA91JZe7d+lirvPn7g42WlNFNK39ad1jvrtrHFQFNTHghi8zag0CofvVrQ1oleYRYJPF2ejbCREu8/PNNb5+5u4dc+/Zz7u3sezic+fTizbeXQjZQQEZdsssWeGGzCNJkpZkj2f0x/gGE9MdhyImqfyZwpoSER2zTJDqcgMM0z9ACaXZbOKgeJExqqzcjNM7t2RBohWC+t54Auic8+e0PHqNDbXgmho5JRicR5MY+o3pimTsiZaRbm00wKnZzgtExMkzMMe+c0Zq2C8OmbidqdDVjP5AQSTLLX+twKvTJN1iBXDUjMfmhCCOoCxxaMpLZDNup6VWrzubZmOoQhBlOeGJVA2e25RWg9GlFGTRFiys4cVXPBtsHwzpRh34TWhaaClt20MXNEXURZMd3CebE5xC4TKU9GBOmdabZ6oqtFFRshaaQcmaZIa52tw3xnqvspCbPYTFdHmLoertxZEsX7QI+nhbuHe07nk40nRLsaGfqjTnazHtl4jF4orjHoFEMdg7bi7+n2hzFrdXzFoUK5WXyo6AuYjgPC64IFs0GhcxhWh/qFcBveVe8veXIiI4gdZCPxnx/i1QJ9t0Sz9+PtjTd+CDSNhtsLuBEZ71Vd5FqPYC4+ZmL+Y9VGGF7cP/FK6yUye8CEg0ylnZRtGD8Iv+/j/+cV1//W46c//SkAn3/++Te+/vnnnx/f++lPf8p3v/vdb15ESrx58+b4md/72LaNbduOf3/48AGAt2+/pq0VqvK1rqxtR8pm0ktByCKkJdmh0JpZgGgnoJyXROg2UU4XyvXCet3Y1pXrdTVoTwtPCpeystaNUBuazL13FtOkV4cjCNmN6BR97lwvK+u60ehkx4A3X5gqgRbMHXVYg9hhZ72Etm02n2KXzdwnWm9kAnt54sPThZ9+9Y7LVpDuuWWPtBbYduW5bXx4utJVWctutirgIsM2RLjvjafSqD77lEI0jyVAa2VvJhc1JXGlZ2NdPV+ebCJeAteyc5oSb9684o/9X/5O7u4fTVGCwJc//zl5Sq4OPR7ejH6xqFNOxBQJLsR66wP026ZU2/whCPGUjdGliSAnVBIxBlJyewuxAVdccw2xYdatXqw/lmb6trnvlTItC0EqUxYe7t+gIdK6sl52al+NFl4S4XSPaCO0QpBKSKbwoGlC24rQmeZMSgY91dY4TbO9zyicH1zCSAJB2xFcJGaaQzWhd9IgLBDpfs+HgneIlvWW3qDa6otRKJvNEXUwRlswtqPEbKO32pFWSMlVGTy77w5txXA7kGrrtLJBM9+xsqkHLo7B0+hU7e69ul4367mFiJIM8kUJ3QKUINCFnU5zDc6YDGJThRoSpT7atSRh6qsFRKA93LsOpZGitr1TWyO0ysPjPXleqNfVHRKaEW8Gww49ejS3k1Kc6j0qKofMBuMP+72uOqAIRtA42HreY7Wu2Fintz4R4HqcHtCG7xwcQcQjhQdArELsL/pkL6DE4cIwfl8kMrQ6tTU0du9Zvwi8DkcaeSTQ1c4aaW5DJDc1jtGbOogpYlJqrdj4zNEwewGfjjCtrimqYvt09PryNPn4xHhPN8jzD+LxrWAV/nv/3r/Hv/Pv/Dt/29c/Xq6gmTkKMXVyFHZXNyYImgJrDFSgSKI8FQwJUKrubLXRGuRuU/BMM/PrhR6ubCHRmVG9sNdO3StFrVmck/DqIXM+3UHM7Bq5fjAJnykpse3U2ClJuJtn7k+RPAV6SHzclK121r0zpeJur8rzbgsAVeT0xOST5kWFFGdiDOhceX+98PX1ma/XZx6XM6c5ssyR13cPECOld37+1XtK30CE892MyEyOiSVPfPe7r9l36wl+fHomNdvY5/OEpMUEg/eVpQnLlLi/m1nOr8ydGeVnX/yUp/1i8k9R+NGv/4jv//D7fP53/BF6uxKDDT//ke99l9ALoRWg0oP3pYKpLaCJmAOBajBNEtNY7NjA45zdBgRCzEyTbfAYFInZm+NAyDaA2prBJroimNpAa7aNQgr0EL1RvnOahfkUOd3NLHNCuQNAWiCkE0jEbEOcuVYrGmZEC0F32+xjE8eF4aoNirTNIJ+QCX3zo8Q14OJsCQ5u566K0FAxkkHQ6tm3N8Lj4mwuO6y0u9NAONl988Oq9wbB9P3Mj8z7FloYGoCihZcnq0brN5t9SkTVDFZVZqRvdgipot2U4yWd6W2/Ze3NnzskREdSaRVlzHdOdihOObeDW8PJq2i3z+nFgkQ40Zr1iFFFyweX8UroJ5/Yne2d9ekj7XJB905uyjQ1cu5IEw8+Ctn0/WJXkhrt3wR5jfig2m5InffItTcnyAQPynLMgCpmd6TaqXWnrLsdziEa9DwGcBULxKiNpaR4HNrWq7XkNIQRMLHvjTgmJjv3InpyjKOkeLAQDzFgjGIhLqYtIpZ4dpvt64cFkJDThDSla4HeSCLusRddk7DTe7W+JtbPDF659eb0+kFXBMCbhQEk+eB3gJCywYhBWRYTA0/ZK8T/f7MK/7ce3/ve9wD42c9+xve///3j6z/72c/4e//ev/f4mS+++OIbv1dr5euvvz5+//c+/o1/49/gX/vX/rXj3x8+fOBHP/oRn7z5lE/uPyFHYcudQmPfizfvzfLjmWqVVYOkk83fSGcS8yFqVYlNiFh20J0W2hGaKkHFm+0LofssTwA6rHtBRSkaiWkiBZvvEelM3q85LzM5GVtrV0+qCMQsRB3K0JaN5ZzIIRJbAzGG4LY3upqQZtk661aozZrHwauWoJgBYhFKU8fE1d0elMe7s2XDHb788q0pZ9QKrZuaeQyHAsHLuY/LWlm3lelSjMQRhLcfn9FulvfxbiYl8wPrLtsk2iF10jwhTaB0dC9jdzI0JG021TJJpaOh0fbNsj01ZeqhyF323eWwAoR4mFSaQaA333uznonuCJUQs8uyKVCo2xXFqpbWhD5Xe7/OlhKEmCZCtv5ILxCGVJBx3S38qFENhmq9esaNqxcoNlNjP2oBahBJbCAMoPprDvjJqeNaGUqpVuBc7XPTSEgzuOeVqIDuL7L4YM85jP0cxkE7SHW0dfPXCnZIdlf9HtY2WlB/fW2rBSYGu8wqV5vfUT/sqkNeFdp6g6kwNXtj3tUbJVsV2J1sU2HYa2CSWa5BxJEAIBw6ft1mjroP9feyU/fNWL15JqZ0aA72Vu399DHDNNaHv3entg4G36B1Mz5TxFivo8B5cQ6JM1oVQWJwQelwg9kOawYzezSVfkWyyT3Z8PGLz8craXDtxNbsNbitAeuDySFar21UeP7cKXiFPp7b1/MxHNxvJKeuTuF3+HRAjFZKopgOY68dbebyPUxkezWPth6TV+12jb0VgnuDDQX6VoxMdJ8WppPe4ETBkoff95T/xR5/oIHr13/91/ne977H//g//o9HoPrw4QN/8S/+Rf7lf/lfBuAf+Af+Ad69e8dv/uZv8vf9fX8fAP/T//Q/0Xvnj//xP/77Pu88z8zz/Ld9/fHxkcfHV9b7SGafUPbd1ctNJaD2zSSWqoJP8yfpRBWf5O9++DtLMNoHbhYpBpeFmIxa3wVcKUN7N9Fd7ZQWuVsmBlunO06dkvXTUKPd73RqG0QhO8R7N0WN3oFkUEkOmdrMgLIUY5uJCrV1l17ChmSDD1oCey2UaqoHigXKKKaWcJ5NMHjbK2/ffWR3VtISI9NkVhy4KkZtnbqbnUprjVIrcSvkmIgh8nTdyNEU8HPO5Cn7QCg+h2KwQjhUMMQ3qgMrekRvjs6B2pyK+XX5EGcfDq5KKzasPMzvgn0CHnQcjlFug5i49E53WKOPYUs7rHo3NpgdaLeufHA2neeyjP6aHWqj5+bvcQSBwZJTPaolO3IsEx4H8aji7GHvx1Tt1Suk7v2ZGxRjgTlZD+wFh1ul++Hq1YMkhsSRupWLHYyDYm2BxpQaoh+YLlDbm/O8/fm6WaVYYHO3NAHa5oPv/h61oxS7RW13YVmDD+nFeygexP09dnVLklbtfkbL8Hurft0jwHmdOg68pq5l2dFqFTCl+udtMmxDZPn39q0OmEs5+lsjYASH7TW4snsYsGxzeHLccLujIUQzIhUzAY3RlTsUDlFDjC7eD+81IzaIB4zjWl7ugOM6fe34642vW1/sxc/08dwOK7v02dhP4r202ws5q1I4lGyMtNOOy/CO17EWx3XHaO4NuEaj9u5yVOPeVLR7X7qbrFRvjXVdOdV2FPm3T/UP5vELB66npyf+l//lfzn+/eMf/5i/8lf+Cm/evOEP/aE/xL/6r/6r/Lv/7r/LH/2jf/Sgw//gBz84Zr3+2B/7Y/zj//g/zr/0L/1L/Gf/2X9GKYU//af/NL/xG7/xCzEKAU7zGUEovdAb4DJPWf3wEbibT+RaWXV3PLvSuonqttKdrmy2EKYe7Sd+U1prSEpHllhaRbrZHNQAkCmtc9mv9LWayKgooRejlas1zgE0CD0trMNK3U4Dei+0srM5qWONkTkH9q2w7YW3Hy98er8w50iMVhkuKaHLzLKYUVsPliXt1YQ0TyERT2fbiN2zJW/Sq1uUhw5rKcw1k2OkAk9PO+tWuFyvNieTEzHN1C7stdH7zpQin3z2itevHvjB61d8/r0f8erNG5Z0pgdTFkjxRL1uhL4bXJgSweNVa0Z7t5tyE9W1c9kzWLFs1tCUYCy5EGzYWzJBsgXEmFECGixYWYAZ6uE3JQRBOEWDRENw/6w+wR4ID29sLmz4QYmzsVI/gocNNs9oiz4jZlCR+uFi7Cw76HoUqzoJhtLp0KQbqgTYa4Af0h5oxDNnF4u2gbNi8j/JmGM2T3XLksUDqw7WliiQX1QUcByM2V9TBEJyZlhwsVgjg2h3eCtm+3fzADTo2HlymMuD+Ph/yA5NCSqmkmK/M/lOVYfqoKdkWbtBBn5Q74hMdmsUeh92NB2VRNcdY26f0Lqie7J+aM/Q/LPzPZ9iwuCIG6wWJ/+ajrPfEiWzJVGGioVEdwvWNGIE2oe6ufWScZmpEMQdy8W3lr7oyYLEIftmEFyMyQPddMB+tzUAMuTkPHGJMX8zQPSXdj4+bC2F4MLOIWVT6/BkL3uQUVXTHIxCb83gzSnbgLNMHmwavY17blDhfL+QpkyebU3GyUWsQzB1FbGdG9zCSHxuT9QqxlNSzueJ03m+9dr8MQLsr/L4hQPXX/7Lf5l/5B/5R45/Dwjvn//n/3n+i//iv+Bf/9f/dZ6fn/lTf+pP8e7dO/6hf+gf4s/9uT93zHAB/Nf/9X/Nn/7Tf5p/9B/9R48B5D/7Z//sL371c0bnbA617UpphVIq0aWFVKDSTFOw9RtdFmFKttlSM1iiVjNlK1ux7CBCiMKUk5kMhhN77eTYbZBSOqqZ0uB5bUxqDWm0QVVaM9+unNUBJ6HnBNHmb8zhoIEGtEVyUes3aGMrO3utbKVSaqNqJ2FaesspMS8zd2fbaAal9IP9oxKIOfJwbxPr2hohRJcEEr7zndc2q1EKX799oqqymecJMUUWhClnTucT0zST54V1vXC5GkHmdJ/49Duf8t3vfMb3P/uEu4fXxDhTy86A07Q1E9/EXHhFoPVRiSWGIrdBFj7TEgyYEz+Qj8RUxGjEo9nsbEv7nWgVRFeQ7pWwwS6HRp2JHdrh3tVcf9Ukf3rZGTRhyy6zZeBqh2RrFQn26Q0acE/VE/abCO1Q5QnHQePPqYvP1gnt2Lim92f/dKjOvmxAW3QPKlWkVRuLiNmUEOJ0C0a9+P3WYygWMSVC8Z/pDv2OND+A34to8LQbGoaYvNoaMJTBiJI6je7ZcqBrPE6c1qqTINR+1ueOuhhJxiqXW08neCC1StfWf/f7KDn4tRiSoBVfA51QrZenvdIHLCuQ58WrH9P++2YVM/6nDqOOb3nioM64q7cK1+SWrKIf79h+Jd4qHdVblQLHZz2ShJvzsM1XdQkGebvqBS6bxO8JWvZcEaQdQRV0XIGtW++34VqPaKCye58uHgFo9CAPmaXj1tjn0L3fpi5kfYMR3a4Fv19d7blruSVeQ7aqGwnDciVXoQ+RoNVVzAplXQ3OLfuLnpwct3vM0/2yj184cP3D//A/fPuAfp+HiPBn/syf4c/8mT/zv/ozb968+YWHjX+/x1YLEgPZeyGGcRvFeWTAUUwQVUM0VpVv1hkFSTZ6oWYXXrWjEc6nCRVcJigyJSMFxAo5dKfbN4REbYIERarDVB2bEwqWkIbkB7EIIQRaMNw9HH2TaEOlMZgAbtkptSJEcoJ5msh5OvyflsXmhIIEquphsSK9E0NCEeYpcz6fySkh2g+dwS7CNE/eNyrsWzdPrzB6R8YASxKZTwspTZ5VOl0YmOfEaTlxPp053d0j4m6pajYLwX2IRhVwQBYv14xvckVvHlsD00dvZ9AICG5e5z92wEBjoPkbXl8vYZYXW0PE4UVncKqLGVvg8j7H6Fk4i6Tv4/vWcB+vGQS6Y/XdNeCO6KPRLzzY5y8QNNBdNBgwtRZPoG4FlAL9CIIdoEe6irPvyu3tWCOC45gZhAKganPtSRnWS/Z8ahqMiJqG3nEfO8Fsk1FXhR8BLPoKHUadzVl2gtCaYM0/pasQul1DBzNQ9c8jOVO0O5w75nWjCBoGaTthojKe4DW7V2ZZYmLHOgK295KiMyyHanofa+L3LB5hVENG++5+r4dc1BH0VC2hCDemnH9CFsxH4HJFiy7BbUv8qrtDbCMouszRCCJGRum31X1kZmOBHld72xvjCl68N0vILADbehyfl774M/aYHO/L/n5rTwSHcLuPnJhShxxU/fHc/aDa3yrvoeavop5IyG1OTjv06ozpG5tSXr7N3/PWf5nHt4JV+L/2+OLnv83dPPEwBcJk2npRhSnNxGikg5CF3pQ+my4fnpnAzrnbgbReK7Iaw286T3zy+sH02kTZt42yb7RayTkhRdBilUVMWIBMwea7WjVWXo6eTpoQ8FCqp1akNHpTSrdDnhiRPHF3vmO7XFnLFZrycDcz5chnrwqnaSaEQO+d+8cHTsvCssz0bjNd27ZRrs8MtekQJk73d+ScSSl4TyEQYzaCgzO67paFWqyvVVonL2fmeeI8L2gT9tK4bhvL6UzOmYf74pVBoJbOvgd4ujKlwmnJlhQoJG30JIRomZhVFr5xm1GKR4O5BaPhhg6SJi8o1A7F3uy+jWAIqFh/TlWoXaxoVYOFOtYjDMFJEOEGtQQi6szG6PYpbd+R7lVhMJhQ4qAaVy5PK7WsQANZwdW/owDBKOWtKt0PqRRMbBRA6eyu4hFUyfPiwdOGp0MIntvaz7fezAXb45uN8WR6NzUTWiMEPxy697LUBoWn+WTXj40o2Nq3+24zVMped9Kw4bCTldabV05WSagOOxt7B8krB6taAmvBYa/oM0S2xkvnkOFqWo+eo0hgyidUO6XsnuVjav0SzcHb1S5aazbaoRVqMVfqKXsVkFBNEJox59T82WzAdz/EpfU4rDlOxojSWjn6Ye0gLw0oWG2WYMz+OlV+jKPYwe+VDNCKV1zN1sjRuwIPgt1fF4vQvaHYyEMdfnMHjCu3QKcWpMa9thaWMlTdDSZ2OPHF6a9e5VryZMxZC2QJpR/EL3HZEFP370fypKP1q8Hjt9PssfvUVQ/HCAXvgdqLW/v6BsdLyIjsiJiOpsRgdjO3y/WC929LUX/hx7c6cP21v/bXSSLMSWhhtqoBE1P1dJge7QMT34h4abw3k9cptfH8ZBpoHRPb/M6nnxCz0KXz9HTh+bKx7cUEaVVICEtQNGTHFIOJe/pCXVs7Cv2myhwtAy5NXbm7u/K2zZYVFeZ5dvkY5dU5cmImpsz9wyPnbJDf5Xo1s8vaTSi4d8/qI3fn82HLEGNmnhdjC6JUrNSPKROiQZ8hWj+gtUprnb02WrFsbN82wGyXl3liSe7zg5In4e7+jnmaaOuFre30PJHDmZxtqNVgimqSOmK9lNYbzTdMa6PPp4RkB1kPHembBREErVa9ald6NfkciaZrjlqzvu12EAxIJAZXwA5DGqcf50O9rhzSP5OtFYmZ9ek92it5OZHPGUmTBXmBUq88fXjL+7df8bs//m0UoxyLdLokFIPENCYkmAjtdr1Sys627TxvG1E6U4Sc7nzzKxDJySq3prCXjVILe9mZU0fHfGAO1FKopaJ1s/ekHCzTKMqclBBOzoLtNDUdzpQincjWCqVW1rWwTBAk0Emm49dMlDbSXBlhwJQ2m3jOAnFGCSbC2wzJSNEO19orpTXWrZHHGteAqimva8WSRYmoJBBbA6owxUBaFmLOSBDKVgh0ltj55O6ex1f3fPLpK6Z5JtAJvUHdKWuhlUJSqNvq3BirVLo6ysGtyldwtYwB/Qmhq3stDvsfQ4oC3mt14gPg5wgeyG6KEUiy6tzJVcb+vFVv5XJlKGdMswckFZuLklslhSdxeEWGBGKeGezbg9BzlJNK24cmpzlPGESMUfp9jbfaD/JNLbujC5Dm2YKTa2B6aWVniapbFzXKVpjOEyEbPN2bCRqQzYFB/Tb0vhnTMiTKdqWWynZZ+d3f/gnz4+ecH+txG0fQ0uMrv/zjWx24fvdnP0erqRYQJgg2jNq7QzjNbCkIA7LxoUcPWB07AK/XnbIbpTfkyLVUQhI6jefnC9fVBnZTCkS1Q20O0MXkiUIING9US+/sQ5ASYydmZ/9VZ8EdfRjUMzHIU2ZKE+d54u7unrvzzHmZmKfEaU4IiWmyRR1TJk3eK6tmD5/FDeoEUozMU3Lpoe4wv2XbdS+mWKE25GoUflhOk7kuu4qHhOGdZEoX6khBnpScow38HmrUg/I9+knee9PuzM0bhGE/5vAq498448qgwwEzHVTlwSYMBrFaM94Gey1weebqTO/BDvNizV4g2MYXge7qCqIRbTuqM4xOpAhIJGaDqkptfPj4xI//1t8yH6pporUCYULCRJqyNU5R0J3tcmXbd9ZtY29KlE4KnSiL9WgAkYxZKimtWzVUm2kxLpPRAFStV1lLpZbCvj7fgjqmdGI+btZr7arWj9JAToEYhdoDpVdqa2x7+0bgUrkdUtp2UjLyAAJBbHB8yaAy0cGNIE0CaPStrEpsrHs/1kMno1psPGLv/nklkERKpvBRuzIlIeRs0kZiii5JlIcl8f03n7CXQp4yDykyp0jIiVY3r0RdJ2/AeMeKsYeKE1dk9P+8XhAjK2FI8NFnUWSgw7ZuPB8aB+1QmRhr9SDCyA2mfik06yAyQy3eXnTAf9ZbHCjCCEjHzx7pzVC0GM/P7e8MMhIOtavnbw77wctSioOxCQymp+rtmsd1Hxgjetwcq6r01j9r4cbghBulXkzncIzF7NtGa2MQ/Pd53N7aL/X4Vgeuv/E7P2NfV6II0zS5T03gUrp5dXUzehwfgFMPAaHvzQ8coL1gpQXh3WX1GZ1m6hfFxHQNZgNcYX0cdCFas3swoUTHwW3IgaMPtl+iHAd2TtZYbk2JKfHp61ecTnf84Huf8rhkpmgBcTkJ8zRxWh6RuNDVbScw2K33TkbZ1o1WGykqpzmahJIEUqnUatJYzx8+EmMkp0TrkIMyTYlXj6+5xieen4XLZTVPKFEPXjAa20EKMSppEqbTDCJGXsnJWXnGgFG5yb+MezG09ky1Pzgrrpmix/BY8lEaaepCo1Yd4gdISEMCKZKT/7C/gL109y+ZQeP4vGOPMPowrXkwawjF+gbBD6Ng2mumN7fQNPDh+cJf+52fUkonxYltvRLziTSdON3b8LL2RtmvlH2nlMK67+Tp5AzHSi8f/ZAFcCFcNVKNab5bsJ2yQ0aYtmIr1o/8+PEdk4gxV2MkzYsNJmuhlucX6y8eB3Up3dexV/XZD2aNxGQKIbV1tvWZyZXvU7KZsRATKXRavxyH5lpwZxCjpI+JnGvFpLtiIobF4E3P2t2xiYCwzJnSlb1DSkJrpt4vAut1JcfAp68eoCgxTbx+8ymvYiKdZuYUWcszcYpIUEJOEMTVO/yDFhkYHcdIRAChe29KHE4fCYoTFrof0kN+yYklCoiVcOBDGDYjrLf1HaxPaezIfqxT00AQv4pmuIe0gzQ09gZja8no4wkqDahGFHrBxhvvUQgQlK72nON5Rbxn7AfPaJuFaNdhZJodY3s6lCwDuvPALhBy8Fma7tdulZ9VcOE4C6yKehF4hz+YowJ27sotQI2P6fbXX/rxrQ5cl+vKvu9EYC1G+TZdM8vSRwPXsGD1krm7ptttbiOJ4b7d4bDevVeS1DFtG7K1gV+DUaYUbg1hDBLsrdNU3f7bA5QEq7iCuMOtfWxKZ8lGqe5dOJ1O/OgH3+VHP/icH37/eyQxzcKnj88sU+ThfuHVJ2/QOJsL/N4ordBqodYKZTUIUATtOzEJKdt9mJcHam2s1yu/9eEd18vFemM7LEvk4f6O/1OayQ93vHl45NPPf8jl8pFtu3C9XPh4/YCqMe/yq8A0BU7LRJoFIZFDcoFj3EDRSAPhYK1lxGFNCRPad4aV+FCGCE6guTG7GsdQZ5hcesY+99GnkW7VEdgGstndMRMknqgo+GuoCHTLV7oaTNRHz3P87IsgKCHTauPju7f89Odfs667sapqJeWFNC2cnu+Zl0yMIK2wbTvrXni+rkxLYUnRLHQOyxYgmMSWSEBjIqWZ1irreqGsY3ZKqDoj3UYm9m0j5ETKmfMyI9n6N1GFTYXazNersPswrlIVcshGFNpXrsVYbVUFCckHTDFV72QjB8uUMVnBxiRDeBnvKbZD7T4FyDmbJc66M8+JaZqZz48m0bZumB+brfspJT55/Yqtd9baTHYMg8wRKGVj3xSl8Ye/9wn5dOLxzWdMMRKc4XawNkWMOJTMs6o1qyhupAQ8GQEI3Fx4HbAKXjEM+46BY3H7v47/OKKCV8eH8oajDN88lOU4U0I0EtQxb+b2MuM8EvAe7JB8amitHnAFfKbKMo/bC9meMHp/21Z62elBDrLOEa1k7JVAIBzkNQHzkxO7p8fzejp9zIPVQi8JrfutevPn7b1ZgBObh+zBwnqYMESLxjxNBO02bD7Qlhf39v/QgWuZzmizN6E+GChBYRgVpmg4mJfd22pN4O6qzDFaPyxKNAJGt005TZPZoiRB2BnikynZzEuUwBTF+Rf2odRWjGZeG/OyWPNdhBiS2bu7P9jAyluvWFwJBAk8Pjzw6vUjD4/3xCmypEzUiZgi93fZAkWAkAKaAnNObFunRqUlCNOJttjgY6uJecqOu0POdnjOOfL6/kTSagaKtVC2xrMqb7/+mrM2Tuc77u9fMU2R3hI1Re4Xm+tBAqc5cZ4nTrPNf9nclvdsAlapYVT3waiTATsMI0Y/TGwDmKhnCCb8Krcdx2AlKspQQui1ELI74wrcmvKWiIz+AQMi9CrPSmQ/zDTb8SARCemWaVtGg1HD7fCw3uDE64cHtrkTQuLp+QMSMiFkkkSCJFIILMtEns6ceufVK1ek6JXeCj0HJ4EEVLLZdgSjjk8xUFslXi7EUA91+lp3QjAq/P1D57ycmHJmmZNpJaJIL2TJ1qsshb25ikqrLGlizpkQAnOtBK3mmVVg3zdSSkzThJwrp8WG/JPrJ4YQSNIJebEMulXipjboXnamaWZKBimGtXA+L8zLifn8ho/v35HnlZwfTI8yJeZp5m6Z2VXYulL31dmNlW19tjGF3ni4P/Gdz7/P4+vXxCDet7ID1wSTk4vkxuPz5TjXfc2MY1EHPOzrzlXu/Zs4BjDqn9uvHJiIraEXhfuL3+UWtbyEkBe/NtbvmO06mImjslEPQh6IhTHr9CL6vjziXxZeglWE3pMaf/TFxdjyv61/C5B+PWOMYQS5EXSP9zp6qWP+7qXahbMuVUwhf/yJmKmkCiFlptM9e+tc9+0GQ8o33tGvFLy+1YHr8fxI7GLMoTShwQR1ZTIZmCkndAoOFXTWebU5BrXyf8p26EZJrJdnSinstTHPMyFFJAZSuFpPxbXJgpjz6RxhjLX03qnVehGtFE5390ZZj4EYJmebATn4IGGj1I2o1fy5UuKTTx55fP3I6f4eSYFlWZhTZLk/cZdhCkKkEYNJsUiMiAotRlQDU0y+yTvlaoexdqEVZQrGpIt3J7732Ws+ToGnOSHxwtPHZ7bLyhdffMHDXnh4XTgtZzPcjCZDdV6ibQ7gPE+cFwtckUhI0RhgSQyuYFBtHW0IgpdWvpk66ocI3sOS4AFEbl5BI1lA5NYL6M3kaGJkyE6bpI/3FjxYcmDw9t8wBvgclhJNpo4ekqtlDHpxO/4YqmyGmtPpnh9873v0btJfP//6J5RuYrXnPBGnTJ4Sj/eZooEUI+c5Ulrgcnni+fkjNc/kGMkhUpkQsefO8x1ZOq005ruV89IotXNZOx/efUmKmRQi59Md57s7crKZqzjdAYq2jTzfo3Wn7ldqS2z7Smk7+XTPnCJRhF0DUyqU1vlw6Xx89xUpT5xO90xZOZ3OTPNCLSsxnXwOrzLdvQag7RemLbKXjb1cOd+/Ygo2VTfvyv3Dwmk5k07fJU6/y3690rbOPAnTtDAtZ0IrFBJFEr08kZcEND58/RNaNWLTp6/v+f6v/SFef/IIWA84NOtZSbA5SOtbhgPtOAKNw2jysl90nM9yrAv1fTIepkto6+hloLLC3hLfQU9/+Tio4DJ6Efbo2m0EwoPuTYT35bHt63f81xO742RXxXpjVjVZwPDXVGtZ9K6HpmIfffUX8W70pQ9pqyNw3QLouBb1N6y+p8y8E4b79UgOgcN+zJCoYHDsOJMwO5603LPWyvO6GqmDMfrAi/f9yz++1YHr7/67/q/sa6HtT2xxoUeDrZbXr5iWhdM0UZJVQ3VfgZ3TsjDlmcu2mfVCCJzyRFuvPF+ufPH2vS0yMUZcW6+mpI0dsDkZK+9uSm5x3tlLdVdhC5ASF6OQdvNamrx/srXmFV+htY0pNEJXQheW8yMpn4hh4u7+gbvziSVG9n1nDoUclZwC13WjlI/s20ZeFs73Dyx39wTJ7NvKfr1SPnzkcn1m3yvXS2O5W7h/vOfVm0/4P/8//u9mJbHvfPGTL/itv/bX+Z3f/gm/+Vf/38SUuTuf+f53vsMPv/eaN29e8d3vfAoSeXp+4vL8TNuvaLmDZhVFwOfUagWCQ2yDZ9scg0/c1ESVuq1OT1ZiXBhZXN93l68JDA04bZ2+bxz9AA9sWm0Oy6SUvFchoM3w/piS+X/56bY9fYTejFAy3xtNN0DZN8Jm1XokGH0/dGo3iaxpuedHf+ffzXd+7e/mfHfP/cMrJomsTbjula9/8rvUbjNQoVU+7jvTkvnOJ6/Ip4WIQIOvS+Xpwzuul2dayNzlmRgTJUQ+vv0atDHNiV/77A1raby9XHn/xe8yzwvLvPAwLfQg7GXj7Rc/JZzuLJnqyk6k7lf6duHx0095df+K8+mOiwpvf/4z9n1lujvxw+98RifwxfsLP/mb/x8kRubzHa/mkylzBPj49mvCNBNSZgZaPhkkvq/IcmKZZu7mEzLPXD5+4Hp5JpzOPJ7OTHlhlxPrr30frYVA4G5ajPmWMuu7d9QYaTFxTpEwQ+s7b7/4Hd58+gmhN37w2Ru++70fsEwRFSOshOTC2RgE2kpj39x0NZmcmWNYPjBrVYeMODCsRXxk4CAk1Js2pzEqLTDovhvrNNhIDdzW4TjgTcNG0GaVIi4VNo7mul5tLnMv5OXkFWJAq7l4j2au6T4qaOGQcXohqqu9HkndGKZum+k01n0n5jPD6qSXMXcYjmpQ1RiOzVV1gov/am3QdkvgMBk1ghEryl7YrqvrY1oQ69UGl5tUeqm36rCvtme6oHHHzKTgshfa+5U8XU0UIkZDGV4QO35P9+4XenyrA9fDZ59iFNOVp56oJJZ54e7hTM4GY9ToNNVeCbIZ40oCOZsatQApJoJETiHxuuMDwwqiLPm7bA32ar5A+CyMiiA5E4GTdpIUywpVUSKl2KbTYIPCoLSQQTpBjUiSTdTMJvhDIueZ03LmbnngfJ6Zs5D3QCISBXJKlPZMq6DdmI9xSqRpAu/L5Sky3y2k00QrldPZ2FnLw739eXyEEDh1JZ/vmE8zn37/Ozx8/zs8Xy/se2HfN3pS4ikzv3ogKKQlc/dwB/3KvMw2IxbjYTYn3e0xht4bo/8kFrgctu8+jN1Nb4cY3DMrRCTemuYSX0AXJj/h/TIbPzDFDffvGhIWngWquJqFX4eKN+CCGHEkGkst5ImQfNP3DrUgZUejUptl2iEF5uXEcmf9m+XuFXOemSVy1+H88EBZnynblefnj2zv3nFaZl598oo0n0gxE0Ni0sD1zXfY15W1NDfdC1QS0+kVop2UoKVAmOH13Sf86Ac/ZMqmEblfV9Za2fadON8jecYILqAxm+WKNu4e7zjPJ6Y8cS+J88MnlLJDFKOeI7yeX/PmzacmIRYiZd2pQFUlL4825jHmzKIdXkE7acpMOTPnTJoXpuWR+boSpuQ9u8gkE/fnhTg8p7rNGTUic5pIrmISUKbZ9BtPe2f5+h19fWarwlYqKRo71oRsPdvXSpAEcTSlXsJ4/jn7ULMcVZJDxd5kGTNKiBp83J3lB7fKKUUPLjaQKw4/awhHZW9rNEKw8YFDr5ZBzgjQAz0Io/4zclE82kVHf2kgED7ZN8QKBHGPPP+6K5QM8o6GQBfxRog4quABL4z7AiS5lV+j7x8Ekzd7gU4kQx6kBySZ+7dKdJo+JlvXbyMmVuR6QJZxfYmYMuf7M3tRtt1FwV8AsONevJSB+kUf3+rAdX51T4wzvRUokULmtCzc302Gj/dOjtNBqEjhelRFEG0wUbvNf0lgEuGxmyoGAiEGXr9+w2VrXNZqH4IfirU2ZFoM7hEl6dUDlyNjbMBOSxGt1lfTOERxG7ELCfWh5R0kktLEPJ9YpjPznJizq83ri35Z3mm1mJL8MhGzqaZ3rYQIMQfmu4UQTROtrisiQj7fkc8n4nxjX07nM/OS+eS7b/jku9/ly7df8vbtO377t3/CdMrEZSLME1mV+bQgIuzre+blRHIlj5DCcYSE+E3YxBamMw292tLe0eH7o77ZxIR1xc0JARte9OclOgQk9vOm5zcWfjD4sTvU4o8D7x84frLBVR3wZIyElLznZJuuOxECoDfjS4cQSDkTp5k4zfQ4UWJG0kwKkYd5pl7esV0SW98JHyHlwHJakLyYtXrMnGRmWu6prRmpqDZaV0QzabkjYIosT89P1pOaMj/6tc+ZfND2i59/Sbte0VxI8xnC5L1CiHkmp8CUAvOcvL8amOJEPj0Y1b4VrlfzaFumxPc++0Nk17b74ouvTF6sd9L8QMfcESx5z0e/OCXr1aYYmPJMZ4JcCFGo1cgnIWSXGzM0Yl0LxYdnrS9niUWt1Vx7UdKpkOd7sw/qtreaunt1cMNGNRHmIJbgvGwq6QvigIbg6vkOIo7D8Uhq+jg5vTAJtzUyDtPoCReGlIyDXmI4FCbGsLChe0OmzFedcDCNCSOE+mF9jE5wew9icODt2jgSZBxpvzH/bG8IxoRSvDU7XhgseB10dg+iGowROfanXSRDqsXQVx8WThFJ4/lHsLLnvrEFx2t6YD3gTRtTWZaZddtNiLwPosmLx0GY+eUe3+rAFZYTaT7BXnn9cEbjRAiZ89mEcdd15Xy+I0U7ZFM0qm4thdo7FFvg0+zahnXmbk6oCnlOTKeJTz/9nOfLztNl4/l5owiU3nl+vpJm88mKIqRuB6OoMofE8/OFy7rClKk+31CdcRi0EVokhEbfd/oK0zwdKvhpyYRo9Nacs5XxDmnM5xPL/ZnX4XPCMtNKo+4bVs6YcOhyd8dy9xpx0snwGtIOT+++tkw7RdLpgfn+nvnhNd/5w/83vvf8NR/efsV3f/zXyZNtpg9fv+XuPPH6zXd4eP0p+/6Ilt3VR4beIIj0A8r45mM0msFF9GyTBoxw0bvBifad2//CAF5ckfsb4P0t4wbrhYWkDImdQ0fpOAWMPt+7HNn1UO7oXYi+80dmffxOTITYQCuXy5WyKrsUnvdOnGbylLmfIql+pO8XSin0VtCWbai7XNhqZyuNTSZO80LOma11nteNUkxhfYqJFG3DX9ed3hpTFPi175nNipog8vPl2Vi0EkknM+oTFVKEHAM5mT7m5bqaqn+cyMlclAGeriu1FKIEfvj5p0yLieA2lKYOY6vQRZzEDsMOQ9Tlmvznojxb/wMhL5lSK9qUKYo5GBCorbM2m90qvZugfTQ1du3CWk0Rv1eASJpn7h5OLKfFTDlzRuTGxut1Z/QpgzMKR9/mCAY6qp5RKb3oIzH6pnr0iHxBHoexeNDQoxLhCAAhZ7PpGUFxPPOL9TZiR8zmgxZDQlp3OZIXkk/H8/pvjh6Uv4kgL/pto8lk2LmNa4RAEE+KozH/9KjtDL6UIxiPapIbhK8+FqIjfxRQ28vBiRz0fljzDFKHfb1ixpfezwv+rnqDbs4S9Mp2eUJFKKUcQWq8+/9D97i+fvsWjU/koOhJLLnvG00nUyVonYRng9vOKTdaLbRWSacTa9lpbWfSmSln4hRIJ1ivz6QUmFNgff7AttlMTG+G4aJOBZVM02DzKO7pFYLDYD40aTCOqaS10umlEdpOrFdiqCak6o62qVdOVM5RyTSiKGkKCJNNuq8b6XRHypMFzTRRt816RlR6KU4+wYKZb2LE7FsAUD9Aeqe1D+ylmxvyxx8TF1Mx+Pz7P0R64fr0zIe3b5lotLsdrZUcJ2q1LSIM+SznSujo2np2NnpVnr0PGRkJgej35IB4cKPHcVrYTgDgmAVR2+CHNuBI+8ZrHhiGZ4ASPZkOvtltu4SUrJEsrhkp4yAaLLCK9uh04W7MU088EINkU4wEEk9PF+4oSK30srHkxDxPZv5XoNEp3SSq1rqxsVlPYt/NsG/K5jHVQQjEVtB9p/XO9f07UjubdNl2IZRKqEoIjVR2o/jTieEO7ZV9N6bsWFPaTV4KnEHbCr3utNa4vH0L54WUIu35Pb1ZVdSbIlFJwSqmEBdE1GSYCNbL65U0zTSMXh97cjcEm/NZL89eKYn1UkKytVp3Q+g0ULSTfG+U1ih7oW8rW2zIJxOhKVp2g4zph/CyLYLug8svP3dbkUPuy5islpwcFdWLtaS9jVLGK4VwyEAhchz6QUbJY7NoEsYYzI3ibV533zySLf6JjQwkq4LpN3mnIXA8FrZ/274Woo+ODCjOBosZ82butZWcdCQEu88jqetY1QmgPot5wHnu0KBmrTP0SWXcQjXCh1kieVXa3OMQMbFkCa4yot7bNzAzxNE/MyHqqpWg5TbH6pv7Vw1a8C0PXOu60kJlzoLmCjRCb5zqzcnU5MOsuZi9+Wjq4cnUAFygMqVAjkIOUAukKOQolLbTWjiyM8HRA3yjI9AhDe+pESKc6lz9310x65RWD2fgw1SvmlxU1E5iKG0YTBaCwWT9KMMnYp5J02Ibpo3hymja7KGafl5tBzxxPDxbHdYHWgrranJAH75+z+nVA/OysJxOqBdxdSvokuz53MDS7F8MohuQx98GVw9o5kUFMzLiA8Hj5c+8KMxERnEGx3f9b8d9flGE4bRi1Dfs6HK8SGmR23Mdl6Ou03dTLtButis63I+H+GjHRGH1Bj/1rnbghkZoHZoahJpMgy92CLWNW09xWbCcgdaPQ6zVhgS1cQBtSLeZm7rvtDLZSEEz409tHQjIYXbYqNWle3onx2S+V9rp6jNOCnO2WToZFj3bRolC6IlebI03jNASvZ9oQtIuvdV8ts31GsUDiEcBV/W3v9fqwd6V1e2eyzfuN9pdCFvpWEWitVI3P6QRp6/5TRp06pfB5htH4Mhmbv8XRjU0vh58DXE8x9ElG2SDQVUUBwQ9oqiqr0k5nu1W+X9jdTEklOj2HoP30L+xB0bUe0EVH23BMXtGV0PR3W5Jfa/dvLgcbfCge2ygF7dG/T0dWtbjHtohdSsz+wj0Nt910PX9cxi14LhNdg4625OBZPj+aY1t202Tcx4edC8+qQMi/OVD2Lc6cIENvom4cnU3KC6mbBppIRwK2RrEyutk1O7rtlNLd/NDSHmyWSQFiZGYAjmaHYiJRUKa7DV6UyQkSvP2r3bzeRKruFpTkESI0Fxrbt8r17VC25ikMkfbAqU09svKq1evDEIJgTQtCCtQGR5MvQuSZ0TMobc1NYafgqSMkIx9VCvaN3qQY+MN+wnrQUQb9lzNEXe97Gx7JeUMrVG3jUsr1MuV6/MTtRaC3CNqRpNlL55lgkhiUOAPqvnQXYIbbCeRl2tVa3E4Z3gQqcMS7SBQMLJR5cDhGRtEYaiUIOkFxMftQHO9SvXf72VzqMPnxjCopTVnPhJoqRGCaQWCJT21Gxu0NKX2TguFqoHSCoROsLrDw3NEUkJyRuJEjrA3gwMLlqEHfxsmmqxc990q5aAkMWdoBj1fjKHVuqIS2Hs3CxoCsVUE09tb1yfL+HvnPGeHQE1Y2m6pBYijVA2BLhZ4a7VqsLTO1nYCEYmmKqMIe63WB+owJU8HVHxWzKuO7o7OTmZ4CU+lKBSg+mGovdNG8LFUi0Qn4ur0pfoSsPXbe3G1e/tYe1PoBlcyLTa36YSJceha5TWCid5YhT4CIb4mzEhSvMVqQVYR6rZasEk+LziCnVcdRyrWOar1Y+0Nyvi+0x1ODzF6/1agOvIhwsHadyhuVH5H30k6w0bl8PoCsyLxIBFStgpeMbPU47k9IbMNZwHFA69Vh6aGcRi+druYWs1xvNVO6tzereptf2s9Am+vGyKREOx8UDV9zS+++JKPX1/AfDj81ug4AkD1tq1/ice3OnCF/Uprz9SPitwH4nzPcjohp4D2RC+CRp8tQtl2PfS0Wox28EZclPbEFAXZlbvpziABCVzXwloqWwXoVBGKwNY7XSwjjAKdRFW1M1CMlioBYkhGu49DTieQpIDj0KQCs7LrMxqupGkjLY2oPhMRDUqz/SmEnLyp7Bs82nBhd9tsEYVkho5+Rh0yLCBIzLRewC6BaU6muE1CkoLYYDCxcD4Lebrn7mFmWiIhdaQ2GzQMwViEMg4htc05MjKxSlHVKx/XLSNGQjDJI0uC7ZpDFK8q7T6jzXo42KCjLX51+Codce2AC7V7lZ3sWgiHB5hIpPnQnfghHqIQsymUxxjd26zZwRvMEmTvna1Wtq2gRIIosyNH5skmSFIWIrRI3xqn08TjdOaT+0eeth0JpsRfKsdBVYtSqprQU4qEbGojIopMEykEgnbOd0aC6aXy9Nx5ujS22ojnjC89OjbSoaIQBY3R1MKDGhlCHH4VgZQIwKSB8+lEduJQqZFSO6XDMiU0xCGYZH0UARWlidHANVi/R5Ld3xgT4WCkiRGKRKgqdElUIo0E2ZCEIH7debKZKw/29RL48Lx6T02BCsHluca6coJgo9OoDB294GaJOBQoNMAU9dX7NyrxdgCHkVy9KLI8AYp5MF2xHlsCg5/TsZZ7LQ5X2lB57+B0X+/JBofj2wufNm6GkXBb4+IMRcV0Ph0WCMFMRNtBc8fFkC0gRXVExmOL3RtPTrwSsudOVm2787moGNV/ynZODKKEurPzlE2+Ddy80s4hG5iPlgDiEmxhiAGIy2EBTYnrlVNQzj54f4v2PgLwK5793+rAJdpt2LVWQisErQR3KGY03kPzLGloaJl0y5A4Hnvamp34h3Jjx9XWGKSYoxmLM3lED98vE5a1QzSKNbx770eWYXiy2TkEGkFleBwSI7ReUC0EKiFYNhi4SSENSM0yLLu+eCzkQIiBJiDBmuvSPJiIe2N5NinRfjZEce+yYLBfmJBkUlS9gkxmrJh6Zlpm16ILvlE8oz4IFHh1N3bQIEq8gAj9LwPeOJq9A0wUXIpHjoNqKB6446C9irieIWOD+1rw9z5e7KWpH+N7Pdx+9sUfjv9bkqP+81YIWiBvvUNMWEfa155HT3tZRXonS2IKkZwzshdbb61jPl12QR0MhFMlMkYALCiL2FBnRo6Rji6w75W9NmrrtA7RdR2VASvfhkiPA8Lfv/j9UExdJBGYnMJe/fqaS6F9UyXhdpi/GOk9nlvGC+D0bVeISc7RMeWtlzNLkaGiEjiewP3ljFnamkNh2n0g1xhtx84b70N4uRsZ/czD4h79xhWPXzgCyOibjnU5FovgIxLhuJcyrE6whEZ6v62f6IQP7bf77uw8FXEx7dta+4ZArYiLG8hxHh0fmGCBIti+Pu78aBi9JIQMWHTcUq+G5HgNQ11EsKox2AxjTDen59vbN0RCwf0Nq1+/7fsQR+93fAYOnSoHVKzukjHM145hb4+pt8cvH76+3YErmMtur5UYjV0las6suBDtFMwSHNXjYFMPbMauuWH2okprO7278yxK6zZYG92WgLGIRuYGPslvfw/B4JFWXUC13gKYafi5jh+KikFTU1Sj9NcKB1tvBIfo1gUGLaR59iFdt5cYdFSUmFZa3aFUU5jQRu87cvQabNFH90DaW0HihIhBjXGKiHRaCERpDBvy5WyqCjFlYqrH/vJPwe9DeNFP+6YHzzcPkNs/ezMK+EGOkEGBF/MpO1TedewMzOhxPLkcu/UmmuqU+xffO4Yyj93Ny4vwjT2e39ZBivHA8OmNfWtoCmjKpqARTVlEazefr25SV1mUHF0nE1NOKK1Y5YLV/vb5jQPHPZq60qigkIOwpEDOiSRCq8K222fbdTTXleDv0cY3wiHufHw8B8wkTvXvboViZqMxRKt+evHDxTzfRmPfUD+zpcH9msZtk6EH6S8mYpDknCMRJ0AIqNqoSQScSvoi4bCrTWlQ+C0IG3EiWiXZh8K4YA7BzauaQfqRbyaVLw9heVFRecCLEm4Jjo4hX6uyb1VR9Iv3+/AiGbglUOMGjTV9658KToeHG1zOgNpGrw9HHwab8MANb0mcMSTQF79/u57AoQavtzmpIwSPJDAIw/MNHDYNwadQAq1UHw+69fQCBh3WsltijDFFjhlNz/bUFvgRvHB9Re1WedZaKLs5HxyJ5C1b+JUe3+rAta07Zd2YkqlNz9mx3r0REmQJJAne6C7EovR9sw8rZaZ875Pxga4ZSZHz3UTrH+1IqQF6sUn92iilgRjlmFII6QwaqLUzR5udUFGm0z1lD65NqSCdEJUcIr3sXkHsSOsEFkK2gUzVmaYT+96RKfuMkZjWXau0dWV9fssQn12WR4MBu7Ele3FcPWanLtvCjtGz2jgm8kE10KsgyZacopRth6PXwAGDrGuhtitpb6MHi+CsQV/HKQJ1kHnhCGgS3H7CHYjh5eCJ+ZKZLLIfircMsvuwqPWNDZLUMAKRQIijXvPXFfN9igq10Q6oMtBlArFkJcYFQqZrRGM2OyfMgFPCDHHyGS+rjejKthUmOXOeF2qCZVpIMXO5VhAbunz1JnH/MHN6fG19ylhRLpYjpWCOBapMPdLKRtdOTosTGYQcFygrc448nhdOpzsjBe2KaiTKTNfOdWtMebG13gt5OvtsYeckC0V3Wq+kODt8LMynk0HRAR5OE/Ny9goi0poQJSEx0+lMMhElUHshx8mUY/ZKlommjVJ3Qp6PecCUZ1tnwXzlolift+xQtoomyFOi6E6SSBJhb9UgKwISJzQtaFogbeS7mXTOEDpttXUTwOYenVIeAN1XH8ANlHoL0tanBiTQe/VD1/ZjEzl6bfbztnL64RxhDMzRcwrEQwiYGC0BUNPz11ExD2ZqsGBn6/1GZa+toi0Sxmv6fhOgvdhQN2sRT94cRu9uKcIIep4YdDXrpiA+LB4s4FgPaxBcDLXoQ2quFJPNQiAm+loMmhUY5BUVm7VUceQIy0KskHINTu8xxynZCEjZifIAwcZI5nnmfB8430/GN/D79asMHb98fKsDVy3N7cRtRql3YzONzz8EMZpuc6quO/QK5sRqFURExdrrZhci1I5BWb6m+vgDLpLrWcOQhtJBjfUBY7HGrlGAGwHbBL07MUASkYkxUiLazVIiTy7v4nbnzSsHuvewqtua2+bte3OyiM1y9TEvMc8YacECnMWvsaADvTbr9XWXfhGATq07rZviPLUcw7cjiHTlhsm7+K0FJ0W6ooNUcGAdfq1DB86FQNVZlDaMrM6mGlWhb9yRoAE2fOrbXTkIAPSB1wwBUa+mdVTP/tpiFYfPAVgwaHbdNsvi1VlzFpdn1na/q6uiGzElpM0NcxOajL257oVg1s70uwmDdI1dGH3IeS871Q0Nk2/8kZXGybzU9tLotaI5Hg19bZXalZASMZl7dgijstTjYOzd3H0N2rZqudZ6rMvrdWXfNjQH+mk2dZGu7KUeyAU+IG/3eSQP7hSsjhw4jNd7MyJUCNRaXY8zkLM/lwOiXZsRoNoN3uoI2hqtCUEG9Rpj9i4L8zwxZdNKLEOgFiU6K3gQPIzE4VqTGPRdXX4tDoLNcYh7UPOrG5b3A3q8cVe5MfREiHiyh6+V1g8Lmd4xs9Re6YMlCvZ+/V51F/YL3a4dtaCF4ooefm3Suclv2L0yC9jguoH9gOQOwel+O4e6k3Nu/arbgK+KEZ96s2qxKy6dNcZnBgvX/t5ap9fm4gbJztZW7H5kJUZTYFERIwJVE+yOxZKE3uD5slF2c5jvQ77qCLovgvMv+fhWB65S1QUfreqR0gkzI68nEKjVTe16NeNHBtbshIkYjY7rdNPaG8VLWxmKD6ovhCI7g+yhvdqHp5XRrQmOjccYSTlTBrTQlSp28AciKc5EpxnTmjXL5xlxNpNqpfVucBDuc+PngS3kwrpdjgNsv1zN5RQIZSHmkx2eOVvl1TtaGyFmevVg7v0bm5+KbOuFuu/s+wq9mnp4ng2D9xsnEjzT9mUoRixoraPJZXIIR1ZoaJ3cqk+g13oMZPY+gtRgKh1xlAFFddrBxntp7AfqKgccQbp7cLI5PjusEOi1WABE6bWDdKQ5EyoEE0yuFZIFYaTTWnXVCRdQ7ldqT+ZjVTo5z7Bf2a7PNocnwt3DQlcjLBzqIkHYrpsL9xpbVXpDtFGKMidTWdjWC6KdJQdvsZhNT62NlBOxGFSYothhKQY1ln33sQ8oEqAbHX7fVhuZQNmePpp23jJR7mZ6N2Hfddus54kiQd093IKfoNS6o4gpk+HBp1daMx+2QOK6rcQ5W+CKQhSnc2uDVi0hrJ2ghgIoQLOxDRUb5IgOsc7TibtlYZ5tzVgQrkflfQSu1sz9IRpDE5cmKtsOWUCSJUMjCR1V+oCxu3mKqUPEQ0FjBGsEJ11kS15bNwPRsSz9+XpvqDRfui8O4mPA3Zis2tQcuT3JRThGNyxRaIyegxIMilU9EnLG7w1oecCcahl1934Sg/gw3ndXlGqjNw7raTe4mXU9xiWCBIehLUlrtZEmc03vXakvdBpjzMchqr3Sa6HtlZp3JEy02vnw8Rl0phV85Ogm7vsHARZ+qwPXfnlmW3cmfaSFjVxNdHQS02VLGRhDl/vGHsyY0dighZQbc7DsIdQdIRFVYd8tO0hmSRLKBtdnpG4kn12SLmx9oyr0tlMN2IaUeD3txLKS2kqotslHQ721SqSSdTc7QVE0Jx5OE6d5Jrky/dAnfGmeKOJZtjeK96ePx+Zod9Wo2E5jDtk05rQrMQeTlio7psYR0T6REwelOU6JebqjtwXt52PeRkSpNZCnmZxnO5ST9SNaU7PBGOLCqk7fx80iOYKR0RFMQinEjkqzge69E9JEcmmjATTWbr0Gm6PBmtdecZlgqT13SCPDxIgmTQ7ow0BKVwVIdmgpSoxKTCaPdZgKIqZE7hRt9MXgaoV9W/n49is+fP3Ete6k+RV5uuOU4fndO+jK60/f8Or7P0TSHSEmogqyV3jeyNfqzsudvl4IwZypewhoWNkvF77+3S8QKvHz7/PZckK3lfXte55//pb2/h2xNUQ7uT9Tr80qvwBVsh0MrVMwhR8JsKdAjp267/zsd7+g98arx1c8hr+L7d4crz9+9Z724UtjHKZMDM3cwVWdGJLc3qKxdgvCMQtJHgmpEeNKe/eOeprR8x3z451B97Wx7Rfq9SNNBI15wBVeNVTi5tW7Fk5szLnzeIq8efOa0xLo5Up9ekvd7HA0pKLT6Q7lgri7MMHZpxF66AgF0UZaJlq1ajvEBF45hCwHQxGHyU0vM0L1mTVRYPiAiRdEt15USNEQAg9EAwIXp00qgiTodbf1OCXUB3+tkqrWXxXoultvCag9OBxnyhUmQehkJ3mxrzoOcQ7Fms5RXXqAUHlxfcN004ezobm6la1/UbHeYQyQApLEE9SGhuYwIhDVe2iYQWsQkE6yAVgiymPqLI8PvPnsNcs8u/aiPUa4/lUe3+rAdXl+Yt12tO/EbWeaT5T1zCf9NafTyRZmLdT1ynq90HI+eiFRKloDIQvzNLNM0bQoS4FWkOCUh16g70jd6WW1qkmE3gPKfmQjUYAYCJqRlgm9EHtlCubGLKIYglAINGJoBOlI6ITQPeu0PyHIGJV0j6tjbXm1Y3/01BwCaMTYnI4uPtA6YDSrLJUbXGN08NFSHfRhc0JGskFRrr7e6k6IwTL+FM2Z2Ikj8bg221QOk1uPwQPL6HoN51eThhqQoDo92tmPA7A5Ll0ZHrL4c9rzOH4visnUWPP4qMLwvFU80FlHgpe26jY46YfQCyKMAGijNfP+2i7PvP/qPb/zt36b92+f+fB+pbadOH0k5TOnOXL9+IHeKl+++5JG5+c//3VTp7hufP31Oz5+/Mh1rTS3uEAhJ0GDctXG9WNne37m66++ZF+fuTx9YH36QNsvXJ5Xnj8+8/TxPcX7K+ZMbAdUDIKGxCClWMCx27GFzkajlsK6PnNdL9T9yjlG5tBorfP0dOF6/Wh9j5RYsid3qPW21NQQunabtgo2nN3kI/SVLQaenj5weYan6T1975xOC73D5bJR626fYLdZxwE59rpR0SOYRe8Pb9vK1z/7LZZJQAsf37+FXg6rnFJsBjAsCy3i8Lei0QkmYj1fWzkd1eQQtK0J+6Bvx+aApsMRqF6QIHByjge30SO2h3qCM57DyzV//rFfBd8jYfz8QBUGuesFsxb7fZthe/k6A4aQF6f+QDvkRmLyHwmCI0TwjRAh433a62uv/qpy9J4Gc9D2bz/IYkczWwTzq/Mg6QQuax900GqfF2o997IfieVAUG73/ZcPX9/qwFXrjnZTfkhYkDlN0OtCbxFt2YZd2465seqtV4WgNSM9MqWFKVqGXrUQnYUTJRDFKqQoDcRU48chLVqNNNB2sxAnWs+mbUjbkVaIR4/AXcVpRBqZTpBGCkpOMAU7kJIvvHAs7nAsbNQzwSDef5rowYYI1VXTQY6qDGwzWs/YMtLeukFCUWyupY+s0zLIEEzxXZtSi9uJSLCZpxg88N1o6cGTvcOVGAb7+QgU34BQfIRAjUvrQVBua3pUmf4c/qXbt4+nerkxRxDzKnEcRmIM+EPLzn9cxl/G0LQ//3FgDFijFXpZ2Z8/8vHdV3z8sPL8XAjSqBVK3KEk9v1Kr5V1e+Jv/LVC36+8efUKjYmnDx/Yt2f263Zg/V0VzSaqei1Xrq2wX6+szx9Yrx/Q7UK/fCTjfataqdX0B3vv9KA2jyN4XzEeN6j35tUCVN0RNehHqGjb2NfKx69+wtvFxI/32qCvBpX1CGGyz1zV1kwbiY1/sGqBa+8bbTdSwHa5or2xhQhrZVnOLjKLu30IvQkhTQxlhVZWOA5tIUdLprTsvPv57zIl+xyvT8+kIKQgloR2W386z7Ta6WJ9SFJ6sfA4IHQ9iEAv1s2xVPQgBgYZqhq2V49kUW8Q97AWUU/+bmvpFnRuK9NQEZujsjGSl9JitzinL36fo0/uszl84/FyI4zA5XT6UeWZlJT6rJy++EVPMIPcgrTafhQNx54Z71HGCM3YIx7xgjM98aB79AP9yFEaopUQLUk26P5FonD0UPmVHt/qwPWHP/8elMpWrjx+8ppXr1/x6z/8IT0YVrttV55D4ZNXE9///IEP7y88X6+s60pZVzQXRFamDFwswwy18uacPUAE7vOJUyxclsL9knl+3ln3ztYCl67U3ui5wboTg1l9hPpEvTzRr09wbUzTiSllsgjTKZnteYc8TUwJlkm5u0u8OWXup0Tq1ajBPuNlsWFI43hmHUwCKHRbnl3kWFw5J2cP+sKqu+mKYX2/kCI9xxeL3Q7HQ3MUsWFpFGkmmCoxIzGRxLNQxqL2RS83Ou4g/JsIrD2pDqgCQZINV+tefKZkaCeMzezbaLjc6k1yxr7urCjxoWbH2w+tNxdlG265rTUjXgQh5RnRjGhCiDaT4/Bg0G66kWrvYJHG61Mgfv+eKJ+zXgplVc4P96zPG2UrzFPm8e4HTFOGKfPzH/8t5Pln/Pb/88/z5td+wJwi3z135OPPicnIDJdSECK1Nbb1LaKFuwDf/zSx3P2Q9eMzz199xW/9lT/Pw/2Zh1cP/KG/4+/g/cePrNuOaGfbLgCkNIM2pimxzJOxxnwg+eP6nhiUtAg/up/Ipx+htbO9e6K/++tM08T9+cSrP/I5V98XOVfWbaNUq1b2/WLCzaeZdTcNRyXy9POfs/VCoLOEiftXr4lx5vnnX/B2W431elp4/el3UEwvNM+TNf2rmtqLWC8yT5k3D9DPmbIVLl/9hKeyo73zcP/KxwICeymczg9My4n5/hVt3yn7yuXpiel0Jk3ZnAuiulV9RVtykpPtndaszxOTDY2HI0myfnnv1jdMKdtIQ6/YKIAf/l5hGUHIqeKMmaojBbL9EDpRHbZOCULmNvJxSwbUoX/7jici3pvE16OM9FXMO842azLnBIcyJQSGBuMRjGX09MSDigkujN4wY3SEQAwJFWPi5mjjEuYAPqNthxCYlhNaPaFVf94xkB0ySiQm5f71A7K84vzJI9M0HdqVt/v0qz2+3YHrj/xhomSiFpiNwdU6TOczUTtd4LPzA6fzmbvTmVevK08fP3K5PHO9PjFHS722fbcaS5VebJ5Kg0Ay1YC97HRgSjN6N5Nmha3YMHEQ5vRIXRu1dlpTYlJSzmidiarHoZxTZJlnphjI0SCdJI0QCmmYxYH1rRj9IoO85ECtnYrYuw346cjiYcxSaX/JAGv0MT/TTXfOYKAOxCMzPeRg8PZOHE3kMXl2YycaGuJAXtMjawyKe2Hpka3Z3A3gV98RkgjaTLjYyCodEWNg2Sym0HxTMLJgT41vw5gOeTpEqKJIt0A5ZrFUOq0rZW9o3UxQNCRC6E6xN3hDjNuMtIqKuUdLDMSYmM9n0rTw6rPv0Su0an2669OV9bqiZefuNJPnTJpOLCI8f3jm+XlFyxUhEQSWOZvjdorMsph8lHbOj4k5CjkElpBYTvc8v//Iu/nElz/7Ka8fz3z65pFPHh+ZlplSK9Ibpa5OdAjQKtnnvpp2hpXGqc/kCEmEKBPz+YFeGs/zO7anDyzLxP3jI3f3D+RpZj5tSK9MOfv1CXWyPuCcE9N8oyZJeGN6GKIsYeL08AkSMokPXD8KMQqPrx94eP2IilHKxWndqhDTRO9GGggjKZFAZObyUbhcN65Pz5R1Z5ln01oMgdoupL3zMJ1IgpGZQmbbKq0bgWBZsrEWa6e1FbCkJcwTvXZabeyrwVkxBlJKlNLNQ680kp8LeUpOnffkCzV1DAZRavRIDQZUdSajGnojantUkpmdDhGQG+39JWxp+4VBRddbVTZmpYzxx6A+WZByNZDehhegCwsrqJrGqfWgbbaqq5rqinDQ5J13Rq96VK0ipiCSp4kQ0tESMFWZ8RoKrVP2SlmNcRvTQi3w5VcfufvknvsqTp45ANVjIPkGZ/7ij2914Pr01T3LfOaUI6t0q37WjRCN9bfMgdP9iWVZWJYZYiYFZZkCl1mIWolYeds8i+ilmqCsQbkgQi07rRVqScYIFGESo+8Om/YaOrVYYztJRyYLWpKE0pXmlcacklndR4PrghpVVtRdRLta8PMqxhjsQ4BnQBH2d9Mg9K8oDJr8sKIYtFk9skI9mH5j81gf4HZPZYRIz8LEB1vlGMh8ocgwSqBjMHv8zEAP9NgUx9cUNPpslb6Yuneqrsqo2DwKDiiRAbNYsmcbYajUOzx05LFyvFZvuOiwmiUXAVXLKk1ZxSgc4kmCJc8KYgoqKUa3uH9j76HZGrmkwDZHyhpY5mxVwZxpbx6IQdm3Fa1XE84NwpyVnO0acgqmcymROCfmFEkSmEK0ioEObaPudzw+LtzdZ3KGU0jkLkgLx5xe7x36zSerG8aMijCFQHJoNpCZT5meI9QTUVemKTMvgRQb02RjHN1NLpNXsN0H8WM0h+gRuPJ8JqIkUTKR6ZxBEuU+E5lIQXi4y5wWS3Bqt1mwg/Eb3UalVbQ2VG2GMEYlpeh9WWyINdhg8jQt1tur1p/Lc7b7tjS20oFga76P9WuEihCDq1BkU2GXdqx/dcJT74O2ja8f0yQNIsf+GCRA33KHwkrw9WY9XQs6Krf9YsHMEr8YgwWXEbi81jKxYYcxx5MDo1l2xDrFkjicVCRjSP8ABP1swFoaB3NxPN3tmo6HYkFPj9TVUIgXCagot6FmVV76eHUPXmHdmU6TyaVthTtctircqszbS4439Ms9vt2Ba9r49CHx8Mn3eJbKc9n4+qsL2r4iJ+H1YyaclM7Get0oe+GUI4+nmdYMCuit8fz+HerW2S01dp/foRaQSL9e2baVr54+MCXTkpsAmbKxx1omY265Mmc7FKcMdw/MIiap002qpzanvhazCJEeYXPH0V3ou1KuHUliygyto8E12YhHif4yjI0F0Kl2eLvfjn2xH/droOlWdQGDtIBj2IdSgKuDB3GrGINRETfyG6/tfSp71hfLsnsIGT0BF0AdTCiNySq+ZIKJXQPahDRNlkHHSOjVs85AClbpjQ1062KNzewbCodLGQOSY56pQY1onInpnoCCRrQH6+k4vCPMCKaa0LfN2KWtIznSytWhxEJ5/oCsF2JZ7TpKpPdAa5HHc2YOMzncsa5f06sd+icfdRAxuHDJgZACeUq0bgK0kUgvG/OsfPe7M69f/wDRhkhl33+X3m08o9Tu80/WHw1ehap2IqbDGGJAUqY1T2LaRluviMD5Dk6ns2Xh/QP79QklWWDSdjx3EAiTfa6tK7OvEzPhnOyI64puFW3vAXi8Vx7vZ0QghULgSwaRrQYbheitocXm3uhKq52t7AYvzpnPXp14XCKPdyd6q6ZGkyLn5Y4uEUmZFBPTcibGyN35jr34fF5XUGMCkjPDoDXGZIaYodInk7iiFehmzZNTJE0nCEPlozNmrMQh64Ci3jO1asGFedXXnO81E7z2Plgwyv7gq5p9j6VcNjKYzfxSLMs61OzFYXLpmJzV2FPJiR4Kvlc49mZGu7OKg7UFDG0xySdjEuIuyPGY2bMgGMxdOmDpYN2pl4LUnXZ+tEtyb8CoYhCkRJBGVyjrjsjFXCtS4m4WXj+eeHw8mzKK3KBC+WYs/aUe3+rAtV4+cInm97NOmUInZijXjX0vlOdOe/9ME+OVUWHKiewSM9PJNlhrO+LqE+v1SmnV50caQTNzDszzvXnYNNsYohA7II3aig08ihsCuktr936QhKFJB10bIrDkmdMyE0WJuvgm6bRto1yfwd1mJQZiGNTsgEjyrA5Cbwd7aUAtIG5B4bj3UKVWhxcFp9fqrfICIPq8llPS/OdNRaD55g2HTtutevNV6BR0y0aNQWmPl1msUc77akogZTUhVBWhS6WWakErRtdpNNXpkG6KH21UcoD1zG6WDooQ1Kw4RMTmr2plWzdoxTLevBLVZG8kRpJXtQrouy9vdIGgfHz/no4S8kRVyCiTdrpullD4cOlxgKtQe6GjpHlikfPRx+nuVoBDNCG8gIDVgkvtjeTJiSl/h5vsFN1JGo3aqkmHBdM11F5umXISoga0R1IcSUy3QD2y/EPnzhTC6cE1PIVaLYEzTyahqiuRq1p274xWibsR03u3kYYxBD7m/BxG7tUO4o5VidqryQ6ht/5NgLu7Eyln5tNCVLOLmdcV0UaMmRCyzVK5MsXl+QPb9eoEKhufGAr5fd+RFMzhYZ4JslrA3ysxmwN4TAnKfgzPSsyk2ecea6GsprCTDq1Qbr1dgCFkLDg64smTiM2mjd6pjNLMgp7obTDZ6OUVRIiD1g/HfhBsQNs7w4iabmVwlrII1FasPpJm61pv/bxjLfv6EBltApvNC7iOJhbkVLIv0W5zWdpptXMuVwt0HUK80ouxluMU7XUUd/OuoGb2Oc3ZhBe6EYo0ujnnQGZ+xcj1rQ5coRXq5T1PpXHNmRKMadj3K9oK2iq7bjQx11X2Tk02dBxToK4zIkK7rmivpo6wGU139Idq3Yg5E7INieY+9DAHdixoM7WOXiK17ERJDK9TkUD0IJEkgBPAsihBq228GBxrtiHH9XqhResThJyIrvCtCCHO3nfCIMYXVb8Gz9bGMKKzgwZbCh/+7Ko+3yteyRg0FgYdTdyYztllrXcIzi48xES5wSYvQDpV2zBd/DD3ObPxqA163exw2DcfLsUUzUs7Xj9Fcz4OwSqu0RMY7MgBqfQX1ZZlxW48Lwal1Vq5XlekVVpXm2/r7Qh+gYZZxYDqbgLMAjFF6r4fAW7ouYHaSFmIlvGKEB2mEe83ipiBYE43Or9ma7jj2XVwmCm6d5f4LQ5ON7eE3tyyRe2+9GSD4jHd1sSAsgbzK4buASaQvMFuyOeN8qyo21kkaEZ9DhqJBHPubUPv06sJwdaS1/lGsEm3xGesO4cRQ0rGdnM1EQvPkahizMXsX/d90YP1E2OIRghA0dSZJp+xFOv/Vh/C1w7aCvtuArBBhNZ9LddKWVckCiFFptMJwQgE63UnzwspZ+vd9HIM7QYxRX4H2z0R9Dm7g1Ax4AUPUGEMV/i6kHAkeLbDvB8dPBkMpqzugy5OArxlnoNtaAEt3O6vkzNGYtjxscYwZNQsG7Uzy5PPoQDDC0z/EJF2QeI6vLJevB6+LnNGVYnTRJ5PrtIhPiJjavxpnBdgxLHRR6eRcsCEEwrHsPQYsNaRzP4fFCo8s8P7D7y7fsWHAHuwRb4kJYhN12/VPigNCb1e/bMUchKevemYaqX26oOWHY2zHTi9ctl2t/AIkBJTSuRgU+YaxejIIRlV10kSOU5e/guyLFY5hWDNzmwzN6Hv9HWnpYjkDFg1VmphfX8lZyGlYDYX3p/qKkxztw0QAunFBy/SjyENcYsTdchBDyUDl6kRD6opobU6nKa3QUtV1M0JR69qZNrZM6Zhl4APAwfGvIvQfWa4dwvEdb+JfNYGtTybQse6oXW3+67C5f17G37tkOd8KOFTV0JI7rU2lEUwWnWzwz+lBCGRos2FQSckobXG0/MVVJmcZNPbaMTviO7EvJg0k27UWhERlmUiTQsxzS5jlLzist8n29yTSIBe7MDcdlSEHAMxZnKeEGxomCkj2RrdogaXAYQcgeQgEsbuxEYVQs5ENXpxKYU8WRIiMZGDBZfem/VAU7LDv+8ucQaSZpcY86qk2BrXEAh5siCvhjSoZGNrSiKyI71a9YVV2zFNSC+uINEhzDBYZ5gCSWtKJRJiJoiSqM7iMzQCCZYoaKMXJ0CFgKSZXjZ6rZRS6a0gvfm677S2uWqGQW42P1toxZ6fDlWDzTSWwuXp6fDRW053lnzVxuXpyrzMpJyZ5hPzBDklcylGjkAeRP1+BsQ/H+slDU8sdWgueQXV0NbNcijaPXQWhg3DZxdWDtnaDx4eJcgBwfcRvEQMJQyTJX610KsNq5t1zE3zMwUnEgXTe6Rut7mqGIi+90WLV9kemqLtH91XWqmYInyie3sgBOX08GgJ5Lxw/uRz9uuz6aG2SqmFFMTOoW5WQN3lonpvRkSbIiobrTuJqA9xZSdTuTrLL/v4Vgeuz37wQ7J+Rv2bX9ClswvEkNF6dWkYK8NDsDmKeJ4PaCwlp4UqNK0Mu+qck228IK5uYP2CkCKBzOS06a5myugNBvNzGrB46Mfk/LZfnHIbmfKZVjjgumMOIwpooJTCU99Yn59J2eappmkhmqMb2oVp2o7NkTKM/lZMgQNzr7sp03tvSkO2Q74WarlVEcvp3vD63tjX1bJFxGG/G5kjuu5iShNGQbcMOy9nklN8RepRBQEEoi1moNSCHJRhg3xK3Xh+fk9dzbRvmk/U3riuV67XlTzPnE5nck6I7vS+eWURmeYzSmcvO+taiCGSUubu7gGo9F7YrxcTCFXTiLSm/xPX6xNTMp+rHBMhD7V9THyZoXpeqa1Aj0yizMvZ7ErEsk6GOaBGtG30aGornWSfA5GY/VCg02I6eof28ENErZIPErxnod4KNMjXWhSRkBSTMQoI7hWlDRFFh8LIgUP6uvBqTMGDjDPPRs/Rob8QFQ0ZDea2HFwwNuCWOGIUbQnqaiSNHuy9BMwmx7L+TlOzKBkVZRBnvoUJ0UCQhtBowSsOrFdiDgjBlOUFY+xKoulK3TdqqdQamaZMiMK+K5fLE6U0hMQ0nXwfcrBcVU39Y9s2tm3lw9MH0jUx5Yn7+0bJo2q10ZT5dM+83JGncEBoc1gMuhZBUqDuFgTMfmbyNd/Y1ivaCzFlpunefbeaMScrSEi2RwXTDqST8mx97pAgNJqPAChKigmC0qVTy5WgEe0wzbNXgKYtqrVbQjkPIdtOK5Uc7bpjnAzWdKFuVSW6yG7tdh70bs4GKd8Rk7jrsknCEaCuV8p+oVU36UwGB0NgXzdKaagKH583eiyoKD/94gOvyol8ZyK+9ol4wuuwcoz5lz77v9WBazonUm+cH080qkskJbR2WovEahjsmIuIt2SJHP0gVRPkjClbQMkR5Eb/nFq2Deu01ySGB4tCyFZxdLWB3dFrCcGprSoGDYZgG8r7qGCldRBoWtC9IkMsVMSO+9q9d10J9UZB721DQkNCJXU7cAUhGfZn2Zj/riq0VtEwqh9bYKab1uh9c/hBaT2S3C5Fx1nljCXbWIlO8oBri08lUbsDdeNrI1io0HqgqpEiGCK0KtaWDjNxvsOUOhIxz9y9SqRlZymF5MEoxIBwOmi7qhCiEQNymNBYjQUXAhqiEWw00CRTW6E22GvntCRSmpnOD5yms9toBEI0YVAVoWMGeiJymBuGZBWlarMDI0azgolmNqgqEK1aTSGaqHO3qkylMvyn1CV2DnepIUgcfA7G+4IGUVnAGUobQSxRULHQ2HtD28bNJNLv+wBuRRgDFNbH6tDNqdnep5Ei8M/e/t+NoKQGmQrNoGdx+OroBTrrU2yNN2ydWzizn6taCQ4PYcvSoDOtRw/SsnQH37S5iHSjqVofdMBL3UlJzpasXlG0DsRMcMNVjSZDFiVxenQihNgM0uSEjk/SgnSD9GKaian78K5N0tcuaDHkQnUYREbr34RuJKXuCUIXWvHPUgXi7L8X2UszvcsARhQS6IFW8fGNCaKiks3gtFviikxosMSwqveZiYTpzi1fAp14MCYJnkR1geIMSCKkcDMDHWIEkszCoTVLUgDSQlrMnTyIENLsfn0wpcl/19i/IZ8Ik81BausHO7F1pTZlrxDVGNEalH3f2fedUstBexc/U8fjV1GK/1YHLsliTrGvzzQK1llK0OU4qNPe8XY9sbaBFDKJiX+iIHnyrNSyiYbbe/sGsxkIE8HM0SwoCGbTrhLpGoyg5LNTURqj6xODElMwmCsbw6erWvAQaN1EXGOeiHG2bChlCzy+OUK1oBlCsEayGCElqx3swfX8uldIverhVGpq4f1WeeQJrc2sWnaDQSUGYp7NjddnSGIaTfhIcEuDpiYebBBecDqzoq7tFlM84ILa1ajoLULMRo6gsrdGI6ExMN3PUCyLkxA5PbzmHju8Q62HDYgddNYza7XbAa8mPzgJBLW5rVKa0Y5DJMwJto3eCtd9Iy+ZMN9xfv0Zd/P5UHBQXRkEC5HZq2O/J9Pkg6NC7ZXQre8oaUZidqgEazjESMwTfb9ALa5LuKLBPvsBMalDQoeqecDgFlW0VVQmY6UipmWnFZFOTJMHl+4H/MVYfykhDjUK3m8ZPRj06FOaGGzExgGc7i1K6GoK4Njno70StLgcmWvmiaJucGEzYgOuckv4ZszQTnBZs3YbeI/Ze6uF3naCmluChEwX7/locxWcTu9CaU5eUIPg1CsyU8q37zUV4nQiZD1mCkNK5JSZ7h4YQ8N1b8RpZhb4ZFrQfXdx4ULOWDWVEq12aoeyNUSMgRiIlBIozXqiIt3ckY2XSnNpoxCUmBbvB2O6h9HIMylPdMIBuacgxnCMFrDsfnWgmhODJLo2enHIj0Ca7g53hL3dyE4hTt7OVrZaDSYPgZiSkVVap2s5rjvEiEqjuauCkAnLYhiAdqvog0AM5FM+hLFLU9J0Jk0zy8Mr+nallZ2yrbQGpSjb3pjEjHdRI/mUulNrsdlCbzH4yc2LBvkv9fhWB673X3/F3dkasHdhYfG+S3BzQu2NUl3aSCKJm4VC0AJDk6KaAKxlwAHJRp9NMbFdPrJfn6n7SsrZ7Km9saghWQM9T6Qwoc0YSq1sFkxCoO67NZwlUAnedzC35STdmWWQ5omYJoIk6r7Su3hj3JqrwVW0t3X3PodZZcQmRBH2upsihYt2RoQQEpqHAr4dhss0UUqjbJW97gfD6ny6O7JvEZ87k0Akcll3x7cLH5oNqMaQKB1q2Wi9sdfKkg3bbxosKLvFxpQmV6QfQdqqqdN8Ym/Ffq40SJF5mpnyTNkKul5pbUcD5JAREUrt9F7sEBdhSRO1NNayc3m6ErO5D79+eMXzuqHPz+zvn/niq6/ZO9zdf2pQmDZ63VmLvTe0s9zNzPNEyomQM/Nk4qCKsG5PrJcLoTdymiw7lmgJTTDGVgoG+/VWqWVDZSVFmwXTOBuNWewQiz4fRzPl9d4avRYIhdYDrUfwoCVBWVw3cGz+1jo6SDsaDVRwObIhozUa6min1B28WmqaCOKsV+kkK2Nt7aN0tcHgRKR3gaA2tN2s8hMitXZrvPdK21eDTImUbs4GhpV3prTQJZi+f72ifYdebb/ExWGI5ooVgpDc9aFB3yn7RgyZ4NVJrzuqjTBn3rx6AALXtXB9fjIV8lY4xTOlC3tV6raSsmlt5jSza6L1jVJXLvtuxAdVtgKljr5eJ2MVeSW4EWKjS+dumlGEvQutm4VKisLD6UytSqmN9Xq1AidFztNMEx8dUGPjzSkzpUzpQmuW5HTp3M0LIURKF29z2PnweH6gN6XWxnW7mtuDCPfzwlbV1dcrSiNHe829Owu1VYjKeV7IObNXJ5X553O3nBEJtK6mSxpsPvX+7p7ahNrMziXPmWk5cffqU2Mf94bWldrg49uv+fKnXzD1yvrhPZImPq4rrySSUz6cC9TRppdK8b/s41sduDRE4rQwn8y4r4XkgXxHfEGV2j37CmgtpBTcWbUa4wropRhFHBsUTPNszMMQmZaF9XqmbBvzsqCtHo3OjkFocZoJIdmBtW+0bTf/pBgp+8VL4kDXRMiT/VtNqzB4QzRPi1VPAjEbzGaHhvVQwPtwmmBIMLkBpQDS/WshoDRHotQp+yPDUpCdkExNIejC0D0M0cw2VdX6dc06VIogzYJmCGqN3u5WFe51ZOBfRet23ENFbLq+N2pbLXNuSisNqkE7uj7TuhEoai3sV2GfJqac6U6eaLXSaGwOTdSODZnSQZQeTDqp1Ebdd0QTqomWzLbCDqgLtRRijHx4/yVJOnVb2a/PEJRpnpnmmXnKzMk0GVMWpsmYYBbEF6TbnNQ0nfwwSn6/rCKOokgwNlarDdULISgpBHqYnSVpcGJySNI8CsWD3WrrQgNRkw9u2v3NU6Iz9OKEXu/svgfoMhmBIkRaL0YGMlqlK453arn4wLpAT0Y2EkvwUsoGL4r43thMWT0KXY1UId7bE7H906tj0dqgrD7wGhCdTGavN1pdSdGuWzQQ54XuDuM5RlTMNy7E4GMiFuRlrU7J7yY5JdZNi2UojBsZIKrYmi0rfV9pvdEwOrx6rzFHCFqR2mjbM2W3sQuh01uhlkor1TQbj2JVDbp30lEOA7VotLI5XCrEPjRlYF+vdvh32yu2J5W6g0xD0smFozR4Yjr2jvXj62YkqB6ijdGIEgJs1yda66ZV2doBCa+9UtWqouDXrXSrgGoxRq8riFxbYQ+RKpbomspV47kUqx7V2hMhCjEHs8oh0LsZlKYUydMT+7bx8PiKnKI9T7JrreuFOcDlw9dUNRf48/3M6X4Zx4+B17/C0PHLx7c6cCGJNC3Mdw/I8kCL2Q7cdiEGZV4yU7W+DiqUbSO57E5v3Q4PsAop+pxRh3k2V9ogQppmwrSw7zun09mCiBqNtPXogWuxrKUV6r5S12pDjymSy2TXqkLvEzFPngQXhHb0hnJeLDBIJyRoLZgCfUj8f8n7k1jZ9uyuH/ysX7d3RJxzbvOazHy20zb2n0ZVQJVAQswAI7AZgT2xxIBGgpGZMECCmQUSA5gAA5ghD2DKgIkRErIoqSwaqxBVJaooGwPOdGa+fPfde08Tsff+NasGa+04z0A1mab+qpTj6ebL++65cSLi7P37/da37VUdNoAszsHF4jenZxloQIITsTTCF7gNxeWvDES6vbcUkThdLygYsLqHx7oY2CNi9onR3uOerGEdSjEKOqyDCfVSumGk+5CBBvNqfZErCTqgWaW9xGI/s80mN90SI2eC33TqC4bJaY23sGXA+JvhcIaokpOSg3qeYkNCJ4RxheNGr7TtYqHIdaEuZ6YpMucTh+OBcji4IlBIJZIngwObBEY0Y2oMQplvaBQ7uGjwjWsQaEicgMBoio4nwu7jCoUQCyFE2mhWByNAHyYoGh2pF7RvBLVDTsrTdXHLyb2ICClmRj8gdKIoVZMJImJEe7V8SzG4PIr7wlI2NeMA7ZYiseOLsbi0HbG8umEcVwzAMDl4kACtmootBUJ9Lk7s6XLlemSYBF9HRzZT5gW1KS2FaJP72EgxoGRUjHsc2pAAJUc0NX+nQlwPjhVArIHkIpc+Ku1ytkZebTZB+gZQK6Rk1+Y8TUg3bxGjIdoJQcklG7+sSu/W1GD9aQmJYvxqiAb5epFjd1O8IpTgqTcGrGCgnpAVpGRisv8GQponnluKcX9iJGn3jUxpfbNnCAFSIfhmFIO9PlElRihpb7iwc0PGpvjkNRImiAmk0MzqkALVxSQauHKxEUE0GhKixo8G1DlQi7lT32q3rtRm0y9jcHM6EkoipQz70Whb0ZzY1oW1dYRoyEpJ1+vk+vifsHd9b29cBOJ0YshsY+0YpmRrHQ0QN8hl9sUcRONVOi3FkhqCBEbbjNvBOOuUTS1EH8RyohzuUFXKfMNoF+MiMAm5hEgsByPTW6XWlTE3cp6IKaO67spYWoWYJgDGqEhwKW0bxDQZryCDoUd6w2CfPKP9BmiE0Blrx8I1C4qR/4IyVjNRGpnaiJ631mtzHs6gupR2fiUh+eALeqOtTwRPAo97tporHnOer0ogFTMlI9ZAvXs0erOeK3XMvatxSJZ8EAlYMkf3Vt7RO9tyJqaJ3hrr5UzvnZwjpfgC4tj4CPEq+w0hu+LR98Lom2ow7vCqcEC51MplWXjx8iWXp0dubm75yvd/lY9ef2wn3GZFi/lwS5omCOO59kGUWLJ9pmmiXp6IIZmyMkwQZ4jWIzbUSjFFLOuutca2rgTJV1/Z2sWDiqNxcgHbUPp2FX3EYArK1hRtguT5yskmT+bfPXgaJsz8BdqM19OQCOIB0aKgle4iBeKRIA3tnmvn0KvVWGRUvIlsdIYmxBdlczKY0bkPS3wfaiWuxrkBZBgN7ZZSIuILMsU4rhARn04hIaMgyaqBkAixECjsPqWQnstBc0hcTdQySDkTc2Gabrg8vKFMMzlmPgjdXr8a/5bjRE4Th9sTWjdG67Q+6EOcPx4WTO1TZ3242MZVsrej2yHDes7sfgdcgWqKR9Vm10rYY6HE/xn2HlzpKeTrQYv4PMHaxd0NhtThyEci5tnga2zqXC9PJhIlUCbbLMxrXR388QqfPZQau/738OgugsiuPI3mDzXQx6iN1hmt22EXs7Js64XRPI0kBt5+85vUdbVg5pevOZxOhBRZ7s+MIWxb40wmhoKEwaefvuX21z9nvn3HD2+Nw+QWHDWjvfHvu4/sO398T29c79+9J4UB8pZ+c2J42+9hiqQAY4UHHb6QKgyXI4vQCeRcKHni7uaOGEzFtW6btX+mTMmF0RYsDiWyLRWooF4R4TdeLBdE4pWw1B5ZZEVCZJ4s4c1IzsC4bCYFzRFc4q0a0G0lxk6QxqhnRjpALAYjqMEafXsiyGSnraakktG2oV6yZz5VIU0z2/ZoODneBCwwYqTWCxIHEgdRgk01rVLXlaenB3Qo03SgFJ9eL5Ux3gI+8U1m3g7BKsXH3urqbn5bkYMbIm3jqmtD2/C69kHbGnXbuFzO5FSuJuFtWzndHLi9O3KYZna3f0rFP69gaqvr7TUI6puwukl1l06PxqUOLsvK2/s3jLpRDpERVsJsUuaJRNgNtLKw9ZWoBs+YHeFk0I0M3r39DLoQKNSmJjCRbEkLwxVz48J57Tw+nnnz5g2vbu8sOmdbaMnUk5boYR5BUIZzqSlFixOTzmVrPC6NrSl3L2453RyJNJZ1M5m2CqId7RtjvaA5E5O9lq4DRgXtNIkmWpDAMU0wKlutPJ4XVBLH0w03d7cOoRksK6rQNq90OTN8kis5sTlnqb1Rc7H7IgQO6cAYBsE9PS1++EkcDwdoiwtPYNuqJaJg8F3z6yTnbNOCYCYKST6dQwqTH1QGy7qRc2Q6zHz5k09o64W6XrgsT8yzmYrnMpswpQ96u/Dw9h1tudihskFXk9/nXOi920F2nuh6IVAYWBN1SMXtDg5vqUPy0TeuEB069czAENnz3UWV5KpEYiIE9yuNzuhnPDrFaI3R3HvV3A6RiLrS6mblr72ybatDtImQj7CLV7ymaN+46uLrSXD5vm9eJtgxL5kSiWGnFzDxVjMJPdotaT4E1vXCulZaV8rhAFEoxyN3H33MfHNHKsVEQrVR18riYch65UgX5mPmdHug5OSvB+fQ9+zF36Ic17c+fcN6eSIijBd36GSLyPGYTYShnaU1q6PuAyXvygo2jaSUmMrMhy8/JDAYOlh7pW0bJWfmMlOXJySYt4sBQjPSvK8sJOPZvNxxtEbfVoaWa6rO6TBZFAuwafITaSBPGemreWokMboSQyeGhtYzOt9Amt342Rh1pS/3kI6m4OqQpsnw/bYR0mywVQjMp4l6fmc3RYo2vYkZg6lnr6o3iEZrQ1tjtI3z+ZEgwt2NgkzWMbUs9OZQi0CZzF9mE+p+4zisE8V4nFTMxzU6fdgU2tbtahjdlpVtrZwvZ1JMtNY4Xxaenp5Y2wmVO9CTcQwilLERR4IYGNcEB5OXB83ORxgntKwL27pRe2NtymVZeTo/EhjUvjG0omGYuTcEpNli3F1h2HfjU06EMHtEATw9vGd92qiL8vb9I3VEOhbAq77wJF14Wq22/Nvf/oyvfPAhfV1Zzo+MeUI8kqu17hsXaLapreTMaT4yJ1ib8tQGrQ3Wy2uWuxuWx/c8nC9stbmEAbRt1PMDMk+2AOdE0+HxVp3msucUEq9Od0iv1Fp5WCshZm5frLQ6+Oybv87l8kRdV3u7vULf0PWMxmgJFDmyjUHvzbjD6eBJHZHb6dY2s9p4eLowhpJz5vb2hu38YIejbsKiSLS0mLHRQjAjvbfuiguQNE3sRTYpHYhuKH/aVkoOnI4HUz/iCtZ6pswvLMJrSjZJjEYfG/XywPr0wLYuPF6sjVxCYj4c0DHI08Q8bmjLQu8bqSfO5wc31zuDtav6dbhJ2yf8ZlmbIhBSYc/6DKokP9wRzUiPmlisVTvQskex9W4Q+6husUjEUqiuiO11pbZuyEDK9LaaH2sMiJnunHsUWC5PpliWPTbNXvOgXzeuIZGg5ueToa7+NQsN2l1lGc1PuWzUPrjtLxi9kctMng8Wm+Ubd69mjl9rYw8P7qNTqwk9sgeR/4+k779lN67/4y/9e45z4uPpxPbiQCsRenPl0iB0ZQuWRlC3ztpclCDCBavAyCHw4nCgXlYkJ6aXNxQZdirsyvndE5qS/Vo3yzXUYfUVOdORaydWUPtAL2S7IHsnAgWTqa5lgmYxNTFGC+pNiTEVdK2E0IlhMKO044GRCgkxTxODMiqPGqyCYbGkjtAbQQcLxiWkIBxvZ8aymLJySlA7TaGiHAUWHazAHCwNXFQ5TIXpkLm7PfFDP/AJvRXauvH0cLZTkuflGTzpZXcxMtwXM80Hbk8HyryHaiaqq4dkDHqt1GWj1cZlWam10tbKqiu1NpbLyje+/Rnn85m6rujH3X5W4EZKMz2Gqbg0eKBBXeTg4psBl4ttiqpK086yVM6PZ0tk36qJPdpGD9jCVB3eHQ0ytogEcVrOOAMdcH54w9f/89f5tV/9Bv/m3/0/eBpCk0BxIUQU4SbBGStn3LaNL794wXpZuH88w+TCIfdrFT9+rmoq10MpvLq94eWcmG5uKbcvefniluVxUM9v+L/8u/8znz48cNk2InDIhaDD0g+yLYL7dh6dR3xstjBMJfPVjz5icqhqTEde3p6Q8cTY3vJvfvGXePP+PY/ns3Er2Edxm6CGPehIqdg0EYAndguB8GI+ID55L1h1e4yBKSfO94+0PqhDXRRhXNBtFLYQ6IJdH35ImaJQU75O8x0fiFU5o8w5cHso/LZf/Y+8vLnldDPz8sNbPnj9iuwt3fV8sZ/x2AiqrJcz796+5b/82mesqwlMDoeZPBVuTidevnjBZVkoKZFC4N3lwTcrsWZo5WqfMCrPhCxj3XyeGYRgAhlj83bI2RJUSBYZNmrl4vB0itn4c7VM1No384eGRCiZuizmhaorYJFYJSde3BytoXooQTLVN64S4HG90LqJ0VLJJtUfgw2Llgsh0iVabVPfOWQAO3gnLKBZUuB8rjwtFoH2lS99wHw8crx9QV27Sd5R+rawrBfO68plrUCnV2U9V97dn7mcLxabdp2wMF+e/uZFGt/TG9c3395znCL36ZHxzvw2Qc3c24ey1W4Qk1o+Xx3PAkzTW9mN93l64vF8IaXIy/snXt/OJBSpjW9+6x21D7qqKRT9b0eUTb38XZVxlbjvirrdQGk5CiYsdD0o5pWwkjmBEOit2+sPlmPYPfU5ACFbkkcOStdwjd0xhZ3JWqtbJUIQYknm91IzxqovAA1PWg+uIAPaZvDQy8PElz96QVR7LTFmNKmR6Ip7TwJK9ZMctGay+nV0HpbBug1iWpH4yOF4tNNwNyVUXZW6BZYF6igMMpoKtVdGHqQwI++fWEfg4Vw5Pngs0lDynDnOhTwFUgqUaNybZOEwn8wEWQdtVMoUSQWiwPtlQVg53pmKLE7J0g885irsJvBeGaPRVSmlEKOZTscwMUaOgVkSfem8ffvEN9+f2Zp1eu3q0yBwHwZSZk6Hma9+/DEvjq/41njD+e0jOewBz5EhhSi20IdqAotpuuHu5Vd4cVt4/dHHfPzJD/C//I4fofXO+3fv+Q//t18lr92SKQIc7z4w1d3lnjkG93cNtjAZlD0aEZuUcz4w336ZttwzTROvP/wSv+d//78h5cJy2fjX//r/CnJBgn1OczlQUjQEAPVIsGr+QlVLkO+WBSkCkm9pYrVAp3xkKuZdI2Selg6tU1SADTCobZN+VdFqbxbJJsIqw7g+TEzRXHY+ereDkNfPfOPzM0gilkxgIk0zMZtKcV1XFCVOE8e7F8TDgen2BY/LYHm8oN1UctNhpuTI6JWn+0daikwlk8IujR8wNsL1rh+0aj93DRDUOtWGDtBq0Kmxeex5lEMCS++GHMSISKM7FbsFR4YAZLjxGqoq0o0Lqq0SJNIJVAmEVt0IbSEDj5cFAV7eHNhGpXkt0ngywVQKYkkgMuh0lurB32pISRBXaoqtSeL+tTkOthBcxy7m92uNfr6nXz4gThNBErkIh4NwexN49fIFvTZCChxiRLqazcV3rT0smB3V+E08vqc3rp1Ir82w5j1rTzybb4xBGDamxhDo8uzIF+VKkNYBbdjozMCITBRxZd7YceC+62V3lZFdaAPorhIb7Iou/zMVq5rApiy5vnDoOgzzV72GkXasZ2t0zKeDXVwxmsnYAgTCVWJKgL2PaU+M3kUmdvp7bthRoHZ/wQJ12KKSRZhz4vZ45O504ni8oUwHUjCnvcGY0TeuZt9LLcEbIlvr7vcwP8monZQifRgBq0OvJZu1D5p3E4FzhMlg23maSMGm011GrUGdvylk/zWVaBGJUShlMm9Xrx4wazqnICCxE9IgzxNaV4MGnZsT7SiWEh9iRJK12YaU3WRrCRhB1ZWWkZgn5uOJL3/pQ+qmtKasy0ou2TiqKOTjkZcv7vj+L3+J0+GFmWFTImaToIcQGaEQ/RpaqkFeNze3fPSl7+NLH9zy6qOP+OjLn/Cl7/9BzueFESZef/QlwuHIVhsxBW5vPwQG9XxvyRq+ca1hMqHEqGzV/FglT7z84GO03XE8zHz08Vf4yld/G6rCu3f3vPrwI4iZ43ImCRwOJ5s+2K48Za+VUSYsEb6zNBPggHB7+oDhSESJE4eD8UOd6LmX1rxba8Vy/yJR234bmFDGJfkwiNEqQnrvnKt50HrfzDclSnGv3jTPzIcjx8OJGJMLoCyNfFfXxZKZjid6H7x4+YIpFbR3QhBKnok5efyXQXGlmCrSmNp+nbzBPHLioSMahBSCbVw+AUUJBIJvRnYoSimim/VwzSXTQzKRiBPPUexaDcHXLDVTeMzC0Ejp0SFm+zWV7OuUbVytWqzd4TATJdOGUpuyrf3a0VZyfOaVsBzXPRA67JFbJLMKpYjEQBJhSKQplJJJIV75STx70EJhzJJRciI675mLqS/r2gz92IO81aXR/vgNVUjf4eN7euN6eXPgOEfa0iiHYoR6FDTaxbEsq+UORlt0K2IY/Rj0amogJNBIDm1EXtzdcrq9IemAbeXu9kzbfAT2GgsjNivVGuFtmon23wNKEjuxd7V07eQY9JQnu1ljQEpCeidIJMVkUnBzRtvJRhLDAunI80yMgSyQ8mywuw5qF6sZ0WEhqWLKqxEMShRgLpkxGlWVdexBqxaku24LReCmZH77D3yJH/z+T/jwow/5+EtfsbqMXjnOE33rHgVjhG+MRqS3tXK5LCxrJZ5XSrLJdms2zfTeGNVgu9E9vueatGAS9uTVHDEF7k4HYoC5JO5ubnxjFlJKBu2UTJ4zZTaPjY5uakAqvXakZKj2/AxcoaWU0t3fZJwMbaAR1GspynwilQnrM/NqCJ+Ye+90rTRJHF684JMfVD780pdpi3C5NP7Lr32Nw+2RwzxxN83kF3e8fP0BP/DVHyTPJ96/+Yzf/s1vUMUW7toGVRLZF8PFP5/j8chHH32JH/mBr3K8u2W+u+X2gy+j5YHjEH7kd/4ulm1jKJQyMR1vkaGM5YnaFz/RwiKRqN3Cc1WptSISeXH7IS/mmdPNkRcffMDH3/cjXLaVNc78yO/4nTzev2dbFqacmeajlTm2C9u2mBK0N8ZcQAz6ffLJAxHK4RVZO0GtsPNwOkEMrH3w4esX1jQwMOm1WNpMaquFr6rxWnXKEByF8PJKHYN3y2reyLrY+6RbSeVh4ksff8wHr17y4QcfEKLxxxacbMiLsWSDmCLz4cAHH31IvbHNWATLZfTw6LF1prkwHSZa21jX1Xrc9uhKtQqQ7uHIhEBMdh/2bofbHItbRwyRiUEoU2Gt3V7DnNkb1betos2uARt2XHQkWMGopVlbWoyqH0ETKZuvUrxG6OlpQYLw8oMXjCB0Hax1cL6/2OuIngSDqZRbF0K0jatXW88ClsO6h9gTTF168uCGkAyuzSmRp9mfa/h93QkEUpromIVAotEyD48rD/crrXZyVjug72W0DsN/t4/v6Y3r9vYlhykS7pQmZhTdthWlmBBhmhnp4KN8J8RE9nesh2i+DgGKhbamkCh5utYrSjxQ7gLRoYqwLjyrWAdJxeWyneqSe1UPyHTJp3jgaAyR4+HIlK32gTLTh5ByZp4mwlhQGsO9ST1a+GsOSjoekGSKI8jsht+9V0xCoNVgVRcMmja2ainNKgnaha7dmpiHtZVua+X+3VtezIEPXxz5Pf+7324XZglc2nvmMJsXbhK6B5dKhEgGPCU7WhBxKsrNVIhin9sMHG8PbFsnLY3L2vyGswr7oxqUOnojBzuVxpxYzr4wpUA5JuajbVYllauEVyJMxcbNoRGNeg0tDRGKWgxsXRfSulFaBwJLmIHCaMKQ6kn9kZyTwYs5oDqBVnRU2uaBqiiqG1o35nni9Ze+zOnmNYQTXRM/+Pg5MRZXbAlVJ25vb/jw+z+m90G5e83dJz/EUzOJ8bYu3D9dHCkJ3ISJksyQO5UD9+nAOjLzJnwgHtYbCq++70f5/H5h64PDHLACTJCbD4h9WOCuNmI3c7wEYc6T+w6VIBPp5oZwmGG6sQSXOoga+eCTH4HTI4/nC6cpQMjm31ErQGV0wtism8pRhRsN7osaSDigu4ZhCGHyUKTWiacXVwk6faVppKknuajBVoGFkwdWqwRiMM8ZfYFlpbZKa9VT+M9I3ygox7sXlNORDXh6vLcDR7XoIxOfKutTvfqVwFCKvWM1FS997YPDaSZPmVyyheGGSGhWJmvRWUpswtbNJpJTIuVo01Yf1D4oyaD4MboVy0ogZqsiijlSJpsqQ4ymMKW6WMwmn6tPMRZrkcCYgNYt3Nimf5u4A6ZOPZxmE89MmTDNDAKlD99cut0zHn+lBKIWUrIPoK+mZNx5RwkWShxiZPMSXXDjeRSrBBKh9xWC7+ghEott+HOyehmSEGQwHQLlIIyxgWauM5b/z3e/bX2Pb1yH44HbU0HGYBPLSes5MkKmqzXFSj54QEAn5t1LAiKJ3QCsZaZ3S+jOIVOSksTOIa01at/ovdpNZpQZrXWLZDJTEiklL+NT5pwdGx6WSaaWdn04HJlytM6j+UjtkHPmcJiIPV83rtrUXnfMTAmib1yWCp0ROkEbU0rXVHAdyeGJwRBlbd7SKgGtE210w8zHYL2shLhSt4WbU+Lu7sjp9sYu0IDDZNUEGKKWGC2BvaacPRkjBTNgmknOJjKxxS0lGCPQkiceiPhzB6zDzKCGlMR+5cA0F3R0M9C68Cp5E7QFytp0tou99hO/FSrav3dZflVLvI6iRBmWsbgruYK6cTQ4tNrNAKvmwRER9/CAEaTGYxhfL2xtIGkgEU63d4Q0MxDOlxXVjCSL7+raaDSWbgcrUiYqxI6R6ARinsnRTOGaskFwW2VEWxQVLFYsTQypDnXb0djEzgFJasnrGlAZ12SFELN7CJWhxpM0hdr7Nfg0RDOiq1wMmhXrGVO3SkgwOEi6pSQo+JQUEN+4Rpjs+dSuAa8QRggEtQoNhlo0lnpNiHp6A8OmoxicxBfLwhzBwq27Q78IdhrthCiUKG7mj2zb6lDWLk8JV0hKdDi07qkgKdj16pu7UQNKytEOUNF8YCFG0q648umvi2WjBs+fTMlzSoOiYdh1HJzXdvm8fcZyreQxsYkSk17rd3ZV7tgT+6NBdoq9voBZVyK2SYY9Y3Xsz+2TYzS/ZJJAnhI6xCFBsXtFHT5NJsUfQQ0qd7FVEMuttH44T4dX47lDKuTJgoSH9ud+N7GJLiUL4G7NqmWaJ3Zody8YX9yqdvLiu398T29cx9OJFy+P9KVaRxaKtkyPhvWuayNOR0uNxkrRmkOFKRVrJQ5CT4V13UAtmWLOwfPECm3bWDahVgi5EZMBENaJFp1sHJTZgnF7V45TZs/njiHRu908h3mm5GRqopsb1k0pJXM8TISeUDGoatk6abqx2oMcSKeDJTgMtZOapwCUtJdECtHNrhIUyZHiUE4I0LeJPipbt2ibILaZttOBm1Pm5nSiTAe29dGYMD/ZjbAbki05QYIJNXZ+0DxC4rBOI6TMXqIcxMNH90nJrSujGzwy1OXsIdoGFZRpSozuPyvP3jP5i3tdYiCGyU/N+wEEYhRy9lgiSwMm6CAFgySb9KviDN9ATWpvajUrEbUqddnrOvZ0bbvHrpxp73C5fyCUQSozh/lAyJkxhGV9QmOwKUWFpsp53Xj38IjmYs+PLcw6Gqj4gg2ISf2XrdKG5RC2au3QEm2BbGP49KGWOOKTf0wGA6gKGuM1bd6yJ+1w1QfU1pHayNtiYiAxVWvHzPutVbrlN5tqblhAtCAGq+Vk3GkbRDe1ogbNu1LI2w8sgUFjsEoPdo+fXwgS0aqE5AtmD5C82buqHxwCaERjZDSryAliYcyRzHTIxDKhImzLmTkVQpQvLMLPP2fE+NCYIjI8XX1XtQXbmyQEV64GP3AadBclAQYT2gTTjJOK4hsX/gSdnIQU1WMADRpH7YBjNUmRzrCNSaNxstjPZwzbxJVn478iDDFhRAymGB7S3Y/tMnzwQ+F+HeE8k/k68WtWh28WwZ7/eiMzXP1oQjBxlayoMrwaqRxn8jyR3dIxholzJNprDyGQU7K0kLXRaqX2bn7KrXmI8Bc3rf3//RaVw5eXP4Dc3TAOK5cRWWpju/8cRvD8uwvlcCSKkrTz8uYjpFdar8yHiW15orWBMHNZV8OpZXAzFQ7zzOn4kt6hvX/HU1O6bEhVGEqreGjqINKZ5UQoZkK0k5x5aSBbEG1vbKPy6qiUMnEz3zEnS2dIh4Kug+Ap38csaJ6QlGzimg5WUhcsAqrWlW15gpgpXiDYm/nWrFvowKEYgdxCYDvfM0ZnFpPetsPMepj4L4/vCV0JDWaBdlnMI7IN5Dh58nykbdXDe+2iF+enetvo68UyILOSxdIr2tZNot2MrwvVuEZVYVsvTCUhadB6RdRsA00bhE4YlUBnjORcrnKcD3YTMhBdGHXfdSJaK3EMYuh0NVJeTdCGBBOtZBQtgekAKawUmUg0KyuMydPlo22S3atXupcBYtyANlNTlpzQHNgksHbl3WffZj5Ytfl5vRBnZWNlHSvbtrDVhbVtFhvlptIQJ2qrIMKcjly2ldCFIpG1beRksV1PyyNJInOKbOuZ8/nMeauoFA7BosO2thFG9nDnjUOZvWOpMZVkvWxdYWSWxyfSRVgukU/OD6SQyJKolzPLWnlqSujKZMMRl9GJw7q7+lo5hpk+BlvtHOfJrpVu4p6tG6yYSuZcFwOmQmKpq03ZIdOroQFD4Fw3ilfFn5eNE5bksbRqQpnR6dtKq6bm3drgVDLnywNhbHx8c+DFnChRWR9WxtrRbCrDjE302gZtU0KZUAI5DEY0vrW7OMcS9r2GiM7oAcYgyYBg3WHXWDE6UxwIjdg28nRzpRVysL8T1Q5l3Tpgroc6Cx72zWw0wmjI2J43UBLROS56RfpkB2ONlGjqzojShknUTenrSj2xgk/RGyBAH8hY3fDcGV3ca+jPLQVTh1XCsCg36S7iChbxVc9nlsuF3jtzuCPMmUCC9si4FHoMaBRGPXOY4KOPb3n18QvSW+VcHzlNAw0XKmfQ7hYWf+xCjd/E43t74zqcSPMNdXgXEw2ZjgSJSGsGJ4nDWSEyHY/kbjCY5IC0lSDKfLjl6enC0/LEw/mBD24+JOXC6faW1jv3y4XxZBUE0SEzCcMxaUCi1YGnbH6t1rA+H0hlYqvm01jXldvZWkmj12VIinaqdkJ6l9Q3x8skGieWkoXPLqoG0exQVrDKh967K6ncVxQjQ9yDExKqZsIO2KbzTLAaPxTdkzWaIpPFM+mwM+BQcXWV1YoEn8pa77Q2aH3QRFmlYd1eg9iG5/WBRHutfbiMvzUES7uW6ERzGObzcYvArthUPAl9+HQlXjE+TJ5sNff2hRZ/Y1ClSOTaYp6K2xUMKrFaD4fadoWp4YDOTu8qTOsi6l1p3SsoRMBmdbpDpt3hyRgiyXmzmDN6sfzF1jZqcKPyUC87tO/ThuUx4pXvAizryrqe6UPJWa7m4q6DrVVaC1ZyiViIqgZvg97rQIw4X1tFe0cIzHNh21aWdeH9uwf+lx/9Kof5AMG5noDDyYHUu3/GndZdau8LsvXMKbVbxJMOW1D3f56TMWGvaLeOLSsxVAbDG6BbDwTdVbDD0vLH8OduplL1a01UWbeN1hpRrVtMotlH+EJXm3Y7sFloAMQAEvwQ5AjCPg2pj9PDp/erbLsPv8AMJvZMbaIIIzpUrv1qkI/7gvSFXE32a8qhWtQyBYNPnkP2a84e+3M9/373B2KNFA71d3+v+OemPjm50JxrC3GzQ+rVM7XfI35t7K9XnZceY9+4bApUb5YW/16jWUpGk+C5pFbLMrrlkIZUsJ494+K3NliWyro0zy59lsN3v0b7b9UGZNu4DsRtIMMWlFAOJkuNZsyrCISA5ECeZiNwR2GTbtlequTpCCFR2+D9wxNb+wCJmfnmxFYroRSHUxzG2Ivc/CHiJuFo0trVf5ioeAK9wS7r1mhePx6ibVwaA0Ps6/amoxQDw1MFZA/kjNbcWnt1bsZwanFjoYr6hipXqMv0+ZbUIaYo8Zf9hSvf3oFFNO2RcGIbF97DNcYX0Oi+V3Cb/H1479YQpXoM7NBBq7Y49K7usn+GTsYw7kF3Dke4SuYFS6e3NT4YhzCciwgu79fnG2FP6rZ70xdM7FoY+1ekPVDWM7rVyxCJfmPb3zc+SJ5/iXEDrZt/Tl2559YWO4XvKlNMGVaSxSOVYi0AuweqRSsX3O28O9TaXEyxQ6yKsG2Vbbv4wcSK/YJXpvTW6N3SRiyxfTBovrkPz4GzxWrrVt6ZQ6BMBpPX2vjszVvWtVqkGbawg9jz9+cFxerWTWSkLuxRP000755j6G9YDIdbPK5J4C7xbkM9nd4qJ3V0v652HsbhJFVaay7p3/kf9YOOxbH5cQph/+yN/1JvVNAhVyVxdMGHXUBxX+3tB8i+mKtN8H5rfHGxD6rsbEzYf0hDPV7Nod8gngXq/qj9nhLxjdd/6xyxeimmffj2DuzA6S9hPPs7A4G9T09b268ev1b3TfH6La6Q9jWKzd+vINdr1v6C+N/zAtJ9OVBTMu/XdnB6oDvMHEKkBzdiB5/kCEh0GFlMeNK6SfLr1q6f5/5rP1wZhPjdPb6nN67jzS3H0y0M4XEdrFtjJINjQAlpJgSrB0DNMByThe4uvXLf72nrCk8rnz9W7s+dpy3wcCmc9Uib73jQR1YSTSL3S6X0TlYlq5KsbJQsEOpGFDHyvQ16tQzAVh94fPuex2VDY2YsDeowr5cKOgJaAzISmcZk1lyCiKUJVCWPZtlnXSgedaTSCSExBZNrLPiJPXhyutrGFppyEDsJabJEjxoTYcqUGM2vNR3ZWqCPACMQiETNJA1EhVH37hzHyaPHLm0XSuuEMYhkQhczUXahR7FFsA3KPKG6EkMlTerqR1sszj3R1Xx0i1dbdAkMZpoWqs4QboFqU2NzJagnaRAmT7juWCeRbUyICSoG5q+RaBkmowsSJkIspiyUgdCR3kGrc+UBNUWIa28GKSZybVA761MnFKHEDDSOGWDw/vzI3d2JV6Xw+vbE8vBIGFDPG10Wkvd2tWHmcRUQPAAaRQXWVtlaR7u1cgdJVjb62BibhTL3vlK7NQH03i15old6W33xtgVhJEC8tytGNBaGZLRHehdaU0brnO8vjDrMt8c+tQVah0RHtNN0o26zTaAqxGFBr4xBrEptpr8o0cQEqL3HMTqqEIZg1Tx23cvo5A4JpetKaOZTCmOQhkUg9bFBhdgqjE6UYX6x0YktoYsiKXCMR7QFrAgxUb3UNbSBpoBEm6RjnBihQsQ4LPGpfZjlwXBDIZBptaJ78C2uE5eAardot4pdIwTntDrdTZTJO/3s+rFqH1Kw4tth95kxgr7xecgyroQ0f6HxcoGIBGja6A1L5wgGY2uvjGZ1Q4iaoEbVk0owsVEQ5+t8k3K1sQjWlIx/Tz/ESpKryCOKHWY7wxsVIOaAjkj3Q04ULCk/CiwrOUYOhwPHEkBXWrvQR7Wm5mSO1jEsZq2136IT1xDb6cGMfFnsBxujoF1odDTauB2i0EZnNFfC+ClWVXh6eqTVlRCE25s75sMtIRW2NqgeU1OSGRNlJ1zDM9FpSiRxM7JSUmYb1hG1rNs1qSGVRMkHcj44fOfTgQiKJSJEV+qYCi776X0iZgseDcPNgwOIdoLqqrZphuinQosVGqrGG4vJe032YCquKJCS5fVFCaSUmRwyEhGTAYuRwylkP7ViN7QYbJhTZMVOiKM29g6jtPtGunViMYrDk5a6ED1ivDvHJGpTp5HUSlNbeFprxG2jlWpqLm+BFjGjsdXJ2+M60MgAAQAASURBVCJ9LU5sHa1mjjaiOVBHu06gajI5n5QcusFhSBUrVwRrU8fKIaXYzybEQBiRIB5hhG3adasGGdZKoBGlIzpIORoPtW30tNi1GAKaLCVBBAbd4Ri5vpeSIimb6q61xrpa3qKOavUd3WA9kb3cURnDuKxGtxM7ltcXMBvEu3fvOF821uVCyUKrlacn5XxeeP/uc5b14uWNxUKAg/FXlt1oXWotGgQ+ukIY12ST5ouYtQQM9kqdNga92c+5DZPrq6tOGTgXZJBUV5dwDyviVPWJC8fUHNIGnyx6s00RSLuwQ5VWKwTvfaMTg1oHXrClLkTryUOE2tp1GhGfYuxQlOw6UtnHKofThh2SWqe3Tt1Wmm/wo20EOkFMWaybTUdjT2XpBi02TdR1o20rKQxX7grD0QbbROx9SWyoeLL8UGu79uBke27zUalC9bDtvSfP2hyshVnEpnX/+Gzaxe4vVUMKUrZIsl3kcfVcGuRCbw2cnpAUvbete9KBSePLlKnbahUxHpU1Wqeum5u09/aL543yu318T29cbdvotSIDi9Tx01UIyUjgHojRWo9tDLdZXESYcybHTJWNbVkRhsXcpNn6uGJxiMC6eaZpJqWMdAevg3FP4qc0FVvsCEJJk5HYW2OrRqay+zdyhpSNBB3OpbiibAho9NcYMkRTfO38xxV68VDYKOF6gUWHWXQI6ukQe8vonqIhEjFH5XNK/i6V7UNNgBG9QsSiO1AiiKkiVUzp5y/c4EXngQw+U5v6JBgv1KycckzjCpGBK6H8de0cR8Q+1jF2KMGz6lpn1GY3YLRNeX/tiDiPsKuqAt7KhKJ+Woba8c/HVZFfuIbEEaOd3xARO20G20wN1hHatrFdOluNXM4bi2SqRLInbBCs701chh+Eq4l7PT9ZFE8y2bBMGdRec6+N2G1Dr+tGW1ebKJJAN+hvOV9Ynh5Qj/sZtTG2zd6zx5GN3ozXSJtPCIK2Rh8mfb+/WIL3qCsyGnVZqItwf//A+eG9bRwxoJ6xucNk5h1SpA/oDSEQHUYWN95q3Cz9/2qWteDYoQbh22HLUuXVDxzaGkNMvk9dCcPk8zqwr/UU+tEGddtodSOMjbZVk4X7YVBIVh/kUKeKbQa6d7jtkCbuqQzxCslJGFeDsdkgHHoXi6US2bnHnevZYWW7L2zfVofT8ecOth5o8E0YF/sY3DaIntImFnPFDvEFf50+hQ/nrILfrRKvWYjPcKbfTfstOXbI0OXx7HBfBJ+chL1FW57XFEck9glt97npF+4zZV9PbHMz9a0BjbsSMSaHsINlK9rBfaVuq2+wO1Ww87C/RaHCx298SnhYKGXi9lSYp8ATibV5AGjeSAFQq96epz02KHM63dHPF9ga56cHbuaMqJBl4nQ8MJeZHGaOhzv6GIQgXB7eseg9oyqUjBzNeLmNSvLmUKaJm5sP6PKW81Z5bI+0YBdpS4k6ZVpOVBKarGahaUBCI8VISmIoX5mpCI/nBz48Hpgxn8rog23ZeHp8JB9vEaotJJj6rxHI5ZZaLQB0BEV7IwKZAsPEFp3B09aIMTNtg7fvH6HZhHGIgRA9DobIsl6u/NkUndMaQg+RFiItDPq+cWiga+Rpq6znC/1yppSJkIxLyfPMNAZhmHN/bcZfjQGhB8s/1EFrtggEoF1W0mGGaJyQeUaiCRYanrBvrcFdOsRGisPgo640CSQmYo+M1TxPgBH8Eml1YdSFQSdOByQVUp5Rkqna1sqbX/sG798tnM+Db759xxZPaDwwxcjxZibnaFP4931CEUthmYn0hwfef/0/8xheME2JacocbyplKoSUaYuSXRG5tPc8PL4zi3mZqeeF7bzw+OYtj9/6BhoKUaH1C3E9umR60Huh1UqvC1OZiZPXrTxmeltodeF8/8i6nglDmYjcf+sto268e/MZl88/Q+YT83xC2hO5W62PREEojFGpW6UghnDEBGNma+YFZK0eU6b0JvR2QejkqJTDEY2ZHibWyxNjWPrHti1UzWbevbzj4N6lWCY0m1dR+8bl/oHz+ZHLckaA1iolRZbTDdKFFCfytMsjFJHONEW3GxisbHymPcbWrgKTQHEpuwL5KtkPkhkhWE9YytdILenuW5NBSMOzFi0eqddma4Cny4DztnVzgZDFVJV0oOROL83l7P79nYJGjeO1rMfwzMvtD9+h7HVAjAOC1fREmeyeTQp9w8Re6kOjyfwliffpCRyxSS+YHSE4zyd4X1uy1xOnmSFmrNw5XkNPhRD0uolqjPRY6PFALDOfPjzAm2/z9PSWV/UVuZh4rffq03b9rtf+7+mN6yuffIXbu1fUZWOZlCgdxpFCsqieLTFk8x+0feC925hc336bT7/5Td69fcvYOsfJyPRaF2KAHIUchTYab999zre+9TV+7eu/TlRlSpH5OPtJX4ghk7KFfIZSOC9n3j0+8vn9k53200QKkREC83zkeLrldHNL7ZbebCnyM1MOHEog58g2MAMoplicDjNTyZyf3iHnYLzTHl0Tg2ckmrkzFRN+9NHYdCWQsCQXIaUAbSH0DRmR5bzxFM6A+cTAuqbqtjk0Z63Qe1V7zLsiDwZCbUJrQs+4UECpqpyfNtbzRl+rl955osOwbMiou0ABn/hwIrjTaufh4ZFpnmglM6vdmLuajxAIDQaVPgTtMBoWyLrXm1dlbc3sRaTrVBZDsoNId/NtLpRUQAdBqz9/QvJE7TBGZVk3VM2keriZuVtXlnCgxwMlDObDTE4J6Y1SMnkqlOnAfFz48KPX/NAP/QBffxjGh6bEze2RbI2eLArHYjlwoyuRIzkGbg4HTqcDWaBeLJS3rk8m/NEVi1TMpBghQZZg9ogpG2dCZ0O5OWaiJO6OGRmvCJhX8Svf9xF9qxymwNe+/l9Yqykgg66MvllXXSnEaFPXFM1HZDxjZ0SI0ggJV6R6PmdQptmyGAODtW3UVqkSoK8EFxPNOZqXMiaml4XiRP/amnNH7l86FMLIRM0oSosWVhs9i1CwyXRcuV2l1e6bh8F/e7SUDK79VTuUvYsXBvsCr1ehxT4QmFrRMhfVEYGxR7SJea9w+N5+Z39RPch0YBNrqx3aZmjIGMTkE4/DaPvEtNPJIrrbvK4qQx0ubFHjj0eA4BNd2xpIuCIseoVim8dSqW1AjizYy3RMvHt4sOoVAoy7wEui9XW1QYsJPd6whxD0aj1uOmBZDKqUmGkSuCyNh/sLn3/7DV/6yleZse8XYyEwSLF812v/9/TG9dGXPub2eMe7N5+zcQZtTMUI0CHKGIHNoT1BkL0ee1iGW708UC9PjAZkg92Wy0KvZ9r2RLtMSD3T1keWywOX5cycEiUHSknEaD6hKBYwmXImxcCybdS2WcCsWFFeCIHLTvb6mB2wTLlAJ0cnUT3rzO6CQXKSNGD13EGxfMJuDnWNK3tahWhCwkBGRUa1jqR2MZ4GD/BlQ7cVXVeyjiv01JbN/B8I2tQ2BByWHA4JjUELtsEPEWof1A7bEFq1u20o1DE4Xzbq0uy52rjK9JFIp2GMdqN3U6v1YQS+dIU6aKsLJepgDZFp6p7KoA7N4Yo2uaqVri2vqu7wr1eYduCy52DiCBkdGZ2mQor28wmiV7NkSLMllwfoIsTjxBQLRSc0BBaOVJnIoVOyJS6UkMkpmjBhW9BeOUyFj16/YovNFaPCPCdSNFtEGIGbORC00+rgJs6kGDnMs0U4yWBKwt3NxPK4oFuD0UnaSCoUzLxNELNNRMBhsiGR01SYSmLURA7RNg4JHA+FkYR+mbk7TNzXxjIqQRta16vPSbNLvgWSVtsOdNAEAo0QleikqQ6hoZxytg4tHca7DGtmiNoMqhK1w2EOlJw4lWSNMmOw1I0NQbXZgW5O5HDgOAW21qyM0nmnEAMhBYOyXYDFrgj0e/4qFBfXV+ypE+DY/rNSFHZKAZ7xQa6KSd8X7e84FG8Q/P6X/PuDb5yufB3DxBjSUbwBXJXgSff7a97TTBjCCK7O/CIfpLgq81nK/wwbOsSOff2eeoFDvcM3Lg0WXG2bethBw+tbvv5b+AIkz/X7qe5aTn9Jan9BxURBKglxtfZWG+fLhft3b+m9XjfM6ErFEJ4n4e/08T29cf3wD3+VQzwwzmfePryh9TM3x5m0NK9KaPTLBfAMrg1yhCiD7bIStidCu1BXRSY7rT89vOP88C3mtJH6I/n8QFjvGfUCamVvuUTubmZLshAhx0iZJqaSmJLw1DfQSpBOT4VpKkgQHh6fGDTGqPTlQugXgptESplt4htC2C70qtCVKWImwS7IuiJrJdZGahU9YxLZ7UITiDEjKdNlMOpKbRvndeEwZzoRGYHYHhiXlfF04dg3BpFQB8vbew4ZQk70paLihlxXdw0nw7c4E5OlSjwsG+cBS4fLqjDMad+78u7+zKiVpEpdOpMUUshILi67rtCf2OpK00FVJdRAWEEWReLGRqOmyjELh9ngRvqAOIhjV1D6vRU8qDSa6ikErGRzNNTVfEEyPcI2GtpgyKDUmRECOUzk+QBeJx5isaiblmjlwvTRK0oLRDnwOmSWWqg9kaTTnt4S6dy+uKXkSN8WHj7/lMv9yiTKJx++Yj4mwIQL4IkmGphG4u4YEQ3US+f25ujdSYl+eYDamELnB7/8GnnzjvszjG1FpJJ0WMeWTMQYrTKlbqg246xk5tUhcjrOaBWOB5vGdAhztJ/TsUS+7/Ut+fGRd8tK1EFtF2iCjAxNkZQoCZIXJyqmAC0ZpmgxSNkTyNeg3B4SUZS+NdIaeOgbrW2UYM3MjMCUAiUpUwncHTMmsVK62iba2srYKiMkppcvSFF493Dm8Xym14bIIM2RNCcz1TIMa9NxnU72xVc8DkzEBDQ70TnEEzHGHlrrCIqYpxE3vSO4ys/yTfeHqm2e4qWKRtDhVkDxL7AEil3paskTngLvMN6+Mz37q3AO2S7uMUzAsyv2r+/P46XYN7Per+KMHV7cN76dOxYZVxFGSNmS+8Wk/uJ2DHMWWXzank2p/tmEnA298M1MJaLB7QbNVJ4SI1GVdb1w/3jPtz/7deq22vUveKapx3h9l4/v6Y3rsi3k2xMffulj8geZS31kOb9nTdmMlz1wmMSNmZAOJ6YslCR8KMKXP/qQdW0sG9zOCW2N88MT8+FELpMZM2n80Fc/4eNPXvO//Z0/ypQLJWVOkxGRirX+5jJbVuBovHp5x/jqJ4wBS4tEtaT0Nw9nXr7+gMPxhsM8kRGadvLopiIUtRSOZFLrNoTL2u15tZJFOEyBfsjUmyOhD5v+ssF5DujDqEQsBaTQGJvdoGNrFL0gtUFf6a1TiqVzrOcHYkmMNbCen5hPB0pKTDlZJXzvtF7Zzk92EyBsiJmrt8q2XHxzgzoCl2VDekXpfPrtzzgeD0zzTDmeOB1m5nlmOibWeWOtnfNWOb9/JKhSYuA0z5xrp+lgbNUWJGcr9ot/rylhKBqGlXq2QRz2eR+mmRAb5+VC7Zi0vG1oE7puaBWetsVM3kE4JDMTgyCh0MlUVyp+9as/iFAI4UhIhWWFbVP65UzfZnpdWc9P/Nqv/keUYd1LFCsMzJkajgTMLrDWjcv5kbU2LiqstydKiuQgvK8X6rqxrQtv33zDjPTNZuVA5zRHSDe+iA5G21jX+6tHDvEakq5UCbzRM5fDgakc6XVCe2d9OvPw/jN63ajnM7VVTqeZ6XYmdvscWx+s5zN1W6ibTaslj2f/maywCSMGcmy0aiKBvq48rtkEEl6JkhPc5cIkiZAikhIxRLa1szy9Z3to3JwmM/CnmZwGKQjEibubO+bjiTLPvF4e+exb3+Z8/0gwKR99W6yxOYohEWMgZfLFXT2f0iYywQRS6grXmIxH1aF0d6EbKJA9X89+hf10lI1L654ik1IgJM/A1IzQnkUe0ZJ+ZQziPHmMUyLkg1UkNdtkg78uYri2iYcghGReT4kFaTsv583FIXjPXiNl2zjzlM0aMgwqbZuZ/MM18t2muJiycVwhoNHsLmAK6RTd3B8jozaqWvpPFIViKudcin1uAejPk18QIZRA38OR1bjwbW28e/uOVk1M00dn6xav13+r+rg+//RbtGUg20aT1fxbvdEuF4+i6WTxtGgBGRvRQztjnohT5pgy7Ri4O8xEEerLD9lWS6NuywJb5ZAj83wi3L6w5AOGnUyxC78U40h0mHEyx0CZCylltpFIYiee4/HWFD6jsj18DtKRFDhMmcOcDHPvHWkWhZRUOEYl9yfCZlUpoW9kGnMY9LYhvaMt0rZBbY+03tmA4hXcxzkzTYF6qTytj/S6mGepd17OQoqDIg2pG5KM7G/eb9ZjoqXoOYAK2tmWM7UOugaYDxyyFc+tT41t2UwxRaBgVSKtVt62dq2EqL1xyJlQCuUw08eZoY2igd7emsl0KClPpLHBsMbV1ht9RDuw18rQyCCQUrTTqMozgT72hc/gq8XTK0a0dBKbaCxDcecTVJXtcnHTrSBhQ+MBQrZMwsOJECZCmIl5ZtoGdetoPdDXA60uLJcj+jZSN2/gDpkyzZT5aJzalV9YoVtGY+jKnAI5CTkqbqVChwX+7nFB0+HEMRjPExQ7basy+oFlMRWgqNL7wtDkkC1MKZJEEdk8nWUQs4JUFPt1uDsyRcz6gNlGWu8sU2Q5W7p8DkIM29VasfXhMDmgjRAt/TsQyVMiiCVQZJnJwMSwtugUPES2kJfNbBRkpmyiAcmeDo9FcZXiLfcMSincvri1yfLpYhyRmBXAhA4gwSFK4Aql7UG2+3SjZng2+PsLKRo+uYjsSfLParqroRrvwgvWNcYwVstM+8G/1lM3HEYT3ziuvw8GLXdPnhfRK+Tm44/DiVgcmexluON6vQIO23lkkytJTUUIHnWDmYnH8/OHYMkiLqf9otpafRLd4UO5vnZ1btIyDQW5qizDdbSz4OFRK9rMR7tH0uzhCDgn+XzbfVF18p09vqc3rm9//dc43y+UEGhlYcjGWBeW958joxNKImYPjRTo6wYbjBgIByXhP4yUeXk7U/KBFE58/ulnPNy/5f78AH2j5EIpE6f5ZAv3ttpF52rZA51tXa7CgDAVpilyLJmGtaqKRI5z4OnhPU/nR97fv2OKwnxz4Cbf8bKcWNfBuq30dr6GXBymSNg2ExuotcbKtlB04aleLO6qBpalc358YFnOPJzPnI433Nzd8uEnX+blBOe1si4PLOfFF2/48CTQmxGlLcGwhP1tXUza7RfrXISSAzEK69MDl3XQNXKMmePNRCby1CqPy+YiC2GWztIqy7JwX4er3zbkLNwcTkxlYsjsC63dIFu9sGwbXcWUfc0ks1utFpvUPF1AB6FH4ghMI5PEPWqjuWTc0vqD2sKT+qCuHc3ZptoQ3SdnnKH5Y6BfHg1uEjE9R5qIKRHLLXMphFisoqIcKFvzpuhEvTzQ20Ybyny8YV1WK9kUpcwHpsOR88PjtUi0hDOH6UQbylYHx+lgIohQzQJRAm1OHA4HtosNFIe7EweHc3KMqFaPHEssT1a3E2Ognt+hITEkUjfzycUAhEYptgimVEglU5NZI443t1cLQU6JbVtpvXPDDefHJ0TVPH71wTjYVFjXek0UX8+PblxNBE0cDoWA0utKKEeHqjo5Gr+iQcnzibZmS/ZIAeqGIqhPYzGYWq/WJ4PWt4akxOnmyDxnthCIJXpUVbNUdseMFRMXGC7gKSDIdbM3gUWnexcabuVQl2mLNt/owjXdZZfWW1+KddP1bjYAEUgxsSew7DyYfWNPaMHEVrafRCQK6sHEYxdEuG9skGDY9KYMgiTbfMaOctqmOLoJY8DEMfsmZF4v23AUHHEyfip4qwBg31N2Of1wY7JvK8MnVM8mRdyz6ukf4g7q4IknNrFFtnU/jHgDQ7TpUXZuX+xe12fC8Lt6fE9vXMfDTG8L3/z8U7Z4ZrAR6uDuODMfCvNsAbfLZeF8XkBNVJFLIsrgadkQhBd3L7mcBy1eyPrA5eE9o27MJTPngGRQrXz+2TfshylCKcJ6f+a8rHy2LGzL5hewUHv3aJ7B0MTpNBNj5PHSCZhn6twaN1MgPgXevPsW/+mX5VpMeZsrl6XS2rDgWk+aiNJYWqPWzrY0Wm8ksU1opMQxJrIbCC/nBy7LI9/+/Nv0+uz5uC3ur6oGUeYYreG0ZL7+a294/3jh3ftHYkrkGJi8psK2BmEuyaacGNjevKGu46omKykwsMJO3TbWWqm1ExN89vYtX/+0cf+08Yv/p1927YmZdFOMFpUUMzeHzN3xwPL0wPuHC0/LgpTG6VBsQjpmaIbVh03RaZCDTV+1DVpd0dFIIVouohf/pdA9BDXQ+wahQAjeuBxN3fb6YzcoO18Xb9BgvWjbekbHky1y+eDUhxCmDG1BVMll5oMvfWBwTR/oYhtAG52bk6WZhxi5u0lUlWtoUZqszmWsTxbTBMQgHF+8ZH14oJ4vpMOJVJIlcgdBevUMlcjt6WAbbM5Q7+jDhDhRvEJHoK9nqi8UiSOHuzvqsrA+PqDRktXFBR7qLcIbifHilhCEaSrQLhaBRSDvSeoCl/fvqd3M4ylAnmezbmwrleS8kfEf6h6tjcRxSkSBPBVCq4wBjUx2TlIF1sezQdFbpV82ttHNIL1nJaKMvppazuHB2p/HkhF38Y79nME3IW81t0XXskPVQ2nrWk1xGoxrBLOUND+MhBAJqZj4Y7ja1vvErLLFGrlRdRjv6HJ3ayEwa8kX/Fc6jHtzP1VKFkitXcwnEr3Y1Mgm2ubN1EMtJ1HM/9Z64zor+TTYdbCcL9iXRRKzrQU7SvcFXclo5o/bg5lDyX6o88+wGQTZqDZpj0ZdVkAIobPoRqud3pTz1uk9kEjkMFtAOCZK2apnmupv0eQMaRvHUmg5cZxvjOeoldubE4epcDgkHj//nC6NJJGcJ+Y5M02ZmA4UsUXp5c0rcjJTZD2fCX3jWDLl9pbjUWjNbhzJySW8gbkM6r1VitcGWQohKElgq9aY3PogxUG/XGhj8PCw2CKdCx/e3nB3ytRaeXy4cLlUq7RHWFJj2RqtDVaAaN1ESRrvzyutm3ovIoYpa0ekMR0PHObMhy9fI7pyvlz49W+/4/5cIVidypJdFjuUqIPTPHOUwNKEd08bnz8sfHZ/Zi6ZHIQcADFeICB8eHvDcRIkDL71/oGny2bxMilRkmmp2rCEjWt1+BRZ18FlG9w/bSxrs1ia3pmyKfFKTrw4AiEgqfPZ2zNrHbSRmMtMj0eaFOjdIozEWm6DHYBBsCBckuvIAlWbpTqIZXNJtP4mJaNqVSEQQTIaLDVk59RVuqUWSCKEZHUVrsbSEKxjbyf+pwKo/3cjteMQuibigKzKaJGQLRpHciHLzr8LsSR0BFo+ULS54EQ4nAqJiZohzBMpWTVLTIm+rTYJSESrdSiFlJARrXkbzEjtzal1MmvHzpPMx0zOkGJnuMpVgDwf6NtmJZgSGJNtIjlnZOyBuTZDRDfgj5qJwyK2girT7Kfq4tVBauf/PJswZ/ROJpj6FSWlTposiq0RiGqvs4/OGhtSBikKYzE4d6uVY5wgNPaEiLFPD8Elg7vaDthbd3WXwWNTV/dNg6He5eZetOH9Ve7fwoUPtXZgINE2jNEbe3ajbpvxW873jLYHEwPR/WRjWKxasCmteqg0aokcKibZl+QltPj91KpTEd1ev9+/w/Uou3JjT6R4zii0YIDRbRPfkVIHCZ+nQ9Vr4K+H2lBVSepwYYhuMHfLw+h+uFOfcE2pu20eN+UbYEQpUUhTvCKl4qrW7pml3+3je3rjWs+PZIqXtnlahnQsh8FmhLZ26ma5WDnamBsUwsDK8IgEjfT1TLtceHz3QO3CfLghHW4oOTE2RdeK1EgIFv0UR6UvnXqp9M0ysdUJx21trNViXsiga6O2yv27J1KK3BxPvL45MQWh1s7l3RMPl5XhpyWNjbUN36AUydGSrmXw/uHCwCCC2UUoo1skUosFLcLtdEKG0s4XHj5/4O15Q1JhPirrHpC538RSkKisFZZqJ6X3S2MgZBGS2M2/k9XHYl1M6ODN54/cX1aqGqyWo31N7+ahOs2F02GGEKk90JqwNWWtanFa7u3ak+CnosSmxDp4+35BYiFNE9PhBOloG1C/XFWCAzHO0h3/XWFgMnMrpOg0p8wlJkyWGK6blgwjqFUiKsngFec5xvUaMg4hebTYfvp0mSIhGawpYHmJVW1jDQpJiB7m28IgREWi1X2YEMBu/pgNxonJNtZdmz1NAdFCzCAle5BzJJVCtUGAEANt82kpBCTM7IkpqBKjedhiFs+Mq2irlCKMmIhxpmOHNnRwOCRqVLt2RejZvlEKGPm/e4PaRkpO6k+CeOI+rZKz2T60JMKo7mdS5oPn9I1ABvpW0dEI0smHExKz5TpUj5NqHQ3Gt4agyDao9cK2bNzcFkS6/ZlYZJS9Z+dvFOeu4JoWIeJmYhPJ76kXihJa94gjRdWTZ3CvpP+77S0F/mdjdCdrTAAWXECUYvQAaruHidVf03AYEPamBfFcRx0WXyZRTHjCnveOe7yMOwv7e/NpbQzbXxELBTDPpFoKhvpBC+fD9s9kj2TT/b5+5rVMVWgipbDv2/7Z7Zvd2HkMHZ6PODwOSx3lsALJKHbd7B1t4FBkCGZ7+U08vuON61/+y3/J3/pbf4tf+qVf4hvf+Ab/5J/8E/7kn/yT1z//s3/2z/JzP/dzv+Hv/PE//sf5+Z//+evvP//8c/7SX/pL/NN/+k8JIfBTP/VT/J2/83e4ubn5jl7L/+Ff/2tKPJBl8H575NJWtmXjMGdKihxyotd2HbNTiXYCiBZu+7Q2WldyjMzRyORlsekkhkgOgSyweTV3G7vhGGYZvHl6ovZOItAEl3l3nmr1G8AikkTNq/SwVEQssbr833/52mO0ePVE8OzDIuaFGsPobj/62IUYzAeRY+LFzWQ3S+/oCJyXzmfvn/jmt7/NaJXHy8p//fyREiOnU7aKDA2s68ZWrciw6QOXVply4u40QVAezgu3x8IUhQlQma8X+aWuLIsdBJ42O/GWGAhTYhIAZUTzoMxlYj7MHMrMYYbT0TLMVo8HCq6+CyFBKPRxoRBITXisZ15/cMurl6/40ve9ZEqZKJ2uF0//N44qpehlloa/q9c52CjmyrN6IaUDMAzyaRsRkzZvGkmqpN7QMsPoXodSYJiJc+gK2hlYMgjDOqNCSuavK9ZvtC1nlse3aGu20OYb968lar8wRiX0QDjdEYOAdrbtAdoZiZmQD0TJ9LbS60LQRC6BkA72ee0TQO9mvhWrahmiMKqtYtPNlUtr9WJciZgYIvRBbQvnx88puRPygTRNyLpSh4X0js1EBuKf6fCJHiCWGyKRNDqP58/RinMeto710bk8viOOiTzNpMMt0oTaG3W7MNYBcUIkE1CW5YG2ns14f/uSNGXSgHW9p69PtOWRsS1XXmpbn6jLE31rRHlB8ESblCO6Lu5XMnTCXtUuHbAD1S6qEGzxlRAIav1c22W5eqRiPprh2Bf1vdYF9s2wmaVCB8HXCuvoa2YQThY91segro2ue+UQkGb2ZgfbPp1xCmKRf7tQ5Bq9zLNiFFsHTIIvBkeOfuXd5Pmt2nQTrMU9lUL3VGenpK4y9z68SyGKHeKjkERYLpW2VbR7A3K0yCl4PsSKr3d73FQqmbYsHnJuB5nWlMf37y07cih9tGv49W/m8R1vXE9PT/ze3/t7+fN//s/zkz/5k//Dr/nxH/9x/uE//IfX30/T9Bv+/E//6T/NN77xDf75P//n1Fr5c3/uz/EX/+Jf5B//43/8Hb2WOszQmVImS6aGztI6j1sn1M65WYyScQEgSycnyzQ8TYlFYEWhVm6CMJXIcb7hoQ6WrdPWzdR7Do+sQxjVsuiOGXRKWFqdWNCqS6nnmKnDLooxrIm3BJhOM10C3WObelBCDMxq3NDubZgKzA48LJ6HFoNSolJypkyFaZ4sWUArSZQP7z6AZNUs3/jmp2YiHY27Y+L25sTpdOLuxR2xb4wx05tyf1k4zhM3x4lXrzPTzQdctmobc4zkAFNUXn3wA9bCOzrf+vo3WM5PtNb44MOXHI8H6wOTiHQLFF63ytNlsXityRb1UCY0ROajfWZRTEUnmk0Q0jufflbpo3Gpyt3xxMuXN3z00Uvubm6hW5pBH7tiy06gxnM856sFMRjPZPUZUZjCRs6ZKSVSEEpKJsyIgZISe+3L6JubOEEjQH9ewA2TQbCKmT0BQn0KE7AYnmrqVkmBLMNNlm6+DWaYzcXgEu3GTwaf3GKJBImMHNCpMN28wCZEqOuF0SpWMz8Rx3MOXZCBkk2gMR0MYtIO/XL1JuUUPXB6IP2O+fSKmGdCLGzhgaCVpo0yzZ6E4JJxn6QkQCmmHBw9UES9MThyDACB3gIyT0zzkTzP5MPMaNHuUYFyuHFlo6vmykRTK5+cpwNpmg3+umRCMZVva0r3UNspTdycbulz53Q8Mk8TIURqV7pWz8Cz12KZmYb7qmdfosOinNjFGZulrDRL+AB1z9dEqxamO4bXqIzuZZ1W6yMRUCUlJSTnh/pexdK9J0zNd9i7vyZL47HJXnwzcdXf6Iw6TJyR7GI2cRQulPBp5aqAVLQNRvQ+LBE7cKgtdnvs1NBBW7drvQghkqIpOyVERvWNczfyYxBt3yojdEYa6JT99RgM3uh+3ULI+3QrkLJxw0noAVpQX+Nskx29kpnNxrK/n+/y8R1vXD/xEz/BT/zET/y//Zppmvjyl7/8P/yz//Af/gM///M/z7/5N/+G3//7fz8Af+/v/T3+xJ/4E/ztv/23+eSTT/6/fi2H4w238x3HJEAk9RkpK+tqmHCMSsrJSHAVqwuPEJIwnQ5MIUM1krPMwpwTpzKznTdG2KhDkGy5emZSxEdmJUxCyUbwVwW5mHRbsIu2Doyw7p0p+gabCxvCunWewoUcuoMBQtVwVf3cThC9Ot5gE4sLOhW1+Kd55nA6MrZK1EoJnbvbE+uATVerX9FOCnBzKLy6O3I8nTjdHKHuh3ZlGZ3jceLmZubmtpCPEyFF7k4TU0gkGZQ4eHF7ghDYWiXFSE6BFCJ3L068fPWCmAtrF+r6yLZVljWSE4SYiamw9UYoGVIixUZhIolJ8Ucv1NbRuj6rq1BKyRwOM8fDTJL4XLjoKjDDPXD12HNkD7Jj+erTsSWPeAGFcxA817sDO/ZyLUsEOznjGD9+QleHmmK8mkB/o4JsmBy4N0KwjeRZgm2y7+DqLKu1sc3OeBorPQxBiJJBMqkcjSvSYUZqJ/wt6X432gb36hgXkUph99CI2kJo0yi2mGtG5wOpHEl5IoSE5oUeAiqekTd2jsU/W29EjP59zK842ItLYzCuI4rQykRKEzFmU24yYCS0RZv6ZIephOw1OykV9xclg/7w15onSi5sfTP+TAIlFzQpuWRiys5mmoEbv0+HQAwGtQ3UBRD+mfiG0725YE96b83gPOu/2vvCngN6bZrrttkEOyBdN8Xr1z2vD+zc0ZVxlS/84xPR9dJTbyq2a+FK/tgoZqryIFeebI+Jun7vrqjD9Az26EN/8v19VufErNbE0DsbuxTnzXQ4atQYvV2TeFBDo1R8g8XUregOA/oTRjELSgyMgCtV8aSM8RuaHES8zuW7fPz/hOP6hV/4BT7++GNevXrFH/kjf4S/8Tf+Bh988AEAv/iLv8jLly+vmxbAH/2jf5QQAv/qX/0r/tSf+lP/3fOt68q6rtff39/fA/B7f/fv5u74GraFZaps0lgu1S70XtF68fwt4zVCjCQGWQaHaeKydNqAUCKTVFtMQ+L09sxlraxb4+VBWNeNZa2smyIRUhLupmgp7wTPtLMq+KAWHGnQoklCs1eEa0qs1Uy7l/XCLOYj6V1ZR/DCyMjLIowQ2BROTw2VwRThrgiXZqfi080t8xTJY0X6ypu3C1/79D2fvT/z9rzw1dvMi0Pm5ubAB69fUo5H0uHI+f3G0iqXavLb6TBx8+KGuxd3fOuze97dP/H+/ZmXN0eGDLZ14+m//hcenjbuHxbenc+8mNywzXAsXego7+8f2GqjqZKBFCOxZPraGWqKqro01t4YrbGtC22YGnDZNj5/fOKYAy8OEx/cHJhypI/Ow9t7Cw2Vikz4CVUYTdC8q8aMnB/aGMPKoSRwrXOol4spOueZtmVT2xFZeyPGTkyDFGeXBzu57YSyKbDsJtPhxsnrL5u2xStWtvUCfXi2oi8favU4iURITorvvEndiJpNNDKsuNFgw4JIBucYCZG6rmivlosZ923YakMEca4vXhe3bblYjUdKtpH6STtOB0RMuTbUwlr76NS60du+IFpsVK0rOoZXAA2fPgfbthK7maBlF6UgpPkABEZTWl0ApXVTwGofrpAFU6IlUp7sfewL+HUSasaf5cJ6WVmXhW0x6CqEyJDEEHvP2u2HZeZbW6A1Ri9xxXgv5fkH6rFPuxJBx7BknRgJMdE3m65jTMScHIJuXFozLU8MpJJcnWedZuZxKtcWY20WJpySGfgtlk0Y1YQNuucsEgxy8xxES4Oxa03UJPMSo0fFWQpi3zZ6c9WiRzDt6kT0N0Y5qYpFuQ2rPio5OZQstF1VKbsiSRm1mmqxD1ISsnO7o3WT34dOyHbIsXSRza/nSB9maYgpM9j7BoVEtq9H6fqs9P3NPP6nb1w//uM/zk/+5E/ywz/8w/zKr/wKf+2v/TV+4id+gl/8xV8kxsg3v/lNPv7449/4IlLi9evXfPOb3/wfPuff/Jt/k5/92Z/97/776XhDQHjcFlPkJJhSIQcjntWjiVpXWlMOaYa+wbB6BzvBC3ThXFf3UsCnb+8RAlPMFmSaAjMOczlWnYOikulqP5C5+JjcjVuJuKJIobdK04ESaNohCMfDyRthO9IHU04mkUVYeyeWbNKRduamFKYIc+gs92dkKInI3fFDwhDqMvjs80/5/P0Dj5eNkiIvXt5wMyXGGIQMgY6uK+t5MdVj6wY9dUUqrOfKw9szTw8XaI3LxaopWm08LJ/zeFk5LxtbG5ymW6Sc6KOwbIPQ7b+fnypdPb09BULIBM1e/ZFdDDO4jMHWlUuF0RptmFBDCEzTibuXL7i9ncl5IGOBYSkgEpxr6OZxIQS0C0MjjIBIM6EBAUYnYDAMoqSUyCmSpJNpnouIeY8kISSvijcMvw11xaZxi2WaCanY1ODZkybG6bDti0dmhMKgsslgSskKHMnUKO4NU6YYGWLqvxozmhNiGDaSTcwygrCNhRCzbT7zLXK4MOrKyImYTBGLmJEVUWIEScU32khLmZiiTbqlEPNkn19dqNJIIZBiIR1emmcuJijZPkuTVpq/STvEhJSjK/caYz7aCTxGpvlkCQ8SCN0huyCQil0DsiJk6u6fC9EUngRom1W4pAwhE2Si5cmSLOgMEYP/LysP796xLBsSE+XwksNNATEaYEhCZZhAptmkodFGjxgs0Li14e8LO3RF66DS7sSPV4ekPF8bhIP37WgISErXdAsTaJh3zkzZnkaBT+di5ujGuAZK2wD1hVoRh7gVrmWqASX24VaD3ZPlAj4Vy/ZUoRPo2glXcYbDz85Fx2AinyHDEjh2H5enbgS3AHR/H+LydiWhKG10YgqkJFYG4DYzjVjWYTDkipGvKsY6LBIvTcVCpwX/DEyRvG/VkS9MnN/l43/6xvXTP/3T1///u3/37+b3/J7fw4/8yI/wC7/wC/zYj/3Yd/Wcf/Wv/lX+8l/+y9ff39/f8wM/8AOoJ7+JWKCqjbuGbccoqMTn0V+7dT/5eN+H+aTaMH/Stm4Gx6jytFzIIZGK0Hrw0/wwHkLsPKRqKfNDLR4ngl0owxi1gdWHgElFjTvpbL3BkOfTIMatitd5K7C2Rkl2qrEqdvsDU+9Y5blqYyoRWjRDdbCEgakEbo4HXt7dcCyRZV18sQhX6CpFu3xaNRhi9M62VEsMqRbb1LpdjL3b9Diw3LIQAtN84Hi8YZoPECJdMQiFYHFMwWDPPgyCUD95DUz51IdYkrvuUaQgEskxMZXCXGaHUcwQGnRYw4M8t3oZRg7wBfx/vx1Ur39+jaRxTssgwj33Tf2kuS8jDvPwHNyLGu0tIVpnUbTpZa83HzporbMnr0kq0G1hGYj3IgmSMhot1637QqYSIZdrYWdXk7Dvr2ZoB03spIpEK+S0/ja5Lmw4dKjBvqerEJA8QRA0eB2FWMCtxGSFjoj71gIhFUKevSLGP1cxszHazT8m3r9GIOTJpmixRdcSJSJExVgQYChJPIA1JDu8DYOIEgGiwanEHbVycDZl0OaqPBMANA/YvSwrEqwS3qA1UwfaR7ZHgO2fgfh0898sKILDnB7/FKxFeoewUin0uj+/cakShJgiobmvC9/Do3kAd3+n3ajNlHMKIjsU/IXXtG9awSKh1LlK6/YL19exT18WIL3/XRwWDrbh7l/L85/L9eZwSC4lozv8ujP16X7Fy/7RGdxLNIFKfZ7yQnThxxc+0+vee/1AcchYkaCOCJgisjU/APp95ave/39tXP/t47f9tt/Ghx9+yC//8i/zYz/2Y3z5y1/m008//Q1f01rj888//3/Ji03T9N8JPAAet41pKkzzgWVUat9YNpMRh6DEaL6CPjprrUyIT1wVdONyaWxdKcVUT8ZrGGzVpJGGcklegsewyB73vNA3NBqXdV4H3Y5u6KiINOqAppDEfGBDB5ICl6WjQ8heaCcyCLKrluwmPJ/PdtoPgafLSuobPUKLanFMUdjaoBSTfo8t8OGrEx1lqZ2PX73k+z9+RU7wdH4g5ExMiRQzx+PEdbfwXqptqyznQLtOPw5iSCDEzM1NJtfCNFlaxIcvX/LxB6+ZTzPL2lhrZUgzkyqDGK3SQEeDICSg0engafKDpgaQh2gmTasKF07TzJwKy7JxqB2dsMUofOFmDvEabhqiwUXROUJrj3VTadgtvupJGdFhF0+RF/EqD/ff7D9bFNGIDBNXpGTQHTHb4m/V06haoWfvau87YH6knq4n3zHMupDnGz+cRGozfx8IqRwZvdK7UKvBlrt/hyCuTu2MrkhIJgRQ6y2zw0pAYrYfqJgsm2HfO5Ub/zzUPEOh+/uOzpMlLzlsKBFC8ZBW8zbFgNXdO1vRhqLD+JKYDwaHdWtozsOulS7ujxqDNhZ6MoFSH0rfOhI6ITSmYdcusTDotDZQbVa9k2cXFTTqutC27VocWavlX9btwrYupJiuFfAhmFCL4YkNdpq8igCCHyQQg3y9s5XoUnjBD2anmbZYWocdHsznlediBzAdDAYSIJVELgU8e9dqefTKB+7QZtgPplF8dNk5UuM8U8k2Osmef+iQnwTrzHIxEOIbSYBefVP0jUiv2DbXDE9EyMWKS5+bke17950L8+dUgSh2X7XVimp3DjWKdd5dE91VoXubgnPzJQfWbu0UOZpystVuik2f7K6f3W9q2/pfYeP62te+xps3b/jKV74CwB/8g3+Qd+/e8Uu/9Ev8vt/3+wD4F//iXzDG4A/8gT/wHT33f/pP/5FXN7d8dDuRDoFjFFK0iybFQE6zjcMHpR4bhzhgFIY2ar8wlZXL2tiqLZ7EQIrCJ6+tC6iIolqZSiKnRB2Ktg1RJUUo2U6gtxkLbx0+S4dgJtyhPJ6r3Vg6OITAixhszMZPP9HSEPpQ2oCuwiHPHPJETIHpznLeokfR3EyZw1x4dSi8OB3oLZNj4ktNOL04MVR5ffuK4yGSgnD34sR5854cDRyPEyUWUkik8kR0lVEqkdcfveTUOsu6sTWr5tZemcLErRfk3b994vbuwN2rE68/+ITlcuZ8fuLNWxNy9DboXazsMCRiyMxTpkqiImy1chimrGz1Av35tP3+/XuCq9/m21e8eP0Rt6cDT+8+pW9KCx10I4TOSFaNmUb2yaxbj9Iw+Xb24ksdagtfLIyYbREYilC9Ql68/TUgFNsyAmQNlDwZjBgyY2u0drbpSpJ1lA27MVP2Ej4ddPrVH/RWH43jSIlt2652h/b+6XkBQS2pQAcaIOX3Bg2GRDkcGF1pbfBwf2/xO/gU69lw1pn1LPrQsFx9XKNXI+Zd5p3L2adOON7cwljotfHw/nO0maeq9U5rNk3aYSddJ/YeKq03KyltC636hA7EtLIXEeacQJW2LpiaDp9crCF5KEi8J2VLYZlyYikYpxQyqrCtBl8vy2aewvnA7SvheHtDCIG7Y0GqNUZv62rik10jo2a61QGCw4fObe0p7jq6Tbp9WFN0q4yxMdrKOWDow9a8xcC8ZBICl3O1CZBGjkIphVIydTVRkapdbyEKfQyW80qeLNg2xkDMrpx0pSDdZfURttUEQfFi96Q1eAfWy0aMgZQjvXZCtjXmcl4prZJKppRyNQj31k255+hSHRt1MxGaQfk23fWhnh5iE9eex9hH5/H9o3F5OdG2I+l4JE0TqFUVDQYhDHRU5wIzpNl7/hpChdAIKVEOigSL37IU/80Flyvf7eM73rgeHx/55V/+5evvf/VXf5V/9+/+Ha9fv+b169f87M/+LD/1Uz/Fl7/8ZX7lV36Fv/JX/go/+qM/yh//438cgN/1u34XP/7jP85f+At/gX/wD/4BtVZ+5md+hp/+6Z/+jhSFAJ+9eUO9LPRlJs0WudI9Wn/fvKbiHqTeecRCcHV0lnbh6bKxbI3lC6WMMQrrVgnqaO9Qsvctbc2c9QG1IrxYnbfCYoQ8OcDgE6X2wZNXrASsSG7s0JQ6uOQRPr2rPY+ag7+qEoNQ60YLkBhEXIobop2iDPMgxMTti1vilBmq3JxOpOjmxhGIQ424JzB6oWTbvEq1vLnkeHeRCKE5th/o3Uj2VCaTPgvEAfNsEM+2razLhW1b2Q3NBkkm5tKIIZFC8cqLXQatSHecoYcr1NN0UJJxiKN3Hh8fePNZ4PxQ2M7vOM2FVIRga6LvdoIGV+ftSqzd8yLhCrGiQquNnPZQ1cFuUu3bZpFPISIz5uHDYTw/caKB9Xzh/PjI08Mjl3VjkKz6ZQi5JNN0jMrSNlq3SaxGm7ZSDFZQKqYMa73bJiRWcd59Im8Mcg5+Qs+UaUJVfON65/FD5lUbnrNZSiKl2VxLYt1h+8YVXCE4xqCNRi7x+n1vbm5AlV4rj+8+v0KqvVr5pqoYvxEyu1+siVBHcwXesFizZiqz4HA1CNO+cW3bFeaSIIh22rDrXALElEnJSiWRTEqJeZpMqSoKbhSvXVlrRcfgdHvH4XDk7sUHjK174ekevYRvXvvksSdncBWh7tCwocR7hbxDW/5nvXXnvSwFQ8LzdLCjcBGfiJwjs/XDDiK7QMMv1C8wWrsKFUc0TLBhxuCxv/jrv0z67iekgB2ywL1krnp0UcUXvp3D7DvUPbwUc1xpEh3hC/eK/6V9IhpekulCHNxDZ4rKboiAKkPcqL/DgCHS6nNh5bpZQ0EWq33CxUtjDLZq10Vr/ytuXP/23/5b/vAf/sPX3+/c05/5M3+Gv//3/z7//t//e37u536Od+/e8cknn/DH/tgf46//9b/+G6C+f/SP/hE/8zM/w4/92I9dDch/9+/+3e/4xX/7zVseH554uJ+MNBTzPO1u+gDMhxuHZZUwzLA3euPSVpatsbXB2i0hwiBmsYUFM1fGHT9XWKqfiELgJpvAemAnl063zU6gqvFabShLV7LYJhjC882xRzYpMESMB3P+IKKEs0GJrVZOU2KKwjFByoFTiKSpWDTNsMnt9sUdcZoYfXA8Hq5dVF0HMST2qBVGYSqTTaOPF5PpR0u4783e32j9ucqkwjTPLiuHU0nMU2aMzv27t5wfH9nqRnXVmHg+XgxqU52XNIq48bN7Q6vfbCHYBEHvTGXfuBpvvv0t3r39nBgjU1S+/+OX3ITJLllxcbs+wyOmyAr7j8quA7UFWIiWsZbMpGuLlXGMdVkIEukx+QRsCj/RaLE0DrFcHh94861P+fSb3+KzN5+j6UDIE3MqpGyR7q2vvHt69FDgxlm74f3B2mkD1sE01AjvECIpTQYVjsGmg6lgm7wkYgoEh/MeH98jYkGuozbfuISchBiMExTttF14IEI+HAnYorXUjVI8NJXA6Xiwn0frnB/fG1dCsDw659liGPZ5+8Lag3GutTdysWtAu123Me28oZn8TZI/nB8Wl3k36lCqQnDRQAiBKSl1s1ipF3cnfvvv/FFe3N1xPBwhFGobXJaVMBoffnTDqw+/xO3rr3B++xk8WpZjq3uKBeD31R5jJE5y7cGzAKoBaHY/igk49vgn7ep+QCGkCCNelaspD8JwzjEYvC1qVoHgB5PoFoYAjJzsuo7JFItqlSCS7MA4mvni+qgM120kr0rZPVkxBktMiYkR8OtyuB5k56yicfjCs81juCB92CE27PyW2JoRw7Md5Jp+L7a5pmTtEjkbrKzDkYG4R5KZBaV35+ClsXS19oYOl3Vz2FpIPkX3oUgfLOsCasrU7/bxHW9cf+gP/aEvEGz//eOf/bN/9v/xOV6/fv0dm43/R4/PHh6dV+pmqnO56B62bzu8vVYR20B2UrK1Zr4hIBCJyS5q7c3TKayqvA+9xrfUZrLZfZrrfc/UFlJ49gctm50sFbsAcrTNSPtv9Ivv6RqKwQpGLYirDb1uXOD2NHNznPjw7sSraWa+veP2w4959eIjtG1o28hTYi1Wv55ypp4brSmbBEs6cte8YCfcEoUSB/N8y3w48urFDe+f7lm2xYRlGlFNqGbSlUNRpnzLGIPLduF8uWerF7p7nojDEkumzCBwKAcO05GUMw/bytO6sfXFoFcN9GKwXe/d6j3IbuqFN/dveHq3ojr44Y/ukOMH5LsjqewqrohGSy0I6nl4UWiS6HvKtmAxYJ5s4ECF59kpKkLJQs62AYS4xzIF8nTDtq60rbGtK+/OT3x6/8jX3jzyzW+/ReSBGAvT6cYCWrVzWe55//hIGwopMoi0ttHqQlvN4AowJeOqzMuSTZQQbMOfZrEeswbbgJIKUSKPj/cOBbl4p9wiovR+4ZRmS2no3XxZYpxsjrNP6ILETCne/9QF4tlSFRCeHt/aYUmFbQxiOhifoitBk52gW6WFwBCHZF1NqwgaJlKxSUQ3g+YEC/kVrIC0tWpqu5gtbSKaPNvgww4NTocD8ZPE9uaeIZk03fLBxz/AdLjlcPOGN//1P7OtG8vTmdNxpZQTOikX3htfe/UwGDw5ZE8b8agi3wiug40kU8hpZ4TnBuGoJiiRHeL0lBNVyJN4Y/e4VnzsU704DxuDcZ7EYKhKTpCSlTA2F8PESBjPKep4U/tAYBcCDUAVSdO1Mw719c2ypxhBGBacgWCb3ZBg1UUu4rCN0A4jIQViekYSgo+konZsVmBMCU3GB8YUKcdi0VX+wQUxftU4PtuUam8sl42Qy1Wyr72ifWM5P7AtD/TtTEoFqSutb2yX++967f+ezircvJE1qtJlID7pINH4T1VPWvAL0GQ+11MFrgRKHh0Eate/eFV5MIhxTyhIyRIIUgikIFSHuRQ7QQpOVopNa4hYWG00JdtoNj7b7aUm6HCXf9Lgp3vQ3kym6uTqUKW2znndOPaJzeXaY5hRcPRKUZuIrh4OT4nvvbJWfc5BrB3pK1Uaj08LaxMWP60+1YXara6l9X6FGnrf7AMXUN1Mkjs6l2anKlNmeRRM6wgrrQ9aFbYqlKycR2Md3aTew34unYBQ6T6FBLWUEtNtBGK2vrPjZInZdvLrvpDYSmSSdSERkJCwIx/+M3GjpKiT9WF/EzaVONy2QzjaG2OXFQ+DSkKI5JzZG2IlWJ/XAOutOl84HC25XtSg0nlKnO7umOYD5/MTD+/f8TQGtVkqdsqBkk0aHkMxP1KyMsC5hD2piqUrcy6kEGCbTQ2IsLbBPJ/sGtoihzLTh1JbI08+OaiS0myckkDMgeIB5jKE7pFmUxBie7oWc659kMvBoNMeEbIhB1tFizwrW+U5kWaEZKWKAqMO1rqZ9wtBm7qx2YQvIWXn5Ly3CoOQ1vNCaxsP948s5yfW9UTtG3OwCV9PN2x3tyiDy/LE0/1bg1GXxRNrnhMlxnDOJvrkje8t41mMgAY7wAyfN9TzAQViTuYB3mG03SQeBPFqHRGHAX2KsWt2Nwl3UxsOIda928vQhTQ/+9ZiwIUvgSim5hMVD9X269dDbPeUEu3dIMb9mnUTsogSUvBkKHuj4ptSCIL2ZwRylwPa4d7N+CFeoU4Qn1K/8B6jb6ghXNdSL5Dx12GVOLjgrCT3itXOw7t72maVT31UOs9G5O/28T29cfVuyp7s8l/rEhxYreeujNslzjzjwf6D2sfslDM52YJvk4ZeYR0Rm7LAQnlLSqQYiAhIv25cqKmejKj2040I02Qp60GUEYTa9klqnwSD+UyE55DMBr3bIrFzD63bxrVWS1a3W75fDbeiyRVBIDHSo0CD3ps19WKXYV83Vt0IA94/LsilEdPC5VzZQkWiMuXEViuqu+12NyqaUlI9zmrbNzdsI659UHulVti2QUqNnBolNQtujUoKmdFtwzOVmkc5XXF+e+4Qo9XPBJin4r4U8YQBNQ+RdEMMMR+WJK4cFnusjP3gXWDgHIEOvzHHF2TGivYO0hgSkeELThCic5wpWidZioGm1mbcakV7gCgENf/M4XDgSx99wM2LO96/fQttY7uc7XugTHNhisnNqRMagp1sD5k5BnDfYRrCnLPBz4+FWCYkJOqA+XCA0akX4TDZxtV6ZzpaGoVlxx0cglYkC8VTKQShh0SJwhyF8TSRkok95qGk4rE8W0RCoQ9lqhUm40B665ALHq1HC4Hkn/dog2VLlr5QTexBDIRkn1tMBYkJ6R0rbDSiv3vG3eXpzLau1rxcFxhWfTLlzM2LW2qzrMnl/AhD6LU6WuEHvbGbx595rufH80K5/9e9j2t4+C7yzHddo5X2a9zX8j0Qd1cv/rfr7xjjuQ5kR2xCR9Wu6dHNPvJFQfi+aVjqxo5x7pL4/bXb80kKbslxFGd47VH8whrY/V1e4T+Pw7qugfbnO+W3y/D3DW+PUttf165cDLv8/krj6XUIKCXZ4OACEEO2Oo8PDyaQ6iZ26S6gur7P7+LxPb1xBTEVmIiisTDEzL94HpnoIGe/+MagbabuA4jTRC7JeadISCafTcHk+a0Nljbo0t1RbzLaDVPHJRrkZMKC1qk9mqqQwXyY/IQuNHVfBGrigtA9eRqbBHfHfhCHNMRxbYPn1tq4bIPz1jmvlRfHEylOfPjhR5ymmTEJ2oVTniAl1L1G7799oVWLwzkdM+fLytv7J37ta9+0i1KxJPinjWW1BOzDMfPq5ZHf8cNf4eHpiRgD0zyxVrHNvUQWcWm7mv9kPkwwBpfzhYfHM60qowXOtVnNSLdDws3xhtubW374+79CXz6nLivrZeHt9uS4fublcabWylYrMU5EtR6nkQ5c1hWlE3Tj7Zv3rLWxqfLi9pYkmUhmPhwJ2aaL45zRBgxjbMLwBPC60reA7snmnltocmI7SSudNla74UUZWplmIU+RkBOrWkjvHDPzYWIo1GXj4fEdlxB4ebjjR3/oY453X+FXB3zt67/G+/OFl3c33N4cEZlp9ULr9r6PHnk0dObh8kBbN9paubu74fK4UNeN87IQRiBPgZvTgUZmrY3HxweWupEdNdj6gbZt9LZxiJOhCSEwmLhs94xW0Tq4uz3Qu/DQjVtTSZCyVdNoNoVrr0xim/ZtijxJprPRpZKmA7Mr39aaaP1Cbw2q8Pp4R6sbbx7f8vhwJpbC4faW4zTRNVCbcrk8cZxsCoy6wboiqhwOyWD19cz6+Tc4hw/s/Wwbx9cfsUcglRbZLiu1N9alW6qImjAipRnxpkO5cj6efKL7AXZYokftbFulqqE3IQhT3jzpXF1Y5YZ3US5P1UKDR6OkiGYLOlguxpft7cdhFWprvH/3iCQoU+Z0nNEeqbVZCkhtiFrCfUqmWGxjoEnI2TitlDLbZb2m6mjvpClDFPq2EsIgJaBXYprNnyfiwg37DEa166n7oV7UlKLEjA7j2LuYbcJS8CtPDw+oJ4TMxa77kDIk89URLKcwpJ05FDRF6jbMexpgw8RQl75yubxlOb9G8sTl4TNa23h8evqu1/7v6Y3r9YcfGYTUFraQ6IiN6dlc21E7GrrneVkSxC4bTofjVXKquhcKAkGpW/XcvEHbVsOUxSTrMdhml8UUfr0P1q0yY4veXoy3E6AQCWopDkE7tbar0qrVik0yDlMOPyUG86GpWJHkUjdar5yXha+9ec+rd488PDVe5kEOmZQydx9/DGLliQ9vv82v/OrX+ca3PuPTt2fKVK6ThapamWaM5KI8XqrFLT2dyU+Bz5/O1NYoOTBNmeOhUo43lJSstbitXC4Lj08LD/cXcjIuMAlGAovBoq1W7s8bD+eNd5eFnDJzmfjVb3yNiJJj4DgXmjY/yl6Ps9dcQHHO4Xw+sxxmskzkOfD6o1e0PljrIMWMNtvsW2sWrYXQ4x6tZNOVwaoWUTRaA6wMc10WigoiiXAy9aMSqM0S/ntXl3wHDjcnvvzJl3j1wYekdCDEArVRe+d8PlN+PfOt8wPTdOT2xUfcfvgxXxWb6F+8+jVOx4njPDMkUZfFDjD5SBLPoDycyNJZLwvL+cLd6cCybtaofH5BTxOSMqd5QvPRDmFf/hI5mzKxbSvp9MIlQ5AON2jza2w+ELt5oi5PZ+YkHvPUmEswr58HERNNzSjbHSlPLhKoTDmbtHw0dL71/HUgH2GY9H67bPw/yfuzHlmy7FoP/eZcjZm5e0TsLrNa8lDkoQ4l4OJCFxe4//8nXEDSASSKPEVWVVZl5m4iwt3NbLV6mMt9Fx9PPilRXthM5s5ovDFba805x/jG29OBWjKH0yMfzhd0mphOJ3zLpGbeR8oboi9IL6TrC/XR4M7v374BdaRS2badeF0HCLdSXy+I9yiOlmFfN/Jq88OaR09BhO5MZCH0m2UKaCbIGBWTVVoWHNlbvV83dBOsgA4YrBtVeqFWqxpoDc8tfVhMgdjbEITdiO625gjdNqBBV88pDbhuM07lqILssFGsLSj+rpRM2z4imbgfrmrOtGyKPidxMP9kYKD03heVoQg0M7Wp/0x9akrGlstdYdhuqtJW7HqvVjUF78e806qwVorJ3RWLGZJBggdSS8PTqJTRsu29s6+FlMwj2lUI8xO+ZWr7CmH47338rDeu48MjIU707QI4CgKt4idn5kw6fbTwaA3v+lcVTpytvSHW8pHRJ7Y7UW/3AEwTpdrNVmo1BI+KDbOdG6eYTAg6BB1WRd0qqo4grSK9or0amaDfnocYBQPzTDEMiQBdjUThvBInGxbnbvOC2szb04aLUpxDQrSxT6ns28rz65nPL2e+nDfCtjMvM8fjgXcf3rHME3OM7KlQxE5SJaqV8TQ+Pr+yLIGlRGrvPM4H68XLOJGVwp5sA9ulE7zycJhY5gmvVv304Kmc7XfUwrrvfDm/8vnlmdMy8XBY+KV/MyLfh8GYfv9Dv8mIO+u2s+fM0gIhzkyHI70brLiWTtkrZbeb/ta6aIPMPZov1uIacQq1VXoFpVPSjoYJ19sYAI3FoZq0t5RGTsafnGPgzdOR6Bd0ekTcRLmu7Dnx+vrCy+cfcNvZErJDYD4svP/wgaCO3O37Y/TkJuR9t5aRW1AqIQSOpwccjbzvpG3n3ZtH1nVjXa9cv3xmFw/O83hYIC44VSId7wM5bezrSjg9GN1FoIeFljMIxMOBnjbStuHCmUNQSkns+0bPEbxDQuA4TbZxIZAWfDCpPbUMw7t9NruLSLPT9nR4oBUzCael8O27N8b/mw9cX17RGPGnE5J3Um2k2pCS8Zqh7lylU7YN74THx0fEKaV21i0zp2zhjqUiZUV9MOJFU3JK5KHgLLWO0ZXgxr0sgxV612z0Nu5J7uKn2/Vh33y7VvqY1VgrcIxkuMWN3EgaeptpYS3GOzy5cUdGCViLVIeZ+CbQGqo+2y9koJeG+EFl4JtumV/9fhCW8Tpavf29DP+c3ONG/uP/Ha3zm4pXvrb77mvcf/ja8fVi89r74X58WWvN1kqwtap//U25FYygcpu/2Xw1JcsjrM0OBMFP9O6IIf/ktf9nvXEtxxMueLIXU+t0o2B7X/AOQlBaAScmPT0s1u5rdLYEwdtpat83rpcrvWFG493EFdF73jy957Inrlui90wTg9+uJXOcTnjnOR46T6cFVRtQrdeNWu00V4E9JWiwePe1hFfT8FSMWk3vNFW6czQRuzmk43zjl+++IQaPd/AUlF/94h3HuSFaKJhQI333r+zblX3buFw3Ht6d+M+PB/5nF/h8vnB4eODbX/6S3/zd33M4PRHjzPPzD2xfnknXK2tJ/P73v+P7H37gv/3+Tzyfd1IvbNrQ7UDXxt42+r4j2jgdI4s6ck1E7/nw9g3vvnnPw+mJp8e34JQvf/7Ij3/8gd/9+IV/++MP/PnjF777/IXWM+o66Bse3zyg3tGk43uhu4YPHfaKD0LD8bwnHkpmEYuzf3z7HvWR1h3rdWU9X7meL2z7PmZ8Yq2UZvO/XKyqE4EspopqvZmRsiW8FLofs1HZsYaPp/ZKLYm2XomScX5n9snmbaHSo5LjjFwy21ZI7ZWyvVCukXL+M55f8/hw4LhELtv1FsTLZd1Gm1nROrMcJpY58ng48PH5MzEuvPn2wD/9l39kP7/y8vkj//V//99soB8j33z7gdpMuBKC5+HwZNdPq8xLNH9dzlyK2Rxi8ByniT/86U80ueL7zK9++0taunL58iM/fvlMVlO/Le/f0VugVrheLkzHp4HhcnRnLMgCvJx3ajWJ/7unN3x+fib3zPx45Ff/+J+ZvOPDj9/zb//+e6rz+OOJ03EmpZ1tW3l9/swsZlGJcUZKJQi8eXyHREg1cT3vHB7ekXNl3xL0Heh4dbx9eLKFViDTSC0jCF7CsL5UREyS7kf8TB0K4y7WzbilWH9NSLZ2WRrfK1gBQ082i6WZClHAeUGcwYdV1NiXIzWAkhm6dPA6WtE2A3Q+mCZEGy54ejVfqdRu8yGEEKx9baMooUrHRY+LHmkNEwx2S/WWm39STLzRsZbtCBqlYYfD23RzjDBsZhuHZ294Pkc7tPaGhGCAYedGQ2SAdU39Ya9Tge5tg+2dmvaBGbMZXKOTW+OadlJaaTnhEPxN7d1++tr/s9648AGdF6Q2ovP3+IoYkhE0nFr5ms3x35uj1EqulVTgsMyEMDazXsk5s10KL89fgLGJpWwUeOdZDkeqqt28eSeEYGSBITstJZG2jZzz/ZTkfbSTTat28w9moQ+OrIqrVlJrdxRRigjCbaE1P8njw4Fvv/mGf/j7v6fnC1HstLVuF3o1mvPzD5/57s8fWbed99888Nv/9A88vn3P8d0HPn73vSUgPz1SquPL5y/UuvPlyzMPh4XTh2/47btf8/jbX/Pblx/5H/74B/7993/ky8uVz88r33/8TP2zVa1Px8DhMDPFyHSInGRmnibeffOeeT7SO5xfnvn4snF+ObOnnX/4u3/g9PYDv3595b98esF5q4RqMaXelhLPrysv+Up0yhQcMTjjGtbGZd3IpVpbdtv4/OkjRoJX1AdyTYMb2Ub7Sky55j21mppPXcT/hRiHERXS7nMQIxv00b6RgfgKPuKXzna2auXy5cy6faJNr/R4QH0g+NGKzJW0bWyXC+cvLzxeX2go27rxb//2fxkt33v2Pk7fKKlcaO2BkgLpeuXjx484gYfDhP7TPzIdJua8kLadL68vVAFfM255IITI5Bx5K/aaReh64vJ65ny58lqFbz+8YwqePWVevrzw+eNHnn/4kfdPM/PsmZ9OhPOVUndySmzrbn7IZsbny+VM2QMsE3oydFhUTyqv5H2jl8J3153z5UotleOyWzKDF1Jv/O53vycJTG+e+PW339AR8ghsXfdXerqyX19IKeGXieV0gNDIRU0JqrcqQbleX60aj4EY30GwKJR929m366iWBHod1ZK13O/pGRUj5DNicPpN8gA4zJ/F4OAOtZ3zUPYhxhAGukmGx+7G1jUVsnMyRgAd5y2R4sY2dQOCPM2eWio5jU0U87p57wjVlmM/VIUiQneekg1c7bynpf0ub3frZs9fBp7Lj+TmZukStxam84NV2Y2teKsSWzfiO2KbLs2sIG4oAm/ajj5EK2BV1KBZjbbrzfgtTNHTtKNSmGJEUUrOXM4rNVl2XuuZ5jrIDVX2E5f+n/yd/w94bNtmHh2MdNCHpBOx7BhQcu6WQpp3UjGVS6mV0p3l+agjBI8LEQPDis2KivW0z5crOi1owBYoN/xBXUnVlFzSKiWv1JJI+44Mib1zbqT1eotUiQHZEnQd8lOTV98kymYSBvxNCgu0Ys85W289BI8XELH2UleQbtzCEDy5VqZlYTksLIcD83Ig+IXSG9u6cr2+GK6nbDw/XyhvHke8+Cu5FLzzvH/3jpp2Jh9pRSj7mWvJpL3QJmMPxoEBmn1kjhPeW1z5ddvZrivffbyyris1F6blHT443rx95JePj6hWUk58/HRGaJSc2K4rre7oFJi84aoq9nmoWv6XG+aRW3RFaeCjWRBu0lrpln9kMRUe05aPBeqmiFK9zzlGo9aG+dZjxIQ9tmiKNxTUhuUU5ZTY9kQXo5trLagEaiuknGD4fmRwD3srpLxxfn428ohzFI22eaqnsJD2nV4KO3B+eSW4ju8TJe128u+N9Xrmy+dPpNYIIpzedGKI7Crsa4FqUaTz44nL5cJ1Xdk0EmTQQVC+fP7C6/ML23oxbJkqXRzb9crzemGrBWpH3UJHKdtGa+BVOcfA9PbAvCxM08L1fGW9Xsj7hhbY94QITCq0nMnSuZzP/On7P7GWint5tufhjD5+eflM315o6UrZrrR9hd7Z9t0WPadIs9myqJn3b7gq571R7ysUtVbzXb09xAn35ldnVFo2r7yBcNsgsdsse1Rhgy7R2piLjtZ9a4M+wTAoC2YJqEP45RylNlBjW9ZabaY12nJfxXztvoneyBR3v+Y4VAH/Abh9s8Pcmt6t2UakN6DwuO5v7cqbSNYgzuPvh7/s1v+098VerxV54791e1/a+O/tVkk10wbcftc9TwuT8zMOTaqOCrjeiX4IzvhaSd4wZ7fW41+tHP7H779nOj7y+HhkV8zBHzy5FmqzAf3LuZC2ZNw0adRhJJQw42ZBomcOBw7HAIdu+l4JXC9n1suF18uG7A18tpZAnDEZT6fvmVoK6fIK+XI/6Z0eH1nmmWlSqCPeQy0Hqbtsg1JuxO7hsep53KSK007wwUQO25Uff3zm+nple33mP/3mG94+LRzeHHj37j1CpdfCr375nm//5oUtFeblkdqE6+VKTj/y8c+fuWxnrvmVL+cdR0F75fVa+HGe8CFQ2v/GtHSOx4lffPjAt4/vWGQmMHEEvrwoZ7fzbg48LTPTFLms2TZMFcq+kvKVL59f+cMfvudPHy/sA3H13Y+vfPur93zz7Xv+03/+O2bp7OtKnP7ED19+oKaVnK5M3vr1ToWoFoeAh4fDgdM8c5hn4nSklURqhVIKtezjYBCgr0i3jl8Qj3o/2iWv1pboFhRocFlruRhtsqK9GDeu5a8+stHfD97xOqT7pRd6EDQ6JHpLZm5CLonn7QU/OQ6nA4enNyzzgS0ZCmx7PZu8u3dwVlmE6YCbZ/KeSXUnbRvn1xfmqEzaWF+/EFTI1wtfPv3An777jkvKrOvKb7bKFAK1Z373z//Otq6UnHHRk2umtE58eM/6/MzbN28IEvjuj3+g5sRh8pyWhdl58pr47vf/xnefX3jeE+/fveEwHXHqyHVnP++0WlFpPL17w5t3b3n34QOf/vSRz89fOJ9faZsREJZl5v3DibytlLXz8Q9/5L/97l/4dL5QnOf3Hz4wDV/alq64siODBag183paWOaJ3xx+jbixcbhI7wWJnUVOhOCYJ9v4U16NxygMQr6CD7ThNaKNjQlrTaXduJIyhEQqxjS8yemNBFJwQ+wgonjx1MEstC7BABdkmzP5YJXQei3EyVBpKRWIY36ljooYHzMXYrOFvNRmlJjW+AqGHpuuqolGsIpsL9liYpySb/YCVVwIFpujMniMtkHAME1X7grmfhNnKQa4Hm3RG2nKzB/2XEqtlFpw3VF6JZeEV7GOlljYqFfQm8F7HAYR28BjhdkH0xCo0J1aqzN4s8M4Tx0AgJ/6+FlvXKqG/unNSMddFD8teLHFX6VT+xc6iguROFkpXXqn4AhxJsSFEOOAlTpinHl4eMd6PXM+P3N9fSGPGA7jl92GtIBz+OiJzqNtohWTITvnLSbCz1aC99sU1A/GkSLeIcWc6yKONrxG3Q3xhgtWJYz+vKJM00SIE3E6Ms8P7HvGYTdgPD3w4fCWjqM1+OPv/8j1/EJZf+B//T9+x14SGuBwnHECvVWen194eQYQSlPWdKU222we50Dwzhb/apEZcfK00rheN9at8Pl55XzZmObI4ThxWTc+fb7w3fdfOG/FWHo+cJgC++XKH/994w9/+I6WNlJKvFwSpaZhJ2g8Ps7kknm9VPpmv1ucZ44WxudcwOOozqGDgq5hqJ7ASA3dZgb2qgSnnikuNhiuZoeouQxw9mD/ja9GHDcqUO9Y0F7vdm0RwEXwE+vlFZGCk8bDspDSzuWys10L/hAIhyPTw1um4yNb+cK6bXw6rziqVZMx0JqQcqP2K8sYrG+5kGojakCneVRknVyh+0BVhwbh/YcPvHn/jloyP37/zPefP5PWFWmVeFiMldgrlwLv379jmSbCKdJFCfPC+1+855tf/YbolcvrC+IH1b03lmWxZG+ElDe2kpHWeZgCh8OBeZoRsXY5YtWYO55orTPNM/F45PDwiPTOdHzEhYjzG02VN48PHJYDznv+9MeVlDOUzOMUcNKZY2Sap0FKtw1FnKLd47xViGYOd1wvyaLrmxDUG9oNBsw22Iy4DyakuiEWSAaYRWg64nIwuoV1GI3GMk8Hm105JXqx/KhWETV6Th0Le2vlLrWvUZhitGpoqPp6Nw9ocAE3fHLSLaxWJrEDn1qsvdJwTkAsjaCq2qZaK4p1HKL3SIyG36rJhB835Z84eql0dFQ2zWjuYp5TdR7UEeP0dQ3rJmEfPT9TUZdEK5UpRJZ5Zj7MzNPIISsNAvdMMwYGSsSSFNa8IjhaxTbYbvir6Ow+bqKkUvnh4ydaLVz+WuXwy2RJsD0lUMWpY/YelUZvhZqLGfYEQ52EGVpHW0dw9BFTUZva8BZHy0LJjdodGhZcrLRum6O0Sg+BLkqpDfEHuzClEZgpeSftG90rXQO3ZrCZnW2m0YdXqzYZ2Fzo6K1ZDs5abl3thtLBDLP2gB9YqSH1HfWCPW5prx1whBBxfudarqzpSmmFefIcHieb2+ZKnB11zI5yLaRiHqotZ6RNzFNkViGIVR12QjUyCaKUXqjN0Rghe9qo2shYiJ/zQpwcLgpdKrlm1i2zX66WrZRGnIRaa9UApTb4tiHDyPYRK3K9A4anxg0Plg/md+u1omqtIKMMCH/ZGrz1C3sfKcMCjAOBKJhFu9j+NTaxXiu9dnLtiLN51+wj+ZpwbHg3cXg8EWrh0iGdN6a4MKlwnCemMKPdUa+NdU14aRAaopU1b6AZ8cLcbC4XUqYjLOpYfCDeou1xuKJMLuIdvD0eeTPPpE147Uro/a4FeJgmUinWdVDh5IQHP/45qo1ZwPdGQIkiuCZE8cyxc4qeyVsrtbRK7B2vwil6HsJEROlbwvdOHNSG6C1nbHIO3wXLGXSWcuBnZp9QpxyiCVBCiPyAUkekx2Ge6MU2nNYshRipdp33ihNLHu/VEqNz6zQfDN46REw+6L1Fj8g9it5mXBZuIwMwbI0xuy9vxuMb8YaBjTPVnLUajbqv4x42s3zN+X7gubfRRstNnXk6pQvibpJ8GW2/UfyPtp2100ZSwGjbNTF1pBmM5Z5crc4iT3qDXsG5W1vahm39dggTw7sh3H2irXNXzYoa4Lv30SjvwICMS9Mxf9J7y1UGq5Dxs2+FUqePe4t7Lp2qHYAMOmygcFUdmKwOalVZzpl9337y2v+z3rgeDjN0x349o8GSlA9OaeLItbKueeBQLEYeP9uAVkBcoGmgouQqlG4fYF8T+XrBMC8e4gOCG8KPAj5a+uiW0XjCOSX4zqxCSjtVr7Se6E5pamIR8cZnu0F0axf7nW1sXIPcLiMvytiUI8QteFwLhGB0dUezthYF5AAMTFWDlDcDW+qBaT6QS+Hzy2f6iKafD8Kbbx6QaobZ43ph37K1NmpFwwCtVu43jzjDUXlVWvDsF0ugdcGB72jo+NiZjorvDrc5ZFK8b8TgmGaPm6wX3prNGve8s6dsG0LvROeYJw9iQ+hlcvjeEX8bzneiF4Lv0NMdgBxjxE+j0q0Jpw2vauZN7+hq8wuzqhhJvtMRMZO6uHFdaKeT6T3Z6VGdZa8VI/u3VFDnmUKkhUg+J6RfUR84hV8gNbKJY3++8nRwzKNincOMr552aWzXhJNKDw7fE1va6V2YpmYWDmcU/8kFHtTx4DxLiHQKqTtCEo5uRqLjw/HI+ymylcZr95y8J1WHduGb04ktJ9acad3xfgp8mDwHB1sMtN6Yc0L2DemBUCohCbNGmD1Pc2ASoZXG3ipNjNr/dgq8CRO9wn7ZmEaLzbXC0hRcJKrDmW8E75RTmDn5A1ssBK+cYuTN4cg0LQQx9awXZ2rD3eHCRK0mYGEcJaWnYT9ppGoEkpwzyc88nE5DGNUI8dbedRYF0yqVMmwn9vnfEsYZYpzbvKmJmWZvhuXcTLLuEEyQOmZHTpEmNAp7GcZhdxexD3g0GItwAKBrs9BPbG49wk/uMytuaCcVE1VgLdLG13tQg0esN2fYMoXu7Geh9vW4sXH1rwR6ZORsqTO0lZgsRZ0fG7jR9+/9QjrarNLszgJiS+0m/rvNwZzai5Sv6crWdrcZsAWimtfPDdEIambs0uzre7eD/5bST177f9Yb15oyopjcOfRBpzaaOQLtfCbGCcETHLQQbLBaIcyznaB6Z6sZwcLWemMs/owTlilgVExub31Z6+e2WsilUbadPLiGpVRcsIu2tU4bxkFx9v13orl8BX92McCpIWssEqOMX0Mu3KwkW9np7UivjZoybb5x+4ZZ8ZrY1o1z+sLLeWevhRIC/+V//nuchxCFx7fvWa8br6Wz7kY/x3sOp0Ds3gQaF2+n8oGu6mMT9QKbKzR1OBd48+6RwxxYlsDheCB1x547798lzmtmDpFlmpimmdIzVIg1wqkxzYFcbZDrnSPGgCuZZZ54+3TCd2vp9i5EdcRoAhewdGt1FsUi0ZNaIQ+zuPeGKe8dq1SHGTcET/QBdZYBdRt+BxfwzjYqZ/JCRJXoHbUas058J9eEREc/LhyX2RBNAp7C4fHAw+sJbY3tfGF7PVP3K/Sdjv1ZvJL3RC6Fzadhl+jk9ImHWWjeI70x65EolaiF6GTMbYVaNt4/TEynhYfTQquJtL2S1s8s0eOZoFVC9JRW8bmSWxqLhwCNKRg2awlCjIqKRUvk/cIhwsNx4eE4U/dE2RN5OyPdMFYxegh2jfZckJbMFDt5tI+DhID2bMkAQYlLpNNwAofJlKKGlhJaTtScUDEZvF8iUww418nbBWTcB9v1brCdZk/r0X4GQhhhi82pwbH7yJwyT6wJOLghm/pQz40EgVZHVWUIq9bqONTA7GVsbGKChy5fPfJiFViczEwM4/W0W/yJ5aB5b17S3ppxC1XwXpnmQC11QJe57Rf3h3WHxIIpVcDd2p+jsqnFuI/ibB47EEvWJrXKqgw+4u353mNZ+leBxe0X3zBrjHVEneJigGseaLcRcjuq1b98yn2wQs23BtNkvk96tcOHKLUUE/Kcr2yXK+t+JZPpMjIQf+LjZ71xbdersc+wFGEnDdczvon1WPPO5M0lrqNVJGqqs+6cLYBibQSTiTqLrt5XO2U5pbkBC8XaDzQZ8mmbSdEVKQkvUMU2IRti2mZnYoNA8IEowhkZ8nfuBs6G9bMZHpHWGpayC7UYPNMNg64bpb+os2rOqLzsOXP+8sLL65UfXlaeLztdleVx4cP7NwRvSkSHp2bYtkYpgqi/q38CAt0RPNTdjJ8pmbTZB9s4Une4Qc4KfmKeJkIM1DrgrgTQ6U4kl1HVGonAEUMglE6TStWO66bAiiFQWiOoI6gjirCM9kxQxyHMzH4yefqtHSQWPVIHJSCMeJYYI/RuB4qakdbwMdjJu1V6wSIr5JYjZZ4om2OMFFgnaM/0lmg5k9ZX0nnn8mnlT9//wOF4GvOIiI/zPbMsDHtEr3W0pSuuWeTL+XJl23dOWx8zko6IZ/Ke2RthZZ9WvGs8Pp0MYNwGD7Amm/mVyqfv/4xm4fX5le8/fuS67uSS6a2yXK5ct81iJbrw48dP0BrHaeGyrgQfOM1vR6JyH6bVSvSRECKvL8+0rdgB6HIFCUDhsm1M60rKhdfzhb120nal7CtaBfU7pVbe5WZBlw3ojlIytSQ0wfOnH+l7JvjI5eULaV8pCpeXq9FculDzhsMwRkGU4OpogFnVPwfLHJOmuAHGNZKNHwq9wcAT+9NGThb31IWx+LZK1xuTsH6Vu8tQg/ZKq+ahaq1gQEO1MUKt1vLrNwLHYKI2azi32um3igsd6jy+tvWtn/cXeVqgqP27gN7A4GNGpDqy7OrgMarNwkzxOCAGza5pwZiZdagee+sm3ii2wfho2VioG0Ga8vX9qgYXSDmT953SCrUb+NkN0HRvStOOHWgx76rcjNCOUqplwqkJzdTbmKGmZD6v3oijs+T+Wjeufd0IoRPD2Eik4XrBNY+rFXImOuyDadYz7zp6zgMM6pxBLMU5vAaiRmqIhmNxULSMEtzo7X2YFW/GRqFDdQSx+UqXjsMiHZxYvpR3nuA8s7cNrXZBG3fptg7ZN+1W6t88JnZB3jw6TmRIgb0ZGRlzt1rY1zOXl1deni/8+cdnns8JDZ53Inz74Rs7dfdkYNLU2Teb3QVxA3vVbPDtFO86qXdatogKP3dL3BVPFo9r1o8/+oAPE855ShVa8/TuaeJtEzZwIKUboFhxlh8VBKHSpOKaEfenEHit1WTvWNzG5D3BO5wGJh+I6vFiG9vt5k2pQjWkzU1qq2qEglwStWZcH/YElYHi+YuZhx9ZYSNDyY2Zl8WVD4hxTZT9ynZeuTxfeH55QdTxWJ4IYUJdREflFoP900CnHe2doCYsSLlwvm5In8gl0QDvZtbrBkFxrdJK4nQ6UoqMPCNrJTs3EoJ75/nzJ9oqnM8XzpcLpeqI36mWSlwypWa6zLy+no26vyzkWpDlaKf1m0cHwUeHmyI+TFzOP9C3TtozuRScm6gN9pSpOZH2jev2Su8LNRVK2iELEspQcepAlw17h/GH0d45Pz/DlvEa2K8Xaimoc5StGig7WGs2eGWKgvSKOX060PAYpbyL0ougvY78PVO71YbJ0nu7y8EZm9CtajIqy9i4OjCMt72bmVjUJPcmjTcTba/VNrLxmdQ2ALn30qMP0chtM8PWChim3Y6OTew2KxIZSRDWhwNkZKA2OwG3se2NDUHgbtq9tQJv8/J++7s+5mq3ANWRLWYjLJP0t2LJGdKxudjglorY6yzFLB8pJWRwVkvyiNp8tHfrAthPFVSt2kOgqRutebMxMDojU/D0mmlD6OPV0YZx+6c+ft4bV9pNKeMDt8DAOM92Oih+GO+yLZl+ZMxkQ8H0XpimI1MMo1LwQ1/EIBvbh6ndTiyoEpaZ5iK1i4EmDXlhpbwfH2Q1XqJzkymkvBBCtM1LHS54tBZg0KXG6cRNE0xuuOFNEq5iJ6TaCr3Dcpg5HA8cjieOp0c8GepIEK6YcnIuOIzOkF4rr68Xvv/zDxyOkTdPB/7+P//d3ah7PB6Jw0i5bubDca5SGhx8oJZGzpXFBXyISIgcBMIUCE6h7lwuCZcbYb7Fk5uqah5x4t4HS3lFxoVqi5L5XZS5KTEGpsMBPzkmO4WQzhvb1UykD8cD+74ZAPnSmB8WEDPIltI5n8+8vrxyXjeW6FmmwMPxgXiYiHHGRbM+qB/tn9bsBlRF5xGRrp44n9AQUR9hOnDokPyG9Avp/EKImfk08f/7f/8TT9/+lof3vyDMC2kz4/lxjjwcF5Z5tp/fG/Nh4dtf/oJ/+h/+hm+fDmzbxpv3vyGvK3QhHJ94ewgEK5+hwuP7D3z4zW9oOFQ7y2Hhf/n//H/ZWyblncvzJ9LUOJ0OfPPuyVSv2eLnf/HrD7bw5ELSA5PrFkJ6WPAusBwfePvtr5iOJ1TgqPCP//g/stPIvaHtgk628L3/8A4fj3hRApWnD2/ovfNNegv+kf3yzH55oZRqleNy4ttf/4ppWfAhcDxO/L/+6X/i+fLC3jL7+RNOA6C8f/uGIHCaJ/7Hv/kbkISLynI68M2H9zjfyfuFnrbBE2wI3pIU6ODdQB91aq5GJhlUh2majPoQvB0OG0MaDxAMRaRKKbsdDjDYgCUym8n6trNb2KOSc2K/XoZsfaCQpmUkIQ+Bxci3qmq/o45oHAthtXZnK7ZOuDFn6qK0Xoeqb/inivnBdHRtRHWMlRoNR82V1q1N6NRStEHN6zk2TO88rVvMVy5maWjtxnC0//XShmZpiL2aKW7zZrE0s5+Y58mUiF3odgId97Id+FupQ8SirL2OzVgGJV7x4ph9pDsltcr5cub1vNJbttf9Ex8/640r9gb7lVIGAJdOX5+QpIR950FhL42Sd0rZ6brh1DOrJ9PwW8EVk3qq8zjxePFQVkzF0UCKZRD1jPRK7QntgkhBeh6tgoyWitQCNVvCq2uIJkgCbUFCAO/QsqF5swsoZ6vS/K294end4VvBdUExOaz0YTqNC8cYWJwQembY+KzF6SJT7BwWePv0hnWzNp93wuPRfFeTBEIRQlUCyiJKwOYedUA/XevUrniNNN8pMgDBCkJj8c4qwC5MMhFQXHNocXRxxF4I2GwpdFOvdRxe7Fjg6Cze4ryVRqSaVDhA9MaP9CIQPXVkb83BmTgjgMdoBuIcfjamnk6KWxzhciU4+9kuOg5PJ+I0o86Ry2pDZAEf453e30OkxwMyHZHpEY0zou7et0c97vRILDvFH6gxcfhl4PD4LfPhDergy/ef+PzlGX+YaeFED4+45R29K85NLMc3fPvrf+B4fENLK99++2vmaUZDJE9HJrFKI6edKQb8vBCPJ67XF3otpO2Kjw0NRxb3xPtvP5C3YpEeaWOv3iT+eed4msd5uHN1CwEbkksXlmUmTAd0Dly3M61mtssZNJndIC787dPf0rItsK1mqlhSsu/ZEGnSmeMCD4/wbqKlR66vKyEE4jSzLI60fSFvcLl84d2jcjw+UGMg6jvoSqvwy188UksnOM+3bx6hrDgnzMcDh+M3dm8lWHOld/Nl6bRgCtJOuQ4IcqvWFqOhTmyzcka2CN6QRTUZ1aSWCuSxQZhK60bloFccVh220gy02zsSnP17tXlrThVxnRADwQ0/Z++kNFSQjI4Jioq9Xzqii4IL1qvpplhUUZqCNkzJNwDTvTRcGF4t541j+BezMBEdVZU9L6qNJxgVtH2RzbbUKQ4/MGfdUohlwBfQIVwyP1aVRmiBWCtz7UxTJMaAD5bCLSPs1IgmtqlTox0gWqfvG6jFC914maKC84HZz0SNNjPtzfiS21+pqjDoLZ8p07AqZX19Nt5fq4RuqbCUbG+SyPCVREpv9L3Rq12YLsQxLHT0vNkpRDuhm2m5ItAKvdvpxsZC1korZTePUKtIK3a6c5UqyXrRCl0qrTmkZqMrt45281oYfshOKNplzGQtloVmpzHFknMn54gquG7DZbuiBVWPasGp4zDPPB0PpJDxCm8OJiaQEW7nEZsliSmn6ILv4xzWwXfwuNHDFroUGM8nqN0g2q3k1wZSO5JBvOJQghok1fWvoNP7hUwnipqwQYTJyX0IrhLu7Rz1Dtfs988xEIPFiAfteGctPo3RbnhvhmAXTMV5C+BzwVm4nljAaB2LVR+y5F4rfcsUEhWPXi74ZF9Tcmbbribt8JHuFYJHYiccjkj05N4ol5WX1xeu65VpWXBxobtIqcL1fCZvG7kUluVEpOHaiW/fveX4+IibZjY/47q14fZtY5rM71LovL58ppVESdvgOy6EUdXl1Gg509LKVj01mck+eCEM6bR3ES928Cm5EWdL0k018/z80Vp/64XeM6IOH5XH4yN1bFw1bewt0GpDaqP38tVuEAWvE3IwH10MFsEhFK7nz/RW2S4XDrOduPVw4GExmXht8M27E9tusRezFyQZZzTOM61YQsPlZeV62VA1a4W6Mrr+jbxtYz41wMxiMx4X1O6/WqlYVEktRjzPqZiysJuZnFtLrff7rLg3oXV3ryT01l6+bTQtj7ZdA2edmd4t6NWPwNhbJTTqsJtGwiTvVtvYDGuoFG+twtv911v/D9/DgANbd9I2xdvcjpFDdv+598OsXee32ZNJKAcjUG5tXL3T6W+qSeeMfxliG0Gno8XehyQ/BFQsSVtdh35rOzeLIBJDbA0JzP35ODEkH83WEcvhKz957f9Zb1zzNFFrJ+9n0n4l5Z1/yVfePDwQQjDKdEnkvA9enaMWK7Gry1wkszv78LqYGbGnyuU6sqimCFi/ujbLJ5I44ULEu0gvlVwqeb/e4xFaq+y1kLLiV2WKk3mKRt5vb3ZBxOCpvtgiqkptoNEjk4kytBZ6gZy5b17OgXc265JuJ+Jb+JtzUPadtG4g8Pj0AHSis7Zdo9uMo3di8ByWyPPLvaywm8yuK6jNVIDj9BaDjmG0+ZpkeEZKtzBJUXCxMc3gpXGYHZdrGe2POpR8Fn9SchuzPduUpmg3f6ljIx94mylEVD0hOB5OC8fjwjxPODUpu45wQtThw4H5cGA+zPTc7HPJie31hXPJXC8XinR88MzLAemFkhJpW7mcn0cqr2cK1dKNu1XqTRx+WVienghO2dfKeq00sWTntFXOnz4ZXaF0fvX+A3Xy9Jb47r/9C71XtnVlvayUAsfDicPphDhYtzPklTZN1tryMC2BtK+s22at3vWMYpVwzTtzKVCPlACCwznQGJhSYW0beT8jXaF7FA91N/PbOISfLy+WpL1mE1Vgiw294lzH7ZXi7HBmoaaFvG7s28Z6eeUweWsHq8P1QoyeGALLPJkYpiTO5ys1rSid4BzTvOBnTzgEDstixnr1SDeI73q5cP7yGa824E974v/6r/9/vnz+yA9/+jPq4XQ8cjwsxAg5F2ppSGrEODEfDjy+e2t0e+k47ex74boOliJAtfYVKPPB1MS1FJuL1UbOBboFVKJC1Mk2ERHAcaO8x+jZdyNo9JrZq3VMuM2mhsqx1DoET0CvFgwrneIEF5f7houM2vg2pOr2HngvYxbXBoSX+/w2tzaI7befP0xd91Yjd9UyDHwVfN0+W6XXSnOF2pSWx542fHKqJnDKudlrG7gsdeYp9d5DT/b8bm2JZvf0YfKk1lm73futWAZiSsnma62S606u+xDD/JWKM/7uwyNP04zyLTVYRRG9ebjqwKrIbLy+w+FAWjfOq6muUquEoHi1VtvxcDQiQOowIsJvCcSGkKpMYSIV4xPu25kuQgjC49tH2HdKzqScSHVAL7XjSMS8oj2TauV4U+H0hAYdiqZkkRFaaMUUZK7b5DaS0JY5MvE2wOKEyQtTVHyd7ALtwiSO4q90NpudxWnEf1dy2qgihBh4PB2I60bfE/VyBQSvjqOfDAzqPYtGfA/Da2GtJsRuCtct3oXSmeQ2ZxRiEA4uog6SU7aacFXx1RPEMTtPUKG7ztF5/KjA1MsQRVQmsdwi753x6sSqq+NhMdGDtxZHKwmaWQKcHzy63uhpx4vDBUf0CylZ28br8AyFyHxYEBdNcdYz6iacdwOK2pkfnnAhMlcLG3XzQjw+cnp4wrkF5w74+WCqq21jO7+iGhAX0biQk53Mg3O8Pv84TpUdtzxyenrL4fiAHx2B1hqplnvk+e2Asm1XrudX0rrSsYPB8eEJ0TBOys6oDarEaaI0e+6WzJvtJK1WWXk31KkpkfLOvq68vjybT20ge06PTzRuqb6NfUvU1gn+iS2NHCd19LKaStZ5CqYG9Xojw9jCN10LKkerBErh+PQWHwLinXU+yk7p6yDAOJx/4PHNA/l6pWYjz5w/d7yfePP2PWEJxMnhvaB5s1aweqZ5siovRkQcUcs9BSJEodRMaY6UC+IC6gM+Loi3yqQMAVDtdn233skjsVkmk+Z755FqbS06eD/RnQVQtlxGoKIpfBexBrmMNmC4RQHUiriGZPOKzW7G3TiltQxhUR0CqdFC7M3ahzYhw3dDk0mr5GZdFRVT3nr6+FMtvX1UbgylrRnwPc13o4S4kYLdbkHxtqW5YSDutdJKo2eLI2o1EzyGulOPcMuzq5ZCXk3WrwjVeWsvl0KqhdRNORwmT5gCfgiXghMoBal/pa3C948PfFgOVs7PHnGOyXlSr+SS2bZEeHrgdHrg4eGJfdt4PZ+5riuFYvwtFdQFlnkBhJJsoNpuLcLaTBxBY4mRLVVLnr1cwVv8+hInSLsZI9NO6YmgVh2BDu6gUFKC+hXmKuLsxNQK0Vu/uQkW246dpDaFLJ0lOOYBn/VObNEYXrA+lFXaOlorrnZCdHdYZxuSYQkjLt45gqrdIIPfF25GTREUj8eRMpTCmKVhC129UZ5HS6BWROz7gxOKWptTR3ulN0Fjx4Mpx27PfwA373HhA9PknWOaJuY4mcXBGVT1niEk1nxpzejWqm4Mh+1UbJloI4Kheauu44RIJ8RIiJGOpzl7L/oU8dEM1doKy+Mb4rxYqzNOuGkhHB54ePsNMZ7w4UBYTtRtpaaNtK2IONRHwvJAumZqLpScqflqPianxNNbjm/eMR9OhsqpmVoLvmTzo9HHIieE6AheKcfFFHECx6d3wG1xwxiNqvhpootHnFWNJW/3Cqu2PGYZlpabhypwmocApVeEyuHhidqKZZCVSogWchmniaVURA2Flrez2URUyXkza4Y65NaCbZUpYD+7deq+M5+WwbGDJmVUK0a0cM6EUNOysCuU3ZGzsOwLzivLMuNnR6fSW7Y5mDfBwuwnpimgLvAfGDLd2tLijDhjmDabN0UfaANtpdLRUZ3cWsulY1E3riFhQGSrzZzAWpFeLYy0tK8ZWoJ5qNx44xVuMHXrLHRr8d1mZYp1GxhiCQbd555D12+ZdLY82M8abbfRGuzckhD4j1/LaC/SRxvQnoWMWZ7ce45mWbiJLHS0KQcR4d7oo/e7rF5qG6MZyxcUHWGZAojSqtl6Wu2kUob60sAQ4h38RYVba6H3v9JW4Tfv3/F2OdjOP5li7xhm5scjtRZeX86cvnnLNB+ZpgOosI/KKIyIcBVB44SKoZbyXii7he/hhLRnfBiBjurYS2XdE58+fcHHQAiR47ygQC2JvO+or0zBMTlHc46cCiVl8vXK5eV1JJVitGrTCdm8xSvdC60kAhbrve+Fz89XDocDcZruPi5TTpp4o/c2TMmJsiVyFuJQ0dUmQxHo0XmxvsDYNJbJI81uaucc5sUXcCajuAUxthvuZfz/5UbRxlR/PgbmOeC9R5N5lvrIDSp0YueO41G8IWTkBua0ticoOZfBY4xM84yKkQwGv33MNzoMYndt1Squ0QbxPowqwF5JnAJutDcqJteOIdgskYhTayGFecJPAS/w9MvfEJcTrRQqiptm5uMThze/wjlrX4qfLWKizMTTaSxsAi6g3tKzpXfmxQbX4pRlsnYp0ih5M99VHx6lMXcRy93AR8/RHxF9HMbahoR4l+2L95R1ROSIzRBN0r6g+5Br90bQaNe2OqTOg4Read98MCFQzUYdAfMq9QrOU/dEbx0Nky1qLuDiTBmCjtoqraxGH1El3nBDvUPecHGy6/Gyso9NuteMnx3qTCDUg+DEcE3TEtE2UaPSmmc5qlWk3Ygml/ML1/OLRWY4j3OBOQSOp0cQZb1uSKsmbB+UDFW77htKTXIXNrTR4TLJ+o16weiX2TaQcyGHggjUlMnZWvLOtTtCSXsdQovxusehj3Et30QS9/yB8btKaYg0+33t63OpNyBw76YLGyb6NkKrVOyn9oapCsUq5I6jd7F7rTab7RoJa4CDLaGgVpuju5EMPrJ1jd429OwiyuBOGZ3fmQ/LNqVuTNGUoGTkhlCrgwsqnY1KKY2a4bomcmmI2oxTnCd3uK4bL68XtFul+VMfP+uN6zdHx/vHQHx4YJ+huE6vhcNRCPHIr//TIwVPqo29vECD02Nkmg9My5H74JBAnMwxklNj24y0XXImhJlWzV1fiwkrpqnz7rcP5HxbyC94VeLJMX84Eebljh0y7p0dh0rK7NtK3TfK6yutbrQ9U6/ZGIcYvTm4yaIlemfbE0fxHE6P/PpXv+bth/dMy0ScIu26W9ZY2nn+8fec/3zh9fPZEmeXE9KFvXVawuYSTHh3MPVS9aQNphAMLHw68bJdWHcLoozefB8lm4zYB0/wnhCFUEbEilNSymZiZiboZDy+FqjFOIgCPIaJIJ5WYN13ZrUKzUnDz5HgA8t0xC2Bw2JJys4HO9VJR5npxVO73g2biEOcR4i2SNaCU/OQVJwhpsCwVRP4HnEhEOIE+2ag4aCoOxAOR1yckJyZ/WwBe12I8wEXZ0JYTIzTMr0mWlm/ziWw9lxrld7OAKZ69CByuB8yWmnUcqW13U6hk0PUD1rHOAk3CxC8582LDcWh22vtI8JHBe9mwKoAozYoSMbP4V7N+ttiep/XjGN5m0Gs3VhLstllK2MjVNo00xGjj49DSu2FMDtEjT7ieINNI20W0secRfuMOG+eoFmYah4CqGjVTjVlG9KpQ3Yt/UIIlRgUDQdq9vRuasHz6zPedYJrNFW894Q4cTocicugUJCJc7RFvVjV3bHrd7+uSA9W1Vezr9jkCpx460R4IaVMLZ2cKkU7PnTzNrUhTR/CLbpVYTk1rJhzOCLq5uENq7hgCb82c2qkkgeBAsRXGo6IKXPNH2XKXjM+W5vT0EqOJpYS0bEOkI9x7M5CGPT11m2zhQWww6pUAId3Nj/3KjhsYxqdf2S4K2mN0qtdq826PlJ3WrOEcXJGphnfgdgHTaWPmKcwksIrly3RRcjdNrheCmIKMBPDtIL2hu+FVq7k/eUnr/0/643LUEMz0+lAWJSkjbRfcF4G4NUj3W6iJp1WqiUkO5tB3bH6td6NgCIdF26udIbSyE4HtSZaydbmQ0Y7rA2hBPTqxwVoc42bz0THleJdRw+R6jo5b7SUTMHUOl6nu5PeuTZafMIUA1PIlkI7L9YLb92QPK9ni3lfN16/vCAox+ORcDwwTYu5/vNGaw1tYpWVBnqDnAopV4K3PnicJkJN5FostE7cYJDZSdZ1Uy7G6ME586qJkHIZ27/i1RN8GIm5kSzFZNjTZPwzGolBFUFp1aozcY7gAsvhyDwkuIgYkYCO93FUOzLUl22oAxURj2izZFV1hoVS+/no8OF1A3waIcOOmE5tIq3BTubOx+EVslNzLdk8OoN20OvN+lDGKV3u6iy7PsxHVcto27pggGTnR06RXUfSq1Wb3UDPMtReNpi3Xo3IV3bcre0n4zXfUpBGT2iY3hk/Y+zpwngOo43KGNw3i0o3b5DZGvoNlyS3l9Tvxl3G67q1YmtLA/Ss+BDvYqTW8vjZXyPtO4oGZ92EG1lhGIMZz6WOSB5jARoCyHuH00hrFS15cKeVGDxMkTBZosNyPAx/kxFs/IDPSjPzeHfW4uyljs3Wrn3cyKnqDRmJD2Y+h3nutpGoVf3Oq6WlyDA5+wnVem999rTTsWtK1Q9s0iBTdLtPReTrvdQaYZqsNaxq9p27CANUvAVaCkMJaH+sndfss7fB9TBUC+ox8dft2r4LrbzN0m/ij7HeiTeEF+K4+bmsA6OoRESqVfYd2kiKbiXb++f8OLR0nDbUYdfO2HCdCBXb7KXdrpkxM83jHml5iNUc3v307ednvXHNx4X54UhcDrhTwOnwZwyYamuWRGonE0eR3QjnxTYhhrS7pkrJt586oJRj4djWq8lpi2XytL3Q6xDaijnomzqohdQV6YXWsxkgvVK3BDRUMRZbmO/+hj4CEH20Ra4W2yClZcrIy7HUXAOP+hBptbDnnZYS2/c/sl1XtuvKy8fPnI4Hnt49MT09UKtj3Tba9ZV6k6aLXSy9NfZ9Y8uFebKLOsZIrNFo2Rhs00yUYyETwXnHPE9I9WPRaohu3E703nmmGDkcZx62mbQXaHBYFlCovVCqZ3YB6UZj6N3agSF4Hh4fLNTTGYSTYqfjME02hFdBarX4BsE+JxnMQsBLNWWXYuxHZySTXpsNg3VUHt5ZcGd3uMmwTap+hJL24Z1aCYdHW1RotLKbwqsOtaWaGEhdoHdbgMt2JSdTUcblgLppbGzW1pExU2m3hZpOFzf2p7GIVRhfdevh2EbZqg1LGLirW9z8vdsyVGS9oV2x6JwhkW6NmnZqWa3aGJlY9m0yNuURaDiYmb1h9P+ahjjDDLs2bwSZRrI3jZKutJqsOg5xQFytDT+mQ2OGozQrMEdLydLJozIWT1tw1XuLn2/GOYzeo9OE9MY0H4izhaTWZNYTP36nXQSG0aI1xFuLzLsR5uojbvJDRV4hN7o2WnUmBY8TCyAy3w8S0cf7DEzUKjxfCz4GynkU3QyP0z1deGwyHVrxaLSNstTGdDjcD0fOObt2mqWiqzdQsg7avvE29Ot8SowWr0NkLxiFxtp6gkk17PNTuZEuOtISwyiCDhixUTwYhy/Ds6kIrjdCbCyHA6jNkc+vzyZUihMaIkEtgcMM8zvj1E8Mgb0kvnIgoZZquYB7oqSE1m2AACJxWn7y2v+z3rjef/iGo4fryytNZ2QKHOKTUdxbpV92emiIejyeKGp+q1pY1xXnZzuZl0RqdhJGPV2Goo3M9fLM5Xxh3xLHhyd83ZCSyXkn1WIjUhdYtKGpk66QLxMhmHFvHiRyRNg2T60XK5nzSsk7NEGa5XF5NfMsHXJSUmmk0s0v1BLrduZyhsvzM5+//zOXH78YSR7F+5m3v/xb3v7iG6aHmedPz7x+/syXz59Y98LRZTRlqnpaWGjxgayevQtb7VxKQ+OB6KJJzJtjXo6cnt6xtUqYA9McmaSR9kStBVR5eHPCucBhPlFVLc9IHG9joKaCVHj45v04I1QO+8zTciK4aO0wNRZjiI748GhmzlpJuaKTIbNycNSxzhawXCxR1AdSwTxdPtDHDEnE3ssWgoURtopzN+OkwH61uZBzplgLM6hju3ZSS/SUKGVH/YQLC4gbJ2vbUDr+riYDj/NWCbViRBZxHucPJh1mgJuH7lhV0G5+IgWrQpyhw6QV/jI+wuDOtsG1jvkEpYN4AnkUWbc2oZ343aiwbqKAr76hbVRtOqq2wb6kDyaisRXxgSAN1IRJ0tv94DKr5Ru43nDNZo8iSqhp+IMUYYIeoI+ZWW93/BUacFLQblWrD9EqnDgT1drZ6iJ+Othpfdqh7tRloZbC5XwhzItRcGTI0jt079hF6VSaFFyA0hspNz5LRcsr2q64doa80EWs7V/73eSb1BGPxt5cwsx2vZDSznOvhOgGi3BD504tlaSZ5Lq9rl7JvtKxSiZEj5rz0w5KKoj3+OCoPZN3sxjUMqql0UqTecyOe7ONqFunw6DRo23sBzO1G7Gn45CuOHF4qWMuVqziFRnzroxGo76b13mIItrXnC6cI0S1e8I7luPJEjRaRz4FDLIhZFlJ1bbU6IQaLcJIKuzXzjVXLqlQB/+xmamLtL6QtzeE4yMvn3+klY31ev3Ja//PeuNKW2L38PK8Utlh8oibkJosJXfb0MVAr6IT0dnJpnVD6ns32U1cMhUbvDofBoFa8K7z+vLMy/Mr67qT90KkIK2QUiJV4xiigRbHmVc64pP14r2nB7nV/micyckMeFGtmimpWlXWOzEIcWRRlVJJe+F82dj3RFfHpx9+JK3ZvC/PL5xfXsilU1EeHoTz64UYIyFd+PL5hZeXF9Y9kXLG7cr1cuH8/EyrlcPxwK9/8YtR9ivX62Vk/gjLNA8PWaXmjHgLtws+4v2wdhS1yBVvp1lRb8NWsbiROL0l75mSLM7Aic0eHk6POPXUCtu24yZPdI6gfmwyY0g92is0SKngnC3ouYzFUKCnbOoo0ZEHBQxzpHNKVqUxNi5vTEbvlWnI7KtTVKbh4RbSulKS5SyVnEnblTpix8ONX1mrQYOth2Wtyp5prbBvu6kw1d6kql9Vn3u2OBjVEY8zTrpOFfXeVHHdlIZ9tG/MVA4IdlCwycx9/sfN9+YC6EiBlnoXAtS82abRKq1cKSnZ+6ERHy62pbXMen21lnBrVn0OK0YumdK+toKDG/lL+sq+XYfCoUJ6Id/COacL3gXolZYupH0z8GrugyTRgMq6Z7Y9m0BJlCUa5UJcNLtCa1Azdf88pPqw58q2vdocqa2UXCilkTZDeDHawl49tVt2V7mJQ1qiXjZ63EYb2uDZoy/KProawXtmP5lxPCd2sdnpmBRiE8f2F/aBzq04bib3Mnq93qgcQlMsHsgrlEZJiZx2215kVKOt34kn9Z7YNbLNxqyzjfb0rYPsVCmjAtcBeKZj7526eySQ1mYJCM5YpLcKyUQs1rY06LgSvDAFpZRCmM135+NESdlM3NuKiDfQdXdWrQ+g717qCF0VSh20j2YdLgMAAM4xH56QtgxU1U97/Kw3rn3f8VW5XK8UFXryOD/Ty07ed7brmXCMqIuImzhEtaFwM5OxikWZUCtdQb3JfkvzxOCYo3K5nHk9v3K9rOR1Z3I2d8mlWIx7F8DTZ/0q7fYJr/bh9skbbNI7AkraTEmmEXJt7Htiu+xI7SyzM1m/CHvKbHvi9eXMXiz/x/3pT6TLTk6JvO1crldL0UXwcebzx0/ktKMzvL6cOa8bl+uVlhPQ8f7M5x9/ABdwXvjw7i37tpFT4nI+m/8pBqajzcdqa6Sc6Bpt/tY6f5n3Izc5NEIu1YCr4++X+cAuiZ3Etu94J8SgHJcTNVX2PfH88ko4zswj+oHV/FmtFAqKNDvxJxLB2UQg5zoqkE4ZmKpBvSE6myeqdrxT9i7UbnRv5919VvIwT/hxkwoF5+x575fLvTpuHbbrGZdMYVdjHBVSp3TjS9rm8xcbVyq0bnO3mvO9sugdtmyqRhPJFBuAC5bW7QNKx3WbMVr3SXEax/yCsaHZIN9pQMcctfVGc9FaPRqQEeTXulVdvVhrm2o+rjqQXsHPQKO3zLZfbHOpjaBGRmCICnIbBIkulpk2FIQ+vt4NqL4n9j0Zny5aK0l6paWNPW8meEiW5KtGl+a6F9ax8VAapynivaOpt0quNqRlnKYRT+/ZamN9vdrimc/jOUNtzkQE9rbh8TabobKnyp6SBRdulRquY75kFV5vnVoam7cNTLoQXDAqSa1sUuzAglVPrpsgRsLNLzU+4ApNbHNRsWpeB2+06vjeG6U9F2opVPlKyDDVoD3qgP7KeD6jHje4bbcxg4rgRLHJom2qYeDYrCjXO6nG0wkjzFaitVE7nS5GtLBDmH19cBC8cjxcmU4PxHkmeEdKhW3bub6+EsKM995arH23NnMbpv0Rk1JumCqgZhOmdLV1cDm8wUm6M2F/yuNnvXG5xaO+MD9FVp/ZZefleiWIzZTCKeInNVWLVnysODUfT25nnl++kPaM9sbh4T3RRwidbd8QEWYnzEtkTo69FP7wwx9ZfCd6ZV6OFnvSIZVMTxU3Tr2KkvKFWgsP0TYVFyOxFl4vO1IrRy207ti2xOV8Yd0yp8VzWiLLNHO+Xnm9rHz345nn15XWIIR/Z/Z2ITuEJXh88EyqnK+f+P3333HeE5dq8FBVIUyBp8PCHDwvL8/887/+qw3NQ8D7CWodczOLAjcSvePNg7Vrtpy5fqnkYhY0H8RaTnQbdGOQ31yN9XaTCwdvhIDWhgxXTdl0mGZqKeSceV3XES+i95OfnRJlKLRMxOCkGeZJDA+Viy0YVsgagsiJ0TcOXpi94r3Q1VGALXV89MToWObIS5DRlnIc55HS6hQXBXGzxaXUTpWTAZKprMHSrr0qLiwGOe2N1pRatzEAj5S6Wn6ZD7TLRzujO89132wmNqjzopMN9cU8g9SEyyviArV3C+PT2YzsTojzo4kNwE7DbDb/DEeK28ZG6lASdAvrS/uZly9f2NcrLl9BF6OdlIS4GekV7RkfH6jdZrjXLePK1T5jnUltH5s/HAOIBDoRH81aktJObFfbPLrQaITpYIKl7Yz4IzfH6+WyInW3NqI7sJU0zP2Cy8nmjjLhJ6EUWygfDkoMM95PJCl8/nLh/Hrl/PnT8D8JPgiv6+1U30wa36xhGuaZbV1Je2Ldb5WuiR+OUxxpHpWX3GlDFt5TNa9Rq+Bu8Ighnb+Zep2O1G6Q1qjVfI1gh57bdawCqQ2Wuph30lbxjrjbzBKgD1sI1tqVW1JzH8IN+3m5gR+/2yvkbi28jhHyb56uOY6TkWAzxFvnIAZ7PWL3TW92fYUw3Jq9ATYXnOaJaYo8HA92WLOXb0Qbrxxmx+FgBw51yvWcqQ2u286eTUmJEzId7x1LjDxMB1K5IgSazj957f9Zb1wdQWLg9DjjfMVT6W4nemtLBefpWo0HqI4pdJM2u4D4zrpby68U62PPy8S0HCi6E7SBNg6nI/Mh8PbdIyX/K0EsqvzwcAKU1iza3fc8Wh3BaNnFJNJbakxqw1bN5T7styhrG9qqM/VSHy3C13xl3Xb2vYx2TUWwNuK+Z1KupFKITllCJIbANTd+eLnyuu7srYxTn7LMgd4awTlEYVuTzXqcUrot+E7gED05281fSuPT66stjKKk3tlSZUuFJn9h9nR2a92qn+h0TE6ELi834RtLiPY9As96GQmp1gbs0sm1kXKlYs85jPmPfcg2swnOKCfOKbncPDh2krzx1FprvCgExWLkp0BD2HIj9Yb3ymGKTNERvMM7Yb++2knTO96+PxCXE6KePRfcdDIYrsDL8w/WCu2dGI73xabWTqk7IjAtJ/xhsUNBrNRtR3Co6wZeluHFoY1WklK7sm07lB2fV3wwD1rpJi6w+ChnCkpngohSEk47zYnR0tXdF6lWh7LPeSZ9on15IeWM7jsueLqYak9DRKhIs1DVyXu7/j5/xkd7nuoP+DqblJvOpNWG/joRl4X2/Jl9342o4hxOPV2UeDxaJawNHx9shqeQ8w82t9GACw+EdttsOqxXtAs+HHDec12v7Cnz8nKmtdVmOVF5vaxcLisvzxcmb+GUds9ZmGjFMtCkjypnBGm6oUC8mexbrWx7vr1td+O6yLDoDjGFODMal9Yp1QI9EIsiUh3tyaFSZESn6LgnLM6kk8pQFzq7ZvoglFib0DarW6acVV8yCv9BYK9tVFgyNle7/lsb7UuxzceUZwx7SL+3o7d6M4xBrkPsIwYidgi1WqzSbdsUabSqpNw5XxIvrxt+ioQQmOfJDs/OZqW1lHEQUJbJcVmtss+lDb8nNmbZMnlL5LyyposBGeqtZfnf//hZb1ypZFKzqkNDsMFxMsmn+iHZVTPBiTq8K4TpgPcTVRr+yyu6pcEJjLhpwi8HZvE2QJfMdBBCONF75bs//Yj0YqeR43HIaIXYgLyOaiPQU0ZHm6vUbJUIUBELA+yCWLKcScFjNMm5QhNh38y811rHj9RYEWWKjrxncq2ct52o5kSPpXLNzVqDe/qLTeTmOclUZyrFWgpSrXK57Na/D06JHEilkEolpcpekgU6Ok9TZd0z614o0tBum5A6HcNga29kvVEtoNh9Yzlg0zJSeBmbnLntg5jfas/mAWlAdMrk3YiBsPeoUgmj2gnOkYotdp1ucfHullDdBgAUFnWEHOmq7LmyloJzQo6ReQpMwTaujz9+QoEYHLjGIdtnsu6ZD9vFosdxfPzhI2nfaKUQhgHZlKFQe0ZUOSwby4dvCDPErgZ1HV60Gk1Grgi1Q9AbuQFSbvRBLy998CtHZWC7kVC7DJGIUBoDWGpQ5i7eKi6g1tUQXYgZgcVTm1gFSbFDS5jwcRobl12TYV5MGfd6tZxB6YiPOPU3eQeO3WT0Gkw1ebnYwL7LoDgYxDpMC0q3JOY42zXvHC6YZcO5jo+LbXi906XTxH5GnI44H6mAO595fd7Z90apEJbAdd3Ztp2UC5MfAoRxeOli95BrY76kSpwC0irSGsWrKfaAXMYcaWw2ccyAnDq6s2u8FKxiKGaTKP1GvbCvC2rkm4YpjJ3afWrX7FCJNhhbxX2z6+Ov7J4YB46hNLwZ9U2haBtbbdw3316tHSpjI5MhzvHOWQek9SHAuJ2thDzoHPQ+3I0ymLt2H9uoclRUdBCDY9dWDDKwFw7dQAWKEkIcOXkKPd8E2MzBs677aLN+5Y7mUofVZ8B1ewFGuvxPfPysN64//vg9D1vkcHzgMh3ZceTdU7Yd7zqHqXF898A8LcRwoNUXmBdkmiAuhAsE90jeVurDG9rxhByfOJ5MadizyYfDYaYLTO8+sW4bhUaMFj3hXWQWz/XyEcEUYlE9bAnddrb1haRC84FwfODgJzwNV1Yblo9r6qFByTtp31gvG+KUeDzwN0+PfH49U2shqCl5wgXoBa/j4+87xynCQ+RpNpXb4xKNgeeUliviwAUxyscIEvzuxzOtmY9Ge2aOwfLJYiW3TMqZl80iK1rp+CEgGcFGqBfD2HRj3vWbsABFB07GAWk9E6bJQgNzo/RCa5W1WpWVilV0y2Q3rFewRcX+qdpxmCeMMV+kGz82iC2ptkh0mjS6NNZW2MdNWoew03fF90pwE9HZDLIOyXAF1ib0LaOYatSkvpGC8vHTmcv5mX0/k3Lj9HTieDgQeiQs5h27rInH0yOTREKB7VopKVFr5eEXv8a1iHShtIzbd1QzPnr2JFAD2iP59YoPkbAsxDLbLKwp5ZyYHhdQZSsTaYSR+roxPR3wEnHiuaQXtBjDcfKB7g/08MC+r6xX+9lv5iOn+Qko1BH3UgBRT2oTpTR6z7An+i0kUyMpfwYa6hIqnR1lrZ5976R0RsXx4d0HFl0M0KqFJkpXD36iuScjtmtDitC8B+eJEmmzM9HDNLMcJyYH07by/Mc/cH69kvbMvM3k3KA2Hg4zH56OzHMkLJHoA+KFuFgyuHfeRCLiOT8/c71cOXsH3lMa7LvhqehWSVWCpXN7Twe2kGxuC2xbppSKD5leTcYeYsCLG4yMxka2lplYTpx627hSqiMSyCq6KdpcrY02Wq9m2/HOVJ3eKcsSCV93HrIWbgGp217xziosB4gL3GC1IZhvr5TKjSZXayPtZbQ7rQKLbqSe36wGI1GcerOsdPZqhwlUOfrIKSwcw8JDPPF0fGszs15p7mpiqArLvLD2K5qs4lUs+NNHZT4FjqeJh+WA70dqq4Yn+4mPn/XG9W+/+yPLcWaOn/minrULOWd6tYiB4+SZHw44H6yl0hJTNOXQ2iufPr9wXW1g/eXlmWWemafDMEmabJ6yIyFQEf71v/2e2gyIeXmYUf9qrbHW2PcVp53ohIRjHSfD3gvBmQIouBerVHrDFxvety6UZiV3GyeSnlYUowS8fTjZTdIcUSDiid5znCOHJXBaZo7zggsL655JuVBK5em4EKeITgst73QauMbD6XA3IJ/+7Y8DZwPfvHtD00jr1kZJdRtpqJnJz+wpWwqufs0q0hApxTiO4hjSZ6E3a2m2Qbk4TkbxdiGS9kQXM9mauXhi2zNfzhdUO8cpcJoDPhy4xVU4ZypAawlGnl8ulFrovfGwDPGNRjrGwFPpBIEugRuKqI924DIFpvloOUNeefftARnE/tP79xwfDnjnqHvm/a9+QcdzORdyK+wpcbls/PHjmfnLlXmKLD7QneF2zpeN+Ls/46MFh6a0mkGzNYr/3w3FVI3pMblxsg7RQM+9QcnkbTOTZ5isQhuikqDCdHpEnOdyuRKjEr2yBKXH45iJdFJaiV6JweGXI+t1peSE1ETdzXP3R/dnQvhn8+H0SssFXY4QZj5/fr7PU4J2Urf30JRvG9orTjrzwwOX68p6XQkRyrbTW+P185Xv/vA9grEKe4OmnuI8r69nO2AoBDpFLcLFCYZKozN7R5gC277x+vLK998/Qyk4OofDgV9+eOC4TMwe5lFtS/BEF+ja0SgEwLuAcwFE7X6qhSBKQigd5tAJDlqrlFxZs7XLj4eZ0oytuafMWjpFiylrNdBqxXvPPE1MwZS0tRS0NW4SUOmgY2VVhNjV7v/g7lFMtTYk3AZolgco3czW8zKZGKlbh6R6q7Q7gsfmt27kzt3IG+rMB0c3nqDz5qWjd467ZX05FQ7HI3M0kshly5zmaKg278eMza7PWtogzUQeHh4ILhDCxOH4YEpY7Lh4ySZe0Q4pGwYr+nCDyg+Vo1WUpcNeKj98eqa1wvbXmsf13Y+fmc+R6DzPKDtmLHRibaCrV/zrq5XvItRWB0xWSCJc1o2cTQRwPZ+J3ptAY6iBnAi9rJQu5Nr54cfP1jJxyrauMBhkrTZyNcq092Jziz2RkuUBBScjIuBsl19v+NYY0UHUZgmrqlb+R6nWMrsJP1obi7gpu8bBkcNh4u3jicfTCR+OpMFV27fE6TATYkTng2WESQMHx9OBWhppSzx/eWbfdkSEt28eaGIbV62FXONdWBE1su1pVJt1zHqwjavaa3TBqqFWoeTOdV2pxZRFD4cDy/GED4F92827JeC9EsPEuu1GxeiV4xw4zRNxOg2FIAadDc4c/90NyGum18bjwwHnJtTFIYUH1U5wlrA8kMkgWMzG5AlxGaR5RfwbU/Q5x/LmDYdH81/13IjLQkqFkjfW7cLr9crreeWHLxf8ZScEzxK9YW5q5eV1RZ5XnDMTdesVRpzD3tQYf0NQEMdhxvvAYTFOotTG9TJUby6Y2Msb9YFWCMsRUUcqmWUKg5TeaTrdT+edTvBKGIZapONEOARHXldDl43U7MH9sc8kLuAj13Ub1YoSpLEPjyG94cR8XErHf36hDAn908PCdt0oKaNtw7q2nZKLvX8oWRx5JAmICtoqVQazUjplLzY/dGp+p1ZI+871uhNUWIL5i54eT7x9OjI5RUZmVgV0Ebp2sI4Z3lmacalmUPZToY+AUGmgvjF5Z6xPLTgacZ6Zjwslm7xe1LL6RGWIZczzFrxnnmbmyZtEfLdWcWOIF1wb94P9HlCCd8xTQJvh40qtZoQf76fzo/3nzOQfg7Eoc7KDaG0mPkJ0QLbV7u+BrXI3oPDNN+cEG3t1+iDCO6ecHo4cDxMA8rLycFxs43J+tMX7fdbuwkScJt68eYSuqASDRNeKjEgcjxFUem20NLiFqvf59ijauE3QRJWSK6Uktn39yWv/z3rj+uc//EB0ggdrxznBe8/x+IBX5dwbud+wKp3XnGm12IKsk7EExdpddAvcu3Ve4xxZDgut7pwvO9fd8P3mWzfZaM126gfINym8Coob8vE2etkKvZO2HcYGFLyzVl634WsphTl4DsEzR8ebo5EAzttKz9VuximwHMNofTXCFAjHhenpRNAjU8+0nNlfr7YoO+hSWY7RiAfBEw8LeU80lOU0VD0dpnmhSxzxDh2PcfZ8tKTUuK6ENQwwpikNXIiWUSYQ50Ad/extzchZqKnRi3B8fGA5nAzdFK72HjpHmCeC9+i6klunpcw8x8FiPOLVNrfD4UiYZ1ChpMxaKi6ZgfT05h1ukNGB+1DbB09OddDB26D126Ko7kbJbizzAR/VWIynmTBgwSyOSmPbz7y8/JEfP37PDx9f+fjlyr9/fGX4bZmngIyU2tfrTpNnDA/kmKK3Vk0ybNLtbg7O00ZTNYjy/u2RJQQmhD99OkMfYpTgCTEiqrxuO61/NDL6PLFMwfxh+44bm4GoAY/7EBh4gceHmcfDRHx85OV8ZdusE7ClAddF+HLehmrMvEZxmgZZw6TifcjrY4xfFXBAjLYYH+LE8+vO5bpCypRayKVxTY1P5+3u4ZnDoEuokFKxOdoIHkx7Hew/ZQ5KdEoMim+VuETiPHE6TLx7/8SH92+Y8WzrlZQS67ozz56uUIbSNcRIcJ6ybkic0Sbgm83Uhow/zjOlNtKWCaERn47Mj0faVpGY8LlYgveNLEJnbw3nPcs0MU+etGX2a2KrlXKLOsHEUMbtM8vDPAVOh0g5b5Raya1S6HhTohOCIMNLNk8zc1RaseeWOmxbom2JODe8mGcrHI74Md+LwVGuaagpO6llq8q80Lz5Dl3wTA9HHt8ekQ6pCIenJzN0o8QIBrC2VqGoR2NkenikZaA7qnqzBKAgDT8fLQmhJ1pdLV5IOk6solUnTAFEzIs5TQtTCNSykcaa+lMeP+uN64fnLxanIUoPhm25xXU7FXPb76NXPTBNNzBq5WqLCWLfI9ZfnqKhUqiZljo//PhMqpXau/WD1ZJF88gbMqIzEAb7qwmlZFMEZmMjKkPKPZR1IkLNjbRno5xXYwKGhwN+CXiF4xJ5PE3MUyTMZuBVJxwORt4OqkzHhXkORnDu2KBdlLiY+qx2SOuGp+C8h+ZoFPYtsV5Xq+TUZLoNBmzVsniIYVjcqjnqvTIFT3GjbaSCnye0e0TBzZ7r1QLiwuQ4lYnsukFPvV1mrVkUg3gH6sjFfES0xhIDzVt71P5bQZzJlo25mOjdssBuB4ImJk4xAGnD0xh5gZRqvfreOtoNEVVFyFnxkzdvTqtUn0zKXQvNCWk3N3/tFR8iNReCDxwPE+t1J+2JcBMCOOE0eZw3wcjzuSNOiF45HgO/ePfI62Xn05eLzQGH7P50WExR1Y1c8cv3jzzMgal3Pj+/4p3ycIi8f/vA4WixIP/1X/7ElgtdOl4ay+QMbHou+NkPW4Dwy2/fmuKsmoH33dsT756O/N0vPvB//nNCeuW4eJw73Wcb//L7j2y5kTtM0bMsFrZ4Oa9Mc+CmhHt8ehiqRoulOB4iD8eZX3/zyPVyZuud42kmBhmG4U7+w0f2bOGbT0eTVzunfP/jM/6G9+uNqJ4boXFywhw9x2XiV49xsAg9jw8m+kjbjvadbb2yZ2NuOpSuJnzJg4Reg6cOdW9tnVyhWUQ2VLtGUm7WAm/dYom2nZZtjppbpYrNhZ2Dpg3ZbN7ko21e2gVqJ28dCZYbJq3jnNEFa2mE4Jgnz2mZKL2z54KOFlt0gt0OnXaP+rG4F9S8VWWrozuhFuSKNfRKKbioNxsWGu2aSMPTpmrxR9NslWfrZsamVlSsdWlQ/GGEv0n+FWoah/hW6aVQi/kheynMatrY2gq1J2N05sIUPXrptGz8vN6sQs17ZjuvXF/PnK/PpJ4NFB3CT177f9YbVynWGlBnbxBAw2TbKkLJiWvKdhGWShtoHbAwyFYH9HOxLCwcA7nUyJhX6Lpnk+vKaAHgTQFUbfDfu/1uF6NdBN3k8abOy8RgHD1VuxgtnNI2lTKEDb11cjEjMc4bq8c7unPk3s1YOE6gLgZ6MzXddDjiw4JIGGomY6U5r9Rs1Ya0PBSAllHU6zhx9oY6IcYwsDcDrNltQVbvB7hTcd1Ubl7NwO1HK0xGBpE6wcVISx3pBp2tzmSyPSjTNJlPSxQfAi5MIGoK3WbwWlU/FjU3cqXaV0Yb3An9a9pHe7cPsKlturR6Z0CaVku+8hRpt36FKbi6/Z2IybyV201dzEhKJ/eCH6owDWppv9FYjFP0dB3sxmVC1FGlgKzIkOEfTwvHN49kLuhlH6F+oz0YAqK2cSngg+VKaTdFWFeHxsjDmwfm44mGUvgzqZkqM9TOms1CcNkzzSlaDXy6ZmtlaTDjuwsW9zMtB4Mj+4DOwdpW3tvv+nEFyfTSrZ2mgaZC6sISoyliS6b7OJBE44IOAUIEP5GqsJXGfHTE44yqxzfF/3ghSzE5f4y4ecEHD/6CG0ivbVvROA01nRJVmIJjmW2zunH5/DSBs1BQW6kdiJ3w0cHrc2KsRvT+PCVYleDEuBS0RldFXLDZj2eIlyIaIyFaoKpkqwrteYl5ugYPUM1gZ3OguRObbYQ3CLR39nVVmlFbvFkQ/ARdiwlXSh5tP2MO1n6LFnE0hoHZO7wvFMuspFHusGjRCGqfYdcAvqLScFLRYpQTcUaIb9SR4+VIxd4+nDfDfDekVG033v9Qz3cTp+zrThuJBaKdpnaYAbOziNp1Fkar8yYkucXeOO8H/9JIIXFQaLz7K924WgfBWg8Vtbhx4JLs7FZyZi/VKq4ykDmCbS653ZNH/QC5qDiaD7RmaqjcHdfSxqal0OwEI1hekFODhubeiG5ALWsldQZnsCI+jkGmEEKwBaB1eldyu/WEHallKnbDdO0U50mq7KXg42IG2jmgwdo1MsF8esL7iIw+N2rU5eCVvGXIyRJegzN4bQjU3L5KaIMfogdPHPOqhi3UIQR00KatTWI0dgXmacL7YIF8ajdmCDNExZHpfSe7gh84qGlaaNWsA94HYjTUVs7mpQFQ5zgcD3hnsvFzvQ5/m9EALAa8cD1fyck8c0s0mXVrVhmqH+R5Z/8kGY6GsTDdyeVit+edhD5kxZZoazTCrVXciHcPwROibawxVmvTDRP3fFyoOFQy6s6ID8R55uHxkeXNOy5Z0HDhFm0jIiYzFxO4iCpNHAWTtucOiFLDxOHtE/7whq0qW1euBQNHB9jXRkqVl61S/E3a3QmvicfHwDFO0ISmSlOFuFDUU3xHlxP+YUHDRJZAOzxTeqJQho8p2Ny3KyEu1spbYdUJeqOIwauz9yT1rEycC7zmxiyO94cTcV5QmZA/fKZrpuNpwdHnE8SJ5r+gy4I6IW2JZV6IMXKYA94p0XVm11lOB5tl1Y5OMz0EuvdonHCp4pogNZvQYWx8cpv3dYuDcTN4H5h8o6dEK4XuLW8sKEyYHSUcFsLhwBwDfk/klEnVmIsqI9BUofZO6Z2mDo1KdI7SHZLSECt1XJgBZXKjq+EjzU242SG+oqFQr7upA70Zg43/CF2U0oc/MQihF0pz5KJ0KYiPd8Uk4/MtLuCkI67hfaOsIM4Owt6JJVu0RieyZpvhN43Uppi0Vmy2Z+4LWjZgec2Zi2JkePX4IPRueDZrvfSxhjriOJiqd3ezvY6ZnY7/BRdZ5gNOhf2vtVUY4mQfuNiG0p0N8f18sPlBiNRkvoIqBZkma1X1hrhOaxZR4OKMVwtezA1qMT+SKkzHoW7Der+VW8aWp47TSe1C6XJvCcZZKbnbKSmEoYa7nRBNVSWDz2c0CMvMER9oeDQ4coUtV0TNP9LFESajN9wC8KxjJmMYOnpkdEqu41TomaYZUU8pkMtuKqsGDofi70GUooqMCxisYqvSB90BO22hNgsUq9BuAI1aOmxpbPCO4COqO4x05SlEsti8BBhVkLUyYogUEUpPtFKRMVRvtbLXTMkmOLZTppjEu5pnxokQp5m0Z5sFtIKr5mlx02yjuHFab3W0KUWpuZhXZUS2N+1WcVfA6jMT0osH7P3KzZO7o4owzxNZjIihYSHVRnOd+XCwFkic0OnIeW/sVRAfbJPsZm5rYlWQjllnJbAW4ZIr4iK1C9e90fwj7vDE7By//Ju/4ctnYwIuU0Snid4aT4+PvPnmkdYaJReOT284HY5MwfP8+c+UHig9cKmg8wNeM/jI/PiNXZvAL377W+LnM68vF/bLhTwMz3E5go90QILQvd1rkw/0XkAdpXl+eF1JTcBFcgOdjuh8oHclHk5kzeQqaPB0P1N9ZD69QScFGn46ICHgppnjwyNv3xzw2tCW2NNq6dneE/1kAAFvfi7E4XwklIaoKeVMaj7EANJAPCoe7/u4z2w8gDgDMwtos81nmg+E6WhzGY342GC9kPadUqslNnQjTJj0wtuVIjAti20MdTilRjCl0WA6dKEWu75ciMxxJudqT+UWYqk3270dbA0fBj5OhNIoOdNLGJEl7mboGhR2u76cOmKMpG21Tk6utL2SigmDWDcTNrXOtmWWZSE482uWmsnF4Aa1jtmbFyiF4Ly91zO0MjLY6DRMGWi4u3GPxsgNgCwIs3o0BppTtpy4rOm+5vzUx89643p4eMD7gPROFU8VC9kr3YyJuMn6zloQV2BEiVjlNLyB44PvOFpXaBY1IE2MTOMshtykqdl8G2qRGEYLF8v2Kt2oEkONeKMk0Ppgi6mV9zLC7HpH3Nf4eqdi2TW5cJwjXQKtu+FNinSJoHG0Qjra+sh60rvZcrwierN4DlVFcdygYSI6pM0edRDChIj1u52Plimm2S5wdeM52wbuRt1IH21E73HN2nEmSrFQxE4lNIs4AFPH+RBtoRsnSj9Ob9YKNHWSSreDyDQTQ2SaJqCaPHgkN9dq4YK3U16cJlOg1Yr6QS1wDh3DE/F2fBQntmGrQUal24Jj0mzzGTUdrRm1qtyN9wmgq8Wl9HGfzTEC0JypV7XZTT5PJgOOzhstpRRc70zerpU+2tS9FeIcid4zeWfEl9oppTAFExnY4tiYvBLmmb//29/w5fHRxBi9ku5L5wPvfvWNnaBLRjQwBYuo2J8FKYWaMq1CDDMda+8FZ59Pb423p0dagpYa6fXVZhSqeHUjesdeX993JHimYBBhGZ9H6jYfierouRNVmZyRF4IqQQ0+K9KpOdH6iBoZBy2vQC0olXn2vDkdkVYoW6UUO+07DYR4QCTQmkN1Amct3zhbpdXEFIW9FwSjzXSnhO7QDqUpzhuWSHvD8q+sLWlalEbJCSUO6HA1gUP0dk/JoMJ0m6HpyMayTaTjnKe6ate1G+17ZypXdUrtnVuKNSLomPGoWqyIjM8UGl3sPRvHLdCAaB35WwHVQVMZe16Tr+257tR+9pini3q82ogixHEv9E6oig8mUvOjOnK+EVobaRU2Nlhm42CqC6Ot7WycoELrh5HJZkz66jwtTrShGEVMODQFz+S9QQRuB/UbzPAnPH7mG9cjolY1pO6HObze1VKoAx9xruKD9ZQr5p1yAtJuCz22qHVHb4PY0IY6TaNBS1ujlUSnow4Ua32JWBx2G4iV28FJ+02A1YfqWKx6UcctPdcNBZxhW0xosBebB3QJdLmd6KKBbvGIUxhzm9vGJVi1yPh9VsZb/pMTsfgUTN5ra7XelVeiFpInziNumH5ruW8sMl4vA5tFt/mJOP0PCBpRf4eBetct+E+M1u9coGmnax+LljeZtwjSM+Ume59m4jQTQmBaZqDiVJiXA43haWkF50bFGmd8sM/fByOPO+9xzt9jx1sz1ZqM7K6uNyzViOxQ6GNOYKikMSdkREbQETEI8y06dgre2kWiQ3zTcYL9fYUo1u/PpeLGgi7OWoUWymhpv8vkmFWhFsrwA03BM9C8JoGXzmkK/O2vf8Hj8WC+rP3Ky2rV8zJNfHj3juAV6ZV9zZYA3Covzk7LdU+2cflo+WQu2vXbOlIaD9OBfUpsceVzbwZlViO69GoVhZNGS2bZmOYJxCjnNSdyBtcMv9VSIyBEBK0VL8N/h4NuX0/Jdv+Z2BcnnTZactPkOM0Gyl43YW3QnUPdhI8zYBzGrn5U/23Ex7Q7JszdugOY8/ye9Fv7CEM0wY6Ix4ugYWjkeyPnhHOB0iz4Mzi18FWw9QFTC7fh2zIqPXQMFK1DTWiZZHb9uIHOaUAbc+MuoN5mRRZ9MuY9HUTaWANu5gNbD8RV2+Q0YKGR1r1BuSPBRC2DzoV4+2HoSFcWMfViFxlIQm/3m3fEG77K7mZ7P22MxxQcXSyCxvlgbFCnhKC0ppSS7UDSCs2NcYuo/Z4xV44jLiWoEJzcYQI/9fGz3rimeUanif2y47qhbSg7Pji6WG6Oc3bRCoYu6mksnuIord5VgbWbd8O+dgx6vdJrHYKGih1hzOlQe8eNjUi9t9DCbjJbqHfckdx7wfYJ6kDoyDjhcVNJYzdxbx10yIRv8vrRt290i7Og26Y7QLRdLI7kVhKIUwYI0eqkZmimft9kbEML3o92iklW1A0WJ6Zakm5nYhnKyNGAgfFcVQd6CItuKMUYjH1URDYAh1wtO0lU6KUbWJXbz7RNq2NKLfXOCNKnIwyqh0wB9gLiiPOMU1Pa4TzOe+KE/Xsro31izynEiVu67v3Fq0nle6sDP5NovQ8lmPXlzXljVbjS8TpI6bXRu46DDDTp7PuAAQ96gX2O3egTOjiOw7PHX7R17Xc5M3unbAefZs+vlUbdM1+ez4TlQncRurKtlmFUcubjpxf2YlaEH19emabANJlxeQmRoIby2taN1pVtbxSU0gp5vfLls+XPFcBr5HJduV6vxqtUtfcrF7Sbsq1Ju3MRnQ/0YaJNqeJnYy6WCmlf2baC+kbuQhPPTZVIs4NOo9NqHWigQt2rVQ3VzM173qHkQRgXGo6ibnQL5GtlIrelT8e12ccGMpTFzmazqTJUccarbLgRV3OD344ZthUjTIeZqUeETgxKSTulNHKzGfa4jAZcutFKozdDisXJk8vgF45nZ4cq+3fvrPpWOi4Gaqt3fJl5/MwCcnttxiTMiHRCdJRs2KY2kHHOfeUrfs0Nq4ToadVsDMi96QIM1JSCLGGAgYe347aJiqGlnA7hiBfbZNVa55a0YJ+FhY/2gety7Gmnl2w0ltqBahioakrXPa2UNmbpbvoJq749ftYb18t5JWQzeGaE0mQMfgyrklMeRAejYKiYlFoGuDXnRC0NDXJXpNnDbrKeG2mYNlut5JzRwROrOVGGKdY580gYmbmyl8y2JfYt4dtIlO3dZOCt2CmsC6XcvGE3KgRAZ9t3lnkigKkHB4pJwHohzU5a3omV8wPUOabSA9bZ71er3RAWQdKr4X1KH4PbEatuW8iN9o61DoZCr+vXkt/rMFPbVmS/QhnKIatme1VrJap5nGh9GE1tA71BcS3h3Nl+2+zuKjnTSiXtmV53RLrlAY0U6lSNUee9xzmh5kIeRtdWk1VyylARml/LPrPRllVGG2McBG72Ce/Gc9GbXmooFLu18bLNCbbWqNpRJwQRWi028xOroG3QDqINFxzqO2hDnLXleh+RF5j6y0Ux3JsKbnL0nNFgENPremW9vBAD9hzShZxXvnx65Xq9sKdMrgZijsEzxcCbpwfcbPgjH0B3N3KwzLTdWmOeA9fLK+oUH6OlDGshukrZN7oP4CqpJLuuBaRXlnmiFaVmk32XWtn2xBwt4qdQ0aBctytNIXUlbRdyqjRR9pa5kUy2bbXLtZutxAerGFtJ1JagWSrVcpxHgjGUbpWtta4Zs16rINqoDG1eVKlD/m23xLiWpQ/SBDBa5Ldr0vrYfWCS6ojn6PTBKjS1oAlp+jiMtCa0Pq4bJ3Q/msG9Id6Uf61DEb23igUGH1FAKqWP+1MHY7DbZ90Z/zKqLoaIqFHsYH2DKlghb2vXbXfqjDajHSRV3b3CsQxK+XovdNswu3Z6rePvu6XEjwmxHZUL6k1B3Hux59gb2hOqnR4EjR71I/x03F9VBIIzDmycCHEmxNn8n/s9dv6/+/Gz3ri2bad2NTm8NAORtoa2m7zZzvW9FXrJBGdvtowToKFXKuIK0gI3j1fHpJvm5xpudOwkzajQLDTRTqKt6r2oEm6+LPujtdFcG4tWG1VWvxlYADvc3ojtqhi2qbY7/dl7Pzho44WPi1jHhsOQ1N82LpO7f32ufZQQt2+/SZpvPXId33frUFllIPf52/9N3r/E2rZlZ5no11vvfYwxX2vtx3lEhB02znszAQt0QSS6DpAQQsguUMNlMBIlK2wJTAGBKPAQWFChZCghU7KQkEBIBiEe4iHACMmFlMCCe5V5b4YfcSLOOXuvx5xzjNFfLQutj7lPZJL3EgcJ5ZGnteU4a68911xzjtFba3/7HxvzDnU3VjndyLUpmwHDrRs2YpzR6cUZFXeL4AN3sxTaoAz6/qPVhlZzQiglo8WKfMkmaSilkHLBa9fMuWJWMr14tWr5aD5sP8r1fVW7dZnajGzR+h8XXN9TbJlHrrt9a++SzUC4NusgczUHctdh4VZqR4jtUHXedfwfXNiGP2s0DCbsTQQ9jML7Dmdh7u3SG5kopJxYlivj1d/iZ7RVUvc/rK2Q18QShJI9ZbUMuUBEhkD0zqDTYDuRUgu1NUYXLQHcC7t+vTs194haS2fQWh6bXTd2qI3R02qmlkQIXaxeKlUbpdm+K4hjSQkVoThv8LoquEDrqEXD8sV8F4K3rdHCIMNasjU7mBGAOMV5PmPK6m7XEmif4jY4vl/CqkYiEG/7TufMicV5RLTvee06Ut2e21CFTVepqlSnpGwHtckluwSmWZFrbnMrlFsKBV0MvkGJHasBOhO6v0hVK8ba76Htt2s9y8rAA6Oqbzeo9vtwu29UjYzib7v6fj6pvtOYvqtntluS7V3s+7o+7dFRJ6c2Edd+j4sqXor9C2cWDc41g1u1gKPvht1tJ74xO3EG60sMRlISu97NAWf7vb77xxe6cOV++gRnXoK1n9K+HxAK/TBst25CtENqnZrs+zLfdUX/duAjDueF/WHXbZmURWZ8NN1Ty9FuwGZJq7nWd5Ecw8BYDFIYJtO0GDQUjFHnFAmeQzSHccSyjcJgh0yujTUlw54HO1pwpgOznCrrLFutHT5s5CzvbojO2jMtU7/BaqNWm/As5aBf1M0osMHbIl4btrTe7mNsYLVu1p7PJpEOrXTwvuR8e+uswPlb92rO3Uacif3AoxUE6c2FdYHLMt8YmLrJBlCuz2cLQGyNkitrDea8LivihFKbWejYr4PUaq4J3VPR97w6L0LwoWcs6bvpu0WjzNMQDTTpu7u+oK+rLfRzNSd7+zmt36hY7EdTtntVXHewCJ2av2nLnC0/y61p6G7bg/kBSpcCqLPiVlU5z9acmetEZJqE0/0J2Q8s68p8uRLHHbVW1px5OM+9wXLE/cC0HxinA4RIyYVlXVlL4fn5Ag7O88xp2jGvFkXhuvkqYqSTd4esvccpG7Qp0veHXsitkXOmpAzBQlBVzOZpGCI0Z76EtL4f9UzB9zy7yrlmJNjBl3NhnVecd6hvxGnqUJRDWndz35qjvl+idMhdTG9VUgLtESTqtpBi0yv2a2AcPSGa+0grnSssNsnkCiVVk9M8LaRccBhbzr27LQBPxeBWrea849ThJULXcb1rKu3zr7jbc7Rc+7bA9VnMLiC3TZLaf0eAqmhp3JZGn/n+rdnMpWfL4W62bJuerfVmVsTR2rt9+K1prb2p0L4WUe1Ikx2XUh0SKnFQxoApi5wCxTw1xbGUK2hlCL5PuLZ62YWICwPVOeY18XBeiLJNx5/v8YUuXPd394iPrMvVOg5nJIVRTPEtrqE+0iTQZJugLF7eBc9hiAQf2B/u2TJ0Uq3UvOIa0GGiOIxMYWCMo2kUgJpWnB9oTUnrfOs0xNnY78Wzmwr7w5GSzfLHh0BplsnlMajJOjO680aj5BUtBaaBMAZCCbiU8T17yGunh2NxFg1bdLpbW2dQ3U1v2xRjPWFwHTZgeNmu656t5TyoCbdDDF3gaTeI0qmrrkOxHXbb4DS7b7Zuq4MUnSrvbn9s4hS2HCJnhUJ6AXVGed5C8txohdSA/A3Hd9RiMN1NZuws16eUvrjuB2L0YvAR9MA8Yy4aoURvnSmhEINlJRn7qx/WBEKI5DSzLDOpGw6neaUWM0iV6DnuInMyqPLpPDPtRvb7kcMgzDRcsx0IHtup1YZXIV1nfC3IGBm9LeCT2lTpBDNSdWrxOKWYg8dB2AVPPB2Qi2mSLmtht7OuVj24UmnnhWXOjCnw/vsfcHc6sB8GJgmkunB9PvP08AzajI4/LLZjqZnQsAOrFPKSmTqkJLXiS8OlQsEozWleqGtm1pm8Ztspi2e5ztScKQTcWrpBa0DGkYqJ73N3Qldsce8y6FKpl0wdcl+5VNrozf7JB0ZRImZo26oxetVpN232BlMNAdcGtCy2K2yZhjEorfu3ZkusW7MVANXMg7HrXhEI0dxIRgjd0GA/jXbYqk2gEnfkoqy5si6zQXnOHNlbS+DE9t8dWHDe2Rq2n19l2z9hUGwPXcC7Zo2ejWT2Pa124lknWqAmG9F+TwukWvHqiMEKpI3xjtqbaLtHbbcKzvwhHfYeSp+UPjP5uU4+GsdI22RAfVhwdEShf63UnjStUOxJ2WzOzDnHgQv4uGOIIw7bGX/exxe6cFkmkpj/4EZVpfYJtSFdUS8OGoZNN9f6UjOYU3yMxBgpq4kHSy6UnKArwlvJNBxBO5W+dniiVrxs7uXBJrzWqFr7wWodHmpTRy12YZaUzQncm3DXNQur005BRTu01TX6vrPfnDbMrcYO7KZym3B0u/r7LdF6p2b/tcEK7y5InBEjNmB0G682aFA66+kGnmww6GdgC/3M17/juemTmbbtX8MNCgJofaFr4IxshaTpTXEvIiZX0M3ZvTsVYLufd7/p9prbzavPeYNoDKmxpbiIkWwc2mEMI1lohyrt97Du04SZIHhCiGhNCOaqYQd6Jef6rvhXo+PnUlh7g9Fq//v+ztRaKaV1c9GKb0J04FrFa2UcRyqNtU/aODqBSGlZSSGjpbJfE3EIOBXm68x8XbheFxOXb/u4oiRg9Q5fI6/vX0ApzOczz88XHp+eeV5mHh4e0aY3XZbzJrye55XmDc5LazZ2pjhU7VDUUqnJRLY5ZZuQSmOeUzeP9TxgdPeCZ10LzkekNAJm/VQVSko326uSK1qMjVjWTFqT7YS08OyV/RBw42Cjf7N3tTVrDoEOBdpn77oQ3/wamx2OKmxx9+Yl2pspcbb+bkbgUWfXqW4Tp3iGMHa3f89pmljWRO15U02rsZe3Rq7/MfarTYDymenDiSVEsxUfkX6f2M79Vrhka6z6yqJbLOkGq6rt63Cx328YFN13Y+q25mu7N23ydOI6acXf4Em33ftbK9jPIFVrDDZXHcvX2SBaNhTe2lptt7BLp9vOvJ8T3fbpRg5xrvMBQMrnLz9f6MKVSgEPS1pJnQbvakFk7A4JXSOhhqkq2jOjYIiB/f6AF89lnnl484acknVLNbOREVotNwU42L7JCdSSGIYd3geCDzfIsKT1lnSKwvm6sC6r5fmEgFZLJ44xMMR+UfebCQx/fnkaGAajm3ovnfFq/mdho+A2K3aK0GQjbnPDt6X3ZU48rtlOT7vdlIHn9vO0Y+Gu78hMFNi/H6Vhr6GrkPnM/WCsOGrfpeltWe69oCkbFCZboXHguvFwK7S++A3RFGKtbYLEbUdmU+TGzGobROl9Z266G+FCnMNvIqv++pqag7Z0OEK3kdRhOPs2cWHwqIUQ+n72CEPw5hNJgXEw78pq5IZSq+VJObi6YgnLyWA0WVbWxXZQYTBIOuVEykpas+VJNSg5so6BmiKHYyNV5eGSePv43Iu4422wwEKnBr9tkTwxeJZSuS6Jp/OVTx6ezJZIbH8XxDEGx3U/sN/tSKWwfPzAf/qf/788PJ25zCvPl4sxQFVvTZbDDnHf5Q6tFuaU8P3AOveJOPYmwvRMlSUrS8q20wU+6YyzolC63jDEgSmYvkhCoFXLuKrVZCauKWU3cHcYkGjNVC2ZhzdvOR1GXt4duDu9tOfqDDhrLqUzAv1N07i1SU0hpwXXIVu8mdii78JM6bvwOHRBrfZpQkz/dDxFhmlkHAaO455f/savsOTMOs+s9YKTEeenfh9LZ9PZ5yw+EOPYWYXuM02ifb5hjJScO8y97d/ts2g9p0v7/soaR9up0gyObq1aJl//e/HvgJIwRIO5a2GrGuIc4xT7mkFp3q5P7zembbHXsdH9g7GV7bmFrQmW0PVjvQnVHip4OIxIKoQrN6+FVhuXy0zLdu8XzYRxIMjw6xcqfPPxx70rXjuF3Pz26pKpNIs4D8XU7LXgQxfCoqyrp+QntDXm60xaF5uMqlI6zVg6sy4Xo02bP17333JQUrvh/7YV7iNQeNe9bKFxDoxEkBKg+JzZjZvuwiaxbeo4TIOp7CtIhdAivo1oiZQh0tXN4Ieb3iN3SrBzoGJO2xU2pvCtY7tdaMBNv7VBjF7sgNd2q20O+s7F3XY0G7W8cvufVkCaoDRUK+Isyj3EaLusUmhazIIn2M1zI0PS8OJNtClWjKrDIEKMCWal27p1072JZbvgbweoqDmhbAXQFucdiw/2vxtQhVshl22kVGGtglNPxw1RGXFScTrA6smr47xWnucEvmDJs8lYbb1oOzF7reN+ZDzcU1Zhfl55fL6w1EIyw0FmJ5Y62xzrOrOUypvzwpLMuxFMlL51v6U2hhjMNDUG1s3hoChryyxqxR3nDFoL5ksZf+UNw7fPfHxNfPrwyJpLJy7Zwdh6Cu62I6y14avtrsTBqgnF4EPnpMfGmA1Wbwm4LrXnoyneC2YQYUQXu/Iyuqw8dIs1ESEGobQOq5WMU2VdBgYx+PX+tOO0O/HJp28gNa5PMw+PT+gd7Kc9IXQBNeZco007sUXwcSSnzJJhWR1Bbfq1Cd8KmtZGzZnr+cKbT98SozBNE7vd3gJNgyWr7w8nDoe9eVT6QE3f4OnxyqefvuF8ndkf7zjdv+D+/o6cFzsraoeptdFyBe8JwRODZ3a9Ke5X/oZ9NLWJnN50gnSXsJuPizUZre/mneVhuT6RRVyHvC11ojWLE6Q1NISbu9DUrdyaVPAZNBC28wQjmXnBZEOYtRXSI4WcaRubgyodQnYC1SbmXAJrgnkNLFlIzd7v7MTg9jUzMvLw9BG0xDqfP/fZ/4UuXDcYrN/cW8eCczdCAmpMLDs4wRiCFrMAVlTWZe2MrY3hxHeMuk03GE9pOEQbTRz0SJNW63ew8G5tv1WvTmYwQsPGLrQlaW/1HbdDRFGqxTh1CANb2vuIk4FbJXJgGpc+afVIb3vR7tZJ95f4jl3Ybg2YkXs+g+i1G7ni3b+z/Ve/mQze5wYYbLRd7YWx7x6auZv25zFHkY5AcKMf94NeNhpzvzG3hZeRHfpnUPUzE2L/3ht9rMOXui24HbcFdv+Z72qt61MFn/k37zrJumE1TqG13rE2Suk5Y6V1D8qNDNBfrtjvVJqxDlMpLEtGRjtkhmEghJXgPcHX2/tRu7mytsKabWJrt6bCmoGG9uW6kVNch9reQS8dKkZvcBSfeW9SqTQy85pu19MWg9GasQK11ltRdzenF3e7l7Yp4LabdEbm2UgnuVpkjHPmLh/6WF5Qkylob6Ja3T7RG3zknAV8Bicm+XDCNA7cHfa8uj/aFOEcQ4zc3d0b2cNtN5d9joYy2PVo6QH9fipGp92IDta0bWeFvX1efCdd2LW7pgzzbFqw4ClOmZe1Z/NVfvnXvsXD2wfOz880HDEXUkpcrgvreiGl1VifpXShv9BKIUbPEALrutrU0vfFtVQj99CTvZsRPFzfQ5k5wnYTGzJhz2usaFXTC1bZNKhQnZBTesfGzZkgnuo9w2ykIG3m1OLFWwZfrrcJvGmjtoqrtvsyNvQGfHdLLUcXSNt7b82AomoOQdvX6LZTqRRKU5yP7A8vQFPftX2+xxe6cI3DjhAjS7W03bp1Js60QarborTTW7tzs6qia7rFeJSy3VB2tHkvvRDZpLCx2zZc93YwuM8eGN16ydS6n9n9vOutQrDwP+1L2ZJtjDcWrW1GxXVPxNpurC0fA34YCHEw2u5WaNpnCjUN1zZmYbv9PtuCdHNnvp3mNh+8oxiL0YDptNjSX7kAotaymnt84wZs9uqmHRLQXnDaFjvSFC22C2zNIDKHu5Em7FVszhbSpyYrqLdxDKV0myfXhcXafx9ofV9l1dj1BsBU/w5t0j8qE4y7/lm1Dm3Si/utHa/v9g0UY8rVlFlWkyfk7mxSTKdgkEzvFpqa6HhNhcucePN4oYSRVCxSJoTAMERUG7Vqd+S2yUdbNSFr7yJEbNqK0d+mo3pzZjEtjai5oL8rKHTrLyPuBGexFWJpgvhokT2tmbh+PwyU1lhyIc2pF0i7/nzwff+jFjLogKCWX9eL/343En3/2THjMcHz6bBjcPZ+LLXy+HQlN5M4aLAbQ52jpNLlH45d8OzGgSlalMnd6cCLF3e8fv2Cu1cHpsG8/fbH18zXCzklkzK6fgU1a95qbWgypmgrtWu73jHoVJvJIRQ2K7BxGnklL1jn2XR6S+JaHzqc7pC3PSolZy7PZ375l3+NvNre8+XrV1RV1pRY1rfM84WUOtGl66eWVJm9ESbCEGml9r2RFcIN3ldnB7y2RsvNqOPbu913R6jR77cDxdUKNd/eh+Y9xQdCU5bzhdwLV1oTIVjhpCaD23G0oozj0PdQ5R1Zop8tssk4WrUGb0NuCrYj7gzY1lcRpRnqFXrenL0PyrrYHrQCfoi898H34l1luTz8lxzz/9nHF7pwHbwQgV0ceZtW5pKoNbPm861bR4vttUrqB71Vn1zqu71PU4YYe4cLLmU2nVKQPspjuwPz3Ouedrdxo+JyN6oVTy16w5prsVj44IVReuOi/ekHoaiS26Zwt73Mq/3IKXqOwfP+fscxOCbfGAbIHQ8XUYtBwM6zpnojhbSqN+SyKZg7BkZyEOzAVkWiuWrcdj/qwBkjanDROm/pv583Eokr7vbetLL16bAJF11fcPvo+u+phnf2gqBa8Hg2Fqa2alCetwBO8eC8GjOyW+X40jFzZweCPXejVYfzPY1ZAVdukRbiXcfs+7+9HfyCaOvviXXg7sYGU7zvhblYHbumxrefF749Zx5SYSkGa8YYCLGHReZieWBjF6O3xny58Px8JfeUgFqsKETvoWR20aAj22Gak8KMIwyeED0hBlyrpFQp2hhG3/c5jtGDlr7j8Z1JCfa+09Nxg79lOMXgOBaYDibhaJhNT6sFcmaK9EOIbm/k+kRYiaOZRDtnHoRD8AxDsF2Vw0hDXnl1f+B02HF/PPH0+Mh1XnCXjI5mKuxCYBr21KaUavu+otbwHcfA+6cjL04HPnzvJbudsF4u/PLzE6/fP/Hq/o7XH3zIOLzkOXiu52euz080eeeHJ15sgk72vqw5cV1X8+1bCl5Cd2NpHXVQfB3MGWI/oUWZ1zNP5wsfvz3fIj5wjd1+jzbl/PjMw8PFctj21jTTGum6ICFQs01aznucBrQ61rlwyYtdekHYD1OPBHJmoeXNGMDfDJ2V5zWT2wKq+AbTNHZDAEEGywDzIgRx5FzJrZBr4hBGa0CdY76eO7HJSPtSG43CkjNUhxfPPu6IQwd0M+x2ncJvWhC7jqInTB5RE1SbldWWwCy2bvY2DDw9X+3z8La3tkLrKMHRokfGgcP+YNqvBst/Rfn5Qheu++OB3bBD64KsiWtOlPXa2W+2xwi4Hho4MnengdqM9eSd3ey5NqYeF9Ka4rpxrI3BXfQnFm5nQ8lWyGI31d189LoVlDfdzeANxxdxhA5JaLHD3LokCz5sHdPLeRMTGvxYSrYDpmUcozHKqqO/MnSjuWI0fIPfOtFi01EIvWBwgwA3NGkzDzYWywbjmZTApjjr7ry3PZS5W8AGV8o2vTTtMEGfLrsnob0kuU1aCGh+R81tmCj0s5EfG8QnnQyiHePU5lFx/XVvkKa7CcWNyVhvU2XrDEHg5lhh0MtGMNHbpKadqeWFW2AnGzU/GKx2dxhY5gl6hlsYAjEGDrvB9HTduslHz36auD/syc2x5kxKGSbByw5UeX66gFo46BAD98c9pTbu7yyI0/d9SK3mIJKTXbO+F4274555SaRkB5HZCNnnnXv+XG0miD7sBg77kcNxz5ITuUsHUgmklFmj+e+JN/bYbpoo2otLToyDmbBGb676tS/8fbT04zGOTEPk7rRjvxsJosQoDMWKesrGTBzGwN1honQ93HWezTxAPKfdwHE3shsD3is5G9y2zgsiFR+s4E7DSl7n7r1pBrCI5bfRdYi12t5ytxsR1wjjhFabXMJg+9amCrmi1ZjGtTXiGDjJkTCNVDyXZSUXy+OKcaDVRoyRu9OBaYq8enHiy9/zPaAWrLmkTIwmnK84LB+1T/pqEoc4Ru6OB8vnco68LNROavJdVGwco0gkWJHNJgrX2otViKYBxAgetRVKyV1SE1B1lvNXq+WUdQTJu56bZz5tRO+ZRt9F2B2eDv42/bVmiEDpE5uoSQ6cN/ZgFSgFWl7ZQJvgxRzyW3fE6O/tdV65nmeWeaVptXT25m+p5Z/n8YUuXC9PR+4Od7iWGFLmmhN5Ofd9hbG5JicM3jEIPF5nruvCkjMK7KIx9pZcu5s5lFwJzgC0igVCqlhHd5giuWGebLUyhIHBW9T4uq6s/SCJIXCYIrshGJmgCx/HEKB461IFmprruAuO4ByXa2ZZK9GDqmWI1dYdIbDJzQqKMYtKt2Pt49I7+EsV21R3Sn/tpAy14uL6YtWEhu+KkbPFmX2/dHGuCr7rkU2g7Pq00rVVTW9U/lbdbbnbWr3pt5pzePcuENL16c8mIytMrSk+tNvuSWTbRfTo+GYToffu5tNm4s3N1b1DRb1wORwS+vuyFS416r0EYZMv0CduhxAG0604sQiOIGYIOo2Bl6cdLddumCuWcTYE7vc7oLPr5gXxnmEcOB32FBWWlJiXlcF7O9i95+0QmJeF1hq7KHzl/XucE14tiVRK3yHJLVY+p8ycE0Ow5uf1y3uuixE5rv25vfc48Tw/X3i6LFzXRHCwnwIvTnv8MHBZF1LKLMtKaZBSYV0TpRUr0jFwPB5J3SFkXhemOBBjYBoi1/PMZVm4zAmngSlOnA4jZYwcD6NBUaWahm4MqA6knPBeGKd3hSuVCq7S1FiTp8PEFALBg7ZMKivzsnC9LGbu62BZV6b43AkIliG3RRHhbaIzCLrY7m03MIzCdLijFb155YkY/KpSyOs7f804RcbDxAnBxYGHp2fbbaFUCXYvTpFxCpxOOz54/yXf9/3fSymVZVn49O0DtQRqzSy5klcroqVWhIj3jnE38uLF0e5jIEXPkrJ95rV1Bq7gg3b3kkZLhcu8GJzXFKl2HoEVoqatMwcNkQDMIJh393yUTQgvlqzs7f+PY7g1p3Q5ge3ZG01NHK5VSTnjt8KllpxQnSK50dK1r008+9PBfnbJmDuR3ZPLvDJfVtKSaDSiN3RLfr0Wrv/+S/e89+KOMO5ZfcEFx4vTnuvbM5frwuP5irbCy/sD77+8Z3m+8HC+8nSdeVovvDwODOKYnzMtRFKpXC9XXu48vuPUlyY858RcM/ugtAKtOYpEisI0eN47TeiystbGXBpzSuyiYxcFr0Lc7ZEQaQVOo+K1oMvKJWeyNoo03rvf8/D2ytPzzOHuAAjTOPH+e+/hsVjxh6fHd3tpAR/ibc+25q2IKb4Vm0aqFafNloa+xK+tp8A6h9RtnVRBt0W6w7nQoU+MQOCsHISNwIAd+llB1aAq6/Aa0hpJsbiP7tpb+jSUGh16crenaZgodSg2rSGV2psHdZtZ1DsycVX7R9IcaNlE/91ax3Y8aCfNvPu12aasmtO22ydsz7zpuEQRV3HFOu68JloM3L9/4nR3ROpAGAecmHv2fjfgtRfKGHj45GNyLSDCXPpeczSW2f4YOYwDXz58wDVn1pS4nJ+JkhmngZcvTxbuWDM5J4boGRGkwcN15uH5Qi4F1StDbHgP0xQBx243cNhP5Afh4Rx5nlfmZaWVmbw6jvs79ruRXDyXc2MaA91GkUvKPM0r13WlrE84PKMT9seBUiH4xmFUjupZwshlMLPeSTKDEybvGV3DayHlzIt9YPAjQTwPzzsuS+KyJnS1jCgKDOosYt5V6G4ZfggQHSXPeNc4ngYWlPnhmY+eLuz8J5zu9rYH2w8kJiOr1EqYdjd6dc4FpCHBMcWjESGccl0Xa4LUdL1hmvDNUbNjOu1M4ySe1196xbJmlnnh7Scf82vffkNeF2JUXr448fLFiQ8+eM17L0+41sjriK8LT88rl5xIy4JFKpvJ9BBsVTCMgTBKZ4lWcktUCurM5b6JBVUGQIIy+MDhxY7KfT/8Z4oWxJsFlgsQpkCVQs4ZH20P6aeR66UyrytLyrghMPjBIPbBelWCI0QzRu7oKbmYnEGcJ06jIS2COZ3EgeCNJRywKKIoSktGKGrqmXZ3rB+/JZ9nBluL3+6/l3c73ntxx8vDC3CFWivXX6/kjO/56pd5cXcCGUk+QYC73cTpdEdKhVeXBVrmMA0c9xPl5T33uZJy4VoX9ub+w7I0VKKpv68XjsHGcBXHUoTVNbJrHKSRCuSqzMmmhOgd97uIuEZTM/pda2Fw5gguLqA+2hVQK4egRKc4CjkrVZQWlH0Qns8z58uCjxb8KM7fnOGzNvLzky3p6ZTkYQL6crdfAw5Farmd1TdyhttYd7XTrVtnNjlLdOg+ZRtLy41G1HA0E22DTbL6TkysFevM6BOUboEy3T1d+wasTzybR6BGc56nNAjKJiMoTrvr/VYcu9uBc9wEmLn/YrfV9cZYVECMOt8hYFy313LbBqi/J878B8Q5M2F2QG1crot5QvY8Ix9GnDh2+x3f99Xvsb0bA7vdnlRXMwrV2pORR47He56+/B7LfDWJheusMoX5ekFoROc4TSMfTntUlcfHTynLatTruwOH00tqK6xppq4Lu3FkGia+Oux4+/TAPF9Zr7M5dwRP8JGcV/vcneBe3fNBtkX/dZkJ0hl5r+4Yxh2lZM7Pj7RaGEJgGgb2hzsueeW8zDw/nnskhcdLZM3GdPMou1f94AqRa7oaNRtFXU+5dY7lPDN4xzhEDqcDX45fYUmZ8/XKcnkmN0fpJrHXuZiYWyujFw7TxOvX92iot53r6j/Dqlwq0xDs/uh2Wa0aFLU8XOyy8D392qRdnOdqivLmqKX1FAjbD5d2NmF4qYSHPml7YRgnvJhl2EdvHvj04YmUEsE7zteF2pR5TXzr4wfAJrnrdWZeZ5aUeL6mG+OuKiZidw7vA5fLDBjJqWUTcnc4wyYagSBGEhpjpO0nDsc7/AixKS1l2xupkZact9Thpo0qllx+GCdLx3DmUJJVabmQW6OoUGsjiMeXbT9utlil0tcLltYeg5gzfK04dVSv4KT7frp3Z0+/R11PP8Y55pStwVS1KW0YkTjQFM7zlVoy5+uvUzr8q1cvOJ0OlGKjr/rWo6J3DFUZdzu8VkvyFMHvBgZMwHtyGd8nj7EALphzej4yajcaDUIuQo0ODY5JG7k5Umlc5gSqBJTJO8JgcSiKJ1GRVvGtoT5sCSOINiZRoldC7EUjOIjOFvZ3K6c1oQppbdRiivSGLcpzWin5Bg4Sx83nzzovsOlJtPKOLw1bdEkzrK4XLu1Kd9dht9pxbcc7P7QO9UnnzGvfAfV9Wquus4tMG+Vad6dwRh/fXOddMwKFqqO1DlW2xpamTGcfNdd6uFxnIW5U9a67aqoUbbiu4nfiu5WUwShOzZXeiZg7Sd/lmUTF3aYsde1W9nzc3O6h1GLwkwgyFMbR9Th4z8uXrxAX8DKw259Y80IqK2VdGHcT0zRxd3rFcByZL2ee3j5QgwVminiu1wt5WdBaOBz3XUzrmE4jT58+4MSxvz9yun9F00ouC9enJ/a7if1+z4u71+xfnrheLzy/ecTvAnEYmMaJvM7GIMuF2AXLqrCmhbyaOep0d2Aa99SaCYNjmReGIXI47Hjv1QcsmpnTytuP31B7ArK4yJoyJWfqunLcTxx2e/b7PZd0Yb0uNpGKYxxGxAkxGKlkHCPHl0devXhNaY3LsvD4yScUHE0CQxyZ58K6FNJ6ZQiB3TTy4u6E3/WGpEKOvUlqMD+tRm53VpScD6hWUlUu18UITmKJ2ypGTLhes+1Z1RFcoOhG+XYsqRhUWEwwKV5603JhCCOtwdvzmSWt3SknsKyJnAvny7Xvjm2PpAqpJwgvq8X2bOSo1nKH8hzrktjQOTOvdTfIfZMzeLF7yNjODj8Ug++d3Fw0qppwWrwQXTDj4GCQaYiRMERiLoRUaM12pLX1abM0ghOemyOEro9s3Q2vX/+tNnQMRO2pCiposOZoGKw4oXqLZNlWFZusJ5XS7+EOEHU/WAWua6Kkhet8+dxn/xe6cN3tPXej8na+gCuspfDxeSGOE8FH6yglcF0Wrtcr3tui1YeIxsC8rEZBVQEsNG4aJp7SSlBHxMSUjogjMLeG95EhChIrozi0FvIy9zeyRxCIZ62ZXHN3STDa+24ItnsIjrthZNxPxNGgp/PzMz4qpzv73cp1ZT5f+dVvfotWUjfJNVJGLmb2OsmljzRK3MVuMmyRJK1bHIWAxb30U9zouDYA5tozxdjYiLUbCkPrIl4HyNAp+n3hb9ESBuHl7uVpESiNII4QHGtRvDNcXZxdaDbvCeTtuZVpDChYdo9LfYJqtLKSb5ow2Jw5iiprKn0PFrrJvt3EufvXWXcrjKNpflKuOE+3knI4yk1/gusJuyEwxGiwqBoMuNvZbkimifvTPeM4sRt3hLhj3L9AnZDSbBlNqpzPmcJAdQcYWjfQHRmGidOL7yOnhZISviUKZqOzu/s+XHhlUgDnuK4BCSMyHGlxIKtjTZ6nWXHD++zi+xDODGMgDpFp2uGjsC4L8+VM2FaWOCR6ci6UUsnraoJgbezihOxW22sNgXMV/Hhkt3tB2L00iFmNo6hqcSjL5czoDYqSOHA6HAnjlXmebSLr8TD7+5es86VT6CcY9uynPadx4uX7XzanmTiw398hYQ/Ok9YLNS20apBXpZov5LJyGoX9/sA07VhXI22kvDI/P4IaTX1PtkiYUnGtIRJJPUmg1EJa7eAPYaRuaIE6akqmmazgB9vZTWPk4Xzhsj6x5kIqmRcvj0RxuFSZs972hEZ7ap2EYc2l2R9tn4E1PZuMpKlyrbUnCBu8fpPd5dz3xva142Gg5MKb5ZmPP302VnIM3J/2N2ulsmbGKTLsRw6HHdJlAOfnMymviMDhMOKSNyu7WsmrZWEVKnNTYgioNtMQqusBkR7KgJfBJs9mzbCrnlRX9n4Ab/kGuWsdmxZaesvT2wceH5/IKaHNSD9xEHxsxmIOIyVXrteZp6fnz332f6EL17//n/4npkG4XAvzYJDeZcmmPRGjD8fcSH2fIK7iQ0BCRGJgKcUW+tWZ6zuKtErKuTMBXfe9M7B3KYVxGIghWsx8LdRSWNeF0ffobB9ocSCVSq6W4Nu5E4TuZehFmQLE7pyBKOclETEtzOF4YD4vXM8zH3/6gPblqyrdDd288UZvJcc5R5yDBRyq3lyq+zBvzCXsgLcSZq8p1XJz2xa3yRexbg6MZuwEH8zKZtN2eDHxaRChdNF2616Kptz3pPoOjgsi3BA5tecHazzHHndfmskToE+NrvUucYunwW7sEGzP02zn5mUjlUBu1ZIA1JJufTdxTSWD6A2uGQM0tAsuG9InI4fvDhAGMfo4Mk0jd6eTTShhYAwRmfaEMPSodsd6PpPWxJxXm2ixmB2pgoSIHwaOu2NPx1XS8zNrzjQao/fmVamNmipJtRsqW8RJXgrnWilv3hD8YB23d7YsF0HGwOjNzqzS8KmSa6GoMo0D6r0tyJ/MuQM1z0A/juRamC9X3pzPPbZdcEMww1t1ljysZpHUaLjVppqiZj9WWnfMSEYBFzGYznViz9s3D/zqtz4hxIFhmJBobLYYArvTPeNwJIQBvGM9P5LWmes88+mnD1wuV86XK8cxcLo7cjod2O0O+MFIVMt1IeeOeviB+xdH2890FptLFSeFUZTDHiPmqDDPKzlnUi2gBnt5L+yPR06nI7vDRBUP5yvqVkJ0vHhxJAaPpop7uuJDJISBGI0A1bRxXVKHrE0DRXeal2AWVd1QilpL93/01LV0uVij5S3uw0GtnE5Hc51IhetacE2puVp8jxo8Lr7hg2McI8O0I81X1mJkjkGwnVSwFYGIEGolttDhVMegRgICYTc4VDZClxJ8BOcpxVGpzGshgcGSaTW5jyjreu3GDsLdLjBfF4ubalusisHWl6eF6/NCron98WCSittK4Lt/fKEL17c/+hZjsHDca/SsDtYt9dj8SPDJOo1UinUNPV5boidXO7xatVEWVUterdb9ejHXis2GaC2FIUZiiIzTjlArWgtrTky+Q1c+wDCY6LJaDpT4nh9c7VATGlmq5S/1I2dOlYAxgNZ1Yb4kljkxz7PRYTsjULDpp7RKEyssThy5lU6t7aa8W9lSboXFsqC2fY8dPNALhfh3acZ8Vgzr8c3fBIXeNbw2pAlBfBeuNrQWqnOIVgrBRLod3xYRRLeJS7ujie0Rcyt2r27WNmqFNUr/XtVbjhOiiHqa2E6rdehjE8U2ZwfsZlhaqkE4pVUc3XXC2XMoeiOTKObuQWvdOd6KalUrnK1Z86O10tYE2ezDxAeInuVyYU0rS05oK7YniB5fpWfDNRZnEI4Tx9qLnGpDg7ETwZiYrdqOsDgI0UTZWjO5FZo0s8yK0r0llVITKlb8CA6y5XVZU9KQGO1ArRUtVpSbA696cweZr4tBRMET24CUTm4RSw43pxJFc+phnubEvzHX6pKMxu1M9jFEo1hrLiS9Ij4wxIFhFxm8J/rAkjO7XSIOE+KF5fzIMl95Ol/45NO3XLqBcBocyzpznS+8fP0e+8PBvP9E+wrUdjzDONq0T2+6pEDwxOg6dOpYcrOdXddwhh49UnGEGBmnkd1uxzSdWVKg1EpVo9HH4EE8w5otZcEL+120YqTNomq0Xzs9API7CpezG62WlQ3yKH3acijNVRqd7dqbRQGqd6ZnxHVDl9KLQmf3tWpnQW8iLFi09Guk74f7ntt5eZf75xxerQm1xHcBEarac7kOz5s1oqUF1N7s1W7yK66R8mJaSITB7VlTJiVDbnQD5J2YM0ex82+IAwFI0/S5z/4vdOF6enhgkAZ+4OqF5Czmu/lN4GrU8VbVdAhaaaX7FqqHitmalMLaeOciUU1ATHA0V5DgzSanKV4b5Gyeg/3iGjaVuRrTzokSK4SmxIARETpPvK+bEIGWC0aSqOyaOc6X1ni8LizFlOjeO3LtF6diJpudnBDBtE3NUZrvlk1b9qldOBvZwqxkTJ/VRKjOWecON12GoY6Gn7kuiFaBNlqCrnO2K9xcBRALt/Ta8H0Xp871CBkxP7jayD0xlQ2ac0ZlNK/DXkyjSRbAvs1vIkZHL5Bdqe97DEgzH7hs2mZbADsHLUNnjmmHR6MLtvB21lUOQ7g5dk99V2IBgEOfMiHSUAn4MDKNe6ubrSGlUJYLya2dBWa0YRHYTwOp2oE1hEDshV98MMPZXHCqVCxw0TkzfbXvc8jOc8LdkpilFlz0EB1HBpoM5kyg1fYMNEopZASvjlDBRU9ZlbpWiI3omjnA70b2qTcgXiwRXAQ3DdyNE0VswtpLJExm11VypQZbqLeU0OAMTmq2OBTtu0SUdTEIit3EoD1CXqCtlVKUVBr7nNEY0OBJlyvlvhKnHa4UljRzvc68efPMNSVUHbthwrnEsqzkUomnE+N+ZOcD+7uRpvteeKzRCN4xRUcTh99nYqscpoHWoGQlPc2wCBQHq3I4GDR8WQp4E81PY2AalN3oaS3wlJW1VVyDKI5hDGhwoMLxbme751xZarXPtzX2UzRowXVTaNd65BFoNIJT1fauodUGwbMmu/+rKFlXnCq5E63EO4vkqdmaPnMQtwYlrzyfHylpIedKw9jN2mrPCVOCmBtKDXb/Bdcd9L0VRNcskaKWZIiTs2bOmt4OZTbFS6avrhDUYpi6g88gR+bZdIu133viPdNhdwtnFTyjCKUnx3/ex3dVuH76p3+av/N3/g7/8T/+R3a7Hb/rd/0u/vJf/sv8xt/4G2/fsywLf+JP/An+1t/6W6zryo/8yI/w1/7aX+PDDz+8fc83vvENfvzHf5x/9s/+GcfjkR/7sR/jp3/6pwnhu6ujPgSOh5GmIwxQxNFctLiTzkQ7Db0fV4e6fmG0zeHB9yGrsuRs01cx3/IYhXEM5KZ2CAWbSEK/AqvC2Eduj5EzNhaRD5EtFjx0rQWYHUr05g4hwaxiRCD2fKmcEnldzX06b9FhjiUv5GqYfXTBwha9EKXbVykQxxsDzwe6nklwMphbBr1wNzWxpretU/C+C4kFcZulT8OH1rdQgsZgNyEQZDsmBN/pu46G10Qt2m8GY6Vt+H0tDY/vtGQhiE0VFtmwJdSC+Ura7xNc/8eAau7Cbwsk3Jz6aeYjuM1cIoJzFuhXu7+hGd8Kztvd1pCb7sucpSxO3nhz7xzGvWv2NRXojUdQxbfKkhIBMfsd2UyDzXuQ1XYf4mCItiMQH3Cbqa2ajRLBmqsojmkYicGbRZOTzfOKlhM9ptAOnZ4r5WkcpgltjbQspH4IDiEQRtPHzGEmDIFxGE03pEqOJgOQ6Dtl2+6D4HsnFQL7w44ohj4sa0LiSKnK0hr7/YklJc7nmdbd1b0LHHZ3XOeF1ir7/dT9ALsFWC3kBi3bQb8/jBz3O3KuQCVdnnl4+whiP+/p8mTkhq49fP1iotVGSYmnt29NanHYcTp4fBhRdX2KSsTgyGNgXZU5LSxp4RIC83Xlel349qfPPD0sptX0jujhMmd+9duPlG/8GnenHafT3li0TmjqeFozdY0MMbALoROMzGqJOprouVgqNLXhRTjtduaO4YTSzLhaZBPoRkoz0+R1LXYtOkVFWZbMOicu80xebIIUMIZi7lZQTs1sOQZEHW1RoJJHS7n2wREaZk7gBKLliMUQzcrLm5Da43A5GURegVSJwbPTiaKNMEx0AAYclHntGszAab/r7FvHp28+Jc8LdSmEOHB/d8dSlKb/C6g1xdEFcIHad2IWKfRf9/iuKsW/+Bf/gq9//ev8zt/5Oyml8Kf/9J/mh3/4h/mlX/olDocDAH/8j/9x/v7f//v87b/9t7m/v+cnfuIn+IN/8A/yr//1vwYsm+oP/IE/wJe+9CX+zb/5N3zzm9/kD//hP0yMkb/0l/7Sd/XijWGDQXSCeaEhuCA3JwEZfHeEcCiGFTtVzKtEOiitCKHvR/ofeefFpWKEi42abl25A286KsX1n+26FsjiNAxutCW9seBChw57oROPdE2E79ZSzikaI+4zhSu0YOm/zdhAm9npIO1Gc1c/dtacTWmqRr0SF3uRtqzW1npH78wux7uet4TvhQugEXxnEjqhyla4DCrEGYPSnL5BqPgWukO60FyPo3dWkLRpNwC1ahHoIZ9Kp4d1QXSPnFHA61a3FDRQm6MieHpaNdh+w5ybrDkRh2x0/D6VgnnS4ewgVWeTnOvsSFdXm+YQqvrecNiOzcgJnXWJw2tFGgQ1AaU66ebH9nMER6qFpq3vWE0gazC0uQm4xi0mR0RMMtEpxLXVfr0afKSOmyB0CBPaN4VO3TtI1G1u/MYQ894xjqO9p04Zu2VTaxURc98PYyCIUmqhlHQTp0owQkvojDFcNGiyNloxF5GoRgopuXZvTnvPg3dUt6W8bWQaLJ+sWKSFFQtzhncirKmQlpX5cmF/nJimgdevX3I5zyb4LZXjfryx87wIpRTWZWEMgcmPbDpGxUTGOWVKFVKuVhiKkrvGcX88QPVobYTgePnyRBxWznPi07ePzLPpPqfDwP54JAwDLkamMZrThNiawaasRPSx73s9zgV0LYgq+3FH7OdBadBcL/Ii1GoQXfAGWZtNm3ZnEEscTikb41g84xCZDnta6knctTAO5jsYpj1I95PELKCCd8Q4Qtl+putxONEaPqnEYbColDQg0s+9UBiHaNccioSRLQXWeU8de1qzCqf91H0whXkuXEug5RlLhDCvwg2+F2fSiGG03MPQ0ZoqAS//jQTI//Af/sPv+O+/+Tf/Jh988AG/+Iu/yO/5Pb+Hx8dH/sbf+Bv83M/9HL/v9/0+AH72Z3+W3/ybfzP/9t/+W37oh36If/SP/hG/9Eu/xD/5J/+EDz/8kN/2234bf+Ev/AX+5J/8k/zZP/tnGYbhv/j1iAg5VfwIxQkFR9eMWyfjhRxjj52wyQrpbtF936+tmW+hM/isONvB1OKozrDr6hrVVVR6rACAjzTZ5K6KVHcTMDas6xdVtGVEuiWNN0cPrw51AUK0gMKWCNheVkKPO49CaQ5tlcFPViilS3H70nMQ29khgcKI6haLACoBnN1QaMFpQbRQ8TT1XRlv0yXO4frF7+iOHhTDNiSA2kJcnWHc4j34HlvhjLYudK8qPBBubuwKuGA0ePoOgNbTuLygvCuKeivw7maWy1ZWosVqosOt36B1rz51PblVMU9y08SYM/C77CCLZfF932ZNgu9FqyFQexHtBdP1os52jrcVMP8+cWKwYxjtcOosR5cWnFZz4PaKBoeLnpZSh44cNEccR2PiWeiYESrmmWPsn4IqrWXWnIz4sjvQtgwZYMmzTTPFDjMvox2eDYYpEseBWhO7cUDEkdZKGCLeB4YxMgblOs+WIdYDGEMQdmOEZixY39/zUDDDYqG7YAxoXd75ca7rhhyTSzYndVVyVeI44nzBpcwUA9FbwzCMkev1yjJfWOcLL14cONzdsb97weObt6RlJi0Lh73HBSMKXLOxBktaSauyPwrO2y4RjIZfS6U0i7FPGaQ0RAKHw8h7X3lJvSy4WvHe8eGre67XhWmKtJa6O39ljJ7Xr+7Yn04sS7GdzLYqGAZyKSzraue6B+cdYZzI5yttzQxxtFsB8zu0pECD0TOGJiiO0VeDybuuzB0DQxhNc9aUaRi4v9vz3pc+gFKp68qyzExioubxcMc1Jaoq0UdCtH1fHCekFksjCAFp+Ra6mzGCmRfBlYr4aIVrXZmm3a0JQ4Lttvp1QBtQtRyv3WAErOA9Tw+NvAQW58yDtMuNfG+uvQjTFNjvR3bTwOCNCVyAIP/lZ/3//vFfteN6fHwE4NWrVwD84i/+Ijlnfv/v//237/lNv+k38X3f9338wi/8Aj/0Qz/EL/zCL/Bbf+tv/Q7o8Ed+5Ef48R//cf7Df/gP/Pbf/tv/Dz9nXVeLA+iPp6cnAN5eFva7gbE1cvFUta67AVvW1Jq7W0JtlGpx2iBo1g5XWQyFItSqpGTLcO8LqRXWVA2aE2E/uBvzTSTQ3Axq0JoLBtEECT2mwTBjh43jOI+GEdHcNRfC5ZoJXtiNntMudC1Vo7pCZUQRnFabPkRweGKIduhq7fa2vt895sOGNtstbdMG0g92YyxltfyitgmNwaa8alT3m3Wg6/BqU4pdjlaGupLYqcGVvnY4oxuXmgxs67xd32ttUSYWCVPaO0IFvfB4zM0A6J7A0skdrgskrSutYnvCPgZZAdMNDrZkY4ehe1tkiceiYkBR17O/nE111A7xopTmbk7rzjkTSxtbBHFqTiC9xFrgaCMG26HU3qSsa7YGYmgs56XvM5R5LtbYhMAQIvumxGjJw7vRBKTzvPL48NaMU0vjMAm5mjD8el2QOOFDYIrC06efcjk/8/j0ZNPENHJ33Nkua3dg2u2Y9gcszLNHf6RC1sz1eqGWhctl5uHpzPPlChKIZeT+rhiJo1Uj+0jfcQClNhOgO2dSAW+Qa3aCK4VcK/NauTsERJUlJ2PlhkB0wrCfaIjtQerMpw/PPD3PPM8rx3VhqHtiHNnd33P36iW7ITKKsUJTqewUjvuJMVrBN40UuKk3GCWjaSUlI9zUVklaOexGpt3A6xcvcPdC9IH9NBEEhuuV5gfYTUa6GkdidMRxh/ORNTWGYbBrPPXGsBRUPK12BxZx7MaJqIEai1kw9cnTsrP65OLAx27b5hzLnGzqxwyyx52yP1bGw87Aa+m7UnGMxz3T65d8z1e+QlkX1vnCt771CYKZIocxMg2ROAyM44hTNfg5ePKy9JNToSd/t9ZMGtOt1MTZNI4TI5+It7BHESQKtGCFSx27OJr5bhTu71ZcqwiV4ziydoJS6bqxVCqPjzPLU7ZcusuT6SjVWM2f9/G5C1drjT/2x/4Yv/t3/25+y2/5LQB89NFHDMPAixcvvuN7P/zwQz766KPb93y2aG1/v/3df+7x0z/90/y5P/fn/g9fj96zi4HDbmI87Wlj7IeDMWhsjJdbNIRqpvfa9EAuu0HTajTtql1zVTv7xnGZrWA6EY5TMOEsAEKTEfqhHgcbxX1nVQXprERyh7E8hIHQHdQRZXqecWIZRqfjvscUFAqBrMH2MSgi3YDWeUPvehERrOCqEyoOcR4Q2/2o9Fmli21VECK+websa9k59Oc2euuGzrnP2ESFza0Wc4h2XWziNqQVc5Sw2iSYH3XnFHVolU203N8LQVHBvOb6pPxOJGywmXYhtCd0ALDHKHADAbfy2J/7MyEyzg7V7fcLHVZW3C07ymlDfOQm/yfeiuEWqS59MoP+njTpX7f31XuTKDR1lGqHhapNJj56ci1oaQQR001525W1lCjaGHcTTu1aPu5HLq7iQyUW2I2OnRpcK+LxMRKHyG4McHcgRmMv1tJuwukgtrtszbRxtZirv7EkTVqQaoZabB9zPIIfQTw+mMxBokebo1QozhtyEMwiqrZG6OzOzZS4KqTsybURfeX+OOK9cHeA6nxHMBqn/c6MAJwjVyUVh/jBLJwOJ6ILXJ/OBl9GY34O+9GYwBiZ5bQ7sBsjoo11SZZkLMVeS+xhpE4paSSthVoyowtE9SzPMyEOuABVMs7Z/TbFyOuXrxjiYJlg2ARUFUroe2y1+J3WAB9gCrg29EieTsIZHM17tEZ8v9pMGmFwptPNOs2aPKZt3lf8YNE5Q1UkTPb1DvUFCQRvRUlwDDEgTNzf3+E3A2YvRG/hrZ5NmmDxN2MM/bYyjqj3vckbLDvLhPvdVNt1wpR0pq1rlNK6HrPv41x3xinVps0hMO2inYS10nK+kb4AVLRD5v2O3QzBN9jrczw+d+H6+te/zr//9/+ef/Wv/tXn/uH/pY8/9af+FD/1Uz91+++npye++tWvMg3m/Xba76gv7+AwMQ4jzoQoqNqH3LBdgVBvePkturc1tKx2k9dGLtkYXx0CeHo+W40Tx2k/4ZtpVGoTmh8NTgJ2ky3BRYQ4mFbF9Etm6IszS5Qh2g5Dyewen1GMin93OiG1QimUJiQVqroeHdEjtdVRelSHE0dQ37t9KJ1e7vqk5Go/vNTSjAW78Frp+UXNIj22w91+RSvK2vdFbFNS6D6Bys0Y0wEitYt4BQ3yHW4Xtdlrv9W8bv/SnE2BzdmNLd7dntv1wqxgOWj9Jzln74X2aafptqPqGxXtf6O9GNJ/H2/TgdVD+/dNTSB9kxc43/eegPNGsOgkCmf4oLE0nUVO2FDby2jf74j0HWrf4zTth100jVlrQJQeJCm42qjrglaLMCkoQZRx8ORqcJ5GGL1pccQJc6633VgMgXA6MO0i03HPOmdqzlAyQSKoUEpDl4THbMm0JVJupt1KCV8TcYicjgdkdO9se8i24/JmRit9vyfO3T47RyM6Y9+LU7SnIQex1IXjbmQcIt6P5CY3W6Xd4Ji603yuQsUzTXuEwhAGalWuzxebZKIntowbLd3XdSxy9IHRR6JRZS17C0fse7YYrPlY1sISLQl88gOD88zPV+LYaLEguTCEjKppJ+/GAzF4BvFmmJxK1w86tNg957UZAcMFCANRByQYiLOuayf92B7Mb4dzK2QJhlK0CmIu9q01iAG/FS7MiikqyBiNwYpRzrVBENMarvNM9GYycHd3RNZCLlaADV0RKIqLYkGvtTGE/v71nSxdXtKk33MYAakWywNTv13zhVYtZHKIre/yzOi39Ekyl4RiBaxlY0ZrMdbi1rNujE3fmwCT0FRqzZ+7JnyuwvUTP/ET/PzP/zz/8l/+S773e7/39vUvfelLpJR4eHj4jqnrW9/6Fl/60pdu3/Pv/t2/+47n+9a3vnX7u//cYxxHWzj/7x6n+yOnuyPIRIwjfhjZjRO5KLlmUk6dduzMuFLEDibV28GHc5QCaBfANjVRnqMv2btIUYTROxPmYTZ71fpA6y4wwkfThjSPb4qXnnXTXR/qmhiIqGusdaUU8+VwCm3pC+mczc9NjdEWPdSSbCFcqyWz9u690tXwrVGbN2+xPlnkUrsGyUSf6kznlHOmVkdr0g9+m1Ryq0bUcDapamdD5lrJZ3dbxDsfqNr1GMFbVycOjY5cs7nHq7E6N41ULt2rDbFD2duhU1tBW7decg7nI1UrVS1mxneYC2nUasxC6sYatd4j5dxvfIf38WZJVVqhFCOf+GiX+RZhsVyxaBnMz61opTRlTaE3g43SMq2VXqDAO4/rRsS1C8+1KYdxoKpQm6UNX/vNGIEghdLM0UN1IIq3PWqpJoIthfP/mqjr0qd8JQSjaKdU8a0QQzChcwOZBnOfb5VXrw7s9zt2xyPzksnzTJ4vPPBoLLxmoljf7PDDtz61GpR+GOB43HO6v2MpHrrGTK9PNg03NXaYDDgcUS35IJVKLpVdFAZxBKfUYgnLqTQysJxGduNInHZU9Z3eH8nPKylapldRwceJ++OOwzTY+79aau+Szdz4khd2LuPjgIrn8Vq5PnxC9DD6SKnWecVpZBwEXCNTqVkJNPZDJIyeu9OBEAKPTzM5mTbymlfuToFpGhmnPW+vV65poaaFNdcuFhfm6ol0jSXYddU8WQcGionHBdZlRl3AOSHSJStqOrnsI04V3zI+DGyhnc0Fomv23JtCX8x/subFJqQYiRFUK3me+dVvfcR+F5l2A/vjyRqy2kjnZ2rteINAaWIyFaqhA2LN56oBqSvaTKZAh9RbC8SaKVrJmIFzVkdRcCWTc4dlUUYXiQ6iKHMyJ5GcC9/z+n1KhWGc2CQz2pR8razZgliLKg/nJ1ytLNf/RpZPqspP/uRP8nf/7t/ln//zf84P/MAPfMff/47f8TuIMfJP/+k/5Ud/9EcB+E//6T/xjW98g6997WsAfO1rX+Mv/sW/yLe//W0++OADAP7xP/7H3N3d8YM/+IPf1Yvf3b8gjIElJer8BO3MVYTQPcRaU+a0ssVRR9/5nc4o0q27vkrPj7EOu4IPPWXX2G+21G+sxeIgnHagTjfBbKPVjUotrFrIfXEtrnTWniO1htMRh5LKSs3mxp4UlvlsFNbaUIRMhwqdefoZsUFvJrJgWiMrPdBcpFWD2jbHCbNvciTp7IJq2HZVoVjwz7uJqE8VJtBp+A02RKH5ji4qWjP0n7uWtjHWMZv3aoQQMWjVenV7j9QpBev4vduAv4p2VqF187Xvk5wt2d3mpLEFUbqO6rXbLso6O/u/qopmExtTCyFEkx44s4mqxUSaS9rYo0qom32vYymhux80aBnhnUisQvdcrAQqpdg+MufFpkG1+6OITYFFG7omau3yC1/JYtNfK8WEvKWwpJXlutCq0cNpjlxMGzRFz2GMFlZZlZxs11dT4XydGKeB3W6yyRxzut/5QMkmnVjXFa3WuY8TpGLXivMOHSdyzVwuFz5+WHCxC5tzwlXT3aXaaM4W6MHZbvKWdluwacEpg6vgLFByyZXH68plDYTrjPO+O+SPlJpYE0QBEFrc4UJkWawLaaXaga3rTR/19vFqZCAnLOrJYjlmufspuiBkIuvac/SaEZDWogZ1otQ3F7QqD+dkDE9AS+VyLcb+DJE1KyWZK//1spig2AkujuaGrg1ppr+q6qh40x124te8rh2eNj1irZtUxcxvHdpTBGQDMhAficZzMh1Vj2hRGdCa7XpNlWEIDOPQ8/wSbx+6C8w4IsMAquRlZj6v1NoJIa3vZVF6CCAbCUtLppVKTkqczC6+FEW02iSE3ddNbP/rVOmmKwD4ZkYJUwy0avdWyoX7w872Wk5vE/zGOk7zA+v1gbbOMF9YlyvPbz/9rs77zz6+q8L19a9/nZ/7uZ/j7/29v8fpdLrtpO7v79ntdtzf3/NH/+gf5ad+6qd49eoVd3d3/ORP/iRf+9rX+KEf+iEAfviHf5gf/MEf5A/9oT/EX/krf4WPPvqIP/Nn/gxf//rX/7NT1f+vR5wmnIeSZnKaLXNHGzvZKONCcpla7cAaghUXJx4XA7XYPiu4avoZgI3+rq1jsO22d8mlUIuayau8M+xEK1qlkzh8n2GMERdcNnqoc6yt4cUSf0tOFsmumGtzzn2MU7wEkrNeaSue1vUL+L40b8pIu02DtdsGNTWNiCF9RiGvvWhRzQqqqFCwwv3Olgk2ynjDEWXD2IWqnd+nBpP5vopqWL6VqkKtPcbA2IUqGzinRNnQfotMMZDI3PObaE+xtffee9uplZ7xpShSbUfkRKhYs7EJzEPfP6kzH0PXcie0bMGUdnjM82LFplTmbHuB1hq+tP7ZC0vrfo80HIWh65yas5RqtOK0MokZ8tZaWfrrRB1eHC1G+/RrpfTCoepwQSidPdnKSulFNJfMmldqqTgtpLXejF/dfmQQs2haUmHpE/C6ZK5zZBgC4xhxPjLGyH6KSIikNbOkYs1RsWshRM+6GPwaRwE3oFpIeeXh4cnE9EHMKLlY/LN52tnOzIti1JRtosXuDZRjrIzBsszUFebccFlwZIYYjSnsahf+G2kpbEauPuCckX1cs4YDzTaplML5uhjRylmUkHOtE3nE9HHRZBYl5974FVSCXeOtN3HXTFkr16xM+8mQl9pIy2z6wN4Q5VxIqXA5L1a0cYy7CU+HjbvzTesb5ujt39UGSzL7JhEYvFCrNTaoGvvYGd/WDv++u+27cHEOrZUQfWfsmu4urYnr88K4GznsRo77CecgJzvTVAKH0w7xjrRmro9Xu3b01uYZMqE9CbwLqFPKlFzJi7I/mvdhrea407Ta2eVMnGzEKjM8psP1rhSi9xymEdeUuV9v13WmKSwt28932868sVyeuD4/cH1+YD0/sszPnB//GxWuv/7X/zoAv/f3/t7v+PrP/uzP8kf+yB8B4K/+1b+KiPCjP/qj3yFA3h7ee37+53+eH//xH+drX/sah8OBH/uxH+PP//k//12/+LUpLQiNQNPaFd1GOTd9k3lI2AWZKcGKiPcBwRlU1woqFTBvQxd7QCL24dfSnda9g1JpqUBVnAwUvHWgtZDE1OnBR1xXraMVz2riPwmUsDHWlFISNYOK0LztAWrKaC54P7FizhmtFERsvxH9gPrOkCyFKpUYIsFHVreQUqJ0O5shmiVRE29fqw1KxUskNUdqpnmhL4FjiCDSmVqN6IxNKT6QVG4HLTSiNy9IvO92TIpHGeNIU2UtiWVJONcQUcYQzVLGmTO1JxMEpmEgqYXt5WrU4BgDMQy2gjQqIhEY44TzgUJhTeaOIaKMPlpisUDOiqcQnDINkafzlVyKpekmm6q9BJJ2Q+FSkdbsM3OBVYNlOdHwvrILwbrgADlbMRMqcYid6dij07uX5BhGCL1HyA3E4SUizrM2Z64tpZLSQk6dQRqMdSgCozjSaoekxzEFk0Nc5syvffuKbROsKJqZsWcabO4eh8BuihxESKmZwDdg71+El6anAADW/0lEQVRw+BD59O2Cc47704h7WRGx3VqaV665sNbCMi94lCCOKYxU3+UK2iw8UzyDt/2tdktxXzJ+NIFrjD10sSq1JLwKmYpKoa0rQiU4OE0T2RWysaKgVQQYEFroPndNSa10tqzjcZ1v5tGH4BE3oAI1VN6+faakZOSmFmhOKOo4XxbmS0YrvH554vTigARv/nmPz6RcSdmiWZaUWda+k94SEuJsBaujM55N0G67KmVzgOemowvO9KqG6GBic2ydsDU52vfSXeKPwwg90ptqWmVdM5fzwv1pz343sptMP7cms94KITJOlg79dJ5ppfUkb3O5B/p90jp93Ux/H89X5tUY0+/fn5jGgWmIZgvGuxgjJ0bESsVc9FvfA1NgF22fqaXycFl5uK588PJMLo2H82KGw62hFeY5861fe8s0fQONgetyBoW0pO/6zN8e3zVU+P/vMU0TP/MzP8PP/MzP/J9+z/d///fzD/7BP/hufvR/9vHLv/JNDruBoApjQILgWyO7fmHUhIvRDqncSFFYMWFe3Adj2DhzdHh6vpoNSxS+9PpkFPFSeP70GRcjfohMTqGoJWrXbCGKzSAKjZAxl+RxqgSnBKfEwVOuhaUmUlPGF0eCd2iuPDzNNCe4IRJpltRaHHPNVG80V2mQHFS1iO5gMbFQK2H0LKWQy4WlOgZvUNRaE2/TxTKVxpHoQLslTc6gcYA4mAtBNm+zNZk5cfCewQteq+WT5UKVaEQTJyxlMfwewQ8jY+8ajaxRaJijfl5Nf6Q0Wr0So1m8SFOCt6JeUmFVc9BorZn+DRM2D4fJfO1E7LBwF5wLuGGCHh+fciKtmRgsJn4QxxBsenysavHrudghULI5VyDE3YiJC/oh0ux117CzQxRjcl41E7xjGD3SLXGcazzWZNOJWBBn7DDlsqy4Lr/QlBlCMD2TVggjroLUilSlJttBBj/waieMAQ6jsAsrzQVcHHnv5Z6ntw88vHlkTtWsx5zgUfZDYIyBYQhcmxBjYD9GXh9HHp4WcjXz4Ltd5LiLvPdqZJ3fUnHsT0devHjJOBg0OkRv3ppqjlnjGAheyKqsybQ8rimDKqsrOFZYncVyqJKl8MRi7MVdZOyWKRXHp8sF1SvgcKUyiCOK8BhWdBhREVwu0EkOwTn8YG4eY5c+1NqoFcYYqFjTcUkmqNeuJ3z7bFCwaGMtBncVhXVpPF8ypSgtJHuvvOAU8lJZU+WyVoqrPUEb4mBMXbolF86K0X4UUu5QoXhcLTeofM3NhPEdrRmjafvWtVokOIZWmP+kFbvBib2HKEOwplHF46bRYE9nU+k4DozTyLCbEF+IMZg3YVHUhe4EFIiD3nbUEgTXP7f99M6jdGkKTnp6guDHgBs81ftN3o4D5qVSKFSFZV77hsUhrjL6gJNA9J737kZe7AOXZeD//r3v862PHzk/XW9WUU6VqoVvf/oR02ng7svv0/RCTivPT79O87g+fXhkmQdjN43R6MDa8E76fqchw4Rr3bfLe9uDOEdIlVEs1HHwyvl5NueC6CmHEeeglMz1skCohFhNUFoxXLdgB5KC10Lp0R9m5ZIZxJaXOcCyZltcV2XAWExLXnnz9mrwUYxMAlqNtp8LtO7OHCiUblW1JhvRhWbJq9H8zdZUWKrjMAaidyw5cV0KFSGMkz13s8lmTQ0XR9wwEVHWki1Yc14I3i7GKXpcd8mf10yVwBgCQ/DMaen7M4ePA7vBum+cMxmycxQnaMk3t/qaq+k+vCegBG9L63VJpGYEDlEjiBi93bE77RmDFa7NWNg5jxv25qnWC9f1uljhitEIA8GIHikXLrPtmDyNVEvPDnKM+71NLI4b7bupo8UMPVPMSaMWi4Efx0DskGcUc2KQaO7zQQuN1j+fBptZc84MQ7S07AYtJmtwaqGUK/Ni+80JR4jmTjCEwP3Rma4o7thNI8/umVTs+QdCF3aKOahwYxYbAcl7DvuB65JtCsXysXZD5LibGKJ5JvoQuruBQVpGIhRCwzp+ZxPOkivXXGgN+3y09N2xQcpdas7qupWYg3gYOAwB7x1VhOdrphaDtqU0BumFwwkaF5teSp8O+144DDAFzz6aWLwUi9QJ+9HiOXJlLpUpVoZpYNzvOBx2tGJi+3qx+2iLxKlNyaVyWRKKMY2nbjQLpk8rfbcag2eagmkdEbPncraX208B1WwZduKRDqe7LgkIPTXBNxiiie5rUdR76NBZDB16bzb9ezXnnN0QyEVtzzxEfLMGRVolhs3OzAqO+IBTYdFKdYKI2UDFLs0Q6c41zeC/adCb1tCpTYbQ05a7y5Dl2Znn6va+0CHUUmz/tn05dPeTEDz7yTP4gf0QeHl/5PHp+o6QjIJCqZXr9cq8rqiD/f5I8sJ8nT/32f+FLlzf/vSN7WA6LEOnjkcfbMclDj/sjJrc9T91Y/g56db/MHoPrdj3x8iLvam711x4Ol+paqK8SENrn7iaUui6ih7jXlol9fC3KNZJrevKmk2Qp3jOjybcPC+Jh3OiKBbEJ6Yjq7UnvjprQQdpNLEinEsliL9Ra51CysUONoS7KTJ4YS6FJZlA2flANKC5O6nbc6v4m1uHPXc2bgXYvgsLsluSec2NMTAEobRmQtTeoUdxNxq+ONeTX7tg+XYDvHOhEFV8B/tzMfdtB0atvWnkIL4dOv5vjYJ2t/rmYl/u2+eYcyfWYI7yXuwwX3MhdXLX6LuBMHY4+WHsuWImTLZ9F2QNbG4dilJK6QXBGKVj9EyDdaYqthcLWoyJ1RrLWijai2wzc1vFdiAJZx0wdjiYtEDYH3a82AVenkZCOPH+yyNNBlKLLJeV5/PK+WrwlRc63GONSMqNpSgJYYyWe3bcRa5j4BI9a25oh4794AmxyyFqIy+ZVZXrkkwHJMI4ePaHgZYLy5p587hwSdZ8BKdwtukn5S7C7zBXdPZ6wBGmwGlnE1sTx/mayEUtAFWbTVR0Jxs1hqN0fVPDik0MjiEI++Fd1I44x24/kTqz8ZobX/3gxIfv3/OV7/uQH/jKC7Rm0jLzy7/2zPMlc5kzV1fJi8fVxvUyo1XRKbK7m7jbDYiD85yI3TVkPwX2p4HrXElF8XHC1UQU5Th5ciq4ZlZeo0S833w2k2ntxBMpxgRsimuQQ6eRq+0XtbleuCKBShQ4TCZ2rghszeDgKEFZV4P5y5rxvjIGY8u2XCniCE7YDyNjMHKY4m0aa0YFDM4if6jmGXoYA26ySbtUOsu10FxhCFa891Fo3gp4TSvD0LPF8ByHgeMU2e08ThrTzjxjx+PBtGZebu4+raqFhaaKE+H0+p7f8NX/jryc+fY3f+Vzn/1f6ML1fF1MWFnNuQ3sULS9T2CIgTp3OxqnFJF3OqHaEGdMw/vjwfz1VJG14nYncsk8XJ44rxa6ppuep1PJnXiL7sAYV4pNNCm/o1BLZxI9zZlcGmNU/P6IiOP6/IbntVHUXvsSuCnTnRPb09FYxQxWmzZSaYiaHQ+qDD6wVJj7QXJ/PzFMkfn5TBXrUk1nZG1hwyIatDljwrlGDJHBO1yNpDlZKKI4DtNkrg1LRqpyOEwc9yNFTZCYa4/D6Pi8Mao8W5SB7/owtn1hKbTSKFqZ+jSpHdaIwTMNgefLpae0msYkd1cBdeZKok6MEu/NXku8MDBSU6asiblWItZBXs0+hSEG7u4PnC9XS6dtipTcO2U16JROx6jJmgKssORS+n9jRasNrNhNWTv0OHrHvK7m4L+J2vtN67pjSIMbYUbE4TtZwthnlSYRGQamw5HhxXu0otTrykdPz1zXTHOew92R0zQQveuGrp2kQ89MGwJ+F/HDjv1JuNfI0+OZoo21GlPWjyPBV1yAN/NsxTo1xsPOEmoRmmucLwu1Kn4MRCk3hlypvRkKRr6lbw6C91yKUqoyaOXVYUTEcZ4L86rvkrxbI3XzaejUDtVOCuBmQlySUryjrnJDTxR4vLxjc6rC4XsmPnj5gh/43g847AK+5+Yd7z/hfF24LAsPD4+c31woufHig/e46zCoA16/2LPmytunhSUXm2acY14z56WSihGRUHOvOA2ecT+YuN/ZVCld73d62WUxamSIyQMoc2lU986tIuBwwRIDQu0+kzTuJs9SbeLy0ezApCkuF/xu17lLjWVdGcViaR6mlThNTLsdr957QZ4zKRXmNZsHZytoK0RNzGtmrQ0XA9M0EIPp4eZiDWrNGUdlioF9jCRVwrhHfOQ6X82mrjnWBqfdwOm449V797jrE5fzhfN5IV+eoCWC7076YunoMcDxIJx2wsnBkUqJA+v+/nOf/V/owiVebh2/9mIRvTBOIzFGhjh0VlQ/AIcuRlallmIL8eh5+eKO87XHc6vy6r3XlFbQ4PnWw2Ljszfqb63dMRzfk4XBSzMNSS7gvbHmBII4RnGstYGrnPYTX/meL1m2jxeu5RNSKeAbYzApYlPrmJraeBQE4uiprXG+JqRmm1DEcb/f8/C80M4LIXg+eP8l93d7preRb396JleLvxgG39mCgsRohrVqHfxuGvFBuC6ZRS7QKuMQ+OD1K9ZUefM0U1Fe3J94cX+iOWVZbSd2vZ6h1ZuY1167FarYd0A4SKXS1pWWjS12d9iBE87XleA9u93A6bBjfI7MS2ZNlXF6Z9jqkF7orFMdgu104hDNtWGeLSYjZ/bjgPcefV6I3nM67vmB7/8Kb96+5el85e3jlWFwmwE7KXFbjxut3ITcIXrmeaWVgmuVaQzc3R25vzsZDJsyThv3xx3ny5l5WXk6L0yTTT5mjhtYs0W551oYo2eMgRj6++8dd/uBly/23L84cDztaWr7MjpiEIL9Gy+BafIEgSINSreZUiMLjGNgtxsYhoFxgn2xvaa4RowWv+K9mZDFGDp5yVhnIcbuIG/XVQxCG4IV2+j6RFqpVW6fiO/Nk6hyCJZivebGfvB8+PpAGDzTOfHRx1eKVNBmUfPd3b8Tte3e7M1B65Be0MrgHdMgjHFgXi2vDlWmrlUcg+flceK0n9hNg92jYu9ZDJ7dLhJG4cWLI+f7J0oqnF69x91+tGmwKi/vdxY6GZ4tHaJaYZyXZCQqcURvnW4Q8KFH4qgZSXtvcJ5xGcQ0hBh1fOiFqwU1e6gOsUo1302JkVgrrZplXByMoGXwpDCMRiALQ2A8noBGqxmlEtFOKnJMY+SwH7m/OzDLgnOG8IwxmDtOc/hu0N2cY5gCu50Z3g4ScbkHqlZBW2EMnilG2lpsbzyOHI8DXoVSladL4nQYOZ323N+fqN7OPXdZbFdcixG27BLu0yg9hiiR5zPX6wOtE74+7+MLXbh248BuDLTcEG8ZWmM0+6Q4jPg40ly86Sn80SIazApnJQRhP0W+/N5LfuWbb0hrYnDKV7//q+Aa9y+O/PJHD0iAYbQLqhVPa0Jp/pbk653igwnx5jnjXbnBHafozRFgTnz5vTt+8Df/D+z2Iy9eHXi6JJa0EiJMY+yFy5Or7zlVljF0vBvJtfLJJ2dcXuyCnSLf894rfvWjR3CPHHaR/9tv+AoffvCSjz89wv/7myxr4nQ3MI4DiiOrQW19i0Pwwt1phw+BN28vXIPHaeV02vPf/8D3c50Lv/LRAy463n//Ne+99xoZhKenhct1YV6f0JyNHdeUebXO2DkYhs72c8r5nCjzmZosPPHD1/egjm99/Gw+jac9r17f8/B05O3jhaenK6e7yGboJM7TMGNggGmwrnG/n1iSMj8/c316pKTEy7sDQ4y4j5447AY+eP8V/8//8f/BRx9/k1/76BP+X//zNzmdvEE8wJocJgE1oWzwjmHwnO52vH17pqwJVxb208CXv/QeX/nKh3z86UJLKxHle7/yHo+Pn/D24Ylv/MqnvPd6IoYNBRh5+7zwydsLa07cHyfuDgOHyfPm2TQ3L44DH7x35MXdnpcvjnz6sJhHojh2+9Fy31qj+sgUDfL2ruEDxjqtljG2myKnw8S4i+zUZBBjFLRlhmjFyHuDvHfTgAZPc838Gl3ENTOXdgq7wXal0QvJVaOUl9KtyqSLbE2/KKrciRW9y1x4cYr8Dz/wPvvDwJvzQk4fkVIxUm7JhDgQoom2TbJioo1BbM9ymTOxJqbgOEye+9ORb79d+PaDNVD3k+c4BV6dRr706sDdccKJkNYrOGNnruuC83A4Dnz/9/8Al8c3rNcFlSOnV/fEEKAo+53n8e0Tz+eCGwLX62yxQktizQaF7+JokJ/brm0xlmNPRvDOmIS1mSnA5j3onLmFBhwuWmFvteBEccHjetPdnPlxdjAIt02xQYgiRBc4HPamayseuS641gNBm+LF3Rq5FMx5JufMYfS2/6wgFCQ5pJi93DjY1OmhW9N5ZBTqalC0OYEUJBaGUXn14h4fAilVlvLIOA1Mu5FpmqiaiZfZJCXaKCWT1/QdRL6mSl4y8+XK08PHxL3gZOJy+XVauJxMeD8yhkoOI0WEVgv4e6JE8+w6vUeplZJWYthT3Yr6zPjyPR6eHnkqjjG+5uKvtOgIo+DuXlh3XzzudId6ocTB2IrDiDahloT4wVhjaSVOIzUkJCxMu6En4mam4wvWmFmWM3M4UKcXcDzhXzbi6SNqSsgQqOLARSDgcsLHAZxQ0kobTmjOqCvIfgJx1BiI7/8Acv1V9DHjDgfqdEce79CjoNOTHUine4rrzg7N9VTcAR8iNa1kOVJwPD59QiMwDBPs7qiHDyjM5DDT4o43KZIeE+PxxPk6sy6KxBO5LSY2DBHncmcFeubrFR8tybWVGRkn3BBZlwV3eg8Rz7g6EMcqgcelors7ZMWIM6eXlFJoVc1LLxfTm407Ss0kF4my43z5lFpBpgOtedjdI9PEcCjoMLDKwCfnGX/6gHHxxMMT8XS8wUJOd5bgKsKwv6emmSF6Pvzyl9gfPkZbQYIwnx/ZffAV9l/+DXzvq7U3ScKHr0/M5zNPj0+Md/8rr770IeI9eV3Zv/gKb9685VsffRMnyhSUKcBhGnD/n1/ler1yuJuoDdairApLMT9N8QF8YHfc42M0Q+ZBzAFBjRziaqNdLRF3KYXzunBeFlJVnFdevdzz9Phk+8FgNmYiINGZh6IIOo5Ibd2r2TEdJ661d+pHY6VpMef6gPkAqsD+7g4tZjM1hIH05te4cOU4HfnqD/6PvP/+ax6fF/7jL/8j1sczqdkUTowU7ymtEHc7RDzLfOV0eInmwnn5Nrv9nleHyAcv9rz88CXX/+UjfuVpRV3j8GLiw5d7vvqle3ajx7Urbz/9iJIS03HP3etXnF4LPkam/Y73Pvwq773+HkqunM9XdtMIrXJ5ejKq/lp5fHhmzolaCqUq424H3vac96cdKRnKMUTz4GwiJlpeFyT0uJelctgHoheenxKhe3oOEijiusONwwfzCgwe9uNIWqygDaPR9y1x2YNaw4BzPD190slNpo2zBgLGAK4VSl7I1ye8gykqd3vP3TGyLCuXlIjepsdtYgSTvDTnyCl3m7rI0pRSMmtTnp4vnK8rj49PuHLl/sULvI/c7cTCV9PK/OZjfJuRfCW6RiuZWowB2qVq1sV6x/Fu4u5uz2l/x94HqjYu/Deiw/9f7iEWiOgQoxuLR3xFu/7ESUAl0jRYpIgfUMyjr3QhrqhjrYrKgERFosP5gdIs1dSF2P0THCIDTW0yUlGa890fL1KdRyXgfESGCS2NirK2fkHSxcBuoBIMzw7RPAO7JkqJKAE8qOtwjgtUFyxV2Ed87GwoMW1QQbqlTkBloLmBpTmaBDQYXRZ5Bw2hrU92m3dFX4C7HusdIj5OFOvTwEecRJBII5KakTtcGMwRv5mbA3ikT0RGDzNyiYrgfERv0Q6eoo7gPGGcUG0WJumiRdJ4e251PWCS/l70p24YJGnuH667J1jOGZIpKmS1n9kQcoVrsgC+giBhNJhHgrkoNBNRsn0+auF/a1ZKdbZT00aqjrXAUiriArmspJLZzwPny8z5upBUWLLBZ0W90d7FE8YduEzTQqqNoTZUHD4GdrsJj8WgFDUyTatG1BEJxMGSCNYMPpjJaY0RP4rp0LLtVcxpPpK7qB1xxhINZjGGGNSqzRbmpsPrmUi5e+c5ZdiJsVqbwzVFmuJ9ZejpAuYLCWHcoT6gPpCrCYJrg1xh2L9gf/ceSa6MhwNxKeTcTB4Qo01762rXlAjODzRvkSzOBUIUdocTL169Yro74McHg3IbZrgbAy56YwnjKEumYhNIa8o47Wm1kufEw9tPacn2q1ldP+gzb968Ie0Hnp+fmJeF1HdcQ4wgimJaNJRbuGgIAVJBnKU0dAzMxMA9Iih4m378bfdr7hO+3wutmMuIa8YK9d52lFvGnjgzy3WuR8OUgutkJ+88ITjaWmm5mpSjNvucugegeOkmvM48N2sl9QZQcMZ87A5CpXQjae9uQabmN2gSlaLlJnhelxUf+vvR98MGfbYeWmumzuKEuPmxmiAOFGIcGMaJMEzMuZlDyfOvUzq861YwYBEmzge8G8y92W+HrQfB3I/FgwaLxC5KM5fJnkk0mBNzVHCBUivLmropptKq0YjBd6eH2MMKARd6VpLgJODEBK2qxvirDdsBNTt0c7UFsB3OndAgVrTModmSVk2ruIVgmkGmiRRtqlnLFhHSJYzO0/peRSWYw7M6gkTLoNIG1VqhbZTvqwrLknLmlRfDYHCqOsvr8aHvJiKlqr3P6vqF7PpSHbYYFGPMhR6TYB6CNzouwZ5DIAxTn1wD3g+sbWUzAlU1N3CkR7BsC/227dPoB9nGIBWcs8DJ3A/9pkaKWNaKOOumxUdqg+g83g8YobvTdkujVYuMuc4L82q+hE0LLSvrmrkuC4OfzKYpz0RpPD685Xq5ck0Vf13wfedTrxfWlK24qvlH5lrwFBqKD94mgFq7jyZ4H7uPXMN3U171jWssmKSoMehAGAwijBm8cwxDF7lXunOCUah9HFAaTXxnfNrB5IOFREqMNBpaNmsoIRXb09RiuxQvjUAXsndDYh/MUb45T8prj8lxRiAKe+J4IhTHtNsTx5XaEsM4oHGgek/N1kCBERVaZ6PiPN4Hpv2R+/c+gL3DD4Md5M3uB7yYHZF4a75SRaN0+nkjjhPL5cJ6vfCxfJN8NVedsL+nDQM5JT59+JSyjpyfL+SUKKkRdiNjHHBBqc0BRvM31/1euKidNh8QL6zrwrqmW3y9FyHEgG+bYbVHmqJOCd6zpmx7djFXDRHpEGcvXFgRaWC2YKkgzn7+EBw+eMpizhdqBFho4MVTan9t0dixxuprlE3SIF2S0g2UU802ifX07Dx4iiqlGMXKHOCNWLMuCd8b4U3rpZ3Zu/2p1QJxhxj6d2yG5hbhE4cRGUau68oyL5yfnz/32f+FLlyjF3b98qdYkEYMHr9NCS7g1LKXQsAslZqADuSq+BpMQ9EC03RPpHAKhbAU3HWBx5kxBVoTand9Fkz/W+rGwFcDpksxJb0KU/PUahoOtyRCc0Q8oYJer6hW/POVKYMUDyEQm+/ToHV6rmf4KMqu2OI4A2OfVmIcGdfEVCs7J5zEI8uCPj8xPp855Uapjqk4RrF5Z6itd2itR5I0dmlFnGPBOve9CB9OE5ITtWSOYgfK1BpDzrhsIk1ULY5e7eLMW+fZAyDVO6Dg8mYCbP5pAcdYCtE5IpimDhhbg3lFlkzMjWGxHDPFsWDeeZv7AECssEsrzroC2w2Mgzk8lsQhmLPB0Crt+YmYR8Z5Zl8bupqf3C44zF7IGgtYUYqxwy6PuPliIlNVpCZkWfDzzG434JqScuHtr/0qeV6gVI4EfBJz59ZCLW+p80Kbr5zGYLEOIRBz41gtR+04HFguZ6SCVGXopAAEUveEEyfso8d5bKlNRZxniMLpIAzOmIrLeWYyYxezSJqmnuLtqMkxSiR0uUBeEsMoDN5CMVvwqDczrlTMHHhNBa8ebzYmSPO3rDDWwSaFBnVZiC4w+AHNgfPDzHl/oc2JuEZCHeyzE0MoWi7mKdmNhdGKrDMtFVLOwMTp9JLv/b7/jk8fv8noI1OMjDuP87ar9fHQaeUBhp0RcppnvRRCWShzMYHx8sDj2yfWNXM8PjN+9SvEIXB/PBEKTFJ5uT/y8XpFNOI19tm70K0DMSsye68Og02v0RlqM+fCMhu8LL6Y04prFuPhvE3Ss1l/1aqk3AjedmaW56WdfesJYWPlmsg6p8z1asSrcXRIjITmWF1PAx+cYb90V3g1t5vdbkBcRlaHE6WqEIfAMERCnECbhZAmEzcPw4AfI6Ob8N4alUOB+WqU23HY3ZigwUOMDh8AZyJoQzQFqjWZtRctu1/tIV4ZxsDxeGT+5iN5vnBd/xuZ7P5f72Fu5a0Ug6ua3pzYzY2h4KMZTIo2QpCb4e42ius20qOmjUmJ6+XC5XLhfFlYc6G5rohnM1NtxjYLfaOKCf/UKVpNE1H6CN02yygsqmOdF5wqy7za93TlPNDNe7HX7c15Hmc3gHbfr1q6i7vVB1Dr1LRW8ppYZ/OnU3t7TCPTNi4YPcLdFsytBzC6btJKt7VxGMTQau3fb2OZtm6+WU2IOojr0OM7AfFmV1TpS18czS23HCKDPeiUc/t+muVEWeZSd+FQuumxdaG1WWPSeQCmx3ImsmzdXkduolxFu87NtS4YFmeddc1EL0hPAjBv4L7oDl1rpT3XuhMWttQArWbZVEu+vT/LZbavVyMXtJT7dJDx6slpJS8LlxqYRnMZmefV4CPx1pH2rlu7B2OMgRAj5bLc4K8QgiVJa+vwkRUnO1D9jYGpjpsfp9Hze3dcGyEYhBOCY10SIpkaOkLQbHLOebXilBp5SVTp4lktODfY7iVnzucLQRpeS09Q8IhYLMzj27cM4nh+vvLw9MR1XszXs4buXmKfz2aZRK0UaheLw7ImlmQms2lJaLXpc9xZvl4tjefnK0OweJN8cX0KFMLgCTH1610ZhuEGGa8Z1p5RNq8Fva4scyJX2O12Ns00ZUnZUtFVkRC63Rmsud7kB4jtMWs1MXaMgdCd/E2+6G4nt3k1WtK5ISbuFlHTWs9sd64jOTaxFdXbebMxaIfR0pedc13i424TFGpC380fcRzfITPiwdymA3EYyOtKq0buGaUbLDo7b9yNzRoYBu3evFucUNvU7nYXN0sKaOrwwVit4bE7dvRz1TmLvzHnnsS6XljKYjlq/vOXny904VLsw27d086KT09sVdMxuWY3eVPbVxks9C7kcHMW720PrcF8XbheV5YlW3RCMFsomv07+3Ft2w7Zwd07krrpXbpvYutwgIn3HMuy0LoLRusuHvKOsmTPT8+REumOBHIzGm1q5qc4d6NCi7WF5FxY1z5B3OAzd/s5bqMudWPP9q6aGIVbb9Kcmy2NQU597G/NNGXNDnNH7CbDvePqN6z0n7kVl+3t3X6nLWm1qRL6DFVtMdP3Yu7mF9noeqitoDl3y+Pamg9rVEC8uT5sbhJbfHgp1QpXtcPIgiRdb2D87dr5TG7zrYDahdahHLg1RNs1lHNG1PfPmp51pGhJfV9V0FrINAY/QPBmqIz9uu12ENjzqnYTaGeffa3NMNEg5pdpOt9uQGzTmQTp+WpmcyRdIrJky3Hzzj478WK7ws7g24TkTqA5+k4lddcJ88Mk9Cui1b6PsWt/SSvRNaJr7MRyxUIMiDieHx8RbTw+nrnMV8tsEst7auK6SbS5OmgDSqO4iraK7wbLudguak0rTZvBqjuTWORiadFuL2gR8pLp/kI4r4SQCYMd9iEYvJ1KQ1LlMid8Fi7XlXaZyalSFIMxVbvA2jKuNv18bdC0sWqxlHU2bZ7Zdmnfgzm3bbb6tatmS+yD4PvZ4cSmRtvr+ltBKF1O4npRBCsIpd//xqyxJHXcu5BX8TbVGSyut9im0GU1uABe++RtK5WmqRsQbzkPcoN6++2MSCAEK1yKnRPawaWN5aPNzJbV2b522o/WHPXbxvXiLWKQY82ZlFZyM6iczdj8czy+0IUrdxsjyIbZB9CaEe2h7QKj2M5CWyZ0IkHti8P9MJnrOB7nPTEMHP2Bp4eF85ypKRAIhDAQhoGSE76nxboYGYNpMFpVc6xGUOd7BxLtA2yVaX9kGM2W5pM3z4h4zove4IEwjeSczdLIW8z5FotOKfShxHZ3IjDYrmDVgMQ9u2NjGLwtPa8ruIgfdwDIbjQHCPEEb/s/o1Rj8QadVutiRGSEcSLjqViGUhgnht2eWgtrTuyGoftKQIvRzDRRXBjxGCUWGqQZxei+TRzVRTQIu51HpqOJibNDBmBbQkukuEIRTx2GblQKDFO3ubFFMrWAw8gvCoQBCdGmxi76dUEZx0gU04uoD2aBFSOH+5PBJSUTxp3tWDoO33I1dw/XCIN1o1otd2s/RaI4au0aF5m4vum2QAiNyhisUJV2xZeBURpuF6AVdrExBWf2YdXCTkvr6QS60YghV8dS4eHhYuexOKbDrruWFDKJthTTMY4jy1ZtBR5KQrBU24+fL7y4m9jHgNQCwfZA5wrZDTg/kEOktB4KGaEufXIBiFgIoSoUs7uSKLR9NIIBRuggePZ3e/wQiAhvPv2Y8+NbLtdk73FPyX2+PON8xInFtAyh4p2iVLQkQBkmKzj4xpyeeDOfyVLZHSN39yPX68J1TZS8JzhzqshqGqRcEus6U9uzUbankXN45uHtmXlOPLkz5+sFHzwAO2zqDdEssHLO1JJtZ6UG4V3nRMqGmqzOjG19pxpt2XG1GZPuOldygVw66Ukshbw1KEVQ9TQNCB51gUv1oJZSviao1ePx+BrIwFwyD9dGIVPFrN9ajaQWKVZBaH6kSOS6VubVip8IpEslZaHKSFZwTahFeJodaRVSFlaEQQVXHbootQi1qK04JNDEUIc5Qxgt4DSEiIZdNw5uND8jI4zjwP7uBcN07jtrbybM4pAYLMJnLaRVEY3QbHf2eR9f6MIlHZ5wGk382qecWu2w16boYtCUaENCJ1qoovHd+JuyOSbUVkEz0yCsKVHLClpoxVGc0lrtWK/Qaia3d+7O6s3JW2u21fvW+QTztKP1cLyL5QutabWdENovar0xJbQ1i1n/38j7syY5kmzPE/udo4uZu0cAyFru0s3pnuEMRUjKUITf/2PwhULKsDlzu++9tWUCiAh3M9Pt8OGoOWoeWW8pFSWQzEoEAhG2qB79r3PMaXoGgLo81jMRZ/b9PJbX2ji0uQpO/ZSBMAl1fEoHh+XMobXROzMPaqrK3OHf56Q/JvR6/7j7NR2dVr1t13DVkf+7QBy0eR0Vo9cySx3h2A4sJq/5DqeB20+qx14mN+gnnto6ff4a5tOcb9w+gNTq18cHNq8WcbgTz3mbo67ZYN8PinjdiPRBrb4ovb9/cDYmU2dKyTwZW69eR18Lj21DgJSDpz6Uwsf97ukXNqDXWczpf34vjdpmCHIrRPxUU2vDRqVGSJJmueRs17U2T394AkgblA5Hg3J4f5SGwMfjoI3uz+isZJEBzSo9jB9wtwwPrUWo90JQo/fEWCLbRBDGwEOVJ7x1/3hQJlm5H4WDROle7Eg3P7kOQ2j+PFVfRJ3Yn2rVCasevTo0GCP7eW+BVt20LsHN/IJP4chglIoFv6ejD16vKzkvxOXqCRIhICqUUud7BY+j8BNKzguXi/M2rRdKzdRmpBC8QFXVT1+zO+71upCXBYkB247J10IpBRveXrGueYoZ4HKJLMs8VfHXkBqsS/bBzWaahxvwHOY3r1sxw6H7uRakYc9Tkid9+H3rE6KXCa/HcKr/XNVXj4N6eOBv651Wx1Mlinke45MQkPhsF9YQicFDpM2EfZrqfbMdc1Muk24ZtNJoR6dPvysG+149HLsObFXf2HsAMWp1FCOIUooXn77cLhMR8ncxiYvWmgn3o/BoQm1OnfytH7/qjStofBKg3qkjJ+rDeea1Yc9omWcK+TA0/1Wrr0212hj0Udn3jdY7IsOTz9XVXMiZCzE8MWLyTjYXDC8nHO5Yn7yWK3miCyIETxCffT1nheEToDtBYTi/qm9Sc6EaAzQ6etzPYzsOUYiMibd3NLp6TM5LccJeNr+u2RNKdWmeQ5weEiDUuYj5NZmRMN1d8WD+TzO0qld140KM3mdCgrlhtbvGgNJcTOAI4YQ6TMAmL3RyfRO+e+L9J8Zoz/f7mUAAIE29Uyswg0KfONqTy3TO0CGu1tvcmJ0+9h6x+uzm0ukDHGIEcU9LCErQ9DzJObcFfTT/szbmJj8otZLUjdNeKf/jFzK7tLrQ8ZJGwYcBm9Mz4j/PCW217hCndFfrjdOUrrNhWgQjTGXpyXG53H2cnNxUuqokP61VH2b6vL+MQSv1qU4bY7aodbDu7b4D93M1c17PTljuDOTFF2mH0W2+es6ViOrkSbxQ8lT/2hwuz+zQ0br/uT4mz+KWCtXs5akEavNYLTGj1U4MHq4cU3Q4cYopQnVjbVTxTNKUsXmyX5IXMkqIlMOvcW/e/aXiMLZL3WcpZHQFnpnzOn2uLQKEGFh6fz73/hnqOYPTPmB2Ugyzry/wgxMU37icJTj7qScCJIpKJEqY65h4QocqmJ/5Wp/3uttM4HAuPmjEhqMzklww1vu8J6bEkBFxPi9KnHL8CVMGRaJ3BorMVBzkpL99TJ/vISdsaIaZ0oYhMRKXxZGKc80xFz+NPqiluDinVezvdeO65MU5FFxmPczLEbMFxOaNC+t0mTfUPAm8DeOmgZEyUZU1rbTmJHOm836/E4JyWQK1RjR6TUBt5wlAaGYeB4Mv7IrfmNGNlHTyXEYgsi5XLC1EKh+PO6iQFg+jjNG9NkedJHdQGP4AGkapuy/Q3Us/omZMInW4uTFIJoROCkbtjVY9ZHjI5Hxi8o1BzrhSeS56zTp9Pju1DzS5vHivnUibUlhPwO74ptx6cVHF/BoyuTtRIIYp+4d6OIehNjuVeiQGgXxBdZkcmhcMmhitV4fvgxAkEvMyU+gd9u0oU9VBm5uNFY+yCaqEGNnud7IE4ty8fEMzWp9ihmDERclr5pxyffQH8QMnQ04/kKe+Jwms0RfPGBfS9EoNvO5jYPReJ5RaycuclEMiXRasdVrwCpXDGkdpZIV7KYDwD73SnzUTL1Q76KNyjINjzNNI7VQTrutCToll8XoTA6/uqNU5x8mXHZOjWYNy/fyJz59febXEx/0rW9/Y6jFPlYPyOCj3Mu0OkRQWendOJrMSDcw6tbgTcc5jZA2k6LBeILudJLpfbr1cWHMmp8zP3+4u8c4rry83LHiz9/dv31yGPwZdpjdpbqbne9QGxPSC6o3BB1vdOfZKsMFP2biuF27rgtlwpSOKsbIkdWtLAFBkVSqV1jqXuBIl07tCH/Sy83jsDCoavMYk5ZWU0hxaxnPRN1MXZeHJFjFFMEUtUEolaSZpJBDA3AJSajvLh+em4s9HDHHewx+80rDmwiBZGAZRO5d49estK1lWTDI5BnrY2Y4HZR9EGaSopHiZYgwhyo/AtNsFau2MPp5+LYDX6vCsJ/F4YSim2FBKLezHTmtl0v/+ji3LhRgyQR0lWpZPXopbG1USLV8Z1+OZLeoZq51eGn0v9PudXA5G2xh/r6rC0qorU1rjUQrHrOKI2+GLTF5IkwgcoxNJ3mI8jKWmKYwI7tMZBeuFOlxJKHO63fZCmO2wtfuE2ztsD4ebxixCXNM6p/lBssW9NCYk9ekS65hVh9PEUwzE2pz8vemYEeYJo83TYaeWHQs+5fZWqFUIlknqNQM2Gr0eBKDX6qco7dRaCEFoUenDW5VDhNErMMA6tZSpghL//k6ll3kDb63ekAuDVgqlFBcUDL8+Y2KQqoGYMmOWx7VWuX98d6UX0FpBo4tmWisOoQyP3bJ20OrBcRxUmUpDGdQUn56tuHiVC/hUXf0c4bl6QYgyUGmzkmHK5s/T8XAFKEGecOPo5Sl8CImnEsXmzzl6pw3jsW3k6L6q6/UFmdXPvQ9afdDKg+/fvmLDp24vIRwseSHlRPIxH8XI2Ru3R++o+TUcBrUZRA+NtWqUGTVE8Oz11pxDvHx+IafoCwbG4+HtvR1Bs0/lY3T/nMD0FfpwFVU49uoNAGaknPnyekFG4/7xYNt2lnVljYmYE+2AqIMlJ4yKdU+0r0dlPwrbdhDWzMvtws0uhOR8rphRSqH3Suvile6lzpDkwJovaF4gpmnSrvReOe4unKql8f39zvtj477t1FoY1inN/XOt36lHIZjxHoWP7UG+eEJGCpBEn5J+nadmUWFJC716MeN6uaGqlNZY1gzgG1+fSr8YSevCcvHNS8VLOf1ZDMTm3qeYwjM0WVVIKXBdF49e6gY9/DgJLfnEaYjDiMml6WmeFMeAMU9kIl4xYwxSClyu3j/n3iy/9ylFWBeO7VSdDlcrRkefVIUQ8kSAjBASIbgJOUavYzH8tCWjE6MbqgcD1YRKhl2cGmG4IPHc1JPzXGGuAyG5vGqo8PKy8v7YiPiJ7azAaa3P9vXunX0ZgkRC+v+v8f6vP37VG1eKcdZ8BF7yhZfgBLnphTOUNV+vM+ixswabuWLDO6eCkELkkpVDAtIjyRZ/ABxgpNsg5kRIkQUY3eW4sJDiKX0dhOEqo9Iaec1PADCr0NQnmTVeiNnNqRqUQyHkxLIuxDwQTpJfCCECzgvpmFwVlRiFvCiXNZADHAdIFNKSkKiYDYcTxX1nac1Ec7l+INAIDgsQWVVQk/niKhpd0BSiv8yozQ3Wo5sy/lK4hJv58nqavQaPI5IBIonrdXWRjEGpBiEQkpKz5+11sfn3JdecBDe7DvPA5GWNnC0nGsRjZBByUpSMgisSh5Kib2DrEgjm5YMjejJ8EMhLxFScbxIjZ089cMWmm82HGTEtPo22Nnmi4X1W60LOTuKHlDDtLOnGui5usajVN+ty8OWnn7zaQZWQAiaNDrO99vAU7r1OWAo0BvIaZ1bdgN0hozBDXMfwE+cSE2vOpOB5itKgNt/wkkbQWYIo6gIclMwgB6+YD8kzGE0MCcqnlwvSC3XvDLmQ08qSV0/EoGHWONos/5TkAqfSeewHH6ufiG/XletlIeriykE6Kb/y5be/4XZZacW8z0sCeb1xWzM9RCrKcs0eIMDg+PiOVK8AipfMl9vKy+0yF/dMzsnz9frsrwJeLs5t5WVx823zBgfTSRHMZ0xsJlRoIMXJrc1TnYi3BQeN9OGckE2uSNWN0PSGWJ9KVc9ylAlzu7rWn38fAE+I8Mw8YgquXJXYn6iJd2q5t2SSDWbPP9MnZ+XCnB+ydptCD1fnhskj/YD0ZSr4dBZinfSHyPy+z79hei1lmqblXKyGR3FL8DQOhvcYqkJ8mqDdSiLd0NHc54gTgjGEZ89dkB/9XX0KjFTP7i9vU/+75biu1ytxDIjw5R/+kdvrK9drYKuJR6m8PT643H7vQRrSCOXBVgvVOi/XCzFCjoHXdeFRBSyyyAWh+vYxGmFPpMvF40pCZxSvcb/UlU+3T64uS8b+y3ce+877cXC5eoOymNEP41Gda/jy+pkYXDjQSuXbthHywnp7JYRBr4NaB00by3KdCfeD8dg59gMenRQTl0vm0+vCaMrRlMMCl9ebP3TWMeuE6nlq18+fCTKhl2pIdTNlCoFbhHEUP02N4p1Ni5BXo5QxY2aUYIGUBYjk4HFXPhouqJ6LQZjWXX+xXz8txDHQ3rkfH1QLEDIvt8Do6pvIUNZ8RRnYcNNzbYU2Oi+fb08BRS0OZQrGLSf0lpDh+UJmSkhKSg67jorXghC4XC+sKZLT6Q1q3ItyfbkRg48I++H5gN3g9fUzKRphNOSx8VYupJD4dHlh2CCuK/Fyo4zO7XLlumSy7th2p5ad7/vG55/+0UNfy87HVhi1EGPl0+2FWh6U/cG2dR96YiCvmeuL14D0ZrANpPtJNcWIzkVyCYmXfGVdMnlJLBrZjsq9dNac8KSERp6b8whwkcEaAlmVyzXy8rKQq7dc//S6Ei3CLfBTACWhJCRfwe6uohvKy4tfw0sWFpStFt72jRgckotBqTV7YO8lcskr//l//B/48umVYJH8eqWRSMtnLtl4K433UkEKL68/uV/t8TPj0Sjbzvv7V34ThH/+h9/xenvh7ah8ernx259eMFPKfpAE/sPvvvDb3/7E9XqlHIW9bF5cGg0bk18RQySQJTGjcScqgQf/huhclTo/19tw20wa2AUY0EvDZpVK6zO1RWxyhGNyro7c9Mlt93ZSs27jMMWLT3unDiOhPxJ5xjzBj8m5inI0m9VBni4SokeijREmaqDOuU+uu08fZJxKyKDiySTdi0vPZmdsxkIxi3VbI8yusFE7jDMbdGC10uvBqIcncwQly2CNiraOjIb2grVjKjMziJe4JvGAZnW/EabyFAIpgVor+76zbY+/ee3/VW9cf/76jXIc6Dj4oPNp/8zvPv+GssCjFd7e3/n+OGawDHx5jZOIN7Z6R4oRVRmjU45GbwVrd8peQA2N4o3B8TuosO2FGDxaJ4QZGIthbbAu8Uncl7ZNE2fDLEwVzqAedy94AwjKcVQ03AmPN46j4XFPLiNdLwcxKQwnmkfvWDOq7XgUTWdY4P7YPH6oHx7tYi4pbs0QLRy9UI7mJDEzTSIJKYnHG+GCg313GBDthF04qniSfhteLDnNh3pJjDo9RCuMY8JcMxIoqJ8Ic8wT61fG49zsGrVUOkatg2M/sL45N2M+GdZSaaU45NPcZ6QxPys6pO/ubcIlAdtRCL1TevV7JQFMaKXy1nbeZ2GlBU/pr7VzPKFC80zHqSx73B9gRoqB37xeuV5vXkoahW9fH3B4ekZvxkf8RgoQ6cToPCAd/vDzn7lvOz//5at3n5khY7CnP/L9+wcf941gA9HI5bJw396p7aCPwV5mc3V3qXCtffq1jF++f/D+5nyR6QknCQ2ekNDojS72zNYU7Xx9e7DXzjUE3h87Hx8b33/+yn99WfnH37/wn/7DZ5aQ+fnnD77+8sHXt40uC8OCR5X1gy0obyJ8+eLJ+7/59IXNGtt2UO8Nk8ZjPzj2nbI/+Ld//3c+7nfWvHJ/7DQOrDQ+rPMwYzdjDWC3g6jK5fYJuUIS4RL/MxcpZFW6DT59vvI//Y//B/7jP3/hePyJ0ZqbqXF13fbYGL3zft8ovdKsguk8XYjDB3549jimmTTRJWC2OyReCu3Ypx9deCuVo1Q/aYvLpNxuNoUQU5wQQqB25xODemhujnG2GZyYjfOnipI0sdWdGhs9T252eg9bqVNUPJA20wnN+UoQggzQQQiJVgu1tanEdb+iivPqJvM02IZztyhHqZxBBRqSUwP48OhezwEzJ6GPzqgO8dajUKt74kqpDAQJB1fxZHmRgEmY75zy9u2N0Y0lr8hf/S+h5PWK5gvFIj+/V8pW2D/2v3nt/1VvXPvHO49jc2l6gLfHBz//8hVbVm+kfTwYU5QQgMdHfBKhcVFvRlblsq7UOuit0MqDVlwoEJPShj+A3Yw65Z5BAxISMDmhPriuCXC6hDBrLZpPRa2Nudh2enMTb8zxaRS2oLTaJywQAEXeT47GyNN30lpHdExXewYC+16cW7l3WvUlK+XgRkkReNPpl/irokfr/r13r0UQoNVOromcE4/dW2v78Nwy7CxCgfxQenflmGRXX50G7tacnA8Bf4HxRfvxsTGCF+jV0ji7eLbtAOqUd7t3qjeHEMLHu19Lw8NwXVuLDuf/dHIBrXaP4orOPbrKTRit8da9GLO17tUS5vdFHg532lRBnQbLMToMNyh/3FZSysQpq9734t6VGGmNyVs2ZAxSOuEZ5bDhmYb3neWSXFlnxi/t4H7fKUdlSUpaMlU6f/xFWHN+CkPcHzRoo2PBxQvDjNqNY2YFDoNl9aw8QuR+1KnsGjQ8tFlEXLl6/+BRD/YU+X4vvH88+OX9ztIKaRU+7Sv37cEf//iNv/zljY978cDqkDwX8H1aVAVev68+NUtga402Cf8QM4/t8KGkN9qA23Xldl359v1BG4qFRBKhCLQAry8rm3XyklkEIv4Oxd98YdFOMWPfH9zrhyf0p0pawabwaiF5cr56g8J6zYTuwdHn/cTExSAFpHsaRJwBsx11mwKGWiAMfS60i3rRYgyBFB0SHDiHRxKXkTdX7yoBHQD2FFd1HK72e+KCFs/znFrfmekpYcon5MfwYcyA5flcelSazYJV36RsWlv6LKF1LbERmSbiv04SmOKPUyFi2FT8nVu7K0LP9cHDeh2GDjFgFkh5ZpXiUv2zJNfj7mYoQq+04foBC2cr+g+hdGvNucpa/DTZGo+/V6iwPB5s+90nr1GR6Mf9kDKip4v9x8PxHqcSTgUJXkcQQyCnRO3QW6UWn+BidMJ1aKROaTLD/AisgaGBY9+f8uDr6pwSEtyjYz9iT8aUfR9HxcYgzD4tQny648V4Buii0X1eraFmLCm4x0h1KpqClwPKhNxaZ5unOjDvDZtyhm7zQZupFaYeQ9RKQcwVRSEoEgK55Ekcb0/bQBtGq3UKHtyE2vrMJ0vZfTZ6ys3PWJgpTTZDbFBKh+CL/mPbXVrbB/tRXJE0OtYG4XwNzQUKZwSN+g/gyRBHQXCuMseZMqHBw42FmWDii82x71MQMYiLp1v0YVOB5kkGIXoKwDDYHhuMQRDh2xLQOaTEabzUIEgQjmIc++ZdWX0Qpy98oJTJOwnK5eabiyBsHx8zGNW4XRMLnWKV+pfC9ZJJObBc/NlqNijdT6+DQRtesHiUMhuAoUkipkAgsx/t+YzLsKf3K4zGPjopBo4ceL9XPu4H37aD3CrrS+LlfeVPf77z73/4ys8/v9GaoVMuTowuAOhuH7jEeW1RjtLnMyUsS/Jw3xnG+vO3N9Yl8Oma2Qr0oXQiOWVGBKKy/e4z8eODGJyfvGjjy+uFy82RjtEa5Tj4ePzs72JUVCqmnuy/aGK9rWhQ9DG43BYWC6B9Fo7OkFiN2CFI8yDiMw+wdoNtnyiDoRLnKUkJ4cJlWVmWxJKFNgVdASNEtxVwFIjRq2FcsUSI0QOW7Ye3zZj+NIzh7LUPeqfSEOfjfHfxX77RRHd2DKbPkNMxMTcg/5om5tClDeITEP2rNKC53iGnmtihO7fR6PzvbgOI01+GKrV3eosIg7xkMOf8YogEdbiRybf5WlG8XYHAUJmnQF8XbHpl9+PgcRzuB+2D4+914/p0XdFobPvOiIGhU7Y9XCE38AncHw6wOW1Z92p56z7Zjvlg2sSMdEbbBDx+X2Q2oUbxNtqJQ+co82vCZfHEwdY7aT5cIi5LPcsr/Tc8FWPNytH8oVaRac51pVztzScaf65Ykk8v9VQrzOdcCDNbz+WwRGdDU8B9QsM/R6OfCIIKrTeCGAT/mkt06fAZj0Uzurgnhenz8iV/eldE6BNXEJvueAIhOVQq4tO5qAtTokQEmzBLpZXhUEY3rNb5dfwFl/MEO4YblcXl7CmG6e/yRc2baZUl6pP4PWOLgnr2Yh3eEKsSICs5utF4P1whB/49qs6opeE+NqKT0Es6vXD+PPXmXhU61KOC+ddHheRrDKV3FD9FS/CCUWzaIhRsbnxr8hWpHZWP1jj2g5SUnD15pU8Y0/BhAaCaPoeJgfsBtShoez63ZhBH/7FxKUioBBG2YexlsNdOB7Y6+OOf39i3jY97ZdurcxTZRSNmnX503/iHt4uPuSn34S0CfuoVrmucvjMX5sSovtEAWKM1oTRP2pckaFN++YMPcWBoNn7/khnlypKF//b+5rmbpTLq+zM+7cuL0qdv7DIi/+m/23h9feF6uTwzGkN0+Op8R0bwzUVG4LosXK5XBnB/PJ7eryZG1xkEjZLXC7fblcu6sK6R7fGYAh68PHEM8nJQeqdIR1CSGq8vN5ac2Y6N3pUex6zbcVhzr5Xr7cJ1zdyuC9clOqfbGmPkyXGJi5EEWnWD95IClzVxu6x0HOkIAscjs6RIjn4yXNZMjIkYlCiJ0Zp7EdOErMWFTphXLjHFEjEGrpcVUQgpoyk57NgKjeHijJTJeeF6u3FLAbFGO3b2fUNGJxB4ua5wPwjWuOZIUqWYD6nHfWe739m274wEkv+OVYXWK+GUcDdzgU4fU3Fz+rumYABDmi9FJuJEqgoiRlSb2XQCEmbGngfHpudkotOwO22CJgQSZyWBnqZY5NnTxeRuwjyGh+RwoszWU4fp1FM/MKybq+dMUHMVkYrn8InM7D2ih/FWVyqJeeI6FjknNsYg4pEtg+QLW2dCCG5slBm4quLKohiUqNmx9XYWTsxA0XOCNIPWiebqQkb4Yew+vVIzy83wDdApanG1Hz75jlIZrSOzcdfVh/IM3GW4lcDMMD+yzgVcMJLfHzO0zxd9eml696lYzAgmNCJjGsetuzE84BOyX++ZDsIMGpY0ZdRg7Xyx/Xqd35eqkIkO1wSD4eZTMIIGLIYJRw9Gm6d7gRgXdFZZ9I5/bRW0K6UbrQzKYa7kZKrLpjHagKE+PjC9Y+gcbNRPZucpszS/LjJVmqJufK8G3ZQxlCjRrRbH4G0UjgoDJ8/bGNjwr6f4hn/+fD6PyWz9zZxG3F49CDoqaHRLQhjQqxB1QbL66XCebggeynqeAEJwzkqHeSpHMVoFa0LZoVSvebExGxpEaWL84ZfvvO+Fy7q6WlMc+j4zOAUhpMRo/tOUY2ErzmU9tp39/qAehXIciHQQP8EveaVXoUiHETn23Y3KuIm9985xHBPt8KHUorIdB214a4CZQ/vb7jyOKxm71+00o+wdGWlaBxpHbc9w7OAGRPrhSTSeZQhlhnDX7rafvVROt1baD15uHnwdzuDRMaawpD9PbSHqHMY8S9RN4HjiPt7vZePg7e2dbXvQ64H1xOWa0BmLdYjDpmfgAeGs0EnA4dy5KszM0SFw1MJxHPQ6iDlQZzv63/rxK9+4uierGy4cmLhrmOnYPo0nX6DNiFJcFWRuJmRuCIo4X6AzGVkVFQfbJEzyUYR6mjyZ8S+THwoqBHxiVfHF9UcEjM20CJnE8IQGGEQJs4co0JsvgDaT612Rc4a79nna0bkR+4MpYzyDbHUaAh2QwP0cEhiSns5+bMzuqjAlv6DW5s/ppYKCzviZ83N1doLNZJDRCcENsJ3AmRhtM3mCCYPMW+LekOjhoGqeokCr0EHG+XMyMRP3ZzUMkfj82mFKjsFfgqhKYCaTn/+z4Ibn4aZnFecwh7gnpnSHYz3BI0zuiHla8aFFZ45tEDdPe0q8EsQ/X21u5s+hAKDRrWHiGxc6kxYmXIxNGXAMz2tI73MbmEKB3un4QuCyYd8gWps5eIYnGszTl9m5aRkEQ6POiy3zpDYVrc3cooBzIm4W9xNlN7AxOIrXXuiMGBqtMc6NS2by+BxaIjMtIwSCpGdUlyBeSCke4ivT/2M2iDE73ErwpgL1QUKD966pClEHKfQfm9eQOWAFelfK0ai1kxd11afCocb3x8bROtfD80kdzjY/+ZlfwyVnT3lAKOUgHwfDYD8qdd8oe2HfD6IaITok3GuhqsFoWEu+SQ0/peyHw5i1VMz872oGjIiGg9q93geM1hr7vnO2MyggcdBKh1Gx4VVIvXdKLZTivVkxehxUrw0PX/b0nDJFSbU2jlLmxuX+xhwDo1aGek4lo/m9bI3eK7X5EBRz5Bm4NjsCHbbrJFNEPCXn4+PuG1erSO8gzrmJhPnUnoWT3ddOCfPZZA6c872e6FObXHPvgyhnC/Pf6calEVbNpGaEVUBhPxq31ytopFkgL94FJKPzqp6c3k0Z+iNtPEX8FKKKRPcs+Emk0y265NSMUGZ/kMhsx/UHK4qwTvy/m/vHTnGGonPjU1wf5B9DB4E0m5FlRq74g2nDuQ3Dw3vzTKe3Mz0afzHX4FAgovSgzwqOEbpvngT6fAHAEJnxL+cGybkQe/KInp0huMzZz0hK1/mQDfHIHhwH1xidZGamVMzTrYmfnLxDOeDUu0uDFViWF9Q8K1fX5Jj9MDRUah2UNiCoT3R9EKSjeJJ27c7ZBDrBAjbO8s7I6AOdPh/xL+HcQu+QFB2GtrnGd5lpGS6GMU54GKIYi3YgIjJbgvtAJx/oqV6+idKEEdwf1S0Qp0rReiOmuSmKPO0RmGF9Wqjn5urKsoEGY3DeA783zKoYHy8GZxr/udm7BsAXbdRTuplRTBpPblPIQbzNeirCevPNFnHrgJ10sDjc5onjSk5+zmUYa56qV5zHYbbvhuz32Bcvge6L8hgNxOGt5kok5yr19Er5z64irJfE5bpwyVdabK72pXP/sMmtKr/9tBBiBgmU7oOSIEQzJpkyT0N9Dhl+DT1Wyi0RYWz+PWqgVRcM7EchWCflhZGV729vngqSIrfLhd6Hi73G3LBmYn/Kq2eKTjHEGI8JVyaGNWqt3O8b9P4M823lcHVjCPQ1To7IOGrhft/pzWPm1vXi4i3zvMtt22lHI6ZErRvHvrFtO2OHFoN3DdofXAWrgc+37OlQZrzfP3xT7Ma6puk/FWodHk5dlbJ771fvlVJ2vn37xuP+oJXCR1SWb+8s68LnT5/4h998JkWdEPvhQ2Lw2KzeHPEqMxzcz7o4XBkCW+vsx6Dsg3r0v3nt/1VvXBfcK3BLOrNifeGK5YDg03iITuqP3iErUXxhsujybWGwzgcNA6ke5zTmEX2M/WREnZDWE/75kQlXxmCkWb+NV3u7cqb7BK8zf02iVwmIw5rDDo9FmQGVJ48U42x7HUbDoE3oUo0ceJ4+xHx6VnH4qTZ/wXoDDW4cdEHCeEInMSnR9Jn5F1D/2lFJ4sq7MrxAMKq3aYzhW1CzgUl8XgNvc/ZwTjq4yNDJptbHhEBPYl2c27Ixyz2FnCDKbLcNxjGMYY1oHRkK3ZtoU0gzRdxPTNYduBrmtSFJByEYtcxOLhWyDOIUxZTusT2nkMUkeDpG69NgOyZR7qezNjd5FW8dTjrLGy24RTwwxS9Ck9MO4DDtVBpjJj4Mne7OMfl3PDVA8Wu4BM94c0DVhwkzm1mBY55aPYncf89PiRLDHCtwKHd+D1HjU1Cmk9MIeop+JmTeHWkYw2g26HhQsYnQbSBD5oZqjizMk3Nt00hr1aONgk7oKTwrT6T7tTHNWFzRkBh4zmErxzTkqrcaJ/+eAp3X64WX68K6ZuJvXjy41gax7uyXQm2VNSdSTjPNP7o5OSqXNE/kBjIWrhePX2MYMWeH780DhHNw/k9DppXDG6ab21hiCsQcXeb9bCx32CuIElHK6E94uZU6kyvAop8SJQopLowRsSao7RxtUGUQ+ngmYDDNyh61JWgFhgc2t9Y52sbZB7dkQZIQ0iDnhaRC0kB9rZ58oV4ymaZVB/w0e9b3XM8ItzFYlujpGaJc8qw6mZvqsiQvsKwXtkdBRudQc843Ry45c1nc9B2DMkbA3I8x1Yyeek/sM3VE57X3tek4GtvHwa2BlspMFfibPn7VG1eOSo6BMgZRJ3E8CX3EFTdB/L+5usfPO46Fn5Dd/PcpNTZTTyi3MQUbPi2J6CxWdPI6BnOj65SqRolzE3FmrbfmuLxjkVOw4M73zvk9OaZhOIH5RMzUTzfYrMQePyZK0Ul6q8NYcsKj5kMXY/JCeibX/0DiFBdimImfeOZUqlPUEDW66VJtSl8degm+UjuvI0woyzfQfi63rhZ5LqSncupUQ3lJ5I/FW+bfq+O8m74YYP41ogaHncxPo0G7w2wiNJtKrVl+aPM0Y8M8uGDg13yeb3XMkNDupy9TeaYNGDY9Zr6xn+WiHcPCcBO1jKnM9M95/nMu9kP0Cf/qlCub/egx+/HzzZ/cXFCi045hMx7HscenJOR5OvdLa7gS7Lx6E5aePMKPz/sBvQo/lKqnQlOY985+/JnzuvJXf5Zpa9DzWx7uXfLn0U+1MSY0ei1GmBCnDmHoabx1LlXnsyc2k/xDIC8LI/izHoAle/htCEq45GeM1uvtQgrCUdWHiOSZeUGXuThPcYyekLEwCE9JeEjOidpMQ0/Jyx41ZnIQZAzqclDLmJ1iAZnpMnqmVoToWkBz5aQLVmbC/7RsnMHVpzjQ3zWvLSrtDN02YgjPBd2fV0dwTvXqGN7e8Px64xSbOS8bVJHkkUvXdWG0OjnU4PYNdZuNxzz5ELfkBSG4qCT5kCHiUv42+ajTYiNAzonb7crojaDeeny9XFnXlfXiyf0hKKP/KOb1aKmIDtBZbnk+/+fPMfpwhfLig2icwra/5eNXvXG9XF1pEx4JKLTmramyuJRXQoCU3WQ4Mfc2PL9sFSa85aen1h1/ZcB2VPopDABSdNgJcGf9XPRb709DomQm7+DihjG8HttmIaCIENUJ3v/dxoU9FxSYm8Nc7P3hdshwDF/swHyBmNOUYXSb4gVxXP+sfkd84Urmt1nmlB/miS14w+BzMQtTMJDNkCEIfg2iBEbwTctPFL7QpXhKdn1TkcnNnG3QfT7QwOTq5JklCNDMaK3MVIKGZYdIUeVyWQjtTN6eJ4UTappKt47zP2MuFiJ+IqgmDOvkMHfY57U923fn8WeeMGycG8ncQMy/toRzQ5J5ovCNY8yfJ4h3LJlOVR8evnryh8L4q7/e25rPzfK5+egkxYfNzcqTDHR+1pic3fPeIs4XzkBiTd7f5vCnPytP0YO65SOoqyFd6erDyjiHnskHPu/dXNTOP+tJ56DN72OfvjKsE0IiJ/XBJM7hZwo/DN/QGzYXsjQ9UTaj2FbaRDXcSxlZs19fCcH7OPvg8+vi7+kmBG0ePbYo13Wldb8eIXkD8Zny0k1I0SGxsCQP166VMrM5NURCjlyWRBIYrbDrIOToDcrxMocKYVndz+dKzcTL9UKtlVIPtn3zpuijTxjX+aqjHqwxkqPycl19DBoDVeHT643eO61Oo7kVUPeYXa8Ll5EQhVqrZ4Xu0z0ts3ZHYQmeqFLWhbobIsaaA6+fXlAN1FKJKfswIXC5Xgmx0nsn5b/iSofQizeyx+zWlpwjMSr9KKxROY7MkoXXT1/Iy0oIC9eXF1TwZA3lyXUutwtjq+TH7hYemfjzHJTFOkYhXq7Ekenj9jev/b/qjes//fQPXtC63TlIdKlsrZEOkDbON5RtL9w/PtBeySmy5MSX6406x6VLCBzmgadHha9vD18wEFIOsCzIEshxls3Vzr43+phmxFqJwGXN5DVSxUA8Qd29OP6rEZ7Fi0yozuH2c+Kdi7rwIwqmNZdTB3e9VzJo9KN9yBNS6RwtUhSvxMZhJsz5Fwk6xQo6o5smtCQenSQ2T6fhhsv1K9m8b8cX5IyMQRwdFyPMKo6Y0dDm5tq93M4UG4E6dt9MDGwEhq6IRI62oylPMUxnHEofjQasiN8wHSyaCcnoodNbnUZOcxgWg8b0LCUIia4JZ4LUVZY0mkTnCEJHmiF9RtuEqTRVTpIIhgvyW6u+KJshJLdYiHoBsSpB4iwQVJq5/0v6QK3TSyEsvgkG84BbG935ne4nFRtGNE/rtxAY0etezhNiM4dH+5zuGQ5/GzLzIH0xSoJXp9SORH3yZ8FOz493Oi1zE2IiCBheVzJcxSrd6LURo4e0apomUwOp47mZj979dNgGvXgH23VJpABonE3kSiE479Ma5di4HweaFvLlhet1nZ1ig/E4qNZAjdslMsJK0+y2if2A0aA3clzpNpwz6UZcFkK4cnn5CVE/IeioyKgOSYnSLdFMKV1YBixpIaQL15+UZbY0D+usCqVXSh80cf4y5sSnLz8RcybGwCWEGYjs4pllWUjrwqovfBZz2FeSv2NzGOhld4hs+u8efXLhQfjt7/6J1jrlOBh1p7WD3ipb8SQcjZHl9eb3bMCoRp3GeCbXxfT2tRCx6WWL15X1t7/xxudakVa9zboVSi2OvQT3WCmOIITo0KJz+/5ct2lgrjJoAVoUlijk28p6eSGEZaIpnSiD1jc/vaXkQhzpBIMUw9PK0QVXPYliRdi3yujGfT/+5rX/V71xXdcrcSiXxcNvCkJpflo4/Ty+Rzg85qGkPg26r2Q8JaGcsI+dtRZz+q6CagdpaJjhl4PnhOu8icfz5GR+c2RKmP8KujF08i2+AMvkJKZqf0J/022uU5Y6YRkM1BRkpuBqRDRhwWsVGMoQTwMwOSdun8wRP5npGYKZzuO9/z0yvz4KQTOg9HYCYTJhrIToQId7VjDvEiMmVwaaGysHw9ebCVuKuVTdJGKzV2m0zpBIn/0/5tSAw7dB5/2ZtgX8urm4JeIwmpslp0NhhtF6O6uY9xk5h+D33E2a/nfJ8++csJn5zipTljv6eBZ2DnM1pK/1J054nuC8DHOYL04y710Qh2JlwoV5pnmc3WfW7SlNHuLPQOqdNBMLCOGpyjo7s2wqK8/TlnNPQoqn1FhnPYYPP2E+x6i4xSGcxYZ+ZcYQRvcTbBAF8dizoIGoYYoldJ7cXY07JuT84yB/9m6dg5ZzJSDTMGvUPthLZdsPtEEnkdLiifbDkDqo1hA1bI0+CBDmrFR4qgrNZtL9PAmGhRhXJGRyvvptaQet4Aks5on5Ml/A3j2pQ2V6lKY6U4eBTKn5jE+yea/7cMGVTKVurV6J4rCau81N1IUQIRFDYlkvBBEYnWMOWY60KE0dGQnBYdIUAhqUfgzYXPnnSIo8Fcga4g9OErdjWBtsu0ee9ckPnebhgdDGVEjH5IpFfLOttTmXNjep889pTOTk9ztO24aN4Sfh8eP0HmaKhzHZFrOnuvpUH6v4dbPpuTtTQPzvOrMKvf36qEarjcej/M1r/69641rWK0kWbk1QK+zAURthZDCd05B33yw5s1VvLe618b4VBId2loaTinIuoD+knXUYVqpPzeoVAL4xzJNCcxd4aZ08vDfqmXYw+aYwifQ+BqVOQyc6I2NO3kGfJzR/uMqzskBnJphGT9SW+cKMqSg0DaekwBdZidMv44kYiMudY0yzzddDQyUET4E20BSJsjBD4TmXKBX3mekYnks2j7Fu6MbNnWbevDv6rAQpLu2eX0c0+ylLI1K7d2sZyAgg/n1EXDLu9SqNOjzmqI9ZGDptAZ7q7Y24OsUB6dy4mofu2vReOYQ5iy41urcpjnm9/MRyUjtmg17rhIxdyReGuRHBI7enIMQXirMs1BFHQTWyqE/s4AvAkrMnrlSll31CosOjktTQYYQSiNHvk06i3GhIdSn8EwedXhkNSk6BNc9qeA0Tmp4w4DBPKZkp8iEGt3eYc5W+qbhhVOdGeKolQ5yT8yyqNDFoFRk+XER1EU3QWYRqbmOTpxz6TFGYSrijspfmSfZdWBY3ChsDK17oqcEbwEuH2F10ECVOWDxQy+NpQM4hsuSVnK8YgZhW4oS3vYXaa2O6uQfTY6/gOOo8JXiyjkcVQRttwn4VlTPw1vh4PHyR7l5p8/HxQamNOvAcvqBIVNqyuActDtbLC6eSprUxnz2X9rfihaMAGt9YskdK9d44jsq2FfZakSBIN47ePLQWGK0T4uJKZ1wh2bs3jzOGWyL6oJbBx/3BpRtrzu7h6nPjKo2U1O0eqpO/8/UgLzd/t2zAqB4S0AqtNmQYUfCBow/vHCOSTgiQc5jz2Kjj8PeeeUg471sQYVlW8roieeFxdMpWuf+9ZhWWCr29c398JQrkurv3oleHXhQ+X6+0JbEn+E+fX3yzCF4g8vax0cpAr4GIv/Dxmh3+GB3pnY/a/ETVGlvxfiYNOmNlZlSTCIsGFoQ0zYV9r7TjQBeZHEogxAvX1Sf9iKdM+9SutG1jIFTEFwLn+qEbMfrXXzUhQ7HSKLUimuZJQFytOIlch52mGqxB7TsjRDQDuPFQROcm5Z6sFBY3RI7OOBoxrj59h0ivOuUKjWAOsPYBrUDZPmjdu6W2/TH5j0BeEgnIIlw0oz0wmjHuG3twBqe1Qq+d0SujF25L9qlUhdFcIjxscLtcWLI33cacOLYNMcgauYhnqNGE/tjQkEETpkKp3Rem8qCN9jRsWxeP96pl4oXnJOmm0dGdH1tj4qKJZUTKxwPVSAzL3NNdCCDMjX6eYik+bNRWyNUmbDaQKlhxyC1zbraBVAX5qJh0jOzpGl2ILfFJVkydHzmqoR1UDY7q4bHqPq3WPKB3LztZletlZV0CMQpSfRIZQ+k0r2Qv3QUu8xhlJvSJSoxWWYMLdbr4tdqOxs/f31hSIsVETInRjPtHpZWN15eFMOPUWh98fGwcpXC0hmnEJFEt8Xbf/XoBKUfMvBGYA8odQgeLxjhmVQaD+tE5CtQWqVvl5RoYKbDvjePrVzQYKY8pfPJUHDOHfL3MFY690EqjG1xiIKdAWhI6Csfm3qk2GoQCGqnbYH8c5BhYo7HXOv1ZcPROH51SCja9j0EzP//8zqJGYHAchyvtBJDIX779Qu0NRPn804OXlxcu68LHX/7A2/sH21FmYscNE2E/jnlqwgVgFritmdd18WSQk6INkVo3em/U+waa+fypk376jNWDWg9qLey1uF9UFKp3d0kf7NtANk/dWZOSg0x6woe7OnCl7n2jVGNdB59/WrHpQWOUybF3+jioVKp1Cju9FTAfAMcYfDw2dL1wecn85d//nePY/Tr9jR+/6o3rX/7yZ5INvt8/iAKP6vUUR21OXYjMaJfueWMpkmMkpUQthUdp7LUyglL7FCLEQIyurjOM0JUeJmQyp1U186nLIIbAuiSWlOjD+H5/8P442I+dUitlQEyJEJ0PiDG7a8hsTmUuS65TWgs+waoGgirrenHyHeG+bT9UYHhqeohONqvmCWFOD9lUEfgGMYUi3cl17yBSJPn0hTjs1UulHIXH9mC3MsvjEox0HjoQGXM/NY6983g8KMVTBI564EKG7Plm+M+27QfMRfPxuDuHZ4Pa6lSjeMlmYPhmqTrVnV4zcRzOqSSDYdN/MkUqPrP1OSnvTuCrS55tKjODRoc8h0DvlNoopVDKMU+op5jBk+VPn42Z16lspXHsFdWZSxgSU2/n8ORw6LSZEYdO+GmwjYrhuggVWHJiIXKJkbx4gva+Hdy3ze+9VYhTzizK7bJOQttPf8P8JFfrgdXpYwreXKtzM2QMWjMOaYweSMMhKLqfksc8ETQbnlmn9ozKYkDDqDYY2l0RqD68nIo3L2KFWiqldY46EFm43G7E6CeJdfW80LSukC+0rnPz2bzfanQGy1TXBWqDY9ux1tjEyG1HJ/8sx3kK9kmuNUc5UgxT5Tvow9vD/eTMrP5g0gA/SlFn8A1NAFUSzjHHEBnaZ2bnDzWqqy39RB2Cq/FOCBphxsq5naL3xl4bMhqt1jmA6RTY9ClYmRLE5z/9l+CxZkEdMXE9zJhiGr9pbt+o2Hzu0wyNxtxy0Wp/vqOqbuIfEwGJ850SkekL62Ae+K0m2Jh0Q8D/+zj9h36q7+MUWdn8uf0aM2kP5mk2poi28VQpP0W1ZowZsJCyeAO5urjob/34VW9c/8sf/+A+mFIIIpTWn1BgNaMhlPf3KTIYLAqZC0kjezt4Pyr3fWPrzQvegk826+Lwmp90p29rPoSuBJpJ6SGQc+TldiWhPI7C9/vGx32jNPdkxGEsw8imaBquUBJ8apny4tYHpbpjfZirsFJMSM68XF9gqpDu9+0pxTXGLD70puc1B05l+bDhvhZxyXdtvihWGs38JUlhckMqk3/zyJrnQlr9lKgxEeVCStHhBt/Tad3YS2PbveL7KB6WqxrIyTeezlnPsTG60Vvnvn9gE0OvvZNCdqWh9HmqGNPbok/F4F4rQzyMuHWozSc51DjaoDfnhN4fG77oez9QDh6quqYVhszN2zzws1SvaphScFX1/iuR2RatVDOs+jR5lDKTJAYxTVLQIEoFmRuXQBzOi/Q26NZB/QS9aGDJyfvfciJfrrQBexl8HG8TQhY0ZlJMLDnzJS0EdXF8FKXWTimV/dipY7YJRFhS9uTzFOjVT8TH5Mj6jDCj4a3Kw+s5/NpOIccUApj477lh1i0JSwqklFgvK2U/ZjJ6oZUKTedCtaF5RaOQQ0avkTOxPL3+xHYY7993vn990MvBaJWhRk4LGtRFUftGPYzaG1cHkxFgZTAtWZg5X1NrZcmLe8pGp9XiSszg0vVuHtup4YQuz19GH4oMYZSGRF+gY4h0+lP8kpg5jzqjjKYtQGcaj2GEOBGLZ9LJoPXKqJXeKoumZ0nrc/3Gpm1gmt1nWkkM6kWwwd/hZJEhMgcv58eE2ZwwOpfpxRrdkOTJQLWPGZjt388p/uq9kWKYCThMHqpNGsLl6707r93nCV5kzEHBN6/eZyP83BTH4If9ZN4nEQ8xPmrzbMOpep4/uBv4zVgW4frpwlIT9Uh/89r/q964/svPf/FUCbNZBeHVDwygCGw7L1/CjCiCmyk/bw/648H7+523bfepcU5QMRkjBO6teHTQKbnGJ7W4RHp38QFAWhbCy4Xbbz77grUd7CHwaIKwAwU0Y5ppBD6qsfX9HPARhU6gxww3oDZPI8gJ8gXLF8bLK1IbVgvog/bYafWgtWMmAxiBznu5O4w0lYCaDNPBUMHsTBIwUqukkMnhSgyJoisxZELO7OtnilSa3qAqMWXS9YLaDWNQpXG0B70fdO20l4hePrO2RqwdswNBEAu89x3mKW8w6PtOm3llomdsryJ6+k1gG8fMSitYqXiSyEDLIHSXMnswZ0MNjm7UdmAWGEPYhp88VAMmCeILIyYkCrUl6jg4BFp0wUdMiVFnQrUAMbs60gyrA6sHSHWRQCt++tCEBifTzQYy6rQfOBdmWnwK7VPhGN3UOi6Jjl+LROJoLiIYywvhpbK0jvZAXi9emSORn7eNlJKT2tGlhKKDpDMb03xzuL68sKZATkKwyDE6pXePNRrVeUETpJ8/aKKaQ2cdn47rcDFTG4Z177BLVrnqQohw+bT4KWfK9kN4weYJwSo8Hh+U4+Cabh4+LH4PHj+/U4bbBpa8eGdWbx4UoNWN4r0jfQGMXgrjcnOTsCpHac59ikOW34/G2A7C4qfeo1Z+/uUbNirLZeXl8yulFHIQkgq1NVpxcYVNf6EcndE6UQvbtvH9+x0JnZw7uXQuq7creFpL9nzCMdAI5fATSZ8oSaPRtPhGaYIQ6LXQpIF2ehf2vVJqodVKf32FFLytPMTJm7rR+dg9joogLpevHg81TCAmLEbqsdOyt35vW8XUrSTbtvP5cqNHxdbAceyzrHHDVBm2YyiaZFbygErGcM4+SCDTGVbpvXB/FOqofprtB/E//jPXq2+4XfxZlxgZdXMaJARfJ4cPkfUcjiaHXEtB++BLfqFdvSTzWP5O0+HLGNMQdfbVOKF9wgYevzId/uLp6r37dHI0n4ZD0Ocucno32vRhySRU0XOiiJQyxz9VUk4zmNQd7poyn778hsHCftZeaJpqHPW0DbzO3k9wP8jNGFc0eEeW5kwI3uW0b4+nSTetF4YJISdSXzBzEYlDI/W0JjkBPju4jP4UIHCKJYIiKcyeq8BACZqISyRkf/lb8TrytCyUI2C9QD8cFVBX+MW8cLn+HlWldrBxeCJ1qbz//EfqvtFrp9ad0erMNZvRQC5hwjCG726+ablZYIpV/DrFWWOvM4ZHZZY3DJf4ikSESF6i8xwWCNGNmKUWail083DU2gdpWVnTCylF53xqwUYjpYS1gkyDbWn1aQwOk9cSiZxSUDVBZApV5kUOGvxaB/e/hTitCynOBBWorc4Q0kDKgd98+TINqxfy8gLik/397U9+OsW/h6hetXNZbsSpat1b45IiKUzZjPn9dnionUcV51kDwFQLoufj8AOSEofXXCYtpJRmjY9zsU2MkHQmkkdizmhMbB+enVdr5X1/wzd1oZMYIdFNqUPo7U6vZZqLlV48UWNEmfmYPs2XozLiIIVIO3ae6lhcPHGUzmPbUbzIUURIeSHGaX/obcZ5eWpDbw4XGgo6Q7GnMIr5+ZHzhBGeir1xKnKn+pXn2cnhxKDhiSSYeWJ+QKA1hjV/9pufsnKMLiQ5zeIDF210t0g4VOy/J1PlPMaY3jNHDm6XlUM8Sq03pz9E/f6q6Gw87v4eTYGRijjXONW5UaIbxOf75ipHT8WI0inFEQzGjPMy80Fw2gF6sylqcoUkY6JTBu0oGC6zn8UXz9NYDB4TV1qnDA95OPa/U1XhGIMKT+mpTUMjNtV66jfLjXCDozZqd2iuD558iht5Z5Ganpl/8wbB09Qb1I2fhvNQ7sL3VPLeDE2ZdV249UhMmVoLg0gpZW6orppjYuwOoHsunE+YEYsgKc+IJKjH4cGwIRDTQryoS7nGYAxxhVFv2DjLBG3K+nE7wCyQO0lxVe/V8R6t8ByJNERyWAkhc72slOLhwhoDfDRGDVgV//56xDDS7crnz6+E7L6ZXg96LZR95/h49/T13rHm1SoE749CfmD/OmEQYXhYyJTSujSYGcIa0Tjd/UueIbguHIBE0DTTyld6V0Z3W8N+FHrzCvJuzXMLzcgps1wurOuFbkI9NkYr5BQZZfcYHVGkHO61m4npbgv+oUjEhpuK5QdzEUJ8WhyyJkL0yheHIb2Q8wymFQ3kEMnpMpu1r8T86jxe79T9u3txRqcPmbJ1YcnKqsEVXEchhfi/q1ARDRPCmoZlmGHGrv7yZAieorDOtGHMoSeqQ8kxRA+xPr1ukzeKKbofcl2IecFqdc6rVtp+Qt7CIEJaPR7LBDMXTzGtAY2GILQWqc1N9GN4uLBf1DET0/0ZjsFj1lodbA+Pj7IxZpfdj6zN0fxZGkFAZj/WkHNresaCyWiAel39TOUXCZPj8WYGp3V8iPKeLZn/zUOIB32KojwEO84UiqN4ck6t/Rm5ZXNS8DgpZhGnnUHu/t09k+31+Tky1dE5Zb8mU5E5xhksjiuPza/PuSkx/GcabfJlagSmGhXf0F11HVhzJmqH0aiT24rzeQ+iPKuIbPrVxKa613nhYTLzCR1FGVPs4z+Tp2nIFO/0k2ct9W9e+3/VG1dvg4bLX5kLAngWIeLVIG0zyii0+kDGmLyV8vLyynV9YUmRJQa+bRu9d0Z1OMBT2v2djcNTBBBIIXrApChrfiFFf3m8PLKhRJbbZ5brC8IghAvf3r6z7QUsMvoDGGhQf20lo7LQ27u/UMHl3MPCcyPykxpUhHT9jS/oMshxofVGa4V632jlTh8+9RAzEhIaF6w/HPqZm1/ON5bllWHFU/MVbrcrOfxEzhfW24qQvPW5N0I/sCWDXBAplO5GxXR5QZILVa4h8dYHIWdely9s9w/y7ROjVqpCIs5g3TtBvVtqKzvj8cFoO709kNCfm5enhjTUuptGUybmzPX6gtkszWxGCpGYLuR4RRlEyZgJv3z/ig1FTSmjOTk8Jcr0QHlU6nYQgsvmhw2kwKjFo4nmZqSaSGvyzVIcnL3XOivkO2M0N4uew0JY/CQ5BsuyIjFCiGhYCNIIOnjNL7BekLQQ4s1bC/pgFGPbPziOzr5PiA9XrtXRGZZp3Xj7uBMvV1T8RMRQhjXMOiFmmrivR9zyh4TBMoOOfXyJWNl8IQue4Sh48kSp1VcFg2aDtjdsgpwRgWoc/cCWylE7Gg7iSPRSqPtBvXsLMqrocmG0isaVmK9eXsr0iOFlqWWWa8qIpGkmj+GCiFCbkfX1Gav2OL6jOlBrfL/XyYQZQud2VbRHr9w5IpKm39HE/Vb46UfwkGiRMFW7hdGjJ8OMhA1v9e7+I9Oq0Zt6DRLQDz/Rn9mINjwXsR7BQ7QDMAKtCLUKrQeWS3ryZ9Yjtfif3zfftG0ooyYGs427O+pAH4waqUTqodQMwoqnWp4SEk/VSEmwkekj08bCsRvHAaUIvXprtcRIwHlFU0eeNCykkEg5e12g+aKnmunDURLoBF0QXcm3z3DcEWuodJhe1tZ8M22idE0kktsPulFFPFeVQJbI9XiHUtj/XrMKx1T9jEmAuip5mna7JzA8tjtjNFo7fEIWn3R1e7hfKCXqkr2Z14wlRq/E7r55NYMYOrFP75B6osQQ4+39YyqAOtZB40JYBzl5bIoGYS87235QSvXj8oQDxvDAWpujrgd7DsYMgWXM7MTRsdZBu5PKfcaBUGdKgf/MOid4JU1Tb0JCJqQFk/ZXfWGB3oYT7WODYgTJ5Nsn9v6NGB+s5ULZZrGeGZQIVHzrrDSbcT5dEPVG0350Ph4fDlGMwcfbv9OOzVMWgnuDfGOuME+K2+POEqexeB5ZTL1MMS8ZRkVsnsrUg0/3vWBjZ9RGPyoWF1QrUXc0GGKB3oxvb99hzDqWGSk1RncI7v2r+7tGm0/N84GC4SWR5wlFQ3RVqLogw8wVqL74GvRK675xdVHsKAgD65X3/uFwikZKfBB0EGTwoRvx8krMV64v8Pq60Efn+/cP3ndvOe6tY2N7Gjkbyq6uthvtQblv7jfUSJ5inzEKohELCYnJyz3F5dNHt1MICfhz7dO+J4a4Wdsn/22vFGnEJbqq0WYyCu5JDBppdYA1JEApg2334kdPUfHLOrrzhW6NL4xmWGswOjmdp/6Exuhm6egqyfu2P/1VF5jncbgs43m/jDO4ekaVib+bMWU0+ueM3t1CoQELOmHz0yjt4bEhBHJM5DS8ESEIS1rcyydMxMbcRzevpRtzB9I6AUdJcgozLsuvcM7JUZRmXNY0FY82K0ucK1rX7IrANgeOoc/BOmDISHC7MCQ+vXM6BSMpJVJpSDjzTr1Icl3TzCMM83sZXFc3/6t6c7rDFUJYw/O03ad4QhXWS/Y9vQu9V8YYxCjzV6BXhS7Pe+2nNlcLPorbkWpvEykYhH7GGYBZ4RiVOpwW+Vs/ftUbl0zI6QcIMENTp1pPp/TTRn9KZJmf31ujzSkojjw9Hw47tP5DidSmP6f/tYJoluHt+wF4ormYErOQNYNVhgWCKbUpfVbdD3Vo0Ce1GeRqNuEjnnyEzDgJX7LC/N49ioXuggXOhI/zsyYkchK9Zh2x6iR56wRzDiHN5GtX4jXG0Qli9Fqdc9CCUSn7D9gxyZWZsDjlx54n7pe9MIYbCo9j9xSA7gtjG0wMv4G6DN8nzwB9OIynZ6GgzsFjbuQaJ8Q525aDp4aMmYTuJlcD8a/T8FoQLExu60BIcyr9EQ57eqxGb4w5UYpOU/kw/zknlKchovPPOLLrJ64T9nW+Qh364gd840V9DdM+zeID68EXcPE4nUwmj0CIB5fVh4neoZT6zMDsvTr/Yd755H1sg9ELTZuLjtJCAWwUej8ceo4DjYPVTv5w0Ks4USf+3NTqpthTfNRiQ1XYj+IRViIsGGJnEWEjiC/IFw0/BDb2I3Gkd5tvofz4vTNl4YSwgTPIOAQvr0xLIiS3dphG2qgOlbZOCuoT/VSkMU3aQaMLC1RIeir0wuwVSw7Jm/PUMUYkCK3zV9SAf+jkuWOY0KpMRdzzpzhlRJ5a5MPneA6vIfgzEFVm0at//lOCrpBixKaU3yvtXcuTY6Q3NxKHmfvlfLjRRzs/iW7qQeB28lk8lbCnCtCYZbDBg309WBmXoM+aGlXfAO3UHwfv7nLF/WDgULbOtJXT1O4Dv00Ds+fXmA2sNx9EUIJ6MobtlVaLd8zN9CHDB4QY3VLQrNNn+v/f+vGr3rhicDmlTum6O9X7JM9n7It6xH8IkXg6vgVSnH4djazLjbePD2rr7EebqQDTN4Mb7GTyXmda9HpN1GM69YdHqqxixFW94K25vHbJr8S4uPZPFnxqHzOK5e7fj/qCeZKeUaJDTiiQZ0AqSFTQPCep7D9KL9jwhIIx/Ra9dVr57sGeIQOedpBiYv3tP7rSsUfKblC6h7X25mqoAaMKl3x1eXE0cnzh3FePqpTtO60VrxoJrh6L14VLdjP1sEzkH+jbg77dae2DMLr3lr2+sqyfqGX3RbPNxVUU/0EdmuomiCZUk0M+yReZRGJYYIQJVajj5aV2aG43CBrIOdG6b2KG0i3QRqOVYyYbTF6QSbSHGcpsDj9F8elfTsyQgOri17Mffm9EkZBo1eHasr0/hQ4CHvSahazBTc820zYUkk15977x8bWQQubz9RUD9jrYi9FLnxl5nb28uzAE3JAsntqdrXKY0s9AYjoaBhoK++PupwgJDpEF5xe2Oijlg9obrXdqO36IBmzM6xG4tRWrLgLYy04Q+PR6JYfE508vECLDlF5dQQmejTnsLMr0IU1UkZzRXjGyp/Mr6LKQ15Xby5Wcr4CH4YbUWUIirfCyLLzfN8q2UUUYcUGXC9d8wfvSvKnBEx4iIsqaV0YrMArSOst6IcaMmBubz+zF8/Tmtg/n72SKRLzXSgg2GOYeS+mdlTjrX6DXOjljIeGLqQBh+OkNCY4oBM8stFrRnIliZAULiW471gdKZ5HZ5UeH2pDuG9oweSqIdRg6m8W9uJQ5dHWPA5OzCHc2OtROWIbzo9Gfn7N0U+YwJgJ2qj3FJfEeSN6orWClYL0h1knS6K3Qjx3qg37saEqEfGFI4NgP7m93jtpoM9qMKFxvmZeXC7fbK50/Ukeh2d+pAfn//n/6nwlpZf/YqFHpdNpj4/Lbn1jWK5flxm//+T9yiZGrwqfPL6SYCKo0Nn750x95PDZaDPzpD79jPwomSjsOLCiaEznmOVQPvv38s59yzBWKZd9mIr3LS2NeCJoQTRNWdFFIiAk0E+ILYsWP9hoQ+R0WEhYy2nef0kfH1Mv6nAC+eM6cBkLKT9+G4JP0mei3D+dnzDqW84zyUSQu0ArBBkng+umTi0wk8Pm3nxh7RQ0ul1dS9OkoRoXuPpiYArXWU+3N5Xbhcrv4QPDplTjz/PYyaMcdYiReLvzmy284Y5Q+vn1lHHdG2SnHTqmdIcLl5ScPfp1gkCQ/TYp1T56f6QkZYRZUzyif5FDlaO7bSZAWQDoqCUwZfKE1F2nYyKy3V8DP5fe9UMuDfjjfGNNKTAtLWlnWxJIWbpdXcrq5ECErrWwuMpDop1p1lVQQY/38QloWrimDGmW78/bzX3i5LtyuV26XK2175/Hxxv54UPbGmBtcQIjRT1j7sZHyClHQZFwvXpBJiARNpGX1E9vju0NJ854kICsOGWZF4+LDlXZqObxuvfimdfTB+1HYNy+/lKSkmL1FWWB/3L2yJCiXJdO2nVIO9u0BCuu6kJNzWnvbOWqn7Z5m7mPheRL3k4GFNFGGMTlbN5WPFrHakLO7KTgPEzSDemmoiZKFmYDSsLYzqsOox8y2jDGQwrPAxlETmXmAmmCyQWf+ZBjOjGkIiDlsJ+A+o+jq2uM4iCGSQiCtCn3SB3NwdfjZSDr9ar0h0lwSP1sDxl8ZdnuZHGv3IaT3PvvC/HuLE21AZl5i79PV4t1cwxzuTMw4uDHjwJgIjLiqsJZCOQ7q4crYpIrl/Dw9np/vz40SQmDMe2P9bDB3mmT0jtWGlebpHbXTSmXfixfTDs9ttLmxtm6Ux0bvIBL52D3+SVHWGPnt73/P59/9nnB5oYzIUeDY/07l8P/Tf/4fuLx8ceXRmugMtvd3Pv/j71muV9a88rt/+g+sMbCq8Pn1hTix39Lu/PHLF94/3ikBfv/bT7Q+QBOjViQldFlIIUxZ7eDnv/yFclSOUvn+8c7b92/s28PTIIYS0kpYLnSZUAWGpuCueBNGSPNBA0JGY4Z0QdIVa49n944/lGGqnIaru4IrFUPyl9GsO/muvkCsFtyfIUK6XVlyIqVMWm+0sqO9k4YnyZ9qyWVdscNfKA1C0bM+xdtnPYTUlUKuX1JycmluSAl9eYVpymQx+hrRGEiXK9fPn30hm6ni9SNSH8G5ET3QFFhfrrTaPKHAOpod9sAGOQTM/PfyqWLCvXVPBVhWUK94CKYMq34qMWUBYhP6cP7RzjSSEFk/32jtoJWHNyyHQIiBJa28fnrh5Xrjd6+/QcMy4Rdh3958yj49K+IvrTD4/Pvf8PL6ym9eX1mWSC07H1+/ssYpiFHleP/Kvt05to2P7wff7w/246Ds21Tsee3FuaCKqhdXLhdiXqdo5rNbDx5fWZRp3DYuKbAG4ZKUvMZ5UlTCqDwed47j4LEXSmtedV87rVyJSyRfVy7LhbwuhKi8v331aX8MN6LuD1o52La7w37qOYh73dj3ynY0rKsn0wzfTMdUIJo4mmDD1YHSu0O0baD5TFP3YSSKPk+9NtNCJMz+7Hgmus+Q7Mlpp5zIKZKjc6RBHMqP6p41HRNec6mKR2DNgGFRY0pvgf6EL2FK04PNEk5P2vCqGQjxLJic+/GESGPyr+XKzB9fy/BkjzF/Jn/Hx/NncckdTIYeEZv//awz6k8rjQZX2z5VrXMdmMfa51DQR8es4a3aE+6T82uPmcwzB+tpdHaucMatEUDqSbw872kfY6JRPjDbhCyHGb1VjtYp9YR5HQGCs2/Niyo1JlrplMMDAP7Wj1/1xvV/+7/8z/zun/6Ry6fPpE9XugjffvnK7//ptyyXjKbAp9unmdht3OLq095o1MeDzy837tuDniBocQw43BBVQsrEvGKz0dgMvn688fHtg/e3d/7lj/+NP/zrv/P16y/8+c9/gJFAF4grdfjiNXolXVY0CCbqip/ioa8SE2FJhOVGXH9HLR8wSfJSNi+vROg0LvlCSom8RJIm5xx6RV8iMRoxgLD4KSglPn35zOvLwuVy5fXlJ+77DrURj8rPP/+BUVyWfPv8Ba3CKJ3v798R6ZQ6qAOIjT52er0TUIzoPUfZe5NSjoTLlUd3OXW6BLIlggZSWmkantXpec3YXWnNW4q7GJKF60836sfm3i+DmG1CbcpFI6178sAyK0uwPuOVhnvwNHhskbkfrdYdmQnYMSujTg9P98JFjYGUE6+//7/6JNzLVM1VRDpZE7/7h9/ymy9f+O9/9w+0Xn1CbYP3j680Bj0I10ugk+kE2qj84z//I7/96Qv/3T/+ht/89MXTIGrF7ncejw/uH98p326Yekr813978P/913/jz7/8zJ8eO60ZosqSM4gRicS4gCWuty+s108sl5Xr538ixIV2fOPzEsjBUB283m5csnJdld/cknO4fXBsO2/v72z7xn37YN8fHH2wDSEF4fb6ypeffuJ6vfDyyf+OX37+d8rbnfLYuN+/oe3BaAd7eVA236jetsJ/+dfvPO4bj70Q00JtngsYQ3r2gp0t0M5JCtYOenUBR9TsCSUEpA+SRHJwnqu0w4eTOIOAo3NFY5insVgnauT1diHnRJCD0f0kFRUnYhrebGze7CAqrGvCziR4BtZcwDXElcndGs2cs5GghDMppnRMxhwenUc2wZ+HPmhjsARBom9eQ89W7OH+wVF9M5lp9IMx/714RuX0Ysnc8EwaFtyL5QNkQMIgJkCGG95FsOJr2QkbDoYHMZgbiW0On2hwelbngDSFY4p4AMzsnIvJByck8DgOr4USQ4LMDXjajdQHTZs8XJsb1d6Mx7FzLzutFex5yu6kMMhR0JTp+86x7zz+XmtN3vc3wv7CwyJaKz3A+/7gv/6//sJje/Dt7Rs/rT+R1kBaoP75g30/aK3z5fWCBWOIR/N0KkEjS7ySXheXcR6dj9aI4rH9dzt4fHtQ9sJQl4HmZeXTb37L42NQqiusHPiKmJpn3M3IFY0JiS6jbvPmmVYs7AwrnD4SiYGcLmjwqb63WSyYdXJInlq+/fIdtKE6WNIr4XYlmPH1D3/ha/LpOIYLu1WkVMKjsq7eBxWAe/8FHgOpEIMgZDSA93ELqkZSY7SFLkIX4fF+0JIQUmXUhSNEd9KjrBfPSNsfG4+3O7ZX2Aqw09++0x93jnKwtwd9VJQCxX0/rR+c9KQEI0V9ilf2fX+eLONMdh/dPLZn8pwiLhG25tPmGBW17EZm9ce8V6PWnSp/8CQCgfLx4OXTlevtyqILpIVjwB/+/O7Etw0vtKRTe6NuHbPFFy2UESr/9f/zL/w3/oX/xwKfX76w3laWzwvHf/t3yvFBLXfCY0fXxFDlz//bnffHg70cIAlm9BAkQnAOz7pS+sHx/Rv2/YMRIuny7s+Q3HkV4+Wy8pvf/kSpg3UVLhW+/+l4Tt19L+xWOerB96/fvFtOnO8Lt8z98eCx7cgSWNeVnDNNDv7yL//G49t32tj5aQ18/nTjn//zf+Z//S//wrfvH/zrn3/hYzs4qhPwMs5FDWh1ijmcuxx1uJdKIkrFhkOKXT39n9oJ94PIBz0d0+unHtF1dMQKx75RykHUxrFvPs7tbhC+XhYui5/O04T3HHZzHHVU34zMhLR6S8EYztvBj5Ds8zQyxmBJfpJLMc60/smLL5mcV0Qgr4n6vWN431qaQ5EKriyd3KiNMRsloHUIs9hTmAZeqf59yCkOEZaQ2I7dN7nRyZGpeHTlcYyBqMJyuLgJPB9wWSLrmlnXzBYCBaP1Sl7yVEjOazKTXfo8FetUV7p61C+dmKdpjBAZreDlq7PCJGUPCD/q1JN5eMPnT5nHcbilZD7RZu67k9pZk/Dln278H//P/5n39w/u9zv/y//7X/+mtf9XvXH95es3SkhIeEfWhY7xfv/g/bhz3+68vX/jz/FPU8ZptK93jqnaul2WZ4tsSOk5UeV8gTiodXAcjWPm/okIlcr+vtMOj1qRobRWeWwfHJsXADYLtPZwmIvmyR1pwoLRMIrLhIt7lgaNbhXwDiJB0aSw3AgxI8noRDRGargwWCYkcDDKBtoYMrCjo8e7T2PdnPgQxUam9AeURjganz6vXFNmTdmNwTWh5mqsuhW2vfDY3hFVcgTNgidTTJOwtlmgGOnycJOnBodYNc7mWecJyv077f07ve/IdsfKjrQdKXf3z4wDq3NqHJWmNjcuKMmlwmD05mS+qDA0IjoYzV8I653Tm2NWEKJDUMYTrg3SKW14txOD+v7NYRhrjDpIoZEDpJcbY9s5auXBnZQTQYBhjPHhZtchHkA7O7SCHoyibqp8FGzbSd8j+ZvSv77R2kZrO0vtUHRuug7xhglfjjZm4oNM/rRRmsPZWMJIjBDo5qdJG9899aQdaIDw6QsMwVrn4+2re/vGYJRBwwsF39/e0eCgWTUlvE0b03CeS4J/7TY29m9vWK28vixIWGgdfv7LV/70l6/8/PWNt48H2+6Qee1eJdPHLLvUH4Gso9v0FXY8z9ELQdX1165+FLzP7YSpDP/eZ3hu1D6FLt6i7Auscd825C2y7Yk1e3v1JWfsdqF2wXrFWsV6m+kliVV1BvM6ZxasctRKqT6cWu9YK376UWOMRK/C+/udPgZ5L4jcJxw4eH9/91itNkjrQukLqsJ9352jNdhKJwR+2DJG42iVIVD2g/1w+4OK52IiYGFwlMpRCttx0Jlq3ODQdJxKv/3YMZnPuLqh30Zj9Ept3iBRSkX1gKODBjT9KEV1o3CbCUPiytzmSt2393dKOei9wmiEdfHg3qksHt1ow9x0jKulQ/DmC5s+tCl98dNp9+u65AshJvKyTB7wb/v4VW9cf/r5Fz5K89bOlGk2ePv2nUcv7HXnsX8QRZ55fmM7PGna3DEu5snJ68snD9KMkbRcObY3Su0e4CreQ2WiiDXqwzPH2qikfMPGYN/eGU1A3UB57J6+LDIIaSEtFzRVmh6E4DUVtXQsCGMEetuBipk75yUYVg9iXshZaB5bD6wwrnMT8e4cpIF0an971g30PtDk33Nrkba/QWuEbrC/Ii+fiNdXYg0Eubm5cHS29w8+3t75+P5HQlq5XFbiy5VljcgoyCj0sIIFpCdaC6hOj1W60kYkXa6sy8r9OCj3r9Rvf6CUO6EXb6ptG7LfvfyvO9nuG8xfh9IKJaUpUVZsRE51SNdESkzC2OhSEIuIBVQqMVwI6r6ZNoOMY5jcirl9YHt8pdWdUTeCriRpZBmkl9/T73d28wTzy/XmE7wIlG/O82nyrrEYZ1X9Tu3Ju4/qzn3bUDGiVHQYY1T6KCiRvrfpH3rxGnuU/RC8isdP8LRGGbC3QW/vIBfQ4TzjcMVjLz+TlitWfWG5xYhVoWnh/vWP1Ob9caMFxAq9V97vO60X2hgcQ7BRqH1QhnkG33BDaj3eWTXwcrnw6fWfiHmlts6f/rd/5V///U98+9h4e+xzwW7T7iDPRUrjlDifIa+jzWO0w1czmRZaQ3Mi6CBKdy5mJl+UVmctSWVNPjTmGInJCNF9lPdjd3g5KikYOSgv1xXo7DO9w3pzU/qSSD3RVeilP5vFs3b26l1YS4yuEMYFFMMapQQeCB8fH7TmocatzlDmIOzbTpl/RlLgUrwKaD8K1zWBwcfjICadEnhBe6VXKL2xbwelVnrvBOleOgpI9m6rfT+47ztnxKSJS/KTGozOY3vM9DQhKm4vaZVad0qplNIoR53ByjBMybeFPC0AIRm9T5FQFC8pnvfpl2/fverFfKPMwxsFWm8OwTYv0C29OZ8aXYjm0fEe9ODSfjCVWStkRL14ukZIxEX+5rX/V71x/bc//4l03zmOweN4Zy8P7h/fCOuNtCxcbguvv/1HbjlwCcb21btr+vAk5yU4yfhxeM5fwJDgUs9SGnvzbp0+iVuh0w5XNQ2rrJcGJoxaacPJZg2ZvK7uLVHv1zKG99PEaaU8J43uCcuYG4rpOFFqMMoHA4eS9seBmRHeAq35AxbV4UKTCuZSVnrxJPmpSDJRF4EwPDdPEvv7V7J1onUudeV9v1MrIJ0///k75ThIUhlmVGlseviJtO6048HWP7CZufhoOlt+jfd7Ib0s3L78xJff/zPf/+3fuH/7A9vbnzEOtBdktOdk2Eells2Tvadc3zdbh2VGDOQcZlL6K2PMt5rBqgmL/rJUcGFAq4TQPeDTBJOBhjS7xwZBXPFmw2jlzmg7o+0c+zf68Z36+Mbr5Ya2g1Z27t/eeLldnJhujU+pYRLoRAaVfLmwrAs/rcr7oWyl89jeaBpIUXhZlHZ0xigYhS/X24yOAqNTzRPc7/cPtPtzeRTDND0HpSxCGYXaKt2Ezh2sE8cH3z6+ebX8ZWURHwra8cH377+4EMBcoZZmFOdRB6VtlD4oAwKDMgZ7G7ReOMXhgU663tDrym3NLFH4/v3B//N/+V/507d39to4WpuwtqtOMSjVnxnnucIsTa3UVqfvB6A/i1237YHiIcV7uBLvDzR4VNR+v1PqwVEPktqzhgc7XNEmLn0Y4obozy8rOZ95kAENHtnl9TPOlfmfZzYEjCnedyi6z3gkRYmaHJZWzy1dVKg5TngPkp6FrxBlZW+NOgY5R09nV8gpunqw9am2VDfWa/grEUujHaffaSDBGxcAQpOpKBbO+p4YhHx277VCr4fDiMFhw6BKq9WRji0TbJCDIDlhNmZzNtxm2r8HUbvvyzFNPwm31jiOjW3biQFS9NbwVhv7Y+f9+ztX5xNcqHP2uTX4+vWN3gY55dn07pt1UuXy+pm43tia0dcXhim0+9+89v+qN6772zv17Z2tVI66UdtBLTu5VnpZoF+5h4UWlYd0xlF9ExK86gJAjLwuDkVJwCywF+9sat0T588DrY1BSl5Pga683D5hw9geH0jMxOVKvLwS9IracMd9zBzz4a5tQPcmXHTg/lk3+o3aJpE66+O7E8ySlaDTbNwNIbuJdjQ0/QisjRLcADv9YE63uts/hMAaE9e0QnTjqprxuL9RmgeXmgw+vS6ET1fWrFyun/yIb25M3Vph2z6o9ldJ/FU4rDlc+tgI+5VePqAdfP3Dv1C27/TygeggjIFO4jgmry2JeZZyTjVlTMlbYFVZlxURDyiNMUJ0NVuKCTGf3nA/Kkkmt9F3Us6TG/QhwFMzDtrwROo2XJEm0wjKaJTjzrDKH/7bf2HUB70elG3j4+NKjF7N3vMgxgVNK9WMww4eR+PxVniUzlE7Ryv0Kd2/x8E1Jr89QWilcxz79ClBFZ3lgZlkrsxL0bh3KM28taAf7pMypYLHgI1OGwdXjWDCUQvvH3ePpOrN+8HstK6LJ4+o8HJd2Zv3c6UO1g+kuTBlnB1kMfDl08qntHJdFj7e38jdv349Ckv0ypfLEritXu6ZU0RG4Oe3d+5HYXRfXIPKtHEw+a+pWIWZ1NCf/91sEAgkzUhakOF5kpdxJVqZKrwZKitjwtiJNSQWjc5RTe84ApHo7xKdWqoPgkNAusu4BWKK3uyMPKFJw6box2vuvc/rHJg84zAEedbX916dax0eExckIHE2VSOYzoxImIOEETV7n1nvT+7M/57Z0WdGLQ7BldKo1UjV6IsLOmJMrtOzSIiBlD2CLsZIzishJD+dT5LJ1NeYoN6tltMMqg4RNLlfEvNUl6C05jUur7cdnfyXt25nNHg+Zhcfnt2ovEwBlFGr877MgRHhhwldZws7wm240vDQ8Dev/b/qjevj441H2dlKceWOeUoG5p0zYvAI789E5YAXLNo0Hpt151FS8tibAdK7b1ytzYXux8sGU94cI5Ij1+vNlT/9IKSFeLmQbjeSvjj8Y42YLkhtSHOcv1sHq4SZIqB6zn4uucUGtVdGxdVHfQbznsdvC+71GgdRpwxWhPhXSQbnCuGErD67f1IKNJlTZqvcHx/zhXJ10XW9sOYL1zVzud2orfGxD1qrHMfGY/9wYnt0x7eL0tqD1pxA13pltANa4e3nf8X6jlDRMH9G80QAjV6UKDGR1CvMW3WPjIQpqc+rm4SntJrg3WAhZI7pJWnNaMG/VhChdiMk95+NELAGNswhsVqmTHde0zGjjKzS6qDWnZ/7f53dYQV6ZT+u5HxhyVd6gbw0cjcKCsck99uDvToEN2l2VAa7NPR2IeREIFOrcRyF49hJI1BnNNOaLu7jEfcgjdYpw9jLYFSvojDRGSYd/d72g2W2WRcbvH/cyRMy09NZavh7MMeuGANJnPuTobTi+QnJBtW832lZEr/56RMvkggmPO53Yik8Hhujd1YnRwgh8tOnVy7rwrokoiVSiny/b56zWCt9uMBmsexVF2NwlMO9hyIwfVF9NiswIEhwNWWujOSLem47tRVqPRgN9yzh0WxLiOTgG5cLITy8V2ceIgJVGjZcXYo0gvgCHWJAunn6jtkzxUFU5rASvRGg9/luem/XmZTehu+SbjYXaumk2J+WhuCNlF5OOSN7vI8r+fNeBiruqUK9jsfU/WF9jGkoH4wBtXkx6TCXt/tIkoipulAjBXJK5LyQUv6RBDPXLA0RmU0FKaUfG5ckZNhM95glus0Hr9v18pTaazibMNIzlMHkhCgTJp02Kqcpn/lOOnjoXNiYNgAZnVgrsXuE2N/68aveuP749Y/enfQk+VzmetSDgRst4mNHdSB2MErxHEANvH99Q3HYoZsyZKahh0QtHxPnBtPkE5RCCBPmagCBPexgLkF93D9gq8j7Qd3/3SEiq4wudAuYRJbbJ6w/nPdonWbqD4+myXM4mVyPjTrGbOT9TN13RnfVlmPWDWhzagqIRpbLhdF3rNdZshgQ6WgZtGj0XSnBo1+OEHlXpfXNN2sJXNYr3+6/TLzaeL8/vLSvdW4vv6WUO8f+5jL/aYGpdWB2YOYvmR2F/eOdj5//QCsfLhwIimqiq85I1MbYNo9MCu6rsuHZcff75kbUmNi3M0tw8M47IV84o/nL/kYrlXo0NK6+uBs+QMzJEs2slxc0KKNvvL87T9GHwfBhwKxS9vv0m0DZH84fis2XTlz5x5tDbjrTMjQTop/s1HZmPjcSMhojQY0+r6HukZAay2dPFiBG7kd1vsUG39uda/SK9bf3jY8yvAJkCNY/ZmyOeL+YTIhm3CkzKstQPu4PPl2vfHm9sUpihk7xsb0R1eHp421zaFnjDF72bTaEwEu8+OS+JCQGanfRxhjGz2/v7EeBIFxz5Hq78vrpE+u6chYtvi6vrK+fKc04CvzXf/tX9lJIaeU//If/REe5H5Wvf/kj275R6sGYGqLtqJiAjl+41MINpjTcF9pPL5ljPziOxHoZHM37zpYlsiwLy5JYL8pRfLN0YFK5rInruvJFXjiq5yh+PPZnjl+IgRyFNbt6MKiXMKaofPly+1F5sjv/FoLweruiEvH6pMplVS9TPTrf7zuh4OpDEa6XDCk+yxyHuTRf1U9q++FQnM6YptuyzqLUTikHKhOxMWMvB/suPJKScuaSI3EJ9NaIyvTACa+vV5a8EEOgfCRqLYBxvSx+ggx+zYLPuE9flppCSn6SXTJLVu4fK0c56L2hAtfLynq5+LUKDmEOPJFDopuubyHR7QOlk+PM75w1NO+/vPP2y1eW3//Mv33/E612ztzWv+XjV71xfbrcfOKR6WvA1Tvr9UrKC2m98OWn3zvJLw5HzFJRIsoYE5fXRBU/XZUGw4pDAB0s4EbeGIkJLpq81j0MXm43hw5otBqowzi68fh20HuZajmhTfn6kAUbC6NXWmnUXjDPTnA4ZLgHhgCjeZvw8nLjcv0ECARox4y1wqXg4lW1hJixUWB0YheIhoZISjdEB0tMXPPqSQXqeLlEI4nXsi+XzNjGVARVjgrdjMbAxgulbBzHne39zb0p1tkPo7aNWiv7vjMtljAGOS1cloV18WbcFJK7+0d1j5r4tUWmoXo06lHnS+5KsLy+kpYLt/UG0UUbUSLNCu1oHA/v2Rp10A/3xSxzWh4KKV0AobVl+rwKrVVG15lVCNYvrh0QQVAul8Vhqnh1Lm+egqAQQiaElbiupPziBZxyIPiUq0vk5XabvrqDqxdfgRq3nLHhUnWVV9psuw17IYw3em0cW+frvrM3ZW+B4/jukVjDqGO2WtugNWHJnxANdB3EkPjy+ZV/+t0XfrpcZ8D3oBwbOTsUVWug010bKhmxMhXjgoTqhZUpejPxUIKBSKEdG9v24Ov3b6jgE/vq9/XcuG7pwgUXADw2475/ZtsPeoNyfBDyldfbCy+XKx8fDz7udx7FfWVjeGyaP8uRYZW+7e4BEuFFbrP4sFNb9a87BiFeMA66DULM1DYhtyn3F/FmBLGBZc+LjFIc0TCjPirXS2JDefSZJTmMNIMCNLh4axzQzKuQ2mPnslyc1wmBvN54lcgLgetWnpL6fd/Zm3M8j9afiRsxBZaXV0ZcaBqpZaP0zlEbQ2cVzIAiAVIixMx1VZbeWXIk5MT3x8GeAkGMt21zsUZUrpb5ogHJmZR9UKwGex9Qqw/eBqNVorlXciDUKXMveycckJIQdPD2cJ4RBp9vKyEJcQ1cX1a0uEIwiNCOAzV5Qv8BoDUPesAh0m7eaPDx8eC31Yg4NdHP8Ni/4eNXvXH99PrZM7lccPuE25bLlZgSmjO3648pIUn8q40r0EdhmNE0UfGSQZ1CBevmvU7BF+GcMzkLF81+U9Lg5eVKSo4JH7uw18ZWqpPtrbifqAvNnM9q5uWGo0WqNKQZNnxatxEwi/RgdCt08bqMlDORxSX50bzh1AZCw0L0jSuIh9Ba9lDRpkj00sa0vKBqrClzW65orCTFG2LXSJJECpHlGuDhSfR9FEoPvsgpHMdKKVdKuXBXpY9KG428QKkLpRwYgWq+WY/mf3fOmXVdCVlIMRMkODc3iz49WSFNWHMwmjyVkSlm4uVGWi5c8guEgQbP/0OFWhr7x0FpD/ru6fRm1aEjVSyYN7wa1GaUrUzOD1eOoXSCtwufG5cELtcXrsuV1+WTG1O7b6hiOyEuhHQlX2+k5BmUSc+NK6JL4PXllRgM5WAxQAYWOosCuK1A9ZUe3X+WjwJ1MEqlRCOkxNYDW4tsuzGq0btRupP82KB1iPkV0/CsnL/dbnz+/JmfXj45nKTQyvHcuEqN9MlndRIqxcn/oKTs6SEEZS87sQciEFOjl43HfWFYc9l2il6BkX1Cl9n5pVOMFBNcLiuGcGxeeChhEJKQ1pVUOvFoUDYPsW7Na4JsGoxHp1U3FBuwLcGhtVa8iXjaGlp3DkvOTXnAUGY9PcQOoeP1N3amVcgMKTFqa2hSF2C1QewylXf+T1UQw7+fyY/WUQlpQScUm0P0k/fk5kZv1FL4uG+U5oHLR22+3ogSNSApE01Y+mCM+uRdjzYA91i1YbNLUEHVrQDJIfT7/qDPHrutVKIIfSgpRupw+NdU3FCN///SBwG/BqW7oVnFQ7/Hmd86ZmGp+Jq3l0LrddIM+qQeNCoc0zCt4iHVuAfNpi9OZ9P008zFVKy2QavOCfYxVcR/48eveuP6n/7jfw+6sG0H97ZhDK450oPSzSh9UFufCiGltuJwkyhlcl7CoJZGnwVvqp4dNoAmQiDMIjhYpjophsi6LLzcFlL2SJ/9+JiVBK60CuJc1NEHgZk1GJJzYipTORr9lDJmBpooakJveaqAlBwCMuZDYL4xu6pQaeKQEO7x9MibWSCgJk4+j06cDdB+6lRSEN+ElwXMgzmTBtLFiXFs51GFjuPgMUSWnGhVkeotx2MYyyXS+8F27DQiejyo9UHtZZLSp2s+z1Ovh9lqTm6EHGPm6gkxwuff/gOXZeWSF0SEvRUv/9w8DilGjzTK60Itg/f1YPv4zlhc76t4ncgJ/Rku/BDz7Dqbja3d/IVEhZzzhIW9cmJdLlyvN675hXTxNtz9XljYPEw0XUnXT2h8QUPmpjt1eBxWiK5qCzJYNDBKfZb3+cHYk7j3Up7BzzkrIWQqg+NRWNLiQp+UuaTukv/mG5d6IxbKSmF1ZWJv5Bx5vbzy+fqF25JZbwtxidTtIGrFbPCxA5IZ+H1N4uWcIUY+vV6pNjh6BQZWBDXlsmbInrD/7ds3HpsPPUtIrtgLDs5/vB+UMTxEWCIxJ/KAWgJGohyDrbwxFD4+Pvj4uPPY7uz7Ozam+Gc/QCJDI/2YhlcR/vTLLy7z7o11HYi6EKcNXxFVhkcHSfRNfbj5WjbDemUrx7O1IIbAEl0QcLTGoPLxfud+P1jjylkW+jgOWlV0dOp+UPbK0Vy4cb1MoXfr1Bh8TZHB9XJzj17o3k3VXTG4bQUVkCVBUupwJOSyXpDugc+tVR8KznSQWtEQaN19Zpfs0XO+hg1a8cLHVvqsVYqY/f/ae/dYWa/yvv+zbu87M3ufffY5Pj4Xg3EMpUTUhqZpYjlRXfTDMhAUpU3+SBPUkqqCxjVVc2mEiJKQpGrdUqmqWkXJf6FSk7SJBEVFaVQI2CnFoQmBEnBxsevGXM7x5Vz2ZWbed92e3x/PmrFPcGzsgE8PnkfaOvvMzN77nTXrXetZz/O9WBaLJRajNIykLtDeegWkOAVrkQvWK6rQNvFqg+CawrwhU1ImjRljBB8soesbkViRkKTU1PLVUFYUxcWYltQiCmZqqFaDcsZsN6HYjv2xsD8a0liR9CJVzrhw6TwxJ2IemceRVIqqK3Q9Zq1JOLQGqSGEflXMog+TZiAoZCy5JoXJ1wbssOp1VClrx+CLInQ+4K0SLWeTJucklaxesQqrHyu58TN81+GbFlpKKoSZSyLlUYVbSyPz5YSgqMacB9UsBM4v99Uh1yuKyDsl7g01Y/uZoqeMQNLTDEh7jbL3Qx1YHiSCDyz7KX1X2Z702OmknQy0J7KIC+RwoMRMqqXJXSltYH6gmXCRzOFyj3FckFJkmUT1yXJmuUzEuNQbtpE4S99T4oSuP8ZsYuk6q4ik0Ii0Rjki1oHzcPxoz9GdIxw5soWzM87vz9mfL1jaBZpdKyUhpkjJFWcK3lcoCSRTGzDCOOW55UGRaCUlDEuMiTgbCVb5R1CIMTY+UiEO2j/rfGB7us321ozgPcE4skTsGLBugV08sV4snpAIVl2di2R855UAIRlfaZBiw9HZlNCp55RlQpXHVX5ocai9vFqxuTLEkZSEmEFqJoualC7GQftdTZQ44UgVhlIwIfDEOccX/rfn9ImTnD55ghPXHGPnyDbLBo0vOWBsVNPDCElGqgvUMGHvcE/LZ7UwXx4iMWNrpg+q+r6YLzj36AWiMdix4BeJrb7XXpF17BAYhzljjIyjsIyRRcxcWkREUIRkLKSigr863pkqK6CG4KslLwfqdsRmLVGtHBOW48gyRWLp2d2ZMusneE+7Ly3O2wbcaXYcuHbqVIPKvBBizApWmuhn4EKvenlJT3a5KlDDe08pWiK3IozAYUykLOqf5YNKUFGaPUwkpxEaOq+WQt8FPX0Yg3VJJZ28w3ZOPcKcw3eOOlcsg3WqomGdgm9yFHKKDEPk4GBBcJ7do9vs7hyh6zokK5oRpyhG33fMtmeEzmO8oZoGNDHo2rC2TGnAtL4j+IBUJc7TRIO90xNfwrLM6tKQrSfMJtqrpuLQsrkmUBXnIZfMWCLLhVrlGE8TAIcVKOvS3h6TCxcIly4iY6TETCwvUluTg8UBQxrJJROL8iOEJmlkmzdPHLWpjjY+V6rNOTajMxHlyIiKQiqtSh1cVyrVzgesVafc6FyTiMnEhUqpFCnUtnEhQlfselPs8gTTNq4ipolQJmIZKVkXEinqjSXNqLCUqG9QREmApVB8IIQAbgUtzuqGYlSY1nrVMBSE6moT2VTeUkwjyTmVbskGVzO2Fq0xZ32N5JF6uKTmQgW6yaSRSCPDciUzVVkOB4zjgpgii0EnXypCjEXfT0N11lLUJyvb1r9rAqBYTFU1BJw0PTMAQ1wuWDijoBJbWQ6DbixxRMgKmHCWYoqWjEb1/iEliEk3RRHlBxHVqK7V8JG6Bl5YoxwgrcFXvQZUoT1WtXehFgyZPgSK76iSsKZ92apJBkYRiDYor0iUoqDiqBkvqgnYeUONA6FTUICphzpWKVGWc4VEi1pj5DiQmzdXbZtJqrmVbnSBEVbJljBWoSadpwZa9j4wDnPmx3ZpkwQpAWf0NJoSIFpziDZgTKEYSwHGPGBSwpSMsxEKLJcD5y8dkKzyDwyWWRcUpeYc0fUsB50TOcGYMsuUORyi6tilwhizJjVreagnUXkWYWFcU3Y3TLxV6w5RJfWclQ/mksNaQ98Feq/9LOMMYsw6sw/BU/GoR5U2EETq2m9vEhr3yIjyL7OWq1a9cttoEooi1h7MCrbvnF17VRmzUqqoT9HkU9qKyArRqxsT1ugGZY0KbjcKyOraQI0anXOUoklcHHLjJ+q9klJSTljrDytqzzTVC4XF+1YRMiuxX1TU13lVJVlb16ywpiLtumVVLVwn32r9J7hSFTxDk51rxq4W5X7pTzSx3zYGIqqIshpCEWEclWOW8ogPDrE9kl+kcPjz+xcZG+zdrFFWWr8uVhWnU66qBE0lZdXgA1hU7ccUUcWC2mabXamSo9BmjMG5DusCKxdY2zKZaAfUrysTx7iGsHa2b9mfwbnA2rbO+ibHk4lZ0XjS+Me5Fi1VCDo5Gseh1kIcB1zOqrhhapscBWKEdsN1k6kKckolMa6h8NEq1NsZyH6JTDvtpywH3HJJXAykYWQclpAKFkPXK3dJS9SCNdNmAlg5XMzJUR1qF4NK/hRBUXBVFwHlkuh7rkVJtzFmTRKsxeemgu7USdl5tfd4/Ow5zgeHDQFjt6hNeT4vRlaOt10XdPNKhTwf9XQ6JiSqjI4TwYlgyDijfSTV9V6FXS8qK+dYtZ+x7fNQRe4UBwZrKCkQfVLVB5V8VaNB68EowCfXxbosok67eoM7UaXy4C3DMKq6gDHEZSQllf4iZz0BoMJVRiGrmGYIOtZCbDJFdWXMZyBVReVVIFU15atSGR9/lMVizuPnn2D7yDZdr+VLildrdlAlFjJjrixT86gPAUJDjCWV+aklksbIMEYO5gu9DhGqqOeT957gHdsuUJp4rbVBrztnDpYjsSRyUXV4gJUeYF3dE6AgJqmkEilScEeOKRS9NsPX3KoSKeGsZdIFZr0jFk2mclEEnsHReU+W1WcgSI3UkqhZy31pohqVtWZqHMkpgagCh/ZpIDQUYWv5tM1FXY6136SvHYfSyMF6r5cCtWrpMgQ9SZr2O2zbvLamHaAbkarmZ2rOdFu6ARVn6LJjsSxQWwXJKX0gxYhxnk4R53pSdZYQlKKj+op2/fdoShd9c3bWed6SSpOgOnJelQhVMkyluQTEtOuDnDJdW8dqrTjTzEFLA3MVgaqb8hB1sw3Gru86EUixNPPSxHR3xkQsk/QiPXEtqx7/nYUmvIJg2sIkVBJWovaTpKqNudVMzVr1dEJokHKVgXI2kDV5ACzVqpozkhDJYD3OWHCwfXQXEZgv9sgpk0who2rM1mrm1rfTkVC1Ll5GpOikbAxILTPUogjDxkb3occao861GNVbm00ge6SqIoORFWrNqD9xsxwAB07W/JHma4rFkZz6TUkIXDiYU4ZBmfiSmE5nWB8o3lJiQZpLMFGRgzFHclyqfA2w4w2DsSpRZJxq3plmYudUP06scmmMaRqCzjeKgcf3nhpm4AziBCONOGygxI5q1YTL9RMqClZx0wnLxZyYC6kKuWWXCMS0pNN3DzXRkai5sD8/oI4DSMFQCQm17ajCzCmfzJiu2adnJbG6wPaRqerjEchlRE0mPSaoySXGMsZDXG23qFXljpXIrG3zrXrL1s4RNbIsidB5cJoUzHwgS8WVyiRVYhJ8FYLApZiYYJha3Sy30Rs2IizwFKPjYyYTYi0scyQax/GjWxzdntG5Dms1vx6laIHHOIxVEenSOI3ZFIYxEQ+Xyj2UqoWlltnHYllE7R+tFDnoOnKpjFU3V+XcVahLcinkUolZieI6J7TqUWmu402BwjQy63S2zWSyxdbWDtV0eh+7Hu+OUOshuczBeKLpGc0UTGCsiVwLURLbYYL1U1VnWFa6IARbGQ+WLLNhyCqCORY9qeacyDEyHyLzMbLLjGoruEI2AeM9Io5F9SyTJmDDWDhcJLppIPSWeU6UpLqZJqa1Xcd8MSpnshYu7i2ZHZ2qMDuVYpzeAyYwiGOZIaZKSBVbNZHFBmKpjE1v0BhIvhBdwZqC8bopHswHuklg5vz6NG+9luFt12PcgDEjzmiilyhI6IlZjT69VfFjGpiiZG0viFgO5iNiMlMXCF6TSbqObDsG0dO991odEiOIa6jnQRhxZGsRu7JUoW1ohi13hKOnjoO1jCk+77X/qt64hMZMb8zz2m42aceY2kS3NZOyTSJJ+QUry3tjjG4Aepe20psOuCofPwky0N5FUdKeUQ6GiIpK1qZWgLEquVO00YooEVkaN6WU/CRJmtKO0loWEqMLoBE9t68/+BWkB7TE0mzJ1bRPM0Op9Snjsjr669+nlSxWpETjDMZZ4jAgOSqhGRo/pSBZUW61JJ3MSdFfMSVKijhrmpSSaSrget0r76PaTh+rhUmJxwWqxYpyrlbcp7IiQAM5S/MyUjSYsVqW0s8sYL0l9D318EAThZxV6ipGSFEb5UIr2ma8UyXwGEckJS0VIpiiEHNBe3xthIGn2KI7p6iu1ZgjrdzSxE5VoRnvg2rMiTSlbX2dx2KN158zrJW+a2mlslVprzYbiirYov0sfQ+sx1RgjRDTOb+CfrczsWjK5o2lNLFaDIwxYlvTfUhW1UdM1ckgKg47ZlHl+zaexqhTtWmakUb0Xokp6RUY09Bu+jcEaRWL2hRQRNF/RctmK4LuqkxlDDhUjsm2zcsaUeRu6PF+gjdq6SPGNENUdcJ2zpOLcr8W46ClUwM2GMQ4KpZYFHjhrMUjzOcDh4cqYWSMGvQE7zFG9D0nRUtKaxMUQQFUyWqftuj8rGKUwF5U6VxsJWftneWYta9eFLEoIgyj+p8NY2JSJqp2L1avDVXLMUaBYiKGlDKmKHqv1qrzXlTObFVadG1ti82lPcYEIgrIsGp2aUqTDxFaIu6QUihZWxU1F6RbUSwyVZzqo4rec0UyMQ0MY8Q6oS8OWvFdlX60uqL4Mo+YSDWqu1mKqvAbbKsCaOK8Qq9iVuAS7Y3V+iKFwwOtiGyobXOwzikLXrSR4Z5Sky4NNbRaMNdQXnlyYxCj9VlliKNHZqmt2ao3ZjVNpHdlJJdWSDZFD0otreZbKeiRGqT1yZofkGiNWGvKbdNbYXBrpZak1yWimylaeK4l62ltbXeP1oucXrDe8KvytW6O1uri6VyrtTttaOe41CE0NM+x1jsC0qggi5Ijrjm35qy+O269cELGtkXVtguR9jsFs1IwaDdFNRlbrcLgBahWictGsFVv3tZWxtqqdjLWqRuzdbjg6SYdlEJdCbGmrNbizf9Mqp4WnKuI84ippJRQ1rjW5k1ZiYC2oyN6iig1aaKjxyG16agrBXqFnT+ZAOiXb0oKpSUOtiEHnXNKVq3qKJvbJltKJtcne61DW/RNK6eVrO7IK6mgglAEnFSGNsLGGES52FrqzpFV2qLlICilMixHnNUtcswOW4IK9iI4k4gNCl5KITWvJe8E77QkZrzRaYaWtmj2F9rn0TFaJSu0k5hIXfeE1uOBXSeXtsG8fegV2daqIyH0BN9jbUewbfxpAtlWHaC9tdQiLIfEcjzQU0NwbLkJxnrduFIrSVlPFeFgf8H+/iGL5RLFCCvIou8DKWv/LeWi93WFUi0pFXLReVsaTB2hIQBLM1tUfb4YM8My4kOic5puiBiGMTKMiXHMSNv8BM+QCkH0ndlWBQFLTLklBbQUW0sPORec1euzBnI1zYQx6sZVlQlqjVfAkWiCSJW1LqSUoifsCjW7tl7Q+uoGK2CK9sNSiiyXC4YhNTHroEl/BcoKAk8zUXWsPdiriiMjYLGkUtYbkzNNts0YdRTIQggWW1+kDsjrBdGCldZyLBnvPN4F+ukWRybTdcZrTKD6DrGenCq1jFAzvmbV9AKoahC3OjFIk2PR/STj29HZhEAXOpIoStC0TUeqZjq1VNZOoaRWe2z6ZlJYua6uGtS6PylqDmj/GqBijW+LpdB1mrnVUvFGsF4Z8UUUDVWl6qJmnd4cxiF1wPWe6ZZ6jU22d5h0HflLhtD1eN8hzmgfrVQkJ0wQqIUiowrpUhv4o5BaM9e1LKwa24AWepJcuaMa5/FYTON1qc8SBCcEB9OpR1wPzmKc0E8m2pl2AZum7aCptiRMPJPphGuOHefxc4/DmDG9B99jUPKobrlFVf8tzCZCTqrtZ1oikYEJOvH1hGwat89QnGuqNYJ3pmmsGXyn3DlvK84lXK+LU6mZkajSXChvBpFGg0j0fqJJhAMTPCKZXA1mMmvJS6UkzcwtFWsryVb8Sgm/9WmMCLEKPZrXNBwcYJo1TiupWuj9BFcMOVamE78Go2wR8F1HNY5cHDEW3Xu9IRKY0LJ/E7lme4tp31E91Gwx7gL13KOaOXtHP+k5cmQL67QkbsZIaUCCnCsHCy0XIqrMQVuENSvw7atp+lmDt9II6po8LUtTL2l6k2NeMqRDapgSZhOO7h7jSDwC+GZLVOhshxOHzQYvHoPq8A1DZhgU9VpKou96qnEkPDlbUjaMsTAshTw6JHZIUR8qkUoeApQpUgs5Okr0qGAAWDPT9SLBcuH0tCtCzo6aAzULMUalkZRAb6aENMOKFrQlTbFlVDu2ZBGcDpO1eFQ13zASRxgCDEF7sopOtowD1OQwxePE4nPXSrJgc0eQghh1Cy8laZI9gjvi6GwgizT9S8GUQu8C1RSoPbkoyKNkgy0OxxRne1y3RY5KLnbGQ+nVcaNUUnZk55B+gmvgkdVC3UllZiq7TnBG+92HdXjeS/9z2rjuvvtu3ve+9/H5z3+e6XTKd33Xd/Ev/sW/4FWvetX6Na973eu49957L/u5v//3/z6/8iu/sv7/I488wp133slHP/pRtre3eetb38rdd9+tu/JziBhjq0lpv0CTPMG4qnZUoWexXAJaAkumg5DB902AVRe5nFMj4il6pqzsDdAj8ArBU4uoQ6oIRgyL+YIUR8blspEXaZ5PvpXzFIFTays1GdPQhprvV2noHGPb6a6qUKhVhI9FRT5rbeAwF7HtBFjrqnSp/ZoV0rB15DTbsTRbEIXnD8sF1cOcQ5IPpJioWUgug9NygHYiLCsNRM0U2yCvsqs2/qvvpZUqpZXFdMIqdFs5Uk042FaMKZg6IlWa6kfVfpg31BQRF8B1qu/oDMYKkgbEO+YhMOwfsnfpEov5QjPPHDFxxKSBkgdsLapJWY0i2bKiyUzbbPWka57ctPSq1+9JS6x6YsG1c0yDOmepCugx2q+oWHIprTyka7O1q/KxYRxjMxC1zU5nJX7cSsIYhXFroQUjZV2elKckNKUlAprnPHkSW58aV2Xkaki5gC3ts2ulS2OgJNWMM45UtDeRRVUNqnGtRGuBwsF8ruadXvtCKSfV57StVBUTwxgJweO9Z+qV9F1cxYgaRSrIZVVaRMvx7XHt75pWmbAYhJhHncsiT1nwKh2QkqplWAMnrrmG669/GdO+58KFQ4ZxSSoLSslNZFflwwxCFwwnTx7X3rQR9veXet/VqqU52rTGkKKe3ktVd2XnFeHbhcDKF251VeYp33tnmXSd+oRVRUHG9rttqzbo705NUUalzoo0IWKjn3bnVBauipBLRKTgrWFr0rMc0/qai9b016V6Kype7VD35zYV1IjVGrKBzvsm7aa+Z9as3oO05FqTbUH1IvugogSWlsjWotxRp3xY61W5wzf4pRhLNdB5lTFzzY2Bpotaq5aTSymIURQu1mBWveHnEc9pp7j33nu56667+I7v+A5yzvz0T/80d9xxB/fffz9bW1vr173tbW/jF3/xF9f/n81m6+9LKbz5zW/m9OnTfPzjH+fs2bP8nb/zdwgh8M/+2T97Thcfc2oLEOuJRCvNVQHrVUuQduIqpmByBZcBjzWaodtcKa3YomAd5VBpXbe204tRz6SqSBpTYTlXW46UG6O/VoXkG78GiFjR8t6q77QCUKxRT0a5R605B9RWD7JrUInuc0Z/jzgQ3fSKVKhJDfBAf9ZIQwAWrVsbpz20nBlFwFtMhWQbClIqxmQFrTiPbyrOtajic86VJEYnXtWNkjbm6w1thY5gVUGzDfmok7btobrA1+bpI8DSYoKo6G611NGoZbmr2NKgzhTyMF+Tuw8uGeYHB8QVMXSYY4ryjkSSwvxF0VBzq+aAuSgnh1ai1fWtla8w602Hhkql9Qdde73ueboR1xWi02oJRcshZtVqVG5NBYpaRGh5MbQ+kPb1VBtb1ou694qCpJVzolHbEVVbaSUe0TOWBVVG0E+7jYsuHFX0Gk1dqUXo6w0VSRUjbbMVpXZU0ITNPiUJKZVlSgyImg6GjlQSzvsGsabp6WmVwTnUKFUq1hZtqa56zm3MLU/iOqXWNtZ1XUUoKB+qVmmir259IhUjao1SVZD2yM4RTp06xfFju2C+wqW9PQ4XiZxGTUGsbfcgOG84fnyX5bBkjIn9gyWCmlWWXHB1leyaVgbMSjPppk0YWm1TdHw06aiySjhbFccZBdtY3TBVZaOo2WObXzkXTXCQRmnxT5bdWn8ba7FWE96aVNfQWOj6jmVUkEtqlkVOZF2q1s9tVZbVErOgZHrj2mYUAl4M1VbElifTz1WJCXVkyKv1yDXIfkO2lpLX6ElrDMbblnC35nlLyoMzDEk3Omuekn6IQutLLeSiyZxyVp9ctZ9rPKeN63d+53cu+/973/teTp48ySc/+Uluu+229eOz2YzTp08/7e/4r//1v3L//ffz4Q9/mFOnTvGX//Jf5p/8k3/CO9/5Tn7+53+eruu+5usp8qTb6irzb88QU2I5LPEh0E+m9NNthc4O++rqicN3E1WC9p5xnLcBt5iuW0/wnOZai27SUkJL7PyIa/DZrpsSqZRxSR3npFXz2XnlscSBWvJaTQJoH55uWNZaummP58mN0+HaCU2zIGcdwRosXdsQMlJgHNXOxfkpYbKF8xOMddQyh5L1OG+aZ49xlDiQBKrrsK7Tk2epxOFQm67W4UNgvr/PsFwwLA4blNeuSzvOt74hihwCPZwI0qwfVLdRoXMqmhv8BOcDYgRnggIIJGp5hw5rAjvHrsF26tK6zXGGqJ5MCwMxHZDSSFokVeDwvfbphnOqBiDQzWbUPJBiYjm/xP55XWnVP4p1djMYo/5NPmjJ0OhCZ5uPFKhoaBd8mx8dcVSR3pwry2GBsepqPd3exvkpFaNzxXZ6kq8N1Wm0iFhNpyVcZzB2htQBqVo2nm1tkWPkYEycO1QldovCr1dzJotwYAzBWWbNd0qAbCD4Tud2LQRUyNeEgAkdkJvauNIwxDgsgeoCrpvQ9zPMVLNfKXB4cJESR3XRjcsGejCqPN5NlSpQEs47+umMra1tJtaT80gtmeB69oeFYnyT0kqssQQf2qa/OqUnPZ218no16EmsRJ2XokCoah2pKLE1W08UiFUwNigQJEUOD/d1g/St4mEMxTiK6+iPHGVrN7FIBvPEJXI7xdZaKbEQYyUW7R+OAqnAZDIlo+K4Cxyj8SSLChKMIx2O3lhiilgTMD6oW3BWvpgWwCwRy2hhbiwzGxj9hDo9inMekzPRdxyK4SDBMjhmsw6kMBdYFE0ypAsks1ARYKuitYhKUw3AwnrmxrOo4PykaQRWpK/kIbE0EeOnumHVTClqwGlWm2VbNyOV5ViIJbMohUOprRfu2UuJmeuw3RTjHTlp71YyjOKb40amZGF/PnBpX5nVK+d4jK6Zy5i4eGGfYycEKZa0vEI9rr29PQCOHz9+2eO/9mu/xr//9/+e06dP873f+7387M/+7PrUdd9993HzzTdz6tSp9evf8IY3cOedd/K5z32Ob/u2b/uqvzOOI+P4pDzI/v4+ACd2j2tJpVZqO+qaKpjOEbxn0vVcc+YkW0d2mW0fg50dai7klDg8HJgf7JHTqM1hd0pvItCGaugwkykikWC0P1PjqBsAYExhe2cH6xRVFo4e1+N3ToxLWOzvsTjcYxkPqcsDtaiohf39A4oUbBfwzaQNLLkkbbKimdtqkXWNL+NDoOsDzkzaOafQz2Z0weGdJzLV0p8xhGNHQRLWWvpuRh7mGNRbx4fCbHuH2fYO3gQmsyldCAQKNQlpiOxfusj5i4+x3L/Icu8JaoWDwwX7hwvENpCHMSpFtcoWnFHidpuoznmc8wTf0XUB14iXzunGGDrPdBbINqi4qDTOt10BP3TjUJTelC4JtU4oO8rf72Yzto8d49j0dZArJWaW+/ssF5dIwz52eYHOGpbzJV946BHMRGHYVgSTW8m2FKy1jQOlC6qzpvkWheZdpHYRR2eqFWeDo+8dy+wYs2GMEdOU64VtOtchJZOHBePyAO+g6x3T2bVgVI2lm+2of5ZkTFoicUGKAztTz5ljvfYiSuWJiwdYB97rhmWLLrqp5FaSBNrJe7VBeOfWiha2gUesd5iuxwWoOMbcEeOI8QHrHbU2cMSko/OG4A1d5zi6M8P2M+bLyNmvPM7WiRNMpj2TzjMcHDAstVTeFXB2QuctR2db9JOOi3t7XNzbZ75YUBufyBjTNPgUjbo66gpPns4QLZEbazFi1savtZUQ9w8Hzj5xicU88viFC8znh8SoosxVLKbqySTGxHKh4q4XLu2zd3CwTkps0wAc4mFTsikqZ5RVaNt5q32vMXN4uNA+aS4URpo+BgZDrq3XKIWo6twqko1lOQyMMSpqMWZyqpRqwDmqUT3EFCs5K3Yr1dwQpVVP2EUBGym2EioqRSfWNt/BqN5YKSunsbC+j4xATkUrsmJJRZVZVkIAK+BaLvo+1kAugTEmDuZz4pjwHkpwlBoQ4xHr1MlhBTaxjuoa57UI8yGSom5oT0Vte6dVnFSE8/sLSgi4Zqr5fON5b1y1Vn7sx36M7/7u7+amm25aP/7DP/zD3HDDDVx33XV85jOf4Z3vfCcPPPAA73vf+wA4d+7cZZsWsP7/uXPnnvZv3X333fzCL/zCVz3+kuteooujMXr8tkpk7bdn9F3HtO858ZIzehLpt2DnKCDUXDnYH3jisa+wWBxQa2IajDYjRbh08YAaAnZri84betep7M+4JFiHs+DIzLa3MS5QxbF1+gyTrmNiLcs5XHjsHBfPP8rh4iI2zjFlxNTK2XOPMqQROnUPFjGUYpjPD7W+LwK0I3lr1AZvCcHS+yYKi6IAjx7bYXs2Y9pPGNlmf2+PmBP90R2cg+A8k8k2w8ElBM2gJh1s7+ywdXSXrekOO7tH2dqasuUdNQrDfMnjj57j0uFx8nKfOr9ErZavnHucr5x7lP3FYVNWr5TUINBo6W3lK6unSNPM7VbeRrqYdkEfC71n2vckArmIakoCtihAxfra0HkKhe9cTxW9gapxbO3ucuKl1/HyMyex1VDGzBNnz7JcXKTEA45wmolzHFzaJ8VE8Vp2MrUS54XlMLIcBkAack7apZtGGnYEp5/RtO84MjH00wn9bMrOzpTD6DgchIsXL1JdaD5VEGwPpZA6T+6dblydY7K1g7FBT2lHjtJ5g5MCw4LFwQVy9OSZZ7e/BoehFnjgoT9pJSNhEiwlqZXM/nLQk79CIbWMZZ5iwe5M24B17vj2WYTOqXJ60jkkVsulKoSrC7YLHdNZx2w24eSJo5h+i9kyUarn2EtfypEjW2xPe/Yff4KLF57gYO8ijJneG6Z94OTucfb3DsmpsFgMDG5E/hTPdF0gknWtRN8LomRp00RdMU/RvLMY4xjGxP7+nLKIzOcLhmEk5aICBK3UKKIiBMuxsH8wV23ExXKtaqE2MUarISU3U0shVSFVoxyuVCmitiWpqPAtuZCDYGslZtMsZxpgIjcDT1HY+ZgyY8prmS7tRXqMC9rKrur3VxqaMVUtCRsU7FTQjWWIcV2il0aByM0tYFVl0t669qgrBsRoTxmD9X7dxV11qVfVB2Glt6JlWessRVR2LDaxBr0+BYQJDme1LqRzzumBoRH5l0W1WVNVJCzASo0kCwy5sLcYsH1P8E4FkJ9nPO+N66677uKzn/0sH/vYxy57/O1vf/v6+5tvvpkzZ87w+te/noceeohXvOIVz+tvvetd7+InfuIn1v/f39/n+uuv57bv+v/YPXaE3SOB6XS7WZl4rrvupUwnKt4qvuP8/j5nz1/gMMOkD+pgnD1nHz/N3sEeh4cHBKPCnmOsCF8kV+29XLO7w9Z0iy5MGOLALPRNgVx13IoIQ60cO7rFsd0dTuzuUJc9j57Y5fHzx9nbe4zdqWErOKZ0fP6h/83F/Uss0qG6gMbKwTJSi0oNSVZZF+u0x4NUJp2hD5ZJcPQTlZmytnLmmuMc3Z6xNZ1COMmXg2P/cB8vhW0fmPQds9kWF8uAGNVqPLLVM9ueMZ3NIARObM84dvQIO1s9rnjSmDixu0WYvpKj2xNOHTuCl4777/8c//OP/yd/8Ok/4tKlPeaHCw7nS1XRX5Vom4Gd9R4sqg7fTbBiCTbo6at3bE0mBOcwuRL8lISAREI0GEkgicnWVLvN1tD7DhMUtZgyRGBmKzs2cc20MvMzvBwhlAUld3T2Wl5+3Q47YcZi75Brd3d5fO9x4jiQY+TLjx5wYf+QYi3DctnQw0Jw2nPxDrY66DxMO8fRrZ7dI4EjOzscPXaMo7tHGHLPfIAvTr7MYhiprfEe3ExhxbOOqT2uvkq20PdHcF2H6zpmR3aY9k4Vuw8nXOoKpUwJwXDTq25gZ7qNl8DHPvbfuLB3gcVygQMOhsLhmFgaYT62BdVafHiyyR88OCc4J0y6wKSH4B2d77UvJgEfpsytWaPB4nIkZSBmrCn03iNeGOcZL2pRf+LEKb7lpd/C9mzKJBjmLnC0C1ycTFhc3KcPhkkf2Nk6Rh/O0blA7z1bk8kaii00iaQKrEBKK7StTc1RoEewFCmUqmoxxqh5ZbAT6pAZDxZanjuMxDEx5tSEtDu8OIIxpBwZUuKx849zaW+P+WLRTjY0sFJRnc3UJKkwLDHMMRS/jXQJSY5lMQzNH80Yw8Q6EioQvRybeaJ4arFKl5HKslaGqgt4tZpUO9/R9dt0fovc+kBDUfPRZAymQEZP+9V7krOMCIsxIY2Q3hlDlIoKoAHeUb2WfYsLJPTUiRRGMdB5BUyUqohQi5bmrSJYCU5PT7WCKXRTjxkqQ0kMJTWbGeUalmJBOibTLeJKBNk5crKsEAIHVC6RuUAmr4E5BpxlP0aGxZLeqbBAF9TM9fnG89q43vGOd/DBD36Q3/u93+OlL33pM772lltuAeDBBx/kFa94BadPn+Z//I//cdlrHn30UYA/sy+mhnH9Vz3+6c99mq2dbY7ubtMZBRaYtiDkWlimxHR7l7FmDsclw7xgvcEGz6zbBW9JJbH/xBMMyz0Vxk2Vg+VhK2XAuVlPH2YE35FNoTdW5W46xyRMMc6RvcE+/CdaCQqWMlfvpzEtOTz/GDBiKXTVcjguiSkyDEtSVXdWEYOqHTYNMidrPk4uql5vsfhQqKOaw0lN/J8vPASmUVn9DmMayTUTrMrAhNDTTY4S80LRQwLdJKhKwWwL6Q1T32lJrDeYwVBzJdaIKQOTiWPrSEedZx574gLnnniCr3z5HMtB+0i1tBKPsWoKaWgeTSv4vJBzIVBJRUkGpUbyctASV01gFxQsWQoTt8RbCNZQy0oQWRBb8bZxgLqeaAzpwh7zg32+/H++gBOHKZZlGUkHF6nDnI9PKl0S4hB57NIF9vf3lUBdCynRwC1Nksc29XynBPFSKmPM+K4j18p8ORLjnIv7c7pHz+MmgWxmFHqiqKGlFGFwgrW1EbmXXBrUTbmWTD/doZv1hL4jPH6RfqI9S58KNS+197g45NITj2HEILFy7sJ59vcPmC+WjDVr5osho+anprkeaNlQs/eYiwq2ijCmpCATA96kRhZwjcIxoscaR9/1WNdhjKeUyKULF7h0QfjKWYvxPXgPXeDLX/6iCsJaIVTR052FepgYHBwYw6XHDjj76KPsHexrGdU6zfKNlq+UL6Xz4UmNPyHmis9F0Xa+Vyh6Vqdr61QNQs0YK0LFWciSGfPI/HCfnAPTrS3CRPvTnWuisjJSmy6klNz4haoNWldKMwZVs2n0h9BbrOsV2ee15yxUgg90fUdwYCSxqM15olGjQMV6UxoakKrxGld6mUZh/iKZXEZyGkhxpKRIP+1Uid0ZvAnM581ItRYFUeVISYFU2umoATGC01O1bYmLqpdo+VKalmJwrrFXwZpOpces+mmlqMazwdm15qNtlR91lxBik6lT0IeW0FNSlKpvSj8Ez+njR4kxcV6y6rmi11SLkr2NT/g8kGqPKUFbDc8zntPGJSL8w3/4D3n/+9/PPffcw4033visP/PpT38agDNnzgBw66238k//6T/lscce4+TJkwB86EMfYmdnh1e/+tXP6eLPnjtLvz/l0t6W0vmMQZwhp5GUM0OObB+9RmWHpJIGFC9gLX3Yw/cBRJjv7TMM+0r+rMIQF2sk3zgMdGHAN45DMA5nLC5YptMjehS3htKQQIVMXdoGY88MeweUskBqwuRKktIWz6QL97rmbpqAppJc7QplmLUsQM0YmzB22RBuGSmx9QAKuJEqK3V4r9BV5wndQJXUlAEE5x3TrSXTrSUmWA5MK0u6CmNTATGFGge8E0JviPOBg/mS/fmC+aFaqddSMKLQb139WZd3jKGpUxSyJHJMVGcotpBtJTZsdymNdS/qe1SayaW3BsmthIFQrHKRrHO46VTh6FJRh+lBBfKLoqby/JAyLDAMMEZyzBxGLQuuEJ3WuDUqyxrfSMUqHbbSA4wmE2JqfC1DH4QxVowtMF9SbaKaDqxQVGoP55SkXmuhlJGyXJLSSM6RaYRJnBD6DmNH+qDv04tgJVPSyDgfqHsDJSXSmDgYRxbDyDBGUknrZrdYLclKQ+VZq+hNiqpwOFcwNuspAZWdSll9pYqo+n+pUZUkbIdzpiHUVZ8wxqgCr1TEquebCb6VhME6mHUd02nPpAvq/Ou0dDmMhWEYNElop6nVAsZKbLk2TmAb69oWdUVILvChaUaWRC2Vzqi6RrCCFU2qLFa/rxGpKrckk4A3WUVnvQKnTFHdRWrBmIqzOl5IQmvELUm0gpgCNmNswQdDaPphYlTDz3tP1xu8A0rFWb3fEFE6QFXEqdSMoTkYg/ZpKepQXgdNZvKSkkdqjSAJbwveFm0LWIMzGdNk5qRJamXR05cxFePauHoQD+L0+qWp/xTXtCyN4L0mxNaoSWR1AlZRndW263eWbCprQo0BsSq2MKSRZR4YykBZIZAbYte7FTrWMptN1O9OVZzbLdbIMVWouahTQ3t8jaV7HvGcNq677rqLX//1X+cDH/gAR44cWfekjh49ynQ65aGHHuLXf/3X+Z7v+R6uueYaPvOZz/DjP/7j3HbbbbzmNa8B4I477uDVr341f/tv/23e8573cO7cOX7mZ36Gu+6662lPVc8UXzn3ZbwNTDv18aliVOGBFYw8s31sQQidSvPYCcZqxpXi3lMg9BapCqfFGIa5enA766gxU0PGOj3aJtc1xr+Qs8rQ0CDWtSQkRT1FFVVckGQw2SAZYo6MMarcj7OsqsVGKiXr4l+twTtRB1oMMVZGyUSrUinVaqZsgFKn+jckI+awidwKPvTah7Pg7IGScwVUN11I25G8THQuaIMVIA/td1WoVe0eSkLywBCXxJxVew6DWhaoh08trUtRmwqQE1ybpNUIKVWcVAhd25gr1NAaxLq4lVLIcaR0Yzt1Cmmyj2+K1ksMpVY91c2muEZvSDVRhwWI2l/YVvCXUqhxTo4jKSdiLtCyToeWWddYAOswTYSks4JktbZZlIiRivMjxg8c29lCjEKGVW5riTBSYkRsp2oQVj3Bas3kGImpMsaBMQ7sZEseEl3XEfH0pomVGoXdl6ryQXVYqOROXHI4JKpt0kfStB+RJ9UcjGnyTzrfyIWxEXmkFsQVgp9h8CxGS47qEZWLqBWHDdjQ0zXxSWPA1MoiZWIuSpb3tm0iGbzFOwXNmCNb2JoxqWNSO0zRSTCOIyUrZ8lYC1ldh6miiUbV90rVebaCzQusHQW8C+2+bJJR3tEZ2AqVkA+xo8FPIbDEmwGL8hFt8UzMyGwyw3tNnFwasDViTcbZivOCswUrI8Zk3cS8gQCEjNgBY5dY5/A1gRdwWkLtusB0S/BWqKkQFrElLRbng3I5a8FIwpmCcdqvdh4wkTTuU+JFSoyUuE8uC6QsMSSmIdLZkc45XC94ouqsinK6ijhSVdq8dQpiErGUrupXyBQfm52TJfZQlFtMsFrWU4WpSiwRodJ1UKNWfKKHsWaiSRSUQykekivsDQccGS7ho2fGlGwhW0MR3Ww1ibL0kwlSKsNcTUKtWYGdjFKUUqYMic46gnUN3PL84jltXL/8y78MwOte97rLHv/VX/1VfuRHfoSu6/jwhz/Mv/7X/5r5fM7111/PD/zAD/AzP/Mz69c65/jgBz/InXfeya233srW1hZvfetbL+N9PZdQXoXyBMDoyag2ol6rsQbfMZ1sscQ2FemKFE8IDmtVzkSKwtPFWHIctSFsHdYUXDP9c6Gq26m1uHaycc2ULZbcFpbMMM96LLcqgitW9e2s7XFWXZgJBlfjUw4rphHzAsGseBCCM47DVJsShUGKkg8770nWU40qNeTSoBFrbkdbnKuaUWKsWleUjGuGld4F4kqfsEZKUu5XaP5l2nRVd9wO1UOrzkLVbNVh8QIYh3Ez3ErCzhim0/bZGOj6QEyFnAqzWY+1vSbgyVC08glG9c3ULVYYYmQaPM45VaaQhBQL0eJC0NJYKeS8pCZ1Xe6c8u0QoWQ18Ouc1x5Jr+rWRoqy+luWKG6iiEukgTGClgeTSjxVEdIwMjt+lOpUGLWkqBp1GUwtZKvN6UJiZfngET39G1pikiB0UNU6pBo9CQyLgd4rwT2LoYwLVf5wjt4LEjziLLZEpSWsSjB2gkIWBGdBvNAHYZ5zQ3A65mPkmqM7zCZTEMNSBkptGnc1s3YcXutzGqbBrftOVRRtB9rUL6WgQsOFOHo6p6fGLG49hitHBOsLtgjgoUmgqUmrwYtlMpuqFmZRyaVlzg2EoFqHtJO7d45cMss4cDhfEkJPEYipEHNSwrWCBRtxXMtlKSnSb7lQZF8tZbVgYByE4OmnHTZmctLqQc1GFXWaJFeMiTiMGDH4oCjYsOYwqeZWbcr91jqoKh/XhUDXKWRcJZ8gLjP7+3OeePwJla1aDqQhQwVvHZO+w3sDVMYhKle08cV0LVDgmQuOrtce+HKZKbESF0n1C0NZzw/NYQ0W1/zDFD2Ym7O7GEsqVY/PVahF0ZzBOfqJU+QwelhdLiJ7ewe4EDhy5BihqjMC1mNNkwyLkTFFUtTPw6z5J2atXSpVqDEzpCaS8PxbXM+9VPhMcf3113+VasbTxQ033MBv//ZvP5c//bQx6adg1N5exUBXDdQeLTNVdo6doO+Vr0VFPWucJUZphLpWMTATJfQZh7Xq9lmLssC1ntyEda2sbcqd04anMRUkA3X9mhWaxpkAbgLS+g5ZrVSqqerv1jLPda0NWNWuBD2uq3tp0xi0TcnDe3rTY0zAMFGyYFW0XyzKurcr3cMVgkhESzJVjSwFtSQvUjASm/eP9j2McbpZm54gvYrBlkpGkGqhqgUKVb2pVghA296Gc47cvHmkfRZYwCunxxiLeIvYSsoWkiC5aTcCWSKxFrxtXLAGNlO3Fy3H1DQ2FKb+nSo0XTuH6XsCerOYXPRktdJPbCdWGlHWGEVsrZQtWpWYalYeTcqn8b5niroC54khF0hxZEha7nKo51mR5osm+sEp0Eb7eDWN2vvxSmmoRknG1hiM9fh+gjdqh5IZKEZlnValoXUBdbU4WKvk51aWEqON8iygMEePm/TshBkhGCYxMp1mYhyIpRKLym+t5Lpwql3nTFONsU3VRVlFWGfXNI7SGvfV6iaNtcr3ms4bvaJgfFsUraLQdF45rt3dYqv3dF63+sVgGFNhMSqKLpdMyoXtyQRrVdbs6PaM4K1Cxkts00mTOCwE7/HGtsTEU23HdDolto1JgEnw+OAx0uFMJXtNqPrgVVy4CuSo/cRxxCFMe08IllnX0VunCwaqSKGST7VRZFD7kEnXFHOEYAOd1WV2HBOHh4cgpgnkVrxX38CJX71OyAW6EOhD0OuyQu89nVPfrb5rBpriCU43kVy0vylimhivVliMpYlUK6G6lEQl6HqRQMQjaF+2tnHrXc/OdEZtKM9SdZM8PBy5tDdnZ3KkzQuHMz1VMrXCOEbEBEI/YTLpMc5TqjSiuma1qnHIGhn9fOOq1CpcbaCT6UQJul1gRQsBJQSvFtGdY9divdU6cRH6vqPrO/qMLmQiUITgJ8o0t50a8S2WpDhiUZSOiG44CplWW4AqaoteayGPCVWQL4r5MYZqjU5GHzBNTrOOKvgqObLSE1AeUVsySwbPWvVeq8XtJNV4MGJVzmUSnCpqO9UkjHlUM7/5oTrAtqx5rKo4voqYzFopJGX9295JU6U2JHH0btq06Zr8SynYUpGUGzR3dULVzUGKklvrChHvzJNq86lo/d+oEglOy7A4VE/NKtggpaWWeasByRRxuOpwYYL3K+RcIa+cdIcBQ17LaKXSEGjW4UKvsjMiZKL2x4UGHNHHNWdQvps1kFafBU0Boa76H5bRKqKss+3EaBRxdbg4JB0OSBawllyzloylAIHS1PnFGJYpUmtlGCNMtA8pRogiOGMJ1uLDlM4qAMhEVWlRAWffwAyqyChVeVHe29UEURh4lQbl1pN+tobiPVvTLZw3zf+psj8/ZDGO0Pg6tRGIi6l6qBItSRm3Ehxu/EVr8L6Rn3NhME0NAi3/9F2H7QOuOGxVwVVjV4mY2m5Muo5T1x7hJdceYXd7ivM9w2LCYll4Ym+fISUWw8DhcuDanR3t1Qj0k14Jxk3ZXlXkS9N2VIXzmgolVU0exejGNebGOYTearlTnFftRlcoQTdsU4UyRobDQ0qG5XLE1ETnDV1wdMaqQHPJlKgABCsWW7VMa6SdirxqM4pBT95oD3e+GDh/4SLOqOJKTrH12FRtxeQGXBmzCgG0ey9YrYJ4ETrfwGFGOYXG6OcwX4wqKeU93nqWQ/vdXvXjgvPr1kPVj1dlsrAt6SuNIwZGPFv9lDEnCgqcOTgcyfUQay9Sr1EeJkY3tpQNyyUc7EdSMVp+7ntcaJoe1uODzqNcC5IL1ZRV+visB6KnCyPP56eucHzpS1/i+uuvv9KXsYlNbGITm/hzxhe/+MVnRaf/6bgqN65aKw888ACvfvWr+eIXv8jOzs6VvqT/52LFdduMz9PHZnyeOTbj8+yxGaNnjmcbHxHh4OCA6667TvEAzyGuylKhtZaXvOQlAOzs7GwmzTPEZnyeOTbj88yxGZ9nj80YPXM80/gcPXr0ef3O568rv4lNbGITm9jEFYjNxrWJTWxiE5u4quKq3bj6vufd7373cyYtv1hiMz7PHJvxeebYjM+zx2aMnjm+keNzVYIzNrGJTWxiEy/euGpPXJvYxCY2sYkXZ2w2rk1sYhOb2MRVFZuNaxOb2MQmNnFVxWbj2sQmNrGJTVxVcVVuXL/0S7/Et3zLtzCZTLjlllu+ypjyxRI///M/v1b1Xn1967d+6/r5YRi46667uOaaa9je3uYHfuAH1qad36zxe7/3e3zv934v1113HcYY/tN/+k+XPS8i/NzP/RxnzpxhOp1y++2384UvfOGy11y4cIG3vOUt7OzssLu7y9/7e39PxVG/CeLZxudHfuRHvmpOvfGNb7zsNd+s43P33XfzHd/xHRw5coSTJ0/yN/7G3+CBBx647DVfyz31yCOP8OY3v5nZbMbJkyf5qZ/6KXLOL+Rb+YbF1zJGr3vd675qDv3oj/7oZa/5847RVbdx/cf/+B/5iZ/4Cd797nfzR3/0R7z2ta/lDW94A4899tiVvrQrEn/pL/0lzp49u/762Mc+tn7ux3/8x/nP//k/81u/9Vvce++9fOUrX+H7v//7r+DVfuNjPp/z2te+ll/6pV962uff85738G/+zb/hV37lV/jEJz7B1tYWb3jDGxiGYf2at7zlLXzuc5/jQx/60Nrp++1vf/sL9Ra+ofFs4wPwxje+8bI59Ru/8RuXPf/NOj733nsvd911F7//+7/Phz70IVJK3HHHHczn8/Vrnu2eKqXw5je/mRgjH//4x/l3/+7f8d73vpef+7mfuxJv6eseX8sYAbztbW+7bA695z3vWT/3dRkjucriO7/zO+Wuu+5a/7+UItddd53cfffdV/Cqrky8+93vlte+9rVP+9ylS5ckhCC/9Vu/tX7sf/2v/yWA3HfffS/QFV7ZAOT973//+v+1Vjl9+rT8y3/5L9ePXbp0Sfq+l9/4jd8QEZH7779fAPmDP/iD9Wv+y3/5L2KMkS9/+csv2LW/EPGnx0dE5K1vfat83/d935/5My+m8XnssccEkHvvvVdEvrZ76rd/+7fFWivnzp1bv+aXf/mXZWdnR8ZxfGHfwAsQf3qMRET++l//6/KP/tE/+jN/5usxRlfViSvGyCc/+Uluv/329WPWWm6//Xbuu+++K3hlVy6+8IUvcN111/Hyl7+ct7zlLTzyyCMAfPKTnySldNlYfeu3fisve9nLXrRj9fDDD3Pu3LnLxuTo0aPccsst6zG577772N3d5a/+1b+6fs3tt9+OtZZPfOITL/g1X4m45557OHnyJK961au48847OX/+/Pq5F9P47O3tAXD8+HHga7un7rvvPm6++WZOnTq1fs0b3vAG9vf3+dznPvcCXv0LE396jFbxa7/2a5w4cYKbbrqJd73rXSwWi/VzX48xuqpEdp944glKKZe9YYBTp07x+c9//gpd1ZWLW265hfe+97286lWv4uzZs/zCL/wCf+2v/TU++9nPcu7cObquY3d397KfOXXqFOfOnbsyF3yFY/W+n27+rJ47d+4cJ0+evOx57z3Hjx9/UYzbG9/4Rr7/+7+fG2+8kYceeoif/umf5k1vehP33XcfzrkXzfjUWvmxH/sxvvu7v5ubbroJ4Gu6p86dO/e082v13DdTPN0YAfzwD/8wN9xwA9dddx2f+cxneOc738kDDzzA+973PuDrM0ZX1ca1icvjTW960/r717zmNdxyyy3ccMMN/OZv/ibT6fQKXtkmrtb4W3/rb62/v/nmm3nNa17DK17xCu655x5e//rXX8Ere2Hjrrvu4rOf/exlPeNNXB5/1hg9td958803c+bMGV7/+tfz0EMP8YpXvOLr8revqlLhiRMncM59FYrn0Ucf5fTp01foqv7fid3dXf7iX/yLPPjgg5w+fZoYI5cuXbrsNS/msVq972eaP6dPn/4qoE/OmQsXLrwox+3lL385J06c4MEHHwReHOPzjne8gw9+8IN89KMfvczg8Gu5p06fPv2082v13DdL/Flj9HRxyy23AFw2h/68Y3RVbVxd1/Ht3/7t/O7v/u76sVorv/u7v8utt956Ba/s/404PDzkoYce4syZM3z7t387IYTLxuqBBx7gkUceedGO1Y033sjp06cvG5P9/X0+8YlPrMfk1ltv5dKlS3zyk59cv+YjH/kItdb1Dfhiii996UucP3+eM2fOAN/c4yMivOMd7+D9738/H/nIR7jxxhsve/5ruaduvfVW/viP//iyzf1DH/oQOzs7vPrVr35h3sg3MJ5tjJ4uPv3pTwNcNof+3GP0PMEkVyz+w3/4D9L3vbz3ve+V+++/X97+9rfL7u7uZQiVF0v85E/+pNxzzz3y8MMPy3//7/9dbr/9djlx4oQ89thjIiLyoz/6o/Kyl71MPvKRj8gf/uEfyq233iq33nrrFb7qb2wcHBzIpz71KfnUpz4lgPyrf/Wv5FOf+pT8yZ/8iYiI/PN//s9ld3dXPvCBD8hnPvMZ+b7v+z658cYbZblcrn/HG9/4Rvm2b/s2+cQnPiEf+9jH5JWvfKX80A/90JV6S1/XeKbxOTg4kH/8j/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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"img = '../resources/input/translation/gt_mask_0.png'\\n\",\n \"\\n\",\n \"# show input image and mask\\n\",\n \"input_img = mmcv.imread(img)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(input_img))\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"result_out_dir = '../resources/output/translation/tutorial_translation_pix2pix_res.png'\\n\",\n \"editor = MMagicInferencer('pix2pix')\\n\",\n \"results = editor.infer(img=img, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.6 Inference of unconditional GAN models\\n\",\n \"\\n\",\n \"Unconditional GAN models do not need input, and output a image. We take 'styleganv1' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth\\n\",\n \"Switch to evaluation style mode: single\\n\",\n \"Switch to evaluation style mode: single\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"result_out_dir = '../resources/output/unconditional/tutorial_unconditional_styleganv1_res.png'\\n\",\n \"editor = MMagicInferencer('styleganv1')\\n\",\n \"results = editor.infer(result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.7 Inference of video interpolation models\\n\",\n \"\\n\",\n \"Video interpolation models take a video as input, and output a interpolated video. We take 'flavr' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth\\n\",\n \"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 22/22, 0.1 task/s, elapsed: 245s, ETA: 0s11/21 00:31:46 - mmengine - \\u001b[4m\\u001b[37mINFO\\u001b[0m - Output video is save at ../resources/output/video_interpolation/tutorial_video_interpolation_flavr_res.mp4.\\n\",\n \"11/21 00:31:46 - mmengine - \\u001b[4m\\u001b[37mINFO\\u001b[0m - Visualization is implemented in forward process.\\n\",\n \"11/21 00:31:46 - mmengine - \\u001b[4m\\u001b[37mINFO\\u001b[0m - Postprocess is implemented in forward process.\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"from mmengine import mkdir_or_exist\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"video = '../resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'\\n\",\n \"result_out_dir = '../resources/output/video_interpolation/tutorial_video_interpolation_flavr_res.mp4'\\n\",\n \"mkdir_or_exist(os.path.dirname(result_out_dir))\\n\",\n \"editor = MMagicInferencer('flavr')\\n\",\n \"results = editor.infer(video=video, result_out_dir=result_out_dir)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Please check the result video in the output directory.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.8 Inference of video restoration models\\n\",\n \"\\n\",\n \"Video restoration models take a video as input, and output a restorated video. We take 'basicvsr' as an example..\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http loads checkpoint from path: https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\\n\",\n \"11/20 18:13:55 - mmengine - \\u001b[4m\\u001b[37mINFO\\u001b[0m - Output video is save at ../resources/output/video_restoration/tutorial_video_restoration_edvr_res.mp4.\\n\",\n \"11/20 18:13:55 - mmengine - \\u001b[4m\\u001b[37mINFO\\u001b[0m - Postprocess is implemented in visualize process.\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"from mmengine import mkdir_or_exist\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"video = '../resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4'\\n\",\n \"result_out_dir = '../resources/output/video_restoration/tutorial_video_restoration_edvr_res.mp4'\\n\",\n \"mkdir_or_exist(os.path.dirname(result_out_dir))\\n\",\n \"editor = MMagicInferencer('edvr', extra_parameters={'window_size':5})\\n\",\n \"results = editor.infer(video=video, result_out_dir=result_out_dir)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Please check the result video in the output directory.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.9 Inference of text-to-image models\\n\",\n \"\\n\",\n \"Text-to-image models take text as input, and output a image. We take 'stable_diffusion' as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \\n\",\n \"```\\n\",\n \"pip install accelerate\\n\",\n \"```\\n\",\n \".\\n\",\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \\n\",\n \"```\\n\",\n \"pip install accelerate\\n\",\n \"```\\n\",\n \".\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"04/24 20:24:33 - mmengine - INFO - Creating ../resources/stable-diffusion-v1-5 by 'HuggingFace'\\n\"\n ]\n },\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"88258b641d97420bbb07a7970929b7da\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \" 0%| | 0/50 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import mmcv\\n\",\n \"import matplotlib.pyplot as plt \\n\",\n \"from mmagic.apis import MMagicInferencer\\n\",\n \"\\n\",\n \"# Create a MMagicInferencer instance and infer\\n\",\n \"editor = MMagicInferencer(model_name='stable_diffusion')\\n\",\n \"text_prompts = 'A panda is having dinner at KFC'\\n\",\n \"result_out_dir = '../resources/output/text2image/tutorial_text2image_sd_res.png'\\n\",\n \"editor.infer(text=text_prompts, result_out_dir=result_out_dir)\\n\",\n \"\\n\",\n \"# plot the result image\\n\",\n \"img = mmcv.imread(result_out_dir)\\n\",\n \"plt.imshow(mmcv.bgr2rgb(img))\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"mmagic\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.15\"\n },\n \"orig_nbformat\": 4,\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"2396e09a98edfab6266f814c68e2f319a2f52e823b3715487b27762471d9be4e\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "demo/singan_demo.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport sys\n\nimport mmcv\nimport torch\nfrom mmengine import Config, print_log\nfrom mmengine.logging import MMLogger\nfrom mmengine.runner import load_checkpoint, set_random_seed\n\n# yapf: disable\nsys.path.append(os.path.abspath(os.path.join(__file__, '../..'))) # isort:skip # noqa\n\nfrom mmagic.engine import * # isort:skip # noqa: F401,F403,E402\nfrom mmagic.datasets import * # isort:skip # noqa: F401,F403,E402\nfrom mmagic.models import * # isort:skip # noqa: F401,F403,E402\n\nfrom mmagic.registry import MODELS # isort:skip # noqa\n\n# yapf: enable\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Evaluate a GAN model')\n parser.add_argument('config', help='evaluation config file path')\n parser.add_argument('checkpoint', help='checkpoint file')\n parser.add_argument('--seed', type=int, default=2021, help='random seed')\n parser.add_argument(\n '--deterministic',\n action='store_true',\n help='whether to set deterministic options for CUDNN backend.')\n parser.add_argument(\n '--samples-path',\n type=str,\n default='./',\n help='path to store images. If not given, remove it after evaluation\\\n finished')\n parser.add_argument(\n '--save-prev-res',\n action='store_true',\n help='whether to store the results from previous stages')\n parser.add_argument(\n '--num-samples',\n type=int,\n default=10,\n help='the number of synthesized samples')\n args = parser.parse_args()\n return args\n\n\ndef _tensor2img(img):\n img = img.permute(1, 2, 0)\n img = img.clamp(0, 255).to(torch.uint8)\n\n return img.cpu().numpy()\n\n\n@torch.no_grad()\ndef main():\n MMLogger.get_instance('mmagic')\n\n args = parse_args()\n cfg = Config.fromfile(args.config)\n # set cudnn_benchmark\n if cfg.get('cudnn_benchmark', False):\n torch.backends.cudnn.benchmark = True\n\n # set random seeds\n if args.seed is not None:\n set_random_seed(args.seed, deterministic=args.deterministic)\n\n # set scope manually\n cfg.model['_scope_'] = 'mmagic'\n # build the model and load checkpoint\n model = MODELS.build(cfg.model)\n\n model.eval()\n\n # load ckpt\n print_log(f'Loading ckpt from {args.checkpoint}')\n _ = load_checkpoint(model, args.checkpoint, map_location='cpu')\n\n # add dp wrapper\n if torch.cuda.is_available():\n model = model.cuda()\n\n for sample_iter in range(args.num_samples):\n outputs = model.test_step(\n dict(inputs=dict(num_batches=1, get_prev_res=args.save_prev_res)))\n\n # store results from previous stages\n if args.save_prev_res:\n fake_img = outputs[0].fake_img.data\n prev_res_list = outputs[0].prev_res_list\n prev_res_list.append(fake_img)\n for i, img in enumerate(prev_res_list):\n img = _tensor2img(img)\n mmcv.imwrite(\n img,\n os.path.join(args.samples_path, f'stage{i}',\n f'rand_sample_{sample_iter}.png'))\n # just store the final result\n else:\n img = _tensor2img(outputs[0].fake_img.data)\n mmcv.imwrite(\n img,\n os.path.join(args.samples_path,\n f'rand_sample_{sample_iter}.png'))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "demo/utils/gradio_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# yapf: disable\nimport gradio as gr\nimport numpy as np\nfrom PIL import Image, ImageDraw\n\n\nclass EasyDict(dict):\n \"\"\"Convenience class that behaves like a dict but allows access with the\n attribute syntax.\"\"\"\n\n def __getattr__(self, name: str):\n try:\n return self[name]\n except KeyError:\n raise AttributeError(name)\n\n def __setattr__(self, name: str, value) -> None:\n self[name] = value\n\n def __delattr__(self, name: str) -> None:\n del self[name]\n\n\nclass ImageMask(gr.components.Image):\n \"\"\"\n Sets: source=\"canvas\", tool=\"sketch\"\n \"\"\"\n\n is_template = True\n\n def __init__(self, **kwargs):\n super().__init__(\n source='upload', tool='sketch', interactive=False, **kwargs)\n\n def preprocess(self, x):\n if x is None:\n return x\n if self.tool == 'sketch' and self.source in ['upload', 'webcam'\n ] and type(x) != dict:\n decode_image = gr.processing_utils.decode_base64_to_image(x)\n width, height = decode_image.size\n mask = np.ones((height, width, 4), dtype=np.uint8)\n mask[..., -1] = 255\n mask = self.postprocess(mask)\n x = {'image': x, 'mask': mask}\n return super().preprocess(x)\n\n\ndef get_valid_mask(mask: np.ndarray):\n \"\"\"Convert mask from gr.Image(0 to 255, RGBA) to binary mask.\"\"\"\n if mask.ndim == 3:\n mask_pil = Image.fromarray(mask).convert('L')\n mask = np.array(mask_pil)\n if mask.max() == 255:\n mask = mask / 255\n return mask\n\n\ndef draw_points_on_image(image,\n points,\n curr_point=None,\n highlight_all=True,\n radius_scale=0.01):\n overlay_rgba = Image.new('RGBA', image.size, 0)\n overlay_draw = ImageDraw.Draw(overlay_rgba)\n for point_key, point in points.items():\n if ((curr_point is not None and curr_point == point_key)\n or highlight_all):\n p_color = (255, 0, 0)\n t_color = (0, 0, 255)\n\n else:\n p_color = (255, 0, 0, 35)\n t_color = (0, 0, 255, 35)\n\n rad_draw = int(image.size[0] * radius_scale)\n\n p_start = point.get('start_temp', point['start'])\n p_target = point['target']\n\n if p_start is not None and p_target is not None:\n p_draw = int(p_start[0]), int(p_start[1])\n t_draw = int(p_target[0]), int(p_target[1])\n\n overlay_draw.line(\n (p_draw[0], p_draw[1], t_draw[0], t_draw[1]),\n fill=(255, 255, 0),\n width=2,\n )\n\n if p_start is not None:\n p_draw = int(p_start[0]), int(p_start[1])\n overlay_draw.ellipse(\n (\n p_draw[0] - rad_draw,\n p_draw[1] - rad_draw,\n p_draw[0] + rad_draw,\n p_draw[1] + rad_draw,\n ),\n fill=p_color,\n )\n\n if curr_point is not None and curr_point == point_key:\n overlay_draw.text(p_draw, 'p', align='center', fill=(0, 0, 0))\n\n if p_target is not None:\n t_draw = int(p_target[0]), int(p_target[1])\n overlay_draw.ellipse(\n (\n t_draw[0] - rad_draw,\n t_draw[1] - rad_draw,\n t_draw[0] + rad_draw,\n t_draw[1] + rad_draw,\n ),\n fill=t_color,\n )\n\n if curr_point is not None and curr_point == point_key:\n overlay_draw.text(t_draw, 't', align='center', fill=(0, 0, 0))\n\n return Image.alpha_composite(image.convert('RGBA'),\n overlay_rgba).convert('RGB')\n\n\ndef draw_mask_on_image(image, mask):\n im_mask = np.uint8(mask * 255)\n im_mask_rgba = np.concatenate(\n (\n np.tile(im_mask[..., None], [1, 1, 3]),\n 45 * np.ones(\n (im_mask.shape[0], im_mask.shape[1], 1), dtype=np.uint8),\n ),\n axis=-1,\n )\n im_mask_rgba = Image.fromarray(im_mask_rgba).convert('RGBA')\n\n return Image.alpha_composite(image.convert('RGBA'),\n im_mask_rgba).convert('RGB')\n\n\ndef on_change_single_global_state(keys,\n value,\n global_state,\n map_transform=None):\n if map_transform is not None:\n value = map_transform(value)\n\n curr_state = global_state\n if isinstance(keys, str):\n last_key = keys\n\n else:\n for k in keys[:-1]:\n curr_state = curr_state[k]\n\n last_key = keys[-1]\n\n curr_state[last_key] = value\n return global_state\n\n\ndef get_latest_points_pair(points_dict):\n if not points_dict:\n return None\n point_idx = list(points_dict.keys())\n latest_point_idx = max(point_idx)\n return latest_point_idx\n" }, { "path": "demo/utils/renderer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this software and related documentation without an express\n# license agreement from NVIDIA CORPORATION is strictly prohibited.\n\nimport sys\nimport traceback\n\nimport matplotlib.cm\nimport matplotlib.font_manager\nimport numpy as np\nimport torch\nimport torch.fft\nimport torch.nn.functional as F\nfrom PIL import Image, ImageDraw, ImageFont\n\nfrom demo.utils.gradio_utils import EasyDict\nfrom mmagic.apis import MMagicInferencer\n\n\nclass CapturedException(Exception):\n\n def __init__(self, msg=None):\n if msg is None:\n _type, value, _traceback = sys.exc_info()\n assert value is not None\n if isinstance(value, CapturedException):\n msg = str(value)\n else:\n msg = traceback.format_exc()\n assert isinstance(msg, str)\n super().__init__(msg)\n\n\n'''\n----------------------------------------------------------------------------\n'''\n\n\nclass CaptureSuccess(Exception):\n\n def __init__(self, out):\n super().__init__()\n self.out = out\n\n\n'''\n----------------------------------------------------------------------------\n'''\n\n\ndef add_watermark_np(input_image_array, watermark_text='AI Generated'):\n image = Image.fromarray(np.uint8(input_image_array)).convert('RGBA')\n\n # Initialize text image\n txt = Image.new('RGBA', image.size, (255, 255, 255, 0))\n ttf_pth = matplotlib.font_manager.findSystemFonts(\n fontpaths=None, fontext='ttf')[-1]\n\n font = ImageFont.truetype(ttf_pth, round(25 / 512 * image.size[0]))\n d = ImageDraw.Draw(txt)\n\n text_width, text_height = font.getsize(watermark_text)\n text_position = (image.size[0] - text_width - 10,\n image.size[1] - text_height - 10)\n text_color = (\n 255, 255, 255, 128\n ) # white color with the alpha channel set to semi-transparent\n\n # Draw the text onto the text canvas\n d.text(text_position, watermark_text, font=font, fill=text_color)\n\n # Combine the image with the watermark\n watermarked = Image.alpha_composite(image, txt)\n watermarked_array = np.array(watermarked)\n return watermarked_array\n\n\nclass Renderer:\n\n def __init__(self, disable_timing=False):\n self._device = torch.device('cuda' if torch.cuda.is_available(\n ) else 'mps' if torch.backends.mps.is_available() else 'cpu')\n self._dtype = torch.float32 \\\n if self._device.type == 'mps' else torch.float64\n self._pkl_data = dict() # {pkl: dict | CapturedException, ...}\n self._networks = dict() # {cache_key: torch.nn.Module, ...}\n self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...}\n self._cmaps = dict() # {name: torch.Tensor, ...}\n self._is_timing = False\n if not disable_timing:\n self._start_event = torch.cuda.Event(enable_timing=True)\n self._end_event = torch.cuda.Event(enable_timing=True)\n self._disable_timing = disable_timing\n self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...}\n\n def render(self, **args):\n if self._disable_timing:\n self._is_timing = False\n else:\n self._start_event.record(torch.cuda.current_stream(self._device))\n self._is_timing = True\n res = EasyDict()\n try:\n init_net = False\n if not hasattr(self, 'G'):\n init_net = True\n if hasattr(self, 'pkl'):\n if self.pkl != args['pkl']:\n init_net = True\n if hasattr(self, 'w_load'):\n if self.w_load is not args['w_load']:\n init_net = True\n if hasattr(self, 'w0_seed'):\n if self.w0_seed != args['w0_seed']:\n init_net = True\n if hasattr(self, 'w_plus'):\n if self.w_plus != args['w_plus']:\n init_net = True\n if args['reset_w']:\n init_net = True\n res.init_net = init_net\n if init_net:\n self.init_network(res, **args)\n self._render_drag_impl(res, **args)\n except CapturedException:\n res.error = CapturedException()\n if not self._disable_timing:\n self._end_event.record(torch.cuda.current_stream(self._device))\n if 'image' in res:\n res.image = self.to_cpu(res.image).detach().numpy()\n res.image = add_watermark_np(res.image, 'AI Generated')\n if 'stats' in res:\n res.stats = self.to_cpu(res.stats).detach().numpy()\n if 'error' in res:\n res.error = str(res.error)\n # if 'stop' in res and res.stop:\n\n if self._is_timing and not self._disable_timing:\n self._end_event.synchronize()\n res.render_time = self._start_event.elapsed_time(\n self._end_event) * 1e-3\n self._is_timing = False\n return res\n\n def get_network(self, pkl, key, **tweak_kwargs):\n data = self._pkl_data.get(pkl, None)\n if data is None:\n print(f'Loading \"{pkl}\"... ', end='', flush=True)\n try:\n print('Done.')\n except CapturedException:\n data = CapturedException()\n print('Failed!')\n self._pkl_data[pkl] = data\n self._ignore_timing()\n if isinstance(data, CapturedException):\n raise data\n\n orig_net = data[key]\n cache_key = (orig_net, self._device,\n tuple(sorted(tweak_kwargs.items())))\n net = self._networks.get(cache_key, None)\n if net is None:\n try:\n if 'stylegan2' in pkl:\n from training.networks_stylegan2 import Generator\n else:\n raise NameError('Cannot infer model type from pkl name!')\n\n print(data[key].init_args)\n print(data[key].init_kwargs)\n if 'stylegan_human' in pkl:\n net = Generator(\n *data[key].init_args,\n **data[key].init_kwargs,\n square=False,\n padding=True)\n else:\n net = Generator(*data[key].init_args,\n **data[key].init_kwargs)\n net.load_state_dict(data[key].state_dict())\n net.to(self._device)\n except CapturedException:\n net = CapturedException()\n self._networks[cache_key] = net\n self._ignore_timing()\n if isinstance(net, CapturedException):\n raise net\n return net\n\n def _get_pinned_buf(self, ref):\n key = (tuple(ref.shape), ref.dtype)\n buf = self._pinned_bufs.get(key, None)\n if buf is None:\n buf = torch.empty(ref.shape, dtype=ref.dtype).pin_memory()\n self._pinned_bufs[key] = buf\n return buf\n\n def to_device(self, buf):\n return self._get_pinned_buf(buf).copy_(buf).to(self._device)\n\n def to_cpu(self, buf):\n return self._get_pinned_buf(buf).copy_(buf).clone()\n\n def _ignore_timing(self):\n self._is_timing = False\n\n def _apply_cmap(self, x, name='viridis'):\n cmap = self._cmaps.get(name, None)\n if cmap is None:\n cmap = matplotlib.cm.get_cmap(name)\n cmap = cmap(np.linspace(0, 1, num=1024), bytes=True)[:, :3]\n cmap = self.to_device(torch.from_numpy(cmap))\n self._cmaps[name] = cmap\n hi = cmap.shape[0] - 1\n x = (x * hi + 0.5).clamp(0, hi).to(torch.int64)\n x = torch.nn.functional.embedding(x, cmap)\n return x\n\n def init_network(self,\n res,\n ckpt_pth=None,\n w0_seed=0,\n w_load=None,\n w_plus=True,\n noise_mode='const',\n trunc_psi=0.7,\n trunc_cutoff=None,\n input_transform=None,\n lr=0.001,\n **kwargs):\n # Dig up network details.\n if '256' in ckpt_pth:\n editor = MMagicInferencer(\n 'draggan',\n model_setting=1,\n model_ckpt=ckpt_pth,\n )\n elif '512' in ckpt_pth:\n editor = MMagicInferencer(\n 'draggan',\n model_setting=0,\n model_ckpt=ckpt_pth,\n )\n elif '1024' in ckpt_pth:\n editor = MMagicInferencer(\n 'draggan',\n model_setting=2,\n model_ckpt=ckpt_pth,\n )\n else:\n raise NotImplementedError\n self.editor = editor\n # Generate random latents.\n self.w0_seed = w0_seed\n self.w_load = w_load\n\n if self.w_load is None:\n # Generate random latents.\n z = torch.from_numpy(np.random.RandomState(w0_seed).randn(\n 1, 512)).to(torch.float32).requires_grad_(True)\n\n # Run mapping network.\n sample_kwargs = {\n 'truncation': trunc_psi,\n 'return_noise': True,\n 'return_features': True\n }\n extra_parameters = {\n 'sample_kwargs': sample_kwargs,\n 'num_batches': 1,\n 'noise': z,\n 'sample_model': 'ema',\n 'infer_with_grad': True\n }\n results = self.editor.infer(extra_parameters=extra_parameters)\n\n w = results[0]['latent'].unsqueeze(0) # [1, 16, n_dim]\n else:\n w = self.w_load.clone().to(self._device)\n\n self.w0 = w.detach().clone()\n self.w_plus = w_plus\n if w_plus:\n self.w = w.detach()\n else:\n self.w = w[:, 0, :].detach()\n self.w.requires_grad = True\n self.w_optim = torch.optim.Adam([self.w], lr=lr)\n\n self.feat_refs = None\n self.points0_pt = None\n\n def update_lr(self, lr):\n\n del self.w_optim\n self.w_optim = torch.optim.Adam([self.w], lr=lr)\n print(f'Rebuild optimizer with lr: {lr}')\n print(' Remain feat_refs and points0_pt')\n\n def _render_drag_impl(self,\n res,\n points=[],\n targets=[],\n mask=None,\n lambda_mask=10,\n reg=0,\n feature_idx=5,\n r1=3,\n r2=12,\n random_seed=0,\n noise_mode='const',\n trunc_psi=0.7,\n force_fp32=False,\n layer_name=None,\n sel_channels=3,\n base_channel=0,\n img_scale_db=0,\n img_normalize=False,\n untransform=False,\n is_drag=False,\n reset=False,\n to_pil=False,\n **kwargs):\n ws = self.w\n if ws.dim() == 2:\n ws = ws.unsqueeze(1).repeat(1, 6, 1)\n ws = torch.cat([ws[:, :6, :], self.w0[:, 6:, :]],\n dim=1) # [1, 16, n_dim]\n if hasattr(self, 'points'):\n if len(points) != len(self.points):\n reset = True\n if reset:\n self.feat_refs = None\n self.points0_pt = None\n self.points = points\n\n # Run synthesis network.\n sample_kwargs = {\n 'truncation': 1,\n 'return_noise': True,\n 'return_features': True,\n 'input_is_latent': True\n }\n extra_parameters = {\n 'sample_kwargs': sample_kwargs,\n 'num_batches': 1,\n 'noise': ws[0][:1],\n 'sample_model': 'ema',\n 'infer_with_grad': True\n }\n\n results = self.editor.infer(extra_parameters=extra_parameters)\n img = results[0]['fake_img'].unsqueeze(\n 0) / 127.5 - 1. # requires_grad==True\n feat_5 = results[0]['feats'].unsqueeze(0) # requires_grad==True\n h, w = img.shape[-1], img.shape[-2]\n\n if is_drag:\n X = torch.linspace(0, h, h)\n Y = torch.linspace(0, w, w)\n xx, yy = torch.meshgrid(X, Y)\n feat_resize = F.interpolate(feat_5, [h, w], mode='bilinear')\n if self.feat_refs is None:\n self.feat0_resize = F.interpolate(\n feat_5.detach(), [h, w], mode='bilinear')\n self.feat_refs = []\n for point in points:\n py, px = round(point[0]), round(point[1])\n self.feat_refs.append(self.feat0_resize[:, :, py, px])\n self.points0_pt = torch.Tensor(points).unsqueeze(0).to(\n self._device) # 1, N, 2\n\n # Point tracking with feature matching\n with torch.no_grad():\n for j, point in enumerate(points):\n r = round(r2 / 512 * h)\n up = max(point[0] - r, 0)\n down = min(point[0] + r + 1, h)\n left = max(point[1] - r, 0)\n right = min(point[1] + r + 1, w)\n feat_patch = feat_resize[:, :, up:down, left:right]\n L2 = torch.linalg.norm(\n feat_patch - self.feat_refs[j].reshape(1, -1, 1, 1),\n dim=1)\n _, idx = torch.min(L2.view(1, -1), -1)\n width = right - left\n point = [\n idx.item() // width + up,\n idx.item() % width + left\n ]\n points[j] = point\n\n res.points = [[point[0], point[1]] for point in points]\n\n # Motion supervision\n loss_motion = 0\n res.stop = True\n for j, point in enumerate(points):\n direction = torch.Tensor(\n [targets[j][1] - point[1], targets[j][0] - point[0]])\n if torch.linalg.norm(direction) > max(2 / 512 * h, 2):\n res.stop = False\n if torch.linalg.norm(direction) > 1:\n distance = ((xx.to(self._device) - point[0])**2 +\n (yy.to(self._device) - point[1])**2)**0.5\n relis, reljs = torch.where(distance < round(r1 / 512 * h))\n direction = direction / (\n torch.linalg.norm(direction) + 1e-7)\n gridh = (relis - direction[1]) / (h - 1) * 2 - 1\n gridw = (reljs - direction[0]) / (w - 1) * 2 - 1\n grid = torch.stack([gridw, gridh],\n dim=-1).unsqueeze(0).unsqueeze(0)\n target = F.grid_sample(\n feat_resize.float(), grid,\n align_corners=True).squeeze(2)\n loss_motion += F.l1_loss(feat_resize[:, :, relis, reljs],\n target.detach())\n\n loss = loss_motion\n if mask is not None:\n if mask.min() == 0 and mask.max() == 1:\n mask_usq = mask.to(self._device).unsqueeze(0).unsqueeze(0)\n loss_fix = F.l1_loss(feat_resize * mask_usq,\n self.feat0_resize * mask_usq)\n loss += lambda_mask * loss_fix\n\n loss += reg * F.l1_loss(ws, self.w0) # latent code regularization\n if not res.stop:\n self.w_optim.zero_grad()\n print(loss)\n loss.backward()\n self.w_optim.step()\n\n # Scale and convert to uint8.\n img = img[0]\n if img_normalize:\n img = img / img.norm(\n float('inf'), dim=[1, 2], keepdim=True).clip(1e-8, 1e8)\n img = img * (10**(img_scale_db / 20))\n img = (img * 127.5 + 128).clamp(0,\n 255).to(torch.uint8).permute(1, 2, 0)\n if to_pil:\n from PIL import Image\n img = img.cpu().numpy()\n img = Image.fromarray(img)\n res.image = img\n res.w = ws.detach().cpu().numpy()\n" }, { "path": "docker/Dockerfile", "content": "ARG PYTORCH=\"1.6.0\"\nARG CUDA=\"10.1\"\nARG CUDA_ALIAS=\"101\"\nARG CUDNN=\"7\"\nARG MMCV=\"2.0.0rc1\"\nARG Usrname=\"xxx\"\nARG token=\"xxx\"\n\nFROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel\n\nENV TORCH_CUDA_ARCH_LIST=\"6.0 6.1 7.0+PTX\"\nENV TORCH_NVCC_FLAGS=\"-Xfatbin -compress-all\"\nENV CMAKE_PREFIX_PATH=\"$(dirname $(which conda))/../\"\n\nRUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys A4B469963BF863CC\nRUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx \\\n && apt-get clean \\\n && rm -rf /var/lib/apt/lists/*\n\n# Install mmagic\nRUN conda clean --all\nRUN git clone https://${Usrname}:${token}@github.com/open-mmlab/mmagic.git /mmagic\nWORKDIR /mmagic\nENV FORCE_CUDA=\"1\"\nRUN pip install openmim\nRUN mim install mmcv==${MMCV}\nRUN pip install -r requirements.txt\nRUN pip install --no-cache-dir -e .\n" }, { "path": "docker/README.md", "content": "# Docker Image\n\nWe provide a [Dockerfile](Dockerfile) to build an image.\n\n```shell\n# build an image with PyTorch 1.6, CUDA 10.1\ndocker build -t mmagic docker/\n```\n\nRun it with\n\n```shell\ndocker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmagic/data mmagic\n```\n\n**Note**:\nVersions defined in this [Dockerfile](Dockerfile) is not up-to-date.\nIf you use this Dockerfile in your project, you probably want to make some updates.\nFeel free to submit an issue or PR for the update.\n" }, { "path": "docs/en/.dev_scripts/update_dataset_zoo.py", "content": "import os\n\nfrom tqdm import tqdm\n\n\ndef update_dataset_zoo():\n\n target_dir = 'dataset_zoo'\n source_dir = '../../tools/dataset_converters'\n os.makedirs(target_dir, exist_ok=True)\n\n # generate overview\n overviewmsg = \"\"\"\n# Overview\n\n\"\"\"\n\n # generate index.rst\n rstmsg = \"\"\"\n.. toctree::\n :maxdepth: 1\n :caption: Dataset Zoo\n\n overview.md\n\"\"\"\n\n subfolders = os.listdir(source_dir)\n for subf in tqdm(subfolders, desc='update dataset zoo'):\n\n target_subf = subf.replace('-', '_').lower()\n target_readme = os.path.join(target_dir, target_subf + '.md')\n source_readme = os.path.join(source_dir, subf, 'README.md')\n if not os.path.exists(source_readme):\n continue\n\n overviewmsg += f'\\n- [{subf}]({target_subf}.md)'\n rstmsg += f'\\n {target_subf}.md'\n\n # generate all tasks dataset_zoo\n command = f'cat {source_readme} > {target_readme}'\n os.popen(command)\n\n with open(os.path.join(target_dir, 'overview.md'), 'w') as f:\n f.write(overviewmsg)\n\n with open(os.path.join(target_dir, 'index.rst'), 'w') as f:\n f.write(rstmsg)\n\n\nif __name__ == '__main__':\n update_dataset_zoo()\n" }, { "path": "docs/en/.dev_scripts/update_model_zoo.py", "content": "#!/usr/bin/env python\nimport os\nfrom glob import glob\nfrom os import path as osp\nfrom pathlib import Path\n\nfrom modelindex.load_model_index import load\nfrom tqdm import tqdm\n\nMMAGIC_ROOT = Path(__file__).absolute().parents[3]\nTARGET_ROOT = Path(__file__).absolute().parents[1] / 'model_zoo'\n\n\ndef write_file(file, content):\n os.makedirs(osp.dirname(file), exist_ok=True)\n with open(file, 'w', encoding='utf-8') as f:\n f.write(content)\n\n\ndef update_model_zoo():\n \"\"\"load collections and models from model index, return summary,\n collections and models.\"\"\"\n model_index_file = MMAGIC_ROOT / 'model-index.yml'\n model_index = load(str(model_index_file))\n model_index.build_models_with_collections()\n\n # parse model_index according to task\n tasks = {}\n full_models = set()\n for model in model_index.models:\n full_models.add(model.full_model)\n for r in model.results:\n _task = r.task.lower().split(', ')\n for t in _task:\n if t not in tasks:\n tasks[t] = set()\n tasks[t].add(model.full_model)\n\n # assert the number of configs with the number of files\n collections = set([m.in_collection for m in full_models])\n assert len(collections) == len(os.listdir(MMAGIC_ROOT / 'configs')) - 1\n\n configs = set([str(MMAGIC_ROOT / m.config) for m in full_models])\n base_configs = glob(\n str(MMAGIC_ROOT / 'configs/_base_/**/*.py'), recursive=True)\n all_configs = glob(str(MMAGIC_ROOT / 'configs/**/*.py'), recursive=True)\n valid_configs = set(all_configs) - set(base_configs)\n untrackable_configs = valid_configs - configs\n assert len(untrackable_configs) == 0, '/n'.join(\n list(untrackable_configs)) + ' are not trackable.'\n\n # write for overview.md\n papers = set()\n checkpoints = set()\n for m in full_models:\n papers.add(m.paper['Title'])\n if m.weights is not None and m.weights.startswith('https:'):\n checkpoints.add(m.weights)\n task_desc = '\\n'.join([\n f\" - [{t}]({t.replace('-', '_').replace(' ', '_')}.md)\"\n for t in list(tasks.keys())\n ])\n\n # write overview.md\n overview = (f'# Overview\\n\\n'\n f'* Number of checkpoints: {len(checkpoints)}\\n'\n f'* Number of configs: {len(configs)}\\n'\n f'* Number of papers: {len(papers)}\\n'\n f' - ALGORITHM: {len(collections)}\\n\\n'\n f'* Tasks:\\n{task_desc}')\n write_file(TARGET_ROOT / 'overview.md', overview)\n\n # write for index.rst\n task_desc = '\\n'.join([\n f\" {t.replace('-', '_').replace(' ', '_')}.md\"\n for t in list(tasks.keys())\n ])\n overview = (f'.. toctree::\\n'\n f' :maxdepth: 1\\n'\n f' :caption: Model Zoo\\n\\n'\n f' overview.md\\n'\n f'{task_desc}')\n write_file(TARGET_ROOT / 'index.rst', overview)\n\n # write for all the tasks\n for task, models in tqdm(tasks.items(), desc='create markdown files'):\n target_md = f\"{task.replace('-', '_').replace(' ', '_')}.md\"\n target_md = TARGET_ROOT / target_md\n models = sorted(models, key=lambda x: -x.data['Year'])\n\n checkpoints = set()\n for m in models:\n if m.weights is not None and m.weights.startswith('https:'):\n checkpoints.add(m.weights)\n collections = set([m.in_collection for m in models])\n\n papers = set()\n for m in models:\n papers.add(m.paper['Title'])\n\n content = ''\n readme = set()\n for m in models:\n if m.readme not in readme:\n readme.add(m.readme)\n with open(MMAGIC_ROOT / m.readme, 'r', encoding='utf-8') as f:\n c = f.read()\n content += c.replace('# ', '## ')\n overview = (f'# {task}\\n\\n'\n f'## Summary\\n'\n f'* Number of checkpoints: {len(checkpoints)}\\n'\n f'* Number of configs: {len(models)}\\n'\n f'* Number of papers: {len(papers)}\\n'\n f' - ALGORITHM: {len(collections)}\\n\\n'\n f'{content}')\n\n write_file(target_md, overview)\n\n\nif __name__ == '__main__':\n update_model_zoo()\n" }, { "path": "docs/en/.gitignore", "content": "model_zoo\ndataset_zoo\nautoapi\n" }, { "path": "docs/en/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\trm -rf _build\n\trm -rf model_zoo\n\trm -rf dataset_zoo\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/en/_static/css/readthedocs.css", "content": ".header-logo {\n background-image: url(\"../image/mmagic-logo.png\");\n background-size: 142px 46px;\n height: 46px;\n width: 142px;\n}\n\ntable.colwidths-auto td {\n width: 50%\n}\n" }, { "path": "docs/en/_templates/404.html", "content": "{% extends \"layout.html\" %}\n\n{% block body %}\n\n

Page Not Found

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\n Oops! The page you are looking for cannot be found.\n

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\n This is likely to happen when you are switching document versions and the page you are reading is moved to another location in the new version. You can look for it in the content table left, or go to the homepage.\n

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\n If you cannot find documentation you want, please open an issue to tell us!\n

\n\n{% endblock %}\n" }, { "path": "docs/en/_templates/python/attribute.rst", "content": "{% extends \"python/data.rst\" %}\n" }, { "path": "docs/en/_templates/python/class.rst", "content": "{% if obj.display %}\n.. py:{{ obj.type }}:: {{ obj.short_name }}{% if obj.args %}({{ obj.args }}){% endif %}\n{% for (args, return_annotation) in obj.overloads %}\n {{ \" \" * (obj.type | length) }} {{ obj.short_name }}{% if args %}({{ args }}){% endif %}\n{% endfor %}\n\n\n {% if obj.bases %}\n {% if \"show-inheritance\" in autoapi_options %}\n Bases: {% for base in obj.bases %}{{ base|link_objs }}{% if not loop.last %}, {% endif %}{% endfor %}\n {% endif %}\n\n\n {% if \"show-inheritance-diagram\" in autoapi_options and obj.bases != [\"object\"] %}\n .. autoapi-inheritance-diagram:: {{ obj.obj[\"full_name\"] }}\n :parts: 1\n {% if \"private-members\" in autoapi_options %}\n :private-bases:\n {% endif %}\n\n {% endif %}\n {% endif %}\n {% if obj.docstring %}\n {{ obj.docstring|indent(3) }}\n {% endif %}\n {% if \"inherited-members\" in autoapi_options %}\n {% set visible_classes = obj.classes|selectattr(\"display\")|list %}\n {% else %}\n {% set visible_classes = obj.classes|rejectattr(\"inherited\")|selectattr(\"display\")|list %}\n {% endif %}\n {% for klass in visible_classes %}\n {{ klass.render()|indent(3) }}\n {% endfor %}\n {% if \"inherited-members\" in autoapi_options %}\n {% set visible_properties = obj.properties|selectattr(\"display\")|list %}\n {% else %}\n {% set visible_properties = obj.properties|rejectattr(\"inherited\")|selectattr(\"display\")|list %}\n {% endif %}\n {% for property in visible_properties %}\n {{ property.render()|indent(3) }}\n {% endfor %}\n {% if \"inherited-members\" in autoapi_options %}\n {% set visible_attributes = obj.attributes|selectattr(\"display\")|list %}\n {% else %}\n {% set visible_attributes = obj.attributes|rejectattr(\"inherited\")|selectattr(\"display\")|list %}\n {% endif %}\n {% for attribute in visible_attributes %}\n {{ attribute.render()|indent(3) }}\n {% endfor %}\n {% if \"inherited-members\" in autoapi_options %}\n {% set visible_methods = obj.methods|selectattr(\"display\")|list %}\n {% else %}\n {% set visible_methods = obj.methods|rejectattr(\"inherited\")|selectattr(\"display\")|list %}\n {% endif %}\n {% for method in visible_methods %}\n {{ method.render()|indent(3) }}\n {% endfor %}\n{% endif %}\n" }, { "path": "docs/en/_templates/python/data.rst", "content": "{% if obj.display %}\n.. py:{{ obj.type }}:: {{ obj.name }}\n {%+ if obj.value is not none or obj.annotation is not none -%}\n :annotation:\n {%- if obj.annotation %} :{{ obj.annotation }}\n {%- endif %}\n {%- if obj.value is not none %} = {%\n if obj.value is string and obj.value.splitlines()|count > 1 -%}\n Multiline-String\n\n .. raw:: html\n\n
Show Value\n\n .. code-block:: text\n :linenos:\n\n {{ obj.value|indent(width=8) }}\n\n .. raw:: html\n\n
\n\n {%- else -%}\n {{ obj.value|string|truncate(100) }}\n {%- endif %}\n {%- endif %}\n {% endif %}\n\n\n {{ obj.docstring|indent(3) }}\n{% endif %}\n" }, { "path": "docs/en/_templates/python/exception.rst", "content": "{% extends \"python/class.rst\" %}\n" }, { "path": "docs/en/_templates/python/function.rst", "content": "{% if obj.display %}\n.. py:function:: {{ obj.short_name }}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %}\n\n{% for (args, return_annotation) in obj.overloads %}\n {{ obj.short_name }}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %}\n\n{% endfor %}\n {% for property in obj.properties %}\n :{{ property }}:\n {% endfor %}\n\n {% if obj.docstring %}\n {{ obj.docstring|indent(3) }}\n {% endif %}\n{% endif %}\n" }, { "path": "docs/en/_templates/python/method.rst", "content": "{%- if obj.display %}\n.. py:method:: {{ obj.short_name }}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %}\n\n{% for (args, return_annotation) in obj.overloads %}\n {{ obj.short_name }}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %}\n\n{% endfor %}\n {% if obj.properties %}\n {% for property in obj.properties %}\n :{{ property }}:\n {% endfor %}\n\n {% else %}\n\n {% endif %}\n {% if obj.docstring %}\n {{ obj.docstring|indent(3) }}\n {% endif %}\n{% endif %}\n" }, { "path": "docs/en/_templates/python/module.rst", "content": "{% if not obj.display %}\n:orphan:\n\n{% endif %}\n:py:mod:`{{ obj.name }}`\n=========={{ \"=\" * obj.name|length }}\n\n.. py:module:: {{ obj.name }}\n\n{% if obj.docstring %}\n.. autoapi-nested-parse::\n\n {{ obj.docstring|indent(3) }}\n\n{% endif %}\n\n{% block subpackages %}\n{% set visible_subpackages = obj.subpackages|selectattr(\"display\")|list %}\n{% if visible_subpackages %}\nSubpackages\n-----------\n.. toctree::\n :titlesonly:\n :maxdepth: 3\n\n{% for subpackage in visible_subpackages %}\n {{ subpackage.short_name }}/index.rst\n{% endfor %}\n\n\n{% endif %}\n{% endblock %}\n{% block submodules %}\n{% set visible_submodules = obj.submodules|selectattr(\"display\")|list %}\n{% if visible_submodules %}\nSubmodules\n----------\n.. toctree::\n :titlesonly:\n :maxdepth: 1\n\n{% for submodule in visible_submodules %}\n {{ submodule.short_name }}/index.rst\n{% endfor %}\n\n\n{% endif %}\n{% endblock %}\n{% block content %}\n{% if obj.all is not none %}\n{% set visible_children = obj.children|selectattr(\"short_name\", \"in\", obj.all)|list %}\n{% elif obj.type is equalto(\"package\") %}\n{% set visible_children = obj.children|selectattr(\"display\")|list %}\n{% else %}\n{% set visible_children = obj.children|selectattr(\"display\")|rejectattr(\"imported\")|list %}\n{% endif %}\n{% if visible_children %}\n{{ obj.type|title }} Contents\n{{ \"-\" * obj.type|length }}---------\n\n{% set visible_classes = visible_children|selectattr(\"type\", \"equalto\", \"class\")|list %}\n{% set visible_functions = visible_children|selectattr(\"type\", \"equalto\", \"function\")|list %}\n{% set visible_attributes = visible_children|selectattr(\"type\", \"equalto\", \"data\")|list %}\n{% if \"show-module-summary\" in autoapi_options and (visible_classes or visible_functions) %}\n{% block classes scoped %}\n{% if visible_classes %}\nClasses\n~~~~~~~\n\n.. autoapisummary::\n\n{% for klass in visible_classes %}\n {{ klass.id }}\n{% endfor %}\n\n\n{% endif %}\n{% endblock %}\n\n{% block functions scoped %}\n{% if visible_functions %}\nFunctions\n~~~~~~~~~\n\n.. autoapisummary::\n\n{% for function in visible_functions %}\n {{ function.id }}\n{% endfor %}\n\n\n{% endif %}\n{% endblock %}\n\n{% block attributes scoped %}\n{% if visible_attributes %}\nAttributes\n~~~~~~~~~~\n\n.. autoapisummary::\n\n{% for attribute in visible_attributes %}\n {{ attribute.id }}\n{% endfor %}\n\n\n{% endif %}\n{% endblock %}\n{% endif %}\n{% for obj_item in visible_children %}\n{{ obj_item.render()|indent(0) }}\n{% endfor %}\n{% endif %}\n{% endblock %}\n" }, { "path": "docs/en/_templates/python/package.rst", "content": "{% extends \"python/module.rst\" %}\n" }, { "path": "docs/en/_templates/python/property.rst", "content": "{%- if obj.display %}\n.. py:property:: {{ obj.short_name }}\n {% if obj.annotation %}\n :type: {{ obj.annotation }}\n {% endif %}\n {% if obj.properties %}\n {% for property in obj.properties %}\n :{{ property }}:\n {% endfor %}\n {% endif %}\n\n {% if obj.docstring %}\n {{ obj.docstring|indent(3) }}\n {% endif %}\n{% endif %}\n" }, { "path": "docs/en/advanced_guides/data_flow.md", "content": "# Data flow\n\n- [Data Flow](#data-flow)\n - [Overview of dataflow](#overview-of-data-flow)\n - [Data flow between dataset and model](#data-flow-between-dataset-and-model)\n - [Data from dataloader](#data-from-dataloader)\n - [Data from data preprocessor](#data-from-data-preprocessor)\n - [Data flow between model output and visualizer](#data-flow-between-model-output-and-visualizer)\n\n## Overview of dataflow\n\nThe [Runner](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/runner.md) is an \"integrator\" in MMEngine. It covers all aspects of the framework and shoulders the responsibility of organizing and scheduling nearly all modules, that means the dataflow between all modules also controlled by the `Runner`. As illustrated in the [Runner document of MMEngine](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html), the following diagram shows the basic dataflow. In this chapter, we will introduce the dataflow and data format convention between the internal modules managed by the [Runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html).\n\n
\n\n
\n\nIn the above diagram, at each training iteration, dataloader loads images from storage and transfer to data preprocessor, data preprocessor would put images to the specific device and stack data to batch, then model accepts the batch data as inputs, finally the outputs of the model would be compute the loss. Since model parameters are freezed when doing evaluation, the model output would be transferred to [Evaluator](./evaluation.md#ioumetric) to compute metrics or seed the data to visualize in [Visualizer](../user_guides/visualization.md).\n\n## Data flow between dataset and model\n\nIn this section, we will introduce the data flow passing in the dataset in MMagic. About [dataset](https://mmagic.readthedocs.io/en/latest/howto/dataset.html) and \\[transforms\\] pipeline (https://mmagic.readthedocs.io/en/latest/howto/transforms.html) related tutorials can be found in the development of guidelines.The data flow between dataloader and model can be generally split into four parts:\n\n1. Read the original information of `XXDataset` collected datasets, and carry out data conversion processing through data transform pipeline;\n\n2. use `PackInputs` to pack data from previous transformations into a dictionar;\n\n3. use `collate_fn` to stack a list of tensors into a batched tensor;\n\n4. use `data preprocessor` to move all these data to target device, e.g. GPUS, and unzip the dictionary from the dataloader\n into a tuple, containing the input images and meta info (`DataSample`).\n\n### Data from transform pipeline\n\nIn MMagic, different types of 'XXDataset' load the data (LQ) and label (GT), and perform data transformation in different data preprocessing pipelines, and finally package the processed data into a dictionary through `PackInputs`, which contains all the data required for training and testing iterations.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
base_edit_model.py base_conditional_gan.py
\n\n```python\n@MODELS.register_module()\nclass BaseEditModel(BaseModel):\n \"\"\"Base model for image and video editing.\n \"\"\"\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs) -> Union[torch.Tensor, List[DataSample], dict]:\n if isinstance(inputs, dict):\n inputs = inputs['img']\n if mode == 'tensor':\n return self.forward_tensor(inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n\n elif mode == 'loss':\n return self.forward_train(inputs, data_samples, **kwargs)\n```\n\n\n\n```python\n@MODELS.register_module()\nclass BaseConditionalGAN(BaseGAN):\n \"\"\"Base class for Conditional GAM models.\n \"\"\"\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n if isinstance(inputs, Tensor):\n noise = inputs\n sample_kwargs = {}\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n num_batches = noise.shape[0]\n\n pass\n ...\n```\n\n
\n\nFor example, in the `BaseEditModel` and `BaseConditionalGAN` models, key input including `img` and `noise` are required. At the same time, the corresponding fields should also be exposed in the configuration file,[cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py](../../../configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py) as an example,\n\n### Data from dataloader\n\nAfter receiving a list of dictionary from dataset, `collect_fn` in dataloader will gather `inputs` in each dict\nand stack them into a batched tensor. In addition, `data_sample` in each dict will be also collected in a list.\nThen, it will output a dict, containing the same keys with those of the dict in the received list. Finally, dataloader\nwill output the dict from the `collect_fn`. Detailed documentation can be reference [DATASET AND DATALOADER](https://mmengine.readthedocs.io/en/latest/tutorials/dataset.html)。\n\n### Data from data preprocessor\n\nData preprocessor is the last step to process the data before feeding into the model. It will apply image normalization, convert BGR to RGB and move all data to the target device, e.g. GPUs. After above steps, it will output a tuple, containing a list of batched images, and a list of data samples. Detailed documentation can be reference [data_preprocessor](./data_preprocessor.md)。\n\n## Data flow between model output and visualizer\n\nMMEngine agreed [Abstract Data Element](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/data_element.md) for data transfer Where [data sample](./structures.md) as a more advanced encapsulation can hold more categories of label data. In MMagic, `ConcatImageVisualizer` for visual comparison also controls the visual content through the `add_datasample` function. The specific configuration is as follows.\n\n```python\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=[dict(type='LocalVisBackend')],\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\n```\n" }, { "path": "docs/en/advanced_guides/data_preprocessor.md", "content": "# Data pre-processor\n\n## The position of the data preprocessor in the training pipeline.\n\nDuring the model training process, image data undergoes data augmentation using the transforms provided by mmcv. The augmented data is then loaded into a dataloader. Subsequently, a preprocessor is used to move the data from the CPU to CUDA (GPU), perform padding, and normalize the data.\n\nBelow is an example of the `train_pipeline` in the complete configuration file using `configs/_base_/datasets/unpaired_imgs_256x256.py`. The train_pipeline typically defines a sequence of transformations applied to training images using the mmcv library. This pipeline is designed to prevent redundancy in the transformation functions across different downstream algorithm libraries.\n\n```python\n...\ntrain_pipeline = [\n dict(color_type='color', key='img_A', type='LoadImageFromFile'),\n dict(color_type='color', key='img_B', type='LoadImageFromFile'),\n dict(auto_remap=True, mapping=dict(img=['img_A', 'img_B',]),\n share_random_params=True,\n transforms=[dict(interpolation='bicubic', scale=(286, 286,), type='Resize'),\n dict(crop_size=(256, 256,), keys=['img',], random_crop=True, type='Crop'),],\n type='TransformBroadcaster'),\n dict(direction='horizontal', keys=['img_A', ], type='Flip'),\n dict(direction='horizontal', keys=['img_B', ], type='Flip'),\n dict(mapping=dict(img_mask='img_B', img_photo='img_A'),\n remapping=dict(img_mask='img_mask', img_photo='img_photo'),\n type='KeyMapper'),\n dict(data_keys=['img_photo', 'img_mask',],\n keys=['img_photo', 'img_mask',], type='PackInputs'),\n]\n...\n```\n\nIn the `train_step` function in the `mmagic/models/editors/cyclegan/cyclegan.py` script, the data preprocessing steps involve moving, concatenating, and normalizing the transformed data before feeding it into the neural network. Below is an example of the relevant code logic:\n\n```python\n...\nmessage_hub = MessageHub.get_current_instance()\ncurr_iter = message_hub.get_info('iter')\ndata = self.data_preprocessor(data, True)\ndisc_optimizer_wrapper = optim_wrapper['discriminators']\n\ninputs_dict = data['inputs']\noutputs, log_vars = dict(), dict()\n...\n```\n\nIn mmagic, the code implementation for the data processor is located at `mmagic/models/data_preprocessors/data_preprocessor.py`. The data processing workflow is as follows:\n![image](https://github.com/jinxianwei/CloudImg/assets/81373517/f52a92ab-f86d-486d-86ac-a2f388a83ced)\n" }, { "path": "docs/en/advanced_guides/evaluator.md", "content": "# Evaluator\n\n## Evaluation Metrics and Evaluators\n\nIn model validation and testing, it is usually necessary to quantitatively evaluate the accuracy of the model. In mmagic, the evaluation metrics and evaluators are implemented to accomplish this functionality.\n\n- Evaluation metrics are used to calculate specific model accuracy indicators based on test data and model prediction results. mmagic provides a variety of built-in metrics, which can be found in the metrics documentation. Additionally, metrics are decoupled from datasets and can be used for multiple datasets.\n- The evaluator is the top-level module for evaluation metrics and usually contains one or more metrics. The purpose of the evaluator is to perform necessary data format conversion and call evaluation metrics to calculate the model accuracy during model evaluation. The evaluator is typically built by a `Runner` or a testing script, which are used for online evaluation and offline evaluation, respectively.\n\nThe evaluator in MMagic inherits from that in MMEngine and has a similar basic usage. For specific information, you can refer to [Model Accuracy Evaluation](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html). However, different from other high-level vision tasks, the evaluation metrics for generative models often have multiple inputs. For example, the input for the Inception Score (IS) metric is only fake images and any number of real images, while the Perceptual Path Length (PPL) requires sampling from the latent space. To accommodate different evaluation metrics, mmagic introduces two important methods, `prepare_metrics` and `prepare_samplers` to meet the above requirements.\n\n## prepare_metrics\n\n```python\nclass Evaluator(Evaluator):\n\t...\n def prepare_metrics(self, module: BaseModel, dataloader: DataLoader):\n \"\"\"Prepare for metrics before evaluation starts. Some metrics use\n pretrained model to extract feature. Some metrics use pretrained model\n to extract feature and input channel order may vary among those models.\n Therefore, we first parse the output color order from data\n preprocessor and set the color order for each metric. Then we pass the\n dataloader to each metrics to prepare pre-calculated items. (e.g.\n inception feature of the real images). If metric has no pre-calculated\n items, :meth:`metric.prepare` will be ignored. Once the function has\n been called, :attr:`self.is_ready` will be set as `True`. If\n :attr:`self.is_ready` is `True`, this function will directly return to\n avoid duplicate computation.\n\n Args:\n module (BaseModel): Model to evaluate.\n dataloader (DataLoader): The dataloader for real images.\n \"\"\"\n if self.metrics is None:\n self.is_ready = True\n return\n\n if self.is_ready:\n return\n\n # prepare metrics\n for metric in self.metrics:\n metric.prepare(module, dataloader)\n self.is_ready = True\n```\n\nThe `prepare_metrics` method needs to be called before the evaluation starts. It is used to preprocess before evaluating each metric, and will sequentially call the prepare method of each metric in the evaluator to prepare any pre-calculated elements needed for that metric (such as features from hidden layers). Additionally, to avoid repeated calls, the `evaluator.is_ready` flag will be set to True after preprocessing for all metrics is completed.\n\n```python\nclass GenMetric(BaseMetric):\n\t...\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n \"\"\"Prepare for the pre-calculating items of the metric. Defaults to do\n nothing.\n\n Args:\n module (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for the real images.\n \"\"\"\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n```\n\n## prepare_samplers\n\nDifferent metrics require different inputs for generative models. For example, FID, KID, and IS only need the generated fake images, while PPL requires vectors from the latent space. Therefore, mmagic groups different evaluation metrics based on the type of input. One or more evaluation metrics in the same group share a data sampler. The sampler mode for each evaluation metric is determined by the `SAMPLER_MODE` attribute of that metric.\n\n```python\nclass GenMetric(BaseMetric):\n\t...\n SAMPLER_MODE = 'normal'\n\nclass GenerativeMetric(GenMetric):\n\t...\n SAMPLER_MODE = 'Generative'\n```\n\nThe `prepare_samplers` method of the evaluator is responsible for preparing the data samplers based on the sampler mode of all evaluation metrics.\n\n```python\nclass Evaluator(Evaluator):\n\t...\n def prepare_samplers(self, module: BaseModel, dataloader: DataLoader\n ) -> List[Tuple[List[BaseMetric], Iterator]]:\n \"\"\"Prepare for the sampler for metrics whose sampling mode are\n different. For generative models, different metric need image\n generated with different inputs. For example, FID, KID and IS need\n images generated with random noise, and PPL need paired images on the\n specific noise interpolation path. Therefore, we first group metrics\n with respect to their sampler's mode (refers to\n :attr:~`GenMetrics.SAMPLER_MODE`), and build a shared sampler for each\n metric group. To be noted that, the length of the shared sampler\n depends on the metric of the most images required in each group.\n\n Args:\n module (BaseModel): Model to evaluate. Some metrics (e.g. PPL)\n require `module` in their sampler.\n dataloader (DataLoader): The dataloader for real image.\n\n Returns:\n List[Tuple[List[BaseMetric], Iterator]]: A list of \"metrics-shared\n sampler\" pair.\n \"\"\"\n if self.metrics is None:\n return [[[None], []]]\n\n # grouping metrics based on `SAMPLER_MODE` and `sample_mode`\n metric_mode_dict = defaultdict(list)\n for metric in self.metrics: # Specify a sampler group for each metric.\n metric_md5 = self._cal_metric_hash(metric)\n metric_mode_dict[metric_md5].append(metric)\n\n metrics_sampler_list = []\n for metrics in metric_mode_dict.values(): # Generate a sampler for each group.\n first_metric = metrics[0]\n metrics_sampler_list.append([\n metrics,\n first_metric.get_metric_sampler(module, dataloader, metrics)\n ])\n\n return metrics_sampler_list\n```\n\nThe method will first check if it has any evaluation metrics to calculate: if not, it will return directly. If there are metrics to calculate, it will iterate through all the evaluation metrics and group them based on the sampler_mode and sample_model. The specific implementation is as follows: it calculates a hash code based on the sampler_mode and sample_model, and puts the evaluation metrics with the same hash code into the same list.\n\n```python\nclass Evaluator(Evaluator):\n\t...\n @staticmethod\n def _cal_metric_hash(metric: GenMetric):\n \"\"\"Calculate a unique hash value based on the `SAMPLER_MODE` and\n `sample_model`.\"\"\"\n sampler_mode = metric.SAMPLER_MODE\n sample_model = metric.sample_model\n metric_dict = {\n 'SAMPLER_MODE': sampler_mode,\n 'sample_model': sample_model\n }\n if hasattr(metric, 'need_cond_input'):\n metric_dict['need_cond_input'] = metric.need_cond_input\n md5 = hashlib.md5(repr(metric_dict).encode('utf-8')).hexdigest()\n return md5\n```\n\nFinally, this method will generate a sampler for each evaluation metric group and add it to a list to return.\n\n## Evaluation process of an evaluator\n\nThe implementation of evaluation process can be found in `mmagic.engine.runner.MultiValLoop.run` and `mmagic.engine.runner.MultiTestLoop.run`. Here we take `mmagic.engine.runner.MultiValLoop.run` as example.\n\n```python\nclass MultiValLoop(BaseLoop):\n\t...\n def run(self):\n\t...\n # 1. prepare all metrics and get the total length\n metrics_sampler_lists = []\n meta_info_list = []\n dataset_name_list = []\n for evaluator, dataloader in zip(self.evaluators, self.dataloaders):\n # 1.1 prepare for metrics\n evaluator.prepare_metrics(module, dataloader)\n # 1.2 prepare for metric-sampler pair\n metrics_sampler_list = evaluator.prepare_samplers(\n module, dataloader)\n metrics_sampler_lists.append(metrics_sampler_list)\n # 1.3 update total length\n self._total_length += sum([\n len(metrics_sampler[1])\n for metrics_sampler in metrics_sampler_list\n ])\n # 1.4 save metainfo and dataset's name\n meta_info_list.append(\n getattr(dataloader.dataset, 'metainfo', None))\n dataset_name_list.append(dataloader.dataset.__class__.__name__)\n```\n\nFirst, the runner will perform preprocessing and obtain the necessary data samplers for evaluation using the `evaluator.prepare_metric` and `evaluator.prepare_samplers` methods. It will also update the total length of samples obtained using the samplers. As the evaluation metrics and dataset in mmagic are separated, some meta_info required for evaluation also needs to be saved and passed to the evaluator.\n\n```python\nclass MultiValLoop(BaseLoop):\n\t...\n def run(self):\n\t...\n # 2. run evaluation\n for idx in range(len(self.evaluators)):\n # 2.1 set self.evaluator for run_iter\n self.evaluator = self.evaluators[idx]\n self.dataloader = self.dataloaders[idx]\n\n # 2.2 update metainfo for evaluator and visualizer\n meta_info = meta_info_list[idx]\n dataset_name = dataset_name_list[idx]\n if meta_info:\n self.evaluator.dataset_meta = meta_info\n self._runner.visualizer.dataset_meta = meta_info\n else:\n warnings.warn(\n f'Dataset {dataset_name} has no metainfo. `dataset_meta` '\n 'in evaluator, metric and visualizer will be None.')\n\n # 2.3 generate images\n metrics_sampler_list = metrics_sampler_lists[idx]\n for metrics, sampler in metrics_sampler_list:\n for data in sampler:\n self.run_iter(idx_counter, data, metrics)\n idx_counter += 1\n\n # 2.4 evaluate metrics and update multi_metric\n metrics = self.evaluator.evaluate()\n if multi_metric and metrics.keys() & multi_metric.keys():\n raise ValueError('Please set different prefix for different'\n ' datasets in `val_evaluator`')\n else:\n multi_metric.update(metrics)\n # 3. finish evaluation and call hooks\n self._runner.call_hook('after_val_epoch', metrics=multi_metric)\n self._runner.call_hook('after_val')\n```\n\nAfter the preparation for evaluation is completed, the runner will iterate through all the evaluators and perform the evaluation one by one. Each evaluator needs to correspond to a data loader to complete the evaluation work for a dataset. Specifically, during the evaluation process for each evaluator, it is necessary to pass the required meta_info to the evaluator, then iterate through all the metrics_samplers of this evaluator to generate the images needed for evaluation, and finally complete the evaluation.\n" }, { "path": "docs/en/advanced_guides/structures.md", "content": "# Data Structure\n\n`DataSample` , the data structure interface of MMagic, inherits from [` BaseDataElement`](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html). The base class has implemented basic add/delete/update/check functions and supports data migration between different devices, as well as dictionary-like and tensor-like operations, which also allows the interfaces of different algorithms to be unified.\n\nSpecifically, an instance of BaseDataElement consists of two components:\n\n- `metainfo`, which contains some meta information,\n e.g., `img_shape`, `img_id`, `color_order`, etc.\n- `data`, which contains the data used in the loop.\n\nThanks to ` DataSample` , the data flow between each module in the algorithm libraries, such as [`visualizer`](https://mmagic.readthedocs.io/en/latest/user_guides/visualization.html), [`evaluator`](https://mmagic.readthedocs.io/en/latest/advanced_guides/evaluator.html), [`model`](https://mmagic.readthedocs.io/en/latest/howto/models.html), is greatly simplified.\n\nThe attributes in `DataSample` are divided into several parts:\n\n```python\n- ``gt_img``: Ground truth image(s).\n- ``pred_img``: Image(s) of model predictions.\n- ``ref_img``: Reference image(s).\n- ``mask``: Mask in Inpainting.\n- ``trimap``: Trimap in Matting.\n- ``gt_alpha``: Ground truth alpha image in Matting.\n- ``pred_alpha``: Predicted alpha image in Matting.\n- ``gt_fg``: Ground truth foreground image in Matting.\n- ``pred_fg``: Predicted foreground image in Matting.\n- ``gt_bg``: Ground truth background image in Matting.\n- ``pred_bg``: Predicted background image in Matting.\n- ``gt_merged``: Ground truth merged image in Matting.\n```\n\nThe following sample code demonstrates the components of `DataSample`:\n\n```python\n >>> import torch\n >>> import numpy as np\n >>> from mmagic.structures import DataSample\n >>> img_meta = dict(img_shape=(800, 1196, 3))\n >>> img = torch.rand((3, 800, 1196))\n >>> data_sample = DataSample(gt_img=img, metainfo=img_meta)\n >>> assert 'img_shape' in data_sample.metainfo_keys()\n >>> data_sample\n\t >>># metainfo and data of DataSample\n \n```\n\nWe also support `stack` and `split` operation to handle a batch of data samples.\n\n1. Stack\n\nStack a list of data samples to one. All tensor fields will be stacked at first dimension. Otherwise the values will be saved in a list.\n\n```\n Args:\n data_samples (Sequence['DataSample']): A sequence of `DataSample` to stack.\n\n Returns:\n DataSample: The stacked data sample.\n```\n\n2. Split\n\nSplit a sequence of data sample in the first dimension.\n\n```\n\tArgs:\n allow_nonseq_value (bool): Whether allow non-sequential data in\n split operation. If True, non-sequential data will be copied\n for all split data samples. Otherwise, an error will be\n raised. Defaults to False.\n\n Returns:\n Sequence[DataSample]: The list of data samples after splitting.\n```\n\nThe following sample code demonstrates the use of `stack` and ` split`:\n\n```py\nimport torch\nimport numpy as np\nfrom mmagic.structures import DataSample\nimg_meta1 = img_meta2 = dict(img_shape=(800, 1196, 3))\nimg1 = torch.rand((3, 800, 1196))\nimg2 = torch.rand((3, 800, 1196))\ndata_sample1 = DataSample(gt_img=img1, metainfo=img_meta1)\ndata_sample2 = DataSample(gt_img=img2, metainfo=img_meta1)\n```\n\n```py\n# stack them and then use as batched-tensor!\ndata_sample = DataSample.stack([data_sample1, data_sample2])\nprint(data_sample.gt_img.shape)\n torch.Size([2, 3, 800, 1196])\nprint(data_sample.metainfo)\n {'img_shape': [(800, 1196, 3), (800, 1196, 3)]}\n\n# split them if you want\ndata_sample1_, data_sample2_ = data_sample.split()\nassert (data_sample1_.gt_img == img1).all()\nassert (data_sample2_.gt_img == img2).all()\n```\n" }, { "path": "docs/en/changelog.md", "content": "# Changelog\n\n**Highlights**\n\n- An advanced and powerful inpainting algorithm named PowerPaint is released in our repository. [Click to View](https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint)\n\n
\n\n
\n\n**New Features & Improvements**\n\n- \\[Release\\] Post release for v1.1.0 by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2043\n- \\[CodeCamp2023-645\\]Add dreambooth new cfg by @YanxingLiu in https://github.com/open-mmlab/mmagic/pull/2042\n- \\[Enhance\\] add new config for _base_ dir by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2053\n- \\[Enhance\\] support using from_pretrained for instance_crop by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2066\n- \\[Enhance\\] update support for latest diffusers with lora by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2067\n- \\[Feature\\] PowerPaint by @zhuang2002 in https://github.com/open-mmlab/mmagic/pull/2076\n- \\[Enhance\\] powerpaint improvement by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2078\n- \\[Enhance\\] Improve powerpaint by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2080\n- \\[Enhance\\] add outpainting to gradio_PowerPaint.py by @zhuang2002 in https://github.com/open-mmlab/mmagic/pull/2084\n- \\[MMSIG\\] Add new configuration files for StyleGAN2 by @xiaomile in https://github.com/open-mmlab/mmagic/pull/2057\n- \\[MMSIG\\] \\[Doc\\] Update data_preprocessor.md by @jinxianwei in https://github.com/open-mmlab/mmagic/pull/2055\n- \\[Enhance\\] Enhance PowerPaint by @zhuang2002 in https://github.com/open-mmlab/mmagic/pull/2093\n\n**Bug Fixes**\n\n- \\[Fix\\] Update README.md by @eze1376 in https://github.com/open-mmlab/mmagic/pull/2048\n- \\[Fix\\] Fix test tokenizer by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2050\n- \\[Fix\\] fix readthedocs building by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2052\n- \\[Fix\\] --local-rank for PyTorch >= 2.0.0 by @youqingxiaozhua in https://github.com/open-mmlab/mmagic/pull/2051\n- \\[Fix\\] animatediff download from openxlab by @JianxinDong in https://github.com/open-mmlab/mmagic/pull/2061\n- \\[Fix\\] fix best practice by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2063\n- \\[Fix\\] try import expand mask from transformers by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2064\n- \\[Fix\\] Update diffusers to v0.23.0 by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2069\n- \\[Fix\\] add openxlab link to powerpaint by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2082\n- \\[Fix\\] Update swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py, use MultiValLoop. by @ashutoshsingh0223 in https://github.com/open-mmlab/mmagic/pull/2085\n- \\[Fix\\] Fix a test expression that has a logical short circuit. by @munahaf in https://github.com/open-mmlab/mmagic/pull/2046\n- \\[Fix\\] Powerpaint to load safetensors by @sdbds in https://github.com/open-mmlab/mmagic/pull/2088\n\n**New Contributors**\n\n- @eze1376 made their first contribution in https://github.com/open-mmlab/mmagic/pull/2048\n- @youqingxiaozhua made their first contribution in https://github.com/open-mmlab/mmagic/pull/2051\n- @JianxinDong made their first contribution in https://github.com/open-mmlab/mmagic/pull/2061\n- @zhuang2002 made their first contribution in https://github.com/open-mmlab/mmagic/pull/2076\n- @ashutoshsingh0223 made their first contribution in https://github.com/open-mmlab/mmagic/pull/2085\n- @jinxianwei made their first contribution in https://github.com/open-mmlab/mmagic/pull/2055\n- @munahaf made their first contribution in https://github.com/open-mmlab/mmagic/pull/2046\n- @sdbds made their first contribution in https://github.com/open-mmlab/mmagic/pull/2088\n\n**Full Changelog**: https://github.com/open-mmlab/mmagic/compare/v1.1.0...v1.2.0\n\n## v1.1.0 (22/09/2023)\n\n**Highlights**\n\nIn this new version of MMagic, we have added support for the following five new algorithms.\n\n- Support ViCo, a new SD personalization method. [Click to View](https://github.com/open-mmlab/mmagic/blob/main/configs/vico/README.md)\n\n\n\n \n \n
\n
\n \n
\n
\n\n- Support AnimateDiff, a popular text2animation method. [Click to View](https://github.com/open-mmlab/mmagic/blob/main/configs/animatediff/README.md)\n\n![512](https://github.com/ElliotQi/mmagic/assets/46469021/54d92aca-dfa9-4eeb-ba38-3f6c981e5399)\n\n- Support SDXL. [Click to View](https://github.com/open-mmlab/mmagic/blob/main/configs/stable_diffusion_xl/README.md)\n\n
\n\n
\n\n- Support DragGAN implementation with MMagic. [Click to View](https://github.com/open-mmlab/mmagic/blob/main/configs/draggan/README.md)\n\n
\n\n
\n\n- Support for FastComposer. [Click to View](https://github.com/open-mmlab/mmagic/blob/main/configs/fastcomposer/README.md)\n\n
\n\n
\n\n**New Features & Improvements**\n\n- \\[Feature\\] Support inference with diffusers pipeline, sd_xl first. by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2023\n- \\[Enhance\\] add negative prompt for sd inferencer by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2021\n- \\[Enhance\\] Update flake8 checking config in setup.cfg by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/2007\n- \\[Enhance\\] Add ‘config_name' as a supplement to the 'model_setting' by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2027\n- \\[Enhance\\] faster test by @okotaku in https://github.com/open-mmlab/mmagic/pull/2034\n- \\[Enhance\\] Add OpenXLab Badge by @ZhaoQiiii in https://github.com/open-mmlab/mmagic/pull/2037\n\n**CodeCamp Contributions**\n\n- \\[CodeCamp2023-643\\] Add new configs of BigGAN by @limafang in https://github.com/open-mmlab/mmagic/pull/2003\n- \\[CodeCamp2023-648\\] MMagic new config GuidedDiffusion by @ooooo-create in https://github.com/open-mmlab/mmagic/pull/2005\n- \\[CodeCamp2023-649\\] MMagic new config Instance Colorization by @ooooo-create in https://github.com/open-mmlab/mmagic/pull/2010\n- \\[CodeCamp2023-652\\] MMagic new config StyleGAN3 by @hhy150 in https://github.com/open-mmlab/mmagic/pull/2018\n- \\[CodeCamp2023-653\\] Add new configs of Real BasicVSR by @RangeKing in https://github.com/open-mmlab/mmagic/pull/2030\n\n**Bug Fixes**\n\n- \\[Fix\\] Fix best practice and back to contents on mainpage, add new models to model zoo by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2001\n- \\[Fix\\] Check CI error and remove main stream gpu test by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2013\n- \\[Fix\\] Check circle ci memory by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2016\n- \\[Fix\\] remove code and fix clip loss ut test by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2017\n- \\[Fix\\] mock infer in diffusers pipeline inferencer ut. by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2026\n- \\[Fix\\] Fix bug caused by merging draggan by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2029\n- \\[Fix\\] Update QRcode by @crazysteeaam in https://github.com/open-mmlab/mmagic/pull/2009\n- \\[Fix\\] Replace the download links in README with OpenXLab version by @FerryHuang in https://github.com/open-mmlab/mmagic/pull/2038\n- \\[Fix\\] Increase docstring coverage by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2039\n\n**New Contributors**\n\n- @limafang made their first contribution in https://github.com/open-mmlab/mmagic/pull/2003\n- @ooooo-create made their first contribution in https://github.com/open-mmlab/mmagic/pull/2005\n- @hhy150 made their first contribution in https://github.com/open-mmlab/mmagic/pull/2018\n- @ZhaoQiiii made their first contribution in https://github.com/open-mmlab/mmagic/pull/2037\n- @ElliotQi made their first contribution in https://github.com/open-mmlab/mmagic/pull/1980\n- @Beaconsyh08 made their first contribution in https://github.com/open-mmlab/mmagic/pull/2012\n\n**Full Changelog**: https://github.com/open-mmlab/mmagic/compare/v1.0.2...v1.0.3\n\n## v1.0.2 (24/08/2023)\n\n**Highlights**\n\n**1. More detailed documentation**\n\nThank you to the community contributors for helping us improve the documentation. We have improved many documents, including both Chinese and English versions. Please refer to the [documentation](https://mmagic.readthedocs.io/en/latest/) for more details.\n\n**2. New algorithms**\n\n- Support Prompt-to-prompt, DDIM Inversion and Null-text Inversion. [Click to View.](https://github.com/open-mmlab/mmagic/blob/main/projects/prompt_to_prompt/README.md)\n\nFrom right to left: origin image, DDIM inversion, Null-text inversion\n\n
\n \n
\n\nPrompt-to-prompt Editing\n\n
\n cat -> dog\n
\n \n
\n\n
\n spider man -> iron man(attention replace)\n
\n \n
\n\n
\n Effel tower -> Effel tower at night (attention refine)\n
\n \n
\n\n
\n blossom sakura tree -> blossom(-3) sakura tree (attention reweight)\n
\n \n
\n\n- Support Textual Inversion. [Click to view.](https://github.com/open-mmlab/mmagic/blob/main/configs/textual_inversion/README.md)\n\n
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\n\n- Support Attention Injection for more stable video generation with controlnet. [Click to view.](https://github.com/open-mmlab/mmagic/blob/main/configs/controlnet_animation/README.md)\n- Support Stable Diffusion Inpainting. [Click to view.](https://github.com/open-mmlab/mmagic/blob/main/configs/stable_diffusion/README.md)\n\n**New Features & Improvements**\n\n- \\[Enhancement\\] Support noise offset in stable diffusion training by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1880\n- \\[Community\\] Support Glide Upsampler by @Taited in https://github.com/open-mmlab/mmagic/pull/1663\n- \\[Enhance\\] support controlnet inferencer by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1891\n- \\[Feature\\] support Albumentations augmentation transformations and pipeline by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1894\n- \\[Feature\\] Add Attention Injection for unet by @liuwenran in https://github.com/open-mmlab/mmagic/pull/1895\n- \\[Enhance\\] update benchmark scripts by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1907\n- \\[Enhancement\\] update mmagic docs by @crazysteeaam in https://github.com/open-mmlab/mmagic/pull/1920\n- \\[Enhancement\\] Support Prompt-to-prompt, ddim inversion and null-text inversion by @FerryHuang in https://github.com/open-mmlab/mmagic/pull/1908\n- \\[CodeCamp2023-302\\] Support MMagic visualization and write a user guide by @aptsunny in https://github.com/open-mmlab/mmagic/pull/1939\n- \\[Feature\\] Support Textual Inversion by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1822\n- \\[Feature\\] Support stable diffusion inpaint by @Taited in https://github.com/open-mmlab/mmagic/pull/1976\n- \\[Enhancement\\] Adopt `BaseModule` for some models by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1543\n- \\[MMSIG\\]支持 DeblurGANv2 inference by @xiaomile in https://github.com/open-mmlab/mmagic/pull/1955\n- \\[CodeCamp2023-647\\] Add new configs of EG3D by @RangeKing in https://github.com/open-mmlab/mmagic/pull/1985\n\n**Bug Fixes**\n\n- Fix dtype error in StableDiffusion and DreamBooth training by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1879\n- Fix gui VideoSlider bug by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1885\n- Fix init_model and glide demo by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1888\n- Fix InstColorization bug when dim=3 by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1901\n- Fix sd and controlnet fp16 bugs by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1914\n- Fix num_images_per_prompt in controlnet by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1936\n- Revise metafile for sd-inpainting to fix inferencer init by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1995\n\n**New Contributors**\n\n- @wyyang23 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1886\n- @yehuixie made their first contribution in https://github.com/open-mmlab/mmagic/pull/1912\n- @crazysteeaam made their first contribution in https://github.com/open-mmlab/mmagic/pull/1920\n- @BUPT-NingXinyu made their first contribution in https://github.com/open-mmlab/mmagic/pull/1921\n- @zhjunqin made their first contribution in https://github.com/open-mmlab/mmagic/pull/1918\n- @xuesheng1031 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1923\n- @wslgqq277g made their first contribution in https://github.com/open-mmlab/mmagic/pull/1934\n- @LYMDLUT made their first contribution in https://github.com/open-mmlab/mmagic/pull/1933\n- @RangeKing made their first contribution in https://github.com/open-mmlab/mmagic/pull/1930\n- @xin-li-67 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1932\n- @chg0901 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1931\n- @aptsunny made their first contribution in https://github.com/open-mmlab/mmagic/pull/1939\n- @YanxingLiu made their first contribution in https://github.com/open-mmlab/mmagic/pull/1943\n- @tackhwa made their first contribution in https://github.com/open-mmlab/mmagic/pull/1937\n- @Geo-Chou made their first contribution in https://github.com/open-mmlab/mmagic/pull/1940\n- @qsun1 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1956\n- @ththth888 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1961\n- @sijiua made their first contribution in https://github.com/open-mmlab/mmagic/pull/1967\n- @MING-ZCH made their first contribution in https://github.com/open-mmlab/mmagic/pull/1982\n- @AllYoung made their first contribution in https://github.com/open-mmlab/mmagic/pull/1996\n\n## v1.0.1 (26/05/2023)\n\n**New Features & Improvements**\n\n- Support tomesd for StableDiffusion speed-up. [#1801](https://github.com/open-mmlab/mmagic/pull/1801)\n- Support all inpainting/matting/image restoration models inferencer. [#1833](https://github.com/open-mmlab/mmagic/pull/1833), [#1873](https://github.com/open-mmlab/mmagic/pull/1873)\n- Support animated drawings at projects. [#1837](https://github.com/open-mmlab/mmagic/pull/1837)\n- Support Style-Based Global Appearance Flow for Virtual Try-On at projects. [#1786](https://github.com/open-mmlab/mmagic/pull/1786)\n- Support tokenizer wrapper and support EmbeddingLayerWithFixe. [#1846](https://github.com/open-mmlab/mmagic/pull/1846)\n\n**Bug Fixes**\n\n- Fix install requirements. [#1819](https://github.com/open-mmlab/mmagic/pull/1819)\n- Fix inst-colorization PackInputs. [#1828](https://github.com/open-mmlab/mmagic/pull/1828), [#1827](https://github.com/open-mmlab/mmagic/pull/1827)\n- Fix inferencer in pip-install. [#1875](https://github.com/open-mmlab/mmagic/pull/1875)\n\n**New Contributors**\n\n- @XDUWQ made their first contribution in https://github.com/open-mmlab/mmagic/pull/1830\n- @FerryHuang made their first contribution in https://github.com/open-mmlab/mmagic/pull/1786\n- @bobo0810 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1851\n- @jercylew made their first contribution in https://github.com/open-mmlab/mmagic/pull/1874\n\n## v1.0.0 (25/04/2023)\n\nWe are excited to announce the release of MMagic v1.0.0 that inherits from [MMEditing](https://github.com/open-mmlab/mmediting) and [MMGeneration](https://github.com/open-mmlab/mmgeneration).\n\n![mmagic-log](https://user-images.githubusercontent.com/49083766/233557648-9034f5a0-c85d-4092-b700-3a28072251b6.png)\n\nSince its inception, MMEditing has been the preferred algorithm library for many super-resolution, editing, and generation tasks, helping research teams win more than 10 top international competitions and supporting over 100 GitHub ecosystem projects. After iterative updates with OpenMMLab 2.0 framework and merged with MMGeneration, MMEditing has become a powerful tool that supports low-level algorithms based on both GAN and CNN.\n\nToday, MMEditing embraces Generative AI and transforms into a more advanced and comprehensive AIGC toolkit: **MMagic** (**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation).\n\nIn MMagic, we have supported 53+ models in multiple tasks such as fine-tuning for stable diffusion, text-to-image, image and video restoration, super-resolution, editing and generation. With excellent training and experiment management support from [MMEngine](https://github.com/open-mmlab/mmengine), MMagic will provide more agile and flexible experimental support for researchers and AIGC enthusiasts, and help you on your AIGC exploration journey. With MMagic, experience more magic in generation! Let's open a new era beyond editing together. More than Editing, Unlock the Magic!\n\n**Highlights**\n\n**1. New Models**\n\nWe support 11 new models in 4 new tasks.\n\n- Text2Image / Diffusion\n - ControlNet\n - DreamBooth\n - Stable Diffusion\n - Disco Diffusion\n - GLIDE\n - Guided Diffusion\n- 3D-aware Generation\n - EG3D\n- Image Restoration\n - NAFNet\n - Restormer\n - SwinIR\n- Image Colorization\n - InstColorization\n\nhttps://user-images.githubusercontent.com/49083766/233564593-7d3d48ed-e843-4432-b610-35e3d257765c.mp4\n\n**2. Magic Diffusion Model**\n\nFor the Diffusion Model, we provide the following \"magic\" :\n\n- Support image generation based on Stable Diffusion and Disco Diffusion.\n\n- Support Finetune methods such as Dreambooth and DreamBooth LoRA.\n\n- Support controllability in text-to-image generation using ControlNet.\n ![de87f16f-bf6d-4a61-8406-5ecdbb9167b6](https://user-images.githubusercontent.com/49083766/233558077-2005e603-c5a8-49af-930f-e7a465ca818b.png)\n\n- Support acceleration and optimization strategies based on xFormers to improve training and inference efficiency.\n\n- Support video generation based on MultiFrame Render.\n MMagic supports the generation of long videos in various styles through ControlNet and MultiFrame Render.\n prompt keywords: a handsome man, silver hair, smiling, play basketball\n\n https://user-images.githubusercontent.com/12782558/227149757-fd054d32-554f-45d5-9f09-319184866d85.mp4\n\n prompt keywords: a girl, black hair, white pants, smiling, play basketball\n\n https://user-images.githubusercontent.com/49083766/233559964-bd5127bd-52f6-44b6-a089-9d7adfbc2430.mp4\n\n prompt keywords: a handsome man\n\n https://user-images.githubusercontent.com/12782558/227152129-d70d5f76-a6fc-4d23-97d1-a94abd08f95a.mp4\n\n- Support calling basic models and sampling strategies through DiffuserWrapper.\n\n- SAM + MMagic = Generate Anything！\n SAM (Segment Anything Model) is a popular model these days and can also provide more support for MMagic! If you want to create your own animation, you can go to [OpenMMLab PlayGround](https://github.com/open-mmlab/playground/blob/main/mmediting_sam/README.md).\n\n https://user-images.githubusercontent.com/49083766/233562228-f39fc675-326c-4ae8-986a-c942059effd0.mp4\n\n**3. Upgraded Framework**\n\nTo improve your \"spellcasting\" efficiency, we have made the following adjustments to the \"magic circuit\":\n\n- By using MMEngine and MMCV of OpenMMLab 2.0 framework, We decompose the editing framework into different modules and one can easily construct a customized editor framework by combining different modules. We can define the training process just like playing with Legos and provide rich components and strategies. In MMagic, you can complete controls on the training process with different levels of APIs.\n- Support for 33+ algorithms accelerated by Pytorch 2.0.\n- Refactor DataSample to support the combination and splitting of batch dimensions.\n- Refactor DataPreprocessor and unify the data format for various tasks during training and inference.\n- Refactor MultiValLoop and MultiTestLoop, supporting the evaluation of both generation-type metrics (e.g. FID) and reconstruction-type metrics (e.g. SSIM), and supporting the evaluation of multiple datasets at once.\n- Support visualization on local files or using tensorboard and wandb.\n\n**New Features & Improvements**\n\n- Support 53+ algorithms, 232+ configs, 213+ checkpoints, 26+ loss functions, and 20+ metrics.\n- Support controlnet animation and Gradio gui. [Click to view.](https://github.com/open-mmlab/mmagic/tree/main/configs/controlnet_animation)\n- Support Inferencer and Demo using High-level Inference APIs. [Click to view.](https://github.com/open-mmlab/mmagic/tree/main/demo)\n- Support Gradio gui of Inpainting inference. [Click to view.](https://github.com/open-mmlab/mmagic/blob/main/demo/gradio-demo.py)\n- Support qualitative comparison tools. [Click to view.](https://github.com/open-mmlab/mmagic/tree/main/tools/gui)\n- Enable projects. [Click to view.](https://github.com/open-mmlab/mmagic/tree/main/projects)\n- Improve converters scripts and documents for datasets. [Click to view.](https://github.com/open-mmlab/mmagic/tree/main/tools/dataset_converters)\n\n## v1.0.0rc7 (07/04/2023)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc7. This release supports 51+ models, 226+ configs and 212+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- Support DiffuserWrapper\n- Support ControlNet (training and inference).\n- Support PyTorch 2.0.\n\n**New Features & Improvements**\n\n- Support DiffuserWrapper. [#1692](https://github.com/open-mmlab/mmagic/pull/1692)\n- Support ControlNet (training and inference). [#1744](https://github.com/open-mmlab/mmagic/pull/1744)\n- Support PyTorch 2.0 (successfully compile 33+ models on 'inductor' backend). [#1742](https://github.com/open-mmlab/mmagic/pull/1742)\n- Support Image Super-Resolution and Video Super-Resolution models inferencer. [#1662](https://github.com/open-mmlab/mmagic/pull/1662), [#1720](https://github.com/open-mmlab/mmagic/pull/1720)\n- Refactor tools/get_flops script. [#1675](https://github.com/open-mmlab/mmagic/pull/1675)\n- Refactor dataset_converters and documents for datasets. [#1690](https://github.com/open-mmlab/mmagic/pull/1690)\n- Move stylegan ops to MMCV. [#1383](https://github.com/open-mmlab/mmagic/pull/1383)\n\n**Bug Fixes**\n\n- Fix disco inferencer. [#1673](https://github.com/open-mmlab/mmagic/pull/1673)\n- Fix nafnet optimizer config. [#1716](https://github.com/open-mmlab/mmagic/pull/1716)\n- Fix tof typo. [#1711](https://github.com/open-mmlab/mmagic/pull/1711)\n\n**Contributors**\n\nA total of 8 developers contributed to this release.\nThanks @LeoXing1996, @Z-Fran, @plyfager, @zengyh1900, @liuwenran, @ryanxingql, @HAOCHENYE, @VongolaWu\n\n**New Contributors**\n\n- @HAOCHENYE made their first contribution in https://github.com/open-mmlab/mmagic/pull/1712\n\n## v1.0.0rc6 (02/03/2023)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc6. This release supports 50+ models, 222+ configs and 209+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- Support Gradio gui of Inpainting inference.\n- Support Colorization, Translationin and GAN models inferencer.\n\n**New Features & Improvements**\n\n- Refactor FileIO. [#1572](https://github.com/open-mmlab/mmagic/pull/1572)\n- Refactor registry. [#1621](https://github.com/open-mmlab/mmagic/pull/1621)\n- Refactor Random degradations. [#1583](https://github.com/open-mmlab/mmagic/pull/1583)\n- Refactor DataSample, DataPreprocessor, Metric and Loop. [#1656](https://github.com/open-mmlab/mmagic/pull/1656)\n- Use mmengine.basemodule instead of nn.module. [#1491](https://github.com/open-mmlab/mmagic/pull/1491)\n- Refactor Main Page. [#1609](https://github.com/open-mmlab/mmagic/pull/1609)\n- Support Gradio gui of Inpainting inference. [#1601](https://github.com/open-mmlab/mmagic/pull/1601)\n- Support Colorization inferencer. [#1588](https://github.com/open-mmlab/mmagic/pull/1588)\n- Support Translation models inferencer. [#1650](https://github.com/open-mmlab/mmagic/pull/1650)\n- Support GAN models inferencer. [#1653](https://github.com/open-mmlab/mmagic/pull/1653), [#1659](https://github.com/open-mmlab/mmagic/pull/1659)\n- Print config tool. [#1590](https://github.com/open-mmlab/mmagic/pull/1590)\n- Improve type hints. [#1604](https://github.com/open-mmlab/mmagic/pull/1604)\n- Update Chinese documents of metrics and datasets. [#1568](https://github.com/open-mmlab/mmagic/pull/1568), [#1638](https://github.com/open-mmlab/mmagic/pull/1638)\n- Update Chinese documents of BigGAN and Disco-Diffusion. [#1620](https://github.com/open-mmlab/mmagic/pull/1620)\n- Update Evaluation and README of Guided-Diffusion. [#1547](https://github.com/open-mmlab/mmagic/pull/1547)\n\n**Bug Fixes**\n\n- Fix the meaning of `momentum` in EMA. [#1581](https://github.com/open-mmlab/mmagic/pull/1581)\n- Fix output dtype of RandomNoise. [#1585](https://github.com/open-mmlab/mmagic/pull/1585)\n- Fix pytorch2onnx tool. [#1629](https://github.com/open-mmlab/mmagic/pull/1629)\n- Fix API documents. [#1641](https://github.com/open-mmlab/mmagic/pull/1641), [#1642](https://github.com/open-mmlab/mmagic/pull/1642)\n- Fix loading RealESRGAN EMA weights. [#1647](https://github.com/open-mmlab/mmagic/pull/1647)\n- Fix arg passing bug of dataset_converters scripts. [#1648](https://github.com/open-mmlab/mmagic/pull/1648)\n\n**Contributors**\n\nA total of 17 developers contributed to this release.\nThanks @plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @liuwenran, @austinmw, @dienachtderwelt, @liangzelong, @i-aki-y, @xiaomile, @Li-Qingyun, @vansin, @Luo-Yihang, @ydengbi, @ruoningYu, @triple-Mu\n\n**New Contributors**\n\n- @dienachtderwelt made their first contribution in https://github.com/open-mmlab/mmagic/pull/1578\n- @i-aki-y made their first contribution in https://github.com/open-mmlab/mmagic/pull/1590\n- @triple-Mu made their first contribution in https://github.com/open-mmlab/mmagic/pull/1618\n- @Li-Qingyun made their first contribution in https://github.com/open-mmlab/mmagic/pull/1640\n- @Luo-Yihang made their first contribution in https://github.com/open-mmlab/mmagic/pull/1648\n- @ydengbi made their first contribution in https://github.com/open-mmlab/mmagic/pull/1557\n\n## v1.0.0rc5 (04/01/2023)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc5. This release supports 49+ models, 180+ configs and 177+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- Support Restormer.\n- Support GLIDE.\n- Support SwinIR.\n- Support Stable Diffusion.\n\n**New Features & Improvements**\n\n- Disco notebook. (#1507)\n- Revise test requirements and CI. (#1514)\n- Recursive generate summary and docstring. (#1517)\n- Enable projects. (#1526)\n- Support mscoco dataset. (#1520)\n- Improve Chinese documents. (#1532)\n- Type hints. (#1481)\n- Update download link of checkpoints. (#1554)\n- Update deployment guide. (#1551)\n\n**Bug Fixes**\n\n- Fix documentation link checker. (#1522)\n- Fix ssim first channel bug. (#1515)\n- Fix extract_gt_data of realesrgan. (#1542)\n- Fix model index. (#1559)\n- Fix config path in disco-diffusion. (#1553)\n- Fix text2image inferencer. (#1523)\n\n**Contributors**\n\nA total of 16 developers contributed to this release.\nThanks @plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @liuwenran, @AlexZou14, @lvhan028, @xiaomile, @ldr426, @austin273, @whu-lee, @willaty, @curiosity654, @Zdafeng, @Taited\n\n**New Contributors**\n\n- @xiaomile made their first contribution in https://github.com/open-mmlab/mmagic/pull/1481\n- @ldr426 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1542\n- @austin273 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1553\n- @whu-lee made their first contribution in https://github.com/open-mmlab/mmagic/pull/1539\n- @willaty made their first contribution in https://github.com/open-mmlab/mmagic/pull/1541\n- @curiosity654 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1556\n- @Zdafeng made their first contribution in https://github.com/open-mmlab/mmagic/pull/1476\n- @Taited made their first contribution in https://github.com/open-mmlab/mmagic/pull/1534\n\n## v1.0.0rc4 (05/12/2022)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc4. This release supports 45+ models, 176+ configs and 175+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- Support High-level APIs.\n- Support diffusion models.\n- Support Text2Image Task.\n- Support 3D-Aware Generation.\n\n**New Features & Improvements**\n\n- Refactor High-level APIs. (#1410)\n- Support disco-diffusion text-2-image. (#1234, #1504)\n- Support EG3D. (#1482, #1493, #1494, #1499)\n- Support NAFNet model. (#1369)\n\n**Bug Fixes**\n\n- fix srgan train config. (#1441)\n- fix cain config. (#1404)\n- fix rdn and srcnn train configs. (#1392)\n\n**Contributors**\n\nA total of 14 developers contributed to this release.\nThanks @plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @gaoyang07, @ChangjianZhao, @zxczrx123, @jackghosts, @liuwenran, @CCODING04, @RoseZhao929, @shaocongliu, @liangzelong.\n\n**New Contributors**\n\n- @gaoyang07 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1372\n- @ChangjianZhao made their first contribution in https://github.com/open-mmlab/mmagic/pull/1461\n- @zxczrx123 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1462\n- @jackghosts made their first contribution in https://github.com/open-mmlab/mmagic/pull/1463\n- @liuwenran made their first contribution in https://github.com/open-mmlab/mmagic/pull/1410\n- @CCODING04 made their first contribution in https://github.com/open-mmlab/mmagic/pull/783\n- @RoseZhao929 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1474\n- @shaocongliu made their first contribution in https://github.com/open-mmlab/mmagic/pull/1470\n- @liangzelong made their first contribution in https://github.com/open-mmlab/mmagic/pull/1488\n\n## v1.0.0rc3 (10/11/2022)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc3. This release supports 43+ models, 170+ configs and 169+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- convert `mmdet` and `clip` to optional requirements.\n\n**New Features & Improvements**\n\n- Support `try_import` for `mmdet`. (#1408)\n- Support `try_import` for `flip`. (#1420)\n- Update `.gitignore`. ($1416)\n- Set `real_feat` to cpu in `inception_utils`. (#1415)\n- Modify README and configs of StyleGAN2 and PEGAN (#1418)\n- Improve the rendering of Docs-API (#1373)\n\n**Bug Fixes**\n\n- Revise config and pretrain model loading in ESRGAN (#1407)\n- Revise config of LSGAN (#1409)\n- Revise config of CAIN (#1404)\n\n**Contributors**\n\nA total of 5 developers contributed to this release.\n@Z-Fran, @zengyh1900, @plyfager, @LeoXing1996, @ruoningYu.\n\n## v1.0.0rc2 (02/11/2022)\n\n**Highlights**\n\nWe are excited to announce the release of MMEditing 1.0.0rc2. This release supports 43+ models, 170+ configs and 169+ checkpoints in MMGeneration and MMEditing. We highlight the following new features\n\n- patch-based and slider-based image and video comparison viewer.\n- image colorization.\n\n**New Features & Improvements**\n\n- Support qualitative comparison tools. (#1303)\n- Support instance aware colorization. (#1370)\n- Support multi-metrics with different sample-model. (#1171)\n- Improve the implementation\n - refactoring evaluation metrics. (#1164)\n - Save gt images in PGGAN's `forward`. (#1332)\n - Improve type and change default number of `preprocess_div2k_dataset.py`. (#1380)\n - Support pixel value clip in visualizer. (#1365)\n - Support SinGAN Dataset and SinGAN demo. (#1363)\n - Avoid cast int and float in GenDataPreprocessor. (#1385)\n- Improve the documentation\n - Update a menu switcher. (#1162)\n - Fix TTSR's README. (#1325)\n\n**Bug Fixes**\n\n- Fix PPL bug. (#1172)\n- Fix RDN number of channels. (#1328)\n- Fix types of exceptions in demos. (#1372)\n- Fix realesrgan ema. (#1341)\n- Improve the assertion to ensuer `GenerateFacialHeatmap` as `np.float32`. (#1310)\n- Fix sampling behavior of `unpaired_dataset.py` and urls in cyclegan's README. (#1308)\n- Fix vsr models in pytorch2onnx. (#1300)\n- Fix incorrect settings in configs. (#1167,#1200,#1236,#1293,#1302,#1304,#1319,#1331,#1336,#1349,#1352,#1353,#1358,#1364,#1367,#1384,#1386,#1391,#1392,#1393)\n\n**New Contributors**\n\n- @gaoyang07 made their first contribution in https://github.com/open-mmlab/mmagic/pull/1372\n\n**Contributors**\n\nA total of 7 developers contributed to this release.\nThanks @LeoXing1996, @Z-Fran, @zengyh1900, @plyfager, @ryanxingql, @ruoningYu, @gaoyang07.\n\n## v1.0.0rc1(23/9/2022)\n\nMMEditing 1.0.0rc1 has merged MMGeneration 1.x.\n\n- Support 42+ algorithms, 169+ configs and 168+ checkpoints.\n- Support 26+ loss functions, 20+ metrics.\n- Support tensorboard, wandb.\n- Support unconditional GANs, conditional GANs, image2image translation and internal learning.\n\n## v1.0.0rc0(31/8/2022)\n\nMMEditing 1.0.0rc0 is the first version of MMEditing 1.x, a part of the OpenMMLab 2.0 projects.\n\nBuilt upon the new [training engine](https://github.com/open-mmlab/mmengine), MMEditing 1.x unifies the interfaces of dataset, models, evaluation, and visualization.\n\nAnd there are some BC-breaking changes. Please check [the migration tutorial](https://mmagic.readthedocs.io/en/latest/migration/overview.html) for more details.\n" }, { "path": "docs/en/community/contributing.md", "content": "# Contributing guidance\n\nWelcome to the MMagic community, we are committed to building a Multimodal Advanced, Generative, and Intelligent Creation Toolbox.\n\nThis section introduces following contents:\n\n- [Contributing guidance](#contributing-guidance)\n - [Pull Request Workflow](#pull-request-workflow)\n - [1. Fork and clone](#1-fork-and-clone)\n - [2. Configure pre-commit](#2-configure-pre-commit)\n - [3. Create a development branch](#3-create-a-development-branch)\n - [4. Commit the code and pass the unit test](#4-commit-the-code-and-pass-the-unit-test)\n - [5. Push the code to remote](#5-push-the-code-to-remote)\n - [6. Create a Pull Request](#6-create-a-pull-request)\n - [7. Resolve conflicts](#7-resolve-conflicts)\n - [Guidance](#guidance)\n - [Unit test](#unit-test)\n - [Document rendering](#document-rendering)\n - [Code style](#code-style)\n - [Python](#python)\n - [C++ and CUDA](#c-and-cuda)\n - [PR Specs](#pr-specs)\n\nAll kinds of contributions are welcomed, including but not limited to\n\n**Fix bug**\n\nYou can directly post a Pull Request to fix typo in code or documents\n\nThe steps to fix the bug of code implementation are as follows.\n\n1. If the modification involve significant changes, you should create an issue first and describe the error information and how to trigger the bug. Other developers will discuss with you and propose an proper solution.\n2. Posting a pull request after fixing the bug and adding corresponding unit test.\n\n**New Feature or Enhancement**\n\n1. If the modification involve significant changes, you should create an issue to discuss with our developers to propose an proper design.\n2. Post a Pull Request after implementing the new feature or enhancement and add corresponding unit test.\n\n**Document**\n\nYou can directly post a pull request to fix documents. If you want to add a document, you should first create an issue to check if it is reasonable.\n\n### Pull Request Workflow\n\nIf you're not familiar with Pull Request, don't worry! The following guidance will tell you how to create a Pull Request step by step. If you want to dive into the develop mode of Pull Request, you can refer to the [official documents](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests)\n\n#### 1. Fork and clone\n\nIf you are posting a pull request for the first time, you should fork the OpenMMLab repositories by clicking the **Fork** button in the top right corner of the GitHub page, and the forked repositories will appear under your GitHub profile.\n\n\n\nThen, you can clone the repositories to local:\n\n```shell\ngit clone git@github.com:{username}/mmagic.git\n```\n\nAfter that, you should ddd official repository as the upstream repository\n\n```bash\ngit remote add upstream git@github.com:open-mmlab/mmagic\n```\n\nCheck whether remote repository has been added successfully by `git remote -v`\n\n```bash\norigin\tgit@github.com:{username}/mmagic.git (fetch)\norigin\tgit@github.com:{username}/mmagic.git (push)\nupstream\tgit@github.com:open-mmlab/mmagic (fetch)\nupstream\tgit@github.com:open-mmlab/mmagic (push)\n```\n\n```{note}\nHere's a brief introduction to origin and upstream. When we use \"git clone\", we create an \"origin\" remote by default, which points to the repository cloned from. As for \"upstream\", we add it ourselves to point to the target repository. Of course, if you don't like the name \"upstream\", you could name it as you wish. Usually, we'll push the code to \"origin\". If the pushed code conflicts with the latest code in official(\"upstream\"), we should pull the latest code from upstream to resolve the conflicts, and then push to \"origin\" again. The posted Pull Request will be updated automatically.\n```\n\n#### 2. Configure pre-commit\n\nYou should configure [pre-commit](https://pre-commit.com/#intro) in the local development environment to make sure the code style matches that of OpenMMLab. **Note**: The following code should be executed under the mmagic directory.\n\n```shell\npip install -U pre-commit\npre-commit install\n```\n\nCheck that pre-commit is configured successfully, and install the hooks defined in `.pre-commit-config.yaml`.\n\n```shell\npre-commit run --all-files\n```\n\n\n\n\n\n```{note}\nChinese users may fail to download the pre-commit hooks due to the network issue. In this case, you could download these hooks from gitee by setting the .pre-commit-config-zh-cn.yaml\n\npre-commit install -c .pre-commit-config-zh-cn.yaml\npre-commit run --all-files -c .pre-commit-config-zh-cn.yaml\n```\n\nIf the installation process is interrupted, you can repeatedly run `pre-commit run ... ` to continue the installation.\n\nIf the code does not conform to the code style specification, pre-commit will raise a warning and fixes some of the errors automatically.\n\n\n\nIf we want to commit our code bypassing the pre-commit hook, we can use the `--no-verify` option(**only for temporarily commit**.\n\n```shell\ngit commit -m \"xxx\" --no-verify\n```\n\n#### 3. Create a development branch\n\nAfter configuring the pre-commit, we should create a branch based on the main branch to develop the new feature or fix the bug. The proposed branch name is `username/pr_name`\n\n```shell\ngit checkout -b yhc/refactor_contributing_doc\n```\n\nIn subsequent development, if the main branch of the local repository is behind the main branch of \"upstream\", we need to pull the upstream for synchronization, and then execute the above command:\n\n```shell\ngit pull upstream main\n```\n\n#### 4. Commit the code and pass the unit test\n\n- mmagic introduces mypy to do static type checking to increase the robustness of the code. Therefore, we need to add Type Hints to our code and pass the mypy check. If you are not familiar with Type Hints, you can refer to [this tutorial](https://docs.python.org/3/library/typing.html).\n\n- The committed code should pass through the unit test\n\n ```shell\n # Pass all unit tests\n pytest tests\n\n # Pass the unit test of runner\n pytest tests/test_runner/test_runner.py\n ```\n\n If the unit test fails for lack of dependencies, you can install the dependencies referring to the [guidance](#unit-test)\n\n- If the documents are modified/added, we should check the rendering result referring to [guidance](#document-rendering)\n\n#### 5. Push the code to remote\n\nWe could push the local commits to remote after passing through the check of unit test and pre-commit. You can associate the local branch with remote branch by adding `-u` option.\n\n```shell\ngit push -u origin {branch_name}\n```\n\nThis will allow you to use the `git push` command to push code directly next time, without having to specify a branch or the remote repository.\n\n#### 6. Create a Pull Request\n\n(1) Create a pull request in GitHub's Pull request interface\n\n\n\n(2) Modify the PR description according to the guidelines so that other developers can better understand your changes\n\n\n\nFind more details about Pull Request description in [pull request guidelines](#pr-specs).\n\n**note**\n\n(a) The Pull Request description should contain the reason for the change, the content of the change, and the impact of the change, and be associated with the relevant Issue (see [documentation](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)\n\n(b) If it is your first contribution, please sign the CLA\n\n\n\n(c) Check whether the Pull Request pass through the CI\n\n\n\nmmagic will run unit test for the posted Pull Request on different platforms (Linux, Window, Mac), based on different versions of Python, PyTorch, CUDA to make sure the code is correct. We can see the specific test information by clicking `Details` in the above image so that we can modify the code.\n\n(3) If the Pull Request passes the CI, then you can wait for the review from other developers. You'll modify the code based on the reviewer's comments, and repeat the steps [4](#4-commit-the-code-and-pass-the-unit-test)-[5](#5-push-the-code-to-remote) until all reviewers approve it. Then, we will merge it ASAP.\n\n\n\n#### 7. Resolve conflicts\n\nIf your local branch conflicts with the latest main branch of \"upstream\", you'll need to resolove them. There are two ways to do this:\n\n```shell\ngit fetch --all --prune\ngit rebase upstream/main\n```\n\nor\n\n```shell\ngit fetch --all --prune\ngit merge upstream/main\n```\n\nIf you are very good at handling conflicts, then you can use rebase to resolve conflicts, as this will keep your commit logs tidy. If you are not familiar with `rebase`, then you can use `merge` to resolve conflicts.\n\n### Guidance\n\n#### Unit test\n\nWe should make sure the committed code will not decrease the coverage of unit test, we could run the following command to check the coverage of unit test:\n\n```shell\npython -m coverage run -m pytest /path/to/test_file\npython -m coverage html\n# check file in htmlcov/index.html\n```\n\n#### Document rendering\n\nIf the documents are modified/added, we should check the rendering result. We could install the dependencies and run the following command to render the documents and check the results:\n\n```shell\npip install -r requirements/docs.txt\ncd docs/zh_cn/\n# or docs/en\nmake html\n# check file in ./docs/zh_cn/_build/html/index.html\n```\n\n### Code style\n\n#### Python\n\nWe adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.\n\nWe use the following tools for linting and formatting:\n\n- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.\n- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.\n- [yapf](https://github.com/google/yapf): A formatter for Python files.\n- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.\n- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.\n- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.\n\nStyle configurations of yapf and isort can be found in [setup.cfg](../../../setup.cfg).\n\nWe use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,\nfixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.\nThe config for a pre-commit hook is stored in [.pre-commit-config](../../../.pre-commit-config.yaml).\n\n#### C++ and CUDA\n\nWe follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).\n\n### PR Specs\n\n1. Use [pre-commit](https://pre-commit.com) hook to avoid issues of code style\n\n2. One short-time branch should be matched with only one PR\n\n3. Accomplish a detailed change in one PR. Avoid large PR\n\n - Bad: Support Faster R-CNN\n - Acceptable: Add a box head to Faster R-CNN\n - Good: Add a parameter to box head to support custom conv-layer number\n\n4. Provide clear and significant commit message\n\n5. Provide clear and meaningful PR description\n\n - Task name should be clarified in title. The general format is: \\[Prefix\\] Short description of the PR (Suffix)\n - Prefix: add new feature \\[Feature\\], fix bug \\[Fix\\], related to documents \\[Docs\\], in developing \\[WIP\\] (which will not be reviewed temporarily)\n - Introduce main changes, results and influences on other modules in short description\n - Associate related issues and pull requests with a milestone\n" }, { "path": "docs/en/community/projects.md", "content": "# MMagic projects\n\nWelcome to the MMagic community!\nThe MMagic ecosystem consists of tutorials, libraries, and projects from a broad set of researchers in academia and industry, ML and application engineers.\nThe goal of this ecosystem is to support, accelerate, and aid in your exploration with MMagic for AIGC such as image, video, 3D content generation, editing and processing.\n\nHere are a few projects that are built upon MMagic. They are examples of how to use MMagic as a library, to make your projects more maintainable.\nPlease find more projects in [MMagic Ecosystem](https://openmmlab.com/ecosystem).\n\n## Show your projects on OpenMMLab Ecosystem\n\nYou can submit your project so that it can be shown on the homepage of [OpenMMLab](https://openmmlab.com/ecosystem).\n\n## Add example projects to MMagic\n\nHere is an [example project](../../../projects/example_project) about how to add your projects to MMagic.\nYou can copy and create your own project from the [example project](../../../projects/example_project).\n\nWe also provide some documentation listed below for your reference:\n\n- [Contribution Guide](https://mmagic.readthedocs.io/en/latest/community/contributing.html)\n\n The guides for new contributors about how to add your projects to MMagic.\n\n- [New Model Guide](https://mmagic.readthedocs.io/en/latest/howto/models.html)\n\n The documentation of adding new models.\n\n- [Discussions](https://github.com/open-mmlab/mmagic/discussions)\n\n Welcome to start a discussion!\n\n## Projects of libraries and toolboxes\n\n- [PowerVQE](https://github.com/ryanxingql/powervqe): Open framework for quality enhancement of compressed videos based on PyTorch and MMagic.\n\n- [VR-Baseline](https://github.com/linjing7/VR-Baseline): Video Restoration Toolbox.\n\n- [Derain-Toolbox](https://github.com/biubiubiiu/derain-toolbox): Single Image Deraining Toolbox and Benchmark\n\n## Projects of research papers\n\n- [Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV 2022](https://arxiv.org/abs/2207.10765)[\\[github\\]](https://github.com/caojiezhang/DAVSR)\n\n- [SepLUT:Separable Image-adaptive Lookup Tables for Real-time Image Enhancement, ECCV 2022](https://arxiv.org/abs/2207.08351)[\\[github\\]](https://github.com/ImCharlesY/SepLUT)\n\n- [TTVSR: Learning Trajectory-Aware Transformer for Video Super-Resolution, CVPR 2022](https://arxiv.org/abs/2204.04216)[\\[github\\]](https://github.com/researchmm/TTVSR)\n\n- [Arbitrary-Scale Image Synthesis, CVPR 2022](https://arxiv.org/pdf/2204.02273.pdf)[\\[github\\]](https://github.com/vglsd/ScaleParty)\n\n- [Investigating Tradeoffs in Real-World Video Super-Resolution(RealBasicVSR), CVPR 2022](https://arxiv.org/abs/2111.12704)[\\[github\\]](https://github.com/ckkelvinchan/RealBasicVSR)\n\n- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022](https://arxiv.org/abs/2104.13371)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR_PlusPlus)\n\n- [Multi-Scale Memory-Based Video Deblurring, CVPR 2022](https://arxiv.org/abs/2204.02977)[\\[github\\]](https://github.com/jibo27/MemDeblur)\n\n- [AdaInt:Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement, CVPR 2022](https://arxiv.org/abs/2204.13983)[\\[github\\]](https://github.com/ImCharlesY/AdaInt)\n\n- [A New Dataset and Transformer for Stereoscopic Video Super-Resolution, CVPRW 2022](https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/papers/Imani_A_New_Dataset_and_Transformer_for_Stereoscopic_Video_Super-Resolution_CVPRW_2022_paper.pdf)[\\[github\\]](https://github.com/H-deep/Trans-SVSR)\n\n- [Liquid warping GAN with attention: A unified framework for human image synthesis, TPAMI 2021](https://arxiv.org/pdf/2011.09055.pdf)[\\[github\\]](https://github.com/iPERDance/iPERCore)\n\n- [BasicVSR:The Search for Essential Components in Video Super-Resolution and Beyond, CVPR 2021](https://arxiv.org/abs/2012.02181)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR-IconVSR)\n\n- [GLEAN:Generative Latent Bank for Large-Factor Image Super-Resolution, CVPR 2021](https://arxiv.org/abs/2012.00739)[\\[github\\]](https://github.com/ckkelvinchan/GLEAN)\n\n- [DAN:Unfolding the Alternating Optimization for Blind Super Resolution, NeurIPS 2020](https://arxiv.org/abs/2010.02631v4)[\\[github\\]](https://github.com/AlexZou14/DAN-Basd-on-Openmmlab)\n" }, { "path": "docs/en/conf.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport subprocess\nimport sys\n\nimport pytorch_sphinx_theme\n\nsys.path.insert(0, os.path.abspath('../..'))\n\n# -- Project information -----------------------------------------------------\n\nproject = 'MMagic'\ncopyright = '2023, MMagic Authors'\nauthor = 'MMagic Authors'\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.autosectionlabel',\n 'sphinx_markdown_tables',\n 'sphinx_copybutton',\n 'sphinx_tabs.tabs',\n 'myst_parser',\n]\n\nextensions.append('notfound.extension') # enable customizing not-found page\n\nextensions.append('autoapi.extension')\nautoapi_type = 'python'\nautoapi_dirs = ['../../mmagic']\nautoapi_add_toctree_entry = False\nautoapi_template_dir = '_templates'\n# autoapi_options = ['members', 'undoc-members', 'show-module-summary']\n\n# # Core library for html generation from docstrings\n# extensions.append('sphinx.ext.autodoc')\n# extensions.append('sphinx.ext.autodoc.typehints')\n# # Enable 'expensive' imports for sphinx_autodoc_typehints\n# set_type_checking_flag = True\n# # Sphinx-native method. Not as good as sphinx_autodoc_typehints\n# autodoc_typehints = \"description\"\n\n# extensions.append('sphinx.ext.autosummary') # Create neat summary tables\n# autosummary_generate = True # Turn on sphinx.ext.autosummary\n# # Add __init__ doc (ie. params) to class summaries\n# autoclass_content = 'both'\n# autodoc_skip_member = []\n# # If no docstring, inherit from base class\n# autodoc_inherit_docstrings = True\n\nautodoc_mock_imports = [\n 'mmagic.version', 'mmcv._ext', 'mmcv.ops.ModulatedDeformConv2d',\n 'mmcv.ops.modulated_deform_conv2d', 'clip', 'resize_right', 'pandas'\n]\n\nsource_suffix = {\n '.rst': 'restructuredtext',\n '.md': 'markdown',\n}\n\n# # Remove 'view source code' from top of page (for html, not python)\n# html_show_sourcelink = False\n# nbsphinx_allow_errors = True # Continue through Jupyter errors\n# add_module_names = False # Remove namespaces from class/method signatures\n\n# Ignore >>> when copying code\ncopybutton_prompt_text = r'>>> |\\.\\.\\. '\ncopybutton_prompt_is_regexp = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = 'sphinx_rtd_theme'\nhtml_theme = 'pytorch_sphinx_theme'\nhtml_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]\n\nhtml_theme_options = {\n 'menu': [\n {\n 'name': 'GitHub',\n 'url': 'https://github.com/open-mmlab/mmagic',\n },\n {\n 'name':\n 'Version',\n 'children': [\n {\n 'name': 'MMagic 1.x',\n 'url': 'https://mmagic.readthedocs.io/en/latest/',\n 'description': 'Main branch'\n },\n {\n 'name': 'MMEditing 0.x',\n 'url': 'https://mmagic.readthedocs.io/en/0.x/',\n 'description': '0.x branch',\n },\n ],\n 'active':\n True,\n },\n ],\n 'menu_lang':\n 'en',\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\nhtml_css_files = ['css/readthedocs.css']\n\nmyst_enable_extensions = ['colon_fence']\nmyst_heading_anchors = 3\n\nlanguage = 'en'\n\n# The master toctree document.\nroot_doc = 'index'\nnotfound_template = '404.html'\n\n\ndef builder_inited_handler(app):\n subprocess.run(['python', './.dev_scripts/update_model_zoo.py'])\n subprocess.run(['python', './.dev_scripts/update_dataset_zoo.py'])\n\n\ndef skip_member(app, what, name, obj, skip, options):\n if what == 'package' or what == 'module':\n skip = True\n return skip\n\n\ndef viewcode_follow_imported(app, modname, attribute):\n fullname = f'{modname}.{attribute}'\n all_objects = app.env.autoapi_all_objects\n if fullname not in all_objects:\n return None\n\n if all_objects[fullname].obj.get('type') == 'method':\n fullname = fullname[:fullname.rfind('.')]\n attribute = attribute[:attribute.rfind('.')]\n while all_objects[fullname].obj.get('original_path', '') != '':\n fullname = all_objects[fullname].obj.get('original_path')\n\n orig_path = fullname\n if orig_path.endswith(attribute):\n return orig_path[:-len(attribute) - 1]\n\n return modname\n\n\ndef setup(app):\n app.connect('builder-inited', builder_inited_handler)\n app.connect('autoapi-skip-member', skip_member)\n if 'viewcode-follow-imported' in app.events.events:\n app.connect(\n 'viewcode-follow-imported', viewcode_follow_imported, priority=0)\n" }, { "path": "docs/en/device/npu.md", "content": "# NPU (HUAWEI Ascend)\n\n## Usage\n\nPlease refer to the [building documentation of MMCV](https://mmcv.readthedocs.io/en/latest/get_started/build.html#build-mmcv-full-on-ascend-npu-machine) to install MMCV and [mmengine](https://mmengine.readthedocs.io/en/latest/get_started/installation.html#build-from-source) on NPU devices.\n\nHere we use 8 NPUs on your computer to train the model with the following command:\n\n```shell\nbash tools/dist_train.sh configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py 8\n```\n\nAlso, you can use only one NPU to train the model with the following command:\n\n```shell\npython tools/train.py configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py\n```\n\n## Models Results\n\n| Model | Dataset | PSNR | SSIM | Download |\n| :----------------------------------------------------------------------------------------: | ------- | :---: | :--- | :--------------------------------------------------------------------------------------------- |\n| [edsr_x2c64b16_1x16_300k_div2k](https://github.com/open-mmlab/mmagic/blob/main/configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py) | DIV2K | 35.83 | 0.94 | [log](https://download.openmmlab.com/mmediting/device/npu/edsr/edsr_x2c64b16_1xb16-300k_div2k.log) |\n\n**Notes:**\n\n- If not specially marked, the results on NPU with amp are the basically same as those on the GPU with FP32.\n\n**All above models are provided by Huawei Ascend group.**\n" }, { "path": "docs/en/docutils.conf", "content": "[html writers]\ntable_style: colwidths-auto\n" }, { "path": "docs/en/faq.md", "content": "# Frequently asked questions\n\nWe list some common troubles faced by many users and their corresponding\nsolutions here. Feel free to enrich the list if you find any frequent issues\nand have ways to help others to solve them. If the contents here do not cover\nyour issue, please create an issue using the\n[provided templates](https://github.com/open-mmlab/mmagic/issues/new/choose)\nand make sure you fill in all required information in the template.\n\n## FAQ\n\n**Q1**: “xxx: ‘yyy is not in the zzz registry’”.\n\n**A1**: The registry mechanism will be triggered only when the file of the module is imported. So you need to import that file somewhere.\n\n**Q2**: What's the folder structure of xxx dataset?\n\n**A2**: You can make sure the folder structure is correct following tutorials of [dataset preparation](user_guides/dataset_prepare.md).\n\n**Q3**: How to use LMDB data to train the model?\n\n**A3**: You can use scripts in `tools/data` to make LMDB files. More details are shown in tutorials of [dataset preparation](user_guides/dataset_prepare.md).\n\n**Q4**: Why `MMCV==xxx is used but incompatible` is raised when import I try to import `mmgen`?\n\n**A4**:\nThis is because the version of MMCV and MMGeneration are incompatible. Compatible MMGeneration and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.\n\n| MMGeneration version | MMCV version |\n| :------------------: | :--------------: |\n| master | mmcv-full>=2.0.0 |\n\nNote: You need to run `pip uninstall mmcv` first if you have mmcv installed.\nIf mmcv and mmcv-full are both installed, there will be `ModuleNotFoundError`.\n\n**Q5**: How can I ignore some fields in the base configs?\n\n**A5**:\nSometimes, you may set `_delete_=True` to ignore some of fields in base configs.\nYou may refer to [MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md#delete-key-in-dict) for simple illustration.\n\nYou may have a careful look at [this tutorial](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md) for better understanding of this feature.\n\n**Q6**:: How can I use intermediate variables in configs?\n\n**A6**:\nSome intermediate variables are used in the config files, like `train_pipeline`/`test_pipeline` in datasets.\nIt's worth noting that when modifying intermediate variables in the children configs, users need to pass the intermediate variables into corresponding fields again.\n" }, { "path": "docs/en/get_started/install.md", "content": "# Installation\n\nIn this section, you will know about:\n\n- [Installation](#installation)\n - [Installation](#installation-1)\n - [Prerequisites](#prerequisites)\n - [Best practices](#best-practices)\n - [Customize installation](#customize-installation)\n - [CUDA Version](#cuda-version)\n - [Install MMCV without MIM](#install-mmcv-without-mim)\n - [Using MMagic with Docker](#using-mmagic-with-docker)\n - [Trouble shooting](#trouble-shooting)\n - [Developing with multiple MMagic versions](#developing-with-multiple-mmagic-versions)\n\n## Installation\n\nWe recommend that users follow our [Best practices](#best-practices) to install MMagic.\nHowever, the whole process is highly customizable. See [Customize installation](#customize-installation) section for more information.\n\n### Prerequisites\n\nIn this section, we demonstrate how to prepare an environment with PyTorch.\n\nMMagic works on Linux, Windows, and macOS. It requires:\n\n- Python >= 3.7\n- [PyTorch](https://pytorch.org/) >= 1.8\n- [MMCV](https://github.com/open-mmlab/mmcv) >= 2.0.0\n\n>\n\nIf you are experienced with PyTorch and have already installed it,\njust skip this part and jump to the [next section](#best-practices). Otherwise, you can follow these steps for the preparation.\n\n**Step 0.**\nDownload and install Miniconda from [official website](https://docs.conda.io/en/latest/miniconda.html).\n\n**Step 1.**\nCreate a [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html#) and activate it\n\n```shell\nconda create --name mmagic python=3.8 -y\nconda activate mmagic\n```\n\n**Step 2.**\nInstall PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.\n\n- On GPU platforms:\n\n ```shell\n conda install pytorch torchvision cudatoolkit=11.3 -c pytorch\n ```\n\n- On CPU platforms:\n\n ```shell\n conda install pytorch=1.10 torchvision cpuonly -c pytorch\n ```\n\n### Best practices\n\n**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).\n\n```shell\npip install -U openmim\nmim install 'mmcv>=2.0.0'\n```\n\n**Step 1.** Install [MMEngine](https://github.com/open-mmlab/mmengine).\n\n```shell\nmim install 'mmengine'\n```\n\nOr\n\n```shell\npip install mmengine\n```\n\nOr\n\n```shell\npip install git+https://github.com/open-mmlab/mmengine.git\n```\n\n**Step 2.** Install MMagic.\n\n```shell\nmim install 'mmagic'\n```\n\nOr\n\n```shell\npip install mmagic\n```\n\nOr install [MMagic](https://github.com/open-mmlab/mmagic) from the source code.\n\n```shell\ngit clone https://github.com/open-mmlab/mmagic.git\ncd mmagic\npip3 install -e . -v\n```\n\n**Step 5.**\nVerify MMagic has been successfully installed.\n\n```shell\ncd ~\npython -c \"import mmagic; print(mmagic.__version__)\"\n# Example output: 1.0.0\n```\n\nThe installation is successful if the version number is output correctly.\n\n```{note}\nYou may be curious about what `-e .` means when supplied with `pip install`.\nHere is the description:\n\n- `-e` means [editable mode](https://pip.pypa.io/en/latest/cli/pip_install/#cmdoption-e).\n When `import mmagic`, modules under the cloned directory are imported.\n If `pip install` without `-e`, pip will copy cloned codes to somewhere like `lib/python/site-package`.\n Consequently, modified code under the cloned directory takes no effect unless `pip install` again.\n Thus, `pip install` with `-e` is particularly convenient for developers. If some codes are modified, new codes will be imported next time without reinstallation.\n- `.` means code in this directory\n\nYou can also use `pip install -e .[all]`, which will install more dependencies, especially for pre-commit hooks and unittests.\n```\n\n### Customize installation\n\n#### CUDA Version\n\nWhen installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:\n\n- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.\n- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.\n\nPlease make sure the GPU driver satisfies the minimum version requirements.\nSee [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information.\n\n**note**\nInstalling CUDA runtime libraries is enough if you follow our best practices,\nbecause no CUDA code will be compiled locally.\nHowever, if you hope to compile MMCV from source or develop other CUDA operators,\nyou need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads),\nand its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.\n\n#### Install MMCV without MIM\n\nMMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way.\nMIM solves such dependencies automatically and makes the installation easier. However, it is not a must.\n\nTo install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).\nThis requires manually specifying a find-url based on PyTorch version and its CUDA version.\n\nFor example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.\n\n```shell\npip install 'mmcv>=2.0.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html\n```\n\n#### Using MMagic with Docker\n\nWe provide a [Dockerfile](https://github.com/open-mmlab/mmagic/blob/main/docker/Dockerfile) to build an image.\nEnsure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.\n\n```shell\n# build an image with PyTorch 1.8, CUDA 11.1\n# If you prefer other versions, just modified the Dockerfile\ndocker build -t mmagic docker/\n```\n\nRun it with\n\n```shell\ndocker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmagic/data mmagic\n```\n\n#### Trouble shooting\n\nIf you have some issues during the installation, please first view the [FAQ](../faq.md) page.\nYou may [open an issue](https://github.com/open-mmlab/mmagic/issues/new/choose) on GitHub if no solution is found.\n\n### Developing with multiple MMagic versions\n\nThe train and test scripts already modify the `PYTHONPATH` to ensure the script uses the `MMagic` in the current directory.\n\nTo use the default MMagic installed in the environment rather than that you are working with, you can remove the following line in those scripts\n\n```shell\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH\n```\n" }, { "path": "docs/en/get_started/overview.md", "content": "# Overview\n\nWelcome to MMagic! In this section, you will know about\n\n- [Overview](#overview)\n - [What is MMagic?](#what-is-mmagic)\n - [Why should I use MMagic?](#why-should-i-use-mmagic)\n - [Get started](#get-started)\n - [User guides](#user-guides)\n - [Advanced guides](#advanced-guides)\n - [How to](#how-to)\n\n## What is MMagic?\n\nMMagic (**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation) is an open-source AIGC toolbox for professional AI researchers and machine learning engineers to explore image and video processing, editing and generation.\n\nMMagic allows researchers and engineers to use pre-trained state-of-the-art models, train and develop new customized models easily.\n\nMMagic supports various foundamental generative models, including:\n\n- Unconditional Generative Adversarial Networks (GANs)\n- Conditional Generative Adversarial Networks (GANs)\n- Internal Learning\n- Diffusion Models\n- And many other generative models are coming soon!\n\nMMagic supports various applications, including:\n\n- Text-to-Image\n- Image-to-image translation\n- 3D-aware generation\n- Image super-resolution\n- Video super-resolution\n- Video frame interpolation\n- Image inpainting\n- Image matting\n- Image restoration\n- Image colorization\n- Image generation\n- And many other applications are coming soon!\n\n
\n \n
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\n\n## Why should I use MMagic?\n\n- **State of the Art Models**\n\n MMagic provides state-of-the-art generative models to process, edit and synthesize images and videos.\n\n- **Powerful and Popular Applications**\n\n MMagic supports popular and contemporary image restoration, text-to-image, 3D-aware generation, inpainting, matting, super-resolution and generation applications. Specifically, MMagic supports fine-tuning for stable diffusion and many exciting diffusion's application such as ControlNet Animation with SAM. MMagic also supports GAN interpolation, GAN projection, GAN manipulations and many other popular GAN’s applications. It’s time to begin your AIGC exploration journey!\n\n- **Efficient Framework**\n\n By using MMEngine and MMCV of OpenMMLab 2.0 framework, MMagic decompose the editing framework into different modules and one can easily construct a customized editor framework by combining different modules. We can define the training process just like playing with Legos and provide rich components and strategies. In MMagic, you can complete controls on the training process with different levels of APIs. With the support of [MMSeparateDistributedDataParallel](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/wrappers/seperate_distributed.py), distributed training for dynamic architectures can be easily implemented.\n\n## Get started\n\nFor installation instructions, please see [Installation](install.md).\n\n## User guides\n\nFor beginners, we suggest learning the basic usage of MMagic from [user_guides](../user_guides/config.md).\n\n### Advanced guides\n\nFor users who are familiar with MMagic, you may want to learn the design of MMagic, as well as how to extend the repo, how to use multiple repos and other advanced usages, please refer to [advanced_guides](../advanced_guides/evaluator.md).\n\n### How to\n\nFor users who want to use MMagic to do something, please refer to [How to](../howto/models.md).\n" }, { "path": "docs/en/get_started/quick_run.md", "content": "# Quick run\n\nAfter installing MMagic successfully, now you are able to play with MMagic! To generate an image from text, you only need several lines of codes by MMagic!\n\n```python\nfrom mmagic.apis import MMagicInferencer\nsd_inferencer = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = 'output/sd_res.png'\nsd_inferencer.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\nOr you can just run the following command.\n\n```bash\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir ./output/sd_res.png\n```\n\nYou will see a new image `sd_res.png` in folder `output/`, which contained generated samples.\n\nWhat's more, if you want to make these photos much more clear,\nyou only need several lines of codes for image super-resolution by MMagic!\n\n```python\nfrom mmagic.apis import MMagicInferencer\nconfig = 'configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py'\ncheckpoint = 'https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth'\nimg_path = 'tests/data/image/lq/baboon_x4.png'\neditor = MMagicInferencer('esrgan', model_config=config, model_ckpt=checkpoint)\noutput = editor.infer(img=img_path,result_out_dir='output.png')\n```\n\nNow, you can check your fancy photos in `output.png`.\n" }, { "path": "docs/en/howto/dataset.md", "content": "# How to prepare your own datasets\n\nIn this document, we will introduce the design of each datasets in MMagic and how users can design their own dataset.\n\n- [How to prepare your own datasets](#how-to-prepare-your-own-datasets)\n - [Supported Data Format](#supported-data-format)\n - [BasicImageDataset](#basicimagedataset)\n - [BasicFramesDataset](#basicframesdataset)\n - [BasicConditonalDataset](#basicconditonaldataset)\n - [1. Annotation file read by line (e.g., txt)](#1-annotation-file-read-by-line-eg-txt)\n - [2. Dict-based annotation file (e.g., json):](#2-dict-based-annotation-file-eg-json)\n - [3. Folder-based annotation (no annotation file need):](#3-folder-based-annotation-no-annotation-file-need)\n - [ImageNet Dataset and CIFAR10 Dataset](#imagenet-dataset-and-cifar10-dataset)\n - [AdobeComp1kDataset](#adobecomp1kdataset)\n - [GrowScaleImgDataset](#growscaleimgdataset)\n - [SinGANDataset](#singandataset)\n - [PairedImageDataset](#pairedimagedataset)\n - [UnpairedImageDataset](#unpairedimagedataset)\n - [Design a new dataset](#design-a-new-dataset)\n - [Repeat dataset](#repeat-dataset)\n\n## Supported Data Format\n\nIn MMagic, all datasets are inherited from `BaseDataset`.\nEach dataset load the list of data info (e.g., data path) by `load_data_list`.\nIn `__getitem__`, `prepare_data` is called to get the preprocessed data.\nIn `prepare_data`, data loading pipeline consists of the following steps:\n\n1. fetch the data info by passed index, implemented by `get_data_info`\n2. apply data transforms to the data, implemented by `pipeline`\n\n### BasicImageDataset\n\n**BasicImageDataset** `mmagic.datasets.BasicImageDataset`\nGeneral image dataset designed for low-level vision tasks with image, such as image super-resolution, inpainting and unconditional image generation. The annotation file is optional.\n\nIf use annotation file, the annotation format can be shown as follows.\n\n```bash\n Case 1 (CelebA-HQ):\n\n 000001.png\n 000002.png\n\n Case 2 (DIV2K):\n\n 0001_s001.png (480,480,3)\n 0001_s002.png (480,480,3)\n 0001_s003.png (480,480,3)\n 0002_s001.png (480,480,3)\n 0002_s002.png (480,480,3)\n\n Case 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n```\n\nHere we give several examples showing how to use `BasicImageDataset`. Assume the file structure as the following:\n\n```md\nmmagic (root)\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ │ ├── image.png\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ │ │ ├── image_x4.png\n│ │ ├── DIV2K_valid_HR\n│ │ ├── DIV2K_valid_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ ├── places\n│ │ ├── test_set\n│ │ ├── train_set\n| | ├── meta\n| | | ├── Places365_train.txt\n| | | ├── Places365_val.txt\n| ├── celebahq\n│ │ ├── imgs_1024\n\n```\n\nCase 1: Loading DIV2K dataset for training a SISR model.\n\n```python\n dataset = BasicImageDataset(\n ann_file='',\n metainfo=dict(\n dataset_type='div2k',\n task_name='sisr'),\n data_root='data/DIV2K',\n data_prefix=dict(\n gt='DIV2K_train_HR', img='DIV2K_train_LR_bicubic/X4'),\n filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=[])\n```\n\nCase 2: Loading places dataset for training an inpainting model.\n\n```python\n dataset = BasicImageDataset(\n ann_file='meta/Places365_train.txt',\n metainfo=dict(\n dataset_type='places365',\n task_name='inpainting'),\n data_root='data/places',\n data_prefix=dict(gt='train_set'),\n pipeline=[])\n```\n\nCase 3: Loading CelebA-HQ dataset for training an PGGAN.\n\n```python\ndataset = BasicImageDataset(\n pipeline=[],\n data_root='./data/celebahq/imgs_1024')\n```\n\n### BasicFramesDataset\n\n**BasicFramesDataset** `mmagic.datasets.BasicFramesDataset`\nGeneral frames dataset designed for low-level vision tasks with frames, such as video super-resolution and video frame interpolation. The annotation file is optional.\n\nIf use annotation file, the annotation format can be shown as follows.\n\n```bash\nCase 1 (Vid4):\n\n calendar 41\n city 34\n foliage 49\n walk 47\n\nCase 2 (REDS):\n\n 000/00000000.png (720, 1280, 3)\n 000/00000001.png (720, 1280, 3)\n\nCase 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n```\n\nAssume the file structure as the following:\n\n```bash\nmmagic (root)\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Vid4\n│ │ ├── BIx4\n│ │ │ ├── city\n│ │ │ │ ├── img1.png\n│ │ ├── GT\n│ │ │ ├── city\n│ │ │ │ ├── img1.png\n│ │ ├── meta_info_Vid4_GT.txt\n│ ├── vimeo-triplet\n│ │ ├── sequences\n| | | ├── 00001\n│ │ │ │ ├── 0389\n│ │ │ │ │ ├── img1.png\n│ │ │ │ │ ├── img2.png\n│ │ │ │ │ ├── img3.png\n│ │ ├── tri_trainlist.txt\n```\n\nCase 1: Loading Vid4 dataset for training a VSR model.\n\n```python\ndataset = BasicFramesDataset(\n ann_file='meta_info_Vid4_GT.txt',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root='data/Vid4',\n data_prefix=dict(img='BIx4', gt='GT'),\n pipeline=[],\n depth=2,\n num_input_frames=5)\n```\n\nCase 2: Loading Vimeo90k dataset for training a VFI model.\n\n```python\ndataset = BasicFramesDataset(\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root='data/vimeo-triplet',\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=[],\n depth=2,\n load_frames_list=dict(\n img=['img1.png', 'img3.png'], gt=['img2.png']))\n```\n\n### BasicConditonalDataset\n\n**BasicConditonalDataset** `mmagic.datasets.BasicConditonalDataset` is designed for conditional GANs (e.g., SAGAN, BigGAN). This dataset support load label for the annotation file. `BasicConditonalDataset` support three kinds of annotation as follow:\n\n#### 1. Annotation file read by line (e.g., txt)\n\nSample files structure:\n\n```\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n```\n\nSample annotation file (the first column is the image path and the second column is the index of category):\n\n```\n folder_1/xxx.png 0\n folder_1/xxy.png 1\n folder_2/123.png 5\n folder_2/nsdf3.png 3\n ...\n```\n\nConfig example for ImageNet dataset:\n\n```python\ndataset=dict(\n type='BasicConditionalDataset,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n```\n\n#### 2. Dict-based annotation file (e.g., json):\n\nSample files structure:\n\n```\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n```\n\nSample annotation file (the key is the image path and the value column\nis the label):\n\n```\n {\n \"folder_1/xxx.png\": [1, 2, 3, 4],\n \"folder_1/xxy.png\": [2, 4, 1, 0],\n \"folder_2/123.png\": [0, 9, 8, 1],\n \"folder_2/nsdf3.png\", [1, 0, 0, 2],\n ...\n }\n```\n\nConfig example for EG3D (shapenet-car) dataset:\n\n```python\ndataset = dict(\n type='BasicConditionalDataset',\n data_root='./data/eg3d/shapenet-car',\n ann_file='annotation.json',\n pipeline=train_pipeline)\n```\n\nIn this kind of annotation, labels can be any type and not restricted to an index.\n\n#### 3. Folder-based annotation (no annotation file need):\n\nSample files structure:\n\n```\n data_prefix/\n ├── class_x\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n │ └── xxz.png\n └── class_y\n ├── 123.png\n ├── nsdf3.png\n ├── ...\n └── asd932_.png\n```\n\nIf the annotation file is specified, the dataset will be generated by the first two ways, otherwise, try the third way.\n\n### ImageNet Dataset and CIFAR10 Dataset\n\n**ImageNet Dataset** `mmagic.datasets.ImageNet` and **CIFAR10 Dataset**`mmagic.datasets.CIFAR10` are datasets specific designed for ImageNet and CIFAR10 datasets. Both two datasets are encapsulation of `BasicConditionalDataset`. You can used them to load data from ImageNet dataset and CIFAR10 dataset easily.\n\nConfig example for ImageNet:\n\n```python\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='RandomCropLongEdge', keys=['img']),\n dict(type='Resize', scale=(128, 128), keys=['img'], backend='pillow'),\n dict(type='Flip', keys=['img'], flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ndataset=dict(\n type='ImageNet',\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=pipeline),\n```\n\nConfig example for CIFAR10:\n\n```python\npipeline = [dict(type='PackInputs')]\n\ndataset = dict(\n type='CIFAR10',\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=pipeline)\n```\n\n### AdobeComp1kDataset\n\n**AdobeComp1kDataset** `mmagic.datasets.AdobeComp1kDataset`\nAdobe composition-1k dataset.\n\nThe dataset loads (alpha, fg, bg) data and apply specified transforms to\nthe data. You could specify whether composite merged image online or load\ncomposited merged image in pipeline.\n\nExample for online comp-1k dataset:\n\n```md\n[\n {\n \"alpha\": 'alpha/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n]\n```\n\nExample for offline comp-1k dataset:\n\n```md\n[\n {\n \"alpha\": 'alpha/000.png',\n \"merged\": 'merged/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"merged\": 'merged/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n]\n```\n\n### GrowScaleImgDataset\n\n`GrowScaleImgDataset` is designed for dynamic GAN models (e.g., PGGAN and StyleGANv1).\nIn this dataset, we support switching the data root during training to load training images of different resolutions.\nThis procedure is implemented by `GrowScaleImgDataset.update_annotations` and is called by `PGGANFetchDataHook.before_train_iter` in the training process.\n\n```python\ndef update_annotations(self, curr_scale):\n # determine if the data root needs to be updated\n if curr_scale == self._actual_curr_scale:\n return False\n\n # fetch new data root by resolution (scale)\n for scale in self._img_scales:\n if curr_scale <= scale:\n self._curr_scale = scale\n break\n if scale == self._img_scales[-1]:\n assert RuntimeError(\n f'Cannot find a suitable scale for {curr_scale}')\n self._actual_curr_scale = curr_scale\n self.data_root = self.data_roots[str(self._curr_scale)]\n\n # reload the data list with new data root\n self.load_data_list()\n\n # print basic dataset information to check the validity\n print_log('Update Dataset: ' + repr(self), 'current')\n return True\n```\n\n### SinGANDataset\n\n`SinGANDataset` is designed for SinGAN's training.\nIn SinGAN's training, we do not iterate the images in the dataset but return a consistent preprocessed image dict.\n\nTherefore, we bypass the default data loading logic of `BaseDataset` because we do not need to load the corresponding image data based on the given index.\n\n```python\ndef load_data_list(self, min_size, max_size, scale_factor_init):\n # load single image\n real = mmcv.imread(self.data_root)\n self.reals, self.scale_factor, self.stop_scale = create_real_pyramid(\n real, min_size, max_size, scale_factor_init)\n\n self.data_dict = {}\n\n # generate multi scale image\n for i, real in enumerate(self.reals):\n self.data_dict[f'real_scale{i}'] = real\n\n self.data_dict['input_sample'] = np.zeros_like(\n self.data_dict['real_scale0']).astype(np.float32)\n\ndef __getitem__(self, index):\n # directly return the transformed data dict\n return self.pipeline(self.data_dict)\n```\n\n### PairedImageDataset\n\n`PairedImageDataset` is designed for translation models that needs paired training data (e.g., Pix2Pix).\nThe directory structure is shown below. Each image files are the concatenation of the image pair.\n\n```\n./data/dataset_name/\n├── test\n│ └── XXX.jpg\n└── train\n └── XXX.jpg\n```\n\nIn `PairedImageDataset`, we scan the file list in `load_data_list` and save path in `pair_path` field to fit the `LoadPairedImageFromFile` transformation.\n\n```python\ndef load_data_list(self):\n data_infos = []\n pair_paths = sorted(self.scan_folder(self.data_root))\n for pair_path in pair_paths:\n # save path in the specific field\n data_infos.append(dict(pair_path=pair_path))\n\n return data_infos\n```\n\n### UnpairedImageDataset\n\n`UnpairedImageDataset` is designed for translation models that do not need paired data (e.g., CycleGAN). The directory structure is shown below.\n\n```\n./data/dataset_name/\n├── testA\n│ └── XXX.jpg\n├── testB\n│ └── XXX.jpg\n├── trainA\n│ └── XXX.jpg\n└── trainB\n └── XXX.jpg\n\n```\n\nIn this dataset, we overwrite `__getitem__` function to load random image pair in the training process.\n\n```python\ndef __getitem__(self, idx):\n if not self.test_mode:\n return self.prepare_train_data(idx)\n\n return self.prepare_test_data(idx)\n\ndef prepare_train_data(self, idx):\n img_a_path = self.data_infos_a[idx % self.len_a]['path']\n idx_b = np.random.randint(0, self.len_b)\n img_b_path = self.data_infos_b[idx_b]['path']\n results = dict()\n results[f'img_{self.domain_a}_path'] = img_a_path\n results[f'img_{self.domain_b}_path'] = img_b_path\n return self.pipeline(results)\n\ndef prepare_test_data(self, idx):\n img_a_path = self.data_infos_a[idx % self.len_a]['path']\n img_b_path = self.data_infos_b[idx % self.len_b]['path']\n results = dict()\n results[f'img_{self.domain_a}_path'] = img_a_path\n results[f'img_{self.domain_b}_path'] = img_b_path\n return self.pipeline(results)\n```\n\n## Design a new dataset\n\nIf you want to create a dataset for a new low level CV task (e.g. denoise, derain, defog, and de-reflection) or existing dataset format doesn't meet your need, you can reorganize new data formats to existing format.\n\nOr create a new dataset in `mmagic/datasets` to load the data.\n\nInheriting from the base class of datasets such as `BasicImageDataset` and `BasicFramesDataset` will make it easier to create a new dataset.\n\nAnd you can create a new dataset inherited from [BaseDataset](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/base_dataset.py) which is the base class of datasets in [MMEngine](https://github.com/open-mmlab/mmengine).\n\nHere is an example of creating a dataset for video frame interpolation:\n\n```python\nfrom .basic_frames_dataset import BasicFramesDataset\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass NewVFIDataset(BasicFramesDataset):\n \"\"\"Introduce the dataset\n\n Examples of file structure.\n\n Args:\n pipeline (list[dict | callable]): A sequence of data transformations.\n folder (str | :obj:`Path`): Path to the folder.\n ann_file (str | :obj:`Path`): Path to the annotation file.\n test_mode (bool): Store `True` when building test dataset.\n Default: `False`.\n \"\"\"\n\n def __init__(self, ann_file, metainfo, data_root, data_prefix,\n pipeline, test_mode=False):\n super().__init__(ann_file, metainfo, data_root, data_prefix,\n pipeline, test_mode)\n self.data_infos = self.load_annotations()\n\n def load_annotations(self):\n \"\"\"Load annoations for the dataset.\n\n Returns:\n list[dict]: A list of dicts for paired paths and other information.\n \"\"\"\n data_infos = []\n ...\n return data_infos\n\n```\n\nWelcome to [submit new dataset classes to MMagic](https://github.com/open-mmlab/mmagic/compare).\n\n### Repeat dataset\n\nWe use [RepeatDataset](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/dataset_wrapper.py) as wrapper to repeat the dataset.\nFor example, suppose the original dataset is Dataset_A, to repeat it, the config looks like the following\n\n```python\ndataset_A_train = dict(\n type='RepeatDataset',\n times=N,\n dataset=dict( # This is the original config of Dataset_A\n type='Dataset_A',\n ...\n pipeline=train_pipeline\n )\n )\n```\n\nYou may refer to [tutorial in MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/basedataset.md).\n" }, { "path": "docs/en/howto/losses.md", "content": "# How to design your own loss functions\n\n`losses` are registered as `LOSSES` in `MMagic`.\nCustomizing losses is similar to customizing any other model.\nThis section is mainly for clarifying the design of loss modules in MMagic.\nImportantly, when writing your own loss modules, you should follow the same design,\nso that the new loss module can be adopted in our framework without extra effort.\n\nThis guides includes:\n\n- [How to design your own loss functions](#how-to-design-your-own-loss-functions)\n - [Introduction to supported losses](#introduction-to-supported-losses)\n - [Design a new loss function](#design-a-new-loss-function)\n - [An example of MSELoss](#an-example-of-mseloss)\n - [An example of DiscShiftLoss](#an-example-of-discshiftloss)\n - [An example of GANWithCustomizedLoss](#an-example-of-ganwithcustomizedloss)\n - [Available losses](#available-losses)\n - [regular losses](#regular-losses)\n - [losses components](#losses-components)\n\n## Introduction to supported losses\n\nFor convenient usage, you can directly use default loss calculation process we set for concrete algorithms like lsgan, biggan, styleganv2 etc.\nTake `stylegan2` as an example, we use R1 gradient penalty and generator path length regularization as configurable losses, and users can adjust\nrelated arguments like `r1_loss_weight` and `g_reg_weight`.\n\n```python\n# stylegan2_base.py\nloss_config = dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2)\n\nmodel = dict(\n type='StyleGAN2',\n xxx,\n loss_config=loss_config)\n```\n\n## Design a new loss function\n\n### An example of MSELoss\n\nIn general, to implement a loss module, we will write a function implementation and then wrap it with a class implementation. Take the MSELoss as an example:\n\n```python\n@masked_loss\ndef mse_loss(pred, target):\n return F.mse_loss(pred, target, reduction='none')\n\n@LOSSES.register_module()\nclass MSELoss(nn.Module):\n\n def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):\n # codes can be found in ``mmagic/models/losses/pixelwise_loss.py``\n\n def forward(self, pred, target, weight=None, **kwargs):\n # codes can be found in ``mmagic/models/losses/pixelwise_loss.py``\n```\n\nGiven the definition of the loss, we can now use the loss by simply defining it in the configuration file:\n\n```python\npixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean')\n```\n\nNote that `pixel_loss` above must be defined in the model. Please refer to `customize_models` for more details. Similar to model customization, in order to use your customized loss, you need to import the loss in `mmagic/models/losses/__init__.py` after writing it.\n\n### An example of DiscShiftLoss\n\nIn general, to implement a loss module, we will write a function implementation and then wrap it with a class implementation.\nHowever, in `MMagic`, we provide another unified interface `data_info` for users to define the mapping between the input argument and data items.\n\n```python\n@weighted_loss\ndef disc_shift_loss(pred):\n return pred**2\n\n@MODULES.register_module()\nclass DiscShiftLoss(nn.Module):\n\n def __init__(self, loss_weight=1.0, data_info=None):\n super(DiscShiftLoss, self).__init__()\n # codes can be found in ``mmagic/models/losses/disc_auxiliary_loss.py``\n\n def forward(self, *args, **kwargs):\n # codes can be found in ``mmagic/models/losses/disc_auxiliary_loss.py``\n```\n\nThe goal of this design for loss modules is to allow for using it automatically in the generative models (`MODELS`), without other complex codes to define the mapping between data and keyword arguments. Thus, different from other frameworks in `OpenMMLab`, our loss modules contain a special keyword, `data_info`, which is a dictionary defining the mapping between the input arguments and data from the generative models. Taking the `DiscShiftLoss` as an example, when writing the config file, users may use this loss as follows:\n\n```python\ndict(type='DiscShiftLoss',\n loss_weight=0.001 * 0.5,\n data_info=dict(pred='disc_pred_real')\n```\n\nThe information in `data_info` tells the module to use the `disc_pred_real` data as the input tensor for `pred` arguments. Once the `data_info` is not `None`, our loss module will automatically build up the computational graph.\n\n```python\n@MODULES.register_module()\nclass DiscShiftLoss(nn.Module):\n\n def __init__(self, loss_weight=1.0, data_info=None):\n super(DiscShiftLoss, self).__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n\n def forward(self, *args, **kwargs):\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n kwargs.update(dict(weight=self.loss_weight))\n return disc_shift_loss(**kwargs)\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n return disc_shift_loss(*args, weight=self.loss_weight, **kwargs)\n\n @staticmethod\n def loss_name():\n return 'loss_disc_shift'\n\n```\n\nAs shown in this part of codes, once users set the `data_info`, the loss module will receive a dictionary containing all of the necessary data and modules, which is provided by the `MODELS` in the training procedure. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using a keyword argument, please name it as `outputs_dict`.\n\n### An example of GANWithCustomizedLoss\n\nTo build the computational graph, the generative models have to provide a dictionary containing all kinds of data. Having a close look at any generative model, you will find that we collect all kinds of features and modules into a dictionary. We provide a customized `GANWithCustomizedLoss` here to show the process.\n\n```python\nclass GANWithCustomizedLoss(BaseModel):\n\n def __init__(self, gan_loss, disc_auxiliary_loss, gen_auxiliary_loss,\n *args, **kwargs):\n # ...\n if gan_loss is not None:\n self.gan_loss = MODULES.build(gan_loss)\n else:\n self.gan_loss = None\n\n if disc_auxiliary_loss:\n self.disc_auxiliary_losses = MODULES.build(disc_auxiliary_loss)\n if not isinstance(self.disc_auxiliary_losses, nn.ModuleList):\n self.disc_auxiliary_losses = nn.ModuleList(\n [self.disc_auxiliary_losses])\n else:\n self.disc_auxiliary_loss = None\n\n if gen_auxiliary_loss:\n self.gen_auxiliary_losses = MODULES.build(gen_auxiliary_loss)\n if not isinstance(self.gen_auxiliary_losses, nn.ModuleList):\n self.gen_auxiliary_losses = nn.ModuleList(\n [self.gen_auxiliary_losses])\n else:\n self.gen_auxiliary_losses = None\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n # ...\n\n # get data dict to compute losses for disc\n data_dict_ = dict(\n iteration=curr_iter,\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs)\n\n loss_disc, log_vars_disc = self._get_disc_loss(data_dict_)\n\n # ...\n\n def _get_disc_loss(self, outputs_dict):\n # Construct losses dict. If you hope some items to be included in the\n # computational graph, you have to add 'loss' in its name. Otherwise,\n # items without 'loss' in their name will just be used to print\n # information.\n losses_dict = {}\n # gan loss\n losses_dict['loss_disc_fake'] = self.gan_loss(\n outputs_dict['disc_pred_fake'], target_is_real=False, is_disc=True)\n losses_dict['loss_disc_real'] = self.gan_loss(\n outputs_dict['disc_pred_real'], target_is_real=True, is_disc=True)\n\n # disc auxiliary loss\n if self.with_disc_auxiliary_loss:\n for loss_module in self.disc_auxiliary_losses:\n loss_ = loss_module(outputs_dict)\n if loss_ is None:\n continue\n\n # the `loss_name()` function return name as 'loss_xxx'\n if loss_module.loss_name() in losses_dict:\n losses_dict[loss_module.loss_name(\n )] = losses_dict[loss_module.loss_name()] + loss_\n else:\n losses_dict[loss_module.loss_name()] = loss_\n loss, log_var = self.parse_losses(losses_dict)\n\n return loss, log_var\n\n```\n\nHere, the `_get_disc_loss` will help to combine all kinds of losses automatically.\n\nTherefore, as long as users design the loss module with the same rules, any kind of loss can be inserted in the training of generative models,\nwithout other modifications in the code of models. What you only need to do is just defining the `data_info` in the config files.\n\n## Available losses\n\nWe list available losses with examples in configs as follows.\n\n### regular losses\n\n\n\n \n \n \n \n \n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n \n\n
MethodclassExample
vanilla gan lossmmagic.models.GANLoss\n\n```python\n# dic gan\nloss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n)\n\n```\n\n
lsgan lossmmagic.models.GANLoss\n
wgan lossmmagic.models.GANLoss\n\n```python\n# deepfillv1\nloss_gan=dict(\n type='GANLoss',\n gan_type='wgan',\n loss_weight=0.0001,\n)\n```\n\n
hinge lossmmagic.models.GANLoss\n\n```python\n# deepfillv2\nloss_gan=dict(\n type='GANLoss',\n gan_type='hinge',\n loss_weight=0.1,\n)\n```\n\n
smgan lossmmagic.models.GANLoss\n\n```python\n# aot-gan\nloss_gan=dict(\n type='GANLoss',\n gan_type='smgan',\n loss_weight=0.01,\n)\n```\n\n
gradient penaltymmagic.models.GradientPenaltyLoss\n\n```python\n# deepfillv1\nloss_gp=dict(type='GradientPenaltyLoss', loss_weight=10.)\n```\n\n
discriminator shift lossmmagic.models.DiscShiftLoss\n\n```python\n# deepfillv1\nloss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001)\n\n```\n\n
clip lossmmagic.models.CLIPLoss
L1 composition lossmmagic.models.L1CompositionLoss
MSE composition lossmmagic.models.MSECompositionLoss
charbonnier composition lossmmagic.models.CharbonnierCompLoss\n\n```python\n# dim\nloss_comp=dict(type='CharbonnierCompLoss', loss_weight=0.5)\n```\n\n
face id Lossmmagic.models.FaceIdLoss
light cnn feature lossmmagic.models.LightCNNFeatureLoss\n\n```python\n# dic gan\nfeature_loss=dict(\n type='LightCNNFeatureLoss',\n pretrained=pretrained_light_cnn,\n loss_weight=0.1,\n criterion='l1')\n```\n\n
gradient lossmmagic.models.GradientLoss
l1 Lossmmagic.models.L1Loss\n\n```python\n# dic gan\npixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')\n```\n\n
mse lossmmagic.models.MSELoss\n\n```python\n# dic gan\nalign_loss=dict(type='MSELoss', loss_weight=0.1, reduction='mean')\n```\n\n
charbonnier lossmmagic.models.CharbonnierLoss\n\n```python\n# dim\nloss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5)\n```\n\n
masked total variation lossmmagic.models.MaskedTVLoss\n\n```python\n# partial conv\nloss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1\n)\n\n```\n\n
perceptual lossmmagic.models.PerceptualLoss\n\n```python\n# real_basicvsr\nperceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False)\n\n```\n\n
transferal perceptual lossmmagic.models.TransferalPerceptualLoss\n\n```python\n# ttsr\ntransferal_perceptual_loss=dict(\n type='TransferalPerceptualLoss',\n loss_weight=1e-2,\n use_attention=False,\n criterion='mse')\n\n```\n\n
\n\n### losses components\n\nFor `GANWithCustomizedLoss`, we provide several components to build customized loss.\n\n| Method | class |\n| ------------------------------------ | ------------------------------------------- |\n| clip loss component | mmagic.models.CLIPLossComps |\n| discriminator shift loss component | mmagic.models. DiscShiftLossComps |\n| gradient penalty loss component | mmagic.models. GradientPenaltyLossComps |\n| r1 gradient penalty component | mmagic.models. R1GradientPenaltyComps |\n| face Id loss component | mmagic.models. FaceIdLossComps |\n| gan loss component | mmagic.models. GANLossComps |\n| generator path regularizer component | mmagic.models.GeneratorPathRegularizerComps |\n" }, { "path": "docs/en/howto/models.md", "content": "# How to design your own models\n\nMMagic is built upon MMEngine and MMCV, which enables users to design new models quickly, train and evaluate them easily.\nIn this section, you will learn how to design your own models.\n\nThe structure of this guide are as follows:\n\n- [How to design your own models](#how-to-design-your-own-models)\n - [Overview of models in MMagic](#overview-of-models-in-mmagic)\n - [An example of SRCNN](#an-example-of-srcnn)\n - [Step 1: Define the network of SRCNN](#step-1-define-the-network-of-srcnn)\n - [Step 2: Define the model of SRCNN](#step-2-define-the-model-of-srcnn)\n - [Step 3: Start training SRCNN](#step-3-start-training-srcnn)\n - [An example of DCGAN](#an-example-of-dcgan)\n - [Step 1: Define the network of DCGAN](#step-1-define-the-network-of-dcgan)\n - [Step 2: Design the model of DCGAN](#step-2-design-the-model-of-dcgan)\n - [Step 3: Start training DCGAN](#step-3-start-training-dcgan)\n - [References](#references)\n\n## Overview of models in MMagic\n\nIn MMagic, one algorithm can be splited two compents: **Model** and **Module**.\n\n- **Model** are topmost wrappers and always inherint from `BaseModel` provided in MMEngine. **Model** is responsible to network forward, loss calculation and backward, parameters updating, etc. In MMagic, **Model** should be registered as `MODELS`.\n- **Module** includes the neural network **architectures** to train or inference, pre-defined **loss classes**, and **data preprocessors** to preprocess the input data batch. **Module** always present as elements of **Model**. In MMagic, **Module** should be registered as **MODULES**.\n\nTake DCGAN model as an example, [generator](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_generator.py) and [discriminator](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_discriminator.py) are the **Module**, which generate images and discriminate real or fake images. [`DCGAN`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan.py) is the **Model**, which take data from dataloader and train generator and discriminator alternatively.\n\nYou can find the implementation of **Model** and **Module** by the following link.\n\n- **Model**:\n - [Editors](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/editors)\n- **Module**:\n - [Layers](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/layers)\n - [Losses](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/losses)\n - [Data Preprocessor](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/data_preprocessors)\n\n## An example of SRCNN\n\nHere, we take the implementation of the classical image super-resolution model, SRCNN \\[1\\], as an example.\n\n### Step 1: Define the network of SRCNN\n\nSRCNN is the first deep learning method for single image super-resolution \\[1\\].\nTo implement the network architecture of SRCNN,\nwe need to create a new file `mmagic/models/editors/srgan/sr_resnet.py` and implement `class MSRResNet`.\n\nIn this step, we implement `class MSRResNet` by inheriting from `mmengine.models.BaseModule` and define the network architecture in `__init__` function.\nIn particular, we need to use `@MODELS.register_module()` to add the implementation of `class MSRResNet` into the registration of MMagic.\n\n```python\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmagic.registry import MODELS\n\nfrom mmagic.models.utils import (PixelShufflePack, ResidualBlockNoBN,\n default_init_weights, make_layer)\n\n\n@MODELS.register_module()\nclass MSRResNet(BaseModule):\n \"\"\"Modified SRResNet.\n\n A compacted version modified from SRResNet in \"Photo-Realistic Single\n Image Super-Resolution Using a Generative Adversarial Network\".\n\n It uses residual blocks without BN, similar to EDSR.\n Currently, it supports x2, x3 and x4 upsampling scale factor.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_blocks (int): Block number in the trunk network. Default: 16.\n upscale_factor (int): Upsampling factor. Support x2, x3 and x4.\n Default: 4.\n \"\"\"\n _supported_upscale_factors = [2, 3, 4]\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4):\n\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.mid_channels = mid_channels\n self.num_blocks = num_blocks\n self.upscale_factor = upscale_factor\n\n self.conv_first = nn.Conv2d(\n in_channels, mid_channels, 3, 1, 1, bias=True)\n self.trunk_net = make_layer(\n ResidualBlockNoBN, num_blocks, mid_channels=mid_channels)\n\n # upsampling\n if self.upscale_factor in [2, 3]:\n self.upsample1 = PixelShufflePack(\n mid_channels,\n mid_channels,\n self.upscale_factor,\n upsample_kernel=3)\n elif self.upscale_factor == 4:\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n else:\n raise ValueError(\n f'Unsupported scale factor {self.upscale_factor}. '\n f'Currently supported ones are '\n f'{self._supported_upscale_factors}.')\n\n self.conv_hr = nn.Conv2d(\n mid_channels, mid_channels, 3, 1, 1, bias=True)\n self.conv_last = nn.Conv2d(\n mid_channels, out_channels, 3, 1, 1, bias=True)\n\n self.img_upsampler = nn.Upsample(\n scale_factor=self.upscale_factor,\n mode='bilinear',\n align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n self.init_weights()\n\n def init_weights(self):\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n strict (boo, optional): Whether strictly load the pretrained model.\n Defaults to True.\n \"\"\"\n\n for m in [self.conv_first, self.conv_hr, self.conv_last]:\n default_init_weights(m, 0.1)\n\n```\n\nThen, we implement the `forward` function of `class MSRResNet`, which takes as input tensor and then returns the results from `MSRResNet`.\n\n```python\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n feat = self.lrelu(self.conv_first(x))\n out = self.trunk_net(feat)\n\n if self.upscale_factor in [2, 3]:\n out = self.upsample1(out)\n elif self.upscale_factor == 4:\n out = self.upsample1(out)\n out = self.upsample2(out)\n\n out = self.conv_last(self.lrelu(self.conv_hr(out)))\n upsampled_img = self.img_upsampler(x)\n out += upsampled_img\n return out\n```\n\nAfter the implementation of `class MSRResNet`, we need to update the model list in `mmagic/models/editors/__init__.py`, so that we can import and use `class MSRResNet` by `mmagic.models.editors`.\n\n```python\nfrom .srgan.sr_resnet import MSRResNet\n```\n\n### Step 2: Define the model of SRCNN\n\nAfter the implementation of the network architecture,\nwe need to define our model `class BaseEditModel` and implement the forward loop of `class BaseEditModel`.\n\nTo implement `class BaseEditModel`,\nwe create a new file `mmagic/models/base_models/base_edit_model.py`.\nSpecifically, `class BaseEditModel` inherits from `mmengine.model.BaseModel`.\nIn the `__init__` function, we define the loss functions, training and testing configurations, networks of `class BaseEditModel`.\n\n```python\nfrom typing import List, Optional\n\nimport torch\nfrom mmengine.model import BaseModel\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass BaseEditModel(BaseModel):\n \"\"\"Base model for image and video editing.\n\n It must contain a generator that takes frames as inputs and outputs an\n interpolated frame. It also has a pixel-wise loss for training.\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n super().__init__(\n init_cfg=init_cfg, data_preprocessor=data_preprocessor)\n\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n # generator\n self.generator = MODELS.build(generator)\n\n # loss\n self.pixel_loss = MODELS.build(pixel_loss)\n```\n\nSince `mmengine.model.BaseModel` provides the basic functions of the algorithmic model,\nsuch as weights initialize, batch inputs preprocess, parse losses, and update model parameters.\nTherefore, the subclasses inherit from BaseModel, i.e., `class BaseEditModel` in this example,\nonly need to implement the forward method,\nwhich implements the logic to calculate loss and predictions.\n\nSpecifically, the implemented `forward` function of `class BaseEditModel` takes as input `batch_inputs` and `data_samples` and return results according to mode arguments.\n\n```python\n def forward(self,\n batch_inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs):\n \"\"\"Returns losses or predictions of training, validation, testing, and\n simple inference process.\n\n ``forward`` method of BaseModel is an abstract method, its subclasses\n must implement this method.\n\n Accepts ``batch_inputs`` and ``data_samples`` processed by\n :attr:`data_preprocessor`, and returns results according to mode\n arguments.\n\n During non-distributed training, validation, and testing process,\n ``forward`` will be called by ``BaseModel.train_step``,\n ``BaseModel.val_step`` and ``BaseModel.val_step`` directly.\n\n During distributed data parallel training process,\n ``MMSeparateDistributedDataParallel.train_step`` will first call\n ``DistributedDataParallel.forward`` to enable automatic\n gradient synchronization, and then call ``forward`` to get training\n loss.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict or tensor for custom use.\n \"\"\"\n\n if mode == 'tensor':\n return self.forward_tensor(batch_inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n return self.forward_inference(batch_inputs, data_samples, **kwargs)\n\n elif mode == 'loss':\n return self.forward_train(batch_inputs, data_samples, **kwargs)\n```\n\nSpecifically, in `forward_tensor`, `class BaseEditModel` returns the forward tensors of the network directly.\n\n```python\n def forward_tensor(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward tensor.\n Returns result of simple forward.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n feats = self.generator(batch_inputs, **kwargs)\n\n return feats\n```\n\nIn `forward_inference` function, `class BaseEditModel` first converts the forward tensors to images and then returns the images as output.\n\n```python\n def forward_inference(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward inference.\n Returns predictions of validation, testing, and simple inference.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[DataSample]: predictions.\n \"\"\"\n\n feats = self.forward_tensor(batch_inputs, data_samples, **kwargs)\n feats = self.data_preprocessor.destructor(feats)\n predictions = []\n for idx in range(feats.shape[0]):\n predictions.append(\n DataSample(\n pred_img=feats[idx].to('cpu'),\n metainfo=data_samples[idx].metainfo))\n\n return predictions\n```\n\nIn `forward_train`, `class BaseEditModel` calculate the loss function and returns a dictionary contains the losses as output.\n\n```python\n def forward_train(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward training.\n Returns dict of losses of training.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n feats = self.forward_tensor(batch_inputs, data_samples, **kwargs)\n gt_imgs = [data_sample.gt_img.data for data_sample in data_samples]\n batch_gt_data = torch.stack(gt_imgs)\n\n loss = self.pixel_loss(feats, batch_gt_data)\n\n return dict(loss=loss)\n\n```\n\nAfter the implementation of `class BaseEditModel`,\nwe need to update the model list in `mmagic/models/__init__.py`,\nso that we can import and use `class BaseEditModel` by `mmagic.models`.\n\n```python\nfrom .base_models.base_edit_model import BaseEditModel\n```\n\n### Step 3: Start training SRCNN\n\nAfter implementing the network architecture and the forward loop of SRCNN,\nnow we can create a new file `configs/srcnn/srcnn_x4k915_g1_1000k_div2k.py`\nto set the configurations needed by training SRCNN.\n\nIn the configuration file, we need to specify the parameters of our model, `class BaseEditModel`, including the generator network architecture, loss function, additional training and testing configuration, and data preprocessor of input tensors. Please refer to the [Introduction to the loss in MMagic](./losses.md) for more details of losses in MMagic.\n\n```python\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SRCNNNet',\n channels=(3, 64, 32, 3),\n kernel_sizes=(9, 1, 5),\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n```\n\nWe also need to specify the training dataloader and testing dataloader according to create your own dataloader.\nFinally we can start training our own model by：\n\n```python\npython tools/train.py configs/srcnn/srcnn_x4k915_g1_1000k_div2k.py\n```\n\n## An example of DCGAN\n\nHere, we take the implementation of the classical gan model, DCGAN \\[2\\], as an example.\n\n### Step 1: Define the network of DCGAN\n\nDCGAN is a classical image generative adversarial network \\[2\\]. To implement the network architecture of DCGAN, we need to create tow new files `mmagic/models/editors/dcgan/dcgan_generator.py` and `mmagic/models/editors/dcgan/dcgan_discriminator.py`, and implement generator (`class DCGANGenerator`) and discriminator (`class DCGANDiscriminator`).\n\nIn this step, we implement `class DCGANGenerator`, `class DCGANDiscriminator` and define the network architecture in `__init__` function.\nIn particular, we need to use `@MODULES.register_module()` to add the generator and discriminator into the registration of MMagic.\n\nTake the following code as example:\n\n```python\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmcv.runner import load_checkpoint\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\nfrom mmengine.logging import MMLogger\nfrom mmengine.model.utils import normal_init\n\nfrom mmagic.models.builder import MODULES\nfrom ..common import get_module_device\n\n\n@MODULES.register_module()\nclass DCGANGenerator(nn.Module):\n def __init__(self,\n output_scale,\n out_channels=3,\n base_channels=1024,\n input_scale=4,\n noise_size=100,\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='ReLU'),\n out_act_cfg=dict(type='Tanh'),\n pretrained=None):\n super().__init__()\n self.output_scale = output_scale\n self.base_channels = base_channels\n self.input_scale = input_scale\n self.noise_size = noise_size\n\n # the number of times for upsampling\n self.num_upsamples = int(np.log2(output_scale // input_scale))\n\n # output 4x4 feature map\n self.noise2feat = ConvModule(\n noise_size,\n base_channels,\n kernel_size=4,\n stride=1,\n padding=0,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg)\n\n # build up upsampling backbone (excluding the output layer)\n upsampling = []\n curr_channel = base_channels\n for _ in range(self.num_upsamples - 1):\n upsampling.append(\n ConvModule(\n curr_channel,\n curr_channel // 2,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n\n curr_channel //= 2\n\n self.upsampling = nn.Sequential(*upsampling)\n\n # output layer\n self.output_layer = ConvModule(\n curr_channel,\n out_channels,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=None,\n act_cfg=out_act_cfg)\n```\n\nThen, we implement the `forward` function of `DCGANGenerator`, which takes as `noise` tensor or `num_batches` and then returns the results from `DCGANGenerator`.\n\n```python\n def forward(self, noise, num_batches=0, return_noise=False):\n noise_batch = noise_batch.to(get_module_device(self))\n x = self.noise2feat(noise_batch)\n x = self.upsampling(x)\n x = self.output_layer(x)\n return x\n```\n\nIf you want to implement specific weights initialization method for you network, you need add `init_weights` function by yourself.\n\n```python\n def init_weights(self, pretrained=None):\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n normal_init(m, 0, 0.02)\n elif isinstance(m, _BatchNorm):\n nn.init.normal_(m.weight.data)\n nn.init.constant_(m.bias.data, 0)\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n```\n\nAfter the implementation of class `DCGANGenerator`, we need to update the model list in `mmagic/models/editors/__init__.py`, so that we can import and use class `DCGANGenerator` by `mmagic.models.editors`.\n\nImplementation of Class `DCGANDiscriminator` follows the similar logic, and you can find the implementation [here](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_discriminator.py).\n\n### Step 2: Design the model of DCGAN\n\nAfter the implementation of the network **Module**, we need to define our **Model** class `DCGAN`.\n\nYour **Model** should inherit from [`BaseModel`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/base_model/base_model.py#L16) provided by MMEngine and implement three functions, `train_step`, `val_step` and `test_step`.\n\n- `train_step`: This function is responsible to update the parameters of the network and called by MMEngine's Loop ([`IterBasedTrainLoop`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L183) or [`EpochBasedTrainLoop`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L18)). `train_step` take data batch and [`OptimWrapper`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/optim_wrapper.md) as input and return a dict of log.\n- `val_step`: This function is responsible for getting output for validation during the training process. and is called by [`MultiValLoop`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/engine/runner/multi_loops.py#L19).\n- `test_step`: This function is responsible for getting output in test process and is called by [`MultiTestLoop`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/engine/runner/multi_loops.py#L274).\n\n> Note that, in `train_step`, `val_step` and `test_step`, `DataPreprocessor` is called to preprocess the input data batch before feed them to the neural network. To know more about `DataPreprocessor` please refer to this [file](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/data_preprocessors/gen_preprocessor.py) and this [tutorial](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/model.md#%E6%95%B0%E6%8D%AE%E5%A4%84%E7%90%86%E5%99%A8datapreprocessor).\n\nFor simplify using, we provide [`BaseGAN`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/base_models/base_gan.py) class in MMagic, which implements generic `train_step`, `val_step` and `test_step` function for GAN models. With `BaseGAN` as base class, each specific GAN algorithm only need to implement `train_generator` and `train_discriminator`.\n\nIn `train_step`, we support data preprocessing, gradient accumulation (realized by [`OptimWrapper`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/optim_wrapper.md)) and expontial moving averate (EMA) realized by [(`ExponentialMovingAverage`)](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/base_models/average_model.py#L19). With `BaseGAN.train_step`, each specific GAN algorithm only need to implement `train_generator` and `train_discriminator`.\n\n```python\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n # train discriminator, use context manager provided by MMEngine\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n # train_discriminator should be implemented!\n log_vars = self.train_discriminator(\n **data, optimizer_wrapper=disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n # update generator, use context manager provided by MMEngine\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n # train_generator should be implemented!\n log_vars_gen = self.train_generator(\n **data, optimizer_wrapper=gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n # return the log dict\n return log_vars\n```\n\nIn `val_step` and `test_step`, we call data preprocessing and `BaseGAN.forward` progressively.\n\n```python\n def val_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n # call `forward`\n outputs = self(**data)\n return outputs\n\n def test_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n # call `orward`\n outputs = self(**data)\n return outputs\n```\n\nThen, we implement `train_generator` and `train_discriminator` in `DCGAN` class.\n\n```python\nfrom typing import Dict, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom .base_gan import BaseGAN\n\n\n@MODELS.register_module()\nclass DCGAN(BaseGAN):\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))\n losses_dict['loss_disc_real'] = F.binary_cross_entropy_with_logits(\n disc_pred_real, 1. * torch.ones_like(disc_pred_real))\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:\n losses_dict = dict()\n losses_dict['loss_gen'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(\n self, inputs, data_sample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n real_imgs = inputs['img']\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs, data_sample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n num_batches = inputs['img'].shape[0]\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n```\n\nAfter the implementation of `class DCGAN`, we need to update the model list in `mmagic/models/__init__.py`, so that we can import and use `class DCGAN` by `mmagic.models`.\n\n### Step 3: Start training DCGAN\n\nAfter implementing the network **Module** and the **Model** of DCGAN,\nnow we can create a new file `configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py`\nto set the configurations needed by training DCGAN.\n\nIn the configuration file, we need to specify the parameters of our model, `class DCGAN`, including the generator network architecture and data preprocessor of input tensors.\n\n```python\n# model settings\nmodel = dict(\n type='DCGAN',\n noise_size=100,\n data_preprocessor=dict(type='GANDataPreprocessor'),\n generator=dict(type='DCGANGenerator', output_scale=64, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=64,\n output_scale=4,\n out_channels=1))\n```\n\nWe also need to specify the training dataloader and testing dataloader according to [create your own dataloader](dataset.md).\nFinally we can start training our own model by：\n\n```python\npython tools/train.py configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\n```\n\n## References\n\n1. Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou. Image Super-Resolution Using Deep Convolutional Networks\\[J\\]. IEEE transactions on pattern analysis and machine intelligence, 2015.\n\n2. Radford, Alec, Luke Metz, and Soumith Chintala. \"Unsupervised representation learning with deep convolutional generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015).\n" }, { "path": "docs/en/howto/transforms.md", "content": "# How to design your own data transforms\n\nIn this tutorial, we introduce the design of transforms pipeline in MMagic.\n\nThe structure of this guide are as follows:\n\n- [How to design your own data transforms](#how-to-design-your-own-data-transforms)\n - [Data pipelines in MMagic](#data-pipelines-in-mmagic)\n - [A simple example of data transform](#a-simple-example-of-data-transform)\n - [An example of BasicVSR](#an-example-of-basicvsr)\n - [An example of Pix2Pix](#an-example-of-pix2pix)\n - [Supported transforms in MMagic](#supported-transforms-in-mmagic)\n - [Data loading](#data-loading)\n - [Pre-processing](#pre-processing)\n - [Formatting](#formatting)\n - [Extend and use custom pipelines](#extend-and-use-custom-pipelines)\n - [A simple example of MyTransform](#a-simple-example-of-mytransform)\n - [An example of flipping](#an-example-of-flipping)\n\n## Data pipelines in MMagic\n\nFollowing typical conventions, we use `Dataset` and `DataLoader` for data loading with multiple workers. `Dataset` returns a dict of data items corresponding the arguments of models' forward method.\n\nThe data preparation pipeline and the dataset is decomposed. Usually a dataset defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.\n\nA pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.\n\nThe operations are categorized into data loading, pre-processing, and formatting\n\nIn MMagic, all data transformations are inherited from `BaseTransform`.\nThe input and output types of transformations are both dict.\n\n### A simple example of data transform\n\n```python\n>>> from mmagic.transforms import LoadPairedImageFromFile\n>>> transforms = LoadPairedImageFromFile(\n>>> key='pair',\n>>> domain_a='horse',\n>>> domain_b='zebra',\n>>> flag='color'),\n>>> data_dict = {'pair_path': './data/pix2pix/facades/train/1.png'}\n>>> data_dict = transforms(data_dict)\n>>> print(data_dict.keys())\ndict_keys(['pair_path', 'pair', 'pair_ori_shape', 'img_mask', 'img_photo', 'img_mask_path', 'img_photo_path', 'img_mask_ori_shape', 'img_photo_ori_shape'])\n```\n\nGenerally, the last step of the transforms pipeline must be `PackInputs`.\n`PackInputs` will pack the processed data into a dict containing two fields: `inputs` and `data_samples`.\n`inputs` is the variable you want to use as the model's input, which can be the type of `torch.Tensor`, dict of `torch.Tensor`, or any type you want.\n`data_samples` is a list of `DataSample`. Each `DataSample` contains groundtruth and necessary information for corresponding input.\n\n### An example of BasicVSR\n\nHere is a pipeline example for BasicVSR.\n\n```python\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='MirrorSequence', keys=['img', 'gt']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='MirrorSequence', keys=['img']),\n dict(type='PackInputs')\n]\n```\n\nFor each operation, we list the related dict fields that are added/updated/removed, the dict fields marked by '\\*' are optional.\n\n### An example of Pix2Pix\n\nHere is a pipeline example for Pix2Pix training on aerial2maps dataset.\n\n```python\nsource_domain = 'aerial'\ntarget_domain = 'map'\n\npipeline = [\n dict(\n type='LoadPairedImageFromFile',\n io_backend='disk',\n key='pair',\n domain_a=domain_a,\n domain_b=domain_b,\n flag='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': [f'img_{domain_a}', f'img_{domain_b}']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='mmagic.Resize', scale=(286, 286),\n interpolation='bicubic'),\n dict(type='mmagic.FixedCrop', crop_size=(256, 256))\n ]),\n dict(\n type='Flip',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n direction='horizontal'),\n dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}', 'pair'])\n```\n\n## Supported transforms in MMagic\n\n### Data loading\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n LoadImageFromFile\n \n - add: img, img_path, img_ori_shape, \\*ori_img\n
\n RandomLoadResizeBg\n \n - add: bg\n
\n LoadMask\n \n - add: mask\n
\n GetSpatialDiscountMask\n \n - add: discount_mask\n
\n\n### Pre-processing\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n Resize\n \n - add: scale_factor, keep_ratio, interpolation, backend\n - update: specified by keys\n
\n MATLABLikeResize\n \n - add: scale, output_shape\n - update: specified by keys\n
\n RandomRotation\n \n - add: degrees\n - update: specified by keys\n \n
\n Flip\n \n - add: flip, flip_direction\n - update: specified by keys\n
\n RandomAffine\n \n - update: specified by keys\n
\n RandomJitter\n \n - update: fg (img)\n
\n ColorJitter\n \n - update: specified by keys\n
\n BinarizeImage\n \n - update: specified by keys\n
\n RandomMaskDilation\n \n - add: img_dilate_kernel_size\n \n
\n RandomTransposeHW\n \n - add: transpose\n
\n RandomDownSampling\n \n - update: scale, gt (img), lq (img)\n
\n RandomBlur\n \n - update: specified by keys\n
\n RandomResize\n \n - update: specified by keys\n
\n RandomNoise\n \n - update: specified by keys\n
\n RandomJPEGCompression\n \n - update: specified by keys\n
\n RandomVideoCompression\n \n - update: specified by keys\n
\n DegradationsWithShuffle\n \n - update: specified by keys\n
\n GenerateFrameIndices\n \n - update: img_path (gt_path, lq_path)\n
\n GenerateFrameIndiceswithPadding\n \n - update: img_path (gt_path, lq_path)\n
\n TemporalReverse\n \n - add: reverse\n - update: specified by keys\n
\n GenerateSegmentIndices\n \n - add: interval\n - update: img_path (gt_path, lq_path)\n
\n MirrorSequence\n \n - update: specified by keys\n
\n CopyValues\n \n - add: specified by dst_key\n
\n UnsharpMasking\n \n - add: img_unsharp\n
\n Crop\n \n - add: img_crop_bbox, crop_size\n - update: specified by keys\n
\n RandomResizedCrop\n \n - add: img_crop_bbox\n - update: specified by keys\n
\n FixedCrop\n \n - add: crop_size, crop_pos\n - update: specified by keys\n
\n PairedRandomCrop\n \n - update: gt (img), lq (img)\n
\n CropAroundCenter\n \n - add: crop_bbox\n - update: fg (img), alpha (img), trimap (img), bg (img)\n
\n CropAroundUnknown\n \n - add: crop_bbox\n - update: specified by keys\n
\n CropAroundFg\n \n - add: crop_bbox\n - update: specified by keys\n
\n ModCrop\n \n - update: gt (img)\n
\n CropLike\n \n - update: specified by target_key\n
\n GetMaskedImage\n \n - add: masked_img\n
\n GenerateFacialHeatmap\n \n - add: heatmap\n
\n GenerateCoordinateAndCell\n \n - add: coord, cell\n - update: gt (img)\n
\n Normalize\n \n - add: img_norm_cfg\n - update: specified by keys\n
\n RescaleToZeroOne\n \n - update: specified by keys\n
\n\n### Formatting\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n ToTensor\n \n update: specified by keys.\n
\n FormatTrimap\n \n - update: trimap\n
\n PackInputs\n \n - add: inputs, data_sample\n - remove: all other keys\n
\n\n### Albumentations\n\nMMagic support adding custom transformations from [Albumentations](https://github.com/albumentations-team/albumentations) library. Please visit https://albumentations.ai/docs/getting_started/transforms_and_targets to get more information.\n\nAn example of Albumentations's `transforms` is as followed:\n\n```python\nalbu_transforms = [\n dict(\n type='Resize',\n height=100,\n width=100,\n ),\n dict(\n type='RandomFog',\n p=0.5,\n ),\n dict(\n type='RandomRain',\n p=0.5\n ),\n dict(\n type='RandomSnow',\n p=0.5,\n ),\n]\npipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Albumentations',\n keys=['img'],\n transforms=albu_transforms),\n dict(type='PackInputs')\n]\n```\n\n## Extend and use custom pipelines\n\n### A simple example of MyTransform\n\n1. Write a new pipeline in a file, e.g., in `my_pipeline.py`. It takes a dict as input and returns a dict.\n\n```python\nimport random\nfrom mmcv.transforms import BaseTransform\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass MyTransform(BaseTransform):\n \"\"\"Add your transform\n\n Args:\n p (float): Probability of shifts. Default 0.5.\n \"\"\"\n\n def __init__(self, p=0.5):\n self.p = p\n\n def transform(self, results):\n if random.random() > self.p:\n results['dummy'] = True\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(p={self.p})')\n\n return repr_str\n```\n\n2. Import and use the pipeline in your config file.\n\nMake sure the import is relative to where your train script is located.\n\n```python\ntrain_pipeline = [\n ...\n dict(type='MyTransform', p=0.2),\n ...\n]\n```\n\n### An example of flipping\n\nHere we use a simple flipping transformation as example:\n\n```python\nimport random\nimport mmcv\nfrom mmcv.transforms import BaseTransform, TRANSFORMS\n\n@TRANSFORMS.register_module()\nclass MyFlip(BaseTransform):\n def __init__(self, direction: str):\n super().__init__()\n self.direction = direction\n\n def transform(self, results: dict) -> dict:\n img = results['img']\n results['img'] = mmcv.imflip(img, direction=self.direction)\n return results\n```\n\nThus, we can instantiate a `MyFlip` object and use it to process the data dict.\n\n```python\nimport numpy as np\n\ntransform = MyFlip(direction='horizontal')\ndata_dict = {'img': np.random.rand(224, 224, 3)}\ndata_dict = transform(data_dict)\nprocessed_img = data_dict['img']\n```\n\nOr, we can use `MyFlip` transformation in data pipeline in our config file.\n\n```python\npipeline = [\n ...\n dict(type='MyFlip', direction='horizontal'),\n ...\n]\n```\n\nNote that if you want to use `MyFlip` in config, you must ensure the file containing `MyFlip` is imported during the program run.\n" }, { "path": "docs/en/index.rst", "content": "Welcome to MMagic's documentation!\n=====================================\n\nLanguages:\n`English `_\n|\n`简体中文 `_\n\nMMagic (**M**\\ultimodal **A**\\dvanced, **G**\\enerative, and **I**\\ntelligent **C**\\reation) is an open-source AIGC toolbox for professional AI researchers and machine learning engineers to explore image and video processing, editing and generation.\n\nMMagic supports various foundamental generative models, including:\n\n* Unconditional Generative Adversarial Networks (GANs)\n* Conditional Generative Adversarial Networks (GANs)\n* Internal Learning\n* Diffusion Models\n* And many other generative models are coming soon!\n\nMMagic supports various applications, including:\n\n- Text-to-Image\n- Image-to-image translation\n- 3D-aware generation\n- Image super-resolution\n- Video super-resolution\n- Video frame interpolation\n- Image inpainting\n- Image matting\n- Image restoration\n- Image colorization\n- Image generation\n- And many other applications are coming soon!\n\nMMagic is based on `PyTorch `_ and is a part of the `OpenMMLab project `_.\nCodes are available on `GitHub `_.\n\n\nDocumentation\n=============\n\n.. toctree::\n :maxdepth: 1\n :caption: Community\n\n community/contributing.md\n community/projects.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: Get Started\n\n get_started/overview.md\n get_started/install.md\n get_started/quick_run.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: User Guides\n\n user_guides/config.md\n user_guides/dataset_prepare.md\n user_guides/inference.md\n user_guides/train_test.md\n user_guides/metrics.md\n user_guides/visualization.md\n user_guides/useful_tools.md\n user_guides/deploy.md\n\n\n.. toctree::\n :maxdepth: 2\n :caption: Advanced Guides\n\n advanced_guides/models.md\n advanced_guides/dataset.md\n advanced_guides/transforms.md\n advanced_guides/losses.md\n advanced_guides/evaluator.md\n advanced_guides/structures.md\n advanced_guides/data_preprocessor.md\n advanced_guides/data_flow.md\n\n\n.. toctree::\n :maxdepth: 2\n :caption: How To\n\n howto/models.md\n howto/dataset.md\n howto/transforms.md\n howto/losses.md\n\n.. toctree::\n :maxdepth: 1\n :caption: FAQ\n\n faq.md\n\n.. toctree::\n :maxdepth: 2\n :caption: Model Zoo\n\n model_zoo/index.rst\n\n\n.. toctree::\n :maxdepth: 1\n :caption: Dataset Zoo\n\n dataset_zoo/index.rst\n\n.. toctree::\n :maxdepth: 1\n :caption: Changelog\n\n changelog.md\n\n.. toctree::\n :maxdepth: 2\n :caption: API Reference\n\n mmagic.apis.inferencers \n mmagic.structures \n mmagic.datasets \n mmagic.datasets.transforms \n mmagic.evaluation \n mmagic.visualization \n mmagic.engine.hooks \n mmagic.engine.logging \n mmagic.engine.optimizers \n mmagic.engine.runner \n mmagic.engine.schedulers \n mmagic.models.archs \n mmagic.models.base_models \n mmagic.models.losses \n mmagic.models.data_preprocessors \n mmagic.models.utils \n mmagic.models.editors \n mmagic.utils \n\n\n.. toctree::\n :maxdepth: 1\n :caption: Migration from MMEdit 0.x\n\n migration/overview.md\n migration/runtime.md\n migration/models.md\n migration/eval_test.md\n migration/schedule.md\n migration/data.md\n migration/distributed_train.md\n migration/optimizers.md\n migration/visualization.md\n migration/amp.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: Device Support\n\n device/npu.md\n\n\n.. toctree::\n :caption: Switch Language\n\n switch_language.md\n\n\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/en/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=_build\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.http://sphinx-doc.org/\n\texit /b 1\n)\n\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\n\n:end\npopd\n" }, { "path": "docs/en/migration/amp.md", "content": "# Migration of AMP Training\n\nIn 0.x, MMEditing do not support AMP training for the entire forward process.\nInstead, users must use `auto_fp16` decorator to warp the specific submodule and convert the parameter of submodule to fp16.\nThis allows for fine-grained control of the model parameters, but is more cumbersome to use.\nIn addition, users need to handle operations such as scaling of the loss function during the training process by themselves.\n\nMMagic 1.x use `AmpOptimWrapper` provided by MMEngine.\nIn `AmpOptimWrapper.update_params`, gradient scaling and `GradScaler` updating is automatically performed.\nAnd in `optim_context` context manager, `auto_cast` is applied to the entire forward process.\n\nSpecifically, the difference between the 0.x and 1.x is as follows:\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x version 1.x Version
\n\n```python\n# config\nrunner = dict(fp16_loss_scaler=dict(init_scale=512))\n```\n\n```python\n# code\nimport torch.nn as nn\nfrom mmedit.models.builder import build_model\nfrom mmedit.core.runners.fp16_utils import auto_fp16\n\n\nclass DemoModule(nn.Module):\n def __init__(self, cfg):\n self.net = build_model(cfg)\n\n @auto_fp16\n def forward(self, x):\n return self.net(x)\n\nclass DemoModel(nn.Module):\n\n def __init__(self, cfg):\n super().__init__(self)\n self.demo_network = DemoModule(cfg)\n\n def train_step(self,\n data_batch,\n optimizer,\n ddp_reducer=None,\n loss_scaler=None,\n use_apex_amp=False,\n running_status=None):\n # get data from data_batch\n inputs = data_batch['img']\n output = self.demo_network(inputs)\n\n optimizer.zero_grad()\n loss, log_vars = self.get_loss(data_dict_)\n\n if ddp_reducer is not None:\n ddp_reducer.prepare_for_backward(_find_tensors(loss_disc))\n\n if loss_scaler:\n # add support for fp16\n loss_scaler.scale(loss_disc).backward()\n elif use_apex_amp:\n from apex import amp\n with amp.scale_loss(loss_disc, optimizer,\n loss_id=0) as scaled_loss_disc:\n scaled_loss_disc.backward()\n else:\n loss_disc.backward()\n\n if loss_scaler:\n loss_scaler.unscale_(optimizer)\n loss_scaler.step(optimizer)\n else:\n optimizer.step()\n```\n\n\n\n```python\n# config\noptim_wrapper = dict(\n constructor='OptimWrapperConstructor',\n generator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-06),\n type='AmpOptimWrapper', # use amp wrapper\n loss_scale='dynamic'),\n discriminator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-06),\n type='AmpOptimWrapper', # use amp wrapper\n loss_scale='dynamic'))\n```\n\n```python\n# code\nimport torch.nn as nn\nfrom mmagic.registry import MODULES\nfrom mmengine.model import BaseModel\n\n\nclass DemoModule(nn.Module):\n def __init__(self, cfg):\n self.net = MODULES.build(cfg)\n\n def forward(self, x):\n return self.net(x)\n\nclass DemoModel(BaseModel):\n def __init__(self, cfg):\n super().__init__(self)\n self.demo_network = DemoModule(cfg)\n\n def train_step(self, data, optim_wrapper):\n # get data from data_batch\n data = self.data_preprocessor(data, True)\n inputs = data['inputs']\n\n with optim_wrapper.optim_context(self.discriminator):\n output = self.demo_network(inputs)\n loss_dict = self.get_loss(output)\n # use parse_loss provide by `BaseModel`\n loss, log_vars = self.parse_loss(loss_dict)\n optimizer_wrapper.update_params(loss)\n\n return log_vars\n```\n\n
\n\nTo avoid user modifications to the configuration file, MMagic provides the `--amp` option in `train.py`, which allows the user to start AMP training without modifying the configuration file.\nUsers can start AMP training by following command:\n\n```bash\nbash tools/dist_train.sh CONFIG GPUS --amp\n\n# for slurm users\nbash tools/slurm_train.sh PARTITION JOB_NAME CONFIG WORK_DIR --amp\n```\n" }, { "path": "docs/en/migration/data.md", "content": "# Migration of Data Settings\n\nThis section introduces the migration of data settings:\n\n- [Migration of Data Settings](#migration-of-data-settings)\n - [Data pipelines](#data-pipelines)\n - [Dataloader](#dataloader)\n\n## Data pipelines\n\nWe update data pipelines settings in MMagic 1.x. Important modifications are as following.\n\n- Remove normalization and color space transforms operations. They are moved from datasets transforms pipelines to data_preprocessor.\n- The original formatting transforms pipelines `Collect` and `ToTensor` are combined as `PackInputs`.\n More details of data pipelines are shown in [transform guides](../howto/transforms.md).\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\ntrain_pipeline = [ # Training data processing pipeline\n dict(type='LoadImageFromFile', # Load images from files\n io_backend='disk', # io backend\n key='lq', # Keys in results to find corresponding path\n flag='unchanged'), # flag for reading images\n dict(type='LoadImageFromFile', # Load images from files\n io_backend='disk', # io backend\n key='gt', # Keys in results to find corresponding path\n flag='unchanged'), # flag for reading images\n dict(type='RescaleToZeroOne', keys=['lq', 'gt']), # Rescale images from [0, 255] to [0, 1]\n dict(type='Normalize', # Augmentation pipeline that normalize the input images\n keys=['lq', 'gt'], # Images to be normalized\n mean=[0, 0, 0], # Mean values\n std=[1, 1, 1], # Standard variance\n to_rgb=True), # Change to RGB channel\n dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='horizontal'), # Flip direction\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip direction\n dict(type='RandomTransposeHW', # Random transpose h and w for images\n keys=['lq', 'gt'], # Images to be transposed\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='Collect', # Pipeline that decides which keys in the data should be passed to the model\n keys=['lq', 'gt'], # Keys to pass to the model\n meta_keys=['lq_path', 'gt_path']), # Meta information keys. In training, meta information is not needed\n dict(type='ToTensor', # Convert images to tensor\n keys=['lq', 'gt']) # Images to be converted to Tensor\n]\ntest_pipeline = [ # Test pipeline\n dict(\n type='LoadImageFromFile', # Load images from files\n io_backend='disk', # io backend\n key='lq', # Keys in results to find corresponding path\n flag='unchanged'), # flag for reading images\n dict(\n type='LoadImageFromFile', # Load images from files\n io_backend='disk', # io backend\n key='gt', # Keys in results to find corresponding path\n flag='unchanged'), # flag for reading images\n dict(type='RescaleToZeroOne', keys=['lq', 'gt']), # Rescale images from [0, 255] to [0, 1]\n dict(\n type='Normalize', # Augmentation pipeline that normalize the input images\n keys=['lq', 'gt'], # Images to be normalized\n mean=[0, 0, 0], # Mean values\n std=[1, 1, 1], # Standard variance\n to_rgb=True), # Change to RGB channel\n dict(type='Collect', # Pipeline that decides which keys in the data should be passed to the model\n keys=['lq', 'gt'], # Keys to pass to the model\n meta_keys=['lq_path', 'gt_path']), # Meta information keys\n dict(type='ToTensor', # Convert images to tensor\n keys=['lq', 'gt']) # Images to be converted to Tensor\n]\n```\n\n\n\n```python\ntrain_pipeline = [ # Training data processing pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='SetValues', dictionary=dict(scale=scale)), # Set value to destination keys\n dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='horizontal'), # Flip direction\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip direction\n dict(type='RandomTransposeHW', # Random transpose h and w for images\n keys=['lq', 'gt'], # Images to be transposed\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='PackInputs') # The config of collecting data from current pipeline\n]\ntest_pipeline = [ # Test pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='PackInputs') # The config of collecting data from current pipeline\n]\n```\n\n
\n\n## Dataloader\n\nWe update dataloader settings in MMagic 1.x. Important modifications are as following.\n\n- The original `data` field is split to `train_dataloader`, `val_dataloader` and `test_dataloader`. This allows us to configure them in fine-grained. For example, you can specify different sampler and batch size during training and test.\n- The `samples_per_gpu` is renamed to `batch_size`.\n- The `workers_per_gpu` is renamed to `num_workers`.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\ndata = dict(\n # train\n samples_per_gpu=16, # Batch size of a single GPU\n workers_per_gpu=4, # Worker to pre-fetch data for each single GPU\n drop_last=True, # Use drop_last in data_loader\n train=dict( # Train dataset config\n type='RepeatDataset', # Repeated dataset for iter-based model\n times=1000, # Repeated times for RepeatDataset\n dataset=dict(\n type=train_dataset_type, # Type of dataset\n lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X2_sub', # Path for lq folder\n gt_folder='data/DIV2K/DIV2K_train_HR_sub', # Path for gt folder\n ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt', # Path for annotation file\n pipeline=train_pipeline, # See above for train_pipeline\n scale=scale)), # Scale factor for upsampling\n # val\n val_samples_per_gpu=1, # Batch size of a single GPU for validation\n val_workers_per_gpu=4, # Worker to pre-fetch data for each single GPU for validation\n val=dict(\n type=val_dataset_type, # Type of dataset\n lq_folder='data/val_set5/Set5_bicLRx2', # Path for lq folder\n gt_folder='data/val_set5/Set5_mod12', # Path for gt folder\n pipeline=test_pipeline, # See above for test_pipeline\n scale=scale, # Scale factor for upsampling\n filename_tmpl='{}'), # filename template\n # test\n test=dict(\n type=val_dataset_type, # Type of dataset\n lq_folder='data/val_set5/Set5_bicLRx2', # Path for lq folder\n gt_folder='data/val_set5/Set5_mod12', # Path for gt folder\n pipeline=test_pipeline, # See above for test_pipeline\n scale=scale, # Scale factor for upsampling\n filename_tmpl='{}')) # filename template\n```\n\n\n\n```python\ndataset_type = 'BasicImageDataset' # The type of dataset\ndata_root = 'data' # Root path of data\ntrain_dataloader = dict(\n batch_size=16,\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler\n dataset=dict( # Train dataset config\n type=dataset_type, # Type of dataset\n ann_file='meta_info_DIV2K800sub_GT.txt', # Path of annotation file\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # Root path of data\n data_prefix=dict( # Prefix of image path\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # Filename template\n pipeline=train_pipeline))\nval_dataloader = dict(\n batch_size=1,\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n drop_last=False, # Whether drop the last incomplete batch\n sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Validation dataset config\n type=dataset_type, # Type of dataset\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # Root path of data\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # Prefix of image path\n pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n```\n\n
\n" }, { "path": "docs/en/migration/distributed_train.md", "content": "# Migration of Distributed Training Settings\n\nWe have merged [MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x) into MMagic. Here is migration of Distributed Training Settings about MMGeneration.\n\nIn 0.x version, MMGeneration uses `DDPWrapper` and `DynamicRunner` to train static and dynamic model (e.g., PGGAN and StyleGANv2) respectively. In 1.x version, we use `MMSeparateDistributedDataParallel` provided by MMEngine to implement distributed training.\n\nThe configuration differences are shown below:\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Static Model in 0.x Version Static Model in 1.x Version
\n\n```python\n# Use DDPWrapper\nuse_ddp_wrapper = True\nfind_unused_parameters = False\n\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=False)\n```\n\n\n\n```python\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=False)\n```\n\n
\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n
Dynamic Model in 0.x Version Dynamic Model in 1.x Version
\n\n```python\nuse_ddp_wrapper = False\nfind_unused_parameters = False\n\n# Use DynamicRunner\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=True)\n```\n\n\n\n```python\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=True) # set `find_unused_parameters` for dynamic models\n```\n\n
\n" }, { "path": "docs/en/migration/eval_test.md", "content": "# Migration of Evaluation and Testing Settings\n\nWe update evaluation settings in MMagic 1.x. Important modifications are as following.\n\n- The evaluation field is split to `val_evaluator` and `test_evaluator`. The `interval` is moved to `train_cfg.val_interval`.\n- The metrics to evaluation are moved from `test_cfg` to `val_evaluator` and `test_evaluator`.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\ntrain_cfg = None # Training config\ntest_cfg = dict( # Test config\n metrics=['PSNR'], # Metrics used during testing\n crop_border=scale) # Crop border during evaluation\n\nevaluation = dict( # The config to build the evaluation hook\n interval=5000, # Evaluation interval\n save_image=True, # Save images during evaluation\n gpu_collect=True) # Use gpu collect\n```\n\n\n\n```python\nval_evaluator = [\n dict(type='PSNR', crop_border=scale), # The name of metrics to evaluate\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # Config of train loop type\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n```\n\n
\n\nWe have merged [MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x) into MMagic. Here is migration of Evaluation and Testing Settings about MMGeneration.\n\nThe evaluation field is splited to `val_evaluator` and `test_evaluator`. And it won't support `interval` and `save_best` arguments. The `interval` is moved to `train_cfg.val_interval`, see [the schedule settings](./schedule.md) and the `save_best` is moved to `default_hooks.checkpoint.save_best`.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x Version 1.x Version
\n\n```python\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=10000,\n metrics=[\n dict(\n type='FID',\n num_images=50000,\n bgr2rgb=True,\n inception_args=dict(type='StyleGAN')),\n dict(type='IS', num_images=50000)\n ],\n best_metric=['fid', 'is'],\n sample_kwargs=dict(sample_model='ema'))\n```\n\n\n\n```python\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(\n type='FID',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig')\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000)])\n# set best config\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n less_keys=['FID-Full-50k/fid'],\n greater_keys=['IS-50k/is'],\n save_optimizer=True,\n save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n rule=['less', 'greater']))\ntest_evaluator = val_evaluator\n```\n\n
\n\nTo evaluate and test the model correctly, we need to set specific loop in `val_cfg` and `test_cfg`.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Static Model in 0.x Version Static Model in 1.x Version
\n\n```python\ntotal_iters = 1000000\n\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=False,\n pass_training_status=True)\n```\n\n\n\n```python\ntrain_cfg = dict(\n by_epoch=False, # use iteration based training\n max_iters=1000000, # max training iteration\n val_begin=1,\n val_interval=10000) # evaluation interval\nval_cfg = dict(type='MultiValLoop') # specific loop in validation\ntest_cfg = dict(type='MultiTestLoop') # specific loop in testing\n```\n\n
\n" }, { "path": "docs/en/migration/models.md", "content": "# Migration of Model Settings\n\nWe update model settings in MMagic 1.x. Important modifications are as following.\n\n- Remove `pretrained` fields.\n- Add `train_cfg` and `test_cfg` fields in model settings.\n- Add `data_preprocessor` fields. Normalization and color space transforms operations are moved from datasets transforms pipelines to data_preprocessor. We will introduce data_preprocessor later.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\nmodel = dict(\n type='BasicRestorer', # Name of the model\n generator=dict( # Config of the generator\n type='EDSR', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pretrained=None,\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')) # Config for pixel loss model training and testing settings\n```\n\n\n\n```python\nmodel = dict(\n type='BaseEditModel', # Name of the model\n generator=dict( # Config of the generator\n type='EDSRNet', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss\n train_cfg=dict(), # Config of training model.\n test_cfg=dict(), # Config of testing model.\n data_preprocessor=dict( # The Config to build data preprocessor\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,\n 255.]))\n```\n\n
\n\nWe refactor models in MMagic 1.x. Important modifications are as following.\n\n- The `models` in MMagic 1.x is refactored to six parts: `archs`, `base_models`, `data_preprocessors`, `editors`, `diffusion_schedulers` and `losses`.\n- Add `data_preprocessor` module in `models`. Normalization and color space transforms operations are moved from datasets transforms pipelines to data_preprocessor. The data out from the data pipeline is transformed by this module and then fed into the model.\n\nMore details of models are shown in [model guides](../howto/models.md).\n" }, { "path": "docs/en/migration/optimizers.md", "content": "# Migration of Optimizers\n\nWe have merged [MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x) into MMagic. Here is migration of Optimizers about MMGeneration.\n\nIn version 0.x, MMGeneration uses PyTorch's native Optimizer, which only provides general parameter optimization.\nIn version 1.x, we use `OptimizerWrapper` provided by MMEngine.\n\nCompared to PyTorch's `Optimizer`, `OptimizerWrapper` supports the following features:\n\n- `OptimizerWrapper.update_params` implement `zero_grad`, `backward` and `step` in a single function.\n- Support gradient accumulation automatically.\n- Provide a context manager named `OptimizerWrapper.optim_context` to warp the forward process. `optim_context` can automatically call `torch.no_sync` according to current number of updating iteration. In AMP (auto mixed precision) training, `autocast` is called in `optim_context` as well.\n\nFor GAN models, generator and discriminator use different optimizer and training schedule.\nTo ensure that the GAN model's function signature of `train_step` is consistent with other models, we use `OptimWrapperDict`, inherited from `OptimizerWrapper`, to wrap the optimizer of the generator and discriminator.\nTo automate this process MMagic implement `MultiOptimWrapperContructor`.\nAnd you should specify this constructor in your config is you want to train GAN model.\n\nThe config for the 0.x and 1.x versions are shown below:\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x Version 1.x Version
\n\n```python\noptimizer = dict(\n generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),\n discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))\n```\n\n\n\n```python\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n```\n\n
\n\n> Note that, in the 1.x, MMGeneration uses `OptimWrapper` to realize gradient accumulation. This make the config of `discriminator_steps` (training trick for updating the generator once after multiple updates of the discriminator) and gradient accumulation different between 0.x and 1.x version.\n\n- In 0.x version, we use `disc_steps`, `gen_steps` and `batch_accumulation_steps` in configs. `disc_steps` and `batch_accumulation_steps` are counted by the number of calls of `train_step` (is also the number of data reads from the dataloader). Therefore the number of consecutive updates of the discriminator is `disc_steps // batch_accumulation_steps`. And for generators, `gen_steps` is the number of times the generator actually updates continuously.\n- In 1.x version, we use `discriminator_steps`, `generator_steps` and `accumulative_counts` in configs. `discriminator_steps` and `generator_steps` are the number of consecutive updates to itself before updating other modules.\n\nTake config of BigGAN-128 as example.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x Version 1.x Version
\n\n```python\nmodel = dict(\n type='BasiccGAN',\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True),\n gan_loss=dict(type='GANLoss', gan_type='hinge'))\n\n# continuous update discriminator for `disc_steps // batch_accumulation_steps = 8 // 8 = 1` times\n# continuous update generator for `gen_steps = 1` times\n# generators and discriminators perform `batch_accumulation_steps = 8` times gradient accumulations before each update\ntrain_cfg = dict(\n disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)\n```\n\n\n\n```python\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True),\n # continuous update discriminator for `discriminator_steps = 1` times\n # continuous update generator for `generator_steps = 1` times\n generator_steps=1,\n discriminator_steps=1)\n\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n # generator perform `accumulative_counts = 8` times gradient accumulations before each update\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n # discriminator perform `accumulative_counts = 8` times gradient accumulations before each update\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n```\n\n
\n" }, { "path": "docs/en/migration/overview.md", "content": "# Overview\n\nThis section introduces the following contents in terms of migration from MMEditing 0.x\n\n- [Overview](#overview)\n - [New dependencies](#new-dependencies)\n - [Overall structures](#overall-structures)\n - [Other config settings](#other-config-settings)\n\n## New dependencies\n\nMMagic 1.x depends on some new packages, you can prepare a new clean environment and install it again according to the [install tutorial](../get_started/install.md).\n\n## Overall structures\n\nWe refactor overall structures in MMagic 1.x as follows.\n\n- The `core` in the old versions of MMEdit is split into `engine`, `evaluation`, `structures`, and `visualization`\n- The `pipelines` of `datasets` in the old versions of MMEdit is refactored to `transforms`\n- The `models` in MMagic 1.x is refactored to six parts: `archs`, `base_models`, `data_preprocessors`, `editors`, `diffusion_schedulers`, and `losses`.\n\n## Other config settings\n\nWe rename the config file to the new template: `{model_settings}_{module_setting}_{training_setting}_{datasets_info}`.\n\nMore details of config are shown in [config guides](../user_guides/config.md).\n" }, { "path": "docs/en/migration/runtime.md", "content": "# Migration of Runtime Settings\n\nWe update runtime settings in MMagic 1.x. Important modifications are as following.\n\n- The `checkpoint_config` is moved to `default_hooks.checkpoint` and the `log_config` is moved to `default_hooks.logger`. And we move many hooks settings from the script code to the `default_hooks` field in the runtime configuration.\n- The `resume_from` is removed. And we use `resume` to replace it.\n - If resume=True and load_from is not None, resume training from the checkpoint in load_from.\n - If resume=True and load_from is None, try to resume from the latest checkpoint in the work directory.\n - If resume=False and load_from is not None, only load the checkpoint, not resume training.\n - If resume=False and load_from is None, do not load nor resume.\n- The `dist_params` field is a sub field of `env_cfg` now. And there are some new configurations in the `env_cfg`.\n- The `workflow` related functionalities are removed.\n- New field `visualizer`: The visualizer is a new design. We use a visualizer instance in the runner to handle results & log visualization and save to different backends, like Local, TensorBoard and Wandb.\n- New field `default_scope`: The start point to search module for all registries.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\ncheckpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.\n interval=5000, # The save interval is 5000 iterations\n save_optimizer=True, # Also save optimizers\n by_epoch=False) # Count by iterations\nlog_config = dict( # Config to register logger hook\n interval=100, # Interval to print the log\n hooks=[\n dict(type='TextLoggerHook', by_epoch=False), # The logger used to record the training process\n dict(type='TensorboardLoggerHook'), # The Tensorboard logger is also supported\n ])\nvisual_config = None # Visual config, we do not use it.\n# runtime settings\ndist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set\nlog_level = 'INFO' # The level of logging\nload_from = None # load models as a pre-trained model from a given path. This will not resume training\nresume_from = None # Resume checkpoints from a given path, the training will be resumed from the iteration when the checkpoint's is saved\nworkflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. Keep this unchanged when training current matting models\n```\n\n\n\n```python\ndefault_hooks = dict( # Used to build default hooks\n checkpoint=dict( # Config to set the checkpoint hook\n type='CheckpointHook',\n interval=5000, # The save interval is 5000 iterations\n save_optimizer=True,\n by_epoch=False, # Count by iterations\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # Config to register logger hook\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\ndefault_scope = 'mmedit' # Used to set registries location\nenv_cfg = dict( # Parameters to setup distributed training, the port can also be set\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # Used to build log processor\nload_from = None # load models as a pre-trained model from a given path. This will not resume training.\nresume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.\n```\n\n
\n" }, { "path": "docs/en/migration/schedule.md", "content": "# Migration of Schedule Settings\n\nWe update schedule settings in MMagic 1.x. Important modifications are as following.\n\n- Now we use `optim_wrapper` field to specify all configuration about the optimization process. And the `optimizer` is a sub field of `optim_wrapper` now.\n- The `lr_config` field is removed and we use new `param_scheduler` to replace it.\n- The `total_iters` field is moved to `train_cfg` as `max_iters`, `val_cfg` and `test_cfg`, which configure the loop in training, validation and test.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\noptimizers = dict(generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch\ntotal_iters = 300000 # Total training iters\nlr_config = dict( # Learning rate scheduler config used to register LrUpdater hook\n policy='Step', by_epoch=False, step=[200000], gamma=0.5) # The policy of scheduler\n```\n\n\n\n```python\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4),\n )\n) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.\nparam_scheduler = dict( # Config of learning policy\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5) # The policy of scheduler\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # Config of train loop type\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n```\n\n
\n\n> More details of schedule settings are shown in [MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/en/migration/param_scheduler.md).\n" }, { "path": "docs/en/migration/visualization.md", "content": "# Migration of Visualization\n\nIn 0.x, MMEditing use `VisualizationHook` to visualize results in training process. In 1.x version, we unify the function of those hooks into `BasicVisualizationHook` / `VisualizationHook`. Additionally, follow the design of MMEngine, we implement `ConcatImageVisualizer` / `Visualizer` and a group of `VisBackend` to draw and save the visualization results.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x version 1.x Version
\n\n```python\nvisual_config = dict(\n type='VisualizationHook',\n output_dir='visual',\n interval=1000,\n res_name_list=['gt_img', 'masked_img', 'fake_res', 'fake_img'],\n)\n```\n\n\n\n```python\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n
\n\nTo learn more about the visualization function, please refers to [this tutorial](../user_guides/visualization.md).\n" }, { "path": "docs/en/switch_language.md", "content": "# English\n\n# 简体中文\n" }, { "path": "docs/en/user_guides/config.md", "content": "# Tutorial 1: Learn about Configs in MMagic\n\nWe incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.\nIf you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.\n\nYou can learn about the usage of our config system according to the following tutorials.\n\n- [Tutorial 1: Learn about Configs in MMagic](#tutorial-1-learn-about-configs-in-mmagic)\n - [Modify config through script arguments](#modify-config-through-script-arguments)\n - [Config file structure](#config-file-structure)\n - [Config name style](#config-name-style)\n - [An example of EDSR](#an-example-of-edsr)\n - [Model config](#model-config)\n - [Data config](#data-config)\n - [Data pipeline](#data-pipeline)\n - [Dataloader](#dataloader)\n - [Evaluation config](#evaluation-config)\n - [Training and testing config](#training-and-testing-config)\n - [Optimization config](#optimization-config)\n - [Hook config](#hook-config)\n - [Runtime config](#runtime-config)\n - [An example of StyleGAN2](#an-example-of-stylegan2)\n - [Model config](#model-config-1)\n - [Dataset and evaluator config](#dataset-and-evaluator-config)\n - [Training and testing config](#training-and-testing-config-1)\n - [Optimization config](#optimization-config-1)\n - [Hook config](#hook-config-1)\n - [Runtime config](#runtime-config-1)\n - [Other examples](#other-examples)\n - [An example of config system for inpainting](#an-example-of-config-system-for-inpainting)\n - [An example of config system for matting](#an-example-of-config-system-for-matting)\n - [An example of config system for restoration](#an-example-of-config-system-for-restoration)\n\n## Modify config through script arguments\n\nWhen submitting jobs using `tools/train.py` or `tools/test.py`, you may specify `--cfg-options` to in-place modify the config.\n\n- Update config keys of dict chains.\n\n The config options can be specified following the order of the dict keys in the original config.\n For example, `--cfg-options test_cfg.use_ema=False` changes the default sampling model to the original generator,\n and `--cfg-options train_dataloader.batch_size=8` changes the batch size of train dataloader.\n\n- Update keys inside a list of configs.\n\n Some config dicts are composed as a list in your config.\n For example, the training pipeline `train_dataloader.dataset.pipeline` is normally a list\n e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline,\n you may specify `--cfg-options train_dataloader.dataset.pipeline.0.type=LoadImageFromWebcam`.\n The training pipeline `train_pipeline` is normally a list\n e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadMask'` in the pipeline,\n you may specify `--cfg-options train_pipeline.0.type=LoadMask`.\n\n- Update values of list/tuples.\n\n If the value to be updated is a list or a tuple. You can set `--cfg-options key=\"[a,b]\"` or `--cfg-options key=a,b`. It also allows nested list/tuple values, e.g., `--cfg-options key=\"[(a,b),(c,d)]\"`. Note that the quotation mark \" is necessary to support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.\n\n## Config file structure\n\nThere are 3 basic component types under `config/_base_`: datasets, models and default_runtime.\nMany methods could be easily constructed with one of each like AOT-GAN, EDVR, GLEAN, StyleGAN2, CycleGAN, SinGAN, etc.\nConfigs consisting of components from `_base_` are called _primitive_.\n\nFor all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.\n\nFor easy understanding, we recommend contributors to inherit from existing methods.\nFor example, if some modification is made base on BasicVSR,\nuser may first inherit the basic BasicVSR structure by specifying `_base_ = ../basicvsr/basicvsr_reds4.py`,\nthen modify the necessary fields in the config files.\nIf some modification is made base on StyleGAN2,\nuser may first inherit the basic StyleGAN2 structure by specifying `_base_ = ../styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py`,\nthen modify the necessary fields in the config files.\n\nIf you are building an entirely new method that does not share the structure with any of the existing methods,\nyou may create a folder `xxx` under `configs`,\n\nPlease refer to [MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md) for detailed documentation.\n\n## Config name style\n\n```\n{model}_[module setting]_{training schedule}_{dataset}\n```\n\n`{xxx}` is required field and `[yyy]` is optional.\n\n- `{model}`: model type like `stylegan`, `dcgan`, `basicvsr`, `dim`, etc.\n Settings referred in the original paper are included in this field as well (e.g., `Stylegan2-config-f`, `edvrm` of `edvrm_8xb4-600k_reds`.)\n- `[module setting]`: specific setting for some modules, including Encoder, Decoder, Generator, Discriminator, Normalization, loss, Activation, etc. E.g. `c64n7` of `basicvsr-pp_c64n7_8xb1-600k_reds4`, learning rate `Glr4e-4_Dlr1e-4` for dcgan, `gamma32.8` for stylegan3, `woReLUInplace` in sagan. In this section, information from different submodules (e.g., generator and discriminator) are connected with `_`.\n- `{training_scheduler}`: specific setting for training, including batch_size, schedule, etc. For example, learning rate (e.g., `lr1e-3`), number of gpu and batch size is used (e.g., `8xb32`), and total iterations (e.g., `160kiter`) or number of images shown in the discriminator (e.g., `12Mimgs`).\n- `{dataset}`: dataset name and data size info like `celeba-256x256` of `deepfillv1_4xb4_celeba-256x256`, `reds4` of `basicvsr_2xb4_reds4`, `ffhq`, `lsun-car`, `celeba-hq`.\n\n## An example of EDSR\n\nTo help the users have a basic idea of a complete config,\nwe make a brief comments on the [config of the EDSR model](https://github.com/open-mmlab/mmagic/blob/main/configs/edsr/edsr_x2c64b16_g1_300k_div2k.py) we implemented as the following.\nFor more detailed usage and the corresponding alternative for each modules,\nplease refer to the API documentation and the [tutorial in MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md).\n\n### Model config\n\nIn MMagic's config, we use model fields to set up a model.\n\n```python\nmodel = dict(\n type='BaseEditModel', # Name of the model\n generator=dict( # Config of the generator\n type='EDSRNet', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss\n train_cfg=dict(), # Config of training model.\n test_cfg=dict(), # Config of testing model.\n data_preprocessor=dict( # The Config to build data preprocessor\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,\n 255.]))\n```\n\n### Data config\n\n[Dataloaders](https://pytorch.org/docs/stable/data.html?highlight=data%20loader#torch.utils.data.DataLoader) are required for the training, validation, and testing of the [runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html).\nDataset and data pipeline need to be set to build the dataloader. Due to the complexity of this part, we use intermediate variables to simplify the writing of dataloader configs.\n\n#### Data pipeline\n\n```python\ntrain_pipeline = [ # Training data processing pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find the corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find the corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='SetValues', dictionary=dict(scale=scale)), # Set value to destination keys\n dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='horizontal'), # Flip direction\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip direction\n dict(type='RandomTransposeHW', # Random transpose h and w for images\n keys=['lq', 'gt'], # Images to be transposed\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='PackInputs') # The config of collecting data from the current pipeline\n]\ntest_pipeline = [ # Test pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='PackInputs') # The config of collecting data from the current pipeline\n]\n```\n\n#### Dataloader\n\n```python\ndataset_type = 'BasicImageDataset' # The type of dataset\ndata_root = 'data' # Root path of data\ntrain_dataloader = dict(\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler\n dataset=dict( # Train dataset config\n type=dataset_type, # Type of dataset\n ann_file='meta_info_DIV2K800sub_GT.txt', # Path of annotation file\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # Root path of data\n data_prefix=dict( # Prefix of image path\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # Filename template\n pipeline=train_pipeline))\nval_dataloader = dict(\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n drop_last=False, # Whether drop the last incomplete batch\n sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Validation dataset config\n type=dataset_type, # Type of dataset\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # Root path of data\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # Prefix of image path\n pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n```\n\n### Evaluation config\n\n[Evaluators](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html) are used to compute the metrics of the trained model on the validation and testing datasets.\nThe config of evaluators consists of one or a list of metric configs:\n\n```python\nval_evaluator = [\n dict(type='MAE'), # The name of metrics to evaluate\n dict(type='PSNR', crop_border=scale), # The name of metrics to evaluate\n dict(type='SSIM', crop_border=scale), # The name of metrics to evaluate\n]\ntest_evaluator = val_evaluator # The config for testing evaluator\n```\n\n### Training and testing config\n\nMMEngine's runner uses Loop to control the training, validation, and testing processes.\nUsers can set the maximum training iteration and validation intervals with these fields.\n\n```python\ntrain_cfg = dict(\n type='IterBasedTrainLoop', # The name of train loop type\n max_iters=300000, # The number of total iterations\n val_interval=5000, # The number of validation interval iterations\n)\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n```\n\n### Optimization config\n\n`optim_wrapper` is the field to configure optimization related settings.\nThe optimizer wrapper not only provides the functions of the optimizer, but also supports functions such as gradient clipping, mixed precision training, etc. Find more in [optimizer wrapper tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html).\n\n```python\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n )\n) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.\n```\n\n`param_scheduler` is a field that configures methods of adjusting optimization hyper-parameters such as learning rate and momentum.\nUsers can combine multiple schedulers to create a desired parameter adjustment strategy.\nFind more in [parameter scheduler tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html).\n\n```python\nparam_scheduler = dict( # Config of learning policy\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n```\n\n### Hook config\n\nUsers can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.\n\n`default_hooks` is a dict of hook configs. `default_hooks` are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.\n\n```python\ndefault_hooks = dict( # Used to build default hooks\n checkpoint=dict( # Config to set the checkpoint hook\n type='CheckpointHook',\n interval=5000, # The save interval is 5000 iterations\n save_optimizer=True,\n by_epoch=False, # Count by iterations\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # Config to register logger hook\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n```\n\n`custom_hooks` is a list of hook configs. Users can develop there own hooks and insert them in this field.\n\n```python\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # Config of visualization hook\n```\n\n### Runtime config\n\n```python\ndefault_scope = 'mmagic' # Used to set registries location\nenv_cfg = dict( # Parameters to setup distributed training, the port can also be set\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # Used to build log processor\nload_from = None # load models as a pre-trained model from a given path. This will not resume training.\nresume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.\n```\n\n## An example of StyleGAN2\n\nTaking [Stylegan2 at 1024x1024 scale](https://github.com/open-mmlab/mmagic/blob/main/configs//styleganv2/stylegan2_c2_8xb4-fp16-global-800kiters_quicktest-ffhq-256x256.py) as an example,\nwe introduce each field in the config according to different function modules.\n\n### Model config\n\nIn addition to neural network components such as generator, discriminator etc, it also requires `data_preprocessor`, `loss_config`, and some of them contains `ema_config`.\n`data_preprocessor` is responsible for processing a batch of data output by dataloader.\n`loss_config` is responsible for weight of loss terms.\n`ema_config` is responsible for exponential moving average (EMA) operation for generator.\n\n```python\nmodel = dict(\n type='StyleGAN2', # The name of the model\n data_preprocessor=dict(type='DataPreprocessor'), # The config of data preprocessor, usually includs image normalization and padding\n generator=dict( # The config for generator\n type='StyleGANv2Generator', # The name of the generator\n out_size=1024, # The output resolution of the generator\n style_channels=512), # The number of style channels of the generator\n discriminator=dict( # The config for discriminator\n type='StyleGAN2Discriminator', # The name of the discriminator\n in_size=1024), # The input resolution of the discriminator\n ema_config=dict( # The config for EMA\n type='ExponentialMovingAverage', # Specific the type of Average model\n interval=1, # The interval of EMA operation\n momentum=0.9977843871238888), # The momentum of EMA operation\n loss_config=dict( # The config for loss terms\n r1_loss_weight=80.0, # The weight for r1 gradient penalty\n r1_interval=16, # The interval of r1 gradient penalty\n norm_mode='HWC', # The normalization mode for r1 gradient penalty\n g_reg_interval=4, # The interval for generator's regularization\n g_reg_weight=8.0, # The weight for generator's regularization\n pl_batch_shrink=2)) # The factor of shrinking the batch size in path length regularization\n```\n\n### Dataset and evaluator config\n\n[Dataloaders](https://pytorch.org/docs/stable/data.html?highlight=data%20loader#torch.utils.data.DataLoader) are required for the training, validation, and testing of the [runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html).\nDataset and data pipeline need to be set to build the dataloader. Due to the complexity of this part, we use intermediate variables to simplify the writing of dataloader configs.\n\n```python\ndataset_type = 'BasicImageDataset' # Dataset type, this will be used to define the dataset\ndata_root = './data/ffhq/' # Root path of data\n\ntrain_pipeline = [ # Training data process pipeline\n dict(type='LoadImageFromFile', key='img'), # First pipeline to load images from file path\n dict(type='Flip', keys=['img'], direction='horizontal'), # Argumentation pipeline that flip the images\n dict(type='PackInputs', keys=['img']) # The last pipeline that formats the annotation data (if have) and decides which keys in the data should be packed into data_samples\n]\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img'), # First pipeline to load images from file path\n dict(type='PackInputs', keys=['img']) # The last pipeline that formats the annotation data (if have) and decides which keys in the data should be packed into data_samples\n]\ntrain_dataloader = dict( # The config of train dataloader\n batch_size=4, # Batch size of a single GPU\n num_workers=8, # Worker to pre-fetch data for each single GPU\n persistent_workers=True, # If ``True``, the dataloader will not shutdown the worker processes after an epoch end, which can accelerate training speed.\n sampler=dict( # The config of training data sampler\n type='InfiniteSampler', # InfiniteSampler for iteratiion-based training. Refers to https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L107\n shuffle=True), # Whether randomly shuffle the training data\n dataset=dict( # The config of the training dataset\n type=dataset_type,\n data_root=data_root,\n pipeline=train_pipeline))\nval_dataloader = dict( # The config of validation dataloader\n batch_size=4, # Batch size of a single GPU\n num_workers=8, # Worker to pre-fetch data for each single GPU\n dataset=dict( # The config of the validation dataset\n type=dataset_type,\n data_root=data_root,\n pipeline=val_pipeline),\n sampler=dict( # The config of validatioin data sampler\n type='DefaultSampler', # DefaultSampler which supports both distributed and non-distributed training. Refer to https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L14\n shuffle=False), # Whether randomly shuffle the validation data\n persistent_workers=True)\ntest_dataloader = val_dataloader # The config of the testing dataloader\n```\n\n[Evaluators](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html) are used to compute the metrics of the trained model on the validation and testing datasets.\nThe config of evaluators consists of one or a list of metric configs:\n\n```python\nval_evaluator = dict( # The config for validation evaluator\n type='Evaluator', # The type of evaluation\n metrics=[ # The config for metrics\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n ])\ntest_evaluator = val_evaluator # The config for testing evaluator\n```\n\n### Training and testing config\n\nMMEngine's runner uses Loop to control the training, validation, and testing processes.\nUsers can set the maximum training iteration and validation intervals with these fields.\n\n```python\ntrain_cfg = dict( # The config for training\n by_epoch=False, # Set `by_epoch` as False to use iteration-based training\n val_begin=1, # Which iteration to start the validation\n val_interval=10000, # Validation intervals\n max_iters=800002) # Maximum training iterations\nval_cfg = dict(type='MultiValLoop') # The validation loop type\ntest_cfg = dict(type='MultiTestLoop') # The testing loop type\n```\n\n### Optimization config\n\n`optim_wrapper` is the field to configure optimization related settings.\nThe optimizer wrapper not only provides the functions of the optimizer, but also supports functions such as gradient clipping, mixed precision training, etc. Find more in [optimizer wrapper tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html).\n\n```python\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n optimizer=dict(type='Adam', lr=0.0016, betas=(0, 0.9919919678228657))),\n discriminator=dict(\n optimizer=dict(\n type='Adam',\n lr=0.0018823529411764706,\n betas=(0, 0.9905854573074332))))\n```\n\n`param_scheduler` is a field that configures methods of adjusting optimization hyperparameters such as learning rate and momentum.\nUsers can combine multiple schedulers to create a desired parameter adjustment strategy.\nFind more in [parameter scheduler tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html).\nSince StyleGAN2 do not use parameter scheduler, we use config in [CycleGAN](https://github.com/open-mmlab/mmagic/blob/main/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) as an example:\n\n```python\n# parameter scheduler in CycleGAN config\nparam_scheduler = dict(\n type='LinearLrInterval', # The type of scheduler\n interval=400, # The interval to update the learning rate\n by_epoch=False, # The scheduler is called by iteration\n start_factor=0.0002, # The number we multiply parameter value in the first iteration\n end_factor=0, # The number we multiply parameter value at the end of linear changing process.\n begin=40000, # The start iteration of the scheduler\n end=80000) # The end iteration of the scheduler\n```\n\n### Hook config\n\nUsers can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.\n\n`default_hooks` is a dict of hook configs. `default_hooks` are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.\n\n```python\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n less_keys=['FID-Full-50k/fid'],\n greater_keys=['IS-50k/is'],\n save_optimizer=True,\n save_best='FID-Full-50k/fid'))\n```\n\n`custom_hooks` is a list of hook configs. Users can develop there own hooks and insert them in this field.\n\n```python\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n```\n\n### Runtime config\n\n```python\ndefault_scope = 'mmagic' # The default registry scope to find modules. Refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html\n\n# config for environment\nenv_cfg = dict(\n cudnn_benchmark=True, # whether to enable cudnn benchmark.\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), # set multi process parameters.\n dist_cfg=dict(backend='nccl'), # set distributed parameters.\n)\n\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(\n type='LogProcessor', # log processor to process runtime logs\n by_epoch=False) # print log by iteration\nload_from = None # load model checkpoint as a pre-trained model for a given path\nresume = False # Whether to resume from the checkpoint define in `load_from`. If `load_from` is `None`, it will resume the latest checkpoint in `work_dir`\n```\n\n## Other examples\n\n### An example of config system for inpainting\n\nTo help the users have a basic idea of a complete config and the modules in a inpainting system,\nwe make brief comments on the config of Global&Local as the following.\nFor more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.\n\n```python\nmodel = dict(\n type='GLInpaintor', # The name of inpaintor\n data_preprocessor=dict(\n type='DataPreprocessor', # The name of data preprocessor\n mean=[127.5], # Mean value used in data normalization\n std=[127.5], # Std value used in data normalization\n ),\n encdec=dict(\n type='GLEncoderDecoder', # The name of encoder-decoder\n encoder=dict(type='GLEncoder', norm_cfg=dict(type='SyncBN')), # The config of encoder\n decoder=dict(type='GLDecoder', norm_cfg=dict(type='SyncBN')), # The config of decoder\n dilation_neck=dict(\n type='GLDilationNeck', norm_cfg=dict(type='SyncBN'))), # The config of dilation neck\n disc=dict(\n type='GLDiscs', # The name of discriminator\n global_disc_cfg=dict(\n in_channels=3, # The input channel of discriminator\n max_channels=512, # The maximum middle channel in discriminator\n fc_in_channels=512 * 4 * 4, # The input channel of last fc layer\n fc_out_channels=1024, # The output channel of last fc channel\n num_convs=6, # The number of convs used in discriminator\n norm_cfg=dict(type='SyncBN') # The config of norm layer\n ),\n local_disc_cfg=dict(\n in_channels=3, # The input channel of discriminator\n max_channels=512, # The maximum middle channel in discriminator\n fc_in_channels=512 * 4 * 4, # The input channel of last fc layer\n fc_out_channels=1024, # The output channel of last fc channel\n num_convs=5, # The number of convs used in discriminator\n norm_cfg=dict(type='SyncBN') # The config of norm layer\n ),\n ),\n loss_gan=dict(\n type='GANLoss', # The name of GAN loss\n gan_type='vanilla', # The type of GAN loss\n loss_weight=0.001 # The weight of GAN loss\n ),\n loss_l1_hole=dict(\n type='L1Loss', # The type of l1 loss\n loss_weight=1.0 # The weight of l1 loss\n ))\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop',# The name of train loop type\n max_iters=500002, # The number of total iterations\n val_interval=50000, # The number of validation interval iterations\n)\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n\nval_evaluator = [\n dict(type='MAE', mask_key='mask', scaling=100), # The name of metrics to evaluate\n dict(type='PSNR'), # The name of metrics to evaluate\n dict(type='SSIM'), # The name of metrics to evaluate\n]\ntest_evaluator = val_evaluator\n\ninput_shape = (256, 256) # The shape of input image\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'), # The config of loading image\n dict(\n type='LoadMask', # The type of loading mask pipeline\n mask_mode='bbox', # The type of mask\n mask_config=dict(\n max_bbox_shape=(128, 128), # The shape of bbox\n max_bbox_delta=40, # The changing delta of bbox height and width\n min_margin=20, # The minimum margin from bbox to the image border\n img_shape=input_shape)), # The input image shape\n dict(\n type='Crop', # The type of crop pipeline\n keys=['gt'], # The keys of images to be cropped\n crop_size=(384, 384), # The size of cropped patch\n random_crop=True, # Whether to use random crop\n ),\n dict(\n type='Resize', # The type of resizing pipeline\n keys=['gt'], # They keys of images to be resized\n scale=input_shape, # The scale of resizing function\n keep_ratio=False, # Whether to keep ratio during resizing\n ),\n dict(\n type='Normalize', # The type of normalizing pipeline\n keys=['gt_img'], # The keys of images to be normed\n mean=[127.5] * 3, # Mean value used in normalization\n std=[127.5] * 3, # Std value used in normalization\n to_rgb=False), # Whether to transfer image channels to rgb\n dict(type='GetMaskedImage'), # The config of getting masked image pipeline\n dict(type='PackInputs'), # The config of collecting data from the current pipeline\n]\n\ntest_pipeline = train_pipeline # Constructing testing/validation pipeline\n\ndataset_type = 'BasicImageDataset' # The type of dataset\ndata_root = 'data/places' # Root path of data\n\ntrain_dataloader = dict(\n batch_size=12, # Batch size of a single GPU\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n sampler=dict(type='InfiniteSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Train dataset config\n type=dataset_type, # Type of dataset\n data_root=data_root, # Root path of data\n data_prefix=dict(gt='data_large'), # Prefix of image path\n ann_file='meta/places365_train_challenge.txt', # Path of annotation file\n test_mode=False,\n pipeline=train_pipeline,\n ))\n\nval_dataloader = dict(\n batch_size=1, # Batch size of a single GPU\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n drop_last=False, # Whether drop the last incomplete batch\n sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Validation dataset config\n type=dataset_type, # Type of dataset\n data_root=data_root, # Root path of data\n data_prefix=dict(gt='val_large'), # Prefix of image path\n ann_file='meta/places365_val.txt', # Path of annotation file\n test_mode=True,\n pipeline=test_pipeline,\n ))\n\ntest_dataloader = val_dataloader\n\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel') # The name of model wrapper\n\noptim_wrapper = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)),\n disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)))\n\ndefault_scope = 'mmagic' # Used to set registries location\nsave_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments\nexp_name = 'gl_places' # The experiment name\n\ndefault_hooks = dict( # Used to build default hooks\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # Config to register logger hook\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict( # Config to set the checkpoint hook\n type='CheckpointHook',\n interval=50000,\n by_epoch=False,\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict( # Parameters to setup distributed training, the port can also be set\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type='LocalVisBackend')] # The name of visualization backend\nvisualizer = dict( # Config used to build visualizer\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # Used to build custom hooks\n\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(type='LogProcessor', by_epoch=False) # Used to build log processor\n\nload_from = None # load models as a pre-trained model from a given path. This will not resume training.\nresume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.\n\nfind_unused_parameters = False # Whether to set find unused parameters in ddp\n```\n\n### An example of config system for matting\n\nTo help the users have a basic idea of a complete config, we make a brief comments on the config of the original DIM model we implemented as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.\n\n```python\n# model settings\nmodel = dict(\n type='DIM', # The name of model (we call mattor).\n data_preprocessor=dict( # The Config to build data preprocessor\n type='DataPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n bgr_to_rgb=True,\n proc_inputs='normalize',\n proc_trimap='rescale_to_zero_one',\n proc_gt='rescale_to_zero_one',\n ),\n backbone=dict( # The config of the backbone.\n type='SimpleEncoderDecoder', # The type of the backbone.\n encoder=dict( # The config of the encoder.\n type='VGG16'), # The type of the encoder.\n decoder=dict( # The config of the decoder.\n type='PlainDecoder')), # The type of the decoder.\n pretrained='./weights/vgg_state_dict.pth', # The pretrained weight of the encoder to be loaded.\n loss_alpha=dict( # The config of the alpha loss.\n type='CharbonnierLoss', # The type of the loss for predicted alpha matte.\n loss_weight=0.5), # The weight of the alpha loss.\n loss_comp=dict( # The config of the composition loss.\n type='CharbonnierCompLoss', # The type of the composition loss.\n loss_weight=0.5), # The weight of the composition loss.\n train_cfg=dict( # Config of training DIM model.\n train_backbone=True, # In DIM stage1, backbone is trained.\n train_refiner=False), # In DIM stage1, refiner is not trained.\n test_cfg=dict( # Config of testing DIM model.\n refine=False, # Whether use refiner output as output, in stage1, we don't use it.\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ),\n)\n\n# data settings\ndataset_type = 'AdobeComp1kDataset' # Dataset type, this will be used to define the dataset.\ndata_root = 'data/adobe_composition-1k' # Root path of data.\n\ntrain_pipeline = [ # Training data processing pipeline.\n dict(\n type='LoadImageFromFile', # Load alpha matte from file.\n key='alpha', # Key of alpha matte in annotation file. The pipeline will read alpha matte from path `alpha_path`.\n color_type='grayscale'), # Load as grayscale image which has shape (height, width).\n dict(\n type='LoadImageFromFile', # Load image from file.\n key='fg'), # Key of image to load. The pipeline will read fg from path `fg_path`.\n dict(\n type='LoadImageFromFile', # Load image from file.\n key='bg'), # Key of image to load. The pipeline will read bg from path `bg_path`.\n dict(\n type='LoadImageFromFile', # Load image from file.\n key='merged'), # Key of image to load. The pipeline will read merged from path `merged_path`.\n dict(\n type='CropAroundUnknown', # Crop images around unknown area (semi-transparent area).\n keys=['alpha', 'merged', 'fg', 'bg'], # Images to crop.\n crop_sizes=[320, 480, 640]), # Candidate crop size.\n dict(\n type='Flip', # Augmentation pipeline that flips the images.\n keys=['alpha', 'merged', 'fg', 'bg']), # Images to be flipped.\n dict(\n type='Resize', # Augmentation pipeline that resizes the images.\n keys=['alpha', 'merged', 'fg', 'bg'], # Images to be resized.\n scale=(320, 320), # Target size.\n keep_ratio=False), # Whether to keep the ratio between height and width.\n dict(\n type='GenerateTrimap', # Generate trimap from alpha matte.\n kernel_size=(1, 30)), # Kernel size range of the erode/dilate kernel.\n dict(type='PackInputs'), # The config of collecting data from the current pipeline\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile', # Load alpha matte.\n key='alpha', # Key of alpha matte in annotation file. The pipeline will read alpha matte from path `alpha_path`.\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile', # Load image from file\n key='trimap', # Key of image to load. The pipeline will read trimap from path `trimap_path`.\n color_type='grayscale', # Load as grayscale image which has shape (height, width).\n save_original_img=True), # Save a copy of trimap for calculating metrics. It will be saved with key `ori_trimap`\n dict(\n type='LoadImageFromFile', # Load image from file\n key='merged'), # Key of image to load. The pipeline will read merged from path `merged_path`.\n dict(type='PackInputs'), # The config of collecting data from the current pipeline\n]\n\ntrain_dataloader = dict(\n batch_size=1, # Batch size of a single GPU\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler\n dataset=dict( # Train dataset config\n type=dataset_type, # Type of dataset\n data_root=data_root, # Root path of data\n ann_file='training_list.json', # Path of annotation file\n test_mode=False,\n pipeline=train_pipeline,\n ))\n\nval_dataloader = dict(\n batch_size=1, # Batch size of a single GPU\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n drop_last=False, # Whether drop the last incomplete batch\n sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Validation dataset config\n type=dataset_type, # Type of dataset\n data_root=data_root, # Root path of data\n ann_file='test_list.json', # Path of annotation file\n test_mode=True,\n pipeline=test_pipeline,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='SAD'), # The name of metrics to evaluate\n dict(type='MattingMSE'), # The name of metrics to evaluate\n dict(type='GradientError'), # The name of metrics to evaluate\n dict(type='ConnectivityError'), # The name of metrics to evaluate\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', # The name of train loop type\n max_iters=1_000_000, # The number of total iterations\n val_interval=40000, # The number of validation interval iterations\n)\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n\n# optimizer\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n )\n) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.\n\ndefault_scope = 'mmagic' # Used to set registries location\nsave_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments.\n\ndefault_hooks = dict( # Used to build default hooks\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # Config to register logger hook\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict( # Config to set the checkpoint hook\n type='CheckpointHook',\n interval=40000, # The save interval is 40000 iterations.\n by_epoch=False, # Count by iterations.\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict( # Parameters to setup distributed training, the port can also be set\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(type='LogProcessor', by_epoch=False) # Used to build log processor\n\nload_from = None # load models as a pre-trained model from a given path. This will not resume training.\nresume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.\n```\n\n### An example of config system for restoration\n\nTo help the users have a basic idea of a complete config, we make a brief comments on the config of the EDSR model we implemented as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.\n\n```python\nexp_name = 'edsr_x2c64b16_1x16_300k_div2k' # The experiment name\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nload_from = None # based on pre-trained x2 model\n\nscale = 2 # Scale factor for upsampling\n# model settings\nmodel = dict(\n type='BaseEditModel', # Name of the model\n generator=dict( # Config of the generator\n type='EDSRNet', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss\n train_cfg=dict(), # Config of training model.\n test_cfg=dict(), # Config of testing model.\n data_preprocessor=dict( # The Config to build data preprocessor\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,\n 255.]))\n\ntrain_pipeline = [ # Training data processing pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='SetValues', dictionary=dict(scale=scale)), # Set value to destination keys\n dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='horizontal'), # Flip direction\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # Images to be flipped\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip direction\n dict(type='RandomTransposeHW', # Random transpose h and w for images\n keys=['lq', 'gt'], # Images to be transposed\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='PackInputs') # The config of collecting data from the current pipeline\n]\ntest_pipeline = [ # Test pipeline\n dict(type='LoadImageFromFile', # Load images from files\n key='img', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # Load images from files\n key='gt', # Keys in results to find corresponding path\n color_type='color', # Color type of image\n channel_order='rgb', # Channel order of image\n imdecode_backend='cv2'), # decode backend\n dict(type='ToTensor', keys=['img', 'gt']), # Convert images to tensor\n dict(type='PackInputs') # The config of collecting data from the current pipeline\n]\n\n# dataset settings\ndataset_type = 'BasicImageDataset' # The type of dataset\ndata_root = 'data' # Root path of data\n\ntrain_dataloader = dict(\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler\n dataset=dict( # Train dataset config\n type=dataset_type, # Type of dataset\n ann_file='meta_info_DIV2K800sub_GT.txt', # Path of annotation file\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # Root path of data\n data_prefix=dict( # Prefix of image path\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # Filename template\n pipeline=train_pipeline))\nval_dataloader = dict(\n num_workers=4, # The number of workers to pre-fetch data for each single GPU\n persistent_workers=False, # Whether maintain the workers Dataset instances alive\n drop_last=False, # Whether drop the last incomplete batch\n sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler\n dataset=dict( # Validation dataset config\n type=dataset_type, # Type of dataset\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # Root path of data\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # Prefix of image path\n pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'), # The name of metrics to evaluate\n dict(type='PSNR', crop_border=scale), # The name of metrics to evaluate\n dict(type='SSIM', crop_border=scale), # The name of metrics to evaluate\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # Config of train loop type\nval_cfg = dict(type='ValLoop') # The name of validation loop type\ntest_cfg = dict(type='TestLoop') # The name of test loop type\n\n# optimizer\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n )\n) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.\n\nparam_scheduler = dict( # Config of learning policy\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n\ndefault_hooks = dict( # Used to build default hooks\n checkpoint=dict( # Config to set the checkpoint hook\n type='CheckpointHook',\n interval=5000, # The save interval is 5000 iterations\n save_optimizer=True,\n by_epoch=False, # Count by iterations\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # Config to register logger hook\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\ndefault_scope = 'mmagic' # Used to set registries location\nsave_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments.\n\nenv_cfg = dict( # Parameters to setup distributed training, the port can also be set\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO' # The level of logging\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # Used to build log processor\n\nload_from = None # load models as a pre-trained model from a given path. This will not resume training.\nresume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.\n```\n" }, { "path": "docs/en/user_guides/dataset_prepare.md", "content": "# Tutorial 2: Prepare datasets\n\nIn this section, we will detail how to prepare data and adopt the proper dataset in our repo for different methods.\n\nWe support multiple datasets of different tasks.\nThere are two ways to use datasets for training and testing models in MMagic:\n\n1. Using downloaded datasets directly\n2. Preprocessing downloaded datasets before using them.\n\nThe structure of this guide is as follows:\n\n- [Tutorial 2: Prepare datasets](#tutorial-2-prepare-datasets)\n - [Download datasets](#download-datasets)\n - [Prepare datasets](#prepare-datasets)\n - [The overview of the datasets in MMagic](#the-overview-of-the-datasets-in-mmagic)\n\n## Download datasets\n\nYou are supposed to download datasets from their homepage first.\nMost datasets are available after downloaded, so you only need to make sure the folder structure is correct and further preparation is not necessary.\nFor example, you can simply prepare Vimeo90K-triplet datasets by downloading datasets from [homepage](http://toflow.csail.mit.edu/).\n\n## Prepare datasets\n\nSome datasets need to be preprocessed before training or testing. We support many scripts to prepare datasets in [tools/dataset_converters](https://github.com/open-mmlab/mmagic/tree/main/tools/dataset_converters). And you can follow the tutorials of every dataset to run scripts.\nFor example, we recommend cropping the DIV2K images to sub-images. We provide a script to prepare cropped DIV2K dataset. You can run the following command:\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K\n```\n\n## The overview of the datasets in MMagic\n\nWe support detailed tutorials and split them according to different tasks.\n\nPlease check our dataset zoo for data preparation of different tasks.\n\nIf you're interested in more details of datasets in MMagic, please check the [advanced guides](../howto/dataset.md).\n" }, { "path": "docs/en/user_guides/deploy.md", "content": "# Tutorial 8: Deploy models in MMagic\n\nThe deployment of OpenMMLab codebases, including MMClassification, MMDetection, MMagic and so on are supported by [MMDeploy](https://github.com/open-mmlab/mmdeploy).\nThe latest deployment guide for MMagic can be found from [here](https://mmdeploy.readthedocs.io/en/latest/04-supported-codebases/mmagic.html).\n\nThis tutorial is organized as follows:\n\n- [Tutorial 8: Deploy models in MMagic](#tutorial-8-deploy-models-in-mmagic)\n - [Installation](#installation)\n - [Convert model](#convert-model)\n - [Model specification](#model-specification)\n - [Model inference](#model-inference)\n - [Backend model inference](#backend-model-inference)\n - [SDK model inference](#sdk-model-inference)\n - [Supported models](#supported-models)\n\n## Installation\n\nPlease follow the [guide](../get_started/install.md) to install mmagic. And then install mmdeploy from source by following [this](https://mmdeploy.readthedocs.io/en/latest/get_started.html#installation) guide.\n\n```{note}\nIf you install mmdeploy prebuilt package, please also clone its repository by 'git clone https://github.com/open-mmlab/mmdeploy.git --depth=1' to get the deployment config files.\n```\n\n## Convert model\n\nSuppose MMagic and mmdeploy repositories are in the same directory, and the working directory is the root path of MMagic.\n\nTake [ESRGAN](../../../configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) model as an example.\nYou can download its checkpoint from [here](https://download.openmmlab.com/MMagic/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth), and then convert it to onnx model as follows:\n\n```python\nfrom mmdeploy.apis import torch2onnx\nfrom mmdeploy.backend.sdk.export_info import export2SDK\n\nimg = 'tests/data/image/face/000001.png'\nwork_dir = 'mmdeploy_models/mmagic/onnx'\nsave_file = 'end2end.onnx'\ndeploy_cfg = '../mmdeploy/configs/mmagic/super-resolution/super-resolution_onnxruntime_dynamic.py'\nmodel_cfg = 'configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py'\nmodel_checkpoint = 'esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth'\ndevice = 'cpu'\n\n# 1. convert model to onnx\ntorch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg,\n model_checkpoint, device)\n\n# 2. extract pipeline info for inference by MMDeploy SDK\nexport2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)\n```\n\nIt is crucial to specify the correct deployment config during model conversion.MMDeploy has already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmagic) of all supported backends for mmagic, under which the config file path follows the pattern:\n\n```\n{task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py\n```\n\n- **{task}:** task in mmagic.\n\n- **{backend}:** inference backend, such as onnxruntime, tensorrt, pplnn, ncnn, openvino, coreml etc.\n\n- **{precision}:** fp16, int8. When it's empty, it means fp32\n\n- **{static | dynamic}:** static shape or dynamic shape\n\n- **{shape}:** input shape or shape range of a model\n\nTherefore, in the above example, you can also convert `ESRGAN` to other backend models by changing the deployment config file, e.g., converting to tensorrt-fp16 model by `super-resolution_tensorrt-fp16_dynamic-32x32-512x512.py`.\n\n```{tip}\nWhen converting mmagic models to tensorrt models, --device should be set to \"cuda\"\n```\n\n## Model specification\n\nBefore moving on to model inference chapter, let's know more about the converted model structure which is very important for model inference.\n\nThe converted model locates in the working directory like `mmdeploy_models/mmagic/onnx` in the previous example. It includes:\n\n```\nmmdeploy_models/mmagic/onnx\n├── deploy.json\n├── detail.json\n├── end2end.onnx\n└── pipeline.json\n```\n\nin which,\n\n- **end2end.onnx**: backend model which can be inferred by ONNX Runtime\n- ***xxx*.json**: the necessary information for mmdeploy SDK\n\nThe whole package **mmdeploy_models/mmagic/onnx** is defined as **mmdeploy SDK model**, i.e., **mmdeploy SDK model** includes both backend model and inference meta information.\n\n## Model inference\n\n### Backend model inference\n\nTake the previous converted `end2end.onnx` model as an example, you can use the following code to inference the model.\n\n```python\nfrom mmdeploy.apis.utils import build_task_processor\nfrom mmdeploy.utils import get_input_shape, load_config\nimport torch\n\ndeploy_cfg = '../mmdeploy/configs/mmagic/super-resolution/super-resolution_onnxruntime_dynamic.py'\nmodel_cfg = 'configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py'\ndevice = 'cpu'\nbackend_model = ['mmdeploy_models/mmagic/onnx/end2end.onnx']\nimage = 'tests/data/image/lq/baboon_x4.png'\n\n# read deploy_cfg and model_cfg\ndeploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)\n\n# build task and backend model\ntask_processor = build_task_processor(model_cfg, deploy_cfg, device)\nmodel = task_processor.build_backend_model(backend_model)\n\n# process input image\ninput_shape = get_input_shape(deploy_cfg)\nmodel_inputs, _ = task_processor.create_input(image, input_shape)\n\n# do model inference\nwith torch.no_grad():\n result = model.test_step(model_inputs)\n\n# visualize results\ntask_processor.visualize(\n image=image,\n model=model,\n result=result[0],\n window_name='visualize',\n output_file='output_restorer.bmp')\n```\n\n### SDK model inference\n\nYou can also perform SDK model inference like following,\n\n```python\nfrom mmdeploy_python import Restorer\nimport cv2\n\nimg = cv2.imread('tests/data/image/lq/baboon_x4.png')\n\n# create a predictor\nrestorer = Restorer(model_path='mmdeploy_models/mmagic/onnx', device_name='cpu', device_id=0)\n# perform inference\nresult = restorer(img)\n\n# visualize inference result\ncv2.imwrite('output_restorer.bmp', result)\n```\n\nBesides python API, MMDeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo).\n\n## Supported models\n\nPlease refer to [here](https://mmdeploy.readthedocs.io/en/latest/04-supported-codebases/mmagic.html#supported-models) for the supported model list.\n" }, { "path": "docs/en/user_guides/inference.md", "content": "# Tutorial 3: Inference with pre-trained models\n\nMMagic provides Hign-level APIs for you to easily play with state-of-the-art models on your own images or videos.\n\nIn the new API, only two lines of code are needed to implement inference:\n\n```python\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance\neditor = MMagicInferencer('pix2pix')\n# Infer a image. Input image path and output image path is needed.\nresults = editor.infer(img='../resources/input/translation/gt_mask_0.png', result_out_dir='../resources/output/translation/tutorial_translation_pix2pix_res.jpg')\n```\n\nMMagic supports various fundamental generative models, including:\n\nunconditional Generative Adversarial Networks (GANs), conditional GANs, diffusion models, etc.\n\nMMagic also supports various applications, including:\n\ntext-to-image, image-to-image translation, 3D-aware generation, image super-resolution, video super-resolution, video frame interpolation, image inpainting, image matting, image restoration, image colorization, image generation, etc.\n\nIn this section, we will specify how to play with our pre-trained models.\n\n- [Tutorial 3: Inference with Pre-trained Models](#tutorial-3-inference-with-pre-trained-models)\n - [Prepare some images or videos for inference](#Prepare-some-images-or-videos-for-inference)\n - [Generative Models](#Generative-Models)\n - [Unconditional Generative Adversarial Networks (GANs)](<#Unconditional-Generative-Adversarial-Networks-(GANs)>)\n - [Conditional Generative Adversarial Networks (GANs)](<#Conditional-Generative-Adversarial-Networks-(GANs)>)\n - [Diffusion Models](#Diffusion-Models)\n - [Applications](#Applications)\n - [Text-to-Image](#Text-to-Image)\n - [Image-to-image translation](#Image-to-image-translation)\n - [3D-aware generation](#3D-aware-generation)\n - [Image super-resolution](#Image-super-resolution)\n - [Video super-resolution](#Video-super-resolution)\n - [Video frame interpolation](Video-frame-interpolation)\n - [Image inpainting](#Image-inpainting)\n - [Image matting](#Image-matting)\n - [Image restoration](#Image-restoration)\n - [Image colorization](#Image-colorization)\n- [Previous Versions](#Previous-Versions)\n\n## Prepare some images or videos for inference\n\nPlease refer to our [tutorials](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_tutorial.ipynb) for details.\n\n## Generative Models\n\n### Unconditional Generative Adversarial Networks (GANs)\n\nMMagic provides high-level APIs for sampling images with unconditional GANs. Unconditional GAN models do not need input, and output a image. We take 'styleganv1' as an example.\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/unconditional/tutorial_unconditional_styleganv1_res.png'\neditor = MMagicInferencer('styleganv1')\nresults = editor.infer(result_out_dir=result_out_dir)\n```\n\nIndeed, we have already provided a more friendly demo script to users. You can use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name styleganv1 \\\n --result-out-dir demo_unconditional_styleganv1_res.jpg\n```\n\n### Conditional Generative Adversarial Networks (GANs)\n\nMMagic provides high-level APIs for sampling images with conditional GANs. Conditional GAN models take a label as input and output a image. We take 'biggan' as an example..\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/conditional/tutorial_conditinal_biggan_res.jpg'\neditor = MMagicInferencer('biggan', model_setting=1)\nresults = editor.infer(label=1, result_out_dir=result_out_dir)\n```\n\nIndeed, we have already provided a more friendly demo script to users. You can use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name biggan \\\n --model-setting 1 \\\n --label 1 \\\n --result-out-dir demo_conditional_biggan_res.jpg\n```\n\n### Diffusion Models\n\nMMagic provides high-level APIs for sampling images with diffusion models. f\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\neditor = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = './resources/output/text2image/tutorial_text2image_sd_res.png'\neditor.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir demo_text2image_stable_diffusion_res.png\n```\n\n## Applications\n\n### Text-to-Image\n\nText-to-image models take text as input, and output a image. We take 'controlnet-canny' as an example.\n\n```python\nimport cv2\nimport numpy as np\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/controlnet/controlnet-canny.py')\ncontrolnet = MODELS.build(cfg.model).cuda()\n\ncontrol_url = 'https://user-images.githubusercontent.com/28132635/230288866-99603172-04cb-47b3-8adb-d1aa532d1d2c.jpg'\ncontrol_img = mmcv.imread(control_url)\ncontrol = cv2.Canny(control_img, 100, 200)\ncontrol = control[:, :, None]\ncontrol = np.concatenate([control] * 3, axis=2)\ncontrol = Image.fromarray(control)\n\nprompt = 'Room with blue walls and a yellow ceiling.'\n\noutput_dict = controlnet.infer(prompt, control=control)\nsamples = output_dict['samples']\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 1 \\\n --text \"Room with blue walls and a yellow ceiling.\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \\\n --result-out-dir demo_text2image_controlnet_canny_res.png\n```\n\n### Image-to-image translation\n\nMMagic provides high-level APIs for translating images by using image translation models. Here is an example of building Pix2Pix and obtaining the translated images.\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\neditor = MMagicInferencer('pix2pix')\nresults = editor.infer(img=img_path, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name pix2pix \\\n --img ${IMAGE_PATH} \\\n --result-out-dir ${SAVE_PATH}\n```\n\n### 3D-aware generation\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/eg3d-output'\neditor = MMagicInferencer('eg3d')\nresults = editor.infer(result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name eg3d \\\n --result-out-dir ./resources/output/eg3d-output\n```\n\n### Image super-resolution\n\nImage super resolution models take a image as input, and output a high resolution image. We take 'esrgan' as an example.\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = './resources/input/restoration/0901x2.png'\nresult_out_dir = './resources/output/restoration/tutorial_restoration_esrgan_res.png'\neditor = MMagicInferencer('esrgan')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name esrgan \\\n --img ${IMAGE_PATH} \\\n --result-out-dir ${SAVE_PATH}\n```\n\n### Video super-resolution\n\n```python\nimport os\nfrom mmagic.apis import MMagicInferencer\nfrom mmengine import mkdir_or_exist\n\n# Create a MMagicInferencer instance and infer\nvideo = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'\nresult_out_dir = './resources/output/video_super_resolution/tutorial_video_super_resolution_basicvsr_res.mp4'\nmkdir_or_exist(os.path.dirname(result_out_dir))\neditor = MMagicInferencer('basicvsr')\nresults = editor.infer(video=video, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name basicvsr \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4\n```\n\n### Video frame interpolation\n\nVideo interpolation models take a video as input, and output a interpolated video. We take 'flavr' as an example.\n\n```python\nimport os\nfrom mmagic.apis import MMagicInferencer\nfrom mmengine import mkdir_or_exist\n\n# Create a MMagicInferencer instance and infer\nvideo = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'\nresult_out_dir = './resources/output/video_interpolation/tutorial_video_interpolation_flavr_res.mp4'\nmkdir_or_exist(os.path.dirname(result_out_dir))\neditor = MMagicInferencer('flavr')\nresults = editor.infer(video=video, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name flavr \\\n --video ${VIDEO_PATH} \\\n --result-out-dir ${SAVE_PATH}\n```\n\n### Image inpainting\n\nInpaiting models take a masked image and mask pair as input, and output a inpainted image. We take 'global_local' as an example.\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\nimg = './resources/input/inpainting/celeba_test.png'\nmask = './resources/input/inpainting/bbox_mask.png'\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/inpainting/tutorial_inpainting_global_local_res.jpg'\neditor = MMagicInferencer('global_local', model_setting=1)\nresults = editor.infer(img=img, mask=mask, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name global_local \\\n --img ./resources/input/inpainting/celeba_test.png \\\n --mask ./resources/input/inpainting/bbox_mask.png \\\n --result-out-dir ./resources/output/inpainting/demo_inpainting_global_local_res.jpg\n```\n\n### Image matting\n\nInpaiting models take a image and trimap pair as input, and output a alpha image. We take 'gca' as an example.\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\nimg = './resources/input/matting/GT05.jpg'\ntrimap = './resources/input/matting/GT05_trimap.jpg'\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/matting/tutorial_matting_gca_res.png'\neditor = MMagicInferencer('gca')\nresults = editor.infer(img=img, trimap=trimap, result_out_dir=result_out_dir)\n```\n\nUse [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name gca \\\n --img ./resources/input/matting/GT05.jpg \\\n --trimap ./resources/input/matting/GT05_trimap.jpg \\\n --result-out-dir ./resources/output/matting/demo_matting_gca_res.png\n```\n\n### Image restoration\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = './resources/input/restoration/0901x2.png'\nresult_out_dir = './resources/output/restoration/tutorial_restoration_nafnet_res.png'\neditor = MMagicInferencer('nafnet')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name nafnet \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_nafnet_res.png\n```\n\n### Image colorization\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg'\nresult_out_dir = './resources/output/colorization/tutorial_colorization_res.png'\neditor = MMagicInferencer('inst_colorization')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name inst_colorization \\\n --img https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg \\\n --result-out-dir demo_colorization_res.png\n```\n\n## Previous Versions\n\nIf you want to use deprecated demos, please use [MMagic v1.0.0rc7](https://github.com/open-mmlab/mmagic/tree/v1.0.0rc7) and reference the [old tutorial](https://github.com/open-mmlab/mmagic/blob/v1.0.0rc7/docs/en/user_guides/inference.md).\n" }, { "path": "docs/en/user_guides/metrics.md", "content": "# Tutorial 5: Using metrics in MMagic\n\nMMagic supports **17 metrics** to assess the quality of models.\n\nPlease refer to [Train and Test in MMagic](../user_guides/train_test.md) for usages.\n\nHere, we will specify the details of different metrics one by one.\n\nThe structure of this guide are as follows:\n\n- [Tutorial 5: Using metrics in MMagic](#tutorial-5-using-metrics-in-mmagic)\n - [MAE](#mae)\n - [MSE](#mse)\n - [PSNR](#psnr)\n - [SNR](#snr)\n - [SSIM](#ssim)\n - [NIQE](#niqe)\n - [SAD](#sad)\n - [MattingMSE](#mattingmse)\n - [GradientError](#gradienterror)\n - [ConnectivityError](#connectivityerror)\n - [FID and TransFID](#fid-and-transfid)\n - [IS and TransIS](#is-and-transis)\n - [Precision and Recall](#precision-and-recall)\n - [PPL](#ppl)\n - [SWD](#swd)\n - [MS-SSIM](#ms-ssim)\n - [Equivarience](#equivarience)\n\n## MAE\n\nMAE is Mean Absolute Error metric for image.\nTo evaluate with MAE, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='MAE'),\n]\n```\n\n## MSE\n\nMSE is Mean Squared Error metric for image.\nTo evaluate with MSE, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='MSE'),\n]\n```\n\n## PSNR\n\nPSNR is Peak Signal-to-Noise Ratio. Our implement refers to https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio.\nTo evaluate with PSNR, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='PSNR'),\n]\n```\n\n## SNR\n\nSNR is Signal-to-Noise Ratio. Our implementation refers to https://en.wikipedia.org/wiki/Signal-to-noise_ratio.\nTo evaluate with SNR, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='SNR'),\n]\n```\n\n## SSIM\n\nSSIM is structural similarity for image, proposed in [Image quality assessment: from error visibility to structural similarity](https://live.ece.utexas.edu/publications/2004/zwang_ssim_ieeeip2004.pdf). The results of our implementation are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/.\nTo evaluate with SSIM, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='SSIM'),\n]\n```\n\n## NIQE\n\nNIQE is Natural Image Quality Evaluator metric, proposed in [Making a \"Completely Blind\" Image Quality Analyzer](http://www.live.ece.utexas.edu/publications/2013/mittal2013.pdf). Our implementation could produce almost the same results as the official MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip.\n\nTo evaluate with NIQE, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='NIQE'),\n]\n```\n\n## SAD\n\nSAD is Sum of Absolute Differences metric for image matting. This metric compute per-pixel absolute difference and sum across all pixels.\nTo evaluate with SAD, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='SAD'),\n]\n```\n\n## MattingMSE\n\nMattingMSE is Mean Squared Error metric for image matting.\nTo evaluate with MattingMSE, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='MattingMSE'),\n]\n```\n\n## GradientError\n\nGradientError is Gradient error for evaluating alpha matte prediction.\nTo evaluate with GradientError, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='GradientError'),\n]\n```\n\n## ConnectivityError\n\nConnectivityError is Connectivity error for evaluating alpha matte prediction.\nTo evaluate with ConnectivityError, please add the following configuration in the config file:\n\n```python\nval_evaluator = [\n dict(type='ConnectivityError'),\n]\n```\n\n## FID and TransFID\n\nFréchet Inception Distance is a measure of similarity between two datasets of images. It was shown to correlate well with the human judgment of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network.\n\nIn `MMagic`, we provide two versions for FID calculation. One is the commonly used PyTorch version and the other one is used in StyleGAN paper. Meanwhile, we have compared the difference between these two implementations in the StyleGAN2-FFHQ1024 model (the details can be found [here](https://github.com/open-mmlab/mmagic/blob/main/configs/styleganv2/README.md)). Fortunately, there is a marginal difference in the final results. Thus, we recommend users adopt the more convenient PyTorch version.\n\n**About PyTorch version and Tero's version:** The commonly used PyTorch version adopts the modified InceptionV3 network to extract features for real and fake images. However, Tero's FID requires a [script module](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for Tensorflow InceptionV3. Note that applying this script module needs `PyTorch >= 1.6.0`.\n\n**About extracting real inception data:** For the users' convenience, the real features will be automatically extracted at test time and saved locally, and the stored features will be automatically read at the next test. Specifically, we will calculate a hash value based on the parameters used to calculate the real features, and use the hash value to mark the feature file, and when testing, if the `inception_pkl` is not set, we will look for the feature in `MMAGIC_CACHE_DIR` (~/.cache/openmmlab/mmagic/). If cached inception pkl is not found, then extracting will be performed.\n\nTo use the FID metric, you should add the metric in a config file like this:\n\n```python\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n```\n\nIf you work on an new machine, then you can copy the `pkl` files in `MMAGIC_CACHE_DIR` and copy them to new machine and set `inception_pkl` field.\n\n```python\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=\n 'work_dirs/inception_pkl/inception_state-capture_mean_cov-full-33ad4546f8c9152e4b3bdb1b0c08dbaf.pkl', # copied from old machine\n sample_model='ema')\n]\n```\n\n`TransFID` has same usage as `FID`, but it's designed for translation models like `Pix2Pix` and `CycleGAN`, which is adapted for our evaluator. You can refer\nto [evaluation](../user_guides/train_test.md) for details.\n\n## IS and TransIS\n\nInception score is an objective metric for evaluating the quality of generated images, proposed in [Improved Techniques for Training GANs](https://arxiv.org/pdf/1606.03498.pdf). It uses an InceptionV3 model to predict the class of the generated images, and suppose that 1) If an image is of high quality, it will be categorized into a specific class. 2) If images are of high diversity, the range of images' classes will be wide. So the KL-divergence of the conditional probability and marginal probability can indicate the quality and diversity of generated images. You can see the complete implementation in `metrics.py`, which refers to https://github.com/sbarratt/inception-score-pytorch/blob/master/inception_score.py.\nIf you want to evaluate models with `IS` metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py\nmetrics = [\n xxx,\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n```\n\nTo be noted that, the selection of Inception V3 and image resize method can significantly influence the final IS score. Therefore, we strongly recommend users may download the [Tero's script model of Inception V3](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) (load this script model need torch >= 1.6) and use `Bicubic` interpolation with `Pillow` backend.\n\nCorresponding to config, you can set `resize_method` and `use_pillow_resize` for image resizing. You can also set `inception_style` as `StyleGAN` for recommended tero's inception model, or `PyTorch` for torchvision's implementation. For environment without internet, you can download the inception's weights, and set `inception_path` to your inception model.\n\nWe also perform a survey on the influence of data loading pipeline and the version of pretrained Inception V3 on the IS result. All IS are evaluated on the same group of images which are randomly selected from the ImageNet dataset.\n\n
Show the Comparison Results \n\n| Code Base | Inception V3 Version | Data Loader Backend | Resize Interpolation Method | IS |\n| :-------------------------------------------------------------: | :------------------: | :-----------------: | :-------------------------: | :-------------------: |\n| [OpenAI (baseline)](https://github.com/openai/improved-gan) | Tensorflow | Pillow | Pillow Bicubic | **312.255 +/- 4.970** |\n| [StyleGAN-Ada](https://github.com/NVlabs/stylegan2-ada-pytorch) | Tero's Script Model | Pillow | Pillow Bicubic | 311.895 +/ 4.844 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | cv2 Bilinear | 322.932 +/- 2.317 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | cv2 Bicubic | 324.604 +/- 5.157 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | Pillow Bicubic | 318.161 +/- 5.330 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | Pillow Bilinear | 313.126 +/- 5.449 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | cv2 Bilinear | 318.021+/-3.864 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | Pillow Bicubic | 317.997 +/- 5.350 |\n| mmagic (Ours) | Tero's Script Model | cv2 | cv2 Bilinear | 318.879 +/- 2.433 |\n| mmagic (Ours) | Tero's Script Model | cv2 | cv2 Bicubic | 316.125 +/- 5.718 |\n| mmagic (Ours) | Tero's Script Model | cv2 | Pillow Bicubic | **312.045 +/- 5.440** |\n| mmagic (Ours) | Tero's Script Model | Pillow | Pillow Bilinear | 308.645 +/- 5.374 |\n| mmagic (Ours) | Tero's Script Model | Pillow | Pillow Bicubic | 311.733 +/- 5.375 |\n\n
\n\n`TransIS` has same usage as `IS`, but it's designed for translation models like `Pix2Pix` and `CycleGAN`, which is adapted for our evaluator. You can refer\nto [evaluation](../user_guides/train_test.md) for details.\n\n## Precision and Recall\n\nOur `Precision and Recall` implementation follows the version used in StyleGAN2. In this metric, a VGG network will be adopted to extract the features for images. Unfortunately, we have not found a PyTorch VGG implementation leading to similar results with Tero's version used in StyleGAN2. (About the differences, please see this [file](https://github.com/open-mmlab/mmagic/blob/main/configs/styleganv2/README.md).) Thus, in our implementation, we adopt [Teor's VGG](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt) network by default. Importantly, applying this script module needs `PyTorch >= 1.6.0`. If with a lower PyTorch version, we will use the PyTorch official VGG network for feature extraction.\n\nTo evaluate with `P&R`, please add the following configuration in the config file:\n\n```python\nmetrics = [\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K')\n]\n```\n\n## PPL\n\nPerceptual path length measures the difference between consecutive images (their VGG16 embeddings) when interpolating between two random inputs. Drastic changes mean that multiple features have changed together and that they might be entangled. Thus, a smaller PPL score appears to indicate higher overall image quality by experiments. \\\nAs a basis for our metric, we use a perceptually-based pairwise image distance that is calculated as a weighted difference between two VGG16 embeddings, where the weights are fit so that the metric agrees with human perceptual similarity judgments.\nIf we subdivide a latent space interpolation path into linear segments, we can define the total perceptual length of this segmented path as the sum of perceptual differences over each segment, and a natural definition for the perceptual path length would be the limit of this sum under infinitely fine subdivision, but in practice we approximate it using a small subdivision `` $`\\epsilon=10^{-4}`$ ``.\nThe average perceptual path length in latent `space` Z, over all possible endpoints, is therefore\n\n`` $$`L_Z = E[\\frac{1}{\\epsilon^2}d(G(slerp(z_1,z_2;t))), G(slerp(z_1,z_2;t+\\epsilon)))]`$$ ``\n\nComputing the average perceptual path length in latent `space` W is carried out in a similar fashion:\n\n`` $$`L_Z = E[\\frac{1}{\\epsilon^2}d(G(slerp(z_1,z_2;t))), G(slerp(z_1,z_2;t+\\epsilon)))]`$$ ``\n\nWhere `` $`z_1, z_2 \\sim P(z)`$ ``, and `` $` t \\sim U(0,1)`$ `` if we set `sampling` to full, `` $` t \\in \\{0,1\\}`$ `` if we set `sampling` to end. `` $` G`$ `` is the generator(i.e. `` $` g \\circ f`$ `` for style-based networks), and `` $` d(.,.)`$ `` evaluates the perceptual distance between the resulting images.We compute the expectation by taking 100,000 samples (set `num_images` to 50,000 in our code).\n\nYou can find the complete implementation in `metrics.py`, which refers to https://github.com/rosinality/stylegan2-pytorch/blob/master/ppl.py.\nIf you want to evaluate models with `PPL` metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py\nmetrics = [\n xxx,\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n```\n\n## SWD\n\nSliced Wasserstein distance is a discrepancy measure for probability distributions, and smaller distance indicates generated images look like the real ones. We obtain the Laplacian pyramids of every image and extract patches from the Laplacian pyramids as descriptors, then SWD can be calculated by taking the sliced Wasserstein distance of the real and fake descriptors.\nYou can see the complete implementation in `metrics.py`, which refers to https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/sliced_wasserstein.py.\nIf you want to evaluate models with `SWD` metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\nmetrics = [\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n```\n\n## MS-SSIM\n\nMulti-scale structural similarity is used to measure the similarity of two images. We use MS-SSIM here to measure the diversity of generated images, and a low MS-SSIM score indicates the high diversity of generated images. You can see the complete implementation in `metrics.py`, which refers to https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py.\nIf you want to evaluate models with `MS-SSIM` metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig')\n]\n```\n\n## Equivarience\n\nEquivarience of generative models refer to the exchangeability of model forward and geometric transformations. Currently this metric is only calculated for StyleGANv3,\nyou can see the complete implementation in `metrics.py`, which refers to https://github.com/NVlabs/stylegan3/blob/main/metrics/equivariance.py.\nIf you want to evaluate models with `Equivarience` metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py\nmetrics = [\n dict(\n type='Equivariance',\n fake_nums=50000,\n sample_mode='ema',\n prefix='EQ',\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))\n]\n```\n" }, { "path": "docs/en/user_guides/train_test.md", "content": "# Tutorial 4: Train and test in MMagic\n\nIn this section, we introduce how to test and train models in MMagic.\n\nIn this section, we provide the following guides:\n\n- [Tutorial 4: Train and test in MMagic](#tutorial-4-train-and-test-in-mmagic)\n - [Prerequisite](#prerequisite)\n - [Test a model in MMagic](#test-a-model-in-mmagic)\n - [Test with a single GPUs](#test-with-a-single-gpus)\n - [Test with multiple GPUs](#test-with-multiple-gpus)\n - [Test with Slurm](#test-with-slurm)\n - [Test with specific metrics](#test-with-specific-metrics)\n - [Train a model in MMagic](#train-a-model-in-mmagic)\n - [Train with a single GPU](#train-with-a-single-gpu)\n - [Train with multiple nodes](#train-with-multiple-nodes)\n - [Train with multiple GPUs](#train-with-multiple-gpus)\n - [Train with Slurm](#train-with-slurm)\n - [Optional arguments](#optional-arguments)\n - [Train with specific evaluation metrics](#train-with-specific-evaluation-metrics)\n\n## Prerequisite\n\nUsers need to [prepare dataset](../user_guides/dataset_prepare.md) first to enable training and testing models in MMagic.\n\n## Test a model in MMagic\n\n### Test with a single GPUs\n\nYou can use the following commands to test a pre-trained model with single GPUs.\n\n```shell\npython tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}\n```\n\nFor example,\n\n```shell\npython tools/test.py configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\n### Test with multiple GPUs\n\nMMagic supports testing with multiple GPUs,\nwhich can largely save your time in testing models.\nYou can use the following commands to test a pre-trained model with multiple GPUs.\n\n```shell\n./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}\n```\n\nFor example,\n\n```shell\n./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\n### Test with Slurm\n\nIf you run MMagic on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_test.sh`. (This script also supports single machine testing.)\n\n```shell\n[GPUS=${GPUS}] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE}\n```\n\nHere is an example of using 8 GPUs to test an example model on the 'dev' partition with the job name 'test'.\n\n```shell\nGPUS=8 ./tools/slurm_test.sh dev test configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\nYou can check [slurm_test.sh](../../../tools/slurm_test.sh) for full arguments and environment variables.\n\n### Test with specific metrics\n\nMMagic provides various **evaluation metrics**, i.e., MS-SSIM, SWD, IS, FID, Precision&Recall, PPL, Equivarience, TransFID, TransIS, etc.\nWe have provided unified evaluation scripts in [tools/test.py](https://github.com/open-mmlab/mmagic/tree/main/tools/test.py) for all models.\nIf users want to evaluate their models with some metrics, you can add the `metrics` into your config file like this:\n\n```python\n# at the end of the configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n```\n\nAs above, `metrics` consist of multiple metric dictionaries. Each metric will contain `type` to indicate the category of the metric. `fake_nums` denotes the number of images generated by the model. Some metrics will output a dictionary of results, you can also set `prefix` to specify the prefix of the results.\nIf you set the prefix of FID as `FID-Full-50k`, then an example of output may be\n\n```bash\nFID-Full-50k/fid: 3.6561 FID-Full-50k/mean: 0.4263 FID-Full-50k/cov: 3.2298\n```\n\nThen users can test models with the command below:\n\n```shell\nbash tools/dist_test.sh ${CONFIG_FILE} ${CKPT_FILE}\n```\n\nIf you are in slurm environment, please switch to the [tools/slurm_test.sh](https://github.com/open-mmlab/mmagic/tree/main/tools/slurm_test.sh) by using the following commands:\n\n```shell\nsh slurm_test.sh ${PLATFORM} ${JOBNAME} ${CONFIG_FILE} ${CKPT_FILE}\n```\n\n## Train a model in MMagic\n\nMMagic supports multiple ways of training:\n\n1. [Train with a single GPU](#train-with-a-single-gpu)\n2. [Train with multiple GPUs](#train-with-multiple-gpus)\n3. [Train with multiple nodes](#train-with-multiple-nodes)\n4. [Train with Slurm](#train-with-slurm)\n\nSpecifically, all outputs (log files and checkpoints) will be saved to the working directory,\nwhich is specified by `work_dir` in the config file.\n\n### Train with a single GPU\n\n```shell\nCUDA_VISIBLE=0 python tools/train.py configs/example_config.py --work-dir work_dirs/example\n```\n\n### Train with multiple nodes\n\nTo launch distributed training on multiple machines, which can be accessed via IPs, run the following commands:\n\nOn the first machine:\n\n```shell\nNNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR tools/dist_train.sh $CONFIG $GPUS\n```\n\nOn the second machine:\n\n```shell\nNNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR tools/dist_train.sh $CONFIG $GPUS\n```\n\nTo speed up network communication, high speed network hardware, such as Infiniband, is recommended.\nPlease refer to [PyTorch docs](https://pytorch.org/docs/1.11/distributed.html#launch-utility) for more information.\n\n### Train with multiple GPUs\n\n```shell\n./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n```\n\n### Train with Slurm\n\nIf you run MMagic on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)\n\n```shell\n[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}\n```\n\nHere is an example of using 8 GPUs to train an inpainting model on the dev partition.\n\n```shell\nGPUS=8 ./tools/slurm_train.sh dev configs/inpainting/gl_places.py /nfs/xxxx/gl_places_256\n```\n\nYou can check [slurm_train.sh](https://github.com/open-mmlab/mmagic/blob/master/tools/slurm_train.sh) for full arguments and environment variables.\n\n### Optional arguments\n\n- `--amp`: This argument is used for fixed-precision training.\n- `--resume`: This argument is used for auto resume if the training is aborted.\n\n## Train with specific evaluation metrics\n\nBenefit from the `mmengine`'s `Runner`. We can evaluate model during training in a simple way as below.\n\n```python\n# define metrics\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN')\n]\n\n# define dataloader\nval_dataloader = dict(\n batch_size=128,\n num_workers=8,\n dataset=dict(\n type='BasicImageDataset',\n data_root='data/celeba-cropped/',\n pipeline=[\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(64, 64)),\n dict(type='PackInputs')\n ]),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\n# define val interval\ntrain_cfg = dict(by_epoch=False, val_begin=1, val_interval=10000)\n\n# define val loop and evaluator\nval_cfg = dict(type='MultiValLoop')\nval_evaluator = dict(type='Evaluator', metrics=metrics)\n```\n\nYou can set `val_begin` and `val_interval` to adjust when to begin validation and interval of validation.\n\nFor details of metrics, refer to [metrics' guide](./metrics.md).\n" }, { "path": "docs/en/user_guides/useful_tools.md", "content": "# Tutorial 7: Useful tools\n\nWe provide lots of useful tools under `tools/` directory.\n\nThe structure of this guide is as follows:\n\n- [Tutorial 7: Useful tools](#tutorial-7-useful-tools)\n - [Get the FLOPs and params](#get-the-flops-and-params)\n - [Publish a model](#publish-a-model)\n - [Print full config](#print-full-config)\n\n## Get the FLOPs and params\n\nWe provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.\n\n```shell\npython tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]\n```\n\nFor example,\n\n```shell\npython tools/analysis_tools/get_flops.py configs/resotorer/srresnet.py --shape 40 40\n```\n\nYou will get the result like this.\n\n```\n==============================\nInput shape: (3, 40, 40)\nFlops: 4.07 GMac\nParams: 1.52 M\n==============================\n```\n\n**Note**: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.\n\n(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 250, 250).\n(2) Some operators are not counted in FLOPs like GN and custom operators.\nYou can add support for new operators by modifying [`mmcv/cnn/utils/flops_counter.py`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py).\n\n## Publish a model\n\nBefore you upload a model to AWS, you may want to\n\n1. convert model weights to CPU tensors\n2. delete the optimizer states and\n3. compute the hash of the checkpoint file and append time and the hash id to the\n filename.\n\n```shell\npython tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}\n```\n\nE.g.,\n\n```shell\npython tools/model_converters/publish_model.py work_dirs/stylegan2/latest.pth stylegan2_c2_8xb4_ffhq-1024x1024.pth\n```\n\nThe final output filename will be `stylegan2_c2_8xb4_ffhq-1024x1024_{time}-{hash id}.pth`.\n\n## Print full config\n\nMMGeneration incorporates config mechanism to set parameters used for training and testing models. With our [config](../user_guides/config.md) mechanism, users can easily conduct extensive experiments without hard coding. If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.\n\nAn Example:\n\n```shell\npython tools/misc/print_config.py configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py\n```\n" }, { "path": "docs/en/user_guides/visualization.md", "content": "# Tutorial 6: Visualization\n\nThe visualization of images is an important way to measure the quality of image processing, editing and synthesis.\nUsing `visualizer` in config file can save visual results when training or testing. You can follow [MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/visualization.md) to learn the usage of visualization. MMagic provides a rich set of visualization functions.\nIn this tutorial, we introduce the usage of the visualization functions provided by MMagic.\n\n- [Tutorial 6: Visualization](#tutorial-6-visualization)\n - [Overview](#overview)\n - [Visualization configuration of GANs](#visualization-configuration-of-gans)\n - [Visualization configuration of image translation models](#visualization-configuration-of-image-translation-models)\n - [Visualization configuration of diffusion models](#visualization-configuration-of-diffusion-models)\n - [Visualization configuration of inpainting models](#visualization-configuration-of-inpainting-models)\n - [Visualization configuration of matting models](#visualization-configuration-of-matting-models)\n - [Visualization configuration of SISR/VSR/VFI models](#visualization-configuration-of-sisrvsrvfi-models)\n - [Visualization Hook](#visualization-hook)\n - [Visualizer](#visualizer)\n - [VisBackend](#visbackend)\n - [Use Different Storage Backends](#use-different-storage-backends)\n\n## Overview\n\nIt is recommended to learn the basic concept of visualization in design documentation.\n\nIn MMagic, the visualization of the training or testing process requires the configuration of three components: `VisualizationHook`, `Visualizer`, and `VisBackend`, The diagram below shows the relationship between Visualizer and VisBackend,\n\n
\n\n
\n\n**VisualizationHook** fetches the visualization results of the model output in fixed intervals during training and passes them to Visualizer.\n**Visualizer** is responsible for converting the original visualization results into the desired type (png, gif, etc.) and then transferring them to **VisBackend** for storage or display.\n\n### Visualization configuration of GANs\n\nFor GAN models, such as StyleGAN and SAGAN, a usual configuration is shown below:\n\n```python\n# VisualizationHook\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000, # visualization interval\n fixed_input=True, # whether use fixed noise input to generate images\n vis_kwargs_list=dict(type='GAN', name='fake_img') # pre-defined visualization arguments for GAN models\n )\n]\n# VisBackend\nvis_backends = [\n dict(type='VisBackend'), # vis_backend for saving images to file system\n dict(type='WandbVisBackend', # vis_backend for uploading images to Wandb\n init_kwargs=dict(\n project='MMagic', # project name for Wandb\n name='GAN-Visualization-Demo' # name of the experiment for Wandb\n ))\n]\n# Visualizer\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\nIf you apply Exponential Moving Average (EMA) to a generator and want to visualize the EMA model, you can modify config of `VisualizationHook` as below:\n\n```python\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig in `fake_img` at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img', # save images with prefix `fake_img`\n sample_model='ema/orig', # specified kwargs for `NoiseSampler`\n target_keys=['ema.fake_img', 'orig.fake_img'] # specific key to visualization\n ))\n]\n```\n\n### Visualization configuration of image translation models\n\nFor Translation models, such as CycleGAN and Pix2Pix, visualization configs can be formed as below:\n\n```python\n# VisualizationHook\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(\n type='Translation', # Visualize results on the training set\n name='trans'), # save images with prefix `trans`\n dict(\n type='Translationval', # Visualize results on the validation set\n name='trans_val'), # save images with prefix `trans_val`\n ])\n]\n# VisBackend\nvis_backends = [\n dict(type='VisBackend'), # vis_backend for saving images to file system\n dict(type='WandbVisBackend', # vis_backend for uploading images to Wandb\n init_kwargs=dict(\n project='MMagic', # project name for Wandb\n name='Translation-Visualization-Demo' # name of the experiment for Wandb\n ))\n]\n# Visualizer\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\n### Visualization configuration of diffusion models\n\nFor Diffusion models, such as Improved-DDPM, we can use the following configuration to visualize the denoising process through a gif:\n\n```python\n# VisualizationHook\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='DDPMDenoising')) # pre-defined visualization argument for DDPM models\n]\n# VisBackend\nvis_backends = [\n dict(type='VisBackend'), # vis_backend for saving images to file system\n dict(type='WandbVisBackend', # vis_backend for uploading images to Wandb\n init_kwargs=dict(\n project='MMagic', # project name for Wandb\n name='Diffusion-Visualization-Demo' # name of the experiment for Wandb\n ))\n]\n# Visualizer\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\n### Visualization configuration of inpainting models\n\nFor inpainting models, such as AOT-GAN and Global&Local, a usual configuration is shown below:\n\n```python\n# VisBackend\nvis_backends = [dict(type='LocalVisBackend')]\n# Visualizer\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\n# VisualizationHook\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n### Visualization configuration of matting models\n\nFor matting models, such as DIM and GCA, a usual configuration is shown below:\n\n```python\n# VisBackend\nvis_backends = [dict(type='LocalVisBackend')]\n# Visualizer\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='trimap_path',\n img_keys=['pred_alpha', 'trimap', 'gt_merged', 'gt_alpha'],\n bgr2rgb=True)\n# VisualizationHook\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n### Visualization configuration of SISR/VSR/VFI models\n\nFor SISR/VSR/VFI models, such as EDSR, EDVR and CAIN, a usual configuration is shown below:\n\n```python\n# VisBackend\nvis_backends = [dict(type='LocalVisBackend')]\n# Visualizer\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=False)\n# VisualizationHook\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\nThe specific configuration of the `VisualizationHook`, `Visualizer` and `VisBackend` components are described below\n\n## Visualization Hook\n\nIn MMagic, we use `BasicVisualizationHook` and `VisualizationHook` as `VisualizationHook`.\n`VisualizationHook` supports three following cases.\n\n(1) Modify `vis_kwargs_list` to visualize the output of the model under specific inputs , which is suitable for visualization of the generated results of GAN and translation results of Image-to-Image-Translation models under specific data input, etc. Below are two typical examples:\n\n```python\n# input as dict\nvis_kwargs_list = dict(\n type='Noise', # use 'Noise' sampler to generate model input\n name='fake_img', # define prefix of saved images\n)\n\n# input as list of dict\nvis_kwargs_list = [\n dict(type='Arguments', # use `Arguments` sampler to generate model input\n name='arg_output', # define prefix of saved images\n vis_mode='gif', # specific visualization mode as GIF\n forward_kwargs=dict(forward_mode='sampling', sample_kwargs=dict(show_pbar=True)) # specific kwargs for `Arguments` sampler\n ),\n dict(type='Data', # use `Data` sampler to feed data in dataloader to model as input\n n_samples=36, # specific how many samples want to generate\n fixed_input=False, # specific do not use fixed input for each visualization process\n )\n]\n```\n\n`vis_kwargs_list` takes dict or list of dict as input. Each of dict must contain a `type` field indicating the **type of sampler** used to generate the model input, and each of the dict must also contain the keyword fields necessary for the sampler (e.g. `ArgumentSampler` requires that the argument dictionary contain `forward_kwargs`).\n\n> To be noted that, this content is checked by the corresponding sampler and is not restricted by `BasicVisualizationHook`.\n\nIn addition, the other fields are generic fields (e.g. `n_samples`, `n_row`, `name`, `fixed_input`, etc.).\nIf not passed in, the default values from the BasicVisualizationHook initialization will be used.\n\nFor the convenience of users, MMagic has pre-defined visualization parameters for **GAN**, **Translation models**, **SinGAN** and **Diffusion models**, and users can directly use the predefined visualization methods by using the following configuration:\n\n```python\nvis_kwargs_list = dict(type='GAN')\nvis_kwargs_list = dict(type='SinGAN')\nvis_kwargs_list = dict(type='Translation')\nvis_kwargs_list = dict(type='TranslationVal')\nvis_kwargs_list = dict(type='TranslationTest')\nvis_kwargs_list = dict(type='DDPMDenoising')\n```\n\n## Visualizer\n\nIn MMagic, we implement `ConcatImageVisualizer` and `Visualizer`, which inherit from `mmengine.Visualizer`.\nThe base class of `Visualizer` is `ManagerMixin` and this makes `Visualizer` a globally unique object.\nAfter being instantiated, `Visualizer` can be called at anywhere of the code by `Visualizer.get_current_instance()`, as shown below:\n\n```python\n# configs\nvis_backends = [dict(type='VisBackend')]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# `get_instance()` is called for globally unique instantiation\nVISUALIZERS.build(cfg.visualizer)\n\n# Once instantiated by the above code, you can call the `get_current_instance` method at any location to get the visualizer\nvisualizer = Visualizer.get_current_instance()\n```\n\nThe core interface of `Visualizer` is `add_datasample`.\nThrough this interface,\nThis interface will call the corresponding drawing function according to the corresponding `vis_mode` to obtain the visualization result in `np.ndarray` type.\nThen `show` or `add_image` will be called to directly show the results or pass the visualization result to the predefined vis_backend.\n\n## VisBackend\n\nIn general, users do not need to manipulate `VisBackend` objects, only when the current visualization storage can not meet the needs, users will want to manipulate the storage backend directly.\nMMagic supports a variety of different visualization backends, including:\n\n- Basic VisBackend of MMEngine: including LocalVisBackend, TensorboardVisBackend and WandbVisBackend. You can follow [MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/visualization.md) to learn more about them\n- VisBackend: Backend for **File System**. Save the visualization results to the corresponding position.\n- TensorboardVisBackend: Backend for **Tensorboard**. Send the visualization results to Tensorboard.\n- WandbVisBackend: Backend for **Wandb**. Send the visualization results to Tensorboard.\n\nOne `Visualizer` object can have access to any number of VisBackends and users can access to the backend by their class name in their code.\n\n```python\n# configs\nvis_backends = [dict(type='Visualizer'), dict(type='WandbVisBackend')]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# code\nVISUALIZERS.build(cfg.visualizer)\nvisualizer = Visualizer.get_current_instance()\n\n# access to the backend by class name\ngen_vis_backend = visualizer.get_backend('VisBackend')\ngen_wandb_vis_backend = visualizer.get_backend('GenWandbVisBackend')\n```\n\nWhen there are multiply VisBackend with the same class name, user must specific name for each VisBackend.\n\n```python\n# configs\nvis_backends = [\n dict(type='VisBackend', name='gen_vis_backend_1'),\n dict(type='VisBackend', name='gen_vis_backend_2')\n]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# code\nVISUALIZERS.build(cfg.visualizer)\nvisualizer = Visualizer.get_current_instance()\n\nlocal_vis_backend_1 = visualizer.get_backend('gen_vis_backend_1')\nlocal_vis_backend_2 = visualizer.get_backend('gen_vis_backend_2')\n```\n\n### Visualize by Different Storage Backends\n\nIf you want to use a different backend (Wandb, Tensorboard, or a custom backend with a remote window), just change the `vis_backends` in the config, as follows:\n\n**Local**\n\n```python\nvis_backends = [dict(type='LocalVisBackend')]\n```\n\n**Tensorboard**\n\n```python\nvis_backends = [dict(type='TensorboardVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\nvis_backends = [dict(type='WandbVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer', vis_backends=vis_backends, name='visualizer')\n```\n" }, { "path": "docs/zh_cn/.dev_scripts/update_dataset_zoo.py", "content": "import os\n\nfrom tqdm import tqdm\n\n\ndef update_dataset_zoo():\n\n target_dir = 'dataset_zoo'\n source_dir = '../../tools/dataset_converters'\n os.makedirs(target_dir, exist_ok=True)\n\n # generate overview\n overviewmsg = \"\"\"\n# 概览\n\n\"\"\"\n\n # generate index.rst\n rstmsg = \"\"\"\n.. toctree::\n :maxdepth: 1\n :caption: Dataset Zoo\n\n overview.md\n\"\"\"\n\n subfolders = os.listdir(source_dir)\n for subf in tqdm(subfolders, desc='update dataset zoo'):\n\n target_subf = subf.replace('-', '_').lower()\n target_readme = os.path.join(target_dir, target_subf + '.md')\n source_readme = os.path.join(source_dir, subf, 'README_zh-CN.md')\n if not os.path.exists(source_readme):\n continue\n\n overviewmsg += f'\\n- [{subf}]({target_subf}.md)'\n rstmsg += f'\\n {target_subf}.md'\n\n # generate all tasks dataset_zoo\n command = f'cat {source_readme} > {target_readme}'\n os.popen(command)\n\n with open(os.path.join(target_dir, 'overview.md'), 'w') as f:\n f.write(overviewmsg)\n\n with open(os.path.join(target_dir, 'index.rst'), 'w') as f:\n f.write(rstmsg)\n\n\nif __name__ == '__main__':\n update_dataset_zoo()\n" }, { "path": "docs/zh_cn/.dev_scripts/update_model_zoo.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\nimport functools as func\nimport glob\nimport os\nimport os.path as osp\nimport re\nfrom os.path import basename, dirname\n\nimport numpy as np\nimport titlecase\nfrom tqdm import tqdm\n\ngithub_link = 'https://github.com/open-mmlab/mmagic/blob/main/'\n\n\ndef anchor(name):\n return re.sub(r'-+', '-',\n re.sub(r'[^a-zA-Z0-9\\+]', '-',\n name.strip().lower())).strip('-')\n\n\ndef summarize(stats, name):\n allpapers = func.reduce(lambda a, b: a.union(b),\n [p for p, _, _, _, _, _, _ in stats])\n allconfigs = func.reduce(lambda a, b: a.union(b),\n [c for _, c, _, _, _, _, _ in stats])\n allckpts = func.reduce(lambda a, b: a.union(b),\n [c for _, _, c, _, _, _, _ in stats])\n alltasks = func.reduce(lambda a, b: a.union(b),\n [t for _, _, _, t, _, _, _ in stats])\n task_desc = '\\n'.join([\n f\" - [{task}]({task.replace('-', '_').replace(' ', '_').lower()}.md)\" # noqa\n for task in list(alltasks)\n ])\n\n # Overview\n papertypes, papercounts = np.unique([t for t, _ in allpapers],\n return_counts=True)\n countstr = '\\n'.join(\n [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])\n countstr = '\\n'.join([f' - ALGORITHM: {len(stats)}'])\n\n summary = f\"\"\"# {name}\n\"\"\"\n\n if name != 'Overview':\n summary += '\\n## 概览'\n\n summary += f\"\"\"\n* 预训练权重个数: {len(allckpts)}\n* 配置文件个数: {len(allconfigs)}\n* 论文个数: {len(allpapers)}\n{countstr}\n \"\"\"\n\n if name == 'Overview':\n summary += f\"\"\"\n* 任务:\n{task_desc}\n\n\"\"\"\n\n return summary\n\n\n# Count algorithms\ndef update_model_zoo():\n\n target_dir = 'model_zoo'\n\n os.makedirs(target_dir, exist_ok=True)\n\n root_dir = dirname(dirname(dirname(dirname(osp.abspath(__file__)))))\n files = sorted(glob.glob(osp.join(root_dir, 'configs/*/README_zh-CN.md')))\n stats = []\n\n for f in tqdm(files, desc='update model zoo'):\n with open(f, 'r') as content_file:\n content = content_file.read()\n\n # title\n title = content.split('\\n')[0].replace('#', '')\n year = title.split('\\'')[-1].split(')')[0]\n\n # count papers\n papers = set(\n (papertype,\n titlecase.titlecase(paper.lower().strip()).replace('+', r'\\+'))\n for (papertype, paper) in re.findall(\n r'\\s*\\n.*?\\btitle\\s*=\\s*{(.*?)}',\n content, re.DOTALL))\n\n # paper links\n revcontent = '\\n'.join(list(reversed(content.splitlines())))\n paperlinks = {}\n for _, p in papers:\n paper_link = osp.join(github_link, 'configs', basename(dirname(f)),\n 'README_zh-CN.md')\n # print(p, paper_link)\n paperlinks[p] = ' '.join(\n (f'[⇨]({paper_link}#{anchor(paperlink)})'\n for paperlink in re.findall(\n rf'\\btitle\\s*=\\s*{{\\s*{p}\\s*}}.*?\\n## (.*?)\\s*[,;]?\\s*\\n',\n revcontent, re.DOTALL | re.IGNORECASE)))\n # print(' ', paperlinks[p])\n paperlist = '\\n'.join(\n sorted(f' - [{t}] {x} ({paperlinks[x]})' for t, x in papers))\n\n # count configs\n configs = set(x.lower().strip()\n for x in re.findall(r'/configs/.*?\\.py', content))\n\n # count ckpts\n ckpts = list(\n x.lower().strip()\n for x in re.findall(r'\\[model\\]\\(https\\:\\/\\/.*\\.pth', content))\n ckpts.extend(\n x.lower().strip()\n for x in re.findall(r'\\[ckpt\\]\\(https\\:\\/\\/.*\\.pth', content))\n ckpts.extend(\n x.lower().strip()\n for x in re.findall(r'\\[模型\\]\\(https\\:\\/\\/.*\\.pth', content))\n ckpts.extend(\n x.lower().strip()\n for x in re.findall(r'\\[权重\\]\\(https\\:\\/\\/.*\\.pth', content))\n ckpts = set(ckpts)\n\n # count tasks\n task_desc = list(\n set(x.lower().strip()\n for x in re.findall(r'\\*\\*任务\\*\\*: .*', content)))\n tasks = set()\n if len(task_desc) > 0:\n tasks = set(task_desc[0].split('**任务**: ')[1].split(', '))\n\n statsmsg = f\"\"\"## {title}\"\"\"\n if len(tasks) > 0:\n statsmsg += f\"\\n* Tasks: {','.join(list(tasks))}\"\n statsmsg += f\"\"\"\n\n* 预训练权重个数: {len(ckpts)}\n* 配置文件个数: {len(configs)}\n* 论文个数: {len(papers)}\n{paperlist}\n\n\"\"\"\n # * We should have: {len(glob.glob(osp.join(dirname(f), '*.py')))}\n content = content.replace('# ', '## ')\n stats.append((papers, configs, ckpts, tasks, year, statsmsg, content))\n\n # overview\n overview = summarize(stats, '概览')\n with open(osp.join(target_dir, 'overview.md'), 'w') as f:\n f.write(overview)\n\n alltasks = func.reduce(lambda a, b: a.union(b),\n [t for _, _, _, t, _, _, _ in stats])\n\n # index.rst\n indexmsg = \"\"\"\n.. toctree::\n :maxdepth: 1\n :caption: 模型库\n\n overview.md\n\"\"\"\n\n for task in alltasks:\n task = task.replace(' ', '_').replace('-', '_').lower()\n indexmsg += f' {task}.md\\n'\n\n with open(osp.join(target_dir, 'index.rst'), 'w') as f:\n f.write(indexmsg)\n\n # task-specific\n for task in alltasks:\n filtered_model = [\n (paper, config, ckpt, tasks, year, x, content)\n for paper, config, ckpt, tasks, year, x, content in stats\n if task in tasks\n ]\n filtered_model = sorted(filtered_model, key=lambda x: x[-3])[::-1]\n overview = summarize(filtered_model, task)\n\n msglist = '\\n'.join(x for _, _, _, _, _, _, x in filtered_model)\n task = task.replace(' ', '_').replace('-', '_').lower()\n with open(osp.join(target_dir, f'{task}.md'), 'w') as f:\n f.write(overview + '\\n' + msglist)\n\n\nif __name__ == '__main__':\n update_model_zoo()\n" }, { "path": "docs/zh_cn/.gitignore", "content": "model_zoo\ndataset_zoo\n" }, { "path": "docs/zh_cn/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\trm -rf _build\n\trm -rf model_zoo\n\trm -rf dataset_zoo\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/zh_cn/_static/css/readthedocs.css", "content": ".header-logo {\n background-image: url(\"../image/mmagic-logo.png\");\n background-size: 142px 46px;\n height: 46px;\n width: 142px;\n}\n" }, { "path": "docs/zh_cn/_templates/404.html", "content": "{% extends \"layout.html\" %}\n\n{% block body %}\n\n

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\n\n{% endblock %}\n" }, { "path": "docs/zh_cn/advanced_guides/data_flow.md", "content": "# 数据流\n\n- [数据流](#数据流)\n - [数据流概述](#数据流概述)\n - [数据集与模型之间的数据流](#数据集与模型之间的数据流)\n - [数据加载器的数据处理](#数据加载器的数据处理)\n - [数据预处理器的数据处理](#数据预处理器的数据处理)\n - [模型输出与可视化器之间的数据流](#模型输出与可视化器之间的数据流)\n\n## 数据流概述\n\n[Runner](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/runner.md) 相当于 MMEngine 中的“集成器”。它覆盖了框架的所有方面，并肩负着组织和调度几乎所有模块的责任，这意味着各模块之间的数据流也由 `Runner` 控制。在本章节中，我们将介绍 [Runner](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/runner.html) 管理的内部模块之间的数据流和数据格式约定。\n\n
\n\n
\n\n在上图中，在训练迭代中，数据加载器（dataloader）从存储中加载图像并传输到数据预处理器（data preprocessor），数据预处理器会将图像放到特定的设备上，并将数据堆叠到批处理中，之后模型接受批处理数据作为输入，最后将模型的输出计算损失函数（loss）。在评估时模型参数会被冻结，模型的输出需要经由数据预处理器（data preprocessor）解构再被传递给 [Evaluator](./evaluation.md#ioumetric)计算指标或者提供给[Visualizer](../user_guides/visualization.md)进行可视化。\n\n## 数据集与模型之间的数据流\n\n在本节中将介绍在MMagic中数据集中的数据流传递，关于[数据集定义](https://mmagic.readthedocs.io/zh_CN/latest/howto/dataset.html)和[数据处理管线](https://mmagic.readthedocs.io/zh_CN/latest/howto/transforms.html)相关的解读详见开发指南。数据集 （dataset） 和模型 （model）之间的数据流传递一般可以分为如下四个步骤 :\n\n1. 读取 `XXDataset` 收集数据集的原始信息，并且通过数据处理管线对数据进行数据转换处理;\n\n2. 使用 `PackInputs` 将转换完成的数据打包成为一个字典;\n\n3. 使用 `collate_fn` 将各个张量集成为一个批处理张量;\n\n4. 使用 `data_preprocessor` 把以上所有数据迁移到 GPUS 等目标设备，并在数据加载器中将之前打包的字典解压为一个元组，该元组包含输入图像与对应的元信息（`DataSample`）。\n\n### 数据处理管线的数据处理\n\n在MMagic中，经由不同类型的`XXDataset`, 分别读取数据(LQ)以及标注(GT)，并且在不同的数据预处理管道中进行数据转换，最后通过`PackInputs`将处理之后的数据打包为字典，此字典包含训练以及测试过程所需的所有数据。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
base_edit_model.py base_conditional_gan.py
\n\n```python\n@MODELS.register_module()\nclass BaseEditModel(BaseModel):\n \"\"\"Base model for image and video editing.\n \"\"\"\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs) -> Union[torch.Tensor, List[DataSample], dict]:\n if isinstance(inputs, dict):\n inputs = inputs['img']\n if mode == 'tensor':\n return self.forward_tensor(inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n\n elif mode == 'loss':\n return self.forward_train(inputs, data_samples, **kwargs)\n```\n\n\n\n```python\n@MODELS.register_module()\nclass BaseConditionalGAN(BaseGAN):\n \"\"\"Base class for Conditional GAM models.\n \"\"\"\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n if isinstance(inputs, Tensor):\n noise = inputs\n sample_kwargs = {}\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n num_batches = noise.shape[0]\n\n pass\n ...\n```\n\n
\n\n例如在`BaseEditModel`和`BaseConditionalGAN`模型中分别需要输入（input）包括 `img` 和 `noise` 的键值输入。同时，相应的字段也应该在配置文件中暴露, 以[cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py](../../../configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py)为例，\n\n```python\ndomain_a = 'photo'\ndomain_b = 'mask'\npack_input = dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n```\n\n### 数据加载器的数据处理\n\n以数据集中的获取字典列表作为输入，数据加载器（dataloader）中的 `collect_fn` 会提取每个字典的`inputs`并将其整合成一个批处理张量；此外，每个字典中的`data_sample`也会被整合为一个列表，从而输出一个与先前字典有相同键的字典；最终数据加载器会通过 `collect_fn` 输出这个字典。详细文档可见[数据集与数据加载器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/dataset.html)。\n\n### 数据预处理器的数据处理\n\n数据预处理是数据输入模型之前，处理数据过程的最后一步。 数据预处理过程会对图像进行归一处理，如把 BGR 模式转换为 RGB 模式，并将所有数据迁移至 GPU 等目标设备中 。上述各步骤完成后，最终会得到一个元组，该元组包含一个批处理图像的列表，和一个数据样本的列表。详细文档可见[数据预处理](./data_preprocessor.md)。\n\n## 模型输出与可视化器之间的数据流\n\nMMEngine约定了[抽象数据接口](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/advanced_tutorials/data_element.md)用于数据传递，其中 [数据样本](./structures.md)(DataSample) 作为一层更加高级封装可以容纳更多类别的标签数据。在MMagic中，用于可视化对比的`ConcatImageVisualizer`同时也通过 `add_datasample` 方法控制可视化具体内容，具体配置如下。\n\n```python\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=[dict(type='LocalVisBackend')],\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\n```\n" }, { "path": "docs/zh_cn/advanced_guides/data_preprocessor.md", "content": "# 数据预处理器\n\n## 数据preprocessor在训练流程中的位置\n\n在模型训练过程中，图片数据先通过mmcv中的transform进行数据增强，并加载为dataloader，而后通过preprocessor将数据从cpu搬运到cuda上，并进行padding和归一化\n\nmmcv中的transform来自各下游算法库中transform的迁移，防止各下游算法库中transform的冗余，以`configs/_base_/datasets/unpaired_imgs_256x256.py`为例，其完整config中的`train_pipeline`如下所示\n\n```python\n...\ntrain_pipeline = [\n dict(color_type='color', key='img_A', type='LoadImageFromFile'),\n dict(color_type='color', key='img_B', type='LoadImageFromFile'),\n dict(auto_remap=True, mapping=dict(img=['img_A', 'img_B',]),\n share_random_params=True,\n transforms=[dict(interpolation='bicubic', scale=(286, 286,), type='Resize'),\n dict(crop_size=(256, 256,), keys=['img',], random_crop=True, type='Crop'),],\n type='TransformBroadcaster'),\n dict(direction='horizontal', keys=['img_A', ], type='Flip'),\n dict(direction='horizontal', keys=['img_B', ], type='Flip'),\n dict(mapping=dict(img_mask='img_B', img_photo='img_A'),\n remapping=dict(img_mask='img_mask', img_photo='img_photo'),\n type='KeyMapper'),\n dict(data_keys=['img_photo', 'img_mask',],\n keys=['img_photo', 'img_mask',], type='PackInputs'),\n]\n...\n```\n\ndata_preprocessor会对transform后的数据进行数据搬移，拼接和归一化，而后输入到网络中，以`mmagic/models/editors/cyclegan/cyclegan.py`中的`train_step`函数为例，代码中的引用逻辑如下\n\n```python\n...\nmessage_hub = MessageHub.get_current_instance()\ncurr_iter = message_hub.get_info('iter')\ndata = self.data_preprocessor(data, True)\ndisc_optimizer_wrapper = optim_wrapper['discriminators']\n\ninputs_dict = data['inputs']\noutputs, log_vars = dict(), dict()\n...\n```\n\n在mmagic中的data_processor，其代码实现路径为`mmagic/models/data_preprocessors/data_preprocessor.py`，其数据处理流程如下图\n![image](https://github.com/jinxianwei/CloudImg/assets/81373517/f52a92ab-f86d-486d-86ac-a2f388a83ced)\n" }, { "path": "docs/zh_cn/advanced_guides/evaluator.md", "content": "# 评估器\n\n## 评测指标与评测器\n\n在模型的验证和测试中，通常需要对模型的精度进行定量的评测。在mmagic中实现了评测指标(metric)和评测器(evaluator)来完成这一功能。\n\n- 评测指标(metric)用于根据测试数据和模型预测结果，特定模型精度指标的计算。在mmagic中内置了多种metric，详见[评价指标](https://mmagic.readthedocs.io/zh_CN/latest/user_guides/metrics.html)。同时metric和数据集解耦，每种metric可以用于多个数据集。\n- 评测器(evaluator)是评测指标的上层模块，通常需要包含一个或者多个指标。评测器的作用是在模型评测时完成必要的数据格式转换，并调用评测指标来计算模型精度。评测器通常由[执行器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/runner.html)或测试脚本构建，分别用于在线评测和离线评测。\n\nmmagic中的评测器继承自mmengine中的评测器，基本使用方法也与mmengine中的评测器类似，具体可以参见[模型精度评测](https://mmengine.readthedocs.io/zh_CN/latest/design/evaluation.html)。但不同于其他上层视觉任务，生成模型的评估指标往往具有多种输入。例如Inception Score（IS）指标的输入仅为虚假图片和任意数量的真实图片；Perceptual path length（PPL) 则需要从隐空间中进行采样。为了对不同的评测指标进行兼容，mmagic设计了两个重要的方法prepare_metrics和prepare_samplers来实现上述要求。\n\n## prepare_metrics\n\n```python\nclass Evaluator(Evaluator):\n\t...\n def prepare_metrics(self, module: BaseModel, dataloader: DataLoader):\n \"\"\"Prepare for metrics before evaluation starts. Some metrics use\n pretrained model to extract feature. Some metrics use pretrained model\n to extract feature and input channel order may vary among those models.\n Therefore, we first parse the output color order from data\n preprocessor and set the color order for each metric. Then we pass the\n dataloader to each metrics to prepare pre-calculated items. (e.g.\n inception feature of the real images). If metric has no pre-calculated\n items, :meth:`metric.prepare` will be ignored. Once the function has\n been called, :attr:`self.is_ready` will be set as `True`. If\n :attr:`self.is_ready` is `True`, this function will directly return to\n avoid duplicate computation.\n\n Args:\n module (BaseModel): Model to evaluate.\n dataloader (DataLoader): The dataloader for real images.\n \"\"\"\n if self.metrics is None:\n self.is_ready = True\n return\n\n if self.is_ready:\n return\n\n # prepare metrics\n for metric in self.metrics:\n metric.prepare(module, dataloader)\n self.is_ready = True\n```\n\nprepare_metrics方法需要在评测开始之前调用。它被用于在每个评测指标开始评测之前进行预处理，会依次调用evaluator的所有评测指标的prepare方法来准备该评测指标的需要预先计算好的元素(例如一些隐藏层的特征)。同时为了避免多次重复调用，在所有评测指标预处理完成之后，evaluator.is_ready 标志位会被设置为True。\n\n```python\nclass GenMetric(BaseMetric):\n\t...\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n \"\"\"Prepare for the pre-calculating items of the metric. Defaults to do\n nothing.\n\n Args:\n module (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for the real images.\n \"\"\"\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n```\n\n## prepare_samplers\n\n对于生成模型而言，不同的metric需要不同的输入。例如FID, KID, IS只需要生成的fake images，而PPL则需要隐空间的向量。因此mmagic将不同的评估指标按照输入的类型进行了分组，属于同一个组的一个或者多个评测指标共享一个数据的采样器，每个评测指标的sampler mode由该评测指标的SAMPLER_MODE属性决定。\n\n```python\nclass GenMetric(BaseMetric):\n\t...\n SAMPLER_MODE = 'normal'\n\nclass GenerativeMetric(GenMetric):\n\t...\n SAMPLER_MODE = 'Generative'\n```\n\n而evaluator的prepare_samplers 方法就是根据所有评测指标的sampler mode来准备好data sampler。\n\n```python\nclass Evaluator(Evaluator):\n\t...\n def prepare_samplers(self, module: BaseModel, dataloader: DataLoader\n ) -> List[Tuple[List[BaseMetric], Iterator]]:\n \"\"\"Prepare for the sampler for metrics whose sampling mode are\n different. For generative models, different metric need image\n generated with different inputs. For example, FID, KID and IS need\n images generated with random noise, and PPL need paired images on the\n specific noise interpolation path. Therefore, we first group metrics\n with respect to their sampler's mode (refers to\n :attr:~`GenMetrics.SAMPLER_MODE`), and build a shared sampler for each\n metric group. To be noted that, the length of the shared sampler\n depends on the metric of the most images required in each group.\n\n Args:\n module (BaseModel): Model to evaluate. Some metrics (e.g. PPL)\n require `module` in their sampler.\n dataloader (DataLoader): The dataloader for real image.\n\n Returns:\n List[Tuple[List[BaseMetric], Iterator]]: A list of \"metrics-shared\n sampler\" pair.\n \"\"\"\n if self.metrics is None:\n return [[[None], []]]\n\n # grouping metrics based on `SAMPLER_MODE` and `sample_mode`\n metric_mode_dict = defaultdict(list)\n for metric in self.metrics: # 为每个metric指定sampler group\n metric_md5 = self._cal_metric_hash(metric)\n metric_mode_dict[metric_md5].append(metric)\n\n metrics_sampler_list = []\n for metrics in metric_mode_dict.values(): #为每个group生成sampler\n first_metric = metrics[0]\n metrics_sampler_list.append([\n metrics,\n first_metric.get_metric_sampler(module, dataloader, metrics)\n ])\n\n return metrics_sampler_list\n```\n\n该方法会首先检查自身是否有需要计算的评测指标：如果没有直接返回，如果有则会遍历所有评测指标，对所有采样指标根据sampler_mode和sample_model进行分组, 具体实现方式为根据sampler_mode和sample_model计算hash码，将具有相同hash码的评测指标放入同一个列表里。\n\n```python\nclass Evaluator(Evaluator):\n\t...\n @staticmethod\n def _cal_metric_hash(metric: GenMetric):\n \"\"\"Calculate a unique hash value based on the `SAMPLER_MODE` and\n `sample_model`.\"\"\"\n sampler_mode = metric.SAMPLER_MODE\n sample_model = metric.sample_model\n metric_dict = {\n 'SAMPLER_MODE': sampler_mode,\n 'sample_model': sample_model\n }\n if hasattr(metric, 'need_cond_input'):\n metric_dict['need_cond_input'] = metric.need_cond_input\n md5 = hashlib.md5(repr(metric_dict).encode('utf-8')).hexdigest()\n return md5\n```\n\n最后该方法会为每一个评测指标组生成一个sampler采样器，添加到列表返回。\n\n## 评测器评测流程\n\n整个评测器的评测流程在方法mmagic.engine.runner.MultiValLoop.run和mmagic.engine.runner.MultiTestLoop.run中实现。以mmagic.engine.runner.MultiTestLoop.run为例：\n\n```python\nclass MultiValLoop(BaseLoop):\n\t...\n def run(self):\n\t...\n # 1. prepare all metrics and get the total length\n metrics_sampler_lists = []\n meta_info_list = []\n dataset_name_list = []\n for evaluator, dataloader in zip(self.evaluators, self.dataloaders):\n # 1.1 prepare for metrics\n evaluator.prepare_metrics(module, dataloader)\n # 1.2 prepare for metric-sampler pair\n metrics_sampler_list = evaluator.prepare_samplers(\n module, dataloader)\n metrics_sampler_lists.append(metrics_sampler_list)\n # 1.3 update total length\n self._total_length += sum([\n len(metrics_sampler[1])\n for metrics_sampler in metrics_sampler_list\n ])\n # 1.4 save metainfo and dataset's name\n meta_info_list.append(\n getattr(dataloader.dataset, 'metainfo', None))\n dataset_name_list.append(dataloader.dataset.__class__.__name__)\n```\n\nrunner首先会通过evaluator.prepare_metrics和evaluator.prepare_samplers两个方法来进行评测所需要的预处理工作和获取评测所需要的数据采样器；同时更新所有采样器的采样总长度。由于mmagic的评测指标和数据集进行了分离，因此一些在评测时所需要的meta_info也需要进行保存并传递给评测器。\n\n```python\nclass MultiValLoop(BaseLoop):\n\t...\n def run(self):\n\t...\n # 2. run evaluation\n for idx in range(len(self.evaluators)):\n # 2.1 set self.evaluator for run_iter\n self.evaluator = self.evaluators[idx]\n self.dataloader = self.dataloaders[idx]\n\n # 2.2 update metainfo for evaluator and visualizer\n meta_info = meta_info_list[idx]\n dataset_name = dataset_name_list[idx]\n if meta_info:\n self.evaluator.dataset_meta = meta_info\n self._runner.visualizer.dataset_meta = meta_info\n else:\n warnings.warn(\n f'Dataset {dataset_name} has no metainfo. `dataset_meta` '\n 'in evaluator, metric and visualizer will be None.')\n\n # 2.3 generate images\n metrics_sampler_list = metrics_sampler_lists[idx]\n for metrics, sampler in metrics_sampler_list:\n for data in sampler:\n self.run_iter(idx_counter, data, metrics)\n idx_counter += 1\n\n # 2.4 evaluate metrics and update multi_metric\n metrics = self.evaluator.evaluate()\n if multi_metric and metrics.keys() & multi_metric.keys():\n raise ValueError('Please set different prefix for different'\n ' datasets in `val_evaluator`')\n else:\n multi_metric.update(metrics)\n # 3. finish evaluation and call hooks\n self._runner.call_hook('after_val_epoch', metrics=multi_metric)\n self._runner.call_hook('after_val')\n```\n\n在完成了评测前的准备之后，runner会遍历所有evaluator，依次进行评估，每个evaluator需要对应一个dataloader，完成一个数据集的评测工作。具体在对每个evaluator进行评测的过程中，首先需要将评测所需要的meta_info传递给评测器，随后遍历该evaluator的所有metrics_sampler，生成评测所需要的图像，最后再完成评测。\n" }, { "path": "docs/zh_cn/advanced_guides/structures.md", "content": "# Data Structure\n\nMMaigc的数据结构接口`DataSample` 继承自 MMEngine 的 [` BaseDataElement`](https://mmengine.readthedocs.io/zh_CN/latest/advanced_tutorials/data_element.html).MMEngine 的抽象数据接口实现了基础的增/删/改/查功能，且支持不同设备间的数据迁移，也支持了类字典和张量的操作，充分满足了数据的日常使用需求，这也使得不同算法的数据接口可以得到统一。\n\n特别的，`BaseDataElement` 中存在两种类型的数据:\n\n- `metainfo` 类型，包含数据的元信息以确保数据的完整性，如 `img_shape`, `img_id` 等数据所在图片的一些基本信息，方便可视化等情况下对数据进行恢复和使用。\n- `data` 类型，如标注框、框的标签、和实例掩码等。\n\n得益于统一的数据封装，算法库内的 [`visualizer`](https://mmagic.readthedocs.io/zh_CN/latest/user_guides/visualization.html), [`evaluator`](https://mmagic.readthedocs.io/zh_CN/latest/advanced_guides/evaluator.html), [`model`](https://mmagic.readthedocs.io/zh_CN/latest/howto/models.html) 等各个模块间的数据流通都得到了极大的简化。\n\n`DataSample`中的数据分为以下几个属性：\n\n```python\n- ``gt_img``: 原始图像\n- ``pred_img``: 模型预测图像\n- ``ref_img``:参考图像\n- ``mask``: 图像修复中的遮挡区域\n- ``trimap``: 图像抠图中的三通道图\n- ``gt_alpha``: 图像抠图中原始Alpha图\n- ``pred_alpha``: 图像抠图中模型预测Alpha图\n- ``gt_fg``: 图像抠图中原始前景图\n- ``pred_fg``: 图像抠图中模型预测前景图\n- ``gt_bg``: 图像抠图中原始背景图\n- ``pred_bg``: 图像抠图中模型预测背景图\n- ``gt_merged``: 图像抠图中原始合并图\n```\n\n以下示例代码展示了 `DataSample` 的组成元素类型：\n\n```python\nimport torch\nimport numpy as np\nfrom mmagic.structures import DataSample\nimg_meta = dict(img_shape=(800, 1196, 3))\nimg = torch.rand((3, 800, 1196))\ndata_sample = DataSample(gt_img=img, metainfo=img_meta)\nassert 'img_shape' in data_sample.metainfo_keys()\ndata_sample\n# `DataSample` 的组成元素类型\n\n```\n\n`DataSample`同样支持`stack`和`split`操作对数据进行批处理:\n\n1. Stack\n\n该函数用于将数据样本列表堆叠成一个。当数据样本堆叠时，所有张量字段都将堆叠在第一维度。如果数据样本中有非张量字段，例如列表或字典，则这些字段的值将保存在列表中。\n\n```\n Args:\n data_samples (Sequence['DataSample']): 待堆叠的数据样本序列\n\n Returns:\n DataSample: 堆叠的数据样本\n```\n\n2. Split\n\n该函数将在第一维度拆分数据样本序列。\n\n```\n\tArgs:\n allow_nonseq_value (bool): 是否允许在拆分操作中使用非顺序数据。如果为 \"True\"，\t\t\t将为所有拆分数据样本复制非序列数据；否则，将引发错误。默认为 \"False\"。\n\n Returns:\n Sequence[DataSample]: 拆分后的数据样本列表。\n```\n\n以下示例代码展示了 `stack`和`split` 的使用方法：\n\n```py\nimport torch\nimport numpy as np\nfrom mmagic.structures import DataSample\nimg_meta1 = img_meta2 = dict(img_shape=(800, 1196, 3))\nimg1 = torch.rand((3, 800, 1196))\nimg2 = torch.rand((3, 800, 1196))\ndata_sample1 = DataSample(gt_img=img1, metainfo=img_meta1)\ndata_sample2 = DataSample(gt_img=img2, metainfo=img_meta1)\n```\n\n```py\n# 堆叠stack\ndata_sample = DataSample.stack([data_sample1, data_sample2])\nprint(data_sample.gt_img.shape)\n torch.Size([2, 3, 800, 1196])\nprint(data_sample.metainfo)\n {'img_shape': [(800, 1196, 3), (800, 1196, 3)]}\n\n# 拆分split\ndata_sample1_, data_sample2_ = data_sample.split()\nassert (data_sample1_.gt_img == img1).all()\nassert (data_sample2_.gt_img == img2).all()\n```\n" }, { "path": "docs/zh_cn/changelog.md", "content": "# 变更日志\n\n**亮点**\n\n- 我们的代码仓库中发布了一个先进而强大的图像 inpainting 算法 PowerPaint。 [Click to View](https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint)\n\n
\n\n
\n\n**新功能和改进**\n\n- \\[CodeCamp2023-645\\] 新增 dreambooth 的new cfg, by @YanxingLiu in https://github.com/open-mmlab/mmagic/pull/2042\n- \\[Enhance\\] 新增 _base_ 目录下的 new config by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2053\n- \\[Enhance\\] 支持了 instance_crop 使用 from_pretrained by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2066\n- \\[Enhance\\] 支持了最新的 diffusers 使用 lora by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2067\n- \\[Enhance\\] 提升了 powerpaint by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2078\n- \\[Enhance\\] 提升了 powerpaint by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2080\n- \\[Enhance\\] 增加了 gradio_PowerPaint.py 的 outpainting by @zhuang2002 in https://github.com/open-mmlab/mmagic/pull/2084\n- \\[MMSIG\\] 增加了 StyleGAN2 的新config by @xiaomile in https://github.com/open-mmlab/mmagic/pull/2057\n- \\[MMSIG\\] \\[Doc\\] 更新了 data_preprocessor.md by @jinxianwei in https://github.com/open-mmlab/mmagic/pull/2055\n- \\[Enhance\\] 提升了 PowerPaint by @zhuang2002 in https://github.com/open-mmlab/mmagic/pull/2093\n\n**Bug 修复**\n\n- \\[Fix\\] 更新了 README.md by @eze1376 in https://github.com/open-mmlab/mmagic/pull/2048\n- \\[Fix\\] 修复了 test tokenizer by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2050\n- \\[Fix\\] 修复了 readthedocs building by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2052\n- \\[Fix\\] 修复了 --local-rank for PyTorch >= 2.0.0 by @youqingxiaozhua in https://github.com/open-mmlab/mmagic/pull/2051\n- \\[Fix\\] 修复了 animatediff download from openxlab by @JianxinDong in https://github.com/open-mmlab/mmagic/pull/2061\n- \\[Fix\\] 修复了 best practice by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2063\n- \\[Fix\\] 修复了 try import expand mask from transformers by @zengyh1900 in https://github.com/open-mmlab/mmagic/pull/2064\n- \\[Fix\\] 更新了 diffusers to v0.23.0 by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2069\n- \\[Fix\\] 修复了 openxlab link to powerpaint by @liuwenran in https://github.com/open-mmlab/mmagic/pull/2082\n- \\[Fix\\] 更新了 swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py, use MultiValLoop. by @ashutoshsingh0223 in https://github.com/open-mmlab/mmagic/pull/2085\n- \\[Fix\\] 修复了 a test expression that has a logical short circuit. by @munahaf in https://github.com/open-mmlab/mmagic/pull/2046\n- \\[Fix\\] 修复了 Powerpaint load safetensors by @sdbds in https://github.com/open-mmlab/mmagic/pull/2088\n\n**新贡献者**\n\n- @eze1376 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2048\n- @youqingxiaozhua 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2051\n- @JianxinDong 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2061\n- @zhuang2002 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2076\n- @ashutoshsingh0223 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2085\n- @jinxianwei 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2055\n- @munahaf 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2046\n- @sdbds 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2088\n\n**完整更新日志**: https://github.com/open-mmlab/mmagic/compare/v1.1.0...v1.2.0\n\n## v1.1.0 (22/09/2023)\n\n**亮点**\n\n在这次的发版中，我们新支持了下面五个新的算法.\n\n- 支持了 ViCo, 一种新的 SD personalization 算法. [点击查看](https://github.com/open-mmlab/mmagic/blob/main/configs/vico/README.md)\n\n\n\n \n \n
\n
\n \n
\n
\n\n- 支持了 AnimateDiff, 一个很火的 text2animation 算法. [点击查看](https://github.com/open-mmlab/mmagic/blob/main/configs/animatediff/README.md)\n\n![512](https://github.com/ElliotQi/mmagic/assets/46469021/54d92aca-dfa9-4eeb-ba38-3f6c981e5399)\n\n- 支持了 SDXL. [点击查看](https://github.com/open-mmlab/mmagic/blob/main/configs/stable_diffusion_xl/README.md)\n\n
\n\n
\n\n- 支持了 DragGAN. [点击查看](https://github.com/open-mmlab/mmagic/blob/main/configs/draggan/README.md)\n\n
\n\n
\n\n- 支持了 FastComposer. [点击查看](https://github.com/open-mmlab/mmagic/blob/main/configs/fastcomposer/README.md)\n\n
\n\n
\n\n**新功能和改进**\n\n- \\[功能\\] 支持使用diffusers pipeline进行推断，首先使用sd_xl。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2023\n- \\[增强\\] 为sd推理器添加负面提示。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2021\n- \\[增强\\] 更新setup.cfg中的flake8检查配置。由@LeoXing1996完成，在https://github.com/open-mmlab/mmagic/pull/2007\n- \\[增强\\] 将'config_name'作为'model_setting'的补充添加。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2027\n- \\[增强\\] 更快的测试。由@okotaku完成，在https://github.com/open-mmlab/mmagic/pull/2034\n- \\[增强\\] 添加OpenXLab徽章。由@ZhaoQiiii完成，在https://github.com/open-mmlab/mmagic/pull/2037\n\n**CodeCamp贡献**\n\n- \\[CodeCamp2023-643\\] 添加BigGAN的新配置。由@limafang完成，在https://github.com/open-mmlab/mmagic/pull/2003\n- \\[CodeCamp2023-648\\] MMagic的新配置GuidedDiffusion。由@ooooo-create完成，在https://github.com/open-mmlab/mmagic/pull/2005\n- \\[CodeCamp2023-649\\] MMagic的新配置Instance Colorization。由@ooooo-create完成，在https://github.com/open-mmlab/mmagic/pull/2010\n- \\[CodeCamp2023-652\\] MMagic的新配置StyleGAN3。由@hhy150完成，在https://github.com/open-mmlab/mmagic/pull/2018\n- \\[CodeCamp2023-653\\] 添加Real BasicVSR的新配置。由@RangeKing完成，在https://github.com/open-mmlab/mmagic/pull/2030\n\n**Bug 修复**\n\n- \\[修复\\] 修复主页上的最佳实践和返回目录，将新模型添加到模型库中。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2001\n- \\[修复\\] 检查CI错误并移除主流GPU测试。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2013\n- \\[修复\\] 检查Circle CI内存。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2016\n- \\[修复\\] 移除代码并修复剪辑损失的单元测试。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2017\n- \\[修复\\] 在diffusers pipeline推理器单元测试中模拟推理。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2026\n- \\[修复\\] 由于合并draggan而修复的错误。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2029\n- \\[修复\\] 更新QR码。由@crazysteeaam完成，在https://github.com/open-mmlab/mmagic/pull/2009\n- \\[修复\\] 用OpenXLab版本替换README中的下载链接。由@FerryHuang完成，在https://github.com/open-mmlab/mmagic/pull/2038\n- \\[修复\\] 增加文档字符串覆盖率。由@liuwenran完成，在https://github.com/open-mmlab/mmagic/pull/2039\n\n**新贡献者**\n\n- @limafang 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2003\n- @ooooo-create 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2005\n- @hhy150 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2018\n- @ZhaoQiiii 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2037\n- @ElliotQi 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1980\n- @Beaconsyh08 首次贡献于 https://github.com/open-mmlab/mmagic/pull/2012\n\n**完整更新日志**: https://github.com/open-mmlab/mmagic/compare/v1.0.2...v1.0.3\n\n## v1.0.2 (24/08/2023)\n\n**亮点**\n\n**1. 更详尽的文档**\n\n感谢社区的贡献者们帮助我们改进文档。我们已经改进了许多文档，包括中文和英文版本。更多详细信息请参考[文档](https://mmagic.readthedocs.io/zh_CN/latest/)。\n\n**2. 新的算法**\n\n- 支持了 Prompt-to-prompt, DDIM Inversion 和 Null-text Inversion. [点击查看.](https://github.com/open-mmlab/mmagic/blob/main/projects/prompt_to_prompt/README.md)\n\n从左到右: origin image, DDIM inversion, Null-text inversion\n\n
\n \n
\n\nPrompt-to-prompt 编辑\n\n
\n cat -> dog\n
\n \n
\n\n
\n spider man -> iron man(attention replace)\n
\n \n
\n\n
\n Effel tower -> Effel tower at night (attention refine)\n
\n \n
\n\n
\n blossom sakura tree -> blossom(-3) sakura tree (attention reweight)\n
\n \n
\n\n- 支持了 Textual Inversion. [点击查看.](https://github.com/open-mmlab/mmagic/blob/main/configs/textual_inversion/README.md)\n\n
\n\n
\n\n- 支持了 Attention Injection 以便使用 controlnet 生成更稳定的视频. [点击查看.](https://github.com/open-mmlab/mmagic/blob/main/configs/controlnet_animation/README.md)\n- 支持了 Stable Diffusion Inpainting. [点击查看.](https://github.com/open-mmlab/mmagic/blob/main/configs/stable_diffusion/README.md)\n\n**新功能和改进**\n\n- \\[增强\\] 支持在稳定扩散训练中的噪声偏移，由 @LeoXing1996 提交于 https://github.com/open-mmlab/mmagic/pull/1880 ↗\n- \\[社区\\] 支持 Glide Upsampler，由 @Taited 提交于 https://github.com/open-mmlab/mmagic/pull/1663 ↗\n- \\[增强\\] 支持 controlnet 推理器，由 @Z-Fran 提交于 https://github.com/open-mmlab/mmagic/pull/1891 ↗\n- \\[功能\\] 支持 Albumentations 增强变换和流水线，由 @Z-Fran 提交于 https://github.com/open-mmlab/mmagic/pull/1894 ↗\n- \\[功能\\] 为 unet 添加注意力注入，由 @liuwenran 提交于 https://github.com/open-mmlab/mmagic/pull/1895 ↗\n- \\[增强\\] 更新基准测试脚本，由 @Z-Fran 提交于 https://github.com/open-mmlab/mmagic/pull/1907 ↗\n- \\[增强\\] 更新 mmagic 文档，由 @crazysteeaam 提交于 https://github.com/open-mmlab/mmagic/pull/1920 ↗\n- \\[增强\\] 支持 Prompt-to-prompt、ddim inversion 和 null-text inversion，由 @FerryHuang 提交于 https://github.com/open-mmlab/mmagic/pull/1908 ↗\n- \\[CodeCamp2023-302\\] 支持 MMagic 可视化并编写用户指南，由 @aptsunny 提交于 https://github.com/open-mmlab/mmagic/pull/1939 ↗\n- \\[功能\\] 支持textual inversion，由 @LeoXing1996 提交于 https://github.com/open-mmlab/mmagic/pull/1822 ↗\n- \\[增强\\] 对一些模型采用 BaseModule，由 @LeoXing1996 提交于 https://github.com/open-mmlab/mmagic/pull/1543 ↗\n- \\[MMSIG\\] 支持 DeblurGANv2 推理，由 @xiaomile 提交于 https://github.com/open-mmlab/mmagic/pull/1955 ↗\n- \\[CodeCamp2023-647\\] 添加 EG3D 的新配置，由 @RangeKing 提交于 https://github.com/open-mmlab/mmagic/pull/1985 ↗\n\n**Bug 修复**\n\n- 修复了 StableDiffusion 和 DreamBooth 训练中的 dtype 错误。 by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1879\n- 修复了 gui VideoSlider 的 bug。 by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1885\n- 修复了 init_model 和 glide demo 的问题。 by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1888\n- 修复了当 dim=3 时的 InstColorization bug。 by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1901\n- 修复了 sd 和 controlnet 的 fp16 bug。 by @Z-Fran in https://github.com/open-mmlab/mmagic/pull/1914\n- 修复了 controlnet 中的 num_images_per_prompt。 by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1936\n- 修正了 sd-inpainting 的 metafile 以修复推理器的初始化。 by @LeoXing1996 in https://github.com/open-mmlab/mmagic/pull/1995\n\n**新贡献者**\n\n- @wyyang23 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1886\n- @yehuixie 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1912\n- @crazysteeaam 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1920\n- @BUPT-NingXinyu 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1921\n- @zhjunqin 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1918\n- @xuesheng1031 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1923\n- @wslgqq277g 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1934\n- @LYMDLUT 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1933\n- @RangeKing 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1930\n- @xin-li-67 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1932\n- @chg0901 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1931\n- @aptsunny 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1939\n- @YanxingLiu 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1943\n- @tackhwa 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1937\n- @Geo-Chou 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1940\n- @qsun1 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1956\n- @ththth888 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1961\n- @sijiua 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1967\n- @MING-ZCH 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1982\n- @AllYoung 首次贡献于 https://github.com/open-mmlab/mmagic/pull/1996\n\n## v1.0.1 (26/05/2023)\n\n**新功能和改进**\n\n- 支持 StableDiffusion tomesd 加速. [#1801](https://github.com/open-mmlab/mmagic/pull/1801)\n- 支持所有 inpainting/matting/image restoration 模型的 inferencer. [#1833](https://github.com/open-mmlab/mmagic/pull/1833), [#1873](https://github.com/open-mmlab/mmagic/pull/1873)\n- 支持 animated drawings. [#1837](https://github.com/open-mmlab/mmagic/pull/1837)\n- 支持 Style-Based Global Appearance Flow for Virtual Try-On at projects. [#1786](https://github.com/open-mmlab/mmagic/pull/1786)\n- 支持 tokenizer wrapper 和 EmbeddingLayerWithFixe. [#1846](https://github.com/open-mmlab/mmagic/pull/1846)\n\n**Bug 修复**\n\n- 修复安装依赖. [#1819](https://github.com/open-mmlab/mmagic/pull/1819)\n- 修复 inst-colorization PackInputs. [#1828](https://github.com/open-mmlab/mmagic/pull/1828), [#1827](https://github.com/open-mmlab/mmagic/pull/1827)\n- 修复 pip install 时 inferencer 无法使用的问题. [#1875](https://github.com/open-mmlab/mmagic/pull/1875)\n\n## v1.0.0 (25/04/2023)\n\n我们正式发布 MMagic v1.0.0 版本，源自 [MMEditing](https://github.com/open-mmlab/mmediting) 和 [MMGeneration](https://github.com/open-mmlab/mmgeneration)。\n\n![mmagic-log](https://user-images.githubusercontent.com/49083766/233557648-9034f5a0-c85d-4092-b700-3a28072251b6.png)\n\n自从 MMEditing 诞生以来，它一直是许多图像超分辨率、编辑和生成任务的首选算法库，帮助多个研究团队取得 10 余 项国际顶级赛事的胜利，支撑了 100 多个 GitHub 生态项目。经过 OpenMMLab 2.0 框架的迭代更新以及与 MMGeneration 的合并，MMEditing 已经成为了一个支持基于 GAN 和 CNN 的底层视觉算法的强大工具。\n\n而今天，MMEditing 将更加拥抱生成式 AI（Generative AI），正式更名为 **MMagic**（**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation），致力于打造更先进、更全面的 AIGC 开源算法库。\n\n在 MMagic 中，我们已经支持了 53+ 模型，分布于 Stable Diffusion 的微调、图文生成、图像及视频修复、超分辨率、编辑和生成等多种任务。配合 [MMEngine](https://github.com/open-mmlab/mmengine) 出色的训练与实验管理支持，MMagic 将为广大研究者与 AIGC 爱好者们提供更加快捷灵活的实验支持，助力你的 AIGC 探索之旅。使用 MMagic，体验更多生成的魔力！让我们一起开启超越编辑的新纪元！ More than Editing, Unlock the Magic!\n\n**主要更新**\n\n**1. 新算法**\n\n我们支持了4个新任务以及11个新算法。\n\n- Text2Image / Diffusion\n - ControlNet\n - DreamBooth\n - Stable Diffusion\n - Disco Diffusion\n - GLIDE\n - Guided Diffusion\n- 3D-aware Generation\n - EG3D\n- Image Restoration\n - NAFNet\n - Restormer\n - SwinIR\n- Image Colorization\n - InstColorization\n\nhttps://user-images.githubusercontent.com/49083766/233564593-7d3d48ed-e843-4432-b610-35e3d257765c.mp4\n\n**2. Magic Diffusion Model**\n\n针对 Diffusion Model，我们提供了以下“魔法”\n\n- 支持基于 Stable Diffusion 与 Disco Diffusion 的图像生成.\n\n- 支持 Dreambooth 以及 DreamBooth LoRA 等 Finetune 方法.\n\n- 支持 ControlNet 进行可控性的文本到图像生成.\n ![de87f16f-bf6d-4a61-8406-5ecdbb9167b6](https://user-images.githubusercontent.com/49083766/233558077-2005e603-c5a8-49af-930f-e7a465ca818b.png)\n\n- 支持 xFormers 加速和优化策略，提高训练与推理效率.\n\n- 支持基于 MultiFrame Render 的视频生成.\n MMagic 支持通过 ControlNet 与多帧渲染法实现长视频的生成。\n prompt keywords: a handsome man, silver hair, smiling, play basketball\n\n https://user-images.githubusercontent.com/12782558/227149757-fd054d32-554f-45d5-9f09-319184866d85.mp4\n\n prompt keywords: a girl, black hair, white pants, smiling, play basketball\n\n https://user-images.githubusercontent.com/49083766/233559964-bd5127bd-52f6-44b6-a089-9d7adfbc2430.mp4\n\n prompt keywords: a handsome man\n\n https://user-images.githubusercontent.com/12782558/227152129-d70d5f76-a6fc-4d23-97d1-a94abd08f95a.mp4\n\n- 支持通过 Wrapper 调用 Diffusers 的基础模型以及采样策略.\n\n- SAM + MMagic = Generate Anything！\n 当下流行的 SAM（Segment Anything Model）也可以为 MMagic 提供更多加持！想制作自己的动画，可以移步至 [OpenMMLab PlayGround](https://github.com/open-mmlab/playground/blob/main/mmediting_sam/README.md)！\n\n https://user-images.githubusercontent.com/49083766/233562228-f39fc675-326c-4ae8-986a-c942059effd0.mp4\n\n**3. 框架升级**\n\n为了提升你的“施法”效率，我们对“魔术回路”做了以下升级:\n\n- 通过 OpenMMLab 2.0 框架的 MMEngine 和 MMCV， MMagic 将编辑框架分解为不同的组件，并且可以通过组合不同的模块轻松地构建自定义的编辑器模型。我们可以像搭建“乐高”一样定义训练流程，提供丰富的组件和策略。在 MMagic 中，你可以使用不同的 APIs 完全控制训练流程.\n- 支持 33+ 算法 Pytorch 2.0 加速.\n- 重构 DataSample，支持 batch 维度的组合与拆分.\n- 重构 DataPreprocessor，并统一各种任务在训练与推理时的数据格式.\n- 重构 MultiValLoop 与 MultiTestLoop，同时支持生成类型指标（e.g. FID）与重建类型指标（e.g. SSIM） 的评测，同时支持一次性评测多个数据集\n- 支持本地可视化以及使用 tensorboard 或 wandb的可视化.\n\n**新功能和改进**\n\n- 支持 53+ 算法，232+ 配置，213+ 模型权重，26+ 损失函数，and 20+ 评价指标.\n- 支持 controlnet 动画生成以及 Gradio gui. [点击查看.](https://github.com/open-mmlab/mmagic/tree/main/configs/controlnet_animation)\n- 支持 Inferencer 和 Demo，使用High-level Inference APIs. [点击查看.](https://github.com/open-mmlab/mmagic/tree/main/demo)\n- 支持 Inpainting 推理的 Gradio gui. [点击查看.](https://github.com/open-mmlab/mmagic/blob/main/demo/gradio-demo.py)\n- 支持可视化图像/视频质量比较工具. [点击查看.](https://github.com/open-mmlab/mmagic/tree/main/tools/gui)\n- 开启 projects，助力社区更快向算法库中添加新算法. [点击查看.](https://github.com/open-mmlab/mmagic/tree/main/projects)\n- 完善数据集的预处理脚本和使用说明文档. [点击查看.](https://github.com/open-mmlab/mmagic/tree/main/tools/dataset_converters)\n\n## v1.0.0rc7 (07/04/2023)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc7 版本。 此版本支持了 MMEditing 和 MMGeneration 的 51+ 模型，226+ configs 和 212+ checkpoints。以下是此次版本发布的重点新功能\n\n- 支持了 DiffuserWrapper.\n- 支持了 ControlNet 的推理与训练.\n- 支持了 PyTorch 2.0.\n\n**新功能和改进**\n\n- 支持了 DiffuserWrapper. [#1692](https://github.com/open-mmlab/mmagic/pull/1692)\n- 支持了 ControlNet 的推理与训练. [#1744](https://github.com/open-mmlab/mmagic/pull/1744)\n- 支持了 PyTorch 2.0 (使用 'inductor' 后端成功编译 33+ 模型) [#1742](https://github.com/open-mmlab/mmagic/pull/1742).\n- 支持了图像超分和视频超分的 inferencer. [#1662](https://github.com/open-mmlab/mmagic/pull/1662), [#1720](https://github.com/open-mmlab/mmagic/pull/1720)\n- 重构 get_flops 脚本. [#1675](https://github.com/open-mmlab/mmagic/pull/1675)\n- 重构数据集的 dataset_converters 脚本和使用文档. [#1690](https://github.com/open-mmlab/mmagic/pull/1690)\n- 迁移 stylegan 算子到 MMCV 中. [#1383](https://github.com/open-mmlab/mmagic/pull/1383)\n\n**Bug 修复**\n\n- 修复 disco inferencer. [#1673](https://github.com/open-mmlab/mmagic/pull/1673)\n- 修复 nafnet optimizer 配置. [#1716](https://github.com/open-mmlab/mmagic/pull/1716)\n- 修复 tof typo. [#1711](https://github.com/open-mmlab/mmagic/pull/1711)\n\n**贡献者**\n\n@LeoXing1996, @Z-Fran, @plyfager, @zengyh1900, @liuwenran, @ryanxingql, @HAOCHENYE, @VongolaWu\n\n## v1.0.0rc6 (02/03/2023)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc6 版本。 此版本支持了 MMEditing 和 MMGeneration 的 50+ 模型，222+ configs 和 209+ checkpoints。以下是此次版本发布的重点新功能\n\n- 支持了 Inpainting 任务推理的 Gradio gui.\n- 支持了图像上色、图像翻译和 GAN 模型的 inferencer.\n\n**新功能和改进**\n\n- 重构了 FileIO. [#1572](https://github.com/open-mmlab/mmagic/pull/1572)\n- 重构了 registry. [#1621](https://github.com/open-mmlab/mmagic/pull/1621)\n- 重构了 Random degradations. [#1583](https://github.com/open-mmlab/mmagic/pull/1583)\n- 重构了 DataSample, DataPreprocessor, Metric 和 Loop. [#1656](https://github.com/open-mmlab/mmagic/pull/1656)\n- 使用 mmengine.basemodule 替换 nn.module. [#1491](https://github.com/open-mmlab/mmagic/pull/1491)\n- 重构了算法库主页. [#1609](https://github.com/open-mmlab/mmagic/pull/1609)\n- 支持了 Inpainting 任务推理的 Gradio gui. [#1601](https://github.com/open-mmlab/mmagic/pull/1601)\n- 支持了图像上色的 inferencer. [#1588](https://github.com/open-mmlab/mmagic/pull/1588)\n- 支持了图像翻译和所有 GAN 模型的 inferencer. [#1650](https://github.com/open-mmlab/mmagic/pull/1650)\n- 支持了 GAN 模型的 inferencer. [#1653](https://github.com/open-mmlab/mmagic/pull/1653), [#1659](https://github.com/open-mmlab/mmagic/pull/1659)\n- 新增 Print config 工具. [#1590](https://github.com/open-mmlab/mmagic/pull/1590)\n- 改进 type hints. [#1604](https://github.com/open-mmlab/mmagic/pull/1604)\n- 更新 metrics 和 datasets 的中文文档. [#1568](https://github.com/open-mmlab/mmagic/pull/1568), [#1638](https://github.com/open-mmlab/mmagic/pull/1638)\n- 更新 BigGAN 和 Disco-Diffusion 的中文文档. [#1620](https://github.com/open-mmlab/mmagic/pull/1620)\n- 更新 Guided-Diffusion 的 Evaluation 和 README. [#1547](https://github.com/open-mmlab/mmagic/pull/1547)\n\n**Bug 修复**\n\n- 修复 EMA `momentum`. [#1581](https://github.com/open-mmlab/mmagic/pull/1581)\n- 修复 RandomNoise 的输出类型. [#1585](https://github.com/open-mmlab/mmagic/pull/1585)\n- 修复 pytorch2onnx 工具. [#1629](https://github.com/open-mmlab/mmagic/pull/1629)\n- 修复 API 文档. [#1641](https://github.com/open-mmlab/mmagic/pull/1641), [#1642](https://github.com/open-mmlab/mmagic/pull/1642)\n- 修复 RealESRGAN 加载 EMA 参数. [#1647](https://github.com/open-mmlab/mmagic/pull/1647)\n- 修复 dataset_converters 脚本的 arg passing bug. [#1648](https://github.com/open-mmlab/mmagic/pull/1648)\n\n**贡献者**\n\n@plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @liuwenran, @austinmw, @dienachtderwelt, @liangzelong, @i-aki-y, @xiaomile, @Li-Qingyun, @vansin, @Luo-Yihang, @ydengbi, @ruoningYu, @triple-Mu\n\n## v1.0.0rc5 (04/01/2023)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc5 版本。 此版本支持了 MMEditing 和 MMGeneration 的 49+ 模型，180+ configs 和 177+ checkpoints。以下是此次版本发布的重点新功能\n\n- 支持了 Restormer 算法.\n- 支持了 GLIDE 算法.\n- 支持了 SwinIR 算法.\n- 支持了 Stable Diffusion 算法.\n\n**新功能和改进**\n\n- 新增 Disco notebook. (#1507)\n- 优化测试 requirements 和 CI. (#1514)\n- 自动生成文档 summary 和 API docstring. (#1517)\n- 开启 projects. (#1526)\n- 支持 mscoco dataset. (#1520)\n- 改进中文文档. (#1532)\n- 添加 Type hints. (#1481)\n- 更新模型权重下载链接. (#1554)\n- 更新部署指南. (#1551)\n\n**Bug 修复**\n\n- 修复文档链接检查. (#1522)\n- 修复 ssim bug. (#1515)\n- 修复 realesrgan 的 `extract_gt_data`. (#1542)\n- 修复算法索引. (#1559)\n- F修复 disco-diffusion 的 config 路径. (#1553)\n- Fix text2image inferencer. (#1523)\n\n**贡献者**\n\n@plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @liuwenran, @AlexZou14, @lvhan028, @xiaomile, @ldr426, @austin273, @whu-lee, @willaty, @curiosity654, @Zdafeng, @Taited\n\n## v1.0.0rc4 (05/12/2022)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc4 版本。 此版本支持了 MMEditing 和 MMGeneration 的 45+ 模型，176+ configs 和 175+ checkpoints。以下是此次版本发布的重点新功能\n\n- 支持了 High-level APIs.\n- 支持了 diffusion 算法.\n- 支持了 Text2Image 任务.\n- 支持了 3D-Aware Generation.\n\n**新功能和改进**\n\n- 支持和重构了 High-level APIs. (#1410)\n- 支持了 disco-diffusion 文生图算法. (#1234, #1504)\n- 支持了 EG3D 算法. (#1482, #1493, #1494, #1499)\n- 支持了 NAFNet 算法. (#1369)\n\n**Bug 修复**\n\n- 修复 srgan 的训练配置. (#1441)\n- 修复 cain 的 config. (#1404)\n- 修复 rdn 和 srcnn 的训练配置. (#1392)\n\n**贡献者**\n\n@plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @gaoyang07, @ChangjianZhao, @zxczrx123, @jackghosts, @liuwenran, @CCODING04, @RoseZhao929, @shaocongliu, @liangzelong.\n\n## v1.0.0rc3 (10/11/2022)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc3 版本。 此版本支持了 MMEditing 和 MMGeneration 的 43+ 模型，170+ configs 和 169+ checkpoints。以下是此次版本发布的重点新功能\n\n- 将 `mmdet` 和 `clip` 改为可选安装需求.\n\n**新功能和改进**\n\n- 支持 `mmdet` 的 `try_import`. (#1408)\n- 支持 `flip` 的 `try_import`. (#1420)\n- 更新 `.gitignore`. ($1416)\n- 设置 `inception_utils` 的 `real_feat` 为 cpu 变量. (#1415)\n- 更新 StyleGAN2 和 PEGAN 的 README 和 configs. (#1418)\n- 改进 API 文档的渲染. (#1373)\n\n**Bug 修复**\n\n- 修复 ESRGAN 的 config 和预训练模型加载. (#1407)\n- 修复 LSGAN 的 config. (#1409)\n- 修复 CAIN 的 config. (#1404)\n\n**贡献者**\n\n@Z-Fran, @zengyh1900, @plyfager, @LeoXing1996, @ruoningYu.\n\n## v1.0.0rc2 (02/11/2022)\n\n**主要更新**\n\n我们很高兴发布 MMEditing 1.0.0rc2 版本。 此版本支持了 MMEditing 和 MMGeneration 的 43+ 模型，170+ configs 和 169+ checkpoints。以下是此次版本发布的重点新功能\n\n- 基于 patch 和 slider 的 图像和视频可视化质量比较工具.\n- 支持了图像上色算法.\n\n**新功能和改进**\n\n- 支持了质量比较工具. (#1303)\n- 支持了 instance aware colorization 上色算法. (#1370)\n- 支持使用不同采样模型的 multi-metrics. (#1171)\n- 改进代码实现\n - 重构 evaluation metrics. (#1164)\n - 在 PGGAN 的 `forward` 中保存 gt 图像. (#1332)\n - 改进 `preprocess_div2k_dataset.py` 脚本的默认参数. (#1380)\n - 支持在 visualizer 中裁剪像素值. (#1365)\n - 支持了 SinGAN 数据集和 SinGAN demo. (#1363)\n - 支持 GenDataPreprocessor 中返回 int 和 float 数据类型. (#1385)\n- 改进文档\n - 更新菜单切换. (#1162)\n - 修复 TTSR README. (#1325)\n\n**Bug 修复**\n\n- 修复 PPL bug. (#1172)\n- 修复 RDN `number of channels` 参数. (#1328)\n- 修复 demo 的 exceptions 类型. (#1372)\n- 修复 realesrgan ema. (#1341)\n- 改进 assertion 检查 `GenerateFacialHeatmap` 的数据类型为 `np.float32`. (#1310)\n- 修复 `unpaired_dataset.py` 的采样方式. (#1308)\n- 修复视频超分模型的 pytorch2onnx 脚本. (#1300)\n- 修复错误的 config 配置. (#1167,#1200,#1236,#1293,#1302,#1304,#1319,#1331,#1336,#1349,#1352,#1353,#1358,#1364,#1367,#1384,#1386,#1391,#1392,#1393)\n\n**贡献者**\n\n@LeoXing1996, @Z-Fran, @zengyh1900, @plyfager, @ryanxingql, @ruoningYu, @gaoyang07.\n\n## v1.0.0rc1(23/9/2022)\n\nMMEditing 1.0.0rc1 已经合并了 MMGeneration 1.x。\n\n- 支持 42+ 算法, 169+ 配置文件 and 168+ 预训练模型参数文件.\n- 支持 26+ loss functions, 20+ metrics.\n- 支持 tensorboard, wandb.\n- 支持 unconditional GANs, conditional GANs, image2image translation 以及 internal learning.\n\n## v1.0.0rc0(31/8/2022)\n\nMMEditing 1.0.0rc0 是 MMEditing 1.x 的第一个版本，是 OpenMMLab 2.0 项目的一部分。\n\n基于新的[训练引擎](https://github.com/open-mmlab/mmengine), MMEditing 1.x 统一了数据、模型、评测和可视化的接口。\n\n该版本存在有一些 BC-breaking 的修改。 请在[迁移指南](https://mmagic.readthedocs.io/zh_CN/latest/migration/overview.html)中查看更多细节。\n" }, { "path": "docs/zh_cn/community/contributing.md", "content": "# 贡献代码\n\n欢迎加入 MMagic 社区，我们致力于打造新一代人工智能内容生成（AIGC）工具箱，我们欢迎任何类型的贡献，包括但不限于\n\n**修复错误**\n\n修复代码实现错误的步骤如下：\n\n1. 如果提交的代码改动较大，建议先提交 issue，并正确描述 issue 的现象、原因和复现方式，讨论后确认修复方案。\n2. 修复错误并补充相应的单元测试，提交拉取请求。\n\n**新增功能或组件**\n\n1. 如果新功能或模块涉及较大的代码改动，建议先提交 issue，确认功能的必要性。\n2. 实现新增功能并添单元测试，提交拉取请求。\n\n**文档补充**\n\n修复文档可以直接提交拉取请求\n\n添加文档或将文档翻译成其他语言步骤如下\n\n1. 提交 issue，确认添加文档的必要性。\n2. 添加文档，提交拉取请求。\n\n### 拉取请求工作流\n\n如果你对拉取请求不了解，没关系，接下来的内容将会从零开始，一步一步地指引你如何创建一个拉取请求。如果你想深入了解拉取请求的开发模式，可以参考 github [官方文档](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests)\n\n#### 1. 复刻仓库\n\n当你第一次提交拉取请求时，先复刻 OpenMMLab 原代码库，点击 GitHub 页面右上角的 **Fork** 按钮，复刻后的代码库将会出现在你的 GitHub 个人主页下。\n\n\n\n将代码克隆到本地\n\n```shell\ngit clone git@github.com:{username}/mmagic.git\n```\n\n添加原代码库为上游代码库\n\n```bash\ngit remote add upstream git@github.com:open-mmlab/mmagic\n```\n\n检查 remote 是否添加成功，在终端输入 `git remote -v`\n\n```bash\norigin\tgit@github.com:{username}/mmagic.git (fetch)\norigin\tgit@github.com:{username}/mmagic.git (push)\nupstream\tgit@github.com:open-mmlab/mmagic (fetch)\nupstream\tgit@github.com:open-mmlab/mmagic (push)\n```\n\n```{note}\n这里对 origin 和 upstream 进行一个简单的介绍，当我们使用 git clone 来克隆代码时，会默认创建一个 origin 的 remote，它指向我们克隆的代码库地址，而 upstream 则是我们自己添加的，用来指向原始代码库地址。当然如果你不喜欢他叫 upstream，也可以自己修改，比如叫 open-mmlab。我们通常向 origin 提交代码（即 fork 下来的远程仓库），然后向 upstream 提交一个 pull request。如果提交的代码和最新的代码发生冲突，再从 upstream 拉取最新的代码，和本地分支解决冲突，再提交到 origin。\n```\n\n#### 2. 配置 pre-commit\n\n在本地开发环境中，我们使用 [pre-commit](https://pre-commit.com/#intro) 来检查代码风格，以确保代码风格的统一。在提交代码，需要先安装 pre-commit（需要在 mmagic 目录下执行）:\n\n```shell\npip install -U pre-commit\npre-commit install\n```\n\n检查 pre-commit 是否配置成功，并安装 `.pre-commit-config.yaml` 中的钩子：\n\n```shell\npre-commit run --all-files\n```\n\n\n\n\n\n```{note}\n如果你是中国用户，由于网络原因，可能会出现安装失败的情况，这时可以使用国内源\n\npre-commit install -c .pre-commit-config-zh-cn.yaml\n\npre-commit run --all-files -c .pre-commit-config-zh-cn.yaml\n```\n\n如果安装过程被中断，可以重复执行 `pre-commit run ...` 继续安装。\n\n如果提交的代码不符合代码风格规范，pre-commit 会发出警告，并自动修复部分错误。\n\n\n\n如果我们想临时绕开 pre-commit 的检查提交一次代码，可以在 `git commit` 时加上 `--no-verify`（需要保证最后推送至远程仓库的代码能够通过 pre-commit 检查）。\n\n```shell\ngit commit -m \"xxx\" --no-verify\n```\n\n#### 3. 创建开发分支\n\n安装完 pre-commit 之后，我们需要基于 main 创建开发分支，建议的分支命名规则为 `username/pr_name`。\n\n```shell\ngit checkout -b yhc/refactor_contributing_doc\n```\n\n在后续的开发中，如果本地仓库的 main 分支落后于 upstream 的 main 分支，我们需要先拉取 upstream 的代码进行同步，再执行上面的命令\n\n```shell\ngit pull upstream main\n```\n\n#### 4. 提交代码并在本地通过单元测试\n\n- mmagic 引入了 mypy 来做静态类型检查，以增加代码的鲁棒性。因此我们在提交代码时，需要补充 Type Hints。具体规则可以参考[教程](https://zhuanlan.zhihu.com/p/519335398)。\n\n- 提交的代码同样需要通过单元测试\n\n ```shell\n # 通过全量单元测试\n pytest tests\n\n # 我们需要保证提交的代码能够通过修改模块的单元测试，以 runner 为例\n pytest tests/test_runner/test_runner.py\n ```\n\n 如果你由于缺少依赖无法运行修改模块的单元测试，可以参考[指引-单元测试](#单元测试)\n\n- 如果修改/添加了文档，参考[指引](#文档渲染)确认文档渲染正常。\n\n#### 5. 推送代码到远程\n\n代码通过单元测试和 pre-commit 检查后，将代码推送到远程仓库，如果是第一次推送，可以在 `git push` 后加上 `-u` 参数以关联远程分支\n\n```shell\ngit push -u origin {branch_name}\n```\n\n这样下次就可以直接使用 `git push` 命令推送代码了，而无需指定分支和远程仓库。\n\n#### 6. 提交拉取请求（PR）\n\n(1) 在 GitHub 的 Pull request 界面创建拉取请求\n\n\n(2) 根据指引修改 PR 描述，以便于其他开发者更好地理解你的修改\n\n\n\n描述规范详见[拉取请求规范](#拉取请求规范)\n\n \n\n**注意事项**\n\n(a) PR 描述应该包含修改理由、修改内容以及修改后带来的影响，并关联相关 Issue（具体方式见[文档](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)）\n\n(b) 如果是第一次为 OpenMMLab 做贡献，需要签署 CLA\n\n\n\n(c) 检查提交的 PR 是否通过 CI（集成测试）\n\n\n\nmmagic 会在不同的平台（Linux、Window、Mac），基于不同版本的 Python、PyTorch、CUDA 对提交的代码进行单元测试，以保证代码的正确性，如果有任何一个没有通过，我们可点击上图中的 `Details` 来查看具体的测试信息，以便于我们修改代码。\n\n(3) 如果 PR 通过了 CI，那么就可以等待其他开发者的 review，并根据 reviewer 的意见，修改代码，并重复 [4](#4-提交代码并本地通过单元测试)-[5](#5-推送代码到远程) 步骤，直到 reviewer 同意合入 PR。\n\n\n\n所有 reviewer 同意合入 PR 后，我们会尽快将 PR 合并到主分支。\n\n#### 7. 解决冲突\n\n随着时间的推移，我们的代码库会不断更新，这时候，如果你的 PR 与主分支存在冲突，你需要解决冲突，解决冲突的方式有两种：\n\n```shell\ngit fetch --all --prune\ngit rebase upstream/main\n```\n\n或者\n\n```shell\ngit fetch --all --prune\ngit merge upstream/main\n```\n\n如果你非常善于处理冲突，那么可以使用 rebase 的方式来解决冲突，因为这能够保证你的 commit log 的整洁。如果你不太熟悉 `rebase` 的使用，那么可以使用 `merge` 的方式来解决冲突。\n\n### 指引\n\n#### 单元测试\n\n在提交修复代码错误或新增特性的拉取请求时，我们应该尽可能的让单元测试覆盖所有提交的代码，计算单元测试覆盖率的方法如下\n\n```shell\npython -m coverage run -m pytest /path/to/test_file\npython -m coverage html\n# check file in htmlcov/index.html\n```\n\n#### 文档渲染\n\n在提交修复代码错误或新增特性的拉取请求时，可能会需要修改/新增模块的 docstring。我们需要确认渲染后的文档样式是正确的。\n本地生成渲染后的文档的方法如下\n\n```shell\npip install -r requirements/docs.txt\ncd docs/zh_cn/\n# or docs/en\nmake html\n# check file in ./docs/zh_cn/_build/html/index.html\n```\n\n### 代码风格\n\n#### Python\n\n[PEP8](https://www.python.org/dev/peps/pep-0008/) 作为 OpenMMLab 算法库首选的代码规范，我们使用以下工具检查和格式化代码\n\n- [flake8](https://github.com/PyCQA/flake8): Python 官方发布的代码规范检查工具，是多个检查工具的封装\n- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具\n- [yapf](https://github.com/google/yapf): Google 发布的代码规范检查工具\n- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误\n- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具\n- [docformatter](https://github.com/myint/docformatter): 格式化 docstring 的工具\n\nyapf 和 isort 的配置可以在 [setup.cfg](../../../setup.cfg) 找到\n\n通过配置 [pre-commit hook](https://pre-commit.com/) ，我们可以在提交代码时自动检查和格式化 `flake8`、`yapf`、`isort`、`trailing whitespaces`、`markdown files`，修复 `end-of-files`、`double-quoted-strings`、`python-encoding-pragma`、`mixed-line-ending`，调整 `requirments.txt` 的包顺序。\npre-commit 钩子的配置可以在 [.pre-commit-config](../../../.pre-commit-config.yaml) 找到。\n\npre-commit 具体的安装使用方式见[拉取请求](#2-配置-pre-commit)。\n\n更具体的规范请参考 [OpenMMLab 代码规范](contributing.md#代码风格)。\n\n#### C++ and CUDA\n\nC++ 和 CUDA 的代码规范遵从 [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)\n\n### 拉取请求规范\n\n1. 使用 [pre-commit hook](https://pre-commit.com)，尽量减少代码风格相关问题\n\n2. 一个`拉取请求`对应一个短期分支\n\n3. 粒度要细，一个`拉取请求`只做一件事情，避免超大的`拉取请求`\n\n - Bad：实现 Faster R-CNN\n - Acceptable：给 Faster R-CNN 添加一个 box head\n - Good：给 box head 增加一个参数来支持自定义的 conv 层数\n\n4. 每次 Commit 时需要提供清晰且有意义 commit 信息\n\n5. 提供清晰且有意义的`拉取请求`描述\n\n - 标题写明白任务名称，一般格式:\\[Prefix\\] Short description of the pull request (Suffix)\n - prefix: 新增功能 \\[Feature\\], 修 bug \\[Fix\\], 文档相关 \\[Docs\\], 开发中 \\[WIP\\] (暂时不会被review)\n - 描述里介绍`拉取请求`的主要修改内容，结果，以及对其他部分的影响, 参考`拉取请求`模板\n - 关联相关的`议题` (issue) 和其他`拉取请求`\n\n6. 如果引入了其他三方库，或借鉴了三方库的代码，请确认他们的许可证和 mmagic 兼容，并在借鉴的代码上补充 `This code is inspired from http://`\n\n## 代码规范\n\n### 代码规范标准\n\n#### PEP 8 —— Python 官方代码规范\n\n[Python 官方的代码风格指南](https://www.python.org/dev/peps/pep-0008/)，包含了以下几个方面的内容：\n\n- 代码布局，介绍了 Python 中空行、断行以及导入相关的代码风格规范。比如一个常见的问题：当我的代码较长，无法在一行写下时，何处可以断行？\n\n- 表达式，介绍了 Python 中表达式空格相关的一些风格规范。\n\n- 尾随逗号相关的规范。当列表较长，无法一行写下而写成如下逐行列表时，推荐在末项后加逗号，从而便于追加选项、版本控制等。\n\n ```python\n # Correct:\n FILES = ['setup.cfg', 'tox.ini']\n # Correct:\n FILES = [\n 'setup.cfg',\n 'tox.ini',\n ]\n # Wrong:\n FILES = ['setup.cfg', 'tox.ini',]\n # Wrong:\n FILES = [\n 'setup.cfg',\n 'tox.ini'\n ]\n ```\n\n- 命名相关规范、注释相关规范、类型注解相关规范，我们将在后续章节中做详细介绍。\n\n \"A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is the most important.\" PEP 8 -- Style Guide for Python Code\n\n:::{note}\nPEP 8 的代码规范并不是绝对的，项目内的一致性要优先于 PEP 8 的规范。OpenMMLab 各个项目都在 setup.cfg 设定了一些代码规范的设置，请遵照这些设置。一个例子是在 PEP 8 中有如下一个例子：\n\n```python\n# Correct:\nhypot2 = x*x + y*y\n# Wrong:\nhypot2 = x * x + y * y\n```\n\n这一规范是为了指示不同优先级，但 OpenMMLab 的设置中通常没有启用 yapf 的 `ARITHMETIC_PRECEDENCE_INDICATION` 选项，因而格式规范工具不会按照推荐样式格式化，以设置为准。\n:::\n\n#### Google 开源项目风格指南\n\n[Google 使用的编程风格指南](https://google.github.io/styleguide/pyguide.html)，包括了 Python 相关的章节。相较于 PEP 8，该指南提供了更为详尽的代码指南。该指南包括了语言规范和风格规范两个部分。\n\n其中，语言规范对 Python 中很多语言特性进行了优缺点的分析，并给出了使用指导意见，如异常、Lambda 表达式、列表推导式、metaclass 等。\n\n风格规范的内容与 PEP 8 较为接近，大部分约定建立在 PEP 8 的基础上，也有一些更为详细的约定，如函数长度、TODO 注释、文件与 socket 对象的访问等。\n\n推荐将该指南作为参考进行开发，但不必严格遵照，一来该指南存在一些 Python 2 兼容需求，例如指南中要求所有无基类的类应当显式地继承 Object, 而在仅使用 Python 3 的环境中，这一要求是不必要的，依本项目中的惯例即可。二来 OpenMMLab 的项目作为框架级的开源软件，不必对一些高级技巧过于避讳，尤其是 MMCV。但尝试使用这些技巧前应当认真考虑是否真的有必要，并寻求其他开发人员的广泛评估。\n\n另外需要注意的一处规范是关于包的导入，在该指南中，要求导入本地包时必须使用路径全称，且导入的每一个模块都应当单独成行，通常这是不必要的，而且也不符合目前项目的开发惯例，此处进行如下约定：\n\n```python\n# Correct\nfrom mmagic.cnn.bricks import (Conv2d, build_norm_layer, DropPath, MaxPool2d,\n Linear)\nfrom ..utils import ext_loader\n\n# Wrong\nfrom mmagic.cnn.bricks import Conv2d, build_norm_layer, DropPath, MaxPool2d, \\\n Linear # 使用括号进行连接，而不是反斜杠\nfrom ...utils import is_str # 最多向上回溯一层，过多的回溯容易导致结构混乱\n```\n\nOpenMMLab 项目使用 pre-commit 工具自动格式化代码，详情见[贡献代码](contributing.md#代码风格)。\n\n### 命名规范\n\n#### 命名规范的重要性\n\n优秀的命名是良好代码可读的基础。基础的命名规范对各类变量的命名做了要求，使读者可以方便地根据代码名了解变量是一个类 / 局部变量 / 全局变量等。而优秀的命名则需要代码作者对于变量的功能有清晰的认识，以及良好的表达能力，从而使读者根据名称就能了解其含义，甚至帮助了解该段代码的功能。\n\n#### 基础命名规范\n\n| 类型 | 公有 | 私有 |\n| --------------- | ---------------- | ------------------ |\n| 模块 | lower_with_under | \\_lower_with_under |\n| 包 | lower_with_under | |\n| 类 | CapWords | \\_CapWords |\n| 异常 | CapWordsError | |\n| 函数（方法） | lower_with_under | \\_lower_with_under |\n| 函数 / 方法参数 | lower_with_under | |\n| 全局 / 类内常量 | CAPS_WITH_UNDER | \\_CAPS_WITH_UNDER |\n| 全局 / 类内变量 | lower_with_under | \\_lower_with_under |\n| 变量 | lower_with_under | \\_lower_with_under |\n| 局部变量 | lower_with_under | |\n\n注意：\n\n- 尽量避免变量名与保留字冲突，特殊情况下如不可避免，可使用一个后置下划线，如 class\\_\n- 尽量不要使用过于简单的命名，除了约定俗成的循环变量 i，文件变量 f，错误变量 e 等。\n- 不会被用到的变量可以命名为 \\_，逻辑检查器会将其忽略。\n\n#### 命名技巧\n\n良好的变量命名需要保证三点：\n\n1. 含义准确，没有歧义\n2. 长短适中\n3. 前后统一\n\n```python\n# Wrong\nclass Masks(metaclass=ABCMeta): # 命名无法表现基类；Instance or Semantic？\n pass\n\n# Correct\nclass BaseInstanceMasks(metaclass=ABCMeta):\n pass\n\n# Wrong，不同地方含义相同的变量尽量用统一的命名\ndef __init__(self, inplanes, planes):\n pass\n\ndef __init__(self, in_channels, out_channels):\n pass\n```\n\n常见的函数命名方法：\n\n- 动宾命名法：crop_img, init_weights\n- 动宾倒置命名法：imread, bbox_flip\n\n注意函数命名与参数的顺序，保证主语在前，符合语言习惯：\n\n- check_keys_exist(key, container)\n- check_keys_contain(container, key)\n\n注意避免非常规或统一约定的缩写，如 nb -> num_blocks，in_nc -> in_channels\n\n### docstring 规范\n\n#### 为什么要写 docstring\n\ndocstring 是对一个类、一个函数功能与 API 接口的详细描述，有两个功能，一是帮助其他开发者了解代码功能，方便 debug 和复用代码；二是在 Readthedocs 文档中自动生成相关的 API reference 文档，帮助不了解源代码的社区用户使用相关功能。\n\n#### 如何写 docstring\n\n与注释不同，一份规范的 docstring 有着严格的格式要求，以便于 Python 解释器以及 sphinx 进行文档解析，详细的 docstring 约定参见 [PEP 257](https://www.python.org/dev/peps/pep-0257/)。此处以例子的形式介绍各种文档的标准格式，参考格式为 [Google 风格](https://zh-google-styleguide.readthedocs.io/en/latest/google-python-styleguide/python_style_rules/#comments)。\n\n1. 模块文档\n\n 代码风格规范推荐为每一个模块（即 Python 文件）编写一个 docstring，但目前 OpenMMLab 项目大部分没有此类 docstring，因此不做硬性要求。\n\n ```python\n \"\"\"A one line summary of the module or program, terminated by a period.\n\n Leave one blank line. The rest of this docstring should contain an\n overall description of the module or program. Optionally, it may also\n contain a brief description of exported classes and functions and/or usage\n examples.\n\n Typical usage example:\n\n foo = ClassFoo()\n bar = foo.FunctionBar()\n \"\"\"\n ```\n\n2. 类文档\n\n 类文档是我们最常需要编写的，此处，按照 OpenMMLab 的惯例，我们使用了与 Google 风格不同的写法。如下例所示，文档中没有使用 Attributes 描述类属性，而是使用 Args 描述 __init__ 函数的参数。\n\n 在 Args 中，遵照 `parameter (type): Description.` 的格式，描述每一个参数类型和功能。其中，多种类型可使用 `(float or str)` 的写法，可以为 None 的参数可以写为 `(int, optional)`。\n\n ```python\n class BaseRunner(metaclass=ABCMeta):\n \"\"\"The base class of Runner, a training helper for PyTorch.\n\n All subclasses should implement the following APIs:\n\n - ``run()``\n - ``train()``\n - ``val()``\n - ``save_checkpoint()``\n\n Args:\n model (:obj:`torch.nn.Module`): The model to be run.\n batch_processor (callable, optional): A callable method that process\n a data batch. The interface of this method should be\n ``batch_processor(model, data, train_mode) -> dict``.\n Defaults to None.\n optimizer (dict or :obj:`torch.optim.Optimizer`, optional): It can be\n either an optimizer (in most cases) or a dict of optimizers\n (in models that requires more than one optimizer, e.g., GAN).\n Defaults to None.\n work_dir (str, optional): The working directory to save checkpoints\n and logs. Defaults to None.\n logger (:obj:`logging.Logger`): Logger used during training.\n Defaults to None. (The default value is just for backward\n compatibility)\n meta (dict, optional): A dict records some import information such as\n environment info and seed, which will be logged in logger hook.\n Defaults to None.\n max_epochs (int, optional): Total training epochs. Defaults to None.\n max_iters (int, optional): Total training iterations. Defaults to None.\n \"\"\"\n\n def __init__(self,\n model,\n batch_processor=None,\n optimizer=None,\n work_dir=None,\n logger=None,\n meta=None,\n max_iters=None,\n max_epochs=None):\n ...\n ```\n\n 另外，在一些算法实现的主体类中，建议加入原论文的链接；如果参考了其他开源代码的实现，则应加入 modified from，而如果是直接复制了其他代码库的实现，则应加入 copied from ，并注意源码的 License。如有必要，也可以通过 .. math:: 来加入数学公式\n\n ```python\n # 参考实现\n # This func is modified from `detectron2\n # `_.\n\n # 复制代码\n # This code was copied from the `ubelt\n # library`_.\n\n # 引用论文 & 添加公式\n class LabelSmoothLoss(nn.Module):\n r\"\"\"Initializer for the label smoothed cross entropy loss.\n\n Refers to `Rethinking the Inception Architecture for Computer Vision\n `_.\n\n This decreases gap between output scores and encourages generalization.\n Labels provided to forward can be one-hot like vectors (NxC) or class\n indices (Nx1).\n And this accepts linear combination of one-hot like labels from mixup or\n cutmix except multi-label task.\n\n Args:\n label_smooth_val (float): The degree of label smoothing.\n num_classes (int, optional): Number of classes. Defaults to None.\n mode (str): Refers to notes, Options are \"original\", \"classy_vision\",\n \"multi_label\". Defaults to \"classy_vision\".\n reduction (str): The method used to reduce the loss.\n Options are \"none\", \"mean\" and \"sum\". Defaults to 'mean'.\n loss_weight (float): Weight of the loss. Defaults to 1.0.\n\n Note:\n if the ``mode`` is \"original\", this will use the same label smooth\n method as the original paper as:\n\n .. math::\n (1-\\epsilon)\\delta_{k, y} + \\frac{\\epsilon}{K}\n\n where :math:`\\epsilon` is the ``label_smooth_val``, :math:`K` is\n the ``num_classes`` and :math:`\\delta_{k,y}` is Dirac delta,\n which equals 1 for k=y and 0 otherwise.\n\n if the ``mode`` is \"classy_vision\", this will use the same label\n smooth method as the `facebookresearch/ClassyVision\n `_ repo as:\n\n .. math::\n \\frac{\\delta_{k, y} + \\epsilon/K}{1+\\epsilon}\n\n if the ``mode`` is \"multi_label\", this will accept labels from\n multi-label task and smoothing them as:\n\n .. math::\n (1-2\\epsilon)\\delta_{k, y} + \\epsilon\n ```\n\n```{note}\n注意 \\`\\`here\\`\\`、\\`here\\`、\"here\" 三种引号功能是不同。\n\n在 reStructured 语法中，\\`\\`here\\`\\` 表示一段代码；\\`here\\` 表示斜体；\"here\" 无特殊含义，一般可用来表示字符串。其中 \\`here\\` 的用法与 Markdown 中不同，需要多加留意。\n另外还有 :obj:\\`type\\` 这种更规范的表示类的写法，但鉴于长度，不做特别要求，一般仅用于表示非常用类型。\n```\n\n3. 方法（函数）文档\n\n 函数文档与类文档的结构基本一致，但需要加入返回值文档。对于较为复杂的函数和类，可以使用 Examples 字段加入示例；如果需要对参数加入一些较长的备注，可以加入 Note 字段进行说明。\n\n 对于使用较为复杂的类或函数，比起看大段大段的说明文字和参数文档，添加合适的示例更能帮助用户迅速了解其用法。需要注意的是，这些示例最好是能够直接在 Python 交互式环境中运行的，并给出一些相对应的结果。如果存在多个示例，可以使用注释简单说明每段示例，也能起到分隔作用。\n\n ```python\n def import_modules_from_strings(imports, allow_failed_imports=False):\n \"\"\"Import modules from the given list of strings.\n\n Args:\n imports (list | str | None): The given module names to be imported.\n allow_failed_imports (bool): If True, the failed imports will return\n None. Otherwise, an ImportError is raise. Defaults to False.\n\n Returns:\n List[module] | module | None: The imported modules.\n All these three lines in docstring will be compiled into the same\n line in readthedocs.\n\n Examples:\n >>> osp, sys = import_modules_from_strings(\n ... ['os.path', 'sys'])\n >>> import os.path as osp_\n >>> import sys as sys_\n >>> assert osp == osp_\n >>> assert sys == sys_\n \"\"\"\n ...\n ```\n\n 如果函数接口在某个版本发生了变化，需要在 docstring 中加入相关的说明，必要时添加 Note 或者 Warning 进行说明，例如：\n\n ```python\n class CheckpointHook(Hook):\n \"\"\"Save checkpoints periodically.\n\n Args:\n out_dir (str, optional): The root directory to save checkpoints. If\n not specified, ``runner.work_dir`` will be used by default. If\n specified, the ``out_dir`` will be the concatenation of\n ``out_dir`` and the last level directory of ``runner.work_dir``.\n Defaults to None. `Changed in version 1.3.15.`\n file_client_args (dict, optional): Arguments to instantiate a\n FileClient. See :class:`mmagic.fileio.FileClient` for details.\n Defaults to None. `New in version 1.3.15.`\n\n Warning:\n Before v1.3.15, the ``out_dir`` argument indicates the path where the\n checkpoint is stored. However, in v1.3.15 and later, ``out_dir``\n indicates the root directory and the final path to save checkpoint is\n the concatenation of out_dir and the last level directory of\n ``runner.work_dir``. Suppose the value of ``out_dir`` is\n \"/path/of/A\" and the value of ``runner.work_dir`` is \"/path/of/B\",\n then the final path will be \"/path/of/A/B\".\n ```\n\n 如果参数或返回值里带有需要展开描述字段的 dict，则应该采用如下格式：\n\n ```python\n def func(x):\n r\"\"\"\n Args:\n x (None): A dict with 2 keys, ``padded_targets``, and ``targets``.\n\n - ``targets`` (list[Tensor]): A list of tensors.\n Each tensor has the shape of :math:`(T_i)`. Each\n element is the index of a character.\n - ``padded_targets`` (Tensor): A tensor of shape :math:`(N)`.\n Each item is the length of a word.\n\n Returns:\n dict: A dict with 2 keys, ``padded_targets``, and ``targets``.\n\n - ``targets`` (list[Tensor]): A list of tensors.\n Each tensor has the shape of :math:`(T_i)`. Each\n element is the index of a character.\n - ``padded_targets`` (Tensor): A tensor of shape :math:`(N)`.\n Each item is the length of a word.\n \"\"\"\n return x\n ```\n\n```{important}\n为了生成 readthedocs 文档，文档的编写需要按照 ReStructrued 文档格式，否则会产生文档渲染错误，在提交 PR 前，最好生成并预览一下文档效果。\n语法规范参考：\n\n- [reStructuredText Primer - Sphinx documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#)\n- [Example Google Style Python Docstrings ‒ napoleon 0.7 documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html#example-google)\n```\n\n### 注释规范\n\n#### 为什么要写注释\n\n对于一个开源项目，团队合作以及社区之间的合作是必不可少的，因而尤其要重视合理的注释。不写注释的代码，很有可能过几个月自己也难以理解，造成额外的阅读和修改成本。\n\n#### 如何写注释\n\n最需要写注释的是代码中那些技巧性的部分。如果你在下次代码审查的时候必须解释一下，那么你应该现在就给它写注释。对于复杂的操作，应该在其操作开始前写上若干行注释。对于不是一目了然的代码，应在其行尾添加注释。\n—— Google 开源项目风格指南\n\n```python\n# We use a weighted dictionary search to find out where i is in\n# the array. We extrapolate position based on the largest num\n# in the array and the array size and then do binary search to\n# get the exact number.\nif i & (i-1) == 0: # True if i is 0 or a power of 2.\n```\n\n为了提高可读性, 注释应该至少离开代码2个空格.\n另一方面, 绝不要描述代码. 假设阅读代码的人比你更懂Python, 他只是不知道你的代码要做什么.\n—— Google 开源项目风格指南\n\n```python\n# Wrong:\n# Now go through the b array and make sure whenever i occurs\n# the next element is i+1\n\n# Wrong:\nif i & (i-1) == 0: # True if i bitwise and i-1 is 0.\n```\n\n在注释中，可以使用 Markdown 语法，因为开发人员通常熟悉 Markdown 语法，这样可以便于交流理解，如可使用单反引号表示代码和变量（注意不要和 docstring 中的 ReStructured 语法混淆）\n\n```python\n# `_reversed_padding_repeated_twice` is the padding to be passed to\n# `F.pad` if needed (e.g., for non-zero padding types that are\n# implemented as two ops: padding + conv). `F.pad` accepts paddings in\n# reverse order than the dimension.\nself._reversed_padding_repeated_twice = _reverse_repeat_tuple(self.padding, 2)\n```\n\n#### 注释示例\n\n1. 出自 `mmcv/utils/registry.py`，对于较为复杂的逻辑结构，通过注释，明确了优先级关系。\n\n ```python\n # self.build_func will be set with the following priority:\n # 1. build_func\n # 2. parent.build_func\n # 3. build_from_cfg\n if build_func is None:\n if parent is not None:\n self.build_func = parent.build_func\n else:\n self.build_func = build_from_cfg\n else:\n self.build_func = build_func\n ```\n\n2. 出自 `mmcv/runner/checkpoint.py`，对于 bug 修复中的一些特殊处理，可以附带相关的 issue 链接，帮助其他人了解 bug 背景。\n\n ```python\n def _save_ckpt(checkpoint, file):\n # The 1.6 release of PyTorch switched torch.save to use a new\n # zipfile-based file format. It will cause RuntimeError when a\n # checkpoint was saved in high version (PyTorch version>=1.6.0) but\n # loaded in low version (PyTorch version<1.6.0). More details at\n # https://github.com/open-mmlab/mmpose/issues/904\n if digit_version(TORCH_VERSION) >= digit_version('1.6.0'):\n torch.save(checkpoint, file, _use_new_zipfile_serialization=False)\n else:\n torch.save(checkpoint, file)\n ```\n\n### 类型注解\n\n#### 为什么要写类型注解\n\n类型注解是对函数中变量的类型做限定或提示，为代码的安全性提供保障、增强代码的可读性、避免出现类型相关的错误。\nPython 没有对类型做强制限制，类型注解只起到一个提示作用，通常你的 IDE 会解析这些类型注解，然后在你调用相关代码时对类型做提示。另外也有类型注解检查工具，这些工具会根据类型注解，对代码中可能出现的问题进行检查，减少 bug 的出现。\n需要注意的是，通常我们不需要注释模块中的所有函数：\n\n1. 公共的 API 需要注释\n2. 在代码的安全性，清晰性和灵活性上进行权衡是否注释\n3. 对于容易出现类型相关的错误的代码进行注释\n4. 难以理解的代码请进行注释\n5. 若代码中的类型已经稳定，可以进行注释. 对于一份成熟的代码，多数情况下，即使注释了所有的函数，也不会丧失太多的灵活性.\n\n#### 如何写类型注解\n\n1. 函数 / 方法类型注解，通常不对 self 和 cls 注释。\n\n ```python\n from typing import Optional, List, Tuple\n\n # 全部位于一行\n def my_method(self, first_var: int) -> int:\n pass\n\n # 另起一行\n def my_method(\n self, first_var: int,\n second_var: float) -> Tuple[MyLongType1, MyLongType1, MyLongType1]:\n pass\n\n # 单独成行（具体的应用场合与行宽有关，建议结合 yapf 自动化格式使用）\n def my_method(\n self, first_var: int, second_var: float\n ) -> Tuple[MyLongType1, MyLongType1, MyLongType1]:\n pass\n\n # 引用尚未被定义的类型\n class MyClass:\n def __init__(self,\n stack: List[\"MyClass\"]) -> None:\n pass\n ```\n\n 注：类型注解中的类型可以是 Python 内置类型，也可以是自定义类，还可以使用 Python 提供的 wrapper 类对类型注解进行装饰，一些常见的注解如下：\n\n ```python\n # 数值类型\n from numbers import Number\n\n # 可选类型，指参数可以为 None\n from typing import Optional\n def foo(var: Optional[int] = None):\n pass\n\n # 联合类型，指同时接受多种类型\n from typing import Union\n def foo(var: Union[float, str]):\n pass\n\n from typing import Sequence # 序列类型\n from typing import Iterable # 可迭代类型\n from typing import Any # 任意类型\n from typing import Callable # 可调用类型\n\n from typing import List, Dict # 列表和字典的泛型类型\n from typing import Tuple # 元组的特殊格式\n # 虽然在 Python 3.9 中，list, tuple 和 dict 本身已支持泛型，但为了支持之前的版本\n # 我们在进行类型注解时还是需要使用 List, Tuple, Dict 类型\n # 另外，在对参数类型进行注解时，尽量使用 Sequence & Iterable & Mapping\n # List, Tuple, Dict 主要用于返回值类型注解\n # 参见 https://docs.python.org/3/library/typing.html#typing.List\n ```\n\n2. 变量类型注解，一般用于难以直接推断其类型时\n\n ```python\n # Recommend: 带类型注解的赋值\n a: Foo = SomeUndecoratedFunction()\n a: List[int]: [1, 2, 3] # List 只支持单一类型泛型，可使用 Union\n b: Tuple[int, int] = (1, 2) # 长度固定为 2\n c: Tuple[int, ...] = (1, 2, 3) # 变长\n d: Dict[str, int] = {'a': 1, 'b': 2}\n\n # Not Recommend：行尾类型注释\n # 虽然这种方式被写在了 Google 开源指南中，但这是一种为了支持 Python 2.7 版本\n # 而补充的注释方式，鉴于我们只支持 Python 3, 为了风格统一，不推荐使用这种方式。\n a = SomeUndecoratedFunction() # type: Foo\n a = [1, 2, 3] # type: List[int]\n b = (1, 2, 3) # type: Tuple[int, ...]\n c = (1, \"2\", 3.5) # type: Tuple[int, Text, float]\n ```\n\n3. 泛型\n\n 上文中我们知道，typing 中提供了 list 和 dict 的泛型类型，那么我们自己是否可以定义类似的泛型呢？\n\n ```python\n from typing import TypeVar, Generic\n\n KT = TypeVar('KT')\n VT = TypeVar('VT')\n\n class Mapping(Generic[KT, VT]):\n def __init__(self, data: Dict[KT, VT]):\n self._data = data\n\n def __getitem__(self, key: KT) -> VT:\n return self._data[key]\n ```\n\n 使用上述方法，我们定义了一个拥有泛型能力的映射类，实际用法如下：\n\n ```python\n mapping = Mapping[str, float]({'a': 0.5})\n value: float = example['a']\n ```\n\n 另外，我们也可以利用 TypeVar 在函数签名中指定联动的多个类型：\n\n ```python\n from typing import TypeVar, List\n\n T = TypeVar('T') # Can be anything\n A = TypeVar('A', str, bytes) # Must be str or bytes\n\n\n def repeat(x: T, n: int) -> List[T]:\n \"\"\"Return a list containing n references to x.\"\"\"\n return [x]*n\n\n\n def longest(x: A, y: A) -> A:\n \"\"\"Return the longest of two strings.\"\"\"\n return x if len(x) >= len(y) else y\n ```\n\n更多关于类型注解的写法请参考 [typing](https://docs.python.org/3/library/typing.html)。\n\n#### 类型注解检查工具\n\n[mypy](https://mypy.readthedocs.io/en/stable/) 是一个 Python 静态类型检查工具。根据你的类型注解，mypy 会检查传参、赋值等操作是否符合类型注解，从而避免可能出现的 bug。\n\n例如如下的一个 Python 脚本文件 test.py:\n\n```python\ndef foo(var: int) -> float:\n return float(var)\n\na: str = foo('2.0')\nb: int = foo('3.0') # type: ignore\n```\n\n运行 mypy test.py 可以得到如下检查结果，分别指出了第 4 行在函数调用和返回值赋值两处类型错误。而第 5 行同样存在两个类型错误，由于使用了 type: ignore 而被忽略了，只有部分特殊情况可能需要此类忽略。\n\n```\ntest.py:4: error: Incompatible types in assignment (expression has type \"float\", variable has type \"int\")\ntest.py:4: error: Argument 1 to \"foo\" has incompatible type \"str\"; expected \"int\"\nFound 2 errors in 1 file (checked 1 source file)\n```\n" }, { "path": "docs/zh_cn/community/projects.md", "content": "# 生态项目\n\n欢迎来到 MMagic 社区！\n\nMMagic 社区由来自学术界和工业界的广大研究人员、机器学习和应用工程师编写的教程、库和项目组成。\n\n该社区的目标是支持、加速和帮助您使用 MMagic 探索 AIGC，例如图像、视频、3D 内容生成、编辑和处理。\n\n这里有一些基于 MMagic 的项目。 它们是如何将 MMagic 用作库的示例，以使您的项目更易于维护。请在[MMagic Ecosystem](https://openmmlab.com/ecosystem)中找到更多项目。\n\n## 在 OpenMMLab 社区上展示您的项目\n\n您可以提交您的项目，以便它可以显示在[OpenMMLab](https://openmmlab.com/ecosystem)的主页上。\n\n## 添加示例项目到 MMagic\n\n这是一个关于如何将项目添加到 MMagic 的[示例项目](../../../projects/example_project)。\n\n您可以从 [example project](../../../projects/example_project) 复制并创建自己的项目。\n\n我们还提供了下面列出的一些文档供您参考：\n\n- [贡献指南](https://mmagic.readthedocs.io/zh_CN/latest/community/contributing.html)\n\n 新贡献者指南，了解如何将您的项目添加到 MMagic。\n\n- [新模型指南](https://mmagic.readthedocs.io/zh_CN/latest/howto/models.html)\n\n 添加新模型的文档。\n\n- [讨论](https://github.com/open-mmlab/mmagic/discussions)\n\n 欢迎开始讨论！\n\n## 库和工具箱的项目\n\n- [PowerVQE](https://github.com/ryanxingql/powervqe)：基于 PyTorch 和 MMagic 的压缩视频质量增强开放框架\n- [VR-Baseline](https://github.com/linjing7/VR-Baseline)：视频修复工具箱\n- [Derain-Toolbox](https://github.com/biubiubiiu/derain-toolbox)：单图像去雨工具箱和基准\n\n## 研究论文项目\n\n- [Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV 2022](https://arxiv.org/abs/2207.10765)[\\[github\\]](https://github.com/caojiezhang/DAVSR)\n\n- [SepLUT:Separable Image-adaptive Lookup Tables for Real-time Image Enhancement, ECCV 2022](https://arxiv.org/abs/2207.08351)[\\[github\\]](https://github.com/ImCharlesY/SepLUT)\n\n- [TTVSR: Learning Trajectory-Aware Transformer for Video Super-Resolution, CVPR 2022](https://arxiv.org/abs/2204.04216)[\\[github\\]](https://github.com/researchmm/TTVSR)\n\n- [Arbitrary-Scale Image Synthesis, CVPR 2022](https://arxiv.org/pdf/2204.02273.pdf)[\\[github\\]](https://github.com/vglsd/ScaleParty)\n\n- [Investigating Tradeoffs in Real-World Video Super-Resolution(RealBasicVSR), CVPR 2022](https://arxiv.org/abs/2111.12704)[\\[github\\]](https://github.com/ckkelvinchan/RealBasicVSR)\n\n- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022](https://arxiv.org/abs/2104.13371)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR_PlusPlus)\n\n- [Multi-Scale Memory-Based Video Deblurring, CVPR 2022](https://arxiv.org/abs/2204.02977)[\\[github\\]](https://github.com/jibo27/MemDeblur)\n\n- [AdaInt:Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement, CVPR 2022](https://arxiv.org/abs/2204.13983)[\\[github\\]](https://github.com/ImCharlesY/AdaInt)\n\n- [A New Dataset and Transformer for Stereoscopic Video Super-Resolution, CVPRW 2022](https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/papers/Imani_A_New_Dataset_and_Transformer_for_Stereoscopic_Video_Super-Resolution_CVPRW_2022_paper.pdf)[\\[github\\]](https://github.com/H-deep/Trans-SVSR)\n\n- [Liquid warping GAN with attention: A unified framework for human image synthesis, TPAMI 2021](https://arxiv.org/pdf/2011.09055.pdf)[\\[github\\]](https://github.com/iPERDance/iPERCore)\n\n- [BasicVSR:The Search for Essential Components in Video Super-Resolution and Beyond, CVPR 2021](https://arxiv.org/abs/2012.02181)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR-IconVSR)\n\n- [GLEAN:Generative Latent Bank for Large-Factor Image Super-Resolution, CVPR 2021](https://arxiv.org/abs/2012.00739)[\\[github\\]](https://github.com/ckkelvinchan/GLEAN)\n\n- [DAN:Unfolding the Alternating Optimization for Blind Super Resolution, NeurIPS 2020](https://arxiv.org/abs/2010.02631v4)[\\[github\\]](https://github.com/AlexZou14/DAN-Basd-on-Openmmlab)\n" }, { "path": "docs/zh_cn/conf.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport subprocess\nimport sys\n\nimport pytorch_sphinx_theme\n\nsys.path.insert(0, os.path.abspath('../..'))\n\n# -- Project information -----------------------------------------------------\n\nproject = 'MMagic'\ncopyright = '2023, MMagic Authors'\nauthor = 'MMagic Authors'\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.autosectionlabel',\n 'sphinx_markdown_tables',\n 'sphinx_copybutton',\n 'sphinx_tabs.tabs',\n 'myst_parser',\n]\n\nextensions.append('notfound.extension') # enable customizing not-found page\n\nextensions.append('autoapi.extension')\nautoapi_type = 'python'\nautoapi_dirs = ['../../mmagic']\nautoapi_add_toctree_entry = False\nautoapi_template_dir = '_templates'\n# autoapi_options = ['members', 'undoc-members', 'show-module-summary']\n\n# # Core library for html generation from docstrings\n# extensions.append('sphinx.ext.autodoc')\n# extensions.append('sphinx.ext.autodoc.typehints')\n# # Enable 'expensive' imports for sphinx_autodoc_typehints\n# set_type_checking_flag = True\n# # Sphinx-native method. Not as good as sphinx_autodoc_typehints\n# autodoc_typehints = \"description\"\n\n# extensions.append('sphinx.ext.autosummary') # Create neat summary tables\n# autosummary_generate = True # Turn on sphinx.ext.autosummary\n# # Add __init__ doc (ie. params) to class summaries\n# autoclass_content = 'both'\n# autodoc_skip_member = []\n# # If no docstring, inherit from base class\n# autodoc_inherit_docstrings = True\n\nautodoc_mock_imports = [\n 'mmagic.version', 'mmcv._ext', 'mmcv.ops.ModulatedDeformConv2d',\n 'mmcv.ops.modulated_deform_conv2d', 'clip', 'resize_right', 'pandas'\n]\n\nsource_suffix = {\n '.rst': 'restructuredtext',\n '.md': 'markdown',\n}\n\n# Ignore >>> when copying code\ncopybutton_prompt_text = r'>>> |\\.\\.\\. '\ncopybutton_prompt_is_regexp = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['../en/_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = 'sphinx_rtd_theme'\nhtml_theme = 'pytorch_sphinx_theme'\nhtml_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]\n\nhtml_theme_options = {\n # 'logo_url': 'https://mmocr.readthedocs.io/en/latest/',\n 'menu': [\n {\n 'name': 'GitHub',\n 'url': 'https://github.com/open-mmlab/mmagic',\n },\n {\n 'name':\n '版本',\n 'children': [\n {\n 'name': 'MMagic 1.x',\n 'url': 'https://mmagic.readthedocs.io/zh_CN/latest/',\n 'description': 'Main 分支文档'\n },\n {\n 'name': 'MMEditing 0.x',\n 'url': 'https://mmagic.readthedocs.io/zh_CN/0.x/',\n 'description': '0.x 分支文档'\n },\n ],\n 'active':\n True,\n },\n ],\n 'menu_lang':\n 'cn'\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\nhtml_css_files = ['css/readthedocs.css']\n\nmyst_enable_extensions = ['colon_fence']\nmyst_heading_anchors = 3\n\nlanguage = 'zh_CN'\n\n# The master toctree document.\nroot_doc = 'index'\nnotfound_template = '404.html'\n\n\ndef builder_inited_handler(app):\n subprocess.run(['python', './.dev_scripts/update_model_zoo.py'])\n subprocess.run(['python', './.dev_scripts/update_dataset_zoo.py'])\n\n\ndef skip_member(app, what, name, obj, skip, options):\n if what == 'package' or what == 'module':\n skip = True\n return skip\n\n\ndef setup(app):\n app.connect('builder-inited', builder_inited_handler)\n app.connect('autoapi-skip-member', skip_member)\n" }, { "path": "docs/zh_cn/device/npu_zh.md", "content": "# NPU (华为昇腾)\n\n## 使用方法\n\n首先，请参考[MMCV](https://mmcv.readthedocs.io/zh_CN/latest/get_started/build.html#npu-mmcv-full) 安装带有 NPU 支持的 MMCV与 [mmengine](https://mmengine.readthedocs.io/en/latest/get_started/installation.html#build-from-source) 。\n\n使用如下命令，可以利用 8 个 NPU 训练模型（以 edsr 为例）：\n\n```shell\nbash tools/dist_train.sh configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py 8\n```\n\n或者，使用如下命令，在一个 NPU 上训练模型（以 edsr 为例）：\n\n```shell\npython tools/train.py configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py\n```\n\n## 经过验证的模型\n\n| Model | Dataset | PSNR | SSIM | Download |\n| :----------------------------------------------------------------------------------------: | ------- | :---: | :--- | :--------------------------------------------------------------------------------------------- |\n| [edsr_x2c64b16_1x16_300k_div2k](https://github.com/open-mmlab/mmagic/blob/main/configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py) | DIV2K | 35.83 | 0.94 | [log](https://download.openmmlab.com/mmediting/device/npu/edsr/edsr_x2c64b16_1xb16-300k_div2k.log) |\n\n**注意:**\n\n- 如果没有特别标记，NPU 上的结果与使用 FP32 的 GPU 上的结果相同。\n\n**以上所有模型权重及训练日志均由华为昇腾团队提供**\n" }, { "path": "docs/zh_cn/faq.md", "content": "# 常见问题解答\n\n我们在此列出了许多用户面临的一些常见问题及其相应的解决方案。如果您发现任何常见问题，并有办法帮助他人解决这些问题，请随时丰富列表内容。如果这里的内容没有涵盖您的问题，请使用[提供的模板](https://github.com/open-mmlab/mmagic/issues/new/choose)创建一个问题，并确保填写了模板中的所有必要信息。\n\n## 常见问题\n\n**问题1：** “xxx: ‘yyy is not in the zzz registry’”.\n\n**回答1：** 只有导入模块文件时，才会触发注册表机制。所以你需要在某个地方导入该文件。\n\n**问题2：** 某个数据集的文件夹结构是什么？\n\n**回答2：** 您可以根据[数据集准备](https://github.com/sijiua/mmagic/blob/dev-1.x/docs/en/user_guides/dataset_prepare.md)教程来确保文件夹结构的正确性。\n\n**问题3：** 如何使用 LMDB 数据训练模型？\n\n**回答3：** 您可以使用工具/数据中的脚本制作 LMDB 文件。更多详情请参见[数据集准备](https://github.com/sijiua/mmagic/blob/dev-1.x/docs/en/user_guides/dataset_prepare.md)教程。\n\n**问题4：** 为什么使用了 MMCV==xxx，但在导入 mmagic 时却出现了不兼容？\n\n**回答4：** 这是因为 MMCV 和 MMagic 的版本不兼容。兼容的 MMagic 和 MMCV 版本如下所示。请选择正确的 MMCV 版本以避免安装问题。\n\n| MMagic版本 | MMCV 版本 |\n| :--------: | :--------------: |\n| master | mmcv-full>=2.0.0 |\n\n注意：如果已安装 mmcv，则需要先运行 pip uninstall mmcv。如果同时安装了 mmcv 和 mmcv-full，则会出现模块未找到错误（ModuleNotFoundError）。\n**问题5：** 如何忽略基本配置中的某些字段？\n\n**回答5：** 有些时候您可以设置 _delete_=True 来忽略基本配置中的某些字段。您可以参考 [MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md#delete-key-in-dict) 的简单说明。\n您可以仔细阅读[本教程](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md)，以便更好地理解这一功能。\n\n**问题6：** 如何在配置中使用中间变量？\n\n**回答6：** 有些中间变量会在配置文件中使用，比如数据集中的 train_pipeline/test_pipeline。值得注意的是，当修改子配置中的中间变量时，用户需要再次将中间变量传递到相应的字段中。\n" }, { "path": "docs/zh_cn/get_started/install.md", "content": "# 安装教程\n\n在本节中，你将了解到：\n\n- [安装教程](#安装教程)\n - [安装](#安装)\n - [前提条件](#前提条件)\n - [最佳实践](#最佳实践)\n - [自定义安装](#自定义安装)\n - [CUDA版本](#cuda版本)\n - [不使用MIM安装MMCV](#不使用mim安装mmcv)\n - [在Docker中使用MMagic](#在docker中使用mmagic)\n - [问题解决](#问题解决)\n - [使用多个MMagic版本开发](#使用多个mmagic版本开发)\n\n## 安装\n\n我们建议用户按照我们的[最佳实践](#最佳实践)来安装MMagic。\n然而，整个过程是高度可定制的。更多信息请参阅[自定义安装](#自定义安装)部分。\n\n### 前提条件\n\n在本节中，我们将演示如何使用PyTorch准备环境。\n\nMMagic可以在Linux, Windows, 和macOS上运行。它要求：\n\n- Python >= 3.7\n- [PyTorch](https://pytorch.org/) >= 1.8\n- [MMCV](https://github.com/open-mmlab/mmcv) >= 2.0.0\n\n>\n\n如果您对PyTorch有经验并且已经安装了它，直接跳过这一部分，跳到[下一节](#最佳实践)。否则, 您可以按照以下步骤来准备环境。\n\n**Step 0.**\n从[官方网站](https://docs.conda.io/en/latest/miniconda.html)下载和安装Miniconda.\n\n**Step 1.**\n创建一个[conda虚拟环境](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html#)并激活它\n\n```shell\nconda create --name mmagic python=3.8 -y\nconda activate mmagic\n```\n\n**Step 2.**\n按照[官方说明](https://pytorch.org/get-started/locally/)安装PyTorch，例如\n\n- 在GPU平台上：\n\n ```shell\n conda install pytorch torchvision cudatoolkit=11.3 -c pytorch\n ```\n\n- 在CPU平台上：\n\n ```shell\n conda install pytorch=1.10 torchvision cpuonly -c pytorch\n ```\n\n### 最佳实践\n\n**Step 0.** 使用[MIM](https://github.com/open-mmlab/mim)安装[MMCV](https://github.com/open-mmlab/mmcv)。\n\n```shell\npip install -U openmim\nmim install 'mmcv>=2.0.0'\n```\n\n**Step 1.** 安装[MMEngine](https://github.com/open-mmlab/mmengine)。\n\n```shell\nmim install 'mmengine'\n```\n\n或者\n\n```shell\npip install mmengine\n```\n\n或者\n\n```shell\npip install git+https://github.com/open-mmlab/mmengine.git\n```\n\n**Step 2.** 安装MMagic。\n\n```shell\nmim install 'mmagic'\n```\n\n或者\n\n```shell\npip install mmagic\n```\n\n或者从源代码安装[MMagic](https://github.com/open-mmlab/mmagic)。\n\n```shell\ngit clone https://github.com/open-mmlab/mmagic.git\ncd mmagic\npip3 install -e . -v\n```\n\n**Step 5.**\n检查MMagic是否安装成功。\n\n```shell\ncd ~\npython -c \"import mmagic; print(mmagic.__version__)\"\n# 示例输出: 1.0.0\n```\n\n显示正确的版本号，则表示安装成功。\n\n```{note}\n你可能想知道附加在`pip install`后面的`-e .`是什么意思。\n下面是说明:\n\n- `-e`表示[可编辑模式](https://pip.pypa.io/en/latest/cli/pip_install/#cmdoption-e).\n 当`import mmagic`时，将导入克隆目录下的模块。\n 如果`pip install`没有附加`-e`, pip会将克隆的代码复制到类似`lib/python/site-package`的地方。\n 因此，除非再次执行`pip install`命令，否则在克隆目录下修改后的代码不会生效。\n 因此，`pip install`命令附带`-e`对于开发人员来说特别方便。如果修改了一些代码，下次导入新的代码时不需要重新安装。\n- `.`表示此目录中的代码。\n\n你也可以使用`pip install -e .[all]`命令，这将安装更多的依赖项，特别是对于预提交hooks和单元测试。\n```\n\n### 自定义安装\n\n#### CUDA版本\n\n安装PyTorch时,您需要指定CUDA的版本。如果您不清楚该选择哪一个，请遵循我们的建议:\n\n- 对于基于Ampere的NVIDIA GPUs，如GeForce 30系列和NVIDIA A100，必须使用CUDA 11。\n- 对于较老的NVIDIA GPUs，是向后兼容的，但CUDA 10.2提供了更好的兼容性，更轻量。\n\n请确保GPU驱动程序满足最低版本要求。\n更多信息请参见[此表](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions)。\n\n**注意**\n如果遵循我们的最佳实践，安装CUDA runtime库就足够了，因为不会在本地编译CUDA代码。\n但是，如果您希望从源代码编译MMCV或开发其他CUDA算子，则需要从NVIDIA的[开发者网站](https://developer.nvidia.com/cuda-downloads)安装完整的CUDA工具包，其版本应与PyTorch的CUDA版本匹配。即，在 `conda install` 命令中指定的cudatoolkit版本。\n\n#### 不使用MIM安装MMCV\n\nMMCV包含c++和CUDA扩展，因此以一种复杂的方式依赖于PyTorch。MIM自动解决了这种依赖关系，并使安装更容易。然而，这并不是必须的。\n\n要使用pip而不是MIM安装MMCV，请遵循[MMCV安装指南](https://mmcv.readthedocs.io/en/latest/get_started/installation.html)。这需要根据PyTorch版本及其CUDA版本手动指定find-url。\n\n例如，以下命令install mmcv-full是针对PyTorch 1.10.x和CUDA 11.3构建的。\n\n```shell\npip install 'mmcv>=2.0.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html\n```\n\n#### 在Docker中使用MMagic\n\n我们提供一个[Dockerfile](https://github.com/open-mmlab/mmagic/blob/main/docker/Dockerfile)来构建一个镜像。请确保您的[docker版本](https://docs.docker.com/engine/install/)>=19.03。\n\n```shell\n# 使用PyTorch 1.8, CUDA 11.1构建一个镜像\n# 如果您喜欢其他版本，只需修改Dockerfile\ndocker build -t mmagic docker/\n```\n\n使用如下命令运行\n\n```shell\ndocker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmagic/data mmagic\n```\n\n#### 问题解决\n\n如果在安装过程中遇到问题，请先查看[FAQ](../faq.md)页面。如果找不到解决方案，可以在GitHub上[open an issue](https://github.com/open-mmlab/mmagic/issues/new/choose)。\n\n### 使用多个MMagic版本开发\n\n训练和测试脚本已经修改了`PYTHONPATH`，以确保脚本使用当前目录中的`MMagic`。\n\n要使用环境中安装的默认MMagic，而不是您正在使用的MMagic，可以删除这些脚本中的以下行\n\n```shell\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH\n```\n" }, { "path": "docs/zh_cn/get_started/overview.md", "content": "# 概述\n\n欢迎来到 MMagic！ 在本节中，您将了解\n\n- [MMagic是什么？](#mmagic-是什么)\n- [为什么要使用 MMagic？](#为什么要使用-mmagic)\n- [新手入门](#新手入门)\n- [基础教程](#基础教程)\n- [进阶教程](#进阶教程)\n\n## MMagic 是什么？\n\nMMagic (**M**ultimodal **A**dvanced, **G**enerative, and **I**ntelligent **C**reation) 是一个供专业人工智能研究人员和机器学习工程师去处理、编辑和生成图像与视频的开源 AIGC 工具箱。\n\nMMagic 允许研究人员和工程师使用最先进的预训练模型，并且可以轻松训练和开发新的定制模型。\n\nMMagic 支持各种基础生成模型，包括：\n\n- 无条件生成对抗网络 (GANs)\n- 条件生成对抗网络 (GANs)\n- 内部学习\n- 扩散模型\n- 还有许多其他生成模型即将推出！\n\nMMagic 支持各种应用程序，包括：\n\n- 图文生成\n- 图像翻译\n- 3D 生成\n- 图像超分辨率\n- 视频超分辨率\n- 视频插帧\n- 图像补全\n- 图像抠图\n- 图像修复\n- 图像上色\n- 图像生成\n- 还有许多其他应用程序即将推出！\n\n
\n \n
\n
\n\n## 为什么要使用 MMagic？\n\n- **SOTA 算法**\n\n MMagic 提供了处理、编辑、生成图像和视频的 SOTA 算法。\n\n- **强有力且流行的应用**\n\n MMagic 支持了流行的图像修复、图文生成、3D生成、图像修补、抠图、超分辨率和生成等任务的应用。特别是 MMagic 支持了 Stable Diffusion 的微调和许多激动人心的 diffusion 应用，例如 ControlNet 动画生成。MMagic 也支持了 GANs 的插值，投影，编辑和其他流行的应用。请立即开始你的 AIGC 探索之旅！\n\n- **高效的框架**\n\n 通过 OpenMMLab 2.0 框架的 MMEngine 和 MMCV， MMagic 将编辑框架分解为不同的组件，并且可以通过组合不同的模块轻松地构建自定义的编辑器模型。我们可以像搭建“乐高”一样定义训练流程，提供丰富的组件和策略。在 MMagic 中，你可以使用不同的 APIs 完全控制训练流程。得益于 [MMSeparateDistributedDataParallel](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/wrappers/seperate_distributed.py), 动态模型结构的分布式训练可以轻松实现。\n\n## 新手入门\n\n安装说明见[安装](install.md)。\n\n## 基础教程\n\n对于初学者，我们建议从 [基础教程](../user_guides/config.md) 学习 MMagic 的基本用法。\n\n## 进阶教程\n\n对于熟悉 MMagic 的用户，可能想了解 MMagic 的进阶实用，以及如何扩展算法库，如何使用多个算法库框架等高级用法，请参考[进阶教程](../advanced_guides/evaluator.md)。\n\n## 开发指南\n\n想要使用 MMagic 进行深度开发的用户，可以参考[开发指南](../howto/models.md)。\n" }, { "path": "docs/zh_cn/get_started/quick_run.md", "content": "# 快速运行\n\n成功安装MMagic后，现在您可以玩转MMagic了！如果您要从文本生成图像，只需要MMagic的几行代码！\n\n```python\nfrom mmagic.apis import MMagicInferencer\nsd_inferencer = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = 'output/sd_res.png'\nsd_inferencer.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\n或者您可以运行以下命令。\n\n```bash\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir ./output/sd_res.png\n```\n\n您将在文件夹`output/`中看到一个新图像`sd_res.png`，其中包含生成的样本。\n\n更重要的是，如果您想让这些照片更清晰，MMagic的超分辨率只需要几行代码！\n\n```python\nfrom mmagic.apis import MMagicInferencer\nconfig = 'configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py'\ncheckpoint = 'https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth'\nimg_path = 'tests/data/image/lq/baboon_x4.png'\neditor = MMagicInferencer('esrgan', model_config=config, model_ckpt=checkpoint)\noutput = editor.infer(img=img_path,result_out_dir='output.png')\n```\n\n现在，您可以在 `output.png` 中查看您想要的图片。\n" }, { "path": "docs/zh_cn/howto/dataset.md", "content": "# 如何自定义数据集\n\n本文档将介绍 MMagic 中每一个数据集的设计方式，以及用户如何设计自定义数据集。\n\n- [如何自定义数据集](#如何自定义数据集)\n - [支持的数据集格式](#支持的数据集格式)\n - [BasicImageDataset](#basicimagedataset)\n - [BasicFramesDataset](#basicframesdataset)\n - [BasicConditonalDataset](#basicconditonaldataset)\n - [1. 逐行读取的标注文件格式（例如 txt 文件）](#1-逐行读取的标注文件格式例如-txt-文件)\n - [2. 基于字典的标注文件格式（例如 json）](#2-基于字典的标注文件格式例如-json)\n - [3. 基于文件夹的标注格式（无需标注文件）](#3-基于文件夹的标注格式无需标注文件)\n - [ImageNet 和 CIFAR10 数据集](#imagenet-和-cifar10-数据集)\n - [AdobeComp1kDataset](#adobecomp1kdataset)\n - [GrowScaleImgDataset](#growscaleimgdataset)\n - [SinGANDataset](#singandataset)\n - [PairedImageDataset](#pairedimagedataset)\n - [UnpairedImageDataset](#unpairedimagedataset)\n - [实现一个新的数据集](#实现一个新的数据集)\n - [重复数据集](#重复数据集)\n\n## 支持的数据集格式\n\n在 MMagic 中，所有的数据集都是从 `BaseDataset` 类继承而来的。\n每个数据集都通过 `load_data_list` 方法来加载数据信息列表（例如数据所在的路径）。\n在 `__getitem__` 方法中，调用 `prepare_data` 来获取前处理后的数据。\n在 `prepare_data` 方法中，数据加载流程包括如下步骤：\n\n1. 通过传入的索引来获取数据信息，由 `get_data_info` 方法实现。\n2. 对数据应用数据转换，由 `pipeline` 方法实现。\n\n### BasicImageDataset\n\n**BasicImageDataset** `mmagic.datasets.BasicImageDataset` 是一个通用图片数据集，是为了底层视觉任务而设计的，比如图像超分辨率，图像修复和无条件图像生成。可以选择是否使用标注文件。\n\n如使用标注文件，标注的格式可以如下所示：\n\n```bash\n Case 1 (CelebA-HQ):\n\n 000001.png\n 000002.png\n\n Case 2 (DIV2K):\n\n 0001_s001.png (480,480,3)\n 0001_s002.png (480,480,3)\n 0001_s003.png (480,480,3)\n 0002_s001.png (480,480,3)\n 0002_s002.png (480,480,3)\n\n Case 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n```\n\n下面我们给出几个如何使用 `BasicImageDataset` 的示例。假定文件结构如下：\n\n```md\nmmagic (root)\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ │ ├── image.png\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ │ │ ├── image_x4.png\n│ │ ├── DIV2K_valid_HR\n│ │ ├── DIV2K_valid_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ ├── places\n│ │ ├── test_set\n│ │ ├── train_set\n| | ├── meta\n| | | ├── Places365_train.txt\n| | | ├── Places365_val.txt\n| ├── celebahq\n│ │ ├── imgs_1024\n\n```\n\n按照以上的文件结构给出 3 个示例。\n\n示例 1: 加载 `DIV2K` 数据集来训练一个 `SISR` 模型。\n\n```python\n dataset = BasicImageDataset(\n ann_file='',\n metainfo=dict(\n dataset_type='div2k',\n task_name='sisr'),\n data_root='data/DIV2K',\n data_prefix=dict(\n gt='DIV2K_train_HR', img='DIV2K_train_LR_bicubic/X4'),\n filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=[])\n```\n\n示例 2: 加载 `places` 数据集来训练一个 `inpainting` 模型.\n\n```python\n dataset = BasicImageDataset(\n ann_file='meta/Places365_train.txt',\n metainfo=dict(\n dataset_type='places365',\n task_name='inpainting'),\n data_root='data/places',\n data_prefix=dict(gt='train_set'),\n pipeline=[])\n```\n\n示例 3: 加载 `CelebA-HQ` 数据集来训练一个 `PGGAN` 模型.\n\n```python\ndataset = BasicImageDataset(\n pipeline=[],\n data_root='./data/celebahq/imgs_1024')\n```\n\n### BasicFramesDataset\n\n**BasicFramesDataset** `mmagic.datasets.BasicFramesDataset` 也是一个通用图片数据集，为视频帧的底层视觉任务而设计的，比如视频的超分辨率和视频帧插值。\n可以选择是否使用标注文件。\n\n如使用标注文件, 标注的格式示例所示：\n\n```bash\nCase 1 (Vid4):\n\n calendar 41\n city 34\n foliage 49\n walk 47\n\nCase 2 (REDS):\n\n 000/00000000.png (720, 1280, 3)\n 000/00000001.png (720, 1280, 3)\n\nCase 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n```\n\n假定文件结构如下：\n\n```bash\nmmagic (root)\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Vid4\n│ │ ├── BIx4\n│ │ │ ├── city\n│ │ │ │ ├── img1.png\n│ │ ├── GT\n│ │ │ ├── city\n│ │ │ │ ├── img1.png\n│ │ ├── meta_info_Vid4_GT.txt\n│ ├── vimeo-triplet\n│ │ ├── sequences\n| | | ├── 00001\n│ │ │ │ ├── 0389\n│ │ │ │ │ ├── img1.png\n│ │ │ │ │ ├── img2.png\n│ │ │ │ │ ├── img3.png\n│ │ ├── tri_trainlist.txt\n```\n\n按照以上的文件结构给出两个示例。\n\n示例 1: 加载 `Vid4` 数据集来训练一个 `VSR` 模型.\n\n```python\ndataset = BasicFramesDataset(\n ann_file='meta_info_Vid4_GT.txt',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root='data/Vid4',\n data_prefix=dict(img='BIx4', gt='GT'),\n pipeline=[],\n depth=2,\n num_input_frames=5)\n```\n\n示例 2: 加载 `Vimeo90k` 数据集来训练一个 `VFI` 模型.\n\n```python\ndataset = BasicFramesDataset(\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root='data/vimeo-triplet',\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=[],\n depth=2,\n load_frames_list=dict(\n img=['img1.png', 'img3.png'], gt=['img2.png']))\n```\n\n### BasicConditonalDataset\n\n**BasicConditonalDataset** `mmagic.datasets.BasicConditonalDataset` 是为条件生成对抗网络而设计的（例如 `SAGAN`、`BigGAN`）。该数据集支持为标注文件加载标签。 `BasicConditonalDataset` 支持如下 3 种标注格式。\n\n#### 1. 逐行读取的标注文件格式（例如 txt 文件）\n\n样本文件结构：\n\n```\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n```\n\n样本标注文件格式（第一列是图像的路径，第二列是类别的索引）\n\n```\n folder_1/xxx.png 0\n folder_1/xxy.png 1\n folder_2/123.png 5\n folder_2/nsdf3.png 3\n ...\n```\n\n`ImageNet` 数据集的配置示例：\n\n```python\ndataset=dict(\n type='BasicConditionalDataset,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n```\n\n#### 2. 基于字典的标注文件格式（例如 json）\n\n样本文件结构：\n\n```\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n```\n\n样本标注文件格式（键为图像的路径，值为标签）。\n\n```\n {\n \"folder_1/xxx.png\": [1, 2, 3, 4],\n \"folder_1/xxy.png\": [2, 4, 1, 0],\n \"folder_2/123.png\": [0, 9, 8, 1],\n \"folder_2/nsdf3.png\", [1, 0, 0, 2],\n ...\n }\n```\n\n`EG3D (shapenet-car) ` 数据集的配置示例：\n\n```python\ndataset = dict(\n type='BasicConditionalDataset',\n data_root='./data/eg3d/shapenet-car',\n ann_file='annotation.json',\n pipeline=train_pipeline)\n```\n\n在这种类型的注释中，标签可以是任何类型，不仅限于索引。\n\n#### 3. 基于文件夹的标注格式（无需标注文件）\n\n样本文件结构：\n\n```\n data_prefix/\n ├── class_x\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n │ └── xxz.png\n └── class_y\n ├── 123.png\n ├── nsdf3.png\n ├── ...\n └── asd932_.png\n```\n\n如果在配置的 `ann_file` 中指定了标注文件，则将使用上面的前两种方式生成数据集，否则将尝试使用第三种方式。\n\n### ImageNet 和 CIFAR10 数据集\n\n**ImageNet 数据集** `mmagic.datasets.ImageNet` 和 **CIFAR10 数据集**`mmagic.datasets.CIFAR10` 是为 `ImageNet` 和 `CIFAR10` 这两个数据集而设计的。\n这两个数据集都是基于 `BasicConditionalDataset` 封装的。您可以使用它们来轻松加载这两个数据集的数据。\n\n`ImageNet` 的配置示例：\n\n```python\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='RandomCropLongEdge', keys=['img']),\n dict(type='Resize', scale=(128, 128), keys=['img'], backend='pillow'),\n dict(type='Flip', keys=['img'], flip_ratio=0.5, direction='horizontal'),\n dict(type='PackInputs')\n]\n\ndataset=dict(\n type='ImageNet',\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=pipeline),\n```\n\n`CIFAR10` 的配置示例:\n\n```python\npipeline = [dict(type='PackInputs')]\n\ndataset = dict(\n type='CIFAR10',\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=pipeline)\n```\n\n### AdobeComp1kDataset\n\n**AdobeComp1kDataset** `mmagic.datasets.AdobeComp1kDataset` 是为 `Adobe composition-1k` 数据集而设计的。\n\n该数据集加载（alpha, fg, bg）数据，并对数据执行指定的变换。您可以在 `pipeline` 中指定在线合成图像或加载离线已合成的图像。\n\n在线合成 `comp-1k` 数据集示例:\n\n```md\n[\n {\n \"alpha\": 'alpha/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n]\n```\n\n离线合成 `comp-1k` 数据集示例:\n\n```md\n[\n {\n \"alpha\": 'alpha/000.png',\n \"merged\": 'merged/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"merged\": 'merged/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n]\n```\n\n### GrowScaleImgDataset\n\n`GrowScaleImgDataset` 是为了动态 GAN 模型（例如 `PGGAN` 和 `StyleGANv1`）而设计的。\n在这个数据集中，我们支持在训练过程中切换数据根目录，来加载不同分辨率的训练图像。\n这个过程是通过 `GrowScaleImgDataset.update_annotations` 方法实现的，并在训练过程中由 `PGGANFetchDataHook.before_train_iter` 调用。\n\n```python\ndef update_annotations(self, curr_scale):\n # 确定是否需要更新数据根目录\n if curr_scale == self._actual_curr_scale:\n return False\n\n # 按图像分辨率（尺度）提取新的数据根目录\n for scale in self._img_scales:\n if curr_scale <= scale:\n self._curr_scale = scale\n break\n if scale == self._img_scales[-1]:\n assert RuntimeError(\n f'Cannot find a suitable scale for {curr_scale}')\n self._actual_curr_scale = curr_scale\n self.data_root = self.data_roots[str(self._curr_scale)]\n\n # 使用新的数据根目录重新加载数据列表\n self.load_data_list()\n\n # print basic dataset information to check the validity\n print_log('Update Dataset: ' + repr(self), 'current')\n return True\n```\n\n### SinGANDataset\n\n`SinGANDataset` 是为 `SinGAN` 模型训练而设计的数据集。在 `SinGAN` 的训练中，我们不会去迭代数据集中的图像，而是返回一个一致的预处理图像字典。\n由于不需要根据给定的索引加载相应的图像数据，我们绕过了 `BaseDataset` 的默认数据加载逻辑。\n\n```python\ndef load_data_list(self, min_size, max_size, scale_factor_init):\n # 加载单张图像\n real = mmcv.imread(self.data_root)\n self.reals, self.scale_factor, self.stop_scale = create_real_pyramid(\n real, min_size, max_size, scale_factor_init)\n\n self.data_dict = {}\n\n # 生成多尺度图像\n for i, real in enumerate(self.reals):\n self.data_dict[f'real_scale{i}'] = real\n\n self.data_dict['input_sample'] = np.zeros_like(\n self.data_dict['real_scale0']).astype(np.float32)\n\ndef __getitem__(self, index):\n # 直接返回转换过的数据字典\n return self.pipeline(self.data_dict)\n```\n\n### PairedImageDataset\n\n`PairedImageDataset` 专为需要成对训练数据的图像转换模型（例如 `Pix2Pix`）设计。\n\n目录结构如下所示，其中每个图像文件都是图像对的拼接。\n\n```\n./data/dataset_name/\n├── test\n│ └── XXX.jpg\n└── train\n └── XXX.jpg\n```\n\n在 `PairedImageDataset` 中，我们在 `load_data_list` 方法中扫描文件列表，然后将路径保存在 `pair_path` 字段中，以适配 `LoadPairedImageFromFile` 中的转换。\n\n```python\ndef load_data_list(self):\n data_infos = []\n pair_paths = sorted(self.scan_folder(self.data_root))\n for pair_path in pair_paths:\n # save path in the specific field\n data_infos.append(dict(pair_path=pair_path))\n\n return data_infos\n```\n\n### UnpairedImageDataset\n\n`UnpairedImageDataset` 是专为不需要成对数据的图像转换模型（例如 `CycleGAN`）设计的数据集。\n\n目录结构如下所示：\n\n```\n./data/dataset_name/\n├── testA\n│ └── XXX.jpg\n├── testB\n│ └── XXX.jpg\n├── trainA\n│ └── XXX.jpg\n└── trainB\n └── XXX.jpg\n\n```\n\n在该数据集中，我们重载了 `__getitem__` 方法，实现了在训练过程中加载随机的图像对。\n\n```python\ndef __getitem__(self, idx):\n if not self.test_mode:\n return self.prepare_train_data(idx)\n\n return self.prepare_test_data(idx)\n\ndef prepare_train_data(self, idx):\n img_a_path = self.data_infos_a[idx % self.len_a]['path']\n idx_b = np.random.randint(0, self.len_b)\n img_b_path = self.data_infos_b[idx_b]['path']\n results = dict()\n results[f'img_{self.domain_a}_path'] = img_a_path\n results[f'img_{self.domain_b}_path'] = img_b_path\n return self.pipeline(results)\n\ndef prepare_test_data(self, idx):\n img_a_path = self.data_infos_a[idx % self.len_a]['path']\n img_b_path = self.data_infos_b[idx % self.len_b]['path']\n results = dict()\n results[f'img_{self.domain_a}_path'] = img_a_path\n results[f'img_{self.domain_b}_path'] = img_b_path\n return self.pipeline(results)\n```\n\n## 实现一个新的数据集\n\n如果您需要为一个新的底层 CV 任务（例如去噪、去雨、去雾和去反射）创建一个数据集，或者现有的数据集格式不符合您的需求，您可以将新的数据格式重新组织成现有的格式，或者在 `mmagic/datasets` 中创建一个新的数据集中来加载数据。\n\n从现有的数据集基类中继承（例如 `BasicImageDataset` 和 `BasicFramesDataset`）会比较容易创建一个新的数据集。\n\n您也可以创建一个继承自 [BaseDataset](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/base_dataset.py) 的新数据集，它是定义在 [MMEngine](https://github.com/open-mmlab/mmengine) 中的数据集基类。\n\n下面是创建一个用于视频帧插值的数据集的示例：\n\n```python\nfrom .basic_frames_dataset import BasicFramesDataset\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass NewVFIDataset(BasicFramesDataset):\n \"\"\"Introduce the dataset\n\n Examples of file structure.\n\n Args:\n pipeline (list[dict | callable]): A sequence of data transformations.\n folder (str | :obj:`Path`): Path to the folder.\n ann_file (str | :obj:`Path`): Path to the annotation file.\n test_mode (bool): Store `True` when building test dataset.\n Default: `False`.\n \"\"\"\n\n def __init__(self, ann_file, metainfo, data_root, data_prefix,\n pipeline, test_mode=False):\n super().__init__(ann_file, metainfo, data_root, data_prefix,\n pipeline, test_mode)\n self.data_infos = self.load_annotations()\n\n def load_annotations(self):\n \"\"\"Load annoations for the dataset.\n\n Returns:\n list[dict]: A list of dicts for paired paths and other information.\n \"\"\"\n data_infos = []\n ...\n return data_infos\n\n```\n\n欢迎[提交新的数据集类到 MMagic](https://github.com/open-mmlab/mmagic/compare)\n\n### 重复数据集\n\n我们使用 [RepeatDataset](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/dataset_wrapper.py) 作为包装器来重复数据集。\n例如，假设原始数据集是 `Dataset_A`，为了重复它，配置文件应该如下所示：\n\n```python\ndataset_A_train = dict(\n type='RepeatDataset',\n times=N,\n dataset=dict( # This is the original config of Dataset_A\n type='Dataset_A',\n ...\n pipeline=train_pipeline\n )\n )\n```\n\n您可以参考 [MMEngine 中的教程](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/basedataset.md)。\n" }, { "path": "docs/zh_cn/howto/losses.md", "content": "# 如何设计自己的损失函数\n\n`losses` 在 `MMagic` 中注册为 `LOSSES`。\n在 MMagic 中设计自己的损失函数，步骤和在 MMagic 中自定义任何其他模型类似。\n本节主要具体介绍了如何在 MMagic 中实现自定义的损失函数。\n本教程建议您在实现自定义的损失函数时，应该遵循本教程相同的设计，这样在我们的框架中使用您新定义的损失函数，就不需要额外的工作。\n\n本指南包括：\n\n- [设计你自己的损失函数](#如何设计自己的损失函数)\n - [支持损失函数介绍](#支持的损失函数介绍)\n - [设计一个新的损失函数](#设计一个新的损失函数)\n - [MSELoss 的一个例子](#MSELoss-的一个例子)\n - [DiscShiftLoss 的一个例子](#DiscShiftLoss-的一个例子)\n - [GANWithCustomizedLoss 的一个例子](#GANWithCustomizedLoss-的一个例子)\n - [可用损失函数](#可用损失函数)\n - [常规损失函数](#常规损失函数)\n - [损失函数组件](#损失函数组件)\n\n## 支持的损失函数介绍\n\n为了方便使用，您可以直接使用我们为具体算法设置的默认损失计算过程，如lsgan、biggan、styleganv2等。\n以`stylegan2`为例，我们使用R1梯度惩罚和生成器路径长度正则化作为可配置损失，用户可以调整相关参数，如 `r1_loss_weight` 和 `g_reg_weight`。\n\n```python\n# stylegan2_base.py\nloss_config =dict(\n r1_loss_weight=10。 / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2。 * g_reg_interval,\n pl_batch_shrink=2)\n\nmodel=dict(\n type='StyleGAN2',\n xxx,\n loss_config=loss_config)\n```\n\n## 设计一个新的损失函数\n\n### MSELoss 的一个例子\n\n一般来说，要实现一个损失模块，我们会编写一个函数实现，然后用类实现包装它。 以MSELoss为例：\n\n```python\n@masked_loss\ndef mse_loss(pred，target)：\n return F.mse_loss（pred，target，reduction='none'）\n\n@LOSSES.register_module()\nClass MSELoss(nn.Module)：\n\n def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):\n # 代码可以在``mmagic/models/losses/pixelwise_loss.py``中找到\n\n def forward(self, pred, target, weight=None, **kwargs):\n # 代码可以在``mmagic/models/losses/pixelwise_loss.py``中找到\n```\n\n根据这个损失函数的定义，我们现在可以简单地通过在配置文件中定义它来使用：\n\n```python\npixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean')\n```\n\n请注意，上面的`pixel_loss`必须在模型中定义。 详情请参考[自定义模型](./models.md)。 与自定义模型类似，为了使用您自己实现的损失函数，您需要在编写后在`mmagic/models/losses/__init__.py`中导入该损失函数。\n\n### DiscShiftLoss 的一个例子\n\n一般来说，要实现一个损失模块，我们会编写一个函数实现，然后用类实现包装它。\n但是，在 MMagic 中，我们提供了另一个统一的接口 data_info 供用户定义输入参数和数据项之间的映射。\n\n```python\n@weighted_loss\ndef disc_shift_loss(pred)：\n return pred**2\n\n@MODULES.register_module()\nClass DiscShiftLoss(nn.Module)：\n\n def __init__(self, loss_weight=1.0, data_info=None):\n super(DiscShiftLoss，self).__init__()\n # 代码可以在``mmagic/models/losses/disc_auxiliary_loss.py``中找到\n\n def forward(self, *args, **kwargs):\n # 代码可以在``mmagic/models/losses/disc_auxiliary_loss.py``中找到\n```\n\n这种损失模块设计的目标是允许在生成模型(`MODELS`)中自动使用它，而无需其他复杂代码来定义数据和关键字参数之间的映射。 因此，与 OpenMMLab 中的其他框架不同，我们的损失模块包含一个特殊的关键字 data_info，它是一个定义输入参数与生成模型数据之间映射的字典。 以`DiscShiftLoss`为例，用户在编写配置文件时，可能会用到这个loss，如下：\n\n```python\ndict(type='DiscShiftLoss',\n loss_weight=0.001 * 0.5,\n data_info=dict(pred='disc_pred_real'))\n```\n\n`data_info` 中的信息告诉模块使用 `disc_pred_real` 数据作为 `pred` 参数的输入张量。 一旦 `data_info` 不为 `None`，我们的损失模块将自动构建计算图。\n\n```python\n@MODULES.register_module()\nclass DiscShiftLoss(nn.Module):\n\n def __init__(self, loss_weight=1.0, data_info=None):\n super(DiscShiftLoss, self).__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n\n def forward(self, *args, **kwargs):\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n kwargs.update(dict(weight=self.loss_weight))\n return disc_shift_loss(**kwargs)\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n return disc_shift_loss(*args, weight=self.loss_weight, **kwargs)\n\n @staticmethod\n def loss_name():\n return 'loss_disc_shift'\n\n```\n\n如这部分代码所示，一旦用户设置了“data_info”，损失模块将收到一个包含所有必要数据和模块的字典，该字典由训练过程中的“MODELS”提供。 如果此字典作为非关键字参数给出，则应将其作为第一个参数提供。 如果您使用关键字参数，请将其命名为 `outputs_dict`。\n\n### GANWithCustomizedLoss 的一个例子\n\n为了构建计算图，生成模型必须提供包含各种数据的字典。 仔细观察任何生成模型，你会发现我们将各种特征和模块收集到字典中。 我们在这里提供了一个自定义的`GANWithCustomizedLoss`来展示这个过程。\n\n```python\nclass GANWithCustomizedLoss(BaseModel):\n\n def __init__(self, gan_loss, disc_auxiliary_loss, gen_auxiliary_loss,\n *args, **kwargs):\n # ...\n if gan_loss is not None:\n self.gan_loss = MODULES.build(gan_loss)\n else:\n self.gan_loss = None\n\n if disc_auxiliary_loss:\n self.disc_auxiliary_losses = MODULES.build(disc_auxiliary_loss)\n if not isinstance(self.disc_auxiliary_losses, nn.ModuleList):\n self.disc_auxiliary_losses = nn.ModuleList(\n [self.disc_auxiliary_losses])\n else:\n self.disc_auxiliary_loss = None\n\n if gen_auxiliary_loss:\n self.gen_auxiliary_losses = MODULES.build(gen_auxiliary_loss)\n if not isinstance(self.gen_auxiliary_losses, nn.ModuleList):\n self.gen_auxiliary_losses = nn.ModuleList(\n [self.gen_auxiliary_losses])\n else:\n self.gen_auxiliary_losses = None\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n # ...\n\n # get data dict to compute losses for disc\n data_dict_ = dict(\n iteration=curr_iter,\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs)\n\n loss_disc, log_vars_disc = self._get_disc_loss(data_dict_)\n\n # ...\n\n def _get_disc_loss(self, outputs_dict):\n # Construct losses dict. If you hope some items to be included in the\n # computational graph, you have to add 'loss' in its name. Otherwise,\n # items without 'loss' in their name will just be used to print\n # information.\n losses_dict = {}\n # gan loss\n losses_dict['loss_disc_fake'] = self.gan_loss(\n outputs_dict['disc_pred_fake'], target_is_real=False, is_disc=True)\n losses_dict['loss_disc_real'] = self.gan_loss(\n outputs_dict['disc_pred_real'], target_is_real=True, is_disc=True)\n\n # disc auxiliary loss\n if self.with_disc_auxiliary_loss:\n for loss_module in self.disc_auxiliary_losses:\n loss_ = loss_module(outputs_dict)\n if loss_ is None:\n continue\n\n # the `loss_name()` function return name as 'loss_xxx'\n if loss_module.loss_name() in losses_dict:\n losses_dict[loss_module.loss_name(\n )] = losses_dict[loss_module.loss_name()] + loss_\n else:\n losses_dict[loss_module.loss_name()] = loss_\n loss, log_var = self.parse_losses(losses_dict)\n\n return loss, log_var\n\n```\n\n在这里，`_get_disc_loss` 将帮助自动组合各种损失函数。\n\n因此，只要用户设计相同规则的损失模块，就可以在生成模型的训练中插入任何一种损失，无需对模型代码进行其他修改。 您只需要在配置文件中定义 `data_info` 即可。\n\n## 可用损失函数\n\n我们在配置中列出了可用的损失示例，如下所示。\n\n### 常规损失函数\n\n\n\n \n \n \n \n \n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n \n\n
MethodclassExample
vanilla gan lossmmagic.models.GANLoss\n\n```python\n# dic gan\nloss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n)\n\n```\n\n
lsgan lossmmagic.models.GANLoss\n
wgan lossmmagic.models.GANLoss\n\n```python\n# deepfillv1\nloss_gan=dict(\n type='GANLoss',\n gan_type='wgan',\n loss_weight=0.0001,\n)\n```\n\n
hinge lossmmagic.models.GANLoss\n\n```python\n# deepfillv2\nloss_gan=dict(\n type='GANLoss',\n gan_type='hinge',\n loss_weight=0.1,\n)\n```\n\n
smgan lossmmagic.models.GANLoss\n\n```python\n# aot-gan\nloss_gan=dict(\n type='GANLoss',\n gan_type='smgan',\n loss_weight=0.01,\n)\n```\n\n
gradient penaltymmagic.models.GradientPenaltyLoss\n\n```python\n# deepfillv1\nloss_gp=dict(type='GradientPenaltyLoss', loss_weight=10.)\n```\n\n
discriminator shift lossmmagic.models.DiscShiftLoss\n\n```python\n# deepfillv1\nloss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001)\n\n```\n\n
clip lossmmagic.models.CLIPLoss
L1 composition lossmmagic.models.L1CompositionLoss
MSE composition lossmmagic.models.MSECompositionLoss
charbonnier composition lossmmagic.models.CharbonnierCompLoss\n\n```python\n# dim\nloss_comp=dict(type='CharbonnierCompLoss', loss_weight=0.5)\n```\n\n
face id Lossmmagic.models.FaceIdLoss
light cnn feature lossmmagic.models.LightCNNFeatureLoss\n\n```python\n# dic gan\nfeature_loss=dict(\n type='LightCNNFeatureLoss',\n pretrained=pretrained_light_cnn,\n loss_weight=0.1,\n criterion='l1')\n```\n\n
gradient lossmmagic.models.GradientLoss
l1 Lossmmagic.models.L1Loss\n\n```python\n# dic gan\npixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')\n```\n\n
mse lossmmagic.models.MSELoss\n\n```python\n# dic gan\nalign_loss=dict(type='MSELoss', loss_weight=0.1, reduction='mean')\n```\n\n
charbonnier lossmmagic.models.CharbonnierLoss\n\n```python\n# dim\nloss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5)\n```\n\n
masked total variation lossmmagic.models.MaskedTVLoss\n\n```python\n# partial conv\nloss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1\n)\n\n```\n\n
perceptual lossmmagic.models.PerceptualLoss\n\n```python\n# real_basicvsr\nperceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False)\n\n```\n\n
transferal perceptual lossmmagic.models.TransferalPerceptualLoss\n\n```python\n# ttsr\ntransferal_perceptual_loss=dict(\n type='TransferalPerceptualLoss',\n loss_weight=1e-2,\n use_attention=False,\n criterion='mse')\n\n```\n\n
\n\n### 损失函数组件\n\n对于“GANWithCustomizedLoss”，我们提供了几个组件来构建自定义损失。\n\n| Method | class |\n| ------------------------------------ | ------------------------------------------- |\n| clip loss component | mmagic.models.CLIPLossComps |\n| discriminator shift loss component | mmagic.models. DiscShiftLossComps |\n| gradient penalty loss component | mmagic.models. GradientPenaltyLossComps |\n| r1 gradient penalty component | mmagic.models. R1GradientPenaltyComps |\n| face Id loss component | mmagic.models. FaceIdLossComps |\n| gan loss component | mmagic.models. GANLossComps |\n| generator path regularizer component | mmagic.models.GeneratorPathRegularizerComps |\n" }, { "path": "docs/zh_cn/howto/models.md", "content": "# 如何设计自己的模型\n\nMMagic建立在MMEngine和MMCV的基础上，使用户能够快速地设计新模型，轻松地地训练和评估它们。\n在本节中，您将学习如何设计自己的模型。\n\n本指南的结构如下:\n\n- [如何设计自己的模型](#如何设计自己的模型)\n - [MMagic中的模型概述](#mmagic中的模型概述)\n - [一个SRCNN的例子](#一个srcnn的例子)\n - [Step 1: 定义SRCNN网络](#step-1-定义srcnn网络)\n - [Step 2: 定义SRCNN的模型](#step-2-定义srcnn的模型)\n - [Step 3: 开始训练SRCNN](#step-3-开始训练srcnn)\n - [一个DCGAN的例子](#一个dcgan的例子)\n - [Step 1: 定义DCGAN的网络](#step-1-定义dcgan的网络)\n - [Step 2: 设计DCGAN的模型](#step-2-设计dcgan的模型)\n - [Step 3: 开始训练DCGAN](#step-3-开始训练dcgan)\n - [参考文献](#参考文献)\n\n## MMagic中的模型概述\n\n在MMagic中，一个算法可以分为两部分: **Model** 和 **Module**.\n\n- **Model** 是最顶层的包装，并且总是继承自MMEngine中提供的 `BaseModel` 。 **Model** 负责网络前向、损耗计算、反向、参数更新等. 在MMagic中, **Model** 应该注册为 `MODELS`.\n- **Module** 模块包括用于训练或推理的 **architectures** , 预定义的 **loss classes**, 以及对批量输入数据预处理的 **data preprocessors** 。 **Module** 总是作为**Model**的元素呈现。 在MMagic中, **Module** 应该注册为 **MODULES**。\n\n以DCGAN model 模型为例，[生成器](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_generator.py) 和 [判别器](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_discriminator.py) 是 **Module**, 分别用于生成图像和鉴别图像真伪。 [`DCGAN`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan.py) 是 **Model**, 它从dataloader中获取数据，交替训练生成器和鉴别器。\n\n您可以通过以下链接找到 **Model** 和 **Module** 的实现。\n\n- **Model**:\n - [Editors](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/editors)\n- **Module**:\n - [Layers](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/layers)\n - [Losses](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/losses)\n - [Data Preprocessor](https://github.com/open-mmlab/mmagic/tree/main/mmagic/models/data_preprocessors)\n\n## 一个SRCNN的例子\n\n这里，我们以经典图像超分辨率模型SRCNN\\[1\\]的实现为例。\n\n### Step 1: 定义SRCNN网络\n\nSRCNN 是第一个用于单幅图像超分辨率\\[1\\]的深度学习方法。为了实现SRCNN的网络架构，我们需要创建一个新文件 `mmagic/models/editors/srgan/sr_resnet.py` 并执行 `class MSRResNet`。\n\n在这一步中，我们通过继承`mmengine.models.BaseModule`来实现 `class MSRResNet`，并在`__init__`函数中定义网络架构。\n特别地，我们需要使用`@MODELS.register_module()`将`class MSRResNet`的实现添加到MMagic的注册中。\n\n```python\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmagic.registry import MODELS\n\nfrom mmagic.models.utils import (PixelShufflePack, ResidualBlockNoBN,\n default_init_weights, make_layer)\n\n\n@MODELS.register_module()\nclass MSRResNet(BaseModule):\n \"\"\"修改后的SRResNet。\n\n 由 \"使用生成对抗网络的照片-现实的单幅图像超级分辨率 \"中的SRResNet修改而来的压缩版本。\n\n 它使用无BN的残差块，类似于EDSR。\n 目前支持x2、x3和x4上采样比例因子。\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_blocks (int): Block number in the trunk network. Default: 16.\n upscale_factor (int): Upsampling factor. Support x2, x3 and x4.\n Default: 4.\n \"\"\"\n _supported_upscale_factors = [2, 3, 4]\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4):\n\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.mid_channels = mid_channels\n self.num_blocks = num_blocks\n self.upscale_factor = upscale_factor\n\n self.conv_first = nn.Conv2d(\n in_channels, mid_channels, 3, 1, 1, bias=True)\n self.trunk_net = make_layer(\n ResidualBlockNoBN, num_blocks, mid_channels=mid_channels)\n\n # upsampling\n if self.upscale_factor in [2, 3]:\n self.upsample1 = PixelShufflePack(\n mid_channels,\n mid_channels,\n self.upscale_factor,\n upsample_kernel=3)\n elif self.upscale_factor == 4:\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n else:\n raise ValueError(\n f'Unsupported scale factor {self.upscale_factor}. '\n f'Currently supported ones are '\n f'{self._supported_upscale_factors}.')\n\n self.conv_hr = nn.Conv2d(\n mid_channels, mid_channels, 3, 1, 1, bias=True)\n self.conv_last = nn.Conv2d(\n mid_channels, out_channels, 3, 1, 1, bias=True)\n\n self.img_upsampler = nn.Upsample(\n scale_factor=self.upscale_factor,\n mode='bilinear',\n align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n self.init_weights()\n\n def init_weights(self):\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n strict (boo, optional): Whether strictly load the pretrained model.\n Defaults to True.\n \"\"\"\n\n for m in [self.conv_first, self.conv_hr, self.conv_last]:\n default_init_weights(m, 0.1)\n\n```\n\n然后，我们实现了`class MSRResNet`的`forward` 函数, 该函数将输入张量作为输入张量，然后返回`MSRResNet`的结果。\n\n```python\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n feat = self.lrelu(self.conv_first(x))\n out = self.trunk_net(feat)\n\n if self.upscale_factor in [2, 3]:\n out = self.upsample1(out)\n elif self.upscale_factor == 4:\n out = self.upsample1(out)\n out = self.upsample2(out)\n\n out = self.conv_last(self.lrelu(self.conv_hr(out)))\n upsampled_img = self.img_upsampler(x)\n out += upsampled_img\n return out\n```\n\n在`class MSRResNet`实现后，我们需要更新`mmagic/models/editors/__init__.py`中的模型列表，以便我们可以通过`mmagic.models.editors`导入和使用`class MSRResNet`。\n\n```python\nfrom .srgan.sr_resnet import MSRResNet\n```\n\n### Step 2: 定义SRCNN的模型\n\n网络架构实现后, 我们需要定义我们的模型`class BaseEditModel` 并实现`class BaseEditModel`的前向循环。\n\n为了实现`class BaseEditModel`,\n我们创建一个新文件`mmagic/models/base_models/base_edit_model.py`。\n具体来说，`class BaseEditModel`继承自`mmengine.model.BaseModel`.\n在`__init__`函数中，我们定义了`class BaseEditModel`的损失函数，训练, 测试配置和网络。\n\n```python\nfrom typing import List, Optional\n\nimport torch\nfrom mmengine.model import BaseModel\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass BaseEditModel(BaseModel):\n \"\"\"用于图像和视频编辑的基本模型。\n\n 它必须包含一个生成器，将帧作为输入并输出插值帧。它也有一个用于训练的pixel-wise损失。\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n super().__init__(\n init_cfg=init_cfg, data_preprocessor=data_preprocessor)\n\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n # generator\n self.generator = MODELS.build(generator)\n\n # loss\n self.pixel_loss = MODELS.build(pixel_loss)\n```\n\n因为`mmengine.model.BaseModel`提供了算法模型的基本功能，例如权重初始化、批量输入预处理、解析损失和更新模型参数。\n因此，子类继承自BaseModel，即本例中的`class BaseEditModel`,\n只需要实现forward方法，该方法实现了计算损失和预测的逻辑。\n\n具体来说，`class BaseEditModel`实现的`forward`函数将`batch_inputs`和`data_samples`作为输入，并根据模式参数返回结果。\n\n```python\n def forward(self,\n batch_inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs):\n \"\"\"返回训练、验证、测试和简单推理过程的损失或预测。\n\n BaseModel的``forward``方法是一个抽象方法，它的子类必须实现这个方法。\n\n 接受由:attr:`data_preprocessor`处理的``batch_inputs`` 和 ``data_samples``, 并根据模式参数返回结果。.\n\n 在非分布式训练、验证和测试过程中，\n ``forward``将被``BaseModel.train_step``,\n ``BaseModel.val_step``和``BaseModel.val_step``直接调用。\n\n 在分布式数据并行训练过程中,``MMSeparateDistributedDataParallel.train_step``将首先调用``DistributedDataParallel.forward``以启用自动梯度同步，然后调用``forward``获得训练损失。\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict or tensor for custom use.\n \"\"\"\n\n if mode == 'tensor':\n return self.forward_tensor(batch_inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n return self.forward_inference(batch_inputs, data_samples, **kwargs)\n\n elif mode == 'loss':\n return self.forward_train(batch_inputs, data_samples, **kwargs)\n```\n\n具体来说，在`forward_tensor`中, `class BaseEditModel`直接返回网络的前向张量。\n\n```python\n def forward_tensor(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward tensor.\n Returns result of simple forward.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n feats = self.generator(batch_inputs, **kwargs)\n\n return feats\n```\n\n在`forward_inference`函数中，`class BaseEditModel`首先将前向张量转换为图像，然后返回该图像作为输出。\n\n```python\n def forward_inference(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward inference.\n Returns predictions of validation, testing, and simple inference.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[DataSample]: predictions.\n \"\"\"\n\n feats = self.forward_tensor(batch_inputs, data_samples, **kwargs)\n feats = self.data_preprocessor.destructor(feats)\n predictions = []\n for idx in range(feats.shape[0]):\n predictions.append(\n DataSample(\n pred_img=feats[idx].to('cpu'),\n metainfo=data_samples[idx].metainfo))\n\n return predictions\n```\n\n在`forward_train`中, `class BaseEditModel`计算损失函数，并返回一个包含损失的字典作为输出。\n\n```python\n def forward_train(self, batch_inputs, data_samples=None, **kwargs):\n \"\"\"Forward training.\n Returns dict of losses of training.\n\n Args:\n batch_inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n feats = self.forward_tensor(batch_inputs, data_samples, **kwargs)\n gt_imgs = [data_sample.gt_img.data for data_sample in data_samples]\n batch_gt_data = torch.stack(gt_imgs)\n\n loss = self.pixel_loss(feats, batch_gt_data)\n\n return dict(loss=loss)\n\n```\n\n在实现了`class BaseEditModel`之后，我们需要更新\n`mmagic/models/__init__.py`中的模型列表，这样我们就可以通过`mmagic.models`导入和使用`class BaseEditModel`。\n\n```python\nfrom .base_models.base_edit_model import BaseEditModel\n```\n\n### Step 3: 开始训练SRCNN\n\n在实现了网络结构和SRCNN的前向循环后、 现在我们可以创建一个新的文件`configs/srcnn/srcnn_x4k915_g1_1000k_div2k.py`\n来设置训练SRCNN所需的配置。\n\n在配置文件中，我们需要指定我们的模型`class BaseEditModel`的参数，包括生成器网络结构、损失函数、额外的训练和测试配置，以及输入张量的数据预处理器。请参考[MMagic中的损失函数介绍](./losses.md)了解MMagic中损失函数的更多细节。\n\n```python\n# model settings\nmodel = dict(\n type='BaseEditModel',\n generator=dict(\n type='SRCNNNet',\n channels=(3, 64, 32, 3),\n kernel_sizes=(9, 1, 5),\n upscale_factor=scale),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n```\n\n我们还需要根据创建自己的数据加载器来指定训练数据加载器和测试数据加载器。\n最后，我们可以开始训练我们自己的模型:\n\n```python\npython tools/train.py configs/srcnn/srcnn_x4k915_g1_1000k_div2k.py\n```\n\n## 一个DCGAN的例子\n\n这里，我们以经典gan模型DCGAN\\[2\\]的实现为例。\n\n### Step 1: 定义DCGAN的网络\n\nDCGAN是一种经典的图像生成对抗网络\\[2\\]。为了实现DCGAN的网络架构，我们需要创建两个新文件`mmagic/models/editors/dcgan/dcgan_generator.py`和`mmagic/models/editors/dcgan/dcgan_discriminator.py`，并实现生成器(`class DCGANGenerator`) 和鉴别器(`class DCGANDiscriminator`)。\n\n在这一步中，我们实现了`class DCGANGenerator`, `class DCGANDiscriminator` 并在`__init__`函数中定义了网络架构。\n特别地，我们需要使用`@MODULES.register_module()`来将生成器和鉴别器添加到MMagic的注册中。\n\n以下面的代码为例:\n\n```python\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmcv.runner import load_checkpoint\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\nfrom mmengine.logging import MMLogger\nfrom mmengine.model.utils import normal_init\n\nfrom mmagic.models.builder import MODULES\nfrom ..common import get_module_device\n\n\n@MODULES.register_module()\nclass DCGANGenerator(nn.Module):\n def __init__(self,\n output_scale,\n out_channels=3,\n base_channels=1024,\n input_scale=4,\n noise_size=100,\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='ReLU'),\n out_act_cfg=dict(type='Tanh'),\n pretrained=None):\n super().__init__()\n self.output_scale = output_scale\n self.base_channels = base_channels\n self.input_scale = input_scale\n self.noise_size = noise_size\n\n # 上采样的次数\n self.num_upsamples = int(np.log2(output_scale // input_scale))\n\n # 输出4x4的特征图\n self.noise2feat = ConvModule(\n noise_size,\n base_channels,\n kernel_size=4,\n stride=1,\n padding=0,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg)\n\n # 建立上采样骨干（不包括输出层）\n upsampling = []\n curr_channel = base_channels\n for _ in range(self.num_upsamples - 1):\n upsampling.append(\n ConvModule(\n curr_channel,\n curr_channel // 2,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n\n curr_channel //= 2\n\n self.upsampling = nn.Sequential(*upsampling)\n\n # 输出层\n self.output_layer = ConvModule(\n curr_channel,\n out_channels,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=None,\n act_cfg=out_act_cfg)\n```\n\n然后，我们实现了`DCGANGenerator`的`forward`函数，该函数接受 `noise`张量或`num_batches`，然后返回`DCGANGenerator`的结果。\n\n```python\n def forward(self, noise, num_batches=0, return_noise=False):\n noise_batch = noise_batch.to(get_module_device(self))\n x = self.noise2feat(noise_batch)\n x = self.upsampling(x)\n x = self.output_layer(x)\n return x\n```\n\n如果你想为你的网络实现特定的权重初始化方法，你需要自己添加`init_weights`函数。\n\n```python\n def init_weights(self, pretrained=None):\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n normal_init(m, 0, 0.02)\n elif isinstance(m, _BatchNorm):\n nn.init.normal_(m.weight.data)\n nn.init.constant_(m.bias.data, 0)\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n```\n\n在实现`DCGANGenerator`类之后，我们需要更新`mmagic/models/editors/__init__.py`中的模型列表，以便我们可以通过`mmagic.models.editors`导入和使用`DCGANGenerator`类。\n\n类`DCGANDiscriminator`的实现遵循类似的逻辑，你可以在[这里](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/editors/dcgan/dcgan_discriminator.py)找到实现。\n\n### Step 2: 设计DCGAN的模型\n\n在实现了网络**Module**之后，我们需要定义我们的**Model**类 `DCGAN`。\n\n你的**Model**应该继承自MMEngine提供的[`BaseModel`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/base_model/base_model.py#L16)，并实现三个函数，`train_step`, `val_step`和`test_step`。\n\n- `train_step`: 这个函数负责更新网络的参数，由MMEngine的Loop ([`IterBasedTrainLoop`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L183)或 [`EpochBasedTrainLoop`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L18))调用。 `train_step`将数据批处理和[`OptimWrapper`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/optim_wrapper.md)作为输入并返回一个日志字典。\n- `val_step`: 该函数负责在训练过程中获取用于验证的输出，由 [`MultiValLoop`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/engine/runner/multi_loops.py#L19)调用。\n- `test_step`: 该函数负责在测试过程中获取输出，由[`MultiTestLoop`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/engine/runner/multi_loops.py#L274)调用。\n\n> 请注意，在`train_step`, `val_step`和`test_step`中，调用`DataPreprocessor`对输入数据进行预处理，然后再将它们提供给神经网络。要了解有关`DataPreprocessor`的更多信息，请参阅此[文件](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/data_preprocessors/gen_preprocessor.py) and 和本[教程](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/model.md#%E6%95%B0%E6%8D%AE%E5%A4%84%E7%90%86%E5%99%A8datapreprocessor)。\n\n为了简化使用，我们在MMagic中提供了[`BaseGAN`](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/base_models/base_gan.py)类，它为GAN模型实现了通用的`train_step`, `val_step`和`test_step`函数。使用`BaseGAN`作为基类，每个特定的GAN算法只需要实现`train_generator` and `train_discriminator`.\n\n在`train_step`中，我们支持数据预处理、梯度累积(由[`OptimWrapper`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/optim_wrapper.md)实现)和指数滑动平均(EMA)通过[(`ExponentialMovingAverage`)](https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/base_models/average_model.py#L19)实现。使用`BaseGAN.train_step`，每个特定的GAN算法只需要实现`train_generator`和`train_discriminator`。\n\n```python\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n # 训练判别器，使用MMEngine提供的上下文管理器\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n # train_discriminator should be implemented!\n log_vars = self.train_discriminator(\n **data, optimizer_wrapper=disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n # update generator, use context manager provided by MMEngine\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n # train_generator should be implemented!\n log_vars_gen = self.train_generator(\n **data, optimizer_wrapper=gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n # return the log dict\n return log_vars\n```\n\n在`val_step`和`test_step`，我们渐进地调用数据预处理和`BaseGAN.forward`。\n\n```python\n def val_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n # call `forward`\n outputs = self(**data)\n return outputs\n\n def test_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n # call `orward`\n outputs = self(**data)\n return outputs\n```\n\n然后，我们在`DCGAN`类中实现`train_generator`和`train_discriminator`。\n\n```python\nfrom typing import Dict, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom .base_gan import BaseGAN\n\n\n@MODELS.register_module()\nclass DCGAN(BaseGAN):\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))\n losses_dict['loss_disc_real'] = F.binary_cross_entropy_with_logits(\n disc_pred_real, 1. * torch.ones_like(disc_pred_real))\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:\n losses_dict = dict()\n losses_dict['loss_gen'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(\n self, inputs, data_sample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n real_imgs = inputs['img']\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs, data_sample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n num_batches = inputs['img'].shape[0]\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n```\n\n在实现了`class DCGAN`之后，我们需要更新`mmagic/models/__init__.py`中的模型列表，以便我们可以通过`mmagic.models`导入和使用`class DCGAN`。\n\n### Step 3: 开始训练DCGAN\n\n在实现了网络**Module**和DCGAN的**Model**之后，现在我们可以创建一个新文件`configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py`\n来设置训练DCGAN所需的配置。\n\n在配置文件中，我们需要指定模型的参数，`class DCGAN`，包括生成器网络架构和输入张量的数据预处理器。\n\n```python\n# model settings\nmodel = dict(\n type='DCGAN',\n noise_size=100,\n data_preprocessor=dict(type='GANDataPreprocessor'),\n generator=dict(type='DCGANGenerator', output_scale=64, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=64,\n output_scale=4,\n out_channels=1))\n```\n\n我们还需要根据[创建自己的数据加载器](dataset.md)指定训练数据加载器和测试数据加载器。\n最后，我们可以开始训练我们自己的模型:\n\n```python\npython tools/train.py configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\n```\n\n## 参考文献\n\n1. Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou. Image Super-Resolution Using Deep Convolutional Networks\\[J\\]. IEEE transactions on pattern analysis and machine intelligence, 2015.\n\n2. Radford, Alec, Luke Metz, and Soumith Chintala. \"Unsupervised representation learning with deep convolutional generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015).\n" }, { "path": "docs/zh_cn/howto/transforms.md", "content": "# 如何设计自己的数据变换\n\n在本教程中，我们将介绍MMagic中变换流水线的设计。\n\nThe structure of this guide are as follows:\n\n- [如何设计自己的数据变换](#如何设计自己的数据变换)\n - [MMagic中的数据流水线](#mmagic中的数据流水线)\n - [数据变换的一个简单示例](#数据变换的一个简单示例)\n - [BasicVSR的一个示例](#basicvsr的一个示例)\n - [Pix2Pix的一个示例](#pix2pix的一个示例)\n - [MMagic中支持的数据变换](#mmagic中支持的数据变换)\n - [数据加载](#数据加载)\n - [预处理](#预处理)\n - [格式化](#格式化)\n - [扩展和使用自定义流水线](#扩展和使用自定义流水线)\n - [一个简单的MyTransform示例](#一个简单的mytransform示例)\n - [一个翻转变换的示例](#一个翻转变换的示例)\n\n## MMagic中的数据流水线\n\n按照典型的惯例，我们使用 `Dataset` 和 `DataLoader` 来加载多个线程的数据。 `Dataset` 返回一个与模型的forward方法的参数相对应的数据项的字典。\n\n数据准备流水线和数据集是分开的。通常，一个数据集定义了如何处理标注，而一个数据管道定义了准备一个数据字典的所有步骤。\n\n一个流水线由一连串的操作组成。每个操作都需要一个字典作为输入，并为下一个变换输出一个字典。\n\n这些操作被分为数据加载、预处理和格式化。\n\n在MMagic中，所有数据变换都继承自 `BaseTransform`。\n变换的输入和输出类型都是字典。\n\n### 数据变换的一个简单示例\n\n```python\n>>> from mmagic.transforms import LoadPairedImageFromFile\n>>> transforms = LoadPairedImageFromFile(\n>>> key='pair',\n>>> domain_a='horse',\n>>> domain_b='zebra',\n>>> flag='color'),\n>>> data_dict = {'pair_path': './data/pix2pix/facades/train/1.png'}\n>>> data_dict = transforms(data_dict)\n>>> print(data_dict.keys())\ndict_keys(['pair_path', 'pair', 'pair_ori_shape', 'img_mask', 'img_photo', 'img_mask_path', 'img_photo_path', 'img_mask_ori_shape', 'img_photo_ori_shape'])\n```\n\n一般来说，变换流水线的最后一步必须是 `PackInputs`.\n`PackInputs` 将把处理过的数据打包成一个包含两个字段的字典：`inputs` 和 `data_samples`.\n`inputs` 是你想用作模型输入的变量，它可以是 `torch.Tensor` 的类型， `torch.Tensor` 的字典，或者你想要的任何类型。\n`data_samples` 是一个 `DataSample` 的列表. 每个 `DataSample` 都包含真实值和对应输入的必要信息。\n\n### BasicVSR的一个示例\n\n下面是一个BasicVSR的流水线示例。\n\n```python\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='SetValues', dictionary=dict(scale=scale)),\n dict(type='PairedRandomCrop', gt_patch_size=256),\n dict(\n type='Flip',\n keys=['img', 'gt'],\n flip_ratio=0.5,\n direction='horizontal'),\n dict(\n type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type='MirrorSequence', keys=['img', 'gt']),\n dict(type='PackInputs')\n]\n\nval_pipeline = [\n dict(type='GenerateSegmentIndices', interval_list=[1]),\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='PackInputs')\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img', channel_order='rgb'),\n dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),\n dict(type='MirrorSequence', keys=['img']),\n dict(type='PackInputs')\n]\n```\n\n对于每个操作，我们列出了添加/更新/删除的相关字典字段，标记为 '\\*' 的字典字段是可选的。\n\n### Pix2Pix的一个示例\n\n下面是一个在aerial2maps数据集上Pix2Pix训练的流水线示例。\n\n```python\nsource_domain = 'aerial'\ntarget_domain = 'map'\n\npipeline = [\n dict(\n type='LoadPairedImageFromFile',\n io_backend='disk',\n key='pair',\n domain_a=domain_a,\n domain_b=domain_b,\n flag='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': [f'img_{domain_a}', f'img_{domain_b}']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='mmagic.Resize', scale=(286, 286),\n interpolation='bicubic'),\n dict(type='mmagic.FixedCrop', crop_size=(256, 256))\n ]),\n dict(\n type='Flip',\n keys=[f'img_{domain_a}', f'img_{domain_b}'],\n direction='horizontal'),\n dict(\n type='PackInputs',\n keys=[f'img_{domain_a}', f'img_{domain_b}', 'pair'])\n```\n\n## MMagic中支持的数据变换\n\n### 数据加载\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n LoadImageFromFile\n \n - add: img, img_path, img_ori_shape, \\*ori_img\n
\n RandomLoadResizeBg\n \n - add: bg\n
\n LoadMask\n \n - add: mask\n
\n GetSpatialDiscountMask\n \n - add: discount_mask\n
\n\n### 预处理\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n Resize\n \n - add: scale_factor, keep_ratio, interpolation, backend\n - update: specified by keys\n
\n MATLABLikeResize\n \n - add: scale, output_shape\n - update: specified by keys\n
\n RandomRotation\n \n - add: degrees\n - update: specified by keys\n \n
\n Flip\n \n - add: flip, flip_direction\n - update: specified by keys\n
\n RandomAffine\n \n - update: specified by keys\n
\n RandomJitter\n \n - update: fg (img)\n
\n ColorJitter\n \n - update: specified by keys\n
\n BinarizeImage\n \n - update: specified by keys\n
\n RandomMaskDilation\n \n - add: img_dilate_kernel_size\n \n
\n RandomTransposeHW\n \n - add: transpose\n
\n RandomDownSampling\n \n - update: scale, gt (img), lq (img)\n
\n RandomBlur\n \n - update: specified by keys\n
\n RandomResize\n \n - update: specified by keys\n
\n RandomNoise\n \n - update: specified by keys\n
\n RandomJPEGCompression\n \n - update: specified by keys\n
\n RandomVideoCompression\n \n - update: specified by keys\n
\n DegradationsWithShuffle\n \n - update: specified by keys\n
\n GenerateFrameIndices\n \n - update: img_path (gt_path, lq_path)\n
\n GenerateFrameIndiceswithPadding\n \n - update: img_path (gt_path, lq_path)\n
\n TemporalReverse\n \n - add: reverse\n - update: specified by keys\n
\n GenerateSegmentIndices\n \n - add: interval\n - update: img_path (gt_path, lq_path)\n
\n MirrorSequence\n \n - update: specified by keys\n
\n CopyValues\n \n - add: specified by dst_key\n
\n UnsharpMasking\n \n - add: img_unsharp\n
\n Crop\n \n - add: img_crop_bbox, crop_size\n - update: specified by keys\n
\n RandomResizedCrop\n \n - add: img_crop_bbox\n - update: specified by keys\n
\n FixedCrop\n \n - add: crop_size, crop_pos\n - update: specified by keys\n
\n PairedRandomCrop\n \n - update: gt (img), lq (img)\n
\n CropAroundCenter\n \n - add: crop_bbox\n - update: fg (img), alpha (img), trimap (img), bg (img)\n
\n CropAroundUnknown\n \n - add: crop_bbox\n - update: specified by keys\n
\n CropAroundFg\n \n - add: crop_bbox\n - update: specified by keys\n
\n ModCrop\n \n - update: gt (img)\n
\n CropLike\n \n - update: specified by target_key\n
\n GetMaskedImage\n \n - add: masked_img\n
\n GenerateFacialHeatmap\n \n - add: heatmap\n
\n GenerateCoordinateAndCell\n \n - add: coord, cell\n - update: gt (img)\n
\n Normalize\n \n - add: img_norm_cfg\n - update: specified by keys\n
\n RescaleToZeroOne\n \n - update: specified by keys\n
\n\n### 格式化\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TransformModification of Results' keys
\n ToTensor\n \n update: specified by keys.\n
\n FormatTrimap\n \n - update: trimap\n
\n PackInputs\n \n - add: inputs, data_sample\n - remove: all other keys\n
\n\n### Albumentations\n\nMMagic 支持添加 [Albumentations](https://github.com/albumentations-team/albumentations) 库中的 transformation，请浏览 https://albumentations.ai/docs/getting_started/transforms_and_targets 获取更多 transformation 的信息。\n\n使用 Albumentations 的示例如下：\n\n```python\nalbu_transforms = [\n dict(\n type='Resize',\n height=100,\n width=100,\n ),\n dict(\n type='RandomFog',\n p=0.5,\n ),\n dict(\n type='RandomRain',\n p=0.5\n ),\n dict(\n type='RandomSnow',\n p=0.5,\n ),\n]\npipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Albumentations',\n keys=['img'],\n transforms=albu_transforms),\n dict(type='PackInputs')\n]\n```\n\n## 扩展和使用自定义流水线\n\n### 一个简单的MyTransform示例\n\n1. 在文件中写入一个新的流水线，例如在 `my_pipeline.py`中。它接受一个字典作为输入，并返回一个字典。\n\n```python\nimport random\nfrom mmcv.transforms import BaseTransform\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass MyTransform(BaseTransform):\n \"\"\"Add your transform\n\n Args:\n p (float): Probability of shifts. Default 0.5.\n \"\"\"\n\n def __init__(self, p=0.5):\n self.p = p\n\n def transform(self, results):\n if random.random() > self.p:\n results['dummy'] = True\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(p={self.p})')\n\n return repr_str\n```\n\n2. 在你的配置文件中导入并使用该流水线。\n\n确保导入相对于你的训练脚本所在的位置。\n\n```python\ntrain_pipeline = [\n ...\n dict(type='MyTransform', p=0.2),\n ...\n]\n```\n\n### 一个翻转变换的示例\n\n这里我们以一个简单的翻转变换为例：\n\n```python\nimport random\nimport mmcv\nfrom mmcv.transforms import BaseTransform, TRANSFORMS\n\n@TRANSFORMS.register_module()\nclass MyFlip(BaseTransform):\n def __init__(self, direction: str):\n super().__init__()\n self.direction = direction\n\n def transform(self, results: dict) -> dict:\n img = results['img']\n results['img'] = mmcv.imflip(img, direction=self.direction)\n return results\n```\n\n因此，我们可以实例化一个 `MyFlip` 对象，用它来处理数据字典。\n\n```python\nimport numpy as np\n\ntransform = MyFlip(direction='horizontal')\ndata_dict = {'img': np.random.rand(224, 224, 3)}\ndata_dict = transform(data_dict)\nprocessed_img = data_dict['img']\n```\n\n或者，我们可以在配置文件的数据流水线中使用 `MyFlip` 变换。\n\n```python\npipeline = [\n ...\n dict(type='MyFlip', direction='horizontal'),\n ...\n]\n```\n\n请注意，如果你想在配置中使用 `MyFlip` ，你必须确保在程序运行过程中导入包含 `MyFlip` 的文件。\n" }, { "path": "docs/zh_cn/index.rst", "content": "欢迎来到 MMagic 的中文文档！\n=====================================\n\n您可以在页面左下角切换中英文文档。\n\n.. note::\n 目前英文版有更多的内容，欢迎加入我们一起提升中文文档！\n 您可以通过 issue，discussion 或者我们的社区群来联系我们！\n\n\n.. toctree::\n :maxdepth: 1\n :caption: MMagic 社区\n\n community/contributing.md\n community/projects.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: 新手入门\n\n 概述 \n 安装 \n 快速运行 \n\n\n.. toctree::\n :maxdepth: 1\n :caption: 基础教程\n\n user_guides/config.md\n user_guides/dataset_prepare.md\n user_guides/inference.md\n user_guides/train_test.md\n user_guides/metrics.md\n user_guides/visualization.md\n user_guides/useful_tools.md\n user_guides/deploy.md\n\n.. toctree::\n :maxdepth: 2\n :caption: 进阶教程\n\n advanced_guides/evaluator.md\n advanced_guides/structures.md\n advanced_guides/data_preprocessor.md\n advanced_guides/data_flow.md\n\n.. toctree::\n :maxdepth: 1\n :caption: 开发指南\n\n howto/models.md\n howto/dataset.md\n howto/transforms.md\n howto/losses.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: 常见问题\n\n faq.md\n\n\n.. toctree::\n :maxdepth: 2\n :caption: 模型库\n\n model_zoo/index.rst\n\n\n.. toctree::\n :maxdepth: 1\n :caption: 数据集库\n\n dataset_zoo/index.rst\n\n.. toctree::\n :maxdepth: 1\n :caption: 变更日志\n\n changelog.md\n\n\n.. toctree::\n :maxdepth: 2\n :caption: 接口文档（英文）\n\n mmagic/apis.inferencers \n mmagic/structures \n mmagic/datasets \n mmagic/datasets.transforms \n mmagic/evaluation \n mmagic/visualization \n mmagic/engine.hooks \n mmagic/engine.logging \n mmagic/engine.optimizers \n mmagic/engine.runner \n mmagic/engine.schedulers \n mmagic/models.archs \n mmagic/models.base_models \n mmagic/models.losses \n mmagic/models.data_preprocessors \n mmagic/models.utils \n mmagic/models.editors \n mmagic/utils \n\n.. toctree::\n :maxdepth: 1\n :caption: 迁移指南\n\n migration/overview.md\n migration/runtime.md\n migration/models.md\n migration/eval_test.md\n migration/schedule.md\n migration/data.md\n migration/distributed_train.md\n migration/optimizers.md\n migration/visualization.md\n migration/amp.md\n\n\n.. toctree::\n :maxdepth: 1\n :caption: 设备支持\n\n device/npu_zh.md\n\n\n.. toctree::\n :caption: 语言切换\n\n switch_language.md\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/zh_cn/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=_build\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.http://sphinx-doc.org/\n\texit /b 1\n)\n\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\n\n:end\npopd\n" }, { "path": "docs/zh_cn/migration/amp.md", "content": "# 混合精度训练的迁移\n\n在 0.x 版中，MMEditing 并不支持对整体前向过程的混合精度训练。相反，用户必须使用 `auto_fp16` 装饰器来适配特定子模块，然后再将子模块的参数转化成 fp16。这样就可以拥有对模型参数的更细粒度的控制，但是该方法使用起来很繁琐，而且用户需要自己处理一些操作，比如训练过程中损失函数的缩放\n\nMMagic 1.x 版使用了 MMEngine 提供的 `AmpOptimWrapper`，在 `AmpOptimWrapper.update_params` 中，梯度缩放和 `GradScaler` 更新将被自动执行，且在 `optim_context` 上下文管理其中，`auto_cast`被应用到整个前向过程中。\n\n具体来说，0.x 版和 1.x 版之间的差异如下所示：\n\n\n\n \n \n \n \n\n\n\n\n\n\n\n\n\n
0.x 版 1.x 版
\n\n```python\n# 配置\nrunner = dict(fp16_loss_scaler=dict(init_scale=512))\n```\n\n```python\n# 代码\nimport torch.nn as nn\nfrom mmedit.models.builder import build_model\nfrom mmedit.core.runners.fp16_utils import auto_fp16\n\n\nclass DemoModule(nn.Module):\n def __init__(self, cfg):\n self.net = build_model(cfg)\n\n @auto_fp16\n def forward(self, x):\n return self.net(x)\n\nclass DemoModel(nn.Module):\n\n def __init__(self, cfg):\n super().__init__(self)\n self.demo_network = DemoModule(cfg)\n\n def train_step(self,\n data_batch,\n optimizer,\n ddp_reducer=None,\n loss_scaler=None,\n use_apex_amp=False,\n running_status=None):\n # 从 data_batch 中获取数据\n inputs = data_batch['img']\n output = self.demo_network(inputs)\n\n optimizer.zero_grad()\n loss, log_vars = self.get_loss(data_dict_)\n\n if ddp_reducer is not None:\n ddp_reducer.prepare_for_backward(_find_tensors(loss_disc))\n\n if loss_scaler:\n # 添加 fp16 支持\n loss_scaler.scale(loss_disc).backward()\n elif use_apex_amp:\n from apex import amp\n with amp.scale_loss(loss_disc, optimizer,\n loss_id=0) as scaled_loss_disc:\n scaled_loss_disc.backward()\n else:\n loss_disc.backward()\n\n if loss_scaler:\n loss_scaler.unscale_(optimizer)\n loss_scaler.step(optimizer)\n else:\n optimizer.step()\n```\n\n\n\n```python\n# 配置\noptim_wrapper = dict(\n constructor='OptimWrapperConstructor',\n generator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-06),\n type='AmpOptimWrapper', # 使用 amp 封装器\n loss_scale='dynamic'),\n discriminator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-06),\n type='AmpOptimWrapper', # 使用 amp 封装器\n loss_scale='dynamic'))\n```\n\n```python\n# 代码\nimport torch.nn as nn\nfrom mmagic.registry import MODULES\nfrom mmengine.model import BaseModel\n\n\nclass DemoModule(nn.Module):\n def __init__(self, cfg):\n self.net = MODULES.build(cfg)\n\n def forward(self, x):\n return self.net(x)\n\nclass DemoModel(BaseModel):\n def __init__(self, cfg):\n super().__init__(self)\n self.demo_network = DemoModule(cfg)\n\n def train_step(self, data, optim_wrapper):\n # 从 data_batch 中获取数据\n data = self.data_preprocessor(data, True)\n inputs = data['inputs']\n\n with optim_wrapper.optim_context(self.discriminator):\n output = self.demo_network(inputs)\n loss_dict = self.get_loss(output)\n # 使用 `BaseModel` 提供的 parse_loss\n loss, log_vars = self.parse_loss(loss_dict)\n optimizer_wrapper.update_params(loss)\n\n return log_vars\n```\n\n
\n\n若要避免用户操作配置文件，MMagic 在 `train.py` 里提供了 `--amp` 选项，其可以让用户在不修改配置文件的情况下启动混合精度训练，用户可以使用以下命令启动混合精度训练：\n\n```bash\nbash tools/dist_train.sh CONFIG GPUS --amp\n\n# 对 slurm 用户\nbash tools/slurm_train.sh PARTITION JOB_NAME CONFIG WORK_DIR --amp\n```\n" }, { "path": "docs/zh_cn/migration/data.md", "content": "# Data Settings 的迁移\n\n本篇文档负责介绍 data settings 的迁移方式：\n\n- \\[Data Settings 的迁移\\](#Data Settings 的迁移)\n - [Data Pipelines](#data-pipelines)\n - [Dataloader](#dataloader)\n\n## Data Pipelines\n\n在 MMagic 1.x 中我们更新了 data pipeline 的设置，有以下几个重要的修改：\n\n- 去除了 `normalization` 和 `color space` 两种数据变换操作，并将它们移动到了 `data_preprocessor` 部分。\n- 原版本中格式化数据变换 pipeline 的 `Collect` 和 `ToTensor` 在新版本中被整合为 `PackInputs`。更多的细节可以在 [数据变换文档](../howto/transforms.md) 中查看。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
原版本 新版本
\n\n```python\ntrain_pipeline = [ # Train pipeline\n dict(type='LoadImageFromFile', # 从文件读取图片\n io_backend='disk', # io backend\n key='lq', # 找到结果对应路径的 keys\n flag='unchanged'), # 读取图片的 flag\n dict(type='LoadImageFromFile', # 从文件读取图片\n io_backend='disk', # io backend\n key='gt', # 找到结果对应路径的 keys\n flag='unchanged'), # 读取图片的 flag\n dict(type='RescaleToZeroOne', keys=['lq', 'gt']), # 将图片从 [0, 255] 缩放到 [0, 1]\n dict(type='Normalize', # normalize 图片的 augmentation pipeline\n keys=['lq', 'gt'], # 需要 normalized 的图片\n mean=[0, 0, 0], # 平均值\n std=[1, 1, 1], # 标准差\n to_rgb=True), # 是否转换到 rgb 通道\n dict(type='PairedRandomCrop', gt_patch_size=96), # PairedRandomCrop\n dict(type='Flip', # 翻转图片\n keys=['lq', 'gt'], # 需要翻转的图片\n flip_ratio=0.5, # 翻转概率\n direction='horizontal'), # Flip 方向\n dict(type='Flip', # Flip 图片\n keys=['lq', 'gt'], # 需要翻转的图片\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip 方向\n dict(type='RandomTransposeHW', # 随即对图片的高和宽转置\n keys=['lq', 'gt'], # 需要 transpose 的图片\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='Collect', # Pipeline that decides which keys in the data should be passed to the model\n keys=['lq', 'gt'], # Keys to pass to the model\n meta_keys=['lq_path', 'gt_path']), # Meta information keys. 训练时 meta information 不是必须的\n dict(type='ToTensor', # 图片转为 tensor\n keys=['lq', 'gt']) # 需要转换为 tensor 的图片\n]\ntest_pipeline = [ # Test pipeline\n dict(\n type='LoadImageFromFile', # 从文件读取图片\n io_backend='disk', # io backend\n key='lq', # 找到结果对应路径的 keys\n flag='unchanged'), # flag for reading images\n dict(\n type='LoadImageFromFile', # 从文件读取图片\n io_backend='disk', # io backend\n key='gt', # 找到结果对应路径的 keys\n flag='unchanged'), # flag for reading images\n dict(type='RescaleToZeroOne', keys=['lq', 'gt']), # 将图片从 [0, 255] 缩放到 [0, 1]\n dict(\n type='Normalize', # 对输入图片执行 normalization 的数据增强 pipeline\n keys=['lq', 'gt'], # 需要 normalized 图片\n mean=[0, 0, 0], # Mean values\n std=[1, 1, 1], # Standard variance\n to_rgb=True), # 是否转为 rgb 格式\n dict(type='Collect', # Pipeline that decides which keys in the data should be passed to the model\n keys=['lq', 'gt'], # Keys to pass to the model\n meta_keys=['lq_path', 'gt_path']), # Meta information keys\n dict(type='ToTensor', # 图片转为 tensor\n keys=['lq', 'gt']) # 需要转换为 tensor 的图片\n]\n```\n\n\n\n```python\ntrain_pipeline = [ # train pipeline\n dict(type='LoadImageFromFile', # 从文件读取图片\n key='img', # 找到结果对应路径的 keys\n color_type='color', # 图片的 color type\n channel_order='rgb', # 图片的 channel 顺序\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # 从文件读取图片\n key='gt', # 找到结果对应路径的 keys\n color_type='color', # 图片的 color type\n channel_order='rgb', # 图片的 channel 顺序\n imdecode_backend='cv2'), # decode backend\n dict(type='SetValues', dictionary=dict(scale=scale)), # 设置 destination keys\n dict(type='PairedRandomCrop', gt_patch_size=96), # PairedRandomCrop\n dict(type='Flip', # 翻转图片\n keys=['lq', 'gt'], # 需要翻转的图片\n flip_ratio=0.5, # Flip ratio\n direction='horizontal'), # Flip 方向\n dict(type='Flip', # Flip images\n keys=['lq', 'gt'], # 需要翻转的图片\n flip_ratio=0.5, # Flip ratio\n direction='vertical'), # Flip 方向\n dict(type='RandomTransposeHW', # 随即对图片的高和宽进行转置\n keys=['lq', 'gt'], # 需要转置的图片\n transpose_ratio=0.5 # Transpose ratio\n ),\n dict(type='PackInputs') # 在当前 pipeline 中收集数据的设置\n]\ntest_pipeline = [ # Test pipeline\n dict(type='LoadImageFromFile', # 从文件读取图片\n key='img', # 找到结果对应路径的 keys\n color_type='color', # 图片的 color type\n channel_order='rgb', # 图片的 channel order\n imdecode_backend='cv2'), # decode backend\n dict(type='LoadImageFromFile', # 从文件读取图片\n key='gt', # 找到结果对应路径的 keys\n color_type='color', # 图片的 color type\n channel_order='rgb', # 图片的 channel order\n imdecode_backend='cv2'), # decode backend\n dict(type='PackInputs') # 在当前 pipeline 中收集数据的设置\n]\n```\n\n
\n\n## Dataloader\n\n在 MMagic 1.x 中我们更新了 dataloader 的设置方式，有以下几个重要的修改：\n\n- 原版本中的 `data` 字段分为了 `train_dataloader` ， `val_dataloader` 和 `test_dataloader` 三个独立的部分。这样我们就可以细粒度的对各部分进行配置。例如用户就可以针对训练和测试制定不同的 sampler 和 batch size 。\n- `samples_per_gpu` 更名为 `batch_size` 。\n- `workers_per_gpu` 更名为 `num_workers` 。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
原版本 新版本
\n\n```python\ndata = dict(\n # train\n samples_per_gpu=16, # 每个 GPU 上的 batch_size\n workers_per_gpu=4, # 每个 GPU 上做 pre-fetch 的 worker 数\n drop_last=True, # 在 data_loader 中使用 drop_last\n train=dict( # Train dataset 配置\n type='RepeatDataset', # 对 iter-based 模型设置为 RepeatDataset\n times=1000, # RepeatDataset 的 repeated times 参数\n dataset=dict(\n type=train_dataset_type, # 数据集类型\n lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X2_sub', # lq 的文件路径\n gt_folder='data/DIV2K/DIV2K_train_HR_sub', # ground truth 的文件路径\n ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt', # 标注文件的路径\n pipeline=train_pipeline, # 参照 train_pipeline\n scale=scale)), # Upsampling 的 scale factor\n # validation\n val_samples_per_gpu=1, # validation 时每个 GPU 上的 batch_size\n val_workers_per_gpu=4, # validation 是每个 GPU 上做 pre-fetch 的 worker 数\n val=dict(\n type=val_dataset_type, # 数据集类型\n lq_folder='data/val_set5/Set5_bicLRx2', # lq 的文件路径\n gt_folder='data/val_set5/Set5_mod12', # ground truth 的文件路径\n pipeline=test_pipeline, # 参照 test_pipeline\n scale=scale, # Upsampling 的 scale factor\n filename_tmpl='{}'), # filename 模板\n # test\n test=dict(\n type=val_dataset_type, # 数据集类型\n lq_folder='data/val_set5/Set5_bicLRx2', # lq 的文件路径\n gt_folder='data/val_set5/Set5_mod12', # ground truth 的文件路径\n pipeline=test_pipeline, # 参照 test_pipeline\n scale=scale, # Upsampling 的 scale factor\n filename_tmpl='{}'), # filename 模板\n)\n```\n\n\n\n```python\ndataset_type = 'BasicImageDataset' # 数据集类型\ndata_root = 'data' # 数据集根目录\ntrain_dataloader = dict(\n batch_size=16,\n num_workers=4, # 每个 GPU 上做 pre-fetch 的 worker 数\n persistent_workers=False, # 是否保持 workers instance 存活\n sampler=dict(type='InfiniteSampler', shuffle=True), # data sampler 类型\n dataset=dict( # 训练数据集 config\n type=dataset_type, # 数据集类型\n ann_file='meta_info_DIV2K800sub_GT.txt', # 标注文件路径\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # 数据根目录\n data_prefix=dict( # 图像文件前缀\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # Filename 模板\n pipeline=train_pipeline))\nval_dataloader = dict(\n batch_size=1,\n num_workers=4, # 每个 GPU 上做 pre-fetch 的 worker 数\n persistent_workers=False, # 是否保持 workers instance 存活\n drop_last=False, # 是否丢弃最后未完成的 batch\n sampler=dict(type='DefaultSampler', shuffle=False), # data sampler 类型\n dataset=dict( # Validation 数据集设置\n type=dataset_type, # 数据集类型\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # 数据根目录\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # 图像文件前缀\n pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n```\n\n
\n" }, { "path": "docs/zh_cn/migration/distributed_train.md", "content": "# 分布式训练的迁移\n\n我们已经将[MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x)合并至MMagic。以下是针对MMGeneration中分布式训练的迁移事项。\n\n在0.x版中，MMGeneration使用`DDPWrapper`和`DynamicRunner`来训练对应的静态和动态模型（例如PGGAN和StyleGANv2），但在1.x 版中，我们使用MMEngine提供的`MMSeparateDistributedDataParallel`来实现分布式训练。\n\n如下是配置前后对比：\n\n\n \n \n \n \n \n \n\n\n\n\n\n\n
0.x版中的静态模型 1.x版中的静态模型
\n\n```python\n# 使用DDPWrapper\nuse_ddp_wrapper = True\nfind_unused_parameters = False\n\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=False)\n```\n\n\n\n```python\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=False)\n```\n\n
\n\n\n\n \n \n \n \n\n\n\n\n\n\n\n\n\n
0.x版中的动态模型 1.x版中的动态模型
\n\n```python\nuse_ddp_wrapper = False\nfind_unused_parameters = False\n\n# 使用DynamicRunner\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=True)\n```\n\n\n\n```python\nmodel_wrapper_cfg = dict(\n type='MMSeparateDistributedDataParallel',\n broadcast_buffers=False,\n find_unused_parameters=True) # 针对动态模型，设置`find_unused_parameters`标志为True\n```\n\n
\n" }, { "path": "docs/zh_cn/migration/eval_test.md", "content": "# 评估与测试设置的迁移\n\n我们更新了 MMagic 1.x 中的评估设置，重要修改如下：\n\n- 评估字段被分为 `val_evaluator` 和 `test_evaluator` ， `interval` 被移动到 `train_cfg.val_interval` 。\n- 评估指标从 `test_cfg` 移至 `val_evaluator` 和 `test_evaluator`\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
原评估配置 新评估配置
\n\n```python\ntrain_cfg = None # 训练配置字典变量设为 None\ntest_cfg = dict( # 测试配置字典变量\n metrics=['PSNR'], # 测试期间使用的指标 PSNR （峰值信噪比）\n crop_border=scale) # 评估期间裁剪边框\n\nevaluation = dict( # 构建评估钩子的配置字典变量\n interval=5000, # 评价间隔\n save_image=True, # 评估期间保存图像\n gpu_collect=True) # 使用 GPU 收集\n```\n\n\n\n```python\nval_evaluator = [\n dict(type='PSNR', crop_border=scale), # 要评估的指标名称\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # 训练循环类型配置\nval_cfg = dict(type='ValLoop') # 验证循环类型的名称\ntest_cfg = dict(type='TestLoop') # 测试循环类型的名称\n```\n\n
\n\n我们已将[MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x)合并到 MMagic 中.\n这里是关于 MMGeneration 的评估和测试设置的迁移。\n\n评估字段分为 `val_evaluator` 和 `test_evaluator` ，并且评估字段不再支持 `interval` 和 `save_best` 参数。\n\n- `interval` 移至 `train_cfg.val_interval`，请参阅[调度设置](./schedule.md)。\n- `save_best` 移至 `default_hooks.checkpoint.save_best`。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x 版本 1.x 新版本
\n\n```python\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=10000,\n metrics=[\n dict(\n type='FID',\n num_images=50000,\n bgr2rgb=True,\n inception_args=dict(type='StyleGAN')),\n dict(type='IS', num_images=50000)\n ],\n best_metric=['fid', 'is'],\n sample_kwargs=dict(sample_model='ema'))\n```\n\n\n\n```python\nval_evaluator = dict(\n type='Evaluator',\n metrics=[\n dict(\n type='FID',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000)])\n# 设置最佳配置\ndefault_hooks = dict(\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n less_keys=['FID-Full-50k/fid'],\n greater_keys=['IS-50k/is'],\n save_optimizer=True,\n save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n rule=['less', 'greater']))\ntest_evaluator = val_evaluator\n```\n\n
\n\n为了正确评估和测试模型，我们需要在 `val_cfg` 和 `test_cfg` 中设置特定的循环。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
0.x 版本中的静态模型 1.x 版本中的静态模型
\n\n```python\ntotal_iters = 1000000\n\nrunner = dict(\n type='DynamicIterBasedRunner',\n is_dynamic_ddp=False,\n pass_training_status=True)\n```\n\n\n\n```python\ntrain_cfg = dict(\n by_epoch=False, # 使用基于迭代的训练\n max_iters=1000000, # 最大训练迭代次数\n val_begin=1,\n val_interval=10000) # 评价间隔\nval_cfg = dict(type='MultiValLoop') # 验证中的特定循环\ntest_cfg = dict(type='MultiTestLoop') # 测试中的特定循环\n```\n\n
\n" }, { "path": "docs/zh_cn/migration/models.md", "content": "# 模型的迁移\n\n我们在 MMagic 1.x. 版本更新了模型设定，其中重要的改动如下所示：\n\n- 删除 `pretrained` 字段.\n- 在模型设定中添加 `train_cfg` 和 `test_cfg` 字段.\n- 添加 `data_preprocessor` 字段. 这里主要是将归一化和颜色空间转换操作从 `dataset transform` 流程中移动到 `data_preprocessor` 中. 我们接下来会介绍`data_preprocessor`.\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\nmodel = dict(\n type='BasicRestorer', # Name of the model\n generator=dict( # Config of the generator\n type='EDSR', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pretrained=None,\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')) # Config for pixel loss model training and testing settings\n```\n\n\n\n```python\nmodel = dict(\n type='BaseEditModel', # Name of the model\n generator=dict( # Config of the generator\n type='EDSRNet', # Type of the generator\n in_channels=3, # Channel number of inputs\n out_channels=3, # Channel number of outputs\n mid_channels=64, # Channel number of intermediate features\n num_blocks=16, # Block number in the trunk network\n upscale_factor=scale, # Upsampling factor\n res_scale=1, # Used to scale the residual in residual block\n rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders\n rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss\n train_cfg=dict(), # Config of training model.\n test_cfg=dict(), # Config of testing model.\n data_preprocessor=dict( # The Config to build data preprocessor\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,\n 255.]))\n```\n\n
\n\n我们在 MMagic 1.x. 版本中对模型进行了重构，其中重要的改动如下所示：\n\n- MMagic 1.x 中的 `models` 被重构为六个部分：`archs`、`base_models`、`data_preprocessors`、`editors`、`diffusion_schedulers` 和 `losses`.\n\n- 在 `models` 中添加了 `data_preprocessor` 模块。这里主要是将归一化和颜色空间转换操作从 `dataset transform` 流程中移动到 `data_preprocessor` 中.此时，数据流经过数据预处理后，会先经过 `data_preprocessor` 模块的转换，然后再输入到模型中.\n\n模型的更多详细信息请参见[模型指南](../howto/models.md).\n" }, { "path": "docs/zh_cn/migration/optimizers.md", "content": "# 优化器的迁移\n\n我们已经将[MMGeneration 1.x](https://github.com/open-mmlab/mmgeneration/tree/1.x)合并至MMagic。以下是针对MMGeneration中优化器的迁移事项。\n\n在0.x版中，MMGeneration使用PyTorch自带的优化器，其只提供了通用参数优化，而在1.x版中，我们则使用了MMEngine提供的`OptimizerWrapper`。\n\n对比PyTorch自带的`Optimizer`，`OptimizerWrapper`可以支持如下功能：\n\n- `OptimizerWrapper.update_params`在一个单一的函数中就实现了`zero_grad`，`backward`和`step`\n- 支持梯度自动累积\n- 提供一个名为`OptimizerWrapper.optim_context`的上下文管理器来封装前向进程，`optim_context`会根据当前更新迭代数目来自动调用`torch.no_sync`，在AMP(Auto Mixed Precision)训练中，`autocast`也会在`optim_context`中被调用。\n\n对GAN模型，生成器和鉴别器采用不同的优化器和训练策略。要使GAN模型的`train_step`函数签名和其它模型的保持一致，我们使用从`OptimizerWrapper`继承下来的`OptimWrapperDict`来封装生成器和鉴别器的优化器，为了便于该流程的自动化MMagic实现了`MultiOptimWrapperContructor`构造器。如你想训练GAN模型，那么应该在你的配置中指定该构造器。\n\n如下是0.x版和1.x版的配置对比\n\n\n\n \n \n \n \n\n\n\n\n\n\n\n\n\n\n
0.x版 1.x版
\n\n```python\noptimizer = dict(\n generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),\n discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))\n```\n\n\n\n```python\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n```\n\n
\n\n> 注意，在1.x版中，MMGeneration使用`OptimWrapper`来实现梯度累加，这就会导致在0.x版和1.x版之间，`discriminator_steps`配置（用于在多次更新鉴别器之后更新一次生成器的训练技巧）与梯度累加均出现不一致问题。\n\n- 在0.x版中，我们在配置里使用`disc_steps`，`gen_steps`和`batch_accumulation_steps` 。`disc_steps`和`batch_accumulation_steps`会根据`train_step`的调用次数来进行统计（亦即dataloader中数据的读取次数）。因此鉴别器的一段连续性更新次数为`disc_steps // batch_accumulation_steps`。且对于生成器，`gen_steps`是生成器实际的一段连续性更新次数\n- 但在1.x版中，我们在配置里则使用了`discriminator_steps`，`generator_steps` 和`accumulative_counts`。`discriminator_steps`和`generator_steps`指的是自身在更新其它模型之前的一段连续性的更新次数\n 以BigGAN-128配置为例。\n\n\n\n \n \n \n \n\n\n\n\n\n\n\n\n\n\n
0.x版 1.x版
\n\n```python\nmodel = dict(\n type='BasiccGAN',\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True),\n gan_loss=dict(type='GANLoss', gan_type='hinge'))\n\n# 连续性更新鉴别器`disc_steps // batch_accumulation_steps = 8 // 8 = 1`次\n# 连续性更新生成器`gen_steps = 1`次\n# 生成器与鉴别器在每次更新之前执行`batch_accumulation_steps = 8`次梯度累加\ntrain_cfg = dict(\n disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)\n```\n\n\n\n```python\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True),\n # 连续性更新鉴别器`discriminator_steps = 1`次\n # 连续性更新生成器`generator_steps = 1`次\n generator_steps=1,\n discriminator_steps=1)\n\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n # 生成器在每次更新之前执行`accumulative_counts = 8`次梯度累加\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n # 鉴别器在每次更新之前执行`accumulative_counts = 8`次梯度累加\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n```\n\n
\n" }, { "path": "docs/zh_cn/migration/overview.md", "content": "# 概览\n\n本节将从以下几个方面介绍如何从 MMEditing 0.x 迁移至 MMagic 1.x：\n\n- [概览](#概览)\n - [新依赖项](#新依赖项)\n - [总体结构](#总体结构)\n - [其他配置设置](#其他配置设置)\n\n## 新依赖项\n\nMMagic 1.x 依赖于一些新的包，您可以按照[安装教程](../get_started/install.md)准备一个新的干净环境并重新安装。\n\n## 总体结构\n\n我们在 MMagic 1.x 中对总体结构进行了重构，具体如下：\n\n- 旧版本 MMEdit 中的 `core` 被拆分为 `engine`、`evaluation`、`structures` 和 `visualization`\n- 旧版本 MMEdit 中 `datasets` 的 `pipelines` 被重构为 `transforms`\n- MMagic 1.x 中的 `models` 被重构为六个部分：`archs`、`base_models`、`data_preprocessors`、`editors`、`diffusion_schedulers` 和 `losses`。\n\n## 其他配置设置\n\n我们将配置文件重命名为新模板：`{model_settings}_{module_setting}_{training_setting}_{datasets_info}`。\n\n更多配置细节请参见[配置指南](../user_guides/config.md)。\n" }, { "path": "docs/zh_cn/migration/runtime.md", "content": "# 运行设置的迁移\n\n我们更新了 MMagic 1.x 中的运行设置，重要修改如下：\n\n- `checkpoint_config` 被移动到 `default_hooks.checkpoint`，`log_config` 被移动到 `default_hooks.logger`。 我们将许多 hooks 设置从脚本代码移动到运行配置的 `default_hooks` 字段中。\n- `resume_from` 被移除，使用 `resume` 替代它。\n - 如果 resume=True 并且 load_from 不是 None, 则从load_from中的检查点恢复训练。\n - 如果 resume=True 且 load_from 为 None，则尝试从工作目录中的最新检查点恢复。\n - 如果 resume=False 且 load_from 不为None，则仅加载检查点，不恢复训练。\n - 如果 resume=False 且 load_from 为 None，则不加载也不恢复。\n- `dist_params` 字段现在是 `env_cfg` 的一个子字段。 并且在 `env_cfg` 还有一些新的配置。\n- `workflow` 相关功能已被删除。\n- 新字段 `visualizer`: 可视化工具是一个新设计。在 runner 中使用可视化器实例来处理结果和日志可视化并保存到不同的后端，例如 Local、TensorBoard 和 Wandb。\n- 新字段 `default_scope`: 所有注册器搜索 module 的起点。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
原始配置 新的配置
\n\n```python\ncheckpoint_config = dict( # 设置检查点 hook 的配置, 参考 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py 完成的\n interval=5000, # 保存间隔为 5000 次迭代\n save_optimizer=True, # 也保存优化器\n by_epoch=False) # 通过 iterations 计数\nlog_config = dict( # 注册日志 hook 的配置\n interval=100, # 打印日志的间隔\n hooks=[\n dict(type='TextLoggerHook', by_epoch=False), # logger 用来记录训练过程\n dict(type='TensorboardLoggerHook'), # 也支持 Tensorboard logger\n ])\nvisual_config = None # 可视化配置，我们不使用它。\n# runtime settings\ndist_params = dict(backend='nccl') # 设置分布式训练的参数，还可以设置端口\nlog_level = 'INFO' # 日志等级\nload_from = None # 从指定路径加载预训练模型，这不会恢复训练\nresume_from = None # 从给定路径恢复检查点，训练将从保存检查点的epoch开始恢复\nworkflow = [('train', 1)] # Runner 的工作流程. [('train', 1)] 意味着只有一个工作流，并且名为“train”的工作流执行一次。 在训练当前的抠图模型时，请保持此项不变\n```\n\n\n\n```python\ndefault_hooks = dict( # 用来创建默认 hooks\n checkpoint=dict( # 设置 checkpoint hook 的配置\n type='CheckpointHook',\n interval=5000, # 保存间隔为5000次迭代\n save_optimizer=True,\n by_epoch=False, # 通过 iterations 计数\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # 注册 logger hook 的配置\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\ndefault_scope = 'mmedit' # 用来设置注册位置\nenv_cfg = dict( # 设置分布式训练的参数，还可以设置端口\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\nlog_level = 'INFO' # 日志等级\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # 用来创建日志处理器\nload_from = None # 从指定路径加载预训练模型，这不会恢复训练\nresume = False # 从给定路径恢复检查点，训练将从保存检查点的epoch开始恢复\n```\n\n
\n" }, { "path": "docs/zh_cn/migration/schedule.md", "content": "# 调度器的迁移\n\n我们更新了MMagic 1.x 中的调度器设置，重要修改如下:\n\n- 现在我们使用 `optim_wrapper` 字段来指定关于优化过程的所有配置。`optimizer` 字段现在是 `optim_wrapper` 的一个子字段。\n- `lr_config` 字段被移除，我们使用新的 `param_scheduler` 来代替它。\n- `total_iters` 字段已移至 `train_cfg`，作为 `max_iters`, `val_cfg` 和 `test_cfg`，用于配置训练、验证和测试中的循环。\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n
Original New
\n\n```python\noptimizers = dict(generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))) # 用于构建优化器的配置，支持 PyTorch 中的所有优化器，其参数与 PyTorch 中的参数相同。\ntotal_iters = 300000 # 总训练迭代次数\nlr_config = dict( # 用于注册 LrUpdater hook 的学习率调度器配置\n policy='Step', by_epoch=False, step=[200000], gamma=0.5) # 调度器的策略\n```\n\n\n\n```python\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4),\n )\n) # 用于构建优化器的配置，支持 PyTorch 中的所有优化器，其参数与 PyTorch 中的参数相同。\nparam_scheduler = dict( # 学习策略的配置\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5) # 调度器的策略\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # 训练循环类型的配置\nval_cfg = dict(type='ValLoop') # 验证循环类型的名称\ntest_cfg = dict(type='TestLoop') # 测试循环类型的名称\n```\n\n
\n\n> 有关调度器设置的更多详细信息可在 [MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/en/migration/param_scheduler.md) 中找到。\n" }, { "path": "docs/zh_cn/migration/visualization.md", "content": "# 可视化的迁移\n\n在0.x版中，MMEditing使用`VisualizationHook`来对训练过程中生成的结果进行可视化，在1.x版中，我们将该功能整合到`BasicVisualizationHook` / `VisualizationHook`中，而且遵循MMEngine的设计，我们实现了`ConcatImageVisualizer` / `Visualizer`和一系列`VisBackend`来绘制和保存可视化结果。\n\n\n\n \n \n \n \n\n\n\n\n\n\n\n\n\n
0.x版 1.x版
\n\n```python\nvisual_config = dict(\n type='VisualizationHook',\n output_dir='visual',\n interval=1000,\n res_name_list=['gt_img', 'masked_img', 'fake_res', 'fake_img'],\n)\n```\n\n\n\n```python\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n
\n\n要了解更多关于可视化的功能，请参阅[这个教程](../user_guides/visualization.md)。\n" }, { "path": "docs/zh_cn/stat.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\nimport functools as func\nimport glob\nimport re\nfrom os.path import basename, splitext\n\nimport numpy as np\nimport titlecase\n\n\ndef anchor(name):\n return re.sub(r'-+', '-',\n re.sub(r'[^a-zA-Z0-9\\+]', '-',\n name.strip().lower())).strip('-')\n\n\n# Count algorithms\n\nfiles = sorted(glob.glob('*_models.md'))\n# files = sorted(glob.glob('docs/*_models.md'))\n\nstats = []\n\nfor f in files:\n with open(f, 'r') as content_file:\n content = content_file.read()\n\n # title\n title = content.split('\\n')[0].replace('#', '')\n\n # count papers\n papers = set(\n (papertype,\n titlecase.titlecase(paper.lower().strip()).replace('+', r'\\+'))\n for (papertype, paper) in re.findall(\n r'\\s*\\n.*?\\btitle\\s*=\\s*{(.*?)}',\n content, re.DOTALL))\n # paper links\n revcontent = '\\n'.join(list(reversed(content.splitlines())))\n paperlinks = {}\n for _, p in papers:\n print(p)\n paperlinks[p] = ' '.join(\n (f'[⇨]({splitext(basename(f))[0]}.html#{anchor(paperlink)})'\n for paperlink in re.findall(\n rf'\\btitle\\s*=\\s*{{\\s*{p}\\s*}}.*?\\n## (.*?)\\s*[,;]?\\s*\\n',\n revcontent, re.DOTALL | re.IGNORECASE)))\n print(' ', paperlinks[p])\n paperlist = '\\n'.join(\n sorted(f' - [{t}] {x} ({paperlinks[x]})' for t, x in papers))\n # count configs\n configs = set(x.lower().strip()\n for x in re.findall(r'https.*configs/.*\\.py', content))\n\n # count ckpts\n ckpts = set(x.lower().strip()\n for x in re.findall(r'https://download.*\\.pth', content)\n if 'mmedit' in x)\n\n statsmsg = f\"\"\"\n## [{title}]({f})\n\n* 模型权重文件数量: {len(ckpts)}\n* 配置文件数量: {len(configs)}\n* 论文数量: {len(papers)}\n{paperlist}\n\n \"\"\"\n\n stats.append((papers, configs, ckpts, statsmsg))\n\nallpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _, _ in stats])\nallconfigs = func.reduce(lambda a, b: a.union(b), [c for _, c, _, _ in stats])\nallckpts = func.reduce(lambda a, b: a.union(b), [c for _, _, c, _ in stats])\n\n# Summarize\n\nmsglist = '\\n'.join(x for _, _, _, x in stats)\npapertypes, papercounts = np.unique([t for t, _ in allpapers],\n return_counts=True)\ncountstr = '\\n'.join(\n [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])\n\nmodelzoo = f\"\"\"\n# 总览\n\n* 模型权重文件数量: {len(allckpts)}\n* 配置文件数量: {len(allconfigs)}\n* 论文数量: {len(allpapers)}\n{countstr}\n\n有关支持的数据集，请参阅 [数据集总览](datasets.md)。\n\n{msglist}\n\n\"\"\"\n\nwith open('modelzoo.md', 'w') as f:\n f.write(modelzoo)\n\n# Count datasets\n\nfiles = sorted(glob.glob('*_datasets.md'))\n\ndatastats = []\n\nfor f in files:\n with open(f, 'r') as content_file:\n content = content_file.read()\n\n # title\n title = content.split('\\n')[0].replace('#', '')\n\n # count papers\n papers = set(\n (papertype,\n titlecase.titlecase(paper.lower().strip()).replace('+', r'\\+'))\n for (papertype, paper) in re.findall(\n r'\\s*\\n.*?\\btitle\\s*=\\s*{(.*?)}',\n content, re.DOTALL))\n # paper links\n revcontent = '\\n'.join(list(reversed(content.splitlines())))\n paperlinks = {}\n for _, p in papers:\n print(p)\n paperlinks[p] = ', '.join(\n (f'[{p} ⇨]({splitext(basename(f))[0]}.html#{anchor(p)})'\n for p in re.findall(\n rf'\\btitle\\s*=\\s*{{\\s*{p}\\s*}}.*?\\n## (.*?)\\s*[,;]?\\s*\\n',\n revcontent, re.DOTALL | re.IGNORECASE)))\n print(' ', paperlinks[p])\n paperlist = '\\n'.join(\n sorted(f' - [{t}] {x} ({paperlinks[x]})' for t, x in papers))\n # count configs\n configs = set(x.lower().strip()\n for x in re.findall(r'https.*configs/.*\\.py', content))\n\n # count ckpts\n ckpts = set(x.lower().strip()\n for x in re.findall(r'https://download.*\\.pth', content)\n if 'mmedit' in x)\n\n statsmsg = f\"\"\"\n## [{title}]({f})\n\n* 论文数量: {len(papers)}\n{paperlist}\n\n \"\"\"\n\n datastats.append((papers, configs, ckpts, statsmsg))\n\nalldatapapers = func.reduce(lambda a, b: a.union(b),\n [p for p, _, _, _ in datastats])\n\n# Summarize\n\nmsglist = '\\n'.join(x for _, _, _, x in stats)\ndatamsglist = '\\n'.join(x for _, _, _, x in datastats)\npapertypes, papercounts = np.unique([t for t, _ in alldatapapers],\n return_counts=True)\ncountstr = '\\n'.join(\n [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])\n\nmodelzoo = f\"\"\"\n# 总览\n\n* 论文数量: {len(alldatapapers)}\n{countstr}\n\n有关支持的算法, 可参见 [模型总览](modelzoo.md).\n\n{datamsglist}\n\"\"\"\n\nwith open('datasets.md', 'w') as f:\n f.write(modelzoo)\n" }, { "path": "docs/zh_cn/switch_language.md", "content": "## English\n\n## 简体中文\n" }, { "path": "docs/zh_cn/user_guides/config.md", "content": "# 教程1 了解MMagic的配置文件\n\n我们在我们的配置系统中采用了模块化和继承设计，方便进行各种实验。\n如果您希望查看配置文件，您可以运行 `python tools/misc/print_config.py /PATH/TO/CONFIG` 来查看完整的配置。\n\n您可以根据以下教程了解我们配置系统的使用方法。\n\n- [教程1：了解MMagic中的配置](#教程1-了解MMagic的配置文件)\n - [通过脚本参数修改配置](#通过脚本参数修改配置)\n - [配置文件结构](#配置文件结构)\n - [配置文件命名风格](#配置文件命名风格)\n - [EDSR的示例](#EDSR的示例)\n - [模型配置](#模型配置)\n - [数据配置](#数据配置)\n - [数据流程](#数据流程)\n - [数据加载器](#数据加载器)\n - [评估配置](#评估配置)\n - [训练和测试配置](#训练和测试配置)\n - [优化配置](#优化配置)\n - [钩子配置](#钩子配置)\n - [运行时配置](#运行时配置)\n - [StyleGAN2的示例](#StyleGAN2的示例)\n - [模型配置](#模型配置)\n - [数据集和评估器配置](#数据集和评估器配置)\n - [训练和测试配置](#训练和测试配置-1)\n - [优化配置](#优化配置-1)\n - [钩子配置](#钩子配置-1)\n - [运行时配置](#运行时配置-1)\n - [其他示例](#其他示例)\n - [修复任务的配置示例](#修复任务的配置示例)\n - [抠图任务的配置示例](#抠图任务的配置示例)\n - [恢复任务的配置示例](#恢复任务的配置示例)\n\n## 通过脚本参数修改配置\n\n使用 `tools/train.py`或 `tools/test.py` 来运行时，您可以通过指定 `--cfg-options` 来临时修改配置。\n\n- 更新字典链中的配置键\n\n 可以按照原始配置中字典键的顺序指定配置选项。例如，`--cfg-options test_cfg.use_ema=False`\n 将默认的采样模型更改为原始生成器，`--cfg-options train_dataloader.batch_size=8` 将训练数据加载器的批大小更改为8。\n\n- 更新配置列表中的键\n\n 您的配置中有些配置字典是作为列表组成的。例如，训练流程 `train_dataloader.dataset.pipeline`\n 通常是一个列表，例如 `[dict(type='LoadImageFromFile'), ...]`。如果您想要在流程中将 `'LoadImageFromFile'`\n 更改为 `'LoadImageFromWebcam'`，可以指定 `--cfg-options train_dataloader.dataset.pipeline.0.type=LoadImageFromWebcam`\n 。训练流程 `train_pipeline` 通常也是一个列表，例如 `[dict(type='LoadImageFromFile'), ...]`\n 。如果您想要将 `'LoadImageFromFile'` 更改为 `'LoadMask'`，可以指定 `--cfg-options train_pipeline.0.type=LoadMask`。\n\n- 更新列表/元组的值\n\n 如果要更新的值是列表或元组，您可以设置 `--cfg-options key=\"[a,b]\"` 或 `--cfg-options key=a,b`\n 。它还允许嵌套的列表/元组值，例如 `--cfg-options key=\"[(a,b),(c,d)]\"`。请注意，为了支持列表/元组数据类型，引号 `\"`\n 是必需的，并且在指定的值内引号之间不允许有空格。\n\n## 配置文件结构\n\n在`config/_base_`\n目录下有三种基本组件类型：数据集（datasets）、模型（models）和默认运行时（default_runtime）。许多方法都可以通过使用其中的每种组件之一进行简单构建，例如AOT-GAN、EDVR、GLEAN、StyleGAN2、CycleGAN、SinGAN等。由`_base_`\n组件组成的配置被称为原始配置。\n\n对于同一文件夹下的所有配置文件，建议只有**一个**原始配置。所有其他配置文件都应该继承自原始配置。同时，最大的继承层级为3。\n\n为了便于理解，我们建议贡献者从现有方法中继承。例如，如果基于BasicVSR进行了某些修改，用户可以通过在配置文件中指定`_base_ = ../basicvsr/basicvsr_reds4.py`\n来首先继承基本的BasicVSR结构，然后修改配置文件中的必要字段。如果基于StyleGAN2进行了某些修改，用户可以通过在配置文件中指定`_base_ = ../styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py`\n来首先继承基本的StyleGAN2结构，然后修改配置文件中的必要字段。\n\n如果您正在构建一种完全不与任何现有方法共享结构的全新方法，您可以在`configs`目录下创建一个名为`xxx`的文件夹。\n\n详细的文档请参考[MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md)。\n\n## 配置文件命名风格\n\n配置文件按照下面的风格命名。我们建议社区贡献者使用同样的风格。\n\n```\n{model}_[module setting]_{training schedule}_{dataset}\n```\n\n`{xxx}` 是必填字段，`[yyy]` 是可选字段。\n\n- `{model}`：模型类型，如 `stylegan`、`dcgan`、`basicvsr`、`dim`\n 等。原始论文中提到的设置也包含在此字段中（例如 `Stylegan2-config-f`、`edvrm` 的 `edvrm_8xb4-600k_reds`）。\n- `[module setting]`：某些模块的具体设置，包括 Encoder、Decoder、Generator、Discriminator、Normalization、loss、Activation\n 等。例如 `c64n7` 的 `basicvsr-pp_c64n7_8xb1-600k_reds4`，dcgan 的学习率 `Glr4e-4_Dlr1e-4`，stylegan3 的 `gamma32.8`，sagan\n 中的 `woReLUInplace`。在这个部分，来自不同子模块（例如 generator 和 discriminator）的信息用 `_` 连接起来。\n- `{training_scheduler}`：训练的特定设置，包括批量大小、训练计划等。例如，学习率（例如 `lr1e-3`），使用的 GPU\n 数量和批量大小（例如 `8xb32`），总迭代次数（例如 `160kiter`）或在 discriminator 中显示的图像数量（例如 `12Mimgs`）。\n- `{dataset}`：数据集名称和数据大小信息，例如 `deepfillv1_4xb4_celeba-256x256` 的 `celeba-256x256`，`basicvsr_2xb4_reds4`\n 的 `reds4`，`ffhq`，`lsun-car`，`celeba-hq`。\n\n## EDSR的示例\n\n为了帮助用户对完整的配置文件有一个基本的了解，我们对我们实现的[EDSR模型的配置文件](https://github.com/open-mmlab/mmagic/blob/main/configs/edsr/edsr_x2c64b16_g1_300k_div2k.py)\n进行简要说明，如下所示。关于每个模块的更详细用法和相应的替代方案，请参考API文档和[MMEngine中的教程](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/config.md)。\n\n### 模型配置\n\n在MMagic的配置文件中，我们使用 `model` 字段来设置模型。\n\n```python\nmodel = dict(\n type='BaseEditModel', # 模型的名称\n generator=dict( # 生成器的配置\n type='EDSRNet', # 生成器的类型\n in_channels=3, # 输入的通道数\n out_channels=3, # 输出的通道数\n mid_channels=64, # 中间特征的通道数\n num_blocks=16, # 主干网络中的块数\n upscale_factor=scale, # 上采样因子\n res_scale=1, # 用于缩放残差块中的残差\n rgb_mean=(0.4488, 0.4371, 0.4040), # RGB图像的均值\n rgb_std=(1.0, 1.0, 1.0)), # RGB图像的标准差\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), # 配置像素损失\n train_cfg=dict(), # 训练模型的配置\n test_cfg=dict(), # 测试模型的配置\n data_preprocessor=dict( # 数据预处理器的配置\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.])\n)\n```\n\n### 数据配置\n\n训练、验证和测试[运行器(runner)](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html)\n都需要使用[数据加载器(Dataloader)](https://pytorch.org/docs/stable/data.html?highlight=data%20loader#torch.utils.data.DataLoader)。\n为了构建数据加载器，需要设置数据集(Dataset)和数据处理流程(data pipeline)。\n由于这部分的复杂性，我们使用中间变量来简化数据加载器配置的编写。\n\n#### 数据流程\n\n```python\ntrain_pipeline = [ # 训练数据处理流程\n dict(type='LoadImageFromFile', # 从文件中加载图像\n key='img', # 在结果中查找对应路径的键名\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='LoadImageFromFile', # 从文件中加载图像\n key='gt', # 在结果中查找对应路径的键名\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='SetValues', dictionary=dict(scale=scale)), # 将值设置给目标键名\n dict(type='PairedRandomCrop', gt_patch_size=96), # 随机裁剪成配对图像\n dict(type='Flip', # 翻转图像\n keys=['lq', 'gt'], # 需要翻转的图像键名\n flip_ratio=0.5, # 翻转比例\n direction='horizontal'), # 翻转方向\n dict(type='Flip', # 翻转图像\n keys=['lq', 'gt'], # 需要翻转的图像键名\n flip_ratio=0.5, # 翻转比例\n direction='vertical'), # 翻转方向\n dict(type='RandomTransposeHW', # 随机交换图像的宽高\n keys=['lq', 'gt'], # 需要交换的图像键名\n transpose_ratio=0.5 # 交换比例\n ),\n dict(type='PackInputs') # 从当前处理流程中收集数据的配置\n]\n\ntest_pipeline = [ # 测试数据处理流程\n dict(type='LoadImageFromFile', # 从文件中加载图像\n key='img', # 在结果中查找对应路径的键名\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='LoadImageFromFile', # 从文件中加载图像\n key='gt', # 在结果中查找对应路径的键名\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='PackInputs') # 从当前处理流程中收集数据的配置\n]\n```\n\n#### 数据加载器\n\n```python\ndataset_type = 'BasicImageDataset' # 数据集的类型\ndata_root = 'data' # 数据的根路径\n\ntrain_dataloader = dict(\n num_workers=4, # 每个 GPU 预取数据的工作进程数\n persistent_workers=False, # 是否保持工作进程中的数据集实例处于活动状态\n sampler=dict(type='InfiniteSampler', shuffle=True), # 数据采样器的类型\n dataset=dict( # 训练数据集配置\n type=dataset_type, # 数据集的类型\n ann_file='meta_info_DIV2K800sub_GT.txt', # 注释文件的路径\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # 数据的根路径\n data_prefix=dict( # 图像路径的前缀\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # 文件名模板\n pipeline=train_pipeline)\n)\n\nval_dataloader = dict(\n num_workers=4, # 每个 GPU 预取数据的工作进程数\n persistent_workers=False, # 是否保持工作进程中的数据集实例处于活动状态\n drop_last=False, # 是否丢弃最后一个不完整的批次\n sampler=dict(type='DefaultSampler', shuffle=False), # 数据采样器的类型\n dataset=dict( # 验证数据集配置\n type=dataset_type, # 数据集的类型\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # 数据的根路径\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # 图像路径的前缀\n pipeline=test_pipeline)\n)\n\ntest_dataloader = val_dataloader\n```\n\n### 评估配置\n\n[评估器](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html)用于计算在验证集和测试集上训练模型的指标。\n评估器的配置包括一个或多个指标配置：\n\n```python\nval_evaluator = [\n dict(type='MAE'), # 要评估的指标名称\n dict(type='PSNR', crop_border=scale), # 要评估的指标名称\n dict(type='SSIM', crop_border=scale), # 要评估的指标名称\n]\n\ntest_evaluator = val_evaluator # 测试评估器的配置与验证评估器相同\n\n```\n\n### 训练和测试配置\n\nMMEngine的运行器使用Loop来控制训练、验证和测试过程。\n用户可以使用这些字段设置最大训练迭代次数和验证间隔。\n\n```python\ntrain_cfg = dict(\n type='IterBasedTrainLoop', # 训练循环类型的名称\n max_iters=300000, # 总迭代次数\n val_interval=5000, # 验证间隔迭代次数\n)\nval_cfg = dict(type='ValLoop') # 验证循环类型的名称\ntest_cfg = dict(type='TestLoop') # 测试循环类型的名称\n```\n\n### 优化配置\n\n`optim_wrapper`是用于配置优化相关设置的字段。\n优化器包装器不仅提供优化器的功能，还支持梯度裁剪、混合精度训练等功能。在[optimizer wrapper教程](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html)\n中可以了解更多信息。\n\n```python\noptim_wrapper = dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n) # 用于构建优化器的配置，支持所有与PyTorch中参数相同的优化器。\n```\n\n`param_scheduler`是一个配置优化超参数（如学习率和动量）调整方法的字段。\n用户可以结合多个调度器来创建所需的参数调整策略。\n在[parameter scheduler教程](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html)中可以了解更多信息。\n\n```python\nparam_scheduler = dict( # 学习策略的配置\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n```\n\n### 钩子配置\n\n用户可以将钩子（hooks）附加到训练、验证和测试循环中，在运行过程中插入一些操作。有两个不同的钩子字段，一个是`default_hooks`\n，另一个是`custom_hooks`。\n\n`default_hooks`是一个包含钩子配置的字典。`default_hooks`\n是运行时必需的钩子，它们具有默认的优先级，不应修改。如果未设置，默认值将被使用。要禁用默认钩子，用户可以将其配置设置为`None`。\n\n```python\ndefault_hooks = dict( # 用于构建默认钩子的配置\n checkpoint=dict( # 配置检查点钩子\n type='CheckpointHook',\n interval=5000, # 保存间隔为5000次迭代\n save_optimizer=True,\n by_epoch=False, # 以迭代次数计数\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # 配置注册日志钩子\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n```\n\n`custom_hooks`是一个钩子配置的列表。用户可以开发自己的钩子并将其插入到该字段中。\n\n```python\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # 可视化钩子的配置\n```\n\n### 运行时配置\n\n```python\ndefault_scope = 'mmagic' # 用于设置注册表位置\nenv_cfg = dict( # 设置分布式训练的参数，端口也可以设置\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\nlog_level = 'INFO' # 日志记录的级别\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # 用于构建日志处理器\nload_from = None # 从给定路径加载模型作为预训练模型。这不会恢复训练。\nresume = False # 从给定路径恢复检查点，训练将从检查点保存的时期继续。\n```\n\n## StyleGAN2的示例\n\n以[Stylegan2 在 1024x1024 分辨率上的配置文件](https://github.com/open-mmlab/mmagic/blob/main/configs//styleganv2/stylegan2_c2_8xb4-fp16-global-800kiters_quicktest-ffhq-256x256.py)\n为例，我们根据不同的功能模块介绍配置中的各个字段。\n\n### 模型配置\n\n除了包括生成器、鉴别器等神经网络组件之外，还需要`data_preprocessor`、`loss_config`等字段，其中一些还包含`ema_config`。\n\n`data_preprocessor`负责处理数据加载器输出的一个批次数据。\n\n`loss_config`负责设置损失项的权重。\n\n`ema_config`负责为生成器执行指数移动平均（EMA）操作。\n\n```python\nmodel = dict(\n type='StyleGAN2', # 模型的名称\n data_preprocessor=dict(type='DataPreprocessor'), # 数据预处理器的配置，通常包括图像归一化和填充\n generator=dict( # 生成器的配置\n type='StyleGANv2Generator', # 生成器的名称\n out_size=1024, # 生成器的输出分辨率\n style_channels=512), # 生成器的风格通道数\n discriminator=dict( # 鉴别器的配置\n type='StyleGAN2Discriminator', # 鉴别器的名称\n in_size=1024), # 鉴别器的输入分辨率\n ema_config=dict( # EMA的配置\n type='ExponentialMovingAverage', # 平均模型的具体类型\n interval=1, # EMA操作的间隔\n momentum=0.9977843871238888), # EMA操作的动量\n loss_config=dict( # 损失项的配置\n r1_loss_weight=80.0, # r1梯度惩罚的权重\n r1_interval=16, # r1梯度惩罚的间隔\n norm_mode='HWC', # r1梯度惩罚的归一化模式\n g_reg_interval=4, # 生成器的正则化间隔\n g_reg_weight=8.0, # 生成器的正则化权重\n pl_batch_shrink=2)) # 路径长度正则化中缩减批次大小的因子\n```\n\n### 数据集和评估器配置\n\n训练、验证和测试[runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html)\n需要使用数据加载器[Dataloaders](https://pytorch.org/docs/stable/data.html?highlight=data%20loader#torch.utils.data.DataLoader)。\n需要设置数据集和数据处理流程来构建数据加载器。由于这部分的复杂性，我们使用中间变量来简化数据加载器配置的编写。\n\n```python\ndataset_type = 'BasicImageDataset' # 数据集类型，将用于定义数据集\ndata_root = './data/ffhq/' # 数据的根目录\n\ntrain_pipeline = [ # 训练数据处理流程\n dict(type='LoadImageFromFile', key='img'), # 第一个处理流程，从文件路径加载图像\n dict(type='Flip', keys=['img'], direction='horizontal'), # 图像翻转的数据增强处理流程\n dict(type='PackInputs', keys=['img']) # 最后一个处理流程，格式化注释数据（如果有）并决定哪些键应该打包到data_samples中\n]\nval_pipeline = [\n dict(type='LoadImageFromFile', key='img'), # 第一个处理流程，从文件路径加载图像\n dict(type='PackInputs', keys=['img']) # 最后一个处理流程，格式化注释数据（如果有）并决定哪些键应该打包到data_samples中\n]\ntrain_dataloader = dict( # 训练数据加载器的配置\n batch_size=4, # 单个GPU的批次大小\n num_workers=8, # 每个单个GPU的数据预取工作线程数\n persistent_workers=True, # 如果为True，则数据加载器将在一个epoch结束后不会关闭工作进程，这可以加速训练速度。\n sampler=dict( # 训练数据采样器的配置\n type='InfiniteSampler',\n # 用于迭代训练的InfiniteSampler。参考 https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L107\n shuffle=True), # 是否随机打乱训练数据\n dataset=dict( # 训练数据集的配置\n type=dataset_type,\n data_root=data_root,\n pipeline=train_pipeline))\nval_dataloader = dict( # 验证数据加载器的配置\n batch_size=4, # 单个GPU的批次大小\n num_workers=8, # 每个单个GPU的数据预取工作线程数\n dataset=dict( # 验证数据集的配置\n type=dataset_type,\n data_root=data_root,\n pipeline=val_pipeline),\n sampler=dict( # 验证数据采样器的配置\n type='DefaultSampler',\n # 支持分布式和非分布式训练的DefaultSampler。参考 https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L14\n shuffle=False), # 是否随机打乱验证数据\n persistent_workers=True)\ntest_dataloader = val_dataloader # 测试数据加载器的配置与验证数据加载器相同\n```\n\n[评估器](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html)用于计算在验证和测试数据集上训练模型的指标。\n评估器的配置由一个或多个指标配置组成：\n\n```python\nval_evaluator = dict( # 验证评估器的配置\n type='Evaluator', # 评估类型\n metrics=[ # 指标的配置\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n ])\ntest_evaluator = val_evaluator # 测试评估器的配置与验证评估器相同\n```\n\n### 训练和测试配置\n\nMMEngine的runner使用Loop来控制训练、验证和测试过程。\n用户可以使用以下字段设置最大训练迭代次数和验证间隔：\n\n```python\ntrain_cfg = dict( # 训练配置\n by_epoch=False, # 设置`by_epoch`为False以使用基于迭代的训练\n val_begin=1, # 开始验证的迭代次数\n val_interval=10000, # 验证间隔\n max_iters=800002) # 最大训练迭代次数\nval_cfg = dict(type='MultiValLoop') # 验证循环类型\ntest_cfg = dict(type='MultiTestLoop') # 测试循环类型\n```\n\n### 优化配置\n\n`optim_wrapper`是配置优化相关设置的字段。\n优化器包装器不仅提供优化器的功能，还支持梯度裁剪、混合精度训练等功能。在[optimizer wrapper tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html)\n中可以找到更多信息。\n\n```python\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n optimizer=dict(type='Adam', lr=0.0016, betas=(0, 0.9919919678228657))),\n discriminator=dict(\n optimizer=dict(\n type='Adam',\n lr=0.0018823529411764706,\n betas=(0, 0.9905854573074332))))\n```\n\n`param_scheduler`是一个配置优化超参数（如学习率和动量）调整方法的字段。\n用户可以组合多个调度器来创建所需的参数调整策略。\n在[parameter scheduler tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html)中可以找到更多信息。\n由于StyleGAN2不使用参数调度器，我们以[CycleGAN](https://github.com/open-mmlab/mmagic/blob/main/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py)\n的配置作为示例：\n\n```python\n# CycleGAN配置中的参数调度器\nparam_scheduler = dict(\n type='LinearLrInterval', # 调度器的类型\n interval=400, # 更新学习率的间隔\n by_epoch=False, # 调度器按迭代调用\n start_factor=0.0002, # 在第一次迭代中乘以参数值的数值\n end_factor=0, # 在线性变化过程结束时乘以参数值的数值\n begin=40000, # 调度器的起始迭代次数\n end=80000) # 调度器的结束迭代次数\n```\n\n### 钩子配置\n\n用户可以在训练、验证和测试循环中附加钩子，以在运行过程中插入一些操作。这里有两个不同的钩子字段，一个是`default_hooks`\n，另一个是`custom_hooks`。\n\n`default_hooks`是一个钩子配置的字典。`default_hooks`\n是在运行时必须的钩子。它们具有默认的优先级，不应该被修改。如果没有设置，运行器将使用默认值。要禁用一个默认钩子，用户可以将其配置设置为`None`。\n\n```python\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),\n checkpoint=dict(\n type='CheckpointHook',\n interval=10000,\n by_epoch=False,\n less_keys=['FID-Full-50k/fid'],\n greater_keys=['IS-50k/is'],\n save_optimizer=True,\n save_best='FID-Full-50k/fid'))\n```\n\n`custom_hooks` 是一个钩子配置的列表。用户可以开发自己的钩子并将它们插入到这个字段中。\n\n```python\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n```\n\n### 运行时配置\n\n```python\ndefault_scope = 'mmagic' # 默认的注册表作用域，用于查找模块。参考 https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html\n\n# 环境配置\nenv_cfg = dict(\n cudnn_benchmark=True, # 是否启用cudnn基准测试\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), # 设置多进程参数\n dist_cfg=dict(backend='nccl') # 设置分布式参数\n)\n\nlog_level = 'INFO' # 日志级别\nlog_processor = dict(\n type='LogProcessor', # 日志处理器，用于处理运行时日志\n by_epoch=False) # 按迭代打印日志\nload_from = None # 从给定路径加载模型检查点作为预训练模型\nresume = False # 是否从`load_from`定义的检查点恢复训练。如果`load_from`为`None`，将恢复`work_dir`中的最新检查点\n```\n\n## 其他示例\n\n### 修复任务的配置示例\n\n为了帮助用户对修复系统的完整配置和模块有一个基本的了解，我们对全局和局部修复的配置进行简要注释，如下所示。有关更详细的用法和每个模块的替代选项，请参考API文档。\n\n```python\nmodel = dict(\n type='GLInpaintor', # 修复模型的名称\n data_preprocessor=dict(\n type='DataPreprocessor', # 数据预处理器的名称\n mean=[127.5], # 数据归一化时使用的均值\n std=[127.5], # 数据归一化时使用的标准差\n ),\n encdec=dict(\n type='GLEncoderDecoder', # 编码器-解码器的名称\n encoder=dict(type='GLEncoder', norm_cfg=dict(type='SyncBN')), # 编码器的配置\n decoder=dict(type='GLDecoder', norm_cfg=dict(type='SyncBN')), # 解码器的配置\n dilation_neck=dict(\n type='GLDilationNeck', norm_cfg=dict(type='SyncBN'))), # 膨胀模块的配置\n disc=dict(\n type='GLDiscs', # 判别器的名称\n global_disc_cfg=dict(\n in_channels=3, # 判别器的输入通道数\n max_channels=512, # 判别器中间通道的最大数量\n fc_in_channels=512 * 4 * 4, # 最后一个全连接层的输入通道数\n fc_out_channels=1024, # 最后一个全连接层的输出通道数\n num_convs=6, # 判别器中使用的卷积层数量\n norm_cfg=dict(type='SyncBN') # 归一化层的配置\n ),\n local_disc_cfg=dict(\n in_channels=3, # 判别器的输入通道数\n max_channels=512, # 判别器中间通道的最大数量\n fc_in_channels=512 * 4 * 4, # 最后一个全连接层的输入通道数\n fc_out_channels=1024, # 最后一个全连接层的输出通道数\n num_convs=5, # 判别器中使用的卷积层数量\n norm_cfg=dict(type='SyncBN') # 归一化层的配置\n ),\n ),\n loss_gan=dict(\n type='GANLoss', # GAN损失的名称\n gan_type='vanilla', # GAN损失的类型\n loss_weight=0.001 # GAN损失的权重\n ),\n loss_l1_hole=dict(\n type='L1Loss', # L1损失的类型\n loss_weight=1.0 # L1损失的权重\n )\n)\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', # 训练循环的类型\n max_iters=500002, # 总迭代次数\n val_interval=50000 # 验证间隔的迭代次数\n)\nval_cfg = dict(type='ValLoop') # 验证循环的类型\ntest_cfg = dict(type='TestLoop') # 测试循环的类型\n\nval_evaluator = [\n dict(type='MAE', mask_key='mask', scaling=100), # 用于评估的指标名称\n dict(type='PSNR'), # 用于评估的指标名称\n dict(type='SSIM'), # 用于评估的指标名称\n]\ntest_evaluator = val_evaluator\n\ninput_shape = (256, 256) # 输入图像的形状\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'), # 加载图像的配置\n dict(\n type='LoadMask', # 加载掩膜的类型\n mask_mode='bbox', # 掩膜的类型\n mask_config=dict(\n max_bbox_shape=(128, 128), # 边界框的形状\n max_bbox_delta=40, # 边界框高度和宽度的变化范围\n min_margin=20, # 边界框与图像边界的最小间距\n img_shape=input_shape)), # 输入图像的形状\n dict(\n type='Crop', # 裁剪的类型\n keys=['gt'], # 需要裁剪的图像的键\n crop_size=(384, 384), # 裁剪后的大小\n random_crop=True, # 是否随机裁剪\n ),\n dict(\n type='Resize', # 调整大小的类型\n keys=['gt'], # 需要调整大小的图像的键\n scale=input_shape, # 调整大小的比例\n keep_ratio=False, # 是否保持比例\n ),\n dict(\n type='Normalize', # 归一化的类型\n keys=['gt_img'], # 需要归一化的图像的键\n mean=[127.5] * 3, # 归一化时使用的均值\n std=[127.5] * 3, # 归一化时使用的标准差\n to_rgb=False), # 是否将图像通道转换为RGB\n dict(type='GetMaskedImage'), # 获取掩膜图像的配置\n dict(type='PackInputs'), # 收集当前流水线中的数据的配置\n]\n\ntest_pipeline = train_pipeline # 构建测试/验证流水线\n\ndataset_type = 'BasicImageDataset' # 数据集的类型\ndata_root = 'data/places' # 数据的根路径\n\ntrain_dataloader = dict(\n batch_size=12, # 单个GPU的批处理大小\n num_workers=4, # 每个单个GPU预取数据的工作线程数\n persistent_workers=False, # 是否保持工作线程的数据集实例\n sampler=dict(type='InfiniteSampler', shuffle=False), # 数据采样器的类型\n dataset=dict( # 训练数据集的配置\n type=dataset_type, # 数据集的类型\n data_root=data_root, # 数据的根路径\n data_prefix=dict(gt='data_large'), # 图像路径的前缀\n ann_file='meta/places365_train_challenge.txt', # 注释文件的路径\n test_mode=False,\n pipeline=train_pipeline,\n ))\n\nval_dataloader = dict(\n batch_size=1, # 单个GPU的批处理大小\n num_workers=4, # 每个单个GPU预取数据的工作线程数\n persistent_workers=False, # 是否保持工作线程的数据集实例\n drop_last=False, # 是否丢弃最后一个不完整的批次\n sampler=dict(type='DefaultSampler', shuffle=False), # 数据采样器的类型\n dataset=dict( # 验证数据集的配置\n type=dataset_type, # 数据集的类型\n data_root=data_root, # 数据的根路径\n data_prefix=dict(gt='val_large'), # 图像路径的前缀\n ann_file='meta/places365_val.txt', # 注释文件的路径\n test_mode=True,\n pipeline=test_pipeline,\n ))\n\ntest_dataloader = val_dataloader\n\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel') # 模型包装器的名称\n\noptim_wrapper = dict( # 用于构建优化器的配置，支持PyTorch中的所有优化器，其参数与PyTorch中的优化器的参数相同\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)),\n disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)))\n\ndefault_scope = 'mmagic' # 用于设置注册表位置\nsave_dir = './work_dirs' # 保存模型检查点和日志的目录\nexp_name = 'gl_places' # 实验名称\n\ndefault_hooks = dict( # 用于构建默认挂钩\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # 注册记录器挂钩的配置\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict( # 设置检查点挂钩的配置\n type='CheckpointHook',\n interval=50000,\n by_epoch=False,\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict( # 用于设置分布式训练的参数，也可以设置端口\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type='LocalVisBackend')] # 可视化后端的名称\nvisualizer = dict( # 用于构建可视化器的配置\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # 用于构建自定义挂钩\n\nlog_level = 'INFO' # 记录级别\nlog_processor = dict(type='LogProcessor', by_epoch=False) # 用于构建日志处理器\n\nload_from = None # 从给定路径加载预训练模型\nresume = False # 从给定路径恢复检查点\n\nfind_unused_parameters = False # 是否在DDP中设置未使用的参数\n```\n\n### 抠图任务的配置示例\n\n为了帮助用户对完整的配置有一个基本的了解，我们对我们实现的原始DIM（Deep Image\nMatting）模型的配置进行了简要的注释，如下所示。有关每个模块的更详细用法和相应的替代方案，请参阅API文档。\n\n```python\n# 模型设置\nmodel = dict(\n type='DIM', # 模型的名称（我们称之为mattor）。\n data_preprocessor=dict( # 数据预处理器的配置\n type='DataPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n bgr_to_rgb=True,\n proc_inputs='normalize',\n proc_trimap='rescale_to_zero_one',\n proc_gt='rescale_to_zero_one',\n ),\n backbone=dict( # 骨干网络的配置。\n type='SimpleEncoderDecoder', # 骨干网络的类型。\n encoder=dict( # 编码器的配置。\n type='VGG16'), # 编码器的类型。\n decoder=dict( # 解码器的配置。\n type='PlainDecoder')), # 解码器的类型。\n pretrained='./weights/vgg_state_dict.pth', # 要加载的编码器的预训练权重。\n loss_alpha=dict( # alpha损失的配置。\n type='CharbonnierLoss', # 预测的alpha融合图像的损失类型。\n loss_weight=0.5), # alpha损失的权重。\n loss_comp=dict( # 合成损失的配置。\n type='CharbonnierCompLoss', # 合成损失的类型。\n loss_weight=0.5), # 合成损失的权重。\n train_cfg=dict( # DIM模型的训练配置。\n train_backbone=True, # 在DIM阶段1中，训练骨干网络。\n train_refiner=False), # 在DIM阶段1中，不训练refiner。\n test_cfg=dict( # DIM模型的测试配置。\n refine=False, # 是否使用refiner输出作为输出，在阶段1中我们不使用它。\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ),\n)\n\n# 数据设置\ndataset_type = 'AdobeComp1kDataset' # 数据集类型，用于定义数据集。\ndata_root = 'data/adobe_composition-1k' # 数据的根路径。\n\ntrain_pipeline = [ # 训练数据处理流程。\n dict(\n type='LoadImageFromFile', # 从文件加载alpha融合图像。\n key='alpha', # 注释文件中alpha融合图像的键。该流程将从路径`alpha_path`读取alpha融合图像。\n color_type='grayscale'), # 加载为灰度图像，具有形状（高度，宽度）。\n dict(\n type='LoadImageFromFile', # 从文件加载图像。\n key='fg'), # 要加载的图像的键。该流程将从路径`fg_path`读取前景图像。\n dict(\n type='LoadImageFromFile', # 从文件加载图像。\n key='bg'), # 要加载的图像的键。该流程将从路径`bg_path`读取背景图像。\n dict(\n type='LoadImageFromFile', # 从文件加载图像。\n key='merged'), # 要加载的图像的键。该流程将从路径`merged_path`读取合并图像。\n dict(\n type='CropAroundUnknown', # 在未知区域（半透明区域）周围裁剪图像。\n keys=['alpha', 'merged', 'fg', 'bg'], # 要裁剪的图像。\n crop_sizes=[320, 480, 640]), # 候选裁剪大小。\n dict(\n type='Flip', # 翻转图像的增强流程。\n keys=['alpha', 'merged', 'fg', 'bg']), # 要翻转的图像。\n dict(\n type='Resize', # 调整图像大小的增强流程。\n keys=['alpha', 'merged', 'fg', 'bg'], # 要调整大小的图像。\n scale=(320, 320), # 目标大小。\n keep_ratio=False), # 是否保持高度和宽度之间的比例。\n dict(\n type='GenerateTrimap', # 从alpha融合图像生成trimap。\n kernel_size=(1, 30)), # 腐蚀/膨胀内核的大小范围。\n dict(type='PackInputs'), # 从当前流程中收集数据的配置\n]\ntest_pipeline = [\n dict(\n type='LoadImageFromFile', # 加载alpha融合图像。\n key='alpha', # 注释文件中alpha融合图像的键。该流程将从路径`alpha_path`读取alpha融合图像。\n color_type='grayscale',\n save_original_img=True),\n dict(\n type='LoadImageFromFile', # 从文件加载图像。\n key='trimap', # 要加载的图像的键。该流程将从路径`trimap_path`读取trimap。\n color_type='grayscale', # 加载为灰度图像，具有形状（高度，宽度）。\n save_original_img=True), # 保存trimap的副本用于计算指标。它将以键`ori_trimap`保存。\n dict(\n type='LoadImageFromFile', # 从文件加载图像。\n key='merged'), # 要加载的图像的键。该流程将从路径`merged_path`读取合并图像。\n dict(type='PackInputs'), # 从当前流程中收集数据的配置\n]\n\ntrain_dataloader = dict(\n batch_size=1, # 单个GPU的批处理大小\n num_workers=4, # 每个单个GPU预提取数据的工作线程数\n persistent_workers=False, # 是否保持工作线程Dataset实例处于活动状态\n sampler=dict(type='InfiniteSampler', shuffle=True), # 数据采样器的类型\n dataset=dict( # 训练数据集的配置\n type=dataset_type, # 数据集的类型\n data_root=data_root, # 数据的根路径\n ann_file='training_list.json', # 注释文件的路径\n test_mode=False,\n pipeline=train_pipeline,\n ))\n\nval_dataloader = dict(\n batch_size=1, # 单个GPU的批处理大小\n num_workers=4, # 每个单个GPU预提取数据的工作线程数\n persistent_workers=False, # 是否保持工作线程Dataset实例处于活动状态\n drop_last=False, # 是否丢弃最后一个不完整的批次\n sampler=dict(type='DefaultSampler', shuffle=False), # 数据采样器的类型\n dataset=dict( # 验证数据集的配置\n type=dataset_type, # 数据集的类型\n data_root=data_root, # 数据的根路径\n ann_file='test_list.json', # 注释文件的路径\n test_mode=True,\n pipeline=test_pipeline,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='SAD'), # 要评估的指标名称\n dict(type='MattingMSE'), # 要评估的指标名称\n dict(type='GradientError'), # 要评估的指标名称\n dict(type='ConnectivityError'), # 要评估的指标名称\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', # 训练循环类型的名称\n max_iters=1_000_000, # 总迭代次数\n val_interval=40000, # 验证间隔迭代次数\n)\nval_cfg = dict(type='ValLoop') # 验证循环类型的名称\ntest_cfg = dict(type='TestLoop') # 测试循环类型的名称\n\n# 优化器\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n )\n) # 用于构建优化器的配置，支持PyTorch中所有优化器，其参数也与PyTorch中的参数相同。\n\ndefault_scope = 'mmagic' # 用于设置注册表位置\nsave_dir = './work_dirs' # 保存当前实验的模型检查点和日志的目录。\n\ndefault_hooks = dict( # 用于构建默认钩子\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # 注册日志记录器钩子的配置\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict( # 配置检查点钩子\n type='CheckpointHook',\n interval=40000, # 保存间隔为40000次迭代。\n by_epoch=False, # 按迭代计数。\n out_dir=save_dir),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\nenv_cfg = dict( # 设置分布式训练的参数，也可以设置端口\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO' # 日志级别\nlog_processor = dict(type='LogProcessor', by_epoch=False) # 用于构建日志处理器的配置。\n\n# 导入模块并设置MMDetection的配置\nload_from = None # 从给定路径加载模型作为预训练模型，这不会恢复训练。\nresume = False # 从给定路径恢复检查点，训练将从检查点保存的时期恢复。\n```\n\n### 恢复任务的配置示例\n\n为了帮助用户对完整配置有一个基本的理解，我们对我们实现的EDSR模型的配置进行了简要的注释，如下所示。有关更详细的用法和每个模块的相应替代方案，请参阅API文档。\n\n```python\nexp_name = 'edsr_x2c64b16_1x16_300k_div2k' # 实验名称\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nload_from = None # 基于预训练的x2模型\n\nscale = 2 # 上采样的比例\n# 模型设置\nmodel = dict(\n type='BaseEditModel', # 模型名称\n generator=dict( # 生成器的配置\n type='EDSRNet', # 生成器的类型\n in_channels=3, # 输入的通道数\n out_channels=3, # 输出的通道数\n mid_channels=64, # 中间特征的通道数\n num_blocks=16, # 主干网络中的块数\n upscale_factor=scale, # 上采样因子\n res_scale=1, # 用于缩放残差块中的残差\n rgb_mean=(0.4488, 0.4371, 0.4040), # 图像的RGB均值\n rgb_std=(1.0, 1.0, 1.0)), # 图像的RGB标准差\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # 像素损失的配置\ntrain_cfg = dict(), # 训练模型的配置\ntest_cfg = dict(), # 测试模型的配置\ndata_preprocessor = dict( # 数据预处理器的配置\n type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.]))\n\ntrain_pipeline = [ # 训练数据处理的流程\n dict(type='LoadImageFromFile', # 从文件加载图像\n key='img', # 结果中寻找对应路径的关键字\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='LoadImageFromFile', # 从文件加载图像\n key='gt', # 结果中寻找对应路径的关键字\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='SetValues', dictionary=dict(scale=scale)), # 设置目标关键字的值\n dict(type='PairedRandomCrop', gt_patch_size=96), # 随机裁剪配对图像\n dict(type='Flip', # 翻转图像\n keys=['lq', 'gt'], # 需要翻转的图像\n flip_ratio=0.5, # 翻转的比例\n direction='horizontal'), # 翻转的方向\n dict(type='Flip', # 翻转图像\n keys=['lq', 'gt'], # 需要翻转的图像\n flip_ratio=0.5, # 翻转的比例\n direction='vertical'), # 翻转的方向\n dict(type='RandomTransposeHW', # 随机转置图像的高度和宽度\n keys=['lq', 'gt'], # 需要转置的图像\n transpose_ratio=0.5 # 转置的比例\n ),\n dict(type='PackInputs') # 收集当前流程中的数据的配置\n]\ntest_pipeline = [ # 测试流程\n dict(type='LoadImageFromFile', # 从文件加载图像\n key='img', # 结果中寻找对应路径的关键字\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='LoadImageFromFile', # 从文件加载图像\n key='gt', # 结果中寻找对应路径的关键字\n color_type='color', # 图像的颜色类型\n channel_order='rgb', # 图像的通道顺序\n imdecode_backend='cv2'), # 解码后端\n dict(type='ToTensor', keys=['img', 'gt']), # 将图像转换为张量\n dict(type='PackInputs') # 收集当前流程中的数据的配置\n]\n\n# 数据集设置\ndataset_type = 'BasicImageDataset' # 数据集的类型\ndata_root = 'data' # 数据的根路径\n\ntrain_dataloader = dict(\n num_workers=4, # 每个GPU预提取数据的工作进程数\n persistent_workers=False, # 是否保持工作进程中的数据集实例处于活动状态\n sampler=dict(type='InfiniteSampler', shuffle=True), # 数据采样器的类型\n dataset=dict( # 训练数据集的配置\n type=dataset_type, # 数据集的类型\n ann_file='meta_info_DIV2K800sub_GT.txt', # 注释文件的路径\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K', # 数据的根路径\n data_prefix=dict( # 图像路径的前缀\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n filename_tmpl=dict(img='{}', gt='{}'), # 文件名模板\n pipeline=train_pipeline))\nval_dataloader = dict(\n num_workers=4, # 每个GPU预提取数据的工作进程数\n persistent_workers=False, # 是否保持工作进程中的数据集实例处于活动状态\n drop_last=False, # 是否丢弃最后一个不完整的批次\n sampler=dict(type='DefaultSampler', shuffle=False), # 数据采样器的类型\n dataset=dict( # 验证数据集的配置\n type=dataset_type, # 数据集的类型\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5', # 数据的根路径\n data_prefix=dict(img='LRbicx2', gt='GTmod12'), # 图像路径的前缀\n pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type='MAE'), # 用于评估的指标的名称\n dict(type='PSNR', crop_border=scale), # 用于评估的指标的名称\n dict(type='SSIM', crop_border=scale), # 用于评估的指标的名称\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(\n type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # 训练循环类型的配置\nval_cfg = dict(type='ValLoop') # 验证循环类型的名称\ntest_cfg = dict(type='TestLoop') # 测试循环类型的名称\n\n# 优化器\noptim_wrapper = dict(\n dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=0.00001),\n )\n) # 用于构建优化器的配置，支持PyTorch中所有优化器，参数与PyTorch中的相同。\n\nparam_scheduler = dict( # 学习策略的配置\n type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)\n\ndefault_hooks = dict( # 用于构建默认钩子\n checkpoint=dict( # 配置保存检查点的钩子\n type='CheckpointHook',\n interval=5000, # 保存间隔为5000次迭代\n save_optimizer=True,\n by_epoch=False, # 以迭代计数\n out_dir=save_dir,\n ),\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=100), # 注册记录器钩子的配置\n param_scheduler=dict(type='ParamSchedulerHook'),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n)\n\ndefault_scope = 'mmagic' # 用于设置注册表位置\nsave_dir = './work_dirs' # 保存当前实验的模型检查点和日志的目录。\n\nenv_cfg = dict( # 设置分布式训练的参数，端口也可以设置\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO' # 记录的级别\nlog_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # 用于构建日志处理器\n\nload_from = None # 从给定路径加载模型作为预训练模型，这不会恢复训练。\nresume = False # 从给定路径恢复检查点，训练将从检查点保存的时期恢复。\n```\n" }, { "path": "docs/zh_cn/user_guides/dataset_prepare.md", "content": "# 教程 2：准备数据集\n\n在本节中，我们将详细介绍如何准备数据并在本仓库的不同任务中采用适当的数据集。\n\n我们支持不同任务的多个数据集。\n在MMagic中，有两种方法可以将数据集用于训练和测试模型：\n\n1. 直接使用下载的数据集\n2. 在使用下载的数据集之前对其进行预处理\n\n本文的结构如下：\n\n- \\[教程 2：准备数据集\\](#教程 2：准备数据集)\n - [下载数据集](#下载数据集)\n - [准备数据集](#准备数据集)\n - [MMagic中的数据集概述](#MMagic中的数据集概述)\n\n## 下载数据集\n\n首先，建议您从官方的页面下载数据集。\n大多数数据集在下载后都是可用的，因此您只需确保文件夹结构正确，无需进一步准备。\n例如，您可以通过从[主页](http://toflow.csail.mit.edu/)下载，来简单地准备Vimeo90K-triplet数据集.\n\n## 准备数据集\n\n一些数据集需要在训练或测试之前进行预处理。我们在\n[tools/dataset_converters](https://github.com/open-mmlab/mmagic/tree/main/tools/dataset_converters)中支持许多用来准备数据集的脚本。\n您可以遵循每个数据集的教程来运行脚本。例如，我们建议将DIV2K图像裁剪为子图像。我们提供了一个脚本来准备裁剪的DIV2K数据集。可以运行以下命令：\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K\n```\n\n## MMagic中的数据集概述\n\n我们支持详细的教程，并根据不同的任务进行拆分。\n\n请查看我们的数据集概览，了解不同任务的数据准备。\n\n如果您对MMagic中数据集的更多细节感兴趣，请查看[进阶教程](../howto/dataset.md)。\n" }, { "path": "docs/zh_cn/user_guides/deploy.md", "content": "# 教程 8：模型部署指南\n\n[MMDeploy](https://github.com/open-mmlab/mmdeploy) 是 OpenMMLab 的部署仓库，负责包括 MMClassification、MMDetection、MMagic 等在内的各算法库的部署工作。\n你可以从[这里](https://mmdeploy.readthedocs.io/zh_CN/latest/04-supported-codebases/mmagic.html)获取 MMDeploy 对 MMClassification 部署支持的最新文档。\n\n本文的结构如下：\n\n- [安装](#安装)\n- [模型转换](#模型转换)\n- [模型规范](#模型规范)\n- [模型推理](#模型推理)\n - [后端模型推理](#后端模型推理)\n - [SDK 模型推理](#sdk-模型推理)\n- [模型支持列表](#模型支持列表)\n\n## 安装\n\n请参考[此处](../get_started/install.md)安装 mmagic。然后，按照[说明](https://mmdeploy.readthedocs.io/zh_CN/latest/get_started.html#mmdeploy)安装 mmdeploy。\n\n```{note}\n如果安装的是 mmdeploy 预编译包，那么也请通过 'git clone https://github.com/open-mmlab/mmdeploy.git --depth=1' 下载 mmdeploy 源码。因为它包含了部署时要用到的配置文件\n```\n\n## 模型转换\n\n假设在安装步骤中，mmagic 和 mmdeploy 代码库在同级目录下，并且当前的工作目录为 mmagic 的根目录，那么以 [ESRGAN](../../../configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) 模型为例，你可以从[此处](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth)下载对应的 checkpoint，并使用以下代码将之转换为 onnx 模型：\n\n```python\nfrom mmdeploy.apis import torch2onnx\nfrom mmdeploy.backend.sdk.export_info import export2SDK\n\nimg = 'tests/data/image/face/000001.png'\nwork_dir = 'mmdeploy_models/mmagic/onnx'\nsave_file = 'end2end.onnx'\ndeploy_cfg = '../mmdeploy/configs/mmagic/super-resolution/super-resolution_onnxruntime_dynamic.py'\nmodel_cfg = 'configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py'\nmodel_checkpoint = 'esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth'\ndevice = 'cpu'\n\n# 1. convert model to onnx\ntorch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg,\n model_checkpoint, device)\n\n# 2. extract pipeline info for inference by MMDeploy SDK\nexport2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)\n```\n\n转换的关键之一是使用正确的配置文件。项目中已内置了各后端部署[配置文件](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmagic)。\n文件的命名模式是：\n\n```\n{task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py\n```\n\n其中：\n\n- **{task}:** mmagic 中的任务\n- **{backend}:** 推理后端名称。比如，onnxruntime、tensorrt、pplnn、ncnn、openvino、coreml 等等\n- **{precision}:** 推理精度。比如，fp16、int8。不填表示 fp32\n- **{static | dynamic}:** 动态、静态 shape\n- **{shape}:** 模型输入的 shape 或者 shape 范围\n\n在上例中，你也可以把 `ESRGAN` 转为其他后端模型。比如使用`super-resolution_tensorrt-fp16_dynamic-32x32-512x512.py`，把模型转为 tensorrt-fp16 模型。\n\n```{tip}\n当转 tensorrt 模型时, --device 需要被设置为 \"cuda\"\n```\n\n## 模型规范\n\n在使用转换后的模型进行推理之前，有必要了解转换结果的结构。 它存放在 `--work-dir` 指定的路路径下。\n\n上例中的`mmdeploy_models/mmagic/onnx`，结构如下：\n\n```\nmmdeploy_models/mmagic/onnx\n├── deploy.json\n├── detail.json\n├── end2end.onnx\n└── pipeline.json\n```\n\n重要的是：\n\n- **end2end.onnx**: 推理引擎文件。可用 ONNX Runtime 推理\n- ***xxx*.json**: mmdeploy SDK 推理所需的 meta 信息\n\n整个文件夹被定义为**mmdeploy SDK model**。换言之，**mmdeploy SDK model**既包括推理引擎，也包括推理 meta 信息。\n\n## 模型推理\n\n### 后端模型推理\n\n以上述模型转换后的 `end2end.onnx` 为例，你可以使用如下代码进行推理：\n\n```python\nfrom mmdeploy.apis.utils import build_task_processor\nfrom mmdeploy.utils import get_input_shape, load_config\nimport torch\n\ndeploy_cfg = '../mmdeploy/configs/mmagic/super-resolution/super-resolution_onnxruntime_dynamic.py'\nmodel_cfg = 'configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py'\ndevice = 'cpu'\nbackend_model = ['mmdeploy_models/mmagic/onnx/end2end.onnx']\nimage = 'tests/data/image/lq/baboon_x4.png'\n\n# read deploy_cfg and model_cfg\ndeploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)\n\n# build task and backend model\ntask_processor = build_task_processor(model_cfg, deploy_cfg, device)\nmodel = task_processor.build_backend_model(backend_model)\n\n# process input image\ninput_shape = get_input_shape(deploy_cfg)\nmodel_inputs, _ = task_processor.create_input(image, input_shape)\n\n# do model inference\nwith torch.no_grad():\n result = model.test_step(model_inputs)\n\n# visualize results\ntask_processor.visualize(\n image=image,\n model=model,\n result=result[0],\n window_name='visualize',\n output_file='output_restorer.bmp')\n```\n\n### SDK 模型推理\n\n你也可以参考如下代码，对 SDK model 进行推理：\n\n```python\nfrom mmdeploy_python import Restorer\nimport cv2\n\nimg = cv2.imread('tests/data/image/lq/baboon_x4.png')\n\n# create a predictor\nrestorer = Restorer(model_path='mmdeploy_models/mmagic/onnx', device_name='cpu', device_id=0)\n# perform inference\nresult = restorer(img)\n\n# visualize inference result\ncv2.imwrite('output_restorer.bmp', result)\n```\n\n除了python API，mmdeploy SDK 还提供了诸如 C、C++、C#、Java等多语言接口。\n你可以参考[样例](https://github.com/open-mmlab/mmdeploy/tree/main/demo)学习其他语言接口的使用方法。\n\n## 模型支持列表\n\n请参考[这里](https://mmdeploy.readthedocs.io/zh_CN/latest/04-supported-codebases/mmagic.html#id7)\n" }, { "path": "docs/zh_cn/user_guides/index.rst", "content": ".. toctree::\n :maxdepth: 3\n\n config.md\n\n.. toctree::\n :maxdepth: 3\n\n datasets/dataset_prepare.md\n\n.. toctree::\n :maxdepth: 3\n\n inference.md\n train_test.md\n useful_tools.md\n visualization.md\n" }, { "path": "docs/zh_cn/user_guides/inference.md", "content": "# 教程3：使用预训练模型推理\n\nMMagic 提供了高级API，让您可以轻松地在自己的图像或视频上使用最先进的模型进行操作。\n在新的API中，仅需两行代码即可进行推理。\n\n```python\nfrom mmagic.apis import MMagicInferencer\n\n# 创建MMagicInferencer实例\neditor = MMagicInferencer('pix2pix')\n# 推理图片.需要输入图片路径与输出图片路径\nresults = editor.infer(img='../resources/input/translation/gt_mask_0.png', result_out_dir='../resources/output/translation/tutorial_translation_pix2pix_res.jpg')\n```\n\nMMagic支持各种基础生成模型，包括无条件生成对抗网络（GANs）、条件GANs、扩散模型等。\nMMagic 同样支持多种应用，包括：文生图、图生图的转换、3D感知生成、图像超分、视频超分、视频帧插值、图像修补、图像抠图、图像恢复、图像上色、图像生成等。\n在本节中，我们将详细说明如何使用我们预训练的模型进行操作。\n\n- 教程3: 使用预训练模型推理\n - [准备一些图片或者视频用于推理](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Prepare-some-images-or-videos-for-inference)\n - 生成模型\n - [无条件生成对抗网络 (GANs)]()\n - [条件生成对抗网络(GANs)]()\n - [扩散模型](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Diffusion-Models)\n - 应用\n - [文生图](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Text-to-Image)\n - [图生图的转换](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-to-image-translation)\n - [3D感知生成](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#3D-aware-generation)\n - [图像超分](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-super-resolution)\n - [视频超分](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Video-super-resolution)\n - [视频帧插值](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/Video-frame-interpolation)\n - [图像修补](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-inpainting)\n - [图像抠图](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-matting)\n - [图像恢复](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-restoration)\n - [图像上色](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Image-colorization)\n- [以前的版本](https://github.com/open-mmlab/mmagic/blob/main/docs/zh_cn/user_guides/inference.md#Previous-Versions)\n\n## 准备一些图片或者视频用于推理\n\n请参考我们的[教程](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_tutorial.ipynb)获取详细信息。\n\n## 生成模型\n\n### 无条件生成对抗网络（GANs）\n\nMMagic提供了用于使用无条件GANs进行图像采样的高级API。无条件GAN模型不需要输入，并输出一张图像。我们以'styleganv1'为例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# 创建MMagicInferencer实例，并进行推理\nresult_out_dir = './resources/output/unconditional/tutorial_unconditional_styleganv1_res.png'\neditor = MMagicInferencer('styleganv1')\nresults = editor.infer(result_out_dir=result_out_dir)\n```\n\n确实，我们已经为用户提供了一个更友好的演示脚本。您可以使用以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)：\n\n```python\npython demo/mmagic_inference_demo.py \\\n --model-name styleganv1 \\\n --result-out-dir demo_unconditional_styleganv1_res.jpg\n```\n\n### 条件生成对抗网络(GANs)\n\nMMagic提供了使用条件GAN进行图像采样的高级API。条件GAN模型接受一个标签作为输入，并输出一张图像。我们以'biggan'为例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# 创建MMagicInferencer实例，并进行推理\nresult_out_dir = './resources/output/conditional/tutorial_conditinal_biggan_res.jpg'\neditor = MMagicInferencer('biggan', model_setting=1)\nresults = editor.infer(label=1, result_out_dir=result_out_dir)\n```\n\n我们已经为用户提供了一个更友好的演示脚本。您可以使用以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)：\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name biggan \\\n --model-setting 1 \\\n --label 1 \\\n --result-out-dir demo_conditional_biggan_res.jpg\n```\n\n### 扩散模型\n\nMMagic提供了使用扩散模型进行图像采样的高级API。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# 创建MMagicInferencer实例，并进行推理\neditor = MMagicInferencer(model_name='stable_diffusion')\ntext_prompts = 'A panda is having dinner at KFC'\nresult_out_dir = './resources/output/text2image/tutorial_text2image_sd_res.png'\neditor.infer(text=text_prompts, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name stable_diffusion \\\n --text \"A panda is having dinner at KFC\" \\\n --result-out-dir demo_text2image_stable_diffusion_res.png\n```\n\n## 应用\n\n### 文生图\n\n文生图模型将文本作为输入，输出一张图片。我们以'controlnet-canny'为例。\n\n```python\nimport cv2\nimport numpy as np\nimport mmcv\nfrom mmengine import Config\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\ncfg = Config.fromfile('configs/controlnet/controlnet-canny.py')\ncontrolnet = MODELS.build(cfg.model).cuda()\n\ncontrol_url = 'https://user-images.githubusercontent.com/28132635/230288866-99603172-04cb-47b3-8adb-d1aa532d1d2c.jpg'\ncontrol_img = mmcv.imread(control_url)\ncontrol = cv2.Canny(control_img, 100, 200)\ncontrol = control[:, :, None]\ncontrol = np.concatenate([control] * 3, axis=2)\ncontrol = Image.fromarray(control)\n\nprompt = 'Room with blue walls and a yellow ceiling.'\n\noutput_dict = controlnet.infer(prompt, control=control)\nsamples = output_dict['samples']\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name controlnet \\\n --model-setting 1 \\\n --text \"Room with blue walls and a yellow ceiling.\" \\\n --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \\\n --result-out-dir demo_text2image_controlnet_canny_res.png\n```\n\n### 图生图的转换\n\nMMagic提供了使用图像翻译模型进行图像翻译的高级API。下面是构建Pix2Pix并获取翻译图像的示例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\neditor = MMagicInferencer('pix2pix')\nresults = editor.infer(img=img_path, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name pix2pix \\\n --img ${IMAGE_PATH} \\\n --result-out-dir ${SAVE_PATH}\n```\n\n### 3D感知生成\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nresult_out_dir = './resources/output/eg3d-output'\neditor = MMagicInferencer('eg3d')\nresults = editor.infer(result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name eg3d \\\n --result-out-dir ./resources/output/eg3d-output\n```\n\n### 图像超分\n\n图像超分辨率模型接受一张图像作为输入，并输出一张高分辨率图像。我们以 'esrgan' 为例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = './resources/input/restoration/0901x2.png'\nresult_out_dir = './resources/output/restoration/tutorial_restoration_esrgan_res.png'\neditor = MMagicInferencer('esrgan')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name esrgan \\\n --img ${IMAGE_PATH} \\\n --result-out-dir ${SAVE_PATH}\n```\n\n### 视频超分\n\n```python\nimport os\nfrom mmagic.apis import MMagicInferencer\nfrom mmengine import mkdir_or_exist\n\n# Create a MMagicInferencer instance and infer\nvideo = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'\nresult_out_dir = './resources/output/video_super_resolution/tutorial_video_super_resolution_basicvsr_res.mp4'\nmkdir_or_exist(os.path.dirname(result_out_dir))\neditor = MMagicInferencer('basicvsr')\nresults = editor.infer(video=video, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name basicvsr \\\n --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \\\n --result-out-dir ./resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4\n```\n\n### 视频帧插值\n\n视频插值模型接受一个视频作为输入，并输出一个插值后的视频。我们以 'flavr' 为例。\n\n```python\nimport os\nfrom mmagic.apis import MMagicInferencer\nfrom mmengine import mkdir_or_exist\n\n# Create a MMagicInferencer instance and infer\nvideo = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'\nresult_out_dir = './resources/output/video_interpolation/tutorial_video_interpolation_flavr_res.mp4'\nmkdir_or_exist(os.path.dirname(result_out_dir))\neditor = MMagicInferencer('flavr')\nresults = editor.infer(video=video, result_out_dir=result_out_dir)\n```\n\n### 图像修补\n\n修复模型接受一对屏蔽图像和屏蔽蒙版作为输入，并输出一个修复后的图像。我们以 'global_local' 为例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\nimg = './resources/input/matting/GT05.jpg'\ntrimap = './resources/input/matting/GT05_trimap.jpg'\n\n# 创建MMagicInferencer实例，并进行推理\nresult_out_dir = './resources/output/matting/tutorial_matting_gca_res.png'\neditor = MMagicInferencer('gca')\nresults = editor.infer(img=img, trimap=trimap, result_out_dir=result_out_dir)\n```\n\n### 图像抠图\n\n**抠图**模型接受一对图像和修剪映射作为输入，并输出一个 alpha 图像。我们以 'gca' 为例。\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\nimg = './resources/input/matting/GT05.jpg'\ntrimap = './resources/input/matting/GT05_trimap.jpg'\n\n# 创建MMagicInferencer实例，并进行推理\nresult_out_dir = './resources/output/matting/tutorial_matting_gca_res.png'\neditor = MMagicInferencer('gca')\nresults = editor.infer(img=img, trimap=trimap, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name gca \\\n --img ./resources/input/matting/GT05.jpg \\\n --trimap ./resources/input/matting/GT05_trimap.jpg \\\n --result-out-dir ./resources/output/matting/demo_matting_gca_res.png\n```\n\n### 图像恢复\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = './resources/input/restoration/0901x2.png'\nresult_out_dir = './resources/output/restoration/tutorial_restoration_nafnet_res.png'\neditor = MMagicInferencer('nafnet')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name nafnet \\\n --img ./resources/input/restoration/0901x2.png \\\n --result-out-dir ./resources/output/restoration/demo_restoration_nafnet_res.png\n```\n\n### 图像上色\n\n```python\nimport mmcv\nimport matplotlib.pyplot as plt\nfrom mmagic.apis import MMagicInferencer\n\n# Create a MMagicInferencer instance and infer\nimg = 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg'\nresult_out_dir = './resources/output/colorization/tutorial_colorization_res.png'\neditor = MMagicInferencer('inst_colorization')\nresults = editor.infer(img=img, result_out_dir=result_out_dir)\n```\n\n通过以下命令使用[demo/mmagic_inference_demo.py](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_demo.py)\n\n```shell\npython demo/mmagic_inference_demo.py \\\n --model-name inst_colorization \\\n --img https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg \\\n --result-out-dir demo_colorization_res.png\n```\n\n## 以前的版本\n\n如果您想使用已弃用的演示，请使用[MMagic v1.0.0rc7](https://github.com/open-mmlab/mmagic/tree/v1.0.0rc7)并参考[旧教程](https://github.com/open-mmlab/mmagic/blob/v1.0.0rc7/docs/en/user_guides/inference.md)。\n" }, { "path": "docs/zh_cn/user_guides/metrics.md", "content": "# 教程 5：使用评价指标\n\nMMagic支持**17个指标**以评估模型质量。\n\n有关用法，请参阅[MMagic中的训练与测试](../user_guides/train_test.md)。\n\n在这里，我们将逐个介绍不同指标的详细信息。\n\n本文的结构如下:\n\n01. [MAE](#mae)\n02. [MSE](#mse)\n03. [PSNR](#psnr)\n04. [SNR](#snr)\n05. [SSIM](#ssim)\n06. [NIQE](#niqe)\n07. [SAD](#sad)\n08. [MattingMSE](#mattingmse)\n09. [GradientError](#gradienterror)\n10. [ConnectivityError](#connectivityerror)\n11. [FID and TransFID](#fid-and-transfid)\n12. [IS and TransIS](#is-and-transis)\n13. [Precision and Recall](#precision-and-recall)\n14. [PPL](#ppl)\n15. [SWD](#swd)\n16. [MS-SSIM](#ms-ssim)\n17. [Equivarience](#equivarience)\n\n## MAE\n\nMAE是图像的平均绝对误差。\n要使用MAE进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='MAE'),\n]\n```\n\n## MSE\n\nMSE是图像的均方误差。\n要使用MSE进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='MSE'),\n]\n```\n\n## PSNR\n\nPSNR是峰值信噪比。我们的实现方法来自https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio。\n要使用PSNR进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='PSNR'),\n]\n```\n\n## SNR\n\nSNR是信噪比。我们的实现方法来自 https://en.wikipedia.org/wiki/Signal-to-noise_ratio。\n要使用SNR进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='SNR'),\n]\n```\n\n## SSIM\n\nSSIM是图像的结构相似度，在[图像质量评估:从错误可见性到结构相似度](https://live.ece.utexas.edu/publications/2004/zwang_ssim_ieeeip2004.pdf)中提出。我们实现的结果与https://ece.uwaterloo.ca/~z70wang/research/ssim/官方发布的MATLAB代码相同。\n要使用SSIM进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='SSIM'),\n]\n```\n\n## NIQE\n\nNIQE是自然图像质量评估指标，在[制作'完全盲'图像质量分析仪](http://www.live.ece.utexas.edu/publications/2013/mittal2013.pdf)中提出。我们的实现可以产生几乎与官方MATLAB代码相同的结果:http://live.ece.utexas.edu/research/quality/niqe_release.zip。\n要使用NIQE进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='NIQE'),\n]\n```\n\n## SAD\n\nSAD是图像抠图的绝对误差和。该指标计算每个像素的绝对差和所有像素的总和。\n要使用SAD进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='SAD'),\n]\n```\n\n## MattingMSE\n\nMattingMSE是图像抠图的均方误差。\n要使用MattingMSE进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='MattingMSE'),\n]\n```\n\n## GradientError\n\nGradientError是用于评估alpha matte预测的梯度误差。\n要使用GradientError进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='GradientError'),\n]\n```\n\n## ConnectivityError\n\nConnectivityError是用于评估alpha matte预测的连通性误差。\n要使用ConnectivityError进行评估，请在配置文件中添加以下配置:\n\n```python\nval_evaluator = [\n dict(type='ConnectivityError'),\n]\n```\n\n## FID 和 TransFID\n\nFréchet初始距离是两个图像数据集之间相似度的度量。它被证明与人类对视觉质量的判断有很好的相关性，最常用于评估生成对抗网络样本的质量。FID是通过计算两个高斯函数之间的Fréchet距离来计算的，这些高斯函数适合于Inception网络的特征表示。\n\n在`MMagic`中，我们提供了两个版本的FID计算。一个是常用的PyTorch版本，另一个用于StyleGAN。同时，我们在StyleGAN2-FFHQ1024模型中比较了这两种实现之间的差异(详细信息可以在这里找到\\[https://github.com/open-mmlab/mmagic/blob/main/configs/styleganv2/README.md\\])。幸运的是，最终结果只是略有不同。因此，我们建议用户采用更方便的PyTorch版本。\n\n**关于PyTorch版本和Tero版本:** 常用的PyTorch版本采用修改后的InceptionV3网络提取真假图像特征。然而，Tero的FID需要Tensorflow InceptionV3的[脚本模块](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt)。注意，应用此脚本模块需要' PyTorch >= 1.6.0 '。\n\n**关于提取真实的初始数据:** 为了方便用户，在测试时自动提取真实的特征并保存在本地，存储的特征在下次测试时自动读取。具体来说，我们将根据用于计算实际特性的参数计算一个哈希值，并使用哈希值来标记特性文件，在测试时，如果' inception_pkl '没有设置，我们将在' MMAGIC_CACHE_DIR ' (~/.cache/openmmlab/mmagic/)中寻找该特性。如果未找到缓存的初始pkl，则将执行提取。\n\n要使用FID指标，请在配置文件中添加以下配置:\n\n```python\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n```\n\n如果您在一台新机器上工作，那么您可以复制'MMAGIC_CACHE_DIR'中的'pkl'文件，将它们复制到新机器并设置'inception_pkl'字段。\n\n```python\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n inception_pkl=\n 'work_dirs/inception_pkl/inception_state-capture_mean_cov-full-33ad4546f8c9152e4b3bdb1b0c08dbaf.pkl', # copied from old machine\n sample_model='ema')\n]\n```\n\n'TransFID'与'FID'的用法相同，但TransFID是为'Pix2Pix'和'CycleGAN'等翻译模型设计的，适用于我们的评估器。更多信息您可以参考[evaluation](../user_guides/train_test.md)。\n\n## IS 和 TransIS\n\nInception评分是评估生成图像质量的客观指标，在[改进的训练GANs技术](https://arxiv.org/pdf/1606.03498.pdf)中提出。它使用一个InceptionV3模型来预测生成的图像的类别，并假设: 1)如果图像质量高，它将被归类到特定的类别。2)如果图像具有较高的多样性，则图像的类别范围将很广。因此，条件概率和边际概率的kl -散度可以指示生成图像的质量和多样性。您可以在'metrics.py'中看到完整的实现，它指向https://github.com/sbarratt/inception-score-pytorch/blob/master/inception_score.py。\n如果您想使用'IS'指标评估模型，请在配置文件中添加以下配置:\n\n```python\n# at the end of the configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py\nmetrics = [\n xxx,\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n```\n\n需要注意的是，Inception V3的选择和图像大小的调整方法会显著影响最终的IS评分。因此，我们强烈建议用户可以下载[Tero's script model of Inception V3](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt)(加载此脚本模型需要torch >= 1.6)，并使用'Bicubic'插值与'Pillow'后端。\n\n对应于config，您可以设置'resize_method'和'use_pillow_resize'用于图像大小的调整。您也可以将'inception_style'设置为'StyleGAN'用于推荐的tero的初始模型，或'PyTorch'用于torchvision的实现。对于没有互联网的环境，您可以下载初始的权重，并将'inception_path'设置为您的初始模型。\n\n我们还调查了数据加载管线和预训练的Inception V3版本对IS结果的影响。所有IS都在同一组图像上进行评估，这些图像是从ImageNet数据集中随机选择的。\n\n
显示对比结果 \n\n| Code Base | Inception V3 Version | Data Loader Backend | Resize Interpolation Method | IS |\n| :-------------------------------------------------------------: | :------------------: | :-----------------: | :-------------------------: | :-------------------: |\n| [OpenAI (baseline)](https://github.com/openai/improved-gan) | Tensorflow | Pillow | Pillow Bicubic | **312.255 +/- 4.970** |\n| [StyleGAN-Ada](https://github.com/NVlabs/stylegan2-ada-pytorch) | Tero's Script Model | Pillow | Pillow Bicubic | 311.895 +/ 4.844 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | cv2 Bilinear | 322.932 +/- 2.317 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | cv2 Bicubic | 324.604 +/- 5.157 |\n| mmagic (Ours) | Pytorch Pretrained | cv2 | Pillow Bicubic | 318.161 +/- 5.330 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | Pillow Bilinear | 313.126 +/- 5.449 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | cv2 Bilinear | 318.021+/-3.864 |\n| mmagic (Ours) | Pytorch Pretrained | Pillow | Pillow Bicubic | 317.997 +/- 5.350 |\n| mmagic (Ours) | Tero's Script Model | cv2 | cv2 Bilinear | 318.879 +/- 2.433 |\n| mmagic (Ours) | Tero's Script Model | cv2 | cv2 Bicubic | 316.125 +/- 5.718 |\n| mmagic (Ours) | Tero's Script Model | cv2 | Pillow Bicubic | **312.045 +/- 5.440** |\n| mmagic (Ours) | Tero's Script Model | Pillow | Pillow Bilinear | 308.645 +/- 5.374 |\n| mmagic (Ours) | Tero's Script Model | Pillow | Pillow Bicubic | 311.733 +/- 5.375 |\n\n
\n\n'TransIS'与'IS'的用法相同，但TransIS是为'Pix2Pix'和'CycleGAN'这样的翻译模型设计的，这是为我们的评估器改编的。更多信息可参考[evaluation](../user_guides/train_test.md)。\n\n## Precision and Recall\n\n我们的'Precision and Recall'实现遵循StyleGAN2中使用的版本。在该度量中，采用VGG网络对图像进行特征提取。不幸的是，我们还没有发现PyTorch VGG实现与StyleGAN2中使用的Tero版本产生类似的结果。(关于差异，请参阅这个[文件](https://github.com/open-mmlab/mmagicing/blob/main/configs/styleganv2/README.md)。)因此，在我们的实现中，我们默认采用[Teor's VGG](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt)网络。需要注意的是，应用这个脚本模块需要'PyTorch >= 1.6.0'。如果使用较低的PyTorch版本，我们将使用PyTorch官方VGG网络进行特征提取。\n要使用' P&R '进行评估，请在配置文件中添加以下配置:\n\n```python\nmetrics = [\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K')\n]\n```\n\n## PPL\n\n当在两个随机输入之间进行插值时，感知路径长度测量连续图像（其VGG16嵌入）之间的差异。剧烈的变化意味着多个特征一起发生了变化，它们可能会叠加在一起。通过实验表明，较小的PPL分数表明整体图像质量较高。\n作为该指标的基础，我们使用基于感知的成对图像距离，该距离被计算为两个VGG16嵌入之间的加权差，其中权重被拟合，从而评价指标与人类的感知相似性判断一致。\n如果我们将潜在空间插值路径细分为线性段，我们可以将该分段路径的总感知长度定义为每个段上感知差异的总和，并且感知路径长度的自然定义将是无限细分下的总和的极限，但在实践中，我们使用一个小的细分`` $`\\epsilon=10^｛-4｝`$ ``来近似它。\n因此，潜在`space`Z中所有可能端点的平均感知路径长度为\n\n`` $$`L_Z = E[\\frac{1}{\\epsilon^2}d(G(slerp(z_1,z_2;t))), G(slerp(z_1,z_2;t+\\epsilon)))]`$$ ``\n\n以类似的方式计算潜在 `space` W中的平均感知路径长度：:\n\n`` $$`L_Z = E[\\frac{1}{\\epsilon^2}d(G(slerp(z_1,z_2;t))), G(slerp(z_1,z_2;t+\\epsilon)))]`$$ ``\n\n当 `` $`z_1, z_2 \\sim P(z)`$ ``， 如果我们设置 `sampling` 为 ` full`， 则 `` $` t \\sim U(0,1)`$ ``， 如果设置 `sampling` 为 `end`，则`` $` t \\in \\{0,1\\}`$ ``。 `` $` G`$ `` 是生成器(i.e. `` $` g \\circ f`$ `` 用于style-based网络)， `` $` d(.,.)`$ `` 用于计算结果图像之间的感知距离。我们通过取100,000个样本来计算期望(在代码中将' num_images '设置为50,000)。\n\n您可以在'metrics.py'中找到完整的实现，参考https://github.com/rosinality/stylegan2-pytorch/blob/master/ppl.py。\n如果您想使用'PPL'指标评估模型，请在配置文件中添加以下配置:\n\n```python\n# at the end of the configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py\nmetrics = [\n xxx,\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n```\n\n## SWD\n\n切片Wasserstein距离是概率分布的差异度量，距离越小表示生成的图像越真实。我们获得每个图像的拉普拉斯金字塔，并从拉普拉斯金字塔中提取小块作为描述符，然后可以通过获取真实和伪描述符切片的Wasserstein距离来计算SWD。\n您可以在'metrics.py'中看到完整的实现，参考https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/sliced_wasserstein.py。\n如果您想使用'SWD'指标评估模型，请在配置文件中添加以下配置:\n\n```python\n# at the end of the configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\nmetrics = [\n dict(\n type='SWD',\n prefix='swd',\n fake_nums=16384,\n sample_model='orig',\n image_shape=(3, 64, 64))\n]\n```\n\n## MS-SSIM\n\n采用多尺度结构相似度来衡量两幅图像的相似度。我们在这里使用MS-SSIM来衡量生成图像的多样性，MS-SSIM得分低表示生成图像的多样性高。您可以在'metrics.py'中看到完整的实现，参考https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py。\n如果您想使用'MS-SSIM'指标评估模型，请在配置文件中添加以下配置:\n\n```python\n# at the end of the configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py\nmetrics = [\n dict(\n type='MS_SSIM', prefix='ms-ssim', fake_nums=10000,\n sample_model='orig')\n]\n```\n\n## Equivarience\n\n生成模型的等价性是指模型正变换和几何变换的互换性。目前这个指标只针对StyleGANv3计算，您可以在'metrics.py'中看到完整的实现，参考https://github.com/NVlabs/stylegan3/blob/main/metrics/equivariance.py。\n如果您想使用'Equivarience'指标评估模型，请在配置文件中添加以下配置:\n\n```python\n# at the end of the configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py\nmetrics = [\n dict(\n type='Equivariance',\n fake_nums=50000,\n sample_mode='ema',\n prefix='EQ',\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))\n]\n```\n" }, { "path": "docs/zh_cn/user_guides/train_test.md", "content": "# 教程 4：在MMagic环境下训练与测试\n\n在该部分中，您将学到如何在MMagic环境下完成训练与测试\n\n我们提供如下教程：\n\n- [预先准备](#预先准备)\n- [在MMagic中测试模型](#在MMagic中测试模型)\n - [在单个GPU上测试](#在单个GPU上测试)\n - [在多个GPU上测试](#在多个GPU上测试)\n - [在Slurm上测试](#在Slurm上测试)\n - [使用特定指标进行测试](#使用特定指标进行测试)\n- [在MMagic中训练模型](#在MMagic中训练模型)\n - [在单个GPU上训练](#在单个GPU上训练)\n - [在多个GPU上训练](#在多个GPU上训练)\n - [在多个节点上训练](#在多个节点上训练)\n - [在Slurm上训练](#在Slurm上训练)\n - [使用特定的评估指标进行训练](#使用特定的评估指标进行训练)\n\n## 预先准备\n\n用户需要首先 [准备数据集](../user_guides/dataset_prepare.md) 从而能够在MMagic环境中训练和测试。\n\n## 在MMagic中测试模型\n\n### 在单个GPU上测试\n\n您可以通过如下命令使用单个GPU来测试预训练模型。\n\n```shell\npython tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}\n```\n\n例如：\n\n```shell\npython tools/test.py configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\n### 在多个GPU上测试\n\nMMagic支持使用多个GPU测试，能够极大地节约模型测试时间。\n可以通过如下命令使用多个GPU来测试预训练模型。\n\n```shell\n./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}\n```\n\n例如：\n\n```shell\n./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\n### 在Slurm上测试\n\n如果您在由 [slurm](https://slurm.schedmd.com/) 管理的集群上运行MMagic，可以使用脚本`slurm_test.sh`。（此脚本还支持单机测试。）\n\n```shell\n[GPUS=${GPUS}] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE}\n```\n\n下面是一个使用8个GPU在“dev”分区上测试一个示例模型的例子，作业名称为“test”。\n\n```shell\nGPUS=8 ./tools/slurm_test.sh dev test configs/example_config.py work_dirs/example_exp/example_model_20200202.pth\n```\n\n您可以检查 [slurm_test.sh](../../../tools/slurm_test.sh) 以获取完整的参数和环境变量。\n\n### 使用特定指标进行测试\n\nMMagic 提供各种评**估值指标**，例如：MS-SSIM、SWD、IS、FID、Precision&Recall、PPL、Equivarience、TransFID、TransIS等。\n我们在[tools/test.py](https://github.com/open-mmlab/mmagic/tree/main/tools/test.py)中为所有模型提供了统一的评估脚本。\n如果用户想用一些指标来评估他们的模型，你可以像这样将 `metrics` 添加到你的配置文件中:\n\n```python\n# 在文件 configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py 的末尾\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),\n dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')\n]\n```\n\n如上所述, `metrics` 由多个指标字典组成。 每个指标包含 `type` 来表示其类别。 `fake_nums` 表示模型生成的图像数量。\n有些指标会输出一个结果字典，您也可以设置 `prefix` 来指定结果的前缀。\n如果将FID的前缀设置为 `FID-Full-50k`，则输出的示例可能是\n\n```bash\nFID-Full-50k/fid: 3.6561 FID-Full-50k/mean: 0.4263 FID-Full-50k/cov: 3.2298\n```\n\n然后用户可以使用下面的命令测试模型:\n\n```shell\nbash tools/dist_test.sh ${CONFIG_FILE} ${CKPT_FILE}\n```\n\n如果您在 slurm 环境中，请使用如下命令切换到 [tools/slurm_test.sh](https://github.com/open-mmlab/mmagic/tree/main/tools/slurm_test.sh)：\n\n```shell\nsh slurm_test.sh ${PLATFORM} ${JOBNAME} ${CONFIG_FILE} ${CKPT_FILE}\n```\n\n## 在MMagic中训练模型\n\nMMagic支持多种训练方式:\n\n1. [在单个GPU上训练](#在单个GPU上训练)\n2. [在单个GPU上训练](#在单个GPU上训练)\n3. [在多个节点上训练](#在多个节点上训练)\n4. [在Slurm上训练](#在Slurm上训练)\n\nSpecifically, all outputs (log files and checkpoints) will be saved to the working directory,\nwhich is specified by `work_dir` in the config file.\n\n### 在单个GPU上训练\n\n```shell\nCUDA_VISIBLE=0 python tools/train.py configs/example_config.py --work-dir work_dirs/example\n```\n\n### 在多个节点上训练\n\n要在多台机器上启动分布式训练，这些机器可以通过IP访问，运行以下命令:\n\n在第一台机器上:\n\n```shell\nNNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR tools/dist_train.sh $CONFIG $GPUS\n```\n\n在第二台机器上:\n\n```shell\nNNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR tools/dist_train.sh $CONFIG $GPUS\n```\n\n为了提高网络通信速度，建议使用高速网络硬件，如Infiniband。\n请参考 [PyTorch docs](https://pytorch.org/docs/1.11/distributed.html#launch-utility) 以获取更多信息。\n\n### 在多个GPU上训练\n\n```shell\n./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n```\n\n### 在Slurm上训练\n\n如果您在由 [slurm](https://slurm.schedmd.com/) 管理的集群上运行MMagic，可以使用脚本`slurm_train.sh`。（此脚本还支持单机测试。）\n\n```shell\n[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}\n```\n\n下面是一个使用8个gpu在dev分区上训练inpainting模型的示例。\n\n```shell\nGPUS=8 ./tools/slurm_train.sh dev configs/inpainting/gl_places.py /nfs/xxxx/gl_places_256\n```\n\n你可以在 [slurm_train.sh](https://github.com/open-mmlab/mmagic/blob/master/tools/slurm_train.sh) 上查阅完整参数和环境变量。\n\n### 可选参数\n\n- `--amp`：此参数用于固定精度训练。\n- `--resume`：此参数用于在训练中止时自动恢复。\n\n## 使用特定的评估指标进行训练\n\n受益于 `mmengine`的 `Runner`，我们可以在训练过程中对模型进行简单的评估，如下所示。\n\n```python\n# 定义指标\nmetrics = [\n dict(\n type='FrechetInceptionDistance',\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN')\n]\n\n# 定义dataloader\nval_dataloader = dict(\n batch_size=128,\n num_workers=8,\n dataset=dict(\n type='BasicImageDataset',\n data_root='data/celeba-cropped/',\n pipeline=[\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(64, 64)),\n dict(type='PackInputs')\n ]),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\n# 定义 val interval\ntrain_cfg = dict(by_epoch=False, val_begin=1, val_interval=10000)\n\n# 定义 val loop 和 evaluator\nval_cfg = dict(type='MultiValLoop')\nval_evaluator = dict(type='Evaluator', metrics=metrics)\n```\n\n可以设置 `val_begin` 和 `val_interval` 来调整何时开始验证和验证间隔。\n\n有关指标的详细信息，请参考 [metrics' guide](./metrics.md).\n" }, { "path": "docs/zh_cn/user_guides/useful_tools.md", "content": "# 教程 7：实用工具（待更新）\n\n我们在 `tools/` 目录下提供了很多有用的工具。\n\n### 获取 FLOP 和参数量（实验性）\n\n我们提供了一个改编自 [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) 的脚本来计算模型的 FLOP 和参数量。\n\n```shell\npython tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]\n```\n\n例如，\n\n```shell\npython tools/get_flops.py configs/resotorer/srresnet.py --shape 40 40\n```\n\n你会得到以下的结果。\n\n```\n==============================\nInput shape: (3, 40, 40)\nFlops: 4.07 GMac\nParams: 1.52 M\n==============================\n```\n\n**注**：此工具仍处于实验阶段，我们不保证数字正确。 您可以将结果用于简单的比较，但在技术报告或论文中采用它之前，请仔细检查它。\n\n(1) FLOPs 与输入形状有关，而参数量与输入形状无关。默认输入形状为 (1, 3, 250, 250)。\n(2) 一些运算符不计入 FLOP，如 GN 和自定义运算符。\n你可以通过修改 [`mmcv/cnn/utils/flops_counter.py`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) 来添加对新运算符的支持。\n\n### 发布模型\n\n在将模型上传到 AWS 之前，您可能需要\n(1) 将模型权重转换为 CPU tensors, (2) 删除优化器状态，和\n(3) 计算模型权重文件的哈希并将哈希 ID 附加到文件名。\n\n```shell\npython tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}\n```\n\n例如，\n\n```shell\npython tools/publish_model.py work_dirs/example_exp/latest.pth example_model_20200202.pth\n```\n\n最终输出文件名将是 `example_model_20200202-{hash id}.pth`.\n\n### 转换为 ONNX（实验性）\n\n我们提供了一个脚本将模型转换为 [ONNX](https://github.com/onnx/onnx) 格式。 转换后的模型可以通过 [Netron](https://github.com/lutzroeder/netron) 等工具进行可视化。此外，我们还支持比较 Pytorch 和 ONNX 模型之间的输出结果。\n\n```bash\npython tools/pytorch2onnx.py\n ${CFG_PATH} \\\n ${CHECKPOINT_PATH} \\\n ${MODEL_TYPE} \\\n ${IMAGE_PATH} \\\n --trimap-path ${TRIMAP_PATH} \\\n --output-file ${OUTPUT_ONNX} \\\n --show \\\n --verify \\\n --dynamic-export\n```\n\n参数说明：\n\n- `config` : 模型配置文件的路径。\n- `checkpoint` : 模型模型权重文件的路径。\n- `model_type` : 配置文件的模型类型，选项： `inpainting`, `mattor`, `restorer`, `synthesizer`。\n- `image_path` : 输入图像文件的路径。\n- `--trimap-path` : 输入三元图文件的路径，用于 mattor 模型。\n- `--output-file`: 输出 ONNX 模型的路径。默认为 `tmp.onnx`。\n- `--opset-version` : ONNX opset 版本。默认为 11。\n- `--show`: 确定是否打印导出模型的架构。默认为 `False`。\n- `--verify`: 确定是否验证导出模型的正确性。默认为 `False`。\n- `--dynamic-export`: 确定是否导出具有动态输入和输出形状的 ONNX 模型。默认为 `False`。\n\n**注**：此工具仍处于试验阶段。目前不支持某些自定义运算符。我们现在只支持 `mattor` 和 `restorer`。\n\n#### 支持导出到 ONNX 的模型列表\n\n下表列出了保证可导出到 ONNX 并可在 ONNX Runtime 中运行的模型。\n\n| 模型 | 配置 | 动态形状 | 批量推理 | 备注 |\n| :------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------: | :------: | :--: |\n| ESRGAN | [esrgan_x4c64b23g32_g1_400k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py) | Y | Y | |\n| ESRGAN | [esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/esrgan/esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py) | Y | Y | |\n| SRCNN | [srcnn_x4k915_g1_1000k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py) | Y | Y | |\n| DIM | [dim_stage3_v16_pln_1x1_1000k_comp1k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/dim/dim_stage3_v16_pln_1x1_1000k_comp1k.py) | Y | Y | |\n| GCA | [gca_r34_4x10_200k_comp1k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/gca/gca_r34_4x10_200k_comp1k.py) | N | Y | |\n| IndexNet | [indexnet_mobv2_1x16_78k_comp1k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/indexnet/indexnet_mobv2_1x16_78k_comp1k.py) | Y | Y | |\n\n**注**：\n\n- *以上所有模型均使用 Pytorch==1.6.0 和 onnxruntime==1.5.1*\n- 如果您遇到上面列出的模型的任何问题，请创建一个 issue，我们会尽快处理。对于列表中未包含的型号，请尝试自行解决。\n- 由于此功能是实验性的并且可能会快速更改，请始终尝试使用最新的 `mmcv` 和 `mmagic`。\n\n### 将 ONNX 转换为 TensorRT（实验性）\n\n我们还提供了将 [ONNX](https://github.com/onnx/onnx) 模型转换为 [TensorRT](https://github.com/NVIDIA/TensorRT) 格式的脚本。 此外，我们支持比较 ONNX 和 TensorRT 模型之间的输出结果。\n\n```bash\npython tools/onnx2tensorrt.py\n ${CFG_PATH} \\\n ${MODEL_TYPE} \\\n ${IMAGE_PATH} \\\n ${INPUT_ONNX} \\\n --trt-file ${OUT_TENSORRT} \\\n --max-shape INT INT INT INT \\\n --min-shape INT INT INT INT \\\n --workspace-size INT \\\n --fp16 \\\n --show \\\n --verify \\\n --verbose\n```\n\n参数说明：\n\n- `config` : 模型配置文件的路径。\n- `model_type` :配置文件的模型类型，选项： `inpainting`, `mattor`, `restorer`, `synthesizer`。\n- `img_path` : 输入图像文件的路径。\n- `onnx_file` : 输入 ONNX 文件的路径。\n- `--trt-file` : 输出 TensorRT 模型的路径。默认为 `tmp.trt`。\n- `--max-shape` : 模型输入的最大形状。\n- `--min-shape` : 模型输入的最小形状。\n- `--workspace-size`: 以 GiB 为单位的最大工作空间大小。默认为 1 GiB。\n- `--fp16`: 确定是否以 fp16 模式导出 TensorRT。默认为 `False`。\n- `--show`: 确定是否显示 ONNX 和 TensorRT 的输出。默认为 `False`。\n- `--verify`: 确定是否验证导出模型的正确性。默认为 `False`。\n- `--verbose`: 确定在创建 TensorRT 引擎时是否详细记录日志消息。默认为 `False`。\n\n**注**：此工具仍处于试验阶段。 目前不支持某些自定义运算符。 我们现在只支持 `restorer`。 在生成 SRCNN 的 ONNX 文件时，将 SCRNN 模型中的 'bicubic' 替换为 'bilinear' \\[此处\\](https://github.com/open-mmlab/mmagic/blob/764e6065e315b7d0033762038fcbf0bb1c570d4d/mmagic.bones/modelsrnn py#L40）。 因为 TensorRT 目前不支持 bicubic 插值，最终性能将下降约 4%。\n\n#### 支持导出到 TensorRT 的模型列表\n\n下表列出了保证可导出到 TensorRT 引擎并可在 TensorRT 中运行的模型。\n\n| 模型 | 配置 | 动态形状 | 批量推理 | 备注 |\n| :----: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :------: | :------: | :-----------------------------------: |\n| ESRGAN | [esrgan_x4c64b23g32_g1_400k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py) | Y | Y | |\n| ESRGAN | [esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/esrgan/esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py) | Y | Y | |\n| SRCNN | [srcnn_x4k915_g1_1000k_div2k.py](https://github.com/open-mmlab/mmagic/blob/master/configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py) | Y | Y | 'bicubic' 上采样必须替换为 'bilinear' |\n\n**注**：\n\n- *以上所有模型均使用 Pytorch==1.8.1、onnxruntime==1.7.0 和 tensorrt==7.2.3.4 进行测试*\n- 如果您遇到上面列出的模型的任何问题，请创建一个问题，我们会尽快处理。 对于列表中未包含的型号，请尝试自行解决。\n- 由于此功能是实验性的并且可能会快速更改，因此请始终尝试使用最新的 `mmcv` 和 `mmagic`。\n\n### 评估 ONNX 和 TensorRT 模型（实验性）\n\n我们在 `tools/deploy_test.py` 中提供了评估 TensorRT 和 ONNX 模型的方法。\n\n#### 先决条件\n\n要评估 ONNX 和 TensorRT 模型，应先安装 onnx、onnxruntime 和 TensorRT。遵循 [mmcv 中的 ONNXRuntime](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) 和 \\[mmcv 中的 TensorRT 插件\\](https://github.com/open-mmlab/mmcv/blob/master/docs/tensorrt_plugin.md%EF%BC%89%E4%BD%BF%E7%94%A8 ONNXRuntime 自定义操作和 TensorRT 插件安装 `mmcv-full`。\n\n#### 用法\n\n```bash\npython tools/deploy_test.py \\\n ${CONFIG_FILE} \\\n ${MODEL_PATH} \\\n ${BACKEND} \\\n --out ${OUTPUT_FILE} \\\n --save-path ${SAVE_PATH} \\\n ----cfg-options ${CFG_OPTIONS} \\\n```\n\n#### 参数说明：\n\n- `config`: 模型配置文件的路径。\n- `model`: TensorRT 或 ONNX 模型文件的路径。\n- `backend`: 用于测试的后端，选择 tensorrt 或 onnxruntime。\n- `--out`: pickle 格式的输出结果文件的路径。\n- `--save-path`: 存储图像的路径，如果没有给出，则不会保存图像。\n- `--cfg-options`: 覆盖使用的配置文件中的一些设置，`xxx=yyy` 格式的键值对将被合并到配置文件中。\n\n#### 结果和模型\n\n\n\t\n\t \n\t \n\t \n\t \n\t \n\t \n \n \n\t\n \n\t \n\t \n\t \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ModelConfigDatasetMetricPyTorchONNX RuntimeTensorRT FP32TensorRT FP16
ESRGAN\n esrgan_x4c64b23g32_g1_400k_div2k.py\n Set5PSNR28.270028.261928.261928.2616
SSIM0.77780.77840.77840.7783
Set14PSNR24.632824.629024.629024.6274
SSIM0.64910.64940.64940.6494
DIV2KPSNR26.653126.653226.653226.6532
SSIM0.73400.73400.73400.7340
ESRGAN\n esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py\n Set5PSNR30.642830.630730.630730.6305
SSIM0.85590.85650.85650.8566
Set14PSNR27.054327.042227.042227.0411
SSIM0.74470.74500.74500.7449
DIV2KPSNR29.335429.335429.335429.3339
SSIM0.82630.82630.82630.8263
SRCNN\n srcnn_x4k915_g1_1000k_div2k.py\n Set5PSNR28.431628.412027.214427.2127
SSIM0.80990.81060.77820.7781
Set14PSNR25.648625.636724.861324.8599
SSIM0.70140.70150.66740.6673
DIV2KPSNR27.746027.746026.989126.9862
SSIM0.78540.785430.76050.7604
\n\n**注**：\n\n- 所有 ONNX 和 TensorRT 模型都使用数据集上的动态形状进行评估，图像根据原始配置文件进行预处理。\n- 此工具仍处于试验阶段，我们目前仅支持 `restorer`。\n" }, { "path": "docs/zh_cn/user_guides/visualization.md", "content": "# 教程 6：可视化\n\n图像的可视化是衡量图像处理、编辑和合成质量的重要手段。\n在配置文件中使用 `visualizer` 可以在训练或测试时保存可视化结果。您可以跟随[MMEngine文档](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/advanced_tutorials/visualization.md)学习可视化的用法。MMagic提供了一套丰富的可视化功能。\n在本教程中，我们将介绍MMagic提供的可视化函数的用法。\n\n- [教程6:可视化](#教程6:可视化)\n - [概述](#概述)\n - [GAN的可视化配置](#gan的可视化配置)\n - [图像翻译模型的可视化配置](#图像翻译模型的可视化配置)\n - [扩散模型的可视化配置](#扩散模型的可视化配置)\n - [图像补全模型的可视化配置](#图像补全模型的可视化配置)\n - [图像抠图模型的可视化配置](#图像抠图模型的可视化配置)\n - [SISR/VSR/VFI等模型的可视化配置](#sisrvsrvfi等模型的可视化配置)\n - [可视化钩子](#可视化钩子)\n - [可视化器](#可视化器)\n - [可视化后端](#可视化后端)\n - [在不同的存储后端可视化](#在不同的存储后端可视化)\n\n## 概述\n\n建议先学习 [设计文档](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/visualization.md) 里关于可视化的基本概念。\n\n在MMagic中，训练或测试过程的可视化需要配置三个组件:`VisualizationHook`、`Visualizer`和`VisBackend`, 如下图表展示了 `Visualizer` 和 `VisBackend` 的关系。\n\n
\n\n
\n\n**VisualizationHook** 在训练期间以固定的间隔获取模型输出的可视化结果，并将其传递给**Visualizer**。\n**Visualizer** 负责将原始可视化结果转换为所需的类型(png, gif等)，然后将其传输到**VisBackend**进行存储或显示。\n\n### GAN的可视化配置\n\n对于像`StyleGAN`和`SAGAN`这样的GAN模型，通常的配置如下所示:\n\n```python\n# 可视化钩子\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000, # 配置可视化勾子的间隔数\n fixed_input=True, # 是否固定噪声输入生成图像\n vis_kwargs_list=dict(type='GAN', name='fake_img') # 对于GAN模型预先定义可视化参数\n )\n]\n# 可视化后端\nvis_backends = [\n dict(type='VisBackend'), # 可视化后端用于存储。\n dict(type='WandbVisBackend', # 可以上传至Wandb的可视化后端\n init_kwargs=dict(\n project='MMagic', # Wandb项目名\n name='GAN-Visualization-Demo' # Wandb实验命名\n ))\n]\n# 可视化器\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\n如果您将指数移动平均(EMA)应用于生成器，并希望可视化EMA模型，您可以修改`VisualizationHook`的配置，如下所示:\n\n```python\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # 同时在`fake_img`中可视化ema以及orig\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img', # 使用`fake_img`保存图片\n sample_model='ema/orig', # 对于`NoiseSampler`特别定义参数\n target_keys=['ema.fake_img', 'orig.fake_img'] # 指定的可视化的键值\n ))\n]\n```\n\n### 图像翻译模型的可视化配置\n\n对于`CycleGAN`、`Pix2Pix`等翻译模型，可以形成如下可视化配置:\n\n```python\n# 可视化钩子\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=[\n dict(\n type='Translation', # 在训练集可视化结果\n name='trans'), # 保存`trans`字段的图像\n dict(\n type='Translationval', # 在验证集可视化结果\n name='trans_val'), # 保存`trans_val`字段的图像\n ])\n]\n# 可视化后端\nvis_backends = [\n dict(type='VisBackend'), # 可视化后端用于存储。\n dict(type='WandbVisBackend', # 可以上传至Wandb的可视化后端\n init_kwargs=dict(\n project='MMagic', # Wandb项目名\n name='Translation-Visualization-Demo' # Wandb实验命名\n ))\n]\n# 可视化器\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\n### 扩散模型的可视化配置\n\n对于扩散模型，例如`Improved-DDPM`，我们可以使用以下配置通过gif来可视化去噪过程:\n\n```python\n# 可视化钩子\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='DDPMDenoising')) # 对于DDPM模型预先定义可视化参数\n]\n# 可视化后端\nvis_backends = [\n dict(type='VisBackend'), # 可视化后端用于存储。\n dict(type='WandbVisBackend', # 可以上传至Wandb的可视化后端\n init_kwargs=dict(\n project='MMagic', # Wandb项目名\n name='Diffusion-Visualization-Demo' # Wandb实验命名\n ))\n]\n# 可视化器\nvisualizer = dict(type='Visualizer', vis_backends=vis_backends)\n```\n\n### 图像补全模型的可视化配置\n\n对于图像补全模型，如`AOT-GAN`和`Global&Local`，通常的配置如下所示:\n\n```python\n# 可视化后端\nvis_backends = [dict(type='LocalVisBackend')]\n# 可视化器\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\n# 可视化钩子\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n### 图像抠图模型的可视化配置\n\n对于`DIM`和`GCA`等图像抠图模型，通常的配置如下所示:\n\n```python\n# 可视化后端\nvis_backends = [dict(type='LocalVisBackend')]\n# 可视化器\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='trimap_path',\n img_keys=['pred_alpha', 'trimap', 'gt_merged', 'gt_alpha'],\n bgr2rgb=True)\n# 可视化钩子\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n### SISR/VSR/VFI等模型的可视化配置\n\n对于SISR/VSR/VFI等模型，如`EDSR`, `EDVR`和`CAIN`，通常的配置如下所示:\n\n```python\n# 可视化后端\nvis_backends = [dict(type='LocalVisBackend')]\n# 可视化器\nvisualizer = dict(\n type='ConcatImageVisualizer',\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=False)\n# 可视化钩子\ncustom_hooks = [dict(type='BasicVisualizationHook', interval=1)]\n```\n\n可视化钩子、可视化器和可视化后端组件的具体配置如下所述.\n\n## 可视化钩子\n\n在MMagic中，我们使用`BasicVisualizationHook`和`VisualizationHook`作为可视化钩子。\n可视化钩子支持以下三种情况。\n\n(1) 修改`vis_kwargs_list`，实现特定输入下模型输出的可视化，适用于特定数据输入下GAN生成结果和图像翻译模型的翻译结果的可视化等。下面是两个典型的例子:\n\n```python\n# input as dict\nvis_kwargs_list = dict(\n type='Noise', # 使用'Noise'采样生成模型输入\n name='fake_img', # 定义保存图像的命名\n)\n\n# input as list of dict\nvis_kwargs_list = [\n dict(type='Arguments', # 使用'Arguments'采样生成模型输入\n name='arg_output', # 定义保存图像的命名\n vis_mode='gif', # 通过gif来可视化\n forward_kwargs=dict(forward_mode='sampling', sample_kwargs=dict(show_pbar=True)) # 为'Arguments'采样定义参数\n ),\n dict(type='Data', # 在dataloader使用'Data'采样提供数据作为可视化输入\n n_samples=36, # 定义多少采样生成可视化结果\n fixed_input=False, # 定义对于可视化过程不固定输入\n )\n]\n```\n\n`vis_kwargs_list`接受字典或字典的列表作为输入。每个字典必须包含一个`type`字段，指示用于生成模型输入的采样器类型，并且每个字典还必须包含采样器所需的关键字字段(例如:`ArgumentSampler`要求参数字典包含`forward_kwargs`)。\n\n> 需要注意的是，此内容由相应的采样器检查，不受`BasicVisualizationHook`的限制。\n\n此外，其他字段是通用字段(例如:`n_samples`、`n_row`,`name`,`fixed_input`等等)。\n如果没有传入，则使用`BasicVisualizationHook`初始化的默认值。\n\n为了方便用户使用，MMagic为**GAN**、**Translation models**、**SinGAN**和**Diffusion models**预置了可视化参数，用户可以通过以下配置直接使用预定义的可视化方法:\n\n```python\nvis_kwargs_list = dict(type='GAN')\nvis_kwargs_list = dict(type='SinGAN')\nvis_kwargs_list = dict(type='Translation')\nvis_kwargs_list = dict(type='TranslationVal')\nvis_kwargs_list = dict(type='TranslationTest')\nvis_kwargs_list = dict(type='DDPMDenoising')\n```\n\n## 可视化器\n\n在MMagic中，我们实现了`ConcatImageVisualizer`和`Visualizer`，它们继承自`mmengine.Visualizer`。\n`Visualizer`的基类是`ManagerMixin`，这使得`Visualizer`成为一个全局唯一的对象。\n在实例化之后，`Visualizer`可以在代码的任何地方通过`Visualizer.get_current_instance()`调用，如下所示:\n\n```python\n# configs\nvis_backends = [dict(type='VisBackend')]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# `get_instance()` 是为全局唯一实例化调用\nVISUALIZERS.build(cfg.visualizer)\n\n# 通过上述代码实例化后，您可以在任何位置调用`get_current_instance`方法来获取可视化器\nvisualizer = Visualizer.get_current_instance()\n```\n\n`Visualizer`的核心接口是`add_datasample`。\n通过这个界面，该接口将根据相应的`vis_mode`调用相应的绘图函数，以获得`np.ndarray`类型的可视化结果。\n然后调用`show`或`add_image`来直接显示结果或将可视化结果传递给预定义的`vis_backend`。\n\n## 可视化后端\n\n- MMEngine的基本VisBackend包括`LocalVisBackend`、`TensorboardVisBackend`和`WandbVisBackend`。您可以关注[MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/advanced_tutorials/visualization.md)了解更多有关它们的信息。\n- `VisBackend`: **File System**的后端。将可视化结果保存到相应位置。\n- `TensorboardVisBackend`: **Tensorboard**的后端。将可视化结果发送到Tensorboard。\n- `WandbVisBackend`: **Wandb**的后端。将可视化结果发送到Tensorboard。\n\n一个`Visualizer`对象可以访问任意数量的`visbackend`，用户可以在代码中通过类名访问后端。\n\n```python\n# 配置文件\nvis_backends = [dict(type='Visualizer'), dict(type='WandbVisBackend')]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# 代码内\nVISUALIZERS.build(cfg.visualizer)\nvisualizer = Visualizer.get_current_instance()\n\n# 通过类名访问后端\ngen_vis_backend = visualizer.get_backend('VisBackend')\ngen_wandb_vis_backend = visualizer.get_backend('GenWandbVisBackend')\n```\n\n当有多个`VisBackend`具有相同的类名时，用户必须为每个`VisBackend`指定名称。\n\n```python\n# 配置文件\nvis_backends = [\n dict(type='VisBackend', name='gen_vis_backend_1'),\n dict(type='VisBackend', name='gen_vis_backend_2')\n]\nvisualizer = dict(\n type='Visualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n```python\n# 代码内\nVISUALIZERS.build(cfg.visualizer)\nvisualizer = Visualizer.get_current_instance()\n\nlocal_vis_backend_1 = visualizer.get_backend('gen_vis_backend_1')\nlocal_vis_backend_2 = visualizer.get_backend('gen_vis_backend_2')\n```\n\n### 在不同的存储后端可视化\n\n如果想用不同的存储后端( Wandb, Tensorboard, 或者远程窗口里常规的后端)，像以下这样改配置文件的 `vis_backends` 就行了：\n\n**Local**\n\n```python\nvis_backends = [dict(type='LocalVisBackend')]\n```\n\n**Tensorboard**\n\n```python\nvis_backends = [dict(type='TensorboardVisBackend')]\nvisualizer = dict(\n type='ConcatImageVisualizer', vis_backends=vis_backends, name='visualizer')\n```\n\n**Wandb**\n\n```python\nvis_backends = [dict(type='WandbVisBackend', init_kwargs=dict(project={PROJECTS}, name={EXPNAME}))]\nvisualizer = dict(\n type='ConcatImageVisualizer', vis_backends=vis_backends, name='visualizer')\n```\n" }, { "path": "mmagic/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmcv\nimport mmengine\n\nfrom .version import __version__, version_info\n\ntry:\n from mmcv.utils import digit_version\nexcept ImportError:\n\n def digit_version(version_str):\n digit_ver = []\n for x in version_str.split('.'):\n if x.isdigit():\n digit_ver.append(int(x))\n elif x.find('rc') != -1:\n patch_version = x.split('rc')\n digit_ver.append(int(patch_version[0]) - 1)\n digit_ver.append(int(patch_version[1]))\n return digit_ver\n\n\nMMCV_MIN = '2.0.0'\nMMCV_MAX = '2.2.0'\nmmcv_min_version = digit_version(MMCV_MIN)\nmmcv_max_version = digit_version(MMCV_MAX)\nmmcv_version = digit_version(mmcv.__version__)\n\nMMENGINE_MIN = '0.4.0'\nMMENGINE_MAX = '1.0.0'\nmmengine_min_version = digit_version(MMENGINE_MIN)\nmmengine_max_version = digit_version(MMENGINE_MAX)\nmmengine_version = digit_version(mmengine.__version__)\n\nassert (mmcv_min_version <= mmcv_version < mmcv_max_version), \\\n f'mmcv=={mmcv.__version__} is used but incompatible. ' \\\n f'Please install mmcv-full>={mmcv_min_version}, <{mmcv_max_version}.'\n\nassert (mmengine_min_version <= mmengine_version < mmengine_max_version), \\\n f'mmengine=={mmengine.__version__} is used but incompatible. ' \\\n f'Please install mmengine>={mmengine_min_version}, ' \\\n f'<{mmengine_max_version}.'\n\n__all__ = ['__version__', 'version_info']\n" }, { "path": "mmagic/apis/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .inferencers.inference_functions import init_model\nfrom .mmagic_inferencer import MMagicInferencer\n\n__all__ = ['MMagicInferencer', 'init_model']\n" }, { "path": "mmagic/apis/inferencers/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Optional, Union\n\nimport torch\n\nfrom mmagic.utils import ConfigType\nfrom .colorization_inferencer import ColorizationInferencer\nfrom .conditional_inferencer import ConditionalInferencer\nfrom .controlnet_animation_inferencer import ControlnetAnimationInferencer\nfrom .diffusers_pipeline_inferencer import DiffusersPipelineInferencer\nfrom .eg3d_inferencer import EG3DInferencer\nfrom .image_super_resolution_inferencer import ImageSuperResolutionInferencer\nfrom .inpainting_inferencer import InpaintingInferencer\nfrom .matting_inferencer import MattingInferencer\nfrom .text2image_inferencer import Text2ImageInferencer\nfrom .translation_inferencer import TranslationInferencer\nfrom .unconditional_inferencer import UnconditionalInferencer\nfrom .video_interpolation_inferencer import VideoInterpolationInferencer\nfrom .video_restoration_inferencer import VideoRestorationInferencer\n\n__all__ = [\n 'ColorizationInferencer', 'ConditionalInferencer', 'EG3DInferencer',\n 'InpaintingInferencer', 'MattingInferencer',\n 'ImageSuperResolutionInferencer', 'Text2ImageInferencer',\n 'TranslationInferencer', 'UnconditionalInferencer',\n 'VideoInterpolationInferencer', 'VideoRestorationInferencer',\n 'ControlnetAnimationInferencer', 'DiffusersPipelineInferencer'\n]\n\n\nclass Inferencers:\n \"\"\"Class to assign task to different inferencers.\n\n Args:\n task (str): Inferencer task.\n config (str or ConfigType): Model config or the path to it.\n ckpt (str, optional): Path to the checkpoint.\n device (str, optional): Device to run inference. If None, the best\n device will be automatically used.\n seed (int): The random seed used in inference. Defaults to 2022.\n \"\"\"\n\n def __init__(self,\n task: Optional[str] = None,\n config: Optional[Union[ConfigType, str]] = None,\n ckpt: Optional[str] = None,\n device: torch.device = None,\n extra_parameters: Optional[Dict] = None,\n seed: int = 2022) -> None:\n self.task = task\n if self.task in ['conditional', 'Conditional GANs']:\n self.inferencer = ConditionalInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['colorization', 'Colorization']:\n self.inferencer = ColorizationInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['unconditional', 'Unconditional GANs', 'DragGAN']:\n self.inferencer = UnconditionalInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['matting', 'Matting']:\n self.inferencer = MattingInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['inpainting', 'Inpainting']:\n self.inferencer = InpaintingInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['translation', 'Image2Image']:\n self.inferencer = TranslationInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['Image super-resolution', 'Image Super-Resolution']:\n self.inferencer = ImageSuperResolutionInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['video_restoration', 'Video Super-Resolution']:\n self.inferencer = VideoRestorationInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['video_interpolation', 'Video Interpolation']:\n self.inferencer = VideoInterpolationInferencer(\n config, ckpt, device, extra_parameters)\n elif self.task in [\n 'text2image', 'Text2Image', 'Text2Image, Image2Image'\n ]:\n self.inferencer = Text2ImageInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['3D_aware_generation', '3D-aware Generation']:\n self.inferencer = EG3DInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['controlnet_animation']:\n self.inferencer = ControlnetAnimationInferencer(config)\n elif self.task in [\n 'Image Restoration', 'Denoising, Deblurring, Deraining',\n 'Image Super-Resolution, Image denoising, JPEG compression '\n 'artifact reduction', 'Deblurring'\n ]:\n self.inferencer = ImageSuperResolutionInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n elif self.task in ['Diffusers Pipeline']:\n self.inferencer = DiffusersPipelineInferencer(\n config, ckpt, device, extra_parameters, seed=seed)\n else:\n raise ValueError(f'Unknown inferencer task: {self.task}')\n\n def __call__(self, **kwargs) -> Union[Dict, List[Dict]]:\n \"\"\"Call the inferencer.\n\n Args:\n kwargs: Keyword arguments for the inferencer.\n\n Returns:\n Union[Dict, List[Dict]]: Results of inference pipeline.\n \"\"\"\n return self.inferencer(**kwargs)\n\n def get_extra_parameters(self) -> List[str]:\n \"\"\"Each inferencer may has its own parameters. Call this function to\n get these parameters.\n\n Returns:\n List[str]: List of unique parameters.\n \"\"\"\n return self.inferencer.get_extra_parameters()\n" }, { "path": "mmagic/apis/inferencers/base_mmagic_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Any, Dict, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\nfrom mmengine.infer import BaseInferencer\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.structures import BaseDataElement\nfrom torchvision import utils\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import ConfigType, SampleList, register_all_modules\nfrom .inference_functions import set_random_seed\n\nInputType = Union[str, int, np.ndarray]\nInputsType = Union[InputType, Sequence[InputType]]\nPredType = Union[BaseDataElement, SampleList]\nImgType = Union[np.ndarray, Sequence[np.ndarray]]\nResType = Union[Dict, List[Dict], BaseDataElement, List[BaseDataElement]]\n\n\nclass BaseMMagicInferencer(BaseInferencer):\n \"\"\"Base inferencer.\n\n Args:\n config (str or ConfigType): Model config or the path to it.\n ckpt (str, optional): Path to the checkpoint.\n device (str, optional): Device to run inference. If None, the best\n device will be automatically used.\n extra_parameters (Dict, optional): Extra parameters for\n different models in inference stage.\n seed (str, optional): Seed for inference.\n \"\"\"\n\n func_kwargs = dict(\n preprocess=[],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=['get_datasample'])\n func_order = dict(preprocess=0, forward=1, visualize=2, postprocess=3)\n\n extra_parameters = dict()\n\n def __init__(self,\n config: Union[ConfigType, str],\n ckpt: Optional[str],\n device: Optional[str] = None,\n extra_parameters: Optional[Dict] = None,\n seed: int = 2022,\n **kwargs) -> None:\n # Load config to cfg\n if device is None:\n device = torch.device(\n 'cuda' if torch.cuda.is_available() else 'cpu')\n self.device = device\n register_all_modules()\n super().__init__(config, ckpt, device)\n\n self._init_extra_parameters(extra_parameters)\n self.base_params = self._dispatch_kwargs(**kwargs)\n self.seed = seed\n set_random_seed(self.seed)\n\n def _init_model(self, cfg: Union[ConfigType, str], ckpt: Optional[str],\n device: str) -> None:\n \"\"\"Initialize the model with the given config and checkpoint on the\n specific device.\"\"\"\n model = MODELS.build(cfg.model)\n if ckpt is not None and ckpt != '':\n ckpt = load_checkpoint(model, ckpt, map_location='cpu')\n if cfg.model.get(\n 'init_cfg') and cfg.model.init_cfg.type == 'convert_from_unet':\n model.init_weights()\n model.cfg = cfg\n model.to(device)\n model.eval()\n return model\n\n def _init_pipeline(self, cfg: ConfigType) -> Compose:\n \"\"\"Initialize the test pipeline.\"\"\"\n if 'test_dataloader' in cfg and \\\n 'dataset' in cfg.test_dataloader and \\\n 'pipeline' in cfg.test_dataloader.dataset:\n pipeline_cfg = cfg.test_dataloader.dataset.pipeline\n return Compose(pipeline_cfg)\n return None\n\n def _init_extra_parameters(self, extra_parameters: Dict) -> None:\n \"\"\"Initialize extra_parameters of each kind of inferencer.\"\"\"\n if extra_parameters is not None:\n for key in self.extra_parameters.keys():\n if key in extra_parameters.keys():\n self.extra_parameters[key] = extra_parameters[key]\n\n def _update_extra_parameters(self, **kwargs) -> None:\n \"\"\"update extra_parameters during run time.\"\"\"\n if 'extra_parameters' in kwargs:\n input_extra_parameters = kwargs['extra_parameters']\n if input_extra_parameters is not None:\n for key in self.extra_parameters.keys():\n if key in input_extra_parameters.keys():\n self.extra_parameters[key] = \\\n input_extra_parameters[key]\n\n def _dispatch_kwargs(self, **kwargs) -> Tuple[Dict, Dict, Dict, Dict]:\n \"\"\"Dispatch kwargs to preprocess(), forward(), visualize() and\n postprocess() according to the actual demands.\"\"\"\n results = [{}, {}, {}, {}]\n dispatched_kwargs = set()\n\n # Dispatch kwargs according to self.func_kwargs\n for func_name, func_kwargs in self.func_kwargs.items():\n for func_kwarg in func_kwargs:\n if func_kwarg in kwargs:\n dispatched_kwargs.add(func_kwarg)\n results[self.func_order[func_name]][func_kwarg] = kwargs[\n func_kwarg]\n\n return results\n\n def __call__(self, **kwargs) -> Union[Dict, List[Dict]]:\n \"\"\"Call the inferencer.\n\n Args:\n kwargs: Keyword arguments for the inferencer.\n\n Returns:\n Union[Dict, List[Dict]]: Results of inference pipeline.\n \"\"\"\n if ('extra_parameters' in kwargs.keys() and kwargs['extra_parameters']\n and 'infer_with_grad' in kwargs['extra_parameters'].keys()\n and kwargs['extra_parameters']['infer_with_grad']):\n results = self.base_call(**kwargs)\n else:\n with torch.no_grad():\n results = self.base_call(**kwargs)\n return results\n\n def base_call(self, **kwargs) -> Union[Dict, List[Dict]]:\n \"\"\"Call the inferencer.\n\n Args:\n kwargs: Keyword arguments for the inferencer.\n\n Returns:\n Union[Dict, List[Dict]]: Results of inference pipeline.\n \"\"\"\n\n self._update_extra_parameters(**kwargs)\n\n params = self._dispatch_kwargs(**kwargs)\n preprocess_kwargs = self.base_params[0].copy()\n preprocess_kwargs.update(params[0])\n forward_kwargs = self.base_params[1].copy()\n forward_kwargs.update(params[1])\n visualize_kwargs = self.base_params[2].copy()\n visualize_kwargs.update(params[2])\n postprocess_kwargs = self.base_params[3].copy()\n postprocess_kwargs.update(params[3])\n\n data = self.preprocess(**preprocess_kwargs)\n preds = self.forward(data, **forward_kwargs)\n imgs = self.visualize(preds, **visualize_kwargs)\n results = self.postprocess(preds, imgs, **postprocess_kwargs)\n return results\n\n def get_extra_parameters(self) -> List[str]:\n \"\"\"Each inferencer may has its own parameters. Call this function to\n get these parameters.\n\n Returns:\n List[str]: List of unique parameters.\n \"\"\"\n return list(self.extra_parameters.keys())\n\n def postprocess(\n self,\n preds: PredType,\n imgs: Optional[List[np.ndarray]] = None,\n is_batch: bool = False,\n get_datasample: bool = False,\n ) -> Union[ResType, Tuple[ResType, np.ndarray]]:\n \"\"\"Postprocess predictions.\n\n Args:\n preds (List[Dict]): Predictions of the model.\n imgs (Optional[np.ndarray]): Visualized predictions.\n is_batch (bool): Whether the inputs are in a batch.\n Defaults to False.\n get_datasample (bool): Whether to use Datasample to store\n inference results. If False, dict will be used.\n\n Returns:\n result (Dict): Inference results as a dict.\n imgs (torch.Tensor): Image result of inference as a tensor or\n tensor list.\n \"\"\"\n results = preds\n if not get_datasample:\n results = []\n for pred in preds:\n result = self._pred2dict(pred)\n results.append(result)\n if not is_batch:\n results = results[0]\n return results, imgs\n\n def _pred2dict(self, pred_tensor: torch.Tensor) -> Dict:\n \"\"\"Extract elements necessary to represent a prediction into a\n dictionary. It's better to contain only basic data elements such as\n strings and numbers in order to guarantee it's json-serializable.\n\n Args:\n pred_tensor (torch.Tensor): The tensor to be converted.\n\n Returns:\n dict: The output dictionary.\n \"\"\"\n result = {}\n result['infer_results'] = pred_tensor\n return result\n\n def visualize(self,\n inputs: list,\n preds: Any,\n show: bool = False,\n result_out_dir: str = '',\n **kwargs) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Customize your visualization by overriding this method. visualize\n should return visualization results, which could be np.ndarray or any\n other objects.\n\n Args:\n inputs (list): Inputs preprocessed by :meth:`_inputs_to_list`.\n preds (Any): Predictions of the model.\n show (bool): Whether to display the image in a popup window.\n Defaults to False.\n result_out_dir (str): Output directory of images. Defaults to ''.\n\n Returns:\n List[np.ndarray]: Visualization results.\n \"\"\"\n results = (preds[:, [2, 1, 0]] + 1.) / 2.\n\n # save images\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n utils.save_image(results, result_out_dir)\n\n return results\n" }, { "path": "mmagic/apis/inferencers/colorization_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\nfrom mmengine.dataset.utils import default_collate as collate\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import tensor2img\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass ColorizationInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with colorization models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['img'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def preprocess(self, img: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n img(InputsType): Image to be translated by models.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n # build the data pipeline\n test_pipeline = Compose(self.model.cfg.test_pipeline)\n # prepare data\n data = dict(img_path=img)\n _data = test_pipeline(data)\n data = dict()\n data['inputs'] = _data['inputs'] / 255.0\n data = collate([data])\n data['data_samples'] = [_data['data_samples']]\n if 'empty_box' not in data['data_samples'][0]:\n data['data_samples'][0].set_data({'empty_box': True})\n if not data['data_samples'][0].empty_box:\n data['data_samples'][0].cropped_img.data = \\\n data['data_samples'][0].cropped_img.data / 255.0\n if 'cuda' in str(self.device):\n data['inputs'] = data['inputs'].cuda()\n data['data_samples'][0] = data['data_samples'][0].cuda()\n data['data_samples'] = DataSample.stack(data['data_samples'])\n return data\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n with torch.no_grad():\n result = self.model(mode='tensor', **inputs)\n return result\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n results = tensor2img(preds[0])\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n mmcv.imwrite(results, result_out_dir)\n\n return results\n" }, { "path": "mmagic/apis/inferencers/conditional_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom torchvision import utils\n\nfrom mmagic.structures import DataSample\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass ConditionalInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with conditional models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['label'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n extra_parameters = dict(num_batches=4, sample_model='orig')\n\n def preprocess(self, label: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n label(InputsType): Input label for condition models.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n num_batches = self.extra_parameters['num_batches']\n sample_model = self.extra_parameters['sample_model']\n\n results = dict(\n num_batches=num_batches, labels=label, sample_model=sample_model)\n\n return results\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n return self.model(inputs)\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n res_list = []\n res_list.extend([item.fake_img.data.cpu() for item in preds])\n results = torch.stack(res_list, dim=0)\n results = results[:, [2, 1, 0]] / 255.\n\n # save images\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n utils.save_image(results, result_out_dir)\n\n return results\n\n def _pred2dict(self, data_sample: DataSample) -> Dict:\n \"\"\"Extract elements necessary to represent a prediction into a\n dictionary. It's better to contain only basic data elements such as\n strings and numbers in order to guarantee it's json-serializable.\n\n Args:\n data_sample (DataSample): The data sample to be converted.\n\n Returns:\n dict: The output dictionary.\n \"\"\"\n result = {}\n result['fake_img'] = data_sample.fake_img.data.cpu()\n result['gt_label'] = data_sample.gt_label.label\n return result\n" }, { "path": "mmagic/apis/inferencers/controlnet_animation_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List, Optional, Union\n\nimport cv2\nimport mmcv\nimport numpy as np\nimport PIL.Image\nimport requests\nimport torch\nfrom controlnet_aux import HEDdetector\nfrom mmengine.config import Config\nfrom mmengine.runner import set_random_seed\n\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.utils import ConfigType\nfrom .base_mmagic_inferencer import BaseMMagicInferencer\n\nVIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi')\nIMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG')\n\n\ndef load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:\n \"\"\"\n Args:\n Loads `image` to a PIL Image.\n image (`str` or `PIL.Image.Image`):\n The image to convert to the PIL Image format.\n Returns:\n `PIL.Image.Image`: A PIL Image.\n \"\"\"\n if isinstance(image, str):\n if image.startswith('http://') or image.startswith('https://'):\n image = PIL.Image.open(requests.get(image, stream=True).raw)\n elif os.path.isfile(image):\n image = PIL.Image.open(image)\n else:\n raise ValueError(\n f'Incorrect path or url, URLs must start with `http://` '\n f'or `https://`, and {image} is not a valid path')\n elif isinstance(image, PIL.Image.Image):\n image = image\n else:\n raise ValueError(\n 'Incorrect format used for image. Should be an url linking'\n ' to an image, a local path, or a PIL image.')\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert('RGB')\n return image\n\n\nclass ControlnetAnimationInferencer(BaseMMagicInferencer):\n \"\"\"Base inferencer.\n\n Args:\n config (str or ConfigType): Model config or the path to it.\n ckpt (str, optional): Path to the checkpoint.\n device (str, optional): Device to run inference. If None, the best\n device will be automatically used.\n result_out_dir (str): Output directory of images. Defaults to ''.\n \"\"\"\n\n func_kwargs = dict(\n preprocess=[],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=['get_datasample'])\n func_order = dict(preprocess=0, forward=1, visualize=2, postprocess=3)\n\n extra_parameters = dict()\n\n def __init__(self,\n config: Union[ConfigType, str],\n device: Optional[str] = None,\n extra_parameters: Optional[Dict] = None,\n dtype=torch.float32,\n **kwargs) -> None:\n cfg = Config.fromfile(config)\n self.hed = HEDdetector.from_pretrained(cfg.control_detector)\n self.inference_method = cfg.inference_method\n if self.inference_method == 'attention_injection':\n cfg.model.attention_injection = True\n self.pipe = MODELS.build(cfg.model).cuda().eval()\n\n control_scheduler_cfg = dict(\n type=cfg.control_scheduler,\n from_config=self.pipe.scheduler.config,\n )\n control_scheduler = DIFFUSION_SCHEDULERS.build(control_scheduler_cfg)\n self.pipe.test_scheduler = control_scheduler\n\n @torch.no_grad()\n def __call__(self,\n prompt=None,\n video=None,\n negative_prompt=None,\n controlnet_conditioning_scale=0.7,\n image_width=512,\n image_height=512,\n save_path=None,\n strength=0.75,\n num_inference_steps=20,\n seed=1,\n output_fps=None,\n reference_img=None,\n **kwargs) -> Union[Dict, List[Dict]]:\n \"\"\"Call the inferencer.\n\n Args:\n kwargs: Keyword arguments for the inferencer.\n\n Returns:\n Union[Dict, List[Dict]]: Results of inference pipeline.\n \"\"\"\n if save_path is None:\n from datetime import datetime\n datestring = datetime.now().strftime('%y%m%d-%H%M%S')\n save_path = '/tmp/' + datestring + '.mp4'\n\n set_random_seed(seed)\n\n latent_width = image_width // 8\n latent_height = image_height // 8\n\n init_noise_shape = (1, 4, latent_height, latent_width)\n init_noise_all_frame = torch.randn(\n init_noise_shape, dtype=self.pipe.controlnet.dtype).cuda()\n\n init_noise_shape_cat = (1, 4, latent_height, latent_width * 3)\n init_noise_all_frame_cat = torch.randn(\n init_noise_shape_cat, dtype=self.pipe.controlnet.dtype).cuda()\n\n latent_mask = torch.zeros(\n (1, 4, image_height // 8, image_width // 8 * 3))\n latent_mask[:, :, :,\n image_width // 8 + 1:image_width // 8 * 2 - 1] = 1.0\n latent_mask = latent_mask.type(self.pipe.controlnet.dtype).cuda()\n\n # load the images\n input_file_extension = os.path.splitext(video)[1]\n from_video = True\n all_images = []\n if input_file_extension in VIDEO_EXTENSIONS:\n video_reader = mmcv.VideoReader(video)\n input_fps = int(video_reader.fps)\n if output_fps is None:\n output_fps = input_fps\n if output_fps > input_fps:\n output_fps = input_fps\n sample_rate = int(input_fps / output_fps)\n\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n video_writer = cv2.VideoWriter(save_path, fourcc, output_fps,\n (image_width, image_height))\n for frame in video_reader:\n all_images.append(np.flip(frame, axis=2))\n else:\n frame_files = os.listdir(video)\n frame_files = [os.path.join(video, f) for f in frame_files]\n frame_files.sort()\n for frame in frame_files:\n frame_extension = os.path.splitext(frame)[1]\n if frame_extension in IMAGE_EXTENSIONS:\n all_images.append(frame)\n\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n\n from_video = False\n\n if self.inference_method == 'multi-frame rendering':\n # first result\n if from_video:\n image = PIL.Image.fromarray(all_images[0])\n else:\n image = load_image(all_images[0])\n image = image.resize((image_width, image_height))\n detect_resolution = min(image_width, image_height)\n hed_image = self.hed(\n image,\n detect_resolution=detect_resolution,\n image_resolution=detect_resolution)\n hed_image = hed_image.resize((image_width, image_height))\n\n result = self.pipe.infer(\n control=hed_image,\n latent_image=image,\n prompt=prompt,\n negative_prompt=negative_prompt,\n strength=strength,\n controlnet_conditioning_scale=controlnet_conditioning_scale,\n num_inference_steps=num_inference_steps,\n latents=init_noise_all_frame)['samples'][0]\n\n first_result = result\n first_hed = hed_image\n last_result = result\n last_hed = hed_image\n\n for ind in range(len(all_images)):\n if from_video:\n if ind % sample_rate > 0:\n continue\n image = PIL.Image.fromarray(all_images[ind])\n else:\n image = load_image(all_images[ind])\n print('processing frame ind ' + str(ind))\n\n image = image.resize((image_width, image_height))\n hed_image = self.hed(image, image_resolution=image_width)\n\n concat_img = PIL.Image.new('RGB',\n (image_width * 3, image_height))\n concat_img.paste(last_result, (0, 0))\n concat_img.paste(image, (image_width, 0))\n concat_img.paste(first_result, (image_width * 2, 0))\n\n concat_hed = PIL.Image.new('RGB',\n (image_width * 3, image_height),\n 'black')\n concat_hed.paste(last_hed, (0, 0))\n concat_hed.paste(hed_image, (image_width, 0))\n concat_hed.paste(first_hed, (image_width * 2, 0))\n\n result = self.pipe.infer(\n control=concat_hed,\n latent_image=concat_img,\n prompt=prompt,\n negative_prompt=negative_prompt,\n strength=strength,\n controlnet_conditioning_scale= # noqa\n controlnet_conditioning_scale,\n num_inference_steps=num_inference_steps,\n latents=init_noise_all_frame_cat,\n latent_mask=latent_mask,\n )['samples'][0]\n result = result.crop(\n (image_width, 0, image_width * 2, image_height))\n\n last_result = result\n last_hed = hed_image\n\n if from_video:\n video_writer.write(np.flip(np.asarray(result), axis=2))\n else:\n frame_name = frame_files[ind].split('/')[-1]\n save_name = os.path.join(save_path, frame_name)\n result.save(save_name)\n\n if from_video:\n video_writer.release()\n else:\n if reference_img is None:\n if from_video:\n image = PIL.Image.fromarray(all_images[0])\n else:\n image = load_image(all_images[0])\n image = image.resize((image_width, image_height))\n detect_resolution = min(image_width, image_height)\n hed_image = self.hed(\n image,\n detect_resolution=detect_resolution,\n image_resolution=detect_resolution)\n hed_image = hed_image.resize((image_width, image_height))\n\n result = self.pipe.infer(\n control=hed_image,\n latent_image=image,\n prompt=prompt,\n negative_prompt=negative_prompt,\n strength=strength,\n controlnet_conditioning_scale= # noqa\n controlnet_conditioning_scale,\n num_inference_steps=num_inference_steps,\n latents=init_noise_all_frame)['samples'][0]\n\n reference_img = result\n else:\n reference_img = load_image(reference_img)\n reference_img = reference_img.resize(\n (image_width, image_height))\n\n for ind in range(len(all_images)):\n if from_video:\n if ind % sample_rate > 0:\n continue\n image = PIL.Image.fromarray(all_images[ind])\n else:\n image = load_image(all_images[ind])\n print('processing frame ind ' + str(ind))\n\n image = image.resize((image_width, image_height))\n hed_image = self.hed(image, image_resolution=image_width)\n\n result = self.pipe.infer(\n control=hed_image,\n latent_image=image,\n prompt=prompt,\n negative_prompt=negative_prompt,\n strength=strength,\n controlnet_conditioning_scale= # noqa\n controlnet_conditioning_scale,\n num_inference_steps=num_inference_steps,\n latents=init_noise_all_frame,\n reference_img=reference_img,\n )['samples'][0]\n\n if from_video:\n video_writer.write(np.flip(np.asarray(result), axis=2))\n else:\n frame_name = frame_files[ind].split('/')[-1]\n save_name = os.path.join(save_path, frame_name)\n result.save(save_name)\n\n if from_video:\n video_writer.release()\n\n return save_path\n" }, { "path": "mmagic/apis/inferencers/diffusers_pipeline_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\nfrom mmengine import mkdir_or_exist\nfrom PIL.Image import Image\nfrom torchvision.utils import save_image\n\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass DiffusersPipelineInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with text2image models.\"\"\"\n\n func_kwargs = dict(\n preprocess=[\n 'text', 'negative_prompt', 'num_inference_steps', 'height', 'width'\n ],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def preprocess(self,\n text: InputsType = None,\n negative_prompt: InputsType = None,\n num_inference_steps: int = 20,\n height=None,\n width=None) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n text(InputsType): text input for text-to-image model.\n negative_prompt(InputsType): negative prompt.\n\n Returns:\n result(Dict): Results of preprocess.\n \"\"\"\n result = self.extra_parameters\n if text:\n result['prompt'] = text\n if negative_prompt:\n result['negative_prompt'] = negative_prompt\n if num_inference_steps:\n result['num_inference_steps'] = num_inference_steps\n if height:\n result['height'] = height\n if width:\n result['width'] = width\n\n return result\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n images = self.model(**inputs).images\n\n return images\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n if type(preds) is list:\n preds = preds[0]\n if type(preds) is Image:\n preds.save(result_out_dir)\n else:\n save_image(preds, result_out_dir, normalize=True)\n\n return preds\n" }, { "path": "mmagic/apis/inferencers/eg3d_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom collections import defaultdict\nfrom typing import Dict, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nfrom mmengine import print_log\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom PIL import Image\nfrom torch.nn import functional as F\nfrom torchvision.utils import make_grid\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import ForwardInputs, try_import\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\nfrom .inference_functions import calculate_grid_size\n\nimageio = try_import('imageio')\nimageio_ffmpeg = try_import('imageio_ffmpeg')\n\n\nclass EG3DInferencer(BaseMMagicInferencer):\n\n func_kwargs = dict(\n preprocess=['inputs'],\n forward=['num_images', 'interpolation'],\n visualize=[\n 'result_out_dir', 'vis_mode', 'save_img', 'save_video',\n 'img_suffix', 'video_suffix'\n ],\n postprocess=[])\n\n extra_parameters = dict(num_batches=4, sample_model='ema', add_noise=False)\n\n def preprocess(self, inputs: InputsType = None) -> ForwardInputs:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n inputs (List[Union[str, np.ndarray]]): The conditional inputs for\n the inferencer. Defaults to None.\n\n Returns:\n ForwardInputs: The preprocessed inputs and data samples.\n \"\"\"\n if isinstance(inputs, Sequence):\n assert all([type(inputs[0]) == type(lab) for lab in inputs\n ]), ('All label inputs must have the same type.')\n if isinstance(inputs[0], list):\n for lab in inputs:\n assert all([isinstance(l_, float) for l_ in lab])\n inputs = np.array(inputs).astype(np.float32)\n elif isinstance(inputs[0], np.ndarray):\n assert all([lab.ndim == 1 for lab in inputs])\n inputs = [input_.astype(np.float32) for input_ in inputs]\n else:\n raise ValueError(\n 'EG3D only support ndarry or list as label input.')\n\n data_sample_list = []\n for lab in inputs:\n data_sample = DataSample()\n data_sample.set_gt_label(lab)\n data_sample_list.append(data_sample.to(self.device))\n self.extra_parameters['num_batches'] = len(inputs)\n else:\n data_sample_list = None\n\n num_batches = self.extra_parameters['num_batches']\n sample_model = self.extra_parameters['sample_model']\n add_noise = self.extra_parameters['add_noise']\n inputs = dict(\n num_batches=num_batches,\n sample_model=sample_model,\n add_noise=add_noise)\n\n if data_sample_list is None:\n data_samples = None\n else:\n data_samples = DataSample.stack(data_sample_list)\n\n return inputs, data_samples\n\n def forward(self,\n inputs: ForwardInputs,\n interpolation: Optional[str] = 'both',\n num_images: int = 100) -> Union[dict, List[dict]]:\n \"\"\"Forward the inputs to the model.\n\n Args:\n inputs (ForwardInputs): Model inputs. If data sample (the second\n element of `inputs`) is not passed, will generate a sequence\n of images corresponding to passed `interpolation` mode.\n interpolation (str): The interpolation mode. Supported choices\n are 'both', 'conditioning', and 'camera'. Defaults to 'both'.\n num_images (int): The number of frames of interpolation.\n Defaults to 500.\n\n Returns:\n Union[dict, List[dict]]: Output dict corresponds to the input\n condition or the list of output dict of each frame during the\n interpolation process.\n \"\"\"\n inputs, data_sample = inputs # unpack the tuple\n # forward as the passed input\n if data_sample is not None:\n outputs = self.model(inputs, data_sample)\n output_dict = defaultdict(list)\n # return outputs\n for output in outputs:\n fake_img = output.fake_img.data\n depth_img = output.depth\n lr_img = output.lr_img.data\n ray_origins = output.ray_origins\n ray_directions = output.ray_directions\n output_dict['fake_img'].append(fake_img)\n output_dict['depth'].append(depth_img)\n output_dict['lr_img'].append(lr_img)\n output_dict['ray_origins'].append(ray_origins)\n output_dict['ray_directions'].append(ray_directions)\n\n for k in output_dict.keys():\n output_dict[k] = torch.stack(output_dict[k], dim=0)\n\n return output_dict\n\n num_batches = inputs['num_batches']\n output_list = self.model.interpolation(num_images, num_batches,\n interpolation)\n return output_list\n\n def visualize(self,\n preds: Union[PredType, List[PredType]],\n vis_mode: str = 'both',\n save_img: bool = True,\n save_video: bool = True,\n img_suffix: str = '.png',\n video_suffix: str = '.mp4',\n result_out_dir: str = 'eg3d_output') -> None:\n \"\"\"Visualize predictions.\n\n Args:\n preds (Union[PredType, List[PredType]]): Prediction os model.\n vis_mode (str, optional): Which output to visualize. Supported\n choices are 'both', 'depth', and 'img'. Defaults to 'all'.\n save_img (bool, optional): Whether save images. Defaults to True.\n save_video (bool, optional): Whether save videos. Defaults to True.\n img_suffix (str, optional): The suffix of saved images.\n Defaults to '.png'.\n video_suffix (str, optional): The suffix of saved videos.\n Defaults to '.mp4'.\n result_out_dir (str, optional): The save director of image and\n videos. Defaults to 'eg3d_output'.\n \"\"\"\n if save_video:\n assert imageio is not None, (\n 'Please install imageio by \\'pip install '\n 'imageio\\' to save video.')\n assert imageio_ffmpeg is not None, (\n 'Please install imageio-ffmpeg by \\'pip install '\n 'imageio-ffmpeg\\' to save video.')\n\n os.makedirs(result_out_dir, exist_ok=True)\n assert vis_mode.upper() in ['BOTH', 'DEPTH', 'IMG']\n if vis_mode.upper() == 'BOTH':\n vis_mode = ['DEPTH', 'IMG']\n if not isinstance(vis_mode, list):\n vis_mode = [vis_mode]\n\n if not isinstance(preds, list):\n preds = [preds]\n if save_video:\n save_video = False\n print_log('Only one frame of output is generated and cannot '\n 'save video. Set \\'save_video\\' to \\'False\\' '\n 'automatically.')\n if not save_img:\n save_img = True\n print_log('Only one frame of output is generated can only save'\n 'image. Set \\'save_img\\' to \\'True\\' automatically.')\n\n # save video\n batch_size = preds[0]['fake_img'].shape[0]\n\n img_dict = {}\n for target in vis_mode:\n target = 'fake_img' if target.upper() == 'IMG' else target\n if target.lower() == 'fake_img':\n imgs = self.preprocess_img(preds)\n else:\n imgs = self.preprocess_depth(preds)\n img_dict[target.lower()] = imgs\n\n nrow = calculate_grid_size(batch_size)\n\n if save_video:\n video_path = osp.join(\n result_out_dir,\n f'{target.lower()}_seed{self.seed}{video_suffix}')\n video_writer = imageio.get_writer(\n video_path,\n mode='I',\n fps=60,\n codec='libx264',\n bitrate='10M')\n\n frame_list = torch.split(imgs, batch_size)\n for idx, frame in enumerate(frame_list):\n # frame: [bz, C, H, W]\n frame_grid = make_grid(\n frame, nrow=nrow).permute(1, 2, 0)[..., (2, 1, 0)]\n frame_grid = frame_grid.numpy().astype(np.uint8)\n if save_video:\n video_writer.append_data(frame_grid)\n\n if save_img:\n if len(frame_list) != 1:\n img_name = (f'{target.lower()}_frame{idx}_'\n f'seed{self.seed}{img_suffix}')\n else:\n img_name = (f'{target.lower()}_seed{self.seed}'\n f'{img_suffix}')\n img_path = osp.join(result_out_dir, img_name)\n Image.fromarray(frame_grid).save(img_path)\n\n if save_video:\n video_writer.close()\n print_log(f'Save video to \\'{video_path}\\'.', 'current')\n\n if len(vis_mode) > 1:\n fake_img = img_dict['fake_img']\n depth_img = img_dict['depth']\n # [num_frame * bz, 3, H, W * 2]\n imgs = torch.cat([fake_img, depth_img], dim=-1)\n nrow = calculate_grid_size(batch_size, aspect_ratio=2)\n\n if save_video:\n video_path = osp.join(\n result_out_dir, f'combine_seed{self.seed}{video_suffix}')\n video_writer = imageio.get_writer(\n video_path,\n mode='I',\n fps=60,\n codec='libx264',\n bitrate='10M')\n\n frame_list = torch.split(imgs, batch_size)\n for idx, frame in enumerate(frame_list):\n frame_grid = make_grid(\n frame, nrow=nrow).permute(1, 2, 0)[..., (2, 1, 0)]\n frame_grid = frame_grid.numpy().astype(np.uint8)\n\n if save_video:\n video_writer.append_data(frame_grid)\n\n if save_img:\n if len(frame_list) != 1:\n img_name = (f'combine_frame{idx}_'\n f'seed{self.seed}{img_suffix}')\n else:\n img_name = (f'combine_seed{self.seed}' f'{img_suffix}')\n img_path = osp.join(result_out_dir, img_name)\n Image.fromarray(frame_grid).save(img_path)\n\n if save_video:\n video_writer.close()\n print_log(f'Save video to \\'{video_path}\\'.', 'current')\n\n def preprocess_img(self, preds: List[dict]) -> torch.Tensor:\n \"\"\"Preprocess images in the predictions.\n\n Args:\n preds (List[dict]): List of prediction dict of each frame.\n\n Returns:\n torch.Tensor: Preprocessed image tensor shape like\n [num_frame * bz, 3, H, W].\n \"\"\"\n imgs = [p['fake_img'].cpu() for p in preds]\n imgs = torch.cat(imgs, dim=0) # [num_frame * bz, 3, H, W]\n imgs = ((imgs + 1) / 2 * 255.).clamp(0, 255)\n return imgs\n\n def preprocess_depth(self, preds: List[dict]) -> torch.Tensor:\n \"\"\"Preprocess depth in the predictions.\n\n Args:\n preds (List[dict]): List of prediction dict of each frame.\n\n Returns:\n torch.Tensor: Preprocessed depth tensor shape like\n [num_frame * bz, 3, H, W].\n \"\"\"\n depth = [p['depth'].cpu() for p in preds]\n\n depth = torch.cat(depth, dim=0)\n depth = -depth\n depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.\n depth = depth.clamp(0, 255).repeat(1, 3, 1, 1)\n\n img_size = preds[0]['fake_img'].shape[-1]\n if img_size != depth.shape[-1]:\n interpolation_kwargs = dict(\n size=img_size, mode='bilinear', align_corners=False)\n if digit_version(TORCH_VERSION) >= digit_version('1.11.0'):\n interpolation_kwargs['antialias'] = True\n depth = F.interpolate(depth, **interpolation_kwargs)\n return depth\n\n def postprocess(self,\n preds: PredType,\n imgs: Optional[List[np.ndarray]] = None,\n is_batch: bool = False,\n get_datasample: bool = False) -> Dict[str, torch.tensor]:\n \"\"\"Postprocess predictions.\n\n Args:\n preds (List[Dict]): Predictions of the model.\n imgs (Optional[np.ndarray]): Visualized predictions.\n is_batch (bool): Whether the inputs are in a batch.\n Defaults to False.\n get_datasample (bool): Whether to use Datasample to store\n inference results. If False, dict will be used.\n\n Returns:\n Dict[str, torch.Tensor]: Inference results as a dict.\n \"\"\"\n if isinstance(preds[0], dict):\n keys = preds[0].keys()\n outputs = defaultdict(list)\n for pred in preds:\n for k in keys:\n outputs[k].append(pred[k])\n for k in keys:\n outputs[k] = torch.stack(outputs[k], dim=0)\n return outputs\n # directly return the dict\n return preds\n" }, { "path": "mmagic/apis/inferencers/image_super_resolution_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\n\nfrom mmagic.utils import tensor2img\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass ImageSuperResolutionInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with restoration models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['img', 'ref'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n img(InputsType): Image to be restored by models.\n ref(InputsType): Reference image for restoration models.\n Defaults to None.\n\n Returns:\n data(Dict): Results of preprocess.\n \"\"\"\n cfg = self.model.cfg\n\n # select the data pipeline\n if cfg.get('inference_pipeline', None):\n test_pipeline = cfg.inference_pipeline\n elif cfg.get('demo_pipeline', None):\n test_pipeline = cfg.demo_pipeline\n elif cfg.get('test_pipeline', None):\n test_pipeline = cfg.test_pipeline\n else:\n test_pipeline = cfg.val_pipeline\n\n keys_to_remove = ['gt', 'gt_path']\n for key in keys_to_remove:\n for pipeline in list(test_pipeline):\n if 'key' in pipeline and key == pipeline['key']:\n test_pipeline.remove(pipeline)\n if 'keys' in pipeline and key in pipeline['keys']:\n pipeline['keys'].remove(key)\n if len(pipeline['keys']) == 0:\n test_pipeline.remove(pipeline)\n if 'meta_keys' in pipeline and key in pipeline['meta_keys']:\n pipeline['meta_keys'].remove(key)\n\n # build the data pipeline\n test_pipeline = Compose(test_pipeline)\n\n # prepare data\n if ref: # Ref-SR\n data = dict(img_path=img, ref_path=ref)\n else: # SISR\n data = dict(img_path=img)\n _data = test_pipeline(data)\n\n data = dict()\n data['inputs'] = [_data['inputs']]\n data['data_samples'] = [_data['data_samples']]\n\n return data\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n inputs = self.model.data_preprocessor(inputs)\n with torch.no_grad():\n result = self.model(mode='predict', **inputs)\n return result\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n result = preds[0].output.pred_img / 255.\n results = tensor2img(result)[..., ::-1]\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n mmcv.imwrite(results, result_out_dir)\n\n return results\n" }, { "path": "mmagic/apis/inferencers/inference_functions.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import Config\nfrom mmengine.config import ConfigDict\nfrom mmengine.fileio import get_file_backend\nfrom mmengine.registry import init_default_scope\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.runner import set_random_seed as set_random_seed_engine\n\nfrom mmagic.registry import MODELS\n\nVIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi')\nFILE_CLIENT = get_file_backend(backend_args={'backend': 'local'})\n\n\ndef set_random_seed(seed, deterministic=False, use_rank_shift=True):\n \"\"\"Set random seed.\n\n In this function, we just modify the default behavior of the similar\n function defined in MMCV.\n\n Args:\n seed (int): Seed to be used.\n deterministic (bool): Whether to set the deterministic option for\n CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`\n to True and `torch.backends.cudnn.benchmark` to False.\n Default: False.\n rank_shift (bool): Whether to add rank number to the random seed to\n have different random seed in different threads. Default: True.\n \"\"\"\n set_random_seed_engine(seed, deterministic, use_rank_shift)\n\n\ndef delete_cfg(cfg, key='init_cfg'):\n \"\"\"Delete key from config object.\n\n Args:\n cfg (str or :obj:`mmengine.Config`): Config object.\n key (str): Which key to delete.\n \"\"\"\n\n if key in cfg:\n cfg.pop(key)\n for _key in cfg.keys():\n if isinstance(cfg[_key], ConfigDict):\n delete_cfg(cfg[_key], key)\n\n\ndef init_model(config, checkpoint=None, device='cuda:0'):\n \"\"\"Initialize a model from config file.\n\n Args:\n config (str or :obj:`mmengine.Config`): Config file path or the config\n object.\n checkpoint (str, optional): Checkpoint path. If left as None, the model\n will not load any weights.\n device (str): Which device the model will deploy. Default: 'cuda:0'.\n\n Returns:\n nn.Module: The constructed model.\n \"\"\"\n\n if isinstance(config, str):\n config = Config.fromfile(config)\n elif not isinstance(config, Config):\n raise TypeError('config must be a filename or Config object, '\n f'but got {type(config)}')\n # config.test_cfg.metrics = None\n delete_cfg(config.model, 'init_cfg')\n\n init_default_scope(config.get('default_scope', 'mmagic'))\n\n model = MODELS.build(config.model)\n\n if checkpoint is not None:\n checkpoint = load_checkpoint(model, checkpoint)\n\n model.cfg = config # save the config in the model for convenience\n model.to(device)\n model.eval()\n\n return model\n\n\ndef pad_sequence(data, window_size):\n \"\"\"Pad frame sequence data.\n\n Args:\n data (Tensor): The frame sequence data.\n window_size (int): The window size used in sliding-window framework.\n\n Returns:\n data (Tensor): The padded result.\n \"\"\"\n\n padding = window_size // 2\n\n data = torch.cat([\n data[:, 1 + padding:1 + 2 * padding].flip(1), data,\n data[:, -1 - 2 * padding:-1 - padding].flip(1)\n ],\n dim=1)\n\n return data\n\n\ndef read_image(filepath):\n \"\"\"Read image from file.\n\n Args:\n filepath (str): File path.\n\n Returns:\n image (np.array): Image.\n \"\"\"\n img_bytes = FILE_CLIENT.get(filepath)\n image = mmcv.imfrombytes(\n img_bytes, flag='color', channel_order='rgb', backend='pillow')\n return image\n\n\ndef read_frames(source, start_index, num_frames, from_video, end_index):\n \"\"\"Read frames from file or video.\n\n Args:\n source (list | mmcv.VideoReader): Source of frames.\n start_index (int): Start index of frames.\n num_frames (int): frames number to be read.\n from_video (bool): Weather read frames from video.\n end_index (int): The end index of frames.\n\n Returns:\n images (np.array): Images.\n \"\"\"\n images = []\n last_index = min(start_index + num_frames, end_index)\n # read frames from video\n if from_video:\n for index in range(start_index, last_index):\n if index >= source.frame_cnt:\n break\n images.append(np.flip(source.get_frame(index), axis=2))\n else:\n files = source[start_index:last_index]\n images = [read_image(f) for f in files]\n return images\n\n\ndef calculate_grid_size(num_batches: int = 1, aspect_ratio: int = 1) -> int:\n \"\"\"Calculate the number of images per row (nrow) to make the grid closer to\n square when formatting a batch of images to grid.\n\n Args:\n num_batches (int, optional): Number of images per batch. Defaults to 1.\n aspect_ratio (int, optional): The aspect ratio (width / height) of\n each image sample. Defaults to 1.\n\n Returns:\n int: Calculated number of images per row.\n \"\"\"\n curr_ncol, curr_nrow = 1, num_batches\n curr_delta = curr_nrow * aspect_ratio - curr_ncol\n\n nrow = curr_nrow\n delta = curr_delta\n\n while curr_delta > 0:\n\n curr_ncol += 1\n curr_nrow = math.ceil(num_batches / curr_ncol)\n\n curr_delta = curr_nrow * aspect_ratio - curr_ncol\n if curr_delta < delta and curr_delta >= 0:\n nrow, delta = curr_nrow, curr_delta\n\n return nrow\n" }, { "path": "mmagic/apis/inferencers/inpainting_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\n\nfrom mmagic.utils import tensor2img\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass InpaintingInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with inpainting models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['img', 'mask'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def _init_pipeline(self, cfg) -> Compose:\n \"\"\"Initialize the test pipeline.\"\"\"\n return None\n\n def preprocess(self, img: InputsType, mask: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n img(InputsType): Image to be inpainted by models.\n mask(InputsType): Image mask for inpainting models.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n infer_pipeline_cfg = [\n dict(type='LoadImageFromFile', key='gt', channel_order='bgr'),\n dict(\n type='LoadMask',\n mask_mode='file',\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n ]\n\n infer_pipeline = Compose(infer_pipeline_cfg)\n\n # prepare data\n _data = infer_pipeline(dict(gt_path=img, mask_path=mask))\n data = dict()\n data['inputs'] = [_data['inputs']]\n data['data_samples'] = [_data['data_samples']]\n return data\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n inputs = self.model.data_preprocessor(inputs)\n with torch.no_grad():\n result = self.model(mode='predict', **inputs)\n return result\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Mask of input image.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n result = preds[0].output.pred_img / 255.\n\n result = tensor2img(result)[..., ::-1]\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n mmcv.imwrite(result, result_out_dir)\n\n return result\n" }, { "path": "mmagic/apis/inferencers/matting_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\n\nfrom mmagic.structures import DataSample\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass MattingInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with matting models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['img', 'trimap'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def preprocess(self, img: InputsType, trimap: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n img(InputsType): Image to be processed by models.\n mask(InputsType): Mask corresponding to the input image.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n # remove alpha from test_pipeline\n keys_to_remove = ['alpha', 'ori_alpha']\n for key in keys_to_remove:\n for pipeline in list(self.cfg.test_pipeline):\n if 'key' in pipeline and key == pipeline['key']:\n self.cfg.test_pipeline.remove(pipeline)\n if 'keys' in pipeline and key in pipeline['keys']:\n pipeline['keys'].remove(key)\n if len(pipeline['keys']) == 0:\n self.cfg.test_pipeline.remove(pipeline)\n if 'meta_keys' in pipeline and key in pipeline['meta_keys']:\n pipeline['meta_keys'].remove(key)\n\n # build the data pipeline\n test_pipeline = Compose(self.cfg.test_pipeline)\n # prepare data\n data = dict(merged_path=img, trimap_path=trimap)\n _data = test_pipeline(data)\n trimap = _data['data_samples'].trimap.data\n preprocess_res = dict()\n preprocess_res['inputs'] = [_data['inputs']]\n preprocess_res['data_samples'] = [_data['data_samples']]\n return preprocess_res\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n inputs = self.model.data_preprocessor(inputs)\n with torch.no_grad():\n return self.model(mode='predict', **inputs)\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n result = preds[0].output\n result = result.pred_alpha.data.cpu()\n\n # save images\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n mmcv.imwrite(result.numpy(), result_out_dir)\n\n return result\n\n def _pred2dict(self, data_sample: DataSample) -> Dict:\n \"\"\"Extract elements necessary to represent a prediction into a\n dictionary. It's better to contain only basic data elements such as\n strings and numbers in order to guarantee it's json-serializable.\n\n Args:\n data_sample (DataSample): The data sample to be converted.\n\n Returns:\n dict: The output dictionary.\n \"\"\"\n result = {}\n result['pred_alpha'] = data_sample.output.pred_alpha.data.cpu()\n return result\n" }, { "path": "mmagic/apis/inferencers/text2image_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport mmcv\nimport numpy as np\nfrom mmengine import mkdir_or_exist\nfrom PIL.Image import Image, fromarray\nfrom torchvision.utils import save_image\n\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass Text2ImageInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with text2image models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['text', 'control', 'negative_prompt'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n extra_parameters = dict(height=None, width=None, seed=1)\n\n def preprocess(self,\n text: InputsType,\n control: str = None,\n negative_prompt: InputsType = None) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n text(InputsType): text input for text-to-image model.\n control(str): control img dir for controlnet.\n negative_prompt(InputsType): negative prompt.\n\n Returns:\n result(Dict): Results of preprocess.\n \"\"\"\n result = self.extra_parameters\n if type(text) is dict:\n result['text_prompts'] = text\n else:\n result['prompt'] = text\n\n if control:\n control_img = mmcv.imread(control)\n control_img = fromarray(control_img)\n result['control'] = control_img\n result.pop('seed', None)\n\n if negative_prompt:\n result['negative_prompt'] = negative_prompt\n\n return result\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n image = self.model.infer(**inputs)['samples']\n\n return image\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n if type(preds) is list:\n preds = preds[0]\n if type(preds) is Image:\n preds.save(result_out_dir)\n else:\n save_image(preds, result_out_dir, normalize=True)\n\n return preds\n" }, { "path": "mmagic/apis/inferencers/translation_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom mmengine.dataset import Compose\nfrom mmengine.dataset.utils import default_collate as collate\nfrom torchvision import utils\n\nfrom mmagic.models.base_models import BaseTranslationModel\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass TranslationInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with translation models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['img'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n def preprocess(self, img: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n img(InputsType): Image to be translated by models.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n assert isinstance(self.model, BaseTranslationModel)\n\n # get source domain and target domain\n self.target_domain = self.model._default_domain\n source_domain = self.model.get_other_domains(self.target_domain)[0]\n\n cfg = self.model.cfg\n # build the data pipeline\n test_pipeline = Compose(cfg.test_pipeline)\n\n # prepare data\n # dirty code to deal with test data pipeline\n data = dict()\n data['pair_path'] = img\n data['img_A_path'] = img\n data['img_B_path'] = img\n data = collate([test_pipeline(data)])\n data = self.model.data_preprocessor(data, False)\n\n inputs_dict = data['inputs']\n results = inputs_dict[f'img_{source_domain}']\n return results\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n with torch.no_grad():\n results = self.model(\n inputs, test_mode=True, target_domain=self.target_domain)\n output = results['target']\n return output\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = None) -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n results = (preds[:, [2, 1, 0]] + 1.) / 2.\n\n # save images\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n utils.save_image(results, result_out_dir)\n\n return results\n" }, { "path": "mmagic/apis/inferencers/unconditional_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\nfrom mmengine import mkdir_or_exist\nfrom torchvision import utils\n\nfrom mmagic.structures import DataSample\nfrom .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType\n\n\nclass UnconditionalInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with unconditional models.\"\"\"\n\n func_kwargs = dict(\n preprocess=[],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n extra_parameters = dict(\n num_batches=4, sample_model='orig', sample_kwargs=None, noise=None)\n\n def preprocess(self) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Returns:\n results(Dict): Results of preprocess.\n \"\"\"\n num_batches = self.extra_parameters['num_batches']\n sample_model = self.extra_parameters['sample_model']\n noise = self.extra_parameters['noise']\n sample_kwargs = self.extra_parameters['sample_kwargs']\n\n results = dict(\n num_batches=num_batches,\n sample_model=sample_model,\n sample_kwargs=sample_kwargs,\n noise=noise)\n\n return results\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\"\"\"\n return self.model(inputs)\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = '') -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n res_list = []\n res_list.extend([item.fake_img.data.cpu() for item in preds])\n results = torch.stack(res_list, dim=0)\n if results.shape[1] == 3:\n results = results[:, [2, 1, 0]] / 255.\n else:\n results = results / 255.\n\n # save images\n if result_out_dir:\n mkdir_or_exist(os.path.dirname(result_out_dir))\n utils.save_image(results, result_out_dir)\n\n return results\n\n def _pred2dict(self, data_sample: DataSample) -> Dict:\n \"\"\"Extract elements necessary to represent a prediction into a\n dictionary. It's better to contain only basic data elements such as\n strings and numbers in order to guarantee it's json-serializable.\n\n Args:\n data_sample (DataSample): The data sample to be converted.\n\n Returns:\n dict: The output dictionary.\n \"\"\"\n result = {}\n result['fake_img'] = data_sample.fake_img.data.cpu()\n result['noise'] = data_sample.noise.data.cpu()\n if hasattr(data_sample, 'latent'):\n result['latent'] = data_sample.latent\n if hasattr(data_sample, 'feats'):\n result['feats'] = data_sample.feats\n return result\n" }, { "path": "mmagic/apis/inferencers/video_interpolation_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport os\nimport os.path as osp\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport cv2\nimport mmcv\nimport mmengine\nimport numpy as np\nimport torch\nfrom mmengine.dataset import Compose\nfrom mmengine.dataset.utils import default_collate as collate\nfrom mmengine.logging import MMLogger\nfrom mmengine.utils import ProgressBar\n\nfrom .base_mmagic_inferencer import (BaseMMagicInferencer, InputsType,\n PredType, ResType)\nfrom .inference_functions import VIDEO_EXTENSIONS, read_frames, read_image\n\n\nclass VideoInterpolationInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with video interpolation models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['video'],\n forward=['result_out_dir'],\n visualize=[],\n postprocess=[])\n\n extra_parameters = dict(\n start_idx=0,\n end_idx=None,\n batch_size=4,\n fps_multiplier=0,\n fps=0,\n filename_tmpl='{:08d}.png')\n\n def preprocess(self, video: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n video(InputsType): Video to be interpolated by models.\n\n Returns:\n video(InputsType): Video to be interpolated by models.\n \"\"\"\n # build the data pipeline\n if self.model.cfg.get('demo_pipeline', None):\n test_pipeline = self.model.cfg.demo_pipeline\n elif self.model.cfg.get('test_pipeline', None):\n test_pipeline = self.model.cfg.test_pipeline\n else:\n test_pipeline = self.model.cfg.val_pipeline\n\n # remove the data loading pipeline\n tmp_pipeline = []\n for pipeline in test_pipeline:\n if pipeline['type'] not in [\n 'GenerateSegmentIndices', 'LoadImageFromFile'\n ]:\n tmp_pipeline.append(pipeline)\n test_pipeline = tmp_pipeline\n\n # compose the pipeline\n self.test_pipeline = Compose(test_pipeline)\n\n return video\n\n def forward(self,\n inputs: InputsType,\n result_out_dir: InputsType = '') -> PredType:\n \"\"\"Forward the inputs to the model.\n\n Args:\n inputs (InputsType): Input video directory.\n result_out_dir (str): Output directory of video.\n Defaults to ''.\n\n Returns:\n PredType: Result of forwarding\n \"\"\"\n # check if the input is a video\n input_file_extension = os.path.splitext(inputs)[1]\n if input_file_extension in VIDEO_EXTENSIONS:\n source = mmcv.VideoReader(inputs)\n input_fps = source.fps\n length = source.frame_cnt\n from_video = True\n h, w = source.height, source.width\n if self.extra_parameters['fps_multiplier']:\n assert self.extra_parameters['fps_multiplier'] > 0, \\\n '`fps_multiplier` cannot be negative'\n output_fps = \\\n self.extra_parameters['fps_multiplier'] * input_fps\n else:\n fps = self.extra_parameters['fps']\n output_fps = fps if fps > 0 else input_fps * 2\n else:\n files = os.listdir(inputs)\n files = [osp.join(inputs, f) for f in files]\n files.sort()\n source = files\n length = files.__len__()\n from_video = False\n example_frame = read_image(files[0])\n h, w = example_frame.shape[:2]\n fps = self.extra_parameters['fps']\n output_fps = fps if fps > 0 else 60\n\n # check if the output is a video\n output_file_extension = os.path.splitext(result_out_dir)[1]\n mmengine.utils.mkdir_or_exist(osp.dirname(result_out_dir))\n if output_file_extension in VIDEO_EXTENSIONS:\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n target = cv2.VideoWriter(result_out_dir, fourcc, output_fps,\n (w, h))\n to_video = True\n else:\n to_video = False\n\n self.extra_parameters['end_idx'] = min(\n self.extra_parameters['end_idx'], length) \\\n if self.extra_parameters['end_idx'] is not None else length\n\n # calculate step args\n step_size = \\\n self.model.step_frames * self.extra_parameters['batch_size']\n lenth_per_step = self.model.required_frames + \\\n self.model.step_frames * (self.extra_parameters['batch_size'] - 1)\n repeat_frame = self.model.required_frames - self.model.step_frames\n\n prog_bar = ProgressBar(\n math.ceil((self.extra_parameters['end_idx'] + step_size -\n lenth_per_step - self.extra_parameters['start_idx']) /\n step_size))\n output_index = self.extra_parameters['start_idx']\n for start_index in range(self.extra_parameters['start_idx'],\n self.extra_parameters['end_idx'], step_size):\n images = read_frames(\n source,\n start_index,\n lenth_per_step,\n from_video,\n end_index=self.extra_parameters['end_idx'])\n\n # data prepare\n data = dict(img=images, inputs_path=None, key=inputs)\n data = self.test_pipeline(data)['inputs'] / 255.0\n data = collate([data])\n # data.shape: [1, t, c, h, w]\n\n # forward the model\n data = self.model.split_frames(data)\n input_tensors = data.clone().detach()\n with torch.no_grad():\n output = self.model(data.to(self.device), mode='tensor')\n if len(output.shape) == 4:\n output = output.unsqueeze(1)\n output_tensors = output.cpu()\n if len(output_tensors.shape) == 4:\n output_tensors = output_tensors.unsqueeze(1)\n result = self.model.merge_frames(input_tensors, output_tensors)\n if not self.extra_parameters['start_idx'] == start_index:\n result = result[repeat_frame:]\n prog_bar.update()\n\n # save frames\n if to_video:\n for frame in result:\n target.write(frame)\n else:\n filename_tmpl = self.extra_parameters['filename_tmpl']\n for frame in result:\n save_path = osp.join(result_out_dir,\n filename_tmpl.format(output_index))\n mmcv.imwrite(frame, save_path)\n output_index += 1\n\n if start_index + lenth_per_step >= \\\n self.extra_parameters['end_idx']:\n break\n\n if to_video:\n target.release()\n\n logger: MMLogger = MMLogger.get_current_instance()\n logger.info(f'Output video is save at {result_out_dir}.')\n\n return {}\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = '') -> List[np.ndarray]:\n \"\"\"Visualize is not needed in this inferencer.\"\"\"\n logger: MMLogger = MMLogger.get_current_instance()\n logger.info('Visualization is implemented in forward process.')\n return None\n\n def postprocess(\n self,\n preds: PredType,\n imgs: Optional[List[np.ndarray]] = None\n ) -> Union[ResType, Tuple[ResType, np.ndarray]]:\n \"\"\"Postprocess is not needed in this inferencer.\"\"\"\n logger: MMLogger = MMLogger.get_current_instance()\n logger.info('Postprocess is implemented in forward process.')\n return None\n" }, { "path": "mmagic/apis/inferencers/video_restoration_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport glob\nimport os\nimport os.path as osp\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport cv2\nimport mmcv\nimport mmengine\nimport numpy as np\nimport torch\nfrom mmengine.dataset import Compose\nfrom mmengine.logging import MMLogger\nfrom mmengine.utils import ProgressBar\n\nfrom mmagic.utils import tensor2img\nfrom .base_mmagic_inferencer import (BaseMMagicInferencer, InputsType,\n PredType, ResType)\nfrom .inference_functions import VIDEO_EXTENSIONS, pad_sequence\n\n\nclass VideoRestorationInferencer(BaseMMagicInferencer):\n \"\"\"inferencer that predicts with video restoration models.\"\"\"\n\n func_kwargs = dict(\n preprocess=['video'],\n forward=[],\n visualize=['result_out_dir'],\n postprocess=[])\n\n extra_parameters = dict(\n start_idx=0,\n filename_tmpl='{:08d}.png',\n window_size=0,\n max_seq_len=None)\n\n def preprocess(self, video: InputsType) -> Dict:\n \"\"\"Process the inputs into a model-feedable format.\n\n Args:\n video(InputsType): Video to be restored by models.\n\n Returns:\n results(InputsType): Results of preprocess.\n \"\"\"\n # build the data pipeline\n if self.model.cfg.get('demo_pipeline', None):\n test_pipeline = self.model.cfg.demo_pipeline\n elif self.model.cfg.get('test_pipeline', None):\n test_pipeline = self.model.cfg.test_pipeline\n else:\n test_pipeline = self.model.cfg.val_pipeline\n\n # check if the input is a video\n file_extension = osp.splitext(video)[1]\n if file_extension in VIDEO_EXTENSIONS:\n video_reader = mmcv.VideoReader(video)\n # load the images\n data = dict(img=[], img_path=None, key=video)\n for frame in video_reader:\n data['img'].append(np.flip(frame, axis=2))\n\n # remove the data loading pipeline\n tmp_pipeline = []\n for pipeline in test_pipeline:\n if pipeline['type'] not in [\n 'GenerateSegmentIndices', 'LoadImageFromFile'\n ]:\n tmp_pipeline.append(pipeline)\n test_pipeline = tmp_pipeline\n else:\n # the first element in the pipeline must be\n # 'GenerateSegmentIndices'\n if test_pipeline[0]['type'] != 'GenerateSegmentIndices':\n raise TypeError('The first element in the pipeline must be '\n f'\"GenerateSegmentIndices\", but got '\n f'\"{test_pipeline[0][\"type\"]}\".')\n\n # specify start_idx and filename_tmpl\n test_pipeline[0]['start_idx'] = self.extra_parameters['start_idx']\n test_pipeline[0]['filename_tmpl'] = \\\n self.extra_parameters['filename_tmpl']\n\n # prepare data\n sequence_length = len(glob.glob(osp.join(video, '*')))\n lq_folder = osp.dirname(video)\n key = osp.basename(video)\n data = dict(\n img_path=lq_folder,\n gt_path='',\n key=key,\n sequence_length=sequence_length)\n\n # compose the pipeline\n test_pipeline = Compose(test_pipeline)\n data = test_pipeline(data)\n results = data['inputs'].unsqueeze(0) / 255.0 # in cpu\n\n return results\n\n def forward(self, inputs: InputsType) -> PredType:\n \"\"\"Forward the inputs to the model.\n\n Args:\n inputs (InputsType): Images array of input video.\n\n Returns:\n PredType: Results of forwarding\n \"\"\"\n with torch.no_grad():\n if self.extra_parameters[\n 'window_size'] > 0: # sliding window framework\n data = pad_sequence(inputs,\n self.extra_parameters['window_size'])\n result = []\n # yapf: disable\n for i in range(0, data.size(1) - 2 * (self.extra_parameters['window_size'] // 2)): # noqa\n # yapf: enable\n data_i = data[:, i:i +\n self.extra_parameters['window_size']].to(\n self.device)\n result.append(\n self.model(inputs=data_i, mode='tensor').cpu())\n result = torch.stack(result, dim=1)\n else: # recurrent framework\n if self.extra_parameters['max_seq_len'] is None:\n result = self.model(\n inputs=inputs.to(self.device), mode='tensor').cpu()\n else:\n result = []\n for i in range(0, inputs.size(1),\n self.extra_parameters['max_seq_len']):\n result.append(\n self.model(\n inputs=inputs[:, i:i + self.\n extra_parameters['max_seq_len']].\n to(self.device),\n mode='tensor').cpu())\n result = torch.cat(result, dim=1)\n return result\n\n def visualize(self,\n preds: PredType,\n result_out_dir: str = '') -> List[np.ndarray]:\n \"\"\"Visualize predictions.\n\n Args:\n preds (List[Union[str, np.ndarray]]): Forward results\n by the inferencer.\n data (List[Dict]): Not needed by this kind of inferencer.\n result_out_dir (str): Output directory of image.\n Defaults to ''.\n\n Returns:\n List[np.ndarray]: Result of visualize\n \"\"\"\n file_extension = os.path.splitext(result_out_dir)[1]\n mmengine.utils.mkdir_or_exist(osp.dirname(result_out_dir))\n prog_bar = ProgressBar(preds.size(1))\n if file_extension in VIDEO_EXTENSIONS: # save as video\n h, w = preds.shape[-2:]\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n video_writer = cv2.VideoWriter(result_out_dir, fourcc, 25, (w, h))\n for i in range(0, preds.size(1)):\n img = tensor2img(preds[:, i, :, :, :])\n video_writer.write(img.astype(np.uint8))\n prog_bar.update()\n cv2.destroyAllWindows()\n video_writer.release()\n else:\n for i in range(self.extra_parameters['start_idx'],\n self.extra_parameters['start_idx'] + preds.size(1)):\n output_i = \\\n preds[:, i - self.extra_parameters['start_idx'], :, :, :]\n output_i = tensor2img(output_i)\n filename_tmpl = self.extra_parameters['filename_tmpl']\n save_path_i = f'{result_out_dir}/{filename_tmpl.format(i)}'\n mmcv.imwrite(output_i, save_path_i)\n prog_bar.update()\n\n logger: MMLogger = MMLogger.get_current_instance()\n logger.info(f'Output video is save at {result_out_dir}.')\n\n return []\n\n def postprocess(\n self,\n preds: PredType,\n imgs: Optional[List[np.ndarray]] = None\n ) -> Union[ResType, Tuple[ResType, np.ndarray]]:\n \"\"\"Postprocess is not needed in this inferencer.\"\"\"\n logger: MMLogger = MMLogger.get_current_instance()\n logger.info('Postprocess is implemented in visualize process.')\n return None\n" }, { "path": "mmagic/apis/mmagic_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport warnings\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport yaml\nfrom mmengine.registry import init_default_scope\n\nfrom .inferencers import Inferencers\nfrom .inferencers.base_mmagic_inferencer import InputsType\n\n\nclass MMagicInferencer:\n \"\"\"MMagicInferencer API for mmagic models inference.\n\n Args:\n model_name (str): Name of the editing model.\n model_setting (str): Setting of a specific model.\n Default to 'a'.\n model_config (str): Path to the config file for the editing model.\n Default to None.\n model_ckpt (str): Path to the checkpoint file for the editing model.\n Default to None.\n config_dir (str): Path to the directory containing config files.\n Default to 'configs/'.\n device (torch.device): Device to use for inference. Default to 'cuda'.\n\n Examples:\n >>> # inference of a conditional model, biggan for example\n >>> editor = MMagicInferencer(model_name='biggan')\n >>> editor.infer(label=1, result_out_dir='./biggan_res.jpg')\n\n >>> # inference of a translation model, pix2pix for example\n >>> editor = MMagicInferencer(model_name='pix2pix')\n >>> editor.infer(img='./test.jpg', result_out_dir='./pix2pix_res.jpg')\n\n >>> # see demo/mmediting_inference_tutorial.ipynb for more examples\n \"\"\"\n # unsupported now\n # singan, liif\n # output should be checked\n # dic, glean\n\n inference_supported_models = [\n # colorization models\n 'inst_colorization',\n\n # conditional models\n 'biggan',\n 'sngan_proj',\n 'sagan',\n\n # unconditional models\n 'dcgan',\n 'deblurganv2',\n 'wgan-gp',\n 'lsgan',\n 'ggan',\n 'pggan',\n 'styleganv1',\n 'styleganv2',\n 'styleganv3',\n\n # matting models\n 'dim',\n 'indexnet',\n 'gca',\n\n # inpainting models\n 'aot_gan',\n 'deepfillv1',\n 'deepfillv2',\n 'global_local',\n 'partial_conv',\n\n # translation models\n 'pix2pix',\n 'cyclegan',\n\n # image super-resolution models\n 'srcnn',\n 'srgan_resnet',\n 'edsr',\n 'esrgan',\n 'rdn',\n 'dic',\n 'ttsr',\n 'glean',\n 'real_esrgan',\n\n # video_interpolation models\n 'flavr',\n 'cain',\n\n # video_restoration models\n 'edvr',\n 'tdan',\n 'basicvsr',\n 'iconvsr',\n 'basicvsr_pp',\n 'real_basicvsr',\n\n # image_restoration models\n 'nafnet',\n 'swinir',\n 'restormer',\n\n # text2image models\n 'controlnet',\n 'disco_diffusion',\n 'stable_diffusion',\n\n # 3D-aware generation\n 'eg3d',\n\n # animation inferencer\n 'controlnet_animation',\n\n # draggan\n 'draggan',\n\n # diffusers pipeline inferencer\n 'diffusers_pipeline',\n ]\n\n inference_supported_models_cfg = {}\n inference_supported_models_cfg_inited = False\n\n def __init__(self,\n model_name: str = None,\n model_setting: int = None,\n config_name: int = None,\n model_config: str = None,\n model_ckpt: str = None,\n device: torch.device = None,\n extra_parameters: Dict = None,\n seed: int = 2022,\n **kwargs) -> None:\n init_default_scope('mmagic')\n MMagicInferencer.init_inference_supported_models_cfg()\n inferencer_kwargs = {}\n inferencer_kwargs.update(\n self._get_inferencer_kwargs(model_name, model_setting, config_name,\n model_config, model_ckpt,\n extra_parameters))\n self.inferencer = Inferencers(\n device=device, seed=seed, **inferencer_kwargs)\n\n def _get_inferencer_kwargs(self, model_name: Optional[str],\n model_setting: Optional[int],\n config_name: Optional[int],\n model_config: Optional[str],\n model_ckpt: Optional[str],\n extra_parameters: Optional[Dict]) -> Dict:\n \"\"\"Get the kwargs for the inferencer.\"\"\"\n kwargs = {}\n\n if model_name is not None:\n cfgs = self.get_model_config(model_name)\n kwargs['task'] = cfgs['task']\n setting_to_use = 0\n if model_setting:\n setting_to_use = model_setting\n config_dir = cfgs['settings'][setting_to_use]['Config']\n if config_name:\n for setting in cfgs['settings']:\n if setting['Name'] == config_name:\n config_dir = setting['Config']\n break\n config_dir = config_dir[config_dir.find('configs'):]\n if osp.exists(\n osp.join(osp.dirname(__file__), '..', '..', config_dir)):\n kwargs['config'] = osp.join(\n osp.dirname(__file__), '..', '..', config_dir)\n else:\n kwargs['config'] = osp.join(\n osp.dirname(__file__), '..', '.mim', config_dir)\n if 'Weights' in cfgs['settings'][setting_to_use].keys():\n kwargs['ckpt'] = cfgs['settings'][setting_to_use]['Weights']\n if model_name == 'controlnet':\n kwargs['ckpt'] = None\n\n if model_config is not None:\n if kwargs.get('config', None) is not None:\n warnings.warn(\n f'{model_name}\\'s default config '\n f'is overridden by {model_config}', UserWarning)\n kwargs['config'] = model_config\n\n if model_ckpt is not None:\n if kwargs.get('ckpt', None) is not None:\n warnings.warn(\n f'{model_name}\\'s default checkpoint '\n f'is overridden by {model_ckpt}', UserWarning)\n kwargs['ckpt'] = model_ckpt\n\n if extra_parameters is not None:\n kwargs['extra_parameters'] = extra_parameters\n\n return kwargs\n\n def print_extra_parameters(self):\n \"\"\"Print the unique parameters of each kind of inferencer.\"\"\"\n extra_parameters = self.inferencer.get_extra_parameters()\n print(extra_parameters)\n\n def infer(self,\n img: InputsType = None,\n video: InputsType = None,\n label: InputsType = None,\n trimap: InputsType = None,\n mask: InputsType = None,\n result_out_dir: str = '',\n **kwargs) -> Union[Dict, List[Dict]]:\n \"\"\"Infer edit model on an image(video).\n\n Args:\n img (str): Img path.\n video (str): Video path.\n label (int): Label for conditional or unconditional models.\n trimap (str): Trimap path for matting models.\n mask (str): Mask path for inpainting models.\n result_out_dir (str): Output directory of result image or video.\n Defaults to ''.\n\n Returns:\n Dict or List[Dict]: Each dict contains the inference result of\n each image or video.\n \"\"\"\n return self.inferencer(\n img=img,\n video=video,\n label=label,\n trimap=trimap,\n mask=mask,\n result_out_dir=result_out_dir,\n **kwargs)\n\n def get_model_config(self, model_name: str) -> Dict:\n \"\"\"Get the model configuration including model config and checkpoint\n url.\n\n Args:\n model_name (str): Name of the model.\n Returns:\n dict: Model configuration.\n \"\"\"\n if model_name not in self.inference_supported_models:\n raise ValueError(f'Model {model_name} is not supported.')\n else:\n return self.inference_supported_models_cfg[model_name]\n\n @staticmethod\n def init_inference_supported_models_cfg() -> None:\n if not MMagicInferencer.inference_supported_models_cfg_inited:\n if osp.exists(\n osp.join(osp.dirname(__file__), '..', '..', 'configs')):\n all_cfgs_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'configs')\n else:\n all_cfgs_dir = osp.join(\n osp.dirname(__file__), '..', '.mim', 'configs')\n for model_name in MMagicInferencer.inference_supported_models:\n meta_file_dir = osp.join(all_cfgs_dir, model_name,\n 'metafile.yml')\n with open(meta_file_dir, 'r') as stream:\n parsed_yaml = yaml.safe_load(stream)\n task = parsed_yaml['Models'][0]['Results'][0]['Task']\n MMagicInferencer.inference_supported_models_cfg[\n model_name] = {}\n MMagicInferencer.inference_supported_models_cfg[model_name][\n 'task'] = task # noqa\n MMagicInferencer.inference_supported_models_cfg[model_name][\n 'settings'] = parsed_yaml['Models'] # noqa\n\n MMagicInferencer.inference_supported_models_cfg_inited = True\n\n @staticmethod\n def get_inference_supported_models() -> List:\n \"\"\"static function for getting inference supported modes.\"\"\"\n return MMagicInferencer.inference_supported_models\n\n @staticmethod\n def get_inference_supported_tasks() -> List:\n \"\"\"static function for getting inference supported tasks.\"\"\"\n if not MMagicInferencer.inference_supported_models_cfg_inited:\n MMagicInferencer.init_inference_supported_models_cfg()\n\n supported_task = set()\n for key in MMagicInferencer.inference_supported_models_cfg.keys():\n if MMagicInferencer.inference_supported_models_cfg[key]['task'] \\\n not in supported_task:\n supported_task.add(MMagicInferencer.\n inference_supported_models_cfg[key]['task'])\n return list(supported_task)\n\n @staticmethod\n def get_task_supported_models(task: str) -> List:\n \"\"\"static function for getting task supported models.\"\"\"\n if not MMagicInferencer.inference_supported_models_cfg_inited:\n MMagicInferencer.init_inference_supported_models_cfg()\n\n supported_models = []\n for key in MMagicInferencer.inference_supported_models_cfg.keys():\n if MMagicInferencer.inference_supported_models_cfg[key][\n 'task'] == task:\n supported_models.append(key)\n return supported_models\n" }, { "path": "mmagic/configs/_base_/datasets/basicvsr_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicFramesDataset\nfrom mmagic.datasets.transforms import (GenerateSegmentIndices,\n LoadImageFromFile, MirrorSequence,\n PackInputs)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\n# configs for REDS4\nreds_data_root = 'data/REDS'\n\nreds_pipeline = [\n dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\nreds_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=reds_data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_val.txt',\n depth=1,\n num_input_frames=100,\n fixed_seq_len=100,\n pipeline=reds_pipeline))\n\nreds_evaluator = [\n dict(type=PSNR, prefix='REDS4-BIx4-RGB'),\n dict(type=SSIM, prefix='REDS4-BIx4-RGB')\n]\n\n# configs for vimeo90k-bd and vimeo90k-bi\nvimeo_90k_data_root = 'data/vimeo90k'\nvimeo_90k_file_list = [\n 'im1.png', 'im2.png', 'im3.png', 'im4.png', 'im5.png', 'im6.png', 'im7.png'\n]\n\nvimeo_90k_pipeline = [\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=MirrorSequence, keys=['img']),\n dict(type=PackInputs)\n]\n\nvimeo_90k_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),\n data_root=vimeo_90k_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_test_GT.txt',\n depth=2,\n num_input_frames=7,\n fixed_seq_len=7,\n load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),\n pipeline=vimeo_90k_pipeline))\n\nvimeo_90k_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),\n data_root=vimeo_90k_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vimeo90K_test_GT.txt',\n depth=2,\n num_input_frames=7,\n fixed_seq_len=7,\n load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),\n pipeline=vimeo_90k_pipeline))\n\nvimeo_90k_bd_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),\n]\n\nvimeo_90k_bi_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),\n]\n\n# config for UDM10 (BDx4)\nudm10_data_root = 'data/UDM10'\n\nudm10_pipeline = [\n dict(\n type=GenerateSegmentIndices,\n interval_list=[1],\n filename_tmpl='{:04d}.png'),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\nudm10_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='udm10', task_name='vsr'),\n data_root=udm10_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n pipeline=udm10_pipeline))\n\nudm10_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='UDM10-BDx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='UDM10-BDx4-Y')\n]\n\n# config for vid4\nvid4_data_root = 'data/Vid4'\n\nvid4_pipeline = [\n dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\nvid4_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=1,\n pipeline=vid4_pipeline))\n\nvid4_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=1,\n pipeline=vid4_pipeline))\n\nvid4_bd_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='VID4-BDx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='VID4-BDx4-Y'),\n]\nvid4_bi_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='VID4-BIx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='VID4-BIx4-Y'),\n]\n\n# config for test\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n reds_dataloader,\n vimeo_90k_bd_dataloader,\n vimeo_90k_bi_dataloader,\n udm10_dataloader,\n vid4_bd_dataloader,\n vid4_bi_dataloader,\n]\ntest_evaluator = [\n reds_evaluator,\n vimeo_90k_bd_evaluator,\n vimeo_90k_bi_evaluator,\n udm10_evaluator,\n vid4_bd_evaluator,\n vid4_bi_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/celeba.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.evaluation import MAE, PSNR, SSIM\n\n# Base config for CelebA-HQ dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/CelebA-HQ'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt=''),\n ann_file='train_celeba_img_list.txt',\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt=''),\n ann_file='val_celeba_img_list.txt',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type=MAE, mask_key='mask', scaling=100),\n # By default, compute with pixel value from 0-1\n # scale=2 to align with 1.0\n # scale=100 seems to align with readme\n dict(type=PSNR),\n dict(type=SSIM),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "mmagic/configs/_base_/datasets/cifar10_noaug.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset.sampler import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.cifar10_dataset import CIFAR10\nfrom mmagic.datasets.transforms.formatting import PackInputs\n\ncifar_pipeline = [dict(type=PackInputs)]\ncifar_dataset = dict(\n type=CIFAR10,\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline)\n\ntrain_dataloader = dict(\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/cifar10_nopad.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets import CIFAR10\nfrom mmagic.datasets.transforms import Flip, PackInputs\n\ncifar_pipeline = [\n dict(type=Flip, keys=['gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type=PackInputs)\n]\ncifar_dataset = dict(\n type=CIFAR10,\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline)\n\n# test dataset do not use flip\ncifar_pipeline_test = [dict(type=PackInputs)]\ncifar_dataset_test = dict(\n type=CIFAR10,\n data_root='./data',\n data_prefix='cifar10',\n test_mode=False,\n pipeline=cifar_pipeline_test)\n\ntrain_dataloader = dict(\n num_workers=2,\n dataset=cifar_dataset,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset_test,\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=32,\n num_workers=2,\n dataset=cifar_dataset_test,\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/comp1k.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.evaluation import SAD, ConnectivityError, GradientError, MattingMSE\n\n# Base config for Composition-1K dataset\n\n# dataset settings\ndataset_type = 'AdobeComp1kDataset'\ndata_root = 'data/adobe_composition-1k'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n ann_file='training_list.json',\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n ann_file='test_list.json',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\n# TODO: matting\nval_evaluator = [\n dict(type=SAD),\n dict(type=MattingMSE),\n dict(type=GradientError),\n dict(type=ConnectivityError),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "mmagic/configs/_base_/datasets/deblurring-defocus_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import MAE, PSNR, SSIM\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='imgL',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='imgR',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\ndpdd_data_root = 'data/DPDD'\n\ndpdd_indoor_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='DPDD-Indoor', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n ann_file='indoor_labels.txt',\n pipeline=test_pipeline))\ndpdd_indoor_evaluator = [\n dict(type=MAE, prefix='DPDD-Indoor'),\n dict(type=PSNR, prefix='DPDD-Indoor'),\n dict(type=SSIM, prefix='DPDD-Indoor'),\n]\n\ndpdd_outdoor_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='DPDD-Outdoor', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n ann_file='outdoor_labels.txt',\n pipeline=test_pipeline))\ndpdd_outdoor_evaluator = [\n dict(type=MAE, prefix='DPDD-Outdoor'),\n dict(type=PSNR, prefix='DPDD-Outdoor'),\n dict(type=SSIM, prefix='DPDD-Outdoor'),\n]\n\ndpdd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='DPDD-Combined', task_name='deblurring'),\n data_root=dpdd_data_root,\n data_prefix=dict(\n img='inputC', imgL='inputL', imgR='inputR', gt='target'),\n pipeline=test_pipeline))\ndpdd_evaluator = [\n dict(type=MAE, prefix='DPDD-Combined'),\n dict(type=PSNR, prefix='DPDD-Combined'),\n dict(type=SSIM, prefix='DPDD-Combined'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n dpdd_indoor_dataloader,\n dpdd_outdoor_dataloader,\n dpdd_dataloader,\n]\ntest_evaluator = [\n dpdd_indoor_evaluator,\n dpdd_outdoor_evaluator,\n dpdd_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/deblurring-motion_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\ngopro_data_root = 'data/gopro/test'\ngopro_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='GoPro', task_name='deblurring'),\n data_root=gopro_data_root,\n data_prefix=dict(img='blur', gt='sharp'),\n pipeline=test_pipeline))\ngopro_evaluator = [\n dict(type=PSNR, prefix='GoPro'),\n dict(type=SSIM, prefix='GoPro'),\n]\n\nhide_data_root = 'data/HIDE'\nhide_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='HIDE', task_name='deblurring'),\n data_root=hide_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nhide_evaluator = [\n dict(type=PSNR, prefix='HIDE'),\n dict(type=SSIM, prefix='HIDE'),\n]\n\nrealblurj_data_root = 'data/RealBlur_J'\nrealblurj_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='RealBlur_J', task_name='deblurring'),\n data_root=realblurj_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrealblurj_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='RealBlurJ'),\n dict(type=SSIM, convert_to='Y', prefix='RealBlurJ'),\n]\n\nrealblurr_data_root = 'data/RealBlur_R'\nrealblurr_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='RealBlur_R', task_name='deblurring'),\n data_root=realblurr_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrealblurr_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='RealBlurR'),\n dict(type=SSIM, convert_to='Y', prefix='RealBlurR'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n gopro_dataloader,\n hide_dataloader,\n realblurj_dataloader,\n realblurr_dataloader,\n]\ntest_evaluator = [\n gopro_evaluator,\n hide_evaluator,\n realblurj_evaluator,\n realblurr_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/decompression_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import (LoadImageFromFile, PackInputs,\n RandomJPEGCompression)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\nquality = 10\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=RandomJPEGCompression,\n params=dict(quality=[quality, quality]),\n bgr2rgb=True,\n keys=['img']),\n dict(type=PackInputs)\n]\n\nclassic5_data_root = 'data/Classic5'\nclassic5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='classic5', task_name='CAR'),\n data_root=classic5_data_root,\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\nclassic5_evaluator = [\n dict(type=PSNR, prefix='Classic5'),\n dict(type=SSIM, prefix='Classic5'),\n]\n\nlive1_data_root = 'data/LIVE1'\nlive1_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='live1', task_name='CAR'),\n data_root=live1_data_root,\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\nlive1_evaluator = [\n dict(type=PSNR, prefix='LIVE1'),\n dict(type=SSIM, prefix='LIVE1'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n classic5_dataloader,\n live1_dataloader,\n]\ntest_evaluator = [\n classic5_evaluator,\n live1_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/denoising-gaussian_color_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import (LoadImageFromFile, PackInputs,\n RandomNoise)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\nsigma = 15\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=RandomNoise,\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=0),\n keys=['img']),\n dict(type=PackInputs)\n]\n\ndata_root = 'data/denoising_gaussian_test'\ncbsd68_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='CBSD68', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='CBSD68', gt='CBSD68'),\n pipeline=test_pipeline))\ncbsd68_evaluator = [\n dict(type=PSNR, prefix='CBSD68'),\n dict(type=SSIM, prefix='CBSD68'),\n]\n\nkodak24_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Kodak24', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Kodak24', gt='Kodak24'),\n pipeline=test_pipeline))\nkodak24_evaluator = [\n dict(type=PSNR, prefix='Kodak24'),\n dict(type=SSIM, prefix='Kodak24'),\n]\n\nmcmaster_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='McMaster', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='McMaster', gt='McMaster'),\n pipeline=test_pipeline))\nmcmaster_evaluator = [\n dict(type=PSNR, prefix='McMaster'),\n dict(type=SSIM, prefix='McMaster'),\n]\n\nurban100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Urban100', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Urban100', gt='Urban100'),\n pipeline=test_pipeline))\nurban100_evaluator = [\n dict(type=PSNR, prefix='Urban100'),\n dict(type=SSIM, prefix='Urban100'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n cbsd68_dataloader,\n kodak24_dataloader,\n mcmaster_dataloader,\n urban100_dataloader,\n]\ntest_evaluator = [\n cbsd68_evaluator,\n kodak24_evaluator,\n mcmaster_evaluator,\n urban100_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/denoising-gaussian_gray_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import (LoadImageFromFile, PackInputs,\n RandomNoise)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\nsigma = 15\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n to_y_channel=True,\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n to_y_channel=True,\n imdecode_backend='cv2'),\n dict(\n type=RandomNoise,\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[sigma, sigma],\n gaussian_gray_noise_prob=1),\n keys=['img']),\n dict(type=PackInputs)\n]\n\ndata_root = 'data/denoising_gaussian_test'\nset12_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Set12', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Set12', gt='Set12'),\n pipeline=test_pipeline))\nset12_evaluator = [\n dict(type=PSNR, prefix='Set12'),\n dict(type=SSIM, prefix='Set12'),\n]\n\nbsd68_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='BSD68', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='BSD68', gt='BSD68'),\n pipeline=test_pipeline))\nbsd68_evaluator = [\n dict(type=PSNR, prefix='BSD68'),\n dict(type=SSIM, prefix='BSD68'),\n]\n\nurban100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Urban100', task_name='denoising'),\n data_root=data_root,\n data_prefix=dict(img='Urban100', gt='Urban100'),\n pipeline=test_pipeline))\nurban100_evaluator = [\n dict(type=PSNR, prefix='Urban100'),\n dict(type=SSIM, prefix='Urban100'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n set12_dataloader,\n bsd68_dataloader,\n urban100_dataloader,\n]\ntest_evaluator = [\n set12_evaluator,\n bsd68_evaluator,\n urban100_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/denoising-real_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\nsidd_data_root = 'data/SIDD/val/'\nsidd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='SIDD', task_name='denoising'),\n data_root=sidd_data_root,\n data_prefix=dict(img='noisy', gt='gt'),\n filename_tmpl=dict(gt='{}_GT', img='{}_NOISY'),\n pipeline=test_pipeline))\nsidd_evaluator = [\n dict(type=PSNR, prefix='SIDD'),\n dict(type=SSIM, prefix='SIDD'),\n]\n\ndnd_data_root = 'data/DND'\ndnd_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='DND', task_name='denoising'),\n data_root=dnd_data_root,\n data_prefix=dict(img='input', gt='groundtruth'),\n pipeline=test_pipeline))\ndnd_evaluator = [\n dict(type=PSNR, prefix='DND'),\n dict(type=SSIM, prefix='DND'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n sidd_dataloader,\n # dnd_dataloader,\n]\ntest_evaluator = [\n sidd_evaluator,\n # dnd_dataloader,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/deraining_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\nrain100h_data_root = 'data/Rain100H'\nrain100h_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Rain100H', task_name='deraining'),\n data_root=rain100h_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrain100h_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Rain100H'),\n dict(type=SSIM, convert_to='Y', prefix='Rain100H'),\n]\n\nrain100l_data_root = 'data/Rain100L'\nrain100l_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Rain100L', task_name='deraining'),\n data_root=rain100l_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\nrain100l_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Rain100L'),\n dict(type=SSIM, convert_to='Y', prefix='Rain100L'),\n]\n\ntest100_data_root = 'data/Test100'\ntest100_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Test100', task_name='deraining'),\n data_root=test100_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest100_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Test100'),\n dict(type=SSIM, convert_to='Y', prefix='Test100'),\n]\n\ntest1200_data_root = 'data/Test1200'\ntest1200_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Test1200', task_name='deraining'),\n data_root=test1200_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest1200_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Test1200'),\n dict(type=SSIM, convert_to='Y', prefix='Test1200'),\n]\n\ntest2800_data_root = 'data/Test2800'\ntest2800_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='Test2800', task_name='deraining'),\n data_root=test2800_data_root,\n data_prefix=dict(img='input', gt='target'),\n pipeline=test_pipeline))\ntest2800_evaluator = [\n dict(type=PSNR, convert_to='Y', prefix='Test2800'),\n dict(type=SSIM, convert_to='Y', prefix='Test2800'),\n]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n rain100h_dataloader,\n rain100l_dataloader,\n test100_dataloader,\n test1200_dataloader,\n test2800_dataloader,\n]\ntest_evaluator = [\n rain100h_evaluator,\n rain100l_evaluator,\n test100_evaluator,\n test1200_evaluator,\n test2800_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/ffhq_flip.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset.sampler import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.basic_image_dataset import BasicImageDataset\nfrom mmagic.datasets.transforms.aug_shape import Flip\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\n\ndataset_type = BasicImageDataset\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=Flip, keys=['gt'], direction='horizontal'),\n dict(type=PackInputs, keys='gt')\n]\n\nval_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=PackInputs, keys=['gt'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets import BasicImageDataset, GrowScaleImgDataset\nfrom mmagic.datasets.transforms import Flip, LoadImageFromFile, PackInputs\n\ndataset_type = 'GrowScaleImgDataset'\n\npipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=Flip, keys='gt', direction='horizontal'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type=GrowScaleImgDataset,\n data_roots={\n '1024': './data/ffhq/images',\n '256': './data/ffhq/ffhq_imgs/ffhq_256',\n },\n gpu_samples_base=4,\n # note that this should be changed with total gpu number\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4,\n '128': 4,\n '256': 4,\n '512': 4,\n '1024': 4\n },\n len_per_stage=300000,\n pipeline=pipeline),\n sampler=dict(type=InfiniteSampler, shuffle=True))\n\ntest_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type=BasicImageDataset,\n data_prefix=dict(gt=''),\n pipeline=pipeline,\n data_root='./data/ffhq/images'),\n sampler=dict(type=DefaultSampler, shuffle=False))\n\nval_dataloader = test_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/imagenet_128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets.transforms import (CenterCropLongEdge, Flip,\n LoadImageFromFile, PackInputs,\n RandomCropLongEdge, Resize)\n\n# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=RandomCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(128, 128), keys='gt', backend='pillow'),\n dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=CenterCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(128, 128), keys='gt', backend='pillow'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/imagenet_256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets.transforms import (CenterCropLongEdge, Flip,\n LoadImageFromFile, PackInputs,\n RandomCropLongEdge, Resize)\n\n# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='img'),\n dict(type=RandomCropLongEdge, keys=['img']),\n dict(type=Resize, scale=(256, 256), keys=['img'], backend='pillow'),\n dict(type=Flip, keys=['img'], flip_ratio=0.5, direction='horizontal'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img'),\n dict(type=CenterCropLongEdge, keys=['img']),\n dict(type=Resize, scale=(256, 256), backend='pillow'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/imagenet_512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset.sampler import DefaultSampler\n\nfrom mmagic.datasets.imagenet_dataset import ImageNet\nfrom mmagic.datasets.transforms.aug_shape import Flip, Resize\nfrom mmagic.datasets.transforms.crop import (CenterCropLongEdge,\n RandomCropLongEdge)\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\n\n# dataset settings\ndataset_type = ImageNet\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=RandomCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(512, 512), keys='gt', backend='pillow'),\n dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=CenterCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(512, 512), keys='gt', backend='pillow'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/imagenet_64.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset.sampler import DefaultSampler\n\nfrom mmagic.datasets.imagenet_dataset import ImageNet\nfrom mmagic.datasets.transforms.aug_shape import Flip, Resize\nfrom mmagic.datasets.transforms.crop import (CenterCropLongEdge,\n RandomCropLongEdge)\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\n\n# dataset settings\ndataset_type = ImageNet\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=RandomCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(64, 64), keys='gt', backend='pillow'),\n dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=CenterCropLongEdge, keys='gt'),\n dict(type=Resize, scale=(64, 64), keys='gt', backend='pillow'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/imagenet_noaug_128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# dataset settings\nfrom mmengine.dataset.sampler import DefaultSampler\n\nfrom mmagic.datasets.imagenet_dataset import ImageNet\nfrom mmagic.datasets.transforms.aug_shape import Resize\nfrom mmagic.datasets.transforms.crop import CenterCropLongEdge\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\n\ndataset_type = ImageNet\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# Remove `RandomFlip` augmentation and change `RandomCropLongEdge` to\n# `CenterCropLongEdge` to eliminate randomness.\n# dataset settings\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='img'),\n dict(type=CenterCropLongEdge),\n dict(type=Resize, scale=(128, 128), backend='pillow'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img'),\n dict(type=CenterCropLongEdge),\n dict(type=Resize, scale=(128, 128), backend='pillow'),\n dict(type=PackInputs)\n]\n\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='data/imagenet',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=True),\n persistent_workers=True)\n\nval_dataloader = dict(\n batch_size=64,\n num_workers=5,\n dataset=dict(\n type=dataset_type,\n data_root='./data/imagenet/',\n ann_file='meta/train.txt',\n data_prefix='train',\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = val_dataloader\n" }, { "path": "mmagic/configs/_base_/datasets/liif_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import (GenerateCoordinateAndCell,\n LoadImageFromFile, PackInputs,\n RandomDownSampling)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\nscale_test_list = [2, 3, 4, 6, 18, 30]\n\ntest_pipelines = [[\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=RandomDownSampling, scale_min=scale_test, scale_max=scale_test),\n dict(type=GenerateCoordinateAndCell, scale=scale_test, reshape_gt=False),\n dict(type=PackInputs)\n] for scale_test in scale_test_list]\n\n# test config for Set5\nset5_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root='data/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\nset5_evaluators = [[\n dict(type=PSNR, crop_border=scale, prefix=f'Set5x{scale}'),\n dict(type=SSIM, crop_border=scale, prefix=f'Set5x{scale}'),\n] for scale in scale_test_list]\n\n# test config for Set14\nset14_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root='data/Set14',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\nset14_evaluators = [[\n dict(type=PSNR, crop_border=scale, prefix=f'Set14x{scale}'),\n dict(type=SSIM, crop_border=scale, prefix=f'Set14x{scale}'),\n] for scale in scale_test_list]\n\n# test config for DIV2K\ndiv2k_dataloaders = [\n dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root='data/DIV2K',\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline)) for test_pipeline in test_pipelines\n]\ndiv2k_evaluators = [[\n dict(type=PSNR, crop_border=scale, prefix=f'DIV2Kx{scale}'),\n dict(type=SSIM, crop_border=scale, prefix=f'DIV2Kx{scale}'),\n] for scale in scale_test_list]\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n *set5_dataloaders,\n *set14_dataloaders,\n *div2k_dataloaders,\n]\ntest_evaluator = [\n *set5_evaluators,\n *set14_evaluators,\n *div2k_evaluators,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/lsun_stylegan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\n\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=PackInputs)\n]\n\nval_pipeline = [dict(type=LoadImageFromFile, key='gt'), dict(type=PackInputs)]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/paired_imgs_256x256_crop.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.transforms import (FixedCrop, Flip,\n LoadPairedImageFromFile, PackInputs,\n Resize)\n\ndataset_type = 'PairedImageDataset'\n# domain_a = None # set by user\n# domain_b = None # set by user\n\ntrain_pipeline = [\n dict(\n type=LoadPairedImageFromFile,\n key='pair',\n domain_a='A',\n domain_b='B',\n color_type='color'),\n dict(\n type=Resize,\n keys=['img_A', 'img_B'],\n scale=(286, 286),\n interpolation='bicubic'),\n dict(type=FixedCrop, keys=['img_A', 'img_B'], crop_size=(256, 256)),\n dict(type=Flip, keys=['img_A', 'img_B'], direction='horizontal'),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type=PackInputs,\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\ntest_pipeline = [\n dict(\n type=LoadPairedImageFromFile,\n key='pair',\n domain_a='A',\n domain_b='B',\n color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type=Resize,\n scale=(256, 256),\n keys='img',\n interpolation='bicubic')\n ]),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type=PackInputs,\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/places.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.evaluation import MAE, PSNR, SSIM\n\n# Base config for places365 dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/Places'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt='data_large'),\n ann_file='meta/places365_train_challenge.txt',\n # Note that Places365-standard (1.8M images) and\n # Place365-challenge (8M images) use different image lists.\n test_mode=False,\n ))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_prefix=dict(gt='val_large'),\n ann_file='meta/places365_val.txt',\n test_mode=True,\n ))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type=MAE, mask_key='mask', scaling=100),\n # By default, compute with pixel value from 0-1\n # scale=2 to align with 1.0\n # scale=100 seems to align with readme\n dict(type=PSNR),\n dict(type=SSIM),\n]\n\ntest_evaluator = val_evaluator\n" }, { "path": "mmagic/configs/_base_/datasets/sisr_x2_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM, Evaluator\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=2, prefix='Set5'),\n dict(type=SSIM, crop_border=2, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx2', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=2, prefix='Set14'),\n dict(type=SSIM, crop_border=2, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n # ann_file='meta_info_DIV2K800sub_GT.txt',\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=2, prefix='DIV2K'),\n dict(type=SSIM, crop_border=2, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/sisr_x3_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM, Evaluator\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=3, prefix='Set5'),\n dict(type=SSIM, crop_border=3, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx3', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=3, prefix='Set14'),\n dict(type=SSIM, crop_border=3, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X3_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=3, prefix='DIV2K'),\n dict(type=SSIM, crop_border=3, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/sisr_x4_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM, Evaluator\n\ntest_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=PackInputs)\n]\n\n# test config for Set5\nset5_data_root = 'data/Set5'\nset5_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=set5_data_root,\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline))\nset5_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=4, prefix='Set5'),\n dict(type=SSIM, crop_border=4, prefix='Set5'),\n ])\n\nset14_data_root = 'data/Set14'\nset14_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n metainfo=dict(dataset_type='set14', task_name='sisr'),\n data_root=set14_data_root,\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=test_pipeline))\nset14_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=4, prefix='Set14'),\n dict(type=SSIM, crop_border=4, prefix='Set14'),\n ])\n\n# test config for DIV2K\ndiv2k_data_root = 'data/DIV2K'\ndiv2k_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicImageDataset,\n ann_file='meta_info_DIV2K100sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=div2k_data_root,\n data_prefix=dict(\n img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),\n pipeline=test_pipeline))\ndiv2k_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=4, prefix='DIV2K'),\n dict(type=SSIM, crop_border=4, prefix='DIV2K'),\n ])\n\n# test config\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n set5_dataloader,\n set14_dataloader,\n div2k_dataloader,\n]\ntest_evaluator = [\n set5_evaluator,\n set14_evaluator,\n div2k_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/tdan_test_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicFramesDataset\nfrom mmagic.datasets.transforms import (GenerateFrameIndiceswithPadding,\n GenerateSegmentIndices,\n LoadImageFromFile, PackInputs)\nfrom mmagic.engine.runner import MultiTestLoop\nfrom mmagic.evaluation import PSNR, SSIM, Evaluator\n\n# configs for SPMCS-30\nSPMC_data_root = 'data/SPMCS'\n\nSPMC_pipeline = [\n dict(type=GenerateFrameIndiceswithPadding, padding='reflection'),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\nSPMC_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='spmcs', task_name='vsr'),\n data_root=SPMC_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_SPMCS_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=SPMC_pipeline))\n\nSPMC_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='spmcs', task_name='vsr'),\n data_root=SPMC_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_SPMCS_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=SPMC_pipeline))\n\nSPMC_bd_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),\n dict(type=SSIM, crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),\n ])\nSPMC_bi_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),\n dict(type=SSIM, crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),\n ])\n\n# config for vid4\nvid4_data_root = 'data/Vid4'\n\nvid4_pipeline = [\n # dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(type=GenerateFrameIndiceswithPadding, padding='reflection'),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\nvid4_bd_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BDx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=vid4_pipeline))\n\nvid4_bi_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root=vid4_data_root,\n data_prefix=dict(img='BIx4', gt='GT'),\n ann_file='meta_info_Vid4_GT.txt',\n depth=2,\n num_input_frames=5,\n pipeline=vid4_pipeline))\n\nvid4_bd_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, convert_to='Y', prefix='VID4-BDx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='VID4-BDx4-Y'),\n ])\nvid4_bi_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=PSNR, convert_to='Y', prefix='VID4-BIx4-Y'),\n dict(type=SSIM, convert_to='Y', prefix='VID4-BIx4-Y'),\n ])\n\n# config for test\ntest_cfg = dict(type=MultiTestLoop)\ntest_dataloader = [\n SPMC_bd_dataloader,\n SPMC_bi_dataloader,\n vid4_bd_dataloader,\n vid4_bi_dataloader,\n]\ntest_evaluator = [\n SPMC_bd_evaluator,\n SPMC_bi_evaluator,\n vid4_bd_evaluator,\n vid4_bi_evaluator,\n]\n" }, { "path": "mmagic/configs/_base_/datasets/unconditional_imgs_128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs, Resize\n\n# dataset_type = 'BasicImageDataset'\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=Resize, keys='gt', scale=(128, 128)),\n dict(type=PackInputs)\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/unconditional_imgs_64x64.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs, Resize\n\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=Resize, keys='gt', scale=(64, 64)),\n dict(type=PackInputs)\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/unconditional_imgs_flip_512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset.sampler import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.basic_image_dataset import BasicImageDataset\nfrom mmagic.datasets.transforms.aug_shape import Flip, Resize\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\n\ndataset_type = BasicImageDataset\n\n# TODO:\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(type=Resize, keys='gt', scale=(512, 512)),\n dict(type=Flip, keys=['gt'], direction='horizontal'), # TODO:\n dict(type=PackInputs)\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='Resize',\n keys='gt',\n scale=(256, 256),\n interpolation='lanczos',\n backend='pillow'),\n dict(type='Flip', keys=['gt'], direction='horizontal'),\n dict(type='PackInputs')\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=None,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=None,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_prefix=dict(gt=''),\n data_root=None, # set by user\n pipeline=train_pipeline),\n sampler=dict(type='DefaultSampler', shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/datasets/unpaired_imgs_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\n\nfrom mmagic.datasets.transforms import Crop, Flip, PackInputs, Resize\n\ndataset_type = 'UnpairedImageDataset'\ndomain_a = None # set by user\ndomain_b = None # set by user\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img_A', color_type='color'),\n dict(type='LoadImageFromFile', key='img_B', color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(type=Resize, scale=(286, 286), interpolation='bicubic'),\n dict(\n type=Crop,\n keys=['img'],\n crop_size=(256, 256),\n random_crop=True),\n ]),\n dict(type=Flip, keys=['img_A'], direction='horizontal'),\n dict(type=Flip, keys=['img_B'], direction='horizontal'),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type=PackInputs,\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='img_A', color_type='color'),\n dict(type='LoadImageFromFile', key='img_B', color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_A', 'img_B']},\n auto_remap=True,\n share_random_params=True,\n transforms=dict(\n type=Resize, scale=(256, 256), interpolation='bicubic'),\n ),\n # NOTE: users should implement their own keyMapper and Pack operation\n # dict(\n # type='KeyMapper',\n # mapping={\n # f'img_{domain_a}': 'img_A',\n # f'img_{domain_b}': 'img_B'\n # },\n # remapping={\n # f'img_{domain_a}': f'img_{domain_a}',\n # f'img_{domain_b}': f'img_{domain_b}'\n # }),\n # dict(\n # type=PackInputs,\n # keys=[f'img_{domain_a}', f'img_{domain_b}'],\n # data_keys=[f'img_{domain_a}', f'img_{domain_b}'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=4,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n test_mode=True,\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=4,\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n test_mode=True,\n pipeline=test_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n" }, { "path": "mmagic/configs/_base_/default_runtime.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, LoggerHook,\n ParamSchedulerHook)\nfrom mmengine.visualization import LocalVisBackend\n\nfrom mmagic.engine import BasicVisualizationHook, IterTimerHook, LogProcessor\nfrom mmagic.visualization import ConcatImageVisualizer\n\ndefault_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n checkpoint=dict(\n type=CheckpointHook,\n interval=5000,\n out_dir=save_dir,\n by_epoch=False,\n max_keep_ckpts=10,\n save_best='PSNR',\n rule='greater',\n ),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nlog_level = 'INFO'\nlog_processor = dict(type=LogProcessor, window_size=100, by_epoch=False)\n\nload_from = None\nresume = False\n\nvis_backends = [dict(type=LocalVisBackend)]\nvisualizer = dict(\n type=ConcatImageVisualizer,\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type=BasicVisualizationHook, interval=1)]\n" }, { "path": "mmagic/configs/_base_/gen_default_runtime.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.hooks import CheckpointHook, LoggerHook\nfrom mmengine.model import MMSeparateDistributedDataParallel\n\nfrom mmagic.engine import (IterTimerHook, LogProcessor,\n MultiOptimWrapperConstructor, MultiTestLoop,\n MultiValLoop)\nfrom mmagic.evaluation import Evaluator\nfrom mmagic.visualization import VisBackend, Visualizer\n\ndefault_scope = 'mmagic'\n\nrandomness = dict(seed=2022, diff_rank_seed=True)\n# env settings\ndist_params = dict(backend='nccl')\n# disable opencv multithreading to avoid system being overloaded\nopencv_num_threads = 0\n# set multi-process start method as `fork` to speed up the training\nmp_start_method = 'fork'\n\n# configure for default hooks\ndefault_hooks = dict(\n # record time of every iteration.\n timer=dict(type=IterTimerHook),\n # print log every 100 iterations.\n logger=dict(type=LoggerHook, interval=100, log_metric_by_epoch=False),\n # save checkpoint per 10000 iterations\n checkpoint=dict(\n type=CheckpointHook,\n interval=10000,\n by_epoch=False,\n max_keep_ckpts=20,\n less_keys=['FID-Full-50k/fid', 'FID-50k/fid', 'swd/avg'],\n greater_keys=['IS-50k/is', 'ms-ssim/avg'],\n save_optimizer=True))\n\n# config for environment\nenv_cfg = dict(\n # whether to enable cudnn benchmark.\n cudnn_benchmark=True,\n # set multi process parameters.\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n # set distributed parameters.\n dist_cfg=dict(backend='nccl'))\n\n# set log level\nlog_level = 'INFO'\nlog_processor = dict(type=LogProcessor, by_epoch=False)\n\n# load from which checkpoint\nload_from = None\n\n# whether to resume training from the loaded checkpoint\nresume = None\n\n# config for model wrapper\nmodel_wrapper_cfg = dict(\n type=MMSeparateDistributedDataParallel,\n broadcast_buffers=False,\n find_unused_parameters=False)\n\n# set visualizer\nvis_backends = [dict(type=VisBackend)]\nvisualizer = dict(type=Visualizer, vis_backends=vis_backends)\n\n# config for training\ntrain_cfg = dict(by_epoch=False, val_begin=1, val_interval=10000)\n\n# config for val\nval_cfg = dict(type=MultiValLoop)\nval_evaluator = dict(type=Evaluator)\n\n# config for test\ntest_cfg = dict(type=MultiTestLoop)\ntest_evaluator = dict(type=Evaluator)\n\n# config for optim_wrapper_constructor\noptim_wrapper = dict(constructor=MultiOptimWrapperConstructor)\n" }, { "path": "mmagic/configs/_base_/inpaint_default_runtime.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,\n LoggerHook, ParamSchedulerHook)\nfrom mmengine.runner import LogProcessor\nfrom mmengine.visualization import LocalVisBackend\n\nfrom mmagic.engine.hooks import BasicVisualizationHook\nfrom mmagic.visualization import ConcatImageVisualizer\n\ndefault_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n checkpoint=dict(\n type=CheckpointHook, interval=50000, by_epoch=False, out_dir=save_dir),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type=LocalVisBackend)]\nvisualizer = dict(\n type=ConcatImageVisualizer,\n vis_backends=vis_backends,\n fn_key='gt_path',\n img_keys=['gt_img', 'input', 'pred_img'],\n bgr2rgb=True)\ncustom_hooks = [dict(type=BasicVisualizationHook, interval=1)]\n\nlog_level = 'INFO'\nlog_processor = dict(type=LogProcessor, by_epoch=False)\n\nload_from = None\nresume = False\n\n# TODO: support auto scaling lr\n" }, { "path": "mmagic/configs/_base_/matting_default_runtime.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,\n LoggerHook, ParamSchedulerHook)\nfrom mmengine.runner import LogProcessor\nfrom mmengine.visualization import LocalVisBackend\n\nfrom mmagic.engine.hooks import BasicVisualizationHook\nfrom mmagic.visualization import ConcatImageVisualizer\n\ndefault_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n checkpoint=dict(\n type=CheckpointHook, interval=10000, by_epoch=False, out_dir=save_dir),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),\n dist_cfg=dict(backend='nccl'),\n)\n\nvis_backends = [dict(type=LocalVisBackend)]\nvisualizer = dict(\n type=ConcatImageVisualizer,\n vis_backends=vis_backends,\n fn_key='trimap_path',\n img_keys=['pred_alpha', 'trimap', 'gt_merged', 'gt_alpha'],\n bgr2rgb=True)\ncustom_hooks = [dict(type=BasicVisualizationHook, interval=2000)]\n\nlog_level = 'INFO'\nlog_processor = dict(type=LogProcessor, by_epoch=False)\n\nload_from = None\nresume = False\n\n# TODO: support auto scaling lr\n" }, { "path": "mmagic/configs/_base_/models/base_cyclegan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.archs import PatchDiscriminator\nfrom mmagic.models.editors import CycleGAN\nfrom mmagic.models.editors.cyclegan import ResnetGenerator\n\n_domain_a = None # set by user\n_domain_b = None # set by user\nmodel = dict(\n type=CycleGAN,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=ResnetGenerator,\n in_channels=3,\n out_channels=3,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type=PatchDiscriminator,\n in_channels=3,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02)),\n default_domain=None, # set by user\n reachable_domains=None, # set by user\n related_domains=None # set by user\n)\n" }, { "path": "mmagic/configs/_base_/models/base_deepfillv1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import MMSeparateDistributedDataParallel\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.archs import MultiLayerDiscriminator\nfrom mmagic.models.editors import (ContextualAttentionNeck, DeepFillDecoder,\n DeepFillEncoder, DeepFillEncoderDecoder,\n DeepFillRefiner, DeepFillv1Discriminators,\n DeepFillv1Inpaintor, GLDilationNeck,\n GLEncoderDecoder)\nfrom mmagic.models.losses import (DiscShiftLoss, GANLoss, GradientPenaltyLoss,\n L1Loss)\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type=MMSeparateDistributedDataParallel)\n\nmodel = dict(\n type=DeepFillv1Inpaintor,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type=DeepFillEncoderDecoder,\n stage1=dict(\n type=GLEncoderDecoder,\n encoder=dict(type=DeepFillEncoder, padding_mode='reflect'),\n decoder=dict(\n type=DeepFillDecoder, in_channels=128, padding_mode='reflect'),\n dilation_neck=dict(\n type=GLDilationNeck,\n in_channels=128,\n act_cfg=dict(type='ELU'),\n padding_mode='reflect')),\n stage2=dict(\n type=DeepFillRefiner,\n encoder_attention=dict(\n type=DeepFillEncoder,\n encoder_type='stage2_attention',\n padding_mode='reflect'),\n encoder_conv=dict(\n type=DeepFillEncoder,\n encoder_type='stage2_conv',\n padding_mode='reflect'),\n dilation_neck=dict(\n type=GLDilationNeck,\n in_channels=128,\n act_cfg=dict(type='ELU'),\n padding_mode='reflect'),\n contextual_attention=dict(\n type=ContextualAttentionNeck,\n in_channels=128,\n padding_mode='reflect'),\n decoder=dict(\n type=DeepFillDecoder, in_channels=256,\n padding_mode='reflect'))),\n disc=dict(\n type=DeepFillv1Discriminators,\n global_disc_cfg=dict(\n type=MultiLayerDiscriminator,\n in_channels=3,\n max_channels=256,\n fc_in_channels=256 * 16 * 16,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n local_disc_cfg=dict(\n type=MultiLayerDiscriminator,\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 8 * 8,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2))),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type=GANLoss,\n gan_type='wgan',\n loss_weight=0.0001,\n ),\n loss_l1_hole=dict(\n type=L1Loss,\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type=L1Loss,\n loss_weight=1.0,\n ),\n loss_gp=dict(type=GradientPenaltyLoss, loss_weight=10.),\n loss_disc_shift=dict(type=DiscShiftLoss, loss_weight=0.001))\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)),\n disc=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)))\n\n# learning policy\n# Fixed\n" }, { "path": "mmagic/configs/_base_/models/base_deepfillv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import MMSeparateDistributedDataParallel\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.archs import MultiLayerDiscriminator\nfrom mmagic.models.base_models import TwoStageInpaintor\nfrom mmagic.models.editors import (ContextualAttentionNeck, DeepFillDecoder,\n DeepFillEncoder, DeepFillEncoderDecoder,\n DeepFillRefiner, GLDilationNeck,\n GLEncoderDecoder)\nfrom mmagic.models.losses import GANLoss, L1Loss\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type=MMSeparateDistributedDataParallel)\n\nmodel = dict(\n type=TwoStageInpaintor,\n disc_input_with_mask=True,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type=DeepFillEncoderDecoder,\n stage1=dict(\n type=GLEncoderDecoder,\n encoder=dict(\n type=DeepFillEncoder,\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n decoder=dict(\n type=DeepFillDecoder,\n conv_type='gated_conv',\n in_channels=96,\n channel_factor=0.75,\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'),\n dilation_neck=dict(\n type=GLDilationNeck,\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect')),\n stage2=dict(\n type=DeepFillRefiner,\n encoder_attention=dict(\n type=DeepFillEncoder,\n encoder_type='stage2_attention',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n encoder_conv=dict(\n type=DeepFillEncoder,\n encoder_type='stage2_conv',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n dilation_neck=dict(\n type=GLDilationNeck,\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect'),\n contextual_attention=dict(\n type=ContextualAttentionNeck,\n in_channels=96,\n conv_type='gated_conv',\n padding_mode='reflect'),\n decoder=dict(\n type=DeepFillDecoder,\n in_channels=192,\n conv_type='gated_conv',\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'))),\n disc=dict(\n type=MultiLayerDiscriminator,\n in_channels=4,\n max_channels=256,\n fc_in_channels=None,\n num_convs=6,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=True,\n ),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type=GANLoss,\n gan_type='hinge',\n loss_weight=0.1,\n ),\n loss_l1_hole=dict(\n type=L1Loss,\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type=L1Loss,\n loss_weight=1.0,\n ),\n)\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)),\n disc=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)))\n\n# learning policy\n# Fixed\n" }, { "path": "mmagic/configs/_base_/models/base_edvr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\nfrom mmengine.hooks import CheckpointHook\nfrom mmengine.optim import OptimWrapper\nfrom mmengine.runner import IterBasedTrainLoop\n\nfrom mmagic.datasets import BasicFramesDataset\nfrom mmagic.datasets.transforms import (Flip, GenerateFrameIndices,\n GenerateFrameIndiceswithPadding,\n GenerateSegmentIndices,\n LoadImageFromFile, PackInputs,\n PairedRandomCrop, RandomTransposeHW,\n SetValues, TemporalReverse)\nfrom mmagic.engine.runner import MultiTestLoop, MultiValLoop\nfrom mmagic.evaluation import PSNR, SSIM\n\n_base_ = '../default_runtime.py'\n\nscale = 4\n\ntrain_pipeline = [\n dict(type=GenerateFrameIndices, interval_list=[1], frames_per_clip=99),\n dict(type=TemporalReverse, keys='img_path', reverse_ratio=0),\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type=SetValues, dictionary=dict(scale=scale)),\n dict(type=PairedRandomCrop, gt_patch_size=256),\n dict(\n type=Flip, keys=['img', 'gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type=Flip, keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type=RandomTransposeHW, keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type=PackInputs)\n]\n\nval_pipeline = [\n dict(type=GenerateFrameIndiceswithPadding, padding='reflection_circle'),\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb'),\n dict(type=PackInputs)\n]\n\ndemo_pipeline = [\n dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(\n type=LoadImageFromFile,\n key='img',\n color_type='color',\n channel_order='rgb'),\n dict(type=PackInputs)\n]\n\ndata_root = 'data/REDS'\nsave_dir = './work_dirs'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=8,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_train.txt',\n depth=2,\n num_input_frames=5,\n num_output_frames=1,\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),\n data_root=data_root,\n data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),\n ann_file='meta_info_reds4_val.txt',\n depth=2,\n num_input_frames=5,\n num_output_frames=1,\n pipeline=val_pipeline))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type=PSNR),\n dict(type=SSIM),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(type=IterBasedTrainLoop, max_iters=600_000, val_interval=5000)\nval_cfg = dict(type=MultiValLoop)\ntest_cfg = dict(type=MultiTestLoop)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type=OptimWrapper,\n optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)),\n)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type=CheckpointHook,\n interval=5000,\n save_optimizer=True,\n out_dir=save_dir,\n by_epoch=False))\n" }, { "path": "mmagic/configs/_base_/models/base_gl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import MMSeparateDistributedDataParallel\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import (GLDecoder, GLDilationNeck, GLEncoder,\n GLEncoderDecoder)\nfrom mmagic.models.editors.global_local import GLDiscs, GLInpaintor\nfrom mmagic.models.losses import GANLoss, L1Loss\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type=MMSeparateDistributedDataParallel)\n\nmodel = dict(\n type=GLInpaintor,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type=GLEncoderDecoder,\n encoder=dict(type=GLEncoder, norm_cfg=dict(type='SyncBN')),\n decoder=dict(type=GLDecoder, norm_cfg=dict(type='SyncBN')),\n dilation_neck=dict(type=GLDilationNeck, norm_cfg=dict(type='SyncBN'))),\n disc=dict(\n type=GLDiscs,\n global_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='SyncBN'),\n ),\n local_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=5,\n norm_cfg=dict(type='SyncBN'),\n ),\n ),\n loss_gan=dict(\n type=GANLoss,\n gan_type='vanilla',\n loss_weight=0.001,\n ),\n loss_l1_hole=dict(\n type=L1Loss,\n loss_weight=1.0,\n ))\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0004)),\n disc=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0004)))\n\n# learning policy\n# Fixed\n" }, { "path": "mmagic/configs/_base_/models/base_glean.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,\n LoggerHook, ParamSchedulerHook)\nfrom mmengine.model import MMSeparateDistributedDataParallel\nfrom mmengine.optim import CosineAnnealingLR, OptimWrapper\nfrom mmengine.runner import IterBasedTrainLoop\n\nfrom mmagic.engine.runner import MultiTestLoop, MultiValLoop\nfrom mmagic.evaluation import MAE, PSNR, SSIM\n\n_base_ = '../default_runtime.py'\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(\n type=MMSeparateDistributedDataParallel, find_unused_parameters=True)\n\nsave_dir = './work_dirs'\n\nval_evaluator = [\n dict(type=MAE),\n dict(type=PSNR),\n dict(type=SSIM),\n]\ntest_evaluator = val_evaluator\n\ntrain_cfg = dict(type=IterBasedTrainLoop, max_iters=300_000, val_interval=5000)\nval_cfg = dict(type=MultiValLoop)\ntest_cfg = dict(type=MultiTestLoop)\n\n# optimizer\noptim_wrapper = dict(\n constructor='MultiOptimWrapperConstructor',\n generator=dict(\n type=OptimWrapper,\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n discriminator=dict(\n type=OptimWrapper,\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n)\n\n# learning policy\nparam_scheduler = dict(\n type=CosineAnnealingLR, by_epoch=False, T_max=600_000, eta_min=1e-7)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type=CheckpointHook,\n interval=5000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n save_best=['MAE', 'PSNR', 'SSIM'],\n rule=['less', 'greater', 'greater']),\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n" }, { "path": "mmagic/configs/_base_/models/base_liif.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,\n LoggerHook, ParamSchedulerHook)\nfrom mmengine.optim import MultiStepLR, OptimWrapper\nfrom mmengine.runner import IterBasedTrainLoop\n\nfrom mmagic.datasets.transforms import (Flip, GenerateCoordinateAndCell,\n LoadImageFromFile, PackInputs,\n RandomDownSampling, RandomTransposeHW)\nfrom mmagic.engine.runner import MultiValLoop\nfrom mmagic.evaluation import MAE, PSNR, SSIM, Evaluator\n\n_base_ = '../default_runtime.py'\nwork_dir = './work_dirs/liif'\nsave_dir = './work_dirs'\n\nscale_min, scale_max = 1, 4\nscale_test = 4\n\ntrain_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type=RandomDownSampling,\n scale_min=scale_min,\n scale_max=scale_max,\n patch_size=48),\n dict(\n type=Flip, keys=['img', 'gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type=Flip, keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),\n dict(type=RandomTransposeHW, keys=['img', 'gt'], transpose_ratio=0.5),\n dict(type=GenerateCoordinateAndCell, sample_quantity=2304),\n dict(type=PackInputs)\n]\nval_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='gt',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(type=RandomDownSampling, scale_min=scale_max, scale_max=scale_max),\n dict(type=GenerateCoordinateAndCell, reshape_gt=False),\n dict(type=PackInputs)\n]\n# test_pipeline = [\n# dict(\n# type=LoadImageFromFile,\n# key='gt',\n# color_type='color',\n# channel_order='rgb',\n# imdecode_backend='cv2'),\n# dict(\n# type=LoadImageFromFile,\n# key='img',\n# color_type='color',\n# channel_order='rgb',\n# imdecode_backend='cv2'),\n# dict(type=GenerateCoordinateAndCell, scale=scale_test,\n# reshape_gt=False),\n# dict(type=PackInputs)\n# ]\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=8,\n batch_size=16,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type,\n ann_file='meta_info_DIV2K800sub_GT.txt',\n metainfo=dict(dataset_type='div2k', task_name='sisr'),\n data_root=data_root + '/DIV2K',\n data_prefix=dict(gt='DIV2K_train_HR_sub'),\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=dataset_type,\n metainfo=dict(dataset_type='set5', task_name='sisr'),\n data_root=data_root + '/Set5',\n data_prefix=dict(img='LRbicx4', gt='GTmod12'),\n pipeline=val_pipeline))\n\nval_evaluator = dict(\n type=Evaluator,\n metrics=[\n dict(type=MAE),\n dict(type=PSNR, crop_border=scale_max),\n dict(type=SSIM, crop_border=scale_max),\n ])\n\ntrain_cfg = dict(\n type=IterBasedTrainLoop, max_iters=1_000_000, val_interval=3000)\nval_cfg = dict(type=MultiValLoop)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type=OptimWrapper,\n optimizer=dict(type='Adam', lr=1e-4))\n\n# learning policy\nparam_scheduler = dict(\n type=MultiStepLR,\n by_epoch=False,\n milestones=[200_000, 400_000, 600_000, 800_000],\n gamma=0.5)\n\ndefault_hooks = dict(\n checkpoint=dict(\n type=CheckpointHook,\n interval=3000,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n" }, { "path": "mmagic/configs/_base_/models/base_pconv.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import MMSeparateDistributedDataParallel\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import (PConvDecoder, PConvEncoder,\n PConvEncoderDecoder, PConvInpaintor)\nfrom mmagic.models.losses import L1Loss, MaskedTVLoss, PerceptualLoss\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(type=MMSeparateDistributedDataParallel)\n\nmodel = dict(\n type=PConvInpaintor,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type=PConvEncoderDecoder,\n encoder=dict(\n type=PConvEncoder,\n norm_cfg=dict(type='SyncBN', requires_grad=False),\n norm_eval=True),\n decoder=dict(type=PConvDecoder, norm_cfg=dict(type='SyncBN'))),\n disc=None,\n loss_composed_percep=dict(\n type=PerceptualLoss,\n vgg_type='vgg16',\n layer_weights={\n '4': 1.,\n '9': 1.,\n '16': 1.,\n },\n perceptual_weight=0.05,\n style_weight=120,\n pretrained=('torchvision://vgg16')),\n loss_out_percep=True,\n loss_l1_hole=dict(\n type=L1Loss,\n loss_weight=6.,\n ),\n loss_l1_valid=dict(\n type=L1Loss,\n loss_weight=1.,\n ),\n loss_tv=dict(\n type=MaskedTVLoss,\n loss_weight=0.1,\n ))\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type=OptimWrapper,\n optimizer=dict(type='Adam', lr=0.00005))\n\n# learning policy\n# Fixed\n" }, { "path": "mmagic/configs/_base_/models/base_pix2pix.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.archs import PatchDiscriminator\nfrom mmagic.models.editors import Pix2Pix\nfrom mmagic.models.editors.pix2pix import UnetGenerator\n\nsource_domain = None # set by user\ntarget_domain = None # set by user\n# model settings\nmodel = dict(\n type=Pix2Pix,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=UnetGenerator,\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type=PatchDiscriminator,\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02)),\n loss_config=dict(pixel_loss_weight=100.0),\n default_domain=target_domain,\n reachable_domains=[target_domain],\n related_domains=[target_domain, source_domain])\n" }, { "path": "mmagic/configs/_base_/models/base_styleganv1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors.stylegan1 import (StyleGAN1, StyleGAN1Discriminator,\n StyleGAN1Generator)\n\nmodel = dict(\n type=StyleGAN1,\n data_preprocessor=dict(type=DataPreprocessor),\n style_channels=512,\n generator=dict(type=StyleGAN1Generator, out_size=None, style_channels=512),\n discriminator=dict(type=StyleGAN1Discriminator, in_size=None))\n" }, { "path": "mmagic/configs/_base_/models/base_styleganv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import ExponentialMovingAverage\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import StyleGAN2\nfrom mmagic.models.editors.stylegan2 import (StyleGAN2Discriminator,\n StyleGAN2Generator)\n\n# define GAN model\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nloss_config = dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2)\n\nmodel = dict(\n type=StyleGAN2,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=StyleGAN2Generator,\n out_size=None, # Need to be set.\n style_channels=512,\n ),\n discriminator=dict(\n type=StyleGAN2Discriminator,\n in_size=None, # Need to be set.\n ),\n ema_config=dict(type=ExponentialMovingAverage),\n loss_config=loss_config)\n" }, { "path": "mmagic/configs/_base_/models/base_styleganv3.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# define GAN model\nfrom mmagic.models.base_models.average_model import ExponentialMovingAverage\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Discriminator\nfrom mmagic.models.editors.stylegan3 import StyleGAN3, StyleGAN3Generator\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nmodel = dict(\n type=StyleGAN3,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=StyleGAN3Generator, # StyleGANv3Generator\n noise_size=512,\n style_channels=512,\n out_size=None, # Need to be set.\n img_channels=3,\n ),\n discriminator=dict(\n type=StyleGAN2Discriminator,\n in_size=None, # Need to be set.\n ),\n ema_config=dict(type=ExponentialMovingAverage),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n" }, { "path": "mmagic/configs/_base_/models/base_tof.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,\n LoggerHook, ParamSchedulerHook)\nfrom mmengine.optim import MultiStepLR, OptimWrapper\nfrom mmengine.runner import IterBasedTrainLoop\n\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine.runner import MultiTestLoop, MultiValLoop\nfrom mmagic.evaluation import MAE, PSNR, SSIM\n\n_base_ = '../default_runtime.py'\n\ntrain_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(\n type=LoadImageFromFile,\n key='gt',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type=PackInputs)\n]\n\ndemo_pipeline = [\n dict(\n type=LoadImageFromFile,\n key='img',\n channel_order='rgb',\n imdecode_backend='pillow'),\n dict(type=PackInputs)\n]\n\n# dataset settings\ntrain_dataset_type = 'BasicFramesDataset'\nval_dataset_type = 'BasicFramesDataset'\ndata_root = 'data/vimeo_triplet'\nsave_dir = './work_dirs'\n\ntrain_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=train_dataset_type,\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\nval_dataloader = dict(\n num_workers=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=val_dataset_type,\n ann_file='tri_testlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root=data_root,\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=train_pipeline,\n depth=2,\n load_frames_list=dict(img=['im1.png', 'im3.png'], gt=['im2.png'])))\n\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n dict(type=MAE),\n dict(type=PSNR),\n dict(type=SSIM),\n]\ntest_evaluator = val_evaluator\n\n# 5000 iters == 1 epoch\nepoch_length = 5000\n\ntrain_cfg = dict(\n type=IterBasedTrainLoop, max_iters=1_000_000, val_interval=epoch_length)\nval_cfg = dict(type=MultiValLoop)\ntest_cfg = dict(type=MultiTestLoop)\n\n# optimizer\noptim_wrapper = dict(\n constructor='DefaultOptimWrapperConstructor',\n type=OptimWrapper,\n optimizer=dict(\n type='Adam',\n lr=5e-5,\n betas=(0.9, 0.99),\n weight_decay=1e-4,\n ),\n)\n\n# learning policy\nparam_scheduler = dict(\n type=MultiStepLR,\n by_epoch=False,\n gamma=0.5,\n milestones=[200000, 400000, 600000, 800000])\n\ndefault_hooks = dict(\n checkpoint=dict(\n type=CheckpointHook,\n interval=epoch_length,\n save_optimizer=True,\n by_epoch=False,\n out_dir=save_dir,\n ),\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n" }, { "path": "mmagic/configs/_base_/models/biggan/base_biggan_128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nmodel = dict(\n type='BigGAN',\n num_classes=1000,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='BigGANGenerator',\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')),\n generator_steps=1,\n discriminator_steps=1)\n" }, { "path": "mmagic/configs/_base_/models/dcgan/base_dcgan_128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import DCGAN\nfrom mmagic.models.editors.dcgan import DCGANDiscriminator, DCGANGenerator\n\n# define GAN model\nmodel = dict(\n type=DCGAN,\n noise_size=100,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(type=DCGANGenerator, output_scale=128, base_channels=1024),\n discriminator=dict(\n type=DCGANDiscriminator,\n input_scale=128,\n output_scale=4,\n out_channels=100))\n" }, { "path": "mmagic/configs/_base_/models/dcgan/base_dcgan_64x64.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import DCGAN\nfrom mmagic.models.editors.dcgan import DCGANDiscriminator, DCGANGenerator\n\n# define GAN model\nmodel = dict(\n type=DCGAN,\n noise_size=100,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(type=DCGANGenerator, output_scale=64, base_channels=1024),\n discriminator=dict(\n type=DCGANDiscriminator,\n input_scale=64,\n output_scale=4,\n out_channels=1))\n" }, { "path": "mmagic/configs/_base_/models/sagan/base_sagan_128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import SAGAN\nfrom mmagic.models.editors.biggan import SelfAttentionBlock\nfrom mmagic.models.editors.sagan import ProjDiscriminator, SNGANGenerator\n\nmodel = dict(\n type=SAGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=SNGANGenerator,\n output_scale=128,\n base_channels=64,\n attention_cfg=dict(type=SelfAttentionBlock),\n attention_after_nth_block=4,\n with_spectral_norm=True),\n discriminator=dict(\n type=ProjDiscriminator,\n input_scale=128,\n base_channels=64,\n attention_cfg=dict(type=SelfAttentionBlock),\n attention_after_nth_block=1,\n with_spectral_norm=True),\n generator_steps=1,\n discriminator_steps=1)\n" }, { "path": "mmagic/configs/_base_/models/sagan/base_sagan_32x32.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import SAGAN\nfrom mmagic.models.editors.biggan import SelfAttentionBlock\nfrom mmagic.models.editors.sagan import ProjDiscriminator, SNGANGenerator\n\nmodel = dict(\n type=SAGAN,\n data_preprocessor=dict(type=DataPreprocessor),\n num_classes=10,\n generator=dict(\n type=SNGANGenerator,\n num_classes=10,\n output_scale=32,\n base_channels=256,\n attention_cfg=dict(type=SelfAttentionBlock),\n attention_after_nth_block=2,\n with_spectral_norm=True),\n discriminator=dict(\n type=ProjDiscriminator,\n num_classes=10,\n input_scale=32,\n base_channels=128,\n attention_cfg=dict(type=SelfAttentionBlock),\n attention_after_nth_block=1,\n with_spectral_norm=True),\n generator_steps=1,\n discriminator_steps=5)\n" }, { "path": "mmagic/configs/_base_/models/sngan_proj/base_sngan_proj_128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors.sagan import (SAGAN, ProjDiscriminator,\n SNGANGenerator)\n\n# define GAN model\nmodel = dict(\n type=SAGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(type=SNGANGenerator, output_scale=128, base_channels=64),\n discriminator=dict(\n type=ProjDiscriminator, input_scale=128, base_channels=64),\n discriminator_steps=2)\n" }, { "path": "mmagic/configs/_base_/models/sngan_proj/base_sngan_proj_32x32.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors.sagan import (SAGAN, ProjDiscriminator,\n SNGANGenerator)\n\n# define GAN model\nmodel = dict(\n type=SAGAN,\n num_classes=10,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(type=SNGANGenerator, output_scale=32, base_channels=256),\n discriminator=dict(\n type=ProjDiscriminator, input_scale=32, base_channels=128),\n discriminator_steps=5)\n" }, { "path": "mmagic/configs/_base_/schedules/.gitkeep", "content": "" }, { "path": "mmagic/configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.biggan import (BigGAN, BigGANDeepDiscriminator,\n BigGANGenerator)\n\nwith read_base():\n from .._base_.datasets.imagenet_noaug_128 import *\n from .._base_.gen_default_runtime import *\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type=BigGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n ema_config=ema_config,\n generator=dict(\n type=BigGANGenerator,\n output_scale=128,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type=BigGANDeepDiscriminator,\n input_scale=128,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.biggan import (BigGAN, BigGANDiscriminator,\n BigGANGenerator)\n\nwith read_base():\n from .._base_.datasets.imagenet_noaug_128 import *\n from .._base_.gen_default_runtime import *\n\n# setting image size to 256x256\ntrain_dataloader.dataset.pipeline[2].scale = (256, 256)\ntest_dataloader.dataset.pipeline[2].scale = (256, 256)\nval_dataloader.dataset.pipeline[2].scale = (256, 256)\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type=BigGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n ema_config=ema_config,\n generator=dict(\n type=BigGANGenerator,\n output_scale=256,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type=BigGANDiscriminator,\n input_scale=256,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.biggan import (BigGAN, BigGANDiscriminator,\n BigGANGenerator)\n\nwith read_base():\n from .._base_.datasets.imagenet_noaug_128 import *\n from .._base_.gen_default_runtime import *\n\n# setting image size to 512x512\ntrain_dataloader.dataset.pipeline[2].scale = (512, 512)\ntest_dataloader.dataset.pipeline[2].scale = (512, 512)\nval_dataloader.dataset.pipeline[2].scale = (512, 512)\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type=BigGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n ema_config=ema_config,\n generator=dict(\n type=BigGANGenerator,\n output_scale=512,\n noise_size=128,\n num_classes=1000,\n base_channels=128,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n concat_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type=BigGANDiscriminator,\n input_scale=512,\n num_classes=1000,\n base_channels=128,\n sn_eps=1e-6,\n sn_style='torch',\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.biggan import (BigGAN, BigGANDiscriminator,\n BigGANGenerator)\n\n# define model\n\nwith read_base():\n from .._base_.datasets.cifar10_noaug import *\n from .._base_.gen_default_runtime import *\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n start_iter=1000)\n\nmodel = dict(\n type=BigGAN,\n num_classes=10,\n data_preprocessor=dict(type=DataPreprocessor, output_channel_order='BGR'),\n generator=dict(\n type=BigGANGenerator,\n output_scale=32,\n noise_size=128,\n num_classes=10,\n base_channels=64,\n with_shared_embedding=False,\n sn_eps=1e-8,\n sn_style='torch',\n split_noise=False,\n auto_sync_bn=False,\n init_cfg=dict(type='N02')),\n discriminator=dict(\n type=BigGANDiscriminator,\n input_scale=32,\n num_classes=10,\n base_channels=64,\n sn_eps=1e-8,\n sn_style='torch',\n with_spectral_norm=True,\n init_cfg=dict(type='N02')),\n generator_steps=1,\n discriminator_steps=4,\n ema_config=ema_config)\n\n# define dataset\ntrain_dataloader = dict(batch_size=25, num_workers=8)\nval_dataloader = dict(batch_size=25, num_workers=8)\ntest_dataloader = dict(batch_size=25, num_workers=8)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n]\n\noptim_wrapper = dict(\n generator=dict(optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999))))\ntrain_cfg = dict(max_iters=500000)\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\n\nwith read_base():\n from .._base_.datasets.imagenet_noaug_128 import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.biggan.base_biggan_128x128 import *\n\n# define model\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(ema_config=ema_config)\ntrain_cfg = dict(max_iters=1500000)\n\n# define dataset\ntrain_dataloader = dict(\n batch_size=32, num_workers=8, dataset=dict(data_root='data/imagenet'))\n\n# define optimizer\noptim_wrapper = dict(\n generator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6)),\n discriminator=dict(\n accumulative_counts=8,\n optimizer=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6)))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=10000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n]\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# save multi best checkpoints\ndefault_hooks = dict(\n checkpoint=dict(\n save_best=['FID-Full-50k/fid', 'IS-50k/is'], rule=['less', 'greater']))\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.biggan import (BigGAN, BigGANDiscriminator,\n BigGANGenerator)\n\nwith read_base():\n from .._base_.datasets.imagenet_noaug_128 import *\n from .._base_.gen_default_runtime import *\n\nema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=0.0001,\n update_buffers=True,\n start_iter=20000)\n\nmodel = dict(\n type=BigGAN,\n num_classes=1000,\n data_preprocessor=dict(type=DataPreprocessor),\n ema_config=ema_config,\n generator=dict(\n type=BigGANGenerator,\n output_scale=128,\n noise_size=120,\n num_classes=1000,\n base_channels=96,\n shared_dim=128,\n with_shared_embedding=True,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n split_noise=True,\n auto_sync_bn=False,\n rgb2bgr=True,\n init_cfg=dict(type='ortho')),\n discriminator=dict(\n type=BigGANDiscriminator,\n input_scale=128,\n num_classes=1000,\n base_channels=96,\n sn_eps=1e-6,\n act_cfg=dict(type='ReLU', inplace=True),\n with_spectral_norm=True,\n init_cfg=dict(type='ortho')))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type='IS',\n prefix='IS-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/dreambooth/dreambooth-finetune_text_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nfrom mmengine.dataset.sampler import InfiniteSampler\nfrom torch.optim import AdamW\n\nfrom mmagic.datasets.dreambooth_dataset import DreamBoothDataset\nfrom mmagic.datasets.transforms.aug_shape import Resize\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper\nfrom mmagic.models.editors.dreambooth import DreamBooth\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\n\nmodel = dict(\n type=DreamBooth,\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='unet',\n ),\n text_encoder=dict(\n type=ClipWrapper,\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n finetune_text_encoder=True,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type=DataPreprocessor),\n val_prompts=val_prompts)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper.update(\n modules='.*unet',\n optimizer=dict(type=AdamW, lr=5e-6),\n accumulative_counts=4 # batch size = 4 * 1 = 4\n)\n\npipeline = [\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=Resize, scale=(512, 512)),\n dict(type=PackInputs)\n]\n\ndataset = dict(\n type=DreamBoothDataset,\n data_root='./data/dreambooth',\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks.update(dict(logger=dict(interval=10)))\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=50,\n fixed_input=True,\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "mmagic/configs/dreambooth/dreambooth-prior_pre.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .dreambooth import *\n\n# config for model\nmodel.update(dict(prior_loss_weight=1, class_prior_prompt='a dog'))\n" }, { "path": "mmagic/configs/dreambooth/dreambooth.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nfrom mmengine.dataset.sampler import InfiniteSampler\nfrom torch.optim import AdamW\n\nfrom mmagic.datasets.dreambooth_dataset import DreamBoothDataset\nfrom mmagic.datasets.transforms.aug_shape import Resize\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper\nfrom mmagic.models.editors.dreambooth import DreamBooth\n\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\n\nmodel = dict(\n type=DreamBooth,\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='unet',\n ),\n text_encoder=dict(\n type=ClipWrapper,\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type=DataPreprocessor),\n val_prompts=val_prompts)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper.update(\n modules='.*unet',\n optimizer=dict(type=AdamW, lr=5e-6),\n accumulative_counts=4 # batch size = 4 * 1 = 4\n)\n\npipeline = [\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=Resize, scale=(512, 512)),\n dict(type=PackInputs)\n]\n\ndataset = dict(\n type=DreamBoothDataset,\n data_root='./data/dreambooth',\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks.update(dict(logger=dict(interval=10)))\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=50,\n fixed_input=True,\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "mmagic/configs/dreambooth/dreambooth_lora-prior_pre.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .dreambooth_lora import *\n\nmodel.update(dict(prior_loss_weight=1, class_prior_prompt='a dog'))\n" }, { "path": "mmagic/configs/dreambooth/dreambooth_lora.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nfrom mmengine.dataset.sampler import InfiniteSampler\nfrom torch.optim import AdamW\n\nfrom mmagic.datasets.dreambooth_dataset import DreamBoothDataset\nfrom mmagic.datasets.transforms.aug_shape import Resize\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper\nfrom mmagic.models.editors.dreambooth import DreamBooth\n\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\nval_prompts = [\n 'a sks dog in basket', 'a sks dog on the mountain',\n 'a sks dog beside a swimming pool', 'a sks dog on the desk',\n 'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'\n]\nlora_config = dict(target_modules=['to_q', 'to_k', 'to_v'])\n\nmodel = dict(\n type=DreamBooth,\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet2DConditionModel',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='unet',\n ),\n text_encoder=dict(\n type=ClipWrapper,\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type=DataPreprocessor),\n prior_loss_weight=0,\n val_prompts=val_prompts,\n lora_config=lora_config)\n\ntrain_cfg = dict(max_iters=1000)\n\noptim_wrapper = dict(\n # Only optimize LoRA mappings\n modules='.*.lora_mapping',\n # NOTE: lr should be larger than dreambooth finetuning\n optimizer=dict(type=AdamW, lr=5e-4),\n accumulative_counts=1)\n\npipeline = [\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=Resize, scale=(512, 512)),\n dict(type=PackInputs)\n]\ndataset = dict(\n type=DreamBoothDataset,\n data_root='./data/dreambooth',\n # TODO: rename to instance\n concept_dir='imgs',\n prompt='a photo of sks dog',\n pipeline=pipeline)\ntrain_dataloader = dict(\n dataset=dataset,\n num_workers=16,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n persistent_workers=True,\n batch_size=1)\nval_cfg = val_evaluator = val_dataloader = None\ntest_cfg = test_evaluator = test_dataloader = None\n\n# hooks\ndefault_hooks.update(dict(logger=dict(interval=10)))\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=50,\n fixed_input=True,\n vis_kwargs_list=dict(type='Data', name='fake_img'),\n n_samples=1)\n]\n" }, { "path": "mmagic/configs/eg3d/eg3d_cvt-official-rgb_afhq-512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\nfrom mmengine.config import read_base\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicConditionalDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.eg3d import EG3D, GaussianCamera, TriplaneGenerator\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nmodel = dict(\n type=EG3D,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=TriplaneGenerator,\n out_size=512,\n triplane_channels=32,\n triplane_size=256,\n num_mlps=2,\n sr_add_noise=False,\n sr_in_size=128,\n neural_rendering_resolution=128,\n renderer_cfg=dict(\n ray_start=2.25,\n ray_end=3.3,\n box_warp=1,\n depth_resolution=48,\n depth_resolution_importance=48,\n white_back=False,\n ),\n rgb2bgr=True),\n camera=dict(\n type=GaussianCamera,\n horizontal_mean=3.14 / 2,\n horizontal_std=0.35,\n vertical_mean=3.14 / 2 - 0.05,\n vertical_std=0.25,\n radius=2.7,\n fov=18.837,\n look_at=[0, 0, 0.2]))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_afhq.pkl'\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img', color_type='color'),\n dict(type=PackInputs)\n]\ntest_dataset = dict(\n type=BasicConditionalDataset,\n data_root='./data/eg3d/afhq',\n ann_file='afhq.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 4` cost nearly **9.5GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "mmagic/configs/eg3d/eg3d_cvt-official-rgb_ffhq-512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\nfrom mmengine.config import read_base\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicConditionalDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.eg3d import EG3D, GaussianCamera, TriplaneGenerator\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nmodel = dict(\n type=EG3D,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=TriplaneGenerator,\n out_size=512,\n triplane_channels=32,\n triplane_size=256,\n num_mlps=2,\n neural_rendering_resolution=128,\n sr_add_noise=False,\n sr_in_size=128,\n # NOTE: double hidden channels and out channels for FFHQ-512\n sr_hidden_channels=256,\n sr_out_channels=128,\n renderer_cfg=dict(\n ray_start=2.25,\n ray_end=3.3,\n box_warp=1,\n depth_resolution=48,\n depth_resolution_importance=48,\n white_back=False,\n ),\n rgb2bgr=True),\n camera=dict(\n type=GaussianCamera,\n horizontal_mean=3.14 / 2,\n horizontal_std=0.35,\n vertical_mean=3.14 / 2 - 0.05,\n vertical_std=0.25,\n radius=2.7,\n fov=18.837,\n look_at=[0, 0, 0.2]))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_ffhq_512.pkl'\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig')\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img', color_type='color'),\n dict(type=PackInputs)\n]\ntest_dataset = dict(\n type=BasicConditionalDataset,\n data_root='./data/eg3d/ffhq_512',\n ann_file='ffhq_512.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 4` cost nearly **9.5GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=4,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "mmagic/configs/eg3d/eg3d_cvt-official-rgb_shapenet-128x128.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\nfrom mmengine.config import read_base\nfrom mmengine.dataset import DefaultSampler\n\nfrom mmagic.datasets import BasicConditionalDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile, PackInputs\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors.eg3d import EG3D, TriplaneGenerator, UniformCamera\n\nwith read_base():\n from .._base_.gen_default_runtime import *\n\nmodel = dict(\n type=EG3D,\n data_preprocessor=dict(type=DataPreprocessor),\n generator=dict(\n type=TriplaneGenerator,\n out_size=128,\n zero_cond_input=True,\n cond_scale=0,\n sr_in_size=64,\n renderer_cfg=dict(\n # Official implementation set ray_start, ray_end and box_warp as\n # 0.1, 2.6 and 1.6 respectively, and FID is 7.2441\n # ray_start=0.1,\n # ray_end=2.6,\n # box_warp=1.6,\n ray_start=0.4,\n ray_end=2.0,\n box_warp=1.7,\n depth_resolution=64,\n depth_resolution_importance=64,\n white_back=True,\n ),\n rgb2bgr=True),\n camera=dict(\n type=UniformCamera,\n horizontal_mean=3.141,\n horizontal_std=3.141,\n vertical_mean=3.141 / 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n up=[0, 0, 1],\n radius=1.2),\n)\n\ntrain_cfg = train_dataloader = optim_wrapper = None\nval_cfg = val_dataloader = val_evaluator = None\n\ninception_pkl = './work_dirs/inception_pkl/eg3d_shapenet.pkl'\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n need_cond_input=True,\n sample_model='orig'),\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Random-Camera',\n fake_nums=50000,\n inception_pkl=inception_pkl,\n sample_model='orig'),\n]\n\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img', color_type='color'),\n dict(type=PackInputs)\n]\ntest_dataset = dict(\n type=BasicConditionalDataset,\n data_root='./data/eg3d/shapenet-car',\n ann_file='shapenet.json',\n pipeline=test_pipeline)\ntest_dataloader = dict(\n # NOTE: `batch_size = 16` cost nearly **12GB** of GPU memory,\n # modification this param by yourself corresponding to your own GPU.\n batch_size=16,\n persistent_workers=False,\n drop_last=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n num_workers=9,\n dataset=test_dataset)\n\ntest_evaluator = dict(metrics=metrics)\n\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n # save_at_test=False,\n vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n" }, { "path": "mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .adm_ddim250_8xb32_imagenet_256x256 import *\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel\n\nmodel.update(\n dict(\n classifier=dict(\n type=EncoderUNetModel,\n image_size=256,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=2,\n attention_resolutions=(8, 16, 32),\n channel_mult=(1, 1, 2, 2, 4, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention')))\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .adm_ddim250_8xb32_imagenet_512x512 import *\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel\n\nmodel.update(\n dict(\n classifier=dict(\n type=EncoderUNetModel,\n image_size=512,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=2,\n attention_resolutions=(16, 32, 64),\n channel_mult=(0.5, 1, 1, 2, 2, 4, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention')))\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_64x64.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .adm_ddim250_8xb32_imagenet_64x64 import *\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel\n\nmodel.update(\n dict(\n classifier=dict(\n type=EncoderUNetModel,\n image_size=64,\n in_channels=3,\n model_channels=128,\n out_channels=1000,\n num_res_blocks=4,\n attention_resolutions=(2, 4, 8),\n channel_mult=(1, 2, 3, 4),\n use_fp16=False,\n num_head_channels=64,\n use_scale_shift_norm=True,\n resblock_updown=True,\n pool='attention')))\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/guided_diffusion/adm_ddim250_8xb32_imagenet_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.imagenet_64 import *\n from .._base_.gen_default_runtime import *\n\nfrom mmagic.engine.hooks.visualization_hook import VisualizationHook\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler\nfrom mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,\n MultiHeadAttentionBlock)\nfrom mmagic.models.editors.guided_diffusion.adm import AblatedDiffusionModel\n\nmodel = dict(\n type=AblatedDiffusionModel,\n data_preprocessor=dict(type=DataPreprocessor),\n unet=dict(\n type=DenoisingUnet,\n image_size=256,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type=MultiHeadAttentionBlock,\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type=EditDDIMScheduler,\n variance_type='learned_range',\n beta_schedule='linear'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader.update(dict(batch_size=32, num_workers=8))\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n\n# VIS_HOOK\ncustom_hooks = [dict(type=VisualizationHook, interval=5000, fixed_input=True)]\n" }, { "path": "mmagic/configs/guided_diffusion/adm_ddim250_8xb32_imagenet_512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.imagenet_512 import *\n from .._base_.gen_default_runtime import *\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler\nfrom mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,\n MultiHeadAttentionBlock)\nfrom mmagic.models.editors.guided_diffusion import AblatedDiffusionModel\n\nmodel = dict(\n type=AblatedDiffusionModel,\n data_preprocessor=dict(type=DataPreprocessor),\n unet=dict(\n type=DenoisingUnet,\n image_size=512,\n in_channels=3,\n base_channels=256,\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type=MultiHeadAttentionBlock,\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=False),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type=EditDDIMScheduler,\n variance_type='learned_range',\n beta_schedule='linear'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader.update(dict(batch_size=32, num_workers=8))\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/guided_diffusion/adm_ddim250_8xb32_imagenet_64x64.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.imagenet_64 import *\n from .._base_.gen_default_runtime import *\n\nfrom mmagic.evaluation.metrics import FrechetInceptionDistance\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler\nfrom mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,\n MultiHeadAttentionBlock)\nfrom mmagic.models.editors.guided_diffusion import AblatedDiffusionModel\n\nmodel = dict(\n type=AblatedDiffusionModel,\n data_preprocessor=dict(type=DataPreprocessor),\n unet=dict(\n type=DenoisingUnet,\n image_size=64,\n in_channels=3,\n base_channels=192,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type=MultiHeadAttentionBlock,\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=True),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type=EditDDIMScheduler,\n variance_type='learned_range',\n beta_schedule='squaredcos_cap_v2'),\n rgb2bgr=True,\n use_fp16=False)\n\ntest_dataloader.update(dict(batch_size=32, num_workers=8))\ntrain_cfg = dict(max_iters=100000)\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='orig',\n sample_kwargs=dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.))\n]\n\nval_evaluator = dict(metrics=metrics)\ntest_evaluator = dict(metrics=metrics)\n" }, { "path": "mmagic/configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.nn.modules import HuberLoss\n\nfrom mmagic.datasets.transforms.aug_shape import Resize\nfrom mmagic.datasets.transforms.crop import InstanceCrop\nfrom mmagic.datasets.transforms.formatting import PackInputs\nfrom mmagic.datasets.transforms.loading import LoadImageFromFile\nfrom mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor\nfrom mmagic.models.editors.inst_colorization.colorization_net import \\\n ColorizationNet\nfrom mmagic.models.editors.inst_colorization.fusion_net import FusionNet\nfrom mmagic.models.editors.inst_colorization.inst_colorization import \\\n InstColorization\n\nwith read_base():\n from .._base_.default_runtime import *\n\nexperiment_name = 'inst-colorization_full_official_cocostuff_256x256'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nstage = 'full'\n\nmodel = dict(\n type=InstColorization,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[127.5],\n std=[127.5],\n ),\n image_model=dict(\n type=ColorizationNet, input_nc=4, output_nc=2, norm_type='batch'),\n instance_model=dict(\n type=ColorizationNet, input_nc=4, output_nc=2, norm_type='batch'),\n fusion_model=dict(\n type=FusionNet, input_nc=4, output_nc=2, norm_type='batch'),\n color_data_opt=dict(\n ab_thresh=0,\n p=1.0,\n sample_PS=[\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n ],\n ab_norm=110,\n ab_max=110.,\n ab_quant=10.,\n l_norm=100.,\n l_cent=50.,\n mask_cent=0.5),\n which_direction='AtoB',\n loss=dict(type=HuberLoss, delta=.01))\n\n# yapf: disable\ntest_pipeline = [\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(\n type=InstanceCrop,\n config_file='mmdet::mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py', # noqa\n finesize=256,\n box_num_upbound=5),\n dict(\n type=Resize,\n keys=['img', 'cropped_img'],\n scale=(256, 256),\n keep_ratio=False),\n dict(\n type=PackInputs,\n data_keys=['box_info', 'box_info_2x', 'box_info_4x', 'box_info_8x']),\n]\n" }, { "path": "mmagic/configs/real_basicvsr/realbasicvsr_c64b20_1x30x8_8xb1_lr5e_5_150k_reds.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\nfrom mmengine.config import read_base\nfrom mmengine.optim.optimizer import OptimWrapper\nfrom mmengine.runner.loops import IterBasedTrainLoop\nfrom torch.optim.adam import Adam\n\nfrom mmagic.engine import MultiOptimWrapperConstructor\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors import (RealBasicVSR, RealBasicVSRNet,\n UNetDiscriminatorWithSpectralNorm)\nfrom mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss\n\nwith read_base():\n from .realbasicvsr_wogan_c64b20_2x30x8_8xb2_lr1e_4_300k_reds import *\n\nexperiment_name = 'realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\n# load_from = 'https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth' # noqa\n\nscale = 4\n\n# model settings\nmodel.update(\n dict(\n type=RealBasicVSR,\n generator=dict(\n type=RealBasicVSRNet,\n mid_channels=64,\n num_propagation_blocks=20,\n num_cleaning_blocks=20,\n dynamic_refine_thres=255, # change to 5 for test\n spynet_pretrained=\n 'https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n is_fix_cleaning=False,\n is_sequential_cleaning=False),\n discriminator=dict(\n type=UNetDiscriminatorWithSpectralNorm,\n in_channels=3,\n mid_channels=64,\n skip_connection=True),\n pixel_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),\n cleaning_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type=PerceptualLoss,\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type=GANLoss,\n gan_type='vanilla',\n loss_weight=5e-2,\n real_label_val=1.0,\n fake_label_val=0),\n is_use_sharpened_gt_in_pixel=True,\n is_use_sharpened_gt_in_percep=True,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=True,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n )))\n\n# optimizer\noptim_wrapper.update(\n dict(\n _delete_=True,\n constructor=MultiOptimWrapperConstructor,\n generator=dict(\n type=OptimWrapper,\n optimizer=dict(type=Adam, lr=5e-5, betas=(0.9, 0.99))),\n discriminator=dict(\n type=OptimWrapper,\n optimizer=dict(type=Adam, lr=1e-4, betas=(0.9, 0.99))),\n ))\n\ntrain_cfg.update(\n dict(type=IterBasedTrainLoop, max_iters=150_000, val_interval=5000))\n" }, { "path": "mmagic/configs/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_8xb2_lr1e_4_300k_reds.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\nfrom mmengine.config import read_base\nfrom mmengine.dataset.sampler import DefaultSampler, InfiniteSampler\nfrom mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, LoggerHook,\n ParamSchedulerHook)\nfrom mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper\nfrom mmengine.runner.loops import IterBasedTrainLoop\nfrom torch.optim.adam import Adam\n\nfrom mmagic.datasets import BasicFramesDataset\nfrom mmagic.datasets.transforms import (Clip, CopyValues,\n DegradationsWithShuffle, FixedCrop,\n Flip, GenerateSegmentIndices,\n LoadImageFromFile, MirrorSequence,\n PackInputs, RandomBlur,\n RandomJPEGCompression, RandomNoise,\n RandomResize, RandomTransposeHW,\n RandomVideoCompression, SetValues,\n UnsharpMasking)\nfrom mmagic.engine import MultiOptimWrapperConstructor\nfrom mmagic.engine.hooks import (BasicVisualizationHook,\n ExponentialMovingAverageHook, IterTimerHook)\nfrom mmagic.evaluation import Evaluator\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors import RealBasicVSR, RealBasicVSRNet\nfrom mmagic.models.losses import L1Loss\n\nwith read_base():\n from .._base_.default_runtime import *\n\nexperiment_name = 'realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds'\nwork_dir = f'./work_dirs/{experiment_name}'\nsave_dir = './work_dirs/'\n\nscale = 4\n\n# model settings\nmodel = dict(\n type=RealBasicVSR,\n generator=dict(\n type=RealBasicVSRNet,\n mid_channels=64,\n num_propagation_blocks=20,\n num_cleaning_blocks=20,\n dynamic_refine_thres=255, # change to 1.5 for test\n spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n 'basicvsr/spynet_20210409-c6c1bd09.pth',\n is_fix_cleaning=False,\n is_sequential_cleaning=False),\n pixel_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),\n cleaning_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),\n is_use_sharpened_gt_in_pixel=True,\n is_use_ema=True,\n data_preprocessor=dict(\n type=DataPreprocessor,\n mean=[0., 0., 0.],\n std=[255., 255., 255.],\n ))\n\ntrain_pipeline = [\n dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=SetValues, dictionary=dict(scale=scale)),\n dict(type=FixedCrop, keys=['gt'], crop_size=(256, 256)),\n dict(type=Flip, keys=['gt'], flip_ratio=0.5, direction='horizontal'),\n dict(type=Flip, keys=['gt'], flip_ratio=0.5, direction='vertical'),\n dict(type=RandomTransposeHW, keys=['gt'], transpose_ratio=0.5),\n dict(type=MirrorSequence, keys=['gt']),\n dict(\n type=UnsharpMasking,\n keys=['gt'],\n kernel_size=51,\n sigma=0,\n weight=0.5,\n threshold=10),\n dict(type=CopyValues, src_keys=['gt_unsharp'], dst_keys=['img']),\n dict(\n type=RandomBlur,\n params=dict(\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 3],\n sigma_y=[0.2, 3],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2],\n sigma_x_step=0.02,\n sigma_y_step=0.02,\n rotate_angle_step=0.31416,\n beta_gaussian_step=0.05,\n beta_plateau_step=0.1,\n omega_step=0.0628),\n keys=['img'],\n ),\n dict(\n type=RandomResize,\n params=dict(\n resize_mode_prob=[0.2, 0.7, 0.1], # up, down, keep\n resize_scale=[0.15, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3.0, 1 / 3.0, 1 / 3.0],\n resize_step=0.015,\n is_size_even=True),\n keys=['img'],\n ),\n dict(\n type=RandomNoise,\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 30],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 3],\n poisson_gray_noise_prob=0.4,\n gaussian_sigma_step=0.1,\n poisson_scale_step=0.005),\n keys=['img'],\n ),\n dict(\n type=RandomJPEGCompression,\n params=dict(quality=[30, 95], quality_step=3),\n keys=['img'],\n ),\n dict(\n type=RandomVideoCompression,\n params=dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5]),\n keys=['img'],\n ),\n dict(\n type=RandomBlur,\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=[\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'\n ],\n kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],\n sigma_x=[0.2, 1.5],\n sigma_y=[0.2, 1.5],\n rotate_angle=[-3.1416, 3.1416],\n beta_gaussian=[0.5, 4],\n beta_plateau=[1, 2],\n sigma_x_step=0.02,\n sigma_y_step=0.02,\n rotate_angle_step=0.31416,\n beta_gaussian_step=0.05,\n beta_plateau_step=0.1,\n omega_step=0.0628),\n keys=['img'],\n ),\n dict(\n type=RandomResize,\n params=dict(\n resize_mode_prob=[0.3, 0.4, 0.3], # up, down, keep\n resize_scale=[0.3, 1.2],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n resize_step=0.03,\n is_size_even=True),\n keys=['img'],\n ),\n dict(\n type=RandomNoise,\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.5, 0.5],\n gaussian_sigma=[1, 25],\n gaussian_gray_noise_prob=0.4,\n poisson_scale=[0.05, 2.5],\n poisson_gray_noise_prob=0.4,\n gaussian_sigma_step=0.1,\n poisson_scale_step=0.005),\n keys=['img'],\n ),\n dict(\n type=RandomJPEGCompression,\n params=dict(quality=[30, 95], quality_step=3),\n keys=['img'],\n ),\n dict(\n type=DegradationsWithShuffle,\n degradations=[\n dict(\n type=RandomVideoCompression,\n params=dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5]),\n keys=['img'],\n ),\n [\n dict(\n type=RandomResize,\n params=dict(\n target_size=(64, 64),\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n ),\n dict(\n type=RandomBlur,\n params=dict(\n prob=0.8,\n kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],\n kernel_list=['sinc'],\n kernel_prob=[1],\n omega=[3.1416 / 3, 3.1416],\n omega_step=0.0628),\n ),\n ]\n ],\n keys=['img'],\n ),\n dict(type=Clip, keys=['img']),\n dict(type=PackInputs)\n]\n\nval_pipeline = [\n dict(\n type=GenerateSegmentIndices,\n interval_list=[1],\n filename_tmpl='{:04d}.png'),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\ntest_pipeline = [\n dict(\n type=GenerateSegmentIndices,\n interval_list=[1],\n filename_tmpl='{:08d}.png'),\n dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\ndemo_pipeline = [\n dict(type=GenerateSegmentIndices, interval_list=[1]),\n dict(type=LoadImageFromFile, key='img', channel_order='rgb'),\n dict(type=PackInputs)\n]\n\ndata_root = 'data'\n\ntrain_dataloader = dict(\n num_workers=10,\n batch_size=2,\n persistent_workers=False,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='reds', task_name='vsr'),\n data_root=f'{data_root}/REDS',\n data_prefix=dict(img='train_sharp_sub', gt='train_sharp_sub'),\n depth=1,\n num_input_frames=15,\n pipeline=train_pipeline))\n\nval_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='udm10', task_name='vsr'),\n data_root=f'{data_root}/UDM10',\n data_prefix=dict(img='BIx4', gt='GT'),\n pipeline=val_pipeline))\n\ntest_dataloader = dict(\n num_workers=1,\n batch_size=1,\n persistent_workers=False,\n sampler=dict(type=DefaultSampler, shuffle=False),\n dataset=dict(\n type=BasicFramesDataset,\n metainfo=dict(dataset_type='video_lq', task_name='vsr'),\n data_root=f'{data_root}/VideoLQ',\n data_prefix=dict(img='', gt=''),\n pipeline=test_pipeline))\n\nval_evaluator = dict(\n type=Evaluator, metrics=[\n dict(type='PSNR'),\n dict(type='SSIM'),\n ])\n\ntest_evaluator = dict(\n type=Evaluator,\n metrics=[dict(type='NIQE', input_order='CHW', convert_to='Y')])\n\ntrain_cfg = dict(type=IterBasedTrainLoop, max_iters=300_000, val_interval=5000)\n\n# optimizer\noptim_wrapper = dict(\n constructor=MultiOptimWrapperConstructor,\n generator=dict(\n type=OptimWrapper,\n optimizer=dict(type=Adam, lr=1e-4, betas=(0.9, 0.99))))\n\n# NO learning policy\n\ndefault_hooks = dict(\n checkpoint=dict(\n type=CheckpointHook,\n interval=5000,\n save_optimizer=True,\n out_dir=save_dir,\n max_keep_ckpts=10,\n save_best='PSNR',\n rule='greater',\n by_epoch=False),\n timer=dict(type=IterTimerHook),\n logger=dict(type=LoggerHook, interval=100),\n param_scheduler=dict(type=ParamSchedulerHook),\n sampler_seed=dict(type=DistSamplerSeedHook),\n)\n\ncustom_hooks = [\n dict(type=BasicVisualizationHook, interval=5),\n dict(\n type=ExponentialMovingAverageHook,\n module_keys=('generator_ema'),\n interval=1,\n interp_cfg=dict(momentum=0.001),\n )\n]\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_800kiters_ffhq_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.optim import Adam\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import * # noqa: F403,F405\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg.update(max_iters=800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/ffhq/ffhq_imgs/ffhq_256'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_cat_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.optim import Adam\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.datasets.lsun_stylegan import * # noqa: F403,F405\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg.update(max_iters=800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-cat'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_church_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.optim import Adam\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.datasets.lsun_stylegan import * # noqa: F403,F405\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg.update(max_iters=800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-church'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_horse_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.optim import Adam\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.datasets.lsun_stylegan import * # noqa: F403,F405\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=256),\n discriminator=dict(in_size=256),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg.update(max_iters=800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/lsun-horse'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_ffhq_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom torch.optim import Adam\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import * # noqa: F403,F405\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=1024),\n discriminator=dict(in_size=1024),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\nextra_parameters = dict(num_batches=1, sample_model='orig')\n\ntrain_cfg.update(max_iters=800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_8xb4_lsun_car_384x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler\nfrom torch.optim import Adam\n\nfrom mmagic.datasets.transforms import (Flip, LoadImageFromFile, NumpyPad,\n PackInputs)\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.evaluation import (FrechetInceptionDistance, PerceptualPathLength,\n PrecisionAndRecall)\nfrom mmagic.models import BaseGAN\n\nwith read_base():\n from .._base_.gen_default_runtime import * # noqa: F403,F405\n from .._base_.models.base_styleganv2 import * # noqa: F403,F405\n\n# reg params\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nema_half_life = 10. # G_smoothing_kimg\n\nmodel.update(\n generator=dict(out_size=512),\n discriminator=dict(in_size=512),\n ema_config=dict(\n type=ExponentialMovingAverage,\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.)))),\n loss_config=dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2))\n\ntrain_cfg.update(max_iters=1800002)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n# DATA\nbatch_size = 4\ndata_root = './data/lsun/images/car'\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, key='gt'),\n dict(\n type=NumpyPad,\n keys='img',\n padding=((64, 64), (0, 0), (0, 0)),\n ),\n dict(type=Flip, keys=['gt'], direction='horizontal'),\n dict(type=PackInputs)\n]\n\nval_pipeline = train_pipeline\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type=InfiniteSampler, shuffle=True),\n dataset=dict(\n type=dataset_type, data_root=data_root, pipeline=train_pipeline))\n\nval_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_root=data_root, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\ntest_dataloader = dict(\n batch_size=4,\n num_workers=8,\n dataset=dict(\n type=dataset_type,\n data_root=data_root, # set by user\n pipeline=val_pipeline),\n sampler=dict(type=DefaultSampler, shuffle=False),\n persistent_workers=True)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-50k',\n fake_nums=50000,\n real_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(type=PrecisionAndRecall, fake_nums=50000, prefix='PR-50K'),\n dict(type=PerceptualPathLength, fake_nums=50000, prefix='ppl-w')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks.update(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom mmengine.optim import AmpOptimWrapper\n\nwith read_base():\n from mmagic.configs.styleganv2.stylegan2_c2_8xb4_800kiters_ffhq_256x256 import *\n\nmodel.update(\n generator=dict(out_size=256, num_fp16_scales=4),\n discriminator=dict(in_size=256, num_fp16_scales=4),\n loss_config=dict(scale_r1_loss=True))\n\noptim_wrapper.update(\n generator=dict(type=AmpOptimWrapper, loss_scale=512),\n discriminator=dict(type=AmpOptimWrapper, loss_scale=512))\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from mmagic.configs.styleganv2.stylegan2_c2_8xb4_800kiters_ffhq_256x256 import *\n\nmodel.update(loss_config=dict(r1_use_apex_amp=False, g_reg_use_apex_amp=False))\n\ntrain_cfg.update(max_iters=800002)\n\n# remain to be refactored\napex_amp = dict(mode='gan', init_args=dict(opt_level='O1', num_losses=2))\nresume_from = None\n" }, { "path": "mmagic/configs/styleganv2/stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\nfrom mmengine.optim import AmpOptimWrapper\n\nwith read_base():\n from mmagic.configs.styleganv2.stylegan2_c2_8xb4_800kiters_ffhq_256x256 import *\n\nmodel.update(\n generator=dict(out_size=256, fp16_enabled=True),\n discriminator=dict(in_size=256, fp16_enabled=False, num_fp16_scales=4),\n)\ntrain_cfg.update(max_iters=800000)\noptim_wrapper.update(\n generator=dict(type=AmpOptimWrapper, loss_scale=512),\n discriminator=dict(type=AmpOptimWrapper, loss_scale=512))\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_r_ada_gamma33_8xb4_fp16_metfaces_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom torch.optim import Adam\n\nfrom mmagic.engine.hooks.visualization_hook import VisualizationHook\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.base_models.average_model import ExponentialMovingAverage\nfrom mmagic.models.base_models.base_gan import BaseGAN\nfrom mmagic.models.editors.stylegan2.stylegan2_discriminator import (\n ADAAug, ADAStyleGAN2Discriminator)\n# 模型的配置\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\nr1_gamma = 3.3 # set by user\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nload_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth' # noqa\n\n# ada settings\naug_kwargs = {\n 'xflip': 1,\n 'rotate90': 1,\n 'xint': 1,\n 'scale': 1,\n 'rotate': 1,\n 'aniso': 1,\n 'xfrac': 1,\n 'brightness': 1,\n 'contrast': 1,\n 'lumaflip': 1,\n 'hue': 1,\n 'saturation': 1\n}\n\nema_half_life = 10. # G_smoothing_kimg\nema_kimg = 10\nema_nimg = ema_kimg * 1000\nema_beta = 0.5**(32 / max(ema_nimg, 1e-8))\n\nema_config = dict(\n type=ExponentialMovingAverage, interval=1, momentum=ema_beta, start_iter=0)\n\nmodel.update(\n generator=dict(\n out_size=1024,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(\n type=ADAStyleGAN2Discriminator,\n in_size=1024,\n input_bgr2rgb=True,\n data_aug=dict(type=ADAAug, aug_pipeline=aug_kwargs, ada_kimg=100)),\n loss_config=dict(\n r1_loss_weight=r1_gamma / 2.0 * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC'),\n ema_config=ema_config)\n\n# 优化配置\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.0025 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\n# 数据集配置\nbatch_size = 4\ndata_root = 'data/metfaces/images/'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\n# 训练配置\ntrain_cfg.update(max_iters=160000)\n# VIS_HOOK hook配置\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img'))\n]\n\n# METRICS 评估配置\nmetrics = [\n dict(\n type=FrechetInceptionDistance, # FID\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n# default_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid')) # 只是加进去\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_r_cvt_official_rgb_8xb4_ffhq_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan2.stylegan2_discriminator import \\\n StyleGAN2Discriminator\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\n\nr1_gamma = 32.8\nd_reg_interval = 16\n\nmodel.update(\n generator=dict(\n out_size=1024,\n img_channels=3,\n synthesis_cfg=synthesis_cfg,\n rgb2bgr=True),\n discriminator=dict(type=StyleGAN2Discriminator, in_size=1024))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\n\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_r_cvt_official_rgb_8xb4_ffhqu_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.unconditional_imgs_flip_lanczos_resize_256x256 \\\n import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 32768,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\nmodel.update(\n generator=dict(\n out_size=256,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1))\n\nbatch_size = 4\ndata_root = './data/ffhqu/images'\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_r_cvt_official_rgb_8xb4x8_afhqv2_512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.unconditional_imgs_flip_512x512 import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan2.stylegan2_discriminator import \\\n StyleGAN2Discriminator\nfrom mmagic.models.editors.stylegan3.stylegan3_generator import \\\n StyleGAN3Generator\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 65536,\n 'channel_max': 1024,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n}\n\nmodel.update(\n generator=dict(\n type=StyleGAN3Generator, # 'StyleGANv3Generator',Registry里面用于区分别名\n noise_size=512,\n style_channels=512,\n out_size=512,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(type=StyleGAN2Discriminator, in_size=512))\n\nbatch_size = 4\ndata_root = 'data/afhqv2/'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_ada_gamma66_8xb4_fp16_metfaces_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom torch.optim import Adam\n\nfrom mmagic.engine.hooks.visualization_hook import VisualizationHook\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.base_models.average_model import ExponentialMovingAverage\nfrom mmagic.models.base_models.base_gan import BaseGAN\nfrom mmagic.models.editors.stylegan2.stylegan2_discriminator import (\n ADAAug, ADAStyleGAN2Discriminator)\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 6.6 # set by user\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\nload_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth' # noqa\n# ada settings\naug_kwargs = {\n 'xflip': 1,\n 'rotate90': 1,\n 'xint': 1,\n 'scale': 1,\n 'rotate': 1,\n 'aniso': 1,\n 'xfrac': 1,\n 'brightness': 1,\n 'contrast': 1,\n 'lumaflip': 1,\n 'hue': 1,\n 'saturation': 1\n}\n\nema_half_life = 10. # G_smoothing_kimg\n\nema_kimg = 10\nema_nimg = ema_kimg * 1000\nema_beta = 0.5**(32 / max(ema_nimg, 1e-8))\n\nema_config = dict(\n type=ExponentialMovingAverage, interval=1, momentum=ema_beta, start_iter=0)\n\nmodel.update(\n generator=dict(\n out_size=1024,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(\n type=ADAStyleGAN2Discriminator,\n in_size=1024,\n input_bgr2rgb=True,\n data_aug=dict(type=ADAAug, aug_pipeline=aug_kwargs, ada_kimg=100)),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.0025 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/metfaces/images/'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\ntrain_cfg.update(max_iters=160000)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img')\n ) # vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_cvt_official_rgb_8xb4_afhqv2_512x512.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.unconditional_imgs_flip_512x512 import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nmodel.update(\n generator=dict(\n out_size=512,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=512))\n\nbatch_size = 4\ndata_root = 'data/afhqv2/'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_cvt_official_rgb_8xb4_ffhq_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\n\nmodel.update(\n generator=dict(\n out_size=1024,\n img_channels=3,\n synthesis_cfg=synthesis_cfg,\n rgb2bgr=True),\n discriminator=dict(in_size=1024))\n\nbatch_size = 4\ndata_root = './data/ffhq/images'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_cvt_official_rgb_8xb4_ffhqu_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.unconditional_imgs_flip_lanczos_resize_256x256 import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\n# synthesis network的配置（generator）\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 16384,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nmodel.update(\n generator=dict(\n out_size=256,\n img_channels=3,\n rgb2bgr=True,\n synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1))\n\nbatch_size = 4\ndata_root = './data/ffhqu/images'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg = train_dataloader = optim_wrapper = None\n\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_gamma20_8xb4_fp16_noaug_ffhq_256x256.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.unconditional_imgs_flip_lanczos_resize_256x256 \\\n import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom torch.optim import Adam\n\nfrom mmagic.engine.hooks.visualization_hook import VisualizationHook\nfrom mmagic.evaluation.metrics.equivariance import Equivariance\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.base_models.average_model import RampUpEMA\nfrom mmagic.models.base_models.base_gan import BaseGAN\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 16384,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 2. # set by user\nd_reg_interval = 16\n\nema_config = dict(\n type=RampUpEMA,\n interval=1,\n ema_kimg=10,\n ema_rampup=0.05,\n batch_size=32,\n eps=1e-8,\n start_iter=0)\n\nmodel.update(\n generator=dict(out_size=256, img_channels=3, synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=256, channel_multiplier=1),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.0025 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/ffhq/images'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg.update(max_iters=800002)\n\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img')\n ) # vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema'),\n dict(\n type=Equivariance,\n fake_nums=50000,\n sample_mode='ema',\n prefix='EQ',\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/configs/styleganv3/stylegan3_t_gamma328_8xb4_fp16_noaug_ffhq_1024x1024.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.config import read_base\n\nwith read_base():\n from .._base_.datasets.ffhq_flip import *\n from .._base_.gen_default_runtime import *\n from .._base_.models.base_styleganv3 import *\n\nfrom torch.optim import Adam\n\nfrom mmagic.engine.hooks.visualization_hook import VisualizationHook\nfrom mmagic.evaluation.metrics.fid import FrechetInceptionDistance\nfrom mmagic.models.base_models.average_model import RampUpEMA\nfrom mmagic.models.base_models.base_gan import BaseGAN\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork\n\nbatch_size = 32\nmagnitude_ema_beta = 0.5**(batch_size / (20 * 1e3))\n\nsynthesis_cfg = {\n 'type': SynthesisNetwork,\n 'channel_base': 32768,\n 'channel_max': 512,\n 'magnitude_ema_beta': 0.999\n}\nr1_gamma = 32.8\nd_reg_interval = 16\n\nema_config = dict(\n type=RampUpEMA,\n interval=1,\n ema_kimg=10,\n ema_rampup=0.05,\n batch_size=batch_size,\n eps=1e-8,\n start_iter=0)\n\nmodel.update(\n generator=dict(out_size=1024, img_channels=3, synthesis_cfg=synthesis_cfg),\n discriminator=dict(in_size=1024),\n loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),\n ema_config=ema_config)\n\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ratio = d_reg_interval / (d_reg_interval + 1)\n\noptim_wrapper.update(\n generator=dict(\n optimizer=dict(\n type=Adam, lr=0.0025 * g_reg_ratio, betas=(0, 0.99**g_reg_ratio))),\n discriminator=dict(\n optimizer=dict(\n type=Adam, lr=0.002 * d_reg_ratio, betas=(0, 0.99**d_reg_ratio))))\n\nbatch_size = 4\ndata_root = 'data/ffhq/images'\n\ntrain_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\nval_dataloader.update(batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntest_dataloader.update(\n batch_size=batch_size, dataset=dict(data_root=data_root))\n\ntrain_cfg.update(max_iters=800002)\n\n# VIS_HOOK\ncustom_hooks = [\n dict(\n type=VisualizationHook,\n interval=5000,\n fixed_input=True,\n vis_kwargs_list=dict(type=BaseGAN, name='fake_img')\n ) # vis_kwargs_list=dict(type='GAN', name='fake_img'))\n]\n\n# METRICS\nmetrics = [\n dict(\n type=FrechetInceptionDistance,\n prefix='FID-Full-50k',\n fake_nums=50000,\n inception_style='StyleGAN',\n sample_model='ema')\n]\n# NOTE: config for save multi best checkpoints\n# default_hooks = dict(\n# checkpoint=dict(\n# save_best=['FID-Full-50k/fid', 'IS-50k/is'],\n# rule=['less', 'greater']))\n\ndefault_hooks.update(checkpoint=dict(save_best='FID-Full-50k/fid'))\nval_evaluator.update(metrics=metrics)\ntest_evaluator.update(metrics=metrics)\n" }, { "path": "mmagic/datasets/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .basic_conditional_dataset import BasicConditionalDataset\nfrom .basic_frames_dataset import BasicFramesDataset\nfrom .basic_image_dataset import BasicImageDataset\nfrom .cifar10_dataset import CIFAR10\nfrom .comp1k_dataset import AdobeComp1kDataset\nfrom .controlnet_dataset import ControlNetDataset\nfrom .dreambooth_dataset import DreamBoothDataset\nfrom .grow_scale_image_dataset import GrowScaleImgDataset\nfrom .imagenet_dataset import ImageNet\nfrom .mscoco_dataset import MSCoCoDataset\nfrom .paired_image_dataset import PairedImageDataset\nfrom .singan_dataset import SinGANDataset\nfrom .textual_inversion_dataset import TextualInversionDataset\nfrom .unpaired_image_dataset import UnpairedImageDataset\n\n__all__ = [\n 'AdobeComp1kDataset', 'BasicImageDataset', 'BasicFramesDataset',\n 'BasicConditionalDataset', 'UnpairedImageDataset', 'PairedImageDataset',\n 'ImageNet', 'CIFAR10', 'GrowScaleImgDataset', 'SinGANDataset',\n 'MSCoCoDataset', 'ControlNetDataset', 'DreamBoothDataset', 'ViCoDataset',\n 'ControlNetDataset', 'SDFinetuneDataset', 'TextualInversionDataset'\n]\n" }, { "path": "mmagic/datasets/basic_conditional_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Sequence, Union\n\nimport mmengine\nimport numpy as np\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import get_file_backend\nfrom mmengine.logging import MMLogger\n\nfrom mmagic.registry import DATASETS\nfrom .data_utils import expanduser, find_folders, get_samples\n\n\n@DATASETS.register_module()\nclass BasicConditionalDataset(BaseDataset):\n \"\"\"Custom dataset for conditional GAN. This class is based on the\n combination of `BaseDataset` (https://github.com/open-\n mmlab/mmclassification/blob/main/mmcls/datasets/base_dataset.py) # noqa\n and `CustomDataset` (https://github.com/open-\n mmlab/mmclassification/blob/main/mmcls/datasets/custom.py). # noqa.\n\n The dataset supports two kinds of annotation format.\n\n 1. A annotation file read by line (e.g., txt) is provided, and each line indicates a sample:\n\n The sample files: ::\n\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n\n The annotation file (the first column is the image path and the second\n column is the index of category): ::\n\n folder_1/xxx.png 0\n folder_1/xxy.png 1\n folder_2/123.png 5\n folder_2/nsdf3.png 3\n ...\n\n Please specify the name of categories by the argument ``classes``\n or ``metainfo``.\n\n 2. A dict-based annotation file (e.g., json) is provided, key and value\n indicate the path and label of the sample:\n\n The sample files: ::\n\n data_prefix/\n ├── folder_1\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n └── folder_2\n ├── 123.png\n ├── nsdf3.png\n └── ...\n\n The annotation file (the key is the image path and the value column\n is the label): ::\n\n {\n \"folder_1/xxx.png\": [1, 2, 3, 4],\n \"folder_1/xxy.png\": [2, 4, 1, 0],\n \"folder_2/123.png\": [0, 9, 8, 1],\n \"folder_2/nsdf3.png\", [1, 0, 0, 2],\n ...\n }\n\n In this kind of annotation, labels can be any type and not restricted to an index.\n\n 3. The samples are arranged in the specific way: ::\n\n data_prefix/\n ├── class_x\n │ ├── xxx.png\n │ ├── xxy.png\n │ └── ...\n │ └── xxz.png\n └── class_y\n ├── 123.png\n ├── nsdf3.png\n ├── ...\n └── asd932_.png\n\n If the ``ann_file`` is specified, the dataset will be generated by the\n first two ways, otherwise, try the third way.\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n metainfo (dict, optional): Meta information for dataset, such as class\n information. Defaults to None.\n data_root (str): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to ''.\n data_prefix (str | dict): Prefix for the data. Defaults to ''.\n extensions (Sequence[str]): A sequence of allowed extensions. Defaults\n to ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif').\n lazy_init (bool): Whether to load annotation during instantiation.\n In some cases, such as visualization, only the meta information of\n the dataset is needed, which is not necessary to load annotation\n file. ``Basedataset`` can skip load annotations to save time by set\n ``lazy_init=False``. Defaults to False.\n **kwargs: Other keyword arguments in :class:`BaseDataset`.\n \"\"\"\n\n def __init__(self,\n ann_file: str = '',\n metainfo: Optional[dict] = None,\n data_root: str = '',\n data_prefix: Union[str, dict] = '',\n extensions: Sequence[str] = ('.jpg', '.jpeg', '.png', '.ppm',\n '.bmp', '.pgm', '.tif'),\n lazy_init: bool = False,\n classes: Union[str, Sequence[str], None] = None,\n **kwargs):\n assert (ann_file or data_prefix or data_root), \\\n 'One of `ann_file`, `data_root` and `data_prefix` must '\\\n 'be specified.'\n if isinstance(data_prefix, str):\n data_prefix = dict(gt_path=expanduser(data_prefix))\n\n ann_file = expanduser(ann_file)\n metainfo = self._compat_classes(metainfo, classes)\n self.extensions = tuple(set([i.lower() for i in extensions]))\n\n super().__init__(\n # The base class requires string ann_file but this class doesn't\n ann_file=ann_file,\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=data_prefix,\n # Force to lazy_init for some modification before loading data.\n lazy_init=True,\n **kwargs)\n\n # Full initialize the dataset.\n if not lazy_init:\n self.full_init()\n\n def _find_samples(self, file_backend):\n \"\"\"find samples from ``data_prefix``.\"\"\"\n classes, folder_to_idx = find_folders(self.img_prefix, file_backend)\n samples, empty_classes = get_samples(\n self.img_prefix,\n folder_to_idx,\n is_valid_file=self.is_valid_file,\n file_backend=file_backend,\n )\n\n if len(samples) == 0:\n raise RuntimeError(\n f'Found 0 files in subfolders of: {self.data_prefix}. '\n f'Supported extensions are: {\",\".join(self.extensions)}')\n\n if self.CLASSES is not None:\n assert len(self.CLASSES) == len(classes), \\\n f\"The number of subfolders ({len(classes)}) doesn't match \" \\\n f'the number of specified classes ({len(self.CLASSES)}). ' \\\n 'Please check the data folder.'\n else:\n self._metainfo['classes'] = tuple(classes)\n\n if empty_classes:\n logger = MMLogger.get_current_instance()\n logger.warning(\n 'Found no valid file in the folder '\n f'{\", \".join(empty_classes)}. '\n f\"Supported extensions are: {', '.join(self.extensions)}\")\n\n self.folder_to_idx = folder_to_idx\n\n return samples\n\n def load_data_list(self):\n \"\"\"Load image paths and gt_labels.\"\"\"\n if self.img_prefix:\n file_backend = get_file_backend(uri=self.img_prefix)\n\n if not self.ann_file:\n samples = self._find_samples(file_backend)\n elif self.ann_file.endswith('json'):\n samples = mmengine.fileio.io.load(self.ann_file)\n samples = [[name, label] for name, label in samples.items()]\n elif self.ann_file.endswith('txt'):\n lines = mmengine.list_from_file(self.ann_file)\n samples = [x.strip().rsplit(' ', 1) for x in lines]\n else:\n raise TypeError('Only support \\'json\\' and \\'txt\\' as annotation.')\n\n def add_prefix(filename, prefix=''):\n if not prefix:\n return filename\n else:\n return file_backend.join_path(prefix, filename)\n\n data_list = []\n for filename, gt_label in samples:\n img_path = add_prefix(filename, self.img_prefix)\n # convert digit label to int\n if isinstance(gt_label, str):\n gt_label = int(gt_label) if gt_label.isdigit() else gt_label\n info = {'gt_path': img_path, 'gt_label': gt_label}\n data_list.append(info)\n return data_list\n\n def is_valid_file(self, filename: str) -> bool:\n \"\"\"Check if a file is a valid sample.\"\"\"\n return filename.lower().endswith(self.extensions)\n\n @property\n def img_prefix(self):\n \"\"\"The prefix of images.\"\"\"\n return self.data_prefix['gt_path']\n\n @property\n def CLASSES(self):\n \"\"\"Return all categories names.\"\"\"\n return self._metainfo.get('classes', None)\n\n @property\n def class_to_idx(self):\n \"\"\"Map mapping class name to class index.\n\n Returns:\n dict: mapping from class name to class index.\n \"\"\"\n\n return {cat: i for i, cat in enumerate(self.CLASSES)}\n\n def get_gt_labels(self):\n \"\"\"Get all ground-truth labels (categories).\n\n Returns:\n np.ndarray: categories for all images.\n \"\"\"\n\n gt_labels = np.array(\n [self.get_data_info(i)['gt_label'] for i in range(len(self))])\n return gt_labels\n\n def get_cat_ids(self, idx: int) -> List[int]:\n \"\"\"Get category id by index.\n\n Args:\n idx (int): Index of data.\n\n Returns:\n cat_ids (List[int]): Image category of specified index.\n \"\"\"\n\n return [int(self.get_data_info(idx)['gt_label'])]\n\n def _compat_classes(self, metainfo, classes):\n \"\"\"Merge the old style ``classes`` arguments to ``metainfo``.\"\"\"\n if isinstance(classes, str):\n # take it as a file path\n class_names = mmengine.list_from_file(expanduser(classes))\n elif isinstance(classes, (tuple, list)):\n class_names = classes\n elif classes is not None:\n raise ValueError(f'Unsupported type {type(classes)} of classes.')\n\n if metainfo is None:\n metainfo = {}\n\n if classes is not None:\n metainfo = {'classes': tuple(class_names), **metainfo}\n\n return metainfo\n\n def full_init(self):\n \"\"\"Load annotation file and set ``BaseDataset._fully_initialized`` to\n True.\"\"\"\n super().full_init()\n\n # To support the standard OpenMMLab 2.0 annotation format. Generate\n # metainfo in internal format from standard metainfo format.\n if 'categories' in self._metainfo and 'classes' not in self._metainfo:\n categories = sorted(\n self._metainfo['categories'], key=lambda x: x['id'])\n self._metainfo['classes'] = tuple(\n [cat['category_name'] for cat in categories])\n\n def __repr__(self):\n \"\"\"Print the basic information of the dataset.\n\n Returns:\n str: Formatted string.\n \"\"\"\n head = 'Dataset ' + self.__class__.__name__\n body = []\n if self._fully_initialized:\n body.append(f'Number of samples: \\t{self.__len__()}')\n else:\n body.append(\"Haven't been initialized\")\n\n if self.CLASSES is not None:\n body.append(f'Number of categories: \\t{len(self.CLASSES)}')\n else:\n body.append('The `CLASSES` meta info is not set.')\n\n body.extend(self.extra_repr())\n\n if len(self.pipeline.transforms) > 0:\n body.append('With transforms:')\n for t in self.pipeline.transforms:\n body.append(f' {t}')\n\n lines = [head] + [' ' * 4 + line for line in body]\n return '\\n'.join(lines)\n\n def extra_repr(self) -> List[str]:\n \"\"\"The extra repr information of the dataset.\"\"\"\n body = []\n body.append(f'Annotation file: \\t{self.ann_file}')\n body.append(f'Prefix of images: \\t{self.img_prefix}')\n return body\n" }, { "path": "mmagic/datasets/basic_frames_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom typing import Callable, List, Optional, Union\n\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import get_file_backend, list_from_file\n\nfrom ..registry import DATASETS\n\n\n@DATASETS.register_module()\nclass BasicFramesDataset(BaseDataset):\n \"\"\"BasicFramesDataset for open source projects in OpenMMLab/MMagic.\n\n This dataset is designed for low-level vision tasks with frames,\n such as video super-resolution and video frame interpolation.\n\n The annotation file is optional.\n\n If use annotation file, the annotation format can be shown as follows.\n\n .. code-block:: none\n\n Case 1 (Vid4):\n\n calendar 41\n city 34\n foliage 49\n walk 47\n\n Case 2 (REDS):\n\n 000/00000000.png (720, 1280, 3)\n 000/00000001.png (720, 1280, 3)\n\n Case 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n metainfo (dict, optional): Meta information for dataset, such as class\n information. Defaults to None.\n data_root (str, optional): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to None.\n data_prefix (dict, optional): Prefix for training data. Defaults to\n dict(img='', gt='').\n pipeline (list, optional): Processing pipeline. Defaults to [].\n test_mode (bool, optional): ``test_mode=True`` means in test phase.\n Defaults to False.\n filename_tmpl (str): Template for each filename. Note that the\n template excludes the file extension. Default: '{}'.\n search_key (str): The key used for searching the folder to get\n data_list. Default: 'gt'.\n backend_args (dict, optional): Arguments to instantiate the prefix of\n uri corresponding backend. Defaults to None.\n depth (int): The depth of path. Default: 1\n num_input_frames (None | int): Number of input frames. Default: None.\n num_output_frames (None | int): Number of output frames. Default: None.\n fixed_seq_len (None | int): The fixed sequence length.\n If None, BasicFramesDataset will obtain the length of each\n sequence.\n Default: None.\n load_frames_list (dict): Load frames list for each key.\n Default: dict().\n\n Examples:\n\n Assume the file structure as the following:\n\n mmagic (root)\n ├── mmagic\n ├── tools\n ├── configs\n ├── data\n │ ├── Vid4\n │ │ ├── BIx4\n │ │ │ ├── city\n │ │ │ │ ├── img1.png\n │ │ ├── GT\n │ │ │ ├── city\n │ │ │ │ ├── img1.png\n │ │ ├── meta_info_Vid4_GT.txt\n │ ├── places\n │ │ ├── sequences\n | | | ├── 00001\n │ │ │ │ ├── 0389\n │ │ │ │ │ ├── img1.png\n │ │ │ │ │ ├── img2.png\n │ │ │ │ │ ├── img3.png\n │ │ ├── tri_trainlist.txt\n\n Case 1: Loading Vid4 dataset for training a VSR model.\n\n .. code-block:: python\n\n dataset = BasicFramesDataset(\n ann_file='meta_info_Vid4_GT.txt',\n metainfo=dict(dataset_type='vid4', task_name='vsr'),\n data_root='data/Vid4',\n data_prefix=dict(img='BIx4', gt='GT'),\n pipeline=[],\n depth=2,\n num_input_frames=5)\n\n Case 2: Loading Vimeo90k dataset for training a VFI model.\n\n .. code-block:: python\n\n dataset = BasicFramesDataset(\n ann_file='tri_trainlist.txt',\n metainfo=dict(dataset_type='vimeo90k', task_name='vfi'),\n data_root='data/vimeo-triplet',\n data_prefix=dict(img='sequences', gt='sequences'),\n pipeline=[],\n depth=2,\n load_frames_list=dict(\n img=['img1.png', 'img3.png'], gt=['img2.png']))\n\n See more details in unittest\n tests/test_datasets/test_base_frames_dataset.py\n TestFramesDatasets().test_version_1_method()\n \"\"\"\n\n METAINFO = dict(dataset_type='base_edit_dataset', task_name='editing')\n\n def __init__(self,\n ann_file: str = '',\n metainfo: Optional[dict] = None,\n data_root: Optional[str] = None,\n data_prefix: dict = dict(img=''),\n pipeline: List[Union[dict, Callable]] = [],\n test_mode: bool = False,\n filename_tmpl: dict = dict(),\n search_key: Optional[str] = None,\n backend_args: Optional[dict] = None,\n depth: int = 1,\n num_input_frames: Optional[int] = None,\n num_output_frames: Optional[int] = None,\n fixed_seq_len: Optional[int] = None,\n load_frames_list: dict = dict(),\n **kwargs):\n\n for key in data_prefix:\n if key not in filename_tmpl:\n filename_tmpl[key] = '{}'\n\n if search_key is None:\n keys = list(data_prefix.keys())\n search_key = keys[0]\n self.search_key = search_key\n self.filename_tmpl = filename_tmpl\n self.use_ann_file = (ann_file != '')\n if backend_args is None:\n self.backend_args = None\n else:\n self.backend_args = backend_args.copy()\n self.depth = depth\n self.seq_lens = dict(fixed_seq_len=fixed_seq_len)\n self.num_input_frames = num_input_frames\n self.num_output_frames = num_output_frames\n self.load_frames_list = load_frames_list\n self.file_backend = get_file_backend(\n uri=data_root, backend_args=backend_args)\n\n super().__init__(\n ann_file=ann_file,\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=data_prefix,\n pipeline=pipeline,\n test_mode=test_mode,\n **kwargs)\n\n def load_data_list(self) -> List[dict]:\n \"\"\"Load data list from folder or annotation file.\n\n Returns:\n list[dict]: A list of annotation.\n \"\"\"\n\n path_list = self._get_path_list()\n self._set_seq_lens()\n\n data_list = []\n for path in path_list:\n basename, _ = osp.splitext(path)\n sequence_length = self.seq_lens['fixed_seq_len']\n if sequence_length is None:\n sequence_length = self.seq_lens[path.split(os.sep)[0]]\n data = dict(\n key=basename,\n num_input_frames=self.num_input_frames,\n num_output_frames=self.num_output_frames,\n sequence_length=sequence_length)\n for key in self.data_prefix:\n if key in self.load_frames_list:\n folder = osp.join(self.data_prefix[key], path)\n data[f'{key}_path'] = self._get_frames_list(key, folder)\n # The list of frames has been loaded,\n # ``sequence_length`` is useless\n # Avoid loading frames by ``sequence_length`` in pipeline\n data['sequence_length'] = None\n # overwrite ``num_input_frames`` and ``num_output_frames``\n if key == 'img':\n data['num_input_frames'] = len(data[f'{key}_path'])\n elif key == 'gt':\n data['num_output_frames'] = len(data[f'{key}_path'])\n else:\n data[f'{key}_path'] = self.data_prefix[key]\n data_list.append(data)\n\n return data_list\n\n def _get_path_list(self):\n \"\"\"Get list of paths from annotation file or folder of dataset.\n\n Returns:\n list[str]: A list of paths.\n \"\"\"\n\n if self.use_ann_file:\n path_list = self._get_path_list_from_ann()\n else:\n path_list = self._get_path_list_from_folder(depth=self.depth)\n\n return path_list\n\n def _get_path_list_from_ann(self):\n \"\"\"Get list of paths from annotation file.\n\n Returns:\n list[str]: A list of paths.\n \"\"\"\n\n ann_list = list_from_file(\n self.ann_file, backend_args=self.backend_args)\n path_list = []\n for ann in ann_list:\n if ann.isspace() or ann == '':\n continue\n path = ann.split(' ')[0]\n # Compatible with Windows file systems\n path = path.replace('/', os.sep)\n splitted_path = path.split(os.sep)\n if self.seq_lens['fixed_seq_len'] is None:\n self.seq_lens[splitted_path[0]] = 0\n ann_depth = len(splitted_path)\n if self.depth > ann_depth:\n # desire \"folder/file\", but the ann_file provides \"folder\".\n sub_path_list = self._get_path_list_from_folder(\n sub_folder=path,\n need_ext=False,\n depth=self.depth - ann_depth)\n path_list.extend(\n [path + os.sep + sub_path for sub_path in sub_path_list])\n elif self.depth < ann_depth:\n # desire \"folder\", while the ann_file provides \"folder/file\".\n desire_path = f'{os.sep}'.join(splitted_path[:self.depth])\n if desire_path not in path_list:\n path_list.append(desire_path)\n else:\n # desire \"folder/file\" and the ann_file provides \"folder/file\".\n # or desire \"folder\" and the ann_file provides \"folder\".\n path_list.append(path)\n\n return path_list\n\n def _get_path_list_from_folder(self,\n sub_folder=None,\n need_ext=True,\n depth=1):\n \"\"\"Get list of paths from folder.\n\n Args:\n sub_folder (None | str): The path of sub_folder. Default: None.\n need_ext (bool): Whether need ext. Default: True.\n depth (int): Residual depth of path, recursively called to\n ``depth == 1``. Default: 1\n\n Returns:\n list[str]: A list of paths.\n \"\"\"\n\n folder = self.data_prefix[self.search_key]\n tmpl = self.filename_tmpl[self.search_key].format('')\n path_list = []\n if sub_folder:\n folder = osp.join(folder, sub_folder)\n listdir = list(self.file_backend.list_dir_or_file(dir_path=folder))\n listdir.sort()\n for path in listdir:\n basename, ext = osp.splitext(path)\n if not (sub_folder or self.seq_lens['fixed_seq_len']):\n self.seq_lens[basename] = 0\n if depth > 1:\n sub_path_list = self._get_path_list_from_folder(\n sub_folder=path)\n path_list.extend(\n [path + os.sep + sub_path for sub_path in sub_path_list])\n elif basename.endswith(tmpl):\n if need_ext:\n path = path.replace(tmpl + ext, ext)\n else:\n path = path.replace(tmpl + ext, '')\n path_list.append(path)\n\n return path_list\n\n def _set_seq_lens(self):\n \"\"\"Get sequence lengths.\"\"\"\n\n if self.seq_lens['fixed_seq_len']:\n return\n folder = self.data_prefix[self.search_key]\n for key in self.seq_lens.keys():\n if key == 'fixed_seq_len':\n continue\n path = osp.join(folder, key)\n num_frames = len(list(self.file_backend.list_dir_or_file(path)))\n self.seq_lens[key] = num_frames\n\n def _get_frames_list(self, key, folder):\n \"\"\"Obtain list of frames.\n\n Args:\n key (str): The key of frames list, e.g. ``img``, ``gt``.\n folder (str): Folder of frames.\n\n Return:\n list[str]: The paths list of frames.\n \"\"\"\n\n if 'all' in self.load_frames_list[key]:\n # load all\n files = list(self.file_backend.list_dir_or_file(dir_path=folder))\n else:\n files = self.load_frames_list[key]\n\n files.sort()\n tmpl = self.filename_tmpl[key]\n files = [tmpl.format(file) for file in files]\n paths = [osp.join(folder, file) for file in files]\n\n return paths\n" }, { "path": "mmagic/datasets/basic_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nimport re\nfrom typing import Callable, List, Optional, Tuple, Union\n\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import get_file_backend, list_from_file\n\nfrom mmagic.registry import DATASETS\n\nIMG_EXTENSIONS = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm',\n '.PPM', '.bmp', '.BMP', '.tif', '.TIF', '.tiff', '.TIFF')\n\n\n@DATASETS.register_module()\nclass BasicImageDataset(BaseDataset):\n \"\"\"BasicImageDataset for open source projects in OpenMMLab/MMagic.\n\n This dataset is designed for low-level vision tasks with image,\n such as super-resolution and inpainting.\n\n The annotation file is optional.\n\n If use annotation file, the annotation format can be shown as follows.\n\n .. code-block:: none\n\n Case 1 (CelebA-HQ):\n\n 000001.png\n 000002.png\n\n Case 2 (DIV2K):\n\n 0001_s001.png (480,480,3)\n 0001_s002.png (480,480,3)\n 0001_s003.png (480,480,3)\n 0002_s001.png (480,480,3)\n 0002_s002.png (480,480,3)\n\n Case 3 (Vimeo90k):\n\n 00001/0266 (256, 448, 3)\n 00001/0268 (256, 448, 3)\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n metainfo (dict, optional): Meta information for dataset, such as class\n information. Defaults to None.\n data_root (str, optional): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to None.\n data_prefix (dict, optional): Prefix for training data. Defaults to\n dict(img=None, ann=None).\n pipeline (list, optional): Processing pipeline. Defaults to [].\n test_mode (bool, optional): ``test_mode=True`` means in test phase.\n Defaults to False.\n filename_tmpl (dict): Template for each filename. Note that the\n template excludes the file extension. Default: dict().\n search_key (str): The key used for searching the folder to get\n data_list. Default: 'gt'.\n backend_args (dict, optional): Arguments to instantiate the prefix of\n uri corresponding backend. Defaults to None.\n suffix (str or tuple[str], optional): File suffix\n that we are interested in. Default: None.\n recursive (bool): If set to True, recursively scan the\n directory. Default: False.\n\n Note:\n\n Assume the file structure as the following:\n\n .. code-block:: none\n\n mmagic (root)\n ├── mmagic\n ├── tools\n ├── configs\n ├── data\n │ ├── DIV2K\n │ │ ├── DIV2K_train_HR\n │ │ │ ├── image.png\n │ │ ├── DIV2K_train_LR_bicubic\n │ │ │ ├── X2\n │ │ │ ├── X3\n │ │ │ ├── X4\n │ │ │ │ ├── image_x4.png\n │ │ ├── DIV2K_valid_HR\n │ │ ├── DIV2K_valid_LR_bicubic\n │ │ │ ├── X2\n │ │ │ ├── X3\n │ │ │ ├── X4\n │ ├── places\n │ │ ├── test_set\n │ │ ├── train_set\n | | ├── meta\n | | | ├── Places365_train.txt\n | | | ├── Places365_val.txt\n\n Examples:\n\n Case 1: Loading DIV2K dataset for training a SISR model.\n\n .. code-block:: python\n\n dataset = BasicImageDataset(\n ann_file='',\n metainfo=dict(\n dataset_type='div2k',\n task_name='sisr'),\n data_root='data/DIV2K',\n data_prefix=dict(\n gt='DIV2K_train_HR', img='DIV2K_train_LR_bicubic/X4'),\n filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=[])\n\n Case 2: Loading places dataset for training an inpainting model.\n\n .. code-block:: python\n\n dataset = BasicImageDataset(\n ann_file='meta/Places365_train.txt',\n metainfo=dict(\n dataset_type='places365',\n task_name='inpainting'),\n data_root='data/places',\n data_prefix=dict(gt='train_set'),\n pipeline=[])\n \"\"\"\n\n METAINFO = dict(dataset_type='basic_image_dataset', task_name='editing')\n\n def __init__(self,\n ann_file: str = '',\n metainfo: Optional[dict] = None,\n data_root: Optional[str] = None,\n data_prefix: dict = dict(img=''),\n pipeline: List[Union[dict, Callable]] = [],\n test_mode: bool = False,\n filename_tmpl: dict = dict(),\n search_key: Optional[str] = None,\n backend_args: Optional[dict] = None,\n img_suffix: Optional[Union[str, Tuple[str]]] = IMG_EXTENSIONS,\n recursive: bool = False,\n **kwards):\n\n for key in data_prefix:\n if key not in filename_tmpl:\n filename_tmpl[key] = '{}'\n\n if search_key is None:\n keys = list(data_prefix.keys())\n search_key = keys[0]\n self.search_key = search_key\n self.filename_tmpl = filename_tmpl\n self.use_ann_file = (ann_file != '')\n if backend_args is None:\n self.backend_args = None\n else:\n self.backend_args = backend_args.copy()\n self.img_suffix = img_suffix\n self.recursive = recursive\n self.file_backend = get_file_backend(\n uri=data_root, backend_args=backend_args)\n\n super().__init__(\n ann_file=ann_file,\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=data_prefix,\n pipeline=pipeline,\n test_mode=test_mode,\n **kwards)\n\n def load_data_list(self) -> List[dict]:\n \"\"\"Load data list from folder or annotation file.\n\n Returns:\n list[dict]: A list of annotation.\n \"\"\"\n\n path_list = self._get_path_list()\n\n data_list = []\n for file in path_list:\n basename, ext = osp.splitext(file)\n if basename.startswith(os.sep):\n # Avoid absolute-path-like annotations\n basename = basename[1:]\n data = dict(key=basename)\n for key in self.data_prefix:\n path = osp.join(self.data_prefix[key],\n (f'{self.filename_tmpl[key].format(basename)}'\n f'{ext}'))\n data[f'{key}_path'] = path\n data_list.append(data)\n\n return data_list\n\n def _get_path_list(self):\n \"\"\"Get list of paths from annotation file or folder of dataset.\n\n Returns:\n list[dict]: A list of paths.\n \"\"\"\n\n path_list = []\n if self.use_ann_file:\n path_list = self._get_path_list_from_ann()\n else:\n path_list = self._get_path_list_from_folder()\n\n return path_list\n\n def _get_path_list_from_ann(self):\n \"\"\"Get list of paths from annotation file.\n\n Returns:\n List: List of paths.\n \"\"\"\n\n ann_list = list_from_file(\n self.ann_file, backend_args=self.backend_args)\n path_list = []\n for ann in ann_list:\n if ann.isspace() or ann == '':\n continue\n path = ann.split(' ')[0]\n # Compatible with Windows file systems\n path = path.replace('/', os.sep)\n path_list.append(path)\n\n return path_list\n\n def _get_path_list_from_folder(self):\n \"\"\"Get list of paths from folder.\n\n Returns:\n List: List of paths.\n \"\"\"\n\n path_list = []\n folder = self.data_prefix[self.search_key]\n tmpl = self.filename_tmpl[self.search_key].format('')\n virtual_path = self.filename_tmpl[self.search_key].format('.*')\n for img_path in self.file_backend.list_dir_or_file(\n dir_path=folder,\n list_dir=False,\n suffix=self.img_suffix,\n recursive=self.recursive,\n ):\n basename, ext = osp.splitext(img_path)\n if re.match(virtual_path, basename):\n img_path = img_path.replace(tmpl + ext, ext)\n path_list.append(img_path)\n\n return path_list\n" }, { "path": "mmagic/datasets/categories.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Pre-defined categories names of various datasets.\n\nIMAGENET_CATEGORIES = (\n 'tench, Tinca tinca',\n 'goldfish, Carassius auratus',\n 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', # noqa: E501\n 'tiger shark, Galeocerdo cuvieri',\n 'hammerhead, hammerhead shark',\n 'electric ray, crampfish, numbfish, torpedo',\n 'stingray',\n 'cock',\n 'hen',\n 'ostrich, Struthio camelus',\n 'brambling, Fringilla montifringilla',\n 'goldfinch, Carduelis carduelis',\n 'house finch, linnet, Carpodacus mexicanus',\n 'junco, snowbird',\n 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n 'robin, American robin, Turdus migratorius',\n 'bulbul',\n 'jay',\n 'magpie',\n 'chickadee',\n 'water ouzel, dipper',\n 'kite',\n 'bald eagle, American eagle, Haliaeetus leucocephalus',\n 'vulture',\n 'great grey owl, great gray owl, Strix nebulosa',\n 'European fire salamander, Salamandra salamandra',\n 'common newt, Triturus vulgaris',\n 'eft',\n 'spotted salamander, Ambystoma maculatum',\n 'axolotl, mud puppy, Ambystoma mexicanum',\n 'bullfrog, Rana catesbeiana',\n 'tree frog, tree-frog',\n 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n 'loggerhead, loggerhead turtle, Caretta caretta',\n 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', # noqa: E501\n 'mud turtle',\n 'terrapin',\n 'box turtle, box tortoise',\n 'banded gecko',\n 'common iguana, iguana, Iguana iguana',\n 'American chameleon, anole, Anolis carolinensis',\n 'whiptail, whiptail lizard',\n 'agama',\n 'frilled lizard, Chlamydosaurus kingi',\n 'alligator lizard',\n 'Gila monster, Heloderma suspectum',\n 'green lizard, Lacerta viridis',\n 'African chameleon, Chamaeleo chamaeleon',\n 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', # noqa: E501\n 'African crocodile, Nile crocodile, Crocodylus niloticus',\n 'American alligator, Alligator mississipiensis',\n 'triceratops',\n 'thunder snake, worm snake, Carphophis amoenus',\n 'ringneck snake, ring-necked snake, ring snake',\n 'hognose snake, puff adder, sand viper',\n 'green snake, grass snake',\n 'king snake, kingsnake',\n 'garter snake, grass snake',\n 'water snake',\n 'vine snake',\n 'night snake, Hypsiglena torquata',\n 'boa constrictor, Constrictor constrictor',\n 'rock python, rock snake, Python sebae',\n 'Indian cobra, Naja naja',\n 'green mamba',\n 'sea snake',\n 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n 'sidewinder, horned rattlesnake, Crotalus cerastes',\n 'trilobite',\n 'harvestman, daddy longlegs, Phalangium opilio',\n 'scorpion',\n 'black and gold garden spider, Argiope aurantia',\n 'barn spider, Araneus cavaticus',\n 'garden spider, Aranea diademata',\n 'black widow, Latrodectus mactans',\n 'tarantula',\n 'wolf spider, hunting spider',\n 'tick',\n 'centipede',\n 'black grouse',\n 'ptarmigan',\n 'ruffed grouse, partridge, Bonasa umbellus',\n 'prairie chicken, prairie grouse, prairie fowl',\n 'peacock',\n 'quail',\n 'partridge',\n 'African grey, African gray, Psittacus erithacus',\n 'macaw',\n 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n 'lorikeet',\n 'coucal',\n 'bee eater',\n 'hornbill',\n 'hummingbird',\n 'jacamar',\n 'toucan',\n 'drake',\n 'red-breasted merganser, Mergus serrator',\n 'goose',\n 'black swan, Cygnus atratus',\n 'tusker',\n 'echidna, spiny anteater, anteater',\n 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', # noqa: E501\n 'wallaby, brush kangaroo',\n 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', # noqa: E501\n 'wombat',\n 'jellyfish',\n 'sea anemone, anemone',\n 'brain coral',\n 'flatworm, platyhelminth',\n 'nematode, nematode worm, roundworm',\n 'conch',\n 'snail',\n 'slug',\n 'sea slug, nudibranch',\n 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n 'chambered nautilus, pearly nautilus, nautilus',\n 'Dungeness crab, Cancer magister',\n 'rock crab, Cancer irroratus',\n 'fiddler crab',\n 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', # noqa: E501\n 'American lobster, Northern lobster, Maine lobster, Homarus americanus', # noqa: E501\n 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', # noqa: E501\n 'crayfish, crawfish, crawdad, crawdaddy',\n 'hermit crab',\n 'isopod',\n 'white stork, Ciconia ciconia',\n 'black stork, Ciconia nigra',\n 'spoonbill',\n 'flamingo',\n 'little blue heron, Egretta caerulea',\n 'American egret, great white heron, Egretta albus',\n 'bittern',\n 'crane',\n 'limpkin, Aramus pictus',\n 'European gallinule, Porphyrio porphyrio',\n 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n 'bustard',\n 'ruddy turnstone, Arenaria interpres',\n 'red-backed sandpiper, dunlin, Erolia alpina',\n 'redshank, Tringa totanus',\n 'dowitcher',\n 'oystercatcher, oyster catcher',\n 'pelican',\n 'king penguin, Aptenodytes patagonica',\n 'albatross, mollymawk',\n 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', # noqa: E501\n 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n 'dugong, Dugong dugon',\n 'sea lion',\n 'Chihuahua',\n 'Japanese spaniel',\n 'Maltese dog, Maltese terrier, Maltese',\n 'Pekinese, Pekingese, Peke',\n 'Shih-Tzu',\n 'Blenheim spaniel',\n 'papillon',\n 'toy terrier',\n 'Rhodesian ridgeback',\n 'Afghan hound, Afghan',\n 'basset, basset hound',\n 'beagle',\n 'bloodhound, sleuthhound',\n 'bluetick',\n 'black-and-tan coonhound',\n 'Walker hound, Walker foxhound',\n 'English foxhound',\n 'redbone',\n 'borzoi, Russian wolfhound',\n 'Irish wolfhound',\n 'Italian greyhound',\n 'whippet',\n 'Ibizan hound, Ibizan Podenco',\n 'Norwegian elkhound, elkhound',\n 'otterhound, otter hound',\n 'Saluki, gazelle hound',\n 'Scottish deerhound, deerhound',\n 'Weimaraner',\n 'Staffordshire bullterrier, Staffordshire bull terrier',\n 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', # noqa: E501\n 'Bedlington terrier',\n 'Border terrier',\n 'Kerry blue terrier',\n 'Irish terrier',\n 'Norfolk terrier',\n 'Norwich terrier',\n 'Yorkshire terrier',\n 'wire-haired fox terrier',\n 'Lakeland terrier',\n 'Sealyham terrier, Sealyham',\n 'Airedale, Airedale terrier',\n 'cairn, cairn terrier',\n 'Australian terrier',\n 'Dandie Dinmont, Dandie Dinmont terrier',\n 'Boston bull, Boston terrier',\n 'miniature schnauzer',\n 'giant schnauzer',\n 'standard schnauzer',\n 'Scotch terrier, Scottish terrier, Scottie',\n 'Tibetan terrier, chrysanthemum dog',\n 'silky terrier, Sydney silky',\n 'soft-coated wheaten terrier',\n 'West Highland white terrier',\n 'Lhasa, Lhasa apso',\n 'flat-coated retriever',\n 'curly-coated retriever',\n 'golden retriever',\n 'Labrador retriever',\n 'Chesapeake Bay retriever',\n 'German short-haired pointer',\n 'vizsla, Hungarian pointer',\n 'English setter',\n 'Irish setter, red setter',\n 'Gordon setter',\n 'Brittany spaniel',\n 'clumber, clumber spaniel',\n 'English springer, English springer spaniel',\n 'Welsh springer spaniel',\n 'cocker spaniel, English cocker spaniel, cocker',\n 'Sussex spaniel',\n 'Irish water spaniel',\n 'kuvasz',\n 'schipperke',\n 'groenendael',\n 'malinois',\n 'briard',\n 'kelpie',\n 'komondor',\n 'Old English sheepdog, bobtail',\n 'Shetland sheepdog, Shetland sheep dog, Shetland',\n 'collie',\n 'Border collie',\n 'Bouvier des Flandres, Bouviers des Flandres',\n 'Rottweiler',\n 'German shepherd, German shepherd dog, German police dog, alsatian',\n 'Doberman, Doberman pinscher',\n 'miniature pinscher',\n 'Greater Swiss Mountain dog',\n 'Bernese mountain dog',\n 'Appenzeller',\n 'EntleBucher',\n 'boxer',\n 'bull mastiff',\n 'Tibetan mastiff',\n 'French bulldog',\n 'Great Dane',\n 'Saint Bernard, St Bernard',\n 'Eskimo dog, husky',\n 'malamute, malemute, Alaskan malamute',\n 'Siberian husky',\n 'dalmatian, coach dog, carriage dog',\n 'affenpinscher, monkey pinscher, monkey dog',\n 'basenji',\n 'pug, pug-dog',\n 'Leonberg',\n 'Newfoundland, Newfoundland dog',\n 'Great Pyrenees',\n 'Samoyed, Samoyede',\n 'Pomeranian',\n 'chow, chow chow',\n 'keeshond',\n 'Brabancon griffon',\n 'Pembroke, Pembroke Welsh corgi',\n 'Cardigan, Cardigan Welsh corgi',\n 'toy poodle',\n 'miniature poodle',\n 'standard poodle',\n 'Mexican hairless',\n 'timber wolf, grey wolf, gray wolf, Canis lupus',\n 'white wolf, Arctic wolf, Canis lupus tundrarum',\n 'red wolf, maned wolf, Canis rufus, Canis niger',\n 'coyote, prairie wolf, brush wolf, Canis latrans',\n 'dingo, warrigal, warragal, Canis dingo',\n 'dhole, Cuon alpinus',\n 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n 'hyena, hyaena',\n 'red fox, Vulpes vulpes',\n 'kit fox, Vulpes macrotis',\n 'Arctic fox, white fox, Alopex lagopus',\n 'grey fox, gray fox, Urocyon cinereoargenteus',\n 'tabby, tabby cat',\n 'tiger cat',\n 'Persian cat',\n 'Siamese cat, Siamese',\n 'Egyptian cat',\n 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', # noqa: E501\n 'lynx, catamount',\n 'leopard, Panthera pardus',\n 'snow leopard, ounce, Panthera uncia',\n 'jaguar, panther, Panthera onca, Felis onca',\n 'lion, king of beasts, Panthera leo',\n 'tiger, Panthera tigris',\n 'cheetah, chetah, Acinonyx jubatus',\n 'brown bear, bruin, Ursus arctos',\n 'American black bear, black bear, Ursus americanus, Euarctos americanus', # noqa: E501\n 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n 'sloth bear, Melursus ursinus, Ursus ursinus',\n 'mongoose',\n 'meerkat, mierkat',\n 'tiger beetle',\n 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n 'ground beetle, carabid beetle',\n 'long-horned beetle, longicorn, longicorn beetle',\n 'leaf beetle, chrysomelid',\n 'dung beetle',\n 'rhinoceros beetle',\n 'weevil',\n 'fly',\n 'bee',\n 'ant, emmet, pismire',\n 'grasshopper, hopper',\n 'cricket',\n 'walking stick, walkingstick, stick insect',\n 'cockroach, roach',\n 'mantis, mantid',\n 'cicada, cicala',\n 'leafhopper',\n 'lacewing, lacewing fly',\n \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\", # noqa: E501\n 'damselfly',\n 'admiral',\n 'ringlet, ringlet butterfly',\n 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n 'cabbage butterfly',\n 'sulphur butterfly, sulfur butterfly',\n 'lycaenid, lycaenid butterfly',\n 'starfish, sea star',\n 'sea urchin',\n 'sea cucumber, holothurian',\n 'wood rabbit, cottontail, cottontail rabbit',\n 'hare',\n 'Angora, Angora rabbit',\n 'hamster',\n 'porcupine, hedgehog',\n 'fox squirrel, eastern fox squirrel, Sciurus niger',\n 'marmot',\n 'beaver',\n 'guinea pig, Cavia cobaya',\n 'sorrel',\n 'zebra',\n 'hog, pig, grunter, squealer, Sus scrofa',\n 'wild boar, boar, Sus scrofa',\n 'warthog',\n 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n 'ox',\n 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n 'bison',\n 'ram, tup',\n 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', # noqa: E501\n 'ibex, Capra ibex',\n 'hartebeest',\n 'impala, Aepyceros melampus',\n 'gazelle',\n 'Arabian camel, dromedary, Camelus dromedarius',\n 'llama',\n 'weasel',\n 'mink',\n 'polecat, fitch, foulmart, foumart, Mustela putorius',\n 'black-footed ferret, ferret, Mustela nigripes',\n 'otter',\n 'skunk, polecat, wood pussy',\n 'badger',\n 'armadillo',\n 'three-toed sloth, ai, Bradypus tridactylus',\n 'orangutan, orang, orangutang, Pongo pygmaeus',\n 'gorilla, Gorilla gorilla',\n 'chimpanzee, chimp, Pan troglodytes',\n 'gibbon, Hylobates lar',\n 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n 'guenon, guenon monkey',\n 'patas, hussar monkey, Erythrocebus patas',\n 'baboon',\n 'macaque',\n 'langur',\n 'colobus, colobus monkey',\n 'proboscis monkey, Nasalis larvatus',\n 'marmoset',\n 'capuchin, ringtail, Cebus capucinus',\n 'howler monkey, howler',\n 'titi, titi monkey',\n 'spider monkey, Ateles geoffroyi',\n 'squirrel monkey, Saimiri sciureus',\n 'Madagascar cat, ring-tailed lemur, Lemur catta',\n 'indri, indris, Indri indri, Indri brevicaudatus',\n 'Indian elephant, Elephas maximus',\n 'African elephant, Loxodonta africana',\n 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n 'barracouta, snoek',\n 'eel',\n 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', # noqa: E501\n 'rock beauty, Holocanthus tricolor',\n 'anemone fish',\n 'sturgeon',\n 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n 'lionfish',\n 'puffer, pufferfish, blowfish, globefish',\n 'abacus',\n 'abaya',\n \"academic gown, academic robe, judge's robe\",\n 'accordion, piano accordion, squeeze box',\n 'acoustic guitar',\n 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n 'airliner',\n 'airship, dirigible',\n 'altar',\n 'ambulance',\n 'amphibian, amphibious vehicle',\n 'analog clock',\n 'apiary, bee house',\n 'apron',\n 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', # noqa: E501\n 'assault rifle, assault gun',\n 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n 'bakery, bakeshop, bakehouse',\n 'balance beam, beam',\n 'balloon',\n 'ballpoint, ballpoint pen, ballpen, Biro',\n 'Band Aid',\n 'banjo',\n 'bannister, banister, balustrade, balusters, handrail',\n 'barbell',\n 'barber chair',\n 'barbershop',\n 'barn',\n 'barometer',\n 'barrel, cask',\n 'barrow, garden cart, lawn cart, wheelbarrow',\n 'baseball',\n 'basketball',\n 'bassinet',\n 'bassoon',\n 'bathing cap, swimming cap',\n 'bath towel',\n 'bathtub, bathing tub, bath, tub',\n 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', # noqa: E501\n 'beacon, lighthouse, beacon light, pharos',\n 'beaker',\n 'bearskin, busby, shako',\n 'beer bottle',\n 'beer glass',\n 'bell cote, bell cot',\n 'bib',\n 'bicycle-built-for-two, tandem bicycle, tandem',\n 'bikini, two-piece',\n 'binder, ring-binder',\n 'binoculars, field glasses, opera glasses',\n 'birdhouse',\n 'boathouse',\n 'bobsled, bobsleigh, bob',\n 'bolo tie, bolo, bola tie, bola',\n 'bonnet, poke bonnet',\n 'bookcase',\n 'bookshop, bookstore, bookstall',\n 'bottlecap',\n 'bow',\n 'bow tie, bow-tie, bowtie',\n 'brass, memorial tablet, plaque',\n 'brassiere, bra, bandeau',\n 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n 'breastplate, aegis, egis',\n 'broom',\n 'bucket, pail',\n 'buckle',\n 'bulletproof vest',\n 'bullet train, bullet',\n 'butcher shop, meat market',\n 'cab, hack, taxi, taxicab',\n 'caldron, cauldron',\n 'candle, taper, wax light',\n 'cannon',\n 'canoe',\n 'can opener, tin opener',\n 'cardigan',\n 'car mirror',\n 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n \"carpenter's kit, tool kit\",\n 'carton',\n 'car wheel',\n 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', # noqa: E501\n 'cassette',\n 'cassette player',\n 'castle',\n 'catamaran',\n 'CD player',\n 'cello, violoncello',\n 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n 'chain',\n 'chainlink fence',\n 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', # noqa: E501\n 'chain saw, chainsaw',\n 'chest',\n 'chiffonier, commode',\n 'chime, bell, gong',\n 'china cabinet, china closet',\n 'Christmas stocking',\n 'church, church building',\n 'cinema, movie theater, movie theatre, movie house, picture palace',\n 'cleaver, meat cleaver, chopper',\n 'cliff dwelling',\n 'cloak',\n 'clog, geta, patten, sabot',\n 'cocktail shaker',\n 'coffee mug',\n 'coffeepot',\n 'coil, spiral, volute, whorl, helix',\n 'combination lock',\n 'computer keyboard, keypad',\n 'confectionery, confectionary, candy store',\n 'container ship, containership, container vessel',\n 'convertible',\n 'corkscrew, bottle screw',\n 'cornet, horn, trumpet, trump',\n 'cowboy boot',\n 'cowboy hat, ten-gallon hat',\n 'cradle',\n 'crane',\n 'crash helmet',\n 'crate',\n 'crib, cot',\n 'Crock Pot',\n 'croquet ball',\n 'crutch',\n 'cuirass',\n 'dam, dike, dyke',\n 'desk',\n 'desktop computer',\n 'dial telephone, dial phone',\n 'diaper, nappy, napkin',\n 'digital clock',\n 'digital watch',\n 'dining table, board',\n 'dishrag, dishcloth',\n 'dishwasher, dish washer, dishwashing machine',\n 'disk brake, disc brake',\n 'dock, dockage, docking facility',\n 'dogsled, dog sled, dog sleigh',\n 'dome',\n 'doormat, welcome mat',\n 'drilling platform, offshore rig',\n 'drum, membranophone, tympan',\n 'drumstick',\n 'dumbbell',\n 'Dutch oven',\n 'electric fan, blower',\n 'electric guitar',\n 'electric locomotive',\n 'entertainment center',\n 'envelope',\n 'espresso maker',\n 'face powder',\n 'feather boa, boa',\n 'file, file cabinet, filing cabinet',\n 'fireboat',\n 'fire engine, fire truck',\n 'fire screen, fireguard',\n 'flagpole, flagstaff',\n 'flute, transverse flute',\n 'folding chair',\n 'football helmet',\n 'forklift',\n 'fountain',\n 'fountain pen',\n 'four-poster',\n 'freight car',\n 'French horn, horn',\n 'frying pan, frypan, skillet',\n 'fur coat',\n 'garbage truck, dustcart',\n 'gasmask, respirator, gas helmet',\n 'gas pump, gasoline pump, petrol pump, island dispenser',\n 'goblet',\n 'go-kart',\n 'golf ball',\n 'golfcart, golf cart',\n 'gondola',\n 'gong, tam-tam',\n 'gown',\n 'grand piano, grand',\n 'greenhouse, nursery, glasshouse',\n 'grille, radiator grille',\n 'grocery store, grocery, food market, market',\n 'guillotine',\n 'hair slide',\n 'hair spray',\n 'half track',\n 'hammer',\n 'hamper',\n 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n 'hand-held computer, hand-held microcomputer',\n 'handkerchief, hankie, hanky, hankey',\n 'hard disc, hard disk, fixed disk',\n 'harmonica, mouth organ, harp, mouth harp',\n 'harp',\n 'harvester, reaper',\n 'hatchet',\n 'holster',\n 'home theater, home theatre',\n 'honeycomb',\n 'hook, claw',\n 'hoopskirt, crinoline',\n 'horizontal bar, high bar',\n 'horse cart, horse-cart',\n 'hourglass',\n 'iPod',\n 'iron, smoothing iron',\n \"jack-o'-lantern\",\n 'jean, blue jean, denim',\n 'jeep, landrover',\n 'jersey, T-shirt, tee shirt',\n 'jigsaw puzzle',\n 'jinrikisha, ricksha, rickshaw',\n 'joystick',\n 'kimono',\n 'knee pad',\n 'knot',\n 'lab coat, laboratory coat',\n 'ladle',\n 'lampshade, lamp shade',\n 'laptop, laptop computer',\n 'lawn mower, mower',\n 'lens cap, lens cover',\n 'letter opener, paper knife, paperknife',\n 'library',\n 'lifeboat',\n 'lighter, light, igniter, ignitor',\n 'limousine, limo',\n 'liner, ocean liner',\n 'lipstick, lip rouge',\n 'Loafer',\n 'lotion',\n 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', # noqa: E501\n \"loupe, jeweler's loupe\",\n 'lumbermill, sawmill',\n 'magnetic compass',\n 'mailbag, postbag',\n 'mailbox, letter box',\n 'maillot',\n 'maillot, tank suit',\n 'manhole cover',\n 'maraca',\n 'marimba, xylophone',\n 'mask',\n 'matchstick',\n 'maypole',\n 'maze, labyrinth',\n 'measuring cup',\n 'medicine chest, medicine cabinet',\n 'megalith, megalithic structure',\n 'microphone, mike',\n 'microwave, microwave oven',\n 'military uniform',\n 'milk can',\n 'minibus',\n 'miniskirt, mini',\n 'minivan',\n 'missile',\n 'mitten',\n 'mixing bowl',\n 'mobile home, manufactured home',\n 'Model T',\n 'modem',\n 'monastery',\n 'monitor',\n 'moped',\n 'mortar',\n 'mortarboard',\n 'mosque',\n 'mosquito net',\n 'motor scooter, scooter',\n 'mountain bike, all-terrain bike, off-roader',\n 'mountain tent',\n 'mouse, computer mouse',\n 'mousetrap',\n 'moving van',\n 'muzzle',\n 'nail',\n 'neck brace',\n 'necklace',\n 'nipple',\n 'notebook, notebook computer',\n 'obelisk',\n 'oboe, hautboy, hautbois',\n 'ocarina, sweet potato',\n 'odometer, hodometer, mileometer, milometer',\n 'oil filter',\n 'organ, pipe organ',\n 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n 'overskirt',\n 'oxcart',\n 'oxygen mask',\n 'packet',\n 'paddle, boat paddle',\n 'paddlewheel, paddle wheel',\n 'padlock',\n 'paintbrush',\n \"pajama, pyjama, pj's, jammies\",\n 'palace',\n 'panpipe, pandean pipe, syrinx',\n 'paper towel',\n 'parachute, chute',\n 'parallel bars, bars',\n 'park bench',\n 'parking meter',\n 'passenger car, coach, carriage',\n 'patio, terrace',\n 'pay-phone, pay-station',\n 'pedestal, plinth, footstall',\n 'pencil box, pencil case',\n 'pencil sharpener',\n 'perfume, essence',\n 'Petri dish',\n 'photocopier',\n 'pick, plectrum, plectron',\n 'pickelhaube',\n 'picket fence, paling',\n 'pickup, pickup truck',\n 'pier',\n 'piggy bank, penny bank',\n 'pill bottle',\n 'pillow',\n 'ping-pong ball',\n 'pinwheel',\n 'pirate, pirate ship',\n 'pitcher, ewer',\n \"plane, carpenter's plane, woodworking plane\",\n 'planetarium',\n 'plastic bag',\n 'plate rack',\n 'plow, plough',\n \"plunger, plumber's helper\",\n 'Polaroid camera, Polaroid Land camera',\n 'pole',\n 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', # noqa: E501\n 'poncho',\n 'pool table, billiard table, snooker table',\n 'pop bottle, soda bottle',\n 'pot, flowerpot',\n \"potter's wheel\",\n 'power drill',\n 'prayer rug, prayer mat',\n 'printer',\n 'prison, prison house',\n 'projectile, missile',\n 'projector',\n 'puck, hockey puck',\n 'punching bag, punch bag, punching ball, punchball',\n 'purse',\n 'quill, quill pen',\n 'quilt, comforter, comfort, puff',\n 'racer, race car, racing car',\n 'racket, racquet',\n 'radiator',\n 'radio, wireless',\n 'radio telescope, radio reflector',\n 'rain barrel',\n 'recreational vehicle, RV, R.V.',\n 'reel',\n 'reflex camera',\n 'refrigerator, icebox',\n 'remote control, remote',\n 'restaurant, eating house, eating place, eatery',\n 'revolver, six-gun, six-shooter',\n 'rifle',\n 'rocking chair, rocker',\n 'rotisserie',\n 'rubber eraser, rubber, pencil eraser',\n 'rugby ball',\n 'rule, ruler',\n 'running shoe',\n 'safe',\n 'safety pin',\n 'saltshaker, salt shaker',\n 'sandal',\n 'sarong',\n 'sax, saxophone',\n 'scabbard',\n 'scale, weighing machine',\n 'school bus',\n 'schooner',\n 'scoreboard',\n 'screen, CRT screen',\n 'screw',\n 'screwdriver',\n 'seat belt, seatbelt',\n 'sewing machine',\n 'shield, buckler',\n 'shoe shop, shoe-shop, shoe store',\n 'shoji',\n 'shopping basket',\n 'shopping cart',\n 'shovel',\n 'shower cap',\n 'shower curtain',\n 'ski',\n 'ski mask',\n 'sleeping bag',\n 'slide rule, slipstick',\n 'sliding door',\n 'slot, one-armed bandit',\n 'snorkel',\n 'snowmobile',\n 'snowplow, snowplough',\n 'soap dispenser',\n 'soccer ball',\n 'sock',\n 'solar dish, solar collector, solar furnace',\n 'sombrero',\n 'soup bowl',\n 'space bar',\n 'space heater',\n 'space shuttle',\n 'spatula',\n 'speedboat',\n \"spider web, spider's web\",\n 'spindle',\n 'sports car, sport car',\n 'spotlight, spot',\n 'stage',\n 'steam locomotive',\n 'steel arch bridge',\n 'steel drum',\n 'stethoscope',\n 'stole',\n 'stone wall',\n 'stopwatch, stop watch',\n 'stove',\n 'strainer',\n 'streetcar, tram, tramcar, trolley, trolley car',\n 'stretcher',\n 'studio couch, day bed',\n 'stupa, tope',\n 'submarine, pigboat, sub, U-boat',\n 'suit, suit of clothes',\n 'sundial',\n 'sunglass',\n 'sunglasses, dark glasses, shades',\n 'sunscreen, sunblock, sun blocker',\n 'suspension bridge',\n 'swab, swob, mop',\n 'sweatshirt',\n 'swimming trunks, bathing trunks',\n 'swing',\n 'switch, electric switch, electrical switch',\n 'syringe',\n 'table lamp',\n 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n 'tape player',\n 'teapot',\n 'teddy, teddy bear',\n 'television, television system',\n 'tennis ball',\n 'thatch, thatched roof',\n 'theater curtain, theatre curtain',\n 'thimble',\n 'thresher, thrasher, threshing machine',\n 'throne',\n 'tile roof',\n 'toaster',\n 'tobacco shop, tobacconist shop, tobacconist',\n 'toilet seat',\n 'torch',\n 'totem pole',\n 'tow truck, tow car, wrecker',\n 'toyshop',\n 'tractor',\n 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', # noqa: E501\n 'tray',\n 'trench coat',\n 'tricycle, trike, velocipede',\n 'trimaran',\n 'tripod',\n 'triumphal arch',\n 'trolleybus, trolley coach, trackless trolley',\n 'trombone',\n 'tub, vat',\n 'turnstile',\n 'typewriter keyboard',\n 'umbrella',\n 'unicycle, monocycle',\n 'upright, upright piano',\n 'vacuum, vacuum cleaner',\n 'vase',\n 'vault',\n 'velvet',\n 'vending machine',\n 'vestment',\n 'viaduct',\n 'violin, fiddle',\n 'volleyball',\n 'waffle iron',\n 'wall clock',\n 'wallet, billfold, notecase, pocketbook',\n 'wardrobe, closet, press',\n 'warplane, military plane',\n 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n 'washer, automatic washer, washing machine',\n 'water bottle',\n 'water jug',\n 'water tower',\n 'whiskey jug',\n 'whistle',\n 'wig',\n 'window screen',\n 'window shade',\n 'Windsor tie',\n 'wine bottle',\n 'wing',\n 'wok',\n 'wooden spoon',\n 'wool, woolen, woollen',\n 'worm fence, snake fence, snake-rail fence, Virginia fence',\n 'wreck',\n 'yawl',\n 'yurt',\n 'web site, website, internet site, site',\n 'comic book',\n 'crossword puzzle, crossword',\n 'street sign',\n 'traffic light, traffic signal, stoplight',\n 'book jacket, dust cover, dust jacket, dust wrapper',\n 'menu',\n 'plate',\n 'guacamole',\n 'consomme',\n 'hot pot, hotpot',\n 'trifle',\n 'ice cream, icecream',\n 'ice lolly, lolly, lollipop, popsicle',\n 'French loaf',\n 'bagel, beigel',\n 'pretzel',\n 'cheeseburger',\n 'hotdog, hot dog, red hot',\n 'mashed potato',\n 'head cabbage',\n 'broccoli',\n 'cauliflower',\n 'zucchini, courgette',\n 'spaghetti squash',\n 'acorn squash',\n 'butternut squash',\n 'cucumber, cuke',\n 'artichoke, globe artichoke',\n 'bell pepper',\n 'cardoon',\n 'mushroom',\n 'Granny Smith',\n 'strawberry',\n 'orange',\n 'lemon',\n 'fig',\n 'pineapple, ananas',\n 'banana',\n 'jackfruit, jak, jack',\n 'custard apple',\n 'pomegranate',\n 'hay',\n 'carbonara',\n 'chocolate sauce, chocolate syrup',\n 'dough',\n 'meat loaf, meatloaf',\n 'pizza, pizza pie',\n 'potpie',\n 'burrito',\n 'red wine',\n 'espresso',\n 'cup',\n 'eggnog',\n 'alp',\n 'bubble',\n 'cliff, drop, drop-off',\n 'coral reef',\n 'geyser',\n 'lakeside, lakeshore',\n 'promontory, headland, head, foreland',\n 'sandbar, sand bar',\n 'seashore, coast, seacoast, sea-coast',\n 'valley, vale',\n 'volcano',\n 'ballplayer, baseball player',\n 'groom, bridegroom',\n 'scuba diver',\n 'rapeseed',\n 'daisy',\n \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\", # noqa: E501\n 'corn',\n 'acorn',\n 'hip, rose hip, rosehip',\n 'buckeye, horse chestnut, conker',\n 'coral fungus',\n 'agaric',\n 'gyromitra',\n 'stinkhorn, carrion fungus',\n 'earthstar',\n 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', # noqa: E501\n 'bolete',\n 'ear, spike, capitulum',\n 'toilet tissue, toilet paper, bathroom tissue')\n\nCIFAR10_CATEGORIES = ('airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',\n 'frog', 'horse', 'ship', 'truck')\n" }, { "path": "mmagic/datasets/cifar10_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pickle\nfrom typing import List, Optional\n\nimport mmengine.dist as dist\nimport numpy as np\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import DATASETS\nfrom .basic_conditional_dataset import BasicConditionalDataset\nfrom .categories import CIFAR10_CATEGORIES\nfrom .data_utils import check_md5, download_and_extract_archive\n\n\n@DATASETS.register_module()\nclass CIFAR10(BasicConditionalDataset):\n \"\"\"`CIFAR10 `_ Dataset.\n\n This implementation is modified from\n https://github.com/pytorch/vision/blob/master/torchvision/datasets/cifar.py\n\n Args:\n data_prefix (str): Prefix for data.\n test_mode (bool): ``test_mode=True`` means in test phase.\n It determines to use the training set or test set.\n metainfo (dict, optional): Meta information for dataset, such as\n categories information. Defaults to None.\n data_root (str): The root directory for ``data_prefix``.\n Defaults to ''.\n download (bool): Whether to download the dataset if not exists.\n Defaults to True.\n **kwargs: Other keyword arguments in :class:`BaseDataset`.\n \"\"\" # noqa: E501\n\n base_folder = 'cifar-10-batches-py'\n url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n filename = 'cifar-10-python.tar.gz'\n tgz_md5 = 'c58f30108f718f92721af3b95e74349a'\n train_list = [\n ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],\n ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],\n ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],\n ['data_batch_4', '634d18415352ddfa80567beed471001a'],\n ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],\n ]\n\n test_list = [\n ['test_batch', '40351d587109b95175f43aff81a1287e'],\n ]\n meta = {\n 'filename': 'batches.meta',\n 'key': 'label_names',\n 'md5': '5ff9c542aee3614f3951f8cda6e48888',\n }\n METAINFO = {'classes': CIFAR10_CATEGORIES}\n\n def __init__(self,\n data_prefix: str,\n test_mode: bool,\n metainfo: Optional[dict] = None,\n data_root: str = '',\n download: bool = True,\n **kwargs):\n self.download = download\n super().__init__(\n # The CIFAR dataset doesn't need specify annotation file\n ann_file='',\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=dict(root=data_prefix),\n test_mode=test_mode,\n **kwargs)\n\n def load_data_list(self):\n \"\"\"Load images and ground truth labels.\"\"\"\n root_prefix = self.data_prefix['root']\n file_backend = get_file_backend(uri=root_prefix)\n\n if dist.is_main_process() and not self._check_integrity():\n if file_backend.name != 'LocalBackend':\n raise RuntimeError(\n f'The dataset on {root_prefix} is not integrated, '\n f'please manually handle it.')\n\n if self.download:\n download_and_extract_archive(\n self.url,\n root_prefix,\n filename=self.filename,\n md5=self.tgz_md5)\n else:\n raise RuntimeError(\n f'Cannot find {self.__class__.__name__} dataset in '\n f\"{self.data_prefix['root']}, you can specify \"\n '`download=True` to download automatically.')\n\n dist.barrier()\n assert self._check_integrity(), \\\n 'Download failed or shared storage is unavailable. Please ' \\\n f'download the dataset manually through {self.url}.'\n\n if not self.test_mode:\n downloaded_list = self.train_list\n else:\n downloaded_list = self.test_list\n\n imgs = []\n gt_labels = []\n\n # load the picked numpy arrays\n for file_name, _ in downloaded_list:\n file_path = file_backend.join_path(root_prefix, self.base_folder,\n file_name)\n content = file_backend.get(file_path)\n entry = pickle.loads(content, encoding='latin1')\n imgs.append(entry['data'])\n if 'labels' in entry:\n gt_labels.extend(entry['labels'])\n else:\n gt_labels.extend(entry['fine_labels'])\n\n imgs = np.vstack(imgs).reshape(-1, 3, 32, 32)\n imgs = imgs.transpose((0, 2, 3, 1)) # convert to HWC\n\n if self.CLASSES is None:\n # The metainfo in the file has the lowest priority, therefore\n # we only need to load it if classes is not specified.\n self._load_meta()\n\n data_list = []\n for img, gt_label in zip(imgs, gt_labels):\n info = {\n 'gt': img,\n 'gt_label': int(gt_label),\n 'gt_channel_order': 'RGB'\n }\n data_list.append(info)\n return data_list\n\n def _load_meta(self):\n \"\"\"Load categories information from metafile.\"\"\"\n root = self.data_prefix['root']\n file_backend = get_file_backend(uri=root)\n\n path = file_backend.join_path(root, self.base_folder,\n self.meta['filename'])\n md5 = self.meta.get('md5', None)\n if not file_backend.exists(path) or (md5 is not None\n and not check_md5(path, md5)):\n raise RuntimeError(\n 'Dataset metadata file not found or corrupted.' +\n ' You can use `download=True` to download it')\n content = file_backend.get(path)\n data = pickle.loads(content, encoding='latin1')\n self._metainfo.setdefault('classes', data[self.meta['key']])\n\n def _check_integrity(self):\n \"\"\"Check the integrity of data files.\"\"\"\n root = self.data_prefix['root']\n file_backend = get_file_backend(uri=root)\n\n for fentry in (self.train_list + self.test_list):\n filename, md5 = fentry[0], fentry[1]\n fpath = file_backend.join_path(root, self.base_folder, filename)\n if not file_backend.exists(fpath):\n return False\n if md5 is not None and not check_md5(\n fpath, md5, file_backend=file_backend):\n return False\n return True\n\n def extra_repr(self) -> List[str]:\n \"\"\"The extra repr information of the dataset.\"\"\"\n body = [f\"Prefix of data: \\t{self.data_prefix['root']}\"]\n return body\n" }, { "path": "mmagic/datasets/comp1k_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport os.path as osp\nfrom typing import List, Union\n\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import load\n\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass AdobeComp1kDataset(BaseDataset):\n \"\"\"Adobe composition-1k dataset.\n\n The dataset loads (alpha, fg, bg) data and apply specified transforms to\n the data. You could specify whether composite merged image online or load\n composited merged image in pipeline.\n\n Example for online comp-1k dataset:\n\n ::\n\n [\n {\n \"alpha\": 'alpha/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n ]\n\n Example for offline comp-1k dataset:\n\n ::\n\n [\n {\n \"alpha\": 'alpha/000.png',\n \"merged\": 'merged/000.png',\n \"fg\": 'fg/000.png',\n \"bg\": 'bg/000.png'\n },\n {\n \"alpha\": 'alpha/001.png',\n \"merged\": 'merged/001.png',\n \"fg\": 'fg/001.png',\n \"bg\": 'bg/001.png'\n },\n ]\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n data_root (str, optional): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to None.\n pipeline (list, optional): Processing pipeline. Defaults to [].\n test_mode (bool, optional): ``test_mode=True`` means in test phase.\n Defaults to False.\n **kwargs: Other arguments passed to\n :class:`mmengine.dataset.BaseDataset`.\n\n Examples:\n See unit-tests\n TODO: Move some codes in unittest here\n \"\"\"\n\n # TODO: Support parsing folder structures without annotation files.\n\n METAINFO = dict(dataset_type='matting_dataset', task_name='matting')\n\n def load_data_list(self) -> List[dict]:\n \"\"\"Load annotations from an annotation file named as ``self.ann_file``\n\n In order to be compatible to both new and old annotation format,\n we copy implementations from mmengine and do some modifications.\n\n Returns:\n list[dict]: A list of annotation.\n \"\"\" # noqa: E501\n # `self.ann_file` denotes the absolute annotation file path if\n # `self.root=None` or relative path if `self.root=/path/to/data/`.\n annotations = load(self.ann_file)\n assert annotations, f'annotation file \"{self.ann_file}\" is empty.'\n if isinstance(annotations, list):\n # Old annotation format, we get data_list only\n raw_data_list = annotations\n elif isinstance(annotations, dict):\n # New annotation format, follow original routine in base class\n if 'data_list' not in annotations or 'metainfo' not in annotations:\n raise ValueError('Annotation must have data_list and metainfo '\n 'keys')\n metainfo = annotations['metainfo']\n raw_data_list = annotations['data_list']\n\n # Meta information load from annotation file will not influence the\n # existed meta information load from `BaseDataset.METAINFO` and\n # `metainfo` arguments defined in constructor.\n for k, v in metainfo.items():\n self._metainfo.setdefault(k, v)\n else:\n raise TypeError(\n f'The annotations loaded from annotation file '\n f'should be a list or dict, but got {type(annotations)}!')\n\n # load and parse data_infos.\n data_list = []\n for raw_data_info in raw_data_list:\n # parse raw data information to target format\n data_info = self.parse_data_info(raw_data_info)\n if isinstance(data_info, dict):\n # For image tasks, `data_info` should information if single\n # image, such as dict(img_path='xxx', width=360, ...)\n data_list.append(data_info)\n elif isinstance(data_info, list):\n # For video tasks, `data_info` could contain image\n # information of multiple frames, such as\n # [dict(video_path='xxx', timestamps=...),\n # dict(video_path='xxx', timestamps=...)]\n for item in data_info:\n if not isinstance(item, dict):\n raise TypeError('data_list must be list of dict, but '\n f'got {type(item)}')\n data_list.extend(data_info)\n else:\n raise TypeError('data_info should be a dict or list of dict, '\n f'but got {type(data_info)}')\n\n return data_list\n\n def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]:\n \"\"\"Join data_root to each path in data_info.\"\"\"\n\n data_info = raw_data_info.copy()\n for key in raw_data_info:\n data_info[key] = osp.join(self.data_root, data_info[key])\n\n return data_info\n" }, { "path": "mmagic/datasets/controlnet_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport json\nimport os\nfrom typing import Callable, List, Union\n\nfrom mmengine.dataset import BaseDataset\n\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass ControlNetDataset(BaseDataset):\n \"\"\"Demo dataset to test ControlNet. Modified from https://github.com/lllyas\n viel/ControlNet/blob/16ea3b5379c1e78a4bc8e3fc9cae8d65c42511b1/tutorial_data\n set.py # noqa.\n\n You can download the demo data from https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip # noqa\n and then unzip the file to the ``data`` folder.\n\n Args:\n ann_file (str): Path to the annotation file. Defaults\n to 'prompt.json' as ControlNet's default.\n data_root (str): Path to the data root. Defaults to './data/fill50k'.\n pipeline (list[dict | callable]): A sequence of data transforms.\n \"\"\"\n\n def __init__(self,\n ann_file: str = 'prompt.json',\n data_root: str = './data/fill50k',\n control_key='source',\n image_key='target',\n pipeline: List[Union[dict, Callable]] = []):\n self.control_key = control_key\n self.image_key = image_key\n super().__init__(\n ann_file=ann_file, data_root=data_root, pipeline=pipeline)\n\n def load_data_list(self) -> List[dict]:\n \"\"\"Load annotations from an annotation file named as ``self.ann_file``\n\n Returns:\n list[dict]: A list of annotation.\n \"\"\"\n data_list = []\n with open(self.ann_file, 'rt') as file:\n anno_list = file.readlines()\n\n for anno in anno_list:\n anno = json.loads(anno)\n # source = anno['source']\n # target = anno['target']\n source = anno[self.control_key]\n target = anno[self.image_key]\n prompt = anno['prompt']\n\n source = os.path.join(self.data_root, source)\n target = os.path.join(self.data_root, target)\n\n data_list.append({\n 'source_path': source,\n 'target_path': target,\n 'prompt': prompt\n })\n\n return data_list\n" }, { "path": "mmagic/datasets/data_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport gzip\nimport hashlib\nimport os\nimport os.path\nimport os.path as osp\nimport shutil\nimport tarfile\nimport tempfile\nimport urllib.error\nimport urllib.request\nimport zipfile\nfrom os import PathLike\nfrom typing import Callable, Dict, List, Tuple\n\nfrom mmengine.fileio.backends import BaseStorageBackend\n\n\n# TODO: we can use FileClient.infer_client to replace this function\ndef infer_io_backend(data_root: str) -> str:\n \"\"\"Infer the io backend from the given data_root.\n\n Args:\n data_root (str): The path of data root.\n\n Returns:\n str: The io backend.\n \"\"\"\n if (data_root.upper().startswith('HTTP')\n or data_root.upper().startswith('HTTPS')):\n backend = 'http'\n elif data_root.upper().startswith('S3') or (\n len(data_root.split(':')) > 2\n and data_root.split(':')[1].upper() == 'S3'):\n # two case:\n # 1. s3://xxxxx (raw petrel path)\n # 2. CONFIG:s3://xxx (petrel path with specific config)\n backend = 'petrel'\n else:\n # use default one\n backend = 'local'\n return backend\n\n\ndef calculate_md5(fpath: str,\n file_backend: BaseStorageBackend = None,\n chunk_size: int = 1024 * 1024) -> str:\n \"\"\"Calculate MD5 of the file.\n\n Args:\n fpath (str): The path of the file.\n file_backend (BaseStorageBackend, optional): The file backend to fetch\n the file. Defaults to None.\n chunk_size (int, optional): The chunk size to calculate MD5. Defaults\n to 1024*1024.\n\n Returns:\n str: The string of MD5.\n \"\"\"\n md5 = hashlib.md5()\n if file_backend is None or file_backend.name == 'LocalBackend':\n with open(fpath, 'rb') as f:\n for chunk in iter(lambda: f.read(chunk_size), b''):\n md5.update(chunk)\n else:\n md5.update(file_backend.get(fpath))\n return md5.hexdigest()\n\n\ndef check_md5(fpath, md5, **kwargs) -> bool:\n \"\"\"Checn whether the MD5 of the file.\n\n Args:\n fpath (str): The path of the file.\n md5 (str): Target MD5 value.\n\n Returns:\n bool: If true, the MD5 of passed file is same as target MD5.\n \"\"\"\n return md5 == calculate_md5(fpath, **kwargs)\n\n\ndef check_integrity(fpath, md5=None) -> bool:\n \"\"\"Check whether the file is integrity by comparing the MD5 of the file\n with target MD5.\n\n Args:\n fpath (str): The path of the file.\n md5 (str, optional): The target MD5 value. Defaults to None.\n\n Returns:\n bool: If true, the passed file is integrity.\n \"\"\"\n if not os.path.isfile(fpath):\n return False\n if md5 is None:\n return True\n return check_md5(fpath, md5)\n\n\ndef download_url_to_file(url, dst, hash_prefix=None, progress=True):\n \"\"\"Download object at the given URL to a local path.\n\n Modified from\n https://pytorch.org/docs/stable/hub.html#torch.hub.download_url_to_file\n\n Args:\n url (str): URL of the object to download\n dst (str): Full path where object will be saved,\n e.g. ``/tmp/temporary_file``\n hash_prefix (string, optional): If not None, the SHA256 downloaded\n file should start with ``hash_prefix``. Defaults to None.\n progress (bool): whether or not to display a progress bar to stderr.\n Defaults to True\n \"\"\"\n file_size = None\n req = urllib.request.Request(url)\n u = urllib.request.urlopen(req)\n meta = u.info()\n if hasattr(meta, 'getheaders'):\n content_length = meta.getheaders('Content-Length')\n else:\n content_length = meta.get_all('Content-Length')\n if content_length is not None and len(content_length) > 0:\n file_size = int(content_length[0])\n\n # We deliberately save it in a temp file and move it after download is\n # complete. This prevents a local file being overridden by a broken\n # download.\n dst = os.path.expanduser(dst)\n dst_dir = os.path.dirname(dst)\n f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)\n\n import rich.progress\n columns = [\n rich.progress.DownloadColumn(),\n rich.progress.BarColumn(bar_width=None),\n rich.progress.TimeRemainingColumn(),\n ]\n try:\n if hash_prefix is not None:\n sha256 = hashlib.sha256()\n with rich.progress.Progress(*columns) as pbar:\n task = pbar.add_task('download', total=file_size, visible=progress)\n while True:\n buffer = u.read(8192)\n if len(buffer) == 0:\n break\n f.write(buffer)\n if hash_prefix is not None:\n sha256.update(buffer)\n pbar.update(task, advance=len(buffer))\n\n f.close()\n if hash_prefix is not None:\n digest = sha256.hexdigest()\n if digest[:len(hash_prefix)] != hash_prefix:\n raise RuntimeError(\n 'invalid hash value (expected \"{}\", got \"{}\")'.format(\n hash_prefix, digest))\n shutil.move(f.name, dst)\n finally:\n f.close()\n if os.path.exists(f.name):\n os.remove(f.name)\n\n\ndef download_url(url, root, filename=None, md5=None):\n \"\"\"Download a file from a url and place it in root.\n\n Args:\n url (str): URL to download file from.\n root (str): Directory to place downloaded file in.\n filename (str | None): Name to save the file under.\n If filename is None, use the basename of the URL.\n md5 (str | None): MD5 checksum of the download.\n If md5 is None, download without md5 check.\n \"\"\"\n root = os.path.expanduser(root)\n if not filename:\n filename = os.path.basename(url)\n fpath = os.path.join(root, filename)\n\n os.makedirs(root, exist_ok=True)\n\n if check_integrity(fpath, md5):\n print(f'Using downloaded and verified file: {fpath}')\n else:\n try:\n print(f'Downloading {url} to {fpath}')\n download_url_to_file(url, fpath)\n except (urllib.error.URLError, IOError) as e:\n if url[:5] == 'https':\n url = url.replace('https:', 'http:')\n print('Failed download. Trying https -> http instead.'\n f' Downloading {url} to {fpath}')\n download_url_to_file(url, fpath)\n else:\n raise e\n # check integrity of downloaded file\n if not check_integrity(fpath, md5):\n raise RuntimeError('File not found or corrupted.')\n\n\ndef _is_tarxz(filename):\n \"\"\"Judge whether the file is `.tar.xz`\"\"\"\n return filename.endswith('.tar.xz')\n\n\ndef _is_tar(filename):\n \"\"\"Judge whether the file is `.tar`\"\"\"\n return filename.endswith('.tar')\n\n\ndef _is_targz(filename):\n \"\"\"Judge whether the file is `.tar.gz`\"\"\"\n return filename.endswith('.tar.gz')\n\n\ndef _is_tgz(filename):\n \"\"\"Judge whether the file is `.tgz`\"\"\"\n return filename.endswith('.tgz')\n\n\ndef _is_gzip(filename):\n \"\"\"Judge whether the file is `.gzip`\"\"\"\n return filename.endswith('.gz') and not filename.endswith('.tar.gz')\n\n\ndef _is_zip(filename):\n \"\"\"Judge whether the file is `.zip`\"\"\"\n return filename.endswith('.zip')\n\n\ndef extract_archive(from_path, to_path=None, remove_finished=False):\n \"\"\"Extract the archive.\"\"\"\n if to_path is None:\n to_path = os.path.dirname(from_path)\n\n if _is_tar(from_path):\n with tarfile.open(from_path, 'r') as tar:\n tar.extractall(path=to_path)\n elif _is_targz(from_path) or _is_tgz(from_path):\n with tarfile.open(from_path, 'r:gz') as tar:\n tar.extractall(path=to_path)\n elif _is_tarxz(from_path):\n with tarfile.open(from_path, 'r:xz') as tar:\n tar.extractall(path=to_path)\n elif _is_gzip(from_path):\n to_path = os.path.join(\n to_path,\n os.path.splitext(os.path.basename(from_path))[0])\n with open(to_path, 'wb') as out_f, gzip.GzipFile(from_path) as zip_f:\n out_f.write(zip_f.read())\n elif _is_zip(from_path):\n with zipfile.ZipFile(from_path, 'r') as z:\n z.extractall(to_path)\n else:\n raise ValueError(f'Extraction of {from_path} not supported')\n\n if remove_finished:\n os.remove(from_path)\n\n\ndef download_and_extract_archive(url,\n download_root,\n extract_root=None,\n filename=None,\n md5=None,\n remove_finished=False):\n \"\"\"Download and extract the archive.\"\"\"\n download_root = os.path.expanduser(download_root)\n if extract_root is None:\n extract_root = download_root\n if not filename:\n filename = os.path.basename(url)\n\n download_url(url, download_root, filename, md5)\n\n archive = os.path.join(download_root, filename)\n print(f'Extracting {archive} to {extract_root}')\n extract_archive(archive, extract_root, remove_finished)\n\n\ndef open_maybe_compressed_file(path: str):\n \"\"\"Return a file object that possibly decompresses 'path' on the fly.\n\n Decompression occurs when argument `path` is a string and ends with '.gz'\n or '.xz'.\n \"\"\"\n if not isinstance(path, str):\n return path\n if path.endswith('.gz'):\n import gzip\n return gzip.open(path, 'rb')\n if path.endswith('.xz'):\n import lzma\n return lzma.open(path, 'rb')\n return open(path, 'rb')\n\n\ndef expanduser(path):\n \"\"\"Expand ~ and ~user constructions.\n\n If user or $HOME is unknown, do nothing.\n \"\"\"\n if isinstance(path, (str, PathLike)):\n return osp.expanduser(path)\n else:\n return path\n\n\ndef find_folders(root: str, file_backend: BaseStorageBackend\n ) -> Tuple[List[str], Dict[str, int]]:\n \"\"\"Find classes by folders under a root.\n\n Args:\n root (string): root directory of folders\n\n Returns:\n Tuple[List[str], Dict[str, int]]:\n\n - folders: The name of sub folders under the root.\n - folder_to_idx: The map from folder name to class idx.\n \"\"\"\n folders = list(\n file_backend.list_dir_or_file(\n root,\n list_dir=True,\n list_file=False,\n recursive=False,\n ))\n folders.sort()\n folder_to_idx = {folders[i]: i for i in range(len(folders))}\n return folders, folder_to_idx\n\n\ndef get_samples(root: str, folder_to_idx: Dict[str, int],\n is_valid_file: Callable, file_backend: BaseStorageBackend):\n \"\"\"Make dataset by walking all images under a root.\n\n Args:\n root (string): root directory of folders\n folder_to_idx (dict): the map from class name to class idx\n is_valid_file (Callable): A function that takes path of a file\n and check if the file is a valid sample file.\n\n Returns:\n Tuple[list, set]:\n\n - samples: a list of tuple where each element is (image, class_idx)\n - empty_folders: The folders don't have any valid files.\n \"\"\"\n samples = []\n available_classes = set()\n\n for folder_name in sorted(list(folder_to_idx.keys())):\n _dir = file_backend.join_path(root, folder_name)\n files = list(\n file_backend.list_dir_or_file(\n _dir,\n list_dir=False,\n list_file=True,\n recursive=True,\n ))\n for file in sorted(list(files)):\n if is_valid_file(file):\n path = file_backend.join_path(folder_name, file)\n item = (path, folder_to_idx[folder_name])\n samples.append(item)\n available_classes.add(folder_name)\n\n empty_folders = set(folder_to_idx.keys()) - available_classes\n\n return samples, empty_folders\n" }, { "path": "mmagic/datasets/dreambooth_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom typing import Callable, List, Union\n\nfrom mmengine import FileClient\nfrom mmengine.dataset import BaseDataset\n\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass DreamBoothDataset(BaseDataset):\n \"\"\"Dataset for DreamBooth.\n\n Args:\n data_root (str): Path to the data root.\n concept_dir (str): Path to the concept images.\n prompt (str): Prompt of the concept.\n pipeline (list[dict | callable]): A sequence of data transforms.\n \"\"\"\n\n def __init__(self,\n data_root: str,\n concept_dir: str,\n prompt: str,\n pipeline: List[Union[dict, Callable]] = []):\n\n data_prefix = dict(img_path=concept_dir)\n\n self.prompt = prompt\n\n super().__init__(\n data_root=data_root, data_prefix=data_prefix, pipeline=pipeline)\n\n def load_data_list(self) -> list:\n \"\"\"Load data list from concept_dir and class_dir.\"\"\"\n data_list = []\n\n img_dir = self.data_prefix['img_path']\n file_client = FileClient.infer_client(uri=img_dir)\n img_dir = osp.abspath(img_dir)\n\n for data_name in file_client.list_dir_or_file(img_dir, list_dir=False):\n data_info = dict(\n img_path=file_client.join_path(img_dir, data_name),\n prompt=self.prompt)\n data_list.append(data_info)\n\n return data_list\n" }, { "path": "mmagic/datasets/grow_scale_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Union\n\nfrom mmengine import print_log\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass GrowScaleImgDataset(BaseDataset):\n \"\"\"Grow Scale Unconditional Image Dataset.\n\n This dataset is similar with ``UnconditionalImageDataset``, but offer\n more dynamic functionalities for the supporting complex algorithms, like\n PGGAN.\n\n Highlight functionalities:\n\n #. Support growing scale dataset. The motivation is to decrease data\n pre-processing load in CPU. In this dataset, you can provide\n ``imgs_roots`` like:\n\n .. code-block:: python\n\n {'64': 'path_to_64x64_imgs',\n '512': 'path_to_512x512_imgs'}\n\n Then, in training scales lower than 64x64, this dataset will set\n ``self.imgs_root`` as 'path_to_64x64_imgs';\n #. Offer ``samples_per_gpu`` according to different scales. In this\n dataset, ``self.samples_per_gpu`` will help runner to know the updated\n batch size.\n\n Basically, This dataset contains raw images for training unconditional\n GANs. Given a root dir, we will recursively find all images in this root.\n The transformation on data is defined by the pipeline.\n\n Args:\n imgs_root (str): Root path for unconditional images.\n pipeline (list[dict | callable]): A sequence of data transforms.\n len_per_stage (int, optional): The length of dataset for each scale.\n This args change the length dataset by concatenating or extracting\n subset. If given a value less than 0., the original length will be\n kept. Defaults to 1e6.\n gpu_samples_per_scale (dict | None, optional): Dict contains\n ``samples_per_gpu`` for each scale. For example, ``{'32': 4}`` will\n set the scale of 32 with ``samples_per_gpu=4``, despite other scale\n with ``samples_per_gpu=self.gpu_samples_base``.\n gpu_samples_base (int, optional): Set default ``samples_per_gpu`` for\n each scale. Defaults to 32.\n io_backend (str, optional): The storage backend type. Options are\n \"disk\", \"ceph\", \"memcached\", \"lmdb\", \"http\" and \"petrel\".\n Default: None.\n test_mode (bool, optional): If True, the dataset will work in test\n mode. Otherwise, in train mode. Default to False.\n \"\"\"\n\n _VALID_IMG_SUFFIX = ('.jpg', '.png', '.jpeg', '.JPEG')\n\n def __init__(self,\n data_roots: dict,\n pipeline,\n len_per_stage=int(1e6),\n gpu_samples_per_scale=None,\n gpu_samples_base=32,\n io_backend: Optional[str] = None,\n file_lists: Optional[Union[str, dict]] = None,\n test_mode=False):\n\n assert isinstance(data_roots, dict)\n self.data_roots = data_roots\n self._img_scales = sorted([int(x) for x in data_roots.keys()])\n self._curr_scale = self._img_scales[0]\n self._actual_curr_scale = self._curr_scale\n self.data_root = self.data_roots[str(self._curr_scale)]\n\n # len_per_stage = -1, keep the original length\n self.len_per_stage = len_per_stage\n self.curr_stage = 0\n self.gpu_samples_per_scale = gpu_samples_per_scale\n if self.gpu_samples_per_scale is not None:\n assert isinstance(self.gpu_samples_per_scale, dict)\n else:\n self.gpu_samples_per_scale = dict()\n self.gpu_samples_base = gpu_samples_base\n\n if io_backend is None:\n data_root_ = list(data_roots.values())[0]\n self.file_backend = get_file_backend(uri=data_root_)\n else:\n self.file_backend = get_file_backend(\n backend_args={'backend': io_backend})\n\n # use current data root to initialize and do not support\n # `serialize_data`\n super().__init__(\n data_root=self.data_root,\n pipeline=pipeline,\n test_mode=test_mode,\n serialize_data=False)\n\n # print basic dataset information to check the validity\n print_log(repr(self), 'current')\n\n def load_data_list(self):\n \"\"\"Load annotations.\"\"\"\n # recursively find all of the valid images from imgs_root\n data_list = self.file_backend.list_dir_or_file(\n self.data_root,\n list_dir=False,\n suffix=self._VALID_IMG_SUFFIX,\n recursive=True)\n self.data_list = [\n self.file_backend.join_path(self.data_root, x) for x in data_list\n ]\n\n if self.len_per_stage > 0:\n self.concat_imgs_list_to(self.len_per_stage)\n self.samples_per_gpu = self.gpu_samples_per_scale.get(\n str(self._actual_curr_scale), self.gpu_samples_base)\n return self.data_list\n\n def update_annotations(self, curr_scale):\n \"\"\"Update annotations.\n\n Args:\n curr_scale (int): Current image scale.\n\n Returns:\n bool: Whether to update.\n \"\"\"\n if curr_scale == self._actual_curr_scale:\n return False\n\n for scale in self._img_scales:\n if curr_scale <= scale:\n self._curr_scale = scale\n break\n if scale == self._img_scales[-1]:\n assert RuntimeError(\n f'Cannot find a suitable scale for {curr_scale}')\n self._actual_curr_scale = curr_scale\n self.data_root = self.data_roots[str(self._curr_scale)]\n self.load_data_list()\n # print basic dataset information to check the validity\n print_log('Update Dataset: ' + repr(self), 'current')\n return True\n\n def concat_imgs_list_to(self, num):\n \"\"\"Concat image list to specified length.\n\n Args:\n num (int): The length of the concatenated image list.\n \"\"\"\n\n if num <= len(self.data_list):\n self.data_list = self.data_list[:num]\n return\n\n concat_factor = (num // len(self.data_list)) + 1\n imgs = self.data_list * concat_factor\n self.data_list = imgs[:num]\n\n def prepare_train_data(self, idx):\n \"\"\"Prepare training data.\n\n Args:\n idx (int): Index of current batch.\n\n Returns:\n dict: Prepared training data batch.\n \"\"\"\n results = dict(gt_path=self.data_list[idx])\n return self.pipeline(results)\n\n def prepare_test_data(self, idx):\n \"\"\"Prepare testing data.\n\n Args:\n idx (int): Index of current batch.\n\n Returns:\n dict: Prepared training data batch.\n \"\"\"\n results = dict(gt_path=self.data_list[idx])\n return self.pipeline(results)\n\n def __getitem__(self, idx):\n \"\"\"Get the idx-th image and data information of dataset after\n ``self.pipeline``, and ``full_init`` will be called if the dataset has\n not been fully initialized.\n\n During training phase, if ``self.pipeline`` get ``None``,\n ``self._rand_another`` will be called until a valid image is fetched or\n the maximum limit of refetch is reached.\n\n Args:\n idx (int): The index of self.data_list.\n\n Returns:\n dict: The idx-th image and data information of dataset after\n ``self.pipeline``.\n \"\"\"\n if not self.test_mode:\n return self.prepare_train_data(idx)\n\n return self.prepare_test_data(idx)\n\n def __repr__(self):\n \"\"\"Print ``self.transforms`` in sequence.\n\n Returns:\n str: Formatted string.\n \"\"\"\n dataset_name = self.__class__\n imgs_root = self.data_root\n num_imgs = len(self)\n return (f'dataset_name: {dataset_name}, total {num_imgs} images in '\n f'imgs_root: {imgs_root}')\n" }, { "path": "mmagic/datasets/imagenet_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Union\n\nfrom mmagic.registry import DATASETS\nfrom .basic_conditional_dataset import BasicConditionalDataset\nfrom .categories import IMAGENET_CATEGORIES\n\n\n@DATASETS.register_module()\nclass ImageNet(BasicConditionalDataset):\n \"\"\"`ImageNet `_ Dataset.\n\n The dataset supports two kinds of annotation format. More details can be\n found in :class:`CustomDataset`.\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n metainfo (dict, optional): Meta information for dataset, such as class\n information. Defaults to None.\n data_root (str): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to ''.\n data_prefix (str | dict): Prefix for training data. Defaults to ''.\n **kwargs: Other keyword arguments in :class:`CustomDataset` and\n :class:`BaseDataset`.\n \"\"\" # noqa: E501\n\n IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif')\n METAINFO = {'classes': IMAGENET_CATEGORIES}\n\n def __init__(self,\n ann_file: str = '',\n metainfo: Optional[dict] = None,\n data_root: str = '',\n data_prefix: Union[str, dict] = '',\n **kwargs):\n kwargs = {'extensions': self.IMG_EXTENSIONS, **kwargs}\n super().__init__(\n ann_file=ann_file,\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=data_prefix,\n **kwargs)\n" }, { "path": "mmagic/datasets/mscoco_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport random\nfrom typing import Optional, Sequence, Union\n\nimport mmengine\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import DATASETS\nfrom .basic_conditional_dataset import BasicConditionalDataset\n\n\n@DATASETS.register_module()\n@DATASETS.register_module('MSCOCO')\nclass MSCoCoDataset(BasicConditionalDataset):\n \"\"\"MSCoCo 2014 dataset.\n\n Args:\n ann_file (str): Annotation file path. Defaults to ''.\n metainfo (dict, optional): Meta information for dataset, such as class\n information. Defaults to None.\n data_root (str): The root directory for ``data_prefix`` and\n ``ann_file``. Defaults to ''.\n drop_caption_rate (float, optional): Rate of dropping caption,\n used for training. Defaults to 0.0.\n phase (str, optional): Subdataset used for certain phase, can be set\n to `train`, `test` and `val`. Defaults to 'train'.\n year (int, optional): Version of CoCo dataset, can be set to 2014\n and 2017. Defaults to 2014.\n data_prefix (str | dict): Prefix for the data. Defaults to ''.\n extensions (Sequence[str]): A sequence of allowed extensions. Defaults\n to ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif').\n lazy_init (bool): Whether to load annotation during instantiation.\n In some cases, such as visualization, only the meta information of\n the dataset is needed, which is not necessary to load annotation\n file. ``Basedataset`` can skip load annotations to save time by set\n ``lazy_init=False``. Defaults to False.\n caption_style (str): If you want to add a style description for each\n caption, you can set caption_style to your style prompt. For\n example, 'realistic style'. Defaults to empty str.\n **kwargs: Other keyword arguments in :class:`BaseDataset`.\n \"\"\"\n METAINFO = dict(dataset_type='text_image_dataset', task_name='editing')\n\n def __init__(self,\n ann_file: str = '',\n metainfo: Optional[dict] = None,\n data_root: str = '',\n drop_caption_rate=0.0,\n phase='train',\n year=2014,\n data_prefix: Union[str, dict] = '',\n extensions: Sequence[str] = ('.jpg', '.jpeg', '.png', '.ppm',\n '.bmp', '.pgm', '.tif'),\n lazy_init: bool = False,\n classes: Union[str, Sequence[str], None] = None,\n caption_style: str = '',\n **kwargs):\n ann_file = os.path.join('annotations', 'captions_' + phase +\n f'{year}.json') if ann_file == '' else ann_file\n self.year = year\n assert self.year == 2014 or self.year == 2017, \\\n 'Caption is only supported in 2014 or 2017.'\n self.image_prename = ''\n if self.year == 2014:\n self.image_prename = 'COCO_' + phase + f'{year}_'\n self.phase = phase\n self.drop_rate = drop_caption_rate\n self.caption_style = caption_style\n\n super().__init__(\n ann_file=ann_file,\n metainfo=metainfo,\n data_root=data_root,\n data_prefix=data_prefix,\n extensions=extensions,\n lazy_init=lazy_init,\n classes=classes,\n **kwargs)\n\n def load_data_list(self):\n \"\"\"Load image paths and gt_labels.\"\"\"\n if self.img_prefix:\n file_backend = get_file_backend(uri=self.img_prefix)\n json_file = mmengine.fileio.io.load(self.ann_file)\n\n def add_prefix(filename, prefix=''):\n if not prefix:\n return filename\n else:\n return file_backend.join_path(prefix, filename)\n\n data_list = []\n for item in json_file['annotations']:\n image_name = self.image_prename + str(\n item['image_id']).zfill(12) + '.jpg'\n img_path = add_prefix(\n os.path.join(self.phase + str(self.year), image_name),\n self.img_prefix)\n caption = item['caption'].lower()\n if self.caption_style != '':\n caption = caption + ' ' + self.caption_style\n info = {\n 'img_path':\n img_path,\n 'gt_prompt':\n caption if (self.phase != 'train' or self.drop_rate < 1e-6\n or random.random() >= self.drop_rate) else ''\n }\n data_list.append(info)\n return data_list\n" }, { "path": "mmagic/datasets/paired_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom typing import Optional\n\nfrom mmengine.dataset import BaseDataset\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import DATASETS\n\nIMG_EXTENSIONS = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm',\n '.PPM', '.bmp', '.BMP', '.tif', '.TIF', '.tiff', '.TIFF')\n\n\n@DATASETS.register_module()\nclass PairedImageDataset(BaseDataset):\n \"\"\"General paired image folder dataset for image generation.\n\n It assumes that the training directory is '/path/to/data/train'.\n During test time, the directory is '/path/to/data/test'. '/path/to/data'\n can be initialized by args 'dataroot'. Each sample contains a pair of\n images concatenated in the w dimension (A|B).\n\n Args:\n dataroot (str | :obj:`Path`): Path to the folder root of paired images.\n pipeline (List[dict | callable]): A sequence of data transformations.\n test_mode (bool): Store `True` when building test dataset.\n Default: `False`.\n test_dir (str): Subfolder of dataroot which contain test images.\n Default: 'test'.\n \"\"\"\n\n def __init__(self,\n data_root,\n pipeline,\n io_backend: Optional[str] = None,\n test_mode=False,\n test_dir='test'):\n phase = test_dir if test_mode else 'train'\n self.data_root = osp.join(str(data_root), phase)\n\n if io_backend is None:\n self.file_backend = get_file_backend(uri=data_root)\n else:\n self.file_backend = get_file_backend(\n backend_args={'backend': io_backend})\n\n super().__init__(\n data_root=self.data_root, pipeline=pipeline, test_mode=test_mode)\n # self.data_infos = self.load_annotations()\n\n def load_data_list(self):\n \"\"\"Load paired image paths.\n\n Returns:\n list[dict]: List that contains paired image paths.\n \"\"\"\n data_infos = []\n pair_paths = sorted(self.scan_folder(self.data_root))\n for pair_path in pair_paths:\n data_infos.append(dict(pair_path=pair_path))\n\n return data_infos\n\n def scan_folder(self, path):\n \"\"\"Obtain image path list (including sub-folders) from a given folder.\n\n Args:\n path (str | :obj:`Path`): Folder path.\n\n Returns:\n list[str]: Image list obtained from the given folder.\n \"\"\"\n imgs_list = self.file_backend.list_dir_or_file(\n path, list_dir=False, suffix=IMG_EXTENSIONS, recursive=True)\n images = [self.file_backend.join_path(path, img) for img in imgs_list]\n assert images, f'{path} has no valid image file.'\n return images\n" }, { "path": "mmagic/datasets/singan_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport mmcv\nimport numpy as np\nfrom mmengine.dataset import BaseDataset\n\nfrom mmagic.registry import DATASETS\n\n\ndef create_real_pyramid(real, min_size, max_size, scale_factor_init):\n \"\"\"Create image pyramid.\n\n This function is modified from the official implementation:\n https://github.com/tamarott/SinGAN/blob/master/SinGAN/functions.py#L221\n\n In this implementation, we adopt the rescaling function from MMCV.\n Args:\n real (np.array): The real image array.\n min_size (int): The minimum size for the image pyramid.\n max_size (int): The maximum size for the image pyramid.\n scale_factor_init (float): The initial scale factor.\n \"\"\"\n\n num_scales = int(\n np.ceil(\n np.log(np.power(min_size / min(real.shape[0], real.shape[1]), 1)) /\n np.log(scale_factor_init))) + 1\n\n scale2stop = int(\n np.ceil(\n np.log(\n min([max_size, max([real.shape[0], real.shape[1]])]) /\n max([real.shape[0], real.shape[1]])) /\n np.log(scale_factor_init)))\n\n stop_scale = num_scales - scale2stop\n\n scale1 = min(max_size / max([real.shape[0], real.shape[1]]), 1)\n real_max = mmcv.imrescale(real, scale1)\n scale_factor = np.power(\n min_size / (min(real_max.shape[0], real_max.shape[1])),\n 1 / (stop_scale))\n\n scale2stop = int(\n np.ceil(\n np.log(\n min([max_size, max([real.shape[0], real.shape[1]])]) /\n max([real.shape[0], real.shape[1]])) /\n np.log(scale_factor_init)))\n stop_scale = num_scales - scale2stop\n\n reals = []\n for i in range(stop_scale + 1):\n scale = np.power(scale_factor, stop_scale - i)\n curr_real = mmcv.imrescale(real, scale)\n reals.append(curr_real)\n\n return reals, scale_factor, stop_scale\n\n\n@DATASETS.register_module()\nclass SinGANDataset(BaseDataset):\n \"\"\"SinGAN Dataset.\n\n In this dataset, we create an image pyramid and save it in the cache.\n\n Args:\n img_path (str): Path to the single image file.\n min_size (int): Min size of the image pyramid. Here, the number will be\n set to the ``min(H, W)``.\n max_size (int): Max size of the image pyramid. Here, the number will be\n set to the ``max(H, W)``.\n scale_factor_init (float): Rescale factor. Note that the actual factor\n we use may be a little bit different from this value.\n num_samples (int, optional): The number of samples (length) in this\n dataset. Defaults to -1.\n \"\"\"\n\n def __init__(self,\n data_root,\n min_size,\n max_size,\n scale_factor_init,\n pipeline,\n num_samples=-1):\n self.min_size = min_size\n self.max_size = max_size\n self.scale_factor_init = scale_factor_init\n self.num_samples = num_samples\n super().__init__(data_root=data_root, pipeline=pipeline)\n\n def full_init(self):\n \"\"\"Skip the full init process for SinGANDataset.\"\"\"\n\n self.load_data_list(self.min_size, self.max_size,\n self.scale_factor_init)\n\n def load_data_list(self, min_size, max_size, scale_factor_init):\n \"\"\"Load annotations for SinGAN Dataset.\n\n Args:\n min_size (int): The minimum size for the image pyramid.\n max_size (int): The maximum size for the image pyramid.\n scale_factor_init (float): The initial scale factor.\n \"\"\"\n real = mmcv.imread(self.data_root)\n self.reals, self.scale_factor, self.stop_scale = create_real_pyramid(\n real, min_size, max_size, scale_factor_init)\n\n self.data_dict = {}\n\n for i, real in enumerate(self.reals):\n self.data_dict[f'real_scale{i}'] = real\n\n self.data_dict['input_sample'] = np.zeros_like(\n self.data_dict['real_scale0']).astype(np.float32)\n\n def __getitem__(self, index):\n \"\"\"Get `:attr:self.data_dict`. For SinGAN, we use single image with\n different resolution to train the model.\n\n Args:\n idx (int): This will be ignored in :class:`SinGANDataset`.\n\n Returns:\n dict: Dict contains input image in different resolution.\n ``self.pipeline``.\n \"\"\"\n return self.pipeline(deepcopy(self.data_dict))\n\n def __len__(self):\n \"\"\"Get the length of filtered dataset and automatically call\n ``full_init`` if the dataset has not been fully init.\n\n Returns:\n int: The length of filtered dataset.\n \"\"\"\n return int(1e6) if self.num_samples < 0 else self.num_samples\n" }, { "path": "mmagic/datasets/textual_inversion_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom random import choice\nfrom typing import Callable, List, Union\n\nfrom mmengine import FileClient\nfrom mmengine.dataset import BaseDataset\n\nfrom mmagic.registry import DATASETS\n\n\n@DATASETS.register_module()\nclass TextualInversionDataset(BaseDataset):\n \"\"\"Dataset for Textual Inversion and ViCo.\n\n Args:\n data_root (str): Path to the data root.\n concept_dir (str): Path to the concept images.\n placeholder (str): A string to denote the concept.\n template (list[str]): A list of strings like 'A photo of {}'.\n with_image_reference (bool): Is used for vico training.\n pipeline (list[dict | callable]): A sequence of data transforms.\n \"\"\"\n\n def __init__(\n self,\n data_root: str,\n concept_dir: str,\n placeholder: str,\n template: str,\n # used for vico training\n with_image_reference: bool = False,\n pipeline: List[Union[dict, Callable]] = []):\n\n data_prefix = dict(img_path=concept_dir)\n\n self.placeholder = placeholder\n if osp.exists(osp.join(data_root, concept_dir)):\n self.num_images = len(os.listdir(osp.join(data_root, concept_dir)))\n if osp.exists(template):\n with open(template, 'r') as file:\n self.template = file.readlines()\n self.with_image_reference = with_image_reference\n\n super().__init__(\n data_root=data_root, data_prefix=data_prefix, pipeline=pipeline)\n\n def load_data_list(self) -> list:\n \"\"\"Load data list from concept_dir and class_dir.\"\"\"\n data_list = []\n\n img_dir = self.data_prefix['img_path']\n file_client = FileClient.infer_client(uri=img_dir)\n img_dir = osp.abspath(img_dir)\n\n for data_name in file_client.list_dir_or_file(img_dir, list_dir=False):\n data_info = dict(\n img_path=file_client.join_path(img_dir, data_name))\n data_list.append(data_info)\n return data_list\n\n def prepare_data(self, idx):\n \"\"\"Get data processed by ``self.pipeline``.\n\n Args:\n idx (int): The index of ``data_info``.\n\n Returns:\n Any: Depends on ``self.pipeline``.\n \"\"\"\n data_info = self.get_data_info(idx)\n\n if self.with_image_reference:\n numbers = list(range(self.num_images))\n if len(numbers) > 1:\n numbers.remove(idx % self.num_images)\n img_dir = self.data_prefix['img_path']\n file_client = FileClient.infer_client(uri=img_dir)\n img_dir = osp.abspath(img_dir)\n data_names = list(\n file_client.list_dir_or_file(img_dir, list_dir=False))\n image_ref_path = file_client.join_path(img_dir,\n data_names[choice(numbers)])\n data_info['img_ref_path'] = image_ref_path\n\n # load random template\n selected_template = choice(self.template)\n prompt = selected_template.format(self.placeholder)\n data_info['prompt'] = prompt\n return self.pipeline(data_info)\n" }, { "path": "mmagic/datasets/transforms/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .albu_function import AlbuCorruptFunction, PairedAlbuTransForms\nfrom .albumentations import Albumentations\nfrom .alpha import GenerateSeg, GenerateSoftSeg\nfrom .aug_frames import MirrorSequence, TemporalReverse\nfrom .aug_pixel import (BinarizeImage, Clip, ColorJitter, RandomAffine,\n RandomMaskDilation, UnsharpMasking)\nfrom .aug_shape import (Flip, NumpyPad, RandomRotation, RandomTransposeHW,\n Resize)\nfrom .crop import (CenterCropLongEdge, Crop, CropAroundCenter, CropAroundFg,\n CropAroundUnknown, CropLike, FixedCrop, InstanceCrop,\n ModCrop, PairedRandomCrop, RandomCropLongEdge,\n RandomResizedCrop)\nfrom .fgbg import (CompositeFg, MergeFgAndBg, PerturbBg, RandomJitter,\n RandomLoadResizeBg)\nfrom .formatting import PackInputs\nfrom .generate_assistant import (GenerateCoordinateAndCell,\n GenerateFacialHeatmap)\nfrom .generate_frame_indices import (GenerateFrameIndices,\n GenerateFrameIndiceswithPadding,\n GenerateSegmentIndices)\nfrom .get_masked_image import GetMaskedImage\nfrom .loading import (GetSpatialDiscountMask, LoadImageFromFile, LoadMask,\n LoadPairedImageFromFile)\nfrom .matlab_like_resize import MATLABLikeResize\nfrom .normalization import Normalize, RescaleToZeroOne\nfrom .random_degradations import (DegradationsWithShuffle, RandomBlur,\n RandomJPEGCompression, RandomNoise,\n RandomResize, RandomVideoCompression)\nfrom .random_down_sampling import RandomDownSampling\nfrom .trimap import (FormatTrimap, GenerateTrimap,\n GenerateTrimapWithDistTransform, TransformTrimap)\nfrom .values import CopyValues, SetValues\n\n__all__ = [\n 'BinarizeImage', 'Clip', 'ColorJitter', 'CopyValues', 'Crop', 'CropLike',\n 'DegradationsWithShuffle', 'LoadImageFromFile', 'LoadMask', 'Flip',\n 'FixedCrop', 'GenerateCoordinateAndCell', 'GenerateFacialHeatmap',\n 'GenerateFrameIndices', 'GenerateFrameIndiceswithPadding',\n 'GenerateSegmentIndices', 'GetMaskedImage', 'GetSpatialDiscountMask',\n 'MATLABLikeResize', 'MirrorSequence', 'ModCrop', 'Normalize', 'PackInputs',\n 'PairedRandomCrop', 'RandomAffine', 'RandomBlur', 'RandomDownSampling',\n 'RandomJPEGCompression', 'RandomMaskDilation', 'RandomNoise',\n 'RandomResize', 'RandomResizedCrop', 'RandomRotation', 'RandomTransposeHW',\n 'RandomVideoCompression', 'RescaleToZeroOne', 'Resize', 'SetValues',\n 'TemporalReverse', 'ToTensor', 'UnsharpMasking', 'CropAroundCenter',\n 'CropAroundFg', 'GenerateSeg', 'CropAroundUnknown', 'GenerateSoftSeg',\n 'FormatTrimap', 'TransformTrimap', 'GenerateTrimap',\n 'GenerateTrimapWithDistTransform', 'CompositeFg', 'RandomLoadResizeBg',\n 'MergeFgAndBg', 'PerturbBg', 'RandomJitter', 'LoadPairedImageFromFile',\n 'CenterCropLongEdge', 'RandomCropLongEdge', 'NumpyPad', 'InstanceCrop',\n 'Albumentations', 'AlbuCorruptFunction', 'PairedAlbuTransForms'\n]\n" }, { "path": "mmagic/datasets/transforms/albu_function.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List\n\nimport albumentations as albu\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass PairedAlbuTransForms(BaseTransform):\n \"\"\"PairedAlbuTransForms augmentation.\n\n Apply the same AlbuTransforms augmentation to paired images.\n \"\"\"\n\n def __init__(self,\n size: int,\n lq_key: str = 'img',\n gt_key: str = 'gt',\n scope: str = 'geometric',\n crop: str = 'random',\n p: float = 0.5):\n self.size = size\n self.lq_key = lq_key\n self.gt_key = gt_key\n self.scope = scope\n self.crop = crop\n self.p = p\n augs = {\n 'weak':\n albu.Compose([\n albu.HorizontalFlip(),\n ], p=self.p),\n 'geometric':\n albu.OneOf([\n albu.HorizontalFlip(always_apply=True),\n albu.ShiftScaleRotate(always_apply=True),\n albu.Transpose(always_apply=True),\n albu.OpticalDistortion(always_apply=True),\n albu.ElasticTransform(always_apply=True),\n ],\n p=self.p)\n }\n aug_fn = augs[self.scope]\n crop_fn = {\n 'random': albu.RandomCrop(self.size, self.size, always_apply=True),\n 'center': albu.CenterCrop(self.size, self.size, always_apply=True)\n }[self.crop]\n pad = albu.PadIfNeeded(self.size, self.size)\n\n self.pipeline = albu.Compose([aug_fn, pad, crop_fn],\n additional_targets={'target': 'image'})\n\n def transform(self, results):\n \"\"\"processing input results according to `self.pipeline`.\n\n Args:\n results (dict): contains the processed data\n through the transform pipeline.\n\n Returns:\n results: the processed data.\n \"\"\"\n r = self.pipeline(\n image=results[self.lq_key], target=results[self.gt_key])\n results[self.lq_key] = r['image']\n results[self.gt_key] = r['target']\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(size={self.size}, '\n f'lq_key={self.lq_key}, '\n f'gt_key={self.gt_key}, '\n f'scope={self.scope}, '\n f'crop={self.crop}, '\n f'p={self.p})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass AlbuTransForms(BaseTransform):\n \"\"\"AlbuTransForms augmentation.\n\n Apply the same AlbuTransForms augmentation to the input images.\n \"\"\"\n\n def __init__(self,\n size: int,\n keys: List,\n scope: str = 'geometric',\n crop: str = 'random',\n p: float = 0.5):\n self.size = size\n self.keys = keys\n self.scope = scope\n self.crop = crop\n self.p = p\n augs = {\n 'weak':\n albu.Compose([\n albu.HorizontalFlip(),\n ]),\n 'geometric':\n albu.OneOf([\n albu.HorizontalFlip(always_apply=True),\n albu.ShiftScaleRotate(always_apply=True),\n albu.Transpose(always_apply=True),\n albu.OpticalDistortion(always_apply=True),\n albu.ElasticTransform(always_apply=True),\n ],\n p=self.p)\n }\n aug_fn = augs[self.scope]\n crop_fn = {\n 'random': albu.RandomCrop(self.size, self.size, always_apply=True),\n 'center': albu.CenterCrop(self.size, self.size, always_apply=True)\n }[self.crop]\n pad = albu.PadIfNeeded(self.size, self.size)\n\n self.pipeline = albu.Compose([aug_fn, pad, crop_fn])\n\n def transform(self, results):\n \"\"\"processing input results according to `self.pipeline`.\n\n Args:\n results (dict): contains the processed data\n through the transform pipeline.\n\n Returns:\n results: the processed data.\n \"\"\"\n for key in self.keys:\n r = self.pipeline(image=results[key])\n results[key] = r['image']\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(size={self.size}, '\n f'keys={self.keys}, '\n f'scope={self.scope}, '\n f'crop={self.crop}, '\n f'p={self.p})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass PairedAlbuNormalize(BaseTransform):\n \"\"\"PairedAlbuNormalize augmentation.\n\n Apply the same AlbuNormalize augmentation to the paired images.\n \"\"\"\n\n def __init__(self,\n lq_key: str,\n gt_key: str,\n mean=(0.5, 0.5, 0.5),\n std=(0.5, 0.5, 0.5),\n max_pixel_value: float = 255.0,\n always_apply: bool = False,\n p: float = 1.0):\n self.lq_key = lq_key\n self.gt_key = gt_key\n self.mean = mean\n self.std = std\n self.max_pixel_value = max_pixel_value\n self.always_apply = always_apply\n self.p = p\n normalize = albu.Normalize(\n mean=self.mean,\n std=self.std,\n max_pixel_value=self.max_pixel_value,\n always_apply=self.always_apply,\n p=self.p)\n self.normalize = albu.Compose([normalize],\n additional_targets={'target': 'image'})\n\n def transform(self, results):\n \"\"\"processing input results according to `self.normalize`.\n\n Args:\n results (dict): contains the processed data\n through the transform pipeline.\n\n Returns:\n results: the processed data.\n \"\"\"\n if self.gt_key not in results.keys():\n r = self.normalize(image=results[self.lq_key])\n else:\n r = self.normalize(\n image=results[self.lq_key], target=results[self.gt_key])\n results[self.gt_key] = r['target']\n results[self.lq_key] = r['image']\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(lq_key={self.lq_key}, '\n f'gt_key={self.gt_key}, '\n f'mean={self.mean}, '\n f'std={self.std}, '\n f'max_pixel_value={self.max_pixel_value}, '\n f'always_apply={self.always_apply}, '\n f'p={self.p}) ')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass AlbuNormalize(BaseTransform):\n \"\"\"AlbuNormalize augmentation.\n\n Apply the same AlbuNormalize augmentation to the input images.\n \"\"\"\n\n def __init__(self,\n keys: List,\n mean=(0.5, 0.5, 0.5),\n std=(0.5, 0.5, 0.5),\n max_pixel_value: float = 255.0,\n always_apply: bool = False,\n p: float = 1.0):\n self.keys = keys\n self.mean = mean\n self.std = std\n self.max_pixel_value = max_pixel_value\n self.always_apply = always_apply\n self.p = p\n normalize = albu.Normalize(\n mean=self.mean,\n std=self.std,\n max_pixel_value=self.max_pixel_value,\n always_apply=self.always_apply,\n p=self.p)\n self.normalize = albu.Compose([normalize])\n\n def transform(self, results):\n \"\"\"processing input results according to `self.normalize`.\n\n Args:\n results (dict): contains the processed data\n through the transform pipeline.\n\n Returns:\n results: the processed data.\n \"\"\"\n for key in self.keys:\n r = self.normalize(image=results[key])\n results[key] = r['image']\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, '\n f'mean={self.mean}, '\n f'std={self.std}, '\n f'max_pixel_value={self.max_pixel_value}, '\n f'always_apply={self.always_apply}, '\n f'p={self.p}) ')\n\n return repr_str\n\n\ndef _resolve_aug_fn(name):\n d = {\n 'cutout': albu.Cutout,\n 'rgb_shift': albu.RGBShift,\n 'hsv_shift': albu.HueSaturationValue,\n 'motion_blur': albu.MotionBlur,\n 'median_blur': albu.MedianBlur,\n 'snow': albu.RandomSnow,\n 'shadow': albu.RandomShadow,\n 'fog': albu.RandomFog,\n 'brightness_contrast': albu.RandomBrightnessContrast,\n 'gamma': albu.RandomGamma,\n 'sun_flare': albu.RandomSunFlare,\n 'sharpen': albu.Sharpen,\n 'jpeg': albu.ImageCompression,\n 'gray': albu.ToGray,\n 'pixelize': albu.Downscale,\n # ToDo: partial gray\n }\n return d[name]\n\n\n@TRANSFORMS.register_module()\nclass AlbuCorruptFunction(BaseTransform):\n \"\"\"AlbuCorruptFunction augmentation.\n\n Apply the same AlbuCorruptFunction augmentation to the input images.\n \"\"\"\n\n def __init__(self, keys: List[str], config: List[dict], p: float = 1.0):\n self.keys = keys\n self.config = config\n self.p = p\n augs = []\n for aug_params in self.config:\n name = aug_params.pop('name')\n cls = _resolve_aug_fn(name)\n prob = aug_params.pop('prob') if 'prob' in aug_params else .5\n augs.append(cls(p=prob, **aug_params))\n\n self.augs = albu.OneOf(augs, p=self.p)\n\n def transform(self, results):\n \"\"\"processing input results according to `self.augs`.\n\n Args:\n results (dict): contains the processed data\n through the transform pipeline.\n\n Returns:\n results: the processed data.\n \"\"\"\n for key in self.keys:\n results[key] = self.augs(image=results[key])['image']\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, '\n f'config={self.config}, '\n f'p={self.p}) ')\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/albumentations.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport inspect\nfrom typing import List\n\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\ntry:\n import albumentations\n from albumentations import Compose\nexcept ImportError:\n albumentations = None\n Compose = None\n\n\n@TRANSFORMS.register_module()\nclass Albumentations(BaseTransform):\n \"\"\"Albumentation augmentation.\n\n Adds custom transformations from Albumentations library.\n Please, visit `https://github.com/albumentations-team/albumentations`\n and `https://albumentations.ai/docs/getting_started/transforms_and_targets`\n to get more information.\n\n An example of ``transforms`` is as followed:\n\n .. code-block::\n\n albu_transforms = [\n dict(\n type='Resize',\n height=100,\n width=100,\n ),\n dict(\n type='RandomFog',\n p=0.5,\n ),\n dict(\n type='RandomRain',\n p=0.5\n ),\n dict(\n type='RandomSnow',\n p=0.5,\n ),\n ]\n pipeline = [\n dict(\n type='LoadImageFromFile',\n key='img',\n color_type='color',\n channel_order='rgb',\n imdecode_backend='cv2'),\n dict(\n type='Albumentations',\n keys=['img'],\n transforms=albu_transforms),\n dict(type='PackInputs')\n ]\n\n Args:\n keys (list[str]): A list specifying the keys whose values are modified.\n transforms (list[dict]): A list of albu transformations.\n \"\"\"\n\n def __init__(self, keys: List[str], transforms: List[dict]) -> None:\n\n if Compose is None:\n raise RuntimeError('Please install albumentations')\n\n self.keys = keys\n\n # Args will be modified later, copying it will be safer\n transforms = copy.deepcopy(transforms)\n self.transforms = transforms\n self.aug = Compose([self.albu_builder(t) for t in self.transforms])\n\n def albu_builder(self, cfg: dict) -> albumentations:\n \"\"\"Import a module from albumentations.\n\n It inherits some of :func:`build_from_cfg` logic.\n\n Args:\n cfg (dict): Config dict. It should at least contain the key \"type\".\n\n Returns:\n obj: The constructed object.\n \"\"\"\n\n assert isinstance(cfg, dict) and 'type' in cfg\n args = cfg.copy()\n obj_type = args.pop('type')\n if isinstance(obj_type, str):\n if albumentations is None:\n raise RuntimeError('Please install albumentations')\n obj_cls = getattr(albumentations, obj_type)\n elif inspect.isclass(obj_type):\n obj_cls = obj_type\n else:\n raise TypeError(\n f'type must be a str or valid type, but got {type(obj_type)}')\n\n if 'transforms' in args:\n args['transforms'] = [\n self.albu_builder(transform)\n for transform in args['transforms']\n ]\n\n return obj_cls(**args)\n\n def _apply_albu(self, imgs):\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n outputs = []\n for img in imgs:\n outputs.append(self.aug(image=img)['image'])\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def transform(self, results):\n \"\"\"Transform function of Albumentations.\"\"\"\n\n for k in self.keys:\n results[k] = self._apply_albu(results[k])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(keys={self.keys}, transforms={self.transforms})'\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/alpha.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Augmentation on alpha matte.\"\"\"\n# Not used in current algorithms\n\nimport random\n\nimport cv2\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.utils import is_list_of, is_tuple_of\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.utils import random_choose_unknown\n\n\n@TRANSFORMS.register_module()\nclass GenerateSeg(BaseTransform):\n \"\"\"Generate segmentation mask from alpha matte.\n\n Args:\n kernel_size (int, optional): Kernel size for both erosion and\n dilation. The kernel will have the same height and width.\n Defaults to 5.\n erode_iter_range (tuple, optional): Iteration of erosion.\n Defaults to (10, 20).\n dilate_iter_range (tuple, optional): Iteration of dilation.\n Defaults to (15, 30).\n num_holes_range (tuple, optional): Range of number of holes to\n randomly select from. Defaults to (0, 3).\n hole_sizes (list, optional): List of (h, w) to be selected as the\n size of the rectangle hole.\n Defaults to [(15, 15), (25, 25), (35, 35), (45, 45)].\n blur_ksizes (list, optional): List of (h, w) to be selected as the\n kernel_size of the gaussian blur.\n Defaults to [(21, 21), (31, 31), (41, 41)].\n \"\"\"\n\n def __init__(self,\n kernel_size=5,\n erode_iter_range=(10, 20),\n dilate_iter_range=(15, 30),\n num_holes_range=(0, 3),\n hole_sizes=[(15, 15), (25, 25), (35, 35), (45, 45)],\n blur_ksizes=[(21, 21), (31, 31), (41, 41)]):\n self.kernel_size = kernel_size\n self.erode_iter_range = erode_iter_range\n self.dilate_iter_range = dilate_iter_range\n self.num_holes_range = num_holes_range\n self.hole_sizes = hole_sizes\n self.blur_ksizes = blur_ksizes\n\n @staticmethod\n def _crop_hole(img, start_point, hole_size):\n \"\"\"Create a all-zero rectangle hole in the image.\n\n Args:\n img (np.ndarray): Source image.\n start_point (tuple[int]): The top-left point of the rectangle.\n hole_size (tuple[int]): The height and width of the rectangle hole.\n\n Return:\n np.ndarray: The cropped image.\n \"\"\"\n top, left = start_point\n bottom = top + hole_size[0]\n right = left + hole_size[1]\n height, weight = img.shape[:2]\n if top < 0 or bottom > height or left < 0 or right > weight:\n raise ValueError(f'crop area {(left, top, right, bottom)} exceeds '\n f'image size {(height, weight)}')\n img[top:bottom, left:right] = 0\n return img\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n alpha = results['alpha']\n trimap = results['trimap']\n\n # generate segmentation mask\n kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (self.kernel_size,\n self.kernel_size))\n seg = (alpha > 0.5).astype(np.float32)\n seg = cv2.erode(\n seg, kernel, iterations=np.random.randint(*self.erode_iter_range))\n seg = cv2.dilate(\n seg, kernel, iterations=np.random.randint(*self.dilate_iter_range))\n\n # generate some holes in segmentation mask\n num_holes = np.random.randint(*self.num_holes_range)\n for _ in range(num_holes):\n hole_size = random.choice(self.hole_sizes)\n unknown = trimap == 128\n start_point = random_choose_unknown(unknown, hole_size)\n seg = self._crop_hole(seg, start_point, hole_size)\n trimap = self._crop_hole(trimap, start_point, hole_size)\n\n # perform gaussian blur to segmentation mask\n seg = cv2.GaussianBlur(seg, random.choice(self.blur_ksizes), 0)\n\n results['seg'] = seg.astype(np.uint8)\n results['num_holes'] = num_holes\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (\n f'(kernel_size={self.kernel_size}, '\n f'erode_iter_range={self.erode_iter_range}, '\n f'dilate_iter_range={self.dilate_iter_range}, '\n f'num_holes_range={self.num_holes_range}, '\n f'hole_sizes={self.hole_sizes}, blur_ksizes={self.blur_ksizes}')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass GenerateSoftSeg(BaseTransform):\n \"\"\"Generate soft segmentation mask from input segmentation mask.\n\n Required key is \"seg\", added key is \"soft_seg\".\n\n Args:\n fg_thr (float, optional): Threshold of the foreground in the normalized\n input segmentation mask. Defaults to 0.2.\n border_width (int, optional): Width of border to be padded to the\n bottom of the mask. Defaults to 25.\n erode_ksize (int, optional): Fixed kernel size of the erosion.\n Defaults to 5.\n dilate_ksize (int, optional): Fixed kernel size of the dilation.\n Defaults to 5.\n erode_iter_range (tuple, optional): Iteration of erosion.\n Defaults to (10, 20).\n dilate_iter_range (tuple, optional): Iteration of dilation.\n Defaults to (3, 7).\n blur_ksizes (list, optional): List of (h, w) to be selected as the\n kernel_size of the gaussian blur.\n Defaults to [(21, 21), (31, 31), (41, 41)].\n \"\"\"\n\n def __init__(self,\n fg_thr=0.2,\n border_width=25,\n erode_ksize=3,\n dilate_ksize=5,\n erode_iter_range=(10, 20),\n dilate_iter_range=(3, 7),\n blur_ksizes=[(21, 21), (31, 31), (41, 41)]):\n if not isinstance(fg_thr, float):\n raise TypeError(f'fg_thr must be a float, but got {type(fg_thr)}')\n if not isinstance(border_width, int):\n raise TypeError(\n f'border_width must be an int, but got {type(border_width)}')\n if not isinstance(erode_ksize, int):\n raise TypeError(\n f'erode_ksize must be an int, but got {type(erode_ksize)}')\n if not isinstance(dilate_ksize, int):\n raise TypeError(\n f'dilate_ksize must be an int, but got {type(dilate_ksize)}')\n if (not is_tuple_of(erode_iter_range, int)\n or len(erode_iter_range) != 2):\n raise TypeError('erode_iter_range must be a tuple of 2 int, '\n f'but got {erode_iter_range}')\n if (not is_tuple_of(dilate_iter_range, int)\n or len(dilate_iter_range) != 2):\n raise TypeError('dilate_iter_range must be a tuple of 2 int, '\n f'but got {dilate_iter_range}')\n if not is_list_of(blur_ksizes, tuple):\n raise TypeError(\n f'blur_ksizes must be a list of tuple, but got {blur_ksizes}')\n\n self.fg_thr = fg_thr\n self.border_width = border_width\n self.erode_ksize = erode_ksize\n self.dilate_ksize = dilate_ksize\n self.erode_iter_range = erode_iter_range\n self.dilate_iter_range = dilate_iter_range\n self.blur_ksizes = blur_ksizes\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n seg = results['seg'].astype(np.float32) / 255\n height, _ = seg.shape[:2]\n seg[seg > self.fg_thr] = 1\n\n # to align with the original repo, pad the bottom of the mask\n seg = cv2.copyMakeBorder(seg, 0, self.border_width, 0, 0,\n cv2.BORDER_REPLICATE)\n\n # erode/dilate segmentation mask\n erode_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (self.erode_ksize,\n self.erode_ksize))\n dilate_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (self.dilate_ksize,\n self.dilate_ksize))\n seg = cv2.erode(\n seg,\n erode_kernel,\n iterations=np.random.randint(*self.erode_iter_range))\n seg = cv2.dilate(\n seg,\n dilate_kernel,\n iterations=np.random.randint(*self.dilate_iter_range))\n\n # perform gaussian blur to segmentation mask\n seg = cv2.GaussianBlur(seg, random.choice(self.blur_ksizes), 0)\n\n # remove the padded rows\n seg = (seg * 255).astype(np.uint8)\n seg = np.delete(seg, range(height, height + self.border_width), 0)\n\n results['soft_seg'] = seg\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(fg_thr={self.fg_thr}, '\n f'border_width={self.border_width}, '\n f'erode_ksize={self.erode_ksize}, '\n f'dilate_ksize={self.dilate_ksize}, '\n f'erode_iter_range={self.erode_iter_range}, '\n f'dilate_iter_range={self.dilate_iter_range}, '\n f'blur_ksizes={self.blur_ksizes})')\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/aug_frames.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass MirrorSequence(BaseTransform):\n \"\"\"Extend short sequences (e.g. Vimeo-90K) by mirroring the sequences.\n\n Given a sequence with N frames (x1, ..., xN), extend the sequence to\n (x1, ..., xN, xN, ..., x1).\n\n Required Keys:\n\n - [KEYS]\n\n Modified Keys:\n\n - [KEYS]\n\n Args:\n keys (list[str]): The frame lists to be extended.\n \"\"\"\n\n def __init__(self, keys):\n\n self.keys = keys\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for key in self.keys:\n if isinstance(results[key], list):\n results[key] = results[key] + results[key][::-1]\n else:\n raise TypeError('The input must be of class list[nparray]. '\n f'Got {type(results[key])}.')\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass TemporalReverse(BaseTransform):\n \"\"\"Reverse frame lists for temporal augmentation.\n\n Required keys are the keys in attributes \"lq\" and \"gt\",\n added or modified keys are \"lq\", \"gt\" and \"reverse\".\n\n Args:\n keys (list[str]): The frame lists to be reversed.\n reverse_ratio (float): The probability to reverse the frame lists.\n Default: 0.5.\n \"\"\"\n\n def __init__(self, keys, reverse_ratio=0.5):\n\n self.keys = keys\n self.reverse_ratio = reverse_ratio\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n reverse = np.random.random() < self.reverse_ratio\n\n if reverse:\n for key in self.keys:\n results[key].reverse()\n\n results['reverse'] = reverse\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(keys={self.keys}, reverse_ratio={self.reverse_ratio})'\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/aug_pixel.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport numbers\nimport random\nfrom typing import Dict\n\nimport cv2\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom mmcv.transforms import BaseTransform\nfrom PIL import Image\n\nfrom mmagic.registry import TRANSFORMS\n\n\nclass BinarizeImage(BaseTransform):\n \"\"\"Binarize image.\n\n Args:\n keys (Sequence[str]): The images to be binarized.\n binary_thr (float): Threshold for binarization.\n a_min (int): Lower limits of pixel value.\n a_max (int): Upper limits of pixel value.\n dtype (np.dtype): Set the data type of the output. Default: np.uint8\n \"\"\"\n\n def __init__(self, keys, binary_thr, a_min=0, a_max=1, dtype=np.uint8):\n\n self.keys = keys\n self.binary_thr = binary_thr\n self.a_min = a_min\n self.a_max = a_max\n self.dtype = dtype\n\n def _binarize(self, img):\n \"\"\"Binarize image.\n\n Args:\n img (np.ndarray): Input image.\n\n Returns:\n img (np.ndarray): Output image.\n \"\"\"\n\n # Binarize to 0/1\n img = (img[..., :] > self.binary_thr).astype(np.uint8)\n\n if self.a_min != 0 or self.a_max != 1 or self.dtype != np.uint8:\n img = img * (self.a_max - self.a_min) + self.a_min\n img = img.astype(self.dtype)\n\n return img\n\n def transform(self, results):\n \"\"\"The transform function of BinarizeImage.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for k in self.keys:\n results[k] = self._binarize(results[k])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (\n f'(keys={self.keys}, binary_thr={self.binary_thr}, '\n f'a_min={self.a_min}, a_max={self.a_max}, dtype={self.dtype})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass Clip(BaseTransform):\n \"\"\"Clip the pixels.\n\n Modified keys are the attributes specified in \"keys\".\n\n Args:\n keys (list[str]): The keys whose values are clipped.\n a_min (int): Lower limits of pixel value.\n a_max (int): Upper limits of pixel value.\n \"\"\"\n\n def __init__(self, keys, a_min=0, a_max=255):\n\n self.keys = keys\n self.a_min = a_min\n self.a_max = a_max\n\n def _clip(self, input_):\n \"\"\"Clip the pixels.\n\n Args:\n input_ (Union[List, np.ndarray]): Pixels to clip.\n\n Returns:\n Union[List, np.ndarray]: Clipped pixels.\n \"\"\"\n is_single_image = False\n if isinstance(input_, np.ndarray):\n is_single_image = True\n input_ = [input_]\n\n # clip\n input_ = [np.clip(v, self.a_min, self.a_max) for v in input_]\n\n if is_single_image:\n input_ = input_[0]\n\n return input_\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict with the values of the specified keys are rounded\n and clipped.\n \"\"\"\n\n for key in self.keys:\n results[key] = self._clip(results[key])\n\n return results\n\n def __repr__(self):\n\n result = self.__class__.__name__\n result += f'(a_min={self.a_min}, a_max={self.a_max})'\n\n return result\n\n\n@TRANSFORMS.register_module()\nclass ColorJitter(BaseTransform):\n \"\"\"An interface for torch color jitter so that it can be invoked in mmagic\n pipeline.\n\n Randomly change the brightness, contrast and saturation of an image.\n Modified keys are the attributes specified in \"keys\".\n\n Required Keys:\n\n - [KEYS]\n\n Modified Keys:\n\n - [KEYS]\n\n Args:\n keys (list[str]): The images to be resized.\n channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.\n Default: 'rgb'.\n\n Notes:\n\n ``**kwards`` follows the args list of\n ``torchvision.transforms.ColorJitter``.\n\n brightness (float or tuple of float (min, max)): How much to jitter\n brightness. brightness_factor is chosen uniformly from\n [max(0, 1 - brightness), 1 + brightness] or the given [min, max].\n Should be non negative numbers.\n contrast (float or tuple of float (min, max)): How much to jitter\n contrast. contrast_factor is chosen uniformly from\n [max(0, 1 - contrast), 1 + contrast] or the given [min, max].\n Should be non negative numbers.\n saturation (float or tuple of float (min, max)): How much to jitter\n saturation. saturation_factor is chosen uniformly from\n [max(0, 1 - saturation), 1 + saturation] or the given [min, max].\n Should be non negative numbers.\n hue (float or tuple of float (min, max)): How much to jitter hue.\n hue_factor is chosen uniformly from [-hue, hue] or the given\n [min, max].\n Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.\n \"\"\"\n\n def __init__(self, keys, channel_order='rgb', **kwargs):\n\n assert keys, 'Keys should not be empty.'\n assert 'to_rgb' not in kwargs, (\n '`to_rgb` is not support in ColorJitter, '\n \"which is replaced by `channel_order` ('rgb' or 'bgr')\")\n\n self.keys = keys\n self.channel_order = channel_order\n self._transform = transforms.ColorJitter(**kwargs)\n\n def _color_jitter(self, image, this_seed):\n \"\"\"Color Jitter Function.\n\n Args:\n image (np.ndarray): Image.\n this_seed (int): Seed of torch.\n\n Returns:\n image (np.ndarray): The output image.\n \"\"\"\n\n if self.channel_order.lower() == 'bgr':\n image = image[..., ::-1]\n\n image = Image.fromarray(image)\n torch.manual_seed(this_seed)\n image = self._transform(image)\n image = np.asarray(image)\n\n if self.channel_order.lower() == 'bgr':\n image = image[..., ::-1]\n\n return image\n\n def transform(self, results: Dict) -> Dict:\n \"\"\"The transform function of ColorJitter.\n\n Args:\n results (dict): The result dict.\n\n Returns:\n dict: The result dict.\n \"\"\"\n\n this_seed = random.randint(0, 2**32)\n\n for k in self.keys:\n if isinstance(results[k], list):\n results[k] = [\n self._color_jitter(v, this_seed) for v in results[k]\n ]\n else:\n results[k] = self._color_jitter(results[k], this_seed)\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, channel_order={self.channel_order}, '\n f'brightness={self._transform.brightness}, '\n f'contrast={self._transform.contrast}, '\n f'saturation={self._transform.saturation}, '\n f'hue={self._transform.hue})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomAffine(BaseTransform):\n \"\"\"Apply random affine to input images.\n\n This class is adopted from\n https://github.com/pytorch/vision/blob/v0.5.0/torchvision/transforms/\n transforms.py#L1015\n It should be noted that in\n https://github.com/Yaoyi-Li/GCA-Matting/blob/master/dataloader/\n data_generator.py#L70\n random flip is added. See explanation of `flip_ratio` below.\n Required keys are the keys in attribute \"keys\", modified keys\n are keys in attribute \"keys\".\n\n Args:\n keys (Sequence[str]): The images to be affined.\n degrees (float | tuple[float]): Range of degrees to select from. If it\n is a float instead of a tuple like (min, max), the range of degrees\n will be (-degrees, +degrees). Set to 0 to deactivate rotations.\n translate (tuple, optional): Tuple of maximum absolute fraction for\n horizontal and vertical translations. For example translate=(a, b),\n then horizontal shift is randomly sampled in the range\n -img_width * a < dx < img_width * a and vertical shift is randomly\n sampled in the range -img_height * b < dy < img_height * b.\n Default: None.\n scale (tuple, optional): Scaling factor interval, e.g (a, b), then\n scale is randomly sampled from the range a <= scale <= b.\n Default: None.\n shear (float | tuple[float], optional): Range of shear degrees to\n select from. If shear is a float, a shear parallel to the x axis\n and a shear parallel to the y axis in the range (-shear, +shear)\n will be applied. Else if shear is a tuple of 2 values, a x-axis\n shear and a y-axis shear in (shear[0], shear[1]) will be applied.\n Default: None.\n flip_ratio (float, optional): Probability of the image being flipped.\n The flips in horizontal direction and vertical direction are\n independent. The image may be flipped in both directions.\n Default: None.\n \"\"\"\n\n def __init__(self,\n keys,\n degrees,\n translate=None,\n scale=None,\n shear=None,\n flip_ratio=None):\n\n self.keys = keys\n if isinstance(degrees, numbers.Number):\n assert degrees >= 0, ('If degrees is a single number, '\n 'it must be positive.')\n self.degrees = (-degrees, degrees)\n else:\n assert isinstance(degrees, tuple) and len(degrees) == 2, \\\n 'degrees should be a tuple and it must be of length 2.'\n self.degrees = degrees\n\n if translate is not None:\n assert isinstance(translate, tuple) and len(translate) == 2, \\\n 'translate should be a tuple and it must be of length 2.'\n for t in translate:\n assert 0.0 <= t <= 1.0, ('translation values should be '\n 'between 0 and 1.')\n self.translate = translate\n\n if scale is not None:\n assert isinstance(scale, tuple) and len(scale) == 2, \\\n 'scale should be a tuple and it must be of length 2.'\n for s in scale:\n assert s > 0, 'scale values should be positive.'\n self.scale = scale\n\n if shear is not None:\n if isinstance(shear, numbers.Number):\n assert shear >= 0, ('If shear is a single number, '\n 'it must be positive.')\n self.shear = (-shear, shear)\n else:\n assert isinstance(shear, tuple) and len(shear) == 2, \\\n 'shear should be a tuple and it must be of length 2.'\n # X-Axis and Y-Axis shear with (min, max)\n self.shear = shear\n else:\n self.shear = shear\n\n if flip_ratio is not None:\n assert isinstance(flip_ratio,\n float), 'flip_ratio should be a float.'\n self.flip_ratio = flip_ratio\n else:\n self.flip_ratio = 0\n\n @staticmethod\n def _get_params(degrees, translate, scale_ranges, shears, flip_ratio,\n img_size):\n \"\"\"Get parameters for affine transformation.\n\n Returns:\n paras (tuple): Params to be passed to the affine transformation.\n \"\"\"\n\n angle = np.random.uniform(degrees[0], degrees[1])\n if translate is not None:\n max_dx = translate[0] * img_size[0]\n max_dy = translate[1] * img_size[1]\n translations = (np.round(np.random.uniform(-max_dx, max_dx)),\n np.round(np.random.uniform(-max_dy, max_dy)))\n else:\n translations = (0, 0)\n\n if scale_ranges is not None:\n scale = (np.random.uniform(scale_ranges[0], scale_ranges[1]),\n np.random.uniform(scale_ranges[0], scale_ranges[1]))\n else:\n scale = (1.0, 1.0)\n\n if shears is not None:\n shear = np.random.uniform(shears[0], shears[1])\n else:\n shear = 0.0\n\n # Because `flip` is used as a multiplier in line 479 and 480,\n # so -1 stands for flip and 1 stands for no flip. Thus `flip`\n # should be an 'inverse' flag as the result of the comparison.\n # See https://github.com/open-mmlab/mmagic/pull/799 for more detail\n flip = (np.random.rand(2) > flip_ratio).astype(np.int32) * 2 - 1\n\n return angle, translations, scale, shear, flip\n\n @staticmethod\n def _get_inverse_affine_matrix(center, angle, translate, scale, shear,\n flip):\n \"\"\"Helper method to compute inverse matrix for affine transformation.\n\n As it is explained in PIL.Image.rotate, we need compute INVERSE of\n affine transformation matrix: M = T * C * RSS * C^-1 where\n T is translation matrix:\n [1, 0, tx | 0, 1, ty | 0, 0, 1];\n C is translation matrix to keep center:\n [1, 0, cx | 0, 1, cy | 0, 0, 1];\n RSS is rotation with scale and shear matrix.\n\n It is different from the original function in torchvision.\n 1. The order are changed to flip -> scale -> rotation -> shear.\n 2. x and y have different scale factors.\n RSS(shear, a, scale, f) =\n [ cos(a + shear)*scale_x*f -sin(a + shear)*scale_y 0]\n [ sin(a)*scale_x*f cos(a)*scale_y 0]\n [ 0 0 1]\n Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1.\n \"\"\"\n\n angle = math.radians(angle)\n shear = math.radians(shear)\n scale_x = 1.0 / scale[0] * flip[0]\n scale_y = 1.0 / scale[1] * flip[1]\n\n # Inverted rotation matrix with scale and shear\n d = math.cos(angle + shear) * math.cos(angle) + math.sin(\n angle + shear) * math.sin(angle)\n matrix = [\n math.cos(angle) * scale_x,\n math.sin(angle + shear) * scale_x, 0, -math.sin(angle) * scale_y,\n math.cos(angle + shear) * scale_y, 0\n ]\n matrix = [m / d for m in matrix]\n\n # Apply inverse of translation and of center translation:\n # RSS^-1 * C^-1 * T^-1\n matrix[2] += matrix[0] * (-center[0] - translate[0]) + matrix[1] * (\n -center[1] - translate[1])\n matrix[5] += matrix[3] * (-center[0] - translate[0]) + matrix[4] * (\n -center[1] - translate[1])\n\n # Apply center translation: C * RSS^-1 * C^-1 * T^-1\n matrix[2] += center[0]\n matrix[5] += center[1]\n\n return matrix\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n h, w = results[self.keys[0]].shape[:2]\n # if image is too small, set degree to 0 to reduce introduced dark area\n if np.maximum(h, w) < 1024:\n params = self._get_params((0, 0), self.translate, self.scale,\n self.shear, self.flip_ratio, (h, w))\n else:\n params = self._get_params(self.degrees, self.translate, self.scale,\n self.shear, self.flip_ratio, (h, w))\n\n center = (w * 0.5 - 0.5, h * 0.5 - 0.5)\n M = self._get_inverse_affine_matrix(center, *params)\n M = np.array(M).reshape((2, 3))\n\n for key in self.keys:\n ori_ndim = results[key].ndim\n results[key] = cv2.warpAffine(\n results[key],\n M, (w, h),\n flags=cv2.INTER_NEAREST + cv2.WARP_INVERSE_MAP)\n\n if ori_ndim == 3 and results[key].ndim == 2:\n results[key] = results[key][..., None]\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, degrees={self.degrees}, '\n f'translate={self.translate}, scale={self.scale}, '\n f'shear={self.shear}, flip_ratio={self.flip_ratio})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomMaskDilation(BaseTransform):\n \"\"\"Randomly dilate binary masks.\n\n Args:\n keys (Sequence[str]): The images to be resized.\n binary_thr (float): Threshold for obtaining binary mask. Default: 0.\n kernel_min (int): Min size of dilation kernel. Default: 9.\n kernel_max (int): Max size of dilation kernel. Default: 49.\n \"\"\"\n\n def __init__(self, keys, binary_thr=0., kernel_min=9, kernel_max=49):\n\n self.keys = keys\n self.kernel_min = kernel_min\n self.kernel_max = kernel_max\n self.binary_thr = binary_thr\n\n def _random_dilate(self, img):\n\n kernel_size = np.random.randint(self.kernel_min, self.kernel_max + 1)\n kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)\n dilate_kernel_size = kernel_size\n img_ = cv2.dilate(img, kernel, iterations=1)\n\n img_ = (img_ > self.binary_thr).astype(np.float32)\n\n return img_, dilate_kernel_size\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for k in self.keys:\n results[k], d_kernel = self._random_dilate(results[k])\n if len(results[k].shape) == 2:\n results[k] = np.expand_dims(results[k], axis=2)\n results[k + '_dilate_kernel_size'] = d_kernel\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, kernel_min={self.kernel_min}, '\n f'kernel_max={self.kernel_max})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass UnsharpMasking(BaseTransform):\n \"\"\"Apply unsharp masking to an image or a sequence of images.\n\n Args:\n kernel_size (int): The kernel_size of the Gaussian kernel.\n sigma (float): The standard deviation of the Gaussian.\n weight (float): The weight of the \"details\" in the final output.\n threshold (float): Pixel differences larger than this value are\n regarded as \"details\".\n keys (list[str]): The keys whose values are processed.\n\n Added keys are \"xxx_unsharp\", where \"xxx\" are the attributes specified\n in \"keys\".\n \"\"\"\n\n def __init__(self, kernel_size, sigma, weight, threshold, keys):\n\n if kernel_size % 2 == 0:\n raise ValueError('kernel_size must be an odd number, but '\n f'got {kernel_size}.')\n\n self.kernel_size = kernel_size\n self.sigma = sigma\n self.weight = weight\n self.threshold = threshold\n self.keys = keys\n\n kernel = cv2.getGaussianKernel(kernel_size, sigma)\n self.kernel = np.matmul(kernel, kernel.transpose())\n\n def _unsharp_masking(self, imgs):\n \"\"\"Unsharp masking function.\"\"\"\n\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n outputs = []\n for img in imgs:\n img = img.astype(np.float32)\n residue = img - cv2.filter2D(img, -1, self.kernel)\n mask = np.float32(np.abs(residue) > self.threshold)\n soft_mask = cv2.filter2D(mask, -1, self.kernel)\n sharpened = np.clip(img + self.weight * residue, 0, 255)\n\n outputs.append(soft_mask * sharpened + (1 - soft_mask) * img)\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n results[f'{key}_unsharp'] = self._unsharp_masking(results[key])\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, kernel_size={self.kernel_size}, '\n f'sigma={self.sigma}, weight={self.weight}, '\n f'threshold={self.threshold})')\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/aug_shape.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\nfrom copy import deepcopy\nfrom typing import Dict, List, Union\n\nimport mmcv\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.utils import is_tuple_of\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass Flip(BaseTransform):\n \"\"\"Flip the input data with a probability.\n\n Reverse the order of elements in the given data with a specific direction.\n The shape of the data is preserved, but the elements are reordered.\n Required keys are the keys in attributes \"keys\", added or modified keys are\n \"flip\", \"flip_direction\" and the keys in attributes \"keys\".\n It also supports flipping a list of images with the same flip.\n\n Required Keys:\n\n - [KEYS]\n\n Modified Keys:\n\n - [KEYS]\n\n Args:\n keys (Union[str, List[str]]): The images to be flipped.\n flip_ratio (float): The probability to flip the images. Default: 0.5.\n direction (str): Flip images horizontally or vertically. Options are\n \"horizontal\" | \"vertical\". Default: \"horizontal\".\n \"\"\"\n _directions = ['horizontal', 'vertical']\n\n def __init__(self, keys, flip_ratio=0.5, direction='horizontal'):\n\n if direction not in self._directions:\n raise ValueError(f'Direction {direction} is not supported.'\n f'Currently support ones are {self._directions}')\n\n self.keys = keys if isinstance(keys, list) else [keys]\n self.flip_ratio = flip_ratio\n self.direction = direction\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n flip = np.random.random() < self.flip_ratio\n\n if flip:\n for key in self.keys:\n if isinstance(results[key], list):\n for v in results[key]:\n mmcv.imflip_(v, self.direction)\n else:\n mmcv.imflip_(results[key], self.direction)\n\n if 'flip_infos' not in results:\n results['flip_infos'] = []\n\n flip_info = dict(\n keys=self.keys,\n direction=self.direction,\n ratio=self.flip_ratio,\n flip=flip)\n results['flip_infos'].append(flip_info)\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, flip_ratio={self.flip_ratio}, '\n f'direction={self.direction})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomRotation(BaseTransform):\n \"\"\"Rotate the image by a randomly-chosen angle, measured in degree.\n\n Args:\n keys (list[str]): The images to be rotated.\n degrees (tuple[float] | tuple[int] | float | int): If it is a tuple,\n it represents a range (min, max). If it is a float or int,\n the range is constructed as (-degrees, degrees).\n \"\"\"\n\n def __init__(self, keys, degrees):\n\n if isinstance(degrees, (int, float)):\n if degrees < 0.0:\n raise ValueError('Degrees must be positive if it is a number.')\n else:\n degrees = (-degrees, degrees)\n elif not is_tuple_of(degrees, (int, float)):\n raise TypeError(f'Degrees must be float | int or tuple of float | '\n 'int, but got '\n f'{type(degrees)}.')\n\n self.keys = keys\n self.degrees = degrees\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n angle = random.uniform(self.degrees[0], self.degrees[1])\n\n for k in self.keys:\n results[k] = mmcv.imrotate(results[k], angle)\n if results[k].ndim == 2:\n results[k] = np.expand_dims(results[k], axis=2)\n results['degrees'] = self.degrees\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, degrees={self.degrees})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomTransposeHW(BaseTransform):\n \"\"\"Randomly transpose images in H and W dimensions with a probability.\n\n (TransposeHW = horizontal flip + anti-clockwise rotation by 90 degrees)\n When used with horizontal/vertical flips, it serves as a way of rotation\n augmentation.\n It also supports randomly transposing a list of images.\n\n Required keys are the keys in attributes \"keys\", added or modified keys are\n \"transpose\" and the keys in attributes \"keys\".\n\n Args:\n keys (list[str]): The images to be transposed.\n transpose_ratio (float): The probability to transpose the images.\n Default: 0.5.\n \"\"\"\n\n def __init__(self, keys, transpose_ratio=0.5):\n\n self.keys = keys if isinstance(keys, list) else [keys]\n self.transpose_ratio = transpose_ratio\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n transpose = np.random.random() < self.transpose_ratio\n\n if transpose:\n for key in self.keys:\n if isinstance(results[key], list):\n results[key] = [v.transpose(1, 0, 2) for v in results[key]]\n else:\n results[key] = results[key].transpose(1, 0, 2)\n\n results['transpose'] = transpose\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (\n f'(keys={self.keys}, transpose_ratio={self.transpose_ratio})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass Resize(BaseTransform):\n \"\"\"Resize data to a specific size for training or resize the images to fit\n the network input regulation for testing.\n\n When used for resizing images to fit network input regulation, the case is\n that a network may have several downsample and then upsample operation,\n then the input height and width should be divisible by the downsample\n factor of the network.\n For example, the network would downsample the input for 5 times with\n stride 2, then the downsample factor is 2^5 = 32 and the height\n and width should be divisible by 32.\n\n Required keys are the keys in attribute \"keys\", added or modified keys are\n \"keep_ratio\", \"scale_factor\", \"interpolation\" and the\n keys in attribute \"keys\".\n\n Required Keys:\n\n - Required keys are the keys in attribute \"keys\"\n\n Modified Keys:\n\n - Modified the keys in attribute \"keys\" or save as new key ([OUT_KEY])\n\n Added Keys:\n\n - [OUT_KEY]_shape\n - keep_ratio\n - scale_factor\n - interpolation\n\n All keys in \"keys\" should have the same shape. \"test_trans\" is used to\n record the test transformation to align the input's shape.\n\n Args:\n keys (str | list[str]): The image(s) to be resized.\n scale (float | tuple[int]): If scale is tuple[int], target spatial\n size (h, w). Otherwise, target spatial size is scaled by input\n size.\n Note that when it is used, `size_factor` and `max_size` are\n useless. Default: None\n keep_ratio (bool): If set to True, images will be resized without\n changing the aspect ratio. Otherwise, it will resize images to a\n given size. Default: False.\n Note that it is used together with `scale`.\n size_factor (int): Let the output shape be a multiple of size_factor.\n Default:None.\n Note that when it is used, `scale` should be set to None and\n `keep_ratio` should be set to False.\n max_size (int): The maximum size of the longest side of the output.\n Default:None.\n Note that it is used together with `size_factor`.\n interpolation (str): Algorithm used for interpolation:\n \"nearest\" | \"bilinear\" | \"bicubic\" | \"area\" | \"lanczos\".\n Default: \"bilinear\".\n backend (str | None): The image resize backend type. Options are `cv2`,\n `pillow`, `None`. If backend is None, the global imread_backend\n specified by ``mmcv.use_backend()`` will be used.\n Default: None.\n output_keys (list[str] | None): The resized images. Default: None\n Note that if it is not `None`, its length should be equal to keys.\n \"\"\"\n\n def __init__(self,\n keys: Union[str, List[str]] = 'img',\n scale=None,\n keep_ratio=False,\n size_factor=None,\n max_size=None,\n interpolation='bilinear',\n backend=None,\n output_keys=None):\n\n assert keys, 'Keys should not be empty.'\n keys = [keys] if not isinstance(keys, list) else keys\n if output_keys:\n assert len(output_keys) == len(keys)\n else:\n output_keys = keys\n if size_factor:\n assert scale is None, ('When size_factor is used, scale should ',\n f'be None. But received {scale}.')\n assert keep_ratio is False, ('When size_factor is used, '\n 'keep_ratio should be False.')\n if max_size:\n assert size_factor is not None, (\n 'When max_size is used, '\n f'size_factor should also be set. But received {size_factor}.')\n if isinstance(scale, float):\n if scale <= 0:\n raise ValueError(f'Invalid scale {scale}, must be positive.')\n elif is_tuple_of(scale, int):\n max_long_edge = max(scale)\n max_short_edge = min(scale)\n if max_short_edge == -1:\n # assign np.inf to long edge for rescaling short edge later.\n scale = (np.inf, max_long_edge)\n elif scale is not None:\n raise TypeError(\n f'Scale must be None, float or tuple of int, but got '\n f'{type(scale)}.')\n\n self.keys = keys\n self.output_keys = output_keys\n self.scale = scale\n self.size_factor = size_factor\n self.max_size = max_size\n self.keep_ratio = keep_ratio\n self.interpolation = interpolation\n self.backend = backend\n\n def _resize(self, img):\n \"\"\"Resize function.\n\n Args:\n img (np.ndarray): Image.\n\n Returns:\n img (np.ndarray): Resized image.\n \"\"\"\n if isinstance(img, list):\n for i, image in enumerate(img):\n size, img[i] = self._resize(image)\n return size, img\n else:\n if self.keep_ratio:\n img, self.scale_factor = mmcv.imrescale(\n img,\n self.scale,\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n else:\n img, w_scale, h_scale = mmcv.imresize(\n img,\n self.scale,\n return_scale=True,\n interpolation=self.interpolation,\n backend=self.backend)\n self.scale_factor = np.array((w_scale, h_scale),\n dtype=np.float32)\n\n if len(img.shape) == 2:\n img = np.expand_dims(img, axis=2)\n return img.shape, img\n\n def transform(self, results: Dict) -> Dict:\n \"\"\"Transform function to resize images.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n if self.size_factor:\n h, w = results[self.keys[0]].shape[:2]\n new_h = h - (h % self.size_factor)\n new_w = w - (w % self.size_factor)\n if self.max_size:\n new_h = min(self.max_size - (self.max_size % self.size_factor),\n new_h)\n new_w = min(self.max_size - (self.max_size % self.size_factor),\n new_w)\n self.scale = (new_w, new_h)\n\n for key, out_key in zip(self.keys, self.output_keys):\n if key in results:\n size, results[out_key] = self._resize(results[key])\n results[f'{out_key}_shape'] = size\n # copy metainfo\n if f'ori_{key}_shape' in results:\n results[f'ori_{out_key}_shape'] = deepcopy(\n results[f'ori_{key}_shape'])\n if f'{key}_channel_order' in results:\n results[f'{out_key}_channel_order'] = deepcopy(\n results[f'{key}_channel_order'])\n if f'{key}_color_type' in results:\n results[f'{out_key}_color_type'] = deepcopy(\n results[f'{key}_color_type'])\n\n results['scale_factor'] = self.scale_factor\n results['keep_ratio'] = self.keep_ratio\n results['interpolation'] = self.interpolation\n results['backend'] = self.backend\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (\n f'(keys={self.keys}, output_keys={self.output_keys}, '\n f'scale={self.scale}, '\n f'keep_ratio={self.keep_ratio}, size_factor={self.size_factor}, '\n f'max_size={self.max_size}, interpolation={self.interpolation})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass NumpyPad(BaseTransform):\n \"\"\"Numpy Padding.\n\n In this augmentation, numpy padding is adopted to customize padding\n augmentation. Please carefully read the numpy manual in:\n https://numpy.org/doc/stable/reference/generated/numpy.pad.html\n\n If you just hope a single dimension to be padded, you must set ``padding``\n like this:\n\n ::\n\n padding = ((2, 2), (0, 0), (0, 0))\n\n In this case, if you adopt an input with three dimension, only the first\n dimension will be padded.\n\n Args:\n keys (Union[str, List[str]]): The images to be padded.\n padding (int | tuple(int)): Please refer to the args ``pad_width`` in\n ``numpy.pad``.\n \"\"\"\n\n def __init__(self, keys, padding, **kwargs):\n if isinstance(keys, str):\n keys = [keys]\n self.keys = keys\n self.padding = padding\n self.kwargs = kwargs\n\n def transform(self, results):\n \"\"\"Call function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n results[key] = np.pad(results[key], self.padding, **self.kwargs)\n\n return results\n\n def __repr__(self) -> str:\n repr_str = self.__class__.__name__\n repr_str += (\n f'(keys={self.keys}, padding={self.padding}, kwargs={self.kwargs})'\n )\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/blur_kernels.py", "content": "# This code is referenced from BasicSR with modifications.\n# Reference: https://github.com/xinntao/BasicSR/blob/master/basicsr/data/degradations.py # noqa\n# Original license: Copyright (c) 2020 xinntao, under the Apache 2.0 license.\n\nimport numpy as np\nfrom scipy import special\n\n\ndef get_rotated_sigma_matrix(sig_x, sig_y, theta):\n \"\"\"Calculate the rotated sigma matrix (two dimensional matrix).\n\n Args:\n sig_x (float): Standard deviation along the horizontal direction.\n sig_y (float): Standard deviation along the vertical direction.\n theta (float): Rotation in radian.\n\n Returns:\n np.ndarray: Rotated sigma matrix.\n \"\"\"\n\n diag = np.array([[sig_x**2, 0], [0, sig_y**2]]).astype(np.float32)\n rot = np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]]).astype(np.float32)\n\n return np.matmul(rot, np.matmul(diag, rot.T))\n\n\ndef _mesh_grid(kernel_size):\n \"\"\"Generate the mesh grid, centering at zero.\n\n Args:\n kernel_size (int): The size of the kernel.\n\n Returns:\n x_grid (np.ndarray): x-coordinates with shape\n (kernel_size, kernel_size).\n y_grid (np.ndarray): y-coordinates with shape\n (kernel_size, kernel_size).\n xy_grid (np.ndarray): stacked coordinates with shape\n (kernel_size, kernel_size, 2).\n \"\"\"\n\n range_ = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)\n x_grid, y_grid = np.meshgrid(range_, range_)\n xy_grid = np.hstack((x_grid.reshape((kernel_size * kernel_size, 1)),\n y_grid.reshape(kernel_size * kernel_size,\n 1))).reshape(kernel_size, kernel_size,\n 2)\n\n return xy_grid, x_grid, y_grid\n\n\ndef calculate_gaussian_pdf(sigma_matrix, grid):\n \"\"\"Calculate PDF of the bivariate Gaussian distribution.\n\n Args:\n sigma_matrix (np.ndarray): The variance matrix with shape (2, 2).\n grid (np.ndarray): Coordinates generated by :func:`_mesh_grid`,\n with shape (K, K, 2), where K is the kernel size.\n\n Returns:\n kernel (np.ndarray): Un-normalized kernel.\n \"\"\"\n\n inverse_sigma = np.linalg.inv(sigma_matrix)\n kernel = np.exp(-0.5 * np.sum(np.matmul(grid, inverse_sigma) * grid, 2))\n\n return kernel\n\n\ndef bivariate_gaussian(kernel_size,\n sig_x,\n sig_y=None,\n theta=None,\n grid=None,\n is_isotropic=True):\n \"\"\"Generate a bivariate isotropic or anisotropic Gaussian kernel.\n\n In isotropic mode, only `sig_x` is used. `sig_y` and `theta` are\n ignored.\n\n Args:\n kernel_size (int): The size of the kernel\n sig_x (float): Standard deviation along horizontal direction.\n sig_y (float | None, optional): Standard deviation along the vertical\n direction. If it is None, 'is_isotropic' must be set to True.\n Default: None.\n theta (float | None, optional): Rotation in radian. If it is None,\n 'is_isotropic' must be set to True. Default: None.\n grid (ndarray, optional): Coordinates generated by :func:`_mesh_grid`,\n with shape (K, K, 2), where K is the kernel size. Default: None\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n\n Returns:\n kernel (np.ndarray): normalized kernel (i.e. sum to 1).\n \"\"\"\n\n if grid is None:\n grid, _, _ = _mesh_grid(kernel_size)\n\n if is_isotropic:\n sigma_matrix = np.array([[sig_x**2, 0], [0,\n sig_x**2]]).astype(np.float32)\n else:\n if sig_y is None:\n raise ValueError('\"sig_y\" cannot be None if \"is_isotropic\" is '\n 'False.')\n\n sigma_matrix = get_rotated_sigma_matrix(sig_x, sig_y, theta)\n\n kernel = calculate_gaussian_pdf(sigma_matrix, grid)\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef bivariate_generalized_gaussian(kernel_size,\n sig_x,\n sig_y=None,\n theta=None,\n beta=1,\n grid=None,\n is_isotropic=True):\n \"\"\"Generate a bivariate generalized Gaussian kernel.\n\n Described in `Parameter Estimation For Multivariate Generalized\n Gaussian Distributions` by Pascal et. al (2013). In isotropic mode,\n only `sig_x` is used. `sig_y` and `theta` is ignored.\n\n Args:\n kernel_size (int): The size of the kernel\n sig_x (float): Standard deviation along horizontal direction\n sig_y (float | None, optional): Standard deviation along the vertical\n direction. If it is None, 'is_isotropic' must be set to True.\n Default: None.\n theta (float | None, optional): Rotation in radian. If it is None,\n 'is_isotropic' must be set to True. Default: None.\n beta (float, optional): Shape parameter, beta = 1 is the normal\n distribution. Default: 1.\n grid (ndarray, optional): Coordinates generated by :func:`_mesh_grid`,\n with shape (K, K, 2), where K is the kernel size. Default: None\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n\n Returns:\n kernel (np.ndarray): normalized kernel.\n \"\"\"\n\n if grid is None:\n grid, _, _ = _mesh_grid(kernel_size)\n\n if is_isotropic:\n sigma_matrix = np.array([[sig_x**2, 0], [0,\n sig_x**2]]).astype(np.float32)\n else:\n sigma_matrix = get_rotated_sigma_matrix(sig_x, sig_y, theta)\n\n inverse_sigma = np.linalg.inv(sigma_matrix)\n kernel = np.exp(\n -0.5 *\n np.power(np.sum(np.matmul(grid, inverse_sigma) * grid, 2), beta))\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef bivariate_plateau(kernel_size,\n sig_x,\n sig_y,\n theta,\n beta,\n grid=None,\n is_isotropic=True):\n \"\"\"Generate a plateau-like anisotropic kernel.\n\n This kernel has a form of 1 / (1+x^(beta)).\n Ref: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution # noqa\n In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.\n\n Args:\n kernel_size (int): The size of the kernel\n sig_x (float): Standard deviation along horizontal direction\n sig_y (float): Standard deviation along the vertical direction.\n theta (float): Rotation in radian.\n beta (float): Shape parameter, beta = 1 is the normal distribution.\n grid (np.ndarray, optional): Coordinates generated by :func:`_mesh_grid`,\n with shape (K, K, 2), where K is the kernel size. Default: None\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n Returns:\n kernel (np.ndarray): normalized kernel (i.e. sum to 1).\n \"\"\"\n if grid is None:\n grid, _, _ = _mesh_grid(kernel_size)\n\n if is_isotropic:\n sigma_matrix = np.array([[sig_x**2, 0], [0,\n sig_x**2]]).astype(np.float32)\n else:\n sigma_matrix = get_rotated_sigma_matrix(sig_x, sig_y, theta)\n\n inverse_sigma = np.linalg.inv(sigma_matrix)\n kernel = np.reciprocal(\n np.power(np.sum(np.matmul(grid, inverse_sigma) * grid, 2), beta) + 1)\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef random_bivariate_gaussian_kernel(kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n noise_range=None,\n is_isotropic=True):\n \"\"\"Randomly generate bivariate isotropic or anisotropic Gaussian kernels.\n\n In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and\n `rotation_range` is ignored.\n\n Args:\n kernel_size (int): The size of the kernel.\n sigma_x_range (tuple): The range of the standard deviation along the\n horizontal direction. Default: [0.6, 5]\n sigma_y_range (tuple): The range of the standard deviation along the\n vertical direction. Default: [0.6, 5]\n rotation_range (tuple): Range of rotation in radian.\n noise_range (tuple, optional): Multiplicative kernel noise.\n Default: None.\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n\n Returns:\n kernel (np.ndarray): The kernel whose parameters are sampled from the\n specified range.\n \"\"\"\n\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\n assert sigma_x_range[0] <= sigma_x_range[1], 'Wrong sigma_x_range.'\n\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\n if is_isotropic is False:\n assert sigma_y_range[0] <= sigma_y_range[1], 'Wrong sigma_y_range.'\n assert rotation_range[0] <= rotation_range[1], 'Wrong rotation_range.'\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\n rotation = np.random.uniform(rotation_range[0], rotation_range[1])\n else:\n sigma_y = sigma_x\n rotation = 0\n\n kernel = bivariate_gaussian(\n kernel_size, sigma_x, sigma_y, rotation, is_isotropic=is_isotropic)\n\n # add multiplicative noise\n if noise_range is not None:\n assert noise_range[0] <= noise_range[1], 'Wrong noise range.'\n noise = np.random.uniform(\n noise_range[0], noise_range[1], size=kernel.shape)\n kernel = kernel * noise\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef random_bivariate_generalized_gaussian_kernel(kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_range,\n noise_range=None,\n is_isotropic=True):\n \"\"\"Randomly generate bivariate generalized Gaussian kernels.\n\n In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and\n `rotation_range` is ignored.\n\n Args:\n kernel_size (int): The size of the kernel.\n sigma_x_range (tuple): The range of the standard deviation along the\n horizontal direction. Default: [0.6, 5]\n sigma_y_range (tuple): The range of the standard deviation along the\n vertical direction. Default: [0.6, 5]\n rotation_range (tuple): Range of rotation in radian.\n beta_range (float): The range of the shape parameter, beta = 1 is the\n normal distribution.\n noise_range (tuple, optional): Multiplicative kernel noise.\n Default: None.\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n\n Returns:\n kernel (np.ndarray): Normalized kernel.\n \"\"\"\n\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\n assert sigma_x_range[0] <= sigma_x_range[1], 'Wrong sigma_x_range.'\n\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\n if is_isotropic is False:\n assert sigma_y_range[0] <= sigma_y_range[1], 'Wrong sigma_y_range.'\n assert rotation_range[0] <= rotation_range[1], 'Wrong rotation_range.'\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\n rotation = np.random.uniform(rotation_range[0], rotation_range[1])\n else:\n sigma_y = sigma_x\n rotation = 0\n\n # assume beta_range[0] <= 1 <= beta_range[1]\n if np.random.uniform() <= 0.5:\n beta = np.random.uniform(beta_range[0], 1)\n else:\n beta = np.random.uniform(1, beta_range[1])\n\n kernel = bivariate_generalized_gaussian(\n kernel_size,\n sigma_x,\n sigma_y,\n rotation,\n beta,\n is_isotropic=is_isotropic)\n\n # add multiplicative noise\n if noise_range is not None:\n assert noise_range[0] <= noise_range[1], 'Wrong noise range.'\n noise = np.random.uniform(\n noise_range[0], noise_range[1], size=kernel.shape)\n kernel = kernel * noise\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef random_bivariate_plateau_kernel(kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_range,\n noise_range=None,\n is_isotropic=True):\n \"\"\"Randomly generate bivariate plateau kernels.\n\n In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and\n `rotation_range` is ignored.\n\n Args:\n kernel_size (int): The size of the kernel.\n sigma_x_range (tuple): The range of the standard deviation along the\n horizontal direction. Default: [0.6, 5]\n sigma_y_range (tuple): The range of the standard deviation along the\n vertical direction. Default: [0.6, 5]\n rotation_range (tuple): Range of rotation in radian.\n beta_range (float): The range of the shape parameter, beta = 1 is the\n normal distribution.\n noise_range (tuple, optional): Multiplicative kernel noise.\n Default: None.\n is_isotropic (bool, optional): Whether to use an isotropic kernel.\n Default: True.\n\n Returns:\n kernel (np.ndarray): Plateau kernel.\n \"\"\"\n\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\n assert sigma_x_range[0] <= sigma_x_range[1], 'Wrong sigma_x_range.'\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\n\n if is_isotropic is False:\n assert sigma_y_range[0] <= sigma_y_range[1], 'Wrong sigma_y_range.'\n assert rotation_range[0] <= rotation_range[1], 'Wrong rotation_range.'\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\n rotation = np.random.uniform(rotation_range[0], rotation_range[1])\n else:\n sigma_y = sigma_x\n rotation = 0\n\n # TODO: this may be not proper\n if np.random.uniform() <= 0.5:\n beta = np.random.uniform(beta_range[0], 1)\n else:\n beta = np.random.uniform(1, beta_range[1])\n\n kernel = bivariate_plateau(\n kernel_size,\n sigma_x,\n sigma_y,\n rotation,\n beta,\n is_isotropic=is_isotropic)\n\n # add multiplicative noise\n if noise_range is not None:\n assert noise_range[0] <= noise_range[1], 'Wrong noise range.'\n noise = np.random.uniform(\n noise_range[0], noise_range[1], size=kernel.shape)\n kernel = kernel * noise\n kernel = kernel / np.sum(kernel)\n\n return kernel\n\n\ndef random_circular_lowpass_kernel(omega_range, kernel_size, pad_to=0):\n \"\"\"Generate a 2D Sinc filter.\n\n Reference: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter # noqa\n\n Args:\n omega_range (tuple): The cutoff frequency in radian (pi is max).\n kernel_size (int): The size of the kernel. It must be an odd number.\n pad_to (int, optional): The size of the padded kernel. It must be odd\n or zero. Default: 0.\n\n Returns:\n kernel (np.ndarray): The Sinc kernel with specified parameters.\n \"\"\"\n err = np.geterr()\n np.seterr(divide='ignore', invalid='ignore')\n\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\n omega = np.random.uniform(omega_range[0], omega_range[-1])\n\n kernel = np.fromfunction(\n lambda x, y: omega * special.j1(omega * np.sqrt(\n (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) /\n (2 * np.pi * np.sqrt((x - (kernel_size - 1) / 2)**2 +\n (y - (kernel_size - 1) / 2)**2)),\n [kernel_size, kernel_size])\n kernel[(kernel_size - 1) // 2,\n (kernel_size - 1) // 2] = omega**2 / (4 * np.pi)\n kernel = kernel / np.sum(kernel)\n\n if pad_to > kernel_size:\n pad_size = (pad_to - kernel_size) // 2\n kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))\n\n np.seterr(**err)\n\n return kernel\n\n\ndef random_mixed_kernels(kernel_list,\n kernel_prob,\n kernel_size,\n sigma_x_range=[0.6, 5],\n sigma_y_range=[0.6, 5],\n rotation_range=[-np.pi, np.pi],\n beta_gaussian_range=[0.5, 8],\n beta_plateau_range=[1, 2],\n omega_range=[0, np.pi],\n noise_range=None):\n \"\"\"Randomly generate a kernel.\n\n Args:\n kernel_list (list): A list of kernel types. Choices are\n 'iso', 'aniso', 'skew', 'generalized_iso', 'generalized_aniso',\n 'plateau_iso', 'plateau_aniso', 'sinc'.\n kernel_prob (list): The probability of choosing of the corresponding\n kernel.\n kernel_size (int): The size of the kernel.\n sigma_x_range (list, optional): The range of the standard deviation\n along the horizontal direction. Default: (0.6, 5).\n sigma_y_range (list, optional): The range of the standard deviation\n along the vertical direction. Default: (0.6, 5).\n rotation_range (list, optional): Range of rotation in radian.\n Default: (-np.pi, np.pi).\n beta_gaussian_range (list, optional): The range of the shape parameter\n for generalized Gaussian. Default: (0.5, 8).\n beta_plateau_range (list, optional): The range of the shape parameter\n for plateau kernel. Default: (1, 2).\n omega_range (list, optional): The range of omega used in Sinc kernel.\n Default: (0, np.pi).\n noise_range (list, optional): Multiplicative kernel noise.\n Default: None.\n\n Returns:\n kernel (np.ndarray): The kernel whose parameters are sampled from the\n specified range.\n \"\"\"\n\n kernel_type = np.random.choice(kernel_list, p=kernel_prob)\n if kernel_type == 'iso':\n kernel = random_bivariate_gaussian_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n noise_range=noise_range,\n is_isotropic=True)\n elif kernel_type == 'aniso':\n kernel = random_bivariate_gaussian_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n noise_range=noise_range,\n is_isotropic=False)\n elif kernel_type == 'generalized_iso':\n kernel = random_bivariate_generalized_gaussian_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_gaussian_range,\n noise_range=noise_range,\n is_isotropic=True)\n elif kernel_type == 'generalized_aniso':\n kernel = random_bivariate_generalized_gaussian_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_gaussian_range,\n noise_range=noise_range,\n is_isotropic=False)\n elif kernel_type == 'plateau_iso':\n kernel = random_bivariate_plateau_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_plateau_range,\n noise_range=None,\n is_isotropic=True)\n elif kernel_type == 'plateau_aniso':\n kernel = random_bivariate_plateau_kernel(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n beta_plateau_range,\n noise_range=None,\n is_isotropic=False)\n elif kernel_type == 'sinc':\n kernel = random_circular_lowpass_kernel(omega_range, kernel_size)\n\n return kernel\n" }, { "path": "mmagic/datasets/transforms/crop.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport random\n\nimport cv2 as cv\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.hub import get_config\nfrom mmengine.registry import DefaultScope\nfrom mmengine.utils import is_list_of, is_tuple_of\nfrom torch.nn.modules.utils import _pair\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.utils import get_box_info, random_choose_unknown, try_import\n\nmmdet_apis = try_import('mmdet.apis')\n\n\n@TRANSFORMS.register_module()\nclass Crop(BaseTransform):\n \"\"\"Crop data to specific size for training.\n\n Args:\n keys (Sequence[str]): The images to be cropped.\n crop_size (Tuple[int]): Target spatial size (h, w).\n random_crop (bool): If set to True, it will random crop\n image. Otherwise, it will work as center crop. Default: True.\n is_pad_zeros (bool, optional): Whether to pad the image with 0 if\n crop_size is greater than image size. Default: False.\n \"\"\"\n\n def __init__(self, keys, crop_size, random_crop=True, is_pad_zeros=False):\n\n if not is_tuple_of(crop_size, int):\n raise TypeError(\n 'Elements of crop_size must be int and crop_size must be'\n f' tuple, but got {type(crop_size[0])} in {type(crop_size)}')\n\n self.keys = keys\n self.crop_size = crop_size\n self.random_crop = random_crop\n self.is_pad_zeros = is_pad_zeros\n\n def _crop(self, data):\n \"\"\"Crop the data.\n\n Args:\n data (Union[List, np.ndarray]): Input data to crop.\n\n Returns:\n tuple: cropped data and corresponding crop box.\n \"\"\"\n if not isinstance(data, list):\n data_list = [data]\n else:\n data_list = data\n\n crop_bbox_list = []\n data_list_ = []\n\n for item in data_list:\n data_h, data_w = item.shape[:2]\n crop_h, crop_w = self.crop_size\n\n if self.is_pad_zeros:\n\n crop_y_offset, crop_x_offset = 0, 0\n\n if crop_h > data_h:\n crop_y_offset = (crop_h - data_h) // 2\n if crop_w > data_w:\n crop_x_offset = (crop_w - data_w) // 2\n\n if crop_y_offset > 0 or crop_x_offset > 0:\n pad_width = [(2 * crop_y_offset, 2 * crop_y_offset),\n (2 * crop_x_offset, 2 * crop_x_offset)]\n if item.ndim == 3:\n pad_width.append((0, 0))\n item = np.pad(\n item,\n tuple(pad_width),\n mode='constant',\n constant_values=0)\n\n data_h, data_w = item.shape[:2]\n\n crop_h = min(data_h, crop_h)\n crop_w = min(data_w, crop_w)\n\n if self.random_crop:\n x_offset = np.random.randint(0, data_w - crop_w + 1)\n y_offset = np.random.randint(0, data_h - crop_h + 1)\n else:\n x_offset = max(0, (data_w - crop_w)) // 2\n y_offset = max(0, (data_h - crop_h)) // 2\n\n crop_bbox = [x_offset, y_offset, crop_w, crop_h]\n item_ = item[y_offset:y_offset + crop_h,\n x_offset:x_offset + crop_w, ...]\n crop_bbox_list.append(crop_bbox)\n data_list_.append(item_)\n\n if not isinstance(data, list):\n return data_list_[0], crop_bbox_list[0]\n return data_list_, crop_bbox_list\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for k in self.keys:\n data_, crop_bbox = self._crop(results[k])\n results[k] = data_\n results[k + '_crop_bbox'] = crop_bbox\n results['crop_size'] = self.crop_size\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'keys={self.keys}, crop_size={self.crop_size}, '\n f'random_crop={self.random_crop}')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass CropLike(BaseTransform):\n \"\"\"Crop/pad the image in the target_key according to the size of image in\n the reference_key .\n\n Args:\n target_key (str): The key needs to be cropped.\n reference_key (str | None): The reference key, need its size.\n Default: None.\n \"\"\"\n\n def __init__(self, target_key, reference_key=None):\n\n assert reference_key and target_key\n self.target_key = target_key\n self.reference_key = reference_key\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n Require self.target_key and self.reference_key.\n\n Returns:\n dict: A dict containing the processed data and information.\n Modify self.target_key.\n \"\"\"\n\n size = results[self.reference_key].shape\n old_image = results[self.target_key]\n old_size = old_image.shape\n h, w = old_size[:2]\n new_size = size[:2] + old_size[2:]\n h_cover, w_cover = min(h, size[0]), min(w, size[1])\n\n format_image = np.zeros(new_size, dtype=old_image.dtype)\n format_image[:h_cover, :w_cover] = old_image[:h_cover, :w_cover]\n results[self.target_key] = format_image\n\n return results\n\n def __repr__(self):\n\n return (self.__class__.__name__ + f' target_key={self.target_key}, ' +\n f'reference_key={self.reference_key}')\n\n\n@TRANSFORMS.register_module()\nclass FixedCrop(BaseTransform):\n \"\"\"Crop paired data (at a specific position) to specific size for training.\n\n Args:\n keys (Sequence[str]): The images to be cropped.\n crop_size (Tuple[int]): Target spatial size (h, w).\n crop_pos (Tuple[int]): Specific position (x, y). If set to None,\n random initialize the position to crop paired data batch.\n Default: None.\n \"\"\"\n\n def __init__(self, keys, crop_size, crop_pos=None):\n\n if not is_tuple_of(crop_size, int):\n raise TypeError(\n 'Elements of crop_size must be int and crop_size must be'\n f' tuple, but got {type(crop_size[0])} in {type(crop_size)}')\n if not is_tuple_of(crop_pos, int) and (crop_pos is not None):\n raise TypeError(\n 'Elements of crop_pos must be int and crop_pos must be'\n f' tuple or None, but got {type(crop_pos[0])} in '\n f'{type(crop_pos)}')\n\n self.keys = keys\n self.crop_size = crop_size\n self.crop_pos = crop_pos\n\n def _crop(self, data, x_offset, y_offset, crop_w, crop_h):\n \"\"\"Crop the data.\n\n Args:\n data (Union[List, np.ndarray]): Input data to crop.\n x_offset (int): The offset of x axis.\n y_offset (int): The offset of y axis.\n crop_w (int): The width of crop bbox.\n crop_h (int): The height of crop bbox.\n\n Returns:\n tuple: cropped data and corresponding crop box.\n \"\"\"\n crop_bbox = [x_offset, y_offset, crop_w, crop_h]\n data_ = data[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w,\n ...]\n\n return data_, crop_bbox\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n if isinstance(results[self.keys[0]], list):\n data_h, data_w = results[self.keys[0]][0].shape[:2]\n else:\n data_h, data_w = results[self.keys[0]].shape[:2]\n crop_h, crop_w = self.crop_size\n crop_h = min(data_h, crop_h)\n crop_w = min(data_w, crop_w)\n\n if self.crop_pos is None:\n x_offset = np.random.randint(0, data_w - crop_w + 1)\n y_offset = np.random.randint(0, data_h - crop_h + 1)\n else:\n x_offset, y_offset = self.crop_pos\n crop_w = min(data_w - x_offset, crop_w)\n crop_h = min(data_h - y_offset, crop_h)\n\n for k in self.keys:\n images = results[k]\n is_list = isinstance(images, list)\n if not is_list:\n images = [images]\n cropped_images = []\n crop_bbox = None\n for image in images:\n # In fixed crop for paired images, sizes should be the same\n if (image.shape[0] != data_h or image.shape[1] != data_w):\n raise ValueError(\n 'The sizes of paired images should be the same. '\n f'Expected ({data_h}, {data_w}), '\n f'but got ({image.shape[0]}, '\n f'{image.shape[1]}).')\n data_, crop_bbox = self._crop(image, x_offset, y_offset,\n crop_w, crop_h)\n cropped_images.append(data_)\n results[k + '_crop_bbox'] = crop_bbox\n if not is_list:\n cropped_images = cropped_images[0]\n results[k] = cropped_images\n results['crop_size'] = self.crop_size\n results['crop_pos'] = self.crop_pos\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'keys={self.keys}, crop_size={self.crop_size}, '\n f'crop_pos={self.crop_pos}')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass ModCrop(BaseTransform):\n \"\"\"Mod crop images, used during testing.\n\n Required keys are \"scale\" and \"KEY\",\n added or modified keys are \"KEY\".\n\n Args:\n key (str): The key of image. Default: 'gt'\n \"\"\"\n\n def __init__(self, key='gt') -> None:\n super().__init__()\n\n self.key = key\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n img = results[self.key].copy()\n scale = results['scale']\n if img.ndim in [2, 3]:\n h, w = img.shape[0], img.shape[1]\n h_remainder, w_remainder = h % scale, w % scale\n img = img[:h - h_remainder, :w - w_remainder, ...]\n else:\n raise ValueError(f'Wrong img ndim: {img.ndim}.')\n results[self.key] = img\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += f'(key={self.key})'\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass PairedRandomCrop(BaseTransform):\n \"\"\"Paired random crop.\n\n It crops a pair of img and gt images with corresponding locations.\n It also supports accepting img list and gt list.\n Required keys are \"scale\", \"lq_key\", and \"gt_key\",\n added or modified keys are \"lq_key\" and \"gt_key\".\n\n Args:\n gt_patch_size (int): cropped gt patch size.\n lq_key (str): Key of LQ img. Default: 'img'.\n gt_key (str): Key of GT img. Default: 'gt'.\n \"\"\"\n\n def __init__(self, gt_patch_size, lq_key='img', gt_key='gt'):\n\n self.gt_patch_size = gt_patch_size\n self.lq_key = lq_key\n self.gt_key = gt_key\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n scale = results['scale']\n lq_patch_size = self.gt_patch_size // scale\n\n lq_is_list = isinstance(results[self.lq_key], list)\n if not lq_is_list:\n results[self.lq_key] = [results[self.lq_key]]\n gt_is_list = isinstance(results[self.gt_key], list)\n if not gt_is_list:\n results[self.gt_key] = [results[self.gt_key]]\n\n h_lq, w_lq, _ = results[self.lq_key][0].shape\n h_gt, w_gt, _ = results[self.gt_key][0].shape\n\n if h_gt != h_lq * scale or w_gt != w_lq * scale:\n raise ValueError(\n f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x '\n f'multiplication of LQ ({h_lq}, {w_lq}).')\n if h_lq < lq_patch_size or w_lq < lq_patch_size:\n raise ValueError(\n f'LQ ({h_lq}, {w_lq}) is smaller than patch size '\n f'({lq_patch_size}, {lq_patch_size}). Please check '\n f'{results[f\"{self.lq_key}_path\"]} and '\n f'{results[f\"{self.gt_key}_path\"]}.')\n\n # randomly choose top and left coordinates for img patch\n top = np.random.randint(h_lq - lq_patch_size + 1)\n left = np.random.randint(w_lq - lq_patch_size + 1)\n # crop img patch\n results[self.lq_key] = [\n v[top:top + lq_patch_size, left:left + lq_patch_size, ...]\n for v in results[self.lq_key]\n ]\n # crop corresponding gt patch\n top_gt, left_gt = int(top * scale), int(left * scale)\n results[self.gt_key] = [\n v[top_gt:top_gt + self.gt_patch_size,\n left_gt:left_gt + self.gt_patch_size, ...]\n for v in results[self.gt_key]\n ]\n\n if not lq_is_list:\n results[self.lq_key] = results[self.lq_key][0]\n if not gt_is_list:\n results[self.gt_key] = results[self.gt_key][0]\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(gt_patch_size={self.gt_patch_size}, '\n f'lq_key={self.lq_key}, '\n f'gt_key={self.gt_key})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomResizedCrop(BaseTransform):\n \"\"\"Crop data to random size and aspect ratio.\n\n A crop of a random proportion of the original image\n and a random aspect ratio of the original aspect ratio is made.\n The cropped image is finally resized to a given size specified\n by 'crop_size'. Modified keys are the attributes specified in \"keys\".\n\n This code is partially adopted from\n torchvision.transforms.RandomResizedCrop:\n [https://pytorch.org/vision/stable/_modules/torchvision/transforms/\\\n transforms.html#RandomResizedCrop].\n\n Args:\n keys (list[str]): The images to be resized and random-cropped.\n crop_size (int | tuple[int]): Target spatial size (h, w).\n scale (tuple[float], optional): Range of the proportion of the original\n image to be cropped. Default: (0.08, 1.0).\n ratio (tuple[float], optional): Range of aspect ratio of the crop.\n Default: (3. / 4., 4. / 3.).\n interpolation (str, optional): Algorithm used for interpolation.\n It can be only either one of the following:\n \"nearest\" | \"bilinear\" | \"bicubic\" | \"area\" | \"lanczos\".\n Default: \"bilinear\".\n \"\"\"\n\n def __init__(self,\n keys,\n crop_size,\n scale=(0.08, 1.0),\n ratio=(3. / 4., 4. / 3.),\n interpolation='bilinear'):\n\n assert keys, 'Keys should not be empty.'\n if isinstance(crop_size, int):\n crop_size = (crop_size, crop_size)\n elif not is_tuple_of(crop_size, int):\n raise TypeError('\"crop_size\" must be an integer '\n 'or a tuple of integers, but got '\n f'{type(crop_size)}')\n if not is_tuple_of(scale, float):\n raise TypeError('\"scale\" must be a tuple of float, '\n f'but got {type(scale)}')\n if not is_tuple_of(ratio, float):\n raise TypeError('\"ratio\" must be a tuple of float, '\n f'but got {type(ratio)}')\n\n self.keys = keys\n self.crop_size = crop_size\n self.scale = scale\n self.ratio = ratio\n self.interpolation = interpolation\n\n def get_params(self, data):\n \"\"\"Get parameters for a random sized crop.\n\n Args:\n data (np.ndarray): Image of type numpy array to be cropped.\n\n Returns:\n A tuple containing the coordinates of the top left corner\n and the chosen crop size.\n \"\"\"\n\n data_h, data_w = data.shape[:2]\n area = data_h * data_w\n\n for _ in range(10):\n target_area = random.uniform(*self.scale) * area\n log_ratio = (math.log(self.ratio[0]), math.log(self.ratio[1]))\n aspect_ratio = math.exp(random.uniform(*log_ratio))\n\n crop_w = int(round(math.sqrt(target_area * aspect_ratio)))\n crop_h = int(round(math.sqrt(target_area / aspect_ratio)))\n\n if 0 < crop_w <= data_w and 0 < crop_h <= data_h:\n top = random.randint(0, data_h - crop_h)\n left = random.randint(0, data_w - crop_w)\n return top, left, crop_h, crop_w\n\n # Fall back to center crop\n in_ratio = float(data_w) / float(data_h)\n if (in_ratio < min(self.ratio)):\n crop_w = data_w\n crop_h = int(round(crop_w / min(self.ratio)))\n elif (in_ratio > max(self.ratio)):\n crop_h = data_h\n crop_w = int(round(crop_h * max(self.ratio)))\n else: # whole image\n crop_w = data_w\n crop_h = data_h\n top = (data_h - crop_h) // 2\n left = (data_w - crop_w) // 2\n\n return top, left, crop_h, crop_w\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for k in self.keys:\n top, left, crop_h, crop_w = self.get_params(results[k])\n crop_bbox = [top, left, crop_w, crop_h]\n results[k] = results[k][top:top + crop_h, left:left + crop_w, ...]\n results[k] = mmcv.imresize(\n results[k],\n self.crop_size,\n return_scale=False,\n interpolation=self.interpolation)\n results[k + '_crop_bbox'] = crop_bbox\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, crop_size={self.crop_size}, '\n f'scale={self.scale}, ratio={self.ratio}, '\n f'interpolation={self.interpolation})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass CropAroundCenter(BaseTransform):\n \"\"\"Randomly crop the images around unknown area in the center 1/4 images.\n\n This cropping strategy is adopted in GCA matting. The `unknown area` is the\n same as `semi-transparent area`.\n https://arxiv.org/pdf/2001.04069.pdf\n\n It retains the center 1/4 images and resizes the images to 'crop_size'.\n Required keys are \"fg\", \"bg\", \"trimap\" and \"alpha\", added or modified keys\n are \"crop_bbox\", \"fg\", \"bg\", \"trimap\" and \"alpha\".\n\n Args:\n crop_size (int | tuple): Desired output size. If int, square crop is\n applied.\n \"\"\"\n\n def __init__(self, crop_size):\n if is_tuple_of(crop_size, int):\n assert len(crop_size) == 2, 'length of crop_size must be 2.'\n elif not isinstance(crop_size, int):\n raise TypeError('crop_size must be int or a tuple of int, but got '\n f'{type(crop_size)}')\n self.crop_size = _pair(crop_size)\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n fg = results['fg']\n alpha = results['alpha']\n trimap = results['trimap']\n bg = results['bg']\n h, w = fg.shape[:2]\n assert bg.shape == fg.shape, (f'shape of bg {bg.shape} should be the '\n f'same as fg {fg.shape}.')\n\n crop_h, crop_w = self.crop_size\n # Make sure h >= crop_h, w >= crop_w. If not, rescale imgs\n rescale_ratio = max(crop_h / h, crop_w / w)\n if rescale_ratio > 1:\n assert alpha.ndim == trimap.ndim\n ext_dim = (alpha.ndim == 3)\n\n new_h = max(int(h * rescale_ratio), crop_h)\n new_w = max(int(w * rescale_ratio), crop_w)\n fg = mmcv.imresize(fg, (new_w, new_h), interpolation='nearest')\n alpha = mmcv.imresize(\n alpha, (new_w, new_h), interpolation='nearest')\n trimap = mmcv.imresize(\n trimap, (new_w, new_h), interpolation='nearest')\n bg = mmcv.imresize(bg, (new_w, new_h), interpolation='bicubic')\n h, w = new_h, new_w\n\n if ext_dim:\n # mmcv.imresize will squeeze\n alpha = alpha[..., None]\n trimap = trimap[..., None]\n\n # resize to 1/4 to ignore small unknown patches\n small_trimap = mmcv.imresize(\n trimap, (w // 4, h // 4), interpolation='nearest')\n assert small_trimap.ndim == 2\n # find unknown area in center 1/4 region\n margin_h, margin_w = crop_h // 2, crop_w // 2\n sample_area = small_trimap[margin_h // 4:(h - margin_h) // 4,\n margin_w // 4:(w - margin_w) // 4]\n unknown_xs, unknown_ys = np.where(sample_area == 128)\n unknown_num = len(unknown_xs)\n if unknown_num < 10:\n # too few unknown area in the center, crop from the whole image\n top = np.random.randint(0, h - crop_h + 1)\n left = np.random.randint(0, w - crop_w + 1)\n else:\n idx = np.random.randint(unknown_num)\n top = unknown_xs[idx] * 4\n left = unknown_ys[idx] * 4\n bottom = top + crop_h\n right = left + crop_w\n\n results['fg'] = fg[top:bottom, left:right]\n results['alpha'] = alpha[top:bottom, left:right]\n results['trimap'] = trimap[top:bottom, left:right]\n results['bg'] = bg[top:bottom, left:right]\n results['crop_bbox'] = (left, top, right, bottom)\n\n return results\n\n def __repr__(self):\n\n return self.__class__.__name__ + f'(crop_size={self.crop_size})'\n\n\n@TRANSFORMS.register_module()\nclass CropAroundFg(BaseTransform):\n \"\"\"Crop around the whole foreground in the segmentation mask.\n\n Required keys are \"seg\" and the keys in argument `keys`.\n Meanwhile, \"seg\" must be in argument `keys`. Added or modified keys are\n \"crop_bbox\" and the keys in argument `keys`.\n\n Args:\n keys (Sequence[str]): The images to be cropped. It must contain\n 'seg'.\n bd_ratio_range (tuple, optional): The range of the boundary (bd) ratio\n to select from. The boundary ratio is the ratio of the boundary to\n the minimal bbox that contains the whole foreground given by\n segmentation. Default to (0.1, 0.4).\n test_mode (bool): Whether use test mode. In test mode, the tight crop\n area of foreground will be extended to the a square.\n Default to False.\n \"\"\"\n\n def __init__(self, keys, bd_ratio_range=(0.1, 0.4), test_mode=False):\n\n if 'seg' not in keys:\n raise ValueError(f'\"seg\" must be in keys, but got {keys}')\n if (not is_tuple_of(bd_ratio_range, float)\n or len(bd_ratio_range) != 2):\n raise TypeError('bd_ratio_range must be a tuple of 2 int, but got '\n f'{bd_ratio_range}')\n self.keys = keys\n self.bd_ratio_range = bd_ratio_range\n self.test_mode = test_mode\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n seg = results['seg']\n height, width = seg.shape[:2]\n\n # get foreground bbox\n fg_coor = np.array(np.where(seg))\n top, left = np.amin(fg_coor, axis=1)\n bottom, right = np.amax(fg_coor, axis=1)\n\n # enlarge bbox\n long_side = np.maximum(bottom - top, right - left)\n if self.test_mode:\n bottom = top + long_side\n right = left + long_side\n boundary_ratio = np.random.uniform(*self.bd_ratio_range)\n boundary = int(np.round(boundary_ratio * long_side))\n # NOTE: Different from the original repo, we keep track of the four\n # corners of the bbox (left, top, right, bottom) while the original\n # repo use (top, left, height, width) to represent bbox. This may\n # introduce an difference of 1 pixel.\n top = max(top - boundary, 0)\n left = max(left - boundary, 0)\n bottom = min(bottom + boundary, height)\n right = min(right + boundary, width)\n\n for key in self.keys:\n results[key] = results[key][top:bottom, left:right]\n results['crop_bbox'] = (left, top, right, bottom)\n\n return results\n\n\n@TRANSFORMS.register_module()\nclass CropAroundUnknown(BaseTransform):\n \"\"\"Crop around unknown area with a randomly selected scale.\n\n Randomly select the w and h from a list of (w, h).\n Required keys are the keys in argument `keys`, added or\n modified keys are \"crop_bbox\" and the keys in argument `keys`.\n This class assumes value of \"alpha\" ranges from 0 to 255.\n\n Args:\n keys (Sequence[str]): The images to be cropped. It must contain\n 'alpha'. If unknown_source is set to 'trimap', then it must also\n contain 'trimap'.\n crop_sizes (list[int | tuple[int]]): List of (w, h) to be selected.\n unknown_source (str, optional): Unknown area to select from. It must be\n 'alpha' or 'trimap'. Default to 'alpha'.\n interpolations (str | list[str], optional): Interpolation method of\n mmcv.imresize. The interpolation operation will be applied when\n image size is smaller than the crop_size. If given as a list of\n str, it should have the same length as `keys`. Or if given as a\n str all the keys will be resized with the same method.\n Default to 'bilinear'.\n \"\"\"\n\n def __init__(self,\n keys,\n crop_sizes,\n unknown_source='alpha',\n interpolations='bilinear'):\n if 'alpha' not in keys:\n raise ValueError(f'\"alpha\" must be in keys, but got {keys}')\n self.keys = keys\n\n if not isinstance(crop_sizes, list):\n raise TypeError(\n f'Crop sizes must be list, but got {type(crop_sizes)}.')\n self.crop_sizes = [_pair(crop_size) for crop_size in crop_sizes]\n if not is_tuple_of(self.crop_sizes[0], int):\n raise TypeError('Elements of crop_sizes must be int or tuple of '\n f'int, but got {type(self.crop_sizes[0][0])}.')\n\n if unknown_source not in ['alpha', 'trimap']:\n raise ValueError('unknown_source must be \"alpha\" or \"trimap\", '\n f'but got {unknown_source}')\n if unknown_source not in keys:\n # it could only be trimap, since alpha is checked before\n raise ValueError(\n 'if unknown_source is \"trimap\", it must also be set in keys')\n self.unknown_source = unknown_source\n\n if isinstance(interpolations, str):\n self.interpolations = [interpolations] * len(self.keys)\n elif is_list_of(interpolations, str) and len(interpolations) == len(\n self.keys):\n self.interpolations = interpolations\n else:\n raise TypeError(\n 'interpolations must be a str or list of str with '\n f'the same length as keys, but got {interpolations}')\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n h, w = results[self.keys[0]].shape[:2]\n\n rand_ind = np.random.randint(len(self.crop_sizes))\n crop_h, crop_w = self.crop_sizes[rand_ind]\n\n # Make sure h >= crop_h, w >= crop_w. If not, rescale imgs\n rescale_ratio = max(crop_h / h, crop_w / w)\n if rescale_ratio > 1:\n\n h = max(int(h * rescale_ratio), crop_h)\n w = max(int(w * rescale_ratio), crop_w)\n for key, interpolation in zip(self.keys, self.interpolations):\n ext_dim = (results[key].ndim == 3) and (results[key].shape[-1]\n == 1)\n results[key] = mmcv.imresize(\n results[key], (w, h), interpolation=interpolation)\n if ext_dim:\n results[key] = results[key][..., None]\n\n # Select the cropping top-left point which is an unknown pixel\n if self.unknown_source == 'alpha':\n unknown = (results['alpha'] > 0) & (results['alpha'] < 255)\n else:\n unknown = results['trimap'] == 128\n top, left = random_choose_unknown(unknown.squeeze(), (crop_h, crop_w))\n\n bottom = top + crop_h\n right = left + crop_w\n\n for key in self.keys:\n results[key] = results[key][top:bottom, left:right]\n results['crop_bbox'] = (left, top, right, bottom)\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, crop_sizes={self.crop_sizes}, '\n f\"unknown_source='{self.unknown_source}', \"\n f'interpolations={self.interpolations})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomCropLongEdge(BaseTransform):\n \"\"\"Random crop the given image by the long edge.\n\n Args:\n keys (list[str]): The images to be cropped.\n \"\"\"\n\n def __init__(self, keys='img'):\n assert keys, 'Keys should not be empty.'\n if not isinstance(keys, list):\n keys = [keys]\n self.keys = keys\n\n def transform(self, results):\n \"\"\"Call function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for key in self.keys:\n img = results[key]\n img_height, img_width = img.shape[:2]\n crop_size = min(img_height, img_width)\n y1 = 0 if img_height == crop_size else \\\n np.random.randint(0, img_height - crop_size)\n x1 = 0 if img_width == crop_size else \\\n np.random.randint(0, img_width - crop_size)\n y2, x2 = y1 + crop_size - 1, x1 + crop_size - 1\n\n img = mmcv.imcrop(img, bboxes=np.array([x1, y1, x2, y2]))\n results[key] = img\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass CenterCropLongEdge(BaseTransform):\n \"\"\"Center crop the given image by the long edge.\n\n Args:\n keys (list[str]): The images to be cropped.\n \"\"\"\n\n def __init__(self, keys='img'):\n assert keys, 'Keys should not be empty.'\n if not isinstance(keys, list):\n keys = [keys]\n self.keys = keys\n\n def transform(self, results):\n \"\"\"Call function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n for key in self.keys:\n img = results[key]\n img_height, img_width = img.shape[:2]\n crop_size = min(img_height, img_width)\n y1 = 0 if img_height == crop_size else \\\n int(round(img_height - crop_size) / 2)\n x1 = 0 if img_width == crop_size else \\\n int(round(img_width - crop_size) / 2)\n y2 = y1 + crop_size - 1\n x2 = x1 + crop_size - 1\n\n img = mmcv.imcrop(img, bboxes=np.array([x1, y1, x2, y2]))\n results[key] = img\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass InstanceCrop(BaseTransform):\n \"\"\"Use maskrcnn to detect instances on image.\n\n Mask R-CNN is used to detect the instance on the image\n pred_bbox is used to segment the instance on the image\n\n Args:\n config_file (str): config file name relative to detectron2's \"configs/\"\n key (str): Unused\n box_num_upbound (int):The upper limit on the number of instances\n in the figure\n \"\"\"\n\n def __init__(self,\n config_file,\n from_pretrained=None,\n key='img',\n box_num_upbound=-1,\n finesize=256):\n\n assert mmdet_apis is not None, (\n \"Cannot import 'mmdet'. Please install 'mmdet' via \"\n \"\\\"mim install 'mmdet >= 3.0.0'\\\".\")\n\n cfg = get_config(config_file, pretrained=True)\n\n # loading checkpoint from local path\n if from_pretrained is not None:\n cfg.model.backbone.init_cfg.checkpoint = from_pretrained\n\n with DefaultScope.overwrite_default_scope('mmdet'):\n self.predictor = mmdet_apis.init_detector(cfg, cfg.model_path)\n\n self.key = key\n self.box_num_upbound = box_num_upbound\n self.final_size = finesize\n\n def transform(self, results: dict) -> dict:\n \"\"\"The transform function of InstanceCrop.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for Conversion\n\n Returns:\n results (dict): A dict containing the processed data\n and information.\n \"\"\"\n # get consistent box prediction based on L channel\n\n full_img = results['img']\n full_img_size = results['ori_img_shape'][:-1][::-1]\n pred_bbox, pred_scores = self.predict_bbox(full_img)\n\n if self.box_num_upbound > 0 and pred_bbox.shape[\n 0] > self.box_num_upbound:\n index_mask = np.argsort(pred_scores, axis=0)\n index_mask = index_mask[pred_scores.shape[0] -\n self.box_num_upbound:pred_scores.shape[0]]\n pred_bbox = pred_bbox[index_mask]\n\n # get cropped images and box info\n cropped_img_list = []\n index_list = range(len(pred_bbox))\n box_info, box_info_2x, box_info_4x, box_info_8x = np.zeros(\n (4, len(index_list), 6))\n for i in index_list:\n startx, starty, endx, endy = pred_bbox[i]\n cropped_img = full_img[starty:endy, startx:endx, :]\n cropped_img_list.append(cropped_img)\n box_info[i] = np.array(\n get_box_info(pred_bbox[i], full_img_size, self.final_size))\n box_info_2x[i] = np.array(\n get_box_info(pred_bbox[i], full_img_size,\n self.final_size // 2))\n box_info_4x[i] = np.array(\n get_box_info(pred_bbox[i], full_img_size,\n self.final_size // 4))\n box_info_8x[i] = np.array(\n get_box_info(pred_bbox[i], full_img_size,\n self.final_size // 8))\n\n # update results\n if len(pred_bbox) > 0:\n results['cropped_img'] = cropped_img_list\n results['box_info'] = torch.from_numpy(box_info).type(torch.long)\n results['box_info_2x'] = torch.from_numpy(box_info_2x).type(\n torch.long)\n results['box_info_4x'] = torch.from_numpy(box_info_4x).type(\n torch.long)\n results['box_info_8x'] = torch.from_numpy(box_info_8x).type(\n torch.long)\n results['empty_box'] = False\n else:\n results['empty_box'] = True\n return results\n\n def predict_bbox(self, image):\n lab_image = cv.cvtColor(image, cv.COLOR_BGR2LAB)\n l_channel, _, _ = cv.split(lab_image)\n l_stack = np.stack([l_channel, l_channel, l_channel], axis=2)\n\n with DefaultScope.overwrite_default_scope('mmdet'):\n with torch.no_grad():\n results = mmdet_apis.inference_detector(\n self.predictor, l_stack)\n\n bboxes = results.pred_instances.bboxes.cpu().numpy().astype(np.int32)\n scores = results.pred_instances.scores.cpu().numpy()\n index_mask = [i for i, x in enumerate(scores) if x >= 0.7]\n scores = np.array(scores[index_mask])\n bboxes = np.array(bboxes[index_mask])\n return bboxes, scores\n" }, { "path": "mmagic/datasets/transforms/fgbg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Augmentation on foreground and background.\"\"\"\n\nimport numbers\nimport os.path as osp\n\nimport mmcv\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.utils import add_gaussian_noise, adjust_gamma\n\n\n@TRANSFORMS.register_module()\nclass CompositeFg(BaseTransform):\n \"\"\"Composite foreground with a random foreground.\n\n This class composites the current training sample with additional data\n randomly (could be from the same dataset). With probability 0.5, the sample\n will be composited with a random sample from the specified directory.\n The composition is performed as:\n\n .. math::\n fg_{new} = \\\\alpha_1 * fg_1 + (1 - \\\\alpha_1) * fg_2\n\n \\\\alpha_{new} = 1 - (1 - \\\\alpha_1) * (1 - \\\\alpha_2)\n\n where :math:`(fg_1, \\\\alpha_1)` is from the current sample and\n :math:`(fg_2, \\\\alpha_2)` is the randomly loaded sample. With the above\n composition, :math:`\\\\alpha_{new}` is still in `[0, 1]`.\n\n Required keys are \"alpha\" and \"fg\". Modified keys are \"alpha\" and \"fg\".\n\n Args:\n fg_dirs (str | list[str]): Path of directories to load foreground\n images from.\n alpha_dirs (str | list[str]): Path of directories to load alpha mattes\n from.\n interpolation (str): Interpolation method of `mmcv.imresize` to resize\n the randomly loaded images. Default: 'nearest'.\n \"\"\"\n\n def __init__(self, fg_dirs, alpha_dirs, interpolation='nearest'):\n # TODO try fetch the path from dataset\n self.fg_dirs = fg_dirs if isinstance(fg_dirs, list) else [fg_dirs]\n self.alpha_dirs = alpha_dirs if isinstance(alpha_dirs,\n list) else [alpha_dirs]\n self.interpolation = interpolation\n\n self.file_backend = get_file_backend(uri=fg_dirs[0])\n\n self.fg_list, self.alpha_list = self._get_file_list(\n self.fg_dirs, self.alpha_dirs)\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n fg = results['fg']\n alpha = results['alpha'] / 255.0 # float64, H, W, 1\n h, w = results['fg'].shape[:2]\n\n # randomly select fg\n if np.random.rand() < 0.5:\n idx = np.random.randint(len(self.fg_list))\n fg2_bytes = self.file_backend.get(self.fg_list[idx])\n fg2 = mmcv.imfrombytes(fg2_bytes)\n alpha2_bytes = self.file_backend.get(self.alpha_list[idx])\n alpha2 = mmcv.imfrombytes(alpha2_bytes, flag='grayscale')\n alpha2 = alpha2 / 255.0 # float64\n fg2 = mmcv.imresize(fg2, (w, h), interpolation=self.interpolation)\n alpha2 = mmcv.imresize(\n alpha2, (w, h), interpolation=self.interpolation)\n alpha2 = alpha2[..., None]\n\n # the overlap of two 50% transparency will be 75%\n alpha_tmp = 1 - (1 - alpha) * (1 - alpha2)\n # if the result alpha is all-one, then we avoid composition\n if np.any(alpha_tmp < 1):\n # composite fg with fg2\n fg = fg * alpha + fg2 * (1 - alpha)\n alpha = alpha_tmp\n\n results['fg'] = fg\n results['alpha'] = alpha * 255\n return results\n\n def _get_file_list(self, fg_dirs, alpha_dirs):\n all_fg_list = list()\n all_alpha_list = list()\n for fg_dir, alpha_dir in zip(fg_dirs, alpha_dirs):\n fg_list = sorted(\n self.file_backend.list_dir_or_file(fg_dir, list_dir=False))\n alpha_list = sorted(\n self.file_backend.list_dir_or_file(alpha_dir, list_dir=False))\n # we assume the file names for fg and alpha are the same\n assert len(fg_list) == len(alpha_list), (\n f'{fg_dir} and {alpha_dir} should have the same number of '\n f'images ({len(fg_list)} differs from ({len(alpha_list)})')\n fg_list = [osp.join(fg_dir, fg) for fg in fg_list]\n alpha_list = [osp.join(alpha_dir, alpha) for alpha in alpha_list]\n\n all_fg_list.extend(fg_list)\n all_alpha_list.extend(alpha_list)\n return all_fg_list, all_alpha_list\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(fg_dirs={repr(self.fg_dirs)}, '\n f'alpha_dirs={repr(self.alpha_dirs)}, '\n f'interpolation={repr(self.interpolation)})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass MergeFgAndBg(BaseTransform):\n \"\"\"Composite foreground image and background image with alpha.\n\n Required keys are \"alpha\", \"fg\" and \"bg\", added key is \"merged\".\n \"\"\"\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n alpha = results['alpha'].astype(np.float32) / 255.\n fg = results['fg']\n bg = results['bg']\n merged = fg * alpha + (1. - alpha) * bg\n results['merged'] = merged\n return results\n\n def __repr__(self) -> str:\n repr_str = f'{self.__class__.__name__}()'\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass PerturbBg(BaseTransform):\n \"\"\"Randomly add gaussian noise or gamma change to background image.\n\n Required key is \"bg\", added key is \"noisy_bg\".\n\n Args:\n gamma_ratio (float, optional): The probability to use gamma correction\n instead of gaussian noise. Defaults to 0.6.\n \"\"\"\n\n def __init__(self, gamma_ratio=0.6):\n if gamma_ratio < 0 or gamma_ratio > 1:\n raise ValueError('gamma_ratio must be a float between [0, 1], '\n f'but got {gamma_ratio}')\n self.gamma_ratio = gamma_ratio\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n if np.random.rand() >= self.gamma_ratio:\n # generate gaussian noise with random gaussian N([-7, 7), [2, 6))\n mu = np.random.randint(-7, 7)\n sigma = np.random.randint(2, 6)\n results['noisy_bg'] = add_gaussian_noise(results['bg'], mu, sigma)\n else:\n # adjust gamma in a range of N(1, 0.12)\n gamma = np.random.normal(1, 0.12)\n results['noisy_bg'] = adjust_gamma(results['bg'], gamma)\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f'(gamma_ratio={self.gamma_ratio})'\n\n\n@TRANSFORMS.register_module()\nclass RandomJitter(BaseTransform):\n \"\"\"Randomly jitter the foreground in hsv space.\n\n The jitter range of hue is adjustable while the jitter ranges of saturation\n and value are adaptive to the images. Side effect: the \"fg\" image will be\n converted to `np.float32`.\n Required keys are \"fg\" and \"alpha\", modified key is \"fg\".\n\n Args:\n hue_range (float | tuple[float]): Range of hue jittering. If it is a\n float instead of a tuple like (min, max), the range of hue\n jittering will be (-hue_range, +hue_range). Default: 40.\n \"\"\"\n\n def __init__(self, hue_range=40):\n\n if isinstance(hue_range, numbers.Number):\n assert hue_range >= 0, ('If hue_range is a single number, '\n 'it must be positive.')\n self.hue_range = (-hue_range, hue_range)\n else:\n assert isinstance(hue_range, tuple) and len(hue_range) == 2, \\\n 'hue_range should be a tuple and it must be of length 2.'\n self.hue_range = hue_range\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n fg, alpha = results['fg'], results['alpha']\n alpha = alpha[:, :, 0]\n\n # convert to HSV space;\n # convert to float32 image to keep precision during space conversion.\n fg = mmcv.bgr2hsv(fg.astype(np.float32) / 255)\n # Hue noise\n hue_jitter = np.random.randint(self.hue_range[0], self.hue_range[1])\n fg[:, :, 0] = np.remainder(fg[:, :, 0] + hue_jitter, 360)\n\n # Saturation noise\n sat_mean = fg[:, :, 1][alpha > 0].mean()\n # jitter saturation within range (1.1 - sat_mean) * [-0.1, 0.1]\n sat_jitter = (1.1 - sat_mean) * (np.random.rand() * 0.2 - 0.1)\n sat = fg[:, :, 1]\n sat = np.abs(sat + sat_jitter)\n sat[sat > 1] = 2 - sat[sat > 1]\n fg[:, :, 1] = sat\n\n # Value noise\n val_mean = fg[:, :, 2][alpha > 0].mean()\n # jitter value within range (1.1 - val_mean) * [-0.1, 0.1]\n val_jitter = (1.1 - val_mean) * (np.random.rand() * 0.2 - 0.1)\n val = fg[:, :, 2]\n val = np.abs(val + val_jitter)\n val[val > 1] = 2 - val[val > 1]\n fg[:, :, 2] = val\n # convert back to BGR space\n fg = mmcv.hsv2bgr(fg)\n results['fg'] = fg * 255\n\n return results\n\n def __repr__(self):\n\n return self.__class__.__name__ + f'hue_range={self.hue_range}'\n\n\n@TRANSFORMS.register_module()\nclass RandomLoadResizeBg(BaseTransform):\n \"\"\"Randomly load a background image and resize it.\n\n Required key is \"fg\", added key is \"bg\".\n\n Args:\n bg_dir (str): Path of directory to load background images from.\n flag (str): Loading flag for images. Default: 'color'.\n channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.\n Default: 'bgr'.\n kwargs (dict): Args for file client.\n \"\"\"\n\n def __init__(self, bg_dir, flag='color', channel_order='bgr'):\n self.bg_dir = bg_dir\n\n self.file_backend = get_file_backend(uri=bg_dir)\n self.bg_list = list(\n self.file_backend.list_dir_or_file(bg_dir, list_dir=False))\n\n self.flag = flag\n self.channel_order = channel_order\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n h, w = results['fg'].shape[:2]\n idx = np.random.randint(len(self.bg_list))\n filepath = f'{self.bg_dir}/{self.bg_list[idx]}'\n img_bytes = self.file_backend.get(filepath)\n img = mmcv.imfrombytes(\n img_bytes, flag=self.flag, channel_order=self.channel_order) # HWC\n bg = mmcv.imresize(img, (w, h), interpolation='bicubic')\n results['bg'] = bg\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f\"(bg_dir='{self.bg_dir}')\"\n" }, { "path": "mmagic/datasets/transforms/formatting.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Tuple\n\nfrom mmcv.transforms.base import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import all_to_tensor\n\n\n@TRANSFORMS.register_module()\nclass PackInputs(BaseTransform):\n \"\"\"Pack data into DataSample for training, evaluation and testing.\n\n MMagic follows the design of data structure from MMEngine.\n Data from the loader will be packed into data field of DataSample.\n More details of DataSample refer to the documentation of MMEngine:\n https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html\n\n Args:\n keys Tuple[List[str], str, None]: The keys to saved in returned\n inputs, which are used as the input of models, default to\n ['img', 'noise', 'merged'].\n data_keys Tuple[List[str], str, None]: The keys to saved in\n `data_field` of the `data_samples`.\n meta_keys Tuple[List[str], str, None]: The meta keys to saved\n in `metainfo` of the `data_samples`. All the other data will\n be packed into the data of the `data_samples`\n \"\"\"\n\n def __init__(\n self,\n keys: Tuple[List[str], str] = ['merged', 'img'],\n meta_keys: Tuple[List[str], str] = [],\n data_keys: Tuple[List[str], str] = [],\n ) -> None:\n\n assert keys is not None, \\\n 'keys in PackInputs can not be None.'\n assert data_keys is not None, \\\n 'data_keys in PackInputs can not be None.'\n assert meta_keys is not None, \\\n 'meta_keys in PackInputs can not be None.'\n\n self.keys = keys if isinstance(keys, List) else [keys]\n self.data_keys = data_keys if isinstance(data_keys,\n List) else [data_keys]\n self.meta_keys = meta_keys if isinstance(meta_keys,\n List) else [meta_keys]\n\n def transform(self, results: dict) -> dict:\n \"\"\"Method to pack the input data.\n\n Args:\n results (dict): Result dict from the data pipeline.\n\n Returns:\n dict: A dict contains\n\n - 'inputs' (obj:`dict`): The forward data of models.\n According to different tasks, the `inputs` may contain images,\n videos, labels, text, etc.\n\n - 'data_samples' (obj:`DataSample`): The annotation info of the\n sample.\n \"\"\"\n\n # prepare inputs\n inputs = dict()\n for k in self.keys:\n value = results.get(k, None)\n if value is not None:\n inputs[k] = all_to_tensor(value)\n\n # return the inputs as tensor, if it has only one item\n if len(inputs.values()) == 1:\n inputs = list(inputs.values())[0]\n\n data_sample = DataSample()\n # prepare metainfo and data in DataSample according to predefined keys\n predefined_data = {\n k: v\n for (k, v) in results.items()\n if k not in (self.data_keys + self.meta_keys)\n }\n data_sample.set_predefined_data(predefined_data)\n\n # prepare metainfo in DataSample according to user-provided meta_keys\n required_metainfo = {\n k: v\n for (k, v) in results.items() if k in self.meta_keys\n }\n data_sample.set_metainfo(required_metainfo)\n\n # prepare metainfo in DataSample according to user-provided data_keys\n required_data = {\n k: v\n for (k, v) in results.items() if k in self.data_keys\n }\n data_sample.set_tensor_data(required_data)\n\n return {'inputs': inputs, 'data_samples': data_sample}\n\n def __repr__(self) -> str:\n\n repr_str = self.__class__.__name__\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/generate_assistant.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nfrom mmcv.transforms.base import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.utils import all_to_tensor, make_coord\n\ntry:\n import face_alignment\n has_face_alignment = True\nexcept ImportError:\n has_face_alignment = False\n\n\n@TRANSFORMS.register_module()\nclass GenerateCoordinateAndCell(BaseTransform):\n \"\"\"Generate coordinate and cell. Generate coordinate from the desired size\n of SR image.\n\n Train or val:\n\n #. Generate coordinate from GT.\n\n #. Reshape GT image to (HgWg, 3) and transpose to (3, HgWg).\n where `Hg` and `Wg` represent the height and width of GT.\n\n Test:\n\n #. Generate coordinate from LQ and scale or target_size.\n\n #. Then generate cell from coordinate.\n\n Args:\n sample_quantity (int | None): The quantity of samples in coordinates.\n To ensure that the GT tensors in a batch have the same dimensions.\n Default: None.\n scale (float): Scale of upsampling.\n Default: None.\n target_size (tuple[int]): Size of target image.\n Default: None.\n reshape_gt (bool): Whether reshape gt to (-1, 3). Default: True\n If sample_quantity is not None, reshape_gt = True.\n\n The priority of getting 'size of target image' is:\n\n #. results['gt'].shape[-2:]\n\n #. results['lq'].shape[-2:] * scale\n\n #. target_size\n \"\"\"\n\n def __init__(self,\n sample_quantity=None,\n scale=None,\n target_size=None,\n reshape_gt=True):\n\n self.sample_quantity = sample_quantity\n self.scale = scale\n self.target_size = target_size\n self.reshape_gt = reshape_gt or sample_quantity is not None\n\n def transform(self, results):\n \"\"\"Call function.\n\n Args:\n results (dict): A dict containing the necessary information\n and data for augmentation.\n Require either in results:\n 1. 'lq' (tensor), whose shape is similar as (3, H, W).\n 2. 'gt' (tensor), whose shape is similar as (3, H, W).\n 3. None, the premise is self.target_size and\n len(self.target_size) >= 2.\n\n Returns:\n dict: A dict containing the processed data and information.\n Reshape 'gt' to (-1, 3) and transpose to (3, -1)\n if 'gt' in results.\n Add 'coord' and 'cell'.\n \"\"\"\n\n # generate hr_coord (and hr_rgb)\n if 'gt' in results:\n crop_hr = results['gt']\n crop_hr = all_to_tensor(crop_hr)\n self.target_size = crop_hr.shape\n if self.reshape_gt:\n hr_rgb = crop_hr.contiguous().view(3, -1).permute(1, 0)\n results['gt'] = hr_rgb\n elif self.scale is not None and 'img' in results:\n _, h_lr, w_lr = results['img'].shape\n self.target_size = (round(h_lr * self.scale),\n round(w_lr * self.scale))\n else:\n assert self.target_size is not None\n assert len(self.target_size) >= 2\n hr_coord = make_coord(self.target_size[-2:])\n\n if self.sample_quantity is not None and 'gt' in results:\n sample_lst = np.random.choice(\n len(hr_coord), self.sample_quantity, replace=False)\n hr_coord = hr_coord[sample_lst]\n results['gt'] = results['gt'][sample_lst]\n\n # Preparations for cell decoding\n cell = torch.ones_like(hr_coord)\n cell[:, 0] *= 2 / self.target_size[-2]\n cell[:, 1] *= 2 / self.target_size[-1]\n\n results['coord'] = hr_coord\n results['cell'] = cell\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(sample_quantity={self.sample_quantity}, '\n f'scale={self.scale}, target_size={self.target_size}, '\n f'reshape_gt={self.reshape_gt})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass GenerateFacialHeatmap(BaseTransform):\n \"\"\"Generate heatmap from keypoint.\n\n Args:\n image_key (str): Key of facial image in dict.\n ori_size (int | Tuple[int]): Original image size of keypoint.\n target_size (int | Tuple[int]): Target size of heatmap.\n sigma (float): Sigma parameter of heatmap. Default: 1.0\n use_cache (bool): If True, load all heatmap at once. Default: True.\n \"\"\"\n\n def __init__(self,\n image_key,\n ori_size,\n target_size,\n sigma=1.0,\n use_cache=True):\n if isinstance(ori_size, int):\n ori_size = (ori_size, ori_size)\n else:\n ori_size = ori_size[:2]\n if isinstance(target_size, int):\n target_size = (target_size, target_size)\n else:\n target_size = target_size[:2]\n self.size_ratio = (target_size[0] / ori_size[0],\n target_size[1] / ori_size[1])\n self.image_key = image_key\n self.sigma = sigma\n self.target_size = target_size\n self.ori_size = ori_size\n self.use_cache = use_cache\n if use_cache:\n self.cache = dict()\n\n assert has_face_alignment, 'please import face-alignment.'\n device = 'cpu'\n self.face_alignment_model = face_alignment.FaceAlignment(\n face_alignment.LandmarksType._2D, device=device, flip_input=False)\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation. Require keypoint.\n\n Returns:\n dict: A dict containing the processed data and information.\n Add 'heatmap'.\n \"\"\"\n\n img = results[self.image_key]\n\n if self.use_cache:\n hash_key = f\"{self.image_key}_{results['key']}\"\n if hash_key in self.cache:\n heatmap = self.cache[hash_key]\n else:\n heatmap = self.generate_heatmap_from_img(img)\n self.cache[hash_key] = heatmap\n else:\n heatmap = self.generate_heatmap_from_img(img)\n\n results[f'{self.image_key}_heatmap'] = heatmap.astype(np.float32)\n\n return results\n\n def generate_heatmap_from_img(self, image):\n \"\"\"Generate heatmap from img.\n\n Args:\n image (np.ndarray): Face image.\n\n results:\n heatmap (np.ndarray): Heatmap the face image.\n \"\"\"\n\n landmark = self._face_alignment_detector(image)\n\n keypoint_list = [(keypoint[0] * self.size_ratio[0],\n keypoint[1] * self.size_ratio[1])\n for keypoint in landmark]\n heatmap_list = [\n self._generate_one_heatmap(keypoint) for keypoint in keypoint_list\n ]\n\n return np.stack(heatmap_list, axis=2)\n\n def _face_alignment_detector(self, image):\n \"\"\"Generate face landmark by face_alignment.\n\n Args:\n image (np.ndarray): Face image.\n\n Returns:\n landmark (Tuple[float]): Location of landmark.\n \"\"\"\n\n faces = self.face_alignment_model.get_landmarks(image)\n index = 0\n max_size = 0\n for i, face in enumerate(faces):\n size = face[8, 1] - face[19, 1]\n if size > max_size:\n max_size = size\n index = i\n landmark = faces[index]\n\n return landmark\n\n def _generate_one_heatmap(self, keypoint):\n \"\"\"Generate One Heatmap.\n\n Args:\n keypoint (Tuple[float]): Location of a landmark.\n\n results:\n heatmap (np.ndarray): A heatmap of landmark.\n \"\"\"\n\n h, w = self.target_size\n\n x_range = np.arange(start=0, stop=w, dtype=int)\n y_range = np.arange(start=0, stop=h, dtype=int)\n grid_x, grid_y = np.meshgrid(x_range, y_range)\n dist2 = (grid_x - keypoint[0])**2 + (grid_y - keypoint[1])**2\n exponent = dist2 / 2.0 / self.sigma / self.sigma\n heatmap = np.exp(-exponent)\n\n return heatmap\n\n def __repr__(self):\n\n return (f'{self.__class__.__name__}'\n f'(image_key={self.image_key}, '\n f'ori_size={self.ori_size}, '\n f'target_size={self.target_size}, '\n f'sigma={self.sigma}, '\n f'use_cache={self.use_cache})')\n" }, { "path": "mmagic/datasets/transforms/generate_frame_indices.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\n\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass GenerateFrameIndices(BaseTransform):\n \"\"\"Generate frame index for REDS datasets. It also performs temporal\n augmentation with random interval.\n\n Required Keys:\n\n - img_path\n - gt_path\n - key\n - num_input_frames\n\n Modified Keys:\n\n - img_path\n - gt_path\n\n Added Keys:\n\n - interval\n - reverse\n\n Args:\n interval_list (list[int]): Interval list for temporal augmentation.\n It will randomly pick an interval from interval_list and sample\n frame index with the interval.\n frames_per_clip(int): Number of frames per clips. Default: 99 for\n REDS dataset.\n \"\"\"\n\n def __init__(self, interval_list, frames_per_clip=99):\n\n self.interval_list = interval_list\n self.frames_per_clip = frames_per_clip\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n clip_name, frame_name = results['key'].split(\n os.sep) # key example: 000/00000000\n center_frame_idx = int(frame_name)\n num_half_frames = results['num_input_frames'] // 2\n\n sequence_length = results.get('sequence_length',\n self.frames_per_clip + 1)\n frames_per_clip = min(self.frames_per_clip, sequence_length - 1)\n\n interval = np.random.choice(self.interval_list)\n # ensure not exceeding the borders\n start_frame_idx = center_frame_idx - num_half_frames * interval\n end_frame_idx = center_frame_idx + num_half_frames * interval\n while (start_frame_idx < 0) or (end_frame_idx > frames_per_clip):\n center_frame_idx = np.random.randint(0, frames_per_clip + 1)\n start_frame_idx = center_frame_idx - num_half_frames * interval\n end_frame_idx = center_frame_idx + num_half_frames * interval\n frame_name = f'{center_frame_idx:08d}'\n neighbor_list = list(\n range(center_frame_idx - num_half_frames * interval,\n center_frame_idx + num_half_frames * interval + 1, interval))\n\n img_path_root = results['img_path']\n gt_path_root = results['gt_path']\n img_path = [\n osp.join(img_path_root, clip_name, f'{v:08d}.png')\n for v in neighbor_list\n ]\n gt_path = [osp.join(gt_path_root, clip_name, f'{frame_name}.png')]\n\n results['img_path'] = img_path\n results['gt_path'] = gt_path\n results['interval'] = interval\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(interval_list={self.interval_list}, '\n f'frames_per_clip={self.frames_per_clip})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass GenerateFrameIndiceswithPadding(BaseTransform):\n \"\"\"Generate frame index with padding for REDS dataset and Vid4 dataset\n during testing.\n\n Required Keys:\n\n - img_path\n - gt_path\n - key\n - num_input_frames\n - sequence_length\n\n Modified Keys:\n\n - img_path\n - gt_path\n\n Args:\n padding (str): padding mode, one of\n 'replicate' | 'reflection' | 'reflection_circle' | 'circle'.\n\n Examples: current_idx = 0, num_input_frames = 5\n The generated frame indices under different padding mode:\n\n replicate: [0, 0, 0, 1, 2]\n reflection: [2, 1, 0, 1, 2]\n reflection_circle: [4, 3, 0, 1, 2]\n circle: [3, 4, 0, 1, 2]\n\n filename_tmpl (str): Template for file name. Default: '{:08d}'.\n \"\"\"\n\n def __init__(self, padding, filename_tmpl='{:08d}'):\n\n if padding not in ('replicate', 'reflection', 'reflection_circle',\n 'circle'):\n raise ValueError(f'Wrong padding mode {padding}.'\n 'Should be \"replicate\", \"reflection\", '\n '\"reflection_circle\", \"circle\"')\n self.padding = padding\n self.filename_tmpl = filename_tmpl\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n clip_name, frame_name = results['key'].split(os.sep)\n current_idx = int(frame_name)\n sequence_length = results['sequence_length'] - 1 # start from 0\n num_input_frames = results['num_input_frames']\n num_pad = num_input_frames // 2\n\n frame_list = []\n for i in range(current_idx - num_pad, current_idx + num_pad + 1):\n if i < 0:\n if self.padding == 'replicate':\n pad_idx = 0\n elif self.padding == 'reflection':\n pad_idx = -i\n elif self.padding == 'reflection_circle':\n pad_idx = current_idx + num_pad - i\n else:\n pad_idx = num_input_frames + i\n elif i > sequence_length:\n if self.padding == 'replicate':\n pad_idx = sequence_length\n elif self.padding == 'reflection':\n pad_idx = sequence_length * 2 - i\n elif self.padding == 'reflection_circle':\n pad_idx = (current_idx - num_pad) - (i - sequence_length)\n else:\n pad_idx = i - num_input_frames\n else:\n pad_idx = i\n frame_list.append(pad_idx)\n\n img_path_root = results['img_path']\n gt_path_root = results['gt_path']\n img_paths = [\n osp.join(img_path_root, clip_name,\n f'{self.filename_tmpl.format(idx)}.png')\n for idx in frame_list\n ]\n gt_paths = [osp.join(gt_path_root, clip_name, f'{frame_name}.png')]\n\n results['img_path'] = img_paths\n results['gt_path'] = gt_paths\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__ + f\"(padding='{self.padding}')\"\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass GenerateSegmentIndices(BaseTransform):\n \"\"\"Generate frame indices for a segment. It also performs temporal\n augmentation with random interval.\n\n Required Keys:\n\n - img_path\n - gt_path\n - key\n - num_input_frames\n - sequence_length\n\n Modified Keys:\n\n - img_path\n - gt_path\n\n Added Keys:\n\n - interval\n - reverse\n\n Args:\n interval_list (list[int]): Interval list for temporal augmentation.\n It will randomly pick an interval from interval_list and sample\n frame index with the interval.\n start_idx (int): The index corresponds to the first frame in the\n sequence. Default: 0.\n filename_tmpl (str): Template for file name. Default: '{:08d}.png'.\n \"\"\"\n\n def __init__(self, interval_list, start_idx=0, filename_tmpl='{:08d}.png'):\n\n self.interval_list = interval_list\n self.filename_tmpl = filename_tmpl\n self.start_idx = start_idx\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n # key example: '000', 'calendar' (sequence name)\n clip_name = results['key']\n interval = np.random.choice(self.interval_list)\n\n self.sequence_length = results['sequence_length']\n num_input_frames = results.get('num_input_frames',\n self.sequence_length)\n if num_input_frames is None:\n num_input_frames = self.sequence_length\n\n # randomly select a frame as start\n if self.sequence_length - num_input_frames * interval < 0:\n raise ValueError('The input sequence is not long enough to '\n 'support the current choice of [interval] or '\n '[num_input_frames].')\n start_frame_idx = np.random.randint(\n 0, self.sequence_length - num_input_frames * interval + 1)\n end_frame_idx = start_frame_idx + num_input_frames * interval\n neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))\n neighbor_list = [v + self.start_idx for v in neighbor_list]\n\n # add the corresponding file paths\n img_path_root = results['img_path']\n gt_path_root = results['gt_path']\n img_path = [\n osp.join(img_path_root, clip_name, self.filename_tmpl.format(v))\n for v in neighbor_list\n ]\n gt_path = [\n osp.join(gt_path_root, clip_name, self.filename_tmpl.format(v))\n for v in neighbor_list\n ]\n\n results['img_path'] = img_path\n results['gt_path'] = gt_path\n results['interval'] = interval\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(interval_list={self.interval_list})')\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/get_masked_image.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nfrom mmcv.transforms.base import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass GetMaskedImage(BaseTransform):\n \"\"\"Get masked image.\n\n Args:\n img_key (str): Key for clean image. Default: 'gt'.\n mask_key (str): Key for mask image. The mask shape should be\n (h, w, 1) while '1' indicate holes and '0' indicate valid\n regions. Default: 'mask'.\n img_key (str): Key for output image. Default: 'img'.\n zero_value (float): Pixel value of masked area.\n \"\"\"\n\n def __init__(self,\n img_key='gt',\n mask_key='mask',\n out_key='img',\n zero_value=127.5):\n self.img_key = img_key\n self.mask_key = mask_key\n self.out_key = out_key\n self.zero_value = zero_value\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n clean_img = results[self.img_key] # uint8\n mask = results[self.mask_key] # uint8\n\n masked_img = clean_img * (1.0 - mask) + self.zero_value * mask\n masked_img = masked_img.astype(np.float32)\n results[self.out_key] = masked_img\n\n # copy metainfo\n if f'ori_{self.img_key}_shape' in results:\n results[f'ori_{self.out_key}_shape'] = deepcopy(\n results[f'ori_{self.img_key}_shape'])\n if f'{self.img_key}_channel_order' in results:\n results[f'{self.out_key}_channel_order'] = deepcopy(\n results[f'{self.img_key}_channel_order'])\n if f'{self.img_key}_color_type' in results:\n results[f'{self.out_key}_color_type'] = deepcopy(\n results[f'{self.img_key}_color_type'])\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + (\n f'(img_key={repr(self.img_key)}, '\n f'mask_key={repr(self.mask_key)}, '\n f'out_key={repr(self.out_key)}, '\n f'zero_value={repr(self.zero_value)})')\n" }, { "path": "mmagic/datasets/transforms/loading.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom typing import List, Optional, Tuple\n\nimport mmcv\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.fileio import get_file_backend, list_from_file\n\nfrom mmagic.registry import TRANSFORMS\nfrom mmagic.utils import (bbox2mask, brush_stroke_mask, get_irregular_mask,\n random_bbox)\n\n\n@TRANSFORMS.register_module()\nclass LoadImageFromFile(BaseTransform):\n \"\"\"Load a single image or image frames from corresponding paths. Required\n Keys:\n - [Key]_path\n\n New Keys:\n - [KEY]\n - ori_[KEY]_shape\n - ori_[KEY]\n\n Args:\n key (str): Keys in results to find corresponding path.\n color_type (str): The flag argument for :func:``mmcv.imfrombytes``.\n Defaults to 'color'.\n channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.\n Default: 'bgr'.\n imdecode_backend (str): The image decoding backend type. The backend\n argument for :func:``mmcv.imfrombytes``.\n See :func:``mmcv.imfrombytes`` for details.\n candidates are 'cv2', 'turbojpeg', 'pillow', and 'tifffile'.\n Defaults to None.\n use_cache (bool): If True, load all images at once. Default: False.\n to_float32 (bool): Whether to convert the loaded image to a float32\n numpy array. If set to False, the loaded image is an uint8 array.\n Defaults to False.\n to_y_channel (bool): Whether to convert the loaded image to y channel.\n Only support 'rgb2ycbcr' and 'rgb2ycbcr'\n Defaults to False.\n backend_args (dict, optional): Arguments to instantiate the prefix of\n uri corresponding backend. Defaults to None.\n \"\"\"\n\n def __init__(\n self,\n key: str,\n color_type: str = 'color',\n channel_order: str = 'bgr',\n imdecode_backend: Optional[str] = None,\n use_cache: bool = False,\n to_float32: bool = False,\n to_y_channel: bool = False,\n save_original_img: bool = False,\n backend_args: Optional[dict] = None,\n ) -> None:\n\n self.key = key\n self.color_type = color_type\n self.channel_order = channel_order\n self.imdecode_backend = imdecode_backend\n self.save_original_img = save_original_img\n\n if backend_args is None:\n # lasy init at loading\n self.backend_args = None\n self.file_backend = None\n else:\n self.backend_args = backend_args.copy()\n self.file_backend = get_file_backend(backend_args=backend_args)\n\n # cache\n self.use_cache = use_cache\n self.cache = dict()\n\n # convert\n self.to_float32 = to_float32\n self.to_y_channel = to_y_channel\n\n def transform(self, results: dict) -> dict:\n \"\"\"Functions to load image or frames.\n\n Args:\n results (dict): Result dict from :obj:``mmcv.BaseDataset``.\n Returns:\n dict: The dict contains loaded image and meta information.\n \"\"\"\n\n filenames = results[f'{self.key}_path']\n\n if not isinstance(filenames, (List, Tuple)):\n filenames = [str(filenames)]\n is_frames = False\n else:\n filenames = [str(v) for v in filenames]\n is_frames = True\n\n images = []\n shapes = []\n if self.save_original_img:\n ori_imgs = []\n\n for filename in filenames:\n img = self._load_image(filename)\n img = self._convert(img)\n images.append(img)\n shapes.append(img.shape)\n if self.save_original_img:\n ori_imgs.append(img.copy())\n\n if not is_frames:\n images = images[0]\n shapes = shapes[0]\n if self.save_original_img:\n ori_imgs = ori_imgs[0]\n\n results[self.key] = images\n results[f'ori_{self.key}_shape'] = shapes\n results[f'{self.key}_channel_order'] = self.channel_order\n results[f'{self.key}_color_type'] = self.color_type\n if self.save_original_img:\n results[f'ori_{self.key}'] = ori_imgs\n\n return results\n\n def _load_image(self, filename):\n \"\"\"Load an image from file.\n\n Args:\n filename (str): Path of image file.\n Returns:\n np.ndarray: Image.\n \"\"\"\n if self.file_backend is None:\n self.file_backend = get_file_backend(\n uri=filename, backend_args=self.backend_args)\n\n if (self.backend_args is not None) and (self.backend_args.get(\n 'backend', None) == 'lmdb'):\n filename, _ = osp.splitext(osp.basename(filename))\n\n if filename in self.cache:\n img_bytes = self.cache[filename]\n else:\n img_bytes = self.file_backend.get(filename)\n if self.use_cache:\n self.cache[filename] = img_bytes\n\n img = mmcv.imfrombytes(\n content=img_bytes,\n flag=self.color_type,\n channel_order=self.channel_order,\n backend=self.imdecode_backend)\n\n return img\n\n def _convert(self, img: np.ndarray):\n \"\"\"Convert an image to the require format.\n\n Args:\n img (np.ndarray): The original image.\n Returns:\n np.ndarray: The converted image.\n \"\"\"\n\n if self.to_y_channel:\n\n if self.channel_order.lower() == 'rgb':\n img = mmcv.rgb2ycbcr(img, y_only=True)\n elif self.channel_order.lower() == 'bgr':\n img = mmcv.bgr2ycbcr(img, y_only=True)\n else:\n raise ValueError('Currently support only \"bgr2ycbcr\" or '\n '\"bgr2ycbcr\".')\n\n if img.ndim == 2:\n img = np.expand_dims(img, axis=2)\n\n if self.to_float32:\n img = img.astype(np.float32)\n\n return img\n\n def __repr__(self):\n\n repr_str = (f'{self.__class__.__name__}('\n f'key={self.key}, '\n f'color_type={self.color_type}, '\n f'channel_order={self.channel_order}, '\n f'imdecode_backend={self.imdecode_backend}, '\n f'use_cache={self.use_cache}, '\n f'to_float32={self.to_float32}, '\n f'to_y_channel={self.to_y_channel}, '\n f'save_original_img={self.save_original_img}, '\n f'backend_args={self.backend_args})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass LoadMask(BaseTransform):\n \"\"\"Load Mask for multiple types.\n\n For different types of mask, users need to provide the corresponding\n config dict.\n\n Example config for bbox:\n\n .. code-block:: python\n\n config = dict(img_shape=(256, 256), max_bbox_shape=128)\n\n Example config for irregular:\n\n .. code-block:: python\n\n config = dict(\n img_shape=(256, 256),\n num_vertices=(4, 12),\n max_angle=4.,\n length_range=(10, 100),\n brush_width=(10, 40),\n area_ratio_range=(0.15, 0.5))\n\n Example config for ff:\n\n .. code-block:: python\n\n config = dict(\n img_shape=(256, 256),\n num_vertices=(4, 12),\n mean_angle=1.2,\n angle_range=0.4,\n brush_width=(12, 40))\n\n Example config for set:\n\n .. code-block:: python\n\n config = dict(\n mask_list_file='xxx/xxx/ooxx.txt',\n prefix='/xxx/xxx/ooxx/',\n io_backend='local',\n color_type='unchanged',\n file_client_kwargs=dict()\n )\n\n The mask_list_file contains the list of mask file name like this:\n test1.jpeg\n test2.jpeg\n ...\n ...\n\n The prefix gives the data path.\n\n Args:\n mask_mode (str): Mask mode in ['bbox', 'irregular', 'ff', 'set',\n 'file']. Default: 'bbox'.\n * bbox: square bounding box masks.\n * irregular: irregular holes.\n * ff: free-form holes from DeepFillv2.\n * set: randomly get a mask from a mask set.\n * file: get mask from 'mask_path' in results.\n mask_config (dict): Params for creating masks. Each type of mask needs\n different configs. Default: None.\n \"\"\"\n\n def __init__(self, mask_mode='bbox', mask_config=None):\n self.mask_mode = mask_mode\n self.mask_config = dict() if mask_config is None else mask_config\n assert isinstance(self.mask_config, dict)\n\n # set init info if needed in some modes\n self._init_info()\n\n def _init_info(self):\n if self.mask_mode == 'set':\n # get mask list information\n self.io_backend = self.mask_config['io_backend']\n self.color_type = self.mask_config['color_type']\n self.file_prefix = self.mask_config['prefix']\n self.file_client_kwargs = self.mask_config['file_client_kwargs']\n self.file_backend = None\n\n mask_list_file = self.mask_config['mask_list_file']\n self.mask_list = list_from_file(\n mask_list_file, backend_args=self.file_client_kwargs)\n self.mask_list = [\n osp.join(self.file_prefix, i) for i in self.mask_list\n ]\n self.mask_set_size = len(self.mask_list)\n elif self.mask_mode == 'file':\n self.io_backend = 'local'\n self.color_type = 'unchanged'\n self.file_client_kwargs = dict()\n self.file_backend = None\n\n def _get_random_mask_from_set(self):\n if self.file_backend is None:\n self.file_backend = get_file_backend(\n backend_args={'backend': self.io_backend})\n # minus 1 to avoid out of range error\n mask_idx = np.random.randint(0, self.mask_set_size)\n mask_bytes = self.file_backend.get(self.mask_list[mask_idx])\n mask = mmcv.imfrombytes(mask_bytes, flag=self.color_type) # HWC, BGR\n if mask.ndim == 2:\n mask = np.expand_dims(mask, axis=2)\n else:\n mask = mask[:, :, 0:1]\n\n mask[mask > 0] = 1.\n return mask\n\n def _get_mask_from_file(self, path):\n if self.file_backend is None:\n backend_args = self.file_client_kwargs.copy()\n backend_args['backend'] = self.io_backend\n self.file_backend = get_file_backend(backend_args=backend_args)\n mask_bytes = self.file_backend.get(path)\n mask = mmcv.imfrombytes(mask_bytes, flag=self.color_type) # HWC, BGR\n if mask.ndim == 2:\n mask = np.expand_dims(mask, axis=2)\n else:\n mask = mask[:, :, 0:1]\n\n mask[mask > 0] = 1.\n return mask\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n if self.mask_mode == 'bbox':\n mask_bbox = random_bbox(**self.mask_config)\n mask = bbox2mask(self.mask_config['img_shape'], mask_bbox)\n results['mask_bbox'] = mask_bbox\n elif self.mask_mode == 'irregular':\n mask = get_irregular_mask(**self.mask_config)\n elif self.mask_mode == 'set':\n mask = self._get_random_mask_from_set()\n elif self.mask_mode == 'ff':\n mask = brush_stroke_mask(**self.mask_config)\n elif self.mask_mode == 'file':\n mask = self._get_mask_from_file(results['mask_path'])\n else:\n raise NotImplementedError(\n f'Mask mode {self.mask_mode} has not been implemented.')\n results['mask'] = mask\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f\"(mask_mode='{self.mask_mode}')\"\n\n\n@TRANSFORMS.register_module()\nclass GetSpatialDiscountMask(BaseTransform):\n \"\"\"Get spatial discounting mask constant.\n\n Spatial discounting mask is first introduced in:\n Generative Image Inpainting with Contextual Attention.\n\n Args:\n gamma (float, optional): Gamma for computing spatial discounting.\n Defaults to 0.99.\n beta (float, optional): Beta for computing spatial discounting.\n Defaults to 1.5.\n \"\"\"\n\n def __init__(self, gamma=0.99, beta=1.5):\n self.gamma = gamma\n self.beta = beta\n\n def spatial_discount_mask(self, mask_width, mask_height):\n \"\"\"Generate spatial discounting mask constant.\n\n Args:\n mask_width (int): The width of bbox hole.\n mask_height (int): The height of bbox height.\n\n Returns:\n np.ndarray: Spatial discounting mask.\n \"\"\"\n w, h = np.meshgrid(np.arange(mask_width), np.arange(mask_height))\n grid_stack = np.stack([h, w], axis=2)\n mask_values = (self.gamma**(np.minimum(\n grid_stack, [mask_height - 1, mask_width - 1] - grid_stack) *\n self.beta)).max(\n axis=2, keepdims=True)\n\n return mask_values\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n\n mask_bbox = results['mask_bbox']\n mask = results['mask']\n mask_height, mask_width = mask_bbox[-2:]\n discount_hole = self.spatial_discount_mask(mask_width, mask_height)\n discount_mask = np.zeros_like(mask)\n discount_mask[mask_bbox[0]:mask_bbox[0] + mask_height,\n mask_bbox[1]:mask_bbox[1] + mask_width,\n ...] = discount_hole\n\n results['discount_mask'] = discount_mask\n\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + (f'(gamma={self.gamma}, '\n f'beta={self.beta})')\n\n\n@TRANSFORMS.register_module()\nclass LoadPairedImageFromFile(LoadImageFromFile):\n \"\"\"Load a pair of images from file.\n\n Each sample contains a pair of images, which are concatenated in the w\n dimension (a|b). This is a special loading class for generation paired\n dataset. It loads a pair of images as the common loader does and crops\n it into two images with the same shape in different domains.\n\n Required key is \"pair_path\". Added or modified keys are \"pair\",\n \"pair_ori_shape\", \"ori_pair\", \"img_{domain_a}\", \"img_{domain_b}\",\n \"img_{domain_a}_path\", \"img_{domain_b}_path\", \"img_{domain_a}_ori_shape\",\n \"img_{domain_b}_ori_shape\", \"ori_img_{domain_a}\" and\n \"ori_img_{domain_b}\".\n\n Args:\n key (str): Keys in results to find corresponding path.\n domain_a (str, Optional): One of the paired image domain. Defaults\n to 'A'.\n domain_b (str, Optional): The other of the paired image domain.\n Defaults to 'B'.\n color_type (str): The flag argument for :func:``mmcv.imfrombytes``.\n Defaults to 'color'.\n channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.\n Default: 'bgr'.\n imdecode_backend (str): The image decoding backend type. The backend\n argument for :func:``mmcv.imfrombytes``.\n See :func:``mmcv.imfrombytes`` for details.\n candidates are 'cv2', 'turbojpeg', 'pillow', and 'tifffile'.\n Defaults to None.\n use_cache (bool): If True, load all images at once. Default: False.\n to_float32 (bool): Whether to convert the loaded image to a float32\n numpy array. If set to False, the loaded image is an uint8 array.\n Defaults to False.\n to_y_channel (bool): Whether to convert the loaded image to y channel.\n Only support 'rgb2ycbcr' and 'rgb2ycbcr'\n Defaults to False.\n backend_args (dict, optional): Arguments to instantiate the prefix of\n uri corresponding backend. Defaults to None.\n io_backend (str, optional): io backend where images are store. Defaults\n to None.\n \"\"\"\n\n def __init__(self,\n key: str,\n domain_a: str = 'A',\n domain_b: str = 'B',\n color_type: str = 'color',\n channel_order: str = 'bgr',\n imdecode_backend: Optional[str] = None,\n use_cache: bool = False,\n to_float32: bool = False,\n to_y_channel: bool = False,\n save_original_img: bool = False,\n backend_args: Optional[dict] = None):\n super().__init__(key, color_type, channel_order, imdecode_backend,\n use_cache, to_float32, to_y_channel,\n save_original_img, backend_args)\n assert isinstance(domain_a, str)\n assert isinstance(domain_b, str)\n self.domain_a = domain_a\n self.domain_b = domain_b\n\n def transform(self, results: dict) -> dict:\n \"\"\"Functions to load paired images.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n filename = results[f'{self.key}_path']\n\n image = self._load_image(filename)\n image = self._convert(image)\n if self.save_original_img:\n ori_image = image.copy()\n shape = image.shape\n\n # crop pair into a and b\n w = shape[1]\n if w % 2 != 0:\n raise ValueError(\n f'The width of image pair must be even number, but got {w}.')\n new_w = w // 2\n image_a = image[:, :new_w, :]\n image_b = image[:, new_w:, :]\n\n results[f'img_{self.domain_a}'] = image_a\n results[f'img_{self.domain_b}'] = image_b\n results[f'img_{self.domain_a}_path'] = filename\n results[f'img_{self.domain_b}_path'] = filename\n results[f'img_{self.domain_a}_ori_shape'] = image_a.shape\n results[f'img_{self.domain_b}_ori_shape'] = image_b.shape\n if self.save_original_img:\n results[f'ori_img_{self.domain_a}'] = image_a.copy()\n results[f'ori_img_{self.domain_b}'] = image_b.copy()\n\n results[self.key] = image\n results[f'ori_{self.key}_shape'] = shape\n results[f'{self.key}_channel_order'] = self.channel_order\n results[f'{self.key}_color_type'] = self.color_type\n if self.save_original_img:\n results[f'ori_{self.key}'] = ori_image\n\n return results\n" }, { "path": "mmagic/datasets/transforms/matlab_like_resize.py", "content": "# This code is referenced from matlab_imresize with modifications\n# Reference:\n# https://github.com/fatheral/matlab_imresize/blob/master/imresize.py\n# Original license: Copyright (c) 2020 fatheral, under the MIT License.\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\ndef get_size_from_scale(input_size, scale_factor):\n \"\"\"Get the output size given input size and scale factor.\n\n Args:\n input_size (tuple): The size of the input image.\n scale_factor (float): The resize factor.\n\n Returns:\n output_shape (list[int]): The size of the output image.\n \"\"\"\n\n output_shape = [\n int(np.ceil(scale * shape))\n for (scale, shape) in zip(scale_factor, input_size)\n ]\n\n return output_shape\n\n\ndef get_scale_from_size(input_size, output_size):\n \"\"\"Get the scale factor given input size and output size.\n\n Args:\n input_size (tuple(int)): The size of the input image.\n output_size (tuple(int)): The size of the output image.\n\n Returns:\n scale (list[float]): The scale factor of each dimension.\n \"\"\"\n\n scale = [\n 1.0 * output_shape / input_shape\n for (input_shape, output_shape) in zip(input_size, output_size)\n ]\n\n return scale\n\n\ndef _cubic(x):\n \"\"\"Cubic function.\n\n Args:\n x (np.ndarray): The distance from the center position.\n\n Returns:\n np.ndarray: The weight corresponding to a particular distance.\n \"\"\"\n\n x = np.array(x, dtype=np.float32)\n x_abs = np.abs(x)\n x_abs_sq = x_abs**2\n x_abs_cu = x_abs_sq * x_abs\n\n # if |x| <= 1: y = 1.5|x|^3 - 2.5|x|^2 + 1\n # if 1 < |x| <= 2: -0.5|x|^3 + 2.5|x|^2 - 4|x| + 2\n f = (1.5 * x_abs_cu - 2.5 * x_abs_sq + 1) * (x_abs <= 1) + (\n -0.5 * x_abs_cu + 2.5 * x_abs_sq - 4 * x_abs + 2) * ((1 < x_abs) &\n (x_abs <= 2))\n\n return f\n\n\ndef get_weights_indices(input_length, output_length, scale, kernel,\n kernel_width):\n \"\"\"Get weights and indices for interpolation.\n\n Args:\n input_length (int): Length of the input sequence.\n output_length (int): Length of the output sequence.\n scale (float): Scale factor.\n kernel (func): The kernel used for resizing.\n kernel_width (int): The width of the kernel.\n\n Returns:\n tuple(list[np.ndarray], list[np.ndarray]): The weights and the indices\n for interpolation.\n \"\"\"\n if scale < 1: # modified kernel for antialiasing\n\n def h(x):\n return scale * kernel(scale * x)\n\n kernel_width = 1.0 * kernel_width / scale\n else:\n h = kernel\n kernel_width = kernel_width\n\n # coordinates of output\n x = np.arange(1, output_length + 1).astype(np.float32)\n\n # coordinates of input\n u = x / scale + 0.5 * (1 - 1 / scale)\n left = np.floor(u - kernel_width / 2) # leftmost pixel\n p = int(np.ceil(kernel_width)) + 2 # maximum number of pixels\n\n # indices of input pixels\n ind = left[:, np.newaxis, ...] + np.arange(p)\n indices = ind.astype(np.int32)\n\n # weights of input pixels\n weights = h(u[:, np.newaxis, ...] - indices - 1)\n\n weights = weights / np.sum(weights, axis=1)[:, np.newaxis, ...]\n\n # remove all-zero columns\n aux = np.concatenate(\n (np.arange(input_length), np.arange(input_length - 1, -1,\n step=-1))).astype(np.int32)\n indices = aux[np.mod(indices, aux.size)]\n ind2store = np.nonzero(np.any(weights, axis=0))\n weights = weights[:, ind2store]\n indices = indices[:, ind2store]\n\n return weights, indices\n\n\ndef resize_along_dim(img_in, weights, indices, dim):\n \"\"\"Resize along a specific dimension.\n\n Args:\n img_in (np.ndarray): The input image.\n weights (ndarray): The weights used for interpolation, computed from\n [get_weights_indices].\n indices (ndarray): The indices used for interpolation, computed from\n [get_weights_indices].\n dim (int): Which dimension to undergo interpolation.\n\n Returns:\n np.ndarray: Interpolated (along one dimension) image.\n \"\"\"\n\n img_in = img_in.astype(np.float32)\n w_shape = weights.shape\n output_shape = list(img_in.shape)\n output_shape[dim] = w_shape[0]\n img_out = np.zeros(output_shape)\n\n if dim == 0:\n for i in range(w_shape[0]):\n w = weights[i, :][np.newaxis, ...]\n ind = indices[i, :]\n img_slice = img_in[ind, :]\n img_out[i] = np.sum(np.squeeze(img_slice, axis=0) * w.T, axis=0)\n elif dim == 1:\n for i in range(w_shape[0]):\n w = weights[i, :][:, :, np.newaxis]\n ind = indices[i, :]\n img_slice = img_in[:, ind]\n img_out[:, i] = np.sum(np.squeeze(img_slice, axis=1) * w.T, axis=1)\n\n if img_in.dtype == np.uint8:\n img_out = np.clip(img_out, 0, 255)\n return np.around(img_out).astype(np.uint8)\n else:\n return img_out\n\n\n@TRANSFORMS.register_module()\nclass MATLABLikeResize(BaseTransform):\n \"\"\"Resize the input image using MATLAB-like downsampling.\n\n Currently support bicubic interpolation only. Note that the output of\n this function is slightly different from the official MATLAB function.\n\n Required keys are the keys in attribute \"keys\". Added or modified keys\n are \"scale\" and \"output_shape\", and the keys in attribute \"keys\".\n\n Args:\n keys (list[str]): A list of keys whose values are modified.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n kernel (str, optional): The kernel for the resize operation.\n Currently support 'bicubic' only. Default: 'bicubic'.\n kernel_width (float): The kernel width. Currently support 4.0 only.\n Default: 4.0.\n \"\"\"\n\n def __init__(self,\n keys,\n scale=None,\n output_shape=None,\n kernel='bicubic',\n kernel_width=4.0):\n\n if kernel.lower() != 'bicubic':\n raise ValueError('Currently support bicubic kernel only.')\n\n if float(kernel_width) != 4.0:\n raise ValueError('Current support only width=4 only.')\n\n if scale is None and output_shape is None:\n raise ValueError('\"scale\" and \"output_shape\" cannot be both None')\n\n self.kernel_func = _cubic\n self.keys = keys\n self.scale = scale\n self.output_shape = output_shape\n self.kernel = kernel\n self.kernel_width = kernel_width\n\n def _resize(self, img):\n \"\"\"resize an image to the require size.\n\n Args:\n img (np.ndarray): The original image.\n Returns:\n output (np.ndarray): The resized image.\n \"\"\"\n weights = {}\n indices = {}\n\n # compute scale and output_size\n if self.scale is not None:\n scale = float(self.scale)\n scale = [scale, scale]\n output_size = get_size_from_scale(img.shape, scale)\n else:\n scale = get_scale_from_size(img.shape, self.output_shape)\n output_size = list(self.output_shape)\n\n # apply cubic interpolation along two dimensions\n order = np.argsort(np.array(scale))\n for k in range(2):\n key = (img.shape[k], output_size[k], scale[k], self.kernel_func,\n self.kernel_width)\n weight, index = get_weights_indices(img.shape[k], output_size[k],\n scale[k], self.kernel_func,\n self.kernel_width)\n weights[key] = weight\n indices[key] = index\n\n output = np.copy(img)\n if output.ndim == 2: # grayscale image\n output = output[:, :, np.newaxis]\n\n for k in range(2):\n dim = order[k]\n key = (img.shape[dim], output_size[dim], scale[dim],\n self.kernel_func, self.kernel_width)\n output = resize_along_dim(output, weights[key], indices[key], dim)\n\n return output\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n is_single_image = False\n if isinstance(results[key], np.ndarray):\n is_single_image = True\n results[key] = [results[key]]\n\n results[key] = [self._resize(img) for img in results[key]]\n\n if is_single_image:\n results[key] = results[key][0]\n\n results['scale'] = self.scale\n results['output_shape'] = self.output_shape\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (\n f'(keys={self.keys}, scale={self.scale}, '\n f'output_shape={self.output_shape}, '\n f'kernel={self.kernel}, kernel_width={self.kernel_width})')\n\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/normalization.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmcv\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass Normalize(BaseTransform):\n \"\"\"Normalize images with the given mean and std value.\n\n Required keys are the keys in attribute \"keys\", added or modified keys are\n the keys in attribute \"keys\" and these keys with postfix '_norm_cfg'.\n It also supports normalizing a list of images.\n\n Args:\n keys (Sequence[str]): The images to be normalized.\n mean (np.ndarray): Mean values of different channels.\n std (np.ndarray): Std values of different channels.\n to_rgb (bool): Whether to convert channels from BGR to RGB.\n Default: False.\n save_original (bool): Whether to save original images. Default: False.\n \"\"\"\n\n def __init__(self, keys, mean, std, to_rgb=False, save_original=False):\n self.keys = keys\n self.mean = np.array(mean, dtype=np.float32)\n self.std = np.array(std, dtype=np.float32)\n self.to_rgb = to_rgb\n self.save_original = save_original\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n if isinstance(results[key], list):\n if self.save_original:\n results[key + '_unnormalised'] = [\n v.copy() for v in results[key]\n ]\n results[key] = [\n mmcv.imnormalize(v, self.mean, self.std, self.to_rgb)\n for v in results[key]\n ]\n else:\n if self.save_original:\n results[key + '_unnormalised'] = results[key].copy()\n results[key] = mmcv.imnormalize(results[key], self.mean,\n self.std, self.to_rgb)\n\n results['img_norm_cfg'] = dict(\n mean=self.mean, std=self.std, to_rgb=self.to_rgb)\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, mean={self.mean}, std={self.std}, '\n f'to_rgb={self.to_rgb})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RescaleToZeroOne(BaseTransform):\n \"\"\"Transform the images into a range between 0 and 1.\n\n Required keys are the keys in attribute \"keys\", added or modified keys are\n the keys in attribute \"keys\".\n It also supports rescaling a list of images.\n\n Args:\n keys (Sequence[str]): The images to be transformed.\n \"\"\"\n\n def __init__(self, keys):\n self.keys = keys\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n if isinstance(results[key], list):\n results[key] = [\n v.astype(np.float32) / 255. for v in results[key]\n ]\n else:\n results[key] = results[key].astype(np.float32) / 255.\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f'(keys={self.keys})'\n" }, { "path": "mmagic/datasets/transforms/random_degradations.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport io\nimport logging\nimport random\n\nimport cv2\nimport numpy as np\n\nfrom mmagic.datasets.transforms import blur_kernels\nfrom mmagic.registry import TRANSFORMS\n\ntry:\n import av\n has_av = True\nexcept ImportError:\n has_av = False\n\n\n@TRANSFORMS.register_module()\nclass RandomBlur:\n \"\"\"Apply random blur to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n def get_kernel(self, num_kernels: int):\n \"\"\"This is the function to create kernel.\n\n Args:\n num_kernels (int): the number of kernels\n\n Returns:\n _type_: _description_\n \"\"\"\n kernel_type = np.random.choice(\n self.params['kernel_list'], p=self.params['kernel_prob'])\n kernel_size = random.choice(self.params['kernel_size'])\n\n sigma_x_range = self.params.get('sigma_x', [0, 0])\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\n sigma_x_step = self.params.get('sigma_x_step', 0)\n\n sigma_y_range = self.params.get('sigma_y', [0, 0])\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\n sigma_y_step = self.params.get('sigma_y_step', 0)\n\n rotate_angle_range = self.params.get('rotate_angle', [-np.pi, np.pi])\n rotate_angle = np.random.uniform(rotate_angle_range[0],\n rotate_angle_range[1])\n rotate_angle_step = self.params.get('rotate_angle_step', 0)\n\n beta_gau_range = self.params.get('beta_gaussian', [0.5, 4])\n beta_gau = np.random.uniform(beta_gau_range[0], beta_gau_range[1])\n beta_gau_step = self.params.get('beta_gaussian_step', 0)\n\n beta_pla_range = self.params.get('beta_plateau', [1, 2])\n beta_pla = np.random.uniform(beta_pla_range[0], beta_pla_range[1])\n beta_pla_step = self.params.get('beta_plateau_step', 0)\n\n omega_range = self.params.get('omega', None)\n omega_step = self.params.get('omega_step', 0)\n if omega_range is None: # follow Real-ESRGAN settings if not specified\n if kernel_size < 13:\n omega_range = [np.pi / 3., np.pi]\n else:\n omega_range = [np.pi / 5., np.pi]\n omega = np.random.uniform(omega_range[0], omega_range[1])\n\n # determine blurring kernel\n kernels = []\n for _ in range(0, num_kernels):\n kernel = blur_kernels.random_mixed_kernels(\n [kernel_type],\n [1],\n kernel_size,\n [sigma_x, sigma_x],\n [sigma_y, sigma_y],\n [rotate_angle, rotate_angle],\n [beta_gau, beta_gau],\n [beta_pla, beta_pla],\n [omega, omega],\n None,\n )\n kernels.append(kernel)\n\n # update kernel parameters\n sigma_x += np.random.uniform(-sigma_x_step, sigma_x_step)\n sigma_y += np.random.uniform(-sigma_y_step, sigma_y_step)\n rotate_angle += np.random.uniform(-rotate_angle_step,\n rotate_angle_step)\n beta_gau += np.random.uniform(-beta_gau_step, beta_gau_step)\n beta_pla += np.random.uniform(-beta_pla_step, beta_pla_step)\n omega += np.random.uniform(-omega_step, omega_step)\n\n sigma_x = np.clip(sigma_x, sigma_x_range[0], sigma_x_range[1])\n sigma_y = np.clip(sigma_y, sigma_y_range[0], sigma_y_range[1])\n rotate_angle = np.clip(rotate_angle, rotate_angle_range[0],\n rotate_angle_range[1])\n beta_gau = np.clip(beta_gau, beta_gau_range[0], beta_gau_range[1])\n beta_pla = np.clip(beta_pla, beta_pla_range[0], beta_pla_range[1])\n omega = np.clip(omega, omega_range[0], omega_range[1])\n\n return kernels\n\n def _apply_random_blur(self, imgs):\n \"\"\"This is the function to apply blur operation on images.\n\n Args:\n imgs (Tensor): images\n\n Returns:\n Tensor: Images applied blur\n \"\"\"\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n # get kernel and blur the input\n kernels = self.get_kernel(num_kernels=len(imgs))\n imgs = [\n cv2.filter2D(img, -1, kernel)\n for img, kernel in zip(imgs, kernels)\n ]\n\n if is_single_image:\n imgs = imgs[0]\n\n return imgs\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_blur(results[key])\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomJPEGCompression:\n \"\"\"Apply random JPEG compression to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n bgr2rgb (str): Whether change channel order. Default: False.\n \"\"\"\n\n def __init__(self, params, keys, color_type='color', bgr2rgb=False):\n self.keys = keys\n self.params = params\n self.color_type = color_type\n self.bgr2rgb = bgr2rgb\n\n def _apply_random_compression(self, imgs):\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n # determine initial compression level and the step size\n quality = self.params['quality']\n quality_step = self.params.get('quality_step', 0)\n jpeg_param = round(np.random.uniform(quality[0], quality[1]))\n\n # apply jpeg compression\n outputs = []\n for img in imgs:\n encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_param]\n if self.bgr2rgb and self.color_type == 'color':\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n _, img_encoded = cv2.imencode('.jpg', img, encode_param)\n\n if self.color_type == 'color':\n img_encoded = cv2.imdecode(img_encoded, 1)\n if self.bgr2rgb:\n img_encoded = cv2.cvtColor(img_encoded, cv2.COLOR_BGR2RGB)\n outputs.append(img_encoded)\n else:\n outputs.append(cv2.imdecode(img_encoded, 0))\n\n # update compression level\n jpeg_param += np.random.uniform(-quality_step, quality_step)\n jpeg_param = round(np.clip(jpeg_param, quality[0], quality[1]))\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_compression(results[key])\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomNoise:\n \"\"\"Apply random noise to the input.\n\n Currently support Gaussian noise and Poisson noise.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n def _apply_gaussian_noise(self, imgs):\n \"\"\"This is the function used to apply gaussian noise on images.\n\n Args:\n imgs (Tensor): images\n\n Returns:\n Tensor: images applied gaussian noise\n \"\"\"\n sigma_range = self.params['gaussian_sigma']\n sigma = np.random.uniform(sigma_range[0], sigma_range[1])\n\n sigma_step = self.params.get('gaussian_sigma_step', 0)\n\n gray_noise_prob = self.params['gaussian_gray_noise_prob']\n is_gray_noise = np.random.uniform() < gray_noise_prob\n\n outputs = []\n for img in imgs:\n noise = np.float32(np.random.randn(*(img.shape))) * sigma\n if is_gray_noise:\n noise = noise[:, :, :1]\n outputs.append(img + noise)\n\n # update noise level\n sigma += np.random.uniform(-sigma_step, sigma_step)\n sigma = np.clip(sigma, sigma_range[0], sigma_range[1])\n\n return outputs\n\n def _apply_poisson_noise(self, imgs):\n scale_range = self.params['poisson_scale']\n scale = np.random.uniform(scale_range[0], scale_range[1])\n\n scale_step = self.params.get('poisson_scale_step', 0)\n\n gray_noise_prob = self.params['poisson_gray_noise_prob']\n is_gray_noise = np.random.uniform() < gray_noise_prob\n\n outputs = []\n for img in imgs:\n noise = np.float32(img.copy())\n if is_gray_noise:\n noise = cv2.cvtColor(noise[..., [2, 1, 0]], cv2.COLOR_BGR2GRAY)\n noise = noise[..., np.newaxis]\n noise = np.clip((noise).round(), 0, 255)\n unique_val = 2**np.ceil(np.log2(len(np.unique(noise))))\n noise = np.random.poisson(noise * unique_val).astype(np.float32) \\\n / unique_val - noise\n\n outputs.append(img + noise * scale)\n\n # update noise level\n scale += np.random.uniform(-scale_step, scale_step)\n scale = np.clip(scale, scale_range[0], scale_range[1])\n\n return outputs\n\n def _apply_random_noise(self, imgs):\n \"\"\"This is the function used to apply random noise on images.\n\n Args:\n imgs (Tensor): training images\n\n Returns:\n _type_: _description_\n \"\"\"\n noise_type = np.random.choice(\n self.params['noise_type'], p=self.params['noise_prob'])\n\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n if noise_type.lower() == 'gaussian':\n imgs = self._apply_gaussian_noise(imgs)\n elif noise_type.lower() == 'poisson':\n imgs = self._apply_poisson_noise(imgs)\n else:\n raise NotImplementedError(f'\"noise_type\" [{noise_type}] is '\n 'not implemented.')\n\n if is_single_image:\n imgs = imgs[0]\n\n return imgs\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_noise(results[key])\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomResize:\n \"\"\"Randomly resize the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n self.resize_dict = dict(\n bilinear=cv2.INTER_LINEAR,\n bicubic=cv2.INTER_CUBIC,\n area=cv2.INTER_AREA,\n lanczos=cv2.INTER_LANCZOS4)\n\n def _random_resize(self, imgs):\n \"\"\"This is the function used to randomly resize images for training\n augmentation.\n\n Args:\n imgs (Tensor): training images.\n\n Returns:\n Tensor: images after randomly resized\n \"\"\"\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n h, w = imgs[0].shape[:2]\n\n resize_opt = self.params['resize_opt']\n resize_prob = self.params['resize_prob']\n resize_opt = np.random.choice(resize_opt, p=resize_prob).lower()\n if resize_opt not in self.resize_dict:\n raise NotImplementedError(f'resize_opt [{resize_opt}] is not '\n 'implemented')\n resize_opt = self.resize_dict[resize_opt]\n\n resize_step = self.params.get('resize_step', 0)\n\n # determine the target size, if not provided\n target_size = self.params.get('target_size', None)\n if target_size is None:\n resize_mode = np.random.choice(['up', 'down', 'keep'],\n p=self.params['resize_mode_prob'])\n resize_scale = self.params['resize_scale']\n if resize_mode == 'up':\n scale_factor = np.random.uniform(1, resize_scale[1])\n elif resize_mode == 'down':\n scale_factor = np.random.uniform(resize_scale[0], 1)\n else:\n scale_factor = 1\n\n # determine output size\n h_out, w_out = h * scale_factor, w * scale_factor\n if self.params.get('is_size_even', False):\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\n target_size = (int(h_out), int(w_out))\n else:\n resize_step = 0\n\n # resize the input\n if resize_step == 0: # same target_size for all input images\n outputs = [\n cv2.resize(img, target_size[::-1], interpolation=resize_opt)\n for img in imgs\n ]\n else: # different target_size for each input image\n outputs = []\n for img in imgs:\n img = cv2.resize(\n img, target_size[::-1], interpolation=resize_opt)\n outputs.append(img)\n\n # update scale\n scale_factor += np.random.uniform(-resize_step, resize_step)\n scale_factor = np.clip(scale_factor, resize_scale[0],\n resize_scale[1])\n\n # determine output size\n h_out, w_out = h * scale_factor, w * scale_factor\n if self.params.get('is_size_even', False):\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\n target_size = (int(h_out), int(w_out))\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._random_resize(results[key])\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass RandomVideoCompression:\n \"\"\"Apply random video compression to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n assert has_av, 'Please install av to use video compression.'\n\n self.keys = keys\n self.params = params\n logging.getLogger('libav').setLevel(50)\n\n def _apply_random_compression(self, imgs):\n \"\"\"This is the function to apply random compression on images.\n\n Args:\n imgs (Tensor): training images\n\n Returns:\n Tensor: images after randomly compressed\n \"\"\"\n codec = random.choices(self.params['codec'],\n self.params['codec_prob'])[0]\n bitrate = self.params['bitrate']\n bitrate = np.random.randint(bitrate[0], bitrate[1] + 1)\n\n buf = io.BytesIO()\n with av.open(buf, 'w', 'mp4') as container:\n stream = container.add_stream(codec, rate=1)\n stream.height = imgs[0].shape[0]\n stream.width = imgs[0].shape[1]\n stream.pix_fmt = 'yuv420p'\n stream.bit_rate = bitrate\n\n for img in imgs:\n img = img.astype(np.uint8)\n frame = av.VideoFrame.from_ndarray(img, format='rgb24')\n frame.pict_type = 'NONE'\n for packet in stream.encode(frame):\n container.mux(packet)\n\n # Flush stream\n for packet in stream.encode():\n container.mux(packet)\n\n outputs = []\n with av.open(buf, 'r', 'mp4') as container:\n if container.streams.video:\n for frame in container.decode(**{'video': 0}):\n outputs.append(frame.to_rgb().to_ndarray().astype(\n np.float32))\n\n return outputs\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_compression(results[key])\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str\n\n\nallowed_degradations = {\n 'RandomBlur': RandomBlur,\n 'RandomResize': RandomResize,\n 'RandomNoise': RandomNoise,\n 'RandomJPEGCompression': RandomJPEGCompression,\n 'RandomVideoCompression': RandomVideoCompression,\n}\n\n\n@TRANSFORMS.register_module()\nclass DegradationsWithShuffle:\n \"\"\"Apply random degradations to input, with degradations being shuffled.\n\n Degradation groups are supported. The order of degradations within the same\n group is preserved. For example, if we have degradations = [a, b, [c, d]]\n and shuffle_idx = None, then the possible orders are\n\n ::\n\n [a, b, [c, d]]\n [a, [c, d], b]\n [b, a, [c, d]]\n [b, [c, d], a]\n [[c, d], a, b]\n [[c, d], b, a]\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n degradations (list[dict]): The list of degradations.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n shuffle_idx (list | None, optional): The degradations corresponding to\n these indices are shuffled. If None, all degradations are shuffled.\n Default: None.\n \"\"\"\n\n def __init__(self, degradations, keys, shuffle_idx=None):\n\n self.keys = keys\n\n self.degradations = self._build_degradations(degradations)\n\n if shuffle_idx is None:\n self.shuffle_idx = list(range(0, len(degradations)))\n else:\n self.shuffle_idx = shuffle_idx\n\n def _build_degradations(self, degradations):\n for i, degradation in enumerate(degradations):\n if isinstance(degradation, (list, tuple)):\n degradations[i] = self._build_degradations(degradation)\n else:\n degradation_ = allowed_degradations[degradation['type']]\n degradations[i] = degradation_(degradation['params'],\n self.keys)\n\n return degradations\n\n def __call__(self, results):\n \"\"\"Call this transform.\"\"\"\n # shuffle degradations\n if len(self.shuffle_idx) > 0:\n shuffle_list = [self.degradations[i] for i in self.shuffle_idx]\n np.random.shuffle(shuffle_list)\n for i, idx in enumerate(self.shuffle_idx):\n self.degradations[idx] = shuffle_list[i]\n\n # apply degradations to input\n for degradation in self.degradations:\n if isinstance(degradation, (tuple, list)):\n for subdegrdation in degradation:\n results = subdegrdation(results)\n else:\n results = degradation(results)\n\n return results\n\n def __repr__(self):\n \"\"\"Print the basic information of the transform.\"\"\"\n repr_str = self.__class__.__name__\n repr_str += (f'(degradations={self.degradations}, '\n f'keys={self.keys}, '\n f'shuffle_idx={self.shuffle_idx})')\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/random_down_sampling.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport numpy as np\nimport torch\nfrom mmcv import imresize\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass RandomDownSampling(BaseTransform):\n \"\"\"Generate LQ image from GT (and crop), which will randomly pick a scale.\n\n Args:\n scale_min (float): The minimum of upsampling scale, inclusive.\n Default: 1.0.\n scale_max (float): The maximum of upsampling scale, exclusive.\n Default: 4.0.\n patch_size (int): The cropped lr patch size.\n Default: None, means no crop.\n interpolation (str): Interpolation method, accepted values are\n \"nearest\", \"bilinear\", \"bicubic\", \"area\", \"lanczos\" for 'cv2'\n backend, \"nearest\", \"bilinear\", \"bicubic\", \"box\", \"lanczos\",\n \"hamming\" for 'pillow' backend.\n Default: \"bicubic\".\n backend (str | None): The image resize backend type. Options are `cv2`,\n `pillow`, `None`. If backend is None, the global imread_backend\n specified by ``mmcv.use_backend()`` will be used.\n Default: \"pillow\".\n\n Scale will be picked in the range of [scale_min, scale_max).\n \"\"\"\n\n def __init__(self,\n scale_min=1.0,\n scale_max=4.0,\n patch_size=None,\n interpolation='bicubic',\n backend='pillow'):\n\n assert scale_max >= scale_min\n self.scale_min = scale_min\n self.scale_max = scale_max\n self.patch_size = patch_size\n self.interpolation = interpolation\n self.backend = backend\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation. 'gt' is required.\n\n Returns:\n dict: A dict containing the processed data and information.\n modified 'gt', supplement 'lq' and 'scale' to keys.\n \"\"\"\n img = results['gt']\n scale = np.random.uniform(self.scale_min, self.scale_max)\n\n if self.patch_size is None:\n h_lr = math.floor(img.shape[-3] / scale + 1e-9)\n w_lr = math.floor(img.shape[-2] / scale + 1e-9)\n img = img[:round(h_lr * scale), :round(w_lr * scale), :]\n img_down = resize_fn(img, (w_lr, h_lr), self.interpolation,\n self.backend)\n crop_lr, crop_hr = img_down, img\n else:\n w_lr = self.patch_size\n w_hr = round(w_lr * scale)\n x0 = np.random.randint(0, img.shape[-3] - w_hr)\n y0 = np.random.randint(0, img.shape[-2] - w_hr)\n crop_hr = img[x0:x0 + w_hr, y0:y0 + w_hr, :]\n crop_lr = resize_fn(crop_hr, w_lr, self.interpolation,\n self.backend)\n results['gt'] = crop_hr\n results['img'] = crop_lr\n results['scale'] = scale\n\n # copy metainfo to lr image\n if 'gt_channel_order' in results:\n results['img_channel_order'] = results['gt_channel_order']\n if 'gt_color_type' in results:\n results['img_color_type'] = results['gt_color_type']\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f' scale_min={self.scale_min}, '\n f'scale_max={self.scale_max}, '\n f'patch_size={self.patch_size}, '\n f'interpolation={self.interpolation}, '\n f'backend={self.backend}')\n\n return repr_str\n\n\ndef resize_fn(img, size, interpolation='bicubic', backend='pillow'):\n \"\"\"Resize the given image to a given size.\n\n Args:\n img (np.ndarray | torch.Tensor): The input image.\n size (int | tuple[int]): Target size w or (w, h).\n interpolation (str): Interpolation method, accepted values are\n \"nearest\", \"bilinear\", \"bicubic\", \"area\", \"lanczos\" for 'cv2'\n backend, \"nearest\", \"bilinear\", \"bicubic\", \"box\", \"lanczos\",\n \"hamming\" for 'pillow' backend.\n Default: \"bicubic\".\n backend (str | None): The image resize backend type. Options are `cv2`,\n `pillow`, `None`. If backend is None, the global imread_backend\n specified by ``mmcv.use_backend()`` will be used.\n Default: \"pillow\".\n\n Returns:\n np.ndarray | Tensor: `resized_img`, whose type is same as `img`.\n \"\"\"\n if isinstance(size, int):\n size = (size, size)\n if isinstance(img, np.ndarray):\n return imresize(\n img, size, interpolation=interpolation, backend=backend)\n elif isinstance(img, torch.Tensor):\n image = imresize(\n img.numpy(), size, interpolation=interpolation, backend=backend)\n return torch.from_numpy(image)\n\n else:\n raise TypeError('img should got np.ndarray or torch.Tensor,'\n f'but got {type(img)}')\n" }, { "path": "mmagic/datasets/transforms/trimap.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Augmentation on trimaps.\"\"\"\n\nimport cv2\nimport numpy as np\nfrom mmcv.transforms import BaseTransform\nfrom mmengine.utils import is_tuple_of\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass FormatTrimap(BaseTransform):\n \"\"\"Convert trimap (tensor) to one-hot representation.\n\n It transforms the trimap label from (0, 128, 255) to (0, 1, 2). If\n ``to_onehot`` is set to True, the trimap will convert to one-hot tensor of\n shape (3, H, W). Required key is \"trimap\", added or modified key are\n \"trimap\" and \"format_trimap_to_onehot\".\n\n Args:\n to_onehot (bool): whether convert trimap to one-hot tensor. Default:\n ``False``.\n \"\"\"\n\n def __init__(self, to_onehot=False):\n self.to_onehot = to_onehot\n\n def transform(self, results):\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n trimap = results['trimap'].squeeze()\n assert trimap.ndim == 2\n\n if self.to_onehot:\n trimap_one_hot = np.zeros((*trimap.shape, 3), dtype=np.uint8)\n trimap_one_hot[..., 0][trimap == 0] = 1\n trimap_one_hot[..., 1][trimap == 128] = 1\n trimap_one_hot[..., 2][trimap == 255] = 1\n results['trimap'] = trimap_one_hot\n else:\n trimap[trimap == 128] = 1\n trimap[trimap == 255] = 2\n results['trimap'] = trimap\n\n results['format_trimap_to_onehot'] = self.to_onehot\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f'(to_onehot={self.to_onehot})'\n\n\n@TRANSFORMS.register_module()\nclass GenerateTrimap(BaseTransform):\n \"\"\"Using random erode/dilate to generate trimap from alpha matte.\n\n Required key is \"alpha\", added key is \"trimap\".\n\n Args:\n kernel_size (int | tuple[int]): The range of random kernel_size of\n erode/dilate; int indicates a fixed kernel_size. If `random` is set\n to False and kernel_size is a tuple of length 2, then it will be\n interpreted as (erode kernel_size, dilate kernel_size). It should\n be noted that the kernel of the erosion and dilation has the same\n height and width.\n iterations (int | tuple[int], optional): The range of random iterations\n of erode/dilate; int indicates a fixed iterations. If `random` is\n set to False and iterations is a tuple of length 2, then it will be\n interpreted as (erode iterations, dilate iterations). Default to 1.\n random (bool, optional): Whether use random kernel_size and iterations\n when generating trimap. See `kernel_size` and `iterations` for more\n information. Default to True.\n \"\"\"\n\n def __init__(self, kernel_size, iterations=1, random=True):\n if isinstance(kernel_size, int):\n kernel_size = kernel_size, kernel_size + 1\n elif not is_tuple_of(kernel_size, int) or len(kernel_size) != 2:\n raise ValueError('kernel_size must be an int or a tuple of 2 int, '\n f'but got {kernel_size}')\n\n if isinstance(iterations, int):\n iterations = iterations, iterations + 1\n elif not is_tuple_of(iterations, int) or len(iterations) != 2:\n raise ValueError('iterations must be an int or a tuple of 2 int, '\n f'but got {iterations}')\n\n self.random = random\n if self.random:\n min_kernel, max_kernel = kernel_size\n self.iterations = iterations\n self.kernels = [\n cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))\n for size in range(min_kernel, max_kernel)\n ]\n else:\n erode_ksize, dilate_ksize = kernel_size\n self.iterations = iterations\n self.kernels = [\n cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (erode_ksize, erode_ksize)),\n cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (dilate_ksize, dilate_ksize))\n ]\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n alpha = results['alpha']\n\n if self.random:\n kernel_num = len(self.kernels)\n erode_kernel_idx = np.random.randint(kernel_num)\n dilate_kernel_idx = np.random.randint(kernel_num)\n min_iter, max_iter = self.iterations\n erode_iter = np.random.randint(min_iter, max_iter)\n dilate_iter = np.random.randint(min_iter, max_iter)\n else:\n erode_kernel_idx, dilate_kernel_idx = 0, 1\n erode_iter, dilate_iter = self.iterations\n\n eroded = cv2.erode(\n alpha, self.kernels[erode_kernel_idx], iterations=erode_iter)\n dilated = cv2.dilate(\n alpha, self.kernels[dilate_kernel_idx], iterations=dilate_iter)\n\n trimap = np.zeros_like(alpha)\n trimap.fill(128)\n trimap[eroded >= 255] = 255\n trimap[dilated <= 0] = 0\n results['trimap'] = trimap\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(kernels={self.kernels}, iterations={self.iterations}, '\n f'random={self.random})')\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass GenerateTrimapWithDistTransform(BaseTransform):\n \"\"\"Generate trimap with distance transform function.\n\n Args:\n dist_thr (int, optional): Distance threshold. Area with alpha value\n between (0, 255) will be considered as initial unknown area. Then\n area with distance to unknown area smaller than the distance\n threshold will also be consider as unknown area. Defaults to 20.\n random (bool, optional): If True, use random distance threshold from\n [1, dist_thr). If False, use `dist_thr` as the distance threshold\n directly. Defaults to True.\n \"\"\"\n\n def __init__(self, dist_thr=20, random=True):\n if not (isinstance(dist_thr, int) and dist_thr >= 1):\n raise ValueError('dist_thr must be an int that is greater than 1, '\n f'but got {dist_thr}')\n self.dist_thr = dist_thr\n self.random = random\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n alpha = results['alpha']\n\n # image dilation implemented by Euclidean distance transform\n known = (alpha == 0) | (alpha == 255)\n dist_to_unknown = cv2.distanceTransform(\n known.astype(np.uint8), cv2.DIST_L2, cv2.DIST_MASK_PRECISE)\n dist_thr = np.random.randint(\n 1, self.dist_thr) if self.random else self.dist_thr\n unknown = dist_to_unknown <= dist_thr\n\n trimap = (alpha == 255).astype(np.uint8) * 255\n trimap[unknown] = 128\n results['trimap'] = trimap\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(dist_thr={self.dist_thr}, random={self.random})'\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass TransformTrimap(BaseTransform):\n \"\"\"Transform trimap into two-channel and six-channel.\n\n This class will generate a two-channel trimap composed of definite\n foreground and background masks and encode it into a six-channel trimap\n using Gaussian blurs of the generated two-channel trimap at three\n different scales. The transformed trimap has 6 channels.\n\n Required key is \"trimap\", added key is \"transformed_trimap\" and\n \"two_channel_trimap\".\n\n Adopted from the following repository:\n https://github.com/MarcoForte/FBA_Matting/blob/master/networks/transforms.py.\n \"\"\"\n\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n trimap = results['trimap']\n assert len(trimap.shape) == 2\n h, w = trimap.shape[:2]\n # generate two-channel trimap\n trimap2 = np.zeros((h, w, 2), dtype=np.uint8)\n trimap2[trimap == 0, 0] = 255\n trimap2[trimap == 255, 1] = 255\n trimap_trans = np.zeros((h, w, 6), dtype=np.float32)\n factor = np.array([[[0.02, 0.08, 0.16]]], dtype=np.float32)\n for k in range(2):\n if np.any(trimap2[:, :, k]):\n dt_mask = -cv2.distanceTransform(255 - trimap2[:, :, k],\n cv2.DIST_L2, 0)**2\n dt_mask = dt_mask[..., None]\n L = 320\n trimap_trans[..., 3 * k:3 * k +\n 3] = np.exp(dt_mask / (2 * ((factor * L)**2)))\n\n results['transformed_trimap'] = trimap_trans\n results['two_channel_trimap'] = trimap2\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n return repr_str\n" }, { "path": "mmagic/datasets/transforms/values.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict\n\nfrom mmcv.transforms import BaseTransform\n\nfrom mmagic.registry import TRANSFORMS\n\n\n@TRANSFORMS.register_module()\nclass CopyValues(BaseTransform):\n \"\"\"Copy the value of source keys to destination keys.\n\n # TODO Change to dict(dst=src)\n\n It does the following: results[dst_key] = results[src_key] for\n (src_key, dst_key) in zip(src_keys, dst_keys).\n\n Added keys are the keys in the attribute \"dst_keys\".\n\n Required Keys:\n\n - [SRC_KEYS]\n\n Added Keys:\n\n - [DST_KEYS]\n\n Args:\n src_keys (list[str]): The source keys.\n dst_keys (list[str]): The destination keys.\n \"\"\"\n\n def __init__(self, src_keys, dst_keys):\n\n if not isinstance(src_keys, list) or not isinstance(dst_keys, list):\n raise AssertionError('\"src_keys\" and \"dst_keys\" must be lists.')\n\n if len(src_keys) != len(dst_keys):\n raise ValueError('\"src_keys\" and \"dst_keys\" should have the same'\n 'number of elements.')\n\n self.src_keys = src_keys\n self.dst_keys = dst_keys\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict with a key added/modified.\n \"\"\"\n\n for (src_key, dst_key) in zip(self.src_keys, self.dst_keys):\n results[dst_key] = deepcopy(results[src_key])\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(src_keys={self.src_keys})')\n repr_str += (f'(dst_keys={self.dst_keys})')\n\n return repr_str\n\n\n@TRANSFORMS.register_module()\nclass SetValues(BaseTransform):\n \"\"\"Set value to destination keys.\n\n It does the following: results[key] = value\n\n Added keys are the keys in the dictionary.\n\n Required Keys:\n\n - None\n\n Added or Modified Keys:\n\n - keys in the dictionary\n\n Args:\n dictionary (dict): The dictionary to update.\n \"\"\"\n\n def __init__(self, dictionary):\n\n self.dictionary = dictionary\n\n def transform(self, results: Dict):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict with a key added/modified.\n \"\"\"\n\n dictionary = deepcopy(self.dictionary)\n results.update(dictionary)\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(dictionary={self.dictionary})')\n\n return repr_str\n" }, { "path": "mmagic/datasets/unpaired_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom typing import Optional\n\nimport numpy as np\nfrom mmengine.dataset import BaseDataset, force_full_init\nfrom mmengine.fileio import get_file_backend\n\nfrom mmagic.registry import DATASETS\n\nIMG_EXTENSIONS = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm',\n '.PPM', '.bmp', '.BMP', '.tif', '.TIF', '.tiff', '.TIFF')\n\n\n@DATASETS.register_module()\nclass UnpairedImageDataset(BaseDataset):\n \"\"\"General unpaired image folder dataset for image generation.\n\n It assumes that the training directory of images from domain A is\n '/path/to/data/trainA', and that from domain B is '/path/to/data/trainB',\n respectively. '/path/to/data' can be initialized by args 'dataroot'.\n During test time, the directory is '/path/to/data/testA' and\n '/path/to/data/testB', respectively.\n\n Args:\n dataroot (str | :obj:`Path`): Path to the folder root of unpaired\n images.\n pipeline (List[dict | callable]): A sequence of data transformations.\n io_backend (str, optional): The storage backend type. Options are\n \"disk\", \"ceph\", \"memcached\", \"lmdb\", \"http\" and \"petrel\".\n Default: None.\n test_mode (bool): Store `True` when building test dataset.\n Default: `False`.\n domain_a (str, optional): Domain of images in trainA / testA.\n Defaults to 'A'.\n domain_b (str, optional): Domain of images in trainB / testB.\n Defaults to 'B'.\n \"\"\"\n\n def __init__(self,\n data_root,\n pipeline,\n io_backend: Optional[str] = None,\n test_mode=False,\n domain_a='A',\n domain_b='B'):\n phase = 'test' if test_mode else 'train'\n self.dataroot_a = osp.join(str(data_root), phase + 'A')\n self.dataroot_b = osp.join(str(data_root), phase + 'B')\n\n if io_backend is None:\n self.file_backend = get_file_backend(uri=data_root)\n else:\n self.file_backend = get_file_backend(\n backend_args={'backend': io_backend})\n\n super().__init__(\n data_root=data_root,\n pipeline=pipeline,\n test_mode=test_mode,\n serialize_data=False)\n self.len_a = len(self.data_infos_a)\n self.len_b = len(self.data_infos_b)\n self.test_mode = test_mode\n assert isinstance(domain_a, str)\n assert isinstance(domain_b, str)\n self.domain_a = domain_a\n self.domain_b = domain_b\n\n def load_data_list(self):\n \"\"\"Load the data list.\n\n Returns:\n list: The data info list of source and target domain.\n \"\"\"\n self.data_infos_a = self._load_domain_data_list(self.dataroot_a)\n self.data_infos_b = self._load_domain_data_list(self.dataroot_b)\n return [self.data_infos_a, self.data_infos_b]\n\n def _load_domain_data_list(self, dataroot):\n \"\"\"Load unpaired image paths of one domain.\n\n Args:\n dataroot (str): Path to the folder root for unpaired images of\n one domain.\n\n Returns:\n list[dict]: List that contains unpaired image paths of one domain.\n \"\"\"\n data_infos = []\n paths = sorted(self.scan_folder(dataroot))\n for path in paths:\n data_infos.append(dict(path=path))\n return data_infos\n\n @force_full_init\n def get_data_info(self, idx) -> dict:\n \"\"\"Get annotation by index and automatically call ``full_init`` if the\n dataset has not been fully initialized.\n\n Args:\n idx (int): The index of data.\n\n Returns:\n dict: The idx-th annotation of the dataset.\n \"\"\"\n img_a_path = self.data_infos_a[idx % self.len_a]['path']\n if not self.test_mode:\n idx_b = np.random.randint(0, self.len_b)\n img_b_path = self.data_infos_b[idx_b]['path']\n else:\n img_b_path = self.data_infos_b[idx % self.len_b]['path']\n data_info = dict()\n data_info[f'img_{self.domain_a}_path'] = img_a_path\n data_info[f'img_{self.domain_b}_path'] = img_b_path\n return data_info\n\n def __len__(self):\n \"\"\"The length of the dataset.\"\"\"\n return max(self.len_a, self.len_b)\n\n def scan_folder(self, path):\n \"\"\"Obtain image path list (including sub-folders) from a given folder.\n\n Args:\n path (str | :obj:`Path`): Folder path.\n\n Returns:\n list[str]: Image list obtained from the given folder.\n \"\"\"\n imgs_list = self.file_backend.list_dir_or_file(\n path, list_dir=False, suffix=IMG_EXTENSIONS, recursive=True)\n images = [self.file_backend.join_path(path, img) for img in imgs_list]\n assert images, f'{path} has no valid image file.'\n return images\n" }, { "path": "mmagic/engine/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .hooks import * # noqa: F401, F403\nfrom .optimizers import * # noqa: F401, F403\nfrom .runner import * # noqa: F401, F403\nfrom .schedulers import * # noqa: F401, F403\n" }, { "path": "mmagic/engine/hooks/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .ema import ExponentialMovingAverageHook\nfrom .iter_time_hook import IterTimerHook\nfrom .pggan_fetch_data_hook import PGGANFetchDataHook\nfrom .pickle_data_hook import PickleDataHook\nfrom .reduce_lr_scheduler_hook import ReduceLRSchedulerHook\nfrom .visualization_hook import BasicVisualizationHook, VisualizationHook\n\n__all__ = [\n 'ReduceLRSchedulerHook', 'BasicVisualizationHook', 'VisualizationHook',\n 'ExponentialMovingAverageHook', 'IterTimerHook', 'PGGANFetchDataHook',\n 'PickleDataHook'\n]\n" }, { "path": "mmagic/engine/hooks/ema.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\nfrom copy import deepcopy\nfrom functools import partial\nfrom typing import Optional, Sequence\n\nimport torch\nfrom mmengine.hooks import Hook\nfrom mmengine.model.wrappers import is_model_wrapper\nfrom mmengine.registry import HOOKS\nfrom mmengine.runner import Runner\nfrom mmengine.utils import is_tuple_of\n\nDATA_BATCH = Optional[Sequence[dict]]\n\n\n@HOOKS.register_module()\nclass ExponentialMovingAverageHook(Hook):\n \"\"\"Exponential Moving Average Hook.\n\n Exponential moving average is a trick that widely used in current GAN\n literature, e.g., PGGAN, StyleGAN, and BigGAN. This general idea of it is\n maintaining a model with the same architecture, but its parameters are\n updated as a moving average of the trained weights in the original model.\n In general, the model with moving averaged weights achieves better\n performance.\n\n Args:\n module_keys (str | tuple[str]): The name of the ema model. Note that we\n require these keys are followed by '_ema' so that we can easily\n find the original model by discarding the last four characters.\n interp_mode (str, optional): Mode of the interpolation method.\n Defaults to 'lerp'.\n interp_cfg (dict | None, optional): Set arguments of the interpolation\n function. Defaults to None.\n interval (int, optional): Evaluation interval (by iterations).\n Default: -1.\n start_iter (int, optional): Start iteration for ema. If the start\n iteration is not reached, the weights of ema model will maintain\n the same as the original one. Otherwise, its parameters are updated\n as a moving average of the trained weights in the original model.\n Default: 0.\n \"\"\"\n\n def __init__(self,\n module_keys,\n interp_mode='lerp',\n interp_cfg=None,\n interval=-1,\n start_iter=0):\n super().__init__()\n assert isinstance(module_keys, str) or is_tuple_of(module_keys, str)\n self.module_keys = (module_keys, ) if isinstance(module_keys,\n str) else module_keys\n # sanity check for the format of module keys\n for k in self.module_keys:\n assert k.endswith(\n '_ema'), 'You should give keys that end with \"_ema\".'\n self.interp_mode = interp_mode\n self.interp_cfg = dict() if interp_cfg is None else deepcopy(\n interp_cfg)\n self.interval = interval\n self.start_iter = start_iter\n\n assert hasattr(\n self, interp_mode\n ), f'Currently, we do not support {self.interp_mode} for EMA.'\n self.interp_func = partial(\n getattr(self, interp_mode), **self.interp_cfg)\n\n @staticmethod\n def lerp(a, b, momentum=0.001, momentum_nontrainable=1., trainable=True):\n \"\"\"Does a linear interpolation of two parameters/ buffers.\n\n Args:\n a (torch.Tensor): Interpolation start point, refer to orig state.\n b (torch.Tensor): Interpolation end point, refer to ema state.\n momentum (float, optional): The weight for the interpolation\n formula. Defaults to 0.001.\n momentum_nontrainable (float, optional): The weight for the\n interpolation formula used for nontrainable parameters.\n Defaults to 1..\n trainable (bool, optional): Whether input parameters is trainable.\n If set to False, momentum_nontrainable will be used.\n Defaults to True.\n Returns:\n torch.Tensor: Interpolation result.\n \"\"\"\n assert 0.0 < momentum < 1.0, 'momentum must be in range (0.0, 1.0)'\\\n f'but got {momentum}'\n assert 0.0 < momentum_nontrainable <= 1.0, (\n 'momentum_nontrainable must be in range (0.0, 1.0] but got '\n f'{momentum_nontrainable}')\n if momentum > 0.5:\n warnings.warn(\n 'The value of momentum in EMA is usually a small number,'\n 'which is different from the conventional notion of '\n f'momentum but got {momentum}. Please make sure the '\n f'value is correct.')\n m = momentum if trainable else momentum_nontrainable\n return b + (a - b) * m\n\n def every_n_iters(self, runner: Runner, n: int):\n \"\"\"This is the function to perform every n iterations.\n\n Args:\n runner (Runner): runner used to drive the whole pipeline\n n (int): the number of iterations\n\n Returns:\n int: the latest iterations\n \"\"\"\n if runner.iter < self.start_iter:\n return True\n return (runner.iter + 1 - self.start_iter) % n == 0 if n > 0 else False\n\n @torch.no_grad()\n def after_train_iter(self,\n runner: Runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None,\n outputs: Optional[dict] = None) -> None:\n \"\"\"This is the function to perform after each training iteration.\n\n Args:\n runner (Runner): runner to drive the pipeline\n batch_idx (int): the id of batch\n data_batch (DATA_BATCH, optional): data batch. Defaults to None.\n outputs (Optional[dict], optional): output. Defaults to None.\n \"\"\"\n\n if not self.every_n_iters(runner, self.interval):\n return\n\n model = runner.model.module if is_model_wrapper(\n runner.model) else runner.model\n\n for key in self.module_keys:\n # get current ema states\n ema_net = getattr(model, key)\n states_ema = ema_net.state_dict(keep_vars=False)\n # get currently original states\n net = getattr(model, key[:-4])\n states_orig = net.state_dict(keep_vars=True)\n\n for k, v in states_orig.items():\n if runner.iter < self.start_iter:\n states_ema[k].data.copy_(v.data)\n else:\n states_ema[k] = self.interp_func(\n v, states_ema[k], trainable=v.requires_grad).detach()\n ema_net.load_state_dict(states_ema, strict=True)\n\n def before_run(self, runner: Runner):\n \"\"\"This is the function perform before each run.\n\n Args:\n runner (Runner): runner used to drive the whole pipeline\n\n Raises:\n RuntimeError: error message\n \"\"\"\n model = runner.model.module if is_model_wrapper(\n runner.model) else runner.model\n # sanity check for ema model\n for k in self.module_keys:\n if not hasattr(model, k) and not hasattr(model, k[:-4]):\n raise RuntimeError(\n f'Cannot find both {k[:-4]} and {k} network for EMA hook.')\n if not hasattr(model, k) and hasattr(model, k[:-4]):\n setattr(model, k, deepcopy(getattr(model, k[:-4])))\n warnings.warn(\n f'We do not suggest construct and initialize EMA model {k}'\n ' in hook. You may explicitly define it by yourself.')\n" }, { "path": "mmagic/engine/hooks/iter_time_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport time\nfrom typing import Optional, Sequence, Union\n\nfrom mmengine.hooks import IterTimerHook as BaseIterTimerHook\nfrom mmengine.structures import BaseDataElement\n\nfrom mmagic.registry import HOOKS\n\nDATA_BATCH = Optional[Sequence[dict]]\n\n\n@HOOKS.register_module()\nclass IterTimerHook(BaseIterTimerHook):\n \"\"\"IterTimerHooks inherits from :class:`mmengine.hooks.IterTimerHook` and\n overwrites :meth:`self._after_iter`.\n\n This hooks should be used along with\n :class:`mmagic.engine.runner.MultiValLoop` and\n :class:`mmagic.engine.runner.MultiTestLoop`.\n \"\"\"\n\n def _after_iter(self,\n runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None,\n outputs: Optional[Union[dict,\n Sequence[BaseDataElement]]] = None,\n mode: str = 'train') -> None:\n \"\"\"Calculating time for an iteration and updating \"time\"\n ``HistoryBuffer`` of ``runner.message_hub``. If `mode` is 'train', we\n take `runner.max_iters` as the total iterations and calculate the rest\n time. If `mode` in `val` or `test`, we use\n `runner.val_loop.total_length` or `runner.test_loop.total_length` as\n total number of iterations. If you want to know how `total_length` is\n calculated, please refers to\n :meth:`mmagic.engine.runner.MultiValLoop.run` and\n :meth:`mmagic.engine.runner.MultiTestLoop.run`.\n\n Args:\n runner (Runner): The runner of the training validation and\n testing process.\n batch_idx (int): The index of the current batch in the loop.\n data_batch (Sequence[dict], optional): Data from dataloader.\n Defaults to None.\n outputs (dict or sequence, optional): Outputs from model. Defaults\n to None.\n mode (str): Current mode of runner. Defaults to 'train'.\n \"\"\"\n # Update iteration time in `runner.message_hub`.\n message_hub = runner.message_hub\n message_hub.update_scalar(f'{mode}/time', time.time() - self.t)\n self.t = time.time()\n window_size = runner.log_processor.window_size\n # Calculate eta every `window_size` iterations. Since test and val\n # loop will not update runner.iter, use `every_n_inner_iters`to check\n # the interval.\n if self.every_n_inner_iters(batch_idx, window_size):\n iter_time = message_hub.get_scalar(f'{mode}/time').mean(\n window_size)\n if mode == 'train':\n self.time_sec_tot += iter_time * window_size\n # Calculate average iterative time.\n time_sec_avg = self.time_sec_tot / (\n runner.iter - self.start_iter + 1)\n # Calculate eta.\n eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1)\n runner.message_hub.update_info('eta', eta_sec)\n else:\n if mode == 'val':\n total_length = runner.val_loop.total_length\n else:\n total_length = runner.test_loop.total_length\n\n eta_sec = iter_time * (total_length - batch_idx - 1)\n runner.message_hub.update_info('eta', eta_sec)\n" }, { "path": "mmagic/engine/hooks/pggan_fetch_data_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Sequence\n\nimport torch\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler, pseudo_collate\nfrom mmengine.hooks import Hook\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.runner import IterBasedTrainLoop\nfrom mmengine.runner.loops import _InfiniteDataloaderIterator\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import HOOKS\n\nDATA_BATCH = Optional[Sequence[dict]]\n\n\n@HOOKS.register_module()\nclass PGGANFetchDataHook(Hook):\n \"\"\"PGGAN Fetch Data Hook.\n\n Args:\n interval (int, optional): The interval of calling this hook. If set\n to -1, the visualization hook will not be called. Defaults to 1.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n def before_train_iter(self,\n runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None) -> None:\n\n _module = runner.model.module if is_model_wrapper(\n runner.model) else runner.model\n _next_scale_int = _module._next_scale_int\n if isinstance(_next_scale_int, torch.Tensor):\n _next_scale_int = _next_scale_int.item()\n\n dataloader_orig = runner.train_loop.dataloader\n new_dataloader = self.update_dataloader(dataloader_orig,\n _next_scale_int)\n if new_dataloader is not None:\n runner.train_loop.dataloader = new_dataloader\n if isinstance(runner.train_loop, IterBasedTrainLoop):\n runner.train_loop.dataloader_iterator = \\\n _InfiniteDataloaderIterator(new_dataloader)\n\n def update_dataloader(self, dataloader: DataLoader,\n curr_scale: int) -> Optional[DataLoader]:\n \"\"\"Update the data loader.\n\n Args:\n dataloader (DataLoader): The dataloader to be updated.\n curr_scale (int): The current scale of the generated image.\n\n Returns:\n Optional[DataLoader]: The updated dataloader. If the dataloader do\n not need to update, return None.\n \"\"\"\n if hasattr(dataloader.dataset, 'update_annotations'):\n update_flag = dataloader.dataset.update_annotations(curr_scale)\n else:\n update_flag = False\n\n if update_flag:\n dataset = dataloader.dataset\n # build new sampler\n sampler_orig = dataloader.sampler\n if isinstance(sampler_orig, DefaultSampler):\n shuffle = sampler_orig.shuffle\n seed = sampler_orig.seed\n round_up = sampler_orig.round_up\n sampler = DefaultSampler(dataset, shuffle, seed, round_up)\n elif isinstance(sampler_orig, InfiniteSampler):\n shuffle = sampler_orig.shuffle\n seed = sampler_orig.seed\n sampler = InfiniteSampler(dataset, shuffle, seed)\n else:\n raise ValueError('MMagic only support \\'DefaultSampler\\' and '\n '\\'InfiniteSampler\\' as sampler. But receive '\n f'\\'{type(sampler_orig)}\\'.')\n\n num_workers = dataloader.num_workers\n worker_init_fn = dataloader.worker_init_fn\n\n dataloader = DataLoader(\n dataset,\n batch_size=dataloader.dataset.samples_per_gpu,\n sampler=sampler,\n num_workers=num_workers,\n collate_fn=pseudo_collate,\n shuffle=False,\n worker_init_fn=worker_init_fn)\n return dataloader\n return None\n" }, { "path": "mmagic/engine/hooks/pickle_data_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport logging\nimport os\nimport pickle\nfrom typing import List, Optional, Sequence, Tuple\n\nimport numpy as np\nimport torch\nfrom mmengine import is_list_of, mkdir_or_exist, print_log\nfrom mmengine.dist import master_only\nfrom mmengine.hooks import Hook\nfrom mmengine.runner import Runner\nfrom torch import Tensor\n\nfrom mmagic.registry import HOOKS\n\nDATA_BATCH = Optional[Sequence[dict]]\n\n\n@HOOKS.register_module()\nclass PickleDataHook(Hook):\n \"\"\"Pickle Useful Data Hook.\n\n This hook will be used in SinGAN training for saving some important data\n that will be used in testing or inference.\n\n Args:\n output_dir (str): The output path for saving pickled data.\n data_name_list (list[str]): The list contains the name of results in\n outputs dict.\n interval (int): The interval of calling this hook. If set to -1,\n the PickleDataHook will not be called during training. Default: -1.\n before_run (bool, optional): Whether to save before running.\n Defaults to False.\n after_run (bool, optional): Whether to save after running.\n Defaults to False.\n filename_tmpl (str, optional): Format string used to save images. The\n output file name will be formatted as this args.\n Defaults to 'iter_{}.pkl'.\n \"\"\"\n\n def __init__(self,\n output_dir,\n data_name_list,\n interval=-1,\n before_run=False,\n after_run=False,\n filename_tmpl='iter_{}.pkl'):\n assert is_list_of(data_name_list, str)\n self.output_dir = output_dir\n self.data_name_list = data_name_list\n self.interval = interval\n self.filename_tmpl = filename_tmpl\n self._before_run = before_run\n self._after_run = after_run\n\n @master_only\n def after_run(self, runner):\n \"\"\"The behavior after each train iteration.\n\n Args:\n runner (object): The runner.\n \"\"\"\n if self._after_run:\n self._pickle_data(runner)\n\n @master_only\n def before_run(self, runner):\n \"\"\"The behavior after each train iteration.\n\n Args:\n runner (object): The runner.\n \"\"\"\n if self._before_run:\n self._pickle_data(runner)\n\n @master_only\n def after_train_iter(self,\n runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None,\n outputs: Optional[dict] = None):\n \"\"\"The behavior after each train iteration.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the train loop.\n data_batch (Sequence[dict], optional): Data from dataloader.\n Defaults to None.\n outputs (dict, optional): Outputs from model.\n Defaults to None.\n \"\"\"\n if not self.every_n_train_iters(runner, self.interval):\n return\n self._pickle_data(runner)\n\n def _pickle_data(self, runner: Runner):\n \"\"\"Save target data to pickle file.\n\n Args:\n runner (Runner): The runner of the training process.\n \"\"\"\n filename = self.filename_tmpl.format(runner.iter + 1)\n if not hasattr(self, '_out_dir'):\n self._out_dir = os.path.join(runner.work_dir, self.output_dir)\n mkdir_or_exist(self._out_dir)\n file_path = os.path.join(self._out_dir, filename)\n with open(file_path, 'wb') as f:\n module = runner.model\n if hasattr(module, 'module'):\n module = module.module\n not_find_keys = []\n data_dict = {}\n for k in self.data_name_list:\n if hasattr(module, k):\n data_dict[k] = self._get_numpy_data(getattr(module, k))\n else:\n not_find_keys.append(k)\n pickle.dump(data_dict, f)\n print_log(f'Pickle data in {filename}', 'current')\n\n if len(not_find_keys) > 0:\n print_log(\n f'Cannot find keys for pickling: {not_find_keys}',\n 'current',\n level=logging.WARN)\n f.flush()\n\n def _get_numpy_data(\n self, data: Tuple[List[Tensor], Tensor, int]\n ) -> Tuple[List[np.ndarray], np.ndarray, int]:\n \"\"\"Convert tensor or list of tensor to numpy or list of numpy.\n\n Args:\n data (Tuple[List[Tensor], Tensor, int]): Data to be converted.\n\n Returns:\n Tuple[List[np.ndarray], np.ndarray, int]: Converted data.\n \"\"\"\n if isinstance(data, list):\n return [self._get_numpy_data(x) for x in data]\n\n if isinstance(data, torch.Tensor):\n return data.cpu().numpy()\n\n return data\n" }, { "path": "mmagic/engine/hooks/reduce_lr_scheduler_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Sequence\n\nfrom mmengine import MessageHub\nfrom mmengine.hooks import ParamSchedulerHook\nfrom mmengine.runner import Runner\n\nfrom mmagic.registry import HOOKS\n\nDATA_BATCH = Optional[Sequence[dict]]\n\n\n@HOOKS.register_module()\nclass ReduceLRSchedulerHook(ParamSchedulerHook):\n \"\"\"A hook to update learning rate.\n\n Args:\n val_metric (str): The metric of validation. If val_metric is not None,\n we check val_metric to reduce learning. Default: None.\n by_epoch (bool): Whether to update by epoch. Default: True.\n interval (int): The interval of iterations to update. Default: 1.\n \"\"\"\n\n def __init__(self,\n val_metric: str = None,\n by_epoch=True,\n interval=1) -> None:\n super().__init__()\n\n self.message_hub = MessageHub.get_instance('reduce_lr')\n\n self.val_metric = val_metric\n self.by_epoch = by_epoch\n self.interval = interval\n self.sum_value = 0\n self.count = 0\n\n def _calculate_average_value(self):\n value = self.sum_value / self.count\n self.sum_value = 0\n self.count = 0\n self.message_hub.update_scalar('value', value)\n\n def after_train_epoch(self, runner: Runner):\n \"\"\"Call step function for each scheduler after each train epoch.\n\n Args:\n runner (Runner): The runner of the training process.\n \"\"\"\n if not self.by_epoch:\n return\n\n # If val_metric is not None, we check val_metric to reduce learning\n if self.val_metric is not None:\n return\n\n if self.every_n_epochs(runner, self.interval):\n self._calculate_average_value()\n super().after_train_epoch(runner=runner)\n\n def after_train_iter(self,\n runner: Runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None,\n outputs: Optional[dict] = None) -> None:\n \"\"\"Call step function for each scheduler after each iteration.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the train loop.\n data_batch (Sequence[dict], optional): Data from dataloader.\n In order to keep this interface consistent with other hooks,\n we keep ``data_batch`` here. Defaults to None.\n outputs (dict, optional): Outputs from model.\n In order to keep this interface consistent with other hooks, we\n keep ``data_batch`` here. Defaults to None.\n \"\"\"\n\n # If val_metric is not None, we check val_metric to reduce learning\n if self.val_metric is not None:\n return\n\n current = runner.message_hub.get_scalar('train/loss').current()\n self.sum_value += current * len(data_batch)\n self.count += len(data_batch)\n\n if self.by_epoch:\n return\n\n if self.every_n_train_iters(runner, self.interval):\n self._calculate_average_value()\n super().after_train_iter(\n runner=runner,\n batch_idx=batch_idx,\n data_batch=data_batch,\n outputs=outputs)\n\n def after_val_epoch(self,\n runner,\n metrics: Optional[Dict[str, float]] = None):\n \"\"\"Call step function for each scheduler after each validation epoch.\n\n Args:\n runner (Runner): The runner of the training process.\n metrics (dict, optional): The metrics of validation. Default: None.\n \"\"\"\n # If val_metric is None, we check training loss to reduce learning\n # rate.\n if self.val_metric is None:\n return\n\n if self.val_metric not in metrics:\n raise KeyError(f'{self.val_metric} is not found in metrics')\n\n self.sum_value += metrics[self.val_metric]\n self.count += 1\n\n if not self.by_epoch or self.every_n_epochs(runner, self.interval):\n # if self.by_epoch is False,\n # call val after several iter\n # and update LR in each ``after_val_epoch``\n self._calculate_average_value()\n\n def step(param_schedulers):\n assert isinstance(param_schedulers, list)\n for scheduler in param_schedulers:\n scheduler.step()\n\n if isinstance(runner.param_schedulers, list):\n step(runner.param_schedulers)\n elif isinstance(runner.param_schedulers, dict):\n for param_schedulers in runner.param_schedulers.values():\n step(param_schedulers)\n else:\n raise TypeError(\n 'runner.param_schedulers should be list of ParamScheduler '\n 'or a dict containing list of ParamScheduler, '\n f'but got {runner.param_schedulers}')\n" }, { "path": "mmagic/engine/hooks/visualization_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport warnings\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Sequence, Tuple, Union\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.dist import master_only\nfrom mmengine.hooks import Hook\nfrom mmengine.registry import HOOKS\nfrom mmengine.runner import Runner\nfrom mmengine.structures import BaseDataElement\nfrom mmengine.utils import is_list_of\nfrom mmengine.visualization import Visualizer\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import get_sampler\n\n\n@HOOKS.register_module()\nclass BasicVisualizationHook(Hook):\n \"\"\"Basic hook that invoke visualizers during validation and test.\n\n Args:\n interval (int | dict): Visualization interval. Default: {}.\n on_train (bool): Whether to call hook during train. Default to False.\n on_val (bool): Whether to call hook during validation. Default to True.\n on_test (bool): Whether to call hook during test. Default to True.\n \"\"\"\n priority = 'NORMAL'\n\n def __init__(self,\n interval: dict = {},\n on_train=False,\n on_val=True,\n on_test=True):\n self._interval = interval\n self._sample_counter = 0\n self._vis_dir = None\n self._on_train = on_train\n self._on_val = on_val\n self._on_test = on_test\n\n def _after_iter(\n self,\n runner,\n batch_idx: int,\n data_batch: Optional[Sequence[dict]],\n outputs: Optional[Sequence[BaseDataElement]],\n mode=None,\n ) -> None:\n \"\"\"Show or Write the predicted results.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the test loop.\n data_batch (Sequence[dict], optional): Data\n from dataloader. Defaults to None.\n outputs (Sequence[BaseDataElement], optional): Outputs from model.\n Defaults to None.\n \"\"\"\n if mode == 'train' and (not self._on_train):\n return\n elif mode == 'val' and (not self._on_val):\n return\n elif mode == 'test' and (not self._on_test):\n return\n\n if isinstance(self._interval, int):\n interval = self._interval\n else:\n interval = self._interval.get(mode, 1)\n\n if self.every_n_inner_iters(batch_idx, interval):\n for data_sample in outputs:\n runner.visualizer.add_datasample(data_sample, step=runner.iter)\n\n\n@HOOKS.register_module()\nclass VisualizationHook(Hook):\n \"\"\"MMagic Visualization Hook. Used to visual output samples in training,\n validation and testing. In this hook, we use a list called\n `sample_kwargs_list` to control how to generate samples and how to\n visualize them. Each element in `sample_kwargs_list`, called\n `sample_kwargs`, may contains the following keywords:\n\n - Required key words:\n - 'type': Value must be string. Denotes what kind of sampler is used to\n generate image. Refers to :meth:`~mmagic.utils.get_sampler`.\n - Optional key words (If not passed, will use the default value):\n - 'n_row': Value must be int. The number of images in one row.\n - 'num_samples': Value must be int. The number of samples to visualize.\n - 'vis_mode': Value must be string. How to visualize the generated\n samples (e.g. image, gif).\n - 'fixed_input': Value must be bool. Whether use the fixed input\n during the loop.\n - 'draw_gt': Value must be bool. Whether save the real images.\n - 'target_keys': Value must be string or list of string. The keys of\n the target image to visualize.\n - 'name': Value must be string. If not passed, will use\n `sample_kwargs['type']` as default.\n\n For convenience, we also define a group of alias of samplers' type for\n models supported in MMagic. Refers to\n `:attr:self.SAMPLER_TYPE_MAPPING`.\n\n Example:\n >>> # for GAN models\n >>> custom_hooks = [\n >>> dict(\n >>> type='VisualizationHook',\n >>> interval=1000,\n >>> fixed_input=True,\n >>> vis_kwargs_list=dict(type='GAN', name='fake_img'))]\n >>> # for Translation models\n >>> custom_hooks = [\n >>> dict(\n >>> type='VisualizationHook',\n >>> interval=10,\n >>> fixed_input=False,\n >>> vis_kwargs_list=[dict(type='Translation',\n >>> name='translation_train',\n >>> n_samples=6, draw_gt=True,\n >>> n_row=3),\n >>> dict(type='TranslationVal',\n >>> name='translation_val',\n >>> n_samples=16, draw_gt=True,\n >>> n_row=4)])]\n\n # NOTE: user-defined vis_kwargs > vis_kwargs_mapping > hook init args\n\n Args:\n interval (int): Visualization interval. Default: 1000.\n sampler_kwargs_list (Tuple[List[dict], dict]): The list of sampling\n behavior to generate images.\n fixed_input (bool): The default action of whether use fixed input to\n generate samples during the loop. Defaults to True.\n n_samples (Optional[int]): The default value of number of samples to\n visualize. Defaults to 64.\n n_row (Optional[int]): The default value of number of images in each\n row in the visualization results. Defaults to None.\n message_hub_vis_kwargs (Optional[Tuple[str, dict, List[str],\n List[Dict]]]): Key arguments visualize images in message hub.\n Defaults to None.\n save_at_test (bool): Whether save images during test. Defaults to True.\n max_save_at_test (int): Maximum number of samples saved at test time.\n If None is passed, all samples will be saved. Defaults to 100.\n show (bool): Whether to display the drawn image. Default to False.\n wait_time (float): The interval of show (s). Defaults to 0.\n \"\"\"\n\n priority = 'NORMAL'\n\n VIS_KWARGS_MAPPING = dict(\n GAN=dict(type='Noise'),\n SinGAN=dict(type='Arguments', forward_kwargs=dict(mode='rand')),\n Translation=dict(type='Data'),\n TranslationVal=dict(type='ValData'),\n TranslationTest=dict(type='TestData'),\n DDPMDenoising=dict(\n type='Arguments',\n name='ddpm_sample',\n n_samples=16,\n n_row=4,\n vis_mode='gif',\n n_skip=100,\n forward_kwargs=dict(\n forward_mode='sampling',\n sample_kwargs=dict(show_pbar=True, save_intermedia=True))))\n\n def __init__(self,\n interval: int = 1000,\n vis_kwargs_list: Tuple[List[dict], dict] = None,\n fixed_input: bool = True,\n n_samples: Optional[int] = 64,\n n_row: Optional[int] = None,\n message_hub_vis_kwargs: Optional[Tuple[str, dict, List[str],\n List[Dict]]] = None,\n save_at_test: bool = True,\n max_save_at_test: int = 100,\n test_vis_keys: Optional[Union[str, List[str]]] = None,\n show: bool = False,\n wait_time: float = 0):\n\n self._visualizer: Visualizer = Visualizer.get_current_instance()\n self.interval = interval\n\n self.vis_kwargs_list = deepcopy(vis_kwargs_list)\n if isinstance(self.vis_kwargs_list, dict):\n self.vis_kwargs_list = [self.vis_kwargs_list]\n\n self.fixed_input = fixed_input\n self.inputs_buffer = defaultdict(list)\n\n self.n_samples = n_samples\n self.n_row = n_row\n\n self.show = show\n if self.show:\n # No need to think about vis backends.\n self._visualizer._vis_backends = {}\n warnings.warn('The show is True, it means that only '\n 'the prediction results are visualized '\n 'without storing data, so vis_backends '\n 'needs to be excluded.')\n\n self.wait_time = wait_time\n self.save_at_test = save_at_test\n self.test_vis_keys_list = test_vis_keys\n self.max_save_at_test = max_save_at_test\n self.message_vis_kwargs = message_hub_vis_kwargs\n\n @master_only\n def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict,\n outputs) -> None:\n \"\"\":class:`VisualizationHook` do not support visualize during\n validation.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the test loop.\n data_batch (Sequence[dict], optional): Data from dataloader.\n Defaults to None.\n outputs: outputs of the generation model\n \"\"\"\n return\n\n @master_only\n def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: dict,\n outputs):\n \"\"\"Visualize samples after test iteration.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the test loop.\n data_batch (dict, optional): Data from dataloader.\n Defaults to None.\n outputs: outputs of the generation model Defaults to None.\n \"\"\"\n if not self.save_at_test:\n return\n\n for idx, sample in enumerate(outputs):\n curr_idx = batch_idx * len(outputs) + idx\n if (self.max_save_at_test is not None\n and curr_idx >= self.max_save_at_test):\n continue\n # NOTE: only support visualize image tensors (ndim == 3)\n if self.test_vis_keys_list is None:\n target_keys = [\n k for k, v in sample.items() if not k.startswith('_')\n and isinstance(v, torch.Tensor) and v.ndim == 3\n ]\n assert len(target_keys), (\n 'Cannot found Tensor in outputs. Please specific '\n '\\'vis_test_keys_list\\'.')\n elif isinstance(self.test_vis_keys_list, str):\n target_keys = [self.test_vis_keys_list]\n else:\n assert is_list_of(self.test_vis_keys_list, str), (\n 'test_vis_keys_list must be str or list of str or None.')\n target_keys = self.test_vis_keys_list\n\n for key in target_keys:\n name = key.replace('.', '_')\n self._visualizer.add_datasample(\n name=f'test_{name}',\n gen_samples=[sample],\n step=curr_idx,\n target_keys=key,\n n_row=1)\n\n @master_only\n def after_train_iter(self,\n runner: Runner,\n batch_idx: int,\n data_batch: dict = None,\n outputs: Optional[dict] = None) -> None:\n \"\"\"Visualize samples after train iteration.\n\n Args:\n runner (Runner): The runner of the training process.\n batch_idx (int): The index of the current batch in the train loop.\n data_batch (dict): Data from dataloader.\n Defaults to None.\n outputs (dict, optional): Outputs from model. Defaults to None.\n \"\"\"\n if self.every_n_inner_iters(batch_idx, self.interval):\n self.vis_sample(runner, batch_idx, data_batch, outputs)\n\n @torch.no_grad()\n def vis_sample(self,\n runner: Runner,\n batch_idx: int,\n data_batch: dict,\n outputs: Optional[dict] = None) -> None:\n \"\"\"Visualize samples.\n\n Args:\n runner (Runner): The runner contains model to visualize.\n batch_idx (int): The index of the current batch in loop.\n data_batch (dict): Data from dataloader.\n Defaults to None.\n outputs (dict, optional): Outputs from model. Defaults to None.\n \"\"\"\n # this function will only called in training process\n num_batches = runner.train_dataloader.batch_size\n\n module = runner.model\n module.eval()\n if hasattr(module, 'module'):\n module = module.module\n\n forward_func = module.val_step\n\n for vis_kwargs in self.vis_kwargs_list:\n # pop the sample-unrelated values\n vis_kwargs_ = deepcopy(vis_kwargs)\n sampler_type = vis_kwargs_['type']\n\n # replace with alias\n for alias in self.VIS_KWARGS_MAPPING.keys():\n if alias.upper() == sampler_type.upper():\n sampler_alias = deepcopy(self.VIS_KWARGS_MAPPING[alias])\n vis_kwargs_['type'] = sampler_alias.pop('type')\n for default_k, default_v in sampler_alias.items():\n vis_kwargs_.setdefault(default_k, default_v)\n break\n # sampler_type = vis_kwargs_.pop('type')\n\n name = vis_kwargs_.pop('name', None)\n if not name:\n name = sampler_type.lower()\n\n n_samples = vis_kwargs_.pop('n_samples', self.n_samples)\n n_row = vis_kwargs_.pop('n_row', self.n_row)\n\n num_iters = math.ceil(n_samples / num_batches)\n vis_kwargs_['max_times'] = num_iters\n vis_kwargs_['num_batches'] = num_batches\n fixed_input = vis_kwargs_.pop('fixed_input', self.fixed_input)\n target_keys = vis_kwargs_.pop('target_keys', None)\n vis_mode = vis_kwargs_.pop('vis_mode', None)\n\n output_list = []\n if fixed_input and self.inputs_buffer[sampler_type]:\n sampler = self.inputs_buffer[sampler_type]\n else:\n sampler = get_sampler(vis_kwargs_, runner)\n need_save = fixed_input and not self.inputs_buffer[sampler_type]\n\n for inputs in sampler:\n output = forward_func(inputs)\n if len(output) != num_batches:\n # one sample contains multiple elements\n output_list.append(output)\n contain_mul_elements = True\n else:\n output_list += [out for out in forward_func(inputs)]\n contain_mul_elements = False\n\n # save inputs\n if need_save:\n self.inputs_buffer[sampler_type].append(inputs)\n\n output_list = output_list[:n_samples]\n if contain_mul_elements:\n output_to_vis = []\n for output in output_list:\n output_to_vis += output\n else:\n output_to_vis = output_list\n n_row = min(n_row, len(output_to_vis)) if n_row else None\n\n self._visualizer.add_datasample(\n name=name,\n gen_samples=output_to_vis,\n target_keys=target_keys,\n vis_mode=vis_mode,\n n_row=n_row,\n show=self.show,\n wait_time=self.wait_time,\n step=batch_idx + 1,\n **vis_kwargs_)\n\n # save images in message_hub\n self.vis_from_message_hub(batch_idx)\n\n module.train()\n\n def vis_from_message_hub(self, batch_idx: int):\n \"\"\"Visualize samples from message hub.\n\n Args:\n batch_idx (int): The index of the current batch in the test loop.\n color_order (str): The color order of generated images.\n target_mean (Sequence[Union[float, int]]): The original mean\n of the image tensor before preprocessing. Image will be\n re-shifted to ``target_mean`` before visualizing.\n target_std (Sequence[Union[float, int]]): The original std of the\n image tensor before preprocessing. Image will be re-scaled to\n ``target_std`` before visualizing.\n \"\"\"\n # TODO: add destruct in this function\n if self.message_vis_kwargs is None:\n return\n\n message_hub = MessageHub.get_current_instance()\n if 'vis_results' not in message_hub.runtime_info:\n raise RuntimeError('Cannot find \\'vis_results\\' in '\n '\\'message_hub.runtime_info\\'. Cannot perform '\n 'visualization from messageHub.')\n\n vis_results = message_hub.get_info('vis_results')\n if isinstance(self.message_vis_kwargs, str):\n target_keys, vis_modes = [self.message_vis_kwargs], [None]\n elif isinstance(self.message_vis_kwargs, dict):\n target_keys = [self.message_vis_kwargs['key']]\n vis_modes = [self.message_vis_kwargs['vis_mode']]\n elif is_list_of(self.message_vis_kwargs, str):\n target_keys = self.message_vis_kwargs\n vis_modes = [None for _ in range(len(target_keys))]\n else:\n # list of dict\n target_keys = [kwargs['key'] for kwargs in self.message_vis_kwargs]\n vis_modes = [\n kwargs.pop('vis_mode', None)\n for kwargs in deepcopy(self.message_vis_kwargs)\n ]\n\n for key, vis_mode in zip(target_keys, vis_modes):\n if key not in vis_results:\n raise RuntimeError(\n f'Cannot find \\'{key}\\' in '\n 'message_hub.runtime_info[\\'vis_results\\'].')\n\n value = vis_results[key]\n # pack to list of DataSample\n if isinstance(value, torch.Tensor):\n gen_samples = []\n num_batches = value.shape[0]\n for idx in range(num_batches):\n gen_sample = DataSample()\n setattr(gen_sample, key, value[idx])\n gen_samples.append(gen_sample)\n elif is_list_of(value, BaseDataElement):\n # already packed\n gen_samples = value\n num_batches = len(gen_samples)\n else:\n raise TypeError(\n 'Only support to visualize Tensor or list of DataSample '\n f'in MessageHub. But \\'{key}\\' is \\'{type(value)}\\'.')\n\n self._visualizer.add_datasample(\n name=f'train_{key}',\n gen_samples=gen_samples,\n target_keys=key,\n vis_mode=vis_mode,\n n_row=min(self.n_row, num_batches) if self.n_row else None,\n show=self.show,\n step=batch_idx)\n" }, { "path": "mmagic/engine/optimizers/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .multi_optimizer_constructor import MultiOptimWrapperConstructor\nfrom .pggan_optimizer_constructor import PGGANOptimWrapperConstructor\nfrom .singan_optimizer_constructor import SinGANOptimWrapperConstructor\n\n__all__ = [\n 'MultiOptimWrapperConstructor',\n 'PGGANOptimWrapperConstructor',\n 'SinGANOptimWrapperConstructor',\n]\n" }, { "path": "mmagic/engine/optimizers/multi_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport re\nfrom typing import Tuple, Union\n\nimport torch.nn as nn\nfrom mmengine import print_log\nfrom mmengine.optim import (DefaultOptimWrapperConstructor, OptimWrapper,\n OptimWrapperDict)\n\nfrom mmagic.registry import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIM_WRAPPERS,\n OPTIMIZERS)\n\n\n@OPTIM_WRAPPER_CONSTRUCTORS.register_module()\nclass MultiOptimWrapperConstructor:\n \"\"\"OptimizerConstructor for GAN models. This class construct optimizer for\n the submodules of the model separately, and return a\n :class:`mmengine.optim.OptimWrapperDict` or\n :class:`mmengine.optim.OptimWrapper`.\n\n Example 1: Build multi optimizers (e.g., GANs):\n >>> # build GAN model\n >>> model = dict(\n >>> type='GANModel',\n >>> num_classes=10,\n >>> generator=dict(type='Generator'),\n >>> discriminator=dict(type='Discriminator'))\n >>> gan_model = MODELS.build(model)\n >>> # build constructor\n >>> optim_wrapper = dict(\n >>> generator=dict(\n >>> type='OptimWrapper',\n >>> accumulative_counts=1,\n >>> optimizer=dict(type='Adam', lr=0.0002,\n >>> betas=(0.5, 0.999))),\n >>> discriminator=dict(\n >>> type='OptimWrapper',\n >>> accumulative_counts=1,\n >>> optimizer=dict(type='Adam', lr=0.0002,\n >>> betas=(0.5, 0.999))))\n >>> optim_dict_builder = MultiOptimWrapperConstructor(optim_wrapper)\n >>> # build optim wrapper dict\n >>> optim_wrapper_dict = optim_dict_builder(gan_model)\n\n Example 2: Build multi optimizers for specific submodules:\n >>> # build model\n >>> class GAN(nn.Module):\n >>> def __init__(self) -> None:\n >>> super().__init__()\n >>> self.generator = nn.Conv2d(3, 3, 1)\n >>> self.discriminator = nn.Conv2d(3, 3, 1)\n >>> class TextEncoder(nn.Module):\n >>> def __init__(self):\n >>> super().__init__()\n >>> self.embedding = nn.Embedding(100, 100)\n >>> class ToyModel(nn.Module):\n >>> def __init__(self) -> None:\n >>> super().__init__()\n >>> self.m1 = GAN()\n >>> self.m2 = nn.Conv2d(3, 3, 1)\n >>> self.m3 = nn.Linear(2, 2)\n >>> self.text_encoder = TextEncoder()\n >>> model = ToyModel()\n >>> # build constructor\n >>> optim_wrapper = {\n >>> '.*embedding': {\n >>> 'type': 'OptimWrapper',\n >>> 'optimizer': {\n >>> 'type': 'Adam',\n >>> 'lr': 1e-4,\n >>> 'betas': (0.9, 0.99)\n >>> }\n >>> },\n >>> 'm1.generator': {\n >>> 'type': 'OptimWrapper',\n >>> 'optimizer': {\n >>> 'type': 'Adam',\n >>> 'lr': 1e-5,\n >>> 'betas': (0.9, 0.99)\n >>> }\n >>> },\n >>> 'm2': {\n >>> 'type': 'OptimWrapper',\n >>> 'optimizer': {\n >>> 'type': 'Adam',\n >>> 'lr': 1e-5,\n >>> }\n >>> }\n >>> }\n >>> optim_dict_builder = MultiOptimWrapperConstructor(optim_wrapper)\n >>> # build optim wrapper dict\n >>> optim_wrapper_dict = optim_dict_builder(model)\n\n Example 3: Build a single optimizer for multi modules (e.g., DreamBooth):\n >>> # build StableDiffusion model\n >>> model = dict(\n >>> type='StableDiffusion',\n >>> unet=dict(type='unet'),\n >>> vae=dict(type='vae'),\n text_encoder=dict(type='text_encoder'))\n >>> diffusion_model = MODELS.build(model)\n >>> # build constructor\n >>> optim_wrapper = dict(\n >>> modules=['unet', 'text_encoder']\n >>> optimizer=dict(type='Adam', lr=0.0002),\n >>> accumulative_counts=1)\n >>> optim_dict_builder = MultiOptimWrapperConstructor(optim_wrapper)\n >>> # build optim wrapper dict\n >>> optim_wrapper_dict = optim_dict_builder(diffusion_model)\n\n Args:\n optim_wrapper_cfg_dict (dict): Config of the optimizer wrapper.\n paramwise_cfg (dict): Config of parameter-wise settings. Default: None.\n \"\"\"\n\n def __init__(self, optim_wrapper_cfg: dict, paramwise_cfg=None):\n\n if not isinstance(optim_wrapper_cfg, dict):\n raise TypeError('optimizer_cfg should be a dict',\n f'but got {type(optim_wrapper_cfg)}')\n assert paramwise_cfg is None, (\n 'paramwise_cfg should be set in each optimizer separately')\n self.optim_cfg = optim_wrapper_cfg\n\n if 'modules' in optim_wrapper_cfg:\n # single optimizer with multi param groups\n cfg_ = optim_wrapper_cfg.copy()\n self.modules = cfg_.pop('modules')\n paramwise_cfg_ = cfg_.pop('paramwise_cfg', None)\n self.constructors = DefaultOptimWrapperConstructor(\n cfg_, paramwise_cfg_)\n else:\n self.constructors = {}\n self.modules = {}\n for key, cfg in self.optim_cfg.items():\n cfg_ = cfg.copy()\n if 'modules' in cfg_:\n self.modules[key] = cfg_.pop('modules')\n paramwise_cfg_ = cfg_.pop('paramwise_cfg', None)\n self.constructors[key] = DefaultOptimWrapperConstructor(\n cfg_, paramwise_cfg_)\n\n def __call__(self,\n module: nn.Module) -> Union[OptimWrapperDict, OptimWrapper]:\n \"\"\"Build optimizer and return a optimizer_wrapper_dict.\"\"\"\n\n optimizers = {}\n if hasattr(module, 'module'):\n module = module.module\n if isinstance(self.constructors, dict):\n for key, constructor in self.constructors.items():\n module_names = self.modules[key] if self.modules else key\n if (isinstance(module_names, str)\n and module_names in module._modules):\n optimizers[key] = constructor(\n module._modules[module_names])\n optim_wrapper_cfg = constructor.optimizer_cfg\n print_log(\n f'Add to optimizer \\'{key}\\' '\n f'({optim_wrapper_cfg}): \\'{key}\\'.', 'current')\n else:\n\n assert not constructor.paramwise_cfg, (\n 'Do not support paramwise_cfg for multi module '\n 'optimizer.')\n\n params, found_names = get_params_by_names(\n module, module_names)\n # build optimizer\n optimizer_cfg = constructor.optimizer_cfg.copy()\n optimizer_cfg['params'] = params\n optimizer = OPTIMIZERS.build(optimizer_cfg)\n\n # build optimizer wrapper\n optim_wrapper_cfg = constructor.optim_wrapper_cfg.copy()\n optim_wrapper_cfg.setdefault('type', 'OptimWrapper')\n optim_wrapper = OPTIM_WRAPPERS.build(\n optim_wrapper_cfg,\n default_args=dict(optimizer=optimizer))\n\n for name in found_names:\n print_log(\n f'Add to optimizer \\'{key}\\' '\n f'({constructor.optimizer_cfg}): \\'{name}\\'.',\n 'current')\n\n optimizers[key] = optim_wrapper\n\n return OptimWrapperDict(**optimizers)\n\n else:\n params, found_names = get_params_by_names(module, self.modules)\n\n constructor = self.constructors\n assert not constructor.paramwise_cfg, (\n 'Do not support paramwise_cfg for multi parameters')\n\n optimizer_cfg = constructor.optimizer_cfg.copy()\n optimizer_cfg['params'] = params\n optimizer = OPTIMIZERS.build(optimizer_cfg)\n for name in found_names:\n print_log(\n f'Add to optimizer ({constructor.optimizer_cfg}): '\n f'\\'{name}\\'.', 'current')\n\n # build optimizer wrapper\n optim_wrapper_cfg = constructor.optim_wrapper_cfg.copy()\n optim_wrapper_cfg.setdefault('type', 'OptimWrapper')\n optim_wrapper = OPTIM_WRAPPERS.build(\n optim_wrapper_cfg, default_args=dict(optimizer=optimizer))\n\n return optim_wrapper\n\n\ndef get_params_by_names(module: nn.Module,\n names: Union[str, list]) -> Tuple[list, list]:\n \"\"\"Support two kinds of name matching:\n 1. matching name from **first-level** submodule.\n 2. matching name by `re.fullmatch`.\n\n Args:\n module (nn.Module): The module to get parameters.\n names (Union[str, list]): The name or a list of names of the\n submodule parameters.\n\n Returns:\n Tuple[list]: A list of parameters and corresponding name for logging.\n \"\"\"\n\n if not isinstance(names, list):\n names = [names]\n\n params = []\n found_names = []\n for name in names:\n if name in module._modules:\n params.extend(module._modules[name].parameters())\n found_names.append(name)\n else:\n for n, m in module.named_modules():\n if re.fullmatch(name, n):\n params.extend(m.parameters())\n found_names.append(n)\n return params, found_names\n" }, { "path": "mmagic/engine/optimizers/pggan_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.optim import DefaultOptimWrapperConstructor, OptimWrapperDict\n\nfrom mmagic.registry import OPTIM_WRAPPER_CONSTRUCTORS\n\n\n@OPTIM_WRAPPER_CONSTRUCTORS.register_module()\nclass PGGANOptimWrapperConstructor:\n \"\"\"OptimizerConstructor for PGGAN models. Set optimizers for each\n stage of PGGAN. All submodule must be contained in a\n :class:`torch.nn.ModuleList` named 'blocks'. And we access each submodule\n by `MODEL.blocks[SCALE]`, where `MODEL` is generator or discriminator, and\n the scale is the index of the resolution scale.\n\n More detail about the resolution scale and naming rule please refers to\n :class:`~mmagic.models.editors.pggan.PGGANGenerator` and\n :class:`~mmagic.models.editors.pggan.PGGANDiscriminator`.\n\n Example:\n >>> # build PGGAN model\n >>> model = dict(\n >>> type='ProgressiveGrowingGAN',\n >>> data_preprocessor=dict(type='GANDataPreprocessor'),\n >>> noise_size=512,\n >>> generator=dict(type='PGGANGenerator', out_scale=1024,\n >>> noise_size=512),\n >>> discriminator=dict(type='PGGANDiscriminator', in_scale=1024),\n >>> nkimgs_per_scale={\n >>> '4': 600,\n >>> '8': 1200,\n >>> '16': 1200,\n >>> '32': 1200,\n >>> '64': 1200,\n >>> '128': 1200,\n >>> '256': 1200,\n >>> '512': 1200,\n >>> '1024': 12000,\n >>> },\n >>> transition_kimgs=600,\n >>> ema_config=dict(interval=1))\n >>> pggan = MODELS.build(model)\n >>> # build constructor\n >>> optim_wrapper = dict(\n >>> generator=dict(optimizer=dict(type='Adam', lr=0.001,\n >>> betas=(0., 0.99))),\n >>> discriminator=dict(\n >>> optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n >>> lr_schedule=dict(\n >>> generator={\n >>> '128': 0.0015,\n >>> '256': 0.002,\n >>> '512': 0.003,\n >>> '1024': 0.003\n >>> },\n >>> discriminator={\n >>> '128': 0.0015,\n >>> '256': 0.002,\n >>> '512': 0.003,\n >>> '1024': 0.003\n >>> }))\n >>> optim_wrapper_dict_builder = PGGANOptimWrapperConstructor(\n >>> optim_wrapper)\n >>> # build optim wrapper dict\n >>> optim_wrapper_dict = optim_wrapper_dict_builder(pggan)\n\n Args:\n optim_wrapper_cfg (dict): Config of the optimizer wrapper.\n paramwise_cfg (Optional[dict]): Parameter-wise options.\n \"\"\"\n\n def __init__(self,\n optim_wrapper_cfg: dict,\n paramwise_cfg: Optional[dict] = None):\n if not isinstance(optim_wrapper_cfg, dict):\n raise TypeError('optimizer_cfg should be a dict',\n f'but got {type(optim_wrapper_cfg)}')\n assert paramwise_cfg is None, (\n 'paramwise_cfg should be set in each optimizer separately')\n self.optim_cfg = deepcopy(optim_wrapper_cfg)\n\n self.reset_optim = self.optim_cfg.pop('reset_optim_for_new_scale',\n True)\n print(self.reset_optim)\n self.lr_schedule = self.optim_cfg.pop('lr_schedule', dict())\n self.constructors = {}\n\n for key, cfg in self.optim_cfg.items():\n cfg_ = cfg.copy()\n paramwise_cfg_ = cfg_.pop('paramwise_cfg', None)\n self.constructors[key] = DefaultOptimWrapperConstructor(\n cfg_, paramwise_cfg_)\n\n def __call__(self, module: nn.Module) -> OptimWrapperDict:\n \"\"\"Build optimizer and return a optimizerwrapperdict.\"\"\"\n optimizers = {}\n if is_model_wrapper(module):\n module = module.module\n\n # module.scales: [int, int]\n scales = [s[0] for s in module.scales]\n\n for key, base_cfg in self.optim_cfg.items():\n submodule = module._modules[key]\n\n cfg_ = base_cfg.copy()\n base_lr = cfg_['optimizer']['lr']\n paramwise_cfg_ = base_cfg.pop('paramwise_cfg', None)\n\n default_constructor = self.constructors[key]\n default_optimizer = default_constructor(submodule)\n for idx, scale in enumerate(scales):\n if self.reset_optim:\n scale_cfg = cfg_.copy()\n scale_lr = self.lr_schedule[key].get(str(scale), base_lr)\n scale_cfg['optimizer']['lr'] = scale_lr\n constructor = DefaultOptimWrapperConstructor(\n scale_cfg, paramwise_cfg_)\n optimizers[f'{key}_{scale}'] = constructor(submodule)\n else:\n optimizers[f'{key}_{scale}'] = default_optimizer\n\n optimizers = OptimWrapperDict(**optimizers)\n return optimizers\n" }, { "path": "mmagic/engine/optimizers/singan_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmengine.optim import DefaultOptimWrapperConstructor, OptimWrapperDict\n\nfrom mmagic.registry import OPTIM_WRAPPER_CONSTRUCTORS\n\n\n@OPTIM_WRAPPER_CONSTRUCTORS.register_module()\nclass SinGANOptimWrapperConstructor:\n \"\"\"OptimizerConstructor for SinGAN models. Set optimizers for each\n submodule of SinGAN. All submodule must be contained in a\n :class:`torch.nn.ModuleList` named 'blocks'. And we access each submodule\n by `MODEL.blocks[SCALE]`, where `MODEL` is generator or discriminator, and\n the scale is the index of the resolution scale.\n\n More detail about the resolution scale and naming rule please refers to\n :class:`~mmagic.models.editors.singan.SinGANMultiScaleGenerator` and\n :class:`~mmagic.models.editors.singan.SinGANMultiScaleDiscriminator`.\n\n Example:\n >>> # build SinGAN model\n >>> model = dict(\n >>> type='SinGAN',\n >>> data_preprocessor=dict(\n >>> type='GANDataPreprocessor',\n >>> non_image_keys=['input_sample']),\n >>> generator=dict(\n >>> type='SinGANMultiScaleGenerator',\n >>> in_channels=3,\n >>> out_channels=3,\n >>> num_scales=2),\n >>> discriminator=dict(\n >>> type='SinGANMultiScaleDiscriminator',\n >>> in_channels=3,\n >>> num_scales=3))\n >>> singan = MODELS.build(model)\n >>> # build constructor\n >>> optim_wrapper = dict(\n >>> generator=dict(optimizer=dict(type='Adam', lr=0.0005,\n >>> betas=(0.5, 0.999))),\n >>> discriminator=dict(\n >>> optimizer=dict(type='Adam', lr=0.0005,\n >>> betas=(0.5, 0.999))))\n >>> optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n >>> optim_wrapper)\n >>> # build optim wrapper dict\n >>> optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n\n Args:\n optim_wrapper_cfg (dict): Config of the optimizer wrapper.\n paramwise_cfg (Optional[dict]): Parameter-wise options.\n \"\"\"\n\n def __init__(self,\n optim_wrapper_cfg: dict,\n paramwise_cfg: Optional[dict] = None):\n if not isinstance(optim_wrapper_cfg, dict):\n raise TypeError('optimizer_cfg should be a dict',\n f'but got {type(optim_wrapper_cfg)}')\n assert paramwise_cfg is None, (\n 'paramwise_cfg should be set in each optimizer separately')\n self.optim_cfg = optim_wrapper_cfg\n self.constructors = {}\n for key, cfg in self.optim_cfg.items():\n cfg_ = cfg.copy()\n paramwise_cfg_ = cfg_.pop('paramwise_cfg', None)\n self.constructors[key] = DefaultOptimWrapperConstructor(\n cfg_, paramwise_cfg_)\n\n def __call__(self, module: nn.Module) -> OptimWrapperDict:\n \"\"\"Build optimizer and return a optimizerwrapperdict.\"\"\"\n optimizers = {}\n if hasattr(module, 'module'):\n module = module.module\n num_scales = module.num_scales\n\n for key, constructor in self.constructors.items():\n for idx in range(num_scales + 1):\n submodule = module._modules[key]\n if hasattr(submodule, 'module'):\n submodule = submodule.module\n optimizers[f'{key}_{idx}'] = constructor(submodule.blocks[idx])\n optimizers = OptimWrapperDict(**optimizers)\n return optimizers\n" }, { "path": "mmagic/engine/runner/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .log_processor import LogProcessor\nfrom .multi_loops import MultiTestLoop, MultiValLoop\n\n__all__ = ['MultiTestLoop', 'MultiValLoop', 'LogProcessor']\n" }, { "path": "mmagic/engine/runner/log_processor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.runner import LogProcessor as BaseLogProcessor\n\nfrom mmagic.registry import LOG_PROCESSORS\n\n\n@LOG_PROCESSORS.register_module() # type: ignore\nclass LogProcessor(BaseLogProcessor):\n \"\"\"LogProcessor inherits from :class:`mmengine.runner.LogProcessor` and\n overwrites :meth:`self.get_log_after_iter`.\n\n This log processor should be used along with\n :class:`mmagic.engine.runner.MultiValLoop` and\n :class:`mmagic.engine.runner.MultiTestLoop`.\n \"\"\"\n\n def _get_dataloader_size(self, runner, mode) -> int:\n \"\"\"Get dataloader size of current loop. In `MultiValLoop` and\n `MultiTestLoop`, we use `total_length` instead of `len(dataloader)` to\n denote the total number of iterations.\n\n Args:\n runner (Runner): The runner of the training/validation/testing\n mode (str): Current mode of runner.\n\n Returns:\n int: The dataloader size of current loop.\n \"\"\"\n if hasattr(self._get_cur_loop(runner, mode), 'total_length'):\n return self._get_cur_loop(runner, mode).total_length\n else:\n return super()._get_dataloader_size(runner, mode)\n" }, { "path": "mmagic/engine/runner/loop_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom logging import WARNING\nfrom typing import Any, Dict, List, Union\n\nfrom mmengine import is_list_of, print_log\nfrom mmengine.evaluator import Evaluator\n\nEVALUATOR_TYPE = Union[Evaluator, Dict, List]\n\n\ndef update_and_check_evaluator(evaluator: EVALUATOR_TYPE\n ) -> Union[Evaluator, dict]:\n \"\"\"Check the whether the evaluator instance or dict config is Evaluator. If\n input is a dict config, attempt to set evaluator type as Evaluator and\n raised warning if it is not allowed. If input is a Evaluator instance,\n check whether it is a Evaluator class, otherwise,\n\n Args:\n evaluator (Union[Evaluator, dict, list]): The evaluator instance or\n config dict.\n \"\"\"\n # check Evaluator instance\n warning_template = ('Evaluator type for current config is \\'{}\\'. '\n 'If you want to use MultiValLoop, we strongly '\n 'recommend you to use \\'Evaluator\\' provided by '\n '\\'MMagic\\'. Otherwise, there maybe some potential '\n 'bugs.')\n if isinstance(evaluator, Evaluator):\n cls_name = evaluator.__class__.__name__\n if cls_name != 'Evaluator':\n print_log(warning_template.format(cls_name), 'current', WARNING)\n return evaluator\n\n # add type for **single evaluator with list of metrics**\n if isinstance(evaluator, list):\n evaluator = dict(type='Evaluator', metrics=evaluator)\n return evaluator\n\n # check and update dict config\n assert isinstance(evaluator, dict), (\n 'Can only conduct check and update for list of metrics, a config dict '\n f'or a Evaluator object. But receives {type(evaluator)}.')\n evaluator.setdefault('type', 'Evaluator')\n evaluator.setdefault('metrics', None) # default as 'dummy evaluator'\n _type = evaluator['type']\n if _type != 'Evaluator':\n print_log(warning_template.format(_type), 'current', WARNING)\n return evaluator\n\n\ndef is_evaluator(evaluator: Any) -> bool:\n \"\"\"Check whether the input is a valid evaluator config or Evaluator object.\n\n Args:\n evaluator (Any): The input to check.\n\n Returns:\n bool: Whether the input is a valid evaluator config or Evaluator\n object.\n \"\"\"\n # Single evaluator with type\n if isinstance(evaluator, dict) and 'metrics' in evaluator:\n return True\n # Single evaluator without type\n elif (is_list_of(evaluator, dict)\n and all(['metrics' not in cfg_ for cfg_ in evaluator])):\n return True\n elif isinstance(evaluator, Evaluator):\n return True\n else:\n return False\n" }, { "path": "mmagic/engine/runner/multi_loops.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\nfrom typing import Dict, List, Sequence, Union\n\nimport torch\nfrom mmengine.evaluator import BaseMetric, Evaluator\nfrom mmengine.runner.amp import autocast\nfrom mmengine.runner.base_loop import BaseLoop\nfrom torch.utils.data import DataLoader\n\nfrom mmagic.registry import LOOPS\nfrom .loop_utils import is_evaluator, update_and_check_evaluator\n\nDATALOADER_TYPE = Union[DataLoader, Dict, List]\nEVALUATOR_TYPE = Union[Evaluator, Dict, List]\n\n\n@LOOPS.register_module()\nclass MultiValLoop(BaseLoop):\n \"\"\"Validation loop for MMagic models which support evaluate multiply\n dataset at the same time. This class support evaluate:\n\n 1. Metrics (metric) on a single dataset (e.g. PSNR and SSIM on DIV2K\n dataset)\n 2. Different metrics on different datasets (e.g. PSNR on DIV2K and SSIM\n and PSNR on SET5)\n\n Use cases:\n\n Case 1: metrics on a single dataset\n\n >>> # add the following lines in your config\n >>> # 1. use `MultiValLoop` instead of `ValLoop` in MMEngine\n >>> val_cfg = dict(type='MultiValLoop')\n >>> # 2. specific MultiEvaluator instead of Evaluator in MMEngine\n >>> val_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=[\n >>> dict(type='PSNR', crop_border=2, prefix='Set5'),\n >>> dict(type='SSIM', crop_border=2, prefix='Set5'),\n >>> ])\n >>> # 3. define dataloader\n >>> val_dataloader = dict(...)\n\n Case 2: different metrics on different datasets\n\n >>> # add the following lines in your config\n >>> # 1. use `MultiValLoop` instead of `ValLoop` in MMEngine\n >>> val_cfg = dict(type='MultiValLoop')\n >>> # 2. specific a list MultiEvaluator\n >>> # do not forget to add prefix for each metric group\n >>> div2k_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=dict(type='SSIM', crop_border=2, prefix='DIV2K'))\n >>> set5_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=[\n >>> dict(type='PSNR', crop_border=2, prefix='Set5'),\n >>> dict(type='SSIM', crop_border=2, prefix='Set5'),\n >>> ])\n >>> # define evaluator config\n >>> val_evaluator = [div2k_evaluator, set5_evaluator]\n >>> # 3. specific a list dataloader for each metric groups\n >>> div2k_dataloader = dict(...)\n >>> set5_dataloader = dict(...)\n >>> # define dataloader config\n >>> val_dataloader = [div2k_dataloader, set5_dataloader]\n\n Args:\n runner (Runner): A reference of runner.\n dataloader (Dataloader or dict or list): A dataloader object or a dict\n to build a dataloader a list of dataloader object or a list of\n config dicts.\n evaluator (Evaluator or dict or list): A evaluator object or a dict to\n build the evaluator or a list of evaluator object or a list of\n config dicts.\n \"\"\"\n\n def __init__(self,\n runner,\n dataloader: DATALOADER_TYPE,\n evaluator: EVALUATOR_TYPE,\n fp16: bool = False):\n self._runner = runner\n\n self.dataloaders = self._build_dataloaders(dataloader)\n self.evaluators = self._build_evaluators(evaluator)\n\n self.fp16 = fp16\n\n assert len(self.dataloaders) == len(self.evaluators), (\n 'Length of dataloaders and evaluators must be same, but receive '\n f'\\'{len(self.dataloaders)}\\' and \\'{len(self.evaluators)}\\''\n 'respectively.')\n\n self._total_length = None # length for all dataloaders\n\n @property\n def total_length(self) -> int:\n if self._total_length is not None:\n return self._total_length\n\n warnings.warn('\\'total_length\\' has not been initialized and return '\n '\\'0\\' for safety. This result is likely to be incorrect'\n ' and we recommend you to call \\'total_length\\' after '\n '\\'self.run\\' is called.')\n return 0\n\n def _build_dataloaders(self,\n dataloader: DATALOADER_TYPE) -> List[DataLoader]:\n \"\"\"Build dataloaders.\n\n Args:\n dataloader (Dataloader or dict or list): A dataloader object or a\n dict to build a dataloader a list of dataloader object or a\n list of config dict.\n\n Returns:\n List[Dataloader]: List of dataloaders for compute metrics.\n \"\"\"\n runner = self._runner\n\n if not isinstance(dataloader, list):\n dataloader = [dataloader]\n\n dataloaders = []\n for loader in dataloader:\n if isinstance(loader, dict):\n dataloaders.append(\n runner.build_dataloader(loader, seed=runner.seed))\n else:\n dataloaders.append(loader)\n\n return dataloaders\n\n def _build_evaluators(self, evaluator: EVALUATOR_TYPE) -> List[Evaluator]:\n \"\"\"Build evaluators.\n\n Args:\n evaluator (Evaluator or dict or list): A evaluator object or a\n dict to build the evaluator or a list of evaluator object or a\n list of config dicts.\n\n Returns:\n List[Evaluator]: List of evaluators for compute metrics.\n \"\"\"\n runner = self._runner\n\n # Input type checking and packing\n # 1. Single evaluator without type: [dict(), dict(), ...]\n # 2. Single evaluator with type: dict(type=xx, metrics=xx)\n # 3. Multi evaluator without type: [[dict, ...], [dict, ...]]\n # 4. Multi evaluator with type: [dict(type=xx, metrics=xx), dict(...)]\n if is_evaluator(evaluator):\n evaluator = [update_and_check_evaluator(evaluator)]\n else:\n assert all([\n is_evaluator(cfg) for cfg in evaluator\n ]), ('Unsupported evaluator type, please check your input and '\n 'the docstring.')\n evaluator = [update_and_check_evaluator(cfg) for cfg in evaluator]\n\n evaluators = [runner.build_evaluator(eval) for eval in evaluator]\n\n return evaluators\n\n def run(self):\n \"\"\"Launch validation. The evaluation process consists of four steps.\n\n 1. Prepare pre-calculated items for all metrics by calling\n :meth:`self.evaluator.prepare_metrics`.\n 2. Get a list of metrics-sampler pair. Each pair contains a list of\n metrics with the same sampler mode and a shared sampler.\n 3. Generate images for the each metrics group. Loop for elements in\n each sampler and feed to the model as input by calling\n :meth:`self.run_iter`.\n 4. Evaluate all metrics by calling :meth:`self.evaluator.evaluate`.\n \"\"\"\n\n self._runner.call_hook('before_val')\n self._runner.call_hook('before_val_epoch')\n self._runner.model.eval()\n\n # access to the true model\n module = self._runner.model\n if hasattr(self.runner.model, 'module'):\n module = module.module\n\n multi_metric = dict()\n idx_counter = 0\n self._total_length = 0\n\n # 1. prepare all metrics and get the total length\n metrics_sampler_lists = []\n meta_info_list = []\n dataset_name_list = []\n for evaluator, dataloader in zip(self.evaluators, self.dataloaders):\n # 1.1 prepare for metrics\n evaluator.prepare_metrics(module, dataloader)\n # 1.2 prepare for metric-sampler pair\n metrics_sampler_list = evaluator.prepare_samplers(\n module, dataloader)\n metrics_sampler_lists.append(metrics_sampler_list)\n # 1.3 update total length\n self._total_length += sum([\n len(metrics_sampler[1])\n for metrics_sampler in metrics_sampler_list\n ])\n # 1.4 save metainfo and dataset's name\n meta_info_list.append(\n getattr(dataloader.dataset, 'metainfo', None))\n dataset_name_list.append(dataloader.dataset.__class__.__name__)\n\n # 2. run evaluation\n for idx in range(len(self.evaluators)):\n # 2.1 set self.evaluator for run_iter\n self.evaluator = self.evaluators[idx]\n self.dataloader = self.dataloaders[idx]\n\n # 2.2 update metainfo for evaluator and visualizer\n meta_info = meta_info_list[idx]\n dataset_name = dataset_name_list[idx]\n if meta_info:\n self.evaluator.dataset_meta = meta_info\n self._runner.visualizer.dataset_meta = meta_info\n else:\n warnings.warn(\n f'Dataset {dataset_name} has no metainfo. `dataset_meta` '\n 'in evaluator, metric and visualizer will be None.')\n\n # 2.3 generate images\n metrics_sampler_list = metrics_sampler_lists[idx]\n for metrics, sampler in metrics_sampler_list:\n for data in sampler:\n self.run_iter(idx_counter, data, metrics)\n idx_counter += 1\n\n # 2.4 evaluate metrics and update multi_metric\n metrics = self.evaluator.evaluate()\n if multi_metric and metrics.keys() & multi_metric.keys():\n raise ValueError('Please set different prefix for different'\n ' datasets in `val_evaluator`')\n else:\n multi_metric.update(metrics)\n\n # 3. finish evaluation and call hooks\n self._runner.call_hook('after_val_epoch', metrics=multi_metric)\n self._runner.call_hook('after_val')\n\n @torch.no_grad()\n def run_iter(self, idx, data_batch: dict, metrics: Sequence[BaseMetric]):\n \"\"\"Iterate one mini-batch and feed the output to corresponding\n `metrics`.\n\n Args:\n idx (int): Current idx for the input data.\n data_batch (dict): Batch of data from dataloader.\n metrics (Sequence[BaseMetric]): Specific metrics to evaluate.\n \"\"\"\n self._runner.call_hook(\n 'before_val_iter', batch_idx=idx, data_batch=data_batch)\n # outputs should be sequence of BaseDataElement\n with autocast(enabled=self.fp16):\n outputs = self._runner.model.val_step(data_batch)\n self.evaluator.process(outputs, data_batch, metrics)\n self._runner.call_hook(\n 'after_val_iter',\n batch_idx=idx,\n data_batch=data_batch,\n outputs=outputs)\n\n\n@LOOPS.register_module()\nclass MultiTestLoop(BaseLoop):\n \"\"\"Test loop for MMagic models which support evaluate multiply dataset at\n the same time. This class support evaluate:\n\n 1. Metrics (metric) on a single dataset (e.g. PSNR and SSIM on DIV2K\n dataset)\n 2. Different metrics on different datasets (e.g. PSNR on DIV2K and SSIM\n and PSNR on SET5)\n\n Use cases:\n\n Case 1: metrics on a single dataset\n\n >>> # add the following lines in your config\n >>> # 1. use `MultiTestLoop` instead of `TestLoop` in MMEngine\n >>> val_cfg = dict(type='MultiTestLoop')\n >>> # 2. specific MultiEvaluator instead of Evaluator in MMEngine\n >>> test_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=[\n >>> dict(type='PSNR', crop_border=2, prefix='Set5'),\n >>> dict(type='SSIM', crop_border=2, prefix='Set5'),\n >>> ])\n >>> # 3. define dataloader\n >>> test_dataloader = dict(...)\n\n Case 2: different metrics on different datasets\n\n >>> # add the following lines in your config\n >>> # 1. use `MultiTestLoop` instead of `TestLoop` in MMEngine\n >>> Test_cfg = dict(type='MultiTestLoop')\n >>> # 2. specific a list MultiEvaluator\n >>> # do not forget to add prefix for each metric group\n >>> div2k_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=dict(type='SSIM', crop_border=2, prefix='DIV2K'))\n >>> set5_evaluator = dict(\n >>> type='MultiEvaluator',\n >>> metrics=[\n >>> dict(type='PSNR', crop_border=2, prefix='Set5'),\n >>> dict(type='SSIM', crop_border=2, prefix='Set5'),\n >>> ])\n >>> # define evaluator config\n >>> test_evaluator = [div2k_evaluator, set5_evaluator]\n >>> # 3. specific a list dataloader for each metric groups\n >>> div2k_dataloader = dict(...)\n >>> set5_dataloader = dict(...)\n >>> # define dataloader config\n >>> test_dataloader = [div2k_dataloader, set5_dataloader]\n\n Args:\n runner (Runner): A reference of runner.\n dataloader (Dataloader or dict or list): A dataloader object or a dict\n to build a dataloader a list of dataloader object or a list of\n config dicts.\n evaluator (Evaluator or dict or list): A evaluator object or a dict to\n build the evaluator or a list of evaluator object or a list of\n config dicts.\n \"\"\"\n\n def __init__(self, runner, dataloader, evaluator, fp16=False):\n self._runner = runner\n\n self.dataloaders = self._build_dataloaders(dataloader)\n self.evaluators = self._build_evaluators(evaluator)\n\n self.fp16 = fp16\n\n assert len(self.dataloaders) == len(self.evaluators), (\n 'Length of dataloaders and evaluators must be same, but receive '\n f'\\'{len(self.dataloaders)}\\' and \\'{len(self.evaluators)}\\''\n 'respectively.')\n\n self._total_length = None\n\n @property\n def total_length(self) -> int:\n if self._total_length is not None:\n return self._total_length\n\n warnings.warn('\\'total_length\\' has not been initialized and return '\n '\\'0\\' for safety. This result is likely to be incorrect'\n ' and we recommend you to call \\'total_length\\' after '\n '\\'self.run\\' is called.')\n return 0\n\n def _build_dataloaders(self,\n dataloader: DATALOADER_TYPE) -> List[DataLoader]:\n \"\"\"Build dataloaders.\n\n Args:\n dataloader (Dataloader or dict or list): A dataloader object or a\n dict to build a dataloader a list of dataloader object or a\n list of config dict.\n\n Returns:\n List[Dataloader]: List of dataloaders for compute metrics.\n \"\"\"\n runner = self._runner\n\n if not isinstance(dataloader, list):\n dataloader = [dataloader]\n\n dataloaders = []\n for loader in dataloader:\n if isinstance(loader, dict):\n dataloaders.append(\n runner.build_dataloader(loader, seed=runner.seed))\n else:\n dataloaders.append(loader)\n\n return dataloaders\n\n def _build_evaluators(self, evaluator: EVALUATOR_TYPE) -> List[Evaluator]:\n \"\"\"Build evaluators.\n\n Args:\n evaluator (Evaluator or dict or list): A evaluator object or a\n dict to build the evaluator or a list of evaluator object or a\n list of config dicts.\n\n Returns:\n List[Evaluator]: List of evaluators for compute metrics.\n \"\"\"\n runner = self._runner\n\n # Input type checking and packing\n # 1. Single evaluator without type: [dict(), dict(), ...]\n # 2. Single evaluator with type: dict(type=xx, metrics=xx)\n # 3. Multi evaluator without type: [[dict, ...], [dict, ...]]\n # 4. Multi evaluator with type: [dict(type=xx, metrics=xx), dict(...)]\n if is_evaluator(evaluator):\n evaluator = [update_and_check_evaluator(evaluator)]\n else:\n assert all([\n is_evaluator(cfg) for cfg in evaluator\n ]), ('Unsupported evaluator type, please check your input and '\n 'the docstring.')\n evaluator = [update_and_check_evaluator(cfg) for cfg in evaluator]\n\n evaluators = [runner.build_evaluator(eval) for eval in evaluator]\n\n return evaluators\n\n def run(self):\n \"\"\"Launch validation. The evaluation process consists of four steps.\n\n 1. Prepare pre-calculated items for all metrics by calling\n :meth:`self.evaluator.prepare_metrics`.\n 2. Get a list of metrics-sampler pair. Each pair contains a list of\n metrics with the same sampler mode and a shared sampler.\n 3. Generate images for the each metrics group. Loop for elements in\n each sampler and feed to the model as input by calling\n :meth:`self.run_iter`.\n 4. Evaluate all metrics by calling :meth:`self.evaluator.evaluate`.\n \"\"\"\n\n self._runner.call_hook('before_test')\n self._runner.call_hook('before_test_epoch')\n self._runner.model.eval()\n\n # access to the true model\n module = self._runner.model\n if hasattr(self._runner.model, 'module'):\n module = module.module\n\n multi_metric = dict()\n idx_counter = 0\n self._total_length = 0\n\n # 1. prepare all metrics and get the total length\n metrics_sampler_lists = []\n meta_info_list = []\n dataset_name_list = []\n for evaluator, dataloader in zip(self.evaluators, self.dataloaders):\n # 1.1 prepare for metrics\n evaluator.prepare_metrics(module, dataloader)\n # 1.2 prepare for metric-sampler pair\n metrics_sampler_list = evaluator.prepare_samplers(\n module, dataloader)\n metrics_sampler_lists.append(metrics_sampler_list)\n # 1.3 update total length\n self._total_length += sum([\n len(metrics_sampler[1])\n for metrics_sampler in metrics_sampler_list\n ])\n # 1.4 save metainfo and dataset's name\n meta_info_list.append(\n getattr(dataloader.dataset, 'metainfo', None))\n dataset_name_list.append(dataloader.dataset.__class__.__name__)\n\n # 2. run evaluation\n for idx in range(len(self.evaluators)):\n # 2.1 set self.evaluator for run_iter\n self.evaluator = self.evaluators[idx]\n self.dataloader = self.dataloaders[idx]\n\n # 2.2 update metainfo for evaluator and visualizer\n meta_info = meta_info_list[idx]\n dataset_name = dataset_name_list[idx]\n if meta_info:\n self.evaluator.dataset_meta = meta_info\n self._runner.visualizer.dataset_meta = meta_info\n else:\n warnings.warn(\n f'Dataset {dataset_name} has no metainfo. `dataset_meta` '\n 'in evaluator, metric and visualizer will be None.')\n\n # 2.3 generate images\n metrics_sampler_list = metrics_sampler_lists[idx]\n for metrics, sampler in metrics_sampler_list:\n for data in sampler:\n self.run_iter(idx_counter, data, metrics)\n idx_counter += 1\n\n # 2.4 evaluate metrics and update multi_metric\n metrics = self.evaluator.evaluate()\n if multi_metric and metrics.keys() & multi_metric.keys():\n raise ValueError('Please set different prefix for different'\n ' datasets in `test_evaluator`')\n else:\n multi_metric.update(metrics)\n\n # 3. finish evaluation and call hooks\n self._runner.call_hook('after_test_epoch', metrics=multi_metric)\n self._runner.call_hook('after_test')\n\n @torch.no_grad()\n def run_iter(self, idx, data_batch: dict, metrics: Sequence[BaseMetric]):\n \"\"\"Iterate one mini-batch and feed the output to corresponding\n `metrics`.\n\n Args:\n idx (int): Current idx for the input data.\n data_batch (dict): Batch of data from dataloader.\n metrics (Sequence[BaseMetric]): Specific metrics to evaluate.\n \"\"\"\n self._runner.call_hook(\n 'before_test_iter', batch_idx=idx, data_batch=data_batch)\n # outputs should be sequence of BaseDataElement\n with autocast(enabled=self.fp16):\n outputs = self._runner.model.test_step(data_batch)\n self.evaluator.process(outputs, data_batch, metrics)\n self._runner.call_hook(\n 'after_test_iter',\n batch_idx=idx,\n data_batch=data_batch,\n outputs=outputs)\n" }, { "path": "mmagic/engine/schedulers/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .linear_lr_scheduler_with_interval import LinearLrInterval\nfrom .reduce_lr_scheduler import ReduceLR\n\n__all__ = [\n 'LinearLrInterval',\n 'ReduceLR',\n]\n" }, { "path": "mmagic/engine/schedulers/linear_lr_scheduler_with_interval.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine import MessageHub\nfrom mmengine.optim import LinearLR\n\nfrom mmagic.registry import PARAM_SCHEDULERS\n\n\n@PARAM_SCHEDULERS.register_module()\nclass LinearLrInterval(LinearLR):\n \"\"\"Linear learning rate scheduler for image generation.\n\n In the beginning, the learning rate is 'start_factor' defined in mmengine.\n We give a target learning rate 'end_factor' and a start point 'begin'.\n If :attr:self.by_epoch is True, 'begin' is calculated by epoch, otherwise,\n calculated by iteration.\" Before 'begin', we fix learning rate as\n 'start_factor'; After 'begin', we linearly update learning rate to\n 'end_factor'.\n\n Args:\n interval (int): The interval to update the learning rate. Default: 1.\n \"\"\"\n\n def __init__(self, *args, interval=1, **kwargs):\n self.interval = interval\n super().__init__(*args, **kwargs)\n\n def _get_value(self):\n \"\"\"Compute value using chainable form of the scheduler.\"\"\"\n if self.last_step == 0:\n return [\n group[self.param_name] * self.start_factor\n for group in self.optimizer.param_groups\n ]\n\n message_hub = MessageHub.get_current_instance()\n if self.by_epoch:\n progress = message_hub.get_info('epoch')\n else:\n progress = message_hub.get_info('iter')\n\n max_progress = self.end\n\n factor = (max(0, progress - self.begin) // self.interval) / (\n (max_progress - self.begin) // self.interval)\n\n return [\n self.start_factor + (self.end_factor - self.start_factor) * factor\n for group in self.optimizer.param_groups\n ]\n" }, { "path": "mmagic/engine/schedulers/reduce_lr_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine import MessageHub\nfrom mmengine.optim import _ParamScheduler\n\nfrom mmagic.registry import PARAM_SCHEDULERS\n\n\n@PARAM_SCHEDULERS.register_module()\nclass ReduceLR(_ParamScheduler):\n \"\"\"Decays the learning rate of each parameter group by linearly changing\n small multiplicative factor until the number of epoch reaches a pre-defined\n milestone: ``end``.\n\n Notice that such decay can happen simultaneously with other changes to the\n learning rate from outside this scheduler.\n\n Note:\n The learning rate of each parameter group will be update at regular\n intervals.\n\n Args:\n optimizer (Optimizer or OptimWrapper): Wrapped optimizer.\n mode (str, optional): One of `min`, `max`. In `min` mode, lr will\n be reduced when the quantity monitored has stopped\n decreasing; in `max` mode it will be reduced when the\n quantity monitored has stopped increasing. Default: 'min'.\n factor (float, optional): Factor by which the learning rate will be\n reduced. new_lr = lr * factor. Default: 0.1.\n patience (int, optional): Number of epochs with no improvement after\n which learning rate will be reduced. For example, if\n `patience = 2`, then we will ignore the first 2 epochs\n with no improvement, and will only decrease the LR after the\n 3rd epoch if the loss still hasn't improved then.\n Default: 10.\n threshold (float, optional): Threshold for measuring the new optimum,\n to only focus on significant changes. Default: 1e-4.\n threshold_mode (str, optional): One of `rel`, `abs`. In `rel` mode,\n dynamic_threshold = best * ( 1 + threshold ) in 'max'\n mode or best * ( 1 - threshold ) in `min` mode.\n In `abs` mode, dynamic_threshold = best + threshold in\n `max` mode or best - threshold in `min` mode. Default: 'rel'.\n cooldown (int, optional): Number of epochs to wait before resuming\n normal operation after lr has been reduced. Default: 0.\n min_lr (float, optional): Minimum LR value to keep. If LR after decay\n is lower than `min_lr`, it will be clipped to this value.\n Default: 0.\n eps (float, optional): Minimal decay applied to lr. If the difference\n between new and old lr is smaller than eps, the update is\n ignored. Default: 1e-8.\n begin (int): Step at which to start updating the learning rate.\n Defaults to 0.\n end (int): Step at which to stop updating the learning rate.\n last_step (int): The index of last step. Used for resume without\n state dict. Defaults to -1.\n by_epoch (bool): Whether the scheduled learning rate is updated by\n epochs. Defaults to True.\n \"\"\"\n\n def __init__(self,\n optimizer,\n mode: str = 'min',\n factor: float = 0.1,\n patience: int = 10,\n threshold: float = 1e-4,\n threshold_mode: str = 'rel',\n cooldown: int = 0,\n min_lr: float = 0.,\n eps: float = 1e-8,\n **kwargs):\n\n super().__init__(optimizer=optimizer, param_name='lr', **kwargs)\n\n self.message_hub = MessageHub.get_instance('reduce_lr')\n\n if mode not in ['min', 'max']:\n raise ValueError(\n 'mode must be one of \"min\" or \"max\", instead got {mode}')\n self.mode = mode\n\n if factor >= 1.0 or factor < 0:\n raise ValueError('Factor should be < 1.0 and >=0')\n self.factor = factor\n\n self.patience = patience\n self.threshold = threshold\n\n if threshold_mode not in ['rel', 'abs']:\n raise ValueError('thresh_mode must be one of \"rel\" or \"abs\",'\n f'instead got {threshold_mode}')\n self.threshold_mode = threshold_mode\n\n self.cooldown = cooldown\n self.cooldown_counter = 0\n self.best = None\n self.num_bad_epochs = None\n self.mode_worse = None # the worse value for the chosen mode\n self.min_lr = min_lr\n self.eps = eps\n self.last_epoch = 0\n self._init_is_better(self.mode)\n self._reset()\n\n def _get_value(self):\n \"\"\"Compute value using chainable form of the scheduler.\"\"\"\n\n if self.last_step == 0:\n return [\n group[self.param_name] for group in self.optimizer.param_groups\n ]\n\n current = self.message_hub.get_scalar('value').current()\n if self.is_better(current, self.best):\n self.best = current\n self.num_bad_epochs = 0\n else:\n self.num_bad_epochs += 1\n\n if self.in_cooldown:\n self.cooldown_counter -= 1\n self.num_bad_epochs = 0\n\n if self.num_bad_epochs > self.patience:\n self.cooldown_counter = self.cooldown\n self.num_bad_epochs = 0\n results = []\n for group in self.optimizer.param_groups:\n regular_lr = group[self.param_name]\n if regular_lr - regular_lr * self.factor > self.eps:\n regular_lr = max(regular_lr * self.factor, self.min_lr)\n results.append(regular_lr)\n return results\n\n else:\n return [\n group[self.param_name] for group in self.optimizer.param_groups\n ]\n\n def _init_is_better(self, mode):\n if mode == 'min':\n self.mode_worse = float('inf')\n else:\n self.mode_worse = float('-inf')\n\n def _reset(self):\n self.best = self.mode_worse\n self.cooldown_counter = 0\n self.num_bad_epochs = 0\n\n def is_better(self, a, best):\n if self.mode == 'min' and self.threshold_mode == 'rel':\n rel_epsilon = 1. - self.threshold\n return a < best * rel_epsilon\n elif self.mode == 'min' and self.threshold_mode == 'abs':\n return a < best - self.threshold\n elif self.mode == 'max' and self.threshold_mode == 'rel':\n rel_epsilon = 1. + self.threshold\n return a > best * rel_epsilon\n else:\n return a > best + self.threshold\n\n @property\n def in_cooldown(self):\n return self.cooldown_counter > 0\n" }, { "path": "mmagic/evaluation/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .evaluator import Evaluator\nfrom .functional import gauss_gradient\nfrom .metrics import (MAE, MSE, NIQE, PSNR, SAD, SNR, SSIM, ConnectivityError,\n Equivariance, FrechetInceptionDistance, GradientError,\n InceptionScore, MattingMSE,\n MultiScaleStructureSimilarity, PerceptualPathLength,\n PrecisionAndRecall, SlicedWassersteinDistance, TransFID,\n TransIS, niqe, psnr, snr, ssim)\n\n__all__ = [\n 'Evaluator',\n 'gauss_gradient',\n 'ConnectivityError',\n 'GradientError',\n 'MAE',\n 'MattingMSE',\n 'MSE',\n 'NIQE',\n 'niqe',\n 'PSNR',\n 'psnr',\n 'SAD',\n 'SNR',\n 'snr',\n 'SSIM',\n 'ssim',\n 'Equivariance',\n 'FrechetInceptionDistance',\n 'InceptionScore',\n 'MultiScaleStructureSimilarity',\n 'PerceptualPathLength',\n 'MultiScaleStructureSimilarity',\n 'PrecisionAndRecall',\n 'SlicedWassersteinDistance',\n 'TransFID',\n 'TransIS',\n]\n" }, { "path": "mmagic/evaluation/evaluator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport hashlib\nfrom collections import defaultdict\nfrom typing import Any, Iterator, List, Optional, Sequence, Tuple, Union\n\nfrom mmengine.evaluator import BaseMetric, Evaluator\nfrom mmengine.model import BaseModel\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import EVALUATORS\nfrom mmagic.structures import DataSample\nfrom .metrics.base_gen_metric import GenMetric\n\n\n@EVALUATORS.register_module()\nclass Evaluator(Evaluator):\n \"\"\"Evaluator for generative models. Unlike high-level vision tasks, metrics\n for generative models have various input types. For example, Inception\n Score (IS, :class:`~mmagic.evaluation.InceptionScore`) only needs to\n take fake images as input. However, Frechet Inception Distance (FID,\n :class:`~mmagic.evaluation.FrechetInceptionDistance`) needs to take\n both real images and fake images as input, and the numbers of real images\n and fake images can be set arbitrarily. For Perceptual path length (PPL,\n :class:`~mmagic.evaluation.PerceptualPathLength`), generator need\n to sample images along a latent path.\n\n In order to be compatible with different metrics, we designed two critical\n functions, :meth:`prepare_metrics` and :meth:`prepare_samplers` to support\n those requirements.\n\n - :meth:`prepare_metrics` set the image images' color order\n and pass the dataloader to all metrics. Therefore metrics need\n pre-processing to prepare the corresponding feature.\n - :meth:`prepare_samplers` pass the dataloader and model to the metrics,\n and get the corresponding sampler of each kind of metrics. Metrics with\n same sample mode can share the sampler.\n\n The whole evaluation process can be found in\n :meth:`mmagic.engine.runner.MultiValLoop.run` and\n :meth:`mmagic.engine.runner.MultiTestLoop.run`.\n\n Args:\n metrics (dict or BaseMetric or Sequence): The config of metrics.\n \"\"\"\n\n def __init__(self, metrics: Union[dict, BaseMetric, Sequence]):\n if metrics is not None:\n super().__init__(metrics)\n else:\n self.metrics = None\n self.is_ready = False\n\n def prepare_metrics(self, module: BaseModel, dataloader: DataLoader):\n \"\"\"Prepare for metrics before evaluation starts. Some metrics use\n pretrained model to extract feature. Some metrics use pretrained model\n to extract feature and input channel order may vary among those models.\n Therefore, we first parse the output color order from data\n preprocessor and set the color order for each metric. Then we pass the\n dataloader to each metrics to prepare pre-calculated items. (e.g.\n inception feature of the real images). If metric has no pre-calculated\n items, :meth:`metric.prepare` will be ignored. Once the function has\n been called, :attr:`self.is_ready` will be set as `True`. If\n :attr:`self.is_ready` is `True`, this function will directly return to\n avoid duplicate computation.\n\n Args:\n module (BaseModel): Model to evaluate.\n dataloader (DataLoader): The dataloader for real images.\n \"\"\"\n if self.metrics is None:\n self.is_ready = True\n return\n\n if self.is_ready:\n return\n\n # prepare metrics\n for metric in self.metrics:\n metric.prepare(module, dataloader)\n self.is_ready = True\n\n @staticmethod\n def _cal_metric_hash(metric: GenMetric):\n \"\"\"Calculate a unique hash value based on the `SAMPLER_MODE` and\n `sample_model`.\"\"\"\n sampler_mode = metric.SAMPLER_MODE\n sample_model = metric.sample_model\n metric_dict = {\n 'SAMPLER_MODE': sampler_mode,\n 'sample_model': sample_model\n }\n if hasattr(metric, 'need_cond_input'):\n metric_dict['need_cond_input'] = metric.need_cond_input\n md5 = hashlib.md5(repr(metric_dict).encode('utf-8')).hexdigest()\n return md5\n\n def prepare_samplers(self, module: BaseModel, dataloader: DataLoader\n ) -> List[Tuple[List[BaseMetric], Iterator]]:\n \"\"\"Prepare for the sampler for metrics whose sampling mode are\n different. For generative models, different metric need image\n generated with different inputs. For example, FID, KID and IS need\n images generated with random noise, and PPL need paired images on the\n specific noise interpolation path. Therefore, we first group metrics\n with respect to their sampler's mode (refers to\n :attr:~`GenMetrics.SAMPLER_MODE`), and build a shared sampler for each\n metric group. To be noted that, the length of the shared sampler\n depends on the metric of the most images required in each group.\n\n Args:\n module (BaseModel): Model to evaluate. Some metrics (e.g. PPL)\n require `module` in their sampler.\n dataloader (DataLoader): The dataloader for real image.\n\n Returns:\n List[Tuple[List[BaseMetric], Iterator]]: A list of \"metrics-shared\n sampler\" pair.\n \"\"\"\n if self.metrics is None:\n return [[[None], []]]\n\n # grouping metrics based on `SAMPLER_MODE` and `sample_mode`\n metric_mode_dict = defaultdict(list)\n for metric in self.metrics:\n metric_md5 = self._cal_metric_hash(metric)\n metric_mode_dict[metric_md5].append(metric)\n\n metrics_sampler_list = []\n for metrics in metric_mode_dict.values():\n first_metric = metrics[0]\n metrics_sampler_list.append([\n metrics,\n first_metric.get_metric_sampler(module, dataloader, metrics)\n ])\n\n return metrics_sampler_list\n\n def process(self, data_samples: Sequence[DataSample],\n data_batch: Optional[Any],\n metrics: Sequence[BaseMetric]) -> None:\n \"\"\"Pass `data_batch` from dataloader and `predictions` (generated\n results) to corresponding `metrics`.\n\n Args:\n data_samples (Sequence[DataSample]): A batch of generated\n results from model.\n data_batch (Optional[Any]): A batch of data from the\n metrics specific sampler or the dataloader.\n metrics (Optional[Sequence[BaseMetric]]): Metrics to evaluate.\n \"\"\"\n if self.metrics is None:\n return\n\n _data_samples = []\n for data_sample in data_samples:\n if isinstance(data_sample, DataSample):\n _data_samples.append(data_sample.to_dict())\n else:\n _data_samples.append(data_sample)\n\n # feed to the specifics metrics\n for metric in metrics:\n metric.process(data_batch, _data_samples)\n\n def evaluate(self) -> dict:\n \"\"\"Invoke ``evaluate`` method of each metric and collect the metrics\n dictionary. Different from `Evaluator.evaluate`, this function does not\n take `size` as input, and elements in `self.metrics` will call their\n own `evaluate` method to calculate the metric.\n\n Returns:\n dict: Evaluation results of all metrics. The keys are the names\n of the metrics, and the values are corresponding results.\n \"\"\"\n if self.metrics is None:\n return {'No Metric': 'Nan'}\n metrics = {}\n for metric in self.metrics:\n _results = metric.evaluate()\n\n # Check metric name conflicts\n for name in _results.keys():\n if name in metrics:\n raise ValueError(\n 'There are multiple evaluation results with the same '\n f'metric name {name}. Please make sure all metrics '\n 'have different prefixes.')\n\n metrics.update(_results)\n return metrics\n" }, { "path": "mmagic/evaluation/functional/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .fid_inception import InceptionV3\nfrom .gaussian_funcs import gauss_gradient\nfrom .inception_utils import (disable_gpu_fuser_on_pt19, load_inception,\n prepare_inception_feat, prepare_vgg_feat)\n\n__all__ = [\n 'gauss_gradient', 'InceptionV3', 'disable_gpu_fuser_on_pt19',\n 'load_inception', 'prepare_vgg_feat', 'prepare_inception_feat'\n]\n" }, { "path": "mmagic/evaluation/functional/fid_inception.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Inception networks used in calculating FID and Inception metrics.\n\nThis code is modified from:\nhttps://github.com/rosinality/stylegan2-pytorch/blob/master/inception.py\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.model_zoo import load_url\nfrom torchvision import models\n\n# Inception weights ported to PyTorch from\n# https://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz\nFID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501\n\n\nclass InceptionV3(nn.Module):\n \"\"\"Pretrained InceptionV3 network returning feature maps.\"\"\"\n\n # Index of default block of inception to return,\n # corresponds to output of final average pooling\n DEFAULT_BLOCK_INDEX = 3\n\n # Maps feature dimensionality to their output blocks indices\n BLOCK_INDEX_BY_DIM = {\n 64: 0, # First max pooling features\n 192: 1, # Second max pooling features\n 768: 2, # Pre-aux classifier features\n 2048: 3 # Final average pooling features\n }\n\n def __init__(self,\n output_blocks=[DEFAULT_BLOCK_INDEX],\n resize_input=True,\n normalize_input=True,\n requires_grad=False,\n use_fid_inception=True,\n load_fid_inception=True):\n \"\"\"Build pretrained InceptionV3.\n\n Args:\n output_blocks (list[int]): Indices of blocks to return features of.\n Possible values are:\n - 0: corresponds to output of first max pooling\n - 1: corresponds to output of second max pooling\n - 2: corresponds to output which is fed to aux classifier\n - 3: corresponds to output of final average pooling\n resize_input (bool): If true, bilinearly resizes input to width and\n height 299 before feeding input to model. As the network\n without fully connected layers is fully convolutional, it\n should be able to handle inputs of arbitrary size, so resizing\n might not be strictly needed.\n normalize_input (bool): If true, scales the input from range (0, 1)\n to the range the pretrained Inception network expects, namely\n (-1, 1).\n requires_grad (bool): If true, parameters of the model require\n gradients. Possibly useful for finetuning the network.\n use_fid_inception (bool): If true, uses the pretrained Inception\n model used in Tensorflow's FID implementation. If false, uses\n the pretrained Inception model available in torchvision. The\n FID Inception model has different weights and a slightly\n different structure from torchvision's Inception model. If you\n want to compute FID scores, you are strongly advised to set\n this parameter to true to get comparable results.\n \"\"\"\n super().__init__()\n\n self.resize_input = resize_input\n self.normalize_input = normalize_input\n self.output_blocks = sorted(output_blocks)\n self.last_needed_block = max(output_blocks)\n\n assert self.last_needed_block <= 3, \\\n 'Last possible output block index is 3'\n\n self.blocks = nn.ModuleList()\n\n if use_fid_inception:\n inception = fid_inception_v3(load_fid_inception)\n else:\n inception = models.inception_v3(pretrained=True)\n\n # Block 0: input to maxpool1\n block0 = [\n inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3,\n inception.Conv2d_2b_3x3,\n nn.MaxPool2d(kernel_size=3, stride=2)\n ]\n self.blocks.append(nn.Sequential(*block0))\n\n # Block 1: maxpool1 to maxpool2\n if self.last_needed_block >= 1:\n block1 = [\n inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3,\n nn.MaxPool2d(kernel_size=3, stride=2)\n ]\n self.blocks.append(nn.Sequential(*block1))\n\n # Block 2: maxpool2 to aux classifier\n if self.last_needed_block >= 2:\n block2 = [\n inception.Mixed_5b,\n inception.Mixed_5c,\n inception.Mixed_5d,\n inception.Mixed_6a,\n inception.Mixed_6b,\n inception.Mixed_6c,\n inception.Mixed_6d,\n inception.Mixed_6e,\n ]\n self.blocks.append(nn.Sequential(*block2))\n\n # Block 3: aux classifier to final avgpool\n if self.last_needed_block >= 3:\n block3 = [\n inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c,\n nn.AdaptiveAvgPool2d(output_size=(1, 1))\n ]\n self.blocks.append(nn.Sequential(*block3))\n\n for param in self.parameters():\n param.requires_grad = requires_grad\n\n def forward(self, inp):\n \"\"\"Get Inception feature maps.\n\n Args:\n inp (torch.Tensor): Input tensor of shape Bx3xHxW.\n Values are expected to be in range (0, 1)\n\n Returns:\n list(torch.Tensor): Corresponding to the selected output \\\n block, sorted ascending by index.\n \"\"\"\n outp = []\n x = inp\n\n if self.resize_input:\n x = F.interpolate(\n x, size=(299, 299), mode='bilinear', align_corners=False)\n\n if self.normalize_input:\n x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1)\n\n for idx, block in enumerate(self.blocks):\n x = block(x)\n if idx in self.output_blocks:\n outp.append(x)\n\n if idx == self.last_needed_block:\n break\n\n return outp\n\n\ndef fid_inception_v3(load_ckpt=True):\n \"\"\"Build pretrained Inception model for FID computation.\n\n The Inception model for FID computation uses a different set of weights\n and has a slightly different structure than torchvision's Inception.\n\n This method first constructs torchvision's Inception and then patches the\n necessary parts that are different in the FID Inception model.\n \"\"\"\n inception = models.inception_v3(\n num_classes=1008, aux_logits=False, pretrained=False)\n inception.Mixed_5b = FIDInceptionA(192, pool_features=32)\n inception.Mixed_5c = FIDInceptionA(256, pool_features=64)\n inception.Mixed_5d = FIDInceptionA(288, pool_features=64)\n inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)\n inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)\n inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)\n inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)\n inception.Mixed_7b = FIDInceptionE_1(1280)\n inception.Mixed_7c = FIDInceptionE_2(2048)\n\n if load_ckpt:\n state_dict = load_url(FID_WEIGHTS_URL, progress=True)\n inception.load_state_dict(state_dict)\n\n return inception\n\n\nclass FIDInceptionA(models.inception.InceptionA):\n \"\"\"InceptionA block patched for FID computation.\"\"\"\n\n def __init__(self, in_channels, pool_features):\n super().__init__(in_channels, pool_features)\n\n def forward(self, x):\n \"\"\"Get InceptionA feature maps.\n\n Args:\n x (torch.Tensor): Input tensor of shape BxCxHxW.\n\n Returns:\n torch.Tensor: Feature Maps of x outputted by this block.\n \"\"\"\n branch1x1 = self.branch1x1(x)\n\n branch5x5 = self.branch5x5_1(x)\n branch5x5 = self.branch5x5_2(branch5x5)\n\n branch3x3dbl = self.branch3x3dbl_1(x)\n branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)\n\n # Patch: Tensorflow's average pool does not use the padded zero's in\n # its average calculation\n branch_pool = F.avg_pool2d(\n x, kernel_size=3, stride=1, padding=1, count_include_pad=False)\n branch_pool = self.branch_pool(branch_pool)\n\n outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]\n return torch.cat(outputs, 1)\n\n\nclass FIDInceptionC(models.inception.InceptionC):\n \"\"\"InceptionC block patched for FID computation.\"\"\"\n\n def __init__(self, in_channels, channels_7x7):\n super().__init__(in_channels, channels_7x7)\n\n def forward(self, x):\n \"\"\"Get InceptionC feature maps.\n\n Args:\n x (torch.Tensor): Input tensor of shape BxCxHxW.\n\n Returns:\n torch.Tensor: Feature Maps of x outputted by this block.\n \"\"\"\n branch1x1 = self.branch1x1(x)\n\n branch7x7 = self.branch7x7_1(x)\n branch7x7 = self.branch7x7_2(branch7x7)\n branch7x7 = self.branch7x7_3(branch7x7)\n\n branch7x7dbl = self.branch7x7dbl_1(x)\n branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)\n branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)\n branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)\n branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)\n\n # Patch: Tensorflow's average pool does not use the padded zero's in\n # its average calculation\n branch_pool = F.avg_pool2d(\n x, kernel_size=3, stride=1, padding=1, count_include_pad=False)\n branch_pool = self.branch_pool(branch_pool)\n\n outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]\n return torch.cat(outputs, 1)\n\n\nclass FIDInceptionE_1(models.inception.InceptionE):\n \"\"\"First InceptionE block patched for FID computation.\"\"\"\n\n def __init__(self, in_channels):\n super().__init__(in_channels)\n\n def forward(self, x):\n \"\"\"Get first InceptionE feature maps.\n\n Args:\n x (torch.Tensor): Input tensor of shape BxCxHxW.\n\n Returns:\n torch.Tensor: Feature Maps of x outputted by this block.\n \"\"\"\n branch1x1 = self.branch1x1(x)\n\n branch3x3 = self.branch3x3_1(x)\n branch3x3 = [\n self.branch3x3_2a(branch3x3),\n self.branch3x3_2b(branch3x3),\n ]\n branch3x3 = torch.cat(branch3x3, 1)\n\n branch3x3dbl = self.branch3x3dbl_1(x)\n branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n branch3x3dbl = [\n self.branch3x3dbl_3a(branch3x3dbl),\n self.branch3x3dbl_3b(branch3x3dbl),\n ]\n branch3x3dbl = torch.cat(branch3x3dbl, 1)\n\n # Patch: Tensorflow's average pool does not use the padded zero's in\n # its average calculation\n branch_pool = F.avg_pool2d(\n x, kernel_size=3, stride=1, padding=1, count_include_pad=False)\n branch_pool = self.branch_pool(branch_pool)\n\n outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]\n return torch.cat(outputs, 1)\n\n\nclass FIDInceptionE_2(models.inception.InceptionE):\n \"\"\"Second InceptionE block patched for FID computation.\"\"\"\n\n def __init__(self, in_channels):\n super().__init__(in_channels)\n\n def forward(self, x):\n \"\"\"Get second InceptionE feature maps.\n\n Args:\n x (torch.Tensor): Input tensor of shape BxCxHxW.\n\n Returns:\n torch.Tensor: Feature Maps of x outputted by this block.\n \"\"\"\n branch1x1 = self.branch1x1(x)\n\n branch3x3 = self.branch3x3_1(x)\n branch3x3 = [\n self.branch3x3_2a(branch3x3),\n self.branch3x3_2b(branch3x3),\n ]\n branch3x3 = torch.cat(branch3x3, 1)\n\n branch3x3dbl = self.branch3x3dbl_1(x)\n branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n branch3x3dbl = [\n self.branch3x3dbl_3a(branch3x3dbl),\n self.branch3x3dbl_3b(branch3x3dbl),\n ]\n branch3x3dbl = torch.cat(branch3x3dbl, 1)\n\n # Patch: The FID Inception model uses max pooling instead of average\n # pooling. This is likely an error in this specific Inception\n # implementation, as other Inception models use average pooling here\n # (which matches the description in the paper).\n branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)\n branch_pool = self.branch_pool(branch_pool)\n\n outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]\n return torch.cat(outputs, 1)\n" }, { "path": "mmagic/evaluation/functional/gaussian_funcs.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport cv2\nimport numpy as np\n\n\ndef gaussian(x, sigma):\n \"\"\"Gaussian function.\n\n Args:\n x (array_like): The independent variable.\n sigma (float): Standard deviation of the gaussian function.\n\n Return:\n np.ndarray or scalar: Gaussian value of `x`.\n \"\"\"\n return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))\n\n\ndef dgaussian(x, sigma):\n \"\"\"Gradient of gaussian.\n\n Args:\n x (array_like): The independent variable.\n sigma (float): Standard deviation of the gaussian function.\n\n Return:\n np.ndarray or scalar: Gradient of gaussian of `x`.\n \"\"\"\n return -x * gaussian(x, sigma) / sigma**2\n\n\ndef gauss_filter(sigma, epsilon=1e-2):\n \"\"\"Gradient of gaussian.\n\n Args:\n sigma (float): Standard deviation of the gaussian kernel.\n epsilon (float): Small value used when calculating kernel size.\n Default: 1e-2.\n\n Return:\n filter_x (np.ndarray): Gaussian filter along x axis.\n filter_y (np.ndarray): Gaussian filter along y axis.\n \"\"\"\n half_size = np.ceil(\n sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon)))\n size = int(2 * half_size + 1)\n\n # create filter in x axis\n filter_x = np.zeros((size, size))\n for i in range(size):\n for j in range(size):\n filter_x[i, j] = gaussian(i - half_size, sigma) * dgaussian(\n j - half_size, sigma)\n\n # normalize filter\n norm = np.sqrt((filter_x**2).sum())\n filter_x = filter_x / norm\n filter_y = np.transpose(filter_x)\n\n return filter_x, filter_y\n\n\ndef gauss_gradient(img, sigma):\n \"\"\"Gaussian gradient.\n\n From https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/\n submissions/8060/versions/2/previews/gaussgradient/gaussgradient.m/\n index.html\n\n Args:\n img (np.ndarray): Input image.\n sigma (float): Standard deviation of the gaussian kernel.\n\n Return:\n np.ndarray: Gaussian gradient of input `img`.\n \"\"\"\n filter_x, filter_y = gauss_filter(sigma)\n img_filtered_x = cv2.filter2D(\n img, -1, filter_x, borderType=cv2.BORDER_REPLICATE)\n img_filtered_y = cv2.filter2D(\n img, -1, filter_y, borderType=cv2.BORDER_REPLICATE)\n return np.sqrt(img_filtered_x**2 + img_filtered_y**2)\n" }, { "path": "mmagic/evaluation/functional/inception_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport hashlib\nimport os\nimport os.path as osp\nimport pickle\nimport sys\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom typing import Optional, Tuple\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine import is_filepath, print_log\nfrom mmengine.dataset import BaseDataset, Compose, pseudo_collate\nfrom mmengine.dist import (all_gather, get_dist_info, get_world_size,\n is_main_process)\nfrom mmengine.evaluator import BaseMetric\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.utils.data.dataset import Dataset\nfrom torchvision.models.inception import inception_v3\n\nfrom mmagic.utils import MMAGIC_CACHE_DIR, download_from_url\nfrom . import InceptionV3\n\nALLOWED_INCEPTION = ['StyleGAN', 'PyTorch']\nTERO_INCEPTION_URL = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt' # noqa\n\n\n@contextmanager\ndef disable_gpu_fuser_on_pt19():\n \"\"\"On PyTorch 1.9 a CUDA fuser bug prevents the Inception JIT model to run.\n\n Refers to:\n https://github.com/GaParmar/clean-fid/blob/5e1e84cdea9654b9ac7189306dfa4057ea2213d8/cleanfid/inception_torchscript.py#L9 # noqa\n https://github.com/GaParmar/clean-fid/issues/5\n https://github.com/pytorch/pytorch/issues/64062\n \"\"\"\n if torch.__version__.startswith('1.9.'):\n old_val = torch._C._jit_can_fuse_on_gpu()\n torch._C._jit_override_can_fuse_on_gpu(False)\n yield\n if torch.__version__.startswith('1.9.'):\n torch._C._jit_override_can_fuse_on_gpu(old_val)\n\n\ndef load_inception(inception_args, metric):\n \"\"\"Load Inception Model from given ``inception_args`` and ``metric``.\n\n This function would try to load Inception under the guidance of 'type'\n given in `inception_args`, if not given, we would try best to load Tero's\n ones. In detail, we would first try to load the model from disk with\n the given 'inception_path', and then try to download the checkpoint from\n 'inception_url'. If both method are failed, pytorch version of Inception\n would be loaded.\n\n Args:\n inception_args (dict): Keyword args for inception net.\n metric (string): Metric to use the Inception. This argument would\n influence the pytorch's Inception loading.\n\n Returns:\n model (torch.nn.Module): Loaded Inception model.\n style (string): The version of the loaded Inception.\n \"\"\"\n\n if not isinstance(inception_args, dict):\n raise TypeError('Receive invalid \\'inception_args\\': '\n f'\\'{inception_args}\\'')\n\n _inception_args = deepcopy(inception_args)\n inception_type = _inception_args.pop('type', None)\n\n if torch.__version__ < '1.6.0':\n print_log(\n 'Current Pytorch Version not support script module, load '\n 'Inception Model from torch model zoo. If you want to use '\n 'Tero\\' script model, please update your Pytorch higher '\n f'than \\'1.6\\' (now is {torch.__version__})', 'current')\n return _load_inception_torch(_inception_args, metric), 'pytorch'\n\n # load pytorch version is specific\n if inception_type != 'StyleGAN':\n return _load_inception_torch(_inception_args, metric), 'pytorch'\n\n # try to load Tero's version\n path = _inception_args.get('inception_path', TERO_INCEPTION_URL)\n if path is None:\n path = TERO_INCEPTION_URL\n\n # try to parse `path` as web url and download\n if 'http' not in path:\n model = _load_inception_from_path(path)\n if isinstance(model, torch.nn.Module):\n return model, 'StyleGAN'\n\n # try to parse `path` as path on disk\n model = _load_inception_from_url(path)\n if isinstance(model, torch.nn.Module):\n return model, 'StyleGAN'\n\n raise RuntimeError('Cannot Load Inception Model, please check the input '\n f'`inception_args`: {inception_args}')\n\n\ndef _load_inception_from_path(inception_path):\n \"\"\"Load inception from passed path.\n\n Args:\n inception_path (str): The path of inception.\n\n Returns:\n nn.Module: The loaded inception.\n \"\"\"\n print_log(\n 'Try to load Tero\\'s Inception Model from '\n f'\\'{inception_path}\\'.', 'current')\n try:\n model = torch.jit.load(inception_path)\n print_log('Load Tero\\'s Inception Model successfully.', 'current')\n except Exception as e:\n model = None\n print_log('Load Tero\\'s Inception Model failed. '\n f'\\'{e}\\' occurs.', 'current')\n return model\n\n\ndef _load_inception_from_url(inception_url: str) -> nn.Module:\n \"\"\"Load Inception network from the give `inception_url`\"\"\"\n inception_url = inception_url if inception_url else TERO_INCEPTION_URL\n print_log(f'Try to download Inception Model from {inception_url}...',\n 'current')\n try:\n path = download_from_url(inception_url, dest_dir=MMAGIC_CACHE_DIR)\n print_log('Download Finished.', 'current')\n return _load_inception_from_path(path)\n except Exception as e:\n print_log(f'Download Failed. {e} occurs.', 'current')\n return None\n\n\ndef _load_inception_torch(inception_args, metric) -> nn.Module:\n \"\"\"Load Inception network from PyTorch's model zoo.\"\"\"\n assert metric in ['FID', 'IS']\n if metric == 'FID':\n inception_model = InceptionV3([3], **inception_args)\n elif metric == 'IS':\n inception_model = inception_v3(pretrained=True, transform_input=False)\n print_log(\n 'Load Inception V3 Network from Pytorch Model Zoo '\n 'for IS calculation. The results can only used '\n 'for monitoring purposes. To get more accuracy IS, '\n 'please use Tero\\'s Inception V3 checkpoints '\n 'and use Bicubic Interpolation with Pillow backend '\n 'for image resizing. More details may refer to '\n 'https://github.com/open-mmlab/MMEditing/blob/master/docs/en/quick_run.md#is.', # noqa\n 'current')\n return inception_model\n\n\ndef get_inception_feat_cache_name_and_args(dataloader: DataLoader,\n metric: BaseMetric, real_nums: int,\n capture_mean_cov: bool,\n capture_all: bool\n ) -> Tuple[str, dict]:\n \"\"\"Get the name and meta info of the inception feature cache file\n corresponding to the input dataloader and metric.\n\n The meta info includes\n 'data_root', 'data_prefix', 'meta_info' and 'pipeline' of the dataset, and\n 'inception_style' and 'inception_args' of the metric. Then we calculate the\n hash value of the meta info dict with md5, and the name of the inception\n feature cache will be 'inception_feat_{HASH}.pkl'.\n Args:\n dataloader (Dataloader): The dataloader of real images.\n metric (BaseMetric): The metric which needs inception features.\n real_nums (int): Number of images used to extract inception feature.\n capture_mean_cov (bool): Whether save the mean and covariance of\n inception feature. Defaults to False.\n capture_all (bool): Whether save the raw inception feature. Defaults\n to False.\n Returns:\n Tuple[str, dict]: Filename and meta info dict of the inception feature\n cache.\n \"\"\"\n\n dataset: BaseDataset = dataloader.dataset\n assert isinstance(dataset, Dataset), (\n f'Only support normal dataset, but receive {type(dataset)}.')\n\n # get dataset info\n data_root = deepcopy(dataset.data_root)\n data_prefix = deepcopy(dataset.data_prefix)\n metainfo = dataset.metainfo\n pipeline = repr(dataset.pipeline)\n\n # get metric info\n inception_style = metric.inception_style\n inception_args = getattr(metric, 'inception_args', None)\n\n real_key = 'gt_img' if metric.real_key is None else metric.real_key\n args = dict(\n data_root=data_root,\n data_prefix=data_prefix,\n metainfo=metainfo,\n pipeline=pipeline,\n inception_style=inception_style,\n inception_args=inception_args,\n # save `num_gpus` because this may influence the data loading order\n num_gpus=get_world_size(),\n capture_mean_cov=capture_mean_cov,\n capture_all=capture_all,\n real_keys=real_key,\n real_nums=real_nums)\n\n real_nums_str = 'full' if real_nums == -1 else str(real_nums)\n md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))\n if capture_all:\n prefix = 'inception_state-capture_all'\n elif capture_mean_cov:\n prefix = 'inception_state-capture_mean_cov'\n else:\n prefix = 'inception_state-capture_all_mean_cov'\n cache_tag = f'{prefix}-{real_nums_str}-{md5.hexdigest()}.pkl'\n return cache_tag, args\n\n\ndef get_vgg_feat_cache_name_and_args(dataloader: DataLoader,\n metric: BaseMetric) -> Tuple[str, dict]:\n \"\"\"Get the name and meta info of the vgg feature cache file corresponding\n to the input dataloader and metric.\n\n The meta info includes 'data_root',\n 'data_prefix', 'meta_info' and 'pipeline' of the dataset, and\n 'use_tero_scirpt' of the metric. Then we calculate the hash value of the\n meta info dict with md5, and the name of the vgg feature cache will be\n 'vgg_feat_{HASH}.pkl'.\n Args:\n dataloader (Dataloader): The dataloader of real images.\n metric (BaseMetric): The metric which needs inception features.\n Returns:\n Tuple[str, dict]: Filename and meta info dict of the inception feature\n cache.\n \"\"\"\n\n dataset: BaseDataset = dataloader.dataset\n assert isinstance(dataset, Dataset), (\n f'Only support normal dataset, but receive {type(dataset)}.')\n\n # get dataset info\n data_root = deepcopy(dataset.data_root)\n data_prefix = deepcopy(dataset.data_prefix)\n metainfo = dataset.metainfo\n pipeline = dataset.pipeline\n if isinstance(pipeline, Compose):\n pipeline_str = repr(pipeline)\n else:\n pipeline_str = ''\n\n # get metric info\n use_tero_scirpt = metric.use_tero_scirpt\n\n args = dict(\n data_root=data_root,\n data_prefix=data_prefix,\n metainfo=metainfo,\n pipeline=pipeline_str,\n use_tero_scirpt=use_tero_scirpt)\n\n md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))\n cache_tag = f'vgg_state-{md5.hexdigest()}.pkl'\n return cache_tag, args\n\n\ndef prepare_inception_feat(dataloader: DataLoader,\n metric: BaseMetric,\n data_preprocessor: Optional[nn.Module] = None,\n capture_mean_cov: bool = False,\n capture_all: bool = False) -> dict:\n \"\"\"Prepare inception feature for the input metric.\n\n - If `metric.inception_pkl` is an online path, try to download and load\n it. If cannot download or load, corresponding error will be raised.\n - If `metric.inception_pkl` is local path and file exists, try to load the\n file. If cannot load, corresponding error will be raised.\n - If `metric.inception_pkl` is local path and file not exists, we will\n extract the inception feature manually and save to 'inception_pkl'.\n - If `metric.inception_pkl` is not defined, we will extract the inception\n feature and save it to default cache dir with default name.\n\n Args:\n dataloader (Dataloader): The dataloader of real images.\n metric (BaseMetric): The metric which needs inception features.\n data_preprocessor (Optional[nn.Module]): Data preprocessor of the\n module. Used to preprocess the real images. If not passed, real\n images will automatically normalized to [-1, 1]. Defaults to None.\n capture_mean_cov (bool): Whether save the mean and covariance of\n inception feature. Defaults to False.\n capture_all (bool): Whether save the raw inception feature. If true,\n it will take a lot of time to save the inception feature. Defaults\n to False.\n\n Returns:\n dict: Dict contains inception feature.\n \"\"\"\n assert capture_mean_cov or capture_all, (\n 'At least one of \\'capture_mean_cov\\' and \\'capture_all\\' is True.')\n if not hasattr(metric, 'inception_pkl'):\n return\n inception_pkl: Optional[str] = metric.inception_pkl\n\n if isinstance(inception_pkl, str):\n if is_filepath(inception_pkl) and osp.exists(inception_pkl):\n with open(inception_pkl, 'rb') as file:\n inception_state = pickle.load(file)\n print_log(\n f'\\'{metric.prefix}\\' successful load inception feature '\n f'from \\'{inception_pkl}\\'', 'current')\n return inception_state\n elif inception_pkl.startswith('s3'):\n try:\n raise NotImplementedError(\n 'Not support download from Ceph currently')\n except Exception as exp:\n raise exp('Not support download from Ceph currently')\n elif inception_pkl.startswith('http'):\n try:\n raise NotImplementedError(\n 'Not support download from url currently')\n except Exception as exp:\n # cannot download, raise error\n raise exp('Not support download from url currently')\n\n # cannot load or download from file, extract manually\n assert hasattr(metric, 'real_nums'), (\n f'Metric \\'{metric.name}\\' must have attribute \\'real_nums\\'.')\n real_nums = metric.real_nums\n if inception_pkl is None:\n inception_pkl, args = get_inception_feat_cache_name_and_args(\n dataloader, metric, real_nums, capture_mean_cov, capture_all)\n inception_pkl = osp.join(MMAGIC_CACHE_DIR, inception_pkl)\n else:\n args = dict()\n if osp.exists(inception_pkl):\n with open(inception_pkl, 'rb') as file:\n real_feat = pickle.load(file)\n print_log(f'load preprocessed feat from {inception_pkl}', 'current')\n return real_feat\n\n assert hasattr(metric, 'inception'), (\n 'Metric must have a inception network to extract inception features.')\n\n real_feat = []\n\n print_log(\n f'Inception pkl \\'{inception_pkl}\\' is not found, extract '\n 'manually.', 'current')\n\n import rich.progress\n\n dataset, batch_size = dataloader.dataset, dataloader.batch_size\n if real_nums == -1:\n num_items = len(dataset)\n else:\n num_items = min(len(dataset), real_nums)\n\n rank, num_gpus = get_dist_info()\n item_subset = [(i * num_gpus + rank) % num_items\n for i in range((num_items - 1) // num_gpus + 1)]\n inception_dataloader = DataLoader(\n dataset,\n batch_size=batch_size,\n sampler=item_subset,\n collate_fn=pseudo_collate,\n shuffle=False,\n drop_last=False)\n # init rich pbar for the main process\n if is_main_process():\n # check the launcher\n slurm_env_name = ['SLURM_PROCID', 'SLURM_NTASKS', 'SLURM_NODELIST']\n if all([n in os.environ for n in slurm_env_name]):\n is_slurm = True\n pbar = mmengine.ProgressBar(len(inception_dataloader))\n else:\n is_slurm = False\n columns = [\n rich.progress.TextColumn('[bold blue]{task.description}'),\n rich.progress.BarColumn(bar_width=40),\n rich.progress.TaskProgressColumn(),\n rich.progress.TimeRemainingColumn(),\n ]\n pbar = rich.progress.Progress(*columns)\n pbar.start()\n task = pbar.add_task(\n 'Calculate Inception Feature.',\n total=len(inception_dataloader),\n visible=True)\n\n for data in inception_dataloader:\n # set training = False to avoid norm + convert to BGR\n data_samples = data_preprocessor(data, False)['data_samples']\n\n real_key = 'gt_img' if metric.real_key is None else metric.real_key\n img = getattr(data_samples, real_key)\n\n real_feat_ = metric.forward_inception(img).cpu()\n real_feat.append(real_feat_)\n\n if is_main_process():\n if is_slurm:\n pbar.update(1)\n else:\n pbar.update(task, advance=1)\n\n # stop the pbar\n if is_main_process():\n if is_slurm:\n sys.stdout.write('\\n')\n else:\n pbar.stop()\n\n # collect results\n real_feat = torch.cat(real_feat)\n # use `all_gather` here, gather tensor is much quicker than gather object.\n real_feat = all_gather(real_feat)\n\n # only cat on the main process\n if is_main_process():\n inception_state = dict(**args)\n if capture_mean_cov:\n real_feat = torch.cat(real_feat, dim=0)[:num_items].numpy()\n real_mean = np.mean(real_feat, 0)\n real_cov = np.cov(real_feat, rowvar=False)\n inception_state['real_mean'] = real_mean\n inception_state['real_cov'] = real_cov\n if capture_all:\n inception_state['raw_feature'] = real_feat\n dir_name = osp.dirname(inception_pkl)\n os.makedirs(dir_name, exist_ok=True)\n print_log(\n f'Saving inception pkl to {inception_pkl}. Please be patient.',\n 'current')\n with open(inception_pkl, 'wb') as file:\n pickle.dump(inception_state, file)\n print_log('Inception pkl Finished.', 'current')\n return inception_state\n\n\ndef prepare_vgg_feat(dataloader: DataLoader,\n metric: BaseMetric,\n data_preprocessor: Optional[nn.Module] = None,\n auto_save=True) -> np.ndarray:\n \"\"\"Prepare vgg feature for the input metric.\n\n - If `metric.vgg_pkl` is an online path, try to download and load\n it. If cannot download or load, corresponding error will be raised.\n - If `metric.vgg_pkl` is local path and file exists, try to load the\n file. If cannot load, corresponding error will be raised.\n - If `metric.vgg_pkl` is local path and file not exists, we will\n extract the vgg feature manually and save to 'vgg_pkl'.\n - If `metric.vgg_pkl` is not defined, we will extract the vgg\n feature and save it to default cache dir with default name.\n\n Args:\n dataloader (Dataloader): The dataloader of real images.\n metric (BaseMetric): The metric which needs vgg features.\n data_preprocessor (Optional[nn.Module]): Data preprocessor of the\n module. Used to preprocess the real images. If not passed, real\n images will automatically normalized to [-1, 1]. Defaults to None.\n Returns:\n np.ndarray: Loaded vgg feature.\n \"\"\"\n if not hasattr(metric, 'vgg16_pkl'):\n return\n vgg_pkl: Optional[str] = metric.vgg16_pkl\n\n if isinstance(vgg_pkl, str):\n if is_filepath(vgg_pkl) and osp.exists(vgg_pkl):\n with open(vgg_pkl, 'rb') as file:\n vgg_state = pickle.load(file)\n print_log(\n f'\\'{metric.prefix}\\' successful load VGG feature '\n f'from \\'{vgg_pkl}\\'', 'current')\n return vgg_state['vgg_feat']\n elif vgg_pkl.startswith('s3'):\n try:\n raise NotImplementedError(\n 'Not support download from Ceph currently')\n except Exception as exp:\n raise exp('Not support download from Ceph currently')\n elif vgg_pkl.startswith('http'):\n try:\n raise NotImplementedError(\n 'Not support download from url currently')\n except Exception as exp:\n # cannot download, raise error\n raise exp('Not support download from url currently')\n\n # cannot load or download from file, extract manually\n if vgg_pkl is None:\n vgg_pkl, args = get_vgg_feat_cache_name_and_args(dataloader, metric)\n vgg_pkl = osp.join(MMAGIC_CACHE_DIR, vgg_pkl)\n else:\n args = dict()\n if osp.exists(vgg_pkl):\n with open(vgg_pkl, 'rb') as file:\n real_feat = pickle.load(file)['vgg_feat']\n print(f'load preprocessed feat from {vgg_pkl}')\n return real_feat\n\n assert hasattr(\n metric,\n 'vgg16'), ('Metric must have a vgg16 network to extract vgg features.')\n\n real_feat = []\n\n print_log(f'Vgg pkl \\'{vgg_pkl}\\' is not found, extract '\n 'manually.', 'current')\n\n import rich.progress\n\n # init rich pbar for the main process\n if is_main_process():\n columns = [\n rich.progress.TextColumn('[bold blue]{task.description}'),\n rich.progress.BarColumn(bar_width=40),\n rich.progress.TaskProgressColumn(),\n rich.progress.TimeRemainingColumn(),\n ]\n pbar = rich.progress.Progress(*columns)\n pbar.start()\n task = pbar.add_task(\n 'Calculate VGG16 Feature.',\n total=len(dataloader.dataset),\n visible=True)\n\n for data in dataloader:\n # set training = False to avoid norm + convert to BGR\n data_samples = data_preprocessor(data, False)['data_samples']\n\n real_key = 'gt_img' if metric.real_key is None else metric.real_key\n img = getattr(data_samples, real_key)\n\n real_feat_ = metric.extract_features(img)\n real_feat.append(real_feat_)\n\n if is_main_process():\n pbar.update(task, advance=len(real_feat_) * get_world_size())\n\n # stop the pbar\n if is_main_process():\n pbar.stop()\n\n # collect results\n real_feat = torch.cat(real_feat)\n # use `all_gather` here, gather tensor is much quicker than gather object.\n real_feat = all_gather(real_feat)\n\n # only cat on the main process\n if is_main_process():\n real_feat = torch.cat(real_feat, dim=0)[:len(dataloader.dataset)].cpu()\n if auto_save:\n vgg_state = dict(vgg_feat=real_feat, **args)\n with open(vgg_pkl, 'wb') as file:\n pickle.dump(vgg_state, file)\n return real_feat\n" }, { "path": "mmagic/evaluation/metrics/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nfrom .connectivity_error import ConnectivityError\nfrom .equivariance import Equivariance\nfrom .fid import FrechetInceptionDistance, TransFID\nfrom .gradient_error import GradientError\nfrom .inception_score import InceptionScore, TransIS\nfrom .mae import MAE\nfrom .matting_mse import MattingMSE\nfrom .ms_ssim import MultiScaleStructureSimilarity\nfrom .mse import MSE\nfrom .niqe import NIQE, niqe\nfrom .ppl import PerceptualPathLength\nfrom .precision_and_recall import PrecisionAndRecall\nfrom .psnr import PSNR, psnr\nfrom .sad import SAD\nfrom .snr import SNR, snr\nfrom .ssim import SSIM, ssim\nfrom .swd import SlicedWassersteinDistance\n\n__all__ = [\n 'MAE',\n 'MSE',\n 'PSNR',\n 'psnr',\n 'SNR',\n 'snr',\n 'SSIM',\n 'ssim',\n 'MultiScaleStructureSimilarity',\n 'FrechetInceptionDistance',\n 'TransFID',\n 'InceptionScore',\n 'TransIS',\n 'SAD',\n 'MattingMSE',\n 'ConnectivityError',\n 'GradientError',\n 'PerceptualPathLength',\n 'PrecisionAndRecall',\n 'SlicedWassersteinDistance',\n 'NIQE',\n 'niqe',\n 'Equivariance',\n]\n" }, { "path": "mmagic/evaluation/metrics/base_gen_metric.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport warnings\nfrom typing import Any, Iterator, List, Optional\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine import is_list_of, print_log\nfrom mmengine.dataset import pseudo_collate\nfrom mmengine.dist import (all_gather, broadcast_object_list, collect_results,\n get_dist_info, get_world_size, is_main_process)\nfrom mmengine.evaluator import BaseMetric\nfrom mmengine.model import is_model_wrapper\nfrom torch import Tensor\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.structures import DataSample\n\n\nclass GenMetric(BaseMetric):\n \"\"\"Metric for MMagic.\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n real_nums (int): Numbers of the real image need for the metric. If `-1`\n is passed means all images from the dataset is need. Defaults to 0.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n real_key (Optional[str]): Key for get real images from the input dict.\n Defaults to 'img'.\n sample_model (str): Sampling model for the generative model. Support\n 'orig' and 'ema'. Defaults to 'ema'.\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n\n SAMPLER_MODE = 'normal'\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = 0,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None) -> None:\n super().__init__(collect_device, prefix)\n self.sample_model = sample_model\n\n self.fake_nums = fake_nums\n self.real_nums = real_nums\n self.real_key = real_key\n self.fake_key = fake_key\n self.real_results: List[Any] = []\n self.fake_results: List[Any] = []\n\n @property\n def real_nums_per_device(self):\n \"\"\"Number of real images need for current device.\"\"\"\n return math.ceil(self.real_nums / get_world_size())\n\n @property\n def fake_nums_per_device(self):\n \"\"\"Number of fake images need for current device.\"\"\"\n return math.ceil(self.fake_nums / get_world_size())\n\n def _collect_target_results(self, target: str) -> Optional[list]:\n \"\"\"Collected results in distributed environments.\n\n Args:\n target (str): Target results to collect.\n\n Returns:\n Optional[list]: The collected results.\n \"\"\"\n assert target in [\n 'fake', 'real'\n ], ('Only support to collect \\'fake\\' or \\'real\\' results.')\n results = getattr(self, f'{target}_results')\n size = getattr(self, f'{target}_nums')\n size = len(results) * get_world_size() if size == -1 else size\n\n if len(results) == 0:\n warnings.warn(\n f'{self.__class__.__name__} got empty `self.{target}_results`.'\n ' Please ensure that the processed results are properly added '\n f'into `self.{target}_results` in `process` method.')\n\n if is_list_of(results, Tensor):\n # apply all_gather for tensor results\n results = torch.cat(results, dim=0)\n results = torch.cat(all_gather(results), dim=0)[:size]\n results = torch.split(results, 1)\n else:\n # apply collect_results (all_gather_object) for non-tensor results\n results = collect_results(results, size, self.collect_device)\n\n # on non-main process, results should be `None`\n if is_main_process() and len(results) != size:\n raise ValueError(f'Length of results is \\'{len(results)}\\', not '\n f'equals to target size \\'{size}\\'.')\n return results\n\n def evaluate(self) -> dict:\n \"\"\"Evaluate the model performance of the whole dataset after processing\n all batches. Different like :class:`~mmengine.evaluator.BaseMetric`,\n this function evaluate the metric with paired results (`results_fake`\n and `results_real`).\n\n Returns:\n dict: Evaluation metrics dict on the val dataset. The keys are the\n names of the metrics, and the values are corresponding results.\n \"\"\"\n\n results_fake = self._collect_target_results(target='fake')\n results_real = self._collect_target_results(target='real')\n\n if is_main_process():\n # pack to list, align with BaseMetrics\n _metrics = self.compute_metrics(results_fake, results_real)\n # Add prefix to metric names\n if self.prefix:\n _metrics = {\n '/'.join((self.prefix, k)): v\n for k, v in _metrics.items()\n }\n metrics = [_metrics]\n else:\n metrics = [None] # type: ignore\n\n broadcast_object_list(metrics)\n\n # reset the results list\n self.real_results.clear()\n self.fake_results.clear()\n\n return metrics[0]\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: List['GenMetric']) -> DataLoader:\n \"\"\"Get sampler for normal metrics. Directly returns the dataloader.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images.\n metrics (List['GenMetric']): Metrics with the same sample mode.\n\n Returns:\n DataLoader: Default sampler for normal metrics.\n \"\"\"\n batch_size = dataloader.batch_size\n dataset_length = len(dataloader.dataset)\n rank, num_gpus = get_dist_info()\n assert self.real_nums <= dataset_length, (\n f'\\'real_nums\\'({self.real_nums}) can not larger than length of '\n f'dataset ({dataset_length}).')\n nums = dataset_length if self.real_nums == -1 else self.real_nums\n item_subset = [(i * num_gpus + rank) % nums\n for i in range((nums - 1) // num_gpus + 1)]\n\n metric_dataloader = DataLoader(\n dataloader.dataset,\n batch_size=batch_size,\n sampler=item_subset,\n collate_fn=pseudo_collate,\n shuffle=False,\n drop_last=False)\n\n return metric_dataloader\n\n def compute_metrics(self, results_fake, results_real) -> dict:\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (list): The processed results of each batch.\n\n Returns:\n dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n \"\"\"Prepare for the pre-calculating items of the metric. Defaults to do\n nothing.\n\n Args:\n module (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for the real images.\n \"\"\"\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n\nclass GenerativeMetric(GenMetric):\n \"\"\"Metric for generative metrics. Except for the preparation phase\n (:meth:`prepare`), generative metrics do not need extra real images.\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n real_nums (int): Numbers of the real image need for the metric. If `-1`\n is passed means all images from the dataset is need. Defaults to 0.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n real_key (Optional[str]): Key for get real images from the input dict.\n Defaults to 'img'.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'ema'.\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n sample_kwargs(dict): Sampling arguments for model test.\n \"\"\"\n SAMPLER_MODE = 'Generative'\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = 0,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'img',\n need_cond_input: bool = False,\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n sample_kwargs: dict = dict()):\n super().__init__(fake_nums, real_nums, fake_key, real_key,\n sample_model, collect_device, prefix)\n self.need_cond_input = need_cond_input\n self.sample_kwargs = sample_kwargs\n if self.need_cond_input:\n print_log('Set \\'need_cond_input\\' as True, this may influence '\n 'the evaluation results of conditional models.')\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: GenMetric):\n \"\"\"Get sampler for generative metrics. Returns a dummy iterator, whose\n return value of each iteration is a dict containing batch size and\n sample mode to generate images.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images. Used to get\n batch size during generate fake images.\n metrics (List['GenMetric']): Metrics with the same sampler mode.\n\n Returns:\n :class:`dummy_iterator`: Sampler for generative metrics.\n \"\"\"\n\n batch_size = dataloader.batch_size\n dataset = dataloader.dataset\n\n sample_model = metrics[0].sample_model\n assert all([metric.sample_model == sample_model for metric in metrics\n ]), ('\\'sample_model\\' between metrics is inconsistency.')\n\n class dummy_iterator:\n\n def __init__(self, batch_size, max_length, sample_model, dataset,\n need_cond, sample_kwargs) -> None:\n self.batch_size = batch_size\n self.max_length = max_length\n self.sample_model = sample_model\n self.dataset = dataset\n self.need_cond = need_cond\n self.sample_kwargs = sample_kwargs\n\n def __iter__(self) -> Iterator:\n self.idx = 0\n return self\n\n def __len__(self) -> int:\n return math.ceil(self.max_length / self.batch_size)\n\n def get_cond(self) -> List[DataSample]:\n\n data_sample_list = []\n for _ in range(self.batch_size):\n data_sample = DataSample()\n cond = self.dataset.get_data_info(\n np.random.randint(len(self.dataset)))['gt_label']\n data_sample.set_gt_label(torch.Tensor(cond))\n data_sample_list.append(data_sample)\n return data_sample_list\n\n def __next__(self) -> dict:\n if self.idx > self.max_length:\n raise StopIteration\n self.idx += batch_size\n\n output_dict = dict(\n inputs=dict(\n sample_model=self.sample_model,\n num_batches=self.batch_size,\n sample_kwargs=self.sample_kwargs))\n\n if self.need_cond:\n output_dict['data_samples'] = self.get_cond()\n\n return output_dict\n\n return dummy_iterator(\n batch_size=batch_size,\n max_length=max([metric.fake_nums_per_device\n for metric in metrics]),\n sample_model=sample_model,\n dataset=dataset,\n need_cond=self.need_cond_input,\n sample_kwargs=self.sample_kwargs)\n\n def evaluate(self) -> dict():\n \"\"\"Evaluate generative metric. In this function we only collect\n :attr:`fake_results` because generative metrics do not need real\n images.\n\n Returns:\n dict: Evaluation metrics dict on the val dataset. The keys are the\n names of the metrics, and the values are corresponding results.\n \"\"\"\n results_fake = self._collect_target_results(target='fake')\n\n if is_main_process():\n # pack to list, align with BaseMetrics\n _metrics = self.compute_metrics(results_fake)\n # Add prefix to metric names\n if self.prefix:\n _metrics = {\n '/'.join((self.prefix, k)): v\n for k, v in _metrics.items()\n }\n metrics = [_metrics]\n else:\n metrics = [None] # type: ignore\n\n broadcast_object_list(metrics)\n\n # reset the results list\n self.fake_results.clear()\n\n return metrics[0]\n\n def compute_metrics(self, results) -> dict:\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (list): The processed results of each batch.\n\n Returns:\n dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n" }, { "path": "mmagic/evaluation/metrics/base_sample_wise_metric.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Evaluation metrics based on each sample.\"\"\"\n\nfrom typing import List, Optional, Sequence\n\nimport torch.nn as nn\nfrom mmengine.evaluator import BaseMetric\nfrom mmengine.model import is_model_wrapper\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom .metrics_utils import average, obtain_data\n\n\n@METRICS.register_module()\nclass BaseSampleWiseMetric(BaseMetric):\n \"\"\"Base sample wise metric of edit.\n\n Subclass must provide process function.\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n mask_key (str, optional): Key of mask, if mask_key is None, calculate\n all regions. Default: None\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n device (str): Device used to place torch tensors to compute metrics.\n Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n scaling (float, optional): Scaling factor for final metric.\n E.g. scaling=100 means the final metric will be amplified by 100\n for output. Default: 1\n \"\"\"\n\n SAMPLER_MODE = 'normal'\n sample_model = 'orig' # TODO: low-level models only support origin model\n metric = None # the name of metric\n\n def __init__(self,\n gt_key: str = 'gt_img',\n pred_key: str = 'pred_img',\n mask_key: Optional[str] = None,\n scaling=1,\n device='cpu',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None) -> None:\n assert self.metric is not None, (\n '\\'metric\\' must be defined for \\'BaseSampleWiseMetric\\'.')\n super().__init__(collect_device, prefix)\n\n self.gt_key = gt_key\n self.pred_key = pred_key\n self.mask_key = mask_key\n self.scaling = scaling\n self.device = device\n\n self.channel_order = 'BGR'\n\n def compute_metrics(self, results: List):\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (List): The processed results of each batch.\n\n Returns:\n Dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n result = average(results, self.metric) * self.scaling\n\n return {self.metric: result}\n\n def process(self, data_batch: Sequence[dict],\n data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data and predictions.\n\n Args:\n data_batch (Sequence[dict]): A batch of data\n from the dataloader.\n predictions (Sequence[dict]): A batch of outputs from\n the model.\n \"\"\"\n\n for data in data_samples:\n prediction = data['output']\n\n gt = obtain_data(data, self.gt_key, self.device)\n pred = obtain_data(prediction, self.pred_key, self.device)\n if self.mask_key is not None:\n mask = obtain_data(data, self.mask_key)\n mask[mask != 0] = 1\n else:\n mask = 1 - pred * 0\n\n if len(gt.shape) <= 3:\n result = self.process_image(gt, pred, mask)\n else:\n result_sum = 0\n for i in range(gt.shape[0]):\n result_sum += self.process_image(gt[i], pred[i], mask[i])\n result = result_sum / gt.shape[0]\n\n self.results.append({self.metric: result})\n\n def process_image(self, gt, pred, mask):\n raise NotImplementedError\n\n def evaluate(self) -> dict:\n assert hasattr(self, 'size'), (\n 'Cannot find \\'size\\', please make sure \\'self.prepare\\' is '\n 'called correctly.')\n return super().evaluate(self.size)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader):\n self.size = len(dataloader.dataset)\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics) -> DataLoader:\n \"\"\"Get sampler for normal metrics. Directly returns the dataloader.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images.\n metrics (List['GenMetric']): Metrics with the same sample mode.\n\n Returns:\n DataLoader: Default sampler for normal metrics.\n \"\"\"\n return dataloader\n" }, { "path": "mmagic/evaluation/metrics/connectivity_error.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Evaluation metrics used in Image Matting.\"\"\"\n\nfrom typing import List, Sequence\n\nimport cv2\nimport numpy as np\nimport torch.nn as nn\nfrom mmengine.model import is_model_wrapper\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import _fetch_data_and_check, average\n\n\n@METRICS.register_module()\nclass ConnectivityError(BaseSampleWiseMetric):\n \"\"\"Connectivity error for evaluating alpha matte prediction.\n\n .. note::\n\n Current implementation assume image / alpha / trimap array in numpy\n format and with pixel value ranging from 0 to 255.\n\n .. note::\n\n pred_alpha should be masked by trimap before passing\n into this metric\n\n Args:\n step (float): Step of threshold when computing intersection between\n `alpha` and `pred_alpha`. Default to 0.1 .\n norm_const (int): Divide the result to reduce its magnitude.\n Default to 1000.\n\n Default prefix: ''\n\n Metrics:\n - ConnectivityError (float): Connectivity Error\n \"\"\"\n\n metric = 'ConnectivityError'\n\n def __init__(\n self,\n step=0.1,\n norm_constant=1000,\n **kwargs,\n ) -> None:\n self.step = step\n self.norm_constant = norm_constant\n super().__init__(**kwargs)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader):\n self.size = len(dataloader.dataset)\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n def process(self, data_batch: Sequence[dict],\n data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.results``, which will be used to\n compute the metrics when all batches have been processed.\n\n Args:\n data_batch (Sequence[dict]): A batch of data from the dataloader.\n predictions (Sequence[dict]): A batch of outputs from\n the model.\n \"\"\"\n\n for data_sample in data_samples:\n pred_alpha, gt_alpha, trimap = _fetch_data_and_check(data_sample)\n\n thresh_steps = np.arange(0, 1 + self.step, self.step)\n round_down_map = -np.ones_like(gt_alpha)\n for i in range(1, len(thresh_steps)):\n gt_alpha_thresh = gt_alpha >= thresh_steps[i]\n pred_alpha_thresh = pred_alpha >= thresh_steps[i]\n intersection = gt_alpha_thresh & pred_alpha_thresh\n intersection = intersection.astype(np.uint8)\n\n # connected components\n _, output, stats, _ = cv2.connectedComponentsWithStats(\n intersection, connectivity=4)\n # start from 1 in dim 0 to exclude background\n size = stats[1:, -1]\n\n # largest connected component of the intersection\n omega = np.zeros_like(gt_alpha)\n if len(size) != 0:\n max_id = np.argmax(size)\n # plus one to include background\n omega[output == max_id + 1] = 1\n\n mask = (round_down_map == -1) & (omega == 0)\n round_down_map[mask] = thresh_steps[i - 1]\n round_down_map[round_down_map == -1] = 1\n\n gt_alpha_diff = gt_alpha - round_down_map\n pred_alpha_diff = pred_alpha - round_down_map\n # only calculate difference larger than or equal to 0.15\n gt_alpha_phi = 1 - gt_alpha_diff * (gt_alpha_diff >= 0.15)\n pred_alpha_phi = 1 - pred_alpha_diff * (pred_alpha_diff >= 0.15)\n\n connectivity_error = np.sum(\n np.abs(gt_alpha_phi - pred_alpha_phi) * (trimap == 128))\n\n # divide by 1000 to reduce the magnitude of the result\n connectivity_error /= self.norm_constant\n\n self.results.append({'conn_err': connectivity_error})\n\n def compute_metrics(self, results: List):\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (dict): The processed results of each batch.\n\n Returns:\n Dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n conn_err = average(results, 'conn_err')\n\n return {'ConnectivityError': conn_err}\n" }, { "path": "mmagic/evaluation/metrics/equivariance.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom typing import Iterator, List, Optional, Sequence\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.dist import all_gather\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom .base_gen_metric import GenerativeMetric\n\n\n@METRICS.register_module('EQ')\n@METRICS.register_module()\nclass Equivariance(GenerativeMetric):\n\n name = 'Equivariance'\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = 0,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n need_cond_input: bool = False,\n sample_mode: str = 'ema',\n sample_kwargs: dict = dict(),\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n eq_cfg=dict()):\n super().__init__(fake_nums, real_nums, fake_key, real_key,\n need_cond_input, sample_mode, collect_device, prefix)\n # set default sampler config\n self._eq_cfg = deepcopy(eq_cfg)\n self._eq_cfg.setdefault('compute_eqt_int', False)\n self._eq_cfg.setdefault('compute_eqt_frac', False)\n self._eq_cfg.setdefault('compute_eqr', False)\n self._eq_cfg.setdefault('translate_max', 0.125)\n self._eq_cfg.setdefault('rotate_max', 1)\n self.SAMPLER_MODE = 'EqSampler'\n\n self.sample_kwargs = sample_kwargs\n # compute numbers of eq\n self.n_sub_metric = 0\n if self._eq_cfg['compute_eqt_int']:\n self.n_sub_metric += 1\n if self._eq_cfg['compute_eqt_frac']:\n self.n_sub_metric += 1\n if self._eq_cfg['compute_eqr']:\n self.n_sub_metric += 1\n\n self.fake_results = defaultdict(list)\n\n @torch.no_grad()\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results``, which will be used\n to compute the metrics when all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n cfg_key_list = ['compute_eqt_int', 'compute_eqt_frac', 'compute_eqr']\n sample_key_list = ['eqt_int', 'eqt_frac', 'eqr']\n for pred in data_samples:\n for cfg_key, sample_key in zip(cfg_key_list, sample_key_list):\n if self._eq_cfg[cfg_key]:\n assert sample_key in pred\n # assert hasattr(pred, sample_key)\n eq_sample = pred[sample_key]\n diff = eq_sample['diff'].to(torch.float64).sum()\n mask = eq_sample['mask'].to(torch.float64).sum()\n self.fake_results[sample_key] += [diff, mask]\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: List[GenerativeMetric]):\n \"\"\"Get sampler for generative metrics. Returns a dummy iterator, whose\n return value of each iteration is a dict containing batch size and\n sample mode to generate images.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images. Used to get\n batch size during generate fake images.\n metrics (List['GenerativeMetric']): Metrics with the same sampler\n mode.\n\n Returns:\n :class:`dummy_iterator`: Sampler for generative metrics.\n \"\"\"\n\n batch_size = dataloader.batch_size\n\n sample_model = metrics[0].sample_model\n assert all([metric.sample_model == sample_model for metric in metrics\n ]), ('\\'sample_model\\' between metrics is inconsistency.')\n\n return eq_iterator(\n batch_size=batch_size,\n max_length=max([metric.fake_nums_per_device\n for metric in metrics]),\n sample_mode=sample_model,\n eq_cfg=self._eq_cfg,\n sample_kwargs=self.sample_kwargs)\n\n def compute_metrics(self, results) -> dict:\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (list): The processed results of each batch.\n\n Returns:\n dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n results = dict()\n for key in ['eqt_int', 'eqt_frac', 'eqr']:\n if key not in self.fake_results:\n continue\n sums = torch.stack(self.fake_results[key], dim=0)\n mses = (sums[0::2] / sums[1::2]).mean()\n psnrs = np.log10(2) * 20 - mses.log10() * 10\n psnrs = psnrs.cpu().numpy()\n results[key] = psnrs\n return results\n\n def _collect_target_results(self, target: str) -> Optional[list]:\n \"\"\"Collect function for Eq metric. This function support collect\n results typing as Dict[List[Tensor]]`.\n\n Args:\n target (str): Target results to collect.\n\n Returns:\n Optional[list]: The collected results.\n \"\"\"\n if target == 'real':\n return\n results = getattr(self, f'{target}_results')\n results_collected = []\n results_collected = dict()\n for key, result in results.items():\n result_collected = torch.stack(result)\n result_collected = torch.cat(all_gather(result_collected), dim=0)\n results_collected[key] = torch.split(result_collected,\n len(result_collected))\n\n return results_collected\n\n\nclass eq_iterator:\n\n def __init__(self, batch_size, max_length, sample_mode, eq_cfg,\n sample_kwargs) -> None:\n self.batch_size = batch_size\n self.max_length = max_length\n self.sample_mode = sample_mode\n self.eq_cfg = deepcopy(eq_cfg)\n self.sample_kwargs = sample_kwargs\n\n def __iter__(self) -> Iterator:\n self.idx = 0\n return self\n\n def __len__(self) -> int:\n return self.max_length // self.batch_size\n\n def __next__(self) -> dict:\n if self.idx >= self.max_length:\n raise StopIteration\n self.idx += self.batch_size\n mode = dict(\n sample_mode=self.sample_mode,\n eq_cfg=self.eq_cfg,\n sample_kwargs=self.sample_kwargs)\n # StyleGAN3 forward will receive eq config from mode\n return dict(inputs=dict(mode=mode, num_batches=self.batch_size))\n" }, { "path": "mmagic/evaluation/metrics/fid.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Sequence, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.dist import is_main_process\nfrom scipy import linalg\nfrom torch import Tensor\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom ..functional import (disable_gpu_fuser_on_pt19, load_inception,\n prepare_inception_feat)\nfrom .base_gen_metric import GenerativeMetric\n\n\n@METRICS.register_module('FID-Full')\n@METRICS.register_module('FID')\n@METRICS.register_module()\nclass FrechetInceptionDistance(GenerativeMetric):\n \"\"\"FID metric. In this metric, we calculate the distance between real\n distributions and fake distributions. The distributions are modeled by the\n real samples and fake samples, respectively. `Inception_v3` is adopted as\n the feature extractor, which is widely used in StyleGAN and BigGAN.\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n real_nums (int): Numbers of the real images need for the metric. If -1\n is passed, means all real images in the dataset will be used.\n Defaults to -1.\n inception_style (str): The target inception style want to load. If the\n given style cannot be loaded successful, will attempt to load a\n valid one. Defaults to 'StyleGAN'.\n inception_path (str, optional): Path the the pretrain Inception\n network. Defaults to None.\n inception_pkl (str, optional): Path to reference inception pickle file.\n If `None`, the statistical value of real distribution will be\n calculated at running time. Defaults to None.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n real_key (Optional[str]): Key for get real images from the input dict.\n Defaults to 'img'.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'orig'.\n collect_device (str, optional): Device name used for collecting results\n from different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n name = 'FID'\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = -1,\n inception_style='StyleGAN',\n inception_path: Optional[str] = None,\n inception_pkl: Optional[str] = None,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n need_cond_input: bool = False,\n sample_model: str = 'orig',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n sample_kwargs: dict = dict()):\n super().__init__(fake_nums, real_nums, fake_key, real_key,\n need_cond_input, sample_model, collect_device, prefix,\n sample_kwargs)\n self.real_mean = None\n self.real_cov = None\n self.device = 'cpu'\n self.inception, self.inception_style = self._load_inception(\n inception_style, inception_path)\n self.inception_pkl = inception_pkl\n\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n \"\"\"Preparing inception feature for the real images.\n\n Args:\n module (nn.Module): The model to evaluate.\n dataloader (DataLoader): The dataloader for real images.\n \"\"\"\n self.device = module.data_preprocessor.device\n self.inception.to(self.device)\n self.inception.eval()\n inception_feat_dict = prepare_inception_feat(\n dataloader, self, module.data_preprocessor, capture_mean_cov=True)\n if is_main_process():\n self.real_mean = inception_feat_dict['real_mean']\n self.real_cov = inception_feat_dict['real_cov']\n\n def _load_inception(self, inception_style: str,\n inception_path: Optional[str]\n ) -> Tuple[nn.Module, str]:\n \"\"\"Load inception and return the successful loaded style.\n\n Args:\n inception_style (str): Target style of Inception network want to\n load.\n inception_path (Optional[str]): The path to the inception.\n\n Returns:\n Tuple[nn.Module, str]: The actually loaded inception network and\n corresponding style.\n \"\"\"\n if inception_style == 'StyleGAN':\n args = dict(type='StyleGAN', inception_path=inception_path)\n else:\n args = dict(type='Pytorch', normalize_input=False)\n inception, style = load_inception(args, 'FID')\n inception.eval()\n return inception, style\n\n def forward_inception(self, image: Tensor) -> Tensor:\n \"\"\"Feed image to inception network and get the output feature.\n\n Args:\n data_samples (Sequence[dict]): A batch of data sample dict used to\n extract inception feature.\n\n Returns:\n Tensor: Image feature extracted from inception.\n \"\"\"\n # image must passed with 'bgr'\n image = image[:, [2, 1, 0]].to(self.device)\n\n if self.inception_style == 'StyleGAN':\n image = image.to(torch.uint8)\n with disable_gpu_fuser_on_pt19():\n feat = self.inception(image, return_features=True)\n else:\n image = (image - 127.5) / 127.5 # to [-1, 1]\n feat = self.inception(image)[0].view(image.shape[0], -1)\n return feat\n\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results``, which will be used\n to compute the metrics when all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n if len(self.fake_results) >= self.fake_nums_per_device:\n return\n\n fake_imgs = []\n for pred in data_samples:\n fake_img_ = pred\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n\n # check whether shape in fake_imgs are same\n img_shape = fake_imgs[0].shape\n if all([img.shape == img_shape for img in fake_imgs]):\n # all images have the same shape, forward inception altogether\n fake_imgs = torch.stack(fake_imgs, dim=0)\n feat = self.forward_inception(fake_imgs)\n feat_list = list(torch.split(feat, 1))\n else:\n # images have different shape, forward separately\n feat_list = [\n self.forward_inception(img[None, ...]) for img in fake_imgs\n ]\n self.fake_results += feat_list\n\n @staticmethod\n def _calc_fid(sample_mean: np.ndarray,\n sample_cov: np.ndarray,\n real_mean: np.ndarray,\n real_cov: np.ndarray,\n eps: float = 1e-6) -> Tuple[float]:\n \"\"\"Refer to the implementation from:\n\n https://github.com/rosinality/stylegan2-pytorch/blob/master/fid.py#L34\n \"\"\"\n cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False)\n\n if not np.isfinite(cov_sqrt).all():\n print('product of cov matrices is singular')\n offset = np.eye(sample_cov.shape[0]) * eps\n cov_sqrt = linalg.sqrtm(\n (sample_cov + offset) @ (real_cov + offset))\n\n if np.iscomplexobj(cov_sqrt):\n if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):\n m = np.max(np.abs(cov_sqrt.imag))\n\n raise ValueError(f'Imaginary component {m}')\n\n cov_sqrt = cov_sqrt.real\n\n mean_diff = sample_mean - real_mean\n mean_norm = mean_diff @ mean_diff\n\n trace = np.trace(sample_cov) + np.trace(\n real_cov) - 2 * np.trace(cov_sqrt)\n\n fid = mean_norm + trace\n\n return float(fid), float(mean_norm), float(trace)\n\n def compute_metrics(self, fake_results: list) -> dict:\n \"\"\"Compute the result of FID metric.\n\n Args:\n fake_results (list): List of image feature of fake images.\n\n Returns:\n dict: A dict of the computed FID metric and its mean and\n covariance.\n \"\"\"\n fake_feats = torch.cat(fake_results, dim=0)\n fake_feats_np = fake_feats.cpu().numpy()\n fake_mean = np.mean(fake_feats_np, 0)\n fake_cov = np.cov(fake_feats_np, rowvar=False)\n\n fid, mean, cov = self._calc_fid(fake_mean, fake_cov, self.real_mean,\n self.real_cov)\n\n return {'fid': fid, 'mean': mean, 'cov': cov}\n\n\n@METRICS.register_module()\nclass TransFID(FrechetInceptionDistance):\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = -1,\n inception_style='StyleGAN',\n inception_path: Optional[str] = None,\n inception_pkl: Optional[str] = None,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'img',\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None):\n # NOTE: set `need_cond` as False since we direct return the original\n # dataloader as sampler\n super().__init__(fake_nums, real_nums, inception_style, inception_path,\n inception_pkl, fake_key, real_key, False,\n sample_model, collect_device, prefix)\n\n self.SAMPLER_MODE = 'normal'\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: List['GenerativeMetric']) -> DataLoader:\n \"\"\"Get sampler for normal metrics. Directly returns the dataloader.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images.\n metrics (List['GenMetric']): Metrics with the same sample mode.\n\n Returns:\n DataLoader: Default sampler for normal metrics.\n \"\"\"\n return dataloader\n" }, { "path": "mmagic/evaluation/metrics/gradient_error.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Sequence\n\nimport cv2\nimport numpy as np\nimport torch.nn as nn\nfrom mmengine.model import is_model_wrapper\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom ..functional import gauss_gradient\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import _fetch_data_and_check, average\n\n\n@METRICS.register_module()\nclass GradientError(BaseSampleWiseMetric):\n \"\"\"Gradient error for evaluating alpha matte prediction.\n\n .. note::\n\n Current implementation assume image / alpha / trimap array in numpy\n format and with pixel value ranging from 0 to 255.\n\n .. note::\n\n pred_alpha should be masked by trimap before passing\n into this metric\n\n Args:\n sigma (float): Standard deviation of the gaussian kernel.\n Defaults to 1.4 .\n norm_const (int): Divide the result to reduce its magnitude.\n Defaults to 1000 .\n\n Default prefix: ''\n\n Metrics:\n - GradientError (float): Gradient Error\n \"\"\"\n\n metric = 'GradientError'\n\n def __init__(\n self,\n sigma=1.4,\n norm_constant=1000,\n **kwargs,\n ) -> None:\n self.sigma = sigma\n self.norm_constant = norm_constant\n super().__init__(**kwargs)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader):\n self.size = len(dataloader.dataset)\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n def process(self, data_batch: Sequence[dict],\n data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.results``, which will be used to\n compute the metrics when all batches have been processed.\n\n Args:\n data_batch (Sequence[dict]): A batch of data from the dataloader.\n predictions (Sequence[dict]): A batch of outputs from\n the model.\n \"\"\"\n\n for data_sample in data_samples:\n pred_alpha, gt_alpha, trimap = _fetch_data_and_check(data_sample)\n\n gt_alpha_normed = np.zeros_like(gt_alpha)\n pred_alpha_normed = np.zeros_like(pred_alpha)\n\n cv2.normalize(gt_alpha, gt_alpha_normed, 1.0, 0.0, cv2.NORM_MINMAX)\n cv2.normalize(pred_alpha, pred_alpha_normed, 1.0, 0.0,\n cv2.NORM_MINMAX)\n\n gt_alpha_grad = gauss_gradient(gt_alpha_normed, self.sigma)\n pred_alpha_grad = gauss_gradient(pred_alpha_normed, self.sigma)\n # this is the sum over n samples\n grad_loss = ((gt_alpha_grad - pred_alpha_grad)**2 *\n (trimap == 128)).sum()\n\n # divide by 1000 to reduce the magnitude of the result\n grad_loss /= self.norm_constant\n\n self.results.append({'grad_err': grad_loss})\n\n def compute_metrics(self, results: List):\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (dict): The processed results of each batch.\n\n Returns:\n Dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n grad_err = average(results, 'grad_err')\n\n return {'GradientError': grad_err}\n" }, { "path": "mmagic/evaluation/metrics/inception_score.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Sequence, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom PIL import Image\nfrom scipy.stats import entropy\nfrom torch import Tensor\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\n# from .inception_utils import disable_gpu_fuser_on_pt19, load_inception\nfrom ..functional import disable_gpu_fuser_on_pt19, load_inception\nfrom .base_gen_metric import GenerativeMetric\n\n\n@METRICS.register_module('IS')\n@METRICS.register_module()\nclass InceptionScore(GenerativeMetric):\n \"\"\"IS (Inception Score) metric. The images are split into groups, and the\n inception score is calculated on each group of images, then the mean and\n standard deviation of the score is reported. The calculation of the\n inception score on a group of images involves first using the inception v3\n model to calculate the conditional probability for each image (p(y|x)). The\n marginal probability is then calculated as the average of the conditional\n probabilities for the images in the group (p(y)). The KL divergence is then\n calculated for each image as the conditional probability multiplied by the\n log of the conditional probability minus the log of the marginal\n probability. The KL divergence is then summed over all images and averaged\n over all classes and the exponent of the result is calculated to give the\n final score.\n\n Ref: https://github.com/sbarratt/inception-score-pytorch/blob/master/inception_score.py # noqa\n\n Note that we highly recommend that users should download the Inception V3\n script module from the following address. Then, the `inception_pkl` can\n be set with user's local path. If not given, we will use the Inception V3\n from pytorch model zoo. However, this may bring significant different in\n the final results.\n\n Tero's Inception V3: https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt # noqa\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n resize (bool, optional): Whether resize image to 299x299. Defaults to\n True.\n splits (int, optional): The number of groups. Defaults to 10.\n inception_style (str): The target inception style want to load. If the\n given style cannot be loaded successful, will attempt to load a\n valid one. Defaults to 'StyleGAN'.\n inception_path (str, optional): Path the the pretrain Inception\n network. Defaults to None.\n resize_method (str): Resize method. If `resize` is False, this will be\n ignored. Defaults to 'bicubic'.\n use_pil_resize (bool): Whether use Bicubic interpolation with\n Pillow's backend. If set as True, the evaluation process may be a\n little bit slow, but achieve a more accurate IS result. Defaults\n to False.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'orig'.\n collect_device (str, optional): Device name used for collecting results\n from different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n name = 'IS'\n\n pil_resize_method_mapping = {\n 'bicubic': Image.BICUBIC,\n 'bilinear': Image.BILINEAR,\n 'nearest': Image.NEAREST,\n 'box': Image.BOX\n }\n\n def __init__(self,\n fake_nums: int = 5e4,\n resize: bool = True,\n splits: int = 10,\n inception_style: str = 'StyleGAN',\n inception_path: Optional[str] = None,\n resize_method='bicubic',\n use_pillow_resize: bool = True,\n fake_key: Optional[str] = None,\n need_cond_input: bool = False,\n sample_model='orig',\n collect_device: str = 'cpu',\n prefix: str = None):\n super().__init__(fake_nums, 0, fake_key, None, need_cond_input,\n sample_model, collect_device, prefix)\n\n self.resize = resize\n self.splits = splits\n self.device = 'cpu'\n\n if not use_pillow_resize:\n print_log(\n 'We strongly recommend to use the bicubic resize with '\n 'Pillow backend. Otherwise, the results maybe '\n 'unreliable', 'current')\n self.use_pillow_resize = use_pillow_resize\n\n if self.use_pillow_resize:\n allowed_resize_method = list(self.pil_resize_method_mapping.keys())\n assert resize_method in self.pil_resize_method_mapping, (\n f'\\'resize_method\\' (\\'{resize_method}\\') is not supported '\n 'for PIL resize. Please select resize method in '\n f'{allowed_resize_method}.')\n self.resize_method = self.pil_resize_method_mapping[resize_method]\n else:\n self.resize_method = resize_method\n\n self.inception, self.inception_style = self._load_inception(\n inception_style, inception_path)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n \"\"\"Prepare for the pre-calculating items of the metric. Defaults to do\n nothing.\n\n Args:\n module (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for the real images.\n \"\"\"\n self.device = module.data_preprocessor.device\n self.inception.to(self.device)\n\n def _load_inception(self, inception_style: str,\n inception_path: Optional[str]\n ) -> Tuple[nn.Module, str]:\n \"\"\"Load pretrain model of inception network.\n Args:\n inception_style (str): Target style of Inception network want to\n load.\n inception_path (Optional[str]): The path to the inception.\n\n Returns:\n Tuple[nn.Module, str]: The actually loaded inception network and\n corresponding style.\n \"\"\"\n inception, style = load_inception(\n dict(type=inception_style, inception_path=inception_path), 'IS')\n inception.eval()\n return inception, style\n\n def _preprocess(self, image: Tensor) -> Tensor:\n \"\"\"Preprocess image before pass to the Inception. Preprocess operations\n contain channel conversion and resize.\n\n Args:\n image (Tensor): Image tensor before preprocess.\n\n Returns:\n Tensor: Image tensor after resize and channel conversion\n (if need.)\n \"\"\"\n # image must passed in 'bgr'\n image = image[:, [2, 1, 0]]\n if not self.resize:\n return image\n if self.use_pillow_resize:\n image = image.to(torch.uint8)\n x_np = [x_.permute(1, 2, 0).detach().cpu().numpy() for x_ in image]\n\n # use bicubic resize as default\n x_pil = [\n Image.fromarray(x_).resize((299, 299),\n resample=self.resize_method)\n for x_ in x_np\n ]\n x_ten = torch.cat(\n [torch.FloatTensor(np.array(x_)[None, ...]) for x_ in x_pil])\n return x_ten.permute(0, 3, 1, 2)\n else:\n return F.interpolate(\n image, size=(299, 299), mode=self.resize_method)\n\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results``, which will be used\n to compute the metrics when all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n if len(self.fake_results) >= self.fake_nums_per_device:\n return\n\n fake_imgs = []\n for pred in data_samples:\n fake_img_ = pred\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n fake_imgs = torch.stack(fake_imgs, dim=0)\n fake_imgs = self._preprocess(fake_imgs).to(self.device)\n\n if self.inception_style == 'StyleGAN':\n fake_imgs = fake_imgs.to(torch.uint8)\n with disable_gpu_fuser_on_pt19():\n feat = self.inception(fake_imgs, no_output_bias=True)\n else:\n fake_imgs = (fake_imgs - 127.5) / 127.5\n feat = F.softmax(self.inception(fake_imgs), dim=1)\n\n # NOTE: feat is shape like (bz, 1000), convert to a list\n self.fake_results += list(torch.split(feat, 1))\n\n def compute_metrics(self, fake_results: list) -> dict:\n \"\"\"Compute the results of Inception Score metric.\n\n Args:\n fake_results (list): List of image feature of fake images.\n\n Returns:\n dict: A dict of the computed IS metric and its standard error\n \"\"\"\n split_scores = []\n preds = torch.cat(fake_results, dim=0).cpu().numpy()\n # check for the size\n assert preds.shape[0] >= self.fake_nums\n preds = preds[:self.fake_nums]\n for k in range(self.splits):\n part = preds[k * (self.fake_nums // self.splits):(k + 1) *\n (self.fake_nums // self.splits), :]\n py = np.mean(part, axis=0)\n scores = []\n for i in range(part.shape[0]):\n pyx = part[i, :]\n scores.append(entropy(pyx, py))\n split_scores.append(np.exp(np.mean(scores)))\n\n mean, std = np.mean(split_scores), np.std(split_scores)\n\n return {'is': float(mean), 'is_std': float(std)}\n\n\n@METRICS.register_module()\nclass TransIS(InceptionScore):\n \"\"\"IS (Inception Score) metric. The images are split into groups, and the\n inception score is calculated on each group of images, then the mean and\n standard deviation of the score is reported. The calculation of the\n inception score on a group of images involves first using the inception v3\n model to calculate the conditional probability for each image (p(y|x)). The\n marginal probability is then calculated as the average of the conditional\n probabilities for the images in the group (p(y)). The KL divergence is then\n calculated for each image as the conditional probability multiplied by the\n log of the conditional probability minus the log of the marginal\n probability. The KL divergence is then summed over all images and averaged\n over all classes and the exponent of the result is calculated to give the\n final score.\n\n Ref: https://github.com/sbarratt/inception-score-pytorch/blob/master/inception_score.py # noqa\n\n Note that we highly recommend that users should download the Inception V3\n script module from the following address. Then, the `inception_pkl` can\n be set with user's local path. If not given, we will use the Inception V3\n from pytorch model zoo. However, this may bring significant different in\n the final results.\n\n Tero's Inception V3: https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt # noqa\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n resize (bool, optional): Whether resize image to 299x299. Defaults to\n True.\n splits (int, optional): The number of groups. Defaults to 10.\n inception_style (str): The target inception style want to load. If the\n given style cannot be loaded successful, will attempt to load a\n valid one. Defaults to 'StyleGAN'.\n inception_path (str, optional): Path the the pretrain Inception\n network. Defaults to None.\n resize_method (str): Resize method. If `resize` is False, this will be\n ignored. Defaults to 'bicubic'.\n use_pil_resize (bool): Whether use Bicubic interpolation with\n Pillow's backend. If set as True, the evaluation process may be a\n little bit slow, but achieve a more accurate IS result. Defaults\n to False.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'ema'.\n collect_device (str, optional): Device name used for collecting results\n from different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n\n def __init__(self,\n fake_nums: int = 50000,\n resize: bool = True,\n splits: int = 10,\n inception_style: str = 'StyleGAN',\n inception_path: Optional[str] = None,\n resize_method='bicubic',\n use_pillow_resize: bool = True,\n fake_key: Optional[str] = None,\n sample_model='ema',\n collect_device: str = 'cpu',\n prefix: str = None):\n # NOTE: set `need_cond` as False since we direct return the original\n # dataloader as sampler\n super().__init__(fake_nums, resize, splits, inception_style,\n inception_path, resize_method, use_pillow_resize,\n fake_key, False, sample_model, collect_device, prefix)\n self.SAMPLER_MODE = 'normal'\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: List['GenerativeMetric']) -> DataLoader:\n \"\"\"Get sampler for normal metrics. Directly returns the dataloader.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images.\n metrics (List['GenMetric']): Metrics with the same sample mode.\n\n Returns:\n DataLoader: Default sampler for normal metrics.\n \"\"\"\n return dataloader\n" }, { "path": "mmagic/evaluation/metrics/mae.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Evaluation metrics based on pixels.\"\"\"\n\nimport numpy as np\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\n\n\n@METRICS.register_module()\nclass MAE(BaseSampleWiseMetric):\n \"\"\"Mean Absolute Error metric for image.\n\n mean(abs(a-b))\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n mask_key (str, optional): Key of mask, if mask_key is None, calculate\n all regions. Default: None\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n\n Metrics:\n - MAE (float): Mean of Absolute Error\n \"\"\"\n\n metric = 'MAE'\n\n def process_image(self, gt, pred, mask):\n \"\"\"Process an image.\n\n Args:\n gt (Tensor | np.ndarray): GT image.\n pred (Tensor | np.ndarray): Pred image.\n mask (Tensor | np.ndarray): Mask of evaluation.\n Returns:\n result (np.ndarray): MAE result.\n \"\"\"\n\n gt = gt / 255.\n pred = pred / 255.\n\n diff = gt - pred\n diff = abs(diff)\n\n if self.mask_key is not None:\n diff *= mask # broadcast for channel dimension\n scale = np.prod(diff.shape) / np.prod(mask.shape)\n result = diff.sum() / (mask.sum() * scale + 1e-12)\n else:\n result = diff.mean()\n\n return result\n" }, { "path": "mmagic/evaluation/metrics/matting_mse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Sequence\n\nimport torch.nn as nn\nfrom mmengine.model import is_model_wrapper\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import _fetch_data_and_check, average\n\n\n@METRICS.register_module()\nclass MattingMSE(BaseSampleWiseMetric):\n \"\"\"Mean Squared Error metric for image matting.\n\n This metric compute per-pixel squared error average across all\n pixels.\n i.e. mean((a-b)^2) / norm_const\n\n .. note::\n\n Current implementation assume image / alpha / trimap array in numpy\n format and with pixel value ranging from 0 to 255.\n\n .. note::\n\n pred_alpha should be masked by trimap before passing\n into this metric\n\n Default prefix: ''\n\n Args:\n norm_const (int): Divide the result to reduce its magnitude.\n Default to 1000.\n\n Metrics:\n - MattingMSE (float): Mean of Squared Error\n \"\"\"\n\n default_prefix = ''\n metric = 'MattingMSE'\n\n def __init__(\n self,\n norm_const=1000,\n **kwargs,\n ) -> None:\n self.norm_const = norm_const\n super().__init__(**kwargs)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader):\n self.size = len(dataloader.dataset)\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n def process(self, data_batch: Sequence[dict],\n data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data and predictions.\n\n Args:\n data_batch (Sequence[dict]): A batch of data\n from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from\n the model.\n \"\"\"\n for data_sample in data_samples:\n pred_alpha, gt_alpha, trimap = _fetch_data_and_check(data_sample)\n\n weight_sum = (trimap == 128).sum()\n if weight_sum != 0:\n mse_result = ((pred_alpha - gt_alpha)**2).sum() / weight_sum\n else:\n mse_result = 0\n\n self.results.append({'mse': mse_result})\n\n def compute_metrics(self, results: List):\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (dict): The processed results of each batch.\n\n Returns:\n Dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n mse = average(results, 'mse')\n\n return {'MattingMSE': mse}\n" }, { "path": "mmagic/evaluation/metrics/metrics_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmcv\nimport numpy as np\nimport torch\n\nfrom mmagic.utils import reorder_image\n\n\ndef _assert_ndim(input, name, ndim, shape_hint):\n if input.ndim != ndim:\n raise ValueError(\n f'{name} should be of shape {shape_hint}, but got {input.shape}.')\n\n\ndef _assert_masked(pred_alpha, trimap):\n if (pred_alpha[trimap == 0] != 0).any() or (pred_alpha[trimap == 255] !=\n 255).any():\n raise ValueError(\n 'pred_alpha should be masked by trimap before evaluation')\n\n\ndef _fetch_data_and_check(data_samples):\n \"\"\"Fetch and check data from one item of data_batch and predictions.\n\n Args:\n data_batch (dict): One item of data_batch.\n predictions (dict): One item of predictions.\n\n Returns:\n pred_alpha (Tensor): Pred_alpha data of predictions.\n ori_alpha (Tensor): Ori_alpha data of data_batch.\n ori_trimap (Tensor): Ori_trimap data of data_batch.\n \"\"\"\n ori_trimap = data_samples['ori_trimap'][0, :, :].cpu().numpy()\n ori_alpha = data_samples['ori_alpha'][0, :, :].cpu().numpy()\n pred_alpha = data_samples['output']['pred_alpha'] # 2D tensor\n pred_alpha = pred_alpha.cpu().numpy()\n\n _assert_ndim(ori_trimap, 'trimap', 2, 'HxW')\n _assert_ndim(ori_alpha, 'gt_alpha', 2, 'HxW')\n _assert_ndim(pred_alpha, 'pred_alpha', 2, 'HxW')\n _assert_masked(pred_alpha, ori_trimap)\n\n # dtype uint8 -> float64\n pred_alpha = pred_alpha / 255.0\n ori_alpha = ori_alpha / 255.0\n # test shows that using float32 vs float64 differs final results at 1e-4\n # speed are comparable, so we choose float64 for accuracy\n\n return pred_alpha, ori_alpha, ori_trimap\n\n\ndef average(results, key):\n \"\"\"Average of key in results(list[dict]).\n\n Args:\n results (list[dict]): A list of dict containing the necessary data.\n key (str): The key of target data.\n\n Returns:\n result: The average result.\n \"\"\"\n\n total = 0\n n = 0\n for batch_result in results:\n batch_size = batch_result.get('batch_size', 1)\n total += batch_result[key] * batch_size\n n += batch_size\n\n return total / n\n\n\ndef img_transform(img,\n crop_border=0,\n input_order='HWC',\n convert_to=None,\n channel_order='rgb'):\n \"\"\"Image transform.\n\n Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio\n\n Args:\n img (np.ndarray): Images with range [0, 255].\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n channel_order (str): The channel order of image. Default: 'rgb'\n\n Returns:\n float: PSNR result.\n \"\"\"\n\n if input_order not in ['HWC', 'CHW']:\n raise ValueError(\n f'Wrong input_order {input_order}. Supported input_orders are '\n '\"HWC\" and \"CHW\"')\n\n img = reorder_image(img, input_order=input_order)\n if isinstance(img, torch.Tensor):\n img = img.numpy()\n img = img.astype(np.float32)\n\n if isinstance(convert_to, str) and convert_to.lower() == 'y':\n if channel_order.upper() == 'RGB':\n img = mmcv.rgb2ycbcr(img / 255., y_only=True) * 255.\n elif channel_order.upper() == 'BGR':\n img = mmcv.bgr2ycbcr(img / 255., y_only=True) * 255.\n else:\n raise ValueError(\n 'Only support `rgb2y` and `bgr2`, but the channel_order '\n f'is {channel_order}')\n img = np.expand_dims(img, axis=2)\n elif convert_to is not None:\n raise ValueError('Wrong color model. Supported values are '\n '\"Y\" and None.')\n\n if crop_border != 0:\n img = img[crop_border:-crop_border, crop_border:-crop_border, ...]\n\n return img\n\n\ndef obtain_data(data_sample, key, device='cpu'):\n \"\"\"Obtain data of key from data_sample and converse data to device.\n Args:\n data_sample (dict): A dict of data sample.\n key (str): The key of data to obtain.\n device (str): Which device the data will deploy. Default: 'cpu'.\n\n Returns:\n result (Tensor | np.ndarray): The data of key.\n \"\"\"\n candidates = ['data_samples', key, 'data']\n\n for k in candidates:\n if k in data_sample:\n result = data_sample[k]\n if isinstance(result, dict):\n return obtain_data(result, key, device)\n else:\n if isinstance(result, torch.Tensor):\n result = result.to(device)\n return result\n\n raise KeyError('Mapping key was not found')\n" }, { "path": "mmagic/evaluation/metrics/ms_ssim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\nfrom typing import List, Optional, Sequence\n\nimport numpy as np\nimport torch\nfrom mmengine.dist import all_gather, get_world_size, is_main_process\nfrom scipy import signal\n\nfrom mmagic.registry import METRICS\nfrom .base_gen_metric import GenerativeMetric\n\n\ndef _f_special_gauss(size, sigma):\n r\"\"\"Return a circular symmetric gaussian kernel.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py # noqa\n\n Args:\n size (int): Size of Gaussian kernel.\n sigma (float): Standard deviation for Gaussian blur kernel.\n\n Returns:\n ndarray: Gaussian kernel.\n \"\"\"\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n g = np.exp(-((x**2 + y**2) / (2.0 * sigma**2)))\n return g / g.sum()\n\n\ndef _hox_downsample(img):\n r\"\"\"Downsample images with factor equal to 0.5.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py # noqa\n\n Args:\n img (ndarray): Images with order \"NHWC\".\n\n Returns:\n ndarray: Downsampled images with order \"NHWC\".\n \"\"\"\n return (img[:, 0::2, 0::2, :] + img[:, 1::2, 0::2, :] +\n img[:, 0::2, 1::2, :] + img[:, 1::2, 1::2, :]) * 0.25\n\n\ndef _ssim_for_multi_scale(img1,\n img2,\n max_val=255,\n filter_size=11,\n filter_sigma=1.5,\n k1=0.01,\n k2=0.03):\n \"\"\"Calculate SSIM (structural similarity) and contrast sensitivity.\n\n Ref:\n Image quality assessment: From error visibility to structural similarity.\n\n The results are the same as that of the official released MATLAB code in\n https://ece.uwaterloo.ca/~z70wang/research/ssim/.\n\n For three-channel images, SSIM is calculated for each channel and then\n averaged.\n\n This function attempts to match the functionality of ssim_index_new.m by\n Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip\n\n Args:\n img1 (ndarray): Images with range [0, 255] and order \"NHWC\".\n img2 (ndarray): Images with range [0, 255] and order \"NHWC\".\n max_val (int): the dynamic range of the images (i.e., the difference\n between the maximum the and minimum allowed values).\n Default to 255.\n filter_size (int): Size of blur kernel to use (will be reduced for\n small images). Default to 11.\n filter_sigma (float): Standard deviation for Gaussian blur kernel (will\n be reduced for small images). Default to 1.5.\n k1 (float): Constant used to maintain stability in the SSIM calculation\n (0.01 in the original paper). Default to 0.01.\n k2 (float): Constant used to maintain stability in the SSIM calculation\n (0.03 in the original paper). Default to 0.03.\n\n Returns:\n tuple: Pair containing the mean SSIM and contrast sensitivity between\n `img1` and `img2`.\n \"\"\"\n if img1.shape != img2.shape:\n raise RuntimeError(\n 'Input images must have the same shape (%s vs. %s).' %\n (img1.shape, img2.shape))\n if img1.ndim != 4:\n raise RuntimeError('Input images must have four dimensions, not %d' %\n img1.ndim)\n\n img1 = img1.astype(np.float32)\n img2 = img2.astype(np.float32)\n _, height, width, _ = img1.shape\n\n # Filter size can't be larger than height or width of images.\n size = min(filter_size, height, width)\n\n # Scale down sigma if a smaller filter size is used.\n sigma = size * filter_sigma / filter_size if filter_size else 0\n\n if filter_size:\n window = np.reshape(_f_special_gauss(size, sigma), (1, size, size, 1))\n mu1 = signal.fftconvolve(img1, window, mode='valid')\n mu2 = signal.fftconvolve(img2, window, mode='valid')\n sigma11 = signal.fftconvolve(img1 * img1, window, mode='valid')\n sigma22 = signal.fftconvolve(img2 * img2, window, mode='valid')\n sigma12 = signal.fftconvolve(img1 * img2, window, mode='valid')\n else:\n # Empty blur kernel so no need to convolve.\n mu1, mu2 = img1, img2\n sigma11 = img1 * img1\n sigma22 = img2 * img2\n sigma12 = img1 * img2\n\n mu11 = mu1 * mu1\n mu22 = mu2 * mu2\n mu12 = mu1 * mu2\n sigma11 -= mu11\n sigma22 -= mu22\n sigma12 -= mu12\n\n # Calculate intermediate values used by both ssim and cs_map.\n c1 = (k1 * max_val)**2\n c2 = (k2 * max_val)**2\n v1 = 2.0 * sigma12 + c2\n v2 = sigma11 + sigma22 + c2\n ssim = np.mean((((2.0 * mu12 + c1) * v1) / ((mu11 + mu22 + c1) * v2)),\n axis=(1, 2, 3)) # Return for each image individually.\n cs = np.mean(v1 / v2, axis=(1, 2, 3))\n return ssim, cs\n\n\ndef ms_ssim(img1,\n img2,\n max_val=255,\n filter_size=11,\n filter_sigma=1.5,\n k1=0.01,\n k2=0.03,\n weights=None,\n reduce_mean=True) -> np.ndarray:\n \"\"\"Calculate MS-SSIM (multi-scale structural similarity).\n\n Ref:\n This function implements Multi-Scale Structural Similarity (MS-SSIM) Image\n Quality Assessment according to Zhou Wang's paper, \"Multi-scale structural\n similarity for image quality assessment\" (2003).\n Link: https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf\n\n Author's MATLAB implementation:\n http://www.cns.nyu.edu/~lcv/ssim/msssim.zip\n\n PGGAN's implementation:\n https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py\n\n Args:\n img1 (ndarray): Images with range [0, 255] and order \"NHWC\".\n img2 (ndarray): Images with range [0, 255] and order \"NHWC\".\n max_val (int): the dynamic range of the images (i.e., the difference\n between the maximum the and minimum allowed values).\n Default to 255.\n filter_size (int): Size of blur kernel to use (will be reduced for\n small images). Default to 11.\n filter_sigma (float): Standard deviation for Gaussian blur kernel (will\n be reduced for small images). Default to 1.5.\n k1 (float): Constant used to maintain stability in the SSIM calculation\n (0.01 in the original paper). Default to 0.01.\n k2 (float): Constant used to maintain stability in the SSIM calculation\n (0.03 in the original paper). Default to 0.03.\n weights (list): List of weights for each level; if none, use five\n levels and the weights from the original paper. Default to None.\n\n Returns:\n np.ndarray: MS-SSIM score between `img1` and `img2`.\n \"\"\"\n if img1.shape != img2.shape:\n raise RuntimeError(\n 'Input images must have the same shape (%s vs. %s).' %\n (img1.shape, img2.shape))\n if img1.ndim != 4:\n raise RuntimeError('Input images must have four dimensions, not %d' %\n img1.ndim)\n\n # Note: default weights don't sum to 1.0 but do match the paper / matlab\n # code.\n weights = np.array(\n weights if weights else [0.0448, 0.2856, 0.3001, 0.2363, 0.1333])\n levels = weights.size\n im1, im2 = [x.astype(np.float32) for x in [img1, img2]]\n mssim = []\n mcs = []\n for _ in range(levels):\n ssim, cs = _ssim_for_multi_scale(\n im1,\n im2,\n max_val=max_val,\n filter_size=filter_size,\n filter_sigma=filter_sigma,\n k1=k1,\n k2=k2)\n mssim.append(ssim)\n mcs.append(cs)\n im1, im2 = [_hox_downsample(x) for x in [im1, im2]]\n\n # Clip to zero. Otherwise we get NaNs.\n mssim = np.clip(np.asarray(mssim), 0.0, np.inf)\n mcs = np.clip(np.asarray(mcs), 0.0, np.inf)\n\n results = np.prod(mcs[:-1, :]**weights[:-1, np.newaxis], axis=0) * \\\n (mssim[-1, :]**weights[-1])\n if reduce_mean:\n # Average over images only at the end.\n results = np.mean(results)\n return results\n\n\n@METRICS.register_module('MS_SSIM')\n@METRICS.register_module()\nclass MultiScaleStructureSimilarity(GenerativeMetric):\n \"\"\"MS-SSIM (Multi-Scale Structure Similarity) metric.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py # noqa\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n real_key (Optional[str]): Key for get real images from the input dict.\n Defaults to 'img'.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'ema'.\n collect_device (str, optional): Device name used for collecting results\n from different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n name = 'MS-SSIM'\n\n def __init__(self,\n fake_nums: int,\n fake_key: Optional[str] = None,\n need_cond_input: bool = False,\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None) -> None:\n super().__init__(fake_nums, 0, fake_key, None, need_cond_input,\n sample_model, collect_device, prefix)\n\n assert fake_nums % 2 == 0\n self.num_pairs = fake_nums // 2\n\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Feed data to the metric.\n\n Args:\n data_batch (dict): Real images from dataloader. Do not be used\n in this metric.\n data_samples (Sequence[dict]): Generated images.\n \"\"\"\n if len(self.fake_results) >= (self.fake_nums_per_device // 2):\n return\n\n fake_imgs = []\n for pred in data_samples:\n fake_img_ = pred\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n minibatch = torch.stack(fake_imgs, dim=0)\n\n assert minibatch.shape[0] % 2 == 0, 'batch size must be divided by 2.'\n\n half1 = minibatch[0::2].cpu().data.numpy().transpose((0, 2, 3, 1))\n half2 = minibatch[1::2].cpu().data.numpy().transpose((0, 2, 3, 1))\n\n scores = ms_ssim(half1, half2, reduce_mean=False)\n self.fake_results += [torch.Tensor([s]) for s in scores.tolist()]\n\n def _collect_target_results(self, target: str) -> Optional[list]:\n \"\"\"Collected results for MS-SSIM metric. Size of `self.fake_results` in\n MS-SSIM does not relay on `self.fake_nums` but `self.num_pairs`.\n\n Args:\n target (str): Target results to collect.\n\n Returns:\n Optional[list]: The collected results.\n \"\"\"\n assert target in 'fake', 'Only support to collect \\'fake\\' results.'\n results = getattr(self, f'{target}_results')\n size = self.num_pairs\n size = len(results) * get_world_size() if size == -1 else size\n\n if len(results) == 0:\n warnings.warn(\n f'{self.__class__.__name__} got empty `self.{target}_results`.'\n ' Please ensure that the processed results are properly added '\n f'into `self.{target}_results` in `process` method.')\n\n # apply all_gather for tensor results\n results = torch.cat(results, dim=0)\n results = torch.cat(all_gather(results), dim=0)[:size]\n results = torch.split(results, 1)\n\n # on non-main process, results should be `None`\n if is_main_process() and len(results) != size:\n raise ValueError(f'Length of results is \\'{len(results)}\\', not '\n f'equals to target size \\'{size}\\'.')\n return results\n\n def compute_metrics(self, results_fake: List):\n \"\"\"Computed the result of MS-SSIM.\n\n Returns:\n dict: Calculated MS-SSIM result.\n \"\"\"\n results = torch.cat(results_fake, dim=0)\n avg = results.sum() / self.num_pairs\n return {'avg': round(avg.item(), 4)}\n" }, { "path": "mmagic/evaluation/metrics/mse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Evaluation metrics based on pixels.\"\"\"\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\n\n\n@METRICS.register_module()\nclass MSE(BaseSampleWiseMetric):\n \"\"\"Mean Squared Error metric for image.\n\n mean((a-b)^2)\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n mask_key (str, optional): Key of mask, if mask_key is None, calculate\n all regions. Default: None\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n\n Metrics:\n - MSE (float): Mean of Squared Error\n \"\"\"\n\n metric = 'MSE'\n\n def process_image(self, gt, pred, mask):\n \"\"\"Process an image.\n\n Args:\n gt (Torch | np.ndarray): GT image.\n pred (Torch | np.ndarray): Pred image.\n mask (Torch | np.ndarray): Mask of evaluation.\n Returns:\n result (np.ndarray): MSE result.\n \"\"\"\n\n gt = gt / 255.\n pred = pred / 255.\n\n diff = gt - pred\n diff *= diff\n\n if self.mask_key is not None:\n diff *= mask\n result = diff.sum() / mask.sum()\n else:\n result = diff.mean()\n\n return result\n" }, { "path": "mmagic/evaluation/metrics/niqe.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nimport os\nfrom typing import Optional\n\nimport cv2\nimport mmcv\nimport numpy as np\nfrom scipy.ndimage import convolve\nfrom scipy.special import gamma\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\nfrom mmagic.registry import METRICS\nfrom mmagic.utils import reorder_image, to_numpy\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\n\n\n@METRICS.register_module()\nclass NIQE(BaseSampleWiseMetric):\n \"\"\"Calculate NIQE (Natural Image Quality Evaluator) metric.\n\n Ref: Making a \"Completely Blind\" Image Quality Analyzer.\n This implementation could produce almost the same results as the official\n MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip\n\n We use the official params estimated from the pristine dataset.\n We use the recommended block size (96, 96) without overlaps.\n\n Args:\n\n key (str): Key of image. Default: 'pred_img'\n is_predicted (bool): If the image is predicted, it will be picked from\n predictions; otherwise, it will be picked from data_batch.\n Default: True\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: 'gray'.\n\n Metrics:\n - NIQE (float): Natural Image Quality Evaluator\n \"\"\"\n\n metric = 'NIQE'\n\n def __init__(self,\n key: str = 'pred_img',\n is_predicted: bool = True,\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n crop_border=0,\n input_order='HWC',\n convert_to='gray') -> None:\n super().__init__(collect_device=collect_device, prefix=prefix)\n\n convert_to = convert_to.lower()\n assert convert_to in [\n 'y', 'gray'\n ], ('Only support gray image, '\n \"``convert_to`` should be selected from ['y', 'gray']\")\n\n self.key = key\n self.is_predicted = is_predicted\n self.crop_border = crop_border\n self.input_order = input_order\n self.convert_to = convert_to\n\n def process_image(self, gt, pred, mask) -> None:\n \"\"\"Process an image.\n\n Args:\n gt (np.ndarray): GT image.\n pred (np.ndarray): Pred image.\n mask (np.ndarray): Mask of evaluation.\n Returns:\n result (np.ndarray): NIQE result.\n \"\"\"\n\n result = niqe(\n img=pred,\n crop_border=self.crop_border,\n input_order=self.input_order,\n convert_to=self.convert_to)\n return result\n\n\ndef estimate_aggd_param(block):\n \"\"\"Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters.\n\n Args:\n block (np.ndarray): 2D Image block.\n\n Returns:\n tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD\n distribution (Estimating the parames in Equation 7 in the paper).\n \"\"\"\n block = block.flatten()\n gam = np.arange(0.2, 10.001, 0.001) # len = 9801\n gam_reciprocal = np.reciprocal(gam)\n r_gam = np.square(gamma(gam_reciprocal * 2)) / (\n gamma(gam_reciprocal) * gamma(gam_reciprocal * 3))\n\n left_std = np.sqrt(np.mean(block[block < 0]**2))\n right_std = np.sqrt(np.mean(block[block > 0]**2))\n gammahat = left_std / right_std\n rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2)\n rhatnorm = (rhat * (gammahat**3 + 1) *\n (gammahat + 1)) / ((gammahat**2 + 1)**2)\n array_position = np.argmin((r_gam - rhatnorm)**2)\n\n alpha = gam[array_position]\n beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))\n beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))\n return (alpha, beta_l, beta_r)\n\n\ndef compute_feature(block):\n \"\"\"Compute features.\n\n Args:\n block (np.ndarray): 2D Image block.\n\n Returns:\n feat (List): Features with length of 18.\n \"\"\"\n feat = []\n alpha, beta_l, beta_r = estimate_aggd_param(block)\n feat.extend([alpha, (beta_l + beta_r) / 2])\n\n # distortions disturb the fairly regular structure of natural images.\n # This deviation can be captured by analyzing the sample distribution of\n # the products of pairs of adjacent coefficients computed along\n # horizontal, vertical and diagonal orientations.\n shifts = [[0, 1], [1, 0], [1, 1], [1, -1]]\n for shift in shifts:\n shifted_block = np.roll(block, shift, axis=(0, 1))\n alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block)\n mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha))\n feat.extend([alpha, mean, beta_l, beta_r])\n return feat\n\n\ndef niqe_core(img,\n mu_pris_param,\n cov_pris_param,\n gaussian_window,\n block_size_h=96,\n block_size_w=96):\n \"\"\"Calculate NIQE (Natural Image Quality Evaluator) metric.\n\n Ref: Making a \"Completely Blind\" Image Quality Analyzer.\n This implementation could produce almost the same results as the official\n MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip\n\n Note that we do not include block overlap height and width, since they are\n always 0 in the official implementation.\n\n For good performance, it is advisable by the official implementation to\n divide the distorted image in to the same size patched as used for the\n construction of multivariate Gaussian model.\n\n Args:\n img (np.ndarray): Input image whose quality needs to be computed. The\n image must be a gray or Y (of YCbCr) image with shape (h, w).\n Range [0, 255] with float type.\n mu_pris_param (np.ndarray): Mean of a pre-defined multivariate Gaussian\n model calculated on the pristine dataset.\n cov_pris_param (np.ndarray): Covariance of a pre-defined multivariate\n Gaussian model calculated on the pristine dataset.\n gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the\n image.\n block_size_h (int): Height of the blocks in to which image is divided.\n Default: 96 (the official recommended value). Default: 96.\n block_size_w (int): Width of the blocks in to which image is divided.\n Default: 96 (the official recommended value). Default: 96.\n\n Returns:\n np.ndarray: NIQE quality.\n \"\"\"\n # crop image\n h, w = img.shape\n num_block_h = math.floor(h / block_size_h)\n num_block_w = math.floor(w / block_size_w)\n img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w]\n\n distparam = [] # dist param is actually the multiscale features\n for scale in (1, 2): # perform on two scales (1, 2)\n mu = convolve(img, gaussian_window, mode='nearest')\n\n sigma = np.sqrt(\n np.abs(\n convolve(np.square(img), gaussian_window, mode='nearest') -\n np.square(mu)))\n # normalize, as in Eq. 1 in the paper\n img_nomalized = (img - mu) / (sigma + 1)\n\n feat = []\n for idx_w in range(num_block_w):\n for idx_h in range(num_block_h):\n # process each block\n block = img_nomalized[idx_h * block_size_h //\n scale:(idx_h + 1) * block_size_h //\n scale, idx_w * block_size_w //\n scale:(idx_w + 1) * block_size_w //\n scale]\n feat.append(compute_feature(block))\n\n distparam.append(np.array(feat))\n\n # matlab-like bicubic downsample with anti-aliasing\n if scale == 1:\n resize = MATLABLikeResize(keys=None, scale=0.5)\n img = resize._resize(img[:, :, np.newaxis] / 255.)[:, :, 0] * 255.\n\n distparam = np.concatenate(distparam, axis=1)\n\n # fit a MVG (multivariate Gaussian) model to distorted patch features\n mu_distparam = np.nanmean(distparam, axis=0)\n distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)]\n cov_distparam = np.cov(distparam_no_nan, rowvar=False)\n\n # compute niqe quality, Eq. 10 in the paper\n invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2)\n quality = np.matmul(\n np.matmul((mu_pris_param - mu_distparam), invcov_param),\n np.transpose((mu_pris_param - mu_distparam)))\n\n return np.squeeze(np.sqrt(quality))\n\n\ndef niqe(img, crop_border, input_order='HWC', convert_to='y'):\n \"\"\"Calculate NIQE (Natural Image Quality Evaluator) metric.\n\n Ref: Making a \"Completely Blind\" Image Quality Analyzer.\n This implementation could produce almost the same results as the official\n MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip\n\n We use the official params estimated from the pristine dataset.\n We use the recommended block size (96, 96) without overlaps.\n\n Args:\n img (np.ndarray): Input image whose quality needs to be computed.\n The input image must be in range [0, 255] with float/int type.\n The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order)\n If the input order is 'HWC' or 'CHW', it will be converted to gray\n or Y (of YCbCr) image according to the ``convert_to`` argument.\n crop_border (int): Cropped pixels in each edge of an image. These\n pixels are not involved in the metric calculation.\n input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'.\n Default: 'y'.\n\n Returns:\n niqe_result (float): NIQE result.\n \"\"\"\n\n # we use the official params estimated from the pristine dataset.\n niqe_pris_params = np.load(\n os.path.join(os.path.dirname(__file__), 'niqe_pris_params.npz'))\n mu_pris_param = niqe_pris_params['mu_pris_param']\n cov_pris_param = niqe_pris_params['cov_pris_param']\n gaussian_window = niqe_pris_params['gaussian_window']\n\n img = to_numpy(img, np.float32)\n\n if input_order != 'HW':\n img = reorder_image(img, input_order=input_order)\n if convert_to == 'y':\n img = mmcv.bgr2ycbcr(img / 255., y_only=True) * 255.\n elif convert_to == 'gray':\n img = mmcv.bgr2gray(img / 255., cv2.COLOR_BGR2GRAY) * 255.\n img = np.squeeze(img)\n\n if crop_border != 0:\n img = img[crop_border:-crop_border, crop_border:-crop_border]\n\n # round to follow official implementation\n img = img.round()\n\n niqe_result = niqe_core(img, mu_pris_param, cov_pris_param,\n gaussian_window)\n\n return niqe_result\n" }, { "path": "mmagic/evaluation/metrics/ppl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Sequence\n\nimport lpips\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.models.utils import get_module_device, normalize_vecs\nfrom mmagic.registry import METRICS\nfrom .base_gen_metric import GenerativeMetric\n\n\ndef slerp(a, b, percent):\n \"\"\"Spherical linear interpolation between two unnormalized vectors.\n\n Args:\n a (Tensor): Tensor with shape [N, C].\n b (Tensor): Tensor with shape [N, C].\n percent (float|Tensor): A float or tensor with shape broadcastable to\n the shape of input Tensors.\n\n Returns:\n Tensor: Spherical linear interpolation result with shape [N, C].\n \"\"\"\n a = normalize_vecs(a)\n b = normalize_vecs(b)\n d = (a * b).sum(-1, keepdim=True)\n p = percent * torch.acos(d)\n c = normalize_vecs(b - d * a)\n d = a * torch.cos(p) + c * torch.sin(p)\n\n return normalize_vecs(d)\n\n\n@METRICS.register_module('PPL')\n@METRICS.register_module()\nclass PerceptualPathLength(GenerativeMetric):\n r\"\"\"Perceptual path length.\n\n Measure the difference between consecutive images (their VGG16\n embeddings) when interpolating between two random inputs. Drastic\n changes mean that multiple features have changed together and that\n they might be entangled.\n\n Ref: https://github.com/rosinality/stylegan2-pytorch/blob/master/ppl.py # noqa\n\n Args:\n num_images (int): The number of evaluated generated samples.\n image_shape (tuple, optional): Image shape in order \"CHW\". Defaults\n to None.\n crop (bool, optional): Whether crop images. Defaults to True.\n epsilon (float, optional): Epsilon parameter for path sampling.\n Defaults to 1e-4.\n space (str, optional): Latent space. Defaults to 'W'.\n sampling (str, optional): Sampling mode, whether sampling in full\n path or endpoints. Defaults to 'end'.\n latent_dim (int, optional): Latent dimension of input noise.\n Defaults to 512.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n \"\"\"\n SAMPLER_MODE = 'path'\n\n def __init__(self,\n fake_nums: int,\n real_nums: int = 0,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n need_cond_input: bool = False,\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n crop=True,\n epsilon=1e-4,\n space='W',\n sampling='end',\n latent_dim=512):\n super().__init__(fake_nums, real_nums, fake_key, real_key,\n need_cond_input, sample_model, collect_device, prefix)\n self.crop = crop\n\n self.epsilon = epsilon\n self.space = space\n self.sampling = sampling\n self.latent_dim = latent_dim\n\n @torch.no_grad()\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results``, which will be used\n to compute the metrics when all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n fake_imgs = []\n for pred in data_samples:\n fake_img_ = pred\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n fake_imgs = torch.stack(fake_imgs, dim=0)\n fake_imgs = (fake_imgs - 127.5) / 127.5 # [0, 255] to [-1, 1]\n feat = self._compute_distance(fake_imgs)\n feat_list = list(torch.split(feat, 1))\n self.fake_results += feat_list\n\n @torch.no_grad()\n def _compute_distance(self, images):\n \"\"\"Feed data to the metric.\n\n Args:\n images (Tensor): Input tensor.\n \"\"\"\n # use minibatch's device type to initialize a lpips calculator\n if not hasattr(self, 'percept'):\n self.percept = lpips.LPIPS(net='vgg').to(images.device)\n # crop and resize images\n if self.crop:\n c = images.shape[2] // 8\n minibatch = images[:, :, c * 3:c * 7, c * 2:c * 6]\n\n factor = minibatch.shape[2] // 256\n if factor > 1:\n minibatch = F.interpolate(\n minibatch,\n size=(256, 256),\n mode='bilinear',\n align_corners=False)\n # calculator and store lpips score\n distance = self.percept(minibatch[::2], minibatch[1::2]).view(\n minibatch.shape[0] // 2) / (\n self.epsilon**2)\n return distance.to('cpu')\n\n @torch.no_grad()\n def compute_metrics(self, fake_results: list) -> dict:\n \"\"\"Summarize the results.\n\n Returns:\n dict | list: Summarized results.\n \"\"\"\n distances = torch.cat(self.fake_results, dim=0).numpy()\n lo = np.percentile(distances, 1, interpolation='lower')\n hi = np.percentile(distances, 99, interpolation='higher')\n filtered_dist = np.extract(\n np.logical_and(lo <= distances, distances <= hi), distances)\n ppl_score = float(filtered_dist.mean())\n return {'ppl_score': ppl_score}\n\n def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,\n metrics: list):\n \"\"\"Get sampler for generative metrics. Returns a dummy iterator, whose\n return value of each iteration is a dict containing batch size and\n sample mode to generate images.\n\n Args:\n model (nn.Module): Model to evaluate.\n dataloader (DataLoader): Dataloader for real images. Used to get\n batch size during generate fake images.\n metrics (list): Metrics with the same sampler mode.\n\n Returns:\n :class:`dummy_iterator`: Sampler for generative metrics.\n \"\"\"\n\n batch_size = dataloader.batch_size\n\n sample_model = metrics[0].sample_model\n assert all([metric.sample_model == sample_model for metric in metrics\n ]), ('\\'sample_model\\' between metrics is inconsistency.')\n\n class PPLSampler:\n \"\"\"StyleGAN series generator's sampling iterator for PPL metric.\n\n Args:\n generator (nn.Module): StyleGAN series' generator.\n num_images (int): The number of evaluated generated samples.\n batch_size (int): Batch size of generated images.\n space (str, optional): Latent space. Defaults to 'W'.\n sampling (str, optional): Sampling mode, whether sampling in\n full path or endpoints. Defaults to 'end'.\n epsilon (float, optional): Epsilon parameter for path sampling.\n Defaults to 1e-4.\n latent_dim (int, optional): Latent dimension of input noise.\n Defaults to 512.\n \"\"\"\n\n def __init__(self,\n generator,\n num_images,\n batch_size,\n space='W',\n sampling='end',\n epsilon=1e-4,\n latent_dim=512):\n assert space in ['Z', 'W']\n assert sampling in ['full', 'end']\n n_batch = num_images // batch_size\n\n resid = num_images - (n_batch * batch_size)\n self.batch_sizes = [batch_size] * n_batch + ([resid] if\n resid > 0 else [])\n self.device = get_module_device(generator)\n self.generator = generator.module if hasattr(\n generator, 'module') else generator\n self.latent_dim = latent_dim\n self.space = space\n self.sampling = sampling\n self.epsilon = epsilon\n\n def __iter__(self):\n self.idx = 0\n return self\n\n def __len__(self):\n return len(self.batch_sizes)\n\n @torch.no_grad()\n def __next__(self):\n if self.idx >= len(self.batch_sizes):\n raise StopIteration\n batch = self.batch_sizes[self.idx]\n injected_noise = self.generator.make_injected_noise()\n inputs = torch.randn([batch * 2, self.latent_dim],\n device=self.device)\n if self.sampling == 'full':\n lerp_t = torch.rand(batch, device=self.device)\n else:\n lerp_t = torch.zeros(batch, device=self.device)\n\n if self.space == 'W':\n assert hasattr(self.generator, 'style_mapping')\n latent = self.generator.style_mapping(inputs)\n latent_t0, latent_t1 = latent[::2], latent[1::2]\n latent_e0 = torch.lerp(latent_t0, latent_t1, lerp_t[:,\n None])\n latent_e1 = torch.lerp(latent_t0, latent_t1,\n lerp_t[:, None] + self.epsilon)\n latent_e = torch.stack([latent_e0, latent_e1],\n 1).view(*latent.shape)\n else:\n latent_t0, latent_t1 = inputs[::2], inputs[1::2]\n latent_e0 = slerp(latent_t0, latent_t1, lerp_t[:, None])\n latent_e1 = slerp(latent_t0, latent_t1,\n lerp_t[:, None] + self.epsilon)\n latent_e = torch.stack([latent_e0, latent_e1],\n 1).view(*inputs.shape)\n\n self.idx += 1\n return dict(\n inputs=dict(\n noise=latent_e,\n sample_kwargs=dict(\n injected_noise=injected_noise,\n input_is_latent=(self.space == 'W'))))\n\n ppl_sampler = PPLSampler(\n model.generator_ema\n if self.sample_model == 'ema' else model.generator,\n num_images=max([metric.fake_nums_per_device\n for metric in metrics]),\n batch_size=batch_size,\n space=self.space,\n sampling=self.sampling,\n epsilon=self.epsilon,\n latent_dim=self.latent_dim)\n return ppl_sampler\n" }, { "path": "mmagic/evaluation/metrics/precision_and_recall.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom typing import Optional, Sequence, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom torch.utils.data.dataloader import DataLoader\nfrom torchvision import models as torchvision_models\n\nfrom mmagic.models.utils import get_module_device\nfrom mmagic.registry import METRICS\nfrom ..functional import prepare_vgg_feat\nfrom .base_gen_metric import GenerativeMetric\n\n\ndef compute_pr_distances(row_features,\n col_features,\n num_gpus=1,\n rank=0,\n col_batch_size=10000):\n r\"\"\"Compute distances between real images and fake images.\n\n This function is used for calculate Precision and Recall metric.\n Refer to:https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/precision_recall.py # noqa\n \"\"\"\n assert 0 <= rank < num_gpus\n num_cols = col_features.shape[0]\n num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus\n col_batches = torch.nn.functional.pad(col_features,\n [0, 0, 0, -num_cols % num_batches\n ]).chunk(num_batches)\n dist_batches = []\n for col_batch in col_batches[rank::num_gpus]:\n dist_batch = torch.cdist(\n row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]\n for src in range(num_gpus):\n dist_broadcast = dist_batch.clone()\n if num_gpus > 1:\n torch.distributed.broadcast(dist_broadcast, src=src)\n dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)\n return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None\n\n\n@METRICS.register_module('PR')\n@METRICS.register_module()\nclass PrecisionAndRecall(GenerativeMetric):\n r\"\"\"Improved Precision and recall metric.\n\n In this metric, we draw real and generated samples respectively, and\n embed them into a high-dimensional feature space using a pre-trained\n classifier network. We use these features to estimate the corresponding\n manifold. We obtain the estimation by calculating pairwise Euclidean\n distances between all feature vectors in the set and, for each feature\n vector, construct a hypersphere with radius equal to the distance to its\n kth nearest neighbor. Together, these hyperspheres define a volume in\n the feature space that serves as an estimate of the true manifold.\n Precision is quantified by querying for each generated image whether\n the image is within the estimated manifold of real images.\n Symmetrically, recall is calculated by querying for each real image\n whether the image is within estimated manifold of generated image.\n\n Ref: https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/precision_recall.py # noqa\n\n Note that we highly recommend that users should download the vgg16\n script module from the following address. Then, the `vgg16_script` can\n be set with user's local path. If not given, we will use the vgg16 from\n pytorch model zoo. However, this may bring significant different in the\n final results.\n\n Tero's vgg16: https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt\n\n Args:\n num_images (int): The number of evaluated generated samples.\n image_shape (tuple): Image shape in order \"CHW\". Defaults to None.\n num_real_need (int | None, optional): The number of real images.\n Defaults to None.\n full_dataset (bool, optional): Whether to use full dataset for\n evaluation. Defaults to False.\n k (int, optional): Kth nearest parameter. Defaults to 3.\n bgr2rgb (bool, optional): Whether to change the order of image\n channel. Defaults to True.\n vgg16_script (str, optional): Path for the Tero's vgg16 module.\n Defaults to 'work_dirs/cache/vgg16.pt'.\n row_batch_size (int, optional): The batch size of row data.\n Defaults to 10000.\n col_batch_size (int, optional): The batch size of col data.\n Defaults to 10000.\n auto_save (bool, optional): Whether save vgg feature automatically.\n need_cond_input (bool): If true, the sampler will return the\n conditional input randomly sampled from the original dataset.\n This require the dataset implement `get_data_info` and field\n `gt_label` must be contained in the return value of\n `get_data_info`. Noted that, for unconditional models, set\n `need_cond_input` as True may influence the result of evaluation\n results since the conditional inputs are sampled from the dataset\n distribution; otherwise will be sampled from the uniform\n distribution. Defaults to False.\n \"\"\"\n name = 'PR'\n\n def __init__(self,\n fake_nums,\n real_nums=-1,\n k=3,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n need_cond_input: bool = False,\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n vgg16_script='work_dirs/cache/vgg16.pt',\n vgg16_pkl=None,\n row_batch_size=10000,\n col_batch_size=10000,\n auto_save=True):\n super().__init__(fake_nums, real_nums, fake_key, real_key,\n need_cond_input, sample_model, collect_device, prefix)\n print_log('loading vgg16 for improved precision and recall...',\n 'current')\n self.vgg16_pkl = vgg16_pkl\n self.vgg16, self.use_tero_scirpt = self._load_vgg(vgg16_script)\n self.k = k\n\n self.auto_save = auto_save\n self.row_batch_size = row_batch_size\n self.col_batch_size = col_batch_size\n\n def _load_vgg(self, vgg16_script: Optional[str]) -> Tuple[nn.Module, bool]:\n \"\"\"Load VGG network from the given path.\n\n Args:\n vgg16_script: The path of script model of VGG network. If None,\n will load the pytorch version.\n\n Returns:\n Tuple[nn.Module, str]: The actually loaded VGG network and\n corresponding style.\n \"\"\"\n if os.path.isfile(vgg16_script):\n vgg16 = torch.jit.load('work_dirs/cache/vgg16.pt').eval()\n use_tero_scirpt = True\n else:\n print_log(\n 'Cannot load Tero\\'s script module. Use official '\n 'vgg16 instead', 'current')\n vgg16 = torchvision_models.vgg16(pretrained=True).eval()\n use_tero_scirpt = False\n return vgg16, use_tero_scirpt\n\n @torch.no_grad()\n def extract_features(self, images: torch.Tensor) -> torch.Tensor:\n \"\"\"Extracting image features.\n\n Args:\n images (torch.Tensor): Images tensor.\n Returns:\n torch.Tensor: Vgg16 features of input images.\n \"\"\"\n # image must passed in 'bgr'\n images = images[:, [2, 1, 0], ...]\n if self.use_tero_scirpt:\n images = images.to(torch.uint8)\n feature = self.vgg16(images, return_features=True)\n else:\n images = (images - 127.5) / 127.5\n batch = F.interpolate(images, size=(224, 224))\n before_fc = self.vgg16.features(batch)\n before_fc = before_fc.view(-1, 7 * 7 * 512)\n feature = self.vgg16.classifier[:4](before_fc)\n\n return feature\n\n @torch.no_grad()\n def compute_metrics(self, results_fake) -> dict:\n \"\"\"compute_metrics.\n\n Returns:\n dict: Summarized results.\n \"\"\"\n gen_features = torch.cat(results_fake, dim=0).to(self.collect_device)\n real_features = self.results_real\n\n self._result_dict = {}\n\n for name, manifold, probes in [\n ('precision', real_features, gen_features),\n ('recall', gen_features, real_features)\n ]:\n kth = []\n for manifold_batch in manifold.split(self.row_batch_size):\n distance = compute_pr_distances(\n row_features=manifold_batch,\n col_features=manifold,\n col_batch_size=self.col_batch_size)\n kth.append(\n distance.to(torch.float32).kthvalue(self.k + 1).values.to(\n torch.float16))\n kth = torch.cat(kth)\n pred = []\n for probes_batch in probes.split(self.row_batch_size):\n distance = compute_pr_distances(\n row_features=probes_batch,\n col_features=manifold,\n col_batch_size=self.col_batch_size)\n pred.append((distance <= kth).any(dim=1))\n self._result_dict[name] = float(\n torch.cat(pred).to(torch.float32).mean())\n\n precision = self._result_dict['precision']\n recall = self._result_dict['recall']\n self._result_str = f'precision: {precision}, recall:{recall}'\n return self._result_dict\n\n @torch.no_grad()\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results``, which will be used\n to compute the metrics when all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n fake_imgs = []\n for pred in data_samples:\n fake_img_ = pred\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n fake_imgs = torch.stack(fake_imgs, dim=0)\n feat = self.extract_features(fake_imgs)\n feat_list = list(torch.split(feat, 1))\n self.fake_results += feat_list\n\n @torch.no_grad()\n def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:\n # move to corresponding device\n device = get_module_device(module)\n self.vgg16.to(device)\n\n vgg_feat = prepare_vgg_feat(dataloader, self, module.data_preprocessor,\n self.auto_save)\n if self.real_nums != -1:\n assert self.real_nums <= vgg_feat.shape[0], (\n f'Need \\'{self.real_nums}\\' of real nums, but only '\n f'\\'{vgg_feat.shape[0]}\\' images be found in the '\n 'inception feature.')\n vgg_feat = vgg_feat[np.random.choice(\n vgg_feat.shape[0], size=self.real_nums, replace=True)]\n self.results_real = vgg_feat\n" }, { "path": "mmagic/evaluation/metrics/psnr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport numpy as np\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import img_transform\n\n\n@METRICS.register_module()\nclass PSNR(BaseSampleWiseMetric):\n \"\"\"Peak Signal-to-Noise Ratio.\n\n Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'CHW'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n\n Metrics:\n - PSNR (float): Peak Signal-to-Noise Ratio\n \"\"\"\n\n metric = 'PSNR'\n\n def __init__(self,\n gt_key: str = 'gt_img',\n pred_key: str = 'pred_img',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n crop_border=0,\n input_order='CHW',\n convert_to=None) -> None:\n super().__init__(\n gt_key=gt_key,\n pred_key=pred_key,\n mask_key=None,\n collect_device=collect_device,\n prefix=prefix)\n\n self.crop_border = crop_border\n self.input_order = input_order\n self.convert_to = convert_to\n\n def process_image(self, gt, pred, mask):\n \"\"\"Process an image.\n\n Args:\n gt (Torch | np.ndarray): GT image.\n pred (Torch | np.ndarray): Pred image.\n mask (Torch | np.ndarray): Mask of evaluation.\n Returns:\n np.ndarray: PSNR result.\n \"\"\"\n\n return psnr(\n img1=gt,\n img2=pred,\n crop_border=self.crop_border,\n input_order=self.input_order,\n convert_to=self.convert_to,\n channel_order=self.channel_order)\n\n\ndef psnr(img1,\n img2,\n crop_border=0,\n input_order='HWC',\n convert_to=None,\n channel_order='rgb'):\n \"\"\"Calculate PSNR (Peak Signal-to-Noise Ratio).\n\n Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio\n\n Args:\n img1 (ndarray): Images with range [0, 255].\n img2 (ndarray): Images with range [0, 255].\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n channel_order (str): The channel order of image. Default: 'rgb'.\n\n Returns:\n result (float): PSNR result.\n \"\"\"\n\n assert img1.shape == img2.shape, (\n f'Image shapes are different: {img1.shape}, {img2.shape}.')\n\n img1 = img_transform(\n img1,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n img2 = img_transform(\n img2,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n\n mse_value = ((img1 - img2)**2).mean()\n if mse_value == 0:\n result = float('inf')\n else:\n result = 20. * np.log10(255. / np.sqrt(mse_value))\n\n return result\n" }, { "path": "mmagic/evaluation/metrics/sad.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Sequence\n\nimport numpy as np\nimport torch.nn as nn\nfrom mmengine.model import is_model_wrapper\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import _fetch_data_and_check, average\n\n\n@METRICS.register_module()\nclass SAD(BaseSampleWiseMetric):\n \"\"\"Sum of Absolute Differences metric for image matting.\n\n This metric compute per-pixel absolute difference and sum across all\n pixels.\n i.e. sum(abs(a-b)) / norm_const\n\n .. note::\n\n Current implementation assume image / alpha / trimap array in numpy\n format and with pixel value ranging from 0 to 255.\n\n .. note::\n\n pred_alpha should be masked by trimap before passing\n into this metric\n\n Default prefix: ''\n\n Args:\n norm_const (int): Divide the result to reduce its magnitude.\n Default to 1000.\n\n Metrics:\n - SAD (float): Sum of Absolute Differences\n \"\"\"\n\n default_prefix = ''\n metric = 'SAD'\n\n def __init__(\n self,\n norm_const=1000,\n **kwargs,\n ) -> None:\n self.norm_const = norm_const\n super().__init__(**kwargs)\n\n def prepare(self, module: nn.Module, dataloader: DataLoader):\n self.size = len(dataloader.dataset)\n if is_model_wrapper(module):\n module = module.module\n self.data_preprocessor = module.data_preprocessor\n\n def process(self, data_batch: Sequence[dict],\n data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data and predictions.\n\n Args:\n data_batch (Sequence[Tuple[Any, dict]]): A batch of data\n from the dataloader.\n predictions (Sequence[dict]): A batch of outputs from\n the model.\n \"\"\"\n for data_sample in data_samples:\n pred_alpha, gt_alpha, _ = _fetch_data_and_check(data_sample)\n\n # divide by 1000 to reduce the magnitude of the result\n sad_sum = np.abs(pred_alpha - gt_alpha).sum() / self.norm_const\n\n result = {'sad': sad_sum}\n\n self.results.append(result)\n\n def compute_metrics(self, results: List):\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (dict): The processed results of each batch.\n\n Returns:\n Dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n\n sad = average(results, 'sad')\n\n return {'SAD': sad}\n" }, { "path": "mmagic/evaluation/metrics/snr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport numpy as np\n\nfrom mmagic.registry import METRICS\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import img_transform\n\n\n@METRICS.register_module()\nclass SNR(BaseSampleWiseMetric):\n \"\"\"Signal-to-Noise Ratio.\n\n Ref: https://en.wikipedia.org/wiki/Signal-to-noise_ratio\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the SNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'CHW'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n\n Metrics:\n - SNR (float): Signal-to-Noise Ratio\n \"\"\"\n\n metric = 'SNR'\n\n def __init__(self,\n gt_key: str = 'gt_img',\n pred_key: str = 'pred_img',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n crop_border=0,\n input_order='CHW',\n convert_to=None) -> None:\n super().__init__(\n gt_key=gt_key,\n pred_key=pred_key,\n mask_key=None,\n collect_device=collect_device,\n prefix=prefix)\n\n self.crop_border = crop_border\n self.input_order = input_order\n self.convert_to = convert_to\n\n def process_image(self, gt, pred, mask):\n \"\"\"Process an image.\n\n Args:\n gt (Torch | np.ndarray): GT image.\n pred (Torch | np.ndarray): Pred image.\n mask (Torch | np.ndarray): Mask of evaluation.\n Returns:\n np.ndarray: SNR result.\n \"\"\"\n\n return snr(\n gt=gt,\n pred=pred,\n crop_border=self.crop_border,\n input_order=self.input_order,\n convert_to=self.convert_to,\n channel_order=self.channel_order)\n\n\ndef snr(gt,\n pred,\n crop_border=0,\n input_order='HWC',\n convert_to=None,\n channel_order='rgb'):\n \"\"\"Calculate PSNR (Peak Signal-to-Noise Ratio).\n\n Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio\n\n Args:\n gt (ndarray): Images with range [0, 255].\n pred (ndarray): Images with range [0, 255].\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n channel_order (str): The channel order of image. Default: 'rgb'.\n\n Returns:\n float: SNR result.\n \"\"\"\n\n assert gt.shape == pred.shape, (\n f'Image shapes are different: {gt.shape}, {pred.shape}.')\n\n gt = img_transform(\n gt,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n pred = img_transform(\n pred,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n\n signal = ((gt)**2).mean()\n noise = ((gt - pred)**2).mean()\n\n result = 10. * np.log10(signal / noise)\n\n return result\n" }, { "path": "mmagic/evaluation/metrics/ssim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport cv2\nimport numpy as np\n\nfrom mmagic.registry import METRICS\nfrom mmagic.utils import to_numpy\nfrom .base_sample_wise_metric import BaseSampleWiseMetric\nfrom .metrics_utils import img_transform\n\n\n@METRICS.register_module()\nclass SSIM(BaseSampleWiseMetric):\n \"\"\"Calculate SSIM (structural similarity).\n\n Ref:\n Image quality assessment: From error visibility to structural similarity\n\n The results are the same as that of the official released MATLAB code in\n https://ece.uwaterloo.ca/~z70wang/research/ssim/.\n\n For three-channel images, SSIM is calculated for each channel and then\n averaged.\n\n Args:\n\n gt_key (str): Key of ground-truth. Default: 'gt_img'\n pred_key (str): Key of prediction. Default: 'pred_img'\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Default: None\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the PSNR calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n\n Metrics:\n - SSIM (float): Structural similarity\n \"\"\"\n\n metric = 'SSIM'\n\n def __init__(self,\n gt_key: str = 'gt_img',\n pred_key: str = 'pred_img',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None,\n crop_border=0,\n input_order='CHW',\n convert_to=None) -> None:\n super().__init__(\n gt_key=gt_key,\n pred_key=pred_key,\n mask_key=None,\n collect_device=collect_device,\n prefix=prefix)\n\n self.crop_border = crop_border\n self.input_order = input_order\n self.convert_to = convert_to\n\n def process_image(self, gt, pred, mask):\n \"\"\"Process an image.\n\n Args:\n gt (Torch | np.ndarray): GT image.\n pred (Torch | np.ndarray): Pred image.\n mask (Torch | np.ndarray): Mask of evaluation.\n Returns:\n np.ndarray: SSIM result.\n \"\"\"\n\n return ssim(\n img1=gt,\n img2=pred,\n crop_border=self.crop_border,\n input_order=self.input_order,\n convert_to=self.convert_to,\n channel_order=self.channel_order)\n\n\ndef _ssim(img1, img2):\n \"\"\"Calculate SSIM (structural similarity) for one channel images.\n\n It is called by func:`ssim`.\n\n Args:\n img1, img2 (np.ndarray): Images with range [0, 255] with order 'HWC'.\n\n Returns:\n float: SSIM result.\n \"\"\"\n\n C1 = (0.01 * 255)**2\n C2 = (0.03 * 255)**2\n\n kernel = cv2.getGaussianKernel(11, 1.5)\n window = np.outer(kernel, kernel.transpose())\n\n mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]\n mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]\n mu1_sq = mu1**2\n mu2_sq = mu2**2\n mu1_mu2 = mu1 * mu2\n sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq\n sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq\n sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2\n\n ssim_map = ((2 * mu1_mu2 + C1) *\n (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *\n (sigma1_sq + sigma2_sq + C2))\n\n return ssim_map.mean()\n\n\ndef ssim(img1,\n img2,\n crop_border=0,\n input_order='HWC',\n convert_to=None,\n channel_order='rgb'):\n \"\"\"Calculate SSIM (structural similarity).\n\n Ref:\n Image quality assessment: From error visibility to structural similarity\n\n The results are the same as that of the official released MATLAB code in\n https://ece.uwaterloo.ca/~z70wang/research/ssim/.\n\n For three-channel images, SSIM is calculated for each channel and then\n averaged.\n\n Args:\n img1 (ndarray): Images with range [0, 255].\n img2 (ndarray): Images with range [0, 255].\n crop_border (int): Cropped pixels in each edges of an image. These\n pixels are not involved in the SSIM calculation. Default: 0.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n Default: 'HWC'.\n convert_to (str): Whether to convert the images to other color models.\n If None, the images are not altered. When computing for 'Y',\n the images are assumed to be in BGR order. Options are 'Y' and\n None. Default: None.\n channel_order (str): The channel order of image. Default: 'rgb'\n\n Returns:\n float: SSIM result.\n \"\"\"\n\n assert img1.shape == img2.shape, (\n f'Image shapes are different: {img1.shape}, {img2.shape}.')\n\n img1 = img_transform(\n img1,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n img2 = img_transform(\n img2,\n crop_border=crop_border,\n input_order=input_order,\n convert_to=convert_to,\n channel_order=channel_order)\n\n img1 = to_numpy(img1)\n img2 = to_numpy(img2)\n\n ssims = []\n for i in range(img1.shape[2]):\n ssims.append(_ssim(img1[..., i], img2[..., i]))\n\n return np.array(ssims).mean()\n" }, { "path": "mmagic/evaluation/metrics/swd.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Sequence\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.dist import all_gather, get_world_size\n\nfrom mmagic.registry import METRICS\nfrom .base_gen_metric import GenMetric\n\n\ndef sliced_wasserstein(distribution_a,\n distribution_b,\n dir_repeats=4,\n dirs_per_repeat=128):\n r\"\"\"sliced Wasserstein distance of two sets of patches.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/ms_ssim.py # noqa\n\n Args:\n distribution_a (Tensor): Descriptors of first distribution.\n distribution_b (Tensor): Descriptors of second distribution.\n dir_repeats (int): The number of projection times. Default to 4.\n dirs_per_repeat (int): The number of directions per projection.\n Default to 128.\n\n Returns:\n float: sliced Wasserstein distance.\n \"\"\"\n if torch.cuda.is_available():\n distribution_b = distribution_b.cuda()\n assert distribution_a.ndim == 2\n assert distribution_a.shape == distribution_b.shape\n assert dir_repeats > 0 and dirs_per_repeat > 0\n distribution_a = distribution_a.to(distribution_b.device)\n results = []\n for _ in range(dir_repeats):\n dirs = torch.randn(distribution_a.shape[1], dirs_per_repeat)\n dirs /= torch.sqrt(torch.sum((dirs**2), dim=0, keepdim=True))\n dirs = dirs.to(distribution_b.device)\n proj_a = torch.matmul(distribution_a, dirs)\n proj_b = torch.matmul(distribution_b, dirs)\n # To save cuda memory, we perform sort in cpu\n proj_a, _ = torch.sort(proj_a.cpu(), dim=0)\n proj_b, _ = torch.sort(proj_b.cpu(), dim=0)\n dists = torch.abs(proj_a - proj_b)\n results.append(torch.mean(dists).item())\n torch.cuda.empty_cache()\n return sum(results) / dir_repeats\n\n\n# Gaussian blur kernel\ndef get_gaussian_kernel():\n \"\"\"Get the gaussian blur kernel.\n\n Returns:\n Tensor: Blur kernel.\n \"\"\"\n kernel = np.array([[1, 4, 6, 4, 1], [4, 16, 24, 16, 4], [6, 24, 36, 24, 6],\n [4, 16, 24, 16, 4], [1, 4, 6, 4, 1]],\n np.float32) / 256.0\n gaussian_k = torch.as_tensor(kernel.reshape(1, 1, 5, 5))\n return gaussian_k\n\n\ndef get_pyramid_layer(image, gaussian_k, direction='down'):\n \"\"\"Get the pyramid layer.\n\n Args:\n image (Tensor): Input image.\n gaussian_k (Tensor): Gaussian kernel\n direction (str, optional): The direction of pyramid. Defaults to\n 'down'.\n\n Returns:\n Tensor: The output of the pyramid.\n \"\"\"\n gaussian_k = gaussian_k.to(image.device)\n if direction == 'up':\n image = F.interpolate(image, scale_factor=2)\n multiband = [\n F.conv2d(\n image[:, i:i + 1, :, :],\n gaussian_k,\n padding=2,\n stride=1 if direction == 'up' else 2) for i in range(3)\n ]\n image = torch.cat(multiband, dim=1)\n return image\n\n\ndef gaussian_pyramid(original, n_pyramids, gaussian_k):\n \"\"\"Get a group of gaussian pyramid.\n\n Args:\n original (Tensor): The input image.\n n_pyramids (int): The number of pyramids.\n gaussian_k (Tensor): The gaussian kernel.\n\n Returns:\n List[Tensor]: The list of output of gaussian pyramid.\n \"\"\"\n x = original\n # pyramid down\n pyramids = [original]\n for _ in range(n_pyramids):\n x = get_pyramid_layer(x, gaussian_k)\n pyramids.append(x)\n return pyramids\n\n\ndef laplacian_pyramid(original, n_pyramids, gaussian_k):\n \"\"\"Calculate Laplacian pyramid.\n\n Ref: https://github.com/koshian2/swd-pytorch/blob/master/swd.py\n\n Args:\n original (Tensor): Batch of Images with range [0, 1] and order \"NCHW\"\n n_pyramids (int): Levels of pyramids minus one.\n gaussian_k (Tensor): Gaussian kernel with shape (1, 1, 5, 5).\n\n Return:\n list[Tensor]. Laplacian pyramids of original.\n \"\"\"\n # create gaussian pyramid\n pyramids = gaussian_pyramid(original, n_pyramids, gaussian_k)\n\n # pyramid up - diff\n laplacian = []\n for i in range(len(pyramids) - 1):\n diff = pyramids[i] - get_pyramid_layer(pyramids[i + 1], gaussian_k,\n 'up')\n laplacian.append(diff)\n # Add last gaussian pyramid\n laplacian.append(pyramids[len(pyramids) - 1])\n return laplacian\n\n\ndef get_descriptors_for_minibatch(minibatch, nhood_size, nhoods_per_image):\n r\"\"\"Get descriptors of one level of pyramids.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/sliced_wasserstein.py # noqa\n\n Args:\n minibatch (Tensor): Pyramids of one level with order \"NCHW\".\n nhood_size (int): Pixel neighborhood size.\n nhoods_per_image (int): The number of descriptors per image.\n\n Return:\n Tensor: Descriptors of images from one level batch.\n \"\"\"\n S = minibatch.shape # (minibatch, channel, height, width)\n assert len(S) == 4 and S[1] == 3\n N = nhoods_per_image * S[0]\n H = nhood_size // 2\n nhood, chan, x, y = np.ogrid[0:N, 0:3, -H:H + 1, -H:H + 1]\n img = nhood // nhoods_per_image\n x = x + np.random.randint(H, S[3] - H, size=(N, 1, 1, 1))\n y = y + np.random.randint(H, S[2] - H, size=(N, 1, 1, 1))\n idx = ((img * S[1] + chan) * S[2] + y) * S[3] + x\n return minibatch.view(-1)[idx]\n\n\ndef finalize_descriptors(desc):\n r\"\"\"Normalize and reshape descriptors.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/sliced_wasserstein.py # noqa\n\n Args:\n desc (list or Tensor): List of descriptors of one level.\n\n Return:\n Tensor: Descriptors after normalized along channel and flattened.\n \"\"\"\n if isinstance(desc, list):\n desc = torch.cat(desc, dim=0)\n assert desc.ndim == 4 # (neighborhood, channel, height, width)\n desc -= torch.mean(desc, dim=(0, 2, 3), keepdim=True)\n desc /= torch.std(desc, dim=(0, 2, 3), keepdim=True)\n desc = desc.reshape(desc.shape[0], -1)\n return desc\n\n\n@METRICS.register_module('SWD')\n@METRICS.register_module()\nclass SlicedWassersteinDistance(GenMetric):\n \"\"\"SWD (Sliced Wasserstein distance) metric. We calculate the SWD of two\n sets of images in the following way. In every 'feed', we obtain the\n Laplacian pyramids of every images and extract patches from the Laplacian\n pyramids as descriptors. In 'summary', we normalize these descriptors along\n channel, and reshape them so that we can use these descriptors to represent\n the distribution of real/fake images. And we can calculate the sliced\n Wasserstein distance of the real and fake descriptors as the SWD of the\n real and fake images.\n\n Ref: https://github.com/tkarras/progressive_growing_of_gans/blob/master/metrics/sliced_wasserstein.py # noqa\n\n Args:\n fake_nums (int): Numbers of the generated image need for the metric.\n image_shape (tuple): Image shape in order \"CHW\".\n fake_key (Optional[str]): Key for get fake images of the output dict.\n Defaults to None.\n real_key (Optional[str]): Key for get real images from the input dict.\n Defaults to 'gt_img'.\n sample_model (str): Sampling mode for the generative model. Support\n 'orig' and 'ema'. Defaults to 'ema'.\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n \"\"\"\n\n name = 'SWD'\n\n def __init__(self,\n fake_nums: int,\n image_shape: tuple,\n fake_key: Optional[str] = None,\n real_key: Optional[str] = 'gt_img',\n sample_model: str = 'ema',\n collect_device: str = 'cpu',\n prefix: Optional[str] = None):\n super().__init__(fake_nums, fake_nums, fake_key, real_key,\n sample_model, collect_device, prefix)\n\n self.nhood_size = 7 # height and width of the extracted patches\n self.nhoods_per_image = 128 # number of extracted patches per image\n self.dir_repeats = 4 # times of sampling directions\n self.dirs_per_repeat = 128 # number of directions per sampling\n self.resolutions = []\n res = image_shape[1]\n self.image_shape = image_shape\n while res >= 16 and len(self.resolutions) < 4:\n self.resolutions.append(res)\n res //= 2\n self.n_pyramids = len(self.resolutions)\n\n self.gaussian_k = get_gaussian_kernel()\n self.real_results = [[] for res in self.resolutions]\n self.fake_results = [[] for res in self.resolutions]\n\n self._num_processed = 0\n\n def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:\n \"\"\"Process one batch of data samples and predictions. The processed\n results should be stored in ``self.fake_results`` and\n ``self.real_results``, which will be used to compute the metrics when\n all batches have been processed.\n\n Args:\n data_batch (dict): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n if self.fake_nums != -1 and (self._num_processed >=\n self.fake_nums_per_device):\n return\n\n real_imgs, fake_imgs = [], []\n for data in data_samples:\n # parse real images\n real_imgs.append(data['gt_img'])\n # parse fake images\n fake_img_ = data\n # get ema/orig results\n if self.sample_model in fake_img_:\n fake_img_ = fake_img_[self.sample_model]\n # get specific fake_keys\n if (self.fake_key is not None and self.fake_key in fake_img_):\n fake_img_ = fake_img_[self.fake_key]\n else:\n # get img tensor\n fake_img_ = fake_img_['fake_img']\n fake_imgs.append(fake_img_)\n real_imgs = torch.stack(real_imgs, dim=0)\n fake_imgs = torch.stack(fake_imgs, dim=0)\n\n # [0, 255] -> [-1, 1]\n real_imgs = (real_imgs - 127.5) / 127.5\n fake_imgs = (fake_imgs - 127.5) / 127.5\n\n # real images\n assert real_imgs.shape[1:] == self.image_shape\n if real_imgs.shape[1] == 1:\n real_imgs = real_imgs.repeat(1, 3, 1, 1)\n real_pyramid = laplacian_pyramid(real_imgs, self.n_pyramids - 1,\n self.gaussian_k)\n # lod: layer_of_descriptors\n if self.real_results == []:\n self.real_results = [[] for res in self.resolutions]\n for lod, level in enumerate(real_pyramid):\n desc = get_descriptors_for_minibatch(level, self.nhood_size,\n self.nhoods_per_image)\n self.real_results[lod].append(desc.cpu())\n\n # fake images\n assert fake_imgs.shape[1:] == self.image_shape\n if fake_imgs.shape[1] == 1:\n fake_imgs = fake_imgs.repeat(1, 3, 1, 1)\n fake_pyramid = laplacian_pyramid(fake_imgs, self.n_pyramids - 1,\n self.gaussian_k)\n # lod: layer_of_descriptors\n if self.fake_results == []:\n self.fake_results = [[] for res in self.resolutions]\n for lod, level in enumerate(fake_pyramid):\n desc = get_descriptors_for_minibatch(level, self.nhood_size,\n self.nhoods_per_image)\n self.fake_results[lod].append(desc.cpu())\n\n self._num_processed += real_imgs.shape[0]\n\n def _collect_target_results(self, target: str) -> Optional[list]:\n \"\"\"Collect function for SWD metric. This function support collect\n results typing as `List[List[Tensor]]`.\n\n Args:\n target (str): Target results to collect.\n\n Returns:\n Optional[list]: The collected results.\n \"\"\"\n assert target in [\n 'fake', 'real'\n ], ('Only support to collect \\'fake\\' or \\'real\\' results.')\n results = getattr(self, f'{target}_results')\n results_collected = []\n world_size = get_world_size()\n for result in results:\n # save the original tensor size\n results_size_list = [res.shape[0] for res in result] * world_size\n result_collected = torch.cat(result, dim=0)\n result_collected = torch.cat(all_gather(result_collected), dim=0)\n # split to tuple\n result_collected = torch.split(result_collected, results_size_list)\n # convert to list\n result_collected = [res for res in result_collected]\n results_collected.append(result_collected)\n\n self._num_processed = 0\n return results_collected\n\n def compute_metrics(self, results_fake, results_real) -> dict:\n \"\"\"Compute the result of SWD metric.\n\n Args:\n fake_results (list): List of image feature of fake images.\n real_results (list): List of image feature of real images.\n\n Returns:\n dict: A dict of the computed SWD metric.\n \"\"\"\n fake_descs = [finalize_descriptors(d) for d in results_fake]\n real_descs = [finalize_descriptors(d) for d in results_real]\n distance = [\n sliced_wasserstein(dreal, dfake, self.dir_repeats,\n self.dirs_per_repeat)\n for dreal, dfake in zip(real_descs, fake_descs)\n ]\n del real_descs\n del fake_descs\n\n distance = [d * 1e3 for d in distance] # multiply by 10^3\n result = distance + [np.mean(distance)]\n\n return {\n f'{resolution}': round(d, 4)\n for resolution, d in zip(self.resolutions + ['avg'], result)\n }\n" }, { "path": "mmagic/models/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .base_models import (BaseConditionalGAN, BaseEditModel, BaseGAN,\n BaseMattor, BaseTranslationModel, BasicInterpolator,\n ExponentialMovingAverage)\nfrom .data_preprocessors import DataPreprocessor, MattorPreprocessor\nfrom .editors import * # noqa: F401, F403\nfrom .losses import * # noqa: F401, F403\n\n__all__ = [\n 'BaseGAN', 'BaseTranslationModel', 'BaseEditModel', 'MattorPreprocessor',\n 'DataPreprocessor', 'BasicInterpolator', 'BaseMattor', 'BasicInterpolator',\n 'ExponentialMovingAverage', 'BaseConditionalGAN'\n]\n" }, { "path": "mmagic/models/archs/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# To register Deconv\nimport warnings\nfrom typing import List\n\nfrom mmagic.utils import try_import\nfrom .all_gather_layer import AllGatherLayer\nfrom .aspp import ASPP\nfrom .attention_injection import AttentionInjection\nfrom .conv import * # noqa: F401, F403\nfrom .downsample import pixel_unshuffle\nfrom .ensemble import SpatialTemporalEnsemble\nfrom .gated_conv_module import SimpleGatedConvModule\nfrom .img_normalize import ImgNormalize\nfrom .linear_module import LinearModule\nfrom .lora import (LoRAWrapper, set_lora, set_lora_disable, set_lora_enable,\n set_only_lora_trainable)\nfrom .multi_layer_disc import MultiLayerDiscriminator\nfrom .patch_disc import PatchDiscriminator\nfrom .resnet import ResNet\nfrom .separable_conv_module import DepthwiseSeparableConvModule\nfrom .simple_encoder_decoder import SimpleEncoderDecoder\nfrom .smpatch_disc import SoftMaskPatchDiscriminator\nfrom .sr_backbone import ResidualBlockNoBN\nfrom .tokenizer import TokenizerWrapper\nfrom .upsample import PixelShufflePack\nfrom .vgg import VGG16\nfrom .wrapper import DiffusersWrapper\n\n\ndef register_diffusers_models() -> List[str]:\n \"\"\"Register models in ``diffusers.models`` to the ``MODELS`` registry.\n Specifically, the registered models from diffusers only defines the network\n forward without training. See more details about diffusers in:\n https://huggingface.co/docs/diffusers/api/models.\n\n Returns:\n List[str]: A list of registered DIFFUSION_MODELS' name.\n \"\"\"\n import inspect\n\n from mmagic.registry import MODELS\n\n diffusers = try_import('diffusers')\n if diffusers is None:\n warnings.warn('Diffusion Models are not registered as expect. '\n 'If you want to use diffusion models, '\n 'please install diffusers>=0.12.0.')\n return None\n\n def gen_wrapped_cls(module, module_name):\n return type(\n module_name, (DiffusersWrapper, ),\n dict(\n _module_cls=module,\n _module_name=module_name,\n __module__=__name__))\n\n DIFFUSERS_MODELS = []\n for module_name in dir(diffusers.models):\n module = getattr(diffusers.models, module_name)\n if inspect.isclass(module):\n wrapped_module = gen_wrapped_cls(module, module_name)\n MODELS.register_module(name=module_name, module=wrapped_module)\n DIFFUSERS_MODELS.append(module_name)\n\n DIFFUSERS_PIPELINES = []\n for pipeline_name in dir(diffusers.pipelines):\n pipeline = getattr(diffusers.pipelines, pipeline_name)\n if (inspect.isclass(pipeline)\n and issubclass(pipeline, diffusers.DiffusionPipeline)):\n wrapped_pipeline = gen_wrapped_cls(pipeline, pipeline_name)\n MODELS.register_module(name=pipeline_name, module=wrapped_pipeline)\n DIFFUSERS_PIPELINES.append(pipeline_name)\n\n return DIFFUSERS_MODELS, DIFFUSERS_PIPELINES\n\n\nREGISTERED_DIFFUSERS_MODELS, REGISTERED_DIFFUSERS_PIPELINES = \\\n register_diffusers_models()\n\n__all__ = [\n 'ASPP', 'DepthwiseSeparableConvModule', 'SimpleGatedConvModule',\n 'LinearModule', 'pixel_unshuffle', 'PixelShufflePack', 'ImgNormalize',\n 'SpatialTemporalEnsemble', 'SoftMaskPatchDiscriminator',\n 'SimpleEncoderDecoder', 'MultiLayerDiscriminator', 'PatchDiscriminator',\n 'VGG16', 'ResNet', 'AllGatherLayer', 'ResidualBlockNoBN', 'LoRAWrapper',\n 'set_lora', 'set_lora_disable', 'set_lora_enable',\n 'set_only_lora_trainable', 'TokenizerWrapper', 'AttentionInjection'\n]\n" }, { "path": "mmagic/models/archs/all_gather_layer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.autograd as autograd\nimport torch.distributed as dist\n\n\nclass AllGatherLayer(autograd.Function):\n \"\"\"All gather layer with backward propagation path.\n\n Indeed, this module is to make ``dist.all_gather()`` in the backward graph.\n Such kind of operation has been widely used in Moco and other contrastive\n learning algorithms.\n \"\"\"\n\n @staticmethod\n def forward(ctx, x):\n \"\"\"Forward function.\"\"\"\n ctx.save_for_backward(x)\n output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]\n dist.all_gather(output, x)\n return tuple(output)\n\n @staticmethod\n def backward(ctx, *grad_outputs):\n \"\"\"Backward function.\"\"\"\n x, = ctx.saved_tensors\n grad_out = torch.zeros_like(x)\n grad_out = grad_outputs[dist.get_rank()]\n return grad_out\n" }, { "path": "mmagic/models/archs/aspp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Sequence\n\nimport torch\nfrom mmcv.cnn import ConvModule\nfrom torch import Tensor, nn\nfrom torch.nn import functional as F\n\nfrom .separable_conv_module import DepthwiseSeparableConvModule\n\n\nclass ASPPPooling(nn.Sequential):\n \"\"\"ASPP Pooling module.\n\n The code is adopted from\n https://github.com/pytorch/vision/blob/master/torchvision/models/\n segmentation/deeplabv3.py\n\n Args:\n in_channels (int): Input channels of the module.\n out_channels (int): Output channels of the module.\n conv_cfg (dict): Config dict for convolution layer. If \"None\",\n nn.Conv2d will be applied.\n norm_cfg (dict): Config dict for normalization layer.\n act_cfg (dict): Config dict for activation layer.\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int,\n conv_cfg: Optional[dict], norm_cfg: Optional[dict],\n act_cfg: Optional[dict]):\n super().__init__(\n nn.AdaptiveAvgPool2d(1),\n ConvModule(\n in_channels,\n out_channels,\n 1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg))\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function for ASPP Pooling module.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n size = x.shape[-2:]\n for mod in self:\n x = mod(x)\n return F.interpolate(\n x, size=size, mode='bilinear', align_corners=False)\n\n\nclass ASPP(nn.Module):\n \"\"\"ASPP module from DeepLabV3.\n\n The code is adopted from\n https://github.com/pytorch/vision/blob/master/torchvision/models/\n segmentation/deeplabv3.py\n\n For more information about the module:\n `\"Rethinking Atrous Convolution for Semantic Image Segmentation\"\n `_.\n\n Args:\n in_channels (int): Input channels of the module.\n out_channels (int): Output channels of the module. Default: 256.\n mid_channels (int): Output channels of the intermediate ASPP conv\n modules. Default: 256.\n dilations (Sequence[int]): Dilation rate of three ASPP conv module.\n Default: [12, 24, 36].\n conv_cfg (dict): Config dict for convolution layer. If \"None\",\n nn.Conv2d will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer.\n Default: dict(type='BN').\n act_cfg (dict): Config dict for activation layer.\n Default: dict(type='ReLU').\n separable_conv (bool): Whether replace normal conv with depthwise\n separable conv which is faster. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n out_channels: int = 256,\n mid_channels: int = 256,\n dilations: Sequence[int] = (12, 24, 36),\n conv_cfg: Optional[dict] = None,\n norm_cfg: Optional[dict] = dict(type='BN'),\n act_cfg: Optional[dict] = dict(type='ReLU'),\n separable_conv: bool = False):\n super().__init__()\n\n if separable_conv:\n conv_module = DepthwiseSeparableConvModule\n else:\n conv_module = ConvModule\n\n modules = []\n modules.append(\n ConvModule(\n in_channels,\n mid_channels,\n 1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg))\n\n for dilation in dilations:\n modules.append(\n conv_module(\n in_channels,\n mid_channels,\n 3,\n padding=dilation,\n dilation=dilation,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg))\n\n modules.append(\n ASPPPooling(in_channels, mid_channels, conv_cfg, norm_cfg,\n act_cfg))\n\n self.convs = nn.ModuleList(modules)\n\n self.project = nn.Sequential(\n ConvModule(\n 5 * mid_channels,\n out_channels,\n 1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg), nn.Dropout(0.5))\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function for ASPP module.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n res = []\n for conv in self.convs:\n res.append(conv(x))\n res = torch.cat(res, dim=1)\n return self.project(res)\n" }, { "path": "mmagic/models/archs/attention_injection.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom enum import Enum\n\nimport torch\nimport torch.nn as nn\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom torch import Tensor\n\nAttentionStatus = Enum('ATTENTION_STATUS', 'READ WRITE DISABLE')\n\n\ndef torch_dfs(model: torch.nn.Module):\n result = [model]\n for child in model.children():\n result += torch_dfs(child)\n return result\n\n\nclass AttentionInjection(nn.Module):\n \"\"\"Wrapper for stable diffusion unet.\n\n Args:\n module (nn.Module): The module to be wrapped.\n \"\"\"\n\n def __init__(self, module: nn.Module, injection_weight=5):\n super().__init__()\n self.attention_status = AttentionStatus.READ\n self.style_cfgs = []\n self.unet = module\n\n attn_inject = self\n\n def transformer_forward_replacement(\n self,\n hidden_states,\n attention_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n timestep=None,\n cross_attention_kwargs=None,\n class_labels=None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( # noqa\n hidden_states,\n timestep,\n class_labels,\n hidden_dtype=hidden_states.dtype)\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n attn_output = None\n self_attention_context = norm_hidden_states\n if attn_inject.attention_status == AttentionStatus.WRITE:\n self.bank.append(self_attention_context.detach().clone())\n if attn_inject.attention_status == AttentionStatus.READ:\n if len(self.bank) > 0:\n self.bank = self.bank * injection_weight\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=torch.cat(\n [self_attention_context] + self.bank, dim=1))\n # attn_output = self.attn1(\n # norm_hidden_states,\n # encoder_hidden_states=self.bank[0])\n self.bank = []\n if attn_output is None:\n attn_output = self.attn1(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n cross_attention_kwargs = cross_attention_kwargs if \\\n cross_attention_kwargs is not None else {}\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep)\n if self.use_ada_layer_norm else self.norm2(hidden_states))\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * \\\n (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n all_modules = torch_dfs(self.unet)\n\n attn_modules = [\n module for module in all_modules\n if isinstance(module, BasicTransformerBlock)\n ]\n for i, module in enumerate(attn_modules):\n if getattr(module, '_original_inner_forward', None) is None:\n module._original_inner_forward = module.forward\n module.forward = transformer_forward_replacement.__get__(\n module, BasicTransformerBlock)\n module.bank = []\n\n def forward(self,\n x: Tensor,\n t,\n encoder_hidden_states=None,\n down_block_additional_residuals=None,\n mid_block_additional_residual=None,\n ref_x=None) -> Tensor:\n \"\"\"Forward and add LoRA mapping.\n\n Args:\n x (Tensor): The input tensor.\n\n Returns:\n Tensor: The output tensor.\n \"\"\"\n if ref_x is not None:\n self.attention_status = AttentionStatus.WRITE\n self.unet(\n ref_x,\n t,\n encoder_hidden_states=encoder_hidden_states,\n down_block_additional_residuals= # noqa\n down_block_additional_residuals,\n mid_block_additional_residual=mid_block_additional_residual)\n self.attention_status = AttentionStatus.READ\n output = self.unet(\n x,\n t,\n encoder_hidden_states=encoder_hidden_states,\n down_block_additional_residuals= # noqa\n down_block_additional_residuals,\n mid_block_additional_residual=mid_block_additional_residual)\n\n return output\n" }, { "path": "mmagic/models/archs/conv.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom torch import nn\n\nfrom mmagic.registry import MODELS\n\nMODELS.register_module('Deconv', module=nn.ConvTranspose2d)\n# TODO: octave conv\n" }, { "path": "mmagic/models/archs/downsample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom torch import Tensor\n\n\ndef pixel_unshuffle(x: Tensor, scale: int) -> Tensor:\n \"\"\"Down-sample by pixel unshuffle.\n\n Args:\n x (Tensor): Input tensor.\n scale (int): Scale factor.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n\n b, c, h, w = x.shape\n if h % scale != 0 or w % scale != 0:\n raise AssertionError(\n f'Invalid scale ({scale}) of pixel unshuffle for tensor '\n f'with shape: {x.shape}')\n h = int(h / scale)\n w = int(w / scale)\n x = x.view(b, c, h, scale, w, scale)\n x = x.permute(0, 1, 3, 5, 2, 4)\n return x.reshape(b, -1, h, w)\n" }, { "path": "mmagic/models/archs/ensemble.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\n\nclass SpatialTemporalEnsemble(nn.Module):\n \"\"\"Apply spatial and temporal ensemble and compute outputs.\n\n Args:\n is_temporal_ensemble (bool, optional): Whether to apply ensemble\n temporally. If True, the sequence will also be flipped temporally.\n If the input is an image, this argument must be set to False.\n Default: False.\n \"\"\"\n\n def __init__(self, is_temporal_ensemble: Optional[bool] = False):\n\n super().__init__()\n\n self.is_temporal_ensemble = is_temporal_ensemble\n\n def _transform(self, imgs: torch.Tensor, mode: str) -> torch.Tensor:\n \"\"\"Apply spatial transform (flip, rotate) to the images.\n\n Args:\n imgs (torch.Tensor): The images to be transformed/\n mode (str): The mode of transform. Supported values are 'vertical',\n 'horizontal', and 'transpose', corresponding to vertical flip,\n horizontal flip, and rotation, respectively.\n\n Returns:\n torch.Tensor: Output of the model with spatial ensemble applied.\n \"\"\"\n\n is_single_image = False\n if imgs.ndim == 4:\n if self.is_temporal_ensemble:\n raise ValueError('\"is_temporal_ensemble\" must be False if '\n 'the input is an image.')\n is_single_image = True\n imgs = imgs.unsqueeze(1)\n\n if mode == 'vertical':\n imgs = imgs.flip(4).clone()\n elif mode == 'horizontal':\n imgs = imgs.flip(3).clone()\n elif mode == 'transpose':\n imgs = imgs.permute(0, 1, 2, 4, 3).clone()\n\n if is_single_image:\n imgs = imgs.squeeze(1)\n\n return imgs\n\n def spatial_ensemble(self, imgs: torch.Tensor,\n model: nn.Module) -> torch.Tensor:\n \"\"\"Apply spatial ensemble.\n\n Args:\n imgs (torch.Tensor): The images to be processed by the model. Its\n size should be either (n, t, c, h, w) or (n, c, h, w).\n model (nn.Module): The model to process the images.\n\n Returns:\n torch.Tensor: Output of the model with spatial ensemble applied.\n \"\"\"\n\n img_list = [imgs.cpu()]\n for mode in ['vertical', 'horizontal', 'transpose']:\n img_list.extend([self._transform(t, mode) for t in img_list])\n\n output_list = [model(t.to(imgs.device)).cpu() for t in img_list]\n for i in range(len(output_list)):\n if i > 3:\n output_list[i] = self._transform(output_list[i], 'transpose')\n if i % 4 > 1:\n output_list[i] = self._transform(output_list[i], 'horizontal')\n if (i % 4) % 2 == 1:\n output_list[i] = self._transform(output_list[i], 'vertical')\n\n outputs = torch.stack(output_list, dim=0)\n outputs = outputs.mean(dim=0, keepdim=False)\n\n return outputs.to(imgs.device)\n\n def forward(self, imgs: torch.Tensor, model: nn.Module) -> torch.Tensor:\n \"\"\"Apply spatial and temporal ensemble.\n\n Args:\n imgs (torch.Tensor): The images to be processed by the model. Its\n size should be either (n, t, c, h, w) or (n, c, h, w).\n model (nn.Module): The model to process the images.\n\n Returns:\n torch.Tensor: Output of the model with spatial ensemble applied.\n \"\"\"\n outputs = self.spatial_ensemble(imgs, model)\n if self.is_temporal_ensemble:\n outputs += self.spatial_ensemble(imgs.flip(1), model).flip(1)\n outputs *= 0.5\n\n return outputs\n" }, { "path": "mmagic/models/archs/gated_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule, build_activation_layer\n\n\nclass SimpleGatedConvModule(nn.Module):\n \"\"\"Simple Gated Convolutional Module.\n\n This module is a simple gated convolutional module. The detailed formula\n is:\n\n .. math::\n y = \\\\phi(conv1(x)) * \\\\sigma(conv2(x)),\n\n where `phi` is the feature activation function and `sigma` is the gate\n activation function. In default, the gate activation function is sigmoid.\n\n Args:\n in_channels (int): Same as nn.Conv2d.\n out_channels (int): The number of channels of the output feature. Note\n that `out_channels` in the conv module is doubled since this module\n contains two convolutions for feature and gate separately.\n kernel_size (int or tuple[int]): Same as nn.Conv2d.\n feat_act_cfg (dict): Config dict for feature activation layer.\n Default: dict(type='ELU').\n gate_act_cfg (dict): Config dict for gate activation layer.\n Default: dict(type='Sigmoid').\n kwargs (keyword arguments): Same as `ConvModule`.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n out_channels: int,\n kernel_size: Union[int, Tuple[int, int]],\n feat_act_cfg: Optional[dict] = dict(type='ELU'),\n gate_act_cfg: Optional[dict] = dict(type='Sigmoid'),\n **kwargs):\n super().__init__()\n # the activation function should specified outside conv module\n kwargs_ = copy.deepcopy(kwargs)\n kwargs_['act_cfg'] = None\n self.with_feat_act = feat_act_cfg is not None\n self.with_gate_act = gate_act_cfg is not None\n\n self.conv = ConvModule(in_channels, out_channels * 2, kernel_size,\n **kwargs_)\n\n if self.with_feat_act:\n self.feat_act = build_activation_layer(feat_act_cfg)\n\n if self.with_gate_act:\n self.gate_act = build_activation_layer(gate_act_cfg)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n x = self.conv(x)\n x, gate = torch.split(x, x.size(1) // 2, dim=1)\n if self.with_feat_act:\n x = self.feat_act(x)\n if self.with_gate_act:\n gate = self.gate_act(gate)\n x = x * gate\n\n return x\n" }, { "path": "mmagic/models/archs/img_normalize.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\n\n\nclass ImgNormalize(nn.Conv2d):\n \"\"\"Normalize images with the given mean and std value.\n\n Based on Conv2d layer, can work in GPU.\n\n Args:\n pixel_range (float): Pixel range of feature.\n img_mean (Tuple[float]): Image mean of each channel.\n img_std (Tuple[float]): Image std of each channel.\n sign (int): Sign of bias. Default -1.\n \"\"\"\n\n def __init__(self,\n pixel_range: float,\n img_mean: Tuple[float, float, float],\n img_std: Tuple[float, float, float],\n sign: int = -1):\n\n assert len(img_mean) == len(img_std)\n num_channels = len(img_mean)\n super().__init__(num_channels, num_channels, kernel_size=1)\n\n std = torch.Tensor(img_std)\n self.weight.data = torch.eye(num_channels).view(\n num_channels, num_channels, 1, 1)\n self.weight.data.div_(std.view(num_channels, 1, 1, 1))\n self.bias.data = sign * pixel_range * torch.Tensor(img_mean)\n self.bias.data.div_(std)\n\n self.weight.requires_grad = False\n self.bias.requires_grad = False\n" }, { "path": "mmagic/models/archs/linear_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Tuple\n\nimport torch.nn as nn\nfrom mmcv.cnn import build_activation_layer\nfrom mmengine.model.weight_init import kaiming_init\nfrom torch import Tensor\n\n\nclass LinearModule(nn.Module):\n \"\"\"A linear block that contains linear/norm/activation layers.\n\n For low level vision, we add spectral norm and padding layer.\n\n Args:\n in_features (int): Same as nn.Linear.\n out_features (int): Same as nn.Linear.\n bias (bool): Same as nn.Linear. Default: True.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n inplace (bool): Whether to use inplace mode for activation.\n Default: True.\n with_spectral_norm (bool): Whether use spectral norm in linear module.\n Default: False.\n order (tuple[str]): The order of linear/activation layers. It is a\n sequence of \"linear\", \"norm\" and \"act\". Examples are\n (\"linear\", \"act\") and (\"act\", \"linear\").\n \"\"\"\n\n def __init__(self,\n in_features: int,\n out_features: int,\n bias: bool = True,\n act_cfg: Optional[dict] = dict(type='ReLU'),\n inplace: bool = True,\n with_spectral_norm: bool = False,\n order: Tuple[str, str] = ('linear', 'act')):\n super().__init__()\n assert act_cfg is None or isinstance(act_cfg, dict)\n self.act_cfg = act_cfg\n self.inplace = inplace\n self.with_spectral_norm = with_spectral_norm\n self.order = order\n assert isinstance(self.order, tuple) and len(self.order) == 2\n assert set(order) == set(['linear', 'act'])\n\n self.with_activation = act_cfg is not None\n self.with_bias = bias\n\n # build linear layer\n self.linear = nn.Linear(in_features, out_features, bias=bias)\n # export the attributes of self.linear to a higher level for\n # convenience\n self.in_features = self.linear.in_features\n self.out_features = self.linear.out_features\n\n if self.with_spectral_norm:\n self.linear = nn.utils.spectral_norm(self.linear)\n\n # build activation layer\n if self.with_activation:\n act_cfg_ = act_cfg.copy()\n act_cfg_.setdefault('inplace', inplace)\n self.activate = build_activation_layer(act_cfg_)\n\n # Use msra init by default\n self.init_weights()\n\n def init_weights(self) -> None:\n \"\"\"Init weights for the model.\"\"\"\n if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':\n nonlinearity = 'leaky_relu'\n a = self.act_cfg.get('negative_slope', 0.01)\n else:\n nonlinearity = 'relu'\n a = 0\n\n kaiming_init(self.linear, a=a, nonlinearity=nonlinearity)\n\n def forward(self, x: Tensor, activate: Optional[bool] = True) -> Tensor:\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of :math:`(n, *, c)`.\n Same as ``torch.nn.Linear``.\n activate (bool, optional): Whether to use activation layer.\n Defaults to True.\n\n Returns:\n torch.Tensor: Same as ``torch.nn.Linear``.\n \"\"\"\n for layer in self.order:\n if layer == 'linear':\n x = self.linear(x)\n elif layer == 'act' and activate and self.with_activation:\n x = self.activate(x)\n return x\n" }, { "path": "mmagic/models/archs/lora.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport re\nfrom typing import Any, List, Optional, Union\n\nimport torch.nn as nn\nfrom mmengine import print_log\nfrom torch import Tensor\n\n\nclass LoRALinear(nn.Module):\n \"\"\"Linear layer for LoRA.\n\n Args:\n in_feat (int): Number of input features.\n out_feat (int): Number of output features.\n rank (int): The rank of LoRA.\n \"\"\"\n\n def __init__(self, in_feat: int, out_feat: int, rank: int = 4):\n super().__init__()\n self.rank = rank\n assert rank < min(in_feat, out_feat)\n\n self.down = nn.Linear(in_feat, rank, bias=False)\n self.up = nn.Linear(rank, out_feat, bias=False)\n\n nn.init.normal_(self.down.weight, std=1 / rank)\n nn.init.zeros_(self.up.weight)\n\n def forward(self, x: Tensor) -> Tensor:\n ori_type = x.dtype\n dtype = self.down.weight.dtype\n\n out = self.down(x.to(dtype))\n out = self.up(out)\n return out.to(ori_type)\n\n\nclass LoRAWrapper(nn.Module):\n \"\"\"Wrapper for LoRA layer.\n\n Args:\n module (nn.Module): The module to be wrapped.\n in_feat (int): Number of input features.\n out_feat (int): Number of output features.\n rank (int): The rank of LoRA.\n scale (float): The scale of LoRA feature.\n names (Union[str, List[str]], optional): The name of LoRA layers. If\n you want to add multi LoRA for one module, names for each LoRA\n mapping must be defined.\n \"\"\"\n\n def __init__(self,\n module: nn.Module,\n in_feat: int,\n out_feat: int,\n rank: int,\n scale: float = 1,\n names: Optional[Union[str, List[str]]] = None):\n super().__init__()\n\n # NOTE: LoRA for linear layer, LoCON will coming soon~\n assert isinstance(\n module,\n nn.Linear), ('Only Support LoRA for linear layer currently. '\n 'LoCON will coming soon~')\n self.wrapped = module\n\n if names is not None:\n # set a list of LoRAs\n if not isinstance(names, list):\n names = [names]\n if isinstance(rank, list):\n assert len(rank) == len(names)\n else:\n rank = [rank] * len(names)\n if isinstance(scale, list):\n assert len(scale) == len(names)\n else:\n scale = [scale] * len(names)\n\n self.names = names\n self.lora_mapping = dict()\n self.scale = dict()\n self.enable = dict()\n self.rank = dict()\n for n, r, s in zip(names, rank, scale):\n self.lora_mapping[n] = LoRALinear(in_feat, out_feat, r)\n self.scale_dict[n] = s\n self.enable[n] = True\n self.rank[n] = r\n self.lora_mapping = nn.ModuleDict(self.lora_mapping)\n\n else:\n # set single LoRA\n self.names = None\n self.lora_mapping = LoRALinear(in_feat, out_feat, rank)\n self.scale = scale\n self.enable = True\n self.rank = rank\n\n self.in_feat, self.out_feat = in_feat, out_feat\n\n def add_lora(self,\n name: str,\n rank: int,\n scale: float = 1,\n state_dict: Optional[dict] = None):\n \"\"\"Add LoRA mapping.\n\n Args:\n name (str): The name of added LoRA.\n rank (int): The rank of added LoRA.\n scale (float, optional): The scale of added LoRA. Defaults to 1.\n state_dict (dict, optional): The state dict of added LoRA.\n Defaults to None.\n \"\"\"\n mapping_to_add = LoRALinear(self.in_feat, self.out_feat, rank)\n if state_dict is not None:\n mapping_to_add.load_state_dict(mapping_to_add)\n # move to device and type\n mapping_to_add.to(self.lora_mapping.weight.dtype)\n\n if isinstance(self.names, list):\n self.names.append(name)\n self.lora_mapping[name] = mapping_to_add\n self.scale[name] = scale\n self.enable[name] = True\n self.rank[name] = rank\n else:\n self.names = ['orig', name]\n self.lora_mapping = nn.ModuleDict({\n 'orig': self.lora_mapping,\n name: mapping_to_add\n })\n self.scale = {'orig': self.scale, name: scale}\n self.enable = {'orig': self.enable, name: True}\n self.rank = {'orig': self.rank, name: rank}\n print_log(\n 'The original LoRA mapping do not have name, '\n 'save as \\'orig\\'.', 'current')\n print_log(f'Add LoRA \\'{name}\\' with rank {rank} and scale {scale}.',\n 'current')\n\n def _set_value(self,\n attr_name: str,\n value: Any,\n name: Optional[str] = None):\n \"\"\"Set value of attribute.\n\n Args:\n attr_name (str): The name of attribute to be set value.\n value (Any): The value to be set.\n name (str, optional): The name of field in `attr_name`. If\n passed, will set value to `attr_name[name]`. Defaults to None.\n \"\"\"\n attr = getattr(self, attr_name)\n\n if isinstance(attr, dict):\n if name is None:\n attr = {k: value for k in self.names}\n print_log(f'Set all value in \\'{attr_name}\\' as \\'{value}\\'.',\n 'current')\n else:\n attr[name] = value\n print_log(f'Set \\'{attr_name}[{name}]\\' as \\'{value}\\'.',\n 'current')\n else:\n attr = value\n print_log(f'Set \\'{attr_name}\\' as \\'{value}\\'.', 'current')\n\n setattr(self, attr_name, attr)\n\n def set_scale(self, scale: float, name: Optional[str] = None):\n \"\"\"Set LoRA scale.\n\n Args:\n scale (float): The scale to be set.\n name (str, optional): The name of LoRA to be set. Defaults to None.\n \"\"\"\n self._set_value('scale', scale, name)\n\n def set_enable(self, name: Optional[str] = None):\n \"\"\"Enable LoRA for the current layer.\n\n Args:\n name (str, optional): The name of LoRA to be set. Defaults to None.\n \"\"\"\n self._set_value('enable', True, name)\n\n def set_disable(self, name: Optional[str] = None):\n \"\"\"Disable LoRA for the current layer.\n\n Args:\n name (str, optional): The name of LoRA to be set. Defaults to None.\n \"\"\"\n self._set_value('enable', False, name)\n\n def forward_lora_mapping(self, x: Tensor) -> Tensor:\n \"\"\"Forward LoRA mapping.\n\n Args:\n x (Tensor): The input tensor.\n\n Returns:\n Tensor: The output tensor.\n \"\"\"\n mapping_out = 0\n if isinstance(self.lora_mapping, dict):\n for name in self.names:\n scale = self.scale[name]\n mapping_layer = self.lora_mapping[name]\n enable = self.enable[name]\n\n if enable:\n mapping_out = scale * mapping_layer(x)\n else:\n if self.enable:\n mapping_out = self.scale * self.lora_mapping(x)\n return mapping_out\n\n def forward(self, x: Tensor, *args, **kwargs) -> Tensor:\n \"\"\"Forward and add LoRA mapping.\n\n Args:\n x (Tensor): The input tensor.\n\n Returns:\n Tensor: The output tensor.\n \"\"\"\n mapping_out = self.forward_lora_mapping(x)\n return mapping_out + self.wrapped(x)\n\n @classmethod\n def wrap_lora(cls, module, rank=4, scale=1, names=None, state_dict=None):\n \"\"\"Wrap LoRA.\n\n Use case:\n >>> linear = nn.Linear(2, 4)\n >>> lora_linear = LoRAWrapper.wrap_lora(linear, 4, 1)\n\n Args:\n module (nn.Module): The module to add LoRA.\n rank (int): The rank for LoRA.\n scale (float):\n\n Returns:\n LoRAWrapper:\n \"\"\"\n assert isinstance(module,\n nn.Linear), 'Only support LoRA for Linear Layer'\n in_feat = module.weight.shape[1]\n out_feat = module.weight.shape[0]\n lora = LoRAWrapper(module, in_feat, out_feat, rank, scale, names)\n\n return lora\n\n\ndef replace_module(parent_module: nn.Module, child_name: str,\n new_module: nn.Module):\n \"\"\"Replace module in parent module.\"\"\"\n setattr(parent_module, child_name, new_module)\n\n\ndef get_submodule(module: nn.Module, key: str):\n \"\"\"Get submodule by key.\"\"\"\n target_name = key.split('.')[-1]\n parent = module.get_submodule('.'.join(key.split('.')[:-1]))\n target = module.get_submodule(key)\n return parent, target, target_name\n\n\ndef set_lora(module: nn.Module,\n config: dict,\n verbose: bool = True) -> nn.Module:\n \"\"\"Set LoRA for module.\n\n Use case:\n >>> 1. set all lora with same parameters\n >>> lora_config = dict(\n >>> rank=4,\n >>> scale=1,\n >>> target_modules=['to_q', 'to_k', 'to_v'])\n\n >>> 2. set lora with different parameters\n >>> lora_config = dict(\n >>> rank=4,\n >>> scale=1,\n >>> target_modules=[\n >>> # set `to_q` the default parameters\n >>> 'to_q',\n >>> # set `to_k` the defined parameters\n >>> dict(target_module='to_k', rank=8, scale=1),\n >>> # set `to_v` the defined `rank` and default `scale`\n >>> dict(target_module='to_v', rank=16)\n >>> ])\n\n Args:\n module (nn.Module): The module to set LoRA.\n config (dict): The config dict.\n verbose (bool): Whether to print log. Defaults to True.\n \"\"\"\n default_rank = config.get('rank', 4)\n default_scale = config.get('scale', 1)\n target_modules = config['target_modules']\n if not isinstance(target_modules, list):\n target_modules = [target_modules]\n\n keys = [k for k, _ in module.named_modules()]\n\n for k in keys:\n for target_module in target_modules:\n if isinstance(target_module, str):\n module_name = target_module\n rank = default_rank\n scale = default_scale\n # pretrained_path = None\n\n elif isinstance(target_module, dict):\n module_name = target_module['target_module']\n rank = target_module.get('rank', default_rank)\n scale = target_module.get('scale', default_scale)\n # pretrained_path = target_module.get('pretrained_path', None)\n\n else:\n raise TypeError('Only support dict or string type for '\n 'target_modules')\n # match keys\n if re.fullmatch(module_name, k):\n if verbose:\n print_log(\n f'Set LoRA for \\'{k}\\' with '\n f'regularization expression match \\'{module_name}\\'.',\n 'current')\n elif k.endswith(module_name):\n if verbose:\n print_log(\n f'Set LoRA for \\'{k}\\' with '\n f'suffix match \\'{module_name}\\'.', 'current')\n else:\n continue\n\n parent, target, target_name = get_submodule(module, k)\n new_module = LoRAWrapper.wrap_lora(target, rank=rank, scale=scale)\n replace_module(parent, target_name, new_module)\n return module\n\n\ndef set_only_lora_trainable(module: nn.Module) -> nn.Module:\n \"\"\"Set only LoRA modules trainable.\"\"\"\n for n, m in module.named_children():\n if isinstance(m, LoRAWrapper):\n m.lora_mapping.requires_grad_(True)\n elif isinstance(m, nn.Module):\n m.requires_grad_(False)\n set_only_lora_trainable(m)\n\n return module\n\n\ndef set_lora_enable(module: nn.Module) -> nn.Module:\n \"\"\"Enable LoRA modules.\"\"\"\n for n, m in module.named_children():\n if isinstance(m, LoRAWrapper):\n m.set_enable()\n elif isinstance(m, nn.Module):\n set_lora_enable(m)\n return module\n\n\ndef set_lora_disable(module: nn.Module) -> nn.Module:\n \"\"\"Disable LoRA modules.\"\"\"\n for n, m in module.named_children():\n if isinstance(m, LoRAWrapper):\n m.set_disable()\n elif isinstance(m, nn.Module):\n set_lora_disable(m)\n return module\n" }, { "path": "mmagic/models/archs/multi_layer_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine import MMLogger\nfrom mmengine.runner import load_checkpoint\nfrom torch import Tensor\n\nfrom mmagic.models.archs import LinearModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass MultiLayerDiscriminator(nn.Module):\n \"\"\"Multilayer Discriminator.\n\n This is a commonly used structure with stacked multiply convolution layers.\n\n Args:\n in_channels (int): Input channel of the first input convolution.\n max_channels (int): The maximum channel number in this structure.\n num_conv (int): Number of stacked intermediate convs (including input\n conv but excluding output conv). Default to 5.\n fc_in_channels (int | None): Input dimension of the fully connected\n layer. If `fc_in_channels` is None, the fully connected layer will\n be removed. Default to None.\n fc_out_channels (int): Output dimension of the fully connected layer.\n Default to 1024.\n kernel_size (int): Kernel size of the conv modules. Default to 5.\n conv_cfg (dict): Config dict to build conv layer.\n norm_cfg (dict): Config dict to build norm layer.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n out_act_cfg (dict): Config dict for output activation, \"relu\" by\n default.\n with_input_norm (bool): Whether add normalization after the input conv.\n Default to True.\n with_out_convs (bool): Whether add output convs to the discriminator.\n The output convs contain two convs. The first out conv has the same\n setting as the intermediate convs but a stride of 1 instead of 2.\n The second out conv is a conv similar to the first out conv but\n reduces the number of channels to 1 and has no activation layer.\n Default to False.\n with_spectral_norm (bool): Whether use spectral norm after the conv\n layers. Default to False.\n kwargs (keyword arguments).\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n max_channels: int,\n num_convs: int = 5,\n fc_in_channels: Optional[int] = None,\n fc_out_channels: int = 1024,\n kernel_size: int = 5,\n conv_cfg: Optional[dict] = None,\n norm_cfg: Optional[dict] = None,\n act_cfg: Optional[dict] = dict(type='ReLU'),\n out_act_cfg: Optional[dict] = dict(type='ReLU'),\n with_input_norm: bool = True,\n with_out_convs: bool = False,\n with_spectral_norm: bool = False,\n **kwargs):\n super().__init__()\n if fc_in_channels is not None:\n assert fc_in_channels > 0\n\n self.max_channels = max_channels\n self.with_fc = fc_in_channels is not None\n self.num_convs = num_convs\n self.with_out_act = out_act_cfg is not None\n self.with_out_convs = with_out_convs\n\n cur_channels = in_channels\n for i in range(num_convs):\n out_ch = min(64 * 2**i, max_channels)\n norm_cfg_ = norm_cfg\n act_cfg_ = act_cfg\n if i == 0 and not with_input_norm:\n norm_cfg_ = None\n elif (i == num_convs - 1 and not self.with_fc\n and not self.with_out_convs):\n norm_cfg_ = None\n act_cfg_ = out_act_cfg\n self.add_module(\n f'conv{i + 1}',\n ConvModule(\n cur_channels,\n out_ch,\n kernel_size=kernel_size,\n stride=2,\n padding=kernel_size // 2,\n norm_cfg=norm_cfg_,\n act_cfg=act_cfg_,\n with_spectral_norm=with_spectral_norm,\n **kwargs))\n cur_channels = out_ch\n\n if self.with_out_convs:\n cur_channels = min(64 * 2**(num_convs - 1), max_channels)\n out_ch = min(64 * 2**num_convs, max_channels)\n self.add_module(\n f'conv{num_convs + 1}',\n ConvModule(\n cur_channels,\n out_ch,\n kernel_size,\n stride=1,\n padding=kernel_size // 2,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n **kwargs))\n self.add_module(\n f'conv{num_convs + 2}',\n ConvModule(\n out_ch,\n 1,\n kernel_size,\n stride=1,\n padding=kernel_size // 2,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n **kwargs))\n\n if self.with_fc:\n self.fc = LinearModule(\n fc_in_channels,\n fc_out_channels,\n bias=True,\n act_cfg=out_act_cfg,\n with_spectral_norm=with_spectral_norm)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w') or (n, c).\n \"\"\"\n input_size = x.size()\n # out_convs has two additional ConvModules\n num_convs = self.num_convs + 2 * self.with_out_convs\n for i in range(num_convs):\n x = getattr(self, f'conv{i + 1}')(x)\n\n if self.with_fc:\n x = x.view(input_size[0], -1)\n x = self.fc(x)\n\n return x\n\n def init_weights(self, pretrained: Optional[str] = None) -> None:\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n # Here, we only initialize the module with fc layer since the\n # conv and norm layers has been initialized in `ConvModule`.\n if isinstance(m, nn.Linear):\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n nn.init.constant_(m.bias.data, 0.0)\n else:\n raise TypeError('pretrained must be a str or None')\n" }, { "path": "mmagic/models/archs/patch_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule, build_conv_layer\nfrom mmengine.model import BaseModule\nfrom torch import Tensor\n\nfrom mmagic.models.utils import generation_init_weights\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass PatchDiscriminator(BaseModule):\n \"\"\"A PatchGAN discriminator.\n\n Args:\n in_channels (int): Number of channels in input images.\n base_channels (int): Number of channels at the first conv layer.\n Default: 64.\n num_conv (int): Number of stacked intermediate convs (excluding input\n and output conv). Default: 3.\n norm_cfg (dict): Config dict to build norm layer. Default:\n `dict(type='BN')`.\n init_cfg (dict): Config dict for initialization.\n `type`: The name of our initialization method. Default: 'normal'.\n `gain`: Scaling factor for normal, xavier and orthogonal.\n Default: 0.02.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n base_channels: int = 64,\n num_conv: int = 3,\n norm_cfg: dict = dict(type='BN'),\n init_cfg: Optional[dict] = dict(type='normal', gain=0.02)):\n super().__init__(init_cfg=init_cfg)\n assert isinstance(norm_cfg, dict), (\"'norm_cfg' should be dict, but\"\n f'got {type(norm_cfg)}')\n assert 'type' in norm_cfg, \"'norm_cfg' must have key 'type'\"\n # We use norm layers in the patch discriminator.\n # Only for IN, use bias since it does not have affine parameters.\n use_bias = norm_cfg['type'] == 'IN'\n\n kernel_size = 4\n padding = 1\n\n # input layer\n sequence = [\n ConvModule(\n in_channels=in_channels,\n out_channels=base_channels,\n kernel_size=kernel_size,\n stride=2,\n padding=padding,\n bias=True,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n ]\n\n # stacked intermediate layers,\n # gradually increasing the number of filters\n multiple_now = 1\n multiple_prev = 1\n for n in range(1, num_conv):\n multiple_prev = multiple_now\n multiple_now = min(2**n, 8)\n sequence += [\n ConvModule(\n in_channels=base_channels * multiple_prev,\n out_channels=base_channels * multiple_now,\n kernel_size=kernel_size,\n stride=2,\n padding=padding,\n bias=use_bias,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n ]\n multiple_prev = multiple_now\n multiple_now = min(2**num_conv, 8)\n sequence += [\n ConvModule(\n in_channels=base_channels * multiple_prev,\n out_channels=base_channels * multiple_now,\n kernel_size=kernel_size,\n stride=1,\n padding=padding,\n bias=use_bias,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n ]\n\n # output one-channel prediction map\n sequence += [\n build_conv_layer(\n dict(type='Conv2d'),\n base_channels * multiple_now,\n 1,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n ]\n\n self.model = nn.Sequential(*sequence)\n self.init_type = 'normal' if init_cfg is None else init_cfg.get(\n 'type', 'normal')\n self.init_gain = 0.02 if init_cfg is None else init_cfg.get(\n 'gain', 0.02)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.model(x)\n\n def init_weights(self) -> None:\n \"\"\"Initialize weights for the model.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Default: None.\n \"\"\"\n if self.init_cfg is None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n generation_init_weights(\n self, init_type=self.init_type, init_gain=self.init_gain)\n self._is_init = True\n" }, { "path": "mmagic/models/archs/resnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Sequence\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import build_activation_layer, build_conv_layer, build_norm_layer\nfrom mmengine import MMLogger\nfrom mmengine.model.weight_init import constant_init, kaiming_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\nfrom torch import Tensor\n\n\nclass BasicBlock(nn.Module):\n \"\"\"Basic block for ResNet.\n\n Args:\n inplanes (int): Number of input channels.\n planes (int): Number of output channels.\n stride (int): Stride of the first block of one stage. Default: 1.\n dilation (int): Dilation of one stage. Default: 1.\n downsample (nn.Module): Downsample module. Default: None.\n act_cfg (dict): Dictionary to construct and config activation layer.\n Default: dict(type='ReLU').\n conv_cfg (dict): Dictionary to construct and config convolution layer.\n Default: None.\n norm_cfg (dict): Dictionary to construct and config norm layer.\n Default: dict(type='BN').\n with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n memory while slowing down the training speed. Default: False.\n \"\"\"\n\n expansion = 1\n\n def __init__(self,\n inplanes: int,\n planes: int,\n stride: int = 1,\n dilation: int = 1,\n downsample: Optional[nn.Module] = None,\n act_cfg: dict = dict(type='ReLU'),\n conv_cfg: Optional[dict] = None,\n norm_cfg: dict = dict(type='BN'),\n with_cp: bool = False):\n super(BasicBlock, self).__init__()\n\n self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)\n self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)\n\n self.conv1 = build_conv_layer(\n conv_cfg,\n inplanes,\n planes,\n 3,\n stride=stride,\n padding=dilation,\n dilation=dilation,\n bias=False)\n self.add_module(self.norm1_name, norm1)\n self.conv2 = build_conv_layer(\n conv_cfg, planes, planes, 3, padding=1, bias=False)\n self.add_module(self.norm2_name, norm2)\n\n self.activate = build_activation_layer(act_cfg)\n self.downsample = downsample\n self.stride = stride\n self.dilation = dilation\n self.with_cp = with_cp\n\n @property\n def norm1(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the first convolution layer\"\"\"\n return getattr(self, self.norm1_name)\n\n @property\n def norm2(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the second convolution layer\"\"\"\n return getattr(self, self.norm2_name)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\"\"\"\n\n def _inner_forward(x: Tensor) -> Tensor:\n identity = x\n\n out = self.conv1(x)\n out = self.norm1(out)\n out = self.activate(out)\n\n out = self.conv2(out)\n out = self.norm2(out)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n\n return out\n\n if self.with_cp and x.requires_grad:\n out = cp.checkpoint(_inner_forward, x)\n else:\n out = _inner_forward(x)\n\n out = self.activate(out)\n\n return out\n\n\nclass Bottleneck(nn.Module):\n \"\"\"Bottleneck block for ResNet.\n\n Args:\n inplanes (int): Number of input channels.\n planes (int): Number of output channels.\n stride (int): Stride of the first block of one stage. Default: 1.\n dilation (int): Dilation of one stage. Default: 1.\n downsample (nn.Module): Downsample module. Default: None.\n act_cfg (dict): Dictionary to construct and config activation layer.\n Default: dict(type='ReLU').\n conv_cfg (dict): Dictionary to construct and config convolution layer.\n Default: None.\n norm_cfg (dict): Dictionary to construct and config norm layer.\n Default: dict(type='BN').\n with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n memory while slowing down the training speed. Default: False.\n \"\"\"\n\n expansion = 4\n\n def __init__(self,\n inplanes: int,\n planes: int,\n stride: int = 1,\n dilation: int = 1,\n downsample: Optional[nn.Module] = None,\n act_cfg: dict = dict(type='ReLU'),\n conv_cfg: Optional[dict] = None,\n norm_cfg: dict = dict(type='BN'),\n with_cp: bool = False):\n super(Bottleneck, self).__init__()\n\n self.inplanes = inplanes\n self.planes = planes\n self.stride = stride\n self.dilation = dilation\n self.act_cfg = act_cfg\n self.conv_cfg = conv_cfg\n self.norm_cfg = norm_cfg\n self.conv1_stride = 1\n self.conv2_stride = stride\n self.with_cp = with_cp\n\n self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)\n self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)\n self.norm3_name, norm3 = build_norm_layer(\n norm_cfg, planes * self.expansion, postfix=3)\n\n self.conv1 = build_conv_layer(\n conv_cfg,\n inplanes,\n planes,\n kernel_size=1,\n stride=self.conv1_stride,\n bias=False)\n self.add_module(self.norm1_name, norm1)\n\n self.conv2 = build_conv_layer(\n conv_cfg,\n planes,\n planes,\n kernel_size=3,\n stride=self.conv2_stride,\n padding=dilation,\n dilation=dilation,\n bias=False)\n\n self.add_module(self.norm2_name, norm2)\n self.conv3 = build_conv_layer(\n conv_cfg,\n planes,\n planes * self.expansion,\n kernel_size=1,\n bias=False)\n self.add_module(self.norm3_name, norm3)\n\n self.activate = build_activation_layer(act_cfg)\n self.downsample = downsample\n\n @property\n def norm1(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the first convolution layer\"\"\"\n return getattr(self, self.norm1_name)\n\n @property\n def norm2(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the second convolution layer\"\"\"\n return getattr(self, self.norm2_name)\n\n @property\n def norm3(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the second convolution layer\"\"\"\n return getattr(self, self.norm3_name)\n\n def forward(self, x: Tensor) -> Tensor:\n identity = x\n\n out = self.conv1(x)\n out = self.norm1(out)\n out = self.activate(out)\n\n out = self.conv2(out)\n out = self.norm2(out)\n out = self.activate(out)\n\n out = self.conv3(out)\n out = self.norm3(out)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.activate(out)\n\n return out\n\n\nclass ResNet(nn.Module):\n \"\"\"General ResNet.\n\n This class is adopted from\n https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/backbones/resnet.py.\n\n Args:\n depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.\n in_channels (int): Number of input image channels. Default\" 3.\n stem_channels (int): Number of stem channels. Default: 64.\n base_channels (int): Number of base channels of res layer. Default: 64.\n num_stages (int): Resnet stages, normally 4.\n strides (Sequence[int]): Strides of the first block of each stage.\n Default: (1, 2, 2, 2).\n dilations (Sequence[int]): Dilation of each stage.\n Default: (1, 1, 2, 4).\n deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n Default: False.\n avg_down (bool): Use AvgPool instead of stride conv when\n downsampling in the bottleneck. Default: False.\n frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n -1 means not freezing any parameters. Default: -1.\n act_cfg (dict): Dictionary to construct and config activation layer.\n Default: dict(type='ReLU').\n conv_cfg (dict): Dictionary to construct and config convolution layer.\n Default: None.\n norm_cfg (dict): Dictionary to construct and config norm layer.\n Default: dict(type='BN').\n with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n memory while slowing down the training speed. Default: False.\n multi_grid (Sequence[int]|None): Multi grid dilation rates of last\n stage. Default: None.\n contract_dilation (bool): Whether contract first dilation of each layer\n Default: False.\n zero_init_residual (bool): Whether to use zero init for last norm layer\n in resblocks to let them behave as identity. Default: True.\n \"\"\"\n\n arch_settings = {\n 18: (BasicBlock, (2, 2, 2, 2)),\n 34: (BasicBlock, (3, 4, 6, 3)),\n 50: (Bottleneck, (3, 4, 6, 3)),\n 101: (Bottleneck, (3, 4, 23, 3)),\n 152: (Bottleneck, (3, 8, 36, 3))\n }\n\n def __init__(self,\n depth: int,\n in_channels: int = 3,\n stem_channels: int = 64,\n base_channels: int = 64,\n num_stages: int = 4,\n strides: Sequence[int] = (1, 2, 2, 2),\n dilations: Sequence[int] = (1, 1, 2, 4),\n deep_stem: bool = False,\n avg_down: bool = False,\n frozen_stages: int = -1,\n act_cfg: dict = dict(type='ReLU'),\n conv_cfg: Optional[dict] = None,\n norm_cfg: dict = dict(type='BN'),\n with_cp: bool = False,\n multi_grid: Optional[Sequence[int]] = None,\n contract_dilation: bool = False,\n zero_init_residual: bool = True):\n super(ResNet, self).__init__()\n from functools import partial\n\n if depth not in self.arch_settings:\n raise KeyError(f'invalid depth {depth} for resnet')\n self.block, stage_blocks = self.arch_settings[depth]\n self.depth = depth\n self.inplanes = stem_channels\n self.stem_channels = stem_channels\n self.base_channels = base_channels\n self.num_stages = num_stages\n assert num_stages >= 1 and num_stages <= 4\n\n self.strides = strides\n self.dilations = dilations\n assert len(strides) == len(dilations) == num_stages\n\n self.deep_stem = deep_stem\n self.avg_down = avg_down\n self.frozen_stages = frozen_stages\n\n self.conv_cfg = conv_cfg\n self.act_cfg = act_cfg\n self.norm_cfg = norm_cfg\n\n self.with_cp = with_cp\n self.multi_grid = multi_grid\n self.contract_dilation = contract_dilation\n self.zero_init_residual = zero_init_residual\n\n self._make_stem_layer(in_channels, stem_channels)\n\n self.layer1 = self._make_layer(\n self.block, 64, stage_blocks[0], stride=strides[0])\n self.layer2 = self._make_layer(\n self.block, 128, stage_blocks[1], stride=strides[1])\n self.layer3 = self._make_layer(\n self.block, 256, stage_blocks[2], stride=strides[2])\n self.layer4 = self._make_layer(\n self.block, 512, stage_blocks[3], stride=strides[3])\n\n self.layer1.apply(partial(self._nostride_dilate, dilate=dilations[0]))\n self.layer2.apply(partial(self._nostride_dilate, dilate=dilations[1]))\n self.layer3.apply(partial(self._nostride_dilate, dilate=dilations[2]))\n self.layer4.apply(partial(self._nostride_dilate, dilate=dilations[3]))\n\n self._freeze_stages()\n\n def _make_stem_layer(self, in_channels: int, stem_channels: int) -> None:\n \"\"\"Make stem layer for ResNet.\"\"\"\n if self.deep_stem:\n self.stem = nn.Sequential(\n build_conv_layer(\n self.conv_cfg,\n in_channels,\n stem_channels // 2,\n kernel_size=3,\n stride=2,\n padding=1,\n bias=False),\n build_norm_layer(self.norm_cfg, stem_channels // 2)[1],\n build_activation_layer(self.act_cfg),\n build_conv_layer(\n self.conv_cfg,\n stem_channels // 2,\n stem_channels // 2,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=False),\n build_norm_layer(self.norm_cfg, stem_channels // 2)[1],\n build_activation_layer(self.act_cfg),\n build_conv_layer(\n self.conv_cfg,\n stem_channels // 2,\n stem_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=False),\n build_norm_layer(self.norm_cfg, stem_channels)[1],\n build_activation_layer(self.act_cfg))\n else:\n self.conv1 = build_conv_layer(\n self.conv_cfg,\n in_channels,\n stem_channels,\n kernel_size=7,\n stride=2,\n padding=3,\n bias=False)\n self.norm1_name, norm1 = build_norm_layer(\n self.norm_cfg, stem_channels, postfix=1)\n self.add_module(self.norm1_name, norm1)\n self.activate = build_activation_layer(self.act_cfg)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n @property\n def norm1(self) -> nn.Module:\n \"\"\"nn.Module: normalization layer after the second convolution layer\"\"\"\n return getattr(self, self.norm1_name)\n\n def _make_layer(self,\n block: BasicBlock,\n planes: int,\n blocks: int,\n stride: int = 1,\n dilation: int = 1) -> nn.Module:\n downsample = None\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = nn.Sequential(\n build_conv_layer(\n self.conv_cfg,\n self.inplanes,\n planes * block.expansion,\n stride=stride,\n kernel_size=1,\n dilation=dilation,\n bias=False),\n build_norm_layer(self.norm_cfg, planes * block.expansion)[1])\n\n layers = []\n layers.append(\n block(\n self.inplanes,\n planes,\n stride,\n downsample=downsample,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg,\n conv_cfg=self.conv_cfg))\n self.inplanes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(\n block(\n self.inplanes,\n planes,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg,\n conv_cfg=self.conv_cfg))\n\n return nn.Sequential(*layers)\n\n def _nostride_dilate(self, m: nn.Module, dilate: int) -> None:\n classname = m.__class__.__name__\n if classname.find('Conv') != -1 and dilate > 1:\n # the convolution with stride\n\n if m.stride == (2, 2):\n m.stride = (1, 1)\n if m.kernel_size == (3, 3):\n m.dilation = (dilate // 2, dilate // 2)\n m.padding = (dilate // 2, dilate // 2)\n # other convolutions\n else:\n if m.kernel_size == (3, 3):\n m.dilation = (dilate, dilate)\n m.padding = (dilate, dilate)\n\n def init_weights(self, pretrained: Optional[str] = None) -> None:\n \"\"\"Init weights for the model.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n kaiming_init(m)\n elif isinstance(m, (_BatchNorm, nn.GroupNorm)):\n constant_init(m, 1)\n if self.zero_init_residual:\n\n for m in self.modules():\n if isinstance(m, Bottleneck):\n constant_init(m.norm3, 0)\n elif isinstance(m, BasicBlock):\n constant_init(m.norm2, 0)\n else:\n raise TypeError('pretrained must be a str or None')\n\n def _freeze_stages(self) -> None:\n \"\"\"Freeze stages param and norm stats.\"\"\"\n if self.frozen_stages >= 0:\n if self.deep_stem:\n self.stem.eval()\n for param in self.stem.parameters():\n param.requires_grad = False\n else:\n self.norm1.eval()\n for m in [self.conv1, self.norm1]:\n for param in m.parameters():\n param.requires_grad = False\n\n for i in range(1, self.frozen_stages + 1):\n m = getattr(self, f'layer{i}')\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def forward(self, x: Tensor) -> List[Tensor]:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n conv_out = [x]\n if self.deep_stem:\n x = self.stem(x)\n else:\n x = self.conv1(x)\n x = self.norm1(x)\n x = self.activate(x)\n conv_out.append(x)\n x = self.maxpool(x)\n x = self.layer1(x)\n conv_out.append(x)\n x = self.layer2(x)\n conv_out.append(x)\n x = self.layer3(x)\n conv_out.append(x)\n x = self.layer4(x)\n conv_out.append(x)\n return conv_out\n" }, { "path": "mmagic/models/archs/separable_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Tuple, Union\n\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom torch import Tensor\n\n\nclass DepthwiseSeparableConvModule(nn.Module):\n \"\"\"Depthwise separable convolution module.\n\n See https://arxiv.org/pdf/1704.04861.pdf for details.\n\n This module can replace a ConvModule with the conv block replaced by two\n conv block: depthwise conv block and pointwise conv block. The depthwise\n conv block contains depthwise-conv/norm/activation layers. The pointwise\n conv block contains pointwise-conv/norm/activation layers. It should be\n noted that there will be norm/activation layer in the depthwise conv block\n if ``norm_cfg`` and ``act_cfg`` are specified.\n\n Args:\n in_channels (int): Same as nn.Conv2d.\n out_channels (int): Same as nn.Conv2d.\n kernel_size (int or tuple[int]): Same as nn.Conv2d.\n stride (int or tuple[int]): Same as nn.Conv2d. Default: 1.\n padding (int or tuple[int]): Same as nn.Conv2d. Default: 0.\n dilation (int or tuple[int]): Same as nn.Conv2d. Default: 1.\n norm_cfg (dict): Default norm config for both depthwise ConvModule and\n pointwise ConvModule. Default: None.\n act_cfg (dict): Default activation config for both depthwise ConvModule\n and pointwise ConvModule. Default: dict(type='ReLU').\n dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is\n 'default', it will be the same as ``norm_cfg``. Default: 'default'.\n dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is\n 'default', it will be the same as ``act_cfg``. Default: 'default'.\n pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is\n 'default', it will be the same as `norm_cfg`. Default: 'default'.\n pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is\n 'default', it will be the same as ``act_cfg``. Default: 'default'.\n kwargs (optional): Other shared arguments for depthwise and pointwise\n ConvModule. See ConvModule for ref.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n out_channels: int,\n kernel_size: Union[int, Tuple[int, int]],\n stride: Union[int, Tuple[int, int]] = 1,\n padding: Union[int, Tuple[int, int]] = 0,\n dilation: Union[int, Tuple[int, int]] = 1,\n norm_cfg: Optional[dict] = None,\n act_cfg: Optional[dict] = dict(type='ReLU'),\n dw_norm_cfg: Union[dict, str] = 'default',\n dw_act_cfg: Union[dict, str] = 'default',\n pw_norm_cfg: Union[dict, str] = 'default',\n pw_act_cfg: Union[dict, str] = 'default',\n **kwargs):\n super().__init__()\n assert 'groups' not in kwargs, 'groups should not be specified'\n\n # if norm/activation config of depthwise/pointwise ConvModule is not\n # specified, use default config.\n dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg\n dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg\n pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg\n pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg\n\n # depthwise convolution\n self.depthwise_conv = ConvModule(\n in_channels,\n in_channels,\n kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=in_channels,\n norm_cfg=dw_norm_cfg,\n act_cfg=dw_act_cfg,\n **kwargs)\n\n self.pointwise_conv = ConvModule(\n in_channels,\n out_channels,\n 1,\n norm_cfg=pw_norm_cfg,\n act_cfg=pw_act_cfg,\n **kwargs)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n x = self.depthwise_conv(x)\n x = self.pointwise_conv(x)\n return x\n" }, { "path": "mmagic/models/archs/simple_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nfrom mmengine.model import BaseModule\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SimpleEncoderDecoder(BaseModule):\n \"\"\"Simple encoder-decoder model from matting.\n\n Args:\n encoder (dict): Config of the encoder.\n decoder (dict): Config of the decoder.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n encoder: dict,\n decoder: dict,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg)\n\n self.encoder = MODELS.build(encoder)\n if hasattr(self.encoder, 'out_channels'):\n decoder['in_channels'] = self.encoder.out_channels\n self.decoder = MODELS.build(decoder)\n\n def forward(self, *args, **kwargs) -> Tensor:\n \"\"\"Forward function.\n\n Returns:\n Tensor: The output tensor of the decoder.\n \"\"\"\n out = self.encoder(*args, **kwargs)\n out = self.decoder(out)\n return out\n" }, { "path": "mmagic/models/archs/smpatch_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\nfrom torch import Tensor\n\nfrom mmagic.models.utils import generation_init_weights\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SoftMaskPatchDiscriminator(BaseModule):\n \"\"\"A Soft Mask-Guided PatchGAN discriminator.\n\n Args:\n in_channels (int): Number of channels in input images.\n base_channels (int, optional): Number of channels at the\n first conv layer. Default: 64.\n num_conv (int, optional): Number of stacked intermediate convs\n (excluding input and output conv). Default: 3.\n norm_cfg (dict, optional): Config dict to build norm layer.\n Default: None.\n init_cfg (dict, optional): Config dict for initialization.\n `type`: The name of our initialization method. Default: 'normal'.\n `gain`: Scaling factor for normal, xavier and orthogonal.\n Default: 0.02.\n with_spectral_norm (bool, optional): Whether use spectral norm\n after the conv layers. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n base_channels: Optional[int] = 64,\n num_conv: Optional[int] = 3,\n norm_cfg: Optional[dict] = None,\n init_cfg: Optional[dict] = dict(type='normal', gain=0.02),\n with_spectral_norm: Optional[bool] = False):\n super().__init__()\n\n kernel_size = 4\n padding = 1\n\n # input layer\n sequence = [\n ConvModule(\n in_channels=in_channels,\n out_channels=base_channels,\n kernel_size=kernel_size,\n stride=2,\n padding=padding,\n bias=False,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=with_spectral_norm)\n ]\n\n # stacked intermediate layers,\n # gradually increasing the number of filters\n multiplier_in = 1\n multiplier_out = 1\n for n in range(1, num_conv):\n multiplier_in = multiplier_out\n multiplier_out = min(2**n, 8)\n sequence += [\n ConvModule(\n in_channels=base_channels * multiplier_in,\n out_channels=base_channels * multiplier_out,\n kernel_size=kernel_size,\n stride=2,\n padding=padding,\n bias=False,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=with_spectral_norm)\n ]\n multiplier_in = multiplier_out\n multiplier_out = min(2**num_conv, 8)\n sequence += [\n ConvModule(\n in_channels=base_channels * multiplier_in,\n out_channels=base_channels * multiplier_out,\n kernel_size=kernel_size,\n stride=1,\n padding=padding,\n bias=False,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=with_spectral_norm)\n ]\n\n # output one-channel prediction map\n sequence += [\n nn.Conv2d(\n base_channels * multiplier_out,\n 1,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n ]\n\n self.model = nn.Sequential(*sequence)\n self.init_type = 'normal' if init_cfg is None else init_cfg.get(\n 'type', 'normal')\n self.init_gain = 0.02 if init_cfg is None else init_cfg.get(\n 'gain', 0.02)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.model(x)\n\n def init_weights(self) -> None:\n \"\"\"Initialize weights for the model.\"\"\"\n\n generation_init_weights(\n self, init_type=self.init_type, init_gain=self.init_gain)\n\n self._is_init = True\n" }, { "path": "mmagic/models/archs/sr_backbone.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom torch import Tensor\n\nfrom ..utils import default_init_weights\n\n# def default_init_weights(module, scale=1):\n# \"\"\"Initialize network weights.\n\n# Args:\n# modules (nn.Module): Modules to be initialized.\n# scale (float): Scale initialized weights, especially for residual\n# blocks. Default: 1.\n# \"\"\"\n# for m in module.modules():\n# if isinstance(m, nn.Conv2d):\n# kaiming_init(m, a=0, mode='fan_in', bias=0)\n# m.weight.data *= scale\n# elif isinstance(m, nn.Linear):\n# kaiming_init(m, a=0, mode='fan_in', bias=0)\n# m.weight.data *= scale\n# elif isinstance(m, _BatchNorm):\n# constant_init(m.weight, val=1, bias=0)\n\n# def make_layer(block, num_blocks, **kwarg):\n# \"\"\"Make layers by stacking the same blocks.\n\n# Args:\n# block (nn.module): nn.module class for basic block.\n# num_blocks (int): number of blocks.\n\n# Returns:\n# nn.Sequential: Stacked blocks in nn.Sequential.\n# \"\"\"\n# layers = []\n# for _ in range(num_blocks):\n# layers.append(block(**kwarg))\n# return nn.Sequential(*layers)\n\n\nclass ResidualBlockNoBN(nn.Module):\n \"\"\"Residual block without BN.\n\n It has a style of:\n\n ::\n\n ---Conv-ReLU-Conv-+-\n |________________|\n\n Args:\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n res_scale (float): Used to scale the residual before addition.\n Default: 1.0.\n \"\"\"\n\n def __init__(self, mid_channels: int = 64, res_scale: float = 1.0):\n super().__init__()\n self.res_scale = res_scale\n self.conv1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=True)\n self.conv2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=True)\n\n self.relu = nn.ReLU(inplace=True)\n\n # if res_scale < 1.0, use the default initialization, as in EDSR.\n # if res_scale = 1.0, use scaled kaiming_init, as in MSRResNet.\n if res_scale == 1.0:\n self.init_weights()\n\n def init_weights(self) -> None:\n \"\"\"Initialize weights for ResidualBlockNoBN.\n\n Initialization methods like `kaiming_init` are for VGG-style modules.\n For modules with residual paths, using smaller std is better for\n stability and performance. We empirically use 0.1. See more details in\n \"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks\"\n \"\"\"\n\n for m in [self.conv1, self.conv2]:\n default_init_weights(m, 0.1)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n identity = x\n out = self.conv2(self.relu(self.conv1(x)))\n return identity + out * self.res_scale\n" }, { "path": "mmagic/models/archs/tokenizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"This a wrapper for tokenizer.\"\"\"\nimport copy\nimport os\nimport random\nfrom logging import WARNING\nfrom typing import Any, List, Optional, Union\n\nfrom mmengine import print_log\n\nfrom mmagic.utils import try_import\n\n\nclass TokenizerWrapper:\n \"\"\"Tokenizer wrapper for CLIPTokenizer. Only support CLIPTokenizer\n currently. This wrapper is modified from https://github.com/huggingface/dif\n fusers/blob/e51f19aee82c8dd874b715a09dbc521d88835d68/src/diffusers/loaders.\n py#L358 # noqa.\n\n Args:\n from_pretrained (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Defaults to None.\n from_config (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Defaults to None.\n\n *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs\n will be passed to `from_pretrained` function. Otherwise, *args\n and **kwargs will be used to initialize the model by\n `self._module_cls(*args, **kwargs)`.\n \"\"\"\n\n def __init__(self,\n from_pretrained: Optional[Union[str, os.PathLike]] = None,\n from_config: Optional[Union[str, os.PathLike]] = None,\n *args,\n **kwargs):\n transformers = try_import('transformers')\n module_cls = transformers.CLIPTokenizer\n\n assert not (from_pretrained and from_config), (\n '\\'from_pretrained\\' and \\'from_config\\' should not be passed '\n 'at the same time.')\n\n if from_config:\n print_log(\n 'Tokenizers from Huggingface transformers do not support '\n '\\'from_config\\'. Will call \\'from_pretrained\\' instead '\n 'with the same argument.', 'current', WARNING)\n from_pretrained = from_config\n\n if from_pretrained:\n self.wrapped = module_cls.from_pretrained(from_pretrained, *args,\n **kwargs)\n else:\n self.wrapper = module_cls(*args, **kwargs)\n\n self._from_pretrained = from_pretrained\n self.token_map = {}\n\n def __getattr__(self, name: str) -> Any:\n if name == 'wrapped':\n return super().__getattr__('wrapped')\n\n try:\n return getattr(self.wrapped, name)\n except AttributeError:\n try:\n return super().__getattr__(name)\n except AttributeError:\n raise AttributeError(\n '\\'name\\' cannot be found in both '\n f'\\'{self.__class__.__name__}\\' and '\n f'\\'{self.__class__.__name__}.tokenizer\\'.')\n\n def try_adding_tokens(self, tokens: Union[str, List[str]], *args,\n **kwargs):\n \"\"\"Attempt to add tokens to the tokenizer.\n\n Args:\n tokens (Union[str, List[str]]): The tokens to be added.\n \"\"\"\n num_added_tokens = self.wrapped.add_tokens(tokens, *args, **kwargs)\n assert num_added_tokens != 0, (\n f'The tokenizer already contains the token {tokens}. Please pass '\n 'a different `placeholder_token` that is not already in the '\n 'tokenizer.')\n\n def get_token_info(self, token: str) -> dict:\n \"\"\"Get the information of a token, including its start and end index in\n the current tokenizer.\n\n Args:\n token (str): The token to be queried.\n\n Returns:\n dict: The information of the token, including its start and end\n index in current tokenizer.\n \"\"\"\n token_ids = self.__call__(token).input_ids\n start, end = token_ids[1], token_ids[-2] + 1\n return {'name': token, 'start': start, 'end': end}\n\n def add_placeholder_token(self,\n placeholder_token: str,\n *args,\n num_vec_per_token: int = 1,\n **kwargs):\n \"\"\"Add placeholder tokens to the tokenizer.\n\n Args:\n placeholder_token (str): The placeholder token to be added.\n num_vec_per_token (int, optional): The number of vectors of\n the added placeholder token.\n *args, **kwargs: The arguments for `self.wrapped.add_tokens`.\n \"\"\"\n output = []\n if num_vec_per_token == 1:\n self.try_adding_tokens(placeholder_token, *args, **kwargs)\n output.append(placeholder_token)\n else:\n output = []\n for i in range(num_vec_per_token):\n ith_token = placeholder_token + f'_{i}'\n self.try_adding_tokens(ith_token, *args, **kwargs)\n output.append(ith_token)\n\n for token in self.token_map:\n if token in placeholder_token:\n raise ValueError(\n f'The tokenizer already has placeholder token {token} '\n f'that can get confused with {placeholder_token} '\n 'keep placeholder tokens independent')\n self.token_map[placeholder_token] = output\n\n def replace_placeholder_tokens_in_text(self,\n text: Union[str, List[str]],\n vector_shuffle: bool = False,\n prop_tokens_to_load: float = 1.0\n ) -> Union[str, List[str]]:\n \"\"\"Replace the keywords in text with placeholder tokens. This function\n will be called in `self.__call__` and `self.encode`.\n\n Args:\n text (Union[str, List[str]]): The text to be processed.\n vector_shuffle (bool, optional): Whether to shuffle the vectors.\n Defaults to False.\n prop_tokens_to_load (float, optional): The proportion of tokens to\n be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0.\n\n Returns:\n Union[str, List[str]]: The processed text.\n \"\"\"\n if isinstance(text, list):\n output = []\n for i in range(len(text)):\n output.append(\n self.replace_placeholder_tokens_in_text(\n text[i], vector_shuffle=vector_shuffle))\n return output\n\n for placeholder_token in self.token_map:\n if placeholder_token in text:\n tokens = self.token_map[placeholder_token]\n tokens = tokens[:1 + int(len(tokens) * prop_tokens_to_load)]\n if vector_shuffle:\n tokens = copy.copy(tokens)\n random.shuffle(tokens)\n text = text.replace(placeholder_token, ' '.join(tokens))\n return text\n\n def replace_text_with_placeholder_tokens(self, text: Union[str, List[str]]\n ) -> Union[str, List[str]]:\n \"\"\"Replace the placeholder tokens in text with the original keywords.\n This function will be called in `self.decode`.\n\n Args:\n text (Union[str, List[str]]): The text to be processed.\n\n Returns:\n Union[str, List[str]]: The processed text.\n \"\"\"\n if isinstance(text, list):\n output = []\n for i in range(len(text)):\n output.append(\n self.replace_text_with_placeholder_tokens(text[i]))\n return output\n\n for placeholder_token, tokens in self.token_map.items():\n merged_tokens = ' '.join(tokens)\n if merged_tokens in text:\n text = text.replace(merged_tokens, placeholder_token)\n return text\n\n def __call__(self,\n text: Union[str, List[str]],\n *args,\n vector_shuffle: bool = False,\n prop_tokens_to_load: float = 1.0,\n **kwargs):\n \"\"\"The call function of the wrapper.\n\n Args:\n text (Union[str, List[str]]): The text to be tokenized.\n vector_shuffle (bool, optional): Whether to shuffle the vectors.\n Defaults to False.\n prop_tokens_to_load (float, optional): The proportion of tokens to\n be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0\n *args, **kwargs: The arguments for `self.wrapped.__call__`.\n \"\"\"\n replaced_text = self.replace_placeholder_tokens_in_text(\n text,\n vector_shuffle=vector_shuffle,\n prop_tokens_to_load=prop_tokens_to_load)\n\n return self.wrapped.__call__(replaced_text, *args, **kwargs)\n\n def encode(self, text: Union[str, List[str]], *args, **kwargs):\n \"\"\"Encode the passed text to token index.\n\n Args:\n text (Union[str, List[str]]): The text to be encode.\n *args, **kwargs: The arguments for `self.wrapped.__call__`.\n \"\"\"\n replaced_text = self.replace_placeholder_tokens_in_text(text)\n return self.wrapped(replaced_text, *args, **kwargs)\n\n def decode(self,\n token_ids,\n return_raw: bool = False,\n *args,\n **kwargs) -> Union[str, List[str]]:\n \"\"\"Decode the token index to text.\n\n Args:\n token_ids: The token index to be decoded.\n return_raw: Whether keep the placeholder token in the text.\n Defaults to False.\n *args, **kwargs: The arguments for `self.wrapped.decode`.\n\n Returns:\n Union[str, List[str]]: The decoded text.\n \"\"\"\n text = self.wrapped.decode(token_ids, *args, **kwargs)\n if return_raw:\n return text\n replaced_text = self.replace_text_with_placeholder_tokens(text)\n return replaced_text\n\n def __repr__(self):\n \"\"\"The representation of the wrapper.\"\"\"\n s = super().__repr__()\n prefix = f'Wrapped Module Class: {self._module_cls}\\n'\n prefix += f'Wrapped Module Name: {self._module_name}\\n'\n if self._from_pretrained:\n prefix += f'From Pretrained: {self._from_pretrained}\\n'\n s = prefix + s\n return s\n" }, { "path": "mmagic/models/archs/upsample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nfrom .sr_backbone import default_init_weights\n\n\nclass PixelShufflePack(nn.Module):\n \"\"\"Pixel Shuffle upsample layer.\n\n Args:\n in_channels (int): Number of input channels.\n out_channels (int): Number of output channels.\n scale_factor (int): Upsample ratio.\n upsample_kernel (int): Kernel size of Conv layer to expand channels.\n\n Returns:\n Upsampled feature map.\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int, scale_factor: int,\n upsample_kernel: int):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.scale_factor = scale_factor\n self.upsample_kernel = upsample_kernel\n self.upsample_conv = nn.Conv2d(\n self.in_channels,\n self.out_channels * scale_factor * scale_factor,\n self.upsample_kernel,\n padding=(self.upsample_kernel - 1) // 2)\n self.init_weights()\n\n def init_weights(self) -> None:\n \"\"\"Initialize weights for PixelShufflePack.\"\"\"\n default_init_weights(self, 1)\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function for PixelShufflePack.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.upsample_conv(x)\n x = F.pixel_shuffle(x, self.scale_factor)\n return x\n" }, { "path": "mmagic/models/archs/vgg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Optional\n\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init, xavier_init\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom ..archs.aspp import ASPP\n\n\n@MODELS.register_module()\nclass VGG16(BaseModule):\n \"\"\"Customized VGG16 Encoder.\n\n A 1x1 conv is added after the original VGG16 conv layers. The indices of\n max pooling layers are returned for unpooling layers in decoders.\n\n Args:\n in_channels (int): Number of input channels.\n batch_norm (bool, optional): Whether use ``nn.BatchNorm2d``.\n Default to False.\n aspp (bool, optional): Whether use ASPP module after the last conv\n layer. Default to False.\n dilations (list[int], optional): Atrous rates of ASPP module.\n Default to None.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n batch_norm: Optional[bool] = False,\n aspp: Optional[bool] = False,\n dilations: Optional[List[int]] = None,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n self.batch_norm = batch_norm\n self.aspp = aspp\n self.dilations = dilations\n\n self.layer1 = self._make_layer(in_channels, 64, 2)\n self.layer2 = self._make_layer(64, 128, 2)\n self.layer3 = self._make_layer(128, 256, 3)\n self.layer4 = self._make_layer(256, 512, 3)\n self.layer5 = self._make_layer(512, 512, 3)\n\n self.conv6 = nn.Conv2d(512, 512, kernel_size=1)\n if self.batch_norm:\n self.bn = nn.BatchNorm2d(512)\n self.relu = nn.ReLU(inplace=True)\n\n if self.aspp:\n self.aspp = ASPP(512, dilations=self.dilations)\n self.out_channels = 256\n else:\n self.out_channels = 512\n\n def _make_layer(self, inplanes: int, planes: int,\n convs_layers: int) -> nn.Module:\n layers = []\n for _ in range(convs_layers):\n conv2d = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1)\n if self.batch_norm:\n bn = nn.BatchNorm2d(planes)\n layers += [conv2d, bn, nn.ReLU(inplace=True)]\n else:\n layers += [conv2d, nn.ReLU(inplace=True)]\n inplanes = planes\n layers += [nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)]\n return nn.Sequential(*layers)\n\n def init_weights(self) -> None:\n \"\"\"Init weights for the model.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n # Default initialization\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m)\n elif isinstance(m, nn.BatchNorm2d):\n constant_init(m, 1)\n\n def forward(self, x: Tensor) -> Dict[str, Tensor]:\n \"\"\"Forward function for ASPP module.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n dict: Dict containing output tensor and maxpooling indices.\n \"\"\"\n out, max_idx_1 = self.layer1(x)\n out, max_idx_2 = self.layer2(out)\n out, max_idx_3 = self.layer3(out)\n out, max_idx_4 = self.layer4(out)\n out, max_idx_5 = self.layer5(out)\n\n out = self.conv6(out)\n if self.batch_norm:\n out = self.bn(out)\n out = self.relu(out)\n if self.aspp:\n out = self.aspp(out)\n\n return {\n 'out': out,\n 'max_idx_1': max_idx_1,\n 'max_idx_2': max_idx_2,\n 'max_idx_3': max_idx_3,\n 'max_idx_4': max_idx_4,\n 'max_idx_5': max_idx_5\n }\n" }, { "path": "mmagic/models/archs/wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom logging import WARNING\nfrom typing import Any, List, Optional, Union\n\nimport torch\nfrom mmengine import print_log\nfrom mmengine.model import BaseModule\nfrom torch import dtype as TORCH_DTYPE\n\ndtype_mapping = {\n 'float32': torch.float32,\n 'float16': torch.float16,\n 'fp32': torch.float32,\n 'fp16': torch.float16,\n 'half': torch.float16,\n}\n\n\nclass DiffusersWrapper(BaseModule):\n \"\"\"Wrapper for models from HuggingFace Diffusers. This wrapper will be set\n a attribute called `_module_cls` by wrapping function and will be used to\n initialize the model structure.\n\n Example:\n >>> 1. Load pretrained model from HuggingFace Space.\n >>> config = dict(\n >>> type='ControlNetModel', # has been registered in `MODELS`\n >>> from_pretrained='lllyasviel/sd-controlnet-canny',\n >>> torch_dtype=torch.float16)\n >>> controlnet = MODELS.build(config)\n\n >>> 2. Initialize model with pre-defined configs.\n >>> config = dict(\n >>> type='ControlNetModel', # has been registered in `MODELS`\n >>> from_config='lllyasviel/sd-controlnet-canny',\n >>> cache_dir='~/.cache/OpenMMLab')\n >>> controlnet = MODELS.build(config)\n\n >>> 3. Initialize model with own defined arguments\n >>> config = dict(\n >>> type='ControlNetModel', # has been registered in `MODELS`\n >>> in_channels=3,\n >>> down_block_types=['DownBlock2D'],\n >>> block_out_channels=(32, ),\n >>> conditioning_embedding_out_channels=(16, ))\n >>> controlnet = MODELS.build(config)\n\n Args:\n from_pretrained (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Please refers to\n `diffusers.model.modeling_utils.ModelMixin.from_pretrained`\n for more detail. Defaults to None.\n from_config (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Please refers to\n `diffusers.configuration_utils.ConfigMixin.load_config`\n for more detail. Defaults to None.\n init_cfg (dict or List[dict], optional): Initialization config dict.\n Noted that, in `DiffuserWrapper`, if you want to load pretrained\n weight from HuggingFace space, please use `from_pretrained`\n argument instead of using `init_cfg`. Defaults to None.\n\n *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs\n will be passed to `from_pretrained` function. If `from_config`\n is passed, *args and **kwargs will be passed to `load_config`\n function. Otherwise, *args and **kwargs will be used to\n initialize the model by `self._module_cls(*args, **kwargs)`.\n \"\"\"\n\n def __init__(self,\n from_pretrained: Optional[Union[str, os.PathLike]] = None,\n from_config: Optional[Union[str, os.PathLike]] = None,\n dtype: Optional[Union[str, TORCH_DTYPE]] = None,\n init_cfg: Union[dict, List[dict], None] = None,\n *args,\n **kwargs):\n super().__init__(init_cfg)\n\n module_cls = self._module_cls\n assert not (from_pretrained and from_config), (\n '\\'from_pretrained\\' and \\'from_config\\' should not be passed '\n 'at the same time.')\n\n self._from_pretrained = from_pretrained\n self._from_config = from_config\n\n if from_pretrained is not None:\n self.model = module_cls.from_pretrained(from_pretrained, *args,\n **kwargs)\n # weight has been initialized from pretrained, therefore we\n # `self._is_init` as True manually\n self._is_init = True\n elif from_config is not None:\n _config = module_cls.load_config(from_config, *args, **kwargs)\n self.model = module_cls(**_config)\n else:\n self.model = module_cls(*args, **kwargs)\n\n if dtype is not None:\n if isinstance(dtype, str):\n assert dtype in dtype_mapping, (\n 'Only support following dtype string: '\n f'{list(dtype_mapping.keys())}, but receive {dtype}.')\n dtype = dtype_mapping[dtype]\n self.model.to(dtype)\n print_log(f'Set model dtype to \\'{dtype}\\'.', 'current')\n\n self.config = self.model.config\n\n def init_weights(self):\n \"\"\"Initialize the weights.\n\n If type is 'Pretrained' but the model has be loaded from `repo_id`, a\n warning will be raised.\n \"\"\"\n if self.init_cfg and self.init_cfg['type'] == 'Pretrained':\n if self._from_pretrained is not None:\n print_log(\n 'Has been loaded from pretrained model from '\n f'\\'{self._from_pretrained}\\'. Your behavior is '\n 'very dangerous.', 'current', WARNING)\n super().init_weights()\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"This function provide a way to access the attributes of the wrapped\n model.\n\n Args:\n name (str): The name of the attribute.\n\n Returns:\n Any: The got attribute.\n \"\"\"\n # Q: why we need end of recursion for 'model'?\n # A: In `nn.Module.__setattr__`, if value is instance of `nn.Module`,\n # it will be removed from `__dict__` and added to\n # `__dict__._modules`. Therefore, `model` cannot be found in\n # `self.__dict__`. When we call `self.model`, python cannot found\n # 'model' in `self.__dict__` and then `self.__getattr__('model')`\n # will be called. If we call `self.model` in `self.__getattr__`\n # which does not have any exit about 'model',`RecursionError`\n # will be raised.\n if name == 'model':\n return super().__getattr__('model')\n\n try:\n return getattr(self.model, name)\n except AttributeError:\n try:\n return super().__getattr__(name)\n except AttributeError:\n raise AttributeError('\\'name\\' cannot be found in both '\n f'\\'{self.__class__.__name__}\\' and '\n f'\\'{self.__class__.__name__}.model\\'.')\n\n def __repr__(self):\n \"\"\"The representation of the wrapper.\"\"\"\n s = super().__repr__()\n prefix = f'Wrapped Module Class: {self._module_cls}\\n'\n prefix += f'Wrapped Module Name: {self._module_name}\\n'\n if self._from_pretrained:\n prefix += f'From Pretrained: {self._from_pretrained}\\n'\n if self._from_config:\n prefix += f'From Config: {self._from_config}\\n'\n s = prefix + s\n return s\n\n def forward(self, *args, **kwargs) -> Any:\n \"\"\"Forward function of wrapped module.\n\n Args:\n *args, **kwargs: The arguments of the wrapped module.\n\n Returns:\n Any: The output of wrapped module's forward function.\n \"\"\"\n return self.model(*args, **kwargs)\n\n def to(\n self,\n torch_device: Optional[Union[str, torch.device]] = None,\n torch_dtype: Optional[torch.dtype] = None,\n ):\n \"\"\"Put wrapped module to device or convert it to torch_dtype. There are\n two to() function. One is nn.module.to() and the other is\n diffusers.pipeline.to(), if both args are passed,\n diffusers.pipeline.to() is called.\n\n Args:\n torch_device: The device to put to.\n torch_dtype: The type to convert to.\n\n Returns:\n self: the wrapped module itself.\n \"\"\"\n if torch_dtype is None:\n self.model.to(torch_device)\n else:\n self.model.to(torch_device, torch_dtype)\n return self\n" }, { "path": "mmagic/models/base_models/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .average_model import ExponentialMovingAverage, RampUpEMA\nfrom .base_conditional_gan import BaseConditionalGAN\nfrom .base_edit_model import BaseEditModel\nfrom .base_gan import BaseGAN\nfrom .base_mattor import BaseMattor\nfrom .base_translation_model import BaseTranslationModel\nfrom .basic_interpolator import BasicInterpolator\nfrom .one_stage import OneStageInpaintor\nfrom .two_stage import TwoStageInpaintor\n\n__all__ = [\n 'BaseEditModel',\n 'BaseGAN',\n 'BaseConditionalGAN',\n 'BaseMattor',\n 'BasicInterpolator',\n 'BaseTranslationModel',\n 'OneStageInpaintor',\n 'TwoStageInpaintor',\n 'ExponentialMovingAverage',\n 'RampUpEMA',\n]\n" }, { "path": "mmagic/models/base_models/average_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport itertools\nimport warnings\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseAveragedModel\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\n\n# NOTICE: Since mmengine do not support loading ``state_dict`` without wrap\n# ema module with ``BaseAveragedModel`` currently, we rewrite\n# ``ExponentialMovingAverage`` and add ``_load_from_state_dict`` temporarily\n\n\n@MODELS.register_module()\nclass ExponentialMovingAverage(BaseAveragedModel):\n r\"\"\"Implements the exponential moving average (EMA) of the model.\n\n All parameters are updated by the formula as below:\n\n .. math::\n\n Xema_{t+1} = (1 - momentum) * Xema_{t} + momentum * X_t\n\n Args:\n model (nn.Module): The model to be averaged.\n momentum (float): The momentum used for updating ema parameter.\n Defaults to 0.0002.\n Ema's parameter are updated with the formula\n :math:`averaged\\_param = (1-momentum) * averaged\\_param +\n momentum * source\\_param`.\n interval (int): Interval between two updates. Defaults to 1.\n device (torch.device, optional): If provided, the averaged model will\n be stored on the :attr:`device`. Defaults to None.\n update_buffers (bool): if True, it will compute running averages for\n both the parameters and the buffers of the model. Defaults to\n False.\n \"\"\" # noqa: W605\n\n def __init__(self,\n model: nn.Module,\n momentum: float = 0.0002,\n interval: int = 1,\n device: Optional[torch.device] = None,\n update_buffers: bool = False) -> None:\n super().__init__(model, interval, device, update_buffers)\n assert 0.0 < momentum < 1.0, 'momentum must be in range (0.0, 1.0)'\\\n f'but got {momentum}'\n if momentum > 0.5:\n warnings.warn(\n 'The value of momentum in EMA is usually a small number,'\n 'which is different from the conventional notion of '\n f'momentum but got {momentum}. Please make sure the '\n f'value is correct.')\n self.momentum = momentum\n\n def avg_func(self, averaged_param: Tensor, source_param: Tensor,\n steps: int) -> None:\n \"\"\"Compute the moving average of the parameters using exponential\n moving average.\n\n Args:\n averaged_param (Tensor): The averaged parameters.\n source_param (Tensor): The source parameters.\n steps (int): The number of times the parameters have been\n updated.\n \"\"\"\n averaged_param.mul_(1 - self.momentum).add_(\n source_param, alpha=self.momentum)\n\n def _load_from_state_dict(self, state_dict: dict, prefix: str,\n local_metadata: dict, strict: bool,\n missing_keys: list, unexpected_keys: list,\n error_msgs: List[str]) -> None:\n \"\"\"Overrides ``nn.Module._load_from_state_dict`` to support loading\n ``state_dict`` without wrap ema module with ``BaseAveragedModel``.\n\n In OpenMMLab 1.0, model will not wrap ema submodule with\n ``BaseAveragedModel``, and the ema weight key in `state_dict` will\n miss `module` prefix. Therefore, ``BaseAveragedModel`` need to\n automatically add the ``module`` prefix if the corresponding key in\n ``state_dict`` misses it.\n\n Args:\n state_dict (dict): A dict containing parameters and\n persistent buffers.\n prefix (str): The prefix for parameters and buffers used in this\n module\n local_metadata (dict): a dict containing the metadata for this\n module.\n strict (bool): Whether to strictly enforce that the keys in\n :attr:`state_dict` with :attr:`prefix` match the names of\n parameters and buffers in this module\n missing_keys (List[str]): if ``strict=True``, add missing keys to\n this list\n unexpected_keys (List[str]): if ``strict=True``, add unexpected\n keys to this list\n error_msgs (List[str]): error messages should be added to this\n list, and will be reported together in\n :meth:`~torch.nn.Module.load_state_dict`.\n \"\"\"\n\n for key, value in list(state_dict.items()):\n # To support load the pretrained model, which does not wrap ema\n # module with `BaseAveragedModel`, `BaseAveragedModel` will\n # automatically add `module` prefix to the `state_dict` which\n # key starts with the custom prefix. For example, the old\n # checkpoint with `state_dict` with keys:\n # ['layer.weight', 'layer.bias', 'ema.steps', 'ema.weight', 'ema.bias'] # noqa: E501\n # will be replaced with:\n # ['layer.weight', 'layer.bias', 'ema.steps', 'ema.module.weight', 'ema.module.bias'] # noqa: E501\n\n # The key added with `module` prefix needs to satisfy\n # three conditions.\n # 1. key starts with current prefix, such as `model.ema`.\n # 2. The content after the prefix does not start with the `module`\n # 3. Key does not end with steps.\n if key.startswith(prefix) and not key[len(prefix):].startswith(\n 'module') and not key.endswith('steps'):\n new_key = key[:len(prefix)] + 'module.' + key[len(prefix):]\n state_dict[new_key] = value\n state_dict.pop(key)\n state_dict.setdefault(prefix + 'steps', torch.tensor(0))\n super()._load_from_state_dict(state_dict, prefix, local_metadata,\n strict, missing_keys, unexpected_keys,\n error_msgs)\n\n def sync_buffers(self, model: nn.Module) -> None:\n \"\"\"Copy buffer from model to averaged model.\n\n Args:\n model (nn.Module): The model whose parameters will be averaged.\n \"\"\"\n # if not update buffer, copy buffer from orig model\n if self.update_buffers:\n warnings.warn(\n '`update_buffers` is set to True in this ema model, and '\n 'buffers will be updated in `update_parameters`.')\n\n avg_buffer = itertools.chain(self.module.buffers())\n orig_buffer = itertools.chain(model.buffers())\n for b_avg, b_orig in zip(avg_buffer, orig_buffer):\n b_avg.data.copy_(b_orig.data)\n\n def sync_parameters(self, model: nn.Module) -> None:\n \"\"\"Copy buffer and parameters from model to averaged model.\n\n Args:\n model (nn.Module): The model whose parameters will be averaged.\n \"\"\"\n # before ema, copy weights from orig\n avg_param = (\n itertools.chain(self.module.parameters(), self.module.buffers()))\n src_param = (itertools.chain(model.parameters(), model.buffers()))\n for p_avg, p_src in zip(avg_param, src_param):\n p_avg.data.copy_(p_src.data)\n\n\n@MODELS.register_module()\nclass RampUpEMA(BaseAveragedModel):\n r\"\"\"Implements the exponential moving average with ramping up momentum.\n\n Ref: https://github.com/NVlabs/stylegan3/blob/master/training/training_loop.py # noqa\n\n Args:\n model (nn.Module): The model to be averaged.\n interval (int): Interval between two updates. Defaults to 1.\n ema_kimg (int, optional): EMA kimgs. Defaults to 10.\n ema_rampup (float, optional): Ramp up rate. Defaults to 0.05.\n batch_size (int, optional): Global batch size. Defaults to 32.\n eps (float, optional): Ramp up epsilon. Defaults to 1e-8.\n start_iter (int, optional): EMA start iter. Defaults to 0.\n device (torch.device, optional): If provided, the averaged model will\n be stored on the :attr:`device`. Defaults to None.\n update_buffers (bool): if True, it will compute running averages for\n both the parameters and the buffers of the model. Defaults to\n False.\n \"\"\" # noqa: W605\n\n def __init__(self,\n model: nn.Module,\n interval: int = 1,\n ema_kimg: int = 10,\n ema_rampup: float = 0.05,\n batch_size: int = 32,\n eps: float = 1e-8,\n start_iter: int = 0,\n device: Optional[torch.device] = None,\n update_buffers: bool = False) -> None:\n \"\"\"_summary_\"\"\"\n super().__init__(model, interval, device, update_buffers)\n self.interval = interval\n self.ema_kimg = ema_kimg\n self.ema_rampup = ema_rampup\n self.batch_size = batch_size\n self.eps = eps\n\n @staticmethod\n def rampup(steps, ema_kimg=10, ema_rampup=0.05, batch_size=4, eps=1e-8):\n \"\"\"Ramp up ema momentum.\n\n Ref: https://github.com/NVlabs/stylegan3/blob/a5a69f58294509598714d1e88c9646c3d7c6ec94/training/training_loop.py#L300-L308 # noqa\n\n Args:\n steps:\n ema_kimg (int, optional): Half-life of the exponential moving\n average of generator weights. Defaults to 10.\n ema_rampup (float, optional): EMA ramp-up coefficient.If set to\n None, then rampup will be disabled. Defaults to 0.05.\n batch_size (int, optional): Total batch size for one training\n iteration. Defaults to 4.\n eps (float, optional): Epsiolon to avoid ``batch_size`` divided by\n zero. Defaults to 1e-8.\n\n Returns:\n dict: Updated momentum.\n \"\"\"\n cur_nimg = (steps + 1) * batch_size\n ema_nimg = ema_kimg * 1000\n if ema_rampup is not None:\n ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)\n ema_beta = 0.5**(batch_size / max(ema_nimg, eps))\n return ema_beta\n\n def avg_func(self, averaged_param: Tensor, source_param: Tensor,\n steps: int) -> None:\n \"\"\"Compute the moving average of the parameters using exponential\n moving average.\n\n Args:\n averaged_param (Tensor): The averaged parameters.\n source_param (Tensor): The source parameters.\n steps (int): The number of times the parameters have been\n updated.\n \"\"\"\n momentum = 1. - self.rampup(self.steps, self.ema_kimg, self.ema_rampup,\n self.batch_size, self.eps)\n if not (0.0 < momentum < 1.0):\n warnings.warn('RampUp momentum must be in range (0.0, 1.0)'\n f'but got {momentum}')\n averaged_param.mul_(1 - momentum).add_(source_param, alpha=momentum)\n\n def _load_from_state_dict(self, state_dict: dict, prefix: str,\n local_metadata: dict, strict: bool,\n missing_keys: list, unexpected_keys: list,\n error_msgs: List[str]) -> None:\n \"\"\"Overrides ``nn.Module._load_from_state_dict`` to support loading\n ``state_dict`` without wrap ema module with ``BaseAveragedModel``.\n\n In OpenMMLab 1.0, model will not wrap ema submodule with\n ``BaseAveragedModel``, and the ema weight key in `state_dict` will\n miss `module` prefix. Therefore, ``BaseAveragedModel`` need to\n automatically add the ``module`` prefix if the corresponding key in\n ``state_dict`` misses it.\n\n Args:\n state_dict (dict): A dict containing parameters and\n persistent buffers.\n prefix (str): The prefix for parameters and buffers used in this\n module\n local_metadata (dict): a dict containing the metadata for this\n module.\n strict (bool): Whether to strictly enforce that the keys in\n :attr:`state_dict` with :attr:`prefix` match the names of\n parameters and buffers in this module\n missing_keys (List[str]): if ``strict=True``, add missing keys to\n this list\n unexpected_keys (List[str]): if ``strict=True``, add unexpected\n keys to this list\n error_msgs (List[str]): error messages should be added to this\n list, and will be reported together in\n :meth:`~torch.nn.Module.load_state_dict`.\n \"\"\"\n\n for key, value in list(state_dict.items()):\n # To support load the pretrained model, which does not wrap ema\n # module with `BaseAveragedModel`, `BaseAveragedModel` will\n # automatically add `module` prefix to the `state_dict` which\n # key starts with the custom prefix. For example, the old\n # checkpoint with `state_dict` with keys:\n # ['layer.weight', 'layer.bias', 'ema.steps', 'ema.weight', 'ema.bias'] # noqa: E501\n # will be replaced with:\n # ['layer.weight', 'layer.bias', 'ema.steps', 'ema.module.weight', 'ema.module.bias'] # noqa: E501\n\n # The key added with `module` prefix needs to satisfy\n # three conditions.\n # 1. key starts with current prefix, such as `model.ema`.\n # 2. The content after the prefix does not start with the `module`\n # 3. Key does not end with steps.\n if key.startswith(prefix) and not key[len(prefix):].startswith(\n 'module') and not key.endswith('steps'):\n new_key = key[:len(prefix)] + 'module.' + key[len(prefix):]\n state_dict[new_key] = value\n state_dict.pop(key)\n state_dict.setdefault(prefix + 'steps', torch.tensor(0))\n super()._load_from_state_dict(state_dict, prefix, local_metadata,\n strict, missing_keys, unexpected_keys,\n error_msgs)\n\n def sync_buffers(self, model: nn.Module) -> None:\n \"\"\"Copy buffer from model to averaged model.\n\n Args:\n model (nn.Module): The model whose parameters will be averaged.\n \"\"\"\n # if not update buffer, copy buffer from orig model\n if self.update_buffers:\n warnings.warn(\n '`update_buffers` is set to True in this ema model, and '\n 'buffers will be updated in `update_parameters`.')\n\n avg_buffer = itertools.chain(self.module.buffers())\n orig_buffer = itertools.chain(model.buffers())\n for b_avg, b_orig in zip(avg_buffer, orig_buffer):\n b_avg.data.copy_(b_orig.data)\n\n def sync_parameters(self, model: nn.Module) -> None:\n \"\"\"Copy buffer and parameters from model to averaged model.\n\n Args:\n model (nn.Module): The model whose parameters will be averaged.\n \"\"\"\n # before ema, copy weights from orig\n avg_param = (\n itertools.chain(self.module.parameters(), self.module.buffers()))\n src_param = (itertools.chain(model.parameters(), model.buffers()))\n for p_avg, p_src in zip(avg_param, src_param):\n p_avg.data.copy_(p_src.data)\n" }, { "path": "mmagic/models/base_models/base_conditional_gan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, LabelVar\nfrom ..utils import get_valid_num_batches, label_sample_fn\nfrom .base_gan import BaseGAN\n\nModelType = Union[Dict, nn.Module]\n\n\nclass BaseConditionalGAN(BaseGAN):\n \"\"\"Base class for Conditional GAM models.\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): The number of times the generator is completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): The number of times the discriminator is\n completely updated before the generator is updated. Defaults to 1.\n noise_size (Optional[int]): Size of the input noise vector.\n Default to None.\n num_classes (Optional[int]): The number classes you would like to\n generate. Defaults to None.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n noise_size: Optional[int] = None,\n num_classes: Optional[int] = None,\n ema_config: Optional[Dict] = None,\n loss_config: Optional[Dict] = None):\n\n self.num_classes = self._get_valid_num_classes(num_classes, generator,\n discriminator)\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, noise_size,\n ema_config, loss_config)\n\n def label_fn(self, label: LabelVar = None, num_batches: int = 1) -> Tensor:\n \"\"\"Sampling function for label. There are three scenarios in this\n function:\n\n - If `label` is a callable function, sample `num_batches` of labels\n with passed `label`.\n - If `label` is `None`, sample `num_batches` of labels in range of\n `[0, self.num_classes-1]` uniformly.\n - If `label` is a `torch.Tensor`, check the range of the tensor is in\n `[0, self.num_classes-1]`. If all values are in valid range,\n directly return `label`.\n\n Args:\n label (Union[Tensor, Callable, List[int], None]): You can directly\n give a batch of label through a ``torch.Tensor`` or offer a\n callable function to sample a batch of label data. Otherwise,\n the ``None`` indicates to use the default label sampler.\n Defaults to `None`.\n num_batches (int, optional): The number of batches label want to\n sample. If `label` is a Tensor, this will be ignored. Defaults\n to 1.\n\n Returns:\n Tensor: Sampled label tensor.\n \"\"\"\n return label_sample_fn(\n label=label,\n num_batches=num_batches,\n num_classes=self.num_classes,\n device=self.device)\n\n def data_sample_to_label(self, data_sample: DataSample\n ) -> Optional[torch.Tensor]:\n \"\"\"Get labels from input `data_sample` and pack to `torch.Tensor`. If\n no label is found in the passed `data_sample`, `None` would be\n returned.\n\n Args:\n data_sample (DataSample): Input data samples.\n\n Returns:\n Optional[torch.Tensor]: Packed label tensor.\n \"\"\"\n # assume all data_sample have the same data fields\n if not data_sample or 'gt_label' not in data_sample.keys():\n return None\n gt_labels = data_sample.gt_label.label\n return gt_labels\n\n @staticmethod\n def _get_valid_num_classes(num_classes: Optional[int],\n generator: ModelType,\n discriminator: Optional[ModelType]) -> int:\n \"\"\"Try to get the value of `num_classes` from input, `generator` and\n `discriminator` and check the consistency of these values. If no\n conflict is found, return the `num_classes`.\n\n Args:\n num_classes (Optional[int]): `num_classes` passed to\n `BaseConditionalGAN_refactor`'s initialize function.\n generator (ModelType): The config or the model of generator.\n discriminator (Optional[ModelType]): The config or model of\n discriminator.\n\n Returns:\n int: The number of classes to be generated.\n \"\"\"\n if isinstance(generator, dict):\n num_classes_gen = generator.get('num_classes', None)\n else:\n num_classes_gen = getattr(generator, 'num_classes', None)\n\n num_classes_disc = None\n if discriminator is not None:\n if isinstance(discriminator, dict):\n num_classes_disc = discriminator.get('num_classes', None)\n else:\n num_classes_disc = getattr(discriminator, 'num_classes', None)\n\n # check consistency between gen and disc\n if num_classes_gen is not None and num_classes_disc is not None:\n assert num_classes_disc == num_classes_gen, (\n '\\'num_classes\\' is inconsistent between generator and '\n f'discriminator. Receive \\'{num_classes_gen}\\' and '\n f'\\'{num_classes_disc}\\'.')\n model_num_classes = num_classes_gen or num_classes_disc\n\n if num_classes is not None and model_num_classes is not None:\n assert num_classes == model_num_classes, (\n 'Input \\'num_classes\\' is inconsistent with '\n f'model\\'s ones. Receive \\'{num_classes}\\' and '\n f'\\'{model_num_classes}\\'.')\n\n num_classes = num_classes or model_num_classes\n return num_classes\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n \"\"\"Sample images with the given inputs. If forward mode is 'ema' or\n 'orig', the image generated by corresponding generator will be\n returned. If forward mode is 'ema/orig', images generated by original\n generator and EMA generator will both be returned in a dict.\n\n Args:\n inputs (ForwardInputs): Dict containing the necessary\n information (e.g. noise, num_batches, mode) to generate image.\n data_samples (Optional[list]): Data samples collated by\n :attr:`data_preprocessor`. Defaults to None.\n mode (Optional[str]): `mode` is not used in\n :class:`BaseConditionalGAN`. Defaults to None.\n\n Returns:\n List[DataSample]: Generated images or image dict.\n \"\"\"\n if isinstance(inputs, Tensor):\n noise = inputs\n sample_kwargs = {}\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n num_batches = noise.shape[0]\n\n labels = self.data_sample_to_label(data_samples)\n if labels is None:\n labels = self.label_fn(num_batches=num_batches)\n\n sample_model = self._get_valid_model(inputs)\n batch_sample_list = []\n if sample_model in ['ema', 'orig']:\n if sample_model == 'ema':\n generator = self.generator_ema\n else:\n generator = self.generator\n outputs = generator(noise, label=labels, return_noise=False)\n outputs = self.data_preprocessor.destruct(outputs, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.fake_img = outputs\n gen_sample.noise = noise\n gen_sample.set_gt_label(labels)\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = sample_model\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n else: # sample model in 'ema/orig'\n outputs_orig = self.generator(\n noise, label=labels, return_noise=False, **sample_kwargs)\n outputs_ema = self.generator_ema(\n noise, label=labels, return_noise=False, **sample_kwargs)\n outputs_orig = self.data_preprocessor.destruct(\n outputs_orig, data_samples)\n outputs_ema = self.data_preprocessor.destruct(\n outputs_ema, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.ema = DataSample(fake_img=outputs_ema)\n gen_sample.orig = DataSample(fake_img=outputs_orig)\n gen_sample.noise = noise\n gen_sample.set_gt_label(labels)\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = 'ema/orig'\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n return batch_sample_list\n\n def train_generator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Training function for discriminator. All GANs should implement this\n function by themselves.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = inputs['img'].shape[0]\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n fake_imgs = self.generator(\n noise=noise, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n fake_imgs=fake_imgs,\n disc_pred_fake_g=disc_pred_fake,\n # iteration=curr_iter,\n batch_size=num_batches,\n fake_label=fake_labels,\n loss_scaler=getattr(optimizer_wrapper, 'loss_scaler', None))\n parsed_loss, log_vars = self._get_gen_loss(data_dict_)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def train_discriminator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Training function for discriminator. All GANs should implement this\n function by themselves.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = inputs['img']\n real_labels = self.data_sample_to_label(data_samples)\n assert real_labels is not None, (\n 'Cannot found \\'gt_label\\' in \\'data_sample\\'.')\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(\n noise=noise_batch, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n disc_pred_real = self.discriminator(real_imgs, label=real_labels)\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs,\n # iteration=curr_iter,\n batch_size=num_batches,\n gt_label=real_labels,\n fake_label=fake_labels,\n loss_scaler=setattr(optimizer_wrapper, 'loss_scaler', None))\n loss, log_vars = self._get_disc_loss(data_dict_)\n\n optimizer_wrapper.update_params(loss)\n return log_vars\n" }, { "path": "mmagic/models/base_models/base_edit_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nfrom mmengine.model import BaseModel\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass BaseEditModel(BaseModel):\n \"\"\"Base model for image and video editing.\n\n It must contain a generator that takes frames as inputs and outputs an\n interpolated frame. It also has a pixel-wise loss for training.\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator: dict,\n pixel_loss: dict,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None,\n init_cfg: Optional[dict] = None,\n data_preprocessor: Optional[dict] = None):\n super().__init__(\n init_cfg=init_cfg, data_preprocessor=data_preprocessor)\n\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n # generator\n self.generator = MODELS.build(generator)\n\n # loss\n self.pixel_loss = MODELS.build(pixel_loss)\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs) -> Union[torch.Tensor, List[DataSample], dict]:\n \"\"\"Returns losses or predictions of training, validation, testing, and\n simple inference process.\n\n ``forward`` method of BaseModel is an abstract method, its subclasses\n must implement this method.\n\n Accepts ``inputs`` and ``data_samples`` processed by\n :attr:`data_preprocessor`, and returns results according to mode\n arguments.\n\n During non-distributed training, validation, and testing process,\n ``forward`` will be called by ``BaseModel.train_step``,\n ``BaseModel.val_step`` and ``BaseModel.val_step`` directly.\n\n During distributed data parallel training process,\n ``MMSeparateDistributedDataParallel.train_step`` will first call\n ``DistributedDataParallel.forward`` to enable automatic\n gradient synchronization, and then call ``forward`` to get training\n loss.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``. Default: 'tensor'.\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict`` or tensor for custom use.\n \"\"\"\n if isinstance(inputs, dict):\n inputs = inputs['img']\n if mode == 'tensor':\n return self.forward_tensor(inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n\n elif mode == 'loss':\n return self.forward_train(inputs, data_samples, **kwargs)\n\n def convert_to_datasample(self, predictions: DataSample,\n data_samples: DataSample,\n inputs: Optional[torch.Tensor]\n ) -> List[DataSample]:\n \"\"\"Add predictions and destructed inputs (if passed) to data samples.\n\n Args:\n predictions (DataSample): The predictions of the model.\n data_samples (DataSample): The data samples loaded from\n dataloader.\n inputs (Optional[torch.Tensor]): The input of model. Defaults to\n None.\n\n Returns:\n List[DataSample]: Modified data samples.\n \"\"\"\n\n if inputs is not None:\n destructed_input = self.data_preprocessor.destruct(\n inputs, data_samples, 'img')\n data_samples.set_tensor_data({'input': destructed_input})\n # split to list of data samples\n data_samples = data_samples.split()\n predictions = predictions.split()\n\n for data_sample, pred in zip(data_samples, predictions):\n data_sample.output = pred\n\n return data_samples\n\n def forward_tensor(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n feats = self.generator(inputs, **kwargs)\n\n return feats\n\n def forward_inference(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n **kwargs) -> DataSample:\n \"\"\"Forward inference. Returns predictions of validation, testing, and\n simple inference.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n DataSample: predictions.\n \"\"\"\n\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n feats = self.data_preprocessor.destruct(feats, data_samples)\n\n # create a stacked data sample here\n predictions = DataSample(pred_img=feats.cpu())\n\n return predictions\n\n def forward_train(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n **kwargs) -> Dict[str, torch.Tensor]:\n \"\"\"Forward training. Returns dict of losses of training.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n batch_gt_data = data_samples.gt_img\n\n loss = self.pixel_loss(feats, batch_gt_data)\n\n return dict(loss=loss)\n" }, { "path": "mmagic/models/base_models/base_gan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom abc import ABCMeta\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import Config, MessageHub\nfrom mmengine.model import BaseModel, is_model_wrapper\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, NoiseVar, SampleList\nfrom ..utils import (get_valid_noise_size, get_valid_num_batches,\n noise_sample_fn, set_requires_grad)\n\nModelType = Union[Dict, nn.Module]\n\n\nclass BaseGAN(BaseModel, metaclass=ABCMeta):\n \"\"\"Base class for GAN models.\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): The number of times the generator is completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): The number of times the discriminator is\n completely updated before the generator is updated. Defaults to 1.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n noise_size: Optional[int] = None,\n ema_config: Optional[Dict] = None,\n loss_config: Optional[Dict] = None):\n super().__init__(data_preprocessor=data_preprocessor)\n\n # get valid noise_size\n noise_size = get_valid_noise_size(noise_size, generator)\n\n # build generator\n if isinstance(generator, dict):\n self._gen_cfg = deepcopy(generator)\n # build generator with default `noise_size` and `num_classes`\n gen_args = dict()\n if noise_size:\n gen_args['noise_size'] = noise_size\n if hasattr(self, 'num_classes') and self.num_classes is not None:\n gen_args['num_classes'] = self.num_classes\n generator = MODELS.build(generator, default_args=gen_args)\n self.generator = generator\n # get noise_size from generator because generator may have default\n # `noise_size` value\n self.noise_size = getattr(self.generator, 'noise_size', noise_size)\n\n # build discriminator\n if discriminator:\n if isinstance(discriminator, dict):\n self._disc_cfg = deepcopy(discriminator)\n # build discriminator with default `num_classes`\n disc_args = dict()\n if hasattr(self, 'num_classes'):\n disc_args['num_classes'] = self.num_classes\n discriminator = MODELS.build(\n discriminator, default_args=disc_args)\n self.discriminator = discriminator\n\n self._gen_steps = generator_steps\n self._disc_steps = discriminator_steps\n\n if ema_config is None:\n self._ema_config = None\n self._with_ema_gen = False\n else:\n self._ema_config = deepcopy(ema_config)\n self._init_ema_model(self._ema_config)\n self._with_ema_gen = True\n\n self._init_loss(loss_config)\n\n @staticmethod\n def gather_log_vars(log_vars_list: List[Dict[str, Tensor]]\n ) -> Dict[str, Tensor]:\n \"\"\"Gather a list of log_vars.\n Args:\n log_vars_list: List[Dict[str, Tensor]]\n\n Returns:\n Dict[str, Tensor]\n \"\"\"\n if len(log_vars_list) == 1:\n return log_vars_list[0]\n\n log_keys = log_vars_list[0].keys()\n\n log_vars = dict()\n for k in log_keys:\n assert all([k in log_vars for log_vars in log_vars_list\n ]), (f'\\'{k}\\' not in some of the \\'log_vars\\'.')\n log_vars[k] = torch.mean(\n torch.stack([log_vars[k] for log_vars in log_vars_list],\n dim=0))\n\n return log_vars\n\n def _init_loss(self, loss_config: Optional[Dict] = None) -> None:\n \"\"\"Initialize customized loss modules.\n\n If loss_config is a dict, we allow kinds of value for each field.\n\n 1. `loss_config` is None: Users will implement all loss calculations\n in their own function. Weights for each loss terms are hard coded.\n 2. `loss_config` is dict of scalar or string: Users will implement all\n loss calculations and use passed `loss_config` to control the\n weight or behavior of the loss calculation. Users will unpack and\n use each field in this dict by themselves.\n\n loss_config = dict(gp_norm_mode='HWC', gp_loss_weight=10)\n\n 3. `loss_config` is dict of dict: Each field in `loss_config` will\n used to build a corresponding loss module. And use loss calculation\n function predefined by :class:`BaseGAN` to calculate the loss.\n\n loss_config = dict()\n\n Example:\n loss_config = dict(\n # `BaseGAN` pre-defined fields\n gan_loss=dict(type='GANLoss', gan_type='wgan-logistic-ns'),\n disc_auxiliary_loss=dict(\n type='R1GradientPenalty',\n loss_weight=10. / 2.,\n interval=2,\n norm_mode='HWC',\n data_info=dict(\n real_data='real_imgs',\n discriminator='disc')),\n gen_auxiliary_loss=dict(\n type='GeneratorPathRegularizer',\n loss_weight=2,\n pl_batch_shrink=2,\n interval=g_reg_interval,\n data_info=dict(\n generator='gen',\n num_batches='batch_size')),\n # user-defined field for loss weights or loss calculation\n my_loss_2=dict(weight=2, norm_mode='L1'),\n my_loss_3=2,\n my_loss_4_norm_type='L2')\n\n\n Args:\n loss_config (Optional[Dict], optional): Loss config used to build\n loss modules or define the loss weights. Defaults to None.\n \"\"\"\n if loss_config is None:\n self.gan_loss = None\n self.gen_auxiliary_losses = None\n self.disc_auxiliary_losses = None\n self.loss_config = dict()\n return\n\n self.loss_config = deepcopy(loss_config)\n\n # build pre-defined losses\n gan_loss = loss_config.get('gan_loss', None)\n if gan_loss is not None:\n self.gan_loss = MODELS.build(gan_loss)\n else:\n self.gan_loss = None\n\n disc_auxiliary_loss = loss_config.get('disc_auxiliary_loss', None)\n if disc_auxiliary_loss:\n if not isinstance(disc_auxiliary_loss, list):\n disc_auxiliary_loss = [disc_auxiliary_loss]\n self.disc_auxiliary_losses = nn.ModuleList(\n [MODELS.build(loss) for loss in disc_auxiliary_loss])\n else:\n self.disc_auxiliary_losses = None\n\n gen_auxiliary_loss = loss_config.get('gen_auxiliary_loss', None)\n if gen_auxiliary_loss:\n if not isinstance(gen_auxiliary_loss, list):\n gen_auxiliary_loss = [gen_auxiliary_loss]\n self.gen_auxiliary_losses = nn.ModuleList(\n [MODELS.build(loss) for loss in gen_auxiliary_loss])\n else:\n self.gen_auxiliary_losses = None\n\n def noise_fn(self, noise: NoiseVar = None, num_batches: int = 1):\n \"\"\"Sampling function for noise. There are three scenarios in this\n function:\n\n - If `noise` is a callable function, sample `num_batches` of noise\n with passed `noise`.\n - If `noise` is `None`, sample `num_batches` of noise from gaussian\n distribution.\n - If `noise` is a `torch.Tensor`, directly return `noise`.\n\n Args:\n noise (Union[Tensor, Callable, List[int], None]): You can directly\n give a batch of label through a ``torch.Tensor`` or offer a\n callable function to sample a batch of label data. Otherwise,\n the ``None`` indicates to use the default noise sampler.\n Defaults to `None`.\n num_batches (int, optional): The number of batches label want to\n sample. If `label` is a Tensor, this will be ignored. Defaults\n to 1.\n\n Returns:\n Tensor: Sampled noise tensor.\n \"\"\"\n return noise_sample_fn(\n noise=noise,\n num_batches=num_batches,\n noise_size=self.noise_size,\n device=self.device)\n\n @property\n def generator_steps(self) -> int:\n \"\"\"int: The number of times the generator is completely updated before\n the discriminator is updated.\"\"\"\n return self._gen_steps\n\n @property\n def discriminator_steps(self) -> int:\n \"\"\"int: The number of times the discriminator is completely updated\n before the generator is updated.\"\"\"\n return self._disc_steps\n\n @property\n def device(self) -> torch.device:\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n @property\n def with_ema_gen(self) -> bool:\n \"\"\"Whether the GAN adopts exponential moving average.\n\n Returns:\n bool: If `True`, means this GAN model is adopted to exponential\n moving average and vice versa.\n \"\"\"\n return self._with_ema_gen\n\n def _init_ema_model(self, ema_config: dict):\n \"\"\"Initialize a EMA model corresponding to the given `ema_config`. If\n `ema_config` is an empty dict or `None`, EMA model will not be\n initialized.\n\n Args:\n ema_config (dict): Config to initialize the EMA model.\n \"\"\"\n ema_config.setdefault('type', 'ExponentialMovingAverage')\n self.ema_start = ema_config.pop('start_iter', 0)\n src_model = self.generator.module if is_model_wrapper(\n self.generator) else self.generator\n self.generator_ema = MODELS.build(\n ema_config, default_args=dict(model=src_model))\n\n def _get_valid_model(self, batch_inputs: ForwardInputs) -> str:\n \"\"\"Try to get the valid forward model from inputs.\n\n - If forward model is defined in `batch_inputs`, it will be used as\n forward model.\n - If forward model is not defined in `batch_inputs`, 'ema' will\n returned if :property:`with_ema_gen` is true. Otherwise, 'orig' will\n be returned.\n\n Args:\n batch_inputs (ForwardInputs): Inputs passed to :meth:`forward`.\n\n Returns:\n str: Forward model to generate image. ('orig', 'ema' or\n 'ema/orig').\n \"\"\"\n if isinstance(batch_inputs, dict):\n sample_model = batch_inputs.get('sample_model', None)\n else: # batch_inputs is a Tensor\n sample_model = None\n\n # set default value\n if sample_model is None:\n if self.with_ema_gen:\n sample_model = 'ema'\n else:\n sample_model = 'orig'\n\n # security checking for mode\n assert sample_model in [\n 'ema', 'ema/orig', 'orig'\n ], ('Only support \\'ema\\', \\'ema/orig\\', \\'orig\\' '\n f'in {self.__class__.__name__}\\'s image sampling.')\n if sample_model in ['ema', 'ema/orig']:\n assert self.with_ema_gen, (\n f'\\'{self.__class__.__name__}\\' do not have EMA model.')\n return sample_model\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> SampleList:\n \"\"\"Sample images with the given inputs. If forward mode is 'ema' or\n 'orig', the image generated by corresponding generator will be\n returned. If forward mode is 'ema/orig', images generated by original\n generator and EMA generator will both be returned in a dict.\n\n Args:\n batch_inputs (ForwardInputs): Dict containing the necessary\n information (e.g. noise, num_batches, mode) to generate image.\n data_samples (Optional[list]): Data samples collated by\n :attr:`data_preprocessor`. Defaults to None.\n mode (Optional[str]): `mode` is not used in :class:`BaseGAN`.\n Defaults to None.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n if isinstance(inputs, Tensor):\n noise = inputs\n sample_kwargs = {}\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n num_batches = noise.shape[0]\n\n sample_model = self._get_valid_model(inputs)\n batch_sample_list = []\n if sample_model in ['ema', 'orig']:\n if sample_model == 'ema':\n generator = self.generator_ema\n else:\n generator = self.generator\n if sample_kwargs:\n if 'return_noise' in sample_kwargs.keys():\n outputs = generator(noise, **sample_kwargs)\n else:\n outputs = generator(\n noise, return_noise=False,\n **sample_kwargs) # yapf: disable\n else:\n sample_kwargs = {}\n outputs = generator(\n noise, return_noise=False,\n **sample_kwargs) # no need to be False all time\n if isinstance(outputs, dict):\n if 'latent' in outputs.keys():\n latent = outputs['latent']\n if 'feats' in outputs.keys():\n feats = outputs['feats']\n outputs = outputs['fake_img']\n\n outputs = self.data_preprocessor.destruct(outputs, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.fake_img = outputs\n gen_sample.noise = noise\n if 'latent' in locals():\n gen_sample.latent = latent\n if 'feats' in locals():\n gen_sample.feats = feats\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = sample_model\n batch_sample_list = gen_sample.split(allow_nonseq_value=True, )\n\n else: # sample model is 'ema/orig\n outputs_orig = self.generator(\n noise, return_noise=False, **sample_kwargs)\n outputs_ema = self.generator_ema(\n noise, return_noise=False, **sample_kwargs)\n outputs_orig = self.data_preprocessor.destruct(\n outputs_orig, data_samples)\n outputs_ema = self.data_preprocessor.destruct(\n outputs_ema, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.ema = DataSample(fake_img=outputs_ema)\n gen_sample.orig = DataSample(fake_img=outputs_orig)\n gen_sample.noise = noise\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = 'ema/orig'\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n return batch_sample_list\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data.\n\n Calls ``self.data_preprocessor(data)`` and\n ``self(inputs, data_sample, mode=None)`` in order. Return the\n generated results which will be passed to evaluator.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n List[DataSample]: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n \"\"\"Train GAN model. In the training of GAN models, generator and\n discriminator are updated alternatively. In MMagic's design,\n `self.train_step` is called with data input. Therefore we always update\n discriminator, whose updating is relay on real data, and then determine\n if the generator needs to be updated based on the current number of\n iterations. More details about whether to update generator can be found\n in :meth:`should_gen_update`.\n\n Args:\n data (dict): Data sampled from dataloader.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n log_vars = self.train_discriminator(\n **data, optimizer_wrapper=disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n log_vars_gen = self.train_generator(\n **data, optimizer_wrapper=gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = self.gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n # if not update buffer, copy buffer from orig model\n if not self.generator_ema.update_buffers:\n self.generator_ema.sync_buffers(\n self.generator.module if is_model_wrapper(\n self.generator) else self.generator)\n elif self.with_ema_gen:\n # before ema, copy weights from orig\n self.generator_ema.sync_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n return log_vars\n\n def _get_gen_loss(self, out_dict):\n losses_dict = {}\n # gan loss\n losses_dict['loss_disc_fake_g'] = self.gan_loss(\n out_dict['disc_pred_fake_g'], target_is_real=True, is_disc=False)\n\n # gen auxiliary loss\n if self.gen_auxiliary_losses is not None:\n for loss_module in self.gen_auxiliary_losses:\n loss_ = loss_module(out_dict)\n if loss_ is None:\n continue\n\n # the `loss_name()` function return name as 'loss_xxx'\n if loss_module.loss_name() in losses_dict:\n losses_dict[loss_module.loss_name(\n )] = losses_dict[loss_module.loss_name()] + loss_\n else:\n losses_dict[loss_module.loss_name()] = loss_\n loss, log_var = self.parse_losses(losses_dict)\n\n return loss, log_var\n\n def _get_disc_loss(self, out_dict):\n # Construct losses dict. If you hope some items to be included in the\n # computational graph, you have to add 'loss' in its name. Otherwise,\n # items without 'loss' in their name will just be used to print\n # information.\n losses_dict = {}\n # gan loss\n losses_dict['loss_disc_fake'] = self.gan_loss(\n out_dict['disc_pred_fake'], target_is_real=False, is_disc=True)\n losses_dict['loss_disc_real'] = self.gan_loss(\n out_dict['disc_pred_real'], target_is_real=True, is_disc=True)\n\n # disc auxiliary loss\n if self.disc_auxiliary_losses is not None:\n for loss_module in self.disc_auxiliary_losses:\n loss_ = loss_module(out_dict)\n if loss_ is None:\n continue\n\n # the `loss_name()` function return name as 'loss_xxx'\n if loss_module.loss_name() in losses_dict:\n losses_dict[loss_module.loss_name(\n )] = losses_dict[loss_module.loss_name()] + loss_\n else:\n losses_dict[loss_module.loss_name()] = loss_\n loss, log_var = self.parse_losses(losses_dict)\n\n return loss, log_var\n\n def train_generator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Training function for discriminator. All GANs should implement this\n function by themselves.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = inputs['img'].shape[0]\n noise = self.noise_fn(num_batches=num_batches)\n\n fake_imgs = self.generator(noise=noise)\n disc_pred_fake_g = self.discriminator(fake_imgs)\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n fake_imgs=fake_imgs,\n disc_pred_fake_g=disc_pred_fake_g,\n # iteration=curr_iter,\n batch_size=num_batches,\n loss_scaler=getattr(optimizer_wrapper, 'loss_scaler', None))\n loss, log_vars = self._get_gen_loss(data_dict_)\n\n optimizer_wrapper.update_params(loss)\n return log_vars\n\n def train_discriminator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Training function for discriminator. All GANs should implement this\n function by themselves.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs, num_batches = inputs['img'], inputs['img'].shape[0]\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise)\n\n # disc pred for fake imgs and real_imgs\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n # get data dict to compute losses for disc\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs,\n # iteration=curr_iter,\n batch_size=num_batches,\n loss_scaler=getattr(optimizer_wrapper, 'loss_scaler', None))\n loss, log_vars = self._get_disc_loss(data_dict_)\n\n optimizer_wrapper.update_params(loss)\n return log_vars\n" }, { "path": "mmagic/models/base_models/base_mattor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom abc import ABCMeta\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.config import Config, ConfigDict\nfrom mmengine.model import BaseModel\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\nDataSamples = Optional[Union[list, torch.Tensor]]\nForwardResults = Union[Dict[str, torch.Tensor], List[DataSample],\n Tuple[torch.Tensor], torch.Tensor]\n\n\ndef _pad(batch_image: torch.Tensor,\n ds_factor: int,\n mode: str = 'reflect') -> Tuple[torch.Tensor, Tuple[int, int]]:\n \"\"\"Pad image to a multiple of give down-sampling factor.\"\"\"\n\n h, w = batch_image.shape[-2:] # NCHW\n\n new_h = ds_factor * ((h - 1) // ds_factor + 1)\n new_w = ds_factor * ((w - 1) // ds_factor + 1)\n\n pad_h = new_h - h\n pad_w = new_w - w\n pad = (pad_h, pad_w)\n if new_h != h or new_w != w:\n pad_width = (0, pad_w, 0, pad_h) # torch.pad in reverse order\n batch_image = F.pad(batch_image, pad_width, mode)\n\n return batch_image, pad\n\n\ndef _interpolate(batch_image: torch.Tensor,\n ds_factor: int,\n mode: str = 'bicubic'\n ) -> Tuple[torch.Tensor, Tuple[int, int]]:\n \"\"\"Resize image to multiple of give down-sampling factor.\"\"\"\n\n h, w = batch_image.shape[-2:] # NCHW\n\n new_h = h - (h % ds_factor)\n new_w = w - (w % ds_factor)\n\n size = (new_h, new_w)\n if new_h != h or new_w != w:\n batch_image = F.interpolate(batch_image, size=size, mode=mode)\n\n return batch_image, size\n\n\nclass BaseMattor(BaseModel, metaclass=ABCMeta):\n \"\"\"Base class for trimap-based matting models.\n\n A matting model must contain a backbone which produces `pred_alpha`,\n a dense prediction with the same height and width of input image.\n In some cases (such as DIM), the model has a refiner which refines\n the prediction of the backbone.\n\n Subclasses should overwrite the following functions:\n\n - :meth:`_forward_train`, to return a loss\n - :meth:`_forward_test`, to return a prediction\n - :meth:`_forward`, to return raw tensors\n\n For test, this base class provides functions to resize inputs and\n post-process pred_alphas to get predictions\n\n Args:\n backbone (dict): Config of backbone.\n data_preprocessor (dict): Config of data_preprocessor.\n See :class:`MattorPreprocessor` for details.\n init_cfg (dict, optional): Initialization config dict.\n train_cfg (dict): Config of training.\n Customized by subclassesCustomized bu In ``train_cfg``,\n ``train_backbone`` should be specified. If the model has a refiner,\n ``train_refiner`` should be specified.\n test_cfg (dict): Config of testing.\n In ``test_cfg``, If the model has a\n refiner, ``train_refiner`` should be specified.\n \"\"\"\n\n def __init__(self,\n data_preprocessor: Union[dict, Config],\n backbone: dict,\n init_cfg: Optional[dict] = None,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None):\n # Build data_preprocessor in BaseModel\n # Initialize weights in BaseModule\n super().__init__(\n data_preprocessor=data_preprocessor, init_cfg=init_cfg)\n\n self.train_cfg = ConfigDict(\n train_cfg) if train_cfg is not None else ConfigDict()\n self.test_cfg = ConfigDict(\n test_cfg) if test_cfg is not None else ConfigDict()\n\n self.backbone = MODELS.build(backbone)\n\n def resize_inputs(self, batch_inputs: torch.Tensor) -> torch.Tensor:\n \"\"\"Pad or interpolate images and trimaps to multiple of given\n factor.\"\"\"\n\n resize_method = self.test_cfg['resize_method']\n resize_mode = self.test_cfg['resize_mode']\n size_divisor = self.test_cfg['size_divisor']\n\n batch_images = batch_inputs[:, :3, :, :]\n batch_trimaps = batch_inputs[:, 3:, :, :]\n\n if resize_method == 'pad':\n batch_images, _ = _pad(batch_images, size_divisor, resize_mode)\n batch_trimaps, _ = _pad(batch_trimaps, size_divisor, resize_mode)\n elif resize_method == 'interp':\n batch_images, _ = _interpolate(batch_images, size_divisor,\n resize_mode)\n batch_trimaps, _ = _interpolate(batch_trimaps, size_divisor,\n 'nearest')\n else:\n raise NotImplementedError\n\n return torch.cat((batch_images, batch_trimaps), dim=1)\n\n def restore_size(self, pred_alpha: torch.Tensor,\n data_sample: DataSample) -> torch.Tensor:\n \"\"\"Restore the predicted alpha to the original shape.\n\n The shape of the predicted alpha may not be the same as the shape of\n original input image. This function restores the shape of the predicted\n alpha.\n\n Args:\n pred_alpha (torch.Tensor): A single predicted alpha of\n shape (1, H, W).\n data_sample (DataSample): Data sample containing\n original shape as meta data.\n\n Returns:\n torch.Tensor: The reshaped predicted alpha.\n \"\"\"\n resize_method = self.test_cfg['resize_method']\n resize_mode = self.test_cfg['resize_mode']\n\n ori_h, ori_w = data_sample.ori_merged_shape[:2]\n if resize_method == 'pad':\n pred_alpha = pred_alpha[:, :ori_h, :ori_w]\n elif resize_method == 'interp':\n pred_alpha = F.interpolate(\n pred_alpha.unsqueeze(0), size=(ori_h, ori_w), mode=resize_mode)\n pred_alpha = pred_alpha[0] # 1,H,W\n\n return pred_alpha\n\n def postprocess(\n self,\n batch_pred_alpha: torch.Tensor, # N, 1, H, W, float32\n data_samples: DataSample,\n ) -> List[DataSample]:\n \"\"\"Post-process alpha predictions.\n\n This function contains the following steps:\n 1. Restore padding or interpolation\n 2. Mask alpha prediction with trimap\n 3. Clamp alpha prediction to 0-1\n 4. Convert alpha prediction to uint8\n 5. Pack alpha prediction into DataSample\n\n Currently only batch_size 1 is actually supported.\n\n Args:\n batch_pred_alpha (torch.Tensor): A batch of predicted alpha\n of shape (N, 1, H, W).\n data_samples (List[DataSample]): List of data samples.\n\n Returns:\n List[DataSample]: A list of predictions.\n Each data sample contains a pred_alpha,\n which is a torch.Tensor with dtype=uint8, device=cuda:0\n \"\"\"\n\n assert batch_pred_alpha.ndim == 4 # N, 1, H, W, float32\n assert len(batch_pred_alpha) == 1\n\n # NOTE: for mattors, we split datasamples here, not in\n # `convert_to_datasample`\n data_samples = data_samples.split()\n predictions = []\n\n for pa, ds in zip(batch_pred_alpha, data_samples):\n pa = self.restore_size(pa, ds) # 1, H, W\n pa = pa[0] # H, W\n\n pa.clamp_(min=0, max=1)\n ori_trimap = ds.ori_trimap[0, :, :] # H, W\n pa[ori_trimap == 255] = 1\n pa[ori_trimap == 0] = 0\n\n pa *= 255\n pa.round_()\n pa = pa.to(dtype=torch.uint8)\n # pa = pa.cpu().numpy()\n pa_sample = DataSample(pred_alpha=pa)\n predictions.append(pa_sample)\n\n return predictions\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: DataSamples = None,\n mode: str = 'tensor') -> List[DataSample]:\n \"\"\"General forward function.\n\n Args:\n inputs (torch.Tensor): A batch of inputs.\n with image and trimap concatenated alone channel dimension.\n data_samples (List[DataSample], optional):\n A list of data samples, containing:\n - Ground-truth alpha / foreground / background to compute loss\n - other meta information\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``. Default: 'tensor'.\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n List[DataElement]:\n Sequence of predictions packed into DataElement\n \"\"\"\n if mode == 'tensor':\n raw = self._forward(inputs)\n return raw\n elif mode == 'predict':\n # Pre-process runs in runner\n inputs = self.resize_inputs(inputs)\n batch_pred_alpha = self._forward_test(inputs)\n predictions = self.postprocess(batch_pred_alpha, data_samples)\n predictions = self.convert_to_datasample(predictions, data_samples)\n return predictions\n elif mode == 'loss':\n loss = self._forward_train(inputs, data_samples)\n return loss\n else:\n raise ValueError('Invalid forward mode.')\n\n def convert_to_datasample(self, predictions: List[DataSample],\n data_samples: DataSample) -> List[DataSample]:\n \"\"\"Add predictions to data samples.\n\n Args:\n predictions (List[DataSample]): The predictions of the model.\n data_samples (DataSample): The data samples loaded from\n dataloader.\n\n Returns:\n List[DataSample]: Modified data samples.\n \"\"\"\n data_samples = data_samples.split()\n for data_sample, pred in zip(data_samples, predictions):\n data_sample.output = pred\n return data_samples\n" }, { "path": "mmagic/models/base_models/base_translation_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom abc import ABCMeta\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom typing import List, Optional\n\nimport torch.nn as nn\nfrom mmengine.model import BaseModel, is_model_wrapper\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass BaseTranslationModel(BaseModel, metaclass=ABCMeta):\n \"\"\"Base Translation Model.\n\n Translation models can transfer images from one domain to\n another. Domain information like `default_domain`,\n `reachable_domains` are needed to initialize the class.\n And we also provide query functions like `is_domain_reachable`,\n `get_other_domains`.\n\n You can get a specific generator based on the domain,\n and by specifying `target_domain` in the forward function,\n you can decide the domain of generated images.\n Considering the difference among different image translation models,\n we only provide the external interfaces mentioned above.\n When you implement image translation with a specific method,\n you can inherit both `BaseTranslationModel`\n and the method (e.g BaseGAN) and implement abstract methods.\n\n Args:\n default_domain (str): Default output domain.\n reachable_domains (list[str]): Domains that can be generated by\n the model.\n related_domains (list[str]): Domains involved in training and\n testing. `reachable_domains` must be contained in\n `related_domains`. However, related_domains may contain\n source domains that are used to retrieve source images from\n data_batch but not in reachable_domains.\n discriminator_steps (int): The number of times the discriminator is\n completely updated before the generator is updated. Defaults to 1.\n disc_init_steps (int): The number of initial steps used only to train\n discriminators.\n \"\"\"\n\n def __init__(self,\n generator,\n discriminator,\n default_domain: str,\n reachable_domains: List[str],\n related_domains: List[str],\n data_preprocessor,\n discriminator_steps: int = 1,\n disc_init_steps: int = 0,\n real_img_key: str = 'real_img',\n loss_config: Optional[dict] = None):\n super().__init__(data_preprocessor)\n self._default_domain = default_domain\n self._reachable_domains = reachable_domains\n self._related_domains = related_domains\n assert self._default_domain in self._reachable_domains\n assert set(self._reachable_domains) <= set(self._related_domains)\n\n self.discriminator_steps = discriminator_steps\n self.disc_init_steps = disc_init_steps\n self.real_img_key = real_img_key\n\n self._gen_cfg = deepcopy(generator)\n # build domain generators\n self.generators = nn.ModuleDict()\n for domain in self._reachable_domains:\n self.generators[domain] = MODELS.build(generator)\n\n self._disc_cfg = deepcopy(discriminator)\n # build domain discriminators\n if discriminator is not None:\n self.discriminators = nn.ModuleDict()\n for domain in self._reachable_domains:\n self.discriminators[domain] = MODELS.build(discriminator)\n # support no discriminator in testing\n else:\n self.discriminators = None\n\n self.loss_config = dict() if loss_config is None else loss_config\n\n def init_weights(self):\n \"\"\"Initialize weights for the module dict.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Default: None.\n \"\"\"\n self._params_init_info = defaultdict(dict)\n\n for _, param in self.named_parameters():\n self._params_init_info[param][\n 'init_info'] = f'The value is the same before and ' \\\n f'after calling `init_weights` ' \\\n f'of {self.__class__.__name__} '\n mean = param.data.mean().cpu()\n self._params_init_info[param]['tmp_mean_value'] = mean\n\n for domain in self._reachable_domains:\n if is_model_wrapper(self.generators):\n gen = self.generators.module[domain]\n else:\n gen = self.generators[domain]\n gen._params_init_info = self._params_init_info\n gen.init_weights()\n\n if self.discriminators is not None:\n if is_model_wrapper(self.discriminators):\n disc = self.discriminators.module[domain]\n else:\n disc = self.discriminators[domain]\n disc._params_init_info = self._params_init_info\n disc.init_weights()\n\n super()._dump_init_info()\n for m in self.modules():\n if hasattr(m, '_params_init_info'):\n del m._params_init_info\n\n def get_module(self, module):\n \"\"\"Get `nn.ModuleDict` to fit the `MMDistributedDataParallel`\n interface.\n\n Args:\n module (MMDistributedDataParallel | nn.ModuleDict): The input\n module that needs processing.\n\n Returns:\n nn.ModuleDict: The ModuleDict of multiple networks.\n \"\"\"\n if is_model_wrapper(module):\n return module.module\n\n return module\n\n def forward(self, img, test_mode=False, **kwargs):\n \"\"\"Forward function.\n\n Args:\n img (tensor): Input image tensor.\n test_mode (bool): Whether in test mode or not. Default: False.\n kwargs (dict): Other arguments.\n \"\"\"\n if not test_mode:\n return self.forward_train(img, **kwargs)\n\n return self.forward_test(img, **kwargs)\n\n def forward_train(self, img, target_domain, **kwargs):\n \"\"\"Forward function for training.\n\n Args:\n img (tensor): Input image tensor.\n target_domain (str): Target domain of output image.\n kwargs (dict): Other arguments.\n\n Returns:\n dict: Forward results.\n \"\"\"\n target = self.translation(img, target_domain=target_domain, **kwargs)\n results = dict(source=img, target=target)\n return results\n\n def forward_test(self, img, target_domain, **kwargs):\n \"\"\"Forward function for testing.\n\n Args:\n img (tensor): Input image tensor.\n target_domain (str): Target domain of output image.\n kwargs (dict): Other arguments.\n\n Returns:\n dict: Forward results.\n \"\"\"\n target = self.translation(img, target_domain=target_domain, **kwargs)\n results = dict(source=img.cpu(), target=target.cpu())\n return results\n\n def is_domain_reachable(self, domain):\n \"\"\"Whether image of this domain can be generated.\"\"\"\n return domain in self._reachable_domains\n\n def get_other_domains(self, domain):\n \"\"\"get other domains.\"\"\"\n return list(set(self._related_domains) - set([domain]))\n\n def _get_target_generator(self, domain):\n \"\"\"get target generator.\"\"\"\n assert self.is_domain_reachable(\n domain\n ), f'{domain} domain is not reachable, available domain list is\\\n {self._reachable_domains}'\n\n return self.get_module(self.generators)[domain]\n\n def _get_target_discriminator(self, domain):\n \"\"\"get target discriminator.\"\"\"\n assert self.is_domain_reachable(\n domain\n ), f'{domain} domain is not reachable, available domain list is\\\n {self._reachable_domains}'\n\n return self.get_module(self.discriminators)[domain]\n\n def translation(self, image, target_domain=None, **kwargs):\n \"\"\"Translation Image to target style.\n\n Args:\n image (tensor): Image tensor with a shape of (N, C, H, W).\n target_domain (str, optional): Target domain of output image.\n Default to None.\n\n Returns:\n dict: Image tensor of target style.\n \"\"\"\n if target_domain is None:\n target_domain = self._default_domain\n _model = self._get_target_generator(target_domain)\n outputs = _model(image, **kwargs)\n return outputs\n" }, { "path": "mmagic/models/base_models/basic_interpolator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import tensor2img\nfrom .base_edit_model import BaseEditModel\n\n# TODO tensor2img will be move\n\n\n@MODELS.register_module()\nclass BasicInterpolator(BaseEditModel):\n \"\"\"Basic model for video interpolation.\n\n It must contain a generator that takes frames as inputs and outputs an\n interpolated frame. It also has a pixel-wise loss for training.\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n required_frames (int): Required frames in each process. Default: 2\n step_frames (int): Step size of video frame interpolation. Default: 1\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`.\n \"\"\"\n\n def __init__(self,\n generator: dict,\n pixel_loss: dict,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None,\n required_frames: int = 2,\n step_frames: int = 1,\n init_cfg: Optional[dict] = None,\n data_preprocessor: Optional[dict] = None):\n\n super().__init__(\n generator=generator,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n # Required frames in each process\n self.required_frames = required_frames\n # Step size of video frame interpolation\n self.step_frames = step_frames\n\n def split_frames(self, input_tensors: torch.Tensor) -> torch.Tensor:\n \"\"\"split input tensors for inference.\n\n Args:\n input_tensors (Tensor): Tensor of input frames with shape\n [1, t, c, h, w]\n\n Returns:\n Tensor: Split tensor with shape [t-1, 2, c, h, w]\n \"\"\"\n\n num_frames = input_tensors.shape[1]\n\n result = [\n input_tensors[:, i:i + self.required_frames]\n for i in range(0, num_frames - self.required_frames +\n 1, self.step_frames)\n ]\n result = torch.cat(result, dim=0)\n\n return result\n\n @staticmethod\n def merge_frames(input_tensors: torch.Tensor,\n output_tensors: torch.Tensor) -> list:\n \"\"\"merge input frames and output frames.\n\n Interpolate a frame between the given two frames.\n\n Merged from\n [[in1, in2], [in2, in3], [in3, in4], ...]\n [[out1], [out2], [out3], ...]\n to\n [in1, out1, in2, out2, in3, out3, in4, ...]\n\n Args:\n input_tensors (Tensor): The input frames with shape [n, 2, c, h, w]\n output_tensors (Tensor): The output frames with shape\n [n, 1, c, h, w].\n\n Returns:\n list[np.array]: The final frames.\n \"\"\"\n\n num_frames = input_tensors.shape[0]\n result = []\n for i in range(num_frames):\n result.append(tensor2img(input_tensors[i, 0]))\n result.append(tensor2img(output_tensors[i, 0]))\n result.append(tensor2img(input_tensors[-1, 1]))\n\n return result\n" }, { "path": "mmagic/models/base_models/one_stage.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom mmengine.config import Config\nfrom mmengine.model import BaseModel\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import SampleList\nfrom ..utils import set_requires_grad\n\nFORWARD_RETURN_TYPE = Union[dict, torch.Tensor,\n Tuple[torch.Tensor, torch.Tensor], SampleList]\n\n\n@MODELS.register_module()\nclass OneStageInpaintor(BaseModel):\n \"\"\"Standard one-stage inpaintor with commonly used losses.\n\n An inpaintor must contain an encoder-decoder style generator to\n inpaint masked regions. A discriminator will be adopted when\n adversarial training is needed.\n\n In this class, we provide a common interface for inpaintors.\n For other inpaintors, only some funcs may be modified to fit the\n input style or training schedule.\n\n Args:\n data_preprocessor (dict): Config of data_preprocessor.\n encdec (dict): Config for encoder-decoder style generator.\n disc (dict): Config for discriminator.\n loss_gan (dict): Config for adversarial loss.\n loss_gp (dict): Config for gradient penalty loss.\n loss_disc_shift (dict): Config for discriminator shift loss.\n loss_composed_percep (dict): Config for perceptual and style loss with\n composed image as input.\n loss_out_percep (dict): Config for perceptual and style loss with\n direct output as input.\n loss_l1_hole (dict): Config for l1 loss in the hole.\n loss_l1_valid (dict): Config for l1 loss in the valid region.\n loss_tv (dict): Config for total variation loss.\n train_cfg (dict): Configs for training scheduler. `disc_step` must be\n contained for indicates the discriminator updating steps in each\n training step.\n test_cfg (dict): Configs for testing scheduler.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n data_preprocessor: Union[dict, Config],\n encdec: dict,\n disc: Optional[dict] = None,\n loss_gan: Optional[dict] = None,\n loss_gp: Optional[dict] = None,\n loss_disc_shift: Optional[dict] = None,\n loss_composed_percep: Optional[dict] = None,\n loss_out_percep: bool = False,\n loss_l1_hole: Optional[dict] = None,\n loss_l1_valid: Optional[dict] = None,\n loss_tv: Optional[dict] = None,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None,\n init_cfg: Optional[dict] = None):\n super().__init__(\n data_preprocessor=data_preprocessor, init_cfg=init_cfg)\n self.with_l1_hole_loss = loss_l1_hole is not None\n self.with_l1_valid_loss = loss_l1_valid is not None\n self.with_tv_loss = loss_tv is not None\n self.with_composed_percep_loss = loss_composed_percep is not None\n self.with_out_percep_loss = loss_out_percep\n self.with_gan = disc is not None and loss_gan is not None\n self.with_gp_loss = loss_gp is not None\n self.with_disc_shift_loss = loss_disc_shift is not None\n self.is_train = train_cfg is not None\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n self.generator = MODELS.build(encdec)\n\n # build loss modules\n if self.with_gan:\n self.disc = MODELS.build(disc)\n self.loss_gan = MODELS.build(loss_gan)\n\n if self.with_l1_hole_loss:\n self.loss_l1_hole = MODELS.build(loss_l1_hole)\n\n if self.with_l1_valid_loss:\n self.loss_l1_valid = MODELS.build(loss_l1_valid)\n\n if self.with_composed_percep_loss:\n self.loss_percep = MODELS.build(loss_composed_percep)\n\n if self.with_gp_loss:\n self.loss_gp = MODELS.build(loss_gp)\n\n if self.with_disc_shift_loss:\n self.loss_disc_shift = MODELS.build(loss_disc_shift)\n\n if self.with_tv_loss:\n self.loss_tv = MODELS.build(loss_tv)\n\n self.disc_step_count = 0\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[SampleList],\n mode: str = 'tensor') -> FORWARD_RETURN_TYPE:\n \"\"\"Forward function.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``. Default: 'tensor'.\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict`` or tensor for custom use.\n \"\"\"\n if mode == 'tensor':\n raw = self.forward_tensor(inputs, data_samples)\n return raw\n elif mode == 'predict':\n # Pre-process runs in BaseModel.val_step / test_step\n predictions = self.forward_test(inputs, data_samples)\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n elif mode == 'loss':\n raise NotImplementedError('This mode should not be used in '\n 'current training schedule. Please use '\n '`train_step` for training.')\n else:\n raise ValueError('Invalid forward mode.')\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> dict:\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n\n 1. get fake res/image\n 2. optimize discriminator (if have)\n 3. optimize generator\n\n If `self.train_cfg.disc_step > 1`, the train step will contain multiple\n iterations for optimizing discriminator with different input data and\n only one iteration for optimizing generator after `disc_step`\n iterations for discriminator.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n masked_img = batch_inputs # float\n # gt_img: float [-1, 1], mask: uint8 [0/1]\n gt_img, mask = data_samples.gt_img, data_samples.mask\n mask = mask.float()\n\n # get common output from encdec\n input_x = torch.cat([masked_img, mask], dim=1)\n fake_res = self.generator(input_x)\n fake_img = gt_img * (1. - mask) + fake_res * mask\n\n # discriminator training step\n if self.train_cfg.disc_step > 0:\n set_requires_grad(self.disc, True)\n disc_losses = self.forward_train_d(\n fake_img.detach(), False, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].zero_grad()\n loss_disc.backward()\n\n disc_losses = self.forward_train_d(gt_img, True, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n loss_disc.backward()\n\n if self.with_gp_loss:\n loss_d_gp = self.loss_gp(\n self.disc, gt_img, fake_img, mask=mask)\n loss_disc, log_vars_d = self.parse_losses(\n dict(loss_gp=loss_d_gp))\n log_vars.update(log_vars_d)\n loss_disc.backward()\n\n optim_wrapper['disc'].step()\n\n self.disc_step_count = (self.disc_step_count +\n 1) % self.train_cfg.disc_step\n if self.disc_step_count != 0:\n # results contain the data for visualization\n results = dict(\n gt_img=gt_img.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=fake_res.cpu(),\n fake_img=fake_img.cpu())\n\n # outputs = dict(\n # log_vars=log_vars,\n # num_samples=len(data_batch['gt_img'].data),\n # results=results)\n\n return log_vars\n\n # generator (encdec) training step, results contain the data\n # for visualization\n if self.with_gan:\n set_requires_grad(self.disc, False)\n results, g_losses = self.generator_loss(fake_res, fake_img, gt_img,\n mask, masked_img)\n loss_g, log_vars_g = self.parse_losses(g_losses)\n log_vars.update(log_vars_g)\n optim_wrapper['generator'].zero_grad()\n loss_g.backward()\n optim_wrapper['generator'].step()\n\n # outputs = dict(\n # log_vars=log_vars,\n # num_samples=len(data_batch['gt_img'].data),\n # results=results)\n\n return log_vars\n\n def forward_train(self, *args, **kwargs) -> None:\n \"\"\"Forward function for training.\n\n In this version, we do not use this interface.\n \"\"\"\n raise NotImplementedError('This interface should not be used in '\n 'current training schedule. Please use '\n '`train_step` for training.')\n\n def forward_train_d(self, data_batch: torch.Tensor, is_real: bool,\n is_disc: bool) -> dict:\n \"\"\"Forward function in discriminator training step.\n\n In this function, we compute the prediction for each data batch (real\n or fake). Meanwhile, the standard gan loss will be computed with\n several proposed losses for stable training.\n\n Args:\n data_batch (torch.Tensor): Batch of real data or fake data.\n is_real (bool): If True, the gan loss will regard this batch as\n real data. Otherwise, the gan loss will regard this batch as\n fake data.\n is_disc (bool): If True, this function is called in discriminator\n training step. Otherwise, this function is called in generator\n training step. This will help us to compute different types of\n adversarial loss, like LSGAN.\n\n Returns:\n dict: Contains the loss items computed in this function.\n \"\"\"\n pred = self.disc(data_batch)\n loss_ = self.loss_gan(pred, is_real, is_disc)\n\n loss = dict(real_loss=loss_) if is_real else dict(fake_loss=loss_)\n\n if self.with_disc_shift_loss:\n loss_d_shift = self.loss_disc_shift(loss_)\n # 0.5 for average the fake and real data\n loss.update(loss_disc_shift=loss_d_shift * 0.5)\n\n return loss\n\n def generator_loss(self, fake_res: torch.Tensor, fake_img: torch.Tensor,\n gt: torch.Tensor, mask: torch.Tensor,\n masked_img: torch.Tensor) -> Tuple[dict, dict]:\n \"\"\"Forward function in generator training step.\n\n In this function, we mainly compute the loss items for generator with\n the given (fake_res, fake_img). In general, the `fake_res` is the\n direct output of the generator and the `fake_img` is the composition of\n direct output and ground-truth image.\n\n Args:\n fake_res (torch.Tensor): Direct output of the generator.\n fake_img (torch.Tensor): Composition of `fake_res` and\n ground-truth image.\n gt (torch.Tensor): Ground-truth image.\n mask (torch.Tensor): Mask image.\n masked_img (torch.Tensor): Composition of mask image and\n ground-truth image.\n\n Returns:\n tuple(dict): Dict contains the results computed within this \\\n function for visualization and dict contains the loss items \\\n computed in this function.\n \"\"\"\n loss = dict()\n\n if self.with_gan:\n g_fake_pred = self.disc(fake_img)\n loss_g_fake = self.loss_gan(g_fake_pred, True, is_disc=False)\n loss['loss_g_fake'] = loss_g_fake\n\n if self.with_l1_hole_loss:\n loss_l1_hole = self.loss_l1_hole(fake_res, gt, weight=mask)\n loss['loss_l1_hole'] = loss_l1_hole\n\n if self.with_l1_valid_loss:\n loss_loss_l1_valid = self.loss_l1_valid(\n fake_res, gt, weight=1. - mask)\n loss['loss_l1_valid'] = loss_loss_l1_valid\n\n if self.with_composed_percep_loss:\n loss_pecep, loss_style = self.loss_percep(fake_img, gt)\n if loss_pecep is not None:\n loss['loss_composed_percep'] = loss_pecep\n if loss_style is not None:\n loss['loss_composed_style'] = loss_style\n\n if self.with_out_percep_loss:\n loss_out_percep, loss_out_style = self.loss_percep(fake_res, gt)\n if loss_out_percep is not None:\n loss['loss_out_percep'] = loss_out_percep\n if loss_out_style is not None:\n loss['loss_out_style'] = loss_out_style\n\n if self.with_tv_loss:\n loss_tv = self.loss_tv(fake_img, mask=mask)\n loss['loss_tv'] = loss_tv\n\n res = dict(\n gt_img=gt.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=fake_res.cpu(),\n fake_img=fake_img.cpu())\n\n return res, loss\n\n def forward_tensor(self, inputs: torch.Tensor, data_samples: SampleList\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Forward function in tensor mode.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_samples (List[dict]): List of data sample dict.\n\n Returns:\n tuple: Direct output of the generator and composition of `fake_res`\n and ground-truth image.\n \"\"\"\n # Pre-process runs in BaseModel.val_step / test_step\n masked_imgs = inputs # N,3,H,W\n\n masks = data_samples.mask # N,1,H,W\n input_xs = torch.cat([masked_imgs, masks], dim=1) # N,4,H,W\n fake_reses = self.generator(input_xs)\n fake_imgs = fake_reses * masks + masked_imgs * (1. - masks)\n return fake_reses, fake_imgs\n\n def forward_test(self, inputs: torch.Tensor,\n data_samples: SampleList) -> DataSample:\n \"\"\"Forward function for testing.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_samples (List[dict]): List of data sample dict.\n\n Returns:\n predictions (List[DataSample]): List of prediction saved in\n DataSample.\n \"\"\"\n fake_reses, fake_imgs = self.forward_tensor(inputs, data_samples)\n\n predictions = []\n fake_reses = self.data_preprocessor.destruct(fake_reses, data_samples)\n fake_imgs = self.data_preprocessor.destruct(fake_imgs, data_samples)\n\n # create a stacked data sample here\n predictions = DataSample(\n fake_res=fake_reses, fake_img=fake_imgs, pred_img=fake_imgs)\n\n return predictions\n\n def convert_to_datasample(self, predictions: DataSample,\n data_samples: DataSample,\n inputs: Optional[torch.Tensor]\n ) -> List[DataSample]:\n \"\"\"Add predictions and destructed inputs (if passed) to data samples.\n\n Args:\n predictions (DataSample): The predictions of the model.\n data_samples (DataSample): The data samples loaded from\n dataloader.\n inputs (Optional[torch.Tensor]): The input of model. Defaults to\n None.\n\n Returns:\n List[DataSample]: Modified data samples.\n \"\"\"\n if inputs is not None:\n destructed_input = self.data_preprocessor.destruct(\n inputs, data_samples, 'img')\n data_samples.set_tensor_data({'input': destructed_input})\n data_samples = data_samples.split()\n predictions = predictions.split()\n\n for data_sample, pred in zip(data_samples, predictions):\n data_sample.output = pred\n\n return data_samples\n\n def forward_dummy(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward dummy function for getting flops.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Results tensor with shape of (n, 3, h, w).\n \"\"\"\n res = self.generator(x)\n\n return res\n" }, { "path": "mmagic/models/base_models/two_stage.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Sequence, Tuple, Union\n\nimport torch\nfrom mmengine.config import Config\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import SampleList\nfrom ..utils import set_requires_grad\nfrom .one_stage import OneStageInpaintor\n\n\n@MODELS.register_module()\nclass TwoStageInpaintor(OneStageInpaintor):\n \"\"\"Standard two-stage inpaintor with commonly used losses. A two-stage\n inpaintor contains two encoder-decoder style generators to inpaint masked\n regions. Currently, we support these loss types in each of two stage\n inpaintors:\n\n ['loss_gan', 'loss_l1_hole', 'loss_l1_valid', 'loss_composed_percep',\\\n 'loss_out_percep', 'loss_tv']\n The `stage1_loss_type` and `stage2_loss_type` should be chosen from these\n loss types.\n\n Args:\n data_preprocessor (dict): Config of data_preprocessor.\n encdec (dict): Config for encoder-decoder style generator.\n disc (dict): Config for discriminator.\n loss_gan (dict): Config for adversarial loss.\n loss_gp (dict): Config for gradient penalty loss.\n loss_disc_shift (dict): Config for discriminator shift loss.\n loss_composed_percep (dict): Config for perceptual and style loss with\n composed image as input.\n loss_out_percep (dict): Config for perceptual and style loss with\n direct output as input.\n loss_l1_hole (dict): Config for l1 loss in the hole.\n loss_l1_valid (dict): Config for l1 loss in the valid region.\n loss_tv (dict): Config for total variation loss.\n train_cfg (dict): Configs for training scheduler. `disc_step` must be\n contained for indicates the discriminator updating steps in each\n training step.\n test_cfg (dict): Configs for testing scheduler.\n init_cfg (dict, optional): Initialization config dict.\n stage1_loss_type (tuple[str]): Contains the loss names used in the\n first stage model. Default: ('loss_l1_hole').\n stage2_loss_type (tuple[str]): Contains the loss names used in the\n second stage model. Default: ('loss_l1_hole', 'loss_gan').\n input_with_ones (bool): Whether to concatenate an extra ones tensor in\n input. Default: True.\n disc_input_with_mask (bool): Whether to add mask as input in\n discriminator. Default: False.\n \"\"\"\n\n def __init__(\n self,\n data_preprocessor: Union[dict, Config],\n encdec: dict,\n disc: Optional[dict] = None,\n loss_gan: Optional[dict] = None,\n loss_gp: Optional[dict] = None,\n loss_disc_shift: Optional[dict] = None,\n loss_composed_percep: Optional[dict] = None,\n loss_out_percep: bool = False,\n loss_l1_hole: Optional[dict] = None,\n loss_l1_valid: Optional[dict] = None,\n loss_tv: Optional[dict] = None,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None,\n init_cfg: Optional[dict] = None,\n stage1_loss_type: Optional[Sequence[str]] = ('loss_l1_hole', ),\n stage2_loss_type: Optional[Sequence[str]] = ('loss_l1_hole',\n 'loss_gan'),\n input_with_ones: bool = True,\n disc_input_with_mask: bool = False):\n super().__init__(\n data_preprocessor=data_preprocessor,\n encdec=encdec,\n disc=disc,\n loss_gan=loss_gan,\n loss_gp=loss_gp,\n loss_disc_shift=loss_disc_shift,\n loss_composed_percep=loss_composed_percep,\n loss_out_percep=loss_out_percep,\n loss_l1_hole=loss_l1_hole,\n loss_l1_valid=loss_l1_valid,\n loss_tv=loss_tv,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg)\n\n self.stage1_loss_type = stage1_loss_type\n self.stage2_loss_type = stage2_loss_type\n self.input_with_ones = input_with_ones\n self.disc_input_with_mask = disc_input_with_mask\n\n if self.train_cfg is not None:\n self.cur_iter = self.train_cfg.start_iter\n\n def forward_tensor(self, inputs: torch.Tensor, data_samples: SampleList\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Forward function in tensor mode.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_samples (List[dict]): List of data sample dict.\n Returns:\n dict: Dict contains output results.\n \"\"\"\n # Pre-process runs in BaseModel.val_step / test_step\n masked_imgs = inputs # N,3,H,W\n\n masks = data_samples.mask\n if self.input_with_ones:\n tmp_ones = torch.ones_like(masks)\n input_xs = torch.cat([masked_imgs, tmp_ones, masks], dim=1)\n else:\n input_xs = torch.cat([masked_imgs, masks], dim=1) # N,4,H,W\n stage1_fake_res, stage2_fake_res = self.generator(input_xs)\n fake_imgs = stage2_fake_res * masks + masked_imgs * (1. - masks)\n return stage2_fake_res, fake_imgs\n\n def two_stage_loss(self, stage1_data: dict, stage2_data: dict,\n gt: torch.Tensor, mask: torch.Tensor,\n masked_img: torch.Tensor) -> Tuple[dict, dict]:\n \"\"\"Calculate two-stage loss.\n\n Args:\n stage1_data (dict): Contain stage1 results.\n stage2_data (dict): Contain stage2 results..\n gt (torch.Tensor): Ground-truth image.\n mask (torch.Tensor): Mask image.\n masked_img (torch.Tensor): Composition of mask image and\n ground-truth image.\n Returns:\n tuple(dict): Dict contains the results computed within this \\\n function for visualization and dict contains the loss items \\\n computed in this function.\n \"\"\"\n\n loss = dict()\n results = dict(\n gt_img=gt.cpu(), mask=mask.cpu(), masked_img=masked_img.cpu())\n # calculate losses for stage1\n if self.stage1_loss_type is not None:\n fake_res = stage1_data['fake_res']\n fake_img = stage1_data['fake_img']\n for type_key in self.stage1_loss_type:\n tmp_loss = self.calculate_loss_with_type(\n type_key, fake_res, fake_img, gt, mask, prefix='stage1_')\n loss.update(tmp_loss)\n\n results.update(\n dict(\n stage1_fake_res=stage1_data['fake_res'].cpu(),\n stage1_fake_img=stage1_data['fake_img'].cpu()))\n\n if self.stage2_loss_type is not None:\n fake_res = stage2_data['fake_res']\n fake_img = stage2_data['fake_img']\n for type_key in self.stage2_loss_type:\n tmp_loss = self.calculate_loss_with_type(\n type_key, fake_res, fake_img, gt, mask, prefix='stage2_')\n loss.update(tmp_loss)\n results.update(\n dict(\n stage2_fake_res=stage2_data['fake_res'].cpu(),\n stage2_fake_img=stage2_data['fake_img'].cpu()))\n\n return results, loss\n\n def calculate_loss_with_type(self,\n loss_type: str,\n fake_res: torch.Tensor,\n fake_img: torch.Tensor,\n gt: torch.Tensor,\n mask: torch.Tensor,\n prefix: Optional[str] = 'stage1_') -> dict:\n \"\"\"Calculate multiple types of losses.\n\n Args:\n loss_type (str): Type of the loss.\n fake_res (torch.Tensor): Direct results from model.\n fake_img (torch.Tensor): Composited results from model.\n gt (torch.Tensor): Ground-truth tensor.\n mask (torch.Tensor): Mask tensor.\n prefix (str, optional): Prefix for loss name.\n Defaults to 'stage1\\_'. # noqa\n Returns:\n dict: Contain loss value with its name.\n \"\"\"\n loss_dict = dict()\n if loss_type == 'loss_gan':\n if self.disc_input_with_mask:\n disc_input_x = torch.cat([fake_img, mask], dim=1)\n else:\n disc_input_x = fake_img\n g_fake_pred = self.disc(disc_input_x)\n loss_g_fake = self.loss_gan(g_fake_pred, True, is_disc=False)\n loss_dict[prefix + 'loss_g_fake'] = loss_g_fake\n elif 'percep' in loss_type:\n loss_pecep, loss_style = self.loss_percep(fake_img, gt)\n if loss_pecep is not None:\n loss_dict[prefix + loss_type] = loss_pecep\n if loss_style is not None:\n loss_dict[prefix + loss_type[:-6] + 'style'] = loss_style\n elif 'tv' in loss_type:\n loss_tv = self.loss_tv(fake_img, mask=mask)\n loss_dict[prefix + loss_type] = loss_tv\n elif 'l1' in loss_type:\n weight = 1. - mask if 'valid' in loss_type else mask\n loss_l1 = getattr(self, loss_type)(fake_res, gt, weight=weight)\n loss_dict[prefix + loss_type] = loss_l1\n else:\n raise NotImplementedError(\n f'Please check your loss type {loss_type}'\n f' and the config dict in init function. '\n f'We cannot find the related loss function.')\n\n return loss_dict\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> dict:\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n 1. get fake res/image\n 2. optimize discriminator (if have)\n 3. optimize generator\n\n If `self.train_cfg.disc_step > 1`, the train step will contain multiple\n iterations for optimizing discriminator with different input data and\n only one iteration for optimizing generator after `disc_step`\n iterations for discriminator.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of \\\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n masked_img = batch_inputs # float\n gt_img = data_samples.gt_img\n mask = data_samples.mask\n mask = mask.float()\n\n # get common output from encdec\n if self.input_with_ones:\n tmp_ones = torch.ones_like(mask)\n input_x = torch.cat([masked_img, tmp_ones, mask], dim=1)\n else:\n input_x = torch.cat([masked_img, mask], dim=1)\n stage1_fake_res, stage2_fake_res = self.generator(input_x)\n stage1_fake_img = masked_img * (1. - mask) + stage1_fake_res * mask\n stage2_fake_img = masked_img * (1. - mask) + stage2_fake_res * mask\n\n # discriminator training step\n # In this version, we only use the results from the second stage to\n # train discriminators, which is a commonly used setting. This can be\n # easily modified to your custom training schedule.\n if self.train_cfg.disc_step > 0:\n set_requires_grad(self.disc, True)\n if self.disc_input_with_mask:\n disc_input_x = torch.cat([stage2_fake_img.detach(), mask],\n dim=1)\n else:\n disc_input_x = stage2_fake_img.detach()\n disc_losses = self.forward_train_d(\n disc_input_x, False, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].zero_grad()\n optim_wrapper['disc'].backward(loss_disc)\n\n if self.disc_input_with_mask:\n disc_input_x = torch.cat([gt_img, mask], dim=1)\n else:\n disc_input_x = gt_img\n disc_losses = self.forward_train_d(\n disc_input_x, True, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].backward(loss_disc)\n\n if self.with_gp_loss:\n # gradient penalty loss should not be used with mask as input\n assert not self.disc_input_with_mask\n loss_d_gp = self.loss_gp(\n self.disc, gt_img, stage2_fake_img, mask=mask)\n loss_disc, log_vars_d = self.parse_losses(\n dict(loss_gp=loss_d_gp))\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].backward(loss_disc)\n\n optim_wrapper['disc'].step()\n\n self.disc_step_count = (self.disc_step_count +\n 1) % self.train_cfg.disc_step\n if self.disc_step_count != 0:\n # results contain the data for visualization\n results = dict(\n gt_img=gt_img.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=stage2_fake_res.cpu(),\n fake_img=stage2_fake_img.cpu())\n\n return log_vars\n\n # prepare stage1 results and stage2 results dict for calculating losses\n stage1_results = dict(\n fake_res=stage1_fake_res, fake_img=stage1_fake_img)\n stage2_results = dict(\n fake_res=stage2_fake_res, fake_img=stage2_fake_img)\n\n # generator (encdec) and refiner training step, results contain the\n # data for visualization\n if self.with_gan:\n set_requires_grad(self.disc, False)\n results, two_stage_losses = self.two_stage_loss(\n stage1_results, stage2_results, gt_img, mask, masked_img)\n loss_two_stage, log_vars_two_stage = self.parse_losses(\n two_stage_losses)\n log_vars.update(log_vars_two_stage)\n optim_wrapper['generator'].zero_grad()\n optim_wrapper['generator'].backward(loss_two_stage)\n optim_wrapper['generator'].step()\n\n return log_vars\n" }, { "path": "mmagic/models/data_preprocessors/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .data_preprocessor import DataPreprocessor\nfrom .mattor_preprocessor import MattorPreprocessor\n\n__all__ = ['DataPreprocessor', 'MattorPreprocessor']\n" }, { "path": "mmagic/models/data_preprocessors/data_preprocessor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom logging import WARNING\nfrom typing import List, Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom mmengine.model import ImgDataPreprocessor\nfrom mmengine.utils import is_seq_of\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\n\nCastData = Union[tuple, dict, DataSample, Tensor, list]\n\n\n@MODELS.register_module()\nclass DataPreprocessor(ImgDataPreprocessor):\n \"\"\"Image pre-processor for generative models. This class provide\n normalization and bgr to rgb conversion for image tensor inputs. The input\n of this classes should be dict which keys are `inputs` and `data_samples`.\n\n Besides to process tensor `inputs`, this class support dict as `inputs`.\n - If the value is `Tensor` and the corresponding key is not contained in\n :attr:`_NON_IMAGE_KEYS`, it will be processed as image tensor.\n - If the value is `Tensor` and the corresponding key belongs to\n :attr:`_NON_IMAGE_KEYS`, it will not remains unchanged.\n - If value is string or integer, it will not remains unchanged.\n\n Args:\n mean (Sequence[float or int], float or int, optional): The pixel mean\n of image channels. Noted that normalization operation is performed\n *after channel order conversion*. If it is not specified, images\n will not be normalized. Defaults None.\n std (Sequence[float or int], float or int, optional): The pixel\n standard deviation of image channels. Noted that normalization\n operation is performed *after channel order conversion*. If it is\n not specified, images will not be normalized. Defaults None.\n pad_size_divisor (int): The size of padded image should be\n divisible by ``pad_size_divisor``. Defaults to 1.\n pad_value (float or int): The padded pixel value. Defaults to 0.\n pad_mode (str): Padding mode for ``torch.nn.functional.pad``.\n Defaults to 'constant'.\n non_image_keys (List[str] or str): Keys for fields that not need to be\n processed (padding, channel conversion and normalization) as\n images. If not passed, the keys in :attr:`_NON_IMAGE_KEYS` will be\n used. This argument will only work when `inputs` is dict or list\n of dict. Defaults to None.\n non_concatenate_keys (List[str] or str): Keys for fields that not need\n to be concatenated. If not passed, the keys in\n :attr:`_NON_CONCATENATE_KEYS` will be used. This argument will only\n work when `inputs` is dict or list of dict. Defaults to None.\n output_channel_order (str, optional): The desired image channel order\n of output the data preprocessor. This is also the desired input\n channel order of model (and this most likely to be the output\n order of model). If not passed, no channel order conversion will\n be performed. Defaults to None.\n data_keys (List[str] or str): Keys to preprocess in data samples.\n Defaults to 'gt_img'.\n input_view (tuple, optional): The view of input tensor. This\n argument maybe deleted in the future. Defaults to None.\n output_view (tuple, optional): The view of output tensor. This\n argument maybe deleted in the future. Defaults to None.\n stack_data_sample (bool): Whether stack a list of data samples to one\n data sample. Only support with input data samples are\n `DataSamples`. Defaults to True.\n \"\"\"\n _NON_IMAGE_KEYS = ['noise']\n _NON_CONCATENATE_KEYS = ['num_batches', 'mode', 'sample_kwargs', 'eq_cfg']\n\n def __init__(self,\n mean: Union[Sequence[Union[float, int]], float, int] = 127.5,\n std: Union[Sequence[Union[float, int]], float, int] = 127.5,\n pad_size_divisor: int = 1,\n pad_value: Union[float, int] = 0,\n pad_mode: str = 'constant',\n non_image_keys: Optional[Tuple[str, List[str]]] = None,\n non_concentate_keys: Optional[Tuple[str, List[str]]] = None,\n output_channel_order: Optional[str] = None,\n data_keys: Union[List[str], str] = 'gt_img',\n input_view: Optional[tuple] = None,\n output_view: Optional[tuple] = None,\n stack_data_sample=True):\n\n if not isinstance(mean, (list, tuple)) and mean is not None:\n mean = [mean]\n if not isinstance(std, (list, tuple)) and std is not None:\n std = [std]\n\n super().__init__(mean, std, pad_size_divisor, pad_value)\n # get channel order\n assert (output_channel_order is None\n or output_channel_order in ['RGB', 'BGR']), (\n 'Only support \\'RGB\\', \\'BGR\\' or None for '\n '\\'output_channel_order\\', but receive '\n f'\\'{output_channel_order}\\'.')\n self.output_channel_order = output_channel_order\n\n # add user defined keys\n if non_image_keys is not None:\n if not isinstance(non_image_keys, list):\n non_image_keys = [non_image_keys]\n self._NON_IMAGE_KEYS += non_image_keys\n if non_concentate_keys is not None:\n if not isinstance(non_concentate_keys, list):\n non_concentate_keys = [non_concentate_keys]\n self._NON_CONCATENATE_KEYS += non_concentate_keys\n\n self.pad_mode = pad_mode\n self.pad_size_dict = dict()\n if data_keys is not None and not isinstance(data_keys, list):\n self.data_keys = [data_keys]\n else:\n self.data_keys = data_keys\n\n # TODO: can be removed since only be used in LIIF\n self.input_view = input_view\n self.output_view = output_view\n\n self._done_padding = False # flag for padding checking\n self._conversion_warning_raised = False # flag for conversion warning\n\n self.stack_data_sample = stack_data_sample\n\n def cast_data(self, data: CastData) -> CastData:\n \"\"\"Copying data to the target device.\n\n Args:\n data (dict): Data returned by ``DataLoader``.\n\n Returns:\n CollatedResult: Inputs and data sample at target device.\n \"\"\"\n if isinstance(data, (str, int, float)):\n return data\n return super().cast_data(data)\n\n @staticmethod\n def _parse_channel_index(inputs) -> int:\n \"\"\"Parse channel index of inputs.\"\"\"\n channel_index_mapping = {2: 1, 3: 0, 4: 1, 5: 2}\n if isinstance(inputs, dict):\n ndim = inputs['fake_img'].ndim\n assert ndim in channel_index_mapping, (\n 'Only support (H*W, C), (C, H, W), (N, C, H, W) or '\n '(N, t, C, H, W) inputs. But received '\n f'\\'({inputs.shape})\\'.')\n channel_index = channel_index_mapping[ndim]\n else:\n assert inputs.ndim in channel_index_mapping, (\n 'Only support (H*W, C), (C, H, W), (N, C, H, W) or '\n '(N, t, C, H, W) inputs. But received '\n f'\\'({inputs.shape})\\'.')\n channel_index = channel_index_mapping[inputs.ndim]\n\n return channel_index\n\n def _parse_channel_order(self,\n key: str,\n inputs: Tensor,\n data_sample: Optional[DataSample] = None) -> str:\n channel_index = self._parse_channel_index(inputs)\n if isinstance(inputs, dict):\n num_color_channels = inputs['fake_img'].shape[channel_index]\n else:\n num_color_channels = inputs.shape[channel_index]\n\n # data sample is None, attempt to infer from input tensor\n if data_sample is None:\n if num_color_channels == 1:\n return 'single'\n else:\n # default as BGR\n return 'BGR'\n\n # data sample is not None, infer from metainfo\n channel_order_key = 'gt_channel_order' if key == 'gt_img' \\\n else f'{key}_channel_order'\n color_type_key = 'gt_color_type' if key == 'gt_img' \\\n else f'{key}_color_type'\n\n # TODO: raise warning here, we can build a dict which fields are keys\n # have been parsed.\n color_flag = data_sample.metainfo.get(color_type_key, None)\n channel_order = data_sample.metainfo.get(channel_order_key, None)\n\n # handle stacked data sample, refers to `DataSample.stack`\n if isinstance(color_flag, list):\n assert all([c == color_flag[0] for c in color_flag])\n color_flag = color_flag[0]\n if isinstance(channel_order, list):\n assert all([c == channel_order[0] for c in channel_order])\n channel_order = channel_order[0]\n\n # NOTE: to handle inputs such as Y, users may modify the following code\n if color_flag == 'grayscale':\n assert num_color_channels == 1\n return 'single'\n elif color_flag == 'unchanged':\n # if inputs is not None:\n return 'single' if num_color_channels == 1 else 'BGR'\n else:\n # inference from channel_order\n if channel_order:\n return channel_order\n else:\n # no channel order, infer from num channels\n return 'single' if num_color_channels == 1 else 'BGR'\n\n def _parse_batch_channel_order(self, key: str, inputs: Sequence,\n data_samples: Optional[Sequence[DataSample]]\n ) -> str:\n \"\"\"Parse channel order of inputs in batch.\"\"\"\n\n assert len(inputs) == len(data_samples)\n batch_inputs_orders = [\n self._parse_channel_order(key, inp, data_sample)\n for inp, data_sample in zip(inputs, data_samples)\n ]\n inputs_order = batch_inputs_orders[0]\n\n # security checking for channel order\n assert all([\n inputs_order == order for order in batch_inputs_orders\n ]), (f'Channel order ({batch_inputs_orders}) of input targets '\n f'(\\'{key}\\') are inconsistent.')\n\n return inputs_order\n\n def _update_metainfo(self,\n padding_info: Tensor,\n channel_order_info: Optional[dict] = None,\n data_samples: Optional[SampleList] = None\n ) -> SampleList:\n \"\"\"Update `padding_info` and `channel_order` to metainfo of.\n\n *a batch of `data_samples`*. For channel order, we consider same field\n among data samples share the same channel order. Therefore\n `channel_order` is passed as a dict, which key and value are field\n name and corresponding channel order. For padding info, we consider\n padding info is same among all field of a sample, but can vary between\n samples. Therefore, we pass `padding_info` as Tensor shape like\n (B, 1, 1).\n\n Args:\n padding_info (Tensor): The padding info of each sample. Shape\n like (B, 1, 1).\n channel_order (dict, Optional): The channel order of target field.\n Key and value are field name and corresponding channel order\n respectively.\n data_samples (List[DataSample], optional): The data samples to\n be updated. If not passed, will initialize a list of empty data\n samples. Defaults to None.\n\n Returns:\n List[DataSample]: The updated data samples.\n \"\"\"\n n_samples = padding_info.shape[0]\n if data_samples is None:\n data_samples = [DataSample() for _ in range(n_samples)]\n else:\n assert len(data_samples) == n_samples, (\n f'The length of \\'data_samples\\'({len(data_samples)}) and '\n f'\\'padding_info\\'({n_samples}) are inconsistent. Please '\n 'check your inputs.')\n\n # update padding info\n for pad_size, data_sample in zip(padding_info, data_samples):\n data_sample.set_metainfo({'padding_size': pad_size})\n\n # update channel order\n if channel_order_info is not None:\n for data_sample in data_samples:\n for key, channel_order in channel_order_info.items():\n data_sample.set_metainfo(\n {f'{key}_output_channel_order': channel_order})\n\n self._done_padding = padding_info.sum() != 0\n return data_samples\n\n def _do_conversion(self,\n inputs: Tensor,\n inputs_order: str = 'BGR',\n target_order: Optional[str] = None\n ) -> Tuple[Tensor, str]:\n \"\"\"Conduct channel order conversion for *a batch of inputs*, and return\n the converted inputs and order after conversion.\n\n inputs_order:\n * RGB / RGB: Convert to target order.\n * SINGLE: Do not change\n \"\"\"\n if (target_order is None\n or inputs_order.upper() == target_order.upper()):\n # order is not changed, return the input one\n return inputs, inputs_order\n\n def conversion(inputs, channel_index):\n if inputs.shape[channel_index] == 4:\n new_index = [2, 1, 0, 3]\n else:\n new_index = [2, 1, 0]\n\n # do conversion\n inputs = torch.index_select(\n inputs, channel_index,\n torch.LongTensor(new_index).to(inputs.device))\n return inputs\n\n channel_index = self._parse_channel_index(inputs)\n\n if inputs_order.upper() in ['RGB', 'BGR']:\n inputs = conversion(inputs, channel_index)\n return inputs, target_order\n elif inputs_order.upper() == 'SINGLE':\n if not self._conversion_warning_raised:\n print_log(\n 'Cannot convert inputs with \\'single\\' channel order '\n f'to \\'output_channel_order\\' ({self.output_channel_order}'\n '). Return without conversion.', 'current', WARNING)\n self._conversion_warning_raised = True\n return inputs, inputs_order\n else:\n raise ValueError(f'Unsupported inputs order \\'{inputs_order}\\'.')\n\n def _do_norm(self,\n inputs: Tensor,\n do_norm: Optional[bool] = None) -> Tensor:\n\n do_norm = self._enable_normalize if do_norm is None else do_norm\n\n if do_norm:\n if self.input_view is None:\n if inputs.ndim == 2: # special case for (H*W, C) tensor\n target_shape = [1, -1]\n else:\n target_shape = [1 for _ in range(inputs.ndim - 3)\n ] + [-1, 1, 1]\n else:\n target_shape = self.input_view\n mean = self.mean.view(target_shape)\n std = self.std.view(target_shape)\n\n # shape checking to avoid broadcast a single channel tensor to 3\n channel_idx = self._parse_channel_index(inputs)\n n_channel_inputs = inputs.shape[channel_idx]\n n_channel_mean = mean.shape[channel_idx]\n n_channel_std = std.shape[channel_idx]\n assert n_channel_mean == 1 or n_channel_mean == n_channel_inputs\n assert n_channel_std == 1 or n_channel_std == n_channel_inputs\n\n inputs = (inputs - mean) / std\n return inputs\n\n def _preprocess_image_tensor(self,\n inputs: Tensor,\n data_samples: Optional[SampleList] = None,\n key: str = 'img'\n ) -> Tuple[Tensor, SampleList]:\n \"\"\"Preprocess a batch of image tensor and update metainfo to\n corresponding data samples.\n\n Args:\n inputs (Tensor): Image tensor with shape (C, H, W), (N, C, H, W) or\n (N, t, C, H, W) to preprocess.\n data_samples (List[DataSample], optional): The data samples\n of corresponding inputs. If not passed, a list of empty data\n samples will be initialized to save metainfo. Defaults to None.\n key (str): The key of image tensor in data samples.\n Defaults to 'img'.\n\n Returns:\n Tuple[Tensor, List[DataSample]]: The preprocessed image tensor\n and updated data samples.\n \"\"\"\n if not data_samples: # none or empty list\n data_samples = [DataSample() for _ in range(inputs.shape[0])]\n\n assert inputs.dim() in [\n 3, 4, 5\n ], ('The input of `_preprocess_image_tensor` should be a (C, H, W), '\n '(N, C, H, W) or (N, t, C, H, W)tensor, but got a tensor with '\n f'shape: {inputs.shape}')\n channel_order = self._parse_batch_channel_order(\n key, inputs, data_samples)\n inputs, output_channel_order = self._do_conversion(\n inputs, channel_order, self.output_channel_order)\n inputs = self._do_norm(inputs)\n h, w = inputs.shape[-2:]\n target_h = math.ceil(h / self.pad_size_divisor) * self.pad_size_divisor\n target_w = math.ceil(w / self.pad_size_divisor) * self.pad_size_divisor\n pad_h = target_h - h\n pad_w = target_w - w\n batch_inputs = F.pad(inputs, (0, pad_w, 0, pad_h), self.pad_mode,\n self.pad_value)\n\n padding_size = torch.FloatTensor((0, pad_h, pad_w))[None, ...]\n padding_size = padding_size.repeat(inputs.shape[0], 1)\n data_samples = self._update_metainfo(padding_size,\n {key: output_channel_order},\n data_samples)\n return batch_inputs, data_samples\n\n def _preprocess_image_list(self,\n tensor_list: List[Tensor],\n data_samples: Optional[SampleList],\n key: str = 'img') -> Tuple[Tensor, SampleList]:\n \"\"\"Preprocess a list of image tensor and update metainfo to\n corresponding data samples.\n\n Args:\n tensor_list (List[Tensor]): Image tensor list to be preprocess.\n data_samples (List[DataSample], optional): The data samples\n of corresponding inputs. If not passed, a list of empty data\n samples will be initialized to save metainfo. Defaults to None.\n key (str): The key of tensor list in data samples.\n Defaults to 'img'.\n\n Returns:\n Tuple[Tensor, List[DataSample]]: The preprocessed image tensor\n and updated data samples.\n \"\"\"\n if not data_samples: # none or empty list\n data_samples = [DataSample() for _ in range(len(tensor_list))]\n\n channel_order = self._parse_batch_channel_order(\n key, tensor_list, data_samples)\n dim = tensor_list[0].dim()\n assert all([\n tensor.ndim == dim for tensor in tensor_list\n ]), ('Expected the dimensions of all tensors must be the same, '\n f'but got {[tensor.ndim for tensor in tensor_list]}')\n\n num_img = len(tensor_list)\n all_sizes: torch.Tensor = torch.Tensor(\n [tensor.shape for tensor in tensor_list])\n max_sizes = torch.ceil(\n torch.max(all_sizes, dim=0)[0] /\n self.pad_size_divisor) * self.pad_size_divisor\n padding_sizes = max_sizes - all_sizes\n # The dim of channel and frame index should not be padded.\n padding_sizes[:, :-2] = 0\n if padding_sizes.sum() == 0:\n stacked_tensor = torch.stack(tensor_list)\n stacked_tensor, output_channel_order = self._do_conversion(\n stacked_tensor, channel_order, self.output_channel_order)\n stacked_tensor = self._do_norm(stacked_tensor)\n data_samples = self._update_metainfo(padding_sizes,\n {key: output_channel_order},\n data_samples)\n return stacked_tensor, data_samples\n\n # `pad` is the second arguments of `F.pad`. If pad is (1, 2, 3, 4),\n # it means that padding the last dim with 1(left) 2(right), padding the\n # penultimate dim to 3(top) 4(bottom). The order of `pad` is opposite\n # of the `padded_sizes`. Therefore, the `padded_sizes` needs to be\n # reversed, and only odd index of pad should be assigned to keep\n # padding \"right\" and \"bottom\".\n pad = torch.zeros(num_img, 2 * dim, dtype=torch.int)\n pad[:, 1::2] = padding_sizes[:, range(dim - 1, -1, -1)]\n batch_tensor = []\n for idx, tensor in enumerate(tensor_list):\n paded_tensor = F.pad(tensor, tuple(pad[idx].tolist()),\n self.pad_mode, self.pad_value)\n batch_tensor.append(paded_tensor)\n stacked_tensor = torch.stack(batch_tensor)\n stacked_tensor, output_channel_order = self._do_conversion(\n stacked_tensor, channel_order, self.output_channel_order)\n stacked_tensor = self._do_norm(stacked_tensor)\n data_samples = self._update_metainfo(padding_sizes,\n {key: output_channel_order},\n data_samples)\n # return stacked_tensor, padding_sizes\n return stacked_tensor, data_samples\n\n def _preprocess_dict_inputs(self,\n batch_inputs: dict,\n data_samples: Optional[SampleList] = None\n ) -> Tuple[dict, SampleList]:\n \"\"\"Preprocess dict type inputs.\n\n Args:\n batch_inputs (dict): Input dict.\n data_samples (List[DataSample], optional): The data samples\n of corresponding inputs. If not passed, a list of empty data\n samples will be initialized to save metainfo. Defaults to None.\n\n Returns:\n Tuple[dict, List[DataSample]]: The preprocessed dict and\n updated data samples.\n \"\"\"\n pad_size_dict = dict()\n for k, inputs in batch_inputs.items():\n # handle concentrate for values in list\n if isinstance(inputs, list):\n if k in self._NON_CONCATENATE_KEYS:\n # use the first value\n assert all([\n inputs[0] == inp for inp in inputs\n ]), (f'NON_CONCENTATE_KEY \\'{k}\\' should be consistency '\n 'among the data list.')\n batch_inputs[k] = inputs[0]\n else:\n assert all([\n isinstance(inp, torch.Tensor) for inp in inputs\n ]), ('Only support stack list of Tensor in inputs dict. '\n f'But \\'{k}\\' is list of \\'{type(inputs[0])}\\'.')\n\n if k not in self._NON_IMAGE_KEYS:\n # preprocess as image\n inputs, data_samples = self._preprocess_image_list(\n inputs, data_samples, k)\n pad_size_dict[k] = [\n data.metainfo.get('padding_size')\n for data in data_samples\n ]\n else:\n # only stack\n inputs = torch.stack(inputs)\n\n batch_inputs[k] = inputs\n\n elif isinstance(inputs, Tensor) and k not in self._NON_IMAGE_KEYS:\n batch_inputs[k], data_samples = \\\n self._preprocess_image_tensor(inputs, data_samples, k)\n pad_size_dict[k] = [\n data.metainfo.get('padding_size') for data in data_samples\n ]\n\n # NOTE: we only support all key shares the same padding size\n if pad_size_dict:\n padding_sizes = list(pad_size_dict.values())[0]\n padding_key = list(pad_size_dict.keys())[0]\n for idx, tar_size in enumerate(padding_sizes):\n for k, sizes in pad_size_dict.items():\n if (tar_size != sizes[idx]).any():\n raise ValueError(\n f'All fields of a data sample should share the '\n 'same padding size, but got different size for '\n f'\\'{k}\\'(\\'{sizes[idx]}\\') and \\'{padding_key}\\''\n f'(\\'{tar_size}\\') at index {idx}.Please check '\n 'your data carefully.')\n\n return batch_inputs, data_samples\n\n def _preprocess_data_sample(self, data_samples: SampleList,\n training: bool) -> DataSample:\n \"\"\"Preprocess data samples. When `training` is True, fields belong to\n :attr:`self.data_keys` will be converted to\n :attr:`self.output_channel_order` and then normalized by `self.mean`\n and `self.std`. When `training` is False, fields belongs to\n :attr:`self.data_keys` will be attempted to convert to 'BGR' without\n normalization. The corresponding metainfo related to normalization,\n channel order conversion will be updated to data sample as well.\n\n Args:\n data_samples (List[DataSample]): A list of data samples to\n preprocess.\n training (bool): Whether in training mode.\n\n Returns:\n list: The list of processed data samples.\n \"\"\"\n if not training:\n # set default order to BGR in test stage\n target_order, do_norm = 'BGR', False\n else:\n # norm in training, conversion as default (None)\n target_order, do_norm = self.output_channel_order, True\n\n for data_sample in data_samples:\n if not self.data_keys:\n break\n for key in self.data_keys:\n if not hasattr(data_sample, key):\n # do not raise error here\n print_log(f'Cannot find key \\'{key}\\' in data sample.',\n 'current', WARNING)\n break\n\n data = data_sample.get(key)\n data_channel_order = self._parse_channel_order(\n key, data, data_sample)\n data, channel_order = self._do_conversion(\n data, data_channel_order, target_order)\n data = self._do_norm(data, do_norm)\n data_sample.set_data({f'{key}': data})\n data_process_meta = {\n f'{key}_enable_norm': self._enable_normalize,\n f'{key}_output_channel_order': channel_order,\n f'{key}_mean': self.mean,\n f'{key}_std': self.std\n }\n data_sample.set_metainfo(data_process_meta)\n\n if self.stack_data_sample:\n assert is_seq_of(data_samples, DataSample), (\n 'Only support \\'stack_data_sample\\' for DataSample '\n 'object. Please refer to \\'DataSample.stack\\'.')\n return DataSample.stack(data_samples)\n return data_samples\n\n def forward(self, data: dict, training: bool = False) -> dict:\n \"\"\"Performs normalization、padding and channel order conversion.\n\n Args:\n data (dict): Input data to process.\n training (bool): Whether to in training mode. Default: False.\n\n Returns:\n dict: Data in the same format as the model input.\n \"\"\"\n data = self.cast_data(data)\n _batch_inputs = data['inputs']\n _batch_data_samples = data.get('data_samples', None)\n\n # process input\n if isinstance(_batch_inputs, torch.Tensor):\n _batch_inputs, _batch_data_samples = \\\n self._preprocess_image_tensor(\n _batch_inputs, _batch_data_samples)\n elif is_seq_of(_batch_inputs, torch.Tensor):\n _batch_inputs, _batch_data_samples = \\\n self._preprocess_image_list(\n _batch_inputs, _batch_data_samples)\n elif isinstance(_batch_inputs, dict):\n _batch_inputs, _batch_data_samples = \\\n self._preprocess_dict_inputs(\n _batch_inputs, _batch_data_samples)\n elif is_seq_of(_batch_inputs, dict):\n # convert list of dict to dict of list\n keys = _batch_inputs[0].keys()\n dict_input = {k: [inp[k] for inp in _batch_inputs] for k in keys}\n _batch_inputs, _batch_data_samples = \\\n self._preprocess_dict_inputs(\n dict_input, _batch_data_samples)\n else:\n raise ValueError('Only support following inputs types: '\n '\\'torch.Tensor\\', \\'List[torch.Tensor]\\', '\n '\\'dict\\', \\'List[dict]\\'. But receive '\n f'\\'{type(_batch_inputs)}\\'.')\n data['inputs'] = _batch_inputs\n\n # process data samples\n if _batch_data_samples:\n _batch_data_samples = self._preprocess_data_sample(\n _batch_data_samples, training)\n\n data['data_samples'] = _batch_data_samples\n\n return data\n\n def destruct(self,\n outputs: Tensor,\n data_samples: Union[SampleList, DataSample, None] = None,\n key: str = 'img') -> Union[list, Tensor]:\n \"\"\"Destruct padding, normalization and convert channel order to BGR if\n could. If `data_samples` is a list, outputs will be destructed as a\n batch of tensor. If `data_samples` is a `DataSample`, `outputs` will be\n destructed as a single tensor.\n\n Before feed model outputs to visualizer and evaluator, users should\n call this function for model outputs and inputs.\n\n Use cases:\n\n >>> # destruct model outputs.\n >>> # model outputs share the same preprocess information with inputs\n >>> # ('img') therefore use 'img' as key\n >>> feats = self.forward_tensor(inputs, data_samples, **kwargs)\n >>> feats = self.data_preprocessor.destruct(feats, data_samples, 'img')\n\n >>> # destruct model inputs for visualization\n >>> for idx, data_sample in enumerate(data_samples):\n >>> destructed_input = self.data_preprocessor.destruct(\n >>> inputs[idx], data_sample, key='img')\n >>> data_sample.set_data({'input': destructed_input})\n\n Args:\n outputs (Tensor): Tensor to destruct.\n data_samples (Union[SampleList, DataSample], optional): Data\n samples (or data sample) corresponding to `outputs`.\n Defaults to None\n key (str): The key of field in data sample. Defaults to 'img'.\n\n Returns:\n Union[list, Tensor]: Destructed outputs.\n \"\"\"\n # NOTE: only support passing tensor sample, if the output of model is\n # a dict, users should call this manually.\n # Since we do not know whether the outputs is image tensor.\n _batch_outputs = self._destruct_norm_and_conversion(\n outputs, data_samples, key)\n _batch_outputs = self._destruct_padding(_batch_outputs, data_samples)\n _batch_outputs = _batch_outputs.clamp_(0, 255)\n return _batch_outputs\n\n def _destruct_norm_and_conversion(self, batch_tensor: Tensor,\n data_samples: Union[SampleList,\n DataSample, None],\n key: str) -> Tensor:\n \"\"\"De-norm and de-convert channel order. Noted that, we de-norm first,\n and then de-conversion, since mean and std used in normalization is\n based on channel order after conversion.\n\n Args:\n batch_tensor (Tensor): Tensor to destruct.\n data_samples (Union[SampleList, DataSample], optional): Data\n samples (or data sample) corresponding to `outputs`.\n key (str): The key of field in data sample.\n\n Returns:\n Tensor: Destructed tensor.\n \"\"\"\n\n output_key = f'{key}_output'\n # get channel order from data sample\n if isinstance(data_samples, list):\n inputs_order = self._parse_batch_channel_order(\n output_key, batch_tensor, data_samples)\n else:\n inputs_order = self._parse_channel_order(output_key, batch_tensor,\n data_samples)\n if self._enable_normalize:\n if self.output_view is None:\n if batch_tensor.ndim == 2: # special case for (H*W, C) tensor\n target_shape = [1, -1]\n else:\n target_shape = [1 for _ in range(batch_tensor.ndim - 3)\n ] + [-1, 1, 1]\n else:\n target_shape = self.output_view\n mean = self.mean.view(target_shape)\n std = self.std.view(target_shape)\n batch_tensor = batch_tensor * std + mean\n\n # convert output to 'BGR' if able\n batch_tensor, _ = self._do_conversion(\n batch_tensor, inputs_order=inputs_order, target_order='BGR')\n\n return batch_tensor\n\n def _destruct_padding(self,\n batch_tensor: Tensor,\n data_samples: Union[SampleList, DataSample, None],\n same_padding: bool = True) -> Union[list, Tensor]:\n \"\"\"Destruct padding of the input tensor.\n\n Args:\n batch_tensor (Tensor): Tensor to destruct.\n data_samples (Union[SampleList, DataSample], optional): Data\n samples (or data sample) corresponding to `outputs`. If\n same_padding (bool): Whether all samples will un-padded with the\n padding info of the first sample, and return a stacked\n un-padded tensor. Otherwise each sample will be unpadded with\n padding info saved in corresponding data samples, and return a\n list of un-padded tensor, since each un-padded tensor may have\n the different shape. Defaults to True.\n\n Returns:\n Union[list, Tensor]: Destructed outputs.\n \"\"\"\n # NOTE: If same padding, batch_tensor will un-padded with the padding\n # info # of the first sample and return a Unpadded tensor. Otherwise,\n # input tensor # will un-padded with the corresponding padding info\n # saved in data samples and return a list of tensor.\n if data_samples is None:\n return batch_tensor\n\n if isinstance(data_samples, list):\n is_batch_data = True\n if 'padding_size' in data_samples[0].metainfo_keys():\n pad_infos = [\n sample.metainfo['padding_size'] for sample in data_samples\n ]\n else:\n pad_infos = None\n else:\n if 'padding_size' in data_samples.metainfo_keys():\n pad_infos = data_samples.metainfo['padding_size']\n else:\n pad_infos = None\n # NOTE: here we assume padding size in metainfo are saved as tensor\n if not isinstance(pad_infos, list):\n pad_infos = [pad_infos]\n is_batch_data = False\n else:\n is_batch_data = True\n if all([pad_info is None for pad_info in pad_infos]):\n pad_infos = None\n\n if not is_batch_data:\n batch_tensor = batch_tensor[None, ...]\n\n if pad_infos is None:\n if self._done_padding:\n print_log(\n 'Cannot find padding information (\\'padding_size\\') in '\n 'meta info of \\'data_samples\\'. Please check whether '\n 'you have called \\'self.forward\\' properly.', 'current',\n WARNING)\n return batch_tensor if is_batch_data else batch_tensor[0]\n\n if same_padding:\n # un-pad with the padding info of the first sample\n padded_h, padded_w = pad_infos[0][-2:]\n padded_h, padded_w = int(padded_h), int(padded_w)\n h, w = batch_tensor.shape[-2:]\n batch_tensor = batch_tensor[..., :h - padded_h, :w - padded_w]\n return batch_tensor if is_batch_data else batch_tensor[0]\n else:\n # un-pad with the corresponding padding info\n unpadded_tensors = []\n for idx, pad_info in enumerate(pad_infos):\n padded_h, padded_w = pad_info[-2:]\n padded_h = int(padded_h)\n padded_w = int(padded_w)\n h, w = batch_tensor[idx].shape[-2:]\n unpadded_tensor = batch_tensor[idx][..., :h - padded_h, :w -\n padded_w]\n unpadded_tensors.append(unpadded_tensor)\n return unpadded_tensors if is_batch_data else unpadded_tensors[0]\n" }, { "path": "mmagic/models/data_preprocessors/mattor_preprocessor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nfrom logging import WARNING\nfrom typing import Dict, List, Optional, Sequence, Tuple, Union\n\nimport torch\nfrom mmengine import print_log\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom .data_preprocessor import DataPreprocessor\n\nDataSamples = Optional[Union[list, torch.Tensor]]\nForwardResults = Union[Dict[str, torch.Tensor], List[DataSample],\n Tuple[torch.Tensor], torch.Tensor]\nMEAN_STD_TYPE = Union[Sequence[Union[float, int]], float, int]\n\n\n@MODELS.register_module()\nclass MattorPreprocessor(DataPreprocessor):\n \"\"\"DataPreprocessor for matting models.\n\n See base class ``DataPreprocessor`` for detailed information.\n\n Workflow as follow :\n\n - Collate and move data to the target device.\n - Convert inputs from bgr to rgb if the shape of input is (3, H, W).\n - Normalize image with defined std and mean.\n - Stack inputs to batch_inputs.\n\n Args:\n mean (Sequence[float or int], float or int, optional): The pixel mean\n of image channels. Noted that normalization operation is performed\n *after channel order conversion*. If it is not specified, images\n will not be normalized. Defaults None.\n std (Sequence[float or int], float or int, optional): The pixel\n standard deviation of image channels. Noted that normalization\n operation is performed *after channel order conversion*. If it is\n not specified, images will not be normalized. Defaults None.\n proc_trimap (str): Methods to process gt tensors.\n Default: 'rescale_to_zero_one'.\n Available options are ``rescale_to_zero_one`` and ``as-is``.\n stack_data_sample (bool): Whether stack a list of data samples to one\n data sample. Only support with input data samples are\n `DataSamples`. Defaults to True.\n \"\"\"\n\n def __init__(self,\n mean: MEAN_STD_TYPE = [123.675, 116.28, 103.53],\n std: MEAN_STD_TYPE = [58.395, 57.12, 57.375],\n output_channel_order: str = 'RGB',\n proc_trimap: str = 'rescale_to_zero_one',\n stack_data_sample=True):\n # specific data_keys for matting task\n data_keys = ['gt_fg', 'gt_bg', 'gt_merged', 'gt_alpha']\n super().__init__(\n mean,\n std,\n output_channel_order=output_channel_order,\n data_keys=data_keys,\n stack_data_sample=stack_data_sample)\n\n self.proc_trimap = proc_trimap\n # self.proc_gt = proc_gt\n\n def _proc_batch_trimap(self, batch_trimaps: torch.Tensor):\n\n if self.proc_trimap == 'rescale_to_zero_one':\n batch_trimaps = batch_trimaps / 255.0 # uint8->float32\n elif self.proc_trimap == 'as_is':\n batch_trimaps = batch_trimaps.to(torch.float32)\n else:\n raise ValueError(\n f'proc_trimap = {self.proc_trimap} is not supported.')\n\n return batch_trimaps\n\n def _preprocess_data_sample(self, data_samples: SampleList,\n training: bool) -> list:\n \"\"\"Preprocess data samples. When `training` is True, fields belong to\n :attr:`self.data_keys` will be converted to\n :attr:`self.output_channel_order` and *divided by 255*. When `training`\n is False, fields belongs to :attr:`self.data_keys` will be attempted\n to convert to 'BGR' without normalization. The corresponding metainfo\n related to normalization, channel order conversion will be updated to\n data sample as well.\n\n Args:\n data_samples (List[DataSample]): A list of data samples to\n preprocess.\n training (bool): Whether in training mode.\n\n Returns:\n list: The list of processed data samples.\n \"\"\"\n if not training:\n # set default order to BGR in test stage\n target_order = 'BGR'\n else:\n # conversion as default (None)\n target_order = self.output_channel_order\n\n for data_sample in data_samples:\n for key in self.data_keys:\n if not hasattr(data_sample, key):\n # do not raise error here\n if key != 'gt_fg' and not training:\n # gt_fg is not required in test stage, therefore do\n # not print log\n print_log(f'Cannot find key \\'{key}\\' in data sample.',\n 'current', WARNING)\n break\n\n data = data_sample.get(key)\n data_channel_order = self._parse_channel_order(\n key, data, data_sample)\n data, channel_order = self._do_conversion(\n data, data_channel_order, target_order)\n if training:\n data = data / 255. # NOTE: divided by 255\n data_sample.set_data({f'{key}': data})\n data_process_meta = {\n f'{key}_enable_norm': self._enable_normalize,\n f'{key}_output_channel_order': channel_order,\n f'{key}_mean': self.mean,\n f'{key}_std': self.std\n }\n data_sample.set_metainfo(data_process_meta)\n\n if self.stack_data_sample:\n return DataSample.stack(data_samples)\n\n return data_samples\n\n def forward(self,\n data: Sequence[dict],\n training: bool = False) -> Tuple[torch.Tensor, list]:\n \"\"\"Pre-process input images, trimaps, ground-truth as configured.\n\n Args:\n data (Sequence[dict]): data sampled from dataloader.\n training (bool): Whether to enable training time augmentation.\n Default: False.\n\n Returns:\n Tuple[torch.Tensor, list]:\n Batched inputs and list of data samples.\n \"\"\"\n if not training:\n # Image may of different size when testing\n assert len(data['data_samples']) == 1, (\n 'only batch_size=1 is supported for testing.')\n data = super().forward(data, training=training)\n\n batch_images = data['inputs']\n batch_trimaps = data['data_samples'].trimap\n batch_trimaps = self._proc_batch_trimap(batch_trimaps)\n\n # Stack image and trimap along channel dimension\n # All existing models do concat at the start of forwarding\n # and data_sample is a very complex data structure\n # so this is a simple work-around to make codes simpler\n # print(f\"batch_trimap.dtype = {batch_trimap.dtype}\")\n\n assert batch_images.ndim == batch_trimaps.ndim == 4\n assert batch_images.shape[-2:] == batch_trimaps.shape[-2:], (\n 'Expect merged.shape[-2:] == trimap.shape[-2:], '\n f'but got {batch_images.shape[-2:]} vs {batch_trimaps.shape[-2:]}')\n\n # N, (4/6), H, W\n batch_inputs = torch.cat((batch_images, batch_trimaps), dim=1)\n\n data['inputs'] = batch_inputs\n return data\n" }, { "path": "mmagic/models/diffusion_schedulers/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\nfrom typing import Any, List\n\nfrom mmagic.utils import try_import\nfrom .ddim_scheduler import EditDDIMScheduler\nfrom .ddpm_scheduler import EditDDPMScheduler\n\n\nclass SchedulerWrapper:\n \"\"\"Wrapper for schedulers from HuggingFace Diffusers. This wrapper will be\n set a attribute called `_scheduler_cls` by wrapping function and will be\n used to initialize the model structure.\n\n Example:\n >>> 1. Load pretrained model from HuggingFace Space.\n >>> config = dict(\n >>> type='DDPMScheduler',\n >>> from_pretrained='lllyasviel/sd-controlnet-canny',\n >>> subfolder='scheduler')\n >>> ddpm_scheduler = DIFFUSION_SCHEDULERS.build(config)\n\n >>> 2. Initialize model with own defined arguments\n >>> config = dict(\n >>> type='EulerDiscreteScheduler',\n >>> num_train_timesteps=2000,\n >>> beta_schedule='scaled_linear')\n >>> euler_scheduler = DIFFUSION_SCHEDULERS.build(config)\n\n Args:\n from_pretrained (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Please refers to\n `diffusers.model.modeling_utils.ModelMixin.from_pretrained`\n for more detail. Defaults to None.\n\n *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs\n will be passed to `from_pretrained` function. Otherwise, *args\n and **kwargs will be used to initialize the model by\n `self._module_cls(*args, **kwargs)`.\n \"\"\"\n\n def __init__(self,\n from_pretrained=None,\n from_config=None,\n *args,\n **kwargs):\n\n scheduler_cls = self._scheduler_cls\n\n self._from_pretrained = from_pretrained\n self._from_config = from_config\n if self._from_pretrained:\n self.scheduler = scheduler_cls.from_pretrained(\n from_pretrained, *args, **kwargs)\n elif self._from_config:\n self.scheduler = scheduler_cls.from_config(from_config, *args,\n **kwargs)\n else:\n self.scheduler = scheduler_cls(*args, **kwargs)\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"This function provide a way to access the attributes of the wrapped\n scheduler.\n\n Args:\n name (str): The name of the attribute.\n\n Returns:\n Any: The got attribute.\n \"\"\"\n\n try:\n return getattr(self.scheduler, name)\n except AttributeError:\n raise AttributeError(f'{name} cannot be found in both '\n f'\\'{self.__class__.__name__}\\' and '\n f'\\'{self.__class__.__name__}.scheduler\\'.')\n\n def __repr__(self):\n \"\"\"The representation of the wrapper.\"\"\"\n s = super().__repr__()\n prefix = f'Wrapped Scheduler Class: {self._scheduler_cls}\\n'\n prefix += f'Wrapped Scheduler Name: {self._scheduler_name}\\n'\n if self._from_pretrained:\n prefix += f'From Pretrained: {self._from_pretrained}\\n'\n if self._from_config:\n prefix += f'From Config: {self._from_config}\\n'\n s = prefix + s\n return s\n\n\ndef register_diffusers_schedulers() -> List[str]:\n \"\"\"Register schedulers in ``diffusers.schedulers`` to the\n ``DIFFUSION_SCHEDULERS`` registry. Specifically, the registered schedulers\n from diffusers define the methodology for iteratively adding noise to an\n image or for updating a sample based on model outputs. See more details\n about schedulers in diffusers here:\n https://huggingface.co/docs/diffusers/api/schedulers/overview.\n\n Returns:\n List[str]: A list of registered DIFFUSION_SCHEDULERS' name.\n \"\"\"\n\n import inspect\n\n from mmagic.registry import DIFFUSION_SCHEDULERS\n\n diffusers = try_import('diffusers')\n if diffusers is None:\n warnings.warn('Diffusion Schedulers are not registered as expect. '\n 'If you want to use diffusion models, '\n 'please install diffusers>=0.12.0.')\n return None\n\n def gen_wrapped_cls(scheduler, scheduler_name):\n return type(\n scheduler_name, (SchedulerWrapper, ),\n dict(\n _scheduler_cls=scheduler,\n _scheduler_name=scheduler_name,\n __module__=__name__))\n\n DIFFUSERS_SCHEDULERS = []\n for module_name in dir(diffusers.schedulers):\n if module_name.startswith('Flax'):\n continue\n elif module_name.endswith('Scheduler'):\n _scheduler = getattr(diffusers.schedulers, module_name)\n if inspect.isclass(_scheduler):\n wrapped_scheduler = gen_wrapped_cls(_scheduler, module_name)\n DIFFUSION_SCHEDULERS.register_module(\n name=module_name, module=wrapped_scheduler)\n DIFFUSERS_SCHEDULERS.append(module_name)\n return DIFFUSERS_SCHEDULERS\n\n\nREGISTERED_DIFFUSERS_SCHEDULERS = register_diffusers_schedulers()\n\n__all__ = ['EditDDIMScheduler', 'EditDDPMScheduler']\n" }, { "path": "mmagic/models/diffusion_schedulers/ddim_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom mmagic.models.utils.diffusion_utils import betas_for_alpha_bar\nfrom mmagic.registry import DIFFUSION_SCHEDULERS\n\n\n@DIFFUSION_SCHEDULERS.register_module()\nclass EditDDIMScheduler:\n \"\"\"```EditDDIMScheduler``` support the diffusion and reverse process\n formulated in https://arxiv.org/abs/2010.02502.\n\n The code is heavily influenced by https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py. # noqa\n The difference is that we ensemble gradient-guided sampling in step function.\n\n Args:\n num_train_timesteps (int, optional): _description_. Defaults to 1000.\n beta_start (float, optional): _description_. Defaults to 0.0001.\n beta_end (float, optional): _description_. Defaults to 0.02.\n beta_schedule (str, optional): _description_. Defaults to \"linear\".\n variance_type (str, optional): _description_. Defaults to 'learned_range'.\n timestep_values (_type_, optional): _description_. Defaults to None.\n clip_sample (bool, optional): _description_. Defaults to True.\n set_alpha_to_one (bool, optional): _description_. Defaults to True.\n \"\"\"\n\n def __init__(\n self,\n num_train_timesteps=1000,\n beta_start=0.0001,\n beta_end=0.02,\n beta_schedule='linear',\n variance_type='learned_range',\n timestep_values=None,\n clip_sample=True,\n set_alpha_to_one=True,\n ):\n self.num_train_timesteps = num_train_timesteps\n self.beta_start = beta_start\n self.beta_end = beta_end\n self.beta_schedule = beta_schedule\n self.variance_type = variance_type\n self.timestep_values = timestep_values\n self.clip_sample = clip_sample\n self.set_alpha_to_one = set_alpha_to_one\n\n if beta_schedule == 'linear':\n self.betas = np.linspace(\n beta_start, beta_end, num_train_timesteps, dtype=np.float32)\n elif beta_schedule == 'scaled_linear':\n # this schedule is very specific to the latent diffusion model.\n self.betas = np.linspace(\n beta_start**0.5,\n beta_end**0.5,\n num_train_timesteps,\n dtype=np.float32)**2\n elif beta_schedule == 'squaredcos_cap_v2':\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(\n f'{beta_schedule} does is not implemented for {self.__class__}'\n )\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = np.cumprod(self.alphas, axis=0)\n\n # At every step in ddim, we are looking into the\n # previous alphas_cumprod. For the final step,\n # there is no previous alphas_cumprod because we are already\n # at 0 `set_alpha_to_one` decides whether we set this parameter\n # simply to one or whether we use the final alpha of the\n # \"non-previous\" one.\n self.final_alpha_cumprod = np.array(\n 1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()\n\n def set_timesteps(self, num_inference_steps, offset=0):\n \"\"\"set time steps.\"\"\"\n\n self.num_inference_steps = num_inference_steps\n self.timesteps = np.arange(\n 0, self.num_train_timesteps,\n self.num_train_timesteps // self.num_inference_steps)[::-1].copy()\n self.timesteps += offset\n\n def scale_model_input(self,\n sample: torch.FloatTensor,\n timestep: Optional[int] = None) -> torch.FloatTensor:\n \"\"\"Ensures interchangeability with schedulers that need to scale the\n denoising model input depending on the current timestep.\n\n Args:\n sample (`torch.FloatTensor`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `torch.FloatTensor`: scaled input sample\n \"\"\"\n\n return sample\n\n def _get_variance(self, timestep, prev_timestep):\n \"\"\"get variance.\"\"\"\n\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[\n prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n variance = (beta_prod_t_prev /\n beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n return variance\n\n def step(\n self,\n model_output: Union[torch.FloatTensor, np.ndarray],\n timestep: int,\n sample: Union[torch.FloatTensor, np.ndarray],\n cond_fn=None,\n cond_kwargs={},\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n ):\n \"\"\"step forward.\"\"\"\n\n output = {}\n if self.num_inference_steps is None:\n raise ValueError(\"Number of inference steps is 'None', '\\\n 'you need to run 'set_timesteps' '\\\n 'after creating the scheduler\")\n\n pred = None\n if isinstance(model_output, dict):\n pred = model_output['pred']\n model_output = model_output['eps']\n elif model_output.shape[1] == sample.shape[\n 1] * 2 and self.variance_type in ['learned', 'learned_range']:\n model_output, _ = torch.split(model_output, sample.shape[1], dim=1)\n else:\n if not model_output.shape == sample.shape:\n raise TypeError\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # noqa\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointingc to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = (\n timestep - self.num_train_timesteps // self.num_inference_steps)\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[\n prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf # noqa\n pred_original_sample = (sample - (\n (beta_prod_t)**(0.5)) * model_output) / alpha_prod_t**(0.5)\n if pred is not None:\n pred_original_sample = pred\n\n gradient = 0.\n if cond_fn is not None:\n if cond_fn.__name__ == 'classifier_grad':\n y = cond_kwargs['y']\n classifier = cond_kwargs['classifier']\n classifier_scale = cond_kwargs['classifier_scale']\n gradient = cond_fn(\n classifier,\n sample,\n timestep,\n y=y,\n classifier_scale=classifier_scale)\n else:\n gradient = cond_fn(\n cond_kwargs.pop('unet'), self, sample, timestep,\n beta_prod_t, cond_kwargs.pop('model_stats'), **cond_kwargs)\n model_output = model_output - (beta_prod_t**0.5) * gradient\n pred_original_sample = (\n sample -\n (beta_prod_t**(0.5)) * model_output) / alpha_prod_t**(0.5)\n # 4. Clip \"predicted x_0\"\n if self.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance**(0.5)\n output.update(dict(sigma=std_dev_t))\n\n if use_clipped_model_output:\n # the model_output is always\n # re-derived from the clipped x_0 in Glide\n model_output = (sample - (alpha_prod_t**(0.5)) *\n pred_original_sample) / beta_prod_t**(0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf # noqa\n pred_sample_direction = (1 - alpha_prod_t_prev -\n std_dev_t**2)**(0.5) * model_output\n\n # 7. compute x_t without \"random noise\" of\n # formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_mean = alpha_prod_t_prev**(\n 0.5) * pred_original_sample + pred_sample_direction\n output.update(dict(mean=prev_mean, prev_sample=prev_mean))\n\n if eta > 0:\n device = model_output.device if torch.is_tensor(\n model_output) else 'cpu'\n noise = torch.randn(\n model_output.shape, generator=generator).to(device)\n variance = std_dev_t * noise\n\n if not torch.is_tensor(model_output):\n variance = variance.numpy()\n\n prev_sample = prev_mean + variance\n output.update({'prev_sample': prev_sample})\n\n # NOTE: this x0 is twice computed\n output.update({\n 'original_sample': pred_original_sample,\n 'beta_prod_t': beta_prod_t\n })\n return output\n\n def add_noise(self, original_samples, noise, timesteps):\n \"\"\"add noise.\"\"\"\n\n sqrt_alpha_prod = self.alphas_cumprod[timesteps]**0.5\n sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps])**0.5\n noisy_samples = (\n sqrt_alpha_prod * original_samples +\n sqrt_one_minus_alpha_prod * noise)\n return noisy_samples\n\n def __len__(self):\n return self.num_train_timesteps\n" }, { "path": "mmagic/models/diffusion_schedulers/ddpm_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom mmagic.models.utils.diffusion_utils import betas_for_alpha_bar\nfrom mmagic.registry import DIFFUSION_SCHEDULERS\n\n\n@DIFFUSION_SCHEDULERS.register_module()\nclass EditDDPMScheduler:\n\n def __init__(self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = 'linear',\n trained_betas: Optional[Union[np.array, list]] = None,\n variance_type='fixed_small',\n clip_sample=True):\n \"\"\"```EditDDPMScheduler``` support the diffusion and reverse process\n formulated in https://arxiv.org/abs/2006.11239.\n\n The code is heavily influenced by https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py. # noqa\n\n Args:\n num_train_timesteps (int, optional): The timesteps for training\n process. Defaults to 1000.\n beta_start (float, optional): The beta value at start. The beta\n values will be interpolated from beta_start to beta_end.\n Defaults to 0.0001.\n beta_end (float, optional): The beta value at the end. The beta\n values will be interpolated from beta_start to beta_end.\n Defaults to 0.02.\n beta_schedule (str, optional): The interpolation schedule for beta\n values. Supported choices are 'linear', 'scaled_linear', and\n 'squaredcos_cap_v2'. Defaults to 'linear'.\n trained_betas (list, np.array, optional): betas directly to the\n constructor to bypass `beta_start`, `beta_end` etc. Defaults to None.\n variance_type (str, optional): How denoising unet output variance\n value. Supported choices are 'fixed_small', 'fixed_small_log',\n 'fixed_large', 'fixed_large_log', 'learned', and 'leanred_range'.\n Defaults to 'fixed_small'.\n clip_sample (bool, optional): Whether clip the value of predicted\n original image (x0) to [-1, 1]. Defaults to True.\n \"\"\"\n self.num_train_timesteps = num_train_timesteps\n if trained_betas is not None:\n self.betas = np.asarray(trained_betas)\n elif beta_schedule == 'linear':\n self.betas = np.linspace(\n beta_start, beta_end, num_train_timesteps, dtype=np.float64)\n elif beta_schedule == 'scaled_linear':\n # this schedule is very specific to the latent diffusion model.\n self.betas = np.linspace(\n beta_start**0.5,\n beta_end**0.5,\n num_train_timesteps,\n dtype=np.float32)**2\n elif beta_schedule == 'squaredcos_cap_v2':\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(\n f'{beta_schedule} does is not implemented for {self.__class__}'\n )\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = np.cumprod(self.alphas, axis=0)\n self.one = np.array(1.0)\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()\n\n self.variance_type = variance_type\n self.clip_sample = clip_sample\n\n def set_timesteps(self, num_inference_steps):\n \"\"\"set timesteps.\"\"\"\n\n num_inference_steps = min(self.num_train_timesteps,\n num_inference_steps)\n self.num_inference_steps = num_inference_steps\n self.timesteps = np.arange(\n 0, self.num_train_timesteps,\n self.num_train_timesteps // self.num_inference_steps)[::-1].copy()\n\n def _get_variance(self, t, predicted_variance=None, variance_type=None):\n \"\"\"get variance.\"\"\"\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # noqa\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample # noqa\n variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]\n\n if t == 0:\n log_variance = (1 - alpha_prod_t_prev) / (\n 1 - alpha_prod_t) * self.betas[1]\n else:\n log_variance = np.log(variance)\n\n if variance_type is None:\n variance_type = self.variance_type\n\n # hacks - were probs added for training stability\n if variance_type == 'fixed_small':\n variance = np.clip(variance, a_min=1e-20, a_max=10000)\n # for rl-diffusion_scheduler https://arxiv.org/abs/2205.09991\n elif variance_type == 'fixed_small_log':\n variance = np.log(np.clip(variance, a_min=1e-20, a_max=10000))\n elif variance_type == 'fixed_large':\n variance = self.betas[t]\n elif variance_type == 'fixed_large_log':\n # Glide max_log\n variance = np.log(self.betas[t])\n elif variance_type == 'learned':\n return predicted_variance\n elif variance_type == 'learned_range':\n min_log = log_variance\n max_log = np.log(self.betas[t])\n frac = (predicted_variance + 1) / 2\n log_variance = frac * max_log + (1 - frac) * min_log\n variance = torch.exp(log_variance)\n\n return variance\n\n def step(self,\n model_output: torch.FloatTensor,\n timestep: int,\n sample: torch.FloatTensor,\n predict_epsilon=True,\n cond_fn=None,\n cond_kwargs={},\n generator=None):\n\n t = timestep\n \"\"\"step forward\"\"\"\n\n if model_output.shape[1] == sample.shape[\n 1] * 2 and self.variance_type in ['learned', 'learned_range']:\n model_output, predicted_variance = torch.split(\n model_output, sample.shape[1], dim=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf # noqa\n if predict_epsilon:\n pred_original_sample = (\n (sample - beta_prod_t**(0.5) * model_output) /\n alpha_prod_t**(0.5))\n else:\n pred_original_sample = model_output\n\n # 3. Clip \"predicted x_0\"\n if self.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # noqa\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev**(0.5) *\n self.betas[t]) / beta_prod_t\n current_sample_coeff = self.alphas[t]**(\n 0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_mean = (\n pred_original_sample_coeff * pred_original_sample +\n current_sample_coeff * sample)\n\n # 6. Add noise\n noise = torch.randn_like(model_output)\n sigma = 0\n if t > 0:\n sigma = self._get_variance(\n t, predicted_variance=predicted_variance)**0.5\n\n pred_prev_sample = pred_prev_mean + sigma * noise\n\n gradient = 0.\n if cond_fn is not None:\n y = cond_kwargs['y']\n classifier = cond_kwargs['classifier']\n classifier_scale = cond_kwargs['classifier_scale']\n gradient = cond_fn(\n classifier,\n sample,\n timestep,\n y=y,\n classifier_scale=classifier_scale)\n\n guided_mean = pred_prev_mean + sigma * gradient\n pred_prev_sample = guided_mean + sigma * noise\n\n return {\n 'prev_sample': pred_prev_sample,\n 'mean': pred_prev_mean,\n 'sigma': sigma,\n 'noise': noise\n }\n\n def add_noise(self, original_samples, noise, timesteps):\n \"\"\"add noise.\"\"\"\n\n sqrt_alpha_prod = self.alphas_cumprod[timesteps]**0.5\n sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps])**0.5\n noisy_samples = (\n sqrt_alpha_prod * original_samples +\n sqrt_one_minus_alpha_prod * noise)\n return noisy_samples\n\n def training_loss(self, model, x_0, t):\n raise NotImplementedError(\n 'This function is supposed to return '\n 'a dict containing loss items giving sampled x0 and timestep.')\n\n def sample_timestep(self):\n raise NotImplementedError\n\n def __len__(self):\n return self.num_train_timesteps\n" }, { "path": "mmagic/models/editors/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .animatediff import AnimateDiff, UNet3DConditionMotionModel\nfrom .aotgan import AOTBlockNeck, AOTEncoderDecoder, AOTInpaintor\nfrom .arcface import IDLossModel\nfrom .basicvsr import BasicVSR, BasicVSRNet\nfrom .basicvsr_plusplus_net import BasicVSRPlusPlusNet\nfrom .biggan import BigGAN\nfrom .cain import CAIN, CAINNet\nfrom .controlnet import ControlStableDiffusion\nfrom .cyclegan import CycleGAN\nfrom .dcgan import DCGAN\nfrom .ddpm import DenoisingUnet\nfrom .deblurganv2 import (DeblurGanV2, DeblurGanV2Discriminator,\n DeblurGanV2Generator)\nfrom .deepfillv1 import (ContextualAttentionModule, ContextualAttentionNeck,\n DeepFillDecoder, DeepFillEncoder, DeepFillRefiner,\n DeepFillv1Discriminators, DeepFillv1Inpaintor)\nfrom .deepfillv2 import DeepFillEncoderDecoder\nfrom .dic import (DIC, DICNet, FeedbackBlock, FeedbackBlockCustom,\n FeedbackBlockHeatmapAttention, LightCNN, MaxFeature)\nfrom .dim import DIM\nfrom .disco_diffusion import ClipWrapper, DiscoDiffusion\nfrom .dreambooth import DreamBooth\nfrom .edsr import EDSRNet\nfrom .edvr import EDVR, EDVRNet\nfrom .eg3d import EG3D\nfrom .esrgan import ESRGAN, RRDBNet\nfrom .fastcomposer import FastComposer\nfrom .fba import FBADecoder, FBAResnetDilated\nfrom .flavr import FLAVR, FLAVRNet\nfrom .gca import GCA\nfrom .ggan import GGAN\nfrom .glean import GLEANStyleGANv2\nfrom .global_local import (GLDecoder, GLDilationNeck, GLEncoder,\n GLEncoderDecoder)\nfrom .guided_diffusion import AblatedDiffusionModel\nfrom .iconvsr import IconVSRNet\nfrom .indexnet import (DepthwiseIndexBlock, HolisticIndexBlock,\n IndexedUpsample, IndexNet, IndexNetDecoder,\n IndexNetEncoder)\nfrom .inst_colorization import InstColorization\nfrom .liif import LIIF, MLPRefiner\nfrom .lsgan import LSGAN\nfrom .mspie import MSPIEStyleGAN2, PESinGAN\nfrom .nafnet import NAFBaseline, NAFBaselineLocal, NAFNet, NAFNetLocal\nfrom .pconv import (MaskConvModule, PartialConv2d, PConvDecoder, PConvEncoder,\n PConvEncoderDecoder, PConvInpaintor)\nfrom .pggan import ProgressiveGrowingGAN\nfrom .pix2pix import Pix2Pix\nfrom .plain import PlainDecoder, PlainRefiner\nfrom .rdn import RDNNet\nfrom .real_basicvsr import RealBasicVSR, RealBasicVSRNet\nfrom .real_esrgan import RealESRGAN, UNetDiscriminatorWithSpectralNorm\nfrom .restormer import Restormer\nfrom .sagan import SAGAN\nfrom .singan import SinGAN\nfrom .srcnn import SRCNNNet\nfrom .srgan import SRGAN, ModifiedVGG, MSRResNet\nfrom .stable_diffusion import StableDiffusion, StableDiffusionInpaint\nfrom .stable_diffusion_xl import StableDiffusionXL\nfrom .stylegan1 import StyleGAN1\nfrom .stylegan2 import StyleGAN2\nfrom .stylegan3 import StyleGAN3, StyleGAN3Generator\nfrom .swinir import SwinIRNet\nfrom .tdan import TDAN, TDANNet\nfrom .textual_inversion import TextualInversion\nfrom .tof import TOFlowVFINet, TOFlowVSRNet, ToFResBlock\nfrom .ttsr import LTE, TTSR, SearchTransformer, TTSRDiscriminator, TTSRNet\nfrom .vico import ViCo\nfrom .wgan_gp import WGANGP\n\n__all__ = [\n 'AOTEncoderDecoder', 'AOTBlockNeck', 'AOTInpaintor',\n 'ContextualAttentionNeck', 'ContextualAttentionModule', 'CAIN', 'CAINNet',\n 'DIM', 'DIC', 'DICNet', 'LightCNN', 'FeedbackBlock',\n 'FeedbackBlockHeatmapAttention', 'FeedbackBlockCustom', 'MaxFeature',\n 'FLAVR', 'FLAVRNet', 'ToFResBlock', 'TOFlowVFINet', 'TOFlowVSRNet',\n 'DeepFillEncoder', 'DeepFillEncoderDecoder', 'DeepFillDecoder',\n 'DeepFillRefiner', 'DeepFillv1Inpaintor', 'DeepFillv1Discriminators',\n 'EDSRNet', 'ESRGAN', 'DepthwiseIndexBlock', 'HolisticIndexBlock',\n 'IndexNet', 'IndexNetEncoder', 'IndexedUpsample', 'IndexNetDecoder', 'GCA',\n 'GLEncoderDecoder', 'GLEncoder', 'GLDecoder', 'GLDilationNeck',\n 'PartialConv2d', 'PConvEncoderDecoder', 'PConvEncoder', 'PConvDecoder',\n 'PConvInpaintor', 'MaskConvModule', 'RRDBNet', 'SRCNNNet', 'RRDBNet',\n 'RealESRGAN', 'UNetDiscriminatorWithSpectralNorm', 'EDVR', 'EDVRNet',\n 'TDAN', 'TDANNet', 'BasicVSR', 'BasicVSRNet', 'BasicVSRPlusPlusNet',\n 'IconVSRNet', 'RealBasicVSR', 'RealBasicVSRNet', 'SRGAN', 'MaxFeature',\n 'ModifiedVGG', 'MSRResNet', 'RDNNet', 'LTE', 'TTSR', 'TTSRNet',\n 'TTSRDiscriminator', 'TTSRNet', 'SearchTransformer', 'GLEANStyleGANv2',\n 'LIIF', 'MLPRefiner', 'PlainRefiner', 'PlainDecoder', 'FBAResnetDilated',\n 'FBADecoder', 'WGANGP', 'CycleGAN', 'SAGAN', 'LSGAN', 'GGAN', 'Pix2Pix',\n 'StyleGAN1', 'StyleGAN2', 'StyleGAN3', 'BigGAN', 'DCGAN',\n 'ProgressiveGrowingGAN', 'SinGAN', 'AblatedDiffusionModel',\n 'DiscoDiffusion', 'IDLossModel', 'PESinGAN', 'MSPIEStyleGAN2',\n 'StyleGAN3Generator', 'InstColorization', 'NAFBaseline',\n 'NAFBaselineLocal', 'NAFNet', 'NAFNetLocal', 'DenoisingUnet',\n 'ClipWrapper', 'EG3D', 'Restormer', 'SwinIRNet', 'StableDiffusion',\n 'ControlStableDiffusion', 'DreamBooth', 'TextualInversion', 'DeblurGanV2',\n 'DeblurGanV2Generator', 'DeblurGanV2Discriminator',\n 'StableDiffusionInpaint', 'ViCo', 'FastComposer', 'AnimateDiff',\n 'UNet3DConditionMotionModel', 'StableDiffusionXL'\n]\n" }, { "path": "mmagic/models/editors/animatediff/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .animatediff import AnimateDiff\nfrom .animatediff_utils import save_videos_grid\nfrom .unet_3d import UNet3DConditionMotionModel\n\n__all__ = ['AnimateDiff', 'save_videos_grid', 'UNet3DConditionMotionModel']\n" }, { "path": "mmagic/models/editors/animatediff/animatediff.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport inspect\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\nfrom mmengine import print_log\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModel\nfrom safetensors import safe_open\nfrom tqdm import tqdm\n\nfrom mmagic.models.archs import TokenizerWrapper, set_lora\nfrom mmagic.models.utils import build_module, set_tomesd, set_xformers\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom .animatediff_utils import (convert_ldm_clip_checkpoint,\n convert_ldm_unet_checkpoint,\n convert_ldm_vae_checkpoint)\n\nlogger = MMLogger.get_current_instance()\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module('animatediff')\n@MODELS.register_module()\nclass AnimateDiff(BaseModel):\n \"\"\"Implementation of `AnimateDiff.\n\n `_ (AnimateDiff).\n\n Args:\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n tokenizer (str): The **name** for CLIP tokenizer.\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n lora_config (dict, optional): The config for LoRA finetuning. Defaults\n to None.\n val_prompts (Union[str, List[str]], optional): The prompts for\n validation. Defaults to None.\n class_prior_prompt (str, optional): The prompt for class prior loss.\n num_class_images (int, optional): The number of images for class prior.\n Defaults to 3.\n prior_loss_weight (float, optional): The weight for class prior loss.\n Defaults to 0.\n fine_tune_text_encoder (bool, optional): Whether to fine-tune text\n encoder. Defaults to False.\n dtype (str, optional): The dtype for the model. Defaults to 'fp16'.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa\n Defaults to 0.\n tomesd_cfg (dict, optional): The config for TOMESD. Please refers to\n https://github.com/dbolya/tomesd and\n https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/utils/tome_utils.py for detail. # noqa\n Defaults to None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Defaults to\n dict(type='DataPreprocessor').\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Defaults to None/\n \"\"\"\n\n def __init__(\n self,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: str = 'fp32',\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor=dict(type='DataPreprocessor'),\n motion_module_cfg: Optional[dict] = None,\n dream_booth_lora_cfg: Optional[dict] = None,\n ):\n super().__init__(data_preprocessor)\n\n default_args = dict()\n if dtype is not None:\n default_args['dtype'] = dtype\n\n self.dtype = torch.float32\n if dtype in ['float16', 'fp16', 'half']:\n self.dtype = torch.float16\n elif dtype == 'bf16':\n self.dtype = torch.bfloat16\n else:\n assert dtype in [\n 'fp32', None\n ], ('dtype must be one of \\'fp32\\', \\'fp16\\', \\'bf16\\' or None.')\n self.vae = build_module(vae, MODELS, default_args=default_args)\n self.unet = build_module(unet, MODELS) # NOTE: initialize unet as fp32\n self._unet_ori_dtype = next(self.unet.parameters()).dtype\n print_log(f'Set UNet dtype to \\'{self._unet_ori_dtype}\\'.', 'current')\n\n self.init_motion_module(motion_module_cfg)\n\n self.scheduler = build_module(scheduler, DIFFUSION_SCHEDULERS)\n if test_scheduler is None:\n self.test_scheduler = deepcopy(self.scheduler)\n else:\n self.test_scheduler = build_module(test_scheduler,\n DIFFUSION_SCHEDULERS)\n self.text_encoder = build_module(text_encoder, MODELS)\n if not isinstance(tokenizer, str):\n self.tokenizer = tokenizer\n else:\n # NOTE: here we assume tokenizer is an string\n self.tokenizer = TokenizerWrapper(tokenizer, subfolder='tokenizer')\n\n self.unet_sample_size = self.unet.sample_size\n self.vae_scale_factor = 2**(len(self.vae.block_out_channels) - 1)\n\n self.enable_noise_offset = noise_offset_weight > 0\n self.noise_offset_weight = noise_offset_weight\n\n self.enable_xformers = enable_xformers\n self.unet.set_use_memory_efficient_attention_xformers(True)\n\n self.tomesd_cfg = tomesd_cfg\n self.set_tomesd()\n\n self.init_dreambooth_lora(dream_booth_lora_cfg)\n\n self.prepare_model()\n\n def set_xformers(self, module: Optional[nn.Module] = None) -> nn.Module:\n \"\"\"Set xformers for the model.\n\n Returns:\n nn.Module: The model with xformers.\n \"\"\"\n if self.enable_xformers:\n if module is None:\n set_xformers(self)\n else:\n set_xformers(module)\n\n def set_tomesd(self) -> nn.Module:\n \"\"\"Set ToMe for the stable diffusion model.\n\n Returns:\n nn.Module: The model with ToMe.\n \"\"\"\n if self.tomesd_cfg is not None:\n set_tomesd(self, **self.tomesd_cfg)\n\n @property\n def device(self):\n \"\"\"Set device for the model.\"\"\"\n return next(self.parameters()).device\n\n def init_motion_module(self, motion_module_cfg):\n if motion_module_cfg is not None:\n if 'path' in motion_module_cfg.keys():\n motion_module_state_dict = torch.load(\n motion_module_cfg['path'], map_location='cpu')\n # if \"global_step\" in motion_module_state_dict:\n # func_args.update({\"global_step\":\n # motion_module_state_dict[\"global_step\"]})\n missing, unexpected = self.unet.load_state_dict(\n motion_module_state_dict, strict=False)\n assert len(unexpected) == 0\n\n def init_dreambooth_lora(self, dream_booth_lora_cfg):\n # TODO: finish\n if dream_booth_lora_cfg is not None:\n if 'path' in dream_booth_lora_cfg.keys():\n state_dict = {}\n with safe_open(\n dream_booth_lora_cfg['path'], framework='pt',\n device='cpu') as f:\n for key in f.keys():\n state_dict[key] = f.get_tensor(key)\n # vae\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(\n state_dict, self.vae.config)\n self.vae.load_state_dict(converted_vae_checkpoint)\n # unet\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n state_dict, self.unet.config)\n self.unet.load_state_dict(\n converted_unet_checkpoint, strict=False)\n # text_model\n self.text_encoder = convert_ldm_clip_checkpoint(state_dict)\n # self.convert_lora(state_dict)\n\n def _encode_prompt(self, prompt, device, num_videos_per_prompt,\n do_classifier_free_guidance, negative_prompt):\n \"\"\"Encodes the prompt into text encoder hidden states.\"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(\n prompt, padding='longest', return_tensors='pt').input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[\n -1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1:-1])\n logger.warning(\n 'The following part of your input was truncated '\n f'because CLIP can only handle sequences up to'\n f' {self.tokenizer.model_max_length} tokens: {removed_text}')\n\n text_encoder = self.text_encoder.module if hasattr(\n self.text_encoder, 'module') else self.text_encoder\n if hasattr(text_encoder.config, 'use_attention_mask'\n ) and text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation\n # per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)\n text_embeddings = text_embeddings.view(\n bs_embed * num_videos_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [''] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f'`negative_prompt` should be the same type '\n f'to `prompt`, but got {type(negative_prompt)} !='\n f' {type(prompt)}.')\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f'`negative_prompt`: {negative_prompt} has '\n f'batch size {len(negative_prompt)}, but `prompt`:'\n f' {prompt} has batch size {batch_size}. Please '\n f'make sure that passed `negative_prompt` matches'\n ' the batch size of `prompt`.')\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n\n if hasattr(text_encoder.config, 'use_attention_mask'\n ) and text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation\n # per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(\n 1, num_videos_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(\n batch_size * num_videos_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings\n # into a single batch to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def decode_latents(self, latents):\n \"\"\"latents decoder.\"\"\"\n video_length = latents.shape[2]\n latents = 1 / 0.18215 * latents\n latents = rearrange(latents, 'b c f h w -> (b f) c h w')\n # video = self.vae.decode(latents).sample\n video = []\n for frame_idx in tqdm(range(latents.shape[0])):\n video.append(\n self.vae.decode(latents[frame_idx:frame_idx + 1]).sample)\n video = torch.cat(video)\n video = rearrange(video, '(b f) c h w -> b c f h w', f=video_length)\n video = (video / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant\n # overhead and is compatible with bfloa16\n video = video.cpu().float().numpy()\n return video\n\n def prepare_extra_step_kwargs(self, generator, eta):\n \"\"\"Prepare extra kwargs for the scheduler step, since not all\n schedulers have the same signature eta (η) is only used with the\n DDIMScheduler, it will be ignored for other schedulers.\"\"\"\n # prepare extra kwargs for the scheduler step, since not all\n # schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will\n # be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = 'eta' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width):\n \"\"\"Check inputs.\n\n Raise error if not correct\n \"\"\"\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f'`prompt` has to be of type `str`'\n f' or `list` but is {type(prompt)}')\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f'`height` and `width` have to be divisible'\n f' by 8 but are {height} and {width}.')\n\n # if (callback_steps is None) or (\n # callback_steps is not None and (not isinstance(callback_steps,\n # int) or callback_steps <= 0)\n # ):\n # raise ValueError(\n # f\"`callback_steps` has to be a positive integer but\n # is {callback_steps} of type\"\n # f\" {type(callback_steps)}.\"\n # )\n\n def convert_lora(self,\n state_dict,\n LORA_PREFIX_UNET='lora_unet',\n LORA_PREFIX_TEXT_ENCODER='lora_te',\n alpha=0.6):\n \"\"\" Convert lora for unet and text_encoder\n TODO: use this function to convert lora\n\n Args:\n state_dict (_type_): _description_\n LORA_PREFIX_UNET (str, optional):\n _description_. Defaults to 'lora_unet'.\n LORA_PREFIX_TEXT_ENCODER (str, optional):\n _description_. Defaults to 'lora_te'.\n alpha (float, optional): _description_. Defaults to 0.6.\n\n Returns:\n TODO: check each output type\n _type_: unet && text_encoder\n \"\"\"\n # load base model\n # pipeline = StableDiffusionPipeline.from_pretrained(base_model_path,\n # torch_dtype=torch.float32)\n\n # load LoRA weight from .safetensors\n # state_dict = load_file(checkpoint_path)\n\n visited = []\n\n # directly update weight in diffusers model\n for key in state_dict:\n # it is suggested to print out the key, it usually\n # will be something like below\n # \"lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight\"\n\n # as we have set the alpha beforehand, so just skip\n if '.alpha' in key or key in visited:\n continue\n\n if 'text' in key:\n layer_infos = key.split('.')[0].split(\n LORA_PREFIX_TEXT_ENCODER + '_')[-1].split('_')\n curr_layer = self.text_encoder\n else:\n layer_infos = key.split('.')[0].split(LORA_PREFIX_UNET +\n '_')[-1].split('_')\n curr_layer = self.unet\n # find the target layer\n temp_name = layer_infos.pop(0)\n while len(layer_infos) > -1:\n try:\n curr_layer = curr_layer.__getattr__(temp_name)\n if len(layer_infos) > 0:\n temp_name = layer_infos.pop(0)\n elif len(layer_infos) == 0:\n break\n except Exception:\n if len(temp_name) > 0:\n temp_name += '_' + layer_infos.pop(0)\n else:\n temp_name = layer_infos.pop(0)\n\n pair_keys = []\n if 'lora_down' in key:\n pair_keys.append(key.replace('lora_down', 'lora_up'))\n pair_keys.append(key)\n else:\n pair_keys.append(key)\n pair_keys.append(key.replace('lora_up', 'lora_down'))\n\n # update weight\n if len(state_dict[pair_keys[0]].shape) == 4:\n weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(\n torch.float32)\n weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(\n 2).to(torch.float32)\n curr_layer.weight.data += alpha * torch.mm(\n weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(\n curr_layer.weight.data.device)\n else:\n weight_up = state_dict[pair_keys[0]].to(torch.float32)\n weight_down = state_dict[pair_keys[1]].to(torch.float32)\n curr_layer.weight.data += alpha * torch.mm(\n weight_up, weight_down).to(curr_layer.weight.data.device)\n\n # update visited list\n for item in pair_keys:\n visited.append(item)\n\n return self.unet, self.text_encoder\n\n def prepare_latents(self,\n batch_size,\n num_channels_latents,\n video_length,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None):\n \"\"\"Prepare latent variables.\"\"\"\n shape = (batch_size, num_channels_latents, video_length,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f'You have passed a list of generators of length '\n f'{len(generator)}, but requested an effective batch'\n f' size of {batch_size}. Make sure the batch size matches the'\n f' length of the generators.')\n if latents is None:\n rand_device = 'cpu' if device.type == 'mps' else device\n\n if isinstance(generator, list):\n shape = shape\n # shape = (1,) + shape[1:]\n latents = [\n torch.randn(\n shape,\n generator=generator[i],\n device=rand_device,\n dtype=dtype) for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0).to(device)\n else:\n latents = torch.randn(\n shape,\n generator=generator,\n device=rand_device,\n dtype=dtype).to(device)\n else:\n if latents.shape != shape:\n raise ValueError(f'Unexpected latents shape, got '\n f'{latents.shape}, expected {shape}')\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation\n # required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_model(self):\n \"\"\"Prepare model for training.\n\n Move model to target dtype and disable gradient for some models.\n \"\"\"\n self.vae.requires_grad_(False)\n print_log('Set VAE untrainable.', 'current')\n self.vae.to(self.dtype)\n print_log(f'Move VAE to {self.dtype}.', 'current')\n # if not self.finetune_text_encoder or self.lora_config:\n if 1:\n self.text_encoder.requires_grad_(False)\n print_log('Set Text Encoder untrainable.', 'current')\n self.text_encoder.to(self.dtype)\n print_log(f'Move Text Encoder to {self.dtype}.', 'current')\n # if self.lora_config:\n if 1:\n self.unet.requires_grad_(False)\n print_log('Set Unet untrainable.', 'current')\n\n def set_lora(self):\n \"\"\"Set LORA for model.\"\"\"\n if self.lora_config:\n set_lora(self.unet, self.lora_config)\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n if self.val_prompts is None:\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples.split() * len(prompt)\n data_samples = DataSample.stack(data_samples.split() * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n if self.val_prompts is None:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples = DataSample.stack(data['data_samples'] * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n video_length: Optional[int] = 16,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator,\n List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n return_type: Optional[str] = 'tensor',\n show_progress: bool = True,\n seed: Optional[int] = 1007):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (str or List[str]): The prompt or prompts to guide\n the video generation.\n video_length (int, Option): The number of frames of the\n generated video. Defaults to 16.\n height (int, Optional): The height in pixels of the generated\n image. If not passed, the height will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n width (int, Optional): The width in pixels of the generated image.\n If not passed, the width will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n num_inference_steps (int): The number of denoising steps.\n More denoising steps usually lead to a higher quality video at\n the expense of slower inference. Defaults to 50.\n guidance_scale (float): Guidance scale as defined in Classifier-\n Free Diffusion Guidance (https://arxiv.org/abs/2207.12598).\n Defaults to 7.5\n negative_prompt (str or List[str], optional): The prompt or\n prompts not to guide the video generation. Ignored when not\n using guidance (i.e., ignored if `guidance_scale` is less\n than 1). Defaults to None.\n num_videos_per_prompt (int): The number of videos to generate\n per prompt. Defaults to 1.\n eta (float): Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n DDIMScheduler, will be ignored for others. Defaults to 0.0.\n generator (torch.Generator, optional): A torch generator to make\n generation deterministic. Defaults to None.\n latents (torch.FloatTensor, optional): Pre-generated noisy latents,\n sampled from a Gaussian distribution, to be used as inputs for\n video generation. Can be used to tweak the same generation with\n different prompts. If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n Defaults to None.\n return_type (str): The return type of the inference results.\n Supported types are 'video', 'numpy', 'tensor'. If 'video'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n #TODO\n Returns:\n dict: A dict containing the generated video\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n if seed != -1:\n torch.manual_seed(seed)\n print_log(f'current seed: {torch.initial_seed()}')\n print_log(f'sampling {prompt} ...')\n\n # 1. Check inputs. Raise error if not correct\n\n self.check_inputs(prompt, height,\n width) # NOTE: aligned with origin repo\n\n # 2. Define call parameters\n batch_size = 1\n if latents is not None:\n batch_size = latents.shape[0]\n if isinstance(prompt, list):\n batch_size = len(prompt)\n device = self.device\n\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n video_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n\n # 3. Encode input prompt\n\n prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size\n if negative_prompt is not None:\n negative_prompt = negative_prompt if isinstance(\n negative_prompt, list) else [negative_prompt] * batch_size\n text_embeddings = self._encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance,\n negative_prompt) # NOTE aligned with origin repo\n\n # 4. Prepare timesteps\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n video_length,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n ) # NOTE aligned with origin repo\n latents_dtype = latents.dtype\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n )['sample'].to(dtype=latents_dtype)\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents,\n **extra_step_kwargs)['prev_sample'] # FIXME: not aligned\n # FIXME: revise config thresholding=False\n # scheduler pred_original_sample not aligned\n # fixed clip_sample=False\n # 8. Post-processing\n video = self.decode_latents(latents.to(video_dtype))\n\n if return_type == 'tensor':\n video = torch.from_numpy(video)\n\n return {'samples': video}\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n" }, { "path": "mmagic/models/editors/animatediff/animatediff_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport re\nfrom typing import Optional\n\nimport imageio\nimport numpy as np\nimport torch\nimport torchvision\nfrom diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder\nfrom diffusers.pipelines.stable_diffusion import StableUnCLIPImageNormalizer\nfrom diffusers.schedulers import DDPMScheduler\nfrom einops import rearrange\nfrom transformers import (CLIPImageProcessor, CLIPTextModel, CLIPVisionConfig,\n CLIPVisionModelWithProjection)\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"Removes segments.\n\n Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return '.'.join(path.split('.')[n_shave_prefix_segments:])\n else:\n return '.'.join(path.split('.')[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"Updates paths inside resnets to the new naming scheme (local\n renaming)\"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace('in_layers.0', 'norm1')\n new_item = new_item.replace('in_layers.2', 'conv1')\n\n new_item = new_item.replace('out_layers.0', 'norm2')\n new_item = new_item.replace('out_layers.3', 'conv2')\n\n new_item = new_item.replace('emb_layers.1', 'time_emb_proj')\n new_item = new_item.replace('skip_connection', 'conv_shortcut')\n\n new_item = shave_segments(\n new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({'old': old_item, 'new': new_item})\n\n return mapping\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"Updates paths inside resnets to the new naming scheme (local\n renaming)\"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace('nin_shortcut', 'conv_shortcut')\n new_item = shave_segments(\n new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({'old': old_item, 'new': new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"Updates paths inside attentions to the new naming scheme (local\n renaming)\"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n mapping.append({'old': old_item, 'new': new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"Updates paths inside attentions to the new naming scheme (local\n renaming)\"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace('norm.weight', 'group_norm.weight')\n new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n new_item = new_item.replace('q.weight', 'query.weight')\n new_item = new_item.replace('q.bias', 'query.bias')\n\n new_item = new_item.replace('k.weight', 'key.weight')\n new_item = new_item.replace('k.bias', 'key.bias')\n\n new_item = new_item.replace('v.weight', 'value.weight')\n new_item = new_item.replace('v.bias', 'value.bias')\n\n new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n new_item = shave_segments(\n new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({'old': old_item, 'new': new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(paths,\n checkpoint,\n old_checkpoint,\n attention_paths_to_split=None,\n additional_replacements=None,\n config=None):\n \"\"\"This does the final conversion step: take locally converted weights and\n apply a global renaming to them. It splits attention layers, and takes into\n account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(\n paths, list\n ), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1,\n channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config['num_head_channels'] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels //\n num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map['query']] = query.reshape(target_shape)\n checkpoint[path_map['key']] = key.reshape(target_shape)\n checkpoint[path_map['value']] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path['new']\n\n # These have already been assigned\n if attention_paths_to_split is not None and (\n new_path in attention_paths_to_split):\n continue\n # Global renaming happens here\n new_path = new_path.replace('middle_block.0', 'mid_block.resnets.0')\n new_path = new_path.replace('middle_block.1', 'mid_block.attentions.0')\n new_path = new_path.replace('middle_block.2', 'mid_block.resnets.1')\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement['old'],\n replacement['new'])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if 'proj_attn.weight' in new_path:\n checkpoint[new_path] = old_checkpoint[path['old']][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path['old']]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = ['query.weight', 'key.weight', 'value.weight']\n for key in keys:\n if '.'.join(key.split('.')[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif 'proj_attn.weight' in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_unet_diffusers_config(original_config,\n image_size: int,\n controlnet=False):\n \"\"\"Creates a config for the diffusers based on the config of the LDM\n model.\"\"\"\n if controlnet:\n unet_params = original_config.model.params.control_stage_config.params\n else:\n unet_params = original_config.model.params.unet_config.params\n\n vae_params = \\\n original_config.model.params.first_stage_config.params.ddconfig\n\n block_out_channels = [\n unet_params.model_channels * mult for mult in unet_params.channel_mult\n ]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = 'CrossAttnDownBlock2D' \\\n if resolution in unet_params.attention_resolutions \\\n else 'DownBlock2D'\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = 'CrossAttnUpBlock2D' \\\n if resolution in unet_params.attention_resolutions \\\n else 'UpBlock2D'\n up_block_types.append(block_type)\n resolution //= 2\n\n vae_scale_factor = 2**(len(vae_params.ch_mult) - 1)\n\n head_dim = unet_params.num_heads if 'num_heads' in unet_params else None\n use_linear_projection = (\n unet_params.use_linear_in_transformer\n if 'use_linear_in_transformer' in unet_params else False)\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim = [5, 10, 20, 20]\n\n class_embed_type = None\n projection_class_embeddings_input_dim = None\n\n if 'num_classes' in unet_params:\n if unet_params.num_classes == 'sequential':\n class_embed_type = 'projection'\n assert 'adm_in_channels' in unet_params\n projection_class_embeddings_input_dim = unet_params.adm_in_channels\n else:\n raise NotImplementedError(\n f'Unknown conditional unet num_classes config: '\n f'{unet_params.num_classes}')\n\n config = {\n 'sample_size':\n image_size // vae_scale_factor,\n 'in_channels':\n unet_params.in_channels,\n 'down_block_types':\n tuple(down_block_types),\n 'block_out_channels':\n tuple(block_out_channels),\n 'layers_per_block':\n unet_params.num_res_blocks,\n 'cross_attention_dim':\n unet_params.context_dim,\n 'attention_head_dim':\n head_dim,\n 'use_linear_projection':\n use_linear_projection,\n 'class_embed_type':\n class_embed_type,\n 'projection_class_embeddings_input_dim':\n projection_class_embeddings_input_dim,\n }\n\n if not controlnet:\n config['out_channels'] = unet_params.out_channels\n config['up_block_types'] = tuple(up_block_types)\n\n return config\n\n\ndef create_vae_diffusers_config(original_config, image_size: int):\n \"\"\"Creates a config for the diffusers based on the config of the LDM\n model.\"\"\"\n vae_params = \\\n original_config.model.params.first_stage_config.params.ddconfig\n _ = original_config.model.params.first_stage_config.params.embed_dim\n\n block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]\n down_block_types = ['DownEncoderBlock2D'] * len(block_out_channels)\n up_block_types = ['UpDecoderBlock2D'] * len(block_out_channels)\n\n config = {\n 'sample_size': image_size,\n 'in_channels': vae_params.in_channels,\n 'out_channels': vae_params.out_ch,\n 'down_block_types': tuple(down_block_types),\n 'up_block_types': tuple(up_block_types),\n 'block_out_channels': tuple(block_out_channels),\n 'latent_channels': vae_params.z_channels,\n 'layers_per_block': vae_params.num_res_blocks,\n }\n return config\n\n\ndef convert_ldm_unet_checkpoint(checkpoint,\n config,\n path=None,\n extract_ema=False,\n controlnet=False):\n \"\"\"Takes a state dict and a config, and returns a converted checkpoint.\"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n if controlnet:\n unet_key = 'control_model.'\n else:\n unet_key = 'model.diffusion_model.'\n\n # at least a 100 parameters have to start with `model_ema`\n # in order for the checkpoint to be EMA\n if sum(k.startswith('model_ema') for k in keys) > 100 and extract_ema:\n print(f'Checkpoint {path} has both EMA and non-EMA weights.')\n print('In this conversion only the EMA weights are extracted. '\n 'If you want to instead extract the non-EMA'\n ' weights (useful to continue fine-tuning), please make sure to '\n 'remove the `--extract_ema` flag.')\n for key in keys:\n if key.startswith('model.diffusion_model'):\n flat_ema_key = 'model_ema.' + ''.join(key.split('.')[1:])\n unet_state_dict[key.replace(unet_key,\n '')] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith('model_ema') for k in keys) > 100:\n print('In this conversion only the non-EMA weights '\n 'are extracted. If you want to instead extract the EMA'\n ' weights (usually better for inference), please'\n ' make sure to add the `--extract_ema` flag.')\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key,\n '')] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint['time_embedding.linear_1.weight'] = unet_state_dict[\n 'time_embed.0.weight']\n new_checkpoint['time_embedding.linear_1.bias'] = unet_state_dict[\n 'time_embed.0.bias']\n new_checkpoint['time_embedding.linear_2.weight'] = unet_state_dict[\n 'time_embed.2.weight']\n new_checkpoint['time_embedding.linear_2.bias'] = unet_state_dict[\n 'time_embed.2.bias']\n\n if config['class_embed_type'] is None:\n # No parameters to port\n ...\n elif config['class_embed_type'] == 'timestep' or config[\n 'class_embed_type'] == 'projection':\n new_checkpoint['class_embedding.linear_1.weight'] = unet_state_dict[\n 'label_emb.0.0.weight']\n new_checkpoint['class_embedding.linear_1.bias'] = unet_state_dict[\n 'label_emb.0.0.bias']\n new_checkpoint['class_embedding.linear_2.weight'] = unet_state_dict[\n 'label_emb.0.2.weight']\n new_checkpoint['class_embedding.linear_2.bias'] = unet_state_dict[\n 'label_emb.0.2.bias']\n else:\n raise NotImplementedError(\n f\"Not implemented `class_embed_type`: {config['class_embed_type']}\"\n )\n\n new_checkpoint['conv_in.weight'] = unet_state_dict[\n 'input_blocks.0.0.weight']\n new_checkpoint['conv_in.bias'] = unet_state_dict['input_blocks.0.0.bias']\n\n if not controlnet:\n new_checkpoint['conv_norm_out.weight'] = unet_state_dict[\n 'out.0.weight']\n new_checkpoint['conv_norm_out.bias'] = unet_state_dict['out.0.bias']\n new_checkpoint['conv_out.weight'] = unet_state_dict['out.2.weight']\n new_checkpoint['conv_out.bias'] = unet_state_dict['out.2.bias']\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\n '.'.join(layer.split('.')[:2])\n for layer in unet_state_dict if 'input_blocks' in layer\n })\n input_blocks = {\n layer_id:\n [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\n '.'.join(layer.split('.')[:2])\n for layer in unet_state_dict if 'middle_block' in layer\n })\n middle_blocks = {\n layer_id:\n [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\n '.'.join(layer.split('.')[:2])\n for layer in unet_state_dict if 'output_blocks' in layer\n })\n output_blocks = {\n layer_id:\n [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config['layers_per_block'] + 1)\n layer_in_block_id = (i - 1) % (config['layers_per_block'] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f'input_blocks.{i}.0' in key\n and f'input_blocks.{i}.0.op' not in key\n ]\n attentions = [\n key for key in input_blocks[i] if f'input_blocks.{i}.1' in key\n ]\n\n if f'input_blocks.{i}.0.op.weight' in unet_state_dict:\n new_checkpoint[\n f'down_blocks.{block_id}.downsamplers.0.conv.weight'] = \\\n unet_state_dict.pop(f'input_blocks.{i}.0.op.weight')\n new_checkpoint[\n f'down_blocks.{block_id}.downsamplers.0.conv.bias'] = \\\n unet_state_dict.pop(f'input_blocks.{i}.0.op.bias')\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\n 'old': f'input_blocks.{i}.0',\n 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n 'old': f'input_blocks.{i}.1',\n 'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}\n assign_to_checkpoint(\n attentions_paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n for i in range(num_output_blocks):\n block_id = i // (config['layers_per_block'] + 1)\n layer_in_block_id = i % (config['layers_per_block'] + 1)\n output_block_layers = [\n shave_segments(name, 2) for name in output_blocks[i]\n ]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split('.')[0], shave_segments(\n layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [\n key for key in output_blocks[i]\n if f'output_blocks.{i}.0' in key\n ]\n attentions = [\n key for key in output_blocks[i]\n if f'output_blocks.{i}.1' in key\n ]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\n 'old': f'output_blocks.{i}.0',\n 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n output_block_list = {\n k: sorted(v)\n for k, v in output_block_list.items()\n }\n if ['conv.bias', 'conv.weight'] in output_block_list.values():\n index = list(output_block_list.values()).index(\n ['conv.bias', 'conv.weight'])\n new_checkpoint[\n f'up_blocks.{block_id}.upsamplers.0.conv.weight'] = \\\n unet_state_dict[\n f'output_blocks.{i}.{index}.conv.weight']\n new_checkpoint[\n f'up_blocks.{block_id}.upsamplers.0.conv.bias'] = \\\n unet_state_dict[\n f'output_blocks.{i}.{index}.conv.bias']\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n 'old': f'output_blocks.{i}.1',\n 'new':\n f'up_blocks.{block_id}.attentions.{layer_in_block_id}',\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config)\n else:\n resnet_0_paths = renew_resnet_paths(\n output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = '.'.join(['output_blocks', str(i), path['old']])\n new_path = '.'.join([\n 'up_blocks',\n str(block_id), 'resnets',\n str(layer_in_block_id), path['new']\n ])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n if controlnet:\n # conditioning embedding\n\n orig_index = 0\n\n new_checkpoint[\n 'controlnet_cond_embedding.conv_in.weight'] = unet_state_dict.pop(\n f'input_hint_block.{orig_index}.weight')\n new_checkpoint[\n 'controlnet_cond_embedding.conv_in.bias'] = unet_state_dict.pop(\n f'input_hint_block.{orig_index}.bias')\n\n orig_index += 2\n\n diffusers_index = 0\n\n while diffusers_index < 6:\n new_checkpoint[f'controlnet_cond_embedding.blocks'\n f'.{diffusers_index}.weight'] = (\n unet_state_dict.pop(\n f'input_hint_block.{orig_index}.weight'))\n new_checkpoint[\n f'controlnet_cond_embedding.blocks.{diffusers_index}.bias'] = (\n unet_state_dict.pop(f'input_hint_block.{orig_index}.bias'))\n diffusers_index += 1\n orig_index += 2\n\n new_checkpoint[\n 'controlnet_cond_embedding.conv_out.weight'] = unet_state_dict.pop(\n f'input_hint_block.{orig_index}.weight')\n new_checkpoint[\n 'controlnet_cond_embedding.conv_out.bias'] = unet_state_dict.pop(\n f'input_hint_block.{orig_index}.bias')\n\n # down blocks\n for i in range(num_input_blocks):\n new_checkpoint[\n f'controlnet_down_blocks.{i}.weight'] = unet_state_dict.pop(\n f'zero_convs.{i}.0.weight')\n new_checkpoint[\n f'controlnet_down_blocks.{i}.bias'] = unet_state_dict.pop(\n f'zero_convs.{i}.0.bias')\n\n # mid block\n new_checkpoint['controlnet_mid_block.weight'] = unet_state_dict.pop(\n 'middle_block_out.0.weight')\n new_checkpoint['controlnet_mid_block.bias'] = unet_state_dict.pop(\n 'middle_block_out.0.bias')\n\n return new_checkpoint\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n vae_key = 'first_stage_model.'\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, '')] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint['encoder.conv_in.weight'] = vae_state_dict[\n 'encoder.conv_in.weight']\n new_checkpoint['encoder.conv_in.bias'] = vae_state_dict[\n 'encoder.conv_in.bias']\n new_checkpoint['encoder.conv_out.weight'] = vae_state_dict[\n 'encoder.conv_out.weight']\n new_checkpoint['encoder.conv_out.bias'] = vae_state_dict[\n 'encoder.conv_out.bias']\n new_checkpoint['encoder.conv_norm_out.weight'] = vae_state_dict[\n 'encoder.norm_out.weight']\n new_checkpoint['encoder.conv_norm_out.bias'] = vae_state_dict[\n 'encoder.norm_out.bias']\n\n new_checkpoint['decoder.conv_in.weight'] = vae_state_dict[\n 'decoder.conv_in.weight']\n new_checkpoint['decoder.conv_in.bias'] = vae_state_dict[\n 'decoder.conv_in.bias']\n new_checkpoint['decoder.conv_out.weight'] = vae_state_dict[\n 'decoder.conv_out.weight']\n new_checkpoint['decoder.conv_out.bias'] = vae_state_dict[\n 'decoder.conv_out.bias']\n new_checkpoint['decoder.conv_norm_out.weight'] = vae_state_dict[\n 'decoder.norm_out.weight']\n new_checkpoint['decoder.conv_norm_out.bias'] = vae_state_dict[\n 'decoder.norm_out.bias']\n\n new_checkpoint['quant_conv.weight'] = vae_state_dict['quant_conv.weight']\n new_checkpoint['quant_conv.bias'] = vae_state_dict['quant_conv.bias']\n new_checkpoint['post_quant_conv.weight'] = vae_state_dict[\n 'post_quant_conv.weight']\n new_checkpoint['post_quant_conv.bias'] = vae_state_dict[\n 'post_quant_conv.bias']\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\n '.'.join(layer.split('.')[:3])\n for layer in vae_state_dict if 'encoder.down' in layer\n })\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f'down.{layer_id}' in key]\n for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\n '.'.join(layer.split('.')[:3])\n for layer in vae_state_dict if 'decoder.up' in layer\n })\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key]\n for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [\n key for key in down_blocks[i]\n if f'down.{i}' in key and f'down.{i}.downsample' not in key\n ]\n\n if f'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:\n new_checkpoint[\n f'encoder.down_blocks.{i}.downsamplers.0.conv.weight'] = \\\n vae_state_dict.pop(f'encoder.down.{i}.downsample.conv.weight')\n new_checkpoint[\n f'encoder.down_blocks.{i}.downsamplers.0.conv.bias'] = \\\n vae_state_dict.pop(f'encoder.down.{i}.downsample.conv.bias')\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\n 'old': f'down.{i}.block',\n 'new': f'down_blocks.{i}.resnets'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n mid_resnets = [key for key in vae_state_dict if 'encoder.mid.block' in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [\n key for key in mid_resnets if f'encoder.mid.block_{i}' in key\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\n 'old': f'mid.block_{i}',\n 'new': f'mid_block.resnets.{i - 1}'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n mid_attentions = [\n key for key in vae_state_dict if 'encoder.mid.attn' in key\n ]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id]\n if f'up.{block_id}' in key and f'up.{block_id}.upsample' not in key\n ]\n\n if f'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:\n new_checkpoint[\n f'decoder.up_blocks.{i}.upsamplers.0.conv.weight'] = \\\n vae_state_dict[f'decoder.up.{block_id}.upsample.conv.weight']\n new_checkpoint[\n f'decoder.up_blocks.{i}.upsamplers.0.conv.bias'] = \\\n vae_state_dict[f'decoder.up.{block_id}.upsample.conv.bias']\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\n 'old': f'up.{block_id}.block',\n 'new': f'up_blocks.{i}.resnets'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n mid_resnets = [key for key in vae_state_dict if 'decoder.mid.block' in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [\n key for key in mid_resnets if f'decoder.mid.block_{i}' in key\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\n 'old': f'mid.block_{i}',\n 'new': f'mid_block.resnets.{i - 1}'\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n\n mid_attentions = [\n key for key in vae_state_dict if 'decoder.mid.attn' in key\n ]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n config=config)\n conv_attn_to_linear(new_checkpoint)\n\n new_checkpoint = {\n 'model.' + key: value\n for key, value in new_checkpoint.items()\n }\n new_checkpoint = {\n key.replace('query', 'to_q'): value\n for key, value in new_checkpoint.items()\n }\n new_checkpoint = {\n key.replace('key', 'to_k'): value\n for key, value in new_checkpoint.items()\n }\n new_checkpoint = {\n key.replace('value', 'to_v'): value\n for key, value in new_checkpoint.items()\n }\n new_checkpoint = {\n key.replace('proj_attn', 'to_out.0'): value\n for key, value in new_checkpoint.items()\n }\n return new_checkpoint\n\n\ndef convert_ldm_clip_checkpoint(checkpoint):\n text_model = CLIPTextModel.from_pretrained('openai/clip-vit-large-patch14')\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n for key in keys:\n if key.startswith('cond_stage_model.transformer'):\n text_model_dict[\n key[len('cond_stage_model.transformer.'):]] = checkpoint[key]\n # Certain text transformers no longer\n # expect position_ids after transformers==4.31\n position_id_key = 'text_model.embeddings.position_ids'\n if position_id_key in text_model_dict and \\\n position_id_key not in text_model.state_dict():\n del text_model_dict[position_id_key]\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ntextenc_conversion_lst = [\n ('cond_stage_model.model.positional_embedding',\n 'text_model.embeddings.position_embedding.weight'),\n ('cond_stage_model.model.token_embedding.weight',\n 'text_model.embeddings.token_embedding.weight'),\n ('cond_stage_model.model.ln_final.weight',\n 'text_model.final_layer_norm.weight'),\n ('cond_stage_model.model.ln_final.bias',\n 'text_model.final_layer_norm.bias'),\n]\ntextenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}\n\ntextenc_transformer_conversion_lst = [\n # (stable-diffusion, HF Diffusers)\n ('resblocks.', 'text_model.encoder.layers.'),\n ('ln_1', 'layer_norm1'),\n ('ln_2', 'layer_norm2'),\n ('.c_fc.', '.fc1.'),\n ('.c_proj.', '.fc2.'),\n ('.attn', '.self_attn'),\n ('ln_final.', 'transformer.text_model.final_layer_norm.'),\n ('token_embedding.weight',\n 'transformer.text_model.embeddings.token_embedding.weight'),\n ('positional_embedding',\n 'transformer.text_model.embeddings.position_embedding.weight'),\n]\nprotected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}\ntextenc_pattern = re.compile('|'.join(protected.keys()))\n\n\ndef convert_paint_by_example_checkpoint(checkpoint):\n config = CLIPVisionConfig.from_pretrained('openai/clip-vit-large-patch14')\n model = PaintByExampleImageEncoder(config)\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n for key in keys:\n if key.startswith('cond_stage_model.transformer'):\n text_model_dict[\n key[len('cond_stage_model.transformer.'):]] = checkpoint[key]\n\n # load clip vision\n model.model.load_state_dict(text_model_dict)\n\n # load mapper\n keys_mapper = {\n k[len('cond_stage_model.mapper.res'):]: v\n for k, v in checkpoint.items()\n if k.startswith('cond_stage_model.mapper')\n }\n\n MAPPING = {\n 'attn.c_qkv': ['attn1.to_q', 'attn1.to_k', 'attn1.to_v'],\n 'attn.c_proj': ['attn1.to_out.0'],\n 'ln_1': ['norm1'],\n 'ln_2': ['norm3'],\n 'mlp.c_fc': ['ff.net.0.proj'],\n 'mlp.c_proj': ['ff.net.2'],\n }\n\n mapped_weights = {}\n for key, value in keys_mapper.items():\n prefix = key[:len('blocks.i')]\n suffix = key.split(prefix)[-1].split('.')[-1]\n name = key.split(prefix)[-1].split(suffix)[0][1:-1]\n mapped_names = MAPPING[name]\n\n num_splits = len(mapped_names)\n for i, mapped_name in enumerate(mapped_names):\n new_name = '.'.join([prefix, mapped_name, suffix])\n shape = value.shape[0] // num_splits\n mapped_weights[new_name] = value[i * shape:(i + 1) * shape]\n\n model.mapper.load_state_dict(mapped_weights)\n\n # load final layer norm\n model.final_layer_norm.load_state_dict({\n 'bias':\n checkpoint['cond_stage_model.final_ln.bias'],\n 'weight':\n checkpoint['cond_stage_model.final_ln.weight'],\n })\n\n # load final proj\n model.proj_out.load_state_dict({\n 'bias': checkpoint['proj_out.bias'],\n 'weight': checkpoint['proj_out.weight'],\n })\n\n # load uncond vector\n model.uncond_vector.data = torch.nn.Parameter(\n checkpoint['learnable_vector'])\n return model\n\n\ndef convert_open_clip_checkpoint(checkpoint):\n text_model = CLIPTextModel.from_pretrained(\n 'stabilityai/stable-diffusion-2', subfolder='text_encoder')\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n if 'cond_stage_model.model.text_projection' in checkpoint:\n d_model = int(\n checkpoint['cond_stage_model.model.text_projection'].shape[0])\n else:\n d_model = 1024\n\n text_model_dict[\n 'text_model.embeddings.position_ids'] = \\\n text_model.text_model.embeddings.get_buffer('position_ids')\n\n for key in keys:\n # Diffusers drops the final layer and\n # only uses the penultimate layer\n if 'resblocks.23' in key:\n continue\n if key in textenc_conversion_map:\n text_model_dict[textenc_conversion_map[key]] = checkpoint[key]\n if key.startswith('cond_stage_model.model.transformer.'):\n new_key = key[len('cond_stage_model.model.transformer.'):]\n if new_key.endswith('.in_proj_weight'):\n new_key = new_key[:-len('.in_proj_weight')]\n new_key = textenc_pattern.sub(\n lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[\n new_key + '.q_proj.weight'] = checkpoint[key][:d_model, :]\n text_model_dict[new_key + '.k_proj.weight'] = checkpoint[key][\n d_model:d_model * 2, :]\n text_model_dict[new_key +\n '.v_proj.weight'] = checkpoint[key][d_model *\n 2:, :]\n elif new_key.endswith('.in_proj_bias'):\n new_key = new_key[:-len('.in_proj_bias')]\n new_key = textenc_pattern.sub(\n lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key +\n '.q_proj.bias'] = checkpoint[key][:d_model]\n text_model_dict[\n new_key +\n '.k_proj.bias'] = checkpoint[key][d_model:d_model * 2]\n text_model_dict[new_key +\n '.v_proj.bias'] = checkpoint[key][d_model * 2:]\n else:\n new_key = textenc_pattern.sub(\n lambda m: protected[re.escape(m.group(0))], new_key)\n\n text_model_dict[new_key] = checkpoint[key]\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ndef stable_unclip_image_encoder(original_config):\n \"\"\"Returns the image processor and clip image encoder for the img2img\n unclip pipeline.\n\n We currently know of two types of stable unclip models which separately use\n the clip and the openclip image encoders.\n \"\"\"\n\n image_embedder_config = original_config.model.params.embedder_config\n\n sd_clip_image_embedder_class = image_embedder_config.target\n sd_clip_image_embedder_class = sd_clip_image_embedder_class.split('.')[-1]\n\n if sd_clip_image_embedder_class == 'ClipImageEmbedder':\n clip_model_name = image_embedder_config.params.model\n\n if clip_model_name == 'ViT-L/14':\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n 'openai/clip-vit-large-patch14')\n else:\n raise NotImplementedError(\n f'Unknown CLIP checkpoint name in stable '\n f'diffusion checkpoint {clip_model_name}')\n\n elif sd_clip_image_embedder_class == 'FrozenOpenCLIPImageEmbedder':\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n 'laion/CLIP-ViT-H-14-laion2B-s32B-b79K')\n else:\n raise NotImplementedError(\n f'Unknown CLIP image embedder class in '\n f'stable diffusion checkpoint {sd_clip_image_embedder_class}')\n\n return feature_extractor, image_encoder\n\n\ndef stable_unclip_image_noising_components(\n original_config,\n clip_stats_path: Optional[str] = None,\n device: Optional[str] = None):\n \"\"\"Returns the noising components for the img2img and txt2img unclip\n pipelines.\n\n Converts the stability noise augmentor into\n 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats\n 2. a `DDPMScheduler` for holding the noise schedule\n\n If the noise augmentor config specifies a clip stats path,\n the `clip_stats_path` must be provided.\n \"\"\"\n noise_aug_config = original_config.model.params.noise_aug_config\n noise_aug_class = noise_aug_config.target\n noise_aug_class = noise_aug_class.split('.')[-1]\n\n if noise_aug_class == 'CLIPEmbeddingNoiseAugmentation':\n noise_aug_config = noise_aug_config.params\n embedding_dim = noise_aug_config.timestep_dim\n max_noise_level = noise_aug_config.noise_schedule_config.timesteps\n beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule\n\n image_normalizer = StableUnCLIPImageNormalizer(\n embedding_dim=embedding_dim)\n image_noising_scheduler = DDPMScheduler(\n num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)\n\n if 'clip_stats_path' in noise_aug_config:\n if clip_stats_path is None:\n raise ValueError(\n 'This stable unclip config requires a `clip_stats_path`')\n\n clip_mean, clip_std = torch.load(\n clip_stats_path, map_location=device)\n clip_mean = clip_mean[None, :]\n clip_std = clip_std[None, :]\n\n clip_stats_state_dict = {\n 'mean': clip_mean,\n 'std': clip_std,\n }\n\n image_normalizer.load_state_dict(clip_stats_state_dict)\n else:\n raise NotImplementedError(\n f'Unknown noise augmentor class: {noise_aug_class}')\n\n return image_normalizer, image_noising_scheduler\n\n\ndef save_videos_grid(videos: torch.Tensor,\n path: str,\n rescale=False,\n n_rows=6,\n fps=8):\n videos = rearrange(videos, 'b c t h w -> t b c h w')\n outputs = []\n for x in videos:\n x = torchvision.utils.make_grid(x, nrow=n_rows)\n x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)\n if rescale:\n x = (x + 1.0) / 2.0 # -1,1 -> 0,1\n x = (x * 255).numpy().astype(np.uint8)\n outputs.append(x)\n\n os.makedirs(os.path.dirname(path), exist_ok=True)\n # imageio v3 doesn't support fps\n if imageio.__version__ < '2.28.0':\n imageio.mimsave(path, outputs, fps=fps)\n else:\n imageio.mimsave(path, outputs, duration=1000 * 1 / fps, loop=10)\n" }, { "path": "mmagic/models/editors/animatediff/attention_3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Adapted from https://github.com/huggingface/diffusers/blob/main/\n# src/diffusers/models/attention.py\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.models.attention import AdaLayerNorm, FeedForward\n# from diffusers.models.cross_attention import CrossAttention\n# from diffusers.models.attention_processor import Attention as CrossAttention\nfrom diffusers.models.modeling_utils import ModelMixin\nfrom diffusers.utils import BaseOutput\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom einops import rearrange, repeat\nfrom torch import nn\n\n\n@dataclass\nclass Transformer3DModelOutput(BaseOutput):\n \"\"\"Output of Transformer3DModel.\"\"\"\n sample: torch.FloatTensor\n\n\nif is_xformers_available():\n \"\"\"Check xformer.\n\n If available use xformers to save memory\n \"\"\"\n import xformers\n import xformers.ops\nelse:\n xformers = None\n\n\nclass Transformer3DModel(ModelMixin, ConfigMixin):\n \"\"\"Transformer model for image-like data. Takes either discrete (classes of\n vector embeddings) or continuous (actual embeddings) inputs.\n\n When input is continuous: First, project the input\n (aka embedding) and reshape to b, t, d. Then apply standard\n transformer action. Finally, reshape to image.\n\n When input is discrete: First, input (classes of latent pixels)\n is converted to embeddings and has positional\n embeddings applied, see `ImagePositionalEmbeddings`.\n Then apply standard transformer action. Finally, predict\n classes of unnoised image.\n\n Note that it is assumed one of the input classes is\n the masked latent pixel. The predicted classes of the unnoised\n image do not contain a prediction for the masked pixel as\n the unnoised image cannot be masked.\n\n Args:\n num_attention_heads (`int`, *optional*, defaults to 16):\n The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88):\n The number of channels in each head.\n in_channels (`int`, *optional*):\n Pass if the input is continuous.\n The number of channels in the input and output.\n num_layers (`int`, *optional*, defaults to 1):\n The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.1):\n The dropout probability to use.\n norm_num_groups (int):\n Norm group num, defaults to 32.\n cross_attention_dim (`int`, *optional*):\n The number of context dimensions to use.\n attention_bias (`bool`, *optional*):\n Configure if the TransformerBlocks' attention should contain\n a bias parameter.\n sample_size (`int`, *optional*):\n Pass if the input is discrete. The width of the latent images.\n Note that this is fixed at training time as it is used for\n learning a number of position embeddings. See\n `ImagePositionalEmbeddings`.\n num_vector_embeds (`int`, *optional*):\n Pass if the input is discrete. The number of classes of\n the vector embeddings of the latent pixels.\n Includes the class for the masked latent pixel.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n use_linear_projection (bool):\n Whether to use linear projection, defaults to False.\n only_cross_attention (bool):\n whether only use cross attention, defaults to False.\n unet_use_temporal_attention (bool):\n whether use temporal attention, defaults to False.\n upcast_attention (bool):\n whether use upcast attention, defaults to False.\n unet_use_cross_frame_attention (bool):\n whether use cross frame attention, defaults to False.\n unet_use_temporal_attention (bool):\n whether use temporal attention, defaults to False.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n activation_fn: str = 'geglu',\n num_embeds_ada_norm: Optional[int] = None,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n ):\n super().__init__()\n self.use_linear_projection = use_linear_projection\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n\n # Define input layers\n self.in_channels = in_channels\n\n self.norm = torch.nn.GroupNorm(\n num_groups=norm_num_groups,\n num_channels=in_channels,\n eps=1e-6,\n affine=True)\n if use_linear_projection:\n self.proj_in = nn.Linear(in_channels, inner_dim)\n else:\n self.proj_in = nn.Conv2d(\n in_channels, inner_dim, kernel_size=1, stride=1, padding=0)\n\n # Define transformers blocks\n self.transformer_blocks = nn.ModuleList([\n BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n attention_bias=attention_bias,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n ) for d in range(num_layers)\n ])\n\n # 4. Define output layers\n if use_linear_projection:\n self.proj_out = nn.Linear(in_channels, inner_dim)\n else:\n self.proj_out = nn.Conv2d(\n inner_dim, in_channels, kernel_size=1, stride=1, padding=0)\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n timestep=None,\n return_dict: bool = True):\n \"\"\"forward function.\n\n Args:\n hidden_states ( When discrete, `torch.LongTensor`\n of shape `(batch size, num latent pixels)`.\n When continuous, `torch.FloatTensor` of shape `\n (batch size, channel, height, width)`): Input\n hidden_states\n encoder_hidden_states ( `torch.LongTensor` of shape\n `(batch size, context dim)`, *optional*):\n Conditional embeddings for cross attention layer.\n If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.long`, *optional*):\n Optional timestep to be applied as an embedding\n in AdaLayerNorm's. Used to indicate denoising step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`Transformer3DModelOutput`]\n instead of a plain tuple.\n\n Returns:\n Dict if `return_dict` is True, otherwise a `tuple`.\n When returning a tuple, the first element is the sample\n tensor.\n \"\"\"\n # Input\n assert hidden_states.dim(\n ) == 5, f'{\"Expected hidden_states to have ndim=5, \"}'\n f'but got ndim={hidden_states.dim()}.'\n video_length = hidden_states.shape[2]\n hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w')\n if encoder_hidden_states is not None:\n encoder_hidden_states = repeat(\n encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)\n\n batch, channel, height, weight = hidden_states.shape\n residual = hidden_states\n\n hidden_states = self.norm(hidden_states)\n if not self.use_linear_projection:\n hidden_states = self.proj_in(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * weight, inner_dim)\n else:\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * weight, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n\n # Blocks\n for block in self.transformer_blocks:\n hidden_states = block(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n timestep=timestep,\n video_length=video_length)\n\n # Output\n if not self.use_linear_projection:\n hidden_states = (\n hidden_states.reshape(batch, height, weight,\n inner_dim).permute(0, 3, 1,\n 2).contiguous())\n hidden_states = self.proj_out(hidden_states)\n else:\n hidden_states = self.proj_out(hidden_states)\n hidden_states = (\n hidden_states.reshape(batch, height, weight,\n inner_dim).permute(0, 3, 1,\n 2).contiguous())\n\n output = hidden_states + residual\n\n output = rearrange(output, '(b f) c h w -> b c f h w', f=video_length)\n if not return_dict:\n return (output, )\n\n return Transformer3DModelOutput(sample=output)\n\n\nclass BasicTransformerBlock(nn.Module):\n \"\"\"A basic Transformer block.\n\n Args:\n dim (int): The number of channels in the input and output.\n num_attention_heads (int): The number of heads to use for\n multi-head attention.\n attention_head_dim (int): The number of channels in each head.\n dropout (float, *optional*, defaults to 0.0):\n The dropout probability to use.\n cross_attention_dim (int, *optional*):\n The size of the context vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n attention_bias (bool, *optional*, defaults to `False`):\n Configure if the attentions should contain a bias parameter.\n only_cross_attention (bool, defaults to False):\n whether to use cross attention only.\n upcast_attention (bool):\n whether use upcast attention, defaults to False.\n unet_use_cross_frame_attention (bool):\n whether use cross frame attention, defaults to False.\n unet_use_temporal_attention (bool):\n whether use temporal attention, defaults to False.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = 'geglu',\n num_embeds_ada_norm: Optional[int] = None,\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n self.use_ada_layer_norm = num_embeds_ada_norm is not None\n self.unet_use_cross_frame_attention = unet_use_cross_frame_attention\n self.unet_use_temporal_attention = unet_use_temporal_attention\n\n # SC-Attn\n assert unet_use_cross_frame_attention is not None\n if unet_use_cross_frame_attention:\n # TODO: cross_frame_attention\n pass\n # self.attn1 = SparseCausalAttention2D(\n # query_dim=dim,\n # heads=num_attention_heads,\n # dim_head=attention_head_dim,\n # dropout=dropout,\n # bias=attention_bias,\n # cross_attention_dim=cross_attention_dim\n # if only_cross_attention else None,\n # upcast_attention=upcast_attention,\n # )\n else:\n # TODO: Check whether replace this with models.editors\n # .ddpm.attention.CrossAttention\n self.attn1 = CrossAttention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n )\n self.norm1 = AdaLayerNorm(\n dim, num_embeds_ada_norm\n ) if self.use_ada_layer_norm else nn.LayerNorm(dim)\n\n # Cross-Attn\n if cross_attention_dim is not None:\n self.attn2 = CrossAttention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n )\n else:\n self.attn2 = None\n\n if cross_attention_dim is not None:\n self.norm2 = AdaLayerNorm(\n dim, num_embeds_ada_norm\n ) if self.use_ada_layer_norm else nn.LayerNorm(dim)\n else:\n self.norm2 = None\n\n # Feed-forward\n self.ff = FeedForward(\n dim, dropout=dropout, activation_fn=activation_fn)\n self.norm3 = nn.LayerNorm(dim)\n\n # Temp-Attn\n assert unet_use_temporal_attention is not None\n if unet_use_temporal_attention:\n self.attn_temp = CrossAttention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n )\n nn.init.zeros_(self.attn_temp.to_out[0].weight.data)\n self.norm_temp = AdaLayerNorm(\n dim, num_embeds_ada_norm\n ) if self.use_ada_layer_norm else nn.LayerNorm(dim)\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n timestep=None,\n attention_mask=None,\n video_length=None):\n \"\"\"forward with hidden states, context and timestep.\"\"\"\n # SparseCausal-Attention\n norm_hidden_states = (\n self.norm1(hidden_states, timestep)\n if self.use_ada_layer_norm else self.norm1(hidden_states))\n\n if self.unet_use_cross_frame_attention:\n hidden_states = self.attn1(\n norm_hidden_states,\n attention_mask=attention_mask,\n video_length=video_length) + hidden_states\n else:\n hidden_states = self.attn1(\n norm_hidden_states,\n attention_mask=attention_mask) + hidden_states\n\n if self.attn2 is not None:\n # Cross-Attention\n norm_hidden_states = (\n self.norm2(hidden_states, timestep)\n if self.use_ada_layer_norm else self.norm2(hidden_states))\n hidden_states = (\n self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask) + hidden_states)\n\n # Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n # Temporal-Attention\n if self.unet_use_temporal_attention:\n d = hidden_states.shape[1]\n hidden_states = rearrange(\n hidden_states, '(b f) d c -> (b d) f c', f=video_length)\n norm_hidden_states = (\n self.norm_temp(hidden_states, timestep)\n if self.use_ada_layer_norm else self.norm_temp(hidden_states))\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\n hidden_states = rearrange(\n hidden_states, '(b d) f c -> (b f) d c', d=d)\n\n return hidden_states\n\n\nclass CrossAttention(nn.Module):\n r\"\"\"\n A cross attention layer.\n\n Parameters:\n query_dim (`int`): The number of channels in the query.\n cross_attention_dim (`int`, *optional*):\n The number of channels in the encoder_hidden_states.\n If not given, defaults to `query_dim`.\n heads (`int`, *optional*, defaults to 8):\n The number of heads to use for multi-head attention.\n dim_head (`int`, *optional*, defaults to 64):\n The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n bias (`bool`, *optional*, defaults to False):\n Set to `True` for the query, key, and value linear\n layers to contain a bias parameter.\n \"\"\"\n\n def __init__(\n self,\n query_dim: int,\n cross_attention_dim: Optional[int] = None,\n heads: int = 8,\n dim_head: int = 64,\n dropout: float = 0.0,\n bias=False,\n upcast_attention: bool = False,\n upcast_softmax: bool = False,\n added_kv_proj_dim: Optional[int] = None,\n norm_num_groups: Optional[int] = None,\n ):\n super().__init__()\n inner_dim = dim_head * heads\n cross_attention_dim = cross_attention_dim \\\n if cross_attention_dim is not None else query_dim\n self.upcast_attention = upcast_attention\n self.upcast_softmax = upcast_softmax\n\n self.scale = dim_head**-0.5\n\n self.heads = heads\n # for slice_size > 0 the attention score computation\n # is split across the batch axis to save memory\n # You can set slice_size with `set_attention_slice`\n self.sliceable_head_dim = heads\n self._slice_size = None\n if xformers is not None:\n self._use_memory_efficient_attention_xformers = True\n else:\n self._use_memory_efficient_attention_xformers = False\n self.added_kv_proj_dim = added_kv_proj_dim\n\n if norm_num_groups is not None:\n self.group_norm = nn.GroupNorm(\n num_channels=inner_dim,\n num_groups=norm_num_groups,\n eps=1e-5,\n affine=True)\n else:\n self.group_norm = None\n\n self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)\n self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)\n self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)\n\n if self.added_kv_proj_dim is not None:\n self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)\n self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)\n\n self.to_out = nn.ModuleList([])\n self.to_out.append(nn.Linear(inner_dim, query_dim))\n self.to_out.append(nn.Dropout(dropout))\n\n def reshape_heads_to_batch_dim(self, tensor):\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size, seq_len, head_size,\n dim // head_size)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size,\n seq_len, dim // head_size)\n return tensor\n\n def reshape_batch_dim_to_heads(self, tensor):\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size // head_size, head_size, seq_len,\n dim)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size,\n seq_len, dim * head_size)\n return tensor\n\n def set_attention_slice(self, slice_size):\n if slice_size is not None and slice_size > self.sliceable_head_dim:\n raise ValueError(f'slice_size {slice_size} has to be smaller \\\n or equal to {self.sliceable_head_dim}.')\n\n self._slice_size = slice_size\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n\n encoder_hidden_states = encoder_hidden_states\n\n if self.group_norm is not None:\n hidden_states = self.group_norm(hidden_states.transpose(\n 1, 2)).transpose(1, 2)\n\n query = self.to_q(hidden_states)\n dim = query.shape[-1]\n query = self.reshape_heads_to_batch_dim(query)\n\n if self.added_kv_proj_dim is not None:\n key = self.to_k(hidden_states)\n value = self.to_v(hidden_states)\n encoder_hidden_states_key_proj = \\\n self.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = \\\n self.add_v_proj(encoder_hidden_states)\n\n key = self.reshape_heads_to_batch_dim(key)\n value = self.reshape_heads_to_batch_dim(value)\n encoder_hidden_states_key_proj = \\\n self.reshape_heads_to_batch_dim(\n encoder_hidden_states_key_proj\n )\n encoder_hidden_states_value_proj = \\\n self.reshape_heads_to_batch_dim(\n encoder_hidden_states_value_proj\n )\n\n key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)\n value = torch.concat([encoder_hidden_states_value_proj, value],\n dim=1)\n else:\n encoder_hidden_states = encoder_hidden_states \\\n if encoder_hidden_states is not None else hidden_states\n key = self.to_k(encoder_hidden_states)\n value = self.to_v(encoder_hidden_states)\n\n key = self.reshape_heads_to_batch_dim(key)\n value = self.reshape_heads_to_batch_dim(value)\n\n if attention_mask is not None:\n if attention_mask.shape[-1] != query.shape[1]:\n target_length = query.shape[1]\n attention_mask = F.pad(\n attention_mask, (0, target_length), value=0.0)\n attention_mask = attention_mask.repeat_interleave(\n self.heads, dim=0)\n\n # attention, what we cannot get enough of\n if self._use_memory_efficient_attention_xformers and\\\n 'cuda' in query.device.type:\n # hidden_states = xformers.ops.memory_efficient_attention(\n # query, key, value, attn_bias=attention_mask,\n # op=self.attention_op, scale=self.scale\n # )\n hidden_states = self._memory_efficient_attention_xformers(\n query, key, value, attention_mask)\n # Some versions of xformers return output in fp32, cast it\n # back to the dtype of the input\n hidden_states = hidden_states.to(query.dtype)\n else:\n if self._slice_size is None or query.shape[\n 0] // self._slice_size == 1:\n hidden_states = self._attention(query, key, value,\n attention_mask)\n else:\n hidden_states = self._sliced_attention(query, key, value,\n sequence_length, dim,\n attention_mask)\n\n # linear proj\n hidden_states = self.to_out[0](hidden_states)\n\n # dropout\n hidden_states = self.to_out[1](hidden_states)\n return hidden_states\n\n def _attention(self, query, key, value, attention_mask=None):\n if self.upcast_attention:\n query = query.float()\n key = key.float()\n\n attention_scores = torch.baddbmm(\n torch.empty(\n query.shape[0],\n query.shape[1],\n key.shape[1],\n dtype=query.dtype,\n device=query.device),\n query,\n key.transpose(-1, -2),\n beta=0,\n alpha=self.scale,\n )\n\n if attention_mask is not None:\n attention_scores = attention_scores + attention_mask\n\n if self.upcast_softmax:\n attention_scores = attention_scores.float()\n\n attention_probs = attention_scores.softmax(dim=-1)\n\n # cast back to the original dtype\n attention_probs = attention_probs.to(value.dtype)\n\n # compute attention output\n hidden_states = torch.bmm(attention_probs, value)\n\n # reshape hidden_states\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n\n def _sliced_attention(self, query, key, value, sequence_length, dim,\n attention_mask):\n batch_size_attention = query.shape[0]\n hidden_states = torch.zeros(\n (batch_size_attention, sequence_length, dim // self.heads),\n device=query.device,\n dtype=query.dtype)\n slice_size = self._slice_size \\\n if self._slice_size is not None else hidden_states.shape[0]\n for i in range(hidden_states.shape[0] // slice_size):\n start_idx = i * slice_size\n end_idx = (i + 1) * slice_size\n\n query_slice = query[start_idx:end_idx]\n key_slice = key[start_idx:end_idx]\n\n if self.upcast_attention:\n query_slice = query_slice.float()\n key_slice = key_slice.float()\n\n attn_slice = torch.baddbmm(\n torch.empty(\n slice_size,\n query.shape[1],\n key.shape[1],\n dtype=query_slice.dtype,\n device=query.device),\n query_slice,\n key_slice.transpose(-1, -2),\n beta=0,\n alpha=self.scale,\n )\n\n if attention_mask is not None:\n attn_slice = attn_slice + attention_mask[start_idx:end_idx]\n\n if self.upcast_softmax:\n attn_slice = attn_slice.float()\n\n attn_slice = attn_slice.softmax(dim=-1)\n\n # cast back to the original dtype\n attn_slice = attn_slice.to(value.dtype)\n attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])\n\n hidden_states[start_idx:end_idx] = attn_slice\n\n # reshape hidden_states\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n\n def _memory_efficient_attention_xformers(self, query, key, value,\n attention_mask):\n # TODO attention_mask\n query = query.contiguous()\n key = key.contiguous()\n value = value.contiguous()\n hidden_states = xformers.ops.memory_efficient_attention(\n query, key, value, attn_bias=attention_mask)\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n" }, { "path": "mmagic/models/editors/animatediff/motion_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Adapted from https://github.com/huggingface/diffusers\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom diffusers.utils import BaseOutput\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom einops import rearrange, repeat\nfrom torch import nn\n\nfrom mmagic.models.editors.ddpm.attention import GEGLU, ApproximateGELU\nfrom .attention_3d import CrossAttention\n\n\ndef zero_module(module):\n \"\"\"Zero out the parameters of a module and return it.\"\"\"\n for p in module.parameters():\n p.detach().zero_()\n return module\n\n\n@dataclass\nclass TemporalTransformer3DModelOutput(BaseOutput):\n \"\"\"Output of TemporalTransformer3DModel.\"\"\"\n sample: torch.FloatTensor\n\n\nif is_xformers_available():\n import xformers\n import xformers.ops\nelse:\n xformers = None\n\n\ndef get_motion_module(in_channels, motion_module_type: str,\n motion_module_kwargs: dict):\n \"\"\"Get motion module.\"\"\"\n if motion_module_type == 'Vanilla':\n return VanillaTemporalModule(\n in_channels=in_channels,\n **motion_module_kwargs,\n )\n else:\n raise ValueError\n\n\nclass VanillaTemporalModule(nn.Module):\n \"\"\"Module which uses transformer to handle 3d motion.\"\"\"\n\n def __init__(\n self,\n in_channels,\n num_attention_heads=8,\n num_transformer_block=2,\n attention_block_types=('Temporal_Self', 'Temporal_Self'),\n cross_frame_attention_mode=None,\n temporal_position_encoding=False,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1,\n zero_initialize=True,\n ):\n super().__init__()\n temp_pos_max_len = temporal_position_encoding_max_len\n self.temporal_transformer = TemporalTransformer3DModel(\n in_channels=in_channels,\n num_attention_heads=num_attention_heads,\n attention_head_dim=in_channels // num_attention_heads //\n temporal_attention_dim_div,\n num_layers=num_transformer_block,\n attention_block_types=attention_block_types,\n cross_frame_attention_mode=cross_frame_attention_mode,\n temporal_position_encoding=temporal_position_encoding,\n temporal_position_encoding_max_len=temp_pos_max_len,\n )\n\n if zero_initialize:\n self.temporal_transformer.proj_out = zero_module(\n self.temporal_transformer.proj_out)\n\n def forward(self,\n input_tensor,\n temb,\n encoder_hidden_states,\n attention_mask=None,\n anchor_frame_idx=None):\n \"\"\"forward with sample.\"\"\"\n hidden_states = input_tensor\n hidden_states = self.temporal_transformer(hidden_states,\n encoder_hidden_states,\n attention_mask)\n\n output = hidden_states\n return output\n\n\nclass TemporalTransformer3DModel(nn.Module):\n \"\"\"Module which uses implement 3D Transformer.\"\"\"\n\n def __init__(\n self,\n in_channels,\n num_attention_heads,\n attention_head_dim,\n num_layers,\n attention_block_types=(\n 'Temporal_Self',\n 'Temporal_Self',\n ),\n dropout=0.0,\n norm_num_groups=32,\n cross_attention_dim=768,\n activation_fn='geglu',\n attention_bias=False,\n upcast_attention=False,\n cross_frame_attention_mode=None,\n temporal_position_encoding=False,\n temporal_position_encoding_max_len=24,\n ):\n super().__init__()\n\n inner_dim = num_attention_heads * attention_head_dim\n\n self.norm = torch.nn.GroupNorm(\n num_groups=norm_num_groups,\n num_channels=in_channels,\n eps=1e-6,\n affine=True)\n self.proj_in = nn.Linear(in_channels, inner_dim)\n temp_pos_max_len = temporal_position_encoding_max_len\n self.transformer_blocks = nn.ModuleList([\n TemporalTransformerBlock(\n dim=inner_dim,\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n attention_block_types=attention_block_types,\n dropout=dropout,\n norm_num_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n attention_bias=attention_bias,\n upcast_attention=upcast_attention,\n cross_frame_attention_mode=cross_frame_attention_mode,\n temporal_position_encoding=temporal_position_encoding,\n temporal_position_encoding_max_len=temp_pos_max_len,\n ) for d in range(num_layers)\n ])\n self.proj_out = nn.Linear(inner_dim, in_channels)\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None):\n \"\"\"forward with hidden states, encoder_hidden_states and\n attention_mask.\"\"\"\n\n assert hidden_states.dim(\n ) == 5, f'{\"Expected hidden_states to have ndim=5, \"}'\n f'but got ndim={hidden_states.dim()}.'\n video_length = hidden_states.shape[2]\n hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w')\n\n batch, channel, height, weight = hidden_states.shape\n residual = hidden_states\n\n hidden_states = self.norm(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * weight, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n\n # Transformer Blocks\n for block in self.transformer_blocks:\n hidden_states = block(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n video_length=video_length)\n\n # output\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.reshape(batch, height,\n weight, inner_dim).permute(\n 0, 3, 1, 2).contiguous()\n\n output = hidden_states + residual\n output = rearrange(output, '(b f) c h w -> b c f h w', f=video_length)\n\n return output\n\n\nclass TemporalTransformerBlock(nn.Module):\n \"\"\"Module which is a component of Temporal 3D Transformer.\"\"\"\n\n def __init__(\n self,\n dim,\n num_attention_heads,\n attention_head_dim,\n attention_block_types=(\n 'Temporal_Self',\n 'Temporal_Self',\n ),\n dropout=0.0,\n norm_num_groups=32,\n cross_attention_dim=768,\n activation_fn='geglu',\n attention_bias=False,\n upcast_attention=False,\n cross_frame_attention_mode=None,\n temporal_position_encoding=False,\n temporal_position_encoding_max_len=24,\n ):\n super().__init__()\n\n attention_blocks = []\n norms = []\n temp_pos_max_len = temporal_position_encoding_max_len\n for block_name in attention_block_types:\n attention_blocks.append(\n VersatileAttention(\n attention_mode=block_name.split('_')[0],\n cross_attention_dim=cross_attention_dim\n if block_name.endswith('_Cross') else None,\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n cross_frame_attention_mode=cross_frame_attention_mode,\n temporal_position_encoding=temporal_position_encoding,\n temporal_position_encoding_max_len=temp_pos_max_len,\n ))\n norms.append(nn.LayerNorm(dim))\n\n self.attention_blocks = nn.ModuleList(attention_blocks)\n self.norms = nn.ModuleList(norms)\n\n self.ff = FeedForward(\n dim, dropout=dropout, activation_fn=activation_fn)\n self.ff_norm = nn.LayerNorm(dim)\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n video_length=None):\n \"\"\"forward with hidden states, encoder_hidden_states and\n attention_mask.\"\"\"\n for attention_block, norm in zip(self.attention_blocks, self.norms):\n norm_hidden_states = norm(hidden_states)\n hidden_states = attention_block(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states\n if attention_block.is_cross_attention else None,\n video_length=video_length,\n ) + hidden_states\n\n hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states\n\n output = hidden_states\n return output\n\n\nclass PositionalEncoding(nn.Module):\n \"\"\"a implementation of PositionEncoding.\"\"\"\n\n def __init__(self, d_model, dropout=0., max_len=24):\n super().__init__()\n self.dropout = nn.Dropout(p=dropout)\n position = torch.arange(max_len).unsqueeze(1)\n div_term = torch.exp(\n torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n pe = torch.zeros(1, max_len, d_model)\n pe[0, :, 0::2] = torch.sin(position * div_term)\n pe[0, :, 1::2] = torch.cos(position * div_term)\n self.register_buffer('pe', pe)\n\n def forward(self, x):\n \"\"\"forward function.\"\"\"\n x = x + self.pe[:, :x.size(1)]\n return self.dropout(x)\n\n\nclass VersatileAttention(CrossAttention):\n \"\"\"a implementation of VersatileAttention.\"\"\"\n\n def __init__(self,\n attention_mode=None,\n cross_frame_attention_mode=None,\n temporal_position_encoding=False,\n temporal_position_encoding_max_len=24,\n *args,\n **kwargs):\n super().__init__(*args, **kwargs)\n assert attention_mode == 'Temporal'\n\n self.attention_mode = attention_mode\n self.is_cross_attention = kwargs['cross_attention_dim'] is not None\n\n self.pos_encoder = PositionalEncoding(\n kwargs['query_dim'],\n dropout=0.,\n max_len=temporal_position_encoding_max_len) if (\n temporal_position_encoding\n and attention_mode == 'Temporal') else None\n\n self._use_memory_efficient_attention_xformers = False\n\n def extra_repr(self):\n \"\"\"return module information.\"\"\"\n return f'(Module Info) Attention_Mode: {self.attention_mode},\\\n Is_Cross_Attention: {self.is_cross_attention}'\n\n def reshape_heads_to_batch_dim(self, tensor):\n \"\"\"reshape heads num to batch dim.\"\"\"\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size, seq_len, head_size,\n dim // head_size)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size,\n seq_len, dim // head_size)\n return tensor\n\n def reshape_batch_dim_to_heads(self, tensor):\n \"\"\"reshape batch dim to heads num.\"\"\"\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size // head_size, head_size, seq_len,\n dim)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size,\n seq_len, dim * head_size)\n return tensor\n\n def _memory_efficient_attention_xformers(self, query, key, value,\n attention_mask):\n \"\"\"use xformers to save memory.\"\"\"\n # TODO attention_mask\n query = query.contiguous()\n key = key.contiguous()\n value = value.contiguous()\n hidden_states = xformers.ops.memory_efficient_attention(\n query, key, value, attn_bias=attention_mask)\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n video_length=None):\n \"\"\"forward with hidden states, encoder_hidden_states and\n attention_mask.\"\"\"\n batch_size, sequence_length, _ = hidden_states.shape\n\n if self.attention_mode == 'Temporal':\n d = hidden_states.shape[1]\n hidden_states = rearrange(\n hidden_states, '(b f) d c -> (b d) f c', f=video_length)\n\n if self.pos_encoder is not None:\n hidden_states = self.pos_encoder(hidden_states)\n\n encoder_hidden_states = repeat(\n encoder_hidden_states, 'b n c -> (b d) n c', d=d\n ) if encoder_hidden_states is not None else encoder_hidden_states\n else:\n raise NotImplementedError\n\n encoder_hidden_states = encoder_hidden_states\n\n if self.group_norm is not None:\n hidden_states = self.group_norm(hidden_states.transpose(\n 1, 2)).transpose(1, 2)\n\n query = self.to_q(hidden_states)\n dim = query.shape[-1]\n query = self.reshape_heads_to_batch_dim(query)\n\n if self.added_kv_proj_dim is not None:\n raise NotImplementedError\n\n encoder_hidden_states = encoder_hidden_states \\\n if encoder_hidden_states is not None else hidden_states\n key = self.to_k(encoder_hidden_states)\n value = self.to_v(encoder_hidden_states)\n\n key = self.reshape_heads_to_batch_dim(key)\n value = self.reshape_heads_to_batch_dim(value)\n\n if attention_mask is not None:\n if attention_mask.shape[-1] != query.shape[1]:\n target_length = query.shape[1]\n attention_mask = F.pad(\n attention_mask, (0, target_length), value=0.0)\n attention_mask = attention_mask.repeat_interleave(\n self.heads, dim=0)\n\n # attention, what we cannot get enough of\n if self._use_memory_efficient_attention_xformers:\n hidden_states = self._memory_efficient_attention_xformers(\n query, key, value, attention_mask)\n # Some versions of xformers return output in fp32,\n # cast it back to the dtype of the input\n hidden_states = hidden_states.to(query.dtype)\n else:\n if self._slice_size is None or query.shape[\n 0] // self._slice_size == 1:\n hidden_states = self._attention(query, key, value,\n attention_mask)\n else:\n hidden_states = self._sliced_attention(query, key, value,\n sequence_length, dim,\n attention_mask)\n\n # linear proj\n hidden_states = self.to_out[0](hidden_states)\n\n # dropout\n hidden_states = self.to_out[1](hidden_states)\n\n if self.attention_mode == 'Temporal':\n hidden_states = rearrange(\n hidden_states, '(b d) f c -> (b f) d c', d=d)\n\n return hidden_states\n\n\nclass FeedForward(nn.Module):\n r\"\"\"\n A feed-forward layer.\n\n Parameters:\n dim (`int`): The number of channels in the input.\n dim_out (`int`, *optional*):\n The number of channels in the output. If not given, defaults to `dim`.\n mult (`int`, *optional*, defaults to 4):\n The multiplier to use for the hidden dimension.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n dim_out: Optional[int] = None,\n mult: int = 4,\n dropout: float = 0.0,\n activation_fn: str = 'geglu',\n ):\n super().__init__()\n inner_dim = int(dim * mult)\n dim_out = dim_out if dim_out is not None else dim\n\n if activation_fn == 'gelu':\n act_fn = GELU(dim, inner_dim)\n elif activation_fn == 'geglu':\n act_fn = GEGLU(dim, inner_dim)\n elif activation_fn == 'geglu-approximate':\n act_fn = ApproximateGELU(dim, inner_dim)\n\n self.net = nn.ModuleList([])\n # project in\n self.net.append(act_fn)\n # project dropout\n self.net.append(nn.Dropout(dropout))\n # project out\n self.net.append(nn.Linear(inner_dim, dim_out))\n\n def forward(self, hidden_states):\n for module in self.net:\n hidden_states = module(hidden_states)\n return hidden_states\n\n\nclass GELU(nn.Module):\n r\"\"\"\n GELU activation function\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out)\n\n def gelu(self, gate):\n if gate.device.type != 'mps':\n return F.gelu(gate)\n # mps: gelu is not implemented for float16\n return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)\n\n def forward(self, hidden_states):\n hidden_states = self.proj(hidden_states)\n hidden_states = self.gelu(hidden_states)\n return hidden_states\n" }, { "path": "mmagic/models/editors/animatediff/resnet_3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Adapted from https://github.com/huggingface/diffusers/blob/main/\n# src/diffusers/models/resnet.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\n\nclass InflatedConv3d(nn.Conv2d):\n \"\"\"An implementation of InflatedConv3d.\"\"\"\n\n def forward(self, x):\n \"\"\"forward function.\"\"\"\n video_length = x.shape[2]\n\n x = rearrange(x, 'b c f h w -> (b f) c h w')\n x = super().forward(x)\n x = rearrange(x, '(b f) c h w -> b c f h w', f=video_length)\n\n return x\n\n\nclass InflatedGroupNorm(nn.GroupNorm):\n\n def forward(self, x):\n video_length = x.shape[2]\n\n x = rearrange(x, 'b c f h w -> (b f) c h w')\n x = super().forward(x)\n x = rearrange(x, '(b f) c h w -> b c f h w', f=video_length)\n\n return x\n\n\nclass Upsample3D(nn.Module):\n \"\"\"An 3D upsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n use_conv_transpose (bool): whether to use conv transpose.\n out_channels (int): output channels.\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n use_conv_transpose=False,\n out_channels=None,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_conv_transpose = use_conv_transpose\n self.name = name\n\n if use_conv_transpose:\n raise NotImplementedError\n elif use_conv:\n self.conv = InflatedConv3d(\n self.channels, self.out_channels, 3, padding=1)\n\n def forward(self, hidden_states, output_size=None):\n \"\"\"forward with hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n\n if self.use_conv_transpose:\n raise NotImplementedError\n\n # Cast to float32 to as 'upsample_nearest2d_out_frame'\n # op does not support bfloat16\n dtype = hidden_states.dtype\n if dtype == torch.bfloat16:\n hidden_states = hidden_states.to(torch.float32)\n\n # upsample_nearest_nhwc fails with large batch sizes.\n # see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n hidden_states = hidden_states.contiguous()\n\n # if `output_size` is passed we force the interpolation output\n # size and do not make use of `scale_factor=2`\n if output_size is None:\n hidden_states = F.interpolate(\n hidden_states, scale_factor=[1.0, 2.0, 2.0], mode='nearest')\n else:\n hidden_states = F.interpolate(\n hidden_states, size=output_size, mode='nearest')\n\n # If the input is bfloat16, we cast back to bfloat16\n if dtype == torch.bfloat16:\n hidden_states = hidden_states.to(dtype)\n\n # if self.use_conv:\n # if self.name == \"conv\":\n # hidden_states = self.conv(hidden_states)\n # else:\n # hidden_states = self.Conv2d_0(hidden_states)\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass Downsample3D(nn.Module):\n \"\"\"A 3D downsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n out_channels (int): output channels\n padding (int): padding num\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n out_channels=None,\n padding=1,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.padding = padding\n stride = 2\n self.name = name\n\n if use_conv:\n self.conv = InflatedConv3d(\n self.channels,\n self.out_channels,\n 3,\n stride=stride,\n padding=padding)\n else:\n raise NotImplementedError\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n if self.use_conv and self.padding == 0:\n raise NotImplementedError\n\n assert hidden_states.shape[1] == self.channels\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass ResnetBlock3D(nn.Module):\n \"\"\"3D resnet block support down sample and up sample.\n\n Args:\n in_channels (int): input channels.\n out_channels (int): output channels.\n conv_shortcut (bool): whether to use conv shortcut.\n dropout (float): dropout rate.\n temb_channels (int): time embedding channels.\n groups (int): conv groups.\n groups_out (int): conv out groups.\n pre_norm (bool): whether to norm before conv. Todo: remove.\n eps (float): eps for groupnorm.\n non_linearity (str): non linearity type.\n time_embedding_norm (str): time embedding norm type.\n output_scale_factor (float): factor to scale input and output.\n use_in_shortcut (bool): whether to use conv in shortcut.\n \"\"\"\n\n def __init__(\n self,\n *,\n in_channels,\n out_channels=None,\n conv_shortcut=False,\n dropout=0.0,\n temb_channels=512,\n groups=32,\n groups_out=None,\n pre_norm=True,\n eps=1e-6,\n non_linearity='swish',\n time_embedding_norm='default',\n output_scale_factor=1.0,\n use_in_shortcut=None,\n use_inflated_groupnorm=None,\n ):\n super().__init__()\n self.pre_norm = pre_norm\n self.pre_norm = True\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.time_embedding_norm = time_embedding_norm\n self.output_scale_factor = output_scale_factor\n\n if groups_out is None:\n groups_out = groups\n\n if use_inflated_groupnorm:\n self.norm1 = InflatedGroupNorm(\n num_groups=groups,\n num_channels=in_channels,\n eps=eps,\n affine=True)\n else:\n self.norm1 = torch.nn.GroupNorm(\n num_groups=groups,\n num_channels=in_channels,\n eps=eps,\n affine=True)\n\n self.conv1 = InflatedConv3d(\n in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if temb_channels is not None:\n if self.time_embedding_norm == 'default':\n time_emb_proj_out_channels = out_channels\n elif self.time_embedding_norm == 'scale_shift':\n time_emb_proj_out_channels = out_channels * 2\n else:\n raise ValueError(f'unknown time_embedding_norm : '\n f'{self.time_embedding_norm} ')\n\n self.time_emb_proj = torch.nn.Linear(temb_channels,\n time_emb_proj_out_channels)\n else:\n self.time_emb_proj = None\n\n if use_inflated_groupnorm:\n self.norm2 = InflatedGroupNorm(\n num_groups=groups_out,\n num_channels=out_channels,\n eps=eps,\n affine=True)\n else:\n self.norm2 = torch.nn.GroupNorm(\n num_groups=groups_out,\n num_channels=out_channels,\n eps=eps,\n affine=True)\n\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = InflatedConv3d(\n out_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if non_linearity == 'swish':\n self.nonlinearity = lambda x: F.silu(x)\n elif non_linearity == 'mish':\n self.nonlinearity = Mish()\n elif non_linearity == 'silu':\n self.nonlinearity = nn.SiLU()\n\n self.use_in_shortcut = self.in_channels != self.out_channels \\\n if use_in_shortcut is None else use_in_shortcut\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = InflatedConv3d(\n in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n\n def forward(self, input_tensor, temb):\n \"\"\"forward with hidden states and time embeddings.\"\"\"\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.conv1(hidden_states)\n\n if temb is not None:\n temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None,\n None, None]\n\n if temb is not None and self.time_embedding_norm == 'default':\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n\n if temb is not None and self.time_embedding_norm == 'scale_shift':\n scale, shift = torch.chunk(temb, 2, dim=1)\n hidden_states = hidden_states * (1 + scale) + shift\n\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = (input_tensor +\n hidden_states) / self.output_scale_factor\n\n return output_tensor\n\n\nclass Mish(torch.nn.Module):\n \"\"\"Mish activation function.\"\"\"\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n return hidden_states * torch.tanh(\n torch.nn.functional.softplus(hidden_states))\n" }, { "path": "mmagic/models/editors/animatediff/unet_3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Adapted from https://github.com/huggingface/diffusers/\n# blob/main/src/diffusers/models/unet_2d_condition.py\n\nimport json\nimport os\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.models.embeddings import TimestepEmbedding, Timesteps\nfrom diffusers.models.modeling_utils import ModelMixin\nfrom diffusers.utils import BaseOutput\nfrom huggingface_hub import snapshot_download\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import constant_init\nfrom safetensors import safe_open\n\nfrom mmagic.registry import MODELS\nfrom .resnet_3d import InflatedConv3d, InflatedGroupNorm\nfrom .unet_block import (CrossAttnDownBlock3D, CrossAttnUpBlock3D, DownBlock3D,\n UNetMidBlock3DCrossAttn, UpBlock3D, get_down_block,\n get_up_block)\n\nlogger = MMLogger.get_current_instance()\n\n\n@dataclass\nclass UNet3DConditionOutput(BaseOutput):\n \"\"\"Output of UNet3DCondtion.\"\"\"\n sample: torch.FloatTensor\n\n\n@MODELS.register_module()\nclass UNet3DConditionMotionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n \"\"\" Implementation of UNet3DConditionMotionModel\"\"\"\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n 'CrossAttnDownBlock3D',\n 'CrossAttnDownBlock3D',\n 'CrossAttnDownBlock3D',\n 'DownBlock3D',\n ),\n mid_block_type: str = 'UNetMidBlock3DCrossAttn',\n up_block_types: Tuple[str] = ('UpBlock3D', 'CrossAttnUpBlock3D',\n 'CrossAttnUpBlock3D',\n 'CrossAttnUpBlock3D'),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = 'silu',\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 768,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = 'default',\n\n # Additional\n use_inflated_groupnorm=False,\n use_motion_module=False,\n motion_module_resolutions=(1, 2, 4, 8),\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type=None,\n motion_module_kwargs={},\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n subfolder=None,\n from_pretrained=None,\n unet_addtion_kwargs=None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # input\n self.conv_in = InflatedConv3d(\n in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos,\n freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim,\n time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds,\n time_embed_dim)\n elif class_embed_type == 'timestep':\n self.class_embedding = TimestepEmbedding(timestep_input_dim,\n time_embed_dim)\n elif class_embed_type == 'identity':\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention\n ] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim, ) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n res = 2**i\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module\n and (res in motion_module_resolutions)\n and (not motion_module_decoder_only),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == 'UNetMidBlock3DCrossAttn':\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module\n and motion_module_mid_block,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n else:\n raise ValueError(f'unknown mid_block_type : {mid_block_type}')\n\n # count how many layers upsample the videos\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n res = 2**(3 - i)\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(\n i + 1,\n len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module\n and (res in motion_module_resolutions),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if use_inflated_groupnorm:\n self.conv_norm_out = InflatedGroupNorm(\n num_channels=block_out_channels[0],\n num_groups=norm_num_groups,\n eps=norm_eps)\n else:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0],\n num_groups=norm_num_groups,\n eps=norm_eps)\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0],\n num_groups=norm_num_groups,\n eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = InflatedConv3d(\n block_out_channels[0], out_channels, kernel_size=3, padding=1)\n self.init_weights(subfolder, from_pretrained)\n\n def init_weights(self, subfolder=None, from_pretrained=None):\n \"\"\"Init weights for models.\n\n We just use the initialization method proposed in the original paper.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if isinstance(from_pretrained, str):\n from diffusers.utils import WEIGHTS_NAME\n model_file = os.path.join(from_pretrained, subfolder, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n cache_file = snapshot_download(\n 'runwayml/stable-diffusion-v1-5',\n allow_patterns=['*.json', '*unet*safetensors'],\n ignore_patterns=[\n '*.fp16.safetensors', '*v1-5*', '*ema.safetensors'\n ])\n from diffusers.utils import SAFETENSORS_WEIGHTS_NAME\n model_file = os.path.join(cache_file, subfolder,\n SAFETENSORS_WEIGHTS_NAME)\n state_dict = {}\n with safe_open(model_file, framework='pt', device='cpu') as f:\n for key in f.keys():\n state_dict[key] = f.get_tensor(key)\n else:\n state_dict = torch.load(model_file, map_location='cpu')\n\n m, u = self.load_state_dict(state_dict, strict=False)\n logger.info(\n f'### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};'\n )\n params = [\n p.numel() if 'temporal' in n else 0\n for n, p in self.named_parameters()\n ]\n logger.info(\n f'### Temporal Module Parameters: {sum(params) / 1e6} M')\n elif from_pretrained is None:\n # As Improved-DDPM, we apply zero-initialization to\n # second conv block in ResBlock (keywords: conv_2)\n # the output layer of the Unet (keywords: 'out' but\n # not 'out_blocks')\n # projection layer in Attention layer (keywords: proj)\n for n, m in self.named_modules():\n if isinstance(m, nn.Conv2d) and ('conv2' in n or\n ('out' in n\n and 'out_blocks' not in n)):\n constant_init(m, 0)\n if isinstance(m, nn.Conv1d) and 'proj' in n:\n constant_init(m, 0)\n else:\n raise TypeError('from_pretrained must be a str or None but'\n f' got {type(from_pretrained)} instead.')\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will\n split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory\n in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*,\n defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads,\n so attention will be computed in two steps. If\n `\"max\"`, maximum amount of memory will be saved by\n running only one slice at a time. If a number is\n provided, uses as many slices as\n `attention_head_dim // slice_size`. In this case,\n `attention_head_dim' must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n \"\"\"set attention slice recursively.\"\"\"\n if hasattr(module, 'set_attention_slice'):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == 'auto':\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == 'max':\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(\n slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f'You have provided {len(slice_size)}, but '\n f'{self.config} has {len(sliceable_head_dims)} different'\n f' attention layers. Make sure to match '\n f'`len(slice_size)` to be {len(sliceable_head_dims)}.')\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(\n f'size {size} has to be smaller or equal to {dim}.')\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module,\n slice_size: List[int]):\n \"\"\"set attention slice recursively.\"\"\"\n\n if hasattr(module, 'set_attention_slice'):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n \"\"\"set gradient checkpoint.\"\"\"\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D,\n CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, channel, height, width)\n noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`):\n (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`):\n (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`UNet3DConditionOutput`]\n instead of a plain tuple.\n\n Returns:\n [`UNet3DConditionOutput`] or `tuple`:\n [`UNet3DConditionOutput`]\n if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the\n # overall upsampling factor. he overall upsampling factor is equal\n # T to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size\n # can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is\n # not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\n 'Forward upsample size to force interpolation output size.')\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # This would be a good case for the `match`\n # statement (Python 3.10+)\n is_mps = sample.device.type == 'mps'\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps],\n dtype=dtype,\n device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way\n # that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will\n # always return f32 tensors\n # but time_embedding might actually be running in fp16.\n # so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\n 'class_labels should be provided when num_class_embeds > 0'\n )\n\n if self.config.class_embed_type == 'timestep':\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # pre-process\n sample = self.conv_in(sample)\n\n # down\n down_block_res_samples = (sample, )\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, 'has_cross_attention'\n ) and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n )\n else:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states)\n\n down_block_res_samples += res_samples\n\n # mid\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask)\n\n # up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets):]\n down_block_res_samples = down_block_res_samples[:-len(\n upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, 'has_cross_attention'\n ) and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n encoder_hidden_states=encoder_hidden_states,\n )\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample, )\n\n return UNet3DConditionOutput(sample=sample)\n\n @classmethod\n def from_pretrained_2d(cls,\n pretrained_model_path,\n subfolder=None,\n unet_additional_kwargs=None):\n \"\"\"a class method for initialization.\"\"\"\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path,\n subfolder)\n logger.info(f\"loaded temporal unet's pretrained weights \\\n from {pretrained_model_path} ...\")\n\n config_file = os.path.join(pretrained_model_path, 'config.json')\n if not os.path.isfile(config_file):\n raise RuntimeError(f'{config_file} does not exist')\n with open(config_file, 'r') as f:\n config = json.load(f)\n config['_class_name'] = cls.__name__\n config['down_block_types'] = [\n 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D',\n 'CrossAttnDownBlock3D', 'DownBlock3D'\n ]\n config['up_block_types'] = [\n 'UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D',\n 'CrossAttnUpBlock3D'\n ]\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config, **unet_additional_kwargs)\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n raise RuntimeError(f'{model_file} does not exist')\n state_dict = torch.load(model_file, map_location='cpu')\n\n m, u = model.load_state_dict(state_dict, strict=False)\n logger.info(\n f'### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};')\n # print(f\"### missing keys:\\n{m}\\n### unexpected keys:\\n{u}\\n\")\n\n params = [\n p.numel() if 'temporal' in n else 0\n for n, p in model.named_parameters()\n ]\n logger.info(f'### Temporal Module Parameters: {sum(params) / 1e6} M')\n\n return model\n" }, { "path": "mmagic/models/editors/animatediff/unet_block.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Adapted from https://github.com/huggingface/diffusers/blob/\n# main/src/diffusers/models/unet_2d_blocks.py\n\nimport torch\nfrom torch import nn\n\nfrom .attention_3d import Transformer3DModel\nfrom .motion_module import get_motion_module\nfrom .resnet_3d import Downsample3D, ResnetBlock3D, Upsample3D\n\n\ndef get_down_block(\n down_block_type,\n num_layers,\n in_channels,\n out_channels,\n temb_channels,\n add_downsample,\n resnet_eps,\n resnet_act_fn,\n attn_num_head_channels,\n resnet_groups=None,\n cross_attention_dim=None,\n downsample_padding=None,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift='default',\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n):\n \"\"\"get unet down path block.\"\"\"\n\n down_block_type = down_block_type[7:] if down_block_type.startswith(\n 'UNetRes') else down_block_type\n if down_block_type == 'DownBlock3D':\n return DownBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n elif down_block_type == 'CrossAttnDownBlock3D':\n if cross_attention_dim is None:\n raise ValueError('cross_attention_dim must be specified \\\n for CrossAttnDownBlock3D')\n return CrossAttnDownBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attn_num_head_channels,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n raise ValueError(f'{down_block_type} does not exist.')\n\n\ndef get_up_block(\n up_block_type,\n num_layers,\n in_channels,\n out_channels,\n prev_output_channel,\n temb_channels,\n add_upsample,\n resnet_eps,\n resnet_act_fn,\n attn_num_head_channels,\n resnet_groups=None,\n cross_attention_dim=None,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift='default',\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n):\n \"\"\"get unet up path block.\"\"\"\n\n up_block_type = up_block_type[7:] if up_block_type.startswith(\n 'UNetRes') else up_block_type\n if up_block_type == 'UpBlock3D':\n return UpBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n elif up_block_type == 'CrossAttnUpBlock3D':\n if cross_attention_dim is None:\n raise ValueError(\n 'cross_attention_dim must be specified for CrossAttnUpBlock3D')\n return CrossAttnUpBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attn_num_head_channels,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n use_inflated_groupnorm=use_inflated_groupnorm,\n use_motion_module=use_motion_module,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n raise ValueError(f'{up_block_type} does not exist.')\n\n\nclass UNetMidBlock3DCrossAttn(nn.Module):\n \"\"\"3D unet mid block built by cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n output_scale_factor=1.0,\n cross_attention_dim=1280,\n dual_cross_attention=False,\n use_linear_projection=False,\n upcast_attention=False,\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n self.attn_num_head_channels = attn_num_head_channels\n resnet_groups = resnet_groups if resnet_groups is not None else min(\n in_channels // 4, 32)\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock3D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n )\n ]\n attentions = []\n motion_modules = []\n\n for _ in range(num_layers):\n if dual_cross_attention:\n raise NotImplementedError\n cfa = unet_use_cross_frame_attention\n attentions.append(\n Transformer3DModel(\n attn_num_head_channels,\n in_channels // attn_num_head_channels,\n in_channels=in_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n unet_use_cross_frame_attention=cfa,\n unet_use_temporal_attention=unet_use_temporal_attention,\n ))\n motion_modules.append(\n get_motion_module(\n in_channels=in_channels,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n ) if use_motion_module else None)\n resnets.append(\n ResnetBlock3D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n ))\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n def forward(self,\n hidden_states,\n temb=None,\n encoder_hidden_states=None,\n attention_mask=None):\n \"\"\"forward with hidden states.\"\"\"\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet, motion_module in zip(self.attentions,\n self.resnets[1:],\n self.motion_modules):\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states).sample\n hidden_states = motion_module(\n hidden_states,\n temb,\n encoder_hidden_states=encoder_hidden_states\n ) if motion_module is not None else hidden_states\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass CrossAttnDownBlock3D(nn.Module):\n \"\"\"Down block built by 3D cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n cross_attention_dim=1280,\n output_scale_factor=1.0,\n downsample_padding=1,\n add_downsample=True,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n ):\n super().__init__()\n resnets = []\n attentions = []\n motion_modules = []\n\n self.has_cross_attention = True\n self.attn_num_head_channels = attn_num_head_channels\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock3D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n ))\n if dual_cross_attention:\n raise NotImplementedError\n cfa = unet_use_cross_frame_attention\n attentions.append(\n Transformer3DModel(\n attn_num_head_channels,\n out_channels // attn_num_head_channels,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n unet_use_cross_frame_attention=cfa,\n unet_use_temporal_attention=unet_use_temporal_attention,\n ))\n motion_modules.append(\n get_motion_module(\n in_channels=out_channels,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n ) if use_motion_module else None)\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([\n Downsample3D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name='op')\n ])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(self,\n hidden_states,\n temb=None,\n encoder_hidden_states=None,\n attention_mask=None):\n \"\"\"forward with hidden states.\"\"\"\n\n output_states = ()\n\n for resnet, attn, motion_module in zip(self.resnets, self.attentions,\n self.motion_modules):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb)\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(attn, return_dict=False),\n hidden_states,\n encoder_hidden_states,\n )[0]\n if motion_module is not None:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(motion_module),\n hidden_states.requires_grad_(), temb,\n encoder_hidden_states)\n\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states).sample\n\n # add motion module\n hidden_states = motion_module(\n hidden_states,\n temb,\n encoder_hidden_states=encoder_hidden_states\n ) if motion_module is not None else hidden_states\n\n output_states += (hidden_states, )\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states, )\n\n return hidden_states, output_states\n\n\nclass DownBlock3D(nn.Module):\n \"\"\"Down block built by 3D resnet.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_downsample=True,\n downsample_padding=1,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n ):\n super().__init__()\n resnets = []\n motion_modules = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock3D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n ))\n motion_modules.append(\n get_motion_module(\n in_channels=out_channels,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n ) if use_motion_module else None)\n\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([\n Downsample3D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name='op')\n ])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(self, hidden_states, temb=None, encoder_hidden_states=None):\n \"\"\"forward with hidden states.\"\"\"\n\n output_states = ()\n\n for resnet, motion_module in zip(self.resnets, self.motion_modules):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb)\n if motion_module is not None:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(motion_module),\n hidden_states.requires_grad_(), temb,\n encoder_hidden_states)\n else:\n hidden_states = resnet(hidden_states, temb)\n\n # add motion module\n hidden_states = motion_module(\n hidden_states,\n temb,\n encoder_hidden_states=encoder_hidden_states\n ) if motion_module is not None else hidden_states\n\n output_states += (hidden_states, )\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states, )\n\n return hidden_states, output_states\n\n\nclass CrossAttnUpBlock3D(nn.Module):\n \"\"\"Up block built by 3D cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n cross_attention_dim=1280,\n output_scale_factor=1.0,\n add_upsample=True,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n use_inflated_groupnorm=None,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n ):\n super().__init__()\n resnets = []\n attentions = []\n motion_modules = []\n\n self.has_cross_attention = True\n self.attn_num_head_channels = attn_num_head_channels\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers -\n 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 \\\n else out_channels\n\n resnets.append(\n ResnetBlock3D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n ))\n if dual_cross_attention:\n raise NotImplementedError\n cfa = unet_use_cross_frame_attention\n attentions.append(\n Transformer3DModel(\n attn_num_head_channels,\n out_channels // attn_num_head_channels,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n unet_use_cross_frame_attention=cfa,\n unet_use_temporal_attention=unet_use_temporal_attention,\n ))\n motion_modules.append(\n get_motion_module(\n in_channels=out_channels,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n ) if use_motion_module else None)\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([\n Upsample3D(\n out_channels, use_conv=True, out_channels=out_channels)\n ])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states,\n res_hidden_states_tuple,\n temb=None,\n encoder_hidden_states=None,\n upsample_size=None,\n attention_mask=None,\n ):\n \"\"\"forward with hidden states and res hidden states.\"\"\"\n\n for resnet, attn, motion_module in zip(self.resnets, self.attentions,\n self.motion_modules):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states],\n dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb)\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(attn, return_dict=False),\n hidden_states,\n encoder_hidden_states,\n )[0]\n if motion_module is not None:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(motion_module),\n hidden_states.requires_grad_(), temb,\n encoder_hidden_states)\n\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states).sample\n\n # add motion module\n hidden_states = motion_module(\n hidden_states,\n temb,\n encoder_hidden_states=encoder_hidden_states\n ) if motion_module is not None else hidden_states\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpBlock3D(nn.Module):\n \"\"\"Up block built by 3D resnet.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_upsample=True,\n use_motion_module=None,\n motion_module_type=None,\n motion_module_kwargs=None,\n use_inflated_groupnorm=None,\n ):\n super().__init__()\n resnets = []\n motion_modules = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers -\n 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 \\\n else out_channels\n\n resnets.append(\n ResnetBlock3D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_inflated_groupnorm=use_inflated_groupnorm,\n ))\n motion_modules.append(\n get_motion_module(\n in_channels=out_channels,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n ) if use_motion_module else None)\n\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([\n Upsample3D(\n out_channels, use_conv=True, out_channels=out_channels)\n ])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states,\n res_hidden_states_tuple,\n temb=None,\n upsample_size=None,\n encoder_hidden_states=None,\n ):\n \"\"\"forward with hidden states and res hidden states.\"\"\"\n\n for resnet, motion_module in zip(self.resnets, self.motion_modules):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states],\n dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb)\n if motion_module is not None:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(motion_module),\n hidden_states.requires_grad_(), temb,\n encoder_hidden_states)\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = motion_module(\n hidden_states,\n temb,\n encoder_hidden_states=encoder_hidden_states\n ) if motion_module is not None else hidden_states\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n" }, { "path": "mmagic/models/editors/aotgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .aot_decoder import AOTDecoder\nfrom .aot_encoder import AOTEncoder\nfrom .aot_encoder_decoder import AOTEncoderDecoder\nfrom .aot_inpaintor import AOTInpaintor\nfrom .aot_neck import AOTBlockNeck\n\n__all__ = [\n 'AOTEncoderDecoder', 'AOTBlockNeck', 'AOTInpaintor', 'AOTEncoder',\n 'AOTDecoder'\n]\n" }, { "path": "mmagic/models/editors/aotgan/aot_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass AOTDecoder(BaseModule):\n \"\"\"Decoder used in AOT-GAN model.\n\n This implementation follows:\n Aggregated Contextual Transformations for High-Resolution Image Inpainting\n\n Args:\n in_channels (int, optional): Channel number of input feature.\n Default: 256.\n mid_channels (int, optional): Channel number of middle feature.\n Default: 128.\n out_channels (int, optional): Channel number of output feature.\n Default 3.\n act_cfg (dict, optional): Config dict for activation layer,\n \"relu\" by default.\n \"\"\"\n\n def __init__(self,\n in_channels=256,\n mid_channels=128,\n out_channels=3,\n act_cfg=dict(type='ReLU')):\n super().__init__()\n\n self.decoder = nn.ModuleList([\n ConvModule(\n in_channels,\n mid_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=act_cfg),\n ConvModule(\n mid_channels,\n mid_channels // 2,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=act_cfg),\n ConvModule(\n mid_channels // 2,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None)\n ])\n self.output_act = nn.Tanh()\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n for i in range(0, len(self.decoder)):\n if i <= 1:\n x = F.interpolate(\n x, scale_factor=2, mode='bilinear', align_corners=True)\n x = self.decoder[i](x)\n\n return self.output_act(x)\n" }, { "path": "mmagic/models/editors/aotgan/aot_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass AOTEncoder(BaseModule):\n \"\"\"Encoder used in AOT-GAN model.\n\n This implementation follows:\n Aggregated Contextual Transformations for High-Resolution Image Inpainting\n\n Args:\n in_channels (int, optional): Channel number of input feature.\n Default: 4.\n mid_channels (int, optional): Channel number of middle feature.\n Default: 64.\n out_channels (int, optional): Channel number of output feature.\n Default: 256.\n act_cfg (dict, optional): Config dict for activation layer,\n \"relu\" by default.\n \"\"\"\n\n def __init__(self,\n in_channels=4,\n mid_channels=64,\n out_channels=256,\n act_cfg=dict(type='ReLU')):\n super().__init__()\n self.encoder = nn.Sequential(\n nn.ReflectionPad2d(3),\n ConvModule(\n in_channels,\n mid_channels,\n kernel_size=7,\n stride=1,\n act_cfg=act_cfg),\n ConvModule(\n mid_channels,\n mid_channels * 2,\n kernel_size=4,\n stride=2,\n padding=1,\n act_cfg=act_cfg),\n ConvModule(\n mid_channels * 2,\n out_channels,\n kernel_size=4,\n stride=2,\n padding=1,\n act_cfg=act_cfg))\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n return self.encoder(x)\n" }, { "path": "mmagic/models/editors/aotgan/aot_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nfrom mmagic.registry import MODELS\nfrom ..global_local import GLEncoderDecoder\n\n\n@MODELS.register_module()\nclass AOTEncoderDecoder(GLEncoderDecoder):\n \"\"\"Encoder-Decoder used in AOT-GAN model.\n\n This implementation follows:\n Aggregated Contextual Transformations for High-Resolution Image Inpainting\n The architecture of the encoder-decoder is:\n (conv2d x 3) --> (dilated conv2d x 8) --> (conv2d or deconv2d x 3).\n\n Args:\n encoder (dict): Config dict to encoder.\n decoder (dict): Config dict to build decoder.\n dilation_neck (dict): Config dict to build dilation neck.\n \"\"\"\n\n def __init__(self,\n encoder=dict(type='AOTEncoder'),\n decoder=dict(type='AOTDecoder'),\n dilation_neck=dict(type='AOTBlockNeck')):\n super().__init__()\n self.encoder = MODELS.build(encoder)\n self.decoder = MODELS.build(decoder)\n self.dilation_neck = MODELS.build(dilation_neck)\n" }, { "path": "mmagic/models/editors/aotgan/aot_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List\n\nimport torch\n\nfrom mmagic.models.base_models import OneStageInpaintor\nfrom mmagic.registry import MODELS\nfrom ...utils import set_requires_grad\n\n\n@MODELS.register_module()\nclass AOTInpaintor(OneStageInpaintor):\n \"\"\"Inpaintor for AOT-GAN method.\n\n This inpaintor is implemented according to the paper: Aggregated Contextual\n Transformations for High-Resolution Image Inpainting\n \"\"\"\n\n def forward_train_d(self, data_batch, is_real, is_disc, mask):\n \"\"\"Forward function in discriminator training step.\n\n In this function, we compute the prediction for each data batch (real\n or fake). Meanwhile, the standard gan loss will be computed with\n several proposed losses for stable training.\n\n Args:\n data_batch (torch.Tensor): Batch of real data or fake data.\n is_real (bool): If True, the gan loss will regard this batch as\n real data. Otherwise, the gan loss will regard this batch as\n fake data.\n is_disc (bool): If True, this function is called in discriminator\n training step. Otherwise, this function is called in generator\n training step. This will help us to compute different types of\n adversarial loss, like LSGAN.\n mask (torch.Tensor): Mask of data.\n\n Returns:\n dict: Contains the loss items computed in this function.\n \"\"\"\n\n pred = self.disc(data_batch)\n loss_ = self.loss_gan(pred, is_real, is_disc, mask=mask)\n\n loss = dict(real_loss=loss_) if is_real else dict(fake_loss=loss_)\n\n if self.with_disc_shift_loss:\n loss_d_shift = self.loss_disc_shift(loss_)\n # 0.5 for average the fake and real data\n loss.update(loss_disc_shift=loss_d_shift * 0.5)\n\n return loss\n\n def generator_loss(self, fake_res, fake_img, gt, mask, masked_img):\n \"\"\"Forward function in generator training step.\n\n In this function, we mainly compute the loss items for generator with\n the given (fake_res, fake_img). In general, the `fake_res` is the\n direct output of the generator and the `fake_img` is the composition of\n direct output and ground-truth image.\n\n Args:\n fake_res (torch.Tensor): Direct output of the generator.\n fake_img (torch.Tensor): Composition of `fake_res` and\n ground-truth image.\n gt (torch.Tensor): Ground-truth image.\n mask (torch.Tensor): Mask image.\n masked_img (torch.Tensor): Composition of mask image and\n ground-truth image.\n\n Returns:\n tuple(dict): Dict contains the results computed within this\n function for visualization and dict contains the loss items\n computed in this function.\n \"\"\"\n loss = dict()\n\n if self.with_gan:\n pred = self.disc(fake_img)\n loss_g_fake = self.loss_gan(pred, True, False, mask=mask)\n loss['loss_g_fake'] = loss_g_fake\n\n if self.with_l1_valid_loss:\n loss_l1_valid = self.loss_l1_valid(fake_res, gt)\n loss['loss_l1_valid'] = loss_l1_valid\n\n if self.with_out_percep_loss:\n loss_out_percep, loss_out_style = self.loss_percep(fake_res, gt)\n if loss_out_percep is not None:\n loss['loss_out_percep'] = loss_out_percep\n if loss_out_style is not None:\n loss['loss_out_style'] = loss_out_style\n\n res = dict(\n gt_img=gt.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=fake_res.cpu(),\n fake_img=fake_img.cpu())\n\n return res, loss\n\n def forward_tensor(self, inputs, data_samples):\n \"\"\"Forward function in tensor mode.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_samples (List[dict]): List of data sample dict.\n\n Returns:\n tuple: Direct output of the generator and composition of `fake_res`\n and ground-truth image.\n \"\"\"\n # Pre-process runs in BaseModel.val_step / test_step\n masks = data_samples.mask\n\n masked_imgs = inputs # N,3,H,W\n masked_imgs = masked_imgs.float() + masks\n\n input_xs = torch.cat([masked_imgs, masks], dim=1) # N,4,H,W\n fake_reses = self.generator(input_xs)\n fake_imgs = fake_reses * masks + masked_imgs * (1. - masks)\n return fake_reses, fake_imgs\n\n def train_step(self, data: List[dict], optim_wrapper):\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n 1. get fake res/image\n 2. compute reconstruction losses for generator\n 3. compute adversarial loss for discriminator\n 4. optimize generator\n 5. optimize discriminator\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n # prepare data for training\n gt_img = data_samples.gt_img\n mask = data_samples.mask\n mask = mask.float()\n\n masked_img = batch_inputs\n masked_img = masked_img.float() + mask\n\n # get common output from encdec\n input_x = torch.cat([masked_img, mask], dim=1)\n fake_res = self.generator(input_x)\n fake_img = gt_img * (1. - mask) + fake_res * mask\n\n # discriminator training step\n if self.train_cfg.disc_step > 0:\n set_requires_grad(self.disc, True)\n\n disc_losses_real = self.forward_train_d(\n gt_img, True, True, mask=mask)\n disc_losses_fake = self.forward_train_d(\n fake_img.detach(), False, True, mask=mask)\n disc_losses_ = disc_losses_real['real_loss'] + disc_losses_fake[\n 'fake_loss']\n disc_losses = dict(disc_losses=disc_losses_)\n\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n optim_wrapper['disc'].backward(loss_disc)\n optim_wrapper['disc'].step()\n optim_wrapper['disc'].zero_grad()\n log_vars.update(log_vars_d)\n\n self.disc_step_count = (self.disc_step_count +\n 1) % self.train_cfg.disc_step\n if self.disc_step_count != 0:\n return log_vars\n\n # generator (encdec) training step, results contain the data\n # for visualization\n if self.with_gan:\n set_requires_grad(self.disc, False)\n results, g_losses = self.generator_loss(fake_res, fake_img, gt_img,\n mask, masked_img)\n loss_g, log_vars_g = self.parse_losses(g_losses)\n log_vars.update(log_vars_g)\n\n optim_wrapper['generator'].backward(loss_g)\n optim_wrapper['generator'].step()\n optim_wrapper['generator'].zero_grad()\n\n return log_vars\n" }, { "path": "mmagic/models/editors/aotgan/aot_neck.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass AOTBlockNeck(BaseModule):\n \"\"\"Dilation backbone used in AOT-GAN model.\n\n This implementation follows:\n Aggregated Contextual Transformations for High-Resolution Image Inpainting\n\n Args:\n in_channels (int, optional): Channel number of input feature.\n Default: 256.\n dilation_rates (Tuple[int], optional): The dilation rates used\n for AOT block. Default: (1, 2, 4, 8).\n num_aotblock (int, optional): Number of AOT blocks. Default: 8.\n act_cfg (dict, optional): Config dict for activation layer,\n \"relu\" by default.\n kwargs (keyword arguments).\n \"\"\"\n\n def __init__(self,\n in_channels=256,\n dilation_rates=(1, 2, 4, 8),\n num_aotblock=8,\n act_cfg=dict(type='ReLU'),\n **kwargs):\n super().__init__()\n\n self.dilation_rates = list(dilation_rates)\n\n self.model = nn.Sequential(*[(AOTBlock(\n in_channels=in_channels,\n dilation_rates=self.dilation_rates,\n act_cfg=act_cfg,\n )) for _ in range(0, num_aotblock)])\n\n def forward(self, x):\n x = self.model(x)\n return x\n\n\nclass AOTBlock(BaseModule):\n \"\"\"AOT Block which constitutes the dilation backbone.\n\n This implementation follows:\n Aggregated Contextual Transformations for High-Resolution Image Inpainting\n\n The AOT Block adopts the split-transformation-merge strategy:\n Splitting: A kernel with 256 output channels is split into four\n 64-channel sub-kernels.\n Transforming: Each sub-kernel performs a different transformation with\n a different dilation rate.\n Splitting: Sub-kernels with different receptive fields are merged.\n\n Args:\n in_channels (int, optional): Channel number of input feature.\n Default: 256.\n dilation_rates (Tuple[int]): The dilation rates used for AOT block.\n Default (1, 2, 4, 8).\n act_cfg (dict, optional): Config dict for activation layer,\n \"relu\" by default.\n kwargs (keyword arguments).\n \"\"\"\n\n def __init__(self,\n in_channels=256,\n dilation_rates=(1, 2, 4, 8),\n act_cfg=dict(type='ReLU'),\n **kwargs):\n super().__init__()\n self.dilation_rates = dilation_rates\n self.blocks = nn.ModuleList([\n nn.Sequential(\n nn.ReflectionPad2d(dilation_rate),\n ConvModule(\n in_channels,\n in_channels // 4,\n kernel_size=3,\n dilation=dilation_rate,\n act_cfg=act_cfg)) for dilation_rate in self.dilation_rates\n ])\n\n self.fuse = nn.Sequential(\n nn.ReflectionPad2d(1),\n ConvModule(in_channels, in_channels, 3, dilation=1, act_cfg=None))\n\n self.gate = nn.Sequential(\n nn.ReflectionPad2d(1),\n ConvModule(in_channels, in_channels, 3, dilation=1, act_cfg=None))\n\n def normalize(self, x):\n mean = x.mean((2, 3), keepdim=True)\n std = x.std((2, 3), keepdim=True) + 1e-9\n x = 2 * (x - mean) / std - 1\n x = 5 * x\n return x\n\n def forward(self, x):\n\n dilate_x = [\n self.blocks[i](x) for i in range(0, len(self.dilation_rates))\n ]\n dilate_x = torch.cat(dilate_x, 1)\n dilate_x = self.fuse(dilate_x)\n mask = self.normalize(self.gate(x))\n mask = torch.sigmoid(mask)\n return x * (1 - mask) + dilate_x * mask\n" }, { "path": "mmagic/models/editors/arcface/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .id_loss import IDLossModel\n\n__all__ = ['IDLossModel']\n" }, { "path": "mmagic/models/editors/arcface/arcface_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom collections import namedtuple\n\nimport torch\nfrom torch.nn import (AdaptiveAvgPool2d, BatchNorm2d, Conv2d, MaxPool2d,\n Module, PReLU, ReLU, Sequential, Sigmoid)\n\n# yapf: disable\n\"\"\"\nArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) # isort:skip # noqa\n\"\"\"\n# yapf: enable\n\n\nclass Flatten(Module):\n \"\"\"Flatten Module.\"\"\"\n\n def forward(self, input):\n return input.view(input.size(0), -1)\n\n\ndef l2_norm(input, axis=1):\n \"\"\"l2 normalization.\n\n Args:\n input (torch.Tensor): The input tensor.\n axis (int, optional): Specifies which axis of input to calculate the\n norm across. Defaults to 1.\n\n Returns:\n Tensor: Tensor after L2 normalization per-instance.\n \"\"\"\n norm = torch.norm(input, 2, axis, True)\n output = torch.div(input, norm)\n return output\n\n\nclass Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):\n \"\"\"A named tuple describing a ResNet block.\"\"\"\n\n\ndef get_block(in_channel, depth, num_units, stride=2):\n \"\"\"Get a single block config.\n\n Args:\n in_channel (int): Input channels.\n depth (int): Output channels.\n num_units (int): Number of unit modules.\n stride (int, optional): Conv2d stride. Defaults to 2.\n\n Returns:\n list: A list of unit modules' config.\n \"\"\"\n return [Bottleneck(in_channel, depth, stride)\n ] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]\n\n\ndef get_blocks(num_layers):\n \"\"\"Get block configs of backbone.\n\n Args:\n num_layers (int): Number of ConvBlock layers in backbone.\n\n Raises:\n ValueError: `num_layers` must be one of [50, 100, 152].\n\n Returns:\n list: A list of block configs.\n \"\"\"\n if num_layers == 50:\n blocks = [\n get_block(in_channel=64, depth=64, num_units=3),\n get_block(in_channel=64, depth=128, num_units=4),\n get_block(in_channel=128, depth=256, num_units=14),\n get_block(in_channel=256, depth=512, num_units=3)\n ]\n elif num_layers == 100:\n blocks = [\n get_block(in_channel=64, depth=64, num_units=3),\n get_block(in_channel=64, depth=128, num_units=13),\n get_block(in_channel=128, depth=256, num_units=30),\n get_block(in_channel=256, depth=512, num_units=3)\n ]\n elif num_layers == 152:\n blocks = [\n get_block(in_channel=64, depth=64, num_units=3),\n get_block(in_channel=64, depth=128, num_units=8),\n get_block(in_channel=128, depth=256, num_units=36),\n get_block(in_channel=256, depth=512, num_units=3)\n ]\n else:\n raise ValueError(\n 'Invalid number of layers: {}. Must be one of [50, 100, 152]'.\n format(num_layers))\n return blocks\n\n\nclass SEModule(Module):\n \"\"\"Squeeze-and-Excitation Modules.\n\n Args:\n channels (int): Input channels.\n reduction (int): Intermediate channels reduction ratio.\n \"\"\"\n\n def __init__(self, channels, reduction):\n super(SEModule, self).__init__()\n self.avg_pool = AdaptiveAvgPool2d(1)\n self.fc1 = Conv2d(\n channels,\n channels // reduction,\n kernel_size=1,\n padding=0,\n bias=False)\n self.relu = ReLU(inplace=True)\n self.fc2 = Conv2d(\n channels // reduction,\n channels,\n kernel_size=1,\n padding=0,\n bias=False)\n self.sigmoid = Sigmoid()\n\n def forward(self, x):\n \"\"\"Forward Function.\"\"\"\n module_input = x\n x = self.avg_pool(x)\n x = self.fc1(x)\n x = self.relu(x)\n x = self.fc2(x)\n x = self.sigmoid(x)\n return module_input * x\n\n\nclass bottleneck_IR(Module):\n \"\"\"Intermediate Resblock of bottleneck.\n\n Args:\n in_channel (int): Input channels.\n depth (int): Output channels.\n stride (int): Conv2d stride.\n \"\"\"\n\n def __init__(self, in_channel, depth, stride):\n \"\"\"Intermediate Resblock of bottleneck.\n\n Args:\n in_channel (int): Input channels.\n depth (int): Output channels.\n stride (int): Conv2d stride.\n \"\"\"\n super(bottleneck_IR, self).__init__()\n if in_channel == depth:\n self.shortcut_layer = MaxPool2d(1, stride)\n else:\n self.shortcut_layer = Sequential(\n Conv2d(in_channel, depth, (1, 1), stride, bias=False),\n BatchNorm2d(depth))\n self.res_layer = Sequential(\n BatchNorm2d(in_channel),\n Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),\n PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False),\n BatchNorm2d(depth))\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n shortcut = self.shortcut_layer(x)\n res = self.res_layer(x)\n return res + shortcut\n\n\nclass bottleneck_IR_SE(Module):\n \"\"\"Intermediate Resblock of bottleneck with SEModule.\n\n Args:\n in_channel (int): Input channels.\n depth (int): Output channels.\n stride (int): Conv2d stride.\n \"\"\"\n\n def __init__(self, in_channel, depth, stride):\n super(bottleneck_IR_SE, self).__init__()\n if in_channel == depth:\n self.shortcut_layer = MaxPool2d(1, stride)\n else:\n self.shortcut_layer = Sequential(\n Conv2d(in_channel, depth, (1, 1), stride, bias=False),\n BatchNorm2d(depth))\n self.res_layer = Sequential(\n BatchNorm2d(in_channel),\n Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),\n PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False),\n BatchNorm2d(depth), SEModule(depth, 16))\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n shortcut = self.shortcut_layer(x)\n res = self.res_layer(x)\n return res + shortcut\n" }, { "path": "mmagic/models/editors/arcface/id_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmengine\nimport torch\nfrom torch import nn\n\nfrom mmagic.registry import MODELS\nfrom .model_irse import Backbone\n\n\n@MODELS.register_module('ArcFace')\nclass IDLossModel(nn.Module):\n \"\"\"Face id loss model.\n\n Args:\n ir_se50_weights (str, optional): Url of ir-se50 weights.\n Defaults to None.\n \"\"\"\n # ir se50 weight download link\n _ir_se50_url = 'https://download.openxlab.org.cn/models/rangoliu/Arcface-IR-SE50/weight/Arcface-IR-SE50' # noqa\n\n def __init__(self, ir_se50_weights=None):\n super(IDLossModel, self).__init__()\n mmengine.print_log('Loading ResNet ArcFace', 'current')\n self.facenet = Backbone(\n input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')\n if ir_se50_weights is None:\n ir_se50_weights = self._ir_se50_url\n self.facenet.load_state_dict(\n torch.hub.load_state_dict_from_url(\n ir_se50_weights, map_location='cpu'))\n self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))\n self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))\n self.facenet = self.facenet.eval()\n\n def extract_feats(self, x):\n \"\"\"Extracting face features.\n\n Args:\n x (torch.Tensor): Image tensor of faces.\n\n Returns:\n torch.Tensor: Face features.\n \"\"\"\n if x.shape[2] != 256:\n x = self.pool(x)\n x = x[:, :, 35:223, 32:220] # Crop interesting region\n x = self.face_pool(x)\n x_feats = self.facenet(x)\n return x_feats\n\n def forward(self, pred=None, gt=None):\n \"\"\"Calculate face loss.\n\n Args:\n pred (torch.Tensor, optional): Predictions of face images.\n Defaults to None.\n gt (torch.Tensor, optional): Ground truth of face images.\n Defaults to None.\n\n Returns:\n Tuple(float, float): A tuple contain face similarity loss and\n improvement.\n \"\"\"\n n_samples = gt.shape[0]\n y_feats = self.extract_feats(\n gt) # Otherwise use the feature from there\n y_hat_feats = self.extract_feats(pred)\n y_feats = y_feats.detach()\n loss = 0\n sim_improvement = 0\n count = 0\n for i in range(n_samples):\n diff_target = y_hat_feats[i].dot(y_feats[i])\n loss += 1 - diff_target\n count += 1\n\n return loss / count, sim_improvement / count\n" }, { "path": "mmagic/models/editors/arcface/model_irse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom torch.nn import (BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear,\n Module, PReLU, Sequential)\n\nfrom .arcface_modules import (Flatten, bottleneck_IR, bottleneck_IR_SE,\n get_blocks, l2_norm)\n\n# yapf: disable\n\"\"\"\nModified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) # isort:skip # noqa\n\"\"\"\n# yapf: enable\n\n\nclass Backbone(Module):\n ''' Arcface backbone.\n There are many repos follow this codes for facial recognition, and we also\n follow this routine.\n Ref: https://github.com/orpatashnik/StyleCLIP/blob/main/models/facial_recognition/helpers.py # noqa\n\n Args:\n input_size (int): Input size of image.\n num_layers (int): Number of layer in backbone.\n mode (str, optional): Bottle neck mode. If set to 'ir_se', then\n SEModule will be applied. Defaults to 'ir'.\n drop_ratio (float, optional): Drop out ratio. Defaults to 0.4.\n affine (bool, optional): Whether use affine in BatchNorm1d.\n Defaults to True.\n '''\n\n def __init__(self,\n input_size,\n num_layers,\n mode='ir',\n drop_ratio=0.4,\n affine=True):\n super(Backbone, self).__init__()\n assert input_size in [112, 224], 'input_size should be 112 or 224'\n assert num_layers in [50, 100,\n 152], 'num_layers should be 50, 100 or 152'\n assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'\n blocks = get_blocks(num_layers)\n if mode == 'ir':\n unit_module = bottleneck_IR\n elif mode == 'ir_se':\n unit_module = bottleneck_IR_SE\n self.input_layer = Sequential(\n Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64),\n PReLU(64))\n if input_size == 112:\n self.output_layer = Sequential(\n BatchNorm2d(512), Dropout(drop_ratio), Flatten(),\n Linear(512 * 7 * 7, 512), BatchNorm1d(512, affine=affine))\n else:\n self.output_layer = Sequential(\n BatchNorm2d(512), Dropout(drop_ratio), Flatten(),\n Linear(512 * 14 * 14, 512), BatchNorm1d(512, affine=affine))\n\n modules = []\n for block in blocks:\n for bottleneck in block:\n modules.append(\n unit_module(bottleneck.in_channel, bottleneck.depth,\n bottleneck.stride))\n self.body = Sequential(*modules)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.input_layer(x)\n x = self.body(x)\n x = self.output_layer(x)\n return l2_norm(x)\n\n\ndef IR_50(input_size):\n \"\"\"Constructs a ir-50 model.\"\"\"\n model = Backbone(\n input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)\n return model\n\n\ndef IR_101(input_size):\n \"\"\"Constructs a ir-101 model.\"\"\"\n model = Backbone(\n input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)\n return model\n\n\ndef IR_152(input_size):\n \"\"\"Constructs a ir-152 model.\"\"\"\n model = Backbone(\n input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)\n return model\n\n\ndef IR_SE_50(input_size):\n \"\"\"Constructs a ir_se-50 model.\"\"\"\n model = Backbone(\n input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)\n return model\n\n\ndef IR_SE_101(input_size):\n \"\"\"Constructs a ir_se-101 model.\"\"\"\n model = Backbone(\n input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)\n return model\n\n\ndef IR_SE_152(input_size):\n \"\"\"Constructs a ir_se-152 model.\"\"\"\n model = Backbone(\n input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)\n return model\n" }, { "path": "mmagic/models/editors/basicvsr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .basicvsr import BasicVSR\nfrom .basicvsr_net import BasicVSRNet\n\n__all__ = ['BasicVSR', 'BasicVSRNet']\n" }, { "path": "mmagic/models/editors/basicvsr/basicvsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models import BaseEditModel\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass BasicVSR(BaseEditModel):\n \"\"\"BasicVSR model for video super-resolution.\n\n Note that this model is used for IconVSR.\n\n Paper:\n BasicVSR: The Search for Essential Components in Video Super-Resolution\n and Beyond, CVPR, 2021\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n ensemble (dict): Config for ensemble. Default: None.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n ensemble=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n super().__init__(\n generator=generator,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n # fix pre-trained networks\n self.fix_iter = train_cfg.get('fix_iter', 0) if train_cfg else 0\n self.is_weight_fixed = False\n\n # count training steps\n self.register_buffer('step_counter', torch.zeros(1))\n\n # ensemble\n self.forward_ensemble = None\n if ensemble is not None:\n if ensemble['type'] == 'SpatialTemporalEnsemble':\n from mmagic.models.archs import SpatialTemporalEnsemble\n is_temporal = ensemble.get('is_temporal_ensemble', False)\n self.forward_ensemble = SpatialTemporalEnsemble(is_temporal)\n else:\n raise NotImplementedError(\n 'Currently support only '\n '\"SpatialTemporalEnsemble\", but got type '\n f'[{ensemble[\"type\"]}]')\n\n def check_if_mirror_extended(self, lrs):\n \"\"\"Check whether the input is a mirror-extended sequence.\n\n If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the\n (t-1-i)-th frame.\n\n Args:\n lrs (tensor): Input LR images with shape (n, t, c, h, w)\n \"\"\"\n\n is_mirror_extended = False\n if lrs.size(1) % 2 == 0:\n lrs_1, lrs_2 = torch.chunk(lrs, 2, dim=1)\n if torch.norm(lrs_1 - lrs_2.flip(1)) == 0:\n is_mirror_extended = True\n\n return is_mirror_extended\n\n def forward_train(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward training. Returns dict of losses of training.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n # fix SPyNet and EDVR at the beginning\n if self.step_counter < self.fix_iter:\n if not self.is_weight_fixed:\n self.is_weight_fixed = True\n for k, v in self.generator.named_parameters():\n if 'spynet' in k or 'edvr' in k:\n v.requires_grad_(False)\n elif self.step_counter == self.fix_iter:\n # train all the parameters\n self.generator.requires_grad_(True)\n\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n batch_gt_data = data_samples.gt_img\n\n loss = self.pixel_loss(feats, batch_gt_data)\n self.step_counter += 1\n\n return dict(loss=loss)\n\n def forward_inference(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward inference. Returns predictions of validation, testing.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n DataSample: predictions.\n \"\"\"\n\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n # feats.shape = [b, t, c, h, w]\n feats = self.data_preprocessor.destruct(feats, data_samples)\n\n # If the GT is an image (i.e. the center frame), the output sequence is\n # turned to an image.\n gt = data_samples.gt_img[0]\n if gt is not None and gt.data.ndim == 3:\n t = feats.size(1)\n if self.check_if_mirror_extended(inputs):\n # with mirror extension\n feats = 0.5 * (feats[:, t // 4] + feats[:, -1 - t // 4])\n else:\n # without mirror extension\n feats = feats[:, t // 2]\n\n # create a stacked data sample\n predictions = DataSample(\n pred_img=feats.cpu(), metainfo=data_samples.metainfo)\n\n return predictions\n" }, { "path": "mmagic/models/editors/basicvsr/basicvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom logging import WARNING\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine import MMLogger, print_log\nfrom mmengine.model import BaseModule\nfrom mmengine.runner import load_checkpoint\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import flow_warp, make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass BasicVSRNet(BaseModule):\n \"\"\"BasicVSR network structure for video super-resolution.\n\n Support only x4 upsampling.\n\n Paper:\n BasicVSR: The Search for Essential Components in Video Super-Resolution\n and Beyond, CVPR, 2021\n\n Args:\n mid_channels (int): Channel number of the intermediate features.\n Default: 64.\n num_blocks (int): Number of residual blocks in each propagation branch.\n Default: 30.\n spynet_pretrained (str): Pre-trained model path of SPyNet.\n Default: None.\n \"\"\"\n\n def __init__(self, mid_channels=64, num_blocks=30, spynet_pretrained=None):\n\n super().__init__()\n\n self.mid_channels = mid_channels\n\n # optical flow network for feature alignment\n self.spynet = SPyNet(pretrained=spynet_pretrained)\n\n # propagation branches\n self.backward_resblocks = ResidualBlocksWithInputConv(\n mid_channels + 3, mid_channels, num_blocks)\n self.forward_resblocks = ResidualBlocksWithInputConv(\n mid_channels + 3, mid_channels, num_blocks)\n\n # upsample\n self.fusion = nn.Conv2d(\n mid_channels * 2, mid_channels, 1, 1, 0, bias=True)\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, 64, 2, upsample_kernel=3)\n self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)\n self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)\n self.img_upsample = nn.Upsample(\n scale_factor=4, mode='bilinear', align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n self._raised_warning = False\n\n def check_if_mirror_extended(self, lrs):\n \"\"\"Check whether the input is a mirror-extended sequence.\n\n If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the\n (t-1-i)-th frame.\n\n Args:\n lrs (tensor): Input LR images with shape (n, t, c, h, w)\n \"\"\"\n\n self.is_mirror_extended = False\n if lrs.size(1) % 2 == 0:\n lrs_1, lrs_2 = torch.chunk(lrs, 2, dim=1)\n if torch.norm(lrs_1 - lrs_2.flip(1)) == 0:\n self.is_mirror_extended = True\n\n def compute_flow(self, lrs):\n \"\"\"Compute optical flow using SPyNet for feature warping.\n\n Note that if the input is an mirror-extended sequence, 'flows_forward'\n is not needed, since it is equal to 'flows_backward.flip(1)'.\n\n Args:\n lrs (tensor): Input LR images with shape (n, t, c, h, w)\n\n Return:\n tuple(Tensor): Optical flow. 'flows_forward' corresponds to the\n flows used for forward-time propagation (current to previous).\n 'flows_backward' corresponds to the flows used for\n backward-time propagation (current to next).\n \"\"\"\n\n n, t, c, h, w = lrs.size()\n lrs_1 = lrs[:, :-1, :, :, :].reshape(-1, c, h, w)\n lrs_2 = lrs[:, 1:, :, :, :].reshape(-1, c, h, w)\n\n flows_backward = self.spynet(lrs_1, lrs_2).view(n, t - 1, 2, h, w)\n\n if self.is_mirror_extended: # flows_forward = flows_backward.flip(1)\n flows_forward = None\n else:\n flows_forward = self.spynet(lrs_2, lrs_1).view(n, t - 1, 2, h, w)\n\n return flows_forward, flows_backward\n\n def forward(self, lrs):\n \"\"\"Forward function for BasicVSR.\n\n Args:\n lrs (Tensor): Input LR sequence with shape (n, t, c, h, w).\n\n Returns:\n Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).\n \"\"\"\n n, t, c, h, w = lrs.size()\n if (h < 64 or w < 64) and not self._raised_warning:\n print_log(\n f'{self.__class__.__name__} is designed for input '\n 'larger than 64x64, but the resolution of current image '\n f'is {h}x{w}. We recommend you to check your input.',\n 'current', WARNING)\n self._raised_warning = True\n\n # check whether the input is an extended sequence\n self.check_if_mirror_extended(lrs)\n\n # compute optical flow\n flows_forward, flows_backward = self.compute_flow(lrs)\n\n # backward-time propagation\n outputs = []\n feat_prop = lrs.new_zeros(n, self.mid_channels, h, w)\n for i in range(t - 1, -1, -1):\n if i < t - 1: # no warping required for the last timestep\n flow = flows_backward[:, i, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n\n feat_prop = torch.cat([lrs[:, i, :, :, :], feat_prop], dim=1)\n feat_prop = self.backward_resblocks(feat_prop)\n\n outputs.append(feat_prop)\n outputs = outputs[::-1]\n\n # forward-time propagation and upsampling\n feat_prop = torch.zeros_like(feat_prop)\n for i in range(0, t):\n lr_curr = lrs[:, i, :, :, :]\n if i > 0: # no warping required for the first timestep\n if flows_forward is not None:\n flow = flows_forward[:, i - 1, :, :, :]\n else:\n flow = flows_backward[:, -i, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n\n feat_prop = torch.cat([lr_curr, feat_prop], dim=1)\n feat_prop = self.forward_resblocks(feat_prop)\n\n # upsampling given the backward and forward features\n out = torch.cat([outputs[i], feat_prop], dim=1)\n out = self.lrelu(self.fusion(out))\n out = self.lrelu(self.upsample1(out))\n out = self.lrelu(self.upsample2(out))\n out = self.lrelu(self.conv_hr(out))\n out = self.conv_last(out)\n base = self.img_upsample(lr_curr)\n out += base\n outputs[i] = out\n\n return torch.stack(outputs, dim=1)\n\n\nclass ResidualBlocksWithInputConv(BaseModule):\n \"\"\"Residual blocks with a convolution in front.\n\n Args:\n in_channels (int): Number of input channels of the first conv.\n out_channels (int): Number of channels of the residual blocks.\n Default: 64.\n num_blocks (int): Number of residual blocks. Default: 30.\n \"\"\"\n\n def __init__(self, in_channels, out_channels=64, num_blocks=30):\n super().__init__()\n\n main = []\n\n # a convolution used to match the channels of the residual blocks\n main.append(nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=True))\n main.append(nn.LeakyReLU(negative_slope=0.1, inplace=True))\n\n # residual blocks\n main.append(\n make_layer(\n ResidualBlockNoBN, num_blocks, mid_channels=out_channels))\n\n self.main = nn.Sequential(*main)\n\n def forward(self, feat):\n \"\"\"Forward function for ResidualBlocksWithInputConv.\n\n Args:\n feat (Tensor): Input feature with shape (n, in_channels, h, w)\n\n Returns:\n Tensor: Output feature with shape (n, out_channels, h, w)\n \"\"\"\n return self.main(feat)\n\n\nclass SPyNet(BaseModule):\n \"\"\"SPyNet network structure.\n\n The difference to the SPyNet in [tof.py] is that\n 1. more SPyNetBasicModule is used in this version, and\n 2. no batch normalization is used in this version.\n\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n\n Args:\n pretrained (str): path for pre-trained SPyNet. Default: None.\n \"\"\"\n\n def __init__(self, pretrained):\n super().__init__()\n\n self.basic_module = nn.ModuleList(\n [SPyNetBasicModule() for _ in range(6)])\n\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=True, logger=logger)\n elif pretrained is not None:\n raise TypeError('[pretrained] should be str or None, '\n f'but got {type(pretrained)}.')\n\n self.register_buffer(\n 'mean',\n torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))\n self.register_buffer(\n 'std',\n torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))\n\n def compute_flow(self, ref, supp):\n \"\"\"Compute flow from ref to supp.\n\n Note that in this function, the images are already resized to a\n multiple of 32.\n\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n n, _, h, w = ref.size()\n\n # normalize the input images\n ref = [(ref - self.mean) / self.std]\n supp = [(supp - self.mean) / self.std]\n\n # generate downsampled frames\n for level in range(5):\n ref.append(\n F.avg_pool2d(\n input=ref[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.append(\n F.avg_pool2d(\n input=supp[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n ref = ref[::-1]\n supp = supp[::-1]\n\n # flow computation\n flow = ref[0].new_zeros(n, 2, h // 32, w // 32)\n for level in range(len(ref)):\n if level == 0:\n flow_up = flow\n else:\n flow_up = F.interpolate(\n input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n\n # add the residue to the upsampled flow\n flow = flow_up + self.basic_module[level](\n torch.cat([\n ref[level],\n flow_warp(\n supp[level],\n flow_up.permute(0, 2, 3, 1),\n padding_mode='border'), flow_up\n ], 1))\n\n return flow\n\n def forward(self, ref, supp):\n \"\"\"Forward function of SPyNet.\n\n This function computes the optical flow from ref to supp.\n\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n\n # upsize to a multiple of 32\n h, w = ref.shape[2:4]\n w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)\n h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)\n ref = F.interpolate(\n input=ref, size=(h_up, w_up), mode='bilinear', align_corners=False)\n supp = F.interpolate(\n input=supp,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n\n # compute flow, and resize back to the original resolution\n flow = F.interpolate(\n input=self.compute_flow(ref, supp),\n size=(h, w),\n mode='bilinear',\n align_corners=False)\n\n # adjust the flow values\n flow[:, 0, :, :] *= float(w) / float(w_up)\n flow[:, 1, :, :] *= float(h) / float(h_up)\n\n return flow\n\n\nclass SPyNetBasicModule(BaseModule):\n \"\"\"Basic Module for SPyNet.\n\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(\n in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n" }, { "path": "mmagic/models/editors/basicvsr_plusplus_net/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .basicvsr_plusplus_net import BasicVSRPlusPlusNet\n\n__all__ = ['BasicVSRPlusPlusNet']\n" }, { "path": "mmagic/models/editors/basicvsr_plusplus_net/basicvsr_plusplus_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init\n\nfrom mmagic.models.archs import PixelShufflePack\nfrom mmagic.models.utils import flow_warp\nfrom mmagic.registry import MODELS\nfrom ..basicvsr.basicvsr_net import ResidualBlocksWithInputConv, SPyNet\n\n\n@MODELS.register_module()\nclass BasicVSRPlusPlusNet(BaseModule):\n \"\"\"BasicVSR++ network structure.\n\n Support either x4 upsampling or same size output.\n\n Paper:\n BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation\n and Alignment\n\n Args:\n mid_channels (int, optional): Channel number of the intermediate\n features. Default: 64.\n num_blocks (int, optional): The number of residual blocks in each\n propagation branch. Default: 7.\n max_residue_magnitude (int): The maximum magnitude of the offset\n residue (Eq. 6 in paper). Default: 10.\n is_low_res_input (bool, optional): Whether the input is low-resolution\n or not. If False, the output resolution is equal to the input\n resolution. Default: True.\n spynet_pretrained (str, optional): Pre-trained model path of SPyNet.\n Default: None.\n cpu_cache_length (int, optional): When the length of sequence is larger\n than this value, the intermediate features are sent to CPU. This\n saves GPU memory, but slows down the inference speed. You can\n increase this number if you have a GPU with large memory.\n Default: 100.\n \"\"\"\n\n def __init__(self,\n mid_channels=64,\n num_blocks=7,\n max_residue_magnitude=10,\n is_low_res_input=True,\n spynet_pretrained=None,\n cpu_cache_length=100):\n\n super().__init__()\n self.mid_channels = mid_channels\n self.is_low_res_input = is_low_res_input\n self.cpu_cache_length = cpu_cache_length\n\n # optical flow\n self.spynet = SPyNet(pretrained=spynet_pretrained)\n\n # feature extraction module\n if is_low_res_input:\n self.feat_extract = ResidualBlocksWithInputConv(3, mid_channels, 5)\n else:\n self.feat_extract = nn.Sequential(\n nn.Conv2d(3, mid_channels, 3, 2, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(mid_channels, mid_channels, 3, 2, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n ResidualBlocksWithInputConv(mid_channels, mid_channels, 5))\n\n # propagation branches\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2']\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * mid_channels,\n mid_channels,\n 3,\n padding=1,\n deform_groups=16,\n max_residue_magnitude=max_residue_magnitude)\n self.backbone[module] = ResidualBlocksWithInputConv(\n (2 + i) * mid_channels, mid_channels, num_blocks)\n\n # upsampling module\n self.reconstruction = ResidualBlocksWithInputConv(\n 5 * mid_channels, mid_channels, 5)\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, 64, 2, upsample_kernel=3)\n self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)\n self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)\n self.img_upsample = nn.Upsample(\n scale_factor=4, mode='bilinear', align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n def check_if_mirror_extended(self, lqs):\n \"\"\"Check whether the input is a mirror-extended sequence.\n\n If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the\n (t-1-i)-th frame.\n\n Args:\n lqs (tensor): Input low quality (LQ) sequence with\n shape (n, t, c, h, w).\n \"\"\"\n\n # check if the sequence is augmented by flipping\n self.is_mirror_extended = False\n if lqs.size(1) % 2 == 0:\n lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1)\n if torch.norm(lqs_1 - lqs_2.flip(1)) == 0:\n self.is_mirror_extended = True\n\n def compute_flow(self, lqs):\n \"\"\"Compute optical flow using SPyNet for feature alignment.\n\n Note that if the input is an mirror-extended sequence, 'flows_forward'\n is not needed, since it is equal to 'flows_backward.flip(1)'.\n\n Args:\n lqs (tensor): Input low quality (LQ) sequence with\n shape (n, t, c, h, w).\n\n Return:\n tuple(Tensor): Optical flow. 'flows_forward' corresponds to the\n flows used for forward-time propagation (current to previous).\n 'flows_backward' corresponds to the flows used for\n backward-time propagation (current to next).\n \"\"\"\n\n n, t, c, h, w = lqs.size()\n lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w)\n lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w)\n\n flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w)\n\n if self.is_mirror_extended: # flows_forward = flows_backward.flip(1)\n flows_forward = None\n else:\n flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w)\n\n if self.cpu_cache:\n flows_backward = flows_backward.cpu()\n flows_forward = flows_forward.cpu()\n\n return flows_forward, flows_backward\n\n def propagate(self, feats, flows, module_name):\n \"\"\"Propagate the latent features throughout the sequence.\n\n Args:\n feats dict(list[tensor]): Features from previous branches. Each\n component is a list of tensors with shape (n, c, h, w).\n flows (tensor): Optical flows with shape (n, t - 1, 2, h, w).\n module_name (str): The name of the propagation branches. Can either\n be 'backward_1', 'forward_1', 'backward_2', 'forward_2'.\n\n Return:\n dict(list[tensor]): A dictionary containing all the propagated\n features. Each key in the dictionary corresponds to a\n propagation branch, which is represented by a list of tensors.\n \"\"\"\n\n n, t, _, h, w = flows.size()\n\n # PyTorch 2.0 could not compile data type of 'range'\n # frame_idx = range(0, t + 1)\n # flow_idx = range(-1, t)\n frame_idx = list(range(0, t + 1))\n flow_idx = list(range(-1, t))\n mapping_idx = list(range(0, len(feats['spatial'])))\n mapping_idx += mapping_idx[::-1]\n\n if 'backward' in module_name:\n frame_idx = frame_idx[::-1]\n flow_idx = frame_idx\n\n feat_prop = flows.new_zeros(n, self.mid_channels, h, w)\n for i, idx in enumerate(frame_idx):\n feat_current = feats['spatial'][mapping_idx[idx]]\n if self.cpu_cache:\n feat_current = feat_current.cuda()\n feat_prop = feat_prop.cuda()\n # second-order deformable alignment\n if i > 0:\n flow_n1 = flows[:, flow_idx[i], :, :, :]\n if self.cpu_cache:\n flow_n1 = flow_n1.cuda()\n\n cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))\n\n # initialize second-order features\n feat_n2 = torch.zeros_like(feat_prop)\n flow_n2 = torch.zeros_like(flow_n1)\n cond_n2 = torch.zeros_like(cond_n1)\n\n if i > 1: # second-order features\n feat_n2 = feats[module_name][-2]\n if self.cpu_cache:\n feat_n2 = feat_n2.cuda()\n\n flow_n2 = flows[:, flow_idx[i - 1], :, :, :]\n if self.cpu_cache:\n flow_n2 = flow_n2.cuda()\n\n flow_n2 = flow_n1 + flow_warp(flow_n2,\n flow_n1.permute(0, 2, 3, 1))\n cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1))\n\n # flow-guided deformable convolution\n cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)\n feat_prop = torch.cat([feat_prop, feat_n2], dim=1)\n feat_prop = self.deform_align[module_name](feat_prop, cond,\n flow_n1, flow_n2)\n\n # concatenate and residual blocks\n feat = [feat_current] + [\n feats[k][idx]\n for k in feats if k not in ['spatial', module_name]\n ] + [feat_prop]\n if self.cpu_cache:\n feat = [f.cuda() for f in feat]\n\n feat = torch.cat(feat, dim=1)\n feat_prop = feat_prop + self.backbone[module_name](feat)\n feats[module_name].append(feat_prop)\n\n if self.cpu_cache:\n feats[module_name][-1] = feats[module_name][-1].cpu()\n torch.cuda.empty_cache()\n\n if 'backward' in module_name:\n feats[module_name] = feats[module_name][::-1]\n\n return feats\n\n def upsample(self, lqs, feats):\n \"\"\"Compute the output image given the features.\n\n Args:\n lqs (tensor): Input low quality (LQ) sequence with\n shape (n, t, c, h, w).\n feats (dict): The features from the propagation branches.\n\n Returns:\n Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).\n \"\"\"\n\n outputs = []\n num_outputs = len(feats['spatial'])\n\n mapping_idx = list(range(0, num_outputs))\n mapping_idx += mapping_idx[::-1]\n\n for i in range(0, lqs.size(1)):\n hr = [feats[k].pop(0) for k in feats if k != 'spatial']\n hr.insert(0, feats['spatial'][mapping_idx[i]])\n hr = torch.cat(hr, dim=1)\n if self.cpu_cache:\n hr = hr.cuda()\n\n hr = self.reconstruction(hr)\n hr = self.lrelu(self.upsample1(hr))\n hr = self.lrelu(self.upsample2(hr))\n hr = self.lrelu(self.conv_hr(hr))\n hr = self.conv_last(hr)\n if self.is_low_res_input:\n hr += self.img_upsample(lqs[:, i, :, :, :])\n else:\n hr += lqs[:, i, :, :, :]\n\n if self.cpu_cache:\n hr = hr.cpu()\n torch.cuda.empty_cache()\n\n outputs.append(hr)\n\n return torch.stack(outputs, dim=1)\n\n def forward(self, lqs):\n \"\"\"Forward function for BasicVSR++.\n\n Args:\n lqs (tensor): Input low quality (LQ) sequence with\n shape (n, t, c, h, w).\n\n Returns:\n Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).\n \"\"\"\n\n n, t, c, h, w = lqs.size()\n\n # whether to cache the features in CPU (no effect if using CPU)\n if t > self.cpu_cache_length and lqs.is_cuda:\n self.cpu_cache = True\n else:\n self.cpu_cache = False\n\n if self.is_low_res_input:\n lqs_downsample = lqs.clone()\n else:\n lqs_downsample = F.interpolate(\n lqs.view(-1, c, h, w), scale_factor=0.25,\n mode='bicubic').view(n, t, c, h // 4, w // 4)\n\n # check whether the input is an extended sequence\n self.check_if_mirror_extended(lqs)\n\n feats = {}\n # compute spatial features\n if self.cpu_cache:\n feats['spatial'] = []\n for i in range(0, t):\n feat = self.feat_extract(lqs[:, i, :, :, :]).cpu()\n feats['spatial'].append(feat)\n torch.cuda.empty_cache()\n else:\n feats_ = self.feat_extract(lqs.view(-1, c, h, w))\n h, w = feats_.shape[2:]\n feats_ = feats_.view(n, t, -1, h, w)\n feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)]\n\n # compute optical flow using the low-res inputs\n assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, (\n 'The height and width of low-res inputs must be at least 64, '\n f'but got {h} and {w}.')\n flows_forward, flows_backward = self.compute_flow(lqs_downsample)\n\n # feature propagation\n for iter_ in [1, 2]:\n for direction in ['backward', 'forward']:\n module = f'{direction}_{iter_}'\n\n feats[module] = []\n\n if direction == 'backward':\n flows = flows_backward\n elif flows_forward is not None:\n flows = flows_forward\n else:\n flows = flows_backward.flip(1)\n\n feats = self.propagate(feats, flows, module)\n if self.cpu_cache:\n del flows\n torch.cuda.empty_cache()\n\n return self.upsample(lqs, feats)\n\n\nclass SecondOrderDeformableAlignment(ModulatedDeformConv2d):\n \"\"\"Second-order deformable alignment module.\n\n Args:\n in_channels (int): Same as nn.Conv2d.\n out_channels (int): Same as nn.Conv2d.\n kernel_size (int or tuple[int]): Same as nn.Conv2d.\n stride (int or tuple[int]): Same as nn.Conv2d.\n padding (int or tuple[int]): Same as nn.Conv2d.\n dilation (int or tuple[int]): Same as nn.Conv2d.\n groups (int): Same as nn.Conv2d.\n bias (bool or str): If specified as `auto`, it will be decided by the\n norm_cfg. Bias will be set as True if norm_cfg is None, otherwise\n False.\n max_residue_magnitude (int): The maximum magnitude of the offset\n residue (Eq. 6 in paper). Default: 10.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)\n\n super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)\n\n self.conv_offset = nn.Sequential(\n nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1),\n )\n\n self.init_offset()\n\n def init_offset(self):\n \"\"\"Init constant offset.\"\"\"\n constant_init(self.conv_offset[-1], val=0, bias=0)\n\n def forward(self, x, extra_feat, flow_1, flow_2):\n \"\"\"Forward function.\"\"\"\n extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)\n out = self.conv_offset(extra_feat)\n o1, o2, mask = torch.chunk(out, 3, dim=1)\n\n # offset\n offset = self.max_residue_magnitude * torch.tanh(\n torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)\n\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n" }, { "path": "mmagic/models/editors/biggan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .biggan import BigGAN\nfrom .biggan_deep_discriminator import BigGANDeepDiscriminator\nfrom .biggan_deep_generator import BigGANDeepGenerator\nfrom .biggan_discriminator import BigGANDiscriminator\nfrom .biggan_generator import BigGANGenerator\n# from .generator_discriminator import BigGANDiscriminator, BigGANGenerator\nfrom .biggan_modules import (BigGANConditionBN, BigGANDeepDiscResBlock,\n BigGANDeepGenResBlock, BigGANDiscResBlock,\n BigGANGenResBlock, SelfAttentionBlock,\n SNConvModule)\n\n__all__ = [\n 'BigGAN',\n 'BigGANGenerator',\n 'BigGANDiscriminator',\n 'BigGANGenResBlock',\n 'BigGANConditionBN',\n 'SelfAttentionBlock',\n 'BigGANDiscResBlock',\n 'BigGANDeepDiscriminator',\n 'BigGANDeepGenerator',\n 'BigGANDeepDiscResBlock',\n 'BigGANDeepGenResBlock',\n 'SNConvModule',\n]\n" }, { "path": "mmagic/models/editors/biggan/biggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseConditionalGAN\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module()\nclass BigGAN(BaseConditionalGAN):\n \"\"\"Implementation of `Large Scale GAN Training for High Fidelity Natural\n Image Synthesis `_ (BigGAN).\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.biggan.BigGANGenerator`\n and\n :class:`~mmagic.models.editors.biggan.BigGANDiscriminator`\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): Number of times the generator was completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): Number of times the discriminator was\n completely updated before the generator is updated. Defaults to 1.\n noise_size (Optional[int]): Size of the input noise vector.\n Default to 128.\n num_classes (Optional[int]): The number classes you would like to\n generate. Defaults to None.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n noise_size: Optional[int] = None,\n num_classes: Optional[int] = None,\n ema_config: Optional[Dict] = None):\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, noise_size,\n num_classes, ema_config)\n\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n r\"\"\"Get disc loss. BigGAN use hinge loss to train\n the discriminator.\n\n .. math:\n L_{D} = -\\mathbb{E}_{\\left(x, y\\right)\\sim{p}_{data}}\n \\left[\\min\\left(0, -1 + D\\left(x, y\\right)\\right)\\right]\n -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\\left[\\min\n \\left(0, -1 - D\\left(G\\left(z\\right), y\\right)\\right)\\right]\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.relu(1 + disc_pred_fake).mean()\n losses_dict['loss_disc_real'] = F.relu(1 - disc_pred_real).mean()\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake):\n r\"\"\"Get disc loss. BigGAN use hinge loss to train\n the generator.\n\n .. math:\n L_{G} = -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\n D\\left(G\\left(z\\right), y\\right)\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n real_labels = self.data_sample_to_label(data_samples)\n assert real_labels is not None, (\n 'Cannot found \\'gt_label\\' in \\'data_sample\\'.')\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(\n noise=noise_batch, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n disc_pred_real = self.discriminator(real_imgs, label=real_labels)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n fake_imgs = self.generator(\n noise=noise, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/biggan/biggan_deep_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport mmengine\nimport torch\nimport torch.nn as nn\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import normal_init, xavier_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\nfrom .biggan_modules import SelfAttentionBlock, SNConvModule\nfrom .biggan_snmodule import SNEmbedding, SNLinear\n\n\n@MODELS.register_module()\nclass BigGANDeepDiscriminator(nn.Module):\n \"\"\"BigGAN-Deep Discriminator. The implementation refers to\n https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGANdeep.py # noqa.\n\n The overall structure of BigGAN's discriminator is the same with\n the projection discriminator.\n\n The main difference between BigGAN and BigGAN-deep is that\n BigGAN-deep use more deeper residual blocks to construct the whole\n model.\n\n More details can be found in: Large Scale GAN Training for High Fidelity\n Natural Image Synthesis (ICLR2019).\n\n The design of the model structure is highly corresponding to the output\n resolution. For origin BigGAN-Deep's generator, you can set ``output_scale``\n as you need and use the default value of ``arch_cfg`` and ``blocks_cfg``.\n If you want to customize the model, you can set the arguments in this way:\n\n ``arch_cfg``: Config for the architecture of this generator. You can refer\n the ``_default_arch_cfgs`` in the ``_get_default_arch_cfg`` function to see\n the format of the ``arch_cfg``. Basically, you need to provide information\n of each block such as the numbers of input and output channels, whether to\n perform upsampling etc.\n\n ``blocks_cfg``: Config for the convolution block. You can adjust block params\n like ``channel_ratio`` here. You can also replace the block type\n to your registered customized block. However, you should notice that some\n params are shared between these blocks like ``act_cfg``, ``with_spectral_norm``,\n ``sn_eps`` etc.\n\n Args:\n input_scale (int): The scale of the input image.\n num_classes (int, optional): The number of conditional classes.\n Defaults to 0.\n in_channels (int, optional): The channel number of the input image.\n Defaults to 3.\n out_channels (int, optional): The channel number of the final output.\n Defaults to 1.\n base_channels (int, optional): The basic channel number of the\n discriminator. The other layers contains channels based on this\n number. Defaults to 96.\n block_depth (int, optional): The repeat times of Residual Blocks in\n each level of architecture. Defaults to 2.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n init_type (str, optional): The name of an initialization method:\n ortho | N02 | xavier. Defaults to 'ortho'.\n act_cfg (dict, optional): Config for the activation layer.\n Defaults to dict(type='ReLU').\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n blocks_cfg (dict, optional): Config for the convolution block.\n Defaults to dict(type='BigGANDiscResBlock').\n arch_cfg (dict, optional): Config for the architecture of this\n discriminator. Defaults to None.\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n \"\"\"\n\n def __init__(self,\n input_scale,\n num_classes=0,\n in_channels=3,\n out_channels=1,\n base_channels=96,\n block_depth=2,\n sn_eps=1e-6,\n sn_style='ajbrock',\n init_type='ortho',\n act_cfg=dict(type='ReLU', inplace=False),\n with_spectral_norm=True,\n blocks_cfg=dict(type='BigGANDeepDiscResBlock'),\n arch_cfg=None,\n pretrained=None):\n super().__init__()\n self.num_classes = num_classes\n self.out_channels = out_channels\n self.input_scale = input_scale\n self.in_channels = in_channels\n self.base_channels = base_channels\n self.block_depth = block_depth\n self.arch = arch_cfg if arch_cfg else self._get_default_arch_cfg(\n self.input_scale, self.base_channels)\n self.blocks_cfg = deepcopy(blocks_cfg)\n self.blocks_cfg.update(\n dict(\n act_cfg=act_cfg,\n sn_eps=sn_eps,\n sn_style=sn_style,\n with_spectral_norm=with_spectral_norm))\n\n self.input_conv = SNConvModule(\n 3,\n self.arch['in_channels'][0],\n kernel_size=3,\n padding=1,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n act_cfg=None)\n\n self.conv_blocks = nn.ModuleList()\n for index, out_ch in enumerate(self.arch['out_channels']):\n for depth in range(self.block_depth):\n # change args to adapt to current block\n block_cfg_ = deepcopy(self.blocks_cfg)\n block_cfg_.update(\n dict(\n in_channels=self.arch['in_channels'][index]\n if depth == 0 else out_ch,\n out_channels=out_ch,\n with_downsample=self.arch['downsample'][index]\n and depth == 0))\n self.conv_blocks.append(MODELS.build(block_cfg_))\n if self.arch['attention'][index]:\n self.conv_blocks.append(\n SelfAttentionBlock(\n out_ch,\n with_spectral_norm=with_spectral_norm,\n sn_eps=sn_eps,\n sn_style=sn_style))\n\n self.activate = MODELS.build(act_cfg)\n\n self.decision = nn.Linear(self.arch['out_channels'][-1], out_channels)\n if with_spectral_norm:\n if sn_style == 'torch':\n self.decision = spectral_norm(self.decision, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.decision = SNLinear(\n self.arch['out_channels'][-1], out_channels, eps=sn_eps)\n else:\n raise NotImplementedError(\n f'{sn_style} style SN is not supported yet')\n\n if self.num_classes > 0:\n self.proj_y = nn.Embedding(self.num_classes,\n self.arch['out_channels'][-1])\n if with_spectral_norm:\n if sn_style == 'torch':\n self.proj_y = spectral_norm(self.proj_y, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.proj_y = SNEmbedding(\n self.num_classes,\n self.arch['out_channels'][-1],\n eps=sn_eps)\n else:\n raise NotImplementedError(\n f'{sn_style} style SN is not supported yet')\n\n self.init_weights(pretrained=pretrained, init_type=init_type)\n\n def _get_default_arch_cfg(self, input_scale, base_channels):\n assert input_scale in [32, 64, 128, 256, 512]\n _default_arch_cfgs = {\n '32': {\n 'in_channels': [base_channels * item for item in [4, 4, 4]],\n 'out_channels': [base_channels * item for item in [4, 4, 4]],\n 'downsample': [True, True, False, False],\n 'resolution': [16, 8, 8, 8],\n 'attention': [False, False, False, False]\n },\n '64': {\n 'in_channels': [base_channels * item for item in [1, 2, 4, 8]],\n 'out_channels':\n [base_channels * item for item in [2, 4, 8, 16]],\n 'downsample': [True] * 4 + [False],\n 'resolution': [32, 16, 8, 4, 4],\n 'attention': [False, False, False, False, False]\n },\n '128': {\n 'in_channels':\n [base_channels * item for item in [1, 2, 4, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [2, 4, 8, 16, 16]],\n 'downsample': [True] * 5 + [False],\n 'resolution': [64, 32, 16, 8, 4, 4],\n 'attention': [True, False, False, False, False, False]\n },\n '256': {\n 'in_channels':\n [base_channels * item for item in [1, 2, 4, 8, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [2, 4, 8, 8, 16, 16]],\n 'downsample': [True] * 6 + [False],\n 'resolution': [128, 64, 32, 16, 8, 4, 4],\n 'attention': [False, True, False, False, False, False]\n },\n '512': {\n 'in_channels':\n [base_channels * item for item in [1, 1, 2, 4, 8, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [1, 2, 4, 8, 8, 16, 16]],\n 'downsample': [True] * 7 + [False],\n 'resolution': [256, 128, 64, 32, 16, 8, 4, 4],\n 'attention': [False, False, False, True, False, False, False]\n }\n }\n\n return _default_arch_cfgs[str(input_scale)]\n\n def forward(self, x, label=None):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n label (torch.Tensor | None): Label Tensor. Defaults to None.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image with\n given label.\n \"\"\"\n x0 = self.input_conv(x)\n for conv_block in self.conv_blocks:\n x0 = conv_block(x0)\n x0 = self.activate(x0)\n x0 = torch.sum(x0, dim=[2, 3])\n out = self.decision(x0)\n\n if self.num_classes > 0:\n w_y = self.proj_y(label)\n out = out + torch.sum(w_y * x0, dim=1, keepdim=True)\n return out\n\n def init_weights(self, pretrained=None, init_type='ortho'):\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose\n necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n init_type (str, optional): The name of an initialization method:\n ortho | N02 | xavier. Defaults to 'ortho'.\n \"\"\"\n\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif isinstance(pretrained, dict):\n ckpt_path = pretrained.get('ckpt_path', None)\n assert ckpt_path is not None\n prefix = pretrained.get('prefix', '')\n map_location = pretrained.get('map_location', 'cpu')\n strict = pretrained.get('strict', True)\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained model from {ckpt_path}')\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n if init_type == 'ortho':\n nn.init.orthogonal_(m.weight)\n elif init_type == 'N02':\n normal_init(m, 0.0, 0.02)\n elif init_type == 'xavier':\n xavier_init(m)\n else:\n raise NotImplementedError(\n f'{init_type} initialization \\\n not supported now.')\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n" }, { "path": "mmagic/models/editors/biggan/biggan_deep_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport mmengine\nimport torch\nimport torch.nn as nn\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import normal_init, xavier_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom .biggan_modules import SelfAttentionBlock, SNConvModule\nfrom .biggan_snmodule import SNLinear\n\n\n@MODELS.register_module()\nclass BigGANDeepGenerator(nn.Module):\n \"\"\"BigGAN-Deep Generator. The implementation refers to\n https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGANdeep.py # noqa.\n\n In BigGAN, we use a SAGAN-based architecture composing of an\n self-attention block and number of convolutional residual blocks\n with spectral normalization. BigGAN-deep follow the same architecture.\n\n The main difference between BigGAN and BigGAN-deep is that\n BigGAN-deep uses deeper residual blocks to construct the whole\n model.\n\n More details can be found in: Large Scale GAN Training for High Fidelity\n Natural Image Synthesis (ICLR2019).\n\n The design of the model structure is highly corresponding to the output\n resolution. For the original BigGAN-Deep's generator, you can set ``output_scale``\n as you need and use the default value of ``arch_cfg`` and ``blocks_cfg``.\n If you want to customize the model, you can set the arguments in this way:\n\n ``arch_cfg``: Config for the architecture of this generator. You can refer\n the ``_default_arch_cfgs`` in the ``_get_default_arch_cfg`` function to see\n the format of the ``arch_cfg``. Basically, you need to provide information\n of each block such as the numbers of input and output channels, whether to\n perform upsampling, etc.\n\n ``blocks_cfg``: Config for the convolution block. You can adjust block params\n like ``channel_ratio`` here. You can also replace the block type\n to your registered customized block. However, you should notice that some\n params are shared among these blocks like ``act_cfg``, ``with_spectral_norm``,\n ``sn_eps``, etc.\n\n Args:\n output_scale (int): Output scale for the generated image.\n noise_size (int, optional): Size of the input noise vector. Defaults\n to 120.\n num_classes (int, optional): The number of conditional classes. If set\n to 0, this model will be degraded to an unconditional model.\n Defaults to 0.\n out_channels (int, optional): Number of channels in output images.\n Defaults to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Defaults to 96.\n block_depth (int, optional): The repeat times of Residual Blocks in\n each level of architecture. Defaults to 2.\n input_scale (int, optional): The scale of the input 2D feature map.\n Defaults to 4.\n with_shared_embedding (bool, optional): Whether to use shared\n embedding. Defaults to True.\n shared_dim (int, optional): The output channels of shared embedding.\n Defaults to 128.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n init_type (str, optional): The name of an initialization method:\n ortho | N02 | xavier. Defaults to 'ortho'.\n concat_noise (bool, optional): Whether to concat input noise vector\n with class vector. Defaults to True.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to dict(type='nearest', scale_factor=2).\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n auto_sync_bn (bool, optional): Whether to use synchronized batch\n normalization. Defaults to True.\n blocks_cfg (dict, optional): Config for the convolution block. Defaults\n to dict(type='BigGANGenResBlock').\n arch_cfg (dict, optional): Config for the architecture of this\n generator. Defaults to None.\n out_norm_cfg (dict, optional): Config for the norm of output layer.\n Defaults to dict(type='BN').\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n rgb2bgr (bool, optional): Whether to reformat the output channels\n with order `bgr`. We provide several pre-trained BigGAN-Deep\n weights whose output channels order is `rgb`. You can set\n this argument to True to use the weights.\n \"\"\"\n\n def __init__(self,\n output_scale,\n noise_size=120,\n num_classes=0,\n out_channels=3,\n base_channels=96,\n block_depth=2,\n input_scale=4,\n with_shared_embedding=True,\n shared_dim=128,\n sn_eps=1e-6,\n sn_style='ajbrock',\n init_type='ortho',\n concat_noise=True,\n act_cfg=dict(type='ReLU', inplace=False),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n with_spectral_norm=True,\n auto_sync_bn=True,\n blocks_cfg=dict(type='BigGANDeepGenResBlock'),\n arch_cfg=None,\n out_norm_cfg=dict(type='BN'),\n pretrained=None,\n rgb2bgr=False):\n super().__init__()\n self.noise_size = noise_size\n self.num_classes = num_classes\n self.shared_dim = shared_dim\n self.with_shared_embedding = with_shared_embedding\n self.output_scale = output_scale\n self.arch = arch_cfg if arch_cfg else self._get_default_arch_cfg(\n self.output_scale, base_channels)\n self.input_scale = input_scale\n self.concat_noise = concat_noise\n self.blocks_cfg = deepcopy(blocks_cfg)\n self.upsample_cfg = deepcopy(upsample_cfg)\n self.block_depth = block_depth\n self.rgb2bgr = rgb2bgr\n self.sn_style = sn_style\n\n # Validity Check\n # If 'num_classes' equals to zero, we shall set 'with_shared_embedding'\n # to False.\n if num_classes == 0:\n assert not self.with_shared_embedding\n assert not self.concat_noise\n elif not self.with_shared_embedding:\n # If not `with_shared_embedding`, we will use `nn.Embedding` to\n # replace the original `Linear` layer in conditional BN.\n # Meanwhile, we do not adopt split noises.\n assert not self.concat_noise\n\n # First linear layer\n if self.concat_noise:\n self.noise2feat = nn.Linear(\n self.noise_size + self.shared_dim,\n self.arch['in_channels'][0] * (self.input_scale**2))\n else:\n self.noise2feat = nn.Linear(\n self.noise_size,\n self.arch['in_channels'][0] * (self.input_scale**2))\n\n if with_spectral_norm:\n if sn_style == 'torch':\n self.noise2feat = spectral_norm(self.noise2feat, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.noise2feat = SNLinear(\n self.noise_size +\n (self.shared_dim if self.concat_noise else 0),\n self.arch['in_channels'][0] * (self.input_scale**2),\n eps=sn_eps)\n else:\n NotImplementedError(f'{sn_style} style SN is not supported')\n\n # If using 'shared_embedding', we will get an unified embedding of\n # label for all blocks. If not, we just pass the label to each\n # block.\n if with_shared_embedding:\n self.shared_embedding = nn.Embedding(num_classes, shared_dim)\n else:\n self.shared_embedding = nn.Identity()\n\n if num_classes > 0:\n if self.concat_noise:\n self.dim_after_concat = (\n self.shared_dim + self.noise_size\n if self.with_shared_embedding else self.num_classes)\n else:\n self.dim_after_concat = (\n self.shared_dim\n if self.with_shared_embedding else self.num_classes)\n else:\n self.dim_after_concat = 0\n self.blocks_cfg.update(\n dict(\n dim_after_concat=self.dim_after_concat,\n act_cfg=act_cfg,\n sn_eps=sn_eps,\n sn_style=sn_style,\n input_is_label=(num_classes > 0)\n and (not with_shared_embedding),\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn))\n\n self.conv_blocks = nn.ModuleList()\n for index, out_ch in enumerate(self.arch['out_channels']):\n for depth in range(self.block_depth):\n # change args to adapt to current block\n block_cfg_ = deepcopy(self.blocks_cfg)\n block_cfg_.update(\n dict(\n in_channels=self.arch['in_channels'][index],\n out_channels=out_ch if depth == (self.block_depth - 1)\n else self.arch['in_channels'][index],\n upsample_cfg=self.upsample_cfg\n if self.arch['upsample'][index]\n and depth == (self.block_depth - 1) else None))\n self.conv_blocks.append(MODELS.build(block_cfg_))\n\n if self.arch['attention'][index]:\n self.conv_blocks.append(\n SelfAttentionBlock(\n out_ch,\n with_spectral_norm=with_spectral_norm,\n sn_eps=sn_eps,\n sn_style=sn_style))\n\n self.output_layer = SNConvModule(\n self.arch['out_channels'][-1],\n out_channels,\n kernel_size=3,\n padding=1,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n act_cfg=act_cfg,\n norm_cfg=out_norm_cfg,\n bias=True,\n order=('norm', 'act', 'conv'))\n\n self.init_weights(pretrained=pretrained, init_type=init_type)\n\n def _get_default_arch_cfg(self, output_scale, base_channels):\n assert output_scale in [32, 64, 128, 256, 512]\n _default_arch_cfgs = {\n '32': {\n 'in_channels': [base_channels * item for item in [4, 4, 4]],\n 'out_channels': [base_channels * item for item in [4, 4, 4]],\n 'upsample': [True] * 3,\n 'resolution': [8, 16, 32],\n 'attention': [False, False, False]\n },\n '64': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 4]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 4, 2]],\n 'upsample': [True] * 4,\n 'resolution': [8, 16, 32, 64],\n 'attention': [False, False, False, True]\n },\n '128': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 4, 2]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 4, 2, 1]],\n 'upsample': [True] * 5,\n 'resolution': [8, 16, 32, 64, 128],\n 'attention': [False, False, False, True, False]\n },\n '256': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 8, 4, 2]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 8, 4, 2, 1]],\n 'upsample': [True] * 6,\n 'resolution': [8, 16, 32, 64, 128, 256],\n 'attention': [False, False, False, True, False, False]\n },\n '512': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 8, 4, 2, 1]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 8, 4, 2, 1, 1]],\n 'upsample': [True] * 7,\n 'resolution': [8, 16, 32, 64, 128, 256, 512],\n 'attention': [False, False, False, True, False, False, False]\n }\n }\n\n return _default_arch_cfgs[str(output_scale)]\n\n def forward(self,\n noise,\n label=None,\n num_batches=0,\n return_noise=False,\n truncation=-1.0,\n use_outside_embedding=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n label (torch.Tensor | callable | None): You can directly give a\n batch of label through a ``torch.Tensor`` or offer a callable\n function to sample a batch of label data. Otherwise, the\n ``None`` indicates to use the default label sampler.\n Defaults to None.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` and\n ``label`` will be returned in a dict with ``fake_img``.\n Defaults to False.\n truncation (float, optional): Truncation factor. Give value not\n less than 0., the truncation trick will be adopted.\n Otherwise, the truncation trick will not be adopted.\n Defaults to -1..\n use_outside_embedding (bool, optional): Whether to use outside\n embedding or use `shared_embedding`. Set to `True` if\n embedding has already be performed outside this function.\n Default to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img``,\n ``noise_batch`` and ``label`` will be returned.\n \"\"\"\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size))\n # perform truncation\n if truncation >= 0.0:\n noise_batch = torch.clamp(noise_batch, -1. * truncation,\n 1. * truncation)\n\n if self.num_classes == 0:\n label_batch = None\n\n elif isinstance(label, torch.Tensor):\n if not use_outside_embedding:\n assert label.ndim == 1, (\n 'The label should be in shape of (n, )'\n f'but got {label.shape}.')\n label_batch = label\n elif callable(label):\n label_generator = label\n assert num_batches > 0\n label_batch = label_generator((num_batches, ))\n else:\n assert num_batches > 0\n label_batch = torch.randint(0, self.num_classes, (num_batches, ))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n if label_batch is not None:\n label_batch = label_batch.to(get_module_device(self))\n if not use_outside_embedding:\n class_vector = self.shared_embedding(label_batch)\n else:\n class_vector = label_batch\n else:\n class_vector = None\n\n # If 'concat noise', concat class vector and noise batch\n if self.concat_noise:\n if class_vector is not None:\n z = torch.cat([noise_batch, class_vector], dim=1)\n y = z\n elif self.num_classes > 0:\n z = noise_batch\n y = class_vector\n else:\n z = noise_batch\n y = None\n\n # First linear layer\n x = self.noise2feat(z)\n # Reshape\n # We use this conversion step to allow for loading TF weights\n # TF convention on shape is [batch, height, width, channels]\n # PT convention on shape is [batch, channels, height, width]\n x = x.view(x.size(0), self.input_scale, self.input_scale, -1)\n x = x.permute(0, 3, 1, 2).contiguous()\n # Loop over blocks\n for idx, conv_block in enumerate(self.conv_blocks):\n if isinstance(conv_block, SelfAttentionBlock):\n x = conv_block(x)\n else:\n x = conv_block(x, y)\n # Apply batchnorm-relu-conv-tanh at output\n x = self.output_layer(x)\n out_img = torch.tanh(x)\n\n if self.rgb2bgr:\n out_img = out_img[:, [2, 1, 0], ...]\n\n if return_noise:\n output = dict(\n fake_img=out_img, noise_batch=noise_batch, label=label_batch)\n return output\n\n return out_img\n\n def init_weights(self, pretrained=None, init_type='ortho'):\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose\n necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n init_type (str, optional): The name of an initialization method:\n ortho | N02 | xavier. Defaults to 'ortho'.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif isinstance(pretrained, dict):\n ckpt_path = pretrained.get('ckpt_path', None)\n assert ckpt_path is not None\n prefix = pretrained.get('prefix', '')\n map_location = pretrained.get('map_location', 'cpu')\n strict = pretrained.get('strict', True)\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained model from {ckpt_path}')\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n if init_type == 'ortho':\n nn.init.orthogonal_(m.weight)\n elif init_type == 'N02':\n normal_init(m, 0.0, 0.02)\n elif init_type == 'xavier':\n xavier_init(m)\n else:\n raise NotImplementedError(\n f'{init_type} initialization \\\n not supported now.')\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n" }, { "path": "mmagic/models/editors/biggan/biggan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import (BaseModule, normal_init, update_init_info,\n xavier_init)\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\nfrom .biggan_modules import SelfAttentionBlock\nfrom .biggan_snmodule import SNEmbedding, SNLinear\n\n\n@MODELS.register_module()\nclass BigGANDiscriminator(BaseModule):\n \"\"\"BigGAN Discriminator. The implementation refers to\n https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGAN.py # noqa.\n\n In BigGAN, we use a SAGAN-based architecture composing of an self-attention\n block and number of convolutional residual blocks with spectral\n normalization.\n\n More details can be found in: Large Scale GAN Training for High Fidelity\n Natural Image Synthesis (ICLR2019).\n\n The design of the model structure is highly corresponding to the output\n resolution. For the original BigGAN's generator, you can set ``output_scale``\n as you need and use the default value of ``arch_cfg`` and ``blocks_cfg``.\n If you want to customize the model, you can set the arguments in this way:\n\n ``arch_cfg``: Config for the architecture of this generator. You can refer\n the ``_default_arch_cfgs`` in the ``_get_default_arch_cfg`` function to see\n the format of the ``arch_cfg``. Basically, you need to provide information\n of each block such as the numbers of input and output channels, whether to\n perform upsampling, etc.\n\n ``blocks_cfg``: Config for the convolution block. You can replace the block\n type to your registered customized block and adjust block params here.\n However, you should notice that some params are shared among these blocks\n like ``act_cfg``, ``with_spectral_norm``, ``sn_eps``, etc.\n\n Args:\n input_scale (int): The scale of the input image.\n num_classes (int, optional): The number of conditional classes.\n Defaults to 0.\n in_channels (int, optional): The channel number of the input image.\n Defaults to 3.\n out_channels (int, optional): The channel number of the final output.\n Defaults to 1.\n base_channels (int, optional): The basic channel number of the\n discriminator. The other layers contains channels based on this\n number. Defaults to 96.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n act_cfg (dict, optional): Config for the activation layer.\n Defaults to dict(type='ReLU').\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n blocks_cfg (dict, optional): Config for the convolution block.\n Defaults to dict(type='BigGANDiscResBlock').\n arch_cfg (dict, optional): Config for the architecture of this\n discriminator. Defaults to None.\n init_cfg (dict, optional): Initialization config dict. If type is\n `Pretrained`, the pretrain model will be loaded. Otherwise, type\n will be parsed as the name of initialization method. Support values\n are 'ortho', 'N02', 'xavier'. Defaults to dict(type='ortho').\n \"\"\"\n\n def __init__(self,\n input_scale,\n num_classes=0,\n in_channels=3,\n out_channels=1,\n base_channels=96,\n sn_eps=1e-6,\n sn_style='ajbrock',\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=True,\n blocks_cfg=dict(type='BigGANDiscResBlock'),\n arch_cfg=None,\n init_cfg=dict(type='ortho')):\n super().__init__(init_cfg=init_cfg)\n self.num_classes = num_classes\n self.out_channels = out_channels\n self.input_scale = input_scale\n self.in_channels = in_channels\n self.base_channels = base_channels\n self.arch = arch_cfg if arch_cfg else self._get_default_arch_cfg(\n self.input_scale, self.in_channels, self.base_channels)\n self.blocks_cfg = deepcopy(blocks_cfg)\n self.blocks_cfg.update(\n dict(\n act_cfg=act_cfg,\n sn_eps=sn_eps,\n sn_style=sn_style,\n with_spectral_norm=with_spectral_norm))\n self.sn_style = sn_style\n\n self.conv_blocks = nn.ModuleList()\n for index, out_ch in enumerate(self.arch['out_channels']):\n # change args to adapt to current block\n self.blocks_cfg.update(\n dict(\n in_channels=self.arch['in_channels'][index],\n out_channels=out_ch,\n with_downsample=self.arch['downsample'][index],\n is_head_block=(index == 0)))\n self.conv_blocks.append(MODELS.build(self.blocks_cfg))\n if self.arch['attention'][index]:\n self.conv_blocks.append(\n SelfAttentionBlock(\n out_ch,\n with_spectral_norm=with_spectral_norm,\n sn_eps=sn_eps,\n sn_style=sn_style))\n\n self.activate = MODELS.build(act_cfg)\n\n self.decision = nn.Linear(self.arch['out_channels'][-1], out_channels)\n if with_spectral_norm:\n if sn_style == 'torch':\n self.decision = spectral_norm(self.decision, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.decision = SNLinear(\n self.arch['out_channels'][-1], out_channels, eps=sn_eps)\n else:\n raise NotImplementedError('sn style')\n\n if self.num_classes > 0:\n self.proj_y = nn.Embedding(self.num_classes,\n self.arch['out_channels'][-1])\n if with_spectral_norm:\n if sn_style == 'torch':\n self.proj_y = spectral_norm(self.proj_y, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.proj_y = SNEmbedding(\n self.num_classes,\n self.arch['out_channels'][-1],\n eps=sn_eps)\n else:\n raise NotImplementedError('sn style')\n\n def _get_default_arch_cfg(self, input_scale, in_channels, base_channels):\n assert input_scale in [32, 64, 128, 256, 512]\n _default_arch_cfgs = {\n '32': {\n 'in_channels':\n [in_channels] + [base_channels * item for item in [4, 4, 4]],\n 'out_channels':\n [base_channels * item for item in [4, 4, 4, 4]],\n 'downsample': [True, True, False, False],\n 'resolution': [16, 8, 8, 8],\n 'attention': [False, False, False, False]\n },\n '64': {\n 'in_channels': [in_channels] +\n [base_channels * item for item in [1, 2, 4, 8]],\n 'out_channels':\n [base_channels * item for item in [1, 2, 4, 8, 16]],\n 'downsample': [True] * 4 + [False],\n 'resolution': [32, 16, 8, 4, 4],\n 'attention': [False, False, False, False, False]\n },\n '128': {\n 'in_channels': [in_channels] +\n [base_channels * item for item in [1, 2, 4, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [1, 2, 4, 8, 16, 16]],\n 'downsample': [True] * 5 + [False],\n 'resolution': [64, 32, 16, 8, 4, 4],\n 'attention': [True, False, False, False, False, False]\n },\n '256': {\n 'in_channels': [in_channels] +\n [base_channels * item for item in [1, 2, 4, 8, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [1, 2, 4, 8, 8, 16, 16]],\n 'downsample': [True] * 6 + [False],\n 'resolution': [128, 64, 32, 16, 8, 4, 4],\n 'attention': [False, True, False, False, False, False]\n },\n '512': {\n 'in_channels': [in_channels] +\n [base_channels * item for item in [1, 1, 2, 4, 8, 8, 16]],\n 'out_channels':\n [base_channels * item for item in [1, 1, 2, 4, 8, 8, 16, 16]],\n 'downsample': [True] * 7 + [False],\n 'resolution': [256, 128, 64, 32, 16, 8, 4, 4],\n 'attention': [False, False, False, True, False, False, False]\n }\n }\n\n return _default_arch_cfgs[str(input_scale)]\n\n def forward(self, x, label=None):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n label (torch.Tensor | None): Label Tensor. Defaults to None.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image with\n given label.\n \"\"\"\n x0 = x\n for conv_block in self.conv_blocks:\n x0 = conv_block(x0)\n x0 = self.activate(x0)\n x0 = torch.sum(x0, dim=[2, 3])\n out = self.decision(x0)\n\n if self.num_classes > 0:\n w_y = self.proj_y(label)\n out = out + torch.sum(w_y * x0, dim=1, keepdim=True)\n return out\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n for m in self.modules():\n init_type = self.init_cfg['type']\n module_name = m.__class__.__name__\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n if init_type == 'ortho':\n nn.init.orthogonal_(m.weight)\n elif init_type == 'N02':\n normal_init(m, 0.0, 0.02)\n elif init_type == 'xavier':\n xavier_init(m)\n else:\n raise NotImplementedError(\n f'{init_type} initialization not supported now.')\n # save init info\n init_info = (f'{module_name} belongs to (nn.Conv2d, '\n 'nn.Linear, nn.Embedding), initialize by '\n f'\\'init_type\\' {init_type}')\n if hasattr(m, '_params_init_info'):\n update_init_info(m, init_info)\n self._is_init = True\n" }, { "path": "mmagic/models/editors/biggan/biggan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import (BaseModule, normal_init, update_init_info,\n xavier_init)\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom .biggan_modules import SelfAttentionBlock, SNConvModule\nfrom .biggan_snmodule import SNLinear\n\n\n@MODELS.register_module()\nclass BigGANGenerator(BaseModule):\n \"\"\"BigGAN Generator. The implementation refers to\n https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGAN.py # noqa.\n\n In BigGAN, we use a SAGAN-based architecture composing of an self-attention\n block and number of convolutional residual blocks with spectral\n normalization.\n\n More details can be found in: Large Scale GAN Training for High Fidelity\n Natural Image Synthesis (ICLR2019).\n\n The design of the model structure is highly corresponding to the output\n resolution. For the original BigGAN's generator, you can set ``output_scale``\n as you need and use the default value of ``arch_cfg`` and ``blocks_cfg``.\n If you want to customize the model, you can set the arguments in this way:\n\n ``arch_cfg``: Config for the architecture of this generator. You can refer\n the ``_default_arch_cfgs`` in the ``_get_default_arch_cfg`` function to see\n the format of the ``arch_cfg``. Basically, you need to provide information\n of each block such as the numbers of input and output channels, whether to\n perform upsampling, etc.\n\n ``blocks_cfg``: Config for the convolution block. You can replace the block\n type to your registered customized block and adjust block params here.\n However, you should notice that some params are shared among these blocks\n like ``act_cfg``, ``with_spectral_norm``, ``sn_eps``, etc.\n\n Args:\n output_scale (int): Output scale for the generated image.\n noise_size (int, optional): Size of the input noise vector. Defaults\n to 120.\n num_classes (int, optional): The number of conditional classes. If set\n to 0, this model will be degraded to an unconditional model.\n Defaults to 0.\n out_channels (int, optional): Number of channels in output images.\n Defaults to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Defaults to 96.\n input_scale (int, optional): The scale of the input 2D feature map.\n Defaults to 4.\n with_shared_embedding (bool, optional): Whether to use shared\n embedding. Defaults to True.\n shared_dim (int, optional): The output channels of shared embedding.\n Defaults to 128.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n split_noise (bool, optional): Whether to split input noise vector.\n Defaults to True.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to dict(type='nearest', scale_factor=2).\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n auto_sync_bn (bool, optional): Whether to use synchronized batch\n normalization. Defaults to True.\n blocks_cfg (dict, optional): Config for the convolution block. Defaults\n to dict(type='BigGANGenResBlock').\n arch_cfg (dict, optional): Config for the architecture of this\n generator. Defaults to None.\n out_norm_cfg (dict, optional): Config for the norm of output layer.\n Defaults to dict(type='BN').\n rgb2bgr (bool, optional): Whether to reformat the output channels\n with order `bgr`. We provide several pre-trained BigGAN\n weights whose output channels order is `rgb`. You can set\n this argument to True to use the weights.\n init_cfg (dict, optional): Initialization config dict. If type is\n `Pretrained`, the pretrain model will be loaded. Otherwise, type\n will be parsed as the name of initialization method. Support values\n are 'ortho', 'N02', 'xavier'. Defaults to dict(type='ortho').\n \"\"\"\n\n def __init__(self,\n output_scale,\n noise_size=120,\n num_classes=0,\n out_channels=3,\n base_channels=96,\n input_scale=4,\n with_shared_embedding=True,\n shared_dim=128,\n sn_eps=1e-6,\n sn_style='ajbrock',\n split_noise=True,\n act_cfg=dict(type='ReLU'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n with_spectral_norm=True,\n auto_sync_bn=True,\n blocks_cfg=dict(type='BigGANGenResBlock'),\n arch_cfg=None,\n out_norm_cfg=dict(type='BN'),\n rgb2bgr=False,\n init_cfg=dict(type='ortho')):\n super().__init__(init_cfg=init_cfg)\n self.noise_size = noise_size\n self.num_classes = num_classes\n self.shared_dim = shared_dim\n self.with_shared_embedding = with_shared_embedding\n self.output_scale = output_scale\n self.arch = arch_cfg if arch_cfg else self._get_default_arch_cfg(\n self.output_scale, base_channels)\n self.input_scale = input_scale\n self.split_noise = split_noise\n self.blocks_cfg = deepcopy(blocks_cfg)\n self.upsample_cfg = deepcopy(upsample_cfg)\n self.rgb2bgr = rgb2bgr\n self.sn_style = sn_style\n\n # Validity Check\n # If 'num_classes' equals to zero, we shall set 'with_shared_embedding'\n # to False.\n if num_classes == 0:\n assert not self.with_shared_embedding\n else:\n if not self.with_shared_embedding:\n # If not `with_shared_embedding`, we will use `nn.Embedding` to\n # replace the original `Linear` layer in conditional BN.\n # Meanwhile, we do not adopt split noises.\n assert not self.split_noise\n\n # If using split latents, we may need to adjust noise_size\n if self.split_noise:\n # Number of places z slots into\n self.num_slots = len(self.arch['in_channels']) + 1\n self.noise_chunk_size = self.noise_size // self.num_slots\n # Recalculate latent dimensionality for even splitting into chunks\n self.noise_size = self.noise_chunk_size * self.num_slots\n else:\n self.num_slots = 1\n self.noise_chunk_size = 0\n\n # First linear layer\n self.noise2feat = nn.Linear(\n self.noise_size // self.num_slots,\n self.arch['in_channels'][0] * (self.input_scale**2))\n if with_spectral_norm:\n if sn_style == 'torch':\n self.noise2feat = spectral_norm(self.noise2feat, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.noise2feat = SNLinear(\n self.noise_size // self.num_slots,\n self.arch['in_channels'][0] * (self.input_scale**2),\n eps=sn_eps)\n else:\n raise NotImplementedError(f'Your {sn_style} is not supported')\n\n # If using 'shared_embedding', we will get an unified embedding of\n # label for all blocks. If not, we just pass the label to each\n # block.\n if with_shared_embedding:\n self.shared_embedding = nn.Embedding(num_classes, shared_dim)\n else:\n self.shared_embedding = nn.Identity()\n\n if num_classes > 0:\n self.dim_after_concat = (\n self.shared_dim + self.noise_chunk_size\n if self.with_shared_embedding else self.num_classes)\n else:\n self.dim_after_concat = self.noise_chunk_size\n\n self.blocks_cfg.update(\n dict(\n dim_after_concat=self.dim_after_concat,\n act_cfg=act_cfg,\n sn_eps=sn_eps,\n sn_style=sn_style,\n input_is_label=(num_classes > 0)\n and (not with_shared_embedding),\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn))\n\n self.conv_blocks = nn.ModuleList()\n for index, out_ch in enumerate(self.arch['out_channels']):\n # change args to adapt to current block\n self.blocks_cfg.update(\n dict(\n in_channels=self.arch['in_channels'][index],\n out_channels=out_ch,\n upsample_cfg=self.upsample_cfg\n if self.arch['upsample'][index] else None))\n self.conv_blocks.append(MODELS.build(self.blocks_cfg))\n if self.arch['attention'][index]:\n self.conv_blocks.append(\n SelfAttentionBlock(\n out_ch,\n with_spectral_norm=with_spectral_norm,\n sn_eps=sn_eps,\n sn_style=sn_style))\n\n self.output_layer = SNConvModule(\n self.arch['out_channels'][-1],\n out_channels,\n kernel_size=3,\n padding=1,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n act_cfg=act_cfg,\n norm_cfg=out_norm_cfg,\n bias=True,\n order=('norm', 'act', 'conv'))\n\n def _get_default_arch_cfg(self, output_scale, base_channels):\n assert output_scale in [32, 64, 128, 256, 512]\n _default_arch_cfgs = {\n '32': {\n 'in_channels': [base_channels * item for item in [4, 4, 4]],\n 'out_channels': [base_channels * item for item in [4, 4, 4]],\n 'upsample': [True] * 3,\n 'resolution': [8, 16, 32],\n 'attention': [False, False, False]\n },\n '64': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 4]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 4, 2]],\n 'upsample': [True] * 4,\n 'resolution': [8, 16, 32, 64],\n 'attention': [False, False, False, True]\n },\n '128': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 4, 2]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 4, 2, 1]],\n 'upsample': [True] * 5,\n 'resolution': [8, 16, 32, 64, 128],\n 'attention': [False, False, False, True, False]\n },\n '256': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 8, 4, 2]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 8, 4, 2, 1]],\n 'upsample': [True] * 6,\n 'resolution': [8, 16, 32, 64, 128, 256],\n 'attention': [False, False, False, True, False, False]\n },\n '512': {\n 'in_channels':\n [base_channels * item for item in [16, 16, 8, 8, 4, 2, 1]],\n 'out_channels':\n [base_channels * item for item in [16, 8, 8, 4, 2, 1, 1]],\n 'upsample': [True] * 7,\n 'resolution': [8, 16, 32, 64, 128, 256, 512],\n 'attention': [False, False, False, True, False, False, False]\n }\n }\n\n return _default_arch_cfgs[str(output_scale)]\n\n def forward(self,\n noise,\n label=None,\n num_batches=0,\n return_noise=False,\n truncation=-1.0,\n use_outside_embedding=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n label (torch.Tensor | callable | None): You can directly give a\n batch of label through a ``torch.Tensor`` or offer a callable\n function to sample a batch of label data. Otherwise, the\n ``None`` indicates to use the default label sampler.\n Defaults to None.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` and\n ``label`` will be returned in a dict with ``fake_img``.\n Defaults to False.\n truncation (float, optional): Truncation factor. Give value not\n less than 0., the truncation trick will be adopted.\n Otherwise, the truncation trick will not be adopted.\n Defaults to -1..\n use_outside_embedding (bool, optional): Whether to use outside\n embedding or use `shared_embedding`. Set to `True` if\n embedding has already be performed outside this function.\n Default to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img``,\n ``noise_batch`` and ``label`` will be returned.\n \"\"\"\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size))\n\n # perform truncation\n if truncation >= 0.0:\n noise_batch = torch.clamp(noise_batch, -1. * truncation,\n 1. * truncation)\n\n if self.num_classes == 0:\n label_batch = None\n\n elif isinstance(label, torch.Tensor):\n if not use_outside_embedding:\n if label.ndim != 1:\n assert all([s == 1 for s in label.shape[1:]])\n label = label.view(-1)\n assert label.ndim == 1, (\n 'The label should be in shape of (n, )'\n f'but got {label.shape}.')\n label_batch = label\n elif callable(label):\n label_generator = label\n assert num_batches > 0\n label_batch = label_generator((num_batches, ))\n else:\n assert num_batches > 0\n label_batch = torch.randint(0, self.num_classes, (num_batches, ))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n if label_batch is not None:\n label_batch = label_batch.to(get_module_device(self))\n if not use_outside_embedding:\n class_vector = self.shared_embedding(label_batch)\n else:\n class_vector = label_batch\n else:\n class_vector = None\n # If 'split noise', concat class vector and noise chunk\n if self.split_noise:\n zs = torch.split(noise_batch, self.noise_chunk_size, dim=1)\n z = zs[0]\n if class_vector is not None:\n ys = [torch.cat([class_vector, item], 1) for item in zs[1:]]\n else:\n ys = zs[1:]\n else:\n ys = [class_vector] * len(self.conv_blocks)\n z = noise_batch\n\n # First linear layer\n x = self.noise2feat(z)\n # Reshape\n x = x.view(x.size(0), -1, self.input_scale, self.input_scale)\n\n # Loop over blocks\n counter = 0\n for conv_block in self.conv_blocks:\n if isinstance(conv_block, SelfAttentionBlock):\n x = conv_block(x)\n else:\n x = conv_block(x, ys[counter])\n counter += 1\n\n # Apply batchnorm-relu-conv-tanh at output\n out_img = torch.tanh(self.output_layer(x))\n\n if self.rgb2bgr:\n out_img = out_img[:, [2, 1, 0], ...]\n\n if return_noise:\n output = dict(\n fake_img=out_img, noise_batch=noise_batch, label=label_batch)\n return output\n\n return out_img\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n for m in self.modules():\n init_type = self.init_cfg['type']\n module_name = m.__class__.__name__\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n if init_type == 'ortho':\n nn.init.orthogonal_(m.weight)\n elif init_type == 'N02':\n normal_init(m, 0.0, 0.02)\n elif init_type == 'xavier':\n xavier_init(m)\n else:\n raise NotImplementedError(\n f'{init_type} initialization not supported now.')\n # save init info\n init_info = (f'{module_name} belongs to (nn.Conv2d, '\n 'nn.Linear, nn.Embedding), initialize by '\n f'\\'init_type\\' {init_type}')\n if hasattr(m, '_params_init_info'):\n update_init_info(m, init_info)\n self._is_init = True\n" }, { "path": "mmagic/models/editors/biggan/biggan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom torch.nn import Parameter\nfrom torch.nn.modules.batchnorm import SyncBatchNorm\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\nfrom .biggan_snmodule import SNConv2d, SNLinear\n\n\nclass SNConvModule(ConvModule):\n \"\"\"Spectral Normalization ConvModule.\n\n In this module, we inherit default ``mmcv.cnn.ConvModule`` and adopt\n spectral normalization. The spectral normalization is proposed in:\n Spectral Normalization for Generative Adversarial Networks.\n\n Args:\n with_spectral_norm (bool, optional): Whether to use Spectral\n Normalization. Defaults to False.\n spectral_norm_cfg (dict, optional): Config for Spectral Normalization.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n *args,\n with_spectral_norm=False,\n spectral_norm_cfg=None,\n **kwargs):\n super().__init__(*args, with_spectral_norm=False, **kwargs)\n self.with_spectral_norm = with_spectral_norm\n self.spectral_norm_cfg = deepcopy(\n spectral_norm_cfg) if spectral_norm_cfg else dict()\n\n self.sn_eps = self.spectral_norm_cfg.get('eps', 1e-6)\n self.sn_style = self.spectral_norm_cfg.get('sn_style', 'torch')\n\n if self.with_spectral_norm:\n if self.sn_style == 'torch':\n self.conv = spectral_norm(self.conv, eps=self.sn_eps)\n elif self.sn_style == 'ajbrock':\n self.snconv_kwargs = deepcopy(kwargs) if kwargs else dict()\n if 'act_cfg' in self.snconv_kwargs.keys():\n self.snconv_kwargs.pop('act_cfg')\n if 'norm_cfg' in self.snconv_kwargs.keys():\n self.snconv_kwargs.pop('norm_cfg')\n if 'order' in self.snconv_kwargs.keys():\n self.snconv_kwargs.pop('order')\n self.conv = SNConv2d(\n *args, **self.snconv_kwargs, eps=self.sn_eps)\n else:\n raise NotImplementedError(\n f'{self.sn_style} style spectral Norm is not supported yet'\n )\n\n\n@MODELS.register_module()\nclass BigGANGenResBlock(nn.Module):\n \"\"\"Residual block used in BigGAN's generator.\n\n Args:\n in_channels (int): The channel number of the input feature map.\n out_channels (int): The channel number of the output feature map.\n dim_after_concat (int): The channel number of the noise concatenated\n with the class vector.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to dict(type='nearest', scale_factor=2).\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization in this block. Defaults to True.\n input_is_label (bool, optional): Whether the input of BNs' linear layer\n is raw label instead of class vector. Defaults to False.\n auto_sync_bn (bool, optional): Whether to use synchronized batch\n normalization. Defaults to True.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n dim_after_concat,\n act_cfg=dict(type='ReLU'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n sn_eps=1e-6,\n sn_style='ajbrock',\n with_spectral_norm=True,\n input_is_label=False,\n auto_sync_bn=True):\n super().__init__()\n self.activation = MODELS.build(act_cfg)\n self.upsample_cfg = deepcopy(upsample_cfg)\n self.with_upsample = upsample_cfg is not None\n if self.with_upsample:\n self.upsample_layer = MODELS.build(self.upsample_cfg)\n self.learnable_sc = in_channels != out_channels or self.with_upsample\n if self.learnable_sc:\n self.shortcut = SNConvModule(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n # Here in_channels of BigGANGenResBlock equal to num_features of\n # BigGANConditionBN\n self.bn1 = BigGANConditionBN(\n in_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n # Here out_channels of BigGANGenResBlock equal to num_features of\n # BigGANConditionBN\n self.bn2 = BigGANConditionBN(\n out_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n\n self.conv1 = SNConvModule(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv2 = SNConvModule(\n in_channels=out_channels,\n out_channels=out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n def forward(self, x, y):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n y (torch.Tensor): Label tensor or class embedding concatenated with\n noise tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n x0 = self.bn1(x, y)\n x0 = self.activation(x0)\n if self.with_upsample:\n x0 = self.upsample_layer(x0)\n x = self.upsample_layer(x)\n x0 = self.conv1(x0)\n x0 = self.bn2(x0, y)\n x0 = self.activation(x0)\n x0 = self.conv2(x0)\n if self.learnable_sc:\n x = self.shortcut(x)\n return x0 + x\n\n\n@MODELS.register_module()\nclass BigGANConditionBN(nn.Module):\n \"\"\"Conditional Batch Normalization used in BigGAN.\n\n Args:\n num_features (int): The channel number of the input feature map tensor.\n linear_input_channels (int): The channel number of the linear layers'\n input tensor.\n bn_eps (float, optional): Epsilon value for batch normalization.\n Defaults to 1e-5.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n momentum (float, optional): The value used for the running_mean and\n running_var computation. Defaults to 0.1.\n input_is_label (bool, optional): Whether the input of BNs' linear layer\n is raw label instead of class vector. Defaults to False.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n auto_sync_bn (bool, optional): Whether to use synchronized batch\n normalization. Defaults to True.\n \"\"\"\n\n def __init__(self,\n num_features,\n linear_input_channels,\n bn_eps=1e-5,\n sn_eps=1e-6,\n sn_style='ajbrock',\n momentum=0.1,\n input_is_label=False,\n with_spectral_norm=True,\n auto_sync_bn=True):\n super().__init__()\n assert num_features > 0\n if linear_input_channels > 0:\n self.use_cbn = True\n else:\n self.use_cbn = False\n # Prepare gain and bias layers\n if self.use_cbn:\n if not input_is_label:\n self.gain = nn.Linear(\n linear_input_channels, num_features, bias=False)\n self.bias = nn.Linear(\n linear_input_channels, num_features, bias=False)\n # please pay attention if shared_embedding is False\n if with_spectral_norm:\n if sn_style == 'torch':\n self.gain = spectral_norm(self.gain, eps=sn_eps)\n self.bias = spectral_norm(self.bias, eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.gain = SNLinear(\n linear_input_channels,\n num_features,\n bias=False,\n eps=sn_eps)\n self.bias = SNLinear(\n linear_input_channels,\n num_features,\n bias=False,\n eps=sn_eps)\n else:\n raise NotImplementedError('sn style')\n else:\n self.gain = nn.Embedding(linear_input_channels, num_features)\n self.bias = nn.Embedding(linear_input_channels, num_features)\n\n self.bn = nn.BatchNorm2d(\n num_features,\n eps=bn_eps,\n momentum=momentum,\n affine=not self.use_cbn)\n\n if auto_sync_bn and dist.is_initialized():\n self.bn = SyncBatchNorm.convert_sync_batchnorm(self.bn)\n\n def forward(self, x, y):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n y (torch.Tensor): Label tensor or class embedding concatenated with\n noise tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n if self.use_cbn:\n # Calculate class-conditional gains and biases\n gain = (1. + self.gain(y)).view(y.size(0), -1, 1, 1)\n bias = self.bias(y).view(y.size(0), -1, 1, 1)\n out = self.bn(x)\n out = out * gain + bias\n else:\n out = self.bn(x)\n return out\n\n\n@MODELS.register_module()\nclass SelfAttentionBlock(nn.Module):\n \"\"\"Self-Attention block used in BigGAN.\n\n Args:\n in_channels (int): The channel number of the input feature map.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n \"\"\"\n\n def __init__(self,\n in_channels,\n with_spectral_norm=True,\n sn_eps=1e-6,\n sn_style='ajbrock'):\n super(SelfAttentionBlock, self).__init__()\n\n self.in_channels = in_channels\n self.theta = SNConvModule(\n self.in_channels,\n self.in_channels // 8,\n kernel_size=1,\n padding=0,\n bias=False,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n self.phi = SNConvModule(\n self.in_channels,\n self.in_channels // 8,\n kernel_size=1,\n padding=0,\n bias=False,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n self.g = SNConvModule(\n self.in_channels,\n self.in_channels // 2,\n kernel_size=1,\n padding=0,\n bias=False,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n self.o = SNConvModule(\n self.in_channels // 2,\n self.in_channels,\n kernel_size=1,\n padding=0,\n bias=False,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n # Learnable gain parameter\n self.gamma = Parameter(torch.tensor(0.), requires_grad=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n # Apply convs\n theta = self.theta(x)\n phi = F.max_pool2d(self.phi(x), [2, 2])\n g = F.max_pool2d(self.g(x), [2, 2])\n # Perform reshapes\n theta = theta.view(-1, self.in_channels // 8, x.shape[2] * x.shape[3])\n phi = phi.view(-1, self.in_channels // 8, x.shape[2] * x.shape[3] // 4)\n g = g.view(-1, self.in_channels // 2, x.shape[2] * x.shape[3] // 4)\n # Matmul and softmax to get attention maps\n beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1)\n # Attention map times g path\n o = self.o(\n torch.bmm(g, beta.transpose(1, 2)).view(-1, self.in_channels // 2,\n x.shape[2], x.shape[3]))\n return self.gamma * o + x\n\n\n@MODELS.register_module()\nclass BigGANDiscResBlock(nn.Module):\n \"\"\"Residual block used in BigGAN's discriminator.\n\n Args:\n in_channels (int): The channel number of the input tensor.\n out_channels (int): The channel number of the output tensor.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU', inplace=False).\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n with_downsample (bool, optional): Whether to use downsampling in this\n block. Defaults to True.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n is_head_block (bool, optional): Whether this block is the first block\n of BigGAN. Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n act_cfg=dict(type='ReLU', inplace=False),\n sn_eps=1e-6,\n sn_style='ajbrock',\n with_downsample=True,\n with_spectral_norm=True,\n is_head_block=False):\n super().__init__()\n self.activation = MODELS.build(act_cfg)\n self.with_downsample = with_downsample\n self.is_head_block = is_head_block\n if self.with_downsample:\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2)\n self.learnable_sc = in_channels != out_channels or self.with_downsample\n if self.learnable_sc:\n self.shortcut = SNConvModule(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv1 = SNConvModule(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv2 = SNConvModule(\n in_channels=out_channels,\n out_channels=out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n def forward_sc(self, x):\n \"\"\"Forward function of shortcut.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n\n Returns:\n torch.Tensor: Output tensor of shortcut.\n \"\"\"\n if self.is_head_block:\n if self.with_downsample:\n x = self.downsample(x)\n if self.learnable_sc:\n x = self.shortcut(x)\n else:\n if self.learnable_sc:\n x = self.shortcut(x)\n if self.with_downsample:\n x = self.downsample(x)\n return x\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n if self.is_head_block:\n x0 = x\n else:\n x0 = self.activation(x)\n x0 = self.conv1(x0)\n x0 = self.activation(x0)\n x0 = self.conv2(x0)\n if self.with_downsample:\n x0 = self.downsample(x0)\n x1 = self.forward_sc(x)\n return x0 + x1\n\n\n@MODELS.register_module()\nclass BigGANDeepGenResBlock(nn.Module):\n \"\"\"Residual block used in BigGAN-Deep's generator.\n\n Args:\n in_channels (int): The channel number of the input feature map.\n out_channels (int): The channel number of the output feature map.\n dim_after_concat (int): The channel number of the noise concatenated\n with the class vector.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to dict(type='nearest', scale_factor=2).\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n bn_eps (float, optional): Epsilon value for batch normalization.\n Defaults to 1e-5.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization in this block. Defaults to True.\n input_is_label (bool, optional): Whether the input of BNs' linear layer\n is raw label instead of class vector. Defaults to False.\n auto_sync_bn (bool, optional): Whether to use synchronized batch\n normalization. Defaults to True.\n channel_ratio (int, optional): The ratio of the input channels' number\n to the hidden channels' number. Defaults to 4.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n dim_after_concat,\n act_cfg=dict(type='ReLU'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n sn_eps=1e-6,\n sn_style='ajbrock',\n bn_eps=1e-5,\n with_spectral_norm=True,\n input_is_label=False,\n auto_sync_bn=True,\n channel_ratio=4):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.hidden_channels = self.in_channels // channel_ratio\n self.activation = MODELS.build(act_cfg)\n self.upsample_cfg = deepcopy(upsample_cfg)\n self.with_upsample = upsample_cfg is not None\n if self.with_upsample:\n self.upsample_layer = MODELS.build(self.upsample_cfg)\n # Here in_channels of BigGANGenResBlock equal to num_features of\n # BigGANConditionBN\n self.bn1 = BigGANConditionBN(\n in_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n bn_eps=bn_eps,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n # Here out_channels of BigGANGenResBlock equal to num_features of\n # BigGANConditionBN\n self.bn2 = BigGANConditionBN(\n self.hidden_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n bn_eps=bn_eps,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n\n self.bn3 = BigGANConditionBN(\n self.hidden_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n bn_eps=bn_eps,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n\n self.bn4 = BigGANConditionBN(\n self.hidden_channels,\n dim_after_concat,\n sn_eps=sn_eps,\n sn_style=sn_style,\n bn_eps=bn_eps,\n input_is_label=input_is_label,\n with_spectral_norm=with_spectral_norm,\n auto_sync_bn=auto_sync_bn)\n\n self.conv1 = SNConvModule(\n in_channels=in_channels,\n out_channels=self.hidden_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv2 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=self.hidden_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv3 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=self.hidden_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv4 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n def forward(self, x, y):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n y (torch.Tensor): Label tensor or class embedding concatenated with\n noise tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n x0 = self.bn1(x, y)\n x0 = self.activation(x0)\n x0 = self.conv1(x0)\n\n x0 = self.bn2(x0, y)\n x0 = self.activation(x0)\n # Drop channels in x if necessary\n if self.in_channels != self.out_channels:\n x = x[:, :self.out_channels]\n # upsample both h and x at this point\n if self.with_upsample:\n x0 = self.upsample_layer(x0)\n x = self.upsample_layer(x)\n x0 = self.conv2(x0)\n\n x0 = self.bn3(x0, y)\n x0 = self.activation(x0)\n x0 = self.conv3(x0)\n\n x0 = self.bn4(x0, y)\n x0 = self.activation(x0)\n x0 = self.conv4(x0)\n return x0 + x\n\n\n@MODELS.register_module()\nclass BigGANDeepDiscResBlock(nn.Module):\n \"\"\"Residual block used in BigGAN-Deep's discriminator.\n\n Args:\n in_channels (int): The channel number of the input tensor.\n out_channels (int): The channel number of the output tensor.\n channel_ratio (int, optional): The ratio of the input channels' number\n to the hidden channels' number. Defaults to 4.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU', inplace=False).\n sn_eps (float, optional): Epsilon value for spectral normalization.\n Defaults to 1e-6.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `ajbrock`.\n with_downsample (bool, optional): Whether to use downsampling in this\n block. Defaults to True.\n with_spectral_norm (bool, optional): Whether to use spectral\n normalization. Defaults to True.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n channel_ratio=4,\n act_cfg=dict(type='ReLU', inplace=False),\n sn_eps=1e-6,\n sn_style='ajbrock',\n with_downsample=True,\n with_spectral_norm=True):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.hidden_channels = self.out_channels // channel_ratio\n self.activation = MODELS.build(act_cfg)\n self.with_downsample = with_downsample\n\n if self.with_downsample:\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2)\n\n self.learnable_sc = (in_channels != out_channels)\n if self.learnable_sc:\n self.shortcut = SNConvModule(\n in_channels=in_channels,\n out_channels=out_channels - in_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n self.conv1 = SNConvModule(\n in_channels=in_channels,\n out_channels=self.hidden_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n order=('act', 'conv', 'norm'))\n\n self.conv2 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=self.hidden_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n order=('act', 'conv', 'norm'))\n\n self.conv3 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=self.hidden_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style),\n order=('act', 'conv', 'norm'))\n\n self.conv4 = SNConvModule(\n in_channels=self.hidden_channels,\n out_channels=out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=dict(eps=sn_eps, sn_style=sn_style))\n\n def forward_sc(self, x):\n \"\"\"Forward function of shortcut.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n\n Returns:\n torch.Tensor: Output tensor of shortcut.\n \"\"\"\n if self.with_downsample:\n x = self.downsample(x)\n if self.learnable_sc:\n x0 = self.shortcut(x)\n x = torch.cat([x, x0], dim=1)\n return x\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n\n x0 = self.conv1(x)\n x0 = self.conv2(x0)\n x0 = self.conv3(x0)\n x0 = self.activation(x0)\n # downsample\n if self.with_downsample:\n x0 = self.downsample(x0)\n x0 = self.conv4(x0)\n x1 = self.forward_sc(x)\n return x0 + x1\n" }, { "path": "mmagic/models/editors/biggan/biggan_snmodule.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# yapf:disable\n'''\n Ref: Functions in this file are borrowed from https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py # noqa\n'''\n# yapf:enable\n\n\ndef proj(x, y):\n \"\"\"Calculate Projection of x onto y.\n\n Args:\n x (torch.Tensor): Projection vector x.\n y (torch.Tensor): Direction vector y.\n\n Returns:\n torch.Tensor: Projection of x onto y.\n \"\"\"\n return torch.mm(y, x.t()) * y / torch.mm(y, y.t())\n\n\ndef gram_schmidt(x, ys):\n \"\"\"Orthogonalize x w.r.t list of vectors ys.\n\n Args:\n x (torch.Tensor): Vector to be added into the\n orthogonal vectors.\n ys (list[torch.Tensor]): A set of orthogonal vectors.\n\n Returns:\n torch.Tensor: Result of Gram–Schmidt orthogonalization.\n \"\"\"\n for y in ys:\n x = x - proj(x, y)\n return x\n\n\n@torch.no_grad()\ndef power_iteration(weight, u_list, update=True, eps=1e-12):\n \"\"\"Power iteration method for calculating spectral norm.\n\n Args:\n weight (torch.Tensor): Module weight.\n u_list (list[torch.Tensor]): list of left singular vector.\n The length of list equals to the simulation times.\n update (bool, optional): Whether update left singular\n vector. Defaults to True.\n eps (float, optional): Vector Normalization epsilon.\n Defaults to 1e-12.\n\n Returns:\n tuple[list[tensor.Tensor]]: Tuple consist of three lists\n which contain singular values, left singular\n vector and right singular vector respectively.\n \"\"\"\n us, vs, svs = [], [], []\n for i, u in enumerate(u_list):\n v = torch.matmul(u, weight)\n v = F.normalize(gram_schmidt(v, vs), eps=eps)\n vs += [v]\n u = torch.matmul(v, weight.t())\n u = F.normalize(gram_schmidt(u, us), eps=eps)\n us += [u]\n if update:\n u_list[i][:] = u\n svs += [\n torch.squeeze(torch.matmul(torch.matmul(v, weight.t()), u.t()))\n ]\n return svs, us, vs\n\n\nclass SpectralNorm(object):\n \"\"\"Spectral normalization base class.\n\n Args:\n num_svs (int): Number of singular values.\n num_iters (int): Number of power iterations per step.\n num_outputs (int): Number of output channels.\n transpose (bool, optional): If set to `True`, weight\n matrix will be transposed before power iteration.\n Defaults to False.\n eps (float, optional): Vector Normalization epsilon for\n avoiding divide by zero. Defaults to 1e-12.\n \"\"\"\n\n def __init__(self,\n num_svs,\n num_iters,\n num_outputs,\n transpose=False,\n eps=1e-12):\n self.num_iters = num_iters\n self.num_svs = num_svs\n self.transpose = transpose\n self.eps = eps\n # Register a singular vector for each sv\n for i in range(self.num_svs):\n self.register_buffer('u%d' % i, torch.randn(1, num_outputs))\n self.register_buffer('sv%d' % i, torch.ones(1))\n\n @property\n def u(self):\n \"\"\"Get left singular vectors.\"\"\"\n return [getattr(self, 'u%d' % i) for i in range(self.num_svs)]\n\n @property\n def sv(self):\n \"\"\"Get singular values.\"\"\"\n return [getattr(self, 'sv%d' % i) for i in range(self.num_svs)]\n\n def sn_weight(self):\n \"\"\"Compute the spectrally-normalized weight.\"\"\"\n W_mat = self.weight.view(self.weight.size(0), -1)\n if self.transpose:\n W_mat = W_mat.t()\n # Apply num_iters power iterations\n for _ in range(self.num_iters):\n svs, us, vs = power_iteration(\n W_mat, self.u, update=self.training, eps=self.eps)\n # Update the svs\n if self.training:\n with torch.no_grad():\n for i, sv in enumerate(svs):\n self.sv[i][:] = sv\n return self.weight / svs[-1]\n\n\nclass SNConv2d(nn.Conv2d, SpectralNorm):\n \"\"\"2D Conv layer with spectral norm.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n out_channels (int): Number of channels produced by the convolution.\n kernel_size (int): Size of the convolving kernel.\n stride (int, optional): Stride of the convolution.. Defaults to 1.\n padding (int, optional): Zero-padding added to both sides of\n the input. Defaults to 0.\n dilation (int, optional): Spacing between kernel elements.\n Defaults to 1.\n groups (int, optional): Number of blocked connections from input\n channels to output channels. Defaults to 1.\n bias (bool, optional): Whether to use bias parameter.\n Defaults to True.\n num_svs (int): Number of singular values.\n num_iters (int): Number of power iterations per step.\n eps (float, optional): Vector Normalization epsilon for\n avoiding divide by zero. Defaults to 1e-12.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n stride=1,\n padding=0,\n dilation=1,\n groups=1,\n bias=True,\n num_svs=1,\n num_iters=1,\n eps=1e-12):\n nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size,\n stride, padding, dilation, groups, bias)\n SpectralNorm.__init__(self, num_svs, num_iters, out_channels, eps=eps)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n return F.conv2d(x, self.sn_weight(), self.bias, self.stride,\n self.padding, self.dilation, self.groups)\n\n\nclass SNLinear(nn.Linear, SpectralNorm):\n \"\"\"Linear layer with spectral norm.\n\n Args:\n in_features (int): Number of channels in the input feature.\n out_features (int): Number of channels in the out feature.\n bias (bool, optional): Whether to use bias parameter.\n Defaults to True.\n num_svs (int): Number of singular values.\n num_iters (int): Number of power iterations per step.\n eps (float, optional): Vector Normalization epsilon for\n avoiding divide by zero. Defaults to 1e-12.\n \"\"\"\n\n def __init__(self,\n in_features,\n out_features,\n bias=True,\n num_svs=1,\n num_iters=1,\n eps=1e-12):\n nn.Linear.__init__(self, in_features, out_features, bias)\n SpectralNorm.__init__(self, num_svs, num_iters, out_features, eps=eps)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n return F.linear(x, self.sn_weight(), self.bias)\n\n\n# We use num_embeddings as the dim instead of embedding_dim here\n# for convenience sake\nclass SNEmbedding(nn.Embedding, SpectralNorm):\n \"\"\"Embedding layer with spectral norm.\n\n Args:\n num_embeddings (int): Size of the dictionary of embeddings.\n embedding_dim (int): The size of each embedding vector.\n padding_idx (int, optional): If specified, the entries at\n padding_idx do not contribute to the gradient; therefore,\n the embedding vector at padding_idx is not updated during\n training, i.e. it remains as a fixed “pad”. For a newly\n constructed Embedding, the embedding vector at padding_idx\n will default to all zeros, but can be updated to another value\n to be used as the padding vector. Defaults to None.\n max_norm (float, optional): If given, each embedding vector with\n norm larger than max_norm is renormalized to have norm\n max_norm. Defaults to None.\n norm_type (int, optional): The p of the p-norm to compute for\n the max_norm option. Default 2.\n scale_grad_by_freq (bool, optional): If given, this will scale\n gradients by the inverse of frequency of the words in the\n mini-batch. Default False.\n sparse (bool, optional): If True, gradient w.r.t. weight matrix\n will be a sparse tensor. See Notes for more details regarding\n sparse gradients. Defaults to False.\n _weight (torch.Tensor, optional): Initial Weight. Defaults to None.\n num_svs (int): Number of singular values.\n num_iters (int): Number of power iterations per step.\n eps (float, optional): Vector Normalization epsilon for\n avoiding divide by zero. Defaults to 1e-12.\n \"\"\"\n\n def __init__(self,\n num_embeddings,\n embedding_dim,\n padding_idx=None,\n max_norm=None,\n norm_type=2,\n scale_grad_by_freq=False,\n sparse=False,\n _weight=None,\n num_svs=1,\n num_iters=1,\n eps=1e-12):\n nn.Embedding.__init__(self, num_embeddings, embedding_dim, padding_idx,\n max_norm, norm_type, scale_grad_by_freq, sparse,\n _weight)\n SpectralNorm.__init__(\n self, num_svs, num_iters, num_embeddings, eps=eps)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n return F.embedding(x, self.sn_weight())\n" }, { "path": "mmagic/models/editors/cain/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .cain import CAIN\nfrom .cain_net import CAINNet\n\n__all__ = [\n 'CAIN',\n 'CAINNet',\n]\n" }, { "path": "mmagic/models/editors/cain/cain.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models.base_models import BasicInterpolator\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass CAIN(BasicInterpolator):\n \"\"\"CAIN model for Video Interpolation.\n\n Paper: Channel Attention Is All You Need for Video Frame Interpolation\n Ref repo: https://github.com/myungsub/CAIN\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n required_frames (int): Required frames in each process. Default: 2\n step_frames (int): Step size of video frame interpolation. Default: 1\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`.\n \"\"\"\n\n def forward_inference(self, inputs, data_samples=None):\n \"\"\"Forward inference. Returns predictions of validation, testing, and\n simple inference.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[DataSample]: predictions.\n \"\"\"\n\n predictions = super().forward_inference(\n inputs, data_samples, padding_flag=True)\n\n return predictions\n" }, { "path": "mmagic/models/editors/cain/cain_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import pixel_unshuffle\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass CAINNet(BaseModule):\n \"\"\"CAIN network structure.\n\n Paper: Channel Attention Is All You Need for Video Frame Interpolation.\n Ref repo: https://github.com/myungsub/CAIN\n\n Args:\n in_channels (int): Channel number of inputs. Default: 3.\n kernel_size (int): Kernel size of CAINNet. Default: 3.\n num_block_groups (int): Number of block groups. Default: 5.\n num_block_layers (int): Number of blocks in a group. Default: 12.\n depth (int): Down scale depth, scale = 2**depth. Default: 3.\n reduction (int): Channel reduction of CA. Default: 16.\n norm (str | None): Normalization layer. If it is None, no\n normalization is performed. Default: None.\n padding (int): Padding of CAINNet. Default: 7.\n act (function): activate function. Default: nn.LeakyReLU(0.2, True).\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels=3,\n kernel_size=3,\n num_block_groups=5,\n num_block_layers=12,\n depth=3,\n reduction=16,\n norm=None,\n padding=7,\n act=nn.LeakyReLU(0.2, True),\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n mid_channels = in_channels * (4**depth)\n self.scale = 2**depth\n self.padding = padding\n\n self.conv_first = nn.Conv2d(mid_channels * 2, mid_channels,\n kernel_size, 1, 1)\n self.body = make_layer(\n ResidualGroup,\n num_block_groups,\n block_layer=ResidualChannelAttention,\n num_block_layers=num_block_layers,\n mid_channels=mid_channels,\n kernel_size=kernel_size,\n reduction=reduction,\n norm=norm,\n act=act)\n self.conv_last = nn.Conv2d(mid_channels, mid_channels, kernel_size, 1,\n 1)\n\n def forward(self, imgs, padding_flag=False):\n \"\"\"Forward function.\n\n Args:\n imgs (Tensor): Input tensor with shape (n, 2, c, h, w).\n padding_flag (bool): Padding or not. Default: False.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n assert imgs.shape[1] == 2\n x1, x2 = imgs[:, 0], imgs[:, 1]\n\n mean1 = x1.mean((2, 3), keepdim=True)\n mean2 = x2.mean((2, 3), keepdim=True)\n x1 -= mean1\n x2 -= mean2\n\n if padding_flag:\n padding_function, depadding_function = get_padding_functions(\n x1, self.padding)\n x1 = padding_function(x1)\n x2 = padding_function(x2)\n\n x1 = pixel_unshuffle(x1, self.scale)\n x2 = pixel_unshuffle(x2, self.scale)\n\n x = torch.cat([x1, x2], dim=1)\n x = self.conv_first(x)\n res = self.body(x)\n res += x\n x = self.conv_last(res)\n x = F.pixel_shuffle(x, self.scale)\n\n if padding_flag:\n x = depadding_function(x)\n\n x += (mean1 + mean2) / 2\n return x\n\n\ndef get_padding_functions(x, padding=7):\n \"\"\"Generate padding function for CAIN.\n\n This function produces two functions to pad and depad a tensor, given the\n number of pixels to be padded. When applying padding and depadding\n sequentially, the original tensor is obtained.\n\n The generated padding function will pad the given tensor to the 'padding'\n power of 2, i.e., pow(2, 'padding').\n\n tensor --padding_function--> padded tensor\n padded tensor --depadding_function--> original tensor\n\n Args:\n x (Tensor): Input tensor.\n padding (int): Padding size. Default: 7.\n\n Returns:\n padding_function (Function): Padding function.\n depadding_function (Function): Depadding function.\n \"\"\"\n\n h, w = x.shape[-2:]\n padding_width, padding_height = 0, 0\n if w != ((w >> padding) << padding):\n padding_width = (((w >> padding) + 1) << padding) - w\n if h != ((h >> padding) << padding):\n padding_height = (((h >> padding) + 1) << padding) - h\n\n left, right = padding_width // 2, padding_width - padding_width // 2\n up, down = padding_height // 2, padding_height - padding_height // 2\n if down >= h or right >= w:\n function = nn.ReplicationPad2d\n else:\n function = nn.ReflectionPad2d\n\n padding_function = function(padding=[left, right, up, down])\n depadding_function = function(\n padding=[0 - left, 0 - right, 0 - up, 0 - down])\n\n return padding_function, depadding_function\n\n\nclass ConvNormWithReflectionPad(BaseModule):\n \"\"\"Apply reflection padding, followed by a convolution, which can be\n followed by an optional normalization.\n\n Args:\n in_channels (int): Channel number of input features.\n out_channels (int): Channel number of output features.\n kernel_size (int): Kernel size of convolution layer.\n norm (str | None): Normalization layer. If it is None, no\n normalization is performed. Default: None.\n \"\"\"\n\n def __init__(self, in_channels, out_channels, kernel_size, norm=None):\n super().__init__()\n\n self.reflection_pad = nn.ReflectionPad2d(kernel_size // 2)\n self.conv = nn.Conv2d(\n in_channels, out_channels, kernel_size=kernel_size, bias=True)\n\n if norm is None:\n self.norm = None\n elif norm.lower() == 'in':\n self.norm = nn.InstanceNorm2d(\n out_channels, track_running_stats=True)\n elif norm.lower() == 'bn':\n self.norm = nn.BatchNorm2d(out_channels)\n else:\n raise ValueError(f\"Invalid value for 'norm': {norm}\")\n\n def forward(self, x):\n \"\"\"Forward function for ConvNormWithReflectionPad.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape (n, c, h, w).\n \"\"\"\n\n out = self.reflection_pad(x)\n out = self.conv(out)\n if self.norm:\n out = self.norm(out)\n return out\n\n\nclass ChannelAttentionLayer(BaseModule):\n \"\"\"Channel Attention (CA) Layer.\n\n Args:\n mid_channels (int): Channel number of the intermediate features.\n reduction (int): Channel reduction of CA. Default: 16.\n \"\"\"\n\n def __init__(self, mid_channels, reduction=16):\n super().__init__()\n\n # global average pooling: (n, c, h, w) --> (n, c, 1, 1)\n self.avg_pool = nn.AdaptiveAvgPool2d(1)\n # channel reduction.\n self.channel_attention = nn.Sequential(\n nn.Conv2d(\n mid_channels,\n mid_channels // reduction,\n 1,\n padding=0,\n bias=True), nn.ReLU(inplace=True),\n nn.Conv2d(\n mid_channels // reduction,\n mid_channels,\n 1,\n padding=0,\n bias=True), nn.Sigmoid())\n\n def forward(self, x):\n \"\"\"Forward function for ChannelAttentionLayer.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape (n, c, h, w).\n \"\"\"\n\n y = self.avg_pool(x)\n y = self.channel_attention(y)\n return x * y\n\n\nclass ResidualChannelAttention(BaseModule):\n \"\"\"Residual Channel Attention Module.\n\n Args:\n mid_channels (int): Channel number of the intermediate features.\n kernel_size (int): Kernel size of convolution layers. Default: 3.\n reduction (int): Channel reduction. Default: 16.\n norm (None | function): Norm layer. If None, no norm layer.\n Default: None.\n act (function): activation function. Default: nn.LeakyReLU(0.2, True).\n \"\"\"\n\n def __init__(self,\n mid_channels,\n kernel_size=3,\n reduction=16,\n norm=None,\n act=nn.LeakyReLU(0.2, True)):\n super().__init__()\n\n self.body = nn.Sequential(\n ConvNormWithReflectionPad(\n mid_channels, mid_channels, kernel_size, norm=norm), act,\n ConvNormWithReflectionPad(\n mid_channels, mid_channels, kernel_size, norm=norm),\n ChannelAttentionLayer(mid_channels, reduction))\n\n def forward(self, x):\n \"\"\"Forward function for ResidualChannelAttention.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape (n, c, h, w).\n \"\"\"\n\n out = self.body(x)\n return out + x\n\n\nclass ResidualGroup(BaseModule):\n \"\"\"Residual Group, consisting of a stack of residual channel attention,\n followed by a convolution.\n\n Args:\n block_layer (nn.Module): nn.Module class for basic block.\n num_block_layers (int): number of blocks.\n mid_channels (int): Channel number of the intermediate features.\n kernel_size (int): Kernel size of ResidualGroup.\n reduction (int): Channel reduction of CA. Default: 16.\n act (function): activation function. Default: nn.LeakyReLU(0.2, True).\n norm (str | None): Normalization layer. If it is None, no\n normalization is performed. Default: None.\n \"\"\"\n\n def __init__(self,\n block_layer,\n num_block_layers,\n mid_channels,\n kernel_size,\n reduction,\n act=nn.LeakyReLU(0.2, True),\n norm=None):\n super().__init__()\n\n self.body = make_layer(\n block_layer,\n num_block_layers,\n mid_channels=mid_channels,\n kernel_size=kernel_size,\n reduction=reduction,\n norm=norm,\n act=act)\n self.conv_after_body = ConvNormWithReflectionPad(\n mid_channels, mid_channels, kernel_size, norm=norm)\n\n def forward(self, x):\n \"\"\"Forward function for ResidualGroup.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Output tensor with shape (n, c, h, w).\n \"\"\"\n\n y = self.body(x)\n y = self.conv_after_body(y)\n return x + y\n" }, { "path": "mmagic/models/editors/controlnet/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .controlnet import ControlStableDiffusion\nfrom .controlnet_utils import change_base_model\n\n__all__ = ['ControlStableDiffusion', 'change_base_model']\n" }, { "path": "mmagic/models/editors/controlnet/controlnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom logging import WARNING\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.optim import OptimWrapperDict\nfrom PIL import Image\nfrom torch import Tensor\nfrom tqdm import tqdm\n\nfrom mmagic.models.archs import AttentionInjection\nfrom mmagic.models.utils import build_module\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ..stable_diffusion import StableDiffusion\nfrom .controlnet_utils import change_base_model\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass ControlStableDiffusion(StableDiffusion):\n \"\"\"Implementation of `ControlNet with Stable Diffusion.\n\n `_ (ControlNet).\n\n Args:\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n tokenizer (str): The **name** for CLIP tokenizer.\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n controlnet (Union[dict, nn.Module]): The config or module for\n ControlNet.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n dtype (str, optional): The dtype for the model. Defaults to 'fp16'.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa\n Defaults to 0.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Defaults to\n dict(type='DataPreprocessor').\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Defaults to None/\n \"\"\"\n\n def __init__(self,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n controlnet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: str = 'fp32',\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg: Optional[dict] = None,\n attention_injection=False):\n super().__init__(vae, text_encoder, tokenizer, unet, scheduler,\n test_scheduler, dtype, enable_xformers,\n noise_offset_weight, tomesd_cfg, data_preprocessor,\n init_cfg)\n\n default_args = dict()\n if dtype is not None:\n default_args['dtype'] = dtype\n\n # NOTE: initialize controlnet as fp32\n self.controlnet = build_module(controlnet, MODELS)\n self._controlnet_ori_dtype = next(self.controlnet.parameters()).dtype\n print_log(\n 'Set ControlNetModel dtype to '\n f'\\'{self._controlnet_ori_dtype}\\'.', 'current')\n self.set_xformers(self.controlnet)\n\n self.vae.requires_grad_(False)\n self.text_encoder.requires_grad_(False)\n self.unet.requires_grad_(False)\n\n if attention_injection:\n self.unet = AttentionInjection(self.unet)\n\n def init_weights(self):\n \"\"\"Initialize the weights. Noted that this function will only be called\n at train. If you want to inference with a different unet model, you can\n call this function manually or use\n `mmagic.models.editors.controlnet.controlnet_utils.change_base_model`\n to convert the weight of ControlNet manually.\n\n Example:\n >>> 1. init controlnet from unet\n >>> init_cfg = dict(type='init_from_unet')\n\n >>> 2. switch controlnet weight from unet\n >>> # base model is not defined, use `runwayml/stable-diffusion-v1-5`\n >>> # as default\n >>> init_cfg = dict(type='convert_from_unet')\n >>> # base model is defined\n >>> init_cfg = dict(\n >>> type='convert_from_unet',\n >>> base_model=dict(\n >>> type='UNet2DConditionModel',\n >>> from_pretrained='REPO_ID',\n >>> subfolder='unet'))\n \"\"\"\n if self.init_cfg is not None:\n init_type = self.init_cfg.get('type', None)\n else:\n init_type = None\n\n if init_type == 'init_from_unet':\n # fetch module\n if is_model_wrapper(self.controlnet):\n controlnet = self.controlnet.module\n else:\n controlnet = self.controlnet\n\n if is_model_wrapper(self.unet):\n unet = self.unet.module\n else:\n unet = self.unet\n\n if controlnet._from_pretrained is not None:\n print_log(\n 'ControlNet has initialized from pretrained '\n f'weight \\'{controlnet._from_pretrained}\\'.'\n ' Re-initialize ControlNet from Unet.', 'current', WARNING)\n\n # copy weight\n log_template = 'Initialize weight ControlNet from Unet: {}'\n for n, p in unet.named_parameters():\n if n in controlnet.state_dict():\n print_log(log_template.format(n), 'current')\n controlnet.state_dict()[n].copy_(p.data)\n\n # check zero_conv\n zero_conv_blocks = controlnet.controlnet_down_blocks\n for n, p in zero_conv_blocks.named_parameters():\n if not (p == 0).all():\n print_log(f'{n} in ControlNet is not initialized with '\n 'zero. Set to zero manually.')\n p.data.zero_()\n\n elif init_type == 'convert_from_unet':\n # fetch module\n if is_model_wrapper(self.controlnet):\n controlnet = self.controlnet.module\n else:\n controlnet = self.controlnet\n\n if is_model_wrapper(self.unet):\n unet = self.unet.module\n else:\n unet = self.unet\n\n # use sd-v15 as base model by default\n base_model_default_cfg = dict(\n type='UNet2DConditionModel',\n from_pretrained='runwayml/stable-diffusion-v1-5',\n subfolder='unet')\n base_model_cfg = self.init_cfg.get('base_model',\n base_model_default_cfg)\n base_model = MODELS.build(base_model_cfg)\n change_base_model(controlnet, unet, base_model)\n\n else:\n assert init_type is None, (\n 'Only support \\'init_from_unet\\', \\'convert_from_unet\\' or '\n f'None. But receive {init_type}.')\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n \"\"\"Train step for ControlNet model.\n Args:\n data (dict): Data sampled from dataloader.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n optimizer = optim_wrapper['controlnet']\n\n with optimizer.optim_context(self.controlnet):\n target = inputs['target']\n control = (inputs['source'] + 1) / 2 # [-1, 1] -> [0, 1]\n prompt = data_samples.prompt\n\n num_batches = target.shape[0]\n\n target = target.to(self.dtype)\n latents = self.vae.encode(target).latent_dist.sample()\n latents = latents * self.vae.config.scaling_factor\n\n noise = torch.randn_like(latents)\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n\n noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)\n\n input_ids = self.tokenizer(\n prompt,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n\n encoder_hidden_states = self.text_encoder(input_ids)[0]\n\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n # forward control\n # NOTE: we train controlnet in fp32, convert to float manually\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float(),\n controlnet_cond=control.float(),\n return_dict=False,\n )\n # Predict the noise residual and compute loss\n # NOTE: we train unet in fp32, convert to float manually\n model_output = self.unet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float(),\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample)\n model_pred = model_output['sample']\n\n loss = F.mse_loss(model_pred.float(), gt.float(), reduction='mean')\n\n optimizer.update_params(loss)\n\n return dict(loss=loss)\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n prompt = data['data_samples'].prompt\n control = data['inputs']['source']\n\n output = self.infer(\n prompt, control=((control + 1) / 2), return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(\n samples, data['data_samples'], key='target')\n control = self.data_preprocessor.destruct(\n control, data['data_samples'], key='source')\n\n data_sample = DataSample(\n fake_img=samples,\n control=control,\n prompt=data['data_samples'].prompt)\n data_sample_list = data_sample.split()\n return data_sample_list\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n prompt = data['data_samples'].prompt\n control = data['inputs']['source']\n\n output = self.infer(\n prompt, control=((control + 1) / 2), return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(\n samples, data['data_samples'], key='target')\n control = self.data_preprocessor.destruct(\n control, data['data_samples'], key='source')\n\n data_sample = DataSample(\n fake_img=samples,\n control=control,\n prompt=data['data_samples'].prompt)\n data_sample_list = data_sample.split()\n return data_sample_list\n\n # NOTE: maybe we should do this in a controlnet preprocessor\n @staticmethod\n def prepare_control(image: Tuple[Image.Image, List[Image.Image], Tensor,\n List[Tensor]], width: int, height: int,\n batch_size: int, num_images_per_prompt: int,\n device: str, dtype: str) -> Tensor:\n \"\"\"A helper function to prepare single control images.\n\n Args:\n image (Tuple[Image.Image, List[Image.Image], Tensor, List[Tensor]]): # noqa\n The input image for control.\n batch_size (int): The number of the prompt. The control will\n be repeated for `batch_size` times.\n num_images_per_prompt (int): The number images generate for one\n prompt.\n device (str): The device of the control.\n dtype (str): The dtype of the control.\n\n Returns:\n Tensor: The control in torch.tensor.\n \"\"\"\n if not isinstance(image, torch.Tensor):\n if isinstance(image, Image.Image):\n image = [image]\n\n if isinstance(image[0], Image.Image):\n image = [\n img.resize((width, height), resample=Image.LANCZOS)\n for img in image\n ]\n image = [np.array(img)[None, :] for img in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size * num_images_per_prompt\n else:\n assert image_batch_size == batch_size, (\n 'The number of Control condition must be 1 or equal to the '\n 'number of prompt.')\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n return image\n\n def train(self, mode: bool = True):\n \"\"\"Set train/eval mode.\n\n Args:\n mode (bool, optional): Whether set train mode. Defaults to True.\n \"\"\"\n if mode:\n if next(self.controlnet.parameters()\n ).dtype != self._controlnet_ori_dtype:\n print_log(\n 'Set ControlNetModel dtype to '\n f'\\'{self._controlnet_ori_dtype}\\' in the train mode.',\n 'current')\n self.controlnet.to(self._controlnet_ori_dtype)\n else:\n self.controlnet.to(self.dtype)\n print_log(\n f'Set ControlNetModel dtype to \\'{self.dtype}\\' '\n 'in the eval mode.', 'current')\n return super().train(mode)\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n control: Optional[Union[str, np.ndarray, torch.Tensor]] = None,\n controlnet_conditioning_scale: float = 1.0,\n num_inference_steps: int = 20,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n return_type='image',\n show_progress=True):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (str or List[str]): The prompt or prompts to guide\n the image generation.\n height (int, Optional): The height in pixels of the generated\n image. If not passed, the height will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n width (int, Optional): The width in pixels of the generated image.\n If not passed, the width will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n num_inference_steps (int): The number of denoising steps.\n More denoising steps usually lead to a higher quality image at\n the expense of slower inference. Defaults to 50.\n guidance_scale (float): Guidance scale as defined in Classifier-\n Free Diffusion Guidance (https://arxiv.org/abs/2207.12598).\n Defaults to 7.5\n negative_prompt (str or List[str], optional): The prompt or\n prompts not to guide the image generation. Ignored when not\n using guidance (i.e., ignored if `guidance_scale` is less\n than 1). Defaults to None.\n num_images_per_prompt (int): The number of images to generate\n per prompt. Defaults to 1.\n eta (float): Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n DDIMScheduler, will be ignored for others. Defaults to 0.0.\n generator (torch.Generator, optional): A torch generator to make\n generation deterministic. Defaults to None.\n latents (torch.FloatTensor, optional): Pre-generated noisy latents,\n sampled from a Gaussian distribution, to be used as inputs for\n image generation. Can be used to tweak the same generation with\n different prompts. If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n Defaults to None.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images and Control image.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n img_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n if is_model_wrapper(self.controlnet):\n control_dtype = self.controlnet.module.dtype\n else:\n control_dtype = self.controlnet.dtype\n controls = self.prepare_control(\n control,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype=control_dtype)\n if do_classifier_free_guidance:\n controls = torch.cat([controls] * 2)\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(prompt, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n\n # 4. Prepare timesteps\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n latent_model_input = latent_model_input.to(control_dtype)\n text_embeddings = text_embeddings.to(control_dtype)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n controlnet_cond=controls,\n return_dict=False,\n )\n\n down_block_res_samples = [\n down_block_res_sample * controlnet_conditioning_scale\n for down_block_res_sample in down_block_res_samples\n ]\n mid_block_res_sample *= controlnet_conditioning_scale\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n )['sample']\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs)['prev_sample']\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n\n if do_classifier_free_guidance:\n controls = torch.split(controls, controls.shape[0] // 2, dim=0)[0]\n\n if return_type == 'image':\n image = self.output_to_pil(image)\n controls = self.output_to_pil(controls * 2 - 1)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n controls = controls.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image, 'controls': controls}\n\n def forward(self, *args, **kwargs):\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n\n\n@MODELS.register_module()\nclass ControlStableDiffusionImg2Img(ControlStableDiffusion):\n\n def _default_height_width(self, height, width, image):\n if isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[3]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[2]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(\n int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.test_scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self,\n image,\n timestep,\n batch_size,\n num_images_per_prompt,\n dtype,\n device,\n generator=None,\n noise=None):\n if not isinstance(image, (torch.Tensor, Image.Image, list)):\n raise ValueError(\n f'`image` has to be of type `torch.Tensor`, '\n f' `PIL.Image.Image` or list but is {type(image)}')\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f'You have passed a list of generators of '\n f' length {len(generator)}, but requested an effective batch'\n f' size of {batch_size}. Make sure the batch size '\n f' matches the length of the generators.')\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i:i +\n 1]).latent_dist.sample(generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n vae_encode_latents = init_latents\n\n if batch_size > init_latents.shape[0] and \\\n batch_size % init_latents.shape[0] == 0:\n raise ValueError(\n f'Cannot duplicate `image` of batch size'\n f' {init_latents.shape[0]} to {batch_size} text prompts.')\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n if noise is None:\n noise = torch.randn(\n shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n return init_latents, vae_encode_latents\n\n def prepare_latent_image(self, image, dtype):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=dtype)\n else:\n # preprocess image\n if isinstance(image, (Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], Image.Image):\n image = [np.array(i.convert('RGB'))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=dtype) / 127.5 - 1.0\n\n return image\n\n @torch.no_grad()\n def infer(\n self,\n prompt: Union[str, List[str]],\n latent_image: Union[torch.FloatTensor, Image.Image,\n List[torch.FloatTensor], List[Image.Image]] = None,\n latent_mask: torch.FloatTensor = None,\n strength: float = 1.0,\n height: Optional[int] = None,\n width: Optional[int] = None,\n control: Optional[Union[str, np.ndarray, torch.Tensor]] = None,\n controlnet_conditioning_scale: float = 1.0,\n num_inference_steps: int = 20,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n return_type='image',\n show_progress=True,\n reference_img: Union[torch.FloatTensor, Image.Image,\n List[torch.FloatTensor],\n List[Image.Image]] = None,\n ):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (str or List[str]): The prompt or prompts to guide\n the image generation.\n height (int, Optional): The height in pixels of the generated\n image. If not passed, the height will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n width (int, Optional): The width in pixels of the generated image.\n If not passed, the width will be\n `self.unet_sample_size * self.vae_scale_factor` Defaults\n to None.\n num_inference_steps (int): The number of denoising steps.\n More denoising steps usually lead to a higher quality image at\n the expense of slower inference. Defaults to 50.\n guidance_scale (float): Guidance scale as defined in Classifier-\n Free Diffusion Guidance (https://arxiv.org/abs/2207.12598).\n Defaults to 7.5\n negative_prompt (str or List[str], optional): The prompt or\n prompts not to guide the image generation. Ignored when not\n using guidance (i.e., ignored if `guidance_scale` is less\n than 1). Defaults to None.\n num_images_per_prompt (int): The number of images to generate\n per prompt. Defaults to 1.\n eta (float): Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n DDIMScheduler, will be ignored for others. Defaults to 0.0.\n generator (torch.Generator, optional): A torch generator to make\n generation deterministic. Defaults to None.\n latents (torch.FloatTensor, optional): Pre-generated noisy latents,\n sampled from a Gaussian distribution, to be used as inputs for\n image generation. Can be used to tweak the same generation with\n different prompts. If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n Defaults to None.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images and Control image.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n\n # 0. Default height and width to unet\n # height = height or self.unet_sample_size * self.vae_scale_factor\n # width = width or self.unet_sample_size * self.vae_scale_factor\n\n height, width = self._default_height_width(height, width, control)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n img_dtype = self.vae.module.dtype if hasattr(\n self.vae, 'module') else self.vae.dtype\n if is_model_wrapper(self.controlnet):\n control_dtype = self.controlnet.module.dtype\n else:\n control_dtype = self.controlnet.dtype\n controls = self.prepare_control(\n control,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype=control_dtype)\n if do_classifier_free_guidance:\n controls = torch.cat([controls] * 2)\n\n latent_image = self.prepare_latent_image(latent_image,\n self.controlnet.dtype)\n\n if reference_img is not None:\n reference_img = self.prepare_latent_image(reference_img,\n self.controlnet.dtype)\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(prompt, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n text_embeddings = text_embeddings.to(control_dtype)\n\n # 4. Prepare timesteps\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps, strength, device)\n\n latent_timestep = timesteps[:1].repeat(batch_size *\n num_images_per_prompt)\n\n # 5. Prepare latent variables\n latents, vae_encode_latents = self.prepare_latents(\n latent_image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n text_embeddings.dtype,\n device,\n generator,\n noise=latents)\n\n if reference_img is not None:\n _, ref_img_vae_latents = self.prepare_latents(\n reference_img,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n text_embeddings.dtype,\n device,\n generator,\n noise=latents)\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_test_scheduler_extra_step_kwargs(\n generator, eta)\n\n # 7. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n latent_model_input = latent_model_input.to(control_dtype)\n\n if reference_img is not None:\n ref_img_vae_latents_t = self.scheduler.add_noise(\n ref_img_vae_latents, torch.randn_like(ref_img_vae_latents),\n t)\n ref_img_vae_latents_model_input = torch.cat(\n [ref_img_vae_latents_t] * 2) if \\\n do_classifier_free_guidance else ref_img_vae_latents_t\n ref_img_vae_latents_model_input = \\\n self.test_scheduler.scale_model_input(\n ref_img_vae_latents_model_input, t)\n ref_img_vae_latents_model_input = \\\n ref_img_vae_latents_model_input.to(control_dtype)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n controlnet_cond=controls,\n return_dict=False,\n )\n\n down_block_res_samples = [\n down_block_res_sample * controlnet_conditioning_scale\n for down_block_res_sample in down_block_res_samples\n ]\n mid_block_res_sample *= controlnet_conditioning_scale\n\n # predict the noise residual\n if reference_img is not None:\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ref_x=ref_img_vae_latents_model_input)['sample']\n else:\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n )['sample']\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs)['prev_sample']\n\n if latent_mask is not None:\n latents = latents * latent_mask + \\\n vae_encode_latents * (1.0 - latent_mask)\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n\n if do_classifier_free_guidance:\n controls = torch.split(controls, controls.shape[0] // 2, dim=0)[0]\n\n if return_type == 'image':\n image = self.output_to_pil(image)\n controls = self.output_to_pil(controls * 2 - 1)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n controls = controls.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image, 'controls': controls}\n" }, { "path": "mmagic/models/editors/controlnet/controlnet_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom logging import ERROR\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmengine import print_log\nfrom mmengine.runner import save_checkpoint\n\n\ndef change_base_model(controlnet: nn.Module,\n curr_model: nn.Module,\n base_model: nn.Module,\n save_path: Optional[str] = None,\n *args,\n **kwargs) -> nn.Module:\n \"\"\"This function is used to change the base model of ControlNet. Refers to\n https://github.com/lllyasviel/ControlNet/blob/main/tool_transfer_control.py\n .\n\n # noqa.\n\n Args:\n controlnet (nn.Module): The model for ControlNet to convert.\n curr_model (nn.Module): The model of current Stable Diffusion's Unet.\n base_model (nn.Module): The model of Stable Diffusion's Unet which\n ControlNet initialized with.\n save_path (str, optional): The path to save the converted model.\n Defaults to None.\n\n *args, **kwargs: Arguments for `save_checkpoint`.\n \"\"\"\n dtype = next(controlnet.parameters()).dtype\n base_state_dict = base_model.state_dict()\n curr_state_dict = curr_model.state_dict()\n\n print_log('Start convert ControlNet to new Unet.', 'current')\n for k, v in controlnet.state_dict().items():\n if k in base_state_dict:\n base_v = base_state_dict[k].cpu()\n curr_v = curr_state_dict[k].cpu()\n try:\n offset = v.cpu() - base_v\n new_v = offset + curr_v\n controlnet.state_dict()[k].data.copy_(new_v.to(dtype))\n print_log(f'Convert success: \\'{k}\\'.', 'current')\n except Exception as exception:\n print_log(\n f'Error occurs when convert \\'{k}\\'. '\n 'Please check that the model structure of '\n '\\'ControlNet\\', \\'BaseModel\\' and \\'CurrentModel\\' '\n 'are consistent.', 'current', ERROR)\n raise exception\n if save_path:\n save_checkpoint(controlnet.state_dict(), save_path, *args, **kwargs)\n\n return controlnet\n" }, { "path": "mmagic/models/editors/cyclegan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .cyclegan import CycleGAN\nfrom .cyclegan_generator import ResnetGenerator\n\n__all__ = ['CycleGAN', 'ResnetGenerator']\n" }, { "path": "mmagic/models/editors/cyclegan/cyclegan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ...base_models import BaseTranslationModel\nfrom ...utils import set_requires_grad\nfrom .cyclegan_modules import GANImageBuffer\n\n\n@MODELS.register_module()\nclass CycleGAN(BaseTranslationModel):\n \"\"\"CycleGAN model for unpaired image-to-image translation.\n\n Ref:\n Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial\n Networks\n \"\"\"\n\n def __init__(self,\n *args,\n buffer_size=50,\n loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.5),\n **kwargs):\n super().__init__(*args, **kwargs)\n # GAN image buffers\n self.image_buffers = dict()\n self.buffer_size = buffer_size\n for domain in self._reachable_domains:\n self.image_buffers[domain] = GANImageBuffer(self.buffer_size)\n\n self.loss_config = loss_config\n\n def forward_test(self, img, target_domain, **kwargs):\n \"\"\"Forward function for testing.\n\n Args:\n img (tensor): Input image tensor.\n target_domain (str): Target domain of output image.\n kwargs (dict): Other arguments.\n\n Returns:\n dict: Forward results.\n \"\"\"\n # This is a trick for CycleGAN\n # ref: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/e1bdf46198662b0f4d0b318e24568205ec4d7aee/test.py#L54 # noqa\n self.train()\n target = self.translation(img, target_domain=target_domain, **kwargs)\n results = dict(source=img, target=target)\n return results\n\n def _get_disc_loss(self, outputs):\n \"\"\"Backward function for the discriminators.\n\n Args:\n outputs (dict): Dict of forward results.\n\n Returns:\n dict: Discriminators' loss and loss dict.\n \"\"\"\n discriminators = self.get_module(self.discriminators)\n\n log_vars_d = dict()\n loss_d = 0\n\n # GAN loss for discriminators['a']\n for domain in self._reachable_domains:\n losses = dict()\n fake_img = self.image_buffers[domain].query(\n outputs[f'fake_{domain}'])\n fake_pred = discriminators[domain](fake_img.detach())\n losses[f'loss_gan_d_{domain}_fake'] = F.mse_loss(\n fake_pred, 0. * torch.ones_like(fake_pred))\n real_pred = discriminators[domain](outputs[f'real_{domain}'])\n losses[f'loss_gan_d_{domain}_real'] = F.mse_loss(\n real_pred, 1. * torch.ones_like(real_pred))\n\n _loss_d, _log_vars_d = self.parse_losses(losses)\n _loss_d *= 0.5\n loss_d += _loss_d\n log_vars_d[f'loss_gan_d_{domain}'] = _log_vars_d['loss'] * 0.5\n\n return loss_d, log_vars_d\n\n def _get_gen_loss(self, outputs):\n \"\"\"Backward function for the generators.\n\n Args:\n outputs (dict): Dict of forward results.\n\n Returns:\n dict: Generators' loss and loss dict.\n \"\"\"\n generators = self.get_module(self.generators)\n discriminators = self.get_module(self.discriminators)\n\n losses = dict()\n # gan loss\n for domain in self._reachable_domains:\n # Identity reconstruction for generators\n outputs[f'identity_{domain}'] = generators[domain](\n outputs[f'real_{domain}'])\n # GAN loss for generators\n fake_pred = discriminators[domain](outputs[f'fake_{domain}'])\n # LSGAN loss\n losses[f'loss_gan_g_{domain}'] = F.mse_loss(\n fake_pred, 1. * torch.ones_like(fake_pred))\n\n # cycle loss\n loss_weight = self.loss_config['cycle_loss_weight']\n losses['cycle_loss'] = 0.\n for domain in self._reachable_domains:\n losses['cycle_loss'] += loss_weight * F.l1_loss(\n outputs[f'cycle_{domain}'],\n outputs[f'real_{domain}'],\n reduction='mean')\n\n # id loss\n loss_weight = self.loss_config['id_loss_weight']\n if loss_weight != 0.:\n losses['id_loss'] = 0.\n for domain in self._reachable_domains:\n losses['id_loss'] += loss_weight * F.l1_loss(\n outputs[f'identity_{domain}'],\n outputs[f'real_{domain}'],\n reduction='mean')\n\n loss_g, log_vars_g = self.parse_losses(losses)\n\n return loss_g, log_vars_g\n\n def _get_opposite_domain(self, domain):\n \"\"\"Get the opposite domain respect to the input domain.\n\n Args:\n domain (str): The input domain.\n\n Returns:\n str: The opposite domain.\n \"\"\"\n for item in self._reachable_domains:\n if item != domain:\n return item\n return None\n\n def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):\n \"\"\"Training step function.\n\n Args:\n data_batch (dict): Dict of the input data batch.\n optimizer (dict[torch.optim.Optimizer]): Dict of optimizers for\n the generators and discriminators.\n ddp_reducer (:obj:`Reducer` | None, optional): Reducer from ddp.\n It is used to prepare for ``backward()`` in ddp. Defaults to\n None.\n running_status (dict | None, optional): Contains necessary basic\n information for training, e.g., iteration number. Defaults to\n None.\n\n Returns:\n dict: Dict of loss, information for logger, the number of samples\\\n and results for visualization.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n disc_optimizer_wrapper = optim_wrapper['discriminators']\n\n inputs_dict = data['inputs']\n outputs, log_vars = dict(), dict()\n\n # forward generators\n with disc_optimizer_wrapper.optim_context(self.discriminators):\n for target_domain in self._reachable_domains:\n # fetch data by domain\n source_domain = self.get_other_domains(target_domain)[0]\n img = inputs_dict[f'img_{source_domain}']\n # translation process\n results = self(\n img, test_mode=False, target_domain=target_domain)\n outputs[f'real_{source_domain}'] = results['source']\n outputs[f'fake_{target_domain}'] = results['target']\n # cycle process\n results = self(\n results['target'],\n test_mode=False,\n target_domain=source_domain)\n outputs[f'cycle_{source_domain}'] = results['target']\n\n # update discriminators\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n loss_d, log_vars_d = self._get_disc_loss(outputs)\n disc_optimizer_wrapper.update_params(loss_d)\n log_vars.update(log_vars_d)\n\n # generators, no updates to discriminator parameters.\n if ((curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0\n and curr_iter >= self.disc_init_steps):\n set_requires_grad(self.discriminators, False)\n # update generator\n gen_optimizer_wrapper = optim_wrapper['generators']\n with gen_optimizer_wrapper.optim_context(self.generators):\n loss_g, log_vars_g = self._get_gen_loss(outputs)\n gen_optimizer_wrapper.update_params(loss_g)\n log_vars.update(log_vars_g)\n\n set_requires_grad(self.discriminators, True)\n\n return log_vars\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n\n outputs = {}\n for src_domain in self._reachable_domains:\n # Identity reconstruction for generators\n target_domain = self.get_other_domains(src_domain)[0]\n target = self.forward_test(\n inputs_dict[f'img_{src_domain}'],\n target_domain=target_domain)['target']\n outputs[f'img_{target_domain}'] = target\n\n batch_sample_list = []\n num_batches = next(iter(outputs.values())).shape[0]\n data_samples = data_samples.split()\n for idx in range(num_batches):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n\n for src_domain in self._reachable_domains:\n target_domain = self.get_other_domains(src_domain)[0]\n fake_img = outputs[f'img_{target_domain}'][idx]\n fake_img = self.data_preprocessor.destruct(\n fake_img, data_samples[idx], f'img_{target_domain}')\n setattr(gen_sample, f'fake_{target_domain}', fake_img)\n\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n\n outputs = {}\n for src_domain in self._reachable_domains:\n # Identity reconstruction for generators\n target_domain = self.get_other_domains(src_domain)[0]\n target = self.forward_test(\n inputs_dict[f'img_{src_domain}'],\n target_domain=target_domain)['target']\n outputs[f'img_{target_domain}'] = target\n\n batch_sample_list = []\n num_batches = next(iter(outputs.values())).shape[0]\n data_samples = data_samples.split()\n for idx in range(num_batches):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n\n for src_domain in self._reachable_domains:\n target_domain = self.get_other_domains(src_domain)[0]\n fake_img = outputs[f'img_{target_domain}'][idx]\n fake_img = self.data_preprocessor.destruct(\n fake_img, data_samples[idx], f'img_{target_domain}')\n gen_sample.set_tensor_data({f'fake_{target_domain}': fake_img})\n\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n" }, { "path": "mmagic/models/editors/cyclegan/cyclegan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.utils import generation_init_weights\nfrom mmagic.registry import MODELS\nfrom .cyclegan_modules import ResidualBlockWithDropout\n\n\n@MODELS.register_module()\nclass ResnetGenerator(BaseModule):\n \"\"\"Construct a Resnet-based generator that consists of residual blocks\n between a few downsampling/upsampling operations.\n\n Args:\n in_channels (int): Number of channels in input images.\n out_channels (int): Number of channels in output images.\n base_channels (int): Number of filters at the last conv layer.\n Default: 64.\n norm_cfg (dict): Config dict to build norm layer. Default:\n `dict(type='IN')`.\n use_dropout (bool): Whether to use dropout layers. Default: False.\n num_blocks (int): Number of residual blocks. Default: 9.\n padding_mode (str): The name of padding layer in conv layers:\n 'reflect' | 'replicate' | 'zeros'. Default: 'reflect'.\n init_cfg (dict): Config dict for initialization.\n `type`: The name of our initialization method. Default: 'normal'.\n `gain`: Scaling factor for normal, xavier and orthogonal.\n Default: 0.02.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02)):\n super().__init__(init_cfg=init_cfg)\n assert num_blocks >= 0, ('Number of residual blocks must be '\n f'non-negative, but got {num_blocks}.')\n assert isinstance(norm_cfg, dict), (\"'norm_cfg' should be dict, but\"\n f'got {type(norm_cfg)}')\n assert 'type' in norm_cfg, \"'norm_cfg' must have key 'type'\"\n # We use norm layers in the resnet generator.\n # Only for IN, use bias to follow cyclegan's original implementation.\n use_bias = norm_cfg['type'] == 'IN'\n\n model = []\n model += [\n ConvModule(\n in_channels=in_channels,\n out_channels=base_channels,\n kernel_size=7,\n padding=3,\n bias=use_bias,\n norm_cfg=norm_cfg,\n padding_mode=padding_mode)\n ]\n\n num_down = 2\n # add downsampling layers\n for i in range(num_down):\n multiple = 2**i\n model += [\n ConvModule(\n in_channels=base_channels * multiple,\n out_channels=base_channels * multiple * 2,\n kernel_size=3,\n stride=2,\n padding=1,\n bias=use_bias,\n norm_cfg=norm_cfg)\n ]\n\n # add residual blocks\n multiple = 2**num_down\n for i in range(num_blocks):\n model += [\n ResidualBlockWithDropout(\n base_channels * multiple,\n padding_mode=padding_mode,\n norm_cfg=norm_cfg,\n use_dropout=use_dropout)\n ]\n\n # add upsampling layers\n for i in range(num_down):\n multiple = 2**(num_down - i)\n model += [\n ConvModule(\n in_channels=base_channels * multiple,\n out_channels=base_channels * multiple // 2,\n kernel_size=3,\n stride=2,\n padding=1,\n bias=use_bias,\n conv_cfg=dict(type='deconv', output_padding=1),\n norm_cfg=norm_cfg)\n ]\n\n model += [\n ConvModule(\n in_channels=base_channels,\n out_channels=out_channels,\n kernel_size=7,\n padding=3,\n bias=True,\n norm_cfg=None,\n act_cfg=dict(type='Tanh'),\n padding_mode=padding_mode)\n ]\n\n self.model = nn.Sequential(*model)\n self.init_type = 'normal' if init_cfg is None else init_cfg.get(\n 'type', 'normal')\n self.init_gain = 0.02 if init_cfg is None else init_cfg.get(\n 'gain', 0.02)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.model(x)\n\n def init_weights(self):\n \"\"\"Initialize weights for the model.\"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n generation_init_weights(\n self, init_type=self.init_type, init_gain=self.init_gain)\n self._is_init = True\n" }, { "path": "mmagic/models/editors/cyclegan/cyclegan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\n\n\nclass ResidualBlockWithDropout(nn.Module):\n \"\"\"Define a Residual Block with dropout layers.\n\n Ref:\n Deep Residual Learning for Image Recognition\n\n A residual block is a conv block with skip connections. A dropout layer is\n added between two common conv modules.\n\n Args:\n channels (int): Number of channels in the conv layer.\n padding_mode (str): The name of padding layer:\n 'reflect' | 'replicate' | 'zeros'.\n norm_cfg (dict): Config dict to build norm layer. Default:\n `dict(type='IN')`.\n use_dropout (bool): Whether to use dropout layers. Default: True.\n \"\"\"\n\n def __init__(self,\n channels,\n padding_mode,\n norm_cfg=dict(type='BN'),\n use_dropout=True):\n super().__init__()\n assert isinstance(norm_cfg, dict), (\"'norm_cfg' should be dict, but\"\n f'got {type(norm_cfg)}')\n assert 'type' in norm_cfg, \"'norm_cfg' must have key 'type'\"\n # We use norm layers in the residual block with dropout layers.\n # Only for IN, use bias to follow cyclegan's original implementation.\n use_bias = norm_cfg['type'] == 'IN'\n\n block = [\n ConvModule(\n in_channels=channels,\n out_channels=channels,\n kernel_size=3,\n padding=1,\n bias=use_bias,\n norm_cfg=norm_cfg,\n padding_mode=padding_mode)\n ]\n\n if use_dropout:\n block += [nn.Dropout(0.5)]\n\n block += [\n ConvModule(\n in_channels=channels,\n out_channels=channels,\n kernel_size=3,\n padding=1,\n bias=use_bias,\n norm_cfg=norm_cfg,\n act_cfg=None,\n padding_mode=padding_mode)\n ]\n\n self.block = nn.Sequential(*block)\n\n def forward(self, x):\n \"\"\"Forward function. Add skip connections without final ReLU.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = x + self.block(x)\n return out\n\n\nclass GANImageBuffer:\n \"\"\"This class implements an image buffer that stores previously generated\n images.\n\n This buffer allows us to update the discriminator using a history of\n generated images rather than the ones produced by the latest generator\n to reduce model oscillation.\n\n Args:\n buffer_size (int): The size of image buffer. If buffer_size = 0,\n no buffer will be created.\n buffer_ratio (float): The chance / possibility to use the images\n previously stored in the buffer. Default: 0.5.\n \"\"\"\n\n def __init__(self, buffer_size, buffer_ratio=0.5):\n self.buffer_size = buffer_size\n # create an empty buffer\n if self.buffer_size > 0:\n self.img_num = 0\n self.image_buffer = []\n self.buffer_ratio = buffer_ratio\n\n def query(self, images):\n \"\"\"Query current image batch using a history of generated images.\n\n Args:\n images (Tensor): Current image batch without history information.\n \"\"\"\n if self.buffer_size == 0: # if the buffer size is 0, do nothing\n return images\n return_images = []\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n # if the buffer is not full, keep inserting current images\n if self.img_num < self.buffer_size:\n self.img_num = self.img_num + 1\n self.image_buffer.append(image)\n return_images.append(image)\n else:\n use_buffer = np.random.random() < self.buffer_ratio\n # by self.buffer_ratio, the buffer will return a previously\n # stored image, and insert the current image into the buffer\n if use_buffer:\n random_id = np.random.randint(0, self.buffer_size)\n image_tmp = self.image_buffer[random_id].clone()\n self.image_buffer[random_id] = image\n return_images.append(image_tmp)\n # by (1 - self.buffer_ratio), the buffer will return the\n # current image\n else:\n return_images.append(image)\n # collect all the images and return\n return_images = torch.cat(return_images, 0)\n return return_images\n" }, { "path": "mmagic/models/editors/dcgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .dcgan import DCGAN\nfrom .dcgan_discriminator import DCGANDiscriminator\nfrom .dcgan_generator import DCGANGenerator\n\n__all__ = ['DCGAN', 'DCGANDiscriminator', 'DCGANGenerator']\n" }, { "path": "mmagic/models/editors/dcgan/dcgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseGAN\n\n\n@MODELS.register_module()\nclass DCGAN(BaseGAN):\n \"\"\"Implementation of `Unsupervised Representation Learning with Deep\n Convolutional Generative Adversarial Networks`.\n\n Paper link:\n `_ (DCGAN).\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.dcgan.DCGANGenerator` # noqa\n and\n :class:`~mmagic.models.editors.dcgan.DCGANDiscriminator` # noqa\n \"\"\"\n\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n r\"\"\"Get disc loss. DCGAN use the vanilla gan loss to train\n the discriminator.\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))\n losses_dict['loss_disc_real'] = F.binary_cross_entropy_with_logits(\n disc_pred_real, 1. * torch.ones_like(disc_pred_real))\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:\n \"\"\"Get gen loss. DCGAN use the vanilla gan loss to train the generator.\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = F.binary_cross_entropy_with_logits(\n disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/dcgan/dcgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule, normal_init, update_init_info\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DCGANDiscriminator(BaseModule):\n \"\"\"Discriminator for DCGAN.\n\n Implementation Details for DCGAN architecture:\n\n #. Adopt convolution in the discriminator;\n #. Use batchnorm in the discriminator except for the input and final \\\n output layer;\n #. Use LeakyReLU in the discriminator in addition to the output layer.\n\n Args:\n input_scale (int): The scale of the input image.\n output_scale (int): The final scale of the convolutional feature.\n out_channels (int): The channel number of the final output layer.\n in_channels (int, optional): The channel number of the input image.\n Defaults to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Defaults to 128.\n default_norm_cfg (dict, optional): Norm config for all of layers\n except for the final output layer. Defaults to ``dict(type='BN')``.\n default_act_cfg (dict, optional): Activation config for all of layers\n except for the final output layer. Defaults to\n ``dict(type='ReLU')``.\n out_act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to ``dict(type='Tanh')``.\n pretrained (str, optional): Path for the pretrained model. Default to\n ``None``.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n input_scale,\n output_scale,\n out_channels,\n in_channels=3,\n base_channels=128,\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='LeakyReLU'),\n out_act_cfg=None,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n self.input_scale = input_scale\n self.output_scale = output_scale\n self.out_channels = out_channels\n self.base_channels = base_channels\n\n # the number of times for downsampling\n self.num_downsamples = int(np.log2(input_scale // output_scale))\n\n # build up downsampling backbone (excluding the output layer)\n downsamples = []\n curr_channels = in_channels\n for i in range(self.num_downsamples):\n # remove norm for the first conv\n norm_cfg_ = None if i == 0 else default_norm_cfg\n in_ch = in_channels if i == 0 else base_channels * 2**(i - 1)\n\n downsamples.append(\n ConvModule(\n in_ch,\n base_channels * 2**i,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='Conv2d'),\n norm_cfg=norm_cfg_,\n act_cfg=default_act_cfg))\n curr_channels = base_channels * 2**i\n\n self.downsamples = nn.Sequential(*downsamples)\n\n # define output layer\n self.output_layer = ConvModule(\n curr_channels,\n out_channels,\n kernel_size=4,\n stride=1,\n padding=0,\n conv_cfg=dict(type='Conv2d'),\n norm_cfg=None,\n act_cfg=out_act_cfg)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image.\n \"\"\"\n\n n = x.shape[0]\n x = self.downsamples(x)\n x = self.output_layer(x)\n\n # reshape to a flatten feature\n return x.view(n, -1)\n\n def init_weights(self):\n \"\"\"Init weights for models.\n\n We just use the initialization method proposed in the original paper.\n \"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n for m in self.modules():\n module_name = m.__class__.__name__\n if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n normal_init(m, 0, 0.02)\n init_info = (f'{module_name} belongs to (nn.Conv2d and '\n 'nn.ConvTranspose2d), initialize by normal '\n 'distribution with 0 mean and 0.02 std.')\n elif isinstance(m, _BatchNorm):\n nn.init.normal_(m.weight.data)\n nn.init.constant_(m.bias.data, 0)\n init_info = (f'{module_name} is BatchNorm, initialize weight '\n 'by normal discribution with unit mean and zero '\n 'std, and initialize bias as 0.')\n # save init info\n update_init_info(m, init_info)\n" }, { "path": "mmagic/models/editors/dcgan/dcgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule, normal_init, update_init_info\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\n\n\n@MODELS.register_module()\nclass DCGANGenerator(BaseModule):\n \"\"\"Generator for DCGAN.\n\n Implementation Details for DCGAN architecture:\n\n #. Adopt transposed convolution in the generator;\n #. Use batchnorm in the generator except for the final output layer;\n #. Use ReLU in the generator in addition to the final output layer.\n\n More details can be found in the original paper:\n Unsupervised Representation Learning with Deep Convolutional\n Generative Adversarial Networks\n http://arxiv.org/abs/1511.06434\n\n Args:\n output_scale (int | tuple[int]): Output scale for the generated\n image. If only a integer is provided, the output image will\n be a square shape. The tuple of two integers will set the\n height and width for the output image, respectively.\n out_channels (int, optional): The channel number of the output feature.\n Default to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Default to 1024.\n input_scale (int | tuple[int], optional): Output scale for the\n generated image. If only a integer is provided, the input feature\n ahead of the convolutional generator will be a square shape. The\n tuple of two integers will set the height and width for the input\n convolutional feature, respectively. Defaults to 4.\n noise_size (int, optional): Size of the input noise\n vector. Defaults to 100.\n default_norm_cfg (dict, optional): Norm config for all of layers\n except for the final output layer. Defaults to ``dict(type='BN')``.\n default_act_cfg (dict, optional): Activation config for all of layers\n except for the final output layer. Defaults to\n ``dict(type='ReLU')``.\n out_act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to ``dict(type='Tanh')``.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n output_scale,\n out_channels=3,\n base_channels=1024,\n input_scale=4,\n noise_size=100,\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='ReLU'),\n out_act_cfg=dict(type='Tanh'),\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n self.output_scale = output_scale\n self.base_channels = base_channels\n self.input_scale = input_scale\n self.noise_size = noise_size\n\n # the number of times for upsampling\n self.num_upsamples = int(np.log2(output_scale // input_scale))\n\n # output 4x4 feature map\n self.noise2feat = ConvModule(\n noise_size,\n base_channels,\n kernel_size=4,\n stride=1,\n padding=0,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg)\n\n # build up upsampling backbone (excluding the output layer)\n upsampling = []\n curr_channel = base_channels\n for _ in range(self.num_upsamples - 1):\n upsampling.append(\n ConvModule(\n curr_channel,\n curr_channel // 2,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n\n curr_channel //= 2\n\n self.upsampling = nn.Sequential(*upsampling)\n\n # output layer\n self.output_layer = ConvModule(\n curr_channel,\n out_channels,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='ConvTranspose2d'),\n norm_cfg=None,\n act_cfg=out_act_cfg)\n\n # self.init_weights(pretrained=pretrained)\n\n def forward(self, noise, num_batches=0, return_noise=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img`` and\n ``noise_batch`` will be returned.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n if noise.ndim == 2:\n noise_batch = noise[:, :, None, None]\n elif noise.ndim == 4:\n noise_batch = noise\n else:\n raise ValueError('The noise should be in shape of (n, c) or '\n f'(n, c, 1, 1), but got {noise.shape}')\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size, 1, 1))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size, 1, 1))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n\n x = self.noise2feat(noise_batch)\n x = self.upsampling(x)\n x = self.output_layer(x)\n\n if return_noise:\n return dict(fake_img=x, noise_batch=noise_batch)\n\n return x\n\n def init_weights(self):\n \"\"\"Init weights for models.\n\n We just use the initialization method proposed in the original paper.\n \"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n normal_init(m, 0, 0.02)\n elif isinstance(m, _BatchNorm):\n nn.init.normal_(m.weight.data)\n nn.init.constant_(m.bias.data, 0)\n # save init info\n update_init_info(\n m, f'Initialize {m.__class__.__name__} by '\n f'\\'init_type\\' {self.init_cfg}.')\n" }, { "path": "mmagic/models/editors/ddpm/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .denoising_unet import DenoisingUnet\n\n__all__ = ['DenoisingUnet']\n" }, { "path": "mmagic/models/editors/ddpm/attention.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom addict import Dict\nfrom torch import nn\n\n\nclass Transformer2DModel(nn.Module):\n \"\"\"Transformer model for image-like data. Takes either discrete (classes of\n vector embeddings) or continuous (actual embeddings) inputs.\n\n When input is continuous: First, project the input\n (aka embedding) and reshape to b, t, d. Then apply standard\n transformer action. Finally, reshape to image.\n\n When input is discrete: First, input (classes of latent pixels)\n is converted to embeddings and has positional\n embeddings applied, see `ImagePositionalEmbeddings`.\n Then apply standard transformer action. Finally, predict\n classes of unnoised image.\n\n Note that it is assumed one of the input classes is\n the masked latent pixel. The predicted classes of the unnoised\n image do not contain a prediction for the masked pixel as\n the unnoised image cannot be masked.\n\n Args:\n num_attention_heads (`int`, *optional*, defaults to 16):\n The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88):\n The number of channels in each head.\n in_channels (`int`, *optional*):\n Pass if the input is continuous.\n The number of channels in the input and output.\n num_layers (`int`, *optional*, defaults to 1):\n The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.1):\n The dropout probability to use.\n norm_num_groups (int):\n Norm group num, defaults to 32.\n cross_attention_dim (`int`, *optional*):\n The number of context dimensions to use.\n attention_bias (`bool`, *optional*):\n Configure if the TransformerBlocks' attention should contain\n a bias parameter.\n sample_size (`int`, *optional*):\n Pass if the input is discrete. The width of the latent images.\n Note that this is fixed at training time as it is used for\n learning a number of position embeddings. See\n `ImagePositionalEmbeddings`.\n num_vector_embeds (`int`, *optional*):\n Pass if the input is discrete. The number of classes of\n the vector embeddings of the latent pixels.\n Includes the class for the masked latent pixel.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n use_linear_projection (bool):\n Whether to use linear projection, defaults to False.\n only_cross_attention (bool):\n whether only use cross attention, defaults to False.\n \"\"\"\n\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n num_vector_embeds: Optional[int] = None,\n activation_fn: str = 'geglu',\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n ):\n super().__init__()\n self.use_linear_projection = use_linear_projection\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n\n # 1. Transformer2DModel can process both standard continuous\n # images of shape `(batch_size, num_channels, width, height)`\n # as well as quantized image embeddings of shape\n # `(batch_size, num_image_vectors)`\n # Define whether input is continuous or discrete\n # depending on configuration\n self.is_input_continuous = in_channels is not None\n self.is_input_vectorized = num_vector_embeds is not None\n\n if self.is_input_continuous and self.is_input_vectorized:\n raise ValueError(\n f'Cannot define both `in_channels`: {in_channels} '\n f'and `num_vector_embeds`: {num_vector_embeds}. Make'\n f' sure that either `in_channels` or `num_vector_embeds` '\n 'is None.')\n elif not self.is_input_continuous and not self.is_input_vectorized:\n raise ValueError(\n f'Has to define either `in_channels`: {in_channels} or'\n f' `num_vector_embeds`: {num_vector_embeds}. Make'\n f' sure that either `in_channels` or '\n '`num_vector_embeds` is not None.')\n\n # 2. Define input layers\n if self.is_input_continuous:\n self.in_channels = in_channels\n\n self.norm = torch.nn.GroupNorm(\n num_groups=norm_num_groups,\n num_channels=in_channels,\n eps=1e-6,\n affine=True)\n if use_linear_projection:\n self.proj_in = nn.Linear(in_channels, inner_dim)\n else:\n self.proj_in = nn.Conv2d(\n in_channels, inner_dim, kernel_size=1, stride=1, padding=0)\n else:\n raise ValueError('input_vectorized not supported now.')\n\n # 3. Define transformers blocks\n self.transformer_blocks = nn.ModuleList([\n BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n attention_bias=attention_bias,\n only_cross_attention=only_cross_attention,\n ) for d in range(num_layers)\n ])\n\n # 4. Define output layers\n if use_linear_projection:\n self.proj_out = nn.Linear(in_channels, inner_dim)\n else:\n self.proj_out = nn.Conv2d(\n inner_dim, in_channels, kernel_size=1, stride=1, padding=0)\n\n def _set_attention_slice(self, slice_size):\n \"\"\"set attention slice.\"\"\"\n\n for block in self.transformer_blocks:\n block._set_attention_slice(slice_size)\n\n def forward(self,\n hidden_states,\n encoder_hidden_states=None,\n timestep=None,\n return_dict: bool = True):\n \"\"\"forward function.\n\n Args:\n hidden_states ( When discrete, `torch.LongTensor`\n of shape `(batch size, num latent pixels)`.\n When continuous, `torch.FloatTensor` of shape `\n (batch size, channel, height, width)`): Input\n hidden_states\n encoder_hidden_states ( `torch.LongTensor` of shape\n `(batch size, context dim)`, *optional*):\n Conditional embeddings for cross attention layer.\n If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.long`, *optional*):\n Optional timestep to be applied as an embedding\n in AdaLayerNorm's. Used to indicate denoising step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`models.unet_2d_condition.UNet2DConditionOutput`]\n instead of a plain tuple.\n\n Returns:\n Dict if `return_dict` is True, otherwise a `tuple`.\n When returning a tuple, the first element is the sample\n tensor.\n \"\"\"\n # 1. Input\n if self.is_input_continuous:\n batch, channel, height, weight = hidden_states.shape\n residual = hidden_states\n\n hidden_states = self.norm(hidden_states)\n if not self.use_linear_projection:\n hidden_states = self.proj_in(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * weight, inner_dim)\n else:\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * weight, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n else:\n raise ValueError('input_vectorized not supported now.')\n\n # 2. Blocks\n for block in self.transformer_blocks:\n hidden_states = block(\n hidden_states,\n context=encoder_hidden_states,\n timestep=timestep)\n\n # 3. Output\n if not self.use_linear_projection:\n hidden_states = (\n hidden_states.reshape(batch, height, weight,\n inner_dim).permute(0, 3, 1,\n 2).contiguous())\n hidden_states = self.proj_out(hidden_states)\n else:\n hidden_states = self.proj_out(hidden_states)\n hidden_states = (\n hidden_states.reshape(batch, height, weight,\n inner_dim).permute(0, 3, 1,\n 2).contiguous())\n\n output = hidden_states + residual\n\n if not return_dict:\n return (output, )\n\n return Dict(sample=output)\n\n\nclass BasicTransformerBlock(nn.Module):\n \"\"\"A basic Transformer block.\n\n Args:\n dim (int): The number of channels in the input and output.\n num_attention_heads (int): The number of heads to use for\n multi-head attention.\n attention_head_dim (int): The number of channels in each head.\n dropout (float, *optional*, defaults to 0.0):\n The dropout probability to use.\n cross_attention_dim (int, *optional*):\n The size of the context vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n attention_bias (bool, *optional*, defaults to `False`):\n Configure if the attentions should contain a bias parameter.\n only_cross_attention (bool, defaults to False):\n whether to use cross attention only.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = 'geglu',\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n self.attn1 = CrossAttention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim\n if only_cross_attention else None,\n ) # is a self-attention\n self.ff = FeedForward(\n dim, dropout=dropout, activation_fn=activation_fn)\n self.attn2 = CrossAttention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n ) # is self-attn if context is none\n\n # layer norms\n self.norm1 = nn.LayerNorm(dim)\n self.norm2 = nn.LayerNorm(dim)\n self.norm3 = nn.LayerNorm(dim)\n\n def _set_attention_slice(self, slice_size):\n \"\"\"set attention slice.\"\"\"\n self.attn1._slice_size = slice_size\n self.attn2._slice_size = slice_size\n\n def forward(self, hidden_states, context=None, timestep=None):\n \"\"\"forward with hidden states, context and timestep.\"\"\"\n # 1. Self-Attention\n norm_hidden_states = (self.norm1(hidden_states))\n\n if self.only_cross_attention:\n hidden_states = self.attn1(norm_hidden_states,\n context) + hidden_states\n else:\n hidden_states = self.attn1(norm_hidden_states) + hidden_states\n\n # 2. Cross-Attention\n norm_hidden_states = (self.norm2(hidden_states))\n hidden_states = self.attn2(\n norm_hidden_states, context=context) + hidden_states\n\n # 3. Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n return hidden_states\n\n\nclass CrossAttention(nn.Module):\n r\"\"\"\n A cross attention layer.\n\n Args:\n query_dim (`int`): The number of channels in the query.\n cross_attention_dim (`int`, *optional*):\n The number of channels in the context.\n If not given, defaults to `query_dim`.\n heads (`int`, *optional*, defaults to 8):\n The number of heads to use for multi-head attention.\n dim_head (`int`, *optional*, defaults to 64):\n The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n bias (`bool`, *optional*, defaults to False):\n Set to `True` for the query, key,\n and value linear layers to contain a bias parameter.\n \"\"\"\n\n def __init__(\n self,\n query_dim: int,\n cross_attention_dim: Optional[int] = None,\n heads: int = 8,\n dim_head: int = 64,\n dropout: float = 0.0,\n bias=False,\n ):\n super().__init__()\n inner_dim = dim_head * heads\n cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim # noqa\n\n self.scale = dim_head**-0.5\n self.heads = heads\n # for slice_size > 0 the attention score computation\n # is split across the batch axis to save memory\n # You can set slice_size with `set_attention_slice`\n self._slice_size = None\n\n self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)\n self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)\n self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)\n\n self.to_out = nn.ModuleList([])\n self.to_out.append(nn.Linear(inner_dim, query_dim))\n self.to_out.append(nn.Dropout(dropout))\n\n def reshape_heads_to_batch_dim(self, tensor):\n \"\"\"reshape heads num to batch dim.\"\"\"\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size, seq_len, head_size,\n dim // head_size)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size,\n seq_len, dim // head_size)\n return tensor\n\n def reshape_batch_dim_to_heads(self, tensor):\n \"\"\"reshape batch dim to heads num.\"\"\"\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size // head_size, head_size, seq_len,\n dim)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size,\n seq_len, dim * head_size)\n return tensor\n\n def forward(self, hidden_states, context=None, mask=None):\n \"\"\"forward with hidden states, context and mask.\"\"\"\n batch_size, sequence_length, _ = hidden_states.shape\n\n query = self.to_q(hidden_states)\n context = context if context is not None else hidden_states\n key = self.to_k(context)\n value = self.to_v(context)\n\n dim = query.shape[-1]\n\n query = self.reshape_heads_to_batch_dim(query)\n key = self.reshape_heads_to_batch_dim(key)\n value = self.reshape_heads_to_batch_dim(value)\n\n # TODO(PVP) - mask is currently never used. Remember\n # to re-implement when used\n\n # attention, what we cannot get enough of\n if self._slice_size is None or query.shape[0] // self._slice_size == 1:\n hidden_states = self._attention(query, key, value)\n else:\n hidden_states = self._sliced_attention(query, key, value,\n sequence_length, dim)\n\n # linear proj\n hidden_states = self.to_out[0](hidden_states)\n # dropout\n hidden_states = self.to_out[1](hidden_states)\n return hidden_states\n\n def _attention(self, query, key, value):\n \"\"\"attention calculation.\"\"\"\n attention_scores = torch.baddbmm(\n torch.empty(\n query.shape[0],\n query.shape[1],\n key.shape[1],\n dtype=query.dtype,\n device=query.device),\n query,\n key.transpose(-1, -2),\n beta=0,\n alpha=self.scale,\n )\n attention_probs = attention_scores.softmax(dim=-1)\n # compute attention output\n\n hidden_states = torch.bmm(attention_probs, value)\n\n # reshape hidden_states\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n\n def _sliced_attention(self, query, key, value, sequence_length, dim):\n \"\"\"sliced attention calculation.\"\"\"\n batch_size_attention = query.shape[0]\n hidden_states = torch.zeros(\n (batch_size_attention, sequence_length, dim // self.heads),\n device=query.device,\n dtype=query.dtype)\n slice_size = self._slice_size if self._slice_size is not None \\\n else hidden_states.shape[0]\n for i in range(hidden_states.shape[0] // slice_size):\n start_idx = i * slice_size\n end_idx = (i + 1) * slice_size\n attn_slice = torch.baddbmm(\n torch.empty(\n slice_size,\n query.shape[1],\n key.shape[1],\n dtype=query.dtype,\n device=query.device),\n query[start_idx:end_idx],\n key[start_idx:end_idx].transpose(-1, -2),\n beta=0,\n alpha=self.scale,\n )\n attn_slice = attn_slice.softmax(dim=-1)\n attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])\n\n hidden_states[start_idx:end_idx] = attn_slice\n\n # reshape hidden_states\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n return hidden_states\n\n\nclass FeedForward(nn.Module):\n r\"\"\"\n A feed-forward layer.\n\n Args:\n dim (int): The number of channels in the input.\n dim_out (int, *optional*):\n The number of channels in the output.\n If not given, defaults to `dim`.\n mult (int, *optional*, defaults to 4):\n The multiplier to use for the hidden dimension.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n dim_out: Optional[int] = None,\n mult: int = 4,\n dropout: float = 0.0,\n activation_fn: str = 'geglu',\n ):\n super().__init__()\n inner_dim = int(dim * mult)\n dim_out = dim_out if dim_out is not None else dim\n\n if activation_fn == 'geglu':\n geglu = GEGLU(dim, inner_dim)\n elif activation_fn == 'geglu-approximate':\n geglu = ApproximateGELU(dim, inner_dim)\n\n self.net = nn.ModuleList([])\n # project in\n self.net.append(geglu)\n # project dropout\n self.net.append(nn.Dropout(dropout))\n # project out\n self.net.append(nn.Linear(inner_dim, dim_out))\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n for module in self.net:\n hidden_states = module(hidden_states)\n return hidden_states\n\n\n# feedforward\nclass GEGLU(nn.Module):\n r\"\"\"\n A variant of the gated linear unit activation function\n from https://arxiv.org/abs/2002.05202.\n\n Args:\n dim_in (`int`): The number of channels in the input.\n dim_out (`int`): The number of channels in the output.\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out * 2)\n\n def gelu(self, gate):\n \"\"\"gelu activation.\"\"\"\n return F.gelu(gate)\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)\n return hidden_states * self.gelu(gate)\n\n\nclass ApproximateGELU(nn.Module):\n \"\"\"The approximate form of Gaussian Error Linear Unit (GELU)\n\n For more details, see section 2: https://arxiv.org/abs/1606.08415\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out)\n\n def forward(self, x):\n \"\"\"forward function.\"\"\"\n x = self.proj(x)\n return x * torch.sigmoid(1.702 * x)\n" }, { "path": "mmagic/models/editors/ddpm/denoising_unet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom copy import deepcopy\nfrom functools import partial\nfrom typing import Tuple\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn.bricks import build_norm_layer\nfrom mmcv.cnn.bricks.conv_module import ConvModule\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModule, constant_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nfrom mmagic.registry import MODELS\nfrom .embeddings import TimestepEmbedding, Timesteps\nfrom .unet_blocks import UNetMidBlock2DCrossAttn, get_down_block, get_up_block\n\nlogger = MMLogger.get_current_instance()\n\n\nclass EmbedSequential(nn.Sequential):\n \"\"\"A sequential module that passes timestep embeddings to the children that\n support it as an extra input.\n\n Modified from\n https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/unet.py#L35\n \"\"\"\n\n def forward(self, x, y, encoder_out=None):\n for layer in self:\n if isinstance(layer, DenoisingResBlock):\n x = layer(x, y)\n elif isinstance(\n layer,\n MultiHeadAttentionBlock) and encoder_out is not None:\n x = layer(x, encoder_out)\n else:\n x = layer(x)\n return x\n\n\n@MODELS.register_module('GN32')\nclass GroupNorm32(nn.GroupNorm):\n\n def __init__(self, num_channels, num_groups=32, **kwargs):\n super().__init__(num_groups, num_channels, **kwargs)\n\n def forward(self, x):\n return super().forward(x.float()).type(x.dtype)\n\n\ndef convert_module_to_f16(layer):\n \"\"\"Convert primitive modules to float16.\"\"\"\n if isinstance(layer, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):\n layer.weight.data = layer.weight.data.half()\n if layer.bias is not None:\n layer.bias.data = layer.bias.data.half()\n\n\ndef convert_module_to_f32(layer):\n \"\"\"Convert primitive modules to float32, undoing\n convert_module_to_f16().\"\"\"\n if isinstance(layer, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):\n layer.weight.data = layer.weight.data.float()\n if layer.bias is not None:\n layer.bias.data = layer.bias.data.float()\n\n\n@MODELS.register_module()\nclass SiLU(BaseModule):\n r\"\"\"Applies the Sigmoid Linear Unit (SiLU) function, element-wise.\n The SiLU function is also known as the swish function.\n Args:\n input (bool, optional): Use inplace operation or not.\n Defaults to `False`.\n \"\"\"\n\n def __init__(self, inplace=False):\n super().__init__()\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0') and inplace:\n mmengine.print_log(\n 'Inplace version of \\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.')\n self.inplace = inplace\n\n def forward(self, x):\n \"\"\"Forward function for SiLU.\n Args:\n x (torch.Tensor): Input tensor.\n\n Returns:\n torch.Tensor: Tensor after activation.\n \"\"\"\n\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n return x * torch.sigmoid(x)\n\n return F.silu(x, inplace=self.inplace)\n\n\n@MODELS.register_module()\nclass MultiHeadAttention(BaseModule):\n \"\"\"An attention block allows spatial position to attend to each other.\n\n Originally ported from here, but adapted to the N-d case.\n https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. # noqa\n\n Args:\n in_channels (int): Channels of the input feature map.\n num_heads (int, optional): Number of heads in the attention.\n norm_cfg (dict, optional): Config for normalization layer. Default\n to ``dict(type='GN', num_groups=32)``\n \"\"\"\n\n def __init__(self,\n in_channels,\n num_heads=1,\n norm_cfg=dict(type='GN', num_groups=32)):\n super().__init__()\n self.num_heads = num_heads\n _, self.norm = build_norm_layer(norm_cfg, in_channels)\n self.qkv = nn.Conv1d(in_channels, in_channels * 3, 1)\n self.proj = nn.Conv1d(in_channels, in_channels, 1)\n self.init_weights()\n\n @staticmethod\n def QKVAttention(qkv):\n channel = qkv.shape[1] // 3\n q, k, v = torch.chunk(qkv, 3, dim=1)\n scale = 1 / np.sqrt(np.sqrt(channel))\n weight = torch.einsum('bct,bcs->bts', q * scale, k * scale)\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n weight = torch.einsum('bts,bcs->bct', weight, v)\n return weight\n\n def forward(self, x):\n \"\"\"Forward function for multi head attention.\n Args:\n x (torch.Tensor): Input feature map.\n\n Returns:\n torch.Tensor: Feature map after attention.\n \"\"\"\n b, c, *spatial = x.shape\n x = x.reshape(b, c, -1)\n qkv = self.qkv(self.norm(x))\n qkv = qkv.reshape(b * self.num_heads, -1, qkv.shape[2])\n h = self.QKVAttention(qkv)\n h = h.reshape(b, -1, h.shape[-1])\n h = self.proj(h)\n return (h + x).reshape(b, c, *spatial)\n\n def init_weights(self):\n constant_init(self.proj, 0)\n\n\n@MODELS.register_module()\nclass MultiHeadAttentionBlock(BaseModule):\n \"\"\"An attention block that allows spatial positions to attend to each\n other.\n\n Originally ported from here, but adapted to the N-d case.\n https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.\n \"\"\"\n\n def __init__(self,\n in_channels,\n num_heads=1,\n num_head_channels=-1,\n use_new_attention_order=False,\n norm_cfg=dict(type='GN32', num_groups=32),\n encoder_channels=None):\n super().__init__()\n self.in_channels = in_channels\n if num_head_channels == -1:\n self.num_heads = num_heads\n else:\n assert (in_channels % num_head_channels == 0), (\n f'q,k,v channels {in_channels} is not divisible by '\n 'num_head_channels {num_head_channels}')\n self.num_heads = in_channels // num_head_channels\n _, self.norm = build_norm_layer(norm_cfg, in_channels)\n self.qkv = nn.Conv1d(in_channels, in_channels * 3, 1)\n\n if use_new_attention_order:\n # split qkv before split heads\n self.attention = QKVAttention(self.num_heads)\n else:\n # split heads before split qkv\n self.attention = QKVAttentionLegacy(self.num_heads)\n\n self.proj_out = nn.Conv1d(in_channels, in_channels, 1)\n if encoder_channels is not None:\n self.encoder_kv = nn.Conv1d(encoder_channels, in_channels * 2, 1)\n\n def forward(self, x, encoder_out=None):\n b, c, *spatial = x.shape\n x = x.reshape(b, c, -1)\n qkv = self.qkv(self.norm(x))\n if encoder_out is not None:\n encoder_out = self.encoder_kv(encoder_out)\n h = self.attention(qkv, encoder_out)\n else:\n h = self.attention(qkv)\n h = self.proj_out(h)\n return (x + h).reshape(b, c, *spatial)\n\n\n@MODELS.register_module()\nclass QKVAttentionLegacy(BaseModule):\n \"\"\"A module which performs QKV attention.\n\n Matches legacy QKVAttention + input/output heads shaping\n \"\"\"\n\n def __init__(self, n_heads):\n super().__init__()\n self.n_heads = n_heads\n\n def forward(self, qkv, encoder_kv=None):\n \"\"\"Apply QKV attention.\n\n :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n \"\"\"\n bs, width, length = qkv.shape\n assert width % (3 * self.n_heads) == 0\n ch = width // (3 * self.n_heads)\n q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(\n ch, dim=1)\n if encoder_kv is not None:\n assert encoder_kv.shape[1] == self.n_heads * ch * 2\n ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(\n ch, dim=1)\n k = torch.cat([ek, k], dim=-1)\n v = torch.cat([ev, v], dim=-1)\n scale = 1 / math.sqrt(math.sqrt(ch))\n weight = torch.einsum(\n 'bct,bcs->bts', q * scale,\n k * scale) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n a = torch.einsum('bts,bcs->bct', weight, v)\n return a.reshape(bs, -1, length)\n\n\n@MODELS.register_module()\nclass QKVAttention(BaseModule):\n \"\"\"A module which performs QKV attention and splits in a different\n order.\"\"\"\n\n def __init__(self, n_heads):\n super().__init__()\n self.n_heads = n_heads\n\n def forward(self, qkv):\n \"\"\"Apply QKV attention.\n\n :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n \"\"\"\n bs, width, length = qkv.shape\n assert width % (3 * self.n_heads) == 0\n ch = width // (3 * self.n_heads)\n q, k, v = qkv.chunk(3, dim=1)\n scale = 1 / math.sqrt(math.sqrt(ch))\n weight = torch.einsum(\n 'bct,bcs->bts',\n (q * scale).view(bs * self.n_heads, ch, length),\n (k * scale).view(bs * self.n_heads, ch, length),\n ) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n a = torch.einsum('bts,bcs->bct', weight,\n v.reshape(bs * self.n_heads, ch, length))\n return a.reshape(bs, -1, length)\n\n\n@MODELS.register_module()\nclass TimeEmbedding(BaseModule):\n \"\"\"Time embedding layer, reference to Two level embedding. First embedding\n time by an embedding function, then feed to neural networks.\n\n Args:\n in_channels (int): The channel number of the input feature map.\n embedding_channels (int): The channel number of the output embedding.\n embedding_mode (str, optional): Embedding mode for the time embedding.\n Defaults to 'sin'.\n embedding_cfg (dict, optional): Config for time embedding.\n Defaults to None.\n act_cfg (dict, optional): Config for activation layer. Defaults to\n ``dict(type='SiLU', inplace=False)``.\n \"\"\"\n\n def __init__(self,\n in_channels,\n embedding_channels,\n embedding_mode='sin',\n embedding_cfg=None,\n act_cfg=dict(type='SiLU', inplace=False)):\n super().__init__()\n self.blocks = nn.Sequential(\n nn.Linear(in_channels, embedding_channels), MODELS.build(act_cfg),\n nn.Linear(embedding_channels, embedding_channels))\n\n # add `dim` to embedding config\n embedding_cfg_ = dict(dim=in_channels)\n if embedding_cfg is not None:\n embedding_cfg_.update(embedding_cfg)\n if embedding_mode.upper() == 'SIN':\n self.embedding_fn = partial(self.sinusodial_embedding,\n **embedding_cfg_)\n else:\n raise ValueError('Only support `SIN` for time embedding, '\n f'but receive {embedding_mode}.')\n\n @staticmethod\n def sinusodial_embedding(timesteps, dim, max_period=10000):\n \"\"\"Create sinusoidal timestep embeddings.\n\n Args:\n timesteps (torch.Tensor): Timestep to embedding. 1-D tensor shape\n as ``[bz, ]``, one per batch element.\n dim (int): The dimension of the embedding.\n max_period (int, optional): Controls the minimum frequency of the\n embeddings. Defaults to ``10000``.\n\n Returns:\n torch.Tensor: Embedding results shape as `[bz, dim]`.\n \"\"\"\n\n half = dim // 2\n freqs = torch.exp(\n -np.log(max_period) *\n torch.arange(start=0, end=half, dtype=torch.float32) /\n half).to(device=timesteps.device)\n args = timesteps[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat(\n [embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n return embedding\n\n def forward(self, t):\n \"\"\"Forward function for time embedding layer.\n Args:\n t (torch.Tensor): Input timesteps.\n\n Returns:\n torch.Tensor: Timesteps embedding.\n\n \"\"\"\n return self.blocks(self.embedding_fn(t))\n\n\n@MODELS.register_module()\nclass DenoisingResBlock(BaseModule):\n \"\"\"Resblock for the denoising network. If `in_channels` not equals to\n `out_channels`, a learnable shortcut with conv layers will be added.\n\n Args:\n in_channels (int): Number of channels of the input feature map.\n embedding_channels (int): Number of channels of the input embedding.\n use_scale_shift_norm (bool): Whether use scale-shift-norm in\n `NormWithEmbedding` layer.\n dropout (float): Probability of the dropout layers.\n out_channels (int, optional): Number of output channels of the\n ResBlock. If not defined, the output channels will equal to the\n `in_channels`. Defaults to `None`.\n norm_cfg (dict, optional): The config for the normalization layers.\n Defaults too ``dict(type='GN', num_groups=32)``.\n act_cfg (dict, optional): The config for the activation layers.\n Defaults to ``dict(type='SiLU', inplace=False)``.\n shortcut_kernel_size (int, optional): The kernel size for the shortcut\n conv. Defaults to ``1``.\n \"\"\"\n\n def __init__(self,\n in_channels,\n embedding_channels,\n use_scale_shift_norm,\n dropout,\n out_channels=None,\n norm_cfg=dict(type='GN', num_groups=32),\n act_cfg=dict(type='SiLU', inplace=False),\n shortcut_kernel_size=1,\n up=False,\n down=False):\n super().__init__()\n out_channels = in_channels if out_channels is None else out_channels\n\n _norm_cfg = deepcopy(norm_cfg)\n\n _, norm_1 = build_norm_layer(_norm_cfg, in_channels)\n conv_1 = [\n norm_1,\n MODELS.build(act_cfg),\n nn.Conv2d(in_channels, out_channels, 3, padding=1)\n ]\n self.conv_1 = nn.Sequential(*conv_1)\n\n norm_with_embedding_cfg = dict(\n in_channels=out_channels,\n embedding_channels=embedding_channels,\n use_scale_shift=use_scale_shift_norm,\n norm_cfg=_norm_cfg)\n self.norm_with_embedding = MODELS.build(\n dict(type='NormWithEmbedding'),\n default_args=norm_with_embedding_cfg)\n\n conv_2 = [\n MODELS.build(act_cfg),\n nn.Dropout(dropout),\n nn.Conv2d(out_channels, out_channels, 3, padding=1)\n ]\n self.conv_2 = nn.Sequential(*conv_2)\n\n assert shortcut_kernel_size in [\n 1, 3\n ], ('Only support `1` and `3` for `shortcut_kernel_size`, but '\n f'receive {shortcut_kernel_size}.')\n\n self.learnable_shortcut = out_channels != in_channels\n\n if self.learnable_shortcut:\n shortcut_padding = 1 if shortcut_kernel_size == 3 else 0\n self.shortcut = nn.Conv2d(\n in_channels,\n out_channels,\n shortcut_kernel_size,\n padding=shortcut_padding)\n\n self.updown = up or down\n\n if up:\n self.h_upd = DenoisingUpsample(in_channels, False)\n self.x_upd = DenoisingUpsample(in_channels, False)\n elif down:\n self.h_upd = DenoisingDownsample(in_channels, False)\n self.x_upd = DenoisingDownsample(in_channels, False)\n else:\n self.h_upd = self.x_upd = nn.Identity()\n\n self.init_weights()\n\n def forward_shortcut(self, x):\n if self.learnable_shortcut:\n return self.shortcut(x)\n return x\n\n def forward(self, x, y):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n y (torch.Tensor): Shared time embedding or shared label embedding.\n\n Returns:\n torch.Tensor : Output feature map tensor.\n \"\"\"\n if self.updown:\n in_rest, in_conv = self.conv_1[:-1], self.conv_1[-1]\n h = in_rest(x)\n h = self.h_upd(h)\n x = self.x_upd(x)\n h = in_conv(h)\n else:\n h = self.conv_1(x)\n\n shortcut = self.forward_shortcut(x)\n h = self.norm_with_embedding(h, y)\n h = self.conv_2(h)\n return h + shortcut\n\n def init_weights(self):\n # apply zero init to last conv layer\n constant_init(self.conv_2[-1], 0)\n\n\n@MODELS.register_module()\nclass NormWithEmbedding(BaseModule):\n \"\"\"Nornalization with embedding layer. If `use_scale_shift == True`,\n embedding results will be chunked and used to re-shift and re-scale\n normalization results. Otherwise, embedding results will directly add to\n input of normalization layer.\n\n Args:\n in_channels (int): Number of channels of the input feature map.\n embedding_channels (int) Number of channels of the input embedding.\n norm_cfg (dict, optional): Config for the normalization operation.\n Defaults to `dict(type='GN', num_groups=32)`.\n act_cfg (dict, optional): Config for the activation layer. Defaults\n to `dict(type='SiLU', inplace=False)`.\n use_scale_shift (bool): If True, the output of Embedding layer will be\n split to 'scale' and 'shift' and map the output of normalization\n layer to ``out * (1 + scale) + shift``. Otherwise, the output of\n Embedding layer will be added with the input before normalization\n operation. Defaults to True.\n \"\"\"\n\n def __init__(self,\n in_channels,\n embedding_channels,\n norm_cfg=dict(type='GN', num_groups=32),\n act_cfg=dict(type='SiLU', inplace=False),\n use_scale_shift=True):\n super().__init__()\n self.use_scale_shift = use_scale_shift\n _, self.norm = build_norm_layer(norm_cfg, in_channels)\n\n embedding_output = in_channels * 2 if use_scale_shift else in_channels\n self.embedding_layer = nn.Sequential(\n MODELS.build(act_cfg),\n nn.Linear(embedding_channels, embedding_output))\n\n def forward(self, x, y):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n y (torch.Tensor): Shared time embedding or shared label embedding.\n\n Returns:\n torch.Tensor : Output feature map tensor.\n \"\"\"\n embedding = self.embedding_layer(y).type(x.dtype)\n embedding = embedding[:, :, None, None]\n if self.use_scale_shift:\n scale, shift = torch.chunk(embedding, 2, dim=1)\n x = self.norm(x)\n x = x * (1 + scale) + shift\n else:\n x = self.norm(x + embedding)\n return x\n\n\n@MODELS.register_module()\nclass DenoisingDownsample(BaseModule):\n \"\"\"Downsampling operation used in the denoising network. Support average\n pooling and convolution for downsample operation.\n\n Args:\n in_channels (int): Number of channels of the input feature map to be\n downsampled.\n with_conv (bool, optional): Whether use convolution operation for\n downsampling. Defaults to `True`.\n \"\"\"\n\n def __init__(self, in_channels, with_conv=True):\n super().__init__()\n if with_conv:\n self.downsample = nn.Conv2d(in_channels, in_channels, 3, 2, 1)\n else:\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2)\n\n def forward(self, x):\n \"\"\"Forward function for downsampling operation.\n Args:\n x (torch.Tensor): Feature map to downsample.\n\n Returns:\n torch.Tensor: Feature map after downsampling.\n \"\"\"\n return self.downsample(x)\n\n\n@MODELS.register_module()\nclass DenoisingUpsample(BaseModule):\n \"\"\"Upsampling operation used in the denoising network. Allows users to\n apply an additional convolution layer after the nearest interpolation\n operation.\n\n Args:\n in_channels (int): Number of channels of the input feature map to be\n downsampled.\n with_conv (bool, optional): Whether apply an additional convolution\n layer after upsampling. Defaults to `True`.\n \"\"\"\n\n def __init__(self, in_channels, with_conv=True):\n super().__init__()\n self.with_conv = with_conv\n if with_conv:\n self.conv = nn.Conv2d(in_channels, in_channels, 3, 1, 1)\n\n def forward(self, x):\n \"\"\"Forward function for upsampling operation.\n Args:\n x (torch.Tensor): Feature map to upsample.\n\n Returns:\n torch.Tensor: Feature map after upsampling.\n \"\"\"\n x = F.interpolate(x, scale_factor=2, mode='nearest')\n if self.with_conv:\n x = self.conv(x)\n return x\n\n\ndef build_down_block_resattn(resblocks_per_downsample, resblock_cfg,\n in_channels_, out_channels_, attention_scale,\n attention_cfg, in_channels_list, level,\n channel_factor_list, embedding_channels,\n use_scale_shift_norm, dropout, norm_cfg,\n resblock_updown, downsample_cfg, scale):\n \"\"\"build unet down path blocks with resnet and attention.\"\"\"\n\n in_blocks = nn.ModuleList()\n\n for _ in range(resblocks_per_downsample):\n layers = [\n MODELS.build(\n resblock_cfg,\n default_args={\n 'in_channels': in_channels_,\n 'out_channels': out_channels_\n })\n ]\n in_channels_ = out_channels_\n\n if scale in attention_scale:\n layers.append(\n MODELS.build(\n attention_cfg, default_args={'in_channels': in_channels_}))\n\n in_channels_list.append(in_channels_)\n in_blocks.append(EmbedSequential(*layers))\n\n if level != len(channel_factor_list) - 1:\n in_blocks.append(\n EmbedSequential(\n DenoisingResBlock(\n out_channels_,\n embedding_channels,\n use_scale_shift_norm,\n dropout,\n norm_cfg=norm_cfg,\n out_channels=out_channels_,\n down=True) if resblock_updown else MODELS.build(\n downsample_cfg,\n default_args={'in_channels': in_channels_})))\n in_channels_list.append(in_channels_)\n scale *= 2\n return in_blocks, scale\n\n\ndef build_mid_blocks_resattn(resblock_cfg, attention_cfg, in_channels_):\n \"\"\"build unet mid blocks with resnet and attention.\"\"\"\n\n return EmbedSequential(\n MODELS.build(resblock_cfg, default_args={'in_channels': in_channels_}),\n MODELS.build(\n attention_cfg, default_args={'in_channels': in_channels_}),\n MODELS.build(resblock_cfg, default_args={'in_channels': in_channels_}),\n )\n\n\ndef build_up_blocks_resattn(\n resblocks_per_downsample,\n resblock_cfg,\n in_channels_,\n in_channels_list,\n base_channels,\n factor,\n scale,\n attention_scale,\n attention_cfg,\n channel_factor_list,\n level,\n embedding_channels,\n use_scale_shift_norm,\n dropout,\n norm_cfg,\n resblock_updown,\n upsample_cfg,\n):\n \"\"\"build up path blocks with resnet and attention.\"\"\"\n\n out_blocks = nn.ModuleList()\n for idx in range(resblocks_per_downsample + 1):\n layers = [\n MODELS.build(\n resblock_cfg,\n default_args={\n 'in_channels': in_channels_ + in_channels_list.pop(),\n 'out_channels': int(base_channels * factor)\n })\n ]\n in_channels_ = int(base_channels * factor)\n if scale in attention_scale:\n layers.append(\n MODELS.build(\n attention_cfg, default_args={'in_channels': in_channels_}))\n if (level != len(channel_factor_list) - 1\n and idx == resblocks_per_downsample):\n out_channels_ = in_channels_\n layers.append(\n DenoisingResBlock(\n in_channels_,\n embedding_channels,\n use_scale_shift_norm,\n dropout,\n norm_cfg=norm_cfg,\n out_channels=out_channels_,\n up=True) if resblock_updown else MODELS.\n build(\n upsample_cfg, default_args={'in_channels': in_channels_}))\n scale //= 2\n out_blocks.append(EmbedSequential(*layers))\n\n return out_blocks, in_channels_, scale\n\n\n@MODELS.register_module()\nclass DenoisingUnet(BaseModule):\n \"\"\"Denoising Unet. This network receives a diffused image ``x_t`` and\n current timestep ``t``, and returns a ``output_dict`` corresponding to the\n passed ``output_cfg``.\n\n ``output_cfg`` defines the number of channels and the meaning of the\n output. ``output_cfg`` mainly contains keys of ``mean`` and ``var``,\n denoting how the network outputs mean and variance required for the\n denoising process.\n For ``mean``:\n 1. ``dict(mean='EPS')``: Model will predict noise added in the\n diffusion process, and the ``output_dict`` will contain a key named\n ``eps_t_pred``.\n 2. ``dict(mean='START_X')``: Model will direct predict the mean of the\n original image `x_0`, and the ``output_dict`` will contain a key named\n ``x_0_pred``.\n 3. ``dict(mean='X_TM1_PRED')``: Model will predict the mean of diffused\n image at `t-1` timestep, and the ``output_dict`` will contain a key\n named ``x_tm1_pred``.\n\n For ``var``:\n 1. ``dict(var='FIXED_SMALL')`` or ``dict(var='FIXED_LARGE')``: Variance in\n the denoising process is regarded as a fixed value. Therefore only\n 'mean' will be predicted, and the output channels will equal to the\n input image (e.g., three channels for RGB image.)\n 2. ``dict(var='LEARNED')``: Model will predict `log_variance` in the\n denoising process, and the ``output_dict`` will contain a key named\n ``log_var``.\n 3. ``dict(var='LEARNED_RANGE')``: Model will predict an interpolation\n factor and the `log_variance` will be calculated as\n `factor * upper_bound + (1-factor) * lower_bound`. The ``output_dict``\n will contain a key named ``factor``.\n\n If ``var`` is not ``FIXED_SMALL`` or ``FIXED_LARGE``, the number of output\n channels will be the double of input channels, where the first half part\n contains predicted mean values and the other part is the predicted\n variance values. Otherwise, the number of output channels equals to the\n input channels, only containing the predicted mean values.\n\n Args:\n image_size (int | list[int]): The size of image to denoise.\n in_channels (int, optional): The input channels of the input image.\n Defaults as ``3``.\n out_channels (int, optional): The output channels of the output\n prediction. Defaults as ``None`` for automaticaaly assigned by\n ``var_mode``.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contain channels based on this number.\n Defaults to ``128``.\n resblocks_per_downsample (int, optional): Number of ResBlock used\n between two downsample operations. The number of ResBlock between\n upsample operations will be the same value to keep symmetry.\n Defaults to 3.\n num_timesteps (int, optional): The total timestep of the denoising\n process and the diffusion process. Defaults to ``1000``.\n use_rescale_timesteps (bool, optional): Whether rescale the input\n timesteps in range of [0, 1000]. Defaults to ``True``.\n dropout (float, optional): The probability of dropout operation of\n each ResBlock. Pass ``0`` to do not use dropout. Defaults as 0.\n embedding_channels (int, optional): The output channels of time\n embedding layer and label embedding layer. If not passed (or\n passed ``-1``), output channels of the embedding layers will set\n as four times of ``base_channels``. Defaults to ``-1``.\n num_classes (int, optional): The number of conditional classes. If set\n to 0, this model will be degraded to an unconditional model.\n Defaults to 0.\n channels_cfg (list | dict[list], optional): Config for input channels\n of the intermediate blocks. If list is passed, each element of the\n list indicates the scale factor for the input channels of the\n current block with regard to the ``base_channels``. For block\n ``i``, the input and output channels should be\n ``channels_cfg[i] * base_channels`` and\n ``channels_cfg[i+1] * base_channels`` If dict is provided, the key\n of the dict should be the output scale and corresponding value\n should be a list to define channels. Default: Please refer to\n ``_default_channels_cfg``.\n output_cfg (dict, optional): Config for output variables. Defaults to\n ``dict(mean='eps', var='learned_range')``.\n norm_cfg (dict, optional): The config for normalization layers.\n Defaults to ``dict(type='GN', num_groups=32)``.\n act_cfg (dict, optional): The config for activation layers. Defaults\n to ``dict(type='SiLU', inplace=False)``.\n shortcut_kernel_size (int, optional): The kernel size for shortcut\n conv in ResBlocks. The value of this argument will overwrite the\n default value of `resblock_cfg`. Defaults to `3`.\n use_scale_shift_norm (bool, optional): Whether perform scale and shift\n after normalization operation. Defaults to True.\n num_heads (int, optional): The number of attention heads. Defaults to\n 4.\n time_embedding_mode (str, optional): Embedding method of\n ``time_embedding``. Defaults to 'sin'.\n time_embedding_cfg (dict, optional): Config for ``time_embedding``.\n Defaults to None.\n resblock_cfg (dict, optional): Config for ResBlock. Defaults to\n ``dict(type='DenoisingResBlock')``.\n attention_cfg (dict, optional): Config for attention operation.\n Defaults to ``dict(type='MultiHeadAttention')``.\n upsample_conv (bool, optional): Whether use conv in upsample block.\n Defaults to ``True``.\n downsample_conv (bool, optional): Whether use conv operation in\n downsample block. Defaults to ``True``.\n upsample_cfg (dict, optional): Config for upsample blocks.\n Defaults to ``dict(type='DenoisingDownsample')``.\n downsample_cfg (dict, optional): Config for downsample blocks.\n Defaults to ``dict(type='DenoisingUpsample')``.\n attention_res (int | list[int], optional): Resolution of feature maps\n to apply attention operation. Defaults to ``[16, 8]``.\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n \"\"\"\n\n _default_channels_cfg = {\n 512: [0.5, 1, 1, 2, 2, 4, 4],\n 256: [1, 1, 2, 2, 4, 4],\n 128: [1, 1, 2, 3, 4],\n 64: [1, 2, 3, 4],\n 32: [1, 2, 2, 2]\n }\n\n def __init__(self,\n image_size,\n in_channels=3,\n out_channels=None,\n base_channels=128,\n resblocks_per_downsample=3,\n num_timesteps=1000,\n use_rescale_timesteps=False,\n dropout=0,\n embedding_channels=-1,\n num_classes=0,\n use_fp16=False,\n channels_cfg=None,\n output_cfg=dict(mean='eps', var='learned_range'),\n norm_cfg=dict(type='GN', num_groups=32),\n act_cfg=dict(type='SiLU', inplace=False),\n shortcut_kernel_size=1,\n use_scale_shift_norm=False,\n resblock_updown=False,\n num_heads=4,\n time_embedding_mode='sin',\n time_embedding_cfg=None,\n resblock_cfg=dict(type='DenoisingResBlock'),\n attention_cfg=dict(type='MultiHeadAttention'),\n encoder_channels=None,\n downsample_conv=True,\n upsample_conv=True,\n downsample_cfg=dict(type='DenoisingDownsample'),\n upsample_cfg=dict(type='DenoisingUpsample'),\n attention_res=[16, 8],\n pretrained=None,\n unet_type='',\n down_block_types: Tuple[str] = (),\n up_block_types: Tuple[str] = (),\n cross_attention_dim=768,\n layers_per_block: int = 2):\n\n super().__init__()\n\n self.unet_type = unet_type\n self.num_classes = num_classes\n self.num_timesteps = num_timesteps\n self.base_channels = base_channels\n self.encoder_channels = encoder_channels\n self.use_rescale_timesteps = use_rescale_timesteps\n self.dtype = torch.float16 if use_fp16 else torch.float32\n\n self.output_cfg = deepcopy(output_cfg)\n self.mean_mode = self.output_cfg.get('mean', 'eps')\n self.var_mode = self.output_cfg.get('var', 'learned_range')\n self.in_channels = in_channels\n\n # double output_channels to output mean and var at same time\n if out_channels is None:\n out_channels = in_channels if 'FIXED' in self.var_mode.upper() \\\n else 2 * in_channels\n self.out_channels = out_channels\n\n # check type of image_size\n if not isinstance(image_size, int) and not isinstance(\n image_size, list):\n raise TypeError(\n 'Only support `int` and `list[int]` for `image_size`.')\n if isinstance(image_size, list):\n assert len(\n image_size) == 2, 'The length of `image_size` should be 2.'\n assert image_size[0] == image_size[\n 1], 'Width and height of the image should be same.'\n image_size = image_size[0]\n self.image_size = image_size\n\n channels_cfg = deepcopy(self._default_channels_cfg) \\\n if channels_cfg is None else deepcopy(channels_cfg)\n if isinstance(channels_cfg, dict):\n if image_size not in channels_cfg:\n raise KeyError(f'`image_size={image_size} is not found in '\n '`channels_cfg`, only support configs for '\n f'{[chn for chn in channels_cfg.keys()]}')\n self.channel_factor_list = channels_cfg[image_size]\n elif isinstance(channels_cfg, list):\n self.channel_factor_list = channels_cfg\n else:\n raise ValueError('Only support list or dict for `channels_cfg`, '\n f'receive {type(channels_cfg)}')\n\n embedding_channels = base_channels * 4 \\\n if embedding_channels == -1 else embedding_channels\n\n # init the channel scale factor\n scale = 1\n ch = int(base_channels * self.channel_factor_list[0])\n self.in_channels_list = [ch]\n\n if self.unet_type == 'stable':\n # time\n self.time_proj = Timesteps(ch)\n self.time_embedding = TimestepEmbedding(base_channels,\n embedding_channels)\n\n self.conv_in = nn.Conv2d(\n in_channels, ch, kernel_size=3, padding=(1, 1))\n else:\n self.time_embedding = TimeEmbedding(\n base_channels,\n embedding_channels=embedding_channels,\n embedding_mode=time_embedding_mode,\n embedding_cfg=time_embedding_cfg,\n act_cfg=act_cfg)\n\n self.in_blocks = nn.ModuleList(\n [EmbedSequential(nn.Conv2d(in_channels, ch, 3, 1, padding=1))])\n\n if self.num_classes != 0:\n self.label_embedding = nn.Embedding(self.num_classes,\n embedding_channels)\n\n self.resblock_cfg = deepcopy(resblock_cfg)\n self.resblock_cfg.setdefault('dropout', dropout)\n self.resblock_cfg.setdefault('norm_cfg', norm_cfg)\n self.resblock_cfg.setdefault('act_cfg', act_cfg)\n self.resblock_cfg.setdefault('embedding_channels', embedding_channels)\n self.resblock_cfg.setdefault('use_scale_shift_norm',\n use_scale_shift_norm)\n self.resblock_cfg.setdefault('shortcut_kernel_size',\n shortcut_kernel_size)\n\n # get scales of ResBlock to apply attention\n attention_scale = [image_size // int(res) for res in attention_res]\n self.attention_cfg = deepcopy(attention_cfg)\n self.attention_cfg.setdefault('num_heads', num_heads)\n self.attention_cfg.setdefault('norm_cfg', norm_cfg)\n\n self.downsample_cfg = deepcopy(downsample_cfg)\n self.downsample_cfg.setdefault('with_conv', downsample_conv)\n self.upsample_cfg = deepcopy(upsample_cfg)\n self.upsample_cfg.setdefault('with_conv', upsample_conv)\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n attention_head_dim = (num_heads, ) * len(down_block_types)\n\n # construct the encoder part of Unet\n for level, factor in enumerate(self.channel_factor_list):\n in_channels_ = ch if level == 0 \\\n else int(base_channels * self.channel_factor_list[level - 1])\n out_channels_ = int(base_channels * factor)\n\n if self.unet_type == 'stable':\n is_final_block = level == len(self.channel_factor_list) - 1\n down_block_type = down_block_types[level]\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=in_channels_,\n out_channels=out_channels_,\n temb_channels=embedding_channels,\n cross_attention_dim=cross_attention_dim,\n add_downsample=not is_final_block,\n resnet_act_fn=act_cfg['type'],\n resnet_groups=norm_cfg['num_groups'],\n attn_num_head_channels=attention_head_dim[level],\n )\n self.down_blocks.append(down_block)\n\n else:\n in_blocks, scale = build_down_block_resattn(\n resblocks_per_downsample=resblocks_per_downsample,\n resblock_cfg=self.resblock_cfg,\n in_channels_=in_channels_,\n out_channels_=out_channels_,\n attention_scale=attention_scale,\n attention_cfg=self.attention_cfg,\n in_channels_list=self.in_channels_list,\n level=level,\n channel_factor_list=self.channel_factor_list,\n embedding_channels=embedding_channels,\n use_scale_shift_norm=use_scale_shift_norm,\n dropout=dropout,\n norm_cfg=norm_cfg,\n resblock_updown=resblock_updown,\n downsample_cfg=self.downsample_cfg,\n scale=scale)\n self.in_blocks.extend(in_blocks)\n\n # construct the bottom part of Unet\n block_out_channels = [\n times * base_channels for times in self.channel_factor_list\n ]\n in_channels_ = self.in_channels_list[-1]\n if self.unet_type == 'stable':\n self.mid_block = UNetMidBlock2DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=embedding_channels,\n cross_attention_dim=cross_attention_dim,\n resnet_act_fn=act_cfg['type'],\n resnet_time_scale_shift='default',\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_cfg['num_groups'],\n )\n else:\n self.mid_blocks = build_mid_blocks_resattn(self.resblock_cfg,\n self.attention_cfg,\n in_channels_)\n\n # stable up parameters\n self.num_upsamplers = 0\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n output_channel = reversed_block_out_channels[0]\n\n # construct the decoder part of Unet\n in_channels_list = deepcopy(self.in_channels_list)\n if self.unet_type != 'stable':\n self.out_blocks = nn.ModuleList()\n for level, factor in enumerate(self.channel_factor_list[::-1]):\n\n if self.unet_type == 'stable':\n is_final_block = level == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[level]\n input_channel = reversed_block_out_channels[min(\n level + 1,\n len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block_type = up_block_types[level]\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=embedding_channels,\n cross_attention_dim=cross_attention_dim,\n add_upsample=add_upsample,\n resnet_act_fn=act_cfg['type'],\n resnet_groups=norm_cfg['num_groups'],\n attn_num_head_channels=reversed_attention_head_dim[level],\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n else:\n out_blocks, in_channels_, scale = build_up_blocks_resattn(\n resblocks_per_downsample,\n self.resblock_cfg,\n in_channels_,\n in_channels_list,\n base_channels,\n factor,\n scale,\n attention_scale,\n self.attention_cfg,\n self.channel_factor_list,\n level,\n embedding_channels,\n use_scale_shift_norm,\n dropout,\n norm_cfg,\n resblock_updown,\n self.upsample_cfg,\n )\n self.out_blocks.extend(out_blocks)\n\n if self.unet_type == 'stable':\n # out\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0],\n num_groups=norm_cfg['num_groups'])\n if digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.conv_act = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.conv_act = nn.ReLU()\n self.conv_out = nn.Conv2d(\n block_out_channels[0],\n self.out_channels,\n kernel_size=3,\n padding=1)\n else:\n self.out = ConvModule(\n in_channels=in_channels_,\n out_channels=out_channels,\n kernel_size=3,\n padding=1,\n act_cfg=act_cfg,\n norm_cfg=norm_cfg,\n bias=True,\n order=('norm', 'act', 'conv'))\n\n if self.unet_type == 'stable':\n self.sample_size = image_size // 8 # NOTE: hard code here\n\n self.init_weights(pretrained)\n\n def forward(self,\n x_t,\n t,\n encoder_hidden_states=None,\n label=None,\n return_noise=False):\n \"\"\"Forward function.\n Args:\n x_t (torch.Tensor): Diffused image at timestep `t` to denoise.\n t (torch.Tensor): Current timestep.\n label (torch.Tensor | callable | None): You can directly give a\n batch of label through a ``torch.Tensor`` or offer a callable\n function to sample a batch of label data. Otherwise, the\n ``None`` indicates to use the default label sampler.\n return_noise (bool, optional): If True, inputted ``x_t`` and ``t``\n will be returned in a dict with output desired by\n ``output_cfg``. Defaults to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``\n \"\"\"\n # By default samples have to be AT least a multiple of t\n # he overall upsampling factor.\n # The overall upsampling factor is equal\n # to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size\n # can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not\n # a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in x_t.shape[-2:]):\n logger.info(\n 'Forward upsample size to force interpolation output size.')\n forward_upsample_size = True\n\n if not torch.is_tensor(t):\n t = torch.tensor([t], dtype=torch.long, device=x_t.device)\n elif torch.is_tensor(t) and len(t.shape) == 0:\n t = t[None].to(x_t.device)\n\n if self.unet_type == 'stable':\n # broadcast to batch dimension in a way that's\n # compatible with ONNX/Core ML\n t = t.expand(x_t.shape[0])\n\n t_emb = self.time_proj(t)\n\n # t does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16.\n # so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n embedding = self.time_embedding(t_emb)\n else:\n embedding = self.time_embedding(t)\n\n if label is not None:\n assert hasattr(self, 'label_embedding')\n embedding = self.label_embedding(label) + embedding\n\n if self.unet_type == 'stable':\n # 2. pre-process\n x_t = self.conv_in(x_t)\n\n # 3. down\n down_block_res_samples = (x_t, )\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, 'attentions'\n ) and downsample_block.attentions is not None:\n x_t, res_samples = downsample_block(\n hidden_states=x_t,\n temb=embedding,\n encoder_hidden_states=encoder_hidden_states,\n )\n else:\n x_t, res_samples = downsample_block(\n hidden_states=x_t, temb=embedding)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n x_t = self.mid_block(\n x_t, embedding, encoder_hidden_states=encoder_hidden_states)\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.\n resnets):]\n down_block_res_samples = down_block_res_samples[:-len(\n upsample_block.resnets)]\n\n # if we have not reached the final block\n # and need to forward the upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, 'attentions'\n ) and upsample_block.attentions is not None:\n x_t = upsample_block(\n hidden_states=x_t,\n temb=embedding,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n )\n else:\n x_t = upsample_block(\n hidden_states=x_t,\n temb=embedding,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size)\n # 6. post-process\n x_t = self.conv_norm_out(x_t)\n x_t = self.conv_act(x_t)\n x_t = self.conv_out(x_t)\n\n outputs = x_t\n else:\n h, hs = x_t, []\n h = h.type(self.dtype)\n # forward downsample blocks\n for block in self.in_blocks:\n h = block(h, embedding)\n hs.append(h)\n\n # forward middle blocks\n h = self.mid_blocks(h, embedding)\n\n # forward upsample blocks\n for block in self.out_blocks:\n h = block(torch.cat([h, hs.pop()], dim=1), embedding)\n h = h.type(x_t.dtype)\n outputs = self.out(h)\n\n return {'sample': outputs}\n\n def init_weights(self, pretrained=None):\n \"\"\"Init weights for models.\n\n We just use the initialization method proposed in the original paper.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n # As Improved-DDPM, we apply zero-initialization to\n # second conv block in ResBlock (keywords: conv_2)\n # the output layer of the Unet (keywords: 'out' but\n # not 'out_blocks')\n # projection layer in Attention layer (keywords: proj)\n for n, m in self.named_modules():\n if isinstance(m, nn.Conv2d) and ('conv_2' in n or\n ('out' in n\n and 'out_blocks' not in n)):\n constant_init(m, 0)\n if isinstance(m, nn.Conv1d) and 'proj' in n:\n constant_init(m, 0)\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n\n def convert_to_fp16(self):\n \"\"\"Convert the precision of the model to float16.\"\"\"\n self.in_blocks.apply(convert_module_to_f16)\n self.mid_blocks.apply(convert_module_to_f16)\n self.out_blocks.apply(convert_module_to_f16)\n\n def convert_to_fp32(self):\n \"\"\"Convert the precision of the model to float32.\"\"\"\n self.in_blocks.apply(convert_module_to_f32)\n self.mid_blocks.apply(convert_module_to_f32)\n self.out_blocks.apply(convert_module_to_f32)\n" }, { "path": "mmagic/models/editors/ddpm/embeddings.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport mmengine\nimport torch\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\nfrom torch import nn\n\n\nclass TimestepEmbedding(nn.Module):\n \"\"\"Module which uses linear to embed timestep.\"\"\"\n\n def __init__(self,\n in_channels: int,\n time_embed_dim: int,\n act_fn: str = 'silu',\n out_dim: int = None):\n super().__init__()\n\n self.linear_1 = nn.Linear(in_channels, time_embed_dim)\n self.act = None\n if act_fn == 'silu' and \\\n digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.act = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.act = nn.ReLU()\n\n if out_dim is not None:\n time_embed_dim_out = out_dim\n else:\n time_embed_dim_out = time_embed_dim\n self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)\n\n def forward(self, sample):\n \"\"\"forward with sample.\"\"\"\n\n sample = self.linear_1(sample)\n\n if self.act is not None:\n sample = self.act(sample)\n\n sample = self.linear_2(sample)\n return sample\n\n\nclass Timesteps(nn.Module):\n \"\"\"A module which transforms timesteps to embedding.\"\"\"\n\n def __init__(self,\n num_channels: int,\n flip_sin_to_cos: bool = True,\n downscale_freq_shift: float = 0):\n super().__init__()\n self.num_channels = num_channels\n self.flip_sin_to_cos = flip_sin_to_cos\n self.downscale_freq_shift = downscale_freq_shift\n self.max_period = 10000\n self.scale = 1\n\n def forward(self, timesteps):\n \"\"\"forward with timesteps.\"\"\"\n\n assert len(timesteps.shape) == 1, 'Timesteps should be a 1d-array'\n\n embedding_dim = self.num_channels\n half_dim = embedding_dim // 2\n exponent = -math.log(self.max_period) * \\\n torch.arange(\n start=0,\n end=half_dim,\n dtype=torch.float32,\n device=timesteps.device)\n exponent = exponent / (half_dim - self.downscale_freq_shift)\n\n emb = torch.exp(exponent)\n emb = timesteps[:, None].float() * emb[None, :]\n\n # scale embeddings\n emb = self.scale * emb\n\n # concat sine and cosine embeddings\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)\n\n # flip sine and cosine embeddings\n if self.flip_sin_to_cos:\n emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)\n\n # zero pad\n if embedding_dim % 2 == 1:\n emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))\n return emb\n" }, { "path": "mmagic/models/editors/ddpm/res_blocks.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmengine\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\n\nclass ResnetBlock2D(nn.Module):\n \"\"\"resnet block support down sample and up sample.\n\n Args:\n in_channels (int): input channels.\n out_channels (int): output channels.\n conv_shortcut (bool): whether to use conv shortcut.\n dropout (float): dropout rate.\n temb_channels (int): time embedding channels.\n groups (int): conv groups.\n groups_out (int): conv out groups.\n pre_norm (bool): whether to norm before conv. Todo: remove.\n eps (float): eps for groupnorm.\n non_linearity (str): non linearity type.\n time_embedding_norm (str): time embedding norm type.\n output_scale_factor (float): factor to scale input and output.\n use_in_shortcut (bool): whether to use conv in shortcut.\n up (bool): whether to upsample.\n down (bool): whether to downsample.\n \"\"\"\n\n def __init__(\n self,\n in_channels,\n out_channels=None,\n conv_shortcut=False,\n dropout=0.0,\n temb_channels=512,\n groups=32,\n groups_out=None,\n pre_norm=True,\n eps=1e-6,\n non_linearity='silu',\n time_embedding_norm='default',\n output_scale_factor=1.0,\n use_in_shortcut=None,\n up=False,\n down=False,\n ):\n super().__init__()\n self.pre_norm = pre_norm\n self.pre_norm = True\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.time_embedding_norm = time_embedding_norm\n self.up = up\n self.down = down\n self.output_scale_factor = output_scale_factor\n\n if groups_out is None:\n groups_out = groups\n\n self.norm1 = torch.nn.GroupNorm(\n num_groups=groups, num_channels=in_channels, eps=eps, affine=True)\n\n self.conv1 = torch.nn.Conv2d(\n in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if temb_channels is not None:\n self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)\n else:\n self.time_emb_proj = None\n\n self.norm2 = torch.nn.GroupNorm(\n num_groups=groups_out,\n num_channels=out_channels,\n eps=eps,\n affine=True)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = torch.nn.Conv2d(\n out_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if non_linearity == 'silu' and \\\n digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.nonlinearity = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.nonlinearity = nn.ReLU()\n\n self.upsample = self.downsample = None\n if self.up:\n self.upsample = Upsample2D(in_channels, use_conv=False)\n elif self.down:\n self.downsample = Downsample2D(\n in_channels, use_conv=False, padding=1, name='op')\n\n self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut # noqa\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = torch.nn.Conv2d(\n in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n\n def forward(self, input_tensor, temb):\n \"\"\"forward with hidden states and time embeddings.\"\"\"\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.upsample is not None:\n # upsample_nearest_nhwc fails with large batch sizes.\n # see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n input_tensor = input_tensor.contiguous()\n hidden_states = hidden_states.contiguous()\n input_tensor = self.upsample(input_tensor)\n hidden_states = self.upsample(hidden_states)\n elif self.downsample is not None:\n input_tensor = self.downsample(input_tensor)\n hidden_states = self.downsample(hidden_states)\n\n hidden_states = self.conv1(hidden_states)\n\n if temb is not None:\n temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None,\n None]\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = (input_tensor +\n hidden_states) / self.output_scale_factor\n\n return output_tensor\n\n\nclass Upsample2D(nn.Module):\n \"\"\"An upsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n use_conv_transpose (bool): whether to use conv transpose.\n out_channels (int): output channels.\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n use_conv_transpose=False,\n out_channels=None,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_conv_transpose = use_conv_transpose\n self.name = name\n\n conv = None\n if use_conv:\n conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)\n else:\n conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)\n\n self.conv = conv\n\n def forward(self, hidden_states, output_size=None):\n \"\"\"forward with hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n\n if self.use_conv_transpose:\n return self.conv(hidden_states)\n\n # if `output_size` is passed we force the interpolation output\n # size and do not make use of `scale_factor=2`\n if output_size is None:\n hidden_states = F.interpolate(\n hidden_states, scale_factor=2.0, mode='nearest')\n else:\n hidden_states = F.interpolate(\n hidden_states, size=output_size, mode='nearest')\n\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass Downsample2D(nn.Module):\n \"\"\"A downsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n out_channels (int): output channels\n padding (int): padding num\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n out_channels=None,\n padding=1,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.padding = padding\n stride = 2\n self.name = name\n\n if use_conv:\n conv = nn.Conv2d(\n self.channels,\n self.out_channels,\n 3,\n stride=stride,\n padding=padding)\n else:\n assert self.channels == self.out_channels\n conv = nn.AvgPool2d(kernel_size=stride, stride=stride)\n\n self.conv = conv\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n if self.use_conv and self.padding == 0:\n pad = (0, 1, 0, 1)\n hidden_states = F.pad(hidden_states, pad, mode='constant', value=0)\n\n assert hidden_states.shape[1] == self.channels\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n" }, { "path": "mmagic/models/editors/ddpm/unet_blocks.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom torch import nn\n\nfrom .attention import Transformer2DModel\nfrom .res_blocks import Downsample2D, ResnetBlock2D, Upsample2D\n\n\ndef get_down_block(\n down_block_type,\n num_layers,\n in_channels,\n out_channels,\n temb_channels,\n add_downsample,\n resnet_act_fn,\n attn_num_head_channels,\n resnet_eps=1e-5,\n resnet_groups=32,\n cross_attention_dim=1280,\n downsample_padding=1,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n):\n \"\"\"get unet down path block.\"\"\"\n\n down_block_type = down_block_type[7:] if down_block_type.startswith(\n 'UNetRes') else down_block_type\n if down_block_type == 'DownBlock2D':\n return DownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n )\n elif down_block_type == 'CrossAttnDownBlock2D':\n return CrossAttnDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attn_num_head_channels,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n )\n raise ValueError(f'{down_block_type} does not exist.')\n\n\ndef get_up_block(\n up_block_type,\n num_layers,\n in_channels,\n out_channels,\n prev_output_channel,\n temb_channels,\n add_upsample,\n resnet_act_fn,\n attn_num_head_channels,\n resnet_eps=1e-5,\n resnet_groups=32,\n cross_attention_dim=1280,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n):\n \"\"\"get unet up path block.\"\"\"\n\n up_block_type = up_block_type[7:] if up_block_type.startswith(\n 'UNetRes') else up_block_type\n if up_block_type == 'UpBlock2D':\n return UpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n )\n elif up_block_type == 'CrossAttnUpBlock2D':\n return CrossAttnUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attn_num_head_channels,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n )\n raise ValueError(f'{up_block_type} does not exist.')\n\n\nclass UNetMidBlock2DCrossAttn(nn.Module):\n \"\"\"unet mid block built by cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-5,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n attention_type='default',\n output_scale_factor=1.0,\n cross_attention_dim=1280,\n dual_cross_attention=False,\n use_linear_projection=False,\n ):\n super().__init__()\n\n self.attention_type = attention_type\n self.attn_num_head_channels = attn_num_head_channels\n resnet_groups = resnet_groups if resnet_groups is not None else min(\n in_channels // 4, 32)\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n for _ in range(num_layers):\n attentions.append(\n Transformer2DModel(\n attn_num_head_channels,\n in_channels // attn_num_head_channels,\n in_channels=in_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n ))\n\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def set_attention_slice(self, slice_size):\n \"\"\"set attention slice.\"\"\"\n\n head_dims = self.attn_num_head_channels\n head_dims = [head_dims] if isinstance(head_dims, int) else head_dims\n if slice_size is not None and any(dim % slice_size != 0\n for dim in head_dims):\n raise ValueError(\n f'Make sure slice_size {slice_size} is a common divisor of '\n f'the number of heads used in cross_attention: {head_dims}')\n\n for attn in self.attentions:\n attn._set_attention_slice(slice_size)\n\n def forward(self, hidden_states, temb=None, encoder_hidden_states=None):\n \"\"\"forward with hidden states.\"\"\"\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n hidden_states = attn(hidden_states, encoder_hidden_states).sample\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass CrossAttnDownBlock2D(nn.Module):\n \"\"\"Down block built by cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-5,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n cross_attention_dim=1280,\n attention_type='default',\n output_scale_factor=1.0,\n downsample_padding=1,\n add_downsample=True,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.attention_type = attention_type\n self.attn_num_head_channels = attn_num_head_channels\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n attentions.append(\n Transformer2DModel(\n attn_num_head_channels,\n out_channels // attn_num_head_channels,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n ))\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name='op')\n ])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def set_attention_slice(self, slice_size):\n \"\"\"set attention slice.\"\"\"\n\n head_dims = self.attn_num_head_channels\n head_dims = [head_dims] if isinstance(head_dims, int) else head_dims\n if slice_size is not None and any(dim % slice_size != 0\n for dim in head_dims):\n raise ValueError(\n f'Make sure slice_size {slice_size} is a common divisor of '\n f'the number of heads used in cross_attention: {head_dims}')\n\n for attn in self.attentions:\n attn._set_attention_slice(slice_size)\n\n def forward(self, hidden_states, temb=None, encoder_hidden_states=None):\n \"\"\"forward with hidden states.\"\"\"\n\n output_states = ()\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states).sample\n\n output_states += (hidden_states, )\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states, )\n\n return hidden_states, output_states\n\n\nclass DownBlock2D(nn.Module):\n \"\"\"Down block built by resnet.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-5,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_downsample=True,\n downsample_padding=1,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name='op')\n ])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(self, hidden_states, temb=None):\n \"\"\"forward with hidden states.\"\"\"\n\n output_states = ()\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb)\n\n output_states += (hidden_states, )\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states, )\n\n return hidden_states, output_states\n\n\nclass CrossAttnUpBlock2D(nn.Module):\n \"\"\"Up block built by cross attention.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-5,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n cross_attention_dim=1280,\n attention_type='default',\n output_scale_factor=1.0,\n add_upsample=True,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.attention_type = attention_type\n self.attn_num_head_channels = attn_num_head_channels\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers -\n 1) else out_channels\n resnet_in_channels = \\\n prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n attentions.append(\n Transformer2DModel(\n attn_num_head_channels,\n out_channels // attn_num_head_channels,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n ))\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([\n Upsample2D(\n out_channels, use_conv=True, out_channels=out_channels)\n ])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n\n def set_attention_slice(self, slice_size):\n \"\"\"set attention slice.\"\"\"\n\n head_dims = self.attn_num_head_channels\n head_dims = [head_dims] if isinstance(head_dims, int) else head_dims\n if slice_size is not None and any(dim % slice_size != 0\n for dim in head_dims):\n raise ValueError(\n f'Make sure slice_size {slice_size} is a common divisor of '\n f'the number of heads used in cross_attention: {head_dims}')\n\n for attn in self.attentions:\n attn._set_attention_slice(slice_size)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states,\n res_hidden_states_tuple,\n temb=None,\n encoder_hidden_states=None,\n upsample_size=None,\n ):\n \"\"\"forward with hidden states and res hidden states.\"\"\"\n\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states],\n dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states).sample\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpBlock2D(nn.Module):\n \"\"\"Up block built by resnet.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-5,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_upsample=True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers -\n 1) else out_channels\n resnet_in_channels = \\\n prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([\n Upsample2D(\n out_channels, use_conv=True, out_channels=out_channels)\n ])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(self,\n hidden_states,\n res_hidden_states_tuple,\n temb=None,\n upsample_size=None):\n \"\"\"forward with hidden states and res hidden states.\"\"\"\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states],\n dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n" }, { "path": "mmagic/models/editors/deblurganv2/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .deblurganv2 import DeblurGanV2\nfrom .deblurganv2_discriminator import DeblurGanV2Discriminator\nfrom .deblurganv2_generator import DeblurGanV2Generator\n\n__all__ = ['DeblurGanV2', 'DeblurGanV2Generator', 'DeblurGanV2Discriminator']\n" }, { "path": "mmagic/models/editors/deblurganv2/deblurganv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nfrom mmengine.model import BaseModel\nfrom mmengine.optim import OptimWrapperDict\nfrom torch import nn\n\nfrom mmagic.models.losses import AdvLoss\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass DeblurGanV2(BaseModel):\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n pixel_loss: Optional[Union[dict, str]] = None,\n disc_loss: Optional[Union[dict, str]] = None,\n adv_lambda: float = 0.001,\n warmup_num: int = 3,\n train_cfg: Optional[dict] = None,\n test_cfg: Optional[dict] = None,\n init_cfg: Optional[dict] = None,\n data_preprocessor: Optional[dict] = None):\n\n super().__init__(\n init_cfg=init_cfg, data_preprocessor=data_preprocessor)\n if isinstance(generator, dict):\n self.generator = MODELS.build(generator)\n else:\n self.generator = generator\n\n if discriminator:\n if isinstance(generator, dict):\n self.discriminator = MODELS.build(discriminator)\n else:\n self.discriminator = discriminator\n else:\n self.discriminator = None\n\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n self.epoch_num = 0\n self.warmup_num = warmup_num\n\n self.adv_lambda = adv_lambda\n\n self.register_buffer('step_counter', torch.tensor(0), False)\n\n if pixel_loss:\n self.pixel_loss = MODELS.build(pixel_loss)\n\n if disc_loss:\n if isinstance(disc_loss, dict):\n self.disc_loss = MODELS.build(disc_loss)\n else:\n self.disc_loss = AdvLoss(disc_loss)\n else:\n self.disc_loss = None\n if self.disc_loss and getattr(self.discriminator, 'full_gan', None):\n self.disc_loss2 = copy.deepcopy(self.disc_loss)\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs) -> Union[torch.Tensor, List[DataSample], dict]:\n \"\"\"Returns losses or predictions of training, validation, testing, and\n simple inference process.\n\n ``forward`` method of BaseModel is an abstract method, its subclasses\n must implement this method.\n\n Accepts ``inputs`` and ``data_samples`` processed by\n :attr:`data_preprocessor`, and returns results according to mode\n arguments.\n\n During non-distributed training, validation, and testing process,\n ``forward`` will be called by ``BaseModel.train_step``,\n ``BaseModel.val_step`` and ``BaseModel.val_step`` directly.\n\n During distributed data parallel training process,\n ``MMSeparateDistributedDataParallel.train_step`` will first call\n ``DistributedDataParallel.forward`` to enable automatic\n gradient synchronization, and then call ``forward`` to get training\n loss.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``. Default: 'tensor'.\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == val``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict`` or tensor for custom use.\n \"\"\"\n if isinstance(inputs, dict):\n inputs = inputs['img']\n if mode == 'tensor':\n return self.forward_tensor(inputs, data_samples, **kwargs)\n\n elif mode == 'val':\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n elif mode == 'predict':\n h, w = data_samples.ori_img_shape[0][0:2]\n block_size = 32\n min_height = (h // block_size + 1) * block_size\n min_width = (w // block_size + 1) * block_size\n pad = torch.nn.ZeroPad2d(\n padding=(0, min_width - w, 0, min_height - h))\n inputs = pad(inputs)\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions.pred_img = predictions.pred_img[:, :, :h, :w]\n predictions = self.convert_to_datasample(predictions, data_samples,\n inputs)\n return predictions\n\n elif mode == 'loss':\n return self.forward_train(inputs, data_samples, **kwargs)\n\n def convert_to_datasample(self, predictions: DataSample,\n data_samples: DataSample,\n inputs: Optional[torch.Tensor]\n ) -> List[DataSample]:\n \"\"\"Add predictions and destructed inputs (if passed) to data samples.\n\n Args:\n predictions (DataSample): The predictions of the model.\n data_samples (DataSample): The data samples loaded from\n dataloader.\n inputs (Optional[torch.Tensor]): The input of model. Defaults to\n None.\n\n Returns:\n List[DataSample]: Modified data samples.\n \"\"\"\n\n if inputs is not None:\n destructed_input = self.data_preprocessor.destruct(\n inputs, data_samples, 'img')\n data_samples.set_tensor_data({'input': destructed_input})\n data_samples = data_samples.split()\n predictions = predictions.split()\n\n for data_sample, pred in zip(data_samples, predictions):\n data_sample.output = pred\n\n return data_samples\n\n def forward_tensor(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n if torch.cuda.is_available():\n inputs = inputs.cuda()\n feats = self.generator(inputs)\n return feats\n\n def forward_inference(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n **kwargs) -> List[DataSample]:\n \"\"\"Forward inference. Returns predictions of validation, testing, and\n simple inference.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[EditDataSample]: predictions.\n \"\"\"\n\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n feats = self.data_preprocessor.destruct(feats, data_samples)\n predictions = DataSample(pred_img=feats.cpu())\n\n return predictions\n\n def forward_train(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward training. Losses of training is calculated in train_step.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: Result of ``forward_tensor`` with ``training=True``.\n \"\"\"\n\n return self.forward_tensor(\n inputs, data_samples, training=True, **kwargs)\n\n def val_step(self, data: Union[tuple, dict, list]) -> list:\n \"\"\"Gets the predictions of given data.\n\n Calls ``self.data_preprocessor(data, False)`` and\n ``self(inputs, data_sample, mode='predict')`` in order. Return the\n predictions which will be passed to evaluator.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n list: The predictions of given data.\n \"\"\"\n data = self.data_preprocessor(data, False)\n return self._run_forward(data, mode='val')\n self.epoch_num += 1\n\n def test_step(self, data: Union[dict, tuple, list]) -> list:\n \"\"\"``BaseModel`` implements ``test_step`` the same as ``val_step``.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n list: The predictions of given data.\n \"\"\"\n data = self.data_preprocessor(data, False)\n return self._run_forward(data, mode='predict')\n\n def _run_forward(self, data: Union[dict, tuple, list],\n mode: str) -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"Unpacks data for :meth:`forward`\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n mode (str): Mode of forward.\n\n Returns:\n dict or list: Results of training or testing mode.\n \"\"\"\n if isinstance(data, dict):\n results = self(**data, mode=mode)\n elif isinstance(data, (list, tuple)):\n results = self(*data, mode=mode)\n else:\n raise TypeError('Output of `data_preprocessor` should be '\n f'list, tuple or dict, but got {type(data)}')\n return results\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step of GAN-based method.\n\n Args:\n data (List[dict]): Data sampled from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance\n used to update model parameters.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n data = self.data_preprocessor(data, True)\n batch_inputs = data['inputs']\n\n data_samples = data['data_samples']\n batch_gt_data = self.extract_gt_data(data_samples)\n\n log_vars = dict()\n\n if self.warmup_num == self.epoch_num:\n self.generator.module.unfreeze()\n\n g_optim_wrapper = optim_wrapper['generator']\n with g_optim_wrapper.optim_context(self):\n batch_outputs = self.forward_train(batch_inputs, data_samples)\n\n log_vars_d = self.d_step_with_optim(\n batch_outputs=batch_outputs.detach(),\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n log_vars_d = self.g_step_with_optim(\n batch_outputs=batch_outputs,\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if 'loss' in log_vars:\n log_vars.pop('loss')\n\n self.step_counter += 1\n\n return log_vars\n\n def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Tensor):\n \"\"\"G step of DobuleGAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n losses = dict()\n loss_gx = self.pixel_loss(batch_outputs, batch_gt_data)\n batch_outputs2 = ((batch_outputs + 1) / 2.0 -\n self.pixel_loss.vgg.mean) / self.pixel_loss.vgg.std\n batch_gt_data2 = ((batch_gt_data + 1) / 2.0 -\n self.pixel_loss.vgg.mean) / self.pixel_loss.vgg.std\n loss_gp = torch.nn.MSELoss()(batch_outputs2, batch_gt_data2)\n losses['loss_g_content'] = 0.006 * loss_gx[0] + 0.5 * loss_gp\n\n if getattr(self.discriminator, 'full_gan', None):\n losses['loss_g_adv'] = self.adv_lambda * (self.disc_loss(\n self.discriminator.patch_gan,\n batch_outputs,\n batch_gt_data,\n model='generator') + self.disc_loss2(\n self.discriminator.full_gan,\n batch_outputs,\n batch_gt_data,\n model='generator')) / 2\n else:\n losses['loss_g_adv'] = self.adv_lambda * (\n self.disc_loss(\n self.discriminator.patch_gan,\n batch_outputs,\n batch_gt_data,\n model='generator'))\n losses['loss_g'] = losses['loss_g_content'] + losses['loss_g_adv']\n\n return losses\n\n def d_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Tensor):\n \"\"\"D step of DobuleGAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n if getattr(self.discriminator, 'full_gan', None):\n loss_d = (self.disc_loss(self.discriminator.patch_gan,\n batch_outputs, batch_gt_data) +\n self.disc_loss2(self.discriminator.full_gan,\n batch_outputs, batch_gt_data)) / 2\n else:\n loss_d = self.disc_loss(self.discriminator.patch_gan,\n batch_outputs, batch_gt_data)\n return loss_d\n\n def g_step_with_optim(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor,\n optim_wrapper: OptimWrapperDict):\n \"\"\"G step with optim of GAN: Calculate losses of generator and run\n optim.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n optim_wrapper (OptimWrapperDict): Optim wrapper dict.\n\n Returns:\n dict: Dict of parsed losses.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n g_optim_wrapper.zero_grad()\n with g_optim_wrapper.optim_context(self):\n losses_g_double = self.g_step(batch_outputs, batch_gt_data)\n\n parsed_losses_g, log_vars_g = self.parse_losses(losses_g_double)\n loss_pix = g_optim_wrapper.scale_loss(parsed_losses_g)\n g_optim_wrapper.backward(loss_pix)\n g_optim_wrapper.step()\n\n return log_vars_g\n\n def d_step_with_optim(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor,\n optim_wrapper: OptimWrapperDict):\n \"\"\"D step with optim of GAN: Calculate losses of discriminator and run\n optim.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n optim_wrapper (OptimWrapperDict): Optim wrapper dict.\n\n Returns:\n dict: Dict of parsed losses.\n \"\"\"\n\n log_vars = dict()\n d_optim_wrapper = optim_wrapper['discriminator']\n d_optim_wrapper.zero_grad()\n with d_optim_wrapper.optim_context(self):\n\n loss_d_double = self.adv_lambda * self.d_step(\n batch_outputs, batch_gt_data)\n\n parsed_losses_df, log_vars_df = self.parse_losses(\n dict(loss_d=loss_d_double))\n log_vars.update(log_vars_df)\n loss_df = d_optim_wrapper.scale_loss(parsed_losses_df)\n d_optim_wrapper.backward(loss_df, retain_graph=True)\n d_optim_wrapper.step()\n\n return log_vars\n\n def extract_gt_data(self, data_samples):\n \"\"\"extract gt data from data samples.\n\n Args:\n data_samples (list): List of DataSample.\n\n Returns:\n Tensor: Extract gt data.\n \"\"\"\n\n batch_gt_data = data_samples.gt_img\n\n return batch_gt_data\n" }, { "path": "mmagic/models/editors/deblurganv2/deblurganv2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport functools\n\nimport numpy as np\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom .deblurganv2_util import get_norm_layer\n\nbackbone_list = ['DoubleGan', 'MultiScale', 'NoGan', 'PatchGan']\n\n\nclass NLayerDiscriminator(nn.Module):\n \"\"\"Defines the PatchGAN discriminator with the specified arguments.\"\"\"\n\n def __init__(self,\n input_nc=3,\n ndf=64,\n n_layers=3,\n norm_layer=nn.BatchNorm2d,\n use_sigmoid=False,\n use_parallel=True):\n super(NLayerDiscriminator, self).__init__()\n self.use_parallel = use_parallel\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n kw = 4\n padw = int(np.ceil((kw - 1) / 2))\n sequence = [\n nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult = 1\n for n in range(1, n_layers):\n nf_mult_prev = nf_mult\n nf_mult = min(2**n, 8)\n sequence += [\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=2,\n padding=padw,\n bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult_prev = nf_mult\n nf_mult = min(2**n_layers, 8)\n sequence += [\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=1,\n padding=padw,\n bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n sequence += [\n nn.Conv2d(\n ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)\n ]\n\n if use_sigmoid:\n sequence += [nn.Sigmoid()]\n\n self.model = nn.Sequential(*sequence)\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n return self.model(input)\n\n\nclass DicsriminatorTail(nn.Module):\n\n def __init__(self,\n nf_mult,\n n_layers,\n ndf=64,\n norm_layer=nn.BatchNorm2d,\n use_parallel=True):\n super(DicsriminatorTail, self).__init__()\n self.use_parallel = use_parallel\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n kw = 4\n padw = int(np.ceil((kw - 1) / 2))\n\n nf_mult_prev = nf_mult\n nf_mult = min(2**n_layers, 8)\n sequence = [\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=1,\n padding=padw,\n bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n sequence += [\n nn.Conv2d(\n ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)\n ]\n\n self.model = nn.Sequential(*sequence)\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n return self.model(input)\n\n\nclass MultiScaleDiscriminator(nn.Module):\n \"\"\"Defines the MultiScale PatchGAN discriminator with the specified\n arguments.\"\"\"\n\n def __init__(self,\n input_nc=3,\n ndf=64,\n norm_layer=nn.BatchNorm2d,\n use_parallel=True):\n super(MultiScaleDiscriminator, self).__init__()\n self.use_parallel = use_parallel\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n kw = 4\n padw = int(np.ceil((kw - 1) / 2))\n sequence = [\n nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult = 1\n for n in range(1, 3):\n nf_mult_prev = nf_mult\n nf_mult = min(2**n, 8)\n sequence += [\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=2,\n padding=padw,\n bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n self.scale_one = nn.Sequential(*sequence)\n self.first_tail = DicsriminatorTail(nf_mult=nf_mult, n_layers=3)\n nf_mult_prev = 4\n nf_mult = 8\n\n self.scale_two = nn.Sequential(\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=2,\n padding=padw,\n bias=use_bias), norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True))\n nf_mult_prev = nf_mult\n self.second_tail = DicsriminatorTail(nf_mult=nf_mult, n_layers=4)\n self.scale_three = nn.Sequential(\n nn.Conv2d(\n ndf * nf_mult_prev,\n ndf * nf_mult,\n kernel_size=kw,\n stride=2,\n padding=padw,\n bias=use_bias), norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True))\n self.third_tail = DicsriminatorTail(nf_mult=nf_mult, n_layers=5)\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x = self.scale_one(input)\n x_1 = self.first_tail(x)\n x = self.scale_two(x)\n x_2 = self.second_tail(x)\n x = self.scale_three(x)\n x = self.third_tail(x)\n return [x_1, x_2, x]\n\n\ndef get_fullD(norm_layer):\n \"\"\"Get a full gan discriminator.\n\n Args:\n norm_layer (Str): norm type\n \"\"\"\n model_d = NLayerDiscriminator(\n n_layers=5,\n norm_layer=get_norm_layer(norm_type=norm_layer),\n use_sigmoid=False)\n return model_d\n\n\nclass DoubleGan(nn.Module):\n \"\"\"Get a discriminator with a patch gan and a full gan.\"\"\"\n\n def __init__(self, norm_layer='instance', d_layers=3):\n super().__init__()\n self.patch_gan = NLayerDiscriminator(\n n_layers=d_layers,\n norm_layer=get_norm_layer(norm_type=norm_layer),\n use_sigmoid=False)\n self.full_gan = get_fullD(norm_layer)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n List(torch.Tensor) : ``List(torch.tensor)`` will be returned.\n \"\"\"\n # d_full_gan = self.model_d['full'](x)\n d_full_gan_output = self.full_gan(x)\n # d_patch_gan = self.model_d['patch'](x)\n d_patch_gan_output = self.patch_gan(x)\n return [d_full_gan_output, d_patch_gan_output]\n\n\nclass PatchGan(nn.Module):\n \"\"\"A patch gan discriminator with the specified arguments.\"\"\"\n\n def __init__(self, norm_layer='instance', d_layers=3):\n super().__init__()\n self.patch_gan = NLayerDiscriminator(\n n_layers=d_layers,\n norm_layer=get_norm_layer(norm_type=norm_layer),\n use_sigmoid=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n d_patch_gan_output = self.patch_gan(x)\n return d_patch_gan_output\n\n\nclass MultiScale(nn.Module):\n \"\"\"A multiscale patch gan discriminator with the specified arguments.\"\"\"\n\n def __init__(self, norm_layer='instance', d_layers=3):\n super().__init__()\n self.model_d = MultiScaleDiscriminator(\n norm_layer=get_norm_layer(norm_type=norm_layer))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n result_d = self.model_d(x)\n return result_d\n\n\n@MODELS.register_module()\nclass DeblurGanV2Discriminator:\n \"\"\"Defines the discriminator for DeblurGanv2 with the specified arguments..\n\n Args:\n model (Str): Type of the discriminator model\n \"\"\"\n\n def __new__(cls, backbone, *args, **kwargs):\n if backbone == 'DoubleGan':\n return DoubleGan(*args, **kwargs)\n elif backbone == 'NoGan' or backbone == '':\n return super().__new__(cls)\n elif backbone == 'PatchGan':\n return PatchGan(*args, **kwargs)\n elif backbone == 'MultiScale':\n return MultiScale(*args, **kwargs)\n else:\n raise Exception('Discriminator model {} not found, '\n 'Please use the following models: '\n '{}'.format(backbone, backbone_list))\n" }, { "path": "mmagic/models/editors/deblurganv2/deblurganv2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\nfrom .deblurganv2_util import MobileNetV2, get_norm_layer, inceptionresnetv2\n\nbackbone_list = ['FPNInception', 'FPNMobileNet', 'FPNInceptionSimple']\n\n\nclass FPNHead(nn.Module):\n \"\"\"Head for FPNInception,FPNInceptionSimple and FPNMobilenet.\"\"\"\n\n def __init__(self, num_in, num_mid, num_out):\n super().__init__()\n\n self.block0 = nn.Conv2d(\n num_in, num_mid, kernel_size=3, padding=1, bias=False)\n self.block1 = nn.Conv2d(\n num_mid, num_out, kernel_size=3, padding=1, bias=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x = nn.functional.relu(self.block0(x), inplace=True)\n x = nn.functional.relu(self.block1(x), inplace=True)\n return x\n\n\nclass FPN_inception(nn.Module):\n\n def __init__(self, norm_layer, num_filter=256, pretrained='imagenet'):\n \"\"\"Creates an `FPN` instance for feature extraction.\n\n Args:\n num_filter: the number of filters in each output pyramid level\n pretrained: use ImageNet pre-trained backbone feature extractor\n \"\"\"\n\n super().__init__()\n self.inception = inceptionresnetv2(\n num_classes=1000, pretrained=pretrained)\n\n self.enc0 = self.inception.conv2d_1a\n self.enc1 = nn.Sequential(\n self.inception.conv2d_2a,\n self.inception.conv2d_2b,\n self.inception.maxpool_3a,\n ) # 64\n self.enc2 = nn.Sequential(\n self.inception.conv2d_3b,\n self.inception.conv2d_4a,\n self.inception.maxpool_5a,\n ) # 192\n self.enc3 = nn.Sequential(\n self.inception.mixed_5b,\n self.inception.repeat,\n self.inception.mixed_6a,\n ) # 1088\n self.enc4 = nn.Sequential(\n self.inception.repeat_1,\n self.inception.mixed_7a,\n ) # 2080\n self.td1 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter), nn.ReLU(inplace=True))\n self.td2 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter), nn.ReLU(inplace=True))\n self.td3 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter), nn.ReLU(inplace=True))\n self.pad = nn.ReflectionPad2d(1)\n self.lateral4 = nn.Conv2d(2080, num_filter, kernel_size=1, bias=False)\n self.lateral3 = nn.Conv2d(1088, num_filter, kernel_size=1, bias=False)\n self.lateral2 = nn.Conv2d(192, num_filter, kernel_size=1, bias=False)\n self.lateral1 = nn.Conv2d(64, num_filter, kernel_size=1, bias=False)\n self.lateral0 = nn.Conv2d(\n 32, num_filter // 2, kernel_size=1, bias=False)\n\n for param in self.inception.parameters():\n param.requires_grad = False\n\n def unfreeze(self):\n \"\"\"Unfreeze params.\"\"\"\n for param in self.inception.parameters():\n param.requires_grad = True\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n # Bottom-up pathway, from ResNet\n enc0 = self.enc0(x)\n\n enc1 = self.enc1(enc0) # 256\n\n enc2 = self.enc2(enc1) # 512\n\n enc3 = self.enc3(enc2) # 1024\n\n enc4 = self.enc4(enc3) # 2048\n\n # Lateral connections\n\n lateral4 = self.pad(self.lateral4(enc4))\n lateral3 = self.pad(self.lateral3(enc3))\n lateral2 = self.lateral2(enc2)\n lateral1 = self.pad(self.lateral1(enc1))\n lateral0 = self.lateral0(enc0)\n\n # Top-down pathway\n pad = (1, 2, 1, 2) # pad last dim by 1 on each side\n pad1 = (0, 1, 0, 1)\n map4 = lateral4\n map3 = self.td1(\n lateral3 +\n nn.functional.upsample(map4, scale_factor=2, mode='nearest'))\n map2 = self.td2(\n F.pad(lateral2, pad, 'reflect') +\n nn.functional.upsample(map3, scale_factor=2, mode='nearest'))\n map1 = self.td3(\n lateral1 +\n nn.functional.upsample(map2, scale_factor=2, mode='nearest'))\n return F.pad(lateral0, pad1, 'reflect'), map1, map2, map3, map4\n\n\nclass FPNInception(nn.Module):\n \"\"\"Feature Pyramid Network (FPN) with four feature maps of resolutions 1/4,\n 1/8, 1/16, 1/32 and `num_filter` filters for all feature maps.\"\"\"\n\n def __init__(self,\n norm_layer,\n output_ch=3,\n num_filter=128,\n num_filter_fpn=256):\n super().__init__()\n\n norm_layer = get_norm_layer(norm_type=norm_layer)\n self.fpn = FPN_inception(\n num_filter=num_filter_fpn, norm_layer=norm_layer)\n\n # The segmentation heads on top of the FPN\n\n self.head1 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head2 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head3 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head4 = FPNHead(num_filter_fpn, num_filter, num_filter)\n\n self.smooth = nn.Sequential(\n nn.Conv2d(4 * num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter),\n nn.ReLU(),\n )\n\n self.smooth2 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter // 2, kernel_size=3, padding=1),\n norm_layer(num_filter // 2),\n nn.ReLU(),\n )\n\n self.final = nn.Conv2d(\n num_filter // 2, output_ch, kernel_size=3, padding=1)\n\n def unfreeze(self):\n \"\"\"Unfreeze params.\"\"\"\n self.fpn.unfreeze()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n map0, map1, map2, map3, map4 = self.fpn(x)\n\n map4 = nn.functional.upsample(\n self.head4(map4), scale_factor=8, mode='nearest')\n map3 = nn.functional.upsample(\n self.head3(map3), scale_factor=4, mode='nearest')\n map2 = nn.functional.upsample(\n self.head2(map2), scale_factor=2, mode='nearest')\n map1 = nn.functional.upsample(\n self.head1(map1), scale_factor=1, mode='nearest')\n\n smoothed = self.smooth(torch.cat([map4, map3, map2, map1], dim=1))\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n smoothed = self.smooth2(smoothed + map0)\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n\n final = self.final(smoothed)\n res = torch.tanh(final) + x\n\n return torch.clamp(res, min=-1, max=1)\n\n\nclass FPN_inceptionsimple(nn.Module):\n\n def __init__(self, norm_layer, num_filters=256):\n \"\"\"Creates an `FPN` instance for feature extraction.\n\n Args:\n num_filters: the number of filters in each output pyramid level\n pretrained: use ImageNet pre-trained backbone feature extractor\n \"\"\"\n\n super().__init__()\n self.inception = inceptionresnetv2(\n num_classes=1000, pretrained='imagenet')\n\n self.enc0 = self.inception.conv2d_1a\n self.enc1 = nn.Sequential(\n self.inception.conv2d_2a,\n self.inception.conv2d_2b,\n self.inception.maxpool_3a,\n ) # 64\n self.enc2 = nn.Sequential(\n self.inception.conv2d_3b,\n self.inception.conv2d_4a,\n self.inception.maxpool_5a,\n ) # 192\n self.enc3 = nn.Sequential(\n self.inception.mixed_5b,\n self.inception.repeat,\n self.inception.mixed_6a,\n ) # 1088\n self.enc4 = nn.Sequential(\n self.inception.repeat_1,\n self.inception.mixed_7a,\n ) # 2080\n\n self.pad = nn.ReflectionPad2d(1)\n self.lateral4 = nn.Conv2d(2080, num_filters, kernel_size=1, bias=False)\n self.lateral3 = nn.Conv2d(1088, num_filters, kernel_size=1, bias=False)\n self.lateral2 = nn.Conv2d(192, num_filters, kernel_size=1, bias=False)\n self.lateral1 = nn.Conv2d(64, num_filters, kernel_size=1, bias=False)\n self.lateral0 = nn.Conv2d(\n 32, num_filters // 2, kernel_size=1, bias=False)\n\n for param in self.inception.parameters():\n param.requires_grad = False\n\n def unfreeze(self):\n \"\"\"Unfreeze params.\"\"\"\n for param in self.inception.parameters():\n param.requires_grad = True\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n # Bottom-up pathway, from ResNet\n enc0 = self.enc0(x)\n\n enc1 = self.enc1(enc0) # 256\n\n enc2 = self.enc2(enc1) # 512\n\n enc3 = self.enc3(enc2) # 1024\n\n enc4 = self.enc4(enc3) # 2048\n\n # Lateral connections\n\n lateral4 = self.pad(self.lateral4(enc4))\n lateral3 = self.pad(self.lateral3(enc3))\n lateral2 = self.lateral2(enc2)\n lateral1 = self.pad(self.lateral1(enc1))\n lateral0 = self.lateral0(enc0)\n\n # Top-down pathway\n pad = (1, 2, 1, 2) # pad last dim by 1 on each side\n pad1 = (0, 1, 0, 1)\n map4 = lateral4\n map3 = lateral3 + nn.functional.upsample(\n map4, scale_factor=2, mode='nearest')\n map2 = F.pad(lateral2, pad, 'reflect') + nn.functional.upsample(\n map3, scale_factor=2, mode='nearest')\n map1 = lateral1 + nn.functional.upsample(\n map2, scale_factor=2, mode='nearest')\n return F.pad(lateral0, pad1, 'reflect'), map1, map2, map3, map4\n\n\nclass FPNInceptionSimple(nn.Module):\n \"\"\"Feature Pyramid Network (FPN) with four feature maps of resolutions 1/4,\n 1/8, 1/16, 1/32 and `num_filter` filters for all feature maps.\"\"\"\n\n def __init__(self,\n norm_layer,\n output_ch=3,\n num_filter=128,\n num_filter_fpn=256):\n super().__init__()\n\n norm_layer = get_norm_layer(norm_type=norm_layer)\n self.fpn = FPN_inceptionsimple(\n num_filter=num_filter_fpn, norm_layer=norm_layer)\n\n # The segmentation heads on top of the FPN\n\n self.head1 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head2 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head3 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head4 = FPNHead(num_filter_fpn, num_filter, num_filter)\n\n self.smooth = nn.Sequential(\n nn.Conv2d(4 * num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter),\n nn.ReLU(),\n )\n\n self.smooth2 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter // 2, kernel_size=3, padding=1),\n norm_layer(num_filter // 2),\n nn.ReLU(),\n )\n\n self.final = nn.Conv2d(\n num_filter // 2, output_ch, kernel_size=3, padding=1)\n\n def unfreeze(self):\n \"\"\"unfreeze the fpn network.\"\"\"\n self.fpn.unfreeze()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n map0, map1, map2, map3, map4 = self.fpn(x)\n\n map4 = nn.functional.upsample(\n self.head4(map4), scale_factor=8, mode='nearest')\n map3 = nn.functional.upsample(\n self.head3(map3), scale_factor=4, mode='nearest')\n map2 = nn.functional.upsample(\n self.head2(map2), scale_factor=2, mode='nearest')\n map1 = nn.functional.upsample(\n self.head1(map1), scale_factor=1, mode='nearest')\n\n smoothed = self.smooth(torch.cat([map4, map3, map2, map1], dim=1))\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n smoothed = self.smooth2(smoothed + map0)\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n\n final = self.final(smoothed)\n res = torch.tanh(final) + x\n\n return torch.clamp(res, min=-1, max=1)\n\n\nclass FPN_mobilenet(nn.Module):\n\n def __init__(self, norm_layer, num_filters=128, pretrained=None):\n \"\"\"Creates an `FPN` instance for feature extraction.\n\n Args:\n num_filters: the number of filters in each output pyramid level\n pretrained: use ImageNet pre-trained backbone feature extractor\n \"\"\"\n\n super().__init__()\n net = MobileNetV2(n_class=1000)\n\n if pretrained:\n # Load weights into the project directory\n if torch.cuda.is_available():\n state_dict = torch.load(\n pretrained) # add map_location='cpu' if no gpu\n else:\n state_dict = torch.load(pretrained, map_location='cpu')\n net.load_state_dict(state_dict)\n self.features = net.features\n\n self.enc0 = nn.Sequential(*self.features[0:2])\n self.enc1 = nn.Sequential(*self.features[2:4])\n self.enc2 = nn.Sequential(*self.features[4:7])\n self.enc3 = nn.Sequential(*self.features[7:11])\n self.enc4 = nn.Sequential(*self.features[11:16])\n\n self.td1 = nn.Sequential(\n nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),\n norm_layer(num_filters), nn.ReLU(inplace=True))\n self.td2 = nn.Sequential(\n nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),\n norm_layer(num_filters), nn.ReLU(inplace=True))\n self.td3 = nn.Sequential(\n nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),\n norm_layer(num_filters), nn.ReLU(inplace=True))\n\n self.lateral4 = nn.Conv2d(160, num_filters, kernel_size=1, bias=False)\n self.lateral3 = nn.Conv2d(64, num_filters, kernel_size=1, bias=False)\n self.lateral2 = nn.Conv2d(32, num_filters, kernel_size=1, bias=False)\n self.lateral1 = nn.Conv2d(24, num_filters, kernel_size=1, bias=False)\n self.lateral0 = nn.Conv2d(\n 16, num_filters // 2, kernel_size=1, bias=False)\n\n for param in self.features.parameters():\n param.requires_grad = False\n\n def unfreeze(self):\n \"\"\"Unfreeze params.\"\"\"\n for param in self.features.parameters():\n param.requires_grad = True\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n # Bottom-up pathway, from ResNet\n enc0 = self.enc0(x)\n\n enc1 = self.enc1(enc0) # 256\n\n enc2 = self.enc2(enc1) # 512\n\n enc3 = self.enc3(enc2) # 1024\n\n enc4 = self.enc4(enc3) # 2048\n\n # Lateral connections\n\n lateral4 = self.lateral4(enc4)\n lateral3 = self.lateral3(enc3)\n lateral2 = self.lateral2(enc2)\n lateral1 = self.lateral1(enc1)\n lateral0 = self.lateral0(enc0)\n\n # Top-down pathway\n map4 = lateral4\n map3 = self.td1(\n lateral3 +\n nn.functional.upsample(map4, scale_factor=2, mode='nearest'))\n map2 = self.td2(\n lateral2 +\n nn.functional.upsample(map3, scale_factor=2, mode='nearest'))\n map1 = self.td3(\n lateral1 +\n nn.functional.upsample(map2, scale_factor=2, mode='nearest'))\n return lateral0, map1, map2, map3, map4\n\n\nclass FPNMobileNet(nn.Module):\n\n def __init__(self,\n norm_layer,\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128,\n pretrained=None):\n super().__init__()\n\n # Feature Pyramid Network (FPN) with four feature maps of resolutions\n # 1/4, 1/8, 1/16, 1/32 and `num_filters` filters for all feature maps.\n norm_layer = get_norm_layer(norm_type=norm_layer)\n self.fpn = FPN_mobilenet(\n num_filters=num_filter_fpn,\n norm_layer=norm_layer,\n pretrained=pretrained)\n\n # The segmentation heads on top of the FPN\n\n self.head1 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head2 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head3 = FPNHead(num_filter_fpn, num_filter, num_filter)\n self.head4 = FPNHead(num_filter_fpn, num_filter, num_filter)\n\n self.smooth = nn.Sequential(\n nn.Conv2d(4 * num_filter, num_filter, kernel_size=3, padding=1),\n norm_layer(num_filter),\n nn.ReLU(),\n )\n\n self.smooth2 = nn.Sequential(\n nn.Conv2d(num_filter, num_filter // 2, kernel_size=3, padding=1),\n norm_layer(num_filter // 2),\n nn.ReLU(),\n )\n\n self.final = nn.Conv2d(\n num_filter // 2, output_ch, kernel_size=3, padding=1)\n\n def unfreeze(self):\n \"\"\"unfreeze the fpn network.\"\"\"\n self.fpn.unfreeze()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n map0, map1, map2, map3, map4 = self.fpn(x)\n\n map4 = nn.functional.upsample(\n self.head4(map4), scale_factor=8, mode='nearest')\n map3 = nn.functional.upsample(\n self.head3(map3), scale_factor=4, mode='nearest')\n map2 = nn.functional.upsample(\n self.head2(map2), scale_factor=2, mode='nearest')\n map1 = nn.functional.upsample(\n self.head1(map1), scale_factor=1, mode='nearest')\n\n smoothed = self.smooth(torch.cat([map4, map3, map2, map1], dim=1))\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n smoothed = self.smooth2(smoothed + map0)\n smoothed = nn.functional.upsample(\n smoothed, scale_factor=2, mode='nearest')\n\n final = self.final(smoothed)\n res = torch.tanh(final) + x\n\n return torch.clamp(res, min=-1, max=1)\n\n\n@MODELS.register_module()\nclass DeblurGanV2Generator:\n \"\"\"Defines the generator for DeblurGanv2 with the specified arguments..\n\n Args:\n model (Str): Type of the generator model\n \"\"\"\n\n def __new__(cls, backbone, *args, **kwargs):\n if backbone == 'FPNInception':\n return FPNInception(*args, **kwargs)\n elif backbone == 'FPNMobileNet':\n return FPNMobileNet(*args, **kwargs)\n elif backbone == 'FPNInceptionSimple':\n return FPNInceptionSimple(*args, **kwargs)\n else:\n raise Exception('Generator model {} not found, '\n 'Please use the following models: '\n '{}'.format(backbone, backbone_list))\n" }, { "path": "mmagic/models/editors/deblurganv2/deblurganv2_util.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom __future__ import absolute_import, division, print_function\nimport functools\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils import model_zoo\n\npretrained_settings = {\n 'inceptionresnetv2': {\n 'imagenet': {\n 'url':\n 'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'\n 'weight/inceptionresnetv2-520b38e4.pth',\n 'input_space': 'RGB',\n 'input_size': [3, 299, 299],\n 'input_range': [0, 1],\n 'mean': [0.5, 0.5, 0.5],\n 'std': [0.5, 0.5, 0.5],\n 'num_classes': 1000\n },\n 'imagenet+background': {\n 'url':\n 'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'\n 'weight/inceptionresnetv2-520b38e4.pth',\n 'input_space': 'RGB',\n 'input_size': [3, 299, 299],\n 'input_range': [0, 1],\n 'mean': [0.5, 0.5, 0.5],\n 'std': [0.5, 0.5, 0.5],\n 'num_classes': 1001\n }\n }\n}\n\n\nclass BasicConv2d(nn.Module):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):\n super(BasicConv2d, self).__init__()\n self.conv = nn.Conv2d(\n in_planes,\n out_planes,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=False) # verify bias false\n self.bn = nn.BatchNorm2d(\n out_planes,\n eps=0.001, # value found in tensorflow\n momentum=0.1, # default pytorch value\n affine=True)\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x = self.conv(x)\n x = self.bn(x)\n x = self.relu(x)\n return x\n\n\nclass Mixed_5b(nn.Module):\n\n def __init__(self):\n super(Mixed_5b, self).__init__()\n\n self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)\n\n self.branch1 = nn.Sequential(\n BasicConv2d(192, 48, kernel_size=1, stride=1),\n BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2))\n\n self.branch2 = nn.Sequential(\n BasicConv2d(192, 64, kernel_size=1, stride=1),\n BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),\n BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1))\n\n self.branch3 = nn.Sequential(\n nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),\n BasicConv2d(192, 64, kernel_size=1, stride=1))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n x3 = self.branch3(x)\n out = torch.cat((x0, x1, x2, x3), 1)\n return out\n\n\nclass Block35(nn.Module):\n\n def __init__(self, scale=1.0):\n super(Block35, self).__init__()\n\n self.scale = scale\n\n self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)\n\n self.branch1 = nn.Sequential(\n BasicConv2d(320, 32, kernel_size=1, stride=1),\n BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1))\n\n self.branch2 = nn.Sequential(\n BasicConv2d(320, 32, kernel_size=1, stride=1),\n BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),\n BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1))\n\n self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n out = torch.cat((x0, x1, x2), 1)\n out = self.conv2d(out)\n out = out * self.scale + x\n out = self.relu(out)\n return out\n\n\nclass Mixed_6a(nn.Module):\n\n def __init__(self):\n super(Mixed_6a, self).__init__()\n\n self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)\n\n self.branch1 = nn.Sequential(\n BasicConv2d(320, 256, kernel_size=1, stride=1),\n BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),\n BasicConv2d(256, 384, kernel_size=3, stride=2))\n\n self.branch2 = nn.MaxPool2d(3, stride=2)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n out = torch.cat((x0, x1, x2), 1)\n return out\n\n\nclass Block17(nn.Module):\n\n def __init__(self, scale=1.0):\n super(Block17, self).__init__()\n\n self.scale = scale\n\n self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)\n\n self.branch1 = nn.Sequential(\n BasicConv2d(1088, 128, kernel_size=1, stride=1),\n BasicConv2d(\n 128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),\n BasicConv2d(\n 160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)))\n\n self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n out = torch.cat((x0, x1), 1)\n out = self.conv2d(out)\n out = out * self.scale + x\n out = self.relu(out)\n return out\n\n\nclass Mixed_7a(nn.Module):\n\n def __init__(self):\n super(Mixed_7a, self).__init__()\n\n self.branch0 = nn.Sequential(\n BasicConv2d(1088, 256, kernel_size=1, stride=1),\n BasicConv2d(256, 384, kernel_size=3, stride=2))\n\n self.branch1 = nn.Sequential(\n BasicConv2d(1088, 256, kernel_size=1, stride=1),\n BasicConv2d(256, 288, kernel_size=3, stride=2))\n\n self.branch2 = nn.Sequential(\n BasicConv2d(1088, 256, kernel_size=1, stride=1),\n BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),\n BasicConv2d(288, 320, kernel_size=3, stride=2))\n\n self.branch3 = nn.MaxPool2d(3, stride=2)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n x3 = self.branch3(x)\n out = torch.cat((x0, x1, x2, x3), 1)\n return out\n\n\nclass Block8(nn.Module):\n\n def __init__(self, scale=1.0, noReLU=False):\n super(Block8, self).__init__()\n\n self.scale = scale\n self.noReLU = noReLU\n\n self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)\n\n self.branch1 = nn.Sequential(\n BasicConv2d(2080, 192, kernel_size=1, stride=1),\n BasicConv2d(\n 192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),\n BasicConv2d(\n 224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)))\n\n self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)\n if not self.noReLU:\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x0 = self.branch0(x)\n x1 = self.branch1(x)\n out = torch.cat((x0, x1), 1)\n out = self.conv2d(out)\n out = out * self.scale + x\n if not self.noReLU:\n out = self.relu(out)\n return out\n\n\nclass InceptionResNetV2(nn.Module):\n \"\"\"Define a inceptionresnetv2 model.\"\"\"\n\n def __init__(self, num_classes=1001):\n super(InceptionResNetV2, self).__init__()\n # Special attributes\n self.input_space = None\n self.input_size = (299, 299, 3)\n self.mean = None\n self.std = None\n # Modules\n self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)\n self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)\n self.conv2d_2b = BasicConv2d(\n 32, 64, kernel_size=3, stride=1, padding=1)\n self.maxpool_3a = nn.MaxPool2d(3, stride=2)\n self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)\n self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)\n self.maxpool_5a = nn.MaxPool2d(3, stride=2)\n self.mixed_5b = Mixed_5b()\n self.repeat = nn.Sequential(\n Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),\n Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),\n Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),\n Block35(scale=0.17))\n self.mixed_6a = Mixed_6a()\n self.repeat_1 = nn.Sequential(\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),\n Block17(scale=0.10), Block17(scale=0.10))\n self.mixed_7a = Mixed_7a()\n self.repeat_2 = nn.Sequential(\n Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),\n Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),\n Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20))\n self.block8 = Block8(noReLU=True)\n self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)\n self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)\n self.last_linear = nn.Linear(1536, num_classes)\n\n def features(self, input):\n \"\"\"Get network features.\n\n Args:\n input (torch.tensor): You can directly input a ``torch.Tensor``.\n \"\"\"\n x = self.conv2d_1a(input)\n x = self.conv2d_2a(x)\n x = self.conv2d_2b(x)\n x = self.maxpool_3a(x)\n x = self.conv2d_3b(x)\n x = self.conv2d_4a(x)\n x = self.maxpool_5a(x)\n x = self.mixed_5b(x)\n x = self.repeat(x)\n x = self.mixed_6a(x)\n x = self.repeat_1(x)\n x = self.mixed_7a(x)\n x = self.repeat_2(x)\n x = self.block8(x)\n x = self.conv2d_7b(x)\n return x\n\n def logits(self, features):\n \"\"\"Get features logits.\n\n Args:\n features (torch.tensor): You can directly input a ``torch.Tensor``.\n \"\"\"\n x = self.avgpool_1a(features)\n x = x.view(x.size(0), -1)\n x = F.dropout(x, training=self.training)\n x = self.last_linear(x)\n return x\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x = self.features(input)\n x = self.logits(x)\n return x\n\n\ndef inceptionresnetv2(num_classes=1000, pretrained='imagenet'):\n \"\"\"return a inceptionresnetv2 network.\"\"\"\n if pretrained:\n settings = pretrained_settings['inceptionresnetv2'][pretrained]\n model = InceptionResNetV2(num_classes=1001)\n model.load_state_dict(model_zoo.load_url(settings['url']))\n\n if pretrained == 'imagenet':\n new_last_linear = nn.Linear(1536, 1000)\n new_last_linear.weight.data = model.last_linear.weight.data[1:]\n new_last_linear.bias.data = model.last_linear.bias.data[1:]\n model.last_linear = new_last_linear\n\n model.input_space = settings['input_space']\n model.input_size = settings['input_size']\n model.input_range = settings['input_range']\n\n model.mean = settings['mean']\n model.std = settings['std']\n else:\n model = InceptionResNetV2(num_classes=num_classes)\n return model\n\n\ndef conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),\n nn.ReLU6(inplace=True))\n\n\ndef conv_1x1_bn(inp, oup):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup),\n nn.ReLU6(inplace=True))\n\n\nclass InvertedResidual(nn.Module):\n\n def __init__(self, inp, oup, stride, expand_ratio):\n super(InvertedResidual, self).__init__()\n self.stride = stride\n assert stride in [1, 2]\n\n hidden_dim = round(inp * expand_ratio)\n self.use_res_connect = self.stride == 1 and inp == oup\n\n if expand_ratio == 1:\n self.conv = nn.Sequential(\n # dw\n nn.Conv2d(\n hidden_dim,\n hidden_dim,\n 3,\n stride,\n 1,\n groups=hidden_dim,\n bias=False),\n nn.BatchNorm2d(hidden_dim),\n nn.ReLU6(inplace=True),\n # pw-linear\n nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n nn.BatchNorm2d(oup),\n )\n else:\n self.conv = nn.Sequential(\n # pw\n nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),\n nn.BatchNorm2d(hidden_dim),\n nn.ReLU6(inplace=True),\n # dw\n nn.Conv2d(\n hidden_dim,\n hidden_dim,\n 3,\n stride,\n 1,\n groups=hidden_dim,\n bias=False),\n nn.BatchNorm2d(hidden_dim),\n nn.ReLU6(inplace=True),\n # pw-linear\n nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n nn.BatchNorm2d(oup),\n )\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n if self.use_res_connect:\n return x + self.conv(x)\n else:\n return self.conv(x)\n\n\nclass MobileNetV2(nn.Module):\n\n def __init__(self, n_class=1000, input_size=224, width_mult=1.):\n super(MobileNetV2, self).__init__()\n block = InvertedResidual\n input_channel = 32\n last_channel = 1280\n interverted_residual_setting = [\n # t, c, n, s\n [1, 16, 1, 1],\n [6, 24, 2, 2],\n [6, 32, 3, 2],\n [6, 64, 4, 2],\n [6, 96, 3, 1],\n [6, 160, 3, 2],\n [6, 320, 1, 1],\n ]\n\n # building first layer\n assert input_size % 32 == 0\n input_channel = int(input_channel * width_mult)\n self.last_channel = int(\n last_channel * width_mult) if width_mult > 1.0 else last_channel\n self.features = [conv_bn(3, input_channel, 2)]\n # building inverted residual blocks\n for t, c, n, s in interverted_residual_setting:\n output_channel = int(c * width_mult)\n for i in range(n):\n if i == 0:\n self.features.append(\n block(\n input_channel, output_channel, s, expand_ratio=t))\n else:\n self.features.append(\n block(\n input_channel, output_channel, 1, expand_ratio=t))\n input_channel = output_channel\n # building last several layers\n self.features.append(conv_1x1_bn(input_channel, self.last_channel))\n # make it nn.Sequential\n self.features = nn.Sequential(*self.features)\n\n # building classifier\n self.classifier = nn.Sequential(\n nn.Dropout(0.2),\n nn.Linear(self.last_channel, n_class),\n )\n\n self._initialize_weights()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n x = self.features(x)\n x = x.mean(3).mean(2)\n x = self.classifier(x)\n return x\n\n def _initialize_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n if m.bias is not None:\n m.bias.data.zero_()\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n n = m.weight.size(1)\n m.weight.data.normal_(0, 0.01)\n m.bias.data.zero_()\n\n\ndef get_norm_layer(norm_type='instance'):\n \"\"\"Returns a norm layer of the specified type.\n\n Args:\n norm_type (Str): norm layer type\n \"\"\"\n if norm_type == 'batch':\n norm_layer = functools.partial(nn.BatchNorm2d, affine=True)\n elif norm_type == 'instance':\n norm_layer = functools.partial(\n nn.InstanceNorm2d, affine=False, track_running_stats=True)\n else:\n raise NotImplementedError('normalization layer [%s] is not found' %\n norm_type)\n return norm_layer\n" }, { "path": "mmagic/models/editors/deepfillv1/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .contextual_attention import ContextualAttentionModule\nfrom .contextual_attention_neck import ContextualAttentionNeck\nfrom .deepfill_decoder import DeepFillDecoder\nfrom .deepfill_disc import DeepFillv1Discriminators\nfrom .deepfill_encoder import DeepFillEncoder\nfrom .deepfill_refiner import DeepFillRefiner\nfrom .deepfillv1 import DeepFillv1Inpaintor\n\n__all__ = [\n 'DeepFillEncoder', 'DeepFillDecoder', 'ContextualAttentionNeck',\n 'DeepFillv1Inpaintor', 'ContextualAttentionModule',\n 'DeepFillv1Discriminators', 'DeepFillRefiner'\n]\n" }, { "path": "mmagic/models/editors/deepfillv1/contextual_attention.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\n\nclass ContextualAttentionModule(BaseModule):\n \"\"\"Contexture attention module.\n\n The details of this module can be found in:\n Generative Image Inpainting with Contextual Attention\n\n Args:\n unfold_raw_kernel_size (int): Kernel size used in unfolding raw\n feature. Default: 4.\n unfold_raw_stride (int): Stride used in unfolding raw feature. Default:\n 2.\n unfold_raw_padding (int): Padding used in unfolding raw feature.\n Default: 1.\n unfold_corr_kernel_size (int): Kernel size used in unfolding\n context for computing correlation maps. Default: 3.\n unfold_corr_stride (int): Stride used in unfolding context for\n computing correlation maps. Default: 1.\n unfold_corr_dilation (int): Dilation used in unfolding context for\n computing correlation maps. Default: 1.\n unfold_corr_padding (int): Padding used in unfolding context for\n computing correlation maps. Default: 1.\n scale (float): The resale factor used in resize input features.\n Default: 0.5.\n fuse_kernel_size (int): The kernel size used in fusion module.\n Default: 3.\n softmax_scale (float): The scale factor for softmax function.\n Default: 10.\n return_attention_score (bool): If True, the attention score will be\n returned. Default: True.\n \"\"\"\n\n def __init__(self,\n unfold_raw_kernel_size=4,\n unfold_raw_stride=2,\n unfold_raw_padding=1,\n unfold_corr_kernel_size=3,\n unfold_corr_stride=1,\n unfold_corr_dilation=1,\n unfold_corr_padding=1,\n scale=0.5,\n fuse_kernel_size=3,\n softmax_scale=10,\n return_attention_score=True):\n super().__init__()\n self.unfold_raw_kernel_size = unfold_raw_kernel_size\n self.unfold_raw_stride = unfold_raw_stride\n self.unfold_raw_padding = unfold_raw_padding\n self.unfold_corr_kernel_size = unfold_corr_kernel_size\n self.unfold_corr_stride = unfold_corr_stride\n self.unfold_corr_dilation = unfold_corr_dilation\n self.unfold_corr_padding = unfold_corr_padding\n self.scale = scale\n self.fuse_kernel_size = fuse_kernel_size\n self.with_fuse_correlation = fuse_kernel_size > 1\n self.softmax_scale = softmax_scale\n self.return_attention_score = return_attention_score\n\n if self.with_fuse_correlation:\n assert fuse_kernel_size % 2 == 1\n fuse_kernel = torch.eye(fuse_kernel_size).view(\n 1, 1, fuse_kernel_size, fuse_kernel_size)\n self.register_buffer('fuse_kernel', fuse_kernel)\n padding = int((fuse_kernel_size - 1) // 2)\n self.fuse_conv = partial(F.conv2d, padding=padding, stride=1)\n self.softmax = nn.Softmax(dim=1)\n\n def forward(self, x, context, mask=None):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Tensor with shape (n, c, h, w).\n context (torch.Tensor): Tensor with shape (n, c, h, w).\n mask (torch.Tensor): Tensor with shape (n, 1, h, w). Default: None.\n\n Returns:\n tuple(torch.Tensor): Features after contextural attention.\n \"\"\"\n # raw features to be used in copy (deconv)\n raw_context = context\n raw_context_cols = self.im2col(\n raw_context,\n kernel_size=self.unfold_raw_kernel_size,\n stride=self.unfold_raw_stride,\n padding=self.unfold_raw_padding,\n normalize=False,\n return_cols=True)\n # resize the feature to reduce computational cost\n x = F.interpolate(x, scale_factor=self.scale)\n context = F.interpolate(context, scale_factor=self.scale)\n\n context_cols = self.im2col(\n context,\n kernel_size=self.unfold_corr_kernel_size,\n stride=self.unfold_corr_stride,\n padding=self.unfold_corr_padding,\n dilation=self.unfold_corr_dilation,\n normalize=True,\n return_cols=True)\n h_unfold, w_unfold = self.calculate_unfold_hw(\n context.size()[-2:],\n kernel_size=self.unfold_corr_kernel_size,\n stride=self.unfold_corr_stride,\n padding=self.unfold_corr_padding,\n dilation=self.unfold_corr_dilation,\n )\n # reshape context_cols to\n # (n*h_unfold*w_unfold, c, unfold_mks, unfold_mks)\n # 'mks' is short for 'mask_kernel_size'\n context_cols = context_cols.reshape(-1, *context_cols.shape[2:])\n\n # the shape of correlation map should be:\n # (n, h_unfold*w_unfold, h', w')\n correlation_map = self.patch_correlation(x, context_cols)\n # fuse correlation map to enlarge consistent attention region.\n if self.with_fuse_correlation:\n correlation_map = self.fuse_correlation_map(\n correlation_map, h_unfold, w_unfold)\n\n correlation_map = self.mask_correlation_map(correlation_map, mask=mask)\n\n attention_score = self.softmax(correlation_map * self.softmax_scale)\n\n raw_context_filter = raw_context_cols.reshape(\n -1, *raw_context_cols.shape[2:])\n output = self.patch_copy_deconv(attention_score, raw_context_filter)\n # deconv will cause overlap and we need to remove the effects of that\n overlap_factor = self.calculate_overlap_factor(attention_score)\n output /= overlap_factor\n\n if self.return_attention_score:\n n, _, h_s, w_s = attention_score.size()\n attention_score = attention_score.view(n, h_unfold, w_unfold, h_s,\n w_s)\n return output, attention_score\n\n return output\n\n def patch_correlation(self, x, kernel):\n \"\"\"Calculate patch correlation.\n\n Args:\n x (torch.Tensor): Input tensor.\n kernel (torch.Tensor): Kernel tensor.\n\n Returns:\n torch.Tensor: Tensor with shape of (n, l, h, w).\n \"\"\"\n n, _, h_in, w_in = x.size()\n\n patch_corr = F.conv2d(\n x.view(1, -1, h_in, w_in),\n kernel,\n stride=self.unfold_corr_stride,\n padding=self.unfold_corr_padding,\n dilation=self.unfold_corr_dilation,\n groups=n)\n h_out, w_out = patch_corr.size()[-2:]\n return patch_corr.view(n, -1, h_out, w_out)\n\n def patch_copy_deconv(self, attention_score, context_filter):\n \"\"\"Copy patches using deconv.\n\n Args:\n attention_score (torch.Tensor): Tensor with shape of (n, l , h, w).\n context_filter (torch.Tensor): Filter kernel.\n\n Returns:\n torch.Tensor: Tensor with shape of (n, c, h, w).\n \"\"\"\n n, _, h, w = attention_score.size()\n attention_score = attention_score.view(1, -1, h, w)\n output = F.conv_transpose2d(\n attention_score,\n context_filter,\n stride=self.unfold_raw_stride,\n padding=self.unfold_raw_padding,\n groups=n)\n h_out, w_out = output.size()[-2:]\n return output.view(n, -1, h_out, w_out)\n\n def fuse_correlation_map(self, correlation_map, h_unfold, w_unfold):\n \"\"\"Fuse correlation map.\n\n This operation is to fuse correlation map for increasing large\n consistent correlation regions.\n\n The mechanism behind this op is simple and easy to understand. A\n standard 'Eye' matrix will be applied as a filter on the correlation\n map in horizontal and vertical direction.\n\n The shape of input correlation map is (n, h_unfold*w_unfold, h, w).\n When adopting fusing, we will apply convolutional filter in the\n reshaped feature map with shape of (n, 1, h_unfold*w_fold, h*w).\n\n A simple specification for horizontal direction is shown below:\n\n .. code-block:: python\n\n (h, (h, (h, (h,\n 0) 1) 2) 3) ...\n (h, 0)\n (h, 1) 1\n (h, 2) 1\n (h, 3) 1\n ...\n \"\"\"\n # horizontal direction\n n, _, h_map, w_map = correlation_map.size()\n map_ = correlation_map.permute(0, 2, 3, 1)\n map_ = map_.reshape(n, h_map * w_map, h_unfold * w_unfold, 1)\n map_ = map_.permute(0, 3, 1, 2).contiguous()\n map_ = self.fuse_conv(map_, self.fuse_kernel)\n\n correlation_map = map_.view(n, h_unfold, w_unfold, h_map, w_map)\n\n # vertical direction\n map_ = correlation_map.permute(0, 2, 1, 4,\n 3).reshape(n, 1, h_unfold * w_unfold,\n h_map * w_map)\n map_ = self.fuse_conv(map_, self.fuse_kernel)\n\n # Note that the dimension should be transposed since the convolution of\n # eye matrix will put the normed scores into the last several dimension\n correlation_map = map_.view(n, w_unfold, h_unfold, w_map,\n h_map).permute(0, 4, 3, 2, 1)\n correlation_map = correlation_map.reshape(n, -1, h_unfold, w_unfold)\n\n return correlation_map\n\n def calculate_unfold_hw(self,\n input_size,\n kernel_size=3,\n stride=1,\n dilation=1,\n padding=0):\n \"\"\"Calculate (h, w) after unfolding.\n\n The official implementation of `unfold` in pytorch will put the\n dimension (h, w) into `L`. Thus, this function is just to calculate the\n (h, w) according to the equation in:\n https://pytorch.org/docs/stable/nn.html#torch.nn.Unfold\n \"\"\"\n h_in, w_in = input_size\n\n h_unfold = int((h_in + 2 * padding - dilation *\n (kernel_size - 1) - 1) / stride + 1)\n\n w_unfold = int((w_in + 2 * padding - dilation *\n (kernel_size - 1) - 1) / stride + 1)\n return h_unfold, w_unfold\n\n def calculate_overlap_factor(self, attention_score):\n \"\"\"Calculate the overlap factor after applying deconv.\n\n Args:\n attention_score (torch.Tensor): The attention score with shape of\n (n, c, h, w).\n\n Returns:\n torch.Tensor: The overlap factor will be returned.\n \"\"\"\n h, w = attention_score.shape[-2:]\n kernel_size = self.unfold_raw_kernel_size\n\n ones_input = torch.ones(1, 1, h, w).to(attention_score)\n ones_filter = torch.ones(1, 1, kernel_size,\n kernel_size).to(attention_score)\n overlap = F.conv_transpose2d(\n ones_input,\n ones_filter,\n stride=self.unfold_raw_stride,\n padding=self.unfold_raw_padding)\n\n # avoid division by zero\n overlap[overlap == 0] = 1.\n return overlap\n\n def mask_correlation_map(self, correlation_map, mask):\n \"\"\"Add mask weight for correlation map.\n\n Add a negative infinity number to the masked regions so that softmax\n function will result in 'zero' in those regions.\n\n Args:\n correlation_map (torch.Tensor): Correlation map with shape of\n (n, h_unfold*w_unfold, h_map, w_map).\n mask (torch.Tensor): Mask tensor with shape of (n, c, h, w). '1'\n in the mask indicates masked region while '0' indicates valid\n region.\n\n Returns:\n torch.Tensor: Updated correlation map with mask.\n \"\"\"\n if mask is not None:\n mask = F.interpolate(mask, scale_factor=self.scale)\n # if any pixel is masked in patch, the patch is considered to be\n # masked\n mask_cols = self.im2col(\n mask,\n kernel_size=self.unfold_corr_kernel_size,\n stride=self.unfold_corr_stride,\n padding=self.unfold_corr_padding,\n dilation=self.unfold_corr_dilation)\n mask_cols = (mask_cols.sum(dim=1, keepdim=True) > 0).float()\n mask_cols = mask_cols.permute(0, 2,\n 1).reshape(mask.size(0), -1, 1, 1)\n # add negative inf will bring zero in softmax\n mask_cols[mask_cols == 1] = -float('inf')\n correlation_map += mask_cols\n return correlation_map\n\n def im2col(self,\n img,\n kernel_size,\n stride=1,\n padding=0,\n dilation=1,\n normalize=False,\n return_cols=False):\n \"\"\"Reshape image-style feature to columns.\n\n This function is used for unfold feature maps to columns. The\n details of this function can be found in:\n https://pytorch.org/docs/1.1.0/nn.html?highlight=unfold#torch.nn.Unfold\n\n Args:\n img (torch.Tensor): Features to be unfolded. The shape of this\n feature should be (n, c, h, w).\n kernel_size (int): In this function, we only support square kernel\n with same height and width.\n stride (int): Stride number in unfolding. Default: 1.\n padding (int): Padding number in unfolding. Default: 0.\n dilation (int): Dilation number in unfolding. Default: 1.\n normalize (bool): If True, the unfolded feature will be normalized.\n Default: False.\n return_cols (bool): The official implementation in PyTorch of\n unfolding will return features with shape of\n (n, c*$prod{kernel_size}$, L). If True, the features will be\n reshaped to (n, L, c, kernel_size, kernel_size). Otherwise,\n the results will maintain the shape as the official\n implementation.\n\n Returns:\n torch.Tensor: Unfolded columns. If `return_cols` is True, the \\\n shape of output tensor is \\\n `(n, L, c, kernel_size, kernel_size)`. Otherwise, the shape \\\n will be `(n, c*$prod{kernel_size}$, L)`.\n \"\"\"\n\n # unfold img to columns with shape (n, c*kernel_size**2, num_cols)\n img_unfold = F.unfold(\n img,\n kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation)\n # normalize the feature map\n if normalize:\n norm = torch.sqrt((img_unfold**2).sum(dim=1, keepdim=True))\n eps = torch.tensor([1e-4]).to(img)\n img_unfold = img_unfold / torch.max(norm, eps)\n\n if return_cols:\n img_unfold_ = img_unfold.permute(0, 2, 1)\n n, num_cols = img_unfold_.size()[:2]\n img_cols = img_unfold_.view(n, num_cols, img.size(1), kernel_size,\n kernel_size)\n return img_cols\n\n return img_unfold\n" }, { "path": "mmagic/models/editors/deepfillv1/contextual_attention_neck.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.models.editors.deepfillv1.contextual_attention import \\\n ContextualAttentionModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass ContextualAttentionNeck(BaseModule):\n \"\"\"Neck with contextual attention module.\n\n Args:\n in_channels (int): The number of input channels.\n conv_type (str): The type of conv module. In DeepFillv1 model, the\n `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type`\n should be 'gated_conv'.\n conv_cfg (dict | None): Config of conv module. Default: None.\n norm_cfg (dict | None): Config of norm module. Default: None.\n act_cfg (dict | None): Config of activation layer. Default:\n dict(type='ELU').\n contextual_attention_args (dict): Config of contextual attention\n module. Default: dict(softmax_scale=10.).\n kwargs (keyword arguments).\n \"\"\"\n _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule)\n\n def __init__(self,\n in_channels,\n conv_type='conv',\n conv_cfg=None,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n contextual_attention_args=dict(softmax_scale=10.),\n **kwargs):\n super().__init__()\n self.contextual_attention = ContextualAttentionModule(\n **contextual_attention_args)\n conv_module = self._conv_type[conv_type]\n self.conv1 = conv_module(\n in_channels,\n in_channels,\n 3,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs)\n self.conv2 = conv_module(\n in_channels,\n in_channels,\n 3,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs)\n\n def forward(self, x, mask):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n mask (torch.Tensor): Input tensor with shape of (n, 1, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n x, offset = self.contextual_attention(x, x, mask)\n x = self.conv1(x)\n x = self.conv2(x)\n\n return x, offset\n" }, { "path": "mmagic/models/editors/deepfillv1/deepfill_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom functools import partial\n\nimport torch\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule, build_activation_layer\nfrom mmengine.model import BaseModule\n\n# from ...modules import SimpleGatedConvModule\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DeepFillDecoder(BaseModule):\n \"\"\"Decoder used in DeepFill model.\n\n This implementation follows:\n Generative Image Inpainting with Contextual Attention\n\n Args:\n in_channels (int): The number of input channels.\n conv_type (str): The type of conv module. In DeepFillv1 model, the\n `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type`\n should be 'gated_conv'.\n norm_cfg (dict): Config dict to build norm layer. Default: None.\n act_cfg (dict): Config dict for activation layer, \"elu\" by default.\n out_act_cfg (dict): Config dict for output activation layer. Here, we\n provide commonly used `clamp` or `clip` operation.\n channel_factor (float): The scale factor for channel size.\n Default: 1.\n kwargs (keyword arguments).\n \"\"\"\n _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule)\n\n def __init__(self,\n in_channels,\n conv_type='conv',\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='clip', min=-1., max=1.),\n channel_factor=1.,\n **kwargs):\n super().__init__()\n self.with_out_activation = out_act_cfg is not None\n\n conv_module = self._conv_type[conv_type]\n channel_list = [128, 128, 64, 64, 32, 16, 3]\n channel_list = [int(x * channel_factor) for x in channel_list]\n # dirty code for assign output channel with 3\n channel_list[-1] = 3\n for i in range(7):\n kwargs_ = copy.deepcopy(kwargs)\n if i == 6:\n act_cfg = None\n if conv_type == 'gated_conv':\n kwargs_['feat_act_cfg'] = None\n self.add_module(\n f'dec{i + 1}',\n conv_module(\n in_channels,\n channel_list[i],\n kernel_size=3,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs_))\n in_channels = channel_list[i]\n\n if self.with_out_activation:\n act_type = out_act_cfg['type']\n if act_type == 'clip':\n act_cfg_ = copy.deepcopy(out_act_cfg)\n act_cfg_.pop('type')\n self.out_act = partial(torch.clamp, **act_cfg_)\n else:\n self.out_act = build_activation_layer(out_act_cfg)\n\n def forward(self, input_dict):\n \"\"\"Forward Function.\n\n Args:\n input_dict (dict | torch.Tensor): Input dict with middle features\n or torch.Tensor.\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h, w).\n \"\"\"\n if isinstance(input_dict, dict):\n x = input_dict['out']\n else:\n x = input_dict\n for i in range(7):\n x = getattr(self, f'dec{i + 1}')(x)\n if i in (1, 3):\n x = F.interpolate(x, scale_factor=2)\n\n if self.with_out_activation:\n x = self.out_act(x)\n return x\n" }, { "path": "mmagic/models/editors/deepfillv1/deepfill_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import normal_init\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DeepFillv1Discriminators(BaseModule):\n \"\"\"Discriminators used in DeepFillv1 model.\n\n In DeepFillv1 model, the discriminators are independent without any\n concatenation like Global&Local model. Thus, we call this model\n `DeepFillv1Discriminators`. There exist a global discriminator and a local\n discriminator with global and local input respectively.\n\n The details can be found in:\n Generative Image Inpainting with Contextual Attention.\n\n Args:\n global_disc_cfg (dict): Config dict for global discriminator.\n local_disc_cfg (dict): Config dict for local discriminator.\n \"\"\"\n\n def __init__(self, global_disc_cfg, local_disc_cfg):\n super().__init__()\n self.global_disc = MODELS.build(global_disc_cfg)\n self.local_disc = MODELS.build(local_disc_cfg)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (tuple[torch.Tensor]): Contains global image and the local image\n patch.\n\n Returns:\n tuple[torch.Tensor]: Contains the prediction from discriminators \\\n in global image and local image patch.\n \"\"\"\n global_img, local_img = x\n\n global_pred = self.global_disc(global_img)\n local_pred = self.local_disc(local_img)\n\n return global_pred, local_pred\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n\n for m in self.modules():\n if isinstance(m, nn.Linear):\n normal_init(m, 0, std=0.02)\n elif isinstance(m, nn.Conv2d):\n normal_init(m, 0.0, std=0.02)\n\n self._is_init = True\n" }, { "path": "mmagic/models/editors/deepfillv1/deepfill_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DeepFillEncoder(BaseModule):\n \"\"\"Encoder used in DeepFill model.\n\n This implementation follows:\n Generative Image Inpainting with Contextual Attention\n\n Args:\n in_channels (int): The number of input channels. Default: 5.\n conv_type (str): The type of conv module. In DeepFillv1 model, the\n `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type`\n should be 'gated_conv'.\n norm_cfg (dict): Config dict to build norm layer. Default: None.\n act_cfg (dict): Config dict for activation layer, \"elu\" by default.\n encoder_type (str): Type of the encoder. Should be one of ['stage1',\n 'stage2_conv', 'stage2_attention']. Default: 'stage1'.\n channel_factor (float): The scale factor for channel size.\n Default: 1.\n kwargs (keyword arguments).\n \"\"\"\n _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule)\n\n def __init__(self,\n in_channels=5,\n conv_type='conv',\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n encoder_type='stage1',\n channel_factor=1.,\n **kwargs):\n super().__init__()\n conv_module = self._conv_type[conv_type]\n channel_list_dict = dict(\n stage1=[32, 64, 64, 128, 128, 128],\n stage2_conv=[32, 32, 64, 64, 128, 128],\n stage2_attention=[32, 32, 64, 128, 128, 128])\n channel_list = channel_list_dict[encoder_type]\n channel_list = [int(x * channel_factor) for x in channel_list]\n kernel_size_list = [5, 3, 3, 3, 3, 3]\n stride_list = [1, 2, 1, 2, 1, 1]\n for i in range(6):\n ks = kernel_size_list[i]\n padding = (ks - 1) // 2\n self.add_module(\n f'enc{i + 1}',\n conv_module(\n in_channels,\n channel_list[i],\n kernel_size=ks,\n stride=stride_list[i],\n padding=padding,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs))\n in_channels = channel_list[i]\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n for i in range(6):\n x = getattr(self, f'enc{i + 1}')(x)\n outputs = dict(out=x)\n return outputs\n" }, { "path": "mmagic/models/editors/deepfillv1/deepfill_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DeepFillRefiner(BaseModule):\n \"\"\"Refiner used in DeepFill model.\n\n This implementation follows:\n Generative Image Inpainting with Contextual Attention.\n\n Args:\n encoder_attention (dict): Config dict for encoder used in branch\n with contextual attention module.\n encoder_conv (dict): Config dict for encoder used in branch with\n just convolutional operation.\n dilation_neck (dict): Config dict for dilation neck in branch with\n just convolutional operation.\n contextual_attention (dict): Config dict for contextual attention\n neck.\n decoder (dict): Config dict for decoder used to fuse and decode\n features.\n \"\"\"\n\n def __init__(self,\n encoder_attention=dict(\n type='DeepFillEncoder', encoder_type='stage2_attention'),\n encoder_conv=dict(\n type='DeepFillEncoder', encoder_type='stage2_conv'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=128,\n act_cfg=dict(type='ELU')),\n contextual_attention=dict(\n type='ContextualAttentionNeck', in_channels=128),\n decoder=dict(type='DeepFillDecoder', in_channels=256)):\n super().__init__()\n self.encoder_attention = MODELS.build(encoder_attention)\n self.encoder_conv = MODELS.build(encoder_conv)\n self.contextual_attention_neck = MODELS.build(contextual_attention)\n self.dilation_neck = MODELS.build(dilation_neck)\n self.decoder = MODELS.build(decoder)\n\n def forward(self, x, mask):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n mask (torch.Tensor): Input tensor with shape of (n, 1, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n # conv branch\n encoder_dict = self.encoder_conv(x)\n conv_x = self.dilation_neck(encoder_dict['out'])\n\n # contextual attention branch\n attention_x = self.encoder_attention(x)['out']\n h_x, w_x = attention_x.shape[-2:]\n # resale mask to a smaller size\n resized_mask = F.interpolate(mask, size=(h_x, w_x))\n attention_x, offset = self.contextual_attention_neck(\n attention_x, resized_mask)\n\n # concat two branches\n x = torch.cat([conv_x, attention_x], dim=1)\n x = self.decoder(dict(out=x))\n\n return x, offset\n" }, { "path": "mmagic/models/editors/deepfillv1/deepfillv1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional\n\nimport torch\n\nfrom mmagic.models.base_models import TwoStageInpaintor\nfrom mmagic.models.utils import extract_around_bbox, extract_bbox_patch\nfrom mmagic.registry import MODELS\nfrom ...utils import set_requires_grad\n\n\n@MODELS.register_module()\nclass DeepFillv1Inpaintor(TwoStageInpaintor):\n \"\"\"Inpaintor for deepfillv1 method.\n\n This inpaintor is implemented according to the paper:\n Generative image inpainting with contextual attention\n\n Importantly, this inpaintor is an example for using custom training\n schedule based on `TwoStageInpaintor`.\n\n The training pipeline of deepfillv1 is as following:\n\n .. code-block:: python\n\n if cur_iter < iter_tc:\n update generator with only l1 loss\n else:\n update discriminator\n if cur_iter > iter_td:\n update generator with l1 loss and adversarial loss\n\n The new attribute `cur_iter` is added for recording current number of\n iteration. The `train_cfg` contains the setting of the training schedule:\n\n .. code-block:: python\n\n train_cfg = dict(\n start_iter=0,\n disc_step=1,\n iter_tc=90000,\n iter_td=100000\n )\n\n `iter_tc` and `iter_td` correspond to the notation :math:`T_C` and\n :math:`T_D` of the original paper.\n\n Args:\n generator (dict): Config for encoder-decoder style generator.\n disc (dict): Config for discriminator.\n loss_gan (dict): Config for adversarial loss.\n loss_gp (dict): Config for gradient penalty loss.\n loss_disc_shift (dict): Config for discriminator shift loss.\n loss_composed_percep (dict): Config for perceptual and style loss with\n composed image as input.\n loss_out_percep (dict): Config for perceptual and style loss with\n direct output as input.\n loss_l1_hole (dict): Config for l1 loss in the hole.\n loss_l1_valid (dict): Config for l1 loss in the valid region.\n loss_tv (dict): Config for total variation loss.\n train_cfg (dict): Configs for training scheduler. `disc_step` must be\n contained for indicates the discriminator updating steps in each\n training step.\n test_cfg (dict): Configs for testing scheduler.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n data_preprocessor: dict,\n encdec: dict,\n disc=None,\n loss_gan=None,\n loss_gp=None,\n loss_disc_shift=None,\n loss_composed_percep=None,\n loss_out_percep=False,\n loss_l1_hole=None,\n loss_l1_valid=None,\n loss_tv=None,\n stage1_loss_type=None,\n stage2_loss_type=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg: Optional[dict] = None):\n super().__init__(\n data_preprocessor=data_preprocessor,\n encdec=encdec,\n disc=disc,\n loss_gan=loss_gan,\n loss_gp=loss_gp,\n loss_disc_shift=loss_disc_shift,\n loss_composed_percep=loss_composed_percep,\n loss_out_percep=loss_out_percep,\n loss_l1_hole=loss_l1_hole,\n loss_l1_valid=loss_l1_valid,\n loss_tv=loss_tv,\n stage1_loss_type=stage1_loss_type,\n stage2_loss_type=stage2_loss_type,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg)\n\n if self.train_cfg is not None:\n self.cur_iter = self.train_cfg.start_iter\n\n def forward_train_d(self, data_batch, is_real, is_disc):\n \"\"\"Forward function in discriminator training step.\n\n In this function, we modify the default implementation with only one\n discriminator. In DeepFillv1 model, they use two separated\n discriminators for global and local consistency.\n\n Args:\n data_batch (torch.Tensor): Batch of real data or fake data.\n is_real (bool): If True, the gan loss will regard this batch as\n real data. Otherwise, the gan loss will regard this batch as\n fake data.\n is_disc (bool): If True, this function is called in discriminator\n training step. Otherwise, this function is called in generator\n training step. This will help us to compute different types of\n adversarial loss, like LSGAN.\n\n Returns:\n dict: Contains the loss items computed in this function.\n \"\"\"\n global_pred, local_pred = self.disc(data_batch)\n loss_global = self.loss_gan(global_pred, is_real, is_disc)\n loss_local = self.loss_gan(local_pred, is_real, is_disc)\n\n if is_real:\n loss = dict(\n real_loss_global=loss_global, real_loss_local=loss_local)\n else:\n loss = dict(\n fake_loss_global=loss_global, fake_loss_local=loss_local)\n\n if self.with_disc_shift_loss:\n loss_d_shift_global = self.loss_disc_shift(loss_global)\n loss_d_shift_local = self.loss_disc_shift(loss_local)\n # 0.5 for average the fake and real data\n loss.update(loss_disc_shift_global=loss_d_shift_global * 0.5)\n loss.update(loss_disc_shift_local=loss_d_shift_local * 0.5)\n\n return loss\n\n def two_stage_loss(self, stage1_data, stage2_data, gt, mask, masked_img):\n \"\"\"Calculate two-stage loss.\n\n Args:\n stage1_data (dict): Contain stage1 results.\n stage2_data (dict): Contain stage2 results.\n gt (torch.Tensor): Ground-truth image.\n mask (torch.Tensor): Mask image.\n masked_img (torch.Tensor): Composition of mask image and\n ground-truth image.\n Returns:\n tuple(dict): Dict contains the results computed within this \\\n function for visualization and dict contains the loss items \\\n computed in this function.\n \"\"\"\n loss = dict()\n results = dict(\n gt_img=gt.cpu(), mask=mask.cpu(), masked_img=masked_img.cpu())\n # calculate losses for stage1\n if self.stage1_loss_type is not None:\n fake_res = stage1_data['fake_res']\n fake_img = stage1_data['fake_img']\n for type_key in self.stage1_loss_type:\n tmp_loss = self.calculate_loss_with_type(\n type_key, fake_res, fake_img, gt, mask, prefix='stage1_')\n loss.update(tmp_loss)\n\n results.update(\n dict(\n stage1_fake_res=stage1_data['fake_res'].cpu(),\n stage1_fake_img=stage1_data['fake_img'].cpu()))\n\n if self.stage2_loss_type is not None:\n fake_res = stage2_data['fake_res']\n fake_img = stage2_data['fake_img']\n fake_local = stage2_data['fake_local']\n for type_key in self.stage2_loss_type:\n tmp_loss = self.calculate_loss_with_type(\n type_key,\n fake_res,\n fake_img,\n gt,\n mask,\n prefix='stage2_',\n fake_local=fake_local)\n loss.update(tmp_loss)\n results.update(\n dict(\n stage2_fake_res=stage2_data['fake_res'].cpu(),\n stage2_fake_img=stage2_data['fake_img'].cpu()))\n\n return results, loss\n\n def calculate_loss_with_type(self,\n loss_type,\n fake_res,\n fake_img,\n gt,\n mask,\n prefix='stage1_',\n fake_local=None):\n \"\"\"Calculate multiple types of losses.\n\n Args:\n loss_type (str): Type of the loss.\n fake_res (torch.Tensor): Direct results from model.\n fake_img (torch.Tensor): Composited results from model.\n gt (torch.Tensor): Ground-truth tensor.\n mask (torch.Tensor): Mask tensor.\n prefix (str, optional): Prefix for loss name.\n Defaults to 'stage1\\_'. # noqa\n fake_local (torch.Tensor, optional): Local results from model.\n Defaults to None.\n\n Returns:\n dict: Contain loss value with its name.\n \"\"\"\n loss_dict = dict()\n if loss_type == 'loss_gan':\n g_fake_global_pred, g_fake_local_pred = self.disc(\n (fake_img, fake_local))\n loss_g_fake_global = self.loss_gan(\n g_fake_global_pred, True, is_disc=False)\n loss_g_fake_local = self.loss_gan(\n g_fake_local_pred, True, is_disc=False)\n loss_dict[prefix +\n 'loss_g_fake'] = loss_g_fake_global + loss_g_fake_local\n elif 'percep' in loss_type:\n loss_pecep, loss_style = self.loss_percep(fake_img, gt)\n if loss_pecep is not None:\n loss_dict[prefix + loss_type] = loss_pecep\n if loss_style is not None:\n loss_dict[prefix + loss_type[:-6] + 'style'] = loss_style\n elif 'tv' in loss_type:\n loss_tv = self.loss_tv(fake_img, mask=mask)\n loss_dict[prefix + loss_type] = loss_tv\n elif 'l1' in loss_type:\n weight = 1. - mask if 'valid' in loss_type else mask\n loss_l1 = getattr(self, loss_type)(fake_res, gt, weight=weight)\n loss_dict[prefix + loss_type] = loss_l1\n else:\n raise NotImplementedError(\n f'Please check your loss type {loss_type}'\n ' and the config dict in init function. '\n 'We cannot find the related loss function.')\n\n return loss_dict\n\n def train_step(self, data: List[dict], optim_wrapper):\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n\n 1. get fake res/image\n 2. optimize discriminator (if have)\n 3. optimize generator\n\n If `self.train_cfg.disc_step > 1`, the train step will contain multiple\n iterations for optimizing discriminator with different input data and\n only one iteration for optimizing generator after `disc_step`\n iterations for discriminator.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of \\\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n masked_img = batch_inputs # float\n gt_img = data_samples.gt_img\n mask = data_samples.mask\n mask = mask.float()\n\n # PyTorch 2.0 could not compile 'data_samples.mask_bbox'\n # bbox_tensor = torch.LongTensor(data_samples.mask_bbox)\n bbox_tensor = torch.LongTensor(data_samples.metainfo['mask_bbox'])\n\n # get common output from encdec\n # input with ones\n tmp_ones = torch.ones_like(mask)\n input_x = torch.cat([masked_img, tmp_ones, mask], dim=1)\n stage1_fake_res, stage2_fake_res = self.generator(input_x)\n stage1_fake_img = masked_img * (1. - mask) + stage1_fake_res * mask\n stage2_fake_img = masked_img * (1. - mask) + stage2_fake_res * mask\n\n stage2_fake_local, bbox_new = extract_around_bbox(\n stage2_fake_img, bbox_tensor, self.train_cfg.local_size)\n gt_local = extract_bbox_patch(bbox_new, gt_img)\n fake_gt_local = torch.cat([stage2_fake_local, gt_local], dim=2)\n # discriminator training step\n # In this version, we only use the results from the second stage to\n # train discriminators, which is a commonly used setting. This can be\n # easily modified to your custom training schedule.\n if self.train_cfg.disc_step > 0 and self.with_gan:\n set_requires_grad(self.disc, True)\n fake_data = (stage2_fake_img.detach(), stage2_fake_local.detach())\n real_data = (gt_img, gt_local)\n\n disc_losses = self.forward_train_d(fake_data, False, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].zero_grad()\n optim_wrapper['disc'].backward(loss_disc)\n\n disc_losses = self.forward_train_d(real_data, True, is_disc=True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].backward(loss_disc)\n\n if self.with_gp_loss:\n if hasattr(self.disc, 'module'):\n global_disc = self.disc.module.global_disc\n local_disc = self.disc.module.local_disc\n else:\n global_disc = self.disc.global_disc\n local_disc = self.disc.local_disc\n\n loss_gp_global = self.loss_gp(\n global_disc, gt_img, stage2_fake_img, mask=mask)\n loss_gp_local = self.loss_gp(local_disc, gt_local,\n stage2_fake_local)\n loss_disc, log_vars_d = self.parse_losses(\n dict(\n loss_gp_global=loss_gp_global,\n loss_gp_local=loss_gp_local))\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].backward(loss_disc)\n\n optim_wrapper['disc'].step()\n\n self.disc_step_count = (self.disc_step_count +\n 1) % self.train_cfg.disc_step\n if self.disc_step_count != 0:\n # results contain the data for visualization\n results = dict(\n gt_img=gt_img.cpu(),\n masked_img=masked_img.cpu(),\n stage1_fake_res=stage1_fake_res.cpu(),\n stage1_fake_img=stage1_fake_img.cpu(),\n stage2_fake_res=stage2_fake_res.cpu(),\n stage2_fake_img=stage2_fake_img.cpu(),\n fake_gt_local=fake_gt_local.cpu(),\n fake_res=stage2_fake_res.cpu(),\n fake_img=stage2_fake_img.cpu())\n\n return log_vars\n\n # prepare stage1 results and stage2 results dict for calculating losses\n stage1_results = dict(\n fake_res=stage1_fake_res, fake_img=stage1_fake_img)\n stage2_results = dict(\n fake_res=stage2_fake_res,\n fake_img=stage2_fake_img,\n fake_local=stage2_fake_local)\n\n # generator (encdec) and refiner training step, results contain the\n # data for visualization\n if self.with_gan:\n set_requires_grad(self.disc, False)\n results, two_stage_losses = self.two_stage_loss(\n stage1_results, stage2_results, gt_img, mask, masked_img)\n loss_two_stage, log_vars_two_stage = self.parse_losses(\n two_stage_losses)\n log_vars.update(log_vars_two_stage)\n optim_wrapper['generator'].zero_grad()\n optim_wrapper['generator'].backward(loss_two_stage)\n optim_wrapper['generator'].step()\n\n results['fake_gt_local'] = fake_gt_local.cpu()\n\n return log_vars\n" }, { "path": "mmagic/models/editors/deepfillv2/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .two_stage_encoder_decoder import DeepFillEncoderDecoder\n\n__all__ = ['DeepFillEncoderDecoder']\n" }, { "path": "mmagic/models/editors/deepfillv2/two_stage_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init, normal_init\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DeepFillEncoderDecoder(BaseModule):\n \"\"\"Two-stage encoder-decoder structure used in DeepFill model.\n\n The details are in:\n Generative Image Inpainting with Contextual Attention\n\n Args:\n stage1 (dict): Config dict for building stage1 model. As\n DeepFill model uses Global&Local model as baseline in first stage,\n the stage1 model can be easily built with `GLEncoderDecoder`.\n stage2 (dict): Config dict for building stage2 model.\n return_offset (bool): Whether to return offset feature in contextual\n attention module. Default: False.\n \"\"\"\n\n def __init__(self,\n stage1=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='DeepFillEncoder'),\n decoder=dict(type='DeepFillDecoder', in_channels=128),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=128,\n act_cfg=dict(type='ELU'))),\n stage2=dict(type='DeepFillRefiner'),\n return_offset=False):\n super().__init__()\n self.stage1 = MODELS.build(stage1)\n self.stage2 = MODELS.build(stage2)\n\n self.return_offset = return_offset\n\n # support fp16\n self.fp16_enabled = False\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): This input tensor has the shape of (n, 5, h, w).\n In channel dimension, we concatenate [masked_img, ones, mask]\n as DeepFillv1 models do.\n\n Returns:\n tuple[torch.Tensor]: The first two item is the results from first \\\n and second stage. If set `return_offset` as True, the offset \\\n will be returned as the third item.\n \"\"\"\n input_x = x.clone()\n masked_img = input_x[:, :3, ...]\n mask = input_x[:, -1:, ...]\n x = self.stage1(x)\n stage1_res = x.clone()\n stage1_img = stage1_res * mask + masked_img * (1. - mask)\n stage2_input = torch.cat([stage1_img, input_x[:, 3:, ...]], dim=1)\n stage2_res, offset = self.stage2(stage2_input, mask)\n\n if self.return_offset:\n return stage1_res, stage2_res, offset\n\n return stage1_res, stage2_res\n\n # TODO: study the effects of init functions\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n normal_init(m, 0, 0.02)\n elif isinstance(m, (_BatchNorm, nn.InstanceNorm2d)):\n constant_init(m, 1)\n\n self._is_init = True\n" }, { "path": "mmagic/models/editors/dic/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .dic import DIC\nfrom .dic_net import (DICNet, FeedbackBlock, FeedbackBlockCustom,\n FeedbackBlockHeatmapAttention)\nfrom .feedback_hour_glass import FeedbackHourglass\nfrom .light_cnn import LightCNN, MaxFeature\n\n__all__ = [\n 'DICNet',\n 'DIC',\n 'FeedbackHourglass',\n 'LightCNN',\n 'MaxFeature',\n 'FeedbackBlock',\n 'FeedbackBlockCustom',\n 'FeedbackBlockHeatmapAttention',\n]\n" }, { "path": "mmagic/models/editors/dic/dic.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List\n\nimport torch\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.models.utils import set_requires_grad\nfrom mmagic.registry import MODELS\nfrom ..srgan import SRGAN\n\n\n@MODELS.register_module()\nclass DIC(SRGAN):\n \"\"\"DIC model for Face Super-Resolution.\n\n Paper: Deep Face Super-Resolution with Iterative Collaboration between\n Attentive Recovery and Landmark Estimation.\n\n Args:\n generator (dict): Config for the generator.\n pixel_loss (dict): Config for the pixel loss.\n align_loss (dict): Config for the align loss.\n discriminator (dict): Config for the discriminator. Default: None.\n gan_loss (dict): Config for the gan loss. Default: None.\n feature_loss (dict): Config for the feature loss. Default: None.\n train_cfg (dict): Config for train. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n align_loss,\n discriminator=None,\n gan_loss=None,\n feature_loss=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n gan_loss=gan_loss,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.align_loss = MODELS.build(align_loss)\n self.feature_loss = MODELS.build(\n feature_loss) if feature_loss else None\n\n self.pixel_init = train_cfg.get('pixel_init', 0) if train_cfg else 0\n\n def forward_tensor(self, inputs, data_samples=None, training=False):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n training (bool): Whether is training. Default: False.\n\n Returns:\n (Tensor | Tuple[List[Tensor]]): results of forward inference and\n forward train.\n \"\"\"\n\n sr_list, heatmap_list = self.generator(inputs)\n\n if training:\n return sr_list, heatmap_list\n else:\n return sr_list[-1]\n\n def if_run_g(self):\n \"\"\"Calculates whether need to run the generator step.\"\"\"\n\n return True\n\n def if_run_d(self):\n \"\"\"Calculates whether need to run the discriminator step.\"\"\"\n\n return self.step_counter >= self.pixel_init and super().if_run_d()\n\n def g_step(self, batch_outputs, batch_gt_data):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n sr_list, heatmap_list = batch_outputs\n gt, gt_heatmap = batch_gt_data\n\n losses = dict()\n\n # pix loss\n for step, (sr, heatmap) in enumerate(zip(sr_list, heatmap_list)):\n losses[f'loss_pixel_v{step}'] = self.pixel_loss(sr, gt)\n losses[f'loss_align_v{step}'] = self.align_loss(\n heatmap, gt_heatmap)\n\n if self.step_counter >= self.pixel_init:\n pred = sr_list[-1]\n\n # perceptual loss\n if self.feature_loss:\n loss_feature = self.feature_loss(pred, gt)\n losses['loss_feature'] = loss_feature\n\n # gan loss for generator\n if self.gan_loss and self.discriminator:\n fake_g_pred = self.discriminator(pred)\n losses['loss_gan'] = self.gan_loss(\n fake_g_pred, target_is_real=True, is_disc=False)\n\n return losses\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step of GAN-based method.\n\n Args:\n data (List[dict]): Data sampled from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance\n used to update model parameters.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n\n data = self.data_preprocessor(data, True)\n batch_inputs = data['inputs']\n data_samples = data['data_samples']\n batch_gt_data = self.extract_gt_data(data_samples)\n\n log_vars = dict()\n\n with g_optim_wrapper.optim_context(self):\n batch_outputs = self.forward_train(batch_inputs, data_samples)\n\n if self.if_run_g():\n set_requires_grad(self.discriminator, False)\n\n log_vars_d = self.g_step_with_optim(\n batch_outputs=batch_outputs,\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if self.if_run_d():\n set_requires_grad(self.discriminator, True)\n\n sr_list, _ = batch_outputs\n gt, _ = batch_gt_data\n\n for _ in range(self.disc_repeat):\n # detach before function call to resolve PyTorch2.0 compile bug\n log_vars_d = self.d_step_with_optim(\n batch_outputs=sr_list[-1].detach(),\n batch_gt_data=gt,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if 'loss' in log_vars:\n log_vars.pop('loss')\n\n self.step_counter += 1\n\n return log_vars\n\n @staticmethod\n def extract_gt_data(data_samples):\n \"\"\"extract gt data from data samples.\n\n Args:\n data_samples (list): List of DataSample.\n\n Returns:\n Tensor: Extract gt data.\n \"\"\"\n\n batch_gt_img = data_samples.gt_img\n batch_gt_heatmap = data_samples.gt_heatmap\n\n return [batch_gt_img, batch_gt_heatmap]\n" }, { "path": "mmagic/models/editors/dic/dic_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\nfrom .feedback_hour_glass import FeedbackHourglass, reduce_to_five_heatmaps\n\n\n@MODELS.register_module()\nclass DICNet(BaseModule):\n \"\"\"DIC network structure for face super-resolution.\n\n Paper: Deep Face Super-Resolution with Iterative Collaboration between\n Attentive Recovery and Landmark Estimation\n\n Args:\n in_channels (int): Number of channels in the input image\n out_channels (int): Number of channels in the output image\n mid_channels (int): Channel number of intermediate features.\n Default: 64\n num_blocks (tuple[int]): Block numbers in the trunk network.\n Default: 6\n hg_mid_channels (int): Channel number of intermediate features\n of HourGlass. Default: 256\n hg_num_keypoints (int): Keypoint number of HourGlass. Default: 68\n num_steps (int): Number of iterative steps. Default: 4\n upscale_factor (int): Upsampling factor. Default: 8\n detach_attention (bool): Detached from the current tensor for heatmap\n or not.\n prelu_init (float): `init` of PReLU. Default: 0.2\n num_heatmaps (int): Number of heatmaps. Default: 5\n num_fusion_blocks (int): Number of fusion blocks. Default: 7\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels,\n num_blocks=6,\n hg_mid_channels=256,\n hg_num_keypoints=68,\n num_steps=4,\n upscale_factor=8,\n detach_attention=False,\n prelu_init=0.2,\n num_heatmaps=5,\n num_fusion_blocks=7,\n init_cfg=None):\n\n super().__init__(init_cfg=init_cfg)\n\n self.num_steps = num_steps\n self.detach_attention = detach_attention\n\n self.conv_first = nn.Sequential(\n nn.Conv2d(in_channels, mid_channels * 4, 3, 1, 1),\n nn.PReLU(init=prelu_init), nn.PixelShuffle(2))\n\n self.first_block = FeedbackBlockCustom(\n in_channels=mid_channels,\n mid_channels=mid_channels,\n num_blocks=num_blocks,\n upscale_factor=upscale_factor)\n\n self.block = FeedbackBlockHeatmapAttention(\n mid_channels=mid_channels,\n num_blocks=num_blocks,\n upscale_factor=upscale_factor,\n num_heatmaps=num_heatmaps,\n num_fusion_blocks=num_fusion_blocks)\n self.block.need_reset = False\n\n self.hour_glass = FeedbackHourglass(\n mid_channels=hg_mid_channels, num_keypoints=hg_num_keypoints)\n\n self.conv_last = nn.Sequential(\n nn.ConvTranspose2d(mid_channels, mid_channels, 8, 4, 2),\n nn.PReLU(init=prelu_init),\n nn.Conv2d(mid_channels, out_channels, 3, 1, 1))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: Forward results.\n sr_outputs (list[Tensor]): forward sr results.\n heatmap_outputs (list[Tensor]): forward heatmap results.\n \"\"\"\n\n inter_res = nn.functional.interpolate(\n x, size=(128, 128), mode='bilinear', align_corners=False)\n\n x = self.conv_first(x)\n\n sr_outputs = []\n heatmap_outputs = []\n last_hidden = None\n heatmap = None\n\n for step in range(self.num_steps):\n if step == 0:\n sr_feature = self.first_block(x)\n self.block.last_hidden = sr_feature\n else:\n heatmap = reduce_to_five_heatmaps(heatmap,\n self.detach_attention)\n sr_feature = self.block(x, heatmap)\n\n sr = self.conv_last(sr_feature)\n sr = torch.add(inter_res, sr)\n heatmap, last_hidden = self.hour_glass(sr, last_hidden)\n\n sr_outputs.append(sr)\n heatmap_outputs.append(heatmap)\n\n return sr_outputs, heatmap_outputs\n\n\nclass FeedbackBlock(nn.Module):\n \"\"\"Feedback Block of DIC.\n\n It has a style of:\n\n .. code-block:: text\n\n ----- Module ----->\n ^ |\n |____________|\n\n Args:\n mid_channels (int): Number of channels in the intermediate features.\n num_blocks (int): Number of blocks.\n upscale_factor (int): upscale factor.\n padding (int): Padding size. Default: 2.\n prelu_init (float): `init` of PReLU. Default: 0.2\n \"\"\"\n\n def __init__(self,\n mid_channels,\n num_blocks,\n upscale_factor,\n padding=2,\n prelu_init=0.2):\n super().__init__()\n\n stride = upscale_factor\n kernel_size = upscale_factor + 4\n\n self.num_blocks = num_blocks\n self.need_reset = True\n self.last_hidden = None\n\n self.conv_first = nn.Sequential(\n nn.Conv2d(2 * mid_channels, mid_channels, kernel_size=1),\n nn.PReLU(init=prelu_init))\n\n self.up_blocks = nn.ModuleList()\n self.down_blocks = nn.ModuleList()\n self.lr_blocks = nn.ModuleList()\n self.hr_blocks = nn.ModuleList()\n\n for idx in range(self.num_blocks):\n self.up_blocks.append(\n nn.Sequential(\n nn.ConvTranspose2d(mid_channels, mid_channels, kernel_size,\n stride, padding),\n nn.PReLU(init=prelu_init)))\n self.down_blocks.append(\n nn.Sequential(\n nn.Conv2d(mid_channels, mid_channels, kernel_size, stride,\n padding), nn.PReLU(init=prelu_init)))\n if idx > 0:\n self.lr_blocks.append(\n nn.Sequential(\n nn.Conv2d(\n mid_channels * (idx + 1),\n mid_channels,\n kernel_size=1), nn.PReLU(init=prelu_init)))\n self.hr_blocks.append(\n nn.Sequential(\n nn.Conv2d(\n mid_channels * (idx + 1),\n mid_channels,\n kernel_size=1), nn.PReLU(init=prelu_init)))\n\n self.conv_last = nn.Sequential(\n nn.Conv2d(num_blocks * mid_channels, mid_channels, kernel_size=1),\n nn.PReLU(init=prelu_init))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.need_reset:\n self.last_hidden = x\n self.need_reset = False\n\n x = torch.cat((x, self.last_hidden), dim=1)\n x = self.conv_first(x)\n\n lr_features = [x]\n hr_features = []\n\n for idx in range(self.num_blocks):\n # when idx == 0, lr_features == [x]\n lr = torch.cat(lr_features, 1)\n if idx > 0:\n lr = self.lr_blocks[idx - 1](lr)\n hr = self.up_blocks[idx](lr)\n\n hr_features.append(hr)\n\n hr = torch.cat(hr_features, 1)\n if idx > 0:\n hr = self.hr_blocks[idx - 1](hr)\n lr = self.down_blocks[idx](hr)\n\n lr_features.append(lr)\n\n output = torch.cat(lr_features[1:], 1)\n output = self.conv_last(output)\n\n self.last_hidden = output\n\n return output\n\n\nclass FeedbackBlockCustom(FeedbackBlock):\n \"\"\"Custom feedback block, will be used as the first feedback block.\n\n Args:\n in_channels (int): Number of channels in the input features.\n mid_channels (int): Number of channels in the intermediate features.\n num_blocks (int): Number of blocks.\n upscale_factor (int): upscale factor.\n \"\"\"\n\n def __init__(self, in_channels, mid_channels, num_blocks, upscale_factor):\n super().__init__(mid_channels, num_blocks, upscale_factor)\n\n prelu_init = 0.2\n self.conv_first = nn.Sequential(\n nn.Conv2d(in_channels, mid_channels, kernel_size=1),\n nn.PReLU(init=prelu_init))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.conv_first(x)\n\n lr_features = [x]\n hr_features = []\n\n for idx in range(self.num_blocks):\n # when idx == 0, lr_features == [x]\n lr = torch.cat(lr_features, 1)\n if idx > 0:\n lr = self.lr_blocks[idx - 1](lr)\n hr = self.up_blocks[idx](lr)\n\n hr_features.append(hr)\n\n hr = torch.cat(hr_features, 1)\n if idx > 0:\n hr = self.hr_blocks[idx - 1](hr)\n lr = self.down_blocks[idx](hr)\n\n lr_features.append(lr)\n\n output = torch.cat(lr_features[1:], 1)\n output = self.conv_last(output)\n\n return output\n\n\nclass GroupResBlock(nn.Module):\n \"\"\"ResBlock with Group Conv.\n\n Args:\n in_channels (int): Channel number of input features.\n out_channels (int): Channel number of output features.\n mid_channels (int): Channel number of intermediate features.\n groups (int): Number of blocked connections from input to output.\n res_scale (float): Used to scale the residual before addition.\n Default: 1.0.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels,\n groups,\n res_scale=1.0):\n super().__init__()\n\n self.res = nn.Sequential(\n nn.Conv2d(in_channels, mid_channels, 3, 1, 1, groups=groups),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(mid_channels, out_channels, 3, 1, 1, groups=groups))\n self.res_scale = res_scale\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n res = self.res(x).mul(self.res_scale)\n return x + res\n\n\nclass FeatureHeatmapFusingBlock(nn.Module):\n \"\"\"Fusing Feature and Heatmap.\n\n Args:\n in_channels (int): Number of channels in the input features.\n num_heatmaps (int): Number of heatmap.\n num_blocks (int): Number of blocks.\n mid_channels (int | None): Number of channels in the intermediate\n features. Default: None\n \"\"\"\n\n def __init__(self,\n in_channels,\n num_heatmaps,\n num_blocks,\n mid_channels=None):\n super().__init__()\n\n self.num_heatmaps = num_heatmaps\n res_block_channel = in_channels * num_heatmaps\n if mid_channels is None:\n self.mid_channels = num_heatmaps * in_channels\n else:\n self.mid_channels = mid_channels\n self.conv_first = nn.Sequential(\n nn.Conv2d(in_channels, res_block_channel, kernel_size=1),\n nn.LeakyReLU(negative_slope=0.2, inplace=True))\n self.body = make_layer(\n GroupResBlock,\n num_blocks,\n in_channels=res_block_channel,\n out_channels=res_block_channel,\n mid_channels=self.mid_channels,\n groups=num_heatmaps)\n\n def forward(self, feature, heatmap):\n \"\"\"Forward function.\n\n Args:\n feature (Tensor): Input feature tensor.\n heatmap (Tensor): Input heatmap tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n assert self.num_heatmaps == heatmap.size(1)\n batch_size = heatmap.size(0)\n w, h = feature.shape[-2:]\n\n feature = self.conv_first(feature)\n # B * (num_heatmaps*in_channels) * h * w\n feature = self.body(feature)\n attention = nn.functional.softmax(\n heatmap, dim=1) # B * num_heatmaps * h * w\n\n feature = feature.view(batch_size, self.num_heatmaps, -1, w,\n h) * attention.unsqueeze(2)\n feature = feature.sum(1)\n return feature\n\n\nclass FeedbackBlockHeatmapAttention(FeedbackBlock):\n \"\"\"Feedback block with HeatmapAttention.\n\n Args:\n in_channels (int): Number of channels in the input features.\n mid_channels (int): Number of channels in the intermediate features.\n num_blocks (int): Number of blocks.\n upscale_factor (int): upscale factor.\n padding (int): Padding size. Default: 2.\n prelu_init (float): `init` of PReLU. Default: 0.2\n \"\"\"\n\n def __init__(self,\n mid_channels,\n num_blocks,\n upscale_factor,\n num_heatmaps,\n num_fusion_blocks,\n padding=2,\n prelu_init=0.2):\n\n super().__init__(\n mid_channels,\n num_blocks,\n upscale_factor,\n padding=padding,\n prelu_init=prelu_init)\n self.fusion_block = FeatureHeatmapFusingBlock(mid_channels,\n num_heatmaps,\n num_fusion_blocks)\n\n def forward(self, x, heatmap):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature tensor.\n heatmap (Tensor): Input heatmap tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.need_reset:\n self.last_hidden = x\n self.need_reset = False\n\n x = torch.cat((x, self.last_hidden), dim=1)\n x = self.conv_first(x)\n\n # fusion\n x = self.fusion_block(x, heatmap)\n\n lr_features = []\n hr_features = []\n lr_features.append(x)\n\n for idx in range(self.num_blocks):\n # when idx == 0, lr_features == [x]\n lr = torch.cat(lr_features, 1)\n if idx > 0:\n lr = self.lr_blocks[idx - 1](lr)\n hr = self.up_blocks[idx](lr)\n\n hr_features.append(hr)\n\n hr = torch.cat(hr_features, 1)\n if idx > 0:\n hr = self.hr_blocks[idx - 1](hr)\n lr = self.down_blocks[idx](hr)\n\n lr_features.append(lr)\n\n output = torch.cat(lr_features[1:], 1)\n output = self.conv_last(output)\n\n self.last_hidden = output\n\n return output\n" }, { "path": "mmagic/models/editors/dic/feedback_hour_glass.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FeedbackHourglass(BaseModule):\n \"\"\"Feedback Hourglass model for face landmark.\n\n It has a style of:\n\n ::\n\n -- preprocessing ----- Hourglass ----->\n ^ |\n |_______________|\n\n Args:\n mid_channels (int): Number of channels in the intermediate features.\n num_keypoints (int): Number of keypoints.\n \"\"\"\n\n def __init__(self, mid_channels, num_keypoints):\n super().__init__()\n self.mid_channels = mid_channels\n self.num_keypoints = num_keypoints\n\n self.pre_conv_block = nn.Sequential(\n nn.Conv2d(3, self.mid_channels // 4, 7, 2, 3),\n nn.ReLU(inplace=True),\n ResBlock(self.mid_channels // 4, self.mid_channels // 2),\n nn.MaxPool2d(2, 2),\n ResBlock(self.mid_channels // 2, self.mid_channels // 2),\n ResBlock(self.mid_channels // 2, self.mid_channels),\n )\n self.first_conv = nn.Conv2d(2 * self.mid_channels,\n 2 * self.mid_channels, 1)\n\n self.hg = Hourglass(4, 2 * self.mid_channels)\n self.last = nn.Sequential(\n ResBlock(self.mid_channels, self.mid_channels),\n nn.Conv2d(self.mid_channels, self.mid_channels, 1),\n nn.ReLU(inplace=True),\n nn.Conv2d(self.mid_channels, self.num_keypoints, 1))\n\n def forward(self, x, last_hidden=None):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n last_hidden (Tensor | None): The feedback of FeedbackHourglass.\n In first step, last_hidden=None. Otherwise, last_hidden is\n the past output of FeedbackHourglass.\n Default: None.\n\n Returns:\n heatmap (Tensor): Heatmap of facial landmark.\n feedback (Tensor): Feedback Tensor.\n \"\"\"\n\n feature = self.pre_conv_block(x)\n if last_hidden is None:\n feature = self.first_conv(torch.cat((feature, feature), dim=1))\n else:\n feature = self.first_conv(torch.cat((feature, last_hidden), dim=1))\n feature = self.hg(feature)\n heatmap = self.last(feature[:, :self.mid_channels]) # first half\n feedback = feature[:, self.mid_channels:] # second half\n return heatmap, feedback\n\n\nclass ResBlock(nn.Module):\n \"\"\"ResBlock for Hourglass.\n\n It has a style of:\n\n ::\n\n ---Conv-ReLU-Conv-Conv-+-\n |_________Conv________|\n\n or\n\n ---Conv-ReLU-Conv-Conv-+-\n |_____________________|\n\n Args:\n in_channels (int): Number of channels in the input features.\n out_channels (int): Number of channels in the output features.\n \"\"\"\n\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.conv_block = nn.Sequential(\n nn.Conv2d(in_channels, out_channels // 2, 1),\n nn.ReLU(inplace=True),\n nn.Conv2d(\n out_channels // 2, out_channels // 2, 3, stride=1, padding=1),\n nn.Conv2d(out_channels // 2, out_channels, 1))\n if in_channels == out_channels:\n self.skip_layer = None\n else:\n self.skip_layer = nn.Conv2d(in_channels, out_channels, 1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n residual = self.conv_block(x)\n if self.skip_layer:\n x = self.skip_layer(x)\n return x + residual\n\n\nclass Hourglass(nn.Module):\n \"\"\"Hourglass model for face landmark.\n\n It is a recursive model.\n\n Args:\n depth (int): Depth of Hourglass, the number of recursions.\n mid_channels (int): Number of channels in the intermediate features.\n \"\"\"\n\n def __init__(self, depth, mid_channels):\n super().__init__()\n self.up1 = ResBlock(mid_channels, mid_channels)\n self.pool = nn.MaxPool2d(2, 2)\n self.low1 = ResBlock(mid_channels, mid_channels)\n if depth == 1:\n self.low2 = ResBlock(mid_channels, mid_channels)\n else:\n self.low2 = Hourglass(depth - 1, mid_channels)\n self.low3 = ResBlock(mid_channels, mid_channels)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n up1 = self.up1(x)\n low1 = self.low1(self.pool(x))\n low2 = self.low2(low1)\n low3 = self.low3(low2)\n up2 = nn.functional.interpolate(\n low3, scale_factor=2, mode='bilinear', align_corners=True)\n return up1 + up2\n\n\ndef reduce_to_five_heatmaps(ori_heatmap, detach):\n \"\"\"Reduce facial landmark heatmaps to 5 heatmaps.\n\n DIC realizes facial SR with the help of key points of the face.\n The number of key points in datasets are different from each other.\n This function reduces the input heatmaps into 5 heatmaps:\n left eye\n right eye\n nose\n mouse\n face silhouette\n\n Args:\n ori_heatmap (Tensor): Input heatmap tensor. (B, N, 32, 32).\n detach (bool): Detached from the current tensor or not.\n\n returns:\n Tensor: New heatmap tensor. (B, 5, 32, 32).\n \"\"\"\n\n heatmap = ori_heatmap.clone()\n max_heat = heatmap.max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]\n max_heat = max_heat.clamp_min_(0.05)\n heatmap /= max_heat\n if heatmap.size(1) == 5:\n return heatmap.detach() if detach else heatmap\n elif heatmap.size(1) == 68:\n new_heatmap = torch.zeros_like(heatmap[:, :5])\n new_heatmap[:, 0] = heatmap[:, 36:42].sum(1) # left eye\n new_heatmap[:, 1] = heatmap[:, 42:48].sum(1) # right eye\n new_heatmap[:, 2] = heatmap[:, 27:36].sum(1) # nose\n new_heatmap[:, 3] = heatmap[:, 48:68].sum(1) # mouse\n new_heatmap[:, 4] = heatmap[:, :27].sum(1) # face silhouette\n return new_heatmap.detach() if detach else new_heatmap\n elif heatmap.size(1) == 194: # Helen\n new_heatmap = torch.zeros_like(heatmap[:, :5])\n tmp_id = torch.cat((torch.arange(134, 153), torch.arange(174, 193)))\n new_heatmap[:, 0] = heatmap[:, tmp_id].sum(1) # left eye\n tmp_id = torch.cat((torch.arange(114, 133), torch.arange(154, 173)))\n new_heatmap[:, 1] = heatmap[:, tmp_id].sum(1) # right eye\n tmp_id = torch.arange(41, 57)\n new_heatmap[:, 2] = heatmap[:, tmp_id].sum(1) # nose\n tmp_id = torch.arange(58, 113)\n new_heatmap[:, 3] = heatmap[:, tmp_id].sum(1) # mouse\n tmp_id = torch.arange(0, 40)\n new_heatmap[:, 4] = heatmap[:, tmp_id].sum(1) # face silhouette\n return new_heatmap.detach() if detach else new_heatmap\n else:\n raise NotImplementedError(\n f'Face landmark number {heatmap.size(1)} not implemented!')\n" }, { "path": "mmagic/models/editors/dic/light_cnn.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine import MMLogger\nfrom mmengine.model import BaseModule\nfrom mmengine.runner import load_checkpoint\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass LightCNN(BaseModule):\n \"\"\"LightCNN discriminator with input size 128 x 128.\n\n It is used to train DICGAN.\n\n Args:\n in_channels (int): Channel number of inputs.\n \"\"\"\n\n def __init__(self, in_channels):\n super().__init__()\n\n self.features = nn.Sequential(\n MaxFeature(in_channels, 48, 5, 1, 2),\n nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),\n MaxFeature(48, 48, 1, 1, 0),\n MaxFeature(48, 96, 3, 1, 1),\n nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),\n MaxFeature(96, 96, 1, 1, 0),\n MaxFeature(96, 192, 3, 1, 1),\n nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),\n MaxFeature(192, 192, 1, 1, 0),\n MaxFeature(192, 128, 3, 1, 1),\n MaxFeature(128, 128, 1, 1, 0),\n MaxFeature(128, 128, 3, 1, 1),\n nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),\n )\n self.classifier = nn.Sequential(\n MaxFeature(8 * 8 * 128, 256, filter_type='linear'),\n nn.LeakyReLU(0.2, True), nn.Linear(256, 1))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x = self.features(x)\n x = x.view(x.size(0), -1)\n out = self.classifier(x)\n return out\n\n def init_weights(self, pretrained=None, strict=True):\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n strict (boo, optional): Whether strictly load the pretrained model.\n Defaults to True.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=strict, logger=logger)\n elif pretrained is not None:\n raise TypeError(f'\"pretrained\" must be a str or None. '\n f'But received {type(pretrained)}.')\n\n\nclass MaxFeature(nn.Module):\n \"\"\"Conv2d or Linear layer with max feature selector.\n\n Generate feature maps with double channels, split them and select the max\n feature.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n kernel_size (int or tuple): Size of the convolving kernel.\n stride (int or tuple, optional): Stride of the convolution. Default: 1\n padding (int or tuple, optional): Zero-padding added to both sides of\n the input. Default: 1\n filter_type (str): Type of filter. Options are 'conv2d' and 'linear'.\n Default: 'conv2d'.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n filter_type='conv2d'):\n super().__init__()\n self.out_channels = out_channels\n filter_type = filter_type.lower()\n if filter_type == 'conv2d':\n self.filter = nn.Conv2d(\n in_channels,\n 2 * out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n elif filter_type == 'linear':\n self.filter = nn.Linear(in_channels, 2 * out_channels)\n else:\n raise ValueError(\"'filter_type' should be 'conv2d' or 'linear', \"\n f'but got {filter_type}')\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x = self.filter(x)\n out = torch.chunk(x, chunks=2, dim=1)\n return torch.max(out[0], out[1])\n" }, { "path": "mmagic/models/editors/dim/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .dim import DIM\n\n__all__ = ['DIM']\n" }, { "path": "mmagic/models/editors/dim/dim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Tuple\n\nimport torch\nfrom mmengine.logging import MMLogger\n\nfrom mmagic.models.base_models import BaseMattor\nfrom mmagic.models.utils import get_unknown_tensor\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass DIM(BaseMattor):\n \"\"\"Deep Image Matting model.\n\n https://arxiv.org/abs/1703.03872\n\n .. note::\n\n For ``(self.train_cfg.train_backbone, self.train_cfg.train_refiner)``:\n\n * ``(True, False)`` corresponds to the encoder-decoder stage in \\\n the paper.\n * ``(False, True)`` corresponds to the refinement stage in the \\\n paper.\n * ``(True, True)`` corresponds to the fine-tune stage in the paper.\n\n Args:\n data_preprocessor (dict, optional): Config of data pre-processor.\n backbone (dict): Config of backbone.\n refiner (dict): Config of refiner.\n loss_alpha (dict): Config of the alpha prediction loss. Default: None.\n loss_comp (dict): Config of the composition loss. Default: None.\n loss_refine (dict): Config of the loss of the refiner. Default: None.\n train_cfg (dict): Config of training. In ``train_cfg``,\n ``train_backbone`` should be specified. If the model has a refiner,\n ``train_refiner`` should be specified.\n test_cfg (dict): Config of testing. In ``test_cfg``, If the model has a\n refiner, ``train_refiner`` should be specified.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n \"\"\"\n\n def __init__(self,\n data_preprocessor,\n backbone,\n refiner=None,\n train_cfg=None,\n test_cfg=None,\n loss_alpha=None,\n loss_comp=None,\n loss_refine=None,\n init_cfg: Optional[dict] = None):\n # Build data _preprocessor and backbone\n # No init here, init at last\n super().__init__(\n backbone=backbone,\n data_preprocessor=data_preprocessor,\n init_cfg=init_cfg,\n train_cfg=train_cfg,\n test_cfg=test_cfg)\n\n # build refiner if it's not None.\n if refiner is None:\n self.train_cfg['train_refiner'] = False\n self.test_cfg['refine'] = False\n else:\n self.refiner = MODELS.build(refiner)\n\n # if argument train_cfg is not None, validate if the config is proper.\n assert hasattr(self.train_cfg, 'train_refiner')\n assert hasattr(self.test_cfg, 'refine')\n if self.test_cfg.refine and not self.train_cfg.train_refiner:\n logger = MMLogger.get_current_instance()\n logger.warning(\n 'You are not training the refiner, but it is used for '\n 'model forwarding.')\n\n if not self.train_cfg.train_backbone:\n self.freeze_backbone()\n\n # Build losses\n if all(v is None for v in (loss_alpha, loss_comp, loss_refine)):\n raise ValueError('Please specify at least one loss for DIM.')\n\n if loss_alpha is not None:\n self.loss_alpha = MODELS.build(loss_alpha)\n if loss_comp is not None:\n self.loss_comp = MODELS.build(loss_comp)\n if loss_refine is not None:\n self.loss_refine = MODELS.build(loss_refine)\n\n def init_weights(self):\n \"\"\"Initialize the model network weights.\"\"\"\n super().init_weights()\n if self.with_refiner:\n self.refiner.init_weights()\n\n @property\n def with_refiner(self):\n \"\"\"Whether the matting model has a refiner.\"\"\"\n return hasattr(self, 'refiner') and self.refiner is not None\n\n def train(self, mode=True):\n \"\"\"Mode switcher.\n\n Args:\n mode (bool): whether to set training mode (``True``) or evaluation\n mode (``False``). Default: ``True``.\n \"\"\"\n super().train(mode)\n if mode and (not self.train_cfg.train_backbone):\n self.backbone.eval()\n\n def freeze_backbone(self):\n \"\"\"Freeze the backbone and only train the refiner.\"\"\"\n self.backbone.eval()\n for param in self.backbone.parameters():\n param.requires_grad = False\n\n def _forward(self,\n x: torch.Tensor,\n *,\n refine: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Raw forward function.\n\n Args:\n x (torch.Tensor): Concatenation of merged image and trimap\n with shape (N, 4, H, W)\n refine (bool): if forward through refiner\n\n Returns:\n torch.Tensor: pred_alpha, with shape (N, 1, H, W)\n torch.Tensor: pred_refine, with shape (N, 4, H, W)\n \"\"\"\n\n raw_alpha = self.backbone(x)\n pred_alpha = raw_alpha.sigmoid()\n\n if refine and hasattr(self, 'refiner'):\n refine_input = torch.cat((x[:, :3, :, :], pred_alpha), 1)\n pred_refine = self.refiner(refine_input, raw_alpha)\n else:\n # As ONNX does not support NoneType for output,\n # we choose to use zero tensor to represent None\n pred_refine = torch.zeros([])\n return pred_alpha, pred_refine\n\n def _forward_test(self, inputs):\n \"\"\"Forward to get alpha prediction.\"\"\"\n pred_alpha, pred_refine = self._forward(inputs)\n if self.test_cfg.refine:\n return pred_refine\n else:\n return pred_alpha\n\n def _forward_train(self, inputs, data_samples):\n \"\"\"Defines the computation performed at every training call.\n\n Args:\n inputs (torch.Tensor): Concatenation of normalized image and trimap\n shape (N, 4, H, W)\n data_samples (list[DataSample]): Data samples containing:\n - gt_alpha (Tensor): Ground-truth of alpha\n shape (N, 1, H, W), normalized to 0 to 1.\n - gt_fg (Tensor): Ground-truth of foreground\n shape (N, C, H, W), normalized to 0 to 1.\n - gt_bg (Tensor): Ground-truth of background\n shape (N, C, H, W), normalized to 0 to 1.\n\n Returns:\n dict: Contains the loss items and batch information.\n \"\"\"\n # merged, trimap, meta, alpha, ori_merged, fg, bg\n\n gt_alpha = data_samples.gt_alpha\n gt_fg = data_samples.gt_fg\n gt_bg = data_samples.gt_bg\n gt_merged = data_samples.gt_merged\n\n pred_alpha, pred_refine = self._forward(\n inputs, refine=self.train_cfg.train_refiner)\n\n trimap = inputs[:, 3:, :, :]\n # Dim should use proc_trimap='rescale_to_zero_one'\n weight = get_unknown_tensor(trimap, unknown_value=128 / 255)\n\n losses = dict()\n if self.train_cfg.train_backbone:\n if self.loss_alpha is not None:\n losses['loss_alpha'] = self.loss_alpha(pred_alpha, gt_alpha,\n weight)\n if self.loss_comp is not None:\n losses['loss_comp'] = self.loss_comp(pred_alpha, gt_fg, gt_bg,\n gt_merged, weight)\n if self.train_cfg.train_refiner:\n losses['loss_refine'] = self.loss_refine(pred_refine, gt_alpha,\n weight)\n return losses\n" }, { "path": "mmagic/models/editors/disco_diffusion/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .clip_wrapper import ClipWrapper\nfrom .disco import DiscoDiffusion\nfrom .guider import ImageTextGuider\nfrom .secondary_model import SecondaryDiffusionImageNet2, alpha_sigma_to_t\n\n__all__ = [\n 'DiscoDiffusion', 'ImageTextGuider', 'ClipWrapper',\n 'SecondaryDiffusionImageNet2', 'alpha_sigma_to_t'\n]\n" }, { "path": "mmagic/models/editors/disco_diffusion/clip_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import print_log\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass ClipWrapper(nn.Module):\n r\"\"\"Clip Models wrapper.\n\n We provide wrappers for the clip models of ``openai`` and\n ``mlfoundations``, where the user can specify ``clip_type``\n as ``clip`` or ``open_clip``, and then initialize a clip model\n using the same arguments as in the original codebase. The\n following clip models settings are provided in the official\n repo of disco diffusion:\n | Setting | Source | Arguments | # noqa\n |:-----------------------------:|-----------|--------------------------------------------------------------| # noqa\n | ViTB32 | clip | name='ViT-B/32', jit=False | # noqa\n | ViTB16 | clip | name='ViT-B/16', jit=False | # noqa\n | ViTL14 | clip | name='ViT-L/14', jit=False | # noqa\n | ViTL14_336px | clip | name='ViT-L/14@336px', jit=False | # noqa\n | RN50 | clip | name='RN50', jit=False | # noqa\n | RN50x4 | clip | name='RN50x4', jit=False | # noqa\n | RN50x16 | clip | name='RN50x16', jit=False | # noqa\n | RN50x64 | clip | name='RN50x64', jit=False | # noqa\n | RN101 | clip | name='RN101', jit=False | # noqa\n | ViTB32_laion2b_e16 | open_clip | name='ViT-B-32', pretrained='laion2b_e16' | # noqa\n | ViTB32_laion400m_e31 | open_clip | model_name='ViT-B-32', pretrained='laion400m_e31' | # noqa\n | ViTB32_laion400m_32 | open_clip | model_name='ViT-B-32', pretrained='laion400m_e32' | # noqa\n | ViTB32quickgelu_laion400m_e31 | open_clip | model_name='ViT-B-32-quickgelu', pretrained='laion400m_e31' | # noqa\n | ViTB32quickgelu_laion400m_e32 | open_clip | model_name='ViT-B-32-quickgelu', pretrained='laion400m_e32' | # noqa\n | ViTB16_laion400m_e31 | open_clip | model_name='ViT-B-16', pretrained='laion400m_e31' | # noqa\n | ViTB16_laion400m_e32 | open_clip | model_name='ViT-B-16', pretrained='laion400m_e32' | # noqa\n | RN50_yffcc15m | open_clip | model_name='RN50', pretrained='yfcc15m' | # noqa\n | RN50_cc12m | open_clip | model_name='RN50', pretrained='cc12m' | # noqa\n | RN50_quickgelu_yfcc15m | open_clip | model_name='RN50-quickgelu', pretrained='yfcc15m' | # noqa\n | RN50_quickgelu_cc12m | open_clip | model_name='RN50-quickgelu', pretrained='cc12m' | # noqa\n | RN101_yfcc15m | open_clip | model_name='RN101', pretrained='yfcc15m' | # noqa\n | RN101_quickgelu_yfcc15m | open_clip | model_name='RN101-quickgelu', pretrained='yfcc15m' | # noqa\n\n An example of a ``clip_modes_cfg`` is as follows:\n\n Examples:\n\n >>> # Use OpenAI's CLIP\n >>> config = dict(\n >>> type='ClipWrapper',\n >>> clip_type='clip',\n >>> name='ViT-B/32',\n >>> jit=False)\n\n >>> # Use OpenCLIP\n >>> config = dict(\n >>> type='ClipWrapper',\n >>> clip_type='open_clip',\n >>> model_name='RN50',\n >>> pretrained='yfcc15m')\n\n >>> # Use CLIP from Hugging Face Transformers\n >>> config = dict(\n >>> type='ClipWrapper',\n >>> clip_type='huggingface',\n >>> pretrained_model_name_or_path='runwayml/stable-diffusion-v1-5',\n >>> subfolder='text_encoder')\n\n Args:\n clip_type (List[Dict]): The original source of the clip model. Whether be\n ``clip``, ``open_clip`` or ``hugging_face``.\n\n *args, **kwargs: Arguments to initialize corresponding clip model.\n \"\"\"\n\n def __init__(self, clip_type, *args, **kwargs):\n\n super().__init__()\n self.clip_type = clip_type\n assert clip_type in ['clip', 'open_clip', 'huggingface']\n\n error_msg = ('{} need to be installed! Run `pip install -r '\n 'requirements/optional.txt` and try again')\n if clip_type == 'clip':\n try:\n import clip\n except ImportError:\n raise ImportError(error_msg.format('\\'clip\\''))\n print_log(f'Creating {kwargs[\"name\"]} by OpenAI', 'current')\n self.model, _ = clip.load(*args, **kwargs)\n elif clip_type == 'open_clip':\n try:\n import open_clip\n except ImportError:\n raise ImportError(error_msg.format('\\'open_clip_torch\\''))\n print_log(f'Creating {kwargs[\"model_name\"]} by '\n 'mlfoundations', 'current')\n self.model = open_clip.create_model(*args, **kwargs)\n\n elif clip_type == 'huggingface':\n try:\n import transformers\n except ImportError:\n raise ImportError(error_msg.format('\\'transforms\\''))\n # NOTE: use CLIPTextModel to adopt stable diffusion pipeline\n model_cls = transformers.CLIPTextModel\n self.model = model_cls.from_pretrained(*args, **kwargs)\n self.config = self.model.config\n print_log(\n f'Creating {self.model.name_or_path} '\n 'by \\'HuggingFace\\'', 'current')\n\n self.model.eval().requires_grad_(False)\n\n def get_embedding_layer(self):\n \"\"\"Function to get embedding layer of the clip model.\n\n Only support for CLIPTextModel currently.\n \"\"\"\n\n if self.clip_type != 'huggingface':\n print_log(\n 'Do not support \\'get_embedding_layer\\' for clip_type: '\n f'\\'{self.clip_type}\\' currently.', 'current')\n return None\n if self.model.__class__.__name__ != 'CLIPTextModel':\n print_log(\n 'Only support \\'get_embedding_layer\\' for '\n 'CLIPTextModel.', 'current')\n return None\n\n return self.model.text_model.embeddings.token_embedding\n\n def add_embedding(self, embeddings: Union[dict, List[dict]]):\n assert self.clip_type == 'huggingface', (\n 'Only support add embedding for HuggingFace transformers.')\n assert self.model.__class__.__name__ == 'CLIPTextModel', (\n 'Only support add embedding for \\'CLIPTextModel\\' (CLIP).')\n\n embedding_layer = self.get_embedding_layer()\n if not isinstance(embedding_layer, EmbeddingLayerWithFixes):\n self.model.embeddings = EmbeddingLayerWithFixes(embedding_layer)\n\n self.model.embeddings.add_embedding(embeddings)\n\n def set_only_embedding_trainable(self):\n func_name = '\\'set_only_embedding_trainable\\''\n assert self.clip_type == 'huggingface', (\n f'Only support {func_name} for HuggingFace transformers.')\n assert self.model.__class__.__name__ == 'CLIPTextModel', (\n f'Only support {func_name} for \\'CLIPTextModel\\' (CLIP).')\n self.model.requires_grad_(False)\n embedding_layer = self.get_embedding_layer()\n if isinstance(embedding_layer, EmbeddingLayerWithFixes):\n embedding_layer.trainable_embeddings.requires_grad_(True)\n print_log('Set only embedding trainable.', 'current')\n else:\n print_log(\n 'Do not found EmbeddingLayerWithFixes. '\n f'{func_name} do nothing.', 'current')\n\n def set_embedding_layer(self):\n assert self.clip_type == 'huggingface', (\n 'Only support add embedding for HuggingFace transformers.')\n assert self.model.__class__.__name__ == 'CLIPTextModel', (\n 'Only support add embedding for \\'CLIPTextModel\\' (CLIP).')\n embedding_layer = self.get_embedding_layer()\n if not isinstance(embedding_layer, EmbeddingLayerWithFixes):\n self.model.text_model.embeddings.token_embedding = \\\n EmbeddingLayerWithFixes(embedding_layer)\n print_log('Set embedding layer to EmbeddingLayerWithFixes', 'current')\n\n def unset_embedding_layer(self):\n wrapped_embedding_layer = self.model.embeddings\n if isinstance(wrapped_embedding_layer, EmbeddingLayerWithFixes):\n self.model.text_model.embeddings.token_embedding = \\\n wrapped_embedding_layer.wrapped\n print_log('Unset embedding layer.', 'current')\n\n def forward(self, *args, **kwargs):\n \"\"\"Forward function.\"\"\"\n return self.model(*args, **kwargs)\n\n\nclass EmbeddingLayerWithFixes(nn.Module):\n \"\"\"The revised embedding layer to support external embeddings. This design\n of this class is inspired by https://github.com/AUTOMATIC1111/stable-\n diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi\n jack.py#L224 # noqa.\n\n Args:\n wrapped (nn.Emebdding): The embedding layer to be wrapped.\n external_embeddings (Union[dict, List[dict]], optional): The external\n embeddings added to this layer. Defaults to None.\n \"\"\"\n\n def __init__(self,\n wrapped: nn.Embedding,\n external_embeddings: Optional[Union[dict,\n List[dict]]] = None):\n super().__init__()\n self.wrapped = wrapped\n self.num_embeddings = wrapped.weight.shape[0]\n\n self.external_embeddings = []\n if external_embeddings:\n self.add_embeddings(external_embeddings)\n\n self.trainable_embeddings = nn.ParameterDict()\n\n @property\n def weight(self):\n \"\"\"Get the weight of wrapped embedding layer.\"\"\"\n return self.wrapped.weight\n\n def check_duplicate_names(self, embeddings: List[dict]):\n \"\"\"Check whether duplicate names exist in list of 'external\n embeddings'.\n\n Args:\n embeddings (List[dict]): A list of embedding to be check.\n \"\"\"\n names = [emb['name'] for emb in embeddings]\n assert len(names) == len(set(names)), (\n 'Found duplicated names in \\'external_embeddings\\'. Name list: '\n f'\\'{names}\\'')\n\n def check_ids_overlap(self, embeddings):\n \"\"\"Check whether overlap exist in token ids of 'external_embeddings'.\n\n Args:\n embeddings (List[dict]): A list of embedding to be check.\n \"\"\"\n ids_range = [[emb['start'], emb['end'], emb['name']]\n for emb in embeddings]\n ids_range.sort() # sort by 'start'\n # check if 'end' has overlapping\n for idx in range(len(ids_range) - 1):\n name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1]\n assert ids_range[idx][1] <= ids_range[idx + 1][0], (\n f'Found ids overlapping between embeddings \\'{name1}\\' '\n f'and \\'{name2}\\'.')\n\n def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):\n \"\"\"Add external embeddings to this layer.\n\n Use case:\n\n >>> 1. Add token to tokenizer and get the token id.\n >>> tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')\n >>> # 'how much' in kiswahili\n >>> tokenizer.add_placeholder_tokens('ngapi', num_vec_per_token=4)\n >>>\n >>> 2. Add external embeddings to the model.\n >>> new_embedding = {\n >>> 'name': 'ngapi', # 'how much' in kiswahili\n >>> 'embedding': torch.ones(1, 15) * 4,\n >>> 'start': tokenizer.get_token_info('kwaheri')['start'],\n >>> 'end': tokenizer.get_token_info('kwaheri')['end'],\n >>> 'trainable': False # if True, will registry as a parameter\n >>> }\n >>> embedding_layer = nn.Embedding(10, 15)\n >>> embedding_layer_wrapper = EmbeddingLayerWithFixes(embedding_layer)\n >>> embedding_layer_wrapper.add_embeddings(new_embedding)\n >>>\n >>> 3. Forward tokenizer and embedding layer!\n >>> input_text = ['hello, ngapi!', 'hello my friend, ngapi?']\n >>> input_ids = tokenizer(\n >>> input_text, padding='max_length', truncation=True,\n >>> return_tensors='pt')['input_ids']\n >>> out_feat = embedding_layer_wrapper(input_ids)\n >>>\n >>> 4. Let's validate the result!\n >>> assert (out_feat[0, 3: 7] == 2.3).all()\n >>> assert (out_feat[2, 5: 9] == 2.3).all()\n\n Args:\n embeddings (Union[dict, list[dict]]): The external embeddings to\n be added. Each dict must contain the following 4 fields: 'name'\n (the name of this embedding), 'embedding' (the embedding\n tensor), 'start' (the start token id of this embedding), 'end'\n (the end token id of this embedding). For example:\n `{name: NAME, start: START, end: END, embedding: torch.Tensor}`\n \"\"\"\n if isinstance(embeddings, dict):\n embeddings = [embeddings]\n\n self.external_embeddings += embeddings\n self.check_duplicate_names(self.external_embeddings)\n self.check_ids_overlap(self.external_embeddings)\n\n # set for trainable\n added_trainable_emb_info = []\n for embedding in embeddings:\n trainable = embedding.get('trainable', False)\n if trainable:\n name = embedding['name']\n embedding['embedding'] = torch.nn.Parameter(\n embedding['embedding'])\n self.trainable_embeddings[name] = embedding['embedding']\n added_trainable_emb_info.append(name)\n\n added_emb_info = [emb['name'] for emb in embeddings]\n added_emb_info = ', '.join(added_emb_info)\n print_log(f'Successfully add external embeddings: {added_emb_info}.',\n 'current')\n\n if added_trainable_emb_info:\n added_trainable_emb_info = ', '.join(added_trainable_emb_info)\n print_log(\n 'Successfully add trainable external embeddings: '\n f'{added_trainable_emb_info}', 'current')\n\n def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:\n \"\"\"Replace external input ids to 0.\n\n Args:\n input_ids (torch.Tensor): The input ids to be replaced.\n\n Returns:\n torch.Tensor: The replaced input ids.\n \"\"\"\n input_ids_fwd = input_ids.clone()\n input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0\n return input_ids_fwd\n\n def replace_embeddings(self, input_ids: torch.Tensor,\n embedding: torch.Tensor,\n external_embedding: dict) -> torch.Tensor:\n \"\"\"Replace external embedding to the embedding layer. Noted that, in\n this function we use `torch.cat` to avoid inplace modification.\n\n Args:\n input_ids (torch.Tensor): The original token ids. Shape like\n [LENGTH, ].\n embedding (torch.Tensor): The embedding of token ids after\n `replace_input_ids` function.\n external_embedding (dict): The external embedding to be replaced.\n\n Returns:\n torch.Tensor: The replaced embedding.\n \"\"\"\n new_embedding = []\n\n name = external_embedding['name']\n start = external_embedding['start']\n end = external_embedding['end']\n target_ids_to_replace = [i for i in range(start, end)]\n ext_emb = external_embedding['embedding']\n\n # do not need to replace\n if not (input_ids == start).any():\n return embedding\n\n # start replace\n s_idx, e_idx = 0, 0\n while e_idx < len(input_ids):\n if input_ids[e_idx] == start:\n if e_idx != 0:\n # add embedding do not need to replace\n new_embedding.append(embedding[s_idx:e_idx])\n\n # check if the next embedding need to replace is valid\n actually_ids_to_replace = [\n int(i) for i in input_ids[e_idx:e_idx + end - start]\n ]\n assert actually_ids_to_replace == target_ids_to_replace, (\n f'Invalid \\'input_ids\\' in position: {s_idx} to {e_idx}. '\n f'Expect \\'{target_ids_to_replace}\\' for embedding '\n f'\\'{name}\\' but found \\'{actually_ids_to_replace}\\'.')\n\n new_embedding.append(ext_emb)\n\n s_idx = e_idx + end - start\n e_idx = s_idx + 1\n else:\n e_idx += 1\n\n if e_idx == len(input_ids):\n new_embedding.append(embedding[s_idx:e_idx])\n\n return torch.cat(new_embedding, dim=0)\n\n def forward(self,\n input_ids: torch.Tensor,\n external_embeddings: Optional[List[dict]] = None):\n \"\"\"The forward function.\n\n Args:\n input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or\n [LENGTH, ].\n external_embeddings (Optional[List[dict]]): The external\n embeddings. If not passed, only `self.external_embeddings`\n will be used. Defaults to None.\n\n input_ids: shape like [bz, LENGTH] or [LENGTH].\n \"\"\"\n assert input_ids.ndim in [1, 2]\n if input_ids.ndim == 1:\n input_ids = input_ids.unsqueeze(0)\n\n if external_embeddings is None and not self.external_embeddings:\n return self.wrapped(input_ids)\n\n input_ids_fwd = self.replace_input_ids(input_ids)\n inputs_embeds = self.wrapped(input_ids_fwd)\n\n vecs = []\n\n if external_embeddings is None:\n external_embeddings = []\n elif isinstance(external_embeddings, dict):\n external_embeddings = [external_embeddings]\n embeddings = self.external_embeddings + external_embeddings\n\n for input_id, embedding in zip(input_ids, inputs_embeds):\n new_embedding = embedding\n for external_embedding in embeddings:\n new_embedding = self.replace_embeddings(\n input_id, new_embedding, external_embedding)\n vecs.append(new_embedding)\n\n return torch.stack(vecs)\n" }, { "path": "mmagic/models/editors/disco_diffusion/disco.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Union\n\nimport mmcv\nimport mmengine\nimport torch\nimport torch.nn as nn\nfrom mmengine.runner import set_random_seed\nfrom mmengine.runner.checkpoint import (_load_checkpoint,\n _load_checkpoint_with_prefix)\nfrom tqdm import tqdm\n\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom .guider import ImageTextGuider\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module('disco')\n@MODELS.register_module('dd')\n@MODELS.register_module()\nclass DiscoDiffusion(nn.Module):\n \"\"\"Disco Diffusion (DD) is a Google Colab Notebook which leverages an AI\n Image generating technique called CLIP-Guided Diffusion to allow you to\n create compelling and beautiful images from just text inputs. Created by\n Somnai, augmented by Gandamu, and building on the work of RiversHaveWings,\n nshepperd, and many others.\n\n Ref:\n Github Repo: https://github.com/alembics/disco-diffusion\n Colab: https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb # noqa\n\n Args:\n unet (ModelType): Config of denoising Unet.\n diffusion_scheduler (ModelType): Config of diffusion_scheduler scheduler.\n secondary_model (ModelType): A smaller secondary diffusion model\n trained by Katherine Crowson to remove noise from intermediate\n timesteps to prepare them for CLIP.\n Ref: https://twitter.com/rivershavewings/status/1462859669454536711 # noqa\n Defaults to None.\n clip_models (list): Config of clip models. Defaults to [].\n use_fp16 (bool): Whether to use fp16 for unet model. Defaults to False.\n pretrained_cfgs (dict): Path Config for pretrained weights. Usually\n this is a dict contains module name and the corresponding ckpt\n path. Defaults to None.\n \"\"\"\n\n def __init__(self,\n unet,\n diffusion_scheduler,\n secondary_model=None,\n clip_models=[],\n use_fp16=False,\n pretrained_cfgs=None):\n super().__init__()\n self.unet = unet if isinstance(unet, nn.Module) else MODELS.build(unet)\n self.diffusion_scheduler = DIFFUSION_SCHEDULERS.build(\n diffusion_scheduler) if isinstance(diffusion_scheduler,\n dict) else diffusion_scheduler\n\n assert len(clip_models) > 0\n if isinstance(clip_models[0], nn.Module):\n _clip_models = clip_models\n else:\n _clip_models = []\n for clip_cfg in clip_models:\n _clip_models.append(MODELS.build(clip_cfg))\n self.guider = ImageTextGuider(_clip_models)\n\n if secondary_model is not None:\n self.secondary_model = secondary_model if isinstance(\n secondary_model, nn.Module) else MODELS.build(secondary_model)\n self.with_secondary_model = True\n else:\n self.with_secondary_model = False\n\n if pretrained_cfgs:\n self.load_pretrained_models(pretrained_cfgs)\n if use_fp16:\n mmengine.print_log('Convert unet modules to floatpoint16')\n self.unet.convert_to_fp16()\n\n def load_pretrained_models(self, pretrained_cfgs):\n \"\"\"Loading pretrained weights to model. ``pretrained_cfgs`` is a dict\n consist of module name as key and checkpoint path as value.\n\n Args:\n pretrained_cfgs (dict): Path Config for pretrained weights.\n Usually this is a dict contains module name and the\n corresponding ckpt path. Defaults to None.\n \"\"\"\n for key, ckpt_cfg in pretrained_cfgs.items():\n prefix = ckpt_cfg.get('prefix', '')\n map_location = ckpt_cfg.get('map_location', 'cpu')\n strict = ckpt_cfg.get('strict', True)\n ckpt_path = ckpt_cfg.get('ckpt_path')\n if prefix:\n state_dict = _load_checkpoint_with_prefix(\n prefix, ckpt_path, map_location)\n else:\n state_dict = _load_checkpoint(ckpt_path, map_location)\n getattr(self, key).load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained {key} from {ckpt_path}')\n\n @property\n def device(self):\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n @torch.no_grad()\n def infer(self,\n scheduler_kwargs=None,\n height=None,\n width=None,\n init_image=None,\n batch_size=1,\n num_inference_steps=100,\n skip_steps=0,\n show_progress=True,\n text_prompts=[],\n image_prompts=[],\n eta=0.8,\n clip_guidance_scale=5000,\n init_scale=1000,\n tv_scale=0.,\n sat_scale=0.,\n range_scale=150,\n cut_overview=[12] * 400 + [4] * 600,\n cut_innercut=[4] * 400 + [12] * 600,\n cut_ic_pow=[1] * 1000,\n cut_icgray_p=[0.2] * 400 + [0] * 600,\n cutn_batches=4,\n seed=None):\n \"\"\"Inference API for disco diffusion.\n\n Args:\n scheduler_kwargs (dict): Args for infer time diffusion\n scheduler. Defaults to None.\n height (int): Height of output image. Defaults to None.\n width (int): Width of output image. Defaults to None.\n init_image (str): Initial image at the start point\n of denoising. Defaults to None.\n batch_size (int): Batch size. Defaults to 1.\n num_inference_steps (int): Number of inference steps.\n Defaults to 1000.\n skip_steps (int): Denoising steps to skip, usually set\n with ``init_image``. Defaults to 0.\n show_progress (bool): Whether to show progress.\n Defaults to False.\n text_prompts (list): Text prompts. Defaults to [].\n image_prompts (list): Image prompts, this is not the same as\n ``init_image``, they works the same way with\n ``text_prompts``. Defaults to [].\n eta (float): Eta for ddim sampling. Defaults to 0.8.\n clip_guidance_scale (int): The Scale of influence of prompts\n on output image. Defaults to 1000.\n seed (int): Sampling seed. Defaults to None.\n \"\"\"\n # set diffusion_scheduler\n if scheduler_kwargs is not None:\n mmengine.print_log('Switch to infer diffusion scheduler!',\n 'current')\n infer_scheduler = DIFFUSION_SCHEDULERS.build(scheduler_kwargs)\n else:\n infer_scheduler = self.diffusion_scheduler\n # set random seed\n if isinstance(seed, int):\n set_random_seed(seed=seed)\n\n # set step values\n if num_inference_steps > 0:\n infer_scheduler.set_timesteps(num_inference_steps)\n\n _ = image_prompts\n\n height = (height // 64) * 64 if height else self.unet.image_size\n width = (width // 64) * 64 if width else self.unet.image_size\n if init_image is None:\n image = torch.randn(\n (batch_size, self.unet.in_channels, height, width))\n image = image.to(self.device)\n else:\n init = mmcv.imread(init_image, channel_order='rgb')\n init = mmcv.imresize(\n init, (width, height), interpolation='lanczos') / 255.\n init_image = torch.as_tensor(\n init,\n dtype=torch.float32).to(self.device).unsqueeze(0).permute(\n 0, 3, 1, 2).mul(2).sub(1)\n image = init_image.clone()\n image = infer_scheduler.add_noise(\n image, torch.randn_like(image),\n infer_scheduler.timesteps[skip_steps])\n # get stats from text prompts and image prompts\n model_stats = self.guider.compute_prompt_stats(\n text_prompts=text_prompts)\n timesteps = infer_scheduler.timesteps[skip_steps:]\n if show_progress:\n timesteps = tqdm(timesteps)\n for t in timesteps:\n # 1. predicted model_output\n model_output = self.unet(image, t)['sample']\n\n # 2. compute previous image: x_t -> x_t-1\n cond_kwargs = dict(\n model_stats=model_stats,\n init_image=init_image,\n unet=self.unet,\n clip_guidance_scale=clip_guidance_scale,\n init_scale=init_scale,\n tv_scale=tv_scale,\n sat_scale=sat_scale,\n range_scale=range_scale,\n cut_overview=cut_overview,\n cut_innercut=cut_innercut,\n cut_ic_pow=cut_ic_pow,\n cut_icgray_p=cut_icgray_p,\n cutn_batches=cutn_batches,\n )\n if self.with_secondary_model:\n cond_kwargs.update(secondary_model=self.secondary_model)\n diffusion_scheduler_output = infer_scheduler.step(\n model_output,\n t,\n image,\n cond_fn=self.guider.cond_fn,\n cond_kwargs=cond_kwargs,\n eta=eta)\n\n image = diffusion_scheduler_output['prev_sample']\n return {'samples': image}\n" }, { "path": "mmagic/models/editors/disco_diffusion/guider.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport lpips\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as TF\nfrom mmengine.utils import digit_version\nfrom resize_right import resize\nfrom torchvision import __version__ as TORCHVISION_VERSION\n\nfrom mmagic.models.losses import tv_loss\nfrom mmagic.utils import try_import\nfrom .secondary_model import alpha_sigma_to_t\n\nclip = try_import('clip')\n\nnormalize = T.Normalize(\n mean=[0.48145466, 0.4578275, 0.40821073],\n std=[0.26862954, 0.26130258, 0.27577711])\n\n\ndef sinc(x):\n \"\"\"\n Sinc function.\n If x equal to 0,\n sinc(x) = 1\n else:\n sinc(x) = sin(x)/ x\n Args:\n x (torch.Tensor): Input Tensor\n\n Returns:\n torch.Tensor: Function output.\n \"\"\"\n return torch.where(x != 0,\n torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))\n\n\ndef lanczos(x, a):\n \"\"\"Lanczos filter's reconstruction kernel L(x).\"\"\"\n cond = torch.logical_and(-a < x, x < a)\n out = torch.where(cond, sinc(x) * sinc(x / a), x.new_zeros([]))\n return out / out.sum()\n\n\ndef ramp(ratio, width):\n \"\"\"_summary_\n\n Args:\n ratio (_type_): _description_\n width (_type_): _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n n = math.ceil(width / ratio + 1)\n out = torch.empty([n])\n cur = 0\n for i in range(out.shape[0]):\n out[i] = cur\n cur += ratio\n return torch.cat([-out[1:].flip([0]), out])[1:-1]\n\n\ndef resample(input, size, align_corners=True):\n \"\"\"Lanczos resampling image.\n\n Args:\n input (torch.Tensor): Input image tensor.\n size (Tuple[int, int]): Output image size.\n align_corners (bool): align_corners argument of F.interpolate.\n Defaults to True.\n\n Returns:\n torch.Tensor: Resampling results.\n \"\"\"\n n, c, h, w = input.shape\n dh, dw = size\n\n input = input.reshape([n * c, 1, h, w])\n\n if dh < h:\n kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)\n pad_h = (kernel_h.shape[0] - 1) // 2\n input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')\n input = F.conv2d(input, kernel_h[None, None, :, None])\n\n if dw < w:\n kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)\n pad_w = (kernel_w.shape[0] - 1) // 2\n input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')\n input = F.conv2d(input, kernel_w[None, None, None, :])\n\n input = input.reshape([n, c, h, w])\n return F.interpolate(\n input, size, mode='bicubic', align_corners=align_corners)\n\n\ndef range_loss(input):\n \"\"\"range loss.\"\"\"\n return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])\n\n\ndef spherical_dist_loss(x, y):\n \"\"\"spherical distance loss.\"\"\"\n x = F.normalize(x, dim=-1)\n y = F.normalize(y, dim=-1)\n return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)\n\n\nclass MakeCutouts(nn.Module):\n \"\"\"Each iteration, the AI cuts the image into smaller pieces known as cuts.\n\n , and compares each cut to the prompt to decide how to guide the next\n diffusion step.\n This classes will randomly cut patches and perform image augmentation to\n these patches.\n\n Args:\n cut_size (int): Size of the patches.\n cutn (int): Number of patches to cut.\n \"\"\"\n\n def __init__(self, cut_size, cutn):\n super().__init__()\n self.cut_size = cut_size\n self.cutn = cutn\n self.augs = T.Compose([\n T.RandomHorizontalFlip(p=0.5),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.RandomAffine(degrees=15, translate=(0.1, 0.1)),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.RandomPerspective(distortion_scale=0.4, p=0.7),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.RandomGrayscale(p=0.15),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n ])\n\n def forward(self, input, skip_augs=False):\n input = T.Pad(input.shape[2] // 4, fill=0)(input)\n sideY, sideX = input.shape[2:4]\n max_size = min(sideX, sideY)\n\n cutouts = []\n for ch in range(self.cutn):\n if ch > self.cutn - self.cutn // 4:\n cutout = input.clone()\n else:\n size = int(max_size * torch.zeros(1, ).normal_(\n mean=.8, std=.3).clip(float(self.cut_size / max_size), 1.))\n offsetx = torch.randint(0, abs(sideX - size + 1), ())\n offsety = torch.randint(0, abs(sideY - size + 1), ())\n cutout = input[:, :, offsety:offsety + size,\n offsetx:offsetx + size]\n\n if not skip_augs:\n cutout = self.augs(cutout)\n cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))\n del cutout\n\n cutouts = torch.cat(cutouts, dim=0)\n return cutouts\n\n\nclass MakeCutoutsDango(nn.Module):\n \"\"\"Dango233(https://github.com/Dango233)'s version of MakeCutouts.\n\n The improvement compared to ``MakeCutouts`` is that it use partial\n greyscale augmentation to capture structure, and partial rotation\n augmentation to capture whole frames.\n\n Args:\n cut_size (int): Size of the patches.\n Overview (int): The total number of overview cuts.\n In details,\n Overview=1, Add whole frame;\n Overview=2, Add grayscaled frame;\n Overview=3, Add horizontal flip frame;\n Overview=4, Add grayscaled horizontal flip frame;\n Overview>4, Repeat add frame Overview times.\n Defaults to 4.\n InnerCrop (int): The total number of inner cuts.\n Defaults to 0.\n IC_Size_Pow (float): This sets the size of the border\n used for inner cuts. High values have larger borders,\n and therefore the cuts themselves will be smaller and\n provide finer details. Defaults to 0.5.\n IC_Grey_P (float): The portion of the inner cuts can be set to be\n grayscale instead of color. This may help with improved\n definition of shapes and edges, especially in the early\n diffusion steps where the image structure is being defined.\n Defaults to 0.2.\n \"\"\"\n\n def __init__(self,\n cut_size,\n Overview=4,\n InnerCrop=0,\n IC_Size_Pow=0.5,\n IC_Grey_P=0.2):\n super().__init__()\n self.cut_size = cut_size\n self.Overview = Overview\n self.InnerCrop = InnerCrop\n self.IC_Size_Pow = IC_Size_Pow\n self.IC_Grey_P = IC_Grey_P\n\n random_affine_args = dict(degrees=10, translate=(0.05, 0.05))\n if digit_version(TORCHVISION_VERSION) >= digit_version('0.9.0'):\n random_affine_args['interpolation'] = T.InterpolationMode.BILINEAR\n else:\n from PIL import Image\n random_affine_args['resample'] = Image.NEAREST\n\n self.augs = T.Compose([\n T.RandomHorizontalFlip(p=0.5),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.RandomAffine(**random_affine_args),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.RandomGrayscale(p=0.1),\n T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n T.ColorJitter(\n brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n ])\n\n def forward(self, input, skip_augs=False):\n \"\"\"Forward function.\"\"\"\n cutouts = []\n gray = T.Grayscale(3)\n sideY, sideX = input.shape[2:4]\n max_size = min(sideX, sideY)\n min_size = min(sideX, sideY, self.cut_size)\n output_shape = [1, 3, self.cut_size, self.cut_size]\n pad_input = F.pad(input,\n ((sideY - max_size) // 2, (sideY - max_size) // 2,\n (sideX - max_size) // 2, (sideX - max_size) // 2))\n cutout = resize(pad_input, out_shape=output_shape)\n\n if self.Overview > 0:\n if self.Overview <= 4:\n if self.Overview >= 1:\n cutouts.append(cutout)\n if self.Overview >= 2:\n cutouts.append(gray(cutout))\n if self.Overview >= 3:\n cutouts.append(TF.hflip(cutout))\n if self.Overview == 4:\n cutouts.append(gray(TF.hflip(cutout)))\n else:\n cutout = resize(pad_input, out_shape=output_shape)\n for _ in range(self.Overview):\n cutouts.append(cutout)\n\n if self.InnerCrop > 0:\n for i in range(self.InnerCrop):\n size = int(\n torch.rand([])**self.IC_Size_Pow * (max_size - min_size) +\n min_size)\n offsetx = torch.randint(0, sideX - size + 1, ())\n offsety = torch.randint(0, sideY - size + 1, ())\n cutout = input[:, :, offsety:offsety + size,\n offsetx:offsetx + size]\n if i <= int(self.IC_Grey_P * self.InnerCrop):\n cutout = gray(cutout)\n cutout = resize(cutout, out_shape=output_shape)\n cutouts.append(cutout)\n cutouts = torch.cat(cutouts)\n if not skip_augs:\n cutouts = self.augs(cutouts)\n return cutouts\n\n\ndef parse_prompt(prompt):\n \"\"\"Parse prompt, return text and text weight.\"\"\"\n if prompt.startswith('http://') or prompt.startswith('https://'):\n vals = prompt.rsplit(':', 2)\n vals = [vals[0] + ':' + vals[1], *vals[2:]]\n else:\n vals = prompt.rsplit(':', 1)\n vals = vals + ['', '1'][len(vals):]\n return vals[0], float(vals[1])\n\n\ndef split_prompts(prompts, max_frames=1):\n \"\"\"Split prompts to a list of prompts.\"\"\"\n prompt_series = pd.Series([np.nan for a in range(max_frames)])\n for i, prompt in prompts.items():\n prompt_series[i] = prompt\n # prompt_series = prompt_series.astype(str)\n prompt_series = prompt_series.ffill().bfill()\n return prompt_series\n\n\nclass ImageTextGuider(nn.Module):\n \"\"\"Disco-Diffusion uses text and images to guide image generation. We will\n use the clip models to extract text and image features as prompts, and then\n during the iteration, the features of the image patches are computed, and\n the similarity loss between the prompts features and the generated features\n is computed. Other losses also include RGB Range loss, total variation\n loss. Using these losses we can guide the image generation towards the\n desired target.\n\n Args:\n clip_models (List[Dict]): List of clip model settings.\n \"\"\"\n\n def __init__(self, clip_models):\n super().__init__()\n\n assert clip is not None, (\n \"Cannot import 'clip'. Please install 'clip' via \"\n \"\\\"pip install git+https://github.com/openai/CLIP.git\\\".\")\n\n self.clip_models = clip_models\n self.lpips_model = lpips.LPIPS(net='vgg')\n\n def frame_prompt_from_text(self, text_prompts, frame_num=0):\n \"\"\"Get current frame prompt.\"\"\"\n prompts_series = split_prompts(text_prompts)\n if prompts_series is not None and frame_num >= len(prompts_series):\n frame_prompt = prompts_series[-1]\n elif prompts_series is not None:\n frame_prompt = prompts_series[frame_num]\n else:\n frame_prompt = []\n return frame_prompt\n\n def compute_prompt_stats(self,\n text_prompts=[],\n image_prompt=None,\n fuzzy_prompt=False,\n rand_mag=0.05):\n \"\"\"Compute prompts statistics.\n\n Args:\n text_prompts (list): Text prompts. Defaults to [].\n image_prompt (list): Image prompts. Defaults to None.\n fuzzy_prompt (bool, optional): Controls whether to add multiple\n noisy prompts to the prompt losses. If True, can increase\n variability of image output. Defaults to False.\n rand_mag (float, optional): Controls the magnitude of the\n random noise added by fuzzy_prompt. Defaults to 0.05.\n \"\"\"\n model_stats = []\n frame_prompt = self.frame_prompt_from_text(text_prompts)\n for clip_model in self.clip_models:\n model_stat = {\n 'clip_model': None,\n 'target_embeds': [],\n 'make_cutouts': None,\n 'weights': []\n }\n model_stat['clip_model'] = clip_model\n\n for prompt in frame_prompt:\n txt, weight = parse_prompt(prompt)\n txt = clip_model.model.encode_text(\n clip.tokenize(prompt).to(self.device)).float()\n\n if fuzzy_prompt:\n for i in range(25):\n model_stat['target_embeds'].append(\n (txt +\n torch.randn(txt.shape).cuda() * rand_mag).clamp(\n 0, 1))\n model_stat['weights'].append(weight)\n else:\n model_stat['target_embeds'].append(txt)\n model_stat['weights'].append(weight)\n model_stat['target_embeds'] = torch.cat(\n model_stat['target_embeds'])\n model_stat['weights'] = torch.tensor(\n model_stat['weights'], device=self.device)\n if model_stat['weights'].sum().abs() < 1e-3:\n raise RuntimeError('The weights must not sum to 0.')\n model_stat['weights'] /= model_stat['weights'].sum().abs()\n model_stats.append(model_stat)\n return model_stats\n\n def cond_fn(self,\n model,\n diffusion_scheduler,\n x,\n t,\n beta_prod_t,\n model_stats,\n secondary_model=None,\n init_image=None,\n clamp_grad=True,\n clamp_max=0.05,\n clip_guidance_scale=5000,\n init_scale=1000,\n tv_scale=0.,\n sat_scale=0.,\n range_scale=150,\n cut_overview=[12] * 400 + [4] * 600,\n cut_innercut=[4] * 400 + [12] * 600,\n cut_ic_pow=[1] * 1000,\n cut_icgray_p=[0.2] * 400 + [0] * 600,\n cutn_batches=4):\n \"\"\"Clip guidance function.\n\n Args:\n model (nn.Module): _description_\n diffusion_scheduler (object): _description_\n x (torch.Tensor): _description_\n t (int): _description_\n beta_prod_t (torch.Tensor): _description_\n model_stats (List[torch.Tensor]): _description_\n secondary_model (nn.Module): A smaller secondary diffusion model\n trained by Katherine Crowson to remove noise from intermediate\n timesteps to prepare them for CLIP.\n Ref: https://twitter.com/rivershavewings/status/1462859669454536711 # noqa\n Defaults to None.\n init_image (torch.Tensor): Initial image for denoising.\n Defaults to None.\n clamp_grad (bool, optional): Whether clamp gradient. Defaults to True.\n clamp_max (float, optional): Clamp max values. Defaults to 0.05.\n clip_guidance_scale (int, optional): The scale of influence of\n clip guidance on image generation. Defaults to 5000.\n \"\"\"\n with torch.enable_grad():\n x_is_NaN = False\n x = x.detach().requires_grad_()\n n = x.shape[0]\n if secondary_model is not None:\n alpha = torch.tensor(\n diffusion_scheduler.alphas_cumprod[t]**0.5,\n dtype=torch.float32)\n sigma = torch.tensor(\n (1 - diffusion_scheduler.alphas_cumprod[t])**0.5,\n dtype=torch.float32)\n cosine_t = alpha_sigma_to_t(alpha, sigma).to(x.device)\n model_output = secondary_model(\n x, cosine_t[None].repeat([x.shape[0]]))\n pred_original_sample = model_output['pred']\n else:\n model_output = model(x, t)['sample']\n model_output, predicted_variance = torch.split(\n model_output, x.shape[1], dim=1)\n alpha_prod_t = 1 - beta_prod_t\n pred_original_sample = (x - beta_prod_t**(0.5) *\n model_output) / alpha_prod_t**(0.5)\n # fac = diffusion_scheduler_output['beta_prod_t']** (0.5)\n # x_in = diffusion_scheduler_output['original_sample'] * fac + x * (1 - fac) # noqa\n fac = beta_prod_t**(0.5)\n x_in = pred_original_sample * fac + x * (1 - fac)\n x_in_grad = torch.zeros_like(x_in)\n for model_stat in model_stats:\n for i in range(cutn_batches):\n t_int = int(t.item()) + 1\n try:\n input_resolution = model_stat[\n 'clip_model'].model.visual.input_resolution\n except AttributeError:\n input_resolution = 224\n\n cuts = MakeCutoutsDango(\n input_resolution,\n Overview=cut_overview[1000 - t_int],\n InnerCrop=cut_innercut[1000 - t_int],\n IC_Size_Pow=cut_ic_pow[1000 - t_int],\n IC_Grey_P=cut_icgray_p[1000 - t_int])\n clip_in = normalize(cuts(x_in.add(1).div(2)))\n image_embeds = model_stat['clip_model'].model.encode_image(\n clip_in).float()\n dists = spherical_dist_loss(\n image_embeds.unsqueeze(1),\n model_stat['target_embeds'].unsqueeze(0))\n dists = dists.view([\n cut_overview[1000 - t_int] +\n cut_innercut[1000 - t_int], n, -1\n ])\n losses = dists.mul(model_stat['weights']).sum(2).mean(0)\n x_in_grad += torch.autograd.grad(\n losses.sum() * clip_guidance_scale,\n x_in)[0] / cutn_batches\n tv_losses = tv_loss(x_in)\n range_losses = range_loss(pred_original_sample)\n sat_losses = torch.abs(x_in - x_in.clamp(min=-1, max=1)).mean()\n loss = tv_losses.sum() * tv_scale + range_losses.sum(\n ) * range_scale + sat_losses.sum() * sat_scale\n if init_image is not None and init_scale:\n init_losses = self.lpips_model(x_in, init_image)\n loss = loss + init_losses.sum() * init_scale\n x_in_grad += torch.autograd.grad(loss, x_in)[0]\n if not torch.isnan(x_in_grad).any():\n grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]\n else:\n x_is_NaN = True\n grad = torch.zeros_like(x)\n if clamp_grad and not x_is_NaN:\n magnitude = grad.square().mean().sqrt()\n return grad * magnitude.clamp(max=clamp_max) / magnitude\n return grad\n\n @property\n def device(self):\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n def forward(self, x):\n \"\"\"forward function.\"\"\"\n raise NotImplementedError('No forward function for disco guider')\n" }, { "path": "mmagic/models/editors/disco_diffusion/secondary_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\n\n# Note: This model is copied from Disco-Diffusion colab.\n# SourceCode: https://colab.research.google.com/drive/1uGKaBOEACeinAA7jX1_zSFtj_ZW-huHS#scrollTo=XIqUfrmvLIhg # noqa\n\n\ndef append_dims(x, n):\n \"\"\"Append dims.\"\"\"\n return x[(Ellipsis, *(None, ) * (n - x.ndim))]\n\n\ndef expand_to_planes(x, shape):\n \"\"\"Expand tensor to planes.\"\"\"\n return append_dims(x, len(shape)).repeat([1, 1, *shape[2:]])\n\n\ndef alpha_sigma_to_t(alpha, sigma):\n \"\"\"convert alpha&sigma to timestep.\"\"\"\n return torch.atan2(sigma, alpha) * 2 / math.pi\n\n\ndef t_to_alpha_sigma(t):\n \"\"\"convert timestep to alpha and sigma.\"\"\"\n return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)\n\n\nclass ConvBlock(nn.Sequential):\n \"\"\"Convolution Block.\n\n Args:\n c_in (int): Input channels.\n c_out (int): Output channels.\n \"\"\"\n\n def __init__(self, c_in, c_out):\n super().__init__(\n nn.Conv2d(c_in, c_out, 3, padding=1),\n nn.ReLU(inplace=True),\n )\n\n\nclass SkipBlock(nn.Module):\n \"\"\"Skip block wrapper. Wrapping main block and skip block and concat their\n outputs together.\n\n Args:\n main (list): A list of main modules.\n skip (nn.Module): Skip Module. If not given,\n set to ``nn.Identity()``. Defaults to None.\n \"\"\"\n\n def __init__(self, main, skip=None):\n super().__init__()\n self.main = nn.Sequential(*main)\n self.skip = skip if skip else nn.Identity()\n\n def forward(self, input):\n \"\"\"Forward function.\"\"\"\n return torch.cat([self.main(input), self.skip(input)], dim=1)\n\n\nclass FourierFeatures(nn.Module):\n \"\"\"Fourier features mapping MLP.\n\n Args:\n in_features (int): Input channels.\n out_features (int): Output channels.\n std (float): Standard deviation. Defaults to 1..\n \"\"\"\n\n def __init__(self, in_features, out_features, std=1.):\n super().__init__()\n assert out_features % 2 == 0\n self.weight = nn.Parameter(\n torch.randn([out_features // 2, in_features]) * std)\n\n def forward(self, input):\n \"\"\"Forward function.\"\"\"\n f = 2 * math.pi * input @ self.weight.T\n return torch.cat([f.cos(), f.sin()], dim=-1)\n\n\n@MODELS.register_module()\nclass SecondaryDiffusionImageNet2(nn.Module):\n \"\"\"A smaller secondary diffusion model trained by Katherine Crowson to\n remove noise from intermediate timesteps to prepare them for CLIP.\n\n Ref: https://twitter.com/rivershavewings/status/1462859669454536711 # noqa\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.in_channels = 3\n c = 64 # The base channel count\n cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]\n\n self.timestep_embed = FourierFeatures(1, 16)\n self.down = nn.AvgPool2d(2)\n self.up = nn.Upsample(\n scale_factor=2, mode='bilinear', align_corners=False)\n\n self.net = nn.Sequential(\n ConvBlock(3 + 16, cs[0]),\n ConvBlock(cs[0], cs[0]),\n SkipBlock([\n self.down,\n ConvBlock(cs[0], cs[1]),\n ConvBlock(cs[1], cs[1]),\n SkipBlock([\n self.down,\n ConvBlock(cs[1], cs[2]),\n ConvBlock(cs[2], cs[2]),\n SkipBlock([\n self.down,\n ConvBlock(cs[2], cs[3]),\n ConvBlock(cs[3], cs[3]),\n SkipBlock([\n self.down,\n ConvBlock(cs[3], cs[4]),\n ConvBlock(cs[4], cs[4]),\n SkipBlock([\n self.down,\n ConvBlock(cs[4], cs[5]),\n ConvBlock(cs[5], cs[5]),\n ConvBlock(cs[5], cs[5]),\n ConvBlock(cs[5], cs[4]),\n self.up,\n ]),\n ConvBlock(cs[4] * 2, cs[4]),\n ConvBlock(cs[4], cs[3]),\n self.up,\n ]),\n ConvBlock(cs[3] * 2, cs[3]),\n ConvBlock(cs[3], cs[2]),\n self.up,\n ]),\n ConvBlock(cs[2] * 2, cs[2]),\n ConvBlock(cs[2], cs[1]),\n self.up,\n ]),\n ConvBlock(cs[1] * 2, cs[1]),\n ConvBlock(cs[1], cs[0]),\n self.up,\n ]),\n ConvBlock(cs[0] * 2, cs[0]),\n nn.Conv2d(cs[0], 3, 3, padding=1),\n )\n\n def forward(self, input, t):\n \"\"\"Forward function.\"\"\"\n timestep_embed = expand_to_planes(\n self.timestep_embed(t[:, None]), input.shape)\n v = self.net(torch.cat([input, timestep_embed], dim=1))\n alphas, sigmas = map(\n partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))\n pred = input * alphas - v * sigmas\n eps = input * sigmas + v * alphas\n return dict(v=v, pred=pred, eps=eps)\n" }, { "path": "mmagic/models/editors/dreambooth/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .dreambooth import DreamBooth\n\n__all__ = ['DreamBooth']\n" }, { "path": "mmagic/models/editors/dreambooth/dreambooth.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\n\nfrom mmagic.models.archs import set_lora\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ..stable_diffusion.stable_diffusion import StableDiffusion\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass DreamBooth(StableDiffusion):\n \"\"\"Implementation of `DreamBooth with Stable Diffusion.\n\n `_ (DreamBooth).\n\n Args:\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n tokenizer (str): The **name** for CLIP tokenizer.\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n lora_config (dict, optional): The config for LoRA finetuning. Defaults\n to None.\n val_prompts (Union[str, List[str]], optional): The prompts for\n validation. Defaults to None.\n class_prior_prompt (str, optional): The prompt for class prior loss.\n num_class_images (int, optional): The number of images for class prior.\n Defaults to 3.\n prior_loss_weight (float, optional): The weight for class prior loss.\n Defaults to 0.\n finetune_text_encoder (bool, optional): Whether to fine-tune text\n encoder. Defaults to False.\n dtype (str, optional): The dtype for the model. Defaults to 'fp16'.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa\n Defaults to 0.\n tomesd_cfg (dict, optional): The config for TOMESD. Please refers to\n https://github.com/dbolya/tomesd and\n https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/utils/tome_utils.py for detail. # noqa\n Defaults to None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Defaults to\n dict(type='DataPreprocessor').\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Defaults to None/\n \"\"\"\n\n def __init__(self,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n lora_config: Optional[dict] = None,\n val_prompts: Union[str, List[str]] = None,\n class_prior_prompt: Optional[str] = None,\n num_class_images: Optional[int] = 3,\n prior_loss_weight: float = 0,\n finetune_text_encoder: bool = False,\n dtype: str = 'fp16',\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n init_cfg: Optional[dict] = None):\n\n super().__init__(vae, text_encoder, tokenizer, unet, scheduler,\n test_scheduler, dtype, enable_xformers,\n noise_offset_weight, tomesd_cfg, data_preprocessor,\n init_cfg)\n self.num_class_images = num_class_images\n self.class_prior_prompt = class_prior_prompt\n self.prior_loss_weight = prior_loss_weight\n self.class_images = []\n\n self.dtype = torch.float32\n if dtype == 'fp16':\n self.dtype = torch.float16\n elif dtype == 'bf16':\n self.dtype = torch.bfloat16\n else:\n assert dtype in [\n 'fp32', None\n ], ('dtype must be one of \\'fp32\\', \\'fp16\\', \\'bf16\\' or None.')\n\n self.finetune_text_encoder = finetune_text_encoder\n self.val_prompts = val_prompts\n self.lora_config = deepcopy(lora_config)\n\n self.prepare_model()\n self.set_lora()\n\n @torch.no_grad()\n def generate_class_prior_images(self, num_batches=None):\n \"\"\"Generate images for class prior loss.\n\n Args:\n num_batches (int): Number of batches to generate images.\n If not passed, all images will be generated in one\n forward. Defaults to None.\n \"\"\"\n if self.prior_loss_weight == 0:\n return\n if self.class_images:\n return\n\n assert self.class_prior_prompt is not None, (\n '\\'class_prior_prompt\\' must be set when \\'prior_loss_weight\\' is '\n 'larger than 0.')\n assert self.num_class_images is not None, (\n '\\'num_class_images\\' must be set when \\'prior_loss_weight\\' is '\n 'larger than 0.')\n\n print_log(\n 'Generating class prior images with prompt: '\n f'{self.class_prior_prompt}', 'current')\n num_batches = num_batches or self.num_class_images\n\n unet_dtype = next(self.unet.parameters()).dtype\n self.unet.to(self.dtype)\n for idx in range(0, self.num_class_images, num_batches):\n prompt = self.class_prior_prompt\n if self.num_class_images > 1:\n prompt += f' {idx + 1} of {self.num_class_images}'\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n self.class_images.append(samples.clamp(-1, 1))\n self.unet.to(unet_dtype)\n\n def prepare_model(self):\n \"\"\"Prepare model for training.\n\n Move model to target dtype and disable gradient for some models.\n \"\"\"\n self.vae.requires_grad_(False)\n print_log('Set VAE untrainable.', 'current')\n self.vae.to(self.dtype)\n print_log(f'Move VAE to {self.dtype}.', 'current')\n if not self.finetune_text_encoder or self.lora_config:\n self.text_encoder.requires_grad_(False)\n print_log('Set Text Encoder untrainable.', 'current')\n self.text_encoder.to(self.dtype)\n print_log(f'Move Text Encoder to {self.dtype}.', 'current')\n if self.lora_config:\n self.unet.requires_grad_(False)\n print_log('Set Unet untrainable.', 'current')\n\n def set_lora(self):\n \"\"\"Set LORA for model.\"\"\"\n if self.lora_config:\n set_lora(self.unet, self.lora_config)\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n if self.val_prompts is None:\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples.split() * len(prompt)\n data_samples = DataSample.stack(data_samples.split() * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n if self.val_prompts is None:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples = DataSample.stack(data['data_samples'] * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n def train_step(self, data, optim_wrapper):\n\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n\n with optim_wrapper.optim_context(self.unet):\n image = inputs # image for new concept\n prompt = data_samples.prompt\n num_batches = image.shape[0]\n\n if self.prior_loss_weight != 0:\n # image and prompt for prior preservation\n self.generate_class_prior_images(num_batches=num_batches)\n class_images_used = []\n for _ in range(num_batches):\n idx = random.randint(0, len(self.class_images) - 1)\n class_images_used.append(self.class_images[idx])\n\n image = torch.cat([image, *class_images_used], dim=0)\n prompt = prompt + [self.class_prior_prompt]\n\n image = image.to(self.dtype)\n latents = self.vae.encode(image).latent_dist.sample()\n latents = latents * self.vae.config.scaling_factor\n\n noise = torch.randn_like(latents)\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n\n noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)\n\n input_ids = self.tokenizer(\n prompt,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n\n encoder_hidden_states = self.text_encoder(input_ids)[0]\n\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n # NOTE: we train unet in fp32, convert to float manually\n model_output = self.unet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float())\n model_pred = model_output['sample']\n\n loss_dict = dict()\n if self.prior_loss_weight != 0:\n model_pred, prior_pred = model_pred.split(2, dim=1)\n gt, prior_gt = gt.split(2, dim=1)\n # calculate loss in FP32\n dreambooth_loss = F.mse_loss(model_pred.float(), gt.float())\n prior_loss = F.mse_loss(prior_pred.float(), prior_gt.float())\n loss_dict['dreambooth_loss'] = dreambooth_loss\n loss_dict['prior_loss'] = prior_loss * self.prior_loss_weight\n\n else:\n # calculate loss in FP32\n dreambooth_loss = F.mse_loss(model_pred.float(), gt.float())\n loss_dict['dreambooth_loss'] = dreambooth_loss\n\n parsed_loss, log_vars = self.parse_losses(loss_dict)\n optim_wrapper.update_params(parsed_loss)\n\n return log_vars\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n" }, { "path": "mmagic/models/editors/duf/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .duf import DynamicUpsamplingFilter\n\n__all__ = ['DynamicUpsamplingFilter']\n" }, { "path": "mmagic/models/editors/duf/duf.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\n\nclass DynamicUpsamplingFilter(BaseModule):\n \"\"\"Dynamic upsampling filter used in DUF.\n\n Ref: https://github.com/yhjo09/VSR-DUF.\n It only supports input with 3 channels. And it applies the same filters\n to 3 channels.\n\n Args:\n filter_size (tuple): Filter size of generated filters.\n The shape is (kh, kw). Default: (5, 5).\n \"\"\"\n\n def __init__(self, filter_size=(5, 5)):\n super().__init__()\n if not isinstance(filter_size, tuple):\n raise TypeError('The type of filter_size must be tuple, '\n f'but got type{filter_size}')\n if len(filter_size) != 2:\n raise ValueError('The length of filter size must be 2, '\n f'but got {len(filter_size)}.')\n # generate a local expansion filter, similar to im2col\n self.filter_size = filter_size\n filter_prod = np.prod(filter_size)\n expansion_filter = torch.eye(int(filter_prod)).view(\n filter_prod, 1, *filter_size) # (kh*kw, 1, kh, kw)\n self.expansion_filter = expansion_filter.repeat(\n 3, 1, 1, 1) # repeat for all the 3 channels\n\n def forward(self, x, filters):\n \"\"\"Forward function for DynamicUpsamplingFilter.\n\n Args:\n x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w).\n filters (Tensor): Generated dynamic filters.\n The shape is (n, filter_prod, upsampling_square, h, w).\n filter_prod: prod of filter kernel size, e.g., 1*5*5=25.\n upsampling_square: similar to pixel shuffle,\n upsampling_square = upsampling * upsampling\n e.g., for x 4 upsampling, upsampling_square= 4*4 = 16\n\n Returns:\n Tensor: Filtered image with shape (n, 3*upsampling, h, w)\n \"\"\"\n n, filter_prod, upsampling_square, h, w = filters.size()\n kh, kw = self.filter_size\n expanded_input = F.conv2d(\n x,\n self.expansion_filter.to(x),\n padding=(kh // 2, kw // 2),\n groups=3) # (n, 3*filter_prod, h, w)\n expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(\n 0, 3, 4, 1, 2) # (n, h, w, 3, filter_prod)\n filters = filters.permute(\n 0, 3, 4, 1, 2) # (n, h, w, filter_prod, upsampling_square]\n out = torch.matmul(expanded_input,\n filters) # (n, h, w, 3, upsampling_square)\n return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w)\n" }, { "path": "mmagic/models/editors/edsr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .edsr_net import EDSRNet\n\n__all__ = ['EDSRNet']\n" }, { "path": "mmagic/models/editors/edsr/edsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass EDSRNet(BaseModule):\n \"\"\"EDSR network structure.\n\n Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution.\n Ref repo: https://github.com/thstkdgus35/EDSR-PyTorch\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_blocks (int): Block number in the trunk network. Default: 16.\n upscale_factor (int): Upsampling factor. Support 2^n and 3.\n Default: 4.\n res_scale (float): Used to scale the residual in residual block.\n Default: 1.\n rgb_mean (list[float]): Image mean in RGB orders.\n Default: [0.4488, 0.4371, 0.4040], calculated from DIV2K dataset.\n rgb_std (list[float]): Image std in RGB orders. In EDSR, it uses\n [1.0, 1.0, 1.0]. Default: [1.0, 1.0, 1.0].\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4,\n res_scale=1,\n rgb_mean=[0.4488, 0.4371, 0.4040],\n rgb_std=[1.0, 1.0, 1.0]):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.mid_channels = mid_channels\n self.num_blocks = num_blocks\n self.upscale_factor = upscale_factor\n\n self.mean = torch.Tensor(rgb_mean).view(1, -1, 1, 1)\n self.std = torch.Tensor(rgb_std).view(1, -1, 1, 1)\n\n self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, padding=1)\n self.body = make_layer(\n ResidualBlockNoBN,\n num_blocks,\n mid_channels=mid_channels,\n res_scale=res_scale)\n self.conv_after_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n self.upsample = UpsampleModule(upscale_factor, mid_channels)\n self.conv_last = nn.Conv2d(\n mid_channels, out_channels, 3, 1, 1, bias=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n self.mean = self.mean.to(x)\n self.std = self.std.to(x)\n\n x = (x - self.mean) / self.std\n x = self.conv_first(x)\n res = self.conv_after_body(self.body(x))\n res += x\n\n x = self.conv_last(self.upsample(res))\n x = x * self.std + self.mean\n\n return x\n\n\nclass UpsampleModule(nn.Sequential):\n \"\"\"Upsample module used in EDSR.\n\n Args:\n scale (int): Scale factor. Supported scales: 2^n and 3.\n mid_channels (int): Channel number of intermediate features.\n \"\"\"\n\n def __init__(self, scale, mid_channels):\n modules = []\n if (scale & (scale - 1)) == 0: # scale = 2^n\n for _ in range(int(math.log(scale, 2))):\n modules.append(\n PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3))\n elif scale == 3:\n modules.append(\n PixelShufflePack(\n mid_channels, mid_channels, scale, upsample_kernel=3))\n else:\n raise ValueError(f'scale {scale} is not supported. '\n 'Supported scales: 2^n and 3.')\n\n super().__init__(*modules)\n" }, { "path": "mmagic/models/editors/edvr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .edvr import EDVR\nfrom .edvr_net import EDVRNet\n\n__all__ = ['EDVR', 'EDVRNet']\n" }, { "path": "mmagic/models/editors/edvr/edvr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models import BaseEditModel\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass EDVR(BaseEditModel):\n \"\"\"EDVR model for video super-resolution.\n\n EDVR: Video Restoration with Enhanced Deformable Convolutional Networks.\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.with_tsa = generator.get('with_tsa', False)\n self.tsa_iter = self.train_cfg.get('tsa_iter',\n None) if self.train_cfg else None\n self.register_buffer('step_counter', torch.tensor(0), False)\n\n def forward_train(self, inputs, data_samples=None):\n \"\"\"Forward training. Returns dict of losses of training.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n if self.step_counter == 0 and self.with_tsa:\n if self.tsa_iter is None:\n raise KeyError(\n 'In TSA mode, train_cfg must contain \"tsa_iter\".')\n # only train TSA module at the beginning if with TSA module\n for k, v in self.generator.named_parameters():\n if 'fusion' not in k:\n v.requires_grad = False\n\n if self.with_tsa and (self.step_counter == self.tsa_iter):\n # train all the parameters\n for v in self.generator.parameters():\n v.requires_grad = True\n self.step_counter += 1\n\n return super().forward_train(inputs, data_samples)\n" }, { "path": "mmagic/models/editors/edvr/edvr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init, kaiming_init\nfrom torch.nn.modules.utils import _pair\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass EDVRNet(BaseModule):\n \"\"\"EDVR network structure for video super-resolution.\n\n Now only support X4 upsampling factor.\n Paper:\n EDVR: Video Restoration with Enhanced Deformable Convolutional Networks.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_frames (int): Number of input frames. Default: 5.\n deform_groups (int): Deformable groups. Defaults: 8.\n num_blocks_extraction (int): Number of blocks for feature extraction.\n Default: 5.\n num_blocks_reconstruction (int): Number of blocks for reconstruction.\n Default: 10.\n center_frame_idx (int): The index of center frame. Frame counting from\n 0. Default: 2.\n with_tsa (bool): Whether to use TSA module. Default: True.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=10,\n center_frame_idx=2,\n with_tsa=True,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n self.center_frame_idx = center_frame_idx\n self.with_tsa = with_tsa\n act_cfg = dict(type='LeakyReLU', negative_slope=0.1)\n\n self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)\n self.feature_extraction = make_layer(\n ResidualBlockNoBN,\n num_blocks_extraction,\n mid_channels=mid_channels)\n\n # generate pyramid features\n self.feat_l2_conv1 = ConvModule(\n mid_channels, mid_channels, 3, 2, 1, act_cfg=act_cfg)\n self.feat_l2_conv2 = ConvModule(\n mid_channels, mid_channels, 3, 1, 1, act_cfg=act_cfg)\n self.feat_l3_conv1 = ConvModule(\n mid_channels, mid_channels, 3, 2, 1, act_cfg=act_cfg)\n self.feat_l3_conv2 = ConvModule(\n mid_channels, mid_channels, 3, 1, 1, act_cfg=act_cfg)\n # pcd alignment\n self.pcd_alignment = PCDAlignment(\n mid_channels=mid_channels, deform_groups=deform_groups)\n # fusion\n if self.with_tsa:\n self.fusion = TSAFusion(\n mid_channels=mid_channels,\n num_frames=num_frames,\n center_frame_idx=self.center_frame_idx)\n else:\n self.fusion = nn.Conv2d(num_frames * mid_channels, mid_channels, 1,\n 1)\n\n # reconstruction\n self.reconstruction = make_layer(\n ResidualBlockNoBN,\n num_blocks_reconstruction,\n mid_channels=mid_channels)\n # upsample\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, 64, 2, upsample_kernel=3)\n # we fix the output channels in the last few layers to 64.\n self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)\n self.conv_last = nn.Conv2d(64, out_channels, 3, 1, 1)\n self.img_upsample = nn.Upsample(\n scale_factor=4, mode='bilinear', align_corners=False)\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n def forward(self, x):\n \"\"\"Forward function for EDVRNet.\n\n Args:\n x (Tensor): Input tensor with shape (n, t, c, h, w).\n\n Returns:\n Tensor: SR center frame with shape (n, c, h, w).\n \"\"\"\n n, t, c, h, w = x.size()\n assert h % 4 == 0 and w % 4 == 0, (\n 'The height and width of inputs should be a multiple of 4, '\n f'but got {h} and {w}.')\n\n x_center = x[:, self.center_frame_idx, :, :, :].contiguous()\n\n # extract LR features\n # L1\n l1_feat = self.lrelu(self.conv_first(x.view(-1, c, h, w)))\n l1_feat = self.feature_extraction(l1_feat)\n # L2\n l2_feat = self.feat_l2_conv2(self.feat_l2_conv1(l1_feat))\n # L3\n l3_feat = self.feat_l3_conv2(self.feat_l3_conv1(l2_feat))\n\n l1_feat = l1_feat.view(n, t, -1, h, w)\n l2_feat = l2_feat.view(n, t, -1, h // 2, w // 2)\n l3_feat = l3_feat.view(n, t, -1, h // 4, w // 4)\n\n # pcd alignment\n ref_feats = [ # reference feature list\n l1_feat[:, self.center_frame_idx, :, :, :].clone(),\n l2_feat[:, self.center_frame_idx, :, :, :].clone(),\n l3_feat[:, self.center_frame_idx, :, :, :].clone()\n ]\n aligned_feat = []\n for i in range(t):\n neighbor_feats = [\n l1_feat[:, i, :, :, :].clone(), l2_feat[:, i, :, :, :].clone(),\n l3_feat[:, i, :, :, :].clone()\n ]\n aligned_feat.append(self.pcd_alignment(neighbor_feats, ref_feats))\n aligned_feat = torch.stack(aligned_feat, dim=1) # (n, t, c, h, w)\n\n if self.with_tsa:\n feat = self.fusion(aligned_feat)\n else:\n aligned_feat = aligned_feat.view(n, -1, h, w)\n feat = self.fusion(aligned_feat)\n\n # reconstruction\n out = self.reconstruction(feat)\n out = self.lrelu(self.upsample1(out))\n out = self.lrelu(self.upsample2(out))\n out = self.lrelu(self.conv_hr(out))\n out = self.conv_last(out)\n base = self.img_upsample(x_center)\n out += base\n return out\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n\n super().init_weights()\n\n init_type = None if self.init_cfg is None else self.init_cfg.get(\n 'type', None)\n if init_type != 'Pretrained' and self.with_tsa:\n for module in [\n self.fusion.feat_fusion, self.fusion.spatial_attn1,\n self.fusion.spatial_attn2, self.fusion.spatial_attn3,\n self.fusion.spatial_attn4, self.fusion.spatial_attn_l1,\n self.fusion.spatial_attn_l2, self.fusion.spatial_attn_l3,\n self.fusion.spatial_attn_add1\n ]:\n kaiming_init(\n module.conv,\n a=0.1,\n mode='fan_out',\n nonlinearity='leaky_relu',\n bias=0,\n distribution='uniform')\n\n\nclass ModulatedDCNPack(ModulatedDeformConv2d):\n \"\"\"Modulated Deformable Convolutional Pack.\n\n Different from the official DCN, which generates offsets and masks from\n the preceding features, this ModulatedDCNPack takes another different\n feature to generate masks and offsets.\n\n Args:\n in_channels (int): Same as nn.Conv2d.\n out_channels (int): Same as nn.Conv2d.\n kernel_size (int or tuple[int]): Same as nn.Conv2d.\n stride (int or tuple[int]): Same as nn.Conv2d.\n padding (int or tuple[int]): Same as nn.Conv2d.\n dilation (int or tuple[int]): Same as nn.Conv2d.\n groups (int): Same as nn.Conv2d.\n bias (bool or str): If specified as `auto`, it will be decided by the\n norm_cfg. Bias will be set as True if norm_cfg is None, otherwise\n False.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.conv_offset = nn.Conv2d(\n self.in_channels,\n self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],\n kernel_size=self.kernel_size,\n stride=_pair(self.stride),\n padding=_pair(self.padding),\n bias=True)\n self.init_offset()\n\n def init_offset(self):\n \"\"\"Init constant offset.\"\"\"\n constant_init(self.conv_offset, val=0, bias=0)\n\n def forward(self, x, extra_feat):\n \"\"\"Forward function.\"\"\"\n out = self.conv_offset(extra_feat)\n o1, o2, mask = torch.chunk(out, 3, dim=1)\n offset = torch.cat((o1, o2), dim=1)\n mask = torch.sigmoid(mask)\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n\n\nclass PCDAlignment(BaseModule):\n \"\"\"Alignment module using Pyramid, Cascading and Deformable convolution\n (PCD). It is used in EDVRNet.\n\n Args:\n mid_channels (int): Number of the channels of middle features.\n Default: 64.\n deform_groups (int): Deformable groups. Defaults: 8.\n act_cfg (dict): Activation function config for ConvModule.\n Default: LeakyReLU with negative_slope=0.1.\n \"\"\"\n\n def __init__(self,\n mid_channels=64,\n deform_groups=8,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):\n super().__init__()\n\n # Pyramid has three levels:\n # L3: level 3, 1/4 spatial size\n # L2: level 2, 1/2 spatial size\n # L1: level 1, original spatial size\n self.offset_conv1 = nn.ModuleDict()\n self.offset_conv2 = nn.ModuleDict()\n self.offset_conv3 = nn.ModuleDict()\n self.dcn_pack = nn.ModuleDict()\n self.feat_conv = nn.ModuleDict()\n for i in range(3, 0, -1):\n level = f'l{i}'\n self.offset_conv1[level] = ConvModule(\n mid_channels * 2, mid_channels, 3, padding=1, act_cfg=act_cfg)\n if i == 3:\n self.offset_conv2[level] = ConvModule(\n mid_channels, mid_channels, 3, padding=1, act_cfg=act_cfg)\n else:\n self.offset_conv2[level] = ConvModule(\n mid_channels * 2,\n mid_channels,\n 3,\n padding=1,\n act_cfg=act_cfg)\n self.offset_conv3[level] = ConvModule(\n mid_channels, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.dcn_pack[level] = ModulatedDCNPack(\n mid_channels,\n mid_channels,\n 3,\n padding=1,\n deform_groups=deform_groups)\n\n if i < 3:\n act_cfg_ = act_cfg if i == 2 else None\n self.feat_conv[level] = ConvModule(\n mid_channels * 2,\n mid_channels,\n 3,\n padding=1,\n act_cfg=act_cfg_)\n\n # Cascading DCN\n self.cas_offset_conv1 = ConvModule(\n mid_channels * 2, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.cas_offset_conv2 = ConvModule(\n mid_channels, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.cas_dcnpack = ModulatedDCNPack(\n mid_channels,\n mid_channels,\n 3,\n padding=1,\n deform_groups=deform_groups)\n\n self.upsample = nn.Upsample(\n scale_factor=2, mode='bilinear', align_corners=False)\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n def forward(self, neighbor_feats, ref_feats):\n \"\"\"Forward function for PCDAlignment.\n\n Align neighboring frames to the reference frame in the feature level.\n\n Args:\n neighbor_feats (list[Tensor]): List of neighboring features. It\n contains three pyramid levels (L1, L2, L3),\n each with shape (n, c, h, w).\n ref_feats (list[Tensor]): List of reference features. It\n contains three pyramid levels (L1, L2, L3),\n each with shape (n, c, h, w).\n\n Returns:\n Tensor: Aligned features.\n \"\"\"\n # The number of pyramid levels is 3.\n assert len(neighbor_feats) == 3 and len(ref_feats) == 3, (\n 'The length of neighbor_feats and ref_feats must be both 3, '\n f'but got {len(neighbor_feats)} and {len(ref_feats)}')\n\n # Pyramids\n upsampled_offset, upsampled_feat = None, None\n for i in range(3, 0, -1):\n level = f'l{i}'\n offset = torch.cat([neighbor_feats[i - 1], ref_feats[i - 1]],\n dim=1)\n offset = self.offset_conv1[level](offset)\n if i == 3:\n offset = self.offset_conv2[level](offset)\n else:\n offset = self.offset_conv2[level](\n torch.cat([offset, upsampled_offset], dim=1))\n offset = self.offset_conv3[level](offset)\n\n feat = self.dcn_pack[level](neighbor_feats[i - 1], offset)\n if i == 3:\n feat = self.lrelu(feat)\n else:\n feat = self.feat_conv[level](\n torch.cat([feat, upsampled_feat], dim=1))\n\n if i > 1:\n # upsample offset and features\n upsampled_offset = self.upsample(offset) * 2\n upsampled_feat = self.upsample(feat)\n\n # Cascading\n offset = torch.cat([feat, ref_feats[0]], dim=1)\n offset = self.cas_offset_conv2(self.cas_offset_conv1(offset))\n feat = self.lrelu(self.cas_dcnpack(feat, offset))\n return feat\n\n\nclass TSAFusion(BaseModule):\n \"\"\"Temporal Spatial Attention (TSA) fusion module. It is used in EDVRNet.\n\n Args:\n mid_channels (int): Number of the channels of middle features.\n Default: 64.\n num_frames (int): Number of frames. Default: 5.\n center_frame_idx (int): The index of center frame. Default: 2.\n act_cfg (dict): Activation function config for ConvModule.\n Default: LeakyReLU with negative_slope=0.1.\n \"\"\"\n\n def __init__(self,\n mid_channels=64,\n num_frames=5,\n center_frame_idx=2,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):\n super().__init__()\n self.center_frame_idx = center_frame_idx\n # temporal attention (before fusion conv)\n self.temporal_attn1 = nn.Conv2d(\n mid_channels, mid_channels, 3, padding=1)\n self.temporal_attn2 = nn.Conv2d(\n mid_channels, mid_channels, 3, padding=1)\n self.feat_fusion = ConvModule(\n num_frames * mid_channels, mid_channels, 1, act_cfg=act_cfg)\n\n # spatial attention (after fusion conv)\n self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)\n self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1)\n self.spatial_attn1 = ConvModule(\n num_frames * mid_channels, mid_channels, 1, act_cfg=act_cfg)\n self.spatial_attn2 = ConvModule(\n mid_channels * 2, mid_channels, 1, act_cfg=act_cfg)\n self.spatial_attn3 = ConvModule(\n mid_channels, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.spatial_attn4 = ConvModule(\n mid_channels, mid_channels, 1, act_cfg=act_cfg)\n self.spatial_attn5 = nn.Conv2d(\n mid_channels, mid_channels, 3, padding=1)\n self.spatial_attn_l1 = ConvModule(\n mid_channels, mid_channels, 1, act_cfg=act_cfg)\n self.spatial_attn_l2 = ConvModule(\n mid_channels * 2, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.spatial_attn_l3 = ConvModule(\n mid_channels, mid_channels, 3, padding=1, act_cfg=act_cfg)\n self.spatial_attn_add1 = ConvModule(\n mid_channels, mid_channels, 1, act_cfg=act_cfg)\n self.spatial_attn_add2 = nn.Conv2d(mid_channels, mid_channels, 1)\n\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n self.upsample = nn.Upsample(\n scale_factor=2, mode='bilinear', align_corners=False)\n\n def forward(self, aligned_feat):\n \"\"\"Forward function for TSAFusion.\n\n Args:\n aligned_feat (Tensor): Aligned features with shape (n, t, c, h, w).\n\n Returns:\n Tensor: Features after TSA with the shape (n, c, h, w).\n \"\"\"\n n, t, c, h, w = aligned_feat.size()\n # temporal attention\n embedding_ref = self.temporal_attn1(\n aligned_feat[:, self.center_frame_idx, :, :, :].clone())\n emb = self.temporal_attn2(aligned_feat.view(-1, c, h, w))\n emb = emb.view(n, t, -1, h, w) # (n, t, c, h, w)\n\n corr_l = [] # correlation list\n for i in range(t):\n emb_neighbor = emb[:, i, :, :, :]\n corr = torch.sum(emb_neighbor * embedding_ref, 1) # (n, h, w)\n corr_l.append(corr.unsqueeze(1)) # (n, 1, h, w)\n corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1)) # (n, t, h, w)\n corr_prob = corr_prob.unsqueeze(2).expand(n, t, c, h, w)\n corr_prob = corr_prob.contiguous().view(n, -1, h, w) # (n, t*c, h, w)\n aligned_feat = aligned_feat.view(n, -1, h, w) * corr_prob\n\n # fusion\n feat = self.feat_fusion(aligned_feat)\n\n # spatial attention\n attn = self.spatial_attn1(aligned_feat)\n attn_max = self.max_pool(attn)\n attn_avg = self.avg_pool(attn)\n attn = self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1))\n # pyramid levels\n attn_level = self.spatial_attn_l1(attn)\n attn_max = self.max_pool(attn_level)\n attn_avg = self.avg_pool(attn_level)\n attn_level = self.spatial_attn_l2(\n torch.cat([attn_max, attn_avg], dim=1))\n attn_level = self.spatial_attn_l3(attn_level)\n attn_level = self.upsample(attn_level)\n\n attn = self.spatial_attn3(attn) + attn_level\n attn = self.spatial_attn4(attn)\n attn = self.upsample(attn)\n attn = self.spatial_attn5(attn)\n attn_add = self.spatial_attn_add2(self.spatial_attn_add1(attn))\n attn = torch.sigmoid(attn)\n\n # after initialization, * 2 makes (attn * 2) to be close to 1.\n feat = feat * attn * 2 + attn_add\n return feat\n" }, { "path": "mmagic/models/editors/eg3d/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .camera import GaussianCamera, UniformCamera\nfrom .dual_discriminator import DualDiscriminator\nfrom .eg3d import EG3D\nfrom .eg3d_generator import TriplaneGenerator\n\n__all__ = [\n 'DualDiscriminator', 'TriplaneGenerator', 'EG3D', 'UniformCamera',\n 'GaussianCamera'\n]\n" }, { "path": "mmagic/models/editors/eg3d/camera.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import List, Optional, Union\n\nimport torch\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.utils import normalize_vecs\nfrom mmagic.registry import MODELS\n\nDeviceType = Optional[Union[str, int]]\nVectorType = Optional[Union[list, torch.Tensor]]\n\n\nclass BaseCamera(object):\n \"\"\"Base camera class. Sample camera position on sphere with specific\n distribution (e.g., Gaussian, Uniform) and return camera-to-world matrix\n and intrinsics matrix.\n\n Args:\n horizontal_mean (Optional[float]): Mean of the horizontal range in\n radian. Defaults to None.\n vertical_mean (Optional[float]): Mean of the vertical range in radian.\n Defaults to None.\n horizontal_std (Optional[float]): Standard deviation of the horizontal\n range in radian. Defaults to None.\n vertical_std (Optional[float]): Standard deviation of the vertical\n range in radian. Defaults to None.\n look_at (Optional[List, torch.Tensor]): The look at position of the\n camera. Defaults to None.\n fov (Optional[float]): The FOV (field-of-view) in degree. Defaults\n to None.\n up (Optional[List, torch.Tensor]): The up direction of the world\n coordinate. Defaults to None.\n radius (Optional[float]): Radius of the sphere. Defaults to None.\n sampling_strategy (Optional[str]): The sampling strategy (distribution)\n of the camera. Support 'Uniform' and 'Gaussian'.\n Defaults to 'Uniform'.\n \"\"\"\n\n def __init__(self,\n horizontal_mean: Optional[float] = None,\n vertical_mean: Optional[float] = None,\n horizontal_std: Optional[float] = 0,\n vertical_std: Optional[float] = 0,\n look_at: VectorType = [0, 0, 0],\n fov: Optional[float] = None,\n focal: Optional[float] = None,\n up: VectorType = [0, 1, 0],\n radius: Optional[float] = 1,\n sampling_strategy: str = 'uniform'):\n super().__init__()\n self.horizontal_mean = horizontal_mean\n self.vertical_mean = vertical_mean\n self.horizontal_std = horizontal_std\n self.vertical_std = vertical_std\n self.look_at = look_at\n self.up = up\n self.radius = radius\n self.sampling_statregy = sampling_strategy\n\n assert ((fov is None) or (focal is None)), (\n '\\'fov\\' and \\'focal\\' should not be passed at the same time.')\n self.fov = fov\n self.focal = focal\n\n def _sample_in_range(self, mean: float, std: float,\n batch_size: int) -> torch.Tensor:\n \"\"\"Sample value with specific mean and std.\n\n Args:\n mean (float): Mean of the sampled value.\n std (float): Standard deviation of the sampled value.\n batch_size (int): The batch size of the sampled result.\n\n Returns:\n torch.Tensor: Sampled results.\n \"\"\"\n if self.sampling_statregy.upper() == 'UNIFORM':\n return (torch.rand((batch_size, 1)) - 0.5) * 2 * std + mean\n elif self.sampling_statregy.upper() == 'GAUSSIAN':\n return torch.randn((batch_size, 1)) * std + mean\n else:\n raise ValueError(\n 'Only support \\'Uniform\\' sampling and \\'Gaussian\\' sampling '\n 'currently. If you want to implement your own sampling '\n 'method, you can overwrite \\'_sample_in_range\\' function by '\n 'yourself.')\n\n def sample_intrinsic(self,\n fov: Optional[float] = None,\n focal: Optional[float] = None,\n device: Optional[DeviceType] = None,\n batch_size: int = 1) -> torch.Tensor:\n \"\"\"Sample intrinsic matrix.\n\n Args:\n fov (Optional[float], optional): FOV (field of view) in degree. If\n not passed, :attr:`self.fov` will be used. Defaults to None.\n focal (Optional[float], optional): Focal in pixel. If\n not passed, :attr:`self.focal` will be used. Defaults to None.\n batch_size (int): The batch size of the output. Defaults to 1.\n device (DeviceType, optional): Device to put the intrinsic\n matrix. If not passed, :attr:`self.device` will be used.\n Defaults to None.\n\n Returns:\n torch.Tensor: Intrinsic matrix.\n \"\"\"\n # 1. check if foc and focal is both passed\n assert (fov is None) or (focal is None), (\n '\\'fov\\' and focal should not be passed at the same time.')\n # 2. if fov and focal is neither not passed, use initialized ones.\n if fov is None and focal is None:\n fov = self.fov if fov is None else fov\n focal = self.focal if focal is None else focal\n\n if fov is None and focal is None:\n raise ValueError(\n '\\'fov\\', \\'focal\\', \\'self.fov\\' and \\'self.focal\\' should '\n 'not be None neither.')\n\n if fov is not None:\n intrinstic = self.fov_to_intrinsic(fov, device)\n else:\n intrinstic = self.focal_to_instrinsic(focal, device)\n return intrinstic[None, ...].repeat(batch_size, 1, 1)\n\n def fov_to_intrinsic(self,\n fov: Optional[float] = None,\n device: DeviceType = None) -> torch.Tensor:\n \"\"\"Calculate intrinsic matrix from FOV (field of view).\n\n Args:\n fov (Optional[float], optional): FOV (field of view) in degree. If\n not passed, :attr:`self.fov` will be used. Defaults to None.\n device (DeviceType, optional): Device to put the intrinsic\n matrix. If not passed, :attr:`self.device` will be used.\n Defaults to None.\n\n Returns:\n torch.Tensor: Intrinsic matrix.\n \"\"\"\n fov = self.fov if fov is None else fov\n assert fov is not None, (\n '\\'fov\\' and \\'self.fov\\' should not be None at the same time.')\n # device = self.device if device is None else device\n # NOTE: EG3D multiplies '1 / 1.414' as `image_width` to `focal`, we\n # retain this operation\n focal = float(1 / (math.tan(fov * math.pi / 360) * 1.414))\n intrinsics = [[focal, 0, 0.5], [0, focal, 0.5], [0, 0, 1]]\n intrinsics = torch.tensor(intrinsics, device=device)\n return intrinsics\n\n def focal_to_instrinsic(self,\n focal: Optional[float] = None,\n device: DeviceType = None) -> torch.Tensor:\n \"\"\"Calculate intrinsic matrix from focal.\n\n Args:\n focal (Optional[float], optional): Focal in degree. If\n not passed, :attr:`self.focal` will be used. Defaults to None.\n device (DeviceType, optional): Device to put the intrinsic\n matrix. If not passed, :attr:`self.device` will be used.\n Defaults to None.\n\n Returns:\n torch.Tensor: Intrinsic matrix.\n \"\"\"\n focal = self.focal if focal is None else focal\n assert focal is not None, (\n '\\'focal\\' and \\'self.focal\\' should not be None at the '\n 'same time.')\n # device = self.device if device is None else device\n intrinsics = [[focal, 0, 0.5], [0, focal, 0.5], [0, 0, 1]]\n intrinsics = torch.tensor(intrinsics, device=device)\n return intrinsics\n\n def sample_theta(self, mean: float, std: float,\n batch_size: int) -> torch.Tensor:\n \"\"\"Sampling the theta (yaw).\n\n Args:\n mean (float): Mean of theta.\n std (float): Standard deviation of theta.\n batch_size (int): Target batch size of theta.\n\n Returns:\n torch.Tensor: Sampled theta.\n \"\"\"\n h = self._sample_in_range(mean, std, batch_size)\n return h\n\n def sample_phi(self, mean: float, std: float,\n batch_size: int) -> torch.Tensor:\n \"\"\"Sampling the phi (pitch). Unlike sampling theta, we uniformly sample\n phi on cosine space to release a spherical uniform sampling.\n\n Args:\n mean (float): Mean of phi.\n std (float): Standard deviation of phi.\n batch_size (int): Target batch size of phi.\n\n Returns:\n torch.Tensor: Sampled phi.\n \"\"\"\n v = self._sample_in_range(mean, std, batch_size)\n v = torch.clamp(v, 1e-5, math.pi - 1e-5)\n\n v = v / math.pi\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n import numpy as np\n phi = torch.from_numpy(np.arccos((1 - 2 * v).numpy()))\n else:\n phi = torch.arccos(1 - 2 * v)\n return phi\n\n def sample_camera2world(self,\n h_mean: Optional[float] = None,\n v_mean: Optional[float] = None,\n h_std: Optional[float] = None,\n v_std: Optional[float] = None,\n look_at: VectorType = None,\n up: VectorType = None,\n radius: Optional[float] = None,\n batch_size: int = 1,\n device: Optional[str] = None) -> torch.Tensor:\n \"\"\"Sample camera-to-world matrix with the passed condition.\n\n Args:\n h_mean (Optional[float], optional): Mean of horizontal range in\n radian. Defaults to None.\n v_mean (Optional[float], optional): Mean of vertical range in\n radian. Defaults to None.\n h_std (Optional[float], optional): Standard deviation of\n horizontal in radian. Defaults to None.\n v_std (Optional[float], optional): Standard deviation of\n horizontal in radian. Defaults to None.\n look_at (Optional[Tuple[list, torch.Tensor]], optional): Look-at\n position. Defaults to None.\n up (Optional[Tuple[list, torch.Tensor]], optional): Up direction\n of the world coordinate. Defaults to None.\n radius (Optional[float]): Radius of the sphere. Defaults to None.\n batch_size (int, optional): Batch size of the results.\n Defaults to 1.\n device (Optional[str], optional): The target device of the results.\n Defaults to None.\n\n Returns:\n torch.Tensor: Sampled camera-to-world matrix.\n \"\"\"\n # parse input\n h_mean = self.horizontal_mean if h_mean is None else h_mean\n v_mean = self.vertical_mean if v_mean is None else v_mean\n h_std = self.horizontal_std if h_std is None else h_std\n v_std = self.vertical_std if v_std is None else v_std\n radius = self.radius if radius is None else radius\n # device = self.device if device is None else device\n look_at = self.look_at if look_at is None else look_at\n if not isinstance(look_at, torch.FloatTensor):\n look_at = torch.FloatTensor(look_at)\n look_at = look_at.to(device)\n up = self.up if up is None else up\n if not isinstance(up, torch.FloatTensor):\n up = torch.FloatTensor(up)\n up = up.to(device)\n\n # sample yaw and pitch\n theta = self.sample_theta(h_mean, h_std, batch_size).to(device)\n phi = self.sample_phi(v_mean, v_std, batch_size).to(device)\n # construct camera origin\n camera_origins = torch.zeros((batch_size, 3), device=device)\n\n camera_origins[:, 0:1] = radius * torch.sin(phi) * torch.cos(math.pi -\n theta)\n camera_origins[:, 2:3] = radius * torch.sin(phi) * torch.sin(math.pi -\n theta)\n camera_origins[:, 1:2] = radius * torch.cos(phi)\n # calculate forward vector and camer2world\n forward_vectors = normalize_vecs(look_at - camera_origins)\n camera2world = create_cam2world_matrix(forward_vectors, camera_origins,\n up)\n return camera2world\n\n def interpolation_cam2world(self,\n num_images: int,\n h_mean: Optional[float] = None,\n v_mean: Optional[float] = None,\n h_std: Optional[float] = None,\n v_std: Optional[float] = None,\n look_at: VectorType = None,\n up: VectorType = None,\n radius: Optional[float] = None,\n batch_size: int = 1,\n device: Optional[str] = None\n ) -> List[torch.Tensor]:\n \"\"\"Interpolation camera original in spherical trajectory and return a\n list of camera-to-world matrix.\n\n Args:\n num_images (int): The number of images in interpolation.\n h_mean (Optional[float], optional): Mean of horizontal range in\n radian. Defaults to None.\n v_mean (Optional[float], optional): Mean of vertical range in\n radian. Defaults to None.\n h_std (Optional[float], optional): Standard deviation of\n horizontal in radian. Defaults to None.\n v_std (Optional[float], optional): Standard deviation of\n horizontal in radian. Defaults to None.\n look_at (Optional[Tuple[list, torch.Tensor]], optional): Look-at\n position. Defaults to None.\n up (Optional[Tuple[list, torch.Tensor]], optional): Up direction\n of the world coordinate. Defaults to None.\n radius (Optional[float]): Radius of the sphere. Defaults to None.\n batch_size (int, optional): Batch size of the results.\n Defaults to 1.\n device (Optional[str], optional): The target device of the results.\n Defaults to None.\n\n Returns:\n List[torch.Tensor]: List of sampled camera-to-world matrix.\n \"\"\"\n h_mean = self.horizontal_mean if h_mean is None else h_mean\n v_mean = self.vertical_mean if v_mean is None else v_mean\n h_std = self.horizontal_std if h_std is None else h_std\n v_std = self.vertical_std if v_std is None else v_std\n radius = self.radius if radius is None else radius\n look_at = self.look_at if look_at is None else look_at\n if not isinstance(look_at, torch.FloatTensor):\n look_at = torch.FloatTensor(look_at)\n look_at = look_at.to(device)\n up = self.up if up is None else up\n if not isinstance(up, torch.FloatTensor):\n up = torch.FloatTensor(up)\n up = up.to(device)\n\n cam2world_list = []\n for idx in range(num_images):\n h = h_mean + h_std * math.sin(2 * math.pi / num_images * idx)\n v = v_mean + v_std * math.cos(2 * math.pi / num_images * idx)\n cam2world = self.sample_camera2world(\n h_mean=h,\n v_mean=v,\n h_std=0,\n v_std=0,\n batch_size=batch_size,\n device=device)\n cam2world_list.append(cam2world)\n\n return cam2world_list\n\n def __repr__(self):\n repr_string = f'{self.__class__.__name__}'\n attribute_list = [\n 'horizontal_mean', 'vertical_mean', 'horizontal_std',\n 'vertical_std', 'FOV', 'focal', 'look_at', 'up', 'radius',\n 'sampling_statregy'\n ]\n for attribute in attribute_list:\n if getattr(self, attribute, None) is not None:\n repr_string += f'\\n {attribute}: {getattr(self, attribute)}'\n return repr_string\n\n\n@MODELS.register_module()\nclass GaussianCamera(BaseCamera):\n \"\"\"Pre-defined camera class. Sample camera position in gaussian\n distribution.\n\n Args:\n horizontal_mean (Optional[float]): Mean of the horizontal range in\n radian. Defaults to None.\n vertical_mean (Optional[float]): Mean of the vertical range in radian.\n Defaults to None.\n horizontal_std (Optional[float]): Standard deviation of the horizontal\n range in radian. Defaults to None.\n vertical_std (Optional[float]): Standard deviation of the vertical\n range in radian. Defaults to None.\n look_at (Optional[List, torch.Tensor]): The look at position of the\n camera. Defaults to None.\n up (Optional[List, torch.Tensor]): The up direction of the world\n coordinate. Defaults to None.\n radius (Optional[float]): Radius of the sphere. Defaults to None.\n \"\"\"\n\n def __init__(self,\n horizontal_mean: Optional[float] = None,\n vertical_mean: Optional[float] = None,\n horizontal_std: Optional[float] = 0,\n vertical_std: Optional[float] = 0,\n look_at: List = [0, 0, 0],\n fov: Optional[float] = None,\n focal: Optional[float] = None,\n up: VectorType = [0, 1, 0],\n radius: Optional[float] = 1):\n super().__init__(horizontal_mean, vertical_mean, horizontal_std,\n vertical_std, look_at, fov, focal, up, radius,\n 'gaussian')\n\n\n@MODELS.register_module()\nclass UniformCamera(BaseCamera):\n \"\"\"Pre-defined camera class. Sample camera position in uniform\n distribution.\n\n Args:\n horizontal_mean (Optional[float]): Mean of the horizontal range in\n radian. Defaults to None.\n vertical_mean (Optional[float]): Mean of the vertical range in radian.\n Defaults to None.\n horizontal_std (Optional[float]): Standard deviation of the horizontal\n range in radian. Defaults to None.\n vertical_std (Optional[float]): Standard deviation of the vertical\n range in radian. Defaults to None.\n look_at (Optional[List, torch.Tensor]): The look at position of the\n camera. Defaults to None.\n up (Optional[List, torch.Tensor]): The up direction of the world\n coordinate. Defaults to None.\n radius (Optional[float]): Radius of the sphere. Defaults to None.\n \"\"\"\n\n def __init__(self,\n horizontal_mean: Optional[float] = None,\n vertical_mean: Optional[float] = None,\n horizontal_std: Optional[float] = 0,\n vertical_std: Optional[float] = 0,\n look_at: List = [0, 0, 0],\n fov: Optional[float] = None,\n focal: Optional[float] = None,\n up: VectorType = [0, 1, 0],\n radius: Optional[float] = 1):\n super().__init__(horizontal_mean, vertical_mean, horizontal_std,\n vertical_std, look_at, fov, focal, up, radius,\n 'uniform')\n\n\ndef create_cam2world_matrix(forward_vector: torch.Tensor, origin: torch.Tensor,\n up: torch.Tensor) -> torch.Tensor:\n \"\"\"Calculate camera-to-world matrix from camera's forward vector, world\n origin and world up direction. The calculation is performed in right-hand\n coordinate system and the returned matrix is in homogeneous coordinates\n (shape like (bz, 4, 4)).\n\n Args:\n forward_vector (torch.Tensor): The forward vector of the camera.\n origin (torch.Tensor): The origin of the world coordinate.\n up (torch.Tensor): The up direction of the world coordinate.\n\n Returns:\n torch.Tensor: Camera-to-world matrix.\n \"\"\"\n\n forward_vector = normalize_vecs(forward_vector)\n up_vector = up.type(torch.float).expand_as(forward_vector)\n right_vector = -normalize_vecs(\n torch.cross(up_vector, forward_vector, dim=-1))\n up_vector = normalize_vecs(\n torch.cross(forward_vector, right_vector, dim=-1))\n\n rotation_matrix = torch.eye(\n 4, device=origin.device).unsqueeze(0).repeat(forward_vector.shape[0],\n 1, 1)\n rotation_matrix[:, :3, :3] = torch.stack(\n (right_vector, up_vector, forward_vector), axis=-1)\n\n translation_matrix = torch.eye(\n 4, device=origin.device).unsqueeze(0).repeat(forward_vector.shape[0],\n 1, 1)\n translation_matrix[:, :3, 3] = origin\n cam2world = (translation_matrix @ rotation_matrix)[:, :, :]\n assert (cam2world.shape[1:] == (4, 4))\n return cam2world\n" }, { "path": "mmagic/models/editors/eg3d/dual_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.runner.amp import autocast\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom ..stylegan2 import StyleGAN2Discriminator\n\n\n@MODELS.register_module('EG3DDiscriminator')\n@MODELS.register_module()\nclass DualDiscriminator(StyleGAN2Discriminator):\n \"\"\"Dual Discriminator for EG3D. DualDiscriminator shares the same network\n structure with StyleGAN2's Discriminator. However, DualDiscriminator take\n volume rendered low-resolution image and super-resolved image at the same\n time. The LR image will be upsampled and concatenate with SR ones, and then\n feed to the discriminator together.\n\n Args:\n img_channels (int): The number of the image channels. Defaults to 3.\n use_dual_disc (bool): Whether use dual discriminator as EG3D. If True,\n the input channel of the first conv block will be set as\n `2 * img_channels`. Defaults to True.\n disc_c_noise (float): The factor of noise's standard deviation add to\n conditional input before passed to mapping network. Defaults to 0.\n *args, **kwargs: Arguments for StyleGAN2Discriminator.\n \"\"\"\n\n def __init__(self,\n img_channels: int = 3,\n use_dual_disc: bool = True,\n disc_c_noise: float = 0,\n *args,\n **kwargs):\n if use_dual_disc:\n img_channels *= 2\n self.use_dual_disc = use_dual_disc\n super().__init__(img_channels=img_channels, *args, **kwargs)\n self.disc_c_noise = disc_c_noise\n\n def forward(self,\n img: Tensor,\n img_raw: Optional[Tensor] = None,\n cond: Optional[Tensor] = None):\n \"\"\"Forward function.\n\n Args:\n img (torch.Tensor): Input high resoluation image tensor.\n img_raw (torch.Tensor): Input raw (low resolution) image tensor.\n Defaults to None.\n cond (torch.Tensor): The conditional input (camera-to-world matrix\n and intrinsics matrix). Defaults to None.\n\n Returns:\n torch.Tensor: Predict score for the input image.\n \"\"\"\n if self.use_dual_disc:\n assert img_raw is not None, (\n '\\'img_raw\\' must be passed when \\'use_dual_disc\\' is True.')\n\n # This setting was used to finetune on converted weights\n if self.input_bgr2rgb:\n img = img[:, [2, 1, 0], ...]\n if img_raw is not None:\n img_raw = img_raw[:, [2, 1, 0], ...]\n\n if img_raw is not None:\n # the official implementation only use 'antialiased' upsampline,\n # therefore we only support 'antialiased' for torch >= 1.11.0\n interpolation_kwargs = dict(\n size=(img.shape[-1], img.shape[-1]),\n mode='bilinear',\n align_corners=False)\n if digit_version(TORCH_VERSION) >= digit_version('1.11.0'):\n interpolation_kwargs['antialias'] = True\n img_raw_sr = F.interpolate(img_raw, **interpolation_kwargs)\n img = torch.cat([img, img_raw_sr], dim=1)\n\n # convs has own fp-16 controller, do not wrap here\n x = self.convs(img)\n x = self.mbstd_layer(x)\n\n fp16_enabled = (\n self.final_conv.fp16_enabled or not self.convert_input_fp32)\n with autocast(enabled=fp16_enabled):\n if not fp16_enabled:\n x = x.to(torch.float32)\n x = self.final_conv(x)\n x = x.view(x.shape[0], -1)\n x = self.final_linear(x)\n\n # conditioning\n if cond is not None:\n assert self.mapping is not None, (\n '\\'mapping\\' network must not be None when conditional '\n 'input is passed.')\n\n # if self.disc_c_noise is not None and self.disc_c_noise > 0:\n if self.disc_c_noise is not None:\n cond = cond + torch.randn_like(\n cond) * cond.std() * self.disc_c_noise\n cmap = self.mapping(None, cond)\n x = (x * cmap).sum(\n dim=1, keepdim=True) * (1 / np.sqrt(cmap.shape[1]))\n return x\n" }, { "path": "mmagic/models/editors/eg3d/eg3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import Config\nfrom mmengine.utils import ProgressBar\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, SampleList\nfrom ...base_models import BaseConditionalGAN\nfrom ...utils import get_valid_num_batches\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass EG3D(BaseConditionalGAN):\n \"\"\"Implementation of `Efficient Geometry-aware 3D Generative Adversarial\n Networks`\n\n _ (EG3D). # noqa\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.eg3d.eg3d_generator.TriplaneGenerator`\n and\n :class:`~mmagic.models.editors.eg3d.dual_discriminator.DualDiscriminator`\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n camera (Optional[ModelType]): The pre-defined camera to sample random\n camera position. If you want to generate images or videos via\n high-level API, you must set this argument. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): Number of times the generator was completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): Number of times the discriminator was\n completely updated before the generator is updated. Defaults to 1.\n noise_size (Optional[int]): Size of the input noise vector.\n Default to 128.\n num_classes (Optional[int]): The number classes you would like to\n generate. Defaults to None.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n loss_config (Optional[Dict]): The config for training losses.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n camera: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n noise_size: Optional[int] = None,\n ema_config: Optional[Dict] = None,\n loss_config: Optional[Dict] = None):\n\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, noise_size,\n None, ema_config, loss_config)\n if isinstance(camera, dict):\n self.camera = MODELS.build(camera)\n elif isinstance(camera, nn.Module):\n self.camera = camera\n else:\n self.camera = None\n\n def label_fn(self,\n label: Optional[Tensor] = None,\n num_batches: int = 1) -> Tensor:\n \"\"\"Label sampling function for EG3D model.\n\n Args:\n label (Optional[Tensor]): Conditional for EG3D model. If not\n passed, :attr:`self.camera` will be used to sample random\n camera-to-world and intrinsics matrix. Defaults to None.\n\n Returns:\n torch.Tensor: Conditional input for EG3D model.\n \"\"\"\n if label is not None:\n return label\n\n # sample random conditional from camera\n assert self.camera is not None, (\n '\\'camera\\' is not defined for \\'EG3D\\'.')\n camera2world = self.camera.sample_camera2world(batch_size=num_batches)\n intrinsics = self.camera.sample_intrinsic(batch_size=num_batches)\n cond = torch.cat(\n [camera2world.reshape(-1, 16),\n intrinsics.reshape(-1, 9)], dim=1).to(self.device)\n return cond\n\n def data_sample_to_label(self, data_sample: SampleList\n ) -> Optional[torch.Tensor]:\n \"\"\"Get labels from input `data_sample` and pack to `torch.Tensor`. If\n no label is found in the passed `data_sample`, `None` would be\n returned.\n\n Args:\n data_sample (List[DataSample]): Input data samples.\n\n Returns:\n Optional[torch.Tensor]: Packed label tensor.\n \"\"\"\n\n if not data_sample or 'gt_label' not in data_sample.keys():\n return None\n return data_sample.gt_label.label\n\n def pack_to_data_sample(\n self,\n output: Dict[str, Tensor],\n # index: int,\n data_sample: Optional[DataSample] = None) -> DataSample:\n \"\"\"Pack output to data sample. If :attr:`data_sample` is not passed, a\n new DataSample will be instantiated. Otherwise, outputs will be added\n to the passed datasample.\n\n Args:\n output (Dict[Tensor]): Output of the model.\n index (int): The index to save.\n data_sample (DataSample, optional): Data sample to save\n outputs. Defaults to None.\n\n Returns:\n DataSample: Data sample with packed outputs.\n \"\"\"\n\n assert isinstance(output,\n dict), ('Output of EG3D generator should be a dict.')\n\n data_sample = DataSample() if data_sample is None else data_sample\n for k, v in output.items():\n assert isinstance(v, torch.Tensor), (\n f'Output must be tensor. But \\'{k}\\' is type of '\n f'\\'{type(v)}\\'.')\n # NOTE: hard code here, we assume all tensor are [bz, ...]\n data_sample.set_tensor_data({k: v})\n\n return data_sample\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n \"\"\"Sample images with the given inputs. If forward mode is 'ema' or\n 'orig', the image generated by corresponding generator will be\n returned. If forward mode is 'ema/orig', images generated by original\n generator and EMA generator will both be returned in a dict.\n\n Args:\n inputs (ForwardInputs): Dict containing the necessary\n information (e.g. noise, num_batches, mode) to generate image.\n data_samples (Optional[list]): Data samples collated by\n :attr:`data_preprocessor`. Defaults to None.\n mode (Optional[str]): `mode` is not used in\n :class:`BaseConditionalGAN`. Defaults to None.\n\n Returns:\n List[DataSample]: Generated images or image dict.\n \"\"\"\n if isinstance(inputs, Tensor):\n noise = inputs\n sample_kwargs = {}\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n num_batches = noise.shape[0]\n\n labels = self.data_sample_to_label(data_samples)\n if labels is None:\n num_batches = get_valid_num_batches(inputs, data_samples)\n labels = self.label_fn(num_batches=num_batches)\n\n sample_model = self._get_valid_model(inputs)\n batch_sample_list = []\n if sample_model in ['ema', 'orig']:\n if sample_model == 'ema':\n generator = self.generator_ema\n else:\n generator = self.generator\n outputs = generator(noise, label=labels)\n outputs['fake_img'] = self.data_preprocessor.destruct(\n outputs['fake_img'], data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample = self.pack_to_data_sample(outputs, gen_sample)\n gen_sample.noise = noise\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = sample_model\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n else:\n outputs_orig = self.generator(noise, label=labels)\n outputs_ema = self.generator_ema(noise, label=labels)\n outputs_orig['fake_img'] = self.data_preprocessor.destruct(\n outputs_orig['fake_img'], data_samples)\n outputs_ema['fake_img'] = self.data_preprocessor.destruct(\n outputs_ema['fake_img'], data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.ema = self.pack_to_data_sample(outputs_ema)\n gen_sample.orig = self.pack_to_data_sample(outputs_orig)\n gen_sample.noise = noise\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n gen_sample.sample_model = sample_model\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n return batch_sample_list\n\n @torch.no_grad()\n def interpolation(self,\n num_images: int,\n num_batches: int = 4,\n mode: str = 'both',\n sample_model: str = 'orig',\n show_pbar: bool = True) -> List[dict]:\n \"\"\"Interpolation input and return a list of output results. We support\n three kinds of interpolation mode:\n\n * 'camera': First generate style code with random noise and forward\n camera. Then synthesis images with interpolated camera position\n and fixed style code.\n\n * 'conditioning': First generate style code with fixed noise and\n interpolated camera. Then synthesis images with style codes and\n forward camera.\n\n * 'both': Generate images with interpolated camera position.\n\n Args:\n num_images (int): The number of images want to generate.\n num_batches (int, optional): The number of batches to generate at\n one time. Defaults to 4.\n mode (str, optional): The interpolation mode. Supported choices\n are 'both', 'camera', and 'conditioning'. Defaults to 'both'.\n sample_model (str, optional): The model used to generate images,\n support 'orig' and 'ema'. Defaults to 'orig'.\n show_pbar (bool, optional): Whether display a progress bar during\n interpolation. Defaults to True.\n\n Returns:\n List[dict]: The list of output dict of each frame.\n \"\"\"\n assert hasattr(self, 'camera'), ('Camera must be defined.')\n assert mode.upper() in ['BOTH', 'CONDITIONING', 'CAMERA']\n assert sample_model in ['ema', 'orig']\n if sample_model == 'orig':\n gen = self.generator\n else:\n assert self.with_ema_gen, (\n '\\'sample_model\\' is EMA, but ema model not found.')\n # generator_ema is wrapped by AverageModel\n gen = self.generator_ema.module\n\n cam2world_forward = self.camera.sample_camera2world(\n h_std=0, v_std=0, batch_size=num_batches, device=self.device)\n intrinsics = self.camera.sample_intrinsic(\n batch_size=num_batches, device=self.device)\n cond_forward = torch.cat([\n cam2world_forward.view(num_batches, -1),\n intrinsics.view(num_batches, -1)\n ],\n dim=1)\n # 1. generate cond list\n if mode.upper() in ['CAMERA', 'BOTH']:\n cam2world_list = self.camera.interpolation_cam2world(\n num_images, batch_size=num_batches, device=self.device)\n cond_list = []\n for cam2world in cam2world_list:\n cond = torch.cat([\n cam2world.view(num_batches, -1),\n intrinsics.view(num_batches, -1)\n ],\n dim=1)\n cond_list.append(cond)\n else:\n cond_list = [cond_forward for _ in range(num_images)]\n\n # 2. generate pre-defined style list\n if mode.upper() == 'CAMERA':\n # same noise + forward cond\n style = gen.backbone.mapping(\n self.noise_fn(num_batches=num_batches), cond_forward)\n style_list = [style for _ in range(num_images)]\n else:\n # same noise + different cond\n noise = self.noise_fn(num_batches=num_batches)\n style_list = [\n gen.backbone.mapping(noise, cond) for cond in cond_list\n ]\n\n # 3. interpolation\n if show_pbar:\n pbar = ProgressBar(num_images)\n output_list = []\n for style, cond in zip(style_list, cond_list):\n # generate image with const noise\n output = gen(\n style,\n cond,\n input_is_latent=True,\n add_noise=True,\n randomize_noise=False) # use fixed noise\n output_list.append({k: v.cpu() for k, v in output.items()})\n\n if show_pbar:\n pbar.update(1)\n if show_pbar:\n print('\\n')\n\n return output_list\n" }, { "path": "mmagic/models/editors/eg3d/eg3d_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Optional, Tuple\n\nimport torch\nfrom mmengine.model import BaseModule\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom .eg3d_modules import SuperResolutionModule, TriPlaneBackbone\nfrom .ray_sampler import sample_rays\nfrom .renderer import EG3DRenderer\n\n\n@MODELS.register_module('EG3DGenerator')\n@MODELS.register_module()\nclass TriplaneGenerator(BaseModule):\n \"\"\"The generator for EG3D.\n\n EG3D generator contains three components:\n\n * A StyleGAN2 based backbone to generate a triplane feature\n * A neural renderer to sample and render low-resolution 2D feature and\n image from generated triplane feature\n * A super resolution module to upsample low-resolution image to\n high-resolution one\n\n Args:\n out_size (int): The resolution of the generated 2D image.\n noise_size (int): The size of the noise vector of the StyleGAN2\n backbone. Defaults to 512.\n style_channels (int): The number of channels for style code.\n Defaults to 512.\n cond_size (int): The size of the conditional input. Defaults to 25\n (first 16 elements are flattened camera-to-world matrix and the\n last 9 elements are flattened intrinsic matrix).\n cond_mapping_channels (Optional[int]): The channels of the\n conditional mapping layers. If not passed, will use the same value\n as :attr:`style_channels`. Defaults to None.\n cond_scale (float): The scale factor is multiple by the conditional\n input. Defaults to 1.\n zero_cond_input (bool): Whether use 'zero tensor' as the conditional\n input. Defaults to False.\n num_mlps (int): The number of MLP layers (mapping network) used in\n backbone. Defaults to 8.\n triplane_size (int): The size of generated triplane feature. Defaults\n to 256.\n triplane_channels (int): The number of channels for each plane of the\n triplane feature. Defaults to 32.\n sr_in_size (int): The input resolution of super resolution module. If\n the input feature not match with the passed `sr_in_size`, bilinear\n interpolation will be used to resize feature to target size.\n Defaults to 64.\n sr_in_channels (int): The number of the input channels of super\n resolution module. Defaults to 32.\n sr_hidden_channels (int): The number of the hidden channels of super\n resolution module. Defaults to 128.\n sr_out_channels (int): The number of the output channels of super\n resolution module. Defaults to 64.\n sr_add_noise (bool): Whether use noise injection to super resolution\n module. Defaults to False.\n neural_rendering_resolution (int): The resolution of the neural\n rendering output. Defaults to 64. Noted that in the training\n process, neural rendering resolution will be changed.\n Defaults to 64.\n renderer_cfg (int): The config to build :class:`EG3DRenderer`.\n Defaults to '{}'.\n rgb2bgr (bool): Whether convert the RGB output to BGR. This is useful\n when pretrained model is trained on RGB dataset. Defaults to False.\n init_cfg (Optional[dict]): Initialization config. Defaults to None.\n \"\"\"\n\n def __init__(self,\n out_size: int,\n noise_size: int = 512,\n style_channels: int = 512,\n cond_size: int = 25,\n cond_mapping_channels: Optional[int] = None,\n cond_scale: float = 1,\n zero_cond_input: bool = False,\n num_mlps: int = 8,\n triplane_size: int = 256,\n triplane_channels: int = 32,\n sr_in_size: int = 64,\n sr_in_channels: int = 32,\n sr_hidden_channels: int = 128,\n sr_out_channels: int = 64,\n sr_antialias: bool = True,\n sr_add_noise: bool = True,\n neural_rendering_resolution: int = 64,\n renderer_cfg: dict = dict(),\n rgb2bgr: bool = False,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n\n self.out_size = out_size\n self.noise_size = noise_size\n self.cond_size = cond_size\n self.style_size = style_channels\n\n self.cond_scale = cond_scale\n self.zero_cond_input = zero_cond_input\n self.sr_add_noise = sr_add_noise\n\n # build StyleGAN2 backbone\n self.triplane_channels = triplane_channels\n self.backbone = TriPlaneBackbone(\n out_size=triplane_size,\n noise_size=noise_size,\n out_channels=triplane_channels * 3,\n style_channels=style_channels,\n num_mlps=num_mlps,\n cond_size=cond_size,\n cond_scale=self.cond_scale,\n cond_mapping_channels=cond_mapping_channels,\n zero_cond_input=self.zero_cond_input)\n\n # build renderer + nerf-decoder and ray sampler\n self.neural_rendering_resolution = neural_rendering_resolution\n renderer_cfg_ = deepcopy(renderer_cfg)\n decoder_cfg_ = renderer_cfg.get('decoder_cfg', dict())\n decoder_cfg_['in_channels'] = triplane_channels\n decoder_cfg_['out_channels'] = sr_in_channels\n renderer_cfg_['decoder_cfg'] = decoder_cfg_\n self.renderer = EG3DRenderer(**deepcopy(renderer_cfg_))\n\n # build super-resolution module\n sr_factor = out_size // sr_in_size\n assert sr_factor in [\n 2, 4, 8\n ], ('Only support super resolution with factor 2, 4 or 8. '\n f'But \\'out_size\\' and \\'sr_in_size\\'are {out_size} and '\n f'{sr_in_size}.')\n self.sr_model = SuperResolutionModule(\n in_channels=sr_in_channels,\n in_size=sr_in_size,\n hidden_size=sr_in_size * 2 if sr_factor in [4, 8] else sr_in_size,\n out_size=out_size,\n style_channels=style_channels,\n hidden_channels=sr_hidden_channels,\n out_channels=sr_out_channels,\n sr_antialias=sr_antialias)\n\n # flag for pretrained models\n self.rgb2bgr = rgb2bgr\n\n def sample_ray(self, cond: torch.Tensor) -> Tuple[Tensor]:\n \"\"\"Sample render points corresponding to the given conditional.\n\n Args:\n cond (torch.Tensor): Conditional inputs.\n\n Returns:\n Tuple[Tensor]: The original and direction vector of sampled rays.\n \"\"\"\n cam2world_matrix = cond[:, :16].view(-1, 4, 4)\n intrinsics = cond[:, 16:25].view(-1, 3, 3)\n\n ray_origin, ray_directions = sample_rays(\n cam2world_matrix, intrinsics, self.neural_rendering_resolution)\n return ray_origin, ray_directions\n\n def forward(self,\n noise: Tensor,\n label: Optional[Tensor] = None,\n truncation: Optional[float] = 1,\n num_truncation_layer: Optional[int] = None,\n input_is_latent: bool = False,\n plane: Optional[Tensor] = None,\n add_noise: bool = True,\n randomize_noise: bool = True,\n render_kwargs: Optional[dict] = None) -> dict:\n \"\"\"The forward function for EG3D generator.\n\n Args:\n noise (Tensor): The input noise vector.\n label (Optional[Tensor]): The conditional input. Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n num_truncation_layer (int, optional): Number of layers use\n truncated latent. Defaults to None.\n input_is_latent (bool): Whether the input latent. Defaults to\n False.\n plane (Optional[Tensor]): The pre-generated triplane feature. If\n passed, will use the passed plane to generate 2D image.\n Defaults to None.\n add_noise (bool): Whether apply noise injection to the triplane\n backbone. Defaults to True.\n randomize_noise (bool, optional): If `False`, images are sampled\n with the buffered noise tensor injected to the style conv\n block. Defaults to True.\n render_kwargs (Optional[dict], optional): The specific kwargs for\n rendering. Defaults to None.\n\n Returns:\n dict: A dict contains 'fake_img', 'lr_img', 'depth',\n 'ray_directions' and 'ray_origins'.\n \"\"\"\n batch_size = noise.shape[0]\n\n if not input_is_latent:\n styles = self.backbone.mapping(\n noise,\n label,\n truncation=truncation,\n num_truncation_layer=num_truncation_layer)\n else:\n styles = noise\n\n ray_origins, ray_directions = self.sample_ray(label)\n\n if plane is None:\n plane = self.backbone.synthesis(\n styles, add_noise=add_noise, randomize_noise=randomize_noise)\n\n # Reshape output into three `triplane_channels`-channel planes\n plane = plane.view(\n len(plane), 3, self.triplane_channels, plane.shape[-2],\n plane.shape[-1])\n\n # Perform volume rendering\n feature_samples, depth_samples, _ = self.renderer(\n plane, ray_origins, ray_directions, render_kwargs=render_kwargs)\n\n # Reshape into 'raw' neural-rendered image\n H = W = self.neural_rendering_resolution\n feature_image = feature_samples.permute(0, 2, 1).reshape(\n batch_size, feature_samples.shape[-1], H, W).contiguous()\n depth_image = depth_samples.permute(0, 2,\n 1).reshape(batch_size, 1, H, W)\n\n # Run super resolution to get final image\n rgb_image = feature_image[:, :3]\n sr_image = self.sr_model(\n rgb_image, feature_image, styles, add_noise=self.sr_add_noise)\n\n if self.rgb2bgr:\n sr_image = sr_image.flip(1)\n rgb_image = rgb_image.flip(1)\n\n output_dict = dict(\n fake_img=sr_image,\n lr_img=rgb_image, # low-resolution images\n depth=depth_image,\n ray_directions=ray_directions,\n ray_origins=ray_origins)\n\n return output_dict\n" }, { "path": "mmagic/models/editors/eg3d/eg3d_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom mmengine.model import BaseModule\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom ..stylegan2 import StyleGAN2Generator\nfrom ..stylegan2.stylegan2_modules import ModulatedStyleConv, ModulatedToRGB\n\n\nclass TriPlaneBackbone(StyleGAN2Generator):\n \"\"\"Tr-plane backbone for EG3D generator. This class is a wrapper of\n StyleGAN2Generator.\n\n Args:\n noise_size (int, optional): The size of (number of channels) the input\n noise. If not passed, will be set the same value as\n :attr:`style_channels`. Defaults to None.\n out_size (int): The output size of the StyleGAN2 generator.\n out_channels (int): The number of channels for output.\n num_mlps (int, optional): The number of MLP layers. Defaults to 8.\n style_channels (int): The number of channels for style code. Defaults\n to 512.\n cond_size (int, optional): The size of the conditional input. If not\n passed or less than 1, no conditional embedding will be used.\n Defaults to None.\n cond_mapping_channels (int, optional): The channels of the\n conditional mapping layers. If not passed, will use the same value\n as :attr:`style_channels`. Defaults to None.\n cond_scale (float): The scale factor is multiple by the conditional\n input. Defaults to 1.\n zero_cond_input (bool): Whether use 'zero tensor' as the conditional\n input. Defaults to False.\n\n *args, **kwargs: Arguments for StyleGAN2Generator.\n \"\"\"\n\n def __init__(self,\n noise_size: int,\n out_size: int,\n out_channels: int,\n num_mlps: int = 8,\n style_channels: int = 512,\n cond_size: Optional[int] = 25,\n cond_mapping_channels: Optional[int] = None,\n cond_scale: float = 0,\n zero_cond_input: bool = False,\n *args,\n **kwargs):\n super().__init__(\n out_size,\n style_channels,\n num_mlps=num_mlps,\n noise_size=noise_size,\n out_channels=out_channels,\n cond_size=cond_size,\n cond_mapping_channels=cond_mapping_channels,\n *args,\n **kwargs)\n\n self.cond_scale = cond_scale\n self.zero_cond_input = zero_cond_input\n\n def mapping(self,\n noise: torch.Tensor,\n label: Optional[torch.Tensor] = None,\n truncation: float = 1,\n num_truncation_layer: Optional[int] = None,\n update_ws: bool = True) -> torch.Tensor:\n \"\"\"Mapping input noise (z) to style space (w).\n\n Args:\n noise (torch.Tensor): Noise input.\n label (Optional[torch.Tensor]): Conditional inputs.\n Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n num_truncation_layer (int, optional): Number of layers use\n truncated latent. Defaults to None.\n update_ws (bool): Whether update latent code with EMA. Only work\n when `w_avg` is registered. Defaults to False.\n\n Returns:\n torch.Tensor: Style codes after mapping.\n \"\"\"\n assert noise.shape[1] == self.noise_size\n noise = self.pixel_norm(noise)\n\n if label is not None:\n assert label.shape[1] == self.cond_size\n if self.zero_cond_input:\n label = torch.zeros_like(label)\n label = label * self.cond_scale\n embedding = self.embed(label)\n embedding = self.pixel_norm(embedding)\n else:\n # generate a zero input even if cond is not passed.\n if self.zero_cond_input:\n assert self.cond_size is not None, (\n '\\'cond_size\\' must be passed when '\n '\\'zero_cond_input\\' is True.')\n label = torch.zeros(\n noise.shape[0], self.cond_size, device=noise.device)\n embedding = self.embed(label)\n embedding = self.pixel_norm(embedding)\n else:\n embedding = None\n mapping_input = noise if embedding is None \\\n else torch.cat([noise, embedding], dim=1)\n\n styles = self.style_mapping(mapping_input)\n\n if hasattr(self, 'w_avg') and update_ws:\n self.w_avg.copy_(styles.detach().mean(\n dim=0).lerp(self.w_avg, self.w_avg_beta))\n\n if truncation < 1:\n truncation_latent = self.get_mean_latent()\n styles = truncation_latent + truncation * (\n styles - truncation_latent)\n\n return styles\n\n def synthesis(self, styles: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"Generate the Triplane feature.\n\n Args:\n styles (torch.Tensor): The input style code.\n *args, **kwargs: Arguments for StyleGAN2Generator's forward.\n\n Returns:\n torch.Tensor: The generated Triplane feature.\n \"\"\"\n outputs = super().forward(\n styles, input_is_latent=True, update_ws=False, *args, **kwargs)\n return outputs\n\n\nclass SuperResolutionModule(BaseModule):\n \"\"\"Super resolution module for EG3D generator.\n\n Args:\n in_channels (int): The channels of the input feature.\n in_size (int): The size of the input feature.\n hidden_size (int): The size of the hidden feature. Only support hidden\n size equals to in_size or in_size times two. Defaults to None.\n out_size (int): The size of the output image. Defaults to None.\n hidden_channels (int): The channels of the hidden feature.\n Defaults to 64.\n style_channels (int): The channels of the style code. Defaults to 512.\n sr_antialias (bool): Whether use antialias interpolation method in\n upsampling. Defaults to True.\n fp16_enable (bool): Whether enable fp16 in this module.\n Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n in_size: Optional[int] = None,\n hidden_size: Optional[int] = None,\n out_size: Optional[int] = None,\n hidden_channels: int = 128,\n out_channels: int = 64,\n style_channels: Optional[int] = 512,\n sr_antialias: bool = True,\n fp16_enable: bool = False):\n super().__init__()\n self.in_size = in_size\n self.sr_antialias = sr_antialias\n self.fp16_enable = fp16_enable\n\n self.style_channels = style_channels\n\n block0_upsample = hidden_size > in_size\n if block0_upsample:\n assert hidden_size == in_size * 2, (\n 'Only support upsampling with factor 2. But \\'in_resolution\\' '\n f'and \\'hidden_resolution\\' are \\'{in_size}\\' and '\n f'\\'{hidden_size}\\'.')\n\n self.block0 = SynthesisBlock(\n in_channels,\n hidden_channels,\n style_channels,\n img_channels=3,\n upsample=block0_upsample,\n fp16_enabled=fp16_enable)\n\n block1_upsample = out_size > hidden_size\n if block1_upsample:\n assert out_size == hidden_size * 2, (\n 'Only support upsampling with factor 2. But '\n '\\'hidden_resolution\\' and \\'out_resolution\\' are '\n f'\\'{in_size}\\' and \\'{hidden_size}\\'.')\n self.block1 = SynthesisBlock(\n hidden_channels,\n out_channels,\n style_channels,\n img_channels=3,\n upsample=block1_upsample,\n fp16_enabled=fp16_enable)\n if digit_version(TORCH_VERSION) < digit_version('1.11.0'):\n print_log(f'Current Pytorch version is {TORCH_VERSION}, lower '\n 'than 1.11.0. \\'sr_antialias\\' is ignored.')\n\n def forward(self,\n img: torch.Tensor,\n feature: torch.Tensor,\n styles: Union[torch.Tensor, List[torch.Tensor]],\n add_noise: bool = False) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n img (torch.Tensor): Image to super resolution.\n x (torch.Tensor): Feature map of the input image.\n styles (torch.Tensor): Style codes in w space.\n add_noise (bool, optional): Whether add noise to image.\n Defaults to False.\n\n Returns:\n torch.Tensor: Image after super resolution.\n \"\"\"\n if isinstance(styles, list):\n styles = styles[-1]\n if styles.ndim == 3:\n styles = styles[-1]\n assert styles.ndim == 2 and styles.shape[-1] == self.style_channels\n styles = styles[:, None, :].repeat(1, 3, 1)\n if feature.shape[-1] != self.in_size:\n interpolation_kwargs = dict(\n size=(self.in_size, self.in_size),\n mode='bilinear',\n align_corners=False)\n if digit_version(TORCH_VERSION) >= digit_version('1.11.0'):\n interpolation_kwargs['antialias'] = self.sr_antialias\n feature = F.interpolate(feature, **interpolation_kwargs)\n img = F.interpolate(img, **interpolation_kwargs)\n feature, img = self.block0(feature, img, styles, add_noise)\n feature, img = self.block1(feature, img, styles, add_noise)\n return img\n\n\nclass SynthesisBlock(BaseModule):\n \"\"\"Synthesis block for EG3D's SuperResolutionModule.\n\n Args:\n in_channels (int): The number of channels for the input feature.\n out_channels (int): The number of channels for the output feature.\n style_channels (int): The number of channels for style code.\n img_channels (int): The number of channels of output image.\n upsample (bool): Whether do upsampling. Defaults to True.\n conv_clamp (float, optional): Whether clamp the convolutional layer\n results to avoid gradient overflow. Defaults to `256.0`.\n fp16_enabled (bool): Whether enable fp16. Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n out_channels: int,\n style_channels: int,\n img_channels: int,\n upsample: bool = True,\n conv_clamp: int = 256,\n fp16_enabled: bool = False):\n super().__init__()\n # architecture is default as 'skip' in EG3D\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.fp16_enabled = fp16_enabled\n\n self.upsample = upsample\n\n self.conv0 = ModulatedStyleConv(\n in_channels,\n out_channels,\n kernel_size=3,\n upsample=upsample,\n style_channels=style_channels,\n fp16_enabled=fp16_enabled,\n conv_clamp=conv_clamp)\n\n self.conv1 = ModulatedStyleConv(\n out_channels,\n out_channels,\n kernel_size=3,\n style_channels=style_channels,\n conv_clamp=conv_clamp)\n\n self.to_rgb = ModulatedToRGB(\n out_channels, style_channels, img_channels, upsample=upsample)\n\n def forward(self,\n x: torch.Tensor,\n img: torch.Tensor,\n styles: torch.Tensor,\n add_noise: bool = False) -> Tuple[torch.Tensor]:\n \"\"\"Forward Synthesis block.\n\n Args:\n x (torch.Tensor): Input feature.\n img (torch.Tensor): Input image.\n styles (torch.Tensor): Input style code.\n add_noise (bool, optional): Whether apply noise injection.\n Defaults to False.\n\n Returns:\n Tuple[torch.Tensor]: Output feature and image.\n \"\"\"\n w_iter = iter(styles.unbind(dim=1))\n x = self.conv0(x, next(w_iter), add_noise=add_noise)\n x = self.conv1(x, next(w_iter), add_noise=add_noise)\n\n img = self.to_rgb(x, next(w_iter), img)\n\n assert img.dtype == torch.float32\n return x, img\n" }, { "path": "mmagic/models/editors/eg3d/eg3d_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Tuple\n\nimport torch\n\n\ndef get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor,\n box_side_length: float\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Author: Petr Kellnhofer\n Intersects rays with the [-1, 1] NDC volume.\n Returns min and max distance of entry.\n Returns -1 for no intersection.\n https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection # noqa\n\n Args:\n rays_o (torch.Tensor): The origin of each ray.\n rays_d (torch.Tensor): The direction vector of each ray.\n box_side_length (float): The side length of axis aligned\n bounding box (AABB).\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Start and end point\n for each ray. Both shape like (bz, res, res, 1).\n \"\"\"\n o_shape = rays_o.shape\n rays_o = rays_o.detach().reshape(-1, 3)\n rays_d = rays_d.detach().reshape(-1, 3)\n\n bb_min = [\n -1 * (box_side_length / 2), -1 * (box_side_length / 2),\n -1 * (box_side_length / 2)\n ]\n bb_max = [\n 1 * (box_side_length / 2), 1 * (box_side_length / 2),\n 1 * (box_side_length / 2)\n ]\n bounds = torch.tensor([bb_min, bb_max],\n dtype=rays_o.dtype,\n device=rays_o.device)\n is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)\n\n # Precompute inverse for stability.\n invdir = 1 / rays_d\n sign = (invdir < 0).long()\n\n # Intersect with YZ plane.\n tmin = (bounds.index_select(0, sign[..., 0])[..., 0] -\n rays_o[..., 0]) * invdir[..., 0]\n tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] -\n rays_o[..., 0]) * invdir[..., 0]\n\n # Intersect with XZ plane.\n tymin = (bounds.index_select(0, sign[..., 1])[..., 1] -\n rays_o[..., 1]) * invdir[..., 1]\n tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] -\n rays_o[..., 1]) * invdir[..., 1]\n\n # Resolve parallel rays.\n is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False\n\n # Use the shortest intersection.\n tmin = torch.max(tmin, tymin)\n tmax = torch.min(tmax, tymax)\n\n # Intersect with XY plane.\n tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] -\n rays_o[..., 2]) * invdir[..., 2]\n tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] -\n rays_o[..., 2]) * invdir[..., 2]\n\n # Resolve parallel rays.\n is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False\n\n # Use the shortest intersection.\n tmin = torch.max(tmin, tzmin)\n tmax = torch.min(tmax, tzmax)\n\n # Mark invalid.\n tmin[torch.logical_not(is_valid)] = -1\n tmax[torch.logical_not(is_valid)] = -2\n\n return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)\n\n\ndef inverse_transform_sampling(bins: torch.Tensor,\n weights: torch.Tensor,\n n_importance: int,\n deterministic: bool = False,\n eps: float = 1e-5) -> torch.Tensor:\n \"\"\"Sample `N_importance` samples from `bins` with distribution defined by\n `weights`.\n\n Args:\n bins (int): (N_points, N_samples+1) where N_samples is the number\n of coarse samples per ray - 2.\n weights (torch.Tensor): Weights shape like (N_points, N_samples-1).\n n_importance (int): The number of samples to draw from the\n distribution.\n deterministic (bool): Whether use deterministic sampling method.\n Defaults to False.\n eps (float): a small number to prevent division by zero.\n Defaults to 1e-5.\n\n Outputs:\n torch.Tensor: the sampled samples.\n \"\"\"\n N_rays, N_samples_ = weights.shape\n # prevent division by zero (don't do inplace op!)\n weights = weights + eps\n # (N_rays, N_samples_)\n pdf = weights / torch.sum(weights, -1, keepdim=True)\n # (N_rays, N_samples), cumulative distribution function\n cdf = torch.cumsum(pdf, -1)\n # (N_rays, N_samples_+1)\n cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf], -1)\n # padded to 0~1 inclusive\n\n if deterministic:\n u = torch.linspace(0, 1, n_importance, device=bins.device)\n u = u.expand(N_rays, n_importance)\n else:\n u = torch.rand(N_rays, n_importance, device=bins.device)\n u = u.contiguous()\n\n inds = torch.searchsorted(cdf, u, right=True)\n below = torch.clamp_min(inds - 1, 0)\n above = torch.clamp_max(inds, N_samples_)\n\n inds_sampled = torch.stack([below, above],\n -1).view(N_rays, 2 * n_importance)\n cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, n_importance, 2)\n bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, n_importance, 2)\n\n denom = cdf_g[..., 1] - cdf_g[..., 0]\n # denom equals 0 means a bin has weight 0, in which case it will not\n # be sampled anyway, therefore any value for it is fine (set to 1 here)\n denom[denom < eps] = 1\n t = (u - cdf_g[..., 0]) / denom\n samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])\n\n return samples\n\n\ndef linspace_batch(start: torch.Tensor, stop: torch.Tensor,\n num: int) -> torch.Tensor:\n \"\"\"Creates a tensor of shape [num, *start.shape] whose values are evenly\n spaced from start to end, inclusive.\n\n Replicates but the multi-dimensional behaviour of numpy.linspace in\n PyTorch.\n\n Args:\n start (torch.Tensor): The start point of each ray. Shape like\n (bz, res, res, 1).\n stop (torch.Tensor): The end point of each ray. Shape like\n (bz, res, res, 1).\n num (int): The number of points to sample.\n\n Returns:\n torch.Tensor: The sampled points. Shape like (num, bz, res, res, 1)\n \"\"\"\n # create a tensor of 'num' steps from 0 to 1\n steps = torch.arange(\n num, dtype=torch.float32, device=start.device) / (\n num - 1)\n\n # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for\n # broadcastings\n # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but\n # torchscript cannot statically infer the expected size of a list in this\n # context, hence the code below\n for i in range(start.ndim):\n steps = steps.unsqueeze(-1)\n\n # the output starts at 'start' and increments until 'stop' in\n # each dimension\n out = start[None] + steps * (stop - start)[None]\n\n return out\n" }, { "path": "mmagic/models/editors/eg3d/ray_sampler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Tuple\n\nimport torch\n\n\ndef sample_rays(cam2world: torch.Tensor, intrinsics: torch.Tensor,\n resolution: int) -> Tuple[torch.Tensor]:\n \"\"\"Sample origin and direction vectors of rays with passed camera-to-world\n matrix and intrinsics matrix. Noted that skew coefficient is not considered\n in this function.\n\n Args:\n cam2world (torch.Tensor): The camera-to-world matrix in homogeneous\n coordinates. Shape like (bz, 4, 4).\n intrinsics (torch.Tensor): The intrinsic matrix. Shape like (bz, 3, 3).\n resolution (int): The expect resolution of the render output.\n\n Returns:\n Tuple[torch.Tensor]: Origins and view directions for rays. Both shape\n like (bz, resolution^2, 3)\n \"\"\"\n batch_size, n_points = cam2world.shape[0], resolution**2\n cam_in_world = cam2world[:, :3, 3]\n fx = intrinsics[:, 0, 0]\n fy = intrinsics[:, 1, 1]\n cx = intrinsics[:, 0, 2]\n cy = intrinsics[:, 1, 2]\n\n device = cam2world.device\n\n # torch.meshgrid has been modified in 1.10.0 (compatibility with previous\n # versions), and will be further modified in 1.12 (Breaking Change)\n if 'indexing' in torch.meshgrid.__code__.co_varnames:\n u, v = torch.meshgrid(\n torch.arange(resolution, dtype=torch.float32, device=device),\n torch.arange(resolution, dtype=torch.float32, device=device),\n indexing='ij')\n else:\n u, v = torch.meshgrid(\n torch.arange(resolution, dtype=torch.float32, device=device),\n torch.arange(resolution, dtype=torch.float32, device=device))\n uv = torch.stack([u, v])\n uv = uv * (1. / resolution) + (0.5 / resolution)\n uv = uv.flip(0).reshape(2, -1).transpose(1, 0)\n uv = uv.unsqueeze(0).repeat(cam2world.shape[0], 1, 1)\n\n x_cam = uv[:, :, 0].view(batch_size, -1)\n y_cam = uv[:, :, 1].view(batch_size, -1)\n z_cam = torch.ones((batch_size, n_points), device=cam2world.device)\n\n x_lift = (x_cam - cx.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam\n y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam\n\n points_in_cam = torch.stack(\n (x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)\n\n # camera coordinate to world coordinate\n points_in_world = torch.bmm(cam2world, points_in_cam.permute(0, 2, 1))\n points_in_world = points_in_world.permute(0, 2, 1)[:, :, :3]\n\n ray_dirs = points_in_world - cam_in_world[:, None, :]\n ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)\n\n ray_origins = cam_in_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1)\n\n return ray_origins, ray_dirs\n" }, { "path": "mmagic/models/editors/eg3d/renderer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"The renderer is a module that takes in rays, decides where to sample along\neach ray, and computes pixel colors using the volume rendering equation.\"\"\"\n\nfrom typing import Any, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom ..stylegan3.stylegan3_modules import FullyConnectedLayer\nfrom .eg3d_utils import (get_ray_limits_box, inverse_transform_sampling,\n linspace_batch)\n\n\nclass EG3DRenderer(BaseModule):\n \"\"\"Renderer for EG3D. This class samples render points on each input ray\n and interpolate the triplane feature corresponding to the points'\n coordinates. Then, predict each point's RGB feature and density (sigma) by\n a neural network and calculate the RGB feature of each ray by integration.\n Different from typical NeRF models, the decoder of EG3DRenderer takes\n triplane feature of each points as input instead of positional encoding of\n the coordinates.\n\n Args:\n decoder_cfg (dict): The config to build neural renderer.\n ray_start (float): The start position of all rays.\n ray_end (float): The end position of all rays.\n box_warp (float): The side length of the cube spanned by the triplanes.\n The box is axis-aligned, centered at the origin. The range of each\n axis is `[-box_warp/2, box_warp/2]`. If `box_warp=1.8`, it has\n vertices at the range of axis is `[-0.9, 0.9]`. Defaults to 1.\n depth_resolution (int): Resolution of depth, as well as the number of\n points per ray. Defaults to 64.\n depth_resolution_importance (int): Resolution of depth in hierarchical\n sampling. Defaults to 64.\n clamp_mode (str): The clamp mode for density predicted by neural\n renderer. Defaults to 'softplus'.\n white_back (bool): Whether render a white background. Defaults to True.\n projection_mode (str): The projection method to mapping coordinates of\n render points to plane feature. The usage of this argument please\n refer to :meth:`self.project_onto_planes` and\n https://github.com/NVlabs/eg3d/issues/67. Defaults to 'Official'.\n \"\"\"\n\n def __init__(self,\n decoder_cfg: dict,\n ray_start: float,\n ray_end: float,\n box_warp: float = 1,\n depth_resolution: int = 64,\n depth_resolution_importance: int = 64,\n density_noise: float = 0,\n clamp_mode: str = 'softplus',\n white_back: bool = True,\n projection_mode: str = 'Official'):\n super().__init__()\n self.decoder = EG3DDecoder(**decoder_cfg)\n\n self.ray_start = ray_start\n self.ray_end = ray_end\n self.box_warp = box_warp\n self.depth_resolution = depth_resolution\n self.depth_resolution_importance = depth_resolution_importance\n self.density_noise = density_noise\n\n self.clamp_mode = clamp_mode\n self.white_back = white_back\n self.projection_mode = projection_mode\n\n def get_value(self,\n target: str,\n render_kwargs: Optional[dict] = None) -> Any:\n \"\"\"Get value of target field.\n\n Args:\n target (str): The key of the target field.\n render_kwargs (Optional[dict], optional): The input key word\n arguments dict. Defaults to None.\n\n Returns:\n Any: The default value of target field.\n \"\"\"\n if render_kwargs is None:\n return getattr(self, target)\n return render_kwargs.get(target, getattr(self, target))\n\n def forward(self,\n planes: torch.Tensor,\n ray_origins: torch.Tensor,\n ray_directions: torch.Tensor,\n render_kwargs: Optional[dict] = None) -> Tuple[torch.Tensor]:\n \"\"\"Render 2D RGB feature, weighed depth and weights with the passed\n triplane features and rays. 'weights' denotes `w` in Equation 5 of the\n NeRF's paper.\n\n Args:\n planes (torch.Tensor): The triplane features shape like\n (bz, 3, TriPlane_feat, TriPlane_res, TriPlane_res).\n ray_origins (torch.Tensor): The original of each ray to render,\n shape like (bz, NeRF_res * NeRF_res, 3).\n ray_directions (torch.Tensor): The direction vector of each ray to\n render, shape like (bz, NeRF_res * NeRF_res, 3).\n render_kwargs (Optional[dict], optional): The specific kwargs for\n rendering. Defaults to None.\n\n Returns:\n Tuple[torch.Tensor]: Renderer RGB feature, weighted depths and\n weights.\n \"\"\"\n ray_start = self.get_value('ray_start', render_kwargs)\n ray_end = self.get_value('ray_end', render_kwargs)\n box_warp = self.get_value('box_warp', render_kwargs)\n depth_resolution = self.get_value('depth_resolution', render_kwargs)\n depth_resolution_importance = self.get_value(\n 'depth_resolution_importance', render_kwargs)\n density_noise = self.get_value('density_noise', render_kwargs)\n\n if ray_start == ray_end == 'auto':\n ray_start, ray_end = get_ray_limits_box(\n ray_origins, ray_directions, box_side_length=box_warp)\n is_ray_valid = ray_end > ray_start\n if torch.any(is_ray_valid).item():\n ray_start[~is_ray_valid] = ray_start[is_ray_valid].min()\n ray_end[~is_ray_valid] = ray_start[is_ray_valid].max()\n depths_coarse = self.sample_stratified(ray_origins, ray_start,\n ray_end, depth_resolution)\n else:\n assert (isinstance(ray_start, float) and isinstance(\n ray_end, float)), (\n '\\'ray_start\\' and \\'ray_end\\' must be both float type or '\n f'both \\'auto\\'. But receive {ray_start} and {ray_end}.')\n assert ray_start < ray_end, (\n '\\'ray_start\\' must less than \\'ray_end\\'.')\n # Create stratified depth samples\n depths_coarse = self.sample_stratified(ray_origins, ray_start,\n ray_end, depth_resolution)\n\n batch_size, num_rays, samples_per_ray, _ = depths_coarse.shape\n\n # Coarse Pass\n sample_coordinates = (\n ray_origins.unsqueeze(-2) +\n depths_coarse * ray_directions.unsqueeze(-2)).reshape(\n batch_size, -1, 3)\n\n out = self.neural_rendering(planes, sample_coordinates, density_noise,\n box_warp)\n colors_coarse = out['rgb']\n densities_coarse = out['sigma']\n colors_coarse = colors_coarse.reshape(batch_size, num_rays,\n samples_per_ray,\n colors_coarse.shape[-1])\n densities_coarse = densities_coarse.reshape(batch_size, num_rays,\n samples_per_ray, 1)\n\n # Fine Pass\n N_importance = depth_resolution_importance\n if N_importance is not None and N_importance > 0:\n _, _, weights = self.volume_rendering(colors_coarse,\n densities_coarse,\n depths_coarse)\n\n depths_fine = self.sample_importance(depths_coarse, weights,\n N_importance)\n sample_coordinates = (\n ray_origins.unsqueeze(-2) +\n depths_fine * ray_directions.unsqueeze(-2)).reshape(\n batch_size, -1, 3)\n\n out = self.neural_rendering(planes, sample_coordinates,\n density_noise, box_warp)\n colors_fine = out['rgb']\n densities_fine = out['sigma']\n colors_fine = colors_fine.reshape(batch_size, num_rays,\n N_importance,\n colors_fine.shape[-1])\n densities_fine = densities_fine.reshape(batch_size, num_rays,\n N_importance, 1)\n\n all_depths, all_colors, all_densities = self.unify_samples(\n depths_coarse, colors_coarse, densities_coarse, depths_fine,\n colors_fine, densities_fine)\n\n # Aggregate\n rgb_final, depth_final, weights = self.volume_rendering(\n all_colors, all_densities, all_depths)\n else:\n rgb_final, depth_final, weights = self.volume_rendering(\n colors_coarse, densities_coarse, depths_coarse)\n\n return rgb_final, depth_final, weights.sum(2)\n\n def sample_stratified(self, ray_origins: torch.Tensor,\n ray_start: Union[float, torch.Tensor],\n ray_end: Union[float, torch.Tensor],\n depth_resolution: int) -> torch.Tensor:\n \"\"\"Return depths of approximately uniformly spaced samples along rays.\n\n Args:\n ray_origins (torch.Tensor): The original of each ray, shape like\n (bz, NeRF_res * NeRF_res, 3). Only used to provide\n device and shape info.\n ray_start (Union[float, torch.Tensor]): The start position of rays.\n If a float is passed, all rays will have the same start\n distance.\n ray_end (Union[float, torch.Tensor]): The end position of rays. If\n a float is passed, all rays will have the same end distance.\n depth_resolution (int): Resolution of depth, as well as the number\n of points per ray.\n\n Returns:\n torch.Tensor: The sampled coarse depth shape like\n (bz, NeRF_res * NeRF_res, 1).\n \"\"\"\n N, M, _ = ray_origins.shape\n if isinstance(ray_start, torch.Tensor):\n # perform linspace for batch of tensor\n depths_coarse = linspace_batch(ray_start, ray_end,\n depth_resolution)\n depths_coarse = depths_coarse.permute(1, 2, 0, 3)\n depth_delta = (ray_end - ray_start) / (depth_resolution - 1)\n depths_coarse += torch.rand_like(depths_coarse) * depth_delta[...,\n None]\n else:\n depths_coarse = torch.linspace(\n ray_start,\n ray_end,\n depth_resolution,\n device=ray_origins.device)\n depths_coarse = depths_coarse.reshape(1, 1, depth_resolution, 1)\n depths_coarse = depths_coarse.repeat(N, M, 1, 1)\n\n depth_delta = (ray_end - ray_start) / (depth_resolution - 1)\n depths_coarse += torch.rand_like(depths_coarse) * depth_delta\n\n return depths_coarse\n\n def neural_rendering(self, planes: torch.Tensor, sample_coordinates: float,\n density_noise: float, box_warp: float) -> dict:\n \"\"\"Predict RGB features and densities of the coordinates by neural\n renderer model and the triplane input.\n\n Args:\n planes (torch.Tensor): Triplane feature shape like\n (bz, 3, TriPlane_feat, TriPlane_res, TriPlane_res).\n sample_coordinates (torch.Tensor): Coordinates of the sampling\n points, shape like (bz, N_depth * NeRF_res * NeRF_res, 1).\n density_noise (float): Strength of noise add to the predicted\n density.\n box_warp (float): The side length of the cube spanned by the\n triplanes.\n\n Returns:\n dict: A dict contains RGB features ('rgb') and densities ('sigma').\n \"\"\"\n sampled_features = self.sample_from_planes(\n planes, sample_coordinates, box_warp=box_warp)\n\n out = self.decoder(sampled_features)\n if density_noise > 0:\n out['sigma'] += torch.randn_like(out['sigma']) * density_noise\n return out\n\n def sample_from_planes(self,\n plane_features: torch.Tensor,\n coordinates: torch.Tensor,\n interp_mode: str = 'bilinear',\n box_warp: float = None) -> torch.Tensor:\n \"\"\"Sample from feature from triplane feature with the passed\n coordinates of render points.\n\n Args:\n plane_features (torch.Tensor): The triplane feature.\n coordinates (torch.Tensor): The coordinates of points to render.\n interp_mode (str): The interpolation mode to sample feature from\n triplane.\n box_warp (float): The side length of the cube spanned by the\n triplanes.\n\n Returns:\n torch.Tensor: The sampled triplane feature of the render points.\n \"\"\"\n N, n_planes, C, H, W = plane_features.shape\n _, M, _ = coordinates.shape\n plane_features = plane_features.view(N * n_planes, C, H, W)\n\n coordinates = (2 / box_warp) * coordinates\n # NOTE: do not support change projection_mode for specific renderer,\n # use self.projection_mode\n projected_coordinates = self.project_onto_planes(coordinates)\n projected_coordinates = projected_coordinates[:, None, ...]\n\n output_features = torch.nn.functional.grid_sample(\n plane_features,\n projected_coordinates.float(),\n mode=interp_mode,\n padding_mode='zeros',\n align_corners=False)\n output_features = output_features.permute(0, 3, 2, 1).reshape(\n N, n_planes, M, C)\n return output_features\n\n def project_onto_planes(self, coordinates: torch.Tensor) -> torch.Tensor:\n \"\"\"Project 3D points to plane formed by coordinate axes. In this\n function, we use indexing operation to replace matrix multiplication to\n achieve higher calculation performance.\n\n In the original implementation, the mapping matrix is incorrect.\n Therefore we support users to define `projection_mode` to control\n projection behavior in the initialization function of\n :class:`~EG3DRenderer`. If you want to run inference with the official\n pretrained model, please remember to set\n `projection_mode = 'official'`. More information please refer to\n https://github.com/NVlabs/eg3d/issues/67.\n\n If the project mode `official`, the equivalent projection matrix is\n inverse matrix of:\n\n [[[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n [[1, 0, 0], [0, 0, 1], [0, 1, 0]],\n [[0, 0, 1], [1, 0, 0], [0, 1, 0]]]\n\n Otherwise, the equivalent projection matrix is inverse matrix of:\n\n [[[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n [[1, 0, 0], [0, 0, 1], [0, 1, 0]],\n [[0, 0, 1], [0, 1, 0], [1, 0, 0]]]\n\n Args:\n coordinates (torch.Tensor): The coordinates of the render points.\n shape like (bz, NeRF_res * NeRF_res * N_depth, 3).\n\n Returns:\n torch.Tensor: The projected coordinates.\n \"\"\"\n N, _, _ = coordinates.shape\n xy_coord = coordinates[:, :, (0, 1)] # (bz, N_points, 3)\n xz_coord = coordinates[:, :, (0, 2)] # (bz, N_points, 3)\n if self.projection_mode.upper() == 'OFFICIAL':\n yz_coord = coordinates[:, :, (2, 0)] # actually zx_coord\n else:\n yz_coord = coordinates[:, :, (2, 1)]\n coord_proejcted = torch.cat([xy_coord, xz_coord, yz_coord], dim=0)\n # create a index list to release the following remapping:\n # [xy, xy, ..., xz, xz, ..., yz, yz, ...] -> [xy, xz, yz, ...]\n index = []\n for n in range(N):\n index += [n, N + n, N * 2 + n]\n return coord_proejcted[index, ...]\n\n def unify_samples(self, depths_c: torch.Tensor, colors_c: torch.Tensor,\n densities_c: torch.Tensor, depths_f: torch.Tensor,\n colors_f: torch.Tensor,\n densities_f: torch.Tensor) -> Tuple[torch.Tensor]:\n \"\"\"Sort and merge coarse samples and fine samples.\n\n Args:\n depths_c (torch.Tensor): Coarse depths shape like\n (bz, NeRF_res * NeRF_res, N_depth, 1).\n colors_c (torch.Tensor): Coarse color features shape like\n (bz, NeRF_res * NeRF_res, N_depth, N_feat).\n densities_c (torch.Tensor): Coarse densities shape like\n (bz, NeRF_res * NeRF_res, N_depth, 1).\n depths_f (torch.Tensor): Fine depths shape like\n (bz, NeRF_res * NeRF_res, N_depth_fine, 1).\n colors_f (torch.Tensor): Fine colors features shape like\n (bz, NeRF_res * NeRF_res, N_depth_fine, N_feat).\n densities_f (torch.Tensor): Fine densities shape like\n (bz, NeRF_res * NeRF_res, N_depth_fine, 1).\n\n Returns:\n Tuple[torch.Tensor]: Unified depths, color features and densities.\n The third dimension of returns are `N_depth + N_depth_fine`.\n \"\"\"\n all_depths = torch.cat([depths_c, depths_f], dim=-2)\n all_colors = torch.cat([colors_c, colors_f], dim=-2)\n all_densities = torch.cat([densities_c, densities_f], dim=-2)\n\n _, indices = torch.sort(all_depths, dim=-2)\n all_depths = torch.gather(all_depths, -2, indices)\n all_colors = torch.gather(\n all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))\n all_densities = torch.gather(all_densities, -2,\n indices.expand(-1, -1, -1, 1))\n\n return all_depths, all_colors, all_densities\n\n def volume_rendering(self, colors: torch.Tensor, densities: torch.Tensor,\n depths: torch.Tensor) -> Tuple[torch.Tensor]:\n \"\"\"Volume rendering.\n\n Args:\n colors (torch.Tensor): Color feature for each points. Shape like\n (bz, N_points, N_depth, N_feature).\n densities (torch.Tensor): Density for each points. Shape like\n (bz, N_points, N_depth, 1).\n depths (torch.Tensor): Depths for each points. Shape like\n (bz, N_points, N_depth, 1).\n\n Returns:\n Tuple[torch.Tensor]: A tuple of color feature\n `(bz, N_points, N_feature)`, weighted depth\n `(bz, N_points, 1)` and weight\n `(bz, N_points, N_depth-1, 1)`.\n \"\"\"\n deltas = depths[:, :, 1:] - depths[:, :, :-1]\n colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2\n densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2\n depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2\n\n if self.clamp_mode == 'softplus':\n # activation bias of -1 makes things initialize better\n densities_mid = F.softplus(densities_mid - 1)\n else:\n assert False, (\n 'EG3DRenderer only supports \\'softplus\\' for \\'clamp_mode\\', '\n f'but receive \\'{self.clamp_mode}\\'.')\n\n density_delta = densities_mid * deltas\n\n alpha = 1 - torch.exp(-density_delta)\n\n alpha_shifted = torch.cat(\n [torch.ones_like(alpha[:, :, :1]), 1 - alpha + 1e-10], -2)\n weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]\n\n composite_rgb = torch.sum(weights * colors_mid, -2)\n weight_total = weights.sum(2)\n composite_depth = torch.sum(weights * depths_mid, -2) / weight_total\n\n # clip the composite to min/max range of depths\n if digit_version(TORCH_VERSION) < digit_version('1.8.0'):\n composite_depth[torch.isnan(composite_depth)] = float('inf')\n else:\n composite_depth = torch.nan_to_num(composite_depth, float('inf'))\n composite_depth = torch.clamp(composite_depth, torch.min(depths),\n torch.max(depths))\n\n if self.white_back:\n composite_rgb = composite_rgb + 1 - weight_total\n\n composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1)\n\n return composite_rgb, composite_depth, weights\n\n @torch.no_grad()\n def sample_importance(self, z_vals: torch.Tensor, weights: torch.Tensor,\n N_importance: int) -> torch.Tensor:\n \"\"\"Return depths of importance sampled points along rays.\n\n Args:\n z_vals (torch.Tensor): Coarse Z value (depth). Shape like\n (bz, N_points, N_depth, N_feature).\n weights (torch.Tensor): Weights of the coarse samples. Shape like\n (bz, N_points, N_depths-1, 1).\n N_importance (int): Number of samples to resample.\n \"\"\"\n batch_size, num_rays, samples_per_ray, _ = z_vals.shape\n z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray)\n # -1 to account for loss of 1 sample in MipRayMarcher\n weights = weights.reshape(batch_size * num_rays, -1)\n\n # smooth weights as MipNeRF\n # max(weights[:-1], weights[1:])\n weights = F.max_pool1d(weights.unsqueeze(1).float(), 2, 1, padding=1)\n # 0.5 * (weights[:-1] + weights[1:])\n weights = F.avg_pool1d(weights, 2, 1).squeeze()\n weights = weights + 0.01 # add resampling padding\n\n z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])\n importance_z_vals = inverse_transform_sampling(z_vals_mid,\n weights[:, 1:-1],\n N_importance).detach()\n importance_z_vals = importance_z_vals.reshape(batch_size, num_rays,\n N_importance, 1)\n return importance_z_vals\n\n\nclass EG3DDecoder(BaseModule):\n \"\"\"Decoder for EG3D model.\n\n Args:\n in_channels (int): The number of input channels.\n out_channels (int): The number of output channels. Defaults to 32.\n hidden_channels (int): The number of channels of hidden layer.\n Defaults to 64.\n lr_multiplier (float, optional): Equalized learning rate multiplier.\n Defaults to 1.\n rgb_padding (float): Padding for RGB output. Defaults to 0.001.\n \"\"\"\n\n def __init__(self,\n in_channels: int,\n out_channels: int = 32,\n hidden_channels: int = 64,\n lr_multiplier: float = 1,\n rgb_padding: float = 0.001):\n super().__init__()\n self.net = nn.Sequential(\n FullyConnectedLayer(\n in_channels, hidden_channels, lr_multiplier=lr_multiplier),\n nn.Softplus(),\n FullyConnectedLayer(\n hidden_channels, 1 + out_channels,\n lr_multiplier=lr_multiplier))\n self.rgb_padding = rgb_padding\n\n def forward(self, sampled_features: torch.Tensor) -> dict:\n \"\"\"Forward function.\n\n Args:\n sampled_features (torch.Tensor): The sampled triplane feature for\n each points. Shape like (batch_size, xxx, xxx, n_ch).\n\n Returns:\n dict: A dict contains rgb feature and sigma value for each point.\n \"\"\"\n sampled_features = sampled_features.mean(1)\n N, M, C = sampled_features.shape\n\n feat = sampled_features.view(-1, C)\n feat = self.net(feat)\n feat = feat.view(N, M, -1)\n\n rgb = torch.sigmoid(\n feat[..., 1:]) * (1 + 2 * self.rgb_padding) - self.rgb_padding\n sigma = feat[..., 0:1]\n\n return {'rgb': rgb, 'sigma': sigma}\n" }, { "path": "mmagic/models/editors/esrgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .esrgan import ESRGAN\nfrom .rrdb_net import RRDBNet\n\n__all__ = [\n 'ESRGAN',\n 'RRDBNet',\n]\n" }, { "path": "mmagic/models/editors/esrgan/esrgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom ..srgan import SRGAN\n\n\n@MODELS.register_module()\nclass ESRGAN(SRGAN):\n \"\"\"Enhanced SRGAN model for single image super-resolution.\n\n Ref:\n ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.\n It uses RaGAN for GAN updates:\n The relativistic discriminator: a key element missing from standard GAN.\n\n Args:\n generator (dict): Config for the generator.\n discriminator (dict): Config for the discriminator. Default: None.\n gan_loss (dict): Config for the gan loss.\n Note that the loss weight in gan loss is only for the generator.\n pixel_loss (dict): Config for the pixel loss. Default: None.\n perceptual_loss (dict): Config for the perceptual loss. Default: None.\n train_cfg (dict): Config for training. Default: None.\n You may change the training of gan by setting:\n `disc_steps`: how many discriminator updates after one generate\n update;\n `disc_init_steps`: how many discriminator updates at the start of\n the training.\n These two keys are useful when training with WGAN.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n \"\"\"\n\n def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Tensor):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n losses = dict()\n\n # pix loss\n if self.pixel_loss:\n losses['loss_pix'] = self.pixel_loss(batch_outputs, batch_gt_data)\n\n # perceptual loss\n if self.perceptual_loss:\n loss_percep, loss_style = self.perceptual_loss(\n batch_outputs, batch_gt_data)\n if loss_percep is not None:\n losses['loss_perceptual'] = loss_percep\n if loss_style is not None:\n losses['loss_style'] = loss_style\n\n # gan loss for generator\n if self.gan_loss and self.discriminator:\n real_d_pred = self.discriminator(batch_gt_data).detach()\n fake_g_pred = self.discriminator(batch_outputs)\n loss_gan_fake = self.gan_loss(\n fake_g_pred - torch.mean(real_d_pred),\n target_is_real=True,\n is_disc=False)\n loss_gan_real = self.gan_loss(\n real_d_pred - torch.mean(fake_g_pred),\n target_is_real=False,\n is_disc=False)\n losses['loss_gan'] = (loss_gan_fake + loss_gan_real) / 2\n\n return losses\n\n def d_step_real(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor):\n \"\"\"D step of real data.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n # real\n fake_d_pred = self.discriminator(batch_outputs)\n real_d_pred = self.discriminator(batch_gt_data)\n loss_d_real = self.gan_loss(\n real_d_pred - torch.mean(fake_d_pred.detach()),\n target_is_real=True,\n is_disc=True\n ) * 0.5 # 0.5 for averaging loss_d_real and loss_d_fake\n\n self.real_d_pred = torch.mean(real_d_pred.detach())\n # for d_step_fake\n\n return loss_d_real\n\n def d_step_fake(self, batch_outputs: torch.Tensor, batch_gt_data):\n \"\"\"D step of fake data.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n # fake\n fake_d_pred = self.discriminator(batch_outputs.detach())\n loss_d_fake = self.gan_loss(\n fake_d_pred - self.real_d_pred, target_is_real=False, is_disc=True\n ) * 0.5 # 0.5 for averaging loss_d_real and loss_d_fake\n\n return loss_d_fake\n" }, { "path": "mmagic/models/editors/esrgan/rrdb_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import pixel_unshuffle\nfrom mmagic.models.utils import default_init_weights, make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass RRDBNet(BaseModule):\n \"\"\"Networks consisting of Residual in Residual Dense Block, which is used\n in ESRGAN and Real-ESRGAN.\n\n ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.\n Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. # noqa: E501\n Currently, it supports [x1/x2/x4] upsampling scale factor.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64\n num_blocks (int): Block number in the trunk network. Defaults: 23\n growth_channels (int): Channels for each growth. Default: 32.\n upscale_factor (int): Upsampling factor. Support x1, x2 and x4.\n Default: 4.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n _supported_upscale_factors = [1, 2, 4]\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32,\n upscale_factor=4,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n if upscale_factor in self._supported_upscale_factors:\n in_channels = in_channels * ((4 // upscale_factor)**2)\n else:\n raise ValueError(f'Unsupported scale factor {upscale_factor}. '\n f'Currently supported ones are '\n f'{self._supported_upscale_factors}.')\n\n self.upscale_factor = upscale_factor\n self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)\n self.body = make_layer(\n RRDB,\n num_blocks,\n mid_channels=mid_channels,\n growth_channels=growth_channels)\n self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n # upsample\n self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)\n\n self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n if self.upscale_factor in [1, 2]:\n feat = pixel_unshuffle(x, scale=4 // self.upscale_factor)\n else:\n feat = x\n\n feat = self.conv_first(feat)\n body_feat = self.conv_body(self.body(feat))\n feat = feat + body_feat\n\n # upsample\n feat = self.lrelu(\n self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))\n feat = self.lrelu(\n self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))\n\n out = self.conv_last(self.lrelu(self.conv_hr(feat)))\n return out\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n if self.init_cfg:\n super().init_weights()\n else:\n # Use smaller std for better stability and performance. We\n # use 0.1. See more details in \"ESRGAN: Enhanced Super-Resolution\n # Generative Adversarial Networks\"\n for m in [\n self.conv_first, self.conv_body, self.conv_up1,\n self.conv_up2, self.conv_hr, self.conv_last\n ]:\n default_init_weights(m, 0.1)\n\n\nclass ResidualDenseBlock(nn.Module):\n \"\"\"Residual Dense Block.\n\n Used in RRDB block in ESRGAN.\n\n Args:\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n growth_channels (int): Channels for each growth. Default: 32.\n \"\"\"\n\n def __init__(self, mid_channels=64, growth_channels=32):\n super().__init__()\n for i in range(5):\n out_channels = mid_channels if i == 4 else growth_channels\n self.add_module(\n f'conv{i+1}',\n nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,\n 1, 1))\n self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n\n self.init_weights()\n\n def init_weights(self):\n \"\"\"Init weights for ResidualDenseBlock.\n\n Use smaller std for better stability and performance. We empirically\n use 0.1. See more details in \"ESRGAN: Enhanced Super-Resolution\n Generative Adversarial Networks\"\n \"\"\"\n for i in range(5):\n default_init_weights(getattr(self, f'conv{i+1}'), 0.1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x1 = self.lrelu(self.conv1(x))\n x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))\n x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))\n x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))\n x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))\n # Empirically, we use 0.2 to scale the residual for better performance\n return x5 * 0.2 + x\n\n\nclass RRDB(nn.Module):\n \"\"\"Residual in Residual Dense Block.\n\n Used in RRDB-Net in ESRGAN.\n\n Args:\n mid_channels (int): Channel number of intermediate features.\n growth_channels (int): Channels for each growth. Default: 32.\n \"\"\"\n\n def __init__(self, mid_channels, growth_channels=32):\n super().__init__()\n self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)\n self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)\n self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = self.rdb1(x)\n out = self.rdb2(out)\n out = self.rdb3(out)\n # Empirically, we use 0.2 to scale the residual for better performance\n return out * 0.2 + x\n" }, { "path": "mmagic/models/editors/fastcomposer/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .fastcomposer import FastComposer\n\n__all__ = ['FastComposer']\n" }, { "path": "mmagic/models/editors/fastcomposer/fastcomposer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom collections import OrderedDict\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom torch import nn\nfrom torch.hub import load_state_dict_from_url\nfrom tqdm import tqdm\nfrom transformers import CLIPTokenizer\n\nfrom mmagic.registry import MODELS\nfrom ..stable_diffusion import StableDiffusion\nfrom .fastcomposer_util import FastComposerModel, get_object_transforms\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass FastComposer(StableDiffusion):\n\n def __init__(self,\n pretrained_cfg: dict,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: str = 'fp32',\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor=dict(type='DataPreprocessor'),\n init_cfg: Optional[dict] = None):\n super().__init__(vae, text_encoder, tokenizer, unet, scheduler,\n test_scheduler, dtype, enable_xformers,\n noise_offset_weight, tomesd_cfg, data_preprocessor,\n init_cfg)\n self.vae = self.vae.model\n self.unet = self.unet.model\n model = FastComposerModel.from_pretrained(pretrained_cfg, self.vae,\n self.unet)\n if pretrained_cfg['finetuned_model_path'][:7] == 'http://' or \\\n pretrained_cfg['finetuned_model_path'][:8] == 'https://':\n model.load_state_dict(\n load_state_dict_from_url(\n pretrained_cfg['finetuned_model_path'],\n map_location='cpu'),\n strict=False)\n elif pretrained_cfg['finetuned_model_path']:\n model.load_state_dict(\n torch.load(\n pretrained_cfg['finetuned_model_path'],\n map_location='cpu'),\n strict=False)\n weight_dtype = torch.float32\n if dtype == 'fp16':\n weight_dtype = torch.float16\n elif dtype == 'bf16':\n weight_dtype = torch.bfloat16\n model = model.to(weight_dtype)\n\n self.unet = model.unet\n\n if pretrained_cfg['enable_xformers_memory_efficient_attention']:\n self.unet.enable_xformers_memory_efficient_attention()\n\n self.text_encoder = model.text_encoder\n self.image_encoder = model.image_encoder\n\n self.postfuse_module = model.postfuse_module\n\n del model\n\n self.special_tokenizer = CLIPTokenizer.from_pretrained(\n pretrained_cfg['pretrained_model_name_or_path'],\n subfolder='tokenizer',\n revision=pretrained_cfg['revision'],\n )\n self.special_tokenizer.add_tokens(['img'], special_tokens=True)\n self.image_token_id = self.special_tokenizer.convert_tokens_to_ids(\n 'img')\n\n self.object_transforms = get_object_transforms(pretrained_cfg)\n\n @torch.no_grad()\n def _tokenize_and_mask_noun_phrases_ends(self, caption):\n \"\"\"Augment the text embedding.\"\"\"\n\n input_ids = self.special_tokenizer.encode(caption)\n noun_phrase_end_mask = [False for _ in input_ids]\n clean_input_ids = []\n clean_index = 0\n\n for i, id in enumerate(input_ids):\n if id == self.image_token_id:\n noun_phrase_end_mask[clean_index - 1] = True\n else:\n clean_input_ids.append(id)\n clean_index += 1\n\n max_len = self.special_tokenizer.model_max_length\n\n if len(clean_input_ids) > max_len:\n clean_input_ids = clean_input_ids[:max_len]\n else:\n clean_input_ids = clean_input_ids + [\n self.tokenizer.pad_token_id\n ] * (\n max_len - len(clean_input_ids))\n\n if len(noun_phrase_end_mask) > max_len:\n noun_phrase_end_mask = noun_phrase_end_mask[:max_len]\n else:\n noun_phrase_end_mask = noun_phrase_end_mask + [False] * (\n max_len - len(noun_phrase_end_mask))\n\n clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long)\n noun_phrase_end_mask = torch.tensor(\n noun_phrase_end_mask, dtype=torch.bool)\n return clean_input_ids.unsqueeze(0), noun_phrase_end_mask.unsqueeze(0)\n\n @torch.no_grad()\n def _encode_augmented_prompt(self, prompt: str,\n reference_images: List[Image.Image],\n device: torch.device,\n weight_dtype: torch.dtype):\n \"\"\"Encode reference images.\n\n Args:\n prompt (str or list(int)): prompt to be encoded.\n reference_images: (List[Image.Image]): List of reference images.\n device (torch.device):torch device.\n weight_dtype (torch.dtype): torch.dtype.\n\n Returns:\n text_embeddings (torch.Tensor): text embeddings generated by\n clip text encoder.\n \"\"\"\n # TODO: check this\n object_pixel_values = []\n for image in reference_images:\n image_tensor = torch.from_numpy(np.array(\n image.convert('RGB'))).permute(2, 0, 1)\n image = self.object_transforms(image_tensor)\n object_pixel_values.append(image)\n\n object_pixel_values = torch.stack(\n object_pixel_values,\n dim=0).to(memory_format=torch.contiguous_format).float()\n object_pixel_values = object_pixel_values.unsqueeze(0).to(\n dtype=weight_dtype, device=device)\n object_embeds = self.image_encoder(object_pixel_values)\n\n id_and_mask = self._tokenize_and_mask_noun_phrases_ends(prompt)\n input_ids, image_token_mask = id_and_mask\n input_ids, image_token_mask = input_ids.to(\n device), image_token_mask.to(device)\n\n num_objects = image_token_mask.sum(dim=1)\n\n augmented_prompt_embeds = self.postfuse_module(\n self.text_encoder(input_ids)[0], object_embeds, image_token_mask,\n num_objects)\n return augmented_prompt_embeds\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator,\n List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = 'pil',\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor],\n None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n alpha_: float = 0.7,\n reference_subject_images: List[Image.Image] = None,\n augmented_prompt_embeds: Optional[torch.FloatTensor] = None,\n show_progress: bool = True):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation.\n If not defined, one has to pass `prompt_embeds`.\n instead.\n height (`int`, *optional*):\n defaults to\n self.unet.config.sample_size * self.vae_scale_factor\n The height in pixels of the generated image.\n width (`int`, *optional*):\n defaults to\n self.unet.config.sample_size * self.vae_scale_factor\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps.\n More denoising steps usually lead to a higher quality image\n at the expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in\n [Classifier-Free Diffusion Guidance]\n (https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation\n 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).\n Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images\n that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n If not defined, one has to pass\n `negative_prompt_embeds` instead.\n Ignored when not using guidance\n (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`,\n *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/\n docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents,\n sampled from a Gaussian distribution,\n to be used as inputs for image generation.\n Can be used to tweak the same generation with\n different prompts. If not provided, a latents tensor\n will ge generated by sampling using\n the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings.\n Can be used to easily tweak text inputs,\n *e.g.* prompt weighting. If not provided,\n text embeddings will be generated from `prompt`\n input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings.\n Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be\n generated from `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/):\n `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`]\n instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps`\n steps during inference. The function will be\n called with the following arguments: `callback(step: int,\n timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called.\n If not specified, the callback will be called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the\n `AttentionProcessor` as defined under `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/\n diffusers/blob/main/src/diffusers/models/cross_attention.py).\n alpha_ (`float`, defaults to 0.7):\n The ratio of subject conditioning. If `alpha_` is 0.7,\n the beginning 30% of denoising steps use text prompts,\n while the last 70% utilize image-augmented prompts.\n Increase alpha for identity preservation,\n decrease it for prompt consistency.\n reference_subject_images (`List[PIL.Image.Image]`):\n a list of PIL images that are used as reference subjects.\n The number of images should be equal to the\n number of augmented tokens in the prompts.\n augmented_prompt_embeds: (`torch.FloatTensor`, *optional*):\n Pre-generated image augmented text embeddings.\n If not provided, embeddings will be generated from `prompt`\n and `reference_subject_images`.\n show_progress: ('bool'):\n show progress or not.\n Examples:\n\n Returns:\n `OrderedDict` or `tuple`:\n `OrderedDict` if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with\n the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding\n generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n )\n\n assert_text = 'Prompt and reference subject images or prompt_embeds ' \\\n 'and augmented_prompt_embeds must be provided.'\n\n assert (prompt and reference_subject_images) or \\\n (prompt_embeds and augmented_prompt_embeds), assert_text\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self.device\n # here `guidance_scale` is defined analog to\n # the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n assert do_classifier_free_guidance\n\n # 3. Encode input prompt\n prompt_text_only = prompt.replace('img', '')\n\n prompt_embeds = self._encode_prompt(prompt_text_only, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n\n if augmented_prompt_embeds is None:\n augmented_prompt_embeds = self._encode_augmented_prompt(\n prompt, reference_subject_images, device, prompt_embeds.dtype)\n augmented_prompt_embeds = augmented_prompt_embeds.repeat(\n num_images_per_prompt, 1, 1)\n\n prompt_embeds = torch.cat([prompt_embeds, augmented_prompt_embeds],\n dim=0)\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n start_subject_conditioning_step = (1 - alpha_) * num_inference_steps\n\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n (null_prompt_embeds, text_prompt_embeds,\n augmented_prompt_embeds) = prompt_embeds.chunk(3)\n\n # 7. Denoising loop\n num_warmup_steps = len(\n timesteps) - num_inference_steps * self.scheduler.order\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n latent_model_input = (\n torch.cat([latents] *\n 2) if do_classifier_free_guidance else latents)\n latent_model_input = self.scheduler.scale_model_input(\n latent_model_input, t)\n\n if i <= start_subject_conditioning_step:\n current_prompt_embeds = torch.cat(\n [null_prompt_embeds, text_prompt_embeds], dim=0)\n else:\n current_prompt_embeds = torch.cat(\n [null_prompt_embeds, augmented_prompt_embeds], dim=0)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=current_prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n else:\n assert 0, 'Not Implemented'\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents,\n **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (i + 1 > num_warmup_steps and\n (i + 1) % self.scheduler.order\n == 0):\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n has_nsfw_concept = None\n if output_type == 'latent':\n image = latents\n has_nsfw_concept = None\n elif output_type == 'pil':\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 10. Convert to PIL\n image = self.output_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # Offload last model to CPU\n if hasattr(\n self,\n 'final_offload_hook') and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return OrderedDict(\n samples=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "mmagic/models/editors/fastcomposer/fastcomposer_util.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport gc\nimport types\nfrom collections import OrderedDict\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nimport torchvision.transforms as T\nfrom torch import nn\nfrom torch.nn import Linear\nfrom transformers import (CLIPModel, CLIPPreTrainedModel, CLIPTextModel,\n CLIPVisionConfig, CLIPVisionModel)\nfrom transformers.modeling_outputs import BaseModelOutputWithPooling\n\nfrom mmagic.utils import try_import\n\n_expand_mask = try_import('transformers.models.clip.modeling_clip')\nif _expand_mask is None:\n _expand_mask = try_import(\n 'ransformers.models.clip.modeling_clip._prepare_4d_attention_mask')\n\n\nclass FastComposerModel(nn.Module):\n \"\"\"FastComposerModel is based on the StableDiffusion Model and the Clip\n Model.\"\"\"\n\n def __init__(self, text_encoder, image_encoder, vae, unet, cfg):\n super().__init__()\n self.text_encoder = text_encoder\n self.image_encoder = image_encoder\n self.vae = vae\n self.unet = unet\n self.use_ema = False\n self.ema_param = None\n self.pretrained_model_name_or_path = cfg[\n 'pretrained_model_name_or_path']\n self.revision = cfg['revision']\n self.non_ema_revision = cfg['non_ema_revision']\n self.object_localization = cfg['object_localization']\n self.object_localization_weight = cfg['object_localization_weight']\n self.localization_layers = cfg['localization_layers']\n self.mask_loss = cfg['mask_loss']\n self.mask_loss_prob = cfg['mask_loss_prob']\n\n embed_dim = text_encoder.config.hidden_size\n\n self.postfuse_module = FastComposerPostfuseModule(embed_dim)\n\n if self.object_localization:\n self.cross_attention_scores = {}\n self.unet = unet_store_cross_attention_scores(\n self.unet, self.cross_attention_scores,\n self.localization_layers)\n self.object_localization_loss_fn = BalancedL1Loss(\n cfg['object_localization_threshold'],\n cfg['object_localization_normalize'],\n )\n\n def _clear_cross_attention_scores(self):\n \"\"\"Delete cross attention scores.\"\"\"\n if hasattr(self, 'cross_attention_scores'):\n keys = list(self.cross_attention_scores.keys())\n for k in keys:\n del self.cross_attention_scores[k]\n\n gc.collect()\n\n @staticmethod\n def from_pretrained(cfg, vae, unet):\n \"\"\"Init FastComposerTextEncoder and FastComposerCLIPImageEncoder.\"\"\"\n\n text_encoder = FastComposerTextEncoder.from_pretrained(\n cfg['pretrained_model_name_or_path'],\n subfolder='text_encoder',\n revision=cfg['revision'],\n )\n if not isinstance(cfg['image_encoder'], dict):\n image_encoder = FastComposerCLIPImageEncoder.from_pretrained(\n cfg['image_encoder'])\n else:\n vision_model = CLIPVisionModel(\n CLIPVisionConfig.from_dict(cfg['image_encoder']))\n visual_projection = Linear(\n in_features=1024, out_features=768, bias=False)\n vision_processor = T.Normalize(\n (0.48145466, 0.4578275, 0.40821073),\n (0.26862954, 0.26130258, 0.27577711),\n )\n image_encoder = FastComposerCLIPImageEncoder(\n vision_model,\n visual_projection,\n vision_processor,\n )\n\n return FastComposerModel(text_encoder, image_encoder, vae, unet, cfg)\n\n def forward(self, batch, noise_scheduler):\n \"\"\"Forward function.\n\n Args:\n batch (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n noise_scheduler (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n\n Returns:\n Dict\n \"\"\"\n\n pixel_values = batch['pixel_values']\n input_ids = batch['input_ids']\n image_token_mask = batch['image_token_mask']\n object_pixel_values = batch['object_pixel_values']\n num_objects = batch['num_objects']\n\n vae_dtype = self.vae.parameters().__next__().dtype\n vae_input = pixel_values.to(vae_dtype)\n\n latents = self.vae.encode(vae_input).latent_dist.sample()\n latents = latents * self.vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0,\n noise_scheduler.num_train_timesteps, (bsz, ),\n device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to\n # the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # (bsz, max_num_objects, num_image_tokens, dim)\n object_embeds = self.image_encoder(object_pixel_values)\n\n encoder_hidden_states = self.text_encoder(\n input_ids, image_token_mask, object_embeds,\n num_objects)[0] # (bsz, seq_len, dim)\n\n encoder_hidden_states = self.postfuse_module(\n encoder_hidden_states,\n object_embeds,\n image_token_mask,\n num_objects,\n )\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == 'epsilon':\n target = noise\n elif noise_scheduler.config.prediction_type == 'v_prediction':\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{noise_scheduler.config.prediction_type}')\n\n pred = self.unet(noisy_latents, timesteps,\n encoder_hidden_states).sample\n\n if self.mask_loss and torch.rand(1) < self.mask_loss_prob:\n object_segmaps = batch['object_segmaps']\n mask = (object_segmaps.sum(dim=1) > 0).float()\n mask = F.interpolate(\n mask.unsqueeze(1),\n size=(pred.shape[-2], pred.shape[-1]),\n mode='bilinear',\n align_corners=False,\n )\n pred = pred * mask\n target = target * mask\n\n denoise_loss = F.mse_loss(\n pred.float(), target.float(), reduction='mean')\n\n return_dict = {'denoise_loss': denoise_loss}\n\n if self.object_localization:\n object_segmaps = batch['object_segmaps']\n image_token_idx = batch['image_token_idx']\n image_token_idx_mask = batch['image_token_idx_mask']\n localization_loss = get_object_localization_loss(\n self.cross_attention_scores,\n object_segmaps,\n image_token_idx,\n image_token_idx_mask,\n self.object_localization_loss_fn,\n )\n return_dict['localization_loss'] = localization_loss\n loss = self.object_localization_weight * localization_loss\n loss += denoise_loss\n self._clear_cross_attention_scores()\n else:\n loss = denoise_loss\n\n return_dict['loss'] = loss\n return return_dict\n\n\nclass FastComposerTextEncoder(CLIPPreTrainedModel):\n \"\"\"TextEncoder for FastComposerModel.\"\"\"\n\n @staticmethod\n def from_pretrained(model_name_or_path, **kwargs):\n \"\"\"Init textEncoder with Stable Diffusion Model name or path.\"\"\"\n model = CLIPTextModel.from_pretrained(model_name_or_path, **kwargs)\n text_model = model.text_model\n return FastComposerTextEncoder(text_model)\n\n def __init__(self, text_model):\n super().__init__(text_model.config)\n self.config = text_model.config\n self.final_layer_norm = text_model.final_layer_norm\n self.embeddings = text_model.embeddings\n self.encoder = text_model.encoder\n self._build_causal_attention_mask = build_causal_attention_mask\n\n def forward(\n self,\n input_ids,\n image_token_mask=None,\n object_embeds=None,\n num_objects=None,\n attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n \"\"\"Forward function.\n\n Args:\n input_ids (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n image_token_mask (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n object_embeds (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n num_objects (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n attention_mask (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n output_attentions (bool ):\n Default to None.\n output_hidden_states (bool ):\n Default to None.\n return_dict (bool ):\n Default to None.\n\n Returns:\n Union[Tuple, BaseModelOutputWithPooling]\n \"\"\"\n\n output_attentions = (\n output_attentions if output_attentions is not None else\n self.config.output_attentions)\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = (\n return_dict\n if return_dict is not None else self.config.use_return_dict)\n\n input_shape = input_ids.size()\n input_ids = input_ids.view(-1, input_shape[-1])\n\n hidden_states = self.embeddings(input_ids)\n\n bsz, seq_len = input_shape\n causal_attention_mask = self._build_causal_attention_mask(\n bsz, seq_len, hidden_states.dtype).to(hidden_states.device)\n\n # expand attention_mask\n if attention_mask is not None:\n # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]\n attention_mask = _expand_mask(attention_mask, hidden_states.dtype)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n last_hidden_state = self.final_layer_norm(last_hidden_state)\n\n # text_embeds.shape = [batch_size, sequence_length, transformer.width]\n # take features from the eot embedding\n # (eot_token is the highest number in each sequence)\n # casting to torch.int for onnx compatibility:\n # argmax doesn't support int64 inputs with opset 14\n pooled_output = last_hidden_state[\n torch.arange(\n last_hidden_state.shape[0], device=last_hidden_state.device),\n input_ids.to(dtype=torch.int, device=last_hidden_state.device\n ).argmax(dim=-1), ]\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n\nclass FastComposerCLIPImageEncoder(CLIPPreTrainedModel):\n \"\"\"CLIPImageEncoder for FastComposerModel.\"\"\"\n\n @staticmethod\n def from_pretrained(global_model_name_or_path):\n \"\"\"Init CLIPModel with Clip model name or path.\"\"\"\n\n model = CLIPModel.from_pretrained(global_model_name_or_path)\n vision_model = model.vision_model\n visual_projection = model.visual_projection\n vision_processor = T.Normalize(\n (0.48145466, 0.4578275, 0.40821073),\n (0.26862954, 0.26130258, 0.27577711),\n )\n return FastComposerCLIPImageEncoder(\n vision_model,\n visual_projection,\n vision_processor,\n )\n\n def __init__(\n self,\n vision_model,\n visual_projection,\n vision_processor,\n ):\n super().__init__(vision_model.config)\n self.vision_model = vision_model\n self.visual_projection = visual_projection\n self.vision_processor = vision_processor\n\n self.image_size = vision_model.config.image_size\n\n def forward(self, object_pixel_values):\n \"\"\"Forward function.\n\n Args:\n object_pixel_values (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n\n b, num_objects, c, h, w = object_pixel_values.shape\n\n object_pixel_values = object_pixel_values.view(b * num_objects, c, h,\n w)\n\n if h != self.image_size or w != self.image_size:\n h, w = self.image_size, self.image_size\n object_pixel_values = F.interpolate(\n object_pixel_values, (h, w), mode='bilinear')\n\n object_pixel_values = self.vision_processor(object_pixel_values)\n object_embeds = self.vision_model(object_pixel_values)[1]\n object_embeds = self.visual_projection(object_embeds)\n object_embeds = object_embeds.view(b, num_objects, 1, -1)\n return object_embeds\n\n\ndef get_object_transforms(cfg):\n \"\"\"Get Object transforms.\"\"\"\n\n if cfg['no_object_augmentation']:\n pre_augmentations = []\n augmentations = []\n else:\n pre_augmentations = [\n (\n 'zoomin',\n T.RandomApply([RandomZoomIn(min_zoom=1.0, max_zoom=2.0)],\n p=0.5),\n ),\n ]\n\n augmentations = [\n (\n 'rotate',\n T.RandomApply(\n [\n T.RandomAffine(\n degrees=30,\n interpolation=T.InterpolationMode.BILINEAR)\n ],\n p=0.75,\n ),\n ),\n ('jitter', T.RandomApply([T.ColorJitter(0.5, 0.5, 0.5, 0.5)],\n p=0.5)),\n ('blur', T.RandomApply([T.GaussianBlur(5, sigma=(0.1, 2.0))],\n p=0.5)),\n ('gray', T.RandomGrayscale(p=0.1)),\n ('flip', T.RandomHorizontalFlip()),\n ('elastic', T.RandomApply([T.ElasticTransform()], p=0.5)),\n ]\n\n object_transforms = torch.nn.Sequential(\n OrderedDict([\n *pre_augmentations,\n ('pad_to_square', PadToSquare(fill=0, padding_mode='constant')),\n (\n 'resize',\n T.Resize(\n (cfg['object_resolution'], cfg['object_resolution']),\n interpolation=T.InterpolationMode.BILINEAR,\n ),\n ),\n *augmentations,\n ('convert_to_float', T.ConvertImageDtype(torch.float32)),\n ]))\n return object_transforms\n\n\nclass FastComposerPostfuseModule(nn.Module):\n \"\"\"Postfuse Module for FastComposerModel.\"\"\"\n\n def __init__(self, embed_dim):\n super().__init__()\n self.mlp1 = MLP(\n embed_dim * 2, embed_dim, embed_dim, use_residual=False)\n self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True)\n self.layer_norm = nn.LayerNorm(embed_dim)\n\n def fuse_fn(self, text_embeds, object_embeds):\n \"\"\"Fuse function.\n\n Args:\n text_embeds (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n object_embeds (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n\n text_object_embeds = torch.cat([text_embeds, object_embeds], dim=-1)\n text_object_embeds = self.mlp1(text_object_embeds) + text_embeds\n text_object_embeds = self.mlp2(text_object_embeds)\n text_object_embeds = self.layer_norm(text_object_embeds)\n return text_object_embeds\n\n def forward(\n self,\n text_embeds,\n object_embeds,\n image_token_mask,\n num_objects,\n ) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n text_embeds (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n object_embeds (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n image_token_mask (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n\n text_object_embeds = fuse_object_embeddings(text_embeds,\n image_token_mask,\n object_embeds, num_objects,\n self.fuse_fn)\n\n return text_object_embeds\n\n\ndef unet_store_cross_attention_scores(unet, attention_scores, layers=5):\n \"\"\"Unet store cross attention scores.\"\"\"\n from diffusers.models.attention_processor import (Attention, AttnProcessor,\n AttnProcessor2_0)\n\n UNET_LAYER_NAMES = [\n 'down_blocks.0',\n 'down_blocks.1',\n 'down_blocks.2',\n 'mid_block',\n 'up_blocks.1',\n 'up_blocks.2',\n 'up_blocks.3',\n ]\n\n start_layer = (len(UNET_LAYER_NAMES) - layers) // 2\n end_layer = start_layer + layers\n applicable_layers = UNET_LAYER_NAMES[start_layer:end_layer]\n\n def make_new_get_attention_scores_fn(name):\n \"\"\"Wrapper Function of create attention scores for unet.\"\"\"\n\n def new_get_attention_scores(module, query, key, attention_mask=None):\n \"\"\"Create attention scores for unet.\"\"\"\n attention_probs = module.old_get_attention_scores(\n query, key, attention_mask)\n attention_scores[name] = attention_probs\n return attention_probs\n\n return new_get_attention_scores\n\n for name, module in unet.named_modules():\n if isinstance(module, Attention) and 'attn2' in name:\n if not any(layer in name for layer in applicable_layers):\n continue\n if isinstance(module.processor, AttnProcessor2_0):\n module.set_processor(AttnProcessor())\n module.old_get_attention_scores = module.get_attention_scores\n module.get_attention_scores = types.MethodType(\n make_new_get_attention_scores_fn(name), module)\n\n return unet\n\n\nclass BalancedL1Loss(nn.Module):\n \"\"\"BalancedL1Loss for object localization.\"\"\"\n\n def __init__(self, threshold=1.0, normalize=False):\n super().__init__()\n self.threshold = threshold\n self.normalize = normalize\n\n def forward(self, object_token_attn_prob, object_segmaps):\n \"\"\"Forward function.\n\n Args:\n object_token_attn_prob (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n object_segmaps (torch.Tensor ):\n You can directly input a ``torch.Tensor``.\n\n Returns:\n float : ``float`` will be returned.\n \"\"\"\n if self.normalize:\n object_token_attn_prob = object_token_attn_prob / (\n object_token_attn_prob.max(dim=2, keepdim=True)[0] + 1e-5)\n background_segmaps = 1 - object_segmaps\n background_segmaps_sum = background_segmaps.sum(dim=2) + 1e-5\n object_segmaps_sum = object_segmaps.sum(dim=2) + 1e-5\n\n background_loss = (object_token_attn_prob * background_segmaps).sum(\n dim=2) / background_segmaps_sum\n\n object_loss = (object_token_attn_prob *\n object_segmaps).sum(dim=2) / object_segmaps_sum\n\n return background_loss - object_loss\n\n\ndef get_object_localization_loss(\n cross_attention_scores,\n object_segmaps,\n image_token_idx,\n image_token_idx_mask,\n loss_fn,\n):\n \"\"\"To obtain the average of the loss for each layer of object\n localization.\"\"\"\n\n num_layers = len(cross_attention_scores)\n loss = 0\n for k, v in cross_attention_scores.items():\n layer_loss = get_object_localization_loss_for_one_layer(\n v, object_segmaps, image_token_idx, image_token_idx_mask, loss_fn)\n loss += layer_loss\n return loss / num_layers\n\n\ndef get_object_localization_loss_for_one_layer(\n cross_attention_scores,\n object_segmaps,\n object_token_idx,\n object_token_idx_mask,\n loss_fn,\n):\n \"\"\"Get object localization loss for one layer.\"\"\"\n\n bxh, num_noise_latents, num_text_tokens = cross_attention_scores.shape\n b, max_num_objects, _, _ = object_segmaps.shape\n size = int(num_noise_latents**0.5)\n\n # Resize the object segmentation maps to\n # the size of the cross attention scores\n object_segmaps = F.interpolate(\n object_segmaps, size=(size, size), mode='bilinear')\n # (b, max_num_objects, size, size)\n\n object_segmaps = object_segmaps.view(\n b, max_num_objects, -1) # (b, max_num_objects, num_noise_latents)\n\n num_heads = bxh // b\n\n cross_attention_scores = cross_attention_scores.view(\n b, num_heads, num_noise_latents, num_text_tokens)\n\n # Gather object_token_attn_prob\n object_token_attn_prob = torch.gather(\n cross_attention_scores,\n dim=3,\n index=object_token_idx.view(b, 1, 1, max_num_objects).expand(\n b, num_heads, num_noise_latents, max_num_objects),\n ) # (b, num_heads, num_noise_latents, max_num_objects)\n\n object_segmaps = (\n object_segmaps.permute(0, 2,\n 1).unsqueeze(1).expand(b, num_heads,\n num_noise_latents,\n max_num_objects))\n\n loss = loss_fn(object_token_attn_prob, object_segmaps)\n\n loss = loss * object_token_idx_mask.view(b, 1, max_num_objects)\n object_token_cnt = object_token_idx_mask.sum(dim=1).view(b, 1) + 1e-5\n loss = (loss.sum(dim=2) / object_token_cnt).mean()\n\n return loss\n\n\nclass RandomZoomIn(nn.Module):\n \"\"\"RandomZoomIn for object transform.\"\"\"\n\n def __init__(self, min_zoom=1.0, max_zoom=1.5):\n super().__init__()\n self.min_zoom = min_zoom\n self.max_zoom = max_zoom\n\n def forward(self, image: torch.Tensor):\n \"\"\"Forward function.\n\n Args:\n image (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n zoom = torch.rand(1) * (self.max_zoom - self.min_zoom) + self.min_zoom\n image = T.functional.resize(\n image,\n (int(zoom * image.shape[1]), int(zoom * image.shape[2])),\n interpolation=T.InterpolationMode.BILINEAR,\n )\n # crop top square\n image = CropTopSquare()(image)\n return image\n\n\nclass PadToSquare(nn.Module):\n \"\"\"If the height of the image is greater than the width, padding will be\n added on both sides of the image to make it a square.\"\"\"\n\n def __init__(self, fill=0, padding_mode='constant'):\n super().__init__()\n self.fill = fill\n self.padding_mode = padding_mode\n\n def forward(self, image: torch.Tensor):\n \"\"\"Forward function.\n\n Args:\n image (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n _, h, w = image.shape\n if h == w:\n return image\n elif h > w:\n padding = (h - w) // 2\n image = torch.nn.functional.pad(\n image,\n (padding, padding, 0, 0),\n self.padding_mode,\n self.fill,\n )\n else:\n padding = (w - h) // 2\n image = torch.nn.functional.pad(\n image,\n (0, 0, padding, padding),\n self.padding_mode,\n self.fill,\n )\n return image\n\n\nclass CropTopSquare(nn.Module):\n \"\"\"If the height of the image is greater than the width, the image will be\n cropped into a square starting from the top of the image.\"\"\"\n\n def __init__(self):\n super().__init__()\n\n def forward(self, image: torch.Tensor):\n \"\"\"Forward function.\n\n Args:\n image (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n _, h, w = image.shape\n if h <= w:\n return image\n return image[:, :w, :]\n\n\nclass MLP(nn.Module):\n \"\"\"Multilayer Perceptron.\"\"\"\n\n def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True):\n super().__init__()\n if use_residual:\n assert in_dim == out_dim\n self.layernorm = nn.LayerNorm(in_dim)\n self.fc1 = nn.Linear(in_dim, hidden_dim)\n self.fc2 = nn.Linear(hidden_dim, out_dim)\n self.use_residual = use_residual\n self.act_fn = nn.GELU()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor ): You can directly input a ``torch.Tensor``.\n\n Returns:\n torch.Tensor : ``torch.tensor`` will be returned.\n \"\"\"\n residual = x\n x = self.layernorm(x)\n x = self.fc1(x)\n x = self.act_fn(x)\n x = self.fc2(x)\n if self.use_residual:\n x = x + residual\n return x\n\n\ndef fuse_object_embeddings(\n inputs_embeds,\n image_token_mask,\n object_embeds,\n num_objects,\n fuse_fn=torch.add,\n):\n \"\"\"Fuse object embeddings.\"\"\"\n\n object_embeds = object_embeds.to(inputs_embeds.dtype)\n\n batch_size, max_num_objects = object_embeds.shape[:2]\n seq_length = inputs_embeds.shape[1]\n flat_object_embeds = object_embeds.view(-1, object_embeds.shape[-2],\n object_embeds.shape[-1])\n\n valid_object_mask = (\n torch.arange(max_num_objects,\n device=flat_object_embeds.device)[None, :] <\n num_objects[:, None])\n\n valid_object_embeds = flat_object_embeds[valid_object_mask.flatten()]\n\n inputs_embeds = inputs_embeds.view(-1, inputs_embeds.shape[-1])\n image_token_mask = image_token_mask.view(-1)\n valid_object_embeds = valid_object_embeds.view(\n -1, valid_object_embeds.shape[-1])\n\n # slice out the image token embeddings\n image_token_embeds = inputs_embeds[image_token_mask]\n valid_object_embeds = fuse_fn(image_token_embeds, valid_object_embeds)\n\n inputs_embeds.masked_scatter_(image_token_mask[:, None],\n valid_object_embeds)\n inputs_embeds = inputs_embeds.view(batch_size, seq_length, -1)\n return inputs_embeds\n\n\ndef build_causal_attention_mask(bsz, seq_len, dtype, device=None):\n \"\"\"The function originally belonged to CLIPTextTransformer, but it has been\n removed in versions of transformers after 4.25.1.\"\"\"\n\n # lazily create causal attention mask,\n # with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype, device=device)\n mask.fill_(torch.tensor(torch.finfo(dtype).min))\n mask.triu_(1) # zero out the lower diagonal\n mask = mask.unsqueeze(1) # expand mask\n return mask\n" }, { "path": "mmagic/models/editors/fba/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .fba_decoder import FBADecoder\nfrom .fba_encoder import FBAResnetDilated\n\n__all__ = [\n 'FBADecoder',\n 'FBAResnetDilated',\n]\n" }, { "path": "mmagic/models/editors/fba/fba_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine import MMLogger\nfrom mmengine.model.weight_init import constant_init, kaiming_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FBADecoder(nn.Module):\n \"\"\"Decoder for FBA matting.\n\n Args:\n pool_scales (tuple[int]): Pooling scales used in\n Pooling Pyramid Module.\n in_channels (int): Input channels.\n channels (int): Channels after modules, before conv_seg.\n conv_cfg (dict|None): Config of conv layers.\n norm_cfg (dict|None): Config of norm layers.\n act_cfg (dict): Config of activation layers.\n align_corners (bool): align_corners argument of F.interpolate.\n \"\"\"\n\n def __init__(self,\n pool_scales,\n in_channels,\n channels,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU'),\n align_corners=False):\n super().__init__()\n\n assert isinstance(pool_scales, (list, tuple))\n # Pyramid Pooling Module\n self.pool_scales = pool_scales\n self.in_channels = in_channels\n self.channels = channels\n self.conv_cfg = conv_cfg\n self.norm_cfg = norm_cfg\n self.act_cfg = act_cfg\n self.align_corners = align_corners\n self.batch_norm = False\n\n self.ppm = []\n for scale in self.pool_scales:\n self.ppm.append(\n nn.Sequential(\n nn.AdaptiveAvgPool2d(scale),\n *(ConvModule(\n self.in_channels,\n self.channels,\n kernel_size=1,\n bias=True,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg).children())))\n self.ppm = nn.ModuleList(self.ppm)\n\n # Followed the author's implementation that\n # concatenate conv layers described in the supplementary\n # material between up operations\n self.conv_up1 = nn.Sequential(*(list(\n ConvModule(\n self.in_channels + len(pool_scales) * 256,\n self.channels,\n padding=1,\n kernel_size=3,\n bias=True,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg).children()) + list(\n ConvModule(\n self.channels,\n self.channels,\n padding=1,\n bias=True,\n kernel_size=3,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg).children())))\n\n self.conv_up2 = nn.Sequential(*(list(\n ConvModule(\n self.channels * 2,\n self.channels,\n padding=1,\n kernel_size=3,\n bias=True,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg).children())))\n\n if (self.norm_cfg['type'] == 'BN'):\n d_up3 = 128\n else:\n d_up3 = 64\n\n self.conv_up3 = nn.Sequential(*(list(\n ConvModule(\n self.channels + d_up3,\n 64,\n padding=1,\n kernel_size=3,\n bias=True,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg).children())))\n\n self.unpool = nn.MaxUnpool2d(2, stride=2)\n\n conv_up4_1 = list(\n ConvModule(\n 64 + 3 + 3 + 2,\n 32,\n padding=1,\n kernel_size=3,\n bias=True,\n act_cfg=self.act_cfg).children())\n conv_up4_2 = list(\n ConvModule(\n 32,\n 16,\n padding=1,\n kernel_size=3,\n bias=True,\n act_cfg=self.act_cfg).children())\n conv_up4_3 = list(\n ConvModule(\n 16, 7, padding=0, kernel_size=1, bias=True,\n act_cfg=None).children())\n self.conv_up4 = nn.Sequential(*(conv_up4_1 + conv_up4_2 + conv_up4_3))\n\n def init_weights(self, pretrained=None):\n \"\"\"Init weights for the model.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n kaiming_init(m)\n elif isinstance(m, (_BatchNorm, nn.GroupNorm)):\n constant_init(m, 1)\n\n else:\n raise TypeError('pretrained must be a str or None')\n\n def forward(self, inputs):\n \"\"\"Forward function.\n\n Args:\n inputs (dict): Output dict of FbaEncoder.\n Returns:\n tuple(Tensor): Predicted alpha, fg and bg of the current batch.\n \"\"\"\n\n conv_out = inputs['conv_out']\n img = inputs['merged']\n two_channel_trimap = inputs['two_channel_trimap']\n conv5 = conv_out[-1]\n input_size = conv5.size()\n ppm_out = [conv5]\n for pool_scale in self.ppm:\n ppm_out.append(\n nn.functional.interpolate(\n pool_scale(conv5), (input_size[2], input_size[3]),\n mode='bilinear',\n align_corners=self.align_corners))\n ppm_out = torch.cat(ppm_out, 1)\n x = self.conv_up1(ppm_out)\n\n x = torch.nn.functional.interpolate(\n x,\n scale_factor=2,\n mode='bilinear',\n align_corners=self.align_corners)\n\n x = torch.cat((x, conv_out[-4]), 1)\n\n x = self.conv_up2(x)\n x = torch.nn.functional.interpolate(\n x,\n scale_factor=2,\n mode='bilinear',\n align_corners=self.align_corners)\n\n x = torch.cat((x, conv_out[-5]), 1)\n x = self.conv_up3(x)\n\n x = torch.nn.functional.interpolate(\n x,\n scale_factor=2,\n mode='bilinear',\n align_corners=self.align_corners)\n\n x = torch.cat((x, conv_out[-6][:, :3], img, two_channel_trimap), 1)\n output = self.conv_up4(x)\n alpha = torch.clamp(output[:, 0:1], 0, 1)\n F = torch.sigmoid(output[:, 1:4])\n B = torch.sigmoid(output[:, 4:7])\n\n return alpha, F, B\n" }, { "path": "mmagic/models/editors/fba/fba_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models.archs import ResNet\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FBAResnetDilated(ResNet):\n \"\"\"ResNet-based encoder for FBA image matting.\"\"\"\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n # x: (merged_t, trimap_t, two_channel_trimap,merged)\n # t refers to transformed.\n two_channel_trimap = x[:, 9:11]\n merged = x[:, 11:14]\n x = x[:, 0:11, ...]\n conv_out = [x]\n if self.deep_stem:\n x = self.stem(x)\n else:\n x = self.conv1(x)\n x = self.norm1(x)\n x = self.activate(x)\n conv_out.append(x)\n x = self.maxpool(x)\n x = self.layer1(x)\n conv_out.append(x)\n x = self.layer2(x)\n conv_out.append(x)\n x = self.layer3(x)\n conv_out.append(x)\n x = self.layer4(x)\n conv_out.append(x)\n return {\n 'conv_out': conv_out,\n 'merged': merged,\n 'two_channel_trimap': two_channel_trimap\n }\n" }, { "path": "mmagic/models/editors/flavr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .flavr import FLAVR\nfrom .flavr_net import FLAVRNet\n\n__all__ = [\n 'FLAVR',\n 'FLAVRNet',\n]\n" }, { "path": "mmagic/models/editors/flavr/flavr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models.base_models import BasicInterpolator\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import tensor2img\n\n# TODO tensor2img will be move\n\n\n@MODELS.register_module()\nclass FLAVR(BasicInterpolator):\n \"\"\"FLAVR model for video interpolation.\n\n Paper:\n FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation\n\n Ref repo: https://github.com/tarun005/FLAVR\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n required_frames (int): Required frames in each process. Default: 2\n step_frames (int): Step size of video frame interpolation. Default: 1\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n\n Attributes:\n init_cfg (dict, optional): Initialization config dict.\n data_preprocessor (:obj:`BaseDataPreprocessor`): Used for\n pre-processing data sampled by dataloader to the format accepted by\n :meth:`forward`.\n \"\"\"\n\n @staticmethod\n def merge_frames(input_tensors, output_tensors):\n \"\"\"merge input frames and output frames.\n\n Interpolate a frame between the given two frames.\n\n Merged from\n [[in1, in2, in3, in4], [in2, in3, in4, in5], ...]\n [[out1], [out2], [out3], ...]\n to\n [in1, in2, out1, in3, out2, ..., in(-3), out(-1), in(-2), in(-1)]\n\n Args:\n input_tensors (Tensor): The input frames with shape [n, 4, c, h, w]\n output_tensors (Tensor): The output frames with shape\n [n, 1, c, h, w].\n\n Returns:\n list[np.array]: The final frames.\n \"\"\"\n\n num_frames = input_tensors.shape[0]\n result = [tensor2img(input_tensors[0, 0])]\n for i in range(num_frames):\n result.append(tensor2img(input_tensors[i, 1]))\n result.append(tensor2img(output_tensors[i, 0]))\n result.append(tensor2img(input_tensors[-1, 2]))\n result.append(tensor2img(input_tensors[-1, 3]))\n\n return result\n" }, { "path": "mmagic/models/editors/flavr/flavr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FLAVRNet(BaseModule):\n \"\"\"PyTorch implementation of FLAVR for video frame interpolation.\n\n Paper:\n FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation\n\n Ref repo: https://github.com/tarun005/FLAVR\n\n Args:\n num_input_frames (int): Number of input frames.\n num_output_frames (int): Number of output frames.\n mid_channels_list (list[int]): List of number of mid channels.\n Default: [512, 256, 128, 64]\n encoder_layers_list (list[int]): List of number of layers in encoder.\n Default: [2, 2, 2, 2]\n bias (bool): If ``True``, adds a learnable bias to the conv layers.\n Default: ``True``\n norm_cfg (dict | None): Config dict for normalization layer.\n Default: None\n join_type (str): Join type of tensors from decoder and encoder.\n Candidates are ``concat`` and ``add``. Default: ``concat``\n up_mode (str): Up-mode UpConv3d, candidates are ``transpose`` and\n ``trilinear``. Default: ``transpose``\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n num_input_frames,\n num_output_frames,\n mid_channels_list=[512, 256, 128, 64],\n encoder_layers_list=[2, 2, 2, 2],\n bias=False,\n norm_cfg=None,\n join_type='concat',\n up_mode='transpose',\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n self.encoder = Encoder(\n block=BasicBlock,\n layers=encoder_layers_list,\n stem_layer=BasicStem,\n mid_channels_list=mid_channels_list[::-1],\n bias=bias,\n norm_cfg=norm_cfg)\n\n self.decoder = Decoder(\n join_type=join_type,\n up_mode=up_mode,\n mid_channels_list=mid_channels_list,\n batchnorm=norm_cfg)\n\n self.feature_fuse = ConvModule(\n mid_channels_list[3] * num_input_frames,\n mid_channels_list[3],\n kernel_size=1,\n stride=1,\n bias=False,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2, inplace=True))\n\n out_channels = 3 * num_output_frames\n self.conv_last = nn.Sequential(\n nn.ReflectionPad2d(3),\n nn.Conv2d(\n mid_channels_list[3],\n out_channels=out_channels,\n kernel_size=7,\n stride=1,\n padding=0))\n\n def forward(self, images: torch.Tensor):\n \"\"\"Forward function.\n\n Args:\n images (Tensor): Input frames tensor with shape (N, T, C, H, W).\n\n Returns:\n out (Tensor): Output tensor.\n \"\"\"\n\n # from [b, t, c, h, w] to [b, c, d, h, w], where t==d\n images = images.permute((0, 2, 1, 3, 4))\n\n # Batch mean normalization works slightly better than global mean\n # normalization, Refer to https://github.com/myungsub/CAIN\n mean_ = images.mean((2, 3, 4), keepdim=True)\n images = images - mean_\n\n xs = self.encoder(images)\n\n dx_out = self.decoder(xs)\n\n out = self.feature_fuse(dx_out)\n out = self.conv_last(out)\n # b, t*c, h, w\n\n b, c_all, h, w = out.shape\n t = c_all // 3\n mean_ = mean_.view(b, 1, 3, 1, 1)\n out = out.view(b, t, 3, h, w)\n out = out + mean_\n\n # if t==1, which means the output only contains one frame.\n out = out.squeeze(1)\n\n return out\n\n\nclass Encoder(nn.Module):\n \"\"\"Encoder of FLAVR.\n\n Args:\n block (nn.Module): Basic block of encoder.\n layers (str): List of layers in encoder.\n stem_layer (nn.Module): stem layer (conv first).\n mid_channels_list (list[int]): List of mid channels.\n norm_cfg (dict | None): Config dict for normalization layer.\n Default: None\n bias (bool): If ``True``, adds a learnable bias to the conv layers.\n Default: ``True``\n \"\"\"\n\n def __init__(self, block, layers, stem_layer, mid_channels_list, norm_cfg,\n bias):\n super().__init__()\n\n self.in_channels = mid_channels_list[0]\n self.bias = bias\n\n self.stem_layer = stem_layer(mid_channels_list[0], bias, norm_cfg)\n\n self.layer1 = self._make_layer(\n block,\n mid_channels_list[0],\n layers[0],\n norm_cfg=norm_cfg,\n stride=1)\n self.layer2 = self._make_layer(\n block,\n mid_channels_list[1],\n layers[1],\n norm_cfg=norm_cfg,\n stride=2,\n temporal_stride=1)\n self.layer3 = self._make_layer(\n block,\n mid_channels_list[2],\n layers[2],\n norm_cfg=norm_cfg,\n stride=2,\n temporal_stride=1)\n self.layer4 = self._make_layer(\n block,\n mid_channels_list[3],\n layers[3],\n norm_cfg=norm_cfg,\n stride=1,\n temporal_stride=1)\n\n # init weights\n self._initialize_weights()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor).\n\n Returns:\n tuple(Tensor): Output tensors.\n \"\"\"\n\n x_0 = self.stem_layer(x)\n x_1 = self.layer1(x_0)\n x_2 = self.layer2(x_1)\n x_3 = self.layer3(x_2)\n x_4 = self.layer4(x_3)\n\n return x_0, x_1, x_2, x_3, x_4\n\n def _make_layer(self,\n block,\n mid_channels,\n num_blocks,\n norm_cfg,\n stride=1,\n temporal_stride=None):\n \"\"\"Make layers by stacking the blocks.\"\"\"\n downsample = None\n\n if stride != 1 or self.in_channels != mid_channels * block.expansion:\n if temporal_stride:\n ds_stride = (temporal_stride, stride, stride)\n else:\n ds_stride = (stride, stride, stride)\n downsample = ConvModule(\n self.in_channels,\n mid_channels * block.expansion,\n kernel_size=1,\n stride=ds_stride,\n bias=False,\n conv_cfg=dict(type='Conv3d'),\n norm_cfg=norm_cfg,\n act_cfg=None)\n stride = ds_stride\n\n layers = []\n layers.append(\n block(\n self.in_channels,\n mid_channels,\n norm_cfg=norm_cfg,\n stride=stride,\n bias=self.bias,\n downsample=downsample))\n\n self.in_channels = mid_channels * block.expansion\n for _ in range(1, num_blocks):\n layers.append(\n block(\n self.in_channels,\n mid_channels,\n norm_cfg=norm_cfg,\n bias=self.bias))\n\n return nn.Sequential(*layers)\n\n def _initialize_weights(self):\n \"\"\"Init weights for models.\"\"\"\n for m in self.modules():\n if isinstance(m, nn.Conv3d):\n nn.init.kaiming_normal_(\n m.weight, mode='fan_out', nonlinearity='relu')\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.BatchNorm3d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.Linear):\n nn.init.normal_(m.weight, 0, 0.01)\n nn.init.constant_(m.bias, 0)\n\n\nclass Decoder(nn.Module):\n \"\"\"Decoder of FLAVR.\n\n Args:\n join_type (str): Join type of tensors from decoder and encoder.\n Candidates are ``concat`` and ``add``. Default: ``concat``\n up_mode (str): Up-mode UpConv3d, candidates are ``transpose`` and\n ``trilinear``. Default: ``transpose``\n mid_channels_list (list[int]): List of mid channels.\n Default: [512, 256, 128, 64]\n batchnorm (bool): Whether contains BatchNorm3d. Default: False.\n \"\"\"\n\n def __init__(self,\n join_type,\n up_mode,\n mid_channels_list=[512, 256, 128, 64],\n batchnorm=False):\n super().__init__()\n\n growth = 2 if join_type == 'concat' else 1\n self.join_type = join_type\n self.lrelu = nn.LeakyReLU(0.2, True)\n\n self.layer0 = Conv3d(\n mid_channels_list[0],\n mid_channels_list[1],\n kernel_size=3,\n padding=1,\n bias=True,\n batchnorm=batchnorm)\n self.layer1 = UpConv3d(\n mid_channels_list[1] * growth,\n mid_channels_list[2],\n kernel_size=(3, 4, 4),\n stride=(1, 2, 2),\n padding=(1, 1, 1),\n up_mode=up_mode,\n batchnorm=batchnorm)\n self.layer2 = UpConv3d(\n mid_channels_list[2] * growth,\n mid_channels_list[3],\n kernel_size=(3, 4, 4),\n stride=(1, 2, 2),\n padding=(1, 1, 1),\n up_mode=up_mode,\n batchnorm=batchnorm)\n self.layer3 = Conv3d(\n mid_channels_list[3] * growth,\n mid_channels_list[3],\n kernel_size=3,\n padding=1,\n bias=True,\n batchnorm=batchnorm)\n self.layer4 = UpConv3d(\n mid_channels_list[3] * growth,\n mid_channels_list[3],\n kernel_size=(3, 4, 4),\n stride=(1, 2, 2),\n padding=(1, 1, 1),\n up_mode=up_mode,\n batchnorm=batchnorm)\n\n def forward(self, xs):\n \"\"\"Forward function.\n\n Args:\n xs (Tensor): Input tensor).\n\n Returns:\n dx_out (Tensor): Output tensor.\n \"\"\"\n\n dx_3 = self.lrelu(self.layer0(xs[4]))\n dx_3 = self._join_tensors(dx_3, xs[3])\n\n dx_2 = self.lrelu(self.layer1(dx_3))\n dx_2 = self._join_tensors(dx_2, xs[2])\n\n dx_1 = self.lrelu(self.layer2(dx_2))\n dx_1 = self._join_tensors(dx_1, xs[1])\n\n dx_0 = self.lrelu(self.layer3(dx_1))\n dx_0 = self._join_tensors(dx_0, xs[0])\n\n dx_out = self.lrelu(self.layer4(dx_0))\n dx_out = torch.cat(torch.unbind(dx_out, 2), 1)\n\n return dx_out\n\n def _join_tensors(self, x1, x2):\n \"\"\"Concat or Add two tensors.\n\n Args:\n x1 (Tensor): The first input tensor.\n x2 (Tensor): The second input tensor.\n \"\"\"\n\n if self.join_type == 'concat':\n return torch.cat([x1, x2], dim=1)\n else:\n return x1 + x2\n\n\nclass UpConv3d(nn.Module):\n \"\"\"A conv block that bundles conv/SEGating/norm layers.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n Same as that in ``nn._ConvNd``.\n out_channels (int): Number of channels produced by the convolution.\n Same as that in ``nn._ConvNd``.\n kernel_size (int | tuple[int]): Size of the convolving kernel.\n Same as that in ``nn._ConvNd``.\n stride (int | tuple[int]): Stride of the convolution.\n Same as that in ``nn._ConvNd``.\n padding (int | tuple[int]): Zero-padding added to both sides of\n the input. Same as that in ``nn._ConvNd``.\n up_mode (str): Up-mode UpConv3d, candidates are ``transpose`` and\n ``trilinear``. Default: ``transpose``.\n batchnorm (bool): Whether contains BatchNorm3d. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n stride,\n padding,\n up_mode='transpose',\n batchnorm=False):\n\n super().__init__()\n\n self.up_mode = up_mode\n\n if self.up_mode == 'transpose':\n self.upconv = nn.ModuleList([\n nn.ConvTranspose3d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding),\n SEGating(out_channels)\n ])\n\n else:\n self.upconv = nn.ModuleList([\n nn.Upsample(\n mode='trilinear',\n scale_factor=(1, 2, 2),\n align_corners=False),\n nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1),\n SEGating(out_channels)\n ])\n\n if batchnorm:\n self.upconv += [nn.BatchNorm3d(out_channels)]\n\n self.upconv = nn.Sequential(*self.upconv)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n return self.upconv(x)\n\n\nclass Conv3d(nn.Module):\n \"\"\"A conv block that bundles conv/SEGating/norm layers.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n Same as that in ``nn._ConvNd``.\n out_channels (int): Number of channels produced by the convolution.\n Same as that in ``nn._ConvNd``.\n kernel_size (int | tuple[int]): Size of the convolving kernel.\n Same as that in ``nn._ConvNd``.\n stride (int | tuple[int]): Stride of the convolution.\n Same as that in ``nn._ConvNd``. Default: 1.\n padding (int | tuple[int]): Zero-padding added to both sides of\n the input. Same as that in ``nn._ConvNd``.\n bias (bool): If ``True``, adds a learnable bias to the conv layer.\n Default: ``True``\n batchnorm (bool): Whether contains BatchNorm3d. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n stride=1,\n padding=0,\n bias=True,\n batchnorm=False):\n\n super().__init__()\n self.conv = [\n nn.Conv3d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=bias),\n SEGating(out_channels)\n ]\n\n if batchnorm:\n self.conv += [nn.BatchNorm3d(out_channels)]\n\n self.conv = nn.Sequential(*self.conv)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n return self.conv(x)\n\n\nclass BasicStem(ConvModule):\n \"\"\"The default conv-batchnorm-relu stem of FLAVR.\n\n Args:\n out_channels (int): Number of output channels. Default: 64\n bias (bool): If ``True``, adds a learnable bias to the conv layer.\n Default: ``False``\n norm_cfg (dict | None): Config dict for normalization layer.\n Default: None.\n \"\"\"\n\n def __init__(self, out_channels=64, bias=False, norm_cfg=None):\n super().__init__(\n 3,\n out_channels,\n kernel_size=(3, 7, 7),\n stride=(1, 2, 2),\n padding=(1, 3, 3),\n bias=bias,\n conv_cfg=dict(type='Conv3d'),\n norm_cfg=norm_cfg,\n inplace=False)\n\n\nclass BasicBlock(nn.Module):\n \"\"\"Basic block of encoder in FLAVR.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n mid_channels (int): Number of middle channels.\n stride (int | tuple[int]): Stride of the first convolution.\n Default: 1.\n norm_cfg (dict | None): Config dict for normalization layer.\n Default: None.\n bias (bool): If ``True``, adds a learnable bias to the conv layers.\n Default: ``True``\n downsample (None | torch.nn.Module): Down-sample layer.\n Default: None.\n \"\"\"\n\n expansion = 1\n\n def __init__(\n self,\n in_channels,\n mid_channels,\n stride=1,\n norm_cfg=None,\n bias=False,\n downsample=None,\n ):\n super().__init__()\n\n self.conv1 = ConvModule(\n in_channels,\n mid_channels,\n kernel_size=(3, 3, 3),\n stride=stride,\n padding=(1, 1, 1),\n bias=bias,\n conv_cfg=dict(type='Conv3d'),\n norm_cfg=norm_cfg)\n self.conv2 = ConvModule(\n mid_channels,\n mid_channels,\n kernel_size=(3, 3, 3),\n stride=(1, 1, 1),\n padding=(1, 1, 1),\n bias=bias,\n conv_cfg=dict(type='Conv3d'),\n norm_cfg=norm_cfg,\n act_cfg=None)\n self.fg = SEGating(mid_channels) # Feature Gating\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n xs (Tensor): Input tensor).\n\n Returns:\n out (Tensor): Output tensor.\n \"\"\"\n\n residual = x\n out = self.conv1(x)\n out = self.conv2(out)\n out = self.fg(out)\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n out = self.relu(out)\n\n return out\n\n\nclass SEGating(nn.Module):\n \"\"\"Gating of SE attention.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n \"\"\"\n\n def __init__(self, in_channels):\n\n super().__init__()\n\n self.pool = nn.AdaptiveAvgPool3d(1)\n self.attn_layer = nn.Sequential(\n nn.Conv3d(\n in_channels, in_channels, kernel_size=1, stride=1, bias=True),\n nn.Sigmoid())\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor).\n\n Returns:\n Tensor: Output tensors.\n \"\"\"\n\n out = self.pool(x)\n y = self.attn_layer(out)\n return x * y\n" }, { "path": "mmagic/models/editors/gca/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .gca import GCA\nfrom .gca_module import GCAModule\nfrom .resgca_dec import ResGCADecoder, ResNetDec, ResShortcutDec\nfrom .resgca_enc import ResGCAEncoder, ResNetEnc, ResShortcutEnc\n\n__all__ = [\n 'GCA', 'GCAModule', 'ResNetEnc', 'ResShortcutEnc', 'ResGCAEncoder',\n 'ResNetDec', 'ResShortcutDec', 'ResGCADecoder'\n]\n" }, { "path": "mmagic/models/editors/gca/gca.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nfrom mmagic.models.base_models import BaseMattor\nfrom mmagic.models.utils import get_unknown_tensor\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GCA(BaseMattor):\n \"\"\"Guided Contextual Attention image matting model.\n\n https://arxiv.org/abs/2001.04069\n\n Args:\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n backbone (dict): Config of backbone.\n loss_alpha (dict): Config of the alpha prediction loss. Default: None.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n train_cfg (dict): Config of training. In ``train_cfg``,\n ``train_backbone`` should be specified. If the model has a refiner,\n ``train_refiner`` should be specified.\n test_cfg (dict): Config of testing. In ``test_cfg``, If the model has a\n refiner, ``train_refiner`` should be specified.\n \"\"\"\n\n def __init__(self,\n data_preprocessor,\n backbone,\n loss_alpha=None,\n init_cfg: Optional[dict] = None,\n train_cfg=None,\n test_cfg=None):\n super().__init__(\n backbone=backbone,\n data_preprocessor=data_preprocessor,\n init_cfg=init_cfg,\n train_cfg=train_cfg,\n test_cfg=test_cfg)\n\n self.loss_alpha = MODELS.build(loss_alpha)\n\n def _forward(self, inputs):\n \"\"\"Forward function.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n raw_alpha = self.backbone(inputs)\n pred_alpha = (raw_alpha.tanh() + 1.0) / 2.0\n return pred_alpha\n\n def _forward_test(self, inputs):\n \"\"\"Forward function for testing GCA model.\n\n Args:\n inputs (torch.Tensor): batch input tensor.\n\n Returns:\n Tensor: Output tensor of model.\n \"\"\"\n return self._forward(inputs)\n\n def _forward_train(self, inputs, data_samples):\n \"\"\"Forward function for training GCA model.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement]): data samples collated by\n :attr:`data_preprocessor`.\n\n Returns:\n dict: Contains the loss items and batch information.\n \"\"\"\n trimap = inputs[:, 3:, :, :]\n gt_alpha = data_samples.gt_alpha\n pred_alpha = self._forward(inputs)\n\n # FormatTrimap(to_onehot=False) will change unknown_value to 1\n # FormatTrimap(to_onehot=True) will shift to 3 dim,\n # get_unknown_tensor can handle that directly without knowing\n # unknown_value.\n weight = get_unknown_tensor(trimap, unknown_value=1)\n\n losses = {'loss': self.loss_alpha(pred_alpha, gt_alpha, weight)}\n return losses\n" }, { "path": "mmagic/models/editors/gca/gca_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model.weight_init import constant_init, xavier_init\nfrom torch.nn import functional as F\n\n\nclass GCAModule(nn.Module):\n \"\"\"Guided Contextual Attention Module.\n\n From https://arxiv.org/pdf/2001.04069.pdf.\n Based on https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting.\n This module use image feature map to augment the alpha feature map with\n guided contextual attention score.\n\n Image feature and alpha feature are unfolded to small patches and later\n used as conv kernel. Thus, we refer the unfolding size as kernel size.\n Image feature patches have a default kernel size 3 while the kernel size of\n alpha feature patches could be specified by `rate` (see `rate` below). The\n image feature patches are used to convolve with the image feature itself\n to calculate the contextual attention. Then the attention feature map is\n convolved by alpha feature patches to obtain the attention alpha feature.\n At last, the attention alpha feature is added to the input alpha feature.\n\n Args:\n in_channels (int): Input channels of the guided contextual attention\n module.\n out_channels (int): Output channels of the guided contextual attention\n module.\n kernel_size (int): Kernel size of image feature patches. Default 3.\n stride (int): Stride when unfolding the image feature. Default 1.\n rate (int): The downsample rate of image feature map. The corresponding\n kernel size and stride of alpha feature patches will be `rate x 2`\n and `rate`. It could be regarded as the granularity of the gca\n module. Default: 2.\n pad_args (dict): Parameters of padding when convolve image feature with\n image feature patches or alpha feature patches. Allowed keys are\n `mode` and `value`. See torch.nn.functional.pad() for more\n information. Default: dict(mode='reflect').\n interpolation (str): Interpolation method in upsampling and\n downsampling.\n penalty (float): Punishment hyperparameter to avoid a large correlation\n between each unknown patch and itself. Default: -1e4.\n eps (float): A small number to avoid dividing by 0 when calculating\n the normed image feature patch. Default: 1e-4.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n rate=2,\n pad_args=dict(mode='reflect'),\n interpolation='nearest',\n penalty=-1e4,\n eps=1e-4):\n super().__init__()\n self.kernel_size = kernel_size\n self.stride = stride\n self.rate = rate\n self.pad_args = pad_args\n self.interpolation = interpolation\n self.penalty = penalty\n self.eps = eps\n\n # reduced the channels of input image feature.\n self.guidance_conv = nn.Conv2d(in_channels, in_channels // 2, 1)\n\n # convolution after the attention alpha feature\n self.out_conv = ConvModule(\n out_channels,\n out_channels,\n 1,\n norm_cfg=dict(type='BN'),\n act_cfg=None)\n\n self.init_weights()\n\n def init_weights(self):\n \"\"\"Init weights for the model.\"\"\"\n xavier_init(self.guidance_conv, distribution='uniform')\n xavier_init(self.out_conv.conv, distribution='uniform')\n constant_init(self.out_conv.norm, 1e-3)\n\n def forward(self, img_feat, alpha_feat, unknown=None, softmax_scale=1.):\n \"\"\"Forward function of GCAModule.\n\n Args:\n img_feat (Tensor): Image feature map of shape\n (N, ori_c, ori_h, ori_w).\n alpha_feat (Tensor): Alpha feature map of shape\n (N, alpha_c, ori_h, ori_w).\n unknown (Tensor, optional): Unknown area map generated by trimap.\n If specified, this tensor should have shape\n (N, 1, ori_h, ori_w).\n softmax_scale (float, optional): The softmax scale of the attention\n if unknown area is not provided in forward. Default: 1.\n\n Returns:\n Tensor: The augmented alpha feature.\n \"\"\"\n\n if alpha_feat.shape[2:4] != img_feat.shape[2:4]:\n raise ValueError(\n 'image feature size does not align with alpha feature size: '\n f'image feature size {img_feat.shape[2:4]}, '\n f'alpha feature size {alpha_feat.shape[2:4]}')\n\n if unknown is not None and unknown.shape[2:4] != img_feat.shape[2:4]:\n raise ValueError(\n 'image feature size does not align with unknown mask size: '\n f'image feature size {img_feat.shape[2:4]}, '\n f'unknown mask size {unknown.shape[2:4]}')\n\n # preprocess image feature\n img_feat = self.guidance_conv(img_feat)\n img_feat = F.interpolate(\n img_feat, scale_factor=1 / self.rate, mode=self.interpolation)\n\n # preprocess unknown mask\n unknown, softmax_scale = self.process_unknown_mask(\n unknown, img_feat, softmax_scale)\n\n img_ps, alpha_ps, unknown_ps = self.extract_feature_maps_patches(\n img_feat, alpha_feat, unknown)\n\n # create self correlation mask with shape:\n # (N, img_h*img_w, img_h, img_w)\n self_mask = self.get_self_correlation_mask(img_feat)\n\n # split tensors by batch dimension; tuple is returned\n img_groups = torch.split(img_feat, 1, dim=0)\n img_ps_groups = torch.split(img_ps, 1, dim=0)\n alpha_ps_groups = torch.split(alpha_ps, 1, dim=0)\n unknown_ps_groups = torch.split(unknown_ps, 1, dim=0)\n scale_groups = torch.split(softmax_scale, 1, dim=0)\n groups = (img_groups, img_ps_groups, alpha_ps_groups,\n unknown_ps_groups, scale_groups)\n\n out = []\n # i is the virtual index of the sample in the current batch\n for img_i, img_ps_i, alpha_ps_i, unknown_ps_i, scale_i in zip(*groups):\n similarity_map = self.compute_similarity_map(img_i, img_ps_i)\n\n gca_score = self.compute_guided_attention_score(\n similarity_map, unknown_ps_i, scale_i, self_mask)\n\n out_i = self.propagate_alpha_feature(gca_score, alpha_ps_i)\n\n out.append(out_i)\n\n out = torch.cat(out, dim=0)\n out.reshape_as(alpha_feat)\n\n out = self.out_conv(out) + alpha_feat\n return out\n\n def extract_feature_maps_patches(self, img_feat, alpha_feat, unknown):\n \"\"\"Extract image feature, alpha feature unknown patches.\n\n Args:\n img_feat (Tensor): Image feature map of shape\n (N, img_c, img_h, img_w).\n alpha_feat (Tensor): Alpha feature map of shape\n (N, alpha_c, ori_h, ori_w).\n unknown (Tensor, optional): Unknown area map generated by trimap of\n shape (N, 1, img_h, img_w).\n\n Returns:\n tuple: 3-tuple of\n\n ``Tensor``: Image feature patches of shape \\\n (N, img_h*img_w, img_c, img_ks, img_ks).\n\n ``Tensor``: Guided contextual attention alpha feature map. \\\n (N, img_h*img_w, alpha_c, alpha_ks, alpha_ks).\n\n ``Tensor``: Unknown mask of shape (N, img_h*img_w, 1, 1).\n \"\"\"\n # extract image feature patches with shape:\n # (N, img_h*img_w, img_c, img_ks, img_ks)\n img_ks = self.kernel_size\n img_ps = self.extract_patches(img_feat, img_ks, self.stride)\n\n # extract alpha feature patches with shape:\n # (N, img_h*img_w, alpha_c, alpha_ks, alpha_ks)\n alpha_ps = self.extract_patches(alpha_feat, self.rate * 2, self.rate)\n\n # extract unknown mask patches with shape: (N, img_h*img_w, 1, 1)\n unknown_ps = self.extract_patches(unknown, img_ks, self.stride)\n unknown_ps = unknown_ps.squeeze(dim=2) # squeeze channel dimension\n unknown_ps = unknown_ps.mean(dim=[2, 3], keepdim=True)\n\n return img_ps, alpha_ps, unknown_ps\n\n def compute_similarity_map(self, img_feat, img_ps):\n \"\"\"Compute similarity between image feature patches.\n\n Args:\n img_feat (Tensor): Image feature map of shape\n (1, img_c, img_h, img_w).\n img_ps (Tensor): Image feature patches tensor of shape\n (1, img_h*img_w, img_c, img_ks, img_ks).\n\n Returns:\n Tensor: Similarity map between image feature patches with shape \\\n (1, img_h*img_w, img_h, img_w).\n \"\"\"\n img_ps = img_ps[0] # squeeze dim 0\n # convolve the feature to get correlation (similarity) map\n escape_NaN = torch.FloatTensor([self.eps]).to(img_feat)\n img_ps_normed = img_ps / torch.max(self.l2_norm(img_ps), escape_NaN)\n img_feat = self.pad(img_feat, self.kernel_size, self.stride)\n similarity_map = F.conv2d(img_feat, img_ps_normed)\n\n return similarity_map\n\n def compute_guided_attention_score(self, similarity_map, unknown_ps, scale,\n self_mask):\n \"\"\"Compute guided attention score.\n\n Args:\n similarity_map (Tensor): Similarity map of image feature with shape\n (1, img_h*img_w, img_h, img_w).\n unknown_ps (Tensor): Unknown area patches tensor of shape\n (1, img_h*img_w, 1, 1).\n scale (Tensor): Softmax scale of known and unknown area:\n [unknown_scale, known_scale].\n self_mask (Tensor): Self correlation mask of shape\n (1, img_h*img_w, img_h, img_w). At (1, i*i, i, i) mask value\n equals -1e4 for i in [1, img_h*img_w] and other area is all\n zero.\n\n Returns:\n Tensor: Similarity map between image feature patches with shape \\\n (1, img_h*img_w, img_h, img_w).\n \"\"\"\n # scale the correlation with predicted scale factor for known and\n # unknown area\n unknown_scale, known_scale = scale[0]\n out = similarity_map * (\n unknown_scale * unknown_ps.gt(0.).float() +\n known_scale * unknown_ps.le(0.).float())\n # mask itself, self-mask only applied to unknown area\n out = out + self_mask * unknown_ps\n gca_score = F.softmax(out, dim=1)\n\n return gca_score\n\n def propagate_alpha_feature(self, gca_score, alpha_ps):\n \"\"\"Propagate alpha feature based on guided attention score.\n\n Args:\n gca_score (Tensor): Guided attention score map of shape\n (1, img_h*img_w, img_h, img_w).\n alpha_ps (Tensor): Alpha feature patches tensor of shape\n (1, img_h*img_w, alpha_c, alpha_ks, alpha_ks).\n\n Returns:\n Tensor: Propagated alpha feature map of shape \\\n (1, alpha_c, alpha_h, alpha_w).\n \"\"\"\n alpha_ps = alpha_ps[0] # squeeze dim 0\n if self.rate == 1:\n gca_score = self.pad(gca_score, kernel_size=2, stride=1)\n alpha_ps = alpha_ps.permute(1, 0, 2, 3)\n out = F.conv2d(gca_score, alpha_ps) / 4.\n else:\n out = F.conv_transpose2d(\n gca_score, alpha_ps, stride=self.rate, padding=1) / 4.\n\n return out\n\n def process_unknown_mask(self, unknown, img_feat, softmax_scale):\n \"\"\"Process unknown mask.\n\n Args:\n unknown (Tensor, optional): Unknown area map generated by trimap of\n shape (N, 1, ori_h, ori_w)\n img_feat (Tensor): The interpolated image feature map of shape\n (N, img_c, img_h, img_w).\n softmax_scale (float, optional): The softmax scale of the attention\n if unknown area is not provided in forward. Default: 1.\n\n Returns:\n tuple: 2-tuple of\n\n ``Tensor``: Interpolated unknown area map of shape \\\n (N, img_h*img_w, img_h, img_w).\n\n ``Tensor``: Softmax scale tensor of known and unknown area of \\\n shape (N, 2).\n \"\"\"\n n, _, h, w = img_feat.shape\n\n if unknown is not None:\n unknown = unknown.clone()\n unknown = F.interpolate(\n unknown, scale_factor=1 / self.rate, mode=self.interpolation)\n unknown_mean = unknown.mean(dim=[2, 3])\n known_mean = 1 - unknown_mean\n unknown_scale = torch.clamp(\n torch.sqrt(unknown_mean / known_mean), 0.1, 10).to(img_feat)\n known_scale = torch.clamp(\n torch.sqrt(known_mean / unknown_mean), 0.1, 10).to(img_feat)\n softmax_scale = torch.cat([unknown_scale, known_scale], dim=1)\n else:\n unknown = torch.ones((n, 1, h, w)).to(img_feat)\n softmax_scale = torch.FloatTensor(\n [softmax_scale,\n softmax_scale]).view(1, 2).repeat(n, 1).to(img_feat)\n\n return unknown, softmax_scale\n\n def extract_patches(self, x, kernel_size, stride):\n \"\"\"Extract feature patches.\n\n The feature map will be padded automatically to make sure the number of\n patches is equal to `(H / stride) * (W / stride)`.\n\n Args:\n x (Tensor): Feature map of shape (N, C, H, W).\n kernel_size (int): Size of each patches.\n stride (int): Stride between patches.\n\n Returns:\n Tensor: Extracted patches of shape \\\n (N, (H / stride) * (W / stride) , C, kernel_size, kernel_size).\n \"\"\"\n n, c, _, _ = x.shape\n x = self.pad(x, kernel_size, stride)\n x = F.unfold(x, (kernel_size, kernel_size), stride=(stride, stride))\n x = x.permute(0, 2, 1)\n x = x.reshape(n, -1, c, kernel_size, kernel_size)\n return x\n\n def pad(self, x, kernel_size, stride):\n \"\"\"Pad input tensor.\n\n Args:\n x (Tensor): Input tensor.\n kernel_size (int): Kernel size of conv layer.\n stride (int): Stride of conv layer.\n\n Returns:\n Tensor: Padded tensor\n \"\"\"\n left = (kernel_size - stride + 1) // 2\n right = (kernel_size - stride) // 2\n pad = (left, right, left, right)\n return F.pad(x, pad, **self.pad_args)\n\n def get_self_correlation_mask(self, img_feat):\n \"\"\"Create self correlation mask.\n\n Args:\n img_feat (Tensor): Input tensor.\n\n Returns:\n Tensor: Mask tensor.\n \"\"\"\n _, _, h, w = img_feat.shape\n # As ONNX does not support dynamic num_classes, we have to convert it\n # into an integer\n self_mask = F.one_hot(\n torch.arange(h * w).view(h, w), num_classes=int(h * w))\n self_mask = self_mask.permute(2, 0, 1).view(1, h * w, h, w)\n # use large negative value to mask out self-correlation before softmax\n self_mask = self_mask * self.penalty\n return self_mask.to(img_feat)\n\n @staticmethod\n def l2_norm(x):\n \"\"\"L2 normalization function.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: L2 normalized output tensor.\n \"\"\"\n\n x = x**2\n x = x.sum(dim=[1, 2, 3], keepdim=True)\n return torch.sqrt(x)\n" }, { "path": "mmagic/models/editors/gca/resgca_dec.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init\n\nfrom mmagic.registry import MODELS\nfrom .gca_module import GCAModule\nfrom .resgca_enc import BasicBlock\n\n\nclass BasicBlockDec(BasicBlock):\n \"\"\"Basic residual block for decoder.\n\n For decoder, we use ConvTranspose2d with kernel_size 4 and padding 1 for\n conv1. And the output channel of conv1 is modified from `out_channels` to\n `in_channels`.\n \"\"\"\n\n def build_conv1(self, in_channels, out_channels, kernel_size, stride,\n conv_cfg, norm_cfg, act_cfg, with_spectral_norm):\n \"\"\"Build conv1 of the block.\n\n Args:\n in_channels (int): The input channels of the ConvModule.\n out_channels (int): The output channels of the ConvModule.\n kernel_size (int): The kernel size of the ConvModule.\n stride (int): The stride of the ConvModule. If stride is set to 2,\n then ``conv_cfg`` will be overwritten as\n ``dict(type='Deconv')`` and ``kernel_size`` will be overwritten\n as 4.\n conv_cfg (dict): The conv config of the ConvModule.\n norm_cfg (dict): The norm config of the ConvModule.\n act_cfg (dict): The activation config of the ConvModule.\n with_spectral_norm (bool): Whether use spectral norm.\n\n Returns:\n nn.Module: The built ConvModule.\n \"\"\"\n if stride == 2:\n conv_cfg = dict(type='Deconv')\n kernel_size = 4\n padding = 1\n else:\n padding = kernel_size // 2\n\n return ConvModule(\n in_channels,\n in_channels,\n kernel_size,\n stride=stride,\n padding=padding,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n\n def build_conv2(self, in_channels, out_channels, kernel_size, conv_cfg,\n norm_cfg, with_spectral_norm):\n \"\"\"Build conv2 of the block.\n\n Args:\n in_channels (int): The input channels of the ConvModule.\n out_channels (int): The output channels of the ConvModule.\n kernel_size (int): The kernel size of the ConvModule.\n conv_cfg (dict): The conv config of the ConvModule.\n norm_cfg (dict): The norm config of the ConvModule.\n with_spectral_norm (bool): Whether use spectral norm.\n\n Returns:\n nn.Module: The built ConvModule.\n \"\"\"\n return ConvModule(\n in_channels,\n out_channels,\n kernel_size,\n stride=1,\n padding=kernel_size // 2,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm)\n\n\n@MODELS.register_module()\nclass ResNetDec(BaseModule):\n \"\"\"ResNet decoder for image matting.\n\n This class is adopted from https://github.com/Yaoyi-Li/GCA-Matting.\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlockDec` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Channel num of input features.\n kernel_size (int): Kernel size of the conv layers in the decoder.\n conv_cfg (dict): dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n with_spectral_norm (bool): Whether use spectral norm after conv.\n Default: False.\n late_downsample (bool): Whether to adopt late downsample strategy,\n Default: False.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n block,\n layers,\n in_channels,\n kernel_size=3,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(\n type='LeakyReLU', negative_slope=0.2, inplace=True),\n with_spectral_norm=False,\n late_downsample=False,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n if block == 'BasicBlockDec':\n block = BasicBlockDec\n else:\n raise NotImplementedError(f'{block} is not implemented.')\n\n self.kernel_size = kernel_size\n self.inplanes = in_channels\n self.midplanes = 64 if late_downsample else 32\n\n self.layer1 = self._make_layer(block, 256, layers[0], conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n self.layer2 = self._make_layer(block, 128, layers[1], conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n self.layer3 = self._make_layer(block, 64, layers[2], conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n self.layer4 = self._make_layer(block, self.midplanes, layers[3],\n conv_cfg, norm_cfg, act_cfg,\n with_spectral_norm)\n\n self.conv1 = ConvModule(\n self.midplanes,\n 32,\n 4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='Deconv'),\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n\n self.conv2 = ConvModule(\n 32,\n 1,\n self.kernel_size,\n padding=self.kernel_size // 2,\n act_cfg=None)\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n for m in self.modules():\n if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n constant_init(m.weight, 1)\n constant_init(m.bias, 0)\n\n # Zero-initialize the last BN in each residual branch, so that the\n # residual branch starts with zeros, and each residual block behaves\n # like an identity. This improves the model by 0.2~0.3% according to\n # https://arxiv.org/abs/1706.02677\n for m in self.modules():\n if isinstance(m, BasicBlockDec):\n constant_init(m.conv2.bn.weight, 0)\n\n def _make_layer(self, block, planes, num_blocks, conv_cfg, norm_cfg,\n act_cfg, with_spectral_norm):\n upsample = nn.Sequential(\n nn.UpsamplingNearest2d(scale_factor=2),\n ConvModule(\n self.inplanes,\n planes * block.expansion,\n 1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm))\n\n layers = [\n block(\n self.inplanes,\n planes,\n kernel_size=self.kernel_size,\n stride=2,\n interpolation=upsample,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n ]\n self.inplanes = planes * block.expansion\n for _ in range(1, num_blocks):\n layers.append(\n block(\n self.inplanes,\n planes,\n kernel_size=self.kernel_size,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm))\n\n return nn.Sequential(*layers)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.conv1(x)\n x = self.conv2(x)\n\n return x\n\n\n@MODELS.register_module()\nclass ResShortcutDec(ResNetDec):\n \"\"\"ResNet decoder for image matting with shortcut connection.\n\n ::\n\n feat1 --------------------------- conv2 --- out\n |\n feat2 ---------------------- conv1\n |\n feat3 ----------------- layer4\n |\n feat4 ------------ layer3\n |\n feat5 ------- layer2\n |\n out --- layer1\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlockDec` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Channel number of input features.\n kernel_size (int): Kernel size of the conv layers in the decoder.\n conv_cfg (dict): Dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n late_downsample (bool): Whether to adopt late downsample strategy,\n Default: False.\n \"\"\"\n\n def forward(self, inputs):\n \"\"\"Forward function of resnet shortcut decoder.\n\n Args:\n inputs (dict): Output dictionary of the ResNetEnc containing:\n\n - out (Tensor): Output of the ResNetEnc.\n - feat1 (Tensor): Shortcut connection from input image.\n - feat2 (Tensor): Shortcut connection from conv2 of ResNetEnc.\n - feat3 (Tensor): Shortcut connection from layer1 of ResNetEnc.\n - feat4 (Tensor): Shortcut connection from layer2 of ResNetEnc.\n - feat5 (Tensor): Shortcut connection from layer3 of ResNetEnc.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n feat1 = inputs['feat1']\n feat2 = inputs['feat2']\n feat3 = inputs['feat3']\n feat4 = inputs['feat4']\n feat5 = inputs['feat5']\n x = inputs['out']\n\n x = self.layer1(x) + feat5\n x = self.layer2(x) + feat4\n x = self.layer3(x) + feat3\n x = self.layer4(x) + feat2\n x = self.conv1(x) + feat1\n x = self.conv2(x)\n\n return x\n\n\n@MODELS.register_module()\nclass ResGCADecoder(ResShortcutDec):\n \"\"\"ResNet decoder with shortcut connection and gca module.\n\n ::\n\n feat1 ---------------------------------------- conv2 --- out\n |\n feat2 ----------------------------------- conv1\n |\n feat3 ------------------------------ layer4\n |\n feat4, img_feat -- gca_module - layer3\n |\n feat5 ------- layer2\n |\n out --- layer1\n\n * gca module also requires unknown tensor generated by trimap which is \\\n ignored in the above graph.\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlockDec` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Channel number of input features.\n kernel_size (int): Kernel size of the conv layers in the decoder.\n conv_cfg (dict): Dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n with_spectral_norm (bool): Whether use spectral norm. Default: False.\n late_downsample (bool): Whether to adopt late downsample strategy,\n Default: False.\n \"\"\"\n\n def __init__(self,\n block,\n layers,\n in_channels,\n kernel_size=3,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(\n type='LeakyReLU', negative_slope=0.2, inplace=True),\n with_spectral_norm=False,\n late_downsample=False):\n super().__init__(block, layers, in_channels, kernel_size, conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm,\n late_downsample)\n self.gca = GCAModule(128, 128)\n\n def forward(self, inputs):\n \"\"\"Forward function of resnet shortcut decoder.\n\n Args:\n inputs (dict): Output dictionary of the ResGCAEncoder containing:\n\n - out (Tensor): Output of the ResGCAEncoder.\n - feat1 (Tensor): Shortcut connection from input image.\n - feat2 (Tensor): Shortcut connection from conv2 of \\\n ResGCAEncoder.\n - feat3 (Tensor): Shortcut connection from layer1 of \\\n ResGCAEncoder.\n - feat4 (Tensor): Shortcut connection from layer2 of \\\n ResGCAEncoder.\n - feat5 (Tensor): Shortcut connection from layer3 of \\\n ResGCAEncoder.\n - img_feat (Tensor): Image feature extracted by guidance head.\n - unknown (Tensor): Unknown tensor generated by trimap.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n img_feat = inputs['img_feat']\n unknown = inputs['unknown']\n feat1 = inputs['feat1']\n feat2 = inputs['feat2']\n feat3 = inputs['feat3']\n feat4 = inputs['feat4']\n feat5 = inputs['feat5']\n x = inputs['out']\n\n x = self.layer1(x) + feat5\n x = self.layer2(x) + feat4\n x = self.gca(img_feat, x, unknown)\n x = self.layer3(x) + feat3\n x = self.layer4(x) + feat2\n x = self.conv1(x) + feat1\n x = self.conv2(x)\n\n return x\n" }, { "path": "mmagic/models/editors/gca/resgca_enc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule, build_activation_layer\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init\n\nfrom mmagic.registry import MODELS\nfrom .gca_module import GCAModule\n\n\nclass BasicBlock(nn.Module):\n \"\"\"Basic residual block.\n\n Args:\n in_channels (int): Input channels of the block.\n out_channels (int): Output channels of the block.\n kernel_size (int): Kernel size of the convolution layers. Default: 3.\n stride (int): Stride of the first conv of the block. Default: 1.\n interpolation (nn.Module, optional): Interpolation module for skip\n connection. Default: None.\n conv_cfg (dict): dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n with_spectral_norm (bool): Whether use spectral norm after conv.\n Default: False.\n \"\"\"\n expansion = 1\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n interpolation=None,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=False):\n super().__init__()\n assert stride in (1, 2), (\n f'stride other than 1 and 2 is not implemented, got {stride}')\n\n assert stride != 2 or interpolation is not None, (\n 'if stride is 2, interpolation should be specified')\n\n self.conv1 = self.build_conv1(in_channels, out_channels, kernel_size,\n stride, conv_cfg, norm_cfg, act_cfg,\n with_spectral_norm)\n self.conv2 = self.build_conv2(in_channels, out_channels, kernel_size,\n conv_cfg, norm_cfg, with_spectral_norm)\n\n self.interpolation = interpolation\n self.activation = build_activation_layer(act_cfg)\n self.stride = stride\n\n def build_conv1(self, in_channels, out_channels, kernel_size, stride,\n conv_cfg, norm_cfg, act_cfg, with_spectral_norm):\n \"\"\"Build conv1 of the block.\n\n Args:\n in_channels (int): The input channels of the ConvModule.\n out_channels (int): The output channels of the ConvModule.\n kernel_size (int): The kernel size of the ConvModule.\n stride (int): The stride of the ConvModule. If stride is set to 2,\n then ``conv_cfg`` will be overwritten as\n ``dict(type='Deconv')`` and ``kernel_size`` will be overwritten\n as 4.\n conv_cfg (dict): The conv config of the ConvModule.\n norm_cfg (dict): The norm config of the ConvModule.\n act_cfg (dict): The activation config of the ConvModule.\n with_spectral_norm (bool): Whether use spectral norm.\n\n Returns:\n nn.Module: The built ConvModule.\n \"\"\"\n return ConvModule(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n padding=kernel_size // 2,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n\n def build_conv2(self, in_channels, out_channels, kernel_size, conv_cfg,\n norm_cfg, with_spectral_norm):\n \"\"\"Build conv2 of the block.\n\n Args:\n in_channels (int): The input channels of the ConvModule.\n out_channels (int): The output channels of the ConvModule.\n kernel_size (int): The kernel size of the ConvModule.\n stride (int): The stride of the ConvModule. If stride is set to 2,\n then ``conv_cfg`` will be overwritten as\n ``dict(type='Deconv')`` and ``kernel_size`` will be overwritten\n as 4.\n conv_cfg (dict): The conv config of the ConvModule.\n norm_cfg (dict): The norm config of the ConvModule.\n act_cfg (dict): The activation config of the ConvModule.\n with_spectral_norm (bool): Whether use spectral norm.\n\n Returns:\n nn.Module: The built ConvModule.\n \"\"\"\n return ConvModule(\n out_channels,\n out_channels,\n kernel_size,\n stride=1,\n padding=kernel_size // 2,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n identity = x\n\n out = self.conv1(x)\n out = self.conv2(out)\n\n if self.interpolation is not None:\n identity = self.interpolation(x)\n\n out += identity\n out = self.activation(out)\n\n return out\n\n\n@MODELS.register_module()\nclass ResNetEnc(BaseModule):\n \"\"\"ResNet encoder for image matting.\n\n This class is adopted from https://github.com/Yaoyi-Li/GCA-Matting.\n Implement and pre-train on ImageNet with the tricks from\n https://arxiv.org/abs/1812.01187\n without the mix-up part.\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlock` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Number of input channels.\n conv_cfg (dict): dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n with_spectral_norm (bool): Whether use spectral norm after conv.\n Default: False.\n late_downsample (bool): Whether to adopt late downsample strategy,\n Default: False.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n block,\n layers,\n in_channels,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=False,\n late_downsample=False,\n init_cfg: Optional[dict] = None):\n\n super().__init__(init_cfg=init_cfg)\n\n if block == 'BasicBlock':\n block = BasicBlock\n else:\n raise NotImplementedError(f'{block} is not implemented.')\n\n self.inplanes = 64\n self.midplanes = 64 if late_downsample else 32\n\n start_stride = [1, 2, 1, 2] if late_downsample else [2, 1, 2, 1]\n self.conv1 = ConvModule(\n in_channels,\n 32,\n 3,\n stride=start_stride[0],\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n self.conv2 = ConvModule(\n 32,\n self.midplanes,\n 3,\n stride=start_stride[1],\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n self.conv3 = ConvModule(\n self.midplanes,\n self.inplanes,\n 3,\n stride=start_stride[2],\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n\n self.layer1 = self._make_layer(block, 64, layers[0], start_stride[3],\n conv_cfg, norm_cfg, act_cfg,\n with_spectral_norm)\n self.layer2 = self._make_layer(block, 128, layers[1], 2, conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n self.layer3 = self._make_layer(block, 256, layers[2], 2, conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n self.layer4 = self._make_layer(block, 512, layers[3], 2, conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm)\n\n self.out_channels = 512\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n if self.init_cfg['type'] == 'Pretrained':\n # if pretrained weight is trained on 3-channel images,\n # initialize other channels with zeros\n self.conv1.conv.weight.data[:, 3:, :, :] = 0\n else:\n for m in self.modules():\n if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n constant_init(m.weight, 1)\n constant_init(m.bias, 0)\n\n # Zero-initialize the last BN in each residual branch, so that the\n # residual branch starts with zeros, and each residual block\n # behaves like an identity. This improves the model by 0.2~0.3%\n # according to https://arxiv.org/abs/1706.02677\n for m in self.modules():\n if isinstance(m, BasicBlock):\n constant_init(m.conv2.bn.weight, 0)\n\n def _make_layer(self, block, planes, num_blocks, stride, conv_cfg,\n norm_cfg, act_cfg, with_spectral_norm):\n downsample = None\n if stride != 1:\n downsample = nn.Sequential(\n nn.AvgPool2d(2, stride),\n ConvModule(\n self.inplanes,\n planes * block.expansion,\n 1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm))\n\n layers = [\n block(\n self.inplanes,\n planes,\n stride=stride,\n interpolation=downsample,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm)\n ]\n self.inplanes = planes * block.expansion\n for _ in range(1, num_blocks):\n layers.append(\n block(\n self.inplanes,\n planes,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm))\n\n return nn.Sequential(*layers)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.conv3(x)\n\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n return x\n\n\n@MODELS.register_module()\nclass ResShortcutEnc(ResNetEnc):\n \"\"\"ResNet backbone for image matting with shortcut connection.\n\n ::\n\n image ---------------- shortcut[0] --- feat1\n |\n conv1-conv2 ---------- shortcut[1] --- feat2\n |\n conv3-layer1 --- shortcut[2] --- feat3\n |\n layer2 -- shortcut[4] --- feat4\n |\n layer3 - shortcut[5] --- feat5\n |\n layer4 ---------------- out\n\n Baseline model of Natural Image Matting via Guided Contextual Attention\n https://arxiv.org/pdf/2001.04069.pdf.\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlock` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Number of input channels.\n conv_cfg (dict): Dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n with_spectral_norm (bool): Whether use spectral norm after conv.\n Default: False.\n late_downsample (bool): Whether to adopt late downsample strategy.\n Default: False.\n order (tuple[str]): Order of `conv`, `norm` and `act` layer in shortcut\n convolution module. Default: ('conv', 'act', 'norm').\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n block,\n layers,\n in_channels,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=False,\n late_downsample=False,\n order=('conv', 'act', 'norm'),\n init_cfg: Optional[dict] = None):\n super().__init__(\n block,\n layers,\n in_channels,\n conv_cfg,\n norm_cfg,\n act_cfg,\n with_spectral_norm,\n late_downsample,\n init_cfg=init_cfg)\n\n # TODO: rename self.midplanes to self.mid_channels in ResNetEnc\n self.shortcut_in_channels = [in_channels, self.midplanes, 64, 128, 256]\n self.shortcut_out_channels = [32, self.midplanes, 64, 128, 256]\n\n self.shortcut = nn.ModuleList()\n for in_channel, out_channel in zip(self.shortcut_in_channels,\n self.shortcut_out_channels):\n self.shortcut.append(\n self._make_shortcut(in_channel, out_channel, conv_cfg,\n norm_cfg, act_cfg, order,\n with_spectral_norm))\n\n def _make_shortcut(self, in_channels, out_channels, conv_cfg, norm_cfg,\n act_cfg, order, with_spectral_norm):\n return nn.Sequential(\n ConvModule(\n in_channels,\n out_channels,\n 3,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n order=order),\n ConvModule(\n out_channels,\n out_channels,\n 3,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n order=order))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n dict: Contains the output tensor and shortcut feature.\n \"\"\"\n out = self.conv1(x)\n x1 = self.conv2(out)\n out = self.conv3(x1)\n\n x2 = self.layer1(out)\n x3 = self.layer2(x2)\n x4 = self.layer3(x3)\n out = self.layer4(x4)\n\n feat1 = self.shortcut[0](x)\n feat2 = self.shortcut[1](x1)\n feat3 = self.shortcut[2](x2)\n feat4 = self.shortcut[3](x3)\n feat5 = self.shortcut[4](x4)\n\n return {\n 'out': out,\n 'feat1': feat1,\n 'feat2': feat2,\n 'feat3': feat3,\n 'feat4': feat4,\n 'feat5': feat5,\n }\n\n\n@MODELS.register_module()\nclass ResGCAEncoder(ResShortcutEnc):\n \"\"\"ResNet backbone with shortcut connection and gca module.\n\n ::\n\n image ---------------- shortcut[0] -------------- feat1\n |\n conv1-conv2 ---------- shortcut[1] -------------- feat2\n |\n conv3-layer1 ---- shortcut[2] -------------- feat3\n |\n | image - guidance_conv ------------ img_feat\n | |\n layer2 --- gca_module - shortcut[4] - feat4\n |\n layer3 -- shortcut[5] - feat5\n |\n layer4 --------------- out\n\n * gca module also requires unknown tensor generated by trimap which is \\\n ignored in the above graph.\n\n Implementation of Natural Image Matting via Guided Contextual Attention\n https://arxiv.org/pdf/2001.04069.pdf.\n\n Args:\n block (str): Type of residual block. Currently only `BasicBlock` is\n implemented.\n layers (list[int]): Number of layers in each block.\n in_channels (int): Number of input channels.\n conv_cfg (dict): Dictionary to construct convolution layer. If it is\n None, 2d convolution will be applied. Default: None.\n norm_cfg (dict): Config dict for normalization layer. \"BN\" by default.\n act_cfg (dict): Config dict for activation layer, \"ReLU\" by default.\n late_downsample (bool): Whether to adopt late downsample strategy.\n Default: False.\n order (tuple[str]): Order of `conv`, `norm` and `act` layer in shortcut\n convolution module. Default: ('conv', 'act', 'norm').\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n block,\n layers,\n in_channels,\n conv_cfg=None,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=False,\n late_downsample=False,\n order=('conv', 'act', 'norm'),\n init_cfg: Optional[dict] = None):\n super().__init__(\n block,\n layers,\n in_channels,\n conv_cfg,\n norm_cfg,\n act_cfg,\n with_spectral_norm,\n late_downsample,\n order,\n init_cfg=init_cfg)\n\n assert in_channels in (4, 6), (\n f'in_channels must be 4 or 6, but got {in_channels}')\n\n self.trimap_channels = in_channels - 3\n\n guidance_in_channels = [3, 16, 32]\n guidance_out_channels = [16, 32, 128]\n\n guidance_head = []\n for in_channel, out_channel in zip(guidance_in_channels,\n guidance_out_channels):\n guidance_head += [\n ConvModule(\n in_channel,\n out_channel,\n 3,\n stride=2,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n with_spectral_norm=with_spectral_norm,\n padding_mode='reflect',\n order=order)\n ]\n self.guidance_head = nn.Sequential(*guidance_head)\n\n self.gca = GCAModule(128, 128)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (N, C, H, W).\n\n Returns:\n dict: Contains the output tensor, shortcut feature and \\\n intermediate feature.\n \"\"\"\n out = self.conv1(x)\n x1 = self.conv2(out)\n out = self.conv3(x1)\n\n img_feat = self.guidance_head(x[:, :3, ...])\n if self.trimap_channels == 3:\n unknown = x[:, 4:5, ...]\n else:\n unknown = x[:, 3:, ...].eq(1).float()\n # same as img_feat, downsample to 1/8\n unknown = F.interpolate(unknown, scale_factor=1 / 8, mode='nearest')\n\n x2 = self.layer1(out)\n x3 = self.layer2(x2)\n x3 = self.gca(img_feat, x3, unknown)\n x4 = self.layer3(x3)\n out = self.layer4(x4)\n\n # shortcut block\n feat1 = self.shortcut[0](x)\n feat2 = self.shortcut[1](x1)\n feat3 = self.shortcut[2](x2)\n feat4 = self.shortcut[3](x3)\n feat5 = self.shortcut[4](x4)\n\n return {\n 'out': out,\n 'feat1': feat1,\n 'feat2': feat2,\n 'feat3': feat3,\n 'feat4': feat4,\n 'feat5': feat5,\n 'img_feat': img_feat,\n 'unknown': unknown\n }\n" }, { "path": "mmagic/models/editors/ggan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .ggan import GGAN\n\n__all__ = ['GGAN']\n" }, { "path": "mmagic/models/editors/ggan/ggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseGAN\n\n\n@MODELS.register_module()\nclass GGAN(BaseGAN):\n \"\"\"Implementation of `Geometric GAN`.\n\n `_(GGAN).\n \"\"\"\n\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n r\"\"\"Get disc loss. GGAN use hinge loss to train\n the discriminator.\n\n .. math:\n L_{D} = -\\mathbb{E}_{\\left(x, y\\right)\\sim{p}_{data}}\n \\left[\\min\\left(0, -1 + D\\left(x, y\\right)\\right)\\right]\n -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\\left[\\min\n \\left(0, -1 - D\\left(G\\left(z\\right), y\\right)\\right)\\right]\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.relu(1 + disc_pred_fake).mean()\n losses_dict['loss_disc_real'] = F.relu(1 - disc_pred_real).mean()\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake):\n r\"\"\"Get disc loss. GGAN use hinge loss to train\n the generator.\n\n .. math:\n L_{G} = -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\n D\\left(G\\left(z\\right), y\\right)\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/glean/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .glean_styleganv2 import GLEANStyleGANv2\n\n__all__ = ['GLEANStyleGANv2']\n" }, { "path": "mmagic/models/editors/glean/glean_styleganv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import PixelShufflePack\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\nfrom ..esrgan.rrdb_net import RRDB\n\n\n@MODELS.register_module()\nclass GLEANStyleGANv2(BaseModule):\n r\"\"\"GLEAN (using StyleGANv2) architecture for super-resolution.\n\n Paper:\n GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution,\n CVPR, 2021\n\n This method makes use of StyleGAN2 and hence the arguments mostly follow\n that in 'StyleGAN2v2Generator'.\n\n In StyleGAN2, we use a static architecture composing of a style mapping\n module and number of convolutional style blocks. More details can be found\n in: Analyzing and Improving the Image Quality of StyleGAN CVPR2020.\n\n You can load pretrained model through passing information into\n ``pretrained`` argument. We have already offered official weights as\n follows:\n\n - stylegan2-ffhq-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa\n - stylegan2-horse-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa\n - stylegan2-car-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa\n - stylegan2-cat-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa\n - stylegan2-church-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa\n\n If you want to load the ema model, you can just use following codes:\n\n .. code-block:: python\n\n # ckpt_http is one of the valid path from http source\n generator = StyleGANv2Generator(1024, 512,\n pretrained=dict(\n ckpt_path=ckpt_http,\n prefix='generator_ema'))\n\n Of course, you can also download the checkpoint in advance and set\n ``ckpt_path`` with local path. If you just want to load the original\n generator (not the ema model), please set the prefix with 'generator'.\n\n Note that our implementation allows to generate BGR image, while the\n original StyleGAN2 outputs RGB images by default. Thus, we provide\n ``bgr2rgb`` argument to convert the image space.\n\n Args:\n in_size (int): The size of the input image.\n out_size (int): The output size of the StyleGAN2 generator.\n img_channels (int): Number of channels of the input images. 3 for RGB\n image and 1 for grayscale image. Default: 3.\n rrdb_channels (int): Number of channels of the RRDB features.\n Default: 64.\n num_rrdbs (int): Number of RRDB blocks in the encoder. Default: 23.\n style_channels (int): The number of channels for style code.\n Default: 512.\n num_mlps (int, optional): The number of MLP layers. Defaults to 8.\n channel_multiplier (int, optional): The multiplier factor for the\n channel number. Defaults to 2.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 3, 3, 1].\n lr_mlp (float, optional): The learning rate for the style mapping\n layer. Defaults to 0.01.\n default_style_mode (str, optional): The default mode of style mixing.\n In training, we adopt mixing style mode in default. However, in the\n evaluation, we use 'single' style mode. `['mix', 'single']` are\n currently supported. Defaults to 'mix'.\n eval_style_mode (str, optional): The evaluation mode of style mixing.\n Defaults to 'single'.\n mix_prob (float, optional): Mixing probability. The value should be\n in range of [0, 1]. Defaults to 0.9.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n bgr2rgb (bool, optional): Whether to flip the image channel dimension.\n Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_size,\n out_size,\n img_channels=3,\n rrdb_channels=64,\n num_rrdbs=23,\n style_channels=512,\n num_mlps=8,\n channel_multiplier=2,\n blur_kernel=[1, 3, 3, 1],\n lr_mlp=0.01,\n default_style_mode='mix',\n eval_style_mode='single',\n mix_prob=0.9,\n init_cfg=None,\n fp16_enabled=False,\n bgr2rgb=False):\n\n super().__init__(init_cfg=init_cfg)\n\n # input size must be strictly smaller than output size\n if in_size >= out_size:\n raise ValueError('in_size must be smaller than out_size, but got '\n f'{in_size} and {out_size}.')\n\n # latent bank (StyleGANv2), with weights being fixed\n self.generator = MODELS.build(\n dict(\n type='StyleGANv2Generator',\n out_size=out_size,\n style_channels=style_channels,\n num_mlps=num_mlps,\n channel_multiplier=channel_multiplier,\n blur_kernel=blur_kernel,\n lr_mlp=lr_mlp,\n default_style_mode=default_style_mode,\n eval_style_mode=eval_style_mode,\n mix_prob=mix_prob,\n fp16_enabled=fp16_enabled,\n bgr2rgb=bgr2rgb))\n self.generator.requires_grad_(False)\n\n self.in_size = in_size\n self.style_channels = style_channels\n channels = self.generator.channels\n\n # encoder\n num_styles = int(np.log2(out_size)) * 2 - 2\n encoder_res = [2**i for i in range(int(np.log2(in_size)), 1, -1)]\n self.encoder = nn.ModuleList()\n self.encoder.append(\n nn.Sequential(\n RRDBFeatureExtractor(\n img_channels, rrdb_channels, num_blocks=num_rrdbs),\n nn.Conv2d(\n rrdb_channels, channels[in_size], 3, 1, 1, bias=True),\n nn.LeakyReLU(negative_slope=0.2, inplace=True)))\n for res in encoder_res:\n in_channels = channels[res]\n if res > 4:\n out_channels = channels[res // 2]\n block = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True),\n nn.LeakyReLU(negative_slope=0.2, inplace=True))\n else:\n block = nn.Sequential(\n nn.Conv2d(in_channels, in_channels, 3, 1, 1, bias=True),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Flatten(),\n nn.Linear(16 * in_channels, num_styles * style_channels))\n self.encoder.append(block)\n\n # additional modules for StyleGANv2\n self.fusion_out = nn.ModuleList()\n self.fusion_skip = nn.ModuleList()\n for res in encoder_res[::-1]:\n num_channels = channels[res]\n self.fusion_out.append(\n nn.Conv2d(num_channels * 2, num_channels, 3, 1, 1, bias=True))\n self.fusion_skip.append(\n nn.Conv2d(num_channels + 3, 3, 3, 1, 1, bias=True))\n\n # decoder\n decoder_res = [\n 2**i\n for i in range(int(np.log2(in_size)), int(np.log2(out_size) + 1))\n ]\n self.decoder = nn.ModuleList()\n for res in decoder_res:\n if res == in_size:\n in_channels = channels[res]\n else:\n in_channels = 2 * channels[res]\n\n if res < out_size:\n out_channels = channels[res * 2]\n self.decoder.append(\n PixelShufflePack(\n in_channels, out_channels, 2, upsample_kernel=3))\n else:\n self.decoder.append(\n nn.Sequential(\n nn.Conv2d(in_channels, 64, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(64, img_channels, 3, 1, 1)))\n\n def forward(self, lq):\n \"\"\"Forward function.\n\n Args:\n lq (Tensor): Input LR image with shape (n, c, h, w).\n\n Returns:\n Tensor: Output HR image.\n \"\"\"\n\n h, w = lq.shape[2:]\n if h != self.in_size or w != self.in_size:\n raise AssertionError(\n f'Spatial resolution must equal in_size ({self.in_size}).'\n f' Got ({h}, {w}).')\n\n # encoder\n feat = lq\n encoder_features = []\n for block in self.encoder:\n feat = block(feat)\n encoder_features.append(feat)\n encoder_features = encoder_features[::-1]\n\n latent = encoder_features[0].view(lq.size(0), -1, self.style_channels)\n encoder_features = encoder_features[1:]\n\n # generator\n injected_noise = [\n getattr(self.generator, f'injected_noise_{i}')\n for i in range(self.generator.num_injected_noises)\n ]\n # 4x4 stage\n out = self.generator.constant_input(latent)\n out = self.generator.conv1(out, latent[:, 0], noise=injected_noise[0])\n skip = self.generator.to_rgb1(out, latent[:, 1])\n\n _index = 1\n\n # 8x8 ---> higher res\n generator_features = []\n for up_conv, conv, noise1, noise2, to_rgb in zip(\n self.generator.convs[::2], self.generator.convs[1::2],\n injected_noise[1::2], injected_noise[2::2],\n self.generator.to_rgbs):\n\n # feature fusion by channel-wise concatenation\n if out.size(2) <= self.in_size:\n fusion_index = (_index - 1) // 2\n feat = encoder_features[fusion_index]\n\n out = torch.cat([out, feat], dim=1)\n out = self.fusion_out[fusion_index](out)\n\n skip = torch.cat([skip, feat], dim=1)\n skip = self.fusion_skip[fusion_index](skip)\n\n # original StyleGAN operations\n out = up_conv(out, latent[:, _index], noise=noise1)\n out = conv(out, latent[:, _index + 1], noise=noise2)\n skip = to_rgb(out, latent[:, _index + 2], skip)\n\n # store features for decoder\n if out.size(2) > self.in_size:\n generator_features.append(out)\n\n _index += 2\n\n # decoder\n hr = encoder_features[-1]\n for i, block in enumerate(self.decoder):\n if i > 0:\n hr = torch.cat([hr, generator_features[i - 1]], dim=1)\n hr = block(hr)\n\n return hr\n\n\nclass RRDBFeatureExtractor(nn.Module):\n \"\"\"Feature extractor composed of Residual-in-Residual Dense Blocks (RRDBs).\n\n It is equivalent to ESRGAN with the upsampling module removed.\n\n Args:\n in_channels (int): Channel number of inputs. Default: 3.\n mid_channels (int): Channel number of intermediate features.\n Default: 64\n num_blocks (int): Block number in the trunk network. Default: 23.\n growth_channels (int): Channels for each growth. Default: 32.\n \"\"\"\n\n def __init__(self,\n in_channels=3,\n mid_channels=64,\n num_blocks=23,\n growth_channels=32):\n\n super().__init__()\n\n self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)\n self.body = make_layer(\n RRDB,\n num_blocks,\n mid_channels=mid_channels,\n growth_channels=growth_channels)\n self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n feat = self.conv_first(x)\n return feat + self.conv_body(self.body(feat))\n" }, { "path": "mmagic/models/editors/global_local/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .gl_decoder import GLDecoder\nfrom .gl_dilation import GLDilationNeck\nfrom .gl_disc import GLDiscs\nfrom .gl_encoder import GLEncoder\nfrom .gl_encoder_decoder import GLEncoderDecoder\nfrom .gl_inpaintor import GLInpaintor\n\n__all__ = [\n 'GLEncoder', 'GLDecoder', 'GLEncoderDecoder', 'GLDilationNeck',\n 'GLInpaintor', 'GLDiscs'\n]\n" }, { "path": "mmagic/models/editors/global_local/gl_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GLDecoder(BaseModule):\n \"\"\"Decoder used in Global&Local model.\n\n This implementation follows:\n Globally and locally Consistent Image Completion\n\n Args:\n in_channels (int): Channel number of input feature.\n norm_cfg (dict): Config dict to build norm layer.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n out_act (str): Output activation type, \"clip\" by default. Noted that\n in our implementation, we clip the output with range [-1, 1].\n \"\"\"\n\n def __init__(self,\n in_channels=256,\n norm_cfg=None,\n act_cfg=dict(type='ReLU'),\n out_act='clip'):\n super().__init__()\n self.dec1 = ConvModule(\n in_channels,\n 256,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec2 = ConvModule(\n 256,\n 256,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec3 = ConvModule(\n 256,\n 128,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='Deconv'),\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec4 = ConvModule(\n 128,\n 128,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec5 = ConvModule(\n 128,\n 64,\n kernel_size=4,\n stride=2,\n padding=1,\n conv_cfg=dict(type='Deconv'),\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec6 = ConvModule(\n 64,\n 32,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg)\n self.dec7 = ConvModule(\n 32,\n 3,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=None,\n act_cfg=None)\n\n if out_act == 'sigmoid':\n self.output_act = nn.Sigmoid()\n elif out_act == 'clip':\n self.output_act = partial(torch.clamp, min=-1, max=1.)\n else:\n raise ValueError(\n f'{out_act} activation for output has not be supported.')\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n for i in range(7):\n x = getattr(self, f'dec{i + 1}')(x)\n x = self.output_act(x)\n return x\n" }, { "path": "mmagic/models/editors/global_local/gl_dilation.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GLDilationNeck(BaseModule):\n \"\"\"Dilation Backbone used in Global&Local model.\n\n This implementation follows:\n Globally and locally Consistent Image Completion\n\n Args:\n in_channels (int): Channel number of input feature.\n conv_type (str): The type of conv module. In DeepFillv1 model, the\n `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type`\n should be 'gated_conv'.\n norm_cfg (dict): Config dict to build norm layer.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n kwargs (keyword arguments).\n \"\"\"\n _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule)\n\n def __init__(self,\n in_channels=256,\n conv_type='conv',\n norm_cfg=None,\n act_cfg=dict(type='ReLU'),\n **kwargs):\n super().__init__()\n conv_module = self._conv_type[conv_type]\n dilation_convs_ = []\n for i in range(4):\n dilation_ = int(2**(i + 1))\n dilation_convs_.append(\n conv_module(\n in_channels,\n in_channels,\n kernel_size=3,\n padding=dilation_,\n dilation=dilation_,\n stride=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs))\n self.dilation_convs = nn.Sequential(*dilation_convs_)\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n x = self.dilation_convs(x)\n return x\n" }, { "path": "mmagic/models/editors/global_local/gl_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import MultiLayerDiscriminator\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GLDiscs(BaseModule):\n \"\"\"Discriminators in Global&Local.\n\n This discriminator contains a local discriminator and a global\n discriminator as described in the original paper:\n Globally and locally Consistent Image Completion\n\n Args:\n global_disc_cfg (dict): Config dict to build global discriminator.\n local_disc_cfg (dict): Config dict to build local discriminator.\n \"\"\"\n\n def __init__(self, global_disc_cfg, local_disc_cfg):\n super().__init__()\n self.global_disc = MultiLayerDiscriminator(**global_disc_cfg)\n self.local_disc = MultiLayerDiscriminator(**local_disc_cfg)\n\n self.fc = nn.Linear(2048, 1, bias=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (tuple[torch.Tensor]): Contains global image and the local image\n patch.\n\n Returns:\n tuple[torch.Tensor]: Contains the prediction from discriminators \\\n in global image and local image patch.\n \"\"\"\n g_img, l_img = x\n g_pred = self.global_disc(g_img)\n l_pred = self.local_disc(l_img)\n\n pred = self.fc(torch.cat([g_pred, l_pred], dim=1))\n\n return pred\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n\n for m in self.modules():\n # Here, we only initialize the module with fc layer since the\n # conv and norm layers has been initialized in `ConvModule`.\n if isinstance(m, nn.Linear):\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n nn.init.constant_(m.bias.data, 0.0)\n\n self._is_init = True\n" }, { "path": "mmagic/models/editors/global_local/gl_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GLEncoder(BaseModule):\n \"\"\"Encoder used in Global&Local model.\n\n This implementation follows:\n Globally and locally Consistent Image Completion\n\n Args:\n norm_cfg (dict): Config dict to build norm layer.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n \"\"\"\n\n def __init__(self, norm_cfg=None, act_cfg=dict(type='ReLU')):\n super().__init__()\n\n channel_list = [64, 128, 128, 256, 256, 256]\n kernel_size_list = [5, 3, 3, 3, 3, 3]\n stride_list = [1, 2, 1, 2, 1, 1]\n in_channels = 4\n for i in range(6):\n ks = kernel_size_list[i]\n padding = (ks - 1) // 2\n self.add_module(\n f'enc{i + 1}',\n ConvModule(\n in_channels,\n channel_list[i],\n kernel_size=ks,\n stride=stride_list[i],\n padding=padding,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg))\n in_channels = channel_list[i]\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n for i in range(6):\n x = getattr(self, f'enc{i + 1}')(x)\n return x\n" }, { "path": "mmagic/models/editors/global_local/gl_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GLEncoderDecoder(BaseModule):\n \"\"\"Encoder-Decoder used in Global&Local model.\n\n This implementation follows:\n Globally and locally Consistent Image Completion\n\n The architecture of the encoder-decoder is:\\\n (conv2d x 6) --> (dilated conv2d x 4) --> (conv2d or deconv2d x 7)\n\n Args:\n encoder (dict): Config dict to encoder.\n decoder (dict): Config dict to build decoder.\n dilation_neck (dict): Config dict to build dilation neck.\n \"\"\"\n\n def __init__(self,\n encoder=dict(type='GLEncoder'),\n decoder=dict(type='GLDecoder'),\n dilation_neck=dict(type='GLDilationNeck')):\n super().__init__()\n self.encoder = MODELS.build(encoder)\n self.decoder = MODELS.build(decoder)\n self.dilation_neck = MODELS.build(dilation_neck)\n\n # support fp16\n self.fp16_enabled = False\n\n def forward(self, x):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n x = self.encoder(x)\n if isinstance(x, dict):\n x = x['out']\n x = self.dilation_neck(x)\n x = self.decoder(x)\n\n return x\n" }, { "path": "mmagic/models/editors/global_local/gl_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional\n\nimport torch\n\nfrom mmagic.models.base_models import OneStageInpaintor\nfrom mmagic.models.utils import extract_around_bbox, extract_bbox_patch\nfrom mmagic.registry import MODELS\nfrom ...utils import set_requires_grad\n\n\n@MODELS.register_module()\nclass GLInpaintor(OneStageInpaintor):\n \"\"\"Inpaintor for global&local method.\n\n This inpaintor is implemented according to the paper:\n Globally and Locally Consistent Image Completion\n\n Importantly, this inpaintor is an example for using custom training\n schedule based on `OneStageInpaintor`.\n\n The training pipeline of global&local is as following:\n\n .. code-block:: python\n\n if cur_iter < iter_tc:\n update generator with only l1 loss\n else:\n update discriminator\n if cur_iter > iter_td:\n update generator with l1 loss and adversarial loss\n\n The new attribute `cur_iter` is added for recording current number of\n iteration. The `train_cfg` contains the setting of the training schedule:\n\n .. code-block:: python\n\n train_cfg = dict(\n start_iter=0,\n disc_step=1,\n iter_tc=90000,\n iter_td=100000\n )\n\n `iter_tc` and `iter_td` correspond to the notation :math:`T_C` and\n :math:`T_D` of the original paper.\n\n Args:\n generator (dict): Config for encoder-decoder style generator.\n disc (dict): Config for discriminator.\n loss_gan (dict): Config for adversarial loss.\n loss_gp (dict): Config for gradient penalty loss.\n loss_disc_shift (dict): Config for discriminator shift loss.\n loss_composed_percep (dict): Config for perceptural and style loss with\n composed image as input.\n loss_out_percep (dict): Config for perceptual and style loss with\n direct output as input.\n loss_l1_hole (dict): Config for l1 loss in the hole.\n loss_l1_valid (dict): Config for l1 loss in the valid region.\n loss_tv (dict): Config for total variation loss.\n train_cfg (dict): Configs for training scheduler. `disc_step` must be\n contained for indicates the discriminator updating steps in each\n training step.\n test_cfg (dict): Configs for testing scheduler.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n data_preprocessor: dict,\n encdec: dict,\n disc=None,\n loss_gan=None,\n loss_gp=None,\n loss_disc_shift=None,\n loss_composed_percep=None,\n loss_out_percep=False,\n loss_l1_hole=None,\n loss_l1_valid=None,\n loss_tv=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg: Optional[dict] = None):\n super().__init__(\n data_preprocessor=data_preprocessor,\n encdec=encdec,\n disc=disc,\n loss_gan=loss_gan,\n loss_gp=loss_gp,\n loss_disc_shift=loss_disc_shift,\n loss_composed_percep=loss_composed_percep,\n loss_out_percep=loss_out_percep,\n loss_l1_hole=loss_l1_hole,\n loss_l1_valid=loss_l1_valid,\n loss_tv=loss_tv,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg)\n\n if self.train_cfg is not None:\n self.cur_iter = self.train_cfg.start_iter\n\n def generator_loss(self, fake_res, fake_img, fake_local, gt, mask,\n masked_img):\n \"\"\"Forward function in generator training step.\n\n In this function, we mainly compute the loss items for generator with\n the given (fake_res, fake_img). In general, the `fake_res` is the\n direct output of the generator and the `fake_img` is the composition of\n direct output and ground-truth image.\n\n Args:\n fake_res (torch.Tensor): Direct output of the generator.\n fake_img (torch.Tensor): Composition of `fake_res` and\n ground-truth image.\n fake_local (torch.Tensor): Local image.\n gt (torch.Tensor): Ground-truth image.\n mask (torch.Tensor): Mask image.\n masked_img (torch.Tensor): Composition of mask image and\n ground-truth image.\n Returns:\n tuple[dict]: A tuple containing two dictionaries. The first one \\\n is the result dict, which contains the results computed \\\n within this function for visualization. The second one is the \\\n loss dict, containing loss items computed in this function.\n \"\"\"\n loss = dict()\n\n # if cur_iter <= iter_td, do not calculate adversarial loss\n if self.with_gan and self.cur_iter > self.train_cfg.iter_td:\n g_fake_pred = self.disc((fake_img, fake_local))\n loss_g_fake = self.loss_gan(g_fake_pred, True, False)\n loss['loss_g_fake'] = loss_g_fake\n\n if self.with_l1_hole_loss:\n loss_l1_hole = self.loss_l1_hole(fake_res, gt, weight=mask)\n loss['loss_l1_hole'] = loss_l1_hole\n\n if self.with_l1_valid_loss:\n loss_l1_valid = self.loss_l1_valid(fake_res, gt, weight=1. - mask)\n loss['loss_l1_valid'] = loss_l1_valid\n\n res = dict(\n gt_img=gt.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=fake_res.cpu(),\n fake_img=fake_img.cpu())\n\n return res, loss\n\n def train_step(self, data: List[dict], optim_wrapper):\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n\n 1. get fake res/image\n 2. optimize discriminator (if in current schedule)\n 3. optimize generator (if in current schedule)\n\n If ``self.train_cfg.disc_step > 1``, the train step will contain\n multiple iterations for optimizing discriminator with different input\n data and sonly one iteration for optimizing generator after `disc_step`\n iterations for discriminator.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of \\\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n masked_img = batch_inputs # float\n gt_img = data_samples.gt_img\n mask = data_samples.mask\n mask = mask.float()\n\n # PyTorch 2.0 could not compile 'data_samples.mask_bbox'\n # bbox_tensor = torch.LongTensor(data_samples.mask_bbox)\n bbox_tensor = torch.LongTensor(data_samples.metainfo['mask_bbox'])\n\n input_x = torch.cat([masked_img, mask], dim=1)\n fake_res = self.generator(input_x)\n fake_img = gt_img * (1. - mask) + fake_res * mask\n\n fake_local, bbox_new = extract_around_bbox(fake_img, bbox_tensor,\n self.train_cfg.local_size)\n gt_local = extract_bbox_patch(bbox_new, gt_img)\n fake_gt_local = torch.cat([fake_local, gt_local], dim=2)\n\n # if cur_iter > iter_tc, update discriminator\n if (self.train_cfg.disc_step > 0\n and self.cur_iter > self.train_cfg.iter_tc):\n # set discriminator requires_grad as True\n set_requires_grad(self.disc, True)\n\n fake_data = (fake_img.detach(), fake_local.detach())\n real_data = (gt_img, gt_local)\n disc_losses = self.forward_train_d(fake_data, False, True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].zero_grad()\n optim_wrapper['disc'].backward(loss_disc)\n\n disc_losses = self.forward_train_d(real_data, True, True)\n loss_disc, log_vars_d = self.parse_losses(disc_losses)\n log_vars.update(log_vars_d)\n optim_wrapper['disc'].backward(loss_disc)\n optim_wrapper['disc'].step()\n self.disc_step_count = (self.disc_step_count +\n 1) % self.train_cfg.disc_step\n\n # if cur_iter <= iter_td, do not update generator\n if (self.disc_step_count != 0\n or self.cur_iter <= self.train_cfg.iter_td):\n results = dict(\n gt_img=gt_img.cpu(),\n masked_img=masked_img.cpu(),\n fake_res=fake_res.cpu(),\n fake_img=fake_img.cpu(),\n fake_gt_local=fake_gt_local.cpu())\n # outputs = dict(**log_vars,**results)\n\n self.cur_iter += 1\n return log_vars\n\n # set discriminators requires_grad as False to avoid extra computation.\n set_requires_grad(self.disc, False)\n # update generator\n if (self.cur_iter <= self.train_cfg.iter_tc\n or self.cur_iter > self.train_cfg.iter_td):\n results, g_losses = self.generator_loss(fake_res, fake_img,\n fake_local, gt_img, mask,\n masked_img)\n loss_g, log_vars_g = self.parse_losses(g_losses)\n log_vars.update(log_vars_g)\n optim_wrapper['generator'].zero_grad()\n optim_wrapper['generator'].backward(loss_g)\n optim_wrapper['generator'].step()\n\n results.update(fake_gt_local=fake_gt_local.cpu())\n # outputs = dict(**log_vars,**results)\n\n self.cur_iter += 1\n\n return log_vars\n" }, { "path": "mmagic/models/editors/guided_diffusion/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .adm import AblatedDiffusionModel\nfrom .classifier import EncoderUNetModel\n\n__all__ = ['AblatedDiffusionModel', 'EncoderUNetModel']\n" }, { "path": "mmagic/models/editors/guided_diffusion/adm.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import List, Optional\n\nimport mmengine\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\nfrom mmengine.model import BaseModel, is_model_wrapper\nfrom mmengine.optim import OptimWrapperDict\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom tqdm import tqdm\n\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, SampleList\n\n\ndef classifier_grad(classifier, x, t, y=None, classifier_scale=1.0):\n \"\"\"compute classification gradient to x.\"\"\"\n assert y is not None\n with torch.enable_grad():\n x_in = x.detach().requires_grad_(True)\n timesteps = torch.ones_like(y) * t\n logits = classifier(x_in, timesteps)\n log_probs = F.log_softmax(logits, dim=-1)\n selected = log_probs[range(len(logits)), y.view(-1)]\n return torch.autograd.grad(selected.sum(), x_in)[0] * classifier_scale\n\n\n@MODELS.register_module('ADM')\n@MODELS.register_module('GuidedDiffusion')\n@MODELS.register_module()\nclass AblatedDiffusionModel(BaseModel):\n \"\"\"Guided diffusion Model.\n\n Args:\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n unet (ModelType): Config of denoising Unet.\n diffusion_scheduler (ModelType): Config of diffusion_scheduler\n scheduler.\n use_fp16 (bool): Whether to use fp16 for unet model. Defaults to False.\n classifier (ModelType): Config of classifier. Defaults to None.\n pretrained_cfgs (dict): Path Config for pretrained weights. Usually\n this is a dict contains module name and the corresponding ckpt\n path.Defaults to None.\n \"\"\"\n\n def __init__(self,\n data_preprocessor,\n unet,\n diffusion_scheduler,\n use_fp16=False,\n classifier=None,\n classifier_scale=1.0,\n rgb2bgr=False,\n pretrained_cfgs=None):\n\n super().__init__(data_preprocessor=data_preprocessor)\n self.unet = MODELS.build(unet)\n self.diffusion_scheduler = DIFFUSION_SCHEDULERS.build(\n diffusion_scheduler)\n if classifier:\n self.classifier = MODELS.build(classifier)\n else:\n self.classifier = None\n self.classifier_scale = classifier_scale\n\n if pretrained_cfgs:\n self.load_pretrained_models(pretrained_cfgs)\n if use_fp16:\n mmengine.print_log('Convert unet modules to floatpoint16')\n self.unet.convert_to_fp16()\n self.rgb2bgr = rgb2bgr\n\n def load_pretrained_models(self, pretrained_cfgs):\n \"\"\"_summary_\n\n Args:\n pretrained_cfgs (_type_): _description_\n \"\"\"\n for key, ckpt_cfg in pretrained_cfgs.items():\n prefix = ckpt_cfg.get('prefix', '')\n map_location = ckpt_cfg.get('map_location', 'cpu')\n strict = ckpt_cfg.get('strict', True)\n ckpt_path = ckpt_cfg.get('ckpt_path')\n if prefix == '':\n state_dict = torch.load(ckpt_path, map_location=map_location)\n else:\n state_dict = _load_checkpoint_with_prefix(\n prefix, ckpt_path, map_location)\n getattr(self, key).load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained {key} from {ckpt_path}')\n\n @property\n def device(self):\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n @torch.no_grad()\n def infer(self,\n scheduler_kwargs=None,\n init_image=None,\n batch_size=1,\n num_inference_steps=1000,\n labels=None,\n classifier_scale=0.0,\n show_progress=False):\n \"\"\"_summary_\n\n Args:\n init_image (_type_, optional): _description_. Defaults to None.\n batch_size (int, optional): _description_. Defaults to 1.\n num_inference_steps (int, optional): _description_.\n Defaults to 1000.\n labels (_type_, optional): _description_. Defaults to None.\n show_progress (bool, optional): _description_. Defaults to False.\n\n Returns:\n _type_: _description_\n \"\"\"\n if scheduler_kwargs is not None:\n mmengine.print_log('Switch to infer diffusion scheduler!',\n 'current')\n infer_scheduler = DIFFUSION_SCHEDULERS.build(scheduler_kwargs)\n else:\n infer_scheduler = self.diffusion_scheduler\n\n # Sample gaussian noise to begin loop\n if init_image is None:\n image = torch.randn(\n (batch_size, self.get_module(self.unet, 'in_channels'),\n self.get_module(self.unet, 'image_size'),\n self.get_module(self.unet, 'image_size')))\n else:\n image = init_image\n image = image.to(self.device)\n\n if isinstance(labels, int):\n labels = torch.tensor(labels).repeat(batch_size)\n elif labels is None:\n labels = torch.randint(\n low=0,\n high=self.get_module(self.unet, 'num_classes'),\n size=(batch_size, ),\n device=self.device)\n labels = labels.to(self.device)\n\n # set step values\n if num_inference_steps > 0:\n infer_scheduler.set_timesteps(num_inference_steps)\n\n timesteps = infer_scheduler.timesteps\n\n if show_progress and mmengine.dist.is_main_process():\n timesteps = tqdm(timesteps)\n for t in timesteps:\n # 1. predicted model_output\n model_output = self.unet(image, t, label=labels)['sample']\n\n # 2. compute previous image: x_t -> x_t-1\n if classifier_scale > 0 and self.classifier is not None:\n cond_fn = classifier_grad\n cond_kwargs = dict(\n y=labels,\n classifier=self.classifier,\n classifier_scale=classifier_scale)\n else:\n cond_fn = None\n cond_kwargs = {}\n diffusion_scheduler_output = infer_scheduler.step(\n model_output,\n t,\n image,\n cond_fn=cond_fn,\n cond_kwargs=cond_kwargs)\n image = diffusion_scheduler_output['prev_sample']\n\n if self.rgb2bgr:\n image = image[:, [2, 1, 0], ...]\n\n return {'samples': image}\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n \"\"\"_summary_\n\n Args:\n inputs (ForwardInputs): _description_\n data_samples (Optional[list], optional): _description_.\n Defaults to None.\n mode (Optional[str], optional): _description_. Defaults to None.\n\n Returns:\n List[DataSample]: _description_\n \"\"\"\n init_image = inputs.get('init_image', None)\n batch_size = inputs.get('num_batches', 1)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n\n labels = sample_kwargs.get('labels', None)\n num_inference_steps = sample_kwargs.get(\n 'num_inference_steps',\n self.diffusion_scheduler.num_train_timesteps)\n show_progress = sample_kwargs.get('show_progress', False)\n classifier_scale = sample_kwargs.get('classifier_scale',\n self.classifier_scale)\n\n outputs = self.infer(\n init_image=init_image,\n batch_size=batch_size,\n num_inference_steps=num_inference_steps,\n show_progress=show_progress,\n classifier_scale=classifier_scale)\n\n batch_sample_list = []\n for idx in range(batch_size):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n if isinstance(outputs, dict):\n gen_sample.fake_img = outputs['samples'][idx]\n gen_sample.set_gt_label(labels[idx])\n\n # Append input condition (noise and sample_kwargs) to\n # batch_sample_list\n if init_image is not None:\n gen_sample.noise = init_image[idx]\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data.\n\n Calls ``self.data_preprocessor(data)`` and\n ``self(inputs, data_sample, mode=None)`` in order. Return the\n generated results which will be passed to evaluator.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n List[DataSample]: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):\n \"\"\"_summary_\n\n Args:\n data (dict): _description_\n optim_wrapper (OptimWrapperDict): _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n\n # sampling x0 and timestep\n data = self.data_preprocessor(data)\n real_imgs = data['inputs']\n timestep = self.diffusion_scheduler.sample_timestep()\n\n # calculating loss\n loss_dict = self.diffusion_scheduler.training_loss(\n self.unet, real_imgs, timestep)\n loss, log_vars = self._parse_losses(loss_dict)\n optim_wrapper['denoising'].update_params(loss)\n\n # update EMA\n if self.with_ema_denoising and (curr_iter + 1) >= self.ema_start:\n self.denoising_ema.update_parameters(\n self.denoising_ema.\n module if is_model_wrapper(self.denoising) else self.denoising)\n # if not update buffer, copy buffer from orig model\n if not self.denoising_ema.update_buffers:\n self.denoising_ema.sync_buffers(\n self.denoising.module\n if is_model_wrapper(self.denoising) else self.denoising)\n elif self.with_ema_denoising:\n # before ema, copy weights from orig\n self.denoising_ema.sync_parameters(\n self.denoising.\n module if is_model_wrapper(self.denoising) else self.denoising)\n\n return log_vars\n\n def get_module(self, model: nn.Module, module_name: str) -> nn.Module:\n \"\"\"Get an inner module from model.\n\n Since we will wrapper DDP for some model, we have to judge whether the\n module can be indexed directly.\n\n Args:\n model (nn.Module): This model may wrapped with DDP or not.\n module_name (str): The name of specific module.\n\n Return:\n nn.Module: Returned sub module.\n \"\"\"\n module = model.module if hasattr(model, 'module') else model\n return getattr(module, module_name)\n" }, { "path": "mmagic/models/editors/guided_diffusion/classifier.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom abc import abstractmethod\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.models.editors.ddpm.denoising_unet import (QKVAttention,\n QKVAttentionLegacy,\n convert_module_to_f16,\n convert_module_to_f32)\nfrom mmagic.registry import MODELS\n\n\ndef checkpoint(func, inputs, params, flag):\n \"\"\"Evaluate a function without caching intermediate activations, allowing\n for reduced memory at the expense of extra compute in the backward pass.\n\n :param func: the function to evaluate.\n :param inputs: the argument sequence to pass to `func`.\n :param params: a sequence of parameters `func` depends on but does not\n explicitly take as arguments.\n :param flag: if False, disable gradient checkpointing.\n \"\"\"\n if flag:\n args = tuple(inputs) + tuple(params)\n return CheckpointFunction.apply(func, len(inputs), *args)\n else:\n return func(*inputs)\n\n\nclass CheckpointFunction(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, run_function, length, *args):\n ctx.run_function = run_function\n ctx.input_tensors = list(args[:length])\n ctx.input_params = list(args[length:])\n with torch.no_grad():\n output_tensors = ctx.run_function(*ctx.input_tensors)\n return output_tensors\n\n @staticmethod\n def backward(ctx, *output_grads):\n ctx.input_tensors = [\n x.detach().requires_grad_(True) for x in ctx.input_tensors\n ]\n with torch.enable_grad():\n # Fixes a bug where the first op in run_function modifies the\n # Tensor storage in place, which is not allowed for detach()'d\n # Tensors.\n shallow_copies = [x.view_as(x) for x in ctx.input_tensors]\n output_tensors = ctx.run_function(*shallow_copies)\n input_grads = torch.autograd.grad(\n output_tensors,\n ctx.input_tensors + ctx.input_params,\n output_grads,\n allow_unused=True,\n )\n del ctx.input_tensors\n del ctx.input_params\n del output_tensors\n return (None, None) + input_grads\n\n\ndef timestep_embedding(timesteps, dim, max_period=10000):\n \"\"\"Create sinusoidal timestep embeddings.\n\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an [N x dim] Tensor of positional embeddings.\n \"\"\"\n half = dim // 2\n freqs = torch.exp(-math.log(max_period) *\n torch.arange(start=0, end=half, dtype=torch.float32) /\n half).to(device=timesteps.device)\n args = timesteps[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding,\n torch.zeros_like(embedding[:, :1])],\n dim=-1)\n return embedding\n\n\ndef zero_module(module):\n \"\"\"Zero out the parameters of a module and return it.\"\"\"\n for p in module.parameters():\n p.detach().zero_()\n return module\n\n\nclass Upsample(nn.Module):\n \"\"\"An upsampling layer with an optional convolution.\n\n :param channels: channels in the inputs and outputs.\n :param use_conv: a bool determining if a convolution is applied.\n :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n upsampling occurs in the inner-two dimensions.\n \"\"\"\n\n def __init__(self, channels, use_conv, dims=2, out_channels=None):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.dims = dims\n if use_conv:\n self.conv = nn.Conv2d(\n self.channels, self.out_channels, 3, padding=1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): The tensor to upsample.\n\n Returns:\n torch.Tensor: The upsample results.\n \"\"\"\n assert x.shape[1] == self.channels\n if self.dims == 3:\n x = F.interpolate(\n x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2),\n mode='nearest')\n else:\n x = F.interpolate(x, scale_factor=2, mode='nearest')\n if self.use_conv:\n x = self.conv(x)\n return x\n\n\nclass TimestepBlock(nn.Module):\n \"\"\"Any module where forward() takes timestep embeddings as a second\n argument.\"\"\"\n\n @abstractmethod\n def forward(self, x, emb):\n \"\"\"Apply the module to `x` given `emb` timestep embeddings.\"\"\"\n\n\nclass AttentionBlock(nn.Module):\n \"\"\"An attention block that allows spatial positions to attend to each\n other.\n\n Originally ported from here, but adapted to the N-d case.\n https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.\n \"\"\"\n\n def __init__(\n self,\n channels,\n num_heads=1,\n num_head_channels=-1,\n use_checkpoint=False,\n use_new_attention_order=False,\n ):\n super().__init__()\n self.channels = channels\n if num_head_channels == -1:\n self.num_heads = num_heads\n else:\n assert (\n channels %\n num_head_channels == 0), f'q,k,v channels {channels} is not '\n 'divisible by num_head_channels {num_head_channels}'\n self.num_heads = channels // num_head_channels\n self.use_checkpoint = use_checkpoint\n self.norm = normalization(channels)\n self.qkv = nn.Conv1d(channels, channels * 3, 1)\n if use_new_attention_order:\n # split qkv before split heads\n self.attention = QKVAttention(self.num_heads)\n else:\n # split heads before split qkv\n self.attention = QKVAttentionLegacy(self.num_heads)\n\n self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))\n\n def forward(self, x):\n \"\"\"Forward function. This function support gradient checkpoint to save\n memory.\n\n Args:\n x (torch.Tensor): The input tensor for attention.\n\n Returns:\n torch.Tensor: The attention results\n \"\"\"\n return checkpoint(self._forward, (x, ), self.parameters(), True)\n\n def _forward(self, x):\n \"\"\"Forward function of attention block.\n\n Args:\n x (torch.Tensor): The input tensor for attention.\n\n Returns:\n torch.Tensor: The attention results\n \"\"\"\n b, c, *spatial = x.shape\n x = x.reshape(b, c, -1)\n qkv = self.qkv(self.norm(x))\n h = self.attention(qkv)\n h = self.proj_out(h)\n return (x + h).reshape(b, c, *spatial)\n\n\nclass TimestepEmbedSequential(nn.Sequential, TimestepBlock):\n \"\"\"A sequential module that passes timestep embeddings to the children that\n support it as an extra input.\"\"\"\n\n def forward(self, x, emb):\n \"\"\"Forward function. This function support sequential forward with\n embedding input.\n\n Args:\n x (torch.Tensor): Input tensor to forward.\n emb (torch.Tensor): Input timestep embedding.\n\n Returns:\n torch.Tensor: The forward results.\n \"\"\"\n for layer in self:\n if isinstance(layer, TimestepBlock):\n x = layer(x, emb)\n else:\n x = layer(x)\n return x\n\n\nclass GroupNorm32(nn.GroupNorm):\n\n def forward(self, x):\n \"\"\"Forward group normalization.\n\n Args:\n x (torch.Tensor): The input tensor.\n\n Returns:\n torch.Tensor: Tensor after group norm.\n \"\"\"\n return super().forward(x.float()).type(x.dtype)\n\n\ndef normalization(channels):\n \"\"\"Make a standard normalization layer.\n\n :param channels: number of input channels.\n :return: an nn.Module for normalization.\n \"\"\"\n return GroupNorm32(32, channels)\n\n\nclass Downsample(nn.Module):\n \"\"\"A downsampling layer with an optional convolution.\n\n :param channels: channels in the inputs and outputs.\n :param use_conv: a bool determining if a convolution is applied.\n :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n downsampling occurs in the inner-two dimensions.\n \"\"\"\n\n def __init__(self, channels, use_conv, dims=2, out_channels=None):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.dims = dims\n stride = 2 if dims != 3 else (1, 2, 2)\n if use_conv:\n self.op = nn.Conv2d(\n self.channels, self.out_channels, 3, stride=stride, padding=1)\n else:\n assert self.channels == self.out_channels\n self.op = nn.AvgPool2d(kernel_size=stride, stride=stride)\n\n def forward(self, x):\n \"\"\"Forward function for downsample.\n\n Args:\n x (torch.Tensor): The input tensor.\n\n Returns:\n torch.Tenor: Results after downsample.\n \"\"\"\n assert x.shape[1] == self.channels\n return self.op(x)\n\n\nclass ResBlock(TimestepBlock):\n \"\"\"A residual block that can optionally change the number of channels.\n\n :param channels: the number of input channels.\n :param emb_channels: the number of timestep embedding channels.\n :param dropout: the rate of dropout.\n :param out_channels: if specified, the number of out channels.\n :param use_conv: if True and out_channels is specified, use a spatial\n convolution instead of a smaller 1x1 convolution to change the\n channels in the skip connection.\n :param dims: determines if the signal is 1D, 2D, or 3D.\n :param use_checkpoint: if True, use gradient checkpointing on this module.\n :param up: if True, use this block for upsampling.\n :param down: if True, use this block for downsampling.\n \"\"\"\n\n def __init__(\n self,\n channels,\n emb_channels,\n dropout,\n out_channels=None,\n use_conv=False,\n use_scale_shift_norm=False,\n dims=2,\n use_checkpoint=False,\n up=False,\n down=False,\n ):\n super().__init__()\n self.channels = channels\n self.emb_channels = emb_channels\n self.dropout = dropout\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_checkpoint = use_checkpoint\n self.use_scale_shift_norm = use_scale_shift_norm\n\n self.in_layers = nn.Sequential(\n normalization(channels),\n nn.SiLU(),\n nn.Conv2d(channels, self.out_channels, 3, padding=1),\n )\n\n self.updown = up or down\n\n if up:\n self.h_upd = Upsample(channels, False, dims)\n self.x_upd = Upsample(channels, False, dims)\n elif down:\n self.h_upd = Downsample(channels, False, dims)\n self.x_upd = Downsample(channels, False, dims)\n else:\n self.h_upd = self.x_upd = nn.Identity()\n\n self.emb_layers = nn.Sequential(\n nn.SiLU(),\n nn.Linear(\n emb_channels,\n 2 * self.out_channels\n if use_scale_shift_norm else self.out_channels,\n ),\n )\n self.out_layers = nn.Sequential(\n normalization(self.out_channels),\n nn.SiLU(),\n nn.Dropout(p=dropout),\n zero_module(\n nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),\n )\n\n if self.out_channels == channels:\n self.skip_connection = nn.Identity()\n elif use_conv:\n self.skip_connection = nn.Conv2d(\n channels, self.out_channels, 3, padding=1)\n else:\n self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)\n\n def forward(self, x, emb):\n \"\"\"Apply the block to a Tensor, conditioned on a timestep embedding.\n\n :param x: an [N x C x ...] Tensor of features.\n :param emb: an [N x emb_channels] Tensor of timestep embeddings.\n :return: an [N x C x ...] Tensor of outputs.\n \"\"\"\n return checkpoint(self._forward, (x, emb), self.parameters(),\n self.use_checkpoint)\n\n def _forward(self, x, emb):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature tensor to forward.\n emb (torch.Tensor): The timesteps embedding to forward.\n\n Returns:\n torch.Tensor: The forward results.\n \"\"\"\n if self.updown:\n in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]\n h = in_rest(x)\n h = self.h_upd(h)\n x = self.x_upd(x)\n h = in_conv(h)\n else:\n h = self.in_layers(x)\n emb_out = self.emb_layers(emb).type(h.dtype)\n while len(emb_out.shape) < len(h.shape):\n emb_out = emb_out[..., None]\n if self.use_scale_shift_norm:\n out_norm, out_rest = self.out_layers[0], self.out_layers[1:]\n scale, shift = torch.chunk(emb_out, 2, dim=1)\n h = out_norm(h) * (1 + scale) + shift\n h = out_rest(h)\n else:\n h = h + emb_out\n h = self.out_layers(h)\n return self.skip_connection(x) + h\n\n\nclass AttentionPool2d(nn.Module):\n \"\"\"Adapted from CLIP:\n\n https://github.com/openai/CLIP/blob/main/clip/model.py.\n \"\"\"\n\n def __init__(\n self,\n spacial_dim: int,\n embed_dim: int,\n num_heads_channels: int,\n output_dim: int = None,\n ):\n super().__init__()\n self.positional_embedding = nn.Parameter(\n torch.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5)\n self.qkv_proj = nn.Conv1d(embed_dim, 3 * embed_dim, 1)\n self.c_proj = nn.Conv1d(embed_dim, output_dim or embed_dim, 1)\n self.num_heads = embed_dim // num_heads_channels\n self.attention = QKVAttention(self.num_heads)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input feature tensor to forward.\n\n Returns:\n torch.Tensor: The forward results.\n \"\"\"\n b, c, *_spatial = x.shape\n x = x.reshape(b, c, -1) # NC(HW)\n x = torch.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)\n x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)\n x = self.qkv_proj(x)\n x = self.attention(x)\n x = self.c_proj(x)\n return x[:, :, 0]\n\n\n@MODELS.register_module()\nclass EncoderUNetModel(nn.Module):\n \"\"\"The half UNet model with attention and timestep embedding.\n\n For usage, see UNet.\n \"\"\"\n\n def __init__(\n self,\n image_size,\n in_channels,\n model_channels,\n out_channels,\n num_res_blocks,\n attention_resolutions,\n dropout=0,\n channel_mult=(1, 2, 4, 8),\n conv_resample=True,\n dims=2,\n use_checkpoint=False,\n use_fp16=False,\n num_heads=1,\n num_head_channels=-1,\n num_heads_upsample=-1,\n use_scale_shift_norm=False,\n resblock_updown=False,\n use_new_attention_order=False,\n pool='adaptive',\n ):\n super().__init__()\n\n if num_heads_upsample == -1:\n num_heads_upsample = num_heads\n\n self.in_channels = in_channels\n self.model_channels = model_channels\n self.out_channels = out_channels\n self.num_res_blocks = num_res_blocks\n self.attention_resolutions = attention_resolutions\n self.dropout = dropout\n self.channel_mult = channel_mult\n self.conv_resample = conv_resample\n self.use_checkpoint = use_checkpoint\n self.dtype = torch.float16 if use_fp16 else torch.float32\n self.num_heads = num_heads\n self.num_head_channels = num_head_channels\n self.num_heads_upsample = num_heads_upsample\n\n time_embed_dim = model_channels * 4\n self.time_embed = nn.Sequential(\n nn.Linear(model_channels, time_embed_dim),\n nn.SiLU(),\n nn.Linear(time_embed_dim, time_embed_dim),\n )\n\n ch = int(channel_mult[0] * model_channels)\n self.input_blocks = nn.ModuleList([\n TimestepEmbedSequential(nn.Conv2d(in_channels, ch, 3, padding=1))\n ])\n self._feature_size = ch\n input_block_chans = [ch]\n ds = 1\n for level, mult in enumerate(channel_mult):\n for _ in range(num_res_blocks):\n layers = [\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=int(mult * model_channels),\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = int(mult * model_channels)\n if ds in attention_resolutions:\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=num_head_channels,\n use_new_attention_order=use_new_attention_order,\n ))\n self.input_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n input_block_chans.append(ch)\n if level != len(channel_mult) - 1:\n out_ch = ch\n self.input_blocks.append(\n TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n down=True,\n ) if resblock_updown else Downsample(\n ch, conv_resample, dims=dims, out_channels=out_ch))\n )\n ch = out_ch\n input_block_chans.append(ch)\n ds *= 2\n self._feature_size += ch\n\n self.middle_block = TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=num_head_channels,\n use_new_attention_order=use_new_attention_order,\n ),\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n )\n self._feature_size += ch\n self.pool = pool\n if pool == 'adaptive':\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n nn.AdaptiveAvgPool2d((1, 1)),\n zero_module(nn.Conv2d(ch, out_channels, 1)),\n nn.Flatten(),\n )\n elif pool == 'attention':\n assert num_head_channels != -1\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n AttentionPool2d((image_size // ds), ch, num_head_channels,\n out_channels),\n )\n elif pool == 'spatial':\n self.out = nn.Sequential(\n nn.Linear(self._feature_size, 2048),\n nn.ReLU(),\n nn.Linear(2048, self.out_channels),\n )\n elif pool == 'spatial_v2':\n self.out = nn.Sequential(\n nn.Linear(self._feature_size, 2048),\n normalization(2048),\n nn.SiLU(),\n nn.Linear(2048, self.out_channels),\n )\n else:\n raise NotImplementedError(f'Unexpected {pool} pooling')\n\n def convert_to_fp16(self):\n \"\"\"Convert the torso of the model to float16.\"\"\"\n self.input_blocks.apply(convert_module_to_f16)\n self.middle_block.apply(convert_module_to_f16)\n\n def convert_to_fp32(self):\n \"\"\"Convert the torso of the model to float32.\"\"\"\n self.input_blocks.apply(convert_module_to_f32)\n self.middle_block.apply(convert_module_to_f32)\n\n def forward(self, x, timesteps):\n \"\"\"Apply the model to an input batch.\n\n :param x: an [N x C x ...] Tensor of inputs.\n :param timesteps: a 1-D batch of timesteps.\n :return: an [N x K] Tensor of outputs.\n \"\"\"\n emb = self.time_embed(\n timestep_embedding(timesteps, self.model_channels))\n\n results = []\n h = x.type(self.dtype)\n for module in self.input_blocks:\n h = module(h, emb)\n if self.pool.startswith('spatial'):\n results.append(h.type(x.dtype).mean(dim=(2, 3)))\n h = self.middle_block(h, emb)\n if self.pool.startswith('spatial'):\n results.append(h.type(x.dtype).mean(dim=(2, 3)))\n h = torch.cat(results, axis=-1)\n return self.out(h)\n else:\n h = h.type(x.dtype)\n return self.out(h)\n" }, { "path": "mmagic/models/editors/iconvsr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .iconvsr_net import IconVSRNet\n\n__all__ = ['IconVSRNet']\n" }, { "path": "mmagic/models/editors/iconvsr/iconvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom logging import WARNING\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine import MMLogger, print_log\nfrom mmengine.model import BaseModule\nfrom mmengine.runner import load_checkpoint\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import flow_warp, make_layer\nfrom mmagic.registry import MODELS\nfrom ..basicvsr.basicvsr_net import ResidualBlocksWithInputConv, SPyNet\nfrom ..edvr.edvr_net import PCDAlignment, TSAFusion\n\n\n@MODELS.register_module()\nclass IconVSRNet(BaseModule):\n \"\"\"IconVSR network structure for video super-resolution.\n\n Support only x4 upsampling.\n\n Paper:\n BasicVSR: The Search for Essential Components in Video Super-Resolution\n and Beyond, CVPR, 2021\n\n Args:\n mid_channels (int): Channel number of the intermediate features.\n Default: 64.\n num_blocks (int): Number of residual blocks in each propagation branch.\n Default: 30.\n keyframe_stride (int): Number determining the keyframes. If stride=5,\n then the (0, 5, 10, 15, ...)-th frame will be the keyframes.\n Default: 5.\n padding (int): Number of frames to be padded at two ends of the\n sequence. 2 for REDS and 3 for Vimeo-90K. Default: 2.\n spynet_pretrained (str): Pre-trained model path of SPyNet.\n Default: None.\n edvr_pretrained (str): Pre-trained model path of EDVR (for refill).\n Default: None.\n \"\"\"\n\n def __init__(self,\n mid_channels=64,\n num_blocks=30,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=None,\n edvr_pretrained=None):\n\n super().__init__()\n\n self.mid_channels = mid_channels\n self.padding = padding\n self.keyframe_stride = keyframe_stride\n\n # optical flow network for alignment\n self.spynet = SPyNet(pretrained=spynet_pretrained)\n\n # information-refill\n self.edvr = EDVRFeatureExtractor(\n num_frames=padding * 2 + 1,\n center_frame_idx=padding,\n pretrained=edvr_pretrained)\n self.backward_fusion = nn.Conv2d(\n 2 * mid_channels, mid_channels, 3, 1, 1, bias=True)\n self.forward_fusion = nn.Conv2d(\n 2 * mid_channels, mid_channels, 3, 1, 1, bias=True)\n\n # propagation branches\n self.backward_resblocks = ResidualBlocksWithInputConv(\n mid_channels + 3, mid_channels, num_blocks)\n self.forward_resblocks = ResidualBlocksWithInputConv(\n 2 * mid_channels + 3, mid_channels, num_blocks)\n\n # upsample\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, 64, 2, upsample_kernel=3)\n self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)\n self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)\n self.img_upsample = nn.Upsample(\n scale_factor=4, mode='bilinear', align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n self._raised_warning = False\n\n def spatial_padding(self, lrs):\n \"\"\"Apply padding spatially.\n\n Since the PCD module in EDVR requires that the resolution is a multiple\n of 4, we apply padding to the input LR images if their resolution is\n not divisible by 4.\n\n Args:\n lrs (Tensor): Input LR sequence with shape (n, t, c, h, w).\n\n Returns:\n Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad).\n \"\"\"\n n, t, c, h, w = lrs.size()\n\n pad_h = (4 - h % 4) % 4\n pad_w = (4 - w % 4) % 4\n\n # padding\n lrs = lrs.view(-1, c, h, w)\n lrs = F.pad(lrs, [0, pad_w, 0, pad_h], mode='reflect')\n\n return lrs.view(n, t, c, h + pad_h, w + pad_w)\n\n def check_if_mirror_extended(self, lrs):\n \"\"\"Check whether the input is a mirror-extended sequence.\n\n If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the\n (t-1-i)-th frame.\n\n Args:\n lrs (tensor): Input LR images with shape (n, t, c, h, w)\n \"\"\"\n\n self.is_mirror_extended = False\n if lrs.size(1) % 2 == 0:\n lrs_1, lrs_2 = torch.chunk(lrs, 2, dim=1)\n if torch.norm(lrs_1 - lrs_2.flip(1)) == 0:\n self.is_mirror_extended = True\n\n def compute_refill_features(self, lrs, keyframe_idx):\n \"\"\"Compute keyframe features for information-refill.\n\n Since EDVR-M is used, padding is performed before feature computation.\n Args:\n lrs (Tensor): Input LR images with shape (n, t, c, h, w)\n keyframe_idx (list(int)): The indices specifying the keyframes.\n Return:\n dict(Tensor): The keyframe features. Each key corresponds to the\n indices in keyframe_idx.\n \"\"\"\n\n if self.padding == 2:\n lrs = [lrs[:, [4, 3]], lrs, lrs[:, [-4, -5]]] # padding\n elif self.padding == 3:\n lrs = [lrs[:, [6, 5, 4]], lrs, lrs[:, [-5, -6, -7]]] # padding\n lrs = torch.cat(lrs, dim=1)\n\n num_frames = 2 * self.padding + 1\n feats_refill = {}\n for i in keyframe_idx:\n feats_refill[i] = self.edvr(lrs[:, i:i + num_frames].contiguous())\n return feats_refill\n\n def compute_flow(self, lrs):\n \"\"\"Compute optical flow using SPyNet for feature warping.\n\n Note that if the input is an mirror-extended sequence, 'flows_forward'\n is not needed, since it is equal to 'flows_backward.flip(1)'.\n\n Args:\n lrs (tensor): Input LR images with shape (n, t, c, h, w)\n\n Return:\n tuple(Tensor): Optical flow. 'flows_forward' corresponds to the\n flows used for forward-time propagation (current to previous).\n 'flows_backward' corresponds to the flows used for\n backward-time propagation (current to next).\n \"\"\"\n\n n, t, c, h, w = lrs.size()\n lrs_1 = lrs[:, :-1, :, :, :].reshape(-1, c, h, w)\n lrs_2 = lrs[:, 1:, :, :, :].reshape(-1, c, h, w)\n\n flows_backward = self.spynet(lrs_1, lrs_2).view(n, t - 1, 2, h, w)\n\n if self.is_mirror_extended: # flows_forward = flows_backward.flip(1)\n flows_forward = None\n else:\n flows_forward = self.spynet(lrs_2, lrs_1).view(n, t - 1, 2, h, w)\n\n return flows_forward, flows_backward\n\n def forward(self, lrs):\n \"\"\"Forward function for IconVSR.\n\n Args:\n lrs (Tensor): Input LR tensor with shape (n, t, c, h, w).\n Returns:\n Tensor: Output HR tensor with shape (n, t, c, 4h, 4w).\n \"\"\"\n\n n, t, c, h_input, w_input = lrs.size()\n if (h_input < 64 or w_input < 64) and not self._raised_warning:\n print_log(\n f'{self.__class__.__name__} is designed for input '\n 'larger than 64x64, but the resolution of current image '\n f'is {h_input}x{w_input}. We recommend you to check your '\n 'input.', 'current', WARNING)\n self._raised_warning = True\n\n # check whether the input is an extended sequence\n self.check_if_mirror_extended(lrs)\n\n lrs = self.spatial_padding(lrs)\n h, w = lrs.size(3), lrs.size(4)\n\n # get the keyframe indices for information-refill\n keyframe_idx = list(range(0, t, self.keyframe_stride))\n if keyframe_idx[-1] != t - 1:\n keyframe_idx.append(t - 1) # the last frame must be a keyframe\n\n # compute optical flow and compute features for information-refill\n flows_forward, flows_backward = self.compute_flow(lrs)\n feats_refill = self.compute_refill_features(lrs, keyframe_idx)\n\n # backward-time propagation\n outputs = []\n feat_prop = lrs.new_zeros(n, self.mid_channels, h, w)\n for i in range(t - 1, -1, -1):\n lr_curr = lrs[:, i, :, :, :]\n if i < t - 1: # no warping for the last timestep\n flow = flows_backward[:, i, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n if i in keyframe_idx:\n feat_prop = torch.cat([feat_prop, feats_refill[i]], dim=1)\n feat_prop = self.backward_fusion(feat_prop)\n feat_prop = torch.cat([lr_curr, feat_prop], dim=1)\n feat_prop = self.backward_resblocks(feat_prop)\n\n outputs.append(feat_prop)\n outputs = outputs[::-1]\n\n # forward-time propagation and upsampling\n feat_prop = torch.zeros_like(feat_prop)\n for i in range(0, t):\n lr_curr = lrs[:, i, :, :, :]\n if i > 0: # no warping for the first timestep\n if flows_forward is not None:\n flow = flows_forward[:, i - 1, :, :, :]\n else:\n flow = flows_backward[:, -i, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n\n if i in keyframe_idx: # information-refill\n feat_prop = torch.cat([feat_prop, feats_refill[i]], dim=1)\n feat_prop = self.forward_fusion(feat_prop)\n\n feat_prop = torch.cat([lr_curr, outputs[i], feat_prop], dim=1)\n feat_prop = self.forward_resblocks(feat_prop)\n\n out = self.lrelu(self.upsample1(feat_prop))\n out = self.lrelu(self.upsample2(out))\n out = self.lrelu(self.conv_hr(out))\n out = self.conv_last(out)\n base = self.img_upsample(lr_curr)\n out += base\n outputs[i] = out\n\n return torch.stack(outputs, dim=1)[:, :, :, :4 * h_input, :4 * w_input]\n\n\nclass EDVRFeatureExtractor(BaseModule):\n \"\"\"EDVR feature extractor for information-refill in IconVSR.\n\n We use EDVR-M in IconVSR. To adopt pretrained models, please\n specify \"pretrained\".\n\n Paper:\n EDVR: Video Restoration with Enhanced Deformable Convolutional Networks.\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_frames (int): Number of input frames. Default: 5.\n deform_groups (int): Deformable groups. Defaults: 8.\n num_blocks_extraction (int): Number of blocks for feature extraction.\n Default: 5.\n num_blocks_reconstruction (int): Number of blocks for reconstruction.\n Default: 10.\n center_frame_idx (int): The index of center frame. Frame counting from\n 0. Default: 2.\n with_tsa (bool): Whether to use TSA module. Default: True.\n pretrained (str): The pretrained model path. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels=3,\n out_channel=3,\n mid_channels=64,\n num_frames=5,\n deform_groups=8,\n num_blocks_extraction=5,\n num_blocks_reconstruction=10,\n center_frame_idx=2,\n with_tsa=True,\n pretrained=None):\n\n super().__init__()\n\n self.center_frame_idx = center_frame_idx\n self.with_tsa = with_tsa\n act_cfg = dict(type='LeakyReLU', negative_slope=0.1)\n\n self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)\n self.feature_extraction = make_layer(\n ResidualBlockNoBN,\n num_blocks_extraction,\n mid_channels=mid_channels)\n\n # generate pyramid features\n self.feat_l2_conv1 = ConvModule(\n mid_channels, mid_channels, 3, 2, 1, act_cfg=act_cfg)\n self.feat_l2_conv2 = ConvModule(\n mid_channels, mid_channels, 3, 1, 1, act_cfg=act_cfg)\n self.feat_l3_conv1 = ConvModule(\n mid_channels, mid_channels, 3, 2, 1, act_cfg=act_cfg)\n self.feat_l3_conv2 = ConvModule(\n mid_channels, mid_channels, 3, 1, 1, act_cfg=act_cfg)\n # pcd alignment\n self.pcd_alignment = PCDAlignment(\n mid_channels=mid_channels, deform_groups=deform_groups)\n # fusion\n if self.with_tsa:\n self.fusion = TSAFusion(\n mid_channels=mid_channels,\n num_frames=num_frames,\n center_frame_idx=self.center_frame_idx)\n else:\n self.fusion = nn.Conv2d(num_frames * mid_channels, mid_channels, 1,\n 1)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=True, logger=logger)\n elif pretrained is not None:\n raise TypeError(f'\"pretrained\" must be a str or None. '\n f'But received {type(pretrained)}.')\n\n def forward(self, x):\n \"\"\"Forward function for EDVRFeatureExtractor.\n\n Args:\n x (Tensor): Input tensor with shape (n, t, 3, h, w).\n Returns:\n Tensor: Intermediate feature with shape (n, mid_channels, h, w).\n \"\"\"\n\n n, t, c, h, w = x.size()\n\n # extract LR features\n # L1\n l1_feat = self.lrelu(self.conv_first(x.view(-1, c, h, w)))\n l1_feat = self.feature_extraction(l1_feat)\n # L2\n l2_feat = self.feat_l2_conv2(self.feat_l2_conv1(l1_feat))\n # L3\n l3_feat = self.feat_l3_conv2(self.feat_l3_conv1(l2_feat))\n\n l1_feat = l1_feat.view(n, t, -1, h, w)\n l2_feat = l2_feat.view(n, t, -1, h // 2, w // 2)\n l3_feat = l3_feat.view(n, t, -1, h // 4, w // 4)\n\n # pcd alignment\n ref_feats = [ # reference feature list\n l1_feat[:, self.center_frame_idx, :, :, :].clone(),\n l2_feat[:, self.center_frame_idx, :, :, :].clone(),\n l3_feat[:, self.center_frame_idx, :, :, :].clone()\n ]\n aligned_feat = []\n for i in range(t):\n neighbor_feats = [\n l1_feat[:, i, :, :, :].clone(), l2_feat[:, i, :, :, :].clone(),\n l3_feat[:, i, :, :, :].clone()\n ]\n aligned_feat.append(self.pcd_alignment(neighbor_feats, ref_feats))\n aligned_feat = torch.stack(aligned_feat, dim=1) # (n, t, c, h, w)\n\n if self.with_tsa:\n feat = self.fusion(aligned_feat)\n else:\n aligned_feat = aligned_feat.view(n, -1, h, w)\n feat = self.fusion(aligned_feat)\n\n return feat\n" }, { "path": "mmagic/models/editors/indexnet/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .indexnet import IndexNet\nfrom .indexnet_decoder import IndexedUpsample, IndexNetDecoder\nfrom .indexnet_encoder import (DepthwiseIndexBlock, HolisticIndexBlock,\n IndexNetEncoder)\n\n__all__ = [\n 'IndexNet', 'IndexedUpsample', 'IndexNetEncoder', 'IndexNetDecoder',\n 'DepthwiseIndexBlock', 'HolisticIndexBlock'\n]\n" }, { "path": "mmagic/models/editors/indexnet/indexnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models.base_models import BaseMattor\nfrom mmagic.models.utils import get_unknown_tensor\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass IndexNet(BaseMattor):\n \"\"\"IndexNet matting model.\n\n This implementation follows:\n Indices Matter: Learning to Index for Deep Image Matting\n\n Args:\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n backbone (dict): Config of backbone.\n train_cfg (dict): Config of training. In 'train_cfg', 'train_backbone'\n should be specified.\n test_cfg (dict): Config of testing.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n loss_alpha (dict): Config of the alpha prediction loss. Default: None.\n loss_comp (dict): Config of the composition loss. Default: None.\n \"\"\"\n\n def __init__(self,\n data_preprocessor,\n backbone,\n loss_alpha=None,\n loss_comp=None,\n init_cfg=None,\n train_cfg=None,\n test_cfg=None):\n super().__init__(\n backbone=backbone,\n data_preprocessor=data_preprocessor,\n init_cfg=init_cfg,\n train_cfg=train_cfg,\n test_cfg=test_cfg)\n\n self.loss_alpha = (\n MODELS.build(loss_alpha) if loss_alpha is not None else None)\n self.loss_comp = (\n MODELS.build(loss_comp) if loss_comp is not None else None)\n\n def _forward(self, inputs):\n \"\"\"Forward function.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n pred_alpha = self.backbone(inputs)\n return pred_alpha\n\n def _forward_test(self, inputs):\n \"\"\"Forward function for testing IndexNet model.\n\n Args:\n inputs (torch.Tensor): batch input tensor.\n\n Returns:\n Tensor: Output tensor of model.\n \"\"\"\n return self._forward(inputs)\n\n def _forward_train(self, inputs, data_samples):\n \"\"\"Forward function for training IndexNet model.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement]): data samples collated by\n :attr:`data_preprocessor`.\n\n Returns:\n dict: Contains the loss items and batch information.\n \"\"\"\n trimap = inputs[:, 3:, :, :]\n gt_alpha = data_samples.gt_alpha\n gt_fg = data_samples.gt_fg\n gt_bg = data_samples.gt_bg\n gt_merged = data_samples.gt_merged\n\n pred_alpha = self.backbone(inputs)\n\n weight = get_unknown_tensor(trimap, unknown_value=128 / 255)\n\n losses = dict()\n\n if self.loss_alpha is not None:\n losses['loss_alpha'] = self.loss_alpha(pred_alpha, gt_alpha,\n weight)\n if self.loss_comp is not None:\n losses['loss_comp'] = self.loss_comp(pred_alpha, gt_fg, gt_bg,\n gt_merged, weight)\n\n return losses\n" }, { "path": "mmagic/models/editors/indexnet/indexnet_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import kaiming_init, normal_init\n\nfrom mmagic.models.archs import DepthwiseSeparableConvModule\nfrom mmagic.registry import MODELS\n\n\nclass IndexedUpsample(BaseModule):\n \"\"\"Indexed upsample module.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int, optional): Kernel size of the convolution layer.\n Defaults to 5.\n norm_cfg (dict, optional): Config dict for normalization layer.\n Defaults to dict(type='BN').\n conv_module (ConvModule | DepthwiseSeparableConvModule, optional):\n Conv module. Defaults to ConvModule.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size=5,\n norm_cfg=dict(type='BN'),\n conv_module=ConvModule,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n\n self.conv = conv_module(\n in_channels,\n out_channels,\n kernel_size,\n padding=(kernel_size - 1) // 2,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6'))\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n kaiming_init(m, mode='fan_in', nonlinearity='leaky_relu')\n\n def forward(self, x, shortcut, dec_idx_feat=None):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape (N, C, H, W).\n shortcut (Tensor): The shortcut connection with shape\n (N, C, H', W').\n dec_idx_feat (Tensor, optional): The decode index feature map with\n shape (N, C, H', W'). Defaults to None.\n\n Returns:\n Tensor: Output tensor with shape (N, C, H', W').\n \"\"\"\n if dec_idx_feat is not None:\n assert shortcut.dim() == 4, (\n 'shortcut must be tensor with 4 dimensions')\n x = dec_idx_feat * F.interpolate(x, size=shortcut.shape[2:])\n out = torch.cat((x, shortcut), dim=1)\n return self.conv(out)\n\n\n@MODELS.register_module()\nclass IndexNetDecoder(BaseModule):\n \"\"\"Decoder for IndexNet.\n\n Please refer to https://arxiv.org/abs/1908.00672.\n\n Args:\n in_channels (int): Input channels of the decoder.\n kernel_size (int, optional): Kernel size of the convolution layer.\n Defaults to 5.\n norm_cfg (None | dict, optional): Config dict for normalization\n layer. Defaults to dict(type='BN').\n separable_conv (bool): Whether to use separable conv. Default: False.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n in_channels,\n kernel_size=5,\n norm_cfg=dict(type='BN'),\n separable_conv=False,\n init_cfg: Optional[dict] = None):\n # TODO: remove in_channels argument\n super().__init__(init_cfg=init_cfg)\n\n if separable_conv:\n conv_module = DepthwiseSeparableConvModule\n else:\n conv_module = ConvModule\n\n blocks_in_channels = [\n in_channels * 2, 96 * 2, 64 * 2, 32 * 2, 24 * 2, 16 * 2, 32 * 2\n ]\n blocks_out_channels = [96, 64, 32, 24, 16, 32, 32]\n\n self.decoder_layers = nn.ModuleList()\n for in_channel, out_channel in zip(blocks_in_channels,\n blocks_out_channels):\n self.decoder_layers.append(\n IndexedUpsample(in_channel, out_channel, kernel_size, norm_cfg,\n conv_module))\n\n self.pred = nn.Sequential(\n conv_module(\n 32,\n 1,\n kernel_size,\n padding=(kernel_size - 1) // 2,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6')),\n nn.Conv2d(\n 1, 1, kernel_size, padding=(kernel_size - 1) // 2, bias=False))\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n std = math.sqrt(2. /\n (m.out_channels * m.kernel_size[0]**2))\n normal_init(m, mean=0, std=std)\n\n def forward(self, inputs):\n \"\"\"Forward function.\n\n Args:\n inputs (dict): Output dict of IndexNetEncoder.\n\n Returns:\n Tensor: Predicted alpha matte of the current batch.\n \"\"\"\n shortcuts = reversed(inputs['shortcuts'])\n dec_idx_feat_list = reversed(inputs['dec_idx_feat_list'])\n out = inputs['out']\n\n group = (self.decoder_layers, shortcuts, dec_idx_feat_list)\n for decode_layer, shortcut, dec_idx_feat in zip(*group):\n out = decode_layer(out, shortcut, dec_idx_feat)\n\n out = self.pred(out)\n\n return out\n" }, { "path": "mmagic/models/editors/indexnet/indexnet_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init, xavier_init\nfrom mmengine.utils.dl_utils.parrots_wrapper import SyncBatchNorm\n\nfrom mmagic.models.archs import ASPP, DepthwiseSeparableConvModule\nfrom mmagic.registry import MODELS\n\n\ndef build_index_block(in_channels,\n out_channels,\n kernel_size,\n stride=2,\n padding=0,\n groups=1,\n norm_cfg=dict(type='BN'),\n use_nonlinear=False,\n expansion=1):\n \"\"\"Build an conv block for IndexBlock.\n\n Args:\n in_channels (int): The input channels of the block.\n out_channels (int): The output channels of the block.\n kernel_size (int): The kernel size of the block.\n stride (int, optional): The stride of the block. Defaults to 2.\n padding (int, optional): The padding of the block. Defaults to 0.\n groups (int, optional): The groups of the block. Defaults to 1.\n norm_cfg (dict, optional): The norm config of the block.\n Defaults to dict(type='BN').\n use_nonlinear (bool, optional): Whether use nonlinearity in the block.\n If true, a ConvModule with kernel size 1 will be appended and an\n ``ReLU6`` nonlinearity will be added to the origin ConvModule.\n Defaults to False.\n expansion (int, optional): Expansion ratio of the middle channels.\n Effective when ``use_nonlinear`` is true. Defaults to 1.\n\n Returns:\n nn.Module: The built conv block.\n \"\"\"\n if use_nonlinear:\n return nn.Sequential(\n ConvModule(\n in_channels,\n in_channels * expansion,\n kernel_size,\n stride=stride,\n padding=padding,\n groups=groups,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6')),\n ConvModule(\n in_channels * expansion,\n out_channels,\n 1,\n stride=1,\n padding=0,\n groups=groups,\n bias=False,\n norm_cfg=None,\n act_cfg=None))\n\n return ConvModule(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n padding=padding,\n groups=groups,\n bias=False,\n norm_cfg=None,\n act_cfg=None)\n\n\nclass HolisticIndexBlock(BaseModule):\n \"\"\"Holistic Index Block.\n\n From https://arxiv.org/abs/1908.00672.\n\n Args:\n in_channels (int): Input channels of the holistic index block.\n norm_cfg (dict): Config dict for normalization layer.\n Default: dict(type='BN').\n use_context (bool, optional): Whether use larger kernel size in index\n block. Refer to the paper for more information. Defaults to False.\n use_nonlinear (bool): Whether add a non-linear conv layer in the index\n block. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n norm_cfg=dict(type='BN'),\n use_context=False,\n use_nonlinear=False):\n super().__init__()\n\n if use_context:\n kernel_size, padding = 4, 1\n else:\n kernel_size, padding = 2, 0\n\n self.index_block = build_index_block(\n in_channels,\n 4,\n kernel_size,\n stride=2,\n padding=padding,\n groups=1,\n norm_cfg=norm_cfg,\n use_nonlinear=use_nonlinear,\n expansion=2)\n\n self.sigmoid = nn.Sigmoid()\n self.softmax = nn.Softmax(dim=1)\n self.pixel_shuffle = nn.PixelShuffle(2)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape (N, C, H, W).\n\n Returns:\n tuple(Tensor): Encoder index feature and decoder index feature.\n \"\"\"\n x = self.index_block(x)\n\n # normalization\n y = self.sigmoid(x)\n z = self.softmax(y)\n # pixel shuffling\n enc_idx_feat = self.pixel_shuffle(z)\n dec_idx_feat = self.pixel_shuffle(y)\n\n return enc_idx_feat, dec_idx_feat\n\n\nclass DepthwiseIndexBlock(BaseModule):\n \"\"\"Depthwise index block.\n\n From https://arxiv.org/abs/1908.00672.\n\n Args:\n in_channels (int): Input channels of the holistic index block.\n norm_cfg (dict): Config dict for normalization layer.\n Default: dict(type='BN').\n use_context (bool, optional): Whether use larger kernel size in index\n block. Refer to the paper for more information. Defaults to False.\n use_nonlinear (bool): Whether add a non-linear conv layer in the index\n blocks. Default: False.\n mode (str): Mode of index block. Should be 'o2o' or 'm2o'. In 'o2o'\n mode, the group of the conv layers is 1; In 'm2o' mode, the group\n of the conv layer is `in_channels`.\n \"\"\"\n\n def __init__(self,\n in_channels,\n norm_cfg=dict(type='BN'),\n use_context=False,\n use_nonlinear=False,\n mode='o2o'):\n super().__init__()\n\n groups = in_channels if mode == 'o2o' else 1\n\n if use_context:\n kernel_size, padding = 4, 1\n else:\n kernel_size, padding = 2, 0\n\n self.index_blocks = nn.ModuleList()\n for _ in range(4):\n self.index_blocks.append(\n build_index_block(\n in_channels,\n in_channels,\n kernel_size,\n stride=2,\n padding=padding,\n groups=groups,\n norm_cfg=norm_cfg,\n use_nonlinear=use_nonlinear))\n\n self.sigmoid = nn.Sigmoid()\n self.softmax = nn.Softmax(dim=2)\n self.pixel_shuffle = nn.PixelShuffle(2)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape (N, C, H, W).\n\n Returns:\n tuple(Tensor): Encoder index feature and decoder index feature.\n \"\"\"\n n, c, h, w = x.shape\n\n feature_list = [\n _index_block(x).unsqueeze(2) for _index_block in self.index_blocks\n ]\n x = torch.cat(feature_list, dim=2)\n\n # normalization\n y = self.sigmoid(x)\n z = self.softmax(y)\n # pixel shuffling\n y = y.view(n, c * 4, h // 2, w // 2)\n z = z.view(n, c * 4, h // 2, w // 2)\n enc_idx_feat = self.pixel_shuffle(z)\n dec_idx_feat = self.pixel_shuffle(y)\n\n return enc_idx_feat, dec_idx_feat\n\n\nclass InvertedResidual(BaseModule):\n \"\"\"Inverted residual layer for indexnet encoder.\n\n It basically is a depthwise separable conv module. If `expand_ratio` is not\n one, then a conv module of kernel_size 1 will be inserted to change the\n input channels to `in_channels * expand_ratio`.\n\n Args:\n in_channels (int): Input channels of the layer.\n out_channels (int): Output channels of the layer.\n stride (int): Stride of the depthwise separable conv module.\n dilation (int): Dilation of the depthwise separable conv module.\n expand_ratio (float): Expand ratio of the input channels of the\n depthwise separable conv module.\n norm_cfg (dict | None): Config dict for normalization layer.\n use_res_connect (bool, optional): Whether use shortcut connection.\n Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n stride,\n dilation,\n expand_ratio,\n norm_cfg,\n use_res_connect=False):\n super().__init__()\n assert stride in [1, 2], 'stride must 1 or 2'\n\n self.use_res_connect = use_res_connect\n self.kernel_size = 3\n self.dilation = dilation\n\n if expand_ratio == 1:\n self.conv = DepthwiseSeparableConvModule(\n in_channels,\n out_channels,\n 3,\n stride=stride,\n dilation=dilation,\n norm_cfg=norm_cfg,\n dw_act_cfg=dict(type='ReLU6'),\n pw_act_cfg=None)\n else:\n hidden_dim = round(in_channels * expand_ratio)\n self.conv = nn.Sequential(\n ConvModule(\n in_channels,\n hidden_dim,\n 1,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6')),\n DepthwiseSeparableConvModule(\n hidden_dim,\n out_channels,\n 3,\n stride=stride,\n dilation=dilation,\n norm_cfg=norm_cfg,\n dw_act_cfg=dict(type='ReLU6'),\n pw_act_cfg=None))\n\n def pad(self, inputs, kernel_size, dilation):\n \"\"\"Pad input tensor.\n\n Args:\n inputs (Tensor): Input tensor.\n kernel_size (int): Kernel size of conv layer.\n dilation (int): Dilation of conv layer.\n\n Returns:\n Tensor: Padded tensor\n \"\"\"\n effective_ksize = kernel_size + (kernel_size - 1) * (dilation - 1)\n left = (effective_ksize - 1) // 2\n right = effective_ksize // 2\n return F.pad(inputs, (left, right, left, right))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n out = self.conv(self.pad(x, self.kernel_size, self.dilation))\n\n if self.use_res_connect:\n out = out + x\n\n return out\n\n\n@MODELS.register_module()\nclass IndexNetEncoder(BaseModule):\n \"\"\"Encoder for IndexNet.\n\n Please refer to https://arxiv.org/abs/1908.00672.\n\n Args:\n in_channels (int, optional): Input channels of the encoder.\n out_stride (int, optional): Output stride of the encoder. For\n example, if `out_stride` is 32, the input feature map or image\n will be downsample to the 1/32 of original size.\n Defaults to 32.\n width_mult (int, optional): Width multiplication factor of channel\n dimension in MobileNetV2. Defaults to 1.\n index_mode (str, optional): Index mode of the index network. It\n must be one of {'holistic', 'o2o', 'm2o'}. If it is set to\n 'holistic', then Holistic index network will be used as the\n index network. If it is set to 'o2o' (or 'm2o'), when O2O\n (or M2O) Depthwise index network will be used as the index\n network. Defaults to 'm2o'.\n aspp (bool, optional): Whether use ASPP module to augment output\n feature. Defaults to True.\n norm_cfg (None | dict, optional): Config dict for normalization\n layer. Defaults to dict(type='BN').\n freeze_bn (bool, optional): Whether freeze batch norm layer.\n Defaults to False.\n use_nonlinear (bool, optional): Whether use nonlinearity in index\n network. Refer to the paper for more information.\n Defaults to True.\n use_context (bool, optional): Whether use larger kernel size in\n index network. Refer to the paper for more information.\n Defaults to True.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n\n Raises:\n ValueError: out_stride must 16 or 32.\n NameError: Supported index_mode are {'holistic', 'o2o', 'm2o'}.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_stride=32,\n width_mult=1,\n index_mode='m2o',\n aspp=True,\n norm_cfg=dict(type='BN'),\n freeze_bn=False,\n use_nonlinear=True,\n use_context=True,\n init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n if out_stride not in [16, 32]:\n raise ValueError(f'out_stride must 16 or 32, got {out_stride}')\n\n self.out_stride = out_stride\n self.width_mult = width_mult\n\n # we name the index network in the paper index_block\n if index_mode == 'holistic':\n index_block = HolisticIndexBlock\n elif index_mode in ('o2o', 'm2o'):\n index_block = partial(DepthwiseIndexBlock, mode=index_mode)\n else:\n raise NameError('Unknown index block mode {}'.format(index_mode))\n\n # default setting\n initial_channels = 32\n inverted_residual_setting = [\n # expand_ratio, input_chn, output_chn, num_blocks, stride, dilation\n [1, initial_channels, 16, 1, 1, 1],\n [6, 16, 24, 2, 2, 1],\n [6, 24, 32, 3, 2, 1],\n [6, 32, 64, 4, 2, 1],\n [6, 64, 96, 3, 1, 1],\n [6, 96, 160, 3, 2, 1],\n [6, 160, 320, 1, 1, 1],\n ]\n\n # update layer setting according to width_mult\n initial_channels = int(initial_channels * width_mult)\n for layer_setting in inverted_residual_setting:\n # update in_channels and out_channels\n layer_setting[1] = int(layer_setting[1] * self.width_mult)\n layer_setting[2] = int(layer_setting[2] * self.width_mult)\n\n if out_stride == 32:\n # It should be noted that layers 0 is not an InvertedResidual layer\n # but a ConvModule. Thus, the index of InvertedResidual layer in\n # downsampled_layers starts at 1.\n self.downsampled_layers = [0, 2, 3, 4, 6]\n else: # out_stride is 16\n self.downsampled_layers = [0, 2, 3, 4]\n # if out_stride is 16, then increase the dilation of the last two\n # InvertedResidual layer to increase the receptive field\n inverted_residual_setting[5][5] = 2\n inverted_residual_setting[6][5] = 2\n\n # build the first layer\n self.layers = nn.ModuleList([\n ConvModule(\n in_channels,\n initial_channels,\n 3,\n padding=1,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6'))\n ])\n # build bottleneck layers\n for layer_setting in inverted_residual_setting:\n self.layers.append(self._make_layer(layer_setting, norm_cfg))\n\n # freeze encoder batch norm layers\n self.freeze_bn = freeze_bn\n\n # build index blocks\n self.index_layers = nn.ModuleList()\n for layer in self.downsampled_layers:\n # inverted_residual_setting begins at layer1, the in_channels\n # of layer1 is the out_channels of layer0\n self.index_layers.append(\n index_block(inverted_residual_setting[layer][1], norm_cfg,\n use_context, use_nonlinear))\n self.avg_pool = nn.AvgPool2d(2, stride=2)\n\n if aspp:\n dilation = (2, 4, 8) if out_stride == 32 else (6, 12, 18)\n self.dconv = ASPP(\n 320 * self.width_mult,\n 160,\n mid_channels=int(256 * self.width_mult),\n dilations=dilation,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6'),\n separable_conv=True)\n else:\n self.dconv = ConvModule(\n 320 * self.width_mult,\n 160,\n 1,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU6'))\n\n self.out_channels = 160\n\n def _make_layer(self, layer_setting, norm_cfg):\n # expand_ratio, in_channels, out_channels, num_blocks, stride, dilation\n (expand_ratio, in_channels, out_channels, num_blocks, stride,\n dilation) = layer_setting\n\n # downsample is now implemented by index block. In those layers that\n # have downsampling originally, use stride of 1 in the first block and\n # decrease the dilation accordingly.\n dilation0 = max(dilation // 2, 1) if stride == 2 else dilation\n layers = [\n InvertedResidual(in_channels, out_channels, 1, dilation0,\n expand_ratio, norm_cfg)\n ]\n\n in_channels = out_channels\n for _ in range(1, num_blocks):\n layers.append(\n InvertedResidual(\n in_channels,\n out_channels,\n 1,\n dilation,\n expand_ratio,\n norm_cfg,\n use_res_connect=True))\n return nn.Sequential(*layers)\n\n def train(self, mode=True):\n \"\"\"Set BatchNorm modules in the model to evaluation mode.\"\"\"\n super().train(mode)\n if mode and self.freeze_bn:\n for m in self.modules():\n if isinstance(m, (nn.BatchNorm2d, SyncBatchNorm)):\n m.eval()\n\n def init_weights(self):\n \"\"\"Init weights for the model.\n\n Initialization is based on self._init_cfg\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n \"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n # Default initialization\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m)\n elif isinstance(m, nn.BatchNorm2d):\n constant_init(m, 1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape (N, C, H, W).\n\n Returns:\n dict: Output tensor, shortcut feature and decoder index feature.\n \"\"\"\n dec_idx_feat_list = list()\n shortcuts = list()\n for i, layer in enumerate(self.layers):\n x = layer(x)\n if i in self.downsampled_layers:\n enc_idx_feat, dec_idx_feat = self.index_layers[\n self.downsampled_layers.index(i)](\n x)\n x = enc_idx_feat * x\n shortcuts.append(x)\n dec_idx_feat_list.append(dec_idx_feat)\n x = 4 * self.avg_pool(x)\n elif i != 7:\n shortcuts.append(x)\n dec_idx_feat_list.append(None)\n\n x = self.dconv(x)\n\n return {\n 'out': x,\n 'shortcuts': shortcuts,\n 'dec_idx_feat_list': dec_idx_feat_list\n }\n" }, { "path": "mmagic/models/editors/inst_colorization/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .colorization_net import ColorizationNet\nfrom .fusion_net import FusionNet\nfrom .inst_colorization import InstColorization\n\n__all__ = [\n 'InstColorization',\n 'ColorizationNet',\n 'FusionNet',\n]\n" }, { "path": "mmagic/models/editors/inst_colorization/color_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\n\ndef xyz2rgb(xyz):\n \"\"\"Conversion images from xyz to rgb.\n\n Args:\n xyz (tensor): The images to be conversion\n\n Returns:\n out (tensor): The converted image\n \"\"\"\n r = 3.24048134 * xyz[:, 0, :, :] - 1.53715152 * xyz[:, 1, :, :] \\\n - 0.49853633 * xyz[:, 2, :, :]\n g = -0.96925495 * xyz[:, 0, :, :] + 1.87599 * xyz[:, 1, :, :] \\\n + .04155593 * xyz[:, 2, :, :]\n b = .05564664 * xyz[:, 0, :, :] - .20404134 * xyz[:, 1, :, :] \\\n + 1.05731107 * xyz[:, 2, :, :]\n\n # sometimes reaches a small negative number, which causes NaNs\n rgb = torch.cat((r[:, None, :, :], g[:, None, :, :], b[:, None, :, :]),\n dim=1)\n rgb = torch.max(rgb, torch.zeros_like(rgb))\n\n mask = (rgb > .0031308).type(torch.FloatTensor)\n if rgb.is_cuda:\n mask = mask.cuda()\n\n rgb = (1.055 * (rgb**(1. / 2.4)) - 0.055) * mask + 12.92 * rgb * (1 - mask)\n return rgb\n\n\ndef lab2xyz(lab):\n \"\"\"Conversion images from lab to xyz.\n\n Args:\n lab (tensor): The images to be conversion\n\n Returns:\n out (tensor): The converted image\n \"\"\"\n y_int = (lab[:, 0, :, :] + 16.) / 116.\n x_int = (lab[:, 1, :, :] / 500.) + y_int\n z_int = y_int - (lab[:, 2, :, :] / 200.)\n if (z_int.is_cuda):\n z_int = torch.max(torch.Tensor((0, )).cuda(), z_int)\n else:\n z_int = torch.max(torch.Tensor((0, )), z_int)\n\n out = torch.cat(\n (x_int[:, None, :, :], y_int[:, None, :, :], z_int[:, None, :, :]),\n dim=1)\n mask = (out > .2068966).type(torch.FloatTensor)\n if (out.is_cuda):\n mask = mask.cuda()\n\n out = (out**3.) * mask + (out - 16. / 116.) / 7.787 * (1 - mask)\n\n sc = torch.Tensor((0.95047, 1., 1.08883))[None, :, None, None]\n sc = sc.to(out.device)\n\n out = out * sc\n return out\n\n\ndef lab2rgb(lab_rs, color_data_opt):\n \"\"\"Conversion images from lab to rgb.\n\n Args:\n lab_rs (tensor): The images to be conversion\n color_data_opt (dict): Config for image colorspace transformation.\n Include: l_norm, ab_norm, l_cent\n\n Returns:\n out (tensor): The converted image\n \"\"\"\n L = lab_rs[:,\n [0], :, :] * color_data_opt['l_norm'] + color_data_opt['l_cent']\n AB = lab_rs[:, 1:, :, :] * color_data_opt['ab_norm']\n lab = torch.cat((L, AB), dim=1)\n out = xyz2rgb(lab2xyz(lab))\n return out\n\n\ndef encode_ab_ind(data_ab, color_data_opt):\n \"\"\"Encode ab value into an index.\n\n Args:\n data_ab: Nx2xHxW from [-1,1]\n color_data_opt: Config for image colorspace transformation.\n ab_max, ab_quant, ab_norm, ab_quant\n Returns:\n Nx1xHxW from [0,Q)\n \"\"\"\n A = 2 * color_data_opt['ab_max'] / color_data_opt['ab_quant'] + 1\n data_ab_rs = torch.round(\n (data_ab * color_data_opt['ab_norm'] + color_data_opt['ab_max']) /\n color_data_opt['ab_quant']) # normalized bin number\n data_q = data_ab_rs[:, [0], :, :] * A + data_ab_rs[:, [1], :, :]\n return data_q\n\n\ndef rgb2xyz(rgb):\n \"\"\"Conversion images from rgb to xyz\n rgb from [0,1]\n xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423],\n [0.212671, 0.715160, 0.072169],\n [0.019334, 0.119193, 0.950227]])\n Args:\n rgb (Tensor): image in rgb colorspace\n\n Returns:\n xyz (Tensor): image in xyz colorspace\n\n \"\"\"\n mask = (rgb > .04045).type(torch.FloatTensor)\n if (rgb.is_cuda):\n mask = mask.cuda()\n\n rgb = (((rgb + .055) / 1.055)**2.4) * mask + rgb / 12.92 * (1 - mask)\n\n x = .412453 * rgb[:, 0, :, :] + .357580 * rgb[:, 1, :, :] \\\n + .180423 * rgb[:, 2, :, :]\n y = .212671 * rgb[:, 0, :, :] + .715160 * rgb[:, 1, :, :] \\\n + .072169 * rgb[:, 2, :, :]\n z = .019334 * rgb[:, 0, :, :] + .119193 * rgb[:, 1, :, :] \\\n + .950227 * rgb[:, 2, :, :]\n out = torch.cat((x[:, None, :, :], y[:, None, :, :], z[:, None, :, :]),\n dim=1)\n\n return out\n\n\ndef xyz2lab(xyz):\n \"\"\"Conversion images from xyz to lab\n xyz from [0,1]\n factors: 0.95047, 1., 1.08883\n\n Args:\n xyz (Tensor): image in xyz colorspace\n\n Returns:\n out (Tensor): Image in lab colorspace\n \"\"\"\n sc = torch.Tensor((0.95047, 1., 1.08883))[None, :, None, None]\n if (xyz.is_cuda):\n sc = sc.cuda()\n\n xyz_scale = xyz / sc\n\n mask = (xyz_scale > .008856).type(torch.FloatTensor)\n if (xyz_scale.is_cuda):\n mask = mask.cuda()\n\n xyz_int = xyz_scale**(1 / 3.) * mask + (7.787 * xyz_scale +\n 16. / 116.) * (1 - mask)\n\n L = 116. * xyz_int[:, 1, :, :] - 16.\n a = 500. * (xyz_int[:, 0, :, :] - xyz_int[:, 1, :, :])\n b = 200. * (xyz_int[:, 1, :, :] - xyz_int[:, 2, :, :])\n out = torch.cat((L[:, None, :, :], a[:, None, :, :], b[:, None, :, :]),\n dim=1)\n\n return out\n\n\ndef rgb2lab(rgb, color_opt):\n \"\"\"Conversion images from rgb to lab.\n\n Args:\n data_raw (tensor): The images to be conversion\n color_opt (dict): Config for image colorspace transformation.\n Include: ab_thresh, ab_norm, sample_PS, mask_cent\n\n Returns:\n out (tensor): The converted image\n \"\"\"\n lab = xyz2lab(rgb2xyz(rgb))\n l_rs = (lab[:, [0], :, :] - color_opt['l_cent']) / color_opt['l_norm']\n ab_rs = lab[:, 1:, :, :] / color_opt['ab_norm']\n out = torch.cat((l_rs, ab_rs), dim=1)\n return out\n\n\ndef get_colorization_data(data_raw, color_opt, num_points=None):\n \"\"\"Conversion images from rgb to lab.\n\n Args:\n data_raw (tensor): The images to be conversion\n color_opt (dict): Config for image colorspace transformation.\n Include: ab_thresh, ab_norm, sample_PS, mask_cent\n\n Returns:\n results (dict): Output in add_color_patches_rand_gt\n \"\"\"\n if len(data_raw.shape) == 3:\n data_raw = data_raw.unsqueeze(0)\n data = {}\n data_lab = rgb2lab(data_raw, color_opt)\n data['A'] = data_lab[:, [\n 0,\n ], :, :]\n data['B'] = data_lab[:, 1:, :, :]\n\n # mask out grayscale images\n if color_opt['ab_thresh'] > 0:\n thresh = 1. * color_opt['ab_thresh'] / color_opt['ab_norm']\n mask = torch.sum(\n torch.abs(\n torch.max(torch.max(data['B'], dim=3)[0], dim=2)[0] -\n torch.min(torch.min(data['B'], dim=3)[0], dim=2)[0]),\n dim=1) >= thresh\n data['A'] = data['A'][mask, :, :, :]\n data['B'] = data['B'][mask, :, :, :]\n if torch.sum(mask) == 0:\n return None\n\n return add_color_patches_rand_gt(\n data, color_opt, p=color_opt['p'], num_points=num_points)\n\n\ndef add_color_patches_rand_gt(data,\n color_opt,\n p=.125,\n num_points=None,\n use_avg=True,\n samp='normal'):\n \"\"\"Add random color points sampled from ground truth based on: Number of\n points.\n\n - if num_points is 0, then sample from geometric distribution,\n drawn from probability p\n - if num_points > 0, then sample that number of points\n Location of points\n - if samp is 'normal', draw from N(0.5, 0.25) of image\n - otherwise, draw from U[0, 1] of image\n\n Args:\n data (tensor): The images to be conversion\n color_opt (dict): Config for image colorspace transformation\n Include: ab_thresh, ab_norm, sample_PS, mask_cent\n p (float): Sampling geometric distribution, 1.0 means no hints\n num_points (int): Certain number of points\n use_avg (bool): Whether to use the mean when add color point\n Default: True.\n samp (str): Geometric distribution or uniform distribution when\n sample location. Default: normal.\n\n Returns:\n results (dict): Result dict from :obj:``mmcv.BaseDataset``.\n \"\"\"\n N, C, H, W = data['B'].shape\n\n data['hint_B'] = torch.zeros_like(data['B'])\n data['mask_B'] = torch.zeros_like(data['A'])\n\n for nn in range(N):\n pp = 0\n cont_cond = True\n while cont_cond:\n # draw from geometric\n if num_points is None:\n cont_cond = np.random.rand() < (1 - p)\n else:\n # add certain number of points\n cont_cond = pp < num_points\n # skip out of loop if condition not met\n if not cont_cond:\n continue\n\n # patch size\n P = np.random.choice(color_opt['sample_PS'])\n # sample location: geometric distribution\n if samp == 'normal':\n h = int(\n np.clip(\n np.random.normal((H - P + 1) / 2., (H - P + 1) / 4.),\n 0, H - P))\n w = int(\n np.clip(\n np.random.normal((W - P + 1) / 2., (W - P + 1) / 4.),\n 0, W - P))\n else: # uniform distribution\n h = np.random.randint(H - P + 1)\n w = np.random.randint(W - P + 1)\n\n # add color point\n if use_avg:\n data['hint_B'][nn, :, h:h + P, w:w + P] = torch.mean(\n torch.mean(\n data['B'][nn, :, h:h + P, w:w + P],\n dim=2,\n keepdim=True),\n dim=1,\n keepdim=True).view(1, C, 1, 1)\n else:\n data['hint_B'][nn, :, h:h + P, w:w + P] = \\\n data['B'][nn, :, h:h + P, w:w + P]\n\n data['mask_B'][nn, :, h:h + P, w:w + P] = 1\n\n # increment counter\n pp += 1\n\n data['mask_B'] -= color_opt['mask_cent']\n\n return data\n" }, { "path": "mmagic/models/editors/inst_colorization/colorization_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .weight_layer import get_norm_layer\n\n\n@MODELS.register_module()\nclass ColorizationNet(BaseModule):\n \"\"\"Real-Time User-Guided Image Colorization with Learned Deep Priors. The\n backbone used for.\n\n https://arxiv.org/abs/1705.02999\n\n Codes adapted from 'https://github.com/ericsujw/InstColorization.git'\n 'InstColorization/blob/master/models/networks.py#L108'\n\n Args:\n input_nc (int): input image channels\n output_nc (int): output image channels\n norm_type (str): instance normalization or batch normalization\n use_tanh (bool): Whether to use nn.Tanh() Default: True.\n classification (bool): backprop trunk using classification,\n otherwise use regression. Default: True\n \"\"\"\n\n def __init__(self,\n input_nc,\n output_nc,\n norm_type,\n use_tanh=True,\n classification=True):\n super().__init__()\n self.input_nc = input_nc\n self.output_nc = output_nc\n self.classification = classification\n\n norm_layer = get_norm_layer(norm_type)\n\n use_bias = True\n\n # Conv1\n self.model1 = nn.Sequential(\n nn.Conv2d(\n input_nc,\n 64,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 64, 64, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(64),\n )\n\n # Conv2\n self.model2 = nn.Sequential(\n nn.Conv2d(\n 64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(128),\n )\n\n # Conv3\n self.model3 = nn.Sequential(\n nn.Conv2d(\n 128, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(256),\n )\n\n # Conv4\n self.model4 = nn.Sequential(\n nn.Conv2d(\n 256, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n # Conv5\n self.model5 = nn.Sequential(\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n # Conv6\n self.model6 = nn.Sequential(\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n # Conv7\n self.model7 = nn.Sequential(\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n # Conv8\n self.model8up = nn.ConvTranspose2d(\n 512, 256, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model3short8 = nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias)\n\n self.model8 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(256),\n )\n\n # Conv9\n self.model9up = nn.ConvTranspose2d(\n 256, 128, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model2short9 = nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias)\n self.model9 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(128),\n )\n\n # Conv10\n self.model10up = nn.ConvTranspose2d(\n 128, 128, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model1short10 = nn.Conv2d(\n 64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias)\n\n self.model10 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 128,\n 128,\n kernel_size=3,\n dilation=1,\n stride=1,\n padding=1,\n bias=use_bias),\n nn.LeakyReLU(negative_slope=.2),\n )\n\n # classification output\n self.model_class = nn.Conv2d(\n 256,\n 529,\n kernel_size=1,\n padding=0,\n dilation=1,\n stride=1,\n bias=use_bias)\n\n # regression output\n model_out = [\n nn.Conv2d(\n 128,\n 2,\n kernel_size=1,\n padding=0,\n dilation=1,\n stride=1,\n bias=use_bias),\n ]\n if (use_tanh):\n model_out += [nn.Tanh()]\n self.model_out = nn.Sequential(*model_out)\n\n self.upsample4 = nn.Upsample(scale_factor=4, mode='nearest')\n self.softmax = nn.Softmax(dim=1)\n\n def forward(self, input_A, input_B, mask_B):\n \"\"\"Forward function.\n\n Args:\n input_A (tensor): Channel of the image in lab color space\n input_B (tensor): Color patch\n mask_B (tensor): Color patch mask\n\n Returns:\n out_class (tensor): Classification output\n out_reg (tensor): Regression output\n feature_map (dict): The full-image feature\n \"\"\"\n conv1_2 = self.model1(torch.cat((input_A, input_B, mask_B), dim=1))\n conv2_2 = self.model2(conv1_2[:, :, ::2, ::2])\n conv3_3 = self.model3(conv2_2[:, :, ::2, ::2])\n conv4_3 = self.model4(conv3_3[:, :, ::2, ::2])\n conv5_3 = self.model5(conv4_3)\n conv6_3 = self.model6(conv5_3)\n conv7_3 = self.model7(conv6_3)\n conv8_up = self.model8up(conv7_3) + self.model3short8(conv3_3)\n conv8_3 = self.model8(conv8_up)\n\n if (self.classification):\n out_class = self.model_class(conv8_3)\n conv9_up = self.model9up(conv8_3.detach()) + self.model2short9(\n conv2_2.detach())\n conv9_3 = self.model9(conv9_up)\n conv10_up = self.model10up(conv9_3) + self.model1short10(\n conv1_2.detach())\n else:\n out_class = self.model_class(conv8_3.detach())\n conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)\n conv9_3 = self.model9(conv9_up)\n conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2)\n\n conv10_2 = self.model10(conv10_up)\n out_reg = self.model_out(conv10_2)\n\n feature_map = {}\n feature_map['conv1_2'] = conv1_2\n feature_map['conv2_2'] = conv2_2\n feature_map['conv3_3'] = conv3_3\n feature_map['conv4_3'] = conv4_3\n feature_map['conv5_3'] = conv5_3\n feature_map['conv6_3'] = conv6_3\n feature_map['conv7_3'] = conv7_3\n feature_map['conv8_up'] = conv8_up\n feature_map['conv8_3'] = conv8_3\n feature_map['conv9_up'] = conv9_up\n feature_map['conv9_3'] = conv9_3\n feature_map['conv10_up'] = conv10_up\n feature_map['conv10_2'] = conv10_2\n feature_map['out_reg'] = out_reg\n\n return (out_class, out_reg, feature_map)\n" }, { "path": "mmagic/models/editors/inst_colorization/fusion_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .weight_layer import WeightLayer, get_norm_layer\n\n\n@MODELS.register_module()\nclass FusionNet(BaseModule):\n \"\"\"Instance-aware Image Colorization.\n\n https://arxiv.org/abs/2005.10825\n\n Codes adapted from 'https://github.com/ericsujw/InstColorization.git'\n 'InstColorization/blob/master/models/networks.py#L314'\n FusionNet: the full image model with weight layer for fusion.\n\n Args:\n input_nc (int): input image channels\n output_nc (int): output image channels\n norm_type (str): instance normalization or batch normalization\n use_tanh (bool): Whether to use nn.Tanh() Default: True.\n classification (bool): backprop trunk using classification,\n otherwise use regression. Default: True\n \"\"\"\n\n def __init__(self,\n input_nc,\n output_nc,\n norm_type,\n use_tanh=True,\n classification=True):\n super().__init__()\n self.input_nc = input_nc\n self.output_nc = output_nc\n self.classification = classification\n\n norm_layer = get_norm_layer(norm_type)\n use_bias = True\n\n # Conv1\n self.model1 = nn.Sequential(\n nn.Conv2d(\n input_nc,\n 64,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 64, 64, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(64),\n )\n\n self.weight_layer = WeightLayer(64)\n\n # Conv2\n self.model2 = nn.Sequential(\n nn.Conv2d(\n 64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(128),\n )\n\n self.weight_layer2 = WeightLayer(128)\n\n # Conv3\n self.model3 = nn.Sequential(\n nn.Conv2d(\n 128, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(256),\n )\n\n self.weight_layer3 = WeightLayer(256)\n\n # Conv4\n self.model4 = nn.Sequential(\n nn.Conv2d(\n 256, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n self.weight_layer4 = WeightLayer(512)\n\n # Conv5\n self.model5 = nn.Sequential(\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n self.weight_layer5 = WeightLayer(512)\n\n # Conv6\n self.model6 = nn.Sequential(\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512,\n 512,\n kernel_size=3,\n dilation=2,\n stride=1,\n padding=2,\n bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n self.weight_layer6 = WeightLayer(512)\n\n # Conv7\n self.model7 = nn.Sequential(\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(512),\n )\n\n self.weight_layer7 = WeightLayer(512)\n\n # Conv8\n self.model8up = nn.ConvTranspose2d(\n 512, 256, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model3short8 = nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias)\n\n self.weight_layer8_1 = WeightLayer(256)\n\n self.model8 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n nn.Conv2d(\n 256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(256),\n )\n\n self.weight_layer8_2 = WeightLayer(256)\n\n # Conv9\n self.model9up = nn.ConvTranspose2d(\n 256, 128, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model2short9 = nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias)\n\n self.weight_layer9_1 = WeightLayer(128)\n\n self.model9 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias),\n nn.ReLU(True),\n norm_layer(128),\n )\n\n self.weight_layer9_2 = WeightLayer(128)\n\n # Conv10\n self.model10up = nn.ConvTranspose2d(\n 128, 128, kernel_size=4, stride=2, padding=1, bias=use_bias)\n\n self.model1short10 = nn.Conv2d(\n 64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias)\n\n self.weight_layer10_1 = WeightLayer(128)\n\n self.model10 = nn.Sequential(\n nn.ReLU(True),\n nn.Conv2d(\n 128,\n 128,\n kernel_size=3,\n dilation=1,\n stride=1,\n padding=1,\n bias=use_bias),\n nn.LeakyReLU(negative_slope=.2),\n )\n\n self.weight_layer10_2 = WeightLayer(128)\n\n # classification output\n self.model_class = nn.Conv2d(\n 256,\n 529,\n kernel_size=1,\n padding=0,\n dilation=1,\n stride=1,\n bias=use_bias)\n\n # regression output\n model_out = [\n nn.Conv2d(\n 128,\n 2,\n kernel_size=1,\n padding=0,\n dilation=1,\n stride=1,\n bias=use_bias),\n ]\n if (use_tanh):\n model_out += [nn.Tanh()]\n self.model_out = nn.Sequential(*model_out)\n\n self.weight_layerout = WeightLayer(2)\n\n self.upsample4 = nn.Upsample(scale_factor=4, mode='nearest')\n self.softmax = nn.Softmax(dim=1)\n\n def forward(self, input_A, input_B, mask_B, instance_feature,\n box_info_list):\n \"\"\"Forward function.\n\n Args:\n input_A (tensor): Channel of the image in lab color space\n input_B (tensor): Color patch\n mask_B (tensor): Color patch mask\n instance_feature (dict): A bunch of instance features\n box_info_list (list): Bounding box information corresponding\n to the instance\n\n Returns:\n out_reg (tensor): Regression output\n \"\"\"\n conv1_2 = self.model1(torch.cat((input_A, input_B, mask_B), dim=1))\n conv1_2 = self.weight_layer(instance_feature['conv1_2'], conv1_2,\n box_info_list[0])\n\n conv2_2 = self.model2(conv1_2[:, :, ::2, ::2])\n conv2_2 = self.weight_layer2(instance_feature['conv2_2'], conv2_2,\n box_info_list[1])\n\n conv3_3 = self.model3(conv2_2[:, :, ::2, ::2])\n conv3_3 = self.weight_layer3(instance_feature['conv3_3'], conv3_3,\n box_info_list[2])\n\n conv4_3 = self.model4(conv3_3[:, :, ::2, ::2])\n conv4_3 = self.weight_layer4(instance_feature['conv4_3'], conv4_3,\n box_info_list[3])\n\n conv5_3 = self.model5(conv4_3)\n conv5_3 = self.weight_layer5(instance_feature['conv5_3'], conv5_3,\n box_info_list[3])\n\n conv6_3 = self.model6(conv5_3)\n conv6_3 = self.weight_layer6(instance_feature['conv6_3'], conv6_3,\n box_info_list[3])\n\n conv7_3 = self.model7(conv6_3)\n conv7_3 = self.weight_layer7(instance_feature['conv7_3'], conv7_3,\n box_info_list[3])\n\n conv8_up = self.model8up(conv7_3) + self.model3short8(conv3_3)\n conv8_up = self.weight_layer8_1(instance_feature['conv8_up'], conv8_up,\n box_info_list[2])\n\n conv8_3 = self.model8(conv8_up)\n conv8_3 = self.weight_layer8_2(instance_feature['conv8_3'], conv8_3,\n box_info_list[2])\n\n conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)\n conv9_up = self.weight_layer9_1(instance_feature['conv9_up'], conv9_up,\n box_info_list[1])\n\n conv9_3 = self.model9(conv9_up)\n conv9_3 = self.weight_layer9_2(instance_feature['conv9_3'], conv9_3,\n box_info_list[1])\n\n conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2)\n conv10_up = self.weight_layer10_1(instance_feature['conv10_up'],\n conv10_up, box_info_list[0])\n\n conv10_2 = self.model10(conv10_up)\n conv10_2 = self.weight_layer10_2(instance_feature['conv10_2'],\n conv10_2, box_info_list[0])\n\n out_reg = self.model_out(conv10_2)\n return out_reg\n" }, { "path": "mmagic/models/editors/inst_colorization/inst_colorization.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nfrom mmengine.config import Config\nfrom mmengine.model import BaseModel\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom .color_utils import get_colorization_data, lab2rgb\n\n\n@MODELS.register_module()\nclass InstColorization(BaseModel):\n \"\"\"Colorization InstColorization method.\n\n This Colorization is implemented according to the paper:\n Instance-aware Image Colorization, CVPR 2020\n\n Adapted from 'https://github.com/ericsujw/InstColorization.git'\n 'InstColorization/models/train_model'\n Copyright (c) 2020, Su, under MIT License.\n\n Args:\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n image_model (dict): Config for single image model\n instance_model (dict): Config for instance model\n fusion_model (dict): Config for fusion model\n color_data_opt (dict): Option for colorspace conversion\n which_direction (str): AtoB or BtoA\n loss (dict): Config for loss.\n init_cfg (str): Initialization config dict. Default: None.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n \"\"\"\n\n def __init__(self,\n data_preprocessor: Union[dict, Config],\n image_model,\n instance_model,\n fusion_model,\n color_data_opt,\n which_direction='AtoB',\n loss=None,\n init_cfg=None,\n train_cfg=None,\n test_cfg=None):\n\n super().__init__(\n init_cfg=init_cfg, data_preprocessor=data_preprocessor)\n\n # colorization networks\n # image_model: used to colorize a single image\n self.image_model = MODELS.build(image_model)\n\n # instance model: used to colorize cropped instance\n self.instance_model = MODELS.build(instance_model)\n\n # fusion model: input a single image with related instance features\n self.fusion_model = MODELS.build(fusion_model)\n\n self.color_data_opt = color_data_opt\n self.which_direction = which_direction\n\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[List[DataSample]] = None,\n mode: str = 'tensor',\n **kwargs):\n \"\"\"Returns losses or predictions of training, validation, testing, and\n simple inference process.\n\n ``forward`` method of BaseModel is an abstract method, its subclasses\n must implement this method.\n\n Accepts ``inputs`` and ``data_samples`` processed by\n :attr:`data_preprocessor`, and returns results according to mode\n arguments.\n\n During non-distributed training, validation, and testing process,\n ``forward`` will be called by ``BaseModel.train_step``,\n ``BaseModel.val_step`` and ``BaseModel.val_step`` directly.\n\n During distributed data parallel training process,\n ``MMSeparateDistributedDataParallel.train_step`` will first call\n ``DistributedDataParallel.forward`` to enable automatic\n gradient synchronization, and then call ``forward`` to get training\n loss.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n mode (str): mode should be one of ``loss``, ``predict`` and\n ``tensor``. Default: 'tensor'.\n\n - ``loss``: Called by ``train_step`` and return loss ``dict``\n used for logging\n - ``predict``: Called by ``val_step`` and ``test_step``\n and return list of ``BaseDataElement`` results used for\n computing metric.\n - ``tensor``: Called by custom use to get ``Tensor`` type\n results.\n\n Returns:\n ForwardResults:\n\n - If ``mode == loss``, return a ``dict`` of loss tensor used\n for backward and logging.\n - If ``mode == predict``, return a ``list`` of\n :obj:`BaseDataElement` for computing metric\n and getting inference result.\n - If ``mode == tensor``, return a tensor or ``tuple`` of tensor\n or ``dict`` or tensor for custom use.\n \"\"\"\n\n if mode == 'tensor':\n return self.forward_tensor(inputs, data_samples, **kwargs)\n\n elif mode == 'predict':\n predictions = self.forward_inference(inputs, data_samples,\n **kwargs)\n predictions = self.convert_to_datasample(data_samples, predictions)\n return predictions\n\n elif mode == 'loss':\n return self.forward_train(inputs, data_samples, **kwargs)\n\n def convert_to_datasample(self, inputs, data_samples):\n \"\"\"Add predictions and destructed inputs (if passed) to data samples.\n\n Args:\n inputs (Optional[torch.Tensor]): The input of model. Defaults to\n None.\n data_samples (List[DataSample]): The data samples loaded from\n dataloader.\n\n Returns:\n List[DataSample]: Modified data samples.\n \"\"\"\n\n for data_sample, output in zip(inputs, data_samples):\n data_sample.output = output\n return inputs\n\n def forward_train(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward function for training.\"\"\"\n raise NotImplementedError(\n 'Instance Colorization has not supported training.')\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step function.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n Returns:\n dict: Dict with loss, information for logger, the number of\n samples and results for visualization.\n \"\"\"\n raise NotImplementedError(\n 'Instance Colorization has not supported training.')\n\n def forward_inference(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward inference. Returns predictions of validation, testing.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[DataSample]: predictions.\n \"\"\"\n feats = self.forward_tensor(inputs, data_samples, **kwargs)\n feats = self.data_preprocessor.destruct(feats, data_samples)\n predictions = []\n for idx in range(feats.shape[0]):\n pred_img = feats[idx].to('cpu')\n predictions.append(\n DataSample(\n pred_img=pred_img, metainfo=data_samples[idx].metainfo))\n\n return predictions\n\n def forward_tensor(self, inputs, data_samples):\n \"\"\"Forward function in tensor mode.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_sample (dict): Dict contains data sample.\n\n Returns:\n dict: Dict contains output results.\n \"\"\"\n\n # prepare data\n\n assert len(data_samples) == 1, \\\n 'fusion model supports only one image due to different numbers '\\\n 'of instances of different images'\n\n full_img_data = get_colorization_data(inputs, self.color_data_opt)\n AtoB = self.which_direction == 'AtoB'\n\n # preprocess input for a single image\n full_real_A = full_img_data['A' if AtoB else 'B']\n full_hint_B = full_img_data['hint_B']\n full_mask_B = full_img_data['mask_B']\n\n if not data_samples.empty_box[0]:\n # preprocess instance input\n cropped_img = data_samples.cropped_img[0]\n box_info_list = [\n data_samples.box_info[0], data_samples.box_info_2x[0],\n data_samples.box_info_4x[0], data_samples.box_info_8x[0]\n ]\n cropped_data = get_colorization_data(cropped_img,\n self.color_data_opt)\n\n real_A = cropped_data['A' if AtoB else 'B']\n hint_B = cropped_data['hint_B']\n mask_B = cropped_data['mask_B']\n\n # network forward\n _, output, feature_map = self.instance_model(\n real_A, hint_B, mask_B)\n output = self.fusion_model(full_real_A, full_hint_B, full_mask_B,\n feature_map, box_info_list)\n\n else:\n _, output, _ = self.image_model(full_real_A, full_hint_B,\n full_mask_B)\n\n output = [\n full_real_A.type(torch.cuda.FloatTensor),\n output.type(torch.cuda.FloatTensor)\n ]\n output = torch.cat(output, dim=1)\n output = torch.clamp(lab2rgb(output, self.color_data_opt), 0.0, 1.0)\n return output\n" }, { "path": "mmagic/models/editors/inst_colorization/weight_layer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport functools\n\nimport torch\nfrom mmengine.model import BaseModule\nfrom torch import nn\n\nfrom mmagic.registry import MODELS\n\n\ndef get_norm_layer(norm_type='instance'):\n \"\"\"Gets the normalization layer.\n\n Args:\n norm_type (str): Type of the normalization layer.\n\n Returns:\n norm_layer (BatchNorm2d or InstanceNorm2d or None):\n normalization layer. Default: instance\n \"\"\"\n if norm_type == 'batch':\n norm_layer = functools.partial(nn.BatchNorm2d, affine=True)\n elif norm_type == 'instance':\n norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)\n elif norm_type == 'none':\n norm_layer = None\n else:\n raise NotImplementedError('normalization layer [%s] is not found' %\n norm_type)\n return norm_layer\n\n\n@MODELS.register_module()\nclass WeightLayer(BaseModule):\n \"\"\"Weight layer of the fusion_net. A small neural network with three\n convolutional layers to predict full-image weight map and perinstance\n weight map.\n\n Args:\n input_ch (int): Number of channels in the input image.\n inner_ch (int): Number of channels produced by the convolution.\n Default: True\n \"\"\"\n\n def __init__(self, input_ch, inner_ch=16):\n super().__init__()\n self.simple_instance_conv = nn.Sequential(\n nn.Conv2d(input_ch, inner_ch, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n nn.Conv2d(inner_ch, inner_ch, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n nn.Conv2d(inner_ch, 1, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n )\n\n self.simple_bg_conv = nn.Sequential(\n nn.Conv2d(input_ch, inner_ch, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n nn.Conv2d(inner_ch, inner_ch, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n nn.Conv2d(inner_ch, 1, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n )\n\n self.normalize = nn.Softmax(1)\n\n def resize_and_pad(self, feauture_maps, info_array):\n \"\"\"Resize the instance feature as well as the weight map to match the\n size of full-image and do zero padding on both of them.\n\n Args:\n feauture_maps (tensor): Feature map\n info_array (tensor): The bounding box\n\n Returns:\n feauture_maps (tensor): Feature maps after resize and padding\n \"\"\"\n feauture_maps = torch.nn.functional.interpolate(\n feauture_maps,\n size=(info_array[5], info_array[4]),\n mode='bilinear')\n feauture_maps = torch.nn.functional.pad(feauture_maps,\n (info_array[0], info_array[1],\n info_array[2], info_array[3]),\n 'constant', 0)\n return feauture_maps\n\n def forward(self, instance_feature, bg_feature, box_info):\n \"\"\"Forward function.\n\n Args:\n instance_feature (tensor): Instance feature obtained from the\n colorization_net\n bg_feature (tensor): full-image feature\n box_info (tensor): The bounding box corresponding to the instance\n\n Returns:\n out (tensor): Fused feature\n \"\"\"\n mask_list = []\n featur_map_list = []\n mask_sum_for_pred = torch.zeros_like(bg_feature)[:1, :1]\n for i in range(instance_feature.shape[0]):\n tmp_crop = torch.unsqueeze(instance_feature[i], 0)\n conv_tmp_crop = self.simple_instance_conv(tmp_crop)\n pred_mask = self.resize_and_pad(conv_tmp_crop, box_info[i])\n\n tmp_crop = self.resize_and_pad(tmp_crop, box_info[i])\n\n mask = torch.zeros_like(bg_feature)[:1, :1]\n mask[0, 0, box_info[i][2]:box_info[i][2] + box_info[i][5],\n box_info[i][0]:box_info[i][0] + box_info[i][4]] = 1.0\n device = mask.device\n mask = mask.type(torch.FloatTensor).to(device)\n\n mask_sum_for_pred = torch.clamp(mask_sum_for_pred + mask, 0.0, 1.0)\n\n mask_list.append(pred_mask)\n featur_map_list.append(tmp_crop)\n\n pred_bg_mask = self.simple_bg_conv(bg_feature)\n mask_list.append(pred_bg_mask + (1 - mask_sum_for_pred) * 100000.0)\n mask_list = self.normalize(torch.cat(mask_list, 1))\n\n mask_list_maskout = mask_list.clone()\n\n featur_map_list.append(bg_feature)\n featur_map_list = torch.cat(featur_map_list, 0)\n mask_list_maskout = mask_list_maskout.permute(1, 0, 2, 3).contiguous()\n out = featur_map_list * mask_list_maskout\n out = torch.sum(out, 0, keepdim=True)\n return out\n" }, { "path": "mmagic/models/editors/liif/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .liif import LIIF\nfrom .liif_net import LIIFEDSRNet, LIIFRDNNet\nfrom .mlp_refiner import MLPRefiner\n\n__all__ = [\n 'LIIF',\n 'LIIFEDSRNet',\n 'LIIFRDNNet',\n 'MLPRefiner',\n]\n" }, { "path": "mmagic/models/editors/liif/liif.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport torch\n\nfrom mmagic.models.base_models import BaseEditModel\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass LIIF(BaseEditModel):\n \"\"\"LIIF model for single image super-resolution.\n\n Paper: Learning Continuous Image Representation with\n Local Implicit Image Function\n\n Args:\n generator (dict): Config for the generator.\n pixel_loss (dict): Config for the pixel loss.\n pretrained (str): Path for pretrained model. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n \"\"\"\n\n def forward_tensor(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n coord = torch.stack(data_samples.metainfo['coord']).to(inputs)\n cell = torch.stack(data_samples.metainfo['cell']).to(inputs)\n\n feats = self.generator(inputs, coord, cell, **kwargs)\n\n return feats\n\n def forward_inference(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward inference. Returns predictions of validation, testing, and\n simple inference.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (BaseDataElement, optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n List[DataSample]: predictions.\n \"\"\"\n # NOTE: feats: shape [bz, N, 3]\n feats = self.forward_tensor(inputs, data_samples, test_mode=True)\n\n # reshape for eval, [bz, N, 3] -> [bz, 3, H, W]\n ih, iw = inputs.shape[-2:]\n # metainfo in stacked data sample is a list, fetch by indexing\n coord_count = data_samples.metainfo['coord'][0].shape[0]\n s = math.sqrt(coord_count / (ih * iw))\n shape = [len(data_samples), round(ih * s), round(iw * s), 3]\n feats = feats.view(shape).permute(0, 3, 1, 2).contiguous()\n\n feats = self.data_preprocessor.destruct(feats, data_samples)\n\n predictions = DataSample(pred_img=feats.cpu())\n\n return predictions\n" }, { "path": "mmagic/models/editors/liif/liif_net.py", "content": "from abc import abstractmethod\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import make_coord\n\n\nclass LIIFNet(BaseModule):\n \"\"\"LIIF net for single image super-resolution, CVPR, 2021.\n\n Paper: Learning Continuous Image Representation with\n Local Implicit Image Function\n\n The subclasses should define `generator` with `encoder` and `imnet`,\n and overwrite the function `gen_feature`.\n If `encoder` does not contain `mid_channels`, `__init__` should be\n overwrite.\n\n Args:\n encoder (dict): Config for the generator.\n imnet (dict): Config for the imnet.\n local_ensemble (bool): Whether to use local ensemble. Default: True.\n feat_unfold (bool): Whether to use feature unfold. Default: True.\n cell_decode (bool): Whether to use cell decode. Default: True.\n eval_bsize (int): Size of batched predict. Default: None.\n \"\"\"\n\n def __init__(self,\n encoder,\n imnet,\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=None):\n super().__init__()\n\n self.local_ensemble = local_ensemble\n self.feat_unfold = feat_unfold\n self.cell_decode = cell_decode\n self.eval_bsize = eval_bsize\n\n # model\n self.encoder = MODELS.build(encoder)\n imnet_in_dim = self.encoder.mid_channels\n if self.feat_unfold:\n imnet_in_dim *= 9\n imnet_in_dim += 2 # attach coordinates\n if self.cell_decode:\n imnet_in_dim += 2\n imnet['in_dim'] = imnet_in_dim\n self.imnet = MODELS.build(imnet)\n\n def forward(self, x, coord, cell, test_mode=False):\n \"\"\"Forward function.\n\n Args:\n x: input tensor.\n coord (Tensor): coordinates tensor.\n cell (Tensor): cell tensor.\n test_mode (bool): Whether in test mode or not. Default: False.\n\n Returns:\n pred (Tensor): output of model.\n \"\"\"\n\n feature = self.gen_feature(x)\n if self.eval_bsize is None or not test_mode:\n pred = self.query_rgb(feature, coord, cell)\n else:\n pred = self.batched_predict(feature, coord, cell)\n\n return pred\n\n def query_rgb(self, feature, coord, cell=None):\n \"\"\"Query RGB value of GT.\n\n Adapted from 'https://github.com/yinboc/liif.git'\n 'liif/models/liif.py'\n Copyright (c) 2020, Yinbo Chen, under BSD 3-Clause License.\n\n Args:\n feature (Tensor): encoded feature.\n coord (Tensor): coord tensor, shape (BHW, 2).\n cell (Tensor | None): cell tensor. Default: None.\n\n Returns:\n result (Tensor): (part of) output.\n \"\"\"\n\n if self.imnet is None:\n coord = coord.type(feature.type())\n result = F.grid_sample(\n feature,\n coord.flip(-1).unsqueeze(1),\n mode='nearest',\n align_corners=False)\n result = result[:, :, 0, :].permute(0, 2, 1)\n return result\n\n if self.feat_unfold:\n feature = F.unfold(\n feature, 3,\n padding=1).view(feature.shape[0], feature.shape[1] * 9,\n feature.shape[2], feature.shape[3])\n\n if self.local_ensemble:\n vx_lst = [-1, 1]\n vy_lst = [-1, 1]\n eps_shift = 1e-6\n else:\n vx_lst, vy_lst, eps_shift = [0], [0], 0\n\n # field radius (global: [-1, 1])\n radius_x = 2 / feature.shape[-2] / 2\n radius_y = 2 / feature.shape[-1] / 2\n\n feat_coord = make_coord(feature.shape[-2:], flatten=False) \\\n .permute(2, 0, 1) \\\n .unsqueeze(0).expand(feature.shape[0], 2, *feature.shape[-2:])\n feat_coord = feat_coord.to(coord)\n\n preds = []\n areas = []\n for vx in vx_lst:\n for vy in vy_lst:\n coord_ = coord.clone()\n coord_[:, :, 0] += vx * radius_x + eps_shift\n coord_[:, :, 1] += vy * radius_y + eps_shift\n coord_.clamp_(-1 + 1e-6, 1 - 1e-6)\n\n coord_ = coord_.type(feature.type())\n query_feat = F.grid_sample(\n feature, coord_.flip(-1).unsqueeze(1),\n mode='nearest', align_corners=False)[:, :, 0, :] \\\n .permute(0, 2, 1)\n\n feat_coord = feat_coord.type(coord_.type())\n query_coord = F.grid_sample(\n feat_coord, coord_.flip(-1).unsqueeze(1),\n mode='nearest', align_corners=False)[:, :, 0, :] \\\n .permute(0, 2, 1)\n rel_coord = coord - query_coord\n rel_coord[:, :, 0] *= feature.shape[-2]\n rel_coord[:, :, 1] *= feature.shape[-1]\n mid_tensor = torch.cat([query_feat, rel_coord], dim=-1)\n\n if self.cell_decode:\n rel_cell = cell.clone()\n rel_cell[:, :, 0] *= feature.shape[-2]\n rel_cell[:, :, 1] *= feature.shape[-1]\n mid_tensor = torch.cat([mid_tensor, rel_cell], dim=-1)\n\n bs, q = coord.shape[:2]\n pred = self.imnet(mid_tensor.view(bs * q, -1)).view(bs, q, -1)\n preds.append(pred)\n\n area = torch.abs(rel_coord[:, :, 0] * rel_coord[:, :, 1])\n areas.append(area + 1e-9)\n\n total_area = torch.stack(areas).sum(dim=0)\n if self.local_ensemble:\n areas = areas[::-1]\n result = 0\n for pred, area in zip(preds, areas):\n result = result + pred * (area / total_area).unsqueeze(-1)\n\n return result\n\n def batched_predict(self, x, coord, cell):\n \"\"\"Batched predict.\n\n Args:\n x (Tensor): Input tensor.\n coord (Tensor): coord tensor.\n cell (Tensor): cell tensor.\n\n Returns:\n pred (Tensor): output of model.\n \"\"\"\n with torch.no_grad():\n n = coord.shape[1]\n left = 0\n preds = []\n while left < n:\n right = min(left + self.eval_bsize, n)\n pred = self.query_rgb(x, coord[:, left:right, :],\n cell[:, left:right, :])\n preds.append(pred)\n left = right\n pred = torch.cat(preds, dim=1)\n return pred\n\n @abstractmethod\n def gen_feature(self, x):\n \"\"\"Generate feature.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n\n@MODELS.register_module()\nclass LIIFEDSRNet(LIIFNet):\n \"\"\"LIIF net based on EDSR.\n\n Paper: Learning Continuous Image Representation with\n Local Implicit Image Function\n\n Args:\n encoder (dict): Config for the generator.\n imnet (dict): Config for the imnet.\n local_ensemble (bool): Whether to use local ensemble. Default: True.\n feat_unfold (bool): Whether to use feature unfold. Default: True.\n cell_decode (bool): Whether to use cell decode. Default: True.\n eval_bsize (int): Size of batched predict. Default: None.\n \"\"\"\n\n def __init__(self,\n encoder,\n imnet,\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=None):\n super().__init__(\n encoder=encoder,\n imnet=imnet,\n local_ensemble=local_ensemble,\n feat_unfold=feat_unfold,\n cell_decode=cell_decode,\n eval_bsize=eval_bsize)\n\n self.conv_first = self.encoder.conv_first\n self.body = self.encoder.body\n self.conv_after_body = self.encoder.conv_after_body\n del self.encoder\n\n def gen_feature(self, x):\n \"\"\"Generate feature.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x = self.conv_first(x)\n res = self.body(x)\n res = self.conv_after_body(res)\n res += x\n\n return res\n\n\n@MODELS.register_module()\nclass LIIFRDNNet(LIIFNet):\n \"\"\"LIIF net based on RDN.\n\n Paper: Learning Continuous Image Representation with\n Local Implicit Image Function\n\n Args:\n encoder (dict): Config for the generator.\n imnet (dict): Config for the imnet.\n local_ensemble (bool): Whether to use local ensemble. Default: True.\n feat_unfold (bool): Whether to use feat unfold. Default: True.\n cell_decode (bool): Whether to use cell decode. Default: True.\n eval_bsize (int): Size of batched predict. Default: None.\n \"\"\"\n\n def __init__(self,\n encoder,\n imnet,\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=None):\n super().__init__(\n encoder=encoder,\n imnet=imnet,\n local_ensemble=local_ensemble,\n feat_unfold=feat_unfold,\n cell_decode=cell_decode,\n eval_bsize=eval_bsize)\n\n self.sfe1 = self.encoder.sfe1\n self.sfe2 = self.encoder.sfe2\n self.rdbs = self.encoder.rdbs\n self.gff = self.encoder.gff\n self.num_blocks = self.encoder.num_blocks\n del self.encoder\n\n def gen_feature(self, x):\n \"\"\"Generate feature.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n sfe1 = self.sfe1(x)\n sfe2 = self.sfe2(sfe1)\n\n x = sfe2\n local_features = []\n for i in range(self.num_blocks):\n x = self.rdbs[i](x)\n local_features.append(x)\n\n x = self.gff(torch.cat(local_features, 1)) + sfe1\n\n return x\n" }, { "path": "mmagic/models/editors/liif/mlp_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass MLPRefiner(BaseModule):\n \"\"\"Multilayer perceptrons (MLPs), refiner used in LIIF.\n\n Args:\n in_dim (int): Input dimension.\n out_dim (int): Output dimension.\n hidden_list (list[int]): List of hidden dimensions.\n \"\"\"\n\n def __init__(self, in_dim, out_dim, hidden_list):\n super().__init__()\n\n layers = []\n last_channels = in_dim\n for hidden in hidden_list:\n layers.append(nn.Linear(last_channels, hidden))\n layers.append(nn.ReLU())\n last_channels = hidden\n layers.append(nn.Linear(last_channels, out_dim))\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): The input of MLP.\n\n Returns:\n Tensor: The output of MLP.\n \"\"\"\n\n shape = x.shape[:-1]\n x = self.layers(x.view(-1, x.shape[-1]))\n\n return x.view(*shape, -1)\n" }, { "path": "mmagic/models/editors/lsgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .lsgan import LSGAN\nfrom .lsgan_discriminator import LSGANDiscriminator\nfrom .lsgan_generator import LSGANGenerator\n\n__all__ = ['LSGAN', 'LSGANDiscriminator', 'LSGANGenerator']\n" }, { "path": "mmagic/models/editors/lsgan/lsgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseGAN\n\n\n@MODELS.register_module()\nclass LSGAN(BaseGAN):\n \"\"\"Implementation of `Least Squares Generative Adversarial Networks`.\n\n Paper link: https://arxiv.org/pdf/1611.04076.pdf\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.lsgan.LSGANGenerator`\n and\n :class:`~mmagic.models.editors.lsgan.LSGANDiscriminator`\n \"\"\"\n\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple:\n r\"\"\"Get disc loss. LSGAN use the least squares loss to train\n the discriminator.\n\n .. math::\n L_{D}=\\left(D\\left(X_{\\text {data }}\\right)-1\\right)^{2}\n +(D(G(z)))^{2}\n\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.mse_loss(\n disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))\n losses_dict['loss_disc_real'] = F.mse_loss(\n disc_pred_real, 1. * torch.ones_like(disc_pred_real))\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:\n \"\"\"Get gen loss. LSGAN use the least squares loss to train the\n generator.\n\n .. math::\n L_{G}=(D(G(z))-1)^{2}\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = F.mse_loss(\n disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/lsgan/lsgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass LSGANDiscriminator(BaseModule):\n \"\"\"Discriminator for LSGAN.\n\n Implementation Details for LSGAN architecture:\n\n #. Adopt convolution in the discriminator;\n #. Use batchnorm in the discriminator except for the input and final \\\n output layer;\n #. Use LeakyReLU in the discriminator in addition to the output layer;\n #. Use fully connected layer in the output layer;\n #. Use 5x5 conv rather than 4x4 conv in DCGAN.\n\n Args:\n input_scale (int, optional): The scale of the input image. Defaults to\n 128.\n output_scale (int, optional): The final scale of the convolutional\n feature. Defaults to 8.\n out_channels (int, optional): The channel number of the final output\n layer. Defaults to 1.\n in_channels (int, optional): The channel number of the input image.\n Defaults to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Defaults to 128.\n conv_cfg (dict, optional): Config for the convolution module used in\n this discriminator. Defaults to dict(type='Conv2d').\n default_norm_cfg (dict, optional): Norm config for all of layers\n except for the final output layer. Defaults to ``dict(type='BN')``.\n default_act_cfg (dict, optional): Activation config for all of layers\n except for the final output layer. Defaults to\n ``dict(type='LeakyReLU', negative_slope=0.2)``.\n out_act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to ``dict(type='Tanh')``.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n input_scale=128,\n output_scale=8,\n out_channels=1,\n in_channels=3,\n base_channels=64,\n conv_cfg=dict(type='Conv2d'),\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act_cfg=None,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n assert input_scale % output_scale == 0\n assert input_scale // output_scale >= 2\n\n self.input_scale = input_scale\n self.output_scale = output_scale\n self.out_channels = out_channels\n self.base_channels = base_channels\n self.with_out_activation = out_act_cfg is not None\n\n self.conv_blocks = nn.ModuleList()\n self.conv_blocks.append(\n ConvModule(\n in_channels,\n base_channels,\n kernel_size=5,\n stride=2,\n padding=2,\n conv_cfg=conv_cfg,\n norm_cfg=None,\n act_cfg=default_act_cfg))\n\n # the number of times for downsampling\n self.num_downsamples = int(np.log2(input_scale // output_scale)) - 1\n\n # build up downsampling backbone (excluding the output layer)\n curr_channels = base_channels\n for _ in range(self.num_downsamples):\n self.conv_blocks.append(\n ConvModule(\n curr_channels,\n curr_channels * 2,\n kernel_size=5,\n stride=2,\n padding=2,\n conv_cfg=conv_cfg,\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n curr_channels = curr_channels * 2\n\n # output layer\n self.decision = nn.Sequential(\n nn.Linear(output_scale * output_scale * curr_channels,\n out_channels))\n if self.with_out_activation:\n self.out_activation = MODELS.build(out_act_cfg)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image.\n \"\"\"\n n = x.shape[0]\n\n for conv in self.conv_blocks:\n x = conv(x)\n\n x = x.reshape(n, -1)\n x = self.decision(x)\n\n if self.with_out_activation:\n x = self.out_activation(x)\n\n return x\n" }, { "path": "mmagic/models/editors/lsgan/lsgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\n\n\n@MODELS.register_module()\nclass LSGANGenerator(BaseModule):\n \"\"\"Generator for LSGAN.\n\n Implementation Details for LSGAN architecture:\n\n #. Adopt transposed convolution in the generator;\n #. Use batchnorm in the generator except for the final output layer;\n #. Use ReLU in the generator in addition to the final output layer;\n #. Keep channels of feature maps unchanged in the convolution backbone;\n #. Use one more 3x3 conv every upsampling in the convolution backbone.\n\n We follow the implementation details of the origin paper:\n Least Squares Generative Adversarial Networks\n https://arxiv.org/pdf/1611.04076.pdf\n\n Args:\n output_scale (int, optional): Output scale for the generated image.\n Defaults to 128.\n out_channels (int, optional): The channel number of the output feature.\n Defaults to 3.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Defaults to 256.\n input_scale (int, optional): The scale of the input 2D feature map.\n Defaults to 8.\n noise_size (int, optional): Size of the input noise\n vector. Defaults to 1024.\n conv_cfg (dict, optional): Config for the convolution module used in\n this generator. Defaults to dict(type='ConvTranspose2d').\n default_norm_cfg (dict, optional): Norm config for all of layers\n except for the final output layer. Defaults to dict(type='BN').\n default_act_cfg (dict, optional): Activation config for all of layers\n except for the final output layer. Defaults to dict(type='ReLU').\n out_act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to dict(type='Tanh').\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n output_scale=128,\n out_channels=3,\n base_channels=256,\n input_scale=8,\n noise_size=1024,\n conv_cfg=dict(type='ConvTranspose2d'),\n default_norm_cfg=dict(type='BN'),\n default_act_cfg=dict(type='ReLU'),\n out_act_cfg=dict(type='Tanh'),\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n assert output_scale % input_scale == 0\n assert output_scale // input_scale >= 4\n\n self.output_scale = output_scale\n self.base_channels = base_channels\n self.input_scale = input_scale\n self.noise_size = noise_size\n\n self.noise2feat_head = nn.Sequential(\n nn.Linear(noise_size, input_scale * input_scale * base_channels))\n self.noise2feat_tail = nn.Sequential(nn.BatchNorm2d(base_channels))\n if default_act_cfg is not None:\n self.noise2feat_tail.add_module('act',\n MODELS.build(default_act_cfg))\n\n # the number of times for upsampling\n self.num_upsamples = int(np.log2(output_scale // input_scale)) - 2\n\n # build up convolution backbone (excluding the output layer)\n self.conv_blocks = nn.ModuleList()\n for _ in range(self.num_upsamples):\n self.conv_blocks.append(\n ConvModule(\n base_channels,\n base_channels,\n kernel_size=3,\n stride=2,\n padding=1,\n conv_cfg=dict(conv_cfg, output_padding=1),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n self.conv_blocks.append(\n ConvModule(\n base_channels,\n base_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n\n # output blocks\n self.conv_blocks.append(\n ConvModule(\n base_channels,\n int(base_channels // 2),\n kernel_size=3,\n stride=2,\n padding=1,\n conv_cfg=dict(conv_cfg, output_padding=1),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n self.conv_blocks.append(\n ConvModule(\n int(base_channels // 2),\n int(base_channels // 4),\n kernel_size=3,\n stride=2,\n padding=1,\n conv_cfg=dict(conv_cfg, output_padding=1),\n norm_cfg=default_norm_cfg,\n act_cfg=default_act_cfg))\n self.conv_blocks.append(\n ConvModule(\n int(base_channels // 4),\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=None,\n act_cfg=out_act_cfg))\n\n def forward(self, noise, num_batches=0, return_noise=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img`` and\n ``noise_batch`` will be returned.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n if noise.ndim == 2:\n noise_batch = noise\n else:\n raise ValueError('The noise should be in shape of (n, c)'\n f'but got {noise.shape}')\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n # noise2feat\n x = self.noise2feat_head(noise_batch)\n x = x.reshape(\n (-1, self.base_channels, self.input_scale, self.input_scale))\n x = self.noise2feat_tail(x)\n # conv module\n for conv in self.conv_blocks:\n x = conv(x)\n\n if return_noise:\n return dict(fake_img=x, noise_batch=noise_batch)\n\n return x\n" }, { "path": "mmagic/models/editors/mspie/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .mspie_stylegan2 import MSPIEStyleGAN2\nfrom .mspie_stylegan2_discriminator import MSStyleGAN2Discriminator\nfrom .mspie_stylegan2_generator import MSStyleGANv2Generator\nfrom .pe_singan import PESinGAN\nfrom .pe_singan_generator import SinGANMSGeneratorPE\nfrom .positional_encoding import CatersianGrid, SinusoidalPositionalEmbedding\n\n__all__ = [\n 'MSPIEStyleGAN2', 'MSStyleGAN2Discriminator', 'MSStyleGANv2Generator',\n 'PESinGAN', 'SinGANMSGeneratorPE', 'CatersianGrid',\n 'SinusoidalPositionalEmbedding'\n]\n" }, { "path": "mmagic/models/editors/mspie/mspie_stylegan2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict, Union\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...utils import set_requires_grad\nfrom ..stylegan2 import StyleGAN2\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module()\nclass MSPIEStyleGAN2(StyleGAN2):\n \"\"\"MS-PIE StyleGAN2.\n\n In this GAN, we adopt the MS-PIE training schedule so that multi-scale\n images can be generated with a single generator. Details can be found in:\n Positional Encoding as Spatial Inductive Bias in GANs, CVPR2021.\n\n Args:\n train_settings (dict): Config for training settings.\n Defaults to `dict()`.\n \"\"\"\n\n def __init__(self, *args, train_settings=dict(), **kwargs):\n super().__init__(*args, **kwargs)\n self.train_settings = deepcopy(train_settings)\n # set the number of upsampling blocks. This value will be used to\n # calculate the current result size according to the size of the input\n # feature map, e.g., positional encoding map\n self.num_upblocks = self.train_settings.get('num_upblocks', 6)\n\n # multiple input scales (a list of int) that will be added to the\n # original starting scale.\n self.multi_input_scales = self.train_settings.get('multi_input_scales')\n self.multi_scale_probability = self.train_settings.get(\n 'multi_scale_probability')\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n \"\"\"Train GAN model. In the training of GAN models, generator and\n discriminator are updated alternatively. In MMagic's design,\n `self.train_step` is called with data input. Therefore we always update\n discriminator, whose updating is relay on real data, and then determine\n if the generator needs to be updated based on the current number of\n iterations. More details about whether to update generator can be found\n in :meth:`should_gen_update`.\n\n Args:\n data (dict): Data sampled from dataloader.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n log_vars = self.train_discriminator(\n **data, optimizer_wrapper=disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n log_vars_gen = self.train_generator(\n **data, optimizer_wrapper=gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = self.gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n # if not update buffer, copy buffer from orig model\n if not self.generator_ema.update_buffers:\n self.generator_ema.sync_buffers(\n self.generator.module if is_model_wrapper(\n self.generator) else self.generator)\n elif self.with_ema_gen:\n # before ema, copy weights from orig\n self.generator_ema.sync_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (TrainInput): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(\n noise, return_noise=False, chosen_scale=self.chosen_scale)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake, num_batches)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (TrainInput): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n if dist.is_initialized():\n # randomly sample a scale for current training iteration\n chosen_scale = np.random.choice(self.multi_input_scales, 1,\n self.multi_scale_probability)[0]\n\n chosen_scale = torch.tensor(chosen_scale, dtype=torch.int).cuda()\n dist.broadcast(chosen_scale, 0)\n chosen_scale = int(chosen_scale.item())\n\n else:\n logger = MMLogger.get_current_instance()\n logger.info(\n 'Distributed training has not been initialized. Degrade to '\n 'the standard stylegan2')\n chosen_scale = 0\n\n curr_size = (4 + chosen_scale) * (2**self.num_upblocks)\n # adjust the shape of images\n if real_imgs.shape[-2:] != (curr_size, curr_size):\n real_imgs = F.interpolate(\n real_imgs,\n size=(curr_size, curr_size),\n mode='bilinear',\n align_corners=True)\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(\n noise_batch, return_noise=False, chosen_scale=chosen_scale)\n # store chosen scale for training generator\n setattr(self, 'chosen_scale', chosen_scale)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real, real_imgs)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n" }, { "path": "mmagic/models/editors/mspie/mspie_stylegan2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ..stylegan1 import EqualLinearActModule\nfrom ..stylegan2 import ConvDownLayer, ModMBStddevLayer, ResBlock\n\n\n@MODELS.register_module()\nclass MSStyleGAN2Discriminator(BaseModule):\n \"\"\"StyleGAN2 Discriminator.\n\n The architecture of this discriminator is proposed in StyleGAN2. More\n details can be found in: Analyzing and Improving the Image Quality of\n StyleGAN CVPR2020.\n\n Args:\n in_size (int): The input size of images.\n channel_multiplier (int, optional): The multiplier factor for the\n channel number. Defaults to 2.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 3, 3, 1].\n mbstd_cfg (dict, optional): Configs for minibatch-stddev layer.\n Defaults to dict(group_size=4, channel_groups=1).\n \"\"\"\n\n def __init__(self,\n in_size,\n channel_multiplier=2,\n blur_kernel=[1, 3, 3, 1],\n mbstd_cfg=dict(group_size=4, channel_groups=1),\n with_adaptive_pool=False,\n pool_size=(2, 2)):\n super().__init__()\n self.with_adaptive_pool = with_adaptive_pool\n self.pool_size = pool_size\n\n channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256 * channel_multiplier,\n 128: 128 * channel_multiplier,\n 256: 64 * channel_multiplier,\n 512: 32 * channel_multiplier,\n 1024: 16 * channel_multiplier,\n }\n\n log_size = int(np.log2(in_size))\n in_channels = channels[in_size]\n convs = [ConvDownLayer(3, channels[in_size], 1)]\n\n for i in range(log_size, 2, -1):\n out_channel = channels[2**(i - 1)]\n convs.append(ResBlock(in_channels, out_channel, blur_kernel))\n\n in_channels = out_channel\n\n self.convs = nn.Sequential(*convs)\n self.mbstd_layer = ModMBStddevLayer(**mbstd_cfg)\n\n self.final_conv = ConvDownLayer(in_channels + 1, channels[4], 3)\n\n if self.with_adaptive_pool:\n self.adaptive_pool = nn.AdaptiveAvgPool2d(pool_size)\n linear_in_channels = channels[4] * pool_size[0] * pool_size[1]\n else:\n linear_in_channels = channels[4] * 4 * 4\n\n self.final_linear = nn.Sequential(\n EqualLinearActModule(\n linear_in_channels,\n channels[4],\n act_cfg=dict(type='fused_bias')),\n EqualLinearActModule(channels[4], 1),\n )\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input image tensor.\n\n Returns:\n torch.Tensor: Predict score for the input image.\n \"\"\"\n x = self.convs(x)\n\n x = self.mbstd_layer(x)\n x = self.final_conv(x)\n if self.with_adaptive_pool:\n x = self.adaptive_pool(x)\n x = x.view(x.shape[0], -1)\n x = self.final_linear(x)\n\n return x\n" }, { "path": "mmagic/models/editors/mspie/mspie_stylegan2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\nfrom copy import deepcopy\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom ..pggan import PixelNorm\nfrom ..stylegan1 import (ConstantInput, EqualLinearActModule, get_mean_latent,\n style_mixing)\nfrom ..stylegan2 import ModulatedToRGB\nfrom .mspie_stylegan2_modules import ModulatedPEStyleConv\n\n\n@MODELS.register_module()\nclass MSStyleGANv2Generator(BaseModule):\n \"\"\"StyleGAN2 Generator.\n\n In StyleGAN2, we use a static architecture composing of a style mapping\n module and number of convolutional style blocks. More details can be found\n in: Analyzing and Improving the Image Quality of StyleGAN CVPR2020.\n\n Args:\n out_size (int): The output size of the StyleGAN2 generator.\n style_channels (int): The number of channels for style code.\n num_mlps (int, optional): The number of MLP layers. Defaults to 8.\n channel_multiplier (int, optional): The multiplier factor for the\n channel number. Defaults to 2.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 3, 3, 1].\n lr_mlp (float, optional): The learning rate for the style mapping\n layer. Defaults to 0.01.\n default_style_mode (str, optional): The default mode of style mixing.\n In training, we adopt mixing style mode in default. However, in the\n evaluation, we use 'single' style mode. `['mix', 'single']` are\n currently supported. Defaults to 'mix'.\n eval_style_mode (str, optional): The evaluation mode of style mixing.\n Defaults to 'single'.\n mix_prob (float, optional): Mixing probability. The value should be\n in range of [0, 1]. Defaults to 0.9.\n \"\"\"\n\n def __init__(self,\n out_size,\n style_channels,\n num_mlps=8,\n channel_multiplier=2,\n blur_kernel=[1, 3, 3, 1],\n lr_mlp=0.01,\n default_style_mode='mix',\n eval_style_mode='single',\n mix_prob=0.9,\n no_pad=False,\n deconv2conv=False,\n interp_pad=None,\n up_config=dict(scale_factor=2, mode='nearest'),\n up_after_conv=False,\n head_pos_encoding=None,\n head_pos_size=(4, 4),\n interp_head=False):\n super().__init__()\n self.out_size = out_size\n self.style_channels = style_channels\n self.num_mlps = num_mlps\n self.channel_multiplier = channel_multiplier\n self.lr_mlp = lr_mlp\n self._default_style_mode = default_style_mode\n self.default_style_mode = default_style_mode\n self.eval_style_mode = eval_style_mode\n self.mix_prob = mix_prob\n self.no_pad = no_pad\n self.deconv2conv = deconv2conv\n self.interp_pad = interp_pad\n self.with_interp_pad = interp_pad is not None\n self.up_config = deepcopy(up_config)\n self.up_after_conv = up_after_conv\n self.head_pos_encoding = head_pos_encoding\n self.head_pos_size = head_pos_size\n self.interp_head = interp_head\n\n # define style mapping layers\n mapping_layers = [PixelNorm()]\n\n for _ in range(num_mlps):\n mapping_layers.append(\n EqualLinearActModule(\n style_channels,\n style_channels,\n equalized_lr_cfg=dict(lr_mul=lr_mlp, gain=1.),\n act_cfg=dict(type='fused_bias')))\n\n self.style_mapping = nn.Sequential(*mapping_layers)\n\n self.channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256 * channel_multiplier,\n 128: 128 * channel_multiplier,\n 256: 64 * channel_multiplier,\n 512: 32 * channel_multiplier,\n 1024: 16 * channel_multiplier,\n }\n\n in_ch = self.channels[4]\n # constant input layer\n if self.head_pos_encoding:\n if self.head_pos_encoding['type'] in [\n 'CatersianGrid', 'CSG', 'CSG2d'\n ]:\n in_ch = 2\n self.head_pos_enc = MODELS.build(self.head_pos_encoding)\n else:\n size_ = 4\n if self.no_pad:\n size_ += 2\n self.constant_input = ConstantInput(self.channels[4], size=size_)\n\n # 4x4 stage\n self.conv1 = ModulatedPEStyleConv(\n in_ch,\n self.channels[4],\n kernel_size=3,\n style_channels=style_channels,\n blur_kernel=blur_kernel,\n deconv2conv=self.deconv2conv,\n no_pad=self.no_pad,\n up_config=self.up_config,\n interp_pad=self.interp_pad)\n self.to_rgb1 = ModulatedToRGB(\n self.channels[4], style_channels, upsample=False)\n\n # generator backbone (8x8 --> higher resolutions)\n self.log_size = int(np.log2(self.out_size))\n\n self.convs = nn.ModuleList()\n self.upsamples = nn.ModuleList()\n self.to_rgbs = nn.ModuleList()\n\n in_channels_ = self.channels[4]\n\n for i in range(3, self.log_size + 1):\n out_channels_ = self.channels[2**i]\n\n self.convs.append(\n ModulatedPEStyleConv(\n in_channels_,\n out_channels_,\n 3,\n style_channels,\n upsample=True,\n blur_kernel=blur_kernel,\n deconv2conv=self.deconv2conv,\n no_pad=self.no_pad,\n up_config=self.up_config,\n interp_pad=self.interp_pad,\n up_after_conv=self.up_after_conv))\n self.convs.append(\n ModulatedPEStyleConv(\n out_channels_,\n out_channels_,\n 3,\n style_channels,\n upsample=False,\n blur_kernel=blur_kernel,\n deconv2conv=self.deconv2conv,\n no_pad=self.no_pad,\n up_config=self.up_config,\n interp_pad=self.interp_pad,\n up_after_conv=self.up_after_conv))\n self.to_rgbs.append(\n ModulatedToRGB(out_channels_, style_channels, upsample=True))\n\n in_channels_ = out_channels_\n\n self.num_latents = self.log_size * 2 - 2\n self.num_injected_noises = self.num_latents - 1\n\n # register buffer for injected noises\n noises = self.make_injected_noise()\n for layer_idx in range(self.num_injected_noises):\n self.register_buffer(f'injected_noise_{layer_idx}',\n noises[layer_idx])\n\n def train(self, mode=True):\n \"\"\"Set train/eval mode.\n\n Args:\n mode (bool, optional): Whether set train mode. Defaults to True.\n \"\"\"\n if mode:\n if self.default_style_mode != self._default_style_mode:\n mmengine.print_log(\n f'Switch to train style mode: {self._default_style_mode}')\n self.default_style_mode = self._default_style_mode\n\n else:\n if self.default_style_mode != self.eval_style_mode:\n mmengine.print_log(\n f'Switch to evaluation style mode: {self.eval_style_mode}')\n self.default_style_mode = self.eval_style_mode\n\n return super(MSStyleGANv2Generator, self).train(mode)\n\n def make_injected_noise(self, chosen_scale=0):\n \"\"\"make noises that will be injected into feature maps.\n\n Args:\n chosen_scale (int, optional): Chosen scale. Defaults to 0.\n\n Returns:\n list[Tensor]: List of layer-wise noise tensor.\n \"\"\"\n device = get_module_device(self)\n\n base_scale = 2**2 + chosen_scale\n\n noises = [torch.randn(1, 1, base_scale, base_scale, device=device)]\n\n for i in range(3, self.log_size + 1):\n for n in range(2):\n _pad = 0\n if self.no_pad and not self.up_after_conv and n == 0:\n _pad = 2\n noises.append(\n torch.randn(\n 1,\n 1,\n base_scale * 2**(i - 2) + _pad,\n base_scale * 2**(i - 2) + _pad,\n device=device))\n\n return noises\n\n def get_mean_latent(self, num_samples=4096, **kwargs):\n \"\"\"Get mean latent of W space in this generator.\n\n Args:\n num_samples (int, optional): Number of sample times. Defaults\n to 4096.\n\n Returns:\n Tensor: Mean latent of this generator.\n \"\"\"\n return get_mean_latent(self, num_samples, **kwargs)\n\n def style_mixing(self,\n n_source,\n n_target,\n inject_index=1,\n truncation_latent=None,\n truncation=0.7,\n chosen_scale=0):\n \"\"\"Generating style mixing images.\n\n Args:\n n_source (int): Number of source images.\n n_target (int): Number of target images.\n inject_index (int, optional): Index from which replace with source\n latent. Defaults to 1.\n truncation_latent (torch.Tensor, optional): Mean truncation latent.\n Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n curr_scale (int): Current image scale. Defaults to -1.\n transition_weight (float, optional): The weight used in resolution\n transition. Defaults to 1.0.\n chosen_scale (int, optional): Chosen scale. Defaults to 0.\n Returns:\n torch.Tensor: Table of style-mixing images.\n \"\"\"\n return style_mixing(\n self,\n n_source=n_source,\n n_target=n_target,\n inject_index=inject_index,\n truncation_latent=truncation_latent,\n truncation=truncation,\n style_channels=self.style_channels,\n chosen_scale=chosen_scale)\n\n def forward(self,\n styles,\n num_batches=-1,\n return_noise=False,\n return_latents=False,\n inject_index=None,\n truncation=1,\n truncation_latent=None,\n input_is_latent=False,\n injected_noise=None,\n randomize_noise=True,\n chosen_scale=0):\n \"\"\"Forward function.\n\n This function has been integrated with the truncation trick. Please\n refer to the usage of `truncation` and `truncation_latent`.\n\n Args:\n styles (torch.Tensor | list[torch.Tensor] | callable | None): In\n StyleGAN2, you can provide noise tensor or latent tensor. Given\n a list containing more than one noise or latent tensors, style\n mixing trick will be used in training. Of course, You can\n directly give a batch of noise through a ``torch.Tensor`` or\n offer a callable function to sample a batch of noise data.\n Otherwise, the ``None`` indicates to use the default noise\n sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n return_latents (bool, optional): If True, ``latent`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n inject_index (int | None, optional): The index number for mixing\n style codes. Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n truncation_latent (torch.Tensor, optional): Mean truncation latent.\n Defaults to None.\n input_is_latent (bool, optional): If `True`, the input tensor is\n the latent tensor. Defaults to False.\n injected_noise (torch.Tensor | None, optional): Given a tensor, the\n random noise will be fixed as this input injected noise.\n Defaults to None.\n randomize_noise (bool, optional): If `False`, images are sampled\n with the buffered noise tensor injected to the style conv\n block. Defaults to True.\n\n Returns:\n torch.Tensor | dict: Generated image tensor or dictionary \\\n containing more data.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(styles, torch.Tensor):\n assert styles.shape[1] == self.style_channels\n styles = [styles]\n elif mmengine.is_seq_of(styles, torch.Tensor):\n for t in styles:\n assert t.shape[-1] == self.style_channels\n # receive a noise generator and sample noise.\n elif callable(styles):\n device = get_module_device(self)\n noise_generator = styles\n assert num_batches > 0\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n noise_generator((num_batches, self.style_channels))\n for _ in range(2)\n ]\n else:\n styles = [noise_generator((num_batches, self.style_channels))]\n styles = [s.to(device) for s in styles]\n # otherwise, we will adopt default noise sampler.\n else:\n device = get_module_device(self)\n assert num_batches > 0 and not input_is_latent\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n torch.randn((num_batches, self.style_channels))\n for _ in range(2)\n ]\n else:\n styles = [torch.randn((num_batches, self.style_channels))]\n styles = [s.to(device) for s in styles]\n\n if not input_is_latent:\n noise_batch = styles\n styles = [self.style_mapping(s) for s in styles]\n else:\n noise_batch = None\n\n if injected_noise is None:\n if randomize_noise:\n injected_noise = [None] * self.num_injected_noises\n elif chosen_scale > 0:\n if not hasattr(self, f'injected_noise_{chosen_scale}_0'):\n noises_ = self.make_injected_noise(chosen_scale)\n for i in range(self.num_injected_noises):\n setattr(self, f'injected_noise_{chosen_scale}_{i}',\n noises_[i])\n injected_noise = [\n getattr(self, f'injected_noise_{chosen_scale}_{i}')\n for i in range(self.num_injected_noises)\n ]\n else:\n injected_noise = [\n getattr(self, f'injected_noise_{i}')\n for i in range(self.num_injected_noises)\n ]\n # use truncation trick\n if truncation < 1:\n style_t = []\n # calculate truncation latent on the fly\n if truncation_latent is None and not hasattr(\n self, 'truncation_latent'):\n self.truncation_latent = self.get_mean_latent()\n truncation_latent = self.truncation_latent\n elif truncation_latent is None and hasattr(self,\n 'truncation_latent'):\n truncation_latent = self.truncation_latent\n\n for style in styles:\n style_t.append(truncation_latent + truncation *\n (style - truncation_latent))\n\n styles = style_t\n # no style mixing\n if len(styles) < 2:\n inject_index = self.num_latents\n\n if styles[0].ndim < 3:\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n\n else:\n latent = styles[0]\n # style mixing\n else:\n if inject_index is None:\n inject_index = random.randint(1, self.num_latents - 1)\n\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n latent2 = styles[1].unsqueeze(1).repeat(\n 1, self.num_latents - inject_index, 1)\n\n latent = torch.cat([latent, latent2], 1)\n\n if isinstance(chosen_scale, int):\n chosen_scale = (chosen_scale, chosen_scale)\n\n # 4x4 stage\n if self.head_pos_encoding:\n if self.interp_head:\n out = self.head_pos_enc.make_grid2d(self.head_pos_size[0],\n self.head_pos_size[1],\n latent.size(0))\n h_in = self.head_pos_size[0] + chosen_scale[0]\n w_in = self.head_pos_size[1] + chosen_scale[1]\n out = F.interpolate(\n out,\n size=(h_in, w_in),\n mode='bilinear',\n align_corners=True)\n else:\n out = self.head_pos_enc.make_grid2d(\n self.head_pos_size[0] + chosen_scale[0],\n self.head_pos_size[1] + chosen_scale[1], latent.size(0))\n out = out.to(latent)\n else:\n out = self.constant_input(latent)\n if chosen_scale[0] != 0 or chosen_scale[1] != 0:\n out = F.interpolate(\n out,\n size=(out.shape[2] + chosen_scale[0],\n out.shape[3] + chosen_scale[1]),\n mode='bilinear',\n align_corners=True)\n\n out = self.conv1(out, latent[:, 0], noise=injected_noise[0])\n skip = self.to_rgb1(out, latent[:, 1])\n\n _index = 1\n\n # 8x8 ---> higher resolutions\n for up_conv, conv, noise1, noise2, to_rgb in zip(\n self.convs[::2], self.convs[1::2], injected_noise[1::2],\n injected_noise[2::2], self.to_rgbs):\n out = up_conv(out, latent[:, _index], noise=noise1)\n out = conv(out, latent[:, _index + 1], noise=noise2)\n skip = to_rgb(out, latent[:, _index + 2], skip)\n\n _index += 2\n\n img = skip\n\n if return_latents or return_noise:\n output_dict = dict(\n fake_img=img,\n latent=latent,\n inject_index=inject_index,\n noise_batch=noise_batch,\n injected_noise=injected_noise)\n return output_dict\n\n return img\n" }, { "path": "mmagic/models/editors/mspie/mspie_stylegan2_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\ntry:\n from mmcv.ops import conv2d, conv_transpose2d\nexcept ImportError:\n conv2d = None\n conv_transpose2d = None\n print('Warning: mmcv.ops.conv2d, mmcv.ops.conv_transpose2d'\n 'are not available.')\n\nfrom ..pggan import equalized_lr\nfrom ..stylegan1 import Blur, EqualLinearActModule, NoiseInjection\nfrom ..stylegan2.stylegan2_modules import _FusedBiasLeakyReLU\n\n\nclass ModulatedPEConv2d(BaseModule):\n r\"\"\"Modulated Conv2d in StyleGANv2 with Positional Encoding (PE).\n\n This module is modified from the ``ModulatedConv2d`` in StyleGAN2 to\n support the experiments in: Positional Encoding as Spatial Inductive Bias\n in GANs, CVPR'2021.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n style_channels (int): Channels for the style codes.\n demodulate (bool, optional): Whether to adopt demodulation.\n Defaults to True.\n upsample (bool, optional): Whether to adopt upsampling in features.\n Defaults to False.\n downsample (bool, optional): Whether to adopt downsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n equalized_lr_cfg (dict | None, optional): Configs for equalized lr.\n Defaults to dict(mode='fan_in', lr_mul=1., gain=1.).\n style_mod_cfg (dict, optional): Configs for style modulation module.\n Defaults to dict(bias_init=1.).\n style_bias (float, optional): Bias value for style code.\n Defaults to 0..\n eps (float, optional): Epsilon value to avoid computation error.\n Defaults to 1e-8.\n no_pad (bool, optional): Whether to removing the padding in\n convolution. Defaults to False.\n deconv2conv (bool, optional): Whether to substitute the transposed conv\n with (conv2d, upsampling). Defaults to False.\n interp_pad (int | None, optional): The padding number of interpolation\n pad. Defaults to None.\n up_config (dict, optional): Upsampling config.\n Defaults to dict(scale_factor=2, mode='nearest').\n up_after_conv (bool, optional): Whether to adopt upsampling after\n convolution. Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n demodulate=True,\n upsample=False,\n downsample=False,\n blur_kernel=[1, 3, 3, 1],\n equalized_lr_cfg=dict(mode='fan_in', lr_mul=1., gain=1.),\n style_mod_cfg=dict(bias_init=1.),\n style_bias=0.,\n eps=1e-8,\n no_pad=False,\n deconv2conv=False,\n interp_pad=None,\n up_config=dict(scale_factor=2, mode='nearest'),\n up_after_conv=False):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.kernel_size = kernel_size\n self.style_channels = style_channels\n self.demodulate = demodulate\n # sanity check for kernel size\n assert isinstance(self.kernel_size,\n int) and (self.kernel_size >= 1\n and self.kernel_size % 2 == 1)\n self.upsample = upsample\n self.downsample = downsample\n self.style_bias = style_bias\n self.eps = eps\n self.no_pad = no_pad\n self.deconv2conv = deconv2conv\n self.interp_pad = interp_pad\n self.with_interp_pad = interp_pad is not None\n self.up_config = deepcopy(up_config)\n self.up_after_conv = up_after_conv\n\n # build style modulation module\n style_mod_cfg = dict() if style_mod_cfg is None else style_mod_cfg\n\n self.style_modulation = EqualLinearActModule(style_channels,\n in_channels,\n **style_mod_cfg)\n # set lr_mul for conv weight\n lr_mul_ = 1.\n if equalized_lr_cfg is not None:\n lr_mul_ = equalized_lr_cfg.get('lr_mul', 1.)\n self.weight = nn.Parameter(\n torch.randn(1, out_channels, in_channels, kernel_size,\n kernel_size).div_(lr_mul_))\n\n # build blurry layer for upsampling\n if upsample and not self.deconv2conv:\n factor = 2\n p = (len(blur_kernel) - factor) - (kernel_size - 1)\n pad0 = (p + 1) // 2 + factor - 1\n pad1 = p // 2 + 1\n self.blur = Blur(blur_kernel, (pad0, pad1), upsample_factor=factor)\n\n # build blurry layer for downsampling\n if downsample:\n factor = 2\n p = (len(blur_kernel) - factor) + (kernel_size - 1)\n pad0 = (p + 1) // 2\n pad1 = p // 2\n self.blur = Blur(blur_kernel, pad=(pad0, pad1))\n\n # add equalized_lr hook for conv weight\n if equalized_lr_cfg is not None:\n equalized_lr(self, **equalized_lr_cfg)\n\n # if `no_pad`, remove all of the padding in conv\n self.padding = kernel_size // 2 if not no_pad else 0\n\n def forward(self, x, style):\n \"\"\"Forward function.\n\n Args:\n x ([Tensor): Input features with shape of (N, C, H, W).\n style (Tensor): Style latent with shape of (N, C).\n\n Returns:\n Tensor: Output feature with shape of (N, C, H, W).\n \"\"\"\n n, c, h, w = x.shape\n # process style code\n style = self.style_modulation(style).view(n, 1, c, 1,\n 1) + self.style_bias\n\n # combine weight and style\n weight = self.weight * style\n if self.demodulate:\n demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)\n weight = weight * demod.view(n, self.out_channels, 1, 1, 1)\n\n weight = weight.view(n * self.out_channels, c, self.kernel_size,\n self.kernel_size)\n\n if self.upsample and not self.deconv2conv:\n x = x.reshape(1, n * c, h, w)\n weight = weight.view(n, self.out_channels, c, self.kernel_size,\n self.kernel_size)\n weight = weight.transpose(1, 2).reshape(n * c, self.out_channels,\n self.kernel_size,\n self.kernel_size)\n x = conv_transpose2d(x, weight, padding=0, stride=2, groups=n)\n x = x.reshape(n, self.out_channels, *x.shape[-2:])\n x = self.blur(x)\n elif self.upsample and self.deconv2conv:\n if self.up_after_conv:\n x = x.reshape(1, n * c, h, w)\n x = conv2d(x, weight, padding=self.padding, groups=n)\n x = x.view(n, self.out_channels, *x.shape[2:4])\n\n if self.with_interp_pad:\n h_, w_ = x.shape[-2:]\n up_cfg_ = deepcopy(self.up_config)\n up_scale = up_cfg_.pop('scale_factor')\n size_ = (h_ * up_scale + self.interp_pad,\n w_ * up_scale + self.interp_pad)\n x = F.interpolate(x, size=size_, **up_cfg_)\n else:\n x = F.interpolate(x, **self.up_config)\n\n if not self.up_after_conv:\n h_, w_ = x.shape[-2:]\n x = x.view(1, n * c, h_, w_)\n x = conv2d(x, weight, padding=self.padding, groups=n)\n x = x.view(n, self.out_channels, *x.shape[2:4])\n\n elif self.downsample:\n x = self.blur(x)\n x = x.view(1, n * self.in_channels, *x.shape[-2:])\n x = conv2d(x, weight, stride=2, padding=0, groups=n)\n x = x.view(n, self.out_channels, *x.shape[-2:])\n else:\n x = x.view(1, n * c, h, w)\n x = conv2d(x, weight, stride=1, padding=self.padding, groups=n)\n x = x.view(n, self.out_channels, *x.shape[-2:])\n\n return x\n\n\nclass ModulatedPEStyleConv(BaseModule):\n \"\"\"Modulated Style Convolution with Positional Encoding.\n\n This module is modified from the ``ModulatedStyleConv`` in StyleGAN2 to\n support the experiments in: Positional Encoding as Spatial Inductive Bias\n in GANs, CVPR'2021.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n style_channels (int): Channels for the style codes.\n demodulate (bool, optional): Whether to adopt demodulation.\n Defaults to True.\n upsample (bool, optional): Whether to adopt upsampling in features.\n Defaults to False.\n downsample (bool, optional): Whether to adopt downsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n equalized_lr_cfg (dict | None, optional): Configs for equalized lr.\n Defaults to dict(mode='fan_in', lr_mul=1., gain=1.).\n style_mod_cfg (dict, optional): Configs for style modulation module.\n Defaults to dict(bias_init=1.).\n style_bias (float, optional): Bias value for style code.\n Defaults to 0..\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n upsample=False,\n blur_kernel=[1, 3, 3, 1],\n demodulate=True,\n style_mod_cfg=dict(bias_init=1.),\n style_bias=0.,\n **kwargs):\n super().__init__()\n\n self.conv = ModulatedPEConv2d(\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n demodulate=demodulate,\n upsample=upsample,\n blur_kernel=blur_kernel,\n style_mod_cfg=style_mod_cfg,\n style_bias=style_bias,\n **kwargs)\n\n self.noise_injector = NoiseInjection()\n self.activate = _FusedBiasLeakyReLU(out_channels)\n\n def forward(self, x, style, noise=None, return_noise=False):\n \"\"\"Forward Function.\n\n Args:\n x ([Tensor): Input features with shape of (N, C, H, W).\n style (Tensor): Style latent with shape of (N, C).\n noise (Tensor, optional): Noise for injection. Defaults to None.\n return_noise (bool, optional): Whether to return noise tensors.\n Defaults to False.\n\n Returns:\n Tensor: Output features with shape of (N, C, H, W)\n \"\"\"\n out = self.conv(x, style)\n if return_noise:\n out, noise = self.noise_injector(\n out, noise=noise, return_noise=return_noise)\n else:\n out = self.noise_injector(\n out, noise=noise, return_noise=return_noise)\n\n out = self.activate(out)\n\n if return_noise:\n return out, noise\n\n return out\n" }, { "path": "mmagic/models/editors/mspie/pe_singan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import Config\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom ..singan import SinGAN\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module()\nclass PESinGAN(SinGAN):\n \"\"\"Positional Encoding in SinGAN.\n\n This modified SinGAN is used to reimplement the experiments in: Positional\n Encoding as Spatial Inductive Bias in GANs, CVPR2021.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType],\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n num_scales: Optional[int] = None,\n fixed_noise_with_pad: bool = False,\n first_fixed_noises_ch: int = 1,\n iters_per_scale: int = 200,\n noise_weight_init: int = 0.1,\n lr_scheduler_args: Optional[dict] = None,\n test_pkl_data: Optional[str] = None,\n ema_confg: Optional[dict] = None):\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, num_scales,\n iters_per_scale, noise_weight_init, lr_scheduler_args,\n test_pkl_data, ema_confg)\n self.fixed_noise_with_pad = fixed_noise_with_pad\n self.first_fixed_noises_ch = first_fixed_noises_ch\n\n def construct_fixed_noises(self):\n \"\"\"Construct the fixed noises list used in SinGAN.\"\"\"\n for i, real in enumerate(self.reals):\n h, w = real.shape[-2:]\n if self.fixed_noise_with_pad:\n pad_ = self.get_module(self.generator, 'pad_head')\n h += 2 * pad_\n w += 2 * pad_\n if i == 0:\n noise = torch.randn(1, self.first_fixed_noises_ch, h,\n w).to(real)\n self.fixed_noises.append(noise)\n else:\n noise = torch.zeros((1, 1, h, w)).to(real)\n self.fixed_noises.append(noise)\n" }, { "path": "mmagic/models/editors/mspie/pe_singan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\nfrom ..singan.singan_generator import SinGANMultiScaleGenerator\nfrom ..singan.singan_modules import GeneratorBlock\n\n\n@MODELS.register_module()\nclass SinGANMSGeneratorPE(SinGANMultiScaleGenerator):\n \"\"\"Multi-Scale Generator used in SinGAN with positional encoding.\n\n More details can be found in: Positional Encoding as Spatial Inductive Bias\n in GANs, CVPR'2021.\n\n Notes:\n\n - In this version, we adopt the interpolation function from the official\n PyTorch APIs, which is different from the original implementation by the\n authors. However, in our experiments, this influence can be ignored.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n num_scales (int): The number of scales/stages in generator. Note\n that this number is counted from zero, which is the same as the\n original paper.\n kernel_size (int, optional): Kernel size, same as :obj:`nn.Conv2d`.\n Defaults to 3.\n padding (int, optional): Padding for the convolutional layer, same as\n :obj:`nn.Conv2d`. Defaults to 0.\n num_layers (int, optional): The number of convolutional layers in each\n generator block. Defaults to 5.\n base_channels (int, optional): The basic channels for convolutional\n layers in the generator block. Defaults to 32.\n min_feat_channels (int, optional): Minimum channels for the feature\n maps in the generator block. Defaults to 32.\n out_act_cfg (dict | None, optional): Configs for output activation\n layer. Defaults to dict(type='Tanh').\n padding_mode (str, optional): The mode of convolutional padding, same\n as :obj:`nn.Conv2d`. Defaults to 'zero'.\n pad_at_head (bool, optional): Whether to add padding at head.\n Defaults to True.\n interp_pad (bool, optional): The padding value of interpolating feature\n maps. Defaults to False.\n noise_with_pad (bool, optional): Whether the input fixed noises are\n with explicit padding. Defaults to False.\n positional_encoding (dict | None, optional): Configs for the positional\n encoding. Defaults to None.\n first_stage_in_channels (int | None, optional): The input channel of\n the first generator block. If None, the first stage will adopt the\n same input channels as other stages. Defaults to None.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n num_scales,\n kernel_size=3,\n padding=0,\n num_layers=5,\n base_channels=32,\n min_feat_channels=32,\n out_act_cfg=dict(type='Tanh'),\n padding_mode='zero',\n pad_at_head=True,\n interp_pad=False,\n noise_with_pad=False,\n positional_encoding=None,\n first_stage_in_channels=None,\n **kwargs):\n super(SinGANMultiScaleGenerator, self).__init__()\n\n self.pad_at_head = pad_at_head\n self.interp_pad = interp_pad\n self.noise_with_pad = noise_with_pad\n\n self.with_positional_encode = positional_encoding is not None\n if self.with_positional_encode:\n self.head_position_encode = MODELS.build(positional_encoding)\n\n self.pad_head = int((kernel_size - 1) / 2 * num_layers)\n self.blocks = nn.ModuleList()\n\n self.upsample = partial(\n F.interpolate, mode='bicubic', align_corners=True)\n\n for scale in range(num_scales + 1):\n base_ch = min(base_channels * pow(2, int(np.floor(scale / 4))),\n 128)\n min_feat_ch = min(\n min_feat_channels * pow(2, int(np.floor(scale / 4))), 128)\n\n if scale == 0:\n in_ch = (\n first_stage_in_channels\n if first_stage_in_channels else in_channels)\n else:\n in_ch = in_channels\n\n self.blocks.append(\n GeneratorBlock(\n in_channels=in_ch,\n out_channels=out_channels,\n kernel_size=kernel_size,\n padding=padding,\n num_layers=num_layers,\n base_channels=base_ch,\n min_feat_channels=min_feat_ch,\n out_act_cfg=out_act_cfg,\n padding_mode=padding_mode,\n **kwargs))\n\n if padding_mode == 'zero':\n self.noise_padding_layer = nn.ZeroPad2d(self.pad_head)\n self.img_padding_layer = nn.ZeroPad2d(self.pad_head)\n self.mask_padding_layer = nn.ReflectionPad2d(self.pad_head)\n elif padding_mode == 'reflect':\n self.noise_padding_layer = nn.ReflectionPad2d(self.pad_head)\n self.img_padding_layer = nn.ReflectionPad2d(self.pad_head)\n self.mask_padding_layer = nn.ReflectionPad2d(self.pad_head)\n mmengine.print_log('Using Reflection padding', 'current')\n else:\n raise NotImplementedError(\n f'Padding mode {padding_mode} is not supported')\n\n def forward(self,\n input_sample,\n fixed_noises,\n noise_weights,\n rand_mode,\n curr_scale,\n num_batches=1,\n get_prev_res=False,\n return_noise=False):\n \"\"\"Forward function.\n\n Args:\n input_sample (Tensor | None): The input for generator. In the\n original implementation, a tensor filled with zeros is adopted.\n If None is given, we will construct it from the first fixed\n noises.\n fixed_noises (list[Tensor]): List of the fixed noises in SinGAN.\n noise_weights (list[float]): List of the weights for random noises.\n rand_mode (str): Choices from ['rand', 'recon']. In ``rand`` mode,\n it will sample from random noises. Otherwise, the\n reconstruction for the single image will be returned.\n curr_scale (int): The scale for the current inference or training.\n num_batches (int, optional): The number of batches. Defaults to 1.\n get_prev_res (bool, optional): Whether to return results from\n previous stages. Defaults to False.\n return_noise (bool, optional): Whether to return noises tensor.\n Defaults to False.\n\n Returns:\n Tensor | dict: Generated image tensor or dictionary containing \\\n more data.\n \"\"\"\n if get_prev_res or return_noise:\n prev_res_list = []\n noise_list = []\n\n if input_sample is None:\n h, w = fixed_noises[0].shape[-2:]\n if self.noise_with_pad:\n h -= 2 * self.pad_head\n w -= 2 * self.pad_head\n input_sample = torch.zeros(\n (num_batches, 3, h, w)).to(fixed_noises[0])\n\n g_res = input_sample\n\n for stage in range(curr_scale + 1):\n if rand_mode == 'recon':\n noise_ = fixed_noises[stage]\n else:\n noise_ = torch.randn(num_batches,\n *fixed_noises[stage].shape[1:]).to(g_res)\n if return_noise:\n noise_list.append(noise_)\n\n if self.with_positional_encode and stage == 0:\n head_grid = self.head_position_encode(fixed_noises[0])\n noise_ = noise_ + head_grid\n\n # add padding at head\n if self.pad_at_head:\n if self.interp_pad:\n if self.noise_with_pad:\n size = noise_.shape[-2:]\n else:\n size = (noise_.size(2) + 2 * self.pad_head,\n noise_.size(3) + 2 * self.pad_head)\n noise_ = self.upsample(noise_, size)\n g_res_pad = self.upsample(g_res, size)\n else:\n if not self.noise_with_pad:\n noise_ = self.noise_padding_layer(noise_)\n g_res_pad = self.img_padding_layer(g_res)\n else:\n g_res_pad = g_res\n\n if stage == 0 and self.with_positional_encode:\n noise = noise_ * noise_weights[stage]\n else:\n noise = noise_ * noise_weights[stage] + g_res_pad\n g_res = self.blocks[stage](noise.detach(), g_res)\n\n if get_prev_res and stage != curr_scale:\n prev_res_list.append(g_res)\n\n # upsample, here we use interpolation from PyTorch\n if stage != curr_scale:\n h_next, w_next = fixed_noises[stage + 1].shape[-2:]\n if self.noise_with_pad:\n # remove the additional padding if noise with pad\n h_next -= 2 * self.pad_head\n w_next -= 2 * self.pad_head\n g_res = self.upsample(g_res, (h_next, w_next))\n\n if get_prev_res or return_noise:\n output_dict = dict(\n fake_img=g_res,\n prev_res_list=prev_res_list,\n noise_batch=noise_list)\n return output_dict\n\n return g_res\n" }, { "path": "mmagic/models/editors/mspie/positional_encoding.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module('SPE')\n@MODELS.register_module('SPE2d')\nclass SinusoidalPositionalEmbedding(BaseModule):\n \"\"\"Sinusoidal Positional Embedding 1D or 2D (SPE/SPE2d).\n\n This module is a modified from:\n https://github.com/pytorch/fairseq/blob/master/fairseq/modules/sinusoidal_positional_embedding.py # noqa\n\n Based on the original SPE in single dimension, we implement a 2D sinusoidal\n positional encoding (SPE2d), as introduced in Positional Encoding as\n Spatial Inductive Bias in GANs, CVPR'2021.\n\n Args:\n embedding_dim (int): The number of dimensions for the positional\n encoding.\n padding_idx (int | list[int]): The index for the padding contents. The\n padding positions will obtain an encoding vector filling in zeros.\n init_size (int, optional): The initial size of the positional buffer.\n Defaults to 1024.\n div_half_dim (bool, optional): If true, the embedding will be divided\n by :math:`d/2`. Otherwise, it will be divided by\n :math:`(d/2 -1)`. Defaults to False.\n center_shift (int | None, optional): Shift the center point to some\n index. Defaults to None.\n \"\"\"\n\n def __init__(self,\n embedding_dim,\n padding_idx,\n init_size=1024,\n div_half_dim=False,\n center_shift=None):\n super().__init__()\n self.embedding_dim = embedding_dim\n self.padding_idx = padding_idx\n self.div_half_dim = div_half_dim\n self.center_shift = center_shift\n\n self.weights = SinusoidalPositionalEmbedding.get_embedding(\n init_size, embedding_dim, padding_idx, self.div_half_dim)\n\n self.register_buffer('_float_tensor', torch.FloatTensor(1))\n\n self.max_positions = int(1e5)\n\n @staticmethod\n def get_embedding(num_embeddings,\n embedding_dim,\n padding_idx=None,\n div_half_dim=False):\n \"\"\"Build sinusoidal embeddings.\n\n This matches the implementation in tensor2tensor, but differs slightly\n from the description in Section 3.5 of \"Attention Is All You Need\".\n \"\"\"\n assert embedding_dim % 2 == 0, (\n 'In this version, we request '\n f'embedding_dim divisible by 2 but got {embedding_dim}')\n\n # there is a little difference from the original paper.\n half_dim = embedding_dim // 2\n if not div_half_dim:\n emb = np.log(10000) / (half_dim - 1)\n else:\n emb = np.log(1e4) / half_dim\n # compute exp(-log10000 / d * i)\n emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)\n emb = torch.arange(\n num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)\n emb = torch.cat([torch.sin(emb), torch.cos(emb)],\n dim=1).view(num_embeddings, -1)\n if padding_idx is not None:\n emb[padding_idx, :] = 0\n\n return emb\n\n def forward(self, input, **kwargs):\n \"\"\"Input is expected to be of size [bsz x seqlen].\n\n Returned tensor is expected to be of size [bsz x seq_len x emb_dim]\n \"\"\"\n assert input.dim() == 2 or input.dim(\n ) == 4, 'Input dimension should be 2 (1D) or 4(2D)'\n\n if input.dim() == 4:\n return self.make_grid2d_like(input, **kwargs)\n\n b, seq_len = input.shape\n max_pos = self.padding_idx + 1 + seq_len\n\n if self.weights is None or max_pos > self.weights.size(0):\n # recompute/expand embedding if needed\n self.weights = SinusoidalPositionalEmbedding.get_embedding(\n max_pos, self.embedding_dim, self.padding_idx)\n self.weights = self.weights.to(self._float_tensor)\n\n positions = self.make_positions(input, self.padding_idx).to(\n self._float_tensor.device)\n\n return self.weights.index_select(0, positions.view(-1)).view(\n b, seq_len, self.embedding_dim).detach()\n\n def make_positions(self, input, padding_idx):\n \"\"\"Make position tensors.\n\n Args:\n input (tensor): Input tensor.\n padding_idx (int | list[int]): The index for the padding contents.\n The padding positions will obtain an encoding vector filling\n in zeros.\n\n Returns:\n tensor: Position tensors.\n \"\"\"\n mask = input.ne(padding_idx).int()\n return (torch.cumsum(mask, dim=1).type_as(mask) *\n mask).long() + padding_idx\n\n def make_grid2d(self, height, width, num_batches=1, center_shift=None):\n \"\"\"Make 2-d grid mask.\n\n Args:\n height (int): Height of the grid.\n width (int): Width of the grid.\n num_batches (int, optional): The number of batch size.\n Defaults to 1.\n center_shift (int | None, optional): Shift the center point to some\n index. Defaults to None.\n\n Returns:\n Tensor: 2-d Grid mask.\n \"\"\"\n h, w = height, width\n # if `center_shift` is not given from the outside, use\n # `self.center_shift`\n if center_shift is None:\n center_shift = self.center_shift\n\n h_shift = 0\n w_shift = 0\n # center shift to the input grid\n if center_shift is not None:\n # if h/w is even, the left center should be aligned with\n # center shift\n if h % 2 == 0:\n h_left_center = h // 2\n h_shift = center_shift - h_left_center\n else:\n h_center = h // 2 + 1\n h_shift = center_shift - h_center\n\n if w % 2 == 0:\n w_left_center = w // 2\n w_shift = center_shift - w_left_center\n else:\n w_center = w // 2 + 1\n w_shift = center_shift - w_center\n\n # Note that the index is started from 1 since zero will be padding idx.\n # axis -- (b, h or w)\n x_axis = torch.arange(1, w + 1).unsqueeze(0).repeat(num_batches,\n 1) + w_shift\n y_axis = torch.arange(1, h + 1).unsqueeze(0).repeat(num_batches,\n 1) + h_shift\n\n # emb -- (b, emb_dim, h or w)\n x_emb = self(x_axis).transpose(1, 2)\n y_emb = self(y_axis).transpose(1, 2)\n\n # make grid for x/y axis\n # Note that repeat will copy data. If use learned emb, expand may be\n # better.\n x_grid = x_emb.unsqueeze(2).repeat(1, 1, h, 1)\n y_grid = y_emb.unsqueeze(3).repeat(1, 1, 1, w)\n\n # cat grid -- (b, 2 x emb_dim, h, w)\n grid = torch.cat([x_grid, y_grid], dim=1)\n return grid.detach()\n\n def make_grid2d_like(self, x, center_shift=None):\n \"\"\"Input tensor with shape of (b, ..., h, w) Return tensor with shape\n of (b, 2 x emb_dim, h, w)\n\n Note that the positional embedding highly depends on the the function,\n ``make_positions``.\n \"\"\"\n h, w = x.shape[-2:]\n\n grid = self.make_grid2d(h, w, x.size(0), center_shift)\n\n return grid.to(x)\n\n\n@MODELS.register_module('CSG2d')\n@MODELS.register_module('CSG')\n@MODELS.register_module()\nclass CatersianGrid(BaseModule):\n \"\"\"Catersian Grid for 2d tensor.\n\n The Catersian Grid is a common-used positional encoding in deep learning.\n In this implementation, we follow the convention of ``grid_sample`` in\n PyTorch. In other words, ``[-1, -1]`` denotes the left-top corner while\n ``[1, 1]`` denotes the right-bottom corner.\n \"\"\"\n\n def forward(self, x, **kwargs):\n assert x.dim() == 4\n return self.make_grid2d_like(x, **kwargs)\n\n def make_grid2d(self, height, width, num_batches=1, requires_grad=False):\n h, w = height, width\n grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))\n grid_x = 2 * grid_x / max(float(w) - 1., 1.) - 1.\n grid_y = 2 * grid_y / max(float(h) - 1., 1.) - 1.\n grid = torch.stack((grid_x, grid_y), 0)\n grid.requires_grad = requires_grad\n\n grid = torch.unsqueeze(grid, 0)\n grid = grid.repeat(num_batches, 1, 1, 1)\n\n return grid\n\n def make_grid2d_like(self, x, requires_grad=False):\n \"\"\"Input tensor with shape of (b, ..., h, w) Return tensor with shape\n of (b, 2 x emb_dim, h, w)\n\n Note that the positional embedding highly depends on the the function,\n ``make_grid2d``.\n \"\"\"\n h, w = x.shape[-2:]\n grid = self.make_grid2d(h, w, x.size(0), requires_grad=requires_grad)\n\n return grid.to(x)\n" }, { "path": "mmagic/models/editors/nafnet/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .nafbaseline_net import NAFBaseline, NAFBaselineLocal\nfrom .nafnet_net import NAFNet, NAFNetLocal\n\n__all__ = [\n 'NAFNet',\n 'NAFNetLocal',\n 'NAFBaseline',\n 'NAFBaselineLocal',\n]\n" }, { "path": "mmagic/models/editors/nafnet/naf_avgpool2d.py", "content": "# Copyright (c) 2022 megvii-model. All Rights Reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\n\nclass NAFAvgPool2d(BaseModule):\n \"\"\"Average Pooling 2D used in NAFNet.\n\n Note: this is different from the normal AvgPool2d in pytorch.\n According to:\n Improving Image Restoration by Revisiting Global Information Aggregation\n statistics are aggregated in a local region for each pixel\n rather than the global average pooling.\n \"\"\"\n\n def __init__(self,\n kernel_size=None,\n base_size=None,\n auto_pad=True,\n fast_imp=False,\n train_size=None):\n super().__init__()\n self.kernel_size = kernel_size\n self.base_size = base_size\n self.auto_pad = auto_pad\n\n # only used for fast implementation\n self.fast_imp = fast_imp\n self.rs = [5, 4, 3, 2, 1]\n self.max_r1 = self.rs[0]\n self.max_r2 = self.rs[0]\n self.train_size = train_size\n\n def extra_repr(self) -> str:\n return 'kernel_size={}, base_size={}, stride={}, fast_imp={}'.format(\n self.kernel_size, self.base_size, self.kernel_size, self.fast_imp)\n\n def forward(self, x):\n if self.kernel_size is None and self.base_size:\n train_size = self.train_size\n if isinstance(self.base_size, int):\n self.base_size = (self.base_size, self.base_size)\n self.kernel_size = list(self.base_size)\n self.kernel_size[\n 0] = x.shape[2] * self.base_size[0] // train_size[-2]\n self.kernel_size[\n 1] = x.shape[3] * self.base_size[1] // train_size[-1]\n\n # only used for fast implementation\n self.max_r1 = max(1, self.rs[0] * x.shape[2] // train_size[-2])\n self.max_r2 = max(1, self.rs[0] * x.shape[3] // train_size[-1])\n\n if self.kernel_size[0] >= x.size(-2) and self.kernel_size[1] >= x.size(\n -1):\n return F.adaptive_avg_pool2d(x, 1)\n\n if self.fast_imp: # Non-equivalent implementation but faster\n h, w = x.shape[2:]\n if self.kernel_size[0] >= h and self.kernel_size[1] >= w:\n out = F.adaptive_avg_pool2d(x, 1)\n else:\n r1 = [r for r in self.rs if h % r == 0][0]\n r2 = [r for r in self.rs if w % r == 0][0]\n # reduction_constraint\n r1 = min(self.max_r1, r1)\n r2 = min(self.max_r2, r2)\n s = x[:, :, ::r1, ::r2].cumsum(dim=-1).cumsum(dim=-2)\n n, c, h, w = s.shape\n k1, k2 = min(h - 1, self.kernel_size[0] //\n r1), min(w - 1, self.kernel_size[1] // r2)\n out = (s[:, :, :-k1, :-k2] - s[:, :, :-k1, k2:] -\n s[:, :, k1:, :-k2] + s[:, :, k1:, k2:]) / (\n k1 * k2)\n out = torch.nn.functional.interpolate(\n out, scale_factor=(r1, r2))\n else:\n n, c, h, w = x.shape\n s = x.cumsum(dim=-1).cumsum_(dim=-2)\n s = torch.nn.functional.pad(s,\n (1, 0, 1, 0)) # pad 0 for convenience\n k1, k2 = min(h, self.kernel_size[0]), min(w, self.kernel_size[1])\n s1, s2, s3, s4 = s[:, :, :-k1, :-k2], s[:, :, :-k1,\n k2:], s[:, :,\n k1:, :-k2], s[:, :,\n k1:,\n k2:]\n out = s4 + s1 - s2 - s3\n out = out / (k1 * k2)\n\n if self.auto_pad:\n n, c, h, w = x.shape\n _h, _w = out.shape[2:]\n pad2d = ((w - _w) // 2, (w - _w + 1) // 2, (h - _h) // 2,\n (h - _h + 1) // 2)\n out = torch.nn.functional.pad(out, pad2d, mode='replicate')\n\n return out\n\n\ndef replace_layers(model, base_size, train_size, fast_imp, **kwargs):\n \"\"\"Replace all layers with AvgPool2d.\"\"\"\n for n, m in model.named_children():\n if len(list(m.children())) > 0:\n # compound module, go inside it\n replace_layers(m, base_size, train_size, fast_imp, **kwargs)\n\n if isinstance(m, nn.AdaptiveAvgPool2d):\n pool = NAFAvgPool2d(\n base_size=base_size, fast_imp=fast_imp, train_size=train_size)\n assert m.output_size == 1\n setattr(model, n, pool)\n\n\nclass Local_Base():\n \"\"\"Local Base class to use global average pooling.\n\n args:\n train_size: training image size\n \"\"\"\n\n def convert(self, *args, train_size, **kwargs):\n replace_layers(self, *args, train_size=train_size, **kwargs)\n imgs = torch.rand(train_size)\n with torch.no_grad():\n self.forward(imgs)\n" }, { "path": "mmagic/models/editors/nafnet/naf_layerNorm2d.py", "content": "# Copyright (c) 2022 megvii-model. All Rights Reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom torch import nn as nn\n\n\nclass LayerNormFunction(torch.autograd.Function):\n \"\"\"Layer normalization.\"\"\"\n\n @staticmethod\n def forward(ctx, x, weight, bias, eps):\n ctx.eps = eps\n N, C, H, W = x.size()\n mu = x.mean(1, keepdim=True)\n var = (x - mu).pow(2).mean(1, keepdim=True)\n y = (x - mu) / (var + eps).sqrt()\n ctx.save_for_backward(y, var, weight)\n y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)\n return y\n\n @staticmethod\n def backward(ctx, grad_output):\n eps = ctx.eps\n\n N, C, H, W = grad_output.size()\n y, var, weight = ctx.saved_variables\n g = grad_output * weight.view(1, C, 1, 1)\n mean_g = g.mean(dim=1, keepdim=True)\n\n mean_gy = (g * y).mean(dim=1, keepdim=True)\n gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)\n return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(\n dim=0), grad_output.sum(dim=3).sum(dim=2).sum(dim=0), None\n\n\nclass LayerNorm2d(nn.Module):\n \"\"\"Layer normalization module.\n\n Note: This is different from the layernorm2d in pytorch.\n The layer norm here will handle different channels respectively.\n For more information, please refer to the issue:\n https://github.com/megvii-research/NAFNet/issues/35\n \"\"\"\n\n def __init__(self, channels, eps=1e-6):\n super(LayerNorm2d, self).__init__()\n self.register_parameter('weight', nn.Parameter(torch.ones(channels)))\n self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))\n self.eps = eps\n\n def forward(self, x):\n return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)\n" }, { "path": "mmagic/models/editors/nafnet/nafbaseline_net.py", "content": "# Copyright (c) 2022 megvii-model. All Rights Reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .naf_avgpool2d import Local_Base\nfrom .naf_layerNorm2d import LayerNorm2d\n\n\n@MODELS.register_module()\nclass NAFBaseline(BaseModule):\n \"\"\"The original version of Baseline model in \"Simple Baseline for Image\n Restoration\".\n\n Args:\n img_channels (int): Channel number of inputs.\n mid_channels (int): Channel number of intermediate features.\n middle_blk_num (int): Number of middle blocks.\n enc_blk_nums (List of int): Number of blocks for each encoder.\n dec_blk_nums (List of int): Number of blocks for each decoder.\n \"\"\"\n\n def __init__(self,\n img_channel=3,\n mid_channels=16,\n middle_blk_num=1,\n enc_blk_nums=[1, 1, 1, 28],\n dec_blk_nums=[1, 1, 1, 1],\n dw_expand=1,\n ffn_expand=2):\n super().__init__()\n\n self.intro = nn.Conv2d(\n in_channels=img_channel,\n out_channels=mid_channels,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=1,\n bias=True)\n self.ending = nn.Conv2d(\n in_channels=mid_channels,\n out_channels=img_channel,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=1,\n bias=True)\n\n self.encoders = nn.ModuleList()\n self.decoders = nn.ModuleList()\n self.middle_blks = nn.ModuleList()\n self.ups = nn.ModuleList()\n self.downs = nn.ModuleList()\n\n chan = mid_channels\n for num in enc_blk_nums:\n self.encoders.append(\n nn.Sequential(*[\n BaselineBlock(chan, dw_expand, ffn_expand)\n for _ in range(num)\n ]))\n self.downs.append(nn.Conv2d(chan, 2 * chan, 2, 2))\n chan = chan * 2\n\n self.middle_blks = \\\n nn.Sequential(\n *[BaselineBlock(chan, dw_expand, ffn_expand)\n for _ in range(middle_blk_num)]\n )\n\n for num in dec_blk_nums:\n self.ups.append(\n nn.Sequential(\n nn.Conv2d(chan, chan * 2, 1, bias=False),\n nn.PixelShuffle(2)))\n chan = chan // 2\n self.decoders.append(\n nn.Sequential(*[\n BaselineBlock(chan, dw_expand, ffn_expand)\n for _ in range(num)\n ]))\n\n self.padder_size = 2**len(self.encoders)\n\n def forward(self, inp):\n \"\"\"Forward function.\n\n args:\n inp: input tensor image with (B, C, H, W) shape\n \"\"\"\n B, C, H, W = inp.shape\n inp = self.check_image_size(inp)\n\n x = self.intro(inp)\n\n encs = []\n\n for encoder, down in zip(self.encoders, self.downs):\n x = encoder(x)\n encs.append(x)\n x = down(x)\n\n x = self.middle_blks(x)\n\n for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]):\n x = up(x)\n x = x + enc_skip\n x = decoder(x)\n\n x = self.ending(x)\n x = x + inp\n\n return x[:, :, :H, :W]\n\n def check_image_size(self, x):\n \"\"\"Check image size and pad images so that it has enough dimension do\n downsample.\n\n args:\n x: input tensor image with (B, C, H, W) shape.\n \"\"\"\n _, _, h, w = x.size()\n mod_pad_h = (self.padder_size -\n h % self.padder_size) % self.padder_size\n mod_pad_w = (self.padder_size -\n w % self.padder_size) % self.padder_size\n x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h))\n return x\n\n\n@MODELS.register_module()\nclass NAFBaselineLocal(Local_Base, NAFBaseline):\n \"\"\"The original version of Baseline model in \"Simple Baseline for Image\n Restoration\".\n\n Args:\n img_channels (int): Channel number of inputs.\n mid_channels (int): Channel number of intermediate features.\n middle_blk_num (int): Number of middle blocks.\n enc_blk_nums (List of int): Number of blocks for each encoder.\n dec_blk_nums (L`ist of int): Number of blocks for each decoder.\n \"\"\"\n\n def __init__(self,\n *args,\n train_size=(1, 3, 256, 256),\n fast_imp=False,\n **kwargs):\n Local_Base.__init__(self)\n NAFBaseline.__init__(self, *args, **kwargs)\n\n N, C, H, W = train_size\n base_size = (int(H * 1.5), int(W * 1.5))\n\n self.eval()\n with torch.no_grad():\n self.convert(\n base_size=base_size, train_size=train_size, fast_imp=fast_imp)\n\n\n# Components for Baseline\nclass BaselineBlock(BaseModule):\n \"\"\"Baseline's Block in paper.\n\n Args:\n in_channels (int): number of channels\n DW_Expand (int): channel expansion factor for part 1\n FFN_Expand (int): channel expansion factor for part 2\n drop_out_rate (float): drop out ratio\n \"\"\"\n\n def __init__(self,\n in_channels,\n DW_Expand=1,\n FFN_Expand=2,\n drop_out_rate=0.):\n super().__init__()\n dw_channel = in_channels * DW_Expand\n self.conv1 = nn.Conv2d(\n in_channels=in_channels,\n out_channels=dw_channel,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n self.conv2 = nn.Conv2d(\n in_channels=dw_channel,\n out_channels=dw_channel,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=dw_channel,\n bias=True)\n self.conv3 = nn.Conv2d(\n in_channels=dw_channel,\n out_channels=in_channels,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n\n # Channel Attention\n self.se = nn.Sequential(\n nn.AdaptiveAvgPool2d(1),\n nn.Conv2d(\n in_channels=dw_channel,\n out_channels=dw_channel // 2,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True), nn.ReLU(inplace=True),\n nn.Conv2d(\n in_channels=dw_channel // 2,\n out_channels=dw_channel,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True), nn.Sigmoid())\n\n # GELU\n self.gelu = nn.GELU()\n\n ffn_channel = FFN_Expand * in_channels\n self.conv4 = nn.Conv2d(\n in_channels=in_channels,\n out_channels=ffn_channel,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n self.conv5 = nn.Conv2d(\n in_channels=ffn_channel,\n out_channels=in_channels,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n\n self.norm1 = LayerNorm2d(in_channels)\n self.norm2 = LayerNorm2d(in_channels)\n\n self.dropout1 = nn.Dropout(\n drop_out_rate) if drop_out_rate > 0. else nn.Identity()\n self.dropout2 = nn.Dropout(\n drop_out_rate) if drop_out_rate > 0. else nn.Identity()\n\n self.beta = nn.Parameter(\n torch.zeros((1, in_channels, 1, 1)), requires_grad=True)\n self.gamma = nn.Parameter(\n torch.zeros((1, in_channels, 1, 1)), requires_grad=True)\n\n def forward(self, inp):\n \"\"\"Forward Function.\n\n Args:\n inp: input tensor image\n \"\"\"\n x = inp\n\n x = self.norm1(x)\n\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.gelu(x)\n x = x * self.se(x)\n x = self.conv3(x)\n\n x = self.dropout1(x)\n\n y = inp + x * self.beta\n\n x = self.conv4(self.norm2(y))\n x = self.gelu(x)\n x = self.conv5(x)\n\n x = self.dropout2(x)\n\n return y + x * self.gamma\n" }, { "path": "mmagic/models/editors/nafnet/nafnet_net.py", "content": "# Copyright (c) 2022 megvii-model. All Rights Reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .naf_avgpool2d import Local_Base\nfrom .naf_layerNorm2d import LayerNorm2d\n\n\n@MODELS.register_module()\nclass NAFNet(BaseModule):\n \"\"\"NAFNet.\n\n The original version of NAFNet in \"Simple Baseline for Image Restoration\".\n\n Args:\n img_channels (int): Channel number of inputs.\n mid_channels (int): Channel number of intermediate features.\n middle_blk_num (int): Number of middle blocks.\n enc_blk_nums (List of int): Number of blocks for each encoder.\n dec_blk_nums (List of int): Number of blocks for each decoder.\n \"\"\"\n\n def __init__(self,\n img_channels=3,\n mid_channels=16,\n middle_blk_num=1,\n enc_blk_nums=[],\n dec_blk_nums=[]):\n super().__init__()\n\n self.intro = nn.Conv2d(\n in_channels=img_channels,\n out_channels=mid_channels,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=1,\n bias=True)\n self.ending = nn.Conv2d(\n in_channels=mid_channels,\n out_channels=img_channels,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=1,\n bias=True)\n\n self.encoders = nn.ModuleList()\n self.decoders = nn.ModuleList()\n self.middle_blks = nn.ModuleList()\n self.ups = nn.ModuleList()\n self.downs = nn.ModuleList()\n\n chan = mid_channels\n for num in enc_blk_nums:\n self.encoders.append(\n nn.Sequential(*[NAFBlock(chan) for _ in range(num)]))\n self.downs.append(nn.Conv2d(chan, 2 * chan, 2, 2))\n chan = chan * 2\n\n self.middle_blks = \\\n nn.Sequential(\n *[NAFBlock(chan) for _ in range(middle_blk_num)]\n )\n\n for num in dec_blk_nums:\n self.ups.append(\n nn.Sequential(\n nn.Conv2d(chan, chan * 2, 1, bias=False),\n nn.PixelShuffle(2)))\n chan = chan // 2\n self.decoders.append(\n nn.Sequential(*[NAFBlock(chan) for _ in range(num)]))\n\n self.padder_size = 2**len(self.encoders)\n\n def forward(self, inp):\n \"\"\"Forward function.\n\n args:\n inp: input tensor image with (B, C, H, W) shape\n \"\"\"\n B, C, H, W = inp.shape\n inp = self.check_image_size(inp)\n\n x = self.intro(inp)\n\n encs = []\n\n for encoder, down in zip(self.encoders, self.downs):\n x = encoder(x)\n encs.append(x)\n x = down(x)\n\n x = self.middle_blks(x)\n\n for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]):\n x = up(x)\n x = x + enc_skip\n x = decoder(x)\n\n x = self.ending(x)\n x = x + inp\n\n return x[:, :, :H, :W]\n\n def check_image_size(self, x):\n \"\"\"Check image size and pad images so that it has enough dimension do\n downsample.\n\n args:\n x: input tensor image with (B, C, H, W) shape.\n \"\"\"\n _, _, h, w = x.size()\n mod_pad_h = (self.padder_size -\n h % self.padder_size) % self.padder_size\n mod_pad_w = (self.padder_size -\n w % self.padder_size) % self.padder_size\n x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h))\n return x\n\n\n@MODELS.register_module()\nclass NAFNetLocal(Local_Base, NAFNet):\n \"\"\"The original version of NAFNetLocal in \"Simple Baseline for Image\n Restoration\".\n\n NAFNetLocal uses local average pooling modules than NAFNet.\n\n Args:\n img_channels (int): Channel number of inputs.\n mid_channels (int): Channel number of intermediate features.\n middle_blk_num (int): Number of middle blocks.\n enc_blk_nums (List of int): Number of blocks for each encoder.\n dec_blk_nums (List of int): Number of blocks for each decoder.\n \"\"\"\n\n def __init__(self,\n *args,\n train_size=(1, 3, 256, 256),\n fast_imp=False,\n **kwargs):\n Local_Base.__init__(self)\n NAFNet.__init__(self, *args, **kwargs)\n\n N, C, H, W = train_size\n base_size = (int(H * 1.5), int(W * 1.5))\n\n self.eval()\n with torch.no_grad():\n self.convert(\n base_size=base_size, train_size=train_size, fast_imp=fast_imp)\n\n\n# Components for NAFNet\n\n\nclass NAFBlock(BaseModule):\n \"\"\"NAFNet's Block in paper.\n\n Simple gate will shrink the channel to a half.\n To keep the number of channels,\n it expands the channels first.\n\n Args:\n in_channels (int): number of channels\n DW_Expand (int): channel expansion factor for part 1\n FFN_Expand (int): channel expansion factor for part 2\n drop_out_rate (float): drop out ratio\n \"\"\"\n\n def __init__(self,\n in_channels,\n DW_Expand=2,\n FFN_Expand=2,\n drop_out_rate=0.):\n super().__init__()\n\n # Part 1\n\n dw_channel = in_channels * DW_Expand\n self.conv1 = nn.Conv2d(\n in_channels=in_channels,\n out_channels=dw_channel,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n self.conv2 = nn.Conv2d(\n in_channels=dw_channel,\n out_channels=dw_channel,\n kernel_size=3,\n padding=1,\n stride=1,\n groups=dw_channel,\n bias=True)\n\n # Simplified Channel Attention\n self.sca = nn.Sequential(\n nn.AdaptiveAvgPool2d(1),\n nn.Conv2d(\n in_channels=dw_channel // 2,\n out_channels=dw_channel // 2,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True),\n )\n\n self.conv3 = nn.Conv2d(\n in_channels=dw_channel // 2,\n out_channels=in_channels,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n\n # Part 2\n\n ffn_channel = FFN_Expand * in_channels\n self.conv4 = nn.Conv2d(\n in_channels=in_channels,\n out_channels=ffn_channel,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n self.conv5 = nn.Conv2d(\n in_channels=ffn_channel // 2,\n out_channels=in_channels,\n kernel_size=1,\n padding=0,\n stride=1,\n groups=1,\n bias=True)\n\n # Simple Gate\n self.sg = SimpleGate()\n\n # Layer Normalization\n self.norm1 = LayerNorm2d(in_channels)\n self.norm2 = LayerNorm2d(in_channels)\n\n # Dropout\n self.dropout1 = nn.Dropout(\n drop_out_rate) if drop_out_rate > 0. else nn.Identity()\n self.dropout2 = nn.Dropout(\n drop_out_rate) if drop_out_rate > 0. else nn.Identity()\n\n # Feature weight ratio\n self.beta = nn.Parameter(\n torch.zeros((1, in_channels, 1, 1)), requires_grad=True)\n self.gamma = nn.Parameter(\n torch.zeros((1, in_channels, 1, 1)), requires_grad=True)\n\n def forward(self, inp):\n \"\"\"Forward Function.\n\n Args:\n inp: input tensor image\n \"\"\"\n x = inp\n # part 1\n x = self.norm1(x)\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.sg(x)\n x = x * self.sca(x)\n x = self.conv3(x)\n\n x = self.dropout1(x)\n y = inp + x * self.beta\n\n # part 2\n x = self.norm2(y)\n x = self.conv4(x)\n x = self.sg(x)\n x = self.conv5(x)\n\n x = self.dropout2(x)\n out = y + x * self.gamma\n\n return out\n\n\nclass SimpleGate(BaseModule):\n \"\"\"The Simple Gate in \"Simple Baseline for Image Restoration\".\n\n Args:\n x: input tensor feature map with (B, 2 * C, H, W)\n\n Return:\n x1 * x2\n (where x1, x2 are two separate parts by simple split x to [B, C, H, W])\n \"\"\"\n\n def forward(self, x):\n x1, x2 = x.chunk(2, dim=1)\n return x1 * x2\n" }, { "path": "mmagic/models/editors/pconv/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .mask_conv_module import MaskConvModule\nfrom .partial_conv import PartialConv2d\nfrom .pconv_decoder import PConvDecoder\nfrom .pconv_encoder import PConvEncoder\nfrom .pconv_encoder_decoder import PConvEncoderDecoder\nfrom .pconv_inpaintor import PConvInpaintor\n\n__all__ = [\n 'PConvEncoder', 'PConvDecoder', 'PConvEncoderDecoder', 'PConvInpaintor',\n 'MaskConvModule', 'PartialConv2d', 'MaskConvModule'\n]\n" }, { "path": "mmagic/models/editors/pconv/mask_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmcv.cnn import ConvModule\n\n\nclass MaskConvModule(ConvModule):\n \"\"\"Mask convolution module.\n\n This is a simple wrapper for mask convolution like: 'partial conv'.\n Convolutions in this module always need a mask as extra input.\n\n Args:\n in_channels (int): Same as nn.Conv2d.\n out_channels (int): Same as nn.Conv2d.\n kernel_size (int or tuple[int]): Same as nn.Conv2d.\n stride (int or tuple[int]): Same as nn.Conv2d.\n padding (int or tuple[int]): Same as nn.Conv2d.\n dilation (int or tuple[int]): Same as nn.Conv2d.\n groups (int): Same as nn.Conv2d.\n bias (bool or str): If specified as `auto`, it will be decided by the\n norm_cfg. Bias will be set as True if norm_cfg is None, otherwise\n False.\n conv_cfg (dict): Config dict for convolution layer.\n norm_cfg (dict): Config dict for normalization layer.\n act_cfg (dict): Config dict for activation layer, \"relu\" by default.\n inplace (bool): Whether to use inplace mode for activation.\n with_spectral_norm (bool): Whether use spectral norm in conv module.\n padding_mode (str): If the `padding_mode` has not been supported by\n current `Conv2d` in Pytorch, we will use our own padding layer\n instead. Currently, we support ['zeros', 'circular'] with official\n implementation and ['reflect'] with our own implementation.\n Default: 'zeros'.\n order (tuple[str]): The order of conv/norm/activation layers. It is a\n sequence of \"conv\", \"norm\" and \"act\". Examples are\n (\"conv\", \"norm\", \"act\") and (\"act\", \"conv\", \"norm\").\n \"\"\"\n supported_conv_list = ['PConv']\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n assert self.conv_cfg['type'] in self.supported_conv_list\n\n self.init_weights()\n\n def forward(self,\n x,\n mask=None,\n activate=True,\n norm=True,\n return_mask=True):\n \"\"\"Forward function for partial conv2d.\n\n Args:\n x (torch.Tensor): Tensor with shape of (n, c, h, w).\n mask (torch.Tensor): Tensor with shape of (n, c, h, w) or\n (n, 1, h, w). If mask is not given, the function will\n work as standard conv2d. Default: None.\n activate (bool): Whether use activation layer.\n norm (bool): Whether use norm layer.\n return_mask (bool): If True and mask is not None, the updated\n mask will be returned. Default: True.\n\n Returns:\n Tensor or tuple: Result Tensor or 2-tuple of\n\n ``Tensor``: Results after partial conv.\n\n ``Tensor``: Updated mask will be returned if mask is given \\\n and `return_mask` is True.\n \"\"\"\n for layer in self.order:\n if layer == 'conv':\n if self.with_explicit_padding:\n x = self.padding_layer(x)\n mask = self.padding_layer(mask)\n if return_mask:\n x, updated_mask = self.conv(\n x, mask, return_mask=return_mask)\n else:\n x = self.conv(x, mask, return_mask=False)\n elif layer == 'norm' and norm and self.with_norm:\n x = self.norm(x)\n elif layer == 'act' and activate and self.with_activation:\n x = self.activate(x)\n\n if return_mask:\n return x, updated_mask\n\n return x\n" }, { "path": "mmagic/models/editors/pconv/partial_conv.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module(name='PConv')\nclass PartialConv2d(nn.Conv2d):\n \"\"\"Implementation for partial convolution.\n\n Image Inpainting for Irregular Holes Using Partial Convolutions\n [https://arxiv.org/abs/1804.07723]\n\n Args:\n multi_channel (bool): If True, the mask is multi-channel. Otherwise,\n the mask is single-channel.\n eps (float): Need to be changed for mixed precision training.\n For mixed precision training, you need change 1e-8 to 1e-6.\n \"\"\"\n\n def __init__(self, *args, multi_channel=False, eps=1e-8, **kwargs):\n super().__init__(*args, **kwargs)\n\n # whether the mask is multi-channel or not\n self.multi_channel = multi_channel\n self.eps = eps\n\n if self.multi_channel:\n out_channels, in_channels = self.out_channels, self.in_channels\n else:\n out_channels, in_channels = 1, 1\n\n self.register_buffer(\n 'weight_mask_updater',\n torch.ones(out_channels, in_channels, self.kernel_size[0],\n self.kernel_size[1]))\n\n self.mask_kernel_numel = np.prod(self.weight_mask_updater.shape[1:4])\n self.mask_kernel_numel = (self.mask_kernel_numel).item()\n\n def forward(self, input, mask=None, return_mask=True):\n \"\"\"Forward function for partial conv2d.\n\n Args:\n input (torch.Tensor): Tensor with shape of (n, c, h, w).\n mask (torch.Tensor): Tensor with shape of (n, c, h, w) or\n (n, 1, h, w). If mask is not given, the function will\n work as standard conv2d. Default: None.\n return_mask (bool): If True and mask is not None, the updated\n mask will be returned. Default: True.\n\n Returns:\n torch.Tensor : Results after partial conv.\\\n torch.Tensor : Updated mask will be returned if mask is given and \\\n ``return_mask`` is True.\n \"\"\"\n assert input.dim() == 4\n if mask is not None:\n assert mask.dim() == 4\n if self.multi_channel:\n assert mask.shape[1] == input.shape[1]\n else:\n assert mask.shape[1] == 1\n\n # update mask and compute mask ratio\n if mask is not None:\n with torch.no_grad():\n\n updated_mask = F.conv2d(\n mask,\n self.weight_mask_updater,\n bias=None,\n stride=self.stride,\n padding=self.padding,\n dilation=self.dilation)\n mask_ratio = self.mask_kernel_numel / (updated_mask + self.eps)\n\n updated_mask = torch.clamp(updated_mask, 0, 1)\n mask_ratio = mask_ratio * updated_mask\n\n # standard conv2d\n if mask is not None:\n input = input * mask\n raw_out = super().forward(input)\n\n if mask is not None:\n if self.bias is None:\n output = raw_out * mask_ratio\n else:\n # compute new bias when mask is given\n bias_view = self.bias.view(1, self.out_channels, 1, 1)\n output = (raw_out - bias_view) * mask_ratio + bias_view\n output = output * updated_mask\n else:\n output = raw_out\n\n if return_mask and mask is not None:\n return output, updated_mask\n\n return output\n" }, { "path": "mmagic/models/editors/pconv/pconv_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .mask_conv_module import MaskConvModule\n\n\n@MODELS.register_module()\nclass PConvDecoder(BaseModule):\n \"\"\"Decoder with partial conv.\n\n About the details for this architecture, pls see:\n Image Inpainting for Irregular Holes Using Partial Convolutions\n\n Args:\n num_layers (int): The number of convolutional layers. Default: 7.\n interpolation (str): The upsample mode. Default: 'nearest'.\n conv_cfg (dict): Config for convolution module. Default:\n {'type': 'PConv', 'multi_channel': True}.\n norm_cfg (dict): Config for norm layer. Default:\n {'type': 'BN'}.\n \"\"\"\n\n def __init__(self,\n num_layers=7,\n interpolation='nearest',\n conv_cfg=dict(type='PConv', multi_channel=True),\n norm_cfg=dict(type='BN')):\n super().__init__()\n self.num_layers = num_layers\n self.interpolation = interpolation\n\n for i in range(4, num_layers):\n name = f'dec{i+1}'\n self.add_module(\n name,\n MaskConvModule(\n 512 + 512,\n 512,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)))\n\n self.dec4 = MaskConvModule(\n 512 + 256,\n 256,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n\n self.dec3 = MaskConvModule(\n 256 + 128,\n 128,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n\n self.dec2 = MaskConvModule(\n 128 + 64,\n 64,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n\n self.dec1 = MaskConvModule(\n 64 + 3,\n 3,\n kernel_size=3,\n stride=1,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=None,\n act_cfg=None)\n\n def forward(self, input_dict):\n \"\"\"Forward Function.\n\n Args:\n input_dict (dict | torch.Tensor): Input dict with middle features\n or torch.Tensor.\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h, w).\n \"\"\"\n hidden_feats = input_dict['hidden_feats']\n hidden_masks = input_dict['hidden_masks']\n h_key = 'h{:d}'.format(self.num_layers)\n h, h_mask = hidden_feats[h_key], hidden_masks[h_key]\n\n for i in range(self.num_layers, 0, -1):\n enc_h_key = f'h{i-1}'\n dec_l_key = f'dec{i}'\n\n h = F.interpolate(h, scale_factor=2, mode=self.interpolation)\n h_mask = F.interpolate(\n h_mask, scale_factor=2, mode=self.interpolation)\n\n h = torch.cat([h, hidden_feats[enc_h_key]], dim=1)\n h_mask = torch.cat([h_mask, hidden_masks[enc_h_key]], dim=1)\n\n h, h_mask = getattr(self, dec_l_key)(h, h_mask)\n\n return h, h_mask\n" }, { "path": "mmagic/models/editors/pconv/pconv_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import BaseModule\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.models.editors.pconv.mask_conv_module import MaskConvModule\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass PConvEncoder(BaseModule):\n \"\"\"Encoder with partial conv.\n\n About the details for this architecture, pls see:\n Image Inpainting for Irregular Holes Using Partial Convolutions\n\n Args:\n in_channels (int): The number of input channels. Default: 3.\n num_layers (int): The number of convolutional layers. Default: 7.\n conv_cfg (dict): Config for convolution module. Default:\n {'type': 'PConv', 'multi_channel': True}.\n norm_cfg (dict): Config for norm layer. Default:\n {'type': 'BN'}.\n norm_eval (bool): Whether to set norm layers to eval mode, namely,\n freeze running stats (mean and var). Note: Effective on Batch Norm\n and its variants only. Default: False.\n \"\"\"\n\n def __init__(self,\n in_channels=3,\n num_layers=7,\n conv_cfg=dict(type='PConv', multi_channel=True),\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=False):\n super().__init__()\n self.num_layers = num_layers\n self.norm_eval = norm_eval\n\n self.enc1 = MaskConvModule(\n in_channels,\n 64,\n kernel_size=7,\n stride=2,\n padding=3,\n conv_cfg=conv_cfg,\n norm_cfg=None,\n act_cfg=dict(type='ReLU'))\n\n self.enc2 = MaskConvModule(\n 64,\n 128,\n kernel_size=5,\n stride=2,\n padding=2,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU'))\n\n self.enc3 = MaskConvModule(\n 128,\n 256,\n kernel_size=5,\n stride=2,\n padding=2,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU'))\n\n self.enc4 = MaskConvModule(\n 256,\n 512,\n kernel_size=3,\n stride=2,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU'))\n\n for i in range(4, num_layers):\n name = f'enc{i+1}'\n self.add_module(\n name,\n MaskConvModule(\n 512,\n 512,\n kernel_size=3,\n stride=2,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU')))\n\n def train(self, mode=True):\n \"\"\"Set BatchNorm modules in the model to evaluation mode.\"\"\"\n super().train(mode)\n if mode and self.norm_eval:\n for m in self.modules():\n # trick: eval have effect on BatchNorm only\n if isinstance(m, _BatchNorm):\n m.eval()\n\n def forward(self, x, mask):\n \"\"\"Forward function for partial conv encoder.\n\n Args:\n x (torch.Tensor): Masked image with shape (n, c, h, w).\n mask (torch.Tensor): Mask tensor with shape (n, c, h, w).\n\n Returns:\n dict: Contains the results and middle level features in this \\\n module. `hidden_feats` contain the middle feature maps and \\\n `hidden_masks` store updated masks.\n \"\"\"\n # dict for hidden layers of main information flow\n hidden_feats = {}\n # dict for hidden layers of mask information flow\n hidden_masks = {}\n\n hidden_feats['h0'], hidden_masks['h0'] = x, mask\n h_key_prev = 'h0'\n\n for i in range(1, self.num_layers + 1):\n l_key = f'enc{i}'\n h_key = f'h{i}'\n hidden_feats[h_key], hidden_masks[h_key] = getattr(self, l_key)(\n hidden_feats[h_key_prev], hidden_masks[h_key_prev])\n h_key_prev = h_key\n outputs = dict(\n out=hidden_feats[f'h{self.num_layers}'],\n hidden_feats=hidden_feats,\n hidden_masks=hidden_masks)\n\n return outputs\n" }, { "path": "mmagic/models/editors/pconv/pconv_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass PConvEncoderDecoder(BaseModule):\n \"\"\"Encoder-Decoder with partial conv module.\n\n Args:\n encoder (dict): Config of the encoder.\n decoder (dict): Config of the decoder.\n \"\"\"\n\n def __init__(self, encoder, decoder):\n super().__init__()\n self.encoder = MODELS.build(encoder)\n self.decoder = MODELS.build(decoder)\n\n # support fp16\n self.fp16_enabled = False\n\n def forward(self, x, mask_in):\n \"\"\"Forward Function.\n\n Args:\n x (torch.Tensor): Input tensor with shape of (n, c, h, w).\n mask_in (torch.Tensor): Input tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Output tensor with shape of (n, c, h', w').\n \"\"\"\n enc_outputs = self.encoder(x, mask_in)\n x, final_mask = self.decoder(enc_outputs)\n\n return x, final_mask\n" }, { "path": "mmagic/models/editors/pconv/pconv_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List\n\nfrom mmagic.models.base_models import OneStageInpaintor\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass PConvInpaintor(OneStageInpaintor):\n \"\"\"Inpaintor for Partial Convolution method.\n\n This inpaintor is implemented according to the paper: Image inpainting for\n irregular holes using partial convolutions\n \"\"\"\n\n def forward_tensor(self, inputs, data_samples):\n \"\"\"Forward function in tensor mode.\n\n Args:\n inputs (torch.Tensor): Input tensor.\n data_sample (dict): Dict contains data sample.\n\n Returns:\n dict: Dict contains output results.\n \"\"\"\n\n masked_img = inputs # N,3,H,W\n masks = data_samples.mask\n masks = 1. - masks\n masks = masks.repeat(1, 3, 1, 1)\n fake_reses, _ = self.generator(masked_img, masks)\n fake_imgs = fake_reses * (1. - masks) + masked_img * masks\n return fake_reses, fake_imgs\n\n def train_step(self, data: List[dict], optim_wrapper):\n \"\"\"Train step function.\n\n In this function, the inpaintor will finish the train step following\n the pipeline:\n\n 1. get fake res/image\n 2. optimize discriminator (if have)\n 3. optimize generator\n\n If `self.train_cfg.disc_step > 1`, the train step will contain multiple\n iterations for optimizing discriminator with different input data and\n only one iteration for optimizing generator after `disc_step`\n iterations for discriminator.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (dict[torch.optim.Optimizer]): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of \\\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data, True)\n batch_inputs, data_samples = data['inputs'], data['data_samples']\n log_vars = {}\n\n masked_img = batch_inputs # float\n gt_img = data_samples.gt_img\n mask = data_samples.mask\n mask = mask.float()\n\n mask_input = mask.expand_as(gt_img)\n mask_input = 1. - mask_input\n\n fake_res, final_mask = self.generator(masked_img, mask_input)\n fake_img = gt_img * (1. - mask) + fake_res * mask\n\n results, g_losses = self.generator_loss(fake_res, fake_img, gt_img,\n mask, masked_img)\n loss_g_, log_vars_g = self.parse_losses(g_losses)\n log_vars.update(log_vars_g)\n optim_wrapper.zero_grad()\n optim_wrapper.backward(loss_g_)\n optim_wrapper.step()\n\n return log_vars\n" }, { "path": "mmagic/models/editors/pggan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .pggan import ProgressiveGrowingGAN\nfrom .pggan_discriminator import PGGANDiscriminator\nfrom .pggan_generator import PGGANGenerator\nfrom .pggan_modules import (EqualizedLR, EqualizedLRConvDownModule,\n EqualizedLRConvModule, EqualizedLRConvUpModule,\n EqualizedLRLinearModule, MiniBatchStddevLayer,\n PGGANNoiseTo2DFeat, PixelNorm, equalized_lr)\n\n__all__ = [\n 'ProgressiveGrowingGAN', 'EqualizedLR', 'equalized_lr',\n 'EqualizedLRConvModule', 'EqualizedLRLinearModule',\n 'EqualizedLRConvUpModule', 'EqualizedLRConvDownModule', 'PixelNorm',\n 'MiniBatchStddevLayer', 'PGGANNoiseTo2DFeat', 'PGGANGenerator',\n 'PGGANDiscriminator'\n]\n" }, { "path": "mmagic/models/editors/pggan/pggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\nfrom mmengine.dist import get_world_size\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, SampleList\nfrom ...base_models import BaseGAN\nfrom ...utils import get_valid_num_batches, set_requires_grad\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module('PGGAN')\n@MODELS.register_module()\nclass ProgressiveGrowingGAN(BaseGAN):\n \"\"\"Progressive Growing Unconditional GAN.\n\n In this GAN model, we implement progressive growing training schedule,\n which is proposed in Progressive Growing of GANs for improved Quality,\n Stability and Variation, ICLR 2018.\n\n We highly recommend to use ``GrowScaleImgDataset`` for saving computational\n load in data pre-processing.\n\n Notes for **using PGGAN**:\n\n #. In official implementation, Tero uses gradient penalty with\n ``norm_mode=\"HWC\"``\n #. We do not implement ``minibatch_repeats`` where has been used in\n official Tensorflow implementation.\n\n Notes for resuming progressive growing GANs:\n Users should specify the ``prev_stage`` in ``train_cfg``. Otherwise, the\n model is possible to reset the optimizer status, which will bring\n inferior performance. For example, if your model is resumed from the\n `256` stage, you should set ``train_cfg=dict(prev_stage=256)``.\n\n Args:\n generator (dict): Config for generator.\n discriminator (dict): Config for discriminator.\n \"\"\"\n\n def __init__(self,\n generator,\n discriminator,\n data_preprocessor,\n nkimgs_per_scale,\n noise_size=None,\n interp_real=None,\n transition_kimgs: int = 600,\n prev_stage: int = 0,\n ema_config: Optional[Dict] = None):\n super().__init__(generator, discriminator, data_preprocessor, 1, 1,\n noise_size, ema_config)\n\n # register necessary training status\n self.register_buffer('shown_nkimg', torch.tensor(0.))\n self.register_buffer('_curr_transition_weight', torch.tensor(1.))\n\n if interp_real is None:\n interp_real = dict(mode='bilinear', align_corners=True)\n self.interp_real_to = partial(F.interpolate, **interp_real)\n\n self.scales, self.nkimgs = [], []\n for k, v in nkimgs_per_scale.items():\n # support for different data types\n if isinstance(k, str):\n k = (int(k), int(k))\n elif isinstance(k, int):\n k = (k, k)\n else:\n assert mmengine.is_tuple_of(k, int)\n\n # sanity check for the order of scales\n assert len(self.scales) == 0 or k[0] > self.scales[-1][0]\n self.scales.append(k)\n self.nkimgs.append(v)\n\n self.cum_nkimgs = np.cumsum(self.nkimgs)\n self.curr_stage = 0\n # dirty workaround for avoiding optimizer bug in resuming\n self.prev_stage = prev_stage\n # actually nkimgs shown at the end of per training stage\n self._actual_nkimgs = []\n # In each scale, transit from previous torgb layer to newer torgb layer\n # with `transition_kimgs` imgs\n self.transition_kimgs = transition_kimgs\n\n # this buffer is used to resume model easily\n self.register_buffer(\n '_next_scale_int',\n torch.tensor(self.scales[0][0], dtype=torch.int32))\n # TODO: init it with the same value as `_next_scale_int`\n # a dirty workaround for testing\n self.register_buffer(\n '_curr_scale_int',\n torch.tensor(self.scales[-1][0], dtype=torch.int32))\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> SampleList:\n \"\"\"Sample images from noises by using the generator.\n\n Args:\n batch_inputs (ForwardInputs): Dict containing the necessary\n information (e.g. noise, num_batches, mode) to generate image.\n data_samples (Optional[list]): Data samples collated by\n :attr:`data_preprocessor`. Defaults to None.\n mode (Optional[str]): `mode` is not used in\n :class:`ProgressiveGrowingGAN`. Defaults to None.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n if isinstance(inputs, Tensor):\n noise = inputs\n curr_scale = transition_weight = None\n else:\n noise = inputs.get('noise', None)\n num_batches = get_valid_num_batches(inputs, data_samples)\n noise = self.noise_fn(noise, num_batches=num_batches)\n\n curr_scale = inputs.get('curr_scale', None)\n transition_weight = inputs.get('transition_weight', None)\n num_batches = noise.shape[0]\n\n # use `self.curr_scale` if curr_scale is None\n if curr_scale is None:\n # in training, 'curr_scale' will be set as attribute\n if hasattr(self, 'curr_scale'):\n curr_scale = self.curr_scale[0]\n # in testing, adopt '_curr_scale_int' from buffer as testing scale\n else:\n curr_scale = self._curr_scale_int.item()\n\n # use `self._curr_transition_weight` if `transition_weight` is None\n if transition_weight is None:\n transition_weight = self._curr_transition_weight.item()\n\n sample_model = self._get_valid_model(inputs)\n batch_sample_list = []\n if sample_model in ['ema', 'orig']:\n if sample_model == 'ema':\n generator = self.generator_ema\n else:\n generator = self.generator\n outputs = generator(\n noise,\n curr_scale=curr_scale,\n transition_weight=transition_weight)\n outputs = self.data_preprocessor.destruct(outputs, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.fake_img = outputs\n gen_sample.sample_model = sample_model\n gen_sample.noise = noise\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n else: # sample model is 'ema/orig'\n outputs_orig = self.generator(\n noise,\n curr_scale=curr_scale,\n transition_weight=transition_weight)\n outputs_ema = self.generator_ema(\n noise,\n curr_scale=curr_scale,\n transition_weight=transition_weight)\n outputs_orig = self.data_preprocessor.destruct(\n outputs_orig, data_samples)\n outputs_ema = self.data_preprocessor.destruct(\n outputs_ema, data_samples)\n\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples)\n if isinstance(inputs, dict) and 'img' in inputs:\n gen_sample.gt_img = inputs['img']\n gen_sample.ema = DataSample(fake_img=outputs_ema)\n gen_sample.orig = DataSample(fake_img=outputs_orig)\n gen_sample.noise = noise\n gen_sample.sample_model = 'ema/orig'\n batch_sample_list = gen_sample.split(allow_nonseq_value=True)\n\n return batch_sample_list\n\n def train_discriminator(self, inputs: Tensor,\n data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (Tensor): Inputs from current resolution training.\n data_samples (List[DataSample]): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = inputs\n num_batches = len(data_samples)\n noise_batch = self.noise_fn(num_batches=num_batches)\n\n with torch.no_grad():\n fake_imgs = self.generator(\n noise_batch,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight,\n return_noise=False)\n\n disc_pred_fake = self.discriminator(\n fake_imgs,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n disc_pred_real = self.discriminator(\n real_imgs,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n\n parsed_loss, log_vars = self.disc_loss(disc_pred_fake, disc_pred_real,\n fake_imgs, real_imgs)\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,\n fake_data: Tensor, real_data: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Get disc loss. PGGAN use WGAN-GP's loss and discriminator shift\n loss to train the discriminator.\n\n .. math:\n L_{D} = \\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n - \\mathbb{E}_{x\\sim{p_{data}}}D\\left\\(x\\right\\) + L_{GP} \\\\\n L_{GP} = \\lambda\\mathbb{E}(\\Vert\\nabla_{\\tilde{x}}D(\\tilde{x})\n \\Vert_2-1)^2 \\\\\n \\tilde{x} = \\epsilon x + (1-\\epsilon)G(z)\n L_{shift} =\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n fake_data (Tensor): Generated images, used to calculate gradient\n penalty.\n real_data (Tensor): Real images, used to calculate gradient\n penalty.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = disc_pred_fake.mean()\n losses_dict['loss_disc_real'] = -disc_pred_real.mean()\n\n # gradient penalty\n batch_size = real_data.size(0)\n alpha = torch.rand(batch_size, 1, 1, 1).to(real_data)\n\n # interpolate between real_data and fake_data\n interpolates = alpha * real_data + (1. - alpha) * fake_data\n interpolates = autograd.Variable(interpolates, requires_grad=True)\n\n disc_interpolates = self.discriminator(\n interpolates,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n gradients = autograd.grad(\n outputs=disc_interpolates,\n inputs=interpolates,\n grad_outputs=torch.ones_like(disc_interpolates),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n # norm_mode is 'HWC'\n gradients_penalty = ((\n gradients.reshape(batch_size, -1).norm(2, dim=1) - 1)**2).mean()\n losses_dict['loss_gp'] = 10 * gradients_penalty\n losses_dict['loss_disc_shift'] = 0.001 * 0.5 * (\n disc_pred_fake**2 + disc_pred_real**2)\n\n parsed_loss, log_vars = self.parse_losses(losses_dict)\n return parsed_loss, log_vars\n\n def train_generator(self, inputs: Tensor, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (Tensor): Inputs from current resolution training.\n data_samples (List[DataSample]): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n num_batches = len(data_samples)\n noise_batch = self.noise_fn(num_batches=num_batches)\n\n fake_imgs = self.generator(\n noise_batch,\n num_batches=num_batches,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n disc_pred_fake_g = self.discriminator(\n fake_imgs,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake_g)\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Generator loss for PGGAN. PGGAN use WGAN's loss to train the\n generator.\n\n .. math:\n L_{G} = -\\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n + L_{MSE}\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n recon_imgs (Tensor): Reconstructive images.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_vars = self.parse_losses(losses_dict)\n return loss, log_vars\n\n def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):\n \"\"\"Train step function.\n\n This function implements the standard training iteration for\n asynchronous adversarial training. Namely, in each iteration, we first\n update discriminator and then compute loss for generator with the newly\n updated discriminator.\n\n As for distributed training, we use the ``reducer`` from ddp to\n synchronize the necessary params in current computational graph.\n\n Args:\n data_batch (dict): Input data from dataloader.\n optimizer (dict): Dict contains optimizer for generator and\n discriminator.\n ddp_reducer (:obj:`Reducer` | None, optional): Reducer from ddp.\n It is used to prepare for ``backward()`` in ddp. Defaults to\n None.\n running_status (dict | None, optional): Contains necessary basic\n information for training, e.g., iteration number. Defaults to\n None.\n\n Returns:\n dict: Contains 'log_vars', 'num_samples', and 'results'.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n\n # update current stage\n self.curr_stage = int(\n min(\n sum(self.cum_nkimgs <= self.shown_nkimg.item()),\n len(self.scales) - 1))\n self.curr_scale = self.scales[self.curr_stage]\n self._curr_scale_int = self._next_scale_int.clone()\n\n if self.curr_stage != self.prev_stage:\n self.prev_stage = self.curr_stage\n self._actual_nkimgs.append(self.shown_nkimg.item())\n\n data = self.data_preprocessor(data, True)\n data_sample = data['data_samples']\n real_imgs = data_sample.gt_img\n\n curr_scale = str(self.curr_scale[0])\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper[\n f'discriminator_{curr_scale}']\n gen_optimizer_wrapper: OptimWrapper = optim_wrapper[\n f'generator_{curr_scale}']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n # update training configs, like transition weight for torgb layers.\n # get current transition weight for interpolating two torgb layers\n if self.curr_stage == 0:\n transition_weight = 1.\n else:\n transition_weight = (\n self.shown_nkimg.item() -\n self._actual_nkimgs[-1]) / self.transition_kimgs\n # clip to [0, 1]\n transition_weight = min(max(transition_weight, 0.), 1.)\n self._curr_transition_weight = torch.tensor(transition_weight).to(\n self._curr_transition_weight)\n\n if real_imgs.shape[2:] == self.curr_scale:\n pass\n elif real_imgs.shape[2] >= self.curr_scale[0] and real_imgs.shape[\n 3] >= self.curr_scale[1]:\n real_imgs = self.interp_real_to(real_imgs, size=self.curr_scale)\n else:\n raise RuntimeError(\n f'The scale of real image {real_imgs.shape[2:]} is smaller '\n f'than current scale {self.curr_scale}.')\n\n # normal gan training process\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n log_vars = self.train_discriminator(real_imgs, data_sample,\n disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n log_vars_gen = self.train_generator(\n real_imgs, data_sample, gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = self.gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n # if not update buffer, copy buffer from orig model\n if not self.generator_ema.update_buffers:\n self.generator_ema.sync_buffers(\n self.generator.module if is_model_wrapper(\n self.generator) else self.generator)\n elif self.with_ema_gen:\n # before ema, copy weights from orig\n self.generator_ema.sync_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n # add batch size info to log_vars\n _batch_size = real_imgs.shape[0] * get_world_size()\n self.shown_nkimg += (_batch_size / 1000.)\n\n log_vars.update(\n dict(\n shown_nkimg=self.shown_nkimg.item(),\n curr_scale=self.curr_scale[0],\n transition_weight=transition_weight))\n\n # check if a new scale will be added in the next iteration\n _curr_stage = int(\n min(\n sum(self.cum_nkimgs <= self.shown_nkimg.item()),\n len(self.scales) - 1))\n # in the next iteration, we will switch to a new scale\n if _curr_stage != self.curr_stage:\n # `self._next_scale_int` is updated at the end of `train_step`\n self._next_scale_int = self._next_scale_int * 2\n\n return log_vars\n" }, { "path": "mmagic/models/editors/pggan/pggan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom functools import partial\n\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .pggan_modules import (EqualizedLRConvDownModule, EqualizedLRConvModule,\n MiniBatchStddevLayer, PGGANDecisionHead)\n\n\n@MODELS.register_module()\nclass PGGANDiscriminator(BaseModule):\n \"\"\"Discriminator for PGGAN.\n\n Args:\n in_scale (int): The scale of the input image.\n label_size (int, optional): Size of the label vector. Defaults to\n 0.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this\n number. Defaults to 8192.\n max_channels (int, optional): Maximum channels for the feature\n maps in the discriminator block. Defaults to 512.\n in_channels (int, optional): Number of channels in input images.\n Defaults to 3.\n channel_decay (float, optional): Decay for channels of feature\n maps. Defaults to 1.0.\n mbstd_cfg (dict, optional): Configs for minibatch-stddev layer.\n Defaults to dict(group_size=4).\n fused_convdown (bool, optional): Whether use fused downconv.\n Defaults to True.\n conv_module_cfg (dict, optional): Config for the convolution\n module used in this generator. Defaults to None.\n fused_convdown_cfg (dict, optional): Config for the fused downconv\n module used in this discriminator. Defaults to None.\n fromrgb_layer_cfg (dict, optional): Config for the fromrgb layer.\n Defaults to None.\n downsample_cfg (dict, optional): Config for the downsampling\n operation. Defaults to None.\n \"\"\"\n _default_fromrgb_cfg = dict(\n conv_cfg=None,\n kernel_size=1,\n stride=1,\n padding=0,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=None,\n order=('conv', 'act', 'norm'))\n\n _default_conv_module_cfg = dict(\n kernel_size=3,\n padding=1,\n stride=1,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n\n _default_convdown_cfg = dict(\n kernel_size=3,\n padding=1,\n stride=2,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n\n def __init__(self,\n in_scale,\n label_size=0,\n base_channels=8192,\n max_channels=512,\n in_channels=3,\n channel_decay=1.0,\n mbstd_cfg=dict(group_size=4),\n fused_convdown=True,\n conv_module_cfg=None,\n fused_convdown_cfg=None,\n fromrgb_layer_cfg=None,\n downsample_cfg=None):\n super().__init__()\n self.in_scale = in_scale\n self.in_log2_scale = int(np.log2(self.in_scale))\n self.label_size = label_size\n self.base_channels = base_channels\n self.max_channels = max_channels\n self.in_channels = in_channels\n self.channel_decay = channel_decay\n self.with_mbstd = mbstd_cfg is not None\n\n self.fused_convdown = fused_convdown\n\n self.conv_module_cfg = deepcopy(self._default_conv_module_cfg)\n if conv_module_cfg is not None:\n self.conv_module_cfg.update(conv_module_cfg)\n\n if self.fused_convdown:\n self.fused_convdown_cfg = deepcopy(self._default_convdown_cfg)\n if fused_convdown_cfg is not None:\n self.fused_convdown_cfg.update(fused_convdown_cfg)\n\n self.fromrgb_layer_cfg = deepcopy(self._default_fromrgb_cfg)\n if fromrgb_layer_cfg:\n self.fromrgb_layer_cfg.update(fromrgb_layer_cfg)\n\n # setup conv blocks\n self.conv_blocks = nn.ModuleList()\n self.fromrgb_layers = nn.ModuleList()\n\n for s in range(2, self.in_log2_scale + 1):\n self.fromrgb_layers.append(\n self._get_fromrgb_layer(self.in_channels, s))\n\n self.conv_blocks.extend(\n self._get_convdown_block(self._num_out_channels(s - 1), s))\n\n # setup downsample layer\n self.downsample_cfg = deepcopy(downsample_cfg)\n if self.downsample_cfg is None or self.downsample_cfg.get(\n 'type', None) == 'avgpool':\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2)\n elif self.downsample_cfg.get('type', None) in ['nearest', 'bilinear']:\n self.downsample = partial(\n F.interpolate,\n mode=self.downsample_cfg.pop('type'),\n **self.downsample_cfg)\n else:\n raise NotImplementedError(\n 'We have not supported the downsampling with type'\n f' {downsample_cfg}.')\n\n # setup minibatch stddev layer\n if self.with_mbstd:\n self.mbstd_layer = MiniBatchStddevLayer(**mbstd_cfg)\n # minibatch stddev layer will concatenate an additional feature map\n # in channel dimension.\n decision_in_channels = self._num_out_channels(1) * 16 + 16\n else:\n decision_in_channels = self._num_out_channels(1) * 16\n\n # setup decision layer\n self.decision = PGGANDecisionHead(decision_in_channels,\n self._num_out_channels(0),\n 1 + self.label_size)\n\n def _num_out_channels(self, log_scale: int) -> int:\n \"\"\"Calculate the number of output channels of the current network from\n logarithm of current scale.\n\n Args:\n log_scale (int): The logarithm of the current scale.\n\n Returns:\n int: The number of output channels.\n \"\"\"\n return min(\n int(self.base_channels / (2.0**(log_scale * self.channel_decay))),\n self.max_channels)\n\n def _get_fromrgb_layer(self, in_channels: int,\n log2_scale: int) -> nn.Module:\n \"\"\"Get the 'fromrgb' layer from logarithm of current scale.\n\n Args:\n in_channels (int): The number of input channels.\n log2_scale (int): The logarithm of the current scale.\n\n Returns:\n nn.Module: The built from-rgb layer.\n \"\"\"\n return EqualizedLRConvModule(in_channels,\n self._num_out_channels(log2_scale - 1),\n **self.fromrgb_layer_cfg)\n\n def _get_convdown_block(self, in_channels: int,\n log2_scale: int) -> nn.Module:\n \"\"\"Get the downsample layer from logarithm of current scale.\n\n Args:\n in_channels (int): The number of input channels.\n log2_scale (int): The logarithm of the current scale.\n\n Returns:\n nn.Module: The built Conv layer.\n \"\"\"\n modules = []\n if log2_scale == 2:\n modules.append(\n EqualizedLRConvModule(in_channels,\n self._num_out_channels(log2_scale - 1),\n **self.conv_module_cfg))\n else:\n modules.append(\n EqualizedLRConvModule(in_channels,\n self._num_out_channels(log2_scale - 1),\n **self.conv_module_cfg))\n\n if self.fused_convdown:\n cfg_ = dict(downsample=dict(type='fused_pool'))\n cfg_.update(self.fused_convdown_cfg)\n else:\n cfg_ = dict(downsample=self.downsample)\n cfg_.update(self.conv_module_cfg)\n modules.append(\n EqualizedLRConvDownModule(\n self._num_out_channels(log2_scale - 1),\n self._num_out_channels(log2_scale - 2), **cfg_))\n return modules\n\n def forward(self, x, transition_weight=1., curr_scale=-1):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input image tensor.\n transition_weight (float, optional): The weight used in resolution\n transition. Defaults to 1.0.\n curr_scale (int, optional): The scale for the current inference or\n training. Defaults to -1.\n\n Returns:\n Tensor: Predict score for the input image.\n \"\"\"\n curr_log2_scale = self.in_log2_scale if curr_scale < 4 else int(\n np.log2(curr_scale))\n\n original_img = x\n\n x = self.fromrgb_layers[curr_log2_scale - 2](x)\n\n for s in range(curr_log2_scale, 2, -1):\n x = self.conv_blocks[2 * s - 5](x)\n x = self.conv_blocks[2 * s - 4](x)\n if s == curr_log2_scale:\n img_down = self.downsample(original_img)\n y = self.fromrgb_layers[curr_log2_scale - 3](img_down)\n x = y + transition_weight * (x - y)\n\n if self.with_mbstd:\n x = self.mbstd_layer(x)\n\n x = self.decision(x)\n\n if self.label_size > 0:\n return x[:, :1], x[:, 1:]\n\n return x\n" }, { "path": "mmagic/models/editors/pggan/pggan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom .pggan_modules import (EqualizedLRConvModule, EqualizedLRConvUpModule,\n PGGANNoiseTo2DFeat)\n\n\n@MODELS.register_module()\nclass PGGANGenerator(BaseModule):\n \"\"\"Generator for PGGAN.\n\n Args:\n noise_size (int): Size of the input noise vector.\n out_scale (int): Output scale for the generated image.\n label_size (int, optional): Size of the label vector.\n Defaults to 0.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this\n number. Defaults to 8192.\n channel_decay (float, optional): Decay for channels of feature maps.\n Defaults to 1.0.\n max_channels (int, optional): Maximum channels for the feature\n maps in the generator block. Defaults to 512.\n fused_upconv (bool, optional): Whether use fused upconv.\n Defaults to True.\n conv_module_cfg (dict, optional): Config for the convolution\n module used in this generator. Defaults to None.\n fused_upconv_cfg (dict, optional): Config for the fused upconv\n module used in this generator. Defaults to None.\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to None.\n \"\"\"\n _default_fused_upconv_cfg = dict(\n conv_cfg=dict(type='deconv'),\n kernel_size=3,\n stride=2,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='PixelNorm'),\n order=('conv', 'act', 'norm'))\n _default_conv_module_cfg = dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='PixelNorm'),\n order=('conv', 'act', 'norm'))\n\n _default_upsample_cfg = dict(type='nearest', scale_factor=2)\n\n def __init__(self,\n noise_size,\n out_scale,\n label_size=0,\n base_channels=8192,\n channel_decay=1.,\n max_channels=512,\n fused_upconv=True,\n conv_module_cfg=None,\n fused_upconv_cfg=None,\n upsample_cfg=None):\n super().__init__()\n self.noise_size = noise_size if noise_size else min(\n base_channels, max_channels)\n self.out_scale = out_scale\n self.out_log2_scale = int(np.log2(out_scale))\n # sanity check for the output scale\n assert out_scale == 2**self.out_log2_scale and out_scale >= 4\n self.label_size = label_size\n self.base_channels = base_channels\n self.channel_decay = channel_decay\n self.max_channels = max_channels\n self.fused_upconv = fused_upconv\n\n # set conv cfg\n self.conv_module_cfg = deepcopy(self._default_conv_module_cfg)\n # update with customized config\n if conv_module_cfg:\n self.conv_module_cfg.update(conv_module_cfg)\n\n if self.fused_upconv:\n self.fused_upconv_cfg = deepcopy(self._default_fused_upconv_cfg)\n # update with customized config\n if fused_upconv_cfg:\n self.fused_upconv_cfg.update(fused_upconv_cfg)\n\n self.upsample_cfg = deepcopy(self._default_upsample_cfg)\n if upsample_cfg is not None:\n self.upsample_cfg.update(upsample_cfg)\n\n self.noise2feat = PGGANNoiseTo2DFeat(noise_size + label_size,\n self._num_out_channels(1))\n\n self.torgb_layers = nn.ModuleList()\n self.conv_blocks = nn.ModuleList()\n for s in range(2, self.out_log2_scale + 1):\n in_ch = self._num_out_channels(\n s - 1) if s == 2 else self._num_out_channels(s - 2)\n # setup torgb layers\n self.torgb_layers.append(\n self._get_torgb_layer(self._num_out_channels(s - 1)))\n # setup upconv or conv blocks\n self.conv_blocks.extend(self._get_upconv_block(in_ch, s))\n\n # build upsample layer for residual path\n self.upsample_layer = MODELS.build(self.upsample_cfg)\n\n def _get_torgb_layer(self, in_channels: int):\n \"\"\"Get the to-rgb layer based on `in_channels`.\n\n Args:\n in_channels (int): Number of input channels.\n\n Returns:\n nn.Module: To-rgb layer.\n \"\"\"\n return EqualizedLRConvModule(\n in_channels,\n 3,\n kernel_size=1,\n stride=1,\n equalized_lr_cfg=dict(gain=1),\n bias=True,\n norm_cfg=None,\n act_cfg=None)\n\n def _num_out_channels(self, log_scale: int):\n \"\"\"Calculate the number of output channels based on logarithm of\n current scale.\n\n Args:\n log_scale (int): The logarithm of the current scale.\n\n Returns:\n int: The current number of output channels.\n \"\"\"\n return min(\n int(self.base_channels / (2.0**(log_scale * self.channel_decay))),\n self.max_channels)\n\n def _get_upconv_block(self, in_channels, log_scale):\n \"\"\"Get the conv block for upsampling.\n\n Args:\n in_channels (int): The number of input channels.\n log_scale (int): The logarithmic of the current scale.\n\n Returns:\n nn.Module: The conv block for upsampling.\n \"\"\"\n modules = []\n # start 4x4 scale\n if log_scale == 2:\n modules.append(\n EqualizedLRConvModule(in_channels,\n self._num_out_channels(log_scale - 1),\n **self.conv_module_cfg))\n # 8x8 --> 1024x1024 scales\n else:\n if self.fused_upconv:\n cfg_ = dict(upsample=dict(type='fused_nn'))\n cfg_.update(self.fused_upconv_cfg)\n else:\n cfg_ = dict(upsample=self.upsample_cfg)\n cfg_.update(self.conv_module_cfg)\n # up + conv\n modules.append(\n EqualizedLRConvUpModule(in_channels,\n self._num_out_channels(log_scale - 1),\n **cfg_))\n # refine conv\n modules.append(\n EqualizedLRConvModule(\n self._num_out_channels(log_scale - 1),\n self._num_out_channels(log_scale - 1),\n **self.conv_module_cfg))\n\n return modules\n\n def forward(self,\n noise,\n label=None,\n num_batches=0,\n return_noise=False,\n transition_weight=1.,\n curr_scale=-1):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n label (Tensor, optional): Label vector with shape [N, C]. Defaults\n to None.\n num_batches (int, optional): The number of batch size. Defaults to\n 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n transition_weight (float, optional): The weight used in resolution\n transition. Defaults to 1.0.\n curr_scale (int, optional): The scale for the current inference or\n training. Defaults to -1.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img`` and\n ``noise_batch`` will be returned.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n # TODO: check pggan default noise type\n noise_batch = torch.randn((num_batches, self.noise_size))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n\n if label is not None:\n noise_batch = torch.cat([noise_batch,\n label.to(noise_batch)],\n dim=1)\n\n # noise vector to 2D feature\n x = self.noise2feat(noise_batch)\n\n # build current computational graph\n curr_log2_scale = self.out_log2_scale if curr_scale < 0 else int(\n np.log2(curr_scale))\n\n # 4x4 scale\n x = self.conv_blocks[0](x)\n if curr_log2_scale <= 3:\n out_img = last_img = self.torgb_layers[0](x)\n\n # 8x8 and larger scales\n for s in range(3, curr_log2_scale + 1):\n x = self.conv_blocks[2 * s - 5](x)\n x = self.conv_blocks[2 * s - 4](x)\n if s + 1 == curr_log2_scale:\n last_img = self.torgb_layers[s - 2](x)\n elif s == curr_log2_scale:\n out_img = self.torgb_layers[s - 2](x)\n residual_img = self.upsample_layer(last_img)\n out_img = residual_img + transition_weight * (\n out_img - residual_img)\n\n if return_noise:\n output = dict(\n fake_img=out_img, noise_batch=noise_batch, label=label)\n return output\n\n return out_img\n" }, { "path": "mmagic/models/editors/pggan/pggan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn.bricks import ConvModule, build_norm_layer\nfrom mmengine.model import BaseModule, normal_init\nfrom torch.nn.init import _calculate_correct_fan\n\nfrom mmagic.models.archs import AllGatherLayer\nfrom mmagic.registry import MODELS\n\n\nclass EqualizedLR:\n r\"\"\"Equalized Learning Rate.\n\n This trick is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n The general idea is to dynamically rescale the weight in training instead\n of in initializing so that the variance of the responses in each layer is\n guaranteed with some statistical properties.\n\n Note that this function is always combined with a convolution module which\n is initialized with :math:`\\mathcal{N}(0, 1)`.\n\n Args:\n name (str | optional): The name of weights. Defaults to 'weight'.\n mode (str, optional): The mode of computing ``fan`` which is the\n same as ``kaiming_init`` in pytorch. You can choose one from\n ['fan_in', 'fan_out']. Defaults to 'fan_in'.\n \"\"\"\n\n def __init__(self, name='weight', gain=2**0.5, mode='fan_in', lr_mul=1.0):\n self.name = name\n self.mode = mode\n self.gain = gain\n self.lr_mul = lr_mul\n\n def compute_weight(self, module):\n \"\"\"Compute weight with equalized learning rate.\n\n Args:\n module (nn.Module): A module that is wrapped with equalized lr.\n\n Returns:\n torch.Tensor: Updated weight.\n \"\"\"\n weight = getattr(module, self.name + '_orig')\n if weight.ndim == 5:\n # weight in shape of [b, out, in, k, k]\n fan = _calculate_correct_fan(weight[0], self.mode)\n else:\n assert weight.ndim <= 4\n fan = _calculate_correct_fan(weight, self.mode)\n weight = weight * torch.tensor(\n self.gain, device=weight.device) * torch.sqrt(\n torch.tensor(1. / fan, device=weight.device)) * self.lr_mul\n\n return weight\n\n def __call__(self, module, inputs):\n \"\"\"Standard interface for forward pre hooks.\"\"\"\n setattr(module, self.name, self.compute_weight(module))\n\n @staticmethod\n def apply(module, name, gain=2**0.5, mode='fan_in', lr_mul=1.):\n \"\"\"Apply function.\n\n This function is to register an equalized learning rate hook in an\n ``nn.Module``.\n\n Args:\n module (nn.Module): Module to be wrapped.\n name (str | optional): The name of weights. Defaults to 'weight'.\n mode (str, optional): The mode of computing ``fan`` which is the\n same as ``kaiming_init`` in pytorch. You can choose one from\n ['fan_in', 'fan_out']. Defaults to 'fan_in'.\n\n Returns:\n nn.Module: Module that is registered with equalized lr hook.\n \"\"\"\n # sanity check for duplicated hooks.\n for _, hook in module._forward_pre_hooks.items():\n if isinstance(hook, EqualizedLR):\n raise RuntimeError(\n 'Cannot register two equalized_lr hooks on the same '\n f'parameter {name} in {module} module.')\n\n fn = EqualizedLR(name, gain=gain, mode=mode, lr_mul=lr_mul)\n weight = module._parameters[name]\n\n delattr(module, name)\n module.register_parameter(name + '_orig', weight)\n\n # We still need to assign weight back as fn.name because all sorts of\n # things may assume that it exists, e.g., when initializing weights.\n # However, we can't directly assign as it could be an nn.Parameter and\n # gets added as a parameter. Instead, we register weight.data as a\n # plain attribute.\n\n setattr(module, name, weight.data)\n module.register_forward_pre_hook(fn)\n\n # TODO: register load state dict hook\n\n return fn\n\n\ndef equalized_lr(module, name='weight', gain=2**0.5, mode='fan_in', lr_mul=1.):\n r\"\"\"Equalized Learning Rate.\n\n This trick is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n The general idea is to dynamically rescale the weight in training instead\n of in initializing so that the variance of the responses in each layer is\n guaranteed with some statistical properties.\n\n Note that this function is always combined with a convolution module which\n is initialized with :math:`\\mathcal{N}(0, 1)`.\n\n Args:\n module (nn.Module): Module to be wrapped.\n name (str | optional): The name of weights. Defaults to 'weight'.\n mode (str, optional): The mode of computing ``fan`` which is the\n same as ``kaiming_init`` in pytorch. You can choose one from\n ['fan_in', 'fan_out']. Defaults to 'fan_in'.\n\n Returns:\n nn.Module: Module that is registered with equalized lr hook.\n \"\"\"\n EqualizedLR.apply(module, name, gain=gain, mode=mode, lr_mul=lr_mul)\n\n return module\n\n\ndef pixel_norm(x, eps=1e-6):\n \"\"\"Pixel Normalization.\n\n This normalization is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n Args:\n x (torch.Tensor): Tensor to be normalized.\n eps (float, optional): Epsilon to avoid dividing zero.\n Defaults to 1e-6.\n\n Returns:\n torch.Tensor: Normalized tensor.\n \"\"\"\n if torch.__version__ >= '1.7.0':\n norm = torch.linalg.norm(x, ord=2, dim=1, keepdim=True)\n # support older pytorch version\n else:\n norm = torch.norm(x, p=2, dim=1, keepdim=True)\n norm = norm / torch.sqrt(torch.tensor(x.shape[1]).to(x))\n\n return x / (norm + eps)\n\n\n@MODELS.register_module()\nclass PixelNorm(BaseModule):\n \"\"\"Pixel Normalization.\n\n This module is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n Args:\n eps (float, optional): Epsilon value. Defaults to 1e-6.\n \"\"\"\n\n _abbr_ = 'pn'\n\n def __init__(self, in_channels=None, eps=1e-6):\n super().__init__()\n self.eps = eps\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Tensor to be normalized.\n\n Returns:\n torch.Tensor: Normalized tensor.\n \"\"\"\n return pixel_norm(x, self.eps)\n\n\n@MODELS.register_module()\nclass EqualizedLRConvModule(ConvModule):\n r\"\"\"Equalized LR ConvModule.\n\n In this module, we inherit default ``mmcv.cnn.ConvModule`` and adopt\n equalized lr in convolution. The equalized learning rate is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n Note that, the initialization of ``self.conv`` will be overwritten as\n :math:`\\mathcal{N}(0, 1)`.\n\n Args:\n equalized_lr_cfg (dict | None, optional): Config for ``EqualizedLR``.\n If ``None``, equalized learning rate is ignored. Defaults to\n dict(mode='fan_in').\n \"\"\"\n\n def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwargs):\n super().__init__(*args, **kwargs)\n self.with_equalized_lr = equalized_lr_cfg is not None\n if self.with_equalized_lr:\n self.conv = equalized_lr(self.conv, **equalized_lr_cfg)\n # initialize the conv weight with standard Gaussian noise.\n self._init_conv_weights()\n\n def _init_conv_weights(self):\n \"\"\"Initialize conv weights as described in PGGAN.\"\"\"\n normal_init(self.conv)\n\n\n@MODELS.register_module()\nclass EqualizedLRConvUpModule(EqualizedLRConvModule):\n r\"\"\"Equalized LR (Upsample + Conv) Module.\n\n In this module, we inherit ``EqualizedLRConvModule`` and adopt\n upsampling before convolution. As for upsampling, in addition to the\n sampling layer in MMCV, we also offer the \"fused_nn\" type. \"fused_nn\"\n denotes fusing upsampling and convolution. The fusion is modified from\n the official Tensorflow implementation in:\n https://github.com/tkarras/progressive_growing_of_gans/blob/master/networks.py#L86\n\n Args:\n upsample (dict | None, optional): Config for upsampling operation. If\n ``None``, upsampling is ignored. If you need a faster fused version as\n the official PGGAN in Tensorflow, you should set it as\n ``dict(type='fused_nn')``. Defaults to\n ``dict(type='nearest', scale_factor=2)``.\n \"\"\"\n\n def __init__(self,\n *args,\n upsample=dict(type='nearest', scale_factor=2),\n **kwargs):\n super().__init__(*args, **kwargs)\n self.with_upsample = upsample is not None\n if self.with_upsample:\n if upsample.get('type') == 'fused_nn':\n assert isinstance(self.conv, nn.ConvTranspose2d)\n self.conv.register_forward_pre_hook(\n EqualizedLRConvUpModule.fused_nn_hook)\n else:\n self.upsample_layer = MODELS.build(upsample)\n\n def forward(self, x, **kwargs):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n if hasattr(self, 'upsample_layer'):\n x = self.upsample_layer(x)\n return super().forward(x, **kwargs)\n\n @staticmethod\n def fused_nn_hook(module, inputs):\n \"\"\"Standard interface for forward pre hooks.\"\"\"\n weight = module.weight\n # pad the last two dimensions\n weight = F.pad(weight, (1, 1, 1, 1))\n weight = weight[..., 1:, 1:] + weight[..., 1:, :-1] + weight[\n ..., :-1, 1:] + weight[..., :-1, :-1]\n module.weight = weight\n\n\n@MODELS.register_module()\nclass EqualizedLRConvDownModule(EqualizedLRConvModule):\n r\"\"\"Equalized LR (Conv + Downsample) Module.\n\n In this module, we inherit ``EqualizedLRConvModule`` and adopt\n downsampling after convolution. As for downsampling, we provide two modes\n of \"avgpool\" and \"fused_pool\". \"avgpool\" denotes the commonly used average\n pooling operation, while \"fused_pool\" represents fusing downsampling and\n convolution. The fusion is modified from the official Tensorflow\n implementation in:\n https://github.com/tkarras/progressive_growing_of_gans/blob/master/networks.py#L109\n\n\n Args:\n downsample (dict | None, optional): Config for downsampling operation.\n If ``None``, downsampling is ignored. Currently, we support the\n types of [\"avgpool\", \"fused_pool\"]. Defaults to\n dict(type='fused_pool').\n \"\"\"\n\n def __init__(self, *args, downsample=dict(type='fused_pool'), **kwargs):\n super().__init__(*args, **kwargs)\n downsample_cfg = deepcopy(downsample)\n self.with_downsample = downsample is not None\n if self.with_downsample:\n type_ = downsample_cfg.pop('type')\n if type_ == 'avgpool':\n self.downsample = nn.AvgPool2d(2, 2)\n elif type_ == 'fused_pool':\n self.conv.register_forward_pre_hook(\n EqualizedLRConvDownModule.fused_avgpool_hook)\n elif callable(downsample):\n self.downsample = downsample\n else:\n raise NotImplementedError(\n 'Currently, we only support [\"avgpool\", \"fused_pool\"] as '\n f'the type of downsample, but got {type_} instead.')\n\n def forward(self, x, **kwargs):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n torch.Tensor: Normalized tensor.\n \"\"\"\n x = super().forward(x, **kwargs)\n if hasattr(self, 'downsample'):\n x = self.downsample(x)\n return x\n\n @staticmethod\n def fused_avgpool_hook(module, inputs):\n \"\"\"Standard interface for forward pre hooks.\"\"\"\n weight = module.weight\n # pad the last two dimensions\n weight = F.pad(weight, (1, 1, 1, 1))\n weight = (weight[..., 1:, 1:] + weight[..., 1:, :-1] +\n weight[..., :-1, 1:] + weight[..., :-1, :-1]) * 0.25\n module.weight = weight\n\n\n@MODELS.register_module()\nclass EqualizedLRLinearModule(nn.Linear):\n r\"\"\"Equalized LR LinearModule.\n\n In this module, we adopt equalized lr in ``nn.Linear``. The equalized\n learning rate is proposed in:\n Progressive Growing of GANs for Improved Quality, Stability, and Variation\n\n Note that, the initialization of ``self.weight`` will be overwritten as\n :math:`\\mathcal{N}(0, 1)`.\n\n Args:\n equalized_lr_cfg (dict | None, optional): Config for ``EqualizedLR``.\n If ``None``, equalized learning rate is ignored. Defaults to\n dict(mode='fan_in').\n \"\"\"\n\n def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwargs):\n super().__init__(*args, **kwargs)\n self.with_equalized_lr = equalized_lr_cfg is not None\n if self.with_equalized_lr:\n self.lr_mul = equalized_lr_cfg.get('lr_mul', 1.)\n else:\n # In fact, lr_mul will only be used in EqualizedLR for\n # initialization\n self.lr_mul = 1.\n if self.with_equalized_lr:\n equalized_lr(self, **equalized_lr_cfg)\n self._init_linear_weights()\n\n def _init_linear_weights(self):\n \"\"\"Initialize linear weights as described in PGGAN.\"\"\"\n nn.init.normal_(self.weight, 0, 1. / self.lr_mul)\n if self.bias is not None:\n nn.init.constant_(self.bias, 0.)\n\n\n@MODELS.register_module()\nclass PGGANNoiseTo2DFeat(BaseModule):\n\n def __init__(self,\n noise_size,\n out_channels,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='PixelNorm'),\n normalize_latent=True,\n order=('linear', 'act', 'norm')):\n super().__init__()\n self.noise_size = noise_size\n self.out_channels = out_channels\n self.normalize_latent = normalize_latent\n self.with_activation = act_cfg is not None\n self.with_norm = norm_cfg is not None\n self.order = order\n assert len(order) == 3 and set(order) == set(['linear', 'act', 'norm'])\n\n # w/o bias, because the bias is added after reshaping the tensor to\n # 2D feature\n self.linear = EqualizedLRLinearModule(\n noise_size,\n out_channels * 16,\n equalized_lr_cfg=dict(gain=np.sqrt(2) / 4),\n bias=False)\n\n if self.with_activation:\n self.activation = MODELS.build(act_cfg)\n\n # add bias for reshaped 2D feature.\n self.register_parameter(\n 'bias', nn.Parameter(torch.zeros(1, out_channels, 1, 1)))\n\n if self.with_norm:\n _, self.norm = build_norm_layer(norm_cfg, out_channels)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input noise tensor with shape (n, c).\n\n Returns:\n Tensor: Forward results with shape (n, c, 4, 4).\n \"\"\"\n assert x.ndim == 2\n if self.normalize_latent:\n x = pixel_norm(x)\n for order in self.order:\n if order == 'linear':\n x = self.linear(x)\n # [n, c, 4, 4]\n x = torch.reshape(x, (-1, self.out_channels, 4, 4))\n x = x + self.bias\n elif order == 'act' and self.with_activation:\n x = self.activation(x)\n elif order == 'norm' and self.with_norm:\n x = self.norm(x)\n\n return x\n\n\nclass PGGANDecisionHead(BaseModule):\n\n def __init__(self,\n in_channels,\n mid_channels,\n out_channels,\n bias=True,\n equalized_lr_cfg=dict(gain=1),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act=None):\n super().__init__()\n self.in_channels = in_channels\n self.mid_channels = mid_channels\n self.out_channels = out_channels\n self.with_activation = act_cfg is not None\n self.with_out_activation = out_act is not None\n\n # setup linear layers\n # dirty code for supporting default mode in PGGAN\n if equalized_lr_cfg:\n equalized_lr_cfg_ = dict(gain=2**0.5)\n else:\n equalized_lr_cfg_ = None\n self.linear0 = EqualizedLRLinearModule(\n self.in_channels,\n self.mid_channels,\n bias=bias,\n equalized_lr_cfg=equalized_lr_cfg_)\n self.linear1 = EqualizedLRLinearModule(\n self.mid_channels,\n self.out_channels,\n bias=bias,\n equalized_lr_cfg=equalized_lr_cfg)\n\n # setup activation layers\n if self.with_activation:\n self.activation = MODELS.build(act_cfg)\n\n if self.with_out_activation:\n self.out_activation = MODELS.build(out_act)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n if x.ndim > 2:\n x = torch.reshape(x, (x.shape[0], -1))\n\n x = self.linear0(x)\n if self.with_activation:\n x = self.activation(x)\n\n x = self.linear1(x)\n if self.with_out_activation:\n x = self.out_activation(x)\n\n return x\n\n\n@MODELS.register_module()\nclass MiniBatchStddevLayer(BaseModule):\n \"\"\"Minibatch standard deviation.\n\n Args:\n group_size (int, optional): The size of groups in batch dimension.\n Defaults to 4.\n eps (float, optional): Epsilon value to avoid computation error.\n Defaults to 1e-8.\n gather_all_batch (bool, optional): Whether gather batch from all GPUs.\n Defaults to False.\n \"\"\"\n\n def __init__(self, group_size=4, eps=1e-8, gather_all_batch=False):\n super().__init__()\n self.group_size = group_size\n self.eps = eps\n self.gather_all_batch = gather_all_batch\n if self.gather_all_batch:\n assert torch.distributed.is_initialized(\n ), 'Only in distributed training can the tensors be all gathered.'\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n if self.gather_all_batch:\n x = torch.cat(AllGatherLayer.apply(x), dim=0)\n\n # batch size should be smaller than or equal to group size. Otherwise,\n # batch size should be divisible by the group size.\n assert x.shape[\n 0] <= self.group_size or x.shape[0] % self.group_size == 0, (\n 'Batch size be smaller than or equal '\n 'to group size. Otherwise,'\n ' batch size should be divisible by the group size.'\n f'But got batch size {x.shape[0]},'\n f' group size {self.group_size}')\n n, c, h, w = x.shape\n group_size = min(n, self.group_size)\n # [G, M, C, H, W]\n y = torch.reshape(x, (group_size, -1, c, h, w))\n # [G, M, C, H, W]\n y = y - y.mean(dim=0, keepdim=True)\n # In pt>=1.7, you can just use `.square()` function.\n # [M, C, H, W]\n y = y.pow(2).mean(dim=0, keepdim=False)\n y = torch.sqrt(y + self.eps)\n # [M, 1, 1, 1]\n y = y.mean(dim=(1, 2, 3), keepdim=True)\n y = y.repeat(group_size, 1, h, w)\n return torch.cat([x, y], dim=1)\n" }, { "path": "mmagic/models/editors/pix2pix/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .pix2pix import Pix2Pix\nfrom .pix2pix_generator import UnetGenerator\n\n__all__ = ['Pix2Pix', 'UnetGenerator']\n" }, { "path": "mmagic/models/editors/pix2pix/pix2pix.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ...base_models import BaseTranslationModel\nfrom ...utils import set_requires_grad\n\n\n@MODELS.register_module()\nclass Pix2Pix(BaseTranslationModel):\n \"\"\"Pix2Pix model for paired image-to-image translation.\n\n Ref:\n Image-to-Image Translation with Conditional Adversarial Networks\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.pixel_loss_weight = self.loss_config.get('pixel_loss_weight', 100)\n\n def forward_test(self, img, target_domain, **kwargs):\n \"\"\"Forward function for testing.\n\n Args:\n img (tensor): Input image tensor.\n target_domain (str): Target domain of output image.\n kwargs (dict): Other arguments.\n\n Returns:\n dict: Forward results.\n \"\"\"\n # This is a trick for Pix2Pix\n # ref: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/e1bdf46198662b0f4d0b318e24568205ec4d7aee/test.py#L54 # noqa\n self.train()\n target = self.translation(img, target_domain=target_domain, **kwargs)\n results = dict(source=img, target=target)\n return results\n\n def _get_disc_loss(self, outputs):\n \"\"\"Get the loss of discriminator.\n\n Args:\n outputs (dict): A dict of output.\n\n Returns:\n Tuple: Loss and a dict of log of loss terms.\n \"\"\"\n # GAN loss for the discriminator\n losses = dict()\n\n discriminators = self.get_module(self.discriminators)\n target_domain = self._default_domain\n source_domain = self.get_other_domains(target_domain)[0]\n fake_ab = torch.cat((outputs[f'real_{source_domain}'],\n outputs[f'fake_{target_domain}']), 1)\n fake_pred = discriminators[target_domain](fake_ab.detach())\n losses['loss_gan_d_fake'] = F.binary_cross_entropy_with_logits(\n fake_pred, 0. * torch.ones_like(fake_pred))\n real_ab = torch.cat((outputs[f'real_{source_domain}'],\n outputs[f'real_{target_domain}']), 1)\n real_pred = discriminators[target_domain](real_ab)\n losses['loss_gan_d_real'] = F.binary_cross_entropy_with_logits(\n real_pred, 1. * torch.ones_like(real_pred))\n\n loss_d, log_vars_d = self.parse_losses(losses)\n loss_d *= 0.5\n\n return loss_d, log_vars_d\n\n def _get_gen_loss(self, outputs):\n \"\"\"Get the loss of generator.\n\n Args:\n outputs (dict): A dict of output.\n\n Returns:\n Tuple: Loss and a dict of log of loss terms.\n \"\"\"\n\n target_domain = self._default_domain\n source_domain = self.get_other_domains(target_domain)[0]\n losses = dict()\n\n discriminators = self.get_module(self.discriminators)\n # GAN loss for the generator\n fake_ab = torch.cat((outputs[f'real_{source_domain}'],\n outputs[f'fake_{target_domain}']), 1)\n fake_pred = discriminators[target_domain](fake_ab)\n losses['loss_gan_g'] = F.binary_cross_entropy_with_logits(\n fake_pred, 1. * torch.ones_like(fake_pred))\n\n # L1 loss for generator\n losses['loss_pixel'] = self.pixel_loss_weight * F.l1_loss(\n outputs[f'real_{target_domain}'],\n outputs[f'fake_{target_domain}'],\n reduce='mean')\n\n loss_g, log_vars_g = self.parse_losses(losses)\n return loss_g, log_vars_g\n\n def train_step(self, data, optim_wrapper=None):\n \"\"\"Training step function.\n\n Args:\n data_batch (dict): Dict of the input data batch.\n optimizer (dict[torch.optim.Optimizer]): Dict of optimizers for\n the generator and discriminator.\n ddp_reducer (:obj:`Reducer` | None, optional): Reducer from ddp.\n It is used to prepare for ``backward()`` in ddp. Defaults to\n None.\n running_status (dict | None, optional): Contains necessary basic\n information for training, e.g., iteration number. Defaults to\n None.\n\n Returns:\n dict: Dict of loss, information for logger, the number of samples\\\n and results for visualization.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n inputs_dict = data['inputs']\n\n disc_optimizer_wrapper = optim_wrapper['discriminators']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n target_domain = self._default_domain\n source_domain = self.get_other_domains(self._default_domain)[0]\n source_image = inputs_dict[f'img_{source_domain}']\n target_image = inputs_dict[f'img_{target_domain}']\n\n # forward generator\n outputs = dict()\n with disc_optimizer_wrapper.optim_context(self.discriminators):\n results = self(\n source_image,\n target_domain=self._default_domain,\n test_mode=False)\n outputs[f'real_{source_domain}'] = results['source']\n outputs[f'fake_{target_domain}'] = results['target']\n outputs[f'real_{target_domain}'] = target_image\n log_vars = dict()\n\n # discriminator\n set_requires_grad(self.discriminators, True)\n # optimize\n loss_d, log_vars_d = self._get_disc_loss(outputs)\n disc_optimizer_wrapper.update_params(loss_d)\n log_vars.update(log_vars_d)\n\n # generator, no updates to discriminator parameters.\n gen_optimizer_wrapper = optim_wrapper['generators']\n if ((curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0\n and curr_iter >= self.disc_init_steps):\n set_requires_grad(self.discriminators, False)\n # optimize\n with gen_optimizer_wrapper.optim_context(self.generators):\n loss_g, log_vars_g = self._get_gen_loss(outputs)\n gen_optimizer_wrapper.update_params(loss_g)\n log_vars.update(log_vars_g)\n\n return log_vars\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n List[DataSample]: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n target_domain = self._reachable_domains[0]\n source_domain = self.get_other_domains(target_domain)[0]\n outputs = self.forward_test(\n inputs_dict[f'img_{source_domain}'], target_domain=target_domain)\n\n batch_sample_list = []\n num_batches = next(iter(outputs.values())).shape[0]\n for idx in range(num_batches):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n target = outputs['target'][idx]\n target = self.data_preprocessor.destruct(target, data_samples[idx],\n f'img_{target_domain}')\n setattr(gen_sample, f'fake_{target_domain}', target)\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n List[DataSample]: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n target_domain = self._reachable_domains[0]\n source_domain = self.get_other_domains(target_domain)[0]\n outputs = self.forward_test(\n inputs_dict[f'img_{source_domain}'], target_domain=target_domain)\n\n batch_sample_list = []\n if data_samples:\n data_samples = data_samples.split()\n num_batches = next(iter(outputs.values())).shape[0]\n for idx in range(num_batches):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n target = outputs['target'][idx]\n target = self.data_preprocessor.destruct(target, data_samples[idx],\n f'img_{target_domain}')\n gen_sample.set_tensor_data({f'fake_{target_domain}': target})\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n" }, { "path": "mmagic/models/editors/pix2pix/pix2pix_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ...utils import generation_init_weights\nfrom .pix2pix_modules import UnetSkipConnectionBlock\n\n\n@MODELS.register_module()\nclass UnetGenerator(BaseModule):\n \"\"\"Construct the Unet-based generator from the innermost layer to the\n outermost layer, which is a recursive process.\n\n Args:\n in_channels (int): Number of channels in input images.\n out_channels (int): Number of channels in output images.\n num_down (int): Number of downsamplings in Unet. If `num_down` is 8,\n the image with size 256x256 will become 1x1 at the bottleneck.\n Default: 8.\n base_channels (int): Number of channels at the last conv layer.\n Default: 64.\n norm_cfg (dict): Config dict to build norm layer. Default:\n `dict(type='BN')`.\n use_dropout (bool): Whether to use dropout layers. Default: False.\n init_cfg (dict): Config dict for initialization.\n `type`: The name of our initialization method. Default: 'normal'.\n `gain`: Scaling factor for normal, xavier and orthogonal.\n Default: 0.02.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=False,\n init_cfg=dict(type='normal', gain=0.02)):\n super().__init__(init_cfg=init_cfg)\n # We use norm layers in the unet generator.\n assert isinstance(norm_cfg, dict), (\"'norm_cfg' should be dict, but\"\n f'got {type(norm_cfg)}')\n assert 'type' in norm_cfg, \"'norm_cfg' must have key 'type'\"\n\n # add the innermost layer\n unet_block = UnetSkipConnectionBlock(\n base_channels * 8,\n base_channels * 8,\n in_channels=None,\n submodule=None,\n norm_cfg=norm_cfg,\n is_innermost=True)\n # add intermediate layers with base_channels * 8 filters\n for _ in range(num_down - 5):\n unet_block = UnetSkipConnectionBlock(\n base_channels * 8,\n base_channels * 8,\n in_channels=None,\n submodule=unet_block,\n norm_cfg=norm_cfg,\n use_dropout=use_dropout)\n # gradually reduce the number of filters\n # from base_channels * 8 to base_channels\n unet_block = UnetSkipConnectionBlock(\n base_channels * 4,\n base_channels * 8,\n in_channels=None,\n submodule=unet_block,\n norm_cfg=norm_cfg)\n unet_block = UnetSkipConnectionBlock(\n base_channels * 2,\n base_channels * 4,\n in_channels=None,\n submodule=unet_block,\n norm_cfg=norm_cfg)\n unet_block = UnetSkipConnectionBlock(\n base_channels,\n base_channels * 2,\n in_channels=None,\n submodule=unet_block,\n norm_cfg=norm_cfg)\n # add the outermost layer\n self.model = UnetSkipConnectionBlock(\n out_channels,\n base_channels,\n in_channels=in_channels,\n submodule=unet_block,\n is_outermost=True,\n norm_cfg=norm_cfg)\n\n self.init_type = 'normal' if init_cfg is None else init_cfg.get(\n 'type', 'normal')\n self.init_gain = 0.02 if init_cfg is None else init_cfg.get(\n 'gain', 0.02)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.model(x)\n\n def init_weights(self):\n \"\"\"Initialize weights for the model.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Default: None.\n strict (bool, optional): Whether to allow different params for the\n model and checkpoint. Default: True.\n \"\"\"\n if self.init_cfg is not None and self.init_cfg['type'] == 'Pretrained':\n super().init_weights()\n return\n generation_init_weights(\n self, init_type=self.init_type, init_gain=self.init_gain)\n self._is_init = True\n" }, { "path": "mmagic/models/editors/pix2pix/pix2pix_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\n\n\nclass UnetSkipConnectionBlock(nn.Module):\n \"\"\"Construct a Unet submodule with skip connections, with the following.\n\n structure: downsampling - `submodule` - upsampling.\n\n Args:\n outer_channels (int): Number of channels at the outer conv layer.\n inner_channels (int): Number of channels at the inner conv layer.\n in_channels (int): Number of channels in input images/features. If is\n None, equals to `outer_channels`. Default: None.\n submodule (UnetSkipConnectionBlock): Previously constructed submodule.\n Default: None.\n is_outermost (bool): Whether this module is the outermost module.\n Default: False.\n is_innermost (bool): Whether this module is the innermost module.\n Default: False.\n norm_cfg (dict): Config dict to build norm layer. Default:\n `dict(type='BN')`.\n use_dropout (bool): Whether to use dropout layers. Default: False.\n \"\"\"\n\n def __init__(self,\n outer_channels,\n inner_channels,\n in_channels=None,\n submodule=None,\n is_outermost=False,\n is_innermost=False,\n norm_cfg=dict(type='BN'),\n use_dropout=False):\n super().__init__()\n # cannot be both outermost and innermost\n assert not (is_outermost and is_innermost), (\n \"'is_outermost' and 'is_innermost' cannot be True\"\n 'at the same time.')\n self.is_outermost = is_outermost\n assert isinstance(norm_cfg, dict), (\"'norm_cfg' should be dict, but\"\n f'got {type(norm_cfg)}')\n assert 'type' in norm_cfg, \"'norm_cfg' must have key 'type'\"\n # We use norm layers in the unet skip connection block.\n # Only for IN, use bias since it does not have affine parameters.\n use_bias = norm_cfg['type'] == 'IN'\n\n kernel_size = 4\n stride = 2\n padding = 1\n if in_channels is None:\n in_channels = outer_channels\n down_conv_cfg = dict(type='Conv2d')\n down_norm_cfg = norm_cfg\n down_act_cfg = dict(type='LeakyReLU', negative_slope=0.2)\n up_conv_cfg = dict(type='Deconv')\n up_norm_cfg = norm_cfg\n up_act_cfg = dict(type='ReLU')\n up_in_channels = inner_channels * 2\n up_bias = use_bias\n middle = [submodule]\n upper = []\n\n if is_outermost:\n down_act_cfg = None\n down_norm_cfg = None\n up_bias = True\n up_norm_cfg = None\n upper = [nn.Tanh()]\n elif is_innermost:\n down_norm_cfg = None\n up_in_channels = inner_channels\n middle = []\n else:\n upper = [nn.Dropout(0.5)] if use_dropout else []\n\n down = [\n ConvModule(\n in_channels=in_channels,\n out_channels=inner_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=use_bias,\n conv_cfg=down_conv_cfg,\n norm_cfg=down_norm_cfg,\n act_cfg=down_act_cfg,\n order=('act', 'conv', 'norm'))\n ]\n up = [\n ConvModule(\n in_channels=up_in_channels,\n out_channels=outer_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=up_bias,\n conv_cfg=up_conv_cfg,\n norm_cfg=up_norm_cfg,\n act_cfg=up_act_cfg,\n order=('act', 'conv', 'norm'))\n ]\n\n model = down + middle + up + upper\n\n self.model = nn.Sequential(*model)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n if self.is_outermost:\n return self.model(x)\n\n # add skip connections\n return torch.cat([x, self.model(x)], 1)\n" }, { "path": "mmagic/models/editors/plain/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .plain_decoder import PlainDecoder\nfrom .plain_refiner import PlainRefiner\n\n__all__ = ['PlainRefiner', 'PlainDecoder']\n" }, { "path": "mmagic/models/editors/plain/plain_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import xavier_init\nfrom torch.autograd import Function\nfrom torch.nn.modules.pooling import _MaxUnpoolNd\nfrom torch.nn.modules.utils import _pair\n\nfrom mmagic.registry import MODELS\n\n\nclass MaxUnpool2dop(Function):\n \"\"\"We warp the `torch.nn.functional.max_unpool2d` with an extra `symbolic`\n method, which is needed while exporting to ONNX.\n\n Users should not call this function directly.\n \"\"\"\n\n @staticmethod\n def forward(ctx, input, indices, kernel_size, stride, padding,\n output_size):\n \"\"\"Forward function of MaxUnpool2dop.\n\n Args:\n input (Tensor): Tensor needed to upsample.\n indices (Tensor): Indices output of the previous MaxPool.\n kernel_size (Tuple): Size of the max pooling window.\n stride (Tuple): Stride of the max pooling window.\n padding (Tuple): Padding that was added to the input.\n output_size (List or Tuple): The shape of output tensor.\n Returns:\n Tensor: Output tensor.\n \"\"\"\n return F.max_unpool2d(input, indices, kernel_size, stride, padding,\n output_size)\n\n @staticmethod\n def symbolic(g, input, indices, kernel_size, stride, padding, output_size):\n \"\"\"This is the function to define the module of MaxUnpool.\n\n Args:\n g (_type_): _description_\n input (Tensor): Tensor needed to upsample.\n indices (Tensor): Indices output of the previous MaxPool.\n kernel_size (int): Size of the max pooling window.\n stride (Tuple): Stride of the max pooling window.\n padding (Tuple): Padding that was added to the input.\n output_size (List or Tuple): The shape of output tensor.\n\n Returns:\n _type_: _description_\n \"\"\"\n # get shape\n input_shape = g.op('Shape', input)\n const_0 = g.op('Constant', value_t=torch.tensor(0))\n const_1 = g.op('Constant', value_t=torch.tensor(1))\n batch_size = g.op('Gather', input_shape, const_0, axis_i=0)\n channel = g.op('Gather', input_shape, const_1, axis_i=0)\n\n # height = (height - 1) * stride + kernel_size\n height = g.op(\n 'Gather',\n input_shape,\n g.op('Constant', value_t=torch.tensor(2)),\n axis_i=0)\n height = g.op('Sub', height, const_1)\n height = g.op('Mul', height,\n g.op('Constant', value_t=torch.tensor(stride[1])))\n height = g.op('Add', height,\n g.op('Constant', value_t=torch.tensor(kernel_size[1])))\n\n # width = (width - 1) * stride + kernel_size\n width = g.op(\n 'Gather',\n input_shape,\n g.op('Constant', value_t=torch.tensor(3)),\n axis_i=0)\n width = g.op('Sub', width, const_1)\n width = g.op('Mul', width,\n g.op('Constant', value_t=torch.tensor(stride[0])))\n width = g.op('Add', width,\n g.op('Constant', value_t=torch.tensor(kernel_size[0])))\n\n # step of channel\n channel_step = g.op('Mul', height, width)\n # step of batch\n batch_step = g.op('Mul', channel_step, channel)\n\n # channel offset\n range_channel = g.op('Range', const_0, channel, const_1)\n range_channel = g.op(\n 'Reshape', range_channel,\n g.op('Constant', value_t=torch.tensor([1, -1, 1, 1])))\n range_channel = g.op('Mul', range_channel, channel_step)\n range_channel = g.op('Cast', range_channel, to_i=7) # 7 is int64\n\n # batch offset\n range_batch = g.op('Range', const_0, batch_size, const_1)\n range_batch = g.op(\n 'Reshape', range_batch,\n g.op('Constant', value_t=torch.tensor([-1, 1, 1, 1])))\n range_batch = g.op('Mul', range_batch, batch_step)\n range_batch = g.op('Cast', range_batch, to_i=7) # 7 is int64\n\n # update indices\n indices = g.op('Add', indices, range_channel)\n indices = g.op('Add', indices, range_batch)\n\n return g.op(\n 'MaxUnpool',\n input,\n indices,\n kernel_shape_i=kernel_size,\n strides_i=stride)\n\n\nclass MaxUnpool2d(_MaxUnpoolNd):\n \"\"\"This module is modified from Pytorch `MaxUnpool2d` module.\n\n Args:\n kernel_size (int or tuple): Size of the max pooling window.\n stride (int or tuple): Stride of the max pooling window.\n Default: None (It is set to `kernel_size` by default).\n padding (int or tuple): Padding that is added to the input.\n Default: 0.\n \"\"\"\n\n def __init__(self, kernel_size, stride=None, padding=0):\n super(MaxUnpool2d, self).__init__()\n self.kernel_size = _pair(kernel_size)\n self.stride = _pair(stride or kernel_size)\n self.padding = _pair(padding)\n\n def forward(self, input, indices, output_size=None):\n \"\"\"Forward function of MaxUnpool2d.\n\n Args:\n input (Tensor): Tensor needed to upsample.\n indices (Tensor): Indices output of the previous MaxPool.\n output_size (List or Tuple): The shape of output tensor.\n Default: None.\n Returns:\n Tensor: Output tensor.\n \"\"\"\n return MaxUnpool2dop.apply(input, indices, self.kernel_size,\n self.stride, self.padding, output_size)\n\n\n@MODELS.register_module()\nclass PlainDecoder(BaseModule):\n \"\"\"Simple decoder from Deep Image Matting.\n\n Args:\n in_channels (int): Channel num of input features.\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self, in_channels, init_cfg: Optional[dict] = None):\n super().__init__(init_cfg=init_cfg)\n\n self.deconv6_1 = nn.Conv2d(in_channels, 512, kernel_size=1)\n self.deconv5_1 = nn.Conv2d(512, 512, kernel_size=5, padding=2)\n self.deconv4_1 = nn.Conv2d(512, 256, kernel_size=5, padding=2)\n self.deconv3_1 = nn.Conv2d(256, 128, kernel_size=5, padding=2)\n self.deconv2_1 = nn.Conv2d(128, 64, kernel_size=5, padding=2)\n self.deconv1_1 = nn.Conv2d(64, 64, kernel_size=5, padding=2)\n\n self.deconv1 = nn.Conv2d(64, 1, kernel_size=5, padding=2)\n\n self.relu = nn.ReLU(inplace=True)\n self.max_unpool2d_for_onnx = MaxUnpool2d(kernel_size=2, stride=2)\n self.max_unpool2d = nn.MaxUnpool2d(kernel_size=2, stride=2)\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n if self.init_cfg is not None:\n super().init_weights()\n else:\n # Default initialization\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m)\n\n def forward(self, inputs):\n \"\"\"Forward function of PlainDecoder.\n\n Args:\n inputs (dict): Output dictionary of the VGG encoder containing:\n\n - out (Tensor): Output of the VGG encoder.\n - max_idx_1 (Tensor): Index of the first maxpooling layer in the\n VGG encoder.\n - max_idx_2 (Tensor): Index of the second maxpooling layer in the\n VGG encoder.\n - max_idx_3 (Tensor): Index of the third maxpooling layer in the\n VGG encoder.\n - max_idx_4 (Tensor): Index of the fourth maxpooling layer in the\n VGG encoder.\n - max_idx_5 (Tensor): Index of the fifth maxpooling layer in the\n VGG encoder.\n\n Returns:\n Tensor: Output tensor.\n \"\"\"\n max_idx_1 = inputs['max_idx_1']\n max_idx_2 = inputs['max_idx_2']\n max_idx_3 = inputs['max_idx_3']\n max_idx_4 = inputs['max_idx_4']\n max_idx_5 = inputs['max_idx_5']\n x = inputs['out']\n\n max_unpool2d = self.max_unpool2d\n if torch.onnx.is_in_onnx_export():\n max_unpool2d = self.max_unpool2d_for_onnx\n\n out = self.relu(self.deconv6_1(x))\n out = max_unpool2d(out, max_idx_5)\n\n out = self.relu(self.deconv5_1(out))\n out = max_unpool2d(out, max_idx_4)\n\n out = self.relu(self.deconv4_1(out))\n out = max_unpool2d(out, max_idx_3)\n\n out = self.relu(self.deconv3_1(out))\n out = max_unpool2d(out, max_idx_2)\n\n out = self.relu(self.deconv2_1(out))\n out = max_unpool2d(out, max_idx_1)\n\n out = self.relu(self.deconv1_1(out))\n raw_alpha = self.deconv1(out)\n return raw_alpha\n" }, { "path": "mmagic/models/editors/plain/plain_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import xavier_init\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass PlainRefiner(BaseModule):\n \"\"\"Simple refiner from Deep Image Matting.\n\n Args:\n conv_channels (int): Number of channels produced by the three main\n convolutional layer. Default: 64.\n pretrained (str): Name of pretrained model. Default: None.\n \"\"\"\n\n def __init__(self, conv_channels=64, init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n # assert pretrained is None, 'pretrained not supported yet'\n\n self.refine_conv1 = nn.Conv2d(\n 4, conv_channels, kernel_size=3, padding=1)\n self.refine_conv2 = nn.Conv2d(\n conv_channels, conv_channels, kernel_size=3, padding=1)\n self.refine_conv3 = nn.Conv2d(\n conv_channels, conv_channels, kernel_size=3, padding=1)\n self.refine_pred = nn.Conv2d(\n conv_channels, 1, kernel_size=3, padding=1)\n\n self.relu = nn.ReLU(inplace=True)\n\n def init_weights(self):\n \"\"\"Init weights for the module.\"\"\"\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m)\n\n def forward(self, x, raw_alpha):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): The input feature map of refiner.\n raw_alpha (Tensor): The raw predicted alpha matte.\n\n Returns:\n Tensor: The refined alpha matte.\n \"\"\"\n out = self.relu(self.refine_conv1(x))\n out = self.relu(self.refine_conv2(out))\n out = self.relu(self.refine_conv3(out))\n raw_refine = self.refine_pred(out)\n pred_refine = torch.sigmoid(raw_alpha + raw_refine)\n return pred_refine\n" }, { "path": "mmagic/models/editors/rdn/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .rdn_net import RDB, RDNNet\n\n__all__ = ['RDB', 'RDNNet']\n" }, { "path": "mmagic/models/editors/rdn/rdn_net.py", "content": "import torch\nfrom mmengine.model import BaseModule\nfrom torch import nn\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass RDNNet(BaseModule):\n \"\"\"RDN model for single image super-resolution.\n\n Paper: Residual Dense Network for Image Super-Resolution\n\n Adapted from 'https://github.com/yjn870/RDN-pytorch.git'\n 'RDN-pytorch/blob/master/models.py'\n Copyright (c) 2021, JaeYun Yeo, under MIT License.\n\n Most of the implementation follows the implementation in:\n 'https://github.com/sanghyun-son/EDSR-PyTorch.git'\n 'EDSR-PyTorch/blob/master/src/model/rdn.py'\n Copyright (c) 2017, sanghyun-son, under MIT license.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_blocks (int): Block number in the trunk network. Default: 16.\n upscale_factor (int): Upsampling factor. Support 2^n and 3.\n Default: 4.\n num_layer (int): Layer number in the Residual Dense Block.\n Default: 8.\n channel_growth(int): Channels growth in each layer of RDB.\n Default: 64.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4,\n num_layers=8,\n channel_growth=64):\n\n super().__init__()\n self.mid_channels = mid_channels\n self.channel_growth = channel_growth\n self.num_blocks = num_blocks\n self.num_layers = num_layers\n\n # shallow feature extraction\n self.sfe1 = nn.Conv2d(\n in_channels, mid_channels, kernel_size=3, padding=3 // 2)\n self.sfe2 = nn.Conv2d(\n mid_channels, mid_channels, kernel_size=3, padding=3 // 2)\n\n # residual dense blocks\n self.rdbs = nn.ModuleList()\n for _ in range(self.num_blocks):\n self.rdbs.append(\n RDB(self.mid_channels, self.channel_growth, self.num_layers))\n\n # global feature fusion\n self.gff = nn.Sequential(\n nn.Conv2d(\n self.mid_channels * self.num_blocks,\n self.mid_channels,\n kernel_size=1),\n nn.Conv2d(\n self.mid_channels,\n self.mid_channels,\n kernel_size=3,\n padding=3 // 2))\n\n # up-sampling\n assert 2 <= upscale_factor <= 4\n if upscale_factor == 2 or upscale_factor == 4:\n self.upscale = []\n for _ in range(upscale_factor // 2):\n self.upscale.extend([\n nn.Conv2d(\n self.mid_channels,\n self.mid_channels * (2**2),\n kernel_size=3,\n padding=3 // 2),\n nn.PixelShuffle(2)\n ])\n self.upscale = nn.Sequential(*self.upscale)\n else:\n self.upscale = nn.Sequential(\n nn.Conv2d(\n self.mid_channels,\n self.mid_channels * (upscale_factor**2),\n kernel_size=3,\n padding=3 // 2), nn.PixelShuffle(upscale_factor))\n\n self.output = nn.Conv2d(\n self.mid_channels, out_channels, kernel_size=3, padding=3 // 2)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n sfe1 = self.sfe1(x)\n sfe2 = self.sfe2(sfe1)\n\n x = sfe2\n local_features = []\n for i in range(self.num_blocks):\n x = self.rdbs[i](x)\n local_features.append(x)\n\n x = self.gff(torch.cat(local_features, 1)) + sfe1\n # global residual learning\n x = self.upscale(x)\n x = self.output(x)\n return x\n\n\nclass DenseLayer(BaseModule):\n \"\"\"Dense layer.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n \"\"\"\n\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.conv = nn.Conv2d(\n in_channels, out_channels, kernel_size=3, padding=3 // 2)\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c_in, h, w).\n\n Returns:\n Tensor: Forward results, tensor with shape (n, c_in+c_out, h, w).\n \"\"\"\n return torch.cat([x, self.relu(self.conv(x))], 1)\n\n\nclass RDB(BaseModule):\n \"\"\"Residual Dense Block of Residual Dense Network.\n\n Args:\n in_channels (int): Channel number of inputs.\n channel_growth (int): Channels growth in each layer.\n num_layers (int): Layer number in the Residual Dense Block.\n \"\"\"\n\n def __init__(self, in_channels, channel_growth, num_layers):\n super().__init__()\n self.layers = nn.Sequential(*[\n DenseLayer(in_channels + channel_growth * i, channel_growth)\n for i in range(num_layers)\n ])\n\n # local feature fusion\n self.lff = nn.Conv2d(\n in_channels + channel_growth * num_layers,\n in_channels,\n kernel_size=1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return x + self.lff(self.layers(x)) # local residual learning\n" }, { "path": "mmagic/models/editors/real_basicvsr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .real_basicvsr import RealBasicVSR\nfrom .real_basicvsr_net import RealBasicVSRNet\n\n__all__ = ['RealBasicVSR', 'RealBasicVSRNet']\n" }, { "path": "mmagic/models/editors/real_basicvsr/real_basicvsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.models.utils import set_requires_grad\nfrom mmagic.registry import MODELS\nfrom ..real_esrgan import RealESRGAN\n\n\n@MODELS.register_module()\nclass RealBasicVSR(RealESRGAN):\n \"\"\"RealBasicVSR model for real-world video super-resolution.\n\n Ref:\n Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv\n\n Args:\n generator (dict): Config for the generator.\n discriminator (dict, optional): Config for the discriminator.\n Default: None.\n gan_loss (dict, optional): Config for the gan loss.\n Note that the loss weight in gan loss is only for the generator.\n pixel_loss (dict, optional): Config for the pixel loss. Default: None.\n cleaning_loss (dict, optional): Config for the image cleaning loss.\n Default: None.\n perceptual_loss (dict, optional): Config for the perceptual loss.\n Default: None.\n is_use_sharpened_gt_in_pixel (bool, optional): Whether to use the image\n sharpened by unsharp masking as the GT for pixel loss.\n Default: False.\n is_use_sharpened_gt_in_percep (bool, optional): Whether to use the\n image sharpened by unsharp masking as the GT for perceptual loss.\n Default: False.\n is_use_sharpened_gt_in_gan (bool, optional): Whether to use the\n image sharpened by unsharp masking as the GT for adversarial loss.\n Default: False.\n train_cfg (dict): Config for training. Default: None.\n You may change the training of gan by setting:\n `disc_steps`: how many discriminator updates after one generate\n update;\n `disc_init_steps`: how many discriminator updates at the start of\n the training.\n These two keys are useful when training with WGAN.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n discriminator=None,\n gan_loss=None,\n pixel_loss=None,\n cleaning_loss=None,\n perceptual_loss=None,\n is_use_sharpened_gt_in_pixel=False,\n is_use_sharpened_gt_in_percep=False,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=False,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n gan_loss=gan_loss,\n pixel_loss=pixel_loss,\n perceptual_loss=perceptual_loss,\n is_use_sharpened_gt_in_pixel=is_use_sharpened_gt_in_pixel,\n is_use_sharpened_gt_in_percep=is_use_sharpened_gt_in_percep,\n is_use_sharpened_gt_in_gan=is_use_sharpened_gt_in_gan,\n is_use_ema=is_use_ema,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.cleaning_loss = MODELS.build(\n cleaning_loss) if cleaning_loss else None\n\n def extract_gt_data(self, data_samples):\n \"\"\"extract gt data from data samples.\n\n Args:\n data_samples (list): List of DataSample.\n\n Returns:\n Tensor: Extract gt data.\n \"\"\"\n\n gt_pixel, gt_percep, gt_gan = super().extract_gt_data(data_samples)\n n, t, c, h, w = gt_pixel.size()\n gt_pixel = gt_pixel.view(-1, c, h, w)\n gt_percep = gt_percep.view(-1, c, h, w)\n gt_gan = gt_gan.view(-1, c, h, w)\n\n if self.cleaning_loss:\n gt_clean = gt_pixel.view(-1, c, h, w)\n gt_clean = F.interpolate(\n gt_clean,\n scale_factor=0.25,\n mode='area',\n recompute_scale_factor=False)\n gt_clean = gt_clean.view(n, t, c, h // 4, w // 4)\n else:\n gt_clean = None\n\n return gt_pixel, gt_percep, gt_gan, gt_clean\n\n def g_step(self, batch_outputs, batch_gt_data):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tuple[Tensor]): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n gt_pixel, gt_percep, gt_gan, gt_clean = batch_gt_data\n fake_g_output, fake_g_lq = batch_outputs\n fake_g_output = fake_g_output.view(gt_pixel.shape)\n\n losses = super().g_step(\n batch_outputs=fake_g_output,\n batch_gt_data=(gt_pixel, gt_percep, gt_gan))\n\n if self.cleaning_loss:\n losses['loss_clean'] = self.cleaning_loss(fake_g_lq, gt_clean)\n\n return losses\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step of GAN-based method.\n\n Args:\n data (List[dict]): Data sampled from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance\n used to update model parameters.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n\n data = self.data_preprocessor(data, True)\n batch_inputs = data['inputs']\n data_samples = data['data_samples']\n batch_gt_data = self.extract_gt_data(data_samples)\n\n log_vars = dict()\n\n with g_optim_wrapper.optim_context(self):\n batch_outputs = self.forward_train(batch_inputs, data_samples)\n\n if self.if_run_g():\n set_requires_grad(self.discriminator, False)\n\n log_vars_d = self.g_step_with_optim(\n batch_outputs=batch_outputs,\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if self.if_run_d():\n set_requires_grad(self.discriminator, True)\n\n gt_pixel, gt_percep, gt_gan, gt_clean = batch_gt_data\n fake_g_output, fake_g_lq = batch_outputs\n fake_g_output = fake_g_output.view(gt_pixel.shape)\n\n for _ in range(self.disc_repeat):\n # detach before function call to resolve PyTorch2.0 compile bug\n log_vars_d = self.d_step_with_optim(\n batch_outputs=fake_g_output.detach(),\n batch_gt_data=(gt_pixel, gt_percep, gt_gan),\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if 'loss' in log_vars:\n log_vars.pop('loss')\n\n self.step_counter += 1\n\n return log_vars\n\n def forward_train(self, batch_inputs, data_samples=None):\n \"\"\"Forward Train.\n\n Run forward of generator with ``return_lqs=True``\n\n Args:\n batch_inputs (Tensor): Batch inputs.\n data_samples (List[DataSample]): Data samples of Editing.\n Default:None\n\n Returns:\n Tuple[Tensor]: Result of generator.\n (outputs, lqs)\n \"\"\"\n\n return self.generator(batch_inputs, return_lqs=True)\n" }, { "path": "mmagic/models/editors/real_basicvsr/real_basicvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ..basicvsr.basicvsr_net import BasicVSRNet, ResidualBlocksWithInputConv\n\n\n@MODELS.register_module()\nclass RealBasicVSRNet(BaseModule):\n \"\"\"RealBasicVSR network structure for real-world video super-resolution.\n\n Support only x4 upsampling.\n\n Paper:\n Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv\n\n Args:\n mid_channels (int, optional): Channel number of the intermediate\n features. Default: 64.\n num_propagation_blocks (int, optional): Number of residual blocks in\n each propagation branch. Default: 20.\n num_cleaning_blocks (int, optional): Number of residual blocks in the\n image cleaning module. Default: 20.\n dynamic_refine_thres (int, optional): Stop cleaning the images when\n the residue is smaller than this value. Default: 255.\n spynet_pretrained (str, optional): Pre-trained model path of SPyNet.\n Default: None.\n is_fix_cleaning (bool, optional): Whether to fix the weights of\n the image cleaning module during training. Default: False.\n is_sequential_cleaning (bool, optional): Whether to clean the images\n sequentially. This is used to save GPU memory, but the speed is\n slightly slower. Default: False.\n \"\"\"\n\n def __init__(self,\n mid_channels=64,\n num_propagation_blocks=20,\n num_cleaning_blocks=20,\n dynamic_refine_thres=255,\n spynet_pretrained=None,\n is_fix_cleaning=False,\n is_sequential_cleaning=False):\n\n super().__init__()\n\n self.dynamic_refine_thres = dynamic_refine_thres / 255.\n self.is_sequential_cleaning = is_sequential_cleaning\n\n # image cleaning module\n self.image_cleaning = nn.Sequential(\n ResidualBlocksWithInputConv(3, mid_channels, num_cleaning_blocks),\n nn.Conv2d(mid_channels, 3, 3, 1, 1, bias=True),\n )\n\n if is_fix_cleaning: # keep the weights of the cleaning module fixed\n self.image_cleaning.requires_grad_(False)\n\n # BasicVSR\n self.basicvsr = BasicVSRNet(mid_channels, num_propagation_blocks,\n spynet_pretrained)\n self.basicvsr.spynet.requires_grad_(False)\n\n def forward(self, lqs, return_lqs=False):\n \"\"\"Forward function for BasicVSR++.\n\n Args:\n lqs (tensor): Input low quality (LQ) sequence with\n shape (n, t, c, h, w).\n return_lqs (bool): Whether to return LQ sequence. Default: False.\n\n Returns:\n Tensor: Output HR sequence.\n \"\"\"\n n, t, c, h, w = lqs.size()\n\n for _ in range(0, 3): # at most 3 cleaning, determined empirically\n if self.is_sequential_cleaning:\n residues = []\n for i in range(0, t):\n residue_i = self.image_cleaning(lqs[:, i, :, :, :])\n lqs[:, i, :, :, :] += residue_i\n residues.append(residue_i)\n residues = torch.stack(residues, dim=1)\n else: # time -> batch, then apply cleaning at once\n lqs = lqs.view(-1, c, h, w)\n residues = self.image_cleaning(lqs)\n lqs = (lqs + residues).view(n, t, c, h, w)\n\n # determine whether to continue cleaning\n if torch.mean(torch.abs(residues)) < self.dynamic_refine_thres:\n break\n\n # Super-resolution (BasicVSR)\n outputs = self.basicvsr(lqs)\n\n if return_lqs:\n return outputs, lqs\n else:\n return outputs\n" }, { "path": "mmagic/models/editors/real_esrgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .real_esrgan import RealESRGAN\nfrom .unet_disc import UNetDiscriminatorWithSpectralNorm\n\n__all__ = [\n 'RealESRGAN',\n 'UNetDiscriminatorWithSpectralNorm',\n]\n" }, { "path": "mmagic/models/editors/real_esrgan/real_esrgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom ..srgan import SRGAN\n\n\n@MODELS.register_module()\nclass RealESRGAN(SRGAN):\n \"\"\"Real-ESRGAN model for single image super-resolution.\n\n Ref:\n Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure\n Synthetic Data, 2021.\n\n Note: generator_ema is realized in EMA_HOOK\n\n Args:\n generator (dict): Config for the generator.\n discriminator (dict, optional): Config for the discriminator.\n Default: None.\n gan_loss (dict, optional): Config for the gan loss.\n Note that the loss weight in gan loss is only for the generator.\n pixel_loss (dict, optional): Config for the pixel loss. Default: None.\n perceptual_loss (dict, optional): Config for the perceptual loss.\n Default: None.\n is_use_sharpened_gt_in_pixel (bool, optional): Whether to use the image\n sharpened by unsharp masking as the GT for pixel loss.\n Default: False.\n is_use_sharpened_gt_in_percep (bool, optional): Whether to use the\n image sharpened by unsharp masking as the GT for perceptual loss.\n Default: False.\n is_use_sharpened_gt_in_gan (bool, optional): Whether to use the\n image sharpened by unsharp masking as the GT for adversarial loss.\n Default: False.\n is_use_ema (bool, optional): When to apply exponential moving average\n on the network weights. Default: True.\n train_cfg (dict): Config for training. Default: None.\n You may change the training of gan by setting:\n `disc_steps`: how many discriminator updates after one generate\n update;\n `disc_init_steps`: how many discriminator updates at the start of\n the training.\n These two keys are useful when training with WGAN.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n discriminator=None,\n gan_loss=None,\n pixel_loss=None,\n perceptual_loss=None,\n is_use_sharpened_gt_in_pixel=False,\n is_use_sharpened_gt_in_percep=False,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=True,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n gan_loss=gan_loss,\n pixel_loss=pixel_loss,\n perceptual_loss=perceptual_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.is_use_sharpened_gt_in_pixel = is_use_sharpened_gt_in_pixel\n self.is_use_sharpened_gt_in_percep = is_use_sharpened_gt_in_percep\n self.is_use_sharpened_gt_in_gan = is_use_sharpened_gt_in_gan\n self.is_use_ema = is_use_ema\n\n if is_use_ema:\n self.generator_ema = deepcopy(self.generator)\n else:\n self.generator_ema = None\n\n if train_cfg is not None: # used for initializing from ema model\n self.start_iter = train_cfg.get('start_iter', -1)\n else:\n self.start_iter = -1\n\n def forward_tensor(self, inputs, data_samples=None, training=False):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n training (bool): Whether is training. Default: False.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n if training or not self.is_use_ema:\n feats = self.generator(inputs)\n else:\n feats = self.generator_ema(inputs)\n\n return feats\n\n def g_step(self, batch_outputs, batch_gt_data):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tuple[Tensor]): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n gt_pixel, gt_percep, _ = batch_gt_data\n losses = dict()\n\n # pix loss\n if self.pixel_loss:\n losses['loss_pix'] = self.pixel_loss(batch_outputs, gt_pixel)\n\n # perceptual loss\n if self.perceptual_loss:\n loss_percep, loss_style = self.perceptual_loss(\n batch_outputs, gt_percep)\n if loss_percep is not None:\n losses['loss_perceptual'] = loss_percep\n if loss_style is not None:\n losses['loss_style'] = loss_style\n\n # gan loss for generator\n if self.gan_loss and self.discriminator:\n fake_g_pred = self.discriminator(batch_outputs)\n losses['loss_gan'] = self.gan_loss(\n fake_g_pred, target_is_real=True, is_disc=False)\n\n return losses\n\n def d_step_real(self, batch_outputs, batch_gt_data: torch.Tensor):\n \"\"\"Real part of D step.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tuple[Tensor]): Batch GT data.\n\n Returns:\n Tensor: Real part of gan_loss for discriminator.\n \"\"\"\n\n _, _, gt_gan = batch_gt_data\n\n # real\n real_d_pred = self.discriminator(gt_gan)\n loss_d_real = self.gan_loss(\n real_d_pred, target_is_real=True, is_disc=True)\n\n return loss_d_real\n\n def d_step_fake(self, batch_outputs, batch_gt_data):\n \"\"\"Fake part of D step.\n\n Args:\n batch_outputs (Tensor): Output of generator.\n batch_gt_data (Tuple[Tensor]): Batch GT data.\n\n Returns:\n Tensor: Fake part of gan_loss for discriminator.\n \"\"\"\n\n # fake\n fake_d_pred = self.discriminator(batch_outputs.detach())\n loss_d_fake = self.gan_loss(\n fake_d_pred, target_is_real=False, is_disc=True)\n\n return loss_d_fake\n\n def extract_gt_data(self, data_samples):\n \"\"\"extract gt data from data samples.\n\n Args:\n data_samples (list): List of DataSample.\n\n Returns:\n Tensor: Extract gt data.\n \"\"\"\n\n gt = data_samples.gt_img\n gt_unsharp = data_samples.gt_unsharp / 255.\n\n gt_pixel, gt_percep, gt_gan = gt.clone(), gt.clone(), gt.clone()\n if self.is_use_sharpened_gt_in_pixel:\n gt_pixel = gt_unsharp.clone()\n if self.is_use_sharpened_gt_in_percep:\n gt_percep = gt_unsharp.clone()\n if self.is_use_sharpened_gt_in_gan:\n gt_gan = gt_unsharp.clone()\n\n return gt_pixel, gt_percep, gt_gan\n" }, { "path": "mmagic/models/editors/real_esrgan/unet_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass UNetDiscriminatorWithSpectralNorm(BaseModule):\n \"\"\"A U-Net discriminator with spectral normalization.\n\n Args:\n in_channels (int): Channel number of the input.\n mid_channels (int, optional): Channel number of the intermediate\n features. Default: 64.\n skip_connection (bool, optional): Whether to use skip connection.\n Default: True.\n \"\"\"\n\n def __init__(self, in_channels, mid_channels=64, skip_connection=True):\n\n super().__init__()\n\n self.skip_connection = skip_connection\n\n self.conv_0 = nn.Conv2d(\n in_channels, mid_channels, kernel_size=3, stride=1, padding=1)\n\n # downsample\n self.conv_1 = spectral_norm(\n nn.Conv2d(mid_channels, mid_channels * 2, 4, 2, 1, bias=False))\n self.conv_2 = spectral_norm(\n nn.Conv2d(mid_channels * 2, mid_channels * 4, 4, 2, 1, bias=False))\n self.conv_3 = spectral_norm(\n nn.Conv2d(mid_channels * 4, mid_channels * 8, 4, 2, 1, bias=False))\n\n # upsample\n self.conv_4 = spectral_norm(\n nn.Conv2d(mid_channels * 8, mid_channels * 4, 3, 1, 1, bias=False))\n self.conv_5 = spectral_norm(\n nn.Conv2d(mid_channels * 4, mid_channels * 2, 3, 1, 1, bias=False))\n self.conv_6 = spectral_norm(\n nn.Conv2d(mid_channels * 2, mid_channels, 3, 1, 1, bias=False))\n\n # final layers\n self.conv_7 = spectral_norm(\n nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=False))\n self.conv_8 = spectral_norm(\n nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=False))\n self.conv_9 = nn.Conv2d(mid_channels, 1, 3, 1, 1)\n\n self.upsample = nn.Upsample(\n scale_factor=2, mode='bilinear', align_corners=False)\n self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n\n def forward(self, img):\n \"\"\"Forward function.\n\n Args:\n img (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n feat_0 = self.lrelu(self.conv_0(img))\n\n # downsample\n feat_1 = self.lrelu(self.conv_1(feat_0))\n feat_2 = self.lrelu(self.conv_2(feat_1))\n feat_3 = self.lrelu(self.conv_3(feat_2))\n\n # upsample\n feat_3 = self.upsample(feat_3)\n feat_4 = self.lrelu(self.conv_4(feat_3))\n if self.skip_connection:\n feat_4 = feat_4 + feat_2\n\n feat_4 = self.upsample(feat_4)\n feat_5 = self.lrelu(self.conv_5(feat_4))\n if self.skip_connection:\n feat_5 = feat_5 + feat_1\n\n feat_5 = self.upsample(feat_5)\n feat_6 = self.lrelu(self.conv_6(feat_5))\n if self.skip_connection:\n feat_6 = feat_6 + feat_0\n\n # final layers\n out = self.lrelu(self.conv_7(feat_6))\n out = self.lrelu(self.conv_8(out))\n\n return self.conv_9(out)\n" }, { "path": "mmagic/models/editors/restormer/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .restormer_net import Restormer\n\n__all__ = ['Restormer']\n" }, { "path": "mmagic/models/editors/restormer/restormer_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport numbers\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\ndef to_3d(x):\n \"\"\"Reshape input tensor.\"\"\"\n return rearrange(x, 'b c h w -> b (h w) c')\n\n\ndef to_4d(x, h, w):\n \"\"\"Reshape input tensor.\"\"\"\n return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)\n\n\nclass BiasFree_LayerNorm(BaseModule):\n \"\"\"Layer normalization without bias.\n\n Args:\n normalized_shape (tuple): The shape of inputs.\n \"\"\"\n\n def __init__(self, normalized_shape):\n super(BiasFree_LayerNorm, self).__init__()\n if isinstance(normalized_shape, numbers.Integral):\n normalized_shape = (normalized_shape, )\n normalized_shape = torch.Size(normalized_shape)\n\n assert len(normalized_shape) == 1\n\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.normalized_shape = normalized_shape\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n sigma = x.var(-1, keepdim=True, unbiased=False)\n return x / torch.sqrt(sigma + 1e-5) * self.weight\n\n\nclass WithBias_LayerNorm(BaseModule):\n \"\"\"Layer normalization with bias. The bias can be learned.\n\n Args:\n normalized_shape (tuple): The shape of inputs.\n \"\"\"\n\n def __init__(self, normalized_shape):\n super(WithBias_LayerNorm, self).__init__()\n if isinstance(normalized_shape, numbers.Integral):\n normalized_shape = (normalized_shape, )\n normalized_shape = torch.Size(normalized_shape)\n\n assert len(normalized_shape) == 1\n\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.normalized_shape = normalized_shape\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n mu = x.mean(-1, keepdim=True)\n sigma = x.var(-1, keepdim=True, unbiased=False)\n return (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight + self.bias\n\n\nclass LayerNorm(BaseModule):\n \"\"\"Layer normalization module.\n\n Note: This is different from the layernorm2d in pytorch.\n The layer norm here can select Layer Normalization type.\n Args:\n dim (int): Channel number of inputs.\n LayerNorm_type (str): Layer Normalization type.\n \"\"\"\n\n def __init__(self, dim, LayerNorm_type):\n super(LayerNorm, self).__init__()\n if LayerNorm_type == 'BiasFree':\n self.body = BiasFree_LayerNorm(dim)\n else:\n self.body = WithBias_LayerNorm(dim)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n h, w = x.shape[-2:]\n return to_4d(self.body(to_3d(x)), h, w)\n\n\nclass FeedForward(BaseModule):\n \"\"\"Gated-Dconv Feed-Forward Network (GDFN)\n\n The original version of GDFN in\n \"Restormer: Efficient Transformer for High-Resolution Image Restoration\".\n\n Args:\n dim (int): Channel number of inputs.\n ffn_expansion_factor (float): channel expansion factor. Default: 2.66\n bias (bool): The bias of convolution.\n \"\"\"\n\n def __init__(self, dim, ffn_expansion_factor, bias):\n super(FeedForward, self).__init__()\n\n hidden_features = int(dim * ffn_expansion_factor)\n\n self.project_in = nn.Conv2d(\n dim, hidden_features * 2, kernel_size=1, bias=bias)\n\n self.dwconv = nn.Conv2d(\n hidden_features * 2,\n hidden_features * 2,\n kernel_size=3,\n stride=1,\n padding=1,\n groups=hidden_features * 2,\n bias=bias)\n\n self.project_out = nn.Conv2d(\n hidden_features, dim, kernel_size=1, bias=bias)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.project_in(x)\n x1, x2 = self.dwconv(x).chunk(2, dim=1)\n x = F.gelu(x1) * x2\n x = self.project_out(x)\n return x\n\n\nclass Attention(BaseModule):\n \"\"\"Multi-DConv Head Transposed Self-Attention (MDTA)\n\n The original version of MDTA in\n \"Restormer: Efficient Transformer for High-Resolution Image Restoration\".\n\n Args:\n dim (int): Channel number of inputs.\n num_heads (int): Number of attention heads.\n bias (bool): The bias of convolution.\n \"\"\"\n\n def __init__(self, dim, num_heads, bias):\n super(Attention, self).__init__()\n self.num_heads = num_heads\n self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))\n\n self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)\n self.qkv_dwconv = nn.Conv2d(\n dim * 3,\n dim * 3,\n kernel_size=3,\n stride=1,\n padding=1,\n groups=dim * 3,\n bias=bias)\n self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n b, c, h, w = x.shape\n\n qkv = self.qkv_dwconv(self.qkv(x))\n q, k, v = qkv.chunk(3, dim=1)\n\n q = rearrange(\n q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)\n k = rearrange(\n k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)\n v = rearrange(\n v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)\n\n q = torch.nn.functional.normalize(q, dim=-1)\n k = torch.nn.functional.normalize(k, dim=-1)\n\n attn = (q @ k.transpose(-2, -1)) * self.temperature\n attn = attn.softmax(dim=-1)\n\n out = (attn @ v)\n\n out = rearrange(\n out,\n 'b head c (h w) -> b (head c) h w',\n head=self.num_heads,\n h=h,\n w=w)\n\n out = self.project_out(out)\n return out\n\n\nclass TransformerBlock(BaseModule):\n \"\"\"Transformer Block.\n\n The original version of Transformer Block in \"Restormer: Efficient\\\n Transformer for High-Resolution Image Restoration\".\n\n Args:\n dim (int): Channel number of inputs.\n num_heads (int): Number of attention heads.\n ffn_expansion_factor (float): channel expansion factor. Default: 2.66\n bias (bool): The bias of convolution.\n LayerNorm_type (str): Layer Normalization type.\n \"\"\"\n\n def __init__(self, dim, num_heads, ffn_expansion_factor, bias,\n LayerNorm_type):\n super(TransformerBlock, self).__init__()\n\n self.norm1 = LayerNorm(dim, LayerNorm_type)\n self.attn = Attention(dim, num_heads, bias)\n self.norm2 = LayerNorm(dim, LayerNorm_type)\n self.ffn = FeedForward(dim, ffn_expansion_factor, bias)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = x + self.attn(self.norm1(x))\n x = x + self.ffn(self.norm2(x))\n\n return x\n\n\nclass OverlapPatchEmbed(BaseModule):\n \"\"\"Overlapped image patch embedding with 3x3 Conv.\n\n Args:\n in_c (int, optional): Channel number of inputs. Default: 3\n embed_dim (int, optional): embedding dimension. Default: 48\n bias (bool, optional): The bias of convolution. Default: False\n \"\"\"\n\n def __init__(self, in_c=3, embed_dim=48, bias=False):\n super(OverlapPatchEmbed, self).__init__()\n\n self.proj = nn.Conv2d(\n in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.proj(x)\n\n return x\n\n\nclass Downsample(BaseModule):\n \"\"\"Downsample modules.\n\n Args:\n n_feat(int): Channel number of features.\n \"\"\"\n\n def __init__(self, n_feat):\n super(Downsample, self).__init__()\n\n self.body = nn.Sequential(\n nn.Conv2d(\n n_feat,\n n_feat // 2,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=False), nn.PixelUnshuffle(2))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.body(x)\n\n\nclass Upsample(BaseModule):\n \"\"\"Upsample modules.\n\n Args:\n n_feat(int): Channel number of features.\n \"\"\"\n\n def __init__(self, n_feat):\n super(Upsample, self).__init__()\n\n self.body = nn.Sequential(\n nn.Conv2d(\n n_feat,\n n_feat * 2,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=False), nn.PixelShuffle(2))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.body(x)\n\n\n@MODELS.register_module()\nclass Restormer(BaseModule):\n \"\"\"Restormer A PyTorch impl of: `Restormer: Efficient Transformer for High-\n Resolution Image Restoration`. Ref repo:\n https://github.com/swz30/Restormer.\n\n Args:\n inp_channels (int): Number of input image channels. Default: 3.\n out_channels (int): Number of output image channels: 3.\n dim (int): Number of feature dimension. Default: 48.\n num_blocks (List(int)): Depth of each Transformer layer.\n Default: [4, 6, 6, 8].\n num_refinement_blocks (int): Number of refinement blocks.\n Default: 4.\n heads (List(int)): Number of attention heads in different layers.\n Default: 7.\n ffn_expansion_factor (float): Ratio of feed forward network expansion.\n Default: 2.66.\n bias (bool): The bias of convolution. Default: False\n LayerNorm_type (str|optional): Select layer Normalization type.\n Optional: 'WithBias','BiasFree'\n Default: 'WithBias'.\n dual_pixel_task (bool): True for dual-pixel defocus deblurring only.\n Also set inp_channels=6. Default: False.\n dual_keys (List): Keys of dual images in inputs.\n Default: ['imgL', 'imgR'].\n \"\"\"\n\n def __init__(self,\n inp_channels=3,\n out_channels=3,\n dim=48,\n num_blocks=[4, 6, 6, 8],\n num_refinement_blocks=4,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False,\n dual_keys=['imgL', 'imgR']):\n\n super(Restormer, self).__init__()\n\n self.patch_embed = OverlapPatchEmbed(inp_channels, dim)\n\n self.encoder_level1 = nn.Sequential(*[\n TransformerBlock(\n dim=dim,\n num_heads=heads[0],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])\n ])\n\n self.down1_2 = Downsample(dim)\n self.encoder_level2 = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**1),\n num_heads=heads[1],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])\n ])\n\n self.down2_3 = Downsample(int(dim * 2**1))\n self.encoder_level3 = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**2),\n num_heads=heads[2],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])\n ])\n\n self.down3_4 = Downsample(int(dim * 2**2))\n self.latent = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**3),\n num_heads=heads[3],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])\n ])\n\n self.up4_3 = Upsample(int(dim * 2**3))\n self.reduce_chan_level3 = nn.Conv2d(\n int(dim * 2**3), int(dim * 2**2), kernel_size=1, bias=bias)\n self.decoder_level3 = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**2),\n num_heads=heads[2],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])\n ])\n\n self.up3_2 = Upsample(int(dim * 2**2))\n self.reduce_chan_level2 = nn.Conv2d(\n int(dim * 2**2), int(dim * 2**1), kernel_size=1, bias=bias)\n self.decoder_level2 = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**1),\n num_heads=heads[1],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])\n ])\n\n self.up2_1 = Upsample(int(dim * 2**1))\n\n self.decoder_level1 = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**1),\n num_heads=heads[0],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])\n ])\n\n self.refinement = nn.Sequential(*[\n TransformerBlock(\n dim=int(dim * 2**1),\n num_heads=heads[0],\n ffn_expansion_factor=ffn_expansion_factor,\n bias=bias,\n LayerNorm_type=LayerNorm_type)\n for i in range(num_refinement_blocks)\n ])\n\n self.dual_pixel_task = dual_pixel_task\n self.dual_keys = dual_keys\n if self.dual_pixel_task:\n self.skip_conv = nn.Conv2d(\n dim, int(dim * 2**1), kernel_size=1, bias=bias)\n\n self.output = nn.Conv2d(\n int(dim * 2**1),\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=bias)\n\n def forward(self, inp_img):\n \"\"\"Forward function.\n\n Args:\n inp_img (Tensor): Input tensor with shape (B, C, H, W).\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.dual_pixel_task:\n dual_images = [inp_img[key] for key in self.dual_keys]\n inp_img = torch.cat(dual_images, dim=1)\n\n _, _, h, w = inp_img.shape\n if h % 8 == 0:\n padding_h = 0\n else:\n padding_h = 8 - h % 8\n if w % 8 == 0:\n padding_w = 0\n else:\n padding_w = 8 - w % 8\n\n inp_img = F.pad(inp_img, (0, padding_w, 0, padding_h), 'reflect')\n inp_enc_level1 = self.patch_embed(inp_img)\n out_enc_level1 = self.encoder_level1(inp_enc_level1)\n\n inp_enc_level2 = self.down1_2(out_enc_level1)\n out_enc_level2 = self.encoder_level2(inp_enc_level2)\n\n inp_enc_level3 = self.down2_3(out_enc_level2)\n out_enc_level3 = self.encoder_level3(inp_enc_level3)\n\n inp_enc_level4 = self.down3_4(out_enc_level3)\n latent = self.latent(inp_enc_level4)\n\n inp_dec_level3 = self.up4_3(latent)\n inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)\n inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)\n out_dec_level3 = self.decoder_level3(inp_dec_level3)\n\n inp_dec_level2 = self.up3_2(out_dec_level3)\n inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)\n inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)\n out_dec_level2 = self.decoder_level2(inp_dec_level2)\n\n inp_dec_level1 = self.up2_1(out_dec_level2)\n inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)\n out_dec_level1 = self.decoder_level1(inp_dec_level1)\n\n out_dec_level1 = self.refinement(out_dec_level1)\n\n if self.dual_pixel_task:\n out_dec_level1 = out_dec_level1 + self.skip_conv(inp_enc_level1)\n out_dec_level1 = self.output(out_dec_level1)\n else:\n out_dec_level1 = self.output(out_dec_level1) + inp_img\n\n return out_dec_level1[:, :, :h, :w]\n" }, { "path": "mmagic/models/editors/sagan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .sagan import SAGAN\nfrom .sagan_discriminator import ProjDiscriminator\nfrom .sagan_generator import SNGANGenerator\nfrom .sagan_modules import (SNGANDiscHeadResBlock, SNGANDiscResBlock,\n SNGANGenResBlock)\n\n__all__ = [\n 'SAGAN', 'SNGANGenerator', 'ProjDiscriminator', 'SNGANDiscHeadResBlock',\n 'SNGANDiscResBlock', 'SNGANGenResBlock'\n]\n" }, { "path": "mmagic/models/editors/sagan/sagan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseConditionalGAN\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module('SNGAN')\n@MODELS.register_module()\nclass SAGAN(BaseConditionalGAN):\n \"\"\"Implementation of `Self-Attention Generative Adversarial Networks`.\n\n `_ (SAGAN), `Spectral Normalization for\n Generative Adversarial Networks `_\n (SNGAN), and `cGANs with Projection Discriminator\n `_ (Proj-GAN).\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.sagan.SNGANGenerator`\n and\n :class:`~mmagic.models.editors.sagan.ProjDiscriminator`\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): Number of times the generator was completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): Number of times the discriminator was\n completely updated before the generator is updated. Defaults to 1.\n noise_size (Optional[int]): Size of the input noise vector.\n Default to 128.\n num_classes (Optional[int]): The number classes you would like to\n generate. Defaults to None.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n noise_size: Optional[int] = 128,\n num_classes: Optional[int] = None,\n ema_config: Optional[Dict] = None):\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, noise_size,\n num_classes, ema_config)\n\n def disc_loss(self, disc_pred_fake: Tensor,\n disc_pred_real: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Get disc loss. SAGAN, SNGAN and Proj-GAN use hinge loss to train\n the discriminator.\n\n .. math:\n L_{D} = -\\mathbb{E}_{\\left(x, y\\right)\\sim{p}_{data}}\n \\left[\\min\\left(0, -1 + D\\left(x, y\\right)\\right)\\right]\n -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\\left[\\min\n \\left(0, -1 - D\\left(G\\left(z\\right), y\\right)\\right)\\right]\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.relu(1 + disc_pred_fake).mean()\n losses_dict['loss_disc_real'] = F.relu(1 - disc_pred_real).mean()\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Get disc loss. SAGAN, SNGAN and Proj-GAN use hinge loss to train\n the generator.\n\n .. math:\n L_{G} = -\\mathbb{E}_{z\\sim{p_{z}}, y\\sim{p_{data}}}\n D\\left(G\\left(z\\right), y\\right)\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n real_labels = self.data_sample_to_label(data_samples)\n assert real_labels is not None, (\n 'Cannot found \\'gt_label\\' in \\'data_sample\\'.')\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(\n noise=noise_batch, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n disc_pred_real = self.discriminator(real_imgs, label=real_labels)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_labels = self.label_fn(num_batches=num_batches)\n fake_imgs = self.generator(\n noise=noise, label=fake_labels, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/sagan/sagan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModule, xavier_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module('SAGANDiscriminator')\n@MODELS.register_module()\nclass ProjDiscriminator(BaseModule):\n r\"\"\"Discriminator for SNGAN / Proj-GAN. The implementation is refer to\n https://github.com/pfnet-research/sngan_projection/tree/master/dis_models\n\n The overall structure of the projection discriminator can be split into a\n ``from_rgb`` layer, a group of ResBlocks, a linear decision layer, and a\n projection layer. To support defining custom layers, we introduce\n ``from_rgb_cfg`` and ``blocks_cfg``.\n\n The design of the model structure is highly corresponding to the output\n resolution. Therefore, we provide `channels_cfg` and `downsample_cfg` to\n control the input channels and the downsample behavior of the intermediate\n blocks.\n\n ``downsample_cfg``: In default config of SNGAN / Proj-GAN, whether to apply\n downsample in each intermediate blocks is quite flexible and\n corresponding to the resolution of the output image. Therefore, we\n support user to define the ``downsample_cfg`` by themselves, and to\n control the structure of the discriminator.\n\n ``channels_cfg``: In default config of SNGAN / Proj-GAN, the number of\n ResBlocks and the channels of those blocks are corresponding to the\n resolution of the output image. Therefore, we allow user to define\n `channels_cfg` for try their own models. We also provide a default\n config to allow users to build the model only from the output\n resolution.\n\n Args:\n input_scale (int): The scale of the input image.\n num_classes (int, optional): The number classes you would like to\n generate. If num_classes=0, no label projection would be used.\n Default to 0.\n base_channels (int, optional): The basic channel number of the\n discriminator. The other layers contains channels based on this\n number. Defaults to 128.\n input_channels (int, optional): Channels of the input image.\n Defaults to 3.\n attention_cfg (dict, optional): Config for the self-attention block.\n Default to ``dict(type='SelfAttentionBlock')``.\n attention_after_nth_block (int | list[int], optional): Self-attention\n block would be added after which *ConvBlock* (including the head\n block). If ``int`` is passed, only one attention block would be\n added. If ``list`` is passed, self-attention blocks would be added\n after multiple ConvBlocks. To be noted that if the input is\n smaller than ``1``, self-attention corresponding to this index\n would be ignored. Default to 0.\n channels_cfg (list | dict[list], optional): Config for input channels\n of the intermediate blocks. If list is passed, each element of the\n list means the input channels of current block is how many times\n compared to the ``base_channels``. For block ``i``, the input and\n output channels should be ``channels_cfg[i]`` and\n ``channels_cfg[i+1]`` If dict is provided, the key of the dict\n should be the output scale and corresponding value should be a list\n to define channels. Default: Please refer to\n ``_defualt_channels_cfg``.\n downsample_cfg (list[bool] | dict[list], optional): Config for\n downsample behavior of the intermediate layers. If a list is\n passed, ``downsample_cfg[idx] == True`` means apply downsample in\n idx-th block, and vice versa. If dict is provided, the key dict\n should be the input scale of the image and corresponding value\n should be a list ti define the downsample behavior. Default: Please\n refer to ``_default_downsample_cfg``.\n from_rgb_cfg (dict, optional): Config for the first layer to convert\n rgb image to feature map. Defaults to\n ``dict(type='SNGANDiscHeadResBlock')``.\n blocks_cfg (dict, optional): Config for the intermediate blocks.\n Defaults to ``dict(type='SNGANDiscResBlock')``\n act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to ``dict(type='ReLU')``.\n with_spectral_norm (bool, optional): Whether use spectral norm for\n all conv blocks or not. Default to True.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n sn_eps (float, optional): eps for spectral normalization operation.\n Defaults to `1e-12`.\n init_cfg (dict, optional): Config for weight initialization.\n Default to ``dict(type='BigGAN')``.\n pretrained (str | dict , optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n \"\"\"\n\n # default channel factors\n _defualt_channels_cfg = {\n 32: [1, 1, 1],\n 64: [2, 4, 8, 16],\n 128: [2, 4, 8, 16, 16],\n }\n\n # default downsample behavior\n _defualt_downsample_cfg = {\n 32: [True, False, False],\n 64: [True, True, True, True],\n 128: [True, True, True, True, False]\n }\n\n def __init__(self,\n input_scale,\n num_classes=0,\n base_channels=128,\n input_channels=3,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=-1,\n channels_cfg=None,\n downsample_cfg=None,\n from_rgb_cfg=dict(type='SNGANDiscHeadResBlock'),\n blocks_cfg=dict(type='SNGANDiscResBlock'),\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=True,\n sn_style='torch',\n sn_eps=1e-12,\n init_cfg=dict(type='BigGAN'),\n pretrained=None):\n\n super().__init__()\n\n self.init_type = init_cfg.get('type', None)\n\n # add SN options and activation function options to cfg\n self.from_rgb_cfg = deepcopy(from_rgb_cfg)\n self.from_rgb_cfg.setdefault('act_cfg', act_cfg)\n self.from_rgb_cfg.setdefault('with_spectral_norm', with_spectral_norm)\n self.from_rgb_cfg.setdefault('sn_style', sn_style)\n self.from_rgb_cfg.setdefault('init_cfg', init_cfg)\n\n # add SN options and activation function options to cfg\n self.blocks_cfg = deepcopy(blocks_cfg)\n self.blocks_cfg.setdefault('act_cfg', act_cfg)\n self.blocks_cfg.setdefault('with_spectral_norm', with_spectral_norm)\n self.blocks_cfg.setdefault('sn_style', sn_style)\n self.blocks_cfg.setdefault('sn_eps', sn_eps)\n self.blocks_cfg.setdefault('init_cfg', init_cfg)\n\n channels_cfg = deepcopy(self._defualt_channels_cfg) \\\n if channels_cfg is None else deepcopy(channels_cfg)\n if isinstance(channels_cfg, dict):\n if input_scale not in channels_cfg:\n raise KeyError(f'`input_scale={input_scale} is not found in '\n '`channel_cfg`, only support configs for '\n f'{[chn for chn in channels_cfg.keys()]}')\n self.channel_factor_list = channels_cfg[input_scale]\n elif isinstance(channels_cfg, list):\n self.channel_factor_list = channels_cfg\n else:\n raise ValueError('Only support list or dict for `channel_cfg`, '\n f'receive {type(channels_cfg)}')\n\n downsample_cfg = deepcopy(self._defualt_downsample_cfg) \\\n if downsample_cfg is None else deepcopy(downsample_cfg)\n if isinstance(downsample_cfg, dict):\n if input_scale not in downsample_cfg:\n raise KeyError(f'`output_scale={input_scale} is not found in '\n '`downsample_cfg`, only support configs for '\n f'{[chn for chn in downsample_cfg.keys()]}')\n self.downsample_list = downsample_cfg[input_scale]\n elif isinstance(downsample_cfg, list):\n self.downsample_list = downsample_cfg\n else:\n raise ValueError('Only support list or dict for `channel_cfg`, '\n f'receive {type(downsample_cfg)}')\n\n if len(self.downsample_list) != len(self.channel_factor_list):\n raise ValueError('`downsample_cfg` should have same length with '\n '`channels_cfg`, but receive '\n f'{len(self.downsample_list)} and '\n f'{len(self.channel_factor_list)}.')\n\n # check `attention_after_nth_block`\n if not isinstance(attention_after_nth_block, list):\n attention_after_nth_block = [attention_after_nth_block]\n if not all([isinstance(idx, int)\n for idx in attention_after_nth_block]):\n raise ValueError('`attention_after_nth_block` only support int or '\n 'a list of int. Please check your input type.')\n\n self.from_rgb = MODELS.build(\n self.from_rgb_cfg,\n default_args=dict(\n in_channels=input_channels, out_channels=base_channels))\n\n self.conv_blocks = nn.ModuleList()\n # add self-attention block after the first block\n if 1 in attention_after_nth_block:\n attn_cfg_ = deepcopy(attention_cfg)\n attn_cfg_['in_channels'] = base_channels\n attn_cfg_['sn_style'] = sn_style\n self.conv_blocks.append(MODELS.build(attn_cfg_))\n\n for idx in range(len(self.downsample_list)):\n factor_input = 1 if idx == 0 else self.channel_factor_list[idx - 1]\n factor_output = self.channel_factor_list[idx]\n\n # get block-specific config\n block_cfg_ = deepcopy(self.blocks_cfg)\n block_cfg_['downsample'] = self.downsample_list[idx]\n block_cfg_['in_channels'] = factor_input * base_channels\n block_cfg_['out_channels'] = factor_output * base_channels\n self.conv_blocks.append(MODELS.build(block_cfg_))\n\n # build self-attention block\n # the first ConvBlock is `from_rgb` block,\n # add 2 to get the index of the ConvBlocks\n if idx + 2 in attention_after_nth_block:\n attn_cfg_ = deepcopy(attention_cfg)\n attn_cfg_['in_channels'] = factor_output * base_channels\n self.conv_blocks.append(MODELS.build(attn_cfg_))\n\n self.decision = nn.Linear(factor_output * base_channels, 1)\n\n if with_spectral_norm:\n self.decision = spectral_norm(self.decision)\n\n self.num_classes = num_classes\n\n # In this case, discriminator is designed for conditional synthesis.\n if num_classes > 0:\n self.proj_y = nn.Embedding(num_classes,\n factor_output * base_channels)\n if with_spectral_norm:\n self.proj_y = spectral_norm(self.proj_y)\n\n self.activate = MODELS.build(act_cfg)\n self.init_weights(pretrained)\n\n def forward(self, x, label=None):\n \"\"\"Forward function. If `self.num_classes` is larger than 0, label\n projection would be used.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n label (torch.Tensor, options): Label correspond to the input image.\n Noted that, if `self.num_classed` is larger than 0,\n `label` should not be None. Default to None.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image.\n \"\"\"\n h = self.from_rgb(x)\n for conv_block in self.conv_blocks:\n h = conv_block(h)\n h = self.activate(h)\n h = torch.sum(h, dim=[2, 3])\n out = self.decision(h)\n\n if self.num_classes > 0:\n w_y = self.proj_y(label)\n out = out + torch.sum(w_y * h, dim=1, keepdim=True)\n return out.view(out.size(0), -1)\n\n def init_weights(self, pretrained=None, strict=True):\n \"\"\"Init weights for SNGAN-Proj and SAGAN. If ``pretrained=None`` and\n weight initialization would follow the ``INIT_TYPE`` in\n ``init_cfg=dict(type=INIT_TYPE)``.\n\n For SNGAN-Proj\n (``INIT_TYPE.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']``),\n we follow the initialization method in the official Chainer's\n implementation (https://github.com/pfnet-research/sngan_projection).\n\n For SAGAN (``INIT_TYPE.upper() == 'SAGAN'``), we follow the\n initialization method in official tensorflow's implementation\n (https://github.com/brain-research/self-attention-gan).\n\n Besides the reimplementation of the official code's initialization, we\n provide BigGAN's and Pytorch-StudioGAN's style initialization\n (``INIT_TYPE.upper() == BIGGAN`` and ``INIT_TYPE.upper() == STUDIO``).\n Please refer to https://github.com/ajbrock/BigGAN-PyTorch and\n https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.\n\n Args:\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose\n necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=strict, logger=logger)\n elif isinstance(pretrained, dict):\n ckpt_path = pretrained.get('ckpt_path', None)\n assert ckpt_path is not None\n prefix = pretrained.get('prefix', '')\n map_location = pretrained.get('map_location', 'cpu')\n strict = pretrained.get('strict', True)\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n elif pretrained is None:\n if self.init_type.upper() == 'STUDIO':\n # initialization method from Pytorch-StudioGAN\n # * weight: orthogonal_init gain=1\n # * bias : 0\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n nn.init.orthogonal_(m.weight, gain=1)\n if hasattr(m, 'bias') and m.bias is not None:\n m.bias.data.fill_(0.)\n elif self.init_type.upper() == 'BIGGAN':\n # initialization method from BigGAN-pytorch\n # * weight: xavier_init gain=1\n # * bias : default\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n xavier_uniform_(m.weight, gain=1)\n elif self.init_type.upper() == 'SAGAN':\n # initialization method from official tensorflow code\n # * weight: xavier_init gain=1\n # * bias : 0\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n xavier_init(m, gain=1, distribution='uniform')\n elif self.init_type.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']:\n # initialization method from the official chainer code\n # * embedding.weight: xavier_init gain=1\n # * conv.weight : xavier_init gain=sqrt(2)\n # * shortcut.weight : xavier_init gain=1\n # * bias : 0\n for n, m in self.named_modules():\n if isinstance(m, nn.Conv2d):\n if 'shortcut' in n:\n xavier_init(m, gain=1, distribution='uniform')\n else:\n xavier_init(\n m, gain=np.sqrt(2), distribution='uniform')\n if isinstance(m, (nn.Linear, nn.Embedding)):\n xavier_init(m, gain=1, distribution='uniform')\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n else:\n raise TypeError(\"'pretrained' must by a str or None. \"\n f'But receive {type(pretrained)}.')\n" }, { "path": "mmagic/models/editors/sagan/sagan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine import is_list_of\nfrom mmengine.dist import is_distributed\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModule, constant_init, xavier_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.models.utils import get_module_device\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module('SAGANGenerator')\n@MODELS.register_module()\nclass SNGANGenerator(BaseModule):\n r\"\"\"Generator for SNGAN / Proj-GAN. The implementation refers to\n https://github.com/pfnet-research/sngan_projection/tree/master/gen_models\n\n In our implementation, we have two notable design. Namely,\n ``channels_cfg`` and ``blocks_cfg``.\n\n ``channels_cfg``: In default config of SNGAN / Proj-GAN, the number of\n ResBlocks and the channels of those blocks are corresponding to the\n resolution of the output image. Therefore, we allow user to define\n ``channels_cfg`` to try their own models. We also provide a default\n config to allow users to build the model only from the output\n resolution.\n\n ``block_cfg``: In reference code, the generator consists of a group of\n ResBlock. However, in our implementation, to make this model more\n generalize, we support defining ``blocks_cfg`` by users and loading\n the blocks by calling the build_module method.\n\n Args:\n output_scale (int): Output scale for the generated image.\n num_classes (int, optional): The number classes you would like to\n generate. This arguments would influence the structure of the\n intermediate blocks and label sampling operation in ``forward``\n (e.g. If num_classes=0, ConditionalNormalization layers would\n degrade to unconditional ones.). This arguments would be passed\n to intermediate blocks by overwrite their config. Defaults to 0.\n base_channels (int, optional): The basic channel number of the\n generator. The other layers contains channels based on this number.\n Default to 64.\n out_channels (int, optional): Channels of the output images.\n Default to 3.\n input_scale (int, optional): Input scale for the features.\n Defaults to 4.\n noise_size (int, optional): Size of the input noise vector.\n Default to 128.\n attention_cfg (dict, optional): Config for the self-attention block.\n Default to ``dict(type='SelfAttentionBlock')``.\n attention_after_nth_block (int | list[int], optional): Self attention\n block would be added after which *ConvBlock*. If ``int`` is passed,\n only one attention block would be added. If ``list`` is passed,\n self-attention blocks would be added after multiple ConvBlocks.\n To be noted that if the input is smaller than ``1``,\n self-attention corresponding to this index would be ignored.\n Default to 0.\n channels_cfg (list | dict[list], optional): Config for input channels\n of the intermediate blocks. If list is passed, each element of the\n list means the input channels of current block is how many times\n compared to the ``base_channels``. For block ``i``, the input and\n output channels should be ``channels_cfg[i]`` and\n ``channels_cfg[i+1]`` If dict is provided, the key of the dict\n should be the output scale and corresponding value should be a list\n to define channels. Default: Please refer to\n ``_defualt_channels_cfg``.\n blocks_cfg (dict, optional): Config for the intermediate blocks.\n Defaults to ``dict(type='SNGANGenResBlock')``\n act_cfg (dict, optional): Activation config for the final output\n layer. Defaults to ``dict(type='ReLU')``.\n use_cbn (bool, optional): Whether use conditional normalization. This\n argument would pass to norm layers. Defaults to True.\n auto_sync_bn (bool, optional): Whether convert Batch Norm to\n Synchronized ones when Distributed training is on. Defaults to\n True.\n with_spectral_norm (bool, optional): Whether use spectral norm for\n conv blocks or not. Default to False.\n with_embedding_spectral_norm (bool, optional): Whether use spectral\n norm for embedding layers in normalization blocks or not. If not\n specified (set as ``None``), ``with_embedding_spectral_norm`` would\n be set as the same value as ``with_spectral_norm``.\n Defaults to None.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n norm_eps (float, optional): eps for Normalization layers (both\n conditional and non-conditional ones). Default to `1e-4`.\n sn_eps (float, optional): eps for spectral normalization operation.\n Defaults to `1e-12`.\n init_cfg (string, optional): Config for weight initialization.\n Defaults to ``dict(type='BigGAN')``.\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n rgb_to_bgr (bool, optional): Whether to reformat the output channels\n with order `bgr`. We provide several pre-trained BigGAN\n weights whose output channels order is `rgb`. You can set\n this argument to True to use the weights.\n \"\"\"\n\n # default channel factors\n _default_channels_cfg = {\n 32: [1, 1, 1],\n 64: [16, 8, 4, 2],\n 128: [16, 16, 8, 4, 2]\n }\n\n def __init__(self,\n output_scale,\n num_classes=0,\n base_channels=64,\n out_channels=3,\n input_scale=4,\n noise_size=128,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=0,\n channels_cfg=None,\n blocks_cfg=dict(type='SNGANGenResBlock'),\n act_cfg=dict(type='ReLU'),\n use_cbn=True,\n auto_sync_bn=True,\n with_spectral_norm=False,\n with_embedding_spectral_norm=None,\n sn_style='torch',\n norm_eps=1e-4,\n sn_eps=1e-12,\n init_cfg=dict(type='BigGAN'),\n pretrained=None,\n rgb_to_bgr=False):\n\n super().__init__()\n\n self.input_scale = input_scale\n self.output_scale = output_scale\n self.noise_size = noise_size\n self.num_classes = num_classes\n self.init_type = init_cfg.get('type', None)\n self.rgb_to_bgr = rgb_to_bgr\n\n self.blocks_cfg = deepcopy(blocks_cfg)\n\n self.blocks_cfg.setdefault('num_classes', num_classes)\n self.blocks_cfg.setdefault('act_cfg', act_cfg)\n self.blocks_cfg.setdefault('use_cbn', use_cbn)\n self.blocks_cfg.setdefault('auto_sync_bn', auto_sync_bn)\n self.blocks_cfg.setdefault('with_spectral_norm', with_spectral_norm)\n\n # set `norm_spectral_norm` as `with_spectral_norm` if not defined\n with_embedding_spectral_norm = with_embedding_spectral_norm \\\n if with_embedding_spectral_norm is not None else with_spectral_norm\n self.blocks_cfg.setdefault('with_embedding_spectral_norm',\n with_embedding_spectral_norm)\n self.blocks_cfg.setdefault('init_cfg', init_cfg)\n self.blocks_cfg.setdefault('sn_style', sn_style)\n self.blocks_cfg.setdefault('norm_eps', norm_eps)\n self.blocks_cfg.setdefault('sn_eps', sn_eps)\n\n channels_cfg = deepcopy(self._default_channels_cfg) \\\n if channels_cfg is None else deepcopy(channels_cfg)\n if isinstance(channels_cfg, dict):\n if output_scale not in channels_cfg:\n raise KeyError(f'`output_scale={output_scale} is not found in '\n '`channel_cfg`, only support configs for '\n f'{[chn for chn in channels_cfg.keys()]}')\n self.channel_factor_list = channels_cfg[output_scale]\n elif isinstance(channels_cfg, list):\n self.channel_factor_list = channels_cfg\n else:\n raise ValueError('Only support list or dict for `channel_cfg`, '\n f'receive {type(channels_cfg)}')\n\n self.noise2feat = nn.Linear(\n noise_size,\n input_scale**2 * base_channels * self.channel_factor_list[0])\n if with_spectral_norm:\n self.noise2feat = spectral_norm(self.noise2feat)\n\n # check `attention_after_nth_block`\n if not isinstance(attention_after_nth_block, list):\n attention_after_nth_block = [attention_after_nth_block]\n if not is_list_of(attention_after_nth_block, int):\n raise ValueError('`attention_after_nth_block` only support int or '\n 'a list of int. Please check your input type.')\n\n self.conv_blocks = nn.ModuleList()\n self.attention_block_idx = []\n for idx in range(len(self.channel_factor_list)):\n factor_input = self.channel_factor_list[idx]\n factor_output = self.channel_factor_list[idx+1] \\\n if idx < len(self.channel_factor_list)-1 else 1\n\n # get block-specific config\n block_cfg_ = deepcopy(self.blocks_cfg)\n block_cfg_['in_channels'] = factor_input * base_channels\n block_cfg_['out_channels'] = factor_output * base_channels\n self.conv_blocks.append(MODELS.build(block_cfg_))\n\n # build self-attention block\n # `idx` is start from 0, add 1 to get the index\n if idx + 1 in attention_after_nth_block:\n self.attention_block_idx.append(len(self.conv_blocks))\n attn_cfg_ = deepcopy(attention_cfg)\n attn_cfg_['in_channels'] = factor_output * base_channels\n attn_cfg_['sn_style'] = sn_style\n self.conv_blocks.append(MODELS.build(attn_cfg_))\n\n to_rgb_norm_cfg = dict(type='BN', eps=norm_eps)\n if is_distributed() and auto_sync_bn:\n to_rgb_norm_cfg['type'] = 'SyncBN'\n\n self.to_rgb = ConvModule(\n factor_output * base_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n norm_cfg=to_rgb_norm_cfg,\n act_cfg=act_cfg,\n order=('norm', 'act', 'conv'),\n with_spectral_norm=with_spectral_norm)\n self.final_act = MODELS.build(dict(type='Tanh'))\n\n self.init_weights(pretrained)\n\n def forward(self, noise, num_batches=0, label=None, return_noise=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n label (torch.Tensor | callable | None): You can directly give a\n batch of label through a ``torch.Tensor`` or offer a callable\n function to sample a batch of label data. Otherwise, the\n ``None`` indicates to use the default label sampler.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output\n image will be returned. Otherwise, a dict contains\n ``fake_image``, ``noise_batch`` and ``label_batch``\n would be returned.\n \"\"\"\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size))\n\n if isinstance(label, torch.Tensor):\n if label.ndim != 1:\n assert all([s == 1 for s in label.shape[1:]])\n label = label.view(-1)\n assert label.ndim == 1, ('The label should be in shape of (n, )'\n f'but got {label.shape}.')\n label_batch = label\n elif callable(label):\n label_generator = label\n assert num_batches > 0\n label_batch = label_generator(num_batches)\n elif self.num_classes == 0:\n label_batch = None\n else:\n assert num_batches > 0\n label_batch = torch.randint(0, self.num_classes, (num_batches, ))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n if label_batch is not None:\n label_batch = label_batch.to(get_module_device(self))\n\n x = self.noise2feat(noise_batch)\n x = x.reshape(x.size(0), -1, self.input_scale, self.input_scale)\n\n for idx, conv_block in enumerate(self.conv_blocks):\n if idx in self.attention_block_idx:\n x = conv_block(x)\n else:\n x = conv_block(x, label_batch)\n\n out_feat = self.to_rgb(x)\n out_img = self.final_act(out_feat)\n\n if self.rgb_to_bgr:\n out_img = out_img[:, [2, 1, 0], ...]\n\n if return_noise:\n return dict(\n fake_img=out_img, noise_batch=noise_batch, label=label_batch)\n return out_img\n\n def init_weights(self, pretrained=None, strict=True):\n \"\"\"Init weights for SNGAN-Proj and SAGAN. If ``pretrained=None``,\n weight initialization would follow the ``INIT_TYPE`` in\n ``init_cfg=dict(type=INIT_TYPE)``.\n\n For SNGAN-Proj,\n (``INIT_TYPE.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']``),\n we follow the initialization method in the official Chainer's\n implementation (https://github.com/pfnet-research/sngan_projection).\n\n For SAGAN (``INIT_TYPE.upper() == 'SAGAN'``), we follow the\n initialization method in official tensorflow's implementation\n (https://github.com/brain-research/self-attention-gan).\n\n Besides the reimplementation of the official code's initialization, we\n provide BigGAN's and Pytorch-StudioGAN's style initialization\n (``INIT_TYPE.upper() == BIGGAN`` and ``INIT_TYPE.upper() == STUDIO``).\n Please refer to https://github.com/ajbrock/BigGAN-PyTorch and\n https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.\n\n Args:\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose\n necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=strict, logger=logger)\n elif isinstance(pretrained, dict):\n ckpt_path = pretrained.get('ckpt_path', None)\n assert ckpt_path is not None\n prefix = pretrained.get('prefix', '')\n map_location = pretrained.get('map_location', 'cpu')\n strict = pretrained.get('strict', True)\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n elif pretrained is None:\n if self.init_type.upper() in 'STUDIO':\n # initialization method from Pytorch-StudioGAN\n # * weight: orthogonal_init gain=1\n # * bias : 0\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n nn.init.orthogonal_(m.weight)\n if hasattr(m, 'bias') and m.bias is not None:\n m.bias.data.fill_(0.)\n elif self.init_type.upper() == 'BIGGAN':\n # initialization method from BigGAN-pytorch\n # * weight: xavier_init gain=1\n # * bias : default\n for n, m in self.named_modules():\n if isinstance(m, (nn.Conv2d, nn.Linear, nn.Embedding)):\n xavier_uniform_(m.weight, gain=1)\n elif self.init_type.upper() == 'SAGAN':\n # initialization method from official tensorflow code\n # * weight : xavier_init gain=1\n # * bias : 0\n # * weight_embedding: 1\n # * bias_embedding : 0\n for n, m in self.named_modules():\n if isinstance(m, (nn.Conv2d, nn.Linear)):\n xavier_init(m, gain=1, distribution='uniform')\n if isinstance(m, nn.Embedding):\n # To be noted that here we initialize the embedding\n # layer in cBN with specific prefix. If you implement\n # your own cBN and want to use this initialization\n # method, please make sure the embedding layers in\n # your implementation have the same prefix as ours.\n if 'weight' in n:\n constant_init(m, 1)\n if 'bias' in n:\n constant_init(m, 0)\n elif self.init_type.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']:\n # initialization method from the official chainer code\n # * conv.weight : xavier_init gain=sqrt(2)\n # * shortcut.weight : xavier_init gain=1\n # * bias : 0\n # * weight_embedding: 1\n # * bias_embedding : 0\n for n, m in self.named_modules():\n if isinstance(m, nn.Conv2d):\n if 'shortcut' in n or 'to_rgb' in n:\n xavier_init(m, gain=1, distribution='uniform')\n else:\n xavier_init(\n m, gain=np.sqrt(2), distribution='uniform')\n if isinstance(m, nn.Linear):\n xavier_init(m, gain=1, distribution='uniform')\n if isinstance(m, nn.Embedding):\n # To be noted that here we initialize the embedding\n # layer in cBN with specific prefix. If you implement\n # your own cBN and want to use this initialization\n # method, please make sure the embedding layers in\n # your implementation have the same prefix as ours.\n if 'weight' in n:\n constant_init(m, 1)\n if 'bias' in n:\n constant_init(m, 0)\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n\n else:\n raise TypeError(\"'pretrained' must be a str or None. \"\n f'But receive {type(pretrained)}.')\n" }, { "path": "mmagic/models/editors/sagan/sagan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch.nn as nn\nfrom mmcv.cnn import build_norm_layer\nfrom mmengine.dist import is_distributed\nfrom mmengine.model import BaseModule, constant_init, xavier_init\nfrom torch import Tensor\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.utils import spectral_norm\n\nfrom mmagic.models.editors.biggan.biggan_modules import SNConvModule\nfrom mmagic.models.editors.biggan.biggan_snmodule import SNEmbedding\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SNGANGenResBlock(BaseModule):\n \"\"\"ResBlock used in Generator of SNGAN / Proj-GAN.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n hidden_channels (int, optional): Input channels of the second Conv\n layer of the block. If ``None`` is given, would be set as\n ``out_channels``. Default to None.\n num_classes (int, optional): Number of classes would like to generate.\n This argument would pass to norm layers and influence the structure\n and behavior of the normalization process. Default to 0.\n use_cbn (bool, optional): Whether use conditional normalization. This\n argument would pass to norm layers. Default to True.\n use_norm_affine (bool, optional): Whether use learnable affine\n parameters in norm operation when cbn is off. Default False.\n act_cfg (dict, optional): Config for activate function. Default\n to ``dict(type='ReLU')``.\n upsample_cfg (dict, optional): Config for the upsample method.\n Default to ``dict(type='nearest', scale_factor=2)``.\n upsample (bool, optional): Whether apply upsample operation in this\n module. Default to True.\n auto_sync_bn (bool, optional): Whether convert Batch Norm to\n Synchronized ones when Distributed training is on. Default to True.\n conv_cfg (dict | None): Config for conv blocks of this module. If pass\n ``None``, would use ``_default_conv_cfg``. Default to ``None``.\n with_spectral_norm (bool, optional): Whether use spectral norm for\n conv blocks and norm layers. Default to True.\n with_embedding_spectral_norm (bool, optional): Whether use spectral\n norm for embedding layers in normalization blocks or not. If not\n specified (set as ``None``), ``with_embedding_spectral_norm`` would\n be set as the same value as ``with_spectral_norm``.\n Default to None.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n norm_eps (float, optional): eps for Normalization layers (both\n conditional and non-conditional ones). Default to `1e-4`.\n sn_eps (float, optional): eps for spectral normalization operation.\n Default to `1e-12`.\n init_cfg (dict, optional): Config for weight initialization.\n Default to ``dict(type='BigGAN')``.\n \"\"\"\n\n _default_conv_cfg = dict(kernel_size=3, stride=1, padding=1, act_cfg=None)\n\n def __init__(self,\n in_channels,\n out_channels,\n hidden_channels=None,\n num_classes=0,\n use_cbn=True,\n use_norm_affine=False,\n act_cfg=dict(type='ReLU'),\n norm_cfg=dict(type='BN'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n upsample=True,\n auto_sync_bn=True,\n conv_cfg=None,\n with_spectral_norm=False,\n with_embedding_spectral_norm=None,\n sn_style='torch',\n norm_eps=1e-4,\n sn_eps=1e-12,\n init_cfg=dict(type='BigGAN')):\n\n super().__init__()\n self.learnable_sc = in_channels != out_channels or upsample\n self.with_upsample = upsample\n self.init_type = init_cfg.get('type', None)\n\n self.activate = MODELS.build(act_cfg)\n hidden_channels = out_channels if hidden_channels is None \\\n else hidden_channels\n\n if self.with_upsample:\n self.upsample = MODELS.build(upsample_cfg)\n\n self.conv_cfg = deepcopy(self._default_conv_cfg)\n if conv_cfg is not None:\n self.conv_cfg.update(conv_cfg)\n\n # set `norm_spectral_norm` as `with_spectral_norm` if not defined\n with_embedding_spectral_norm = with_embedding_spectral_norm \\\n if with_embedding_spectral_norm is not None else with_spectral_norm\n\n sn_cfg = dict(eps=sn_eps, sn_style=sn_style)\n self.conv_1 = SNConvModule(\n in_channels,\n hidden_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n self.conv_2 = SNConvModule(\n hidden_channels,\n out_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n\n self.norm_1 = SNConditionNorm(in_channels, num_classes, use_cbn,\n norm_cfg, use_norm_affine, auto_sync_bn,\n with_embedding_spectral_norm, sn_style,\n norm_eps, sn_eps, init_cfg)\n self.norm_2 = SNConditionNorm(hidden_channels, num_classes, use_cbn,\n norm_cfg, use_norm_affine, auto_sync_bn,\n with_embedding_spectral_norm, sn_style,\n norm_eps, sn_eps, init_cfg)\n\n if self.learnable_sc:\n # use hyperparameters-fixed shortcut here\n self.shortcut = SNConvModule(\n in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg)\n self.init_weights()\n\n def forward(self, x, y=None):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n y (Tensor): Input label with shape (n, ).\n Default None.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n out = self.norm_1(x, y)\n out = self.activate(out)\n if self.with_upsample:\n out = self.upsample(out)\n out = self.conv_1(out)\n\n out = self.norm_2(out, y)\n out = self.activate(out)\n out = self.conv_2(out)\n\n shortcut = self.forward_shortcut(x)\n return out + shortcut\n\n def forward_shortcut(self, x):\n \"\"\"Forward the shortcut branch.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = x\n if self.learnable_sc:\n if self.with_upsample:\n out = self.upsample(out)\n out = self.shortcut(out)\n return out\n\n def init_weights(self):\n \"\"\"Initialize weights for the model.\"\"\"\n if self.init_type.upper() == 'STUDIO':\n nn.init.orthogonal_(self.conv_1.conv.weight)\n nn.init.orthogonal_(self.conv_2.conv.weight)\n self.conv_1.conv.bias.data.fill_(0.)\n self.conv_2.conv.bias.data.fill_(0.)\n if self.learnable_sc:\n nn.init.orthogonal_(self.shortcut.conv.weight)\n self.shortcut.conv.bias.data.fill_(0.)\n elif self.init_type.upper() == 'BIGGAN':\n xavier_uniform_(self.conv_1.conv.weight, gain=1)\n xavier_uniform_(self.conv_2.conv.weight, gain=1)\n if self.learnable_sc:\n xavier_uniform_(self.shortcut.conv.weight, gain=1)\n elif self.init_type.upper() == 'SAGAN':\n xavier_init(self.conv_1, gain=1, distribution='uniform')\n xavier_init(self.conv_2, gain=1, distribution='uniform')\n if self.learnable_sc:\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n elif self.init_type.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']:\n xavier_init(self.conv_1, gain=np.sqrt(2), distribution='uniform')\n xavier_init(self.conv_2, gain=np.sqrt(2), distribution='uniform')\n if self.learnable_sc:\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n\n\n@MODELS.register_module()\nclass SNGANDiscResBlock(BaseModule):\n \"\"\"resblock used in discriminator of sngan / proj-gan.\n\n args:\n in_channels (int): input channels.\n out_channels (int): output channels.\n hidden_channels (int, optional): input channels of the second conv\n layer of the block. if ``none`` is given, would be set as\n ``out_channels``. Defaults to none.\n downsample (bool, optional): whether apply downsample operation in this\n module. Defaults to false.\n act_cfg (dict, optional): config for activate function. default\n to ``dict(type='relu')``.\n conv_cfg (dict | none): config for conv blocks of this module. if pass\n ``none``, would use ``_default_conv_cfg``. default to ``none``.\n with_spectral_norm (bool, optional): whether use spectral norm for\n conv blocks and norm layers. Defaults to true.\n sn_eps (float, optional): eps for spectral normalization operation.\n Default to `1e-12`.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n init_cfg (dict, optional): Config for weight initialization.\n Defaults to ``dict(type='BigGAN')``.\n \"\"\"\n\n _default_conv_cfg = dict(kernel_size=3, stride=1, padding=1, act_cfg=None)\n\n def __init__(self,\n in_channels,\n out_channels,\n hidden_channels=None,\n downsample=False,\n act_cfg=dict(type='ReLU'),\n conv_cfg=None,\n with_spectral_norm=True,\n sn_style='torch',\n sn_eps=1e-12,\n init_cfg=dict(type='BigGAN')):\n\n super().__init__()\n hidden_channels = out_channels if hidden_channels is None \\\n else hidden_channels\n self.with_downsample = downsample\n self.init_type = init_cfg.get('type', None)\n\n self.conv_cfg = deepcopy(self._default_conv_cfg)\n if conv_cfg is not None:\n self.conv_cfg.update(conv_cfg)\n\n self.activate = MODELS.build(act_cfg)\n\n sn_cfg = dict(eps=sn_eps, sn_style=sn_style)\n self.conv_1 = SNConvModule(\n in_channels,\n hidden_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n self.conv_2 = SNConvModule(\n hidden_channels,\n out_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n\n if self.with_downsample:\n self.downsample = nn.AvgPool2d(2, 2)\n\n self.learnable_sc = in_channels != out_channels or downsample\n if self.learnable_sc:\n # use hyperparameters-fixed shortcut here\n self.shortcut = SNConvModule(\n in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg)\n self.init_weights()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n out = self.activate(x)\n out = self.conv_1(out)\n out = self.activate(out)\n out = self.conv_2(out)\n if self.with_downsample:\n out = self.downsample(out)\n\n shortcut = self.forward_shortcut(x)\n return out + shortcut\n\n def forward_shortcut(self, x: Tensor) -> Tensor:\n \"\"\"Forward the shortcut branch.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = x\n if self.learnable_sc:\n out = self.shortcut(out)\n if self.with_downsample:\n out = self.downsample(out)\n return out\n\n def init_weights(self):\n \"\"\"Initialize weights.\"\"\"\n if self.init_type.upper() == 'STUDIO':\n nn.init.orthogonal_(self.conv_1.conv.weight)\n nn.init.orthogonal_(self.conv_2.conv.weight)\n self.conv_1.conv.bias.data.fill_(0.)\n self.conv_2.conv.bias.data.fill_(0.)\n if self.learnable_sc:\n nn.init.orthogonal_(self.shortcut.conv.weight)\n self.shortcut.conv.bias.data.fill_(0.)\n elif self.init_type.upper() == 'BIGGAN':\n xavier_uniform_(self.conv_1.conv.weight, gain=1)\n xavier_uniform_(self.conv_2.conv.weight, gain=1)\n if self.learnable_sc:\n xavier_uniform_(self.shortcut.conv.weight, gain=1)\n elif self.init_type.upper() == 'SAGAN':\n xavier_init(self.conv_1, gain=1, distribution='uniform')\n xavier_init(self.conv_2, gain=1, distribution='uniform')\n if self.learnable_sc:\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n elif self.init_type.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']:\n xavier_init(self.conv_1, gain=np.sqrt(2), distribution='uniform')\n xavier_init(self.conv_2, gain=np.sqrt(2), distribution='uniform')\n if self.learnable_sc:\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n\n\n@MODELS.register_module()\nclass SNGANDiscHeadResBlock(BaseModule):\n \"\"\"The first ResBlock used in discriminator of sngan / proj-gan. Compared\n to ``SNGANDisResBlock``, this module has a different forward order.\n\n args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n downsample (bool, optional): whether apply downsample operation in this\n module. default to false.\n conv_cfg (dict | none): config for conv blocks of this module. if pass\n ``none``, would use ``_default_conv_cfg``. default to ``none``.\n act_cfg (dict, optional): config for activate function. default\n to ``dict(type='relu')``.\n with_spectral_norm (bool, optional): whether use spectral norm for\n conv blocks and norm layers. default to true.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n sn_eps (float, optional): eps for spectral normalization operation.\n Default to `1e-12`.\n init_cfg (dict, optional): Config for weight initialization.\n Default to ``dict(type='BigGAN')``.\n \"\"\"\n\n _default_conv_cfg = dict(kernel_size=3, stride=1, padding=1, act_cfg=None)\n\n def __init__(self,\n in_channels,\n out_channels,\n conv_cfg=None,\n act_cfg=dict(type='ReLU'),\n with_spectral_norm=True,\n sn_eps=1e-12,\n sn_style='torch',\n init_cfg=dict(type='BigGAN')):\n\n super().__init__()\n\n self.init_type = init_cfg.get('type', None)\n self.conv_cfg = deepcopy(self._default_conv_cfg)\n if conv_cfg is not None:\n self.conv_cfg.update(conv_cfg)\n\n self.activate = MODELS.build(act_cfg)\n\n sn_cfg = dict(eps=sn_eps, sn_style=sn_style)\n self.conv_1 = SNConvModule(\n in_channels,\n out_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n self.conv_2 = SNConvModule(\n out_channels,\n out_channels,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg,\n **self.conv_cfg)\n\n self.downsample = nn.AvgPool2d(2, 2)\n\n # use hyperparameters-fixed shortcut here\n self.shortcut = SNConvModule(\n in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n act_cfg=None,\n with_spectral_norm=with_spectral_norm,\n spectral_norm_cfg=sn_cfg)\n self.init_weights()\n\n def forward(self, x: Tensor) -> Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = self.conv_1(x)\n out = self.activate(out)\n out = self.conv_2(out)\n out = self.downsample(out)\n\n shortcut = self.forward_shortcut(x)\n return out + shortcut\n\n def forward_shortcut(self, x: Tensor) -> Tensor:\n \"\"\"Forward the shortcut branch.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n out = self.downsample(x)\n out = self.shortcut(out)\n return out\n\n def init_weights(self):\n \"\"\"Initialize weights.\"\"\"\n if self.init_type.upper() == 'STUDIO':\n for m in [self.conv_1, self.conv_2, self.shortcut]:\n nn.init.orthogonal_(m.conv.weight)\n m.conv.bias.data.fill_(0.)\n elif self.init_type.upper() == 'BIGGAN':\n xavier_uniform_(self.conv_1.conv.weight, gain=1)\n xavier_uniform_(self.conv_2.conv.weight, gain=1)\n xavier_uniform_(self.shortcut.conv.weight, gain=1)\n elif self.init_type.upper() == 'SAGAN':\n xavier_init(self.conv_1, gain=1, distribution='uniform')\n xavier_init(self.conv_2, gain=1, distribution='uniform')\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n elif self.init_type.upper() in ['SNGAN', 'SNGAN-PROJ', 'GAN-PROJ']:\n xavier_init(self.conv_1, gain=np.sqrt(2), distribution='uniform')\n xavier_init(self.conv_2, gain=np.sqrt(2), distribution='uniform')\n xavier_init(self.shortcut, gain=1, distribution='uniform')\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n\n\n@MODELS.register_module()\nclass SNConditionNorm(BaseModule):\n \"\"\"Conditional Normalization for SNGAN / Proj-GAN. The implementation\n refers to.\n\n https://github.com/pfnet-research/sngan_projection/blob/master/source/links/conditional_batch_normalization.py # noda\n\n and\n\n https://github.com/POSTECH-CVLab/PyTorch-StudioGAN/blob/master/src/utils/model_ops.py # noqa\n\n Args:\n in_channels (int): Number of the channels of the input feature map.\n num_classes (int): Number of the classes in the dataset. If ``use_cbn``\n is True, ``num_classes`` must larger than 0.\n use_cbn (bool, optional): Whether use conditional normalization. If\n ``use_cbn`` is True, two embedding layers would be used to mapping\n label to weight and bias used in normalization process.\n norm_cfg (dict, optional): Config for normalization method. Defaults\n to ``dict(type='BN')``.\n cbn_norm_affine (bool): Whether set ``affine=True`` when use conditional batch norm.\n This argument only work when ``use_cbn`` is True. Defaults to False.\n auto_sync_bn (bool, optional): Whether convert Batch Norm to\n Synchronized ones when Distributed training is on. Defaults to True.\n with_spectral_norm (bool, optional): whether use spectral norm for\n conv blocks and norm layers. Defaults to true.\n norm_eps (float, optional): eps for Normalization layers (both\n conditional and non-conditional ones). Defaults to `1e-4`.\n sn_style (str, optional): The style of spectral normalization.\n If set to `ajbrock`, implementation by\n ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py)\n will be adopted.\n If set to `torch`, implementation by `PyTorch` will be adopted.\n Defaults to `torch`.\n sn_eps (float, optional): eps for spectral normalization operation.\n Defaults to `1e-12`.\n init_cfg (dict, optional): Config for weight initialization.\n Defaults to ``dict(type='BigGAN')``.\n \"\"\"\n\n def __init__(self,\n in_channels,\n num_classes,\n use_cbn=True,\n norm_cfg=dict(type='BN'),\n cbn_norm_affine=False,\n auto_sync_bn=True,\n with_spectral_norm=False,\n sn_style='torch',\n norm_eps=1e-4,\n sn_eps=1e-12,\n init_cfg=dict(type='BigGAN')):\n super().__init__()\n self.use_cbn = use_cbn\n self.init_type = init_cfg.get('type', None)\n\n norm_cfg = deepcopy(norm_cfg)\n norm_type = norm_cfg['type']\n\n if norm_type not in ['IN', 'BN', 'SyncBN']:\n raise ValueError('Only support `IN` (InstanceNorm), '\n '`BN` (BatcnNorm) and `SyncBN` for '\n 'Class-conditional bn. '\n f'Receive norm_type: {norm_type}')\n\n if self.use_cbn:\n norm_cfg.setdefault('affine', cbn_norm_affine)\n norm_cfg.setdefault('eps', norm_eps)\n if is_distributed() and auto_sync_bn and norm_type == 'BN':\n norm_cfg['type'] = 'SyncBN'\n\n _, self.norm = build_norm_layer(norm_cfg, in_channels)\n\n if self.use_cbn:\n if num_classes <= 0:\n raise ValueError('`num_classes` must be larger '\n 'than 0 with `use_cbn=True`')\n self.reweight_embedding = (\n self.init_type.upper() == 'BIGGAN'\n or self.init_type.upper() == 'STUDIO')\n if with_spectral_norm:\n if sn_style == 'torch':\n self.weight_embedding = spectral_norm(\n nn.Embedding(num_classes, in_channels), eps=sn_eps)\n self.bias_embedding = spectral_norm(\n nn.Embedding(num_classes, in_channels), eps=sn_eps)\n elif sn_style == 'ajbrock':\n self.weight_embedding = SNEmbedding(\n num_classes, in_channels, eps=sn_eps)\n self.bias_embedding = SNEmbedding(\n num_classes, in_channels, eps=sn_eps)\n else:\n raise NotImplementedError(\n f'{sn_style} style spectral Norm is not '\n 'supported yet')\n else:\n self.weight_embedding = nn.Embedding(num_classes, in_channels)\n self.bias_embedding = nn.Embedding(num_classes, in_channels)\n\n self.init_weights()\n\n def forward(self, x, y=None):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n y (Tensor, optional): Input label with shape (n, ).\n Default None.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n out = self.norm(x)\n if self.use_cbn:\n weight = self.weight_embedding(y)[:, :, None, None]\n bias = self.bias_embedding(y)[:, :, None, None]\n if self.reweight_embedding:\n # print('reweight_called --> correct')\n weight = weight + 1.\n out = out * weight + bias\n return out\n\n def init_weights(self):\n \"\"\"Initialize weights.\"\"\"\n if self.use_cbn:\n if self.init_type.upper() == 'STUDIO':\n nn.init.orthogonal_(self.weight_embedding.weight)\n nn.init.orthogonal_(self.bias_embedding.weight)\n elif self.init_type.upper() == 'BIGGAN':\n xavier_uniform_(self.weight_embedding.weight, gain=1)\n xavier_uniform_(self.bias_embedding.weight, gain=1)\n elif self.init_type.upper() in [\n 'SNGAN', 'SNGAN-PROJ', 'GAN-PROJ', 'SAGAN'\n ]:\n constant_init(self.weight_embedding, 1)\n constant_init(self.bias_embedding, 0)\n else:\n raise NotImplementedError('Unknown initialization method: '\n f'\\'{self.init_type}\\'')\n" }, { "path": "mmagic/models/editors/singan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .singan import SinGAN\nfrom .singan_discriminator import SinGANMultiScaleDiscriminator\nfrom .singan_generator import SinGANMultiScaleGenerator\n\n__all__ = [\n 'SinGAN', 'SinGANMultiScaleDiscriminator', 'SinGANMultiScaleGenerator'\n]\n" }, { "path": "mmagic/models/editors/singan/singan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pickle\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import Config, MessageHub, print_log\nfrom mmengine.model import is_model_wrapper\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch import Tensor\n\nfrom mmagic.models.utils import get_module_device\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import ForwardInputs, SampleList\nfrom ...base_models import BaseGAN\nfrom ...utils import set_requires_grad\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module()\nclass SinGAN(BaseGAN):\n \"\"\"SinGAN.\n\n This model implement the single image generative adversarial model proposed\n in: Singan: Learning a Generative Model from a Single Natural Image,\n ICCV'19.\n\n Notes for training:\n\n - This model should be trained with our dataset ``SinGANDataset``.\n - In training, the ``total_iters`` arguments is related to the number of\n scales in the image pyramid and ``iters_per_scale`` in the ``train_cfg``.\n You should set it carefully in the training config file.\n\n Notes for model architectures:\n\n - The generator and discriminator need ``num_scales`` in initialization.\n However, this arguments is generated by ``create_real_pyramid`` function\n from the ``singan_dataset.py``. The last element in the returned list\n (``stop_scale``) is the value for ``num_scales``. Pay attention that this\n scale is counted from zero. Please see our tutorial for SinGAN to obtain\n more details or our standard config for reference.\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): The number of times the generator is completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): The number of times the discriminator is\n completely updated before the generator is updated. Defaults to 1.\n num_scales (int): The number of scales/stages in generator/\n discriminator. Note that this number is counted from zero, which\n is the same as the original paper. Defaults to None.\n iters_per_scale (int): The training iteration for each resolution\n scale. Defaults to 2000.\n noise_weight_init (float): The initialize weight of fixed noise.\n Defaults to 0.1\n lr_scheduler_args (Optional[dict]): Arguments for learning schedulers.\n Note that in SinGAN, we use MultiStepLR, which is the same as the\n original paper. If not passed, no learning schedule will be used.\n Defaults to None.\n test_pkl_data (Optional[str]): The path of pickle file which contains\n fixed noise and noise weight. This is must for test. Defaults to\n None.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n num_scales: Optional[int] = None,\n iters_per_scale: int = 2000,\n noise_weight_init: int = 0.1,\n lr_scheduler_args: Optional[dict] = None,\n test_pkl_data: Optional[str] = None,\n ema_confg: Optional[dict] = None):\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, None, ema_confg)\n self.iters_per_scale = iters_per_scale\n self.noise_weight_init = noise_weight_init\n if lr_scheduler_args:\n self.lr_scheduler_args = deepcopy(lr_scheduler_args)\n else:\n self.lr_scheduler_args = None\n\n # related to train\n self.num_scales = num_scales\n self.curr_stage = -1\n self.noise_weights = [1]\n self.fixed_noises = []\n self.reals = []\n\n # related to test\n self.loaded_test_pkl = False\n self.pkl_data = test_pkl_data\n\n def load_test_pkl(self):\n \"\"\"Load pickle for test.\"\"\"\n if self.pkl_data is not None:\n with open(self.pkl_data, 'rb') as f:\n data = pickle.load(f)\n self.fixed_noises = self._from_numpy(data['fixed_noises'])\n self.noise_weights = self._from_numpy(data['noise_weights'])\n self.curr_stage = data['curr_stage']\n print_log(f'Load pkl data from {self.pkl_data}', 'current')\n self.pkl_data = self.pkl_data\n self.loaded_test_pkl = True\n\n def _from_numpy(self,\n data: Tuple[list,\n np.ndarray]) -> Tuple[Tensor, List[Tensor]]:\n \"\"\"Convert input numpy array or list of numpy array to Tensor or list\n of Tensor.\n\n Args:\n data (Tuple[list, np.ndarray]): Input data to convert.\n\n Returns:\n Tuple[Tensor, List[Tensor]]: Converted Tensor or list of tensor.\n \"\"\"\n if isinstance(data, list):\n return [self._from_numpy(x) for x in data]\n\n if isinstance(data, np.ndarray):\n data = torch.from_numpy(data)\n device = get_module_device(self.generator)\n data = data.to(device)\n return data\n\n return data\n\n def get_module(self, model: nn.Module, module_name: str) -> nn.Module:\n \"\"\"Get an inner module from model.\n\n Since we will wrapper DDP for some model, we have to judge whether the\n module can be indexed directly.\n\n Args:\n model (nn.Module): This model may wrapped with DDP or not.\n module_name (str): The name of specific module.\n\n Return:\n nn.Module: Returned sub module.\n \"\"\"\n module = model.module if hasattr(model, 'module') else model\n return getattr(module, module_name)\n\n def construct_fixed_noises(self):\n \"\"\"Construct the fixed noises list used in SinGAN.\"\"\"\n for i, real in enumerate(self.reals):\n h, w = real.shape[-2:]\n if i == 0:\n noise = torch.randn(1, 1, h, w).to(real)\n self.fixed_noises.append(noise)\n else:\n noise = torch.zeros_like(real)\n self.fixed_noises.append(noise)\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode=None) -> List[DataSample]:\n \"\"\"Forward function for SinGAN. For SinGAN, `inputs` should be a dict\n contains 'num_batches', 'mode' and other input arguments for the\n generator.\n\n Args:\n inputs (dict): Dict containing the necessary information\n (e.g., noise, num_batches, mode) to generate image.\n data_samples (Optional[list]): Data samples collated by\n :attr:`data_preprocessor`. Defaults to None.\n mode (Optional[str]): `mode` is not used in\n :class:`BaseConditionalGAN`. Defaults to None.\n \"\"\"\n\n # handle batch_inputs\n assert isinstance(inputs, dict), (\n 'SinGAN only support dict type inputs in forward function.')\n gen_kwargs = deepcopy(inputs)\n num_batches = gen_kwargs.pop('num_batches', 1)\n assert num_batches == 1, (\n 'SinGAN only support \\'num_batches\\' as 1, but receive '\n f'{num_batches}.')\n sample_model = self._get_valid_model(inputs)\n gen_kwargs.pop('sample_model', None) # remove sample_model\n\n mode = gen_kwargs.pop('mode', mode)\n mode = 'rand' if mode is None else mode\n curr_scale = gen_kwargs.pop('curr_scale', self.curr_stage)\n\n self.fixed_noises = [\n x.to(self.data_preprocessor.device) for x in self.fixed_noises\n ]\n\n batch_sample_list = []\n if sample_model in ['ema', 'orig']:\n if sample_model == 'ema':\n generator = self.generator_ema\n else:\n generator = self.generator\n\n outputs = generator(\n None,\n fixed_noises=self.fixed_noises,\n noise_weights=self.noise_weights,\n rand_mode=mode,\n num_batches=1,\n curr_scale=curr_scale,\n **gen_kwargs)\n\n gen_sample = DataSample()\n # destruct\n if isinstance(outputs, dict):\n outputs['fake_img'] = self.data_preprocessor.destruct(\n outputs['fake_img'], data_samples)\n outputs['prev_res_list'] = [\n self.data_preprocessor.destruct(r, data_samples)\n for r in outputs['prev_res_list']\n ]\n gen_sample.fake_img = self.data_preprocessor.destruct(\n outputs['fake_img'], data_samples)\n # gen_sample.prev_res_list = self.data_preprocessor.destruct(\n # outputs['fake_img'], data_samples)\n else:\n outputs = self.data_preprocessor.destruct(\n outputs, data_samples)\n\n # save to data sample\n for idx in range(num_batches):\n gen_sample = DataSample()\n # save inputs to data sample\n if data_samples:\n gen_sample.update(data_samples[idx])\n if isinstance(outputs, dict):\n gen_sample.fake_img = outputs['fake_img'][idx]\n gen_sample.prev_res_list = [\n r[idx] for r in outputs['prev_res_list']\n ]\n else:\n gen_sample.fake_img = outputs[idx]\n\n gen_sample.sample_model = sample_model\n batch_sample_list.append(gen_sample)\n\n else: # sample model is 'ema/orig'\n\n outputs_orig = self.generator(\n None,\n fixed_noises=self.fixed_noises,\n noise_weights=self.noise_weights,\n rand_mode=mode,\n num_batches=1,\n curr_scale=curr_scale,\n **gen_kwargs)\n outputs_ema = self.generator_ema(\n None,\n fixed_noises=self.fixed_noises,\n noise_weights=self.noise_weights,\n rand_mode=mode,\n num_batches=1,\n curr_scale=curr_scale,\n **gen_kwargs)\n\n # destruct\n if isinstance(outputs_orig, dict):\n outputs_orig['fake_img'] = self.data_preprocessor.destruct(\n outputs_orig['fake_img'], data_samples)\n outputs_orig['prev_res_list'] = [\n self.data_preprocessor.destruct(r, data_samples)\n for r in outputs_orig['prev_res_list']\n ]\n outputs_ema['fake_img'] = self.data_preprocessor.destruct(\n outputs_ema['fake_img'], data_samples)\n outputs_ema['prev_res_list'] = [\n self.data_preprocessor.destruct(r, data_samples)\n for r in outputs_ema['prev_res_list']\n ]\n else:\n outputs_orig = self.data_preprocessor.destruct(\n outputs_orig, data_samples)\n outputs_ema = self.data_preprocessor.destruct(\n outputs_ema, data_samples)\n\n # save to data sample\n for idx in range(num_batches):\n gen_sample = DataSample()\n gen_sample.ema = DataSample()\n gen_sample.orig = DataSample()\n # save inputs to data sample\n if data_samples:\n gen_sample.update(data_samples[idx])\n if isinstance(outputs_orig, dict):\n gen_sample.ema.fake_img = outputs_ema['fake_img'][idx]\n gen_sample.ema.prev_res_list = [\n r[idx] for r in outputs_ema['prev_res_list']\n ]\n gen_sample.orig.fake_img = outputs_orig['fake_img'][idx]\n gen_sample.orig.prev_res_list = [\n r[idx] for r in outputs_orig['prev_res_list']\n ]\n else:\n gen_sample.ema.fake_img = outputs_ema[idx]\n gen_sample.orig.fake_img = outputs_orig[idx]\n gen_sample.sample_model = sample_model\n\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n\n def gen_loss(self, disc_pred_fake: Tensor,\n recon_imgs: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Generator loss for SinGAN. SinGAN use WGAN's loss and MSE loss to\n train the generator.\n\n .. math:\n L_{D} = -\\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n + L_{MSE}\n L_{MSE} = \\text{mean} \\Vert x - G(z) \\Vert_2\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n recon_imgs (Tensor): Reconstructive images.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n losses_dict['loss_mse'] = 10 * F.mse_loss(recon_imgs,\n self.reals[self.curr_stage])\n loss, log_vars = self.parse_losses(losses_dict)\n return loss, log_vars\n\n def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,\n fake_data: Tensor, real_data: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Get disc loss. SAGAN, SNGAN and Proj-GAN use hinge loss to train\n the generator.\n\n .. math:\n L_{D} = \\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n - \\mathbb{E}_{x\\sim{p_{data}}}D\\left\\(x\\right\\) + L_{GP} \\\\\n L_{GP} = \\lambda\\mathbb{E}(\\Vert\\nabla_{\\tilde{x}}D(\\tilde{x})\n \\Vert_2-1)^2 \\\\\n \\tilde{x} = \\epsilon x + (1-\\epsilon)G(z)\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n fake_data (Tensor): Generated images, used to calculate gradient\n penalty.\n real_data (Tensor): Real images, used to calculate gradient\n penalty.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = disc_pred_fake.mean()\n losses_dict['loss_disc_real'] = -disc_pred_real.mean()\n\n # gradient penalty\n batch_size = real_data.size(0)\n alpha = torch.rand(batch_size, 1, 1, 1).to(real_data)\n\n # interpolate between real_data and fake_data\n interpolates = alpha * real_data + (1. - alpha) * fake_data\n interpolates = autograd.Variable(interpolates, requires_grad=True)\n\n disc_interpolates = self.discriminator(\n interpolates, curr_scale=self.curr_stage)\n gradients = autograd.grad(\n outputs=disc_interpolates,\n inputs=interpolates,\n grad_outputs=torch.ones_like(disc_interpolates),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n # norm_mode is 'pixel'\n gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()\n losses_dict['loss_gp'] = 0.1 * gradients_penalty\n\n parsed_loss, log_vars = self.parse_losses(losses_dict)\n return parsed_loss, log_vars\n\n def train_generator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n fake_imgs = self.generator(\n inputs['input_sample'],\n self.fixed_noises,\n self.noise_weights,\n rand_mode='rand',\n curr_scale=self.curr_stage)\n disc_pred_fake_g = self.discriminator(\n fake_imgs, curr_scale=self.curr_stage)\n\n recon_imgs = self.generator(\n inputs['input_sample'],\n self.fixed_noises,\n self.noise_weights,\n rand_mode='recon',\n curr_scale=self.curr_stage)\n\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake_g, recon_imgs)\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def train_discriminator(self, inputs: dict, data_samples: List[DataSample],\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (List[DataSample]): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n input_sample = inputs['input_sample']\n fake_imgs = self.generator(\n input_sample,\n self.fixed_noises,\n self.noise_weights,\n rand_mode='rand',\n curr_scale=self.curr_stage)\n\n # disc pred for fake imgs and real_imgs\n real_imgs = self.reals[self.curr_stage]\n disc_pred_fake = self.discriminator(fake_imgs.detach(),\n self.curr_stage)\n disc_pred_real = self.discriminator(real_imgs, self.curr_stage)\n parsed_loss, log_vars = self.disc_loss(disc_pred_fake, disc_pred_real,\n fake_imgs, real_imgs)\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def train_gan(self, inputs_dict: dict, data_sample: List[DataSample],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train GAN model. In the training of GAN models, generator and\n discriminator are updated alternatively. In MMagic's design,\n `self.train_step` is called with data input. Therefore we always update\n discriminator, whose updating is relay on real data, and then determine\n if the generator needs to be updated based on the current number of\n iterations. More details about whether to update generator can be found\n in :meth:`should_gen_update`.\n\n Args:\n data (dict): Data sampled from dataloader.\n data_sample (List[DataSample]): List of data sample contains GT\n and meta information.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n with disc_optimizer_wrapper.optim_context(self.discriminator):\n log_vars = self.train_discriminator(inputs_dict, data_sample,\n disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n for _ in range(self.generator_steps * gen_accu_iters):\n with gen_optimizer_wrapper.optim_context(self.generator):\n log_vars_gen = self.train_generator(\n inputs_dict, data_sample, gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = self.gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n # if not update buffer, copy buffer from orig model\n if not self.generator_ema.update_buffers:\n self.generator_ema.sync_buffers(\n self.generator.module if is_model_wrapper(\n self.generator) else self.generator)\n elif self.with_ema_gen:\n # before ema, copy weights from orig\n self.generator_ema.sync_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n return log_vars\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n \"\"\"Train step for SinGAN model. SinGAN is trained with multi-resolution\n images, and each resolution is trained for `:attr:self.iters_per_scale`\n times.\n\n We initialize the weight and learning rate scheduler of the\n corresponding module at the start of each resolution's training. At\n the end of each resolution's training, we update the weight of the\n noise of current resolution by mse loss between reconstructed image and\n real image.\n\n Args:\n data (dict): Data sampled from dataloader.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n\n if curr_iter % (self.iters_per_scale * self.discriminator_steps) == 0:\n self.curr_stage += 1\n # load weights from prev scale\n self.get_module(self.generator, 'check_and_load_prev_weight')(\n self.curr_stage)\n self.get_module(self.discriminator, 'check_and_load_prev_weight')(\n self.curr_stage)\n\n # assert grad_accumulation step is 1\n curr_gen_optim = optim_wrapper[f'generator_{self.curr_stage}']\n curr_disc_optim = optim_wrapper[f'discriminator_{self.curr_stage}']\n _warning_msg = ('SinGAN do set batch size as 1 during training '\n 'and do not support gradient accumulation.')\n assert curr_gen_optim._accumulative_counts == 1, _warning_msg\n assert curr_disc_optim._accumulative_counts == 1, _warning_msg\n\n # build parameters scheduler manually, because parameters_schedule\n # hook update all scheduler at the same\n if self.lr_scheduler_args:\n self.gen_scheduler = torch.optim.lr_scheduler.MultiStepLR(\n curr_gen_optim.optimizer, **self.lr_scheduler_args)\n self.disc_scheduler = torch.optim.lr_scheduler.MultiStepLR(\n curr_disc_optim.optimizer, **self.lr_scheduler_args)\n\n # get current optimizer_wrapper\n curr_optimizer_wrapper = OptimWrapperDict(\n generator=optim_wrapper[f'generator_{self.curr_stage}'],\n discriminator=optim_wrapper[f'discriminator_{self.curr_stage}'])\n\n # handle inputs\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n # setup fixed noises and reals pyramid\n if curr_iter == 0 or len(self.reals) == 0:\n keys = [k for k in inputs_dict.keys() if 'real_scale' in k]\n scales = len(keys)\n self.reals = [inputs_dict[f'real_scale{s}'] for s in range(scales)]\n\n # here we do not padding fixed noises\n self.construct_fixed_noises()\n\n # standard train step\n log_vars = self.train_gan(inputs_dict, data_samples,\n curr_optimizer_wrapper)\n log_vars['curr_stage'] = self.curr_stage\n\n # update noise weight\n if ((curr_iter + 1) % (self.iters_per_scale * self.discriminator_steps)\n == 0) and (self.curr_stage < len(self.reals) - 1):\n with torch.no_grad():\n g_recon = self.generator(\n inputs_dict['input_sample'],\n self.fixed_noises,\n self.noise_weights,\n rand_mode='recon',\n curr_scale=self.curr_stage)\n if isinstance(g_recon, dict):\n g_recon = g_recon['fake_img']\n g_recon = F.interpolate(\n g_recon, self.reals[self.curr_stage + 1].shape[-2:])\n\n mse = F.mse_loss(g_recon.detach(), self.reals[self.curr_stage + 1])\n rmse = torch.sqrt(mse)\n self.noise_weights.append(self.noise_weight_init * rmse.item())\n\n # call scheduler when all submodules are fully updated.\n if (curr_iter + 1) % self.discriminator_steps == 0:\n if self.lr_scheduler_args:\n self.disc_scheduler.step()\n self.gen_scheduler.step()\n\n return log_vars\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data in test progress. Before\n generate images, we call `:meth:self.load_test_pkl` to load the fixed\n noise and current stage of the model from the pickle file.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n if not self.loaded_test_pkl:\n self.load_test_pkl()\n return super().test_step(data)\n" }, { "path": "mmagic/models/editors/singan/singan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch.nn as nn\nfrom mmengine import print_log\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .singan_modules import DiscriminatorBlock\n\n\n@MODELS.register_module()\nclass SinGANMultiScaleDiscriminator(BaseModule):\n \"\"\"Multi-Scale Discriminator used in SinGAN.\n\n More details can be found in: Singan: Learning a Generative Model from a\n Single Natural Image, ICCV'19.\n\n Args:\n in_channels (int): Input channels.\n num_scales (int): The number of scales/stages in generator. Note\n that this number is counted from zero, which is the same as the\n original paper.\n kernel_size (int, optional): Kernel size, same as :obj:`nn.Conv2d`.\n Defaults to 3.\n padding (int, optional): Padding for the convolutional layer, same as\n :obj:`nn.Conv2d`. Defaults to 0.\n num_layers (int, optional): The number of convolutional layers in each\n generator block. Defaults to 5.\n base_channels (int, optional): The basic channels for convolutional\n layers in the generator block. Defaults to 32.\n min_feat_channels (int, optional): Minimum channels for the feature\n maps in the generator block. Defaults to 32.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n in_channels,\n num_scales,\n kernel_size=3,\n padding=0,\n num_layers=5,\n base_channels=32,\n min_feat_channels=32,\n init_cfg=None,\n **kwargs):\n super().__init__(init_cfg=init_cfg)\n self.blocks = nn.ModuleList()\n for scale in range(num_scales + 1):\n base_ch = min(base_channels * pow(2, int(np.floor(scale / 4))),\n 128)\n min_feat_ch = min(\n min_feat_channels * pow(2, int(np.floor(scale / 4))), 128)\n self.blocks.append(\n DiscriminatorBlock(\n in_channels=in_channels,\n kernel_size=kernel_size,\n padding=padding,\n num_layers=num_layers,\n base_channels=base_ch,\n min_feat_channels=min_feat_ch,\n **kwargs))\n\n def forward(self, x, curr_scale):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map.\n curr_scale (int): Current scale for discriminator. If in testing,\n you need to set it to the last scale.\n\n Returns:\n Tensor: Discriminative results.\n \"\"\"\n out = self.blocks[curr_scale](x)\n return out\n\n def check_and_load_prev_weight(self, curr_scale):\n if curr_scale == 0:\n return\n prev_ch = self.blocks[curr_scale - 1].base_channels\n curr_ch = self.blocks[curr_scale].base_channels\n if prev_ch == curr_ch:\n self.blocks[curr_scale].load_state_dict(\n self.blocks[curr_scale - 1].state_dict())\n print_log('Successfully load pretrained model from last scale.')\n else:\n print_log('Cannot load pretrained model from last scale since'\n f' prev_ch({prev_ch}) != curr_ch({curr_ch})')\n" }, { "path": "mmagic/models/editors/singan/singan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModule\nfrom mmengine.runner import load_state_dict\n\nfrom mmagic.registry import MODELS\nfrom .singan_modules import GeneratorBlock\n\n\n@MODELS.register_module()\nclass SinGANMultiScaleGenerator(BaseModule):\n \"\"\"Multi-Scale Generator used in SinGAN.\n\n More details can be found in: Singan: Learning a Generative Model from a\n Single Natural Image, ICCV'19.\n\n Notes:\n\n - In this version, we adopt the interpolation function from the official\n PyTorch APIs, which is different from the original implementation by the\n authors. However, in our experiments, this influence can be ignored.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n num_scales (int): The number of scales/stages in generator. Note\n that this number is counted from zero, which is the same as the\n original paper.\n kernel_size (int, optional): Kernel size, same as :obj:`nn.Conv2d`.\n Defaults to 3.\n padding (int, optional): Padding for the convolutional layer, same as\n :obj:`nn.Conv2d`. Defaults to 0.\n num_layers (int, optional): The number of convolutional layers in each\n generator block. Defaults to 5.\n base_channels (int, optional): The basic channels for convolutional\n layers in the generator block. Defaults to 32.\n min_feat_channels (int, optional): Minimum channels for the feature\n maps in the generator block. Defaults to 32.\n out_act_cfg (dict | None, optional): Configs for output activation\n layer. Defaults to dict(type='Tanh').\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n num_scales,\n kernel_size=3,\n padding=0,\n num_layers=5,\n base_channels=32,\n min_feat_channels=32,\n out_act_cfg=dict(type='Tanh'),\n init_cfg=None,\n **kwargs):\n super().__init__(init_cfg=init_cfg)\n\n self.pad_head = int((kernel_size - 1) / 2 * num_layers)\n self.blocks = nn.ModuleList()\n\n self.upsample = partial(\n F.interpolate, mode='bicubic', align_corners=True)\n\n for scale in range(num_scales + 1):\n base_ch = min(base_channels * pow(2, int(np.floor(scale / 4))),\n 128)\n min_feat_ch = min(\n min_feat_channels * pow(2, int(np.floor(scale / 4))), 128)\n\n self.blocks.append(\n GeneratorBlock(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n padding=padding,\n num_layers=num_layers,\n base_channels=base_ch,\n min_feat_channels=min_feat_ch,\n out_act_cfg=out_act_cfg,\n **kwargs))\n\n self.noise_padding_layer = nn.ZeroPad2d(self.pad_head)\n self.img_padding_layer = nn.ZeroPad2d(self.pad_head)\n\n def forward(self,\n input_sample,\n fixed_noises,\n noise_weights,\n rand_mode,\n curr_scale,\n num_batches=1,\n get_prev_res=False,\n return_noise=False):\n \"\"\"Forward function.\n\n Args:\n input_sample (Tensor | None): The input for generator. In the\n original implementation, a tensor filled with zeros is adopted.\n If None is given, we will construct it from the first fixed\n noises.\n fixed_noises (list[Tensor]): List of the fixed noises in SinGAN.\n noise_weights (list[float]): List of the weights for random noises.\n rand_mode (str): Choices from ['rand', 'recon']. In ``rand`` mode,\n it will sample from random noises. Otherwise, the\n reconstruction for the single image will be returned.\n curr_scale (int): The scale for the current inference or training.\n num_batches (int, optional): The number of batches. Defaults to 1.\n get_prev_res (bool, optional): Whether to return results from\n previous stages. Defaults to False.\n return_noise (bool, optional): Whether to return noises tensor.\n Defaults to False.\n\n Returns:\n Tensor | dict: Generated image tensor or dictionary containing \\\n more data.\n \"\"\"\n if get_prev_res or return_noise:\n prev_res_list = []\n noise_list = []\n\n if input_sample is None:\n input_sample = torch.zeros(\n (num_batches, 3, fixed_noises[0].shape[-2],\n fixed_noises[0].shape[-1])).to(fixed_noises[0])\n\n g_res = input_sample\n\n for stage in range(curr_scale + 1):\n if rand_mode == 'recon':\n noise_ = fixed_noises[stage]\n else:\n noise_ = torch.randn(num_batches,\n *fixed_noises[stage].shape[1:]).to(g_res)\n if return_noise:\n noise_list.append(noise_)\n\n # add padding at head\n pad_ = (self.pad_head, ) * 4\n noise_ = F.pad(noise_, pad_)\n g_res_pad = F.pad(g_res, pad_)\n noise = noise_ * noise_weights[stage] + g_res_pad\n\n g_res = self.blocks[stage](noise.detach(), g_res)\n\n if get_prev_res and stage != curr_scale:\n prev_res_list.append(g_res)\n\n # upsample, here we use interpolation from PyTorch\n if stage != curr_scale:\n h_next, w_next = fixed_noises[stage + 1].shape[-2:]\n g_res = self.upsample(g_res, (h_next, w_next))\n\n if get_prev_res or return_noise:\n output_dict = dict(\n fake_img=g_res,\n prev_res_list=prev_res_list,\n # noise_batch=noise_list\n )\n return output_dict\n\n return g_res\n\n def check_and_load_prev_weight(self, curr_scale):\n logger = MMLogger.get_current_instance()\n if curr_scale == 0:\n return\n prev_ch = self.blocks[curr_scale - 1].base_channels\n curr_ch = self.blocks[curr_scale].base_channels\n\n prev_in_ch = self.blocks[curr_scale - 1].in_channels\n curr_in_ch = self.blocks[curr_scale].in_channels\n if prev_ch == curr_ch and prev_in_ch == curr_in_ch:\n load_state_dict(\n self.blocks[curr_scale],\n self.blocks[curr_scale - 1].state_dict(),\n logger=MMLogger.get_current_instance())\n logger.info('Successfully load pretrained model from last scale.')\n else:\n logger.info(\n 'Cannot load pretrained model from last scale since'\n f' prev_ch({prev_ch}) != curr_ch({curr_ch})'\n f' or prev_in_ch({prev_in_ch}) != curr_in_ch({curr_in_ch})')\n" }, { "path": "mmagic/models/editors/singan/singan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import constant_init, normal_init\nfrom mmengine.runner import load_checkpoint\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\n\nclass GeneratorBlock(nn.Module):\n \"\"\"Generator block used in SinGAN.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n num_scales (int): The number of scales/stages in generator. Note\n that this number is counted from zero, which is the same as the\n original paper.\n kernel_size (int, optional): Kernel size, same as :obj:`nn.Conv2d`.\n Defaults to 3.\n padding (int, optional): Padding for the convolutional layer, same as\n :obj:`nn.Conv2d`. Defaults to 0.\n num_layers (int, optional): The number of convolutional layers in each\n generator block. Defaults to 5.\n base_channels (int, optional): The basic channels for convolutional\n layers in the generator block. Defaults to 32.\n min_feat_channels (int, optional): Minimum channels for the feature\n maps in the generator block. Defaults to 32.\n out_act_cfg (dict | None, optional): Configs for output activation\n layer. Defaults to dict(type='Tanh').\n stride (int, optional): Same as :obj:`nn.Conv2d`. Defaults to 1.\n allow_no_residual (bool, optional): Whether to allow no residual link\n in this block. Defaults to False.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n padding,\n num_layers,\n base_channels,\n min_feat_channels,\n out_act_cfg=dict(type='Tanh'),\n stride=1,\n allow_no_residual=False,\n **kwargs):\n super().__init__()\n self.in_channels = in_channels\n\n self.base_channels = base_channels\n\n self.kernel_size = kernel_size\n self.num_layers = num_layers\n self.allow_no_residual = allow_no_residual\n\n self.head = ConvModule(\n in_channels=in_channels,\n out_channels=base_channels,\n kernel_size=kernel_size,\n padding=padding,\n stride=1,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n **kwargs)\n\n self.body = nn.Sequential()\n\n for i in range(num_layers - 2):\n feat_channels_ = int(base_channels / pow(2, (i + 1)))\n block = ConvModule(\n max(2 * feat_channels_, min_feat_channels),\n max(feat_channels_, min_feat_channels),\n kernel_size=kernel_size,\n padding=padding,\n stride=stride,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n **kwargs)\n self.body.add_module(f'block{i+1}', block)\n\n self.tail = ConvModule(\n max(feat_channels_, min_feat_channels),\n out_channels,\n kernel_size=kernel_size,\n padding=padding,\n stride=1,\n norm_cfg=None,\n act_cfg=out_act_cfg,\n **kwargs)\n\n self.init_weights()\n\n def forward(self, x, prev):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map.\n prev (Tensor): Previous feature map.\n\n Returns:\n Tensor: Output feature map with the shape of (N, C, H, W).\n \"\"\"\n x = self.head(x)\n x = self.body(x)\n x = self.tail(x)\n\n # if prev and x are not in the same shape at the channel dimension\n if self.allow_no_residual and x.shape[1] != prev.shape[1]:\n return x\n\n return x + prev\n\n def init_weights(self, pretrained=None):\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n normal_init(m, 0, 0.02)\n elif isinstance(m, (_BatchNorm, nn.InstanceNorm2d)):\n constant_init(m, 1)\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n\n\nclass DiscriminatorBlock(nn.Module):\n \"\"\"Discriminator Block used in SinGAN.\n\n Args:\n in_channels (int): Input channels.\n base_channels (int): Base channels for this block.\n min_feat_channels (int): The minimum channels for feature map.\n kernel_size (int): Size of convolutional kernel, same as\n :obj:`nn.Conv2d`.\n padding (int): Padding for convolutional layer, same as\n :obj:`nn.Conv2d`.\n num_layers (int): The number of convolutional layers in this block.\n norm_cfg (dict | None, optional): Config for the normalization layer.\n Defaults to dict(type='BN').\n act_cfg (dict | None, optional): Config for the activation layer.\n Defaults to dict(type='LeakyReLU', negative_slope=0.2).\n stride (int, optional): The stride for the convolutional layer, same as\n :obj:`nn.Conv2d`. Defaults to 1.\n \"\"\"\n\n def __init__(self,\n in_channels,\n base_channels,\n min_feat_channels,\n kernel_size,\n padding,\n num_layers,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n stride=1,\n **kwargs):\n super().__init__()\n\n self.base_channels = base_channels\n self.stride = stride\n self.head = ConvModule(\n in_channels,\n base_channels,\n kernel_size=kernel_size,\n padding=padding,\n stride=1,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs)\n\n self.body = nn.Sequential()\n\n for i in range(num_layers - 2):\n feat_channels_ = int(base_channels / pow(2, (i + 1)))\n block = ConvModule(\n max(2 * feat_channels_, min_feat_channels),\n max(feat_channels_, min_feat_channels),\n kernel_size=kernel_size,\n padding=padding,\n stride=stride,\n conv_cfg=None,\n norm_cfg=norm_cfg,\n act_cfg=act_cfg,\n **kwargs)\n self.body.add_module(f'block{i+1}', block)\n\n self.tail = ConvModule(\n max(feat_channels_, min_feat_channels),\n 1,\n kernel_size=kernel_size,\n padding=padding,\n stride=1,\n norm_cfg=None,\n act_cfg=None,\n **kwargs)\n\n self.init_weights()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n x = self.head(x)\n x = self.body(x)\n x = self.tail(x)\n\n return x\n\n # TODO: study the effects of init functions\n def init_weights(self, pretrained=None):\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=False, logger=logger)\n elif pretrained is None:\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n normal_init(m, 0, 0.02)\n elif isinstance(m, (_BatchNorm, nn.InstanceNorm2d)):\n constant_init(m, 1)\n else:\n raise TypeError('pretrained must be a str or None but'\n f' got {type(pretrained)} instead.')\n" }, { "path": "mmagic/models/editors/srcnn/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .srcnn_net import SRCNNNet\n\n__all__ = ['SRCNNNet']\n" }, { "path": "mmagic/models/editors/srcnn/srcnn_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SRCNNNet(BaseModule):\n \"\"\"SRCNN network structure for image super resolution.\n\n SRCNN has three conv layers. For each layer, we can define the\n `in_channels`, `out_channels` and `kernel_size`.\n The input image will first be upsampled with a bicubic upsampler, and then\n super-resolved in the HR spatial size.\n\n Paper: Learning a Deep Convolutional Network for Image Super-Resolution.\n\n Args:\n channels (tuple[int]): A tuple of channel numbers for each layer\n including channels of input and output . Default: (3, 64, 32, 3).\n kernel_sizes (tuple[int]): A tuple of kernel sizes for each conv layer.\n Default: (9, 1, 5).\n upscale_factor (int): Upsampling factor. Default: 4.\n \"\"\"\n\n def __init__(self,\n channels=(3, 64, 32, 3),\n kernel_sizes=(9, 1, 5),\n upscale_factor=4):\n super().__init__()\n assert len(channels) == 4, ('The length of channel tuple should be 4, '\n f'but got {len(channels)}')\n assert len(kernel_sizes) == 3, (\n 'The length of kernel tuple should be 3, '\n f'but got {len(kernel_sizes)}')\n self.upscale_factor = upscale_factor\n self.img_upsampler = nn.Upsample(\n scale_factor=self.upscale_factor,\n mode='bicubic',\n align_corners=False)\n\n self.conv1 = nn.Conv2d(\n channels[0],\n channels[1],\n kernel_size=kernel_sizes[0],\n padding=kernel_sizes[0] // 2)\n self.conv2 = nn.Conv2d(\n channels[1],\n channels[2],\n kernel_size=kernel_sizes[1],\n padding=kernel_sizes[1] // 2)\n self.conv3 = nn.Conv2d(\n channels[2],\n channels[3],\n kernel_size=kernel_sizes[2],\n padding=kernel_sizes[2] // 2)\n\n self.relu = nn.ReLU()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x = self.img_upsampler(x)\n out = self.relu(self.conv1(x))\n out = self.relu(self.conv2(out))\n out = self.conv3(out)\n return out\n" }, { "path": "mmagic/models/editors/srgan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .modified_vgg import ModifiedVGG\nfrom .sr_resnet import MSRResNet\nfrom .srgan import SRGAN\n\n__all__ = [\n 'ModifiedVGG',\n 'MSRResNet',\n 'SRGAN',\n]\n" }, { "path": "mmagic/models/editors/srgan/modified_vgg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass ModifiedVGG(BaseModule):\n \"\"\"A modified VGG discriminator with input size 128 x 128.\n\n It is used to train SRGAN and ESRGAN.\n\n Args:\n in_channels (int): Channel number of inputs. Default: 3.\n mid_channels (int): Channel number of base intermediate features.\n Default: 64.\n \"\"\"\n\n def __init__(self, in_channels, mid_channels):\n super().__init__()\n\n self.conv0_0 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1, bias=True)\n self.conv0_1 = nn.Conv2d(\n mid_channels, mid_channels, 4, 2, 1, bias=False)\n self.bn0_1 = nn.BatchNorm2d(mid_channels, affine=True)\n\n self.conv1_0 = nn.Conv2d(\n mid_channels, mid_channels * 2, 3, 1, 1, bias=False)\n self.bn1_0 = nn.BatchNorm2d(mid_channels * 2, affine=True)\n self.conv1_1 = nn.Conv2d(\n mid_channels * 2, mid_channels * 2, 4, 2, 1, bias=False)\n self.bn1_1 = nn.BatchNorm2d(mid_channels * 2, affine=True)\n\n self.conv2_0 = nn.Conv2d(\n mid_channels * 2, mid_channels * 4, 3, 1, 1, bias=False)\n self.bn2_0 = nn.BatchNorm2d(mid_channels * 4, affine=True)\n self.conv2_1 = nn.Conv2d(\n mid_channels * 4, mid_channels * 4, 4, 2, 1, bias=False)\n self.bn2_1 = nn.BatchNorm2d(mid_channels * 4, affine=True)\n\n self.conv3_0 = nn.Conv2d(\n mid_channels * 4, mid_channels * 8, 3, 1, 1, bias=False)\n self.bn3_0 = nn.BatchNorm2d(mid_channels * 8, affine=True)\n self.conv3_1 = nn.Conv2d(\n mid_channels * 8, mid_channels * 8, 4, 2, 1, bias=False)\n self.bn3_1 = nn.BatchNorm2d(mid_channels * 8, affine=True)\n\n self.conv4_0 = nn.Conv2d(\n mid_channels * 8, mid_channels * 8, 3, 1, 1, bias=False)\n self.bn4_0 = nn.BatchNorm2d(mid_channels * 8, affine=True)\n self.conv4_1 = nn.Conv2d(\n mid_channels * 8, mid_channels * 8, 4, 2, 1, bias=False)\n self.bn4_1 = nn.BatchNorm2d(mid_channels * 8, affine=True)\n\n self.linear1 = nn.Linear(mid_channels * 8 * 4 * 4, 100)\n self.linear2 = nn.Linear(100, 1)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n assert x.size(2) == 128 and x.size(3) == 128, (\n f'Input spatial size must be 128x128, '\n f'but received {x.size()}.')\n\n feat = self.lrelu(self.conv0_0(x))\n feat = self.lrelu(self.bn0_1(\n self.conv0_1(feat))) # output spatial size: (64, 64)\n\n feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))\n feat = self.lrelu(self.bn1_1(\n self.conv1_1(feat))) # output spatial size: (32, 32)\n\n feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))\n feat = self.lrelu(self.bn2_1(\n self.conv2_1(feat))) # output spatial size: (16, 16)\n\n feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))\n feat = self.lrelu(self.bn3_1(\n self.conv3_1(feat))) # output spatial size: (8, 8)\n\n feat = self.lrelu(self.bn4_0(self.conv4_0(feat)))\n feat = self.lrelu(self.bn4_1(\n self.conv4_1(feat))) # output spatial size: (4, 4)\n\n feat = feat.view(feat.size(0), -1)\n feat = self.lrelu(self.linear1(feat))\n out = self.linear2(feat)\n return out\n" }, { "path": "mmagic/models/editors/srgan/sr_resnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import default_init_weights, make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass MSRResNet(BaseModule):\n \"\"\"Modified SRResNet.\n\n A compacted version modified from SRResNet in \"Photo-Realistic Single\n Image Super-Resolution Using a Generative Adversarial Network\".\n\n It uses residual blocks without BN, similar to EDSR.\n Currently, it supports x2, x3 and x4 upsampling scale factor.\n\n Args:\n in_channels (int): Channel number of inputs.\n out_channels (int): Channel number of outputs.\n mid_channels (int): Channel number of intermediate features.\n Default: 64.\n num_blocks (int): Block number in the trunk network. Default: 16.\n upscale_factor (int): Upsampling factor. Support x2, x3 and x4.\n Default: 4.\n \"\"\"\n _supported_upscale_factors = [2, 3, 4]\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4):\n\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.mid_channels = mid_channels\n self.num_blocks = num_blocks\n self.upscale_factor = upscale_factor\n\n self.conv_first = nn.Conv2d(\n in_channels, mid_channels, 3, 1, 1, bias=True)\n self.trunk_net = make_layer(\n ResidualBlockNoBN, num_blocks, mid_channels=mid_channels)\n\n # upsampling\n if self.upscale_factor in [2, 3]:\n self.upsample1 = PixelShufflePack(\n mid_channels,\n mid_channels,\n self.upscale_factor,\n upsample_kernel=3)\n elif self.upscale_factor == 4:\n self.upsample1 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n self.upsample2 = PixelShufflePack(\n mid_channels, mid_channels, 2, upsample_kernel=3)\n else:\n raise ValueError(\n f'Unsupported scale factor {self.upscale_factor}. '\n f'Currently supported ones are '\n f'{self._supported_upscale_factors}.')\n\n self.conv_hr = nn.Conv2d(\n mid_channels, mid_channels, 3, 1, 1, bias=True)\n self.conv_last = nn.Conv2d(\n mid_channels, out_channels, 3, 1, 1, bias=True)\n\n self.img_upsampler = nn.Upsample(\n scale_factor=self.upscale_factor,\n mode='bilinear',\n align_corners=False)\n\n # activation function\n self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n\n self.init_weights()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n feat = self.lrelu(self.conv_first(x))\n out = self.trunk_net(feat)\n\n if self.upscale_factor in [2, 3]:\n out = self.upsample1(out)\n elif self.upscale_factor == 4:\n out = self.upsample1(out)\n out = self.upsample2(out)\n\n out = self.conv_last(self.lrelu(self.conv_hr(out)))\n upsampled_img = self.img_upsampler(x)\n out += upsampled_img\n return out\n\n def init_weights(self):\n \"\"\"Init weights for models.\"\"\"\n for m in [self.conv_first, self.conv_hr, self.conv_last]:\n default_init_weights(m, 0.1)\n" }, { "path": "mmagic/models/editors/srgan/srgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List\n\nimport torch\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.models.base_models import BaseEditModel\nfrom mmagic.models.utils import set_requires_grad\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SRGAN(BaseEditModel):\n \"\"\"SRGAN model for single image super-resolution.\n\n Ref:\n Photo-Realistic Single Image Super-Resolution Using a Generative\n Adversarial Network.\n\n Args:\n generator (dict): Config for the generator.\n discriminator (dict): Config for the discriminator. Default: None.\n gan_loss (dict): Config for the gan loss.\n Note that the loss weight in gan loss is only for the generator.\n pixel_loss (dict): Config for the pixel loss. Default: None.\n perceptual_loss (dict): Config for the perceptual loss. Default: None.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n discriminator=None,\n gan_loss=None,\n pixel_loss=None,\n perceptual_loss=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n # discriminator\n self.discriminator = MODELS.build(\n discriminator) if discriminator else None\n\n # loss\n self.gan_loss = MODELS.build(gan_loss) if gan_loss else None\n self.perceptual_loss = MODELS.build(\n perceptual_loss) if perceptual_loss else None\n\n self.disc_steps = 1 if self.train_cfg is None else self.train_cfg.get(\n 'disc_steps', 1)\n self.disc_repeat = 1 if self.train_cfg is None else self.train_cfg.get(\n 'disc_repeat', 1)\n self.disc_init_steps = (0 if self.train_cfg is None else\n self.train_cfg.get('disc_init_steps', 0))\n\n self.register_buffer('step_counter', torch.tensor(0), False)\n\n if self.discriminator is None or self.gan_loss is None:\n # No GAN model or loss.\n self.disc_repeat = 0\n\n def forward_train(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward training. Losses of training is calculated in train_step.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n Tensor: Result of ``forward_tensor`` with ``training=True``.\n \"\"\"\n\n return self.forward_tensor(\n inputs, data_samples, training=True, **kwargs)\n\n def forward_tensor(self, inputs, data_samples=None, training=False):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n training (bool): Whether is training. Default: False.\n\n Returns:\n Tensor: result of simple forward.\n \"\"\"\n\n feats = self.generator(inputs)\n\n return feats\n\n def if_run_g(self):\n \"\"\"Calculates whether need to run the generator step.\"\"\"\n\n return (self.step_counter % self.disc_steps == 0\n and self.step_counter >= self.disc_init_steps)\n\n def if_run_d(self):\n \"\"\"Calculates whether need to run the discriminator step.\"\"\"\n\n return self.discriminator and self.gan_loss\n\n def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Tensor):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n losses = dict()\n\n # pix loss\n if self.pixel_loss:\n losses['loss_pix'] = self.pixel_loss(batch_outputs, batch_gt_data)\n\n # perceptual loss\n if self.perceptual_loss:\n loss_percep, loss_style = self.perceptual_loss(\n batch_outputs, batch_gt_data)\n if loss_percep is not None:\n losses['loss_perceptual'] = loss_percep\n if loss_style is not None:\n losses['loss_style'] = loss_style\n\n # gan loss for generator\n if self.gan_loss and self.discriminator:\n fake_g_pred = self.discriminator(batch_outputs)\n losses['loss_gan'] = self.gan_loss(\n fake_g_pred, target_is_real=True, is_disc=False)\n\n return losses\n\n def d_step_real(self, batch_outputs, batch_gt_data: torch.Tensor):\n \"\"\"Real part of D step.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n Tensor: Real part of gan_loss for discriminator.\n \"\"\"\n\n # real\n real_d_pred = self.discriminator(batch_gt_data)\n loss_d_real = self.gan_loss(\n real_d_pred, target_is_real=True, is_disc=True)\n\n return loss_d_real\n\n def d_step_fake(self, batch_outputs: torch.Tensor, batch_gt_data):\n \"\"\"Fake part of D step.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n Tensor: Fake part of gan_loss for discriminator.\n \"\"\"\n\n # fake\n fake_d_pred = self.discriminator(batch_outputs.detach())\n loss_d_fake = self.gan_loss(\n fake_d_pred, target_is_real=False, is_disc=True)\n\n return loss_d_fake\n\n def g_step_with_optim(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor,\n optim_wrapper: OptimWrapperDict):\n \"\"\"G step with optim of GAN: Calculate losses of generator and run\n optim.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n optim_wrapper (OptimWrapperDict): Optim wrapper dict.\n\n Returns:\n dict: Dict of parsed losses.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n\n with g_optim_wrapper.optim_context(self):\n losses_g = self.g_step(batch_outputs, batch_gt_data)\n\n parsed_losses_g, log_vars_g = self.parse_losses(losses_g)\n g_optim_wrapper.update_params(parsed_losses_g)\n\n return log_vars_g\n\n def d_step_with_optim(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor,\n optim_wrapper: OptimWrapperDict):\n \"\"\"D step with optim of GAN: Calculate losses of discriminator and run\n optim.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n optim_wrapper (OptimWrapperDict): Optim wrapper dict.\n\n Returns:\n dict: Dict of parsed losses.\n \"\"\"\n\n log_vars = dict()\n d_optim_wrapper = optim_wrapper['discriminator']\n\n with d_optim_wrapper.optim_context(self):\n loss_d_real = self.d_step_real(batch_outputs, batch_gt_data)\n\n parsed_losses_dr, log_vars_dr = self.parse_losses(\n dict(loss_d_real=loss_d_real))\n log_vars.update(log_vars_dr)\n loss_dr = d_optim_wrapper.scale_loss(parsed_losses_dr)\n d_optim_wrapper.backward(loss_dr)\n\n with d_optim_wrapper.optim_context(self):\n loss_d_fake = self.d_step_fake(batch_outputs, batch_gt_data)\n\n parsed_losses_df, log_vars_df = self.parse_losses(\n dict(loss_d_fake=loss_d_fake))\n log_vars.update(log_vars_df)\n loss_df = d_optim_wrapper.scale_loss(parsed_losses_df)\n d_optim_wrapper.backward(loss_df)\n\n if d_optim_wrapper.should_update():\n d_optim_wrapper.step()\n d_optim_wrapper.zero_grad()\n\n return log_vars\n\n def extract_gt_data(self, data_samples):\n \"\"\"extract gt data from data samples.\n\n Args:\n data_samples (list): List of DataSample.\n\n Returns:\n Tensor: Extract gt data.\n \"\"\"\n\n batch_gt_data = data_samples.gt_img\n\n return batch_gt_data\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step of GAN-based method.\n\n Args:\n data (List[dict]): Data sampled from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance\n used to update model parameters.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n\n data = self.data_preprocessor(data, True)\n batch_inputs = data['inputs']\n data_samples = data['data_samples']\n batch_gt_data = self.extract_gt_data(data_samples)\n\n log_vars = dict()\n\n with g_optim_wrapper.optim_context(self):\n batch_outputs = self.forward_train(batch_inputs, data_samples)\n\n if self.if_run_g():\n set_requires_grad(self.discriminator, False)\n\n log_vars_d = self.g_step_with_optim(\n batch_outputs=batch_outputs,\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if self.if_run_d():\n set_requires_grad(self.discriminator, True)\n\n for _ in range(self.disc_repeat):\n # detach before function call to resolve PyTorch2.0 compile bug\n log_vars_d = self.d_step_with_optim(\n batch_outputs=batch_outputs.detach(),\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if 'loss' in log_vars:\n log_vars.pop('loss')\n\n self.step_counter += 1\n\n return log_vars\n" }, { "path": "mmagic/models/editors/stable_diffusion/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .stable_diffusion import StableDiffusion\nfrom .stable_diffusion_inpaint import StableDiffusionInpaint\nfrom .vae import AutoencoderKL\n\n__all__ = ['StableDiffusion', 'AutoencoderKL', 'StableDiffusionInpaint']\n" }, { "path": "mmagic/models/editors/stable_diffusion/clip_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.logging import MMLogger\n\nfrom mmagic.utils import try_import\n\ntransformers = try_import('transformers')\n\nif transformers is not None:\n from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel\n from transformers.models.clip.feature_extraction_clip import \\\n CLIPFeatureExtractor # noqa\n from transformers.models.clip.modeling_clip import CLIPTextModel\n from transformers.models.clip.tokenization_clip import CLIPTokenizer\n\n logger = MMLogger.get_current_instance()\n\n def cosine_distance(image_embeds, text_embeds):\n \"\"\"compute the cosine distance of image embeddings and text\n embeddings.\"\"\"\n normalized_image_embeds = nn.functional.normalize(image_embeds)\n normalized_text_embeds = nn.functional.normalize(text_embeds)\n return torch.mm(normalized_image_embeds, normalized_text_embeds.t())\n\n class StableDiffusionSafetyChecker(PreTrainedModel):\n config_class = CLIPConfig\n _no_split_modules = ['CLIPEncoderLayer']\n\n def __init__(self, config: CLIPConfig):\n \"\"\"check result image for stable diffusion to prevent NSFW content\n generated.\n\n Args:\n config(CLIPConfig): config for transformers clip.\n \"\"\"\n\n super().__init__(config)\n\n self.vision_model = CLIPVisionModel(config.vision_config)\n self.visual_projection = nn.Linear(\n config.vision_config.hidden_size,\n config.projection_dim,\n bias=False)\n\n self.concept_embeds = nn.Parameter(\n torch.ones(17, config.projection_dim), requires_grad=False)\n self.special_care_embeds = nn.Parameter(\n torch.ones(3, config.projection_dim), requires_grad=False)\n\n self.concept_embeds_weights = nn.Parameter(\n torch.ones(17), requires_grad=False)\n self.special_care_embeds_weights = nn.Parameter(\n torch.ones(3), requires_grad=False)\n\n @torch.no_grad()\n def forward(self, clip_input, images):\n \"\"\"return black image if input image has nsfw content.\n\n Args:\n clip_input(torch.Tensor):\n image feature extracted by clip feature extractor.\n images(torch.Tensor):\n image generated by stable diffusion.\n\n Returns:\n images(torch.Tensor):\n black images if input images have nsfw content,\n otherwise return input images.\n has_nsfw_concepts(list[bool]):\n flag list to indicate whether input images have\n nsfw content.\n \"\"\"\n pooled_output = self.vision_model(clip_input)[1]\n image_embeds = self.visual_projection(pooled_output)\n\n # we always cast to float32 as this does not cause\n # significant overhead and is compatible with bfloa16\n special_cos_dist = cosine_distance(\n image_embeds, self.special_care_embeds).cpu().float().numpy()\n cos_dist = cosine_distance(\n image_embeds, self.concept_embeds).cpu().float().numpy()\n\n result = []\n batch_size = image_embeds.shape[0]\n for i in range(batch_size):\n result_img = {\n 'special_scores': {},\n 'special_care': [],\n 'concept_scores': {},\n 'bad_concepts': []\n }\n\n # increase this value to create a stronger `nfsw` filter\n # at the cost of increasing the possibility of\n # filtering benign images\n adjustment = 0.0\n\n for concept_idx in range(len(special_cos_dist[0])):\n concept_cos = special_cos_dist[i][concept_idx]\n concept_threshold = self.special_care_embeds_weights[\n concept_idx].item()\n result_img['special_scores'][concept_idx] = round(\n concept_cos - concept_threshold + adjustment, 3)\n if result_img['special_scores'][concept_idx] > 0:\n result_img['special_care'].append({\n concept_idx,\n result_img['special_scores'][concept_idx]\n })\n adjustment = 0.01\n\n for concept_idx in range(len(cos_dist[0])):\n concept_cos = cos_dist[i][concept_idx]\n concept_threshold = self.concept_embeds_weights[\n concept_idx].item()\n result_img['concept_scores'][concept_idx] = round(\n concept_cos - concept_threshold + adjustment, 3)\n if result_img['concept_scores'][concept_idx] > 0:\n result_img['bad_concepts'].append(concept_idx)\n\n result.append(result_img)\n\n has_nsfw_concepts = [\n len(res['bad_concepts']) > 0 for res in result\n ]\n\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n images[idx] = torch.zeros(images[idx].shape) # black image\n\n if any(has_nsfw_concepts):\n logger.warning(\n 'NSFW content was detected in one or more images.'\n ' A black image will be returned instead.'\n ' Try again with a different prompt and/or seed.')\n\n return images, has_nsfw_concepts\n\n def load_clip_submodels(init_cfg, submodels, requires_safety_checker):\n \"\"\"\n Args:\n init_cfg (dict):\n ckpt path of clip models.\n submodels (List):\n list of stable diffusion submodels.\n requires_safety_checker (bool):\n whether to load safety checker\n\n Returns:\n tokenizer(CLIPTokenizer):\n tokenizer with ckpt loaded.\n feature_extractor(CLIPFeatureExtractor):\n feature_extractor with ckpt loaded.\n text_encoder(CLIPTextModel):\n text_encoder with ckpt loaded.\n safety_checker(StableDiffusionSafetyChecker):\n safety_checker with ckpt loaded.\n\n \"\"\"\n pretrained_model_path = init_cfg.get('pretrained_model_path', None)\n\n tokenizer, feature_extractor, text_encoder, safety_checker = \\\n None, None, None, None\n if pretrained_model_path:\n tokenizer = CLIPTokenizer.from_pretrained(\n os.path.join(pretrained_model_path, 'tokenizer'))\n\n feature_extractor = CLIPFeatureExtractor.from_pretrained(\n os.path.join(pretrained_model_path, 'feature_extractor'))\n\n text_encoder = CLIPTextModel.from_pretrained(\n os.path.join(pretrained_model_path, 'text_encoder'))\n\n if requires_safety_checker:\n submodels.append('safety_checker')\n safety_checker = StableDiffusionSafetyChecker.from_pretrained(\n os.path.join(pretrained_model_path, 'safety_checker'))\n\n return tokenizer, feature_extractor, text_encoder, safety_checker\n\nelse:\n\n def load_clip_submodels(init_cfg, submodels, requires_safety_checker):\n raise ImportError('Please install transformers via '\n '\\'pip install transformers\\'')\n" }, { "path": "mmagic/models/editors/stable_diffusion/stable_diffusion.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport inspect\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModel\nfrom mmengine.runner import set_random_seed\nfrom PIL import Image\nfrom tqdm.auto import tqdm\n\nfrom mmagic.models.archs import TokenizerWrapper\nfrom mmagic.models.utils import build_module, set_tomesd, set_xformers\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\n\nlogger = MMLogger.get_current_instance()\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module('sd')\n@MODELS.register_module()\nclass StableDiffusion(BaseModel):\n \"\"\"Class for Stable Diffusion. Refers to https://github.com/Stability-\n AI/stablediffusion and https://github.com/huggingface/diffusers/blob/main/s\n rc/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_an\n d_excite.py # noqa.\n\n Args:\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n tokenizer (str): The **name** for CLIP tokenizer.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n dtype (str, optional): The dtype for the model This argument will not work\n when dtype is defined for submodels. Defaults to None.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise\n Defaults to 0.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n \"\"\"\n\n def __init__(self,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: Optional[str] = None,\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n init_cfg: Optional[dict] = None):\n\n # TODO: support `from_pretrained` for this class\n super().__init__(data_preprocessor, init_cfg)\n\n default_args = dict()\n if dtype is not None:\n default_args['dtype'] = dtype\n\n self.dtype = torch.float32\n if dtype in ['float16', 'fp16', 'half']:\n self.dtype = torch.float16\n elif dtype == 'bf16':\n self.dtype = torch.bfloat16\n else:\n assert dtype in [\n 'fp32', None\n ], ('dtype must be one of \\'fp32\\', \\'fp16\\', \\'bf16\\' or None.')\n\n self.vae = build_module(vae, MODELS, default_args=default_args)\n self.unet = build_module(unet, MODELS) # NOTE: initialize unet as fp32\n self._unet_ori_dtype = next(self.unet.parameters()).dtype\n print_log(f'Set UNet dtype to \\'{self._unet_ori_dtype}\\'.', 'current')\n self.scheduler = build_module(scheduler, DIFFUSION_SCHEDULERS)\n if test_scheduler is None:\n self.test_scheduler = deepcopy(self.scheduler)\n else:\n self.test_scheduler = build_module(test_scheduler,\n DIFFUSION_SCHEDULERS)\n self.text_encoder = build_module(text_encoder, MODELS)\n if not isinstance(tokenizer, str):\n self.tokenizer = tokenizer\n else:\n # NOTE: here we assume tokenizer is an string\n self.tokenizer = TokenizerWrapper(tokenizer, subfolder='tokenizer')\n\n self.unet_sample_size = self.unet.sample_size\n self.vae_scale_factor = 2**(len(self.vae.block_out_channels) - 1)\n\n self.enable_noise_offset = noise_offset_weight > 0\n self.noise_offset_weight = noise_offset_weight\n\n self.enable_xformers = enable_xformers\n self.set_xformers()\n\n self.tomesd_cfg = tomesd_cfg\n self.set_tomesd()\n\n def set_xformers(self, module: Optional[nn.Module] = None) -> nn.Module:\n \"\"\"Set xformers for the model.\n\n Returns:\n nn.Module: The model with xformers.\n \"\"\"\n if self.enable_xformers:\n if module is None:\n set_xformers(self)\n else:\n set_xformers(module)\n\n def set_tomesd(self) -> nn.Module:\n \"\"\"Set ToMe for the stable diffusion model.\n\n Returns:\n nn.Module: The model with ToMe.\n \"\"\"\n if self.tomesd_cfg is not None:\n set_tomesd(self, **self.tomesd_cfg)\n\n @property\n def device(self):\n return next(self.parameters()).device\n\n def train(self, mode: bool = True):\n \"\"\"Set train/eval mode.\n\n Args:\n mode (bool, optional): Whether set train mode. Defaults to True.\n \"\"\"\n if mode:\n if next(self.unet.parameters()).dtype != self._unet_ori_dtype:\n print_log(\n f'Set UNet dtype to \\'{self._unet_ori_dtype}\\' '\n 'in the train mode.', 'current')\n self.unet.to(self._unet_ori_dtype)\n else:\n self.unet.to(self.dtype)\n print_log(f'Set UNet dtype to \\'{self.dtype}\\' in the eval mode.',\n 'current')\n return super().train(mode)\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n show_progress=True,\n seed=1,\n return_type='image'):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps.\n More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in\n [Classifier-Free Diffusion Guidance]\n (https://arxiv.org/abs/2207.12598).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator] to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents,\n sampled from a Gaussian distribution,\n to be used as inputs for image generation.\n Can be used to tweak the same generation\n with different prompts.\n If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n set_random_seed(seed=seed)\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n\n img_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n latent_dtype = next(self.unet.parameters()).dtype\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(prompt, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n\n # 4. Prepare timesteps\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare latent variables\n if hasattr(self.unet, 'module'):\n num_channels_latents = self.unet.module.in_channels\n else:\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n # TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n latent_model_input = latent_model_input.to(latent_dtype)\n text_embeddings = text_embeddings.to(latent_dtype)\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input, t,\n encoder_hidden_states=text_embeddings)['sample']\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs)['prev_sample']\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n if return_type == 'image':\n image = self.output_to_pil(image)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image}\n\n def output_to_pil(self, image) -> List[Image.Image]:\n \"\"\"Convert output tensor to PIL image. Output tensor will be de-normed\n to [0, 255] by `DataPreprocessor.destruct`. Due to no `data_samples` is\n passed, color order conversion will not be performed.\n\n Args:\n image (torch.Tensor): The output tensor of the decoder.\n\n Returns:\n List[Image.Image]: The list of processed PIL images.\n \"\"\"\n image = self.data_preprocessor.destruct(image)\n image = image.permute(0, 2, 3, 1).to(torch.uint8).cpu().numpy()\n image = [Image.fromarray(img) for img in image]\n return image\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt,\n do_classifier_free_guidance, negative_prompt):\n \"\"\"Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (str or list(int)): prompt to be encoded.\n device: (torch.device): torch device.\n num_images_per_prompt (int): number of images that should be\n generated per prompt.\n do_classifier_free_guidance (`bool`): whether to use classifier\n free guidance or not.\n negative_prompt (str or List[str]): The prompt or prompts not\n to guide the image generation. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is less than `1`).\n\n Returns:\n text_embeddings (torch.Tensor): text embeddings generated by\n clip text encoder.\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(\n prompt, padding='max_length', return_tensors='pt').input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1:-1])\n logger.warning(\n 'The following part of your input was truncated because CLIP'\n ' can only handle sequences up to'\n f' {self.tokenizer.model_max_length} tokens: {removed_text}')\n\n text_encoder = self.text_encoder.module if hasattr(\n self.text_encoder, 'module') else self.text_encoder\n if hasattr(text_encoder.config, 'use_attention_mask'\n ) and text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation per prompt,\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(\n bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [''] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f'`negative_prompt` should be the same type to `prompt`,'\n f'but got {type(negative_prompt)} !='\n f' {type(prompt)}.')\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f'`negative_prompt`: {negative_prompt} has '\n f'batch size {len(negative_prompt)}, but `prompt`:'\n f' {prompt} has batch size {batch_size}.'\n f' Please make sure that passed `negative_prompt` matches'\n ' the batch size of `prompt`.')\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n\n if hasattr(text_encoder.config, 'use_attention_mask'\n ) and text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for\n # each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(\n 1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(\n batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional\n # and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def decode_latents(self, latents):\n \"\"\"use vae to decode latents.\n\n Args:\n latents (torch.Tensor): latents to decode.\n\n Returns:\n image (torch.Tensor): image result.\n \"\"\"\n latents = 1 / 0.18215 * latents\n if hasattr(self.vae, 'module'):\n image = self.vae.module.decode(latents)['sample']\n else:\n image = self.vae.decode(latents)['sample']\n # we always cast to float32 as this does not cause\n # significant overhead and is compatible with bfloa16\n return image.float()\n\n def prepare_extra_step_kwargs(self, generator, eta):\n \"\"\"prepare extra kwargs for the scheduler step.\n\n Args:\n generator (torch.Generator):\n generator for random functions.\n eta (float):\n eta (η) is only used with the DDIMScheduler,\n it will be ignored for other schedulers.\n eta corresponds to η in DDIM paper:\n https://arxiv.org/abs/2010.02502\n and should be between [0, 1]\n\n Return:\n extra_step_kwargs (dict):\n dict contains 'generator' and 'eta'\n \"\"\"\n accepts_eta = 'eta' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def prepare_test_scheduler_extra_step_kwargs(self, generator, eta):\n \"\"\"prepare extra kwargs for the scheduler step.\n\n Args:\n generator (torch.Generator):\n generator for random functions.\n eta (float):\n eta (η) is only used with the DDIMScheduler,\n it will be ignored for other schedulers.\n eta corresponds to η in DDIM paper:\n https://arxiv.org/abs/2010.02502\n and should be between [0, 1]\n\n Return:\n extra_step_kwargs (dict):\n dict contains 'generator' and 'eta'\n \"\"\"\n accepts_eta = 'eta' in set(\n inspect.signature(self.test_scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.test_scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width):\n \"\"\"check whether inputs are in suitable format or not.\"\"\"\n\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f'`prompt` has to be of '\n f'type `str` or `list` but is {type(prompt)}')\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f'`height` and `width` have to be divisible '\n f'by 8 but are {height} and {width}.')\n\n def prepare_latents(self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None):\n \"\"\"prepare latents for diffusion to run in latent space.\n\n Args:\n batch_size (int): batch size.\n num_channels_latents (int): latent channel nums.\n height (int): image height.\n width (int): image width.\n dtype (torch.dtype): float type.\n device (torch.device): torch device.\n generator (torch.Generator):\n generator for random functions, defaults to None.\n latents (torch.Tensor):\n Pre-generated noisy latents, defaults to None.\n\n Return:\n latents (torch.Tensor): prepared latents.\n \"\"\"\n shape = (batch_size, num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n if latents is None:\n latents = torch.randn(\n shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f'Unexpected latents shape, '\n f'got {latents.shape}, expected {shape}')\n latents = latents.to(device)\n\n # scale the initial noise by the standard\n # deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n gt_img = self.data_preprocessor.destruct(data['inputs'], data_samples)\n\n out_data_sample = DataSample(\n fake_img=samples, gt_img=gt_img, prompt=prompt)\n\n # out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n gt_img = self.data_preprocessor.destruct(data['inputs'], data_samples)\n\n out_data_sample = DataSample(\n fake_img=samples, gt_img=gt_img, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n def train_step(self, data, optim_wrapper_dict):\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n\n vae = self.vae.module if hasattr(self.vae, 'module') else self.vae\n\n optim_wrapper = optim_wrapper_dict['unet']\n with optim_wrapper.optim_context(self.unet):\n image = inputs\n prompt = data_samples.prompt\n num_batches = image.shape[0]\n\n image = image.to(self.dtype)\n latents = vae.encode(image).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n noise = torch.randn_like(latents)\n\n if self.enable_noise_offset:\n noise = noise + self.noise_offset_weight * torch.randn(\n latents.shape[0],\n latents.shape[1],\n 1,\n 1,\n device=noise.device)\n\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n\n noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)\n\n input_ids = self.tokenizer(\n prompt,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n\n encoder_hidden_states = self.text_encoder(input_ids)[0]\n\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n # NOTE: we train unet in fp32, convert to float manually\n model_output = self.unet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float())\n model_pred = model_output['sample']\n\n loss_dict = dict()\n # calculate loss in FP32\n loss_mse = F.mse_loss(model_pred.float(), gt.float())\n loss_dict['loss_mse'] = loss_mse\n\n parsed_loss, log_vars = self.parse_losses(loss_dict)\n optim_wrapper.update_params(parsed_loss)\n\n return log_vars\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n" }, { "path": "mmagic/models/editors/stable_diffusion/stable_diffusion_inpaint.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# Copyright 2023 The HuggingFace Team. All rights reserved.\nfrom typing import Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.logging import MMLogger\nfrom mmengine.runner import set_random_seed\nfrom PIL import Image\nfrom tqdm.auto import tqdm\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils.typing import SampleList\nfrom .stable_diffusion import StableDiffusion\n\nlogger = MMLogger.get_current_instance()\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module('sd-inpaint')\n@MODELS.register_module()\nclass StableDiffusionInpaint(StableDiffusion):\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initializes the current class using the same parameters as its\n parent, StableDiffusion.\n\n This constructor is primarily a pass-through to the parent class's\n constructor. All arguments and keyword arguments provided are directly\n passed to the parent class, StableDiffusion.\n \"\"\"\n super().__init__(*args, **kwargs)\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n image: Union[torch.FloatTensor, Image.Image] = None,\n mask_image: Union[torch.FloatTensor, Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n show_progress=True,\n seed=1,\n return_type='image'):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`Union[torch.FloatTensor, Image.Image]`):\n The image to inpaint.\n mask_image (`Union[torch.FloatTensor, Image.Image]`):\n The mask to apply to the image, i.e. regions to inpaint.\n height (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps.\n More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in\n [Classifier-Free Diffusion Guidance]\n (https://arxiv.org/abs/2207.12598).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator] to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents,\n sampled from a Gaussian distribution,\n to be used as inputs for image generation.\n Can be used to tweak the same generation\n with different prompts.\n If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n set_random_seed(seed=seed)\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n\n img_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n latent_dtype = next(self.unet.parameters()).dtype\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(prompt, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n\n # 4. Prepare timesteps\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare mask and image\n mask, masked_image = prepare_mask_and_masked_image(\n image, mask_image, height, width)\n\n # 6. Prepare latent variables\n if hasattr(self.unet, 'module'):\n num_channels_latents = self.vae.module.latent_channels\n num_channels_unet = self.unet.module.in_channels\n else:\n num_channels_latents = self.vae.latent_channels\n num_channels_unet = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare masked image latents\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n total_channels = num_channels_latents + \\\n num_channels_masked_image + num_channels_mask\n if total_channels != self.unet.in_channels:\n raise ValueError(\n 'Incorrect configuration settings! The config of '\n f'`pipeline.unet`: {self.unet.config} expects'\n f' {self.unet.in_channels} but received '\n f'`num_channels_latents`: {num_channels_latents} +'\n f' `num_channels_mask`: {num_channels_mask} + '\n '`num_channels_masked_image`: '\n f'{num_channels_masked_image} = {total_channels}.'\n 'Please verify the config of `pipeline.unet` '\n 'or your `mask_image` or `image` input.')\n elif num_channels_unet != 4:\n raise ValueError(\n f'The unet {self.unet.__class__} should have either 4 or 9 '\n f'input channels, not {self.unet.config.in_channels}.')\n\n # 9. Prepare extra step kwargs.\n # TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n # concat latents with mask\n if num_channels_unet == 9:\n latent_model_input = torch.cat(\n [latent_model_input, mask, masked_image_latents], dim=1)\n latent_model_input = latent_model_input.to(latent_dtype)\n text_embeddings = text_embeddings.to(latent_dtype)\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input, t,\n encoder_hidden_states=text_embeddings)['sample']\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs)['prev_sample']\n\n if num_channels_unet == 4:\n assert NotImplementedError\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n if return_type == 'image':\n image = self.output_to_pil(image)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image}\n\n def prepare_mask_latents(self, mask, masked_image, batch_size,\n num_channels_latents, height, width, dtype,\n device, generator, do_classifier_free_guidance):\n \"\"\"prepare latents for diffusion to run in latent space.\n\n Args:\n mask (torch.Tensor): The mask to apply to the image, i.e. regions\n to inpaint.\n image (torch.Tensor): The image to be masked.\n batch_size (int): batch size.\n num_channels_latents (int): latent channel nums.\n height (int): image height.\n width (int): image width.\n dtype (torch.dtype): float type.\n device (torch.device): torch device.\n generator (torch.Generator):\n generator for random functions, defaults to None.\n latents (torch.Tensor):\n Pre-generated noisy latents, defaults to None.\n do_classifier_free_guidance (bool): Whether to apply\n classifier-free guidance.\n\n Return:\n latents (torch.Tensor): prepared latents.\n \"\"\"\n shape = (batch_size, num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n mask = F.interpolate(\n mask, size=shape[2:]).to(\n device=device, dtype=dtype)\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n masked_image_latents = self.vae.encode(\n masked_image).latent_dist.sample(generator)\n masked_image_latents = self.vae.config.scaling_factor * \\\n masked_image_latents\n\n # duplicate mask and masked_image_latents for each generation per\n # prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match.\"\n 'Masks are supposed to be duplicated to a total batch'\n f' size of {batch_size}, but {mask.shape[0]} masks were '\n 'passed. Make sure the number of masks that you pass'\n ' is divisible by the total requested batch size.')\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't \"\n 'match. Images are supposed to be duplicated to a total'\n f' batch size of {batch_size}, but '\n f'{masked_image_latents.shape[0]} images were passed.'\n ' Make sure the number of images that you pass is'\n 'divisible by the total requested batch size.')\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2)\n if do_classifier_free_guidance else masked_image_latents)\n\n # aligning device to prevent device errors when concatenating it with\n # the latent model input\n masked_image_latents = masked_image_latents.to(\n device=device, dtype=dtype)\n return mask, masked_image_latents\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Performs a validation step on the provided data.\n\n This method is decorated with `torch.no_grad()` which indicates no\n gradients will be computed during the operations. This ensures\n efficient memory usage during testing.\n\n Args:\n data (dict): Dictionary containing input data for testing.\n\n Returns:\n SampleList: List of samples processed during the testing step.\n\n Raises:\n NotImplementedError: This method has not been implemented.\n \"\"\"\n raise NotImplementedError\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Performs a testing step on the provided data.\n\n This method is decorated with `torch.no_grad()` which indicates no\n gradients will be computed during the operations. This ensures\n efficient memory usage during testing.\n\n Args:\n data (dict): Dictionary containing input data for testing.\n\n Returns:\n SampleList: List of samples processed during the testing step.\n\n Raises:\n NotImplementedError: This method has not been implemented.\n \"\"\"\n raise NotImplementedError\n\n def train_step(self, data, optim_wrapper_dict):\n \"\"\"Performs a training step on the provided data.\n\n Args:\n data: Input data for training.\n optim_wrapper_dict: Dictionary containing optimizer wrappers\n which may contain optimizers, schedulers, etc. required\n for the training step.\n\n Raises:\n NotImplementedError: This method has not been implemented.\n \"\"\"\n raise NotImplementedError\n\n\ndef prepare_mask_and_masked_image(image: torch.Tensor,\n mask: torch.Tensor,\n height: int = 512,\n width: int = 512,\n return_image: bool = False):\n \"\"\"Prepare latents for diffusion to run in latent space.\n\n Args:\n image (torch.Tensor): The image to be masked.\n mask (torch.Tensor): The mask to apply to the image, i.e. regions\n to inpaint.\n height (int): Image height.\n width (int): Image width.\n return_image (bool): Whether to return the original image.\n Default to `False`.\n\n Returns:\n mask (torch.Tensor): A binary mask image.\n masked_image (torch.Tensor): An image that applied mask.\n \"\"\"\n\n if image is None:\n raise ValueError('`image` input cannot be undefined.')\n\n if mask is None:\n raise ValueError('`mask_image` input cannot be undefined.')\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError('`image` is a torch.Tensor but `mask` (type: '\n f'{type(mask)} is not')\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[\n 0] == 3, 'Image outside a batch should be of shape (3, H, W)'\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask\n # not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert (image.ndim == 4\n and mask.ndim == 4), 'Image and Mask must have 4 dimensions'\n assert image.shape[-2:] == mask.shape[\n -2:], 'Image and Mask must have the same spatial dimensions'\n assert image.shape[0] == mask.shape[\n 0], 'Image and Mask must have the same batch size'\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError('Image should be in [-1, 1] range')\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError('Mask should be in [0, 1] range')\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(\n f'`mask` is a torch.Tensor but `image` (type: {type(image)} is not'\n )\n else:\n # preprocess image\n if isinstance(image, (Image.Image, np.ndarray)):\n image = [image]\n if isinstance(image, list) and isinstance(image[0], Image.Image):\n # resize all images w.r.t passed height an width\n image = [\n i.resize((width, height), resample=Image.LANCZOS)\n for i in image\n ]\n image = [np.array(i.convert('RGB'))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], Image.Image):\n mask = [\n i.resize((width, height), resample=Image.LANCZOS) for i in mask\n ]\n mask = np.concatenate(\n [np.array(m.convert('L'))[None, None, :] for m in mask],\n axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n # n.b. ensure backwards compatibility as old function does not return image\n if return_image:\n return mask, masked_image, image\n\n return mask, masked_image\n" }, { "path": "mmagic/models/editors/stable_diffusion/vae.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import Optional, Tuple, Union\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom addict import Dict\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nfrom mmagic.registry import MODELS\n\n\nclass Downsample2D(nn.Module):\n \"\"\"A downsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n out_channels (int): output channels\n padding (int): padding num\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n out_channels=None,\n padding=1,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.padding = padding\n stride = 2\n self.name = name\n\n if use_conv:\n conv = nn.Conv2d(\n self.channels,\n self.out_channels,\n 3,\n stride=stride,\n padding=padding)\n else:\n assert self.channels == self.out_channels\n conv = nn.AvgPool2d(kernel_size=stride, stride=stride)\n\n self.conv = conv\n\n def forward(self, hidden_states):\n \"\"\"forward hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n if self.use_conv and self.padding == 0:\n pad = (0, 1, 0, 1)\n hidden_states = F.pad(hidden_states, pad, mode='constant', value=0)\n\n assert hidden_states.shape[1] == self.channels\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass Upsample2D(nn.Module):\n \"\"\"An upsampling layer with an optional convolution.\n\n Args:\n channels (int): channels in the inputs and outputs.\n use_conv (bool): a bool determining if a convolution is applied.\n use_conv_transpose (bool): whether to use conv transpose.\n out_channels (int): output channels.\n \"\"\"\n\n def __init__(self,\n channels,\n use_conv=False,\n use_conv_transpose=False,\n out_channels=None,\n name='conv'):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_conv_transpose = use_conv_transpose\n self.name = name\n\n conv = None\n if use_conv:\n conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)\n else:\n conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)\n\n self.conv = conv\n\n def forward(self, hidden_states, output_size=None):\n \"\"\"forward with hidden states.\"\"\"\n assert hidden_states.shape[1] == self.channels\n\n if self.use_conv_transpose:\n return self.conv(hidden_states)\n\n # if `output_size` is passed we force the interpolation output\n # size and do not make use of `scale_factor=2`\n if output_size is None:\n hidden_states = F.interpolate(\n hidden_states, scale_factor=2.0, mode='nearest')\n else:\n hidden_states = F.interpolate(\n hidden_states, size=output_size, mode='nearest')\n\n # TODO(Suraj, Patrick)\n # - clean up after weight dicts are correctly renamed\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass ResnetBlock2D(nn.Module):\n \"\"\"resnet block support down sample and up sample.\n\n Args:\n in_channels (int): input channels.\n out_channels (int): output channels.\n conv_shortcut (bool): whether to use conv shortcut.\n dropout (float): dropout rate.\n temb_channels (int): time embedding channels.\n groups (int): conv groups.\n groups_out (int): conv out groups.\n pre_norm (bool): whether to norm before conv. Todo: remove.\n eps (float): eps for groupnorm.\n non_linearity (str): non linearity type.\n time_embedding_norm (str): time embedding norm type.\n output_scale_factor (float): factor to scale input and output.\n use_in_shortcut (bool): whether to use conv in shortcut.\n up (bool): whether to upsample.\n down (bool): whether to downsample.\n \"\"\"\n\n def __init__(\n self,\n in_channels,\n out_channels=None,\n conv_shortcut=False,\n dropout=0.0,\n temb_channels=512,\n groups=32,\n groups_out=None,\n pre_norm=True,\n eps=1e-6,\n non_linearity='silu',\n time_embedding_norm='default',\n kernel=None,\n output_scale_factor=1.0,\n use_in_shortcut=None,\n up=False,\n down=False,\n ):\n super().__init__()\n self.pre_norm = pre_norm\n self.pre_norm = True\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.time_embedding_norm = time_embedding_norm\n self.up = up\n self.down = down\n self.output_scale_factor = output_scale_factor\n\n if groups_out is None:\n groups_out = groups\n\n self.norm1 = torch.nn.GroupNorm(\n num_groups=groups, num_channels=in_channels, eps=eps, affine=True)\n\n self.conv1 = torch.nn.Conv2d(\n in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if temb_channels is not None:\n self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)\n else:\n self.time_emb_proj = None\n\n self.norm2 = torch.nn.GroupNorm(\n num_groups=groups_out,\n num_channels=out_channels,\n eps=eps,\n affine=True)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = torch.nn.Conv2d(\n out_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if non_linearity == 'silu' and \\\n digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.nonlinearity = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.nonlinearity = nn.ReLU()\n\n self.upsample = self.downsample = None\n if self.up:\n self.upsample = Upsample2D(in_channels, use_conv=False)\n elif self.down:\n self.downsample = \\\n Downsample2D(\n in_channels, use_conv=False, padding=1, name='op')\n\n self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut # noqa\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = torch.nn.Conv2d(\n in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n\n def forward(self, input_tensor, temb):\n \"\"\"forward with hidden states and time embeddings.\"\"\"\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.upsample is not None:\n # upsample_nearest_nhwc fails with large batch sizes.\n # see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n input_tensor = input_tensor.contiguous()\n hidden_states = hidden_states.contiguous()\n input_tensor = self.upsample(input_tensor)\n hidden_states = self.upsample(hidden_states)\n elif self.downsample is not None:\n input_tensor = self.downsample(input_tensor)\n hidden_states = self.downsample(hidden_states)\n\n hidden_states = self.conv1(hidden_states)\n\n if temb is not None:\n temb = \\\n self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = \\\n (input_tensor + hidden_states) / self.output_scale_factor\n\n return output_tensor\n\n\nclass AttentionBlock(nn.Module):\n \"\"\"An attention block that allows spatial positions to attend to each\n other. Originally ported from here, but adapted to the N-d case.\n https://github.com/hojonathanho/diffusion/blob/\n 1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.\n Uses three q, k, v linear layers to compute attention.\n\n Args:\n channels (int): The number of channels in the input and output.\n num_head_channels (int, *optional*):\n The number of channels in each head. If None, then `num_heads` = 1.\n norm_num_groups (int, *optional*, defaults to 32):\n The number of groups to use for group norm.\n rescale_output_factor (float, *optional*, defaults to 1.0):\n The factor to rescale the output by.\n eps (float, *optional*, defaults to 1e-5):\n The epsilon value to use for group norm.\n \"\"\"\n\n def __init__(\n self,\n channels: int,\n num_head_channels: Optional[int] = None,\n norm_num_groups: int = 32,\n rescale_output_factor: float = 1.0,\n eps: float = 1e-5,\n ):\n super().__init__()\n self.channels = channels\n\n self.num_heads = channels // num_head_channels if num_head_channels is not None else 1 # noqa\n self.num_head_size = num_head_channels\n self.group_norm = nn.GroupNorm(\n num_channels=channels,\n num_groups=norm_num_groups,\n eps=eps,\n affine=True)\n\n # define q,k,v as linear layers\n self.query = nn.Linear(channels, channels)\n self.key = nn.Linear(channels, channels)\n self.value = nn.Linear(channels, channels)\n\n self.rescale_output_factor = rescale_output_factor\n self.proj_attn = nn.Linear(channels, channels, 1)\n\n def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:\n \"\"\"transpose projection.\"\"\"\n new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)\n # move heads to 2nd position (B, T, H * D)\n # -> (B, T, H, D) -> (B, H, T, D)\n new_projection = \\\n projection.view(new_projection_shape).permute(0, 2, 1, 3)\n return new_projection\n\n def forward(self, hidden_states):\n \"\"\"forward hidden states.\"\"\"\n residual = hidden_states\n batch, channel, height, width = hidden_states.shape\n\n # norm\n hidden_states = self.group_norm(hidden_states)\n\n hidden_states = hidden_states.view(batch, channel,\n height * width).transpose(1, 2)\n\n # proj to q, k, v\n query_proj = self.query(hidden_states)\n key_proj = self.key(hidden_states)\n value_proj = self.value(hidden_states)\n\n scale = 1 / math.sqrt(self.channels / self.num_heads)\n\n # get scores\n if self.num_heads > 1:\n query_states = self.transpose_for_scores(query_proj)\n key_states = self.transpose_for_scores(key_proj)\n value_states = self.transpose_for_scores(value_proj)\n\n attention_scores = torch.matmul(\n query_states, key_states.transpose(-1, -2)) * scale\n else:\n query_states, key_states, value_states = \\\n query_proj, key_proj, value_proj\n\n attention_scores = torch.baddbmm(\n torch.empty(\n query_states.shape[0],\n query_states.shape[1],\n key_states.shape[1],\n dtype=query_states.dtype,\n device=query_states.device,\n ),\n query_states,\n key_states.transpose(-1, -2),\n beta=0,\n alpha=scale,\n )\n\n attention_probs = torch.softmax(\n attention_scores.float(), dim=-1).type(attention_scores.dtype)\n\n # compute attention output\n if self.num_heads > 1:\n hidden_states = torch.matmul(attention_probs, value_states)\n hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()\n new_hidden_states_shape = \\\n hidden_states.size()[:-2] + (self.channels,)\n hidden_states = hidden_states.view(new_hidden_states_shape)\n else:\n hidden_states = torch.bmm(attention_probs, value_states)\n\n # compute next hidden_states\n hidden_states = self.proj_attn(hidden_states)\n hidden_states = hidden_states.transpose(-1, -2).reshape(\n batch, channel, height, width)\n\n # res connect and rescale\n hidden_states = \\\n (hidden_states + residual) / self.rescale_output_factor\n return hidden_states\n\n\nclass UNetMidBlock2D(nn.Module):\n \"\"\"middle block in unet.\n\n Args:\n in_channels (int): input channels.\n temb_channels (int): time embedding channels.\n dropout (float): dropout rate, defaults to 0.0.\n num_layers (int): layer num.\n resnet_eps (float): resnet eps, defaults to 1e-6.\n resnet_time_scale_shift (str):\n time scale shift, defaults to 'default'.\n resnet_act_fn (str):\n act function in resnet, defaults to 'silu'.\n resnet_groups (int):\n conv groups in resnet, defaults to 32.\n resnet_pre_norm (bool):\n pre norm in resnet, defaults to True.\n attn_num_head_channels (int):\n attention head channels, defaults to 1.\n attention_type (str):\n attention type ,defaults to 'default'.\n output_scale_factor (float):\n output scale factor, defaults to 1.0.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'silu',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attn_num_head_channels=1,\n attention_type='default',\n output_scale_factor=1.0,\n ):\n super().__init__()\n\n self.attention_type = attention_type\n resnet_groups = resnet_groups if resnet_groups is not None else min(\n in_channels // 4, 32) # noqa\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n for _ in range(num_layers):\n attentions.append(\n AttentionBlock(\n in_channels,\n num_head_channels=attn_num_head_channels,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=resnet_groups,\n ))\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states, temb=None, encoder_states=None):\n \"\"\"forward with hidden states, time embedding and encoder states.\"\"\"\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if self.attention_type == 'default':\n hidden_states = attn(hidden_states)\n else:\n hidden_states = attn(hidden_states, encoder_states)\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass DownEncoderBlock2D(nn.Module):\n \"\"\"Down encoder block in vae.\n\n Args:\n in_channels (int): input channels.\n out_channels (int): output channels.\n dropout (float): dropout rate, defaults to 0.0.\n num_layers (int): layer nums, defaults to 1.\n resnet_eps (float): resnet eps, defaults to 1e-6.\n resnet_time_scale_shift (str):\n time scale shift in resnet, defaults to 'default'.\n resnet_act_fn (str):\n act function in resnet, defaults to 'silu'.\n resnet_groups (int):\n group num in resnet, defaults to 32.\n resnet_pre_norm (bool):\n whether to pre norm in resnet, defaults to True.\n output_scale_factor (float):\n output scale factor, defaults to 1.0.\n add_downsample (bool):\n whether to add downsample, defaults to True,\n downsample_padding (int):\n downsample padding num, defaults to 1.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'silu',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_downsample=True,\n downsample_padding=1,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name='op')\n ])\n else:\n self.downsamplers = None\n\n def forward(self, hidden_states):\n \"\"\"forward with hidden states.\"\"\"\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb=None)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n return hidden_states\n\n\nclass Encoder(nn.Module):\n \"\"\"construct encoder in vae.\"\"\"\n\n def __init__(\n self,\n in_channels=3,\n out_channels=3,\n down_block_types=('DownEncoderBlock2D', ),\n block_out_channels=(64, ),\n layers_per_block=2,\n norm_num_groups=32,\n act_fn='silu',\n double_z=True,\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = torch.nn.Conv2d(\n in_channels,\n block_out_channels[0],\n kernel_size=3,\n stride=1,\n padding=1)\n\n self.mid_block = None\n self.down_blocks = nn.ModuleList([])\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = DownEncoderBlock2D(\n num_layers=self.layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n add_downsample=not is_final_block,\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n downsample_padding=0,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n output_scale_factor=1,\n resnet_time_scale_shift='default',\n attn_num_head_channels=None,\n resnet_groups=norm_num_groups,\n temb_channels=None,\n )\n\n # out\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[-1],\n num_groups=norm_num_groups,\n eps=1e-6)\n if digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.conv_act = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.conv_act = nn.ReLU()\n\n conv_out_channels = 2 * out_channels if double_z else out_channels\n self.conv_out = nn.Conv2d(\n block_out_channels[-1], conv_out_channels, 3, padding=1)\n\n def forward(self, x):\n \"\"\"encoder forward.\"\"\"\n sample = x\n sample = self.conv_in(sample)\n\n # down\n for down_block in self.down_blocks:\n sample = down_block(sample)\n\n # middle\n sample = self.mid_block(sample)\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass UpDecoderBlock2D(nn.Module):\n \"\"\"construct up decoder block.\"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = 'default',\n resnet_act_fn: str = 'swish',\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor=1.0,\n add_upsample=True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n input_channels = in_channels if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n ))\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([\n Upsample2D(\n out_channels, use_conv=True, out_channels=out_channels)\n ])\n else:\n self.upsamplers = None\n\n def forward(self, hidden_states):\n \"\"\"forward hidden states.\"\"\"\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb=None)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass Decoder(nn.Module):\n \"\"\"construct decoder in vae.\"\"\"\n\n def __init__(\n self,\n in_channels=3,\n out_channels=3,\n up_block_types=('UpDecoderBlock2D', ),\n block_out_channels=(64, ),\n layers_per_block=2,\n norm_num_groups=32,\n act_fn='silu',\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = nn.Conv2d(\n in_channels,\n block_out_channels[-1],\n kernel_size=3,\n stride=1,\n padding=1)\n\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n output_scale_factor=1,\n resnet_time_scale_shift='default',\n attn_num_head_channels=None,\n resnet_groups=norm_num_groups,\n temb_channels=None,\n )\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = UpDecoderBlock2D(\n num_layers=self.layers_per_block + 1,\n in_channels=prev_output_channel,\n out_channels=output_channel,\n add_upsample=not is_final_block,\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0],\n num_groups=norm_num_groups,\n eps=1e-6)\n if digit_version(TORCH_VERSION) > digit_version('1.6.0'):\n self.conv_act = nn.SiLU()\n else:\n mmengine.print_log('\\'SiLU\\' is not supported for '\n f'torch < 1.6.0, found \\'{torch.version}\\'.'\n 'Use ReLu instead but result maybe wrong')\n self.conv_act = nn.ReLU()\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, 3, padding=1)\n\n def forward(self, z):\n \"\"\"decoder forward.\"\"\"\n sample = z\n sample = self.conv_in(sample)\n\n # middle\n sample = self.mid_block(sample)\n\n # up\n for up_block in self.up_blocks:\n sample = up_block(sample)\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass DiagonalGaussianDistribution(object):\n \"\"\"Calculate diagonal gaussian distribution.\"\"\"\n\n def __init__(self, parameters, deterministic=False):\n self.parameters = parameters\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = torch.exp(0.5 * self.logvar)\n self.var = torch.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = torch.zeros_like(\n self.mean,\n device=self.parameters.device,\n dtype=self.parameters.dtype)\n\n def sample(self, generator: Optional[torch.Generator] = None) \\\n -> torch.FloatTensor:\n \"\"\"sample function.\"\"\"\n device = self.parameters.device\n sample_device = device\n sample = torch.randn(\n self.mean.shape, generator=generator, device=sample_device)\n # make sure sample is on the same device\n # as the parameters and has same dtype\n sample = sample.to(device=device, dtype=self.parameters.dtype)\n x = self.mean + self.std * sample\n return x\n\n def kl(self, other=None):\n \"\"\"calculate kl divergence.\"\"\"\n if self.deterministic:\n return torch.Tensor([0.0])\n else:\n if other is None:\n return 0.5 * torch.sum(\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\n dim=[1, 2, 3])\n else:\n return 0.5 * torch.sum(\n torch.pow(self.mean - other.mean, 2) / other.var +\n self.var / other.var - 1.0 - self.logvar + other.logvar,\n dim=[1, 2, 3],\n )\n\n def nll(self, sample, dims=[1, 2, 3]):\n \"\"\"calculate negative log likelihood.\"\"\"\n if self.deterministic:\n return torch.Tensor([0.0])\n logtwopi = np.log(2.0 * np.pi)\n return 0.5 * torch.sum(\n logtwopi + self.logvar +\n torch.pow(sample - self.mean, 2) / self.var,\n dim=dims) # noqa\n\n def mode(self):\n \"\"\"return self.mean.\"\"\"\n return self.mean\n\n\n@MODELS.register_module('EditAutoencoderKL')\nclass AutoencoderKL(nn.Module):\n r\"\"\"Variational Autoencoder (VAE) model with KL loss\n from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma\n and Max Welling.\n\n Args:\n in_channels (int, *optional*, defaults to 3):\n Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3):\n Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to :\n obj:`(\"DownEncoderBlock2D\",)`):\n Tuple of downsample block types.\n up_block_types (`Tuple[str]`, *optional*, defaults to :\n obj:`(\"UpDecoderBlock2D\",)`): Tuple of upsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to :\n obj:`(64,)`): Tuple of block output channels.\n act_fn (`str`, *optional*, defaults to `\"silu\"`):\n The activation function to use.\n latent_channels (`int`, *optional*, defaults to `4`):\n Number of channels in the latent space.\n sample_size (`int`, *optional*, defaults to `32`):\n sample size is now not supported.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str] = ('DownEncoderBlock2D', ),\n up_block_types: Tuple[str] = ('UpDecoderBlock2D', ),\n block_out_channels: Tuple[int] = (64, ),\n layers_per_block: int = 1,\n act_fn: str = 'silu',\n latent_channels: int = 4,\n norm_num_groups: int = 32,\n sample_size: int = 32,\n ):\n super().__init__()\n\n self.block_out_channels = block_out_channels\n self.latent_channels = latent_channels\n\n # pass init params to Encoder\n self.encoder = Encoder(\n in_channels=in_channels,\n out_channels=latent_channels,\n down_block_types=down_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n double_z=True,\n )\n\n # pass init params to Decoder\n self.decoder = Decoder(\n in_channels=latent_channels,\n out_channels=out_channels,\n up_block_types=up_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n norm_num_groups=norm_num_groups,\n act_fn=act_fn,\n )\n\n self.quant_conv = torch.nn.Conv2d(2 * latent_channels,\n 2 * latent_channels, 1)\n self.post_quant_conv = torch.nn.Conv2d(latent_channels,\n latent_channels, 1)\n\n @property\n def dtype(self):\n \"\"\"The data type of the parameters of VAE.\"\"\"\n return next(self.parameters()).dtype\n\n def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> Dict:\n \"\"\"encode input.\"\"\"\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior, )\n\n return Dict(latent_dist=posterior)\n\n def decode(self, z: torch.FloatTensor, return_dict: bool = True) \\\n -> Union[Dict, torch.FloatTensor]:\n \"\"\"decode z.\"\"\"\n z = self.post_quant_conv(z)\n dec = self.decoder(z)\n\n if not return_dict:\n return (dec, )\n\n return Dict(sample=dec)\n\n def forward(\n self,\n sample: torch.FloatTensor,\n sample_posterior: bool = False,\n return_dict: bool = True,\n generator: Optional[torch.Generator] = None,\n ) -> Union[Dict, torch.FloatTensor]:\n \"\"\"\n Args:\n sample (torch.FloatTensor): Input sample.\n sample_posterior (bool):\n Whether to sample from the posterior.\n defaults to `False`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`Dict`] instead of a plain tuple.\n Returns:\n Dict(sample=dec): decode results.\n \"\"\"\n x = sample\n posterior = self.encode(x).latent_dist\n if sample_posterior:\n z = posterior.sample(generator=generator)\n else:\n z = posterior.mode()\n dec = self.decode(z).sample\n\n if not return_dict:\n return (dec, )\n\n return Dict(sample=dec)\n" }, { "path": "mmagic/models/editors/stable_diffusion_xl/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .stable_diffusion_xl import StableDiffusionXL\n\n__all__ = ['StableDiffusionXL']\n" }, { "path": "mmagic/models/editors/stable_diffusion_xl/stable_diffusion_xl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport inspect\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import print_log\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModel\nfrom mmengine.optim import OptimWrapperDict\nfrom mmengine.runner import set_random_seed\nfrom PIL import Image\nfrom tqdm.auto import tqdm\n\nfrom mmagic.models.archs import TokenizerWrapper, set_lora\nfrom mmagic.models.utils import build_module, set_tomesd, set_xformers\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\n\nlogger = MMLogger.get_current_instance()\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module('sdxl')\n@MODELS.register_module()\nclass StableDiffusionXL(BaseModel):\n \"\"\"Class for Stable Diffusion XL. Refers to https://github.com/Stability-\n AI.\n\n /generative-models and https://github.com/huggingface/diffusers/blob/main/\n src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py\n\n Args:\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n text_encoder_one (Union[dict, nn.Module]): The config or module for\n text encoder.\n tokenizer_one (str): The **name** for CLIP tokenizer.\n text_encoder_two (Union[dict, nn.Module]): The config or module for\n text encoder.\n tokenizer_two (str): The **name** for CLIP tokenizer.\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n dtype (str, optional): The dtype for the model This argument will not\n work when dtype is defined for submodels. Defaults to None.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in\n https://www.crosslabs.org/blog/diffusion-with-offset-noise\n Defaults to 0.\n tomesd_cfg (dict, optional): The config for TOMESD. Please refers to\n https://github.com/dbolya/tomesd and\n https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/utils/tome_utils.py for detail. # noqa\n Defaults to None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n lora_config (dict, optional): The config for LoRA finetuning. Defaults\n to None.\n val_prompts (Union[str, List[str]], optional): The prompts for\n validation. Defaults to None.\n finetune_text_encoder (bool, optional): Whether to fine-tune text\n encoder. Defaults to False.\n force_zeros_for_empty_prompt (bool): Whether the negative prompt\n embeddings shall be forced to always be set to 0.\n Defaults to True.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n \"\"\"\n\n def __init__(self,\n vae: ModelType,\n text_encoder_one: ModelType,\n tokenizer_one: str,\n text_encoder_two: ModelType,\n tokenizer_two: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: Optional[str] = None,\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n lora_config: Optional[dict] = None,\n val_prompts: Union[str, List[str]] = None,\n finetune_text_encoder: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n init_cfg: Optional[dict] = None):\n\n # TODO: support `from_pretrained` for this class\n super().__init__(data_preprocessor, init_cfg)\n\n default_args = dict()\n if dtype is not None:\n default_args['dtype'] = dtype\n\n self.dtype = torch.float32\n if dtype in ['float16', 'fp16', 'half']:\n self.dtype = torch.float16\n elif dtype == 'bf16':\n self.dtype = torch.bfloat16\n else:\n assert dtype in [\n 'fp32', None\n ], ('dtype must be one of \\'fp32\\', \\'fp16\\', \\'bf16\\' or None.')\n\n self.vae = build_module(vae, MODELS, default_args=default_args)\n self.unet = build_module(unet, MODELS) # NOTE: initialize unet as fp32\n self._unet_ori_dtype = next(self.unet.parameters()).dtype\n print_log(f'Set UNet dtype to \\'{self._unet_ori_dtype}\\'.', 'current')\n self.scheduler = build_module(scheduler, DIFFUSION_SCHEDULERS)\n if test_scheduler is None:\n self.test_scheduler = deepcopy(self.scheduler)\n else:\n self.test_scheduler = build_module(test_scheduler,\n DIFFUSION_SCHEDULERS)\n self.text_encoder_one = build_module(text_encoder_one, MODELS)\n if not isinstance(tokenizer_one, str):\n self.tokenizer_one = tokenizer_one\n else:\n # NOTE: here we assume tokenizer is an string\n self.tokenizer_one = TokenizerWrapper(\n tokenizer_one, subfolder='tokenizer')\n\n self.text_encoder_two = build_module(text_encoder_two, MODELS)\n if not isinstance(tokenizer_two, str):\n self.tokenizer_two = tokenizer_two\n else:\n # NOTE: here we assume tokenizer is an string\n self.tokenizer_two = TokenizerWrapper(\n tokenizer_two, subfolder='tokenizer_2')\n\n self.unet_sample_size = self.unet.sample_size\n self.vae_scale_factor = 2**(len(self.vae.block_out_channels) - 1)\n\n self.enable_noise_offset = noise_offset_weight > 0\n self.noise_offset_weight = noise_offset_weight\n\n self.finetune_text_encoder = finetune_text_encoder\n self.val_prompts = val_prompts\n self.lora_config = deepcopy(lora_config)\n self.force_zeros_for_empty_prompt = force_zeros_for_empty_prompt\n\n self.prepare_model()\n self.set_lora()\n\n self.enable_xformers = enable_xformers\n self.set_xformers()\n\n self.tomesd_cfg = tomesd_cfg\n self.set_tomesd()\n\n def prepare_model(self):\n \"\"\"Prepare model for training.\n\n Move model to target dtype and disable gradient for some models.\n \"\"\"\n self.vae.requires_grad_(False)\n print_log('Set VAE untrainable.', 'current')\n self.vae.to(self.dtype)\n print_log(f'Move VAE to {self.dtype}.', 'current')\n if not self.finetune_text_encoder or self.lora_config:\n self.text_encoder_one.requires_grad_(False)\n self.text_encoder_two.requires_grad_(False)\n print_log('Set Text Encoder untrainable.', 'current')\n self.text_encoder_one.to(self.dtype)\n self.text_encoder_two.to(self.dtype)\n print_log(f'Move Text Encoder to {self.dtype}.', 'current')\n if self.lora_config:\n self.unet.requires_grad_(False)\n print_log('Set Unet untrainable.', 'current')\n\n def set_lora(self):\n \"\"\"Set LORA for model.\"\"\"\n if self.lora_config:\n set_lora(self.unet, self.lora_config)\n\n def set_xformers(self, module: Optional[nn.Module] = None) -> nn.Module:\n \"\"\"Set xformers for the model.\n\n Returns:\n nn.Module: The model with xformers.\n \"\"\"\n if self.enable_xformers:\n if module is None:\n set_xformers(self)\n else:\n set_xformers(module)\n\n def set_tomesd(self) -> nn.Module:\n \"\"\"Set ToMe for the stable diffusion model.\n\n Returns:\n nn.Module: The model with ToMe.\n \"\"\"\n if self.tomesd_cfg is not None:\n set_tomesd(self, **self.tomesd_cfg)\n\n @property\n def device(self):\n return next(self.parameters()).device\n\n def train(self, mode: bool = True):\n \"\"\"Set train/eval mode.\n\n Args:\n mode (bool, optional): Whether set train mode. Defaults to True.\n \"\"\"\n if mode:\n if next(self.unet.parameters()).dtype != self._unet_ori_dtype:\n print_log(\n f'Set UNet dtype to \\'{self._unet_ori_dtype}\\' '\n 'in the train mode.', 'current')\n self.unet.to(self._unet_ori_dtype)\n else:\n self.unet.to(self.dtype)\n print_log(f'Set UNet dtype to \\'{self.dtype}\\' in the eval mode.',\n 'current')\n return super().train(mode)\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n show_progress: bool = True,\n seed: int = 1,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n return_type='image'):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n prompt2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_two` and\n `text_encoder_two`. If not defined, `prompt` is used in both\n text-encoders. Defaults to None.\n height (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps.\n More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0)\n of the total denoising process to be completed before it is\n intentionally prematurely terminated. As a result, the returned\n sample will still retain a substantial amount of noise as\n determined by the discrete timesteps selected by the scheduler.\n The denoising_end parameter should ideally be utilized when\n this pipeline forms a part of a \"Mixture of Denoisers\"\n multi-pipeline setup, as elaborated in\n [**Refining the Image Output**](\n https://huggingface.co/docs/diffusers/api/pipelines/\n stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in\n [Classifier-Free Diffusion Guidance]\n (https://arxiv.org/abs/2207.12598).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*)):\n The `negative_prompt` to be sent to the `tokenizer_two` and\n `text_encoder_two`. If not defined, `negative_prompt` is used\n in both text-encoders. Defaults to None.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator] to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents,\n sampled from a Gaussian distribution,\n to be used as inputs for image generation.\n Can be used to tweak the same generation\n with different prompts.\n If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n show_progress (bool): Whether to show progress.\n Defaults to False.\n seed (int): Seed to be used. Defaults to 1.\n original_size (`Tuple[int]`, *optional*):\n If `original_size` is not the same as `target_size` the image\n will appear to be down- or upsampled. If `original_size` is\n `(width, height)` if not specified.\n Defaults to None.\n crops_coords_top_left (`Tuple[int]`, *optional*):\n `crops_coords_top_left` can be used to generate an image that\n appears to be \"cropped\" from the position. Favorable,\n well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0).\n Defaults to (0, 0).\n target_size (`Tuple[int]`, *optional*):\n For most cases, `target_size` should be set to the desired\n height and width of the generated image. If not specified it\n will be `(width, height)`. Defaults to None.\n negative_original_size (`Tuple[int]`, *optional*):\n To negatively condition the generation process based on a\n specific image resolution. For more information, refer to this\n issue thread:\n https://github.com/huggingface/diffusers/issues/4208.\n Defaults to None.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*):\n To negatively condition the generation process based on a\n specific crop coordinates. For more information, refer to this\n issue thread:\n https://github.com/huggingface/diffusers/issues/4208.\n Defaults to (0, 0).\n negative_target_size (`Tuple[int]`, *optional*):\n To negatively condition the generation process based on a\n target image resolution. It should be as same as the\n `target_size` for most cases. For more information,\n refer to this issue thread:\n https://github.com/huggingface/diffusers/issues/4208.\n Defaults to None.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n set_random_seed(seed=seed)\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n\n img_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n latent_dtype = next(self.unet.parameters()).dtype\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self._encode_prompt(prompt, prompt_2, device,\n num_images_per_prompt,\n do_classifier_free_guidance, negative_prompt,\n negative_prompt_2)\n\n # 4. Prepare timesteps\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare latent variables\n if hasattr(self.unet, 'module'):\n num_channels_latents = self.unet.module.in_channels\n else:\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n # TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype)\n if (negative_original_size is not None) and (negative_target_size\n is not None):\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds],\n dim=0)\n add_text_embeds = torch.cat(\n [negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids],\n dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(\n batch_size * num_images_per_prompt, 1)\n\n # 9 Apply denoising_end\n if denoising_end is not None and isinstance(\n denoising_end,\n float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(self.scheduler.config.num_train_timesteps -\n (denoising_end *\n self.scheduler.config.num_train_timesteps)))\n num_inference_steps = len(\n list(\n filter(lambda ts: ts >= discrete_timestep_cutoff,\n timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 10. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n\n latent_model_input = latent_model_input.to(latent_dtype)\n prompt_embeds = prompt_embeds.to(latent_dtype)\n # predict the noise residual\n added_cond_kwargs = {\n 'text_embeds': add_text_embeds,\n 'time_ids': add_time_ids\n }\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs)['prev_sample']\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n if return_type == 'image':\n image = self.output_to_pil(image)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image}\n\n def _get_add_time_ids(self, original_size: Optional[Tuple[int, int]],\n crops_coords_top_left: Tuple[int, int],\n target_size: Optional[Tuple[int, int]], dtype):\n \"\"\"Get `add_time_ids`.\n\n Args:\n original_size (`Tuple[int]`, *optional*):\n If `original_size` is not the same as `target_size` the image\n will appear to be down- or upsampled. If `original_size` is\n `(width, height)` if not specified.\n Defaults to None.\n crops_coords_top_left (`Tuple[int]`, *optional*):\n `crops_coords_top_left` can be used to generate an image that\n appears to be \"cropped\" from the position. Favorable,\n well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0).\n Defaults to (0, 0).\n target_size (`Tuple[int]`, *optional*):\n For most cases, `target_size` should be set to the desired\n height and width of the generated image. If not specified it\n will be `(width, height)`. Defaults to None.\n dtype (str, optional): The dtype for the embeddings.\n\n Returns:\n add_time_ids (torch.Tensor): time ids for time embeddings layer.\n \"\"\"\n add_time_ids = list(original_size + crops_coords_top_left +\n target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) +\n self.text_encoder_two.config.projection_dim)\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n 'Model expects an added time embedding vector of length '\n f'{expected_add_embed_dim}, but a vector of '\n f'{passed_add_embed_dim} was created. The model has an '\n 'incorrect config. Please check '\n '`unet.config.time_embedding_type` and '\n '`text_encoder_2.config.projection_dim`.')\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n def output_to_pil(self, image) -> List[Image.Image]:\n \"\"\"Convert output tensor to PIL image. Output tensor will be de-normed\n to [0, 255] by `DataPreprocessor.destruct`. Due to no `data_samples` is\n passed, color order conversion will not be performed.\n\n Args:\n image (torch.Tensor): The output tensor of the decoder.\n\n Returns:\n List[Image.Image]: The list of processed PIL images.\n \"\"\"\n image = self.data_preprocessor.destruct(image)\n image = image.permute(0, 2, 3, 1).to(torch.uint8).cpu().numpy()\n image = [Image.fromarray(img) for img in image]\n return image\n\n def _encode_prompt(self, prompt, prompt_2, device, num_images_per_prompt,\n do_classifier_free_guidance, negative_prompt,\n negative_prompt_2):\n \"\"\"Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (str or list(int)): prompt to be encoded.\n prompt_2 (str or list(int)): prompt to be encoded. Send to the\n `tokenizer_two` and `text_encoder_two`. If not defined,\n `prompt` is used in both text-encoders.\n device: (torch.device): torch device.\n num_images_per_prompt (int): number of images that should be\n generated per prompt.\n do_classifier_free_guidance (`bool`): whether to use classifier\n free guidance or not.\n negative_prompt (str or List[str]): The prompt or prompts not\n to guide the image generation. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is less than `1`).\n negative_prompt_2 (str or List[str]): The prompt or prompts not\n to guide the image generation. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is less than `1`).\n Send to `tokenizer_two` and `text_encoder_two`. If not defined,\n `negative_prompt` is used in both text-encoders\n\n Returns:\n text_embeddings (torch.Tensor): text embeddings generated by\n clip text encoder.\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n prompt_2 = prompt_2 or prompt\n tokenizers = [self.tokenizer_one, self.tokenizer_two]\n text_encoders = [self.text_encoder_one, self.text_encoder_two]\n prompts = [prompt, prompt_2]\n prompt_embeds_list = []\n for tokenizer, text_encoder, prompt in zip(tokenizers, text_encoders,\n prompts):\n text_inputs = tokenizer(\n prompt,\n padding='max_length',\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(\n prompt, padding='max_length', return_tensors='pt').input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = tokenizer.batch_decode(\n untruncated_ids[:, tokenizer.model_max_length - 1:-1])\n logger.warning(\n 'The following part of your input was truncated because '\n ' CLIP can only handle sequences up to'\n f' {tokenizer.model_max_length} tokens: {removed_text}')\n\n text_encoder = text_encoder.module if hasattr(\n text_encoder, 'module') else text_encoder\n text_embeddings = text_encoder(\n text_input_ids.to(device),\n output_hidden_states=True,\n )\n pooled_prompt_embeds = text_embeddings.pooler_output\n text_embeddings = text_embeddings.hidden_states[-2]\n\n prompt_embeds_list.append(text_embeddings)\n\n text_embeddings = torch.concat(prompt_embeds_list, dim=-1)\n\n # duplicate text embeddings for each generation per prompt,\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(\n bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and self.force_zeros_for_empty_prompt:\n negative_prompt_embeds = torch.zeros_like(text_embeddings)\n negative_pooled_prompt_embeds = torch.zeros_like(\n pooled_prompt_embeds)\n elif do_classifier_free_guidance:\n negative_prompt = negative_prompt or ''\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(\n negative_prompt):\n raise TypeError(\n '`negative_prompt` should be the same type to `prompt`, '\n f'but got {type(negative_prompt)} != {type(prompt)}.')\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt, negative_prompt_2]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f'`negative_prompt`: {negative_prompt} has batch size '\n f'{len(negative_prompt)}, but `prompt`: {prompt} has batch'\n f' size {batch_size}. Please make sure that passed '\n '`negative_prompt` matches the batch size of `prompt`.')\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(\n uncond_tokens, tokenizers, text_encoders):\n max_length = text_embeddings.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the\n # final text encoder\n negative_pooled_prompt_embeds = (\n negative_prompt_embeds.pooler_output)\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[\n -2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(\n negative_prompt_embeds_list, dim=-1)\n\n bs_embed, seq_len, _ = text_embeddings.shape\n # duplicate text embeddings for each generation per prompt, using mps\n # friendly method\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(\n bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt\n # ,using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(\n 1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(\n batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(\n 1, num_images_per_prompt).view(bs_embed * num_images_per_prompt,\n -1)\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = (\n negative_pooled_prompt_embeds.repeat(\n 1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1))\n\n return (text_embeddings, negative_prompt_embeds, pooled_prompt_embeds,\n negative_pooled_prompt_embeds)\n\n def decode_latents(self, latents):\n \"\"\"use vae to decode latents.\n\n Args:\n latents (torch.Tensor): latents to decode.\n\n Returns:\n image (torch.Tensor): image result.\n \"\"\"\n latents = 1 / 0.18215 * latents\n if hasattr(self.vae, 'module'):\n image = self.vae.module.decode(latents)['sample']\n else:\n image = self.vae.decode(latents)['sample']\n # we always cast to float32 as this does not cause\n # significant overhead and is compatible with bfloa16\n return image.float()\n\n def prepare_extra_step_kwargs(self, generator, eta):\n \"\"\"prepare extra kwargs for the scheduler step.\n\n Args:\n generator (torch.Generator):\n generator for random functions.\n eta (float):\n eta (η) is only used with the DDIMScheduler,\n it will be ignored for other schedulers.\n eta corresponds to η in DDIM paper:\n https://arxiv.org/abs/2010.02502\n and should be between [0, 1]\n\n Return:\n extra_step_kwargs (dict):\n dict contains 'generator' and 'eta'\n \"\"\"\n accepts_eta = 'eta' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def prepare_test_scheduler_extra_step_kwargs(self, generator, eta):\n \"\"\"prepare extra kwargs for the scheduler step.\n\n Args:\n generator (torch.Generator):\n generator for random functions.\n eta (float):\n eta (η) is only used with the DDIMScheduler,\n it will be ignored for other schedulers.\n eta corresponds to η in DDIM paper:\n https://arxiv.org/abs/2010.02502\n and should be between [0, 1]\n\n Return:\n extra_step_kwargs (dict):\n dict contains 'generator' and 'eta'\n \"\"\"\n accepts_eta = 'eta' in set(\n inspect.signature(self.test_scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.test_scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width):\n \"\"\"check whether inputs are in suitable format or not.\"\"\"\n\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f'`prompt` has to be of '\n f'type `str` or `list` but is {type(prompt)}')\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f'`height` and `width` have to be divisible '\n f'by 8 but are {height} and {width}.')\n\n def prepare_latents(self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None):\n \"\"\"prepare latents for diffusion to run in latent space.\n\n Args:\n batch_size (int): batch size.\n num_channels_latents (int): latent channel nums.\n height (int): image height.\n width (int): image width.\n dtype (torch.dtype): float type.\n device (torch.device): torch device.\n generator (torch.Generator):\n generator for random functions, defaults to None.\n latents (torch.Tensor):\n Pre-generated noisy latents, defaults to None.\n\n Return:\n latents (torch.Tensor): prepared latents.\n \"\"\"\n shape = (batch_size, num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n if latents is None:\n latents = torch.randn(\n shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f'Unexpected latents shape, '\n f'got {latents.shape}, expected {shape}')\n latents = latents.to(device)\n\n # scale the initial noise by the standard\n # deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n if self.val_prompts is None:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples = DataSample.stack(data['data_samples'] * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n if self.val_prompts is None:\n gt_img = self.data_preprocessor.destruct(data['inputs'],\n data_samples)\n\n out_data_sample = DataSample(\n fake_img=samples, gt_img=gt_img, prompt=prompt)\n else:\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n if self.val_prompts is None:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples = DataSample.stack(data['data_samples'] * len(prompt))\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n if self.val_prompts is None:\n gt_img = self.data_preprocessor.destruct(data['inputs'],\n data_samples)\n\n out_data_sample = DataSample(\n fake_img=samples, gt_img=gt_img, prompt=prompt)\n else:\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n def encode_prompt_train(self, text_one, text_two):\n \"\"\"Encode prompt for training.\n\n Args:\n text_one (torch.tensor): Input ids from tokenizer_one.\n text_two (torch.tensor): Input ids from tokenizer_two.\n\n Returns:\n prompt_embeds (torch.tensor): Prompt embedings.\n pooled_prompt_embeds (torch.tensor): Pooled prompt embeddings.\n \"\"\"\n prompt_embeds_list = []\n\n text_encoders = [self.text_encoder_one, self.text_encoder_two]\n texts = [text_one, text_two]\n for text_encoder, text in zip(text_encoders, texts):\n\n prompt_embeds = text_encoder(\n text,\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the\n # final text encoder\n pooled_prompt_embeds = prompt_embeds.pooler_output\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n def train_step(self, data: List[dict], optim_wrapper: OptimWrapperDict):\n \"\"\"Train step function.\n\n Args:\n data (List[dict]): Batch of data as input.\n optim_wrapper (OptimWrapperDict): Dict with optimizers\n for generator and discriminator (if have).\n\n Returns:\n dict: Dict with loss, information for logger, the number of \\\n samples and results for visualization.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n\n vae = self.vae.module if hasattr(self.vae, 'module') else self.vae\n\n with optim_wrapper.optim_context(self.unet):\n image = inputs\n prompt = data_samples.prompt\n num_batches = image.shape[0]\n\n image = image.to(self.dtype)\n latents = vae.encode(image).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n noise = torch.randn_like(latents)\n\n if self.enable_noise_offset:\n noise = noise + self.noise_offset_weight * torch.randn(\n latents.shape[0],\n latents.shape[1],\n 1,\n 1,\n device=noise.device)\n\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n\n noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)\n\n input_ids_one = self.tokenizer_one(\n prompt,\n max_length=self.tokenizer_one.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n input_ids_two = self.tokenizer_two(\n prompt,\n max_length=self.tokenizer_two.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n\n (encoder_hidden_states,\n pooled_prompt_embeds) = self.encode_prompt_train(\n input_ids_one, input_ids_two)\n unet_added_conditions = {\n 'time_ids': data['time_ids'],\n 'text_embeds': pooled_prompt_embeds\n }\n\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n # NOTE: we train unet in fp32, convert to float manually\n model_output = self.unet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float(),\n added_cond_kwargs=unet_added_conditions)\n model_pred = model_output['sample']\n\n loss_dict = dict()\n # calculate loss in FP32\n loss_mse = F.mse_loss(model_pred.float(), gt.float())\n loss_dict['loss_mse'] = loss_mse\n\n parsed_loss, log_vars = self.parse_losses(loss_dict)\n optim_wrapper.update_params(parsed_loss)\n\n return log_vars\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n" }, { "path": "mmagic/models/editors/stylegan1/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .stylegan1 import StyleGAN1\nfrom .stylegan1_discriminator import StyleGAN1Discriminator\nfrom .stylegan1_generator import StyleGAN1Generator\nfrom .stylegan1_modules import (Blur, ConstantInput, EqualLinearActModule,\n NoiseInjection, make_kernel)\nfrom .stylegan_utils import get_mean_latent, style_mixing\n\n__all__ = [\n 'Blur', 'ConstantInput', 'EqualLinearActModule', 'make_kernel',\n 'StyleGAN1', 'StyleGAN1Discriminator', 'StyleGAN1Generator',\n 'get_mean_latent', 'style_mixing', 'NoiseInjection'\n]\n" }, { "path": "mmagic/models/editors/stylegan1/stylegan1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import Config\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom ..pggan import ProgressiveGrowingGAN\n\nModelType = Union[Dict, nn.Module]\nTrainInput = Union[dict, Tensor]\n\n\n@MODELS.register_module('StyleGANV1')\n@MODELS.register_module('StyleGANv1')\n@MODELS.register_module()\nclass StyleGAN1(ProgressiveGrowingGAN):\n \"\"\"Implementation of `A Style-Based Generator Architecture for Generative\n Adversarial Networks`.\n\n `_ # noqa\n (StyleGANv1). This class is inherited from\n :class:`~ProgressiveGrowingGAN` to support progressive training.\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.stylegan1.StyleGAN1Generator`\n and\n :class:`~mmagic.models.editors.stylegan1.StyleGAN1Discriminator`\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n style_channels (int): The number of channels for style code. Defaults\n to 128.\n nkimgs_per_scale (dict): The number of images need for each\n resolution's training. Defaults to `{}`.\n intep_real (dict, optional): The config of interpolation method for\n real images. If not passed, bilinear interpolation with\n align_corners will be used. Defaults to None.\n transition_kimgs (int, optional): The number of images during used to\n transit from the previous torgb layer to newer torgb layer.\n Defaults to 600.\n prev_stage (int, optional): The resolution of previous stage. Used for\n resume training. Defaults to 0.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n style_channels: int = 512,\n nkimgs_per_scale: dict = {},\n interp_real: Optional[dict] = None,\n transition_kimgs: int = 600,\n prev_stage: int = 0,\n ema_config: Optional[Dict] = None):\n\n # get valid style_channels\n if isinstance(generator, dict):\n model_style_channels = generator.get('style_channels', None)\n else:\n model_style_channels = getattr(generator, 'style_channels', None)\n\n if style_channels is not None and model_style_channels is not None:\n assert style_channels == model_style_channels, (\n 'Input \\'style_channels\\' is inconsistent with '\n f'\\'generator.style_channels\\'. Receive \\'{style_channels}\\' '\n f'and \\'{model_style_channels}\\'.')\n else:\n style_channels = style_channels or model_style_channels\n\n super().__init__(generator, discriminator, data_preprocessor,\n nkimgs_per_scale, None, interp_real, transition_kimgs,\n prev_stage, ema_config)\n\n self.noise_size = style_channels\n\n def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,\n fake_data: Tensor, real_data: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Get disc loss. StyleGANv1 use non-saturating gan loss and R1\n gradient penalty. loss to train the discriminator.\n\n .. math:\n L_{D} = \\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n - \\mathbb{E}_{x\\sim{p_{data}}}D\\left\\(x\\right\\) + L_{GP} \\\\\n L_{GP} = \\lambda\\mathbb{E}(\\Vert\\nabla_{\\tilde{x}}D(\\tilde{x})\n \\Vert_2-1)^2 \\\\\n \\tilde{x} = \\epsilon x + (1-\\epsilon)G(z)\n L_{shift} =\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n fake_data (Tensor): Generated images, used to calculate gradient\n penalty.\n real_data (Tensor): Real images, used to calculate gradient\n penalty.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = F.softplus(disc_pred_fake).mean()\n losses_dict['loss_disc_real'] = F.softplus(-disc_pred_real).mean()\n\n # R1 gradient penalty\n batch_size = real_data.size(0)\n real_data_ = real_data.clone().requires_grad_()\n disc_pred = self.discriminator(\n real_data_,\n curr_scale=self.curr_scale[0],\n transition_weight=self._curr_transition_weight)\n gradients = autograd.grad(\n outputs=disc_pred,\n inputs=real_data_,\n grad_outputs=torch.ones_like(disc_pred),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n # norm_mode is 'HWC'\n gradients_penalty = gradients.pow(2).reshape(batch_size,\n -1).sum(1).mean()\n losses_dict['loss_r1_gp'] = 10 * gradients_penalty\n\n parsed_loss, log_vars = self.parse_losses(losses_dict)\n return parsed_loss, log_vars\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple[Tensor, dict]:\n r\"\"\"Generator loss for PGGAN. PGGAN use WGAN's loss to train the\n generator.\n\n .. math:\n L_{G} = -\\mathbb{E}_{z\\sim{p_{z}}}D\\left\\(G\\left\\(z\\right\\)\\right\\)\n + L_{MSE}\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n Tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_vars = self.parse_losses(losses_dict)\n return loss, log_vars\n" }, { "path": "mmagic/models/editors/stylegan1/stylegan1_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ..pggan import (EqualizedLRConvDownModule, EqualizedLRConvModule,\n MiniBatchStddevLayer)\nfrom .stylegan1_modules import Blur, EqualLinearActModule\n\n\n@MODELS.register_module('StyleGANv1Discriminator')\n@MODELS.register_module()\nclass StyleGAN1Discriminator(BaseModule):\n \"\"\"StyleGAN1 Discriminator.\n\n The architecture of this discriminator is proposed in StyleGAN1. More\n details can be found in: A Style-Based Generator Architecture for\n Generative Adversarial Networks CVPR2019.\n\n Args:\n in_size (int): The input size of images.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 2, 1].\n mbstd_cfg (dict, optional): Configs for minibatch-stddev layer.\n Defaults to dict(group_size=4).\n \"\"\"\n\n def __init__(self,\n in_size,\n blur_kernel=[1, 2, 1],\n mbstd_cfg=dict(group_size=4)):\n super().__init__()\n\n self.with_mbstd = mbstd_cfg is not None\n channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256,\n 128: 128,\n 256: 64,\n 512: 32,\n 1024: 16,\n }\n\n log_size = int(np.log2(in_size))\n self.log_size = log_size\n in_channels = channels[in_size]\n\n self.convs = nn.ModuleList()\n self.from_rgb = nn.ModuleList()\n\n for i in range(log_size, 2, -1):\n out_channel = channels[2**(i - 1)]\n self.from_rgb.append(\n EqualizedLRConvModule(\n 3,\n in_channels,\n kernel_size=3,\n padding=1,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)))\n self.convs.append(\n nn.Sequential(\n EqualizedLRConvModule(\n in_channels,\n out_channel,\n kernel_size=3,\n padding=1,\n bias=True,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n Blur(blur_kernel, pad=(1, 1)),\n EqualizedLRConvDownModule(\n out_channel,\n out_channel,\n kernel_size=3,\n stride=2,\n padding=1,\n act_cfg=None),\n nn.LeakyReLU(negative_slope=0.2, inplace=True)))\n\n in_channels = out_channel\n\n self.from_rgb.append(\n EqualizedLRConvModule(\n 3,\n in_channels,\n kernel_size=3,\n padding=0,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)))\n self.convs.append(\n nn.Sequential(\n EqualizedLRConvModule(\n in_channels + 1,\n 512,\n kernel_size=3,\n padding=1,\n bias=True,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n EqualizedLRConvModule(\n 512,\n 512,\n kernel_size=4,\n padding=0,\n bias=True,\n norm_cfg=None,\n act_cfg=None),\n ))\n\n if self.with_mbstd:\n self.mbstd_layer = MiniBatchStddevLayer(**mbstd_cfg)\n\n self.final_linear = nn.Sequential(EqualLinearActModule(channels[4], 1))\n\n self.n_layer = len(self.convs)\n\n def forward(self, input, transition_weight=1., curr_scale=-1):\n \"\"\"Forward function.\n\n Args:\n input (torch.Tensor): Input image tensor.\n transition_weight (float, optional): The weight used in resolution\n transition. Defaults to 1..\n curr_scale (int, optional): The resolution scale of image tensor.\n -1 means the max resolution scale of the StyleGAN1.\n Defaults to -1.\n\n Returns:\n torch.Tensor: Predict score for the input image.\n \"\"\"\n curr_log_size = self.log_size if curr_scale < 0 else int(\n np.log2(curr_scale))\n step = curr_log_size - 2\n for i in range(step, -1, -1):\n index = self.n_layer - i - 1\n\n if i == step:\n out = self.from_rgb[index](input)\n\n # minibatch standard deviation\n if i == 0:\n out = self.mbstd_layer(out)\n\n out = self.convs[index](out)\n\n if i > 0:\n if i == step and 0 <= transition_weight < 1:\n skip_rgb = F.avg_pool2d(input, 2)\n skip_rgb = self.from_rgb[index + 1](skip_rgb)\n\n out = (1 - transition_weight\n ) * skip_rgb + transition_weight * out\n\n out = out.view(out.shape[0], -1)\n out = self.final_linear(out)\n return out\n" }, { "path": "mmagic/models/editors/stylegan1/stylegan1_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom ..pggan import EqualizedLRConvModule, PixelNorm\nfrom .stylegan1_modules import EqualLinearActModule, StyleConv\nfrom .stylegan_utils import get_mean_latent, style_mixing\n\n\n@MODELS.register_module('StyleGANv1Generator')\n@MODELS.register_module()\nclass StyleGAN1Generator(BaseModule):\n \"\"\"StyleGAN1 Generator.\n\n In StyleGAN1, we use a progressive growing architecture composing of a\n style mapping module and number of convolutional style blocks. More details\n can be found in: A Style-Based Generator Architecture for Generative\n Adversarial Networks CVPR2019.\n\n Args:\n out_size (int): The output size of the StyleGAN1 generator.\n style_channels (int): The number of channels for style code.\n num_mlps (int, optional): The number of MLP layers. Defaults to 8.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 2, 1].\n lr_mlp (float, optional): The learning rate for the style mapping\n layer. Defaults to 0.01.\n default_style_mode (str, optional): The default mode of style mixing.\n In training, we adopt mixing style mode in default. However, in the\n evaluation, we use 'single' style mode. `['mix', 'single']` are\n currently supported. Defaults to 'mix'.\n eval_style_mode (str, optional): The evaluation mode of style mixing.\n Defaults to 'single'.\n mix_prob (float, optional): Mixing probability. The value should be\n in range of [0, 1]. Defaults to 0.9.\n \"\"\"\n\n def __init__(self,\n out_size,\n style_channels,\n num_mlps=8,\n blur_kernel=[1, 2, 1],\n lr_mlp=0.01,\n default_style_mode='mix',\n eval_style_mode='single',\n mix_prob=0.9):\n super().__init__()\n self.out_size = out_size\n self.style_channels = style_channels\n self.num_mlps = num_mlps\n self.lr_mlp = lr_mlp\n self._default_style_mode = default_style_mode\n self.default_style_mode = default_style_mode\n self.eval_style_mode = eval_style_mode\n self.mix_prob = mix_prob\n\n # define style mapping layers\n mapping_layers = [PixelNorm()]\n\n for _ in range(num_mlps):\n mapping_layers.append(\n EqualLinearActModule(\n style_channels,\n style_channels,\n equalized_lr_cfg=dict(lr_mul=lr_mlp, gain=1.),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2)))\n\n self.style_mapping = nn.Sequential(*mapping_layers)\n\n self.channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256,\n 128: 128,\n 256: 64,\n 512: 32,\n 1024: 16,\n }\n\n # generator backbone (8x8 --> higher resolutions)\n self.log_size = int(np.log2(self.out_size))\n\n self.convs = nn.ModuleList()\n self.to_rgbs = nn.ModuleList()\n\n in_channels_ = self.channels[4]\n\n for i in range(2, self.log_size + 1):\n out_channels_ = self.channels[2**i]\n self.convs.append(\n StyleConv(\n in_channels_,\n out_channels_,\n 3,\n style_channels,\n initial=(i == 2),\n upsample=True,\n fused=True))\n self.to_rgbs.append(\n EqualizedLRConvModule(out_channels_, 3, 1, act_cfg=None))\n\n in_channels_ = out_channels_\n\n self.num_latents = self.log_size * 2 - 2\n self.num_injected_noises = self.num_latents\n\n # register buffer for injected noises\n for layer_idx in range(self.num_injected_noises):\n res = (layer_idx + 4) // 2\n shape = [1, 1, 2**res, 2**res]\n self.register_buffer(f'injected_noise_{layer_idx}',\n torch.randn(*shape))\n\n def train(self, mode=True):\n if mode:\n if self.default_style_mode != self._default_style_mode:\n mmengine.print_log(\n f'Switch to train style mode: {self._default_style_mode}')\n self.default_style_mode = self._default_style_mode\n\n else:\n if self.default_style_mode != self.eval_style_mode:\n mmengine.print_log(\n f'Switch to evaluation style mode: {self.eval_style_mode}')\n self.default_style_mode = self.eval_style_mode\n\n return super(StyleGAN1Generator, self).train(mode)\n\n def make_injected_noise(self):\n \"\"\"make noises that will be injected into feature maps.\n\n Returns:\n list[Tensor]: List of layer-wise noise tensor.\n \"\"\"\n device = get_module_device(self)\n\n # noises = [torch.randn(1, 1, 2**2, 2**2, device=device)]\n noises = []\n\n for i in range(2, self.log_size + 1):\n for _ in range(2):\n noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))\n\n return noises\n\n def get_mean_latent(self, num_samples=4096, **kwargs):\n \"\"\"Get mean latent of W space in this generator.\n\n Args:\n num_samples (int, optional): Number of sample times. Defaults\n to 4096.\n\n Returns:\n Tensor: Mean latent of this generator.\n \"\"\"\n return get_mean_latent(self, num_samples, **kwargs)\n\n def style_mixing(self,\n n_source,\n n_target,\n inject_index=1,\n truncation_latent=None,\n truncation=0.7,\n curr_scale=-1,\n transition_weight=1):\n return style_mixing(\n self,\n n_source=n_source,\n n_target=n_target,\n inject_index=inject_index,\n truncation=truncation,\n truncation_latent=truncation_latent,\n style_channels=self.style_channels,\n curr_scale=curr_scale,\n transition_weight=transition_weight)\n\n def forward(self,\n styles,\n num_batches=-1,\n return_noise=False,\n return_latents=False,\n inject_index=None,\n truncation=1,\n truncation_latent=None,\n input_is_latent=False,\n injected_noise=None,\n randomize_noise=True,\n transition_weight=1.,\n curr_scale=-1):\n \"\"\"Forward function.\n\n This function has been integrated with the truncation trick. Please\n refer to the usage of `truncation` and `truncation_latent`.\n\n Args:\n styles (torch.Tensor | list[torch.Tensor] | callable | None): In\n StyleGAN1, you can provide noise tensor or latent tensor. Given\n a list containing more than one noise or latent tensors, style\n mixing trick will be used in training. Of course, You can\n directly give a batch of noise through a ``torch.Tensor`` or\n offer a callable function to sample a batch of noise data.\n Otherwise, the ``None`` indicates to use the default noise\n sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n return_latents (bool, optional): If True, ``latent`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n inject_index (int | None, optional): The index number for mixing\n style codes. Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n truncation_latent (torch.Tensor, optional): Mean truncation latent.\n Defaults to None.\n input_is_latent (bool, optional): If `True`, the input tensor is\n the latent tensor. Defaults to False.\n injected_noise (torch.Tensor | None, optional): Given a tensor, the\n random noise will be fixed as this input injected noise.\n Defaults to None.\n randomize_noise (bool, optional): If `False`, images are sampled\n with the buffered noise tensor injected to the style conv\n block. Defaults to True.\n transition_weight (float, optional): The weight used in resolution\n transition. Defaults to 1..\n curr_scale (int, optional): The resolution scale of generated image\n tensor. -1 means the max resolution scale of the StyleGAN1.\n Defaults to -1.\n\n Returns:\n torch.Tensor | dict: Generated image tensor or dictionary \\\n containing more data.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(styles, torch.Tensor):\n assert styles.shape[1] == self.style_channels\n styles = [styles]\n elif mmengine.is_seq_of(styles, torch.Tensor):\n for t in styles:\n assert t.shape[-1] == self.style_channels\n # receive a noise generator and sample noise.\n elif callable(styles):\n device = get_module_device(self)\n noise_generator = styles\n assert num_batches > 0\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n noise_generator((num_batches, self.style_channels))\n for _ in range(2)\n ]\n else:\n styles = [noise_generator((num_batches, self.style_channels))]\n styles = [s.to(device) for s in styles]\n # otherwise, we will adopt default noise sampler.\n else:\n device = get_module_device(self)\n assert num_batches > 0 and not input_is_latent\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n torch.randn((num_batches, self.style_channels))\n for _ in range(2)\n ]\n else:\n styles = [torch.randn((num_batches, self.style_channels))]\n styles = [s.to(device) for s in styles]\n\n if not input_is_latent:\n noise_batch = styles\n styles = [self.style_mapping(s) for s in styles]\n else:\n noise_batch = None\n\n if injected_noise is None:\n if randomize_noise:\n injected_noise = [None] * self.num_injected_noises\n else:\n injected_noise = [\n getattr(self, f'injected_noise_{i}')\n for i in range(self.num_injected_noises)\n ]\n # use truncation trick\n if truncation < 1:\n style_t = []\n # calculate truncation latent on the fly\n if truncation_latent is None and not hasattr(\n self, 'truncation_latent'):\n self.truncation_latent = self.get_mean_latent()\n truncation_latent = self.truncation_latent\n elif truncation_latent is None and hasattr(self,\n 'truncation_latent'):\n truncation_latent = self.truncation_latent\n\n for style in styles:\n style_t.append(truncation_latent + truncation *\n (style - truncation_latent))\n\n styles = style_t\n # no style mixing\n if len(styles) < 2:\n inject_index = self.num_latents\n\n if styles[0].ndim < 3:\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n\n else:\n latent = styles[0]\n # style mixing\n else:\n if inject_index is None:\n inject_index = random.randint(1, self.num_latents - 1)\n\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n latent2 = styles[1].unsqueeze(1).repeat(\n 1, self.num_latents - inject_index, 1)\n\n latent = torch.cat([latent, latent2], 1)\n\n curr_log_size = self.log_size if curr_scale < 0 else int(\n np.log2(curr_scale))\n step = curr_log_size - 2\n\n _index = 0\n out = latent\n # 4x4 ---> higher resolutions\n for i, (conv, to_rgb) in enumerate(zip(self.convs, self.to_rgbs)):\n if i > 0 and step > 0:\n out_prev = out\n out = conv(\n out,\n latent[:, _index],\n latent[:, _index + 1],\n noise1=injected_noise[2 * i],\n noise2=injected_noise[2 * i + 1])\n if i == step:\n out = to_rgb(out)\n\n if i > 0 and 0 <= transition_weight < 1:\n skip_rgb = self.to_rgbs[i - 1](out_prev)\n skip_rgb = F.interpolate(\n skip_rgb, scale_factor=2, mode='nearest')\n out = (1 - transition_weight\n ) * skip_rgb + transition_weight * out\n break\n\n _index += 2\n\n img = out\n\n if return_latents or return_noise:\n output_dict = dict(\n fake_img=img,\n latent=latent,\n inject_index=inject_index,\n noise_batch=noise_batch)\n return output_dict\n\n return img\n" }, { "path": "mmagic/models/editors/stylegan1/stylegan1_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom functools import partial\n\nimport mmengine\nimport torch\nimport torch.nn as nn\nfrom mmcv.ops.fused_bias_leakyrelu import fused_bias_leakyrelu\nfrom mmcv.ops.upfirdn2d import upfirdn2d\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom ..pggan import (EqualizedLRConvModule, EqualizedLRConvUpModule,\n EqualizedLRLinearModule)\n\n\nclass EqualLinearActModule(BaseModule):\n \"\"\"Equalized LR Linear Module with Activation Layer.\n\n This module is modified from ``EqualizedLRLinearModule`` defined in PGGAN.\n The major features updated in this module is adding support for activation\n layers used in StyleGAN2.\n\n Args:\n equalized_lr_cfg (dict | None, optional): Config for equalized lr.\n Defaults to dict(gain=1., lr_mul=1.).\n bias (bool, optional): Whether to use bias item. Defaults to True.\n bias_init (float, optional): The value for bias initialization.\n Defaults to ``0.``.\n act_cfg (dict | None, optional): Config for activation layer.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n *args,\n equalized_lr_cfg=dict(gain=1., lr_mul=1.),\n bias=True,\n bias_init=0.,\n act_cfg=None,\n **kwargs):\n super().__init__()\n self.with_activation = act_cfg is not None\n # w/o bias in linear layer\n self.linear = EqualizedLRLinearModule(\n *args, bias=False, equalized_lr_cfg=equalized_lr_cfg, **kwargs)\n\n if equalized_lr_cfg is not None:\n self.lr_mul = equalized_lr_cfg.get('lr_mul', 1.)\n else:\n self.lr_mul = 1.\n\n # define bias outside linear layer\n if bias:\n self.bias = nn.Parameter(\n torch.zeros(self.linear.out_features).fill_(bias_init))\n else:\n self.bias = None\n\n if self.with_activation:\n act_cfg = deepcopy(act_cfg)\n if act_cfg['type'] == 'fused_bias':\n self.act_type = act_cfg.pop('type')\n assert self.bias is not None\n self.activate = partial(fused_bias_leakyrelu, **act_cfg)\n else:\n self.act_type = 'normal'\n self.activate = MODELS.build(act_cfg)\n else:\n self.act_type = None\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, ...).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n if x.ndim >= 3:\n x = x.reshape(x.size(0), -1)\n x = self.linear(x)\n\n if self.with_activation and self.act_type == 'fused_bias':\n x = self.activate(x, self.bias * self.lr_mul)\n elif self.bias is not None and self.with_activation:\n x = self.activate(x + self.bias * self.lr_mul)\n elif self.bias is not None:\n x = x + self.bias * self.lr_mul\n elif self.with_activation:\n x = self.activate(x)\n\n return x\n\n\nclass NoiseInjection(BaseModule):\n \"\"\"Noise Injection Module.\n\n In StyleGAN2, they adopt this module to inject spatial random noise map in\n the generators.\n\n Args:\n noise_weight_init (float, optional): Initialization weight for noise\n injection. Defaults to ``0.``.\n fixed_noise (bool, optional): Whether to inject a fixed noise. Defaults\n to ``False``.\n \"\"\"\n\n def __init__(self, noise_weight_init=0., fixed_noise=False):\n super().__init__()\n self.weight = nn.Parameter(torch.zeros(1).fill_(noise_weight_init))\n self.fixed_noise = fixed_noise\n\n def forward(self, image, noise=None, return_noise=False):\n \"\"\"Forward Function.\n\n Args:\n image (Tensor): Spatial features with a shape of (N, C, H, W).\n noise (Tensor, optional): Noises from the outside.\n Defaults to None.\n return_noise (bool, optional): Whether to return noise tensor.\n Defaults to False.\n\n Returns:\n Tensor: Output features.\n \"\"\"\n if noise is None:\n batch, _, height, width = image.shape\n noise = image.new_empty(batch, 1, height, width).normal_()\n if self.fixed_noise:\n torch.manual_seed(1024)\n noise = torch.randn(batch, 1, height, width).cuda()\n\n noise = noise.to(image.dtype)\n if return_noise:\n return image + self.weight.to(image.dtype) * noise, noise\n\n return image + self.weight.to(image.dtype) * noise\n\n\nclass ConstantInput(BaseModule):\n \"\"\"Constant Input.\n\n In StyleGAN2, they substitute the original head noise input with such a\n constant input module.\n\n Args:\n channel (int): Channels for the constant input tensor.\n size (int, optional): Spatial size for the constant input.\n Defaults to 4.\n \"\"\"\n\n def __init__(self, channel, size=4):\n super().__init__()\n if isinstance(size, int):\n size = [size, size]\n elif mmengine.is_seq_of(size, int):\n assert len(\n size\n ) == 2, f'The length of size should be 2 but got {len(size)}'\n else:\n raise ValueError(f'Got invalid value in size, {size}')\n\n self.input = nn.Parameter(torch.randn(1, channel, *size))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, ...).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n batch = x.shape[0]\n out = self.input.repeat(batch, 1, 1, 1)\n\n return out\n\n\ndef make_kernel(k):\n k = torch.tensor(k, dtype=torch.float32)\n if k.ndim == 1:\n k = k[None, :] * k[:, None]\n\n k /= k.sum()\n\n return k\n\n\nclass Blur(BaseModule):\n \"\"\"Blur module.\n\n This module is adopted rightly after upsampling operation in StyleGAN2.\n\n Args:\n kernel (Array): Blur kernel/filter used in UpFIRDn.\n pad (list[int]): Padding for features.\n upsample_factor (int, optional): Upsampling factor. Defaults to 1.\n \"\"\"\n\n def __init__(self, kernel, pad, upsample_factor=1):\n super().__init__()\n kernel = make_kernel(kernel)\n if upsample_factor > 1:\n kernel = kernel * (upsample_factor**2)\n\n self.register_buffer('kernel', kernel)\n\n self.pad = pad\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n\n # In Tero's implementation, he uses fp32\n return upfirdn2d(x, self.kernel.to(x.dtype), padding=self.pad)\n\n\nclass AdaptiveInstanceNorm(BaseModule):\n r\"\"\"Adaptive Instance Normalization Module.\n\n Ref: https://github.com/rosinality/style-based-gan-pytorch/blob/master/model.py # noqa\n\n Args:\n in_channel (int): The number of input's channel.\n style_dim (int): Style latent dimension.\n \"\"\"\n\n def __init__(self, in_channel, style_dim):\n super().__init__()\n\n self.norm = nn.InstanceNorm2d(in_channel)\n self.affine = EqualizedLRLinearModule(style_dim, in_channel * 2)\n\n self.affine.bias.data[:in_channel] = 1\n self.affine.bias.data[in_channel:] = 0\n\n def forward(self, input, style):\n \"\"\"Forward function.\n\n Args:\n input (Tensor): Input tensor with shape (n, c, h, w).\n style (Tensor): Input style tensor with shape (n, c).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n style = self.affine(style).unsqueeze(2).unsqueeze(3)\n gamma, beta = style.chunk(2, 1)\n\n out = self.norm(input)\n out = gamma * out + beta\n\n return out\n\n\nclass StyleConv(BaseModule):\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n padding=1,\n initial=False,\n blur_kernel=[1, 2, 1],\n upsample=False,\n fused=False):\n \"\"\"Convolutional style blocks composing of noise injector, AdaIN module\n and convolution layers.\n\n Args:\n in_channels (int): The channel number of the input tensor.\n out_channels (itn): The channel number of the output tensor.\n kernel_size (int): The kernel size of convolution layers.\n style_channels (int): The number of channels for style code.\n padding (int, optional): Padding of convolution layers.\n Defaults to 1.\n initial (bool, optional): Whether this is the first StyleConv of\n StyleGAN's generator. Defaults to False.\n blur_kernel (list, optional): The blurry kernel.\n Defaults to [1, 2, 1].\n upsample (bool, optional): Whether perform upsampling.\n Defaults to False.\n fused (bool, optional): Whether use fused upconv.\n Defaults to False.\n \"\"\"\n super().__init__()\n\n if initial:\n self.conv1 = ConstantInput(in_channels)\n else:\n if upsample:\n if fused:\n self.conv1 = nn.Sequential(\n EqualizedLRConvUpModule(\n in_channels,\n out_channels,\n kernel_size,\n padding=padding,\n act_cfg=dict(type='LeakyReLU',\n negative_slope=0.2)),\n Blur(blur_kernel, pad=(1, 1)),\n )\n else:\n self.conv1 = nn.Sequential(\n nn.Upsample(scale_factor=2, mode='nearest'),\n EqualizedLRConvModule(\n in_channels,\n out_channels,\n kernel_size,\n padding=padding,\n act_cfg=None), Blur(blur_kernel, pad=(1, 1)))\n else:\n self.conv1 = EqualizedLRConvModule(\n in_channels,\n out_channels,\n kernel_size,\n padding=padding,\n act_cfg=None)\n\n self.noise_injector1 = NoiseInjection()\n self.activate1 = nn.LeakyReLU(0.2)\n self.adain1 = AdaptiveInstanceNorm(out_channels, style_channels)\n\n self.conv2 = EqualizedLRConvModule(\n out_channels,\n out_channels,\n kernel_size,\n padding=padding,\n act_cfg=None)\n self.noise_injector2 = NoiseInjection()\n self.activate2 = nn.LeakyReLU(0.2)\n self.adain2 = AdaptiveInstanceNorm(out_channels, style_channels)\n\n def forward(self,\n x,\n style1,\n style2,\n noise1=None,\n noise2=None,\n return_noise=False):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor.\n style1 (Tensor): Input style tensor with shape (n, c).\n style2 (Tensor): Input style tensor with shape (n, c).\n noise1 (Tensor, optional): Noise tensor with shape (n, c, h, w).\n Defaults to None.\n noise2 (Tensor, optional): Noise tensor with shape (n, c, h, w).\n Defaults to None.\n return_noise (bool, optional): If True, ``noise1`` and ``noise2``\n will be returned with ``out``. Defaults to False.\n\n Returns:\n Tensor | tuple[Tensor]: Forward results.\n \"\"\"\n out = self.conv1(x)\n if return_noise:\n out, noise1 = self.noise_injector1(\n out, noise=noise1, return_noise=return_noise)\n else:\n out = self.noise_injector1(\n out, noise=noise1, return_noise=return_noise)\n out = self.activate1(out)\n out = self.adain1(out, style1)\n\n out = self.conv2(out)\n if return_noise:\n out, noise2 = self.noise_injector2(\n out, noise=noise2, return_noise=return_noise)\n else:\n out = self.noise_injector2(\n out, noise=noise2, return_noise=return_noise)\n out = self.activate2(out)\n out = self.adain2(out, style2)\n\n if return_noise:\n return out, noise1, noise2\n\n return out\n" }, { "path": "mmagic/models/editors/stylegan1/stylegan_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom ...utils import get_module_device\n\n\n@torch.no_grad()\ndef get_mean_latent(generator, num_samples=4096, bs_per_repeat=1024):\n \"\"\"Get mean latent of W space in Style-based GANs.\n\n Args:\n generator (BaseModule): Generator of a Style-based GAN.\n num_samples (int, optional): Number of sample times. Defaults to 4096.\n bs_per_repeat (int, optional): Batch size of noises per sample.\n Defaults to 1024.\n\n Returns:\n Tensor: Mean latent of this generator.\n \"\"\"\n device = get_module_device(generator)\n mean_style = None\n n_repeat = num_samples // bs_per_repeat\n assert n_repeat * bs_per_repeat == num_samples\n\n for _ in range(n_repeat):\n style = generator.style_mapping(\n torch.randn(bs_per_repeat,\n generator.style_channels).to(device)).mean(\n 0, keepdim=True)\n if mean_style is None:\n mean_style = style\n else:\n mean_style += style\n mean_style /= float(n_repeat)\n\n return mean_style\n\n\n@torch.no_grad()\ndef style_mixing(generator,\n n_source,\n n_target,\n inject_index=1,\n truncation_latent=None,\n truncation=0.7,\n style_channels=512,\n **kwargs):\n device = get_module_device(generator)\n source_code = torch.randn(n_source, style_channels).to(device)\n target_code = torch.randn(n_target, style_channels).to(device)\n\n source_image = generator(\n source_code,\n truncation_latent=truncation_latent,\n truncation=truncation,\n **kwargs)\n\n h, w = source_image.shape[-2:]\n images = [torch.ones(1, 3, h, w).to(device) * -1]\n\n target_image = generator(\n target_code,\n truncation_latent=truncation_latent,\n truncation=truncation,\n **kwargs)\n\n images.append(source_image)\n\n for i in range(n_target):\n image = generator(\n [target_code[i].unsqueeze(0).repeat(n_source, 1), source_code],\n truncation_latent=truncation_latent,\n truncation=truncation,\n inject_index=inject_index,\n **kwargs)\n images.append(target_image[i].unsqueeze(0))\n images.append(image)\n\n images = torch.cat(images, 0)\n\n return images\n" }, { "path": "mmagic/models/editors/stylegan2/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .stylegan2 import StyleGAN2\nfrom .stylegan2_discriminator import (ADAAug, ADAStyleGAN2Discriminator,\n StyleGAN2Discriminator)\nfrom .stylegan2_generator import StyleGAN2Generator\nfrom .stylegan2_modules import (ConvDownLayer, ModMBStddevLayer,\n ModulatedToRGB, ResBlock)\n\n__all__ = [\n 'StyleGAN2', 'StyleGAN2Discriminator', 'StyleGAN2Generator',\n 'ADAStyleGAN2Discriminator', 'ADAAug', 'ConvDownLayer', 'ModMBStddevLayer',\n 'ModulatedToRGB', 'ResBlock'\n]\n" }, { "path": "mmagic/models/editors/stylegan2/ada/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n" }, { "path": "mmagic/models/editors/stylegan2/ada/augment.py", "content": "# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this software and related documentation without an express\n# license agreement from NVIDIA CORPORATION is strictly prohibited.\n\nimport numpy as np\nimport scipy.signal\nimport torch\n\ntry:\n from mmcv.ops import conv2d\nexcept ImportError:\n conv2d = None\n print('Warning: mmcv.ops.conv2d are not available.')\n\nfrom . import grid_sample_gradfix, misc, upfirdn2d\n\n# ----------------------------------------------------------------------------\n# Coefficients of various wavelet decomposition low-pass filters.\n\nwavelets = {\n 'haar': [0.7071067811865476, 0.7071067811865476],\n 'db1': [0.7071067811865476, 0.7071067811865476],\n 'db2': [\n -0.12940952255092145, 0.22414386804185735, 0.836516303737469,\n 0.48296291314469025\n ],\n 'db3': [\n 0.035226291882100656, -0.08544127388224149, -0.13501102001039084,\n 0.4598775021193313, 0.8068915093133388, 0.3326705529509569\n ],\n 'db4': [\n -0.010597401784997278, 0.032883011666982945, 0.030841381835986965,\n -0.18703481171888114, -0.02798376941698385, 0.6308807679295904,\n 0.7148465705525415, 0.23037781330885523\n ],\n 'db5': [\n 0.003335725285001549, -0.012580751999015526, -0.006241490213011705,\n 0.07757149384006515, -0.03224486958502952, -0.24229488706619015,\n 0.13842814590110342, 0.7243085284385744, 0.6038292697974729,\n 0.160102397974125\n ],\n 'db6': [\n -0.00107730108499558, 0.004777257511010651, 0.0005538422009938016,\n -0.031582039318031156, 0.02752286553001629, 0.09750160558707936,\n -0.12976686756709563, -0.22626469396516913, 0.3152503517092432,\n 0.7511339080215775, 0.4946238903983854, 0.11154074335008017\n ],\n 'db7': [\n 0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274,\n 0.012550998556013784, -0.01657454163101562, -0.03802993693503463,\n 0.0806126091510659, 0.07130921926705004, -0.22403618499416572,\n -0.14390600392910627, 0.4697822874053586, 0.7291320908465551,\n 0.39653931948230575, 0.07785205408506236\n ],\n 'db8': [\n -0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771,\n -0.00487035299301066, 0.008746094047015655, 0.013981027917015516,\n -0.04408825393106472, -0.01736930100202211, 0.128747426620186,\n 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893,\n 0.5853546836548691, 0.6756307362980128, 0.3128715909144659,\n 0.05441584224308161\n ],\n 'sym2': [\n -0.12940952255092145, 0.22414386804185735, 0.836516303737469,\n 0.48296291314469025\n ],\n 'sym3': [\n 0.035226291882100656, -0.08544127388224149, -0.13501102001039084,\n 0.4598775021193313, 0.8068915093133388, 0.3326705529509569\n ],\n 'sym4': [\n -0.07576571478927333, -0.02963552764599851, 0.49761866763201545,\n 0.8037387518059161, 0.29785779560527736, -0.09921954357684722,\n -0.012603967262037833, 0.0322231006040427\n ],\n 'sym5': [\n 0.027333068345077982, 0.029519490925774643, -0.039134249302383094,\n 0.1993975339773936, 0.7234076904024206, 0.6339789634582119,\n 0.01660210576452232, -0.17532808990845047, -0.021101834024758855,\n 0.019538882735286728\n ],\n 'sym6': [\n 0.015404109327027373, 0.0034907120842174702, -0.11799011114819057,\n -0.048311742585633, 0.4910559419267466, 0.787641141030194,\n 0.3379294217276218, -0.07263752278646252, -0.021060292512300564,\n 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148\n ],\n 'sym7': [\n 0.002681814568257878, -0.0010473848886829163, -0.01263630340325193,\n 0.03051551316596357, 0.0678926935013727, -0.049552834937127255,\n 0.017441255086855827, 0.5361019170917628, 0.767764317003164,\n 0.2886296317515146, -0.14004724044296152, -0.10780823770381774,\n 0.004010244871533663, 0.010268176708511255\n ],\n 'sym8': [\n -0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298,\n 0.007607487324917605, -0.1432942383508097, -0.061273359067658524,\n 0.4813596512583722, 0.7771857517005235, 0.3644418948353314,\n -0.05194583810770904, -0.027219029917056003, 0.049137179673607506,\n 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668,\n 0.0018899503327594609\n ],\n}\n\n# ----------------------------------------------------------------------------\n# Helpers for constructing transformation matrices.\n\n\ndef matrix(*rows, device=None):\n \"\"\"Constructing transformation matrices.\n\n Args:\n device (str|torch.device, optional): Matrix device. Defaults to None.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n assert all(len(row) == len(rows[0]) for row in rows)\n elems = [x for row in rows for x in row]\n ref = [x for x in elems if isinstance(x, torch.Tensor)]\n if len(ref) == 0:\n return misc.constant(np.asarray(rows), device=device)\n assert device is None or device == ref[0].device\n # change `x.float()` to support pt1.5\n elems = [\n x.float() if isinstance(x, torch.Tensor) else misc.constant(\n x, shape=ref[0].shape, device=ref[0].device) for x in elems\n ]\n return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))\n\n\ndef translate2d(tx, ty, **kwargs):\n \"\"\"Construct 2d translation matrix.\n\n Args:\n tx (float): X-direction translation amount.\n ty (float): Y-direction translation amount.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return matrix([1, 0, tx], [0, 1, ty], [0, 0, 1], **kwargs)\n\n\ndef translate3d(tx, ty, tz, **kwargs):\n \"\"\"Construct 3d translation matrix.\n\n Args:\n tx (float): X-direction translation amount.\n ty (float): Y-direction translation amount.\n tz (float): Z-direction translation amount.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return matrix([1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1],\n **kwargs)\n\n\ndef scale2d(sx, sy, **kwargs):\n \"\"\"Construct 2d scaling matrix.\n\n Args:\n sx (float): X-direction scaling coefficient.\n sy (float): Y-direction scaling coefficient.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return matrix([sx, 0, 0], [0, sy, 0], [0, 0, 1], **kwargs)\n\n\ndef scale3d(sx, sy, sz, **kwargs):\n \"\"\"Construct 3d scaling matrix.\n\n Args:\n sx (float): X-direction scaling coefficient.\n sy (float): Y-direction scaling coefficient.\n sz (float): Z-direction scaling coefficient.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return matrix([sx, 0, 0, 0], [0, sy, 0, 0], [0, 0, sz, 0], [0, 0, 0, 1],\n **kwargs)\n\n\ndef rotate2d(theta, **kwargs):\n \"\"\"Construct 2d rotating matrix.\n\n Args:\n theta (float): Counter-clock wise rotation angle.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return matrix([torch.cos(theta), torch.sin(-theta), 0],\n [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1], **kwargs)\n\n\ndef rotate3d(v, theta, **kwargs):\n \"\"\"Constructing 3d rotating matrix.\n\n Args:\n v (torch.Tensor): Luma axis.\n theta (float): Rotate theta counter-clock wise with ``v`` as the axis.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n vx = v[..., 0]\n vy = v[..., 1]\n vz = v[..., 2]\n s = torch.sin(theta)\n c = torch.cos(theta)\n cc = 1 - c\n return matrix(\n [vx * vx * cc + c, vx * vy * cc - vz * s, vx * vz * cc + vy * s, 0],\n [vy * vx * cc + vz * s, vy * vy * cc + c, vy * vz * cc - vx * s, 0],\n [vz * vx * cc - vy * s, vz * vy * cc + vx * s, vz * vz * cc + c, 0],\n [0, 0, 0, 1], **kwargs)\n\n\ndef translate2d_inv(tx, ty, **kwargs):\n \"\"\"Construct inverse matrix of 2d translation matrix.\n\n Args:\n tx (float): X-direction translation amount.\n ty (float): Y-direction translation amount.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return translate2d(-tx, -ty, **kwargs)\n\n\ndef scale2d_inv(sx, sy, **kwargs):\n \"\"\"Construct inverse matrix of 2d scaling matrix.\n\n Args:\n sx (float): X-direction scaling coefficient.\n sy (float): Y-direction scaling coefficient.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return scale2d(1 / sx, 1 / sy, **kwargs)\n\n\ndef rotate2d_inv(theta, **kwargs):\n \"\"\"Construct inverse matrix of 2d rotating matrix.\n\n Args:\n theta (float): Counter-clock wise rotation angle.\n Returns:\n ndarry | Tensor : Transformation matrices in np.ndarry or torch.Tensor\n format.\n \"\"\"\n return rotate2d(-theta, **kwargs)\n\n\n# ----------------------------------------------------------------------------\n# Versatile image augmentation pipeline from the paper\n# \"Training Generative Adversarial Networks with Limited Data\".\n#\n# All augmentations are disabled by default; individual augmentations can\n# be enabled by setting their probability multipliers to 1.\n\n\nclass AugmentPipe(torch.nn.Module):\n \"\"\"Augmentation pipeline include multiple geometric and color\n transformations.\n\n Note: The meaning of arguments are written in the comments of\n ``__init__`` function.\n \"\"\"\n\n def __init__(\n self,\n xflip=0,\n rotate90=0,\n xint=0,\n xint_max=0.125,\n scale=0,\n rotate=0,\n aniso=0,\n xfrac=0,\n scale_std=0.2,\n rotate_max=1,\n aniso_std=0.2,\n xfrac_std=0.125,\n brightness=0,\n contrast=0,\n lumaflip=0,\n hue=0,\n saturation=0,\n brightness_std=0.2,\n contrast_std=0.5,\n hue_max=1,\n saturation_std=1,\n imgfilter=0,\n imgfilter_bands=[1, 1, 1, 1],\n imgfilter_std=1,\n noise=0,\n cutout=0,\n noise_std=0.1,\n cutout_size=0.5,\n ):\n super().__init__()\n self.register_buffer('p', torch.ones(\n [])) # Overall multiplier for augmentation probability.\n\n # Pixel blitting.\n self.xflip = float(xflip) # Probability multiplier for x-flip.\n self.rotate90 = float(\n rotate90) # Probability multiplier for 90 degree rotations.\n self.xint = float(\n xint) # Probability multiplier for integer translation.\n self.xint_max = float(\n xint_max\n ) # Range of integer translation, relative to image dimensions.\n\n # General geometric transformations.\n self.scale = float(\n scale) # Probability multiplier for isotropic scaling.\n self.rotate = float(\n rotate) # Probability multiplier for arbitrary rotation.\n self.aniso = float(\n aniso) # Probability multiplier for anisotropic scaling.\n self.xfrac = float(\n xfrac) # Probability multiplier for fractional translation.\n self.scale_std = float(\n scale_std) # Log2 standard deviation of isotropic scaling.\n self.rotate_max = float(\n rotate_max) # Range of arbitrary rotation, 1 = full circle.\n self.aniso_std = float(\n aniso_std) # Log2 standard deviation of anisotropic scaling.\n self.xfrac_std = float(\n xfrac_std\n ) # Standard deviation of frational translation, relative to img dims.\n\n # Color transformations.\n self.brightness = float(\n brightness) # Probability multiplier for brightness.\n self.contrast = float(contrast) # Probability multiplier for contrast.\n self.lumaflip = float(\n lumaflip) # Probability multiplier for luma flip.\n self.hue = float(hue) # Probability multiplier for hue rotation.\n self.saturation = float(\n saturation) # Probability multiplier for saturation.\n self.brightness_std = float(\n brightness_std) # Standard deviation of brightness.\n self.contrast_std = float(\n contrast_std) # Log2 standard deviation of contrast.\n self.hue_max = float(\n hue_max) # Range of hue rotation, 1 = full circle.\n self.saturation_std = float(\n saturation_std) # Log2 standard deviation of saturation.\n\n # Image-space filtering.\n self.imgfilter = float(\n imgfilter) # Probability multiplier for image-space filtering.\n self.imgfilter_bands = list(\n imgfilter_bands\n ) # Probability multipliers for individual frequency bands.\n self.imgfilter_std = float(\n imgfilter_std\n ) # Log2 standard deviation of image-space filter amplification.\n\n # Image-space corruptions.\n self.noise = float(\n noise) # Probability multiplier for additive RGB noise.\n self.cutout = float(cutout) # Probability multiplier for cutout.\n self.noise_std = float(\n noise_std) # Standard deviation of additive RGB noise.\n self.cutout_size = float(\n cutout_size\n ) # Size of the cutout rectangle, relative to image dimensions.\n\n # Setup orthogonal lowpass filter for geometric augmentations.\n self.register_buffer('Hz_geom',\n upfirdn2d.setup_filter(wavelets['sym6']))\n\n # Construct filter bank for image-space filtering.\n Hz_lo = np.asarray(wavelets['sym2']) # H(z)\n Hz_hi = Hz_lo * ((-1)**np.arange(Hz_lo.size)) # H(-z)\n Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2\n Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2\n Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i)\n for i in range(1, Hz_fbank.shape[0]):\n Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)\n ]).reshape(Hz_fbank.shape[0], -1)[:, :-1]\n Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])\n Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) //\n 2:(Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2\n self.register_buffer('Hz_fbank',\n torch.as_tensor(Hz_fbank, dtype=torch.float32))\n\n def forward(self, images, debug_percentile=None):\n assert isinstance(images, torch.Tensor) and images.ndim == 4\n batch_size, num_channels, height, width = images.shape\n device = images.device\n if debug_percentile is not None:\n debug_percentile = torch.as_tensor(\n debug_percentile, dtype=torch.float32, device=device)\n\n # -------------------------------------\n # Select parameters for pixel blitting.\n # -------------------------------------\n\n # Initialize inverse homogeneous 2D transform:\n # G_inv @ pixel_out ==> pixel_in\n I_3 = torch.eye(3, device=device)\n G_inv = I_3\n\n # Apply x-flip with probability (xflip * strength).\n if self.xflip > 0:\n i = torch.floor(torch.rand([batch_size], device=device) * 2)\n i = torch.where(\n torch.rand([batch_size], device=device) < self.xflip * self.p,\n i, torch.zeros_like(i))\n if debug_percentile is not None:\n i = torch.full_like(i, torch.floor(debug_percentile * 2))\n G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)\n\n # Apply 90 degree rotations with probability (rotate90 * strength).\n if self.rotate90 > 0:\n i = torch.floor(torch.rand([batch_size], device=device) * 4)\n i = torch.where(\n torch.rand([batch_size], device=device) <\n self.rotate90 * self.p, i, torch.zeros_like(i))\n if debug_percentile is not None:\n i = torch.full_like(i, torch.floor(debug_percentile * 4))\n G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)\n\n # Apply integer translation with probability (xint * strength).\n if self.xint > 0:\n t = (torch.rand([batch_size, 2], device=device) * 2 -\n 1) * self.xint_max\n t = torch.where(\n torch.rand([batch_size, 1], device=device) <\n self.xint * self.p, t, torch.zeros_like(t))\n if debug_percentile is not None:\n t = torch.full_like(t,\n (debug_percentile * 2 - 1) * self.xint_max)\n G_inv = G_inv @ translate2d_inv(\n torch.round(t[:, 0] * width), torch.round(t[:, 1] * height))\n\n # --------------------------------------------------------\n # Select parameters for general geometric transformations.\n # --------------------------------------------------------\n\n # support for pt1.5 (pt1.5 does not contain exp2)\n _scalor_log2 = torch.log(\n torch.tensor(2., device=images.device, dtype=images.dtype))\n\n # Apply isotropic scaling with probability (scale * strength).\n if self.scale > 0:\n s = torch.exp(\n torch.randn([batch_size], device=device) * self.scale_std *\n _scalor_log2)\n s = torch.where(\n torch.rand([batch_size], device=device) < self.scale * self.p,\n s, torch.ones_like(s))\n if debug_percentile is not None:\n s = torch.full_like(\n s,\n torch.exp2(\n torch.erfinv(debug_percentile * 2 - 1) *\n self.scale_std))\n G_inv = G_inv @ scale2d_inv(s, s)\n\n # Apply pre-rotation with probability p_rot.\n p_rot = 1 - torch.sqrt(\n (1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p\n if self.rotate > 0:\n theta = (torch.rand([batch_size], device=device) * 2 -\n 1) * np.pi * self.rotate_max\n theta = torch.where(\n torch.rand([batch_size], device=device) < p_rot, theta,\n torch.zeros_like(theta))\n if debug_percentile is not None:\n theta = torch.full_like(theta, (debug_percentile * 2 - 1) *\n np.pi * self.rotate_max)\n G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.\n\n # Apply anisotropic scaling with probability (aniso * strength).\n if self.aniso > 0:\n s = torch.exp(\n torch.randn([batch_size], device=device) * self.aniso_std *\n _scalor_log2)\n s = torch.where(\n torch.rand([batch_size], device=device) < self.aniso * self.p,\n s, torch.ones_like(s))\n if debug_percentile is not None:\n s = torch.full_like(\n s,\n torch.exp2(\n torch.erfinv(debug_percentile * 2 - 1) *\n self.aniso_std))\n G_inv = G_inv @ scale2d_inv(s, 1 / s)\n\n # Apply post-rotation with probability p_rot.\n if self.rotate > 0:\n theta = (torch.rand([batch_size], device=device) * 2 -\n 1) * np.pi * self.rotate_max\n theta = torch.where(\n torch.rand([batch_size], device=device) < p_rot, theta,\n torch.zeros_like(theta))\n if debug_percentile is not None:\n theta = torch.zeros_like(theta)\n G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.\n\n # Apply fractional translation with probability (xfrac * strength).\n if self.xfrac > 0:\n t = torch.randn([batch_size, 2], device=device) * self.xfrac_std\n t = torch.where(\n torch.rand([batch_size, 1], device=device) <\n self.xfrac * self.p, t, torch.zeros_like(t))\n if debug_percentile is not None:\n t = torch.full_like(\n t,\n torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)\n G_inv = G_inv @ translate2d_inv(t[:, 0] * width, t[:, 1] * height)\n\n # ----------------------------------\n # Execute geometric transformations.\n # ----------------------------------\n\n # Execute if the transform is not identity.\n if G_inv is not I_3:\n\n # Calculate padding.\n cx = (width - 1) / 2\n cy = (height - 1) / 2\n cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1],\n device=device) # [idx, xyz]\n cp = G_inv @ cp.t() # [batch, xyz, idx]\n Hz_pad = self.Hz_geom.shape[0] // 4\n margin = cp[:, :2, :].permute(1, 0,\n 2).flatten(1) # [xy, batch * idx]\n margin = torch.cat([-margin,\n margin]).max(dim=1).values # [x0, y0, x1, y1]\n margin = margin + misc.constant(\n [Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)\n margin = margin.max(misc.constant([0, 0] * 2, device=device))\n margin = margin.min(\n misc.constant([width - 1, height - 1] * 2, device=device))\n mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)\n\n # Pad image and adjust origin.\n images = torch.nn.functional.pad(\n input=images, pad=[mx0, mx1, my0, my1], mode='reflect')\n G_inv = translate2d(\n torch.true_divide(mx0 - mx1, 2), torch.true_divide(\n my0 - my1, 2)) @ G_inv\n\n # Upsample.\n images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)\n G_inv = scale2d(\n 2, 2, device=device) @ G_inv @ scale2d_inv(\n 2, 2, device=device)\n G_inv = translate2d(\n -0.5, -0.5, device=device) @ G_inv @ translate2d_inv(\n -0.5, -0.5, device=device)\n\n # Execute transformation.\n shape = [\n batch_size, num_channels, (height + Hz_pad * 2) * 2,\n (width + Hz_pad * 2) * 2\n ]\n G_inv = scale2d(\n 2 / images.shape[3], 2 / images.shape[2],\n device=device) @ G_inv @ scale2d_inv(\n 2 / shape[3], 2 / shape[2], device=device)\n grid = torch.nn.functional.affine_grid(\n theta=G_inv[:, :2, :], size=shape, align_corners=False)\n images = grid_sample_gradfix.grid_sample(images, grid)\n\n # Downsample and crop.\n images = upfirdn2d.downsample2d(\n x=images,\n f=self.Hz_geom,\n down=2,\n padding=-Hz_pad * 2,\n flip_filter=True)\n\n # --------------------------------------------\n # Select parameters for color transformations.\n # --------------------------------------------\n\n # Initialize homogeneous 3D transformation matrix:\n # C @ color_in ==> color_out\n I_4 = torch.eye(4, device=device)\n C = I_4\n\n # Apply brightness with probability (brightness * strength).\n if self.brightness > 0:\n b = torch.randn([batch_size], device=device) * self.brightness_std\n b = torch.where(\n torch.rand([batch_size], device=device) <\n self.brightness * self.p, b, torch.zeros_like(b))\n if debug_percentile is not None:\n b = torch.full_like(\n b,\n torch.erfinv(debug_percentile * 2 - 1) *\n self.brightness_std)\n C = translate3d(b, b, b) @ C\n\n # Apply contrast with probability (contrast * strength).\n if self.contrast > 0:\n c = torch.exp(\n torch.randn([batch_size], device=device) * self.contrast_std *\n _scalor_log2)\n c = torch.where(\n torch.rand([batch_size], device=device) <\n self.contrast * self.p, c, torch.ones_like(c))\n if debug_percentile is not None:\n c = torch.full_like(\n c,\n torch.exp2(\n torch.erfinv(debug_percentile * 2 - 1) *\n self.contrast_std))\n C = scale3d(c, c, c) @ C\n\n # Apply luma flip with probability (lumaflip * strength).\n v = misc.constant(\n np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.\n if self.lumaflip > 0:\n i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)\n i = torch.where(\n torch.rand([batch_size, 1, 1], device=device) <\n self.lumaflip * self.p, i, torch.zeros_like(i))\n if debug_percentile is not None:\n i = torch.full_like(i, torch.floor(debug_percentile * 2))\n C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.\n\n # Apply hue rotation with probability (hue * strength).\n if self.hue > 0 and num_channels > 1:\n theta = (torch.rand([batch_size], device=device) * 2 -\n 1) * np.pi * self.hue_max\n theta = torch.where(\n torch.rand([batch_size], device=device) < self.hue * self.p,\n theta, torch.zeros_like(theta))\n if debug_percentile is not None:\n theta = torch.full_like(theta, (debug_percentile * 2 - 1) *\n np.pi * self.hue_max)\n C = rotate3d(v, theta) @ C # Rotate around v.\n\n # Apply saturation with probability (saturation * strength).\n if self.saturation > 0 and num_channels > 1:\n s = torch.exp(\n torch.randn([batch_size, 1, 1], device=device) *\n self.saturation_std * _scalor_log2)\n s = torch.where(\n torch.rand([batch_size, 1, 1], device=device) <\n self.saturation * self.p, s, torch.ones_like(s))\n if debug_percentile is not None:\n s = torch.full_like(\n s,\n torch.exp2(\n torch.erfinv(debug_percentile * 2 - 1) *\n self.saturation_std))\n C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C\n\n # ------------------------------\n # Execute color transformations.\n # ------------------------------\n\n # Execute if the transform is not identity.\n if C is not I_4:\n images = images.reshape([batch_size, num_channels, height * width])\n if num_channels == 3:\n images = C[:, :3, :3] @ images + C[:, :3, 3:]\n elif num_channels == 1:\n C = C[:, :3, :].mean(dim=1, keepdims=True)\n images = images * C[:, :, :3].sum(\n dim=2, keepdims=True) + C[:, :, 3:]\n else:\n raise ValueError(\n 'Image must be RGB (3 channels) or L (1 channel)')\n images = images.reshape([batch_size, num_channels, height, width])\n\n # ----------------------\n # Image-space filtering.\n # ----------------------\n\n if self.imgfilter > 0:\n num_bands = self.Hz_fbank.shape[0]\n assert len(self.imgfilter_bands) == num_bands\n expected_power = misc.constant(\n np.array([10, 1, 1, 1]) / 13,\n device=device) # Expected power spectrum (1/f).\n\n # Apply amplification for each band with probability\n # (imgfilter * strength * band_strength).\n g = torch.ones([batch_size, num_bands],\n device=device) # Global gain vector (identity).\n for i, band_strength in enumerate(self.imgfilter_bands):\n t_i = torch.exp(\n torch.randn([batch_size], device=device) *\n self.imgfilter_std * _scalor_log2)\n t_i = torch.where(\n torch.rand([batch_size], device=device) <\n self.imgfilter * self.p * band_strength, t_i,\n torch.ones_like(t_i))\n if debug_percentile is not None:\n t_i = torch.full_like(\n t_i,\n torch.exp2(\n torch.erfinv(debug_percentile * 2 - 1) *\n self.imgfilter_std)\n ) if band_strength > 0 else torch.ones_like(t_i)\n t = torch.ones([batch_size, num_bands],\n device=device) # Temporary gain vector.\n t[:, i] = t_i # Replace i'th element.\n t = t / (expected_power * t.square()).sum(\n dim=-1, keepdims=True).sqrt() # Normalize power.\n g = g * t # Accumulate into global gain.\n\n # Construct combined amplification filter.\n Hz_prime = g @ self.Hz_fbank # [batch, tap]\n Hz_prime = Hz_prime.unsqueeze(1).repeat(\n [1, num_channels, 1]) # [batch, channels, tap]\n Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1,\n -1]) # [batch * channels, 1, tap]\n\n # Apply filter.\n p = self.Hz_fbank.shape[1] // 2\n images = images.reshape(\n [1, batch_size * num_channels, height, width])\n images = torch.nn.functional.pad(\n input=images, pad=[p, p, p, p], mode='reflect')\n images = conv2d(\n input=images,\n weight=Hz_prime.unsqueeze(2),\n groups=batch_size * num_channels)\n images = conv2d(\n input=images,\n weight=Hz_prime.unsqueeze(3),\n groups=batch_size * num_channels)\n images = images.reshape([batch_size, num_channels, height, width])\n\n # ------------------------\n # Image-space corruptions.\n # ------------------------\n\n # Apply additive RGB noise with probability (noise * strength).\n if self.noise > 0:\n sigma = torch.randn([batch_size, 1, 1, 1],\n device=device).abs() * self.noise_std\n sigma = torch.where(\n torch.rand([batch_size, 1, 1, 1], device=device) <\n self.noise * self.p, sigma, torch.zeros_like(sigma))\n if debug_percentile is not None:\n sigma = torch.full_like(\n sigma,\n torch.erfinv(debug_percentile) * self.noise_std)\n images = images + torch.randn(\n [batch_size, num_channels, height, width],\n device=device) * sigma\n\n # Apply cutout with probability (cutout * strength).\n if self.cutout > 0:\n size = torch.full([batch_size, 2, 1, 1, 1],\n self.cutout_size,\n device=device)\n size = torch.where(\n torch.rand([batch_size, 1, 1, 1, 1], device=device) <\n self.cutout * self.p, size, torch.zeros_like(size))\n center = torch.rand([batch_size, 2, 1, 1, 1], device=device)\n if debug_percentile is not None:\n size = torch.full_like(size, self.cutout_size)\n center = torch.full_like(center, debug_percentile)\n coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])\n coord_y = torch.arange(\n height, device=device).reshape([1, 1, -1, 1])\n mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >=\n size[:, 0] / 2)\n mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >=\n size[:, 1] / 2)\n mask = torch.logical_or(mask_x, mask_y).to(torch.float32)\n images = images * mask\n\n return images\n" }, { "path": "mmagic/models/editors/stylegan2/ada/grid_sample_gradfix.py", "content": "# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this software and related documentation without an express\n# license agreement from NVIDIA CORPORATION is strictly prohibited.\n\"\"\"Custom replacement for `torch.nn.functional.grid_sample` that supports\narbitrarily high order gradients between the input and output.\n\nOnly works on 2D images and assumes `mode='bilinear'`, `padding_mode='zeros'`,\n`align_corners=False`.\n\"\"\"\n\nimport warnings\n\nimport torch\n\n# pylint: disable=redefined-builtin\n# pylint: disable=arguments-differ\n# pylint: disable=protected-access\n\n# ----------------------------------------------------------------------------\n\nenabled = True # Enable the custom op by setting this to true.\n\n# ----------------------------------------------------------------------------\n\n\ndef grid_sample(input, grid):\n if _should_use_custom_op():\n return _GridSample2dForward.apply(input, grid)\n return torch.nn.functional.grid_sample(\n input=input,\n grid=grid,\n mode='bilinear',\n padding_mode='zeros',\n align_corners=False)\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _should_use_custom_op():\n if not enabled:\n return False\n if any(\n torch.__version__.startswith(x)\n for x in ['1.5.', '1.6.', '1.7.', '1.8.', '1.9.', '1.10.']):\n return True\n warnings.warn(\n f'grid_sample_gradfix not supported on PyTorch {torch.__version__}.'\n ' Falling back to torch.nn.functional.grid_sample().')\n return False\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _GridSample2dForward(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, input, grid):\n assert input.ndim == 4\n assert grid.ndim == 4\n output = torch.nn.functional.grid_sample(\n input=input,\n grid=grid,\n mode='bilinear',\n padding_mode='zeros',\n align_corners=False)\n ctx.save_for_backward(input, grid)\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n input, grid = ctx.saved_tensors\n grad_input, grad_grid = _GridSample2dBackward.apply(\n grad_output, input, grid)\n return grad_input, grad_grid\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _GridSample2dBackward(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, grad_output, input, grid):\n op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')\n grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)\n ctx.save_for_backward(grid)\n return grad_input, grad_grid\n\n @staticmethod\n def backward(ctx, grad2_grad_input, grad2_grad_grid):\n _ = grad2_grad_grid # unused\n grid, = ctx.saved_tensors\n grad2_grad_output = None\n grad2_input = None\n grad2_grid = None\n\n if ctx.needs_input_grad[0]:\n grad2_grad_output = _GridSample2dForward.apply(\n grad2_grad_input, grid)\n\n assert not ctx.needs_input_grad[2]\n return grad2_grad_output, grad2_input, grad2_grid\n" }, { "path": "mmagic/models/editors/stylegan2/ada/misc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\n# Cached construction of constant tensors. Avoids CPU=>GPU copy when the\n# same constant is used multiple times.\n\n_constant_cache = dict()\n\n\ndef constant(value, shape=None, dtype=None, device=None, memory_format=None):\n value = np.asarray(value)\n if shape is not None:\n shape = tuple(shape)\n if dtype is None:\n dtype = torch.get_default_dtype()\n if device is None:\n device = torch.device('cpu')\n if memory_format is None:\n memory_format = torch.contiguous_format\n\n key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device,\n memory_format)\n tensor = _constant_cache.get(key, None)\n if tensor is None:\n tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)\n if shape is not None:\n tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))\n tensor = tensor.contiguous(memory_format=memory_format)\n _constant_cache[key] = tensor\n return tensor\n" }, { "path": "mmagic/models/editors/stylegan2/ada/upfirdn2d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\ntry:\n from mmcv.ops import upfirdn2d\nexcept ImportError:\n upfirdn2d = None\n print('Warning: mmcv.ops.upfirdn2d is not available.')\n\n\ndef _parse_scaling(scaling):\n if isinstance(scaling, int):\n scaling = [scaling, scaling]\n assert isinstance(scaling, (list, tuple))\n assert all(isinstance(x, int) for x in scaling)\n sx, sy = scaling\n assert sx >= 1 and sy >= 1\n return sx, sy\n\n\ndef _parse_padding(padding):\n if isinstance(padding, int):\n padding = [padding, padding]\n assert isinstance(padding, (list, tuple))\n assert all(isinstance(x, int) for x in padding)\n if len(padding) == 2:\n padx, pady = padding\n padding = [padx, padx, pady, pady]\n padx0, padx1, pady0, pady1 = padding\n return padx0, padx1, pady0, pady1\n\n\ndef _get_filter_size(f):\n if f is None:\n return 1, 1\n assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]\n fw = f.shape[-1]\n fh = f.shape[0]\n fw = int(fw)\n fh = int(fh)\n assert fw >= 1 and fh >= 1\n return fw, fh\n\n\ndef upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):\n r\"\"\"Upsample a batch of 2D images using the given 2D FIR filter.\n By default, the result is padded so that its shape is a multiple of the\n input.\n User-specified padding is applied on top of that, with negative values\n indicating cropping. Pixels outside the image are assumed to be zero.\n Args:\n x: Float32/float64/float16 input tensor of the shape\n `[batch_size, num_channels, in_height, in_width]`.\n f: Float32 FIR filter of the shape\n `[filter_height, filter_width]` (non-separable),\n `[filter_taps]` (separable), or\n `None` (identity).\n up: Integer upsampling factor. Can be a single int or a\n list/tuple\n `[x, y]` (default: 1).\n padding: Padding with respect to the output. Can be a single\n number or a\n list/tuple `[x, y]` or `[x_before, x_after, y_before,\n y_after]`\n (default: 0).\n flip_filter: False = convolution, True = correlation (default: False).\n gain: Overall scaling factor for signal magnitude (default: 1).\n impl: Implementation to use. Can be `'ref'` or `'cuda'`\n (default: `'cuda'`).\n Returns:\n Tensor of the shape `[batch_size, num_channels, out_height, out_width]`\n \"\"\"\n upx, upy = _parse_scaling(up)\n padx0, padx1, pady0, pady1 = _parse_padding(padding)\n fw, fh = _get_filter_size(f)\n p = [\n padx0 + (fw + upx - 1) // 2,\n padx1 + (fw - upx) // 2,\n pady0 + (fh + upy - 1) // 2,\n pady1 + (fh - upy) // 2,\n ]\n\n gain = gain * upx * upy\n f = f * (gain**(f.ndim / 2))\n if flip_filter:\n f = f.flip(list(range(f.ndim)))\n if f.ndim == 1:\n x = upfirdn2d(\n x, f.unsqueeze(0), up=(upx, 1), padding=(p[0], p[1], 0, 0))\n x = upfirdn2d(\n x, f.unsqueeze(1), up=(1, upy), padding=(0, 0, p[2], p[3]))\n return x\n\n\ndef setup_filter(f,\n device=torch.device('cpu'),\n normalize=True,\n flip_filter=False,\n gain=1,\n separable=None):\n r\"\"\"Convenience function to setup 2D FIR filter for `upfirdn2d()`.\n Args:\n f: Torch tensor, numpy array, or python list of the shape\n `[filter_height, filter_width]` (non-separable),\n `[filter_taps]` (separable),\n `[]` (impulse), or\n `None` (identity).\n device: Result device (default: cpu).\n normalize: Normalize the filter so that it retains the magnitude\n for constant input signal (DC)? (default: True).\n flip_filter: Flip the filter? (default: False).\n gain: Overall scaling factor for signal magnitude (default: 1).\n separable: Return a separable filter? (default: select automatically)\n Returns:\n Float32 tensor of the shape\n `[filter_height, filter_width]` (non-separable) or\n `[filter_taps]` (separable).\n \"\"\"\n # Validate.\n if f is None:\n f = 1\n f = torch.as_tensor(f, dtype=torch.float32)\n assert f.ndim in [0, 1, 2]\n assert f.numel() > 0\n if f.ndim == 0:\n f = f[np.newaxis]\n\n # Separable?\n if separable is None:\n separable = (f.ndim == 1 and f.numel() >= 8)\n if f.ndim == 1 and not separable:\n f = f.ger(f)\n assert f.ndim == (1 if separable else 2)\n\n # Apply normalize, flip, gain, and device.\n if normalize:\n f /= f.sum()\n if flip_filter:\n f = f.flip(list(range(f.ndim)))\n f = f * (gain**(f.ndim / 2))\n f = f.to(device=device)\n return f\n\n\ndef downsample2d(x,\n f,\n down=2,\n padding=0,\n flip_filter=False,\n gain=1,\n impl='cuda'):\n r\"\"\"Downsample a batch of 2D images using the given 2D FIR filter.\n By default, the result is padded so that its shape is a fraction of the\n input.\n User-specified padding is applied on top of that, with negative values\n indicating cropping. Pixels outside the image are assumed to be zero.\n Args:\n x: Float32/float64/float16 input tensor of the shape\n `[batch_size, num_channels, in_height, in_width]`.\n f: Float32 FIR filter of the shape\n `[filter_height, filter_width]` (non-separable),\n `[filter_taps]` (separable), or\n `None` (identity).\n down: Integer downsampling factor. Can be a single int or a\n list/tuple\n `[x, y]` (default: 1).\n padding: Padding with respect to the input. Can be a single number\n or a\n list/tuple `[x, y]` or `[x_before, x_after, y_before,\n y_after]`\n (default: 0).\n flip_filter: False = convolution, True = correlation (default: False).\n gain: Overall scaling factor for signal magnitude (default: 1).\n impl: Implementation to use. Can be `'ref'` or `'cuda'`\n (default: `'cuda'`).\n Returns:\n Tensor of the shape `[batch_size, num_channels, out_height, out_width]`\n \"\"\"\n downx, downy = _parse_scaling(down)\n padx0, padx1, pady0, pady1 = _parse_padding(padding)\n fw, fh = _get_filter_size(f)\n p = [\n padx0 + (fw - downx + 1) // 2,\n padx1 + (fw - downx) // 2,\n pady0 + (fh - downy + 1) // 2,\n pady1 + (fh - downy) // 2,\n ]\n if flip_filter:\n f = f.flip(list(range(f.ndim)))\n if f.ndim == 1:\n x = upfirdn2d(\n x, f.unsqueeze(0), down=(downx, 1), padding=(p[0], p[1], 0, 0))\n x = upfirdn2d(\n x, f.unsqueeze(1), down=(1, downy), padding=(0, 0, p[2], p[3]))\n return x\n" }, { "path": "mmagic/models/editors/stylegan2/stylegan2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import Config, MessageHub\nfrom mmengine.model import BaseModel, is_model_wrapper\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ...base_models import BaseGAN\nfrom ...losses import gen_path_regularizer, r1_gradient_penalty_loss\nfrom ...utils import set_requires_grad\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass StyleGAN2(BaseGAN):\n \"\"\"Implementation of `Analyzing and Improving the Image Quality of\n Stylegan`. # noqa.\n\n Paper link: https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html. # noqa\n\n :class:`~mmagic.models.editors.stylegan2.StyleGAN2Generator`\n and\n :class:`~mmagic.models.editors.stylegan2.StyleGAN2Discriminator`\n\n Args:\n generator (ModelType): The config or model of the generator.\n discriminator (Optional[ModelType]): The config or model of the\n discriminator. Defaults to None.\n data_preprocessor (Optional[Union[dict, Config]]): The pre-process\n config or :class:`~mmagic.models.DataPreprocessor`.\n generator_steps (int): The number of times the generator is completely\n updated before the discriminator is updated. Defaults to 1.\n discriminator_steps (int): The number of times the discriminator is\n completely updated before the generator is updated. Defaults to 1.\n ema_config (Optional[Dict]): The config for generator's exponential\n moving average setting. Defaults to None.\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n ema_config: Optional[Dict] = None,\n loss_config=dict()):\n BaseModel.__init__(self, data_preprocessor=data_preprocessor)\n\n # build generator\n if isinstance(generator, dict):\n self._gen_cfg = deepcopy(generator)\n generator = MODELS.build(generator)\n self.generator = generator\n\n # get valid noise_size\n self.noise_size = getattr(self.generator, 'style_channels', 512)\n\n # build discriminator\n if discriminator:\n if isinstance(discriminator, dict):\n self._disc_cfg = deepcopy(discriminator)\n # build discriminator with default `num_classes`\n disc_args = dict()\n if hasattr(self, 'num_classes'):\n disc_args['num_classes'] = self.num_classes\n discriminator = MODELS.build(\n discriminator, default_args=disc_args)\n self.discriminator = discriminator\n\n self._gen_steps = generator_steps\n self._disc_steps = discriminator_steps\n\n if ema_config is None:\n self._ema_config = None\n self._with_ema_gen = False\n else:\n self._ema_config = deepcopy(ema_config)\n self._init_ema_model(self._ema_config)\n self._with_ema_gen = True\n\n # loss config\n self.loss_config = deepcopy(loss_config)\n # r1 settings\n self.r1_loss_weight = self.loss_config.get('r1_loss_weight', 80.0)\n self.r1_interval = self.loss_config.get('r1_interval', 16)\n self.norm_mode = self.loss_config.get('norm_mode', 'pixel')\n self.r1_use_apex_amp = self.loss_config.get('r1_use_apex_amp', False)\n self.scale_r1_loss = self.loss_config.get('scale_r1_loss', False)\n # gen path reg settings\n self.g_reg_interval = self.loss_config.get('g_reg_interval', 4)\n self.g_reg_weight = self.loss_config.get('g_reg_weight', 8.)\n self.pl_batch_shrink = self.loss_config.get('pl_batch_shrink', 2)\n self.g_reg_use_apex_amp = self.loss_config.get('g_reg_use_apex_amp',\n False)\n self.register_buffer('mean_path_length', torch.tensor(0.))\n\n def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,\n real_imgs: Tensor) -> Tuple:\n r\"\"\"Get disc loss. StyleGANv2 use the non-saturating loss and R1\n gradient penalty to train the discriminator.\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n real_imgs (Tensor): Input real images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n\n losses_dict = dict()\n # no-saturating gan loss\n losses_dict['loss_disc_fake'] = F.softplus(disc_pred_fake).mean()\n losses_dict['loss_disc_real'] = F.softplus(-disc_pred_real).mean()\n # R1 Gradient Penalty\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n if curr_iter % self.r1_interval == 0:\n losses_dict[\n 'loss_r1_gp'] = self.r1_loss_weight * r1_gradient_penalty_loss(\n self.discriminator,\n real_imgs,\n norm_mode=self.norm_mode,\n use_apex_amp=self.r1_use_apex_amp)\n\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor, batch_size: int) -> Tuple:\n \"\"\"Get gen loss. StyleGANv2 use the non-saturating loss and generator\n path regularization to train the generator.\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n batch_size (int): Batch size for generating fake images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n # no-saturating gan loss\n losses_dict['loss_gen'] = F.softplus(-disc_pred_fake).mean()\n\n # Generator Path Regularizer\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n if curr_iter % self.g_reg_interval == 0:\n path_penalty, self.mean_path_length, _ = gen_path_regularizer(\n self.generator,\n batch_size,\n self.mean_path_length,\n pl_batch_shrink=self.pl_batch_shrink,\n use_apex_amp=self.g_reg_use_apex_amp)\n losses_dict['loss_path_regular'] = self.g_reg_weight * path_penalty\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real, real_imgs)\n optimizer_wrapper.update_params(parsed_losses)\n # save ada info\n message_hub = MessageHub.get_current_instance()\n message_hub.update_info('disc_pred_real', disc_pred_real)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake, num_batches)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def train_step(self, data: dict,\n optim_wrapper: OptimWrapperDict) -> Dict[str, Tensor]:\n \"\"\"Train GAN model. In the training of GAN models, generator and\n discriminator are updated alternatively. In MMagic's design,\n `self.train_step` is called with data input. Therefore we always update\n discriminator, whose updating is relay on real data, and then determine\n if the generator needs to be updated based on the current number of\n iterations. More details about whether to update generator can be found\n in :meth:`should_gen_update`.\n\n Args:\n data (dict): Data sampled from dataloader.\n optim_wrapper (OptimWrapperDict): OptimWrapperDict instance\n contains OptimWrapper of generator and discriminator.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n data = self.data_preprocessor(data, True)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n\n disc_optimizer_wrapper: OptimWrapper = optim_wrapper['discriminator']\n disc_accu_iters = disc_optimizer_wrapper._accumulative_counts\n\n # NOTE: Do not use context manager of optim_wrapper. Because\n # in mixed-precision training, StyleGAN2 only enable fp16 in\n # specified blocks (refers to `:attr:enable_fp16` in\n # :class:`~StyleGANv2Generator` and :class:`~StyleGAN2Discriminator`\n # for more details), but in :func:`~AmpOptimWrapper.optim_context`,\n # fp16 is applied to all modules. This may slow down gradient\n # accumulation because `no_sycn` in\n # :func:`~OptimWrapper.optim_context` will not be called any more.\n log_vars = self.train_discriminator(inputs_dict, data_samples,\n disc_optimizer_wrapper)\n\n # add 1 to `curr_iter` because iter is updated in train loop.\n # Whether to update the generator. We update generator with\n # discriminator is fully updated for `self.n_discriminator_steps`\n # iterations. And one full updating for discriminator contains\n # `disc_accu_counts` times of grad accumulations.\n if (curr_iter + 1) % (self.discriminator_steps * disc_accu_iters) == 0:\n set_requires_grad(self.discriminator, False)\n gen_optimizer_wrapper = optim_wrapper['generator']\n gen_accu_iters = gen_optimizer_wrapper._accumulative_counts\n\n log_vars_gen_list = []\n # init optimizer wrapper status for generator manually\n gen_optimizer_wrapper.initialize_count_status(\n self.generator, 0, self.generator_steps * gen_accu_iters)\n for _ in range(self.generator_steps * gen_accu_iters):\n log_vars_gen = self.train_generator(inputs_dict, data_samples,\n gen_optimizer_wrapper)\n\n log_vars_gen_list.append(log_vars_gen)\n log_vars_gen = self.gather_log_vars(log_vars_gen_list)\n log_vars_gen.pop('loss', None) # remove 'loss' from gen logs\n\n set_requires_grad(self.discriminator, True)\n\n # only do ema after generator update\n if self.with_ema_gen and (curr_iter + 1) >= (\n self.ema_start * self.discriminator_steps *\n disc_accu_iters):\n self.generator_ema.update_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n # if not update buffer, copy buffer from orig model\n if not self.generator_ema.update_buffers:\n self.generator_ema.sync_buffers(\n self.generator.module if is_model_wrapper(\n self.generator) else self.generator)\n elif self.with_ema_gen:\n # before ema, copy weights from orig\n self.generator_ema.sync_parameters(\n self.generator.module\n if is_model_wrapper(self.generator) else self.generator)\n\n log_vars.update(log_vars_gen)\n\n batch_size = len(data['data_samples'])\n # update ada p\n if hasattr(self.discriminator,\n 'with_ada') and self.discriminator.with_ada:\n self.discriminator.ada_aug.log_buffer[0] += batch_size\n self.discriminator.ada_aug.log_buffer[1] += message_hub.get_info(\n 'disc_pred_real').sign().sum()\n\n self.discriminator.ada_aug.update(\n iteration=curr_iter, num_batches=batch_size)\n log_vars['augment'] = (\n self.discriminator.ada_aug.aug_pipeline.p.data.cpu())\n\n return log_vars\n" }, { "path": "mmagic/models/editors/stylegan2/stylegan2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.runner.amp import autocast\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom ..stylegan1 import EqualLinearActModule\nfrom ..stylegan3.stylegan3_modules import MappingNetwork\nfrom .ada.augment import AugmentPipe\nfrom .ada.misc import constant\nfrom .stylegan2_modules import ConvDownLayer, ModMBStddevLayer, ResBlock\n\n\n@MODELS.register_module('StyleGANv2Discriminator')\n@MODELS.register_module()\nclass StyleGAN2Discriminator(BaseModule):\n \"\"\"StyleGAN2 Discriminator.\n\n The architecture of this discriminator is proposed in StyleGAN2. More\n details can be found in: Analyzing and Improving the Image Quality of\n StyleGAN CVPR2020.\n\n You can load pretrained model through passing information into\n ``pretrained`` argument. We have already offered official weights as\n follows:\n\n - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa\n - stylegan2-horse-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa\n - stylegan2-car-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa\n - stylegan2-cat-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa\n - stylegan2-church-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa\n\n If you want to load the ema model, you can just use following codes:\n\n .. code-block:: python\n\n # ckpt_http is one of the valid path from http source\n discriminator = StyleGAN2Discriminator(1024, 512,\n pretrained=dict(\n ckpt_path=ckpt_http,\n prefix='discriminator'))\n\n Of course, you can also download the checkpoint in advance and set\n ``ckpt_path`` with local path.\n\n Note that our implementation adopts BGR image as input, while the\n original StyleGAN2 provides RGB images to the discriminator. Thus, we\n provide ``bgr2rgb`` argument to convert the image space. If your images\n follow the RGB order, please set it to ``True`` accordingly.\n\n Args:\n in_size (int): The input size of images.\n img_channels (int): The number of channels of the input image. Defaults to 3.\n channel_multiplier (int, optional): The multiplier factor for the\n channel number. Defaults to 2.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 3, 3, 1].\n mbstd_cfg (dict, optional): Configs for minibatch-stddev layer.\n Defaults to dict(group_size=4, channel_groups=1).\n cond_size (int, optional): The size of conditional input. If None or\n less than 1, no conditional mapping will be applied. Defaults to None.\n cond_mapping_channels (int, optional): The dimension of the output of\n conditional mapping. Only work when :attr:`c_dim` is larger than 0.\n If :attr:`c_dim` is larger than 0 and :attr:`cmap_dim` is None, will.\n Defaults to None.\n cond_mapping_layers (int, optional): The number of mapping layer used to\n map conditional input. Only work when c_dim is larger than 0. If\n :attr:`cmapping_layer` is None and :attr:`c_dim` is larger than 0,\n cmapping_layer will set as 8. Defaults to None.\n num_fp16_scales (int, optional): The number of resolutions to use auto\n fp16 training. Defaults to 0.\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n out_fp32 (bool, optional): Whether to convert the output feature map to\n `torch.float32`. Defaults to `True`.\n convert_input_fp32 (bool, optional): Whether to convert input type to\n fp32 if not `fp16_enabled`. This argument is designed to deal with\n the cases where some modules are run in FP16 and others in FP32.\n Defaults to True.\n input_bgr2rgb (bool, optional): Whether to reformat the input channels\n with order `rgb`. Since we provide several converted weights,\n whose input order is `rgb`. You can set this argument to True if\n you want to finetune on converted weights. Defaults to False.\n pretrained (dict | None, optional): Information for pretrained models.\n The necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n in_size,\n img_channels=3,\n channel_multiplier=2,\n blur_kernel=[1, 3, 3, 1],\n mbstd_cfg=dict(group_size=4, channel_groups=1),\n cond_size=None,\n cond_mapping_channels=None,\n cond_mapping_layers=None,\n num_fp16_scales=0,\n fp16_enabled=False,\n out_fp32=True,\n convert_input_fp32=True,\n input_bgr2rgb=False,\n init_cfg=None,\n pretrained=None):\n # TODO: pretrained can be deleted later\n super().__init__(init_cfg=init_cfg)\n self.num_fp16_scale = num_fp16_scales\n self.fp16_enabled = fp16_enabled\n self.convert_input_fp32 = convert_input_fp32\n self.out_fp32 = out_fp32\n\n channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256 * channel_multiplier,\n 128: 128 * channel_multiplier,\n 256: 64 * channel_multiplier,\n 512: 32 * channel_multiplier,\n 1024: 16 * channel_multiplier,\n }\n\n log_size = int(np.log2(in_size))\n\n in_channels = channels[in_size]\n\n _use_fp16 = num_fp16_scales > 0 or fp16_enabled\n convs = [\n ConvDownLayer(\n img_channels, channels[in_size], 1, fp16_enabled=_use_fp16)\n ]\n\n for i in range(log_size, 2, -1):\n out_channel = channels[2**(i - 1)]\n\n # add fp16 training for higher resolutions\n _use_fp16 = (log_size - i) < num_fp16_scales or fp16_enabled\n\n convs.append(\n ResBlock(\n in_channels,\n out_channel,\n blur_kernel,\n fp16_enabled=_use_fp16,\n convert_input_fp32=convert_input_fp32))\n\n in_channels = out_channel\n\n if cond_size is not None and cond_size > 0:\n cond_mapping_channels = 512 if cond_mapping_channels is None \\\n else cond_mapping_channels\n cond_mapping_layers = 8 if cond_mapping_layers is None \\\n else cond_mapping_layers\n self.mapping = MappingNetwork(\n noise_size=0,\n style_channels=cond_mapping_channels,\n cond_size=cond_size,\n num_ws=None,\n num_layers=cond_mapping_layers,\n w_avg_beta=None)\n\n self.convs = nn.Sequential(*convs)\n\n self.mbstd_layer = ModMBStddevLayer(**mbstd_cfg)\n\n self.final_conv = ConvDownLayer(\n in_channels + 1, channels[4], 3, fp16_enabled=fp16_enabled)\n\n if cond_size is None or cond_size <= 0:\n final_linear_out_channels = 1\n else:\n final_linear_out_channels = cond_mapping_channels\n self.final_linear = nn.Sequential(\n EqualLinearActModule(\n channels[4] * 4 * 4,\n channels[4],\n act_cfg=dict(type='fused_bias')),\n EqualLinearActModule(channels[4], final_linear_out_channels),\n )\n\n self.input_bgr2rgb = input_bgr2rgb\n if pretrained is not None:\n self._load_pretrained_model(**pretrained)\n\n def _load_pretrained_model(self,\n ckpt_path,\n prefix='',\n map_location='cpu',\n strict=True):\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained model from {ckpt_path}')\n\n def forward(self, x: Tensor, label: Optional[Tensor] = None):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Input image tensor.\n label (torch.Tensor, optional): The conditional input feed to\n mapping layer. Defaults to None.\n\n Returns:\n torch.Tensor: Predict score for the input image.\n \"\"\"\n # This setting was used to finetune on converted weights\n if self.input_bgr2rgb:\n x = x[:, [2, 1, 0], ...]\n\n # convs has own fp-16 controller, do not wrap here\n x = self.convs(x)\n\n x = self.mbstd_layer(x)\n\n fp16_enabled = (\n self.final_conv.fp16_enabled or not self.convert_input_fp32)\n with autocast(enabled=fp16_enabled):\n if not fp16_enabled:\n x = x.to(torch.float32)\n x = self.final_conv(x)\n x = x.view(x.shape[0], -1)\n x = self.final_linear(x)\n\n # conditioning\n if label is not None:\n assert self.mapping is not None, (\n '\\'self.mapping\\' must not be None when conditional input '\n 'is passed.')\n cmap = self.mapping(None, label)\n x = (x * cmap).sum(\n dim=1, keepdim=True) * (1 / np.sqrt(cmap.shape[1]))\n\n return x\n\n\n@MODELS.register_module()\nclass ADAStyleGAN2Discriminator(StyleGAN2Discriminator):\n\n def __init__(self, in_size, *args, data_aug=None, **kwargs):\n \"\"\"StyleGANv2 Discriminator with adaptive augmentation.\n\n Args:\n in_size (int): The input size of images.\n data_aug (dict, optional): Config for data\n augmentation. Defaults to None.\n \"\"\"\n super().__init__(in_size, *args, **kwargs)\n self.with_ada = data_aug is not None and data_aug != dict()\n if self.with_ada:\n self.ada_aug = MODELS.build(data_aug)\n self.ada_aug.requires_grad = False\n self.log_size = int(np.log2(in_size))\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n if self.with_ada:\n x = self.ada_aug.aug_pipeline(x)\n return super().forward(x)\n\n\n@MODELS.register_module()\nclass ADAAug(BaseModule):\n \"\"\"Data Augmentation Module for Adaptive Discriminator augmentation.\n\n Args:\n aug_pipeline (dict, optional): Config for augmentation pipeline.\n Defaults to None.\n update_interval (int, optional): Interval for updating\n augmentation probability. Defaults to 4.\n augment_initial_p (float, optional): Initial augmentation\n probability. Defaults to 0..\n ada_target (float, optional): ADA target. Defaults to 0.6.\n ada_kimg (int, optional): ADA training duration. Defaults to 500.\n \"\"\"\n\n def __init__(self,\n aug_pipeline=None,\n update_interval=4,\n augment_initial_p=0.,\n ada_target=0.6,\n ada_kimg=500):\n super().__init__()\n aug_pipeline = dict() if aug_pipeline is None else aug_pipeline\n self.aug_pipeline = AugmentPipe(**aug_pipeline)\n self.update_interval = update_interval\n self.ada_kimg = ada_kimg\n self.ada_target = ada_target\n\n self.aug_pipeline.p.copy_(torch.tensor(augment_initial_p))\n\n # this log buffer stores two numbers: num_scalars, sum_scalars.\n self.register_buffer('log_buffer', torch.zeros((2, )))\n\n def update(self, iteration=0, num_batches=0):\n \"\"\"Update Augment probability.\n\n Args:\n iteration (int, optional): Training iteration.\n Defaults to 0.\n num_batches (int, optional): The number of reals batches.\n Defaults to 0.\n \"\"\"\n\n if (iteration + 1) % self.update_interval == 0:\n\n adjust_step = float(num_batches * self.update_interval) / float(\n self.ada_kimg * 1000.)\n\n # get the mean value as the ada heuristic\n ada_heuristic = self.log_buffer[1] / self.log_buffer[0]\n adjust = np.sign(ada_heuristic.item() -\n self.ada_target) * adjust_step\n # update the augment p\n # Note that p may be bigger than 1.0\n self.aug_pipeline.p.copy_(\n (self.aug_pipeline.p +\n adjust).max(constant(0, device=self.log_buffer.device)))\n\n self.log_buffer = self.log_buffer * 0.\n" }, { "path": "mmagic/models/editors/stylegan2/stylegan2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\n\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\nfrom mmengine.runner.amp import autocast\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom ..pggan import PixelNorm\nfrom ..stylegan1 import (ConstantInput, EqualLinearActModule, get_mean_latent,\n style_mixing)\nfrom .stylegan2_modules import ModulatedStyleConv, ModulatedToRGB\n\n\n@MODELS.register_module('StyleGANv2Generator')\n@MODELS.register_module()\nclass StyleGAN2Generator(BaseModule):\n r\"\"\"StyleGAN2 Generator.\n\n In StyleGAN2, we use a static architecture composing of a style mapping\n module and number of convolutional style blocks. More details can be found\n in: Analyzing and Improving the Image Quality of StyleGAN CVPR2020.\n\n You can load pretrained model through passing information into\n ``pretrained`` argument. We have already offered official weights as\n follows:\n\n - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa\n - stylegan2-horse-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa\n - stylegan2-car-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa\n - stylegan2-cat-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa\n - stylegan2-church-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa\n\n If you want to load the ema model, you can just use following codes:\n\n .. code-block:: python\n\n # ckpt_http is one of the valid path from http source\n generator = StyleGANv2Generator(1024, 512,\n pretrained=dict(\n ckpt_path=ckpt_http,\n prefix='generator_ema'))\n\n Of course, you can also download the checkpoint in advance and set\n ``ckpt_path`` with local path. If you just want to load the original\n generator (not the ema model), please set the prefix with 'generator'.\n\n Note that our implementation allows to generate BGR image, while the\n original StyleGAN2 outputs RGB images by default. Thus, we provide\n ``bgr2rgb`` argument to convert the image space.\n\n Args:\n out_size (int): The output size of the StyleGAN2 generator.\n style_channels (int): The number of channels for style code.\n out_channels (int): The number of channels for output. Defaults to 3.\n noise_size (int, optional): The size of (number of channels) the input\n noise. If not passed, will be set the same value as\n :attr:`style_channels`. Defaults to None.\n cond_size (int, optional): The size of the conditional input. If not\n passed or less than 1, no conditional embedding will be used.\n Defaults to None.\n cond_mapping_channels (int, optional): The channels of the\n conditional mapping layers. If not passed, will use the same value\n as :attr:`style_channels`. Defaults to None.\n num_mlps (int, optional): The number of MLP layers. Defaults to 8.\n channel_multiplier (int, optional): The multiplier factor for the\n channel number. Defaults to 2.\n blur_kernel (list, optional): The blurry kernel. Defaults\n to [1, 3, 3, 1].\n lr_mlp (float, optional): The learning rate for the style mapping\n layer. Defaults to 0.01.\n default_style_mode (str, optional): The default mode of style mixing.\n In training, we adopt mixing style mode in default. However, in the\n evaluation, we use 'single' style mode. `['mix', 'single']` are\n currently supported. Defaults to 'mix'.\n eval_style_mode (str, optional): The evaluation mode of style mixing.\n Defaults to 'single'.\n mix_prob (float, optional): Mixing probability. The value should be\n in range of [0, 1]. Defaults to ``0.9``.\n update_mean_latent_with_ema (bool, optional): Whether update mean\n latent code (w) with EMA. Defaults to False.\n w_avg_beta (float, optional): The value used for update `w_avg`.\n Defaults to 0.998.\n num_fp16_scales (int, optional): The number of resolutions to use auto\n fp16 training. Different from ``fp16_enabled``, this argument\n allows users to adopt FP16 training only in several blocks.\n This behaviour is much more similar to the official implementation\n by Tero. Defaults to 0.\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. If this flag is `True`, the whole module will be wrapped\n with ``auto_fp16``. Defaults to False.\n pretrained (dict | None, optional): Information for pretrained models.\n The necessary key is 'ckpt_path'. Besides, you can also provide\n 'prefix' to load the generator part from the whole state dict.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n out_size,\n style_channels,\n out_channels=3,\n noise_size=None,\n cond_size=None,\n cond_mapping_channels=None,\n num_mlps=8,\n channel_multiplier=2,\n blur_kernel=[1, 3, 3, 1],\n lr_mlp=0.01,\n default_style_mode='mix',\n eval_style_mode='single',\n norm_eps=1e-6,\n mix_prob=0.9,\n update_mean_latent_with_ema=False,\n w_avg_beta=0.998,\n num_fp16_scales=0,\n fp16_enabled=False,\n bgr2rgb=False,\n pretrained=None,\n fixed_noise=False):\n super().__init__()\n self.out_size = out_size\n self.style_channels = style_channels\n self.out_channels = out_channels\n self.num_mlps = num_mlps\n self.channel_multiplier = channel_multiplier\n self.lr_mlp = lr_mlp\n self._default_style_mode = default_style_mode\n self.default_style_mode = default_style_mode\n self.eval_style_mode = eval_style_mode\n self.mix_prob = mix_prob\n self.num_fp16_scales = num_fp16_scales\n self.fp16_enabled = fp16_enabled\n self.bgr2rgb = bgr2rgb\n\n self.noise_size = style_channels if noise_size is None else noise_size\n\n self.cond_size = cond_size\n if self.cond_size is not None and self.cond_size > 0:\n cond_mapping_channels = style_channels \\\n if cond_mapping_channels is None else cond_mapping_channels\n self.embed = EqualLinearActModule(cond_size, cond_mapping_channels)\n # NOTE: conditional input is passed, do 2nd moment norm for\n # embedding and noise input respectively, therefore mapping layer\n # start with FC layer\n mapping_layers = []\n else:\n cond_mapping_channels = 0\n # NOTE: conditional input is not passed, put 2nd moment norm at\n # the start of mapping layers\n mapping_layers = [PixelNorm(eps=norm_eps)]\n in_feat = cond_mapping_channels + self.noise_size\n\n # define pixel norm\n self.pixel_norm = PixelNorm(eps=norm_eps)\n\n # define style mapping layers\n for idx in range(num_mlps):\n mapping_layers.append(\n EqualLinearActModule(\n in_feat if idx == 0 else style_channels,\n style_channels,\n equalized_lr_cfg=dict(lr_mul=lr_mlp, gain=1.),\n act_cfg=dict(type='fused_bias')))\n\n self.style_mapping = nn.Sequential(*mapping_layers)\n\n self.channels = {\n 4: 512,\n 8: 512,\n 16: 512,\n 32: 512,\n 64: 256 * channel_multiplier,\n 128: 128 * channel_multiplier,\n 256: 64 * channel_multiplier,\n 512: 32 * channel_multiplier,\n 1024: 16 * channel_multiplier,\n }\n\n # constant input layer\n self.constant_input = ConstantInput(self.channels[4])\n # 4x4 stage\n self.conv1 = ModulatedStyleConv(\n self.channels[4],\n self.channels[4],\n kernel_size=3,\n style_channels=style_channels,\n blur_kernel=blur_kernel,\n fp16_enabled=fp16_enabled,\n fixed_noise=fixed_noise)\n self.to_rgb1 = ModulatedToRGB(\n self.channels[4],\n style_channels,\n out_channels=out_channels,\n upsample=False,\n fp16_enabled=fp16_enabled)\n\n # generator backbone (8x8 --> higher resolutions)\n self.log_size = int(np.log2(self.out_size))\n\n self.convs = nn.ModuleList()\n self.upsamples = nn.ModuleList()\n self.to_rgbs = nn.ModuleList()\n\n blk_in_channels_ = self.channels[4] # in channels of the conv blocks\n\n for i in range(3, self.log_size + 1):\n blk_out_channels_ = self.channels[2**i]\n\n # If `fp16_enabled` is True, all of layers will be run in auto\n # FP16. In the case of `num_fp16_scales` > 0, only partial\n # layers will be run in fp16.\n _use_fp16 = (self.log_size - i) < num_fp16_scales or fp16_enabled\n\n self.convs.append(\n ModulatedStyleConv(\n blk_in_channels_,\n blk_out_channels_,\n 3,\n style_channels,\n upsample=True,\n blur_kernel=blur_kernel,\n fp16_enabled=_use_fp16))\n self.convs.append(\n ModulatedStyleConv(\n blk_out_channels_,\n blk_out_channels_,\n 3,\n style_channels,\n upsample=False,\n blur_kernel=blur_kernel,\n fp16_enabled=_use_fp16))\n self.to_rgbs.append(\n ModulatedToRGB(\n blk_out_channels_,\n style_channels,\n out_channels=out_channels,\n upsample=True,\n fp16_enabled=_use_fp16)) # set to global fp16\n\n blk_in_channels_ = blk_out_channels_\n\n self.num_latents = self.log_size * 2 - 2\n self.num_injected_noises = self.num_latents - 1\n\n # register buffer for injected noises\n for layer_idx in range(self.num_injected_noises):\n res = (layer_idx + 5) // 2\n shape = [1, 1, 2**res, 2**res]\n self.register_buffer(f'injected_noise_{layer_idx}',\n torch.randn(*shape))\n\n if (self.cond_size is not None\n and self.cond_size > 0) or update_mean_latent_with_ema:\n # Due to `get_mean_latent` cannot handle conditional input,\n # assign avg style code here and update with EMA.\n self.register_buffer('w_avg', torch.zeros([style_channels]))\n self.w_avg_beta = w_avg_beta\n mmengine.print_log('Mean latent code (w) is updated with EMA.')\n\n if pretrained is not None:\n self._load_pretrained_model(**pretrained)\n\n def _load_pretrained_model(self,\n ckpt_path,\n prefix='',\n map_location='cpu',\n strict=True):\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained model from {ckpt_path}')\n\n def train(self, mode=True):\n if mode:\n if self.default_style_mode != self._default_style_mode:\n mmengine.print_log(\n f'Switch to train style mode: {self._default_style_mode}')\n self.default_style_mode = self._default_style_mode\n\n else:\n if self.default_style_mode != self.eval_style_mode:\n mmengine.print_log(\n f'Switch to evaluation style mode: {self.eval_style_mode}')\n self.default_style_mode = self.eval_style_mode\n\n return super(StyleGAN2Generator, self).train(mode)\n\n def make_injected_noise(self):\n \"\"\"make noises that will be injected into feature maps.\n\n Returns:\n list[Tensor]: List of layer-wise noise tensor.\n \"\"\"\n device = get_module_device(self)\n\n noises = [torch.randn(1, 1, 2**2, 2**2, device=device)]\n\n for i in range(3, self.log_size + 1):\n for _ in range(2):\n noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))\n\n return noises\n\n def get_mean_latent(self, num_samples=4096, **kwargs):\n \"\"\"Get mean latent of W space in this generator.\n\n Args:\n num_samples (int, optional): Number of sample times. Defaults\n to 4096.\n\n Returns:\n Tensor: Mean latent of this generator.\n \"\"\"\n if hasattr(self, 'w_avg'):\n mmengine.print_log('Get latent code (w) which is updated by EMA.')\n return self.w_avg\n return get_mean_latent(self, num_samples, **kwargs)\n\n def style_mixing(self,\n n_source,\n n_target,\n inject_index=1,\n truncation_latent=None,\n truncation=0.7):\n return style_mixing(\n self,\n n_source=n_source,\n n_target=n_target,\n inject_index=inject_index,\n truncation=truncation,\n truncation_latent=truncation_latent,\n style_channels=self.style_channels)\n\n # @auto_fp16()\n def forward(self,\n styles,\n label=None,\n num_batches=-1,\n return_noise=False,\n return_latents=False,\n inject_index=None,\n truncation=1,\n truncation_latent=None,\n input_is_latent=False,\n injected_noise=None,\n add_noise=True,\n randomize_noise=True,\n update_ws=False,\n return_features=False,\n feat_idx=5,\n return_latent_only=False):\n \"\"\"Forward function.\n\n This function has been integrated with the truncation trick. Please\n refer to the usage of `truncation` and `truncation_latent`.\n\n Args:\n styles (torch.Tensor | list[torch.Tensor] | callable | None): In\n StyleGAN2, you can provide noise tensor or latent tensor. Given\n a list containing more than one noise or latent tensors, style\n mixing trick will be used in training. Of course, You can\n directly give a batch of noise through a ``torch.Tensor`` or\n offer a callable function to sample a batch of noise data.\n Otherwise, the ``None`` indicates to use the default noise\n sampler.\n label (torch.Tensor, optional): Conditional inputs for the\n generator. Defaults to None.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n return_latents (bool, optional): If True, ``latent`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n inject_index (int | None, optional): The index number for mixing\n style codes. Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n truncation_latent (torch.Tensor, optional): Mean truncation latent.\n Defaults to None.\n input_is_latent (bool, optional): If `True`, the input tensor is\n the latent tensor. Defaults to False.\n injected_noise (torch.Tensor | None, optional): Given a tensor, the\n random noise will be fixed as this input injected noise.\n Defaults to None.\n add_noise (bool): Whether apply noise injection. Defaults to True.\n randomize_noise (bool, optional): If `False`, images are sampled\n with the buffered noise tensor injected to the style conv\n block. Defaults to True.\n update_ws (bool): Whether update latent code with EMA. Only work\n when `w_avg` is registered. Defaults to False.\n\n Returns:\n torch.Tensor | dict: Generated image tensor or dictionary \\\n containing more data.\n \"\"\"\n # device = styles.device\n input_dim = self.style_channels if input_is_latent else self.noise_size\n # receive noise and conduct sanity check.\n if isinstance(styles, torch.Tensor):\n assert styles.shape[1] == input_dim\n styles = [styles]\n elif mmengine.is_seq_of(styles, torch.Tensor):\n for t in styles:\n assert t.shape[-1] == input_dim\n # receive a noise generator and sample noise.\n elif callable(styles):\n device = get_module_device(self)\n noise_generator = styles\n assert num_batches > 0\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n noise_generator((num_batches, input_dim)) for _ in range(2)\n ]\n else:\n styles = [noise_generator((num_batches, input_dim))]\n styles = [s.to(device) for s in styles]\n # otherwise, we will adopt default noise sampler.\n else:\n device = get_module_device(self)\n assert num_batches > 0 and not input_is_latent\n if self.default_style_mode == 'mix' and random.random(\n ) < self.mix_prob:\n styles = [\n torch.randn((num_batches, input_dim)) for _ in range(2)\n ]\n else:\n styles = [torch.randn((num_batches, input_dim))]\n styles = [s.to(device) for s in styles]\n\n # no amp for style-mapping and condition-embedding\n if not input_is_latent:\n noise_batch = styles\n if self.cond_size is not None and self.cond_size > 0:\n assert label is not None, (\n '\\'cond_channels\\' is not None, \\'cond\\' must be passed.')\n assert label.shape[1] == self.cond_size\n embedding = self.embed(label)\n # NOTE: If conditional input is passed, do norm for cond\n # embedding and noise input respectively\n # do pixel_norm (2nd_moment_norm) to cond embedding\n embedding = self.pixel_norm(embedding)\n # do pixel_norm (2nd_moment_norm) to noise input\n styles = [self.pixel_norm(s) for s in styles]\n\n styles_list = []\n for s in styles:\n if self.cond_size is not None and self.cond_size > 0:\n s = torch.cat([s, embedding], dim=1)\n styles_list.append(self.style_mapping(s))\n\n styles = styles_list\n else:\n noise_batch = None\n\n # update w_avg during training, if need\n if hasattr(self, 'w_avg') and self.training and update_ws:\n # only update w_avg with the first style code\n self.w_avg.copy_(styles[0].detach().mean(\n dim=0).lerp(self.w_avg, self.w_avg_beta))\n\n if injected_noise is None:\n if randomize_noise:\n injected_noise = [None] * self.num_injected_noises\n else:\n injected_noise = [\n getattr(self, f'injected_noise_{i}')\n for i in range(self.num_injected_noises)\n ]\n # use truncation trick\n if truncation < 1:\n style_t = []\n # calculate truncation latent on the fly\n if truncation_latent is None and not hasattr(\n self, 'truncation_latent'):\n self.truncation_latent = self.get_mean_latent()\n truncation_latent = self.truncation_latent\n elif truncation_latent is None and hasattr(self,\n 'truncation_latent'):\n truncation_latent = self.truncation_latent\n\n for style in styles:\n style_t.append(truncation_latent + truncation *\n (style - truncation_latent))\n\n styles = style_t\n # no style mixing\n if len(styles) < 2:\n inject_index = self.num_latents\n\n if styles[0].ndim < 3:\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n\n else:\n latent = styles[0]\n # style mixing\n else:\n if inject_index is None:\n inject_index = random.randint(1, self.num_latents - 1)\n\n latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n latent2 = styles[1].unsqueeze(1).repeat(\n 1, self.num_latents - inject_index, 1)\n\n latent = torch.cat([latent, latent2], 1)\n\n if return_latent_only:\n return latent\n\n feats = []\n with autocast(enabled=self.fp16_enabled):\n # 4x4 stage\n out = self.constant_input(latent)\n if self.fp16_enabled:\n out = out.to(torch.float16)\n out = self.conv1(\n out,\n latent[:, 0],\n noise=injected_noise[0],\n add_noise=add_noise)\n feats.append(out)\n skip = self.to_rgb1(out, latent[:, 1])\n\n _index = 1\n\n # 8x8 ---> higher resolutions\n for up_conv, conv, noise1, noise2, to_rgb in zip(\n self.convs[::2], self.convs[1::2], injected_noise[1::2],\n injected_noise[2::2], self.to_rgbs):\n out = up_conv(\n out, latent[:, _index], noise=noise1, add_noise=add_noise)\n out = conv(\n out,\n latent[:, _index + 1],\n noise=noise2,\n add_noise=add_noise)\n feats.append(out)\n skip = to_rgb(out, latent[:, _index + 2], skip)\n _index += 2\n\n # make sure the output image is torch.float32 to avoid RunTime Error\n # in other modules\n img = skip.to(torch.float32)\n if self.bgr2rgb:\n img = torch.flip(img, dims=[1])\n\n if return_latents or return_noise:\n if return_features:\n output_dict = dict(\n fake_img=img,\n latent=latent,\n inject_index=inject_index,\n noise_batch=noise_batch,\n feats=feats[feat_idx])\n return output_dict\n\n output_dict = dict(\n fake_img=img,\n latent=latent,\n inject_index=inject_index,\n noise_batch=noise_batch)\n return output_dict\n\n return img\n" }, { "path": "mmagic/models/editors/stylegan2/stylegan2_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmengine\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.ops.fused_bias_leakyrelu import (FusedBiasLeakyReLU,\n fused_bias_leakyrelu)\nfrom mmcv.ops.upfirdn2d import upfirdn2d\nfrom mmengine.dist import get_dist_info\nfrom mmengine.model import BaseModule\nfrom mmengine.runner.amp import autocast\n\nfrom mmagic.models.archs import AllGatherLayer\nfrom ..pggan import EqualizedLRConvModule, equalized_lr\nfrom ..stylegan1 import Blur, EqualLinearActModule, NoiseInjection, make_kernel\n\ntry:\n from mmcv.ops import conv2d, conv_transpose2d\nexcept ImportError:\n import torch.nn.functional as F\n conv2d = F.conv2d\n conv_transpose2d = F.conv_transpose2d\n print('Warning: mmcv.ops.conv2d, mmcv.ops.conv_transpose2d'\n ' and mmcv.ops.upfirdn2d are not available.')\n\n\nclass _FusedBiasLeakyReLU(FusedBiasLeakyReLU):\n \"\"\"Wrap FusedBiasLeakyReLU to support FP16 training.\"\"\"\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, ...).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n return fused_bias_leakyrelu(x, self.bias.to(x.dtype),\n self.negative_slope, self.scale)\n\n\nclass UpsampleUpFIRDn(BaseModule):\n \"\"\"UpFIRDn for Upsampling.\n\n This module is used in the ``to_rgb`` layers in StyleGAN2 for upsampling\n the images.\n\n Args:\n kernel (Array): Blur kernel/filter used in UpFIRDn.\n factor (int, optional): Upsampling factor. Defaults to 2.\n \"\"\"\n\n def __init__(self, kernel, factor=2):\n super().__init__()\n\n self.factor = factor\n kernel = make_kernel(kernel) * (factor**2)\n self.register_buffer('kernel', kernel)\n\n p = kernel.shape[0] - factor\n\n pad0 = (p + 1) // 2 + factor - 1\n pad1 = p // 2\n\n self.pad = (pad0, pad1, pad0, pad1)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n out = upfirdn2d(\n x,\n self.kernel.to(x.dtype),\n up=self.factor,\n down=1,\n padding=self.pad)\n\n return out\n\n\nclass DownsampleUpFIRDn(BaseModule):\n \"\"\"UpFIRDn for Downsampling.\n\n This module is mentioned in StyleGAN2 for dowampling the feature maps.\n\n Args:\n kernel (Array): Blur kernel/filter used in UpFIRDn.\n factor (int, optional): Downsampling factor. Defaults to 2.\n \"\"\"\n\n def __init__(self, kernel, factor=2):\n super().__init__()\n\n self.factor = factor\n kernel = make_kernel(kernel)\n self.register_buffer('kernel', kernel)\n\n p = kernel.shape[0] - factor\n\n pad0 = (p + 1) // 2\n pad1 = p // 2\n\n self.pad = (pad0, pad1)\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n out = upfirdn2d(\n input,\n self.kernel.to(input.dtype),\n up=1,\n down=self.factor,\n padding=self.pad)\n\n return out\n\n\nclass ModulatedConv2d(BaseModule):\n r\"\"\"Modulated Conv2d in StyleGANv2.\n\n This module implements the modulated convolution layers proposed in\n StyleGAN2. Details can be found in Analyzing and Improving the Image\n Quality of StyleGAN, CVPR2020.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n style_channels (int): Channels for the style codes.\n demodulate (bool, optional): Whether to adopt demodulation.\n Defaults to True.\n upsample (bool, optional): Whether to adopt upsampling in features.\n Defaults to False.\n downsample (bool, optional): Whether to adopt downsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n equalized_lr_cfg (dict | None, optional): Configs for equalized lr.\n Defaults to dict(mode='fan_in', lr_mul=1., gain=1.).\n style_mod_cfg (dict, optional): Configs for style modulation module.\n Defaults to dict(bias_init=1.).\n style_bias (float, optional): Bias value for style code.\n Defaults to 0..\n eps (float, optional): Epsilon value to avoid computation error.\n Defaults to 1e-8.\n \"\"\"\n\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n demodulate=True,\n upsample=False,\n downsample=False,\n blur_kernel=[1, 3, 3, 1],\n equalized_lr_cfg=dict(mode='fan_in', lr_mul=1., gain=1.),\n style_mod_cfg=dict(bias_init=1.),\n style_bias=0.,\n padding=None, # self define padding\n eps=1e-8,\n fp16_enabled=False):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.kernel_size = kernel_size\n self.style_channels = style_channels\n self.demodulate = demodulate\n self.fp16_enabled = fp16_enabled\n # sanity check for kernel size\n assert isinstance(self.kernel_size,\n int) and (self.kernel_size >= 1\n and self.kernel_size % 2 == 1)\n self.upsample = upsample\n self.downsample = downsample\n self.style_bias = style_bias\n self.eps = eps\n\n # build style modulation module\n style_mod_cfg = dict() if style_mod_cfg is None else style_mod_cfg\n\n self.style_modulation = EqualLinearActModule(style_channels,\n in_channels,\n **style_mod_cfg)\n # set lr_mul for conv weight\n lr_mul_ = 1.\n if equalized_lr_cfg is not None:\n lr_mul_ = equalized_lr_cfg.get('lr_mul', 1.)\n self.weight = nn.Parameter(\n torch.randn(1, out_channels, in_channels, kernel_size,\n kernel_size).div_(lr_mul_))\n\n # build blurry layer for upsampling\n if upsample:\n factor = 2\n p = (len(blur_kernel) - factor) - (kernel_size - 1)\n pad0 = (p + 1) // 2 + factor - 1\n pad1 = p // 2 + 1\n self.blur = Blur(blur_kernel, (pad0, pad1), upsample_factor=factor)\n # build blurry layer for downsampling\n if downsample:\n factor = 2\n p = (len(blur_kernel) - factor) + (kernel_size - 1)\n pad0 = (p + 1) // 2\n pad1 = p // 2\n self.blur = Blur(blur_kernel, pad=(pad0, pad1))\n\n # add equalized_lr hook for conv weight\n if equalized_lr_cfg is not None:\n equalized_lr(self, **equalized_lr_cfg)\n\n self.padding = padding if padding else (kernel_size // 2)\n\n def forward(self, x, style, input_gain=None):\n n, c, h, w = x.shape\n weight = self.weight\n # Pre-normalize inputs to avoid FP16 overflow.\n # if x.dtype == torch.float16 and self.demodulate:\n if self.fp16_enabled and self.demodulate:\n weight = weight * (\n 1 / np.sqrt(\n self.in_channels * self.kernel_size * self.kernel_size) /\n weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)\n ) # max_Ikk\n style = style / style.norm(\n float('inf'), dim=1, keepdim=True) # max_I\n with autocast(enabled=self.fp16_enabled):\n # process style code\n style = self.style_modulation(style).view(n, 1, c, 1,\n 1) + self.style_bias\n # combine weight and style\n weight = weight * style\n if self.demodulate:\n demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)\n weight = weight * demod.view(n, self.out_channels, 1, 1, 1)\n\n if input_gain is not None:\n # input_gain shape [batch, in_ch]\n input_gain = input_gain.expand(n, self.in_channels)\n # weight shape [batch, out_ch, in_ch, kernel_size, kernel_size]\n weight = weight * input_gain.unsqueeze(1).unsqueeze(\n 3).unsqueeze(4)\n\n weight = weight.view(n * self.out_channels, c, self.kernel_size,\n self.kernel_size)\n\n if self.fp16_enabled:\n weight = weight.to(torch.float16)\n x = x.to(torch.float16)\n\n if self.upsample:\n x = x.reshape(1, n * c, h, w)\n weight = weight.view(n, self.out_channels, c, self.kernel_size,\n self.kernel_size)\n weight = weight.transpose(1,\n 2).reshape(n * c, self.out_channels,\n self.kernel_size,\n self.kernel_size)\n x = conv_transpose2d(x, weight, padding=0, stride=2, groups=n)\n x = x.reshape(n, self.out_channels, *x.shape[-2:])\n x = self.blur(x)\n\n elif self.downsample:\n x = self.blur(x)\n x = x.view(1, n * self.in_channels, *x.shape[-2:])\n x = conv2d(x, weight, stride=2, padding=0, groups=n)\n x = x.view(n, self.out_channels, *x.shape[-2:])\n else:\n x = x.reshape(1, n * c, h, w)\n x = conv2d(x, weight, stride=1, padding=self.padding, groups=n)\n x = x.view(n, self.out_channels, *x.shape[-2:])\n return x\n\n\nclass ModulatedStyleConv(BaseModule):\n \"\"\"Modulated Style Convolution.\n\n In this module, we integrate the modulated conv2d, noise injector and\n activation layers into together.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n style_channels (int): Channels for the style codes.\n demodulate (bool, optional): Whether to adopt demodulation.\n Defaults to True.\n upsample (bool, optional): Whether to adopt upsampling in features.\n Defaults to False.\n downsample (bool, optional): Whether to adopt downsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n equalized_lr_cfg (dict | None, optional): Configs for equalized lr.\n Defaults to dict(mode='fan_in', lr_mul=1., gain=1.).\n style_mod_cfg (dict, optional): Configs for style modulation module.\n Defaults to dict(bias_init=1.).\n style_bias (float, optional): Bias value for style code.\n Defaults to ``0.``.\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n conv_clamp (float, optional): Clamp the convolutional layer results to\n avoid gradient overflow. Defaults to `256.0`.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n upsample=False,\n blur_kernel=[1, 3, 3, 1],\n demodulate=True,\n style_mod_cfg=dict(bias_init=1.),\n style_bias=0.,\n fp16_enabled=False,\n conv_clamp=256,\n fixed_noise=False):\n super().__init__()\n\n # add support for fp16\n self.fp16_enabled = fp16_enabled\n self.conv_clamp = float(conv_clamp)\n\n self.conv = ModulatedConv2d(\n in_channels,\n out_channels,\n kernel_size,\n style_channels,\n demodulate=demodulate,\n upsample=upsample,\n blur_kernel=blur_kernel,\n style_mod_cfg=style_mod_cfg,\n style_bias=style_bias,\n fp16_enabled=fp16_enabled)\n\n self.noise_injector = NoiseInjection(fixed_noise=fixed_noise)\n self.activate = _FusedBiasLeakyReLU(out_channels)\n\n def forward(self,\n x,\n style,\n noise=None,\n add_noise=True,\n return_noise=False):\n \"\"\"Forward Function.\n\n Args:\n x ([Tensor): Input features with shape of (N, C, H, W).\n style (Tensor): Style latent with shape of (N, C).\n noise (Tensor, optional): Noise for injection. Defaults to None.\n add_noise (bool, optional): Whether apply noise injection to\n feature. Defaults to True.\n return_noise (bool, optional): Whether to return noise tensors.\n Defaults to False.\n\n Returns:\n Tensor: Output features with shape of (N, C, H, W)\n \"\"\"\n with autocast(enabled=self.fp16_enabled):\n out = self.conv(x, style)\n\n if add_noise:\n if return_noise:\n out, noise = self.noise_injector(\n out, noise=noise, return_noise=return_noise)\n else:\n out = self.noise_injector(\n out, noise=noise, return_noise=return_noise)\n\n # TODO: FP16 in activate layers\n out = self.activate(out)\n if self.fp16_enabled:\n out = torch.clamp(\n out, min=-self.conv_clamp, max=self.conv_clamp)\n\n if return_noise:\n return out, noise\n\n return out\n\n\nclass ModulatedToRGB(BaseModule):\n \"\"\"To RGB layer.\n\n This module is designed to output image tensor in StyleGAN2.\n\n Args:\n in_channels (int): Input channels.\n style_channels (int): Channels for the style codes.\n out_channels (int, optional): Output channels. Defaults to 3.\n upsample (bool, optional): Whether to adopt upsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n style_mod_cfg (dict, optional): Configs for style modulation module.\n Defaults to dict(bias_init=1.).\n style_bias (float, optional): Bias value for style code.\n Defaults to 0..\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n conv_clamp (float, optional): Clamp the convolutional layer results to\n avoid gradient overflow. Defaults to `256.0`.\n out_fp32 (bool, optional): Whether to convert the output feature map to\n `torch.float32`. Defaults to `True`.\n \"\"\"\n\n def __init__(self,\n in_channels,\n style_channels,\n out_channels=3,\n upsample=True,\n blur_kernel=[1, 3, 3, 1],\n style_mod_cfg=dict(bias_init=1.),\n style_bias=0.,\n fp16_enabled=False,\n conv_clamp=256,\n out_fp32=True):\n super().__init__()\n\n if upsample:\n self.upsample = UpsampleUpFIRDn(blur_kernel)\n\n # add support for fp16\n self.fp16_enabled = fp16_enabled\n self.conv_clamp = float(conv_clamp)\n\n self.conv = ModulatedConv2d(\n in_channels,\n out_channels=out_channels,\n kernel_size=1,\n style_channels=style_channels,\n demodulate=False,\n style_mod_cfg=style_mod_cfg,\n style_bias=style_bias,\n fp16_enabled=fp16_enabled)\n\n self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))\n\n # enforece the output to be fp32 (follow Tero's implementation)\n self.out_fp32 = out_fp32\n\n # @auto_fp16(apply_to=('x', 'style'))\n def forward(self, x, style, skip=None):\n \"\"\"Forward Function.\n\n Args:\n x ([Tensor): Input features with shape of (N, C, H, W).\n style (Tensor): Style latent with shape of (N, C).\n skip (Tensor, optional): Tensor for skip link. Defaults to None.\n\n Returns:\n Tensor: Output features with shape of (N, C, H, W)\n \"\"\"\n with autocast(enabled=self.fp16_enabled):\n out = self.conv(x, style)\n out = out + self.bias.to(x.dtype)\n\n if self.fp16_enabled:\n out = torch.clamp(\n out, min=-self.conv_clamp, max=self.conv_clamp)\n\n # Here, Tero adopts FP16 at `skip`.\n if skip is not None:\n if hasattr(self, 'upsample'):\n skip = self.upsample(skip)\n out = out + skip\n if self.out_fp32:\n out = out.to(torch.float32)\n return out\n\n\nclass ConvDownLayer(nn.Sequential):\n \"\"\"Convolution and Downsampling layer.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n downsample (bool, optional): Whether to adopt downsampling in features.\n Defaults to False.\n blur_kernel (list[int], optional): Blurry kernel.\n Defaults to [1, 3, 3, 1].\n bias (bool, optional): Whether to use bias parameter. Defaults to True.\n act_cfg (dict, optional): Activation configs.\n Defaults to dict(type='fused_bias').\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n conv_clamp (float, optional): Clamp the convolutional layer results to\n avoid gradient overflow. Defaults to `256.0`.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n kernel_size,\n downsample=False,\n blur_kernel=[1, 3, 3, 1],\n bias=True,\n act_cfg=dict(type='fused_bias'),\n fp16_enabled=False,\n conv_clamp=256.):\n\n self.fp16_enabled = fp16_enabled\n self.conv_clamp = float(conv_clamp)\n layers = []\n\n if downsample:\n factor = 2\n p = (len(blur_kernel) - factor) + (kernel_size - 1)\n pad0 = (p + 1) // 2\n pad1 = p // 2\n\n layers.append(Blur(blur_kernel, pad=(pad0, pad1)))\n\n stride = 2\n self.padding = 0\n else:\n stride = 1\n self.padding = kernel_size // 2\n\n self.with_fused_bias = act_cfg is not None and act_cfg.get(\n 'type') == 'fused_bias'\n if self.with_fused_bias:\n conv_act_cfg = None\n else:\n conv_act_cfg = act_cfg\n layers.append(\n EqualizedLRConvModule(\n in_channels,\n out_channels,\n kernel_size,\n padding=self.padding,\n stride=stride,\n bias=bias and not self.with_fused_bias,\n norm_cfg=None,\n act_cfg=conv_act_cfg,\n equalized_lr_cfg=dict(mode='fan_in', gain=1.)))\n if self.with_fused_bias:\n layers.append(_FusedBiasLeakyReLU(out_channels))\n\n super(ConvDownLayer, self).__init__(*layers)\n\n # @auto_fp16(apply_to=('x', ))\n def forward(self, x):\n with autocast(enabled=self.fp16_enabled):\n x = super().forward(x)\n if self.fp16_enabled:\n x = torch.clamp(x, min=-self.conv_clamp, max=self.conv_clamp)\n return x\n\n\nclass ResBlock(BaseModule):\n \"\"\"Residual block used in the discriminator of StyleGAN2.\n\n Args:\n in_channels (int): Input channels.\n out_channels (int): Output channels.\n kernel_size (int): Kernel size, same as :obj:`nn.Con2d`.\n fp16_enabled (bool, optional): Whether to use fp16 training in this\n module. Defaults to False.\n convert_input_fp32 (bool, optional): Whether to convert input type to\n fp32 if not `fp16_enabled`. This argument is designed to deal with\n the cases where some modules are run in FP16 and others in FP32.\n Defaults to True.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n blur_kernel=[1, 3, 3, 1],\n fp16_enabled=False,\n convert_input_fp32=True):\n super().__init__()\n\n self.fp16_enabled = fp16_enabled\n self.convert_input_fp32 = convert_input_fp32\n\n self.conv1 = ConvDownLayer(\n in_channels,\n in_channels,\n 3,\n fp16_enabled=fp16_enabled,\n blur_kernel=blur_kernel)\n self.conv2 = ConvDownLayer(\n in_channels,\n out_channels,\n 3,\n downsample=True,\n fp16_enabled=fp16_enabled,\n blur_kernel=blur_kernel)\n\n self.skip = ConvDownLayer(\n in_channels,\n out_channels,\n 1,\n downsample=True,\n act_cfg=None,\n bias=False,\n fp16_enabled=fp16_enabled,\n blur_kernel=blur_kernel)\n\n def forward(self, input):\n \"\"\"Forward function.\n\n Args:\n input (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map.\n \"\"\"\n # TODO: study whether this explicit datatype transfer will harm the\n # apex training speed\n if not self.fp16_enabled and self.convert_input_fp32:\n input = input.to(torch.float32)\n\n with autocast(enabled=self.fp16_enabled):\n out = self.conv1(input)\n out = self.conv2(out)\n\n skip = self.skip(input)\n out = (out + skip) / np.sqrt(2)\n\n return out\n\n\nclass ModMBStddevLayer(BaseModule):\n \"\"\"Modified MiniBatch Stddev Layer.\n\n This layer is modified from ``MiniBatchStddevLayer`` used in PGGAN. In\n StyleGAN2, the authors add a new feature, `channel_groups`, into this\n layer.\n\n Note that to accelerate the training procedure, we also add a new feature\n of ``sync_std`` to achieve multi-nodes/machine training. This feature is\n still in beta version and we have tested it on 256 scales.\n\n Args:\n group_size (int, optional): The size of groups in batch dimension.\n Defaults to 4.\n channel_groups (int, optional): The size of groups in channel\n dimension. Defaults to 1.\n sync_std (bool, optional): Whether to use synchronized std feature.\n Defaults to False.\n sync_groups (int | None, optional): The size of groups in node\n dimension. Defaults to None.\n eps (float, optional): Epsilon value to avoid computation error.\n Defaults to 1e-8.\n \"\"\"\n\n def __init__(self,\n group_size=4,\n channel_groups=1,\n sync_std=False,\n sync_groups=None,\n eps=1e-8):\n super().__init__()\n self.group_size = group_size\n self.eps = eps\n self.channel_groups = channel_groups\n self.sync_std = sync_std\n self.sync_groups = group_size if sync_groups is None else sync_groups\n\n if self.sync_std:\n assert torch.distributed.is_initialized(\n ), 'Only in distributed training can the sync_std be activated.'\n mmengine.print_log('Adopt synced minibatch stddev layer', 'mmagic')\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input feature map with shape of (N, C, H, W).\n\n Returns:\n Tensor: Output feature map with shape of (N, C+1, H, W).\n \"\"\"\n\n if self.sync_std:\n # concatenate all features\n all_features = torch.cat(AllGatherLayer.apply(x), dim=0)\n # get the exact features we need in calculating std-dev\n rank, ws = get_dist_info()\n local_bs = all_features.shape[0] // ws\n start_idx = local_bs * rank\n # avoid the case where start idx near the tail of features\n if start_idx + self.sync_groups > all_features.shape[0]:\n start_idx = all_features.shape[0] - self.sync_groups\n end_idx = min(local_bs * rank + self.sync_groups,\n all_features.shape[0])\n\n x = all_features[start_idx:end_idx]\n\n # batch size should be smaller than or equal to group size. Otherwise,\n # batch size should be divisible by the group size.\n assert x.shape[\n 0] <= self.group_size or x.shape[0] % self.group_size == 0, (\n 'Batch size be smaller than or equal '\n 'to group size. Otherwise,'\n ' batch size should be divisible by the group size.'\n f'But got batch size {x.shape[0]},'\n f' group size {self.group_size}')\n assert x.shape[1] % self.channel_groups == 0, (\n '\"channel_groups\" must be divided by the feature channels. '\n f'channel_groups: {self.channel_groups}, '\n f'feature channels: {x.shape[1]}')\n\n n, c, h, w = x.shape\n group_size = min(n, self.group_size)\n # [G, M, Gc, C', H, W]\n y = torch.reshape(x, (group_size, -1, self.channel_groups,\n c // self.channel_groups, h, w))\n y = torch.var(y, dim=0, unbiased=False)\n y = torch.sqrt(y + self.eps)\n # [M, 1, 1, 1]\n y = y.mean(dim=(2, 3, 4), keepdim=True).squeeze(2)\n y = y.repeat(group_size, 1, h, w)\n return torch.cat([x, y], dim=1)\n" }, { "path": "mmagic/models/editors/stylegan3/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .stylegan3 import StyleGAN3\nfrom .stylegan3_generator import StyleGAN3Generator\nfrom .stylegan3_modules import SynthesisInput, SynthesisLayer, SynthesisNetwork\n\n__all__ = [\n 'StyleGAN3',\n 'StyleGAN3Generator',\n 'SynthesisInput',\n 'SynthesisLayer',\n 'SynthesisNetwork',\n]\n" }, { "path": "mmagic/models/editors/stylegan3/stylegan3.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmengine import Config, MessageHub\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ...utils import get_module_device, get_valid_num_batches\nfrom ..stylegan2 import StyleGAN2\nfrom .stylegan3_utils import (apply_fractional_pseudo_rotation,\n apply_fractional_rotation,\n apply_fractional_translation,\n apply_integer_translation, rotation_matrix)\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass StyleGAN3(StyleGAN2):\n \"\"\"Implementation of `Alias-Free Generative Adversarial Networks`. # noqa.\n\n Paper link: https://nvlabs-fi-cdn.nvidia.com/stylegan3/stylegan3-paper.pdf # noqa\n\n Detailed architecture can be found in\n\n :class:`~mmagic.models.editors.stylegan3.StyleGAN3Generator`\n and\n :class:`~mmagic.models.editors.stylegan2.StyleGAN2Discriminator`\n \"\"\"\n\n def __init__(self,\n generator: ModelType,\n discriminator: Optional[ModelType] = None,\n data_preprocessor: Optional[Union[dict, Config]] = None,\n generator_steps: int = 1,\n discriminator_steps: int = 1,\n forward_kwargs: Optional[Dict] = None,\n ema_config: Optional[Dict] = None,\n loss_config=dict()):\n super().__init__(generator, discriminator, data_preprocessor,\n generator_steps, discriminator_steps, ema_config,\n loss_config)\n\n self.noise_size = getattr(self.generator, 'noise_size', 512)\n forward_kwargs = dict() if forward_kwargs is None else forward_kwargs\n disc_default_forward_kwargs = dict(update_emas=True, force_fp32=False)\n gen_default_forward_kwargs = dict(force_fp32=False)\n forward_kwargs.setdefault('disc', disc_default_forward_kwargs)\n forward_kwargs.setdefault('gen', gen_default_forward_kwargs)\n self.forward_kwargs = forward_kwargs\n\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n # hard code to compute equivarience\n if 'mode' in inputs_dict and 'eq_cfg' in inputs_dict['mode']:\n batch_size = get_valid_num_batches(inputs_dict, data_samples)\n outputs = self.sample_equivarience_pairs(\n batch_size,\n sample_mode=inputs_dict['mode']['sample_mode'],\n eq_cfg=inputs_dict['mode']['eq_cfg'],\n sample_kwargs=inputs_dict['mode']['sample_kwargs'])\n else:\n outputs = self(inputs_dict, data_samples)\n return outputs\n\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: A list of ``DataSample`` contain generated results.\n \"\"\"\n data = self.data_preprocessor(data)\n inputs_dict, data_samples = data['inputs'], data['data_samples']\n # hard code to compute equivarience\n if 'mode' in inputs_dict and 'eq_cfg' in inputs_dict['mode']:\n batch_size = get_valid_num_batches(inputs_dict, data_samples)\n outputs = self.sample_equivarience_pairs(\n batch_size,\n sample_mode=inputs_dict['mode']['sample_mode'],\n eq_cfg=inputs_dict['mode']['eq_cfg'],\n sample_kwargs=inputs_dict['mode']['sample_kwargs'])\n else:\n outputs = self(inputs_dict, data_samples)\n return outputs\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(\n noise_batch, return_noise=False, **self.forward_kwargs['disc'])\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(disc_pred_fake,\n disc_pred_real, real_imgs)\n optimizer_wrapper.update_params(parsed_losses)\n # save ada info\n message_hub = MessageHub.get_current_instance()\n message_hub.update_info('disc_pred_real', disc_pred_real)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(\n noise, return_noise=False, **self.forward_kwargs['gen'])\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake, num_batches)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n\n def sample_equivarience_pairs(self,\n batch_size,\n sample_mode='ema',\n eq_cfg=dict(\n compute_eqt_int=False,\n compute_eqt_frac=False,\n compute_eqr=False,\n translate_max=0.125,\n rotate_max=1),\n sample_kwargs=dict()):\n generator = self.generator if (sample_mode\n == 'orig') else self.generator_ema\n if hasattr(generator, 'module'):\n generator = generator.module\n\n device = get_module_device(generator)\n identity_matrix = torch.eye(3, device=device)\n\n # Run mapping network.\n z = torch.randn([batch_size, self.noise_size], device=device)\n ws = generator.style_mapping(z=z)\n transform_matrix = getattr(\n getattr(getattr(generator, 'synthesis', None), 'input', None),\n 'transform', None)\n\n # Generate reference image.\n transform_matrix[:] = identity_matrix\n orig = generator.synthesis(ws=ws, **sample_kwargs)\n\n batch_sample = [DataSample() for _ in range(batch_size)]\n # Integer translation (EQ-T).\n if eq_cfg['compute_eqt_int']:\n t = (torch.rand(2, device=device) * 2 -\n 1) * eq_cfg['translate_max']\n t = (t * generator.out_size).round() / generator.out_size\n transform_matrix[:] = identity_matrix\n transform_matrix[:2, 2] = -t\n img = generator.synthesis(ws=ws, **sample_kwargs)\n ref, mask = apply_integer_translation(orig, t[0], t[1])\n\n diff = (ref - img).square() * mask\n for idx in range(batch_size):\n data_sample = batch_sample[idx]\n setattr(data_sample, 'eqt_int',\n DataSample(diff=diff, mask=mask))\n\n # Fractional translation (EQ-T_frac).\n if eq_cfg['compute_eqt_frac']:\n t = (torch.rand(2, device=device) * 2 -\n 1) * eq_cfg['translate_max']\n transform_matrix[:] = identity_matrix\n transform_matrix[:2, 2] = -t\n img = generator.synthesis(ws=ws, **sample_kwargs)\n ref, mask = apply_fractional_translation(orig, t[0], t[1])\n\n diff = (ref - img).square() * mask\n for idx in range(batch_size):\n data_sample = batch_sample[idx]\n setattr(data_sample, 'eqt_frac',\n DataSample(diff=diff, mask=mask))\n\n # Rotation (EQ-R).\n if eq_cfg['compute_eqr']:\n angle = (torch.rand([], device=device) * 2 - 1) * (\n eq_cfg['rotate_max'] * np.pi)\n transform_matrix[:] = rotation_matrix(-angle)\n img = generator.synthesis(ws=ws, **sample_kwargs)\n ref, ref_mask = apply_fractional_rotation(orig, angle)\n pseudo, pseudo_mask = apply_fractional_pseudo_rotation(img, angle)\n mask = ref_mask * pseudo_mask\n\n diff = (ref - pseudo).square() * mask\n for idx in range(batch_size):\n data_sample = batch_sample[idx]\n setattr(data_sample, 'eqr', DataSample(diff=diff, mask=mask))\n\n return batch_sample\n" }, { "path": "mmagic/models/editors/stylegan3/stylegan3_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport mmengine\nimport torch\nfrom mmengine.model import BaseModule\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\n\nfrom mmagic.registry import MODELS\nfrom ...utils import get_module_device\nfrom ..stylegan1 import get_mean_latent\n\n\n@MODELS.register_module('StyleGANv3Generator')\n@MODELS.register_module()\nclass StyleGAN3Generator(BaseModule):\n \"\"\"StyleGAN3 Generator.\n\n In StyleGAN3, we make several changes to StyleGANv2's generator which\n include transformed fourier features, filtered nonlinearity and\n non-critical sampling, etc. More details can be found in: Alias-Free\n Generative Adversarial Networks NeurIPS'2021.\n\n Ref: https://github.com/NVlabs/stylegan3\n\n Args:\n out_size (int): The output size of the StyleGAN3 generator.\n style_channels (int): The number of channels for style code.\n img_channels (int): The number of output's channels.\n noise_size (int, optional): Size of the input noise vector.\n Defaults to 512.\n rgb2bgr (bool, optional): Whether to reformat the output channels\n with order `bgr`. We provide several pre-trained StyleGAN3\n weights whose output channels order is `rgb`. You can set\n this argument to True to use the weights.\n pretrained (str | dict, optional): Path for the pretrained model or\n dict containing information for pretrained models whose necessary\n key is 'ckpt_path'. Besides, you can also provide 'prefix' to load\n the generator part from the whole state dict. Defaults to None.\n synthesis_cfg (dict, optional): Config for synthesis network. Defaults\n to dict(type='SynthesisNetwork').\n mapping_cfg (dict, optional): Config for mapping network. Defaults to\n dict(type='MappingNetwork').\n \"\"\"\n\n def __init__(self,\n out_size,\n style_channels,\n img_channels,\n noise_size=512,\n rgb2bgr=False,\n pretrained=None,\n synthesis_cfg=dict(type='SynthesisNetwork'),\n mapping_cfg=dict(type='MappingNetwork')):\n super().__init__()\n self.noise_size = noise_size\n self.style_channels = style_channels\n self.out_size = out_size\n self.img_channels = img_channels\n self.rgb2bgr = rgb2bgr\n\n self._synthesis_cfg = deepcopy(synthesis_cfg)\n self._synthesis_cfg.setdefault('style_channels', style_channels)\n self._synthesis_cfg.setdefault('out_size', out_size)\n self._synthesis_cfg.setdefault('img_channels', img_channels)\n self.synthesis = MODELS.build(self._synthesis_cfg)\n\n self.num_ws = self.synthesis.num_ws\n self._mapping_cfg = deepcopy(mapping_cfg)\n self._mapping_cfg.setdefault('noise_size', noise_size)\n self._mapping_cfg.setdefault('style_channels', style_channels)\n self._mapping_cfg.setdefault('num_ws', self.num_ws)\n self.style_mapping = MODELS.build(self._mapping_cfg)\n\n if pretrained is not None:\n self._load_pretrained_model(**pretrained)\n\n def _load_pretrained_model(self,\n ckpt_path,\n prefix='',\n map_location='cpu',\n strict=True):\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n self.load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained model from {ckpt_path}')\n\n def forward(self,\n noise,\n num_batches=0,\n input_is_latent=False,\n truncation=1,\n num_truncation_layer=None,\n update_emas=False,\n force_fp32=True,\n return_noise=False,\n return_latents=False):\n \"\"\"Forward Function for stylegan3.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n num_batches (int, optional): The number of batch size.\n Defaults to 0.\n input_is_latent (bool, optional): If `True`, the input tensor is\n the latent tensor. Defaults to False.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n num_truncation_layer (int, optional): Number of layers use\n truncated latent. Defaults to None.\n update_emas (bool, optional): Whether update moving average of\n mean latent. Defaults to False.\n force_fp32 (bool, optional): Force fp32 ignore the weights.\n Defaults to True.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n return_latents (bool, optional): If True, ``latent`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n Returns:\n torch.Tensor | dict: Generated image tensor or dictionary \\\n containing more data.\n \"\"\"\n # if input is latent, set noise size as the style_channels\n noise_size = (\n self.style_channels if input_is_latent else self.noise_size)\n\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, noise_size))\n\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, noise_size))\n\n device = get_module_device(self)\n noise_batch = noise_batch.to(device)\n\n if input_is_latent:\n ws = noise_batch.unsqueeze(1).repeat([1, self.num_ws, 1])\n else:\n ws = self.style_mapping(\n noise_batch,\n truncation=truncation,\n num_truncation_layer=num_truncation_layer,\n update_emas=update_emas)\n out_img = self.synthesis(\n ws, update_emas=update_emas, force_fp32=force_fp32)\n\n if self.rgb2bgr:\n out_img = out_img[:, [2, 1, 0], ...]\n\n if return_noise or return_latents:\n output = dict(fake_img=out_img, noise_batch=noise_batch, latent=ws)\n return output\n\n return out_img\n\n def get_mean_latent(self, num_samples=4096, **kwargs):\n \"\"\"Get mean latent of W space in this generator.\n\n Args:\n num_samples (int, optional): Number of sample times. Defaults\n to 4096.\n\n Returns:\n Tensor: Mean latent of this generator.\n \"\"\"\n if hasattr(self.style_mapping, 'w_avg'):\n return self.style_mapping.w_avg\n return get_mean_latent(self, num_samples, **kwargs)\n\n def get_training_kwargs(self, phase):\n \"\"\"Get training kwargs. In StyleGANv3, we enable fp16, and update\n magnitude ema during training of discriminator. This function is used\n to pass related arguments.\n\n Args:\n phase (str): Current training phase.\n\n Returns:\n dict: Training kwargs.\n \"\"\"\n if phase == 'disc':\n return dict(update_emas=True, force_fp32=False)\n if phase == 'gen':\n return dict(force_fp32=False)\n return {}\n" }, { "path": "mmagic/models/editors/stylegan3/stylegan3_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport scipy\nimport torch\nfrom mmengine.model import BaseModule\n\ntry:\n from mmcv.ops import bias_act, conv2d_gradfix, filtered_lrelu\nexcept ImportError:\n bias_act = None\n conv2d_gradfix = None\n filtered_lrelu = None\n print('Warning: mmcv.ops.bias_act, mmcv.ops.conv2d_gradfix'\n ' and mmcv.ops.filtered_lrelu are not available.')\n\nfrom mmengine.runner.amp import autocast\n\nfrom mmagic.registry import MODELS\n\n\ndef modulated_conv2d(\n x,\n w,\n s,\n demodulate=True,\n padding=0,\n input_gain=None,\n):\n \"\"\"Modulated Conv2d in StyleGANv3.\n\n Args:\n x (torch.Tensor): Input tensor with shape (batch_size, in_channels,\n height, width).\n w (torch.Tensor): Weight of modulated convolution with shape\n (out_channels, in_channels, kernel_height, kernel_width).\n s (torch.Tensor): Style tensor with shape (batch_size, in_channels).\n demodulate (bool): Whether apply weight demodulation. Defaults to True.\n padding (int or list[int]): Convolution padding. Defaults to 0.\n input_gain (list[int]): Scaling factors for input. Defaults to None.\n\n Returns:\n torch.Tensor: Convolution Output.\n \"\"\"\n\n batch_size = int(x.shape[0])\n _, in_channels, kh, kw = w.shape\n\n # Pre-normalize inputs.\n if demodulate:\n w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt()\n s = s * s.square().mean().rsqrt()\n\n # Modulate weights.\n w = w.unsqueeze(0) # [NOIkk]\n w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]\n\n # Demodulate weights.\n if demodulate:\n dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]\n w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]\n\n # Apply input scaling.\n if input_gain is not None:\n input_gain = input_gain.expand(batch_size, in_channels) # [NI]\n w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]\n\n # Execute as one fused op using grouped convolution.\n x = x.reshape(1, -1, *x.shape[2:])\n w = w.reshape(-1, in_channels, kh, kw)\n x = conv2d_gradfix.conv2d(\n input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)\n x = x.reshape(batch_size, -1, *x.shape[2:])\n return x\n\n\nclass FullyConnectedLayer(BaseModule):\n \"\"\"Fully connected layer used in StyleGANv3.\n\n Args:\n in_features (int): Number of channels in the input feature.\n out_features (int): Number of channels in the out feature.\n activation (str, optional): Activation function with choices 'relu',\n 'lrelu', 'linear'. 'linear' means no extra activation.\n Defaults to 'linear'.\n bias (bool, optional): Whether to use additive bias. Defaults to True.\n lr_multiplier (float, optional): Equalized learning rate multiplier.\n Defaults to 1..\n weight_init (float, optional): Weight multiplier for initialization.\n Defaults to 1..\n bias_init (float, optional): Initial bias. Defaults to 0..\n \"\"\"\n\n def __init__(self,\n in_features,\n out_features,\n activation='linear',\n bias=True,\n lr_multiplier=1.,\n weight_init=1.,\n bias_init=0.):\n super().__init__()\n self.in_features = in_features\n self.out_features = out_features\n self.activation = activation\n self.weight = torch.nn.Parameter(\n torch.randn([out_features, in_features]) *\n (weight_init / lr_multiplier))\n bias_init = np.broadcast_to(\n np.asarray(bias_init, dtype=np.float32), [out_features])\n self.bias = torch.nn.Parameter(\n torch.from_numpy(bias_init / lr_multiplier)) if bias else None\n self.weight_gain = lr_multiplier / np.sqrt(in_features)\n self.bias_gain = lr_multiplier\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n w = self.weight.to(x.dtype) * self.weight_gain\n b = self.bias\n if b is not None:\n b = b.to(x.dtype)\n if self.bias_gain != 1:\n b = b * self.bias_gain\n if self.activation == 'linear' and b is not None:\n x = torch.addmm(b.unsqueeze(0), x, w.t())\n else:\n x = x.matmul(w.t())\n x = bias_act(x, b, act=self.activation)\n return x\n\n\n@MODELS.register_module()\nclass MappingNetwork(BaseModule):\n \"\"\"Style mapping network used in StyleGAN3. The main difference between it\n and styleganv1,v2 is that mean latent is registered as a buffer and dynamic\n updated during training.\n\n Args:\n noise_size (int, optional): Size of the input noise vector.\n style_channels (int): The number of channels for style code.\n num_ws (int | None): The repeat times of w latent. If None is passed,\n the output will shape like (batch_size, 1), otherwise the output\n will shape like (bz, num_ws, 1).\n cond_size (int, optional): Size of the conditional input.\n Defaults to None.\n num_layers (int, optional): The number of layers of mapping network.\n Defaults to 2.\n lr_multiplier (float, optional): Equalized learning rate multiplier.\n Defaults to 0.01.\n w_avg_beta (float, optional): The value used for update `w_avg`.\n Defaults to 0.998.\n \"\"\"\n\n def __init__(self,\n noise_size,\n style_channels,\n num_ws,\n cond_size=0,\n num_layers=2,\n lr_multiplier=0.01,\n w_avg_beta=0.998):\n super().__init__()\n self.noise_size = noise_size\n self.cond_size = cond_size\n self.style_channels = style_channels\n self.num_ws = num_ws\n self.num_layers = num_layers\n self.w_avg_beta = w_avg_beta\n\n # Construct layers.\n if self.cond_size is not None and self.cond_size > 0:\n self.embed = FullyConnectedLayer(self.cond_size,\n self.style_channels)\n\n features = [\n self.noise_size +\n (self.style_channels if self.cond_size > 0 else 0)\n ] + [self.style_channels] * self.num_layers\n for idx, in_features, out_features in zip(\n range(num_layers), features[:-1], features[1:]):\n layer = FullyConnectedLayer(\n in_features,\n out_features,\n activation='lrelu',\n lr_multiplier=lr_multiplier)\n setattr(self, f'fc{idx}', layer)\n if w_avg_beta is not None:\n self.register_buffer('w_avg', torch.zeros([style_channels]))\n\n def forward(self,\n z,\n label=None,\n truncation=1,\n num_truncation_layer=None,\n update_emas=False):\n \"\"\"Style mapping function.\n\n Args:\n z (torch.Tensor): Input noise tensor.\n label (torch.Tensor, optional): The conditional input.\n Defaults to None.\n truncation (float, optional): Truncation factor. Give value less\n than 1., the truncation trick will be adopted. Defaults to 1.\n num_truncation_layer (int, optional): Number of layers use\n truncated latent. Defaults to None.\n update_emas (bool, optional): Whether update moving average of\n mean latent. Defaults to False.\n\n Returns:\n torch.Tensor: W-plus latent.\n \"\"\"\n\n if num_truncation_layer is None:\n num_truncation_layer = self.num_ws\n\n x = None\n # Embed, normalize, and concatenate inputs.\n if self.noise_size > 0:\n assert z is not None, (\n '\\'z\\' must be passed since \\'self.noise_size\\''\n f'({self.noise_size}) larger than 0.')\n x = z.to(torch.float32)\n x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()\n if self.cond_size > 0:\n y = self.embed(label.to(torch.float32))\n y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()\n x = torch.cat([x, y], dim=1) if x is not None else y\n\n # Execute layers.\n for idx in range(self.num_layers):\n x = getattr(self, f'fc{idx}')(x)\n\n # Update moving average of W.\n if update_emas and self.w_avg_beta is not None:\n self.w_avg.copy_(x.detach().mean(\n dim=0).lerp(self.w_avg, self.w_avg_beta))\n\n # Broadcast and apply truncation.\n if self.num_ws is not None:\n x = x.unsqueeze(1).repeat([1, self.num_ws, 1])\n if truncation != 1:\n assert hasattr(self, 'w_avg'), (\n '\\'w_avg\\' must not be None when truncation trick is used.')\n if num_truncation_layer is None:\n x = self.w_avg.lerp(x, truncation)\n else:\n x[:, :num_truncation_layer] = self.w_avg.lerp(\n x[:, :num_truncation_layer], truncation)\n return x\n\n\nclass SynthesisInput(BaseModule):\n \"\"\"Module which generate input for synthesis layer.\n\n Args:\n style_channels (int): The number of channels for style code.\n channels (int): The number of output channel.\n size (int): The size of sampling grid.\n sampling_rate (int): Sampling rate for construct sampling grid.\n bandwidth (float): Bandwidth of random frequencies.\n \"\"\"\n\n def __init__(self, style_channels, channels, size, sampling_rate,\n bandwidth):\n super().__init__()\n self.style_channels = style_channels\n self.channels = channels\n self.size = np.broadcast_to(np.asarray(size), [2])\n self.sampling_rate = sampling_rate\n self.bandwidth = bandwidth\n\n # Draw random frequencies from uniform 2D disc.\n freqs = torch.randn([self.channels, 2])\n radii = freqs.square().sum(dim=1, keepdim=True).sqrt()\n freqs /= radii * radii.square().exp().pow(0.25)\n freqs *= bandwidth\n phases = torch.rand([self.channels]) - 0.5\n\n # Setup parameters and buffers.\n self.weight = torch.nn.Parameter(\n torch.randn([self.channels, self.channels]))\n self.affine = FullyConnectedLayer(\n style_channels, 4, weight_init=0, bias_init=[1, 0, 0, 0])\n self.register_buffer('transform', torch.eye(\n 3, 3)) # User-specified inverse transform wrt. resulting image.\n self.register_buffer('freqs', freqs)\n self.register_buffer('phases', phases)\n\n def forward(self, w):\n \"\"\"Forward function.\"\"\"\n # Introduce batch dimension.\n transforms = self.transform.unsqueeze(0) # [batch, row, col]\n freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]\n phases = self.phases.unsqueeze(0) # [batch, channel]\n\n # Apply learned transformation.\n t = self.affine(w) # t = (r_c, r_s, t_x, t_y)\n t = t / t[:, :2].norm(\n dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)\n m_r = torch.eye(\n 3, device=w.device).unsqueeze(0).repeat(\n [w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.\n m_r[:, 0, 0] = t[:, 0] # r'_c\n m_r[:, 0, 1] = -t[:, 1] # r'_s\n m_r[:, 1, 0] = t[:, 1] # r'_s\n m_r[:, 1, 1] = t[:, 0] # r'_c\n m_t = torch.eye(\n 3, device=w.device).unsqueeze(0).repeat(\n [w.shape[0], 1,\n 1]) # Inverse translation wrt. resulting image.\n m_t[:, 0, 2] = -t[:, 2] # t'_x\n m_t[:, 1, 2] = -t[:, 3] # t'_y\n\n # First rotate resulting image, then translate\n # and finally apply user-specified transform.\n transforms = m_r @ m_t @ transforms\n\n # Transform frequencies.\n phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)\n freqs = freqs @ transforms[:, :2, :2]\n\n # Dampen out-of-band frequencies\n # that may occur due to the user-specified transform.\n amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) /\n (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)\n\n # Construct sampling grid.\n theta = torch.eye(2, 3, device=w.device)\n theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate\n theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate\n grids = torch.nn.functional.affine_grid(\n theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]],\n align_corners=False)\n\n # Compute Fourier features.\n x = (grids.unsqueeze(3) @ freqs.permute(\n 0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(\n 3) # [batch, height, width, channel]\n x = x + phases.unsqueeze(1).unsqueeze(2)\n x = torch.sin(x * (np.pi * 2))\n x = x * amplitudes.unsqueeze(1).unsqueeze(2)\n\n # Apply trainable mapping.\n weight = self.weight / np.sqrt(self.channels)\n x = x @ weight.t()\n\n # Ensure correct shape.\n x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]\n return x\n\n\nclass SynthesisLayer(BaseModule):\n \"\"\"Layer of Synthesis network for stylegan3.\n\n Args:\n style_channels (int): The number of channels for style code.\n is_torgb (bool): Whether output of this layer is transformed to\n rgb image.\n is_critically_sampled (bool): Whether filter cutoff is set exactly\n at the bandlimit.\n use_fp16 (bool, optional): Whether to use fp16 training in this\n module. If this flag is `True`, the whole module will be wrapped\n with ``auto_fp16``.\n in_channels (int): The channel number of the input feature map.\n out_channels (int): The channel number of the output feature map.\n in_size (int): The input size of feature map.\n out_size (int): The output size of feature map.\n in_sampling_rate (int): Sampling rate for upsampling filter.\n out_sampling_rate (int): Sampling rate for downsampling filter.\n in_cutoff (float): Cutoff frequency for upsampling filter.\n out_cutoff (float): Cutoff frequency for downsampling filter.\n in_half_width (float): The approximate width of the transition region\n for upsampling filter.\n out_half_width (float): The approximate width of the transition region\n for downsampling filter.\n conv_kernel (int, optional): The kernel of modulated convolution.\n Defaults to 3.\n filter_size (int, optional): Base filter size. Defaults to 6.\n lrelu_upsampling (int, optional): Upsamling rate for `filtered_lrelu`.\n Defaults to 2.\n use_radial_filters (bool, optional): Whether use radially symmetric\n jinc-based filter in downsamping filter. Defaults to False.\n conv_clamp (int, optional): Clamp bound for convolution.\n Defaults to 256.\n magnitude_ema_beta (float, optional): Beta coefficient for calculating\n input magnitude ema. Defaults to 0.999.\n \"\"\"\n\n def __init__(\n self,\n style_channels,\n is_torgb,\n is_critically_sampled,\n use_fp16,\n in_channels,\n out_channels,\n in_size,\n out_size,\n in_sampling_rate,\n out_sampling_rate,\n in_cutoff,\n out_cutoff,\n in_half_width,\n out_half_width,\n conv_kernel=3,\n filter_size=6,\n lrelu_upsampling=2,\n use_radial_filters=False,\n conv_clamp=256,\n magnitude_ema_beta=0.999,\n ):\n super().__init__()\n self.style_channels = style_channels\n self.is_torgb = is_torgb\n self.is_critically_sampled = is_critically_sampled\n self.use_fp16 = use_fp16\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.in_size = np.broadcast_to(np.asarray(in_size), [2])\n self.out_size = np.broadcast_to(np.asarray(out_size), [2])\n self.in_sampling_rate = in_sampling_rate\n self.out_sampling_rate = out_sampling_rate\n self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (\n 1 if is_torgb else lrelu_upsampling)\n self.in_cutoff = in_cutoff\n self.out_cutoff = out_cutoff\n self.in_half_width = in_half_width\n self.out_half_width = out_half_width\n self.conv_kernel = 1 if is_torgb else conv_kernel\n self.conv_clamp = conv_clamp\n self.magnitude_ema_beta = magnitude_ema_beta\n\n # Setup parameters and buffers.\n self.affine = FullyConnectedLayer(\n self.style_channels, self.in_channels, bias_init=1)\n self.weight = torch.nn.Parameter(\n torch.randn([\n self.out_channels, self.in_channels, self.conv_kernel,\n self.conv_kernel\n ]))\n self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))\n self.register_buffer('magnitude_ema', torch.ones([]))\n\n # Design upsampling filter.\n self.up_factor = int(\n np.rint(self.tmp_sampling_rate / self.in_sampling_rate))\n assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate\n self.up_taps = (\n filter_size *\n self.up_factor if self.up_factor > 1 and not self.is_torgb else 1)\n self.register_buffer(\n 'up_filter',\n self.design_lowpass_filter(\n numtaps=self.up_taps,\n cutoff=self.in_cutoff,\n width=self.in_half_width * 2,\n fs=self.tmp_sampling_rate))\n\n # Design downsampling filter.\n self.down_factor = int(\n np.rint(self.tmp_sampling_rate / self.out_sampling_rate))\n assert (self.out_sampling_rate *\n self.down_factor == self.tmp_sampling_rate)\n self.down_taps = (\n filter_size * self.down_factor\n if self.down_factor > 1 and not self.is_torgb else 1)\n self.down_radial = (\n use_radial_filters and not self.is_critically_sampled)\n self.register_buffer(\n 'down_filter',\n self.design_lowpass_filter(\n numtaps=self.down_taps,\n cutoff=self.out_cutoff,\n width=self.out_half_width * 2,\n fs=self.tmp_sampling_rate,\n radial=self.down_radial))\n\n # Compute padding.\n pad_total = (\n self.out_size - 1\n ) * self.down_factor + 1 # Desired output size before downsampling.\n pad_total -= (self.in_size + self.conv_kernel -\n 1) * self.up_factor # Input size after upsampling.\n pad_total += self.up_taps + self.down_taps - 2\n pad_lo = (pad_total + self.up_factor) // 2\n pad_hi = pad_total - pad_lo\n self.padding = [\n int(pad_lo[0]),\n int(pad_hi[0]),\n int(pad_lo[1]),\n int(pad_hi[1])\n ]\n\n def forward(self, x, w, force_fp32=False, update_emas=False):\n \"\"\"Forward function for synthesis layer.\n\n Args:\n x (torch.Tensor): Input feature map tensor.\n w (torch.Tensor): Input style tensor.\n force_fp32 (bool, optional): Force fp32 ignore the weights.\n Defaults to True.\n update_emas (bool, optional): Whether update moving average of\n input magnitude. Defaults to False.\n\n Returns:\n torch.Tensor: Output feature map tensor.\n \"\"\"\n\n # Track input magnitude.\n if update_emas:\n with torch.autograd.profiler.record_function(\n 'update_magnitude_ema'):\n magnitude_cur = x.detach().to(torch.float32).square().mean()\n self.magnitude_ema.copy_(\n magnitude_cur.lerp(self.magnitude_ema,\n self.magnitude_ema_beta))\n input_gain = self.magnitude_ema.rsqrt()\n\n # Execute affine layer.\n styles = self.affine(w)\n if self.is_torgb:\n weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel**2))\n styles = styles * weight_gain\n\n # Execute modulated conv2d.\n dtype = torch.float16 if (self.use_fp16 and not force_fp32 and\n x.device.type == 'cuda') else torch.float32\n with autocast(enabled=dtype == torch.float16):\n x = modulated_conv2d(\n x=x.to(dtype),\n w=self.weight,\n s=styles,\n padding=self.conv_kernel - 1,\n demodulate=(not self.is_torgb),\n input_gain=input_gain)\n\n # Execute bias, filtered leaky ReLU, and clamping.\n gain = 1 if self.is_torgb else np.sqrt(2)\n slope = 1 if self.is_torgb else 0.2\n x = filtered_lrelu(\n input=x,\n filter_up=self.up_filter,\n filter_down=self.down_filter,\n bias=self.bias.to(x.dtype),\n up=self.up_factor,\n down=self.down_factor,\n padding=self.padding,\n gain=gain,\n slope=slope,\n clamp=self.conv_clamp)\n\n # Ensure correct shape and dtype.\n assert x.dtype == dtype\n return x\n\n @staticmethod\n def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):\n \"\"\"Design lowpass filter giving related arguments.\n\n Args:\n numtaps (int): Length of the filter. `numtaps` must be odd if a\n passband includes the Nyquist frequency.\n cutoff (float): Cutoff frequency of filter\n width (float): The approximate width of the transition region.\n fs (float): The sampling frequency of the signal.\n radial (bool, optional): Whether use radially symmetric jinc-based\n filter. Defaults to False.\n\n Returns:\n torch.Tensor: Kernel of lowpass filter.\n \"\"\"\n assert numtaps >= 1\n\n # Identity filter.\n if numtaps == 1:\n return None\n\n # Separable Kaiser low-pass filter.\n if not radial:\n f = scipy.signal.firwin(\n numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)\n return torch.as_tensor(f, dtype=torch.float32)\n\n # Radially symmetric jinc-based filter.\n x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs\n r = np.hypot(*np.meshgrid(x, x))\n f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)\n beta = scipy.signal.kaiser_beta(\n scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))\n w = np.kaiser(numtaps, beta)\n f *= np.outer(w, w)\n f /= np.sum(f)\n return torch.as_tensor(f, dtype=torch.float32)\n\n\n@MODELS.register_module()\nclass SynthesisNetwork(BaseModule):\n \"\"\"Synthesis network for stylegan3.\n\n Args:\n style_channels (int): The number of channels for style code.\n out_size (int): The resolution of output image.\n img_channels (int): The number of channels for output image.\n channel_base (int, optional): Overall multiplier for the number of\n channels. Defaults to 32768.\n channel_max (int, optional): Maximum number of channels in any layer.\n Defaults to 512.\n num_layers (int, optional): Total number of layers, excluding Fourier\n features and ToRGB. Defaults to 14.\n num_critical (int, optional): Number of critically sampled layers at\n the end. Defaults to 2.\n first_cutoff (int, optional): Cutoff frequency of the first layer.\n Defaults to 2.\n first_stopband (int, optional): Minimum stopband of the first layer.\n Defaults to 2**2.1.\n last_stopband_rel (float, optional): Minimum stopband of the last\n layer, expressed relative to the cutoff. Defaults to 2**0.3.\n margin_size (int, optional): Number of additional pixels outside the\n image. Defaults to 10.\n output_scale (float, optional): Scale factor for output value.\n Defaults to 0.25.\n num_fp16_res (int, optional): Number of first few layers use fp16.\n Defaults to 4.\n \"\"\"\n\n def __init__(\n self,\n style_channels,\n out_size,\n img_channels,\n channel_base=32768,\n channel_max=512,\n num_layers=14,\n num_critical=2,\n first_cutoff=2,\n first_stopband=2**2.1,\n last_stopband_rel=2**0.3,\n margin_size=10,\n output_scale=0.25,\n num_fp16_res=4,\n **layer_kwargs,\n ):\n super().__init__()\n self.style_channels = style_channels\n self.num_ws = num_layers + 2\n self.out_size = out_size\n self.img_channels = img_channels\n self.num_layers = num_layers\n self.num_critical = num_critical\n self.margin_size = margin_size\n self.output_scale = output_scale\n self.num_fp16_res = num_fp16_res\n\n # Geometric progression of layer cutoffs and min. stopbands.\n last_cutoff = self.out_size / 2 # f_{c,N}\n last_stopband = last_cutoff * last_stopband_rel # f_{t,N}\n exponents = np.minimum(\n np.arange(self.num_layers + 1) /\n (self.num_layers - self.num_critical), 1)\n cutoffs = first_cutoff * (last_cutoff /\n first_cutoff)**exponents # f_c[i]\n stopbands = first_stopband * (last_stopband /\n first_stopband)**exponents # f_t[i]\n\n # Compute remaining layer parameters.\n sampling_rates = np.exp2(\n np.ceil(np.log2(np.minimum(stopbands * 2, self.out_size)))) # s[i]\n half_widths = np.maximum(stopbands,\n sampling_rates / 2) - cutoffs # f_h[i]\n sizes = sampling_rates + self.margin_size * 2\n sizes[-2:] = self.out_size\n channels = np.rint(\n np.minimum((channel_base / 2) / cutoffs, channel_max))\n channels[-1] = self.img_channels\n\n # Construct layers.\n self.input = SynthesisInput(\n style_channels=self.style_channels,\n channels=int(channels[0]),\n size=int(sizes[0]),\n sampling_rate=sampling_rates[0],\n bandwidth=cutoffs[0])\n self.layer_names = []\n for idx in range(self.num_layers + 1):\n prev = max(idx - 1, 0)\n is_torgb = (idx == self.num_layers)\n is_critically_sampled = (\n idx >= self.num_layers - self.num_critical)\n use_fp16 = (\n sampling_rates[idx] * (2**self.num_fp16_res) > self.out_size)\n layer = SynthesisLayer(\n style_channels=self.style_channels,\n is_torgb=is_torgb,\n is_critically_sampled=is_critically_sampled,\n use_fp16=use_fp16,\n in_channels=int(channels[prev]),\n out_channels=int(channels[idx]),\n in_size=int(sizes[prev]),\n out_size=int(sizes[idx]),\n in_sampling_rate=int(sampling_rates[prev]),\n out_sampling_rate=int(sampling_rates[idx]),\n in_cutoff=cutoffs[prev],\n out_cutoff=cutoffs[idx],\n in_half_width=half_widths[prev],\n out_half_width=half_widths[idx],\n **layer_kwargs)\n name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'\n setattr(self, name, layer)\n self.layer_names.append(name)\n\n def forward(self, ws, **layer_kwargs):\n \"\"\"Forward function.\"\"\"\n ws = ws.to(torch.float32).unbind(dim=1)\n\n # Execute layers.\n x = self.input(ws[0])\n for name, w in zip(self.layer_names, ws[1:]):\n x = getattr(self, name)(x, w, **layer_kwargs)\n if self.output_scale != 1:\n x = x * self.output_scale\n\n # Ensure correct shape and dtype.\n x = x.to(torch.float32)\n return x\n" }, { "path": "mmagic/models/editors/stylegan3/stylegan3_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\ntry:\n from mmcv.ops import filter2d, upsample2d\nexcept ImportError:\n filter2d = None\n upsample2d = None\n print(\n 'Warning: mmcv.ops.filter2d and mmcv.ops.upsample2d are not available.'\n )\n\n\ndef apply_integer_translation(x, tx, ty):\n _N, _C, H, W = x.shape\n tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device)\n ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device)\n ix = tx.round().to(torch.int64)\n iy = ty.round().to(torch.int64)\n\n z = torch.zeros_like(x)\n m = torch.zeros_like(x)\n if abs(ix) < W and abs(iy) < H:\n y = x[:, :, max(-iy, 0):H + min(-iy, 0), max(-ix, 0):W + min(-ix, 0)]\n z[:, :, max(iy, 0):H + min(iy, 0), max(ix, 0):W + min(ix, 0)] = y\n m[:, :, max(iy, 0):H + min(iy, 0), max(ix, 0):W + min(ix, 0)] = 1\n return z, m\n\n\ndef sinc(x):\n y = (x * np.pi).abs()\n z = torch.sin(y) / y.clamp(1e-30, float('inf'))\n return torch.where(y < 1e-30, torch.ones_like(x), z)\n\n\ndef apply_fractional_translation(x, tx, ty, a=3):\n _N, _C, H, W = x.shape\n tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device)\n ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device)\n ix = tx.floor().to(torch.int64)\n iy = ty.floor().to(torch.int64)\n fx = tx - ix\n fy = ty - iy\n b = a - 1\n\n z = torch.zeros_like(x)\n zx0 = max(ix - b, 0)\n zy0 = max(iy - b, 0)\n zx1 = min(ix + a, 0) + W\n zy1 = min(iy + a, 0) + H\n if zx0 < zx1 and zy0 < zy1:\n taps = torch.arange(a * 2, device=x.device) - b\n filter_x = (sinc(taps - fx) * sinc((taps - fx) / a)).unsqueeze(0)\n filter_y = (sinc(taps - fy) * sinc((taps - fy) / a)).unsqueeze(1)\n y = x\n y = filter2d(y, filter_x / filter_x.sum(), padding=[b, a, 0, 0])\n y = filter2d(y, filter_y / filter_y.sum(), padding=[0, 0, b, a])\n y = y[:, :,\n max(b - iy, 0):H + b + a + min(-iy - a, 0),\n max(b - ix, 0):W + b + a + min(-ix - a, 0)]\n z[:, :, zy0:zy1, zx0:zx1] = y\n\n m = torch.zeros_like(x)\n mx0 = max(ix + a, 0)\n my0 = max(iy + a, 0)\n mx1 = min(ix - b, 0) + W\n my1 = min(iy - b, 0) + H\n if mx0 < mx1 and my0 < my1:\n m[:, :, my0:my1, mx0:mx1] = 1\n return z, m\n\n\ndef rotation_matrix(angle):\n angle = torch.as_tensor(angle).to(torch.float32)\n mat = torch.eye(3, device=angle.device)\n mat[0, 0] = angle.cos()\n mat[0, 1] = angle.sin()\n mat[1, 0] = -angle.sin()\n mat[1, 1] = angle.cos()\n return mat\n\n\ndef lanczos_window(x, a):\n x = x.abs() / a\n return torch.where(x < 1, sinc(x), torch.zeros_like(x))\n\n\ndef construct_affine_bandlimit_filter(mat,\n a=3,\n amax=16,\n aflt=64,\n up=4,\n cutoff_in=1,\n cutoff_out=1):\n assert a <= amax < aflt\n mat = torch.as_tensor(mat).to(torch.float32)\n\n # Construct 2D filter taps in input & output coordinate spaces.\n taps = ((torch.arange(aflt * up * 2 - 1, device=mat.device) + 1) / up -\n aflt).roll(1 - aflt * up)\n yi, xi = torch.meshgrid(taps, taps)\n xo, yo = (torch.stack([xi, yi], dim=2) @ mat[:2, :2].t()).unbind(2)\n\n # Convolution of two oriented 2D sinc filters.\n fin = sinc(xi * cutoff_in) * sinc(yi * cutoff_in)\n fout = sinc(xo * cutoff_out) * sinc(yo * cutoff_out)\n f = torch.fft.ifftn(torch.fft.fftn(fin) * torch.fft.fftn(fout)).real\n\n # Convolution of two oriented 2D Lanczos windows.\n wi = lanczos_window(xi, a) * lanczos_window(yi, a)\n wo = lanczos_window(xo, a) * lanczos_window(yo, a)\n w = torch.fft.ifftn(torch.fft.fftn(wi) * torch.fft.fftn(wo)).real\n\n # Construct windowed FIR filter.\n f = f * w\n\n # Finalize.\n c = (aflt - amax) * up\n f = f.roll([aflt * up - 1] * 2, dims=[0, 1])[c:-c, c:-c]\n f = torch.nn.functional.pad(f,\n [0, 1, 0, 1]).reshape(amax * 2, up, amax * 2,\n up)\n f = f / f.sum([0, 2], keepdim=True) / (up**2)\n f = f.reshape(amax * 2 * up, amax * 2 * up)[:-1, :-1]\n return f\n\n\ndef apply_affine_transformation(x, mat, up=4, **filter_kwargs):\n _N, _C, H, W = x.shape\n mat = torch.as_tensor(mat).to(dtype=torch.float32, device=x.device)\n\n # Construct filter.\n f = construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs)\n assert f.ndim == 2 and f.shape[0] == f.shape[1] and f.shape[0] % 2 == 1\n p = f.shape[0] // 2\n\n # Construct sampling grid.\n theta = mat.inverse()\n theta[:2, 2] *= 2\n theta[0, 2] += 1 / up / W\n theta[1, 2] += 1 / up / H\n theta[0, :] *= W / (W + p / up * 2)\n theta[1, :] *= H / (H + p / up * 2)\n theta = theta[:2, :3].unsqueeze(0).repeat([x.shape[0], 1, 1])\n g = torch.nn.functional.affine_grid(theta, x.shape, align_corners=False)\n\n # Resample image.\n y = upsample2d(input=x, filter=f, up=up, padding=p)\n z = torch.nn.functional.grid_sample(\n y, g, mode='bilinear', padding_mode='zeros', align_corners=False)\n\n # Form mask.\n m = torch.zeros_like(y)\n c = p * 2 + 1\n m[:, :, c:-c, c:-c] = 1\n m = torch.nn.functional.grid_sample(\n m, g, mode='nearest', padding_mode='zeros', align_corners=False)\n return z, m\n\n\ndef apply_fractional_rotation(x, angle, a=3, **filter_kwargs):\n angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device)\n mat = rotation_matrix(angle)\n return apply_affine_transformation(\n x, mat, a=a, amax=a * 2, **filter_kwargs)\n\n\ndef apply_fractional_pseudo_rotation(x, angle, a=3, **filter_kwargs):\n angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device)\n mat = rotation_matrix(-angle)\n f = construct_affine_bandlimit_filter(\n mat, a=a, amax=a * 2, up=1, **filter_kwargs)\n y = filter2d(input=x, filter=f)\n m = torch.zeros_like(y)\n c = f.shape[0] // 2\n m[:, :, c:-c, c:-c] = 1\n return y, m\n" }, { "path": "mmagic/models/editors/swinir/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .swinir_net import SwinIRNet\n\n__all__ = ['SwinIRNet']\n" }, { "path": "mmagic/models/editors/swinir/swinir_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport torch.nn as nn\n\nfrom .swinir_utils import to_2tuple\n\n\nclass PatchEmbed(nn.Module):\n r\"\"\" Image to Patch Embedding\n Args:\n img_size (int): Image size. Default: 224.\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels.\n Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self,\n img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n norm_layer=None):\n super().__init__()\n img_size = to_2tuple(img_size)\n patch_size = to_2tuple(patch_size)\n patches_resolution = [\n img_size[0] // patch_size[0], img_size[1] // patch_size[1]\n ]\n self.img_size = img_size\n self.patch_size = patch_size\n self.patches_resolution = patches_resolution\n self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, Ph, Pw).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = x.flatten(2).transpose(1, 2) # B Ph*Pw C\n if self.norm is not None:\n x = self.norm(x)\n return x\n\n\nclass PatchUnEmbed(nn.Module):\n r\"\"\" Image to Patch Unembedding\n Args:\n img_size (int): Image size. Default: 224.\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels.\n Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self,\n img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n norm_layer=None):\n super().__init__()\n img_size = to_2tuple(img_size)\n patch_size = to_2tuple(patch_size)\n patches_resolution = [\n img_size[0] // patch_size[0], img_size[1] // patch_size[1]\n ]\n self.img_size = img_size\n self.patch_size = patch_size\n self.patches_resolution = patches_resolution\n self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n def forward(self, x, x_size):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n x_size (tuple[int]): Resolution of input feature.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n B, HW, C = x.shape\n x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0],\n x_size[1]) # B Ph*Pw C\n return x\n\n\nclass Upsample(nn.Sequential):\n \"\"\"Upsample module.\n\n Args:\n scale (int): Scale factor. Supported scales: 2^n and 3.\n num_feat (int): Channel number of intermediate features.\n \"\"\"\n\n def __init__(self, scale, num_feat):\n m = []\n if (scale & (scale - 1)) == 0: # scale = 2^n\n for _ in range(int(math.log(scale, 2))):\n m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n m.append(nn.PixelShuffle(2))\n elif scale == 3:\n m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n m.append(nn.PixelShuffle(3))\n else:\n raise ValueError(f'scale {scale} is not supported. '\n 'Supported scales: 2^n and 3.')\n super(Upsample, self).__init__(*m)\n\n\nclass UpsampleOneStep(nn.Sequential):\n \"\"\"UpsampleOneStep module (the difference with Upsample is that it always\n only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters.\n\n Args:\n scale (int): Scale factor. Supported scales: 2^n and 3.\n num_feat (int): Channel number of intermediate features.\n num_out_ch (int): Channel number for PixelShuffle.\n input_resolution (tuple[int], optional): Input resolution.\n Default: None\n \"\"\"\n\n def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):\n self.num_feat = num_feat\n self.input_resolution = input_resolution\n m = []\n m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))\n m.append(nn.PixelShuffle(scale))\n super(UpsampleOneStep, self).__init__(*m)\n" }, { "path": "mmagic/models/editors/swinir/swinir_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import trunc_normal_\n\nfrom mmagic.registry import MODELS\nfrom .swinir_modules import PatchEmbed, PatchUnEmbed, Upsample, UpsampleOneStep\nfrom .swinir_rstb import RSTB\n\n\n@MODELS.register_module()\nclass SwinIRNet(BaseModule):\n r\"\"\" SwinIR\n A PyTorch impl of: `SwinIR: Image Restoration Using Swin Transformer`,\n based on Swin Transformer.\n Ref repo: https://github.com/JingyunLiang/SwinIR\n\n Args:\n img_size (int | tuple(int)): Input image size. Default 64\n patch_size (int | tuple(int)): Patch size. Default: 1\n in_chans (int): Number of input image channels. Default: 3\n embed_dim (int): Patch embedding dimension. Default: 96\n depths (tuple(int)): Depth of each Swin Transformer layer.\n Default: [6, 6, 6, 6]\n num_heads (tuple(int)): Number of attention heads in different layers.\n Default: [6, 6, 6, 6]\n window_size (int): Window size. Default: 7\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n Default: True\n qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n Default: None\n drop_rate (float): Dropout rate. Default: 0\n attn_drop_rate (float): Attention dropout rate. Default: 0\n drop_path_rate (float): Stochastic depth rate. Default: 0.1\n norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n ape (bool): If True, add absolute position embedding to the\n patch embedding. Default: False\n patch_norm (bool): If True, add normalization after patch embedding.\n Default: True\n use_checkpoint (bool): Whether to use checkpointing to save memory.\n Default: False\n upscale (int): Upscale factor. 2/3/4/8 for image SR, 1 for denoising\n and compress artifact reduction. Default: 2\n img_range (float): Image range. 1. or 255. Default: 1.0\n upsampler (string, optional): The reconstruction module.\n 'pixelshuffle' / 'pixelshuffledirect' /'nearest+conv'/None.\n Default: ''\n resi_connection (string): The convolutional block before residual\n connection. '1conv'/'3conv'. Default: '1conv'\n \"\"\"\n\n def __init__(self,\n img_size=64,\n patch_size=1,\n in_chans=3,\n embed_dim=96,\n depths=[6, 6, 6, 6],\n num_heads=[6, 6, 6, 6],\n window_size=7,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.,\n attn_drop_rate=0.,\n drop_path_rate=0.1,\n norm_layer=nn.LayerNorm,\n ape=False,\n patch_norm=True,\n use_checkpoint=False,\n upscale=2,\n img_range=1.,\n upsampler='',\n resi_connection='1conv',\n **kwargs):\n super(SwinIRNet, self).__init__()\n num_in_ch = in_chans\n num_out_ch = in_chans\n num_feat = 64\n self.img_range = img_range\n if in_chans == 3:\n rgb_mean = (0.4488, 0.4371, 0.4040)\n self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n else:\n self.mean = torch.zeros(1, 1, 1, 1)\n self.upscale = upscale\n self.upsampler = upsampler\n self.window_size = window_size\n\n # 1, shallow feature extraction\n self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n\n # 2, deep feature extraction\n self.num_layers = len(depths)\n self.embed_dim = embed_dim\n self.ape = ape\n self.patch_norm = patch_norm\n self.num_features = embed_dim\n self.mlp_ratio = mlp_ratio\n\n # split image into non-overlapping patches\n self.patch_embed = PatchEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=embed_dim,\n embed_dim=embed_dim,\n norm_layer=norm_layer if self.patch_norm else None)\n num_patches = self.patch_embed.num_patches\n patches_resolution = self.patch_embed.patches_resolution\n self.patches_resolution = patches_resolution\n\n # merge non-overlapping patches into image\n self.patch_unembed = PatchUnEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=embed_dim,\n embed_dim=embed_dim,\n norm_layer=norm_layer if self.patch_norm else None)\n\n # absolute position embedding\n if self.ape:\n self.absolute_pos_embed = nn.Parameter(\n torch.zeros(1, num_patches, embed_dim))\n trunc_normal_(self.absolute_pos_embed, std=.02)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n # stochastic depth decay rule\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n ]\n\n # build Residual Swin Transformer blocks (RSTB)\n self.layers = nn.ModuleList()\n for i_layer in range(self.num_layers):\n layer = RSTB(\n dim=embed_dim,\n input_resolution=(patches_resolution[0],\n patches_resolution[1]),\n depth=depths[i_layer],\n num_heads=num_heads[i_layer],\n window_size=window_size,\n mlp_ratio=self.mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop_rate,\n attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],\n norm_layer=norm_layer,\n downsample=None,\n use_checkpoint=use_checkpoint,\n img_size=img_size,\n patch_size=patch_size,\n resi_connection=resi_connection)\n self.layers.append(layer)\n self.norm = norm_layer(self.num_features)\n\n # build the last conv layer in deep feature extraction\n if resi_connection == '1conv':\n self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n elif resi_connection == '3conv':\n # to save parameters and memory\n self.conv_after_body = nn.Sequential(\n nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))\n\n # 3, high quality image reconstruction\n if self.upsampler == 'pixelshuffle':\n # for classical SR\n self.conv_before_upsample = nn.Sequential(\n nn.Conv2d(embed_dim, num_feat, 3, 1, 1),\n nn.LeakyReLU(inplace=True))\n self.upsample = Upsample(upscale, num_feat)\n self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n elif self.upsampler == 'pixelshuffledirect':\n # for lightweight SR (to save parameters)\n self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,\n (patches_resolution[0],\n patches_resolution[1]))\n elif self.upsampler == 'nearest+conv':\n # for real-world SR (less artifacts)\n self.conv_before_upsample = nn.Sequential(\n nn.Conv2d(embed_dim, num_feat, 3, 1, 1),\n nn.LeakyReLU(inplace=True))\n self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n if self.upscale == 4:\n self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n else:\n # for image denoising and JPEG compression artifact reduction\n self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)\n\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n @torch.jit.ignore\n def no_weight_decay(self):\n return {'absolute_pos_embed'}\n\n @torch.jit.ignore\n def no_weight_decay_keywords(self):\n return {'relative_position_bias_table'}\n\n def check_image_size(self, x):\n \"\"\"Check image size and pad images so that it has enough dimension do\n window size.\n\n args:\n x: input tensor image with (B, C, H, W) shape.\n \"\"\"\n _, _, h, w = x.size()\n mod_pad_h = (self.window_size -\n h % self.window_size) % self.window_size\n mod_pad_w = (self.window_size -\n w % self.window_size) % self.window_size\n x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')\n return x\n\n def forward_features(self, x):\n \"\"\"Forward function of Deep Feature Extraction.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x_size = (x.shape[2], x.shape[3])\n x = self.patch_embed(x)\n if self.ape:\n x = x + self.absolute_pos_embed\n x = self.pos_drop(x)\n\n for layer in self.layers:\n x = layer(x, x_size)\n\n x = self.norm(x) # B L C\n x = self.patch_unembed(x, x_size)\n\n return x\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, C, H, W).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n H, W = x.shape[2:]\n x = self.check_image_size(x)\n\n self.mean = self.mean.type_as(x)\n x = (x - self.mean) * self.img_range\n\n if self.upsampler == 'pixelshuffle':\n # for classical SR\n x = self.conv_first(x)\n x = self.conv_after_body(self.forward_features(x)) + x\n x = self.conv_before_upsample(x)\n x = self.conv_last(self.upsample(x))\n elif self.upsampler == 'pixelshuffledirect':\n # for lightweight SR\n x = self.conv_first(x)\n x = self.conv_after_body(self.forward_features(x)) + x\n x = self.upsample(x)\n elif self.upsampler == 'nearest+conv':\n # for real-world SR\n x = self.conv_first(x)\n x = self.conv_after_body(self.forward_features(x)) + x\n x = self.conv_before_upsample(x)\n x = self.lrelu(\n self.conv_up1(\n torch.nn.functional.interpolate(\n x, scale_factor=2, mode='nearest')))\n if self.upscale == 4:\n x = self.lrelu(\n self.conv_up2(\n torch.nn.functional.interpolate(\n x, scale_factor=2, mode='nearest')))\n x = self.conv_last(self.lrelu(self.conv_hr(x)))\n else:\n # for image denoising and JPEG compression artifact reduction\n x_first = self.conv_first(x)\n res = self.conv_after_body(\n self.forward_features(x_first)) + x_first\n x = x + self.conv_last(res)\n\n x = x / self.img_range + self.mean\n\n return x[:, :, :H * self.upscale, :W * self.upscale]\n" }, { "path": "mmagic/models/editors/swinir/swinir_rstb.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint as checkpoint\nfrom mmengine.model.weight_init import trunc_normal_\n\nfrom .swinir_modules import PatchEmbed, PatchUnEmbed\nfrom .swinir_utils import (drop_path, to_2tuple, window_partition,\n window_reverse)\n\n\nclass DropPath(nn.Module):\n \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of\n residual blocks).\"\"\"\n\n def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n self.scale_by_keep = scale_by_keep\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)\n\n def extra_repr(self):\n return f'drop_prob={round(self.drop_prob, 3):0.3f}'\n\n\nclass Mlp(nn.Module):\n \"\"\"Multilayer Perceptron layer.\n\n Args:\n in_features (int): Number of input channels.\n hidden_features (int | None, optional): Number of hidden layer\n channels. Default: None\n out_features (int | None, optional): Number of output channels.\n Default: None\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n \"\"\"\n\n def __init__(self,\n in_features,\n hidden_features=None,\n out_features=None,\n act_layer=nn.GELU,\n drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass WindowAttention(nn.Module):\n r\"\"\" Window based multi-head self attention (W-MSA)\n module with relative position bias.\n It supports both of shifted and non-shifted window.\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to\n query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale\n of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight.\n Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(self,\n dim,\n window_size,\n num_heads,\n qkv_bias=True,\n qk_scale=None,\n attn_drop=0.,\n proj_drop=0.):\n\n super().__init__()\n self.dim = dim\n self.window_size = window_size # Wh, Ww\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim**-0.5\n\n # define a parameter table of relative position bias\n # 2*Wh-1 * 2*Ww-1, nH\n self.relative_position_bias_table = nn.Parameter(\n torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),\n num_heads))\n\n # get pair-wise relative position index\n # for each token inside the window\n coords_h = torch.arange(self.window_size[0])\n coords_w = torch.arange(self.window_size[1])\n coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww\n coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww\n relative_coords = \\\n coords_flatten[:, :, None] - coords_flatten[:, None, :]\n # Wh*Ww, Wh*Ww, 2\n relative_coords = relative_coords.permute(1, 2, 0).contiguous()\n # shift to start from 0\n relative_coords[:, :, 0] += self.window_size[0] - 1\n relative_coords[:, :, 1] += self.window_size[1] - 1\n relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww\n self.register_buffer('relative_position_index',\n relative_position_index)\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n\n self.proj_drop = nn.Dropout(proj_drop)\n\n trunc_normal_(self.relative_position_bias_table, std=.02)\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x, mask=None):\n \"\"\"\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of\n (num_windows, Wh*Ww, Wh*Ww) or None\n \"\"\"\n B_, N, C = x.shape\n qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,\n C // self.num_heads).permute(2, 0, 3, 1, 4)\n # make torchscript happy (cannot use tensor as tuple)\n q, k, v = qkv[0], qkv[1], qkv[2]\n\n q = q * self.scale\n attn = (q @ k.transpose(-2, -1))\n\n relative_position_bias = self.relative_position_bias_table[\n self.relative_position_index.view(-1)].view(\n self.window_size[0] * self.window_size[1],\n self.window_size[0] * self.window_size[1],\n -1) # Wh*Ww,Wh*Ww,nH\n relative_position_bias = relative_position_bias.permute(\n 2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N,\n N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n def extra_repr(self) -> str:\n return f'dim={self.dim}, window_size={self.window_size}, ' \\\n f'num_heads={self.num_heads}'\n\n\nclass SwinTransformerBlock(nn.Module):\n r\"\"\" Swin Transformer Block.\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int]): Input resolution.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias\n to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk\n scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer.\n Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self,\n dim,\n input_resolution,\n num_heads,\n window_size=7,\n shift_size=0,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop=0.,\n attn_drop=0.,\n drop_path=0.,\n act_layer=nn.GELU,\n norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.num_heads = num_heads\n self.window_size = window_size\n self.shift_size = shift_size\n self.mlp_ratio = mlp_ratio\n if min(self.input_resolution) <= self.window_size:\n # if window size is larger than input resolution,\n # we don't partition windows\n self.shift_size = 0\n self.window_size = min(self.input_resolution)\n assert 0 <= self.shift_size < self.window_size, \\\n 'shift_size must in 0-window_size'\n\n self.norm1 = norm_layer(dim)\n self.attn = WindowAttention(\n dim,\n window_size=to_2tuple(self.window_size),\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n attn_drop=attn_drop,\n proj_drop=drop)\n\n self.drop_path = DropPath(\n drop_path) if drop_path > 0. else nn.Identity()\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(\n in_features=dim,\n hidden_features=mlp_hidden_dim,\n act_layer=act_layer,\n drop=drop)\n\n if self.shift_size > 0:\n attn_mask = self.calculate_mask(self.input_resolution)\n else:\n attn_mask = None\n\n self.register_buffer('attn_mask', attn_mask)\n\n def calculate_mask(self, x_size):\n # calculate attention mask for SW-MSA\n \"\"\"Calculate attention mask for SW-MSA.\n\n Args:\n x_size (tuple[int]): Resolution of input feature.\n\n Returns:\n Tensor: Attention mask\n \"\"\"\n H, W = x_size\n img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1\n h_slices = (slice(0, -self.window_size),\n slice(-self.window_size,\n -self.shift_size), slice(-self.shift_size, None))\n w_slices = (slice(0, -self.window_size),\n slice(-self.window_size,\n -self.shift_size), slice(-self.shift_size, None))\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(\n img_mask, self.window_size) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1,\n self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0,\n float(-100.0)).masked_fill(\n attn_mask == 0, float(0.0))\n\n return attn_mask\n\n def forward(self, x, x_size):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n x_size (tuple[int]): Resolution of input feature.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n H, W = x_size\n B, L, C = x.shape\n # assert L == H * W, \"input feature has wrong size\"\n\n shortcut = x\n x = self.norm1(x)\n x = x.view(B, H, W, C)\n\n # cyclic shift\n if self.shift_size > 0:\n shifted_x = torch.roll(\n x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n else:\n shifted_x = x\n\n # partition windows\n x_windows = window_partition(\n shifted_x, self.window_size) # nW*B, window_size, window_size, C\n x_windows = x_windows.view(-1, self.window_size * self.window_size,\n C) # nW*B, window_size*window_size, C\n\n # W-MSA/SW-MSA (to be compatible for testing on images\n # whose shapes are the multiple of window size\n if self.input_resolution == x_size:\n attn_windows = self.attn(\n x_windows,\n mask=self.attn_mask) # nW*B, window_size*window_size, C\n else:\n attn_windows = self.attn(\n x_windows, mask=self.calculate_mask(x_size).to(x.device))\n\n # merge windows\n attn_windows = attn_windows.view(-1, self.window_size,\n self.window_size, C)\n shifted_x = window_reverse(attn_windows, self.window_size, H,\n W) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(\n shifted_x,\n shifts=(self.shift_size, self.shift_size),\n dims=(1, 2))\n else:\n x = shifted_x\n x = x.view(B, H * W, C)\n\n # FFN\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n def extra_repr(self) -> str:\n return f'dim={self.dim}, ' \\\n f'input_resolution={self.input_resolution}, ' \\\n f'num_heads={self.num_heads}, ' \\\n f'window_size={self.window_size}, ' \\\n f'shift_size={self.shift_size}, ' \\\n f'mlp_ratio={self.mlp_ratio}'\n\n\nclass BasicLayer(nn.Module):\n \"\"\"A basic Swin Transformer layer for one stage.\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int]): Input resolution.\n depth (int): Number of blocks.\n num_heads (int): Number of attention heads.\n window_size (int): Local window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias\n to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk\n scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate.\n Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer.\n Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the\n end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory.\n Default: False.\n \"\"\"\n\n def __init__(self,\n dim,\n input_resolution,\n depth,\n num_heads,\n window_size,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop=0.,\n attn_drop=0.,\n drop_path=0.,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False):\n\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n SwinTransformerBlock(\n dim=dim,\n input_resolution=input_resolution,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i]\n if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer) for i in range(depth)\n ])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(\n input_resolution, dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, x_size):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n x_size (tuple[int]): Resolution of input feature.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n for blk in self.blocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, x_size)\n else:\n x = blk(x, x_size)\n if self.downsample is not None:\n x = self.downsample(x)\n return x\n\n def extra_repr(self) -> str:\n return f'dim={self.dim}, ' \\\n f'input_resolution={self.input_resolution}, ' \\\n f'depth={self.depth}'\n\n\nclass RSTB(nn.Module):\n \"\"\"Residual Swin Transformer Block (RSTB).\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int]): Input resolution.\n depth (int): Number of blocks.\n num_heads (int): Number of attention heads.\n window_size (int): Local window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n Default: 4.0\n qkv_bias (bool, optional): If True, add a learnable bias to\n query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale\n of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate.\n Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer.\n Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the\n end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory.\n Default: False.\n img_size (int): Input image size. Default: 224\n patch_size (int): Patch size. Default: 4\n resi_connection (string): The convolutional block before\n residual connection. Default: '1conv'\n \"\"\"\n\n def __init__(self,\n dim,\n input_resolution,\n depth,\n num_heads,\n window_size,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop=0.,\n attn_drop=0.,\n drop_path=0.,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False,\n img_size=224,\n patch_size=4,\n resi_connection='1conv'):\n super(RSTB, self).__init__()\n\n self.dim = dim\n self.input_resolution = input_resolution\n\n self.residual_group = BasicLayer(\n dim=dim,\n input_resolution=input_resolution,\n depth=depth,\n num_heads=num_heads,\n window_size=window_size,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path,\n norm_layer=norm_layer,\n downsample=downsample,\n use_checkpoint=use_checkpoint)\n\n if resi_connection == '1conv':\n self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n elif resi_connection == '3conv':\n # to save parameters and memory\n self.conv = nn.Sequential(\n nn.Conv2d(dim, dim // 4, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n nn.Conv2d(dim // 4, dim, 3, 1, 1))\n\n self.patch_embed = PatchEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=0,\n embed_dim=dim,\n norm_layer=None)\n\n self.patch_unembed = PatchUnEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=0,\n embed_dim=dim,\n norm_layer=None)\n\n def forward(self, x, x_size):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (B, L, C).\n x_size (tuple[int]): Resolution of input feature.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n return self.patch_embed(\n self.conv(\n self.patch_unembed(self.residual_group(x, x_size),\n x_size))) + x\n" }, { "path": "mmagic/models/editors/swinir/swinir_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport collections.abc\nfrom itertools import repeat\n\n\n# From PyTorch internals\ndef _ntuple(n):\n \"\"\"A `to_tuple` function generator. It returns a function, this function\n will repeat the input to a tuple of length ``n`` if the input is not an\n Iterable object, otherwise, return the input directly.\n\n Args:\n n (int): The number of the target length.\n \"\"\"\n\n def parse(x):\n if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):\n return x\n return tuple(repeat(x, n))\n\n return parse\n\n\nto_2tuple = _ntuple(2)\n\n\ndef drop_path(x,\n drop_prob: float = 0.,\n training: bool = False,\n scale_by_keep: bool = True):\n \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of\n residual blocks).\n\n This is the same as the DropConnect impl I created for\n EfficientNet, etc networks, however, the original name is misleading\n as 'Drop Connect' is a different form of dropout in a separate paper...\n See discussion:\n https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956\n I've opted for changing the layer and argument names to 'drop path'\n rather than mix DropConnect as a layer name and use\n 'survival rate' as the argument.\n \"\"\"\n if drop_prob == 0. or not training:\n return x\n keep_prob = 1 - drop_prob\n # work with diff dim tensors, not just 2D ConvNets\n shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)\n random_tensor = x.new_empty(shape).bernoulli_(keep_prob)\n if keep_prob > 0.0 and scale_by_keep:\n random_tensor.div_(keep_prob)\n return x * random_tensor\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size,\n C)\n windows = x.permute(0, 1, 3, 2, 4,\n 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size,\n window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n" }, { "path": "mmagic/models/editors/tdan/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .tdan import TDAN\nfrom .tdan_net import TDANNet\n\n__all__ = ['TDAN', 'TDANNet']\n" }, { "path": "mmagic/models/editors/tdan/tdan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models import BaseEditModel\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass TDAN(BaseEditModel):\n \"\"\"TDAN model for video super-resolution.\n\n Paper:\n TDAN: Temporally-Deformable Alignment Network for Video Super-\n Resolution, CVPR, 2020\n\n Args:\n generator (dict): Config for the generator structure.\n pixel_loss (dict): Config for pixel-wise loss.\n lq_pixel_loss (dict): Config for pixel-wise loss for the LQ images.\n train_cfg (dict): Config for training. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`.\n \"\"\"\n\n def __init__(self,\n generator,\n pixel_loss,\n lq_pixel_loss,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n super().__init__(\n generator=generator,\n pixel_loss=pixel_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.lq_pixel_loss = MODELS.build(lq_pixel_loss)\n\n def forward_train(self, inputs, data_samples=None, **kwargs):\n \"\"\"Forward training. Returns dict of losses of training.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n feats, aligned_img = self.forward_tensor(\n inputs, data_samples, training=True, **kwargs)\n batch_gt_data = data_samples.gt_img\n\n losses = dict()\n # loss on the HR image\n losses['loss_pix'] = self.pixel_loss(feats, batch_gt_data)\n # loss on the aligned LR images\n t = aligned_img.size(1)\n lq_ref = inputs[:,\n t // 2:t // 2 + 1, :, :, :].expand(-1, t, -1, -1, -1)\n loss_pix_lq = self.lq_pixel_loss(aligned_img, lq_ref)\n losses['loss_pix_lq'] = loss_pix_lq\n\n return losses\n\n def forward_tensor(self,\n inputs,\n data_samples=None,\n training=False,\n **kwargs):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n training (bool): Whether is training. Default: False.\n\n Returns:\n (Tensor | List[Tensor]): results of forward inference and\n forward train.\n \"\"\"\n\n outputs = self.generator(inputs, **kwargs)\n\n return outputs if training else outputs[0]\n" }, { "path": "mmagic/models/editors/tdan/tdan_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmcv.ops import DeformConv2d, DeformConv2dPack, deform_conv2d\nfrom mmengine.model import BaseModule\nfrom mmengine.model.weight_init import constant_init\nfrom torch.nn.modules.utils import _pair\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass TDANNet(BaseModule):\n \"\"\"TDAN network structure for video super-resolution.\n\n Support only x4 upsampling.\n\n Paper:\n TDAN: Temporally-Deformable Alignment Network for Video Super-\n Resolution, CVPR, 2020\n\n Args:\n in_channels (int): Number of channels of the input image. Default: 3.\n mid_channels (int): Number of channels of the intermediate features.\n Default: 64.\n out_channels (int): Number of channels of the output image. Default: 3.\n num_blocks_before_align (int): Number of residual blocks before\n temporal alignment. Default: 5.\n num_blocks_after_align (int): Number of residual blocks after\n temporal alignment. Default: 10.\n \"\"\"\n\n def __init__(self,\n in_channels=3,\n mid_channels=64,\n out_channels=3,\n num_blocks_before_align=5,\n num_blocks_after_align=10):\n\n super().__init__()\n\n self.feat_extract = nn.Sequential(\n ConvModule(in_channels, mid_channels, 3, padding=1),\n make_layer(\n ResidualBlockNoBN,\n num_blocks_before_align,\n mid_channels=mid_channels))\n\n self.feat_aggregate = nn.Sequential(\n nn.Conv2d(mid_channels * 2, mid_channels, 3, padding=1, bias=True),\n DeformConv2dPack(\n mid_channels, mid_channels, 3, padding=1, deform_groups=8),\n DeformConv2dPack(\n mid_channels, mid_channels, 3, padding=1, deform_groups=8))\n self.align_1 = AugmentedDeformConv2dPack(\n mid_channels, mid_channels, 3, padding=1, deform_groups=8)\n self.align_2 = DeformConv2dPack(\n mid_channels, mid_channels, 3, padding=1, deform_groups=8)\n self.to_rgb = nn.Conv2d(mid_channels, 3, 3, padding=1, bias=True)\n\n self.reconstruct = nn.Sequential(\n ConvModule(in_channels * 5, mid_channels, 3, padding=1),\n make_layer(\n ResidualBlockNoBN,\n num_blocks_after_align,\n mid_channels=mid_channels),\n PixelShufflePack(mid_channels, mid_channels, 2, upsample_kernel=3),\n PixelShufflePack(mid_channels, mid_channels, 2, upsample_kernel=3),\n nn.Conv2d(mid_channels, out_channels, 3, 1, 1, bias=False))\n\n def forward(self, lrs):\n \"\"\"Forward function for TDANNet.\n\n Args:\n lrs (Tensor): Input LR sequence with shape (n, t, c, h, w).\n\n Returns:\n tuple[Tensor]: Output HR image with shape (n, c, 4h, 4w) and\n aligned LR images with shape (n, t, c, h, w).\n \"\"\"\n n, t, c, h, w = lrs.size()\n lr_center = lrs[:, t // 2, :, :, :] # LR center frame\n\n # extract features\n feats = self.feat_extract(lrs.view(-1, c, h, w)).view(n, t, -1, h, w)\n\n # alignment of LR frames\n feat_center = feats[:, t // 2, :, :, :].contiguous()\n aligned_lrs = []\n for i in range(0, t):\n if i == t // 2:\n aligned_lrs.append(lr_center)\n else:\n feat_neig = feats[:, i, :, :, :].contiguous()\n feat_agg = torch.cat([feat_center, feat_neig], dim=1)\n feat_agg = self.feat_aggregate(feat_agg)\n\n aligned_feat = self.align_2(self.align_1(feat_neig, feat_agg))\n aligned_lrs.append(self.to_rgb(aligned_feat))\n\n aligned_lrs = torch.cat(aligned_lrs, dim=1)\n\n # output HR center frame and the aligned LR frames\n return self.reconstruct(aligned_lrs), aligned_lrs.view(n, t, c, h, w)\n\n\nclass AugmentedDeformConv2dPack(DeformConv2d):\n \"\"\"Augmented Deformable Convolution Pack.\n\n Different from DeformConv2dPack, which generates offsets from the\n preceding feature, this AugmentedDeformConv2dPack takes another feature to\n generate the offsets.\n\n Args:\n in_channels (int): Number of channels in the input feature.\n out_channels (int): Number of channels produced by the convolution.\n kernel_size (int or tuple[int]): Size of the convolving kernel.\n stride (int or tuple[int]): Stride of the convolution. Default: 1.\n padding (int or tuple[int]): Zero-padding added to both sides of the\n input. Default: 0.\n dilation (int or tuple[int]): Spacing between kernel elements.\n Default: 1.\n groups (int): Number of blocked connections from input channels to\n output channels. Default: 1.\n deform_groups (int): Number of deformable group partitions.\n bias (bool or str): If specified as `auto`, it will be decided by the\n norm_cfg. Bias will be set as True if norm_cfg is None, otherwise\n False.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.conv_offset = nn.Conv2d(\n self.in_channels,\n self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1],\n kernel_size=self.kernel_size,\n stride=_pair(self.stride),\n padding=_pair(self.padding),\n bias=True)\n\n self.init_offset()\n\n def init_offset(self):\n \"\"\"Init constant offset.\"\"\"\n constant_init(self.conv_offset, val=0, bias=0)\n\n def forward(self, x, extra_feat):\n \"\"\"Forward function.\"\"\"\n offset = self.conv_offset(extra_feat)\n return deform_conv2d(x, offset, self.weight, self.stride, self.padding,\n self.dilation, self.groups, self.deform_groups)\n" }, { "path": "mmagic/models/editors/textual_inversion/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .textual_inversion import TextualInversion\n\n__all__ = ['TextualInversion']\n" }, { "path": "mmagic/models/editors/textual_inversion/textual_inversion.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Optional, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.logging import MMLogger\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ..stable_diffusion.stable_diffusion import StableDiffusion\n\nlogger = MMLogger.get_current_instance()\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass TextualInversion(StableDiffusion):\n \"\"\"Implementation of `An Image is Worth One Word: Personalizing Text-to-\n Image Generation using Textual Inversion.\n\n `_ (Textual Inversion).\n\n Args:\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n tokenizer (str): The **name** for CLIP tokenizer.\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n dtype (str, optional): The dtype for the model. Defaults to 'fp16'.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa\n Defaults to 0.\n tomesd_cfg (dict, optional): The config for TOMESD. Please refers to\n https://github.com/dbolya/tomesd and\n https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/utils/tome_utils.py for detail. # noqa\n Defaults to None.\n initialize_token (str, optional): The initialization token for textual\n embedding to train. Defaults to None.\n num_vefctor_per_token (int): The length of the learnable embedding.\n Defaults to 1.\n val_prompts (Union[str, List[str]], optional): The prompts for\n validation. Defaults to None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Defaults to\n dict(type='DataPreprocessor').\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Defaults to None/\n \"\"\"\n\n def __init__(self,\n placeholder_token: str,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n dtype: Optional[str] = None,\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n initialize_token: Optional[str] = None,\n num_vectors_per_token: int = 1,\n val_prompts=None,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n init_cfg: Optional[dict] = None):\n\n super().__init__(vae, text_encoder, tokenizer, unet, scheduler,\n test_scheduler, dtype, enable_xformers,\n noise_offset_weight, tomesd_cfg, data_preprocessor,\n init_cfg)\n\n self.val_prompts = val_prompts\n self.placeholder_token = placeholder_token\n self.add_tokens(placeholder_token, initialize_token,\n num_vectors_per_token)\n self.prepare_models()\n\n def prepare_models(self):\n \"\"\"Disable gradient for untrainable modules to save memory.\"\"\"\n self.vae.requires_grad_(False)\n self.unet.requires_grad_(False)\n self.text_encoder.set_only_embedding_trainable()\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n if self.val_prompts is None:\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples.split() * len(prompt)\n data_samples = DataSample.stack(data_samples.split() * len(prompt))\n\n unet_dtype = next(self.unet.parameters()).dtype\n self.unet.to(self.dtype)\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n self.unet.to(unet_dtype)\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n if self.val_prompts is None:\n data = self.data_preprocessor(data)\n data_samples = data['data_samples']\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples = DataSample.stack(data['data_samples'] * len(prompt))\n\n unet_dtype = next(self.unet.parameters()).dtype\n self.unet.to(self.dtype)\n\n output = self.infer(prompt, return_type='tensor')\n samples = output['samples']\n\n self.unet.to(unet_dtype)\n\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n def add_tokens(self,\n placeholder_token: str,\n initialize_token: str = None,\n num_vectors_per_token: int = 1):\n \"\"\"Add token for training.\n\n # TODO: support add tokens as dict, then we can load pretrained tokens.\n \"\"\"\n self.tokenizer.add_placeholder_token(\n placeholder_token, num_vec_per_token=num_vectors_per_token)\n\n self.text_encoder.set_embedding_layer()\n embedding_layer = self.text_encoder.get_embedding_layer()\n assert embedding_layer is not None, (\n 'Do not support get embedding layer for current text encoder. '\n 'Please check your configuration.')\n\n if initialize_token:\n init_id = self.tokenizer(initialize_token).input_ids[1]\n initialize_embedding = embedding_layer.weight[init_id]\n initialize_embedding = initialize_embedding[None, ...].repeat(\n num_vectors_per_token, 1)\n else:\n emb_dim = embedding_layer.weight.shape[1]\n initialize_embedding = torch.zeros(num_vectors_per_token, emb_dim)\n\n token_info = self.tokenizer.get_token_info(placeholder_token)\n token_info['embedding'] = initialize_embedding\n token_info['trainable'] = True\n self.token_info = token_info\n embedding_layer.add_embeddings(token_info)\n\n def train_step(self, data, optim_wrapper):\n \"\"\"Training step.\"\"\"\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n\n vae = self.vae.module if hasattr(self.vae, 'module') else self.vae\n vae_dtype = next(vae.parameters()).dtype\n unet_dtype = next(self.unet.parameters()).dtype\n\n with optim_wrapper.optim_context(self.unet):\n image = inputs # image for new concept\n prompt = data_samples.prompt\n num_batches = image.shape[0]\n\n image = image.to(vae_dtype)\n latents = vae.encode(image).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n noise = torch.randn_like(latents)\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n\n noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)\n\n input_ids = self.tokenizer(\n prompt,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n\n encoder_hidden_states = self.text_encoder(input_ids)[0]\n\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n model_output = self.unet(\n noisy_latents.to(unet_dtype),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.to(unet_dtype))\n model_pred = model_output['sample']\n\n loss_dict = dict()\n\n # calculate loss in FP32\n loss = F.mse_loss(model_pred.float(), gt.float())\n loss_dict['loss'] = loss\n\n parsed_loss, log_vars = self.parse_losses(loss_dict)\n optim_wrapper.update_params(parsed_loss)\n\n return log_vars\n" }, { "path": "mmagic/models/editors/tof/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .tof_vfi_net import TOFlowVFINet, ToFResBlock\nfrom .tof_vsr_net import TOFlowVSRNet\n\n__all__ = ['TOFlowVFINet', 'TOFlowVSRNet', 'ToFResBlock']\n" }, { "path": "mmagic/models/editors/tof/tof_vfi_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine import MMLogger\nfrom mmengine.model import BaseModule\nfrom mmengine.runner import load_checkpoint\n\nfrom mmagic.models.utils import flow_warp\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass TOFlowVFINet(BaseModule):\n \"\"\"PyTorch implementation of TOFlow for video frame interpolation.\n\n Paper: Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018\n Code reference:\n\n 1. https://github.com/anchen1011/toflow\n 2. https://github.com/Coldog2333/pytoflow\n\n Args:\n rgb_mean (list[float]): Image mean in RGB orders.\n Default: [0.485, 0.456, 0.406]\n rgb_std (list[float]): Image std in RGB orders.\n Default: [0.229, 0.224, 0.225]\n flow_cfg (dict): Config of SPyNet.\n Default: dict(norm_cfg=None, pretrained=None)\n init_cfg (dict, optional): Initialization config dict. Default: None.\n \"\"\"\n\n def __init__(self,\n rgb_mean=[0.485, 0.456, 0.406],\n rgb_std=[0.229, 0.224, 0.225],\n flow_cfg=dict(norm_cfg=None, pretrained=None),\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n # The mean and std are for img with range (0, 1)\n self.register_buffer('mean', torch.Tensor(rgb_mean).view(1, -1, 1, 1))\n self.register_buffer('std', torch.Tensor(rgb_std).view(1, -1, 1, 1))\n\n # flow estimation module\n self.spynet = SPyNet(**flow_cfg)\n\n # reconstruction module\n self.resnet = ToFResBlock()\n\n def forward(self, imgs):\n \"\"\"\n Args:\n imgs: Input frames with shape of (b, 2, 3, h, w).\n\n Returns:\n Tensor: Interpolated frame with shape of (b, 3, h, w).\n \"\"\"\n\n flow_10 = self.spynet(imgs[:, 0], imgs[:, 1]).permute(0, 2, 3, 1)\n flow_01 = self.spynet(imgs[:, 1], imgs[:, 0]).permute(0, 2, 3, 1)\n\n warp_frame0 = flow_warp(imgs[:, 0], flow_01 / 2)\n warp_frame1 = flow_warp(imgs[:, 1], flow_10 / 2)\n\n warp_frames = torch.stack([warp_frame0, warp_frame1], dim=1)\n output = self.resnet(warp_frames)\n\n return output\n\n\nclass BasicModule(nn.Module):\n \"\"\"Basic module of SPyNet.\n\n Note that unlike the common spynet architecture, the basic module\n here could contain batch normalization.\n\n Args:\n norm_cfg (dict | None): Config of normalization.\n \"\"\"\n\n def __init__(self, norm_cfg):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(\n in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=norm_cfg,\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n\n Returns:\n Tensor: Estimated flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\nclass SPyNet(nn.Module):\n \"\"\"SPyNet architecture.\n\n Note that this implementation is specifically for TOFlow. It differs from\n the common SPyNet in the following aspects:\n 1. The basic modules in paper of TOFlow contain BatchNorm.\n 2. Normalization and denormalization are not done here, as\n they are done in TOFlow.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network\n Code reference:\n https://github.com/Coldog2333/pytoflow\n\n Args:\n norm_cfg (dict | None): Config of normalization.\n pretrained (str): path for pre-trained SPyNet. Default: None.\n \"\"\"\n\n def __init__(self, norm_cfg, pretrained=None):\n super().__init__()\n\n self.basic_module = nn.ModuleList(\n [BasicModule(norm_cfg) for _ in range(4)])\n\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=True, logger=logger)\n elif pretrained is not None:\n raise TypeError('[pretrained] should be str or None, '\n f'but got {type(pretrained)}.')\n\n def forward(self, ref, supp):\n \"\"\"\n Args:\n ref (Tensor): Reference image with shape of (b, 3, h, w).\n supp: The supporting image to be warped: (b, 3, h, w).\n\n Returns:\n Tensor: Estimated optical flow: (b, 2, h, w).\n \"\"\"\n\n num_batches, _, h, w = ref.size()\n ref = [ref]\n supp = [supp]\n\n # generate downsampled frames\n for _ in range(3):\n ref.insert(\n 0,\n F.avg_pool2d(\n input=ref[0],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.insert(\n 0,\n F.avg_pool2d(\n input=supp[0],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n\n # flow computation\n flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16)\n for i in range(4):\n flow_up = F.interpolate(\n input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n flow = flow_up + self.basic_module[i](\n torch.cat([\n ref[i],\n flow_warp(\n supp[i],\n flow_up.permute(0, 2, 3, 1),\n padding_mode='border'), flow_up\n ], 1))\n\n return flow\n\n\nclass ToFResBlock(nn.Module):\n \"\"\"ResNet architecture.\n\n Three-layers ResNet/ResBlock\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n self.res_block = nn.Sequential(\n nn.Conv2d(6, 64, kernel_size=9, stride=1, padding=4),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=1, stride=1, padding=0),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 3, kernel_size=1, stride=1, padding=0))\n\n def forward(self, frames):\n \"\"\"\n Args:\n frames (Tensor): Tensor with shape of (b, 2, 3, h, w).\n\n Returns:\n Tensor: Interpolated frame with shape of (b, 3, h, w).\n \"\"\"\n\n num_batches, _, _, h, w = frames.size()\n average = frames.mean(dim=1)\n x = frames.view(num_batches, -1, h, w)\n result = self.res_block(x)\n return result + average\n" }, { "path": "mmagic/models/editors/tof/tof_vsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.utils import flow_warp\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass TOFlowVSRNet(BaseModule):\n \"\"\"PyTorch implementation of TOFlow.\n\n In TOFlow, the LR frames are pre-upsampled and have the same size with\n the GT frames.\n\n Paper: Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018\n Code reference:\n\n 1. https://github.com/anchen1011/toflow\n 2. https://github.com/Coldog2333/pytoflow\n\n Args:\n adapt_official_weights (bool): Whether to adapt the weights translated\n from the official implementation. Set to false if you want to\n train from scratch. Default: False\n \"\"\"\n\n def __init__(self, adapt_official_weights=False, init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n self.adapt_official_weights = adapt_official_weights\n self.ref_idx = 0 if adapt_official_weights else 3\n\n # flow estimation module\n self.spynet = SPyNet()\n\n # reconstruction module\n self.conv1 = nn.Conv2d(3 * 7, 64, 9, 1, 4)\n self.conv2 = nn.Conv2d(64, 64, 9, 1, 4)\n self.conv3 = nn.Conv2d(64, 64, 1)\n self.conv4 = nn.Conv2d(64, 3, 1)\n\n # activation function\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, lrs):\n \"\"\"\n Args:\n lrs: Input lr frames: (b, 7, 3, h, w).\n\n Returns:\n Tensor: SR frame: (b, 3, h, w).\n \"\"\"\n # In the official implementation, the 0-th frame is the reference frame\n if self.adapt_official_weights:\n lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :]\n\n num_batches, _, _, h, w = lrs.size()\n\n lr_ref = lrs[:, self.ref_idx, :, :, :]\n lr_aligned = []\n for i in range(7): # 7 frames\n if i == self.ref_idx:\n lr_aligned.append(lr_ref)\n else:\n lr_supp = lrs[:, i, :, :, :]\n flow = self.spynet(lr_ref, lr_supp)\n lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1)))\n\n # reconstruction\n hr = torch.stack(lr_aligned, dim=1)\n hr = hr.view(num_batches, -1, h, w)\n hr = self.relu(self.conv1(hr))\n hr = self.relu(self.conv2(hr))\n hr = self.relu(self.conv3(hr))\n hr = self.conv4(hr) + lr_ref\n\n return hr\n\n\nclass BasicModule(nn.Module):\n \"\"\"Basic module of SPyNet.\n\n Note that unlike the common spynet architecture, the basic module here\n contains batch normalization.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(\n in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='ReLU')),\n ConvModule(\n in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n\n Returns:\n Tensor: Estimated flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\nclass SPyNet(nn.Module):\n \"\"\"SPyNet architecture.\n\n Note that this implementation is specifically for TOFlow. It differs from\n the common SPyNet in the following aspects:\n 1. The basic modules here contain BatchNorm.\n 2. Normalization and denormalization are not done here, as\n they are done in TOFlow.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network\n Code reference:\n https://github.com/Coldog2333/pytoflow\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)])\n\n def forward(self, ref, supp):\n \"\"\"\n Args:\n ref (Tensor): Reference image with shape of (b, 3, h, w).\n supp: The supporting image to be warped: (b, 3, h, w).\n\n Returns:\n Tensor: Estimated optical flow: (b, 2, h, w).\n \"\"\"\n num_batches, _, h, w = ref.size()\n ref = [ref]\n supp = [supp]\n\n # generate downsampled frames\n for _ in range(3):\n ref.insert(\n 0,\n F.avg_pool2d(\n input=ref[0],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.insert(\n 0,\n F.avg_pool2d(\n input=supp[0],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n\n # flow computation\n flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16)\n for i in range(4):\n flow_up = F.interpolate(\n input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n flow = flow_up + self.basic_module[i](\n torch.cat([\n ref[i],\n flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up\n ], 1))\n return flow\n" }, { "path": "mmagic/models/editors/ttsr/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .lte import LTE\nfrom .search_transformer import SearchTransformer\nfrom .ttsr import TTSR\nfrom .ttsr_disc import TTSRDiscriminator\nfrom .ttsr_net import TTSRNet\n\n__all__ = [\n 'LTE',\n 'SearchTransformer',\n 'TTSR',\n 'TTSRDiscriminator',\n 'TTSRNet',\n]\n" }, { "path": "mmagic/models/editors/ttsr/lte.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine.model import BaseModule\nfrom torchvision import models\n\nfrom mmagic.models.archs import ImgNormalize\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass LTE(BaseModule):\n \"\"\"Learnable Texture Extractor.\n\n Based on pretrained VGG19. Generate features in 3 levels.\n\n Args:\n requires_grad (bool): Require grad or not. Default: True.\n pixel_range (float): Pixel range of feature. Default: 1.\n load_pretrained_vgg (bool): Load pretrained VGG from torchvision.\n Default: True.\n Train: must load pretrained VGG.\n Eval: needn't load pretrained VGG, because we will load pretrained\n LTE.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n requires_grad=True,\n pixel_range=1.,\n load_pretrained_vgg=True,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n vgg_mean = (0.485, 0.456, 0.406)\n vgg_std = (0.229 * pixel_range, 0.224 * pixel_range,\n 0.225 * pixel_range)\n self.img_normalize = ImgNormalize(\n pixel_range=pixel_range, img_mean=vgg_mean, img_std=vgg_std)\n\n # use vgg19 weights to initialize\n vgg_pretrained_features = models.vgg19(\n pretrained=load_pretrained_vgg).features\n\n self.slice1 = torch.nn.Sequential()\n self.slice2 = torch.nn.Sequential()\n self.slice3 = torch.nn.Sequential()\n\n for x in range(2):\n self.slice1.add_module(str(x), vgg_pretrained_features[x])\n for x in range(2, 7):\n self.slice2.add_module(str(x), vgg_pretrained_features[x])\n for x in range(7, 12):\n self.slice3.add_module(str(x), vgg_pretrained_features[x])\n\n if not requires_grad:\n for param in self.slice1.parameters():\n param.requires_grad = requires_grad\n for param in self.slice2.parameters():\n param.requires_grad = requires_grad\n for param in self.slice3.parameters():\n param.requires_grad = requires_grad\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, 3, h, w).\n\n Returns:\n Tuple[Tensor]: Forward results in 3 levels.\n x_level3: Forward results in level 3 (n, 256, h/4, w/4).\n x_level2: Forward results in level 2 (n, 128, h/2, w/2).\n x_level1: Forward results in level 1 (n, 64, h, w).\n \"\"\"\n\n x = self.img_normalize(x)\n\n x_level1 = x = self.slice1(x)\n x_level2 = x = self.slice2(x)\n x_level3 = x = self.slice3(x)\n\n return [x_level3, x_level2, x_level1]\n" }, { "path": "mmagic/models/editors/ttsr/search_transformer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass SearchTransformer(BaseModule):\n \"\"\"Search texture reference by transformer.\n\n Include relevance embedding, hard-attention and soft-attention.\n \"\"\"\n\n def gather(self, inputs, dim, index):\n \"\"\"Hard Attention. Gathers values along an axis specified by dim.\n\n Args:\n inputs (Tensor): The source tensor. (N, C*k*k, H*W)\n dim (int): The axis along which to index.\n index (Tensor): The indices of elements to gather. (N, H*W)\n\n results:\n outputs (Tensor): The result tensor. (N, C*k*k, H*W)\n \"\"\"\n\n views = [inputs.size(0)\n ] + [1 if i != dim else -1 for i in range(1, inputs.ndim)]\n expansion = [\n -1 if i in (0, dim) else d for i, d in enumerate(inputs.size())\n ]\n index = index.view(views).expand(expansion)\n outputs = torch.gather(inputs, dim, index)\n\n return outputs\n\n def forward(self, img_lq, ref_lq, refs):\n \"\"\"Texture transformer.\n\n Q = LTE(img_lq)\n K = LTE(ref_lq)\n V = LTE(ref), from V_level_n to V_level_1\n\n Relevance embedding aims to embed the relevance between the LQ and\n Ref image by estimating the similarity between Q and K.\n Hard-Attention: Only transfer features from the most relevant position\n in V for each query.\n Soft-Attention: synthesize features from the transferred GT texture\n features T and the LQ features F from the backbone.\n\n Args:\n All args are features come from extractor (such as LTE).\n These features contain 3 levels.\n When upscale_factor=4, the size ratio of these features is\n level3:level2:level1 = 1:2:4.\n img_lq (Tensor): Tensor of 4x bicubic-upsampled lq image.\n (N, C, H, W)\n ref_lq (Tensor): Tensor of ref_lq. ref_lq is obtained\n by applying bicubic down-sampling and up-sampling with factor\n 4x on ref. (N, C, H, W)\n refs (Tuple[Tensor]): Tuple of ref tensors.\n [(N, C, H, W), (N, C/2, 2H, 2W), ...]\n\n Returns:\n tuple: tuple contains:\n soft_attention (Tensor): Soft-Attention tensor. (N, 1, H, W) \\n\n textures (Tuple[Tensor]): Transferred GT textures.\n [(N, C, H, W), (N, C/2, 2H, 2W), ...]\n \"\"\"\n\n levels = len(refs)\n # query\n query = F.unfold(img_lq, kernel_size=(3, 3), padding=1)\n\n # key\n key = F.unfold(ref_lq, kernel_size=(3, 3), padding=1)\n key_t = key.permute(0, 2, 1)\n\n # values\n values = [\n F.unfold(\n refs[i],\n kernel_size=3 * pow(2, i),\n padding=pow(2, i),\n stride=pow(2, i)) for i in range(levels)\n ]\n\n key_t = F.normalize(key_t, dim=2) # [N, H*W, C*k*k]\n query = F.normalize(query, dim=1) # [N, C*k*k, H*W]\n\n # Relevance embedding\n rel_embedding = torch.bmm(key_t, query) # [N, H*W, H*W]\n max_val, max_index = torch.max(rel_embedding, dim=1) # [N, H*W]\n\n # hard-attention\n textures = [self.gather(value, 2, max_index) for value in values]\n\n # to tensor\n h, w = img_lq.size()[-2:]\n textures = [\n F.fold(\n textures[i],\n output_size=(h * pow(2, i), w * pow(2, i)),\n kernel_size=3 * pow(2, i),\n padding=pow(2, i),\n stride=pow(2, i)) / 9. for i in range(levels)\n ]\n\n soft_attention = max_val.view(max_val.size(0), 1, h, w)\n\n return soft_attention, textures\n" }, { "path": "mmagic/models/editors/ttsr/ttsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List\n\nimport torch\nfrom mmengine.optim import OptimWrapperDict\n\nfrom mmagic.models.utils import set_requires_grad\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom ..srgan import SRGAN\n\n\n@MODELS.register_module()\nclass TTSR(SRGAN):\n \"\"\"TTSR model for Reference-based Image Super-Resolution.\n\n Paper: Learning Texture Transformer Network for Image Super-Resolution.\n\n Args:\n generator (dict): Config for the generator.\n extractor (dict): Config for the extractor.\n transformer (dict): Config for the transformer.\n pixel_loss (dict): Config for the pixel loss.\n discriminator (dict): Config for the discriminator. Default: None.\n perceptual_loss (dict): Config for the perceptual loss. Default: None.\n transferal_perceptual_loss (dict): Config for the transferal perceptual\n loss. Default: None.\n gan_loss (dict): Config for the GAN loss. Default: None\n train_cfg (dict): Config for train. Default: None.\n test_cfg (dict): Config for testing. Default: None.\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Default: None.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Default: None.\n \"\"\"\n\n def __init__(self,\n generator,\n extractor,\n transformer,\n pixel_loss,\n discriminator=None,\n perceptual_loss=None,\n transferal_perceptual_loss=None,\n gan_loss=None,\n train_cfg=None,\n test_cfg=None,\n init_cfg=None,\n data_preprocessor=None):\n\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n gan_loss=gan_loss,\n pixel_loss=pixel_loss,\n perceptual_loss=perceptual_loss,\n train_cfg=train_cfg,\n test_cfg=test_cfg,\n init_cfg=init_cfg,\n data_preprocessor=data_preprocessor)\n\n self.transformer = MODELS.build(transformer)\n self.extractor = MODELS.build(extractor)\n extractor['requires_grad'] = False\n self.extractor_copy = MODELS.build(extractor)\n set_requires_grad(self.extractor_copy, False)\n\n if transferal_perceptual_loss:\n self.transferal_perceptual_loss = MODELS.build(\n transferal_perceptual_loss)\n else:\n self.transferal_perceptual_loss = None\n\n self.pixel_init = train_cfg.get('pixel_init', 0) if train_cfg else 0\n\n def forward_tensor(self, inputs, data_samples=None, training=False):\n \"\"\"Forward tensor. Returns result of simple forward.\n\n Args:\n inputs (torch.Tensor): batch input tensor collated by\n :attr:`data_preprocessor`.\n data_samples (List[BaseDataElement], optional):\n data samples collated by :attr:`data_preprocessor`.\n training (bool): Whether is training. Default: False.\n\n Returns:\n (Tensor | Tuple[List[Tensor]]): results of forward inference and\n forward train.\n \"\"\"\n\n img_lq = []\n ref_lq = []\n ref = []\n\n img_lq = data_samples.img_lq / 255.\n ref_lq = data_samples.ref_lq / 255.\n ref = data_samples.ref_img / 255.\n\n img_lq, _, _ = self.extractor(img_lq)\n ref_lq, _, _ = self.extractor(ref_lq)\n refs = self.extractor(ref)\n\n soft_attention, textures = self.transformer(img_lq, ref_lq, refs)\n\n pred = self.generator(inputs, soft_attention, textures)\n\n if training:\n return pred, soft_attention, textures\n else:\n return pred\n\n def if_run_g(self):\n \"\"\"Calculates whether need to run the generator step.\"\"\"\n\n return True\n\n def if_run_d(self):\n \"\"\"Calculates whether need to run the discriminator step.\"\"\"\n\n return self.step_counter >= self.pixel_init and super().if_run_d()\n\n def g_step(self, batch_outputs, batch_gt_data: DataSample):\n \"\"\"G step of GAN: Calculate losses of generator.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n\n Returns:\n dict: Dict of losses.\n \"\"\"\n\n losses = dict()\n pred, soft_attention, textures = batch_outputs\n\n # pix loss\n if self.pixel_loss:\n losses['loss_pix'] = self.pixel_loss(pred, batch_gt_data)\n\n if self.step_counter >= self.pixel_init:\n # perceptual loss\n if self.perceptual_loss:\n loss_percep, loss_style = self.perceptual_loss(\n pred, batch_gt_data)\n if loss_percep is not None:\n losses['loss_perceptual'] = loss_percep\n if loss_style is not None:\n losses['loss_style'] = loss_style\n\n # transform loss\n if self.transferal_perceptual_loss:\n state_dict = self.extractor.module.state_dict() if hasattr(\n self.extractor, 'module') else self.extractor.state_dict()\n self.extractor_copy.load_state_dict(state_dict)\n sr_textures = self.extractor_copy((pred + 1.) / 2.)\n losses['loss_transferal'] = self.transferal_perceptual_loss(\n sr_textures, soft_attention, textures)\n\n # gan loss for generator\n if self.gan_loss and self.discriminator:\n fake_g_pred = self.discriminator(pred)\n losses['loss_gan'] = self.gan_loss(\n fake_g_pred, target_is_real=True, is_disc=False)\n\n return losses\n\n def g_step_with_optim(self, batch_outputs: torch.Tensor,\n batch_gt_data: torch.Tensor,\n optim_wrapper: OptimWrapperDict):\n \"\"\"G step with optim of GAN: Calculate losses of generator and run\n optim.\n\n Args:\n batch_outputs (Tensor): Batch output of generator.\n batch_gt_data (Tensor): Batch GT data.\n optim_wrapper (OptimWrapperDict): Optim wrapper dict.\n\n Returns:\n dict: Dict of parsed losses.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n e_optim_wrapper = optim_wrapper['extractor']\n\n losses_g = self.g_step(batch_outputs, batch_gt_data)\n parsed_losses_g, log_vars_g = self.parse_losses(losses_g)\n\n if g_optim_wrapper.should_update():\n g_optim_wrapper.backward(parsed_losses_g)\n g_optim_wrapper.step()\n g_optim_wrapper.zero_grad()\n\n if e_optim_wrapper.should_update():\n e_optim_wrapper.step()\n e_optim_wrapper.zero_grad()\n\n return log_vars_g\n\n def train_step(self, data: List[dict],\n optim_wrapper: OptimWrapperDict) -> Dict[str, torch.Tensor]:\n \"\"\"Train step of GAN-based method.\n\n Args:\n data (List[dict]): Data sampled from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance\n used to update model parameters.\n\n Returns:\n Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n\n g_optim_wrapper = optim_wrapper['generator']\n\n data = self.data_preprocessor(data, True)\n batch_inputs = data['inputs']\n data_samples = data['data_samples']\n batch_gt_data = self.extract_gt_data(data_samples)\n\n log_vars = dict()\n\n with g_optim_wrapper.optim_context(self):\n batch_outputs = self.forward_train(batch_inputs, data_samples)\n\n if self.if_run_g():\n set_requires_grad(self.discriminator, False)\n\n log_vars_d = self.g_step_with_optim(\n batch_outputs=batch_outputs,\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if self.if_run_d():\n set_requires_grad(self.discriminator, True)\n\n pred, _, _ = batch_outputs\n\n for _ in range(self.disc_repeat):\n # detach before function call to resolve PyTorch2.0 compile bug\n log_vars_d = self.d_step_with_optim(\n batch_outputs=pred.detach(),\n batch_gt_data=batch_gt_data,\n optim_wrapper=optim_wrapper)\n\n log_vars.update(log_vars_d)\n\n if 'loss' in log_vars:\n log_vars.pop('loss')\n\n self.step_counter += 1\n\n return log_vars\n" }, { "path": "mmagic/models/editors/ttsr/ttsr_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch.nn as nn\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass TTSRDiscriminator(BaseModule):\n \"\"\"A discriminator for TTSR.\n\n Args:\n in_channels (int): Channel number of inputs. Default: 3.\n in_size (int): Size of input image. Default: 160.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self, in_channels=3, in_size=160, init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n self.body = nn.Sequential(\n nn.Conv2d(in_channels, 32, 3, 1, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(32, 32, 3, 2, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(32, 64, 3, 1, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(64, 64, 3, 2, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(64, 128, 3, 1, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(128, 128, 3, 2, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(128, 256, 3, 1, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(256, 256, 3, 2, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(256, 512, 3, 1, 1), nn.LeakyReLU(0.2),\n nn.Conv2d(512, 512, 3, 2, 1), nn.LeakyReLU(0.2))\n\n self.last = nn.Sequential(\n nn.Linear(in_size // 32 * in_size // 32 * 512, 1024),\n nn.LeakyReLU(0.2), nn.Linear(1024, 1))\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x = self.body(x)\n x = x.view(x.size(0), -1)\n x = self.last(x)\n\n return x\n" }, { "path": "mmagic/models/editors/ttsr/ttsr_net.py", "content": "from functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import build_conv_layer\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.archs import PixelShufflePack, ResidualBlockNoBN\nfrom mmagic.models.utils import make_layer\nfrom mmagic.registry import MODELS\n\n# Use partial to specify some default arguments\n_conv3x3_layer = partial(\n build_conv_layer, dict(type='Conv2d'), kernel_size=3, padding=1)\n_conv1x1_layer = partial(\n build_conv_layer, dict(type='Conv2d'), kernel_size=1, padding=0)\n\n\n@MODELS.register_module()\nclass TTSRNet(BaseModule):\n \"\"\"TTSR network structure (main-net) for reference-based super-resolution.\n\n Paper: Learning Texture Transformer Network for Image Super-Resolution\n\n Adapted from 'https://github.com/researchmm/TTSR.git'\n 'https://github.com/researchmm/TTSR'\n Copyright permission at 'https://github.com/researchmm/TTSR/issues/38'.\n\n Args:\n in_channels (int): Number of channels in the input image\n out_channels (int): Number of channels in the output image\n mid_channels (int): Channel number of intermediate features.\n Default: 64\n texture_channels (int): Number of texture channels. Default: 64.\n num_blocks (tuple[int]): Block numbers in the trunk network.\n Default: (16, 16, 8, 4)\n res_scale (float): Used to scale the residual in residual block.\n Default: 1.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n mid_channels=64,\n texture_channels=64,\n num_blocks=(16, 16, 8, 4),\n res_scale=1.0,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n\n self.texture_channels = texture_channels\n\n self.sfe = SFE(in_channels, mid_channels, num_blocks[0], res_scale)\n\n # stage 1\n self.conv_first1 = _conv3x3_layer(4 * texture_channels + mid_channels,\n mid_channels)\n\n self.res_block1 = make_layer(\n ResidualBlockNoBN,\n num_blocks[1],\n mid_channels=mid_channels,\n res_scale=res_scale)\n\n self.conv_last1 = _conv3x3_layer(mid_channels, mid_channels)\n\n # up-sampling 1 -> 2\n self.up1 = PixelShufflePack(\n in_channels=mid_channels,\n out_channels=mid_channels,\n scale_factor=2,\n upsample_kernel=3)\n\n # stage 2\n self.conv_first2 = _conv3x3_layer(2 * texture_channels + mid_channels,\n mid_channels)\n\n self.csfi2 = CSFI2(mid_channels)\n\n self.res_block2_1 = make_layer(\n ResidualBlockNoBN,\n num_blocks[2],\n mid_channels=mid_channels,\n res_scale=res_scale)\n self.res_block2_2 = make_layer(\n ResidualBlockNoBN,\n num_blocks[2],\n mid_channels=mid_channels,\n res_scale=res_scale)\n\n self.conv_last2_1 = _conv3x3_layer(mid_channels, mid_channels)\n self.conv_last2_2 = _conv3x3_layer(mid_channels, mid_channels)\n\n # up-sampling 2 -> 3\n self.up2 = PixelShufflePack(\n in_channels=mid_channels,\n out_channels=mid_channels,\n scale_factor=2,\n upsample_kernel=3)\n\n # stage 3\n self.conv_first3 = _conv3x3_layer(texture_channels + mid_channels,\n mid_channels)\n\n self.csfi3 = CSFI3(mid_channels)\n\n self.res_block3_1 = make_layer(\n ResidualBlockNoBN,\n num_blocks[3],\n mid_channels=mid_channels,\n res_scale=res_scale)\n self.res_block3_2 = make_layer(\n ResidualBlockNoBN,\n num_blocks[3],\n mid_channels=mid_channels,\n res_scale=res_scale)\n self.res_block3_3 = make_layer(\n ResidualBlockNoBN,\n num_blocks[3],\n mid_channels=mid_channels,\n res_scale=res_scale)\n\n self.conv_last3_1 = _conv3x3_layer(mid_channels, mid_channels)\n self.conv_last3_2 = _conv3x3_layer(mid_channels, mid_channels)\n self.conv_last3_3 = _conv3x3_layer(mid_channels, mid_channels)\n\n # end, merge features\n self.merge_features = MergeFeatures(mid_channels, out_channels)\n\n def forward(self, x, soft_attention, textures):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n soft_attention (Tensor): Soft-Attention tensor with shape\n (n, 1, h, w).\n textures (Tuple[Tensor]): Transferred HR texture tensors.\n [(N, C, H, W), (N, C/2, 2H, 2W), ...]\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n assert textures[-1].shape[1] == self.texture_channels\n\n x1 = self.sfe(x)\n\n # stage 1\n x1_res = torch.cat((x1, textures[0]), dim=1)\n x1_res = self.conv_first1(x1_res)\n\n # soft-attention\n x1 = x1 + x1_res * soft_attention\n\n x1_res = self.res_block1(x1)\n x1_res = self.conv_last1(x1_res)\n\n x1 = x1 + x1_res\n\n # stage 2\n x21 = x1\n x22 = self.up1(x1)\n x22 = F.relu(x22)\n\n x22_res = torch.cat((x22, textures[1]), dim=1)\n x22_res = self.conv_first2(x22_res)\n\n # soft-attention\n x22_res = x22_res * F.interpolate(\n soft_attention,\n scale_factor=2,\n mode='bicubic',\n align_corners=False)\n x22 = x22 + x22_res\n\n x21_res, x22_res = self.csfi2(x21, x22)\n\n x21_res = self.res_block2_1(x21_res)\n x22_res = self.res_block2_2(x22_res)\n\n x21_res = self.conv_last2_1(x21_res)\n x22_res = self.conv_last2_2(x22_res)\n\n x21 = x21 + x21_res\n x22 = x22 + x22_res\n\n # stage 3\n x31 = x21\n x32 = x22\n x33 = self.up2(x22)\n x33 = F.relu(x33)\n\n x33_res = torch.cat((x33, textures[2]), dim=1)\n x33_res = self.conv_first3(x33_res)\n\n # soft-attention\n x33_res = x33_res * F.interpolate(\n soft_attention,\n scale_factor=4,\n mode='bicubic',\n align_corners=False)\n x33 = x33 + x33_res\n\n x31_res, x32_res, x33_res = self.csfi3(x31, x32, x33)\n\n x31_res = self.res_block3_1(x31_res)\n x32_res = self.res_block3_2(x32_res)\n x33_res = self.res_block3_3(x33_res)\n\n x31_res = self.conv_last3_1(x31_res)\n x32_res = self.conv_last3_2(x32_res)\n x33_res = self.conv_last3_3(x33_res)\n\n x31 = x31 + x31_res\n x32 = x32 + x32_res\n x33 = x33 + x33_res\n x = self.merge_features(x31, x32, x33)\n\n return x\n\n\nclass SFE(nn.Module):\n \"\"\"Structural Feature Encoder.\n\n Backbone of Texture Transformer Network for Image Super-Resolution.\n\n Args:\n in_channels (int): Number of channels in the input image\n mid_channels (int): Channel number of intermediate features\n num_blocks (int): Block number in the trunk network\n res_scale (float): Used to scale the residual in residual block.\n Default: 1.\n \"\"\"\n\n def __init__(self, in_channels, mid_channels, num_blocks, res_scale):\n super().__init__()\n\n self.num_blocks = num_blocks\n self.conv_first = _conv3x3_layer(in_channels, mid_channels)\n\n self.body = make_layer(\n ResidualBlockNoBN,\n num_blocks,\n mid_channels=mid_channels,\n res_scale=res_scale)\n\n self.conv_last = _conv3x3_layer(mid_channels, mid_channels)\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n x1 = x = F.relu(self.conv_first(x))\n x = self.body(x)\n x = self.conv_last(x)\n x = x + x1\n return x\n\n\nclass CSFI2(nn.Module):\n \"\"\"Cross-Scale Feature Integration between 1x and 2x features.\n\n Cross-Scale Feature Integration in Texture Transformer Network for\n Image Super-Resolution.\n It is cross-scale feature integration between 1x and 2x features.\n For example, `conv2to1` means conv layer from 2x feature to 1x\n feature. Down-sampling is achieved by conv layer with stride=2,\n and up-sampling is achieved by bicubic interpolate and conv layer.\n\n Args:\n mid_channels (int): Channel number of intermediate features\n \"\"\"\n\n def __init__(self, mid_channels):\n super().__init__()\n self.conv1to2 = _conv1x1_layer(mid_channels, mid_channels)\n self.conv2to1 = _conv3x3_layer(mid_channels, mid_channels, stride=2)\n\n self.conv_merge1 = _conv3x3_layer(mid_channels * 2, mid_channels)\n self.conv_merge2 = _conv3x3_layer(mid_channels * 2, mid_channels)\n\n def forward(self, x1, x2):\n \"\"\"Forward function.\n\n Args:\n x1 (Tensor): Input tensor with shape (n, c, h, w).\n x2 (Tensor): Input tensor with shape (n, c, 2h, 2w).\n\n Returns:\n x1 (Tensor): Output tensor with shape (n, c, h, w).\n x2 (Tensor): Output tensor with shape (n, c, 2h, 2w).\n \"\"\"\n\n x12 = F.interpolate(\n x1, scale_factor=2, mode='bicubic', align_corners=False)\n x12 = F.relu(self.conv1to2(x12))\n x21 = F.relu(self.conv2to1(x2))\n\n x1 = F.relu(self.conv_merge1(torch.cat((x1, x21), dim=1)))\n x2 = F.relu(self.conv_merge2(torch.cat((x2, x12), dim=1)))\n\n return x1, x2\n\n\nclass CSFI3(nn.Module):\n \"\"\"Cross-Scale Feature Integration between 1x, 2x, and 4x features.\n\n Cross-Scale Feature Integration in Texture Transformer Network for\n Image Super-Resolution.\n It is cross-scale feature integration between 1x and 2x features.\n For example, `conv2to1` means conv layer from 2x feature to 1x\n feature. Down-sampling is achieved by conv layer with stride=2,\n and up-sampling is achieved by bicubic interpolate and conv layer.\n\n Args:\n mid_channels (int): Channel number of intermediate features\n \"\"\"\n\n def __init__(self, mid_channels):\n super().__init__()\n self.conv1to2 = _conv1x1_layer(mid_channels, mid_channels)\n self.conv1to4 = _conv1x1_layer(mid_channels, mid_channels)\n\n self.conv2to1 = _conv3x3_layer(mid_channels, mid_channels, stride=2)\n self.conv2to4 = _conv1x1_layer(mid_channels, mid_channels)\n\n self.conv4to1_1 = _conv3x3_layer(mid_channels, mid_channels, stride=2)\n self.conv4to1_2 = _conv3x3_layer(mid_channels, mid_channels, stride=2)\n self.conv4to2 = _conv3x3_layer(mid_channels, mid_channels, stride=2)\n\n self.conv_merge1 = _conv3x3_layer(mid_channels * 3, mid_channels)\n self.conv_merge2 = _conv3x3_layer(mid_channels * 3, mid_channels)\n self.conv_merge4 = _conv3x3_layer(mid_channels * 3, mid_channels)\n\n def forward(self, x1, x2, x4):\n \"\"\"Forward function.\n\n Args:\n x1 (Tensor): Input tensor with shape (n, c, h, w).\n x2 (Tensor): Input tensor with shape (n, c, 2h, 2w).\n x4 (Tensor): Input tensor with shape (n, c, 4h, 4w).\n\n Returns:\n x1 (Tensor): Output tensor with shape (n, c, h, w).\n x2 (Tensor): Output tensor with shape (n, c, 2h, 2w).\n x4 (Tensor): Output tensor with shape (n, c, 4h, 4w).\n \"\"\"\n\n x12 = F.interpolate(\n x1, scale_factor=2, mode='bicubic', align_corners=False)\n x12 = F.relu(self.conv1to2(x12))\n x14 = F.interpolate(\n x1, scale_factor=4, mode='bicubic', align_corners=False)\n x14 = F.relu(self.conv1to4(x14))\n\n x21 = F.relu(self.conv2to1(x2))\n x24 = F.interpolate(\n x2, scale_factor=2, mode='bicubic', align_corners=False)\n x24 = F.relu(self.conv2to4(x24))\n\n x41 = F.relu(self.conv4to1_1(x4))\n x41 = F.relu(self.conv4to1_2(x41))\n x42 = F.relu(self.conv4to2(x4))\n\n x1 = F.relu(self.conv_merge1(torch.cat((x1, x21, x41), dim=1)))\n x2 = F.relu(self.conv_merge2(torch.cat((x2, x12, x42), dim=1)))\n x4 = F.relu(self.conv_merge4(torch.cat((x4, x14, x24), dim=1)))\n\n return x1, x2, x4\n\n\nclass MergeFeatures(nn.Module):\n \"\"\"Merge Features. Merge 1x, 2x, and 4x features.\n\n Final module of Texture Transformer Network for Image Super-Resolution.\n\n Args:\n mid_channels (int): Channel number of intermediate features\n out_channels (int): Number of channels in the output image\n \"\"\"\n\n def __init__(self, mid_channels, out_channels):\n super().__init__()\n self.conv1to4 = _conv1x1_layer(mid_channels, mid_channels)\n self.conv2to4 = _conv1x1_layer(mid_channels, mid_channels)\n self.conv_merge = _conv3x3_layer(mid_channels * 3, mid_channels)\n self.conv_last1 = _conv3x3_layer(mid_channels, mid_channels // 2)\n self.conv_last2 = _conv1x1_layer(mid_channels // 2, out_channels)\n\n def forward(self, x1, x2, x4):\n \"\"\"Forward function.\n\n Args:\n x1 (Tensor): Input tensor with shape (n, c, h, w).\n x2 (Tensor): Input tensor with shape (n, c, 2h, 2w).\n x4 (Tensor): Input tensor with shape (n, c, 4h, 4w).\n\n Returns:\n x (Tensor): Output tensor with shape (n, c_out, 4h, 4w).\n \"\"\"\n\n x14 = F.interpolate(\n x1, scale_factor=4, mode='bicubic', align_corners=False)\n x14 = F.relu(self.conv1to4(x14))\n x24 = F.interpolate(\n x2, scale_factor=2, mode='bicubic', align_corners=False)\n x24 = F.relu(self.conv2to4(x24))\n\n x = F.relu(self.conv_merge(torch.cat((x4, x14, x24), dim=1)))\n x = self.conv_last1(x)\n x = self.conv_last2(x)\n x = torch.clamp(x, -1, 1)\n\n return x\n" }, { "path": "mmagic/models/editors/vico/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .vico import ViCo\nfrom .vico_utils import set_vico_modules\n\n__all__ = ['ViCo', 'set_vico_modules']\n" }, { "path": "mmagic/models/editors/vico/vico.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.runner import set_random_seed\nfrom PIL import Image\nfrom tqdm.auto import tqdm\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\nfrom ..stable_diffusion.stable_diffusion import StableDiffusion\nfrom .vico_utils import set_vico_modules\n\nModelType = Union[Dict, nn.Module]\n\n\n@MODELS.register_module()\nclass ViCo(StableDiffusion):\n \"\"\"Implementation of `ViCo with Stable Diffusion.\n\n `_ (ViCo).\n\n Args:\n vae (Union[dict, nn.Module]): The config or module for VAE model.\n text_encoder (Union[dict, nn.Module]): The config or module for text\n encoder.\n tokenizer (str): The **name** for CLIP tokenizer.\n unet (Union[dict, nn.Module]): The config or module for Unet model.\n schedule (Union[dict, nn.Module]): The config or module for diffusion\n scheduler.\n test_scheduler (Union[dict, nn.Module], optional): The config or\n module for diffusion scheduler in test stage (`self.infer`). If not\n passed, will use the same scheduler as `schedule`. Defaults to\n None.\n val_prompts (Union[str, List[str]], optional): The prompts for\n validation. Defaults to None.\n num_class_images (int, optional): The number of images for class prior.\n Defaults to 3.\n dtype (str, optional): The dtype for the model. Defaults to 'fp16'.\n enable_xformers (bool, optional): Whether to use xformers.\n Defaults to True.\n noise_offset_weight (bool, optional): The weight of noise offset\n introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa\n Defaults to 0.\n data_preprocessor (dict, optional): The pre-process config of\n :class:`BaseDataPreprocessor`. Defaults to\n dict(type='DataPreprocessor').\n init_cfg (dict, optional): The weight initialized config for\n :class:`BaseModule`. Defaults to None/\n image_cross_layers (List[int], optional): The layers to use image\n cross attention. Defaults to None.\n reg_loss_weight (float, optional): The weight of regularization loss.\n Defaults to 0.\n placeholder (str, optional): The placeholder token. Defaults to None.\n initialize_token (str, optional): The token to initialize the\n placeholder. Defaults to None.\n num_vectors_per_token (int, optional): The number of vectors per token.\n \"\"\"\n\n def __init__(self,\n vae: ModelType,\n text_encoder: ModelType,\n tokenizer: str,\n unet: ModelType,\n scheduler: ModelType,\n test_scheduler: Optional[ModelType] = None,\n val_prompts: Union[str, List[str]] = None,\n dtype: str = 'fp16',\n enable_xformers: bool = True,\n noise_offset_weight: float = 0,\n tomesd_cfg: Optional[dict] = None,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n init_cfg: Optional[dict] = None,\n image_cross_layers: List[int] = None,\n reg_loss_weight: float = 0,\n placeholder: str = None,\n initialize_token: str = None,\n num_vectors_per_token: int = 1):\n\n super().__init__(vae, text_encoder, tokenizer, unet, scheduler,\n test_scheduler, dtype, enable_xformers,\n noise_offset_weight, tomesd_cfg, data_preprocessor,\n init_cfg)\n self.reg_loss_weight = reg_loss_weight\n self.placeholder = placeholder\n\n self.dtype = torch.float32\n if dtype == 'fp16':\n self.dtype = torch.float16\n elif dtype == 'bf16':\n self.dtype = torch.bfloat16\n else:\n assert dtype in [\n 'fp32', None\n ], ('dtype must be one of \\'fp32\\', \\'fp16\\', \\'bf16\\' or None.')\n\n self.val_prompts = val_prompts\n self.add_tokens(placeholder, initialize_token, num_vectors_per_token)\n self.set_vico(image_cross_layers)\n self.prepare_models()\n\n def prepare_models(self):\n \"\"\"Prepare model for training.\n\n Move model to target dtype and disable gradient for some models.\n \"\"\"\n \"\"\"Disable gradient for untrainable modules to save memory.\"\"\"\n self.vae.requires_grad_(False)\n self.unet.requires_grad_(False)\n self.text_encoder.set_only_embedding_trainable()\n self.set_only_imca_trainable()\n\n def set_vico(self, have_image_cross_attention: List[int]):\n \"\"\"Set ViCo for model.\"\"\"\n set_vico_modules(self.unet, have_image_cross_attention)\n\n def set_only_imca_trainable(self):\n \"\"\"Set only image cross attention trainable.\"\"\"\n for _, layer in self.unet.named_modules():\n if layer.__class__.__name__ == 'ViCoTransformer2D':\n if hasattr(layer, 'image_cross_attention'):\n layer.image_cross_attention.train()\n for name, param in (\n layer.image_cross_attention.named_parameters()):\n param.requires_grad = True\n\n def add_tokens(self,\n placeholder_token: str,\n initialize_token: str = None,\n num_vectors_per_token: int = 1):\n \"\"\"Add token for training.\n\n # TODO: support add tokens as dict, then we can load pretrained tokens.\n \"\"\"\n self.tokenizer.add_placeholder_token(\n placeholder_token, num_vec_per_token=num_vectors_per_token)\n\n self.text_encoder.set_embedding_layer()\n embedding_layer = self.text_encoder.get_embedding_layer()\n assert embedding_layer is not None, (\n 'Do not support get embedding layer for current text encoder. '\n 'Please check your configuration.')\n\n if initialize_token:\n init_id = self.tokenizer(initialize_token).input_ids[1]\n initialize_embedding = embedding_layer.weight[init_id]\n initialize_embedding = initialize_embedding[None, ...].repeat(\n num_vectors_per_token, 1)\n else:\n emb_dim = embedding_layer.weight.shape[1]\n initialize_embedding = torch.zeros(num_vectors_per_token, emb_dim)\n\n token_info = self.tokenizer.get_token_info(placeholder_token)\n token_info['embedding'] = initialize_embedding\n token_info['trainable'] = True\n self.token_info = token_info\n embedding_layer.add_embeddings(token_info)\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n image_reference = data['inputs']['img_ref']\n data_samples = data['data_samples']\n if self.val_prompts is None:\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples.split() * len(prompt)\n data_samples = DataSample.stack(data_samples.split() * len(prompt))\n\n output = self.infer(prompt, image_reference, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Calls\n ``self.data_preprocessor`` and ``self.infer`` in order. Return the\n generated results which will be passed to evaluator or visualizer.\n\n Args:\n data (dict or tuple or list): Data sampled from dataset.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n image_reference = data['inputs']['img_ref']\n data_samples = data['data_samples']\n\n if self.val_prompts is None:\n prompt = data_samples.prompt\n else:\n prompt = self.val_prompts\n # construct a fake data_sample for destruct\n data_samples.split() * len(prompt)\n data_samples = DataSample.stack(data_samples.split() * len(prompt))\n\n output = self.infer(prompt, image_reference, return_type='tensor')\n samples = output['samples']\n samples = self.data_preprocessor.destruct(samples, data_samples)\n\n out_data_sample = DataSample(fake_img=samples, prompt=prompt)\n data_sample_list = out_data_sample.split()\n return data_sample_list\n\n def prepare_reference(self,\n image_ref: Union[Image.Image, torch.Tensor],\n height: Optional[int] = 512,\n width: Optional[int] = 512):\n if isinstance(image_ref, Image.Image):\n if not image_ref.mode == 'RGB':\n image_ref = image_ref.convert('RGB')\n img = np.array(image_ref).astype(np.uint8)\n image_ref = Image.fromarray(img)\n image_ref = image_ref.resize((height, width),\n resample=Image.BILINEAR)\n\n image_ref = np.array(image_ref).astype(np.uint8)\n image_ref = (image_ref / 127.5 - 1.0).astype(np.float32)\n image_ref = torch.from_numpy(image_ref).permute(2, 0,\n 1).unsqueeze(0)\n\n return image_ref\n\n def train_step(self, data, optim_wrapper):\n \"\"\"Training step.\"\"\"\n data = self.data_preprocessor(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n\n with optim_wrapper.optim_context(self.unet):\n image = inputs['img'] # image for new concept\n num_batches = image.shape[0]\n\n image_ref = inputs['img_ref']\n # cat image and image reference to avoid forward twice\n image = torch.cat([image, image_ref], dim=0)\n prompt_init = data_samples.prompt\n placeholder_string = self.placeholder\n\n image = image.to(self.dtype)\n latents = self.vae.encode(image).latent_dist.sample()\n latents = latents * self.vae.config.scaling_factor\n\n noise = torch.randn_like(latents[:num_batches, ...])\n timesteps = torch.randint(\n 0,\n self.scheduler.num_train_timesteps, (num_batches, ),\n device=self.device)\n timesteps = timesteps.long()\n # image reference shares the same timesteps\n\n # only add noise to source image\n noisy_latents = self.scheduler.add_noise(\n latents[:num_batches, ...], noise, timesteps)\n noisy_latents = torch.cat(\n [noisy_latents, latents[num_batches:, ...]], dim=0)\n timesteps = torch.cat([timesteps, timesteps])\n\n input_ids = self.tokenizer(\n prompt_init,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n ph_tokens = self.tokenizer(\n [placeholder_string],\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids'].to(self.device)\n ph_tok = ph_tokens[0, 1]\n placeholder_idx = torch.where(input_ids == ph_tok)\n clip_eot_token_id = self.tokenizer.encode(\n self.tokenizer.eos_token)['input_ids'][1]\n endoftext_idx = (torch.arange(input_ids.shape[0]),\n (input_ids == clip_eot_token_id).nonzero(\n as_tuple=False)[0, 1])\n placeholder_position = [placeholder_idx, endoftext_idx]\n\n encoder_hidden_states = self.text_encoder(input_ids)[0]\n encoder_hidden_states = torch.cat(\n [encoder_hidden_states, encoder_hidden_states], dim=0)\n if self.scheduler.config.prediction_type == 'epsilon':\n gt = noise\n elif self.scheduler.config.prediction_type == 'v_prediction':\n gt = self.scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError('Unknown prediction type '\n f'{self.scheduler.config.prediction_type}')\n\n # NOTE: we train unet in fp32, convert to float manually\n model_output = self.unet(\n noisy_latents.float(),\n timesteps,\n encoder_hidden_states=encoder_hidden_states.float(),\n placeholder_position=placeholder_position)\n model_pred = model_output['sample']\n loss_reg = model_output['loss_reg']\n\n loss_dict = dict()\n if loss_reg != 0:\n loss_dict['loss_reg'] = loss_reg * self.reg_loss_weight\n vico_loss = F.mse_loss(model_pred[:1].float(), gt.float())\n loss_dict['vico_loss'] = vico_loss\n parsed_loss, log_vars = self.parse_losses(loss_dict)\n optim_wrapper.update_params(parsed_loss)\n\n return log_vars\n\n @torch.no_grad()\n def infer(self,\n prompt: Union[str, List[str]],\n image_reference: Image.Image = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n show_progress=True,\n seed=1,\n return_type='image'):\n \"\"\"Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*,\n defaults to self.unet_sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps.\n More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in\n [Classifier-Free Diffusion Guidance]\n (https://arxiv.org/abs/2207.12598).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator] to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents,\n sampled from a Gaussian distribution,\n to be used as inputs for image generation.\n Can be used to tweak the same generation\n with different prompts.\n If not provided, a latents tensor will be\n generated by sampling using the supplied random `generator`.\n return_type (str): The return type of the inference results.\n Supported types are 'image', 'numpy', 'tensor'. If 'image'\n is passed, a list of PIL images will be returned. If 'numpy'\n is passed, a numpy array with shape [N, C, H, W] will be\n returned, and the value range will be same as decoder's\n output range. If 'tensor' is passed, the decoder's output\n will be returned. Defaults to 'image'.\n\n Returns:\n dict: A dict containing the generated images.\n \"\"\"\n assert return_type in ['image', 'tensor', 'numpy']\n set_random_seed(seed=seed)\n\n # 0. Default height and width to unet\n height = height or self.unet_sample_size * self.vae_scale_factor\n width = width or self.unet_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self.device\n\n img_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \\\n else self.vae.dtype\n latent_dtype = next(self.unet.parameters()).dtype\n # here `guidance_scale` is defined analog to the\n # guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(prompt, device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt)\n uncond_embeddings, text_embeddings = text_embeddings.chunk(2)\n uncond_embeddings = torch.cat([uncond_embeddings] * 2)\n text_embeddings = torch.cat([text_embeddings] * 2)\n ph_tokens = self.tokenizer(\n num_images_per_prompt * [self.placeholder],\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids']\n input_ids = self.tokenizer(\n num_images_per_prompt * prompt,\n max_length=self.tokenizer.model_max_length,\n return_tensors='pt',\n padding='max_length',\n truncation=True)['input_ids']\n ph_tok = ph_tokens[0, 1]\n # TODO fix hard code\n clip_eot_token_id = 49407\n endoftext_idx = (torch.arange(input_ids.shape[0]),\n torch.nonzero(input_ids == clip_eot_token_id)\n [:batch_size, 1].repeat(num_images_per_prompt))\n placeholder_idx = torch.where(input_ids == ph_tok)\n if self.placeholder in prompt[0]:\n ph_pos = [placeholder_idx, endoftext_idx]\n else:\n ph_pos = [endoftext_idx, endoftext_idx]\n\n # 4. Prepare timesteps\n self.test_scheduler.set_timesteps(num_inference_steps)\n timesteps = self.test_scheduler.timesteps\n\n # 5. Prepare latent variables\n if hasattr(self.unet, 'module'):\n num_channels_latents = self.unet.module.in_channels\n else:\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n image_reference = self.prepare_reference(\n image_reference,\n height,\n width,\n )\n image_reference = self.vae.encode(\n image_reference.to(dtype=img_dtype,\n device=device)).latent_dist.sample()\n image_reference = image_reference.expand(\n batch_size * num_images_per_prompt, -1, -1, -1)\n image_reference = image_reference * self.vae.config.scaling_factor\n\n # 6. Prepare extra step kwargs.\n # TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n if show_progress:\n timesteps = tqdm(timesteps)\n for i, t in enumerate(timesteps):\n latents = torch.cat([latents, image_reference], dim=0)\n latent_model_input = self.test_scheduler.scale_model_input(\n latents, t)\n latent_model_input = latent_model_input.to(latent_dtype)\n text_embeddings = text_embeddings.to(latent_dtype)\n uncond_embeddings = uncond_embeddings.to(latent_dtype)\n # predict the noise residual\n\n noise_pred_uncond = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=uncond_embeddings,\n placeholder_position=ph_pos)['sample'][:batch_size *\n num_images_per_prompt]\n noise_pred_text = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n placeholder_position=ph_pos)['sample'][:batch_size *\n num_images_per_prompt]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_to_denoise = latents[:batch_size *\n num_images_per_prompt, ...]\n latents = self.test_scheduler.step(\n noise_pred, t, latents_to_denoise,\n **extra_step_kwargs)['prev_sample']\n\n # 8. Post-processing\n image = self.decode_latents(latents.to(img_dtype))\n if return_type == 'image':\n image = self.output_to_pil(image)\n elif return_type == 'numpy':\n image = image.cpu().numpy()\n else:\n assert return_type == 'tensor', (\n 'Only support \\'image\\', \\'numpy\\' and \\'tensor\\' for '\n f'return_type, but receive {return_type}')\n\n return {'samples': image}\n\n def forward(self,\n inputs: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:\n \"\"\"forward is not implemented now.\"\"\"\n raise NotImplementedError(\n 'Forward is not implemented now, please use infer.')\n" }, { "path": "mmagic/models/editors/vico/vico_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom diffusers import Transformer2DModel\nfrom diffusers.models.attention import Attention, BasicTransformerBlock\nfrom diffusers.models.unet_2d_condition import UNet2DConditionOutput\nfrom diffusers.utils import BaseOutput, is_torch_version\n\n\nclass ViCoCrossAttnProcessor:\n \"\"\"Processor for implementing attention for the ViCo method.\"\"\"\n\n def __call__(self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None):\n \"\"\"\n Args:\n attn (Attention): Attention module.\n hidden_states (torch.Tensor): Input hidden states.\n encoder_hidden_states (torch.Tensor): Encoder hidden states.\n attention_mask (torch.Tensor): Attention mask.\n Returns:\n torch.Tensor: Output hidden states.\n \"\"\"\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(\n attention_mask, sequence_length, batch_size=batch_size)\n\n query = attn.to_q(hidden_states)\n\n encoder_hidden_states = encoder_hidden_states \\\n if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n # new bookkeeping to save the attn probs\n attn.attn_probs = attention_probs\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\ndef replace_cross_attention(unet):\n \"\"\"Replace Cross Attention processor in UNet.\"\"\"\n for name, module in unet.named_modules():\n name: str\n if name.endswith('attn2'):\n module.set_processor(ViCoCrossAttnProcessor())\n\n\n@dataclass\nclass ViCoTransformer2DModelOutput(BaseOutput):\n \"\"\"Output for ViCoTransformer2DModel.\"\"\"\n sample: torch.FloatTensor\n loss_reg: torch.FloatTensor\n\n\ndef otsu(mask_in):\n \"\"\"Apply otsu for mask.\n\n Args:\n mask_in (torch.Tensor): Input mask.\n \"\"\"\n\n # normalize\n mask_norm = (mask_in - mask_in.min(-1, keepdim=True)[0]) / \\\n (mask_in.max(-1, keepdim=True)[0] - mask_in.min(-1, keepdim=True)[0])\n\n bs = mask_in.shape[0]\n h = mask_in.shape[1]\n mask = []\n for i in range(bs):\n threshold_t = 0.\n max_g = 0.\n for t in range(10):\n mask_i = mask_norm[i]\n low = mask_i[mask_i < t * 0.1]\n high = mask_i[mask_i >= t * 0.1]\n low_num = low.shape[0] / h\n high_num = high.shape[0] / h\n low_mean = low.mean()\n high_mean = high.mean()\n\n g = low_num * high_num * ((low_mean - high_mean)**2)\n if g > max_g:\n max_g = g\n threshold_t = t * 0.1\n\n mask_i[mask_i < threshold_t] = 0\n mask_i[mask_i > threshold_t] = 1\n mask.append(mask_i)\n mask_out = torch.stack(mask, dim=0)\n\n return mask_out\n\n\nclass ViCoTransformer2D(nn.Module):\n \"\"\"New ViCo-Transformer2D to replace the original Transformer2D model.\"\"\"\n\n def __init__(self, org_transformer2d: Transformer2DModel,\n have_image_cross) -> None:\n \"\"\"\n Args:\n org_transformer2d (Transformer2DModel): Original\n Transformer2DModel.\n have_image_cross (bool): Flag indicating if the model has\n image_cross_attention modules.\n \"\"\"\n super().__init__()\n self.transformer_blocks = org_transformer2d.transformer_blocks\n self.is_input_continuous = org_transformer2d.is_input_continuous\n self.norm = org_transformer2d.norm\n self.use_linear_projection = org_transformer2d.use_linear_projection\n self.proj_in = org_transformer2d.proj_in\n self.proj_out = org_transformer2d.proj_out\n self.is_input_vectorized = org_transformer2d.is_input_vectorized\n self.is_input_patches = org_transformer2d.is_input_patches\n\n num_attention_heads = org_transformer2d.num_attention_heads\n attention_head_dim = org_transformer2d.attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n\n self.have_image_cross = have_image_cross\n if self.have_image_cross:\n image_cross_attention = BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n cross_attention_dim=inner_dim)\n self.image_cross_attention = image_cross_attention.to(\n org_transformer2d.device, dtype=org_transformer2d.dtype)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n placeholder_position: list = None,\n class_labels: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ):\n if attention_mask is not None and attention_mask.ndim == 2:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (\n 1 - attention_mask.to(hidden_states.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n if encoder_attention_mask is not None and (encoder_attention_mask.ndim\n == 2):\n encoder_attention_mask = (\n 1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 1. Input\n if self.is_input_continuous:\n batch, _, height, width = hidden_states.shape\n residual = hidden_states\n\n hidden_states = self.norm(hidden_states)\n if not self.use_linear_projection:\n hidden_states = self.proj_in(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * width, inner_dim)\n else:\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(\n batch, height * width, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n elif self.is_input_vectorized:\n hidden_states = self.latent_image_embedding(hidden_states)\n elif self.is_input_patches:\n hidden_states = self.pos_embed(hidden_states)\n\n # 2. Blocks\n for block in self.transformer_blocks:\n hidden_states = block(\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n attention_probs = block.attn2.attn_probs[batch // 2:batch, ...]\n\n loss_reg = None\n if self.have_image_cross:\n # 2.5. image cross attention\n ph_idx, eot_idx = placeholder_position[\n 0], placeholder_position[1]\n attn = attention_probs.transpose(1, 2)\n attn_ph = attn[ph_idx].squeeze(1) # bs, n_patch\n attn_eot = attn[eot_idx].squeeze(1).detach()\n\n # ########################\n # attention reg\n if self.image_cross_attention.training:\n loss_reg = F.mse_loss(\n attn_ph / attn_ph.max(-1, keepdim=True)[0],\n attn_eot / attn_eot.max(-1, keepdim=True)[0])\n # ########################\n\n mask = attn_ph.detach()\n mask = otsu(mask)\n if mask.dim() == 2:\n mask = mask.unsqueeze(1)\n\n hidden_states, image_reference = hidden_states[:batch // 2], \\\n hidden_states[batch // 2:]\n hidden_states = self.image_cross_attention(\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=image_reference,\n encoder_attention_mask=mask,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n hidden_states = torch.cat([hidden_states, image_reference],\n dim=0)\n\n # 3. Output\n if self.is_input_continuous:\n if not self.use_linear_projection:\n hidden_states = hidden_states.reshape(batch, height, width,\n inner_dim).permute(\n 0, 3, 1,\n 2).contiguous()\n hidden_states = self.proj_out(hidden_states)\n else:\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.reshape(batch, height, width,\n inner_dim).permute(\n 0, 3, 1,\n 2).contiguous()\n\n output = hidden_states + residual\n elif self.is_input_vectorized:\n hidden_states = self.norm_out(hidden_states)\n logits = self.out(hidden_states)\n # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)\n logits = logits.permute(0, 2, 1)\n\n # log(p(x_0))\n output = F.log_softmax(logits.double(), dim=1).float()\n elif self.is_input_patches:\n # TODO: cleanup!\n conditioning = self.transformer_blocks[0].norm1.emb(\n timestep, class_labels, hidden_dtype=hidden_states.dtype)\n shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(\n 2, dim=1)\n hidden_states = self.norm_out(hidden_states) * (\n 1 + scale[:, None]) + shift[:, None]\n hidden_states = self.proj_out_2(hidden_states)\n\n # unpatchify\n height = width = int(hidden_states.shape[1]**0.5)\n hidden_states = hidden_states.reshape(\n shape=(-1, height, width, self.patch_size, self.patch_size,\n self.out_channels))\n hidden_states = torch.einsum('nhwpqc->nchpwq', hidden_states)\n output = hidden_states.reshape(\n shape=(-1, self.out_channels, height * self.patch_size,\n width * self.patch_size))\n\n if not return_dict:\n return (output, loss_reg)\n\n return ViCoTransformer2DModelOutput(sample=output, loss_reg=loss_reg)\n\n\ndef replace_transformer2d(module: nn.Module,\n have_image_cross: Dict[str, List[bool]]):\n \"\"\"Replace the the Transformer2DModel in UNet.\n\n Args:\n module (nn.Module): Parent module of Transformer2D.\n have_image_cross (List): List of flag indicating which\n transformer2D modules have image_cross_attention modules.\n \"\"\"\n down_transformer2d_modules = [(k.rsplit('.', 1), v)\n for k, v in module.named_modules()\n if isinstance(v, Transformer2DModel)]\n for i, ((parent, k), v) in enumerate(down_transformer2d_modules):\n parent = module.get_submodule(parent)\n setattr(parent, k, ViCoTransformer2D(v, have_image_cross[i]))\n\n\nclass ViCoBlockWrapper(nn.Module):\n \"\"\"Wrapper for ViCo blocks.\"\"\"\n\n def apply_to(self, org_module):\n self.org_module = org_module\n self.org_module.forward = self.forward\n\n\nclass ViCoCrossAttnDownBlock2D(ViCoBlockWrapper):\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n placeholder_position: torch.Tensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n \"\"\"\n Args:\n hidden_states (torch.FloatTensor): Hidden states.\n temb (Optional[torch.FloatTensor]): Time embedding.\n encoder_hidden_states (Optional[torch.FloatTensor]): Encoder\n hidden states.\n placeholder_position (torch.Tensor): Placeholder position.\n attention_mask (Optional[torch.FloatTensor]): Attention mask.\n cross_attention_kwargs (Optional[Dict[str, Any]]): Cross attention\n keyword arguments.\n encoder_attention_mask (Optional[torch.FloatTensor]): Encoder\n attention mask.\n Returns:\n torch.FloatTensor: Output hidden states.\n Tuple[torch.FloatTensor]: Output hidden states of each block.\n torch.FloatTensor: Attention regularization loss.\n \"\"\"\n output_states = ()\n loss_reg_all = 0.0\n\n for resnet, attn in zip(self.org_module.resnets,\n self.org_module.attentions):\n attn: ViCoTransformer2D\n if self.org_module.training and (\n self.org_module.gradient_checkpointing):\n\n def create_custom_forward(module, return_dict=None):\n\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\n 'use_reentrant': False\n } if is_torch_version('>=', '1.11.0') else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states, loss_reg = torch.utils.checkpoint.checkpoint(\n create_custom_forward(attn, return_dict=False),\n hidden_states,\n encoder_hidden_states,\n None, # timestep\n placeholder_position,\n None, # class_labels\n cross_attention_kwargs,\n attention_mask,\n encoder_attention_mask,\n **ckpt_kwargs,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states, loss_reg = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n placeholder_position=placeholder_position,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )\n\n output_states = output_states + (hidden_states, )\n if loss_reg is not None:\n loss_reg_all += loss_reg\n\n if self.org_module.downsamplers is not None:\n for downsampler in self.org_module.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states, )\n\n return hidden_states, output_states, loss_reg_all\n\n\nclass ViCoUNetMidBlock2DCrossAttn(ViCoBlockWrapper):\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n placeholder_position: torch.Tensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ) -> torch.FloatTensor:\n \"\"\"\n Args:\n hidden_states (torch.FloatTensor): Hidden states.\n temb (Optional[torch.FloatTensor]): Time embedding.\n encoder_hidden_states (Optional[torch.FloatTensor]): Encoder\n hidden states.\n placeholder_position (torch.Tensor): Placeholder position.\n attention_mask (Optional[torch.FloatTensor]): Attention mask.\n cross_attention_kwargs (Optional[Dict[str, Any]]): Cross attention\n keyword arguments.\n encoder_attention_mask (Optional[torch.FloatTensor]): Encoder\n attention mask.\n Returns:\n torch.FloatTensor: Output hidden states.\n torch.FloatTensor: Attention regularization loss.\n \"\"\"\n loss_reg_all = 0.0\n hidden_states = self.org_module.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.org_module.attentions,\n self.org_module.resnets[1:]):\n hidden_states, loss_reg = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n placeholder_position=placeholder_position,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )\n hidden_states = resnet(hidden_states, temb)\n if loss_reg is not None:\n loss_reg_all += loss_reg\n\n return hidden_states, loss_reg_all\n\n\nclass ViCoCrossAttnUpBlock2D(ViCoBlockWrapper):\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n placeholder_position: torch.Tensor = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n \"\"\"Performs the forward pass through the ViCoCrossAttnUpBlock2D module.\n\n Args:\n hidden_states (torch.FloatTensor): Input hidden states.\n res_hidden_states_tuple (Tuple[torch.FloatTensor, ...]):\n Tuple of residual hidden states.\n temb (Optional[torch.FloatTensor], optional):\n Temporal embeddings. Defaults to None.\n encoder_hidden_states (Optional[torch.FloatTensor], optional):\n Encoder hidden states. Defaults to None.\n placeholder_position (torch.Tensor, optional):\n Placeholder positions. Defaults to None.\n cross_attention_kwargs (Optional[Dict[str, Any]], optional):\n Keyword arguments for cross-attention. Defaults to None.\n upsample_size (Optional[int], optional): Upsample size.\n attention_mask (Optional[torch.FloatTensor], optional):\n Attention mask.\n encoder_attention_mask (Optional[torch.FloatTensor], optional):\n Encoder attention mask.\n\n Returns:\n Tuple[torch.FloatTensor, torch.FloatTensor]:\n A tuple containing the output hidden states and\n the total regularization loss.\n \"\"\"\n loss_reg_all = 0.0\n for resnet, attn in zip(self.org_module.resnets,\n self.org_module.attentions):\n attn: ViCoTransformer2D\n\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states],\n dim=1)\n\n if self.org_module.training and (\n self.org_module.gradient_checkpointing):\n\n def create_custom_forward(module, return_dict=None):\n\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\n 'use_reentrant': False\n } if is_torch_version('>=', '1.11.0') else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states, loss_reg = torch.utils.checkpoint.checkpoint(\n create_custom_forward(attn, return_dict=False),\n hidden_states,\n encoder_hidden_states,\n None, # timestep\n placeholder_position,\n None, # class_labels\n cross_attention_kwargs,\n attention_mask,\n encoder_attention_mask,\n **ckpt_kwargs,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states, loss_reg = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n placeholder_position=placeholder_position,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )\n if loss_reg is not None:\n loss_reg_all += loss_reg\n\n if self.org_module.upsamplers is not None:\n for upsampler in self.org_module.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states, loss_reg_all\n\n\nclass ViCoUNet2DConditionOutput(BaseOutput):\n \"\"\"Output for ViCoUNet2DConditionModel.\"\"\"\n sample: torch.FloatTensor\n loss_reg: torch.FloatTensor\n\n\nclass ViCoUNet2DConditionModel(ViCoBlockWrapper):\n \"\"\"UNet2DConditionModel for ViCo Method.\"\"\"\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n placeholder_position: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n \"\"\"Performs the forward pass through the ViCoBlock2D module.\n\n Args:\n sample (torch.FloatTensor): Input sample.\n timestep (Union[torch.Tensor, float, int]): Timestep value.\n encoder_hidden_states (torch.Tensor): Encoder hidden states.\n placeholder_position (torch.Tensor): Placeholder positions.\n class_labels (Optional[torch.Tensor], optional): Class labels.\n Defaults to None.\n timestep_cond (Optional[torch.Tensor], optional):\n Timestep condition. Defaults to None.\n attention_mask (Optional[torch.Tensor], optional):\n Attention mask. Defaults to None.\n cross_attention_kwargs (Optional[Dict[str, Any]], optional):\n Keyword arguments for cross-attention. Defaults to None.\n added_cond_kwargs (Optional[Dict[str, torch.Tensor]], optional):\n Additional condition arguments. Defaults to None.\n down_block_additional_residuals\n (Optional[Tuple[torch.Tensor]], optional):\n Additional residuals for down-blocks. Defaults to None.\n mid_block_additional_residual (Optional[torch.Tensor], optional):\n Additional residual for mid-block. Defaults to None.\n encoder_attention_mask (Optional[torch.Tensor], optional):\n Encoder attention mask. Defaults to None.\n return_dict (bool, optional):\n Whether to return a dictionary or a tuple.\n\n Returns:\n Union[UNet2DConditionOutput, Tuple]:\n The output of the forward pass, which can be either\n a UNet2DConditionOutput object or a tuple of tensors.\n \"\"\"\n default_overall_up_factor = 2**self.org_module.num_upsamplers\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n forward_upsample_size = True\n\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n if encoder_attention_mask is not None:\n encoder_attention_mask = (\n 1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.org_module.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n is_mps = sample.device.type == 'mps'\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps],\n dtype=dtype,\n device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n t_emb = self.org_module.time_proj(timesteps)\n\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.org_module.time_embedding(t_emb, timestep_cond)\n\n if self.org_module.class_embedding is not None:\n if class_labels is None:\n raise ValueError('class_labels should be provided \\\n when num_class_embeds > 0')\n\n if self.org_module.config.class_embed_type == 'timestep':\n class_labels = self.org_module.time_proj(class_labels)\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.org_module.class_embedding(class_labels).to(\n dtype=sample.dtype)\n\n if self.org_module.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n if self.org_module.config.addition_embed_type == 'text':\n aug_emb = self.org_module.add_embedding(encoder_hidden_states)\n emb = emb + aug_emb\n elif self.org_module.config.addition_embed_type == 'text_image':\n # Kadinsky 2.1 - style\n if 'image_embeds' not in added_cond_kwargs:\n raise ValueError(\n f\"{self.org_module.__class__} has the config param \\\n `addition_embed_type` set to 'text_image' which \\\n requires the keyword argument `image_embeds` \\\n to be passed in `added_cond_kwargs`\")\n\n image_embs = added_cond_kwargs.get('image_embeds')\n text_embs = added_cond_kwargs.get('text_embeds',\n encoder_hidden_states)\n\n aug_emb = self.org_module.add_embedding(text_embs, image_embs)\n emb = emb + aug_emb\n\n if self.org_module.time_embed_act is not None:\n emb = self.org_module.time_embed_act(emb)\n\n if self.org_module.encoder_hid_proj is not None and (\n self.org_module.config.encoder_hid_dim_type == 'text_proj'):\n encoder_hidden_states = self.org_module.encoder_hid_proj(\n encoder_hidden_states)\n elif self.org_module.encoder_hid_proj is not None and (\n self.org_module.config.encoder_hid_dim_type\n == 'text_image_proj'):\n # Kadinsky 2.1 - style\n if 'image_embeds' not in added_cond_kwargs:\n raise ValueError(\n f\"{self.org_module.__class__} has the config param \\\n `encoder_hid_dim_type` set to 'text_image_proj' which \\\n requires the keyword argument `image_embeds` to be \\\n passed in `added_conditions`\")\n\n image_embeds = added_cond_kwargs.get('image_embeds')\n encoder_hidden_states = self.org_module.encoder_hid_proj(\n encoder_hidden_states, image_embeds)\n\n # 2. pre-process\n sample = self.org_module.conv_in(sample)\n\n loss_reg_all = 0.0 if self.training else None\n\n # 3. down\n down_block_res_samples = (sample, )\n for downsample_block in self.org_module.down_blocks:\n if hasattr(downsample_block, 'has_cross_attention'\n ) and downsample_block.has_cross_attention:\n sample, res_samples, loss_reg = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n placeholder_position=placeholder_position,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n if self.training:\n loss_reg_all += loss_reg\n else:\n sample, res_samples = downsample_block(\n hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n if down_block_additional_residuals is not None:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals):\n down_block_res_sample = down_block_res_sample + \\\n down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (\n down_block_res_sample, )\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.org_module.mid_block is not None:\n sample, loss_reg = self.org_module.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n if self.training:\n loss_reg_all += loss_reg\n if mid_block_additional_residual is not None:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.org_module.up_blocks):\n is_final_block = i == len(self.org_module.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets):]\n down_block_res_samples = down_block_res_samples[:-len(\n upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, 'has_cross_attention'\n ) and upsample_block.has_cross_attention:\n sample, loss_reg = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n placeholder_position=placeholder_position,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n )\n if self.training:\n loss_reg_all += loss_reg\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size)\n\n # 6. post-process\n if self.org_module.conv_norm_out:\n sample = self.org_module.conv_norm_out(sample)\n sample = self.org_module.conv_act(sample)\n sample = self.org_module.conv_out(sample)\n\n if not return_dict:\n return (sample, loss_reg_all)\n\n return ViCoUNet2DConditionOutput(sample=sample, loss_reg=loss_reg_all)\n\n\ndef set_vico_modules(unet, image_cross_layers):\n \"\"\"Set all modules for ViCo method after the UNet initialized normally.\n\n Args:\n unet (nn.Module): UNet model.\n image_cross_layers (List): List of flag indicating which\n transformer2D modules have image_cross_attention modules.\n \"\"\"\n # replace transformer2d blocks\n replace_transformer2d(unet, image_cross_layers)\n\n # replace cross attention layer\n replace_cross_attention(unet)\n\n # replace forward\n for _, layer in unet.named_modules():\n if layer.__class__.__name__ == 'UNet2DConditionModel':\n vico_unet = ViCoUNet2DConditionModel()\n vico_unet.apply_to(unet)\n elif layer.__class__.__name__ == 'CrossAttnDownBlock2D':\n vico_down_block = ViCoCrossAttnDownBlock2D()\n vico_down_block.apply_to(layer)\n elif layer.__class__.__name__ == 'UNetMidBlock2DCrossAttn':\n vico_mid_block = ViCoUNetMidBlock2DCrossAttn()\n vico_mid_block.apply_to(layer)\n elif layer.__class__.__name__ == 'CrossAttnUpBlock2D':\n vico_up_block = ViCoCrossAttnUpBlock2D()\n vico_up_block.apply_to(layer)\n" }, { "path": "mmagic/models/editors/wgan_gp/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .wgan_discriminator import WGANGPDiscriminator\nfrom .wgan_generator import WGANGPGenerator\nfrom .wgan_gp import WGANGP\n\n__all__ = ['WGANGPDiscriminator', 'WGANGPGenerator', 'WGANGP']\n" }, { "path": "mmagic/models/editors/wgan_gp/wgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.registry import MODELS\nfrom .wgan_gp_module import ConvLNModule, WGANDecisionHead\n\n\n@MODELS.register_module()\nclass WGANGPDiscriminator(BaseModule):\n r\"\"\"Discriminator for WGANGP.\n\n Implementation Details for WGANGP discriminator the same as training\n configuration (a) described in PGGAN paper:\n PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION\n https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf # noqa\n\n #. Adopt convolution architecture specified in appendix A.2;\n #. Add layer normalization to all conv3x3 and conv4x4 layers;\n #. Use LeakyReLU in the discriminator except for the final output layer;\n #. Initialize all weights using He’s initializer.\n\n Args:\n in_channel (int): The channel number of the input image.\n in_scale (int): The scale of the input image.\n conv_module_cfg (dict, optional): Config for the convolution module\n used in this discriminator. Defaults to None.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n _default_channels_per_scale = {\n '4': 512,\n '8': 512,\n '16': 256,\n '32': 128,\n '64': 64,\n '128': 32\n }\n _default_conv_module_cfg = dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='LN2d'),\n order=('conv', 'norm', 'act'))\n\n _default_upsample_cfg = dict(type='nearest', scale_factor=2)\n\n def __init__(self,\n in_channel,\n in_scale,\n conv_module_cfg=None,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n # set initial params\n self.in_channel = in_channel\n self.in_scale = in_scale\n self.conv_module_cfg = deepcopy(self._default_conv_module_cfg)\n if conv_module_cfg is not None:\n self.conv_module_cfg.update(conv_module_cfg)\n # set from_rgb head\n self.from_rgb = ConvModule(\n 3,\n kernel_size=1,\n out_channels=self._default_channels_per_scale[str(self.in_scale)],\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2))\n # set conv_blocks\n self.conv_blocks = nn.ModuleList()\n\n log2scale = int(np.log2(self.in_scale))\n for i in range(log2scale, 2, -1):\n self.conv_blocks.append(\n ConvLNModule(\n self._default_channels_per_scale[str(2**i)],\n self._default_channels_per_scale[str(2**i)],\n feature_shape=(self._default_channels_per_scale[str(2**i)],\n 2**i, 2**i),\n **self.conv_module_cfg))\n self.conv_blocks.append(\n ConvLNModule(\n self._default_channels_per_scale[str(2**i)],\n self._default_channels_per_scale[str(2**(i - 1))],\n feature_shape=(self._default_channels_per_scale[str(\n 2**(i - 1))], 2**i, 2**i),\n **self.conv_module_cfg))\n self.conv_blocks.append(nn.AvgPool2d(kernel_size=2, stride=2))\n self.decision = WGANDecisionHead(\n self._default_channels_per_scale['4'],\n self._default_channels_per_scale['4'],\n 1,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=self.conv_module_cfg['norm_cfg'])\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (torch.Tensor): Fake or real image tensor.\n\n Returns:\n torch.Tensor: Prediction for the reality of the input image.\n \"\"\"\n # noise vector to 2D feature\n x = self.from_rgb(x)\n for conv in self.conv_blocks:\n x = conv(x)\n x = self.decision(x)\n return x\n" }, { "path": "mmagic/models/editors/wgan_gp/wgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule\nfrom mmengine.model import BaseModule\n\nfrom mmagic.models.utils import get_module_device\nfrom mmagic.registry import MODELS\nfrom .wgan_gp_module import WGANNoiseTo2DFeat\n\n\n@MODELS.register_module()\nclass WGANGPGenerator(BaseModule):\n r\"\"\"Generator for WGANGP.\n\n Implementation Details for WGANGP generator the same as training\n configuration (a) described in PGGAN paper:\n PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION\n https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf # noqa\n\n #. Adopt convolution architecture specified in appendix A.2;\n #. Use batchnorm in the generator except for the final output layer;\n #. Use ReLU in the generator except for the final output layer;\n #. Use Tanh in the last layer;\n #. Initialize all weights using He’s initializer.\n\n Args:\n noise_size (int): Size of the input noise vector.\n out_scale (int): Output scale for the generated image.\n conv_module_cfg (dict, optional): Config for the convolution\n module used in this generator. Defaults to None.\n upsample_cfg (dict, optional): Config for the upsampling operation.\n Defaults to None.\n init_cfg (dict, optional): Initialization config dict.\n \"\"\"\n _default_channels_per_scale = {\n '4': 512,\n '8': 512,\n '16': 256,\n '32': 128,\n '64': 64,\n '128': 32\n }\n _default_conv_module_cfg = dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='ReLU'),\n norm_cfg=dict(type='BN'),\n order=('conv', 'norm', 'act'))\n\n _default_upsample_cfg = dict(type='nearest', scale_factor=2)\n\n def __init__(self,\n noise_size,\n out_scale,\n conv_module_cfg=None,\n upsample_cfg=None,\n init_cfg=None):\n super().__init__(init_cfg=init_cfg)\n # set initial params\n self.noise_size = noise_size\n self.out_scale = out_scale\n self.conv_module_cfg = deepcopy(self._default_conv_module_cfg)\n if conv_module_cfg is not None:\n self.conv_module_cfg.update(conv_module_cfg)\n self.upsample_cfg = upsample_cfg if upsample_cfg else deepcopy(\n self._default_upsample_cfg)\n # set noise2feat head\n self.noise2feat = WGANNoiseTo2DFeat(\n self.noise_size, self._default_channels_per_scale['4'])\n # set conv_blocks\n self.conv_blocks = nn.ModuleList()\n self.conv_blocks.append(ConvModule(512, 512, **self.conv_module_cfg))\n\n log2scale = int(np.log2(self.out_scale))\n for i in range(3, log2scale + 1):\n self.conv_blocks.append(MODELS.build(self._default_upsample_cfg))\n self.conv_blocks.append(\n ConvModule(self._default_channels_per_scale[str(2**(i - 1))],\n self._default_channels_per_scale[str(2**i)],\n **self.conv_module_cfg))\n self.conv_blocks.append(\n ConvModule(self._default_channels_per_scale[str(2**i)],\n self._default_channels_per_scale[str(2**i)],\n **self.conv_module_cfg))\n self.to_rgb = ConvModule(\n self._default_channels_per_scale[str(self.out_scale)],\n kernel_size=1,\n out_channels=3,\n act_cfg=dict(type='Tanh'))\n\n def forward(self, noise, num_batches=0, return_noise=False):\n \"\"\"Forward function.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the\n ``None`` indicates to use the default noise sampler.\n num_batches (int, optional): The number of batch size. Defaults to\n 0.\n return_noise (bool, optional): If True, ``noise_batch`` will be\n returned in a dict with ``fake_img``. Defaults to False.\n\n Returns:\n torch.Tensor | dict: If not ``return_noise``, only the output image\n will be returned. Otherwise, a dict contains ``fake_img`` and\n ``noise_batch`` will be returned.\n \"\"\"\n # receive noise and conduct sanity check.\n if isinstance(noise, torch.Tensor):\n assert noise.shape[1] == self.noise_size\n assert noise.ndim == 2, ('The noise should be in shape of (n, c), '\n f'but got {noise.shape}')\n noise_batch = noise\n # receive a noise generator and sample noise.\n elif callable(noise):\n noise_generator = noise\n assert num_batches > 0\n noise_batch = noise_generator((num_batches, self.noise_size))\n # otherwise, we will adopt default noise sampler.\n else:\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, self.noise_size))\n\n # dirty code for putting data on the right device\n noise_batch = noise_batch.to(get_module_device(self))\n\n # noise vector to 2D feature\n x = self.noise2feat(noise_batch)\n for conv in self.conv_blocks:\n x = conv(x)\n out_img = self.to_rgb(x)\n\n if return_noise:\n output = dict(fake_img=out_img, noise_batch=noise_batch)\n return output\n\n return out_img\n" }, { "path": "mmagic/models/editors/wgan_gp/wgan_gp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Dict, Tuple\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch import Tensor\n\nfrom mmagic.models.base_models import BaseGAN\nfrom mmagic.models.losses import gradient_penalty_loss\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@MODELS.register_module()\nclass WGANGP(BaseGAN):\n \"\"\"Implementation of `Improved Training of Wasserstein GANs`.\n\n Paper link: https://arxiv.org/pdf/1704.00028\n\n Detailed architecture can be found in\n :class:`~mmagic.models.editors.wgan_gp.WGANGPGenerator`\n and\n :class:`~mmagic.models.editors.wgan_gp.WGANGPDiscriminator`\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n # gradient penalty loss settings\n self.gp_norm_mode = self.loss_config.get('norm_mode', 'HWC')\n self.gp_loss_weight = self.loss_config.get('loss_weight', 10)\n\n def disc_loss(self, real_data: Tensor, fake_data: Tensor,\n disc_pred_fake: Tensor, disc_pred_real: Tensor) -> Tuple:\n r\"\"\"Get disc loss. WGAN-GP use the wgan loss and gradient penalty to\n train the discriminator.\n\n Args:\n real_data (Tensor): Real input data.\n fake_data (Tensor): Fake input data.\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n disc_pred_real (Tensor): Discriminator's prediction of the real\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_disc_fake'] = disc_pred_fake.mean()\n losses_dict['loss_disc_real'] = -disc_pred_real.mean()\n\n # Gradient Penalty loss\n losses_dict['loss_gp'] = self.gp_loss_weight * gradient_penalty_loss(\n self.discriminator,\n real_data,\n fake_data,\n norm_mode=self.gp_norm_mode)\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:\n \"\"\"Get gen loss. DCGAN use the wgan loss to train the generator.\n\n Args:\n disc_pred_fake (Tensor): Discriminator's prediction of the fake\n images.\n\n Returns:\n tuple[Tensor, dict]: Loss value and a dict of log variables.\n \"\"\"\n losses_dict = dict()\n losses_dict['loss_gen'] = -disc_pred_fake.mean()\n loss, log_var = self.parse_losses(losses_dict)\n return loss, log_var\n\n def train_discriminator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper\n ) -> Dict[str, Tensor]:\n \"\"\"Train discriminator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n real_imgs = data_samples.gt_img\n\n num_batches = real_imgs.shape[0]\n\n noise_batch = self.noise_fn(num_batches=num_batches)\n with torch.no_grad():\n fake_imgs = self.generator(noise=noise_batch, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n disc_pred_real = self.discriminator(real_imgs)\n\n parsed_losses, log_vars = self.disc_loss(real_imgs, fake_imgs,\n disc_pred_fake,\n disc_pred_real)\n optimizer_wrapper.update_params(parsed_losses)\n return log_vars\n\n def train_generator(self, inputs: dict, data_samples: DataSample,\n optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:\n \"\"\"Train generator.\n\n Args:\n inputs (dict): Inputs from dataloader.\n data_samples (DataSample): Data samples from dataloader.\n Do not used in generator's training.\n optim_wrapper (OptimWrapper): OptimWrapper instance used to update\n model parameters.\n\n Returns:\n Dict[str, Tensor]: A ``dict`` of tensor for logging.\n \"\"\"\n num_batches = len(data_samples)\n\n noise = self.noise_fn(num_batches=num_batches)\n fake_imgs = self.generator(noise=noise, return_noise=False)\n\n disc_pred_fake = self.discriminator(fake_imgs)\n parsed_loss, log_vars = self.gen_loss(disc_pred_fake)\n\n optimizer_wrapper.update_params(parsed_loss)\n return log_vars\n" }, { "path": "mmagic/models/editors/wgan_gp/wgan_gp_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule, build_norm_layer\nfrom mmengine.model import constant_init\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass WGANNoiseTo2DFeat(nn.Module):\n \"\"\"Module used in WGAN-GP to transform 1D noise tensor in order [N, C] to\n 2D shape feature tensor in order [N, C, H, W].\n\n Args:\n noise_size (int): Size of the input noise vector.\n out_channels (int): The channel number of the output feature.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n norm_cfg (dict, optional): Config dict to build norm layer. Defaults to\n dict(type='BN').\n order (tuple, optional): The order of conv/norm/activation layers. It\n is a sequence of \"conv\", \"norm\" and \"act\". Common examples are\n (\"conv\", \"norm\", \"act\") and (\"act\", \"conv\", \"norm\"). Defaults to\n ('linear', 'act', 'norm').\n \"\"\"\n\n def __init__(self,\n noise_size,\n out_channels,\n act_cfg=dict(type='ReLU'),\n norm_cfg=dict(type='BN'),\n order=('linear', 'act', 'norm')):\n super().__init__()\n self.noise_size = noise_size\n self.out_channels = out_channels\n self.with_activation = act_cfg is not None\n self.with_norm = norm_cfg is not None\n self.order = order\n assert len(order) == 3 and set(order) == set(['linear', 'act', 'norm'])\n\n # w/o bias, because the bias is added after reshaping the tensor to\n # 2D feature\n self.linear = nn.Linear(noise_size, out_channels * 16, bias=False)\n\n if self.with_activation:\n self.activation = MODELS.build(act_cfg)\n\n # add bias for reshaped 2D feature.\n self.register_parameter(\n 'bias', nn.Parameter(torch.zeros(1, out_channels, 1, 1)))\n\n if self.with_norm:\n _, self.norm = build_norm_layer(norm_cfg, out_channels)\n self._init_weight()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input noise tensor with shape (n, c).\n\n Returns:\n Tensor: Forward results with shape (n, c, 4, 4).\n \"\"\"\n assert x.ndim == 2\n for order in self.order:\n if order == 'linear':\n x = self.linear(x)\n # [n, c, 4, 4]\n x = torch.reshape(x, (-1, self.out_channels, 4, 4))\n x = x + self.bias\n elif order == 'act' and self.with_activation:\n x = self.activation(x)\n elif order == 'norm' and self.with_norm:\n x = self.norm(x)\n\n return x\n\n def _init_weight(self):\n \"\"\"Initialize weights for the model.\"\"\"\n nn.init.normal_(self.linear.weight, 0., 1.)\n if self.bias is not None:\n nn.init.constant_(self.bias, 0.)\n if self.with_norm:\n constant_init(self.norm, 1, bias=0)\n\n\nclass WGANDecisionHead(nn.Module):\n \"\"\"Module used in WGAN-GP to get the final prediction result with 4x4\n resolution input tensor in the bottom of the discriminator.\n\n Args:\n in_channels (int): Number of channels in input feature map.\n mid_channels (int): Number of channels in feature map after\n convolution.\n out_channels (int): The channel number of the final output layer.\n bias (bool, optional): Whether to use bias parameter. Defaults to True.\n act_cfg (dict, optional): Config for the activation layer. Defaults to\n dict(type='ReLU').\n out_act (dict, optional): Config for the activation layer of output\n layer. Defaults to None.\n norm_cfg (dict, optional): Config dict to build norm layer. Defaults to\n dict(type='LN2d').\n \"\"\"\n\n def __init__(self,\n in_channels,\n mid_channels,\n out_channels,\n bias=True,\n act_cfg=dict(type='ReLU'),\n out_act=None,\n norm_cfg=dict(type='LN2d')):\n super().__init__()\n self.in_channels = in_channels\n self.mid_channels = mid_channels\n self.out_channels = out_channels\n self.with_out_activation = out_act is not None\n\n # setup conv layer\n self.conv = ConvLNModule(\n in_channels,\n feature_shape=(mid_channels, 1, 1),\n kernel_size=4,\n out_channels=mid_channels,\n act_cfg=act_cfg,\n norm_cfg=norm_cfg,\n order=('conv', 'norm', 'act'))\n # setup linear layer\n self.linear = nn.Linear(\n self.mid_channels, self.out_channels, bias=bias)\n\n if self.with_out_activation:\n self.out_activation = MODELS.build(out_act)\n\n self._init_weight()\n\n def forward(self, x):\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n x = self.conv(x)\n x = torch.reshape(x, (x.shape[0], -1))\n x = self.linear(x)\n if self.with_out_activation:\n x = self.out_activation(x)\n return x\n\n def _init_weight(self):\n \"\"\"Initialize weights for the model.\"\"\"\n nn.init.normal_(self.linear.weight, 0., 1.)\n nn.init.constant_(self.linear.bias, 0.)\n\n\n@MODELS.register_module()\nclass ConvLNModule(ConvModule):\n r\"\"\"ConvModule with Layer Normalization.\n\n In this module, we inherit default ``mmcv.cnn.ConvModule`` and deal with\n the situation that 'norm_cfg' is 'LN2d' or 'GN'. We adopt 'GN' as a\n replacement for layer normalization referring to:\n https://github.com/LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Pytorch/blob/master/module.py # noqa\n\n Args:\n feature_shape (tuple): The shape of feature map that will be.\n \"\"\"\n\n def __init__(self, *args, feature_shape=None, **kwargs):\n if 'norm_cfg' in kwargs and kwargs['norm_cfg'] is not None and kwargs[\n 'norm_cfg']['type'] in ['LN2d', 'GN']:\n nkwargs = deepcopy(kwargs)\n nkwargs['norm_cfg'] = None\n super().__init__(*args, **nkwargs)\n self.with_norm = True\n self.norm_name = kwargs['norm_cfg']['type']\n if self.norm_name == 'LN2d':\n norm = nn.LayerNorm(feature_shape)\n self.add_module(self.norm_name, norm)\n else:\n norm = nn.GroupNorm(1, feature_shape[0])\n self.add_module(self.norm_name, norm)\n else:\n super().__init__(*args, **kwargs)\n" }, { "path": "mmagic/models/losses/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .adv_loss import AdvLoss\nfrom .clip_loss import CLIPLoss\nfrom .composition_loss import (CharbonnierCompLoss, L1CompositionLoss,\n MSECompositionLoss)\nfrom .face_id_loss import FaceIdLoss\nfrom .feature_loss import LightCNNFeatureLoss\nfrom .gan_loss import (DiscShiftLoss, GANLoss, GaussianBlur,\n GradientPenaltyLoss, disc_shift_loss,\n gen_path_regularizer, gradient_penalty_loss,\n r1_gradient_penalty_loss)\nfrom .gradient_loss import GradientLoss\nfrom .loss_comps import (CLIPLossComps, DiscShiftLossComps, FaceIdLossComps,\n GANLossComps, GeneratorPathRegularizerComps,\n GradientPenaltyLossComps, R1GradientPenaltyComps)\nfrom .loss_wrapper import mask_reduce_loss, reduce_loss\nfrom .perceptual_loss import (PerceptualLoss, PerceptualVGG,\n TransferalPerceptualLoss)\nfrom .pixelwise_loss import (CharbonnierLoss, L1Loss, MaskedTVLoss, MSELoss,\n PSNRLoss, tv_loss)\n\n__all__ = [\n 'L1Loss', 'MSELoss', 'CharbonnierLoss', 'L1CompositionLoss',\n 'MSECompositionLoss', 'CharbonnierCompLoss', 'GANLoss', 'GaussianBlur',\n 'GradientPenaltyLoss', 'PerceptualLoss', 'PerceptualVGG', 'reduce_loss',\n 'mask_reduce_loss', 'DiscShiftLoss', 'MaskedTVLoss', 'GradientLoss',\n 'TransferalPerceptualLoss', 'LightCNNFeatureLoss', 'gradient_penalty_loss',\n 'r1_gradient_penalty_loss', 'gen_path_regularizer', 'FaceIdLoss',\n 'CLIPLoss', 'CLIPLossComps', 'DiscShiftLossComps', 'FaceIdLossComps',\n 'GANLossComps', 'GeneratorPathRegularizerComps',\n 'GradientPenaltyLossComps', 'R1GradientPenaltyComps', 'disc_shift_loss',\n 'tv_loss', 'PSNRLoss', 'AdvLoss'\n]\n" }, { "path": "mmagic/models/losses/adv_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport random\nfrom collections import deque\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom .gan_loss import GANLoss, gradient_penalty_loss\n\n\nclass ImagePool:\n \"\"\"Defined a image pool for RelativisticDiscLoss.\"\"\"\n\n def __init__(self, pool_size):\n self.pool_size = pool_size\n self.sample_size = pool_size\n if self.pool_size > 0:\n self.num_imgs = 0\n self.images = deque()\n\n def add(self, images):\n if self.pool_size == 0:\n return images\n for image in images.data:\n image = torch.unsqueeze(image, 0)\n if self.num_imgs < self.pool_size:\n self.num_imgs = self.num_imgs + 1\n self.images.append(image)\n else:\n self.images.popleft()\n self.images.append(image)\n\n def query(self):\n if len(self.images) > self.sample_size:\n return_images = list(random.sample(self.images, self.sample_size))\n else:\n return_images = list(self.images)\n return torch.cat(return_images, 0)\n\n\nclass DiscLoss(nn.Module):\n \"\"\"Defined a criterion to calculator loss.\"\"\"\n\n def name(self):\n \"\"\"return name of criterion.\"\"\"\n return 'DiscLoss'\n\n def __init__(self, gan_type='vanilla'):\n super(DiscLoss, self).__init__()\n\n self.criterionGAN = GANLoss(gan_type=gan_type)\n\n def forward(self, net, fakeB, realB, model='discriminator'):\n \"\"\"Get discriminator or generator loss.\"\"\"\n if model == 'discriminator':\n self.pred_fake = net.forward(fakeB.detach())\n self.loss_D_fake = self.criterionGAN(self.pred_fake, 0)\n\n # Real\n self.pred_real = net.forward(realB)\n self.loss_D_real = self.criterionGAN(self.pred_real, 1)\n\n # Combined loss\n self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5\n return self.loss_D\n else:\n pred_fake = net.forward(fakeB)\n return self.criterionGAN(pred_fake, 1)\n\n\nclass RelativisticDiscLoss(nn.Module):\n \"\"\"Defined a criterion to calculator loss.\"\"\"\n\n def name(self):\n \"\"\"return name of criterion.\"\"\"\n return 'RelativisticDiscLoss'\n\n def __init__(self):\n super(RelativisticDiscLoss, self).__init__()\n\n self.criterionGAN = GANLoss(gan_type='vanilla')\n self.fake_pool = ImagePool(50)\n self.real_pool = ImagePool(50)\n\n def forward(self, net, fakeB, realB, model='discriminator'):\n \"\"\"Get discriminator or generator loss.\"\"\"\n if model == 'discriminator':\n self.fake_B = fakeB.detach()\n self.real_B = realB\n self.pred_fake = net.forward(self.fake_B)\n self.fake_pool.add(self.pred_fake)\n\n # Real\n self.pred_real = net.forward(self.real_B)\n self.real_pool.add(self.pred_real)\n\n # Combined loss\n self.loss_D = (self.criterionGAN(\n self.pred_real - torch.mean(self.fake_pool.query()), 1) +\n self.criterionGAN(\n self.pred_fake -\n torch.mean(self.real_pool.query()), 0)) / 2\n return self.loss_D\n else:\n self.pred_fake = net.forward(fakeB)\n\n # Real\n self.pred_real = net.forward(realB)\n errG = (self.criterionGAN(\n self.pred_real - torch.mean(self.fake_pool.query()), 0) +\n self.criterionGAN(\n self.pred_fake - torch.mean(self.real_pool.query()),\n 1)) / 2\n return errG\n\n\nclass RelativisticDiscLossLS(nn.Module):\n \"\"\"Defined a criterion to calculator loss.\"\"\"\n\n def name(self):\n \"\"\"return name of criterion.\"\"\"\n return 'RelativisticDiscLossLS'\n\n def __init__(self):\n super(RelativisticDiscLossLS, self).__init__()\n\n self.criterionGAN = GANLoss(gan_type='l1')\n self.fake_pool = ImagePool(50)\n self.real_pool = ImagePool(50)\n\n def forward(self, net, fakeB, realB, model='discriminator'):\n \"\"\"Get discriminator or generator loss.\"\"\"\n if model == 'discriminator':\n self.fake_B = fakeB.detach()\n self.real_B = realB\n self.pred_fake = net.forward(fakeB.detach())\n self.fake_pool.add(self.pred_fake)\n\n # Real\n self.pred_real = net.forward(realB)\n self.real_pool.add(self.pred_real)\n\n # Combined loss\n ex_pdata = torch.mean(\n (self.pred_real - torch.mean(self.fake_pool.query()) - 1)**2)\n ex_pz = torch.mean(\n (self.pred_fake - torch.mean(self.real_pool.query()) + 1)**2)\n self.loss_D = (ex_pdata + ex_pz) / 2\n return self.loss_D\n else:\n self.pred_fake = net.forward(fakeB)\n\n # Real\n self.pred_real = net.forward(realB)\n ex_pdata = torch.mean(\n (self.pred_real - torch.mean(self.fake_pool.query()) + 1)**2)\n ez_pz = torch.mean(\n (self.pred_fake - torch.mean(self.real_pool.query()) - 1)**2)\n errG = (ex_pdata + ez_pz) / 2\n return errG\n\n\nclass DiscLossWGANGP(DiscLoss):\n \"\"\"Defined a criterion to calculator loss.\"\"\"\n\n def name(self):\n \"\"\"return name of criterion.\"\"\"\n return 'DiscLossWGAN-GP'\n\n def __init__(self):\n super(DiscLossWGANGP, self).__init__()\n self.LAMBDA = 10\n\n def forward(self, net, fakeB, realB, model='discriminator'):\n \"\"\"Get discriminator or generator loss.\"\"\"\n if model == 'discriminator':\n self.D_fake = net.forward(fakeB.detach())\n self.D_fake = self.D_fake.mean()\n\n # Real\n self.D_real = net.forward(realB)\n self.D_real = self.D_real.mean()\n # Combined loss\n self.loss_D = self.D_fake - self.D_real\n gradient_penalty = gradient_penalty_loss(net, realB.data,\n fakeB.data) * self.LAMBDA\n return self.loss_D + gradient_penalty\n else:\n self.D_fake = net.forward(fakeB)\n return -self.D_fake.mean()\n\n\n@MODELS.register_module()\nclass AdvLoss:\n \"\"\"Returns the loss of discriminator with the specified type for\n DeblurGanv2.\n\n Args:\n loss_type (Str): One of value in [wgan-gp,lsgan,gan,ragan,ragan-ls].\n \"\"\"\n\n def __new__(cls, loss_type: str):\n if loss_type == 'wgan-gp':\n disc_loss = DiscLossWGANGP()\n elif loss_type == 'lsgan':\n disc_loss = DiscLoss(gan_type='l1')\n elif loss_type == 'gan':\n disc_loss = DiscLoss()\n elif loss_type == 'ragan':\n disc_loss = RelativisticDiscLoss()\n elif loss_type == 'ragan-ls':\n disc_loss = RelativisticDiscLossLS()\n else:\n raise ValueError('GAN Loss [%s] not recognized.' % loss_type)\n return disc_loss\n" }, { "path": "mmagic/models/losses/clip_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import try_import\n\nclip = try_import('clip')\n\n\nclass CLIPLossModel(torch.nn.Module):\n \"\"\"Wrapped clip model to calculate clip loss.\n\n Ref: https://github.com/orpatashnik/StyleCLIP/blob/main/criteria/clip_loss.py # noqa\n\n Args:\n in_size (int, optional): Input image size. Defaults to 1024.\n scale_factor (int, optional): Unsampling factor. Defaults to 7.\n pool_size (int, optional): Pooling output size. Defaults to 224.\n clip_type (str, optional): A model name listed by\n `clip.available_models()`, or the path to a model checkpoint\n containing the state_dict. For more details, you can refer to\n https://github.com/openai/CLIP/blob/573315e83f07b53a61ff5098757e8fc885f1703e/clip/clip.py#L91 # noqa\n Defaults to 'ViT-B/32'.\n \"\"\"\n\n def __init__(self,\n in_size: int = 1024,\n scale_factor: int = 7,\n pool_size: int = 224,\n clip_type: str = 'ViT-B/32') -> None:\n super(CLIPLossModel, self).__init__()\n try:\n import clip\n except ImportError:\n raise 'To use clip loss, openai clip need to be installed first'\n\n assert clip is not None, (\n \"Cannot import 'clip'. Please install 'clip' via \"\n \"\\\"pip install git+https://github.com/openai/CLIP.git\\\".\")\n self.model, self.preprocess = clip.load(clip_type, device='cpu')\n self.upsample = torch.nn.Upsample(scale_factor=scale_factor)\n self.avg_pool = torch.nn.AvgPool2d(\n kernel_size=(scale_factor * in_size // pool_size))\n\n def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\"\"\"\n assert image is not None\n assert text is not None\n image = self.avg_pool(self.upsample(image))\n loss = 1 - self.model(image, text)[0] / 100\n return loss\n\n\n@MODELS.register_module()\nclass CLIPLoss(nn.Module):\n \"\"\"Clip loss. In styleclip, this loss is used to optimize the latent code\n to generate image that match the text.\n\n In this loss, we may need to provide ``image``, ``text``. Thus,\n an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n image='fake_imgs',\n text='descriptions')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n clip_model (dict, optional): Kwargs for clip loss model. Defaults to\n dict().\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_clip'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n data_info: Optional[dict] = None,\n clip_model: dict = dict(),\n loss_name: str = 'loss_clip') -> None:\n\n super(CLIPLoss, self).__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n self.net = CLIPLossModel(**clip_model)\n self._loss_name = loss_name\n\n def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``third_party_net_loss``.\n \"\"\"\n return self.net(image, text) * self.loss_weight\n" }, { "path": "mmagic/models/losses/composition_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom .pixelwise_loss import charbonnier_loss, l1_loss, mse_loss\n\n_reduction_modes = ['none', 'mean', 'sum']\n\n\n@MODELS.register_module()\nclass L1CompositionLoss(nn.Module):\n \"\"\"L1 composition loss.\n\n Args:\n loss_weight (float): Loss weight for L1 loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n\n def forward(self,\n pred_alpha: torch.Tensor,\n fg: torch.Tensor,\n bg: torch.Tensor,\n ori_merged: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"\n Args:\n pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.\n fg (Tensor): of shape (N, 3, H, W). Tensor of foreground object.\n bg (Tensor): of shape (N, 3, H, W). Tensor of background object.\n ori_merged (Tensor): of shape (N, 3, H, W). Tensor of origin merged\n image before normalized by ImageNet mean and std.\n weight (Tensor, optional): of shape (N, 1, H, W). It is an\n indicating matrix: weight[trimap == 128] = 1. Default: None.\n \"\"\"\n pred_merged = pred_alpha * fg + (1. - pred_alpha) * bg\n if weight is not None:\n weight = weight.expand(-1, 3, -1, -1)\n return self.loss_weight * l1_loss(\n pred_merged,\n ori_merged,\n weight,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n\n\n@MODELS.register_module()\nclass MSECompositionLoss(nn.Module):\n \"\"\"MSE (L2) composition loss.\n\n Args:\n loss_weight (float): Loss weight for MSE loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n\n def forward(self,\n pred_alpha: torch.Tensor,\n fg: torch.Tensor,\n bg: torch.Tensor,\n ori_merged: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"\n Args:\n pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.\n fg (Tensor): of shape (N, 3, H, W). Tensor of foreground object.\n bg (Tensor): of shape (N, 3, H, W). Tensor of background object.\n ori_merged (Tensor): of shape (N, 3, H, W). Tensor of origin merged\n image before normalized by ImageNet mean and std.\n weight (Tensor, optional): of shape (N, 1, H, W). It is an\n indicating matrix: weight[trimap == 128] = 1. Default: None.\n \"\"\"\n pred_merged = pred_alpha * fg + (1. - pred_alpha) * bg\n if weight is not None:\n weight = weight.expand(-1, 3, -1, -1)\n return self.loss_weight * mse_loss(\n pred_merged,\n ori_merged,\n weight,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n\n\n@MODELS.register_module()\nclass CharbonnierCompLoss(nn.Module):\n \"\"\"Charbonnier composition loss.\n\n Args:\n loss_weight (float): Loss weight for L1 loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n eps (float): A value used to control the curvature near zero.\n Default: 1e-12.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False,\n eps: bool = 1e-12) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n self.eps = eps\n\n def forward(self,\n pred_alpha: torch.Tensor,\n fg: torch.Tensor,\n bg: torch.Tensor,\n ori_merged: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"\n Args:\n pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.\n fg (Tensor): of shape (N, 3, H, W). Tensor of foreground object.\n bg (Tensor): of shape (N, 3, H, W). Tensor of background object.\n ori_merged (Tensor): of shape (N, 3, H, W). Tensor of origin merged\n image before normalized by ImageNet mean and std.\n weight (Tensor, optional): of shape (N, 1, H, W). It is an\n indicating matrix: weight[trimap == 128] = 1. Default: None.\n \"\"\"\n pred_merged = pred_alpha * fg + (1. - pred_alpha) * bg\n if weight is not None:\n weight = weight.expand(-1, 3, -1, -1)\n return self.loss_weight * charbonnier_loss(\n pred_merged,\n ori_merged,\n weight,\n eps=self.eps,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n" }, { "path": "mmagic/models/losses/face_id_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FaceIdLoss(nn.Module):\n \"\"\"Face similarity loss. Generally this loss is used to keep the id\n consistency of the input face image and output face image.\n\n In this loss, we may need to provide ``gt``, ``pred`` and ``x``. Thus,\n an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n gt='real_imgs',\n pred='fake_imgs')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n facenet (dict, optional): Config dict for facenet. Defaults to\n dict(type='ArcFace', ir_se50_weights=None, device='cuda').\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_id'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n data_info: Optional[dict] = None,\n facenet: dict = dict(type='ArcFace', ir_se50_weights=None),\n loss_name: str = 'loss_id') -> None:\n\n super(FaceIdLoss, self).__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n self.net = MODELS.build(facenet)\n self._loss_name = loss_name\n\n def forward(self, pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\"\"\"\n\n # NOTE: only return the loss term\n return self.net(pred, gt)[0] * self.loss_weight\n" }, { "path": "mmagic/models/losses/feature_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import MMLogger\nfrom mmengine.runner import load_checkpoint\n\nfrom mmagic.models.editors.dic import LightCNN\nfrom mmagic.registry import MODELS\n\n\nclass LightCNNFeature(nn.Module):\n \"\"\"Feature of LightCNN.\n\n It is used to train DICGAN.\n \"\"\"\n\n def __init__(self) -> None:\n super().__init__()\n\n model = LightCNN(3)\n self.features = nn.Sequential(*list(model.features.children()))\n self.features.requires_grad_(False)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n return self.features(x)\n\n def init_weights(self,\n pretrained: Optional[str] = None,\n strict: bool = True) -> None:\n \"\"\"Init weights for models.\n\n Args:\n pretrained (str, optional): Path for pretrained weights. If given\n None, pretrained weights will not be loaded. Defaults to None.\n strict (boo, optional): Whether strictly load the pretrained model.\n Defaults to True.\n \"\"\"\n if isinstance(pretrained, str):\n logger = MMLogger.get_current_instance()\n load_checkpoint(self, pretrained, strict=strict, logger=logger)\n elif pretrained is not None:\n raise TypeError(f'\"pretrained\" must be a str or None. '\n f'But received {type(pretrained)}.')\n\n\n@MODELS.register_module()\nclass LightCNNFeatureLoss(nn.Module):\n \"\"\"Feature loss of DICGAN, based on LightCNN.\n\n Args:\n pretrained (str): Path for pretrained weights.\n loss_weight (float): Loss weight. Default: 1.0.\n criterion (str): Criterion type. Options are 'l1' and 'mse'.\n Default: 'l1'.\n \"\"\"\n\n def __init__(self,\n pretrained: str,\n loss_weight: float = 1.0,\n criterion: str = 'l1') -> None:\n super().__init__()\n self.model = LightCNNFeature()\n if not isinstance(pretrained, str):\n warnings.warn('`LightCNNFeature` model in FeatureLoss ' +\n 'should be pretrained')\n self.model.init_weights(pretrained)\n self.model.eval()\n self.loss_weight = loss_weight\n if criterion == 'l1':\n self.criterion = torch.nn.L1Loss()\n elif criterion == 'mse':\n self.criterion = torch.nn.MSELoss()\n else:\n raise ValueError(\"'criterion' should be 'l1' or 'mse', \"\n f'but got {criterion}')\n\n def forward(self, pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n pred (Tensor): Predicted tensor.\n gt (Tensor): GT tensor.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n self.model.eval()\n pred_feature = self.model(pred)\n gt_feature = self.model(gt).detach()\n feature_loss = self.criterion(pred_feature, gt_feature)\n\n return feature_loss * self.loss_weight\n" }, { "path": "mmagic/models/losses/gan_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine.dist import is_distributed\nfrom torch.cuda.amp.grad_scaler import GradScaler\nfrom torch.nn.functional import conv2d\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GANLoss(nn.Module):\n \"\"\"Define GAN loss.\n\n Args:\n gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge', 'l1'.\n real_label_val (float): The value for real label. Default: 1.0.\n fake_label_val (float): The value for fake label. Default: 0.0.\n loss_weight (float): Loss weight. Default: 1.0.\n Note that loss_weight is only for generators; and it is always 1.0\n for discriminators.\n \"\"\"\n\n def __init__(self,\n gan_type: str,\n real_label_val: float = 1.0,\n fake_label_val: float = 0.0,\n loss_weight: float = 1.0) -> None:\n super().__init__()\n self.gan_type = gan_type\n self.real_label_val = real_label_val\n self.fake_label_val = fake_label_val\n self.loss_weight = loss_weight\n if self.gan_type == 'smgan':\n self.gaussian_blur = GaussianBlur()\n\n if self.gan_type == 'vanilla':\n self.loss = nn.BCEWithLogitsLoss()\n elif self.gan_type == 'lsgan' or self.gan_type == 'smgan':\n self.loss = nn.MSELoss()\n elif self.gan_type == 'wgan':\n self.loss = self._wgan_loss\n elif self.gan_type == 'hinge':\n self.loss = nn.ReLU()\n elif self.gan_type == 'l1':\n self.loss = nn.L1Loss()\n else:\n raise NotImplementedError(\n f'GAN type {self.gan_type} is not implemented.')\n\n def _wgan_loss(self, input: torch.Tensor, target: bool) -> torch.Tensor:\n \"\"\"wgan loss.\n\n Args:\n input (Tensor): Input tensor.\n target (bool): Target label.\n\n Returns:\n Tensor: wgan loss.\n \"\"\"\n\n return -input.mean() if target else input.mean()\n\n def get_target_label(self, input: torch.Tensor,\n target_is_real: bool) -> Union[bool, torch.Tensor]:\n \"\"\"Get target label.\n\n Args:\n input (Tensor): Input tensor.\n target_is_real (bool): Whether the target is real or fake.\n\n Returns:\n (bool | Tensor): Target tensor. Return bool for wgan, otherwise,\n return Tensor.\n \"\"\"\n\n if self.gan_type == 'wgan':\n return target_is_real\n target_val = (\n self.real_label_val if target_is_real else self.fake_label_val)\n return input.new_ones(input.size()) * target_val\n\n def forward(self,\n input: torch.Tensor,\n target_is_real: bool,\n is_disc: bool = False,\n mask: Optional[torch.Tensor] = None) -> torch.Tensor:\n \"\"\"\n Args:\n input (Tensor): The input for the loss module, i.e., the network\n prediction.\n target_is_real (bool): Whether the target is real or fake.\n is_disc (bool): Whether the loss for discriminators or not.\n Default: False.\n mask (Tensor): The mask tensor. Default: None.\n\n Returns:\n Tensor: GAN loss value.\n \"\"\"\n\n target_label = self.get_target_label(input, target_is_real)\n if self.gan_type == 'hinge':\n if is_disc: # for discriminators in hinge-gan\n input = -input if target_is_real else input\n loss = self.loss(1 + input).mean()\n else: # for generators in hinge-gan\n loss = -input.mean()\n elif self.gan_type == 'smgan':\n\n input_height, input_width = input.shape[2:]\n mask_height, mask_width = mask.shape[2:]\n\n # Handle inconsistent size between outputs and masks\n if input_height != mask_height or input_width != mask_width:\n input = F.interpolate(\n input,\n size=(mask_height, mask_width),\n mode='bilinear',\n align_corners=True)\n\n target_label = self.get_target_label(input, target_is_real)\n\n if is_disc:\n if target_is_real:\n target_label = target_label\n else:\n target_label = self.gaussian_blur(mask).detach().cuda(\n ) if mask.is_cuda else self.gaussian_blur(\n mask).detach().cpu()\n # target_label = self.gaussian_blur(mask).detach().cpu()\n loss = self.loss(input, target_label)\n else:\n loss = self.loss(input, target_label) * mask / mask.mean()\n loss = loss.mean()\n else: # other gan types\n loss = self.loss(input, target_label)\n\n # loss_weight is always 1.0 for discriminators\n return loss if is_disc else loss * self.loss_weight\n\n\n@MODELS.register_module()\nclass GaussianBlur(nn.Module):\n \"\"\"A Gaussian filter which blurs a given tensor with a two-dimensional\n gaussian kernel by convolving it along each channel. Batch operation is\n supported.\n\n This function is modified from kornia.filters.gaussian:\n ``.\n\n Args:\n kernel_size (tuple[int]): The size of the kernel. Default: (71, 71).\n sigma (tuple[float]): The standard deviation of the kernel.\n Default (10.0, 10.0)\n\n Returns:\n Tensor: The Gaussian-blurred tensor.\n\n Shape:\n - input: Tensor with shape of (n, c, h, w)\n - output: Tensor with shape of (n, c, h, w)\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, int] = (71, 71),\n sigma: Tuple[float, float] = (10.0, 10.0)\n ) -> None:\n super(GaussianBlur, self).__init__()\n self.kernel_size = kernel_size\n self.sigma = sigma\n self.padding = self.compute_zero_padding(kernel_size)\n self.kernel = self.get_2d_gaussian_kernel(kernel_size, sigma)\n\n @staticmethod\n def compute_zero_padding(kernel_size: Tuple[int, int]) -> tuple:\n \"\"\"Compute zero padding tuple.\n\n Args:\n kernel_size (tuple[int]): The size of the kernel.\n\n Returns:\n tuple: Padding of height and weight.\n \"\"\"\n\n padding = [(ks - 1) // 2 for ks in kernel_size]\n\n return padding[0], padding[1]\n\n def get_2d_gaussian_kernel(self, kernel_size: Tuple[int, int],\n sigma: Tuple[float, float]) -> torch.Tensor:\n \"\"\"Get the two-dimensional Gaussian filter matrix coefficients.\n\n Args:\n kernel_size (tuple[int]): Kernel filter size in the x and y\n direction. The kernel sizes\n should be odd and positive.\n sigma (tuple[int]): Gaussian standard deviation in\n the x and y direction.\n\n Returns:\n kernel_2d (Tensor): A 2D torch tensor with gaussian filter\n matrix coefficients.\n \"\"\"\n\n if not isinstance(kernel_size, tuple) or len(kernel_size) != 2:\n raise TypeError(\n 'kernel_size must be a tuple of length two. Got {}'.format(\n kernel_size))\n if not isinstance(sigma, tuple) or len(sigma) != 2:\n raise TypeError(\n 'sigma must be a tuple of length two. Got {}'.format(sigma))\n\n kernel_size_x, kernel_size_y = kernel_size\n sigma_x, sigma_y = sigma\n\n kernel_x = self.get_1d_gaussian_kernel(kernel_size_x, sigma_x)\n kernel_y = self.get_1d_gaussian_kernel(kernel_size_y, sigma_y)\n kernel_2d = torch.matmul(\n kernel_x.unsqueeze(-1),\n kernel_y.unsqueeze(-1).t())\n\n return kernel_2d\n\n def get_1d_gaussian_kernel(self, kernel_size: int,\n sigma: float) -> torch.Tensor:\n \"\"\"Get the Gaussian filter coefficients in one dimension (x or y\n direction).\n\n Args:\n kernel_size (int): Kernel filter size in x or y direction.\n Should be odd and positive.\n sigma (float): Gaussian standard deviation in x or y direction.\n\n Returns:\n kernel_1d (Tensor): A 1D torch tensor with gaussian filter\n coefficients in x or y direction.\n \"\"\"\n\n if not isinstance(kernel_size,\n int) or kernel_size % 2 == 0 or kernel_size <= 0:\n raise TypeError(\n 'kernel_size must be an odd positive integer. Got {}'.format(\n kernel_size))\n\n kernel_1d = self.gaussian(kernel_size, sigma)\n return kernel_1d\n\n def gaussian(self, kernel_size: int, sigma: float) -> torch.Tensor:\n \"\"\"Gaussian function.\n\n Args:\n kernel_size (int): Kernel filter size in x or y direction.\n Should be odd and positive.\n sigma (float): Gaussian standard deviation in x or y direction.\n\n Returns:\n Tensor: A 1D torch tensor with gaussian filter\n coefficients in x or y direction.\n \"\"\"\n\n def gauss_arg(x):\n return -(x - kernel_size // 2)**2 / float(2 * sigma**2)\n\n gauss = torch.stack([\n torch.exp(torch.tensor(gauss_arg(x))) for x in range(kernel_size)\n ])\n return gauss / gauss.sum()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Tensor with shape (n, c, h, w)\n\n Returns:\n Tensor: The Gaussian-blurred tensor.\n \"\"\"\n if not torch.is_tensor(x):\n raise TypeError(\n 'Input x type is not a torch.Tensor. Got {}'.format(type(x)))\n if not len(x.shape) == 4:\n raise ValueError(\n 'Invalid input shape, we expect BxCxHxW. Got: {}'.format(\n x.shape))\n _, c, _, _ = x.shape\n tmp_kernel = self.kernel.to(x.device).to(x.dtype)\n kernel = tmp_kernel.repeat(c, 1, 1, 1)\n\n return conv2d(x, kernel, padding=self.padding, stride=1, groups=c)\n\n\ndef gradient_penalty_loss(discriminator: nn.Module,\n real_data: torch.Tensor,\n fake_data: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n norm_mode: str = 'pixel') -> torch.Tensor:\n \"\"\"Calculate gradient penalty for wgan-gp.\n\n Args:\n discriminator (nn.Module): Network for the discriminator.\n real_data (Tensor): Real input data.\n fake_data (Tensor): Fake input data.\n mask (Tensor): Masks for inpainting. Default: None.\n\n Returns:\n Tensor: A tensor for gradient penalty.\n \"\"\"\n\n batch_size = real_data.size(0)\n alpha = torch.rand(batch_size, 1, 1, 1).to(real_data)\n\n # interpolate between real_data and fake_data\n interpolates = alpha * real_data + (1. - alpha) * fake_data\n interpolates = autograd.Variable(interpolates, requires_grad=True)\n\n disc_interpolates = discriminator(interpolates)\n gradients = autograd.grad(\n outputs=disc_interpolates,\n inputs=interpolates,\n grad_outputs=torch.ones_like(disc_interpolates),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n\n if mask is not None:\n gradients = gradients * mask\n\n if norm_mode == 'pixel':\n gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()\n elif norm_mode == 'HWC':\n gradients_penalty = ((\n gradients.reshape(batch_size, -1).norm(2, dim=1) - 1)**2).mean()\n else:\n raise NotImplementedError(\n 'Currently, we only support [\"pixel\", \"HWC\"] '\n f'norm mode but got {norm_mode}.')\n if mask is not None:\n gradients_penalty /= torch.mean(mask)\n\n return gradients_penalty\n\n\n@MODELS.register_module()\nclass GradientPenaltyLoss(nn.Module):\n \"\"\"Gradient penalty loss for wgan-gp.\n\n Args:\n loss_weight (float): Loss weight. Default: 1.0.\n \"\"\"\n\n def __init__(self, loss_weight: float = 1.) -> None:\n super().__init__()\n self.loss_weight = loss_weight\n\n def forward(self,\n discriminator: nn.Module,\n real_data: torch.Tensor,\n fake_data: torch.Tensor,\n mask: Optional[torch.Tensor] = None) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n discriminator (nn.Module): Network for the discriminator.\n real_data (Tensor): Real input data.\n fake_data (Tensor): Fake input data.\n mask (Tensor): Masks for inpainting. Default: None.\n\n Returns:\n Tensor: Loss.\n \"\"\"\n loss = gradient_penalty_loss(\n discriminator, real_data, fake_data, mask=mask)\n\n return loss * self.loss_weight\n\n\ndef disc_shift_loss(pred: torch.Tensor) -> torch.Tensor:\n \"\"\"Disc Shift loss.\n\n This loss is proposed in PGGAN as an auxiliary loss for discriminator.\n\n Args:\n pred (Tensor): Input tensor.\n\n Returns:\n torch.Tensor: loss tensor.\n \"\"\"\n return pred**2\n\n\n@MODELS.register_module()\nclass DiscShiftLoss(nn.Module):\n \"\"\"Disc shift loss.\n\n Args:\n loss_weight (float, optional): Loss weight. Defaults to 1.0.\n \"\"\"\n\n def __init__(self, loss_weight: float = 0.1) -> None:\n super().__init__()\n self.loss_weight = loss_weight\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Tensor with shape (n, c, h, w)\n\n Returns:\n Tensor: Loss.\n \"\"\"\n loss = torch.mean(disc_shift_loss(x))\n\n return loss * self.loss_weight\n\n\ndef r1_gradient_penalty_loss(discriminator: nn.Module,\n real_data: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n norm_mode: str = 'pixel',\n loss_scaler: Optional[GradScaler] = None,\n use_apex_amp: bool = False) -> torch.Tensor:\n \"\"\"Calculate R1 gradient penalty for WGAN-GP.\n\n R1 regularizer comes from:\n \"Which Training Methods for GANs do actually Converge?\" ICML'2018\n\n Different from original gradient penalty, this regularizer only penalized\n gradient w.r.t. real data.\n\n Args:\n discriminator (nn.Module): Network for the discriminator.\n real_data (Tensor): Real input data.\n mask (Tensor): Masks for inpainting. Default: None.\n norm_mode (str): This argument decides along which dimension the norm\n of the gradients will be calculated. Currently, we support [\"pixel\"\n , \"HWC\"]. Defaults to \"pixel\".\n\n Returns:\n Tensor: A tensor for gradient penalty.\n \"\"\"\n batch_size = real_data.shape[0]\n\n real_data = real_data.clone().requires_grad_()\n\n disc_pred = discriminator(real_data)\n if loss_scaler:\n disc_pred = loss_scaler.scale(disc_pred)\n elif use_apex_amp:\n from apex.amp._amp_state import _amp_state\n _loss_scaler = _amp_state.loss_scalers[0]\n disc_pred = _loss_scaler.loss_scale() * disc_pred.float()\n\n gradients = autograd.grad(\n outputs=disc_pred,\n inputs=real_data,\n grad_outputs=torch.ones_like(disc_pred),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n\n if loss_scaler:\n # unscale the gradient\n inv_scale = 1. / loss_scaler.get_scale()\n gradients = gradients * inv_scale\n elif use_apex_amp:\n inv_scale = 1. / _loss_scaler.loss_scale()\n gradients = gradients * inv_scale\n\n if mask is not None:\n gradients = gradients * mask\n\n if norm_mode == 'pixel':\n gradients_penalty = ((gradients.norm(2, dim=1))**2).mean()\n elif norm_mode == 'HWC':\n gradients_penalty = gradients.pow(2).reshape(batch_size,\n -1).sum(1).mean()\n else:\n raise NotImplementedError(\n 'Currently, we only support [\"pixel\", \"HWC\"] '\n f'norm mode but got {norm_mode}.')\n if mask is not None:\n gradients_penalty /= torch.mean(mask)\n\n return gradients_penalty\n\n\ndef gen_path_regularizer(generator: nn.Module,\n num_batches: int,\n mean_path_length: torch.Tensor,\n pl_batch_shrink: int = 1,\n decay: float = 0.01,\n weight: float = 1.,\n pl_batch_size: Optional[int] = None,\n sync_mean_buffer: bool = False,\n loss_scaler: Optional[GradScaler] = None,\n use_apex_amp: bool = False) -> Tuple[torch.Tensor]:\n \"\"\"Generator Path Regularization.\n\n Path regularization is proposed in StyleGAN2, which can help the improve\n the continuity of the latent space. More details can be found in:\n Analyzing and Improving the Image Quality of StyleGAN, CVPR2020.\n\n Args:\n generator (nn.Module): The generator module. Note that this loss\n requires that the generator contains ``return_latents`` interface,\n with which we can get the latent code of the current sample.\n num_batches (int): The number of samples used in calculating this loss.\n mean_path_length (Tensor): The mean path length, calculated by moving\n average.\n pl_batch_shrink (int, optional): The factor of shrinking the batch size\n for saving GPU memory. Defaults to 1.\n decay (float, optional): Decay for moving average of mean path length.\n Defaults to 0.01.\n weight (float, optional): Weight of this loss item. Defaults to ``1.``.\n pl_batch_size (int | None, optional): The batch size in calculating\n generator path. Once this argument is set, the ``num_batches`` will\n be overridden with this argument and won't be affected by\n ``pl_batch_shrink``. Defaults to None.\n sync_mean_buffer (bool, optional): Whether to sync mean path length\n across all of GPUs. Defaults to False.\n\n Returns:\n tuple[Tensor]: The penalty loss, detached mean path tensor, and \\\n current path length.\n \"\"\"\n # reduce batch size for conserving GPU memory\n if pl_batch_shrink > 1:\n num_batches = max(1, num_batches // pl_batch_shrink)\n\n # reset the batch size if pl_batch_size is not None\n if pl_batch_size is not None:\n num_batches = pl_batch_size\n\n # get output from different generators\n output_dict = generator(None, num_batches=num_batches, return_latents=True)\n fake_img, latents = output_dict['fake_img'], output_dict['latent']\n\n noise = torch.randn_like(fake_img) / np.sqrt(\n fake_img.shape[2] * fake_img.shape[3])\n\n if loss_scaler:\n loss = loss_scaler.scale((fake_img * noise).sum())[0]\n grad = autograd.grad(\n outputs=loss,\n inputs=latents,\n grad_outputs=torch.ones(()).to(loss),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n\n # unscale the grad\n inv_scale = 1. / loss_scaler.get_scale()\n grad = grad * inv_scale\n elif use_apex_amp:\n from apex.amp._amp_state import _amp_state\n\n # by default, we use loss_scalers[0] for discriminator and\n # loss_scalers[1] for generator\n _loss_scaler = _amp_state.loss_scalers[1]\n loss = _loss_scaler.loss_scale() * ((fake_img * noise).sum()).float()\n\n grad = autograd.grad(\n outputs=loss,\n inputs=latents,\n grad_outputs=torch.ones(()).to(loss),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n\n # unscale the grad\n inv_scale = 1. / _loss_scaler.loss_scale()\n grad = grad * inv_scale\n else:\n grad = autograd.grad(\n outputs=(fake_img * noise).sum(),\n inputs=latents,\n grad_outputs=torch.ones(()).to(fake_img),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n\n path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))\n # update mean path\n path_mean = mean_path_length + decay * (\n path_lengths.mean() - mean_path_length)\n\n if sync_mean_buffer and is_distributed():\n dist.all_reduce(path_mean)\n path_mean = path_mean / float(dist.get_world_size())\n\n path_penalty = (path_lengths - path_mean).pow(2).mean() * weight\n\n return path_penalty, path_mean.detach(), path_lengths\n" }, { "path": "mmagic/models/losses/gradient_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\nfrom .pixelwise_loss import l1_loss\n\n_reduction_modes = ['none', 'mean', 'sum']\n\n\n@MODELS.register_module()\nclass GradientLoss(nn.Module):\n \"\"\"Gradient loss.\n\n Args:\n loss_weight (float): Loss weight for L1 loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean') -> None:\n super().__init__()\n self.loss_weight = loss_weight\n self.reduction = reduction\n if self.reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {self.reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n def forward(self,\n pred: torch.Tensor,\n target: torch.Tensor,\n weight: Optional[torch.Tensor] = None) -> torch.Tensor:\n \"\"\"\n Args:\n pred (Tensor): of shape (N, C, H, W). Predicted tensor.\n target (Tensor): of shape (N, C, H, W). Ground truth tensor.\n weight (Tensor, optional): of shape (N, C, H, W). Element-wise\n weights. Default: None.\n \"\"\"\n kx = torch.Tensor([[1, 0, -1], [2, 0, -2],\n [1, 0, -1]]).view(1, 1, 3, 3).to(target)\n ky = torch.Tensor([[1, 2, 1], [0, 0, 0],\n [-1, -2, -1]]).view(1, 1, 3, 3).to(target)\n\n pred_grad_x = F.conv2d(pred, kx, padding=1)\n pred_grad_y = F.conv2d(pred, ky, padding=1)\n target_grad_x = F.conv2d(target, kx, padding=1)\n target_grad_y = F.conv2d(target, ky, padding=1)\n\n loss = (\n l1_loss(\n pred_grad_x, target_grad_x, weight, reduction=self.reduction) +\n l1_loss(\n pred_grad_y, target_grad_y, weight, reduction=self.reduction))\n return loss * self.loss_weight\n" }, { "path": "mmagic/models/losses/loss_comps/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .clip_loss_comps import CLIPLossComps\nfrom .disc_auxiliary_loss_comps import (DiscShiftLossComps,\n GradientPenaltyLossComps,\n R1GradientPenaltyComps)\nfrom .face_id_loss_comps import FaceIdLossComps\nfrom .gan_loss_comps import GANLossComps\nfrom .gen_auxiliary_loss_comps import GeneratorPathRegularizerComps\n\n__all__ = [\n 'CLIPLossComps', 'DiscShiftLossComps', 'GradientPenaltyLossComps',\n 'R1GradientPenaltyComps', 'FaceIdLossComps', 'GANLossComps',\n 'GeneratorPathRegularizerComps'\n]\n" }, { "path": "mmagic/models/losses/loss_comps/clip_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom ..clip_loss import CLIPLossModel\n\n\n@MODELS.register_module()\nclass CLIPLossComps(nn.Module):\n \"\"\"Clip loss. In styleclip, this loss is used to optimize the latent code\n to generate image that match the text.\n\n In this loss, we may need to provide ``image``, ``text``. Thus,\n an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n image='fake_imgs',\n text='descriptions')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n clip_model (dict, optional): Kwargs for clip loss model. Defaults to\n dict().\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_clip'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n data_info: Optional[dict] = None,\n clip_model: dict = dict(),\n loss_name: str = 'loss_clip') -> None:\n\n super().__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n self.net = CLIPLossModel(**clip_model)\n self._loss_name = loss_name\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``third_party_net_loss``.\n \"\"\"\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n return self.net(*args, **kwargs) * self.loss_weight\n\n @staticmethod\n def loss_name() -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return 'clip_loss'\n" }, { "path": "mmagic/models/losses/loss_comps/disc_auxiliary_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom ..gan_loss import (disc_shift_loss, gradient_penalty_loss,\n r1_gradient_penalty_loss)\n\n\n@MODELS.register_module()\nclass DiscShiftLossComps(nn.Module):\n \"\"\"Disc Shift Loss.\n\n This loss is proposed in PGGAN as an auxiliary loss for discriminator.\n\n **Note for the design of ``data_info``:**\n In ``MMagic``, almost all of loss modules contain the argument\n ``data_info``, which can be used for constructing the link between the\n input items (needed in loss calculation) and the data from the generative\n model. For example, in the training of GAN model, we will collect all of\n important data/modules into a dictionary:\n\n .. code-block:: python\n :caption: Code from StaticUnconditionalGAN, train_step\n :linenos:\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs,\n iteration=curr_iter,\n batch_size=batch_size)\n\n But in this loss, we will need to provide ``pred`` as input. Thus, an\n example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n pred='disc_pred_fake')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n In addition, in general, ``disc_shift_loss`` will be applied over real and\n fake data. In this case, users just need to add this loss module twice, but\n with different ``data_info``. Our model will automatically add these two\n items.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_disc_shift'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n data_info: Optional[dict] = None,\n loss_name: str = 'loss_disc_shift') -> None:\n super().__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n self._loss_name = loss_name\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function, ``disc_shift_loss``.\n \"\"\"\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n return disc_shift_loss(**kwargs) * self.loss_weight\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n return disc_shift_loss(*args, **kwargs) * self.loss_weight\n\n def loss_name(self) -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return self._loss_name\n\n\n@MODELS.register_module()\nclass GradientPenaltyLossComps(nn.Module):\n \"\"\"Gradient Penalty for WGAN-GP.\n\n In the detailed implementation, there are two streams where one uses the\n pixel-wise gradient norm, but the other adopts normalization along instance\n (HWC) dimensions. Thus, ``norm_mode`` are offered to define which mode you\n want.\n\n **Note for the design of ``data_info``:**\n In ``MMagic``, almost all of loss modules contain the argument\n ``data_info``, which can be used for constructing the link between the\n input items (needed in loss calculation) and the data from the generative\n model. For example, in the training of GAN model, we will collect all of\n important data/modules into a dictionary:\n\n .. code-block:: python\n :caption: Code from StaticUnconditionalGAN, train_step\n :linenos:\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs,\n iteration=curr_iter,\n batch_size=batch_size)\n\n But in this loss, we will need to provide ``discriminator``, ``real_data``,\n and ``fake_data`` as input. Thus, an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n discriminator='disc',\n real_data='real_imgs',\n fake_data='fake_imgs')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n norm_mode (str): This argument decides along which dimension the norm\n of the gradients will be calculated. Currently, we support [\"pixel\"\n , \"HWC\"]. Defaults to \"pixel\".\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_gp'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n norm_mode: str = 'pixel',\n data_info: Optional[dict] = None,\n loss_name: str = 'loss_gp') -> None:\n super().__init__()\n self.loss_weight = loss_weight\n self.norm_mode = norm_mode\n self.data_info = data_info\n self._loss_name = loss_name\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``gradient_penalty_loss``.\n \"\"\"\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n # kwargs.update(\n # dict(weight=self.loss_weight, norm_mode=self.norm_mode))\n kwargs.update(dict(norm_mode=self.norm_mode))\n return gradient_penalty_loss(**kwargs) * self.loss_weight\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n # return gradient_penalty_loss(\n # *args, weight=self.loss_weight, **kwargs)\n return gradient_penalty_loss(*args, **kwargs) * self.loss_weight\n\n def loss_name(self) -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return self._loss_name\n\n\n@MODELS.register_module()\nclass R1GradientPenaltyComps(nn.Module):\n \"\"\"R1 gradient penalty for WGAN-GP.\n\n R1 regularizer comes from:\n \"Which Training Methods for GANs do actually Converge?\" ICML'2018\n\n Different from original gradient penalty, this regularizer only penalized\n gradient w.r.t. real data.\n\n **Note for the design of ``data_info``:**\n In ``MMagic``, almost all of loss modules contain the argument\n ``data_info``, which can be used for constructing the link between the\n input items (needed in loss calculation) and the data from the generative\n model. For example, in the training of GAN model, we will collect all of\n important data/modules into a dictionary:\n\n .. code-block:: python\n :caption: Code from StaticUnconditionalGAN, train_step\n :linenos:\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n disc_pred_fake=disc_pred_fake,\n disc_pred_real=disc_pred_real,\n fake_imgs=fake_imgs,\n real_imgs=real_imgs,\n iteration=curr_iter,\n batch_size=batch_size)\n\n But in this loss, we will need to provide ``discriminator`` and\n ``real_data`` as input. Thus, an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n discriminator='disc',\n real_data='real_imgs')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n norm_mode (str): This argument decides along which dimension the norm\n of the gradients will be calculated. Currently, we support [\"pixel\"\n , \"HWC\"]. Defaults to \"pixel\".\n interval (int, optional): The interval of calculating this loss.\n Defaults to 1.\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_r1_gp'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n norm_mode: str = 'pixel',\n interval: int = 1,\n data_info: Optional[dict] = None,\n use_apex_amp: bool = False,\n loss_name: str = 'loss_r1_gp') -> None:\n super().__init__()\n self.loss_weight = loss_weight\n self.norm_mode = norm_mode\n self.interval = interval\n self.data_info = data_info\n self.use_apex_amp = use_apex_amp\n self._loss_name = loss_name\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``r1_gradient_penalty_loss``.\n \"\"\"\n if self.interval > 1:\n assert self.data_info is not None\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n if self.interval > 1 and outputs_dict[\n 'iteration'] % self.interval != 0:\n return None\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n kwargs.update(\n dict(norm_mode=self.norm_mode, use_apex_amp=self.use_apex_amp))\n return r1_gradient_penalty_loss(**kwargs) * self.loss_weight\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n return r1_gradient_penalty_loss(\n *args, norm_mode=self.norm_mode, **kwargs) * self.loss_weight\n\n def loss_name(self) -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return self._loss_name\n" }, { "path": "mmagic/models/losses/loss_comps/face_id_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass FaceIdLossComps(nn.Module):\n \"\"\"Face similarity loss. Generally this loss is used to keep the id\n consistency of the input face image and output face image.\n\n In this loss, we may need to provide ``gt``, ``pred`` and ``x``. Thus,\n an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n gt='real_imgs',\n pred='fake_imgs')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n facenet (dict, optional): Config dict for facenet. Defaults to\n dict(type='ArcFace', ir_se50_weights=None).\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_id'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n data_info: Optional[dict] = None,\n facenet: dict = dict(type='ArcFace', ir_se50_weights=None),\n loss_name: str = 'loss_id') -> None:\n\n super().__init__()\n self.loss_weight = loss_weight\n self.data_info = data_info\n self.net = MODELS.build(facenet)\n self._loss_name = loss_name\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``third_party_net_loss``.\n \"\"\"\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n\n # NOTE: only return the loss term\n return self.net(*args, **kwargs)[0] * self.loss_weight\n\n def loss_name(self) -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return self._loss_name\n" }, { "path": "mmagic/models/losses/loss_comps/gan_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass GANLossComps(nn.Module):\n \"\"\"Define GAN loss.\n\n Args:\n gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge',\n 'wgan-logistic-ns'.\n real_label_val (float): The value for real label. Default: 1.0.\n fake_label_val (float): The value for fake label. Default: 0.0.\n loss_weight (float): Loss weight. Default: 1.0.\n Note that loss_weight is only for generators; and it is always 1.0\n for discriminators.\n \"\"\"\n\n def __init__(self,\n gan_type: str,\n real_label_val: float = 1.0,\n fake_label_val: float = 0.0,\n loss_weight: float = 1.0) -> None:\n super().__init__()\n self.gan_type = gan_type\n self.loss_weight = loss_weight\n self.real_label_val = real_label_val\n self.fake_label_val = fake_label_val\n\n if self.gan_type == 'vanilla':\n self.loss = nn.BCEWithLogitsLoss()\n elif self.gan_type == 'lsgan':\n self.loss = nn.MSELoss()\n elif self.gan_type == 'wgan':\n self.loss = self._wgan_loss\n elif self.gan_type == 'wgan-logistic-ns':\n self.loss = self._wgan_logistic_ns_loss\n elif self.gan_type == 'hinge':\n self.loss = nn.ReLU()\n else:\n raise NotImplementedError(\n f'GAN type {self.gan_type} is not implemented.')\n\n def _wgan_loss(self, input: torch.Tensor, target: bool) -> torch.Tensor:\n \"\"\"wgan loss.\n\n Args:\n input (Tensor): Input tensor.\n target (bool): Target label.\n\n Returns:\n Tensor: wgan loss.\n \"\"\"\n return -input.mean() if target else input.mean()\n\n def _wgan_logistic_ns_loss(self, input: torch.Tensor,\n target: bool) -> torch.Tensor:\n \"\"\"WGAN loss in logistically non-saturating mode.\n\n This loss is widely used in StyleGANv2.\n\n Args:\n input (Tensor): Input tensor.\n target (bool): Target label.\n\n Returns:\n Tensor: wgan loss.\n \"\"\"\n\n return F.softplus(-input).mean() if target else F.softplus(\n input).mean()\n\n def get_target_label(self, input: torch.Tensor,\n target_is_real: bool) -> Union[bool, torch.Tensor]:\n \"\"\"Get target label.\n\n Args:\n input (Tensor): Input tensor.\n target_is_real (bool): Whether the target is real or fake.\n\n Returns:\n (bool | Tensor): Target tensor. Return bool for wgan, otherwise, \\\n return Tensor.\n \"\"\"\n\n if self.gan_type in ['wgan', 'wgan-logistic-ns']:\n return target_is_real\n target_val = (\n self.real_label_val if target_is_real else self.fake_label_val)\n return input.new_ones(input.size()) * target_val\n\n def forward(self,\n input: torch.Tensor,\n target_is_real: bool,\n is_disc: bool = False) -> torch.Tensor:\n \"\"\"\n Args:\n input (Tensor): The input for the loss module, i.e., the network\n prediction.\n target_is_real (bool): Whether the targe is real or fake.\n is_disc (bool): Whether the loss for discriminators or not.\n Default: False.\n\n Returns:\n Tensor: GAN loss value.\n \"\"\"\n target_label = self.get_target_label(input, target_is_real)\n if self.gan_type == 'hinge':\n if is_disc: # for discriminators in hinge-gan\n input = -input if target_is_real else input\n loss = self.loss(1 + input).mean()\n else: # for generators in hinge-gan\n loss = -input.mean()\n else: # other gan types\n loss = self.loss(input, target_label)\n\n # loss_weight is always 1.0 for discriminators\n return loss if is_disc else loss * self.loss_weight\n" }, { "path": "mmagic/models/losses/loss_comps/gen_auxiliary_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\nfrom ..gan_loss import gen_path_regularizer\n\n\n@MODELS.register_module()\nclass GeneratorPathRegularizerComps(nn.Module):\n \"\"\"Generator Path Regularizer.\n\n Path regularization is proposed in StyleGAN2, which can help the improve\n the continuity of the latent space. More details can be found in:\n Analyzing and Improving the Image Quality of StyleGAN, CVPR2020.\n\n Users can achieve lazy regularization by setting ``interval`` arguments\n here.\n\n **Note for the design of ``data_info``:**\n In ``MMagic``, almost all of loss modules contain the argument\n ``data_info``, which can be used for constructing the link between the\n input items (needed in loss calculation) and the data from the generative\n model. For example, in the training of GAN model, we will collect all of\n important data/modules into a dictionary:\n\n .. code-block:: python\n :caption: Code from StaticUnconditionalGAN, train_step\n :linenos:\n\n data_dict_ = dict(\n gen=self.generator,\n disc=self.discriminator,\n fake_imgs=fake_imgs,\n disc_pred_fake_g=disc_pred_fake_g,\n iteration=curr_iter,\n batch_size=batch_size)\n\n But in this loss, we will need to provide ``generator`` and ``num_batches``\n as input. Thus an example of the ``data_info`` is:\n\n .. code-block:: python\n :linenos:\n\n data_info = dict(\n generator='gen',\n num_batches='batch_size')\n\n Then, the module will automatically construct this mapping from the input\n data dictionary.\n\n Args:\n loss_weight (float, optional): Weight of this loss item.\n Defaults to ``1.``.\n pl_batch_shrink (int, optional): The factor of shrinking the batch size\n for saving GPU memory. Defaults to 1.\n decay (float, optional): Decay for moving average of mean path length.\n Defaults to 0.01.\n pl_batch_size (int | None, optional): The batch size in calculating\n generator path. Once this argument is set, the ``num_batches`` will\n be overridden with this argument and won't be affected by\n ``pl_batch_shrink``. Defaults to None.\n sync_mean_buffer (bool, optional): Whether to sync mean path length\n across all of GPUs. Defaults to False.\n interval (int, optional): The interval of calculating this loss. This\n argument is used to support lazy regularization. Defaults to 1.\n data_info (dict, optional): Dictionary contains the mapping between\n loss input args and data dictionary. If ``None``, this module will\n directly pass the input data to the loss function.\n Defaults to None.\n loss_name (str, optional): Name of the loss item. If you want this loss\n item to be included into the backward graph, `loss_` must be the\n prefix of the name. Defaults to 'loss_path_regular'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.,\n pl_batch_shrink: int = 1,\n decay: float = 0.01,\n pl_batch_size: Optional[int] = None,\n sync_mean_buffer: bool = False,\n interval: int = 1,\n data_info: Optional[dict] = None,\n use_apex_amp: bool = False,\n loss_name: str = 'loss_path_regular') -> None:\n super().__init__()\n self.loss_weight = loss_weight\n self.pl_batch_shrink = pl_batch_shrink\n self.decay = decay\n self.pl_batch_size = pl_batch_size\n self.sync_mean_buffer = sync_mean_buffer\n self.interval = interval\n self.data_info = data_info\n self.use_apex_amp = use_apex_amp\n self._loss_name = loss_name\n\n self.register_buffer('mean_path_length', torch.tensor(0.))\n\n def forward(self, *args, **kwargs) -> torch.Tensor:\n \"\"\"Forward function.\n\n If ``self.data_info`` is not ``None``, a dictionary containing all of\n the data and necessary modules should be passed into this function.\n If this dictionary is given as a non-keyword argument, it should be\n offered as the first argument. If you are using keyword argument,\n please name it as `outputs_dict`.\n\n If ``self.data_info`` is ``None``, the input argument or key-word\n argument will be directly passed to loss function,\n ``gen_path_regularizer``.\n \"\"\"\n if self.interval > 1:\n assert self.data_info is not None\n # use data_info to build computational path\n if self.data_info is not None:\n # parse the args and kwargs\n if len(args) == 1:\n assert isinstance(args[0], dict), (\n 'You should offer a dictionary containing network outputs '\n 'for building up computational graph of this loss module.')\n outputs_dict = args[0]\n elif 'outputs_dict' in kwargs:\n assert len(args) == 0, (\n 'If the outputs dict is given in keyworded arguments, no'\n ' further non-keyworded arguments should be offered.')\n outputs_dict = kwargs.pop('outputs_dict')\n else:\n raise NotImplementedError(\n 'Cannot parsing your arguments passed to this loss module.'\n ' Please check the usage of this module')\n if self.interval > 1 and outputs_dict[\n 'iteration'] % self.interval != 0:\n return None\n # link the outputs with loss input args according to self.data_info\n loss_input_dict = {\n k: outputs_dict[v]\n for k, v in self.data_info.items()\n }\n kwargs.update(loss_input_dict)\n kwargs.update(\n dict(\n # weight=self.loss_weight,\n mean_path_length=self.mean_path_length,\n pl_batch_shrink=self.pl_batch_shrink,\n decay=self.decay,\n use_apex_amp=self.use_apex_amp,\n pl_batch_size=self.pl_batch_size,\n sync_mean_buffer=self.sync_mean_buffer))\n path_penalty, self.mean_path_length, _ = gen_path_regularizer(\n **kwargs)\n else:\n # if you have not define how to build computational graph, this\n # module will just directly return the loss as usual.\n path_penalty, self.mean_path_length, _ = gen_path_regularizer(\n *args, **kwargs)\n return path_penalty * self.loss_weight\n\n def loss_name(self) -> str:\n \"\"\"Loss Name.\n\n This function must be implemented and will return the name of this\n loss function. This name will be used to combine different loss items\n by simple sum operation. In addition, if you want this loss item to be\n included into the backward graph, `loss_` must be the prefix of the\n name.\n\n Returns:\n str: The name of this loss item.\n \"\"\"\n return self._loss_name\n" }, { "path": "mmagic/models/losses/loss_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport functools\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef reduce_loss(loss: torch.Tensor, reduction: str) -> torch.Tensor:\n \"\"\"Reduce loss as specified.\n\n Args:\n loss (Tensor): Elementwise loss tensor.\n reduction (str): Options are \"none\", \"mean\" and \"sum\".\n\n Returns:\n Tensor: Reduced loss tensor.\n \"\"\"\n reduction_enum = F._Reduction.get_enum(reduction)\n # none: 0, elementwise_mean:1, sum: 2\n if reduction_enum == 0:\n return loss\n if reduction_enum == 1:\n return loss.mean()\n if reduction_enum == 2:\n return loss.sum()\n raise ValueError(f'reduction type {reduction} not supported')\n\n\ndef mask_reduce_loss(loss: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n reduction: str = 'mean',\n sample_wise: bool = False) -> torch.Tensor:\n \"\"\"Apply element-wise weight and reduce loss.\n\n Args:\n loss (Tensor): Element-wise loss.\n weight (Tensor): Element-wise weights. Default: None.\n reduction (str): Same as built-in losses of PyTorch. Options are\n \"none\", \"mean\" and \"sum\". Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n\n Returns:\n Tensor: Processed loss values.\n \"\"\"\n # if weight is specified, apply element-wise weight\n if weight is not None:\n assert weight.dim() == loss.dim()\n assert weight.size(1) == 1 or weight.size(1) == loss.size(1)\n loss = loss * weight\n\n # if weight is not specified or reduction is sum, just reduce the loss\n if weight is None or reduction == 'sum':\n loss = reduce_loss(loss, reduction)\n # if reduction is mean, then compute mean over masked region\n elif reduction == 'mean':\n # expand weight from N1HW to NCHW\n if weight.size(1) == 1:\n weight = weight.expand_as(loss)\n # small value to prevent division by zero\n eps = 1e-12\n\n # perform sample-wise mean\n if sample_wise:\n weight = weight.sum(dim=[1, 2, 3], keepdim=True) # NCHW to N111\n loss = (loss / (weight + eps)).sum() / weight.size(0)\n # perform pixel-wise mean\n else:\n loss = loss.sum() / (weight.sum() + eps)\n\n return loss\n\n\ndef masked_loss(loss_func):\n \"\"\"Create a masked version of a given loss function.\n\n To use this decorator, the loss function must have the signature like\n `loss_func(pred, target, **kwargs)`. The function only needs to compute\n element-wise loss without any reduction. This decorator will add weight\n and reduction arguments to the function. The decorated function will have\n the signature like `loss_func(pred, target, weight=None, reduction='mean',\n avg_factor=None, **kwargs)`.\n\n :Example:\n\n >>> import torch\n >>> @masked_loss\n >>> def l1_loss(pred, target):\n >>> return (pred - target).abs()\n\n >>> pred = torch.Tensor([0, 2, 3])\n >>> target = torch.Tensor([1, 1, 1])\n >>> weight = torch.Tensor([1, 0, 1])\n\n >>> l1_loss(pred, target)\n tensor(1.3333)\n >>> l1_loss(pred, target, weight)\n tensor(1.5000)\n >>> l1_loss(pred, target, reduction='none')\n tensor([1., 1., 2.])\n >>> l1_loss(pred, target, weight, reduction='sum')\n tensor(3.)\n \"\"\"\n\n @functools.wraps(loss_func)\n def wrapper(pred: torch.Tensor,\n target: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n reduction: str = 'mean',\n sample_wise: bool = False,\n **kwargs) -> torch.Tensor:\n # get element-wise loss\n loss = loss_func(pred, target, **kwargs)\n loss = mask_reduce_loss(loss, weight, reduction, sample_wise)\n return loss\n\n return wrapper\n" }, { "path": "mmagic/models/losses/perceptual_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torchvision.models.vgg as vgg\nfrom mmengine import MMLogger\nfrom mmengine.runner import load_checkpoint\nfrom torch.nn import functional as F\n\nfrom mmagic.registry import MODELS\n\n\nclass PerceptualVGG(nn.Module):\n \"\"\"VGG network used in calculating perceptual loss.\n\n In this implementation, we allow users to choose whether use normalization\n in the input feature and the type of vgg network. Note that the pretrained\n path must fit the vgg type.\n\n Args:\n layer_name_list (list[str]): According to the name in this list,\n forward function will return the corresponding features. This\n list contains the name each layer in `vgg.feature`. An example\n of this list is ['4', '10'].\n vgg_type (str): Set the type of vgg network. Default: 'vgg19'.\n use_input_norm (bool): If True, normalize the input image.\n Importantly, the input feature must in the range [0, 1].\n Default: True.\n pretrained (str): Path for pretrained weights. Default:\n 'torchvision://vgg19'\n \"\"\"\n\n def __init__(self,\n layer_name_list: List[str],\n vgg_type: str = 'vgg19',\n use_input_norm: bool = True,\n pretrained: str = 'torchvision://vgg19') -> None:\n super().__init__()\n if pretrained.startswith('torchvision://'):\n assert vgg_type in pretrained\n self.layer_name_list = layer_name_list\n self.use_input_norm = use_input_norm\n\n # get vgg model and load pretrained vgg weight\n # remove _vgg from attributes to avoid `find_unused_parameters` bug\n _vgg = getattr(vgg, vgg_type)(pretrained=True)\n # self.init_weights(_vgg, pretrained) #TODO urlopen error\n num_layers = max(map(int, layer_name_list)) + 1\n assert len(_vgg.features) >= num_layers\n # only borrow layers that will be used from _vgg to avoid unused params\n self.vgg_layers = _vgg.features[:num_layers]\n\n if self.use_input_norm:\n # the mean is for image with range [0, 1]\n self.register_buffer(\n 'mean',\n torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))\n # the std is for image with range [-1, 1]\n self.register_buffer(\n 'std',\n torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))\n\n for v in self.vgg_layers.parameters():\n v.requires_grad = False\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.use_input_norm:\n x = (x - self.mean) / self.std\n output = {}\n\n for name, module in self.vgg_layers.named_children():\n x = module(x)\n if name in self.layer_name_list:\n output[name] = x.clone()\n return output\n\n def init_weights(self, model: nn.Module, pretrained: str) -> None:\n \"\"\"Init weights.\n\n Args:\n model (nn.Module): Models to be inited.\n pretrained (str): Path for pretrained weights.\n \"\"\"\n logger = MMLogger.get_current_instance()\n load_checkpoint(model, pretrained, logger=logger)\n\n\n@MODELS.register_module()\nclass PerceptualLoss(nn.Module):\n \"\"\"Perceptual loss with commonly used style loss.\n\n Args:\n layers_weights (dict): The weight for each layer of vgg feature for\n perceptual loss. Here is an example: {'4': 1., '9': 1., '18': 1.},\n which means the 5th, 10th and 18th feature layer will be\n extracted with weight 1.0 in calculating losses.\n layers_weights_style (dict): The weight for each layer of vgg feature\n for style loss. If set to 'None', the weights are set equal to\n the weights for perceptual loss. Default: None.\n vgg_type (str): The type of vgg network used as feature extractor.\n Default: 'vgg19'.\n use_input_norm (bool): If True, normalize the input image in vgg.\n Default: True.\n perceptual_weight (float): If `perceptual_weight > 0`, the perceptual\n loss will be calculated and the loss will multiplied by the\n weight. Default: 1.0.\n style_weight (float): If `style_weight > 0`, the style loss will be\n calculated and the loss will multiplied by the weight.\n Default: 1.0.\n norm_img (bool): If True, the image will be normed to [0, 1]. Note that\n this is different from the `use_input_norm` which norm the input in\n in forward function of vgg according to the statistics of dataset.\n Importantly, the input image must be in range [-1, 1].\n pretrained (str): Path for pretrained weights. Default:\n 'torchvision://vgg19'.\n criterion (str): Criterion type. Options are 'l1' and 'mse'.\n Default: 'l1'.\n \"\"\"\n\n def __init__(self,\n layer_weights: dict,\n layer_weights_style: Optional[dict] = None,\n vgg_type: str = 'vgg19',\n use_input_norm: bool = True,\n perceptual_weight: float = 1.0,\n style_weight: float = 1.0,\n norm_img: bool = True,\n pretrained: str = 'torchvision://vgg19',\n criterion: str = 'l1') -> None:\n super().__init__()\n self.norm_img = norm_img\n self.perceptual_weight = perceptual_weight\n self.style_weight = style_weight\n self.layer_weights = layer_weights\n self.layer_weights_style = layer_weights_style\n\n self.vgg = PerceptualVGG(\n layer_name_list=list(self.layer_weights.keys()),\n vgg_type=vgg_type,\n use_input_norm=use_input_norm,\n pretrained=pretrained)\n\n if self.layer_weights_style is not None and \\\n self.layer_weights_style != self.layer_weights:\n self.vgg_style = PerceptualVGG(\n layer_name_list=list(self.layer_weights_style.keys()),\n vgg_type=vgg_type,\n use_input_norm=use_input_norm,\n pretrained=pretrained)\n else:\n self.layer_weights_style = self.layer_weights\n self.vgg_style = None\n\n criterion = criterion.lower()\n if criterion == 'l1':\n self.criterion = torch.nn.L1Loss()\n elif criterion == 'mse':\n self.criterion = torch.nn.MSELoss()\n else:\n raise NotImplementedError(\n f'{criterion} criterion has not been supported in'\n ' this version.')\n\n def forward(self, x: torch.Tensor,\n gt: torch.Tensor) -> Tuple[torch.Tensor]:\n \"\"\"Forward function.\n\n Args:\n x (Tensor): Input tensor with shape (n, c, h, w).\n gt (Tensor): Ground-truth tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.norm_img:\n x = (x + 1.) * 0.5\n gt = (gt + 1.) * 0.5\n # extract vgg features\n x_features = self.vgg(x)\n gt_features = self.vgg(gt.detach())\n\n # calculate perceptual loss\n if self.perceptual_weight > 0:\n percep_loss = 0\n for k in x_features.keys():\n percep_loss += self.criterion(\n x_features[k], gt_features[k]) * self.layer_weights[k]\n percep_loss *= self.perceptual_weight\n else:\n percep_loss = None\n\n # calculate style loss\n if self.style_weight > 0:\n if self.vgg_style is not None:\n x_features = self.vgg_style(x)\n gt_features = self.vgg_style(gt.detach())\n\n style_loss = 0\n for k in x_features.keys():\n style_loss += self.criterion(\n self._gram_mat(x_features[k]),\n self._gram_mat(\n gt_features[k])) * self.layer_weights_style[k]\n style_loss *= self.style_weight\n else:\n style_loss = None\n\n return percep_loss, style_loss\n\n def _gram_mat(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Calculate Gram matrix.\n\n Args:\n x (torch.Tensor): Tensor with shape of (n, c, h, w).\n\n Returns:\n torch.Tensor: Gram matrix.\n \"\"\"\n (n, c, h, w) = x.size()\n features = x.view(n, c, w * h)\n features_t = features.transpose(1, 2)\n gram = features.bmm(features_t) / (c * h * w)\n return gram\n\n\n@MODELS.register_module()\nclass TransferalPerceptualLoss(nn.Module):\n \"\"\"Transferal perceptual loss.\n\n Args:\n loss_weight (float): Loss weight. Default: 1.0.\n use_attention (bool): If True, use soft-attention tensor. Default: True\n criterion (str): Criterion type. Options are 'l1' and 'mse'.\n Default: 'mse'.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n use_attention: bool = True,\n criterion: str = 'mse') -> None:\n super().__init__()\n self.use_attention = use_attention\n self.loss_weight = loss_weight\n criterion = criterion.lower()\n if criterion == 'l1':\n self.loss_function = torch.nn.L1Loss()\n elif criterion == 'mse':\n self.loss_function = torch.nn.MSELoss()\n else:\n raise ValueError(\n f\"criterion should be 'l1' or 'mse', but got {criterion}\")\n\n def forward(self, maps: Tuple[torch.Tensor], soft_attention: torch.Tensor,\n textures: Tuple[torch.Tensor]) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n maps (Tuple[Tensor]): Input tensors.\n soft_attention (Tensor): Soft-attention tensor.\n textures (Tuple[Tensor]): Ground-truth tensors.\n\n Returns:\n Tensor: Forward results.\n \"\"\"\n\n if self.use_attention:\n h, w = soft_attention.shape[-2:]\n softs = [torch.sigmoid(soft_attention)]\n for i in range(1, len(maps)):\n softs.append(\n F.interpolate(\n soft_attention,\n size=(h * pow(2, i), w * pow(2, i)),\n mode='bicubic',\n align_corners=False))\n else:\n softs = [1., 1., 1.]\n\n loss_texture = 0\n for map, soft, texture in zip(maps, softs, textures):\n loss_texture += self.loss_function(map * soft, texture * soft)\n\n return loss_texture * self.loss_weight\n" }, { "path": "mmagic/models/losses/pixelwise_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\nfrom .loss_wrapper import masked_loss\n\n_reduction_modes = ['none', 'mean', 'sum']\n\n\n@masked_loss\ndef l1_loss(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n \"\"\"L1 loss.\n\n Args:\n pred (Tensor): Prediction Tensor with shape (n, c, h, w).\n target ([type]): Target Tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Calculated L1 loss.\n \"\"\"\n return F.l1_loss(pred, target, reduction='none')\n\n\n@masked_loss\ndef mse_loss(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n \"\"\"MSE loss.\n\n Args:\n pred (Tensor): Prediction Tensor with shape (n, c, h, w).\n target ([type]): Target Tensor with shape (n, c, h, w).\n\n Returns:\n Tensor: Calculated MSE loss.\n \"\"\"\n return F.mse_loss(pred, target, reduction='none')\n\n\n@masked_loss\ndef charbonnier_loss(pred: torch.Tensor,\n target: torch.Tensor,\n eps: float = 1e-12) -> torch.Tensor:\n \"\"\"Charbonnier loss.\n\n Args:\n pred (Tensor): Prediction Tensor with shape (n, c, h, w).\n target ([type]): Target Tensor with shape (n, c, h, w).\n eps (float): A value used to control the curvature near zero.\n Default: 1e-12.\n\n Returns:\n Tensor: Calculated Charbonnier loss.\n \"\"\"\n return torch.sqrt((pred - target)**2 + eps)\n\n\ndef tv_loss(input: torch.Tensor) -> torch.Tensor:\n \"\"\"L2 total variation loss, as in Mahendran et al.\"\"\"\n input = F.pad(input, (0, 1, 0, 1), 'replicate')\n x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]\n y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]\n return (x_diff**2 + y_diff**2).mean([1, 2, 3])\n\n\n@MODELS.register_module()\nclass L1Loss(nn.Module):\n \"\"\"L1 (mean absolute error, MAE) loss.\n\n Args:\n loss_weight (float): Loss weight for L1 loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduce loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n\n def forward(self,\n pred: torch.Tensor,\n target: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"Forward Function.\n\n Args:\n pred (Tensor): of shape (N, C, H, W). Predicted tensor.\n target (Tensor): of shape (N, C, H, W). Ground truth tensor.\n weight (Tensor, optional): of shape (N, C, H, W). Element-wise\n weights. Default: None.\n \"\"\"\n return self.loss_weight * l1_loss(\n pred,\n target,\n weight,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n\n\n@MODELS.register_module()\nclass MSELoss(nn.Module):\n \"\"\"MSE (L2) loss.\n\n Args:\n loss_weight (float): Loss weight for MSE loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n\n def forward(self,\n pred: torch.Tensor,\n target: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"Forward Function.\n\n Args:\n pred (Tensor): of shape (N, C, H, W). Predicted tensor.\n target (Tensor): of shape (N, C, H, W). Ground truth tensor.\n weight (Tensor, optional): of shape (N, C, H, W). Element-wise\n weights. Default: None.\n \"\"\"\n return self.loss_weight * mse_loss(\n pred,\n target,\n weight,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n\n\n@MODELS.register_module()\nclass CharbonnierLoss(nn.Module):\n \"\"\"Charbonnier loss (one variant of Robust L1Loss, a differentiable variant\n of L1Loss).\n\n Described in \"Deep Laplacian Pyramid Networks for Fast and Accurate\n Super-Resolution\".\n\n Args:\n loss_weight (float): Loss weight for L1 loss. Default: 1.0.\n reduction (str): Specifies the reduction to apply to the output.\n Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.\n sample_wise (bool): Whether calculate the loss sample-wise. This\n argument only takes effect when `reduction` is 'mean' and `weight`\n (argument of `forward()`) is not None. It will first reduces loss\n with 'mean' per-sample, and then it means over all the samples.\n Default: False.\n eps (float): A value used to control the curvature near zero.\n Default: 1e-12.\n \"\"\"\n\n def __init__(self,\n loss_weight: float = 1.0,\n reduction: str = 'mean',\n sample_wise: bool = False,\n eps: float = 1e-12) -> None:\n super().__init__()\n if reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n self.loss_weight = loss_weight\n self.reduction = reduction\n self.sample_wise = sample_wise\n self.eps = eps\n\n def forward(self,\n pred: torch.Tensor,\n target: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n **kwargs) -> torch.Tensor:\n \"\"\"Forward Function.\n\n Args:\n pred (Tensor): of shape (N, C, H, W). Predicted tensor.\n target (Tensor): of shape (N, C, H, W). Ground truth tensor.\n weight (Tensor, optional): of shape (N, C, H, W). Element-wise\n weights. Default: None.\n \"\"\"\n return self.loss_weight * charbonnier_loss(\n pred,\n target,\n weight,\n eps=self.eps,\n reduction=self.reduction,\n sample_wise=self.sample_wise)\n\n\n@MODELS.register_module()\nclass MaskedTVLoss(L1Loss):\n \"\"\"Masked TV loss.\n\n Args:\n loss_weight (float, optional): Loss weight. Defaults to 1.0.\n \"\"\"\n\n def __init__(self, loss_weight: float = 1.0) -> None:\n super().__init__(loss_weight=loss_weight)\n\n def forward(self,\n pred: torch.Tensor,\n mask: Optional[torch.Tensor] = None) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n pred (torch.Tensor): Tensor with shape of (n, c, h, w).\n mask (torch.Tensor, optional): Tensor with shape of (n, 1, h, w).\n Defaults to None.\n\n Returns:\n [type]: [description]\n \"\"\"\n y_diff = super().forward(\n pred[:, :, :-1, :], pred[:, :, 1:, :], weight=mask[:, :, :-1, :])\n x_diff = super().forward(\n pred[:, :, :, :-1], pred[:, :, :, 1:], weight=mask[:, :, :, :-1])\n\n loss = x_diff + y_diff\n\n return loss\n\n\n@MODELS.register_module()\nclass PSNRLoss(nn.Module):\n \"\"\"PSNR Loss in \"HINet: Half Instance Normalization Network for Image\n Restoration\".\n\n Args:\n loss_weight (float, optional): Loss weight. Defaults to 1.0.\n reduction: reduction for PSNR. Can only be mean here.\n toY: change to calculate the PSNR of Y channel in YCbCr format\n \"\"\"\n\n def __init__(self, loss_weight: float = 1.0, toY: bool = False) -> None:\n super(PSNRLoss, self).__init__()\n self.loss_weight = loss_weight\n import numpy as np\n self.scale = 10 / np.log(10)\n self.toY = toY\n self.coef = torch.tensor([65.481, 128.553, 24.966]).reshape(1, 3, 1, 1)\n self.first = True\n\n def forward(self, pred: torch.Tensor,\n target: torch.Tensor) -> torch.Tensor:\n assert len(pred.size()) == 4\n\n return self.loss_weight * self.scale * torch.log((\n (pred - target)**2).mean(dim=(1, 2, 3)) + 1e-8).mean()\n" }, { "path": "mmagic/models/utils/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nfrom .bbox_utils import extract_around_bbox, extract_bbox_patch\nfrom .flow_warp import flow_warp\nfrom .model_utils import (build_module, default_init_weights,\n generation_init_weights, get_module_device,\n get_valid_noise_size, get_valid_num_batches,\n make_layer, remove_tomesd, set_requires_grad,\n set_tomesd, set_xformers, xformers_is_enable)\nfrom .sampling_utils import label_sample_fn, noise_sample_fn\nfrom .tensor_utils import get_unknown_tensor, normalize_vecs\n\n__all__ = [\n 'default_init_weights', 'make_layer', 'flow_warp',\n 'generation_init_weights', 'set_requires_grad', 'extract_bbox_patch',\n 'extract_around_bbox', 'get_unknown_tensor', 'noise_sample_fn',\n 'label_sample_fn', 'get_valid_num_batches', 'get_valid_noise_size',\n 'get_module_device', 'normalize_vecs', 'build_module', 'set_xformers',\n 'xformers_is_enable', 'set_tomesd', 'remove_tomesd'\n]\n" }, { "path": "mmagic/models/utils/bbox_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\n\ndef extract_bbox_patch(bbox, img, channel_first=True):\n \"\"\"Extract patch from a given bbox.\n\n Args:\n bbox (torch.Tensor | numpy.array): Bbox with (top, left, h, w). If\n `img` has batch dimension, the `bbox` must be stacked at first\n dimension. The shape should be (4,) or (n, 4).\n img (torch.Tensor | numpy.array): Image data to be extracted. If\n organized in batch dimension, the batch dimension must be the first\n order like (n, h, w, c) or (n, c, h, w).\n channel_first (bool): If True, the channel dimension of img is before\n height and width, e.g. (c, h, w). Otherwise, the img shape (samples\n in the batch) is like (h, w, c). Default: True.\n\n Returns:\n (torch.Tensor | numpy.array): Extracted patches. The dimension of the \\\n output should be the same as `img`.\n \"\"\"\n\n def _extract(bbox, img):\n assert len(bbox) == 4\n t, l, h, w = bbox\n if channel_first:\n img_patch = img[..., t:t + h, l:l + w]\n else:\n img_patch = img[t:t + h, l:l + w, ...]\n\n return img_patch\n\n input_size = img.shape\n assert len(input_size) == 3 or len(input_size) == 4\n bbox_size = bbox.shape\n assert bbox_size == (4, ) or (len(bbox_size) == 2\n and bbox_size[0] == input_size[0])\n\n # images with batch dimension\n if len(input_size) == 4:\n output_list = []\n for i in range(input_size[0]):\n img_patch_ = _extract(bbox[i], img[i:i + 1, ...])\n output_list.append(img_patch_)\n if isinstance(img, torch.Tensor):\n img_patch = torch.cat(output_list, dim=0)\n else:\n img_patch = np.concatenate(output_list, axis=0)\n # standardize image\n else:\n img_patch = _extract(bbox, img)\n\n return img_patch\n\n\ndef scale_bbox(bbox, target_size):\n \"\"\"Modify bbox to target size.\n\n The original bbox will be enlarged to the target size with the original\n bbox in the center of the new bbox.\n\n Args:\n bbox (np.ndarray | torch.Tensor): Bboxes to be modified. Bbox can\n be in batch or not. The shape should be (4,) or (n, 4).\n target_size (tuple[int]): Target size of final bbox.\n\n Returns:\n (np.ndarray | torch.Tensor): Modified bboxes.\n \"\"\"\n\n def _mod(bbox, target_size):\n top_ori, left_ori, h_ori, w_ori = bbox\n h, w = target_size\n assert h >= h_ori and w >= w_ori\n top = int(max(0, top_ori - (h - h_ori) // 2))\n left = int(max(0, left_ori - (w - w_ori) // 2))\n\n if isinstance(bbox, torch.Tensor):\n bbox_new = torch.Tensor([top, left, h, w]).type_as(bbox)\n else:\n bbox_new = np.asarray([top, left, h, w])\n\n return bbox_new\n\n if isinstance(bbox, torch.Tensor):\n bbox_new = torch.zeros_like(bbox)\n elif isinstance(bbox, np.ndarray):\n bbox_new = np.zeros_like(bbox)\n else:\n raise TypeError('bbox mush be torch.Tensor or numpy.ndarray'\n f'but got type {type(bbox)}')\n bbox_shape = list(bbox.shape)\n\n if len(bbox_shape) == 2:\n for i in range(bbox_shape[0]):\n bbox_new[i, :] = _mod(bbox[i], target_size)\n else:\n bbox_new = _mod(bbox, target_size)\n\n return bbox_new\n\n\ndef extract_around_bbox(img, bbox, target_size, channel_first=True):\n \"\"\"Extract patches around the given bbox.\n\n Args:\n img (torch.Tensor | numpy.array): Image data to be extracted. If\n organized in batch dimension, the batch dimension must be the first\n order like (n, h, w, c) or (n, c, h, w).\n bbox (np.ndarray | torch.Tensor): Bboxes to be modified. Bbox can\n be in batch or not.\n target_size (List(int)): Target size of final bbox.\n channel_first (bool): If True, the channel dimension of img is before\n height and width, e.g. (c, h, w). Otherwise, the img shape (samples\n in the batch) is like (h, w, c). Default: True.\n\n Returns:\n (torch.Tensor | np.ndarray): Extracted patches. The dimension of the \\\n output should be the same as `img`.\n \"\"\"\n bbox_new = scale_bbox(bbox, target_size)\n img_patch = extract_bbox_patch(bbox_new, img, channel_first=channel_first)\n\n return img_patch, bbox_new\n" }, { "path": "mmagic/models/utils/diffusion_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport numpy as np\n\n\ndef betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):\n \"\"\"Create a beta schedule that discretized the given alpha_t_bar\n function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Source: https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py#L49 # noqa\n \"\"\"\n\n def alpha_bar(time_step):\n return math.cos((time_step + 0.008) / 1.008 * math.pi / 2)**2\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))\n return np.array(betas, dtype=np.float64)\n" }, { "path": "mmagic/models/utils/flow_warp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn.functional as F\n\n\ndef flow_warp(x,\n flow,\n interpolation='bilinear',\n padding_mode='zeros',\n align_corners=True):\n \"\"\"Warp an image or a feature map with optical flow.\n\n Args:\n x (Tensor): Tensor with size (n, c, h, w).\n flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is\n a two-channel, denoting the width and height relative offsets.\n Note that the values are not normalized to [-1, 1].\n interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.\n Default: 'bilinear'.\n padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.\n Default: 'zeros'.\n align_corners (bool): Whether align corners. Default: True.\n\n Returns:\n Tensor: Warped image or feature map.\n \"\"\"\n if x.size()[-2:] != flow.size()[1:3]:\n raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and '\n f'flow ({flow.size()[1:3]}) are not the same.')\n _, _, h, w = x.size()\n # create mesh grid\n device = flow.device\n # torch.meshgrid has been modified in 1.10.0 (compatibility with previous\n # versions), and will be further modified in 1.12 (Breaking Change)\n if 'indexing' in torch.meshgrid.__code__.co_varnames:\n grid_y, grid_x = torch.meshgrid(\n torch.arange(0, h, device=device, dtype=x.dtype),\n torch.arange(0, w, device=device, dtype=x.dtype),\n indexing='ij')\n else:\n grid_y, grid_x = torch.meshgrid(\n torch.arange(0, h, device=device, dtype=x.dtype),\n torch.arange(0, w, device=device, dtype=x.dtype))\n grid = torch.stack((grid_x, grid_y), 2) # h, w, 2\n grid.requires_grad = False\n\n grid_flow = grid + flow\n # scale grid_flow to [-1,1]\n grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0\n grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0\n grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3)\n grid_flow = grid_flow.type(x.type())\n output = F.grid_sample(\n x,\n grid_flow,\n mode=interpolation,\n padding_mode=padding_mode,\n align_corners=align_corners)\n return output\n" }, { "path": "mmagic/models/utils/model_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport logging\nfrom typing import Any, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import print_log\nfrom mmengine.model.weight_init import (constant_init, kaiming_init,\n normal_init, update_init_info,\n xavier_init)\nfrom mmengine.registry import Registry\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\nfrom torch import Tensor\nfrom torch.nn import init\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs\nfrom .tome_utils import (add_tome_cfg_hook, build_mmagic_tomesd_block,\n build_mmagic_wrapper_tomesd_block, isinstance_str)\n\n\ndef default_init_weights(module, scale=1):\n \"\"\"Initialize network weights.\n\n Args:\n modules (nn.Module): Modules to be initialized.\n scale (float): Scale initialized weights, especially for residual\n blocks. Default: 1.\n \"\"\"\n for m in module.modules():\n if isinstance(m, nn.Conv2d):\n kaiming_init(m, a=0, mode='fan_in', bias=0)\n m.weight.data *= scale\n elif isinstance(m, nn.Linear):\n kaiming_init(m, a=0, mode='fan_in', bias=0)\n m.weight.data *= scale\n elif isinstance(m, _BatchNorm):\n constant_init(m.weight, val=1, bias=0)\n\n\ndef make_layer(block, num_blocks, **kwarg):\n \"\"\"Make layers by stacking the same blocks.\n\n Args:\n block (nn.module): nn.module class for basic block.\n num_blocks (int): number of blocks.\n\n Returns:\n nn.Sequential: Stacked blocks in nn.Sequential.\n \"\"\"\n layers = []\n for _ in range(num_blocks):\n layers.append(block(**kwarg))\n return nn.Sequential(*layers)\n\n\ndef get_module_device(module):\n \"\"\"Get the device of a module.\n\n Args:\n module (nn.Module): A module contains the parameters.\n\n Returns:\n torch.device: The device of the module.\n \"\"\"\n try:\n next(module.parameters())\n except StopIteration:\n raise ValueError('The input module should contain parameters.')\n\n if next(module.parameters()).is_cuda:\n return next(module.parameters()).get_device()\n else:\n return torch.device('cpu')\n\n\ndef set_requires_grad(nets, requires_grad=False):\n \"\"\"Set requires_grad for all the networks.\n\n Args:\n nets (nn.Module | list[nn.Module]): A list of networks or a single\n network.\n requires_grad (bool): Whether the networks require gradients or not\n \"\"\"\n if not isinstance(nets, list):\n nets = [nets]\n for net in nets:\n if net is not None:\n for param in net.parameters():\n param.requires_grad = requires_grad\n\n\ndef generation_init_weights(module, init_type='normal', init_gain=0.02):\n \"\"\"Default initialization of network weights for image generation.\n\n By default, we use normal init, but xavier and kaiming might work\n better for some applications.\n\n Args:\n module (nn.Module): Module to be initialized.\n init_type (str): The name of an initialization method:\n normal | xavier | kaiming | orthogonal. Default: 'normal'.\n init_gain (float): Scaling factor for normal, xavier and\n orthogonal. Default: 0.02.\n \"\"\"\n\n def init_func(m):\n \"\"\"Initialization function.\n\n Args:\n m (nn.Module): Module to be initialized.\n \"\"\"\n classname = m.__class__.__name__\n if hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n normal_init(m, 0.0, init_gain)\n elif init_type == 'xavier':\n xavier_init(m, gain=init_gain, distribution='normal')\n elif init_type == 'kaiming':\n kaiming_init(\n m,\n a=0,\n mode='fan_in',\n nonlinearity='leaky_relu',\n distribution='normal')\n elif init_type == 'orthogonal':\n init.orthogonal_(m.weight, gain=init_gain)\n init.constant_(m.bias.data, 0.0)\n else:\n raise NotImplementedError(\n f\"Initialization method '{init_type}' is not implemented\")\n init_info = (f'Initialize {m.__class__.__name__} by \\'init_type\\' '\n f'{init_type}.')\n elif classname.find('BatchNorm2d') != -1:\n # BatchNorm Layer's weight is not a matrix;\n # only normal distribution applies.\n normal_init(m, 1.0, init_gain)\n init_info = (f'{m.__class__.__name__} is BatchNorm2d, initialize '\n 'by Norm initialization with mean=1, '\n f'std={init_gain}')\n\n if hasattr(m, '_params_init_info'):\n update_init_info(module, init_info)\n\n module.apply(init_func)\n\n\ndef get_valid_noise_size(noise_size: Optional[int],\n generator: Union[Dict, nn.Module]) -> Optional[int]:\n \"\"\"Get the value of `noise_size` from input, `generator` and check the\n consistency of these values. If no conflict is found, return that value.\n\n Args:\n noise_size (Optional[int]): `noise_size` passed to\n `BaseGAN_refactor`'s initialize function.\n generator (ModelType): The config or the model of generator.\n\n Returns:\n int | None: The noise size feed to generator.\n \"\"\"\n if isinstance(generator, dict):\n model_noise_size = generator.get('noise_size', None)\n else:\n model_noise_size = getattr(generator, 'noise_size', None)\n\n # get noise_size\n if noise_size is not None and model_noise_size is not None:\n assert noise_size == model_noise_size, (\n 'Input \\'noise_size\\' is inconsistent with '\n f'\\'generator.noise_size\\'. Receive \\'{noise_size}\\' and '\n f'\\'{model_noise_size}\\'.')\n else:\n noise_size = noise_size or model_noise_size\n\n return noise_size\n\n\ndef get_valid_num_batches(batch_inputs: Optional[ForwardInputs] = None,\n data_samples: List[DataSample] = None) -> int:\n \"\"\"Try get the valid batch size from inputs.\n\n - If some values in `batch_inputs` are `Tensor` and 'num_batches' is in\n `batch_inputs`, we check whether the value of 'num_batches' and the the\n length of first dimension of all tensors are same. If the values are not\n same, `AssertionError` will be raised. If all values are the same,\n return the value.\n - If no values in `batch_inputs` is `Tensor`, 'num_batches' must be\n contained in `batch_inputs`. And this value will be returned.\n - If some values are `Tensor` and 'num_batches' is not contained in\n `batch_inputs`, we check whether all tensor have the same length on the\n first dimension. If the length are not same, `AssertionError` will be\n raised. If all length are the same, return the length as batch size.\n - If batch_inputs is a `Tensor`, directly return the length of the first\n dimension as batch size.\n\n Args:\n batch_inputs (ForwardInputs): Inputs passed to :meth:`forward`.\n\n Returns:\n int: The batch size of samples to generate.\n \"\"\"\n # attempt to infer num_batches from batch_inputs\n if batch_inputs is not None:\n if isinstance(batch_inputs, Tensor):\n return batch_inputs.shape[0]\n\n # get num_batches from batch_inputs\n num_batches_dict = {\n k: v.shape[0]\n for k, v in batch_inputs.items() if isinstance(v, Tensor)\n }\n if 'num_batches' in batch_inputs:\n num_batches_dict['num_batches'] = batch_inputs['num_batches']\n\n if num_batches_dict:\n num_batches_inputs = list(num_batches_dict.values())[0]\n # ensure all num_batches are same\n assert all([\n bz == num_batches_inputs for bz in num_batches_dict.values()\n ]), ('\\'num_batches\\' is inconsistency among the preprocessed '\n f'input. \\'num_batches\\' parsed results: {num_batches_dict}')\n else:\n num_batches_inputs = None\n else:\n num_batches_inputs = None\n\n # attempt to infer num_batches from data_samples\n if data_samples is not None:\n num_batches_samples = len(data_samples)\n else:\n num_batches_samples = None\n\n if not (num_batches_inputs or num_batches_samples):\n print_log(\n 'Cannot get \\'num_batches\\' from both \\'inputs\\' and '\n '\\'data_samples\\', automatically set \\'num_batches\\' as 1. '\n 'This may leads to potential error.', 'current', logging.WARNING)\n return 1\n elif num_batches_inputs and num_batches_samples:\n assert num_batches_inputs == num_batches_samples, (\n '\\'num_batches\\' inferred from \\'inputs\\' and \\'data_samples\\' '\n f'are different, ({num_batches_inputs} vs. {num_batches_samples}).'\n ' Please check your input carefully.')\n\n return num_batches_inputs or num_batches_samples\n\n\ndef build_module(module: Union[dict, nn.Module], builder: Registry, *args,\n **kwargs) -> Any:\n \"\"\"Build module from config or return the module itself.\n\n Args:\n module (Union[dict, nn.Module]): The module to build.\n builder (Registry): The registry to build module.\n *args, **kwargs: Arguments passed to build function.\n\n Returns:\n Any: The built module.\n \"\"\"\n if isinstance(module, dict):\n return builder.build(module, *args, **kwargs)\n elif isinstance(module, nn.Module):\n return module\n else:\n raise TypeError(\n f'Only support dict and nn.Module, but got {type(module)}.')\n\n\ndef xformers_is_enable(verbose: bool = False) -> bool:\n \"\"\"Check whether xformers is installed.\n Args:\n verbose (bool): Whether to print the log.\n\n Returns:\n bool: Whether xformers is installed.\n \"\"\"\n from mmagic.utils import try_import\n xformers = try_import('xformers')\n if xformers is None and verbose:\n print_log('Do not support Xformers.', 'current')\n return xformers is not None\n\n\ndef set_xformers(module: nn.Module, prefix: str = '') -> nn.Module:\n \"\"\"Set xformers' efficient Attention for attention modules.\n\n Args:\n module (nn.Module): The module to set xformers.\n prefix (str): The prefix of the module name.\n\n Returns:\n nn.Module: The module with xformers' efficient Attention.\n \"\"\"\n\n if not xformers_is_enable():\n print_log('Do not support Xformers. Please install Xformers first. '\n 'The program will run without Xformers.')\n return\n\n for n, m in module.named_children():\n if hasattr(m, 'set_use_memory_efficient_attention_xformers'):\n # set xformers for Diffusers' Cross Attention\n m.set_use_memory_efficient_attention_xformers(True)\n module_name = f'{prefix}.{n}' if prefix else n\n print_log(\n 'Enable Xformers for HuggingFace Diffusers\\' '\n f'module \\'{module_name}\\'.', 'current')\n else:\n set_xformers(m, prefix=n)\n\n return module\n\n\ndef set_tomesd(model: torch.nn.Module,\n ratio: float = 0.5,\n max_downsample: int = 1,\n sx: int = 2,\n sy: int = 2,\n use_rand: bool = True,\n merge_attn: bool = True,\n merge_crossattn: bool = False,\n merge_mlp: bool = False):\n \"\"\"Patches a stable diffusion model with ToMe. Apply this to the highest\n level stable diffusion object.\n\n Refer to: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L173 # noqa\n\n Args:\n model (torch.nn.Module): A top level Stable Diffusion module to patch in place.\n ratio (float): The ratio of tokens to merge. I.e., 0.4 would reduce the total\n number of tokens by 40%.The maximum value for this is 1-(1/(`sx` * `sy`)). By default,\n the max ratio is 0.75 (usually <= 0.5 is recommended). Higher values result in more speed-up,\n but with more visual quality loss.\n max_downsample (int): Apply ToMe to layers with at most this amount of downsampling.\n E.g., 1 only applies to layers with no downsampling, while 8 applies to all layers.\n Should be chosen from [1, 2, 4, or 8]. 1 and 2 are recommended.\n sx, sy (int, int): The stride for computing dst sets. A higher stride means you can merge\n more tokens, default setting of (2, 2) works well in most cases.\n `sx` and `sy` do not need to divide image size.\n use_rand (bool): Whether or not to allow random perturbations when computing dst sets.\n By default: True, but if you're having weird artifacts you can try turning this off.\n merge_attn (bool): Whether or not to merge tokens for attention (recommended).\n merge_crossattn (bool): Whether or not to merge tokens for cross attention (not recommended).\n merge_mlp (bool): Whether or not to merge tokens for the mlp layers (particular not recommended).\n\n Returns:\n model (torch.nn.Module): Model patched by ToMe.\n \"\"\"\n\n # Make sure the module is not currently patched\n remove_tomesd(model)\n\n is_mmagic = isinstance_str(model, 'StableDiffusion') or isinstance_str(\n model, 'BaseModel')\n\n if is_mmagic:\n # Supports \"StableDiffusion.unet\" and \"unet\"\n diffusion_model = model.unet if hasattr(model, 'unet') else model\n if isinstance_str(diffusion_model, 'DenoisingUnet'):\n is_wrapper = False\n else:\n is_wrapper = True\n else:\n if not hasattr(model, 'model') or not hasattr(model.model,\n 'diffusion_model'):\n # Provided model not supported\n print('Expected a Stable Diffusion / Latent Diffusion model.')\n raise RuntimeError('Provided model was not supported.')\n diffusion_model = model.model.diffusion_model\n # TODO: can support more diffusion models, like Stability AI\n is_wrapper = None\n\n diffusion_model._tome_info = {\n 'size': None,\n 'hooks': [],\n 'args': {\n 'ratio': ratio,\n 'max_downsample': max_downsample,\n 'sx': sx,\n 'sy': sy,\n 'use_rand': use_rand,\n 'merge_attn': merge_attn,\n 'merge_crossattn': merge_crossattn,\n 'merge_mlp': merge_mlp\n }\n }\n add_tome_cfg_hook(diffusion_model)\n\n for _, module in diffusion_model.named_modules():\n if isinstance_str(module, 'BasicTransformerBlock'):\n # TODO: can support more stable diffusion based models\n if is_mmagic:\n if is_wrapper is None:\n raise NotImplementedError(\n 'Specific ToMe block not implemented')\n elif not is_wrapper:\n make_tome_block_fn = build_mmagic_tomesd_block\n elif is_wrapper:\n make_tome_block_fn = build_mmagic_wrapper_tomesd_block\n else:\n raise TypeError(\n 'Currently `tome` only support *stable-diffusion* model!')\n module.__class__ = make_tome_block_fn(module.__class__)\n module._tome_info = diffusion_model._tome_info\n\n return model\n\n\ndef remove_tomesd(model: torch.nn.Module):\n \"\"\"Removes a patch from a ToMe Diffusion module if it was already patched.\n\n Refer to: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L251 # noqa\n \"\"\"\n # For mmagic Stable Diffusion models\n model = model.unet if hasattr(model, 'unet') else model\n\n for _, module in model.named_modules():\n if hasattr(module, '_tome_info'):\n for hook in module._tome_info['hooks']:\n hook.remove()\n module._tome_info['hooks'].clear()\n\n if module.__class__.__name__ == 'ToMeBlock':\n module.__class__ = module._parent\n\n return model\n" }, { "path": "mmagic/models/utils/sampling_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Callable, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nfrom mmengine import is_list_of\nfrom torch import Tensor\n\n\ndef noise_sample_fn(noise: Union[Tensor, Callable, None] = None,\n *,\n num_batches: int = 1,\n noise_size: Union[int, Sequence[int], None] = None,\n device: Optional[str] = None) -> Tensor:\n \"\"\"Sample noise with respect to the given `num_batches`, `noise_size` and\n `device`.\n\n Args:\n noise (torch.Tensor | callable | None): You can directly give a\n batch of noise through a ``torch.Tensor`` or offer a callable\n function to sample a batch of noise data. Otherwise, the ``None``\n indicates to use the default noise sampler. Defaults to None.\n num_batches (int, optional): The number of batch size. Defaults to 1.\n noise_size (Union[int, Sequence[int], None], optional): The size of\n random noise. Defaults to None.\n device (Optional[str], optional): The target device of the random\n noise. Defaults to None.\n\n Returns:\n Tensor: Sampled random noise.\n \"\"\"\n if isinstance(noise, torch.Tensor):\n noise_batch = noise\n # unsqueeze if need\n if noise_batch.ndim == 1:\n noise_batch = noise_batch[None, ...]\n else:\n # generate noise automatically, prepare and check `num_batches` and\n # `noise_size`\n # num_batches = 1 if num_batches is None else num_batches\n assert num_batches > 0, (\n 'If you want to generate noise automatically, \\'num_batches\\' '\n 'must larger than 0.')\n assert noise_size is not None, (\n 'If you want to generate noise automatically, \\'noise_size\\' '\n 'must not be None.')\n noise_size = [noise_size] if isinstance(noise_size, int) \\\n else noise_size\n\n if callable(noise):\n # receive a noise generator and sample noise.\n noise_generator = noise\n noise_batch = noise_generator((num_batches, *noise_size))\n else:\n # otherwise, we will adopt default noise sampler.\n assert num_batches > 0\n noise_batch = torch.randn((num_batches, *noise_size))\n\n # Check the shape if `noise_size` is passed. Ignore `num_batches` here\n # because `num_batches` has default value.\n if noise_size is not None:\n if isinstance(noise_size, int):\n noise_size = [noise_size]\n assert list(noise_batch.shape[1:]) == list(noise_size), (\n 'Size of the input noise is inconsistency with \\'noise_size\\'\\'. '\n f'Receive \\'{noise_batch.shape[1:]}\\' and \\'{noise_size}\\' '\n 'respectively.')\n\n if device is not None:\n return noise_batch.to(device)\n return noise_batch\n\n\ndef label_sample_fn(label: Union[Tensor, Callable, List[int], None] = None,\n *,\n num_batches: int = 1,\n num_classes: Optional[int] = None,\n device: Optional[str] = None) -> Union[Tensor, None]:\n \"\"\"Sample random label with respect to `num_batches`, `num_classes` and\n `device`.\n\n Args:\n label (Union[Tensor, Callable, List[int], None], optional): You can\n directly give a batch of label through a ``torch.Tensor`` or offer\n a callable function to sample a batch of label data. Otherwise, the\n ``None`` indicates to use the default label sampler.\n Defaults to None.\n num_batches (int, optional): The number of batch size. Defaults to 1.\n num_classes (Optional[int], optional): The number of classes. Defaults\n to None.\n device (Optional[str], optional): The target device of the label.\n Defaults to None.\n\n Returns:\n Union[Tensor, None]: Sampled random label.\n \"\"\"\n # label is not passed and do not have `num_classes` to sample label\n if (num_classes is None or num_classes <= 0) and (label is None):\n return None\n\n if isinstance(label, Tensor):\n label_batch = label\n elif isinstance(label, np.ndarray):\n label_batch = torch.from_numpy(label).long()\n elif isinstance(label, list):\n if is_list_of(label, (int, np.ndarray)):\n label = [torch.LongTensor([lab]).squeeze() for lab in label]\n else:\n assert is_list_of(label, torch.Tensor), (\n 'Only support \\'int\\', \\'np.ndarray\\' and \\'torch.Tensor\\' '\n 'type for list input. But receives '\n f'\\'{[type(lab) for lab in label]}\\'')\n label = [lab.squeeze() for lab in label]\n label_batch = torch.stack(label, dim=0)\n else:\n # generate label_batch manually, prepare and check `num_batches`\n assert num_batches > 0, (\n 'If you want to generate label automatically, \\'num_batches\\' '\n 'must larger than 0.')\n\n if callable(label):\n # receive a noise generator and sample noise.\n label_generator = label\n label_batch = label_generator(num_batches)\n else:\n # otherwise, we will adopt default label sampler.\n label_batch = torch.randint(0, num_classes, (num_batches, ))\n\n # Check whether is LongTensor (torch.int64) and shape like [bz, ]\n assert label_batch.dtype == torch.int64, (\n 'Input label cannot convert to torch.LongTensor (torch.int64). Please '\n 'check your input.')\n assert label_batch.ndim == 1, (\n 'Input label must shape like \\'[num_batches, ]\\', but shape like '\n f'({label_batch.shape})')\n\n # Check the label if `num_classes` is passed. Ignore `num_batches` because\n # `num_batches` has default value.\n if num_classes is not None:\n invalid_index = torch.logical_or(label_batch < 0,\n label_batch >= num_classes)\n assert not (invalid_index).any(), (\n f'Labels in label_batch must be in range [0, num_classes - 1] '\n f'([0, {num_classes}-1]). But found {label_batch[invalid_index]} '\n 'are out of range.')\n\n if device is not None:\n return label_batch.to(device)\n return label_batch\n" }, { "path": "mmagic/models/utils/tensor_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\n\ndef get_unknown_tensor(trimap, unknown_value=128 / 255):\n \"\"\"Get 1-channel unknown area tensor from the 3 or 1-channel trimap tensor.\n\n Args:\n trimap (Tensor): Tensor with shape (N, 3, H, W) or (N, 1, H, W).\n unknown_value (float): Scalar value indicating unknown region in\n trimap.\n If trimap is pre-processed using `'rescale_to_zero_one'`, then\n 0 for bg, 128/255 for unknown, 1 for fg,\n and unknown_value should set to 128 / 255.\n If trimap is pre-processed by\n :meth:`FormatTrimap(to_onehot=False)`, then\n 0 for bg, 1 for unknown, 2 for fg\n and unknown_value should set to 1.\n If trimap is pre-processed by\n :meth:`FormatTrimap(to_onehot=True)`, then\n trimap is 3-channeled, and this value is not used.\n\n Returns:\n Tensor: Unknown area mask of shape (N, 1, H, W).\n \"\"\"\n if trimap.shape[1] == 3:\n # The three channels correspond to (bg mask, unknown mask, fg mask)\n # respectively.\n weight = trimap[:, 1:2, :, :].float()\n # elif 'to_onehot' in meta[0]:\n # key 'to_onehot' is added by pipeline `FormatTrimap`\n # 0 for bg, 1 for unknown, 2 for fg\n # weight = trimap.eq(1).float()\n else:\n # trimap is simply processed by pipeline `RescaleToZeroOne`\n # 0 for bg, 128/255 for unknown, 1 for fg\n weight = trimap.eq(unknown_value).float()\n return weight\n\n\ndef normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:\n \"\"\"Normalize vector with it's lengths at the last dimension. If `vector` is\n two-dimension tensor, this function is same as L2 normalization.\n\n Args:\n vector (torch.Tensor): Vectors to be normalized.\n\n Returns:\n torch.Tensor: Vectors after normalization.\n \"\"\"\n return vectors / (torch.norm(vectors, dim=-1, keepdim=True))\n" }, { "path": "mmagic/models/utils/tome_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import Any, Callable, Dict, Tuple, Type\n\nimport torch\n\n\ndef add_tome_cfg_hook(model: torch.nn.Module):\n \"\"\"Add a forward pre hook to get the image size. This hook can be removed\n with remove_patch.\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L158 # noqa\n \"\"\"\n\n def hook(module, args):\n module._tome_info['size'] = (args[0].shape[2], args[0].shape[3])\n return None\n\n model._tome_info['hooks'].append(model.register_forward_pre_hook(hook))\n\n\ndef build_mmagic_wrapper_tomesd_block(block_class: Type[torch.nn.Module]\n ) -> Type[torch.nn.Module]:\n \"\"\"Make a patched class for a DiffusersWrapper model in mmagic. This patch\n applies ToMe to the forward function of the block.\n\n Refer to: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L67 # noqa\n Args:\n block_class (torch.nn.Module): original class need tome speedup.\n\n Returns:\n ToMeBlock (torch.nn.Module): patched class based on the original class.\n \"\"\"\n\n class ToMeBlock(block_class):\n # Save for unpatching later\n _parent = block_class\n\n def forward(\n self,\n hidden_states,\n encoder_hidden_states=None,\n timestep=None,\n attention_mask=None,\n cross_attention_kwargs=None,\n class_labels=None,\n ):\n # -> (1) ToMeBlock\n m_a, m_c, m_m, u_a, u_c, u_m = build_merge(hidden_states,\n self._tome_info)\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa,\\\n shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states,\n timestep,\n class_labels,\n hidden_dtype=hidden_states.dtype)\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # -> (2) ToMe m_a\n norm_hidden_states = m_a(norm_hidden_states)\n\n # 1. Self-Attention\n if cross_attention_kwargs is not None:\n cross_attention_kwargs = cross_attention_kwargs\n else:\n cross_attention_kwargs = {}\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states\n if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n\n # -> (3) ToMe u_a\n hidden_states = u_a(attn_output) + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep)\n if self.use_ada_layer_norm else self.norm2(hidden_states))\n # -> (4) ToMe m_c\n norm_hidden_states = m_c(norm_hidden_states)\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n # -> (5) ToMe u_c\n hidden_states = u_c(attn_output) + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (\n 1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n # -> (6) ToMe m_m\n norm_hidden_states = m_m(norm_hidden_states)\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n # -> (7) ToMe u_m\n hidden_states = u_m(ff_output) + hidden_states\n\n return hidden_states\n\n return ToMeBlock\n\n\ndef build_mmagic_tomesd_block(block_class: Type[torch.nn.Module]\n ) -> Type[torch.nn.Module]:\n \"\"\"Make a patched class for a mmagic StableDiffusion model. This patch\n applies ToMe to the forward function of the block.\n\n Refer to: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L67 # noqa\n Args:\n block_class (torch.nn.Module): original class need tome speedup.\n\n Returns:\n ToMeBlock (torch.nn.Module): patched class based on the original class.\n \"\"\"\n\n class ToMeBlock(block_class):\n # Save for unpatching later\n _parent = block_class\n\n def forward(self, hidden_states, context=None, timestep=None):\n # ->(1) ToMeBlock\n m_a, m_c, m_m, u_a, u_c, u_m = build_merge(hidden_states,\n self._tome_info)\n\n # 1. Self-Attention\n # ->(2) ToMe m_a\n norm_hidden_states = (m_a(self.norm1(hidden_states)))\n\n # ->(3) ToMe u_a\n if self.only_cross_attention:\n hidden_states = u_a(self.attn1(norm_hidden_states,\n context)) + hidden_states\n else:\n hidden_states = u_a(\n self.attn1(norm_hidden_states)) + hidden_states\n\n # 2. Cross-Attention\n # ->(4) ToMe m_c\n norm_hidden_states = (m_c(self.norm2(hidden_states)))\n # ->(5) ToMe u_c\n hidden_states = u_c(\n self.attn2(norm_hidden_states,\n context=context)) + hidden_states\n\n # 3. Feed-forward\n # ->(6) ToMe m_m, u_m\n hidden_states = u_m(self.ff(m_m(\n self.norm3(hidden_states)))) + hidden_states\n\n return hidden_states\n\n return ToMeBlock\n\n\ndef isinstance_str(x: object, cls_name: str):\n \"\"\"Checks whether `x` has any class *named* `cls_name` in its ancestry.\n Doesn't require access to the class's implementation.\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/utils.py#L3 # noqa\n \"\"\"\n\n for _cls in x.__class__.__mro__:\n if _cls.__name__ == cls_name:\n return True\n\n return False\n\n\ndef do_nothing(x: torch.Tensor, mode: str = None):\n \"\"\"Build identical mapping function.\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/merge.py#L5 # noqa\n \"\"\"\n return x\n\n\ndef mps_gather_workaround(input, dim, index):\n \"\"\"Gather function specific for `mps` backend (Metal Performance Shaders).\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/merge.py#L9 # noqa\n \"\"\"\n if input.shape[-1] == 1:\n return torch.gather(\n input.unsqueeze(-1), dim - 1 if dim < 0 else dim,\n index.unsqueeze(-1)).squeeze(-1)\n else:\n return torch.gather(input, dim, index)\n\n\ndef bipartite_soft_matching_random2d(metric: torch.Tensor,\n w: int,\n h: int,\n sx: int,\n sy: int,\n r: int,\n no_rand: bool = False\n ) -> Tuple[Callable, Callable]:\n \"\"\"Partitions the tokens into src and dst and merges r tokens from src to\n dst, dst tokens are partitioned by choosing one randomy in each (`sx`,\n `sy`) region. More details refer to `Token Merging: Your ViT But Faster`,\n paper link: `_ # noqa.\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/merge.py#20 # noqa\n\n Args:\n metric (torch.Tensor): metric with size (B, N, C) for similarity computation.\n w (int): image width in tokens.\n h (int): image height in tokens.\n sx (int): stride in the x dimension for dst, must divide `w`.\n sy (int): stride in the y dimension for dst, must divide `h`.\n r (int): number of tokens to remove (by merging).\n no_rand (bool): if true, disable randomness (use top left corner only).\n\n Returns:\n merge (Callable): token merging function.\n unmerge (Callable): token unmerging function.\n \"\"\"\n B, N, _ = metric.shape\n\n if r <= 0:\n return do_nothing, do_nothing\n\n if metric.device.type == 'mps':\n gather = mps_gather_workaround\n else:\n gather = torch.gather\n\n with torch.no_grad():\n\n hsy, wsx = h // sy, w // sx\n\n # For each sy by sx kernel, randomly assign one token to\n # be dst and the rest src\n if no_rand:\n rand_idx = torch.zeros(\n hsy, wsx, 1, device=metric.device, dtype=torch.int64)\n else:\n rand_idx = torch.randint(\n sy * sx, size=(hsy, wsx, 1), device=metric.device)\n\n # The image might not divide sx and sy, so we need to work\n # on a view of the top left if the idx buffer instead\n idx_buffer_view = torch.zeros(\n hsy, wsx, sy * sx, device=metric.device, dtype=torch.int64)\n idx_buffer_view.scatter_(\n dim=2,\n index=rand_idx,\n src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))\n idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(\n 1, 2).reshape(hsy * sy, wsx * sx)\n\n # Image is not divisible by sx or sy so we need to move it\n # into a new buffer\n if (hsy * sy) < h or (wsx * sx) < w:\n idx_buffer = torch.zeros(\n h, w, device=metric.device, dtype=torch.int64)\n idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view\n else:\n idx_buffer = idx_buffer_view\n\n # We set dst tokens to be -1 and src to be 0, so an argsort\n # gives us dst|src indices\n rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)\n\n # We're finished with these\n del idx_buffer, idx_buffer_view\n\n # rand_idx is currently dst|src, so split them\n num_dst = hsy * wsx\n a_idx = rand_idx[:, num_dst:, :] # src\n b_idx = rand_idx[:, :num_dst, :] # dst\n\n def split(x):\n C = x.shape[-1]\n src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))\n dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C))\n return src, dst\n\n # Cosine similarity between A and B\n metric = metric / metric.norm(dim=-1, keepdim=True)\n a, b = split(metric)\n scores = a @ b.transpose(-1, -2)\n\n # Can't reduce more than the # tokens in src\n r = min(a.shape[1], r)\n\n # Find the most similar greedily\n node_max, node_idx = scores.max(dim=-1)\n edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]\n\n unm_idx = edge_idx[..., r:, :] # Unmerged Tokens\n src_idx = edge_idx[..., :r, :] # Merged Tokens\n dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx)\n\n def merge(x: torch.Tensor, mode='mean') -> torch.Tensor:\n src, dst = split(x)\n n, t1, c = src.shape\n\n unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))\n src = gather(src, dim=-2, index=src_idx.expand(n, r, c))\n\n if not hasattr(torch.Tensor,\n 'scatter_reduce') or torch.__version__ < '1.12.1':\n raise ImportError(\n 'Please upgrade torch >= 1.12.1 to enable \\'scatter_reduce\\'')\n dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)\n\n return torch.cat([unm, dst], dim=1)\n\n def unmerge(x: torch.Tensor) -> torch.Tensor:\n unm_len = unm_idx.shape[1]\n unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]\n _, _, c = unm.shape\n\n src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c))\n\n # Combine back to the original shape\n out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)\n out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)\n out.scatter_(\n dim=-2,\n index=gather(\n a_idx.expand(B, a_idx.shape[1], 1), dim=1,\n index=unm_idx).expand(B, unm_len, c),\n src=unm)\n out.scatter_(\n dim=-2,\n index=gather(\n a_idx.expand(B, a_idx.shape[1], 1), dim=1,\n index=src_idx).expand(B, r, c),\n src=src)\n\n return out\n\n return merge, unmerge\n\n\ndef build_merge(x: torch.Tensor, tome_info: Dict[str,\n Any]) -> Tuple[Callable, ...]:\n \"\"\"Build the merge and unmerge functions for a given setting from\n `tome_info`.\n\n Source: https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L10 # noqa\n \"\"\"\n original_h, original_w = tome_info['size']\n original_tokens = original_h * original_w\n downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))\n\n args = tome_info['args']\n\n if downsample <= args['max_downsample']:\n w = int(math.ceil(original_w / downsample))\n h = int(math.ceil(original_h / downsample))\n r = int(x.shape[1] * args['ratio'])\n # If the batch size is odd, then it's not possible for promted and\n # unprompted images to be in the same batch, which causes artifacts\n # with use_rand, so force it to be off.\n use_rand = False if x.shape[0] % 2 == 1 else args['use_rand']\n m, u = bipartite_soft_matching_random2d(x, w, h, args['sx'],\n args['sy'], r, not use_rand)\n else:\n m, u = (do_nothing, do_nothing)\n\n m_a, u_a = (m, u) if args['merge_attn'] else (do_nothing, do_nothing)\n m_c, u_c = (m, u) if args['merge_crossattn'] else (do_nothing, do_nothing)\n m_m, u_m = (m, u) if args['merge_mlp'] else (do_nothing, do_nothing)\n\n return m_a, m_c, m_m, u_a, u_c, u_m\n" }, { "path": "mmagic/registry.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"Registries and utilities in MMagic.\n\nMMagic provides 17 registry nodes to support using modules across projects.\nEach node is a child of the root registry in MMEngine.\n\nMore details can be found at\nhttps://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.\n\"\"\"\n\nfrom mmengine.registry import DATA_SAMPLERS as MMENGINE_DATA_SAMPLERS\nfrom mmengine.registry import DATASETS as MMENGINE_DATASETS\nfrom mmengine.registry import EVALUATOR as MMENGINE_EVALUATOR\nfrom mmengine.registry import HOOKS as MMENGINE_HOOKS\nfrom mmengine.registry import LOG_PROCESSORS as MMENGINE_LOG_PROCESSORS\nfrom mmengine.registry import LOOPS as MMENGINE_LOOPS\nfrom mmengine.registry import METRICS as MMENGINE_METRICS\nfrom mmengine.registry import MODEL_WRAPPERS as MMENGINE_MODEL_WRAPPERS\nfrom mmengine.registry import MODELS as MMENGINE_MODELS\nfrom mmengine.registry import \\\n OPTIM_WRAPPER_CONSTRUCTORS as MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS\nfrom mmengine.registry import OPTIM_WRAPPERS as MMENGINE_OPTIM_WRAPPERS\nfrom mmengine.registry import OPTIMIZERS as MMENGINE_OPTIMIZERS\nfrom mmengine.registry import PARAM_SCHEDULERS as MMENGINE_PARAM_SCHEDULERS\nfrom mmengine.registry import \\\n RUNNER_CONSTRUCTORS as MMENGINE_RUNNER_CONSTRUCTORS\nfrom mmengine.registry import RUNNERS as MMENGINE_RUNNERS\nfrom mmengine.registry import TASK_UTILS as MMENGINE_TASK_UTILS\nfrom mmengine.registry import TRANSFORMS as MMENGINE_TRANSFORMS\nfrom mmengine.registry import VISBACKENDS as MMENGINE_VISBACKENDS\nfrom mmengine.registry import VISUALIZERS as MMENGINE_VISUALIZERS\nfrom mmengine.registry import \\\n WEIGHT_INITIALIZERS as MMENGINE_WEIGHT_INITIALIZERS\nfrom mmengine.registry import Registry\n\n__all__ = [\n 'RUNNERS', 'RUNNER_CONSTRUCTORS', 'LOOPS', 'HOOKS', 'LOG_PROCESSORS',\n 'OPTIMIZERS', 'OPTIM_WRAPPERS', 'OPTIM_WRAPPER_CONSTRUCTORS',\n 'PARAM_SCHEDULERS', 'DATASETS', 'DATA_SAMPLERS', 'TRANSFORMS', 'MODELS',\n 'MODEL_WRAPPERS', 'WEIGHT_INITIALIZERS', 'TASK_UTILS',\n 'DIFFUSION_SCHEDULERS', 'METRICS', 'EVALUATORS', 'VISUALIZERS',\n 'VISBACKENDS'\n]\n\n#######################################################################\n# mmagic.engine #\n#######################################################################\n\n# Runners like `EpochBasedRunner` and `IterBasedRunner`\nRUNNERS = Registry(\n 'runner',\n parent=MMENGINE_RUNNERS,\n locations=['mmagic.engine'],\n)\n# Runner constructors that define how to initialize runners\nRUNNER_CONSTRUCTORS = Registry(\n 'runner constructor',\n parent=MMENGINE_RUNNER_CONSTRUCTORS,\n locations=['mmagic.engine'],\n)\n# Loops which define the training or test process, like `EpochBasedTrainLoop`\nLOOPS = Registry(\n 'loop',\n parent=MMENGINE_LOOPS,\n locations=['mmagic.engine'],\n)\n# Hooks to add additional functions during running, like `CheckpointHook`\nHOOKS = Registry(\n 'hook',\n parent=MMENGINE_HOOKS,\n locations=['mmagic.engine'],\n)\n# Log processors to process the scalar log data.\nLOG_PROCESSORS = Registry(\n 'log processor',\n parent=MMENGINE_LOG_PROCESSORS,\n locations=['mmagic.engine'],\n)\n# Optimizers to optimize the model weights, like `SGD` and `Adam`.\nOPTIMIZERS = Registry(\n 'optimizer',\n parent=MMENGINE_OPTIMIZERS,\n locations=['mmagic.engine'],\n)\n# Optimizer wrappers to enhance the optimization process.\nOPTIM_WRAPPERS = Registry(\n 'optimizer_wrapper',\n parent=MMENGINE_OPTIM_WRAPPERS,\n locations=['mmagic.engine'],\n)\n# Optimizer constructors to customize the hyper-parameters of optimizers.\nOPTIM_WRAPPER_CONSTRUCTORS = Registry(\n 'optimizer wrapper constructor',\n parent=MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS,\n locations=['mmagic.engine'],\n)\n# Parameter schedulers to dynamically adjust optimization parameters.\nPARAM_SCHEDULERS = Registry(\n 'parameter scheduler',\n parent=MMENGINE_PARAM_SCHEDULERS,\n locations=['mmagic.engine'],\n)\n\n#######################################################################\n# mmagic.datasets #\n#######################################################################\n\n# Datasets like `ImageNet` and `CIFAR10`.\nDATASETS = Registry(\n 'dataset',\n parent=MMENGINE_DATASETS,\n locations=['mmagic.datasets'],\n)\n# Samplers to sample the dataset.\nDATA_SAMPLERS = Registry(\n 'data sampler',\n parent=MMENGINE_DATA_SAMPLERS,\n locations=['mmagic.datasets'],\n)\n# Transforms to process the samples from the dataset.\nTRANSFORMS = Registry(\n 'transform',\n parent=MMENGINE_TRANSFORMS,\n locations=['mmagic.datasets.transforms'],\n)\n\n#######################################################################\n# mmagic.models #\n#######################################################################\n\n# Neural network modules inheriting `nn.Module`.\nMODELS = Registry(\n 'model',\n parent=MMENGINE_MODELS,\n locations=['mmagic.models'],\n)\n# Model wrappers like 'MMDistributedDataParallel'\nMODEL_WRAPPERS = Registry(\n 'model_wrapper',\n parent=MMENGINE_MODEL_WRAPPERS,\n locations=['mmagic.models'],\n)\n# Weight initialization methods like uniform, xavier.\nWEIGHT_INITIALIZERS = Registry(\n 'weight initializer',\n parent=MMENGINE_WEIGHT_INITIALIZERS,\n locations=['mmagic.models'],\n)\n# Task-specific modules like anchor generators and box coders\nTASK_UTILS = Registry(\n 'task util',\n parent=MMENGINE_TASK_UTILS,\n locations=['mmagic.models'],\n)\n# modules for diffusion models that support adding noise and denoising\nDIFFUSION_SCHEDULERS = Registry(\n 'diffusion scheduler',\n locations=['mmagic.models.diffusion_schedulers'],\n)\n\n#######################################################################\n# mmagic.evaluation #\n#######################################################################\n\n# Metrics to evaluate the model prediction results.\nMETRICS = Registry(\n 'metric',\n parent=MMENGINE_METRICS,\n locations=['mmagic.evaluation'],\n)\n# Evaluators to define the evaluation process.\nEVALUATORS = Registry(\n 'evaluator',\n parent=MMENGINE_EVALUATOR,\n locations=['mmagic.evaluation'],\n)\n\n#######################################################################\n# mmagic.visualization #\n#######################################################################\n\n# Visualizers to display task-specific results.\nVISUALIZERS = Registry(\n 'visualizer',\n parent=MMENGINE_VISUALIZERS,\n locations=['mmagic.visualization'],\n)\n# Backends to save the visualization results, like TensorBoard, WandB.\nVISBACKENDS = Registry(\n 'vis_backend',\n parent=MMENGINE_VISBACKENDS,\n locations=['mmagic.visualization'],\n)\n" }, { "path": "mmagic/structures/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .data_sample import DataSample\n\n__all__ = ['DataSample']\n" }, { "path": "mmagic/structures/data_sample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom collections import abc\nfrom copy import deepcopy\nfrom itertools import chain\nfrom numbers import Number\nfrom typing import Any, Sequence, Union\n\nimport mmengine\nimport numpy as np\nimport torch\nfrom mmengine.structures import BaseDataElement, LabelData\n\nfrom mmagic.utils import all_to_tensor\n\n\ndef format_label(value: Union[torch.Tensor, np.ndarray, Sequence, int],\n num_classes: int = None) -> LabelData:\n \"\"\"Convert label of various python types to :obj:`mmengine.LabelData`.\n\n Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,\n :class:`Sequence`, :class:`int`.\n\n Args:\n value (torch.Tensor | numpy.ndarray | Sequence | int): Label value.\n num_classes (int, optional): The number of classes. If not None, set\n it to the metainfo. Defaults to None.\n\n Returns:\n :obj:`mmengine.LabelData`: The formatted label data.\n \"\"\"\n # Handle single number\n if isinstance(value, (torch.Tensor, np.ndarray)) and value.ndim == 0:\n value = int(value.item())\n\n if isinstance(value, np.ndarray):\n value = torch.from_numpy(value)\n elif isinstance(value, Sequence) and not mmengine.is_str(value):\n value = torch.tensor(value)\n elif isinstance(value, int):\n value = torch.LongTensor([value])\n elif not isinstance(value, torch.Tensor):\n raise TypeError(f'Type {type(value)} is not an available label type.')\n\n metainfo = {}\n if num_classes is not None:\n metainfo['num_classes'] = num_classes\n if value.max() >= num_classes:\n raise ValueError(f'The label data ({value}) should not '\n f'exceed num_classes ({num_classes}).')\n label = LabelData(label=value, metainfo=metainfo)\n return label\n\n\ndef is_splitable_var(var: Any) -> bool:\n \"\"\"Check whether input is a splitable variable.\n\n Args:\n var (Any): The input variable to check.\n\n Returns:\n bool: Whether input variable is a splitable variable.\n \"\"\"\n if isinstance(var, DataSample):\n return True\n if isinstance(var, torch.Tensor):\n return True\n if isinstance(var, np.ndarray):\n return True\n if isinstance(var, abc.Sequence) and not isinstance(var, str):\n return True\n return False\n\n\nclass DataSample(BaseDataElement):\n \"\"\"A data structure interface of MMagic. They are used as interfaces\n between different components, e.g., model, visualizer, evaluator, etc.\n Typically, DataSample contains all the information and data from ground-\n truth and predictions.\n\n `DataSample` inherits from `BaseDataElement`. See more details in:\n https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html\n Specifically, an instance of BaseDataElement consists of two components,\n - ``metainfo``, which contains some meta information,\n e.g., `img_shape`, `img_id`, `color_order`, etc.\n - ``data``, which contains the data used in the loop.\n\n The attributes in ``DataSample`` are divided into several parts:\n\n - ``gt_img``: Ground truth image(s).\n - ``pred_img``: Image(s) of model predictions.\n - ``ref_img``: Reference image(s).\n - ``mask``: Mask in Inpainting.\n - ``trimap``: Trimap in Matting.\n - ``gt_alpha``: Ground truth alpha image in Matting.\n - ``pred_alpha``: Predicted alpha image in Matting.\n - ``gt_fg``: Ground truth foreground image in Matting.\n - ``pred_fg``: Predicted foreground image in Matting.\n - ``gt_bg``: Ground truth background image in Matting.\n - ``pred_bg``: Predicted background image in Matting.\n - ``gt_merged``: Ground truth merged image in Matting.\n\n Examples::\n\n >>> import torch\n >>> import numpy as np\n >>> from mmagic.structures import DataSample\n >>> img_meta = dict(img_shape=(800, 1196, 3))\n >>> img = torch.rand((3, 800, 1196))\n >>> data_sample = DataSample(gt_img=img, metainfo=img_meta)\n >>> assert 'img_shape' in data_sample.metainfo_keys()\n >>> data_sample\n \n\n We also support `stack` and `split` operation to handle a batch of data\n samples:\n >>> import torch\n >>> import numpy as np\n >>> from mmagic.structures import DataSample\n >>> img_meta1 = img_meta2 = dict(img_shape=(800, 1196, 3))\n >>> img1 = torch.rand((3, 800, 1196))\n >>> img2 = torch.rand((3, 800, 1196))\n >>> data_sample1 = DataSample(gt_img=img1, metainfo=img_meta1)\n >>> data_sample2 = DataSample(gt_img=img2, metainfo=img_meta1)\n\n >>> # stack them and then use as batched-tensor!\n >>> data_sample = DataSample.stack([data_sample1, data_sample2])\n >>> print(data_sample.gt_img.shape)\n torch.Size([2, 3, 800, 1196])\n >>> print(data_sample.metainfo)\n {'img_shape': [(800, 1196, 3), (800, 1196, 3)]}\n\n >>> # split them if you want\n >>> data_sample1_, data_sample2_ = data_sample.split()\n >>> assert (data_sample1_.gt_img == img1).all()\n >>> assert (data_sample2_.gt_img == img2).all()\n \"\"\"\n\n # source_key_in_results: target_key_in_metainfo\n META_KEYS = {\n 'img_path': 'img_path',\n 'gt_path': 'gt_path',\n 'merged_path': 'merged_path',\n 'trimap_path': 'trimap_path',\n 'ori_shape': 'ori_shape',\n 'img_shape': 'img_shape',\n 'ori_merged_shape': 'ori_merged_shape',\n 'ori_trimap_shape': 'ori_trimap_shape',\n 'trimap_channel_order': 'trimap_channel_order',\n 'empty_box': 'empty_box',\n 'ori_img_shape': 'ori_img_shape',\n 'ori_gt_shape': 'ori_gt_shape',\n 'img_channel_order': 'img_channel_order',\n 'gt_channel_order': 'gt_channel_order',\n 'gt_color_type': 'gt_color_type',\n 'img_color_type': 'img_color_type',\n 'sample_idx': 'sample_idx',\n 'num_input_frames': 'num_input_frames',\n 'num_output_frames': 'num_output_frames',\n 'mask_bbox': 'mask_bbox',\n # for LIIF\n 'coord': 'coord',\n 'cell': 'cell',\n }\n\n # source_key_in_results: target_key_in_datafield\n DATA_KEYS = {\n 'gt': 'gt_img',\n 'gt_label': 'gt_label',\n 'gt_heatmap': 'gt_heatmap',\n 'gt_unsharp': 'gt_unsharp',\n 'merged': 'gt_merged',\n 'ori_alpha': 'ori_alpha',\n 'fg': 'gt_fg',\n 'bg': 'gt_bg',\n 'gt_rgb': 'gt_rgb',\n 'alpha': 'gt_alpha',\n 'img_lq': 'img_lq',\n 'ref': 'ref_img',\n 'ref_lq': 'ref_lq',\n 'mask': 'mask',\n 'trimap': 'trimap',\n 'gray': 'gray',\n 'cropped_img': 'cropped_img',\n 'pred_img': 'pred_img',\n 'ori_trimap': 'ori_trimap',\n # For text to images\n 'prompt': 'prompt',\n # For StyleGAN\n 'latent': 'latent',\n 'feats': 'feats'\n }\n\n def set_predefined_data(self, data: dict) -> None:\n \"\"\"set or change pre-defined key-value pairs in ``data_field`` by\n parameter ``data``.\n\n Args:\n data (dict): A dict contains annotations of image or\n model predictions.\n \"\"\"\n\n metainfo = {\n self.META_KEYS[k]: v\n for (k, v) in data.items() if k in self.META_KEYS\n }\n self.set_metainfo(metainfo)\n\n data = {\n self.DATA_KEYS[k]: v\n for (k, v) in data.items() if k in self.DATA_KEYS\n }\n self.set_tensor_data(data)\n\n def set_tensor_data(self, data: dict) -> None:\n \"\"\"convert input data to tensor, and then set or change key-value pairs\n in ``data_field`` by parameter ``data``.\n\n Args:\n data (dict): A dict contains annotations of image or\n model predictions.\n \"\"\"\n assert isinstance(data,\n dict), f'data should be a `dict` but got {data}'\n for k, v in data.items():\n if k == 'gt_label':\n self.set_gt_label(v)\n elif k == 'prompt':\n self.set_field(v, k, dtype=(str, list))\n else:\n self.set_field(all_to_tensor(v), k, dtype=torch.Tensor)\n\n def set_gt_label(\n self, value: Union[np.ndarray, torch.Tensor, Sequence[Number], Number]\n ) -> 'DataSample':\n \"\"\"Set label of ``gt_label``.\"\"\"\n label = format_label(value, self.get('num_classes'))\n if 'gt_label' in self:\n self.gt_label.label = label.label\n else:\n self.gt_label = label\n return self\n\n @property\n def gt_label(self):\n \"\"\"This the function to fetch gt label.\n\n Returns:\n LabelData: gt label.\n \"\"\"\n return self._gt_label\n\n @gt_label.setter\n def gt_label(self, value: LabelData):\n \"\"\"This is the function to set gt label.\n\n Args:\n value (LabelData): gt label.\n \"\"\"\n self.set_field(value, '_gt_label', dtype=LabelData)\n\n @gt_label.deleter\n def gt_label(self):\n \"\"\"Delete gt label.\"\"\"\n del self._gt_label\n\n @classmethod\n def stack(cls, data_samples: Sequence['DataSample']) -> 'DataSample':\n \"\"\"Stack a list of data samples to one. All tensor fields will be\n stacked at first dimension. Otherwise the values will be saved in a\n list.\n\n Args:\n data_samples (Sequence['DataSample']): A sequence of\n `DataSample` to stack.\n\n Returns:\n DataSample: The stacked data sample.\n \"\"\"\n # 1. check key consistency\n keys = data_samples[0].keys()\n assert all([data.keys() == keys for data in data_samples])\n\n meta_keys = data_samples[0].metainfo_keys()\n assert all(\n [data.metainfo_keys() == meta_keys for data in data_samples])\n\n # 2. stack data\n stacked_data_sample = DataSample()\n for k in keys:\n values = [getattr(data, k) for data in data_samples]\n # 3. check type consistent\n value_type = type(values[0])\n assert all([type(val) == value_type for val in values])\n\n # 4. stack\n if isinstance(values[0], torch.Tensor):\n stacked_value = torch.stack(values)\n elif isinstance(values[0], LabelData):\n labels = [data.label for data in values]\n values = torch.stack(labels)\n stacked_value = LabelData(label=values)\n else:\n stacked_value = values\n stacked_data_sample.set_field(stacked_value, k)\n\n # 5. stack metainfo\n for k in meta_keys:\n values = [data.metainfo[k] for data in data_samples]\n stacked_data_sample.set_metainfo({k: values})\n\n return stacked_data_sample\n\n def split(self,\n allow_nonseq_value: bool = False) -> Sequence['DataSample']:\n \"\"\"Split a sequence of data sample in the first dimension.\n\n Args:\n allow_nonseq_value (bool): Whether allow non-sequential data in\n split operation. If True, non-sequential data will be copied\n for all split data samples. Otherwise, an error will be\n raised. Defaults to False.\n\n Returns:\n Sequence[DataSample]: The list of data samples after splitting.\n \"\"\"\n # 1. split\n data_sample_list = [DataSample() for _ in range(len(self))]\n for k in self.all_keys():\n stacked_value = self.get(k)\n if isinstance(stacked_value, torch.Tensor):\n # split tensor shape like (N, *shape) to N (*shape) tensors\n values = [v for v in stacked_value]\n elif isinstance(stacked_value, LabelData):\n # split tensor shape like (N, *shape) to N (*shape) tensors\n labels = [l_ for l_ in stacked_value.label]\n values = [LabelData(label=l_) for l_ in labels]\n elif isinstance(stacked_value, DataSample):\n values = stacked_value.split()\n else:\n if is_splitable_var(stacked_value):\n values = stacked_value\n elif allow_nonseq_value:\n values = [deepcopy(stacked_value)] * len(self)\n else:\n raise TypeError(\n f'\\'{k}\\' is non-sequential data and '\n '\\'allow_nonseq_value\\' is False. Please check your '\n 'data sample or set \\'allow_nonseq_value\\' as True '\n f'to copy field \\'{k}\\' for all split data sample.')\n\n field = 'metainfo' if k in self.metainfo_keys() else 'data'\n for data, v in zip(data_sample_list, values):\n data.set_field(v, k, field_type=field)\n\n return data_sample_list\n\n def __len__(self):\n \"\"\"Get the length of the data sample.\"\"\"\n\n value_length = []\n for v in chain(self.values(), self.metainfo_values()):\n if isinstance(v, LabelData):\n value_length.append(v.label.shape[0])\n elif is_splitable_var(v):\n value_length.append(len(v))\n else:\n continue\n\n # NOTE: If length of values are not same or the current data sample\n # is empty, return length as 1\n if len(list(set(value_length))) != 1:\n return 1\n\n length = value_length[0]\n return length\n" }, { "path": "mmagic/utils/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .cli import modify_args\nfrom .img_utils import (all_to_tensor, can_convert_to_image, get_box_info,\n reorder_image, tensor2img, to_numpy)\nfrom .io_utils import MMAGIC_CACHE_DIR, download_from_url\n# TODO replace with engine's API\nfrom .logger import print_colored_log\nfrom .sampler import get_sampler\nfrom .setup_env import register_all_modules, try_import\nfrom .trans_utils import (add_gaussian_noise, adjust_gamma, bbox2mask,\n brush_stroke_mask, get_irregular_mask, make_coord,\n random_bbox, random_choose_unknown)\nfrom .typing import ConfigType, ForwardInputs, LabelVar, NoiseVar, SampleList\n\n__all__ = [\n 'modify_args', 'print_colored_log', 'register_all_modules', 'try_import',\n 'ForwardInputs', 'SampleList', 'NoiseVar', 'ConfigType', 'LabelVar',\n 'MMAGIC_CACHE_DIR', 'download_from_url', 'get_sampler', 'tensor2img',\n 'random_choose_unknown', 'add_gaussian_noise', 'adjust_gamma',\n 'make_coord', 'bbox2mask', 'brush_stroke_mask', 'get_irregular_mask',\n 'random_bbox', 'reorder_image', 'to_numpy', 'get_box_info',\n 'can_convert_to_image', 'all_to_tensor'\n]\n" }, { "path": "mmagic/utils/cli.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport re\nimport sys\nimport warnings\n\n\ndef modify_args():\n \"\"\"Modify args of argparse.ArgumentParser.\"\"\"\n for i, v in enumerate(sys.argv):\n if i == 0:\n assert v.endswith('.py')\n elif re.match(r'--\\w+_.*', v):\n new_arg = v.replace('_', '-')\n warnings.warn(\n f'command line argument {v} is deprecated, '\n f'please use {new_arg} instead.',\n category=DeprecationWarning,\n )\n sys.argv[i] = new_arg\n" }, { "path": "mmagic/utils/collect_env.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmcv.utils import collect_env as collect_base_env\nfrom mmengine.utils import get_git_hash\n\nimport mmagic\n\n\ndef collect_env():\n \"\"\"Collect the information of the running environments.\"\"\"\n env_info = collect_base_env()\n env_info['MMagic'] = f'{mmagic.__version__}+{get_git_hash()[:7]}'\n\n return env_info\n\n\nif __name__ == '__main__':\n for name, val in collect_env().items():\n print('{}: {}'.format(name, val))\n" }, { "path": "mmagic/utils/img_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom mmcv.transforms import to_tensor\nfrom torchvision.utils import make_grid\n\n\ndef can_convert_to_image(value):\n \"\"\"Judge whether the input value can be converted to image tensor via\n :func:`images_to_tensor` function.\n\n Args:\n value (any): The input value.\n\n Returns:\n bool: If true, the input value can convert to image with\n :func:`images_to_tensor`, and vice versa.\n \"\"\"\n if isinstance(value, (List, Tuple)):\n return all([can_convert_to_image(v) for v in value])\n elif isinstance(value, np.ndarray) and len(value.shape) > 1:\n return True\n elif isinstance(value, torch.Tensor):\n return True\n else:\n return False\n\n\ndef image_to_tensor(img):\n \"\"\"Trans image to tensor.\n\n Args:\n img (np.ndarray): The original image.\n\n Returns:\n Tensor: The output tensor.\n \"\"\"\n\n if len(img.shape) < 3:\n img = np.expand_dims(img, -1)\n img = np.ascontiguousarray(img)\n tensor = to_tensor(img).permute(2, 0, 1).contiguous()\n\n return tensor\n\n\ndef all_to_tensor(value):\n \"\"\"Trans image and sequence of frames to tensor.\n\n Args:\n value (np.ndarray | list[np.ndarray] | Tuple[np.ndarray]):\n The original image or list of frames.\n\n Returns:\n Tensor: The output tensor.\n \"\"\"\n\n if not can_convert_to_image(value):\n return value\n\n if isinstance(value, (List, Tuple)):\n # sequence of frames\n if len(value) == 1:\n tensor = image_to_tensor(value[0])\n else:\n frames = [image_to_tensor(v) for v in value]\n tensor = torch.stack(frames, dim=0)\n elif isinstance(value, np.ndarray):\n tensor = image_to_tensor(value)\n else:\n # Maybe the data has been converted to Tensor.\n tensor = to_tensor(value)\n\n return tensor\n\n\ndef tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):\n \"\"\"Convert torch Tensors into image numpy arrays.\n\n After clamping to (min, max), image values will be normalized to [0, 1].\n\n For different tensor shapes, this function will have different behaviors:\n\n 1. 4D mini-batch Tensor of shape (N x 3/1 x H x W):\n Use `make_grid` to stitch images in the batch dimension, and then\n convert it to numpy array.\n 2. 3D Tensor of shape (3/1 x H x W) and 2D Tensor of shape (H x W):\n Directly change to numpy array.\n\n Note that the image channel in input tensors should be RGB order. This\n function will convert it to cv2 convention, i.e., (H x W x C) with BGR\n order.\n\n Args:\n tensor (Tensor | list[Tensor]): Input tensors.\n out_type (numpy type): Output types. If ``np.uint8``, transform outputs\n to uint8 type with range [0, 255]; otherwise, float type with\n range [0, 1]. Default: ``np.uint8``.\n min_max (tuple): min and max values for clamp.\n\n Returns:\n (Tensor | list[Tensor]): 3D ndarray of shape (H x W x C) or 2D ndarray\n of shape (H x W).\n \"\"\"\n if not (torch.is_tensor(tensor) or\n (isinstance(tensor, list)\n and all(torch.is_tensor(t) for t in tensor))):\n raise TypeError(\n f'tensor or list of tensors expected, got {type(tensor)}')\n\n if torch.is_tensor(tensor):\n tensor = [tensor]\n result = []\n for _tensor in tensor:\n # Squeeze two times so that:\n # 1. (1, 1, h, w) -> (h, w) or\n # 3. (1, 3, h, w) -> (3, h, w) or\n # 2. (n>1, 3/1, h, w) -> (n>1, 3/1, h, w)\n _tensor = _tensor.squeeze(0).squeeze(0)\n _tensor = _tensor.float().detach().cpu().clamp_(*min_max)\n _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])\n n_dim = _tensor.dim()\n if n_dim == 4:\n img_np = make_grid(\n _tensor, nrow=int(math.sqrt(_tensor.size(0))),\n normalize=False).numpy()\n img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))\n elif n_dim == 3:\n img_np = _tensor.numpy()\n img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))\n elif n_dim == 2:\n img_np = _tensor.numpy()\n else:\n raise ValueError('Only support 4D, 3D or 2D tensor. '\n f'But received with dimension: {n_dim}')\n if out_type == np.uint8:\n # Unlike MATLAB, numpy.unit8() WILL NOT round by default.\n img_np = (img_np * 255.0).round()\n img_np = img_np.astype(out_type)\n result.append(img_np)\n result = result[0] if len(result) == 1 else result\n return result\n\n\ndef reorder_image(img, input_order='HWC'):\n \"\"\"Reorder images to 'HWC' order.\n\n If the input_order is (h, w), return (h, w, 1);\n If the input_order is (c, h, w), return (h, w, c);\n If the input_order is (h, w, c), return as it is.\n\n Args:\n img (np.ndarray): Input image.\n input_order (str): Whether the input order is 'HWC' or 'CHW'.\n If the input image shape is (h, w), input_order will not have\n effects. Default: 'HWC'.\n\n Returns:\n np.ndarray: Reordered image.\n \"\"\"\n\n if input_order not in ['HWC', 'CHW']:\n raise ValueError(\n f'Wrong input_order {input_order}. Supported input_orders are '\n '\"HWC\" and \"CHW\"')\n if len(img.shape) == 2:\n img = img[..., None]\n return img\n if input_order == 'CHW':\n if isinstance(img, np.ndarray):\n img = img.transpose(1, 2, 0)\n elif isinstance(img, torch.Tensor):\n img = img.permute(1, 2, 0)\n return img\n\n\ndef to_numpy(img, dtype=np.float64):\n \"\"\"Convert data into numpy arrays of dtype.\n\n Args:\n img (Tensor | np.ndarray): Input data.\n dtype (np.dtype): Set the data type of the output. Default: np.float64\n\n Returns:\n img (np.ndarray): Converted numpy arrays data.\n \"\"\"\n if isinstance(img, torch.Tensor):\n img = img.cpu().numpy()\n elif not isinstance(img, np.ndarray):\n raise TypeError('Only support torch.tensor and np.ndarray, '\n f'but got type {type(img)}')\n\n img = img.astype(dtype)\n\n return img\n\n\ndef get_box_info(pred_bbox, original_shape, final_size):\n \"\"\"\n\n Args:\n pred_bbox: The bounding box for the instance\n original_shape: Original image shape\n final_size: Size of the final output\n\n Returns:\n List: [L_pad, R_pad, T_pad, B_pad, rh, rw]\n \"\"\"\n assert len(pred_bbox) == 4\n resize_startx = int(pred_bbox[0] / original_shape[0] * final_size)\n resize_starty = int(pred_bbox[1] / original_shape[1] * final_size)\n resize_endx = int(pred_bbox[2] / original_shape[0] * final_size)\n resize_endy = int(pred_bbox[3] / original_shape[1] * final_size)\n rh = resize_endx - resize_startx\n rw = resize_endy - resize_starty\n if rh < 1:\n if final_size - resize_endx > 1:\n resize_endx += 1\n else:\n resize_startx -= 1\n rh = 1\n if rw < 1:\n if final_size - resize_endy > 1:\n resize_endy += 1\n else:\n resize_starty -= 1\n rw = 1\n L_pad = resize_startx\n R_pad = final_size - resize_endx\n T_pad = resize_starty\n B_pad = final_size - resize_endy\n return [L_pad, R_pad, T_pad, B_pad, rh, rw]\n" }, { "path": "mmagic/utils/io_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport hashlib\nimport os\n\nimport click\nimport mmengine\nimport requests\nimport torch.distributed as dist\nfrom mmengine.dist import get_dist_info\nfrom requests.exceptions import InvalidURL, RequestException, Timeout\n\nMMAGIC_CACHE_DIR = os.path.expanduser('~') + '/.cache/openmmlab/mmagic/'\n\n\ndef get_content_from_url(url, timeout=15, stream=False):\n \"\"\"Get content from url.\n\n Args:\n url (str): Url for getting content.\n timeout (int): Set the socket timeout. Default: 15.\n \"\"\"\n try:\n response = requests.get(url, timeout=timeout, stream=stream)\n except InvalidURL as err:\n raise err # type: ignore\n except Timeout as err:\n raise err # type: ignore\n except RequestException as err:\n raise err # type: ignore\n except Exception as err:\n raise err # type: ignore\n return response\n\n\ndef download_from_url(url,\n dest_path=None,\n dest_dir=MMAGIC_CACHE_DIR,\n hash_prefix=None):\n \"\"\"Download object at the given URL to a local path.\n\n Args:\n url (str): URL of the object to download.\n dest_path (str): Path where object will be saved.\n dest_dir (str): The directory of the destination. Defaults to\n ``'~/.cache/openmmlab/mmagic/'``.\n hash_prefix (string, optional): If not None, the SHA256 downloaded\n file should start with `hash_prefix`. Default: None.\n\n Return:\n str: path for the downloaded file.\n \"\"\"\n # get the exact destination path\n if dest_path is None:\n filename = url.split('/')[-1]\n dest_path = os.path.join(dest_dir, filename)\n\n if dest_path.startswith('~'):\n dest_path = os.path.expanduser('~') + dest_path[1:]\n\n # avoid downloading existed file\n if os.path.exists(dest_path):\n return dest_path\n\n rank, ws = get_dist_info()\n\n # only download from the master process\n if rank == 0:\n # mkdir\n _dir = os.path.dirname(dest_path)\n mmengine.mkdir_or_exist(_dir)\n\n if hash_prefix is not None:\n sha256 = hashlib.sha256()\n\n response = get_content_from_url(url, stream=True)\n size = int(response.headers.get('content-length'))\n with open(dest_path, 'wb') as fw:\n content_iter = response.iter_content(chunk_size=1024)\n with click.progressbar(content_iter, length=size / 1024) as chunks:\n for chunk in chunks:\n if chunk:\n fw.write(chunk)\n fw.flush()\n if hash_prefix is not None:\n sha256.update(chunk)\n\n if hash_prefix is not None:\n digest = sha256.hexdigest()\n if digest[:len(hash_prefix)] != hash_prefix:\n raise RuntimeError(\n f'invalid hash value, expected \"{hash_prefix}\", but got '\n f'\"{digest}\"')\n\n # sync the other processes\n if ws > 1:\n dist.barrier()\n\n return dest_path\n" }, { "path": "mmagic/utils/logger.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport logging\n\nfrom mmengine.logging import print_log\nfrom termcolor import colored\n\n\ndef print_colored_log(msg, level=logging.INFO, color='magenta'):\n \"\"\"Print colored log with default logger.\n\n Args:\n msg (str): Message to log.\n level (int): The root logger level. Note that only the process of\n rank 0 is affected, while other processes will set the level to\n \"Error\" and be silent most of the time.Log level,\n default to 'info'.\n color (str, optional): Color 'magenta'.\n \"\"\"\n print_log(colored(msg, color), 'current', level)\n" }, { "path": "mmagic/utils/sampler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Optional\n\nfrom mmengine.dataset import pseudo_collate\nfrom mmengine.runner import Runner\nfrom torch.utils.data import ConcatDataset, DataLoader\n\n\ndef _check_keys(sample_kwargs: dict, key: str) -> None:\n \"\"\"Check whether target `key` is in the `sample_kwargs`.\"\"\"\n assert key in sample_kwargs, (\n f'\\'{key}\\' must be set in \\'sample_kwargs\\'. But only find '\n f'following keys: \\'{list(sample_kwargs.keys())}\\'.')\n\n\ndef get_sampler(sample_kwargs: dict, runner: Optional[Runner]):\n \"\"\"Get a sampler to loop input data.\n\n Args:\n sample_kwargs (dict): _description_\n runner (Optional[Runner]): _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n _check_keys(sample_kwargs, 'type')\n sampler_kwargs_ = deepcopy(sample_kwargs)\n sampler_type = sampler_kwargs_.pop('type')\n sampler = eval(f'{sampler_type}Sampler')(sampler_kwargs_, runner)\n return sampler\n\n\nclass ArgumentsSampler:\n \"\"\"Dummy sampler only return input args multiple times.\"\"\"\n\n def __init__(self,\n sample_kwargs: dict,\n runner: Optional[Runner] = None) -> None:\n _check_keys(sample_kwargs, 'max_times')\n assert isinstance(sample_kwargs['max_times'], int), (\n '\\'max_times\\' in \\'sample_kwargs\\' must be type of int.\\'.')\n\n self.sample_kwargs = deepcopy(sample_kwargs)\n self.max_times = self.sample_kwargs.pop('max_times')\n self.forward_kwargs = self.sample_kwargs.pop('forward_kwargs')\n # set default num_batches from forward_kwargs\n self.forward_kwargs.setdefault('num_batches',\n self.sample_kwargs['num_batches'])\n self.idx = 0\n\n def __iter__(self):\n self.idx = 0\n return self\n\n def __next__(self):\n if self.idx >= self.max_times:\n raise StopIteration\n self.idx += 1\n return dict(inputs=deepcopy(self.forward_kwargs))\n\n\nclass NoiseSampler:\n \"\"\"Noise sampler to by call `models.noise_fn` to generate noise.\"\"\"\n\n def __init__(self, sample_kwargs: dict, runner: Runner) -> None:\n _check_keys(sample_kwargs, 'max_times')\n _check_keys(sample_kwargs, 'num_batches')\n\n self.sample_kwargs = deepcopy(sample_kwargs)\n self.max_times = self.sample_kwargs.pop('max_times')\n self.num_batches = self.sample_kwargs.pop('num_batches')\n\n module = runner.model\n if hasattr(module, 'module'):\n module = module.module\n self.module = module\n\n self.idx = 0\n\n def __iter__(self):\n self.idx = 0\n return self\n\n def __next__(self):\n if self.idx >= self.max_times:\n raise StopIteration\n self.idx += 1\n\n noise = self.module.noise_fn(num_batches=self.num_batches)\n sample_kwargs = deepcopy(self.sample_kwargs)\n sample_kwargs['noise'] = noise\n # return sample_kwargs\n return dict(inputs=sample_kwargs)\n\n\nclass DataSampler:\n \"\"\"Sampler loop the train_dataloader.\"\"\"\n\n def __init__(self, sample_kwargs: dict, runner: Runner) -> None:\n _check_keys(sample_kwargs, 'max_times')\n\n self.sample_kwargs = deepcopy(sample_kwargs)\n self.max_times = self.sample_kwargs.pop('max_times')\n\n # build a new vanilla dataloader, because we should not reset the one\n # used in the training process.\n dataset = runner.train_dataloader.dataset\n batch_size = runner.train_dataloader.batch_size\n self._dataloader = DataLoader(\n dataset, batch_size=batch_size, collate_fn=pseudo_collate)\n self._iterator = iter(self._dataloader)\n\n def __iter__(self):\n self.idx = 0\n return self\n\n def __next__(self):\n if self.idx >= self.max_times:\n self._iterator = iter(self._dataloader)\n raise StopIteration\n self.idx += 1\n return next(self._iterator)\n\n\nclass ValDataSampler:\n \"\"\"Sampler loop the val_dataloader.\"\"\"\n\n def __init__(self, sample_kwargs: dict, runner: Runner) -> None:\n _check_keys(sample_kwargs, 'max_times')\n\n self.sample_kwargs = deepcopy(sample_kwargs)\n self.max_times = self.sample_kwargs.pop('max_times')\n\n # build a new vanilla dataloader, because we should not reset the one\n # used in the training process.\n if hasattr(runner.val_loop, 'dataloader'):\n dataset = runner.val_loop.dataloader.dataset\n batch_size = runner.val_loop.dataloader.batch_size\n else:\n # MultiValLoop use `dataloaders` instead `dataloader`\n loaders = runner.val_loop.dataloaders\n dataset = ConcatDataset([loader.dataset for loader in loaders])\n batch_size = loaders[0].batch_size\n\n self._dataloader = DataLoader(\n dataset, batch_size=batch_size, collate_fn=pseudo_collate)\n self._iterator = iter(self._dataloader)\n\n def __iter__(self):\n self.idx = 0\n return self\n\n def __next__(self):\n if self.idx >= self.max_times:\n self._iterator = iter(self._dataloader)\n raise StopIteration\n self.idx += 1\n return next(self._iterator)\n" }, { "path": "mmagic/utils/setup_env.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport datetime\nimport importlib\nimport warnings\nfrom types import ModuleType\nfrom typing import Optional\n\nfrom mmengine import DefaultScope\n\n\ndef register_all_modules(init_default_scope: bool = True) -> None:\n \"\"\"Register all modules in mmagic into the registries.\n\n Args:\n init_default_scope (bool): Whether initialize the mmagic default scope.\n When `init_default_scope=True`, the global default scope will be\n set to `mmagic`, and all registries will build modules from\n mmagic's registry node.\n To understand more about the registry, please refer\n to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html\n Defaults to True.\n \"\"\" # noqa\n import mmagic.datasets # noqa: F401,F403\n import mmagic.engine # noqa: F401,F403\n import mmagic.evaluation # noqa: F401,F403\n import mmagic.models # noqa: F401,F403\n import mmagic.visualization # noqa: F401,F403\n\n if init_default_scope:\n never_created = DefaultScope.get_current_instance() is None \\\n or not DefaultScope.check_instance_created('mmagic')\n if never_created:\n DefaultScope.get_instance('mmagic', scope_name='mmagic')\n return\n current_scope = DefaultScope.get_current_instance()\n if current_scope.scope_name != 'mmagic':\n warnings.warn('The current default scope '\n f'\"{current_scope.scope_name}\" is not \"mmagic\", '\n '`register_all_modules` will force the current'\n 'default scope to be \"mmagic\". If this is not '\n 'expected, please set `init_default_scope=False`.')\n # avoid name conflict\n new_instance_name = f'mmagic-{datetime.datetime.now()}'\n DefaultScope.get_instance(new_instance_name, scope_name='mmagic')\n\n\ndef try_import(name: str) -> Optional[ModuleType]:\n \"\"\"Try to import a module.\n\n Args:\n name (str): Specifies what module to import in absolute or relative\n terms (e.g. either pkg.mod or ..mod).\n Returns:\n ModuleType or None: If importing successfully, returns the imported\n module, otherwise returns None.\n \"\"\"\n try:\n return importlib.import_module(name)\n except ImportError:\n return None\n" }, { "path": "mmagic/utils/trans_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport logging\nimport math\n\nimport cv2\nimport numpy as np\nimport torch\nfrom mmengine.logging import print_log\nfrom mmengine.utils import is_tuple_of\nfrom PIL import Image, ImageDraw\n\n\ndef make_coord(shape, ranges=None, flatten=True):\n \"\"\"Make coordinates at grid centers.\n\n Args:\n shape (tuple): shape of image.\n ranges (tuple): range of coordinate value. Default: None.\n flatten (bool): flatten to (n, 2) or Not. Default: True.\n\n Returns:\n coord (Tensor): coordinates.\n \"\"\"\n coord_seqs = []\n for i, n in enumerate(shape):\n if ranges is None:\n v0, v1 = -1, 1\n else:\n v0, v1 = ranges[i]\n r = (v1 - v0) / (2 * n)\n seq = v0 + r + (2 * r) * torch.arange(n).float()\n coord_seqs.append(seq)\n\n if 'indexing' in torch.meshgrid.__code__.co_varnames:\n coord = torch.meshgrid(*coord_seqs, indexing='ij')\n else:\n coord = torch.meshgrid(*coord_seqs)\n coord = torch.stack(coord, dim=-1)\n if flatten:\n coord = coord.view(-1, coord.shape[-1])\n return coord\n\n\ndef bbox2mask(img_shape, bbox, dtype='uint8'):\n \"\"\"Generate mask in np.ndarray from bbox.\n\n The returned mask has the shape of (h, w, 1). '1' indicates the\n hole and '0' indicates the valid regions.\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n Args:\n img_shape (tuple[int]): The size of the image.\n bbox (tuple[int]): Configuration tuple, (top, left, height, width)\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n height, width = img_shape[:2]\n\n mask = np.zeros((height, width, 1), dtype=dtype)\n mask[bbox[0]:bbox[0] + bbox[2], bbox[1]:bbox[1] + bbox[3], :] = 1\n\n return mask\n\n\ndef brush_stroke_mask(img_shape,\n num_vertices=(4, 12),\n mean_angle=2 * math.pi / 5,\n angle_range=2 * math.pi / 15,\n brush_width=(12, 40),\n max_loops=4,\n dtype='uint8'):\n \"\"\"Generate free-form mask.\n\n The method of generating free-form mask is in the following paper:\n Free-Form Image Inpainting with Gated Convolution.\n\n When you set the config of this type of mask. You may note the usage of\n `np.random.randint` and the range of `np.random.randint` is [left, right).\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n TODO: Rewrite the implementation of this function.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n num_vertices (int | tuple[int]): Min and max number of vertices. If\n only give an integer, we will fix the number of vertices.\n Default: (4, 12).\n mean_angle (float): Mean value of the angle in each vertex. The angle\n is measured in radians. Default: 2 * math.pi / 5.\n angle_range (float): Range of the random angle.\n Default: 2 * math.pi / 15.\n brush_width (int | tuple[int]): (min_width, max_width). If only give\n an integer, we will fix the width of brush. Default: (12, 40).\n max_loops (int): The max number of for loops of drawing strokes.\n Default: 4.\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'.\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n img_h, img_w = img_shape[:2]\n if isinstance(num_vertices, int):\n min_num_vertices, max_num_vertices = num_vertices, num_vertices + 1\n elif isinstance(num_vertices, tuple):\n min_num_vertices, max_num_vertices = num_vertices\n else:\n raise TypeError('The type of num_vertices should be int'\n f'or tuple[int], but got type: {num_vertices}')\n\n if isinstance(brush_width, tuple):\n min_width, max_width = brush_width\n elif isinstance(brush_width, int):\n min_width, max_width = brush_width, brush_width + 1\n else:\n raise TypeError('The type of brush_width should be int'\n f'or tuple[int], but got type: {brush_width}')\n\n average_radius = math.sqrt(img_h * img_h + img_w * img_w) / 8\n mask = Image.new('L', (img_w, img_h), 0)\n\n loop_num = np.random.randint(1, max_loops)\n num_vertex_list = np.random.randint(\n min_num_vertices, max_num_vertices, size=loop_num)\n angle_min_list = np.random.uniform(0, angle_range, size=loop_num)\n angle_max_list = np.random.uniform(0, angle_range, size=loop_num)\n\n for loop_n in range(loop_num):\n num_vertex = num_vertex_list[loop_n]\n angle_min = mean_angle - angle_min_list[loop_n]\n angle_max = mean_angle + angle_max_list[loop_n]\n angles = []\n vertex = []\n\n # set random angle on each vertex\n angles = np.random.uniform(angle_min, angle_max, size=num_vertex)\n reverse_mask = (np.arange(num_vertex, dtype=np.float32) % 2) == 0\n angles[reverse_mask] = 2 * math.pi - angles[reverse_mask]\n\n h, w = mask.size\n\n # set random vertices\n vertex.append((np.random.randint(0, w), np.random.randint(0, h)))\n r_list = np.random.normal(\n loc=average_radius, scale=average_radius // 2, size=num_vertex)\n for i in range(num_vertex):\n r = np.clip(r_list[i], 0, 2 * average_radius)\n new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)\n new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)\n vertex.append((int(new_x), int(new_y)))\n # draw brush strokes according to the vertex and angle list\n draw = ImageDraw.Draw(mask)\n width = np.random.randint(min_width, max_width)\n draw.line(vertex, fill=1, width=width)\n for v in vertex:\n draw.ellipse((v[0] - width // 2, v[1] - width // 2,\n v[0] + width // 2, v[1] + width // 2),\n fill=1)\n # randomly flip the mask\n if np.random.normal() > 0:\n mask.transpose(Image.FLIP_LEFT_RIGHT)\n if np.random.normal() > 0:\n mask.transpose(Image.FLIP_TOP_BOTTOM)\n mask = np.array(mask).astype(dtype=getattr(np, dtype))\n mask = mask[:, :, None]\n return mask\n\n\ndef random_bbox(img_shape, max_bbox_shape, max_bbox_delta=40, min_margin=20):\n \"\"\"Generate a random bbox for the mask on a given image.\n\n In our implementation, the max value cannot be obtained since we use\n `np.random.randint`. And this may be different with other standard scripts\n in the community.\n\n Args:\n img_shape (tuple[int]): The size of a image, in the form of (h, w).\n max_bbox_shape (int | tuple[int]): Maximum shape of the mask box,\n in the form of (h, w). If it is an integer, the mask box will be\n square.\n max_bbox_delta (int | tuple[int]): Maximum delta of the mask box,\n in the form of (delta_h, delta_w). If it is an integer, delta_h\n and delta_w will be the same. Mask shape will be randomly sampled\n from the range of `max_bbox_shape - max_bbox_delta` and\n `max_bbox_shape`. Default: (40, 40).\n min_margin (int | tuple[int]): The minimum margin size from the\n edges of mask box to the image boarder, in the form of\n (margin_h, margin_w). If it is an integer, margin_h and margin_w\n will be the same. Default: (20, 20).\n\n Returns:\n tuple[int]: The generated box, (top, left, h, w).\n \"\"\"\n if not isinstance(max_bbox_shape, tuple):\n max_bbox_shape = (max_bbox_shape, max_bbox_shape)\n if not isinstance(max_bbox_delta, tuple):\n max_bbox_delta = (max_bbox_delta, max_bbox_delta)\n if not isinstance(min_margin, tuple):\n min_margin = (min_margin, min_margin)\n assert is_tuple_of(max_bbox_shape, int)\n assert is_tuple_of(max_bbox_delta, int)\n assert is_tuple_of(min_margin, int)\n\n img_h, img_w = img_shape[:2]\n max_mask_h, max_mask_w = max_bbox_shape\n max_delta_h, max_delta_w = max_bbox_delta\n margin_h, margin_w = min_margin\n\n if max_mask_h > img_h or max_mask_w > img_w:\n raise ValueError(f'mask shape {max_bbox_shape} should be smaller than '\n f'image shape {img_shape}')\n if (max_delta_h // 2 * 2 >= max_mask_h\n or max_delta_w // 2 * 2 >= max_mask_w):\n raise ValueError(f'mask delta {max_bbox_delta} should be smaller than'\n f'mask shape {max_bbox_shape}')\n if img_h - max_mask_h < 2 * margin_h or img_w - max_mask_w < 2 * margin_w:\n raise ValueError(f'Margin {min_margin} cannot be satisfied for img'\n f'shape {img_shape} and mask shape {max_bbox_shape}')\n\n # get the max value of (top, left)\n max_top = img_h - margin_h - max_mask_h\n max_left = img_w - margin_w - max_mask_w\n # randomly select a (top, left)\n top = np.random.randint(margin_h, max_top)\n left = np.random.randint(margin_w, max_left)\n # randomly shrink the shape of mask box according to `max_bbox_delta`\n # the center of box is fixed\n delta_top = np.random.randint(0, max_delta_h // 2 + 1)\n delta_left = np.random.randint(0, max_delta_w // 2 + 1)\n top = top + delta_top\n left = left + delta_left\n h = max_mask_h - delta_top\n w = max_mask_w - delta_left\n return (top, left, h, w)\n\n\ndef random_irregular_mask(img_shape,\n num_vertices=(4, 8),\n max_angle=4,\n length_range=(10, 100),\n brush_width=(10, 40),\n dtype='uint8'):\n \"\"\"Generate random irregular masks.\n\n This is a modified version of free-form mask implemented in\n 'brush_stroke_mask'.\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n TODO: Rewrite the implementation of this function.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n num_vertices (int | tuple[int]): Min and max number of vertices. If\n only give an integer, we will fix the number of vertices.\n Default: (4, 8).\n max_angle (float): Max value of angle at each vertex. Default 4.0.\n length_range (int | tuple[int]): (min_length, max_length). If only give\n an integer, we will fix the length of brush. Default: (10, 100).\n brush_width (int | tuple[int]): (min_width, max_width). If only give\n an integer, we will fix the width of brush. Default: (10, 40).\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n h, w = img_shape[:2]\n\n mask = np.zeros((h, w), dtype=dtype)\n if isinstance(length_range, int):\n min_length, max_length = length_range, length_range + 1\n elif isinstance(length_range, tuple):\n min_length, max_length = length_range\n else:\n raise TypeError('The type of length_range should be int'\n f'or tuple[int], but got type: {length_range}')\n if isinstance(num_vertices, int):\n min_num_vertices, max_num_vertices = num_vertices, num_vertices + 1\n elif isinstance(num_vertices, tuple):\n min_num_vertices, max_num_vertices = num_vertices\n else:\n raise TypeError('The type of num_vertices should be int'\n f'or tuple[int], but got type: {num_vertices}')\n\n if isinstance(brush_width, int):\n min_brush_width, max_brush_width = brush_width, brush_width + 1\n elif isinstance(brush_width, tuple):\n min_brush_width, max_brush_width = brush_width\n else:\n raise TypeError('The type of brush_width should be int'\n f'or tuple[int], but got type: {brush_width}')\n\n num_v = np.random.randint(min_num_vertices, max_num_vertices)\n\n for i in range(num_v):\n start_x = np.random.randint(w)\n start_y = np.random.randint(h)\n # from the start point, randomly setlect n \\in [1, 6] directions.\n direction_num = np.random.randint(1, 6)\n angle_list = np.random.randint(0, max_angle, size=direction_num)\n length_list = np.random.randint(\n min_length, max_length, size=direction_num)\n brush_width_list = np.random.randint(\n min_brush_width, max_brush_width, size=direction_num)\n for direct_n in range(direction_num):\n angle = 0.01 + angle_list[direct_n]\n if i % 2 == 0:\n angle = 2 * math.pi - angle\n length = length_list[direct_n]\n brush_w = brush_width_list[direct_n]\n # compute end point according to the random angle\n end_x = (start_x + length * np.sin(angle)).astype(np.int32)\n end_y = (start_y + length * np.cos(angle)).astype(np.int32)\n\n cv2.line(mask, (start_y, start_x), (end_y, end_x), 1, brush_w)\n start_x, start_y = end_x, end_y\n mask = np.expand_dims(mask, axis=2)\n\n return mask\n\n\ndef get_irregular_mask(img_shape, area_ratio_range=(0.15, 0.5), **kwargs):\n \"\"\"Get irregular mask with the constraints in mask ratio.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n area_ratio_range (tuple(float)): Contain the minimum and maximum area\n ratio. Default: (0.15, 0.5).\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n mask = random_irregular_mask(img_shape, **kwargs)\n min_ratio, max_ratio = area_ratio_range\n\n while not min_ratio < (np.sum(mask) /\n (img_shape[0] * img_shape[1])) < max_ratio:\n mask = random_irregular_mask(img_shape, **kwargs)\n\n return mask\n\n\n_integer_types = (\n np.byte,\n np.ubyte, # 8 bits\n np.short,\n np.ushort, # 16 bits\n np.intc,\n np.uintc, # 16 or 32 or 64 bits\n np.int_,\n np.uint, # 32 or 64 bits\n np.longlong,\n np.ulonglong) # 64 bits\n\n_integer_ranges = {\n t: (np.iinfo(t).min, np.iinfo(t).max)\n for t in _integer_types\n}\n\ndtype_range = {\n np.bool_: (False, True),\n np.bool8: (False, True),\n np.float16: (-1, 1),\n np.float32: (-1, 1),\n np.float64: (-1, 1)\n}\ndtype_range.update(_integer_ranges)\n\n\ndef dtype_limits(image, clip_negative=False):\n \"\"\"Return intensity limits, i.e. (min, max) tuple, of the image's dtype.\n\n This function is adopted from skimage:\n https://github.com/scikit-image/scikit-image/blob/\n 7e4840bd9439d1dfb6beaf549998452c99f97fdd/skimage/util/dtype.py#L35\n\n Args:\n image (np.ndarray): Input image.\n clip_negative (bool, optional): If True, clip the negative range\n (i.e. return 0 for min intensity) even if the image dtype allows\n negative values. Default: False.\n\n Returns\n tuple: Lower and upper intensity limits.\n \"\"\"\n imin, imax = dtype_range[image.dtype.type]\n if clip_negative:\n imin = 0\n return imin, imax\n\n\ndef adjust_gamma(image, gamma=1, gain=1):\n \"\"\"Performs Gamma Correction on the input image.\n\n This function is adopted from skimage:\n https://github.com/scikit-image/scikit-image/blob/\n 7e4840bd9439d1dfb6beaf549998452c99f97fdd/skimage/exposure/\n exposure.py#L439-L494\n\n Also known as Power Law Transform.\n This function transforms the input image pixelwise according to the\n equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1.\n\n Args:\n image (np.ndarray): Input image.\n gamma (float, optional): Non negative real number. Defaults to 1.\n gain (float, optional): The constant multiplier. Defaults to 1.\n\n Returns:\n np.ndarray: Gamma corrected output image.\n \"\"\"\n if np.any(image < 0):\n raise ValueError('Image Correction methods work correctly only on '\n 'images with non-negative values. Use '\n 'skimage.exposure.rescale_intensity.')\n\n dtype = image.dtype.type\n\n if gamma < 0:\n raise ValueError('Gamma should be a non-negative real number.')\n\n scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])\n\n out = ((image / scale)**gamma) * scale * gain\n return out.astype(dtype)\n\n\ndef add_gaussian_noise(img: np.ndarray, mu, sigma):\n \"\"\"Add Gaussian Noise on the input image.\n\n Args:\n img (np.ndarray): Input image.\n mu (float): The mu value of the Gaussian function.\n sigma (float): The sigma value of the Gaussian function.\n\n Returns:\n noisy_img (np.ndarray): Gaussian noisy output image.\n \"\"\"\n img = img.astype(np.float32)\n gauss_noise = np.random.normal(mu, sigma, img.shape)\n noisy_img = img + gauss_noise\n noisy_img = np.clip(noisy_img, 0, 255)\n return noisy_img\n\n\ndef random_choose_unknown(unknown, crop_size):\n \"\"\"Randomly choose an unknown start (top-left) point for a given crop_size.\n\n Args:\n unknown (np.ndarray): The binary unknown mask.\n crop_size (tuple[int]): The given crop size.\n\n Returns:\n tuple[int]: The top-left point of the chosen bbox.\n \"\"\"\n h, w = unknown.shape\n crop_h, crop_w = crop_size\n delta_h = center_h = crop_h // 2\n delta_w = center_w = crop_w // 2\n\n # mask out the validate area for selecting the cropping center\n mask = np.zeros_like(unknown)\n mask[delta_h:h - delta_h, delta_w:w - delta_w] = 1\n if np.any(unknown & mask):\n center_h_list, center_w_list = np.where(unknown & mask)\n elif np.any(unknown):\n center_h_list, center_w_list = np.where(unknown)\n else:\n print_log('No unknown pixels found!', level=logging.WARNING)\n center_h_list = [center_h]\n center_w_list = [center_w]\n num_unknowns = len(center_h_list)\n rand_ind = np.random.randint(num_unknowns)\n center_h = center_h_list[rand_ind]\n center_w = center_w_list[rand_ind]\n\n # make sure the top-left point is valid\n top = np.clip(center_h - delta_h, 0, h - crop_h)\n left = np.clip(center_w - delta_w, 0, w - crop_w)\n\n return top, left\n" }, { "path": "mmagic/utils/typing.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import Callable, Dict, List, Sequence, Tuple, Union\n\nfrom mmengine.config import ConfigDict\nfrom mmengine.structures import BaseDataElement\nfrom torch import Tensor\n\nForwardInputs = Tuple[Dict[str, Union[Tensor, str, int]], Tensor]\nSampleList = Sequence[BaseDataElement]\n\nNoiseVar = Union[Tensor, Callable, None]\nLabelVar = Union[Tensor, Callable, List[int], None]\n\nConfigType = Union[ConfigDict, Dict]\n" }, { "path": "mmagic/version.py", "content": "# Copyright (c) Open-MMLab. All rights reserved.\n\n__version__ = '1.2.0dev0'\n\n\ndef parse_version_info(version_str):\n ver_info = []\n for x in version_str.split('.'):\n if x.isdigit():\n ver_info.append(int(x))\n elif x.find('rc') != -1:\n patch_version = x.split('rc')\n ver_info.append(int(patch_version[0]))\n ver_info.append(f'rc{patch_version[1]}')\n return tuple(ver_info)\n\n\nversion_info = parse_version_info(__version__)\n" }, { "path": "mmagic/visualization/__init__.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom .concat_visualizer import ConcatImageVisualizer\nfrom .vis_backend import (PaviVisBackend, TensorboardVisBackend, VisBackend,\n WandbVisBackend)\nfrom .visualizer import Visualizer\n\n__all__ = [\n 'ConcatImageVisualizer', 'Visualizer', 'VisBackend', 'PaviVisBackend',\n 'TensorboardVisBackend', 'WandbVisBackend'\n]\n" }, { "path": "mmagic/visualization/concat_visualizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport logging\nimport re\nfrom typing import Sequence\n\nimport numpy as np\nimport torch\nfrom mmengine.visualization import Visualizer\n\nfrom mmagic.registry import VISUALIZERS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import print_colored_log\n\n\n@VISUALIZERS.register_module()\nclass ConcatImageVisualizer(Visualizer):\n \"\"\"Visualize multiple images by concatenation.\n\n This visualizer will horizontally concatenate images belongs to different\n keys and vertically concatenate images belongs to different frames to\n visualize.\n\n Image to be visualized can be:\n - torch.Tensor or np.array\n - Image sequences of shape (T, C, H, W)\n - Multi-channel image of shape (1/3, H, W)\n - Single-channel image of shape (C, H, W)\n\n Args:\n fn_key (str): key used to determine file name for saving image.\n Usually it is the path of some input image. If the value is\n `dir/basename.ext`, the name used for saving will be basename.\n img_keys (str): keys, values of which are images to visualize.\n pixel_range (dict): min and max pixel value used to denormalize images,\n note that only float array or tensor will be denormalized,\n uint8 arrays are assumed to be unnormalized.\n bgr2rgb (bool): whether to convert the image from BGR to RGB.\n name (str): name of visualizer. Default: 'visualizer'.\n *args and \\**kwargs: Other arguments are passed to `Visualizer`. # noqa\n \"\"\"\n\n def __init__(self,\n fn_key: str,\n img_keys: Sequence[str],\n pixel_range={},\n bgr2rgb=False,\n name: str = 'visualizer',\n *args,\n **kwargs) -> None:\n super().__init__(name, *args, **kwargs)\n self.fn_key = fn_key\n self.img_keys = img_keys\n self.pixel_range = pixel_range\n self.bgr2rgb = bgr2rgb\n\n def add_datasample(self, data_sample: DataSample, step=0) -> None:\n \"\"\"Concatenate image and draw.\n\n Args:\n input (torch.Tensor): Single input tensor from data_batch.\n data_sample (DataSample): Single data_sample from data_batch.\n output (DataSample): Single prediction output by model.\n step (int): Global step value to record. Default: 0.\n \"\"\"\n # Note:\n # with LocalVisBackend and default arguments, we have:\n # self.save_dir == runner._log_dir / 'vis_data'\n\n merged_dict = {\n **data_sample.to_dict(),\n }\n\n if 'output' in merged_dict.keys():\n merged_dict.update(**merged_dict['output'])\n\n fn = merged_dict[self.fn_key]\n if isinstance(fn, list):\n fn = fn[0]\n fn = re.split(r' |/|\\\\', fn)[-1]\n fn = fn.split('.')[0]\n\n img_list = []\n for k in self.img_keys:\n if k not in merged_dict:\n print_colored_log(\n f'Key \"{k}\" not in data_sample or outputs',\n level=logging.WARN)\n continue\n\n img = merged_dict[k]\n\n # PixelData\n if isinstance(img, dict) and ('data' in img):\n img = img['data']\n\n # Tensor to array\n if isinstance(img, torch.Tensor):\n img = img.detach().cpu().numpy()\n if img.ndim == 3:\n img = img.transpose(1, 2, 0)\n elif img.ndim == 4:\n img = img.transpose(0, 2, 3, 1)\n\n # concat frame vertically\n if img.ndim == 4:\n img = np.concatenate(img, axis=0)\n\n # gray to 3 channel\n if (img.ndim == 3 and img.shape[2] == 1):\n img = np.concatenate((img, img, img), axis=2)\n\n # gray to 3 channel\n if img.ndim == 2:\n img = np.stack((img, img, img), axis=2)\n\n if self.bgr2rgb:\n img = img[..., ::-1]\n\n if img.dtype != np.uint8:\n # We assume uint8 type are not normalized\n if k in self.pixel_range:\n min_, max_ = self.pixel_range.get(k)\n img = ((img - min_) / (max_ - min_)) * 255\n img = img.clip(0, 255).round().astype(np.uint8)\n\n img_list.append(img)\n\n max_height = max(img.shape[0] for img in img_list)\n\n for i, img in enumerate(img_list):\n if img.shape[0] < max_height:\n img_list[i] = np.concatenate([\n img,\n np.ones((max_height - img.shape[0], *img.shape[1:]),\n dtype=img.dtype) * 127\n ],\n axis=0)\n\n img_cat = np.concatenate(img_list, axis=1)\n\n for vis_backend in self._vis_backends.values():\n vis_backend.add_image(fn, img_cat, step)\n" }, { "path": "mmagic/visualization/vis_backend.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom typing import Optional, Union\n\nimport cv2\nimport imageio\nimport numpy as np\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.config import Config\nfrom mmengine.fileio import dump, get_file_backend\nfrom mmengine.visualization import BaseVisBackend\nfrom mmengine.visualization import \\\n TensorboardVisBackend as BaseTensorboardVisBackend\nfrom mmengine.visualization import WandbVisBackend as BaseWandbVisBackend\nfrom mmengine.visualization.vis_backend import force_init_env\n\nfrom mmagic.registry import VISBACKENDS\n\n\n@VISBACKENDS.register_module()\nclass VisBackend(BaseVisBackend):\n \"\"\"MMagic visualization backend class. It can write image, config, scalars,\n etc. to the local hard disk and ceph path. You can get the drawing backend\n through the experiment property for custom drawing.\n\n Examples:\n >>> from mmagic.visualization import VisBackend\n >>> import numpy as np\n >>> vis_backend = VisBackend(save_dir='temp_dir',\n >>> ceph_path='s3://temp-bucket')\n >>> img = np.random.randint(0, 256, size=(10, 10, 3))\n >>> vis_backend.add_image('img', img)\n >>> vis_backend.add_scalar('mAP', 0.6)\n >>> vis_backend.add_scalars({'loss': [1, 2, 3], 'acc': 0.8})\n >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))\n >>> vis_backend.add_config(cfg)\n Args:\n save_dir (str): The root directory to save the files produced by the\n visualizer.\n img_save_dir (str): The directory to save images.\n Default to 'vis_image'.\n config_save_file (str): The file name to save config.\n Default to 'config.py'.\n scalar_save_file (str): The file name to save scalar values.\n Default to 'scalars.json'.\n ceph_path (Optional[str]): The remote path of Ceph cloud storage.\n Defaults to None.\n delete_local (bool): Whether delete local after uploading to ceph or\n not. If ``ceph_path`` is None, this will be ignored. Defaults to\n True.\n \"\"\"\n\n def __init__(self,\n save_dir: str,\n img_save_dir: str = 'vis_image',\n config_save_file: str = 'config.py',\n scalar_save_file: str = 'scalars.json',\n ceph_path: Optional[str] = None,\n delete_local_image: bool = True):\n assert config_save_file.split('.')[-1] == 'py'\n assert scalar_save_file.split('.')[-1] == 'json'\n super().__init__(save_dir)\n self._img_save_dir = img_save_dir\n self._config_save_file = config_save_file\n self._scalar_save_file = scalar_save_file\n\n self._ceph_path = ceph_path\n self._file_client = None\n self._delete_local_image = delete_local_image\n\n self._cfg = None\n\n def _init_env(self):\n if self._env_initialized:\n return\n self._env_initialized = True\n \"\"\"Init save dir.\"\"\"\n os.makedirs(self._save_dir, exist_ok=True)\n self._img_save_dir = osp.join(\n self._save_dir, # type: ignore\n self._img_save_dir)\n self._config_save_file = osp.join(\n self._save_dir, # type: ignore\n self._config_save_file)\n self._scalar_save_file = osp.join(\n self._save_dir, # type: ignore\n self._scalar_save_file)\n\n if self._ceph_path is not None:\n # work_dir: A/B/.../C/D\n # ceph_path: s3://a/b\n # local_files: A/B/.../C/D/TIME_STAMP/vis_data/\n # remote files: s3://a/b/D/TIME_STAMP/vis_data/\n if self._cfg is None or self._cfg.get('work_dir', None) is None:\n message_hub = MessageHub.get_current_instance()\n cfg_str = message_hub.get_info('cfg')\n self._cfg = Config.fromstring(cfg_str, '.py')\n full_work_dir = osp.abspath(self._cfg['work_dir'])\n if full_work_dir.endswith('/'):\n full_work_dir = full_work_dir[:-1]\n\n # NOTE: handle src_path with `os.sep`, because may windows\n # and linux may have different separate.\n src_path = os.sep.join(full_work_dir.split(os.sep)[:-1])\n # NOTE: handle tar_path with '/', because ceph use linux\n # environment\n tar_path = self._ceph_path[:-1] if \\\n self._ceph_path.endswith('/') else self._ceph_path\n\n backend_args = dict(\n backend='petrel', path_mapping={src_path: tar_path})\n self._file_client = get_file_backend(backend_args=backend_args)\n\n @property # type: ignore\n @force_init_env\n def experiment(self) -> 'VisBackend':\n \"\"\"Return the experiment object associated with this visualization\n backend.\"\"\"\n return self\n\n def add_config(self, config: Config, **kwargs) -> None:\n \"\"\"Record the config to disk.\n\n Args:\n config (Config): The Config object\n \"\"\"\n assert isinstance(config, Config)\n self._cfg = config\n self._init_env()\n config.dump(self._config_save_file)\n self._upload(self._config_save_file)\n\n @force_init_env\n def add_image(self,\n name: str,\n image: np.array,\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Record the image to disk.\n\n Args:\n name (str): The image identifier.\n image (np.ndarray): The image to be saved. The format\n should be RGB. Default to None.\n step (int): Global step value to record. Default to 0.\n \"\"\"\n assert image.dtype == np.uint8\n os.makedirs(self._img_save_dir, exist_ok=True)\n if image.ndim == 3:\n drawn_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n save_file_name = f'{name}_{step}.png'\n cv2.imwrite(\n osp.join(self._img_save_dir, save_file_name), drawn_image)\n elif image.ndim == 4:\n n_skip = kwargs.get('n_skip', 1)\n fps = kwargs.get('fps', 60)\n save_file_name = f'{name}_{step}.gif'\n save_file_path = osp.join(self._img_save_dir, save_file_name)\n\n frames_list = []\n for frame in image[::n_skip]:\n frames_list.append(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))\n if not (image.shape[0] % n_skip == 0):\n frames_list.append(image[-1])\n imageio.mimsave(\n save_file_path, frames_list, 'GIF', duration=1000. / fps)\n else:\n raise ValueError(\n 'Only support visualize image with dimension of 3 or 4. But '\n f'receive input with shape \\'{image.shape}\\'.')\n self._upload(\n osp.join(self._img_save_dir, save_file_name),\n self._delete_local_image)\n\n @force_init_env\n def add_scalar(self,\n name: str,\n value: Union[int, float, torch.Tensor, np.ndarray],\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Record the scalar data to disk.\n\n Args:\n name (str): The scalar identifier.\n value (int, float, torch.Tensor, np.ndarray): Value to save.\n step (int): Global step value to record. Default to 0.\n \"\"\"\n if isinstance(value, torch.Tensor):\n value = value.item()\n self._dump({name: value, 'step': step}, self._scalar_save_file, 'json')\n\n @force_init_env\n def add_scalars(self,\n scalar_dict: dict,\n step: int = 0,\n file_path: Optional[str] = None,\n **kwargs) -> None:\n \"\"\"Record the scalars to disk.\n\n The scalar dict will be written to the default and\n specified files if ``file_path`` is specified.\n\n Args:\n scalar_dict (dict): Key-value pair storing the tag and\n corresponding values. The value must be dumped\n into json format.\n step (int): Global step value to record. Default to 0.\n file_path (str, optional): The scalar's data will be\n saved to the ``file_path`` file at the same time\n if the ``file_path`` parameter is specified.\n Default to None.\n \"\"\"\n assert isinstance(scalar_dict, dict)\n scalar_dict_new = dict()\n for k, v in scalar_dict.items():\n if isinstance(v, torch.Tensor):\n scalar_dict_new[k] = v.item()\n else:\n scalar_dict_new[k] = v\n scalar_dict_new.setdefault('step', step)\n\n if file_path is not None:\n assert file_path.split('.')[-1] == 'json'\n new_save_file_path = osp.join(\n self._save_dir, # type: ignore\n file_path)\n assert new_save_file_path != self._scalar_save_file, \\\n '``file_path`` and ``scalar_save_file`` have the ' \\\n 'same name, please set ``file_path`` to another value'\n self._dump(scalar_dict_new, new_save_file_path, 'json')\n self._dump(scalar_dict_new, self._scalar_save_file, 'json')\n self._upload(self._scalar_save_file)\n\n def _dump(self, value_dict: dict, file_path: str,\n file_format: str) -> None:\n \"\"\"dump dict to file.\n\n Args:\n value_dict (dict) : The dict data to saved.\n file_path (str): The file path to save data.\n file_format (str): The file format to save data.\n \"\"\"\n with open(file_path, 'a+') as f:\n dump(value_dict, f, file_format=file_format)\n f.write('\\n')\n\n def _upload(self, path: str, delete_local=False) -> None:\n \"\"\"Upload file at path to remote.\n\n Args:\n path (str): Path of file to upload.\n \"\"\"\n if self._file_client is None:\n return\n with open(path, 'rb') as file:\n self._file_client.put(file, path)\n if delete_local:\n os.remove(path)\n\n\n@VISBACKENDS.register_module()\nclass TensorboardVisBackend(BaseTensorboardVisBackend):\n\n @force_init_env\n def add_image(self, name: str, image: np.array, step: int = 0, **kwargs):\n \"\"\"Record the image to Tensorboard. Additional support upload gif\n files.\n\n Args:\n name (str): The image identifier.\n image (np.ndarray): The image to be saved. The format\n should be RGB.\n step (int): Useless parameter. Wandb does not\n need this parameter. Default to 0.\n \"\"\"\n\n if image.ndim == 4:\n n_skip = kwargs.get('n_skip', 1)\n fps = kwargs.get('fps', 60)\n\n frames_list = []\n for frame in image[::n_skip]:\n frames_list.append(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))\n if not (image.shape[0] % n_skip == 0):\n frames_list.append(image[-1])\n\n frames_np = np.transpose(\n np.stack(frames_list, axis=0), (0, 3, 1, 2))\n frames_tensor = torch.from_numpy(frames_np)[None, ...]\n self._tensorboard.add_video(\n name, frames_tensor, global_step=step, fps=fps)\n else:\n # write normal image\n self._tensorboard.add_image(name, image, step, dataformats='HWC')\n\n\n@VISBACKENDS.register_module()\nclass PaviVisBackend(BaseVisBackend):\n \"\"\"Visualization backend for Pavi.\"\"\"\n\n def __init__(self,\n save_dir: str,\n exp_name: Optional[str] = None,\n labels: Optional[str] = None,\n project: Optional[str] = None,\n model: Optional[str] = None,\n description: Optional[str] = None):\n self.save_dir = save_dir\n\n self._name = exp_name\n self._labels = labels\n self._project = project\n self._model = model\n self._description = description\n\n def _init_env(self):\n \"\"\"Init save dir.\"\"\"\n try:\n import pavi\n except ImportError:\n raise ImportError(\n 'To use \\'PaviVisBackend\\' Pavi must be installed.')\n self._pavi = pavi.SummaryWriter(\n name=self._name,\n labels=self._labels,\n project=self._project,\n model=self._model,\n description=self._description,\n log_dir=self.save_dir)\n\n @property # type: ignore\n @force_init_env\n def experiment(self) -> 'VisBackend':\n \"\"\"Return the experiment object associated with this visualization\n backend.\"\"\"\n return self._pavi\n\n @force_init_env\n def add_image(self,\n name: str,\n image: np.array,\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Record the image to Pavi.\n\n Args:\n name (str): The image identifier.\n image (np.ndarray): The image to be saved. The format\n should be RGB. Default to None.\n step (int): Global step value to record. Default to 0.\n \"\"\"\n assert image.dtype == np.uint8\n drawn_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n self._pavi.add_image(name, drawn_image, step)\n\n @force_init_env\n def add_scalar(self,\n name: str,\n value: Union[int, float, torch.Tensor, np.ndarray],\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Record the scalar data to Pavi.\n\n Args:\n name (str): The scalar identifier.\n value (int, float, torch.Tensor, np.ndarray): Value to save.\n step (int): Global step value to record. Default to 0.\n \"\"\"\n if isinstance(value, torch.Tensor):\n value = value.item()\n self._pavi.add_scalar(name, value, step)\n\n @force_init_env\n def add_scalars(self,\n scalar_dict: dict,\n step: int = 0,\n file_path: Optional[str] = None,\n **kwargs) -> None:\n \"\"\"Record the scalars to Pavi.\n\n The scalar dict will be written to the default and\n specified files if ``file_path`` is specified.\n\n Args:\n scalar_dict (dict): Key-value pair storing the tag and\n corresponding values. The value must be dumped\n into json format.\n step (int): Global step value to record. Default to 0.\n file_path (str, optional): The scalar's data will be\n saved to the ``file_path`` file at the same time\n if the ``file_path`` parameter is specified.\n Default to None.\n \"\"\"\n assert isinstance(scalar_dict, dict)\n for name, value in scalar_dict.items():\n self.add_scalar(name, value, step)\n\n\n@VISBACKENDS.register_module()\nclass WandbVisBackend(BaseWandbVisBackend):\n \"\"\"Wandb visualization backend for MMagic.\"\"\"\n\n def _init_env(self):\n \"\"\"Setup env for wandb.\"\"\"\n if not os.path.exists(self._save_dir):\n os.makedirs(self._save_dir, exist_ok=True) # type: ignore\n if self._init_kwargs is None:\n self._init_kwargs = {'dir': self._save_dir}\n else:\n self._init_kwargs.setdefault('dir', self._save_dir)\n try:\n import wandb\n except ImportError:\n raise ImportError(\n 'Please run \"pip install wandb\" to install wandb')\n\n # add timestamp at the end of name\n timestamp = self._save_dir.split('/')[-2]\n orig_name = self._init_kwargs.get('name', None)\n if orig_name:\n self._init_kwargs['name'] = f'{orig_name}_{timestamp}'\n wandb.init(**self._init_kwargs)\n self._wandb = wandb\n\n @force_init_env\n def add_image(self, name: str, image: np.array, step: int = 0, **kwargs):\n \"\"\"Record the image to wandb. Additional support upload gif files.\n\n Args:\n name (str): The image identifier.\n image (np.ndarray): The image to be saved. The format\n should be RGB.\n step (int): Useless parameter. Wandb does not\n need this parameter. Default to 0.\n \"\"\"\n try:\n import wandb\n except ImportError:\n raise ImportError(\n 'Please run \"pip install wandb\" to install wandb')\n\n if image.ndim == 4:\n n_skip = kwargs.get('n_skip', 1)\n fps = kwargs.get('fps', 60)\n\n frames_list = []\n for frame in image[::n_skip]:\n frames_list.append(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))\n if not (image.shape[0] % n_skip == 0):\n frames_list.append(image[-1])\n\n frames_np = np.transpose(\n np.stack(frames_list, axis=0), (0, 3, 1, 2))\n self._wandb.log(\n {name: wandb.Video(frames_np, fps=fps, format='gif')},\n commit=self._commit)\n else:\n # write normal image\n self._wandb.log({name: wandb.Image(image)}, commit=self._commit)\n" }, { "path": "mmagic/visualization/visualizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom typing import Dict, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom mmengine.dist import master_only\nfrom mmengine.visualization import Visualizer as BaseVisualizer\nfrom torch import Tensor\nfrom torchvision.utils import make_grid\n\nfrom mmagic.registry import VISUALIZERS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import SampleList\n\nmean_std_type = Optional[Sequence[Union[float, int]]]\n\n\n@VISUALIZERS.register_module()\nclass Visualizer(BaseVisualizer):\n \"\"\"MMagic Visualizer.\n\n Args:\n name (str): Name of the instance. Defaults to 'visualizer'.\n vis_backends (list, optional): Visual backend config list.\n Defaults to None.\n save_dir (str, optional): Save file dir for all storage backends.\n If it is None, the backend storage will not save any data.\n\n Examples::\n\n >>> # Draw image\n >>> vis = Visualizer()\n >>> vis.add_datasample(\n >>> 'random_noise',\n >>> gen_samples=torch.rand(2, 3, 10, 10),\n >>> gt_samples=dict(imgs=torch.randn(2, 3, 10, 10)),\n >>> gt_keys='imgs',\n >>> vis_mode='image',\n >>> n_rows=2,\n >>> step=10)\n \"\"\"\n\n def __init__(self,\n name='visualizer',\n vis_backends: Optional[List[Dict]] = None,\n save_dir: Optional[str] = None) -> None:\n super().__init__(name, vis_backends=vis_backends, save_dir=save_dir)\n\n @staticmethod\n def _post_process_image(image: Tensor) -> Tensor:\n \"\"\"Post process images.\n\n Args:\n image (Tensor): Image to post process. The value range of\n image should be in [0, 255] and the channel order should\n be BGR.\n\n Returns:\n Tensor: Image in RGB color order.\n \"\"\"\n # input image should be [0, 255] and BGR\n if image.shape[1] == 1:\n image = torch.cat([image, image, image], dim=1)\n image = image[:, [2, 1, 0], ...]\n return image\n\n @staticmethod\n def _get_n_row_and_padding(samples: Tuple[dict, Tensor],\n n_row: Optional[int] = None\n ) -> Tuple[int, Optional[Tensor]]:\n \"\"\"Get number of sample in each row and tensor for padding the empty\n position.\n\n Args:\n samples (Tuple[dict, Tensor]): Samples to visualize.\n n_row (int, optional): Number of images displayed in each row of.\n If not passed, n_row will be set as ``int(sqrt(batch_size))``.\n\n Returns:\n Tuple[int, Optional[int]]: Number of sample in each row and tensor\n for padding the empty position.\n \"\"\"\n if isinstance(samples, dict):\n for sample in iter(samples.values()):\n # NOTE: dirty way to get the shape of image tensor\n if isinstance(sample, Tensor) and sample.ndim in [4, 5]:\n sample_shape = sample.shape\n break\n else:\n sample_shape = samples.shape\n\n n_samples = sample_shape[0]\n if n_row is None:\n n_row = math.ceil(math.sqrt(n_samples))\n if n_samples % n_row == 0:\n n_padding = 0\n else:\n n_padding = n_row - (n_samples % n_row)\n if n_padding:\n return n_row, -1.0 * torch.ones(n_padding, *sample_shape[1:])\n return n_row, None\n\n def _vis_gif_sample(self, gen_samples: SampleList,\n target_keys: Union[str, List[str],\n None], n_row: int) -> np.ndarray:\n \"\"\"Visualize gif samples.\n\n Args:\n gen_samples (SampleList): List of data samples to visualize\n target_keys (Union[str, List[str], None]): Keys of the\n visualization target in data samples.\n n_rows (int, optional): Number of images in one row.\n\n Returns:\n np.ndarray: The visualization results.\n \"\"\"\n\n def post_process_sequence(samples):\n num_timesteps = samples.shape[1]\n seq_list = [\n self._post_process_image(samples[:, t, ...].cpu())\n for t in range(num_timesteps)\n ]\n return torch.stack(seq_list, dim=1)\n\n if target_keys is None:\n # get all the keys that are tensors with 4 dimensions\n target_keys = [\n k for k, v in gen_samples[0].items()\n if ((not k.startswith('_')) and (isinstance(v, Tensor)) and (\n v.data.ndim == 4))\n ]\n target_keys = [target_keys] if isinstance(target_keys, str) \\\n else target_keys\n\n sample_list = list()\n for sample in gen_samples:\n sample_dict = dict()\n for k in target_keys:\n sample_dict[k] = self._get_vis_data_by_key(sample, k)\n sample_list.append(sample_dict)\n\n for k in sample_list[0].keys():\n sample_ = torch.stack([samp[k] for samp in sample_list], dim=0)\n sample_ = post_process_sequence(sample_.cpu())\n sample_dict[k] = sample_\n\n n_row, padding_tensor = self._get_n_row_and_padding(sample_dict, n_row)\n num_timesteps = next(iter(sample_dict.values())).shape[1]\n\n vis_results = []\n for sample in sample_dict.values():\n if padding_tensor is not None:\n vis_results.append(torch.cat([sample, padding_tensor], dim=0))\n else:\n vis_results.append(sample)\n\n # concatenate along batch size\n vis_results = torch.cat(vis_results)\n vis_results = [\n make_grid(vis_results[:, t, ...].cpu(),\n nrow=n_row).cpu().permute(1, 2, 0)\n for t in range(num_timesteps)\n ]\n vis_results = torch.stack(vis_results, dim=0) # [t, H, W, 3]\n vis_results = vis_results.numpy().astype(np.uint8)\n return vis_results\n\n def _vis_image_sample(self, gen_samples: SampleList,\n target_keys: Union[str, List[str],\n None], n_row: int) -> np.ndarray:\n \"\"\"Visualize image samples.\n\n Args:\n gen_samples (SampleList): List of data samples to visualize\n target_keys (Union[str, List[str], None]): Keys of the\n visualization target in data samples.\n color_order (str): The color order of the passed images.\n target_mean (Sequence[Union[float, int]]): The target mean of the\n visualization results.\n target_std (Sequence[Union[float, int]]): The target std of the\n visualization results.\n n_rows (int, optional): Number of images in one row.\n\n Returns:\n np.ndarray: The visualization results.\n \"\"\"\n if target_keys is None:\n # get all key of image tensor automatically\n target_keys = [\n k for k, v in gen_samples[0].items()\n if ((not k.startswith('_')) and (isinstance(v, Tensor)) and (\n v.data.ndim == 3))\n ]\n target_keys = [target_keys] if isinstance(target_keys, str) \\\n else target_keys\n\n sample_list = list()\n for sample in gen_samples:\n sample_dict = dict()\n for k in target_keys:\n sample_dict[k] = self._get_vis_data_by_key(sample, k)\n sample_list.append(sample_dict)\n\n for k in sample_list[0].keys():\n sample_ = torch.stack([samp[k] for samp in sample_list], dim=0)\n sample_ = self._post_process_image(sample_.cpu())\n sample_dict[k] = sample_\n\n n_row, padding_tensor = self._get_n_row_and_padding(sample_dict, n_row)\n\n vis_results = []\n for sample in sample_dict.values():\n if padding_tensor is not None:\n vis_results.append(torch.cat([sample, padding_tensor], dim=0))\n else:\n vis_results.append(sample)\n\n # concatenate along batch size\n vis_results = torch.cat(vis_results, dim=0)\n vis_results = make_grid(vis_results, nrow=n_row).cpu().permute(1, 2, 0)\n vis_results = vis_results.numpy().astype(np.uint8)\n return vis_results\n\n def _get_vis_data_by_key(self, sample: DataSample, key: str) -> Tensor:\n \"\"\"Get tensor in ``DataSample`` by the given key.\n\n Args:\n sample (DataSample): Input data sample.\n key (str): Name of the target tensor.\n\n Returns:\n Tensor: Tensor from the data sample.\n \"\"\"\n if '.' in key:\n key_list = key.split('.')\n else:\n key_list = [key]\n vis_data = sample\n for k in key_list:\n # get vis data step by step\n assert hasattr(vis_data, k)\n vis_data = getattr(vis_data, k)\n\n if isinstance(vis_data, Tensor):\n return vis_data\n else:\n # check only one tensor in current datasample to visualize\n elements = [\n element for k, element in vis_data.items()\n if not k.startswith('_') and isinstance(element, Tensor)\n ]\n assert len(elements) == 1, (\n f'Find {len(elements)} Tensor in DataSample with '\n f'key {key}.')\n vis_data = elements[0]\n\n assert isinstance(\n vis_data,\n Tensor), (f'Element with key \\'{key}\\' is not a Tensor.')\n return vis_data\n\n def add_datasample(self,\n name: str,\n *,\n gen_samples: Sequence[DataSample],\n target_keys: Optional[Tuple[str, List[str]]] = None,\n vis_mode: Optional[str] = None,\n n_row: Optional[int] = None,\n show: bool = False,\n wait_time: int = 0,\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Draw datasample and save to all backends.\n\n If GT and prediction are plotted at the same time, they\n are displayed in a stitched image where the left image is the\n ground truth and the right image is the prediction.\n\n If ``show`` is True, all storage backends are ignored,\n and the images will be displayed in a local window.\n\n Args:\n name (str): The image identifier.\n gen_samples (List[DataSample]): Data samples to visualize.\n vis_mode (str, optional): Visualization mode. If not passed, will\n visualize results as image. Defaults to None.\n n_rows (int, optional): Number of images in one row.\n Defaults to None.\n color_order (str): The color order of the passed images. Defaults\n to 'bgr'.\n target_mean (Sequence[Union[float, int]]): The target mean of the\n visualization results. Defaults to 127.5.\n target_std (Sequence[Union[float, int]]): The target std of the\n visualization results. Defaults to 127.5.\n show (bool): Whether to display the drawn image. Default to False.\n wait_time (float): The interval of show (s). Defaults to 0.\n step (int): Global step value to record. Defaults to 0.\n \"\"\"\n\n # get visualize function\n if vis_mode is None:\n vis_func = self._vis_image_sample\n else:\n vis_func = getattr(self, f'_vis_{vis_mode}_sample')\n\n vis_sample = vis_func(gen_samples, target_keys, n_row)\n\n if show:\n self.show(vis_sample, win_name=name, wait_time=wait_time)\n\n self.add_image(name, vis_sample, step, **kwargs)\n\n @master_only\n def add_image(self,\n name: str,\n image: np.ndarray,\n step: int = 0,\n **kwargs) -> None:\n \"\"\"Record the image. Support input kwargs.\n\n Args:\n name (str): The image identifier.\n image (np.ndarray, optional): The image to be saved. The format\n should be RGB. Default to None.\n step (int): Global step value to record. Default to 0.\n \"\"\"\n for vis_backend in self._vis_backends.values():\n vis_backend.add_image(name, image, step, **kwargs) # type: ignore\n" }, { "path": "model-index.yml", "content": "Import:\n- configs/animatediff/metafile.yml\n- configs/aot_gan/metafile.yml\n- configs/basicvsr/metafile.yml\n- configs/basicvsr_pp/metafile.yml\n- configs/biggan/metafile.yml\n- configs/cain/metafile.yml\n- configs/controlnet/metafile.yml\n- configs/controlnet_animation/metafile.yml\n- configs/cyclegan/metafile.yml\n- configs/dcgan/metafile.yml\n- configs/deblurganv2/metafile.yml\n- configs/deepfillv1/metafile.yml\n- configs/deepfillv2/metafile.yml\n- configs/dic/metafile.yml\n- configs/diffusers_pipeline/metafile.yml\n- configs/dim/metafile.yml\n- configs/disco_diffusion/metafile.yml\n- configs/draggan/metafile.yml\n- configs/dreambooth/metafile.yml\n- configs/edsr/metafile.yml\n- configs/edvr/metafile.yml\n- configs/eg3d/metafile.yml\n- configs/esrgan/metafile.yml\n- configs/fastcomposer/metafile.yml\n- configs/flavr/metafile.yml\n- configs/gca/metafile.yml\n- configs/ggan/metafile.yml\n- configs/glean/metafile.yml\n- configs/global_local/metafile.yml\n- configs/guided_diffusion/metafile.yml\n- configs/iconvsr/metafile.yml\n- configs/indexnet/metafile.yml\n- configs/inst_colorization/metafile.yml\n- configs/liif/metafile.yml\n- configs/lsgan/metafile.yml\n- configs/nafnet/metafile.yml\n- configs/partial_conv/metafile.yml\n- configs/pggan/metafile.yml\n- configs/pix2pix/metafile.yml\n- configs/positional_encoding_in_gans/metafile.yml\n- configs/rdn/metafile.yml\n- configs/real_basicvsr/metafile.yml\n- configs/real_esrgan/metafile.yml\n- configs/restormer/metafile.yml\n- configs/sagan/metafile.yml\n- configs/singan/metafile.yml\n- configs/sngan_proj/metafile.yml\n- configs/srcnn/metafile.yml\n- configs/srgan_resnet/metafile.yml\n- configs/stable_diffusion/metafile.yml\n- configs/stable_diffusion_xl/metafile.yml\n- configs/styleganv1/metafile.yml\n- configs/styleganv2/metafile.yml\n- configs/styleganv3/metafile.yml\n- configs/swinir/metafile.yml\n- configs/tdan/metafile.yml\n- configs/textual_inversion/metafile.yml\n- configs/tof/metafile.yml\n- configs/ttsr/metafile.yml\n- configs/vico/metafile.yml\n- configs/wgan-gp/metafile.yml\n" }, { "path": "projects/README.md", "content": "# Welcome to Projects of MMagic\n\nWelcome to the MMagic community!\nThe MMagic ecosystem consists of tutorials, libraries, and projects from a broad set of researchers in academia and industry, ML and application engineers.\nThe goal of this ecosystem is to support, accelerate, and aid in your AIGC exploration with MMagic for image, video, 3D content generation, editing and processing.\n\nHere are a few projects that are built upon MMagic. They are examples of how to use MMagic as a library, to make your projects more maintainable.\nPlease find more projects in [MMagic Ecosystem](https://openmmlab.com/ecosystem).\n\n## Show your projects on OpenMMLab Ecosystem\n\nYou can submit your project so that it can be shown on the homepage of [OpenMMLab](https://openmmlab.com/ecosystem).\n\n## Add example projects to MMagic\n\nHere is an [example project](./example_project) about how to add your projects to MMagic.\nYou can copy and create your own project from the [example project](./example_project).\n\nWe also provide some documentation listed below for your reference:\n\n- [Contribution Guide](https://mmagic.readthedocs.io/en/latest/community/contributing.html)\n\n The guides for new contributors about how to add your projects to MMagic.\n\n- [New Model Guide](https://mmagic.readthedocs.io/en/latest/howto/models.html)\n\n The documentation of adding new models.\n\n- [Discussions](https://github.com/open-mmlab/mmagic/discussions)\n\n Welcome to start a discussion!\n\n## Projects of libraries and toolboxes\n\n- [PowerVQE](https://github.com/ryanxingql/powervqe): Open framework for quality enhancement of compressed videos based on PyTorch and MMagic.\n\n- [VR-Baseline](https://github.com/linjing7/VR-Baseline): Video Restoration Toolbox.\n\n- [Derain-Toolbox](https://github.com/biubiubiiu/derain-toolbox): Single Image Deraining Toolbox and Benchmark\n\n## Projects of research papers\n\n- [Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV 2022](https://arxiv.org/abs/2207.10765)[\\[github\\]](https://github.com/caojiezhang/DAVSR)\n\n- [SepLUT:Separable Image-adaptive Lookup Tables for Real-time Image Enhancement, ECCV 2022](https://arxiv.org/abs/2207.08351)[\\[github\\]](https://github.com/ImCharlesY/SepLUT)\n\n- [TTVSR: Learning Trajectory-Aware Transformer for Video Super-Resolution, CVPR 2022](https://arxiv.org/abs/2204.04216)[\\[github\\]](https://github.com/researchmm/TTVSR)\n\n- [Arbitrary-Scale Image Synthesis, CVPR 2022](https://arxiv.org/pdf/2204.02273.pdf)[\\[github\\]](https://github.com/vglsd/ScaleParty)\n\n- [Investigating Tradeoffs in Real-World Video Super-Resolution(RealBasicVSR), CVPR 2022](https://arxiv.org/abs/2111.12704)[\\[github\\]](https://github.com/ckkelvinchan/RealBasicVSR)\n\n- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022](https://arxiv.org/abs/2104.13371)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR_PlusPlus)\n\n- [Multi-Scale Memory-Based Video Deblurring, CVPR 2022](https://arxiv.org/abs/2204.02977)[\\[github\\]](https://github.com/jibo27/MemDeblur)\n\n- [AdaInt:Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement, CVPR 2022](https://arxiv.org/abs/2204.13983)[\\[github\\]](https://github.com/ImCharlesY/AdaInt)\n\n- [A New Dataset and Transformer for Stereoscopic Video Super-Resolution, CVPRW 2022](https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/papers/Imani_A_New_Dataset_and_Transformer_for_Stereoscopic_Video_Super-Resolution_CVPRW_2022_paper.pdf)[\\[github\\]](https://github.com/H-deep/Trans-SVSR)\n\n- [Liquid warping GAN with attention: A unified framework for human image synthesis, TPAMI 2021](https://arxiv.org/pdf/2011.09055.pdf)[\\[github\\]](https://github.com/iPERDance/iPERCore)\n\n- [BasicVSR:The Search for Essential Components in Video Super-Resolution and Beyond, CVPR 2021](https://arxiv.org/abs/2012.02181)[\\[github\\]](https://github.com/ckkelvinchan/BasicVSR-IconVSR)\n\n- [GLEAN:Generative Latent Bank for Large-Factor Image Super-Resolution, CVPR 2021](https://arxiv.org/abs/2012.00739)[\\[github\\]](https://github.com/ckkelvinchan/GLEAN)\n\n- [DAN:Unfolding the Alternating Optimization for Blind Super Resolution, NeurIPS 2020](https://arxiv.org/abs/2010.02631v4)[\\[github\\]](https://github.com/AlexZou14/DAN-Basd-on-Openmmlab)\n" }, { "path": "projects/animated_drawings/README.md", "content": "# Animated Drawings (SIGGRAPH'2023)\n\n> [A Method for Animating Children's Drawings of The Human Figure](https://arxiv.org/abs/2303.12741)\n\n> **Task**: Drawing\n\n\n\n## Abstract\n\n\n\nChildren’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.\n\n\n\n
\n \n
\n\n## Quick Start\n\n### 1. Install Animated Drawings\n\n```shell\ncd mmagic/projects/animated_drawings\npip install -e git+https://github.com/facebookresearch/AnimatedDrawings.git#egg=animated_drawings\n```\n\n### 2. Download resources\n\n```shell\ncd mmagic/projects/animated_drawings\nmkdir resources\n# download image\nwget -O sakuragi.png https://user-images.githubusercontent.com/12782558/236157945-452fb9d0-e02e-4f36-8338-34f0ca0fe962.png\n# download mask image\nwget -O sakuragi_mask.png https://user-images.githubusercontent.com/12782558/236157965-539a5467-edae-40d0-a9da-7bb5906bcdc4.png\n# download background image\nwget -O basketball_playground.png https://user-images.githubusercontent.com/12782558/236190727-0e456482-2ae3-4304-9e6c-6ba7d319ea71.png\n```\n\n### 3. Generate character\n\nBy running following codes, you will get texture and mask images for animated rendering in characters/slamdunk directory.\n\n```shell\ncd mmagic/projects/animated_drawings\npython tools/generate_character.py\n```\n\n### 3. Generate video\n\nBy running following codes, you will get a Sakuragi moving like a zombie.\n\n```shell\ncd mmagic/projects/animated_drawings\npython tools/generate_video.py\n```\n\nThe output video will be saved at resources dir, and it looks like this:\n\n
\n \n
\n\n## Citation\n\n```bibtex\n@misc{smith2023method,\n title={A Method for Animating Children's Drawings of the Human Figure},\n author={Harrison Jesse Smith and Qingyuan Zheng and Yifei Li and Somya Jain and Jessica K. Hodgins},\n year={2023},\n eprint={2303.12741},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n" }, { "path": "projects/animated_drawings/bvh/zombie.bvh", "content": "HIERARCHY\nROOT Hips\n{\n\tOFFSET 0.00 0.00 0.00\n\tCHANNELS 6 Xposition Yposition Zposition Xrotation Yrotation Zrotation\n\tJOINT Spine\n\t{\n\t\tOFFSET 0.00000 1.15266 1.72900\n\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\tJOINT Spine1\n\t\t{\n\t\t\tOFFSET 0.00000 0.40032 2.27031\n\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\tJOINT Spine2\n\t\t\t{\n\t\t\t\tOFFSET 0.00000 0.20092 2.29656\n\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\tJOINT Spine3\n\t\t\t\t{\n\t\t\t\t\tOFFSET 0.00000 0.00000 2.30533\n\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\tJOINT Neck\n\t\t\t\t\t{\n\t\t\t\t\t\tOFFSET 0.00000 -0.86449 6.05149\n\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\tJOINT Head\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tOFFSET 0.00000 -0.55043 3.12167\n\t\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\t\tEnd Site\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tOFFSET 0.00000 0.00000 3.73175\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tJOINT RightShoulder\n\t\t\t\t\t{\n\t\t\t\t\t\tOFFSET -0.78828 -1.31380 4.25672\n\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\tJOINT RightArm\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tOFFSET -3.91513 0.00000 0.00000\n\t\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\t\tJOINT RightForeArm\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tOFFSET -6.91491 0.00000 0.00000\n\t\t\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\t\t\tJOINT RightHand\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tOFFSET -6.09622 0.00000 0.00000\n\t\t\t\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation Zrotation\n\t\t\t\t\t\t\t\t\tJOINT RightHandEnd\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tOFFSET -1.74995 0.00000 0.00000\n\t\t\t\t\t\t\t\t\t\tCHANNELS 3 Xrotation Yrotation 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-3.8767 1.1527 5.4629 1.4924 0.0000 0.0000 -5.9064 1.3899 3.2082 1.6263 0.0000 0.0000 -5.4511 0.6957 5.5090 -9.2646 0.0000 -0.0000\n 0.2420 -113.9146 24.4288 -6.1188 0.7917 173.2982 0.5363 1.9180 0.3488 -0.0271 0.1817 0.2321 0.7414 1.3984 5.4928 -1.7408 -0.6109 1.2994 -3.6654 -3.5618 -5.0923 2.8594 -1.7095 -10.0361 -5.6160 -1.7818 -2.7589 -10.0446 -1.4897 9.5578 0.0000 0.0000 11.3061 -0.4324 4.9555 -15.6011 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -8.9955 3.4111 0.5091 -6.8569 -0.5092 -14.2754 -0.0000 0.0000 2.8518 5.1706 10.1253 5.7091 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -14.7801 0.5899 -3.0923 11.7029 0.0000 0.0000 -3.7873 1.2071 5.5951 1.4869 0.0000 0.0000 -5.9499 1.2958 3.0909 1.5855 0.0000 0.0000 -5.4294 0.7562 5.6147 -9.3178 0.0000 -0.0000\n 0.2712 -113.9187 24.4312 -6.1838 0.7218 173.3996 0.5738 1.9106 0.3473 0.0200 0.1645 0.2314 0.6859 1.5568 5.4148 -1.7911 -0.5919 1.2639 -3.8391 -3.6634 -5.1571 2.8636 -1.6903 -10.0971 -5.6888 -1.9671 -2.7721 -10.0770 -1.4197 9.4497 0.0000 0.0000 11.3496 -0.3483 5.0395 -15.6018 -0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -9.0535 3.5225 0.5176 -6.9983 -0.8424 -14.3179 -0.0000 0.0000 2.9184 5.1070 9.8719 5.7745 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -14.6764 0.4705 -3.3916 11.4085 0.0000 0.0000 -3.6681 1.2830 5.7672 1.4936 0.0000 0.0000 -6.0082 1.1494 3.0084 1.5642 0.0000 0.0000 -5.4171 0.8449 5.5847 -9.3146 0.0000 -0.0000\n 0.3040 -113.9178 24.4351 -6.2027 0.5965 173.5468 0.6404 1.8950 0.3414 0.0654 0.1558 0.2291 0.6454 1.6328 5.2974 -1.8441 -0.6020 1.2190 -3.9517 -3.6616 -5.1907 2.8679 -1.6676 -10.1435 -5.6461 -2.2122 -2.7639 -10.1461 -1.1994 9.4622 0.0000 0.0000 11.3388 -0.3198 5.0073 -15.6028 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -9.0539 3.6723 0.5602 -7.1105 -1.1401 -14.3977 -0.0000 0.0000 2.9446 5.1060 9.7146 5.8473 -0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -14.3949 0.3047 -3.8132 10.9068 0.0000 0.0000 -3.4737 1.3685 6.0077 1.4820 0.0000 0.0000 -6.0238 0.9457 2.9523 1.5817 0.0000 0.0000 -5.4212 0.9492 5.4913 -9.2978 0.0000 -0.0000\n 0.3365 -113.9197 24.4398 -6.2969 0.4872 173.6508 0.6294 1.8357 0.3489 0.0604 0.1206 0.2295 0.7110 1.7393 5.2503 -1.8797 -0.5737 1.1906 -4.1902 -3.5924 -5.2512 2.9499 -1.6351 -10.1711 -5.6337 -2.4874 -2.8312 -10.2082 -0.9222 9.5500 0.0000 0.0000 11.3347 -0.3452 5.0212 -15.6599 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -9.1270 3.7503 0.6247 -7.2266 -1.2801 -14.5397 -0.0000 0.0000 2.9665 5.1227 9.6310 5.9137 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 -14.1569 0.1386 -4.2702 10.3084 0.0000 0.0000 -3.2189 1.4388 6.3281 1.4592 0.0000 0.0000 -6.1252 0.7455 2.8820 1.6036 0.0000 0.0000 -5.4087 1.0492 5.4572 -9.2844 0.0000 -0.0000\n 0.3671 -113.9236 24.4443 -6.4370 0.4124 173.6916 0.6172 1.7661 0.3623 0.0542 0.0781 0.2329 0.7682 1.8603 5.2666 -1.9300 -0.5312 1.1820 -4.4935 -3.4743 -5.3009 3.0863 -1.6238 -10.1646 -5.6417 -2.7079 -2.9592 -10.2472 -0.6994 9.6659 0.0000 0.0000 11.3585 -0.3574 5.0988 -15.6979 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -9.2215 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1.8445 0.3445 0.3275 0.1453 0.2362 0.1010 1.7419 5.2112 -2.2406 -0.6437 1.1497 -4.4992 -3.0370 -5.1999 3.1748 -1.8003 -10.1790 -5.2329 -2.9265 -3.1606 -10.3840 -0.3740 10.1244 0.0000 0.0000 11.3523 -0.3721 5.0428 -15.7032 0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -8.9216 4.0557 1.7004 -7.3737 -1.4745 -15.4890 -0.0000 0.0000 3.0566 5.1093 9.6551 6.0432 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -13.5463 -0.2912 -5.2286 8.5856 0.0000 0.0000 -2.5248 1.6552 7.1577 1.4785 0.0000 0.0000 -6.2901 0.3082 2.2985 1.3590 0.0000 0.0000 -5.2849 1.2546 5.9939 -9.1846 0.0000 -0.0000\n 0.4524 -113.9256 24.4536 -6.6868 0.2585 173.7179 1.3061 1.8805 0.3278 0.4810 0.1864 0.2402 -0.3043 1.6803 5.1956 -2.3890 -0.6867 1.1422 -4.4342 -2.8256 -5.2233 3.1543 -1.9163 -10.2495 -4.9913 -3.0136 -3.2897 -10.5014 -0.2404 10.3785 0.0000 0.0000 11.3526 -0.4129 5.0415 -15.7572 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -8.7815 4.1169 2.1288 -7.4525 -1.5209 -15.6992 -0.0000 0.0000 3.0966 5.1777 9.6128 6.1403 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-4.9254 -2.5413 -5.5078 3.2517 -2.0666 -10.3943 -4.9745 -3.0396 -3.6613 -10.6049 -0.1501 10.5043 0.0000 0.0000 11.4782 -0.3872 5.2181 -15.7367 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -8.9929 3.8708 2.6129 -7.4061 -1.4634 -15.5674 -0.0000 0.0000 2.9690 5.2323 9.2725 6.3708 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -13.1951 -0.6883 -5.9296 7.3190 0.0000 0.0000 -1.9928 1.7768 7.7697 1.3804 0.0000 0.0000 -6.6699 -0.1081 2.5668 1.5106 0.0000 0.0000 -5.3723 1.5348 5.6769 -9.3563 0.0000 -0.0000\n 0.5391 -113.9220 24.4599 -7.0765 0.1103 173.8044 1.5004 1.7886 0.3099 0.5916 0.0895 0.2526 -0.3083 1.8261 5.2803 -2.5722 -0.5594 1.1946 -5.1048 -2.4027 -5.5820 3.3337 -2.1640 -10.4357 -4.9819 -3.0162 -3.8077 -10.6388 -0.1128 10.4753 0.0000 0.0000 11.5239 -0.3674 5.1930 -15.7386 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -9.0599 3.7849 2.8326 -7.3342 -1.4552 -15.4014 -0.0000 0.0000 2.9042 5.1802 9.1987 6.3858 0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -13.0942 -0.8156 -6.2861 6.8878 0.0000 0.0000 -1.8180 1.8023 8.0516 1.3822 0.0000 0.0000 -6.8866 -0.2577 2.8123 1.6793 0.0000 0.0000 -5.4660 1.6338 5.3587 -9.3408 0.0000 -0.0000\n 0.5602 -113.9209 24.4613 -7.3134 0.0922 173.8970 1.5364 1.7373 0.3023 0.6262 0.0372 0.2543 -0.4650 1.9318 5.2979 -2.6329 -0.5211 1.1745 -5.0942 -2.3241 -5.5511 3.3976 -2.2423 -10.3682 -4.9733 -3.0902 -3.8873 -10.7173 0.0050 10.3926 0.0000 0.0000 11.5094 -0.4141 5.1503 -15.6963 -0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -9.1022 3.7662 2.9864 -7.2589 -1.5049 -15.2073 -0.0000 0.0000 2.8837 5.1867 9.2018 6.3797 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -13.0908 -0.8844 -6.7933 6.4506 0.0000 0.0000 -1.6622 1.8048 8.4522 1.3913 0.0000 0.0000 -7.2744 -0.3605 3.1222 1.9691 0.0000 0.0000 -5.5875 1.7264 4.9549 -9.3258 0.0000 -0.0000\n 0.5737 -113.9114 24.4628 -7.5119 0.0539 174.0700 1.5665 1.6190 0.2975 0.6664 -0.0543 0.2495 -0.6076 2.0895 5.3202 -2.6699 -0.4409 1.1371 -5.0102 -2.3101 -5.4068 3.4476 -2.2327 -10.1049 -5.0435 -3.2214 -3.9167 -10.7642 0.1934 10.2263 0.0000 0.0000 11.4503 -0.5246 5.1314 -15.5529 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -9.1851 3.8129 3.0490 -7.1458 -1.5839 -15.0243 -0.0000 0.0000 2.8609 5.2140 9.1103 6.3424 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -12.9642 -0.9378 -7.3920 5.9078 0.0000 0.0000 -1.5036 1.7633 8.8663 1.4027 0.0000 0.0000 -7.5866 -0.4453 3.2587 2.2115 0.0000 0.0000 -5.6944 1.8067 4.6395 -9.3021 0.0000 -0.0000\n 0.5776 -113.8918 24.4651 -7.6344 -0.0110 174.2775 1.5928 1.4765 0.2951 0.7114 -0.1514 0.2394 -0.6477 2.1985 5.3588 -2.7014 -0.3554 1.1126 -4.9711 -2.2938 -5.1600 3.4806 -2.1285 -9.6509 -5.2219 -3.2975 -3.9258 -10.6973 0.3718 10.0763 0.0000 0.0000 11.3663 -0.6369 5.0976 -15.4640 0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -9.2684 3.9385 3.0493 -7.0044 -1.6115 -14.9159 -0.0000 0.0000 2.8553 5.1510 8.7910 6.1625 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -12.7285 -0.9906 -7.8671 5.3534 0.0000 0.0000 -1.3255 1.6959 9.1247 1.4133 0.0000 0.0000 -7.7389 -0.5103 3.2043 2.3311 0.0000 0.0000 -5.7572 1.8506 4.4824 -9.2675 0.0000 -0.0000\n 0.5691 -113.8764 24.4671 -7.7922 -0.0409 174.3972 1.5864 1.3288 0.2915 0.7107 -0.2325 0.2362 -0.6151 2.3108 5.4648 -2.7265 -0.2983 1.1158 -4.9942 -2.2622 -4.8483 3.4543 -2.0050 -9.0939 -5.4167 -3.3204 -3.8783 -10.5534 0.4774 10.0662 0.0000 0.0000 11.2585 -0.6810 5.0620 -15.5458 -0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -9.2769 4.0911 2.9499 -6.8495 -1.5536 -14.8993 -0.0000 0.0000 2.9265 5.0091 8.3387 5.8590 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -12.6905 -0.9993 -8.0444 5.0559 0.0000 0.0000 -1.1845 1.6443 9.1763 1.4132 0.0000 0.0000 -7.9760 -0.5213 3.2595 2.5248 0.0000 0.0000 -5.8183 1.8489 4.3268 -9.2649 0.0000 -0.0000\n 0.5478 -113.8785 24.4664 -8.1405 0.0304 174.4152 1.4032 1.1957 0.2941 0.5931 -0.2774 0.2439 -0.4284 2.4749 5.6243 -2.7236 -0.2473 1.1251 -5.1090 -2.2363 -4.5006 3.3516 -1.9453 -8.5175 -5.5060 -3.3753 -3.6765 -10.4325 0.5246 10.2132 0.0000 0.0000 11.1418 -0.6392 5.0533 -15.6459 -0.0000 -0.0000 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-24.6034 18.2560 11.9749 4.6967 7.5002 5.1917 -7.2543 -2.2715 15.1365 -10.0491 -9.0138 70.5012 0.0000 0.0000 12.8567 0.3649 -9.1638 -1.9134 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -3.2875 -2.3155 -16.6776 6.2989 15.3697 -80.4371 -0.0000 0.0000 7.7544 6.4788 7.8267 -3.4624 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -18.3649 -1.2161 1.2387 27.3554 0.0000 0.0000 -13.9382 6.8991 -9.6139 1.6419 0.0000 0.0000 -4.8525 -6.3618 10.8109 41.0588 0.0000 0.0000 -11.7156 13.1743 -10.3566 -7.6791 0.0000 -0.0000\n 1.4308 -103.7554 23.9494 -4.3323 3.4037 -174.6713 -2.5915 -4.0189 0.4409 -1.9882 -3.1310 -0.3063 6.8944 9.5135 1.8580 -4.3249 7.0784 -2.6299 -24.3606 16.5062 11.0095 4.2160 6.7636 4.5903 -6.9396 -1.6353 15.3359 -9.5249 -8.8779 71.2572 0.0000 0.0000 12.0445 0.5228 -10.2980 -2.0074 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -3.1213 -2.1786 -16.8189 5.7942 15.7730 -79.7201 -0.0000 0.0000 7.1883 4.7615 7.1497 -3.5888 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -17.1780 -1.4153 1.4369 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-1.1614 -0.3190 0.4409 -13.2020 -10.8496 -5.9279 8.2257 -7.2978 -7.5240 -7.2402 -0.7040 10.4037 -11.0571 -24.8859 71.7234 0.0000 0.0000 23.9827 -0.8853 1.6060 2.1924 -0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -3.2100 3.8603 -13.2178 15.2141 16.7573 -87.4741 -0.0000 0.0000 3.8780 3.2334 6.9594 -4.9887 -0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -2.8315 -10.2271 6.2505 27.0802 0.0000 0.0000 -17.7871 4.9274 2.2093 1.4887 0.0000 0.0000 -22.3729 -17.5795 7.0927 46.9261 0.0000 0.0000 -13.1212 6.0050 3.5455 -7.5010 0.0000 -0.0000\n 4.3270 -76.3075 23.8648 6.7198 -4.2175 178.7986 -2.5414 -4.5541 -0.0125 -2.2134 -1.2642 0.0686 12.4130 3.9780 4.1178 -0.9023 -0.5589 0.6950 -12.7556 -11.4922 -5.8261 7.9683 -7.6893 -7.2710 -6.8354 -0.4263 10.4139 -9.5115 -24.3849 72.0518 0.0000 0.0000 22.5165 -1.0025 0.1644 3.2564 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -3.6486 3.1943 -13.4558 15.0566 18.4420 -86.9496 -0.0000 0.0000 3.6501 3.0864 7.1286 -5.3009 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 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8.3072 -8.4025 -7.0958 -5.8894 0.1257 11.3705 -6.5330 -23.2977 72.5666 0.0000 0.0000 20.4748 -2.9823 -1.8575 5.1705 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -4.7876 2.6346 -14.0461 14.3680 21.2172 -85.0503 -0.0000 0.0000 3.7349 5.0489 8.3118 -5.7790 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 1.6346 -8.5243 6.3955 24.4320 0.0000 0.0000 -19.2595 4.5152 5.7135 1.7790 0.0000 0.0000 -24.3553 -16.0470 8.2545 31.3663 0.0000 0.0000 -7.4900 11.3783 -2.7959 -10.3100 0.0000 -0.0000\n 4.2115 -75.1100 23.8875 6.2801 -3.5795 176.3990 -2.4515 -4.9661 -0.1995 -2.1222 -1.1395 0.1005 12.7789 4.0469 5.0309 -0.3838 -0.6365 0.9734 -12.3727 -11.7064 -5.8719 8.5870 -8.5904 -7.2076 -5.2826 0.4853 11.6315 -5.2520 -22.8086 72.9898 0.0000 0.0000 19.9521 -2.9798 -2.2058 5.8301 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -5.1133 2.4087 -14.5865 13.7159 22.5098 -83.9783 -0.0000 0.0000 3.8643 6.4818 8.2822 -6.4653 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 2.8720 -7.9926 6.2343 23.7561 0.0000 0.0000 -19.9781 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-3.2856 -20.5540 74.3674 0.0000 0.0000 18.9478 -2.6680 -3.2707 5.8176 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -5.5962 2.4934 -15.5665 12.4018 25.4991 -82.0928 -0.0000 0.0000 3.9209 7.6494 7.6045 -8.3931 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 5.1926 -7.7468 3.9036 20.6412 0.0000 0.0000 -20.6487 3.6236 8.0252 2.0859 0.0000 0.0000 -24.9338 -12.9100 7.6640 13.7434 0.0000 0.0000 1.9760 12.3241 -9.5583 -10.1046 0.0000 -0.0000\n 4.0368 -73.6748 23.8081 3.9436 -4.6904 178.4920 -3.4899 -4.9756 -0.8550 -2.6187 -0.7870 0.0234 12.4576 2.3094 3.4926 -0.3358 -1.0853 0.7549 -12.4709 -12.1880 -6.6852 8.1935 -8.5216 -7.9571 -3.2973 -1.1851 11.1554 -2.2892 -19.5553 74.6842 0.0000 0.0000 18.5626 -1.9658 -3.4475 5.6786 -0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -5.6517 2.7958 -16.0909 11.7772 26.0384 -81.5288 -0.0000 0.0000 3.6198 7.6866 7.4123 -9.1619 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 7.2083 -7.9698 1.9072 17.8029 0.0000 0.0000 -20.7939 3.1728 9.3855 2.0872 0.0000 0.0000 -24.4757 -11.5969 7.2956 8.1803 0.0000 0.0000 6.3855 15.0095 -8.1920 -9.7336 0.0000 -0.0000\n 3.9511 -73.0944 23.7500 3.0905 -4.9018 179.2476 -3.8605 -4.5594 -1.1052 -2.7973 -0.6504 0.0301 12.3207 1.6826 3.2097 -0.5525 -1.4184 0.8706 -13.1256 -12.2845 -7.3411 8.1118 -8.3006 -8.3941 -2.8749 -2.1860 10.4547 -1.1680 -18.6657 75.1064 0.0000 0.0000 18.4937 -0.4593 -3.0345 5.3388 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -5.8615 3.6271 -16.9368 10.8959 26.7685 -80.7730 -0.0000 0.0000 3.5942 8.5775 7.6622 -10.2366 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 9.3624 -7.7405 -0.1282 14.8608 0.0000 0.0000 -21.0201 2.3558 11.0937 2.1640 0.0000 0.0000 -23.1063 -9.6227 7.3740 3.7088 0.0000 0.0000 6.4602 16.6131 -3.1284 -8.8846 0.0000 -0.0000\n 3.8328 -72.4808 23.6831 1.7174 -4.7185 179.2845 -4.5848 -3.6048 -1.2722 -3.2389 -0.8346 -0.0021 12.4962 1.0903 3.2009 -0.9105 -1.6739 1.0112 -14.2398 -12.2110 -7.8320 7.8099 -7.8816 -8.8551 -2.3698 -3.8911 9.6748 0.3004 -17.5928 75.8059 0.0000 0.0000 18.7985 1.1888 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0.0000 0.0000 -5.0803 8.3710 -10.8139 6.1422 9.5795 -84.5696 -0.0000 -0.0000 -0.9834 3.0179 3.8672 -9.0951 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 13.1505 0.9011 -6.2615 14.0171 0.0000 0.0000 -23.5831 -3.1424 17.3256 0.0144 0.0000 0.0000 -18.6305 1.3133 0.7329 -2.8758 0.0000 -0.0000 16.4864 -0.5469 5.4141 -8.1621 0.0000 -0.0000\n 2.9074 -69.5382 23.6144 0.6869 4.3616 177.6716 -5.6298 3.2583 -0.8325 -4.3704 -0.1457 0.2202 14.5459 1.7380 4.5524 -1.8925 -1.1820 1.0830 -18.2401 -8.0462 -9.1428 3.3750 -4.8832 -12.8210 0.3261 -11.1996 11.8622 10.9421 -8.8682 75.3219 0.0000 0.0000 10.4236 -3.3660 -3.1935 1.4612 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -4.9137 8.0632 -9.2986 3.7847 5.9882 -85.7595 -0.0000 -0.0000 -3.4783 1.6492 1.3193 -8.7961 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 15.0965 6.2552 -7.4445 15.7319 0.0000 0.0000 -22.1738 -5.4211 18.3031 -2.0186 0.0000 -0.0000 -16.1572 5.6170 -0.8165 -1.7956 0.0000 -0.0000 15.4578 -0.1666 9.4720 -5.7611 0.0000 -0.0000\n 2.5600 -68.6986 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-21.3958 -4.0998 -10.9553 2.8141 -8.3675 -12.2196 3.4301 -6.6841 12.6477 22.0740 -19.2063 77.2357 0.0000 0.0000 7.0568 0.7296 -10.8470 0.4828 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -7.2904 1.4169 -14.8203 -10.3780 11.2440 -82.8775 -0.0000 -0.0000 -5.2456 1.6579 -7.8056 -5.0842 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -28.8697 16.4444 12.3034 70.2385 0.0000 0.0000 -9.4185 -4.2675 -3.7851 -2.1307 0.0000 -0.0000 -1.2628 19.1314 7.4850 1.5034 0.0000 0.0000 -3.4962 -6.5363 3.9581 -5.5213 0.0000 -0.0000\n 1.4535 -63.0776 24.1638 2.0780 11.8384 175.2033 -1.8954 10.1158 0.3897 -1.8819 2.6088 0.7765 11.5554 1.9583 4.8994 -2.5032 -1.9677 2.0535 -20.8461 -4.7888 -11.3615 2.9641 -7.9540 -11.6999 2.3925 -6.4526 11.8489 21.5169 -18.8649 77.1459 0.0000 0.0000 6.6844 2.1250 -14.4086 0.5716 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -7.1354 0.7719 -16.4269 -7.9530 10.8319 -82.9315 -0.0000 -0.0000 -3.8064 2.2142 -6.3000 -5.4926 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -31.2744 14.4366 10.9937 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-4.0060 -179.2587 -6.7154 1.3174 2.4286 -5.7745 0.4569 0.0393 17.9388 -6.1808 -0.8754 -3.4723 -0.1702 0.0401 -22.3469 1.0023 -2.2154 4.2383 -0.4869 -3.8153 -5.2703 -11.4228 7.0985 -3.6007 -15.4603 69.3221 0.0000 0.0000 17.3518 4.9324 -15.0563 -1.0729 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -2.3888 11.5994 -12.2372 8.0435 6.3319 -82.0104 -0.0000 0.0000 4.1935 3.5073 9.6349 -7.7780 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -21.2694 -10.1442 -2.3432 -2.1470 0.0000 -0.0000 20.4709 5.7430 6.2113 0.4001 0.0000 0.0000 13.6257 -4.1367 13.2210 17.2587 0.0000 0.0000 -30.0094 3.1232 -6.9558 -6.2278 0.0000 -0.0000\n 3.2116 -53.1851 23.3764 -2.8766 -6.9596 178.4015 -5.1991 1.0136 1.6135 -4.1267 -0.7330 -0.2798 13.8830 -8.5851 0.0048 -3.6203 -1.0050 0.7378 -22.1391 -0.8621 -1.2810 2.6328 -1.5750 -2.1962 -4.5502 -8.7112 7.5446 -6.7133 -18.6017 68.9335 0.0000 0.0000 20.6379 8.9807 -8.4373 -1.2357 -0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -2.1404 8.8397 -10.9556 5.2064 9.6038 -79.1410 -0.0000 0.0000 2.6949 0.9787 7.1609 -6.8673 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -19.8777 -14.0895 -4.1636 -2.2113 0.0000 -0.0000 18.0739 2.5315 9.2290 0.6616 0.0000 0.0000 11.4343 -7.4451 14.1869 23.8735 0.0000 0.0000 -27.3516 1.1995 -4.7147 -9.4331 0.0000 -0.0000\n 3.3150 -52.5733 23.5636 -4.3630 -7.6907 177.5908 -4.2749 0.6935 0.8149 -3.0743 -1.1107 -0.3424 10.6899 -7.9829 -0.3293 -3.3991 -2.1988 1.1118 -21.9502 -4.5197 -1.7314 0.9842 -2.7144 -0.7441 -4.3908 -6.1976 7.4793 -9.6988 -23.9805 67.8010 0.0000 0.0000 24.8432 11.4856 -1.1743 -1.5657 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -2.4195 3.3958 -11.2390 2.3149 10.6517 -74.8427 -0.0000 0.0000 1.2680 0.1231 5.6752 -6.6517 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -20.0352 -15.0084 -7.2572 -1.2138 0.0000 -0.0000 16.1849 -0.1715 13.5287 0.3867 0.0000 0.0000 5.3317 -9.3294 14.2594 34.5349 0.0000 0.0000 -22.2744 -0.1738 -2.7607 -11.1799 0.0000 -0.0000\n 3.4134 -51.9833 23.7026 -5.1397 -8.6080 178.3871 -3.4365 -0.3899 0.3481 -2.4869 -1.1384 -0.3550 8.9584 -7.2497 -1.2481 -3.3193 -2.8658 1.1496 -21.7267 -7.8396 -2.6872 -0.0124 -3.7309 -0.2952 -4.5662 -4.7012 7.1146 -12.9243 -27.1758 66.3168 0.0000 0.0000 28.5530 13.0983 3.3545 -1.8687 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -2.7703 0.0273 -11.9463 -0.9048 7.2710 -70.8487 -0.0000 -0.0000 -0.2353 1.2811 4.3508 -6.9294 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -19.4802 -15.7485 -9.4642 -0.8692 0.0000 -0.0000 14.5723 -1.2618 15.4688 0.0891 0.0000 0.0000 -1.5349 -12.3525 14.5835 46.4435 0.0000 0.0000 -17.5553 -0.2195 -1.1622 -10.5473 0.0000 -0.0000\n 3.5138 -51.3584 23.7967 -5.0091 -10.4300 179.2931 -2.5724 -2.1023 0.1892 -2.0562 -1.4285 -0.4112 8.1863 -7.4082 -1.5673 -3.4908 -3.1798 1.1914 -21.0115 -9.4014 -3.3641 0.1966 -4.3300 -0.8754 -4.8725 -4.0726 6.7116 -15.7818 -27.0080 65.1639 0.0000 0.0000 30.8466 14.5519 5.6275 -1.9044 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -2.8130 -0.1266 -11.7736 -4.8337 4.2914 -68.7432 -0.0000 -0.0000 -1.9609 2.2166 0.7413 -6.6290 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -17.5269 -17.4536 -10.7127 -1.4447 0.0000 -0.0000 12.9297 -2.9154 15.9249 -0.2085 0.0000 -0.0000 -7.1090 -16.6491 15.3289 56.5184 0.0000 0.0000 -15.7268 -0.6281 0.5053 -10.1685 0.0000 -0.0000\n 3.5725 -50.7153 23.8909 -4.5071 -11.9238 179.4013 -2.0930 -3.3820 0.1560 -1.7683 -1.8376 -0.4599 8.0549 -8.1740 -0.9440 -3.5489 -3.4802 1.4881 -19.7900 -9.4984 -3.6474 1.3830 -4.3358 -1.8456 -5.3905 -3.4638 6.3110 -17.5023 -25.8881 64.3922 0.0000 0.0000 31.7229 15.2591 7.4402 -1.2928 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -2.9418 0.4452 -10.7936 -9.0513 6.3566 -68.1996 -0.0000 -0.0000 -3.7917 -0.0244 -4.1006 -5.9597 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -14.8585 -18.9127 -12.1847 -2.1528 0.0000 -0.0000 11.6495 -5.9035 17.7308 -0.6543 0.0000 -0.0000 -11.2144 -20.4498 16.5145 63.0560 0.0000 0.0000 -16.4271 -1.9444 1.4885 -11.0054 0.0000 -0.0000\n 3.5876 -50.0882 23.9997 -3.8355 -12.4210 179.3436 -1.9843 -4.0665 0.1272 -1.6700 -1.8994 -0.4445 8.3372 -8.7323 -0.2178 -3.2333 -3.8563 1.9052 -18.4440 -9.3934 -3.8673 2.7850 -3.8276 -2.6274 -6.0575 -2.7778 5.7828 -17.9775 -25.7378 63.8946 0.0000 0.0000 31.5180 14.7807 9.4411 -0.4305 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -3.5495 -0.1426 -9.9931 -12.0228 10.5584 -67.7676 -0.0000 -0.0000 -5.2764 -4.2297 -7.3844 -6.3175 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -12.3133 -19.3355 -14.7162 -2.1639 0.0000 -0.0000 10.6256 -8.1474 21.1224 -1.0435 0.0000 -0.0000 -14.5930 -22.7713 17.2650 66.1449 0.0000 0.0000 -17.9463 -2.9657 1.2349 -11.2971 0.0000 -0.0000\n 3.5771 -49.4885 24.1093 -2.9085 -12.3029 179.9947 -1.9061 -4.5853 0.0413 -1.6291 -1.5982 -0.3969 8.6960 -8.8595 -0.1118 -2.7755 -4.1170 2.1414 -17.2137 -9.6178 -4.0814 3.8201 -3.2313 -3.0767 -6.5133 -2.4962 5.2684 -17.6008 -25.9135 63.7607 0.0000 0.0000 30.5779 13.7404 10.6715 0.0493 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -4.3207 -1.0880 -9.7437 -13.2513 10.9650 -67.4640 -0.0000 -0.0000 -6.0028 -5.9416 -7.8121 -6.9532 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -10.1098 -19.0750 -17.4404 -1.4897 0.0000 -0.0000 9.0671 -8.3605 23.7994 -1.2300 0.0000 -0.0000 -17.9376 -23.7517 16.5782 66.6341 0.0000 0.0000 -19.2272 -2.4216 0.9734 -10.2229 0.0000 -0.0000\n 3.5431 -48.9185 24.2028 -1.9263 -12.0071 -178.9420 -1.7013 -5.0551 -0.0909 -1.5256 -1.3279 -0.3714 8.8937 -8.8472 -0.3545 -2.4527 -4.1296 2.1421 -16.1319 -9.6593 -4.0835 4.2908 -3.0682 -3.3459 -6.6119 -2.3146 4.9665 -16.5330 -25.4009 63.9559 0.0000 0.0000 29.1794 12.7388 10.3726 0.1879 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -4.8509 -1.5187 -9.4171 -13.5029 7.7579 -67.8484 -0.0000 -0.0000 -6.3597 -3.8960 -6.8065 -7.2543 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -8.1302 -18.5973 -18.8586 -0.6072 0.0000 -0.0000 7.0153 -7.8305 24.6226 -1.2853 0.0000 -0.0000 -21.2577 -23.8981 15.1798 65.0382 0.0000 0.0000 -19.9808 -1.1715 2.1703 -8.9589 0.0000 -0.0000\n 3.4893 -48.3876 24.2732 -1.2610 -11.6544 -178.3475 -1.4999 -5.3926 -0.1737 -1.4121 -1.3214 -0.3774 8.9978 -8.7853 -0.2889 -2.2557 -3.9844 2.1150 -15.3348 -9.2579 -3.9509 4.1634 -3.5084 -3.6823 -6.4952 -1.7920 4.9038 -14.8743 -24.0394 64.2716 0.0000 0.0000 27.3140 11.4677 8.9331 0.4859 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -5.2968 -1.8816 -8.6177 -13.7529 5.5866 -68.8516 -0.0000 -0.0000 -6.6718 -1.5457 -6.1495 -7.5098 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -6.3577 -18.0671 -18.8193 0.0630 0.0000 0.0000 5.2897 -7.8666 24.6359 -1.2806 0.0000 -0.0000 -24.2843 -23.7228 14.5564 61.5062 0.0000 0.0000 -19.9968 -1.2825 4.2490 -8.0504 0.0000 -0.0000\n 3.4319 -47.8926 24.3225 -0.9060 -11.2703 -178.3072 -1.3314 -5.5973 -0.1936 -1.3179 -1.4127 -0.3742 9.1066 -8.5744 0.2027 -2.1061 -3.8837 2.1934 -14.9395 -8.8919 -4.0540 3.5675 -4.3968 -4.3442 -6.2725 -1.1821 5.0171 -12.9046 -22.2377 64.5334 0.0000 0.0000 24.8059 9.4629 6.9542 0.8813 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -5.8701 -2.4920 -7.7955 -14.2727 6.1461 -70.1012 -0.0000 -0.0000 -6.8904 -1.9783 -5.6427 -7.8584 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -4.6859 -17.5966 -18.2098 0.2919 0.0000 0.0000 3.9354 -8.0254 24.6546 -1.1635 0.0000 -0.0000 -26.8636 -23.3217 14.6137 56.1969 0.0000 0.0000 -19.2464 -2.4232 5.1033 -7.3097 0.0000 -0.0000\n 3.3727 -47.4181 24.3567 -0.7009 -10.8736 -178.3562 -1.1452 -5.6691 -0.2095 -1.1873 -1.4311 -0.3398 9.1615 -8.2815 0.8014 -2.0374 -3.9128 2.3402 -14.8993 -8.9678 -4.6106 2.8242 -5.4822 -5.3866 -6.1360 -0.6096 5.2259 -10.5920 -20.9961 64.6298 0.0000 0.0000 21.7104 6.8357 4.4568 0.8753 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -6.4629 -3.0137 -7.4491 -14.8966 7.4641 -71.1207 -0.0000 -0.0000 -7.2942 -3.3567 -5.0454 -8.1614 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -3.1080 -17.2178 -17.5758 0.2391 0.0000 0.0000 2.5091 -7.5507 24.4977 -0.8937 0.0000 -0.0000 -28.9365 -22.2531 13.8975 49.2834 0.0000 0.0000 -18.2239 -2.0471 3.6498 -8.0937 0.0000 -0.0000\n 3.2922 -46.9524 24.3804 -0.6974 -10.5166 -178.5179 -0.9415 -5.6121 -0.2304 -1.0717 -1.4165 -0.3085 9.1449 -8.1681 1.4208 -2.0639 -4.0371 2.5474 -15.0961 -9.2345 -5.4008 2.2027 -6.4967 -6.5604 -6.1324 0.1967 5.4526 -7.7026 -20.6153 64.8746 0.0000 0.0000 17.8753 3.5854 1.2926 0.6225 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -6.8040 -3.2106 -7.4401 -15.5235 7.7108 -71.7667 -0.0000 -0.0000 -8.1138 -3.1721 -4.5593 -8.3410 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -1.8322 -16.8138 -16.6817 0.3463 0.0000 0.0000 0.9980 -6.8647 24.1032 -0.5535 0.0000 -0.0000 -30.3849 -20.3530 11.7069 40.9295 0.0000 0.0000 -16.8680 0.2140 0.8641 -11.1028 0.0000 -0.0000\n 3.1824 -46.4857 24.3898 -0.9755 -10.2928 -179.0955 -0.8181 -5.5446 -0.2704 -0.9964 -1.3967 -0.2796 9.1300 -8.3542 2.1738 -2.1059 -4.2353 2.8616 -15.4144 -9.3137 -6.0604 1.8203 -7.2162 -7.5115 -6.3647 1.3143 5.7395 -4.1375 -20.8397 65.4776 0.0000 0.0000 13.2674 0.2601 -2.7870 0.4589 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -7.0917 -3.4091 -7.5168 -15.8853 7.0520 -72.0865 -0.0000 -0.0000 -8.9377 -2.0949 -4.1534 -8.5329 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.9388 -16.3466 -15.3862 0.8239 0.0000 0.0000 -0.4636 -6.6149 23.8522 -0.1929 0.0000 -0.0000 -31.2315 -18.1398 8.7352 31.7743 0.0000 0.0000 -15.0568 1.7207 -1.7947 -12.7517 0.0000 -0.0000\n 3.0547 -45.9877 24.3829 -1.2080 -10.1246 -179.8926 -0.6620 -5.5329 -0.3344 -0.9149 -1.2445 -0.2356 9.1448 -8.6472 2.9437 -2.1259 -4.4917 3.2033 -15.6672 -9.2421 -6.4646 1.7487 -7.6184 -8.0952 -6.5658 2.0217 6.1528 -0.4559 -21.1234 66.1647 0.0000 0.0000 8.6422 -1.3516 -7.6297 0.0505 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -7.5837 -3.8232 -7.7491 -15.9362 6.1982 -72.2351 -0.0000 -0.0000 -9.1224 -1.6888 -3.4924 -8.6744 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 0.1152 -15.8433 -14.1978 1.3240 0.0000 0.0000 -2.0410 -6.4975 23.9787 0.1492 0.0000 0.0000 -31.4361 -16.0616 6.1480 22.8280 0.0000 0.0000 -13.1788 2.1601 -3.7467 -11.2065 0.0000 -0.0000\n 2.9242 -45.4327 24.3610 -1.2906 -9.9820 179.3900 -0.4950 -5.5897 -0.4150 -0.8112 -0.9600 -0.1877 9.1060 -8.8674 3.4910 -2.1278 -4.6985 3.4385 -15.7037 -9.0919 -6.6189 1.9937 -7.8797 -8.3985 -6.3310 1.9667 6.6634 2.8967 -21.3288 66.5038 0.0000 0.0000 5.3066 0.0737 -11.8207 -0.6527 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -8.2083 -4.3182 -8.0812 -16.0063 5.7825 -72.4008 -0.0000 -0.0000 -8.6750 -1.6217 -2.5467 -8.5167 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 1.5616 -15.3973 -13.5129 1.5588 0.0000 0.0000 -3.7674 -6.0251 24.3478 0.3242 0.0000 0.0000 -31.3236 -14.2407 4.6420 15.0340 0.0000 0.0000 -10.7002 3.5977 -4.7588 -9.4824 0.0000 -0.0000\n 2.7953 -44.8298 24.3238 -1.5733 -9.9376 178.5120 -0.4706 -5.6685 -0.5154 -0.7809 -0.7877 -0.1731 8.9979 -9.0157 3.8759 -2.1093 -4.7118 3.5150 -15.5827 -8.6994 -6.4219 2.3707 -8.0995 -8.5017 -5.5803 1.4536 7.3793 5.8881 -21.5263 66.4466 0.0000 0.0000 3.6682 3.1720 -14.3641 -0.9852 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -8.7738 -4.6662 -8.0977 -16.2770 5.8231 -72.6974 -0.0000 -0.0000 -8.0899 -1.2361 -1.7602 -8.1871 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 2.8872 -15.0219 -12.9534 1.8358 0.0000 0.0000 -5.4283 -5.4727 24.8240 0.2473 0.0000 0.0000 -31.4917 -12.7269 3.9988 9.0819 0.0000 0.0000 -6.7919 5.7118 -4.2651 -9.4201 0.0000 -0.0000\n 2.6713 -44.1893 24.2661 -2.1964 -9.8852 177.2465 -0.6038 -5.5851 -0.6557 -0.8533 -0.7859 -0.1794 8.8963 -9.1400 4.2104 -2.1044 -4.5456 3.4894 -15.4835 -8.0005 -5.8950 2.6639 -8.2127 -8.4366 -4.5813 0.5226 8.3972 8.3160 -21.9045 66.1062 0.0000 0.0000 2.9395 5.2412 -15.8068 -0.7529 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -9.1729 -4.6887 -7.6699 -16.5677 5.4910 -73.1511 -0.0000 -0.0000 -7.5371 -1.3431 -1.3441 -8.0700 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 3.8217 -14.5390 -12.0052 2.4661 0.0000 0.0000 -7.1936 -5.1210 25.2955 0.1327 0.0000 0.0000 -32.1210 -11.5699 3.7764 5.4071 0.0000 0.0000 -2.1517 6.9519 -2.3815 -9.6013 0.0000 -0.0000\n 2.5645 -43.4726 24.1829 -2.7417 -9.6132 175.8697 -0.8032 -5.2657 -0.7820 -0.9511 -0.7130 -0.1602 8.8651 -9.0460 4.4017 -2.1541 -4.3463 3.3912 -15.4760 -7.2573 -5.3955 2.8570 -8.1851 -8.2949 -3.5847 -0.8233 9.3297 10.2126 -22.7148 65.7594 0.0000 0.0000 2.4801 5.0888 -16.6301 -0.4463 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -9.3359 -4.4740 -7.2848 -16.7635 4.7290 -73.6706 -0.0000 -0.0000 -6.9053 -2.3936 -1.2640 -8.0423 -0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 5.0007 -13.8181 -10.8186 3.1749 0.0000 0.0000 -9.4878 -4.5891 25.6599 0.2715 0.0000 0.0000 -32.5805 -10.7413 4.0743 4.1973 0.0000 0.0000 1.3312 7.4850 -0.7795 -9.3473 0.0000 -0.0000\n 2.4870 -42.6301 24.0685 -3.0566 -9.3031 174.3093 -1.1862 -4.9868 -0.8320 -1.0821 -0.5740 -0.1208 8.8796 -8.5243 4.5449 -2.1634 -4.1232 3.2383 -15.5028 -6.6669 -5.0568 2.9455 -8.0803 -8.1802 -2.7044 -1.7512 9.7103 12.4447 -23.8737 66.1394 0.0000 0.0000 2.4882 3.6148 -16.4242 -0.4882 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -9.3227 -4.3343 -7.1270 -16.6528 4.5616 -74.1478 -0.0000 -0.0000 -6.4081 -2.8629 -1.7515 -7.7662 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 6.7614 -13.0164 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-7.7704 -7.4177 -1.8589 -2.4677 9.1604 18.9664 -22.7784 71.5020 0.0000 0.0000 2.8738 2.1110 -15.6159 -0.8105 0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -9.1491 -1.8723 -4.7639 -12.9213 -1.3257 -76.1687 -0.0000 -0.0000 -6.4492 2.0897 -5.5346 -7.8907 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 7.8468 -9.1061 -8.0236 8.5412 0.0000 0.0000 -17.9811 -5.3168 28.9911 1.5952 0.0000 0.0000 -26.7594 -9.4608 4.1900 1.2851 0.0000 0.0000 10.5558 2.3439 -0.6050 -10.9339 0.0000 -0.0000\n 2.0879 -39.5491 23.6503 -3.9449 -2.5271 168.4015 -4.0091 1.4176 -1.4567 -3.1182 0.4882 0.1001 9.2908 -7.4800 3.9088 -2.0524 -2.6051 2.2028 -15.7429 -5.6485 -2.8106 1.9379 -7.4034 -6.6292 -2.0950 -4.6308 7.9947 21.5050 -20.3102 75.3015 0.0000 0.0000 2.3423 2.5647 -16.0131 -0.8956 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -8.2585 1.6666 -3.1519 -9.7000 -9.7787 -77.6539 -0.0000 -0.0000 -6.3112 6.0358 -9.6415 -7.9280 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 8.2335 -2.2691 -8.2959 13.1146 0.0000 0.0000 -21.9193 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-2.0414 7.6621 33.4742 12.4955 -4.2189 4.4934 -0.7057 -24.0947 3.8513 -0.6484 6.6562 2.1290 -4.3347 1.2079 -22.2550 9.5251 -3.8071 -14.1242 49.0900 0.0000 0.0000 31.4719 -8.1108 15.4457 7.9046 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.7823 21.6963 -7.1394 24.0816 34.0682 -61.4563 -0.0000 0.0000 8.5905 2.2937 -1.4157 -0.5278 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -29.4642 2.8077 -4.3989 -3.1876 0.0000 -0.0000 22.8151 2.7782 7.5536 0.2747 0.0000 0.0000 7.9172 1.5260 7.6178 13.3841 0.0000 0.0000 -18.0565 2.5749 -15.5035 -12.1728 0.0000 -0.0000\n -0.5526 39.3614 23.5894 -10.9836 4.0850 170.4386 -4.2350 -6.3837 2.6014 -2.4269 -13.5132 -1.6507 6.4846 26.7843 14.2445 -4.9709 5.1028 0.0853 -26.9818 4.6002 1.3865 4.9125 0.9215 -3.5986 3.3227 -21.4066 12.6078 2.8488 -15.5150 48.5858 0.0000 0.0000 26.7095 -6.5925 19.1313 6.4022 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 0.3978 23.3632 -7.3792 21.7249 33.6120 -58.4902 -0.0000 0.0000 8.0819 3.1675 -1.6029 -0.4676 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -30.5345 -0.8371 -4.0789 -1.2602 0.0000 -0.0000 19.6459 3.3280 9.3365 0.2152 0.0000 0.0000 5.9458 -0.6700 10.9272 16.8575 0.0000 0.0000 -15.8527 2.6701 -15.6098 -15.1090 0.0000 -0.0000\n -0.2817 39.9226 23.7290 -11.5997 3.4681 168.3228 -3.5156 -4.9957 1.9893 -2.3232 -7.9760 -0.5795 7.8557 15.3547 13.8233 -5.2165 4.8292 1.0427 -29.8288 5.1516 3.7000 1.7525 -0.5892 -2.9400 5.1365 -15.9496 15.5701 8.5405 -20.7069 49.3337 0.0000 0.0000 20.0788 -7.3517 14.8297 3.3208 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 2.4309 20.5888 -7.9772 20.6460 36.9445 -59.0776 -0.0000 0.0000 7.3383 -0.0559 -2.3107 -1.5945 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 -30.4611 -2.8387 -3.8437 0.9437 0.0000 0.0000 17.1955 3.3441 10.6331 -0.0183 0.0000 -0.0000 3.9718 -2.3367 13.0520 21.8303 0.0000 0.0000 -12.9647 2.5775 -16.0771 -17.8563 0.0000 -0.0000\n 0.0284 40.4558 23.8681 -11.2420 2.0010 168.8711 -2.3530 -3.8730 1.4132 -1.9907 -4.6801 -0.0713 9.3785 8.4105 12.3354 -4.9312 3.0639 1.6339 -32.1833 3.5422 3.7921 -1.3713 -2.1783 -2.3127 6.2721 -10.5610 17.4947 13.4523 -25.6318 50.5704 0.0000 0.0000 13.3944 -8.8209 5.2082 0.9669 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 4.0501 14.5434 -9.9641 20.5256 40.7387 -60.3090 -0.0000 0.0000 5.7820 -4.8689 -1.2098 -3.5001 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -28.3494 -4.3202 -3.7605 1.2564 0.0000 0.0000 15.8067 3.4326 9.2912 -0.0399 0.0000 -0.0000 1.1904 -5.7397 13.4391 29.6031 0.0000 0.0000 -9.3276 3.3525 -17.7909 -17.4660 0.0000 -0.0000\n 0.3663 40.9476 23.9653 -11.2242 -0.5269 171.4942 -1.5435 -3.4019 1.0037 -1.7652 -3.6665 -0.0081 10.8112 4.4367 10.5590 -4.5804 1.0611 1.9878 -33.6946 0.3890 1.3121 -2.8520 -4.2471 -2.1725 7.1518 -8.3376 18.1272 18.3641 -27.0961 50.9967 0.0000 0.0000 8.7642 -5.6514 -4.5997 -0.4661 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 4.6656 10.6730 -12.5138 19.8558 39.8223 -60.0507 -0.0000 0.0000 4.2586 -5.9712 2.2915 -4.6530 -0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -25.9961 -6.1705 -4.2065 0.3734 0.0000 0.0000 14.3561 3.5864 6.5096 0.0092 0.0000 0.0000 -3.9808 -10.9778 11.8730 39.9070 0.0000 0.0000 -4.9022 5.4364 -19.4478 -13.8807 0.0000 -0.0000\n 0.6827 41.4098 24.0071 -11.8647 -3.1868 174.3346 -1.5429 -3.3933 0.7451 -1.7768 -3.3677 -0.0342 11.8509 1.5470 9.3768 -4.3979 -0.7467 2.4114 -33.4296 -3.4555 -2.8823 -2.7277 -6.5759 -2.7552 7.9510 -8.9349 17.4369 22.9569 -25.9862 50.5737 0.0000 0.0000 6.5181 1.2107 -12.1084 -0.5439 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 4.6387 9.7597 -14.3485 18.8705 35.0290 -59.4097 -0.0000 0.0000 3.6319 -2.1293 6.0148 -5.1226 -0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -24.2970 -8.2572 -4.7653 -1.0200 0.0000 -0.0000 13.1001 3.6086 3.8517 -0.1369 0.0000 -0.0000 -10.7752 -16.0886 9.4873 49.7937 0.0000 0.0000 -1.6420 7.8659 -18.8941 -11.3904 0.0000 -0.0000\n 0.9028 41.8922 24.0515 -11.7965 -4.7734 176.0246 -1.5055 -3.2795 0.6832 -1.7703 -2.8922 0.0230 12.6807 -0.6116 9.0016 -4.1603 -2.4530 3.0297 -31.2616 -7.2532 -7.2187 -1.8590 -8.3819 -3.5341 8.3078 -10.7407 15.5138 26.2281 -24.3523 50.2131 0.0000 0.0000 5.6063 5.8009 -15.3488 -0.4174 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 3.9793 9.1485 -15.1961 18.0100 30.8070 -59.0896 -0.0000 0.0000 3.4504 2.0556 7.4658 -5.4313 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -22.1300 -9.6381 -4.8682 -2.3383 0.0000 -0.0000 12.2244 3.6674 2.1456 -0.3920 0.0000 -0.0000 -15.8436 -19.0957 8.3089 56.3509 0.0000 0.0000 -2.2224 8.9429 -15.0109 -11.4383 0.0000 -0.0000\n 0.9781 42.4018 24.1469 -10.4041 -5.0134 176.6939 -0.8043 -2.9994 0.6905 -1.4926 -2.1819 0.1415 13.3520 -2.2347 8.8709 -3.6158 -3.9932 3.6676 -27.8981 -10.2080 -9.8877 -0.4004 -9.0251 -3.8787 7.9259 -12.3119 12.9289 27.6092 -23.6075 50.6672 0.0000 0.0000 5.1718 5.8118 -14.0186 -1.1577 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 2.8573 7.4535 -15.3672 16.7101 29.2367 -58.2562 -0.0000 0.0000 3.1857 1.8046 6.4929 -5.3734 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -19.3086 -9.7802 -4.5847 -2.4325 0.0000 -0.0000 10.8671 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25.4636 -25.6452 52.1713 0.0000 0.0000 6.5858 -1.1202 -6.3144 -1.2002 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 0.9448 4.5151 -14.3211 11.7828 25.7744 -54.0170 -0.0000 0.0000 2.0367 -3.3745 4.5725 -4.8763 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -15.2256 -6.9880 -3.5967 0.6678 0.0000 0.0000 6.5053 4.2318 -0.0336 -0.7881 0.0000 -0.0000 -19.4201 -16.6241 10.5585 53.7506 0.0000 0.0000 -9.7009 4.2492 -3.5425 -11.6164 0.0000 -0.0000\n 0.7620 43.8090 24.5183 -6.1663 -2.0684 179.3099 1.3672 -2.4323 0.4110 -0.5266 -1.0231 0.1747 14.6957 -4.6703 7.0868 -1.7567 -5.4322 3.9680 -19.2160 -11.8173 -9.1175 5.0038 -7.9686 -4.2306 6.0250 -13.8365 8.5204 23.2655 -27.9603 51.8595 0.0000 0.0000 8.9404 -6.2552 -2.4659 -0.4008 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 0.2567 3.8713 -13.5876 8.9972 21.9662 -52.5867 -0.0000 0.0000 1.3050 -2.3739 3.6587 -4.6598 0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -12.9458 -5.4908 -3.3976 0.9296 0.0000 0.0000 5.1425 4.4837 -1.0185 -0.8533 0.0000 -0.0000 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0.0000 0.0000 -3.2016 5.3837 -13.4341 -8.1466 0.0000 -0.0000\n 0.8140 45.2827 24.4821 -6.1796 -5.7265 -176.1964 2.7459 -5.1253 0.5581 0.0270 -0.9397 0.0574 13.5242 -6.4117 6.4939 -0.7803 -6.7875 4.6274 -15.4575 -16.6880 -13.0860 5.4763 -10.6806 -9.0549 5.1035 -11.8151 7.4176 15.2403 -36.4508 51.4983 0.0000 0.0000 15.5752 -9.1996 8.9104 0.1045 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -1.9696 -0.0029 -14.0134 -0.1886 14.9466 -50.3856 -0.0000 -0.0000 -2.8256 -6.0937 -4.0047 -5.0376 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -8.5389 -8.7349 -7.7940 -0.4644 0.0000 -0.0000 1.2971 5.0712 -1.1431 -0.7963 0.0000 -0.0000 -29.0947 -15.3539 13.2446 18.9890 0.0000 0.0000 3.0208 7.7477 -15.8052 -12.5786 0.0000 -0.0000\n 0.8419 45.7477 24.4172 -5.6577 -7.3126 -175.8547 3.3618 -5.8774 0.6807 0.3052 -0.5762 0.0636 13.2744 -6.9255 6.2575 -0.5294 -7.5250 4.9009 -15.0294 -18.5521 -14.0565 6.0775 -11.2398 -9.0297 4.9627 -12.7054 6.6461 13.6533 -37.1165 52.1203 0.0000 0.0000 16.6096 -9.2903 9.3424 -0.1624 -0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -2.3193 -1.4479 -14.9455 -0.7007 14.5294 -49.5236 -0.0000 -0.0000 -3.8161 -6.1865 -4.6946 -5.7161 -0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -6.7842 -10.5391 -7.8688 -0.7408 0.0000 -0.0000 0.0807 5.3563 -1.3202 -0.6119 0.0000 -0.0000 -30.3247 -15.7108 12.9840 12.3360 0.0000 0.0000 8.4118 10.6185 -15.4842 -16.3526 0.0000 -0.0000\n 0.8414 46.2446 24.3659 -5.5346 -8.4354 -176.0729 3.7444 -6.1766 0.7320 0.4309 -0.3073 0.0781 13.1515 -7.2254 6.1977 -0.2735 -8.0770 5.1339 -14.7449 -19.8321 -14.6358 6.6535 -11.6684 -8.7400 5.0066 -13.3199 6.1031 12.3898 -38.8238 52.4066 0.0000 0.0000 17.9664 -9.2086 10.0830 0.3227 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -2.5447 -2.5237 -15.7836 -0.3186 14.1207 -49.3253 -0.0000 -0.0000 -3.9572 -5.9931 -4.7814 -5.9404 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -5.6591 -11.7972 -7.7852 -0.2195 0.0000 -0.0000 -1.5708 5.5591 -1.0519 -0.4650 0.0000 -0.0000 -31.8965 -15.6860 13.6645 8.4034 0.0000 0.0000 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-0.0000 -2.3555 -3.7625 -5.3922 -5.9805 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -29.8295 13.9370 -24.2614 1.6501 0.0000 0.0000 11.3448 1.3066 0.0693 -1.4754 0.0000 -0.0000 5.6906 4.5603 11.8316 13.8305 0.0000 0.0000 -17.7572 5.7657 -14.6586 -4.5067 0.0000 -0.0000\n -0.2386 67.3676 23.3913 -7.3437 3.2847 -163.4882 -6.1000 -0.9581 0.9036 -5.0117 -0.0606 -0.2725 17.6212 1.6132 -4.3454 0.4023 2.8431 -3.1851 -6.6253 21.4863 -0.2187 11.3311 12.7134 -5.4099 -4.2835 -14.0428 4.0531 -35.6370 -7.3055 10.0147 -0.0000 0.0001 49.9282 10.1485 20.4993 7.9835 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -1.5329 21.9810 2.6475 22.7241 29.7545 -45.7305 -0.0000 -0.0000 -1.5255 -1.9022 2.6787 -6.2097 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -25.4468 9.3450 -26.3434 -1.7256 0.0000 -0.0000 17.7106 3.4036 -0.6936 -0.5305 0.0000 -0.0000 4.9431 -1.7524 12.8058 18.5510 0.0000 0.0000 -21.0517 7.4118 -17.0530 -5.0317 0.0000 -0.0000\n -0.0454 68.3520 23.3485 -5.1148 -1.7519 -165.6822 -6.1871 -6.6489 0.8207 -5.6761 -3.8437 -0.9737 19.3551 1.9064 -0.4167 -0.3777 3.6016 -2.1070 -10.4195 21.6797 0.9273 10.6145 12.2872 -5.9697 -4.3845 -15.6828 6.8060 -33.4420 -5.5913 11.4631 -0.0000 0.0000 38.7770 4.4455 20.3891 4.0102 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -3.4214 24.8941 1.8017 23.0480 28.0442 -43.8985 -0.0000 -0.0000 -0.3302 -0.6470 9.6417 -5.7248 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -20.7571 1.1841 -27.3403 -5.9487 0.0000 -0.0000 20.5507 5.3775 -0.3950 0.6033 0.0000 0.0000 6.1884 -8.9369 15.0378 23.0664 0.0000 0.0000 -21.8339 8.4464 -17.2051 -8.1040 0.0000 -0.0000\n 0.0678 69.0905 23.5066 -4.9670 -3.9461 -169.1877 -5.7686 -9.1207 0.5923 -5.0192 -7.0618 -1.4532 18.2394 1.8559 3.9651 -0.7760 4.3360 -0.8864 -12.7596 19.9583 1.9722 9.5982 10.8895 -6.4579 -3.8938 -15.6224 10.1229 -31.7100 -8.0117 12.6722 0.0000 0.0000 26.9667 -0.5501 16.9531 -4.5705 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -4.1280 23.6460 1.5030 22.7681 30.0349 -43.7793 -0.0000 0.0000 1.6651 0.4287 12.5267 -4.7459 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -19.7386 -2.9573 -27.6926 -5.7291 0.0000 -0.0000 17.8652 5.9518 2.4985 1.1049 0.0000 0.0000 3.8702 -12.4523 16.1648 30.6443 0.0000 0.0000 -17.1627 8.8127 -16.0935 -13.4960 0.0000 -0.0000\n 0.1528 69.6724 23.7646 -5.2223 -2.8845 -170.1118 -4.5905 -8.8974 0.3880 -3.5043 -6.3721 -1.0611 15.1210 0.8100 5.9080 -0.9675 3.6444 -0.0442 -13.4864 14.2617 1.4070 8.0695 7.6385 -7.0902 -2.7860 -14.9795 12.6971 -28.8389 -13.3585 13.3881 0.0000 0.0000 18.3801 -3.2243 7.5713 -10.2990 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -3.2810 18.3076 0.3328 22.0497 32.3391 -44.2669 -0.0000 0.0000 3.0197 2.2950 12.9496 -4.4029 0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -18.9484 -2.2669 -28.9027 -3.8359 0.0000 -0.0000 14.4101 4.4959 5.5894 0.9022 0.0000 0.0000 -1.9734 -13.3243 13.9156 41.1781 0.0000 0.0000 -7.9113 9.9038 -15.7735 -16.2526 0.0000 -0.0000\n 0.3119 70.2051 23.9782 -4.4639 -1.5971 -168.1176 -2.7067 -8.0382 0.2711 -2.2372 -4.2604 -0.5869 12.3103 0.1535 5.3904 -1.3774 1.8379 0.3772 -14.1088 4.9718 -1.1684 6.0191 2.3789 -8.1891 -0.4678 -13.9292 13.6554 -20.1892 -18.1053 16.6902 0.0000 0.0000 14.7824 -2.7728 -5.2488 -8.4038 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -1.9045 13.2295 -2.0936 19.6834 29.7623 -42.8035 -0.0000 0.0000 2.5775 3.8510 12.7789 -4.9060 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -16.7192 -0.9204 -29.9006 -2.8575 0.0000 -0.0000 12.6836 2.7255 5.5650 0.3742 0.0000 0.0000 -8.8903 -14.5184 9.6298 51.7659 0.0000 0.0000 -0.0465 11.2778 -17.4212 -14.3130 0.0000 -0.0000\n 0.5090 70.6952 24.1072 -3.3103 -1.4116 -166.3583 -0.8845 -7.5987 0.3220 -1.4465 -2.9582 -0.3321 11.0750 0.0100 5.0747 -1.8844 -0.1126 0.8801 -15.7248 -4.7952 -5.1284 3.2400 -3.2168 -9.6701 3.0132 -12.2276 13.5559 -8.3307 -18.6582 24.5859 0.0000 0.0000 14.4555 0.3715 -13.2659 -0.9176 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.7762 11.7084 -5.0320 14.9547 22.4491 -39.2950 -0.0000 0.0000 1.1247 2.7762 9.9560 -4.9016 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -14.4471 -1.1863 -29.4645 -2.3458 0.0000 -0.0000 11.1630 2.2671 3.6309 -0.1304 0.0000 -0.0000 -15.2760 -16.6880 6.2900 59.8764 0.0000 0.0000 2.4573 12.1839 -19.6830 -12.3046 0.0000 -0.0000\n 0.6513 71.1455 24.1714 -2.4792 -1.6280 -166.7813 0.1666 -7.2567 0.4654 -0.9651 -2.6184 -0.1939 11.0565 -0.3886 6.1694 -2.1718 -1.6288 1.7591 -17.4218 -12.1813 -9.0185 0.6376 -6.4108 -11.0174 5.8376 -10.1212 13.0546 -2.0849 -17.2971 33.3710 0.0000 0.0000 13.9737 1.2103 -15.9779 3.0430 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.7054 11.2116 -7.4949 9.1740 16.8685 -36.3006 -0.0000 0.0000 0.1402 -1.1644 3.1310 -4.1323 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -12.7286 -2.3471 -28.0312 -2.1133 0.0000 -0.0000 9.5027 3.0641 2.8410 -0.4765 0.0000 -0.0000 -20.1713 -18.9889 5.6877 64.1211 0.0000 0.0000 1.6048 12.7143 -20.3131 -11.4937 0.0000 -0.0000\n 0.7126 71.5685 24.1994 -1.9217 -1.6247 -168.4760 0.5416 -6.9693 0.5894 -0.6437 -2.6905 -0.1336 11.4739 -1.4422 7.5920 -2.1719 -2.4948 2.6318 -17.6548 -15.2848 -10.9628 -0.2058 -6.7932 -12.1786 6.3593 -8.7749 13.1224 -1.6529 -17.8367 40.4126 0.0000 0.0000 12.8564 1.8826 -16.4459 1.8810 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -1.8604 9.1777 -8.6142 3.8310 15.6140 -35.1695 -0.0000 -0.0000 -0.0128 -5.4806 -3.5477 -4.8200 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -10.9000 -3.4107 -26.3891 -2.6926 0.0000 -0.0000 8.6700 3.9993 3.2622 -0.7267 0.0000 -0.0000 -23.5396 -20.3117 6.4295 64.3997 0.0000 0.0000 0.7585 12.9587 -18.4927 -10.7362 0.0000 -0.0000\n 0.7209 71.9611 24.2275 -1.4066 -1.4351 -169.9302 0.7702 -7.1207 0.6262 -0.4109 -2.9716 -0.1584 11.8703 -2.5727 8.3775 -2.0641 -2.6679 3.0275 -16.6509 -14.4731 -10.8253 0.8110 -5.8111 -13.3491 5.7691 -9.4189 14.3283 -3.0128 -18.7797 44.9197 0.0000 0.0000 13.4701 4.0124 -11.2468 0.4618 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -3.6757 6.5989 -8.8799 0.6467 14.8460 -35.2233 -0.0000 -0.0000 -0.3272 -7.6746 -5.6100 -7.0794 0.0000 -0.0000 0.0000 -0.0000 -0.0000 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-15.7685 -10.2959 -9.5017 2.4077 -4.1684 -14.7293 6.4222 -11.0820 16.5988 -6.4119 -20.0523 49.1286 0.0000 0.0000 20.2775 -1.1419 4.2046 3.9956 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -6.2751 4.1258 -9.0805 -1.5979 8.2162 -38.4032 -0.0000 -0.0000 -3.3788 -7.9110 -6.0543 -10.9060 -0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -7.7581 -3.0066 -20.4798 -3.2005 0.0000 -0.0000 6.6239 4.2230 1.6658 -1.4340 0.0000 -0.0000 -27.8556 -15.7259 4.5938 44.6480 0.0000 0.0000 -2.4468 15.6138 -7.4461 -7.8935 0.0000 -0.0000\n 0.5291 72.9097 24.4260 -2.5166 0.3745 -173.2393 0.9684 -7.4147 0.5851 -0.2330 -3.5401 -0.2489 11.9396 -3.0367 9.1331 -1.8413 -1.8706 2.9893 -15.7379 -9.2705 -9.2469 2.2847 -3.9323 -14.6069 6.4958 -10.2848 16.2261 -8.4053 -23.7919 50.4602 0.0000 0.0000 23.6019 -5.2208 5.5264 5.4018 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -6.3286 2.8023 -9.1683 -4.0074 7.0206 -41.1502 -0.0000 -0.0000 -4.6650 -9.3424 -8.5534 -11.2236 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -8.3150 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-0.2722 -0.7628 -1.4246 0.1419 0.1266 13.8902 -8.3549 4.6278 -2.0267 -5.3929 3.5757 -23.8721 -3.7811 0.9726 -3.1999 -0.1544 5.1853 3.6543 -11.1649 15.0787 5.7121 -21.0078 56.0418 0.0000 0.0000 15.1673 -3.6926 -3.9898 -0.9416 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -15.3262 -14.3952 -23.8353 4.6854 16.8581 -68.8725 -0.0000 -0.0000 -0.6169 -0.7064 4.2744 -5.5332 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 7.9848 -3.1704 8.9068 18.4864 0.0000 0.0000 -22.3832 0.5808 5.8664 0.1347 0.0000 0.0000 -25.6263 -11.3392 -2.7227 26.1138 0.0000 0.0000 -1.0827 5.2932 4.7142 -9.9464 0.0000 -0.0000\n 0.3029 71.5471 23.4543 -1.3239 5.6181 -9.0489 -0.1056 0.7192 -0.8598 -1.6386 0.5257 0.1503 14.0710 -8.9576 3.8949 -2.1957 -5.5508 3.4012 -24.9438 -3.3286 0.8906 -3.5200 0.2148 4.9803 3.1749 -11.8999 15.2898 4.5014 -21.6010 54.9809 0.0000 0.0000 15.2189 -2.3240 -4.6897 -1.1391 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -14.9355 -14.0698 -24.0578 4.2366 16.9856 -69.7885 -0.0000 -0.0000 -0.5210 -1.0164 3.1893 -5.5037 -0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 7.4132 -1.6640 9.2941 19.7538 0.0000 0.0000 -22.3271 -0.7163 5.3137 -0.7305 0.0000 -0.0000 -26.8803 -8.7147 -6.7386 29.2655 0.0000 0.0000 -4.6211 5.0788 9.2692 -8.6758 0.0000 -0.0000\n 0.6085 70.9877 23.4944 -1.0757 5.5495 -9.6145 -0.0152 0.7124 -0.7769 -1.5506 0.6412 0.1603 14.0648 -8.6818 3.6955 -2.3249 -5.4198 3.2749 -25.8643 -2.7100 0.6146 -3.6965 0.6117 4.5035 3.2308 -11.5861 15.3381 3.4107 -22.1394 53.8018 0.0000 0.0000 16.2000 -2.1734 -3.7227 -1.5422 0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -14.3219 -14.3514 -24.4251 3.8331 18.1288 -71.0087 -0.0000 -0.0000 -0.6203 -1.6040 1.8171 -5.4756 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 7.4297 -0.7907 10.7061 21.2802 0.0000 0.0000 -21.1490 -1.6471 4.7201 -1.8178 0.0000 -0.0000 -27.1070 -7.9229 -6.0527 31.9757 0.0000 0.0000 -6.5527 4.4821 9.7192 -7.6115 0.0000 -0.0000\n 0.9113 70.4205 23.5734 -0.8447 5.2297 -10.7878 0.0154 0.7549 -0.6739 -1.5389 0.5673 0.1562 13.8555 -7.9721 3.6719 -2.4251 -5.0498 3.0885 -26.2026 -2.1230 0.6276 -3.4209 0.7176 4.0824 3.8199 -10.9794 15.5047 3.3381 -21.3809 53.2897 0.0000 0.0000 17.7073 -2.9852 -1.8206 -1.6349 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -13.4810 -14.2093 -24.3833 4.2128 18.7916 -72.3223 -0.0000 -0.0000 -0.6655 -1.7628 0.9291 -5.7616 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 6.7727 0.3365 11.9037 23.6908 0.0000 0.0000 -19.7089 -1.6111 4.7518 -2.7228 0.0000 -0.0000 -26.0063 -7.7269 -2.5057 32.7206 0.0000 0.0000 -7.9730 3.0286 7.9002 -7.3691 0.0000 -0.0000\n 1.2226 69.8878 23.6813 -0.2262 4.0798 -12.0716 -0.4581 1.4703 -0.6126 -1.7763 0.6431 0.1684 13.6266 -7.3588 3.3665 -2.4736 -4.6287 2.8519 -26.0574 -1.4238 0.9268 -2.8394 0.7852 3.9153 4.1258 -10.7028 15.8598 3.3325 -20.8207 53.0475 0.0000 0.0000 19.1316 -3.3434 -1.1969 -1.3921 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -12.5020 -13.1766 -24.1172 5.0350 18.5670 -73.0688 -0.0000 -0.0000 -0.5612 -1.1962 0.7216 -6.1796 -0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 4.3608 2.1846 12.2218 27.7813 0.0000 0.0000 -18.1272 -1.5283 5.5308 -3.3214 0.0000 -0.0000 -24.5200 -6.5274 0.4686 31.5731 0.0000 0.0000 -9.2203 1.6426 6.9621 -7.5447 0.0000 -0.0000\n 1.5105 69.4252 23.8034 0.6355 2.5829 -13.2679 -1.2096 2.4417 -0.5278 -2.1152 0.9204 0.2126 13.4831 -6.8510 2.9555 -2.4762 -4.2521 2.6640 -25.8344 -0.4702 1.2182 -2.3325 1.1503 3.8795 3.7196 -10.4913 16.1366 2.5352 -21.6165 52.6359 0.0000 0.0000 20.4808 -2.4889 -2.5194 -1.2157 0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -11.6014 -11.9540 -23.9937 5.6619 18.0795 -73.1630 -0.0000 -0.0000 -0.2962 -0.5703 0.7374 -6.3292 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.1066 3.8950 12.8893 34.0739 0.0000 0.0000 -15.5669 -2.4615 6.6586 -3.3621 0.0000 -0.0000 -23.4255 -4.7146 2.0316 29.8387 0.0000 0.0000 -9.6977 0.7917 7.4417 -7.5735 0.0000 -0.0000\n 1.7428 69.0322 23.9039 1.3178 1.2207 -14.5834 -1.7214 3.3527 -0.4014 -2.3226 1.1609 0.2743 13.2766 -6.1262 2.7619 -2.5052 -3.8600 2.5310 -25.6328 0.4867 1.4428 -2.0670 1.6584 3.8178 3.0309 -9.9440 16.0807 1.4200 -22.5558 52.4769 0.0000 0.0000 22.0778 -0.8060 -4.0576 -1.2383 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -10.9190 -11.3178 -23.9349 6.0073 17.5705 -73.0672 -0.0000 0.0000 0.1442 -0.4487 0.6118 -6.1760 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -4.9484 4.7494 14.1529 41.6846 0.0000 0.0000 -12.4030 -2.9190 7.0143 -2.9817 0.0000 -0.0000 -22.4612 -3.2445 3.8956 28.3431 0.0000 0.0000 -9.6633 0.0257 7.5972 -7.5788 0.0000 -0.0000\n 1.9134 68.6677 23.9687 1.3956 0.1352 -15.9397 -1.7610 4.2512 -0.3091 -2.3148 1.2708 0.3194 12.9839 -5.2121 2.7389 -2.5776 -3.3896 2.3653 -25.2914 1.2293 1.7459 -1.9315 2.0319 3.7212 2.4225 -9.1182 15.7650 0.4307 -22.7695 52.7674 0.0000 0.0000 24.0801 1.3319 -4.1850 -1.2032 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -10.3419 -11.0002 -23.6445 6.0625 17.0875 -72.8662 -0.0000 0.0000 0.3564 -0.4209 0.1686 -6.0689 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -9.1053 5.0003 14.5377 48.3279 0.0000 0.0000 -10.9917 -0.4469 3.8376 -3.2223 0.0000 -0.0000 -20.9128 -2.1352 6.4125 26.8469 0.0000 0.0000 -9.9614 -0.8279 6.9507 -7.7307 0.0000 -0.0000\n 2.0259 68.3133 24.0241 1.0689 -0.3647 -17.1016 -1.6662 4.8697 -0.2429 -2.2229 1.2557 0.3323 12.9107 -4.3954 2.7874 -2.5961 -2.9048 2.2060 -24.8597 1.9141 2.0954 -1.8379 2.3529 3.5848 1.7215 -8.1713 15.4635 -1.2281 -23.5751 52.4432 0.0000 0.0000 26.8000 3.8704 -3.1778 -1.0928 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -9.5637 -10.2964 -23.0196 5.2702 17.4913 -72.3102 -0.0000 -0.0000 -0.5014 0.3172 -1.0360 -6.1662 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -11.8151 4.8465 13.1669 51.9816 0.0000 0.0000 -11.7909 4.5500 -4.3062 -3.4777 0.0000 -0.0000 -18.9440 -1.3705 8.1409 25.2973 0.0000 0.0000 -10.6614 -1.3731 6.7260 -7.7926 0.0000 -0.0000\n 2.0927 67.9828 24.0928 0.6971 -0.1020 -18.1307 -1.6152 4.9598 -0.1540 -2.1496 1.1184 0.3250 13.1421 -3.8280 3.0845 -2.4883 -2.5031 2.1844 -24.5042 2.6685 2.3167 -1.8017 2.7624 3.3667 0.8269 -6.9717 15.2043 -4.2819 -26.1332 50.5199 0.0000 0.0000 30.8685 6.5258 -1.8614 -0.9991 -0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -8.3901 -9.2106 -22.0692 3.1559 19.8722 -71.4760 -0.0000 -0.0000 -3.0598 2.2007 -3.2650 -6.2007 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -13.6038 4.2608 10.6302 51.9990 0.0000 0.0000 -12.9086 9.4471 -13.5987 -2.0893 0.0000 -0.0000 -17.0194 -1.2347 8.8682 24.0884 0.0000 0.0000 -11.2358 -1.6054 7.1788 -7.7167 0.0000 -0.0000\n 2.1377 67.6980 24.1606 0.5823 0.5039 -19.1202 -1.6216 4.8383 -0.0732 -2.1062 0.9094 0.3206 13.4004 -3.4815 3.5953 -2.3499 -2.2781 2.3110 -24.2260 3.2816 2.3829 -1.8051 3.1115 3.1761 -0.0257 -5.6095 14.7846 -8.2545 -29.3877 47.4597 0.0000 0.0000 36.4522 9.0194 -0.2433 -0.8750 -0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -7.0366 -8.1993 -20.7746 0.2983 23.9961 -70.6072 -0.0000 -0.0000 -7.2545 5.1406 -5.9883 -5.8835 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -15.2883 3.5565 7.9811 49.6592 0.0000 0.0000 -14.2318 11.3335 -17.5631 -0.0175 0.0000 -0.0000 -15.5260 -1.6116 9.7378 23.3424 0.0000 0.0000 -11.5980 -1.8812 7.2475 -7.8166 0.0000 -0.0000\n 2.1770 67.4458 24.2088 0.4991 0.9141 -19.7588 -1.4700 5.0669 -0.0715 -2.0382 0.7815 0.3226 13.4753 -3.3994 3.9619 -2.3129 -2.2306 2.4175 -23.8701 3.5401 2.3768 -1.7650 3.2600 3.1539 -0.5025 -4.7150 14.0039 -12.2947 -31.8769 44.7908 -0.0000 0.0000 42.6890 11.6097 1.6495 -0.7858 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -5.6791 -7.3435 -19.2173 -2.4047 28.0182 -69.8708 -0.0000 -0.0000 -12.2537 8.3364 -8.6618 -5.3488 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -16.7688 3.2739 5.4966 46.2393 0.0000 0.0000 -15.7216 9.5375 -14.8464 0.7739 0.0000 0.0000 -14.2433 -1.8210 10.8067 22.6678 0.0000 0.0000 -12.0542 -2.1946 6.7624 -8.0966 0.0000 -0.0000\n 2.2165 67.2019 24.2375 0.1942 0.9013 -19.7816 -1.1804 5.7404 -0.1208 -1.9329 0.8274 0.3314 13.5021 -3.5301 3.9833 -2.3603 -2.2765 2.4658 -23.3659 3.6098 2.2745 -1.6391 3.3534 3.2191 -0.8082 -4.4482 13.0136 -16.2927 -34.0872 42.6954 -0.0000 0.0000 48.9174 14.9109 3.0625 -0.9936 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -4.5254 -6.5890 -17.8183 -4.6850 31.2135 -69.1218 -0.0000 -0.0000 -17.3235 11.1090 -11.3460 -4.8528 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -17.8123 3.5926 3.2065 41.9974 0.0000 0.0000 -15.8008 6.4780 -9.7710 0.3726 0.0000 0.0000 -12.9453 -1.3480 11.1464 21.8008 0.0000 0.0000 -12.5489 -2.3559 6.6235 -8.2174 0.0000 -0.0000\n 2.2529 66.9643 24.2568 -0.2173 0.6311 -19.4232 -0.9673 6.4255 -0.1567 -1.8290 0.9411 0.3487 13.6501 -3.6220 3.9401 -2.3603 -2.2913 2.5115 -22.8353 3.7679 2.0596 -1.4565 3.5638 3.1472 -1.3340 -4.1857 12.1590 -20.4350 -36.8630 40.1056 -0.0000 0.0001 55.1859 18.8038 3.7263 -1.4584 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -3.8209 -6.1838 -16.8220 -6.5573 34.4270 -68.2516 -0.0000 -0.0000 -22.2455 13.4663 -13.8732 -4.3658 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -18.5445 4.0312 1.3675 36.9505 0.0000 0.0000 -14.5769 4.9234 -6.3005 -0.1688 0.0000 -0.0000 -11.6604 -0.4479 10.7853 20.8219 0.0000 0.0000 -12.7919 -2.5202 6.8994 -8.1169 0.0000 -0.0000\n 2.2778 66.7422 24.2738 -0.5369 0.3486 -18.9264 -0.8780 6.8203 -0.1557 -1.7679 1.0024 0.3601 13.8931 -3.5354 3.9747 -2.2576 -2.2052 2.5396 -22.4050 3.9853 1.8331 -1.2849 3.8375 2.9053 -2.1489 -3.4901 11.5779 -24.3755 -39.8229 36.8016 -0.0000 0.0001 61.3185 22.1847 4.4497 -1.7439 -0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -3.6522 -6.3359 -16.0019 -7.8417 38.0472 -67.4667 -0.0000 -0.0000 -26.9264 15.7825 -15.7919 -3.7386 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -19.2110 4.1184 0.2501 31.4518 0.0000 0.0000 -13.0126 4.7081 -4.9145 -0.3629 0.0000 -0.0000 -10.4052 0.3756 10.5924 19.7538 0.0000 0.0000 -12.7877 -2.9262 6.6950 -7.9777 0.0000 -0.0000\n 2.2820 66.5283 24.2937 -0.7838 0.1669 -18.0065 -0.7933 7.0284 -0.1517 -1.7154 1.0399 0.3486 14.1460 -3.4534 3.7896 -2.1133 -2.0627 2.4433 -22.0360 4.1179 1.6790 -1.1621 4.0467 2.6155 -2.9383 -2.7357 11.0752 -27.4448 -42.0587 34.0457 -0.0000 0.0001 66.4802 23.9691 5.8765 -1.4974 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -3.6999 -6.7197 -15.1418 -8.5834 41.0581 -67.0213 -0.0000 -0.0000 -31.1227 18.0046 -17.2055 -3.0822 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -19.8050 3.9512 -0.6355 26.0203 0.0000 0.0000 -11.3411 4.3450 -4.1403 -0.3981 0.0000 -0.0000 -9.0873 0.9709 10.6514 18.5107 0.0000 0.0000 -12.7346 -3.4113 5.6719 -7.7624 0.0000 -0.0000\n 2.2633 66.3032 24.3185 -1.1441 0.0355 -16.4005 -0.6127 7.2873 -0.1609 -1.6426 1.1194 0.3249 14.4226 -3.5990 3.1952 -1.9741 -1.9304 2.2440 -21.6206 4.2123 1.5346 -1.0218 4.2062 2.3132 -3.5030 -2.3657 10.5446 -29.4433 -43.2449 32.5537 -0.0000 0.0001 70.2111 24.4471 7.5779 -0.8543 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -3.7105 -6.9707 -14.3738 -9.0582 42.9244 -66.8537 -0.0000 -0.0000 -34.7490 19.7652 -18.5497 -2.6424 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -20.2291 3.9109 -2.0779 21.2379 0.0000 0.0000 -9.6516 3.4388 -2.8352 -0.7168 0.0000 -0.0000 -7.5607 1.5122 10.3177 17.0859 0.0000 0.0000 -12.6279 -3.6277 4.4016 -7.3836 0.0000 -0.0000\n 2.2264 66.0663 24.3424 -1.6052 -0.1297 -14.4964 -0.3916 7.6418 -0.1762 -1.5701 1.1621 0.2823 14.7208 -3.8755 2.5885 -1.8451 -1.8099 2.0310 -21.1366 4.3694 1.3502 -0.7700 4.4169 1.9704 -3.8528 -2.2867 10.2101 -30.7934 -43.4407 31.6922 -0.0000 0.0001 72.7560 24.9840 8.8765 -0.3293 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -3.6800 -7.1020 -13.7880 -9.3958 44.2104 -66.8778 -0.0000 -0.0000 -37.8979 20.8591 -19.7094 -2.4683 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -20.7487 4.0350 -3.7870 17.6661 0.0000 0.0000 -8.2359 2.5418 -0.9093 -1.2769 0.0000 -0.0000 -5.9204 2.0681 9.4293 15.6568 0.0000 0.0000 -12.4319 -3.5597 3.4212 -7.0737 0.0000 -0.0000\n 2.1784 65.8330 24.3544 -2.0184 -0.4039 -12.8784 -0.1866 8.0020 -0.1763 -1.4618 1.1585 0.2525 14.8686 -4.0754 2.2651 -1.7418 -1.7020 1.9295 -20.6151 4.5513 1.1850 -0.4317 4.7230 1.6282 -4.0952 -2.1509 10.2010 -31.6344 -42.9593 30.9483 -0.0000 0.0001 74.2474 26.1122 9.6618 -0.2086 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -3.7126 -7.3080 -13.2873 -9.5173 45.4445 -67.1209 -0.0000 -0.0000 -40.5651 21.5689 -20.2152 -2.3627 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -21.7301 4.1702 -4.5257 15.5879 0.0000 0.0000 -7.2931 2.0914 0.9678 -1.5843 0.0000 -0.0000 -4.3905 2.6197 8.4720 14.3882 0.0000 0.0000 -12.3272 -3.4312 2.7358 -7.0379 0.0000 -0.0000\n 2.1183 65.6017 24.3541 -2.4279 -0.7566 -11.5081 0.1460 8.2850 -0.1943 -1.2895 1.1665 0.2357 14.7741 -4.1261 2.1412 -1.7051 -1.6037 1.8761 -20.0515 4.6734 1.0598 -0.1447 5.0849 1.3434 -4.1483 -2.0677 10.2891 -31.7014 -42.1270 30.8900 -0.0000 0.0001 74.2757 26.8380 10.1093 -0.2303 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -3.8167 -7.6279 -12.8399 -9.4819 46.3454 -67.5210 -0.0000 -0.0000 -42.6123 22.4337 -20.0736 -2.0398 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -22.9064 4.2881 -4.0444 14.6242 0.0000 0.0000 -6.6103 2.1203 2.0873 -1.5369 0.0000 -0.0000 -2.9345 3.1225 7.7253 13.3101 0.0000 0.0000 -12.4335 -3.2769 2.0683 -7.0810 0.0000 -0.0000\n 2.0464 65.3578 24.3499 -2.8179 -1.2299 -10.0426 0.5359 8.5189 -0.2202 -1.0928 1.2268 0.2272 14.5761 -4.1297 2.0090 -1.7291 -1.5140 1.7987 -19.4873 4.8809 0.9545 0.0189 5.4581 1.0227 -3.8972 -2.3277 10.2900 -31.1240 -41.1280 31.7855 -0.0000 0.0001 72.8056 26.3501 10.1694 -0.0309 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -3.8931 -7.8020 -12.4578 -9.4352 46.4915 -68.0586 -0.0000 -0.0000 -44.0722 23.7364 -19.5408 -1.3405 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -23.9865 4.5528 -3.2575 14.2441 0.0000 0.0000 -5.9657 2.1916 2.3209 -1.5169 0.0000 -0.0000 -1.4728 3.6301 7.0521 12.2283 0.0000 0.0000 -12.5218 -2.9663 1.2526 -6.9964 0.0000 -0.0000\n 1.9623 65.0849 24.3478 -3.1899 -1.7609 -8.4694 0.8847 8.6822 -0.2109 -0.9215 1.2626 0.2117 14.4615 -4.1770 1.9103 -1.7602 -1.3922 1.7234 -19.0640 5.3790 0.8989 0.1285 5.8381 0.5697 -3.5159 -2.7808 10.4013 -30.5248 -40.1809 32.5378 -0.0000 0.0001 70.9509 25.3565 9.6264 0.2857 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -3.9496 -7.7702 -12.0187 -9.2911 46.3344 -68.7402 -0.0000 -0.0000 -45.4687 25.3240 -18.4409 -0.3781 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -24.7866 4.9430 -2.8620 14.1118 0.0000 0.0000 -5.4497 1.8347 2.1679 -1.6798 0.0000 -0.0000 0.2048 4.1172 6.2326 10.8508 0.0000 0.0000 -12.4331 -2.4716 0.4161 -6.9507 0.0000 -0.0000\n 1.8642 64.7864 24.3386 -3.3899 -2.2944 -6.9205 1.0884 8.7649 -0.1853 -0.7838 1.2205 0.1901 14.4243 -4.2567 1.9640 -1.7644 -1.2548 1.7052 -18.9210 6.1111 0.9044 0.2549 6.2593 0.0348 -3.2102 -3.0882 10.6969 -30.2508 -39.5546 32.2251 -0.0000 0.0001 69.9893 24.9108 8.6804 0.3544 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -4.1319 -7.8416 -11.5322 -8.9560 46.6124 -69.3039 -0.0000 -0.0000 -47.4342 26.8155 -16.7225 0.4205 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -25.4027 5.3393 -2.8888 14.0679 0.0000 0.0000 -5.1556 1.2658 2.1926 -1.7615 0.0000 -0.0000 1.8641 4.5104 5.1426 9.3694 0.0000 0.0000 -12.4403 -1.9513 -0.1669 -7.0897 0.0000 -0.0000\n 1.7467 64.4742 24.3146 -3.3014 -2.8440 -5.2706 1.1801 8.7982 -0.1542 -0.6964 1.1993 0.1816 14.4182 -4.3677 2.0653 -1.7823 -1.1682 1.7238 -19.0666 6.8318 0.8872 0.3458 6.7298 -0.4765 -2.9938 -3.3235 10.9081 -30.1311 -39.2862 31.2340 -0.0000 0.0001 70.1469 24.7889 7.8449 0.2016 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -4.3493 -8.0459 -11.2365 -8.7363 46.9265 -69.4917 -0.0000 -0.0000 -49.9126 27.8610 -15.3067 0.6637 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -25.8381 5.7568 -3.1530 13.9176 0.0000 0.0000 -4.8049 0.9859 2.3037 -1.5868 0.0000 -0.0000 3.0607 4.7509 3.9590 8.3567 0.0000 0.0000 -12.7957 -1.4962 -0.6826 -7.2154 0.0000 -0.0000\n 1.6063 64.1498 24.2841 -2.9174 -3.3949 -3.4763 1.1831 8.7553 -0.0686 -0.7131 1.2588 0.1903 14.5207 -4.5189 2.1059 -1.8584 -1.1359 1.7260 -19.4287 7.4745 0.8711 0.3076 7.1993 -0.9333 -2.7008 -3.8829 10.9525 -30.0200 -39.0167 30.3996 -0.0000 0.0001 70.9155 24.2215 7.3567 0.1619 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -4.4574 -8.0207 -11.2135 -8.8561 46.6084 -69.3148 -0.0000 -0.0000 -52.4502 28.3953 -15.0212 0.3901 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -26.1368 6.1794 -3.3248 13.7356 0.0000 0.0000 -3.9049 0.6589 1.9986 -1.3281 0.0000 -0.0000 3.7100 4.8067 2.9889 7.9167 0.0000 0.0000 -13.3095 -0.9690 -1.5226 -7.1841 0.0000 -0.0000\n 1.4433 63.7951 24.2536 -2.3355 -3.8113 -1.8598 1.0504 8.5403 0.0634 -0.8175 1.2886 0.1987 14.7478 -4.7124 2.2045 -1.9600 -1.0817 1.7215 -19.9499 8.1647 1.0581 0.1286 7.5846 -1.2998 -2.3113 -4.6667 11.1652 -30.0715 -38.6487 29.6331 -0.0000 0.0001 72.1275 23.4785 6.8481 0.3343 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -4.3953 -7.6123 -11.1426 -9.1380 45.9886 -69.0018 -0.0000 -0.0000 -54.9189 28.5477 -15.1595 0.0117 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -26.4537 6.3850 -3.0707 14.0110 0.0000 0.0000 -2.1109 -0.4323 1.3989 -1.2111 0.0000 -0.0000 4.2142 4.6320 2.2477 7.5359 0.0000 0.0000 -13.8379 -0.2321 -2.4794 -7.1824 0.0000 -0.0000\n 1.2622 63.3883 24.2084 -1.6172 -4.1689 -0.5309 0.7381 8.2298 0.1751 -0.9060 1.2140 0.1878 14.9363 -4.8272 2.4039 -2.0195 -0.9828 1.7218 -20.6311 8.7501 1.4504 -0.1551 7.8104 -1.5058 -1.9517 -5.2354 11.6570 -30.4095 -38.4824 28.4974 -0.0000 0.0001 73.8656 23.4273 5.7198 0.4526 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -4.5273 -7.4501 -10.9870 -9.2349 46.1886 -68.3470 -0.0000 -0.0000 -57.6556 28.6604 -14.3058 -0.1779 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -26.7385 6.4393 -2.5036 14.9332 0.0000 0.0000 0.6189 -1.7813 1.3300 -1.2302 0.0000 -0.0000 4.8242 4.3735 1.5101 6.9604 0.0000 0.0000 -14.5882 0.5931 -3.1035 -7.2985 0.0000 -0.0000\n 1.0621 62.9201 24.1371 -0.7780 -4.7096 0.9045 0.3554 7.9638 0.3050 -1.0481 1.2303 0.1955 15.1080 -4.7973 2.4659 -2.0650 -0.9238 1.6800 -21.5157 8.9343 1.7328 -0.5913 7.8360 -1.5679 -1.5735 -5.6815 12.0310 -30.8501 -38.3420 27.4942 -0.0000 0.0001 75.7647 24.0832 3.8168 0.3026 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -4.8514 -7.5562 -10.9831 -9.3320 46.5445 -67.5332 -0.0000 -0.0000 -60.2966 28.8344 -12.5490 -0.3588 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -26.4896 6.6852 -1.8549 15.7819 0.0000 0.0000 3.8411 -2.0155 1.7843 -1.2161 0.0000 -0.0000 5.4742 4.2210 0.6541 6.3910 0.0000 0.0000 -15.5017 1.3456 -3.5799 -7.3498 0.0000 -0.0000\n 0.8329 62.3956 24.0543 0.3333 -5.2581 2.4155 -0.2005 7.5030 0.6236 -1.4107 1.4961 0.2575 15.5313 -4.7339 2.3940 -2.2044 -0.9444 1.5947 -22.6491 8.8881 1.8562 -1.3075 7.7167 -1.5841 -1.1012 -6.4533 12.1547 -30.9890 -37.4816 27.4283 -0.0000 0.0001 77.2939 24.7631 1.5539 -0.0266 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -4.9545 -6.7997 -11.0054 -9.6686 45.0894 -67.3538 -0.0000 -0.0001 -62.0099 28.8184 -10.9843 -0.8391 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -25.8680 6.8372 -0.6572 16.2072 0.0000 0.0000 6.1570 -1.2396 1.4127 -1.0049 0.0000 -0.0000 5.9919 3.9564 -0.1917 5.7822 0.0000 0.0000 -16.2065 2.1745 -4.1777 -7.3600 0.0000 -0.0000\n 0.5554 61.8208 23.9815 1.4984 -4.8771 3.0287 -0.7955 6.1737 1.0683 -1.9253 1.5987 0.3162 16.1079 -4.7464 2.7666 -2.4721 -0.9278 1.6258 -23.9502 9.0426 2.2578 -2.3612 7.5197 -1.5418 -0.8032 -7.2066 12.5710 -30.6290 -35.9462 27.7200 -0.0000 0.0001 78.5818 25.2377 -0.5893 -0.3934 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -4.5475 -4.6572 -10.5725 -10.2563 41.5599 -68.0120 -0.0000 -0.0001 -62.6625 28.3034 -9.6832 -1.6859 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -25.6670 5.5782 1.4916 17.1777 0.0000 0.0000 6.1049 -0.8001 -0.5101 -0.5549 0.0000 -0.0000 6.5391 2.7136 -0.5772 5.1585 0.0000 0.0000 -17.1077 3.3444 -4.3519 -7.6339 0.0000 -0.0000\n 0.2269 61.2409 23.9133 2.4884 -3.2481 2.2831 -1.0240 4.1763 1.2767 -2.1474 0.9108 0.2487 16.0759 -4.6719 3.9404 -2.7591 -0.8787 1.8961 -25.2039 9.0082 2.9972 -3.6615 7.0649 -1.3197 -0.8490 -6.9404 13.6334 -30.2297 -35.1453 26.8805 -0.0000 0.0001 80.2493 25.8387 -2.4154 -0.8179 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -4.0852 -2.8609 -9.9132 -10.9962 38.4759 -68.3295 -0.0000 -0.0001 -62.9173 27.7107 -8.2429 -2.8300 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -26.5799 2.5290 3.3959 19.7941 0.0000 0.0000 3.7082 -0.8640 -2.5298 0.0636 0.0000 0.0000 6.4188 0.2266 0.5303 6.3233 0.0000 0.0000 -19.2757 4.8405 -4.2517 -8.1384 0.0000 -0.0000\n -0.1292 60.7184 23.8497 3.2068 -1.3261 1.5997 -0.6296 2.5832 0.9936 -1.8007 -0.2057 0.1021 15.1635 -4.2503 5.2019 -2.9216 -1.1683 2.2846 -26.2006 7.4529 3.1068 -5.1181 5.9277 -0.9032 -0.7461 -5.5221 14.8400 -30.8731 -36.1967 24.3655 -0.0000 0.0001 82.5217 26.6930 -3.8235 -1.3411 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -3.8703 -3.4159 -10.0888 -12.1217 37.4261 -67.4494 -0.0000 -0.0001 -62.7660 27.6913 -8.1533 -4.2720 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -27.7556 -0.6498 3.3426 23.0012 0.0000 0.0000 0.6807 -0.4361 -3.2423 0.6693 0.0000 0.0000 4.3488 -2.7841 2.3961 11.7050 0.0000 0.0000 -22.2449 6.1765 -4.7868 -8.4058 0.0000 -0.0000\n -0.4889 60.2580 23.8200 3.3890 0.2088 2.5257 0.1660 1.6121 0.5220 -1.2888 -0.6489 0.0707 14.2225 -3.7558 5.5633 -2.8963 -1.9753 2.5569 -26.8124 3.8176 1.5918 -6.7424 3.7708 -0.3844 0.1167 -3.8210 15.3731 -33.1566 -38.6671 20.8877 -0.0000 0.0001 84.9858 27.6048 -4.6842 -1.8317 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -3.7349 -5.9395 -11.8407 -13.9084 37.0842 -65.8078 -0.0000 -0.0001 -61.5743 27.7142 -11.3682 -5.9455 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -27.6570 -2.6933 1.6398 24.8303 0.0000 0.0000 -1.5919 0.6767 -3.4070 1.0792 0.0000 0.0000 0.4752 -5.6438 1.8381 20.5223 0.0000 0.0000 -23.6578 6.3966 -5.2999 -8.3881 0.0000 -0.0000\n -0.8178 59.8304 23.8615 3.2786 1.1901 4.5893 0.6930 1.1987 0.2407 -1.0413 -0.4280 0.1225 13.9223 -3.5555 5.2199 -2.6758 -3.0638 2.7810 -27.0424 -0.6769 -1.2290 -8.4135 0.7833 0.1455 1.3884 -2.4799 14.9453 -35.9270 -41.6894 17.5459 -0.0000 0.0001 87.1570 28.5744 -5.4322 -2.2041 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -3.5110 -8.1565 -14.4508 -15.5565 35.7402 -64.7522 -0.0000 -0.0000 -59.2850 26.6557 -16.6515 -7.6147 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -26.2733 -3.7002 -0.4965 24.7200 0.0000 0.0000 -2.9858 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-37.1176 -46.9507 14.6073 -0.0000 0.0001 89.5617 31.2144 -10.2523 -3.9580 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -3.3405 -8.5803 -17.0598 -14.8566 31.7040 -66.5194 -0.0000 -0.0000 -52.8389 24.1752 -21.1690 -9.9719 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -22.0511 -4.4170 -4.4540 21.8462 0.0000 0.0000 -4.4936 1.2657 -0.6326 1.3007 0.0000 0.0000 -10.0999 -8.9686 -1.4272 39.9294 0.0000 0.0000 -20.6824 0.0061 -1.2711 -6.0682 0.0000 -0.0000\n -1.3783 58.5500 24.2122 2.6703 1.1383 4.6854 0.6501 1.0580 0.2134 -0.9938 -0.1947 0.3049 14.1372 -2.9613 6.7712 -1.6181 -5.1020 3.9249 -24.7615 -9.3761 -7.3236 -8.7417 -6.2139 -0.1348 2.2551 -0.8188 13.3473 -35.2296 -48.2329 16.5388 -0.0000 0.0001 88.7193 32.7409 -13.2483 -5.2825 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -3.4539 -7.9046 -17.0825 -13.7472 28.5788 -68.2292 -0.0000 -0.0000 -48.0483 24.8633 -19.7389 -9.4699 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -20.2045 -4.1462 -6.4343 20.9766 0.0000 0.0000 -5.2227 0.7265 2.5101 1.4391 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-0.0000 0.0000 3.1368 -5.4066 6.6285 21.6532 0.0000 0.0000 -18.1998 7.9197 -1.0272 0.4547 0.0000 0.0000 -24.2333 -16.4147 -1.7225 10.2785 0.0000 0.0000 -14.7334 13.5778 -11.2087 -10.2338 0.0000 -0.0000\n 0.8111 20.7141 24.1444 -6.7558 -0.3260 7.0916 1.4988 -4.2997 0.1189 -0.2853 1.2962 0.3688 13.2289 3.3864 2.7783 -0.0040 -0.6384 0.9434 -11.2986 -8.1241 -6.1577 6.7015 -12.9223 -3.6045 3.4360 -2.2707 10.5607 17.7847 -28.3715 62.0378 0.0000 0.0000 8.6075 -2.7715 -1.2230 3.3388 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -6.6931 1.6355 -14.9905 9.1835 15.1121 -80.6322 -0.0000 0.0000 1.8818 5.0305 0.1491 -4.9852 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 4.9485 -4.5972 7.5236 22.1754 0.0000 0.0000 -19.5826 7.6089 -0.6012 0.5506 0.0000 0.0000 -25.5047 -14.9993 -2.5750 9.7343 0.0000 0.0000 -15.1868 13.3923 -10.7962 -9.0431 0.0000 -0.0000\n 1.0656 20.1792 24.0942 -7.0699 -0.6289 5.5622 1.3343 -4.0963 0.0864 -0.3362 1.2432 0.3838 13.4721 3.7014 3.0015 0.1119 -0.4600 0.9315 -11.0398 -7.7422 -5.7569 7.0978 -12.8071 -3.2877 3.2892 -2.4945 10.5623 17.6082 -28.3688 61.9638 0.0000 0.0000 8.6378 -3.1271 -1.1562 3.5214 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -6.7603 1.7659 -14.9057 9.8202 15.4188 -80.5968 -0.0000 0.0000 2.2910 4.9775 0.4875 -5.0758 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 6.6457 -3.5416 8.0808 22.3419 0.0000 0.0000 -20.8348 7.3330 0.2820 0.4582 0.0000 0.0000 -26.3760 -13.6690 -3.6489 8.8994 0.0000 0.0000 -15.2564 13.6851 -10.0586 -8.6232 0.0000 -0.0000\n 1.3172 19.6238 24.0328 -7.2285 -0.7501 4.1503 1.0891 -3.9147 0.0391 -0.4053 1.2646 0.3975 13.6726 3.9471 2.9972 0.1976 -0.3104 0.8649 -10.8762 -7.4775 -5.3591 7.5074 -12.7611 -2.8807 3.0939 -2.7730 10.5519 17.6344 -28.1351 62.1459 0.0000 0.0000 8.6266 -2.9592 -1.0788 3.6689 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -6.9780 1.7591 -14.9208 10.7153 15.1344 -80.4505 -0.0000 0.0000 2.8823 4.9641 1.4693 -5.0858 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 8.2228 -2.5968 8.6187 22.3950 0.0000 0.0000 -21.9457 6.9668 0.9529 0.2338 0.0000 0.0000 -27.0059 -12.5602 -4.5334 8.3124 0.0000 0.0000 -15.5740 14.5026 -9.3112 -8.7891 0.0000 -0.0000\n 1.5500 19.0699 23.9566 -7.0917 -0.4724 2.8689 0.8287 -3.8064 -0.0196 -0.5141 1.2336 0.3935 13.7901 4.0568 2.8258 0.2035 -0.1645 0.7634 -10.9635 -7.2266 -5.0043 7.8204 -12.6897 -2.4712 3.0245 -3.0275 10.6361 17.3557 -28.0708 62.1347 0.0000 0.0000 8.8728 -2.9825 -1.1143 3.6816 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -7.1230 1.7747 -14.9818 11.4014 14.5348 -80.1721 -0.0000 0.0000 3.3767 5.2189 2.6353 -4.9850 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 9.3504 -2.0686 9.2962 22.5979 0.0000 0.0000 -22.8319 6.3623 1.3788 0.0955 0.0000 0.0000 -27.7046 -11.8222 -4.5451 8.4537 0.0000 0.0000 -16.8372 14.8734 -9.1755 -8.7150 0.0000 -0.0000\n 1.7677 18.5170 23.8815 -6.4997 0.0851 1.7045 0.4208 -3.6472 -0.1356 -0.7394 1.1158 0.3760 13.9384 4.0881 2.5826 0.1039 -0.0406 0.6531 -11.3276 -6.9880 -4.7062 7.9835 -12.5382 -2.1252 3.0163 -3.6910 10.8191 16.7004 -27.8960 61.6701 0.0000 0.0000 9.2116 -3.7359 -1.3023 3.6477 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -7.0715 2.0722 -15.0119 11.7295 13.8673 -79.8332 -0.0000 0.0000 3.6543 5.5173 3.3805 -4.9125 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 10.0066 -1.7585 9.7131 22.6961 0.0000 0.0000 -23.3865 5.6112 2.1152 -0.1602 0.0000 -0.0000 -28.4489 -11.3027 -4.0897 8.8269 0.0000 0.0000 -18.2624 14.2782 -9.0910 -8.8052 0.0000 -0.0000\n 1.9908 17.9294 23.8227 -5.6067 0.6754 0.9100 -0.2007 -3.4320 -0.3191 -1.0231 1.0905 0.3648 14.1925 4.0891 2.1556 -0.0993 0.0219 0.5038 -11.8935 -6.7167 -4.4977 8.1959 -12.3788 -1.8661 2.9382 -4.9112 10.9459 16.1064 -27.2185 60.9844 0.0000 0.0000 9.2552 -4.4428 -1.5825 3.6648 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -6.9138 2.6628 -15.0736 11.9836 13.1083 -79.5416 -0.0000 0.0000 3.7802 5.5698 3.7193 -4.9591 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 10.6942 -1.3879 9.4402 22.2443 0.0000 0.0000 -23.4568 4.8513 3.2537 -0.9452 0.0000 -0.0000 -28.8337 -10.6514 -3.6338 8.9024 0.0000 0.0000 -18.9844 13.0370 -8.2376 -9.3862 0.0000 -0.0000\n 2.2224 17.2788 23.7696 -4.7013 1.3202 0.4665 -0.8129 -3.2631 -0.5129 -1.2808 1.2013 0.3682 14.4304 4.0454 1.5888 -0.3689 0.0716 0.3241 -12.7384 -6.2677 -4.3633 8.6423 -12.2169 -1.6819 2.8004 -6.0488 11.0376 15.6519 -26.7923 60.1240 0.0000 0.0000 9.0700 -4.1960 -1.8453 3.7827 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -6.7660 3.2583 -15.2921 12.3182 12.4707 -79.3514 -0.0000 0.0000 3.8938 5.5522 4.0946 -5.0679 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 11.7966 -1.0950 8.8595 21.2761 0.0000 0.0000 -23.0130 4.2162 4.2729 -2.0685 0.0000 -0.0000 -28.3443 -9.8511 -2.8103 8.7562 0.0000 0.0000 -19.8596 11.1701 -6.8271 -9.6958 0.0000 -0.0000\n 2.4366 16.5935 23.6958 -3.3101 2.2749 -0.1396 -1.6027 -3.1149 -0.6922 -1.6611 1.1671 0.3581 14.5996 3.9995 1.2307 -0.7206 0.1772 0.1913 -14.1433 -5.6113 -4.1854 9.1175 -11.9238 -1.5254 2.6650 -6.8243 11.3733 14.8504 -27.3066 58.6051 0.0000 0.0000 9.0273 -3.7921 -2.0560 4.0026 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -6.6504 3.9019 -15.6403 12.5708 11.8841 -79.2104 -0.0000 0.0000 4.0452 5.9568 4.6739 -5.2090 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 12.4295 -1.2396 8.7442 20.4233 0.0000 0.0000 -22.3716 3.8814 4.8533 -3.0012 0.0000 -0.0000 -27.4098 -9.3998 -1.6569 8.2802 0.0000 0.0000 -21.5956 8.2568 -5.3936 -9.3416 0.0000 -0.0000\n 2.6360 15.8973 23.6146 -1.1890 2.9775 -0.6732 -2.6888 -2.3519 -0.9152 -2.3374 0.9425 0.3366 14.9607 4.1525 0.9491 -1.2615 0.2635 0.0654 -16.3015 -5.0291 -4.0272 9.2168 -11.4706 -1.4151 2.5534 -8.3379 12.0574 13.2600 -27.8188 55.7464 0.0000 0.0000 9.3791 -4.5272 -2.2597 4.1647 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -6.4775 5.0121 -16.1199 12.6407 10.4980 -79.0302 -0.0000 0.0000 4.1406 6.7684 5.1960 -5.3979 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 12.2510 -1.2226 8.3686 19.7480 0.0000 0.0000 -21.7342 3.6011 5.4347 -3.8611 0.0000 -0.0000 -26.4914 -8.9608 -1.3906 6.5573 0.0000 0.0000 -22.0979 5.2923 -3.4953 -9.0033 0.0000 -0.0000\n 2.8604 15.1698 23.5725 0.9285 2.3390 0.3784 -3.5825 -0.4337 -1.1565 -3.0748 1.0850 0.3785 15.7235 4.8521 0.0115 -2.0767 0.2040 -0.2634 -19.1555 -4.6525 -4.3381 8.7895 -10.9585 -1.6612 2.7382 -11.4644 12.7932 10.6743 -27.3763 51.1582 0.0000 0.0000 10.3546 -5.9746 -2.0985 4.1188 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -6.1012 6.4445 -17.0462 12.6092 8.2788 -78.9617 -0.0000 0.0000 4.0341 7.4015 5.2086 -5.5357 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 12.3669 -0.0316 6.1134 18.7856 0.0000 0.0000 -20.7294 2.8746 6.2924 -5.1643 0.0000 -0.0000 -25.4760 -7.3173 -2.7480 3.1619 0.0000 0.0000 -18.6248 5.3606 -0.8167 -9.3277 0.0000 -0.0000\n 3.1258 14.3899 23.5899 2.1085 0.4283 3.5721 -3.2911 1.6536 -1.2575 -3.1399 1.8955 0.5240 16.2414 6.5516 -1.4625 -3.0298 0.2641 -0.8141 -22.2537 -3.7260 -5.3758 7.8804 -10.1603 -2.7913 3.6369 -14.7553 13.2976 6.8712 -27.4642 44.7034 0.0000 0.0000 12.2299 -6.9847 -0.9231 4.0475 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -5.1951 7.2117 -18.6577 12.4099 7.0675 -79.5563 -0.0000 0.0000 3.5895 7.6436 4.1314 -5.6198 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 13.8348 1.9826 2.4161 17.8292 0.0000 0.0000 -18.9067 1.9631 6.8078 -6.7369 0.0000 -0.0000 -23.9981 -4.2867 -5.2556 -0.5187 0.0000 -0.0000 -12.4442 9.5454 1.7433 -10.1698 0.0000 -0.0000\n 3.3770 13.6261 23.6712 3.0956 -1.1040 6.8070 -2.1846 2.2955 -1.1075 -2.4938 2.4223 0.6314 15.8975 9.0814 -1.9242 -3.8429 1.1256 -1.2296 -25.0949 -0.9499 -6.2889 6.3671 -8.4333 -4.9783 5.0700 -16.1871 13.6612 1.7629 -29.6611 36.3924 0.0000 0.0000 14.6805 -7.9972 0.9445 4.1710 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -3.5396 7.3200 -20.2840 11.9885 7.6528 -81.1636 -0.0000 0.0000 2.6298 7.6910 1.7476 -5.9850 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 14.5308 3.2333 0.1126 18.6057 0.0000 0.0000 -16.3576 1.6304 6.3371 -8.0845 0.0000 -0.0000 -23.2152 -1.5551 -7.5495 -2.0817 0.0000 -0.0000 -7.6970 13.4715 2.7269 -11.1351 0.0000 -0.0000\n 3.5506 12.9208 23.8341 4.6425 -1.1566 7.7532 -1.3151 1.6351 -0.7755 -1.9636 1.8298 0.6262 14.9533 11.7818 -0.3422 -4.3668 2.6887 -1.1738 -27.2742 3.8022 -5.7215 4.1674 -5.3560 -7.4683 6.3233 -16.0361 14.0022 -3.3875 -32.4219 27.7079 0.0000 0.0000 16.4264 -9.9720 1.9160 4.1508 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -1.5753 7.7296 -20.6916 12.0134 8.5032 -83.2950 -0.0000 0.0000 1.3146 7.0849 -1.1083 -6.6724 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 12.2836 3.4286 0.3656 22.0189 0.0000 0.0000 -12.9726 1.1241 5.7133 -9.4455 0.0000 -0.0000 -23.3975 -0.5939 -8.2713 -1.8896 0.0000 -0.0000 -5.8900 13.1963 1.9229 -12.3737 0.0000 -0.0000\n 3.6627 12.2557 24.0534 6.6332 -0.1403 6.5082 -1.0341 0.6239 -0.4056 -1.7798 0.7342 0.5969 13.6217 14.1815 2.2255 -4.6065 4.0558 -0.8768 -28.4273 8.9118 -3.5504 1.5400 -1.4342 -8.9269 6.7662 -15.8685 14.0459 -6.5114 -33.9565 21.5359 0.0000 0.0000 16.4122 -11.8917 1.0067 3.1897 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.2016 8.2401 -19.4257 13.2434 8.4304 -85.1660 -0.0000 0.0000 0.9369 4.9754 -3.1796 -7.0267 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 7.6499 3.2370 1.5274 27.2305 0.0000 0.0000 -8.0476 -1.7144 6.4053 -10.6001 0.0000 -0.0000 -23.9313 -1.3649 -8.2755 -1.4888 0.0000 -0.0000 -5.8063 10.4881 1.3683 -13.4038 0.0000 -0.0000\n 3.7850 11.6112 24.2642 8.3408 0.7803 4.9369 -0.9832 0.1196 -0.1042 -1.7085 0.2814 0.6153 12.3657 15.5038 3.8828 -4.6156 4.7359 -0.7515 -28.6142 12.9491 -0.7455 -0.8562 2.0905 -8.8263 6.3272 -15.9265 13.6551 -8.3917 -34.8043 18.0246 0.0000 0.0000 15.3359 -12.7638 -1.2069 1.2885 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0035 7.8600 -17.3807 15.5338 7.9362 -86.2137 -0.0000 0.0000 2.7812 2.1939 -3.8306 -6.4824 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 2.6156 3.5991 1.7265 33.0891 0.0000 0.0000 -1.7359 -7.2106 8.8963 -9.6208 0.0000 -0.0000 -24.2173 -2.4221 -8.2424 -1.1061 0.0000 -0.0000 -5.7584 9.2619 1.6970 -13.8404 0.0000 -0.0000\n 3.9504 10.9929 24.4175 9.2852 0.9858 3.9002 -0.9236 0.5226 0.1045 -1.6308 0.6441 0.6806 11.7235 15.3739 4.2268 -4.4392 4.9520 -0.8292 -28.2594 15.8637 1.9467 -2.1103 4.5625 -7.8434 5.6315 -15.5031 13.1034 -11.8333 -35.6842 14.7378 0.0000 0.0000 14.7429 -14.3846 -3.0691 -0.5609 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.6475 6.5915 -15.7192 18.0652 7.8394 -86.1589 -0.0000 0.0000 6.0861 1.2785 -3.0206 -5.7318 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -1.7800 5.0490 0.8165 39.0591 0.0000 0.0000 3.3380 -12.5346 11.7811 -6.5577 0.0000 -0.0000 -23.9982 -2.6675 -7.5884 -0.3342 0.0000 -0.0000 -5.4998 9.7175 1.6411 -13.9953 0.0000 -0.0000\n 4.1244 10.4116 24.5286 9.6088 0.8543 2.8167 -0.8821 1.4863 0.2266 -1.5840 0.9817 0.7093 11.6635 14.5493 4.3598 -4.1628 5.0647 -0.8698 -27.6338 18.1099 4.2312 -1.9921 6.3871 -6.7939 5.2310 -14.7172 12.6861 -17.4230 -35.8210 10.4005 0.0000 0.0000 15.5537 -17.9465 -3.2446 -1.6099 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -1.1573 5.4736 -14.4288 19.9268 7.5204 -85.2205 -0.0000 0.0000 8.5842 2.9606 -1.8847 -6.0299 0.0000 -0.0000 0.0000 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6.8285 -0.3047 9.3789 -5.2023 5.0765 -13.7151 11.8535 -25.5368 -31.3469 4.4981 0.0000 0.0000 20.2488 -17.0136 0.3458 -1.0518 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.6737 5.1721 -10.9296 21.3479 5.6249 -83.6536 -0.0000 0.0000 8.6574 3.1944 -3.1208 -7.9668 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -12.3372 9.7098 -5.0432 49.6365 0.0000 0.0000 -3.8184 -12.2178 3.0198 -4.7264 0.0000 -0.0000 -20.5688 -3.0263 -4.5839 1.7227 0.0000 0.0000 -5.1066 7.9302 -0.3000 -14.5124 0.0000 -0.0000\n 4.5118 8.8107 24.8743 8.4463 2.2185 0.6527 -0.6987 3.0516 0.2147 -1.3803 0.7056 0.6011 12.9250 11.8967 5.1124 -2.8232 5.1430 -0.7291 -22.8645 21.7564 7.0075 0.7869 10.0670 -4.5953 5.0808 -12.9827 11.2562 -26.9602 -28.0129 3.9217 0.0000 0.0000 23.0462 -10.9097 2.7908 -0.3088 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.5833 5.0443 -9.3645 21.6439 7.0033 -83.7898 -0.0000 0.0000 8.4219 0.8031 -4.2825 -8.1979 -0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -14.3807 10.7730 -8.8853 48.0267 0.0000 0.0000 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4.6542 5.3144 25.2042 3.2483 5.9096 2.7234 -0.3259 5.0960 -0.1162 -1.2234 0.0106 0.2736 15.5729 5.2308 3.6147 0.4024 2.9567 -0.3616 -7.8532 16.1919 0.8218 10.4015 8.5591 -4.8523 0.9093 -7.1821 7.3771 -35.2120 -15.4742 10.7545 -0.0000 0.0000 41.4079 10.3745 11.8566 1.2292 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.2686 3.9600 -5.7376 26.1352 13.1930 -88.4974 -0.0000 0.0000 7.9626 2.4331 -3.9241 -7.1139 0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 -22.3859 12.5416 -17.9568 6.7406 0.0000 0.0000 5.6611 -5.0520 5.3221 -0.9475 0.0000 -0.0000 -7.9296 -6.9144 4.3784 5.9980 0.0000 0.0000 -4.4423 2.4365 -12.4091 -8.9958 0.0000 -0.0000\n 4.6209 4.7538 25.2015 1.9034 5.3847 4.0062 0.0657 5.5294 -0.1394 -0.9602 -0.0565 0.2425 15.6242 4.8767 3.4468 0.8534 2.7556 -0.3239 -5.0614 14.5546 -1.0160 11.4138 7.4011 -6.1123 0.4274 -6.4400 6.6694 -34.7636 -15.6052 12.7194 -0.0000 0.0000 42.7598 11.8424 12.2958 1.3291 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.5758 3.9392 -5.2050 27.4604 13.3080 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0.0890 0.2220 16.2597 3.9355 2.9312 1.7794 1.9308 -0.1144 -0.2165 9.7801 -4.5518 13.1520 4.9991 -9.0788 -0.2194 -5.1553 4.9518 -32.0858 -16.1896 17.6050 -0.0000 0.0000 43.3962 13.7549 12.8578 1.1494 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -1.0671 3.6048 -4.5867 29.8141 12.9254 -92.2532 -0.0000 0.0000 7.3308 3.0142 -2.6499 -6.7534 -0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -26.3587 16.7267 -13.8174 9.2076 0.0000 0.0000 7.1740 -0.6616 3.3673 -1.1208 0.0000 -0.0000 1.1257 -6.5164 2.1782 4.4293 0.0000 0.0000 -7.0949 4.0134 -13.5608 -8.6208 0.0000 -0.0000\n 4.3593 2.9067 25.1252 -1.9471 4.3717 9.2135 0.4247 5.9956 -0.0305 -0.6939 0.1855 0.2276 16.9644 3.4105 2.6561 2.1818 1.4005 0.0443 1.2938 7.1164 -5.8618 13.8509 3.8138 -10.3601 -0.4106 -5.0800 4.2394 -30.4617 -16.1742 20.1072 -0.0000 0.0000 43.1684 14.8511 12.9136 0.9807 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -1.2111 3.7986 -4.5588 30.5271 12.3616 -92.7122 -0.0000 0.0000 6.9993 2.7300 -2.2772 -6.8472 -0.0000 0.0000 0.0000 0.0000 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1.1627 -12.1217 -0.5497 -4.8242 3.6996 -26.4986 -16.0155 24.1318 -0.0000 0.0000 42.2381 16.2038 12.3640 1.0089 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -1.4848 4.1994 -4.8762 31.1836 12.6192 -92.3692 -0.0000 0.0000 6.8118 2.3556 -0.9408 -7.2710 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -27.0755 16.8648 -6.2317 7.8768 0.0000 0.0000 7.0997 -0.5803 5.0793 -1.0802 0.0000 -0.0000 4.8563 -8.9879 3.5995 8.0227 0.0000 0.0000 -12.1516 5.8795 -18.3427 -7.8509 0.0000 -0.0000\n 3.8713 0.9128 24.8630 -2.4997 3.6602 12.6272 -1.1732 5.7904 0.1766 -1.3269 0.0317 0.2155 17.9303 2.0855 2.7063 2.9162 0.0580 0.6064 2.5138 0.4457 -8.0749 15.0223 -0.1232 -12.6749 -0.3407 -5.0053 3.5682 -24.1491 -15.2760 26.1992 -0.0000 0.0000 41.3523 15.9091 11.5503 1.3672 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -1.6427 4.0250 -5.3651 30.8933 12.5846 -91.4669 -0.0000 0.0000 6.7990 2.5877 -0.2068 -7.3919 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -27.0539 16.6849 -6.9931 7.0539 0.0000 0.0000 7.0890 -0.9451 6.4337 -1.2160 0.0000 -0.0000 5.4248 -9.4257 4.1672 9.4876 0.0000 0.0000 -13.8505 6.6895 -19.6141 -7.6526 0.0000 -0.0000\n 3.6799 0.2117 24.7138 -1.9944 2.7740 13.2396 -1.7641 5.7756 0.3147 -1.6196 0.2012 0.2570 18.0561 1.9985 2.7482 2.8880 -0.2880 0.7383 1.8443 -1.3012 -8.5715 15.1898 -1.4479 -13.0596 0.1633 -5.6878 3.6700 -22.1170 -13.9443 28.1916 -0.0000 0.0000 40.1612 15.5264 10.6079 1.7435 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -1.8777 4.0321 -5.8623 30.1492 12.1919 -90.0635 -0.0000 0.0000 6.8062 2.7791 0.2365 -7.2645 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -27.0796 16.4086 -7.7591 6.5695 0.0000 0.0000 7.1426 -1.5032 6.1644 -1.6433 0.0000 -0.0000 6.2013 -9.5095 4.6055 10.3859 0.0000 0.0000 -15.2304 7.6241 -20.5093 -7.5525 0.0000 -0.0000\n 3.4747 -0.5508 24.5419 -1.4487 1.6504 13.5985 -2.3506 5.8407 0.4623 -1.8742 0.2575 0.2871 18.0280 2.0841 2.8151 2.8086 -0.5250 0.8456 0.8811 -2.8257 -9.0177 15.4205 -2.8677 -13.2359 0.7470 -6.4386 3.9471 -20.2736 -12.9280 29.9445 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7.5412 2.8256 -22.6528 -1.9284 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 2.7827 10.7344 -10.4978 41.5520 16.7680 -63.0285 -0.0000 0.0000 7.6465 13.8229 18.8013 -3.5994 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -9.5180 -21.0332 -13.6577 -0.2950 0.0000 -0.0000 8.8306 10.0866 -4.6780 0.7949 0.0000 0.0000 2.9901 -27.6001 4.9730 54.0631 0.0000 0.0000 -0.9396 6.6877 -20.4900 -11.8485 0.0000 -0.0000\n 1.6385 -87.0543 23.6079 -2.9757 15.7326 6.3360 -1.3174 -6.3009 0.0631 -2.1042 -2.6500 -0.4946 14.7431 -4.0798 0.3926 -1.8617 0.9969 -0.1795 -20.6227 11.5682 7.0940 3.0699 3.7507 -0.3264 7.6834 -15.1402 10.5407 28.8578 -43.9598 39.9994 0.0000 0.0000 9.0278 2.1827 -26.3187 1.8086 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 3.3130 8.3895 -11.4198 40.5298 13.9117 -62.9232 -0.0000 0.0000 5.6674 11.9389 21.7213 -4.7537 0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -5.9277 -21.5737 -14.7762 -1.1213 0.0000 -0.0000 6.9343 10.8038 -6.5975 0.8674 0.0000 0.0000 -7.6752 -27.3752 -5.5257 66.7721 0.0000 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-0.0000 -0.0000 0.0000 -0.0000 -0.0000 1.8618 13.1554 -10.3810 30.9391 12.9347 -60.7890 -0.0000 0.0000 2.5243 2.5188 7.2481 -5.0384 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -1.5646 -22.8034 -12.4387 -0.5063 0.0000 -0.0000 2.7538 10.3858 -7.2610 0.9256 0.0000 0.0000 -23.2720 -24.4173 -11.7150 79.9299 0.0000 0.0000 -2.5713 2.1625 -13.7494 -7.9727 0.0000 -0.0000\n 1.6253 -89.1584 23.7783 -5.3707 14.8040 4.7263 0.2854 -8.4768 0.4281 -0.9329 -4.7320 -0.3568 15.1307 0.8444 6.7200 -1.6288 1.9023 1.3149 -15.5847 9.1160 2.0155 6.7723 2.5164 -3.8661 6.2756 -15.7948 9.2079 21.3028 -35.4224 35.3772 0.0000 0.0000 9.8854 0.9510 -9.8805 -2.3228 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 1.8674 13.1325 -9.6002 26.2510 14.5073 -61.7864 -0.0000 0.0000 0.7424 0.1226 -2.2343 -6.9648 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 2.4905 -21.4054 -11.9331 0.4546 0.0000 0.0000 0.3356 10.1208 -5.1048 0.5512 0.0000 0.0000 -25.0551 -21.2147 -10.2978 78.6954 0.0000 0.0000 -4.7827 1.2357 -8.8608 -9.3098 0.0000 -0.0000\n 1.7808 -89.9432 23.8777 -7.2914 13.0184 3.9889 0.8672 -8.1734 0.2993 -0.5754 -4.5632 -0.2975 16.0078 1.5433 7.2638 -1.1367 1.9739 1.4263 -13.8036 9.0837 1.7599 8.6752 2.0790 -3.7641 5.1184 -14.8048 9.2514 18.1648 -31.9204 35.5194 0.0000 0.0000 11.6831 2.2239 -1.0185 -2.5557 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 1.4347 12.7884 -9.2304 23.1306 13.9945 -62.2707 -0.0000 -0.0000 -1.0551 -1.8454 -6.4542 -7.2468 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 6.3356 -19.2833 -11.1704 1.5574 0.0000 0.0000 -2.2083 9.5581 -4.1883 0.1190 0.0000 0.0000 -25.4872 -17.5159 -9.0417 73.2130 0.0000 0.0000 -5.9098 1.3560 -4.0229 -10.0795 0.0000 -0.0000\n 1.9128 -90.5372 23.9067 -7.6663 12.1354 3.5616 0.4736 -7.9223 0.0786 -0.7079 -4.5282 -0.3364 16.6138 1.5392 7.0099 -0.7474 2.0027 1.3025 -12.3024 9.2076 1.8752 10.1362 1.4977 -3.1585 3.6145 -14.2721 9.2407 12.8109 -29.9112 34.4326 0.0000 0.0000 15.1344 -2.2745 4.0976 -1.3962 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0099 12.9520 -9.0959 20.8325 12.6204 -61.8383 -0.0000 -0.0000 -2.4094 -1.3846 -3.9806 -5.5388 -0.0000 -0.0000 0.0000 0.0000 -0.0000 0.0000 7.8042 -17.9122 -10.2617 3.4240 0.0000 0.0000 -4.9132 8.7153 -4.3884 -0.0354 0.0000 -0.0000 -27.0115 -15.4333 -7.9220 64.6040 0.0000 0.0000 -7.1562 1.3277 -0.8374 -9.2837 0.0000 -0.0000\n 1.9965 -91.1668 23.8613 -6.7615 12.2042 3.3633 -0.3832 -7.8439 -0.1140 -1.0687 -4.4998 -0.4188 16.3991 0.8937 6.3670 -0.4912 2.0074 1.1124 -11.1257 9.1922 2.1334 11.3246 1.0428 -2.3587 2.1416 -13.4357 9.1454 7.0000 -29.6526 33.2485 0.0000 0.0000 19.9005 -6.2442 5.6095 0.7308 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -1.5944 12.4525 -9.0377 18.6517 12.4347 -61.4284 -0.0000 -0.0000 -3.3432 0.4923 0.7740 -4.3607 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 7.7749 -17.5982 -10.1135 5.2295 0.0000 0.0000 -7.3592 7.9745 -4.2620 -0.1538 0.0000 -0.0000 -29.0269 -15.2964 -6.0882 53.3320 0.0000 0.0000 -7.2845 0.6276 -0.4657 -7.7958 0.0000 -0.0000\n 2.0618 -91.9382 23.7880 -5.6333 12.7096 3.6207 -1.3727 -7.9009 -0.3296 -1.4820 -4.2435 -0.4966 15.9653 -0.1048 5.4164 -0.2530 1.8862 0.9025 -10.3447 9.0631 2.3699 12.2933 0.8165 -1.6393 1.1033 -12.7014 9.2326 2.1816 -29.2450 32.6476 0.0000 0.0000 24.9252 -5.1599 5.8937 2.7526 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -2.6172 11.4849 -9.1677 16.5453 12.5587 -61.2671 -0.0000 -0.0000 -4.0464 1.5877 3.1316 -4.4664 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -0.0000 8.1762 -17.9264 -10.5284 5.6844 0.0000 0.0000 -9.2145 7.4052 -3.7199 -0.6106 0.0000 -0.0000 -29.9685 -15.9637 -3.3227 39.7225 0.0000 0.0000 -4.9915 -0.1685 -3.7236 -6.7876 0.0000 -0.0000\n 2.1726 -92.6896 23.7019 -4.4090 13.1377 3.9212 -2.9138 -7.8380 -0.7782 -2.2753 -4.0767 -0.6110 16.5174 -1.0687 4.5179 0.0124 1.8038 0.6937 -10.1405 9.1934 2.6093 12.9511 0.6529 -1.1254 0.0726 -12.6671 9.6513 -1.0651 -27.4291 32.8992 0.0000 0.0000 28.9228 -0.8280 6.3631 3.7361 0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -3.5428 11.8669 -9.4103 14.7115 11.3218 -60.7673 -0.0000 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-4.0568 -0.8384 18.7269 -1.8132 4.0037 0.2336 2.2729 0.3742 -10.6598 9.8894 3.1570 14.0198 -0.0052 -0.5146 -2.8863 -12.4961 10.7270 -3.3234 -24.1086 34.8974 0.0000 0.0000 32.6327 3.7867 6.5090 2.7067 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -5.9834 15.0137 -9.2124 11.6279 8.8752 -59.6604 -0.0000 -0.0000 -6.1099 -0.6731 1.7625 -4.3598 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 14.0796 -15.9171 -10.8269 2.4877 0.0000 0.0000 -14.2925 3.6656 -1.0317 -0.7373 0.0000 -0.0000 -27.4358 -13.6658 -7.1481 1.2500 0.0000 0.0000 8.9541 6.3351 -6.4810 -8.9662 0.0000 -0.0000\n 2.8315 -95.4686 23.3012 -2.5097 11.3518 1.4079 -6.0356 -6.6785 -1.5882 -3.9173 -4.0755 -0.8201 18.2412 -1.2639 4.3992 0.0129 2.3665 0.3690 -10.6651 9.8195 3.1567 14.7932 -0.2402 -0.4624 -3.9070 -12.3047 11.1014 -3.7926 -23.5318 35.6395 0.0000 0.0000 32.8457 3.2090 5.1936 2.4924 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -6.7759 15.6286 -8.9853 10.5881 8.9763 -59.8942 -0.0000 -0.0000 -6.8511 -1.2947 1.2749 -4.5109 0.0000 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0.0000 19.2961 -0.0918 -4.8970 -1.6215 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -4.5008 17.3515 -14.0425 6.7565 7.7335 -49.3263 -0.0000 -0.0000 -3.5685 -4.4332 3.3441 -3.7201 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -18.1557 16.2946 2.5488 64.1377 0.0000 0.0000 -1.3504 -1.5204 -3.4423 -2.7947 0.0000 -0.0000 -13.6592 4.4384 7.9785 -4.1421 0.0000 -0.0000 9.4816 -1.4770 14.6320 -6.9864 0.0000 -0.0000\n 5.8944 -104.3130 23.9717 1.2487 -4.5054 -13.4927 0.4074 2.6457 -0.3109 -0.0144 -3.4705 -0.2084 8.4033 5.5765 8.8879 -7.0950 4.1358 0.3645 -32.8348 10.5515 9.6115 -1.0212 2.8342 0.0550 -4.7170 -13.2660 16.6312 2.2641 -22.5547 48.7436 0.0000 0.0000 21.0284 -2.2301 -3.9969 -0.6938 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -3.8813 17.3352 -13.3445 6.2387 10.0659 -49.7194 -0.0000 -0.0000 -3.2693 -5.3774 1.5292 -3.0804 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -17.8728 15.8523 0.1112 62.5626 0.0000 0.0000 -5.4892 -0.7927 -5.4421 -3.0658 0.0000 -0.0000 -11.2299 4.8420 7.2033 -3.3559 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-6.7981 10.3606 2.3626 -23.3335 64.8746 0.0000 0.0000 17.2988 5.0156 1.6300 -2.7379 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -6.8844 -0.4558 -17.5029 4.2355 9.7160 -58.2023 -0.0000 -0.0000 -1.7193 -1.2394 2.7620 -7.0172 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 -15.6558 5.0554 -6.2133 16.9192 0.0000 0.0000 -8.1808 4.4014 5.7119 3.3748 0.0000 0.0000 -24.3006 -5.7293 6.8612 21.4806 0.0000 0.0000 -0.3494 10.6350 -16.9256 -8.0527 0.0000 -0.0000\n -0.2866 -113.2515 24.5090 -5.2842 5.7032 172.2359 2.4259 -1.5428 -0.0011 0.0271 0.5416 0.1071 10.1117 -4.7972 4.1107 -2.3740 -5.5168 3.3362 -18.0984 -16.8303 -5.3887 -0.0252 -7.1848 0.4151 -6.7278 -6.7150 10.3875 2.5489 -23.7940 64.8187 0.0000 0.0000 16.8309 4.6580 1.3368 -2.5586 0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -7.2488 -1.1605 -17.7839 3.0739 9.9366 -58.8729 -0.0000 -0.0000 -2.1254 -2.2554 2.4738 -7.1481 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -15.7374 5.3827 -6.9452 16.8385 0.0000 0.0000 -7.9452 3.9035 5.9329 3.0336 0.0000 0.0000 -24.3166 -5.2238 7.1243 20.4661 0.0000 0.0000 -0.4316 10.8342 -15.9923 -7.9684 0.0000 -0.0000\n -0.3615 -113.2502 24.5123 -5.4158 5.7336 172.6257 2.6491 -1.3957 -0.0368 0.1386 0.5951 0.0970 9.7802 -5.1354 4.0181 -2.5424 -5.7032 3.4205 -18.1264 -17.1043 -5.6130 -0.0968 -7.3063 0.2810 -6.5624 -6.6417 10.4975 2.7728 -23.7460 64.8386 0.0000 0.0000 16.2798 4.6508 0.7391 -2.3613 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -7.6009 -1.7164 -17.9262 2.3245 9.8861 -59.6951 -0.0000 -0.0000 -2.4082 -2.4817 2.4036 -7.2972 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -15.8931 5.7076 -7.1956 16.9505 0.0000 0.0000 -7.8001 3.4034 5.8189 2.7293 0.0000 0.0000 -24.3108 -4.7013 7.0830 19.8481 0.0000 0.0000 -0.5675 10.7659 -15.2203 -8.0341 0.0000 -0.0000\n -0.4442 -113.2380 24.5131 -5.5032 5.6135 172.9739 2.7755 -1.3334 -0.0568 0.2085 0.5776 0.0659 9.6053 -5.3996 3.9098 -2.6300 -5.8355 3.4659 -18.1125 -17.3228 -5.7531 -0.1684 -7.4078 0.1459 -6.3862 -6.6495 10.6653 2.9382 -23.6144 64.9031 0.0000 0.0000 15.5709 4.8274 0.0578 -2.1691 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -7.9351 -2.1871 -18.0298 1.8640 9.6221 -60.5245 -0.0000 -0.0000 -2.6451 -2.0370 2.5365 -7.3937 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -15.9233 5.8870 -7.3957 16.9963 0.0000 0.0000 -7.7096 2.9681 5.6766 2.5465 0.0000 0.0000 -24.3024 -4.3901 6.8362 19.6779 0.0000 0.0000 -0.5123 10.6025 -15.0206 -8.0608 0.0000 -0.0000\n -0.5272 -113.2217 24.5086 -5.5756 5.2818 173.2059 2.8566 -1.3743 -0.0714 0.2630 0.5696 0.0592 9.4977 -5.6889 3.8082 -2.6461 -5.9716 3.5220 -18.1139 -17.5446 -5.8352 -0.1272 -7.4854 0.0545 -6.3770 -6.5943 10.8615 3.1109 -23.9154 64.8499 0.0000 0.0000 14.9388 4.7670 -0.3710 -2.1774 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -8.2410 -2.7114 -18.1118 1.5326 9.5784 -61.3038 -0.0000 -0.0000 -2.8457 -1.6141 2.7668 -7.3777 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -15.8091 5.8612 -7.8653 16.8427 0.0000 0.0000 -7.5665 2.5823 5.8532 2.4842 0.0000 0.0000 -24.3348 -4.4402 6.7428 19.9935 0.0000 0.0000 -0.1943 10.6357 -15.3718 -8.0846 0.0000 -0.0000\n -0.6210 -113.2160 24.4942 -5.6918 4.7710 173.2006 3.0375 -1.3972 -0.0641 0.3508 0.5567 0.0498 9.2923 -5.9754 3.8234 -2.6853 -6.2256 3.6663 -18.2022 -17.8631 -5.9027 0.0040 -7.4715 0.0425 -6.5182 -6.5179 10.9905 3.3124 -24.4449 64.6949 0.0000 0.0000 14.4251 4.5974 -0.5108 -2.4134 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -8.5140 -3.2532 -18.1812 1.2513 9.9919 -62.0961 -0.0000 -0.0000 -2.9812 -1.4894 2.9358 -7.3344 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -15.7506 5.6321 -8.3196 16.6225 0.0000 0.0000 -7.3817 2.2044 6.2132 2.4889 0.0000 0.0000 -24.6283 -4.8400 6.8939 21.0763 0.0000 0.0000 0.1618 10.9584 -15.7593 -8.3452 0.0000 -0.0000\n -0.7363 -113.2214 24.4698 -5.7661 4.1148 172.9605 3.4120 -1.3947 -0.0193 0.5325 0.5887 0.0610 8.9811 -6.2993 3.9836 -2.7911 -6.5942 3.9179 -18.4075 -18.3297 -5.9883 0.0363 -7.3314 0.1088 -6.7388 -6.5153 11.0759 3.3897 -24.9418 64.4041 0.0000 0.0000 13.8670 4.8841 -0.3620 -2.6552 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -8.7841 -3.7569 -18.2509 1.0255 10.7910 -62.9842 -0.0000 -0.0000 -3.0946 -1.5014 3.0016 -7.3397 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -15.7199 5.2191 -8.3437 16.4321 0.0000 0.0000 -7.2272 1.8333 6.3626 2.5028 0.0000 0.0000 -25.2693 -5.4904 7.0926 23.2082 0.0000 0.0000 0.3463 11.2713 -15.6923 -8.7232 0.0000 -0.0000\n -0.8691 -113.2246 24.4368 -5.6905 3.2484 172.5958 3.9226 -1.5055 0.0542 0.7869 0.5808 0.0704 8.7267 -6.6149 4.2730 -2.9042 -6.9980 4.2326 -18.6648 -18.9224 -6.0631 -0.1041 -7.1520 0.2404 -6.8767 -6.6474 11.1859 3.1512 -25.1967 63.9708 0.0000 0.0000 13.2452 5.4540 0.2646 -2.8891 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -9.1169 -4.2813 -18.3338 0.9611 11.6249 -63.9384 -0.0000 -0.0000 -3.2539 -1.6157 3.2319 -7.3587 0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 -15.4882 4.5741 -7.9698 16.1660 0.0000 0.0000 -7.0610 1.4918 6.1737 2.4986 0.0000 0.0000 -26.0617 -6.3807 7.2578 26.1900 0.0000 0.0000 0.1832 11.1191 -15.3150 -8.7410 0.0000 -0.0000\n -1.0306 -113.2160 24.3971 -5.5750 2.2767 172.1607 4.3776 -1.6770 0.1241 1.0049 0.5605 0.0767 8.6612 -6.9861 4.5963 -2.9643 -7.3951 4.5506 -18.9058 -19.6167 -6.0917 -0.2455 -7.0580 0.4180 -6.9522 -6.8791 11.2777 2.7806 -25.3362 63.5555 0.0000 0.0000 12.7553 5.6442 1.2482 -3.4196 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -9.5504 -4.8096 -18.4089 1.1838 12.0060 -64.8730 -0.0000 -0.0000 -3.3792 -1.8077 3.8191 -7.3969 0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -15.1759 3.8814 -7.4821 15.8718 0.0000 0.0000 -6.8573 1.1019 5.8999 2.4786 0.0000 0.0000 -26.7684 -7.3152 7.5307 29.2761 0.0000 0.0000 -0.7314 10.3813 -14.9624 -8.3753 0.0000 -0.0000\n -1.2454 -113.1950 24.3535 -5.6508 1.3878 171.7100 4.6196 -1.7186 0.1594 1.1013 0.5984 0.0823 8.7396 -7.4287 4.8055 -2.9812 -7.8194 4.8249 -19.1400 -20.4272 -6.1516 -0.2364 -7.0562 0.5921 -7.0664 -7.2209 11.2684 2.5731 -25.5835 63.3825 0.0000 0.0000 12.3295 5.3575 1.9419 -4.0661 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -10.0204 -5.2474 -18.5023 1.6006 11.8177 -65.6821 -0.0000 -0.0000 -3.3157 -1.7326 4.5093 -7.5298 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -15.1473 3.4193 -7.0595 15.7533 0.0000 0.0000 -6.6820 0.5214 5.7558 2.4454 0.0000 0.0000 -27.3739 -7.9653 7.8330 31.7106 0.0000 0.0000 -2.5997 9.5543 -14.3243 -8.2659 0.0000 -0.0000\n -1.5090 -113.1567 24.3060 -5.8651 0.6122 171.3215 4.7287 -1.6421 0.1810 1.1644 0.7115 0.0896 8.7708 -7.8733 4.8007 -2.9932 -8.2631 5.0387 -19.3525 -21.3177 -6.3024 -0.1535 -7.0104 0.7205 -7.1587 -7.6371 11.2817 2.5031 -25.9894 63.3388 0.0000 0.0000 11.7110 5.0030 2.2526 -4.0166 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -10.3822 -5.5829 -18.6490 1.8778 11.4993 -66.2481 -0.0000 -0.0000 -3.1168 -1.2172 5.0565 -7.6831 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 -15.3710 3.2147 -6.6182 15.8927 0.0000 0.0000 -6.6341 -0.2243 5.6074 2.4255 0.0000 0.0000 -27.6668 -8.2435 7.9479 32.8610 0.0000 0.0000 -4.6398 9.1712 -12.8919 -8.8088 0.0000 -0.0000\n -1.7863 -113.1008 24.2411 -6.2042 -0.2932 170.9659 4.7962 -1.6730 0.2111 1.2124 0.8121 0.0808 8.6396 -8.3143 4.6978 -3.0450 -8.6806 5.1941 -19.4676 -22.2426 -6.4371 -0.1162 -6.7991 0.8314 -7.0027 -8.0941 11.5034 2.3911 -26.3146 63.1544 0.0000 0.0000 10.9068 4.5530 3.5225 -3.0575 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 -10.5963 -5.7949 -18.8274 1.8942 11.3625 -66.4370 -0.0000 -0.0000 -3.1351 -0.7584 5.5511 -7.6733 0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000 -15.7702 2.8793 -5.9332 16.2618 0.0000 0.0000 -6.7580 -0.9424 5.1542 2.4500 0.0000 0.0000 -27.4614 -8.6120 7.9874 32.4616 0.0000 0.0000 -5.9235 9.0376 -11.2851 -9.4232 0.0000 -0.0000\n -2.0794 -113.0299 24.1507 -6.8805 -1.3788 170.4801 4.7027 -1.6579 0.2053 1.1655 0.9296 0.0692 8.3650 -8.9239 4.6366 -3.1562 -9.1370 5.3344 -19.5729 -23.2843 -6.5262 -0.0730 -6.4967 0.9644 -6.5598 -8.8408 11.7749 2.3262 -26.3292 62.9330 0.0000 0.0000 10.0790 3.3101 7.1431 -2.1788 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -10.6953 -5.6717 -19.0896 1.9754 10.8718 -66.1553 -0.0000 -0.0000 -3.6931 -1.0008 5.7890 -7.5582 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 -16.5662 2.3134 -5.0334 16.8543 0.0000 0.0000 -7.0443 -1.6560 4.5228 2.4634 0.0000 0.0000 -26.8981 -9.2904 8.3650 30.8461 0.0000 0.0000 -6.6891 8.5081 -10.8510 -9.2187 0.0000 -0.0000\n -2.4270 -112.9466 24.0570 -8.0371 -2.2435 169.9019 4.2131 -1.0467 0.0911 0.9020 1.2072 0.0863 8.1546 -9.7572 4.5549 -3.2742 -9.7660 5.5026 -20.0235 -24.5230 -6.9225 0.0724 -6.3238 1.0895 -6.2040 -10.2128 11.7096 2.5607 -26.1881 62.9682 0.0000 0.0000 9.1337 1.4543 12.6997 -2.2492 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 -10.6992 -5.0900 -19.7499 2.4942 9.3694 -65.3342 -0.0000 -0.0000 -4.8057 -1.8859 4.9009 -7.6259 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 -17.8783 2.0764 -4.4197 17.4740 0.0000 0.0000 -7.3372 -2.6668 4.2969 2.3335 0.0000 0.0000 -26.5322 -9.6428 8.9723 28.9242 0.0000 0.0000 -7.8374 7.3520 -11.6254 -8.1777 0.0000 -0.0000\n -2.7957 -112.8545 24.0007 -9.1618 -2.5305 169.5924 3.4844 0.2447 -0.1027 0.5052 1.6113 0.1324 8.2166 -10.4683 4.3002 -3.3004 -10.5162 5.6783 -21.0047 -25.7229 -7.9316 0.2661 -6.3794 1.1397 -6.2894 -11.9344 11.2194 3.0223 -26.2301 63.2269 0.0000 0.0000 7.9699 0.3563 17.9863 -3.1337 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -10.6367 -4.3916 -21.0184 3.3554 7.2801 -64.0320 -0.0000 -0.0000 -6.1852 -2.8049 2.5370 -7.9483 -0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -18.9928 2.6280 -4.5304 17.6728 0.0000 0.0000 -7.4071 -3.9720 4.7432 2.0511 0.0000 0.0000 -26.5675 -9.0491 9.1003 27.5870 0.0000 0.0000 -9.3007 5.9461 -12.1104 -7.0772 0.0000 -0.0000\n -3.0587 -112.7812 23.9912 -9.7384 -2.4169 169.7111 2.8909 1.3978 -0.2279 0.2159 1.9806 0.1824 8.4843 -10.7557 3.9761 -3.2297 -11.0579 5.7862 -22.0824 -26.4590 -9.1423 0.3553 -6.5326 1.1151 -6.6959 -13.1551 10.6297 3.3972 -26.5336 63.4323 0.0000 0.0000 6.9529 0.3824 20.8984 -3.8625 -0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -10.5862 -4.0609 -22.4528 4.0487 5.7802 -62.7192 -0.0000 -0.0000 -7.3311 -3.3573 -0.0031 -8.2389 0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -19.3537 3.4984 -5.0777 17.4115 0.0000 0.0000 -7.2628 -5.0103 5.3638 1.7985 0.0000 0.0000 -26.8311 -8.0164 8.6075 27.1461 0.0000 0.0000 -10.1003 4.8465 -11.5585 -6.5691 0.0000 -0.0000\n" }, { "path": "projects/animated_drawings/characters/slamdunk/char_cfg.yaml", "content": "height: 512\nskeleton:\n- loc:\n - 260\n - 230\n name: root\n parent: null\n- loc:\n - 260\n - 230\n name: hip\n parent: root\n- loc:\n - 257\n - 93\n name: torso\n parent: hip\n- loc:\n - 250\n - 54\n name: neck\n parent: torso\n- loc:\n - 213\n - 95\n name: right_shoulder\n parent: torso\n- loc:\n - 172\n - 157\n name: right_elbow\n parent: right_shoulder\n- loc:\n - 145\n - 228\n name: right_hand\n parent: right_elbow\n- loc:\n - 302\n - 88\n name: left_shoulder\n parent: torso\n- loc:\n - 342\n - 150\n name: left_elbow\n parent: left_shoulder\n- loc:\n - 373\n - 212\n name: left_hand\n parent: left_elbow\n- loc:\n - 231\n - 230\n name: right_hip\n parent: root\n- loc:\n - 212\n - 341\n name: right_knee\n parent: right_hip\n- loc:\n - 192\n - 454\n name: right_foot\n parent: right_knee\n- loc:\n - 290\n - 231\n name: left_hip\n parent: root\n- loc:\n - 303\n - 338\n name: left_knee\n parent: left_hip\n- loc:\n - 325\n - 452\n name: left_foot\n parent: left_knee\nwidth: 512\n" }, { "path": "projects/animated_drawings/configs/motion/zombie.yaml", "content": "filepath: bvh/zombie.bvh\nstart_frame_idx: 0\nend_frame_idx: 320\ngroundplane_joint: LeftFoot\nforward_perp_joint_vectors:\n - - LeftShoulder\n - RightShoulder\n - - LeftUpLeg\n - RightUpLeg\nscale: 0.025\nup: +z\n" }, { "path": "projects/animated_drawings/configs/mvc/slamdunk.yaml", "content": "scene:\n ANIMATED_CHARACTERS:\n - character_cfg: characters/slamdunk/char_cfg.yaml\n motion_cfg: configs/motion/zombie.yaml\n retarget_cfg: configs/retarget/fair1_spf.yaml\nview:\n CAMERA_POS: [1.6, 1.7, 4.0]\n CAMERA_FWD: [0.0, 0.5, 2.0]\n BACKGROUND_IMAGE: resources/basketball_playground.png\n\ncontroller:\n MODE: video_render\n OUTPUT_VIDEO_PATH: ./resources/slamdunk_video.mp4\n OUTPUT_VIDEO_CODEC: mp4v\n" }, { "path": "projects/animated_drawings/configs/retarget/fair1_spf.yaml", "content": "char_starting_location: [0.0, 0.0, 0.0]\nbvh_projection_bodypart_groups:\n- bvh_joint_names:\n - RightShoulder\n - RightArm\n - RightForeArm\n - RightHand\n - RightHandEnd\n - LeftShoulder\n - LeftArm\n - LeftForeArm\n - LeftHand\n - LeftHandEnd\n method: sagittal\n name: Upper Limbs\n- bvh_joint_names:\n - RightUpLeg\n - RightLeg\n - RightFoot\n - RightToeBase\n - LeftUpLeg\n - LeftLeg\n - LeftFoot\n - LeftToeBase\n method: pca\n name: Lower Limbs\n- bvh_joint_names:\n - Hips\n - Spine\n - Spine1\n - Spine2\n - Spine3\n - Neck\n - Head\n method: frontal\n name: Trunk\nchar_bodypart_groups:\n- bvh_depth_drivers:\n - Hips\n char_joints:\n - right_shoulder\n - left_shoulder\n - right_hip\n - left_hip\n - hip\n - torso\n - neck\n- bvh_depth_drivers:\n - LeftHand\n char_joints:\n - left_elbow\n - left_hand\n- bvh_depth_drivers:\n - RightHand\n char_joints:\n - right_elbow\n - right_hand\n- bvh_depth_drivers:\n - LeftFoot\n char_joints:\n - left_knee\n - left_foot\n- bvh_depth_drivers:\n - RightFoot\n char_joints:\n - right_knee\n - right_foot\nchar_bvh_root_offset:\n bvh_projection_bodypart_group_for_offset: Lower Limbs\n bvh_joints:\n - - RightFoot\n - RightLeg\n - RightUpLeg\n - - LeftFoot\n - LeftLeg\n - LeftUpLeg\n char_joints:\n - - left_foot\n - left_knee\n - left_hip\n - - right_foot\n - right_knee\n - right_hip\nchar_joint_bvh_joints_mapping:\n left_elbow: !!python/tuple\n - LeftArm\n - LeftForeArm\n left_foot: !!python/tuple\n - LeftLeg\n - LeftFoot\n left_hand: !!python/tuple\n - LeftForeArm\n - LeftHand\n left_knee: !!python/tuple\n - LeftUpLeg\n - LeftLeg\n torso: !!python/tuple\n - Hips\n - Spine3\n neck: !!python/tuple\n - Hips\n - Neck\n right_elbow: !!python/tuple\n - RightArm\n - RightForeArm\n right_foot: !!python/tuple\n - RightLeg\n - RightFoot\n right_hand: !!python/tuple\n - RightForeArm\n - RightHand\n right_knee: !!python/tuple\n - RightUpLeg\n - RightLeg\nchar_runtime_checks:\n - - above\n - neck\n - right_shoulder\n - left_shoulder\n" }, { "path": "projects/animated_drawings/tools/generate_character.py", "content": "import os\nfrom argparse import ArgumentParser\n\nimport cv2\nimport numpy as np\nimport PIL.Image as Image\nimport torch\nimport yaml\nfrom controlnet_aux import OpenposeDetector\nfrom controlnet_aux.open_pose.face import faceDetect\nfrom controlnet_aux.open_pose.hand import handDetect\nfrom controlnet_aux.open_pose.util import (HWC3, draw_bodypose, draw_facepose,\n draw_handpose, resize_image)\n\n\ndef draw_pose(pose, H, W, draw_body=True, draw_hand=True, draw_face=True):\n bodies = pose['bodies']\n faces = pose['faces']\n hands = pose['hands']\n candidate = bodies['candidate']\n subset = bodies['subset']\n canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)\n\n if draw_body:\n canvas = draw_bodypose(canvas, candidate, subset)\n\n if draw_hand:\n canvas = draw_handpose(canvas, hands)\n\n if draw_face:\n canvas = draw_facepose(canvas, faces)\n\n return canvas\n\n\nclass OpenposeDetectorPoint(OpenposeDetector):\n\n def __call__(self,\n input_image,\n detect_resolution=512,\n image_resolution=512,\n hand_and_face=False,\n return_pil=True):\n # hand = False\n if not isinstance(input_image, np.ndarray):\n input_image = np.array(input_image, dtype=np.uint8)\n\n input_image = HWC3(input_image)\n input_image = resize_image(input_image, detect_resolution)\n input_image = input_image[:, :, ::-1].copy()\n H, W, C = input_image.shape\n with torch.no_grad():\n candidate, subset = self.body_estimation(input_image)\n hands = []\n faces = []\n if hand_and_face:\n # Hand\n hands_list = handDetect(candidate, subset, input_image)\n for x, y, w, is_left in hands_list:\n peaks = self.hand_estimation(\n input_image[y:y + w, x:x + w, :]).astype(np.float32)\n if peaks.ndim == 2 and peaks.shape[1] == 2:\n peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1,\n peaks[:, 0] + x) / float(W)\n peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1,\n peaks[:, 1] + y) / float(H)\n hands.append(peaks.tolist())\n # Face\n faces_list = faceDetect(candidate, subset, input_image)\n for x, y, w in faces_list:\n heatmaps = self.face_estimation(input_image[y:y + w,\n x:x + w, :])\n peaks = self.face_estimation.compute_peaks_from_heatmaps(\n heatmaps).astype(np.float32)\n if peaks.ndim == 2 and peaks.shape[1] == 2:\n peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1,\n peaks[:, 0] + x) / float(W)\n peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1,\n peaks[:, 1] + y) / float(H)\n faces.append(peaks.tolist())\n\n if candidate.ndim == 2 and candidate.shape[1] == 4:\n candidate = candidate[:, :2]\n candidate[:, 0] /= float(W)\n candidate[:, 1] /= float(H)\n\n bodies = dict(candidate=candidate.tolist(), subset=subset.tolist())\n pose = dict(bodies=bodies, hands=hands, faces=faces)\n\n canvas = draw_pose(pose, H, W)\n\n detected_map = HWC3(canvas)\n img = resize_image(input_image, image_resolution)\n H, W, C = img.shape\n\n detected_map = cv2.resize(\n detected_map, (W, H), interpolation=cv2.INTER_NEAREST)\n\n if return_pil:\n detected_map = Image.fromarray(detected_map)\n\n return detected_map, candidate, subset\n\n\ndef parse_args():\n parser = ArgumentParser()\n parser.add_argument(\n '--chardir',\n type=str,\n default='characters',\n help='Characters root dir.')\n parser.add_argument(\n '--charname', type=str, default='slamdunk', help='Character name.')\n parser.add_argument(\n '--img', type=str, default='resources/sakuragi.png', help='Img name.')\n parser.add_argument(\n '--mask',\n type=str,\n default='resources/sakuragi_mask.png',\n help='Img mask name.')\n\n args, unknown = parser.parse_known_args()\n\n return args, unknown\n\n\ndef main():\n args, unknown = parse_args()\n\n detect_resolution = 512\n control_detector = 'lllyasviel/ControlNet'\n posedet = OpenposeDetectorPoint.from_pretrained(control_detector)\n\n char_root_dir = args.chardir\n char_name = args.charname\n image_name = args.img\n mask_name = args.mask\n\n image = Image.open(image_name)\n detected_map, candidate, subset = posedet(image)\n pose_name = 'pose.png'\n detected_map.save(os.path.join(char_root_dir, char_name, pose_name))\n\n # resize image\n image_np = np.array(image, dtype=np.uint8)\n image_np = HWC3(image_np)\n image_np = resize_image(image_np, detect_resolution)\n image_resized = Image.fromarray(image_np)\n image_resized.save(os.path.join(char_root_dir, char_name, 'texture.png'))\n\n # resize mask image\n mask = Image.open(mask_name)\n mask_np = np.array(mask, dtype=np.uint8)\n mask_np = HWC3(mask_np)\n mask_np = resize_image(mask_np, detect_resolution)\n image_resized = Image.fromarray(mask_np)\n image_resized.save(os.path.join(char_root_dir, char_name, 'mask.png'))\n\n W, H = image_resized.size\n\n config_file = os.path.join(char_root_dir, char_name, 'char_cfg.yaml')\n config_dict = {}\n config_dict['width'] = detected_map.size[0]\n config_dict['height'] = detected_map.size[1]\n\n # yapf: disable\n kpts = candidate\n kpts[:, 0] = kpts[:, 0] * W\n kpts[:, 1] = kpts[:, 1] * H\n skeleton = []\n skeleton.append({'loc' : [round(x) for x in (kpts[8]+kpts[11])/2], 'name': 'root' , 'parent': None}) # noqa\n skeleton.append({'loc' : [round(x) for x in (kpts[8]+kpts[11])/2], 'name': 'hip' , 'parent': 'root'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[1] ], 'name': 'torso' , 'parent': 'hip'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[0] ], 'name': 'neck' , 'parent': 'torso'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[2] ], 'name': 'right_shoulder', 'parent': 'torso'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[3] ], 'name': 'right_elbow' , 'parent': 'right_shoulder'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[4] ], 'name': 'right_hand' , 'parent': 'right_elbow'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[5] ], 'name': 'left_shoulder' , 'parent': 'torso'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[6] ], 'name': 'left_elbow' , 'parent': 'left_shoulder'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[7] ], 'name': 'left_hand' , 'parent': 'left_elbow'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[8] ], 'name': 'right_hip' , 'parent': 'root'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[9] ], 'name': 'right_knee' , 'parent': 'right_hip'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[10] ], 'name': 'right_foot' , 'parent': 'right_knee'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[11] ], 'name': 'left_hip' , 'parent': 'root'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[12] ], 'name': 'left_knee' , 'parent': 'left_hip'}) # noqa\n skeleton.append({'loc' : [round(x) for x in kpts[13] ], 'name': 'left_foot' , 'parent': 'left_knee'}) # noqa\n # yapf: enable\n config_dict['skeleton'] = skeleton\n\n with open(config_file, 'w') as file:\n yaml.dump(config_dict, file)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "projects/animated_drawings/tools/generate_video.py", "content": "from animated_drawings import render\n\nrender.start('./configs/mvc/slamdunk.yaml')\n" }, { "path": "projects/example_project/README.md", "content": "# Example Project\n\nThis is an example README for community `projects/`. You can write your README in your own project. Here are\nsome recommended parts of a README for others to understand and use your project, you can copy or modify them\naccording to your project.\n\n## Description \\[required\\]\n\nYou can share any information you would like others to know. For example:\n\n```\nAuthor: @xxx.\n\nThis is an implementation of \\[XXX\\].\n```\n\n## Usage \\[required\\]\n\n### Setup Environment \\[required\\]\n\nPlease refer to [Get Started](https://mmagic.readthedocs.io/en/latest/get_started/I.html) to install\nMMagic.\n\nAt first, add the current folder to `PYTHONPATH`, so that Python can find your code. Run command in the current directory to add it.\n\n> Please run it every time after you opened a new shell.\n\n```shell\nexport PYTHONPATH=`pwd`:$PYTHONPATH\n```\n\n### Data Preparation \\[optional\\]\n\nPrepare the ImageNet-2012 dataset according to the [instruction](https://mmagic.readthedocs.io/en/latest/user_guides/dataset_prepare.html#imagenet).\n\n### Training commands \\[optional\\]\n\n**To train with single GPU:**\n\n```bash\nmim train mmagic configs/examplenet_8xb32_in1k.py\n```\n\n**To train with multiple GPUs:**\n\n```bash\nmim train mmagic configs/examplenet_8xb32_in1k.py --launcher pytorch --gpus 8\n```\n\n**To train with multiple GPUs by slurm:**\n\n```bash\nmim train mmagic configs/examplenet_8xb32_in1k.py --launcher slurm \\\n --gpus 16 --gpus-per-node 8 --partition $PARTITION\n```\n\n### Testing commands \\[required\\]\n\n**To test with single GPU:**\n\n```bash\nmim test mmagic configs/examplenet_8xb32_in1k.py $CHECKPOINT\n```\n\n**To test with multiple GPUs:**\n\n```bash\nmim test mmagic configs/examplenet_8xb32_in1k.py $CHECKPOINT --launcher pytorch --gpus 8\n```\n\n**To test with multiple GPUs by slurm:**\n\n```bash\nmim test mmagic configs/examplenet_8xb32_in1k.py $CHECKPOINT --launcher slurm \\\n --gpus 16 --gpus-per-node 8 --partition $PARTITION\n```\n\n## Results \\[required\\]\n\n| Model | Pretrain | Top-1 (%) | Top-5 (%) | Config | Download |\n| :----------------: | :----------: | :-------: | :-------: | :----------------------------------------: | :------------------------------------: |\n| ExampleNet-tiny | From scratch | 82.33 | 96.15 | [config](configs/examplenet_8xb32_in1k.py) | [model](MODEL-LINK) \\| [log](LOG-LINK) |\n| ExampleNet-small\\* | From scratch | 83.63 | 96.51 | [config](configs/examplenet_8xb32_in1k.py) | [model](MODEL-LINK) |\n| ExampleNet-base\\* | From scratch | 84.34 | 96.86 | [config](configs/examplenet_8xb32_in1k.py) | [model](MODEL-LINK) |\n\n*Models with * are converted from the [official repo](REPO-LINK). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*\n\nYou can also paste some visual results here if the model doesn't have quantitative results.\n\n## Citation \\[required\\]\n\n\n\n```bibtex\n@misc{mmagic2023,\n title = {{MMagic}: {OpenMMLab} Multimodal Advanced, Generative, and Intelligent Creation Toolbox},\n author = {{MMagic Contributors}},\n howpublished = {\\url{https://github.com/open-mmlab/mmagic}},\n year = {2023}\n}\n```\n\n## Checklist \\[required\\]\n\nHere is a checklist of this project's progress. And you can ignore this part if you don't plan to contribute\nto MMagic projects.\n\n- [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.\n\n - [ ] Finish the code\n\n \n\n - [ ] Basic docstrings & proper citation\n\n \n\n - [ ] Converted checkpoint and results (Only for reproduction)\n\n \n\n- [ ] Milestone 2: Indicates a successful model implementation.\n\n - [ ] Training results\n\n \n\n- [ ] Milestone 3: Good to be a part of our core package!\n\n - [ ] Unit tests\n\n \n\n - [ ] Code style\n\n \n\n - [ ] `metafile.yml` and `README.md`\n\n \n" }, { "path": "projects/example_project/configs/examplenet_8xb32_in1k.py", "content": "# Directly inherit the entire recipe you want to use.\n_base_ = 'mmagic::srcnn/srcnn_x4k915_1xb16-1000k_div2k.py'\n\n# This line is to import your own modules.\ncustom_imports = dict(imports='models')\n\n# Set your model, training, testing configurations.\n" }, { "path": "projects/example_project/models/__init__.py", "content": "from .example_net import ExampleNet\n\n__all__ = ['ExampleNet']\n" }, { "path": "projects/example_project/models/example_net.py", "content": "from mmagic.models import ResNet\nfrom mmagic.registry import MODELS\n\n\n# Register your model to the `MODELS`.\n@MODELS.register_module()\nclass ExampleNet(ResNet):\n \"\"\"Implements an example backbone.\n\n Implement the backbone network just like a normal pytorch network.\n \"\"\"\n\n def __init__(self, **kwargs) -> None:\n print('#############################\\n'\n '# Hello MMagic! #\\n'\n '#############################')\n super().__init__(**kwargs)\n\n def forward(self, x):\n \"\"\"The forward method of the network.\n\n Args:\n x (torch.Tensor): A tensor of image batch with shape\n ``(batch_size, num_channels, height, width)``.\n\n Returns:\n Tuple[torch.Tensor]: Please return a tuple of tensors and every\n tensor is a feature map of specified scale. If you only want the\n final feature map, simply return a tuple with one item.\n \"\"\"\n return super().forward(x)\n" }, { "path": "projects/flow_style_vton/.gitignore", "content": "*.jpg\n*.zip\n" }, { "path": "projects/flow_style_vton/README.md", "content": "# Style-Based Global Appearance Flow for Virtual Try-On (CVPR 2022)\n\n## Description\n\nAwesome try-on desplays are like this:\n\n![image1](examples/000010_0.png)\n\n```\nAuthor: @FerryHuang.\n\nThis is an implementation of https://github.com/SenHe/Flow-Style-VTON adapting to mmediting. Only inference is supported so far.\n```\n\n## Usage\n\n### Setup Environment\n\nPlease refer to [Get Started](https://mmediting.readthedocs.io/en/latest/get_started/I.html) to install\nMMEditing.\n\nAt first, add the current folder to `PYTHONPATH`, so that Python can find your code. Run command in the current directory to add it.\n\n> Please run it every time after you opened a new shell.\n\n```shell\nexport PYTHONPATH=`pwd`:$PYTHONPATH\n```\n\n### Data Preparation\n\nPlease check the [official repo](https://github.com/SenHe/Flow-Style-VTON) and download test-set and pretrained checkpoints and put them under the folder projects/flow_style_vton\n\n### Testing commands\n\n**To test with single GPU:**\n\n```bash\ncd projects/flow_style_vton\npython inference.py\n```\n\nExpectedly, two folders will be made im_gar_flow_wg and our_t_results, containing the\ntry-on procedures and the final results, respectively.\n\n## Citation\n\n\n\n```bibtex\n@inproceedings{he2022fs_vton,\n title={Style-Based Global Appearance Flow for Virtual Try-On},\n author={He, Sen and Song, Yi-Zhe and Xiang, Tao},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n## Checklist \\[required\\]\n\nHere is a checklist of this project's progress. And you can ignore this part if you don't plan to contribute\nto MMediting projects.\n\n- [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.\n\n - [ ] Finish the code\n\n \n\n - [ ] Basic docstrings & proper citation\n\n \n\n - [ ] Converted checkpoint and results (Only for reproduction)\n\n \n\n- [ ] Milestone 2: Indicates a successful model implementation.\n\n - [ ] Training results\n\n \n\n- [ ] Milestone 3: Good to be a part of our core package!\n\n - [ ] Unit tests\n\n \n\n - [ ] Code style\n\n \n\n - [ ] `metafile.yml` and `README.md`\n\n \n" }, { "path": "projects/flow_style_vton/configs/flow_style_vton_PFAFN_epoch_101.py", "content": "model = dict(\n type='FlowStyleVTON',\n warp_model=dict(type='AFWM', input_nc=3),\n gen_model=dict(\n type='ResUnetGenerator', input_nc=7, output_nc=4, num_downs=5),\n pretrained_cfgs=dict(\n warp_model=dict(ckpt_path='ckp/aug/PFAFN_warp_epoch_101.pth'),\n gen_model=dict(ckpt_path='ckp/aug/PFAFN_gen_epoch_101.pth')))\n" }, { "path": "projects/flow_style_vton/inference.py", "content": "import os\nfrom argparse import ArgumentParser\n\nimport torch\nfrom mmengine.registry import init_default_scope\nfrom torch.utils.data import DataLoader\nfrom torchvision.utils import save_image\nfrom tqdm import tqdm\nfrom vton_dataset import AlignedDataset\n\nfrom mmagic.apis.inferencers.inference_functions import init_model\nfrom projects.flow_style_vton.models import FlowStyleVTON\n\ninit_default_scope('mmagic')\n\nconfig = 'configs/flow_style_vton_PFAFN_epoch_101.py'\n\nparser = ArgumentParser()\nparser.add_argument('--gpu_id', type=int, default=0)\nparser.add_argument(\n '--loadSize', type=int, default=512, help='scale images to this size')\nparser.add_argument(\n '--fineSize', type=int, default=512, help='then crop to this size')\nparser.add_argument('--dataroot', type=str, default='VITON_test')\nparser.add_argument('--test_pairs', type=str, default='test_pairs.txt')\nparser.add_argument(\n '--resize_or_crop',\n type=str,\n default='scale_width',\n help='scaling and cropping of images at load time \\\n [resize_and_crop|crop|scale_width|scale_width_and_crop]')\nparser.add_argument('--phase', type=str, default='test')\nparser.add_argument('--isTrain', default=False)\nparser.add_argument(\n '--no_flip',\n action='store_true',\n help='if specified, do not flip the images for data argumentation')\nparser.add_argument('--batch_size', type=int, default=1)\nparser.add_argument('--num_workers', type=int, default=4)\n\nparser.add_argument('--output_dir', type=str, default='inference_results')\nopt = parser.parse_args()\n\ndataset = AlignedDataset(opt)\ndataloader = DataLoader(dataset, opt.batch_size, num_workers=opt.num_workers)\n\ndevice = torch.device(\n f'cuda:{opt.gpu_id}' if torch.cuda.is_available() else 'cpu')\n# pretrained is set inside the config\nmodel = init_model(config).to(device).eval()\nassert isinstance(model, FlowStyleVTON)\n\nos.makedirs('our_t_results', exist_ok=True)\nos.makedirs('im_gar_flow_wg', exist_ok=True)\nfor i, data in enumerate(tqdm(dataloader)):\n p_tryon, combine = model.infer(data)\n save_image(\n p_tryon,\n os.path.join('our_t_results', data['p_name'][0]),\n nrow=int(1),\n normalize=True,\n value_range=(-1, 1))\n save_image(\n combine,\n os.path.join('im_gar_flow_wg', data['p_name'][0]),\n nrow=int(1),\n normalize=True,\n range=(-1, 1),\n )\n" }, { "path": "projects/flow_style_vton/models/__init__.py", "content": "from .afwm import AFWM\nfrom .flow_style_vton_model import FlowStyleVTON\nfrom .generator import ResUnetGenerator\n\n__all__ = ['FlowStyleVTON', 'AFWM', 'ResUnetGenerator']\n" }, { "path": "projects/flow_style_vton/models/afwm.py", "content": "from math import sqrt\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass AFWM(nn.Module):\n\n def __init__(self, input_nc):\n super(AFWM, self).__init__()\n num_filters = [64, 128, 256, 256, 256]\n self.image_features = FeatureEncoder(3, num_filters)\n self.cond_features = FeatureEncoder(input_nc, num_filters)\n self.image_FPN = RefinePyramid(num_filters)\n self.cond_FPN = RefinePyramid(num_filters)\n self.aflow_net = AFlowNet(len(num_filters))\n\n def forward(self, cond_input, image_input):\n\n # import ipdb; ipdb.set_trace()\n cond_pyramids = self.cond_FPN(\n self.cond_features(cond_input)) # maybe use nn.Sequential\n image_pyramids = self.image_FPN(self.image_features(image_input))\n\n x_warp, last_flow = self.aflow_net(image_input, image_pyramids,\n cond_pyramids)\n\n return x_warp, last_flow\n\n\ndef apply_offset(offset):\n sizes = list(offset.size()[2:])\n grid_list = torch.meshgrid(\n [torch.arange(size, device=offset.device) for size in sizes])\n grid_list = reversed(grid_list)\n # apply offset\n grid_list = [\n grid.float().unsqueeze(0) + offset[:, dim, ...]\n for dim, grid in enumerate(grid_list)\n ]\n # normalize\n grid_list = [\n grid / ((size - 1.0) / 2.0) - 1.0\n for grid, size in zip(grid_list, reversed(sizes))\n ]\n\n return torch.stack(grid_list, dim=-1)\n\n\ndef TVLoss(x):\n tv_h = x[:, :, 1:, :] - x[:, :, :-1, :]\n tv_w = x[:, :, :, 1:] - x[:, :, :, :-1]\n\n return torch.mean(torch.abs(tv_h)) + torch.mean(torch.abs(tv_w))\n\n\n# backbone\nclass EqualLR:\n\n def __init__(self, name):\n self.name = name\n\n def compute_weight(self, module):\n weight = getattr(module, self.name + '_orig')\n fan_in = weight.data.size(1) * weight.data[0][0].numel()\n\n return weight * sqrt(2 / fan_in)\n\n @staticmethod\n def apply(module, name):\n fn = EqualLR(name)\n\n weight = getattr(module, name)\n del module._parameters[name]\n module.register_parameter(name + '_orig', nn.Parameter(weight.data))\n module.register_forward_pre_hook(fn)\n\n return fn\n\n def __call__(self, module, input):\n weight = self.compute_weight(module)\n setattr(module, self.name, weight)\n\n\ndef equal_lr(module, name='weight'):\n EqualLR.apply(module, name)\n\n return module\n\n\nclass EqualLinear(nn.Module):\n\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n linear = nn.Linear(in_dim, out_dim)\n linear.weight.data.normal_()\n linear.bias.data.zero_()\n\n self.linear = equal_lr(linear)\n\n def forward(self, input):\n return self.linear(input)\n\n\nclass ModulatedConv2d(nn.Module):\n\n def __init__(self,\n fin,\n fout,\n kernel_size,\n padding_type='zero',\n upsample=False,\n downsample=False,\n latent_dim=512,\n normalize_mlp=False):\n super(ModulatedConv2d, self).__init__()\n self.in_channels = fin\n self.out_channels = fout\n self.kernel_size = kernel_size\n padding_size = kernel_size // 2\n\n if kernel_size == 1:\n self.demudulate = False\n else:\n self.demudulate = True\n\n self.weight = nn.Parameter(\n torch.Tensor(fout, fin, kernel_size, kernel_size))\n self.bias = nn.Parameter(torch.Tensor(1, fout, 1, 1))\n\n if not normalize_mlp:\n self.mlp_class_std = EqualLinear(latent_dim, fin)\n if padding_type == 'reflect':\n self.padding = nn.ReflectionPad2d(padding_size)\n else:\n self.padding = nn.ZeroPad2d(padding_size)\n\n self.weight.data.normal_()\n self.bias.data.zero_()\n\n def forward(self, input, latent):\n fan_in = self.weight.data.size(1) * self.weight.data[0][0].numel()\n weight = self.weight * sqrt(2 / fan_in)\n weight = weight.view(1, self.out_channels, self.in_channels,\n self.kernel_size, self.kernel_size)\n\n s = self.mlp_class_std(latent).view(-1, 1, self.in_channels, 1, 1)\n weight = s * weight\n if self.demudulate:\n d = torch.rsqrt((weight**2).sum(4).sum(3).sum(2) +\n 1e-5).view(-1, self.out_channels, 1, 1, 1)\n weight = (d * weight).view(-1, self.in_channels, self.kernel_size,\n self.kernel_size)\n else:\n weight = weight.view(-1, self.in_channels, self.kernel_size,\n self.kernel_size)\n\n batch, _, height, width = input.shape\n\n input = input.view(1, -1, height, width)\n input = self.padding(input)\n out = F.conv2d(\n input, weight, groups=batch).view(batch, self.out_channels, height,\n width) + self.bias\n\n return out\n\n\nclass StyledConvBlock(nn.Module):\n\n def __init__(self,\n fin,\n fout,\n latent_dim=256,\n padding='zero',\n actvn='lrelu',\n normalize_affine_output=False,\n modulated_conv=False):\n super(StyledConvBlock, self).__init__()\n if not modulated_conv:\n if padding == 'reflect':\n padding_layer = nn.ReflectionPad2d\n else:\n padding_layer = nn.ZeroPad2d\n\n if modulated_conv:\n conv2d = ModulatedConv2d\n\n if modulated_conv:\n self.actvn_gain = sqrt(2)\n else:\n self.actvn_gain = 1.0\n\n self.modulated_conv = modulated_conv\n\n if actvn == 'relu':\n activation = nn.ReLU(True)\n else:\n activation = nn.LeakyReLU(0.2, True)\n\n if self.modulated_conv:\n self.conv0 = conv2d(\n fin,\n fout,\n kernel_size=3,\n padding_type=padding,\n upsample=False,\n latent_dim=latent_dim,\n normalize_mlp=normalize_affine_output)\n else:\n conv0 = conv2d(fin, fout, kernel_size=3)\n\n seq0 = [padding_layer(1), conv0]\n self.conv0 = nn.Sequential(*seq0)\n\n self.actvn0 = activation\n\n if self.modulated_conv:\n self.conv1 = conv2d(\n fout,\n fout,\n kernel_size=3,\n padding_type=padding,\n downsample=False,\n latent_dim=latent_dim,\n normalize_mlp=normalize_affine_output)\n else:\n conv1 = conv2d(fout, fout, kernel_size=3)\n seq1 = [padding_layer(1), conv1]\n self.conv1 = nn.Sequential(*seq1)\n\n self.actvn1 = activation\n\n def forward(self, input, latent=None):\n if self.modulated_conv:\n out = self.conv0(input, latent)\n else:\n out = self.conv0(input)\n\n out = self.actvn0(out) * self.actvn_gain\n\n if self.modulated_conv:\n out = self.conv1(out, latent)\n else:\n out = self.conv1(out)\n\n out = self.actvn1(out) * self.actvn_gain\n\n return out\n\n\nclass Styled_F_ConvBlock(nn.Module):\n\n def __init__(self,\n fin,\n fout,\n latent_dim=256,\n padding='zero',\n actvn='lrelu',\n normalize_affine_output=False,\n modulated_conv=False):\n super(Styled_F_ConvBlock, self).__init__()\n if not modulated_conv:\n if padding == 'reflect':\n padding_layer = nn.ReflectionPad2d\n else:\n padding_layer = nn.ZeroPad2d\n\n if modulated_conv:\n conv2d = ModulatedConv2d\n\n if modulated_conv:\n self.actvn_gain = sqrt(2)\n else:\n self.actvn_gain = 1.0\n\n self.modulated_conv = modulated_conv\n\n if actvn == 'relu':\n activation = nn.ReLU(True)\n else:\n activation = nn.LeakyReLU(0.2, True)\n\n if self.modulated_conv:\n self.conv0 = conv2d(\n fin,\n 128,\n kernel_size=3,\n padding_type=padding,\n upsample=False,\n latent_dim=latent_dim,\n normalize_mlp=normalize_affine_output)\n else:\n conv0 = conv2d(fin, 128, kernel_size=3)\n\n seq0 = [padding_layer(1), conv0]\n self.conv0 = nn.Sequential(*seq0)\n\n self.actvn0 = activation\n\n if self.modulated_conv:\n self.conv1 = conv2d(\n 128,\n fout,\n kernel_size=3,\n padding_type=padding,\n downsample=False,\n latent_dim=latent_dim,\n normalize_mlp=normalize_affine_output)\n else:\n conv1 = conv2d(128, fout, kernel_size=3)\n seq1 = [padding_layer(1), conv1]\n self.conv1 = nn.Sequential(*seq1)\n\n def forward(self, input, latent=None):\n if self.modulated_conv:\n out = self.conv0(input, latent)\n else:\n out = self.conv0(input)\n\n out = self.actvn0(out) * self.actvn_gain\n\n if self.modulated_conv:\n out = self.conv1(out, latent)\n else:\n out = self.conv1(out)\n\n return out\n\n\nclass ResBlock(nn.Module):\n\n def __init__(self, in_channels):\n super(ResBlock, self).__init__()\n self.block = nn.Sequential(\n nn.BatchNorm2d(in_channels), nn.ReLU(inplace=True),\n nn.Conv2d(\n in_channels, in_channels, kernel_size=3,\n padding=1, bias=False), nn.BatchNorm2d(in_channels),\n nn.ReLU(inplace=True),\n nn.Conv2d(\n in_channels, in_channels, kernel_size=3, padding=1,\n bias=False))\n\n def forward(self, x):\n return self.block(x) + x\n\n\nclass DownSample(nn.Module):\n\n def __init__(self, in_channels, out_channels):\n super(DownSample, self).__init__()\n self.block = nn.Sequential(\n nn.BatchNorm2d(in_channels), nn.ReLU(inplace=True),\n nn.Conv2d(\n in_channels,\n out_channels,\n kernel_size=3,\n stride=2,\n padding=1,\n bias=False))\n\n def forward(self, x):\n return self.block(x)\n\n\nclass FeatureEncoder(nn.Module):\n\n def __init__(self, in_channels, chns=[64, 128, 256, 256, 256]):\n super(FeatureEncoder, self).__init__()\n self.encoders = []\n for i, out_chns in enumerate(chns):\n if i == 0:\n encoder = nn.Sequential(\n DownSample(in_channels, out_chns), ResBlock(out_chns),\n ResBlock(out_chns))\n else:\n encoder = nn.Sequential(\n DownSample(chns[i - 1], out_chns), ResBlock(out_chns),\n ResBlock(out_chns))\n\n self.encoders.append(encoder)\n\n self.encoders = nn.ModuleList(self.encoders)\n\n def forward(self, x):\n encoder_features = []\n for encoder in self.encoders:\n x = encoder(x)\n encoder_features.append(x)\n return encoder_features\n\n\nclass RefinePyramid(nn.Module):\n\n def __init__(self, chns=[64, 128, 256, 256, 256], fpn_dim=256):\n super(RefinePyramid, self).__init__()\n self.chns = chns\n\n # adaptive\n self.adaptive = []\n for in_chns in list(reversed(chns)):\n adaptive_layer = nn.Conv2d(in_chns, fpn_dim, kernel_size=1)\n self.adaptive.append(adaptive_layer)\n self.adaptive = nn.ModuleList(self.adaptive)\n # output conv\n self.smooth = []\n for i in range(len(chns)):\n smooth_layer = nn.Conv2d(\n fpn_dim, fpn_dim, kernel_size=3, padding=1)\n self.smooth.append(smooth_layer)\n self.smooth = nn.ModuleList(self.smooth)\n\n def forward(self, x):\n conv_ftr_list = x\n\n feature_list = []\n last_feature = None\n for i, conv_ftr in enumerate(list(reversed(conv_ftr_list))):\n # adaptive\n feature = self.adaptive[i](conv_ftr)\n # fuse\n if last_feature is not None:\n feature = feature + F.interpolate(\n last_feature, scale_factor=2, mode='nearest')\n # smooth\n feature = self.smooth[i](feature)\n last_feature = feature\n feature_list.append(feature)\n\n return tuple(reversed(feature_list))\n\n\nclass AFlowNet(nn.Module):\n\n def __init__(self, num_pyramid, fpn_dim=256):\n super(AFlowNet, self).__init__()\n\n padding_type = 'zero'\n actvn = 'lrelu'\n normalize_mlp = False\n modulated_conv = True\n\n self.netRefine = []\n\n self.netStyle = []\n\n self.netF = []\n\n for i in range(num_pyramid):\n\n netRefine_layer = torch.nn.Sequential(\n torch.nn.Conv2d(\n 2 * fpn_dim,\n out_channels=128,\n kernel_size=3,\n stride=1,\n padding=1),\n torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),\n torch.nn.Conv2d(\n in_channels=128,\n out_channels=64,\n kernel_size=3,\n stride=1,\n padding=1),\n torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),\n torch.nn.Conv2d(\n in_channels=64,\n out_channels=32,\n kernel_size=3,\n stride=1,\n padding=1),\n torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),\n torch.nn.Conv2d(\n in_channels=32,\n out_channels=2,\n kernel_size=3,\n stride=1,\n padding=1))\n\n style_block = StyledConvBlock(\n 256,\n 49,\n latent_dim=256,\n padding=padding_type,\n actvn=actvn,\n normalize_affine_output=normalize_mlp,\n modulated_conv=modulated_conv)\n\n style_F_block = Styled_F_ConvBlock(\n 49,\n 2,\n latent_dim=256,\n padding=padding_type,\n actvn=actvn,\n normalize_affine_output=normalize_mlp,\n modulated_conv=modulated_conv)\n\n self.netRefine.append(netRefine_layer)\n self.netStyle.append(style_block)\n self.netF.append(style_F_block)\n\n self.netRefine = nn.ModuleList(self.netRefine)\n self.netStyle = nn.ModuleList(self.netStyle)\n self.netF = nn.ModuleList(self.netF)\n\n self.cond_style = torch.nn.Sequential(\n torch.nn.Conv2d(256, 128, kernel_size=(8, 6), stride=1, padding=0),\n torch.nn.LeakyReLU(inplace=False, negative_slope=0.1))\n\n self.image_style = torch.nn.Sequential(\n torch.nn.Conv2d(256, 128, kernel_size=(8, 6), stride=1, padding=0),\n torch.nn.LeakyReLU(inplace=False, negative_slope=0.1))\n\n def forward(self, x, x_warps, x_conds, warp_feature=True):\n last_flow = None\n\n B = x_conds[len(x_warps) - 1].shape[0]\n\n cond_style = self.cond_style(x_conds[len(x_warps) - 1]).view(B, -1)\n image_style = self.image_style(x_warps[len(x_warps) - 1]).view(B, -1)\n style = torch.cat([cond_style, image_style], 1)\n\n for i in range(len(x_warps)):\n x_warp = x_warps[len(x_warps) - 1 - i]\n x_cond = x_conds[len(x_warps) - 1 - i]\n\n if last_flow is not None and warp_feature:\n x_warp_after = F.grid_sample(\n x_warp,\n last_flow.detach().permute(0, 2, 3, 1),\n mode='bilinear',\n padding_mode='border')\n else:\n x_warp_after = x_warp\n\n stylemap = self.netStyle[i](x_warp_after, style)\n\n flow = self.netF[i](stylemap, style)\n flow = apply_offset(flow)\n if last_flow is not None:\n flow = F.grid_sample(\n last_flow, flow, mode='bilinear', padding_mode='border')\n else:\n flow = flow.permute(0, 3, 1, 2)\n\n last_flow = flow\n x_warp = F.grid_sample(\n x_warp,\n flow.permute(0, 2, 3, 1),\n mode='bilinear',\n padding_mode='border')\n concat = torch.cat([x_warp, x_cond], 1)\n flow = self.netRefine[i](concat)\n flow = apply_offset(flow)\n flow = F.grid_sample(\n last_flow, flow, mode='bilinear', padding_mode='border')\n\n last_flow = F.interpolate(flow, scale_factor=2, mode='bilinear')\n\n x_warp = F.grid_sample(\n x,\n last_flow.permute(0, 2, 3, 1),\n mode='bilinear',\n padding_mode='border')\n return x_warp, last_flow\n" }, { "path": "projects/flow_style_vton/models/flow_style_vton_model.py", "content": "import mmengine\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.model import BaseModel\nfrom mmengine.runner.checkpoint import _load_checkpoint\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils.typing import ForwardInputs\n\n\n@MODELS.register_module()\nclass FlowStyleVTON(BaseModel):\n\n def __init__(self, warp_model, gen_model, pretrained_cfgs=None):\n super().__init__()\n self.warp_model = MODELS.build(warp_model)\n self.gen_model = MODELS.build(gen_model)\n if pretrained_cfgs:\n self.load_pretrained_models(pretrained_cfgs)\n\n def load_pretrained_models(self, pretrained_cfgs):\n \"\"\"_summary_\n\n Args:\n pretrained_cfgs (_type_): _description_\n \"\"\"\n for key, ckpt_cfg in pretrained_cfgs.items():\n map_location = ckpt_cfg.get('map_location', 'cpu')\n strict = ckpt_cfg.get('strict', False)\n ckpt_path = ckpt_cfg.get('ckpt_path')\n state_dict = _load_checkpoint(ckpt_path, map_location)\n getattr(self, key).load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained {key} from {ckpt_path}')\n\n @property\n def device(self):\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n def infer(self, inputs):\n\n real_image = inputs['image']\n clothes = inputs['clothes']\n edge = inputs['edge']\n edge = torch.FloatTensor(\n (edge.detach().numpy() > 0.5).astype(np.int64))\n clothes = clothes * edge\n\n device = self.device\n real_image, clothes, edge = real_image.to(device), clothes.to(\n device), edge.to(device)\n warped_cloth, last_flow = self.warp_model(real_image, clothes)\n warped_edge = F.grid_sample(\n edge,\n last_flow.permute(0, 2, 3, 1),\n mode='bilinear',\n padding_mode='zeros')\n\n gen_inputs = torch.cat([real_image, warped_cloth, warped_edge], 1)\n gen_outputs = self.gen_model(gen_inputs)\n p_rendered, m_composite = torch.split(gen_outputs, [3, 1], 1)\n p_rendered = torch.tanh(p_rendered)\n m_composite = torch.sigmoid(m_composite)\n m_composite = m_composite * warped_edge\n p_tryon = warped_cloth * m_composite + p_rendered * (1 - m_composite)\n\n flow_offset = de_offset(last_flow)\n flow_color = flow2color()(flow_offset)\n combine = torch.cat([\n real_image[0], clothes[0],\n flow_color.to(device),\n warped_cloth.to(device)[0], p_tryon[0]\n ], 2).squeeze()\n return p_tryon, combine\n\n def forward(self, inputs: ForwardInputs):\n return self.infer(inputs)\n\n\ndef make_colorwheel():\n \"\"\"Generates a color wheel for optical flow visualization as presented in:\n\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\"\n (ICCV, 2007) URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n According to the C++ source code of Daniel Scharstein\n According to the Matlab source code of Deqing Sun\n \"\"\"\n RY = 15\n YG = 6\n GC = 4\n CB = 11\n BM = 13\n MR = 6\n\n ncols = RY + YG + GC + CB + BM + MR\n colorwheel = np.zeros((ncols, 3))\n col = 0\n\n # RY\n colorwheel[0:RY, 0] = 255\n colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)\n col = col + RY\n # YG\n colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)\n colorwheel[col:col + YG, 1] = 255\n col = col + YG\n # GC\n colorwheel[col:col + GC, 1] = 255\n colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)\n col = col + GC\n # CB\n colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)\n colorwheel[col:col + CB, 2] = 255\n col = col + CB\n # BM\n colorwheel[col:col + BM, 2] = 255\n colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)\n col = col + BM\n # MR\n colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)\n colorwheel[col:col + MR, 0] = 255\n return colorwheel\n\n\nclass flow2color():\n # code from: https://github.com/tomrunia/OpticalFlow_Visualization\n # MIT License\n #\n # Copyright (c) 2018 Tom Runia\n #\n # Permission is hereby granted, free of charge, to any person obtaining\n # a copy of this software and associated documentation files\n # (the \"Software\"), to deal in the Software without restriction,\n # including without limitation the rights to use, copy, modify, merge,\n # publish, distribute, sublicense, and/or sell copies of the Software,\n # and to permit persons to whom the Software is\n # furnished to do so, subject to conditions.\n #\n # Author: Tom Runia\n # Date Created: 2018-08-03\n def __init__(self):\n self.colorwheel = make_colorwheel()\n\n def flow_compute_color(self, u, v, convert_to_bgr=False):\n \"\"\"Applies the flow color wheel to (possibly clipped) flow components u\n and v. According to the C++ source code of Daniel Scharstein According\n to the Matlab source code of Deqing Sun.\n\n :param u: np.ndarray, input horizontal flow\n :param v: np.ndarray, input vertical flow\n :param convert_to_bgr: bool, whether to change ordering and output BGR\n instead of RGB\n :return:\n \"\"\"\n flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)\n ncols = self.colorwheel.shape[0]\n\n rad = np.sqrt(np.square(u) + np.square(v))\n a = np.arctan2(-v, -u) / np.pi\n fk = (a + 1) / 2 * (ncols - 1)\n k0 = np.floor(fk).astype(np.int32)\n k1 = k0 + 1\n k1[k1 == ncols] = 0\n f = fk - k0\n\n for i in range(self.colorwheel.shape[1]):\n\n tmp = self.colorwheel[:, i]\n col0 = tmp[k0] / 255.0\n col1 = tmp[k1] / 255.0\n col = (1 - f) * col0 + f * col1\n\n idx = (rad <= 1)\n col[idx] = 1 - rad[idx] * (1 - col[idx])\n col[~idx] = col[~idx] * 0.75 # out of range?\n\n # Note the 2-i => BGR instead of RGB\n ch_idx = 2 - i if convert_to_bgr else i\n flow_image[:, :, ch_idx] = np.floor(255 * col)\n\n return flow_image\n\n def __call__(self, flow_uv, clip_flow=None, convert_to_bgr=False):\n \"\"\"Expects a two dimensional flow image of shape [H,W,2] According to\n the C++ source code of Daniel Scharstein According to the Matlab source\n code of Deqing Sun.\n\n :param flow_uv: np.ndarray of shape [H,W,2]\n :param clip_flow: float, maximum clipping value for flow\n :return:\n \"\"\"\n if len(flow_uv.size()) != 3:\n flow_uv = flow_uv[0]\n flow_uv = flow_uv.permute(1, 2, 0).cpu().detach().numpy()\n\n assert flow_uv.ndim == 3, 'input flow must have three dimensions'\n assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'\n\n if clip_flow is not None:\n flow_uv = np.clip(flow_uv, 0, clip_flow)\n\n u = flow_uv[:, :, 1]\n v = flow_uv[:, :, 0]\n\n rad = np.sqrt(np.square(u) + np.square(v))\n rad_max = np.max(rad)\n\n epsilon = 1e-5\n u = u / (rad_max + epsilon)\n v = v / (rad_max + epsilon)\n image = self.flow_compute_color(u, v, convert_to_bgr)\n image = torch.tensor(image).float().permute(2, 0, 1) / 255.0 * 2 - 1\n return image\n\n\ndef de_offset(s_grid):\n [b, _, h, w] = s_grid.size()\n\n x = torch.arange(w).view(1, -1).expand(h, -1).float()\n y = torch.arange(h).view(-1, 1).expand(-1, w).float()\n x = 2 * x / (w - 1) - 1\n y = 2 * y / (h - 1) - 1\n grid = torch.stack([x, y], dim=0).float().cuda()\n grid = grid.unsqueeze(0).expand(b, -1, -1, -1)\n\n offset = grid - s_grid\n\n offset_x = offset[:, 0, :, :] * (w - 1) / 2\n offset_y = offset[:, 1, :, :] * (h - 1) / 2\n\n offset = torch.cat((offset_y, offset_x), 0)\n\n return offset\n" }, { "path": "projects/flow_style_vton/models/generator.py", "content": "import os\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass ResUnetGenerator(nn.Module):\n\n def __init__(self,\n input_nc,\n output_nc,\n num_downs,\n ngf=64,\n norm_layer=nn.BatchNorm2d,\n use_dropout=False):\n super(ResUnetGenerator, self).__init__()\n # construct unet structure\n unet_block = ResUnetSkipConnectionBlock(\n ngf * 8,\n ngf * 8,\n input_nc=None,\n submodule=None,\n norm_layer=norm_layer,\n innermost=True)\n\n for i in range(num_downs - 5):\n unet_block = ResUnetSkipConnectionBlock(\n ngf * 8,\n ngf * 8,\n input_nc=None,\n submodule=unet_block,\n norm_layer=norm_layer,\n use_dropout=use_dropout)\n unet_block = ResUnetSkipConnectionBlock(\n ngf * 4,\n ngf * 8,\n input_nc=None,\n submodule=unet_block,\n norm_layer=norm_layer)\n unet_block = ResUnetSkipConnectionBlock(\n ngf * 2,\n ngf * 4,\n input_nc=None,\n submodule=unet_block,\n norm_layer=norm_layer)\n unet_block = ResUnetSkipConnectionBlock(\n ngf,\n ngf * 2,\n input_nc=None,\n submodule=unet_block,\n norm_layer=norm_layer)\n unet_block = ResUnetSkipConnectionBlock(\n output_nc,\n ngf,\n input_nc=input_nc,\n submodule=unet_block,\n outermost=True,\n norm_layer=norm_layer)\n\n self.model = unet_block\n\n def forward(self, input):\n return self.model(input)\n\n\nclass ResidualBlock(nn.Module):\n\n def __init__(self, in_features=64, norm_layer=nn.BatchNorm2d):\n super(ResidualBlock, self).__init__()\n self.relu = nn.ReLU(True)\n if norm_layer is None:\n self.block = nn.Sequential(\n nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),\n nn.ReLU(inplace=True),\n nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),\n )\n else:\n self.block = nn.Sequential(\n nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),\n norm_layer(in_features), nn.ReLU(inplace=True),\n nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),\n norm_layer(in_features))\n\n def forward(self, x):\n residual = x\n out = self.block(x)\n out += residual\n out = self.relu(out)\n return out\n\n\n# Defines the submodule with skip connection.\n# X -------------------identity---------------------- X\n# |-- downsampling -- |submodule| -- upsampling --|\nclass ResUnetSkipConnectionBlock(nn.Module):\n\n def __init__(self,\n outer_nc,\n inner_nc,\n input_nc=None,\n submodule=None,\n outermost=False,\n innermost=False,\n norm_layer=nn.BatchNorm2d,\n use_dropout=False):\n super(ResUnetSkipConnectionBlock, self).__init__()\n self.outermost = outermost\n use_bias = norm_layer == nn.InstanceNorm2d\n\n if input_nc is None:\n input_nc = outer_nc\n downconv = nn.Conv2d(\n input_nc,\n inner_nc,\n kernel_size=3,\n stride=2,\n padding=1,\n bias=use_bias)\n # add two resblock\n res_downconv = [\n ResidualBlock(inner_nc, norm_layer),\n ResidualBlock(inner_nc, norm_layer)\n ]\n res_upconv = [\n ResidualBlock(outer_nc, norm_layer),\n ResidualBlock(outer_nc, norm_layer)\n ]\n\n downrelu = nn.ReLU(True)\n uprelu = nn.ReLU(True)\n if norm_layer is not None:\n downnorm = norm_layer(inner_nc)\n upnorm = norm_layer(outer_nc)\n\n if outermost:\n upsample = nn.Upsample(scale_factor=2, mode='nearest')\n upconv = nn.Conv2d(\n inner_nc * 2,\n outer_nc,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias)\n down = [downconv, downrelu] + res_downconv\n up = [upsample, upconv]\n model = down + [submodule] + up\n elif innermost:\n upsample = nn.Upsample(scale_factor=2, mode='nearest')\n upconv = nn.Conv2d(\n inner_nc,\n outer_nc,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias)\n down = [downconv, downrelu] + res_downconv\n if norm_layer is None:\n up = [upsample, upconv, uprelu] + res_upconv\n else:\n up = [upsample, upconv, upnorm, uprelu] + res_upconv\n model = down + up\n else:\n upsample = nn.Upsample(scale_factor=2, mode='nearest')\n upconv = nn.Conv2d(\n inner_nc * 2,\n outer_nc,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias)\n if norm_layer is None:\n down = [downconv, downrelu] + res_downconv\n up = [upsample, upconv, uprelu] + res_upconv\n else:\n down = [downconv, downnorm, downrelu] + res_downconv\n up = [upsample, upconv, upnorm, uprelu] + res_upconv\n\n if use_dropout:\n model = down + [submodule] + up + [nn.Dropout(0.5)]\n else:\n model = down + [submodule] + up\n\n self.model = nn.Sequential(*model)\n\n def forward(self, x):\n if self.outermost:\n return self.model(x)\n else:\n return torch.cat([x, self.model(x)], 1)\n\n\ndef load_checkpoint(model, checkpoint_path):\n\n if not os.path.exists(checkpoint_path):\n print('No checkpoint!')\n return\n\n checkpoint = torch.load(checkpoint_path)\n checkpoint_new = model.state_dict()\n for param in checkpoint_new:\n checkpoint_new[param] = checkpoint[param]\n\n model.load_state_dict(checkpoint_new)\n" }, { "path": "projects/flow_style_vton/test_pairs.txt", "content": "000001_0.jpg 001744_1.jpg\n000010_0.jpg 004325_1.jpg\n000020_0.jpg 002022_1.jpg\n000028_0.jpg 014652_1.jpg\n000038_0.jpg 010143_1.jpg\n000048_0.jpg 010608_1.jpg\n000057_0.jpg 012578_1.jpg\n000066_0.jpg 009595_1.jpg\n000074_0.jpg 015392_1.jpg\n000082_0.jpg 003291_1.jpg\n000097_0.jpg 019522_1.jpg\n000109_0.jpg 018778_1.jpg\n000118_0.jpg 005533_1.jpg\n000129_0.jpg 007365_1.jpg\n000143_0.jpg 016246_1.jpg\n000154_0.jpg 003898_1.jpg\n000164_0.jpg 003514_1.jpg\n000174_0.jpg 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008590_1.jpg\n019446_0.jpg 006571_1.jpg\n019454_0.jpg 012228_1.jpg\n019465_0.jpg 010003_1.jpg\n019475_0.jpg 006304_1.jpg\n019486_0.jpg 013019_1.jpg\n019494_0.jpg 010929_1.jpg\n019505_0.jpg 010753_1.jpg\n019514_0.jpg 012054_1.jpg\n019522_0.jpg 018102_1.jpg\n019531_0.jpg 015077_1.jpg\n019540_0.jpg 008151_1.jpg\n019552_0.jpg 003787_1.jpg\n019561_0.jpg 006090_1.jpg\n019573_0.jpg 008608_1.jpg\n019581_0.jpg 006081_1.jpg\n019590_0.jpg 012964_1.jpg\n" }, { "path": "projects/flow_style_vton/vton_dataset.py", "content": "import os.path\nimport random\n\nimport numpy as np\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\n\nclass AlignedDataset(Dataset):\n\n def __init__(self, opt):\n self.opt = opt\n self.root = opt.dataroot\n\n self.fine_height = 256\n self.fine_width = 192\n\n self.text = opt.test_pairs\n\n dir_I = '_img'\n self.dir_I = os.path.join(opt.dataroot, opt.phase + dir_I)\n\n dir_C = '_clothes'\n self.dir_C = os.path.join(opt.dataroot, opt.phase + dir_C)\n\n dir_E = '_edge'\n self.dir_E = os.path.join(opt.dataroot, opt.phase + dir_E)\n\n self.im_name = []\n self.c_name = []\n self.e_name = []\n self.get_file_name()\n self.dataset_size = len(self.im_name)\n\n def get_file_name(self):\n\n with open(self.text, 'r') as f:\n for line in f.readlines():\n im_name, c_name = line.strip().split()\n self.im_name.append(os.path.join(self.dir_I, im_name))\n self.c_name.append(os.path.join(self.dir_C, c_name))\n self.e_name.append(os.path.join(self.dir_E, c_name))\n\n def __getitem__(self, index):\n\n I_path = os.path.join(self.im_name[index])\n img = Image.open(I_path).convert('RGB')\n\n params = get_params(self.opt, img.size)\n transform = get_transform(self.opt, params)\n transform_E = get_transform(\n self.opt, params, method=Image.NEAREST, normalize=False)\n\n I_tensor = transform(img)\n C_path = os.path.join(self.c_name[index])\n C = Image.open(C_path).convert('RGB')\n C_tensor = transform(C)\n\n E_path = os.path.join(self.e_name[index])\n E = Image.open(E_path).convert('L')\n E_tensor = transform_E(E)\n\n input_dict = {\n 'image': I_tensor,\n 'clothes': C_tensor,\n 'edge': E_tensor,\n 'p_name': self.im_name[index].split('/')[-1]\n }\n return input_dict\n\n def __len__(self):\n return self.dataset_size\n\n\ndef get_params(opt, size):\n w, h = size\n new_h = h\n new_w = w\n if opt.resize_or_crop == 'resize_and_crop':\n new_h = new_w = opt.loadSize\n elif opt.resize_or_crop == 'scale_width_and_crop':\n new_w = opt.loadSize\n new_h = opt.loadSize * h // w\n\n x = random.randint(0, np.maximum(0, new_w - opt.fineSize))\n y = random.randint(0, np.maximum(0, new_h - opt.fineSize))\n\n flip = 0\n return {'crop_pos': (x, y), 'flip': flip}\n\n\ndef get_transform_resize(opt, params, method=Image.BICUBIC, normalize=True):\n transform_list = []\n transform_list.append(\n transforms.Lambda(\n lambda img: __scale_width(img, opt.loadSize, method)))\n osize = [256, 192]\n transform_list.append(transforms.Scale(osize, method))\n if 'crop' in opt.resize_or_crop:\n transform_list.append(\n transforms.Lambda(\n lambda img: __crop(img, params['crop_pos'], opt.fineSize)))\n\n if opt.resize_or_crop == 'none':\n base = float(2**opt.n_downsample_global)\n if opt.netG == 'local':\n base *= (2**opt.n_local_enhancers)\n transform_list.append(\n transforms.Lambda(lambda img: __make_power_2(img, base, method)))\n\n if opt.isTrain and not opt.no_flip:\n transform_list.append(\n transforms.Lambda(lambda img: __flip(img, params['flip'])))\n\n transform_list += [transforms.ToTensor()]\n\n if normalize:\n transform_list += [\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ]\n return transforms.Compose(transform_list)\n\n\ndef get_transform(opt, params, method=Image.BICUBIC, normalize=True):\n transform_list = []\n if 'resize' in opt.resize_or_crop:\n osize = [opt.loadSize, opt.loadSize]\n transform_list.append(transforms.Resize(osize, method))\n elif 'scale_width' in opt.resize_or_crop:\n transform_list.append(\n transforms.Lambda(\n lambda img: __scale_width(img, opt.loadSize, method)))\n osize = [256, 192]\n transform_list.append(transforms.Resize(osize, method))\n if 'crop' in opt.resize_or_crop:\n transform_list.append(\n transforms.Lambda(\n lambda img: __crop(img, params['crop_pos'], opt.fineSize)))\n\n if opt.resize_or_crop == 'none':\n base = float(16)\n transform_list.append(\n transforms.Lambda(lambda img: __make_power_2(img, base, method)))\n\n if opt.isTrain and not opt.no_flip:\n transform_list.append(\n transforms.Lambda(lambda img: __flip(img, params['flip'])))\n\n transform_list += [transforms.ToTensor()]\n\n if normalize:\n transform_list += [\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ]\n return transforms.Compose(transform_list)\n\n\ndef normalize():\n return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n\n\ndef __make_power_2(img, base, method=Image.BICUBIC):\n ow, oh = img.size\n h = int(round(oh / base) * base)\n w = int(round(ow / base) * base)\n if (h == oh) and (w == ow):\n return img\n return img.resize((w, h), method)\n\n\ndef __scale_width(img, target_width, method=Image.BICUBIC):\n ow, oh = img.size\n if (ow == target_width):\n return img\n w = target_width\n h = int(target_width * oh / ow)\n return img.resize((w, h), method)\n\n\ndef __crop(img, pos, size):\n ow, oh = img.size\n x1, y1 = pos\n tw = th = size\n if (ow > tw or oh > th):\n return img.crop((x1, y1, x1 + tw, y1 + th))\n return img\n\n\ndef __flip(img, flip):\n if flip:\n return img.transpose(Image.FLIP_LEFT_RIGHT)\n return img\n" }, { "path": "projects/glide/configs/README.md", "content": "# GLIDE (Arxiv'2021)\n\n> [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://papers.nips.cc/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf)\n\n> **Task**: Text2Image, diffusion\n\n\n\n## Abstract\n\n\n\nDiffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifierfree guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im.\n\n\n\n
\n \n
\n\n## Results and models\n\n
\n an oil painting of a corgi\n
\n \n
\n\n
\n an cartoon painting of a cat\n
\n \n
\n\n**Laion**\n\n| Method | Resolution | Config | Weights |\n| ------ | ---------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |\n| Glide | 64x64 | [config](projects/glide/configs/glide_ddim-classifier-free_laion-64x64.py) | [model](https://download.openmmlab.com/mmediting/glide/glide_laion-64x64-02afff47.pth) |\n| Glide | 64x64 -> 256x256 | [config](projects/glide/configs/glide_ddim-classifier-free_laion-64-256.py) | [model](https://download.openxlab.org.cn/models/mmediting/GLIDE/weight/glide_laion-64-256) |\n\n## Quick Start\n\nYou can run glide as follows:\n\n```python\nimport torch\nfrom mmagic.apis import init_model\nfrom mmengine.registry import init_default_scope\nfrom projects.glide.models import *\n\ninit_default_scope('mmagic')\n\nconfig = 'projects/glide/configs/glide_ddim-classifier-free_laion-64x64.py'\nckpt = 'https://download.openmmlab.com/mmagic/glide/glide_laion-64x64-02afff47.pth'\nmodel = init_model(config, ckpt).cuda().eval()\nprompt = \"an oil painting of a corgi\"\n\nwith torch.no_grad():\n samples = model.infer(init_image=None,\n prompt=prompt,\n batch_size=16,\n guidance_scale=3.,\n num_inference_steps=100,\n labels=None,\n classifier_scale=0.0,\n show_progress=True)['samples']\n```\n\nYou can synthesis images with 256x256 resolution:\n\n```python\nimport torch\nfrom torchvision.utils import save_image\nfrom mmagic.apis import init_model\nfrom mmengine.registry import init_default_scope\nfrom projects.glide.models import *\n\ninit_default_scope('mmagic')\n\nconfig = 'projects/glide/configs/glide_ddim-classifier-free_laion-64-256.py'\nckpt = 'https://download.openxlab.org.cn/models/mmediting/GLIDE/weight/glide_laion-64-256'\nmodel = init_model(config, ckpt).cuda().eval()\nprompt = \"an oil painting of a corgi\"\n\nwith torch.no_grad():\n samples = model.infer(init_image=None,\n prompt=prompt,\n batch_size=16,\n guidance_scale=3.,\n num_inference_steps=100,\n labels=None,\n classifier_scale=0.0,\n show_progress=True)['samples']\nsave_image(samples, \"corgi.png\", nrow=4, normalize=True, value_range=(-1, 1))\n```\n\n## Citation\n\n```bibtex\n@article{2021GLIDE,\n title={GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models},\n author={ Nichol, A. and Dhariwal, P. and Ramesh, A. and Shyam, P. and Mishkin, P. and Mcgrew, B. and Sutskever, I. and Chen, M. },\n year={2021},\n}\n```\n" }, { "path": "projects/glide/configs/glide_ddim-classifier-free_laion-64-256.py", "content": "unet_cfg = dict(\n type='Text2ImUNet',\n image_size=64,\n base_channels=192,\n in_channels=3,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=0,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=1,\n num_head_channels=64,\n use_new_attention_order=False,\n encoder_channels=512),\n use_scale_shift_norm=True,\n text_ctx=128,\n xf_width=512,\n xf_layers=16,\n xf_heads=8,\n xf_final_ln=True,\n xf_padding=True,\n)\nunet_up_cfg = dict(\n type='SuperResText2ImUNet',\n image_size=256,\n base_channels=192,\n in_channels=3,\n output_cfg=dict(var='FIXED'),\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=0,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=1,\n num_head_channels=64,\n use_new_attention_order=False,\n encoder_channels=512),\n use_scale_shift_norm=True,\n text_ctx=128,\n xf_width=512,\n xf_layers=16,\n xf_heads=8,\n xf_final_ln=True,\n xf_padding=True,\n)\n\nmodel = dict(\n type='Glide',\n data_preprocessor=dict(type='DataPreprocessor', mean=[127.5], std=[127.5]),\n unet=unet_cfg,\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='squaredcos_cap_v2'),\n unet_up=unet_up_cfg,\n diffusion_scheduler_up=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='linear'),\n use_fp16=False)\n" }, { "path": "projects/glide/configs/glide_ddim-classifier-free_laion-64x64.py", "content": "model = dict(\n type='Glide',\n data_preprocessor=dict(type='DataPreprocessor', mean=[127.5], std=[127.5]),\n unet=dict(\n type='Text2ImUNet',\n image_size=64,\n base_channels=192,\n in_channels=3,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=0,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=1,\n num_head_channels=64,\n use_new_attention_order=False,\n encoder_channels=512),\n use_scale_shift_norm=True,\n text_ctx=128,\n xf_width=512,\n xf_layers=16,\n xf_heads=8,\n xf_final_ln=True,\n xf_padding=True,\n ),\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='squaredcos_cap_v2'),\n use_fp16=False)\n" }, { "path": "projects/glide/models/__init__.py", "content": "from .glide import Glide\nfrom .text2im_unet import SuperResText2ImUNet, Text2ImUNet\n\n__all__ = ['Text2ImUNet', 'Glide', 'SuperResText2ImUNet']\n" }, { "path": "projects/glide/models/glide.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Union\n\nimport mmengine\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\nfrom mmengine.model import BaseModel, is_model_wrapper\nfrom mmengine.optim import OptimWrapperDict\nfrom mmengine.runner.checkpoint import _load_checkpoint_with_prefix\nfrom tqdm import tqdm\n\nfrom mmagic.registry import DIFFUSION_SCHEDULERS, MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils.typing import ForwardInputs, SampleList\n\nModelType = Union[Dict, nn.Module]\n\n\ndef classifier_grad(classifier, x, t, y=None, classifier_scale=1.0):\n \"\"\"compute classification gradient to x.\"\"\"\n assert y is not None\n with torch.enable_grad():\n x_in = x.detach().requires_grad_(True)\n logits = classifier(x_in, t)\n log_probs = F.log_softmax(logits, dim=-1)\n selected = log_probs[range(len(logits)), y.view(-1)]\n return torch.autograd.grad(selected.sum(), x_in)[0] * classifier_scale\n\n\n@MODELS.register_module('GLIDE')\n@MODELS.register_module()\nclass Glide(BaseModel):\n \"\"\"GLIDE: Guided language to image diffusion for generation and editing.\n Refer to: https://github.com/openai/glide-text2im.\n\n\n Args:\n data_preprocessor (dict, optional): The pre-process configuration for\n :class:`BaseDataPreprocessor`.\n unet (ModelType): Configuration for the denoising Unet.\n diffusion_scheduler (ModelType): Configuration for the diffusion\n scheduler.\n unet_up (ModelType, optional): Configuration for the upsampling\n denoising UNet. Defaults to None.\n diffusion_scheduler_up (ModelType, optional): Configuration for\n the upsampling diffusion scheduler. Defaults to None.\n use_fp16 (bool, optional): Whether to use fp16 for the unet model.\n Defaults to False.\n classifier (ModelType, optional): Configuration for the classifier.\n Defaults to None.\n classifier_scale (float): Classifier scale for classifier guidance.\n Defaults to 1.0.\n data_preprocessor (Optional[ModelType]): Configuration for the data\n preprocessor.\n pretrained_cfgs (dict, optional): Path configuration for pretrained\n weights. Usually, this is a dict containing the module name and\n the corresponding ckpt path. Defaults to None.\n \"\"\"\n\n def __init__(self,\n unet: ModelType,\n diffusion_scheduler: ModelType,\n unet_up: Optional[ModelType] = None,\n diffusion_scheduler_up: Optional[ModelType] = None,\n use_fp16: Optional[bool] = False,\n classifier: Optional[dict] = None,\n classifier_scale: float = 1.0,\n data_preprocessor: Optional[ModelType] = dict(\n type='DataPreprocessor'),\n pretrained_cfgs: Optional[dict] = None):\n\n super().__init__(data_preprocessor=data_preprocessor)\n self.unet = unet if isinstance(unet, nn.Module) else MODELS.build(unet)\n self.diffusion_scheduler = DIFFUSION_SCHEDULERS.build(\n diffusion_scheduler) if isinstance(diffusion_scheduler,\n dict) else diffusion_scheduler\n\n self.unet_up = None\n self.diffusion_scheduler_up = None\n if unet_up:\n self.unet_up = unet_up if isinstance(\n unet_up, nn.Module) else MODELS.build(unet_up)\n if diffusion_scheduler_up:\n self.diffusion_scheduler_up = DIFFUSION_SCHEDULERS.build(\n diffusion_scheduler_up) if isinstance(\n diffusion_scheduler_up,\n dict) else diffusion_scheduler_up\n else:\n self.diffusion_scheduler_up = deepcopy(\n self.diffusion_scheduler)\n\n if classifier:\n self.classifier = MODELS.build(classifier)\n else:\n self.classifier = None\n self.classifier_scale = classifier_scale\n\n if pretrained_cfgs:\n self.load_pretrained_models(pretrained_cfgs)\n if use_fp16:\n mmengine.print_log('Convert unet modules to floatpoint16')\n self.unet.convert_to_fp16()\n\n def load_pretrained_models(self, pretrained_cfgs):\n \"\"\"_summary_\n\n Args:\n pretrained_cfgs (_type_): _description_\n \"\"\"\n for key, ckpt_cfg in pretrained_cfgs.items():\n prefix = ckpt_cfg.get('prefix', '')\n map_location = ckpt_cfg.get('map_location', 'cpu')\n strict = ckpt_cfg.get('strict', True)\n ckpt_path = ckpt_cfg.get('ckpt_path')\n state_dict = _load_checkpoint_with_prefix(prefix, ckpt_path,\n map_location)\n getattr(self, key).load_state_dict(state_dict, strict=strict)\n mmengine.print_log(f'Load pretrained {key} from {ckpt_path}')\n\n @property\n def device(self):\n \"\"\"Get current device of the model.\n\n Returns:\n torch.device: The current device of the model.\n \"\"\"\n return next(self.parameters()).device\n\n @torch.no_grad()\n def infer(self,\n init_image: Optional[torch.Tensor] = None,\n prompt: str = None,\n batch_size: Optional[int] = 1,\n guidance_scale: float = 3.,\n num_inference_steps: int = 50,\n num_inference_steps_up: Optional[int] = 27,\n labels: Optional[torch.Tensor] = None,\n classifier_scale: float = 0.0,\n show_progress: Optional[bool] = False):\n \"\"\"Inference function for guided diffusion.\n\n Args:\n init_image (torch.Tensor, optional): Starting noise for diffusion.\n Defaults to None.\n prompt (str): The prompt to guide the image generation.\n batch_size (int, optional): Batch size for generation.\n Defaults to 1.\n num_inference_steps (int, optional): The number of denoising steps.\n Defaults to 50.\n num_inference_steps_up (int, optional): The number of upsampling\n denoising steps. Defaults to 27.\n labels (torch.Tensor, optional): Labels for the classifier.\n Defaults to None.\n show_progress (bool, optional): Whether to show the progress bar.\n Defaults to False.\n\n Returns:\n torch.Tensor: Generated images.\n \"\"\"\n # Sample gaussian noise to begin loop\n if init_image is None:\n image = torch.randn((2 * batch_size, self.unet.in_channels,\n self.unet.image_size, self.unet.image_size))\n image = image.to(self.device)\n else:\n image = init_image\n\n # set step values\n if num_inference_steps > 0:\n self.diffusion_scheduler.set_timesteps(num_inference_steps)\n\n timesteps = self.diffusion_scheduler.timesteps\n\n # text embedding\n tokens = self.unet.tokenizer.encode(prompt)\n tokens, mask = self.unet.tokenizer.padded_tokens_and_mask(tokens, 128)\n\n # Create the classifier-free guidance tokens (empty)\n # full_batch_size = batch_size * 2\n uncond_tokens, uncond_mask = \\\n self.unet.tokenizer.padded_tokens_and_mask(\n [], 128)\n\n tokens = torch.tensor(\n [tokens] * batch_size + [uncond_tokens] * batch_size,\n device=self.device)\n mask = torch.tensor(\n [mask] * batch_size + [uncond_mask] * batch_size,\n dtype=torch.bool,\n device=self.device)\n\n if show_progress and mmengine.dist.is_main_process():\n timesteps = tqdm(timesteps)\n\n for t in timesteps:\n # 1. predicted model_output\n half = image[:len(image) // 2]\n combined = torch.cat([half, half], dim=0)\n model_output = self.unet(combined, t, tokens=tokens, mask=mask)\n eps, rest = model_output[:, :3], model_output[:, 3:]\n cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)\n half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)\n eps = torch.cat([half_eps, half_eps], dim=0)\n noise_pred = torch.cat([eps, rest], dim=1)\n\n # 2. compute previous image: x_t -> x_t-1\n diffusion_scheduler_output = self.diffusion_scheduler.step(\n noise_pred, t, image)\n\n # 3. applying classifier guide\n if self.classifier and classifier_scale != 0.0:\n gradient = classifier_grad(\n self.classifier,\n image,\n t,\n labels,\n classifier_scale=classifier_scale)\n guided_mean = (\n diffusion_scheduler_output['mean'].float() +\n diffusion_scheduler_output['sigma'] * gradient.float())\n image = guided_mean + diffusion_scheduler_output[\n 'sigma'] * diffusion_scheduler_output['noise']\n else:\n image = diffusion_scheduler_output['prev_sample']\n\n # abandon unconditional image\n image = image[:image.shape[0] // 2]\n\n if self.unet_up:\n image = self.infer_up(\n low_res_img=image,\n batch_size=batch_size,\n prompt=prompt,\n num_inference_steps=num_inference_steps_up)\n\n return {'samples': image}\n\n @torch.no_grad()\n def infer_up(self,\n low_res_img: torch.Tensor,\n batch_size: int = 1,\n init_image: Optional[torch.Tensor] = None,\n prompt: Optional[str] = None,\n num_inference_steps: int = 27,\n show_progress: bool = False):\n \"\"\"Inference function for upsampling guided diffusion.\n\n Args:\n low_res_img (torch.Tensor): Low resolution image\n (shape: [B, C, H, W]) for upsampling.\n batch_size (int, optional): Batch size for generation.\n Defaults to 1.\n init_image (torch.Tensor, optional): Starting noise\n (shape: [B, C, H, W]) for diffusion. Defaults to None.\n prompt (str, optional): The text prompt to guide the image\n generation. Defaults to None.\n num_inference_steps (int, optional): The number of denoising\n steps. Defaults to 27.\n show_progress (bool, optional): Whether to show the progress bar.\n Defaults to False.\n\n Returns:\n torch.Tensor: Generated upsampled images (shape: [B, C, H, W]).\n \"\"\"\n if init_image is None:\n image = torch.randn(\n (batch_size, self.unet_up.in_channels // 2,\n self.unet_up.image_size, self.unet_up.image_size))\n image = image.to(self.device)\n else:\n image = init_image\n\n # set step values\n if num_inference_steps > 0:\n self.diffusion_scheduler_up.set_timesteps(num_inference_steps)\n timesteps = self.diffusion_scheduler_up.timesteps\n\n # text embedding\n tokens = self.unet.tokenizer.encode(prompt)\n tokens, mask = self.unet.tokenizer.padded_tokens_and_mask(tokens, 128)\n tokens = torch.tensor(\n [tokens] * batch_size, dtype=torch.bool, device=self.device)\n mask = torch.tensor(\n [mask] * batch_size, dtype=torch.bool, device=self.device)\n\n if show_progress and mmengine.dist.is_main_process():\n timesteps = tqdm(timesteps)\n\n for t in timesteps:\n noise_pred = self.unet_up(\n image, t, low_res=low_res_img, tokens=tokens, mask=mask)\n # compute previous image: x_t -> x_t-1\n diffusion_scheduler_output = self.diffusion_scheduler_up.step(\n noise_pred, t, image)\n image = diffusion_scheduler_output['prev_sample']\n\n return image\n\n def forward(self,\n inputs: ForwardInputs,\n data_samples: Optional[list] = None,\n mode: Optional[str] = None) -> List[DataSample]:\n \"\"\"_summary_\n\n Args:\n inputs (ForwardInputs): _description_\n data_samples (Optional[list], optional): _description_.\n Defaults to None.\n mode (Optional[str], optional): _description_. Defaults to None.\n\n Returns:\n List[DataSample]: _description_\n \"\"\"\n init_image = inputs.get('init_image', None)\n batch_size = inputs.get('batch_size', 1)\n labels = data_samples.get('labels', None)\n sample_kwargs = inputs.get('sample_kwargs', dict())\n\n num_inference_steps = sample_kwargs.get(\n 'num_inference_steps',\n self.diffusion_scheduler.num_train_timesteps)\n show_progress = sample_kwargs.get('show_progress', False)\n classifier_scale = sample_kwargs.get('classifier_scale',\n self.classifier_scale)\n\n outputs = self.infer(\n init_image=init_image,\n batch_size=batch_size,\n num_inference_steps=num_inference_steps,\n show_progress=show_progress,\n classifier_scale=classifier_scale)\n\n batch_sample_list = []\n for idx in range(batch_size):\n gen_sample = DataSample()\n if data_samples:\n gen_sample.update(data_samples[idx])\n if isinstance(outputs, dict):\n gen_sample.ema = DataSample(\n fake_img=outputs['ema'][idx], sample_model='ema')\n gen_sample.orig = DataSample(\n fake_img=outputs['orig'][idx], sample_model='orig')\n gen_sample.sample_model = 'ema/orig'\n gen_sample.set_gt_label(labels[idx])\n gen_sample.ema.set_gt_label(labels[idx])\n gen_sample.orig.set_gt_label(labels[idx])\n else:\n gen_sample.fake_img = outputs[idx]\n gen_sample.set_gt_label(labels[idx])\n\n # Append input condition (noise and sample_kwargs) to\n # batch_sample_list\n if init_image is not None:\n gen_sample.noise = init_image[idx]\n gen_sample.sample_kwargs = deepcopy(sample_kwargs)\n batch_sample_list.append(gen_sample)\n return batch_sample_list\n\n @torch.no_grad()\n def val_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data.\n\n Calls ``self.data_preprocessor(data)`` and\n ``self(inputs, data_sample, mode=None)`` in order. Return the\n generated results which will be passed to evaluator.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n SampleList: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n @torch.no_grad()\n def test_step(self, data: dict) -> SampleList:\n \"\"\"Gets the generated image of given data. Same as :meth:`val_step`.\n\n Args:\n data (dict): Data sampled from metric specific\n sampler. More details in `Metrics` and `Evaluator`.\n\n Returns:\n List[DataSample]: Generated image or image dict.\n \"\"\"\n data = self.data_preprocessor(data)\n outputs = self(**data)\n return outputs\n\n def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):\n \"\"\"_summary_\n\n Args:\n data (dict): _description_\n optim_wrapper (OptimWrapperDict): _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n message_hub = MessageHub.get_current_instance()\n curr_iter = message_hub.get_info('iter')\n\n # sampling x0 and timestep\n data = self.data_preprocessor(data)\n real_imgs = data['inputs']\n timestep = self.diffusion_scheduler.sample_timestep()\n\n # calculating loss\n loss_dict = self.diffusion_scheduler.training_loss(\n self.unet, real_imgs, timestep)\n loss, log_vars = self._parse_losses(loss_dict)\n optim_wrapper['denoising'].update_params(loss)\n\n # update EMA\n if self.with_ema_denoising and (curr_iter + 1) >= self.ema_start:\n self.denoising_ema.update_parameters(\n self.denoising_ema.\n module if is_model_wrapper(self.denoising) else self.denoising)\n # if not update buffer, copy buffer from orig model\n if not self.denoising_ema.update_buffers:\n self.denoising_ema.sync_buffers(\n self.denoising.module\n if is_model_wrapper(self.denoising) else self.denoising)\n elif self.with_ema_denoising:\n # before ema, copy weights from orig\n self.denoising_ema.sync_parameters(\n self.denoising.\n module if is_model_wrapper(self.denoising) else self.denoising)\n\n return log_vars\n" }, { "path": "projects/glide/models/glide_modules.py", "content": "import math\n\nimport torch\nimport torch.nn as nn\n\n\nclass QKVMultiheadAttention(nn.Module):\n\n def __init__(self, n_heads: int, n_ctx: int):\n super().__init__()\n self.n_heads = n_heads\n self.n_ctx = n_ctx\n\n def forward(self, qkv):\n bs, n_ctx, width = qkv.shape\n attn_ch = width // self.n_heads // 3\n scale = 1 / math.sqrt(math.sqrt(attn_ch))\n qkv = qkv.view(bs, n_ctx, self.n_heads, -1)\n q, k, v = torch.split(qkv, attn_ch, dim=-1)\n weight = torch.einsum(\n 'bthc,bshc->bhts', q * scale,\n k * scale) # More stable with f16 than dividing afterwards\n wdtype = weight.dtype\n weight = torch.softmax(weight.float(), dim=-1).type(wdtype)\n return torch.einsum('bhts,bshc->bthc', weight,\n v).reshape(bs, n_ctx, -1)\n\n\nclass MultiheadAttention(nn.Module):\n\n def __init__(self, n_ctx, width, heads):\n super().__init__()\n self.n_ctx = n_ctx\n self.width = width\n self.heads = heads\n self.c_qkv = nn.Linear(width, width * 3)\n self.c_proj = nn.Linear(width, width)\n self.attention = QKVMultiheadAttention(heads, n_ctx)\n\n def forward(self, x):\n x = self.c_qkv(x)\n x = self.attention(x)\n x = self.c_proj(x)\n return x\n\n\nclass MLP(nn.Module):\n\n def __init__(self, width):\n super().__init__()\n self.width = width\n self.c_fc = nn.Linear(width, width * 4)\n self.c_proj = nn.Linear(width * 4, width)\n self.gelu = nn.GELU()\n\n def forward(self, x):\n return self.c_proj(self.gelu(self.c_fc(x)))\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(\n self,\n n_ctx: int,\n width: int,\n heads: int,\n ):\n super().__init__()\n\n self.attn = MultiheadAttention(\n n_ctx,\n width,\n heads,\n )\n # TODO should the LayerNorm support fp15 gain\n # and bias but fp32 mean and std?\n self.ln_1 = nn.LayerNorm(width)\n self.mlp = MLP(width)\n self.ln_2 = nn.LayerNorm(width)\n\n def forward(self, x: torch.Tensor):\n x = x + self.attn(self.ln_1(x))\n x = x + self.mlp(self.ln_2(x))\n return x\n\n\nclass Transformer(nn.Module):\n\n def __init__(\n self,\n n_ctx: int,\n width: int,\n layers: int,\n heads: int,\n ):\n super().__init__()\n self.n_ctx = n_ctx\n self.width = width\n self.layers = layers\n self.resblocks = nn.ModuleList([\n ResidualAttentionBlock(\n n_ctx,\n width,\n heads,\n ) for _ in range(layers)\n ])\n\n def forward(self, x: torch.Tensor):\n for block in self.resblocks:\n x = block(x)\n return x\n" }, { "path": "projects/glide/models/glide_tokenizer/__init__.py", "content": "from .bpe import get_encoder\n\n__all__ = ['get_encoder']\n" }, { "path": "projects/glide/models/glide_tokenizer/bpe.py", "content": "\"\"\"Byte pair encoding utilities adapted from:\n\nhttps://github.com/openai/gpt-2/blob/master/src/encoder.py\n\"\"\"\n\nimport gzip\nimport json\nimport os\nfrom functools import lru_cache\nfrom typing import List, Tuple\n\nimport regex as re\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a\n large # of unicode characters in your vocab if you want to avoid UNKs. When\n you're at something like a 10B token dataset you end up needing around 5K\n for decent coverage. This is a significant percentage of your normal, say,\n 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and\n unicode strings. And avoids mapping to whitespace/control characters the\n bpe code barfs on.\n \"\"\"\n bs = (\n list(range(ord('!'),\n ord('~') + 1)) + list(range(ord('¡'),\n ord('¬') + 1)) +\n list(range(ord('®'),\n ord('ÿ') + 1)))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length\n strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\nclass Encoder:\n\n def __init__(self, encoder, bpe_merges, errors='replace'):\n self.encoder = encoder\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.errors = errors # how to handle errors in decoding\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))\n self.cache = {}\n\n # Should haved added re.IGNORECASE so BPE merges can happen for\n # capitalized versions of contractions\n self.pat = re.compile(\n r\"\"\"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]\n +|\\s+(?!\\S)|\\s+\"\"\")\n\n @property\n def n_vocab(self) -> int:\n return len(self.encoder)\n\n @property\n def end_token(self) -> int:\n return self.n_vocab - 1\n\n def padded_tokens_and_mask(self, tokens: List[int],\n text_ctx: int) -> Tuple[List[int], List[bool]]:\n tokens = tokens[:text_ctx]\n padding = text_ctx - len(tokens)\n padded_tokens = tokens + [self.end_token] * padding\n mask = [True] * len(tokens) + [False] * padding\n return padded_tokens, mask\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token)\n pairs = get_pairs(word)\n\n if not pairs:\n return token\n\n while True:\n bigram = min(\n pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[\n i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n text = text.lower()\n bpe_tokens = []\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b]\n for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token]\n for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode(\n 'utf-8', errors=self.errors)\n return text\n\n\ndef get_encoder():\n root_dir = os.path.dirname(os.path.abspath(__file__))\n with gzip.open(os.path.join(root_dir, 'encoder.json.gz'), 'r') as f:\n encoder = json.load(f)\n with gzip.open(os.path.join(root_dir, 'vocab.bpe.gz'), 'r') as f:\n bpe_data = str(f.read(), 'utf-8')\n bpe_merges = [\n tuple(merge_str.split()) for merge_str in bpe_data.split('\\n')[1:-1]\n ]\n return Encoder(\n encoder=encoder,\n bpe_merges=bpe_merges,\n )\n" }, { "path": "projects/glide/models/glide_tokenizer/simple_tokenizer.py", "content": "\"\"\"Copied from: https://github.com/openai/CLIP/blob/573315e83f07b53a61ff5098757\ne8fc885f1703e/clip/simple_tokenizer.py.\"\"\"\n\nimport gzip\nimport html\nimport os\nfrom functools import lru_cache\nfrom typing import List, Tuple\n\nimport ftfy\nimport regex as re\n\n\n@lru_cache()\ndef default_bpe():\n return os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n 'bpe_simple_vocab_16e6.txt.gz')\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a\n large # of unicode characters in your vocab if you want to avoid UNKs. When\n you're at something like a 10B token dataset you end up needing around 5K\n for decent coverage. This is a significant percentage of your normal, say,\n 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and\n unicode strings. And avoids mapping to whitespace/control characters the\n bpe code barfs on.\n \"\"\"\n bs = (\n list(range(ord('!'),\n ord('~') + 1)) + list(range(ord('¡'),\n ord('¬') + 1)) +\n list(range(ord('®'),\n ord('ÿ') + 1)))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length\n strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {\n '<|startoftext|>': '<|startoftext|>',\n '<|endoftext|>': '<|endoftext|>'\n }\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p\n {L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE,\n )\n\n @property\n def start_token(self):\n return self.encoder['<|startoftext|>']\n\n @property\n def end_token(self):\n return self.encoder['<|endoftext|>']\n\n def padded_tokens_and_len(self, tokens: List[int],\n text_ctx: int) -> Tuple[List[int], int]:\n tokens = [self.start_token] + tokens[:text_ctx - 2] + [self.end_token]\n text_len = len(tokens)\n padding = text_ctx - len(tokens)\n padded_tokens = tokens + [0] * padding\n return padded_tokens, text_len\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + '', )\n pairs = get_pairs(word)\n\n if not pairs:\n return token + ''\n\n while True:\n bigram = min(\n pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[\n i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b]\n for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token]\n for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = (\n bytearray([self.byte_decoder[c] for c in text\n ]).decode('utf-8',\n errors='replace').replace('', ' '))\n return text\n" }, { "path": "projects/glide/models/text2im_unet.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmagic.models import DenoisingUnet\nfrom mmagic.registry import MODELS\nfrom .glide_modules import Transformer\nfrom .glide_tokenizer import get_encoder\n\n\n@MODELS.register_module()\nclass Text2ImUNet(DenoisingUnet):\n \"\"\"A UNetModel used in GLIDE that conditions on text with an encoding\n transformer. Expects an extra kwarg `tokens` of text.\n\n Args:\n text_ctx (int): Number of text tokens to expect.\n xf_width (int): Width of the transformer.\n xf_layers (int): Depth of the transformer.\n xf_heads (int): Number of heads in the transformer.\n xf_final_ln (bool): Whether to use a LayerNorm after the output layer.\n tokenizer (callable, optional): Text tokenizer for sampling/vocab\n size. Defaults to get_encoder().\n cache_text_emb (bool, optional): Whether to cache text embeddings.\n Defaults to False.\n xf_ar (float, optional): Autoregressive weight for the transformer.\n Defaults to 0.0.\n xf_padding (bool, optional): Whether to use padding in the transformer.\n Defaults to False.\n share_unemb (bool, optional): Whether to share UNet embeddings.\n Defaults to False.\n \"\"\"\n\n def __init__(\n self,\n text_ctx,\n xf_width,\n xf_layers,\n xf_heads,\n xf_final_ln,\n *args,\n tokenizer=get_encoder(),\n cache_text_emb=False,\n xf_ar=0.0,\n xf_padding=False,\n share_unemb=False,\n **kwargs,\n ):\n self.text_ctx = text_ctx\n self.xf_width = xf_width\n self.xf_ar = xf_ar\n self.xf_padding = xf_padding\n self.tokenizer = tokenizer\n\n if not xf_width:\n super().__init__(*args, **kwargs, encoder_channels=None)\n else:\n super().__init__(*args, **kwargs, encoder_channels=xf_width)\n\n if self.xf_width:\n self.transformer = Transformer(\n text_ctx,\n xf_width,\n xf_layers,\n xf_heads,\n )\n if xf_final_ln:\n self.final_ln = nn.LayerNorm(xf_width)\n else:\n self.final_ln = None\n\n self.token_embedding = nn.Embedding(self.tokenizer.n_vocab,\n xf_width)\n self.positional_embedding = nn.Parameter(\n torch.empty(text_ctx, xf_width, dtype=torch.float32))\n self.transformer_proj = nn.Linear(xf_width, self.base_channels * 4)\n\n if self.xf_padding:\n self.padding_embedding = nn.Parameter(\n torch.empty(text_ctx, xf_width, dtype=torch.float32))\n if self.xf_ar:\n self.unemb = nn.Linear(xf_width, self.tokenizer.n_vocab)\n if share_unemb:\n self.unemb.weight = self.token_embedding.weight\n\n self.cache_text_emb = cache_text_emb\n self.cache = None\n\n def get_text_emb(self, tokens, mask):\n assert tokens is not None\n\n if self.cache_text_emb and self.cache is not None:\n assert (tokens == self.cache['tokens']).all(\n ), f\"Tokens {tokens.cpu().numpy().tolist()} do not match \\\n cache {self.cache['tokens'].cpu().numpy().tolist()}\"\n\n return self.cache\n\n xf_in = self.token_embedding(tokens.long())\n xf_in = xf_in + self.positional_embedding[None]\n if self.xf_padding:\n assert mask is not None\n xf_in = torch.where(mask[..., None], xf_in,\n self.padding_embedding[None])\n xf_out = self.transformer(xf_in.to(self.dtype))\n if self.final_ln is not None:\n xf_out = self.final_ln(xf_out)\n xf_proj = self.transformer_proj(xf_out[:, -1])\n xf_out = xf_out.permute(0, 2, 1) # NLC -> NCL\n\n outputs = dict(xf_proj=xf_proj, xf_out=xf_out)\n\n if self.cache_text_emb:\n self.cache = dict(\n tokens=tokens,\n xf_proj=xf_proj.detach(),\n xf_out=xf_out.detach() if xf_out is not None else None,\n )\n\n return outputs\n\n def del_cache(self):\n self.cache = None\n\n def forward(self, x, timesteps, tokens=None, mask=None):\n hs = []\n if not torch.is_tensor(timesteps):\n timesteps = torch.tensor([timesteps],\n dtype=torch.long,\n device=x.device)\n elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(x.device)\n\n if timesteps.shape[0] != x.shape[0]:\n timesteps = timesteps.repeat(x.shape[0])\n emb = self.time_embedding(timesteps)\n if self.xf_width:\n text_outputs = self.get_text_emb(tokens, mask)\n xf_proj, xf_out = text_outputs['xf_proj'], text_outputs['xf_out']\n emb = emb + xf_proj.to(emb)\n else:\n xf_out = None\n h = x.type(self.dtype)\n for module in self.in_blocks:\n h = module(h, emb, xf_out)\n hs.append(h)\n h = self.mid_blocks(h, emb, xf_out)\n for module in self.out_blocks:\n h = torch.cat([h, hs.pop()], dim=1)\n h = module(h, emb, xf_out)\n h = h.type(x.dtype)\n h = self.out(h)\n return h\n\n\n@MODELS.register_module()\nclass SuperResText2ImUNet(Text2ImUNet):\n \"\"\"A UNetModel that performs super-resolution.\n\n Expects an extra kwarg `low_res` to condition on a low-resolution image.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n if 'in_channels' in kwargs:\n kwargs = dict(kwargs)\n kwargs['in_channels'] = kwargs['in_channels'] * 2\n else:\n args = list(args)\n args[1] = args[1] * 2\n super().__init__(*args, **kwargs)\n\n def forward(self, x, timesteps, low_res=None, **kwargs):\n _, _, new_height, new_width = x.shape\n upsampled = F.interpolate(\n low_res, (new_height, new_width),\n mode='bilinear',\n align_corners=False)\n x = torch.cat([x, upsampled], dim=1)\n return super().forward(x, timesteps, **kwargs)\n" }, { "path": "projects/magicmaker/index.html", "content": "\n \n \n \n \n My static Space\n \n \n \n
\n \n
\n \n \n" }, { "path": "projects/powerpaint/README.md", "content": "# A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting\n\n### [Project Page](https://powerpaint.github.io/) | [Paper](https://arxiv.org/abs/2312.03594) | [Online Demo(OpenXlab)](https://openxlab.org.cn/apps/detail/rangoliu/PowerPaint#basic-information)\n\nThis README provides a step-by-step guide to download the repository, set up the required virtual environment named \"PowerPaint\" using conda, and run PowerPaint with or without ControlNet.\n\n## News\n\n**December 18, 2023**\n\n*Enhanced PowerPaint Model*\n\n- We are delighted to announce the release of more stable model weights. These refined weights can now be accessed on [Hugging Face](https://huggingface.co/JunhaoZhuang/PowerPaint-v1/tree/main). The `gradio_PowerPaint.py` file and [Online Demo](https://openxlab.org.cn/apps/detail/rangoliu/PowerPaint) have also been updated as part of this release.\n\n**December 22, 2023**\n\n- The logical error in loading ControlNet has been rectified. The `gradio_PowerPaint.py` file and [Online Demo](https://openxlab.org.cn/apps/detail/rangoliu/PowerPaint) have also been updated.\n\n## Next\n\n**Stronger Model Weights Coming Soon！**\n\n______________________________________________________________________\n\n\n\n## Getting Started\n\n```bash\n# Clone the Repository\ngit clone https://github.com/open-mmlab/mmagic.git\n\n# Navigate to the Repository\ncd projects/powerpaint\n\n# Create Virtual Environment with Conda\nconda create --name PowerPaint python=3.9\nconda activate PowerPaint\n\n# Install Dependencies\npip install -r requirements.txt\n\n# Create Models Folder\nmkdir models\n\n# Set up Git LFS\ngit lfs install\n\n# Clone PowerPaint Model\ngit lfs clone https://huggingface.co/JunhaoZhuang/PowerPaint-v1/ ./models\n```\n\n## Run PowerPaint\n\nTo run PowerPaint, execute the following command:\n\n```bash\npython gradio_PowerPaint.py\n```\n\nThis command will launch the Gradio interface for PowerPaint.\n\nFeel free to explore and create stunning images with PowerPaint!\n\n## BibTeX\n\n```\n@misc{zhuang2023task,\n title={A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting},\n author={Junhao Zhuang and Yanhong Zeng and Wenran Liu and Chun Yuan and Kai Chen},\n year={2023},\n eprint={2312.03594},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n" }, { "path": "projects/powerpaint/gradio_PowerPaint.py", "content": "import random\n\nimport cv2\nimport gradio as gr\nimport numpy as np\nimport torch\nfrom controlnet_aux import HEDdetector, OpenposeDetector\nfrom diffusers.pipelines.controlnet.pipeline_controlnet import ControlNetModel\nfrom PIL import Image, ImageFilter\nfrom pipeline.pipeline_PowerPaint import \\\n StableDiffusionInpaintPipeline as Pipeline\nfrom pipeline.pipeline_PowerPaint_ControlNet import \\\n StableDiffusionControlNetInpaintPipeline as controlnetPipeline\nfrom safetensors.torch import load_model\nfrom transformers import DPTFeatureExtractor, DPTForDepthEstimation\nfrom utils.utils import TokenizerWrapper, add_tokens\n\ntorch.set_grad_enabled(False)\n\nweight_dtype = torch.float16\nglobal pipe\npipe = Pipeline.from_pretrained(\n 'runwayml/stable-diffusion-inpainting', torch_dtype=weight_dtype)\npipe.tokenizer = TokenizerWrapper(\n from_pretrained='runwayml/stable-diffusion-v1-5',\n subfolder='tokenizer',\n revision=None)\n\nadd_tokens(\n tokenizer=pipe.tokenizer,\n text_encoder=pipe.text_encoder,\n placeholder_tokens=['P_ctxt', 'P_shape', 'P_obj'],\n initialize_tokens=['a', 'a', 'a'],\n num_vectors_per_token=10)\n\nload_model(pipe.unet, './models/unet/diffusion_pytorch_model.safetensors')\npipe.text_encoder.load_state_dict(\n torch.load('./models/text_encoder/pytorch_model.bin'), strict=False)\npipe = pipe.to('cuda')\n\ndepth_estimator = DPTForDepthEstimation.from_pretrained(\n 'Intel/dpt-hybrid-midas').to('cuda')\nfeature_extractor = DPTFeatureExtractor.from_pretrained(\n 'Intel/dpt-hybrid-midas')\nopenpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\nhed = HEDdetector.from_pretrained('lllyasviel/ControlNet')\n\nglobal current_control\ncurrent_control = 'canny'\n# controlnet_conditioning_scale = 0.8\n\n\ndef set_seed(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n\ndef get_depth_map(image):\n image = feature_extractor(\n images=image, return_tensors='pt').pixel_values.to('cuda')\n with torch.no_grad(), torch.autocast('cuda'):\n depth_map = depth_estimator(image).predicted_depth\n\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=(1024, 1024),\n mode='bicubic',\n align_corners=False,\n )\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_map = (depth_map - depth_min) / (depth_max - depth_min)\n image = torch.cat([depth_map] * 3, dim=1)\n\n image = image.permute(0, 2, 3, 1).cpu().numpy()[0]\n image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))\n return image\n\n\ndef add_task(prompt, negative_prompt, control_type):\n # print(control_type)\n if control_type == 'object-removal':\n promptA = prompt + ' P_ctxt'\n promptB = prompt + ' P_ctxt'\n negative_promptA = negative_prompt + ' P_obj'\n negative_promptB = negative_prompt + ' P_obj'\n elif control_type == 'shape-guided':\n promptA = prompt + ' P_shape'\n promptB = prompt + ' P_ctxt'\n negative_promptA = negative_prompt\n negative_promptB = negative_prompt\n elif control_type == 'image-outpainting':\n promptA = prompt + ' P_ctxt'\n promptB = prompt + ' P_ctxt'\n negative_promptA = negative_prompt + ' P_obj'\n negative_promptB = negative_prompt + ' P_obj'\n else:\n promptA = prompt + ' P_obj'\n promptB = prompt + ' P_obj'\n negative_promptA = negative_prompt\n negative_promptB = negative_prompt\n\n return promptA, promptB, negative_promptA, negative_promptB\n\n\ndef predict(input_image, prompt, fitting_degree, ddim_steps, scale, seed,\n negative_prompt, task, vertical_expansion_ratio,\n horizontal_expansion_ratio):\n size1, size2 = input_image['image'].convert('RGB').size\n\n if task != 'image-outpainting':\n if size1 < size2:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (640, int(size2 / size1 * 640)))\n else:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (int(size1 / size2 * 640), 640))\n else:\n if size1 < size2:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (512, int(size2 / size1 * 512)))\n else:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (int(size1 / size2 * 512), 512))\n\n if (vertical_expansion_ratio is not None\n and horizontal_expansion_ratio is not None):\n o_W, o_H = input_image['image'].convert('RGB').size\n c_W = int(horizontal_expansion_ratio * o_W)\n c_H = int(vertical_expansion_ratio * o_H)\n\n expand_img = np.ones((c_H, c_W, 3), dtype=np.uint8) * 127\n original_img = np.array(input_image['image'])\n expand_img[int((c_H - o_H) / 2.0):int((c_H - o_H) / 2.0) + o_H,\n int((c_W - o_W) / 2.0):int((c_W - o_W) / 2.0) +\n o_W, :] = original_img\n\n blurry_gap = 10\n\n expand_mask = np.ones((c_H, c_W, 3), dtype=np.uint8) * 255\n if vertical_expansion_ratio == 1 and horizontal_expansion_ratio != 1:\n expand_mask[int((c_H - o_H) / 2.0):int((c_H - o_H) / 2.0) + o_H,\n int((c_W - o_W) / 2.0) +\n blurry_gap:int((c_W - o_W) / 2.0) + o_W -\n blurry_gap, :] = 0 # noqa\n elif vertical_expansion_ratio != 1 and horizontal_expansion_ratio != 1:\n expand_mask[int((c_H - o_H) / 2.0) +\n blurry_gap:int((c_H - o_H) / 2.0) + o_H - blurry_gap,\n int((c_W - o_W) / 2.0) +\n blurry_gap:int((c_W - o_W) / 2.0) + o_W -\n blurry_gap, :] = 0 # noqa\n elif vertical_expansion_ratio != 1 and horizontal_expansion_ratio == 1:\n expand_mask[int((c_H - o_H) / 2.0) +\n blurry_gap:int((c_H - o_H) / 2.0) + o_H - blurry_gap,\n int((c_W - o_W) /\n 2.0):int((c_W - o_W) / 2.0) + o_W, :] = 0 # noqa\n\n input_image['image'] = Image.fromarray(expand_img)\n input_image['mask'] = Image.fromarray(expand_mask)\n\n promptA, promptB, negative_promptA, negative_promptB = add_task(\n prompt, negative_prompt, task)\n print(promptA, promptB, negative_promptA, negative_promptB)\n img = np.array(input_image['image'].convert('RGB'))\n\n W = int(np.shape(img)[0] - np.shape(img)[0] % 8)\n H = int(np.shape(img)[1] - np.shape(img)[1] % 8)\n input_image['image'] = input_image['image'].resize((H, W))\n input_image['mask'] = input_image['mask'].resize((H, W))\n set_seed(seed)\n global pipe\n result = pipe(\n promptA=promptA,\n promptB=promptB,\n tradoff=fitting_degree,\n tradoff_nag=fitting_degree,\n negative_promptA=negative_promptA,\n negative_promptB=negative_promptB,\n image=input_image['image'].convert('RGB'),\n mask_image=input_image['mask'].convert('RGB'),\n width=H,\n height=W,\n guidance_scale=scale,\n num_inference_steps=ddim_steps).images[0]\n mask_np = np.array(input_image['mask'].convert('RGB'))\n red = np.array(result).astype('float') * 1\n red[:, :, 0] = 180.0\n red[:, :, 2] = 0\n red[:, :, 1] = 0\n result_m = np.array(result)\n result_m = Image.fromarray(\n (result_m.astype('float') * (1 - mask_np.astype('float') / 512.0) +\n mask_np.astype('float') / 512.0 * red).astype('uint8'))\n m_img = input_image['mask'].convert('RGB').filter(\n ImageFilter.GaussianBlur(radius=3))\n m_img = np.asarray(m_img) / 255.0\n img_np = np.asarray(input_image['image'].convert('RGB')) / 255.0\n ours_np = np.asarray(result) / 255.0\n ours_np = ours_np * m_img + (1 - m_img) * img_np\n result_paste = Image.fromarray(np.uint8(ours_np * 255))\n\n dict_res = [input_image['mask'].convert('RGB'), result_m]\n\n dict_out = [input_image['image'].convert('RGB'), result_paste]\n\n return dict_out, dict_res\n\n\ndef predict_controlnet(input_image, input_control_image, control_type, prompt,\n ddim_steps, scale, seed, negative_prompt,\n controlnet_conditioning_scale):\n promptA = prompt + ' P_obj'\n promptB = prompt + ' P_obj'\n negative_promptA = negative_prompt\n negative_promptB = negative_prompt\n size1, size2 = input_image['image'].convert('RGB').size\n\n if size1 < size2:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (640, int(size2 / size1 * 640)))\n else:\n input_image['image'] = input_image['image'].convert('RGB').resize(\n (int(size1 / size2 * 640), 640))\n img = np.array(input_image['image'].convert('RGB'))\n W = int(np.shape(img)[0] - np.shape(img)[0] % 8)\n H = int(np.shape(img)[1] - np.shape(img)[1] % 8)\n input_image['image'] = input_image['image'].resize((H, W))\n input_image['mask'] = input_image['mask'].resize((H, W))\n\n global current_control\n global pipe\n\n base_control = ControlNetModel.from_pretrained(\n 'lllyasviel/sd-controlnet-canny', torch_dtype=weight_dtype)\n control_pipe = controlnetPipeline(pipe.vae, pipe.text_encoder,\n pipe.tokenizer, pipe.unet, base_control,\n pipe.scheduler, None, None, False)\n control_pipe = control_pipe.to('cuda')\n current_control = 'canny'\n if current_control != control_type:\n if control_type == 'canny' or control_type is None:\n control_pipe.controlnet = ControlNetModel.from_pretrained(\n 'lllyasviel/sd-controlnet-canny', torch_dtype=weight_dtype)\n elif control_type == 'pose':\n control_pipe.controlnet = ControlNetModel.from_pretrained(\n 'lllyasviel/sd-controlnet-openpose', torch_dtype=weight_dtype)\n elif control_type == 'depth':\n control_pipe.controlnet = ControlNetModel.from_pretrained(\n 'lllyasviel/sd-controlnet-depth', torch_dtype=weight_dtype)\n else:\n control_pipe.controlnet = ControlNetModel.from_pretrained(\n 'lllyasviel/sd-controlnet-hed', torch_dtype=weight_dtype)\n control_pipe = control_pipe.to('cuda')\n current_control = control_type\n\n controlnet_image = input_control_image\n if current_control == 'canny':\n controlnet_image = controlnet_image.resize((H, W))\n controlnet_image = np.array(controlnet_image)\n controlnet_image = cv2.Canny(controlnet_image, 100, 200)\n controlnet_image = controlnet_image[:, :, None]\n controlnet_image = np.concatenate(\n [controlnet_image, controlnet_image, controlnet_image], axis=2)\n controlnet_image = Image.fromarray(controlnet_image)\n elif current_control == 'pose':\n controlnet_image = openpose(controlnet_image)\n elif current_control == 'depth':\n controlnet_image = controlnet_image.resize((H, W))\n controlnet_image = get_depth_map(controlnet_image)\n else:\n controlnet_image = hed(controlnet_image)\n\n mask_np = np.array(input_image['mask'].convert('RGB'))\n controlnet_image = controlnet_image.resize((H, W))\n set_seed(seed)\n result = control_pipe(\n promptA=promptB,\n promptB=promptA,\n tradoff=1.0,\n tradoff_nag=1.0,\n negative_promptA=negative_promptA,\n negative_promptB=negative_promptB,\n image=input_image['image'].convert('RGB'),\n mask_image=input_image['mask'].convert('RGB'),\n control_image=controlnet_image,\n width=H,\n height=W,\n guidance_scale=scale,\n controlnet_conditioning_scale=controlnet_conditioning_scale,\n num_inference_steps=ddim_steps).images[0]\n red = np.array(result).astype('float') * 1\n red[:, :, 0] = 180.0\n red[:, :, 2] = 0\n red[:, :, 1] = 0\n result_m = np.array(result)\n result_m = Image.fromarray(\n (result_m.astype('float') * (1 - mask_np.astype('float') / 512.0) +\n mask_np.astype('float') / 512.0 * red).astype('uint8'))\n\n mask_np = np.array(input_image['mask'].convert('RGB'))\n m_img = input_image['mask'].convert('RGB').filter(\n ImageFilter.GaussianBlur(radius=4))\n m_img = np.asarray(m_img) / 255.0\n img_np = np.asarray(input_image['image'].convert('RGB')) / 255.0\n ours_np = np.asarray(result) / 255.0\n ours_np = ours_np * m_img + (1 - m_img) * img_np\n result_paste = Image.fromarray(np.uint8(ours_np * 255))\n return [input_image['image'].convert('RGB'),\n result_paste], [controlnet_image, result_m]\n\n\ndef infer(input_image, text_guided_prompt, text_guided_negative_prompt,\n shape_guided_prompt, shape_guided_negative_prompt, fitting_degree,\n ddim_steps, scale, seed, task, enable_control, input_control_image,\n control_type, vertical_expansion_ratio, horizontal_expansion_ratio,\n outpaint_prompt, outpaint_negative_prompt,\n controlnet_conditioning_scale, removal_prompt,\n removal_negative_prompt):\n if task == 'text-guided':\n prompt = text_guided_prompt\n negative_prompt = text_guided_negative_prompt\n elif task == 'shape-guided':\n prompt = shape_guided_prompt\n negative_prompt = shape_guided_negative_prompt\n elif task == 'object-removal':\n prompt = removal_prompt\n negative_prompt = removal_negative_prompt\n elif task == 'image-outpainting':\n prompt = outpaint_prompt\n negative_prompt = outpaint_negative_prompt\n return predict(input_image, prompt, fitting_degree, ddim_steps, scale,\n seed, negative_prompt, task, vertical_expansion_ratio,\n horizontal_expansion_ratio)\n else:\n task = 'text-guided'\n prompt = text_guided_prompt\n negative_prompt = text_guided_negative_prompt\n\n if enable_control and task == 'text-guided':\n return predict_controlnet(input_image, input_control_image,\n control_type, prompt, ddim_steps, scale,\n seed, negative_prompt,\n controlnet_conditioning_scale)\n else:\n return predict(input_image, prompt, fitting_degree, ddim_steps, scale,\n seed, negative_prompt, task, None, None)\n\n\ndef select_tab_text_guided():\n return 'text-guided'\n\n\ndef select_tab_object_removal():\n return 'object-removal'\n\n\ndef select_tab_image_outpainting():\n return 'image-outpainting'\n\n\ndef select_tab_shape_guided():\n return 'shape-guided'\n\n\nwith gr.Blocks(css='style.css') as demo:\n with gr.Row():\n gr.Markdown(\n \"
PowerPaint: High-Quality Versatile Image Inpainting
\" # noqa\n )\n with gr.Row():\n gr.Markdown(\n \"
Project Page  \" # noqa\n \"Paper  \"\n \"Code
\" # noqa\n )\n with gr.Row():\n gr.Markdown(\n '**Note:** Due to network-related factors, the page may experience occasional bugs！ If the inpainting results deviate significantly from expectations, consider toggling between task options to refresh the content.' # noqa\n )\n with gr.Row():\n with gr.Column():\n gr.Markdown('### Input image and draw mask')\n input_image = gr.Image(source='upload', tool='sketch', type='pil')\n\n task = gr.Radio([\n 'text-guided', 'object-removal', 'shape-guided',\n 'image-outpainting'\n ],\n show_label=False,\n visible=False)\n\n # Text-guided object inpainting\n with gr.Tab('Text-guided object inpainting') as tab_text_guided:\n enable_text_guided = gr.Checkbox(\n label='Enable text-guided object inpainting',\n value=True,\n interactive=False)\n text_guided_prompt = gr.Textbox(label='Prompt')\n text_guided_negative_prompt = gr.Textbox(\n label='negative_prompt')\n gr.Markdown('### Controlnet setting')\n enable_control = gr.Checkbox(\n label='Enable controlnet',\n info='Enable this if you want to use controlnet')\n controlnet_conditioning_scale = gr.Slider(\n label='controlnet conditioning scale',\n minimum=0,\n maximum=1,\n step=0.05,\n value=0.5,\n )\n control_type = gr.Radio(['canny', 'pose', 'depth', 'hed'],\n label='Control type')\n input_control_image = gr.Image(source='upload', type='pil')\n tab_text_guided.select(\n fn=select_tab_text_guided, inputs=None, outputs=task)\n\n # Object removal inpainting\n with gr.Tab('Object removal inpainting') as tab_object_removal:\n enable_object_removal = gr.Checkbox(\n label='Enable object removal inpainting',\n value=True,\n info='The recommended configuration for '\n 'the Guidance Scale is 10 or higher.'\n 'If undesired objects appear in the masked area, '\n 'you can address this by specifically increasing '\n 'the Guidance Scale.',\n interactive=False)\n removal_prompt = gr.Textbox(label='Prompt')\n removal_negative_prompt = gr.Textbox(label='negative_prompt')\n tab_object_removal.select(\n fn=select_tab_object_removal, inputs=None, outputs=task)\n\n # Object image outpainting\n with gr.Tab('Image outpainting') as tab_image_outpainting:\n enable_object_removal = gr.Checkbox(\n label='Enable image outpainting',\n value=True,\n info='The recommended configuration for the Guidance '\n 'Scale is 10 or higher. '\n 'If unwanted random objects appear in '\n 'the extended image region, '\n 'you can enhance the cleanliness of the extension '\n 'area by increasing the Guidance Scale.',\n interactive=False)\n outpaint_prompt = gr.Textbox(label='Outpainting_prompt')\n outpaint_negative_prompt = gr.Textbox(\n label='Outpainting_negative_prompt')\n horizontal_expansion_ratio = gr.Slider(\n label='horizontal expansion ratio',\n minimum=1,\n maximum=4,\n step=0.05,\n value=1,\n )\n vertical_expansion_ratio = gr.Slider(\n label='vertical expansion ratio',\n minimum=1,\n maximum=4,\n step=0.05,\n value=1,\n )\n tab_image_outpainting.select(\n fn=select_tab_image_outpainting, inputs=None, outputs=task)\n\n # Shape-guided object inpainting\n with gr.Tab('Shape-guided object inpainting') as tab_shape_guided:\n enable_shape_guided = gr.Checkbox(\n label='Enable shape-guided object inpainting',\n value=True,\n interactive=False)\n shape_guided_prompt = gr.Textbox(label='shape_guided_prompt')\n shape_guided_negative_prompt = gr.Textbox(\n label='shape_guided_negative_prompt')\n fitting_degree = gr.Slider(\n label='fitting degree',\n minimum=0,\n maximum=1,\n step=0.05,\n value=1,\n )\n tab_shape_guided.select(\n fn=select_tab_shape_guided, inputs=None, outputs=task)\n\n run_button = gr.Button(label='Run')\n with gr.Accordion('Advanced options', open=False):\n ddim_steps = gr.Slider(\n label='Steps', minimum=1, maximum=50, value=45, step=1)\n scale = gr.Slider(\n label='Guidance Scale',\n info='For object removal and image outpainting, '\n 'it is recommended to set the value at 10 or above.',\n minimum=0.1,\n maximum=30.0,\n value=7.5,\n step=0.1)\n seed = gr.Slider(\n label='Seed',\n minimum=0,\n maximum=2147483647,\n step=1,\n randomize=True,\n )\n with gr.Column():\n gr.Markdown('### Inpainting result')\n inpaint_result = gr.Gallery(\n label='Generated images', show_label=False, columns=2)\n gr.Markdown('### Mask')\n gallery = gr.Gallery(\n label='Generated masks', show_label=False, columns=2)\n\n run_button.click(\n fn=infer,\n inputs=[\n input_image, text_guided_prompt, text_guided_negative_prompt,\n shape_guided_prompt, shape_guided_negative_prompt, fitting_degree,\n ddim_steps, scale, seed, task, enable_control, input_control_image,\n control_type, vertical_expansion_ratio, horizontal_expansion_ratio,\n outpaint_prompt, outpaint_negative_prompt,\n controlnet_conditioning_scale, removal_prompt,\n removal_negative_prompt\n ],\n outputs=[inpaint_result, gallery])\n\ndemo.queue()\ndemo.launch(share=False, server_name='0.0.0.0', server_port=7860)\n" }, { "path": "projects/powerpaint/pipeline/pipeline_PowerPaint.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport torch\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import (FromSingleFileMixin, LoraLoaderMixin,\n TextualInversionLoaderMixin)\nfrom diffusers.models import (AsymmetricAutoencoderKL, AutoencoderKL,\n UNet2DConditionModel)\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import \\\n StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (deprecate, is_accelerate_available,\n is_accelerate_version, logging)\nfrom diffusers.utils.torch_utils import randn_tensor\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef prepare_mask_and_masked_image(image,\n mask,\n height,\n width,\n return_image: bool = False):\n \"\"\"Prepares a pair (image, mask) to be consumed by the Stable Diffusion\n pipeline. This means that those inputs will be converted to\n ``torch.Tensor`` with shapes ``batch x channels x height x width`` where\n ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized\n to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]):\n The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3``\n ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width``\n ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to\n inpaint.\n It can be a ``PIL.Image``, or a ``height x width``\n ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width``\n ``torch.Tensor``.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]``\n range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and\n ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (or the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, masked_image) as\n ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n\n if image is None:\n raise ValueError('`image` input cannot be undefined.')\n\n if mask is None:\n raise ValueError('`mask_image` input cannot be undefined.')\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError('`image` is a torch.Tensor but '\n f'`mask` (type: {type(mask)} is not')\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[\n 0] == 3, 'Image outside a batch should be of shape (3, H, W)'\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask\n # not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \\\n 'Image and Mask must have 4 dimensions'\n assert image.shape[-2:] == mask.shape[\n -2:], 'Image and Mask must have the same spatial dimensions'\n assert image.shape[0] == mask.shape[\n 0], 'Image and Mask must have the same batch size'\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError('Image should be in [-1, 1] range')\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError('Mask should be in [0, 1] range')\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(\n f'`mask` is a torch.Tensor but `image` (type: {type(image)} is not'\n )\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n # resize all images w.r.t passed height an width\n image = [\n i.resize((width, height), resample=PIL.Image.LANCZOS)\n for i in image\n ]\n image = [np.array(i.convert('RGB'))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [\n i.resize((width, height), resample=PIL.Image.LANCZOS)\n for i in mask\n ]\n mask = np.concatenate(\n [np.array(m.convert('L'))[None, None, :] for m in mask],\n axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n # n.b. ensure backwards compatibility as old function does not return image\n if return_image:\n return mask, masked_image, image\n\n return mask, masked_image\n\n\nclass StableDiffusionInpaintPipeline(DiffusionPipeline,\n TextualInversionLoaderMixin,\n LoraLoaderMixin, FromSingleFileMixin):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the\n superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving,\n running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for\n loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for\n saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode\n images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14]\n (https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise\n the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images\n could be considered offensive or harmful.\n Please refer to the [model card]\n (https://huggingface.co/runwayml/stable-diffusion-v1-5)\n for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated\n images; used as inputs to the `safety_checker`.\n \"\"\"\n _optional_components = ['safety_checker', 'feature_extractor']\n\n def __init__(\n self,\n vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config,\n 'steps_offset') and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f'The configuration file of this scheduler: {scheduler} is '\n ' outdated. `steps_offset`'\n ' should be set to 1 instead of '\n f' {scheduler.config.steps_offset}. Please make sure '\n 'to update the config accordingly as leaving `steps_offset` '\n ' might led to incorrect results'\n ' in future versions. If you have downloaded this checkpoint '\n ' from the Hugging Face Hub,'\n ' it would be very nice if you could open a Pull request for '\n ' the `scheduler/scheduler_config.json`'\n ' file')\n deprecate(\n 'steps_offset!=1',\n '1.0.0',\n deprecation_message,\n standard_warn=False)\n new_config = dict(scheduler.config)\n new_config['steps_offset'] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(\n scheduler.config,\n 'skip_prk_steps') and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f'The configuration file of this scheduler: {scheduler} has '\n ' not set the configuration'\n ' `skip_prk_steps`. `skip_prk_steps` should be set to True in '\n ' the configuration file. Please make'\n ' sure to update the config accordingly as not setting '\n ' `skip_prk_steps` in the config might lead to'\n ' incorrect results in future versions. '\n ' If you have downloaded '\n ' this checkpoint from the Hugging Face'\n ' Hub, it would be very nice if you could open a Pull '\n ' request for the'\n ' `scheduler/scheduler_config.json` file')\n deprecate(\n 'skip_prk_steps not set',\n '1.0.0',\n deprecation_message,\n standard_warn=False)\n new_config = dict(scheduler.config)\n new_config['skip_prk_steps'] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f'You have disabled the safety checker for {self.__class__} '\n ' by passing `safety_checker=None`. Ensure'\n ' that you abide to the conditions of the Stable Diffusion '\n ' license and do not expose unfiltered'\n ' results in services or applications open to the public. '\n ' Both the diffusers team and Hugging Face'\n ' strongly recommend to keep the safety filter enabled in '\n ' all public facing circumstances, disabling'\n ' it only for use-cases that involve analyzing network '\n ' behavior or auditing its results. For more'\n ' information, please have a look at'\n ' https://github.com/huggingface/diffusers/pull/254 .')\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n 'Make sure to define a feature extractor when '\n 'loading {self.__class__} if you want to use the safety'\n ' checker. If you do not want to use the safety checker, '\n \"you can pass `'safety_checker=None'` instead.\")\n\n is_unet_version_less_0_9_0 = hasattr(\n unet.config, '_diffusers_version') and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse('0.9.0.dev0')\n is_unet_sample_size_less_64 = hasattr(\n unet.config, 'sample_size') and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n 'The configuration file of the unet has set the default '\n '`sample_size` to smaller than'\n \" 64 which seems highly unlikely .If you're checkpoint is \"\n ' a fine-tuned version of any of the'\n ' following: \\n- CompVis/stable-diffusion-v1-4 \\n- '\n ' CompVis/stable-diffusion-v1-3 \\n-'\n ' CompVis/stable-diffusion-v1-2 \\n- '\n ' CompVis/stable-diffusion-v1-1 \\n- '\n ' runwayml/stable-diffusion-v1-5'\n ' \\n- runwayml/stable-diffusion-inpainting \\n you should '\n \" change 'sample_size' to 64 in the\"\n ' configuration file. Please make sure to update the config '\n ' accordingly as leaving `sample_size=32`'\n ' in the config might lead to incorrect results in future '\n ' versions. If you have downloaded this'\n ' checkpoint from the Hugging Face Hub, it would be very nice '\n ' if you could open a Pull request for'\n ' the `unet/config.json` file')\n deprecate(\n 'sample_size<64',\n '1.0.0',\n deprecation_message,\n standard_warn=False)\n new_config = dict(unet.config)\n new_config['sample_size'] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n # Check shapes, assume num_channels_latents == 4,\n # num_channels_mask == 1, num_channels_masked == 4\n if unet.config.in_channels != 9:\n logger.info(\n f'You have loaded a UNet with {unet.config.in_channels} '\n 'input channels which.')\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2**(\n len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(\n requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.\n # StableDiffusionPipeline.enable_model_cpu_offload\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offload all models to CPU to reduce memory usage with a low impact\n on performance. Moves one whole model at a\n time to the GPU when its `forward` method is called, and the model\n remains in GPU until the next model runs.\n Memory savings are lower than using `enable_sequential_cpu_offload`,\n but performance is much better due to the\n iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\n '>=', '0.17.0.dev0'):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\n '`enable_model_cpu_offload` requires `accelerate v0.17.0` '\n 'or higher.')\n\n device = torch.device(f'cuda:{gpu_id}')\n\n if self.device.type != 'cpu':\n self.to('cpu', silence_dtype_warnings=True)\n torch.cuda.empty_cache()\n # otherwise we don't see the memory savings\n # (but they probably exist)\n\n hook = None\n for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:\n _, hook = cpu_offload_with_hook(\n cpu_offloaded_model, device, prev_module_hook=hook)\n\n if self.safety_checker is not None:\n _, hook = cpu_offload_with_hook(\n self.safety_checker, device, prev_module_hook=hook)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_\n # stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n promptA,\n promptB,\n t,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_promptA=None,\n negative_promptB=None,\n t_nag=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak\n text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt`\n input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily\n tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be\n generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the\n text encoder if LoRA layers are loaded.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n prompt = promptA\n negative_prompt = negative_promptA\n\n if promptA is not None and isinstance(promptA, str):\n batch_size = 1\n elif promptA is not None and isinstance(promptA, list):\n batch_size = len(promptA)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n promptA = self.maybe_convert_prompt(promptA, self.tokenizer)\n\n text_inputsA = self.tokenizer(\n promptA,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_inputsB = self.tokenizer(\n promptB,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_input_idsA = text_inputsA.input_ids\n text_input_idsB = text_inputsB.input_ids\n untruncated_ids = self.tokenizer(\n promptA, padding='longest', return_tensors='pt').input_ids\n\n if untruncated_ids.shape[-1] >= text_input_idsA.shape[\n -1] and not torch.equal(text_input_idsA, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1:-1])\n logger.warning(\n 'The following part of your input was truncated because '\n 'CLIP can only handle sequences up to'\n f' {self.tokenizer.model_max_length} '\n f'tokens: {removed_text}')\n\n if hasattr(self.text_encoder.config, 'use_attention_mask'\n ) and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputsA.attention_mask.to(device)\n else:\n attention_mask = None\n\n # print(\"text_input_idsA: \",text_input_idsA)\n # print(\"text_input_idsB: \",text_input_idsB)\n # print('t: ',t)\n\n prompt_embedsA = self.text_encoder(\n text_input_idsA.to(device),\n attention_mask=attention_mask,\n )\n prompt_embedsA = prompt_embedsA[0]\n\n prompt_embedsB = self.text_encoder(\n text_input_idsB.to(device),\n attention_mask=attention_mask,\n )\n prompt_embedsB = prompt_embedsB[0]\n prompt_embeds = prompt_embedsA * (t) + (1 - t) * prompt_embedsB\n # print(\"prompt_embeds: \",prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(\n dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt,\n # using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,\n seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokensA: List[str]\n uncond_tokensB: List[str]\n if negative_prompt is None:\n uncond_tokensA = [''] * batch_size\n uncond_tokensB = [''] * batch_size\n elif prompt is not None and type(prompt) is not type(\n negative_prompt):\n raise TypeError(\n f'`negative_prompt` should be the same type to `prompt`, '\n ' but got {type(negative_prompt)} !='\n f' {type(prompt)}.')\n elif isinstance(negative_prompt, str):\n uncond_tokensA = [negative_promptA]\n uncond_tokensB = [negative_promptB]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f'`negative_prompt`: {negative_prompt} has batch '\n f' size {len(negative_prompt)}, but `prompt`:'\n f' {prompt} has batch size {batch_size}. Please make '\n ' sure that passed `negative_prompt` matches'\n ' the batch size of `prompt`.')\n else:\n uncond_tokensA = negative_promptA\n uncond_tokensB = negative_promptB\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokensA = self.maybe_convert_prompt(\n uncond_tokensA, self.tokenizer)\n uncond_tokensB = self.maybe_convert_prompt(\n uncond_tokensB, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_inputA = self.tokenizer(\n uncond_tokensA,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n uncond_inputB = self.tokenizer(\n uncond_tokensB,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n\n if hasattr(self.text_encoder.config, 'use_attention_mask'\n ) and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_inputA.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embedsA = self.text_encoder(\n uncond_inputA.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embedsB = self.text_encoder(\n uncond_inputB.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embedsA[0] * (t_nag) + (\n 1 - t_nag) * negative_prompt_embedsB[0]\n\n # negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per\n # prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(\n dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(\n 1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(\n batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings\n # into a single batch\n # to avoid doing two forward passes\n # print(\"prompt_embeds: \",prompt_embeds)\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.\n # StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(\n image, output_type='pil')\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(\n image)\n safety_checker_input = self.feature_extractor(\n feature_extractor_input, return_tensors='pt').to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype))\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.\n # StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all\n # schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be\n # ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = 'eta' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(\n f'The value of strength should in [0.0, 1.0] but is {strength}'\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f'`height` and `width` have to be divisible by 8 '\n f' but are {height} and {width}.')\n\n if (callback_steps is None) or (callback_steps is not None and\n (not isinstance(callback_steps, int)\n or callback_steps <= 0)):\n raise ValueError(\n f'`callback_steps` has to be a positive integer but '\n f' is {callback_steps} of type'\n f' {type(callback_steps)}.')\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f'Cannot forward both `prompt`: {prompt} and `prompt_embeds`: '\n ' {prompt_embeds}. Please make sure to'\n ' only forward one of the two.')\n elif prompt is None and prompt_embeds is None:\n raise ValueError('Provide either `prompt` or `prompt_embeds`. '\n ' Cannot leave both '\n ' `prompt` and `prompt_embeds` undefined.')\n elif prompt is not None and (not isinstance(prompt, str)\n and not isinstance(prompt, list)):\n raise ValueError(f'`prompt` has to be of type `str` or '\n f' `list` but is {type(prompt)}')\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f'Cannot forward both `negative_prompt`: {negative_prompt} '\n 'and `negative_prompt_embeds`:'\n f' {negative_prompt_embeds}. Please make sure to only '\n ' forward one of the two.')\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n '`prompt_embeds` and `negative_prompt_embeds` '\n 'must have the same shape when passed directly, but'\n f' got: `prompt_embeds` {prompt_embeds.shape} != '\n ' `negative_prompt_embeds`'\n f' {negative_prompt_embeds.shape}.')\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (batch_size, num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f'You have passed a list of generators of '\n f' length {len(generator)},'\n ' but requested an effective batch'\n f' size of {batch_size}. Make sure the batch size '\n ' matches the length of the generators.')\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n 'Since strength < 1. initial latents are to be initialised'\n ' as a combination of Image + Noise.'\n 'However, either the image or the noise timestep '\n 'has not been provided.')\n\n if return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(\n image=image, generator=generator)\n\n if latents is None:\n noise = randn_tensor(\n shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise,\n # else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(\n image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the\n # Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma \\\n if is_strength_max else latents\n else:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n\n outputs = (latents, )\n\n if return_noise:\n outputs += (noise, )\n\n if return_image_latents:\n outputs += (image_latents, )\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor,\n generator: torch.Generator):\n if isinstance(generator, list):\n image_latents = [\n self.vae.encode(image[i:i + 1]).latent_dist.sample(\n generator=generator[i]) for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = self.vae.encode(image).latent_dist.sample(\n generator=generator)\n\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(self, mask, masked_image, batch_size, height,\n width, dtype, device, generator,\n do_classifier_free_guidance):\n # resize the mask to latents shape as we concatenate the\n # mask to the latents\n # we do that before converting to dtype to avoid breaking in\n # case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask,\n size=(height // self.vae_scale_factor,\n width // self.vae_scale_factor))\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(\n masked_image, generator=generator)\n\n # duplicate mask and masked_image_latents for each generation per\n # prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. \"\n ' Masks are supposed to be duplicated to'\n f' a total batch size of {batch_size}, '\n f' but {mask.shape[0]} '\n ' masks were passed. Make sure the number'\n ' of masks that you pass is divisible by the '\n ' total requested batch size.')\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't \"\n ' match. Images are supposed to be duplicated'\n f' to a total batch size of {batch_size}, '\n f' but {masked_image_latents.shape[0]} images were passed.'\n ' Make sure the number of images that you pass is '\n ' divisible by the total requested batch size.')\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2)\n if do_classifier_free_guidance else masked_image_latents)\n\n # aligning device to prevent device errors when concatenating it\n # with the latent model input\n masked_image_latents = masked_image_latents.to(\n device=device, dtype=dtype)\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion_img2img.\n # StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(\n int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:]\n\n return timesteps, num_inference_steps - t_start\n\n @torch.no_grad()\n def __call__(\n self,\n promptA: Union[str, List[str]] = None,\n promptB: Union[str, List[str]] = None,\n image: Union[torch.FloatTensor, PIL.Image.Image] = None,\n mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 1.0,\n tradoff: float = 1.0,\n tradoff_nag: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_promptA: Optional[Union[str, List[str]]] = None,\n negative_promptB: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator,\n List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = 'pil',\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor],\n None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n task_class: Union[torch.Tensor, float, int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If\n not defined, you need to pass `prompt_embeds`.\n image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to be\n inpainted (which parts of the image to be masked\n out with `mask_image` and repainted according to `prompt`).\n mask_image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to mask\n `image`. White pixels in the mask are repainted\n while black pixels are preserved. If `mask_image` is a\n PIL image, it is converted to a single channel\n (luminance) before use. If it's a tensor, it should contain\n one color channel (L) instead of 3, so the\n expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to `self.unet.config.\n sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.\n sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be\n between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the\n `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1,\n added noise is maximum and the denoising\n process runs for the full number of iterations specified in\n `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually\n lead to a higher quality image at the\n expense of slower inference. This parameter is modulated\n by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to\n generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale\n is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in\n image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not\n using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM]\n (https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is\n ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`,\n *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable\n /generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian\n distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation\n with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied\n random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily\n tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the\n `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to\n easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated\n from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between\n `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.\n StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during\n inference. The function is called with the\n following arguments: `callback(step: int, timestep: int,\n latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called.\n If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the\n [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers\n /blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionInpaintPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/\n latent-diffusion/main/data/inpainting_examples\n /overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/\n latent-diffusion/main/data/inpainting_examples/\n overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n\n >>> pipe = StableDiffusionInpaintPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"Face of a yellow cat, high resolution,\n sitting on a park bench\"\n >>> image = pipe(prompt=prompt, image=init_image,\n mask_image=mask_image).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`]\n or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.\n StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is\n a list with the generated images and the\n second element is a list of `bool`s indicating whether the\n corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n prompt = promptA\n negative_prompt = negative_promptA\n # 1. Check inputs\n self.check_inputs(\n prompt,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance\n # weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get('scale', None)\n if cross_attention_kwargs is not None else None)\n prompt_embeds = self._encode_prompt(\n promptA,\n promptB,\n tradoff,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_promptA,\n negative_promptB,\n tradoff_nag,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps,\n strength=strength,\n device=device)\n # check that number of inference steps is not < 1 -\n # as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f'After adjusting the num_inference_steps by strength '\n f' parameter: {strength}, the number of pipeline'\n f'steps is {num_inference_steps} which is < 1 and not '\n 'appropriate for this pipeline.')\n # at which timestep to set the initial noise (n.b. 50% if\n # strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size *\n num_images_per_prompt)\n # create a boolean to check if the strength is set to 1.\n # if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n mask, masked_image, init_image = prepare_mask_and_masked_image(\n image, mask_image, height, width, return_image=True)\n mask_condition = mask.clone()\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + \\\n num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n 'Incorrect configuration settings! The '\n f' config of `pipeline.unet`: {self.unet.config} expects'\n f' {self.unet.config.in_channels} but received '\n f' `num_channels_latents`: {num_channels_latents} +'\n f' `num_channels_mask`: {num_channels_mask} + '\n ' `num_channels_masked_image`:'\n f' {num_channels_masked_image}'\n f' = {num_channels_latents+num_channels_masked_image+ num_channels_mask}. Please verify the config of' # noqa\n ' `pipeline.unet` or your `mask_image` or `image` input.')\n elif num_channels_unet != 4:\n raise ValueError(\n f'The unet {self.unet.__class__} should have either 4 or 9 '\n f'input channels, not {self.unet.config.in_channels}.')\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just\n # be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n num_warmup_steps = len(\n timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents\n # in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(\n latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat(\n [latent_model_input, mask, masked_image_latents],\n dim=1)\n\n # predict the noise residual\n if task_class is not None:\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n task_class=task_class,\n )[0]\n else:\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n **extra_step_kwargs,\n return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise,\n torch.tensor([noise_timestep]))\n\n latents = (1 - init_mask\n ) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and\n (i + 1) % self.scheduler.order == 0): # noqa\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == 'latent':\n condition_kwargs = {}\n if isinstance(self.vae, AsymmetricAutoencoderKL):\n init_image = init_image.to(\n device=device, dtype=masked_image_latents.dtype)\n init_image_condition = init_image.clone()\n init_image = self._encode_vae_image(\n init_image, generator=generator)\n mask_condition = mask_condition.to(\n device=device, dtype=masked_image_latents.dtype)\n condition_kwargs = {\n 'image': init_image_condition,\n 'mask': mask_condition\n }\n image = self.vae.decode(\n latents / self.vae.config.scaling_factor,\n return_dict=False,\n **condition_kwargs)[0]\n image, has_nsfw_concept = self.run_safety_checker(\n image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(\n self,\n 'final_offload_hook') and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(\n images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "projects/powerpaint/pipeline/pipeline_PowerPaint_ControlNet.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/ # noqa\n\nimport inspect\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import (FromSingleFileMixin, LoraLoaderMixin,\n TextualInversionLoaderMixin)\nfrom diffusers.models import (AsymmetricAutoencoderKL, AutoencoderKL,\n ControlNetModel, UNet2DConditionModel)\nfrom diffusers.pipelines.controlnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import \\\n StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (is_accelerate_available, is_accelerate_version,\n logging, replace_example_docstring)\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install transformers accelerate\n >>> from diffusers import (StableDiffusionControlNetInpaintPipeline,\n ControlNetModel, DDIMScheduler)\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/\" +\n \"test-arrays/resolve/main/stable_diffusion_inpaint/boy.png\")\n >>> init_image = init_image.resize((512, 512))\n\n >>> generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n >>> mask_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/\" +\n \"test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png\")\n >>> mask_image = mask_image.resize((512, 512))\n\n\n >>> def make_inpaint_condition(image, image_mask):\n ... image = np.array(\n image.convert(\"RGB\")).astype(np.float32) / 255.0\n ... image_mask = np.array(\n image_mask.convert(\"L\")).astype(np.float32) / 255.0\n\n ... assert image.shape[0:1] == image_mask.shape[0:1], \\\n \"image and image_mask must have the same image size\"\n ... image[image_mask > 0.5] = -1.0 # set as masked pixel\n ... image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)\n ... image = torch.from_numpy(image)\n ... return image\n\n\n >>> control_image = make_inpaint_condition(init_image, mask_image)\n\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"lllyasviel/control_v11p_sd15_inpaint\",\n ... torch_dtype=torch.float16\n ... )\n >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet,\n ... torch_dtype=torch.float16\n ... )\n\n >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> image = pipe(\n ... \"a handsome man with ray-ban sunglasses\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... eta=1.0,\n ... image=init_image,\n ... mask_image=mask_image,\n ... control_image=control_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image # noqa\ndef prepare_mask_and_masked_image(image,\n mask,\n height,\n width,\n return_image=False):\n \"\"\"Prepares a pair (image, mask) to be consumed by the Stable Diffusion\n pipeline. This means that those inputs will be converted to\n ``torch.Tensor`` with shapes ``batch x channels x height x width`` where\n ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized to be\n in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``\n or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width``\n ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or\n a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width``\n ``torch.Tensor``.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range.\n ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image``\n should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (or the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, masked_image) as\n ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n\n if image is None:\n raise ValueError('`image` input cannot be undefined.')\n\n if mask is None:\n raise ValueError('`mask_image` input cannot be undefined.')\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError('`image` is a torch.Tensor '\n f'but `mask` (type: {type(mask)} is not')\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[\n 0] == 3, 'Image outside a batch should be of shape (3, H, W)'\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not\n # batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \\\n 'Image and Mask must have 4 dimensions'\n assert image.shape[-2:] == mask.shape[\n -2:], 'Image and Mask must have the same spatial dimensions'\n assert image.shape[0] == mask.shape[\n 0], 'Image and Mask must have the same batch size'\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError('Image should be in [-1, 1] range')\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError('Mask should be in [0, 1] range')\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(\n f'`mask` is a torch.Tensor but `image` (type: {type(image)} is not'\n )\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n # resize all images w.r.t passed height an width\n image = [\n i.resize((width, height), resample=PIL.Image.LANCZOS)\n for i in image\n ]\n image = [np.array(i.convert('RGB'))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [\n i.resize((width, height), resample=PIL.Image.LANCZOS)\n for i in mask\n ]\n mask = np.concatenate(\n [np.array(m.convert('L'))[None, None, :] for m in mask],\n axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n # n.b. ensure backwards compatibility as old function does not return image\n if return_image:\n return mask, masked_image, image\n\n return mask, masked_image\n\n\nclass StableDiffusionControlNetInpaintPipeline(DiffusionPipeline,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n FromSingleFileMixin):\n r\"\"\"\n Pipeline for text-to-image generation using\n Stable Diffusion with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the\n superclass documentation for the generic methods the\n library implements for all the pipelines\n (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [\n `loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n\n \n\n This pipeline can be used both with checkpoints that\n have been specifically fine-tuned for inpainting, such as\n [runwayml/stable-diffusion-inpainting]\n (https://huggingface.co/runwayml/stable-diffusion-inpainting)\n as well as default text-to-image stable diffusion checkpoints, such as\n [runwayml/stable-diffusion-v1-5]\n (https://huggingface.co/runwayml/stable-diffusion-v1-5).\n Default text-to-image stable diffusion checkpoints might be preferable for\n controlnets that have been fine-tuned on\n those, such as [lllyasviel/control_v11p_sd15_inpaint]\n (https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).\n\n \n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images\n to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#\n transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/\n clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/\n en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to\n denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the unet during the\n denoising process. If you set multiple ControlNets\n as a list, the outputs from each ControlNet are added together\n to create one combined additional\n conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise\n the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images\n could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/\n runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as\n inputs for the `safety_checker`.\n \"\"\"\n _optional_components = ['safety_checker', 'feature_extractor']\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel],\n Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f'You have disabled the safety checker for {self.__class__} by'\n ' passing `safety_checker=None`. Ensure'\n ' that you abide to the conditions of the Stable Diffusion'\n ' license and do not expose unfiltered'\n ' results in services or applications open to the public.'\n ' Both the diffusers team and Hugging Face'\n ' strongly recommend to keep the safety filter enabled'\n ' in all public facing circumstances, disabling'\n ' it only for use-cases that involve analyzing network'\n ' behavior or auditing its results. For more'\n ' information, please have a look at'\n ' https://github.com/huggingface/diffusers/pull/254 .')\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n 'Make sure to define a feature extractor when '\n 'loading {self.__class__} if you want to use the safety'\n ' checker. If you do not want to use the safety checker,'\n \" you can pass `'safety_checker=None'` instead.\")\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2**(\n len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor,\n do_convert_rgb=True,\n do_normalize=False)\n self.register_to_config(\n requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled,\n the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful\n to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing`\n was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled,\n the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps.\n This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was\n previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory\n usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole\n model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model\n runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due\n to the iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\n '>=', '0.17.0.dev0'):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError('`enable_model_cpu_offload` '\n 'requires `accelerate v0.17.0` or higher.')\n\n device = torch.device(f'cuda:{gpu_id}')\n\n hook = None\n for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:\n _, hook = cpu_offload_with_hook(\n cpu_offloaded_model, device, prev_module_hook=hook)\n\n if self.safety_checker is not None:\n # the safety checker can offload the vae again\n _, hook = cpu_offload_with_hook(\n self.safety_checker, device, prev_module_hook=hook)\n\n # control net hook has be manually offloaded as it alternates with unet\n cpu_offload_with_hook(self.controlnet, device)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n promptA,\n promptB,\n t,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_promptA=None,\n negative_promptB=None,\n t_nag=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation.\n If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak\n text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt`\n input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily\n tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be\n generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the\n text encoder if LoRA layers are loaded.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n prompt = promptA\n negative_prompt = negative_promptA\n\n if promptA is not None and isinstance(promptA, str):\n batch_size = 1\n elif promptA is not None and isinstance(promptA, list):\n batch_size = len(promptA)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n promptA = self.maybe_convert_prompt(promptA, self.tokenizer)\n\n text_inputsA = self.tokenizer(\n promptA,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_inputsB = self.tokenizer(\n promptB,\n padding='max_length',\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_input_idsA = text_inputsA.input_ids\n text_input_idsB = text_inputsB.input_ids\n untruncated_ids = self.tokenizer(\n promptA, padding='longest', return_tensors='pt').input_ids\n\n if untruncated_ids.shape[-1] >= text_input_idsA.shape[\n -1] and not torch.equal(text_input_idsA, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1:-1])\n logger.warning(\n 'The following part of your input was truncated because'\n 'CLIP can only handle sequences up to'\n f' {self.tokenizer.model_max_length} '\n f' tokens: {removed_text}')\n\n if hasattr(self.text_encoder.config, 'use_attention_mask'\n ) and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputsA.attention_mask.to(device)\n else:\n attention_mask = None\n\n # print(\"text_input_idsA: \",text_input_idsA)\n # print(\"text_input_idsB: \",text_input_idsB)\n # print('t: ',t)\n\n prompt_embedsA = self.text_encoder(\n text_input_idsA.to(device),\n attention_mask=attention_mask,\n )\n prompt_embedsA = prompt_embedsA[0]\n\n prompt_embedsB = self.text_encoder(\n text_input_idsB.to(device),\n attention_mask=attention_mask,\n )\n prompt_embedsB = prompt_embedsB[0]\n prompt_embeds = prompt_embedsA * (t) + (1 - t) * prompt_embedsB\n # print(\"prompt_embeds: \",prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(\n dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt,\n # using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,\n seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokensA: List[str]\n uncond_tokensB: List[str]\n if negative_prompt is None:\n uncond_tokensA = [''] * batch_size\n uncond_tokensB = [''] * batch_size\n elif prompt is not None and type(prompt) is not type(\n negative_prompt):\n raise TypeError(\n f'`negative_prompt` should be the same type to `prompt`, '\n f'but got {type(negative_prompt)} !='\n f' {type(prompt)}.')\n elif isinstance(negative_prompt, str):\n uncond_tokensA = [negative_promptA]\n uncond_tokensB = [negative_promptB]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f'`negative_prompt`: {negative_prompt} has '\n f'batch size {len(negative_prompt)}, but `prompt`:'\n f' {prompt} has batch size {batch_size}. Please make'\n ' sure that passed `negative_prompt` matches'\n ' the batch size of `prompt`.')\n else:\n uncond_tokensA = negative_promptA\n uncond_tokensB = negative_promptB\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokensA = self.maybe_convert_prompt(\n uncond_tokensA, self.tokenizer)\n uncond_tokensB = self.maybe_convert_prompt(\n uncond_tokensB, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_inputA = self.tokenizer(\n uncond_tokensA,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n uncond_inputB = self.tokenizer(\n uncond_tokensB,\n padding='max_length',\n max_length=max_length,\n truncation=True,\n return_tensors='pt',\n )\n\n if hasattr(self.text_encoder.config, 'use_attention_mask'\n ) and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_inputA.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embedsA = self.text_encoder(\n uncond_inputA.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embedsB = self.text_encoder(\n uncond_inputB.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embedsA[0] * (t_nag) + (\n 1 - t_nag) * negative_prompt_embedsB[0]\n\n # negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation\n # per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(\n dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(\n 1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(\n batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings\n # into a single batch\n # to avoid doing two forward passes\n # print(\"prompt_embeds: \",prompt_embeds)\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(\n image, output_type='pil')\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(\n image)\n safety_checker_input = self.feature_extractor(\n feature_extractor_input, return_tensors='pt').to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype))\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n warnings.warn(\n 'The decode_latents method is deprecated and will '\n 'be removed in a future version. Please'\n ' use VaeImageProcessor instead',\n FutureWarning,\n )\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant\n # overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion.StableDiffusionPipeline.\n # prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not\n # all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it\n # will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = 'eta' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs['eta'] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = 'generator' in set(\n inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs['generator'] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion_img2img.\n # StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(\n int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:]\n\n return timesteps, num_inference_steps - t_start\n\n def check_inputs(\n self,\n prompt,\n image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError('`height` and `width` have to be divisible '\n f' by 8 but are {height} and {width}.')\n\n if (callback_steps is None) or (callback_steps is not None and\n (not isinstance(callback_steps, int)\n or callback_steps <= 0)):\n raise ValueError(f'`callback_steps` has to be a positive '\n f' integer but is {callback_steps} of type'\n f' {type(callback_steps)}.')\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f'Cannot forward both `prompt`: {prompt} and '\n f'`prompt_embeds`: {prompt_embeds}. Please make sure to'\n ' only forward one of the two.')\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n 'Provide either `prompt` or `prompt_embeds`.'\n 'Cannot leave both `prompt` and `prompt_embeds` undefined.')\n elif prompt is not None and (not isinstance(prompt, str)\n and not isinstance(prompt, list)):\n raise ValueError(f'`prompt` has to be of '\n f'type `str` or `list` but is {type(prompt)}')\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f'Cannot forward both `negative_prompt`: {negative_prompt} '\n f'and `negative_prompt_embeds`:'\n f' {negative_prompt_embeds}. Please make'\n f' sure to only forward one of the two.')\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n '`prompt_embeds` and `negative_prompt_embeds` must'\n f' have the same shape when passed directly, but'\n f' got: `prompt_embeds` {prompt_embeds.shape} != '\n '`negative_prompt_embeds`'\n f' {negative_prompt_embeds.shape}.')\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f'You have {len(self.controlnet.nets)} ControlNets '\n ' and you have passed {len(prompt)}'\n ' prompts. The conditionings will be '\n ' fixed across the prompts.')\n\n # Check `image`\n is_compiled = hasattr(\n F, 'scaled_dot_product_attention') and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule)\n\n if (isinstance(self.controlnet, ControlNetModel) or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)):\n self.check_image(image, prompt, prompt_embeds)\n elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):\n if not isinstance(image, list):\n raise TypeError(\n 'For multiple controlnets: `image` must be type `list`')\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1],\n # [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError('A single batch of multiple conditionings '\n 'are supported at the moment.')\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n 'For multiple controlnets: `image` must have the '\n 'same length as the number of controlnets, but got'\n f' {len(image)} images '\n f' and {len(self.controlnet.nets)} ControlNets.')\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (isinstance(self.controlnet, ControlNetModel) or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\n 'For single controlnet: '\n '`controlnet_conditioning_scale` must be type `float`.')\n elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):\n if isinstance(controlnet_conditioning_scale, list):\n if any(\n isinstance(i, list)\n for i in controlnet_conditioning_scale):\n raise ValueError(\n 'A single batch of '\n 'multiple conditionings are supported at the moment.')\n elif isinstance(\n controlnet_conditioning_scale,\n list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets):\n raise ValueError(\n 'For multiple controlnets: When '\n '`controlnet_conditioning_scale` is specified as '\n '`list`, it must have'\n ' the same length as the number of controlnets')\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f'`control_guidance_start` has {len(control_guidance_start)} '\n ' elements, but `control_guidance_end` has'\n f' {len(control_guidance_end)} elements. Make sure to'\n f' provide the same number of elements to each list.')\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f'`control_guidance_start`: {control_guidance_start} has'\n f' {len(control_guidance_start)} elements but there '\n f' are {len(self.controlnet.nets)} controlnets available. '\n f' Make sure to provide {len(self.controlnet.nets)}.')\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f'control guidance start: {start} cannot be larger or '\n f'equal to control guidance end: {end}.')\n if start < 0.0:\n raise ValueError(\n f\"control guidance start: {start} can't be smaller than 0.\"\n )\n if end > 1.0:\n raise ValueError(\n f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.\n # StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(\n image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(\n image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(\n image[0], np.ndarray)\n\n if (not image_is_pil and not image_is_tensor and not image_is_np\n and not image_is_pil_list and not image_is_tensor_list\n and not image_is_np_list):\n raise TypeError(\n 'image must be passed and be one of PIL image, numpy array,'\n f' torch tensor, list of PIL images, list of numpy arrays or'\n f'list of torch tensors, but is {type(image)}')\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n 'If image batch size is not 1, image batch size must be '\n 'same as prompt batch size. '\n f' image batch size: {image_batch_size}, '\n f' prompt batch size: {prompt_batch_size}')\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.\n # StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(\n image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion_inpaint.\n # StableDiffusionInpaintPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (batch_size, num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n 'You have passed a list of generators of '\n f'length {len(generator)}, but requested an effective batch'\n f' size of {batch_size}. Make sure the batch size '\n 'matches the length of the generators.')\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n 'Since strength < 1. initial latents are to be initialised'\n ' as a combination of Image + Noise.'\n 'However, either the image or the noise timestep has '\n ' not been provided.')\n\n if return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(\n image=image, generator=generator)\n\n if latents is None:\n noise = randn_tensor(\n shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise,\n # else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(\n image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the\n # Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma \\\n if is_strength_max else latents\n else:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n\n outputs = (latents, )\n\n if return_noise:\n outputs += (noise, )\n\n if return_image_latents:\n outputs += (image_latents, )\n\n return outputs\n\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[2]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[3]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion_inpaint.\n # StableDiffusionInpaintPipeline.prepare_mask_latents\n def prepare_mask_latents(self, mask, masked_image, batch_size, height,\n width, dtype, device, generator,\n do_classifier_free_guidance):\n # resize the mask to latents shape as we\n # concatenate the mask to the latents\n # we do that before converting to dtype to\n # avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask,\n size=(height // self.vae_scale_factor,\n width // self.vae_scale_factor))\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(\n masked_image, generator=generator)\n\n # duplicate mask and masked_image_latents for each generation per\n # prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and required batch size don't match.'\"\n ' Masks are supposed to be duplicated to'\n f' total batch size of {batch_size}, but {mask.shape[0]} '\n ' masks were passed. Make sure the number'\n ' of masks that you pass is divisible by the total '\n ' requested batch size.')\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n 'The passed images and the required batch size '\n \" don't match. Images are supposed to be duplicated\"\n f' to a total batch size of {batch_size}, but '\n f' {masked_image_latents.shape[0]} images were passed.'\n ' Make sure the number of images that you pass is '\n ' divisible by the total requested batch size.')\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2)\n if do_classifier_free_guidance else masked_image_latents)\n\n # aligning device to prevent device errors when\n # concatenating it with the latent model input\n masked_image_latents = masked_image_latents.to(\n device=device, dtype=dtype)\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.\n # pipeline_stable_diffusion_inpaint.\n # StableDiffusionInpaintPipeline._encode_vae_image\n def _encode_vae_image(self, image: torch.Tensor,\n generator: torch.Generator):\n if isinstance(generator, list):\n image_latents = [\n self.vae.encode(image[i:i + 1]).latent_dist.sample(\n generator=generator[i]) for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = self.vae.encode(image).latent_dist.sample(\n generator=generator)\n\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n @torch.no_grad()\n def predict_woControl(\n self,\n promptA: Union[str, List[str]] = None,\n promptB: Union[str, List[str]] = None,\n image: Union[torch.FloatTensor, PIL.Image.Image] = None,\n mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 1.0,\n tradoff: float = 1.0,\n tradoff_nag: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_promptA: Optional[Union[str, List[str]]] = None,\n negative_promptB: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator,\n List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = 'pil',\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor],\n None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n task_class: Union[torch.Tensor, float, int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not\n defined, you need to pass `prompt_embeds`.\n image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to be inpainted\n (which parts of the image to be masked\n out with `mask_image` and repainted according to `prompt`).\n mask_image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to\n mask `image`.\n White pixels in the mask are repainted\n while black pixels are preserved. If `mask_image` is a\n PIL image, it is converted to a single channel\n (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the\n expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to `self.unet.config.\n sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to\n `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`.\n Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the\n `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength`\n is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified\n in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps\n usually lead to a higher quality image at the\n expense of slower inference. This parameter is\n modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to\n generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance\n scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image\n generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not\n using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM]\n (https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is\n ignored in other schedulers.\n generator (`torch.Generator` or\n `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/\n generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian\n distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation\n with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied\n random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily\n tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the\n `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used\n to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated\n from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between\n `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.\n StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during\n inference. The function is called with the\n following arguments: `callback(step: int, timestep: int,\n latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called.\n If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to\n the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/\n blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionInpaintPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/\n latent-diffusion/main/data/inpainting_examples/\n overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/\n latent-diffusion/main/data/inpainting_examples/\n overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n\n >>> pipe = StableDiffusionInpaintPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"Face of a yellow cat, high resolution,\n sitting on a park bench\"\n >>> image = pipe(prompt=prompt, image=init_image,\n mask_image=mask_image).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`]\n or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.\n StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a\n list with the generated images and the\n second element is a list of `bool`s indicating whether the\n corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n prompt = promptA\n negative_prompt = negative_promptA\n # 1. Check inputs\n self.check_inputs(\n prompt,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance\n # weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get('scale', None)\n if cross_attention_kwargs is not None else None)\n prompt_embeds = self._encode_prompt(\n promptA,\n promptB,\n tradoff,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_promptA,\n negative_promptB,\n tradoff_nag,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps,\n strength=strength,\n device=device)\n # check that number of inference steps is not < 1 -\n # as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n 'After adjusting the num_inference_steps by strength '\n f'parameter: {strength}, the number of pipeline'\n f'steps is {num_inference_steps} which is < 1 and '\n f'not appropriate for this pipeline.')\n # at which timestep to set the initial noise\n # (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size *\n num_images_per_prompt)\n # create a boolean to check if the strength\n # is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n mask, masked_image, init_image = prepare_mask_and_masked_image(\n image, mask_image, height, width, return_image=True)\n mask_condition = mask.clone()\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if (num_channels_latents + num_channels_mask +\n num_channels_masked_image) != self.unet.config.in_channels:\n raise ValueError(\n 'Incorrect configuration settings! The config '\n f'of `pipeline.unet`: {self.unet.config} expects'\n f' {self.unet.config.in_channels} but received '\n f'`num_channels_latents`: {num_channels_latents} +'\n f' `num_channels_mask`: {num_channels_mask} + '\n f'`num_channels_masked_image`: {num_channels_masked_image}'\n f' = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of' # noqa\n ' `pipeline.unet` or your `mask_image` or `image` input.')\n elif num_channels_unet != 4:\n raise ValueError(\n f'The unet {self.unet.__class__} should have either 4 '\n f'or 9 input channels, not {self.unet.config.in_channels}.')\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally\n # just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n num_warmup_steps = len(\n timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents\n # in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(\n latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat(\n [latent_model_input, mask, masked_image_latents],\n dim=1)\n\n # predict the noise residual\n if task_class is not None:\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n task_class=task_class,\n )[0]\n else:\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n **extra_step_kwargs,\n return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise,\n torch.tensor([noise_timestep]))\n\n latents = (1 - init_mask\n ) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and\n (i + 1) % self.scheduler.order == 0): # noqa\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == 'latent':\n condition_kwargs = {}\n if isinstance(self.vae, AsymmetricAutoencoderKL):\n init_image = init_image.to(\n device=device, dtype=masked_image_latents.dtype)\n init_image_condition = init_image.clone()\n init_image = self._encode_vae_image(\n init_image, generator=generator)\n mask_condition = mask_condition.to(\n device=device, dtype=masked_image_latents.dtype)\n condition_kwargs = {\n 'image': init_image_condition,\n 'mask': mask_condition\n }\n image = self.vae.decode(\n latents / self.vae.config.scaling_factor,\n return_dict=False,\n **condition_kwargs)[0]\n image, has_nsfw_concept = self.run_safety_checker(\n image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(\n self,\n 'final_offload_hook') and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(\n images=image, nsfw_content_detected=has_nsfw_concept)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n promptA: Union[str, List[str]] = None,\n promptB: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n control_image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray,\n List[torch.FloatTensor], List[PIL.Image.Image],\n List[np.ndarray], ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 1.0,\n tradoff: float = 1.0,\n tradoff_nag: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_promptA: Optional[Union[str, List[str]]] = None,\n negative_promptB: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator,\n List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = 'pil',\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor],\n None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.5,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If\n not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.FloatTensor`, `PIL.Image.Image`,\n `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,\n `List[List[torch.FloatTensor]]`, or\n `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input\n condition to generate guidance to Unet. If\n the type is specified as `Torch.FloatTensor`, it is passed to\n ControlNet as is. `PIL.Image.Image` can\n also be accepted as an image. The dimensions of the output\n image defaults to `image`'s dimensions. If\n height and/or width are passed, `image` is resized according\n to them. If multiple ControlNets are\n specified in init, images must be passed as a list such that\n each element of the list can be correctly\n batched for input to a single controlnet.\n height (`int`, *optional*, defaults to self.unet.config.\n sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.\n sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 1.):\n Conceptually, indicates how much to transform the masked\n portion of the reference `image`. Must be\n between 0 and 1. `image` will be used as a starting point,\n adding more noise to it the larger the\n `strength`. The number of denoising steps depends on the\n amount of noise initially added. When\n `strength` is 1, added noise will be maximum and the\n denoising process will run for the full number of\n iterations specified in `num_inference_steps`. A value of 1,\n therefore, essentially ignores the masked\n portion of the reference `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually\n lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion\n Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale\n is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that\n are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If\n not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using\n guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper:\n https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or\n `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)]\n (https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian\n distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with\n different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied\n random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily\n tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from\n `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to\n easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will\n be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/):\n `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.\n StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps\n during inference. The function will be\n called with the following arguments: `callback(step: int,\n timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called.\n If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the\n `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor]\n (https://github.com/huggingface/diffusers/blob/main/src\n /diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`,\n *optional*, defaults to 0.5):\n The outputs of the controlnet are multiplied by\n `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets\n are specified in init, you can set the\n corresponding scale as a list. Note that by default, we use a\n smaller conditioning scale for inpainting\n than for [`~StableDiffusionControlNetPipeline.__call__`].\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to\n recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0\n and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*,\n defaults to 0.0):\n The percentage of total steps at which the controlnet\n starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*,\n defaults to 1.0):\n The percentage of total steps at which the controlnet\n stops applying.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`]\n or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`]\n if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the\n generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated\n image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n controlnet = self.controlnet._orig_mod if is_compiled_module(\n self.controlnet) else self.controlnet\n\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, image)\n\n prompt = promptA\n negative_prompt = negative_promptA\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(\n control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [\n control_guidance_start\n ]\n elif not isinstance(control_guidance_end, list) and isinstance(\n control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [\n control_guidance_end\n ]\n elif not isinstance(control_guidance_start, list) and not isinstance(\n control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(\n controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = mult * [\n control_guidance_start\n ], mult * [control_guidance_end]\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n control_image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance\n # weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf .\n # `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(\n controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale\n ] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions if isinstance(\n controlnet, ControlNetModel) else\n controlnet.nets[0].config.global_pool_conditions)\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get('scale', None)\n if cross_attention_kwargs is not None else None)\n prompt_embeds = self._encode_prompt(\n promptA,\n promptB,\n tradoff,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_promptA,\n negative_promptB,\n tradoff_nag,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 4. Preprocess mask and image - resizes image and\n # mask w.r.t height and width\n mask, masked_image, init_image = prepare_mask_and_masked_image(\n image, mask_image, height, width, return_image=True)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps,\n strength=strength,\n device=device)\n # at which timestep to set the initial noise (n.b.\n # 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size *\n num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so\n # then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally\n # just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or\n (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(\n keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(\n timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(\n latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(\n control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [\n c * s for c, s in zip(controlnet_conditioning_scale,\n controlnet_keep[i])\n ]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Inferred ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the\n # unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [\n torch.cat([torch.zeros_like(d), d])\n for d in down_block_res_samples\n ]\n mid_block_res_sample = torch.cat([\n torch.zeros_like(mid_block_res_sample),\n mid_block_res_sample\n ])\n\n # predict the noise residual\n if num_channels_unet == 9:\n latent_model_input = torch.cat(\n [latent_model_input, mask, masked_image_latents],\n dim=1)\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n **extra_step_kwargs,\n return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise,\n torch.tensor([noise_timestep]))\n\n latents = (1 - init_mask\n ) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and\n (i + 1) % self.scheduler.order == 0): # noqa\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # If we do sequential model offloading, let's\n # offload unet and controlnet\n # manually for max memory savings\n if hasattr(\n self,\n 'final_offload_hook') and self.final_offload_hook is not None:\n self.unet.to('cpu')\n self.controlnet.to('cpu')\n torch.cuda.empty_cache()\n\n if not output_type == 'latent':\n image = self.vae.decode(\n latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(\n image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(\n self,\n 'final_offload_hook') and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(\n images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "projects/powerpaint/requirements.txt", "content": "controlnet-aux==0.0.3\ndiffusers==0.23.1\ngradio==3.41.0\nmmengine\nopencv-python\ntorch\ntorchvision\ntransformers\n" }, { "path": "projects/powerpaint/utils/utils.py", "content": "import copy\nimport importlib\nimport os\nimport random\nfrom logging import WARNING\nfrom typing import Any, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom mmengine import print_log\n\n\ndef try_import(name: str):\n \"\"\"Try to import a module.\n\n Args:\n name (str): Specifies what module to import in absolute or relative\n terms (e.g. either pkg.mod or ..mod).\n Returns:\n ModuleType or None: If importing successfully, returns the imported\n module, otherwise returns None.\n \"\"\"\n try:\n return importlib.import_module(name)\n except ImportError:\n return None\n\n\nclass TokenizerWrapper:\n \"\"\"Tokenizer wrapper for CLIPTokenizer. Only support CLIPTokenizer\n currently. This wrapper is modified from https://github.com/huggingface/dif\n fusers/blob/e51f19aee82c8dd874b715a09dbc521d88835d68/src/diffusers/loaders.\n py#L358 # noqa.\n\n Args:\n from_pretrained (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Defaults to None.\n from_config (Union[str, os.PathLike], optional): The *model id*\n of a pretrained model or a path to a *directory* containing\n model weights and config. Defaults to None.\n\n *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs\n will be passed to `from_pretrained` function. Otherwise, *args\n and **kwargs will be used to initialize the model by\n `self._module_cls(*args, **kwargs)`.\n \"\"\"\n\n def __init__(self,\n from_pretrained: Optional[Union[str, os.PathLike]] = None,\n from_config: Optional[Union[str, os.PathLike]] = None,\n *args,\n **kwargs):\n transformers = try_import('transformers')\n module_cls = transformers.CLIPTokenizer\n\n assert not (from_pretrained and from_config), (\n '\\'from_pretrained\\' and \\'from_config\\' should not be passed '\n 'at the same time.')\n\n if from_config:\n print_log(\n 'Tokenizers from Huggingface transformers do not support '\n '\\'from_config\\'. Will call \\'from_pretrained\\' instead '\n 'with the same argument.', 'current', WARNING)\n from_pretrained = from_config\n\n if from_pretrained:\n self.wrapped = module_cls.from_pretrained(from_pretrained, *args,\n **kwargs)\n else:\n self.wrapper = module_cls(*args, **kwargs)\n\n self._from_pretrained = from_pretrained\n self.token_map = {}\n\n def __getattr__(self, name: str) -> Any:\n if name == 'wrapped':\n return super().__getattr__('wrapped')\n\n try:\n return getattr(self.wrapped, name)\n except AttributeError:\n try:\n return super().__getattr__(name)\n except AttributeError:\n raise AttributeError(\n '\\'name\\' cannot be found in both '\n f'\\'{self.__class__.__name__}\\' and '\n f'\\'{self.__class__.__name__}.tokenizer\\'.')\n\n def try_adding_tokens(self, tokens: Union[str, List[str]], *args,\n **kwargs):\n \"\"\"Attempt to add tokens to the tokenizer.\n\n Args:\n tokens (Union[str, List[str]]): The tokens to be added.\n \"\"\"\n num_added_tokens = self.wrapped.add_tokens(tokens, *args, **kwargs)\n assert num_added_tokens != 0, (\n f'The tokenizer already contains the token {tokens}. Please pass '\n 'a different `placeholder_token` that is not already in the '\n 'tokenizer.')\n\n def get_token_info(self, token: str) -> dict:\n \"\"\"Get the information of a token, including its start and end index in\n the current tokenizer.\n\n Args:\n token (str): The token to be queried.\n\n Returns:\n dict: The information of the token, including its start and end\n index in current tokenizer.\n \"\"\"\n token_ids = self.__call__(token).input_ids\n start, end = token_ids[1], token_ids[-2] + 1\n return {'name': token, 'start': start, 'end': end}\n\n def add_placeholder_token(self,\n placeholder_token: str,\n *args,\n num_vec_per_token: int = 1,\n **kwargs):\n \"\"\"Add placeholder tokens to the tokenizer.\n\n Args:\n placeholder_token (str): The placeholder token to be added.\n num_vec_per_token (int, optional): The number of vectors of\n the added placeholder token.\n *args, **kwargs: The arguments for `self.wrapped.add_tokens`.\n \"\"\"\n output = []\n if num_vec_per_token == 1:\n self.try_adding_tokens(placeholder_token, *args, **kwargs)\n output.append(placeholder_token)\n else:\n output = []\n for i in range(num_vec_per_token):\n ith_token = placeholder_token + f'_{i}'\n self.try_adding_tokens(ith_token, *args, **kwargs)\n output.append(ith_token)\n\n for token in self.token_map:\n if token in placeholder_token:\n raise ValueError(\n f'The tokenizer already has placeholder token {token} '\n f'that can get confused with {placeholder_token} '\n 'keep placeholder tokens independent')\n self.token_map[placeholder_token] = output\n\n def replace_placeholder_tokens_in_text(self,\n text: Union[str, List[str]],\n vector_shuffle: bool = False,\n prop_tokens_to_load: float = 1.0\n ) -> Union[str, List[str]]:\n \"\"\"Replace the keywords in text with placeholder tokens. This function\n will be called in `self.__call__` and `self.encode`.\n\n Args:\n text (Union[str, List[str]]): The text to be processed.\n vector_shuffle (bool, optional): Whether to shuffle the vectors.\n Defaults to False.\n prop_tokens_to_load (float, optional): The proportion of tokens to\n be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0.\n\n Returns:\n Union[str, List[str]]: The processed text.\n \"\"\"\n if isinstance(text, list):\n output = []\n for i in range(len(text)):\n output.append(\n self.replace_placeholder_tokens_in_text(\n text[i], vector_shuffle=vector_shuffle))\n return output\n\n for placeholder_token in self.token_map:\n if placeholder_token in text:\n tokens = self.token_map[placeholder_token]\n tokens = tokens[:1 + int(len(tokens) * prop_tokens_to_load)]\n if vector_shuffle:\n tokens = copy.copy(tokens)\n random.shuffle(tokens)\n text = text.replace(placeholder_token, ' '.join(tokens))\n return text\n\n def replace_text_with_placeholder_tokens(self, text: Union[str, List[str]]\n ) -> Union[str, List[str]]:\n \"\"\"Replace the placeholder tokens in text with the original keywords.\n This function will be called in `self.decode`.\n\n Args:\n text (Union[str, List[str]]): The text to be processed.\n\n Returns:\n Union[str, List[str]]: The processed text.\n \"\"\"\n if isinstance(text, list):\n output = []\n for i in range(len(text)):\n output.append(\n self.replace_text_with_placeholder_tokens(text[i]))\n return output\n\n for placeholder_token, tokens in self.token_map.items():\n merged_tokens = ' '.join(tokens)\n if merged_tokens in text:\n text = text.replace(merged_tokens, placeholder_token)\n return text\n\n def __call__(self,\n text: Union[str, List[str]],\n *args,\n vector_shuffle: bool = False,\n prop_tokens_to_load: float = 1.0,\n **kwargs):\n \"\"\"The call function of the wrapper.\n\n Args:\n text (Union[str, List[str]]): The text to be tokenized.\n vector_shuffle (bool, optional): Whether to shuffle the vectors.\n Defaults to False.\n prop_tokens_to_load (float, optional): The proportion of tokens to\n be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0\n *args, **kwargs: The arguments for `self.wrapped.__call__`.\n \"\"\"\n replaced_text = self.replace_placeholder_tokens_in_text(\n text,\n vector_shuffle=vector_shuffle,\n prop_tokens_to_load=prop_tokens_to_load)\n\n return self.wrapped.__call__(replaced_text, *args, **kwargs)\n\n def encode(self, text: Union[str, List[str]], *args, **kwargs):\n \"\"\"Encode the passed text to token index.\n\n Args:\n text (Union[str, List[str]]): The text to be encode.\n *args, **kwargs: The arguments for `self.wrapped.__call__`.\n \"\"\"\n replaced_text = self.replace_placeholder_tokens_in_text(text)\n return self.wrapped(replaced_text, *args, **kwargs)\n\n def decode(self,\n token_ids,\n return_raw: bool = False,\n *args,\n **kwargs) -> Union[str, List[str]]:\n \"\"\"Decode the token index to text.\n\n Args:\n token_ids: The token index to be decoded.\n return_raw: Whether keep the placeholder token in the text.\n Defaults to False.\n *args, **kwargs: The arguments for `self.wrapped.decode`.\n\n Returns:\n Union[str, List[str]]: The decoded text.\n \"\"\"\n text = self.wrapped.decode(token_ids, *args, **kwargs)\n if return_raw:\n return text\n replaced_text = self.replace_text_with_placeholder_tokens(text)\n return replaced_text\n\n def __repr__(self):\n \"\"\"The representation of the wrapper.\"\"\"\n s = super().__repr__()\n prefix = f'Wrapped Module Class: {self._module_cls}\\n'\n prefix += f'Wrapped Module Name: {self._module_name}\\n'\n if self._from_pretrained:\n prefix += f'From Pretrained: {self._from_pretrained}\\n'\n s = prefix + s\n return s\n\n\nclass EmbeddingLayerWithFixes(nn.Module):\n \"\"\"The revised embedding layer to support external embeddings. This design\n of this class is inspired by https://github.com/AUTOMATIC1111/stable-\n diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi\n jack.py#L224 # noqa.\n\n Args:\n wrapped (nn.Emebdding): The embedding layer to be wrapped.\n external_embeddings (Union[dict, List[dict]], optional): The external\n embeddings added to this layer. Defaults to None.\n \"\"\"\n\n def __init__(self,\n wrapped: nn.Embedding,\n external_embeddings: Optional[Union[dict,\n List[dict]]] = None):\n super().__init__()\n self.wrapped = wrapped\n self.num_embeddings = wrapped.weight.shape[0]\n\n self.external_embeddings = []\n if external_embeddings:\n self.add_embeddings(external_embeddings)\n\n self.trainable_embeddings = nn.ParameterDict()\n\n @property\n def weight(self):\n \"\"\"Get the weight of wrapped embedding layer.\"\"\"\n return self.wrapped.weight\n\n def check_duplicate_names(self, embeddings: List[dict]):\n \"\"\"Check whether duplicate names exist in list of 'external\n embeddings'.\n\n Args:\n embeddings (List[dict]): A list of embedding to be check.\n \"\"\"\n names = [emb['name'] for emb in embeddings]\n assert len(names) == len(set(names)), (\n 'Found duplicated names in \\'external_embeddings\\'. Name list: '\n f'\\'{names}\\'')\n\n def check_ids_overlap(self, embeddings):\n \"\"\"Check whether overlap exist in token ids of 'external_embeddings'.\n\n Args:\n embeddings (List[dict]): A list of embedding to be check.\n \"\"\"\n ids_range = [[emb['start'], emb['end'], emb['name']]\n for emb in embeddings]\n ids_range.sort() # sort by 'start'\n # check if 'end' has overlapping\n for idx in range(len(ids_range) - 1):\n name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1]\n assert ids_range[idx][1] <= ids_range[idx + 1][0], (\n f'Found ids overlapping between embeddings \\'{name1}\\' '\n f'and \\'{name2}\\'.')\n\n def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):\n \"\"\"Add external embeddings to this layer.\n\n Use case:\n\n >>> 1. Add token to tokenizer and get the token id.\n >>> tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')\n >>> # 'how much' in kiswahili\n >>> tokenizer.add_placeholder_tokens('ngapi', num_vec_per_token=4)\n >>>\n >>> 2. Add external embeddings to the model.\n >>> new_embedding = {\n >>> 'name': 'ngapi', # 'how much' in kiswahili\n >>> 'embedding': torch.ones(1, 15) * 4,\n >>> 'start': tokenizer.get_token_info('kwaheri')['start'],\n >>> 'end': tokenizer.get_token_info('kwaheri')['end'],\n >>> 'trainable': False # if True, will registry as a parameter\n >>> }\n >>> embedding_layer = nn.Embedding(10, 15)\n >>> embedding_layer_wrapper = EmbeddingLayerWithFixes(embedding_layer)\n >>> embedding_layer_wrapper.add_embeddings(new_embedding)\n >>>\n >>> 3. Forward tokenizer and embedding layer!\n >>> input_text = ['hello, ngapi!', 'hello my friend, ngapi?']\n >>> input_ids = tokenizer(\n >>> input_text, padding='max_length', truncation=True,\n >>> return_tensors='pt')['input_ids']\n >>> out_feat = embedding_layer_wrapper(input_ids)\n >>>\n >>> 4. Let's validate the result!\n >>> assert (out_feat[0, 3: 7] == 2.3).all()\n >>> assert (out_feat[2, 5: 9] == 2.3).all()\n\n Args:\n embeddings (Union[dict, list[dict]]): The external embeddings to\n be added. Each dict must contain the following 4 fields: 'name'\n (the name of this embedding), 'embedding' (the embedding\n tensor), 'start' (the start token id of this embedding), 'end'\n (the end token id of this embedding). For example:\n `{name: NAME, start: START, end: END, embedding: torch.Tensor}`\n \"\"\"\n if isinstance(embeddings, dict):\n embeddings = [embeddings]\n\n self.external_embeddings += embeddings\n self.check_duplicate_names(self.external_embeddings)\n self.check_ids_overlap(self.external_embeddings)\n\n # set for trainable\n added_trainable_emb_info = []\n for embedding in embeddings:\n trainable = embedding.get('trainable', False)\n if trainable:\n name = embedding['name']\n embedding['embedding'] = torch.nn.Parameter(\n embedding['embedding'])\n self.trainable_embeddings[name] = embedding['embedding']\n added_trainable_emb_info.append(name)\n\n added_emb_info = [emb['name'] for emb in embeddings]\n added_emb_info = ', '.join(added_emb_info)\n print_log(f'Successfully add external embeddings: {added_emb_info}.',\n 'current')\n\n if added_trainable_emb_info:\n added_trainable_emb_info = ', '.join(added_trainable_emb_info)\n print_log(\n 'Successfully add trainable external embeddings: '\n f'{added_trainable_emb_info}', 'current')\n\n def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:\n \"\"\"Replace external input ids to 0.\n\n Args:\n input_ids (torch.Tensor): The input ids to be replaced.\n\n Returns:\n torch.Tensor: The replaced input ids.\n \"\"\"\n input_ids_fwd = input_ids.clone()\n input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0\n return input_ids_fwd\n\n def replace_embeddings(self, input_ids: torch.Tensor,\n embedding: torch.Tensor,\n external_embedding: dict) -> torch.Tensor:\n \"\"\"Replace external embedding to the embedding layer. Noted that, in\n this function we use `torch.cat` to avoid inplace modification.\n\n Args:\n input_ids (torch.Tensor): The original token ids. Shape like\n [LENGTH, ].\n embedding (torch.Tensor): The embedding of token ids after\n `replace_input_ids` function.\n external_embedding (dict): The external embedding to be replaced.\n\n Returns:\n torch.Tensor: The replaced embedding.\n \"\"\"\n new_embedding = []\n\n name = external_embedding['name']\n start = external_embedding['start']\n end = external_embedding['end']\n target_ids_to_replace = [i for i in range(start, end)]\n ext_emb = external_embedding['embedding']\n\n # do not need to replace\n if not (input_ids == start).any():\n return embedding\n\n # start replace\n s_idx, e_idx = 0, 0\n while e_idx < len(input_ids):\n if input_ids[e_idx] == start:\n if e_idx != 0:\n # add embedding do not need to replace\n new_embedding.append(embedding[s_idx:e_idx])\n\n # check if the next embedding need to replace is valid\n actually_ids_to_replace = [\n int(i) for i in input_ids[e_idx:e_idx + end - start]\n ]\n assert actually_ids_to_replace == target_ids_to_replace, (\n f'Invalid \\'input_ids\\' in position: {s_idx} to {e_idx}. '\n f'Expect \\'{target_ids_to_replace}\\' for embedding '\n f'\\'{name}\\' but found \\'{actually_ids_to_replace}\\'.')\n\n new_embedding.append(ext_emb)\n\n s_idx = e_idx + end - start\n e_idx = s_idx + 1\n else:\n e_idx += 1\n\n if e_idx == len(input_ids):\n new_embedding.append(embedding[s_idx:e_idx])\n\n return torch.cat(new_embedding, dim=0)\n\n def forward(self,\n input_ids: torch.Tensor,\n external_embeddings: Optional[List[dict]] = None):\n \"\"\"The forward function.\n\n Args:\n input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or\n [LENGTH, ].\n external_embeddings (Optional[List[dict]]): The external\n embeddings. If not passed, only `self.external_embeddings`\n will be used. Defaults to None.\n\n input_ids: shape like [bz, LENGTH] or [LENGTH].\n \"\"\"\n assert input_ids.ndim in [1, 2]\n if input_ids.ndim == 1:\n input_ids = input_ids.unsqueeze(0)\n\n if external_embeddings is None and not self.external_embeddings:\n return self.wrapped(input_ids)\n\n input_ids_fwd = self.replace_input_ids(input_ids)\n inputs_embeds = self.wrapped(input_ids_fwd)\n\n vecs = []\n\n if external_embeddings is None:\n external_embeddings = []\n elif isinstance(external_embeddings, dict):\n external_embeddings = [external_embeddings]\n embeddings = self.external_embeddings + external_embeddings\n\n for input_id, embedding in zip(input_ids, inputs_embeds):\n new_embedding = embedding\n for external_embedding in embeddings:\n new_embedding = self.replace_embeddings(\n input_id, new_embedding, external_embedding)\n vecs.append(new_embedding)\n\n return torch.stack(vecs)\n\n\ndef add_tokens(tokenizer,\n text_encoder,\n placeholder_tokens: list,\n initialize_tokens: list = None,\n num_vectors_per_token: int = 1):\n \"\"\"Add token for training.\n\n # TODO: support add tokens as dict, then we can load pretrained tokens.\n \"\"\"\n if initialize_tokens is not None:\n assert len(initialize_tokens) == len(placeholder_tokens), (\n 'placeholder_token should be the same length as initialize_token')\n for ii in range(len(placeholder_tokens)):\n\n tokenizer.add_placeholder_token(\n placeholder_tokens[ii], num_vec_per_token=num_vectors_per_token)\n\n # text_encoder.set_embedding_layer()\n embedding_layer = text_encoder.text_model.embeddings.token_embedding\n text_encoder.text_model.embeddings.token_embedding = \\\n EmbeddingLayerWithFixes(embedding_layer)\n embedding_layer = text_encoder.text_model.embeddings.token_embedding\n\n assert embedding_layer is not None, (\n 'Do not support get embedding layer for current text encoder. '\n 'Please check your configuration.')\n initialize_embedding = []\n if initialize_tokens is not None:\n for ii in range(len(placeholder_tokens)):\n init_id = tokenizer(initialize_tokens[ii]).input_ids[1]\n temp_embedding = embedding_layer.weight[init_id]\n initialize_embedding.append(temp_embedding[None, ...].repeat(\n num_vectors_per_token, 1))\n else:\n for ii in range(len(placeholder_tokens)):\n init_id = tokenizer('a').input_ids[1]\n temp_embedding = embedding_layer.weight[init_id]\n len_emb = temp_embedding.shape[0]\n init_weight = (torch.rand(num_vectors_per_token, len_emb) -\n 0.5) / 2.0\n initialize_embedding.append(init_weight)\n\n # initialize_embedding = torch.cat(initialize_embedding,dim=0)\n\n token_info_all = []\n for ii in range(len(placeholder_tokens)):\n token_info = tokenizer.get_token_info(placeholder_tokens[ii])\n token_info['embedding'] = initialize_embedding[ii]\n token_info['trainable'] = True\n token_info_all.append(token_info)\n embedding_layer.add_embeddings(token_info_all)\n" }, { "path": "projects/prompt_to_prompt/README.md", "content": "# Inversions & Editing (DDIM Inversion & Null-Text Inversion & Prompt-to-Prompt Editing)\n\n```\nAuthor: @FerryHuang\n\nThis is an implementation of the papers:\n```\n\n> [PROMPT-TO-PROMPT IMAGE EDITING\n> WITH CROSS-ATTENTION CONTROL](https://prompt-to-prompt.github.io/ptp_files/Prompt-to-Prompt_preprint.pdf)\n\n> [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/pdf/2211.09794.pdf)\n\n> **Task**: Text2Image, diffusion, inversion, editing\n\n\n\n## Abstract\n\n\n\nDiffusion's inversion basically means you put an image (with or without a prompt) into a method and it will return a latent code which can be later turned back to a image with high simmilarity as the original one. Of course we want this latent code for an editing purpose, that's also why we always implement inversion methods together with the editing methods.\n\nThis project contains **Two inversion methods** and **One editing method**.\n\n## From right to left: origin image, DDIM inversion, Null-text inversion\n\n
\n \n
\n\n## Prompt-to-prompt Editing\n\n
\n cat -> dog\n
\n \n
\n\n
\n spider man -> iron man(attention replace)\n
\n \n
\n\n
\n Effel tower -> Effel tower at night (attention refine)\n
\n \n
\n\n
\n blossom sakura tree -> blossom(-3) sakura tree (attention reweight)\n
\n \n
\n\n## Quick Start\n\nA walkthrough of the project is provided [here](visualize.ipynb)\n\nor you can just run the following scripts to get the results:\n\n```python\n# load the mmagic SD1.5\nfrom mmengine import MODELS, Config\nfrom mmengine.registry import init_default_scope\n\ninit_default_scope('mmagic')\n\nconfig = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py'\nconfig = Config.fromfile(config).copy()\n\nStableDiffuser = MODELS.build(config.model)\nStableDiffuser = StableDiffuser.to('cuda')\n```\n\n```python\n# inversion\nimage_path = 'projects/prompt_to_prompt/assets/gnochi_mirror.jpeg'\nprompt = \"a cat sitting next to a mirror\"\nimage_tensor = ptp_utils.load_512(image_path).to('cuda')\n\nfrom inversions.null_text_inversion import NullTextInversion\nfrom models.ptp import EmptyControl\nfrom models import ptp_utils\n\nnull_inverter = NullTextInversion(StableDiffuser)\nnull_inverter.init_prompt(prompt)\nddim_latents = null_inverter.ddim_inversion(image_tensor)\nx_t = ddim_latents[-1]\nuncond_embeddings = null_inverter.null_optimization(ddim_latents, num_inner_steps=10, epsilon=1e-5)\nnull_text_rec, _ = ptp_utils.text2image_ldm_stable(StableDiffuser, [prompt], EmptyControl(), latent=x_t, uncond_embeddings=uncond_embeddings)\nptp_utils.view_images(null_text_rec)\n```\n\n```python\n# prompt-to-prompt editing\nprompts = [\"A cartoon of spiderman\",\n \"A cartoon of ironman\"]\nimport torch\nfrom models.ptp import LocalBlend, AttentionReplace\nfrom models.ptp_utils import text2image_ldm_stable\ng = torch.Generator().manual_seed(2023616)\nlb = LocalBlend(prompts, (\"spiderman\", \"ironman\"), model=StableDiffuser)\ncontroller = AttentionReplace(prompts, 50,\n cross_replace_steps={\"default_\": 1., \"ironman\": .2},\n self_replace_steps=0.4,\n local_blend=lb, model=StableDiffuser)\nimages, x_t = text2image_ldm_stable(StableDiffuser, prompts, controller, latent=None,\n num_inference_steps=50, guidance_scale=7.5, uncond_embeddings=None, generator=g)\n```\n\n## Citation\n\n```bibtex\n@article{hertz2022prompt,\n title = {Prompt-to-Prompt Image Editing with Cross Attention Control},\n author = {Hertz, Amir and Mokady, Ron and Tenenbaum, Jay and Aberman, Kfir and Pritch, Yael and Cohen-Or, Daniel},\n journal = {arXiv preprint arXiv:2208.01626},\n year = {2022},\n}\n@article{mokady2022null,\n title={Null-text Inversion for Editing Real Images using Guided Diffusion Models},\n author={Mokady, Ron and Hertz, Amir and Aberman, Kfir and Pritch, Yael and Cohen-Or, Daniel},\n journal={arXiv preprint arXiv:2211.09794},\n year={2022}\n}\n```\n" }, { "path": "projects/prompt_to_prompt/inversions/__init__.py", "content": "" }, { "path": "projects/prompt_to_prompt/inversions/ddim_inversion.py", "content": "\"\"\"This code was originally taken from https://github.com/google/prompt-to-\nprompt and modified by Ferry.\"\"\"\n\n# Copyright 2022 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom mmagic.models.diffusion_schedulers import EditDDIMScheduler\n\n\nclass DDIMInversion:\n\n def __init__(self, model, **kwargs):\n scheduler = EditDDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule='scaled_linear',\n clip_sample=False,\n set_alpha_to_one=False)\n self.model = model\n self.num_ddim_steps = kwargs.pop('num_ddim_steps', 50)\n self.guidance_scale = kwargs.pop('guidance_scale', 7.5)\n self.tokenizer = self.model.tokenizer\n self.model.scheduler = scheduler\n self.model.scheduler.set_timesteps(self.num_ddim_steps)\n self.prompt = None\n self.context = None\n\n def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray],\n timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):\n prev_timestep = timestep - (\n self.scheduler.num_train_timesteps //\n self.scheduler.num_inference_steps)\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n self.scheduler.alphas_cumprod[prev_timestep]\n if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod)\n beta_prod_t = 1 - alpha_prod_t\n pred_original_sample = (\n sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n pred_sample_direction = (1 - alpha_prod_t_prev)**0.5 * model_output\n prev_sample = alpha_prod_t_prev**0.5 * pred_original_sample \\\n + pred_sample_direction\n return prev_sample\n\n def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray],\n timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):\n timestep, next_timestep = min(\n timestep - self.scheduler.num_train_timesteps //\n self.scheduler.num_inference_steps, 999), timestep\n alpha_prod_t = self.scheduler.alphas_cumprod[\n timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod\n alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]\n beta_prod_t = 1 - alpha_prod_t\n next_original_sample = (\n sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n next_sample_direction = (1 - alpha_prod_t_next)**0.5 * model_output\n next_sample = alpha_prod_t_next**0.5 * next_original_sample \\\n + next_sample_direction\n return next_sample\n\n def get_noise_pred_single(self, latents, t, context):\n noise_pred = self.model.unet(\n latents, t, encoder_hidden_states=context)['sample']\n return noise_pred\n\n def get_noise_pred(self, latents, t, is_forward=True, context=None):\n latents_input = torch.cat([latents] * 2)\n if context is None:\n context = self.context\n guidance_scale = 1 if is_forward else self.guidance_scale\n noise_pred = self.model.unet(\n latents_input, t, encoder_hidden_states=context)['sample']\n noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_prediction_text - noise_pred_uncond)\n if is_forward:\n latents = self.next_step(noise_pred, t, latents)\n else:\n latents = self.prev_step(noise_pred, t, latents)\n return latents\n\n @torch.no_grad()\n def latent2image(self, latents, return_type='np'):\n latents = 1 / 0.18215 * latents.detach()\n image = self.model.vae.decode(latents, return_dict=False)[0]\n if return_type == 'np':\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()[0]\n image = (image * 255).astype(np.uint8)\n return image\n\n @torch.no_grad()\n def image2latent(self, image):\n with torch.no_grad():\n if type(image) is Image:\n image = np.array(image)\n if type(image) is torch.Tensor and image.dim() == 4:\n latents = image\n else:\n image = torch.from_numpy(image).float() / 127.5 - 1\n image = image.permute(2, 0,\n 1).unsqueeze(0).to(self.model.device)\n latents = self.model.vae.encode(image)['latent_dist'].mean\n latents = latents * 0.18215\n return latents\n\n @torch.no_grad()\n def init_prompt(self, prompt: str):\n uncond_input = self.model.tokenizer(\n [''],\n padding='max_length',\n max_length=self.model.tokenizer.model_max_length,\n return_tensors='pt')\n uncond_embeddings = self.model.text_encoder(\n uncond_input.input_ids.to(self.model.device))[0]\n text_input = self.model.tokenizer(\n [prompt],\n padding='max_length',\n max_length=self.model.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_embeddings = self.model.text_encoder(\n text_input.input_ids.to(self.model.device))[0]\n self.context = torch.cat([uncond_embeddings, text_embeddings])\n self.prompt = prompt\n\n @torch.no_grad()\n def ddim_loop(self, latent):\n uncond_embeddings, cond_embeddings = self.context.chunk(2)\n all_latent = [latent]\n latent = latent.clone().detach()\n for i in range(self.num_ddim_steps):\n t = self.model.scheduler.timesteps[\n len(self.model.scheduler.timesteps) - i - 1]\n noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)\n latent = self.next_step(noise_pred, t, latent)\n all_latent.append(latent)\n return all_latent\n\n @property\n def scheduler(self):\n return self.model.scheduler\n\n @torch.no_grad()\n def ddim_inversion(self, image):\n latent = self.model.vae.encode(image)['latent_dist'].mean\n latent = latent * 0.18215\n # image_rec = self.latent2image(latent)\n ddim_latents = self.ddim_loop(latent)\n return ddim_latents\n" }, { "path": "projects/prompt_to_prompt/inversions/null_text_inversion.py", "content": "import torch\nimport torch.nn.functional as F\nfrom inversions.ddim_inversion import DDIMInversion\nfrom torch.optim import Adam\nfrom tqdm import tqdm\n\n\nclass NullTextInversion(DDIMInversion):\n\n # basically the only thing null_text_inversion does is\n # to add a null_text_optimization method over ddim inversion\n\n def null_optimization(self, latents, num_inner_steps, epsilon):\n uncond_embeddings, cond_embeddings = self.context.chunk(2)\n uncond_embeddings_list = []\n latent_cur = latents[-1]\n bar = tqdm(total=num_inner_steps * self.num_ddim_steps)\n for i in range(self.num_ddim_steps):\n uncond_embeddings = uncond_embeddings.clone().detach()\n uncond_embeddings.requires_grad = True\n optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))\n latent_prev = latents[len(latents) - i - 2]\n t = self.model.scheduler.timesteps[i]\n with torch.no_grad():\n noise_pred_cond = self.get_noise_pred_single(\n latent_cur, t, cond_embeddings)\n for j in range(num_inner_steps):\n noise_pred_uncond = self.get_noise_pred_single(\n latent_cur, t, uncond_embeddings)\n noise_pred = noise_pred_uncond + self.guidance_scale * (\n noise_pred_cond - noise_pred_uncond)\n latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)\n loss = F.mse_loss(latents_prev_rec, latent_prev)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n loss_item = loss.item()\n bar.update()\n if loss_item < epsilon + i * 2e-5:\n break\n for j in range(j + 1, num_inner_steps):\n bar.update()\n uncond_embeddings_list.append(uncond_embeddings[:1].detach())\n with torch.no_grad():\n context = torch.cat([uncond_embeddings, cond_embeddings])\n latent_cur = self.get_noise_pred(latent_cur, t, False, context)\n bar.close()\n return uncond_embeddings_list\n" }, { "path": "projects/prompt_to_prompt/models/ptp.py", "content": "\"\"\"This code was originally taken from https://github.com/google/prompt-to-\nprompt.\"\"\"\nimport abc\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as nnf\nfrom PIL import Image\n\nimport projects.prompt_to_prompt.models.ptp_utils as ptp_utils\nimport projects.prompt_to_prompt.models.seq_aligner as seq_aligner\n\nLOW_RESOURCE = False\nMAX_NUM_WORDS = 77\n\n\ndef make_controller(prompts: List[str],\n is_replace_controller: bool,\n cross_replace_steps: Dict[str, float],\n self_replace_steps: float,\n blend_words=None,\n equilizer_params=None,\n model=None):\n num_ddim_steps = 50\n if blend_words is None:\n lb = None\n else:\n lb = LocalBlend(prompts, blend_words, model=model)\n if is_replace_controller:\n controller = AttentionReplace(\n prompts,\n num_ddim_steps,\n cross_replace_steps=cross_replace_steps,\n self_replace_steps=self_replace_steps,\n local_blend=lb,\n model=model)\n else:\n controller = AttentionRefine(\n prompts,\n num_ddim_steps,\n cross_replace_steps=cross_replace_steps,\n self_replace_steps=self_replace_steps,\n local_blend=lb,\n model=model)\n if equilizer_params is not None:\n eq = get_equalizer(prompts[1], equilizer_params['words'],\n equilizer_params['values'])\n controller = AttentionReweight(\n prompts,\n num_ddim_steps,\n cross_replace_steps=cross_replace_steps,\n self_replace_steps=self_replace_steps,\n equalizer=eq,\n local_blend=lb,\n controller=controller)\n return controller\n\n\nclass LocalBlend:\n\n def __call__(self, x_t, attention_store):\n k = 1\n maps = attention_store['down_cross'][2:4] + attention_store[\n 'up_cross'][:3]\n maps = [\n item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16,\n MAX_NUM_WORDS) for item in maps\n ]\n maps = torch.cat(maps, dim=1)\n maps = (maps * self.alpha_layers).sum(-1).mean(1)\n mask = nnf.max_pool2d(\n maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))\n mask = nnf.interpolate(mask, size=(x_t.shape[2:]))\n mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]\n mask = mask.gt(self.threshold)\n mask = (mask[:1] + mask[1:]).float()\n x_t = x_t[:1] + mask * (x_t - x_t[:1])\n return x_t\n\n def __init__(self, prompts: List[str], words, threshold=.3, model=None):\n device, tokenizer = model.device, model.tokenizer\n alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)\n for i, (prompt, words_) in enumerate(zip(prompts, words)):\n if type(words_) is str:\n words_ = [words_]\n for word in words_:\n ind = ptp_utils.get_word_inds(prompt, word, tokenizer)\n alpha_layers[i, :, :, :, :, ind] = 1\n self.alpha_layers = alpha_layers.to(device)\n self.threshold = threshold\n\n\nclass AttentionControl(abc.ABC):\n\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n @property\n def num_uncond_att_layers(self):\n return self.num_att_layers if LOW_RESOURCE else 0\n\n @abc.abstractmethod\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n raise NotImplementedError\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= self.num_uncond_att_layers:\n if LOW_RESOURCE:\n attn = self.forward(attn, is_cross, place_in_unet)\n else:\n h = attn.shape[0]\n attn[h // 2:] = self.forward(attn[h // 2:], is_cross,\n place_in_unet)\n self.cur_att_layer += 1\n if self.cur_att_layer == (self.num_att_layers +\n self.num_uncond_att_layers):\n self.cur_att_layer = 0\n self.cur_step += 1\n self.between_steps()\n return attn\n\n def reset(self):\n self.cur_step = 0\n self.cur_att_layer = 0\n\n def __init__(self):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n\n\nclass EmptyControl:\n\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n return attn\n\n\nclass AttentionStore(AttentionControl):\n\n @staticmethod\n def get_empty_store():\n return {\n 'down_cross': [],\n 'mid_cross': [],\n 'up_cross': [],\n 'down_self': [],\n 'mid_self': [],\n 'up_self': []\n }\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n if attn.shape[1] <= 32**2: # avoid memory overhead\n self.step_store[key].append(attn)\n return attn\n\n def between_steps(self):\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = {\n key: [item / self.cur_step for item in self.attention_store[key]]\n for key in self.attention_store\n }\n return average_attention\n\n def reset(self):\n super(AttentionStore, self).reset()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n def __init__(self):\n super(AttentionStore, self).__init__()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n\nclass AttentionControlEdit(AttentionStore, abc.ABC):\n\n def step_callback(self, x_t):\n if self.local_blend is not None:\n x_t = self.local_blend(x_t, self.attention_store)\n return x_t\n\n def replace_self_attention(self, attn_base, att_replace):\n if att_replace.shape[2] <= 16**2:\n return attn_base.unsqueeze(0).expand(att_replace.shape[0],\n *attn_base.shape)\n else:\n return att_replace\n\n @abc.abstractmethod\n def replace_cross_attention(self, attn_base, att_replace):\n raise NotImplementedError\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n super(AttentionControlEdit, self).forward(attn, is_cross,\n place_in_unet)\n if is_cross or (self.num_self_replace[0] <= self.cur_step <\n self.num_self_replace[1]):\n h = attn.shape[0] // (self.batch_size)\n attn = attn.reshape(self.batch_size, h, *attn.shape[1:])\n attn_base, attn_repalce = attn[0], attn[1:]\n if is_cross:\n alpha_words = self.cross_replace_alpha[self.cur_step]\n attn_repalce_new = self.replace_cross_attention(\n attn_base, attn_repalce) * alpha_words + (\n 1 - alpha_words) * attn_repalce\n attn[1:] = attn_repalce_new\n else:\n attn[1:] = self.replace_self_attention(attn_base, attn_repalce)\n attn = attn.reshape(self.batch_size * h, *attn.shape[2:])\n return attn\n\n def __init__(self, prompts, num_steps: int,\n cross_replace_steps: Union[float, Tuple[float, float],\n Dict[str, Tuple[float, float]]],\n self_replace_steps: Union[float, Tuple[float, float]],\n local_blend: Optional[LocalBlend], model):\n super(AttentionControlEdit, self).__init__()\n self.batch_size = len(prompts)\n self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps,\n model.tokenizer).to(model.device)\n if type(self_replace_steps) is float:\n self_replace_steps = 0, self_replace_steps\n self.num_self_replace = int(num_steps * self_replace_steps[0]), int(\n num_steps * self_replace_steps[1])\n self.local_blend = local_blend\n\n\nclass AttentionReplace(AttentionControlEdit):\n\n def replace_cross_attention(self, attn_base, att_replace):\n return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)\n\n def __init__(self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n model=None):\n super(AttentionReplace, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n model=model)\n self.mapper = seq_aligner.get_replacement_mapper(\n prompts, model.tokenizer).to(model.device)\n\n\nclass AttentionRefine(AttentionControlEdit):\n\n def replace_cross_attention(self, attn_base, att_replace):\n attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)\n attn_replace = attn_base_replace * self.alphas + att_replace * (\n 1 - self.alphas)\n return attn_replace\n\n def __init__(self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n model=None):\n super(AttentionRefine, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n model=model)\n self.mapper, alphas = seq_aligner.get_refinement_mapper(\n prompts, model.tokenizer)\n self.mapper, alphas = self.mapper.to(model.device), alphas.to(\n model.device)\n self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])\n\n\nclass AttentionReweight(AttentionControlEdit):\n\n def replace_cross_attention(self, attn_base, att_replace):\n if self.prev_controller is not None:\n attn_base = self.prev_controller.replace_cross_attention(\n attn_base, att_replace)\n attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None,\n None, :]\n return attn_replace\n\n def __init__(self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n equalizer,\n local_blend: Optional[LocalBlend] = None,\n controller: Optional[AttentionControlEdit] = None,\n model=None):\n super(AttentionReweight, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n model=model)\n self.equalizer = equalizer.to(model.device)\n self.prev_controller = controller\n\n\ndef get_equalizer(text: str,\n word_select: Union[int, Tuple[int, ...]],\n values: Union[List[float], Tuple[float, ...]],\n tokenizer=None):\n if type(word_select) is int or type(word_select) is str:\n word_select = (word_select, )\n equalizer = torch.ones(len(values), 77)\n values = torch.tensor(values, dtype=torch.float32)\n for word in word_select:\n inds = ptp_utils.get_word_inds(text, word, tokenizer)\n equalizer[:, inds] = values\n return equalizer\n\n\ndef aggregate_attention(attention_store: AttentionStore,\n res: int,\n from_where: List[str],\n is_cross: bool,\n select: int,\n prompts=None):\n out = []\n attention_maps = attention_store.get_average_attention()\n num_pixels = res**2\n for location in from_where:\n for item in attention_maps[\n f\"{location}_{'cross' if is_cross else 'self'}\"]:\n if item.shape[1] == num_pixels:\n cross_maps = item.reshape(\n len(prompts), -1, res, res, item.shape[-1])[select]\n out.append(cross_maps)\n out = torch.cat(out, dim=0)\n out = out.sum(0) / out.shape[0]\n return out.cpu()\n\n\ndef show_cross_attention(attention_store: AttentionStore,\n res: int,\n from_where: List[str],\n select: int = 0,\n prompts=None,\n tokenizer=None):\n tokens = tokenizer.encode(prompts[select])\n decoder = tokenizer.decode\n attention_maps = aggregate_attention(attention_store, res, from_where,\n True, select, prompts)\n images = []\n for i in range(len(tokens)):\n image = attention_maps[:, :, i]\n image = 255 * image / image.max()\n image = image.unsqueeze(-1).expand(*image.shape, 3)\n image = image.numpy().astype(np.uint8)\n image = np.array(Image.fromarray(image).resize((256, 256)))\n image = ptp_utils.text_under_image(image, decoder(int(tokens[i])))\n images.append(image)\n return (ptp_utils.view_images(np.stack(images, axis=0)))\n\n\ndef show_self_attention_comp(attention_store: AttentionStore,\n res: int,\n from_where: List[str],\n max_com=10,\n select: int = 0):\n attention_maps = aggregate_attention(attention_store, res, from_where,\n False, select).numpy().reshape(\n (res**2, res**2))\n u, s, vh = np.linalg.svd(attention_maps -\n np.mean(attention_maps, axis=1, keepdims=True))\n images = []\n for i in range(max_com):\n image = vh[i].reshape(res, res)\n image = image - image.min()\n image = 255 * image / image.max()\n image = np.repeat(\n np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)\n image = Image.fromarray(image).resize((256, 256))\n image = np.array(image)\n images.append(image)\n ptp_utils.view_images(np.concatenate(images, axis=1))\n\n\ndef run_and_display(model,\n prompts,\n controller,\n latent=None,\n run_baseline=False,\n generator=None):\n if run_baseline:\n print('w.o. prompt-to-prompt')\n images, latent = run_and_display(\n model,\n prompts,\n EmptyControl(),\n latent=latent,\n run_baseline=False,\n generator=generator)\n print('with prompt-to-prompt')\n images, x_t = ptp_utils.text2image_ld\n" }, { "path": "projects/prompt_to_prompt/models/ptp_utils.py", "content": "\"\"\"This code was originally taken from https://github.com/google/prompt-to-\nprompt.\"\"\"\n\n# Copyright 2022 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport cv2\nimport numpy as np\nimport torch\nfrom IPython.display import display\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\ndef tensor_to_nparray(image: torch.Tensor):\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.detach().cpu().permute(0, 2, 3, 1).numpy()[0]\n image = (image * 255).astype(np.uint8)\n return image\n\n\ndef text_under_image(image: np.ndarray,\n text: str,\n text_color: Tuple[int, int, int] = (0, 0, 0)):\n h, w, c = image.shape\n offset = int(h * .2)\n img = np.ones((h + offset, w, c), dtype=np.uint8) * 255\n font = cv2.FONT_HERSHEY_SIMPLEX\n img[:h] = image\n textsize = cv2.getTextSize(text, font, 1, 2)[0]\n text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2\n cv2.putText(img, text, (text_x, text_y), font, 1, text_color, 2)\n return img\n\n\ndef view_images(images, num_rows=1, offset_ratio=0.02):\n if type(images) is list:\n num_empty = len(images) % num_rows\n elif images.ndim == 4:\n num_empty = images.shape[0] % num_rows\n else:\n images = [images]\n num_empty = 0\n\n empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255\n images = [image.astype(np.uint8)\n for image in images] + [empty_images] * num_empty\n num_items = len(images)\n\n h, w, c = images[0].shape\n offset = int(h * offset_ratio)\n num_cols = num_items // num_rows\n image_ = np.ones(\n (h * num_rows + offset * (num_rows - 1), w * num_cols + offset *\n (num_cols - 1), 3),\n dtype=np.uint8) * 255\n for i in range(num_rows):\n for j in range(num_cols):\n image_[i * (h + offset):i * (h + offset) + h:, j * (w + offset):j *\n (w + offset) + w] = images[i * num_cols + j]\n\n pil_img = Image.fromarray(image_)\n display(pil_img)\n # return pil_img\n\n\ndef diffusion_step(model,\n controller,\n latents,\n context,\n t,\n guidance_scale,\n low_resource=False):\n if low_resource:\n noise_pred_uncond = model.unet(\n latents, t, encoder_hidden_states=context[0])['sample']\n noise_prediction_text = model.unet(\n latents, t, encoder_hidden_states=context[1])['sample']\n else:\n latents_input = torch.cat([latents] * 2)\n noise_pred = model.unet(\n latents_input, t, encoder_hidden_states=context)['sample']\n noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (\n noise_prediction_text - noise_pred_uncond)\n latents = model.scheduler.step(noise_pred, t, latents)['prev_sample']\n latents = controller.step_callback(latents)\n return latents\n\n\ndef latent2image(vae, latents):\n latents = 1 / 0.18215 * latents\n image = vae.decode(latents)['sample']\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n image = (image * 255).astype(np.uint8)\n return image\n\n\ndef init_latent(latent, model, height, width, generator, batch_size):\n if latent is None:\n latent = torch.randn(\n (1, model.unet.in_channels, height // 8, width // 8),\n generator=generator,\n )\n latents = latent.expand(batch_size, model.unet.in_channels, height // 8,\n width // 8).to(model.device)\n return latent, latents\n\n\n@torch.no_grad()\ndef text2image_ldm_stable(model,\n prompt: List[str],\n controller,\n num_inference_steps: int = 50,\n guidance_scale: Optional[float] = 7.5,\n generator: Optional[torch.Generator] = None,\n latent: Optional[torch.FloatTensor] = None,\n uncond_embeddings=None,\n start_time=50,\n return_type='image'):\n batch_size = len(prompt)\n register_attention_control(model, controller)\n height = width = 512\n\n text_input = model.tokenizer(\n prompt,\n padding='max_length',\n max_length=model.tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt',\n )\n text_embeddings = model.text_encoder(\n text_input.input_ids.to(model.device))[0]\n max_length = text_input.input_ids.shape[-1]\n if uncond_embeddings is None:\n uncond_input = model.tokenizer(\n [''] * batch_size,\n padding='max_length',\n max_length=max_length,\n return_tensors='pt')\n uncond_embeddings_ = model.text_encoder(\n uncond_input.input_ids.to(model.device))[0]\n else:\n uncond_embeddings_ = None\n\n latent, latents = init_latent(latent, model, height, width, generator,\n batch_size)\n model.scheduler.set_timesteps(num_inference_steps)\n for i, t in enumerate(tqdm(model.scheduler.timesteps[-start_time:])):\n if uncond_embeddings_ is None:\n context = torch.cat([\n uncond_embeddings[i].expand(*text_embeddings.shape),\n text_embeddings\n ])\n else:\n context = torch.cat([uncond_embeddings_, text_embeddings])\n latents = diffusion_step(\n model,\n controller,\n latents,\n context,\n t,\n guidance_scale,\n low_resource=False)\n\n if return_type == 'image':\n image = latent2image(model.vae, latents)\n else:\n image = latents\n return image, latent\n\n\ndef register_attention_control(model, controller):\n\n def ca_forward(self, place_in_unet):\n to_out = self.to_out\n if type(to_out) is torch.nn.modules.container.ModuleList:\n to_out = self.to_out[0]\n else:\n to_out = self.to_out\n\n def forward(x, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, dim = x.shape\n h = self.heads\n q = self.to_q(x)\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if is_cross else x\n k = self.to_k(encoder_hidden_states)\n v = self.to_v(encoder_hidden_states)\n q = self.head_to_batch_dim(q)\n k = self.head_to_batch_dim(k)\n v = self.head_to_batch_dim(v)\n\n sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale\n\n if attention_mask is not None:\n attention_mask = attention_mask.reshape(batch_size, -1)\n max_neg_value = -torch.finfo(sim.dtype).max\n attention_mask = attention_mask[:, None, :].repeat(h, 1, 1)\n sim.masked_fill_(~attention_mask, max_neg_value)\n\n # attention, what we cannot get enough of\n attn = sim.softmax(dim=-1)\n attn = controller(attn, is_cross, place_in_unet)\n out = torch.einsum('b i j, b j d -> b i d', attn, v)\n out = self.batch_to_head_dim(out)\n return to_out(out)\n\n return forward\n\n class DummyController:\n\n def __call__(self, *args):\n return args[0]\n\n def __init__(self):\n self.num_att_layers = 0\n\n if controller is None:\n controller = DummyController()\n\n # def register_recr(net_, count, place_in_unet):\n # if net_.__class__.__name__ == 'CrossAttention':\n # net_.forward = ca_forward(net_, place_in_unet)\n # return count + 1\n # elif hasattr(net_, 'children'):\n # for net__ in net_.children():\n # count = register_recr(net__, count, place_in_unet)\n # return count\n\n # cross_att_count = 0\n # sub_nets = model.unet.named_children()\n # for net in sub_nets:\n # if \"down\" in net[0]:\n # cross_att_count += register_recr(net[1], 0, \"down\")\n # elif \"up\" in net[0]:\n # cross_att_count += register_recr(net[1], 0, \"up\")\n # elif \"mid\" in net[0]:\n # cross_att_count += register_recr(net[1], 0, \"mid\")\n\n # controller.num_att_layers = cross_att_count\n\n def register_recr(net_name: str, net_, count, place_in_unet):\n if net_name.endswith('attn2') or net_name.endswith('attn1'):\n net_.forward = ca_forward(net_, place_in_unet)\n return count + 1\n return count\n\n cross_att_count = 0\n sub_nets = model.unet.named_modules()\n for net in sub_nets:\n if 'down' in net[0]:\n cross_att_count += register_recr(net[0], net[1], 0, 'down')\n elif 'up' in net[0]:\n cross_att_count += register_recr(net[0], net[1], 0, 'up')\n elif 'mid' in net[0]:\n cross_att_count += register_recr(net[0], net[1], 0, 'mid')\n\n controller.num_att_layers = cross_att_count\n\n\ndef get_word_inds(text: str, word_place: int, tokenizer):\n split_text = text.split(' ')\n if type(word_place) is str:\n word_place = [\n i for i, word in enumerate(split_text) if word_place == word\n ]\n elif type(word_place) is int:\n word_place = [word_place]\n out = []\n if len(word_place) > 0:\n words_encode = [\n tokenizer.decode([item]).strip('#')\n for item in tokenizer.encode(text)['input_ids']\n ][1:-1]\n cur_len, ptr = 0, 0\n\n for i in range(len(words_encode)):\n cur_len += len(words_encode[i])\n if ptr in word_place:\n out.append(i + 1)\n if cur_len >= len(split_text[ptr]):\n ptr += 1\n cur_len = 0\n return np.array(out)\n\n\ndef update_alpha_time_word(alpha,\n bounds: Union[float, Tuple[float, float]],\n prompt_ind: int,\n word_inds: Optional[torch.Tensor] = None):\n if type(bounds) is float:\n bounds = 0, bounds\n start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] *\n alpha.shape[0])\n if word_inds is None:\n word_inds = torch.arange(alpha.shape[2])\n alpha[:start, prompt_ind, word_inds] = 0\n alpha[start:end, prompt_ind, word_inds] = 1\n alpha[end:, prompt_ind, word_inds] = 0\n return alpha\n\n\ndef get_time_words_attention_alpha(\n prompts,\n num_steps,\n cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],\n tokenizer,\n max_num_words=77):\n if type(cross_replace_steps) is not dict:\n cross_replace_steps = {'default_': cross_replace_steps}\n if 'default_' not in cross_replace_steps:\n cross_replace_steps['default_'] = (0., 1.)\n alpha_time_words = torch.zeros(num_steps + 1,\n len(prompts) - 1, max_num_words)\n for i in range(len(prompts) - 1):\n alpha_time_words = update_alpha_time_word(\n alpha_time_words, cross_replace_steps['default_'], i)\n for key, item in cross_replace_steps.items():\n if key != 'default_':\n inds = [\n get_word_inds(prompts[i], key, tokenizer)\n for i in range(1, len(prompts))\n ]\n for i, ind in enumerate(inds):\n if len(ind) > 0:\n alpha_time_words = update_alpha_time_word(\n alpha_time_words, item, i, ind)\n alpha_time_words = alpha_time_words.reshape(num_steps + 1,\n len(prompts) - 1, 1, 1,\n max_num_words)\n return alpha_time_words\n\n\ndef load_512(image_path, left=0, right=0, top=0, bottom=0, device=None):\n if type(image_path) is str:\n image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3]\n else:\n image = image_path\n h, w, c = image.shape\n left = min(left, w - 1)\n right = min(right, w - left - 1)\n top = min(top, h - left - 1)\n bottom = min(bottom, h - top - 1)\n image = image[top:h - bottom, left:w - right]\n h, w, c = image.shape\n if h < w:\n offset = (w - h) // 2\n image = image[:, offset:offset + h]\n elif w < h:\n offset = (h - w) // 2\n image = image[offset:offset + w]\n image = np.array(Image.fromarray(image).resize((512, 512)))\n image = torch.from_numpy(image).float() / 127.5 - 1\n image = image.permute(2, 0, 1).unsqueeze(0).to(device)\n\n return image\n" }, { "path": "projects/prompt_to_prompt/models/seq_aligner.py", "content": "\"\"\"This code was originally taken from https://github.com/google/prompt-to-\nprompt.\"\"\"\n\nimport numpy as np\n# Copyright 2022 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport torch\n\n\nclass ScoreParams:\n\n def __init__(self, gap, match, mismatch):\n self.gap = gap\n self.match = match\n self.mismatch = mismatch\n\n def mis_match_char(self, x, y):\n if x != y:\n return self.mismatch\n else:\n return self.match\n\n\ndef get_matrix(size_x, size_y, gap):\n matrix = []\n for i in range(size_x + 1):\n sub_matrix = []\n for j in range(size_y + 1):\n sub_matrix.append(0)\n matrix.append(sub_matrix)\n for j in range(1, size_y + 1):\n matrix[0][j] = j * gap\n for i in range(1, size_x + 1):\n matrix[i][0] = i * gap\n return np.array(matrix)\n\n\n# def get_matrix(size_x, size_y, gap):\n# matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n# matrix[0, 1:] = (np.arange(size_y) + 1) * gap\n# matrix[1:, 0] = (np.arange(size_x) + 1) * gap\n# return matrix\n\n\ndef get_traceback_matrix(size_x, size_y):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = 1\n matrix[1:, 0] = 2\n matrix[0, 0] = 4\n return matrix\n\n\ndef global_align(x, y, score):\n matrix = get_matrix(len(x), len(y), score.gap)\n trace_back = get_traceback_matrix(len(x), len(y))\n for i in range(1, len(x) + 1):\n for j in range(1, len(y) + 1):\n left = matrix[i, j - 1] + score.gap\n up = matrix[i - 1, j] + score.gap\n diag = matrix[i - 1, j - 1] + score.mis_match_char(\n x[i - 1], y[j - 1])\n matrix[i, j] = max(left, up, diag)\n if matrix[i, j] == left:\n trace_back[i, j] = 1\n elif matrix[i, j] == up:\n trace_back[i, j] = 2\n else:\n trace_back[i, j] = 3\n return matrix, trace_back\n\n\ndef get_aligned_sequences(x, y, trace_back):\n x_seq = []\n y_seq = []\n i = len(x)\n j = len(y)\n mapper_y_to_x = []\n while i > 0 or j > 0:\n if trace_back[i, j] == 3:\n x_seq.append(x[i - 1])\n y_seq.append(y[j - 1])\n i = i - 1\n j = j - 1\n mapper_y_to_x.append((j, i))\n elif trace_back[i][j] == 1:\n x_seq.append('-')\n y_seq.append(y[j - 1])\n j = j - 1\n mapper_y_to_x.append((j, -1))\n elif trace_back[i][j] == 2:\n x_seq.append(x[i - 1])\n y_seq.append('-')\n i = i - 1\n elif trace_back[i][j] == 4:\n break\n mapper_y_to_x.reverse()\n return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)\n\n\ndef get_mapper(x: str, y: str, tokenizer, max_len=77):\n x_seq = tokenizer.encode(x)['input_ids']\n y_seq = tokenizer.encode(y)['input_ids']\n score = ScoreParams(0, 1, -1)\n matrix, trace_back = global_align(x_seq, y_seq, score)\n mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]\n alphas = torch.ones(max_len)\n alphas[:mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()\n mapper = torch.zeros(max_len, dtype=torch.int64)\n mapper[:mapper_base.shape[0]] = mapper_base[:, 1]\n mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len -\n len(y_seq))\n return mapper, alphas\n\n\ndef get_refinement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers, alphas = [], []\n for i in range(1, len(prompts)):\n mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n alphas.append(alpha)\n return torch.stack(mappers), torch.stack(alphas)\n\n\ndef get_word_inds(text: str, word_place: int, tokenizer):\n split_text = text.split(' ')\n if type(word_place) is str:\n word_place = [\n i for i, word in enumerate(split_text) if word_place == word\n ]\n elif type(word_place) is int:\n word_place = [word_place]\n out = []\n if len(word_place) > 0:\n words_encode = [\n tokenizer.decode([item]).strip('#')\n for item in tokenizer.encode(text)['input_ids']\n ][1:-1]\n cur_len, ptr = 0, 0\n\n for i in range(len(words_encode)):\n cur_len += len(words_encode[i])\n if ptr in word_place:\n out.append(i + 1)\n if cur_len >= len(split_text[ptr]):\n ptr += 1\n cur_len = 0\n return np.array(out)\n\n\ndef get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):\n words_x = x.split(' ')\n words_y = y.split(' ')\n if len(words_x) != len(words_y):\n raise ValueError()\n inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]\n inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]\n inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]\n mapper = np.zeros((max_len, max_len))\n i = j = 0\n cur_inds = 0\n while i < max_len and j < max_len:\n if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:\n inds_source_, inds_target_ = inds_source[cur_inds], inds_target[\n cur_inds]\n if len(inds_source_) == len(inds_target_):\n mapper[inds_source_, inds_target_] = 1\n else:\n ratio = 1 / len(inds_target_)\n for i_t in inds_target_:\n mapper[inds_source_, i_t] = ratio\n cur_inds += 1\n i += len(inds_source_)\n j += len(inds_target_)\n elif cur_inds < len(inds_source):\n mapper[i, j] = 1\n i += 1\n j += 1\n else:\n mapper[j, j] = 1\n i += 1\n j += 1\n\n return torch.from_numpy(mapper).float()\n\n\ndef get_replacement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers = []\n for i in range(1, len(prompts)):\n mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n return torch.stack(mappers)\n" }, { "path": "projects/prompt_to_prompt/visualize.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### A Walkthrough of DDIM inversion and Null-text inversion and Prompt-to-Prompt editing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/mnt/wsl/PHYSICALDRIVE4/htr/mmagic\\n\"\n ]\n }\n ],\n \"source\": [\n \"# cd into the mmagic directory to make sure mmagic models work fine!!\\n\",\n \"%cd ../..\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Load Stable Diffusion from MMagic\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/ssd2/priv/miniconda3/envs/mmagic/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \\n\",\n \"```\\n\",\n \"pip install accelerate\\n\",\n \"```\\n\",\n \".\\n\",\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \\n\",\n \"```\\n\",\n \"pip install accelerate\\n\",\n \"```\\n\",\n \".\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"06/17 13:45:38 - mmengine - INFO - Creating runwayml/stable-diffusion-v1-5 by 'HuggingFace'\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/mnt/wsl/PHYSICALDRIVE4/htr/mmagic/mmagic/models/archs/wrapper.py:149: FutureWarning: Accessing config attribute `block_out_channels` directly via 'AutoencoderKL' object attribute is deprecated. Please access 'block_out_channels' over 'AutoencoderKL's config object instead, e.g. 'unet.config.block_out_channels'.\\n\",\n \" return getattr(self.model, name)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from mmengine import MODELS, Config\\n\",\n \"from mmengine.registry import init_default_scope\\n\",\n \"\\n\",\n \"init_default_scope('mmagic')\\n\",\n \"\\n\",\n \"config = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py'\\n\",\n \"config = Config.fromfile(config).copy()\\n\",\n \"# change the 'pretrained_model_path' if you have downloaded the weights manually\\n\",\n \"# config.model.unet.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\\n\",\n \"# config.model.vae.from_pretrained = '/path/to/your/stable-diffusion-v1-5'\\n\",\n \"\\n\",\n \"StableDiffuser = MODELS.build(config.model)\\n\",\n \"StableDiffuser = StableDiffuser.to('cuda')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### VAE vs DDIM Inversion vs Null-Text Inversion\\n\",\n \"Diffusion's inversion basically means you put an image (with or without a prompt) into a method and it will return a latent code which can be later turned back to a image with high simmilarity as the original one. Of course we want this latent code for an editing purpose, that's also why we always implement inversion methods together with the editing methods.\\n\",\n \"\\n\",\n \"Let's first see how ddim inversion and null-text inversion works on the same image and prompt. Note here we put the result of vae as the upper bound of the inversion result. (For further knowledge please read the original paper of null-text inversion proposed by google ：））\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. VAE reconstruction as the standard result\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from models import ptp_utils\\n\",\n \"import torch\\n\",\n \"import mmcv\\n\",\n \"\\n\",\n \"image = mmcv.imread('https://github.com/open-mmlab/mmagic/assets/12782558/c851dcf8-bcfa-424f-9eeb-d01a48ea7127', channel_order='rgb')\\n\",\n \"prompt = \\\"a cat sitting next to a mirror\\\"\\n\",\n \"# using vae to reconstruct the image\\n\",\n \"image_tensor = ptp_utils.load_512(image).to('cuda')\\n\",\n \"vae = StableDiffuser.vae\\n\",\n \"with torch.no_grad():\\n\",\n \" vae_rec = vae(image_tensor, return_dict=False)[0]\\n\",\n \"ptp_utils.view_images(ptp_utils.tensor_to_nparray(vae_rec))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Use ddim inversion to invert the image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/mnt/wsl/PHYSICALDRIVE4/htr/mmagic/mmagic/models/archs/wrapper.py:149: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\\n\",\n \" return getattr(self.model, name)\\n\",\n \"100%|██████████| 50/50 [00:07<00:00, 6.57it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from models.ptp import EmptyControl\\n\",\n \"from inversions.ddim_inversion import DDIMInversion\\n\",\n \"\\n\",\n \"ddim_inverter = DDIMInversion(StableDiffuser)\\n\",\n \"ddim_inverter.init_prompt(prompt)\\n\",\n \"ddim_latents = ddim_inverter.ddim_inversion(image_tensor)\\n\",\n \"x_t = ddim_latents[-1]\\n\",\n \"ddim_rec, _ = ptp_utils.text2image_ldm_stable(StableDiffuser, [prompt], EmptyControl(), latent=x_t)\\n\",\n \"ptp_utils.view_images(ddim_rec)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Use null-text inversion to invert the image\\n\",\n \"Note null-text inversion uses the same inversion process as ddim inversion but additionally optimizes the **unconditional embedding** to make the result more similar to the original image.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 0%| | 0/500 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from inversions.null_text_inversion import NullTextInversion\\n\",\n \"null_inverter = NullTextInversion(StableDiffuser)\\n\",\n \"null_inverter.init_prompt(prompt)\\n\",\n \"uncond_embeddings = null_inverter.null_optimization(ddim_latents, num_inner_steps=10, epsilon=1e-5)\\n\",\n \"null_text_rec, _ = ptp_utils.text2image_ldm_stable(StableDiffuser, [prompt], EmptyControl(), latent=x_t, uncond_embeddings=uncond_embeddings)\\n\",\n \"ptp_utils.view_images(null_text_rec)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 4. Conclusion\\n\",\n \"Let's put all these results together and it's obvious to see that null-text inversion is better than ddim inversion.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"vae reconstruction -> ddim reconstruction -> null text reconstruction\\n\"\n ]\n }\n ],\n \"source\": [\n \"ptp_utils.view_images([ptp_utils.tensor_to_nparray(vae_rec), ddim_rec, null_text_rec])\\n\",\n \"print(\\\"vae reconstruction -> ddim reconstruction -> null text reconstruction\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prompt-to-prompt Editing\\n\",\n \"prompt-to-prompt controls the attention map in diffusion process to edit a user-provided image or generated image. Let's use the example above to see how it works.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Same process as above, we get the noise latent x_t and use new prompt to edit the cat into a dog **while keeping the orther features unchanged**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import gc\\n\",\n \"gc.collect()\\n\",\n \"torch.cuda.empty_cache()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 0%| | 0/50 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ptp_utils.view_images(images)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Of course ptp can also be used to edit a generated image(no inversion needed).\\n\",\n \"Three types of attention controller are provided here:\\n\",\n \"1. AttentionReplace: replace the attention map of the original word with that of new inserted word. (above example)\\n\",\n \"2. AttentionRefine: extend the prompt with adjective words or something.\\n\",\n \"3. AttentionReweighting: strengthen or weaken some features' presence in an image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 0%| | 0/50 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ptp_utils.view_images(images)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 50/50 [00:13<00:00, 3.84it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"prompts = [\\\"a photo of Eiffel Tower\\\",\\n\",\n \" \\\"a photo of Eiffel Tower at night\\\"]\\n\",\n \"from models.ptp import AttentionRefine\\n\",\n \"controller = AttentionRefine(prompts, 50, cross_replace_steps=1.0,\\n\",\n \" self_replace_steps=.4, model=StableDiffuser)\\n\",\n \"images, _ = text2image_ldm_stable(StableDiffuser, prompts, controller, latent=None)\\n\",\n \"ptp_utils.view_images(images)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 0%| | 0/50 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"prompts = [\\\"a blossom sakura tree\\\"] * 2\\n\",\n \"\\n\",\n \"from models.ptp import AttentionReweight, get_equalizer\\n\",\n \"### pay 3 times more attention to the word \\\"roses\\\"\\n\",\n \"equalizer = get_equalizer(prompts[1], (\\\"blossom\\\",), (-3,), StableDiffuser.tokenizer)\\n\",\n \"controller = AttentionReweight(prompts, 50, cross_replace_steps=.8,\\n\",\n \" self_replace_steps=.4,\\n\",\n \" equalizer=equalizer, model=StableDiffuser)\\n\",\n \"images, _ = text2image_ldm_stable(StableDiffuser, prompts, controller, latent=None)\\n\",\n \"ptp_utils.view_images(images)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"mmagic\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.16\"\n },\n \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "requirements/docs.txt", "content": "docutils==0.16.0\nmodelindex\nmyst_parser\n-e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme\nrequests<=2.29.0\nsphinx==4.5.0\nsphinx-autoapi\nsphinx-copybutton\nsphinx-notfound-page\nsphinx-tabs\nsphinx_markdown_tables\n" }, { "path": "requirements/mminstall.txt", "content": "mmcv>=2.0.0\nmmengine>=0.4.0\n" }, { "path": "requirements/optional.txt", "content": "albumentations\n-e git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1#egg=clip\nimageio-ffmpeg==0.4.4\nmmdet >= 3.0.0\nopen_clip_torch\nPyQt5\n" }, { "path": "requirements/readthedocs.txt", "content": "lmdb\nlpips\nmmcv >= 2.0.0rc1\nmmdet >= 3.0.0\nmmengine\nprettytable\nPygments\nregex\nscikit-image\ntabulate\ntitlecase\ntorch\ntorchvision\ntqdm\n" }, { "path": "requirements/runtime.txt", "content": "av\nav==8.0.3; python_version < '3.7'\nclick # required by mmagic/utils/io_utils.py\ncontrolnet_aux\ndiffusers>=0.23.0\neinops\nface-alignment<=1.3.4\nfacexlib\nlmdb\nlpips\nmediapipe\nnumpy\n# MMCV depends opencv-python instead of headless, thus we install opencv-python\n# Due to a bug from upstream, we skip this two version\n# https://github.com/opencv/opencv-python/issues/602\n# https://github.com/opencv/opencv/issues/21366\n# It seems to be fixed in https://github.com/opencv/opencv/pull/21382\nopencv-python!=4.5.5.62,!=4.5.5.64\npandas # required by mmagic/models/editors/disco_diffusion/guider.py\nPillow\nresize_right\ntensorboard\ntransformers>=4.27.4\n" }, { "path": "requirements/tests.txt", "content": "albumentations\n-e git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1#egg=clip\ncontrolnet_aux\n# codecov\n# flake8\n# isort==5.10.1\n# onnxruntime\n# pytest\n# pytest-runner\n# yapf\ncoverage < 7.0.0\nimageio-ffmpeg==0.4.4\ninterrogate\nmmdet >= 3.0.0\npytest\ntransformers>=4.27.4\n" }, { "path": "requirements.txt", "content": "-r requirements/runtime.txt\n-r requirements/tests.txt\n-r requirements/optional.txt\n" }, { "path": "setup.cfg", "content": "[bdist_wheel]\nuniversal=1\n\n[aliases]\ntest=pytest\n\n[tool:pytest]\ntestpaths = tests/\n\n[yapf]\nbased_on_style = pep8\nblank_line_before_nested_class_or_def = true\nsplit_before_expression_after_opening_paren = true\nsplit_penalty_import_names=0\nSPLIT_PENALTY_AFTER_OPENING_BRACKET=888\n\n[isort]\nline_length = 79\nmulti_line_output = 0\nextra_standard_library = setuptools\nknown_first_party = mmagic\nknown_third_party = PIL,cv2,lmdb,mmcv,numpy,onnx,onnxruntime,packaging,pymatting,pytest,pytorch_sphinx_theme,requests,scipy,titlecase,torch,torchvision,ts\nno_lines_before = STDLIB,LOCALFOLDER\ndefault_section = THIRDPARTY\n\n[flake8]\n# The E251 check is conflict with yapf in some situation.\n# See https://github.com/google/yapf/issues/393\nextend-ignore = E251\n# The F401 check is wrong if the `__all__` variable is modified\n# in `__init__.py`\nper-file-ignores =\n */__init__.py: F401\n mmagic/configs/*: F401,F403,F405,E501\n" }, { "path": "setup.py", "content": "import os\nimport os.path as osp\nimport shutil\nimport subprocess\nimport sys\nimport warnings\nfrom setuptools import find_packages, setup\n\nimport torch\nfrom torch.utils.cpp_extension import (BuildExtension, CppExtension,\n CUDAExtension)\n\n\ndef readme():\n with open('README.md', encoding='utf-8') as f:\n content = f.read()\n return content\n\n\nversion_file = 'mmagic/version.py'\n\n\ndef get_git_hash():\n\n def _minimal_ext_cmd(cmd):\n # construct minimal environment\n env = {}\n for k in ['SYSTEMROOT', 'PATH', 'HOME']:\n v = os.environ.get(k)\n if v is not None:\n env[k] = v\n # LANGUAGE is used on win32\n env['LANGUAGE'] = 'C'\n env['LANG'] = 'C'\n env['LC_ALL'] = 'C'\n out = subprocess.Popen(\n cmd, stdout=subprocess.PIPE, env=env).communicate()[0]\n return out\n\n try:\n out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])\n sha = out.strip().decode('ascii')\n except OSError:\n sha = 'unknown'\n\n return sha\n\n\ndef get_hash():\n if os.path.exists('.git'):\n sha = get_git_hash()[:7]\n elif os.path.exists(version_file):\n try:\n from mmagic.version import __version__\n sha = __version__.split('+')[-1]\n except ImportError:\n raise ImportError('Unable to get git version')\n else:\n sha = 'unknown'\n\n return sha\n\n\ndef get_version():\n with open(version_file, 'r') as f:\n exec(compile(f.read(), version_file, 'exec'))\n return locals()['__version__']\n\n\ndef parse_requirements(fname='requirements.txt', with_version=True):\n \"\"\"Parse the package dependencies listed in a requirements file but strips\n specific versioning information.\n\n Args:\n fname (str): path to requirements file\n with_version (bool, default=False): if True include version specs\n\n Returns:\n List[str]: list of requirements items\n\n CommandLine:\n python -c \"import setup; print(setup.parse_requirements())\"\n \"\"\"\n import re\n import sys\n from os.path import exists\n require_fpath = fname\n\n def parse_line(line):\n \"\"\"Parse information from a line in a requirements text file.\"\"\"\n if line.startswith('-r '):\n # Allow specifying requirements in other files\n target = line.split(' ')[1]\n for info in parse_require_file(target):\n yield info\n else:\n info = {'line': line}\n if line.startswith('-e '):\n info['package'] = line.split('#egg=')[1]\n elif '@git+' in line:\n info['package'] = line\n else:\n # Remove versioning from the package\n pat = '(' + '|'.join(['>=', '<=', '==', '>', '<']) + ')'\n parts = re.split(pat, line, maxsplit=1)\n parts = [p.strip() for p in parts]\n\n info['package'] = parts[0]\n if len(parts) > 1:\n op, rest = parts[1:]\n if ';' in rest:\n # Handle platform specific dependencies\n # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies\n version, platform_deps = map(str.strip,\n rest.split(';'))\n info['platform_deps'] = platform_deps\n else:\n version = rest # NOQA\n info['version'] = (op, version)\n yield info\n\n def parse_require_file(fpath):\n with open(fpath, 'r') as f:\n for line in f.readlines():\n line = line.strip()\n if line and not line.startswith('#'):\n for info in parse_line(line):\n yield info\n\n def gen_packages_items():\n if exists(require_fpath):\n for info in parse_require_file(require_fpath):\n parts = [info['package']]\n if with_version and 'version' in info:\n parts.extend(info['version'])\n if not sys.version.startswith('3.4'):\n # apparently package_deps are broken in 3.4\n platform_deps = info.get('platform_deps')\n if platform_deps is not None:\n parts.append(';' + platform_deps)\n item = ''.join(parts)\n yield item\n\n packages = list(gen_packages_items())\n return packages\n\n\ndef make_cuda_ext(name, module, sources, sources_cuda=[]):\n\n define_macros = []\n extra_compile_args = {'cxx': []}\n\n if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':\n define_macros += [('WITH_CUDA', None)]\n extension = CUDAExtension\n extra_compile_args['nvcc'] = [\n '-D__CUDA_NO_HALF_OPERATORS__',\n '-D__CUDA_NO_HALF_CONVERSIONS__',\n '-D__CUDA_NO_HALF2_OPERATORS__',\n ]\n sources += sources_cuda\n else:\n print(f'Compiling {name} without CUDA')\n extension = CppExtension\n\n return extension(\n name=f'{module}.{name}',\n sources=[os.path.join(*module.split('.'), p) for p in sources],\n define_macros=define_macros,\n extra_compile_args=extra_compile_args)\n\n\ndef add_mim_extension():\n \"\"\"Add extra files that are required to support MIM into the package.\n\n These files will be added by creating a symlink to the originals if the\n package is installed in `editable` mode (e.g. pip install -e .), or by\n copying from the originals otherwise.\n \"\"\"\n\n # parse installment mode\n if 'develop' in sys.argv:\n # installed by `pip install -e .`\n mode = 'symlink'\n elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:\n # installed by `pip install .`\n # or create source distribution by `python setup.py sdist`\n mode = 'copy'\n else:\n return\n\n filenames = ['tools', 'configs', 'demo', 'model-index.yml']\n repo_path = osp.dirname(__file__)\n mim_path = osp.join(repo_path, 'mmagic', '.mim')\n os.makedirs(mim_path, exist_ok=True)\n\n for filename in filenames:\n if osp.exists(filename):\n src_path = osp.join(repo_path, filename)\n tar_path = osp.join(mim_path, filename)\n\n if osp.isfile(tar_path) or osp.islink(tar_path):\n os.remove(tar_path)\n elif osp.isdir(tar_path):\n shutil.rmtree(tar_path)\n\n if mode == 'symlink':\n src_relpath = osp.relpath(src_path, osp.dirname(tar_path))\n try:\n os.symlink(src_relpath, tar_path)\n except OSError:\n # Creating a symbolic link on windows may raise an\n # `OSError: [WinError 1314]` due to privilege. If\n # the error happens, the src file will be copied\n mode = 'copy'\n warnings.warn(\n f'Failed to create a symbolic link for {src_relpath}, '\n f'and it will be copied to {tar_path}')\n else:\n continue\n if mode == 'copy':\n if osp.isfile(src_path):\n shutil.copyfile(src_path, tar_path)\n elif osp.isdir(src_path):\n shutil.copytree(src_path, tar_path)\n else:\n warnings.warn(f'Cannot copy file {src_path}.')\n else:\n raise ValueError(f'Invalid mode {mode}')\n\n\nif __name__ == '__main__':\n add_mim_extension()\n setup(\n name='mmagic',\n version=get_version(),\n description='OpenMMLab Multimodal Advanced, Generative, and '\n 'Intelligent Creation Toolbox',\n long_description=readme(),\n long_description_content_type='text/markdown',\n maintainer='MMagic Contributors',\n maintainer_email='openmmlab@gmail.com',\n keywords='computer vision, super resolution, video interpolation, '\n 'inpainting, matting, SISR, RefSR, VSR, GAN, VFI',\n url='https://github.com/open-mmlab/mmagic',\n packages=find_packages(exclude=('configs', 'tools', 'demo')),\n include_package_data=True,\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Intended Audience :: Education',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3.9',\n 'Programming Language :: Python :: 3.10',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'Topic :: Scientific/Engineering :: Image Processing',\n ],\n license='Apache License 2.0',\n install_requires=parse_requirements('requirements/runtime.txt'),\n cmdclass={'build_ext': BuildExtension},\n extras_require={\n 'all': parse_requirements('requirements.txt'),\n 'tests': parse_requirements('requirements/tests.txt'),\n 'mim': parse_requirements('requirements/mminstall.txt'),\n },\n zip_safe=False)\n" }, { "path": "tests/configs/aot_test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nmodel = dict(\n type='AOTInpaintor',\n train_cfg=dict(\n disc_step=1,\n start_iter=0,\n ),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='AOTEncoderDecoder',\n encoder=dict(type='AOTEncoder'),\n decoder=dict(type='AOTDecoder'),\n dilation_neck=dict(\n type='AOTBlockNeck', dilation_rates=(1, 2, 4, 8), num_aotblock=8)),\n disc=dict(\n type='SoftMaskPatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n with_spectral_norm=True,\n ),\n loss_gan=dict(\n type='GANLoss',\n gan_type='smgan',\n loss_weight=0.01,\n ),\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg19',\n layer_weights={\n '1': 1.,\n '6': 1.,\n '11': 1.,\n '20': 1.,\n '29': 1.,\n },\n layer_weights_style={\n '8': 1.,\n '17': 1.,\n '26': 1.,\n '31': 1.,\n },\n perceptual_weight=0.1,\n style_weight=250),\n loss_out_percep=True,\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.,\n ),\n loss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001),\n)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/configs/deepfillv1_test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nmodel = dict(\n type='DeepFillv1Inpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(type='DeepFillEncoderDecoder'),\n disc=dict(\n type='DeepFillv1Discriminators',\n global_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=256,\n fc_in_channels=256 * 16 * 16,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n local_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 8 * 8,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2))),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_composed_percep',\n 'loss_tv'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type='GANLoss',\n gan_type='hinge',\n loss_weight=1,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg16',\n layer_weights={\n '4': 1.,\n '9': 1.,\n '16': 1.,\n },\n perceptual_weight=0.05,\n style_weight=120,\n pretrained=('torchvision://vgg16')),\n loss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1,\n ),\n loss_gp=dict(type='GradientPenaltyLoss', loss_weight=10.),\n loss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001),\n train_cfg=dict(disc_step=2, start_iter=0, local_size=(128, 128)),\n)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/configs/diffuser_wrapper_cfg/config.json", "content": "{\n \"_class_name\": \"ControlNetModel\",\n \"_diffusers_version\": \"0.14.0\",\n \"act_fn\": \"silu\",\n \"attention_head_dim\": 2,\n \"block_out_channels\": [\n 32\n ],\n \"class_embed_type\": null,\n \"conditioning_embedding_out_channels\": [\n 16\n ],\n \"controlnet_conditioning_channel_order\": \"rgb\",\n \"cross_attention_dim\": 16,\n \"down_block_types\": [\n \"DownBlock2D\"\n ],\n \"downsample_padding\": 1,\n \"flip_sin_to_cos\": true,\n \"freq_shift\": 0,\n \"in_channels\": 4,\n \"layers_per_block\": 2,\n \"mid_block_scale_factor\": 1,\n \"norm_eps\": 1e-05,\n \"norm_num_groups\": 32,\n \"num_class_embeds\": null,\n \"only_cross_attention\": false,\n \"projection_class_embeddings_input_dim\": null,\n \"resnet_time_scale_shift\": \"default\",\n \"upcast_attention\": false,\n \"use_linear_projection\": false\n}\n" }, { "path": "tests/configs/gl_test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\ninput_shape = (256, 256)\n\nglobal_disc_cfg = dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='BN'))\nlocal_disc_cfg = dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=5,\n norm_cfg=dict(type='BN'))\n\nmodel = dict(\n type='GLInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='GLEncoder'),\n decoder=dict(type='GLDecoder'),\n dilation_neck=dict(type='GLDilationNeck')),\n disc=dict(\n type='GLDiscs',\n global_disc_cfg=global_disc_cfg,\n local_disc_cfg=local_disc_cfg),\n loss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n ),\n loss_l1_hole=dict(type='L1Loss', loss_weight=1.0),\n loss_l1_valid=dict(type='L1Loss', loss_weight=1.0),\n train_cfg=dict(\n disc_step=1, start_iter=0, iter_tc=2, iter_td=3,\n local_size=(128, 128)))\n\nmodel_dirty = dict(\n type='GLInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='GLEncoder'),\n decoder=dict(type='GLDecoder'),\n dilation_neck=dict(type='GLDilationNeck')),\n disc=dict(\n type='GLDiscs',\n global_disc_cfg=global_disc_cfg,\n local_disc_cfg=local_disc_cfg),\n loss_gan=None,\n loss_l1_hole=None,\n loss_l1_valid=None)\n\nmodel_inference = dict(\n type='GLInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='GLEncoder'),\n decoder=dict(type='GLDecoder'),\n dilation_neck=dict(type='GLDilationNeck')),\n disc=dict(\n type='GLDiscs',\n global_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='SyncBN'),\n ),\n local_disc_cfg=dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=5,\n norm_cfg=dict(type='SyncBN'),\n ),\n ),\n loss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ))\n\ntest_pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(\n type='LoadMask',\n mask_mode='bbox',\n mask_config=dict(\n max_bbox_shape=(128, 128),\n max_bbox_delta=40,\n min_margin=20,\n img_shape=input_shape)),\n dict(\n type='Crop',\n keys=['gt'],\n crop_size=(384, 384),\n random_crop=True,\n ),\n dict(\n type='Resize',\n keys=['gt'],\n scale=input_shape,\n keep_ratio=False,\n ),\n dict(type='GetMaskedImage'),\n dict(type='PackInputs'),\n]\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/configs/one_stage_gl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nmodel = dict(\n type='OneStageInpaintor',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='GLEncoderDecoder',\n encoder=dict(type='GLEncoder'),\n decoder=dict(type='GLDecoder'),\n dilation_neck=dict(type='GLDilationNeck')),\n disc=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='BN'),\n ),\n loss_gan=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.001,\n ),\n loss_gp=dict(\n type='GradientPenaltyLoss',\n loss_weight=1.,\n ),\n loss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001),\n loss_composed_percep=dict(\n type='PerceptualLoss',\n layer_weights={'0': 1.},\n perceptual_weight=0.1,\n style_weight=0,\n ),\n loss_out_percep=True,\n loss_l1_hole=dict(type='L1Loss', loss_weight=1.0),\n loss_l1_valid=dict(type='L1Loss', loss_weight=1.0),\n loss_tv=dict(type='MaskedTVLoss', loss_weight=0.01),\n train_cfg=dict(disc_step=1))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/configs/pconv_test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nmodel = dict(\n type='PConvInpaintor',\n train_cfg=dict(\n disc_step=0,\n start_iter=0,\n ),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='PConvEncoderDecoder',\n encoder=dict(\n type='PConvEncoder',\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True),\n decoder=dict(type='PConvDecoder', norm_cfg=dict(type='BN'))),\n disc=None,\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg16',\n layer_weights={\n '4': 1.,\n '9': 1.,\n '16': 1.,\n },\n perceptual_weight=0.05,\n style_weight=120,\n pretrained=('torchvision://vgg16')),\n loss_out_percep=True,\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=6.,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.,\n ),\n loss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1,\n ))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/configs/two_stage_test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nmodel = dict(\n type='TwoStageInpaintor',\n disc_input_with_mask=True,\n train_cfg=dict(disc_step=1, start_iter=0),\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n encdec=dict(\n type='DeepFillEncoderDecoder',\n stage1=dict(\n type='GLEncoderDecoder',\n encoder=dict(\n type='DeepFillEncoder',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n conv_type='gated_conv',\n in_channels=96,\n channel_factor=0.75,\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect')),\n stage2=dict(\n type='DeepFillRefiner',\n encoder_attention=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_attention',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n encoder_conv=dict(\n type='DeepFillEncoder',\n encoder_type='stage2_conv',\n conv_type='gated_conv',\n channel_factor=0.75,\n padding_mode='reflect'),\n dilation_neck=dict(\n type='GLDilationNeck',\n in_channels=96,\n conv_type='gated_conv',\n act_cfg=dict(type='ELU'),\n padding_mode='reflect'),\n contextual_attention=dict(\n type='ContextualAttentionNeck',\n in_channels=96,\n conv_type='gated_conv',\n padding_mode='reflect'),\n decoder=dict(\n type='DeepFillDecoder',\n in_channels=192,\n conv_type='gated_conv',\n out_act_cfg=dict(type='Tanh'),\n padding_mode='reflect'))),\n disc=dict(\n type='MultiLayerDiscriminator',\n in_channels=4,\n max_channels=256,\n fc_in_channels=None,\n num_convs=6,\n norm_cfg=None,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n with_spectral_norm=True,\n ),\n stage1_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_composed_percep',\n 'loss_tv'),\n stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),\n loss_gan=dict(\n type='GANLoss',\n gan_type='hinge',\n loss_weight=0.1,\n ),\n loss_l1_hole=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_l1_valid=dict(\n type='L1Loss',\n loss_weight=1.0,\n ),\n loss_composed_percep=dict(\n type='PerceptualLoss',\n vgg_type='vgg16',\n layer_weights={\n '4': 1.,\n '9': 1.,\n '16': 1.,\n },\n perceptual_weight=0.05,\n style_weight=120,\n pretrained=('torchvision://vgg16')),\n loss_tv=dict(\n type='MaskedTVLoss',\n loss_weight=0.1,\n ),\n loss_disc_shift=dict(type='DiscShiftLoss', loss_weight=0.001),\n)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/data/coco/annotations/captions_train2014.json", "content": "{\n \"annotations\": [{\"image_id\": 9, \"caption\": \"a good meal\"}]\n}" }, { "path": "tests/data/coco/annotations/captions_val2014.json", "content": "{\n \"annotations\": [{\"image_id\": 42, \"caption\": \"a pair of slippers\"}]\n}" }, { "path": "tests/data/controlnet/prompt.json", "content": "{\"source\": \"source/0.png\", \"target\": \"target/0.png\", \"prompt\": \"pale golden rod circle with old lace background\"}\n{\"source\": \"source/1.png\", \"target\": \"target/1.png\", \"prompt\": \"light coral circle with white background\"}\n" }, { "path": "tests/data/dataset/anno.json", "content": "{\n \"a/1.JPG\": [\n 1,\n 2,\n 3,\n 4\n ],\n \"b/2.jpeg\": [\n 1,\n 4,\n 5,\n 3\n ]\n}" }, { "path": "tests/data/dataset/anno.txt", "content": "a/1.JPG 0\nb/2.jpeg 1\nb/subb/3.jpg 1" }, { "path": "tests/data/dataset/c/c.not_support_type", "content": "" }, { "path": "tests/data/dataset/classes.txt", "content": "bus\ncar" }, { "path": "tests/data/dataset/wrong.yml", "content": "" }, { "path": "tests/data/frames/ann1.txt", "content": "sequence_1\n\nsequence_2\n" }, { "path": "tests/data/frames/ann2.txt", "content": "sequence_1/00000000.png\nsequence_1/00000001.png\nsequence_2/00000000.png\n" }, { "path": "tests/data/frames/ann3.txt", "content": "gt/sequence_1\ngt/sequence_2\n" }, { "path": "tests/data/image/train.txt", "content": "\nbaboon.png (480,500,3)\n/baboon.png (480,500,3)\n" }, { "path": "tests/data/inpainting/mask_list.txt", "content": "mask/test.png\n" }, { "path": "tests/data/inpainting/mask_list_single_ch.txt", "content": "mask/test_single_ch.png\n" }, { "path": "tests/data/lq.lmdb/meta_info.txt", "content": "baboon.png (120,125,3) 1\n" }, { "path": "tests/data/matting_dataset/ann.json", "content": "{\n \"metainfo\": {\n \"dataset_type\": \"test_dataset\",\n \"task_name\": \"test_task\"\n },\n \"data_list\": [\n {\n \"alpha_path\": \"alpha/GT05.jpg\",\n \"trimap_path\": \"trimap/GT05.jpg\",\n \"bg_path\": \"bg/GT26r.jpg\",\n \"fg_path\": \"fg/GT05.jpg\",\n \"merged_path\": \"merged/GT05.jpg\"\n },\n {\n \"alpha_path\": \"alpha/GT05.jpg\",\n \"bg_path\": \"bg/GT26r.jpg\",\n \"fg_path\": \"fg/GT05.jpg\"\n }\n ]\n}\n" }, { "path": "tests/data/matting_dataset/ann_old.json", "content": "[\n {\n \"alpha_path\": \"alpha/GT05.jpg\",\n \"trimap_path\": \"trimap/GT05.jpg\",\n \"bg_path\": \"bg/GT26r.jpg\",\n \"fg_path\": \"fg/GT05.jpg\",\n \"merged_path\": \"merged/GT05.jpg\"\n },\n {\n \"alpha_path\": \"alpha/GT05.jpg\",\n \"bg_path\": \"bg/GT26r.jpg\",\n \"fg_path\": \"fg/GT05.jpg\"\n }\n]\n" }, { "path": "tests/data/textual_inversion/imagenet_templates_small.txt", "content": "a photo of a {}\na rendering of a {}\na cropped photo of the {}\nthe photo of a {}\na photo of a clean {}\na photo of a dirty {}\na dark photo of the {}\na photo of my {}\na photo of the cool {}\na close-up photo of a {}\na bright photo of the {}\na cropped photo of a {}\na photo of the {}\na good photo of the {}\na photo of one {}\na close-up photo of the {}\na rendition of the {}\na photo of the clean {}\na rendition of a {}\na photo of a nice {}\na good photo of a {}\na photo of the nice {}\na photo of the small {}\na photo of the weird {}\na photo of the large {}\na photo of a cool {}\na photo of a small {}" }, { "path": "tests/test_apis/test_inferencers/test_base_mmagic_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nimport pytest\n\nfrom mmagic.apis.inferencers.base_mmagic_inferencer import BaseMMagicInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_base_mmagic_inferencer():\n with pytest.raises(Exception):\n BaseMMagicInferencer(1, None)\n\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'sngan_proj',\n 'sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py')\n\n with pytest.raises(Exception):\n BaseMMagicInferencer(cfg, 'test')\n\n inferencer_instance = BaseMMagicInferencer(cfg, None)\n extra_parameters = inferencer_instance.get_extra_parameters()\n assert len(extra_parameters) == 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_colorization_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport unittest\n\nimport torch\n\nfrom mmagic.apis.inferencers.colorization_inferencer import \\\n ColorizationInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_colorization_inferencer():\n\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs',\n 'inst_colorization',\n 'inst-colorizatioon_full_official_cocostuff-256x256.py')\n data_path = osp.join(\n osp.dirname(__file__), '..', '..', 'data', 'unpaired', 'trainA',\n '1.jpg')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'inst_colorization_result.jpg')\n\n inferencer_instance = \\\n ColorizationInferencer(cfg, None)\n del inferencer_instance.model.cfg.test_pipeline[1]\n\n inferencer_instance.preprocess(img=data_path)\n\n preds = torch.rand((1, 3, 256, 256))\n result_img = inferencer_instance.visualize(preds=preds)\n result_img = inferencer_instance.visualize(\n preds=preds, result_out_dir=result_out_dir)\n\n if not torch.cuda.is_available():\n # RoI pooling only support in GPU\n return unittest.skip('test requires GPU and torch+cuda')\n\n inferencer_instance(img=data_path)\n inference_result = inferencer_instance(\n img=data_path, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.shape == (256, 256, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_conditional_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.conditional_inferencer import \\\n ConditionalInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_conditional_inferencer():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'sngan_proj',\n 'sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'conditional_result.png')\n inferencer_instance = \\\n ConditionalInferencer(cfg,\n None,\n extra_parameters={'sample_model': 'orig'})\n inference_result = inferencer_instance(label=1)\n inference_result = inferencer_instance(\n label=1, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.shape == (4, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_diffusers_pipeline_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.apis.inferencers.diffusers_pipeline_inferencer import \\\n DiffusersPipelineInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower()\n or digit_version(TORCH_VERSION) <= digit_version('1.8.1'),\n reason='skip on windows due to limited RAM'\n 'and get_submodule requires torch >= 1.9.0')\ndef test_diffusers_pipeline_inferencer():\n cfg = dict(\n model=dict(\n type='DiffusionPipeline', from_pretrained='google/ddpm-cat-256'))\n\n inferencer_instance = DiffusersPipelineInferencer(cfg, None)\n result = inferencer_instance()\n assert result[1][0].size == (256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_eg3d_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nimport shutil\n\nimport numpy as np\nimport pytest\nfrom mmengine import Config\n\nfrom mmagic.apis.inferencers.eg3d_inferencer import EG3DInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nconfig = dict(\n model=dict(\n type='EG3D',\n generator=dict(\n type='TriplaneGenerator',\n out_size=32,\n noise_size=8,\n style_channels=8,\n num_mlps=1,\n triplane_size=8,\n triplane_channels=4,\n sr_in_size=8,\n sr_in_channels=8,\n neural_rendering_resolution=5,\n cond_scale=1,\n renderer_cfg=dict(\n ray_start=0.1,\n ray_end=2.6,\n box_warp=1.6,\n depth_resolution=4,\n white_back=True,\n depth_resolution_importance=4,\n ),\n rgb2bgr=True),\n camera=dict(\n type='UniformCamera',\n horizontal_mean=3.141,\n horizontal_std=3.141,\n vertical_mean=3.141 / 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n up=[0, 0, 1],\n radius=1.2),\n data_preprocessor=dict(type='DataPreprocessor')))\n\n\ndef test_eg3d_inferencer():\n cfg = Config(config)\n\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out', 'eg3d_output')\n\n inferencer_instance = EG3DInferencer(cfg, None)\n output = inferencer_instance(\n vis_mode='both',\n save_img=False,\n save_video=True,\n interpolation='camera',\n num_images=10,\n result_out_dir=result_out_dir)\n # contrust target file list\n target_file_list = [\n 'fake_img_seed2022.mp4', 'depth_seed2022.mp4', 'combine_seed2022.mp4'\n ]\n assert set(target_file_list) == set(os.listdir(result_out_dir))\n assert isinstance(output, dict)\n shutil.rmtree(result_out_dir)\n\n output = inferencer_instance(\n vis_mode='both',\n save_img=True,\n save_video=False,\n interpolation='camera',\n num_images=10,\n result_out_dir=result_out_dir)\n # contrust target file list\n target_file_list = [\n f'fake_img_frame{idx}_seed2022.png' for idx in range(10)\n ]\n target_file_list += [f'depth_frame{idx}_seed2022.png' for idx in range(10)]\n target_file_list += [\n f'combine_frame{idx}_seed2022.png' for idx in range(10)\n ]\n assert set(target_file_list) == set(os.listdir(result_out_dir))\n assert isinstance(output, dict)\n shutil.rmtree(result_out_dir)\n\n output = inferencer_instance(\n vis_mode='depth',\n save_img=False,\n interpolation='camera',\n num_images=2,\n result_out_dir=result_out_dir)\n # contrust target file list\n target_file_list = ['depth_seed2022.mp4']\n assert set(target_file_list) == set(os.listdir(result_out_dir))\n assert isinstance(output, dict)\n shutil.rmtree(result_out_dir)\n\n output = inferencer_instance(\n vis_mode='depth',\n save_video=False,\n save_img=False,\n interpolation='camera',\n sample_model='orig',\n num_images=2,\n result_out_dir=result_out_dir)\n assert len(os.listdir(result_out_dir)) == 0\n\n # test cond input\n inferencer_instance = EG3DInferencer(\n cfg, None, extra_parameters=dict(sample_model='orig'))\n cond_input = [np.random.randn(25) for _ in range(2)]\n output = inferencer_instance(\n inputs=cond_input,\n vis_mode='both',\n save_video=True,\n save_img=False,\n interpolation='camera',\n num_images=2,\n result_out_dir=result_out_dir)\n # contrust target file list\n target_file_list = [\n 'fake_img_seed2022.png',\n 'depth_seed2022.png',\n 'combine_seed2022.png',\n ]\n assert set(target_file_list) == set(os.listdir(result_out_dir))\n assert isinstance(output, dict)\n shutil.rmtree(result_out_dir)\n\n cond_input = [[0.111 for _ in range(25)] for _ in range(2)]\n output = inferencer_instance(\n inputs=cond_input,\n vis_mode='depth',\n save_video=False,\n save_img=True,\n interpolation='camera',\n num_images=2,\n result_out_dir=result_out_dir)\n target_file_list = ['depth_seed2022.png']\n assert set(target_file_list) == set(os.listdir(result_out_dir))\n assert isinstance(output, dict)\n shutil.rmtree(result_out_dir)\n\n cond_input = [['wrong'], ['input']]\n with pytest.raises(AssertionError):\n inferencer_instance(\n inputs=cond_input,\n vis_mode='depth',\n save_video=False,\n save_img=False,\n interpolation='camera',\n num_images=2,\n result_out_dir=result_out_dir)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_image_super_resolution_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.apis.inferencers.image_super_resolution_inferencer import \\\n ImageSuperResolutionInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_image_super_resolution_inferencer():\n data_root = osp.join(osp.dirname(__file__), '../../../')\n config = data_root + 'configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py' # noqa\n img_path = data_root + 'tests/data/image/lq/baboon_x4.png'\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'image_super_resolution_result.png')\n\n inferencer_instance = \\\n ImageSuperResolutionInferencer(config, None)\n inferencer_instance(img=img_path)\n inference_result = inferencer_instance(\n img=img_path, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.shape == (480, 500, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_inference_functions.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\n\nfrom mmagic.apis.inferencers.inference_functions import (calculate_grid_size,\n init_model,\n set_random_seed)\n\n\ndef test_init_model():\n set_random_seed(1)\n\n with pytest.raises(TypeError):\n init_model(['dog'])\n\n\ndef test_calculate_grid_size():\n inp_batch_size = (10, 13, 20, 1, 4)\n target_nrow = (4, 4, 5, 1, 2)\n for bz, tar in zip(inp_batch_size, target_nrow):\n assert calculate_grid_size(bz) == tar\n\n # test aspect_ratio is not None\n inp_batch_size = (10, 13, 20, 1, 4)\n aspect_ratio = (2, 3, 3, 4, 3)\n target_nrow = (3, 3, 3, 1, 2)\n for bz, ratio, tar in zip(inp_batch_size, aspect_ratio, target_nrow):\n assert calculate_grid_size(bz, ratio) == tar\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_inpainting_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.inpainting_inferencer import InpaintingInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_inpainting_inferencer():\n data_root = osp.join(osp.dirname(__file__), '../../')\n masked_img_path = data_root + 'data/inpainting/celeba_test.png'\n mask_path = data_root + 'data/inpainting/bbox_mask.png'\n cfg = osp.join(\n osp.dirname(__file__),\n '..',\n '..',\n '..',\n 'configs',\n 'aot_gan',\n 'aot-gan_smpgan_4xb4_places-512x512.py',\n )\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out', 'inpainting_result.png')\n\n inferencer_instance = \\\n InpaintingInferencer(cfg, None)\n inferencer_instance(img=masked_img_path, mask=mask_path)\n inference_result = inferencer_instance(\n img=masked_img_path, mask=mask_path, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.shape == (256, 256, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_matting_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.matting_inferencer import MattingInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_matting_inferencer():\n data_root = osp.join(osp.dirname(__file__), '../../../')\n config = data_root + 'configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py'\n img_path = data_root + 'tests/data/matting_dataset/merged/GT05.jpg'\n trimap_path = data_root + 'tests/data/matting_dataset/trimap/GT05.png'\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out', 'matting_result.png')\n\n inferencer_instance = \\\n MattingInferencer(config, None)\n inferencer_instance(img=img_path, trimap=trimap_path)\n inference_result = inferencer_instance(\n img=img_path, trimap=trimap_path, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.numpy().shape == (552, 800)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_mmedit_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nimport pytest\n\nfrom mmagic.apis.inferencers import Inferencers\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_mmagic_inferencer():\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('colorization', ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('unconditional', ['error_type'],\n None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('matting', ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('inpainting', ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('translation', ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('restoration', ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('video_restoration', ['error_type'],\n None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('video_interpolation',\n ['error_type'], None)\n\n with pytest.raises(Exception) as e_info:\n inferencer_instance = Inferencers('dog', ['error_type'], None)\n\n print(e_info)\n\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'sngan_proj',\n 'sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py')\n inferencer_instance = \\\n Inferencers('conditional',\n cfg,\n None,\n extra_parameters={'sample_model': 'orig'})\n inference_result = inferencer_instance(label=1)\n result_img = inference_result[1]\n assert result_img.shape == (4, 3, 32, 32)\n\n extra_parameters = inferencer_instance.get_extra_parameters()\n assert len(extra_parameters) == 2\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_text2image_inferencers.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport unittest\nfrom unittest import TestCase\nfrom unittest.mock import patch\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom torchvision.version import __version__ as TV_VERSION\n\nfrom mmagic.apis.inferencers.text2image_inferencer import Text2ImageInferencer\nfrom mmagic.models import DenoisingUnet, DiscoDiffusion\nfrom mmagic.models.diffusion_schedulers import EditDDIMScheduler\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass clip_mock(nn.Module):\n\n def __init__(self, device='cuda'):\n super().__init__()\n self.register_buffer('tensor', torch.randn([1, 512]))\n\n def encode_image(self, inputs):\n return inputs.mean() * self.tensor.repeat(inputs.shape[0], 1).to(\n inputs.device)\n\n def encode_text(self, inputs):\n return self.tensor.repeat(inputs.shape[0], 1).to(inputs.device)\n\n def forward(self, x):\n return x\n\n\nclass clip_mock_wrapper(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.model = clip_mock()\n\n def forward(self, x):\n return x\n\n\nclass TestTranslationInferencer(TestCase):\n\n def setUp(self):\n self.unet32 = DenoisingUnet(\n image_size=32,\n in_channels=3,\n base_channels=8,\n resblocks_per_downsample=2,\n attention_res=(8, ),\n norm_cfg=dict(type='GN32', num_groups=8),\n dropout=0.0,\n num_classes=0,\n use_fp16=True,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=2,\n num_head_channels=8,\n use_new_attention_order=False),\n use_scale_shift_norm=True)\n # mock clip\n self.clip_models = [clip_mock_wrapper(), clip_mock_wrapper()]\n # diffusion_scheduler\n self.diffusion_scheduler = EditDDIMScheduler(\n variance_type='learned_range',\n beta_schedule='linear',\n clip_sample=False)\n\n unet32 = self.unet32\n diffusion_scheduler = self.diffusion_scheduler\n clip_models = self.clip_models\n self.disco_diffusion = DiscoDiffusion(\n unet=unet32,\n diffusion_scheduler=diffusion_scheduler,\n secondary_model=None,\n clip_models=clip_models,\n use_fp16=True).cuda()\n\n @unittest.skipIf(\n digit_version(TV_VERSION) <= digit_version('0.7.0'),\n reason='torchvision version limiation')\n @unittest.skipIf(not torch.cuda.is_available(), reason='requires cuda')\n def test_translation(self):\n cfg_root = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs',\n 'disco_diffusion')\n cfg = osp.join(cfg_root,\n 'disco-diffusion_adm-u-finetuned_imagenet-512x512.py')\n text = {0: ['sad']}\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out', 'disco_result.png')\n\n with patch.object(Text2ImageInferencer, '_init_model'):\n inferencer_instance = Text2ImageInferencer(\n cfg, None, extra_parameters={'num_inference_steps': 2})\n # mock model\n inferencer_instance.model = self.disco_diffusion\n inferencer_instance(text=text)\n inference_result = inferencer_instance(\n text=text, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img[0].cpu().numpy().shape == (3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_translation_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.translation_inferencer import \\\n TranslationInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_translation_inferencer():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'pix2pix',\n 'pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py')\n data_path = osp.join(\n osp.dirname(__file__), '..', '..', 'data', 'unpaired', 'trainA',\n '1.jpg')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'translation_result.png')\n\n inferencer_instance = \\\n TranslationInferencer(cfg, None)\n inferencer_instance(img=data_path)\n inference_result = inferencer_instance(\n img=data_path, result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img[0].cpu().numpy().shape == (3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_unconditional_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nimport torch\n\nfrom mmagic.apis.inferencers.unconditional_inferencer import \\\n UnconditionalInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_unconditional_inferencer():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'styleganv1',\n 'styleganv1_ffhq-256x256_8xb4-25Mimgs.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'unconditional_result.png')\n\n inferencer_instance = \\\n UnconditionalInferencer(cfg,\n None,\n extra_parameters={\n 'num_batches': 1,\n 'sample_model': 'orig'})\n # test no result_out_dir\n inferencer_instance()\n inference_result = inferencer_instance(result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.detach().numpy().shape == (1, 3, 256, 256)\n\n # test additional input for extra parameters\n noise = torch.randn(1, 512)\n sample_kwargs = sample_kwargs = {\n 'return_noise': True,\n 'input_is_latent': False\n }\n\n inferencer_instance = \\\n UnconditionalInferencer(cfg,\n None,\n extra_parameters={\n 'num_batches': 1,\n 'sample_model': 'orig',\n 'noise': noise,\n 'sample_kwargs': sample_kwargs})\n # test no result_out_dir\n inferencer_instance()\n inference_result = inferencer_instance(result_out_dir=result_out_dir)\n result_img = inference_result[1]\n assert result_img.detach().numpy().shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_video_interpolation_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.video_interpolation_inferencer import \\\n VideoInterpolationInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_video_interpolation_inferencer():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'flavr',\n 'flavr_in4out1_8xb4_vimeo90k-septuplet.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'video_interpolation_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n video_path = data_root + 'tests/data/frames/test_inference.mp4'\n\n inferencer_instance = \\\n VideoInterpolationInferencer(cfg,\n None)\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef test_video_interpolation_inferencer_input_dir():\n data_root = osp.join(osp.dirname(__file__), '../../../')\n config = data_root + 'configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py'\n video_path = data_root + 'tests/data/frames/sequence/gt/sequence_1'\n result_out_dir = data_root + 'tests/data/out'\n\n inferencer_instance = \\\n VideoInterpolationInferencer(config,\n None,\n extra_parameters={'fps': 60})\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef test_video_interpolation_inferencer_fps_multiplier():\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data',\n 'video_interpolation_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n cfg = data_root + 'configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py'\n video_path = data_root + 'tests/data/frames/test_inference.mp4'\n\n inferencer_instance = \\\n VideoInterpolationInferencer(cfg,\n None,\n extra_parameters={'fps_multiplier': 2})\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_inferencers/test_video_restoration_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.apis.inferencers.video_restoration_inferencer import \\\n VideoRestorationInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_video_restoration_inferencer():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'basicvsr',\n 'basicvsr_2xb4_reds4.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'video_restoration_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n video_path = data_root + 'tests/data/frames/test_inference.mp4'\n\n inferencer_instance = \\\n VideoRestorationInferencer(\n cfg,\n None)\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef test_video_restoration_inferencer_input_dir():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'basicvsr',\n 'basicvsr_2xb4_reds4.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'video_restoration_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n input_dir = osp.join(data_root, 'tests/data/frames/sequence/gt/sequence_1')\n result_out_dir = data_root + 'tests/data/out'\n\n inferencer_instance = \\\n VideoRestorationInferencer(\n cfg,\n None)\n inference_result = inferencer_instance(\n video=input_dir, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef test_video_restoration_inferencer_window_size():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'basicvsr',\n 'basicvsr_2xb4_reds4.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'video_restoration_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n video_path = data_root + 'tests/data/frames/test_inference.mp4'\n\n extra_parameters = {'window_size': 3}\n\n inferencer_instance = \\\n VideoRestorationInferencer(\n cfg,\n None,\n extra_parameters=extra_parameters)\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef test_video_restoration_inferencer_max_seq_len():\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', '..', 'configs', 'basicvsr',\n 'basicvsr_2xb4_reds4.py')\n result_out_dir = osp.join(\n osp.dirname(__file__), '..', '..', 'data/out',\n 'video_restoration_result.mp4')\n data_root = osp.join(osp.dirname(__file__), '../../../')\n video_path = data_root + 'tests/data/frames/test_inference.mp4'\n\n extra_parameters = {'max_seq_len': 3}\n\n inferencer_instance = \\\n VideoRestorationInferencer(\n cfg,\n None,\n extra_parameters=extra_parameters)\n inference_result = inferencer_instance(\n video=video_path, result_out_dir=result_out_dir)\n assert inference_result is None\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_apis/test_mmagic_inferencer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nimport pytest\n\nfrom mmagic.apis import MMagicInferencer\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_edit():\n with pytest.raises(Exception):\n MMagicInferencer('dog', ['error_type'], None)\n\n with pytest.raises(Exception):\n MMagicInferencer()\n\n with pytest.raises(Exception):\n MMagicInferencer(model_setting=1)\n\n supported_models = MMagicInferencer.get_inference_supported_models()\n MMagicInferencer.inference_supported_models_cfg_inited = False\n supported_models = MMagicInferencer.get_inference_supported_models()\n\n supported_tasks = MMagicInferencer.get_inference_supported_tasks()\n MMagicInferencer.inference_supported_models_cfg_inited = False\n supported_tasks = MMagicInferencer.get_inference_supported_tasks()\n\n task_supported_models = \\\n MMagicInferencer.get_task_supported_models('Image2Image Translation')\n MMagicInferencer.inference_supported_models_cfg_inited = False\n task_supported_models = \\\n MMagicInferencer.get_task_supported_models('Image2Image Translation')\n\n print(supported_models)\n print(supported_tasks)\n print(task_supported_models)\n\n cfg = osp.join(\n osp.dirname(__file__), '..', '..', 'configs', 'biggan',\n 'biggan_2xb25-500kiters_cifar10-32x32.py')\n\n mmagic_instance = MMagicInferencer(\n 'biggan',\n model_ckpt='',\n model_config=cfg,\n extra_parameters={'sample_model': 'ema'})\n mmagic_instance.print_extra_parameters()\n inference_result = mmagic_instance.infer(label=1)\n result_img = inference_result[1]\n assert result_img.shape == (4, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_basic_conditional_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n# import os\nimport os.path as osp\nfrom unittest import TestCase\n\nimport numpy as np\n\nfrom mmagic.datasets import BasicConditionalDataset\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nDATA_DIR = osp.abspath(osp.join(osp.dirname(__file__), '../data/dataset/'))\n\n\nclass TestBasicConditonalDataset(TestCase):\n\n def test_init(self):\n ann_file = osp.abspath(osp.join(DATA_DIR, 'anno.txt'))\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR,\n ann_file=ann_file,\n metainfo={'classes': ('bus', 'car')})\n\n self.assertEqual(dataset.CLASSES, ('bus', 'car'))\n self.assertFalse(dataset.test_mode)\n self.assertNotIn('With transforms:', repr(dataset))\n\n classes_file = osp.abspath(osp.join(DATA_DIR, 'classes.txt'))\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR, ann_file=ann_file, classes=classes_file)\n self.assertEqual(dataset.CLASSES, ('bus', 'car'))\n self.assertEqual(dataset.class_to_idx, {'bus': 0, 'car': 1})\n\n ann_file = osp.abspath(osp.join(DATA_DIR, 'wrong.yml'))\n with self.assertRaises(TypeError):\n BasicConditionalDataset(data_root=DATA_DIR, ann_file=ann_file)\n\n gt_labels = dataset.get_gt_labels()\n print(type(gt_labels))\n self.assertTrue((gt_labels == np.array([0, 1, 1])).all())\n\n for idx, tar_ids in enumerate([0, 1, 1]):\n cat_ids = dataset.get_cat_ids(idx)\n self.assertEqual(cat_ids, [tar_ids])\n\n data = dataset[0]\n self.assertEqual(data['sample_idx'], 0)\n self.assertEqual(data['gt_label'], 0)\n self.assertIn('a/1.JPG', data['gt_path'])\n\n # test data prefix --> b/subb\n dataset = BasicConditionalDataset(data_root=DATA_DIR, data_prefix='b')\n self.assertIn('subb', dataset.CLASSES)\n\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR, data_prefix={'gt_path': 'b'})\n self.assertIn('subb', dataset.CLASSES)\n\n # test runtime error --> no samples\n with self.assertRaises(RuntimeError):\n dataset = BasicConditionalDataset(data_root=osp.dirname(__file__))\n\n # test assert error --> class list is not same\n with self.assertRaises(AssertionError):\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR, classes=['1', '2', '3', '4'])\n\n # test Value error --> wrong classes type input\n with self.assertRaises(ValueError):\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR, classes=dict(a=1))\n\n # test raise warning --> find empty classes -->\n # TODO: how to get logger's output?\n dataset = BasicConditionalDataset(data_root=DATA_DIR)\n\n # test lazy init\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR,\n lazy_init=True,\n pipeline=[dict(type='PackInputs')])\n self.assertFalse(dataset._fully_initialized)\n self.assertIn(\"Haven't been initialized\", repr(dataset))\n self.assertIn('With transforms:', repr(dataset))\n\n # test load label from json file\n ann_file = osp.abspath(osp.join(DATA_DIR, 'anno.json'))\n dataset = BasicConditionalDataset(\n data_root=DATA_DIR,\n ann_file=ann_file,\n lazy_init=True,\n pipeline=[dict(type='PackInputs')])\n self.assertEqual(dataset[0]['data_samples'].gt_label.label.tolist(),\n [1, 2, 3, 4])\n self.assertEqual(dataset[1]['data_samples'].gt_label.label.tolist(),\n [1, 4, 5, 3])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_basic_frames_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom pathlib import Path\n\nfrom mmagic.datasets import BasicFramesDataset\n\n\nclass TestFramesDatasets:\n\n # TODO add a param for depth of file (for sequence_length).\n\n @classmethod\n def setup_class(cls):\n cls.data_root = Path(__file__).parent.parent / 'data' / 'frames'\n\n def test_version_1_method(self):\n\n # test SRREDSDataset and SRREDSMultipleGTDataset\n # need to split the data set ahead of schedule\n dataset = BasicFramesDataset(\n ann_file='ann2.txt',\n metainfo=dict(dataset_type='SRREDSDataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n backend_args=dict(backend='local'),\n pipeline=[],\n depth=1,\n num_input_frames=5,\n fixed_seq_len=100)\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=5,\n num_output_frames=None,\n sequence_length=100,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n\n # test SRVimeo90KDataset and SRVimeo90KMultipleGTDataset\n # Each clip of Vimeo90K has 7 frames starting from 1.\n # So we use 9 for generating frame_index_list in Version 1.0:\n # N | frame_index_list\n # 1 | 4\n # 3 | 3,4,5\n # 5 | 2,3,4,5,6\n # 7 | 1,2,3,4,5,6,7\n # In Version 2.0, we load the list of frames directly\n dataset = BasicFramesDataset(\n ann_file='ann3.txt',\n metainfo=dict(dataset_type='SRVimeo90KDataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(img='sequence', gt='sequence'),\n pipeline=[],\n depth=2,\n load_frames_list=dict(\n img=['all'], gt=['00000000.png', '00000001.png']))\n assert dataset[0] == dict(\n key=f'gt{os.sep}sequence_1',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n sample_idx=0)\n\n # test SRVid4Dataset\n dataset = BasicFramesDataset(\n ann_file='ann1.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n\n # test SRTestMultipleGTDataset and SRFolderMultipleGTDataset\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n pipeline=[],\n num_input_frames=1,\n depth=1)\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=1,\n num_output_frames=None,\n sequence_length=2,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n dataset = BasicFramesDataset(\n ann_file='ann1.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n pipeline=[],\n num_input_frames=1,\n depth=1)\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=1,\n num_output_frames=None,\n sequence_length=2,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n\n # test SRFolderVideoDataset\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n pipeline=[],\n num_input_frames=5,\n depth=2)\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=5,\n num_output_frames=None,\n sequence_length=2,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n dataset = BasicFramesDataset(\n ann_file='ann2.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n pipeline=[],\n num_input_frames=5,\n depth=2)\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=5,\n num_output_frames=None,\n sequence_length=2,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n\n # test VFIVimeo90KDataset\n dataset = BasicFramesDataset(\n ann_file='ann3.txt',\n metainfo=dict(dataset_type='vfi_folder_dataset', task_name='vfi'),\n data_root=self.data_root,\n data_prefix=dict(img='sequence', gt='sequence'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3,\n load_frames_list=dict(\n img=['00000000.png', '00000001.png'], gt=['00000000.png']))\n assert dataset[0] == dict(\n key=f'gt{os.sep}sequence_1',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png')\n ],\n sample_idx=0)\n\n def test_vsr_folder_dataset(self):\n # case 1: deep_path\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key=f'sequence_1{os.sep}00000001',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n assert dataset[2] == dict(\n key=f'sequence_2{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=2)\n\n # case 2: not deep_path\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n\n # case 3: no fixed_seq_len\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=None)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=2,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n\n # case 4: not deep_path, load_frames_list\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3,\n load_frames_list=dict(img=['all'], gt=['00000000.png']))\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png')\n ],\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=3,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000001.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000002.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png')\n ],\n sample_idx=1)\n\n # case 5: deep_path, load_frames_list\n dataset = BasicFramesDataset(\n ann_file='',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(img='sequence', gt='sequence'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3,\n load_frames_list=dict(img=['00000000.png'], gt=['all']))\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / 'sequence'),\n gt=str(self.data_root / 'sequence'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key=f'gt{os.sep}sequence_1',\n num_input_frames=1,\n num_output_frames=2,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n sample_idx=0)\n assert dataset[1] == dict(\n key=f'gt{os.sep}sequence_2',\n num_input_frames=1,\n num_output_frames=3,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000001.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000002.png')\n ],\n sample_idx=1)\n\n def test_vsr_ann_dataset(self):\n # case 1: deep_path, not deep_ann\n dataset = BasicFramesDataset(\n ann_file='ann1.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n assert dataset.ann_file == str(self.data_root / 'ann1.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key=f'sequence_1{os.sep}00000001',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n assert dataset[2] == dict(\n key=f'sequence_2{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=2)\n\n # case 2: deep_path, deep_ann\n dataset = BasicFramesDataset(\n ann_file='ann2.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n assert dataset.ann_file == str(self.data_root / 'ann2.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key=f'sequence_1{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key=f'sequence_1{os.sep}00000001',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n assert dataset[2] == dict(\n key=f'sequence_2{os.sep}00000000',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=2)\n\n # case 3: not deep_path, not deep_ann\n dataset = BasicFramesDataset(\n ann_file='ann1.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n assert dataset.ann_file == str(self.data_root / 'ann1.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n\n # case 4: not deep_path, deep_ann\n dataset = BasicFramesDataset(\n ann_file='ann2.txt',\n metainfo=dict(dataset_type='vsr_folder_dataset', task_name='vsr'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3)\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n assert dataset.ann_file == str(self.data_root / 'ann2.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=2,\n num_output_frames=2,\n sequence_length=3,\n img_path=str(self.data_root / f'sequence{os.sep}gt'),\n gt_path=str(self.data_root / f'sequence{os.sep}gt'),\n sample_idx=1)\n\n def test_vfi_ann_dataset(self):\n # case 1: not deep_path\n dataset = BasicFramesDataset(\n ann_file='ann1.txt',\n metainfo=dict(dataset_type='vfi_folder_dataset', task_name='vfi'),\n data_root=self.data_root,\n data_prefix=dict(\n img=f'sequence{os.sep}gt', gt=f'sequence{os.sep}gt'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=1,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3,\n load_frames_list=dict(\n img=['00000000.png', '00000001.png'], gt=['00000000.png']))\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / f'sequence{os.sep}gt'),\n gt=str(self.data_root / f'sequence{os.sep}gt'))\n assert dataset.ann_file == str(self.data_root / 'ann1.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='sequence_1',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png')\n ],\n sample_idx=0)\n assert dataset[1] == dict(\n key='sequence_2',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png')\n ],\n sample_idx=1)\n\n # case 2: deep_path\n dataset = BasicFramesDataset(\n ann_file='ann3.txt',\n metainfo=dict(dataset_type='vfi_folder_dataset', task_name='vfi'),\n data_root=self.data_root,\n data_prefix=dict(img='sequence', gt='sequence'),\n filename_tmpl=dict(img='{}', gt='{}'),\n pipeline=[],\n depth=2,\n num_input_frames=2,\n num_output_frames=2,\n fixed_seq_len=3,\n load_frames_list=dict(\n img=['00000000.png', '00000001.png'], gt=['00000000.png']))\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / 'sequence'),\n gt=str(self.data_root / 'sequence'))\n assert dataset.ann_file == str(self.data_root / 'ann3.txt')\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key=f'gt{os.sep}sequence_1',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_1' /\n '00000000.png')\n ],\n sample_idx=0)\n assert dataset[1] == dict(\n key=f'gt{os.sep}sequence_2',\n num_input_frames=2,\n num_output_frames=1,\n sequence_length=None,\n img_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png'),\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000001.png')\n ],\n gt_path=[\n str(self.data_root / f'sequence{os.sep}gt' / 'sequence_2' /\n '00000000.png')\n ],\n sample_idx=1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_basic_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom pathlib import Path\n\nimport mmcv\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import LoadImageFromFile\n\n\nclass TestImageDatasets:\n\n @classmethod\n def setup_class(cls):\n cls.data_root = Path(__file__).parent.parent / 'data' / 'image'\n\n def test_version_1_method(self):\n\n # test SRAnnotationDataset\n dataset = BasicImageDataset(\n ann_file='train.txt',\n metainfo=dict(\n dataset_type='sr_annotation_dataset', task_name='sisr'),\n data_root=self.data_root,\n data_prefix=dict(img='lq', gt='gt'),\n filename_tmpl=dict(img='{}_x4'),\n backend_args=dict(backend='local'),\n pipeline=[])\n assert dataset[0] == dict(\n key='baboon',\n img_path=str(self.data_root / 'lq' / 'baboon_x4.png'),\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n\n # test SRFolderDataset\n dataset = BasicImageDataset(\n metainfo=dict(dataset_type='sr_folder_dataset', task_name='sisr'),\n data_root=self.data_root,\n data_prefix=dict(img='lq', gt='gt'),\n filename_tmpl=dict(img='{}_x4'),\n pipeline=[])\n assert dataset[0] == dict(\n key='baboon',\n img_path=str(self.data_root / 'lq' / 'baboon_x4.png'),\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n\n # test SRFolderGTDataset\n dataset = BasicImageDataset(\n metainfo=dict(dataset_type='sr_folder_dataset', task_name='sisr'),\n data_root=self.data_root,\n data_prefix=dict(gt='gt'),\n filename_tmpl=dict(),\n pipeline=[])\n assert dataset[0] == dict(\n key='baboon',\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n\n # test SRLmdbDataset\n # The reconstructed LoadImageFromFile supports process images in LMDB\n # backend, which require similar img_path as that of disk backend.\n # Thus SRLmdbDataset is useless.\n # We can build the dataset in the same way with SRAnnotationDataset:\n pipeline = [\n LoadImageFromFile(\n key='img',\n backend_args=dict(\n backend='lmdb',\n db_path=Path(__file__).parent.parent / 'data' / 'lq.lmdb'))\n ]\n dataset = BasicImageDataset(\n ann_file=f'lq.lmdb{os.sep}meta_info.txt',\n metainfo=dict(\n dataset_type='sr_annotation_dataset', task_name='sisr'),\n data_prefix=dict(gt='', img=''),\n data_root=self.data_root.parent,\n pipeline=pipeline)\n assert dataset.ann_file == str(self.data_root.parent / 'lq.lmdb' /\n 'meta_info.txt')\n path_baboon_x4 = Path(\n __file__).parent.parent / 'data' / 'image' / 'lq' / 'baboon_x4.png'\n img_baboon_x4 = mmcv.imread(str(path_baboon_x4), flag='color')\n h, w, _ = img_baboon_x4.shape\n assert dataset[0]['img'].shape == (h, w, 3)\n assert dataset[0]['ori_img_shape'] == (h, w, 3)\n dataset[0]['img_path'] == str(self.data_root.parent / 'baboon.png')\n dataset[0]['gt_path'] == str(self.data_root.parent / 'baboon.png')\n\n # test ImgInpaintingDataset\n # test SRFacialLandmarkDataset i.e. SRAnnGTDataset\n dataset = BasicImageDataset(\n ann_file='train.txt',\n metainfo=dict(\n dataset_type='ImgInpaintingDataset', task_name='inpainting'),\n data_root=self.data_root,\n data_prefix=dict(gt='gt'),\n filename_tmpl=dict(),\n pipeline=[])\n assert dataset[0] == dict(\n key='baboon',\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n assert dataset[1] == dict(\n key='baboon',\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=1)\n\n def test_sisr_annotation_dataset(self):\n # setup\n dataset = BasicImageDataset(\n ann_file='train.txt',\n metainfo=dict(\n dataset_type='sisr_annotation_dataset', task_name='sisr'),\n data_root=self.data_root,\n data_prefix=dict(img='lq', gt='gt'),\n filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=[])\n\n assert dataset.ann_file == str(self.data_root / 'train.txt')\n assert dataset.data_prefix == dict(\n img=str(self.data_root / 'lq'), gt=str(self.data_root / 'gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='baboon',\n img_path=str(self.data_root / 'lq' / 'baboon_x4.png'),\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0), dataset[0]\n\n def test_sisr_folder_dataset(self):\n # setup\n dataset = BasicImageDataset(\n metainfo=dict(\n dataset_type='sisr_folder_dataset', task_name='sisr'),\n data_root=self.data_root,\n data_prefix=dict(img='lq', gt='gt'),\n filename_tmpl=dict(img='{}_x4', gt='{}'),\n pipeline=[])\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / 'lq'), gt=str(self.data_root / 'gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='baboon',\n img_path=str(self.data_root / 'lq' / 'baboon_x4.png'),\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n\n def test_refsr_folder_dataset(self):\n # setup\n dataset = BasicImageDataset(\n metainfo=dict(\n dataset_type='refsr_folder_dataset', task_name='refsr'),\n data_root=self.data_root,\n data_prefix=dict(img='lq', gt='gt', ref='gt'),\n filename_tmpl=dict(img='{}_x4', gt='{}', ref='{}'),\n pipeline=[])\n\n assert dataset.data_prefix == dict(\n img=str(self.data_root / 'lq'),\n gt=str(self.data_root / 'gt'),\n ref=str(self.data_root / 'gt'))\n # Serialize ``self.data_list`` to save memory\n assert dataset.data_list == []\n assert dataset[0] == dict(\n key='baboon',\n img_path=str(self.data_root / 'lq' / 'baboon_x4.png'),\n gt_path=str(self.data_root / 'gt' / 'baboon.png'),\n ref_path=str(self.data_root / 'gt' / 'baboon.png'),\n sample_idx=0)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_categories.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.datasets.categories import CIFAR10_CATEGORIES, IMAGENET_CATEGORIES\n\n\ndef test_cifar10_categories():\n assert len(CIFAR10_CATEGORIES) == 10\n\n\ndef test_imagenet_categories():\n assert len(IMAGENET_CATEGORIES) == 1000\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_cifar10_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nimport pickle\nimport tempfile\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nimport numpy as np\n\nfrom mmagic.registry import DATASETS\nfrom mmagic.utils import register_all_modules\n\nDATA_DIR = osp.abspath(osp.join(osp.dirname(__file__), '../data/dataset/'))\n\nregister_all_modules()\n\n\nclass TestCIFAR10(TestCase):\n DATASET_TYPE = 'CIFAR10'\n\n @classmethod\n def setUpClass(cls) -> None:\n super().setUpClass()\n\n tmpdir = tempfile.TemporaryDirectory()\n cls.tmpdir = tmpdir\n data_prefix = tmpdir.name\n cls.DEFAULT_ARGS = dict(\n data_prefix=data_prefix, pipeline=[], test_mode=False)\n\n dataset_class = DATASETS.get(cls.DATASET_TYPE)\n base_folder = osp.join(data_prefix, dataset_class.base_folder)\n os.mkdir(base_folder)\n\n cls.fake_imgs = np.random.randint(\n 0, 255, size=(6, 3 * 32 * 32), dtype=np.uint8)\n cls.fake_labels = np.random.randint(0, 10, size=(6, ))\n cls.fake_classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n batch1 = dict(\n data=cls.fake_imgs[:2], labels=cls.fake_labels[:2].tolist())\n with open(osp.join(base_folder, 'data_batch_1'), 'wb') as f:\n f.write(pickle.dumps(batch1))\n\n batch2 = dict(\n data=cls.fake_imgs[2:4], labels=cls.fake_labels[2:4].tolist())\n with open(osp.join(base_folder, 'data_batch_2'), 'wb') as f:\n f.write(pickle.dumps(batch2))\n\n test_batch = dict(\n data=cls.fake_imgs[4:], fine_labels=cls.fake_labels[4:].tolist())\n with open(osp.join(base_folder, 'test_batch'), 'wb') as f:\n f.write(pickle.dumps(test_batch))\n\n meta = {dataset_class.meta['key']: cls.fake_classes}\n meta_filename = dataset_class.meta['filename']\n with open(osp.join(base_folder, meta_filename), 'wb') as f:\n f.write(pickle.dumps(meta))\n\n dataset_class.train_list = [['data_batch_1', None],\n ['data_batch_2', None]]\n dataset_class.test_list = [['test_batch', None]]\n dataset_class.meta['md5'] = None\n\n def test_initialize(self):\n dataset_class = DATASETS.get(self.DATASET_TYPE)\n\n # Test overriding metainfo by `metainfo` argument\n cfg = {**self.DEFAULT_ARGS, 'metainfo': {'classes': ('bus', 'car')}}\n dataset = dataset_class(**cfg)\n self.assertEqual(dataset.CLASSES, ('bus', 'car'))\n\n # Test overriding metainfo by `classes` argument\n cfg = {**self.DEFAULT_ARGS, 'classes': ['bus', 'car']}\n dataset = dataset_class(**cfg)\n self.assertEqual(dataset.CLASSES, ('bus', 'car'))\n\n classes_file = osp.join(DATA_DIR, 'classes.txt')\n cfg = {**self.DEFAULT_ARGS, 'classes': classes_file}\n dataset = dataset_class(**cfg)\n self.assertEqual(dataset.CLASSES, ('bus', 'car'))\n self.assertEqual(dataset.class_to_idx, {'bus': 0, 'car': 1})\n\n # Test invalid classes\n cfg = {**self.DEFAULT_ARGS, 'classes': dict(classes=1)}\n with self.assertRaisesRegex(ValueError, \"type \"):\n dataset_class(**cfg)\n\n def test_load_data_list(self):\n dataset_class = DATASETS.get(self.DATASET_TYPE)\n\n # Test default behavior\n dataset = dataset_class(**self.DEFAULT_ARGS)\n self.assertEqual(len(dataset), 4)\n self.assertEqual(dataset.CLASSES, dataset_class.METAINFO['classes'])\n\n data_info = dataset[0]\n fake_img = self.fake_imgs[0].reshape(3, 32, 32).transpose(1, 2, 0)\n np.testing.assert_equal(data_info['gt'], fake_img)\n np.testing.assert_equal(data_info['gt_label'], self.fake_labels[0])\n assert data_info['gt_channel_order'] == 'RGB'\n\n # Test with test_mode=True\n cfg = {**self.DEFAULT_ARGS, 'test_mode': True}\n dataset = dataset_class(**cfg)\n self.assertEqual(len(dataset), 2)\n\n data_info = dataset[0]\n fake_img = self.fake_imgs[4].reshape(3, 32, 32).transpose(1, 2, 0)\n np.testing.assert_equal(data_info['gt'], fake_img)\n np.testing.assert_equal(data_info['gt_label'], self.fake_labels[4])\n assert data_info['gt_channel_order'] == 'RGB'\n\n # Test load meta\n cfg = {**self.DEFAULT_ARGS, 'lazy_init': True}\n dataset = dataset_class(**cfg)\n dataset._metainfo = {}\n dataset.full_init()\n self.assertEqual(dataset.CLASSES, self.fake_classes)\n\n cfg = {**self.DEFAULT_ARGS, 'lazy_init': True}\n dataset = dataset_class(**cfg)\n dataset._metainfo = {}\n dataset.meta['filename'] = 'invalid'\n with self.assertRaisesRegex(RuntimeError, 'not found or corrupted'):\n dataset.full_init()\n\n # Test automatically download\n with patch(\n 'mmagic.datasets.cifar10_dataset.download_and_extract_archive'\n ) as mock:\n cfg = {**self.DEFAULT_ARGS, 'lazy_init': True, 'test_mode': True}\n dataset = dataset_class(**cfg)\n dataset.test_list = [['invalid_batch', None]]\n with self.assertRaisesRegex(AssertionError, 'Download failed'):\n dataset.full_init()\n mock.assert_called_once_with(\n dataset.url,\n dataset.data_prefix['root'],\n filename=dataset.filename,\n md5=dataset.tgz_md5)\n\n with self.assertRaisesRegex(RuntimeError, '`download=True`'):\n cfg = {\n **self.DEFAULT_ARGS, 'lazy_init': True,\n 'test_mode': True,\n 'download': False\n }\n dataset = dataset_class(**cfg)\n dataset.test_list = [['test_batch', 'invalid_md5']]\n dataset.full_init()\n\n # Test different backend\n cfg = {\n **self.DEFAULT_ARGS, 'lazy_init': True,\n 'data_prefix': 'http://openmmlab/cifar'\n }\n dataset = dataset_class(**cfg)\n dataset._check_integrity = MagicMock(return_value=False)\n with self.assertRaisesRegex(RuntimeError, 'http://openmmlab/cifar'):\n dataset.full_init()\n\n def test_extra_repr(self):\n dataset_class = DATASETS.get(self.DATASET_TYPE)\n cfg = {**self.DEFAULT_ARGS, 'lazy_init': True}\n dataset = dataset_class(**cfg)\n\n self.assertIn(f'Prefix of data: \\t{dataset.data_prefix[\"root\"]}',\n repr(dataset))\n\n @classmethod\n def tearDownClass(cls):\n cls.tmpdir.cleanup()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_comp1k_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport os.path as osp\nfrom pathlib import Path\n\nfrom mmagic.datasets import AdobeComp1kDataset\n\n\nclass TestMattingDatasets:\n\n # path to a minimal test dataset under 'tests/data'\n DATA_ROOT = Path(__file__).parent.parent / 'data' / 'matting_dataset'\n\n # cls.pipeline = [\n # dict(type='LoadImageFromFile', key='alpha', flag='grayscale')\n # ]\n\n def test_comp1k_dataset(self):\n \"\"\"Verify AdobeComp1kDataset reads dataset correctly.\n\n AdobeComp1kDataset should support both new and old annotation formats.\n \"\"\"\n\n ann_new = 'ann.json'\n ann_old = 'ann_old.json'\n\n for ann in ann_new, ann_old:\n # TODO: we may add an actual pipeline later\n ds = AdobeComp1kDataset(ann, data_root=self.DATA_ROOT, pipeline=[])\n\n assert len(ds) == 2\n assert ds.metainfo == dict(\n dataset_type='matting_dataset', task_name='matting')\n\n data0 = ds[0]\n data1 = ds[1]\n assert osp.isfile(data0['alpha_path'])\n assert osp.isfile(data1['alpha_path'])\n\n is_correct_ntpath = data0 == {\n 'alpha_path': f'{self.DATA_ROOT}\\\\alpha/GT05.jpg',\n 'trimap_path': f'{self.DATA_ROOT}\\\\trimap/GT05.jpg',\n 'bg_path': f'{self.DATA_ROOT}\\\\bg/GT26r.jpg',\n 'fg_path': f'{self.DATA_ROOT}\\\\fg/GT05.jpg',\n 'merged_path': f'{self.DATA_ROOT}\\\\merged/GT05.jpg',\n 'sample_idx': 0,\n }\n is_correct_posixpath = data0 == {\n 'alpha_path': f'{self.DATA_ROOT}/alpha/GT05.jpg',\n 'trimap_path': f'{self.DATA_ROOT}/trimap/GT05.jpg',\n 'bg_path': f'{self.DATA_ROOT}/bg/GT26r.jpg',\n 'fg_path': f'{self.DATA_ROOT}/fg/GT05.jpg',\n 'merged_path': f'{self.DATA_ROOT}/merged/GT05.jpg',\n 'sample_idx': 0,\n }\n assert is_correct_ntpath or is_correct_posixpath\n\n is_correct_ntpath = data1 == {\n 'alpha_path': f'{self.DATA_ROOT}\\\\alpha/GT05.jpg',\n 'bg_path': f'{self.DATA_ROOT}\\\\bg/GT26r.jpg',\n 'fg_path': f'{self.DATA_ROOT}\\\\fg/GT05.jpg',\n 'sample_idx': 1,\n }\n is_correct_posixpath = data1 == {\n 'alpha_path': f'{self.DATA_ROOT}/alpha/GT05.jpg',\n 'bg_path': f'{self.DATA_ROOT}/bg/GT26r.jpg',\n 'fg_path': f'{self.DATA_ROOT}/fg/GT05.jpg',\n 'sample_idx': 1,\n }\n assert is_correct_ntpath or is_correct_posixpath\n\n # TODO: after mmcv.transform becomes ready in CI\n # assert 'alpha' in first_data\n # assert isinstance(first_data['alpha'], np.ndarray)\n # assert first_data['alpha'].shape == (552, 800)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_controlnet_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\n\nfrom mmagic.datasets import ControlNetDataset\n\ndata_dir = os.path.join(__file__, '../', '../', 'data', 'controlnet')\ndata_dir = os.path.abspath(data_dir)\nanno_path = os.path.join(data_dir, 'prompt.json')\nsource_path = os.path.join(data_dir, 'source')\ntarget_path = os.path.join(data_dir, 'target')\n\n\ndef test_controlnet_dataset():\n print(os.path.abspath(data_dir))\n dataset = ControlNetDataset(data_root=data_dir)\n assert len(dataset) == 2\n prompts = [\n 'pale golden rod circle with old lace background',\n 'light coral circle with white background'\n ]\n for idx, data in enumerate(dataset):\n assert 'source_path' in data\n assert 'target_path' in data\n assert data['prompt'] == prompts[idx]\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_data_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.datasets.data_utils import infer_io_backend\n\n\ndef test_infer_io_backend():\n path = 'http://openmmlab/xxx'\n assert infer_io_backend(path) == 'http'\n\n path = 'https://torchvision/xxx'\n assert infer_io_backend(path) == 'http'\n\n path = 'MYCLUSTER:s3://openmmlab/datasets/mmediting/lsun/'\n assert infer_io_backend(path) == 'petrel'\n\n path = 's3://work_dirs/'\n assert infer_io_backend(path) == 'petrel'\n\n path = 'this/is/a/test'\n assert infer_io_backend(path) == 'local'\n\n\n# TODO: add more uts\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_dreambooth_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\n\nfrom mmagic.datasets import DreamBoothDataset\n\n# we use controlnet's dataset to test\ndata_dir = os.path.join(__file__, '../', '../', 'data', 'controlnet')\nconcept_dir = os.path.join(data_dir, 'source')\n\n\ndef test_dreambooth_dataset():\n print(os.path.abspath(data_dir))\n dataset = DreamBoothDataset(\n data_root=data_dir,\n concept_dir=concept_dir,\n prompt='a sks ball',\n )\n assert len(dataset) == 2\n for data in dataset:\n assert data['prompt'] == 'a sks ball'\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_grow_scale_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nimport pytest\n\nfrom mmagic.datasets import GrowScaleImgDataset\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestGrowScaleImgDataset:\n\n @classmethod\n def setup_class(cls):\n cls.imgs_root = osp.join(osp.dirname(__file__), '..', 'data/image')\n cls.imgs_roots = {\n '4': cls.imgs_root,\n '8': osp.join(cls.imgs_root, 'img_root'),\n '32': osp.join(cls.imgs_root, 'img_root', 'grass')\n }\n cls.default_pipeline = [dict(type='LoadImageFromFile', key='gt')]\n cls.len_per_stage = 10\n cls.gpu_samples_base = 2\n\n def test_dynamic_unconditional_img_dataset(self):\n dataset = GrowScaleImgDataset(\n self.imgs_roots,\n self.default_pipeline,\n self.len_per_stage,\n io_backend='local',\n gpu_samples_base=self.gpu_samples_base)\n assert len(dataset) == 10\n img = dataset[2]['gt']\n assert img.ndim == 3\n assert repr(dataset) == (\n f'dataset_name: {dataset.__class__}, '\n f'total {10} images in imgs_root: {self.imgs_root}')\n assert dataset.samples_per_gpu == 2\n\n dataset.update_annotations(8)\n assert len(dataset) == 10\n img = dataset[2]['gt']\n assert img.ndim == 3\n assert repr(dataset) == (f'dataset_name: {dataset.__class__}, '\n f'total {10} images in imgs_root:'\n f' {osp.join(self.imgs_root, \"img_root\")}')\n assert dataset.samples_per_gpu == 2\n\n dataset = GrowScaleImgDataset(\n self.imgs_roots,\n self.default_pipeline,\n 20,\n gpu_samples_base=self.gpu_samples_base,\n gpu_samples_per_scale={\n '4': 10,\n '16': 13\n })\n assert len(dataset) == 20\n img = dataset[2]['gt']\n assert img.ndim == 3\n assert repr(dataset) == (\n f'dataset_name: {dataset.__class__}, '\n f'total {20} images in imgs_root: {self.imgs_root}')\n assert dataset.samples_per_gpu == 10\n\n dataset.update_annotations(8)\n assert len(dataset) == 20\n img = dataset[2]['gt']\n assert img.ndim == 3\n assert repr(dataset) == (f'dataset_name: {dataset.__class__}, '\n f'total {20} images in imgs_root:'\n f' {osp.join(self.imgs_root, \"img_root\")}')\n assert dataset.samples_per_gpu == 2\n\n dataset = GrowScaleImgDataset(\n self.imgs_roots, self.default_pipeline, 5, test_mode=True)\n assert len(dataset) == 5\n img = dataset[2]['gt']\n assert img.ndim == 3\n assert repr(dataset) == (\n f'dataset_name: {dataset.__class__}, '\n f'total {5} images in imgs_root: {self.imgs_root}')\n\n dataset.update_annotations(24)\n assert len(dataset) == 5\n img = dataset[2]['gt']\n assert img.ndim == 3\n _path_str = osp.join(self.imgs_root, 'img_root', 'grass')\n assert repr(dataset) == (f'dataset_name: {dataset.__class__}, '\n f'total {5} images in imgs_root: {_path_str}')\n\n with pytest.raises(AssertionError):\n _ = GrowScaleImgDataset(\n self.imgs_root,\n self.default_pipeline,\n 10,\n gpu_samples_per_scale=10)\n\n with pytest.raises(AssertionError):\n _ = GrowScaleImgDataset(10, self.default_pipeline, 10.)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_imagenet_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom unittest import TestCase\n\nfrom mmagic.datasets import ImageNet\n\nDATA_DIR = osp.abspath(osp.join(osp.dirname(__file__), '../data/dataset/'))\n\n\nclass TestImageNetDataset(TestCase):\n\n def test_load_data_list(self):\n\n with self.assertRaisesRegex(\n AssertionError, r\"\\(3\\) doesn't match .* classes \\(1000\\)\"):\n ImageNet(data_root=DATA_DIR, data_prefix=DATA_DIR)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_mscoco_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom pathlib import Path\n\nfrom mmagic.datasets import MSCoCoDataset\n\n\nclass TestMSCoCoDatasets:\n\n @classmethod\n def setup_class(cls):\n cls.data_root = Path(__file__).parent.parent / 'data' / 'coco'\n\n def test_mscoco(self):\n\n # test basic usage\n dataset = MSCoCoDataset(data_root=self.data_root, pipeline=[])\n assert dataset[0] == dict(\n gt_prompt='a good meal',\n img_path=os.path.join(self.data_root, 'train2014',\n 'COCO_train2014_000000000009.jpg'),\n sample_idx=0)\n\n # test with different phase\n dataset = MSCoCoDataset(\n data_root=self.data_root, phase='val', pipeline=[])\n assert dataset[0] == dict(\n gt_prompt='a pair of slippers',\n img_path=os.path.join(self.data_root, 'val2014',\n 'COCO_val2014_000000000042.jpg'),\n sample_idx=0)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_paired_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.datasets import PairedImageDataset\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestPairedImageDataset(object):\n\n @classmethod\n def setup_class(cls):\n cls.imgs_root = osp.join(\n osp.dirname(osp.dirname(__file__)), 'data/paired')\n cls.default_pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='a',\n domain_b='b'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_a', 'img_b']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='Resize',\n scale=(286, 286),\n interpolation='bicubic'),\n dict(type='FixedCrop', keys=['img'], crop_size=(256, 256))\n ]),\n dict(type='Flip', direction='horizontal', keys=['img_a', 'img_b']),\n dict(type='PackInputs', keys=['img_a', 'img_b'])\n ]\n\n def test_paired_image_dataset(self):\n dataset = PairedImageDataset(\n self.imgs_root, pipeline=self.default_pipeline)\n assert len(dataset) == 2\n img = dataset[0]['inputs']['img_a']\n assert img.ndim == 3\n img = dataset[0]['inputs']['img_b']\n assert img.ndim == 3\n\n dataset = PairedImageDataset(\n self.imgs_root, pipeline=self.default_pipeline, io_backend='local')\n assert len(dataset) == 2\n img = dataset[0]['inputs']['img_a']\n assert img.ndim == 3\n img = dataset[0]['inputs']['img_b']\n assert img.ndim == 3\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_singan_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.datasets import SinGANDataset\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestSinGANDataset(object):\n\n @classmethod\n def setup_class(cls):\n cls.imgs_root = osp.join(\n osp.dirname(osp.dirname(__file__)), 'data/image/gt/baboon.png')\n cls.min_size = 25\n cls.max_size = 250\n cls.num_scales = 10\n cls.scale_factor_init = 0.75\n cls.pipeline = [\n dict(\n type='PackInputs',\n keys=[f'real_scale{i}' for i in range(cls.num_scales)])\n ]\n\n def test_singan_dataset(self):\n dataset = SinGANDataset(\n self.imgs_root,\n min_size=self.min_size,\n max_size=self.max_size,\n scale_factor_init=self.scale_factor_init,\n pipeline=self.pipeline)\n assert len(dataset) == 1000000\n\n data_dict = dataset[0]['inputs']\n assert all(\n [f'real_scale{i}' in data_dict for i in range(self.num_scales)])\n\n\na = TestSinGANDataset()\na.setup_class()\na.test_singan_dataset()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_textual_inversion_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\n\nfrom mmagic.datasets import TextualInversionDataset\n\ndata_dir = os.path.join(__file__, '../', '../', 'data', 'controlnet')\nconcept_dir = os.path.join(data_dir, 'source')\nplaceholder = 'S*'\ntemplate = os.path.join(__file__, '../', '../', 'data', 'textual_inversion',\n 'imagenet_templates_small.txt')\nwith_image_reference = True\n\n\ndef test_textual_inversion_dataset():\n print(os.path.abspath(data_dir))\n dataset = TextualInversionDataset(\n data_root=data_dir,\n concept_dir=concept_dir,\n placeholder=placeholder,\n template=template,\n with_image_reference=with_image_reference)\n assert len(dataset) == 2\n" }, { "path": "tests/test_datasets/test_transforms/test_albumentations.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\n\nfrom mmagic.datasets.transforms import Albumentations\n\n\ndef test_albumentations():\n results = {}\n results['img'] = np.ones((8, 8, 3)).astype(np.uint8)\n model = Albumentations(\n keys=['img'], transforms=[\n dict(type='Resize', height=4, width=4),\n ])\n results = model(results)\n assert results['img'].shape == (4, 4, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_alpha.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import GenerateSeg, GenerateSoftSeg\n\n\ndef assert_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n assert set(target_keys).issubset(set(result_keys))\n\n\ndef test_generate_seg():\n with pytest.raises(ValueError):\n # crop area should not exceed the image size\n img = np.random.rand(32, 32, 3)\n GenerateSeg._crop_hole(img, (0, 0), (64, 64))\n\n target_keys = ['alpha', 'trimap', 'seg', 'num_holes']\n alpha = np.random.randint(0, 255, (64, 64))\n # previously this is 32 by 32, but default hole size contains 45\n trimap = np.zeros_like(alpha)\n trimap[(alpha > 0) & (alpha < 255)] = 128\n trimap[alpha == 255] = 255\n results = dict(alpha=alpha, trimap=trimap)\n for _ in range(3):\n # test multiple times as it contain some random operations\n generate_seg = GenerateSeg(num_holes_range=(1, 3))\n generate_seg_results = generate_seg(results)\n assert_keys_contain(generate_seg_results.keys(), target_keys)\n assert generate_seg_results['seg'].shape == alpha.shape\n assert isinstance(generate_seg_results['num_holes'], int)\n assert generate_seg_results['num_holes'] < 3\n\n # check repr string and the default setting\n assert repr(generate_seg) == generate_seg.__class__.__name__ + (\n '(kernel_size=5, erode_iter_range=(10, 20), '\n 'dilate_iter_range=(15, 30), num_holes_range=(1, 3), '\n 'hole_sizes=[(15, 15), (25, 25), (35, 35), (45, 45)], '\n 'blur_ksizes=[(21, 21), (31, 31), (41, 41)]')\n\n\ndef test_generate_soft_seg():\n with pytest.raises(TypeError):\n # fg_thr must be a float\n GenerateSoftSeg(fg_thr=[0.2])\n with pytest.raises(TypeError):\n # border_width must be an int\n GenerateSoftSeg(border_width=25.)\n with pytest.raises(TypeError):\n # erode_ksize must be an int\n GenerateSoftSeg(erode_ksize=5.)\n with pytest.raises(TypeError):\n # dilate_ksize must be an int\n GenerateSoftSeg(dilate_ksize=5.)\n with pytest.raises(TypeError):\n # erode_iter_range must be a tuple of 2 int\n GenerateSoftSeg(erode_iter_range=(3, 5, 7))\n with pytest.raises(TypeError):\n # dilate_iter_range must be a tuple of 2 int\n GenerateSoftSeg(dilate_iter_range=(3, 5, 7))\n with pytest.raises(TypeError):\n # blur_ksizes must be a list of tuple\n GenerateSoftSeg(blur_ksizes=[21, 21])\n\n target_keys = ['seg', 'soft_seg']\n\n seg = np.random.randint(0, 255, (512, 512))\n results = dict(seg=seg)\n\n generate_soft_seg = GenerateSoftSeg(\n erode_ksize=3,\n dilate_ksize=3,\n erode_iter_range=(1, 2),\n dilate_iter_range=(1, 2),\n blur_ksizes=[(11, 11)])\n generate_soft_seg_results = generate_soft_seg(results)\n assert_keys_contain(generate_soft_seg_results.keys(), target_keys)\n assert generate_soft_seg_results['soft_seg'].shape == seg.shape\n\n repr_str = generate_soft_seg.__class__.__name__ + (\n '(fg_thr=0.2, border_width=25, erode_ksize=3, dilate_ksize=3, '\n 'erode_iter_range=(1, 2), dilate_iter_range=(1, 2), '\n 'blur_ksizes=[(11, 11)])')\n assert repr(generate_soft_seg) == repr_str\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_aug_frames.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import MirrorSequence, TemporalReverse\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n\n cls.results = dict()\n cls.gt = np.random.randint(0, 256, (256, 128, 3), dtype=np.uint8)\n cls.img = np.random.randint(0, 256, (64, 32, 3), dtype=np.uint8)\n\n cls.results = dict(\n img=cls.img,\n gt=cls.gt,\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n\n cls.results['ori_img'] = np.random.randint(\n 0, 256, (256, 256, 3), dtype=np.uint8)\n cls.results['mask'] = np.random.randint(\n 0, 256, (256, 256, 1), dtype=np.uint8)\n # cls.results['img_tensor'] = torch.rand((3, 256, 256))\n # cls.results['mask_tensor'] = torch.zeros((1, 256, 256))\n # cls.results['mask_tensor'][:, 50:150, 40:140] = 1.\n\n @staticmethod\n def assert_img_equal(img, ref_img, ratio_thr=0.999):\n \"\"\"Check if img and ref_img are matched approximately.\"\"\"\n\n assert img.shape == ref_img.shape\n assert img.dtype == ref_img.dtype\n area = ref_img.shape[-1] * ref_img.shape[-2]\n diff = np.abs(img.astype('int32') - ref_img.astype('int32'))\n assert np.sum(diff <= 1) / float(area) > ratio_thr\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n\n return set(target_keys).issubset(set(result_keys))\n\n def test_mirror_sequence(self):\n imgs = [np.random.rand(4, 4, 3) for _ in range(0, 5)]\n gts = [np.random.rand(16, 16, 3) for _ in range(0, 5)]\n\n target_keys = ['img', 'gt']\n mirror_sequence = MirrorSequence(keys=['img', 'gt'])\n results = dict(img=imgs, gt=gts)\n results = mirror_sequence(results)\n\n assert self.check_keys_contain(results.keys(), target_keys)\n for i in range(0, 5):\n np.testing.assert_almost_equal(results['img'][i],\n results['img'][-i - 1])\n np.testing.assert_almost_equal(results['gt'][i],\n results['gt'][-i - 1])\n\n assert repr(mirror_sequence) == mirror_sequence.__class__.__name__ + (\n \"(keys=['img', 'gt'])\")\n\n # each key should contain a list of nparray\n with pytest.raises(TypeError):\n results = dict(img=0, gt=gts)\n mirror_sequence(results)\n\n def test_temporal_reverse(self):\n img_lq1 = np.random.rand(4, 4, 3).astype(np.float32)\n img_lq2 = np.random.rand(4, 4, 3).astype(np.float32)\n img_gt = np.random.rand(8, 8, 3).astype(np.float32)\n results = dict(lq=[img_lq1, img_lq2], gt=[img_gt])\n\n target_keys = ['lq', 'gt', 'reverse']\n temporal_reverse = TemporalReverse(keys=['lq', 'gt'], reverse_ratio=1)\n results = temporal_reverse(results)\n assert self.check_keys_contain(results.keys(), target_keys)\n np.testing.assert_almost_equal(results['lq'][0], img_lq2)\n np.testing.assert_almost_equal(results['lq'][1], img_lq1)\n np.testing.assert_almost_equal(results['gt'][0], img_gt)\n\n assert repr(\n temporal_reverse) == temporal_reverse.__class__.__name__ + (\n f\"(keys={['lq', 'gt']}, reverse_ratio=1)\")\n\n results = dict(lq=[img_lq1, img_lq2], gt=[img_gt])\n temporal_reverse = TemporalReverse(keys=['lq', 'gt'], reverse_ratio=0)\n results = temporal_reverse(results)\n assert self.check_keys_contain(results.keys(), target_keys)\n np.testing.assert_almost_equal(results['lq'][0], img_lq1)\n np.testing.assert_almost_equal(results['lq'][1], img_lq2)\n np.testing.assert_almost_equal(results['gt'][0], img_gt)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_aug_pixel.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\n\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (BinarizeImage, Clip, ColorJitter,\n RandomAffine, RandomMaskDilation,\n UnsharpMasking)\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n\n cls.results = dict()\n cls.gt = np.random.randint(0, 256, (256, 128, 3), dtype=np.uint8)\n cls.img = np.random.randint(0, 256, (64, 32, 3), dtype=np.uint8)\n\n cls.results = dict(\n img=cls.img,\n gt=cls.gt,\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n\n cls.results['ori_img'] = np.random.randint(\n 0, 256, (256, 256, 3), dtype=np.uint8)\n cls.results['mask'] = np.random.randint(\n 0, 256, (256, 256, 1), dtype=np.uint8)\n # cls.results['img_tensor'] = torch.rand((3, 256, 256))\n # cls.results['mask_tensor'] = torch.zeros((1, 256, 256))\n # cls.results['mask_tensor'][:, 50:150, 40:140] = 1.\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n\n return set(target_keys).issubset(set(result_keys))\n\n def test_binarize(self):\n\n mask_ = np.zeros((5, 5, 1))\n mask_[2, 2, :] = 0.6\n gt_mask = mask_.copy()\n gt_mask[2, 2, :] = 1.\n results = dict(mask=mask_.copy())\n binarize = BinarizeImage(['mask'], 0.5, dtype=np.float32)\n results = binarize(results)\n assert np.array_equal(results['mask'], gt_mask.astype(np.float32))\n\n results = dict(mask=mask_.copy())\n binarize = BinarizeImage(['mask'], 0.5)\n results = binarize(results)\n assert np.array_equal(results['mask'], gt_mask.astype(np.uint8))\n assert repr(binarize) == (\n binarize.__class__.__name__ + f\"(keys={['mask']}, binary_thr=0.5, \"\n f'a_min=0, a_max=1, dtype={np.uint8})')\n\n results = dict(mask=mask_.copy())\n binarize = BinarizeImage(['mask'], 0.5, a_max=3, a_min=1)\n results = binarize(results)\n assert np.array_equal(results['mask'],\n gt_mask.astype(np.uint8) * 2 + 1)\n assert repr(binarize) == (\n binarize.__class__.__name__ + f\"(keys={['mask']}, binary_thr=0.5, \"\n f'a_min=1, a_max=3, dtype={np.uint8})')\n\n def test_clip(self):\n results = {}\n\n # clip\n results['gt'] = (1.2 - 0.1) * self.results['gt']\n model = Clip(keys=['gt'])\n assert np.array_equal(model(results)['gt'], results['gt'].clip(0, 255))\n\n def test_color_jitter(self):\n\n results = copy.deepcopy(self.results)\n results['gt'] = (results['gt'] * 255).astype(np.uint8)\n results['img'] = [results['gt'], results['gt']]\n\n target_keys = ['gt', 'img']\n\n color_jitter = ColorJitter(\n keys=['gt', 'img'],\n brightness=0.5,\n contrast=0.5,\n saturation=0.5,\n hue=0.5)\n color_jitter_results = color_jitter(results)\n\n assert self.check_keys_contain(color_jitter_results.keys(),\n target_keys)\n assert color_jitter_results['gt'].shape == self.gt.shape\n color_jitter = ColorJitter(\n keys=['gt', 'img'],\n channel_order='bgr',\n brightness=0.5,\n contrast=0.5,\n saturation=0.5,\n hue=0.5)\n color_jitter_results = color_jitter(results)\n assert self.check_keys_contain(color_jitter_results.keys(),\n target_keys)\n assert color_jitter_results['gt'].shape == self.gt.shape\n assert np.abs(color_jitter_results['gt'] - self.gt.shape).mean() > 0\n\n assert repr(color_jitter) == color_jitter.__class__.__name__ + (\n f'(keys={color_jitter.keys}, '\n f'channel_order={color_jitter.channel_order}, '\n f'brightness={color_jitter._transform.brightness}, '\n f'contrast={color_jitter._transform.contrast}, '\n f'saturation={color_jitter._transform.saturation}, '\n f'hue={color_jitter._transform.hue})')\n\n with pytest.raises(AssertionError):\n color_jitter = ColorJitter(\n keys=['gt', 'img'],\n channel_order='bgr',\n to_rgb=True,\n brightness=0.5,\n contrast=0.5,\n saturation=0.5,\n hue=0.5)\n\n def test_random_affine(self):\n with pytest.raises(AssertionError):\n RandomAffine(None, -1)\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, translate='Not a tuple')\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, translate=(0, 0, 0))\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, translate=(0, 2))\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, scale='Not a tuple')\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, scale=(0.8, 1., 1.2))\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, scale=(-0.8, 1.))\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, shear=-1)\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, shear=(0, 1, 2))\n\n with pytest.raises(AssertionError):\n RandomAffine(None, 0, flip_ratio='Not a float')\n\n target_keys = ['fg', 'alpha']\n\n # Test identical transformation\n alpha = np.random.rand(4, 4).astype(np.float32)\n fg = np.random.rand(4, 4).astype(np.float32)\n results = dict(alpha=alpha, fg=fg)\n random_affine = RandomAffine(['fg', 'alpha'],\n degrees=0,\n flip_ratio=0.0)\n random_affine_results = random_affine(results)\n assert np.allclose(alpha, random_affine_results['alpha'])\n assert np.allclose(fg, random_affine_results['fg'])\n\n # Test flip in both direction\n fg = np.random.rand(4, 4).astype(np.float32)\n alpha = np.random.rand(4, 4).astype(np.float32)\n results = dict(alpha=alpha, fg=fg)\n random_affine = RandomAffine(['fg', 'alpha'],\n degrees=0,\n flip_ratio=1.0)\n random_affine_results = random_affine(results)\n assert np.allclose(alpha[::-1, ::-1], random_affine_results['alpha'])\n assert np.allclose(fg[::-1, ::-1], random_affine_results['fg'])\n\n # test random affine with different valid setting combinations\n # only shape are tested\n alpha = np.random.rand(240, 320, 1).astype(np.float32)\n fg = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(alpha=alpha, fg=fg)\n random_affine = RandomAffine(['fg', 'alpha'],\n degrees=30,\n translate=(0, 1),\n shear=(10, 20),\n flip_ratio=0.5)\n random_affine_results = random_affine(results)\n assert self.check_keys_contain(random_affine_results.keys(),\n target_keys)\n assert random_affine_results['fg'].shape == (240, 320, 3)\n assert random_affine_results['alpha'].shape == (240, 320, 1)\n\n alpha = np.random.rand(240, 320, 1).astype(np.float32)\n fg = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(alpha=alpha, fg=fg)\n random_affine = RandomAffine(['fg', 'alpha'],\n degrees=(-30, 30),\n scale=(0.8, 1.25),\n shear=10,\n flip_ratio=0.5)\n random_affine_results = random_affine(results)\n assert self.check_keys_contain(random_affine_results.keys(),\n target_keys)\n assert random_affine_results['fg'].shape == (240, 320, 3)\n assert random_affine_results['alpha'].shape == (240, 320, 1)\n\n alpha = np.random.rand(240, 320, 1).astype(np.float32)\n fg = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(alpha=alpha, fg=fg)\n random_affine = RandomAffine(['fg', 'alpha'], degrees=30)\n random_affine_results = random_affine(results)\n assert self.check_keys_contain(random_affine_results.keys(),\n target_keys)\n assert random_affine_results['fg'].shape == (240, 320, 3)\n assert random_affine_results['alpha'].shape == (240, 320, 1)\n\n assert repr(random_affine) == random_affine.__class__.__name__ + (\n f'(keys={target_keys}, degrees={(-30, 30)}, '\n f'translate={None}, scale={None}, '\n f'shear={None}, flip_ratio={0})')\n\n def test_random_dilation(self):\n mask = np.zeros((3, 3, 1), dtype=np.float32)\n mask[1, 1] = 1\n gt_mask = np.ones_like(mask)\n results = dict(mask=mask.copy())\n dilation = RandomMaskDilation(['mask'],\n binary_thr=0.5,\n kernel_min=3,\n kernel_max=3)\n results = dilation(results)\n assert np.array_equal(results['mask'], gt_mask)\n assert results['mask_dilate_kernel_size'] == 3\n assert str(dilation) == (\n dilation.__class__.__name__ +\n f\"(keys={['mask']}, kernel_min=3, kernel_max=3)\")\n\n def test_unsharp_masking(self):\n results = {}\n\n unsharp_masking = UnsharpMasking(\n kernel_size=15, sigma=0, weight=0.5, threshold=10, keys=['gt'])\n\n # single image\n results['gt'] = np.zeros((8, 8, 3)).astype(np.float32)\n results = unsharp_masking(results)\n assert isinstance(results['gt_unsharp'], np.ndarray)\n\n # sequence of images\n results['gt'] = [np.zeros((8, 8, 3)).astype(np.float32)] * 2\n results = unsharp_masking(results)\n assert isinstance(results['gt_unsharp'], list)\n\n assert repr(unsharp_masking) == unsharp_masking.__class__.__name__ + (\n \"(keys=['gt'], kernel_size=15, sigma=0, weight=0.5, threshold=10)\")\n\n # kernel_size must be odd\n with pytest.raises(ValueError):\n unsharp_masking = UnsharpMasking(\n kernel_size=10, sigma=0, weight=0.5, threshold=10, keys=['gt'])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_aug_shape.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\n\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (CenterCropLongEdge, Flip, NumpyPad,\n RandomCropLongEdge, RandomRotation,\n RandomTransposeHW, Resize)\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n\n cls.results = dict()\n cls.gt = np.random.randint(0, 256, (256, 128, 3), dtype=np.uint8)\n cls.img = np.random.randint(0, 256, (64, 32, 3), dtype=np.uint8)\n\n cls.results = dict(\n img=cls.img,\n gt=cls.gt,\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n\n cls.results['ori_img'] = np.random.randint(\n 0, 256, (256, 256, 3), dtype=np.uint8)\n cls.results['mask'] = np.random.randint(\n 0, 256, (256, 256, 1), dtype=np.uint8)\n # cls.results['img_tensor'] = torch.rand((3, 256, 256))\n # cls.results['mask_tensor'] = torch.zeros((1, 256, 256))\n # cls.results['mask_tensor'][:, 50:150, 40:140] = 1.\n\n @staticmethod\n def check_flip(origin_img, result_img, flip_direction):\n \"\"\"Check if the origin_img are flipped correctly into result_img in\n different flip_directions.\n\n Args:\n origin_img (np.ndarray): Original image.\n result_img (np.ndarray): Result image.\n flip_direction (str): Direction of flip.\n\n Returns:\n bool: Whether origin_img == result_img.\n \"\"\"\n\n if flip_direction == 'horizontal':\n diff = result_img[:, ::-1] - origin_img\n else:\n diff = result_img[::-1, :] - origin_img\n\n return diff.max() < 1e-8\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n\n return set(target_keys).issubset(set(result_keys))\n\n @staticmethod\n def check_transposehw(origin_img, result_img):\n \"\"\"Check if the origin_imgs are transposed correctly.\"\"\"\n\n h, w, c = origin_img.shape\n for i in range(c):\n for j in range(h):\n for k in range(w):\n if result_img[k, j, i] != origin_img[j, k, i]: # noqa:E501\n return False\n return True\n\n def test_flip(self):\n results = copy.deepcopy(self.results)\n\n trans = Flip(keys='img', flip_ratio=0)\n assert trans.keys == ['img']\n\n with pytest.raises(ValueError):\n Flip(keys=['img', 'gt'], direction='vertically')\n\n # horizontal\n np.random.seed(1)\n target_keys = ['img', 'gt', 'flip_infos']\n flip = Flip(keys=['img', 'gt'], flip_ratio=1, direction='horizontal')\n assert 'flip_infos' not in results\n results = flip(results)\n assert results['flip_infos'] == [\n dict(\n keys=['img', 'gt'], direction='horizontal', ratio=1, flip=True)\n ]\n assert self.check_keys_contain(results.keys(), target_keys)\n assert results['img'].shape == self.img.shape\n assert results['gt'].shape == self.gt.shape\n assert self.check_flip(self.img, results['img'],\n results['flip_infos'][-1]['direction'])\n assert self.check_flip(self.gt, results['gt'],\n results['flip_infos'][-1]['direction'])\n results = flip(results)\n assert results['flip_infos'] == [\n dict(\n keys=['img', 'gt'], direction='horizontal', ratio=1,\n flip=True),\n dict(\n keys=['img', 'gt'], direction='horizontal', ratio=1,\n flip=True),\n ]\n\n # vertical\n results = copy.deepcopy(self.results)\n flip = Flip(keys=['img', 'gt'], flip_ratio=1, direction='vertical')\n results = flip(results)\n assert self.check_keys_contain(results.keys(), target_keys)\n assert results['img'].shape == self.img.shape\n assert results['gt'].shape == self.gt.shape\n assert self.check_flip(self.img, results['img'],\n results['flip_infos'][-1]['direction'])\n assert self.check_flip(self.gt, results['gt'],\n results['flip_infos'][-1]['direction'])\n assert repr(flip) == flip.__class__.__name__ + (\n f\"(keys={['img', 'gt']}, flip_ratio=1, \"\n f\"direction={results['flip_infos'][-1]['direction']})\")\n\n # flip a list\n # horizontal\n flip = Flip(keys=['img', 'gt'], flip_ratio=1, direction='horizontal')\n results = dict(\n img=[self.img, np.copy(self.img)],\n gt=[self.gt, np.copy(self.gt)],\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n flip_rlt = flip(copy.deepcopy(results))\n assert self.check_keys_contain(flip_rlt.keys(), target_keys)\n assert self.check_flip(self.img, flip_rlt['img'][0],\n flip_rlt['flip_infos'][-1]['direction'])\n assert self.check_flip(self.gt, flip_rlt['gt'][0],\n flip_rlt['flip_infos'][-1]['direction'])\n np.testing.assert_almost_equal(flip_rlt['gt'][0], flip_rlt['gt'][1])\n np.testing.assert_almost_equal(flip_rlt['img'][0], flip_rlt['img'][1])\n\n # vertical\n flip = Flip(keys=['img', 'gt'], flip_ratio=1, direction='vertical')\n flip_rlt = flip(copy.deepcopy(results))\n assert self.check_keys_contain(flip_rlt.keys(), target_keys)\n assert self.check_flip(self.img, flip_rlt['img'][0],\n flip_rlt['flip_infos'][-1]['direction'])\n assert self.check_flip(self.gt, flip_rlt['gt'][0],\n flip_rlt['flip_infos'][-1]['direction'])\n np.testing.assert_almost_equal(flip_rlt['gt'][0], flip_rlt['gt'][1])\n np.testing.assert_almost_equal(flip_rlt['img'][0], flip_rlt['img'][1])\n\n # no flip\n flip = Flip(keys=['img', 'gt'], flip_ratio=0, direction='vertical')\n results = flip(copy.deepcopy(results))\n assert self.check_keys_contain(results.keys(), target_keys)\n np.testing.assert_almost_equal(results['gt'][0], self.gt)\n np.testing.assert_almost_equal(results['img'][0], self.img)\n np.testing.assert_almost_equal(results['gt'][0], results['gt'][1])\n np.testing.assert_almost_equal(results['img'][0], results['img'][1])\n\n def test_random_rotation(self):\n with pytest.raises(ValueError):\n RandomRotation(None, degrees=-10.0)\n with pytest.raises(TypeError):\n RandomRotation(None, degrees=('0.0', '45.0'))\n\n target_keys = ['degrees']\n results = copy.deepcopy(self.results)\n\n random_rotation = RandomRotation(['ori_img'], degrees=(0, 45))\n random_rotation_results = random_rotation(results)\n assert self.check_keys_contain(random_rotation_results.keys(),\n target_keys)\n assert random_rotation_results['ori_img'].shape == (256, 256, 3)\n assert random_rotation_results['degrees'] == (0, 45)\n assert repr(random_rotation) == random_rotation.__class__.__name__ + (\n \"(keys=['ori_img'], degrees=(0, 45))\")\n\n # test single degree integer\n random_rotation = RandomRotation(['ori_img'], degrees=45)\n random_rotation_results = random_rotation(results)\n assert self.check_keys_contain(random_rotation_results.keys(),\n target_keys)\n assert random_rotation_results['ori_img'].shape == (256, 256, 3)\n assert random_rotation_results['degrees'] == (-45, 45)\n\n # test image dim == 2\n grey_scale_ori_img = np.random.rand(256, 256).astype(np.float32)\n results = dict(ori_img=grey_scale_ori_img.copy())\n random_rotation = RandomRotation(['ori_img'], degrees=(0, 45))\n random_rotation_results = random_rotation(results)\n assert self.check_keys_contain(random_rotation_results.keys(),\n target_keys)\n assert random_rotation_results['ori_img'].shape == (256, 256, 1)\n\n def test_random_transposehw(self):\n\n trans = RandomTransposeHW(keys='img', transpose_ratio=1)\n assert trans.keys == ['img']\n\n results = self.results.copy()\n target_keys = ['img', 'gt', 'transpose']\n transposehw = RandomTransposeHW(keys=['img', 'gt'], transpose_ratio=1)\n results = transposehw(results)\n assert self.check_keys_contain(results.keys(), target_keys)\n assert self.check_transposehw(self.img, results['img'])\n assert self.check_transposehw(self.gt, results['gt'])\n assert results['img'].shape == (32, 64, 3)\n assert results['gt'].shape == (128, 256, 3)\n\n assert repr(transposehw) == transposehw.__class__.__name__ + (\n f\"(keys={['img', 'gt']}, transpose_ratio=1)\")\n\n # for image list\n ori_results = dict(\n img=[self.img, np.copy(self.img)],\n gt=[self.gt, np.copy(self.gt)],\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n target_keys = ['img', 'gt', 'transpose']\n transposehw = RandomTransposeHW(keys=['img', 'gt'], transpose_ratio=1)\n results = transposehw(ori_results.copy())\n assert self.check_keys_contain(results.keys(), target_keys)\n assert self.check_transposehw(self.img, results['img'][0])\n assert self.check_transposehw(self.gt, results['gt'][1])\n np.testing.assert_almost_equal(results['gt'][0], results['gt'][1])\n np.testing.assert_almost_equal(results['img'][0], results['img'][1])\n\n # no transpose\n target_keys = ['img', 'gt', 'transpose']\n transposehw = RandomTransposeHW(keys=['img', 'gt'], transpose_ratio=0)\n results = transposehw(ori_results.copy())\n assert self.check_keys_contain(results.keys(), target_keys)\n np.testing.assert_almost_equal(results['gt'][0], self.gt)\n np.testing.assert_almost_equal(results['img'][0], self.img)\n np.testing.assert_almost_equal(results['gt'][0], results['gt'][1])\n np.testing.assert_almost_equal(results['img'][0], results['img'][1])\n\n def test_resize(self):\n with pytest.raises(AssertionError):\n Resize([], scale=0.5)\n with pytest.raises(AssertionError):\n Resize(['gt_img'], size_factor=32, scale=0.5)\n with pytest.raises(AssertionError):\n Resize(['gt_img'], size_factor=32, keep_ratio=True)\n with pytest.raises(AssertionError):\n Resize(['gt_img'], max_size=32, size_factor=None)\n with pytest.raises(ValueError):\n Resize(['gt_img'], scale=-0.5)\n with pytest.raises(TypeError):\n Resize(['gt_img'], (0.4, 0.2))\n with pytest.raises(TypeError):\n Resize(['gt_img'], dict(test=None))\n\n target_keys = ['alpha']\n\n alpha = np.random.rand(240, 320).astype(np.float32)\n results = dict(alpha=alpha)\n resize = Resize(keys=['alpha'], size_factor=32, max_size=None)\n resize_results = resize(results)\n assert self.check_keys_contain(resize_results.keys(), target_keys)\n assert resize_results['alpha'].shape == (224, 320, 1)\n resize = Resize(keys=['alpha'], size_factor=32, max_size=320)\n resize_results = resize(results)\n assert self.check_keys_contain(resize_results.keys(), target_keys)\n assert resize_results['alpha'].shape == (224, 320, 1)\n\n resize = Resize(keys=['alpha'], size_factor=32, max_size=200)\n resize_results = resize(results)\n assert self.check_keys_contain(resize_results.keys(), target_keys)\n assert resize_results['alpha'].shape == (192, 192, 1)\n\n resize = Resize(['gt_img'], (-1, 200))\n assert resize.scale == (np.inf, 200)\n\n results = dict(gt_img=self.results['ori_img'].copy())\n resize_keep_ratio = Resize(['gt_img'], scale=0.5, keep_ratio=True)\n results = resize_keep_ratio(results)\n assert results['gt_img'].shape[:2] == (128, 128)\n assert results['scale_factor'] == 0.5\n\n results = dict(gt_img=self.results['ori_img'].copy())\n resize_keep_ratio = Resize(['gt_img'],\n scale=(128, 128),\n keep_ratio=False)\n results = resize_keep_ratio(results)\n assert results['gt_img'].shape[:2] == (128, 128)\n\n # test input with shape (256, 256)\n results = dict(\n gt_img=self.results['ori_img'][..., 0].copy(), alpha=alpha)\n resize = Resize(['gt_img', 'alpha'],\n scale=(128, 128),\n keep_ratio=False,\n output_keys=['img', 'beta'])\n results = resize(results)\n assert results['gt_img'].shape == (256, 256)\n assert results['img'].shape == (128, 128, 1)\n assert results['alpha'].shape == (240, 320)\n assert results['beta'].shape == (128, 128, 1)\n\n name_ = str(resize_keep_ratio)\n assert name_ == resize_keep_ratio.__class__.__name__ + (\n \"(keys=['gt_img'], output_keys=['gt_img'], \"\n 'scale=(128, 128), '\n f'keep_ratio={False}, size_factor=None, '\n 'max_size=None, interpolation=bilinear)')\n\n # test input with shape (256, 256) + out keys and metainfo copy\n results = dict(\n gt_img=self.results['ori_img'][..., 0].copy(),\n alpha=alpha,\n ori_alpha_shape=[3, 3],\n gt_img_channel_order='rgb',\n alpha_color_type='grayscale')\n resize = Resize(['gt_img', 'alpha'],\n scale=(128, 128),\n keep_ratio=False,\n output_keys=['img', 'beta'])\n results = resize(results)\n assert results['gt_img'].shape == (256, 256)\n assert results['img'].shape == (128, 128, 1)\n assert results['alpha'].shape == (240, 320)\n assert results['beta'].shape == (128, 128, 1)\n assert results['ori_beta_shape'] == [3, 3]\n assert results['img_channel_order'] == 'rgb'\n assert results['beta_color_type'] == 'grayscale'\n\n name_ = str(resize_keep_ratio)\n assert name_ == resize_keep_ratio.__class__.__name__ + (\n \"(keys=['gt_img'], output_keys=['gt_img'], \"\n 'scale=(128, 128), '\n f'keep_ratio={False}, size_factor=None, '\n 'max_size=None, interpolation=bilinear)')\n\n\ndef test_random_long_edge_crop():\n results = dict(img=np.random.rand(256, 128, 3).astype(np.float32))\n crop = RandomCropLongEdge(['img'])\n results = crop(results)\n assert results['img'].shape == (128, 128, 3)\n\n repr_str = crop.__class__.__name__\n repr_str += (f'(keys={crop.keys})')\n\n assert str(crop) == repr_str\n\n\ndef test_center_long_edge_crop():\n results = dict(img=np.random.rand(256, 128, 3).astype(np.float32))\n crop = CenterCropLongEdge(['img'])\n results = crop(results)\n assert results['img'].shape == (128, 128, 3)\n\n\ndef test_numpy_pad():\n results = dict(img=np.zeros((5, 5, 1)))\n\n pad = NumpyPad(['img'], ((2, 2), (0, 0), (0, 0)))\n results = pad(results)\n assert results['img'].shape == (9, 5, 1)\n\n repr_str = pad.__class__.__name__\n repr_str += (\n f'(keys={pad.keys}, padding={pad.padding}, kwargs={pad.kwargs})')\n\n assert str(pad) == repr_str\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_blur_kernels.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.datasets.transforms import blur_kernels\n\n\ndef test_blur_kernels():\n kernels = [\n 'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso',\n 'plateau_aniso', 'sinc'\n ]\n for kernel_type in kernels:\n kernel = blur_kernels.random_mixed_kernels([kernel_type], [1], 5)\n assert kernel.shape == (5, 5)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_crop.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport os.path as osp\nimport unittest\n\nimport cv2\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.datasets.transforms import (Crop, CropLike, FixedCrop,\n InstanceCrop, ModCrop,\n PairedRandomCrop, RandomResizedCrop)\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n cls.results = dict()\n cls.img_gt = np.random.rand(256, 128, 3).astype(np.float32)\n cls.img_lq = np.random.rand(64, 32, 3).astype(np.float32)\n\n cls.results = dict(\n img=cls.img_lq,\n gt=cls.img_gt,\n scale=4,\n lq_path='fake_lq_path',\n gt_path='fake_gt_path')\n\n cls.results['img'] = np.random.rand(256, 256, 3).astype(np.float32)\n\n cls.results['img_a'] = np.random.rand(286, 286, 3).astype(np.float32)\n cls.results['img_b'] = np.random.rand(286, 286, 3).astype(np.float32)\n\n def test_crop(self):\n with pytest.raises(TypeError):\n Crop(['img'], (0.23, 0.1))\n\n # test center crop\n results = copy.deepcopy(self.results)\n center_crop = Crop(['img'], crop_size=(128, 128), random_crop=False)\n results = center_crop(results)\n assert results['img_crop_bbox'] == [64, 64, 128, 128]\n assert np.array_equal(self.results['img'][64:192, 64:192, :],\n results['img'])\n\n # test random crop\n results = copy.deepcopy(self.results)\n random_crop = Crop(['img'], crop_size=(128, 128), random_crop=True)\n results = random_crop(results)\n assert 0 <= results['img_crop_bbox'][0] <= 128\n assert 0 <= results['img_crop_bbox'][1] <= 128\n assert results['img_crop_bbox'][2] == 128\n assert results['img_crop_bbox'][3] == 128\n\n # test random crop for larger size than the original shape\n results = copy.deepcopy(self.results)\n random_crop = Crop(['img'], crop_size=(512, 512), random_crop=True)\n results = random_crop(results)\n assert np.array_equal(self.results['img'], results['img'])\n assert str(random_crop) == (\n random_crop.__class__.__name__ +\n \"keys=['img'], crop_size=(512, 512), random_crop=True\")\n\n # test center crop for size larger than original shape\n results = copy.deepcopy(self.results)\n center_crop = Crop(['img'],\n crop_size=(512, 512),\n random_crop=False,\n is_pad_zeros=True)\n gt_pad = np.pad(\n copy.deepcopy(self.results)['img'],\n ((128, 128), (128, 128), (0, 0)),\n mode='constant',\n constant_values=0)\n results = center_crop(results)\n assert results['img_crop_bbox'] == [128, 128, 512, 512]\n assert np.array_equal(gt_pad, results['img'])\n\n def test_random_resized_crop(self):\n with pytest.raises(TypeError):\n RandomResizedCrop(['img'], crop_size=(0.23, 0.1))\n with pytest.raises(TypeError):\n RandomResizedCrop(['img'], crop_size=(128, 128), scale=(1, 1))\n with pytest.raises(TypeError):\n RandomResizedCrop(['img'],\n crop_size=(128, 128),\n scale=(0.5, 0.5),\n ratio=(1, 2))\n\n # test random crop\n results = copy.deepcopy(self.results)\n random_resized_crop = RandomResizedCrop(['img'], crop_size=(128, 128))\n results = random_resized_crop(results)\n assert 0 <= results['img_crop_bbox'][0] <= 256\n assert 0 <= results['img_crop_bbox'][1] <= 256\n assert results['img_crop_bbox'][2] <= 256\n assert results['img_crop_bbox'][3] <= 256\n assert results['img'].shape == (128, 128, 3)\n\n # test random crop with integer crop size\n results = copy.deepcopy(self.results)\n random_resized_crop = RandomResizedCrop(['img'], crop_size=128)\n results = random_resized_crop(results)\n assert 0 <= results['img_crop_bbox'][0] <= 256\n assert 0 <= results['img_crop_bbox'][1] <= 256\n assert results['img_crop_bbox'][2] <= 256\n assert results['img_crop_bbox'][3] <= 256\n assert results['img'].shape == (128, 128, 3)\n assert str(random_resized_crop) == (\n random_resized_crop.__class__.__name__ +\n \"(keys=['img'], crop_size=(128, 128), scale=(0.08, 1.0), \"\n f'ratio={(3. / 4., 4. / 3.)}, interpolation=bilinear)')\n\n # test random crop for larger size than the original shape\n results = copy.deepcopy(self.results)\n random_resized_crop = RandomResizedCrop(['img'], crop_size=(512, 512))\n results = random_resized_crop(results)\n assert results['img'].shape == (512, 512, 3)\n assert str(random_resized_crop) == (\n random_resized_crop.__class__.__name__ +\n \"(keys=['img'], crop_size=(512, 512), scale=(0.08, 1.0), \"\n f'ratio={(3. / 4., 4. / 3.)}, interpolation=bilinear)')\n\n # test center crop for in_ratio < min(self.ratio)\n results = copy.deepcopy(self.results)\n center_crop = RandomResizedCrop(['img'],\n crop_size=(128, 128),\n ratio=(100.0, 200.0))\n results = center_crop(results)\n assert results['img_crop_bbox'] == [126, 0, 256, 3]\n assert results['img'].shape == (128, 128, 3)\n\n # test center crop for in_ratio > max(self.ratio)\n results = copy.deepcopy(self.results)\n center_crop = RandomResizedCrop(['img'],\n crop_size=(128, 128),\n ratio=(0.01, 0.02))\n results = center_crop(results)\n assert results['img_crop_bbox'] == [0, 125, 5, 256]\n assert results['img'].shape == (128, 128, 3)\n\n def test_fixed_crop(self):\n with pytest.raises(TypeError):\n FixedCrop(['img_a', 'img_b'], (0.23, 0.1))\n with pytest.raises(TypeError):\n FixedCrop(['img_a', 'img_b'], (256, 256), (0, 0.1))\n\n # test shape consistency\n results = copy.deepcopy(self.results)\n fixed_crop = FixedCrop(['img_a', 'img'], crop_size=(128, 128))\n with pytest.raises(ValueError):\n results = fixed_crop(results)\n\n # test sequence\n results = copy.deepcopy(self.results)\n results['img_a'] = [results['img_a'], results['img_a']]\n results['img_b'] = [results['img_b'], results['img_b']]\n fixed_crop = FixedCrop(['img_a', 'img_b'], crop_size=(128, 128))\n results = fixed_crop(results)\n for img in results['img_a']:\n assert img.shape == (128, 128, 3)\n for img in results['img_b']:\n assert img.shape == (128, 128, 3)\n\n # test given pos crop\n results = copy.deepcopy(self.results)\n given_pos_crop = FixedCrop(['img_a', 'img_b'],\n crop_size=(256, 256),\n crop_pos=(1, 1))\n results = given_pos_crop(results)\n assert results['img_a_crop_bbox'] == [1, 1, 256, 256]\n assert results['img_b_crop_bbox'] == [1, 1, 256, 256]\n assert np.array_equal(self.results['img_a'][1:257, 1:257, :],\n results['img_a'])\n assert np.array_equal(self.results['img_b'][1:257, 1:257, :],\n results['img_b'])\n\n # test given pos crop if pos > suitable pos\n results = copy.deepcopy(self.results)\n given_pos_crop = FixedCrop(['img_a', 'img_b'],\n crop_size=(256, 256),\n crop_pos=(280, 280))\n results = given_pos_crop(results)\n assert results['img_a_crop_bbox'] == [280, 280, 6, 6]\n assert results['img_b_crop_bbox'] == [280, 280, 6, 6]\n assert np.array_equal(self.results['img_a'][280:, 280:, :],\n results['img_a'])\n assert np.array_equal(self.results['img_b'][280:, 280:, :],\n results['img_b'])\n assert str(given_pos_crop) == (\n given_pos_crop.__class__.__name__ +\n \"keys=['img_a', 'img_b'], crop_size=(256, 256), \" +\n 'crop_pos=(280, 280)')\n\n # test random initialized fixed crop\n results = copy.deepcopy(self.results)\n random_fixed_crop = FixedCrop(['img_a', 'img_b'],\n crop_size=(256, 256),\n crop_pos=None)\n results = random_fixed_crop(results)\n assert 0 <= results['img_a_crop_bbox'][0] <= 30\n assert 0 <= results['img_a_crop_bbox'][1] <= 30\n assert results['img_a_crop_bbox'][2] == 256\n assert results['img_a_crop_bbox'][3] == 256\n x_offset, y_offset, crop_w, crop_h = results['img_a_crop_bbox']\n assert x_offset == results['img_b_crop_bbox'][0]\n assert y_offset == results['img_b_crop_bbox'][1]\n assert crop_w == results['img_b_crop_bbox'][2]\n assert crop_h == results['img_b_crop_bbox'][3]\n assert np.array_equal(\n self.results['img_a'][y_offset:y_offset + crop_h,\n x_offset:x_offset + crop_w, :],\n results['img_a'])\n assert np.array_equal(\n self.results['img_b'][y_offset:y_offset + crop_h,\n x_offset:x_offset + crop_w, :],\n results['img_b'])\n\n # test given pos crop for lager size than the original shape\n results = copy.deepcopy(self.results)\n given_pos_crop = FixedCrop(['img_a', 'img_b'],\n crop_size=(512, 512),\n crop_pos=(1, 1))\n results = given_pos_crop(results)\n assert results['img_a_crop_bbox'] == [1, 1, 285, 285]\n assert results['img_b_crop_bbox'] == [1, 1, 285, 285]\n assert np.array_equal(self.results['img_a'][1:, 1:, :],\n results['img_a'])\n assert np.array_equal(self.results['img_b'][1:, 1:, :],\n results['img_b'])\n assert str(given_pos_crop) == (\n given_pos_crop.__class__.__name__ +\n \"keys=['img_a', 'img_b'], crop_size=(512, 512), crop_pos=(1, 1)\")\n\n # test random initialized fixed crop for lager size\n # than the original shape\n results = copy.deepcopy(self.results)\n random_fixed_crop = FixedCrop(['img_a', 'img_b'],\n crop_size=(512, 512),\n crop_pos=None)\n results = random_fixed_crop(results)\n assert results['img_a_crop_bbox'] == [0, 0, 286, 286]\n assert results['img_b_crop_bbox'] == [0, 0, 286, 286]\n assert np.array_equal(self.results['img_a'], results['img_a'])\n assert np.array_equal(self.results['img_b'], results['img_b'])\n assert str(random_fixed_crop) == (\n random_fixed_crop.__class__.__name__ +\n \"keys=['img_a', 'img_b'], crop_size=(512, 512), crop_pos=None\")\n\n def test_modcrop(self):\n # color image\n results = dict(gt=np.random.randn(257, 258, 3), scale=4)\n modcrop = ModCrop()\n results = modcrop(results)\n assert results['gt'].shape == (256, 256, 3)\n\n # gray image\n results = dict(gt=np.random.randn(257, 258), scale=4)\n results = modcrop(results)\n assert results['gt'].shape == (256, 256)\n\n # Wrong img ndim\n with pytest.raises(ValueError):\n results = dict(gt=np.random.randn(1, 257, 258, 3), scale=4)\n results = modcrop(results)\n\n assert repr(modcrop) == (\n modcrop.__class__.__name__ + f'(key={modcrop.key})')\n\n def test_paired_random_crop(self):\n results = dict(\n gt=np.random.randn(256, 128, 3),\n img=np.random.randn(64, 32, 3),\n scale=4,\n gt_path='fake_gt_path',\n img_path='fake_lq_path')\n pairedrandomcrop = PairedRandomCrop(128)\n results = pairedrandomcrop(results)\n assert results['gt'].shape == (128, 128, 3)\n assert results['img'].shape == (32, 32, 3)\n\n # Scale mismatches. GT (h, w) is not {scale} multiplication of LQ's.\n with pytest.raises(ValueError):\n results = dict(\n gt=np.random.randn(128, 128, 3),\n img=np.random.randn(64, 64, 3),\n scale=4,\n gt_path='fake_gt_path',\n img_path='fake_lq_path')\n results = pairedrandomcrop(results)\n\n # LQ (h, w) is smaller than patch size.\n with pytest.raises(ValueError):\n results = dict(\n gt=np.random.randn(32, 32, 3),\n img=np.random.randn(8, 8, 3),\n scale=4,\n gt_path='fake_gt_path',\n img_path='fake_lq_path')\n results = pairedrandomcrop(results)\n\n assert repr(pairedrandomcrop) == (\n pairedrandomcrop.__class__.__name__ +\n (f'(gt_patch_size={pairedrandomcrop.gt_patch_size}, '\n f'lq_key={pairedrandomcrop.lq_key}, '\n f'gt_key={pairedrandomcrop.gt_key})'))\n\n # for image list\n results = dict(\n img=[self.img_lq, self.img_lq],\n gt=[self.img_gt, self.img_gt],\n scale=4,\n img_path='fake_lq_path',\n gt_path='fake_gt_path')\n pairedrandomcrop = PairedRandomCrop(128)\n results = pairedrandomcrop(results)\n for v in results['gt']:\n assert v.shape == (128, 128, 3)\n for v in results['img']:\n assert v.shape == (32, 32, 3)\n np.testing.assert_almost_equal(results['gt'][0], results['gt'][1])\n np.testing.assert_almost_equal(results['img'][0], results['img'][1])\n\n\ndef test_crop_like():\n img = np.uint8(np.random.randn(480, 640, 3) * 255)\n img_ref = np.uint8(np.random.randn(512, 512, 3) * 255)\n\n inputs = dict(gt=img, ref=img_ref)\n crop_like = CropLike(target_key='gt', reference_key='ref')\n results = crop_like(inputs)\n assert set(list(results.keys())) == set(['gt', 'ref'])\n assert repr(crop_like) == (\n crop_like.__class__.__name__ +\n f' target_key={crop_like.target_key}, ' +\n f'reference_key={crop_like.reference_key}')\n assert results['gt'].shape == (512, 512, 3)\n sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512]))\n assert sum_diff < 1e-6\n\n inputs = dict(gt=img, ref=img_ref[:, :, 0])\n crop_like = CropLike(target_key='gt', reference_key='ref')\n results = crop_like(inputs)\n assert set(list(results.keys())) == set(['gt', 'ref'])\n assert results['gt'].shape == (512, 512, 3)\n sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512]))\n assert sum_diff < 1e-6\n\n inputs = dict(gt=img[:, :, 0], ref=img_ref)\n crop_like = CropLike(target_key='gt', reference_key='ref')\n results = crop_like(inputs)\n assert set(list(results.keys())) == set(['gt', 'ref'])\n assert results['gt'].shape == (512, 512)\n sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512, 0]))\n assert sum_diff < 1e-6\n\n\ndef test_instance_crop():\n\n if not torch.cuda.is_available():\n # RoI pooling only support in GPU\n return unittest.skip('test requires GPU and torch+cuda')\n\n croper = InstanceCrop(\n key='img',\n finesize=256,\n box_num_upbound=2,\n config_file='mmdet::mask_rcnn/'\n 'mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py') # noqa\n\n img_path = osp.join(\n osp.dirname(__file__), '..', '..',\n 'data/image/img_root/horse/horse.jpeg')\n img = cv2.imread(img_path)\n data = dict(img=img, ori_img_shape=img.shape, img_channel_order='rgb')\n\n results = croper(data)\n\n assert 'empty_box' in results\n if results['empty_box']:\n cropped_img = results['cropped_img']\n assert len(cropped_img) == 0\n assert len(cropped_img) <= 2\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_fgbg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nfrom mmengine.fileio import load\n\nfrom mmagic.datasets.transforms import (CompositeFg, MergeFgAndBg, PerturbBg,\n RandomJitter, RandomLoadResizeBg)\n\ntest_root = Path(__file__).parent.parent.parent\ndata_root = test_root / 'data' / 'matting_dataset'\n\n\ndef assert_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n assert set(target_keys).issubset(set(result_keys))\n\n\ndef test_composite_fg():\n target_keys = ['alpha', 'fg', 'bg']\n\n fg = np.random.rand(32, 32, 3).astype(np.float32)\n bg = np.random.rand(32, 32, 3).astype(np.float32)\n alpha = np.random.rand(32, 32, 1).astype(np.float32)\n results = dict(alpha=alpha, fg=fg, bg=bg)\n # use merged dir as fake fg dir, trimap dir as fake alpha dir for unittest\n composite_fg = CompositeFg(\n [str(data_root / 'fg'),\n str(data_root / 'merged')],\n [str(data_root / 'alpha'),\n str(data_root / 'trimap')])\n correct_fg_list = [\n str(data_root / 'fg' / 'GT05.jpg'),\n str(data_root / 'merged' / 'GT05.jpg')\n ]\n correct_alpha_list = [\n str(data_root / 'alpha' / 'GT05.jpg'),\n str(data_root / 'trimap' / 'GT05.png')\n ]\n assert composite_fg.fg_list == correct_fg_list\n assert composite_fg.alpha_list == correct_alpha_list\n for _ in range(3): # to test randomness\n composite_fg_results = composite_fg(results)\n assert_keys_contain(composite_fg_results.keys(), target_keys)\n assert composite_fg_results['fg'].shape == (32, 32, 3)\n assert composite_fg_results['alpha'].shape == (32, 32, 1)\n\n fg = np.random.rand(32, 32, 3).astype(np.float32)\n bg = np.random.rand(32, 32, 3).astype(np.float32)\n alpha = np.random.rand(32, 32, 1).astype(np.float32)\n results = dict(alpha=alpha, fg=fg, bg=bg)\n composite_fg = CompositeFg(\n str(data_root / 'fg'),\n str(data_root / 'alpha'),\n interpolation='bilinear')\n for _ in range(3): # to test randomness\n composite_fg_results = composite_fg(results)\n assert_keys_contain(composite_fg_results.keys(), target_keys)\n assert composite_fg_results['fg'].shape == (32, 32, 3)\n assert composite_fg_results['alpha'].shape == (32, 32, 1)\n\n _fg_dirs = str(data_root / 'fg')\n _alpha_dirs = str(data_root / 'alpha')\n strs = (f\"(fg_dirs=['{_fg_dirs}'], \"\n f\"alpha_dirs=['{_alpha_dirs}'], \"\n \"interpolation='bilinear')\")\n\n assert repr(composite_fg) == 'CompositeFg' + strs.replace('\\\\', '\\\\\\\\')\n\n\ndef test_merge_fg_and_bg():\n target_keys = ['fg', 'bg', 'alpha', 'merged']\n\n fg = np.random.randn(32, 32, 3)\n bg = np.random.randn(32, 32, 3)\n alpha = np.random.randn(32, 32, 1)\n results = dict(fg=fg, bg=bg, alpha=alpha)\n merge_fg_and_bg = MergeFgAndBg()\n merge_fg_and_bg_results = merge_fg_and_bg(results)\n\n assert_keys_contain(merge_fg_and_bg_results.keys(), target_keys)\n assert merge_fg_and_bg_results['merged'].shape == fg.shape\n assert merge_fg_and_bg_results['alpha'].shape == (32, 32, 1)\n\n\ndef test_perturb_bg():\n with pytest.raises(ValueError):\n # gammma_ratio must be a float between [0, 1]\n PerturbBg(-0.5)\n\n with pytest.raises(ValueError):\n # gammma_ratio must be a float between [0, 1]\n PerturbBg(1.1)\n\n target_keys = ['bg', 'noisy_bg']\n # set a random seed to make sure the test goes through every branch\n np.random.seed(123)\n\n img_shape = (32, 32, 3)\n results = dict(bg=np.random.randint(0, 255, img_shape))\n perturb_bg = PerturbBg(0.6)\n perturb_bg_results = perturb_bg(results)\n assert_keys_contain(perturb_bg_results.keys(), target_keys)\n assert perturb_bg_results['noisy_bg'].shape == img_shape\n\n img_shape = (32, 32, 3)\n results = dict(bg=np.random.randint(0, 255, img_shape))\n perturb_bg = PerturbBg(0.6)\n perturb_bg_results = perturb_bg(results)\n assert_keys_contain(perturb_bg_results.keys(), target_keys)\n assert perturb_bg_results['noisy_bg'].shape == img_shape\n\n repr_str = perturb_bg.__class__.__name__ + '(gamma_ratio=0.6)'\n assert repr(perturb_bg) == repr_str\n\n\ndef test_random_jitter():\n with pytest.raises(AssertionError):\n RandomJitter(-40)\n\n with pytest.raises(AssertionError):\n RandomJitter((-40, 40, 40))\n\n target_keys = ['fg']\n\n fg = np.random.rand(240, 320, 3).astype(np.float32)\n alpha = np.random.rand(240, 320, 1).astype(np.float32)\n results = dict(fg=fg.copy(), alpha=alpha)\n random_jitter = RandomJitter(40)\n random_jitter_results = random_jitter(results)\n assert_keys_contain(random_jitter_results.keys(), target_keys)\n assert random_jitter_results['fg'].shape == (240, 320, 3)\n\n fg = np.random.rand(240, 320, 3).astype(np.float32)\n alpha = np.random.rand(240, 320, 1).astype(np.float32)\n results = dict(fg=fg.copy(), alpha=alpha)\n random_jitter = RandomJitter((-50, 50))\n random_jitter_results = random_jitter(results)\n assert_keys_contain(random_jitter_results.keys(), target_keys)\n assert random_jitter_results['fg'].shape == (240, 320, 3)\n\n assert repr(random_jitter) == random_jitter.__class__.__name__ + (\n 'hue_range=(-50, 50)')\n\n\ndef test_random_load_resize_bg():\n ann_file = data_root / 'ann_old.json'\n bg_dir = data_root / 'bg'\n data_infos = load(ann_file)\n results = dict()\n for data_info in data_infos:\n for key in data_info:\n results[key] = str(data_root / data_info[key])\n target_keys = ['bg']\n\n results = dict(fg=np.random.rand(128, 128))\n random_load_bg = RandomLoadResizeBg(bg_dir=bg_dir)\n for _ in range(2):\n random_load_bg_results = random_load_bg(results)\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n assert set(target_keys).issubset(set(random_load_bg_results.keys()))\n assert isinstance(random_load_bg_results['bg'], np.ndarray)\n assert random_load_bg_results['bg'].shape == (128, 128, 3)\n\n assert repr(\n random_load_bg) == f\"RandomLoadResizeBg(bg_dir='{str(bg_dir)}')\"\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_formatting.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nfrom mmcv.transforms import to_tensor\n\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.structures.data_sample import DataSample\n\n\ndef assert_tensor_equal(img, ref_img, ratio_thr=0.999):\n \"\"\"Check if img and ref_img are matched approximately.\"\"\"\n assert img.shape == ref_img.shape\n assert img.dtype == ref_img.dtype\n area = ref_img.shape[-1] * ref_img.shape[-2]\n diff = torch.abs(img - ref_img)\n assert torch.sum(diff <= 1) / float(area) > ratio_thr\n\n\ndef test_pack_inputs():\n\n pack_inputs = PackInputs(meta_keys='a', data_keys='numpy')\n assert repr(pack_inputs) == 'PackInputs'\n\n ori_results = dict(\n img=np.random.rand(64, 64, 3),\n gt=[np.random.rand(64, 61, 3),\n np.random.rand(64, 61, 3)],\n img_lq=np.random.rand(64, 64, 3),\n ref=np.random.rand(64, 62, 3),\n ref_lq=np.random.rand(64, 62, 3),\n mask=np.random.rand(64, 63, 3),\n gt_heatmap=np.random.rand(64, 65, 3),\n gt_unsharp=np.random.rand(64, 65, 3),\n merged=np.random.rand(64, 64, 3),\n trimap=np.random.rand(64, 66, 3),\n alpha=np.random.rand(64, 67, 3),\n fg=np.random.rand(64, 68, 3),\n bg=np.random.rand(64, 69, 3),\n img_shape=(64, 64),\n a='b',\n numpy=np.random.rand(48, 48, 3))\n\n results = ori_results.copy()\n\n packed_results = pack_inputs(results)\n\n target_keys = ['inputs', 'data_samples']\n assert set(target_keys).issubset(set(packed_results.keys()))\n\n data_sample = packed_results['data_samples']\n assert isinstance(data_sample, DataSample)\n\n assert data_sample.img_shape == (64, 64)\n assert data_sample.a == 'b'\n\n numpy_tensor = to_tensor(ori_results['numpy'])\n numpy_tensor = numpy_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.numpy, numpy_tensor)\n\n gt_tensors = [to_tensor(v) for v in ori_results['gt']]\n gt_tensors = [v.permute(2, 0, 1) for v in gt_tensors]\n gt_tensor = torch.stack(gt_tensors, dim=0)\n assert_tensor_equal(data_sample.gt_img, gt_tensor)\n\n img_lq_tensor = to_tensor(ori_results['ref'])\n img_lq_tensor = img_lq_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.ref_img, img_lq_tensor)\n\n ref_lq_tensor = to_tensor(ori_results['ref'])\n ref_lq_tensor = ref_lq_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.ref_img, ref_lq_tensor)\n\n ref_tensor = to_tensor(ori_results['ref'])\n ref_tensor = ref_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.ref_img, ref_tensor)\n\n mask_tensor = to_tensor(ori_results['mask'])\n mask_tensor = mask_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.mask, mask_tensor)\n\n gt_heatmap_tensor = to_tensor(ori_results['gt_heatmap'])\n gt_heatmap_tensor = gt_heatmap_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_heatmap, gt_heatmap_tensor)\n\n gt_unsharp_tensor = to_tensor(ori_results['gt_heatmap'])\n gt_unsharp_tensor = gt_unsharp_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_heatmap, gt_unsharp_tensor)\n\n gt_merged_tensor = to_tensor(ori_results['merged'])\n gt_merged_tensor = gt_merged_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_merged, gt_merged_tensor)\n\n trimap_tensor = to_tensor(ori_results['trimap'])\n trimap_tensor = trimap_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.trimap, trimap_tensor)\n\n gt_alpha_tensor = to_tensor(ori_results['alpha'])\n gt_alpha_tensor = gt_alpha_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_alpha, gt_alpha_tensor)\n\n gt_fg_tensor = to_tensor(ori_results['fg'])\n gt_fg_tensor = gt_fg_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_fg, gt_fg_tensor)\n\n gt_bg_tensor = to_tensor(ori_results['bg'])\n gt_bg_tensor = gt_bg_tensor.permute(2, 0, 1)\n assert_tensor_equal(data_sample.gt_bg, gt_bg_tensor)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_generate_assistant.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.datasets.transforms import (GenerateCoordinateAndCell,\n GenerateFacialHeatmap,\n LoadImageFromFile)\n\n\ndef test_generate_coordinate_and_cell():\n\n tensor1 = torch.randn((3, 64, 48))\n inputs1 = dict(lq=tensor1)\n coordinate1 = GenerateCoordinateAndCell(scale=3.1, target_size=(128, 96))\n results1 = coordinate1(inputs1)\n assert set(list(results1.keys())) == set(['lq', 'coord', 'cell'])\n assert repr(coordinate1) == (\n coordinate1.__class__.__name__ +\n f'(sample_quantity={coordinate1.sample_quantity}, ' +\n f'scale={coordinate1.scale}, ' +\n f'target_size={coordinate1.target_size}, ' +\n f'reshape_gt={coordinate1.reshape_gt})')\n\n tensor2 = torch.randn((3, 64, 48))\n inputs2 = dict(gt=tensor2)\n coordinate2 = GenerateCoordinateAndCell(\n sample_quantity=64 * 48, scale=3.1, target_size=(128, 96))\n results2 = coordinate2(inputs2)\n assert set(list(results2.keys())) == set(['gt', 'coord', 'cell'])\n assert results2['gt'].shape == (64 * 48, 3)\n\n inputs3 = dict()\n coordinate3 = GenerateCoordinateAndCell(\n sample_quantity=64 * 48, scale=3.1, target_size=(128, 96))\n results3 = coordinate3(inputs3)\n assert set(list(results3.keys())) == set(['coord', 'cell'])\n\n\ndef test_generate_facial_heatmap():\n\n results = dict(gt_path='tests/data/image/face/000001.png', key='000001')\n loader = LoadImageFromFile(key='gt', channel_order='rgb')\n results = loader(results)\n\n generate_heatmap = GenerateFacialHeatmap('gt', 256, 32)\n results1 = generate_heatmap(results)\n results1 = generate_heatmap(results)\n assert set(list(results1.keys())) == set([\n 'gt_path', 'gt', 'ori_gt_shape', 'gt_heatmap', 'key',\n 'gt_channel_order', 'gt_color_type'\n ])\n assert results1['gt_heatmap'].shape == (32, 32, 68)\n\n generate_heatmap = GenerateFacialHeatmap('gt', (256, 256), (32, 64))\n results2 = generate_heatmap(results)\n assert set(list(results2.keys())) == set([\n 'gt_path', 'gt', 'ori_gt_shape', 'gt_heatmap', 'key',\n 'gt_channel_order', 'gt_color_type'\n ])\n assert results2['gt_heatmap'].shape == (32, 64, 68)\n\n generate_heatmap = GenerateFacialHeatmap(\n 'gt', (256, 256), (32, 64), use_cache=False)\n results2 = generate_heatmap(results)\n assert set(list(results2.keys())) == set([\n 'gt_path', 'gt', 'ori_gt_shape', 'gt_heatmap', 'key',\n 'gt_channel_order', 'gt_color_type'\n ])\n assert results2['gt_heatmap'].shape == (32, 64, 68)\n\n assert repr(generate_heatmap) == (\n generate_heatmap.__class__.__name__ +\n f'(image_key={generate_heatmap.image_key}, ' +\n f'ori_size={generate_heatmap.ori_size}, ' +\n f'target_size={generate_heatmap.target_size}, ' +\n f'sigma={generate_heatmap.sigma}, '\n f'use_cache={generate_heatmap.use_cache})')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_generate_frame_indices.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport os.path as osp\n\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (GenerateFrameIndices,\n GenerateFrameIndiceswithPadding,\n GenerateSegmentIndices)\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n\n cls.results = dict()\n cls.gt = np.random.randint(0, 256, (256, 128, 3), dtype=np.uint8)\n cls.img = np.random.randint(0, 256, (64, 32, 3), dtype=np.uint8)\n\n cls.results = dict(\n img=cls.img,\n gt=cls.gt,\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n\n cls.results['ori_img'] = np.random.randint(\n 0, 256, (256, 256, 3), dtype=np.uint8)\n cls.results['mask'] = np.random.randint(\n 0, 256, (256, 256, 1), dtype=np.uint8)\n # cls.results['img_tensor'] = torch.rand((3, 256, 256))\n # cls.results['mask_tensor'] = torch.zeros((1, 256, 256))\n # cls.results['mask_tensor'][:, 50:150, 40:140] = 1.\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n\n return set(target_keys).issubset(set(result_keys))\n\n def test_frame_index_generator(self):\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key=osp.join('000', '00000010'),\n num_input_frames=3)\n target_keys = ['img_path', 'gt_path', 'key', 'interval']\n frame_index_generator = GenerateFrameIndices(\n interval_list=[1], frames_per_clip=99)\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n\n name_ = repr(frame_index_generator)\n assert name_ == frame_index_generator.__class__.__name__ + (\n '(interval_list=[1], frames_per_clip=99)')\n\n # index out of range\n frame_index_generator = GenerateFrameIndices(interval_list=[10])\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n\n # index out of range\n results['key'] = osp.join('000', '00000099')\n frame_index_generator = GenerateFrameIndices(interval_list=[2, 3])\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n\n def test_frame_index_generation_with_padding(self):\n with pytest.raises(ValueError):\n # Wrong padding mode\n GenerateFrameIndiceswithPadding(padding='fake')\n\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key=osp.join('000', '00000000'),\n sequence_length=100,\n num_input_frames=5)\n target_keys = ['img_path', 'gt_path', 'key']\n replicate_idx = [0, 0, 0, 1, 2]\n reflection_idx = [2, 1, 0, 1, 2]\n reflection_circle_idx = [4, 3, 0, 1, 2]\n circle_idx = [3, 4, 0, 1, 2]\n\n # replicate\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in replicate_idx\n ]\n gt_paths = [osp.join('fake_gt_root', '000', '00000000.png')]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='replicate')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # reflection\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in reflection_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='reflection')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # reflection_circle\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in reflection_circle_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='reflection_circle')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # circle\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in circle_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='circle')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key=osp.join('000', '00000099'),\n sequence_length=100,\n num_input_frames=5)\n target_keys = ['img_path', 'gt_path', 'key']\n replicate_idx = [97, 98, 99, 99, 99]\n reflection_idx = [97, 98, 99, 98, 97]\n reflection_circle_idx = [97, 98, 99, 96, 95]\n circle_idx = [97, 98, 99, 95, 96]\n\n # replicate\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in replicate_idx\n ]\n gt_paths = [osp.join('fake_gt_root', '000', '00000099.png')]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='replicate')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # reflection\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in reflection_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='reflection')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # reflection_circle\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in reflection_circle_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='reflection_circle')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n # circle\n img_paths = [\n osp.join('fake_img_root', '000', f'{v:08d}.png')\n for v in circle_idx\n ]\n frame_index_generator = GenerateFrameIndiceswithPadding(\n padding='circle')\n rlt = frame_index_generator(copy.deepcopy(results))\n assert rlt['img_path'] == img_paths\n assert rlt['gt_path'] == gt_paths\n\n name_ = repr(frame_index_generator)\n assert name_ == frame_index_generator.__class__.__name__ + (\n \"(padding='circle')\")\n\n def test_frame_index_generation_for_recurrent(self):\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key='000',\n num_input_frames=10,\n sequence_length=100)\n\n target_keys = [\n 'img_path',\n 'gt_path',\n 'key',\n 'interval',\n 'num_input_frames',\n 'sequence_length',\n ]\n frame_index_generator = GenerateSegmentIndices(interval_list=[1, 5, 9])\n rlt = frame_index_generator(copy.deepcopy(results))\n assert self.check_keys_contain(rlt.keys(), target_keys)\n\n name_ = repr(frame_index_generator)\n assert name_ == frame_index_generator.__class__.__name__ + (\n '(interval_list=[1, 5, 9])')\n\n # interval too large\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key='000',\n num_input_frames=11,\n sequence_length=100)\n\n frame_index_generator = GenerateSegmentIndices(interval_list=[10])\n with pytest.raises(ValueError):\n frame_index_generator(copy.deepcopy(results))\n\n # num_input_frames is None\n results = dict(\n img_path='fake_img_root',\n gt_path='fake_gt_root',\n key='000',\n num_input_frames=None,\n sequence_length=100)\n\n frame_index_generator = GenerateSegmentIndices(interval_list=[1])\n rlt = frame_index_generator(copy.deepcopy(results))\n assert len(rlt['img_path']) == 100\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_get_masked_image.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\n\nfrom mmagic.datasets.transforms import GetMaskedImage\n\n\ndef test_masked_img():\n img = np.random.rand(4, 4, 3).astype(np.float32)\n mask = np.zeros((4, 4, 1), dtype=np.float32)\n mask[1, 1] = 1\n\n results = dict(gt=img, mask=mask)\n get_masked_img = GetMaskedImage(zero_value=0)\n results = get_masked_img(results)\n masked_img = img * (1. - mask)\n assert np.array_equal(results['img'], masked_img)\n\n name_ = repr(get_masked_img)\n class_name = get_masked_img.__class__.__name__\n assert name_ == class_name + (\"(img_key='gt', mask_key='mask'\"\n \", out_key='img', zero_value=0)\")\n\n # test copy meta info\n results = dict(\n gt=img,\n mask=mask,\n ori_gt_shape=img.shape,\n gt_channel_order='rgb',\n gt_color_type='color')\n get_masked_img = GetMaskedImage(zero_value=0)\n results = get_masked_img(results)\n assert results['ori_img_shape'] == img.shape\n assert results['img_channel_order'] == 'rgb'\n assert results['img_color_type'] == 'color'\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_loading.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom pathlib import Path\n\nimport mmcv\nimport numpy as np\nimport pytest\nfrom mmengine.fileio.backends import LocalBackend\n\nfrom mmagic.datasets.transforms import (GetSpatialDiscountMask,\n LoadImageFromFile, LoadMask)\n\n\ndef test_load_image_from_file():\n\n path_baboon = Path(\n __file__).parent.parent.parent / 'data' / 'image' / 'gt' / 'baboon.png'\n img_baboon = mmcv.imread(str(path_baboon), flag='color')\n h, w, _ = img_baboon.shape\n\n path_baboon_x4 = Path(\n __file__\n ).parent.parent.parent / 'data' / 'image' / 'lq' / 'baboon_x4.png'\n img_baboon_x4 = mmcv.imread(str(path_baboon_x4), flag='color')\n\n # read gt image\n # input path is Path object\n results = dict(gt_path=path_baboon)\n config = dict(key='gt')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['gt'].shape == (h, w, 3)\n assert results['ori_gt_shape'] == (h, w, 3)\n np.testing.assert_almost_equal(results['gt'], img_baboon)\n assert results['gt_path'] == path_baboon\n # input path is str\n results = dict(gt_path=str(path_baboon))\n results = image_loader(results)\n assert results['gt'].shape == (h, w, 3)\n assert results['ori_gt_shape'] == (h, w, 3)\n np.testing.assert_almost_equal(results['gt'], img_baboon)\n assert results['gt_path'] == str(path_baboon)\n assert results['gt_channel_order'] == 'bgr'\n assert results['gt_color_type'] == 'color'\n\n # read input image\n # input path is Path object\n results = dict(img_path=path_baboon_x4)\n config = dict(key='img')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['img'].shape == (h // 4, w // 4, 3)\n np.testing.assert_almost_equal(results['img'], img_baboon_x4)\n assert results['img_path'] == path_baboon_x4\n # input path is str\n results = dict(img_path=str(path_baboon_x4))\n results = image_loader(results)\n assert results['img'].shape == (h // 4, w // 4, 3)\n np.testing.assert_almost_equal(results['img'], img_baboon_x4)\n assert results['img_path'] == str(path_baboon_x4)\n assert repr(image_loader) == (\n image_loader.__class__.__name__ +\n ('(key=img, color_type=color, channel_order=bgr, '\n 'imdecode_backend=None, use_cache=False, to_float32=False, '\n 'to_y_channel=False, save_original_img=False, '\n 'backend_args=None)'))\n assert isinstance(image_loader.file_backend, LocalBackend)\n\n # test save_original_img\n results = dict(img_path=path_baboon)\n config = dict(key='img', color_type='grayscale', save_original_img=True)\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['img'].shape == (h, w, 1)\n assert results['ori_img_shape'] == (h, w, 1)\n np.testing.assert_almost_equal(results['ori_img'], results['img'])\n assert id(results['ori_img']) != id(results['img'])\n assert results['img_channel_order'] == 'bgr'\n assert results['img_color_type'] == 'grayscale'\n\n # test: use_cache\n results = dict(gt_path=path_baboon)\n config = dict(key='gt', use_cache=True)\n image_loader = LoadImageFromFile(**config)\n assert image_loader.cache == dict()\n assert repr(image_loader) == (\n image_loader.__class__.__name__ +\n ('(key=gt, color_type=color, channel_order=bgr, '\n 'imdecode_backend=None, use_cache=True, to_float32=False, '\n 'to_y_channel=False, save_original_img=False, '\n 'backend_args=None)'))\n results = image_loader(results)\n assert image_loader.cache is not None\n assert str(path_baboon) in image_loader.cache\n assert results['gt'].shape == (h, w, 3)\n assert results['gt_path'] == path_baboon\n np.testing.assert_almost_equal(results['gt'], img_baboon)\n assert isinstance(image_loader.file_backend, LocalBackend)\n assert results['gt_channel_order'] == 'bgr'\n assert results['gt_color_type'] == 'color'\n\n # convert to y-channel (bgr2y)\n results = dict(gt_path=path_baboon)\n config = dict(key='gt', to_y_channel=True, to_float32=True)\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['gt'].shape == (h, w, 1)\n img_baboon_y = mmcv.bgr2ycbcr(img_baboon, y_only=True)\n img_baboon_y = np.expand_dims(img_baboon_y, axis=2)\n np.testing.assert_almost_equal(results['gt'], img_baboon_y)\n assert results['gt_path'] == path_baboon\n assert results['gt_channel_order'] == 'bgr'\n assert results['gt_color_type'] == 'color'\n\n # convert to y-channel (rgb2y)\n results = dict(gt_path=path_baboon)\n config = dict(\n key='gt', channel_order='rgb', to_y_channel=True, to_float32=True)\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['gt'].shape == (h, w, 1)\n img_baboon_y = mmcv.bgr2ycbcr(img_baboon, y_only=True)\n img_baboon_y = np.expand_dims(img_baboon_y, axis=2)\n np.testing.assert_almost_equal(results['gt'], img_baboon_y)\n\n # test frames\n # input path is Path object\n results = dict(gt_path=[path_baboon])\n config = dict(key='gt', save_original_img=True)\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['gt'][0].shape == (h, w, 3)\n assert results['ori_gt_shape'] == [(h, w, 3)]\n np.testing.assert_almost_equal(results['gt'][0], img_baboon)\n assert results['gt_path'] == [path_baboon]\n # input path is str\n results = dict(gt_path=[str(path_baboon)])\n results = image_loader(results)\n assert results['gt'][0].shape == (h, w, 3)\n assert results['ori_gt_shape'] == [(h, w, 3)]\n np.testing.assert_almost_equal(results['gt'][0], img_baboon)\n assert results['gt_path'] == [str(path_baboon)]\n\n # test lmdb\n results = dict(img_path=path_baboon)\n config = dict(\n key='img',\n backend_args=dict(\n backend='lmdb',\n db_path=Path(__file__).parent.parent.parent / 'data' / 'lq.lmdb'))\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['img'].shape == (h // 4, w // 4, 3)\n assert results['ori_img_shape'] == (h // 4, w // 4, 3)\n assert results['img_path'] == path_baboon\n\n # convert to y-channel (ValueError)\n results = dict(gt_path=path_baboon)\n config = dict(key='gt', to_y_channel=True, channel_order='gg')\n image_loader = LoadImageFromFile(**config)\n with pytest.raises(ValueError):\n results = image_loader(results)\n\n # test infer from s3\n from mmengine.fileio import PetrelBackend\n PetrelBackend.get = lambda self, filepath: filepath\n results = dict(img_path='openmmlab:s3://abcd/efg/')\n config = dict(key='img')\n image_loader = LoadImageFromFile(**config)\n\n try:\n import petrel_client # noqa: F401\n except ImportError:\n with pytest.raises(ImportError) as excinfo:\n results = image_loader(results)\n assert 'Please install petrel_client to enable PetrelBackend.' in str(\n excinfo.value)\n else:\n results = image_loader(results)\n assert results['img'] == 'openmmlab:s3://abcd/efg/'\n\n\ndef test_dct_mask():\n mask = np.zeros((64, 64, 1))\n mask[20:40, 20:40] = 1.\n mask_bbox = [20, 20, 20, 20]\n results = dict(mask=mask, mask_bbox=mask_bbox)\n\n dct_mask = GetSpatialDiscountMask()\n results = dct_mask(results)\n assert 'discount_mask' in results\n assert results['discount_mask'].shape == (64, 64, 1)\n\n mask_height = mask_width = 20\n gamma = 0.99\n beta = 1.5\n mask_values = np.ones((mask_width, mask_height, 1))\n for i in range(mask_width):\n for j in range(mask_height):\n mask_values[i,\n j] = max(gamma**(min(i, mask_width - i - 1) * beta),\n gamma**(min(j, mask_height - j - 1) * beta))\n dct_mask_test = np.zeros_like(mask)\n dct_mask_test[20:40, 20:40, ...] = mask_values\n\n np.testing.assert_almost_equal(dct_mask_test, results['discount_mask'])\n repr_str = dct_mask.__class__.__name__ + (f'(gamma={dct_mask.gamma}, '\n f'beta={dct_mask.beta})')\n assert repr_str == repr(dct_mask)\n\n\nclass TestInpaintLoading:\n\n @classmethod\n def setup_class(cls):\n cls.img_path = Path(__file__).parent.parent.parent.joinpath(\n 'data/image/test.png')\n cls.results = dict(img_info=dict(filename=cls.img_path))\n\n def test_load_mask(self):\n\n # test mask mode: set\n mask_config = dict(\n mask_list_file='tests/data/inpainting/mask_list.txt',\n prefix='tests/data/inpainting/',\n io_backend='local',\n color_type='unchanged',\n file_client_kwargs=dict())\n\n set_loader = LoadMask('set', mask_config)\n class_name = set_loader.__class__.__name__\n assert repr(set_loader) == class_name + \"(mask_mode='set')\"\n for _ in range(2):\n results = dict()\n results = set_loader(results)\n gt_mask = mmcv.imread(\n 'tests/data/inpainting/mask/test.png', flag='unchanged')\n assert np.array_equal(results['mask'], gt_mask[..., 0:1] / 255.)\n\n mask_config = dict(\n mask_list_file='tests/data/inpainting/mask_list_single_ch.txt',\n prefix='tests/data/inpainting/',\n io_backend='local',\n color_type='unchanged',\n file_client_kwargs=dict())\n\n # test mask mode: set with input as single channel image\n set_loader = LoadMask('set', mask_config)\n results = dict()\n results = set_loader(results)\n gt_mask = mmcv.imread(\n 'tests/data/inpainting/mask/test_single_ch.png', flag='unchanged')\n gt_mask = np.expand_dims(gt_mask, axis=2)\n assert np.array_equal(results['mask'], gt_mask[..., 0:1] / 255.)\n\n # test mask mode: ff\n mask_config = dict(\n img_shape=(256, 256),\n num_vertices=(4, 12),\n mean_angle=1.2,\n angle_range=0.4,\n brush_width=(12, 40))\n\n ff_loader = LoadMask('ff', mask_config)\n results = dict()\n results = ff_loader(results)\n assert results['mask'].shape == (256, 256, 1)\n\n # test mask mode: irregular holes\n mask_config = dict(\n img_shape=(256, 256),\n num_vertices=(4, 12),\n max_angle=4.,\n length_range=(10, 100),\n brush_width=(10, 40),\n area_ratio_range=(0.15, 0.5))\n\n irregular_loader = LoadMask('irregular', mask_config)\n results = dict()\n results = irregular_loader(results)\n assert results['mask'].shape == (256, 256, 1)\n\n # test mask mode: bbox\n mask_config = dict(img_shape=(256, 256), max_bbox_shape=128)\n\n bbox_loader = LoadMask('bbox', mask_config)\n results = dict()\n results = bbox_loader(results)\n assert results['mask'].shape == (256, 256, 1)\n\n # test mask mode: file\n mask_loader = LoadMask('file')\n mask = mask_loader(\n dict(mask_path='tests/data/inpainting/mask/test_single_ch.png'))\n assert mask['mask'].shape == (256, 256, 1)\n\n with pytest.raises(NotImplementedError):\n loader = LoadMask('xxxx', mask_config)\n results = loader(results)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_matlab_like_resize.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\n\n\ndef test_matlab_like_resize():\n results = {}\n\n # give scale\n results['lq'] = np.ones((16, 16, 3))\n imresize = MATLABLikeResize(keys=['lq'], scale=0.25)\n results = imresize(results)\n assert results['lq'].shape == (4, 4, 3)\n\n # give scale\n results['lq'] = np.ones((16, 16, 3))\n imresize = MATLABLikeResize(keys=['lq'], output_shape=(6, 6))\n results = imresize(results)\n assert results['lq'].shape == (6, 6, 3)\n\n # kernel must equal 'bicubic'\n with pytest.raises(ValueError):\n MATLABLikeResize(keys=['lq'], kernel='abc')\n\n # kernel_width must equal 4.0\n with pytest.raises(ValueError):\n MATLABLikeResize(keys=['lq'], kernel_width=10)\n\n # scale and output_shape cannot be both None\n with pytest.raises(ValueError):\n MATLABLikeResize(keys=['lq'])\n\n assert repr(imresize) == imresize.__class__.__name__ \\\n + \"(keys=['lq'], scale=None, output_shape=(6, 6), \" \\\n + 'kernel=bicubic, kernel_width=4.0)'\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_normalization.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import Normalize, RescaleToZeroOne\n\n\nclass TestAugmentations:\n\n @staticmethod\n def assert_img_equal(img, ref_img, ratio_thr=0.999):\n \"\"\"Check if img and ref_img are matched approximately.\"\"\"\n assert img.shape == ref_img.shape\n assert img.dtype == ref_img.dtype\n area = ref_img.shape[-1] * ref_img.shape[-2]\n diff = np.abs(img.astype('int32') - ref_img.astype('int32'))\n assert np.sum(diff <= 1) / float(area) > ratio_thr\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n return set(target_keys).issubset(set(result_keys))\n\n def check_normalize(self, origin_img, result_img, norm_cfg):\n \"\"\"Check if the origin_img are normalized correctly into result_img in\n a given norm_cfg.\"\"\"\n target_img = result_img.copy()\n target_img *= norm_cfg['std'][None, None, :]\n target_img += norm_cfg['mean'][None, None, :]\n if norm_cfg['to_rgb']:\n target_img = target_img[:, ::-1, ...].copy()\n self.assert_img_equal(origin_img, target_img)\n\n def test_normalize(self):\n with pytest.raises(TypeError):\n Normalize(['alpha'], dict(mean=[123.675, 116.28, 103.53]),\n [58.395, 57.12, 57.375])\n\n with pytest.raises(TypeError):\n Normalize(['alpha'], [123.675, 116.28, 103.53],\n dict(std=[58.395, 57.12, 57.375]))\n\n target_keys = ['merged', 'img_norm_cfg']\n\n merged = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(merged=merged)\n config = dict(\n keys=['merged'],\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=False)\n normalize = Normalize(**config)\n normalize_results = normalize(results)\n assert self.check_keys_contain(normalize_results.keys(), target_keys)\n self.check_normalize(merged, normalize_results['merged'],\n normalize_results['img_norm_cfg'])\n\n merged = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(merged=merged)\n config = dict(\n keys=['merged'],\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True)\n normalize = Normalize(**config)\n normalize_results = normalize(results)\n assert self.check_keys_contain(normalize_results.keys(), target_keys)\n self.check_normalize(merged, normalize_results['merged'],\n normalize_results['img_norm_cfg'])\n\n assert normalize.__repr__() == (\n normalize.__class__.__name__ +\n f\"(keys={ ['merged']}, mean={np.array([123.675, 116.28, 103.53])},\"\n f' std={np.array([58.395, 57.12, 57.375])}, to_rgb=True)')\n\n # input is an image list\n merged = np.random.rand(240, 320, 3).astype(np.float32)\n merged_2 = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(merged=[merged, merged_2])\n config = dict(\n keys=['merged'],\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=False)\n normalize = Normalize(**config)\n normalize_results = normalize(results)\n assert self.check_keys_contain(normalize_results.keys(), target_keys)\n self.check_normalize(merged, normalize_results['merged'][0],\n normalize_results['img_norm_cfg'])\n self.check_normalize(merged_2, normalize_results['merged'][1],\n normalize_results['img_norm_cfg'])\n\n merged = np.random.rand(240, 320, 3).astype(np.float32)\n merged_2 = np.random.rand(240, 320, 3).astype(np.float32)\n results = dict(merged=[merged, merged_2])\n config = dict(\n keys=['merged'],\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True)\n normalize = Normalize(**config)\n normalize_results = normalize(results)\n assert self.check_keys_contain(normalize_results.keys(), target_keys)\n self.check_normalize(merged, normalize_results['merged'][0],\n normalize_results['img_norm_cfg'])\n self.check_normalize(merged_2, normalize_results['merged'][1],\n normalize_results['img_norm_cfg'])\n\n def test_rescale_to_zero_one(self):\n target_keys = ['alpha']\n\n alpha = np.random.rand(240, 320).astype(np.float32)\n results = dict(alpha=alpha)\n rescale_to_zero_one = RescaleToZeroOne(keys=['alpha'])\n rescale_to_zero_one_results = rescale_to_zero_one(results)\n assert self.check_keys_contain(rescale_to_zero_one_results.keys(),\n target_keys)\n assert rescale_to_zero_one_results['alpha'].shape == (240, 320)\n np.testing.assert_almost_equal(rescale_to_zero_one_results['alpha'],\n alpha / 255.)\n assert repr(rescale_to_zero_one) == (\n rescale_to_zero_one.__class__.__name__ + f\"(keys={['alpha']})\")\n\n # input is image list\n alpha = np.random.rand(240, 320).astype(np.float32)\n alpha_2 = np.random.rand(240, 320).astype(np.float32)\n results = dict(alpha=[alpha, alpha_2])\n rescale_to_zero_one = RescaleToZeroOne(keys=['alpha'])\n rescale_to_zero_one_results = rescale_to_zero_one(results)\n assert rescale_to_zero_one_results['alpha'][0].shape == (240, 320)\n assert rescale_to_zero_one_results['alpha'][1].shape == (240, 320)\n np.testing.assert_almost_equal(rescale_to_zero_one_results['alpha'][0],\n alpha / 255.)\n np.testing.assert_almost_equal(rescale_to_zero_one_results['alpha'][1],\n alpha_2 / 255.)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_random_degradations.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (DegradationsWithShuffle, RandomBlur,\n RandomJPEGCompression, RandomNoise,\n RandomResize, RandomVideoCompression)\n\n\ndef test_random_noise():\n results = {}\n results['lq'] = np.ones((8, 8, 3)).astype(np.uint8)\n\n # Gaussian noise\n model = RandomNoise(\n params=dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[0, 50],\n gaussian_gray_noise_prob=1),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # Poisson noise\n model = RandomNoise(\n params=dict(\n noise_type=['poisson'],\n noise_prob=[1],\n poisson_scale=[0, 1],\n poisson_gray_noise_prob=1),\n keys=['lq'])\n results = model(results)\n shape, dtype = results['lq'].shape, results['lq'].dtype\n assert {'shape': shape, 'dtype': dtype} == \\\n {'shape': (8, 8, 3), 'dtype': np.float32}\n\n # skip degradations with prob < 1\n params = dict(\n noise_type=['gaussian'],\n noise_prob=[1],\n gaussian_sigma=[0, 50],\n gaussian_gray_noise_prob=1,\n prob=0)\n model = RandomNoise(params=params, keys=['lq'])\n assert model(results) == results\n\n assert repr(model) == model.__class__.__name__ + f'(params={params}, ' \\\n + \"keys=['lq'])\"\n\n\ndef test_random_jpeg_compression():\n results = {}\n results['lq'] = np.ones((8, 8, 3)).astype(np.uint8)\n\n model = RandomJPEGCompression(\n params=dict(quality=[5, 50], color_type='color'), keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # skip degradations with prob < 1\n params = dict(quality=[5, 50], color_type='color', prob=0)\n model = RandomJPEGCompression(params=params, keys=['lq'])\n assert model(results) == results\n\n model = RandomJPEGCompression(params=params, keys=['lq'], bgr2rgb=True)\n assert model(results)['lq'].shape == results['lq'].shape\n\n assert repr(model) == model.__class__.__name__ + f'(params={params}, ' \\\n + \"keys=['lq'])\"\n\n\ndef test_random_video_compression():\n results = {}\n results['lq'] = [np.ones((8, 8, 3)).astype(np.float32)] * 5\n\n model = RandomVideoCompression(\n params=dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5]),\n keys=['lq'])\n results = model(results)\n assert results['lq'][0].shape == (8, 8, 3)\n assert len(results['lq']) == 5\n\n # skip degradations with prob < 1\n params = dict(\n codec=['libx264', 'h264', 'mpeg4'],\n codec_prob=[1 / 3., 1 / 3., 1 / 3.],\n bitrate=[1e4, 1e5],\n prob=0)\n model = RandomVideoCompression(params=params, keys=['lq'])\n assert model(results) == results\n\n assert repr(model) == model.__class__.__name__ + f'(params={params}, ' \\\n + \"keys=['lq'])\"\n\n\ndef test_random_resize():\n results = {}\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n\n # upscale\n model = RandomResize(\n params=dict(\n resize_mode_prob=[1, 0, 0],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape[0] >= 8 and results['lq'].shape[1] >= 8\n\n # downscale\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n model = RandomResize(\n params=dict(\n resize_mode_prob=[0, 1, 0],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape[0] <= 8 and results['lq'].shape[1] <= 8\n\n # keep size\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n model = RandomResize(\n params=dict(\n resize_mode_prob=[0, 0, 1],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape[0] == 8 and results['lq'].shape[1] == 8\n\n # given target_size\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n model = RandomResize(\n params=dict(\n resize_mode_prob=[0, 0, 1],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n target_size=(16, 32)),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (16, 32, 3)\n\n # step_size > 0\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n model = RandomResize(\n params=dict(\n resize_mode_prob=[0, 0, 1],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n resize_step=0.05),\n keys=['lq'])\n results = model(results)\n\n # is_size_even is True\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n model = RandomResize(\n params=dict(\n resize_mode_prob=[0, 1, 0],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n resize_step=0.05,\n is_size_even=True),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape[0] % 2 == 0\n assert results['lq'].shape[1] % 2 == 0\n\n # skip degradation\n model = RandomResize(\n params=dict(\n resize_mode_prob=[1, 0, 0],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n prob=0),\n keys=['lq'])\n assert model(results) == results\n\n with pytest.raises(NotImplementedError):\n params = dict(\n resize_mode_prob=[1],\n resize_scale=[1],\n resize_opt=['abc'],\n resize_prob=[1])\n model = RandomResize(params=params, keys=['lq'])\n results = model(results)\n\n assert repr(model) == model.__class__.__name__ + f'(params={params}, ' \\\n + \"keys=['lq'])\"\n\n\ndef test_random_blur():\n results = {}\n results['lq'] = np.ones((8, 8, 3)).astype(np.float32)\n\n # isotropic Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['iso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # anisotropic Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['aniso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # isotropic generalized Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['generalized_iso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # anisotropic generalized Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['generalized_aniso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # isotropic plateau Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['plateau_iso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # anisotropic plateau Gaussian\n model = RandomBlur(\n params=dict(\n kernel_size=[41],\n kernel_list=['plateau_aniso'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # sinc (kernel size < 13)\n model = RandomBlur(\n params=dict(\n kernel_size=[11],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # sinc (kernel size >= 13)\n model = RandomBlur(\n params=dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # sinc (given omega)\n model = RandomBlur(\n params=dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n omega=[0.1, 0.1]),\n keys=['lq'])\n results = model(results)\n assert results['lq'].shape == (8, 8, 3)\n\n # skip degradation\n params = dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n prob=0)\n model = RandomBlur(params=params, keys=['lq'])\n assert model(results) == results\n\n assert repr(model) == model.__class__.__name__ + f'(params={params}, ' \\\n + \"keys=['lq'])\"\n\n\ndef test_degradations_with_shuffle():\n results = {}\n results['lq'] = np.ones((8, 8, 3)).astype(np.uint8)\n\n # shuffle all\n model = DegradationsWithShuffle(\n degradations=[\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n omega=[0.1, 0.1])),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0, 0, 1],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n target_size=(16, 16))),\n [\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[5, 10], color_type='color')),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[15, 20], color_type='color'))\n ]\n ],\n keys=['lq'],\n shuffle_idx=None)\n model(results)\n\n # shuffle last 2\n degradations = [\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n omega=[0.1, 0.1])),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0, 0, 1],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n target_size=(16, 16))),\n [\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[5, 10], color_type='color')),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[15, 20], color_type='color'))\n ]\n ]\n model = DegradationsWithShuffle(\n degradations=degradations, keys=['lq'], shuffle_idx=(1, 2))\n model(results)\n\n assert repr(model) == model.__class__.__name__ \\\n + f'(degradations={degradations}, ' \\\n + \"keys=['lq'], \" \\\n + 'shuffle_idx=(1, 2))'\n\n # shuffle all image degradations multiple times\n degradations = [\n dict(\n type='RandomBlur',\n params=dict(\n kernel_size=[15],\n kernel_list=['sinc'],\n kernel_prob=[1],\n sigma_x=[0.2, 10],\n sigma_y=[0.2, 10],\n rotate_angle=[-3.1416, 3.1416],\n omega=[0.1, 0.1])),\n dict(\n type='RandomResize',\n params=dict(\n resize_mode_prob=[0.4, 0.4, 0.2],\n resize_scale=[0.5, 1.5],\n resize_opt=['bilinear', 'area', 'bicubic'],\n resize_prob=[1 / 3., 1 / 3., 1 / 3.],\n resize_step=0.05)),\n dict(\n type='RandomNoise',\n params=dict(\n noise_type=['gaussian', 'poisson'],\n noise_prob=[0.4, 0.6],\n gaussian_sigma=[0, 20],\n gaussian_gray_noise_prob=1.,\n gaussian_sigma_step=0.05,\n poisson_scale=[0., 1.],\n poisson_gray_noise_prob=1.,\n scale_step=0.05)),\n dict(\n type='RandomJPEGCompression',\n params=dict(quality=[5, 10], color_type='color'))\n ] * 10\n model = DegradationsWithShuffle(degradations=degradations, keys=['lq'])\n model(results)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_random_down_sampling.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\n\nfrom mmagic.datasets.transforms import RandomDownSampling\n\n\ndef test_random_down_sampling():\n img1 = np.uint8(np.random.randn(480, 640, 3) * 255)\n inputs1 = dict(gt=img1)\n down_sampling1 = RandomDownSampling(\n scale_min=1, scale_max=4, patch_size=None)\n results1 = down_sampling1(inputs1)\n assert set(list(results1.keys())) == set(['gt', 'img', 'scale'])\n assert repr(down_sampling1) == (\n down_sampling1.__class__.__name__ +\n f' scale_min={down_sampling1.scale_min}, ' +\n f'scale_max={down_sampling1.scale_max}, ' +\n f'patch_size={down_sampling1.patch_size}, ' +\n f'interpolation={down_sampling1.interpolation}, ' +\n f'backend={down_sampling1.backend}')\n\n img2 = np.uint8(np.random.randn(480, 640, 3) * 255)\n inputs2 = dict(gt=img2)\n down_sampling2 = RandomDownSampling(\n scale_min=1, scale_max=4, patch_size=48)\n results2 = down_sampling2(inputs2)\n assert set(list(results2.keys())) == set(['gt', 'img', 'scale'])\n assert repr(down_sampling2) == (\n down_sampling2.__class__.__name__ +\n f' scale_min={down_sampling2.scale_min}, ' +\n f'scale_max={down_sampling2.scale_max}, ' +\n f'patch_size={down_sampling2.patch_size}, ' +\n f'interpolation={down_sampling2.interpolation}, ' +\n f'backend={down_sampling2.backend}')\n\n # test copy meta info\n img3 = np.uint8(np.random.randn(480, 640, 3) * 255)\n inputs3 = dict(gt=img3, gt_channel_order='rgb', gt_color_type='color')\n down_sampling3 = RandomDownSampling(\n scale_min=1, scale_max=4, patch_size=48)\n results3 = down_sampling3(inputs3)\n assert results3['img_channel_order'] == 'rgb'\n assert results3['img_color_type'] == 'color'\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_trimap.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport cv2\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (FormatTrimap, GenerateTrimap,\n GenerateTrimapWithDistTransform,\n TransformTrimap)\n\n\ndef assert_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n assert set(target_keys).issubset(set(result_keys))\n\n\ndef generate_ref_trimap(alpha, kernel_size, iterations, random):\n \"\"\"Check if a trimap's value is correct.\"\"\"\n if isinstance(kernel_size, int):\n kernel_size = kernel_size, kernel_size + 1\n if isinstance(iterations, int):\n iterations = iterations, iterations + 1\n\n if random:\n min_kernel, max_kernel = kernel_size\n kernel_num = max_kernel - min_kernel\n erode_ksize = min_kernel + np.random.randint(kernel_num)\n erode_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (erode_ksize, erode_ksize))\n dilate_ksize = min_kernel + np.random.randint(kernel_num)\n dilate_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (dilate_ksize, dilate_ksize))\n\n min_iteration, max_iteration = iterations\n erode_iter = np.random.randint(min_iteration, max_iteration)\n dilate_iter = np.random.randint(min_iteration, max_iteration)\n else:\n erode_ksize, dilate_ksize = kernel_size\n erode_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (erode_ksize, erode_ksize))\n dilate_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (dilate_ksize, dilate_ksize))\n erode_iter, dilate_iter = iterations\n\n h, w = alpha.shape\n\n # erode\n erode_kh = erode_kw = erode_ksize\n eroded = np.zeros_like(alpha)\n src = alpha\n pad = ((erode_kh // 2, (erode_kh - 1) // 2), (erode_kw // 2,\n (erode_kw - 1) // 2))\n for _ in range(erode_iter):\n src = np.pad(src, pad, 'constant', constant_values=np.max(src))\n for i in range(h):\n for j in range(w):\n target = src[i:i + erode_kh, j:j + erode_kw]\n eroded[i, j] = np.min(\n (target * erode_kernel)[erode_kernel == 1])\n src = eroded\n\n # dilate\n dilate_kh = dilate_kw = dilate_ksize\n dilated = np.zeros_like(alpha)\n src = alpha\n pad = ((dilate_kh // 2, (dilate_kh - 1) // 2), (dilate_kw // 2,\n (dilate_kw - 1) // 2))\n for _ in range(dilate_iter):\n src = np.pad(src, pad, constant_values=np.min(src))\n for i in range(h):\n for j in range(w):\n target = src[i:i + dilate_kh, j:j + dilate_kw]\n dilated[i, j] = np.max(\n (target * dilate_kernel)[dilate_kernel == 1])\n src = dilated\n\n ref_trimap = np.zeros_like(alpha)\n ref_trimap.fill(128)\n ref_trimap[eroded >= 255] = 255\n ref_trimap[dilated <= 0] = 0\n return ref_trimap\n\n\ndef test_format_trimap():\n ori_trimap = np.random.randint(3, size=(64, 64))\n ori_trimap[ori_trimap == 1] = 128\n ori_trimap[ori_trimap == 2] = 255\n\n results = dict(trimap=ori_trimap.copy())\n format_trimap = FormatTrimap(to_onehot=False)\n results = format_trimap(results)\n result_trimap = results['trimap']\n assert repr(format_trimap) == format_trimap.__class__.__name__ + (\n '(to_onehot=False)')\n assert result_trimap.shape == (64, 64)\n assert ((result_trimap == 0) == (ori_trimap == 0)).all()\n assert ((result_trimap == 1) == (ori_trimap == 128)).all()\n assert ((result_trimap == 2) == (ori_trimap == 255)).all()\n assert results['format_trimap_to_onehot'] is False\n\n results = dict(trimap=ori_trimap.copy())\n format_trimap = FormatTrimap(to_onehot=True)\n results = format_trimap(results)\n result_trimap = results['trimap']\n assert repr(format_trimap) == format_trimap.__class__.__name__ + (\n '(to_onehot=True)')\n assert result_trimap.shape == (64, 64, 3)\n assert ((result_trimap[..., 0] == 1) == (ori_trimap == 0)).all()\n assert ((result_trimap[..., 1] == 1) == (ori_trimap == 128)).all()\n assert ((result_trimap[..., 2] == 1) == (ori_trimap == 255)).all()\n assert results['format_trimap_to_onehot'] is True\n\n\ndef test_generate_trimap():\n\n with pytest.raises(ValueError):\n # kernel_size must be an int or a tuple of 2 int\n GenerateTrimap(1.5)\n\n with pytest.raises(ValueError):\n # kernel_size must be an int or a tuple of 2 int\n GenerateTrimap((3, 3, 3))\n\n with pytest.raises(ValueError):\n # iterations must be an int or a tuple of 2 int\n GenerateTrimap(3, iterations=1.5)\n\n with pytest.raises(ValueError):\n # iterations must be an int or a tuple of 2 int\n GenerateTrimap(3, iterations=(3, 3, 3))\n\n target_keys = ['alpha', 'trimap']\n\n # check random mode\n kernel_size = (3, 5)\n iterations = (3, 5)\n random = True\n alpha = np.random.randn(32, 32)\n results = dict(alpha=alpha)\n generate_trimap = GenerateTrimap(kernel_size, iterations, random)\n np.random.seed(123)\n generate_trimap_results = generate_trimap(results)\n trimap = generate_trimap_results['trimap']\n\n assert_keys_contain(generate_trimap_results.keys(), target_keys)\n assert trimap.shape == alpha.shape\n np.random.seed(123)\n ref_trimap = generate_ref_trimap(alpha, kernel_size, iterations, random)\n assert (trimap == ref_trimap).all()\n\n # check non-random mode\n kernel_size = (3, 5)\n iterations = (5, 3)\n random = False\n generate_trimap = GenerateTrimap(kernel_size, iterations, random)\n generate_trimap_results = generate_trimap(results)\n trimap = generate_trimap_results['trimap']\n\n assert_keys_contain(generate_trimap_results.keys(), target_keys)\n assert trimap.shape == alpha.shape\n ref_trimap = generate_ref_trimap(alpha, kernel_size, iterations, random)\n assert (trimap == ref_trimap).all()\n\n # check repr string\n kernel_size = 1\n iterations = 1\n generate_trimap = GenerateTrimap(kernel_size, iterations)\n kernels = [\n cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (kernel_size, kernel_size))\n ]\n assert repr(generate_trimap) == (\n generate_trimap.__class__.__name__ +\n f'(kernels={kernels}, iterations={(iterations, iterations + 1)}, '\n f'random=True)')\n\n\ndef test_generate_trimap_with_dist_transform():\n with pytest.raises(ValueError):\n # dist_thr must be an float that is greater than 1\n GenerateTrimapWithDistTransform(dist_thr=-1)\n\n target_keys = ['alpha', 'trimap']\n\n alpha = np.random.randint(0, 256, (32, 32))\n alpha[:8, :8] = 0\n alpha[-8:, -8:] = 255\n results = dict(alpha=alpha)\n generate_trimap = GenerateTrimapWithDistTransform(dist_thr=3, random=False)\n generate_trimap_results = generate_trimap(results)\n trimap = generate_trimap_results['trimap']\n assert_keys_contain(generate_trimap_results.keys(), target_keys)\n assert trimap.shape == alpha.shape\n\n alpha = np.random.randint(0, 256, (32, 32))\n results = dict(alpha=alpha)\n generate_trimap = GenerateTrimapWithDistTransform(dist_thr=3, random=True)\n generate_trimap_results = generate_trimap(results)\n trimap = generate_trimap_results['trimap']\n assert_keys_contain(generate_trimap_results.keys(), target_keys)\n assert trimap.shape == alpha.shape\n\n assert repr(generate_trimap) == (\n generate_trimap.__class__.__name__ + '(dist_thr=3, random=True)')\n\n\ndef test_transform_trimap():\n results = dict()\n transform = TransformTrimap()\n target_keys = ['trimap', 'transformed_trimap']\n\n with pytest.raises(KeyError):\n results_transformed = transform(results)\n\n with pytest.raises(AssertionError):\n dummy_trimap = np.zeros((100, 100, 1), dtype=np.uint8)\n results['trimap'] = dummy_trimap\n results_transformed = transform(results)\n\n results = dict()\n # generate dummy trimap with shape (100,100)\n dummy_trimap = np.zeros((100, 100), dtype=np.uint8)\n dummy_trimap[:50, :50] = 255\n results['trimap'] = dummy_trimap\n results_transformed = transform(results)\n assert_keys_contain(results_transformed.keys(), target_keys)\n assert results_transformed['trimap'].shape == dummy_trimap.shape\n assert results_transformed[\n 'transformed_trimap'].shape[:2] == dummy_trimap.shape\n repr_str = transform.__class__.__name__\n assert repr(transform) == repr_str\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_transforms/test_values.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import CopyValues, SetValues\n\n\nclass TestAugmentations:\n\n @classmethod\n def setup_class(cls):\n\n cls.results = dict()\n cls.gt = np.random.randint(0, 256, (256, 128, 3), dtype=np.uint8)\n cls.img = np.random.randint(0, 256, (64, 32, 3), dtype=np.uint8)\n\n cls.results = dict(\n img=cls.img,\n gt=cls.gt,\n scale=4,\n img_path='fake_img_path',\n gt_path='fake_gt_path')\n\n cls.results['ori_img'] = np.random.randint(\n 0, 256, (256, 256, 3), dtype=np.uint8)\n cls.results['mask'] = np.random.randint(\n 0, 256, (256, 256, 1), dtype=np.uint8)\n # cls.results['img_tensor'] = torch.rand((3, 256, 256))\n # cls.results['mask_tensor'] = torch.zeros((1, 256, 256))\n # cls.results['mask_tensor'][:, 50:150, 40:140] = 1.\n\n def test_copy_value(self):\n\n with pytest.raises(AssertionError):\n CopyValues(src_keys='gt', dst_keys='img')\n with pytest.raises(ValueError):\n CopyValues(src_keys=['gt', 'gt'], dst_keys=['img'])\n\n results = {}\n results['gt'] = np.zeros((1)).astype(np.float32)\n\n copy_ = CopyValues(src_keys=['gt'], dst_keys=['img'])\n assert np.array_equal(copy_(results)['img'], results['gt'])\n assert repr(copy_) == copy_.__class__.__name__ + (\"(src_keys=['gt'])\"\n \"(dst_keys=['img'])\")\n\n def test_set_value(self):\n\n with pytest.raises(AssertionError):\n CopyValues(src_keys='gt', dst_keys='img')\n with pytest.raises(ValueError):\n CopyValues(src_keys=['gt', 'gt'], dst_keys=['img'])\n\n results = {}\n results['gt'] = np.zeros((1)).astype(np.float32)\n dictionary = dict(a='b')\n\n set_values = SetValues(dictionary=dictionary)\n new_results = set_values(results)\n for key in dictionary.keys():\n assert new_results[key] == dictionary[key]\n assert repr(set_values) == (\n set_values.__class__.__name__ + f'(dictionary={dictionary})')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_datasets/test_unpaired_image_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\n\nfrom mmagic.datasets import UnpairedImageDataset\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestUnpairedImageDataset(object):\n\n @classmethod\n def setup_class(cls):\n cls.imgs_root = osp.join(\n osp.dirname(osp.dirname(__file__)), 'data/unpaired')\n cls.default_pipeline = [\n dict(type='LoadImageFromFile', key='img_a', color_type='color'),\n dict(type='LoadImageFromFile', key='img_b', color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_a', 'img_b']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='Resize',\n scale=(286, 286),\n interpolation='bicubic')\n ]),\n dict(\n type='Crop',\n keys=['img_a', 'img_b'],\n crop_size=(256, 256),\n random_crop=True),\n dict(type='Flip', direction='horizontal', keys=['img_a', 'img_b']),\n dict(type='PackInputs', keys=['img_a', 'img_b']),\n ]\n\n def test_unpaired_image_dataset(self):\n dataset = UnpairedImageDataset(\n self.imgs_root,\n pipeline=self.default_pipeline,\n domain_a='a',\n domain_b='b')\n assert len(dataset) == 2\n img = dataset[0]['inputs']['img_a']\n assert img.ndim == 3\n img = dataset[0]['inputs']['img_b']\n assert img.ndim == 3\n\n dataset = UnpairedImageDataset(\n self.imgs_root,\n pipeline=self.default_pipeline,\n io_backend='local',\n domain_a='a',\n domain_b='b')\n assert len(dataset) == 2\n img = dataset[0]['inputs']['img_a']\n assert img.ndim == 3\n img = dataset[0]['inputs']['img_b']\n assert img.ndim == 3\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_ema.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import MMDistributedDataParallel\nfrom packaging import version\n\nfrom mmagic.engine import ExponentialMovingAverageHook\n\n\nclass SimpleModule(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.a = nn.Parameter(torch.tensor([1., 2.]))\n if version.parse(torch.__version__) >= version.parse('1.7.0'):\n self.register_buffer('b', torch.tensor([2., 3.]), persistent=True)\n self.register_buffer('c', torch.tensor([0., 1.]), persistent=False)\n else:\n self.register_buffer('b', torch.tensor([2., 3.]))\n self.c = torch.tensor([0., 1.])\n\n\nclass SimpleModel(nn.Module):\n\n def __init__(self) -> None:\n super().__init__()\n self.module_a = SimpleModule()\n self.module_b = SimpleModule()\n\n self.module_a_ema = SimpleModule()\n self.module_b_ema = SimpleModule()\n\n\nclass SimpleModelNoEMA(nn.Module):\n\n def __init__(self) -> None:\n super().__init__()\n self.module_a = SimpleModule()\n self.module_b = SimpleModule()\n\n\nclass SimpleRunner:\n\n def __init__(self):\n self.model = SimpleModel()\n self.iter = 0\n\n\nclass TestEMA:\n\n @classmethod\n def setup_class(cls):\n cls.default_config = dict(\n module_keys=('module_a_ema', 'module_b_ema'),\n interval=1,\n interp_cfg=dict(momentum=0.5))\n cls.runner = SimpleRunner()\n\n @torch.no_grad()\n def test_ema_hook(self):\n cfg_ = deepcopy(self.default_config)\n cfg_['interval'] = -1\n ema = ExponentialMovingAverageHook(**cfg_)\n ema.before_run(self.runner)\n ema.after_train_iter(self.runner, 1)\n\n module_a = self.runner.model.module_a\n module_a_ema = self.runner.model.module_a_ema\n\n ema_states = module_a_ema.state_dict()\n assert torch.equal(ema_states['a'], torch.tensor([1., 2.]))\n\n ema = ExponentialMovingAverageHook(**self.default_config)\n ema.after_train_iter(self.runner, 1)\n\n ema_states = module_a_ema.state_dict()\n assert torch.equal(ema_states['a'], torch.tensor([1., 2.]))\n\n module_a.b /= 2.\n module_a.a.data /= 2.\n module_a.c /= 2.\n\n self.runner.iter += 1\n ema.after_train_iter(self.runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(self.runner.model.module_a.a,\n torch.tensor([0.5, 1.]))\n assert torch.equal(ema_states['a'], torch.tensor([0.75, 1.5]))\n assert torch.equal(ema_states['b'], torch.tensor([1., 1.5]))\n assert 'c' not in ema_states\n\n # check for the validity of args\n with pytest.raises(AssertionError):\n _ = ExponentialMovingAverageHook(module_keys=['a'])\n\n with pytest.raises(AssertionError):\n _ = ExponentialMovingAverageHook(module_keys=('a'))\n\n with pytest.raises(AssertionError):\n _ = ExponentialMovingAverageHook(\n module_keys=('module_a_ema'), interp_mode='xxx')\n\n # test before run\n ema = ExponentialMovingAverageHook(**self.default_config)\n self.runner.model = SimpleModelNoEMA()\n self.runner.iter = 0\n ema.before_run(self.runner)\n assert hasattr(self.runner.model, 'module_a_ema')\n\n module_a = self.runner.model.module_a\n module_a_ema = self.runner.model.module_a_ema\n\n ema.after_train_iter(self.runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(ema_states['a'], torch.tensor([1., 2.]))\n\n module_a.b /= 2.\n module_a.a.data /= 2.\n module_a.c /= 2.\n\n self.runner.iter += 1\n ema.after_train_iter(self.runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(self.runner.model.module_a.a,\n torch.tensor([0.5, 1.]))\n assert torch.equal(ema_states['a'], torch.tensor([0.75, 1.5]))\n assert torch.equal(ema_states['b'], torch.tensor([1., 1.5]))\n assert 'c' not in ema_states\n\n # test ema with simple warm up\n runner = SimpleRunner()\n cfg_ = deepcopy(self.default_config)\n cfg_.update(dict(start_iter=3, interval=1))\n ema = ExponentialMovingAverageHook(**cfg_)\n ema.before_run(runner)\n\n module_a = runner.model.module_a\n module_a_ema = runner.model.module_a_ema\n\n module_a.a.data /= 2.\n\n runner.iter += 1\n ema.after_train_iter(runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(runner.model.module_a.a, torch.tensor([0.5, 1.]))\n assert torch.equal(ema_states['a'], torch.tensor([0.5, 1.]))\n\n module_a.a.data /= 2\n runner.iter += 2\n ema.after_train_iter(runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(runner.model.module_a.a, torch.tensor([0.25, 0.5]))\n assert torch.equal(ema_states['a'], torch.tensor([0.375, 0.75]))\n\n # test warning\n with pytest.warns(UserWarning):\n default_config = dict(\n module_keys=('module_a_ema', 'module_b_ema'),\n interval=1,\n interp_cfg=dict(momentum=0.6))\n cfg_ = deepcopy(default_config)\n ema = ExponentialMovingAverageHook(**cfg_)\n ema.lerp(\n torch.tensor([0.25, 0.5]),\n torch.tensor([0.25, 0.5]),\n momentum=0.6)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_ema_hook_cuda(self):\n ema = ExponentialMovingAverageHook(**self.default_config)\n cuda_runner = SimpleRunner()\n cuda_runner.model = cuda_runner.model.cuda()\n ema.after_train_iter(cuda_runner, 1)\n\n module_a = cuda_runner.model.module_a\n module_a_ema = cuda_runner.model.module_a_ema\n\n ema_states = module_a_ema.state_dict()\n assert torch.equal(ema_states['a'], torch.tensor([1., 2.]).cuda())\n\n module_a.b /= 2.\n module_a.a.data /= 2.\n module_a.c /= 2.\n\n cuda_runner.iter += 1\n ema.after_train_iter(cuda_runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(cuda_runner.model.module_a.a,\n torch.tensor([0.5, 1.]).cuda())\n assert torch.equal(ema_states['a'], torch.tensor([0.75, 1.5]).cuda())\n assert torch.equal(ema_states['b'], torch.tensor([1., 1.5]).cuda())\n assert 'c' not in ema_states\n\n # test before run\n ema = ExponentialMovingAverageHook(**self.default_config)\n model_ = SimpleModelNoEMA().cuda()\n self.runner.model = MagicMock(spec=MMDistributedDataParallel)\n self.runner.model.module = model_\n\n self.runner.iter = 0\n ema.before_run(self.runner)\n assert hasattr(self.runner.model.module, 'module_a_ema')\n\n module_a = self.runner.model.module.module_a\n module_a_ema = self.runner.model.module.module_a_ema\n\n ema.after_train_iter(self.runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(ema_states['a'], torch.tensor([1., 2.]).cuda())\n\n module_a.b /= 2.\n module_a.a.data /= 2.\n module_a.c /= 2.\n\n self.runner.iter += 1\n ema.after_train_iter(self.runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(self.runner.model.module.module_a.a,\n torch.tensor([0.5, 1.]).cuda())\n assert torch.equal(ema_states['a'], torch.tensor([0.75, 1.5]).cuda())\n assert torch.equal(ema_states['b'], torch.tensor([1., 1.5]).cuda())\n assert 'c' not in ema_states\n\n # test ema with simple warm up\n runner = SimpleRunner()\n runner.model = runner.model.cuda()\n cfg_ = deepcopy(self.default_config)\n cfg_.update(dict(start_iter=3, interval=1))\n ema = ExponentialMovingAverageHook(**cfg_)\n ema.before_run(runner)\n\n module_a = runner.model.module_a\n module_a_ema = runner.model.module_a_ema\n\n module_a.a.data /= 2.\n\n runner.iter += 1\n ema.after_train_iter(runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(runner.model.module_a.a,\n torch.tensor([0.5, 1.]).cuda())\n assert torch.equal(ema_states['a'], torch.tensor([0.5, 1.]).cuda())\n\n module_a.a.data /= 2\n runner.iter += 2\n ema.after_train_iter(runner, 1)\n ema_states = module_a_ema.state_dict()\n assert torch.equal(runner.model.module_a.a,\n torch.tensor([0.25, 0.5]).cuda())\n assert torch.equal(ema_states['a'], torch.tensor([0.375, 0.75]).cuda())\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_iter_time_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, Mock, patch\n\nfrom mmengine.logging import MessageHub\n\nfrom mmagic.engine import IterTimerHook\n\n\ndef time_patch():\n if not hasattr(time_patch, 'time'):\n time_patch.time = 0\n else:\n time_patch.time += 1\n return time_patch.time\n\n\nclass TestIterTimerHook(TestCase):\n\n def setUp(self) -> None:\n self.hook = IterTimerHook()\n\n def test_init(self):\n assert self.hook.time_sec_tot == 0\n assert self.hook.start_iter == 0\n\n def test_before_train(self):\n runner = MagicMock()\n runner.iter = 1\n self.hook.before_train(runner)\n assert self.hook.start_iter == 1\n\n def test_before_epoch(self):\n runner = Mock()\n self.hook._before_epoch(runner)\n assert isinstance(self.hook.t, float)\n\n @patch('time.time', MagicMock(return_value=1))\n def test_before_iter(self):\n runner = MagicMock()\n runner.log_buffer = dict()\n self.hook._before_epoch(runner)\n for mode in ('train', 'val', 'test'):\n self.hook._before_iter(runner, batch_idx=1, mode=mode)\n runner.message_hub.update_scalar.assert_called_with(\n f'{mode}/data_time', 0)\n\n @patch('time.time', time_patch)\n def test_after_iter(self):\n runner = MagicMock()\n runner.log_buffer = dict()\n runner.log_processor.window_size = 10\n runner.max_iters = 100\n runner.iter = 0\n runner.test_loop.total_length = 20\n runner.val_loop.total_length = 20\n self.hook._before_epoch(runner)\n self.hook.before_run(runner)\n self.hook._after_iter(runner, batch_idx=1)\n runner.message_hub.update_scalar.assert_called()\n runner.message_hub.get_log.assert_not_called()\n runner.message_hub.update_info.assert_not_called()\n runner.message_hub = MessageHub.get_instance('test_iter_timer_hook')\n runner.iter = 9\n # eta = (100 - 10) / 1\n self.hook._after_iter(runner, batch_idx=89)\n assert runner.message_hub.get_info('eta') == 90\n self.hook._after_iter(runner, batch_idx=9, mode='val')\n assert runner.message_hub.get_info('eta') == 10\n self.hook._after_iter(runner, batch_idx=19, mode='test')\n assert runner.message_hub.get_info('eta') == 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_pggan_fetch_data_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nfrom mmengine.dataset import DefaultSampler, InfiniteSampler, pseudo_collate\nfrom mmengine.runner import IterBasedTrainLoop\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.engine import PGGANFetchDataHook\nfrom mmagic.registry import DATASETS, MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestPGGANFetchDataHook(TestCase):\n\n pggan_cfg = dict(\n type='ProgressiveGrowingGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n noise_size=512,\n generator=dict(type='PGGANGenerator', out_scale=8),\n discriminator=dict(type='PGGANDiscriminator', in_scale=8),\n nkimgs_per_scale={\n '4': 600,\n '8': 1200,\n '16': 1200,\n '32': 1200,\n '64': 1200,\n '128': 12000\n },\n transition_kimgs=600,\n ema_config=dict(interval=1))\n\n imgs_root = osp.join(osp.dirname(__file__), '..', '..', 'data/image')\n grow_scale_dataset_cfg = dict(\n type='GrowScaleImgDataset',\n data_roots={\n '4': imgs_root,\n '8': osp.join(imgs_root, 'img_root'),\n '32': osp.join(imgs_root, 'img_root', 'grass')\n },\n gpu_samples_base=4,\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4\n },\n len_per_stage=10,\n pipeline=[dict(type='LoadImageFromFile', key='img')])\n\n def test_before_train_iter(self):\n runner = MagicMock()\n model = MODELS.build(self.pggan_cfg)\n dataset = DATASETS.build(self.grow_scale_dataset_cfg)\n\n # test default sampler\n default_sampler = DefaultSampler(dataset)\n dataloader = DataLoader(\n batch_size=64,\n dataset=dataset,\n sampler=default_sampler,\n collate_fn=pseudo_collate)\n\n runner.train_loop = MagicMock(spec=IterBasedTrainLoop)\n runner.train_loop.dataloader = dataloader\n runner.model = model\n\n hooks = PGGANFetchDataHook()\n hooks.before_train_iter(runner, 0, None)\n\n for scale, target_bz in self.grow_scale_dataset_cfg[\n 'gpu_samples_per_scale'].items():\n\n model._next_scale_int = torch.tensor(int(scale), dtype=torch.int32)\n hooks.before_train_iter(runner, 0, None)\n self.assertEqual(runner.train_loop.dataloader.batch_size,\n target_bz)\n # check attribute of default sampler\n sampler = runner.train_loop.dataloader.sampler\n self.assertEqual(sampler.seed, default_sampler.seed)\n self.assertEqual(sampler.shuffle, default_sampler.shuffle)\n self.assertEqual(sampler.round_up, default_sampler.round_up)\n\n # set `_next_scale_int` as int\n delattr(model, '_next_scale_int')\n setattr(model, '_next_scale_int', 128)\n hooks.before_train_iter(runner, 0, None)\n self.assertEqual(runner.train_loop.dataloader.batch_size, 4)\n\n # test InfinitySampler\n infinite_sampler = InfiniteSampler(dataset)\n dataloader = DataLoader(\n batch_size=64,\n dataset=dataset,\n sampler=infinite_sampler,\n collate_fn=pseudo_collate)\n runner.train_loop.dataloader = dataloader\n for scale, target_bz in self.grow_scale_dataset_cfg[\n 'gpu_samples_per_scale'].items():\n\n model._next_scale_int = torch.tensor(int(scale), dtype=torch.int32)\n hooks.before_train_iter(runner, 0, None)\n self.assertEqual(runner.train_loop.dataloader.batch_size,\n target_bz)\n # check attribute of infinite sampler\n sampler = runner.train_loop.dataloader.sampler\n self.assertEqual(sampler.seed, infinite_sampler.seed)\n self.assertEqual(sampler.shuffle, infinite_sampler.shuffle)\n\n # test do not update + `IterBasedTrainLoop`\n hooks.before_train_iter(runner, 1, None)\n\n # test not `IterBasedTrainLoop`\n runner.train_loop = MagicMock()\n runner.train_loop.dataloader = dataloader\n runner.model = model\n model._next_scale_int = 8\n # test update\n hooks.before_train_iter(runner, 0, None)\n # test do not update\n hooks.before_train_iter(runner, 1, None)\n\n # test invalid sampler type\n dataloader = DataLoader(\n batch_size=64, dataset=dataset, collate_fn=pseudo_collate)\n runner.train_loop.dataloader = dataloader\n model._next_scale_int = 4\n self.assertRaises(ValueError, hooks.before_train_iter, runner, 0, None)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_pickle_data_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.engine.hooks import PickleDataHook\n\n\nclass ToyModel(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.a = torch.randn(10, 10)\n self.b = np.random.random((10, 10))\n\n\ndef test_PickleDataHook():\n hook = PickleDataHook(\n output_dir='./', data_name_list=['a', 'b', 'c'], interval=3)\n runner = MagicMock()\n runner.work_dir = './test/data'\n runner.iter = 0\n runner.model = ToyModel()\n\n # test after train iter\n hook.after_train_iter(runner, 0, None, None)\n hook.data_name_list = ['a', 'b']\n hook.after_train_iter(runner, 0, None, None)\n\n runner.model = MagicMock()\n runner.model.module = ToyModel()\n hook._pickle_data(runner)\n\n runner.iter = 2\n hook.after_train_iter(runner, 0, None, None)\n\n # test after run\n hook.after_run(runner)\n\n # test before run\n hook.before_run(runner)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_reduce_lr_scheduler_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import Mock\n\nimport pytest\nimport torch\nimport torch.nn.functional as F\nfrom mmengine import MessageHub\n\nfrom mmagic.engine.hooks import ReduceLRSchedulerHook\n\n\nclass ToyModel(torch.nn.Module):\n\n def __init__(self):\n super().__init__()\n self.conv1 = torch.nn.Conv2d(1, 1, 1)\n self.conv2 = torch.nn.Conv2d(1, 1, 1)\n\n def forward(self, x):\n return self.conv2(F.relu(self.conv1(x)))\n\n\ndef test_reduce_lr_scheduler_hook():\n\n scheduler = [Mock()]\n scheduler[0].step = Mock()\n\n runner = Mock()\n runner.epoch = 0\n runner.iter = 0\n runner.param_schedulers = scheduler\n runner.message_hub = MessageHub.get_instance('test-reduce-lr-scheduler')\n runner.message_hub.update_scalar('train/loss', 0.1)\n\n hook = ReduceLRSchedulerHook(val_metric=None, by_epoch=True, interval=2)\n scheduler[0].by_epoch = True\n hook.after_train_iter(runner, 0, data_batch=[dict(a=1)] * 3)\n runner.message_hub.update_scalar('train/loss', 0.2)\n hook.after_train_iter(runner, 0, data_batch=[dict(a=1)] * 2)\n assert abs(hook.sum_value - 0.7) < 1e-8\n assert hook.count == 5\n hook.after_train_epoch(runner)\n runner.epoch += 1\n assert abs(hook.sum_value - 0.7) < 1e-8\n assert hook.count == 5\n hook.after_train_epoch(runner)\n scheduler[0].step.assert_called()\n assert abs(hook.sum_value - 0) < 1e-8\n assert hook.count == 0\n value = hook.message_hub.get_scalar('value').current()\n assert abs(value - 0.14) < 1e-8\n hook.after_val_iter(runner, 0, data_batch=[dict(a=1)] * 2)\n hook.after_val_epoch(runner)\n\n hook = ReduceLRSchedulerHook(val_metric=None, by_epoch=False, interval=2)\n scheduler[0].by_epoch = False\n runner.message_hub.update_scalar('train/loss', 0.1)\n hook.after_train_iter(runner, 0, data_batch=[dict(a=1)] * 3)\n runner.iter += 1\n assert abs(hook.sum_value - 0.3) < 1e-8\n assert hook.count == 3\n runner.message_hub.update_scalar('train/loss', 0.3)\n hook.after_train_iter(runner, 0, data_batch=[dict(a=1)] * 2)\n scheduler[0].step.assert_called()\n value = hook.message_hub.get_scalar('value').current()\n assert abs(value - 0.18) < 1e-8\n assert abs(hook.sum_value - 0) < 1e-8\n assert hook.count == 0\n hook.after_train_epoch(runner)\n hook.after_val_iter(runner, 0, data_batch=[dict(a=1)] * 2)\n hook.after_val_epoch(runner)\n\n hook = ReduceLRSchedulerHook(val_metric='PSNR', by_epoch=True, interval=2)\n scheduler[0].by_epoch = False\n hook.after_train_iter(runner, 0)\n hook.after_train_epoch(runner)\n runner.epoch = 0\n hook.after_val_epoch(runner, metrics=dict(PSNR=40))\n assert abs(hook.sum_value - 40) < 1e-8, hook.sum_value\n assert hook.count == 1\n runner.epoch = 1\n hook.after_val_epoch(runner, metrics=dict(PSNR=50))\n scheduler[0].step.assert_called()\n value = hook.message_hub.get_scalar('value').current()\n assert abs(value - 45) < 1e-8\n assert abs(hook.sum_value - 0) < 1e-8, hook.sum_value\n assert hook.count == 0\n\n hook = ReduceLRSchedulerHook(val_metric='PSNR', by_epoch=False, interval=2)\n scheduler[0].by_epoch = False\n hook.after_train_iter(runner, 0)\n hook.after_train_epoch(runner)\n runner.epoch = 0\n hook.after_val_epoch(runner, metrics=dict(PSNR=40))\n scheduler[0].step.assert_called()\n value = hook.message_hub.get_scalar('value').current()\n assert abs(value - 40) < 1e-8\n assert abs(hook.sum_value - 0) < 1e-8, hook.sum_value\n assert hook.count == 0\n runner.epoch = 1\n runner.param_schedulers = dict(scheduler=scheduler)\n hook.after_val_epoch(runner, metrics=dict(PSNR=50))\n scheduler[0].step.assert_called()\n value = hook.message_hub.get_scalar('value').current()\n assert abs(value - 50) < 1e-8\n assert abs(hook.sum_value - 0) < 1e-8, hook.sum_value\n assert hook.count == 0\n\n with pytest.raises(AssertionError):\n runner.param_schedulers = dict(a='')\n hook.after_val_epoch(runner, metrics=dict(PSNR=50))\n\n with pytest.raises(TypeError):\n runner.param_schedulers = ''\n hook.after_val_epoch(runner, metrics=dict(PSNR=50))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_hooks/test_visualization_hook.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, Mock\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.testing import assert_allclose\nfrom mmengine.visualization import Visualizer\nfrom torch.utils.data.dataset import Dataset\n\nfrom mmagic.engine import VisualizationHook\nfrom mmagic.engine.hooks import BasicVisualizationHook\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\nfrom mmagic.visualization import ConcatImageVisualizer\n\nfrom mmagic.registry import MODELS # isort:skip # noqa\n\nregister_all_modules()\n\n\nclass TestBasicVisualizationHook(TestCase):\n\n def setUp(self) -> None:\n input = torch.rand(2, 3, 32, 32)\n data_sample = DataSample(\n path_rgb='rgb.png',\n tensor3d=torch.ones(3, 32, 32) *\n torch.tensor([[[0.1]], [[0.2]], [[0.3]]]),\n array3d=np.ones(shape=(32, 32, 3)) * [0.4, 0.5, 0.6],\n tensor4d=torch.ones(2, 3, 32, 32) * torch.tensor(\n [[[[0.1]], [[0.2]], [[0.3]]], [[[0.4]], [[0.5]], [[0.6]]]]),\n pixdata=torch.ones(1, 32, 32) * 0.6)\n self.data_batch = {'inputs': input, 'data_samples': [data_sample] * 2}\n\n output = copy.deepcopy(data_sample)\n output.outpixdata = np.ones(shape=(32, 32)) * 0.8\n self.outputs = [output] * 2\n\n self.vis = ConcatImageVisualizer(\n fn_key='path_rgb',\n img_keys=[\n 'tensor3d',\n 'array3d',\n 'pixdata',\n 'tensor4d',\n 'outpixdata',\n ],\n vis_backends=[dict(type='LocalVisBackend')],\n save_dir='work_dirs')\n\n def test_after_iter(self):\n runner = Mock()\n runner.iter = 1\n runner.visualizer = self.vis\n hook = BasicVisualizationHook()\n hook._after_iter(runner, 1, self.data_batch, self.outputs)\n\n img = mmcv.imread('work_dirs/vis_data/vis_image/rgb_1.png')\n assert img.shape == (64, 160, 3)\n\n hook._on_train = hook._on_test = hook._on_val = True\n runner.visualizer = Mock()\n runner.visualizer.add_datasample = Mock()\n hook._after_iter(\n runner, 1, self.data_batch, self.outputs, mode='train')\n runner.visualizer.add_datasample.assert_called\n hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='val')\n runner.visualizer.add_datasample.assert_called\n hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='test')\n assert runner.visualizer.assert_called\n\n hook._on_train = hook._on_test = hook._on_val = False\n hook._after_iter(\n runner, 1, self.data_batch, self.outputs, mode='train')\n assert runner.visualizer.assert_not_called\n hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='val')\n assert runner.visualizer.assert_not_called\n hook._after_iter(runner, 1, self.data_batch, self.outputs, mode='test')\n assert runner.visualizer.assert_not_called\n\n\nclass TestVisualizationHook(TestCase):\n\n Visualizer.get_instance('test-gen-visualizer')\n MessageHub.get_instance('test-gen-visualizer')\n\n def test_init(self):\n hook = VisualizationHook(\n interval=10, vis_kwargs_list=dict(type='Noise'))\n self.assertEqual(hook.interval, 10)\n self.assertEqual(hook.vis_kwargs_list, [dict(type='Noise')])\n self.assertEqual(hook.n_samples, 64)\n self.assertFalse(hook.show)\n\n hook = VisualizationHook(\n interval=10,\n vis_kwargs_list=[dict(type='Noise'),\n dict(type='Translation')])\n self.assertEqual(len(hook.vis_kwargs_list), 2)\n\n hook = VisualizationHook(\n interval=10, vis_kwargs_list=dict(type='GAN'), show=True)\n self.assertEqual(hook._visualizer._vis_backends, {})\n\n def test_vis_sample_with_gan_alias(self):\n gan_model_cfg = dict(\n type='DCGAN',\n noise_size=10,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='DCGANGenerator', output_scale=32, base_channels=32))\n model = MODELS.build(gan_model_cfg)\n runner = MagicMock()\n runner.model = model\n runner.train_dataloader = MagicMock()\n runner.train_dataloader.batch_size = 10\n\n hook = VisualizationHook(\n interval=10, vis_kwargs_list=dict(type='GAN'), n_samples=9)\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n # build a empty data sample\n data_batch = [\n dict(inputs=None, data_samples=DataSample()) for idx in range(10)\n ]\n hook.vis_sample(runner, 0, data_batch, None)\n _, called_kwargs = mock_visualuzer.add_datasample.call_args\n self.assertEqual(called_kwargs['name'], 'gan')\n self.assertEqual(called_kwargs['target_keys'], None)\n self.assertEqual(called_kwargs['vis_mode'], None)\n gen_batches = called_kwargs['gen_samples']\n self.assertEqual(len(gen_batches), 9)\n noise_in_gen_batches = torch.stack(\n [gen_batches[idx].noise for idx in range(9)], 0)\n noise_in_buffer = torch.cat([\n buffer['inputs']['noise'] for buffer in hook.inputs_buffer['GAN']\n ],\n dim=0)[:9]\n self.assertTrue((noise_in_gen_batches == noise_in_buffer).all())\n\n hook.vis_sample(runner, 1, data_batch, None)\n _, called_kwargs = mock_visualuzer.add_datasample.call_args\n gen_batches = called_kwargs['gen_samples']\n noise_in_gen_batches_new = torch.stack(\n [gen_batches[idx].noise for idx in range(9)], 0)\n self.assertTrue((noise_in_gen_batches_new == noise_in_buffer).all())\n\n def test_vis_sample_with_translation_alias(self):\n translation_cfg = dict(\n type='CycleGAN',\n data_preprocessor=dict(\n type='DataPreprocessor', data_keys=['img_photo', 'img_mask']),\n generator=dict(\n type='ResnetGenerator',\n in_channels=3,\n out_channels=3,\n base_channels=8,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=4,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=3,\n base_channels=8,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02)),\n default_domain='photo',\n reachable_domains=['photo', 'mask'],\n related_domains=['photo', 'mask'])\n model = MODELS.build(translation_cfg)\n\n class naive_dataset(Dataset):\n\n def __init__(self, max_len, train=False):\n self.max_len = max_len\n self.train = train\n\n def __len__(self):\n return self.max_len\n\n def __getitem__(self, index):\n weight = index if self.train else -index\n img_photo = torch.ones(3, 32, 32) * weight\n img_mask = torch.ones(3, 32, 32) * (weight + 1)\n return dict(\n inputs=dict(img_photo=img_photo, img_mask=img_mask),\n data_samples=DataSample(\n img_photo=img_photo, img_mask=img_mask))\n\n train_dataloader = MagicMock()\n train_dataloader.batch_size = 4\n train_dataloader.dataset = naive_dataset(max_len=15, train=True)\n val_dataloader = MagicMock()\n val_dataloader.batch_size = 4\n val_dataloader.dataset = naive_dataset(max_len=17)\n\n runner = MagicMock()\n runner.model = model\n runner.train_dataloader = train_dataloader\n runner.val_loop = MagicMock()\n runner.val_loop.dataloader = val_dataloader\n\n hook = VisualizationHook(\n interval=10,\n vis_kwargs_list=[\n dict(type='Translation'),\n dict(type='TranslationVal', name='cyclegan_val')\n ],\n n_samples=9)\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n # build a empty data sample\n data_batch = [\n dict(inputs=None, data_samples=DataSample()) for idx in range(4)\n ]\n hook.vis_sample(runner, 0, data_batch, None)\n called_kwargs_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(len(called_kwargs_list), 2)\n # trans_called_kwargs, trans_val_called_kwargs = called_kwargs_list\n _, trans_called_kwargs = called_kwargs_list[0]\n _, trans_val_called_kwargs = called_kwargs_list[1]\n self.assertEqual(trans_called_kwargs['name'], 'translation')\n self.assertEqual(trans_val_called_kwargs['name'], 'cyclegan_val')\n\n # test train gen samples\n trans_gen_sample = trans_called_kwargs['gen_samples']\n print(trans_gen_sample[0].keys())\n trans_gt_mask_list = [samp.img_mask for samp in trans_gen_sample]\n trans_gt_photo_list = [samp.img_photo for samp in trans_gen_sample]\n\n self.assertEqual(len(trans_gen_sample), 9)\n for idx, (mask, photo) in enumerate(\n zip(trans_gt_mask_list, trans_gt_photo_list)):\n sample_from_dataset = train_dataloader.dataset[idx]['inputs']\n # data sample in test mode --> do not normed\n assert_allclose(mask, sample_from_dataset['img_mask'])\n assert_allclose(photo, sample_from_dataset['img_photo'])\n\n # test val gen samples\n trans_gen_sample = trans_val_called_kwargs['gen_samples']\n trans_gt_mask_list = [samp.img_mask for samp in trans_gen_sample]\n trans_gt_photo_list = [samp.img_photo for samp in trans_gen_sample]\n\n self.assertEqual(len(trans_gen_sample), 9)\n for idx, (mask, photo) in enumerate(\n zip(trans_gt_mask_list, trans_gt_photo_list)):\n sample_from_dataset = val_dataloader.dataset[idx]['inputs']\n assert_allclose(mask, sample_from_dataset['img_mask'])\n assert_allclose(photo, sample_from_dataset['img_photo'])\n\n # check input buffer\n input_buffer = hook.inputs_buffer\n input_buffer['translation']\n\n # TODO: uncomment after support DDPM\n # def test_vis_ddpm_alias_with_user_defined_args(self):\n # ddpm_cfg = dict(\n # type='BasicGaussianDiffusion',\n # num_timesteps=4,\n # data_preprocessor=dict(type='DataPreprocessor'),\n # betas_cfg=dict(type='cosine'),\n # denoising=dict(\n # type='DenoisingUnet',\n # image_size=32,\n # in_channels=3,\n # base_channels=128,\n # resblocks_per_downsample=3,\n # attention_res=[16, 8],\n # use_scale_shift_norm=True,\n # dropout=0.3,\n # num_heads=4,\n # use_rescale_timesteps=True,\n # output_cfg=dict(mean='eps', var='learned_range')),\n # timestep_sampler=dict(type='UniformTimeStepSampler'))\n # model = MODELS.build(ddpm_cfg)\n # runner = MagicMock()\n # runner.model = model\n # runner.train_dataloader = MagicMock()\n # runner.train_dataloader.batch_size = 10\n\n # hook = VisualizationHook(\n # interval=10,\n # n_samples=2,\n # vis_kwargs_list=dict(\n # type='DDPMDenoising', vis_mode='gif', name='ddpm',\n # n_samples=3))\n # mock_visualuzer = MagicMock()\n # mock_visualuzer.add_datasample = MagicMock()\n # hook._visualizer = mock_visualuzer\n\n # # build a empty data sample\n # data_batch = [\n # dict(inputs=None, data_sample=DataSample())\n # for idx in range(10)\n # ]\n # hook.vis_sample(runner, 0, data_batch, None)\n # _, called_kwargs = mock_visualuzer.add_datasample.call_args\n # gen_samples = called_kwargs['gen_samples']\n # self.assertEqual(len(gen_samples), 3)\n # self.assertEqual(called_kwargs['n_row'], min(hook.n_row, 3))\n\n # # test user defined vis kwargs\n # hook.vis_kwargs_list = [\n # dict(\n # type='Arguments',\n # forward_mode='sampling',\n # name='ddpm_sample',\n # n_samples=2,\n # n_row=4,\n # vis_mode='gif',\n # n_skip=1,\n # forward_kwargs=dict(\n # forward_mode='sampling',\n # sample_kwargs=dict(show_pbar=True, save_intermedia=True))) # noqa\n # ]\n # mock_visualuzer = MagicMock()\n # mock_visualuzer.add_datasample = MagicMock()\n # hook._visualizer = mock_visualuzer\n\n # # build a empty data sample\n # data_batch = [\n # dict(inputs=None, data_sample=DataSample())\n # for idx in range(10)\n # ]\n # hook.vis_sample(runner, 0, data_batch, None)\n # _, called_kwargs = mock_visualuzer.add_datasample.call_args\n # gen_samples = called_kwargs['gen_samples']\n # self.assertEqual(len(gen_samples), 2)\n # self.assertEqual(called_kwargs['n_row'], min(hook.n_row, 2))\n\n def test_after_val_iter(self):\n model = MagicMock()\n hook = VisualizationHook(\n interval=10, n_samples=2, vis_kwargs_list=dict(type='GAN'))\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n runner = MagicMock()\n runner.model = model\n\n hook.after_val_iter(runner, 0, [dict()], [DataSample()])\n mock_visualuzer.assert_not_called()\n\n def test_after_train_iter(self):\n gan_model_cfg = dict(\n type='DCGAN',\n noise_size=10,\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='DCGANGenerator', output_scale=32, base_channels=32))\n model = MODELS.build(gan_model_cfg)\n runner = MagicMock()\n runner.model = model\n runner.train_dataloader = MagicMock()\n runner.train_dataloader.batch_size = 10\n\n hook = VisualizationHook(\n interval=2, vis_kwargs_list=dict(type='GAN'), n_samples=9)\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n # build a empty data sample\n data_batch = [\n dict(inputs=None, data_samples=DataSample()) for idx in range(10)\n ]\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n self.assertEqual(mock_visualuzer.add_datasample.call_count, 1)\n\n # test vis with messagehub info --> str\n mock_visualuzer.add_datasample.reset_mock()\n message_hub = MessageHub.get_current_instance()\n\n feat_map = torch.randn(4, 16, 4, 4)\n vis_results = dict(feat_map=feat_map)\n message_hub.update_info('vis_results', vis_results)\n\n hook.message_vis_kwargs = 'feat_map'\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n called_args_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(len(called_args_list), 2) # outputs + messageHub\n _, messageHub_vis_args = called_args_list[1]\n self.assertEqual(messageHub_vis_args['name'], 'train_feat_map')\n self.assertEqual(len(messageHub_vis_args['gen_samples']), 4)\n self.assertEqual(messageHub_vis_args['vis_mode'], None)\n self.assertEqual(messageHub_vis_args['n_row'], None)\n\n # test vis with messagehub info --> list[str]\n mock_visualuzer.add_datasample.reset_mock()\n\n hook.message_vis_kwargs = ['feat_map']\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n called_args_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(len(called_args_list), 2) # outputs + messageHub\n _, messageHub_vis_args = called_args_list[1]\n self.assertEqual(messageHub_vis_args['name'], 'train_feat_map')\n self.assertEqual(len(messageHub_vis_args['gen_samples']), 4)\n self.assertEqual(messageHub_vis_args['vis_mode'], None)\n self.assertEqual(messageHub_vis_args['n_row'], None)\n\n # test vis with messagehub info --> dict\n mock_visualuzer.add_datasample.reset_mock()\n\n hook.message_vis_kwargs = dict(key='feat_map', vis_mode='feature_map')\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n called_args_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(len(called_args_list), 2) # outputs + messageHub\n _, messageHub_vis_args = called_args_list[1]\n self.assertEqual(messageHub_vis_args['name'], 'train_feat_map')\n self.assertEqual(len(messageHub_vis_args['gen_samples']), 4)\n self.assertEqual(messageHub_vis_args['vis_mode'], 'feature_map')\n self.assertEqual(messageHub_vis_args['n_row'], None)\n\n # test vis with messagehub info --> list[dict]\n mock_visualuzer.add_datasample.reset_mock()\n\n feat_map = torch.randn(4, 16, 4, 4)\n x_t = [DataSample(info='x_t')]\n vis_results = dict(feat_map=feat_map, x_t=x_t)\n message_hub.update_info('vis_results', vis_results)\n\n hook.message_vis_kwargs = [\n dict(key='feat_map', vis_mode='feature_map'),\n dict(key='x_t')\n ]\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n called_args_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(len(called_args_list), 3) # outputs + messageHub\n # output_vis_args = called_args_list[0].kwargs\n _, feat_map_vis_args = called_args_list[1]\n self.assertEqual(feat_map_vis_args['name'], 'train_feat_map')\n self.assertEqual(len(feat_map_vis_args['gen_samples']), 4)\n self.assertEqual(feat_map_vis_args['vis_mode'], 'feature_map')\n self.assertEqual(feat_map_vis_args['n_row'], None)\n\n _, x_t_vis_args = called_args_list[2]\n self.assertEqual(x_t_vis_args['name'], 'train_x_t')\n self.assertEqual(len(x_t_vis_args['gen_samples']), 1)\n self.assertEqual(x_t_vis_args['vis_mode'], None)\n self.assertEqual(x_t_vis_args['n_row'], None)\n\n # test vis messageHub info --> errors\n hook.message_vis_kwargs = 'error'\n with self.assertRaises(RuntimeError):\n hook.after_train_iter(runner, 1, data_batch, None)\n\n message_hub.runtime_info.clear()\n with self.assertRaises(RuntimeError):\n hook.after_train_iter(runner, 1, data_batch, None)\n\n hook.message_vis_kwargs = dict(key='feat_map', vis_mode='feature_map')\n message_hub.update_info('vis_results', dict(feat_map='feat_map'))\n with self.assertRaises(TypeError):\n hook.after_train_iter(runner, 1, data_batch, None)\n\n def test_after_train_iter_contain_mul_elements(self):\n # test contain_mul_elements + n_row != None\n # n_row = 8, n_samples = 3, batch_size = 2, model_n_samples = 4\n # run math.ceil(3 / 2) = 2 times, visualize 2 * 4 = 8 samples,\n class MockModel:\n\n def __init__(self, n_samples):\n self.n_samples = n_samples\n\n def noise_fn(self, *args, **kwargs):\n return torch.randn(2, 2)\n\n def val_step(self, *args, **kwargs):\n return [DataSample() for _ in range(self.n_samples)]\n\n def eval(self):\n return self\n\n def train(self):\n return self\n\n runner = MagicMock()\n runner.model = MockModel(n_samples=4)\n runner.train_dataloader = MagicMock()\n runner.train_dataloader.batch_size = 2\n\n hook = VisualizationHook(\n interval=2, vis_kwargs_list=dict(type='GAN'), n_samples=3, n_row=8)\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n # build a empty data sample\n data_batch = [\n dict(inputs=None, data_samples=DataSample()) for idx in range(10)\n ]\n\n for idx in range(3):\n hook.after_train_iter(runner, idx, data_batch, None)\n self.assertEqual(mock_visualuzer.add_datasample.call_count, 1)\n called_args_list = mock_visualuzer.add_datasample.call_args_list[0]\n self.assertEqual(len(called_args_list[1]['gen_samples']), 8)\n\n def test_after_test_iter(self):\n model = MagicMock()\n hook = VisualizationHook(\n interval=10,\n n_samples=2,\n max_save_at_test=None,\n test_vis_keys=['ema', 'orig', 'new_model.x_t', 'gt_img'],\n vis_kwargs_list=dict(type='GAN'))\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n runner = MagicMock()\n runner.model = model\n\n gt_list = [torch.randn(3, 6, 6) for _ in range(4)]\n ema_list = [torch.randn(3, 6, 6) for _ in range(4)]\n orig_list = [torch.randn(3, 6, 6) for _ in range(4)]\n x_t_list = [torch.randn(3, 6, 6) for _ in range(4)]\n\n outputs = []\n for gt, ema, orig, x_t in zip(gt_list, ema_list, orig_list, x_t_list):\n gen_sample = DataSample(\n gt_img=gt,\n ema=DataSample(fake_img=ema),\n orig=DataSample(fake_img=orig),\n new_model=DataSample(x_t=x_t))\n outputs.append(gen_sample)\n\n hook.after_test_iter(runner, 42, [], outputs)\n args_list = mock_visualuzer.add_datasample.call_args_list\n self.assertEqual(\n len(args_list),\n len(hook.test_vis_keys_list) * len(gt_list))\n # check target consistency\n for idx, args in enumerate(args_list):\n _, called_kwargs = args\n gen_samples = called_kwargs['gen_samples']\n name = called_kwargs['name']\n batch_idx = called_kwargs['step']\n target_keys = called_kwargs['target_keys']\n\n self.assertEqual(len(gen_samples), 1)\n idx_in_outputs = idx // 4\n self.assertEqual(batch_idx, idx_in_outputs + 42 * len(outputs))\n self.assertEqual(outputs[idx_in_outputs], gen_samples[0])\n\n # check ema\n if idx % 4 == 0:\n self.assertEqual(target_keys, 'ema')\n self.assertEqual(name, 'test_ema')\n # check orig\n elif idx % 4 == 1:\n self.assertEqual(target_keys, 'orig')\n self.assertEqual(name, 'test_orig')\n # check x_t\n elif idx % 4 == 2:\n self.assertEqual(target_keys, 'new_model.x_t')\n self.assertEqual(name, 'test_new_model_x_t')\n # check gt\n else:\n self.assertEqual(target_keys, 'gt_img')\n self.assertEqual(name, 'test_gt_img')\n\n # test get target key automatically\n hook.test_vis_keys_list = None\n mock_visualuzer.add_datasample.reset_mock()\n hook.after_test_iter(runner, 42, [], outputs)\n\n kwargs_list = [\n args[1] for args in mock_visualuzer.add_datasample.call_args_list\n ]\n self.assertTrue(all([kwargs['target_keys'] for kwargs in kwargs_list]))\n\n # test get target key automatically with error\n outputs = [DataSample(ema=DataSample(fake_img=torch.randn(3, 6, 6)))]\n with self.assertRaises(AssertionError):\n hook.after_test_iter(runner, 42, [], outputs)\n\n # test max save time\n hook = VisualizationHook(\n interval=10,\n n_samples=2,\n test_vis_keys='ema',\n vis_kwargs_list=dict(type='GAN'),\n max_save_at_test=3)\n\n mock_visualuzer = MagicMock()\n mock_visualuzer.add_datasample = MagicMock()\n hook._visualizer = mock_visualuzer\n\n runner = MagicMock()\n runner.model = model\n\n ema_list = [torch.randn(3, 6, 6) for _ in range(4)]\n outputs = [\n DataSample(ema=DataSample(fake_img=ema)) for ema in ema_list\n ]\n hook.after_test_iter(runner, 42, [], outputs)\n mock_visualuzer.add_datasample.assert_not_called()\n\n hook.after_test_iter(runner, 0, [], outputs)\n assert mock_visualuzer.add_datasample.call_count == 3\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_optimizers/test_multi_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch.nn as nn\nfrom mmengine import MMLogger\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.engine.optimizers import MultiOptimWrapperConstructor\n\nlogger = MMLogger.get_instance('test_multi_optimizer_constructor')\n\n\nclass ToyModel(nn.Module):\n\n def __init__(self) -> None:\n super().__init__()\n\n self.generator = nn.Conv2d(3, 3, 1)\n self.discriminator = nn.Conv2d(3, 3, 1)\n\n\nclass TextEncoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.embedding = nn.Embedding(100, 100)\n\n\nclass ToyModel2(nn.Module):\n\n def __init__(self) -> None:\n super().__init__()\n\n self.m1 = ToyModel()\n self.m2 = nn.Conv2d(3, 3, 1)\n self.m3 = nn.Linear(2, 2)\n self.text_encoder = TextEncoder()\n\n\ndef test_optimizer_constructor():\n\n # test optimizer wrapper cfg is a dict\n optim_wrapper_constructor = MultiOptimWrapperConstructor(\n optim_wrapper_cfg=dict(\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n discriminator=dict(\n type='OptimWrapper', optimizer=dict(type='SGD', lr=0.1))))\n model = ToyModel()\n\n optim_wrapper_dict = optim_wrapper_constructor(model)\n assert optim_wrapper_dict.__class__.__name__ == 'OptimWrapperDict'\n assert set(optim_wrapper_dict.optim_wrappers) == set(\n ['generator', 'discriminator'])\n\n # test optimizer wrapper is dict of **modules**\n optim_wrapper = {\n '.*embedding': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-4,\n 'betas': (0.9, 0.99)\n }\n },\n 'm1.generator': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-5,\n 'betas': (0.9, 0.99)\n }\n },\n 'm2': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-5,\n }\n }\n }\n optim_wrapper_constructor = MultiOptimWrapperConstructor(\n optim_wrapper_cfg=dict(\n generator=dict(\n type='OptimWrapper',\n optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),\n discriminator=dict(\n type='OptimWrapper', optimizer=dict(type='SGD', lr=0.1))))\n\n optim_wrapper_cfg = {\n '.*embedding': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-4,\n 'betas': (0.9, 0.99)\n }\n },\n 'm1.generator': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-5,\n 'betas': (0.9, 0.99)\n }\n },\n 'm2': {\n 'type': 'OptimWrapper',\n 'optimizer': {\n 'type': 'Adam',\n 'lr': 1e-5,\n }\n }\n }\n optim_wrapper_constructor = MultiOptimWrapperConstructor(optim_wrapper_cfg)\n model = ToyModel2()\n optim_wrapper_dict = optim_wrapper_constructor(model)\n\n # optim_wrapper_cfg should be a dict\n with pytest.raises(TypeError):\n MultiOptimWrapperConstructor(1)\n\n # parawise_cfg should be set in each optimizer separately\n with pytest.raises(AssertionError):\n MultiOptimWrapperConstructor(dict(), dict())\n\n # test optimizer wrapper with multi param groups\n optim_wrapper_constructor = MultiOptimWrapperConstructor(\n optim_wrapper_cfg=dict(\n modules=['.*text_encoder', '.*generator', 'm2'],\n optimizer=dict(\n type='Adam',\n lr=1e-4,\n betas=(0.9, 0.99),\n ),\n accumulative_counts=4))\n model = ToyModel2()\n optim_wrapper = optim_wrapper_constructor(model)\n assert isinstance(optim_wrapper, OptimWrapper)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_optimizers/test_pggan_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nfrom mmengine.model import MMSeparateDistributedDataParallel\n\nfrom mmagic.engine import PGGANOptimWrapperConstructor\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestPGGANOptimWrapperConstructor(TestCase):\n\n pggan_cfg = dict(\n type='ProgressiveGrowingGAN',\n data_preprocessor=dict(type='DataPreprocessor'),\n noise_size=512,\n generator=dict(type='PGGANGenerator', out_scale=8),\n discriminator=dict(type='PGGANDiscriminator', in_scale=8),\n nkimgs_per_scale={\n '4': 600,\n '8': 1200\n },\n transition_kimgs=600,\n ema_config=dict(interval=1))\n\n base_lr = 0.001\n lr_schedule = dict(generator={'8': 0.0015}, discriminator={'8': 0.0015})\n optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=lr_schedule)\n\n def test(self):\n pggan = MODELS.build(self.pggan_cfg)\n optim_wrapper_dict_builder = PGGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(pggan)\n optim_keys = set(optim_wrapper_dict.keys())\n\n scales = self.pggan_cfg['nkimgs_per_scale'].keys()\n self.assertEqual(\n optim_keys,\n set([\n f'{model}_{scale}' for model in ['generator', 'discriminator']\n for scale in scales\n ]))\n\n # check lr\n for scale in scales:\n gen_optim = optim_wrapper_dict[f'generator_{scale}']\n gen_lr = gen_optim.optimizer.param_groups[0]['lr']\n\n disc_optim = optim_wrapper_dict[f'discriminator_{scale}']\n disc_lr = disc_optim.optimizer.param_groups[0]['lr']\n\n self.assertEqual(\n gen_lr,\n self.lr_schedule['generator'].get(str(scale), self.base_lr))\n self.assertEqual(\n disc_lr, self.lr_schedule['discriminator'].get(\n str(scale), self.base_lr))\n\n # test pggan is Wrapper\n pggan_with_wrapper = MagicMock(\n module=pggan, spec=MMSeparateDistributedDataParallel)\n optim_wrapper_dict = optim_wrapper_dict_builder(pggan_with_wrapper)\n\n # test raise error\n with self.assertRaises(TypeError):\n optim_wrapper_dict_builder = PGGANOptimWrapperConstructor(\n 'optim_wrapper')\n\n # test same optimizer\n optim_wrapper_cfg = deepcopy(self.optim_wrapper_cfg)\n optim_wrapper_cfg['reset_optim_for_new_scale'] = False\n optim_wrapper_dict_builder = PGGANOptimWrapperConstructor(\n optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(pggan)\n # check id are same\n gen_optims = [optim_wrapper_dict[k] for k in optim_keys if 'gen' in k]\n disc_optims = [\n optim_wrapper_dict[k] for k in optim_keys if 'disc' in k\n ]\n self.assertTrue(all([id(gen_optims[0]) == id(o) for o in gen_optims]))\n self.assertTrue(\n all([id(disc_optims[0]) == id(o) for o in disc_optims]))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_optimizers/test_singan_optimizer_constructor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nfrom mmagic.engine import SinGANOptimWrapperConstructor\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestSinGANOptimWrapperConstructor(TestCase):\n\n singan_cfg = dict(\n type='SinGAN',\n num_scales=2,\n data_preprocessor=dict(\n type='DataPreprocessor', non_image_keys=['input_sample']),\n generator=dict(\n type='SinGANMultiScaleGenerator',\n in_channels=3,\n out_channels=3,\n num_scales=2),\n discriminator=dict(\n type='SinGANMultiScaleDiscriminator', in_channels=3, num_scales=2),\n noise_weight_init=0.1,\n iters_per_scale=20,\n test_pkl_data=None)\n\n optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))))\n\n def test(self):\n singan = MODELS.build(self.singan_cfg)\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n optim_keys = set(optim_wrapper_dict.keys())\n self.assertEqual(\n optim_keys,\n set([\n f'{model}_{scale}' for model in ['generator', 'discriminator']\n for scale in range(2 + 1)\n ]))\n\n # test singan is Wrapper\n singan_with_wrapper = MagicMock(\n module=singan,\n generator=MagicMock(module=singan.generator),\n discriminator=MagicMock(module=singan.discriminator))\n optim_wrapper_dict = optim_wrapper_dict_builder(singan_with_wrapper)\n\n # test raise error\n with self.assertRaises(TypeError):\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor('test')\n\n with self.assertRaises(AssertionError):\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg, 'test')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_runner/test_log_processor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock\n\nfrom mmagic.engine import LogProcessor as LogProcessor\n\n\nclass TestLogProcessor:\n\n def setup(self):\n runner = MagicMock()\n runner.epoch = 1\n runner.iter = 10\n runner.max_epochs = 10\n runner.max_iters = 50\n runner.train_dataloader = [0] * 20\n runner.val_dataloader = [0] * 10\n runner.test_dataloader = [0] * 5\n runner.train_loop.dataloader = [0] * 20\n runner.val_loop.total_length = 10\n runner.test_loop.total_length = 5\n self.runner = runner\n\n def test_get_dataloader_size(self):\n log_processor = LogProcessor(by_epoch=True)\n del self.runner.train_loop.total_length\n assert log_processor._get_dataloader_size(self.runner, 'train') == 20\n assert log_processor._get_dataloader_size(self.runner, 'val') == 10\n assert log_processor._get_dataloader_size(self.runner, 'test') == 5\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_runner/test_loop_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock\n\nimport pytest\nfrom mmengine.evaluator import Evaluator as BaseEvaluator\n\nfrom mmagic.engine.runner.loop_utils import (is_evaluator,\n update_and_check_evaluator)\nfrom mmagic.evaluation import Evaluator\n\n\ndef test_is_evaluator():\n evaluator = dict(type='Evaluator', metrics=[dict(type='PSNR')])\n assert is_evaluator(evaluator)\n\n evaluator = [dict(type='PSNR'), dict(type='SSIM')]\n assert is_evaluator(evaluator)\n\n evaluator = MagicMock(spec=BaseEvaluator)\n assert is_evaluator(evaluator)\n\n evaluator = 'SSIM'\n assert not is_evaluator(evaluator)\n\n evaluator = [dict(metrics='PSNR'), dict(metrics='SSIM')]\n assert not is_evaluator(evaluator)\n\n evaluator = dict(type='PSNR')\n assert not is_evaluator(evaluator)\n\n\ndef test_update_and_check_evaluator():\n\n evaluator = MagicMock(spec=BaseEvaluator)\n assert evaluator == update_and_check_evaluator(evaluator)\n\n evaluator = MagicMock(spec=Evaluator)\n assert evaluator == update_and_check_evaluator(evaluator)\n\n evaluator = [dict(type='PSNR'), dict(type='SSIM')]\n evaluator = update_and_check_evaluator(evaluator)\n assert isinstance(evaluator, dict)\n assert evaluator['type'] == 'Evaluator'\n\n evaluator = 'this is wrong'\n with pytest.raises(AssertionError):\n update_and_check_evaluator(evaluator)\n\n evaluator = dict(metrics=[dict(type='PSNR')])\n evaluator = update_and_check_evaluator(evaluator)\n assert 'type' in evaluator\n assert evaluator['type'] == 'Evaluator'\n\n evaluator = dict(type='Evaluator', metrics=[dict(type='PSNR')])\n evaluator = update_and_check_evaluator(evaluator)\n assert evaluator['type'] == 'Evaluator'\n\n evaluator = dict(type='Evaluator', metrics=[dict(type='PSNR')])\n evaluator = update_and_check_evaluator(evaluator)\n assert evaluator['type'] == 'Evaluator'\n\n evaluator = dict(type='Evaluator')\n evaluator = update_and_check_evaluator(evaluator)\n assert evaluator['metrics'] is None\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_runner/test_multi_loops.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nfrom mmengine.evaluator import Evaluator as BaseEvaluator\n\nfrom mmagic.engine import MultiTestLoop, MultiValLoop\nfrom mmagic.evaluation import Evaluator\n\n\ndef build_dataloader(loader, **kwargs):\n if isinstance(loader, dict):\n dataset = MagicMock()\n dataloader = MagicMock()\n dataloader.dataset = dataset\n return dataloader\n else:\n return loader\n\n\ndef build_metrics(metrics):\n if isinstance(metrics, dict):\n return [MagicMock(**metrics)]\n elif isinstance(metrics, list):\n return [MagicMock(**metric) for metric in metrics]\n else:\n raise ValueError('Unsupported metrics type in MockRunner.')\n\n\ndef build_evaluator(evaluator):\n if isinstance(evaluator, BaseEvaluator):\n return evaluator\n\n if isinstance(evaluator, dict):\n\n # a dirty way to check Evaluator type\n if 'type' in evaluator and evaluator['type'] == 'Evaluator':\n spec = Evaluator\n else:\n spec = BaseEvaluator\n\n # if `metrics` in dict keys, it means to build customized evalutor\n if 'metrics' in evaluator:\n evaluator_ = MagicMock(spec=spec)\n evaluator_.metrics = build_metrics(evaluator['metrics'])\n return evaluator_\n # otherwise, default evalutor will be built\n else:\n evaluator_ = MagicMock(spec=spec)\n evaluator_.metrics = build_metrics(evaluator)\n return evaluator_\n\n elif isinstance(evaluator, list):\n # use the default `Evaluator`\n evaluator_ = MagicMock(spec=Evaluator)\n evaluator_.metrics = build_metrics(evaluator)\n return evaluator_\n else:\n raise TypeError(\n 'evaluator should be one of dict, list of dict, and Evaluator'\n f', but got {evaluator}')\n\n\ndef build_mock_runner():\n runner = MagicMock()\n runner.build_evaluator = build_evaluator\n runner.build_dataloader = build_dataloader\n return runner\n\n\nclass TestLoop(TestCase):\n\n def _test_init(self, is_val):\n LOOP_CLS = MultiValLoop if is_val else MultiTestLoop\n\n # test init with single evaluator\n runner = build_mock_runner()\n dataloaders = MagicMock()\n evaluators = [dict(prefix='m1'), dict(prefix='m2')]\n loop = LOOP_CLS(runner, dataloaders, evaluators)\n self.assertEqual(len(loop.evaluators), 1)\n self.assertIsInstance(loop.evaluators[0], Evaluator)\n self.assertEqual(loop.evaluators[0].metrics[0].prefix, 'm1')\n self.assertEqual(loop.evaluators[0].metrics[1].prefix, 'm2')\n\n # test init with single evaluator and dataloader is list\n runner = build_mock_runner()\n dataloaders = [MagicMock()]\n evaluators = dict(\n type='Evaluator', metrics=[dict(prefix='m1'),\n dict(prefix='m2')])\n loop = LOOP_CLS(runner, dataloaders, evaluators)\n self.assertEqual(len(loop.evaluators), 1)\n self.assertIsInstance(loop.evaluators[0], BaseEvaluator)\n self.assertEqual(loop.evaluators[0].metrics[0].prefix, 'm1')\n self.assertEqual(loop.evaluators[0].metrics[1].prefix, 'm2')\n\n # test init with multi evaluators\n runner = build_mock_runner()\n dataloaders = [MagicMock(), MagicMock()]\n evaluators = [\n dict(\n type='Evaluator',\n metrics=[dict(prefix='m1'),\n dict(prefix='m2')]),\n dict(metrics=dict(prefix='m3'))\n ]\n loop = LOOP_CLS(runner, dataloaders, evaluators)\n self.assertEqual(len(loop.evaluators), 2)\n self.assertIsInstance(loop.evaluators[0], BaseEvaluator)\n self.assertIsInstance(loop.evaluators[1], Evaluator)\n self.assertEqual(loop.evaluators[0].metrics[0].prefix, 'm1')\n self.assertEqual(loop.evaluators[0].metrics[1].prefix, 'm2')\n self.assertEqual(loop.evaluators[1].metrics[0].prefix, 'm3')\n\n # test call total length before self.run\n self.assertEqual(loop.total_length, 0)\n\n def test_init(self):\n self._test_init(True) # val\n self._test_init(False) # test\n\n def _test_run(self, is_val):\n # since we have tested init, we direct use predefined mock object to\n # test run function\n LOOP_CLS = MultiValLoop if is_val else MultiTestLoop\n\n # test single evaluator\n runner = build_mock_runner()\n\n dataloader = MagicMock()\n dataloader.batch_size = 3\n\n metric1, metric2, metric3 = MagicMock(), MagicMock(), MagicMock()\n\n evaluator = MagicMock(spec=Evaluator)\n evaluator.prepare_metrics = MagicMock()\n evaluator.prepare_samplers = MagicMock(\n return_value=[[[metric1, metric2],\n [dict(inputs=1), dict(\n inputs=2)]], [[metric3], [dict(inputs=4)]]])\n\n loop = LOOP_CLS(\n runner=runner, dataloader=dataloader, evaluator=evaluator)\n assert len(loop.evaluators) == 1\n assert loop.evaluators[0] == evaluator\n\n # test run\n loop.run()\n\n assert loop.total_length == 3\n call_args_list = evaluator.call_args_list\n for idx, call_args in enumerate(call_args_list):\n if idx == 0:\n inputs = dict(inputs=1)\n elif idx == 1:\n inputs = dict(inputs=2)\n else:\n inputs = dict(inputs=4)\n assert call_args[1] == inputs\n\n # test multi evaluator\n runner = build_mock_runner()\n dataloader = MagicMock()\n dataloader.batch_size = 3\n\n metric11, metric12, metric13 = MagicMock(), MagicMock(), MagicMock()\n metric21 = MagicMock()\n evaluator1 = MagicMock(spec=Evaluator)\n evaluator1.prepare_metrics = MagicMock()\n evaluator1.prepare_samplers = MagicMock(\n return_value=[[[metric11, metric12],\n [dict(inputs=1), dict(\n inputs=2)]], [[metric13], [dict(inputs=4)]]])\n evaluator2 = MagicMock(spec=Evaluator)\n evaluator2.prepare_metrics = MagicMock()\n evaluator2.prepare_samplers = MagicMock(\n return_value=[[[metric21], [dict(inputs=3)]]])\n loop = LOOP_CLS(\n runner=runner,\n dataloader=[dataloader, dataloader],\n evaluator=[evaluator1, evaluator2])\n assert len(loop.evaluators) == 2\n assert loop.evaluators[0] == evaluator1\n assert loop.evaluators[1] == evaluator2\n\n loop.run()\n\n assert loop.total_length == 4\n call_args_list = evaluator1.call_args_list\n for idx, call_args in enumerate(call_args_list):\n if idx == 0:\n inputs = dict(inputs=1)\n elif idx == 1:\n inputs = dict(inputs=2)\n else:\n inputs = dict(inputs=4)\n assert call_args[1] == inputs\n call_args_list = evaluator2.call_args_list\n for idx, call_args in enumerate(call_args_list):\n if idx == 0:\n inputs = dict(inputs=3)\n assert call_args[1] == inputs\n\n def test_run(self):\n self._test_run(True) # val\n self._test_run(False) # test\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_schedulers/test_linear_lr_scheduler_with_interval.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom mmengine import MessageHub\n\nfrom mmagic.engine.schedulers import LinearLrInterval\n\n\nclass ToyModel(torch.nn.Module):\n\n def __init__(self):\n super().__init__()\n self.conv1 = torch.nn.Conv2d(1, 1, 1)\n self.conv2 = torch.nn.Conv2d(1, 1, 1)\n\n def forward(self, x):\n return self.conv2(F.relu(self.conv1(x)))\n\n\nclass TestLinearLrInterval(TestCase):\n\n def setUp(self):\n \"\"\"Setup the model and optimizer which are used in every test method.\n\n TestCase calls functions in this order: setUp() -> testMethod() ->\n tearDown() -> cleanUp()\n \"\"\"\n self.model = ToyModel()\n lr = 1\n self.optimizer = optim.SGD([{\n 'params': self.model.conv1.parameters()\n }],\n lr=lr,\n momentum=0.01,\n weight_decay=5e-4)\n\n def test_step(self):\n targets = torch.linspace(1., 0., 11)\n param_scheduler = LinearLrInterval(\n self.optimizer,\n interval=1,\n by_epoch=False,\n start_factor=1.0,\n end_factor=0,\n begin=0,\n end=10)\n messageHub = MessageHub.get_current_instance()\n for step in range(10):\n messageHub.update_info('iter', step)\n param_scheduler.step()\n np.testing.assert_almost_equal(param_scheduler._get_value(),\n targets[step].item())\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_engine/test_schedulers/test_reduce_lr_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom mmengine import MessageHub\n\nfrom mmagic.engine.schedulers import ReduceLR\n\n\nclass ToyModel(torch.nn.Module):\n\n def __init__(self):\n super().__init__()\n self.conv1 = torch.nn.Conv2d(1, 1, 1)\n self.conv2 = torch.nn.Conv2d(1, 1, 1)\n\n def forward(self, x):\n return self.conv2(F.relu(self.conv1(x)))\n\n\nclass TestLRScheduler(TestCase):\n\n def setUp(self):\n \"\"\"Setup the model and optimizer which are used in every test method.\n\n TestCase calls functions in this order: setUp() -> testMethod() ->\n tearDown() -> cleanUp()\n \"\"\"\n self.model = ToyModel()\n self.optimizer = optim.SGD(\n self.model.parameters(), lr=0.05, momentum=0.01, weight_decay=5e-4)\n\n def test_reduce_lr_scheduler(self):\n\n message_hub = MessageHub.get_instance('reduce_lr')\n\n scheduler = ReduceLR(self.optimizer, patience=1)\n scheduler.last_step = 0\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n scheduler.last_step = 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert abs(results[0] - 0.005) < 1e-8\n assert scheduler.num_bad_epochs == 0\n\n scheduler = ReduceLR(self.optimizer, patience=1, mode='max')\n scheduler.last_step = 0\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n scheduler.last_step = 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert abs(results[0] - 0.005) < 1e-8\n assert scheduler.num_bad_epochs == 0\n\n scheduler = ReduceLR(\n self.optimizer, patience=1, mode='max', threshold_mode='abs')\n scheduler.last_step = 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 1\n\n scheduler = ReduceLR(\n self.optimizer, patience=1, mode='min', threshold_mode='abs')\n scheduler.last_step = 1\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 0\n message_hub.update_scalar('value', 1)\n results = scheduler._get_value()\n assert results == [0.05]\n assert scheduler.num_bad_epochs == 1\n\n with pytest.raises(ValueError):\n ReduceLR(self.optimizer, mode='ysli')\n with pytest.raises(ValueError):\n ReduceLR(self.optimizer, threshold_mode='ysli')\n with pytest.raises(ValueError):\n ReduceLR(self.optimizer, factor=1.5)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_evaluator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nfrom mmagic.evaluation import (Evaluator, FrechetInceptionDistance,\n InceptionScore)\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nfid_loading_str = 'mmagic.evaluation.metrics.fid.FrechetInceptionDistance._load_inception' # noqa\nis_loading_str = 'mmagic.evaluation.metrics.inception_score.InceptionScore._load_inception' # noqa\n\nloading_mock = MagicMock(return_value=(MagicMock(), 'StyleGAN'))\n\n\nclass TestEvaluator(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.metrics = [\n dict(\n type='InceptionScore',\n fake_nums=10,\n inception_style='pytorch',\n sample_model='orig'),\n dict(\n type='FrechetInceptionDistance',\n fake_nums=11,\n inception_style='pytorch',\n sample_model='orig'),\n dict(type='TransFID', fake_nums=10),\n ]\n\n @patch(is_loading_str, loading_mock)\n @patch(fid_loading_str, loading_mock)\n def test_init(self):\n evaluator = Evaluator(self.metrics)\n self.assertFalse(evaluator.is_ready)\n\n @patch(is_loading_str, loading_mock)\n @patch(fid_loading_str, loading_mock)\n def test_prepare_metric(self):\n evaluator = Evaluator(self.metrics)\n model = MagicMock()\n model.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n with patch('mmagic.evaluation.metrics.fid.prepare_inception_feat'):\n evaluator.prepare_metrics(model, dataloader)\n self.assertTrue(evaluator.is_ready)\n\n evaluator = Evaluator(self.metrics)\n evaluator.metrics = [MagicMock()]\n evaluator.is_ready = True\n evaluator.prepare_metrics(model, dataloader)\n evaluator.metrics[0].assert_not_called()\n\n @patch(is_loading_str, loading_mock)\n @patch(fid_loading_str, loading_mock)\n def test_prepare_samplers(self):\n evaluator = Evaluator(self.metrics)\n\n model = MagicMock()\n model.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n dataloader.batch_size = 2\n\n metric_sampler_list = evaluator.prepare_samplers(model, dataloader)\n self.assertEqual(len(metric_sampler_list), 2)\n for metric_cls in [FrechetInceptionDistance, InceptionScore]:\n self.assertTrue(\n any([\n isinstance(m, metric_cls)\n for m in metric_sampler_list[0][0]\n ]))\n self.assertEqual(metric_sampler_list[0][1].max_length, 11)\n self.assertEqual(len(metric_sampler_list[0][1]), 6)\n\n # test prepare metrics with different `sample_model`\n cfg = deepcopy(self.metrics)\n cfg.append(\n dict(\n type='FrechetInceptionDistance',\n fake_nums=12,\n inception_style='pytorch',\n sample_model='ema'))\n evaluator = Evaluator(cfg)\n\n # mock metrics\n model = MagicMock()\n model.data_preprocessor.device = 'cpu'\n\n dataloader = MagicMock()\n dataloader.batch_size = 2\n\n metric_sampler_list = evaluator.prepare_samplers(model, dataloader)\n self.assertEqual(len(metric_sampler_list), 3)\n\n # test prepare sampler with metric.need_cond_input = True\n cfg = deepcopy(self.metrics)\n cfg += [\n dict(\n type='FrechetInceptionDistance',\n fake_nums=12,\n inception_style='pytorch',\n sample_model='ema'),\n dict(\n type='FrechetInceptionDistance',\n fake_nums=12,\n inception_style='pytorch',\n sample_model='ema',\n need_cond_input=True),\n dict(\n type='InceptionScore',\n fake_nums=10,\n inception_style='pytorch',\n sample_model='ema',\n need_cond_input=True),\n dict(\n type='InceptionScore',\n fake_nums=10,\n inception_style='pytorch',\n sample_model='orig',\n need_cond_input=True),\n ]\n # all metrics (5 groups): [[IS-orig, FID-orig], [TransFID-orig],\n # [FID-ema], [FID-cond-ema, IS-cond-ema],\n # [IS-cond-orig]]\n evaluator = Evaluator(cfg)\n\n # mock metrics\n model = MagicMock()\n model.data_preprocessor.device = 'cpu'\n\n dataloader = MagicMock()\n dataloader.batch_size = 2\n\n metric_sampler_list = evaluator.prepare_samplers(model, dataloader)\n self.assertEqual(len(metric_sampler_list), 5)\n\n @patch(is_loading_str, loading_mock)\n @patch(fid_loading_str, loading_mock)\n def test_process(self):\n evaluator = Evaluator(self.metrics)\n metrics_mock = [MagicMock(), MagicMock()]\n\n data_samples = [DataSample(a=1, b=2), dict(c=3, d=4)]\n\n # NOTE: data_batch is not used in evaluation\n evaluator.process(\n data_batch=None, data_samples=data_samples, metrics=metrics_mock)\n\n for metric in metrics_mock:\n metric.process.assert_called_with(None, [{\n 'a': 1,\n 'b': 2\n }, {\n 'c': 3,\n 'd': 4\n }])\n\n @patch(is_loading_str, loading_mock)\n @patch(fid_loading_str, loading_mock)\n def test_evaluate(self):\n evaluator = Evaluator(self.metrics)\n\n # mock metrics\n metric_mock1, metric_mock2 = MagicMock(), MagicMock()\n metric_mock1.evaluate = MagicMock(return_value=dict(m1=233))\n metric_mock2.evaluate = MagicMock(return_value=dict(m2=42))\n evaluator.metrics = [metric_mock1, metric_mock2]\n\n res = evaluator.evaluate()\n self.assertEqual(res, dict(m1=233, m2=42))\n\n # test raise value error with duplicate keys\n metric_mock3, metric_mock4 = MagicMock(), MagicMock()\n metric_mock3.evaluate = MagicMock(return_value=dict(m=3))\n metric_mock4.evaluate = MagicMock(return_value=dict(m=4))\n evaluator.metrics = [metric_mock3, metric_mock4]\n with self.assertRaises(ValueError):\n evaluator.evaluate()\n\n\nclass TestNonMetricEvaluator(TestCase):\n\n def test_init(self):\n evaluator = Evaluator(None)\n self.assertIsNone(evaluator.metrics)\n\n def test_prepare_metrics(self):\n evaluator = Evaluator(None)\n evaluator.prepare_metrics(None, None)\n self.assertTrue(evaluator.is_ready)\n\n def test_prepare_samplers(self):\n evaluator = Evaluator(None)\n metric_sampler_list = evaluator.prepare_samplers(None, None)\n self.assertEqual(metric_sampler_list, [[[None], []]])\n\n def test_process(self):\n evaluator = Evaluator(None)\n evaluator.process(None, None, None)\n\n def test_evalute(self):\n evaluator = Evaluator(None)\n output = evaluator.evaluate()\n self.assertEqual(output, {'No Metric': 'Nan'})\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_functional/test_fid_inception.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.evaluation.functional.fid_inception import (InceptionV3,\n fid_inception_v3)\n\n\nclass TestFIDInception:\n\n @classmethod\n def setup_class(cls):\n cls.load_fid_inception = False\n\n def test_fid_inception(self):\n inception = InceptionV3(load_fid_inception=self.load_fid_inception)\n imgs = torch.randn((2, 3, 256, 256))\n out = inception(imgs)[0]\n assert out.shape == (2, 2048, 1, 1)\n\n imgs = torch.randn((2, 3, 512, 512))\n out = inception(imgs)[0]\n assert out.shape == (2, 2048, 1, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_fid_inception_cuda(self):\n inception = InceptionV3(\n load_fid_inception=self.load_fid_inception).cuda()\n imgs = torch.randn((2, 3, 256, 256)).cuda()\n out = inception(imgs)[0]\n assert out.shape == (2, 2048, 1, 1)\n\n imgs = torch.randn((2, 3, 512, 512)).cuda()\n out = inception(imgs)[0]\n assert out.shape == (2, 2048, 1, 1)\n\n\ndef test_load_fid_inception():\n fid_net = fid_inception_v3(load_ckpt=False)\n\n inputs = torch.randn(1, 3, 299, 299)\n outputs = fid_net(inputs)\n assert outputs.shape == (1, 1008)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_functional/test_gaussian_funcs.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\n\nfrom mmagic.evaluation.functional import gauss_gradient\n\n\ndef test_gauss_gradient():\n img = np.random.randint(0, 255, size=(8, 8, 3))\n grad = gauss_gradient(img, 1.4)\n assert grad.shape == (8, 8, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_functional/test_inception_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock\n\nfrom torch.utils.data import Dataset\n\nfrom mmagic.datasets.transforms import LoadImageFromFile\nfrom mmagic.evaluation.functional.inception_utils import (\n get_inception_feat_cache_name_and_args, get_vgg_feat_cache_name_and_args)\n\n\ndef test_inception_feat_cache_name_args():\n dataloader = MagicMock()\n dataloader.dataset = MagicMock(spec=Dataset)\n dataloader.dataset.data_root = 'test_root'\n dataloader.dataset.data_prefix = 'test_prefix'\n dataloader.dataset.metainfo = dict(meta_info='test_meta')\n dataloader.dataset.pipeline = LoadImageFromFile(key='img')\n\n metric = MagicMock()\n metric.inception_style = 'test_style'\n metric.real_key = 'test_key'\n metric.inception_args = dict(args='test_args')\n cache_tag_1, args_1 = get_inception_feat_cache_name_and_args(\n dataloader, metric, 10, True, True)\n\n dataloader = MagicMock()\n dataloader.dataset = MagicMock(spec=Dataset)\n dataloader.dataset.data_root = 'test_root'\n dataloader.dataset.data_prefix = 'test_prefix'\n dataloader.dataset.metainfo = dict(meta_info='test_meta')\n dataloader.dataset.pipeline = LoadImageFromFile(key='img')\n\n metric = MagicMock()\n metric.inception_style = 'test_style'\n metric.real_key = 'test_key'\n metric.inception_args = dict(args='test_args')\n cache_tag_2, args_2 = get_inception_feat_cache_name_and_args(\n dataloader, metric, 10, True, True)\n # check whether cache name are same with the same inputs\n assert cache_tag_1 == cache_tag_2\n assert args_1 == args_2\n\n\ndef test_vgg_feat_cache_name_args():\n dataloader = MagicMock()\n dataloader.dataset = MagicMock(spec=Dataset)\n dataloader.dataset.data_root = 'test_root'\n dataloader.dataset.data_prefix = 'test_prefix'\n dataloader.dataset.metainfo = dict(meta_info='test_meta')\n dataloader.dataset.pipeline = LoadImageFromFile(key='img')\n\n metric = MagicMock()\n metric.inception_style = 'test_style'\n cache_tag_1, args_1 = get_vgg_feat_cache_name_and_args(dataloader, metric)\n\n dataloader = MagicMock()\n dataloader.dataset = MagicMock(spec=Dataset)\n dataloader.dataset.data_root = 'test_root'\n dataloader.dataset.data_prefix = 'test_prefix'\n dataloader.dataset.metainfo = dict(meta_info='test_meta')\n dataloader.dataset.pipeline = LoadImageFromFile(key='img')\n cache_tag_2, args_2 = get_vgg_feat_cache_name_and_args(dataloader, metric)\n\n # check whether cache name are same with the same inputs\n assert cache_tag_1 == cache_tag_2\n assert args_1 == args_2\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_base_gen_metric.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock, patch\n\nimport pytest\nimport torch\nfrom mmengine.model import MMDistributedDataParallel\n\nfrom mmagic.evaluation.metrics.base_gen_metric import (GenerativeMetric,\n GenMetric)\n\n\ndef mock_collect_fn(results, *args, **kwargs):\n return results[0]\n\n\n@patch('mmagic.evaluation.metrics.base_gen_metric.collect_results',\n mock_collect_fn)\nclass ToyMetric(GenMetric):\n\n def process(self, data_batch, data_samples):\n return\n\n def compute_metrics(self, results):\n return dict(score=1)\n\n\n@patch('mmagic.evaluation.metrics.base_gen_metric.collect_results',\n mock_collect_fn)\nclass ToyGenerativeMetric(GenerativeMetric):\n\n def process(self, data_batch, data_samples):\n return\n\n def compute_metrics(self, results):\n return dict(score=1)\n\n\ndef test_GenMetric():\n metric = ToyMetric(10, 10)\n assert metric.real_nums_per_device == 10\n\n metric.real_results = []\n\n # test get_metric_sampler\n model = MagicMock()\n dataset = MagicMock()\n dataset.__len__.return_value = 10\n dataloader = MagicMock()\n dataloader.batch_size = 4\n dataloader.dataset = dataset\n sampler = metric.get_metric_sampler(model, dataloader, [metric])\n assert sampler.dataset == dataset\n\n # test prepare\n model = MagicMock(spec=MMDistributedDataParallel)\n preprocessor = MagicMock()\n model.module = MagicMock()\n model.module.data_preprocessor = preprocessor\n metric.prepare(model, dataloader)\n assert metric.data_preprocessor == preprocessor\n\n # test raise error with dataset is length than real_nums\n dataset.__len__.return_value = 5\n with pytest.raises(AssertionError):\n metric.get_metric_sampler(model, dataloader, [metric])\n\n\ndef test_GenerativeMetric():\n metric = ToyGenerativeMetric(11, need_cond_input=True)\n assert metric.need_cond_input\n assert metric.real_nums == 0\n assert metric.fake_nums == 11\n\n # NOTE: only test whether returned sampler is correct in this UT\n def side_effect(index):\n return {'gt_label': [i for i in range(index, index + 3)]}\n\n dataset = MagicMock()\n dataset.__len__ = MagicMock(return_value=2)\n dataset.get_data_info.side_effect = side_effect\n dataloader = MagicMock()\n dataloader.batch_size = 10\n dataloader.dataset = dataset\n\n model = MagicMock()\n sampler = metric.get_metric_sampler(model, dataloader, [metric])\n assert sampler.dataset == dataset\n assert sampler.batch_size == 10\n assert sampler.max_length == 11\n assert sampler.sample_model == 'ema'\n\n # index passed to `side_effect` can only be 0 or 1\n assert len(sampler) == 2\n\n iterator = iter(sampler)\n output = next(iterator)\n assert output['inputs'] == dict(\n sample_model='ema', num_batches=10, sample_kwargs={})\n assert len(output['data_samples']) == 10\n\n target_label_list = [\n torch.FloatTensor([0, 1, 2]),\n torch.FloatTensor([1, 2, 3])\n ]\n # check if all cond in target label list\n for data in output['data_samples']:\n label = data.gt_label.label\n assert any([(label == tar).all() for tar in target_label_list])\n\n # test with sample kwargs\n sample_kwargs = dict(\n num_inference_steps=250, show_progress=True, classifier_scale=1.)\n metric = ToyGenerativeMetric(\n 11, need_cond_input=True, sample_kwargs=sample_kwargs)\n sampler = metric.get_metric_sampler(model, dataloader, [metric])\n iterator = iter(sampler)\n output = next(iterator)\n assert output['inputs'] == dict(\n sample_model='ema', num_batches=10, sample_kwargs=sample_kwargs)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_base_sample_wise_metric.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest.mock import MagicMock\n\nimport numpy as np\nimport torch\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.evaluation.metrics.base_sample_wise_metric import \\\n BaseSampleWiseMetric\n\n\nclass BaseSampleWiseMetricMock(BaseSampleWiseMetric):\n\n metric = 'metric'\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def process_image(self, *args, **kwargs):\n return 0\n\n\ndef test_compute_metrics():\n metric = BaseSampleWiseMetricMock()\n\n results = []\n key = 'metric'\n total = 0\n n = 0\n for i in range(10):\n results.append({'batch_size': i, key: i})\n total += i * i\n n += i\n average = metric.compute_metrics(results)\n assert average[key] == total / n\n\n\ndef test_process():\n metric = BaseSampleWiseMetricMock()\n\n mask = np.ones((32, 32, 3)) * 2\n mask[:16] *= 0\n gt = np.ones((32, 32, 3)) * 2\n data_sample = dict(gt_img=gt, mask=mask)\n data_batch = [dict(data_samples=data_sample)]\n predictions = [dict(pred_img=np.ones((32, 32, 3)))]\n\n data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt), mask=torch.from_numpy(mask))))\n\n predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in predictions[0].items()\n })\n\n for d, p in zip(data_batch, predictions):\n d['output'] = p\n\n predictions = data_batch\n metric.process(data_batch, predictions)\n assert len(metric.results) == 2\n assert metric.results[0]['metric'] == 0\n\n\ndef test_prepare():\n data_root = 'tests/data/image/'\n dataset = BasicImageDataset(\n metainfo=dict(dataset_type='sr_annotation_dataset', task_name='sisr'),\n data_root=data_root,\n data_prefix=dict(img='lq', gt='gt'),\n filename_tmpl=dict(img='{}_x4'),\n pipeline=[])\n dataloader = DataLoader(dataset)\n\n metric = BaseSampleWiseMetricMock()\n model = MagicMock()\n model.data_preprocessor = MagicMock()\n\n metric.prepare(model, dataloader)\n assert metric.SAMPLER_MODE == 'normal'\n assert metric.sample_model == 'orig'\n assert metric.size == 1\n assert metric.data_preprocessor == model.data_preprocessor\n\n metric.get_metric_sampler(None, dataloader, [])\n assert dataloader == dataloader\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_connectivity_error.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.datasets.transforms import LoadImageFromFile\nfrom mmagic.evaluation.metrics import ConnectivityError\n\n\nclass TestMattingMetrics:\n\n @classmethod\n def setup_class(cls):\n # Make sure these values are immutable across different test cases.\n\n # This test depends on the interface of loading\n # if loading is changed, data should be change accordingly.\n test_path = Path(__file__).parent.parent.parent\n alpha_path = (\n test_path / 'data' / 'matting_dataset' / 'alpha' / 'GT05.jpg')\n\n results = dict(alpha_path=alpha_path)\n config = dict(key='alpha')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['alpha'].ndim == 3\n\n gt_alpha = np.ones((32, 32), dtype=np.uint8) * 255\n trimap = np.zeros((32, 32), dtype=np.uint8)\n trimap[:16, :16] = 128\n trimap[16:, 16:] = 255\n # non-masked pred_alpha\n pred_alpha = torch.zeros((32, 32), dtype=torch.uint8)\n # masked pred_alpha\n masked_pred_alpha = pred_alpha.clone()\n masked_pred_alpha[trimap == 0] = 0\n masked_pred_alpha[trimap == 255] = 255\n\n gt_alpha = gt_alpha[None, ...]\n trimap = trimap[None, ...]\n\n cls.data_batch = [{\n 'inputs': [],\n 'data_samples': {\n 'ori_trimap': torch.from_numpy(trimap),\n 'ori_alpha': torch.from_numpy(gt_alpha),\n },\n }]\n\n cls.data_samples = [d_['data_samples'] for d_ in cls.data_batch]\n\n cls.bad_preds1_ = [{'pred_alpha': pred_alpha}]\n # pred_alpha should be masked by trimap before evaluation\n cls.bad_preds1 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds1, cls.bad_preds1_):\n d['output'] = p\n\n cls.bad_preds2_ = [{'pred_alpha': pred_alpha[0]}]\n # pred_alpha should be 3 dimensional\n cls.bad_preds2 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds2, cls.bad_preds2_):\n d['output'] = p\n\n cls.good_preds_ = [{'pred_alpha': masked_pred_alpha}]\n cls.good_preds = copy.deepcopy((cls.data_samples))\n for d, p in zip(cls.good_preds, cls.good_preds_):\n d['output'] = p\n\n def test_connectivity_error(self):\n \"\"\"Test connectivity error for evaluating predicted alpha matte.\"\"\"\n\n data_batch, bad_pred1, bad_pred2, good_pred = (\n self.data_batch,\n self.bad_preds1,\n self.bad_preds2,\n self.good_preds,\n )\n\n conn_err = ConnectivityError()\n\n with pytest.raises(ValueError):\n conn_err.process(data_batch, bad_pred1)\n\n with pytest.raises(ValueError):\n conn_err.process(data_batch, bad_pred2)\n\n # process 2 batches\n conn_err.process(data_batch, good_pred)\n conn_err.process(data_batch, good_pred)\n\n assert conn_err.results == [\n {\n 'conn_err': 0.256,\n },\n {\n 'conn_err': 0.256,\n },\n ]\n\n res = conn_err.compute_metrics(conn_err.results)\n\n assert list(res.keys()) == ['ConnectivityError']\n assert np.allclose(res['ConnectivityError'], 0.256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_equivariance.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nfrom mmengine.runner import Runner\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.evaluation import Equivariance\nfrom mmagic.models import DataPreprocessor, StyleGAN3\nfrom mmagic.models.editors.stylegan3 import StyleGAN3Generator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef process_fn(data_batch, predictions):\n\n _predictions = []\n for pred in predictions:\n _predictions.append(pred.to_dict())\n return data_batch, _predictions\n\n\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) < digit_version('1.8.0'),\n reason='version limitation')\nclass TestEquivariance:\n\n @classmethod\n def setup_class(cls):\n pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(64, 64)),\n PackInputs()\n ]\n dataset = BasicImageDataset(\n data_root='./tests/data/image/img_root',\n pipeline=pipeline,\n test_mode=True)\n cls.dataloader = Runner.build_dataloader(\n dict(\n batch_size=2,\n dataset=dataset,\n sampler=dict(type='DefaultSampler')))\n gan_data_preprocessor = DataPreprocessor()\n generator = StyleGAN3Generator(64, 8, 3, noise_size=8)\n cls.module = StyleGAN3(\n generator, data_preprocessor=gan_data_preprocessor)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n @torch.no_grad()\n def test_eq_cuda(self):\n eq = Equivariance(\n 2,\n eq_cfg=dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True),\n sample_mode='orig')\n self.module.cuda()\n sampler = eq.get_metric_sampler(self.module, self.dataloader, [eq])\n eq.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n eq.process(_data_batch, _predictions)\n eq_res = eq.compute_metrics(eq.fake_results)\n isinstance(eq_res['eqt_int'], float) and isinstance(\n eq_res['eqt_frac'], float) and isinstance(eq_res['eqr'], float)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_fid.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nimport pickle\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nimport numpy as np\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.runner import Runner\n\nfrom mmagic.datasets import PairedImageDataset\nfrom mmagic.evaluation import FrechetInceptionDistance, TransFID\nfrom mmagic.models import DataPreprocessor, Pix2Pix\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef process_fn(data_batch, predictions):\n\n _predictions = []\n for pred in predictions:\n _predictions.append(pred.to_dict())\n return data_batch, _predictions\n\n\ndef construct_inception_pkl(inception_path):\n data_root = osp.dirname(inception_path)\n os.makedirs(data_root, exist_ok=True)\n with open(inception_path, 'wb') as file:\n feat = np.random.rand(10, 2048)\n mean = np.mean(feat, 0)\n cov = np.cov(feat, rowvar=False)\n inception_feat = dict(raw_feature=feat, real_mean=mean, real_cov=cov)\n pickle.dump(inception_feat, file)\n\n\nclass inception_mock(nn.Module):\n\n def __init__(self, style):\n super().__init__()\n self.style = style\n\n def forward(self, x, *args, **kwargs):\n mock_feat = torch.randn(x.shape[0], 2048)\n if self.style.upper() in ['STYLEGAN', 'IS']:\n return mock_feat\n else:\n return [mock_feat]\n\n\nclass TestFID(TestCase):\n\n inception_pkl = osp.join(\n osp.dirname(__file__), '..', '..',\n 'data/inception_pkl/inception_feat.pkl')\n\n mock_inception_stylegan = MagicMock(\n return_value=(inception_mock('StyleGAN'), 'StyleGAN'))\n mock_inception_pytorch = MagicMock(\n return_value=(inception_mock('PyTorch'), 'PyTorch'))\n\n def test_init(self):\n construct_inception_pkl(self.inception_pkl)\n\n with patch.object(FrechetInceptionDistance, '_load_inception',\n self.mock_inception_stylegan):\n\n fid = FrechetInceptionDistance(\n fake_nums=2,\n real_key='real',\n fake_key='fake',\n inception_pkl=self.inception_pkl)\n\n self.assertFalse(fid.need_cond_input)\n self.assertIsNone(fid.real_mean)\n self.assertIsNone(fid.real_cov)\n\n module = MagicMock()\n module.data_preprocessor = MagicMock()\n module.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n fid.prepare(module, dataloader)\n\n self.assertIsNotNone(fid.real_mean)\n self.assertIsNotNone(fid.real_cov)\n\n def test_prepare(self):\n\n module = MagicMock()\n module.data_preprocessor = MagicMock()\n module.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n\n with patch.object(FrechetInceptionDistance, '_load_inception',\n self.mock_inception_stylegan):\n fid = FrechetInceptionDistance(\n fake_nums=2,\n real_nums=2,\n real_key='real',\n fake_key='fake',\n inception_pkl=self.inception_pkl)\n fid.prepare(module, dataloader)\n\n # NOTE: do not test load inception network to save time\n # def test_load_inception(self):\n # fid = FrechetInceptionDistance(\n # fake_nums=2,\n # real_nums=2,\n # real_key='real',\n # fake_key='fake',\n # inception_style='PyTorch',\n # inception_pkl=self.inception_pkl)\n # self.assertEqual(fid.inception_style.upper(), 'PYTORCH')\n\n def test_process_and_compute(self):\n with patch.object(FrechetInceptionDistance, '_load_inception',\n self.mock_inception_stylegan):\n fid = FrechetInceptionDistance(\n fake_nums=2,\n real_nums=2,\n real_key='real',\n fake_key='fake',\n inception_pkl=self.inception_pkl)\n gen_images = torch.randn(4, 3, 2, 2)\n gen_samples = [\n DataSample(fake=(gen_images[i])).to_dict() for i in range(4)\n ]\n fid.process(None, gen_samples)\n fid.process(None, gen_samples)\n\n fid.fake_results.clear()\n gen_sample = [\n DataSample(orig=DataSample(fake=torch.randn(3, 2, 2))).to_dict()\n ]\n fid.process(None, gen_sample)\n gen_sample = [\n DataSample(orig=DataSample(\n fake_img=torch.randn(3, 2, 2))).to_dict()\n ]\n fid.process(None, gen_sample)\n\n with patch.object(FrechetInceptionDistance, '_load_inception',\n self.mock_inception_pytorch):\n fid = FrechetInceptionDistance(\n fake_nums=2,\n real_nums=2,\n real_key='real',\n fake_key='fake',\n inception_style='PyTorch',\n inception_pkl=self.inception_pkl)\n module = MagicMock()\n module.data_preprocessor = MagicMock()\n module.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n fid.prepare(module, dataloader)\n gen_samples = [\n DataSample(fake_img=torch.randn(3, 2, 2)).to_dict()\n for _ in range(4)\n ]\n fid.process(None, gen_samples)\n\n metric = fid.evaluate()\n self.assertIsInstance(metric, dict)\n self.assertTrue('fid' in metric)\n self.assertTrue('mean' in metric)\n self.assertTrue('cov' in metric)\n\n\nclass TestTransFID:\n inception_pkl = osp.join(\n osp.dirname(__file__), '..', '..',\n 'data/inception_pkl/inception_feat.pkl')\n\n mock_inception_stylegan = MagicMock(\n return_value=(inception_mock('StyleGAN'), 'StyleGAN'))\n mock_inception_pytorch = MagicMock(\n return_value=(inception_mock('PyTorch'), 'PyTorch'))\n\n @classmethod\n def setup_class(cls):\n pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='edge',\n domain_b='shoe',\n color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_edge', 'img_shoe']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='Resize',\n scale=(256, 256),\n interpolation='bicubic'),\n dict(type='FixedCrop', keys=['img'], crop_size=(256, 256))\n ]),\n dict(type='PackInputs', keys=['img_edge', 'img_shoe', 'pair'])\n ]\n dataset = PairedImageDataset(\n data_root='tests/data/paired', pipeline=pipeline, test_mode=True)\n cls.dataloader = Runner.build_dataloader(\n dict(\n batch_size=2,\n dataset=dataset,\n sampler=dict(type='DefaultSampler')))\n gan_data_preprocessor = DataPreprocessor()\n generator = dict(\n type='UnetGenerator',\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02))\n discriminator = dict(\n type='PatchDiscriminator',\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02))\n cls.module = Pix2Pix(\n generator,\n discriminator,\n data_preprocessor=gan_data_preprocessor,\n default_domain='shoe',\n reachable_domains=['shoe'],\n related_domains=['shoe', 'edge'])\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_trans_fid_cuda(self):\n return\n with patch.object(TransFID, '_load_inception',\n self.mock_inception_stylegan):\n fid = TransFID(\n prefix='FID-Full',\n fake_nums=2,\n real_key='img_shoe',\n fake_key='fake_shoe',\n inception_style='PyTorch')\n self.module.cuda()\n sampler = fid.get_metric_sampler(self.module, self.dataloader, [fid])\n fid.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n fid.process(_data_batch, _predictions)\n fid_res = fid.compute_metrics(fid.fake_results)\n assert fid_res['fid'] >= 0 and fid_res['mean'] >= 0 and fid_res[\n 'cov'] >= 0\n\n def test_trans_fid_cpu(self):\n return\n with patch.object(TransFID, '_load_inception',\n self.mock_inception_stylegan):\n fid = TransFID(\n prefix='FID-Full',\n fake_nums=2,\n real_key='img_shoe',\n fake_key='fake_shoe',\n inception_style='PyTorch')\n sampler = fid.get_metric_sampler(self.module, self.dataloader, [fid])\n fid.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n fid.process(_data_batch, _predictions)\n fid_res = fid.compute_metrics(fid.fake_results)\n assert fid_res['fid'] >= 0 and fid_res['mean'] >= 0 and fid_res[\n 'cov'] >= 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_gradient_error.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.datasets.transforms import LoadImageFromFile\nfrom mmagic.evaluation.metrics import GradientError\n\n\nclass TestMattingMetrics:\n\n @classmethod\n def setup_class(cls):\n # Make sure these values are immutable across different test cases.\n\n # This test depends on the interface of loading\n # if loading is changed, data should be change accordingly.\n test_path = Path(__file__).parent.parent.parent\n alpha_path = (\n test_path / 'data' / 'matting_dataset' / 'alpha' / 'GT05.jpg')\n\n results = dict(alpha_path=alpha_path)\n config = dict(key='alpha')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['alpha'].ndim == 3\n\n gt_alpha = np.ones((32, 32), dtype=np.uint8) * 255\n trimap = np.zeros((32, 32), dtype=np.uint8)\n trimap[:16, :16] = 128\n trimap[16:, 16:] = 255\n # non-masked pred_alpha\n pred_alpha = torch.zeros((32, 32), dtype=torch.uint8)\n # masked pred_alpha\n masked_pred_alpha = pred_alpha.clone()\n masked_pred_alpha[trimap == 0] = 0\n masked_pred_alpha[trimap == 255] = 255\n\n gt_alpha = gt_alpha[None, ...]\n trimap = trimap[None, ...]\n\n cls.data_batch = [{\n 'inputs': [],\n 'data_samples': {\n 'ori_trimap': torch.from_numpy(trimap),\n 'ori_alpha': torch.from_numpy(gt_alpha),\n },\n }]\n\n cls.data_samples = [d_['data_samples'] for d_ in cls.data_batch]\n\n cls.bad_preds1_ = [{'pred_alpha': pred_alpha}]\n # pred_alpha should be masked by trimap before evaluation\n cls.bad_preds1 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds1, cls.bad_preds1_):\n d['output'] = p\n\n cls.bad_preds2_ = [{'pred_alpha': pred_alpha[0]}]\n # pred_alpha should be 3 dimensional\n cls.bad_preds2 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds2, cls.bad_preds2_):\n d['output'] = p\n\n cls.good_preds_ = [{'pred_alpha': masked_pred_alpha}]\n cls.good_preds = copy.deepcopy((cls.data_samples))\n for d, p in zip(cls.good_preds, cls.good_preds_):\n d['output'] = p\n\n def test_gradient_error(self):\n \"\"\"Test gradient error for evaluating predicted alpha matte.\"\"\"\n\n data_batch, bad_pred1, bad_pred2, good_pred = (\n self.data_batch,\n self.bad_preds1,\n self.bad_preds2,\n self.good_preds,\n )\n\n grad_err = GradientError()\n\n with pytest.raises(ValueError):\n grad_err.process(data_batch, bad_pred1)\n\n with pytest.raises(ValueError):\n grad_err.process(data_batch, bad_pred2)\n\n # process 2 batches\n grad_err.process(data_batch, good_pred)\n grad_err.process(data_batch, good_pred)\n\n assert len(grad_err.results) == 2\n for el in grad_err.results:\n assert list(el.keys()) == ['grad_err']\n np.testing.assert_almost_equal(el['grad_err'], 0.0028887)\n\n res = grad_err.compute_metrics(grad_err.results)\n\n assert list(res.keys()) == ['GradientError']\n np.testing.assert_almost_equal(el['grad_err'], 0.0028887)\n # assert np.allclose(res['GradientError'], 0.0028887)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_inception_score.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.runner import Runner\n\nfrom mmagic.datasets import PairedImageDataset\nfrom mmagic.evaluation import InceptionScore, TransIS\nfrom mmagic.models import DataPreprocessor, Pix2Pix\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef process_fn(data_batch, predictions):\n\n _predictions = []\n for pred in predictions:\n _predictions.append(pred.to_dict())\n return data_batch, _predictions\n\n\nclass inception_mock(nn.Module):\n\n def __init__(self, style):\n super().__init__()\n self.style = style\n\n def forward(self, x, *args, **kwargs):\n mock_feat = torch.randn(x.shape[0], 2048)\n if self.style.upper() in ['STYLEGAN', 'IS']:\n return mock_feat\n else:\n return [mock_feat]\n\n\nclass TestIS(TestCase):\n\n mock_inception_stylegan = MagicMock(\n return_value=(inception_mock('IS'), 'StyleGAN'))\n mock_inception_pytorch = MagicMock(\n return_value=(inception_mock('IS'), 'PyTorch'))\n\n def test_init(self):\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_stylegan):\n IS = InceptionScore(fake_nums=2, fake_key='fake')\n\n from PIL import Image\n self.assertEqual(IS.resize, True)\n self.assertEqual(IS.splits, 10)\n self.assertEqual(IS.resize_method, Image.BICUBIC)\n\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_stylegan):\n IS = InceptionScore(\n fake_nums=2, fake_key='fake', use_pillow_resize=False)\n self.assertEqual(IS.use_pillow_resize, False)\n self.assertEqual(IS.resize_method, 'bicubic')\n\n module = MagicMock()\n module.data_preprocessor = MagicMock()\n module.data_preprocessor.device = 'cpu'\n dataloader = MagicMock()\n IS.prepare(module, dataloader)\n\n # NOTE: do not test load inception network to save time\n # def test_load_inception(self):\n # IS = InceptionScore(fake_nums=2, inception_style='PyTorch')\n # self.assertEqual(IS.inception_style.upper(), 'PYTORCH')\n\n def test_process_and_compute(self):\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_stylegan):\n IS = InceptionScore(fake_nums=2, fake_key='fake')\n gen_images = torch.randn(4, 3, 2, 2)\n gen_samples = [\n DataSample(fake_img=img).to_dict() for img in gen_images\n ]\n IS.process(None, gen_samples)\n IS.process(None, gen_samples)\n\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_pytorch):\n IS = InceptionScore(\n fake_nums=2, fake_key='fake', inception_style='PyTorch')\n gen_images = torch.randn(4, 3, 2, 2)\n gen_samples = [\n DataSample(fake_img=img).to_dict() for img in gen_images\n ]\n IS.process(None, gen_samples)\n\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_stylegan):\n IS = InceptionScore(\n fake_nums=2, fake_key='fake', sample_model='orig')\n gen_samples = [\n DataSample(\n ema=DataSample(fake_img=torch.randn(3, 2, 2)),\n orig=DataSample(fake_img=torch.randn(3, 2, 2))).to_dict()\n ]\n IS.process(None, gen_samples)\n gen_samples = [\n DataSample(orig=DataSample(fake=torch.randn(3, 2, 2))).to_dict()\n ]\n IS.process(None, gen_samples)\n\n with patch.object(InceptionScore, '_load_inception',\n self.mock_inception_stylegan):\n IS = InceptionScore(\n fake_nums=2, fake_key='fake', sample_model='orig')\n gen_samples = [\n DataSample(fake_img=torch.randn(3, 2, 2)).to_dict()\n for _ in range(4)\n ]\n IS.process(None, gen_samples)\n\n metric = IS.evaluate()\n self.assertIsInstance(metric, dict)\n self.assertTrue('is' in metric)\n self.assertTrue('is_std' in metric)\n\n\nclass TestTransIS:\n inception_pkl = osp.join(\n osp.dirname(__file__), '..', '..',\n 'data/inception_pkl/inception_feat.pkl')\n\n mock_inception_stylegan = MagicMock(\n return_value=(inception_mock('StyleGAN'), 'StyleGAN'))\n mock_inception_pytorch = MagicMock(\n return_value=(inception_mock('PyTorch'), 'PyTorch'))\n\n @classmethod\n def setup_class(cls):\n pipeline = [\n dict(\n type='LoadPairedImageFromFile',\n key='pair',\n domain_a='edge',\n domain_b='shoe',\n color_type='color'),\n dict(\n type='TransformBroadcaster',\n mapping={'img': ['img_edge', 'img_shoe']},\n auto_remap=True,\n share_random_params=True,\n transforms=[\n dict(\n type='Resize',\n scale=(286, 286),\n interpolation='bicubic'),\n dict(type='FixedCrop', keys=['img'], crop_size=(256, 256))\n ]),\n dict(type='PackInputs', keys=['img_edge', 'img_shoe', 'pair'])\n ]\n dataset = PairedImageDataset(\n data_root='tests/data/paired', pipeline=pipeline, test_mode=True)\n cls.dataloader = Runner.build_dataloader(\n dict(\n batch_size=2,\n dataset=dataset,\n sampler=dict(type='DefaultSampler')))\n gan_data_preprocessor = DataPreprocessor()\n generator = dict(\n type='UnetGenerator',\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02))\n discriminator = dict(\n type='PatchDiscriminator',\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02))\n cls.module = Pix2Pix(\n generator,\n discriminator,\n data_preprocessor=gan_data_preprocessor,\n default_domain='shoe',\n reachable_domains=['shoe'],\n related_domains=['shoe', 'edge'])\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_trans_is_cuda(self):\n return\n with patch.object(TransIS, '_load_inception',\n self.mock_inception_stylegan):\n IS = TransIS(\n prefix='IS-Full',\n fake_nums=2,\n inception_style='PyTorch',\n fake_key='fake_shoe',\n sample_model='orig')\n self.module.cuda()\n sampler = IS.get_metric_sampler(self.module, self.dataloader, [IS])\n IS.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n IS.process(_data_batch, _predictions)\n IS_res = IS.compute_metrics(IS.fake_results)\n assert 'is' in IS_res and 'is_std' in IS_res\n\n def test_trans_is_cpu(self):\n return\n with patch.object(TransIS, '_load_inception',\n self.mock_inception_stylegan):\n IS = TransIS(\n prefix='IS-Full',\n fake_nums=2,\n inception_style='PyTorch',\n fake_key='fake_shoe',\n sample_model='orig')\n sampler = IS.get_metric_sampler(self.module, self.dataloader, [IS])\n IS.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n IS.process(_data_batch, _predictions)\n IS_res = IS.compute_metrics(IS.fake_results)\n assert 'is' in IS_res and 'is_std' in IS_res\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_mae.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\n\nfrom mmagic.evaluation.metrics import MAE\n\n\nclass TestPixelMetrics:\n\n @classmethod\n def setup_class(cls):\n\n mask = np.ones((32, 32, 3)) * 2\n mask[:16] *= 0\n gt = np.ones((32, 32, 3)) * 2\n data_sample = dict(gt_img=gt, mask=mask, gt_channel_order='bgr')\n cls.data_batch = [dict(data_samples=data_sample)]\n cls.predictions = [dict(pred_img=np.ones((32, 32, 3)))]\n\n cls.data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt),\n mask=torch.from_numpy(mask),\n img_channel_order='bgr')))\n cls.predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in cls.predictions[0].items()\n })\n\n for d, p in zip(cls.data_batch, cls.predictions):\n d['output'] = p\n cls.predictions = cls.data_batch\n\n def test_mae(self):\n\n # Single MAE\n mae = MAE()\n mae.process(self.data_batch, self.predictions)\n result = mae.compute_metrics(mae.results)\n assert 'MAE' in result\n np.testing.assert_almost_equal(result['MAE'], 0.003921568627)\n\n # Masked MAE\n mae = MAE(mask_key='mask', prefix='MAE')\n mae.process(self.data_batch, self.predictions)\n result = mae.compute_metrics(mae.results)\n assert 'MAE' in result\n np.testing.assert_almost_equal(result['MAE'], 0.003921568627)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_matting_mse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.datasets.transforms import LoadImageFromFile\nfrom mmagic.evaluation.metrics import MattingMSE\n\n\nclass TestMattingMetrics:\n\n @classmethod\n def setup_class(cls):\n # Make sure these values are immutable across different test cases.\n\n # This test depends on the interface of loading\n # if loading is changed, data should be change accordingly.\n test_path = Path(__file__).parent.parent.parent\n alpha_path = (\n test_path / 'data' / 'matting_dataset' / 'alpha' / 'GT05.jpg')\n\n results = dict(alpha_path=alpha_path)\n config = dict(key='alpha')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['alpha'].ndim == 3\n\n gt_alpha = np.ones((32, 32), dtype=np.uint8) * 255\n trimap = np.zeros((32, 32), dtype=np.uint8)\n trimap[:16, :16] = 128\n trimap[16:, 16:] = 255\n # non-masked pred_alpha\n pred_alpha = torch.zeros((32, 32), dtype=torch.uint8)\n # masked pred_alpha\n masked_pred_alpha = pred_alpha.clone()\n masked_pred_alpha[trimap == 0] = 0\n masked_pred_alpha[trimap == 255] = 255\n\n gt_alpha = gt_alpha[None, ...]\n trimap = trimap[None, ...]\n\n cls.data_batch = [{\n 'inputs': [],\n 'data_samples': {\n 'ori_trimap': torch.from_numpy(trimap),\n 'ori_alpha': torch.from_numpy(gt_alpha),\n },\n }]\n\n cls.data_samples = [d_['data_samples'] for d_ in cls.data_batch]\n\n cls.bad_preds1_ = [{'pred_alpha': pred_alpha}]\n # pred_alpha should be masked by trimap before evaluation\n cls.bad_preds1 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds1, cls.bad_preds1_):\n d['output'] = p\n\n cls.bad_preds2_ = [{'pred_alpha': pred_alpha[0]}]\n # pred_alpha should be 3 dimensional\n cls.bad_preds2 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds2, cls.bad_preds2_):\n d['output'] = p\n\n cls.good_preds_ = [{'pred_alpha': masked_pred_alpha}]\n cls.good_preds = copy.deepcopy((cls.data_samples))\n for d, p in zip(cls.good_preds, cls.good_preds_):\n d['output'] = p\n\n def test_mse(self):\n \"\"\"Test MattingMSE for evaluating predicted alpha matte.\"\"\"\n\n data_batch, bad_pred1, bad_pred2, good_pred = (\n self.data_batch,\n self.bad_preds1,\n self.bad_preds2,\n self.good_preds,\n )\n\n mse = MattingMSE()\n\n with pytest.raises(ValueError):\n mse.process(data_batch, bad_pred1)\n\n with pytest.raises(ValueError):\n mse.process(data_batch, bad_pred2)\n\n # process 2 batches\n mse.process(data_batch, good_pred)\n mse.process(data_batch, good_pred)\n\n assert mse.results == [\n {\n 'mse': 3.0,\n },\n {\n 'mse': 3.0,\n },\n ]\n\n res = mse.compute_metrics(mse.results)\n\n assert list(res.keys()) == ['MattingMSE']\n np.testing.assert_almost_equal(res['MattingMSE'], 3.0)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_metrics_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\n\nfrom mmagic.evaluation.metrics import metrics_utils\nfrom mmagic.evaluation.metrics.metrics_utils import reorder_image\n\n\ndef test_average():\n results = []\n key = 'data'\n total = 0\n n = 0\n for i in range(10):\n results.append({'batch_size': i, key: i})\n total += i * i\n n += i\n average = metrics_utils.average(results, key)\n assert average == total / n\n\n\ndef test_img_transform():\n img = np.random.randint(0, 255, size=(4, 4, 3))\n new_img = metrics_utils.img_transform(img, 0, 'HWC', None, 'rgb')\n assert new_img.shape == (4, 4, 3)\n\n\ndef test_obtain_data():\n img = np.random.randint(0, 255, size=(4, 4, 3))\n key = 'img'\n data_sample = {'data_samples': {key: img}}\n result = metrics_utils.obtain_data(data_sample, key)\n assert not (result - img).any()\n\n\ndef test_reorder_image():\n img_hw = np.ones((32, 32))\n img_hwc = np.ones((32, 32, 3))\n img_chw = np.ones((3, 32, 32))\n\n with pytest.raises(ValueError):\n reorder_image(img_hw, 'HH')\n\n output = reorder_image(img_hw)\n assert output.shape == (32, 32, 1)\n\n output = reorder_image(img_hwc)\n assert output.shape == (32, 32, 3)\n\n output = reorder_image(img_chw, input_order='CHW')\n assert output.shape == (32, 32, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_ms_ssim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport torch\n\nfrom mmagic.evaluation import MultiScaleStructureSimilarity\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestMS_SSIM(TestCase):\n\n def test_init(self):\n MS_SSIM = MultiScaleStructureSimilarity(\n fake_nums=10, fake_key='fake', sample_model='ema')\n self.assertEqual(MS_SSIM.num_pairs, 5)\n\n with self.assertRaises(AssertionError):\n MultiScaleStructureSimilarity(fake_nums=9)\n\n def test_process_and_evaluate(self):\n MS_SSIM = MultiScaleStructureSimilarity(\n fake_nums=4, fake_key='fake', sample_model='ema')\n\n input_batch_size = 6\n input_pairs = 6 // 2\n gen_images = torch.randint(0, 255, (input_batch_size, 3, 32, 32))\n gen_samples = [\n DataSample(fake_img=img).to_dict() for img in gen_images\n ]\n\n MS_SSIM.process(None, gen_samples)\n MS_SSIM.process(None, gen_samples)\n self.assertEqual(len(MS_SSIM.fake_results), input_pairs)\n metric_1 = MS_SSIM.evaluate()\n self.assertTrue('avg' in metric_1)\n\n MS_SSIM.fake_results.clear()\n MS_SSIM.process(None, gen_samples[:4])\n self.assertEqual(len(MS_SSIM.fake_results), 4 // 2)\n metric_2 = MS_SSIM.evaluate()\n self.assertTrue('avg' in metric_2)\n\n MS_SSIM.fake_results.clear()\n gen_samples = [\n DataSample(\n ema=DataSample(fake_img=torch.randint(0, 255, (3, 32, 32))),\n orig=DataSample(\n fake_img=torch.randint(0, 255, (3, 32, 32)))).to_dict()\n ] * 2\n MS_SSIM.process(None, gen_samples)\n\n gen_samples = [\n DataSample(\n ema=DataSample(\n fake=torch.randint(0, 255, (3, 32, 32)))).to_dict()\n ] * 2\n MS_SSIM.process(None, gen_samples)\n\n # test prefix\n MS_SSIM = MultiScaleStructureSimilarity(\n fake_nums=4, fake_key='fake', sample_model='ema', prefix='ms-ssim')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_mse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\n\nfrom mmagic.evaluation.metrics import MSE\n\n\nclass TestPixelMetrics:\n\n @classmethod\n def setup_class(cls):\n\n mask = np.ones((32, 32, 3)) * 2\n mask[:16] *= 0\n gt = np.ones((32, 32, 3)) * 2\n data_sample = dict(gt_img=gt, mask=mask, gt_channel_order='bgr')\n cls.data_batch = [dict(data_samples=data_sample)]\n cls.predictions = [dict(pred_img=np.ones((32, 32, 3)))]\n\n cls.data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt),\n mask=torch.from_numpy(mask),\n img_channel_order='bgr')))\n cls.predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in cls.predictions[0].items()\n })\n\n for d, p in zip(cls.data_batch, cls.predictions):\n d['output'] = p\n cls.predictions = cls.data_batch\n\n def test_mse(self):\n\n # Single MSE\n mae = MSE()\n mae.process(self.data_batch, self.predictions)\n result = mae.compute_metrics(mae.results)\n assert 'MSE' in result\n np.testing.assert_almost_equal(result['MSE'], 0.000015378700496)\n\n # Masked MSE\n mae = MSE(mask_key='mask', prefix='MSE')\n mae.process(self.data_batch, self.predictions)\n result = mae.compute_metrics(mae.results)\n assert 'MSE' in result\n np.testing.assert_almost_equal(result['MSE'], 0.000015378700496)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_niqe.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport mmcv\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.evaluation.metrics import NIQE, niqe\n\n\ndef test_niqe():\n img = mmcv.imread('tests/data/image/gt/baboon.png')\n\n predictions = [dict(pred_img=img)]\n predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in predictions[0].items()\n })\n\n data_batch = [\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(img), gt_channel_order='bgr'))\n ]\n data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(img), img_channel_order='bgr')))\n\n data_samples = [d_['data_samples'] for d_ in data_batch]\n for d, p in zip(data_samples, predictions):\n d['output'] = p\n\n niqe_ = NIQE()\n niqe_.process(data_batch, data_samples)\n result = niqe_.compute_metrics(niqe_.results)\n assert 'NIQE' in result\n np.testing.assert_almost_equal(result['NIQE'], 5.731541051885604)\n\n niqe_ = NIQE(key='gt_img', is_predicted=False)\n niqe_.process(data_batch, data_samples)\n result = niqe_.compute_metrics(niqe_.results)\n assert 'NIQE' in result\n np.testing.assert_almost_equal(result['NIQE'], 5.731541051885604)\n\n with pytest.raises(AssertionError):\n niqe_ = NIQE(convert_to='a')\n\n\ndef test_calculate_niqe():\n img = mmcv.imread('tests/data/image/gt/baboon.png')\n\n result = niqe(img[:, :, 0], crop_border=0, input_order='HW')\n np.testing.assert_almost_equal(result, 5.62525, decimal=5)\n result = niqe(img, crop_border=0, input_order='HWC', convert_to='y')\n np.testing.assert_almost_equal(result, 5.72957, decimal=5)\n result = niqe(img, crop_border=0, input_order='HWC', convert_to='gray')\n np.testing.assert_almost_equal(result, 5.73154, decimal=5)\n result = niqe(\n img.transpose(2, 0, 1),\n crop_border=0,\n input_order='CHW',\n convert_to='y')\n np.testing.assert_almost_equal(result, 5.72957, decimal=5)\n result = niqe(\n img.transpose(2, 0, 1),\n crop_border=0,\n input_order='CHW',\n convert_to='gray')\n np.testing.assert_almost_equal(result, 5.73154, decimal=5)\n\n result = niqe(img[:, :, 0], crop_border=6, input_order='HW')\n np.testing.assert_almost_equal(result, 5.82981, decimal=5)\n result = niqe(img, crop_border=6, input_order='HWC', convert_to='y')\n np.testing.assert_almost_equal(result, 6.10074, decimal=5)\n result = niqe(\n img.transpose(2, 0, 1),\n crop_border=6,\n input_order='CHW',\n convert_to='y')\n np.testing.assert_almost_equal(result, 6.10074, decimal=5)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_ppl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nfrom mmengine.runner import Runner\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.evaluation import PerceptualPathLength\nfrom mmagic.models import LSGAN, DataPreprocessor\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Generator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef process_fn(data_batch, predictions):\n\n _predictions = []\n for pred in predictions:\n _predictions.append(pred.to_dict())\n return data_batch, _predictions\n\n\nclass LPIPS_mock:\n\n def __init__(self, *args, **kwargs):\n pass\n\n def to(self, *args, **kwargs):\n return self\n\n def __call__(self, x1, x2, *args, **kwargs):\n num_batche = x1.shape[0]\n return torch.rand(num_batche, 1, 1, 1)\n\n\nclass TestPPL:\n\n @classmethod\n def setup_class(cls):\n pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Resize', scale=(64, 64)),\n PackInputs()\n ]\n dataset = BasicImageDataset(\n data_root='tests/data/image/img_root',\n pipeline=pipeline,\n test_mode=True)\n cls.dataloader = Runner.build_dataloader(\n dict(\n batch_size=2,\n dataset=dataset,\n sampler=dict(type='DefaultSampler')))\n gan_data_preprocessor = DataPreprocessor()\n generator = StyleGAN2Generator(64, 8)\n cls.module = LSGAN(generator, data_preprocessor=gan_data_preprocessor)\n\n @patch('lpips.LPIPS', LPIPS_mock)\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_ppl_cuda(self):\n ppl = PerceptualPathLength(\n fake_nums=2,\n prefix='ppl-z',\n space='Z',\n sample_model='orig',\n latent_dim=8)\n self.module.cuda()\n sampler = ppl.get_metric_sampler(self.module, self.dataloader, [ppl])\n ppl.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n ppl.process(_data_batch, _predictions)\n ppl_res = ppl.compute_metrics(ppl.fake_results)\n assert ppl_res['ppl_score'] >= 0\n ppl = PerceptualPathLength(\n fake_nums=2,\n prefix='ppl-w',\n space='W',\n sample_model='orig',\n latent_dim=8)\n sampler = ppl.get_metric_sampler(self.module, self.dataloader, [ppl])\n ppl.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n ppl.process(_data_batch, _predictions)\n ppl_res = ppl.compute_metrics(ppl.fake_results)\n assert ppl_res['ppl_score'] >= 0\n\n @patch('lpips.LPIPS', LPIPS_mock)\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_ppl_cpu(self):\n ppl = PerceptualPathLength(\n fake_nums=2,\n prefix='ppl-z',\n space='Z',\n sample_model='orig',\n latent_dim=8)\n sampler = ppl.get_metric_sampler(self.module, self.dataloader, [ppl])\n ppl.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n ppl.process(_data_batch, _predictions)\n ppl_res = ppl.compute_metrics(ppl.fake_results)\n assert ppl_res['ppl_score'] >= 0\n ppl = PerceptualPathLength(\n fake_nums=2,\n prefix='ppl-w',\n space='W',\n sample_model='orig',\n latent_dim=8)\n sampler = ppl.get_metric_sampler(self.module, self.dataloader, [ppl])\n ppl.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n _data_batch, _predictions = process_fn(data_batch, predictions)\n ppl.process(_data_batch, _predictions)\n ppl_res = ppl.compute_metrics(ppl.fake_results)\n assert ppl_res['ppl_score'] >= 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_precision_and_recall.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock, patch\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.runner import Runner\n\nfrom mmagic.datasets import BasicImageDataset\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.evaluation import PrecisionAndRecall\nfrom mmagic.models import LSGAN, DataPreprocessor\nfrom mmagic.models.editors.dcgan import DCGANGenerator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass vgg_pytorch_classifier(nn.Module):\n\n def __init__(self):\n super().__init__()\n\n def forward(self, x):\n return torch.randn(x.shape[0], 4096)\n\n\nclass vgg_mock(nn.Module):\n\n def __init__(self, style):\n super().__init__()\n self.classifier = nn.Sequential(nn.Identity(), nn.Identity(),\n nn.Identity(),\n vgg_pytorch_classifier())\n self.style = style\n\n def forward(self, x, *args, **kwargs):\n if self.style.upper() == 'STYLEGAN':\n return torch.randn(x.shape[0], 4096)\n else: # torch\n return torch.randn(x.shape[0], 7 * 7 * 512)\n\n\nclass TestPR:\n\n @classmethod\n def setup_class(cls):\n pipeline = [\n dict(type='LoadImageFromFile', key='gt'),\n dict(type='Resize', keys='gt', scale=(128, 128)),\n PackInputs()\n ]\n dataset = BasicImageDataset(\n data_root='tests/data/image/img_root',\n pipeline=pipeline,\n data_prefix=dict(gt=''),\n test_mode=True,\n recursive=True)\n cls.dataloader = Runner.build_dataloader(\n dict(\n batch_size=2,\n dataset=dataset,\n sampler=dict(type='DefaultSampler')))\n gan_data_preprocessor = DataPreprocessor()\n generator = DCGANGenerator(128, noise_size=10, base_channels=20)\n cls.module = LSGAN(\n generator, data_preprocessor=gan_data_preprocessor) # noqa\n\n cls.mock_vgg_pytorch = MagicMock(\n return_value=(vgg_mock('PyTorch'), 'False'))\n\n @pytest.mark.skipif(\n not torch.cuda.is_available(), reason='requires cuda') # noqa\n def test_pr_cuda(self):\n pr = PrecisionAndRecall(10, sample_model='orig', auto_save=False)\n self.module.cuda()\n sampler = pr.get_metric_sampler(self.module, self.dataloader, [pr])\n pr.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n predictions = self.module.test_step(data_batch)\n predictions = [pred.to_dict() for pred in predictions]\n pr.process(data_batch, predictions)\n pr_score = pr.compute_metrics(pr.fake_results)\n print(pr_score)\n assert pr_score['precision'] >= 0 and pr_score['recall'] >= 0\n\n def test_pr_cpu(self):\n with patch.object(PrecisionAndRecall, '_load_vgg',\n self.mock_vgg_pytorch):\n pr = PrecisionAndRecall(10, sample_model='orig', auto_save=False)\n sampler = pr.get_metric_sampler(self.module, self.dataloader, [pr])\n pr.prepare(self.module, self.dataloader)\n for data_batch in sampler:\n data_samples = self.module.test_step(data_batch)\n data_samples = [pred.to_dict() for pred in data_samples]\n pr.process(data_batch, data_samples)\n pr_score = pr.evaluate()\n print(pr_score)\n assert pr_score['precision'] >= 0 and pr_score['recall'] >= 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_psnr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.evaluation.metrics import PSNR, psnr\n\n\nclass TestPixelMetrics:\n\n @classmethod\n def setup_class(cls):\n\n mask = np.ones((32, 32, 3)) * 2\n mask[:16] *= 0\n gt = np.ones((32, 32, 3)) * 2\n data_sample = dict(gt_img=gt, mask=mask, gt_channel_order='bgr')\n cls.data_batch = [dict(data_samples=data_sample)]\n cls.predictions = [dict(pred_img=np.ones((32, 32, 3)))]\n\n cls.data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt),\n mask=torch.from_numpy(mask),\n img_channel_order='bgr')))\n cls.predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in cls.predictions[0].items()\n })\n\n for d, p in zip(cls.data_batch, cls.predictions):\n d['output'] = p\n cls.predictions = cls.data_batch\n\n def test_psnr(self):\n\n psnr_ = PSNR()\n psnr_.process(self.data_batch, self.predictions)\n result = psnr_.compute_metrics(psnr_.results)\n assert 'PSNR' in result\n np.testing.assert_almost_equal(result['PSNR'], 48.1308036)\n\n\ndef test_psnr():\n img_hw_1 = np.ones((32, 32))\n img_hwc_1 = np.ones((32, 32, 3))\n img_chw_1 = np.ones((3, 32, 32))\n img_hw_2 = np.ones((32, 32)) * 2\n img_hwc_2 = np.ones((32, 32, 3)) * 2\n img_chw_2 = np.ones((3, 32, 32)) * 2\n\n with pytest.raises(ValueError):\n psnr(img_hw_1, img_hw_2, crop_border=0, input_order='HH')\n\n with pytest.raises(ValueError):\n psnr(img_hw_1, img_hw_2, crop_border=0, convert_to='ABC')\n\n psnr_result = psnr(img_hw_1, img_hw_2, crop_border=0)\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n psnr_result = psnr(img_hwc_1, img_hwc_2, crop_border=0, input_order='HWC')\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n psnr_result = psnr(img_chw_1, img_chw_2, crop_border=0, input_order='CHW')\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n\n psnr_result = psnr(img_hw_1, img_hw_2, crop_border=2)\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n psnr_result = psnr(img_hwc_1, img_hwc_2, crop_border=3, input_order='HWC')\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n psnr_result = psnr(img_chw_1, img_chw_2, crop_border=4, input_order='CHW')\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n\n psnr_result = psnr(img_hwc_1, img_hwc_2, crop_border=0, convert_to=None)\n np.testing.assert_almost_equal(psnr_result, 48.1308036)\n psnr_result = psnr(img_hwc_1, img_hwc_2, crop_border=0, convert_to='Y')\n np.testing.assert_almost_equal(psnr_result, 49.4527218)\n\n # test float inf\n psnr_result = psnr(img_hw_1, img_hw_1, crop_border=0)\n assert psnr_result == float('inf')\n\n # test uint8\n img_hw_1 = np.zeros((32, 32), dtype=np.uint8)\n img_hw_2 = np.ones((32, 32), dtype=np.uint8) * 255\n psnr_result = psnr(img_hw_1, img_hw_2, crop_border=0)\n assert psnr_result == 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_sad.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.datasets.transforms import LoadImageFromFile\nfrom mmagic.evaluation.metrics import SAD\n\n\nclass TestMattingMetrics:\n\n @classmethod\n def setup_class(cls):\n # Make sure these values are immutable across different test cases.\n\n # This test depends on the interface of loading\n # if loading is changed, data should be change accordingly.\n test_path = Path(__file__).parent.parent.parent\n alpha_path = (\n test_path / 'data' / 'matting_dataset' / 'alpha' / 'GT05.jpg')\n\n results = dict(alpha_path=alpha_path)\n config = dict(key='alpha')\n image_loader = LoadImageFromFile(**config)\n results = image_loader(results)\n assert results['alpha'].ndim == 3\n\n gt_alpha = np.ones((32, 32), dtype=np.uint8) * 255\n trimap = np.zeros((32, 32), dtype=np.uint8)\n trimap[:16, :16] = 128\n trimap[16:, 16:] = 255\n # non-masked pred_alpha\n pred_alpha = torch.zeros((32, 32), dtype=torch.uint8)\n # masked pred_alpha\n masked_pred_alpha = pred_alpha.clone()\n masked_pred_alpha[trimap == 0] = 0\n masked_pred_alpha[trimap == 255] = 255\n\n gt_alpha = gt_alpha[None, ...]\n trimap = trimap[None, ...]\n\n cls.data_batch = [{\n 'inputs': [],\n 'data_samples': {\n 'ori_trimap': torch.from_numpy(trimap),\n 'ori_alpha': torch.from_numpy(gt_alpha),\n },\n }]\n\n cls.data_samples = [d_['data_samples'] for d_ in cls.data_batch]\n\n cls.bad_preds1_ = [{'pred_alpha': pred_alpha}]\n # pred_alpha should be masked by trimap before evaluation\n cls.bad_preds1 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds1, cls.bad_preds1_):\n d['output'] = p\n\n cls.bad_preds2_ = [{'pred_alpha': pred_alpha[0]}]\n # pred_alpha should be 3 dimensional\n cls.bad_preds2 = copy.deepcopy(cls.data_samples)\n for d, p in zip(cls.bad_preds2, cls.bad_preds2_):\n d['output'] = p\n\n cls.good_preds_ = [{'pred_alpha': masked_pred_alpha}]\n cls.good_preds = copy.deepcopy((cls.data_samples))\n for d, p in zip(cls.good_preds, cls.good_preds_):\n d['output'] = p\n\n def test_sad(self):\n \"\"\"Test SAD for evaluating predicted alpha matte.\"\"\"\n\n data_batch, bad_pred1, bad_pred2, good_pred = (\n self.data_batch,\n self.bad_preds1,\n self.bad_preds2,\n self.good_preds,\n )\n\n sad = SAD()\n\n with pytest.raises(ValueError):\n sad.process(data_batch, bad_pred1)\n\n with pytest.raises(ValueError):\n sad.process(data_batch, bad_pred2)\n\n # process 2 batches\n sad.process(data_batch, good_pred)\n sad.process(data_batch, good_pred)\n\n assert sad.results == [\n {\n 'sad': 0.768,\n },\n {\n 'sad': 0.768,\n },\n ]\n\n res = sad.compute_metrics(sad.results)\n\n assert list(res.keys()) == ['SAD']\n np.testing.assert_almost_equal(res['SAD'], 0.768)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_snr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.evaluation.metrics import SNR, snr\n\n\nclass TestPixelMetrics:\n\n @classmethod\n def setup_class(cls):\n\n mask = np.ones((32, 32, 3)) * 2\n mask[:16] *= 0\n gt = np.ones((32, 32, 3)) * 2\n data_sample = dict(gt_img=gt, mask=mask, gt_channel_order='bgr')\n cls.data_batch = [dict(data_samples=data_sample)]\n cls.predictions = [dict(pred_img=np.ones((32, 32, 3)))]\n\n cls.data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt),\n mask=torch.from_numpy(mask),\n img_channel_order='bgr')))\n cls.predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in cls.predictions[0].items()\n })\n\n for d, p in zip(cls.data_batch, cls.predictions):\n d['output'] = p\n cls.predictions = cls.data_batch\n\n def test_snr(self):\n\n snr_ = SNR()\n snr_.process(self.data_batch, self.predictions)\n result = snr_.compute_metrics(snr_.results)\n assert 'SNR' in result\n np.testing.assert_almost_equal(result['SNR'], 6.0206001996994)\n\n\ndef test_snr():\n img_hw_1 = np.ones((32, 32)) * 2\n img_hwc_1 = np.ones((32, 32, 3)) * 2\n img_chw_1 = np.ones((3, 32, 32)) * 2\n img_hw_2 = np.ones((32, 32))\n img_hwc_2 = np.ones((32, 32, 3))\n img_chw_2 = np.ones((3, 32, 32))\n\n with pytest.raises(ValueError):\n snr(img_hw_1, img_hw_2, crop_border=0, input_order='HH')\n\n with pytest.raises(ValueError):\n snr(img_hw_1, img_hw_2, crop_border=0, convert_to='ABC')\n\n snr_result = snr(img_hw_1, img_hw_2, crop_border=0)\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n snr_result = snr(img_hwc_1, img_hwc_2, crop_border=0, input_order='HWC')\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n print(snr_result)\n snr_result = snr(img_chw_1, img_chw_2, crop_border=0, input_order='CHW')\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n print(snr_result)\n\n snr_result = snr(img_hw_1, img_hw_2, crop_border=2)\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n snr_result = snr(img_hwc_1, img_hwc_2, crop_border=3, input_order='HWC')\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n snr_result = snr(img_chw_1, img_chw_2, crop_border=4, input_order='CHW')\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n\n snr_result = snr(img_hwc_1, img_hwc_2, crop_border=0, convert_to=None)\n np.testing.assert_almost_equal(snr_result, 6.020600199699402)\n snr_result = snr(img_hwc_1, img_hwc_2, crop_border=0, convert_to='Y')\n np.testing.assert_almost_equal(snr_result, 26.290040016174316)\n\n # test float inf\n snr_result = snr(img_hw_1, img_hw_1, crop_border=0)\n assert snr_result == float('inf')\n\n # test uint8\n img_hw_1 = np.ones((32, 32), dtype=np.uint8)\n img_hw_2 = np.zeros((32, 32), dtype=np.uint8)\n snr_result = snr(img_hw_1, img_hw_2, crop_border=0)\n assert snr_result == 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_ssim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.evaluation.metrics import SSIM, ssim\n\n\ndef test_ssim():\n\n mask = np.ones((3, 32, 32)) * 2\n mask[:16] *= 0\n gt_img = np.ones((3, 32, 32)) * 2\n predictions = [dict(pred_img=np.ones((3, 32, 32)))]\n\n data_batch = [\n dict(\n data_samples=dict(\n gt_img=gt_img, mask=mask, gt_channel_order='bgr'))\n ]\n data_batch.append(\n dict(\n data_samples=dict(\n gt_img=torch.from_numpy(gt_img),\n mask=torch.from_numpy(mask),\n img_channel_order='bgr')))\n predictions.append({\n k: torch.from_numpy(deepcopy(v))\n for (k, v) in predictions[0].items()\n })\n\n data_samples = [d_['data_samples'] for d_ in data_batch]\n for d, p in zip(data_samples, predictions):\n d['output'] = p\n\n ssim_ = SSIM()\n ssim_.process(data_batch, data_samples)\n result = ssim_.compute_metrics(ssim_.results)\n assert 'SSIM' in result\n np.testing.assert_almost_equal(result['SSIM'], 0.913062377743969)\n\n\ndef test_calculate_ssim():\n img_hw_1 = np.ones((32, 32))\n img_hwc_1 = np.ones((32, 32, 3))\n img_chw_1 = np.ones((3, 32, 32))\n img_hw_2 = np.ones((32, 32)) * 2\n img_hwc_2 = np.ones((32, 32, 3)) * 2\n img_chw_2 = np.ones((3, 32, 32)) * 2\n\n with pytest.raises(ValueError):\n ssim(img_hw_1, img_hw_2, crop_border=0, input_order='HH')\n\n with pytest.raises(ValueError):\n ssim(img_hw_1, img_hw_2, crop_border=0, input_order='ABC')\n\n ssim_result = ssim(img_hw_1, img_hw_2, crop_border=0)\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n ssim_result = ssim(img_hwc_1, img_hwc_2, crop_border=0, input_order='HWC')\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n ssim_result = ssim(img_chw_1, img_chw_2, crop_border=0, input_order='CHW')\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n\n ssim_result = ssim(img_hw_1, img_hw_2, crop_border=2)\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n ssim_result = ssim(img_hwc_1, img_hwc_2, crop_border=3, input_order='HWC')\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n ssim_result = ssim(img_chw_1, img_chw_2, crop_border=4, input_order='CHW')\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n\n ssim_result = ssim(img_hwc_1, img_hwc_2, crop_border=0, convert_to=None)\n np.testing.assert_almost_equal(ssim_result, 0.9130623)\n ssim_result = ssim(img_hwc_1, img_hwc_2, crop_border=0, convert_to='Y')\n np.testing.assert_almost_equal(ssim_result, 0.9987801)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_evaluation/test_metrics/test_swd.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport numpy as np\nimport torch\n\nfrom mmagic.evaluation import SlicedWassersteinDistance\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.structures import DataSample\n\n\nclass TestSWD(TestCase):\n\n def test_init(self):\n swd = SlicedWassersteinDistance(fake_nums=10, image_shape=(3, 32, 32))\n self.assertEqual(len(swd.real_results), 2)\n\n def test_prosess(self):\n model = MagicMock()\n model.data_preprocessor = DataPreprocessor()\n swd = SlicedWassersteinDistance(fake_nums=100, image_shape=(3, 32, 32))\n swd.prepare(model, None)\n\n torch.random.manual_seed(42)\n real_samples = [\n dict(inputs=torch.rand(3, 32, 32) * 255.) for _ in range(100)\n ]\n fake_samples = [\n DataSample(\n fake_img=(torch.rand(3, 32, 32) * 255),\n gt_img=(torch.rand(3, 32, 32) * 255)).to_dict()\n for _ in range(100)\n ]\n\n swd.process(real_samples, fake_samples)\n # 100 samples are passed in 1 batch, _num_processed should be 100\n self.assertEqual(swd._num_processed, 100)\n # _num_processed(100) > fake_nums(4), _num_processed should be\n # unchanged\n swd.process(real_samples, fake_samples)\n self.assertEqual(swd._num_processed, 100)\n\n output = swd.evaluate()\n result = [16.495922580361366, 24.15413036942482, 20.325026474893093]\n output = [item / 100 for item in output.values()]\n result = [item / 100 for item in result]\n np.testing.assert_almost_equal(output, result, decimal=1)\n\n swd = SlicedWassersteinDistance(\n fake_nums=4,\n fake_key='fake',\n real_key='img',\n sample_model='orig',\n image_shape=(3, 32, 32))\n swd.prepare(model, None)\n\n # test gray scale input\n swd.image_shape = (1, 32, 32)\n real_samples = [\n dict(inputs=torch.rand(1, 32, 32) * 255.) for _ in range(100)\n ]\n fake_samples = [\n DataSample(\n fake_img=torch.rand(1, 32, 32) * 255,\n gt_img=torch.rand(1, 32, 32) * 255).to_dict()\n for _ in range(100)\n ]\n swd.process(real_samples, fake_samples)\n\n # test fake_nums is -1\n swd = SlicedWassersteinDistance(\n fake_nums=-1,\n fake_key='fake',\n real_key='img',\n sample_model='orig',\n image_shape=(3, 32, 32))\n fake_samples = [\n DataSample(\n fake_img=torch.rand(3, 32, 32) * 255,\n gt_img=torch.rand(3, 32, 32) * 255).to_dict()\n for _ in range(10)\n ]\n for _ in range(3):\n swd.process(None, fake_samples)\n # fake_nums is -1, all samples (10 * 3 = 30) is processed\n self.assertEqual(swd._num_processed, 30)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_aspp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import ASPP\n\n\ndef test_aspp():\n # test aspp with normal conv\n aspp = ASPP(128, out_channels=512, mid_channels=128, dilations=(6, 12, 18))\n assert aspp.convs[0].activate.__class__.__name__ == 'ReLU'\n assert aspp.convs[0].conv.out_channels == 128\n assert aspp.convs[1].__class__.__name__ == 'ConvModule'\n for conv_idx in range(1, 4):\n assert aspp.convs[conv_idx].conv.dilation[0] == 6 * conv_idx\n x = torch.rand(2, 128, 8, 8)\n output = aspp(x)\n assert output.shape == (2, 512, 8, 8)\n\n # test aspp with separable conv\n aspp = ASPP(128, separable_conv=True)\n assert aspp.convs[1].__class__.__name__ == 'DepthwiseSeparableConvModule'\n x = torch.rand(2, 128, 8, 8)\n output = aspp(x)\n assert output.shape == (2, 256, 8, 8)\n\n # test aspp with ReLU6\n aspp = ASPP(128, dilations=(12, 24, 36), act_cfg=dict(type='ReLU6'))\n assert aspp.convs[0].activate.__class__.__name__ == 'ReLU6'\n for conv_idx in range(1, 4):\n assert aspp.convs[conv_idx].conv.dilation[0] == 12 * conv_idx\n x = torch.rand(2, 128, 8, 8)\n output = aspp(x)\n assert output.shape == (2, 256, 8, 8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_conv.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.models.archs import conv\n\n\ndef test_conv():\n assert 'Deconv' in conv.MODELS.module_dict\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_downsample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.archs import pixel_unshuffle\n\n\ndef test_pixel_unshuffle():\n # test on cpu\n x = torch.rand(1, 3, 20, 20)\n y = pixel_unshuffle(x, scale=2)\n assert y.shape == (1, 12, 10, 10)\n with pytest.raises(AssertionError):\n y = pixel_unshuffle(x, scale=3)\n\n # test on gpu\n if torch.cuda.is_available():\n x = x.cuda()\n y = pixel_unshuffle(x, scale=2)\n assert y.shape == (1, 12, 10, 10)\n\n with pytest.raises(AssertionError):\n y = pixel_unshuffle(x, scale=3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_ensemble.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import SpatialTemporalEnsemble\n\n\ndef test_ensemble_cpu():\n model = nn.Identity()\n\n # spatial ensemble of an image\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)\n inputs = torch.rand(1, 3, 4, 4)\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())\n\n # spatial ensemble of a sequence\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)\n inputs = torch.rand(1, 2, 3, 4, 4)\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())\n\n # spatial and temporal ensemble of a sequence\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)\n inputs = torch.rand(1, 2, 3, 4, 4)\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())\n\n # spatial and temporal ensemble of an image\n with pytest.raises(ValueError):\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)\n inputs = torch.rand(1, 3, 4, 4)\n outputs = ensemble(inputs, model)\n\n\ndef test_ensemble_cuda():\n if torch.cuda.is_available():\n model = nn.Identity().cuda()\n\n # spatial ensemble of an image\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)\n inputs = torch.rand(1, 3, 4, 4).cuda()\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.cpu().numpy(),\n outputs.cpu().numpy())\n\n # spatial ensemble of a sequence\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)\n inputs = torch.rand(1, 2, 3, 4, 4).cuda()\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.cpu().numpy(),\n outputs.cpu().numpy())\n\n # spatial and temporal ensemble of a sequence\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)\n inputs = torch.rand(1, 2, 3, 4, 4).cuda()\n outputs = ensemble(inputs, model)\n np.testing.assert_almost_equal(inputs.cpu().numpy(),\n outputs.cpu().numpy())\n\n # spatial and temporal ensemble of an image\n with pytest.raises(ValueError):\n ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)\n inputs = torch.rand(1, 3, 4, 4).cuda()\n outputs = ensemble(inputs, model)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_gated_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import SimpleGatedConvModule\n\n\ndef test_gated_conv():\n conv = SimpleGatedConvModule(3, 10, 3, padding=1)\n x = torch.rand((2, 3, 10, 10))\n assert not conv.conv.with_activation\n assert conv.with_feat_act\n assert conv.with_gate_act\n assert isinstance(conv.feat_act, nn.ELU)\n assert isinstance(conv.gate_act, nn.Sigmoid)\n assert conv.conv.out_channels == 20\n\n out = conv(x)\n assert out.shape == (2, 10, 10, 10)\n\n conv = SimpleGatedConvModule(\n 3, 10, 3, padding=1, feat_act_cfg=None, gate_act_cfg=None)\n assert not conv.with_gate_act\n out = conv(x)\n assert out.shape == (2, 10, 10, 10)\n\n with pytest.raises(AssertionError):\n conv = SimpleGatedConvModule(\n 3, 1, 3, padding=1, order=('linear', 'act', 'norm'))\n\n conv = SimpleGatedConvModule(3, out_channels=10, kernel_size=3, padding=1)\n assert conv.conv.out_channels == 20\n out = conv(x)\n assert out.shape == (2, 10, 10, 10)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_img_normalize.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import ImgNormalize\n\n\ndef test_normalize_layer():\n rgb_mean = (1, 2, 3)\n rgb_std = (1, 0.5, 0.25)\n layer = ImgNormalize(1, rgb_mean, rgb_std)\n x = torch.randn((2, 3, 64, 64))\n y = layer(x)\n x = x.permute((1, 0, 2, 3)).reshape((3, -1))\n y = y.permute((1, 0, 2, 3)).reshape((3, -1))\n rgb_mean = torch.tensor(rgb_mean)\n rgb_std = torch.tensor(rgb_std)\n mean_x = x.mean(dim=1)\n mean_y = y.mean(dim=1)\n std_x = x.std(dim=1)\n std_y = y.std(dim=1)\n assert sum(torch.div(std_x, std_y) - rgb_std) < 1e-5\n assert sum(torch.div(mean_x - rgb_mean, rgb_std) - mean_y) < 1e-5\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_linear_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import LinearModule\n\n\ndef test_linear_module():\n linear = LinearModule(10, 20)\n linear.init_weights()\n x = torch.rand((3, 10))\n assert linear.with_bias\n assert not linear.with_spectral_norm\n assert linear.out_features == 20\n assert linear.in_features == 10\n assert isinstance(linear.activate, nn.ReLU)\n\n y = linear(x)\n assert y.shape == (3, 20)\n\n linear = LinearModule(10, 20, act_cfg=None, with_spectral_norm=True)\n\n assert hasattr(linear.linear, 'weight_orig')\n assert not linear.with_activation\n y = linear(x)\n assert y.shape == (3, 20)\n\n linear = LinearModule(\n 10, 20, act_cfg=dict(type='LeakyReLU'), with_spectral_norm=True)\n y = linear(x)\n assert y.shape == (3, 20)\n assert isinstance(linear.activate, nn.LeakyReLU)\n\n linear = LinearModule(\n 10, 20, bias=False, act_cfg=None, with_spectral_norm=True)\n y = linear(x)\n assert y.shape == (3, 20)\n assert not linear.with_bias\n\n linear = LinearModule(\n 10,\n 20,\n bias=False,\n act_cfg=None,\n with_spectral_norm=True,\n order=('act', 'linear'))\n\n assert linear.order == ('act', 'linear')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_lora.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.archs import (LoRAWrapper, set_lora, set_lora_disable,\n set_lora_enable, set_only_lora_trainable)\n\n\nclass ToyAttn(nn.Module):\n\n def __init__(self, in_dim, context_dim):\n super().__init__()\n self.to_q = nn.Linear(in_dim, in_dim)\n self.to_k = nn.Linear(context_dim, in_dim)\n self.to_v = nn.Linear(context_dim, in_dim)\n\n def forward(self, x, context=None):\n\n b, c, h, w = x.shape\n x = x.view(b, c, h * w).permute(0, 2, 1)\n if context is None:\n context = x\n\n q = self.to_q(x)\n k = self.to_k(context)\n v = self.to_v(context)\n attn_mask = torch.softmax(q @ k.transpose(-2, -1), dim=-1)\n out = attn_mask @ v\n return out.view(b, h, w, c).permute(0, 3, 1, 2)\n\n\nclass ToySubModule(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.net = nn.Identity()\n self.attn1 = ToyAttn(4, 3)\n\n def forward(self, x, context=None):\n x = self.net(x)\n x = self.attn1(x, context)\n return x\n\n\nclass ToyModel(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.n1 = ToySubModule()\n self.attn2 = ToyAttn(4, 4)\n\n def forward(self, x, context=None):\n out = self.n1(x, context)\n out = self.attn2(out)\n return out\n\n\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.8.1'),\n reason='get_submodule requires torch >= 1.9.0')\ndef test_set_lora():\n model = ToyModel()\n\n img = torch.randn(2, 4, 3, 3)\n context = torch.randn(2, 11, 3)\n\n config = dict(rank=2, scale=1, target_modules='to_q')\n model: ToyModel = set_lora(model, config, True)\n isinstance(model.attn2.to_q, LoRAWrapper)\n isinstance(model.n1.attn1.to_q, LoRAWrapper)\n\n out = model(img, context)\n assert out.shape == (2, 4, 3, 3)\n\n model = ToyModel()\n config = dict(\n rank=2,\n scale=1,\n target_modules=[\n 'to_q',\n dict(target_module='.*attn1.to_v', rank=1),\n dict(target_module='to_k', scale=2.5)\n ])\n out_wo_lora = model(img, context)\n set_lora(model, config)\n\n assert isinstance(model.attn2.to_q, LoRAWrapper)\n assert model.attn2.to_q.scale == 1\n assert model.attn2.to_q.rank == 2\n assert isinstance(model.attn2.to_k, LoRAWrapper)\n assert model.attn2.to_k.scale == 2.5\n assert model.attn2.to_k.rank == 2\n assert isinstance(model.attn2.to_v, nn.Linear)\n\n assert isinstance(model.n1.attn1.to_q, LoRAWrapper)\n assert model.n1.attn1.to_q.scale == 1\n assert model.n1.attn1.to_q.rank == 2\n assert isinstance(model.n1.attn1.to_k, LoRAWrapper)\n assert model.n1.attn1.to_k.scale == 2.5\n assert model.n1.attn1.to_k.rank == 2\n assert isinstance(model.n1.attn1.to_v, LoRAWrapper)\n assert model.n1.attn1.to_v.scale == 1\n assert model.n1.attn1.to_v.rank == 1\n\n out_w_lora = model(img, context)\n assert out_w_lora.shape == (2, 4, 3, 3)\n\n model.n1.attn1.to_v.set_scale(10)\n assert model.n1.attn1.to_v.scale == 10\n\n # test set onlyu lora trainable\n set_only_lora_trainable(model)\n for n, m in model.named_parameters():\n if 'lora_' in n:\n assert m.requires_grad\n else:\n assert not m.requires_grad\n\n # test enable and disable\n set_lora_disable(model)\n out_lora_disable = model(img, context)\n assert (out_lora_disable == out_wo_lora).all()\n set_lora_enable(model)\n out_lora_enable = model(img, context)\n assert (out_lora_enable == out_w_lora).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_multi_layer_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import MultiLayerDiscriminator\n\n\ndef test_multi_layer_disc():\n with pytest.raises(AssertionError):\n # fc_in_channels must be greater than 0\n multi_disc = MultiLayerDiscriminator(\n 3, 236, fc_in_channels=-100, out_act_cfg=None)\n\n with pytest.raises(TypeError):\n # stride_list must be a tuple of int with length of 1 or\n # length of num_conv\n multi_disc = MultiLayerDiscriminator(\n 3, 256, num_convs=3, stride_list=(1, 2))\n\n input_g = torch.randn(1, 3, 256, 256)\n # test multi-layer discriminators without fc layer\n multi_disc = MultiLayerDiscriminator(\n in_channels=3, max_channels=256, fc_in_channels=None)\n multi_disc.init_weights()\n disc_pred = multi_disc(input_g)\n assert disc_pred.shape == (1, 256, 8, 8)\n multi_disc = MultiLayerDiscriminator(\n in_channels=3, max_channels=256, fc_in_channels=100)\n assert isinstance(multi_disc.fc.activate, nn.ReLU)\n\n multi_disc = MultiLayerDiscriminator(3, 236, fc_in_channels=None)\n assert multi_disc.with_out_act\n assert not multi_disc.with_fc\n assert isinstance(multi_disc.conv5.activate, nn.ReLU)\n\n multi_disc = MultiLayerDiscriminator(\n 3, 236, fc_in_channels=None, out_act_cfg=None)\n assert not multi_disc.conv5.with_activation\n with pytest.raises(TypeError):\n multi_disc.init_weights(pretrained=dict(igccc=4396))\n\n input_g = torch.randn(1, 3, 16, 16)\n multi_disc = MultiLayerDiscriminator(\n in_channels=3,\n max_channels=256,\n num_convs=2,\n fc_in_channels=4 * 4 * 128,\n fc_out_channels=10,\n with_spectral_norm=True)\n multi_disc.init_weights()\n disc_pred = multi_disc(input_g)\n assert disc_pred.shape == (1, 10)\n assert multi_disc.conv1.with_spectral_norm\n assert multi_disc.conv2.with_spectral_norm\n assert hasattr(multi_disc.fc.linear, 'weight_orig')\n\n num_convs = 3\n multi_disc = MultiLayerDiscriminator(\n in_channels=64,\n max_channels=512,\n num_convs=num_convs,\n kernel_size=4,\n norm_cfg=dict(type='BN'),\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n out_act_cfg=dict(type='ReLU'),\n with_input_norm=False,\n with_out_convs=True)\n # check input conv\n assert not multi_disc.conv1.with_norm\n assert isinstance(multi_disc.conv1.activate, nn.LeakyReLU)\n assert multi_disc.conv1.stride == (2, 2)\n\n # check intermediate conv\n for i in range(1, num_convs):\n assert getattr(multi_disc, f'conv{i + 1}').with_norm\n assert isinstance(\n getattr(multi_disc, f'conv{i + 1}').activate, nn.LeakyReLU)\n assert getattr(multi_disc, f'conv{i + 1}').stride == (2, 2)\n\n # check out_conv\n assert multi_disc.conv4.with_norm\n assert multi_disc.conv4.with_activation\n assert multi_disc.conv4.stride == (1, 1)\n assert not multi_disc.conv5.with_norm\n assert not multi_disc.conv5.with_activation\n assert multi_disc.conv5.stride == (1, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_patch_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\n\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_patch_discriminator():\n # color, BN\n cfg = dict(\n type='PatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02))\n net = MODELS.build(cfg)\n # cpu\n input_shape = (1, 3, 64, 64)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 1, 6, 6)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 1, 6, 6)\n\n # gray, IN\n cfg = dict(\n type='PatchDiscriminator',\n in_channels=1,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02))\n net = MODELS.build(cfg)\n # cpu\n input_shape = (1, 1, 64, 64)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 1, 6, 6)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 1, 6, 6)\n\n # test norm_cfg assertions\n bad_cfg = copy.deepcopy(cfg)\n bad_cfg['norm_cfg'] = None\n with pytest.raises(AssertionError):\n _ = MODELS.build(bad_cfg)\n bad_cfg['norm_cfg'] = dict(tp='BN')\n with pytest.raises(AssertionError):\n _ = MODELS.build(bad_cfg)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_resnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import ResNet\n\n\ndef test_resnet():\n resnet = ResNet(18, 3, 16, 16)\n resnet.init_weights()\n input = torch.rand((2, 3, 128, 128))\n output = resnet(input)\n assert output[0].detach().numpy().shape == (2, 3, 128, 128)\n\n resnet = ResNet(50, 3, 16, 16)\n resnet.init_weights()\n input = torch.rand((2, 3, 128, 128))\n output = resnet(input)\n assert output[0].detach().numpy().shape == (2, 3, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_separable_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import DepthwiseSeparableConvModule\n\n\ndef test_depthwise_separable_conv():\n with pytest.raises(AssertionError):\n # conv_cfg must be a dict or None\n DepthwiseSeparableConvModule(4, 8, 2, groups=2)\n\n # test default config\n conv = DepthwiseSeparableConvModule(3, 8, 2)\n assert conv.depthwise_conv.conv.groups == 3\n assert conv.pointwise_conv.conv.kernel_size == (1, 1)\n assert not conv.depthwise_conv.with_norm\n assert not conv.pointwise_conv.with_norm\n assert conv.depthwise_conv.activate.__class__.__name__ == 'ReLU'\n assert conv.pointwise_conv.activate.__class__.__name__ == 'ReLU'\n x = torch.rand(1, 3, 256, 256)\n output = conv(x)\n assert output.shape == (1, 8, 255, 255)\n\n # test\n conv = DepthwiseSeparableConvModule(3, 8, 2, dw_norm_cfg=dict(type='BN'))\n assert conv.depthwise_conv.norm_name == 'bn'\n assert not conv.pointwise_conv.with_norm\n x = torch.rand(1, 3, 256, 256)\n output = conv(x)\n assert output.shape == (1, 8, 255, 255)\n\n conv = DepthwiseSeparableConvModule(3, 8, 2, pw_norm_cfg=dict(type='BN'))\n assert not conv.depthwise_conv.with_norm\n assert conv.pointwise_conv.norm_name == 'bn'\n x = torch.rand(1, 3, 256, 256)\n output = conv(x)\n assert output.shape == (1, 8, 255, 255)\n\n # add test for ['norm', 'conv', 'act']\n conv = DepthwiseSeparableConvModule(3, 8, 2, order=('norm', 'conv', 'act'))\n x = torch.rand(1, 3, 256, 256)\n output = conv(x)\n assert output.shape == (1, 8, 255, 255)\n\n conv = DepthwiseSeparableConvModule(\n 3, 8, 3, padding=1, with_spectral_norm=True)\n assert hasattr(conv.depthwise_conv.conv, 'weight_orig')\n assert hasattr(conv.pointwise_conv.conv, 'weight_orig')\n output = conv(x)\n assert output.shape == (1, 8, 256, 256)\n\n conv = DepthwiseSeparableConvModule(\n 3, 8, 3, padding=1, padding_mode='reflect')\n assert isinstance(conv.depthwise_conv.padding_layer, nn.ReflectionPad2d)\n output = conv(x)\n assert output.shape == (1, 8, 256, 256)\n\n conv = DepthwiseSeparableConvModule(\n 3, 8, 3, padding=1, dw_act_cfg=dict(type='LeakyReLU'))\n assert conv.depthwise_conv.activate.__class__.__name__ == 'LeakyReLU'\n assert conv.pointwise_conv.activate.__class__.__name__ == 'ReLU'\n output = conv(x)\n assert output.shape == (1, 8, 256, 256)\n\n conv = DepthwiseSeparableConvModule(\n 3, 8, 3, padding=1, pw_act_cfg=dict(type='LeakyReLU'))\n assert conv.depthwise_conv.activate.__class__.__name__ == 'ReLU'\n assert conv.pointwise_conv.activate.__class__.__name__ == 'LeakyReLU'\n output = conv(x)\n assert output.shape == (1, 8, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_simple_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\nfrom mmagic.models.archs import SimpleEncoderDecoder\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef test_encoder_decoder():\n \"\"\"Test SimpleEncoderDecoder.\"\"\"\n # check DIM with only alpha loss\n encoder = dict(type='VGG16', in_channels=4)\n decoder = dict(type='PlainDecoder')\n\n model = SimpleEncoderDecoder(encoder, decoder)\n model.init_weights()\n model.train()\n fg, bg, merged, alpha, trimap = _demo_inputs_pair()\n prediction = model(torch.cat([merged, trimap], 1))\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # check DIM with only composition loss\n encoder = dict(type='VGG16', in_channels=4)\n decoder = dict(type='PlainDecoder')\n\n model = SimpleEncoderDecoder(encoder, decoder)\n model.init_weights()\n model.train()\n fg, bg, merged, alpha, trimap = _demo_inputs_pair()\n prediction = model(torch.cat([merged, trimap], 1))\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # check DIM with both alpha and composition loss\n encoder = dict(type='VGG16', in_channels=4)\n decoder = dict(type='PlainDecoder')\n model = SimpleEncoderDecoder(encoder, decoder)\n model.init_weights()\n model.train()\n fg, bg, merged, alpha, trimap = _demo_inputs_pair()\n prediction = model(torch.cat([merged, trimap], 1))\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n encoder = dict(type='VGG16', in_channels=4)\n decoder = dict(type='PlainDecoder')\n\n model = SimpleEncoderDecoder(encoder, decoder)\n model.init_weights()\n model.train()\n fg, bg, merged, alpha, trimap = _demo_inputs_pair(cuda=True)\n model.cuda()\n prediction = model(torch.cat([merged, trimap], 1))\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n\ndef _demo_inputs_pair(img_shape=(64, 64), batch_size=1, cuda=False):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n fg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n bg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n alpha = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n trimap = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n if cuda:\n fg = fg.cuda()\n bg = bg.cuda()\n merged = merged.cuda()\n alpha = alpha.cuda()\n trimap = trimap.cuda()\n return fg, bg, merged, alpha, trimap\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_smpatch_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_smpatch_discriminator():\n # color, BN\n cfg = dict(\n type='SoftMaskPatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n with_spectral_norm=True)\n net = MODELS.build(cfg)\n # cpu\n input_shape = (1, 3, 64, 64)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 1, 6, 6)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 1, 6, 6)\n\n # gray, IN\n cfg = dict(\n type='SoftMaskPatchDiscriminator',\n in_channels=1,\n base_channels=64,\n num_conv=3,\n with_spectral_norm=True)\n net = MODELS.build(cfg)\n # cpu\n input_shape = (1, 1, 64, 64)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 1, 6, 6)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 1, 6, 6)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_sr_backbone.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import ResidualBlockNoBN\nfrom mmagic.models.utils import make_layer\n\n\ndef test_sr_backbone_utils():\n block = make_layer(ResidualBlockNoBN, 3)\n input = torch.rand((2, 64, 128, 128))\n output = block(input)\n assert output.detach().numpy().shape == (2, 64, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_tokenizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nfrom mmagic.models.archs import TokenizerWrapper\n\nPREFIX = '<|startoftext|>'\nSUFFIX = ' <|endoftext|>'\n\n\nclass TestTokenizerWrapper(TestCase):\n\n def setUp(self):\n \"\"\"Test add placeholder tokens in this function.\"\"\"\n tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')\n\n # 'Goodbye' in kiswahili\n tokenizer.add_placeholder_token('kwaheri', num_vec_per_token=1)\n # 'how much' in kiswahili\n tokenizer.add_placeholder_token('ngapi', num_vec_per_token=4)\n\n with self.assertRaises(AssertionError):\n tokenizer.add_placeholder_token('hello', num_vec_per_token=1)\n\n self.tokenizer = tokenizer\n\n def test_encode_and_decode_and_call(self):\n # test single token\n text = 'Nice bro, kwaheri!'\n input_ids = self.tokenizer.encode(text).input_ids\n self.assertEqual(input_ids[-3:-2],\n self.tokenizer.encode('kwaheri').input_ids[1:-1])\n text_recon = self.tokenizer.decode(input_ids)\n text_recon_raw = self.tokenizer.decode(input_ids, return_raw=True)\n self.assertEqual(text_recon, f'{PREFIX}{text}{SUFFIX}'.lower())\n self.assertEqual(text_recon_raw, f'{PREFIX}{text}{SUFFIX}'.lower())\n self.assertEqual(input_ids, self.tokenizer(text).input_ids)\n\n # test multi token\n text = 'This apple seems delicious, ngapi?'\n input_ids = self.tokenizer.encode(text).input_ids\n self.assertEqual(input_ids[6:-2],\n self.tokenizer.encode('ngapi').input_ids[1:-1])\n text_recon = self.tokenizer.decode(input_ids)\n text_recon_raw = self.tokenizer.decode(input_ids, return_raw=True)\n self.assertEqual(text_recon, f'{PREFIX}{text}{SUFFIX}'.lower())\n for idx in range(4):\n self.assertIn(f'ngapi_{idx}', text_recon_raw)\n self.assertEqual(input_ids, self.tokenizer(text).input_ids)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_upsample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import PixelShufflePack\n\n\ndef test_pixel_shuffle():\n\n # test on cpu\n model = PixelShufflePack(3, 3, 2, 3)\n model.init_weights()\n x = torch.rand(1, 3, 16, 16)\n y = model(x)\n assert y.shape == (1, 3, 32, 32)\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n x = x.cuda()\n y = model(x)\n assert y.shape == (1, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_vgg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport numpy as np\nimport pytest\nimport torch\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.models.archs import VGG16\n\n\ndef check_norm_state(modules, train_state):\n \"\"\"Check if norm layer is in correct train state.\"\"\"\n for mod in modules:\n if isinstance(mod, _BatchNorm):\n if mod.training != train_state:\n return False\n return True\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef assert_mid_feat_shape(mid_feat, target_shape):\n assert len(mid_feat) == 5\n for i in range(5):\n assert_tensor_with_shape(mid_feat[i], torch.Size(target_shape[i]))\n\n\ndef _demo_inputs(input_shape=(2, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_vgg16_encoder():\n \"\"\"Test VGG16 encoder.\"\"\"\n target_shape = [(2, 64, 32, 32), (2, 128, 16, 16), (2, 256, 8, 8),\n (2, 512, 4, 4), (2, 512, 2, 2)]\n\n model = VGG16(4)\n model.init_weights()\n model.train()\n img = _demo_inputs()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n\n model = VGG16(4, batch_norm=True)\n model.init_weights()\n model.train()\n img = _demo_inputs()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n\n model = VGG16(4, aspp=True, dilations=[6, 12, 18])\n model.init_weights()\n model.train()\n img = _demo_inputs()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 256, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n assert check_norm_state(model.modules(), True)\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = VGG16(4)\n model.init_weights()\n model.train()\n model.cuda()\n img = _demo_inputs().cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n\n model = VGG16(4, batch_norm=True)\n model.init_weights()\n model.train()\n model.cuda()\n img = _demo_inputs().cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n\n model = VGG16(4, aspp=True, dilations=[6, 12, 18])\n model.init_weights()\n model.train()\n model.cuda()\n img = _demo_inputs().cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 256, 2, 2))\n assert_tensor_with_shape(outputs['max_idx_1'], target_shape[0])\n assert_tensor_with_shape(outputs['max_idx_2'], target_shape[1])\n assert_tensor_with_shape(outputs['max_idx_3'], target_shape[2])\n assert_tensor_with_shape(outputs['max_idx_4'], target_shape[3])\n assert_tensor_with_shape(outputs['max_idx_5'], target_shape[4])\n assert check_norm_state(model.modules(), True)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_archs/test_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nimport platform\nimport shutil\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\ntest_dir = osp.join(osp.dirname(__file__), '..', '..', '..', 'tests')\nconfig_path = osp.join(test_dir, 'configs', 'diffuser_wrapper_cfg')\nmodel_path = osp.join(test_dir, 'configs', 'tmp_weight')\nckpt_path = osp.join(test_dir, 'configs', 'ckpt')\n\nregister_all_modules()\n\n\nclass TestWrapper(TestCase):\n\n def test_build(self):\n # mock SiLU\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n from mmagic.models.editors.ddpm.denoising_unet import SiLU\n torch.nn.SiLU = SiLU\n\n # 1. test from config\n model = MODELS.build(\n dict(type='ControlNetModel', from_config=config_path))\n model.init_weights() # test init_weights without warning\n model_str = repr(model)\n self.assertIn(\n 'Wrapped Module Class: ', model_str)\n self.assertIn('Wrapped Module Name: ControlNetModel', model_str)\n self.assertIn(f'From Config: {config_path}', model_str)\n\n # 2. test save as diffuser\n os.makedirs(model_path, exist_ok=True)\n if digit_version(TORCH_VERSION) < digit_version('2.0.1'):\n model.save_pretrained(model_path, safe_serialization=False)\n else:\n model.save_pretrained(model_path)\n\n # 3. test from_pretrained\n model = MODELS.build(\n dict(\n type='ControlNetModel',\n from_pretrained=model_path,\n torch_dtype=torch.float16))\n assert all([p.dtype == torch.float16 for p in model.parameters()])\n model_str = repr(model)\n self.assertIn(f'From Pretrained: {model_path}', model_str)\n\n # save ckpt to test init_weights\n os.makedirs(ckpt_path, exist_ok=True)\n torch.save(model.state_dict(), osp.join(ckpt_path, 'model.pth'))\n\n # test raise warning when init_cfg is passed\n model = MODELS.build(\n dict(\n type='ControlNetModel',\n from_pretrained=model_path,\n torch_dtype=torch.float16,\n init_cfg=dict(\n type='Pretrained',\n checkpoint=osp.join(ckpt_path, 'model.pth'))))\n model.init_weights()\n\n # delete saved model to save space\n if 'win' not in platform.system().lower():\n shutil.rmtree(model_path)\n shutil.rmtree(ckpt_path)\n\n # 4. test loading without repo_id\n model = MODELS.build(\n dict(\n type='ControlNetModel',\n in_channels=3,\n down_block_types=['DownBlock2D'],\n block_out_channels=(32, ),\n cross_attention_dim=16,\n attention_head_dim=2,\n conditioning_embedding_out_channels=(16, )), )\n model_str = repr(model)\n self.assertNotIn('From Config:', model_str)\n self.assertNotIn('From Pretrained:', model_str)\n\n # 5. test attribute error for a unknown attribute\n with self.assertRaises(AttributeError):\n model.unkonwn_attr('what\\'s this?')\n\n # 6. test init_weights\n model.init_weights()\n\n # 7. test forward function\n forward_mock = MagicMock()\n model.model.forward = forward_mock\n model(**dict(t='t', control='control'))\n _, called_kwargs = forward_mock.call_args\n self.assertEqual(called_kwargs['t'], 't')\n self.assertEqual(called_kwargs['control'], 'control')\n\n # 8. test other attribute share with BaseModule and model\n register_buffer_mock = MagicMock()\n model.model.registrer_buffer = register_buffer_mock\n model.registrer_buffer('buffer', 123)\n called_args, _ = register_buffer_mock.call_args\n self.assertEqual(called_args, ('buffer', 123))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_average_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.model import BaseModel\nfrom mmengine.testing import assert_allclose\n\nfrom mmagic.models.base_models import ExponentialMovingAverage, RampUpEMA\n\n\nclass ToyModule(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.conv = nn.Conv2d(1, 1, 3)\n self.register_buffer('buffer', torch.randn(2, 2))\n\n def update_buffer(self):\n for buffer in self.buffers():\n buffer.add_(torch.randn(*buffer.size()))\n\n def update_params(self):\n for param in self.parameters():\n param.add(torch.randn(*param.size()))\n\n def update(self):\n self.update_params()\n self.update_buffer()\n\n def forward(self, x):\n return self.conv(x)\n\n\nclass ToyModel_old(BaseModel):\n\n def __init__(self):\n super().__init__()\n self.ema = ToyModule()\n self.module = ToyModule()\n\n def forward(self, x):\n return\n\n\nclass ToyEMAModel(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.module = ToyModule()\n\n def forward(self, x):\n return self.module(x)\n\n\nclass ToyModel(BaseModel):\n\n def __init__(self):\n super().__init__()\n self.ema = ToyEMAModel()\n self.module = ToyModule()\n\n def forward(self, x):\n return\n\n\nclass TestExponentialMovingAverage(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.default_cfg = dict(\n interval=1, momentum=0.0001, update_buffers=True)\n\n def test_init(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n average_model = ExponentialMovingAverage(model=model, **cfg)\n self.assertEqual(average_model.momentum, 0.0001)\n self.assertTrue(\n (average_model.module.conv.weight == model.conv.weight).all())\n\n cfg['momentum'] = 10\n with self.assertRaises(AssertionError):\n average_model = ExponentialMovingAverage(model, **cfg)\n\n cfg['momentum'] = -2\n with self.assertRaises(AssertionError):\n average_model = ExponentialMovingAverage(model, **cfg)\n\n # test warning\n with pytest.warns(UserWarning):\n cfg = deepcopy(self.default_cfg)\n cfg['momentum'] = 0.6\n model = ToyModule()\n average_model = ExponentialMovingAverage(model=model, **cfg)\n self.assertEqual(average_model.momentum, 0.6)\n\n def test_avg_func(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n average_model = ExponentialMovingAverage(model, **cfg)\n src_tensor = torch.randn(1, 3, 2, 2)\n tar_tensor = torch.randn(1, 3, 2, 2)\n tar_tensor_backup = tar_tensor.clone()\n average_model.avg_func(tar_tensor, src_tensor, steps=42)\n assert_allclose(tar_tensor,\n tar_tensor_backup * 0.9999 + src_tensor * 0.0001)\n\n def test_sync_buffer_and_parameters(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n average_model = ExponentialMovingAverage(model, **cfg)\n model.update()\n average_model.sync_parameters(model)\n\n assert_allclose(model.conv.weight, average_model.module.conv.weight)\n assert_allclose(model.conv.bias, average_model.module.conv.bias)\n assert_allclose(model.buffer, average_model.module.buffer)\n\n model.update()\n average_model.sync_buffers(model)\n assert_allclose(model.buffer, average_model.module.buffer)\n\n cfg['update_buffers'] = True\n average_model = ExponentialMovingAverage(model, **cfg)\n with self.assertWarns(Warning):\n average_model.sync_buffers(model)\n\n def test_load_from_state_dict(self):\n cfg = deepcopy(self.default_cfg)\n\n model = ToyModel()\n average_model = ExponentialMovingAverage(model, **cfg)\n\n old_state_dict = ToyModel_old().state_dict()\n average_model._load_from_state_dict(\n old_state_dict,\n 'ema.',\n local_metadata={},\n strict=True,\n missing_keys=[],\n unexpected_keys=[],\n error_msgs=[])\n\n\nclass TestRamUpEMA(TestCase):\n\n @classmethod\n def setUpClass(cls):\n\n cls.default_cfg = dict(\n interval=1, ema_kimg=10, ema_rampup=0.05, batch_size=32, eps=1e-8)\n\n def test_init(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n RampUpEMA(model, **cfg)\n\n def test_avg_func(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n average_model = RampUpEMA(model, **cfg)\n src_tensor = torch.randn(1, 3, 2, 2)\n tar_tensor = torch.randn(1, 3, 2, 2)\n average_model.avg_func(tar_tensor, src_tensor, steps=42)\n\n # model = ToyModule()\n cfg['ema_rampup'] = None\n average_model = RampUpEMA(model, **cfg)\n src_tensor = torch.randn(1, 3, 2, 2)\n tar_tensor = torch.randn(1, 3, 2, 2)\n average_model.avg_func(tar_tensor, src_tensor, steps=42)\n\n # test warning\n with pytest.warns(UserWarning):\n cfg = deepcopy(self.default_cfg)\n cfg['batch_size'] = 0\n model = ToyModule()\n average_model = RampUpEMA(model, **cfg)\n src_tensor = torch.randn(1, 3, 2, 2)\n tar_tensor = torch.randn(1, 3, 2, 2)\n average_model.avg_func(tar_tensor, src_tensor, steps=42)\n\n def test_sync_buffer_and_parameters(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModule()\n average_model = RampUpEMA(model, **cfg)\n model.update()\n average_model.sync_parameters(model)\n\n assert_allclose(model.conv.weight, average_model.module.conv.weight)\n assert_allclose(model.conv.bias, average_model.module.conv.bias)\n assert_allclose(model.buffer, average_model.module.buffer)\n\n model.update()\n average_model.sync_buffers(model)\n assert_allclose(model.buffer, average_model.module.buffer)\n\n cfg['update_buffers'] = True\n average_model = RampUpEMA(model, **cfg)\n with self.assertWarns(Warning):\n average_model.sync_buffers(model)\n\n def test_load_from_state_dict(self):\n cfg = deepcopy(self.default_cfg)\n model = ToyModel()\n average_model = RampUpEMA(model, **cfg)\n\n old_state_dict = ToyModel_old().state_dict()\n average_model._load_from_state_dict(\n old_state_dict,\n 'ema.',\n local_metadata={},\n strict=True,\n missing_keys=[],\n unexpected_keys=[],\n error_msgs=[])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_base_conditional_gan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.structures import LabelData\nfrom mmengine.testing import assert_allclose\nfrom torch.nn import ModuleList\n\nfrom mmagic.models import BaseConditionalGAN, DataPreprocessor\nfrom mmagic.models.losses import (DiscShiftLossComps, GANLossComps,\n GeneratorPathRegularizerComps,\n GradientPenaltyLossComps)\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='SAGANGenerator',\n output_scale=32,\n base_channels=32,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=2,\n with_spectral_norm=True)\ndiscriminator = dict(\n type='ProjDiscriminator',\n input_scale=32,\n base_channels=32,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=1,\n with_spectral_norm=True)\n\n\nclass ToyCGAN(BaseConditionalGAN):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def train_generator(self, inputs, data_samples, optimizer_wrapper):\n return dict(loss_gen=1)\n\n def train_discriminator(self, inputs, data_samples, optimizer_wrapper):\n return dict(loss_disc=2)\n\n\nclass TestBaseGAN(TestCase):\n\n def test_val_step_and_test_step(self):\n gan = ToyCGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor())\n gan.eval()\n\n # no mode\n inputs = dict(inputs=dict(num_batches=3))\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 3)\n self.assertEqual(len(outputs_test), 3)\n for out_val, out_test in zip(outputs_val, outputs_test):\n self.assertEqual(out_val.fake_img.shape, (3, 32, 32))\n self.assertEqual(out_test.fake_img.shape, (3, 32, 32))\n\n # set mode\n inputs = dict(inputs=dict(num_batches=4, sample_model='orig'))\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 4)\n self.assertEqual(len(outputs_test), 4)\n for out_val, out_test in zip(outputs_val, outputs_test):\n self.assertEqual(out_val.sample_model, 'orig')\n self.assertEqual(out_test.sample_model, 'orig')\n self.assertEqual(out_val.fake_img.shape, (3, 32, 32))\n self.assertEqual(out_test.fake_img.shape, (3, 32, 32))\n\n inputs = dict(inputs=dict(num_batches=4, sample_model='orig/ema'))\n self.assertRaises(AssertionError, gan.val_step, inputs)\n\n inputs = dict(inputs=dict(num_batches=4, sample_model='ema'))\n self.assertRaises(AssertionError, gan.val_step, inputs)\n\n # set noise and label input\n gt_label = torch.randint(0, 10, (1, ))\n inputs = dict(\n inputs=dict(noise=torch.randn(1, 10)),\n data_samples=[DataSample(gt_label=LabelData(label=gt_label))])\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 1)\n self.assertEqual(len(outputs_val), 1)\n for idx in range(1):\n test_fake_img = outputs_test[idx].fake_img\n val_fake_img = outputs_val[idx].fake_img\n test_label = outputs_test[idx].gt_label.label\n val_label = outputs_val[idx].gt_label.label\n self.assertEqual(test_label, gt_label)\n self.assertEqual(val_label, gt_label)\n assert_allclose(test_fake_img, val_fake_img)\n\n def test_forward(self):\n # set a gan w/o EMA\n gan = ToyCGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor())\n gan.eval()\n inputs = dict(num_batches=3)\n outputs = gan(inputs, None)\n self.assertEqual(len(outputs), 3)\n for out in outputs:\n self.assertEqual(out.fake_img.shape, (3, 32, 32))\n\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for out in outputs:\n self.assertEqual(out.fake_img.shape, (3, 32, 32))\n\n outputs = gan(torch.randn(3, 10))\n self.assertEqual(len(outputs), 3)\n for out in outputs:\n self.assertEqual(out.fake_img.shape, (3, 32, 32))\n\n # set a gan w EMA\n gan = ToyCGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor(),\n ema_config=dict(interval=1))\n gan.eval()\n # inputs = dict(inputs=dict(num_batches=3))\n inputs = dict(num_batches=3)\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for out in outputs:\n self.assertEqual(out.fake_img.shape, (3, 32, 32))\n\n # inputs = dict(inputs=dict(num_batches=3, sample_model='ema/orig'))\n inputs = dict(num_batches=3, sample_model='ema/orig')\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for out in outputs:\n ema_img = out.ema\n orig_img = out.orig\n self.assertEqual(ema_img.fake_img.shape, orig_img.fake_img.shape)\n self.assertTrue(out.sample_model, 'ema/orig')\n\n # inputs = dict(inputs=dict(noise=torch.randn(4, 10)))\n inputs = dict(noise=torch.randn(4, 10))\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 4)\n for out in outputs:\n self.assertEqual(out.fake_img.shape, (3, 32, 32))\n\n # test data sample input\n # inputs = dict(inputs=dict(noise=torch.randn(3, 10)))\n inputs = dict(noise=torch.randn(3, 10))\n label = [torch.randint(0, 10, (1, )) for _ in range(3)]\n data_sample = [DataSample() for _ in range(3)]\n for idx, sample in enumerate(data_sample):\n sample.set_gt_label(label[idx])\n print(DataSample.stack(data_sample))\n outputs = gan(inputs, DataSample.stack(data_sample))\n self.assertEqual(len(outputs), 3)\n for idx, output in enumerate(outputs):\n self.assertEqual(output.gt_label.label, label[idx])\n\n def test_custom_loss(self):\n message_hub = MessageHub.get_instance('basegan-test-custom-loss')\n message_hub.update_info('iter', 10)\n\n gan_loss = dict(type='GANLossComps', gan_type='vanilla')\n\n # test loss config is dict()\n gan = BaseConditionalGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=dict())\n self.assertIsNone(gan.gan_loss)\n self.assertIsNone(gan.disc_auxiliary_losses)\n self.assertIsNone(gan.gen_auxiliary_losses)\n\n # test loss config is list\n disc_auxiliary_loss_list = [\n dict(type='DiscShiftLossComps'),\n dict(type='GradientPenaltyLossComps')\n ]\n gen_auxiliary_loss_list = [dict(type='GeneratorPathRegularizerComps')]\n loss_config = dict(\n gan_loss=gan_loss,\n disc_auxiliary_loss=disc_auxiliary_loss_list,\n gen_auxiliary_loss=gen_auxiliary_loss_list)\n gan = BaseConditionalGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n self.assertIsInstance(gan.disc_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.disc_auxiliary_losses[0], DiscShiftLossComps)\n self.assertIsInstance(gan.disc_auxiliary_losses[1],\n GradientPenaltyLossComps)\n self.assertIsInstance(gan.gen_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.gen_auxiliary_losses[0],\n GeneratorPathRegularizerComps)\n\n # test loss config is single dict\n disc_auxiliary_loss = dict(\n type='DiscShiftLossComps', data_info=dict(pred='disc_pred_fake'))\n gen_auxiliary_loss = dict(\n type='GeneratorPathRegularizerComps',\n data_info=dict(generator='gen', num_batches='batch_size'))\n loss_config = dict(\n gan_loss=gan_loss,\n disc_auxiliary_loss=disc_auxiliary_loss,\n gen_auxiliary_loss=gen_auxiliary_loss)\n\n gan = BaseConditionalGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n self.assertIsInstance(gan.gan_loss, GANLossComps)\n self.assertIsInstance(gan.disc_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.disc_auxiliary_losses[0], DiscShiftLossComps)\n self.assertIsInstance(gan.gen_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.gen_auxiliary_losses[0],\n GeneratorPathRegularizerComps)\n\n # test forward custom loss terms\n gan = BaseConditionalGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n # mock gen aux loss to avoid build styleGAN Generator\n gen_aux_loss_mock = MagicMock(return_value=torch.Tensor([1.]))\n gen_aux_loss_mock.loss_name = MagicMock(return_value='loss_gen_aux')\n gan._modules['gen_auxiliary_losses'] = [gen_aux_loss_mock]\n # mock optim wrapper\n optimizer_wrapper = {\n 'discriminator': MagicMock(),\n 'generator': MagicMock()\n }\n optimizer_wrapper['discriminator']._accumulative_counts = 1\n optimizer_wrapper['generator']._accumulative_counts = 1\n\n inputs = dict(img=torch.randn(3, 3, 32, 32))\n label = [torch.randint(0, 10, (1, )) for _ in range(3)]\n data_sample = [DataSample() for _ in range(3)]\n for idx, sample in enumerate(data_sample):\n sample.set_gt_label(label[idx])\n data = dict(inputs=inputs, data_samples=data_sample)\n\n log_vars = gan.train_step(data, optim_wrapper=optimizer_wrapper)\n self.assertIn('loss', log_vars)\n self.assertIn('loss_disc_fake', log_vars)\n self.assertIn('loss_disc_real', log_vars)\n self.assertIn('loss_disc_fake_g', log_vars)\n self.assertIn('loss_gen_aux', log_vars)\n self.assertIn('loss_disc_shift', log_vars)\n\n # test forward with only gan loss\n loss_config = dict(gan_loss=gan_loss)\n gan = BaseConditionalGAN(\n noise_size=10,\n num_classes=10,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n log_vars = gan.train_step(data, optim_wrapper=optimizer_wrapper)\n self.assertIn('loss', log_vars)\n self.assertIn('loss_disc_fake', log_vars)\n self.assertIn('loss_disc_real', log_vars)\n self.assertIn('loss_disc_fake_g', log_vars)\n self.assertNotIn('loss_gen_aux', log_vars)\n self.assertNotIn('loss_disc_shift', log_vars)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_base_edit_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch import nn\nfrom torch.optim import Adam\n\nfrom mmagic.models import BaseEditModel, DataPreprocessor\nfrom mmagic.models.losses import L1Loss\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\n@MODELS.register_module()\nclass ToyBaseModel(nn.Module):\n \"\"\"An example of interpolate network for testing BasicInterpolator.\"\"\"\n\n def __init__(self):\n super().__init__()\n self.layer = nn.Conv2d(3, 3, 3, 1, 1)\n\n def forward(self, x):\n return self.layer(x)\n\n def init_weights(self, pretrained=None):\n pass\n\n\ndef test_base_edit_model():\n\n model = BaseEditModel(\n generator=dict(type='ToyBaseModel'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=DataPreprocessor())\n\n assert model.__class__.__name__ == 'BaseEditModel'\n assert isinstance(model.generator, ToyBaseModel)\n assert isinstance(model.pixel_loss, L1Loss)\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # prepare data\n inputs = torch.rand(1, 3, 20, 20)\n target = torch.rand(3, 20, 20)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars['loss'], torch.Tensor)\n save_loss = log_vars['loss']\n log_vars = model.train_step(data, optim_wrapper)\n log_vars = model.train_step(data, optim_wrapper)\n assert save_loss > log_vars['loss']\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 20, 20)\n\n # feat\n output = model(torch.rand(1, 3, 20, 20), mode='tensor')\n assert output.shape == (1, 3, 20, 20)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_base_gan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom mmengine.testing import assert_allclose\nfrom torch.nn import ModuleList\nfrom torch.optim import SGD\n\nfrom mmagic.models import BaseGAN, DataPreprocessor\nfrom mmagic.models.losses import (DiscShiftLossComps, GANLossComps,\n GeneratorPathRegularizerComps,\n GradientPenaltyLossComps)\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(type='DCGANGenerator', output_scale=8, base_channels=8)\ndiscriminator = dict(\n type='DCGANDiscriminator',\n base_channels=8,\n input_scale=8,\n output_scale=4,\n out_channels=1)\n\n\nclass ToyGAN(BaseGAN):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def train_generator(self, inputs, data_samples, optimizer_wrapper):\n return dict(loss_gen=1)\n\n def train_discriminator(self, inputs, data_samples, optimizer_wrapper):\n return dict(loss_disc=2)\n\n\nclass TestBaseGAN(TestCase):\n\n def test_init(self):\n gan = ToyGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor())\n self.assertIsInstance(gan, BaseGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = ToyGAN(\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = ToyGAN(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = ToyGAN(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n self.assertIsNone(gan.gan_loss)\n self.assertIsNone(gan.gen_auxiliary_losses)\n self.assertIsNone(gan.disc_auxiliary_losses)\n self.assertEqual(gan.loss_config, dict())\n\n def test_train_step(self):\n # prepare model\n accu_iter = 2\n n_disc = 2\n message_hub = MessageHub.get_instance('basegan-test')\n gan = ToyGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n ToyGAN.train_discriminator = MagicMock(\n return_value=dict(loss_disc=torch.Tensor(1), loss=torch.Tensor(1)))\n ToyGAN.train_generator = MagicMock(\n return_value=dict(loss_gen=torch.Tensor(2), loss=torch.Tensor(2)))\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n inputs = torch.randn(1, 3, 4, 4)\n data = dict(inputs=inputs)\n\n # simulate train_loop here\n disc_update_times = 0\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(ToyGAN.train_generator.call_count, accu_iter)\n self.assertEqual(\n set(log.keys()), set(['loss', 'loss_disc', 'loss_gen']))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(ToyGAN.train_generator.call_count, 0)\n self.assertEqual(log.keys(), set(['loss', 'loss_disc']))\n\n # disc should update once for each iteration\n disc_update_times += 1\n self.assertEqual(ToyGAN.train_discriminator.call_count,\n disc_update_times)\n\n def test_update_ema(self):\n # prepare model\n n_gen = 4\n n_disc = 2\n accu_iter = 2\n ema_interval = 3\n message_hub = MessageHub.get_instance('basegan-test-ema')\n gan = ToyGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc,\n generator_steps=n_gen,\n ema_config=dict(interval=ema_interval))\n gan.train_discriminator = MagicMock(\n return_value=dict(loss_disc=torch.Tensor(1), loss=torch.Tensor(1)))\n gan.train_generator = MagicMock(\n return_value=dict(loss_gen=torch.Tensor(2), loss=torch.Tensor(2)))\n\n self.assertTrue(gan.with_ema_gen)\n # mock generator_ema with MagicMock\n del gan.generator_ema\n setattr(gan, 'generator_ema', MagicMock())\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n inputs = torch.randn(1, 3, 4, 4)\n data = dict(inputs=inputs)\n\n # simulate train_loop here\n ema_times = 0\n gen_update_times = 0\n disc_update_times = 0\n for idx in range(n_disc * accu_iter * ema_interval):\n message_hub.update_info('iter', idx)\n gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) % (n_disc * accu_iter) == 0:\n ema_times += 1\n gen_update_times += accu_iter * n_gen\n\n disc_update_times += 1\n self.assertEqual(gan.generator_ema.update_parameters.call_count,\n ema_times)\n self.assertEqual(gan.train_generator.call_count, gen_update_times)\n # disc should update once for each iteration\n self.assertEqual(gan.train_discriminator.call_count,\n disc_update_times)\n\n def test_val_step_and_test_step(self):\n gan = ToyGAN(\n noise_size=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor())\n\n # no mode\n inputs = dict(inputs=dict(num_batches=3))\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 3)\n self.assertEqual(len(outputs_test), 3)\n for idx in range(3):\n self.assertEqual(outputs_val[idx].fake_img.shape, (3, 8, 8))\n self.assertEqual(outputs_test[idx].fake_img.shape, (3, 8, 8))\n\n # set mode\n inputs = dict(inputs=dict(num_batches=4, sample_model='orig'))\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 4)\n self.assertEqual(len(outputs_test), 4)\n for idx in range(4):\n self.assertEqual(outputs_val[idx].sample_model, 'orig')\n self.assertEqual(outputs_test[idx].sample_model, 'orig')\n self.assertEqual(outputs_val[idx].fake_img.shape, (3, 8, 8))\n self.assertEqual(outputs_test[idx].fake_img.shape, (3, 8, 8))\n\n inputs = dict(inputs=dict(num_batches=4, sample_model='orig/ema'))\n self.assertRaises(AssertionError, gan.val_step, inputs)\n\n inputs = dict(inputs=dict(num_batches=4, sample_model='ema'))\n self.assertRaises(AssertionError, gan.val_step, inputs)\n\n # set noise input\n inputs = dict(inputs=dict(noise=torch.randn(4, 10)))\n outputs_val = gan.val_step(inputs)\n outputs_test = gan.test_step(inputs)\n self.assertEqual(len(outputs_val), 4)\n self.assertEqual(len(outputs_val), 4)\n for idx in range(4):\n test_fake_img = outputs_test[idx].fake_img\n val_fake_img = outputs_val[idx].fake_img\n assert_allclose(test_fake_img, val_fake_img)\n\n def test_forward(self):\n # set a gan w/o EMA\n gan = ToyGAN(\n noise_size=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor())\n inputs = dict(num_batches=3)\n outputs = gan(inputs, None)\n self.assertEqual(len(outputs), 3)\n for idx in range(3):\n self.assertTrue(outputs[idx].fake_img.shape == (3, 8, 8))\n\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for idx in range(3):\n self.assertEqual(outputs[idx].fake_img.shape, (3, 8, 8))\n\n outputs = gan(torch.randn(3, 10))\n self.assertEqual(len(outputs), 3)\n for idx in range(3):\n self.assertEqual(outputs[idx].fake_img.shape, (3, 8, 8))\n\n # set a gan w EMA\n gan = ToyGAN(\n noise_size=10,\n generator=deepcopy(generator),\n data_preprocessor=DataPreprocessor(),\n ema_config=dict(interval=1))\n inputs = dict(num_batches=3)\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for idx in range(3):\n self.assertEqual(outputs[idx].fake_img.shape, (3, 8, 8))\n\n inputs = dict(num_batches=3, sample_model='ema/orig')\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 3)\n for idx in range(3):\n ema_img = outputs[idx].ema\n orig_img = outputs[idx].orig\n self.assertEqual(ema_img.fake_img.shape, orig_img.fake_img.shape)\n self.assertTrue(outputs[idx].sample_model, 'ema/orig')\n\n inputs = dict(noise=torch.randn(4, 10))\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 4)\n for idx in range(4):\n self.assertEqual(outputs[idx].fake_img.shape, (3, 8, 8))\n\n # test additional sample kwargs\n sample_kwargs = dict(return_noise=True)\n inputs = dict(noise=torch.randn(4, 10), sample_kwargs=sample_kwargs)\n outputs = gan(inputs)\n self.assertEqual(len(outputs), 4)\n for idx in range(4):\n self.assertEqual(outputs[idx].fake_img.shape, (3, 8, 8))\n\n # test when data samples is not None\n inputs = dict(num_batches=3, sample_model='ema/orig')\n data_samples = [DataSample(id=1), DataSample(id=2), DataSample(id=3)]\n outputs = gan(inputs, DataSample.stack(data_samples))\n self.assertEqual(len(outputs), 3)\n for idx, output in enumerate(outputs):\n self.assertEqual(output.id, idx + 1)\n\n def test_custom_loss(self):\n message_hub = MessageHub.get_instance('basegan-test-custom-loss')\n message_hub.update_info('iter', 10)\n\n gan_loss = dict(type='GANLossComps', gan_type='vanilla')\n\n # test loss config is dict()\n gan = BaseGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=dict())\n self.assertIsNone(gan.gan_loss)\n self.assertIsNone(gan.disc_auxiliary_losses)\n self.assertIsNone(gan.gen_auxiliary_losses)\n\n # test loss config is list\n disc_auxiliary_loss_list = [\n dict(type='DiscShiftLossComps'),\n dict(type='GradientPenaltyLossComps')\n ]\n gen_auxiliary_loss_list = [dict(type='GeneratorPathRegularizerComps')]\n loss_config = dict(\n gan_loss=gan_loss,\n disc_auxiliary_loss=disc_auxiliary_loss_list,\n gen_auxiliary_loss=gen_auxiliary_loss_list)\n gan = BaseGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n self.assertIsInstance(gan.disc_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.disc_auxiliary_losses[0], DiscShiftLossComps)\n self.assertIsInstance(gan.disc_auxiliary_losses[1],\n GradientPenaltyLossComps)\n self.assertIsInstance(gan.gen_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.gen_auxiliary_losses[0],\n GeneratorPathRegularizerComps)\n\n # test loss config is single dict\n disc_auxiliary_loss = dict(\n type='DiscShiftLossComps', data_info=dict(pred='disc_pred_fake'))\n gen_auxiliary_loss = dict(\n type='GeneratorPathRegularizerComps',\n data_info=dict(generator='gen', num_batches='batch_size'))\n loss_config = dict(\n gan_loss=gan_loss,\n disc_auxiliary_loss=disc_auxiliary_loss,\n gen_auxiliary_loss=gen_auxiliary_loss)\n\n gan = BaseGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n self.assertIsInstance(gan.gan_loss, GANLossComps)\n self.assertIsInstance(gan.disc_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.disc_auxiliary_losses[0], DiscShiftLossComps)\n self.assertIsInstance(gan.gen_auxiliary_losses, ModuleList)\n self.assertIsInstance(gan.gen_auxiliary_losses[0],\n GeneratorPathRegularizerComps)\n\n # test forward custom loss terms\n gan = BaseGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n # mock gen aux loss to avoid build styleGAN Generator\n gen_aux_loss_mock = MagicMock(return_value=torch.Tensor([1.]))\n gen_aux_loss_mock.loss_name = MagicMock(return_value='loss_gen_aux')\n gan._modules['gen_auxiliary_losses'] = [gen_aux_loss_mock]\n # mock optim wrapper\n optimizer_wrapper = {\n 'discriminator': MagicMock(),\n 'generator': MagicMock()\n }\n optimizer_wrapper['discriminator']._accumulative_counts = 1\n optimizer_wrapper['generator']._accumulative_counts = 1\n\n data = dict(inputs=dict(img=torch.randn(2, 3, 32, 32)))\n log_vars = gan.train_step(data, optim_wrapper=optimizer_wrapper)\n self.assertIn('loss', log_vars)\n self.assertIn('loss_disc_fake', log_vars)\n self.assertIn('loss_disc_real', log_vars)\n self.assertIn('loss_disc_fake_g', log_vars)\n self.assertIn('loss_gen_aux', log_vars)\n self.assertIn('loss_disc_shift', log_vars)\n\n # test forward with only gan loss\n loss_config = dict(gan_loss=gan_loss)\n gan = BaseGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor(),\n loss_config=loss_config)\n data = dict(inputs=dict(img=torch.randn(2, 3, 32, 32)))\n log_vars = gan.train_step(data, optim_wrapper=optimizer_wrapper)\n self.assertIn('loss', log_vars)\n self.assertIn('loss_disc_fake', log_vars)\n self.assertIn('loss_disc_real', log_vars)\n self.assertIn('loss_disc_fake_g', log_vars)\n self.assertNotIn('loss_gen_aux', log_vars)\n self.assertNotIn('loss_disc_shift', log_vars)\n\n def test_gather_log_vars(self):\n gan = ToyGAN(\n noise_size=5,\n generator=deepcopy(generator),\n discriminator=deepcopy(discriminator),\n data_preprocessor=DataPreprocessor())\n log_dict_list = [\n dict(loss=torch.Tensor([2.33]), loss_disc=torch.Tensor([1.14514]))\n ]\n self.assertDictEqual(log_dict_list[0],\n gan.gather_log_vars(log_dict_list))\n\n log_dict_list = [\n dict(loss=torch.Tensor([2]), loss_disc=torch.Tensor([2])),\n dict(loss=torch.Tensor([3]), loss_disc=torch.Tensor([5]))\n ]\n self.assertDictEqual(\n dict(loss=torch.Tensor([2.5]), loss_disc=torch.Tensor([3.5])),\n gan.gather_log_vars(log_dict_list))\n\n # test raise error\n with self.assertRaises(AssertionError):\n gan.gather_log_vars([dict(a=1), dict(b=2)])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_base_mattor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nfrom unittest.mock import patch\n\nimport mmcv\nimport numpy as np\nimport torch\nfrom mmengine.config import ConfigDict\n\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.models.base_models import BaseMattor\nfrom mmagic.models.editors import DIM\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef _get_model_cfg(fname):\n \"\"\"Grab configs necessary to create a model.\n\n These are deep copied to allow for safe modification of parameters without\n influencing other tests.\n \"\"\"\n config_dpath = 'configs'\n config_fpath = osp.join(config_dpath, fname)\n if not osp.exists(config_dpath):\n raise Exception('Cannot find config path')\n config = mmcv.Config.fromfile(config_fpath)\n return config.model\n\n\ndef _demo_input_train(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n inputs = torch.cat((merged, trimap), dim=1)\n if cuda:\n inputs = inputs.cuda()\n\n results = dict(\n alpha=np.random.random(\n (img_shape[0], img_shape[1], 1)).astype(np.float32),\n merged=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32),\n fg=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32),\n bg=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32))\n\n data_samples = []\n packinputs = PackInputs()\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef _demo_input_test(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n test_trans (str): what test transformation is used in data pipeline.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n ori_shape = (img_shape[0], img_shape[1], 1)\n\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n inputs = torch.cat((merged, trimap), dim=1)\n if cuda:\n inputs = inputs.cuda()\n\n results = dict(\n ori_alpha=np.random.random(ori_shape).astype(np.float32),\n ori_trimap=np.random.randint(256, size=ori_shape).astype(np.float32),\n ori_merged_shape=img_shape)\n\n data_samples = []\n packinputs = PackInputs()\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef assert_pred_alpha(predictions, batch_size):\n assert isinstance(predictions, list)\n assert isinstance(predictions[0], DataSample)\n pred_alpha = predictions[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == batch_size\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\n@patch.multiple(BaseMattor, __abstractmethods__=set())\ndef test_base_mattor():\n \"\"\"Test resize and restore size.\"\"\"\n\n data_preprocessor = dict(type='MattorPreprocessor')\n backbone = dict(\n type='SimpleEncoderDecoder',\n encoder=dict(type='VGG16', in_channels=4),\n decoder=dict(type='PlainDecoder'))\n test_cfgs = [\n dict(resize_method='pad', resize_mode='reflect', size_divisor=32),\n dict(resize_method='interp', resize_mode='bilinear', size_divisor=32),\n ]\n out_shapes = (64, 64), (32, 32)\n\n inputs = torch.rand((1, 4, 48, 48))\n for test_cfg, out_shape in zip(test_cfgs, out_shapes):\n mattor = BaseMattor(\n data_preprocessor=data_preprocessor,\n backbone=backbone,\n test_cfg=test_cfg,\n )\n\n out = mattor.resize_inputs(inputs)\n assert out.shape[-2:] == out_shape\n out = mattor.restore_size(out[0],\n ConfigDict(ori_merged_shape=(48, 48)))\n assert out.shape[-2:] == (48, 48)\n\n\ndef test_dim_config():\n\n data_preprocessor = dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n # bgr_to_rgb=True,\n # proc_inputs='normalize',\n proc_trimap='rescale_to_zero_one',\n # proc_gt='rescale_to_zero_one',\n )\n backbone = dict(\n type='SimpleEncoderDecoder',\n encoder=dict(type='VGG16', in_channels=4),\n decoder=dict(type='PlainDecoder'))\n refiner = dict(type='PlainRefiner')\n train_cfg = dict(train_backbone=True, train_refiner=True)\n test_cfg = dict(refine=True)\n loss_alpha = dict(type='L1Loss')\n\n # build mattor without refiner\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner=None,\n loss_alpha=loss_alpha,\n train_cfg=train_cfg,\n test_cfg=test_cfg.copy())\n assert not mattor.with_refiner\n assert not mattor.test_cfg.refine\n\n # only train the refiner, this will freeze the backbone\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner,\n loss_alpha=loss_alpha,\n train_cfg=dict(train_backbone=False, train_refiner=True),\n test_cfg=test_cfg.copy())\n assert not mattor.train_cfg.train_backbone\n assert mattor.train_cfg.train_refiner\n assert mattor.test_cfg.refine\n\n # only train the backbone while the refiner is used for inference but not\n # trained, this behavior is allowed currently but will cause a warning.\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner,\n loss_alpha=loss_alpha,\n train_cfg=dict(train_backbone=True, train_refiner=False),\n test_cfg=test_cfg.copy())\n assert mattor.train_cfg.train_backbone\n assert not mattor.train_cfg.train_refiner\n assert mattor.test_cfg.refine\n\n\ndef test_dim():\n model_cfg = ConfigDict(\n type='DIM',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n # bgr_to_rgb=True,\n # proc_inputs='normalize',\n proc_trimap='rescale_to_zero_one',\n # proc_gt='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(type='VGG16', in_channels=4),\n decoder=dict(type='PlainDecoder')),\n refiner=dict(type='PlainRefiner'),\n loss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5),\n loss_comp=dict(type='CharbonnierCompLoss', loss_weight=0.5),\n loss_refine=dict(type='CharbonnierLoss'),\n train_cfg=dict(train_backbone=True, train_refiner=True),\n test_cfg=dict(\n refine=False,\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ),\n )\n\n # 1. test dim model with refiner\n model_cfg.train_cfg.train_refiner = True\n model_cfg.test_cfg.refine = True\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64))\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train,\n ['loss_alpha', 'loss_comp', 'loss_refine'])\n\n # test model forward in train mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_train = _demo_input_train((64, 64), cuda=True)\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train,\n ['loss_alpha', 'loss_comp', 'loss_refine'])\n\n # test model forward in test mode\n with torch.no_grad():\n model = MODELS.build(model_cfg)\n input_test = _demo_input_test((48, 48))\n output_test = model(*input_test, mode='predict')\n assert isinstance(output_test, list)\n assert isinstance(output_test[0], DataSample)\n pred_alpha = output_test[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == (48, 48)\n\n # test model forward in test mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = model(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # 2. test dim model without refiner\n model_cfg.refiner = None\n model_cfg.test_cfg.refine = True\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64))\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train, ['loss_alpha', 'loss_comp'])\n\n # test model forward in train mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_train = _demo_input_train((64, 64), cuda=True)\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train, ['loss_alpha', 'loss_comp'])\n\n # test model forward in test mode\n with torch.no_grad():\n model = MODELS.build(model_cfg)\n input_test = _demo_input_test((48, 48))\n output_test = model(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # check test with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = model(*input_test, mode='predict')\n\n # test forward_raw\n model.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n model.forward(inputs)\n\n\ndef test_indexnet():\n model_cfg = ConfigDict(\n type='IndexNet',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n # bgr_to_rgb=True,\n # proc_inputs='normalize',\n proc_trimap='rescale_to_zero_one',\n # proc_gt='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='IndexNetEncoder',\n in_channels=4,\n freeze_bn=True,\n ),\n decoder=dict(type='IndexNetDecoder')),\n loss_alpha=dict(\n type='CharbonnierLoss', loss_weight=0.5, sample_wise=True),\n loss_comp=dict(\n type='CharbonnierCompLoss', loss_weight=1.5, sample_wise=True),\n test_cfg=dict(\n resize_method='interp',\n resize_mode='bicubic',\n size_divisor=32,\n ),\n )\n\n model_cfg.backbone.encoder.init_cfg = None\n\n # test indexnet inference\n with torch.no_grad():\n indexnet = MODELS.build(model_cfg)\n indexnet.eval()\n input_test = _demo_input_test((48, 48))\n output_test = indexnet(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # test inference with gpu\n if torch.cuda.is_available():\n indexnet = MODELS.build(model_cfg).cuda()\n indexnet.eval()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = indexnet(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # test forward train though we do not guarantee the training for present\n model_cfg.loss_alpha = None\n model_cfg.loss_comp = dict(type='L1CompositionLoss')\n indexnet = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64), batch_size=2)\n output_train = indexnet(*input_train, mode='loss')\n # assert output_train['num_samples'] == 2\n assert_dict_keys_equal(output_train, ['loss_comp'])\n\n if torch.cuda.is_available():\n model_cfg.loss_alpha = dict(type='L1Loss')\n model_cfg.loss_comp = None\n indexnet = MODELS.build(model_cfg).cuda()\n input_train = _demo_input_train((64, 64), batch_size=2, cuda=True)\n output_train = indexnet(*input_train, mode='loss')\n # assert output_train['num_samples'] == 2\n assert_dict_keys_equal(output_train, ['loss_alpha'])\n\n # test forward\n indexnet.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n indexnet.forward(inputs)\n\n\ndef test_gca():\n model_cfg = ConfigDict(\n type='GCA',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n # bgr_to_rgb=True,\n # proc_inputs='normalize',\n proc_trimap='as_is',\n # proc_gt='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='ResGCAEncoder',\n block='BasicBlock',\n layers=[3, 4, 4, 2],\n in_channels=6,\n with_spectral_norm=True,\n ),\n decoder=dict(\n type='ResGCADecoder',\n block='BasicBlockDec',\n layers=[2, 3, 3, 2],\n with_spectral_norm=True)),\n loss_alpha=dict(type='L1Loss'),\n test_cfg=dict(\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ))\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n meta = {'format_trimap_to_onehot': True}\n inputs, data_samples = _demo_input_train((64, 64), batch_size=2, meta=meta)\n inputs6 = torch.cat((inputs, inputs[:, 3:, :, :], inputs[:, 3:, :, :]),\n dim=1)\n outputs = model(inputs6, data_samples, mode='loss')\n assert_dict_keys_equal(outputs, ['loss'])\n\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n inputs, data_samples = _demo_input_train((64, 64),\n batch_size=2,\n cuda=True,\n meta=meta)\n inputs6 = torch.cat((inputs, inputs[:, 3:, :, :], inputs[:, 3:, :, :]),\n dim=1)\n outputs = model(inputs6, data_samples, mode='loss')\n assert_dict_keys_equal(outputs, ['loss'])\n\n # test model forward in test mode\n with torch.no_grad():\n model_cfg.backbone.encoder.in_channels = 4\n model = MODELS.build(model_cfg)\n inputs = _demo_input_test((48, 48), meta=meta)\n outputs = model(*inputs, mode='predict')\n assert_pred_alpha(outputs, (48, 48))\n\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n inputs = _demo_input_test((48, 48), cuda=True, meta=meta)\n outputs = model(*inputs, mode='predict')\n assert_pred_alpha(outputs, (48, 48))\n\n # test forward_dummy\n model.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n model.forward(inputs)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_base_translation_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nfrom mmengine.model import MMDistributedDataParallel\n\nfrom mmagic.models import BaseTranslationModel\n\n\nclass ToyTranslationModel(BaseTranslationModel):\n\n def __init__(self, generator, discriminator=None):\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n default_domain='A',\n reachable_domains=['A'],\n related_domains=['A', 'B'],\n data_preprocessor=None)\n\n\nclass TestBaseTranslationModel(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.generator = dict(\n type='ResnetGenerator',\n in_channels=3,\n out_channels=3,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02))\n cls.discriminator = dict(\n type='PatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02))\n\n def test_init(self):\n # test disc is None\n model = ToyTranslationModel(\n generator=self.generator, discriminator=None)\n self.assertIsNone(model.discriminators)\n\n # test disc is not None\n model = ToyTranslationModel(\n generator=self.generator, discriminator=self.discriminator)\n self.assertIsNotNone(model.discriminators)\n\n def test_get_module(self):\n generator_mock = MagicMock(spec=MMDistributedDataParallel)\n generator_mock_module = MagicMock()\n generator_mock.module = generator_mock_module\n\n model = ToyTranslationModel(\n generator=self.generator, discriminator=None)\n module = model.get_module(generator_mock)\n self.assertEqual(module, generator_mock_module)\n\n def test_forward(self):\n model = ToyTranslationModel(\n generator=self.generator, discriminator=None)\n res = model.forward_test(\n img=torch.randn(1, 3, 64, 64), target_domain=None)\n self.assertEqual(res['target'].shape, (1, 3, 64, 64))\n self.assertEqual(res['source'].shape, (1, 3, 64, 64))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_basic_interpolator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom torch import nn\n\nfrom mmagic.models import BasicInterpolator\nfrom mmagic.models.losses import L1Loss\nfrom mmagic.registry import MODELS\n\n\n@MODELS.register_module()\nclass InterpolateExample(nn.Module):\n \"\"\"An example of interpolate network for testing BasicInterpolator.\"\"\"\n\n def __init__(self):\n super().__init__()\n self.layer = nn.Conv2d(3, 3, 3, 1, 1)\n\n def forward(self, x):\n return self.layer(x[:, 0])\n\n def init_weights(self, pretrained=None):\n pass\n\n\ndef test_basic_interpolator():\n\n model = BasicInterpolator(\n generator=dict(type='InterpolateExample'),\n pixel_loss=dict(type='L1Loss'))\n assert model.__class__.__name__ == 'BasicInterpolator'\n assert isinstance(model.generator, InterpolateExample)\n assert isinstance(model.pixel_loss, L1Loss)\n\n input_tensors = torch.rand((1, 9, 3, 16, 16))\n input_tensors = model.split_frames(input_tensors)\n assert input_tensors.shape == (8, 2, 3, 16, 16)\n\n output_tensors = torch.rand((8, 1, 3, 16, 16))\n result = model.merge_frames(input_tensors, output_tensors)\n assert len(result) == 17\n assert result[0].shape == (16, 16, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_one_stage.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom os.path import dirname, join\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nfrom mmengine import Config\n\nfrom mmagic.models import GLEncoderDecoder\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\ndef test_one_stage_inpaintor():\n register_all_modules()\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n config_file = join(\n dirname(__file__), '..', '..', 'configs', 'one_stage_gl.py')\n cfg = Config.fromfile(config_file)\n\n # mock perceptual loss for test speed\n cfg.model.loss_composed_percep = None\n inpaintor = MODELS.build(cfg.model)\n\n # modify attributes for mocking\n inpaintor.with_composed_percep_loss = True\n inpaintor.loss_percep = None\n\n # test attributes\n assert inpaintor.__class__.__name__ == 'OneStageInpaintor'\n assert isinstance(inpaintor.generator, GLEncoderDecoder)\n assert inpaintor.with_l1_hole_loss\n assert inpaintor.with_l1_valid_loss\n assert inpaintor.with_tv_loss\n assert inpaintor.with_composed_percep_loss\n assert inpaintor.with_out_percep_loss\n assert inpaintor.with_gan\n assert inpaintor.with_gp_loss\n assert inpaintor.with_disc_shift_loss\n assert inpaintor.is_train\n assert inpaintor.train_cfg['disc_step'] == 1\n assert inpaintor.disc_step_count == 0\n\n with patch.object(\n inpaintor, 'loss_percep', return_value=(torch.tensor(1.0), None)):\n\n input_x = torch.randn(1, 3, 256, 256)\n with pytest.raises(NotImplementedError):\n inpaintor.forward_train(input_x)\n\n inpaintor.to(device=device)\n\n # construct inputs\n gt_img = torch.randn(3, 256, 256).to(device=device)\n mask = torch.zeros_like(gt_img)[0:1, ...]\n mask[..., 20:100, 100:120] = 1.\n masked_img = gt_img * (1. - mask)\n mask_bbox = [100, 100, 110, 110]\n inputs = masked_img.unsqueeze(0)\n data_sample = DataSample(\n mask=mask,\n mask_bbox=mask_bbox,\n gt_img=gt_img,\n )\n data_samples = DataSample.stack([data_sample])\n data_batch = {'inputs': inputs, 'data_samples': [data_sample]}\n\n # test forward_tensor\n fake_reses, fake_imgs = inpaintor.forward_tensor(inputs, data_samples)\n assert fake_reses.shape == fake_imgs.shape == (1, 3, 256, 256)\n\n # test forward test\n predictions = inpaintor.forward_test(inputs, data_samples)\n assert predictions.fake_img.shape == (1, 3, 256, 256)\n\n # test train_step\n optim_g = torch.optim.SGD(inpaintor.generator.parameters(), lr=0.1)\n optim_d = torch.optim.SGD(inpaintor.disc.parameters(), lr=0.1)\n optim_dict = dict(generator=optim_g, disc=optim_d)\n\n log_vars = inpaintor.train_step(data_batch, optim_dict)\n assert 'loss_l1_hole' in log_vars\n assert 'loss_l1_valid' in log_vars\n assert 'loss_composed_percep' in log_vars\n assert 'loss_composed_style' not in log_vars\n assert 'loss_out_percep' in log_vars\n assert 'loss_out_style' not in log_vars\n assert 'loss_tv' in log_vars\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_g_fake' in log_vars\n\n # Another test\n cfg_ = copy.deepcopy(cfg)\n cfg_.model.train_cfg.disc_step = 2\n inpaintor = MODELS.build(cfg_.model)\n inpaintor.to(device=device)\n assert inpaintor.train_cfg.disc_step == 2\n log_vars = inpaintor.train_step(data_batch, optim_dict)\n assert 'loss_l1_hole' not in log_vars\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_base_models/test_two_stage.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport platform\nfrom os.path import dirname, join\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\n# TODO: this test is same as `test_two_stage_encoder_decoder.py`\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_two_stage_inpaintor():\n register_all_modules()\n\n config_file = join(dirname(__file__), '../../configs', 'two_stage_test.py')\n cfg = Config.fromfile(config_file)\n\n inpaintor = MODELS.build(cfg.model)\n\n assert inpaintor.__class__.__name__ == 'TwoStageInpaintor'\n\n if torch.cuda.is_available():\n inpaintor.cuda()\n\n # check architecture\n assert inpaintor.stage1_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_composed_percep', 'loss_tv')\n assert inpaintor.stage2_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_gan')\n assert inpaintor.with_l1_hole_loss\n assert inpaintor.with_l1_valid_loss\n assert inpaintor.with_composed_percep_loss\n assert not inpaintor.with_out_percep_loss\n assert inpaintor.with_gan\n\n inpaintor._modules['loss_percep'] = MagicMock(\n return_value=(torch.Tensor([1]), torch.Tensor([2])))\n\n # prepare data\n gt_img = torch.rand((3, 256, 256))\n mask = torch.zeros((1, 256, 256))\n mask[..., 50:180, 60:170] = 1.\n masked_img = gt_img.unsqueeze(0) * (1. - mask)\n mask_bbox = [100, 100, 110, 110]\n data_batch = {\n 'inputs':\n masked_img,\n 'data_samples':\n [DataSample(\n mask=mask,\n mask_bbox=mask_bbox,\n gt_img=gt_img,\n )]\n }\n\n optim_g = torch.optim.Adam(inpaintor.generator.parameters(), lr=0.0001)\n optim_d = torch.optim.Adam(inpaintor.disc.parameters(), lr=0.0001)\n optims = dict(generator=OptimWrapper(optim_g), disc=OptimWrapper(optim_d))\n\n # check train_step with standard two_stage model\n for i in range(5):\n log_vars = inpaintor.train_step(data_batch, optims)\n\n if i % 2 == 0:\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_disc_shift' in log_vars\n assert 'loss' in log_vars\n else:\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_disc_shift' in log_vars\n assert 'loss' in log_vars\n assert 'stage1_loss_l1_hole' in log_vars\n assert 'stage1_loss_l1_valid' in log_vars\n assert 'stage2_loss_l1_hole' in log_vars\n assert 'stage2_loss_l1_valid' in log_vars\n\n # check for forward_test\n data = inpaintor.data_preprocessor(data_batch, True)\n data_inputs, data_sample = data['inputs'], data['data_samples']\n output = inpaintor.forward_test(data_inputs, data_sample)\n prediction = output\n assert 'fake_res' in prediction\n assert 'fake_img' in prediction\n assert 'pred_img' in prediction\n assert prediction.pred_img.shape == (1, 3, 256, 256)\n\n # check for gp_loss\n cfg_copy = copy.deepcopy(cfg)\n cfg_copy.model.disc_input_with_mask = False\n cfg_copy.model.disc.in_channels = 3\n cfg_copy.model.loss_gp = dict(type='GradientPenaltyLoss', loss_weight=10.)\n inpaintor = MODELS.build(cfg_copy.model)\n assert inpaintor.with_gp_loss\n\n log_vars = inpaintor.train_step(data_batch, optims)\n assert 'real_loss' in log_vars\n assert 'stage1_loss_l1_hole' in log_vars\n assert 'stage1_loss_l1_valid' in log_vars\n assert 'stage2_loss_l1_hole' in log_vars\n assert 'stage2_loss_l1_valid' in log_vars\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_data_preprocessors/test_data_preprocessor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport torch\nimport torch.nn.functional as F\nfrom mmengine.testing import assert_allclose\n\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.structures import DataSample\n\n\nclass TestBaseDataPreprocessor(TestCase):\n\n def test_init(self):\n data_preprocessor = DataPreprocessor(\n mean=[0, 0, 0],\n std=[255, 255, 255],\n pad_size_divisor=16,\n pad_value=10)\n\n self.assertEqual(data_preprocessor._device.type, 'cpu')\n assert_allclose(data_preprocessor.mean,\n torch.tensor([0, 0, 0]).view(-1, 1, 1))\n assert_allclose(data_preprocessor.std,\n torch.tensor([255, 255, 255]).view(-1, 1, 1))\n assert_allclose(data_preprocessor.pad_value, torch.tensor(10))\n self.assertEqual(data_preprocessor.pad_size_divisor, 16)\n self.assertEqual(data_preprocessor.pad_mode, 'constant')\n\n # test non-image-keys\n data_preprocessor = DataPreprocessor(non_image_keys='feature')\n self.assertIn('feature', data_preprocessor._NON_IMAGE_KEYS)\n data_preprocessor = DataPreprocessor(non_image_keys=['feature'])\n self.assertIn('feature', data_preprocessor._NON_IMAGE_KEYS)\n\n # test non-concentate-keys\n data_preprocessor = DataPreprocessor(non_concentate_keys='n_imgs')\n self.assertIn('n_imgs', data_preprocessor._NON_CONCATENATE_KEYS)\n data_preprocessor = DataPreprocessor(non_concentate_keys=['n_imgs'])\n self.assertIn('n_imgs', data_preprocessor._NON_CONCATENATE_KEYS)\n\n def test_parse_channel_index(self):\n data_preprocessor = DataPreprocessor()\n self.assertEqual(\n data_preprocessor._parse_channel_index(torch.rand(3, 5, 5)), 0)\n self.assertEqual(\n data_preprocessor._parse_channel_index(torch.rand(2, 3, 5, 5)), 1)\n self.assertEqual(\n data_preprocessor._parse_channel_index(torch.rand(2, 10, 3, 5, 5)),\n 2)\n self.assertEqual(\n data_preprocessor._parse_channel_index(torch.rand(5, 5)), 1)\n with self.assertRaises(AssertionError):\n data_preprocessor._parse_channel_index(\n torch.rand(1, 2, 10, 3, 5, 5, 5))\n\n # test dict input\n inputs = dict(fake_img=torch.rand(2, 3, 5, 5))\n self.assertEqual(data_preprocessor._parse_channel_index(inputs), 1)\n\n def test_parse_channel_order(self):\n data_preprocessor = DataPreprocessor()\n parse_fn = data_preprocessor._parse_channel_order\n # test no data sample\n self.assertEqual(parse_fn('img', torch.rand(3, 5, 5)), 'BGR')\n self.assertEqual(parse_fn('img', torch.rand(1, 3, 3, 4)), 'BGR')\n self.assertEqual(parse_fn('img', torch.rand(1, 1, 3, 4)), 'single')\n\n # test dict input\n inputs = dict(fake_img=torch.rand(1, 3, 3, 4))\n self.assertEqual(parse_fn('img', inputs), 'BGR')\n\n # test data sample is not None\n data_sample = DataSample()\n\n # test gt_img key\n # - RGB in channel order --> RGB\n data_sample.set_metainfo({'gt_channel_order': 'RGB'})\n self.assertEqual(\n parse_fn('gt_img', torch.rand(3, 5, 5), data_sample), 'RGB')\n # - BGR in channel order --> BGR\n data_sample.set_metainfo({'gt_channel_order': 'BGR'})\n self.assertEqual(\n parse_fn('gt_img', torch.rand(3, 5, 5), data_sample), 'BGR')\n\n # - grayscale in color_type --> single\n data_sample.set_metainfo({'gt_color_type': 'grayscale'})\n self.assertEqual(\n parse_fn('gt_img', torch.rand(1, 5, 5), data_sample), 'single')\n with self.assertRaises(AssertionError):\n parse_fn('gt_img', torch.rand(3, 5, 5), data_sample)\n\n # - unchanged in color_type --> BGR / single\n data_sample.set_metainfo({'gt_color_type': 'unchanged'})\n self.assertEqual(\n parse_fn('gt_img', torch.rand(1, 5, 5), data_sample), 'single')\n self.assertEqual(\n parse_fn('gt_img', torch.rand(3, 5, 5), data_sample), 'BGR')\n\n # test non-gt keys\n # - RGB in channel order --> RGB\n data_sample.set_metainfo({'AAA_channel_order': 'RGB'})\n self.assertEqual(\n parse_fn('AAA', torch.rand(3, 5, 5), data_sample), 'RGB')\n # - BGR in channel order --> BGR\n data_sample.set_metainfo({'BBC_channel_order': 'BGR'})\n self.assertEqual(\n parse_fn('BBC', torch.rand(3, 5, 5), data_sample), 'BGR')\n\n # - grayscale in color_type --> single\n data_sample.set_metainfo({'mm_img_color_type': 'grayscale'})\n data_sample.set_metainfo({'mm_img_channel_order': 'RGB'})\n self.assertEqual(\n parse_fn('mm_img', torch.rand(1, 5, 5), data_sample), 'single')\n with self.assertRaises(AssertionError):\n parse_fn('mm_img', torch.rand(3, 5, 5), data_sample)\n\n # - unchanged in color_type --> BGR / single\n data_sample.set_metainfo({'mm_img_color_type': 'unchanged'})\n self.assertEqual(\n parse_fn('mm_img', torch.rand(1, 5, 5), data_sample), 'single')\n self.assertEqual(\n parse_fn('mm_img', torch.rand(3, 5, 5), data_sample), 'BGR')\n\n # - color_type and channel_order both None --> BGR / single\n self.assertEqual(\n parse_fn('dk', torch.rand(1, 5, 5), data_sample), 'single')\n self.assertEqual(\n parse_fn('dk', torch.rand(3, 5, 5), data_sample), 'BGR')\n\n # test parse channel order for a stacked data sample\n stacked_data_sample = DataSample.stack([data_sample, data_sample])\n self.assertEqual(\n parse_fn('mm_img', torch.rand(1, 5, 5), stacked_data_sample),\n 'single')\n self.assertEqual(\n parse_fn('AAA', torch.rand(3, 5, 5), data_sample), 'RGB')\n\n def test_parse_batch_channel_order(self):\n data_preprocessor = DataPreprocessor()\n parse_fn = data_preprocessor._parse_batch_channel_order\n\n with self.assertRaises(AssertionError):\n parse_fn('img', torch.rand(1, 3, 5, 5), [DataSample()] * 2)\n\n inputs_batch = torch.randn(2, 3, 5, 5)\n data_sample_batch = [\n DataSample(metainfo=dict(gt_channel_order='RGB')),\n DataSample(metainfo=dict(gt_channel_order='RGB'))\n ]\n self.assertTrue(parse_fn('gt', inputs_batch, data_sample_batch))\n\n data_sample_batch = [\n DataSample(metainfo=dict(mm_img_channel_order='RGB')),\n DataSample(metainfo=dict(mm_img_channel_order='BGR'))\n ]\n with self.assertRaises(AssertionError):\n parse_fn(parse_fn('mm_img', inputs_batch, data_sample_batch))\n\n def test_do_conversion(self):\n data_preprocessor = DataPreprocessor()\n cov_fn = data_preprocessor._do_conversion\n\n # RGB -> BGR\n inputs = torch.rand(2, 3, 5, 5)\n target_outputs = inputs[:, [2, 1, 0], ...]\n outputs, order = cov_fn(inputs, 'RGB', 'BGR')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'BGR')\n\n # BGR -> RGB\n outputs, order = cov_fn(inputs, 'BGR', 'RGB')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'RGB')\n\n # RGB -> None\n target_outputs = inputs.clone()\n outputs, order = cov_fn(inputs, 'RGB', None)\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'RGB')\n\n # BGR -> None\n target_outputs = inputs.clone()\n outputs, order = cov_fn(inputs, 'BGR', None)\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'BGR')\n\n # single inputs -> None\n inputs = torch.rand(1, 10, 1, 5, 5)\n target_outputs = inputs.clone()\n outputs, order = cov_fn(inputs, 'single', None)\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'single')\n\n # single inputs -> BGR / RGB\n outputs, order = cov_fn(inputs, 'single', None)\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'single')\n outputs, order = cov_fn(inputs, 'single', 'RGB')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'single')\n outputs, order = cov_fn(inputs, 'single', 'BGR')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'single')\n\n with self.assertRaises(ValueError):\n cov_fn(inputs, 'RGBA', 'RGB')\n\n # RGBA inputs -> BGR / RGB\n inputs = torch.rand(2, 4, 5, 5)\n target_outputs = inputs.clone()\n outputs, order = cov_fn(inputs, 'RGB', 'RGB')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'RGB')\n target_outputs = inputs[:, [2, 1, 0, 3], ...]\n outputs, order = cov_fn(inputs, 'RGB', 'BGR')\n self.assertTrue((outputs == target_outputs).all())\n self.assertEqual(order, 'BGR')\n\n def test_update_metainfo(self):\n data_preprocessor = DataPreprocessor()\n update_fn = data_preprocessor._update_metainfo\n\n with self.assertRaises(AssertionError):\n padding_info = torch.randint(0, 10, (3, 2))\n data_samples = [DataSample() for _ in range(2)]\n update_fn(padding_info, None, data_samples)\n\n padding_info = torch.randint(0, 10, (3, 2))\n channel_order = dict(aaa='RGB', BBB='single')\n output = update_fn(padding_info, channel_order)\n self.assertEqual(len(output), 3)\n for data, padding_tar in zip(output, padding_info):\n self.assertTrue(\n (data.metainfo['padding_size'] == padding_tar).all())\n self.assertEqual(data.metainfo['aaa_output_channel_order'], 'RGB')\n self.assertEqual(data.metainfo['BBB_output_channel_order'],\n 'single')\n\n # channel_order is None + data_sample is not None (use last output)\n padding_info = torch.randint(0, 10, (3, 2))\n output = update_fn(padding_info, None, output)\n self.assertEqual(len(output), 3)\n for data, padding_tar in zip(output, padding_info):\n self.assertTrue(\n (data.metainfo['padding_size'] == padding_tar).all())\n # channel order in last output remains\n self.assertEqual(data.metainfo['aaa_output_channel_order'], 'RGB')\n self.assertEqual(data.metainfo['BBB_output_channel_order'],\n 'single')\n\n def test_preprocess_image_tensor(self):\n \"\"\"We test five cases:\n\n 1. raise error when input is not 4D tensor\n 2. no metainfo + output channel order is None: no conversion\n 3. no metainfo + output channel order is RGB: do conversion to RGB\n 4. metainfo (BGR) + output channel order is RGB: do conversion to RGB\n 5. metainfo (single) + output channel order is RGB: no conversion\n 6. datasample is None: return new data sample with metainfo\n \"\"\"\n data_preprocessor = DataPreprocessor()\n process_fn = data_preprocessor._preprocess_image_tensor\n # 1. raise error\n with self.assertRaises(AssertionError):\n process_fn(torch.rand(2, 25), None)\n # [0, 0, 0] for all time\n target_padding_info = torch.FloatTensor([0, 0, 0])\n\n # 2. no metainfo + output channel order is None\n inputs = torch.randint(0, 255, (4, 3, 5, 5))\n targets = (inputs - 127.5) / 127.5\n data_samples = [\n DataSample(metainfo=dict(mm_img_channel_order='RGB'))\n for _ in range(4)\n ]\n outputs, data_samples = process_fn(inputs, data_samples)\n assert_allclose(outputs, targets)\n for data in data_samples:\n self.assertEqual(data.metainfo['img_output_channel_order'], 'BGR')\n padding_size = data.metainfo['padding_size']\n self.assertEqual(padding_size.shape, (3, ))\n self.assertTrue((padding_size == target_padding_info).all())\n\n # 3. no metainfo + output channel order is RGB -> do conversion\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n process_fn = data_preprocessor._preprocess_image_tensor\n inputs = torch.randint(0, 255, (4, 3, 5, 5))\n targets = ((inputs - 127.5) / 127.5)[:, [2, 1, 0]]\n data_samples = [DataSample() for _ in range(4)]\n outputs, data_samples = process_fn(inputs, data_samples)\n assert_allclose(outputs, targets)\n for data in data_samples:\n self.assertEqual(data.metainfo['img_output_channel_order'], 'RGB')\n padding_size = data.metainfo['padding_size']\n self.assertEqual(padding_size.shape, (3, ))\n self.assertTrue((padding_size == target_padding_info).all())\n\n # 4. metainfo (BGR) + output channel order is RGB -> do conversion\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n process_fn = data_preprocessor._preprocess_image_tensor\n inputs = torch.randint(0, 255, (4, 3, 5, 5))\n targets = ((inputs - 127.5) / 127.5)[:, [2, 1, 0]]\n data_samples = [\n DataSample(metainfo=dict(gt_channel_order='BGR')) for _ in range(4)\n ]\n outputs, data_samples = process_fn(inputs, data_samples, 'gt_img')\n assert_allclose(outputs, targets)\n for data in data_samples:\n self.assertEqual(data.metainfo['gt_img_output_channel_order'],\n 'RGB')\n padding_size = data.metainfo['padding_size']\n self.assertEqual(padding_size.shape, (3, ))\n self.assertTrue((padding_size == target_padding_info).all())\n\n # 5. metainfo (single) + output channel order is RGB -> no conversion\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n process_fn = data_preprocessor._preprocess_image_tensor\n inputs = torch.randint(0, 255, (4, 1, 5, 5))\n targets = ((inputs - 127.5) / 127.5)\n data_samples = [\n DataSample(metainfo=dict(sin_channel_order='single'))\n for _ in range(4)\n ]\n outputs, data_samples = process_fn(inputs, data_samples, 'sin')\n assert_allclose(outputs, targets)\n for data in data_samples:\n self.assertEqual(data.metainfo['sin_output_channel_order'],\n 'single')\n padding_size = data.metainfo['padding_size']\n self.assertEqual(padding_size.shape, (3, ))\n self.assertTrue((padding_size == target_padding_info).all())\n\n # 6. data sample is None + video inputs\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n inputs = torch.randint(0, 255, (4, 5, 3, 5, 5))\n targets = ((inputs - 127.5) / 127.5)[:, :, [2, 1, 0]]\n outputs, data_samples = process_fn(inputs, None, 'sin')\n for data in data_samples:\n self.assertEqual(data.metainfo['sin_output_channel_order'], 'RGB')\n self.assertTrue(\n (data.metainfo['padding_size'] == target_padding_info).all())\n\n def test_preprocess_image_list(self):\n \"\"\"We test four cases:\n\n 1. no metainfo + output channel order is None: no conversion\n 2. no metainfo + output channel order is RGB + padding:\n do conversion to RGB\n 3. metainfo (RGB) + output channel order is RGB + padding:\n no conversion\n 4. metainfo (single) + output channel order is RGB: no conversion\n 5. data_sample is None\n \"\"\"\n # no metainfo + output channel order is None\n data_preprocessor = DataPreprocessor()\n process_fn = data_preprocessor._preprocess_image_list\n input1 = torch.randint(0, 255, (3, 3, 5))\n input2 = torch.randint(0, 255, (3, 3, 5))\n inputs = [input1, input2]\n data_samples = [DataSample() for _ in range(2)]\n target = torch.stack([(input1 - 127.5) / 127.5,\n (input2 - 127.5) / 127.5],\n dim=0)\n target_padding_info = torch.FloatTensor([0, 0, 0])\n outputs, data_samples = process_fn(inputs, data_samples)\n assert_allclose(outputs, target)\n for data in data_samples:\n self.assertEqual(data.metainfo['img_output_channel_order'], 'BGR')\n self.assertTrue(\n (data.metainfo['padding_size'] == target_padding_info).all())\n\n # no metainfo + output channel order is RGB\n data_preprocessor = DataPreprocessor(\n output_channel_order='RGB', pad_value=42)\n process_fn = data_preprocessor._preprocess_image_list\n input1 = torch.randint(0, 255, (3, 3, 5))\n input2 = torch.randint(0, 255, (3, 5, 5))\n inputs = [input1, input2]\n data_samples = [DataSample() for _ in range(2)]\n target1 = F.pad(input1.clone(), (0, 0, 0, 2), value=42) # pad H\n target2 = input2.clone()\n target = torch.stack([target1, target2], dim=0)[:, [2, 1, 0]]\n target = (target - 127.5) / 127.5\n target_padding_info = [\n torch.FloatTensor([0, 2, 0]),\n torch.FloatTensor([0, 0, 0])\n ]\n outputs, data_samples = process_fn(inputs, data_samples)\n assert_allclose(outputs, target)\n for data, pad in zip(data_samples, target_padding_info):\n self.assertEqual(data.metainfo['img_output_channel_order'], 'RGB')\n self.assertTrue((data.metainfo['padding_size'] == pad).all())\n\n # meta info + output channel order is RGB\n data_preprocessor = DataPreprocessor(\n output_channel_order='RGB', pad_value=42)\n process_fn = data_preprocessor._preprocess_image_list\n input1 = torch.randint(0, 255, (3, 3, 5))\n input2 = torch.randint(0, 255, (3, 5, 3))\n inputs = [input1, input2]\n data_samples = [\n DataSample(metainfo=dict(gt_channel_order='RGB')) for _ in range(2)\n ]\n target1 = F.pad(input1.clone(), (0, 0, 0, 2), value=42) # pad H\n target2 = F.pad(input2.clone(), (0, 2, 0, 0), value=42) # pad W\n target = torch.stack([target1, target2], dim=0)\n target = (target - 127.5) / 127.5\n target_padding_info = [\n torch.FloatTensor([0, 2, 0]),\n torch.FloatTensor([0, 0, 2])\n ]\n outputs, data_samples = process_fn(inputs, data_samples, 'gt_img')\n assert_allclose(outputs, target)\n for data, pad in zip(data_samples, target_padding_info):\n self.assertEqual(data.metainfo['gt_img_output_channel_order'],\n 'RGB')\n self.assertTrue((data.metainfo['padding_size'] == pad).all())\n\n # meta info (single) + output channel order is RGB\n data_preprocessor = DataPreprocessor(\n output_channel_order='RGB', pad_value=42)\n process_fn = data_preprocessor._preprocess_image_list\n input1 = torch.randint(0, 255, (1, 3, 5))\n input2 = torch.randint(0, 255, (1, 5, 3))\n inputs = [input1, input2]\n data_samples = [\n DataSample(metainfo=dict(AA_channel_order='single'))\n for _ in range(2)\n ]\n target1 = F.pad(input1.clone(), (0, 0, 0, 2), value=42) # pad H\n target2 = F.pad(input2.clone(), (0, 2, 0, 0), value=42) # pad W\n target = torch.stack([target1, target2], dim=0)\n target = (target - 127.5) / 127.5\n target_padding_info = [\n torch.FloatTensor([0, 2, 0]),\n torch.FloatTensor([0, 0, 2])\n ]\n outputs, data_samples = process_fn(inputs, data_samples, 'AA')\n assert_allclose(outputs, target)\n for data, pad in zip(data_samples, target_padding_info):\n self.assertEqual(data.metainfo['AA_output_channel_order'],\n 'single')\n self.assertTrue((data.metainfo['padding_size'] == pad).all())\n\n # test data sample is None\n data_preprocessor = DataPreprocessor(\n output_channel_order='RGB', pad_value=42)\n process_fn = data_preprocessor._preprocess_image_list\n input1 = torch.randint(0, 255, (1, 3, 5))\n input2 = torch.randint(0, 255, (1, 5, 3))\n inputs = [input1, input2]\n target1 = F.pad(input1.clone(), (0, 0, 0, 2), value=42) # pad H\n target2 = F.pad(input2.clone(), (0, 2, 0, 0), value=42) # pad W\n target = torch.stack([target1, target2], dim=0)\n target = (target - 127.5) / 127.5\n target_padding_info = [\n torch.FloatTensor([0, 2, 0]),\n torch.FloatTensor([0, 0, 2])\n ]\n outputs, data_samples = process_fn(inputs, None, 'test')\n assert_allclose(outputs, target)\n for data, pad in zip(data_samples, target_padding_info):\n self.assertEqual(data.metainfo['test_output_channel_order'],\n 'single')\n self.assertTrue((data.metainfo['padding_size'] == pad).all())\n\n def test_preprocess_dict_inputs(self):\n \"\"\"Since preprocess of dict inputs are based on\n `_preprocess_image_list` and `_preprocess_image_tensor`, we just test a\n simple case for translation model and padding behavior.\"\"\"\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n process_fn = data_preprocessor._preprocess_dict_inputs\n\n inputs = dict(\n img_A=[torch.randint(0, 255, (3, 5, 5)) for _ in range(3)],\n img_B=[torch.randint(0, 255, (3, 5, 5)) for _ in range(3)],\n noise=[torch.randn(16) for _ in range(3)],\n num_batches=3,\n tensor=torch.randint(0, 255, (3, 4, 5, 5)),\n mode=['ema', 'ema', 'ema'],\n )\n data_samples = [\n DataSample(\n metainfo=dict(\n img_A_channel_order='RGB', img_B_channel_order='BGR'))\n for _ in range(3)\n ]\n target_A = (torch.stack(inputs['img_A']) - 127.5) / 127.5\n target_B = (torch.stack(inputs['img_B']) - 127.5) / 127.5\n target_B = target_B[:, [2, 1, 0]]\n target_noise = torch.stack(inputs['noise'])\n # no metainfo, parse as BGR, do conversion\n target_tensor = ((inputs['tensor'] - 127.5) / 127.5)[:, [2, 1, 0, 3]]\n\n outputs, data_samples = process_fn(inputs, data_samples)\n assert_allclose(outputs['img_A'], target_A)\n assert_allclose(outputs['img_B'], target_B)\n assert_allclose(outputs['noise'], target_noise)\n assert_allclose(outputs['tensor'], target_tensor)\n self.assertEqual(outputs['mode'], 'ema')\n self.assertEqual(outputs['num_batches'], 3)\n\n # test no padding checking --> no tensor inputs\n inputs = dict(num_batches=3, mode=['ema', 'ema', 'ema'])\n outputs, data_samples = process_fn(inputs, data_samples)\n self.assertEqual(outputs['mode'], 'ema')\n self.assertEqual(outputs['num_batches'], 3)\n\n # test error in padding checking\n data_samples = [DataSample() for _ in range(2)]\n inputs = dict(\n img_A=[\n torch.randint(0, 255, (3, 3, 5)),\n torch.randint(0, 255, (3, 5, 5))\n ],\n img_B=[\n torch.randint(0, 255, (3, 5, 5)),\n torch.randint(0, 255, (3, 5, 5))\n ],\n )\n with self.assertRaises(ValueError):\n process_fn(inputs, data_samples)\n\n def test_prerprocess_data_sample(self):\n \"\"\"Only test training and testint mode in this test case.\"\"\"\n data_preprocessor = DataPreprocessor(\n data_keys=['gt_img', 'AA', 'dk'], output_channel_order='RGB')\n cov_fn = data_preprocessor._preprocess_data_sample\n\n # test training is True\n metainfo = dict(gt_channel_order='RGB', tar_channel_order='BGR')\n data_samples = [\n DataSample(\n gt_img=torch.randint(0, 255, (3, 5, 5)),\n AA=torch.randint(0, 255, (3, 5, 5)),\n metainfo=metainfo,\n ) for _ in range(3)\n ]\n tar_AA = torch.stack([data.AA for data in data_samples])\n tar_AA = ((tar_AA - 127.5) / 127.5)[:, [2, 1, 0]]\n tar_gt = torch.stack([data.gt_img for data in data_samples])\n tar_gt = (tar_gt - 127.5) / 127.5\n outputs = cov_fn(data_samples, True)\n assert_allclose(outputs.gt_img, tar_gt)\n assert_allclose(outputs.AA, tar_AA)\n\n # test training is False\n data_samples = [\n DataSample(\n gt_img=torch.randint(0, 255, (3, 5, 5)),\n AA=torch.randint(0, 255, (3, 5, 5)),\n metainfo=metainfo,\n ) for _ in range(3)\n ]\n tar_AA = torch.stack([data.AA for data in data_samples])\n tar_AA = tar_AA # BGR, no conversion\n tar_gt = torch.stack([data.gt_img for data in data_samples])\n tar_gt = tar_gt[:, [2, 1, 0]] # RGB -> BGR\n outputs = cov_fn(data_samples, False)\n assert_allclose(outputs.gt_img, tar_gt)\n assert_allclose(outputs.AA, tar_AA)\n\n # test no stack\n data_preprocessor.stack_data_sample = False\n data_samples = [\n DataSample(\n gt_img=torch.randint(0, 255, (3, 5, 5)),\n AA=torch.randint(0, 255, (3, 5, 5)),\n metainfo=metainfo,\n ) for _ in range(3)\n ]\n tar_AA = torch.stack([data.AA for data in data_samples])\n tar_AA = tar_AA # BGR, no conversion\n tar_gt = torch.stack([data.gt_img for data in data_samples])\n tar_gt = tar_gt[:, [2, 1, 0]] # RGB -> BGR\n outputs = cov_fn(data_samples, False)\n output_gt = torch.stack([out.gt_img for out in outputs])\n output_AA = torch.stack([out.AA for out in outputs])\n assert_allclose(output_gt, tar_gt)\n assert_allclose(output_AA, tar_AA)\n\n def test_destruct_tensor_norm_and_conversion(self):\n \"\"\"Test batch inputs, single input and batch inputs + no norm in this\n unit test.\"\"\"\n data_preprocessor = DataPreprocessor()\n cov_fn = data_preprocessor._destruct_norm_and_conversion\n\n # test batch\n metainfo = dict(\n img_output_channel_order='RGB',\n aaa_output_channel_order='single',\n bbb_output_channel_order='BGR',\n )\n img = torch.randn([3, 3, 5, 5])\n aaa = torch.randn([3, 1, 5, 5])\n bbb = torch.randn([3, 3, 5, 5])\n ccc = torch.randn([3, 3, 5, 5])\n data_samples = [DataSample(metainfo=metainfo) for _ in range(3)]\n tar_img = ((img * 127.5) + 127.5)[:, [2, 1, 0]]\n tar_aaa = (aaa * 127.5) + 127.5\n tar_bbb = (bbb * 127.5) + 127.5\n tar_ccc = (ccc * 127.5) + 127.5\n assert_allclose(cov_fn(img, data_samples, 'img'), tar_img)\n assert_allclose(cov_fn(aaa, data_samples, 'aaa'), tar_aaa)\n assert_allclose(cov_fn(bbb, data_samples, 'bbb'), tar_bbb)\n assert_allclose(cov_fn(ccc, data_samples, 'bbb'), tar_ccc)\n\n # test single sample\n img = torch.randn([3, 5, 5])\n aaa = torch.randn([1, 5, 5])\n bbb = torch.randn([3, 5, 5])\n ccc = torch.randn([3, 5, 5])\n data_samples = DataSample(metainfo=metainfo)\n tar_img = ((img * 127.5) + 127.5)[[2, 1, 0], ...]\n tar_aaa = (aaa * 127.5) + 127.5\n tar_bbb = (bbb * 127.5) + 127.5\n tar_ccc = (ccc * 127.5) + 127.5\n assert_allclose(cov_fn(img, data_samples, 'img'), tar_img)\n assert_allclose(cov_fn(aaa, data_samples, 'aaa'), tar_aaa)\n assert_allclose(cov_fn(bbb, data_samples, 'bbb'), tar_bbb)\n assert_allclose(cov_fn(ccc, data_samples, 'bbb'), tar_ccc)\n\n # test no norm\n data_preprocessor._enable_normalize = False\n metainfo = dict(img_output_channel_order='RGB', )\n img = torch.randn([3, 3, 5, 5])\n data_samples = [DataSample(metainfo=metainfo) for _ in range(3)]\n tar_img = img[:, [2, 1, 0]]\n assert_allclose(cov_fn(img, data_samples, 'img'), tar_img)\n\n def test_destruct_tensor_padding(self):\n \"\"\"Test five cases in this unit test.\n\n 1. batch inputs + same padidng = True\n 2. batch inputs + same padding = False\n 3. single inputs\n 4. data_samples is None\n 5. padding_size is not found in metainfo\n 6. one data sample + padding size is not found in metainfo\n 7. list of sample + padding size is not found in metainfo\n\n 8. data sample is stacked\n 9. data sample is stacked + padding size not found in metainfo\n \"\"\"\n data_preprocessor = DataPreprocessor()\n cov_fn = data_preprocessor._destruct_padding\n\n # data sample is None, no un-padding\n batch_tensor = torch.randint(0, 255, (2, 3, 3, 3))\n tar_output = batch_tensor.clone()\n output = cov_fn(batch_tensor, None)\n assert_allclose(tar_output, output)\n\n # batch inputs + same_padding is True\n batch_tensor = torch.randint(0, 255, (2, 3, 5, 5))\n metainfo_1 = dict(padding_size=torch.FloatTensor([2, 0]))\n metainfo_2 = dict(padding_size=torch.FloatTensor([0, 2]))\n data_samples = [\n DataSample(metainfo=metainfo_1),\n DataSample(metainfo=metainfo_2)\n ]\n output = cov_fn(batch_tensor, data_samples)\n self.assertEqual(output.shape, (2, 3, 3, 5))\n\n # batch inputs + same padding is False\n batch_tensor = torch.randint(0, 255, (2, 3, 5, 5))\n metainfo_1 = dict(padding_size=torch.FloatTensor([2, 0]))\n metainfo_2 = dict(padding_size=torch.FloatTensor([0, 2]))\n data_samples = [\n DataSample(metainfo=metainfo_1),\n DataSample(metainfo=metainfo_2)\n ]\n output = cov_fn(batch_tensor, data_samples, False)\n self.assertIsInstance(output, list)\n self.assertEqual(output[0].shape, (3, 3, 5))\n self.assertEqual(output[1].shape, (3, 5, 3))\n\n # single inputs\n batch_tensor = torch.randint(0, 255, (3, 5, 5))\n data_samples = DataSample(metainfo=metainfo_1)\n output = cov_fn(batch_tensor, data_samples, False)\n self.assertEqual(output.shape, (3, 3, 5))\n\n # single data sample + test metainfo not found\n batch_tensor = torch.randint(0, 255, (3, 5, 5))\n data_preprocessor._done_padding = False\n output = cov_fn(batch_tensor, DataSample(), False)\n assert_allclose(output, batch_tensor)\n\n # list of data sample + test metainfo not found\n batch_tensor = torch.randint(0, 255, (2, 3, 5, 5))\n output = cov_fn(batch_tensor, [DataSample()] * 2, False)\n assert_allclose(output, batch_tensor)\n\n data_preprocessor._done_padding = True\n output = cov_fn(batch_tensor, DataSample(), False)\n assert_allclose(output, batch_tensor)\n\n # test stacked data sample\n data_samples = [\n DataSample(metainfo=metainfo_1),\n DataSample(metainfo=metainfo_2)\n ]\n stacked_data_sample = DataSample.stack(data_samples)\n batch_tensor = torch.randint(0, 255, (2, 3, 5, 5))\n output = cov_fn(batch_tensor, stacked_data_sample, False)\n self.assertIsInstance(output, list)\n self.assertEqual(output[0].shape, (3, 3, 5))\n self.assertEqual(output[1].shape, (3, 5, 3))\n\n # test stacked data sample + metainfo is None\n stacked_data_sample = DataSample()\n batch_tensor = torch.randint(0, 255, (2, 3, 5, 5))\n output = cov_fn(batch_tensor, stacked_data_sample, True)\n self.assertEqual(output.shape, (2, 3, 5, 5))\n\n def test_forward(self):\n \"\"\"Test five cases in this unit test.\n\n We do not cover all possible\n cases, as we test them in test cases for other functions.\n 1. Input is tensor.\n 2. Input is list of tensors.\n 3. Input is a list of dict.\n 4. Input is dict.\n 5. Input is tensor + no norm\n 6. data samples behavior in training = False / True\n 7. Input is wrong type.\n \"\"\"\n data_preprocessor = DataPreprocessor()\n\n # 1. input is tensor\n inputs = torch.randint(0, 255, (2, 3, 5, 5))\n data = dict(inputs=inputs)\n tar_output = (inputs.clone() - 127.5) / 127.5\n data_preprocessor = DataPreprocessor()\n data = data_preprocessor(data)\n assert_allclose(data['inputs'], tar_output)\n\n # 2. input is list of tensor\n input1 = torch.randn(3, 3, 5)\n input2 = torch.randn(3, 3, 5)\n\n data = dict(inputs=[input1, input2])\n\n data = data_preprocessor(data)\n\n self.assertEqual(data['inputs'].shape, (2, 3, 3, 5))\n\n target_input1 = (input1.clone() - 127.5) / 127.5\n target_input2 = (input2.clone() - 127.5) / 127.5\n assert_allclose(target_input1, data['inputs'][0])\n assert_allclose(target_input2, data['inputs'][1])\n\n # 3. input is list of dict\n imgA1 = torch.randn(3, 3, 5)\n imgA2 = torch.randn(3, 3, 5)\n imgB1 = torch.randn(3, 3, 5)\n imgB2 = torch.randn(3, 3, 5)\n data = dict(inputs=[\n dict(imgA=imgA1, imgB=imgB1),\n dict(imgA=imgA2, imgB=imgB2)\n ])\n data = data_preprocessor(data)\n self.assertEqual(list(data['inputs'].keys()), ['imgA', 'imgB'])\n\n img1 = torch.randn(3, 4, 4)\n img2 = torch.randn(3, 4, 4)\n noise1 = torch.randn(3, 4, 4)\n noise2 = torch.randn(3, 4, 4)\n target_input1 = (img1[[2, 1, 0], ...].clone() - 127.5) / 127.5\n target_input2 = (img2[[2, 1, 0], ...].clone() - 127.5) / 127.5\n\n data = dict(inputs=[\n dict(noise=noise1, img=img1, num_batches=2, mode='ema'),\n dict(noise=noise2, img=img2, num_batches=2, mode='ema'),\n ])\n data_preprocessor = DataPreprocessor(output_channel_order='RGB')\n data = data_preprocessor(data)\n\n self.assertEqual(\n list(data['inputs'].keys()),\n ['noise', 'img', 'num_batches', 'mode'])\n assert_allclose(data['inputs']['noise'][0], noise1)\n assert_allclose(data['inputs']['noise'][1], noise2)\n assert_allclose(data['inputs']['img'][0], target_input1)\n assert_allclose(data['inputs']['img'][1], target_input2)\n self.assertEqual(data['inputs']['num_batches'], 2)\n self.assertEqual(data['inputs']['mode'], 'ema')\n\n # 4. nput is dict\n sampler_results = dict(inputs=dict(num_batches=2, mode='ema'))\n data = data_preprocessor(sampler_results)\n self.assertEqual(data['inputs'], sampler_results['inputs'])\n self.assertIsNone(data['data_samples'])\n\n # test dict input with tensor\n sampler_results = dict(inputs=dict(fake_img=torch.randn(2, 3, 10, 10)))\n data = data_preprocessor(sampler_results)\n\n # # 5. input is tensor + no norm\n data_preprocessor = DataPreprocessor(mean=None, std=None)\n input1 = torch.randn(3, 3, 5)\n input2 = torch.randn(3, 3, 5)\n data = dict(inputs=torch.stack([input1, input2], dim=0))\n data = data_preprocessor(data)\n self.assertEqual(data['inputs'].shape, (2, 3, 3, 5))\n self.assertTrue((data['inputs'] == torch.stack([input1, input2],\n dim=0)).all())\n\n # 6. data samples behavior in training = False / True\n data_preprocessor = DataPreprocessor()\n gt_inp1 = torch.randint(0, 255, (3, 5, 5))\n gt_inp2 = torch.randint(0, 255, (3, 5, 5))\n\n data_samples = [DataSample(gt_img=gt_inp1), DataSample(gt_img=gt_inp2)]\n data = dict(inputs=[input1, input2], data_samples=data_samples)\n data = data_preprocessor(data, training=False)\n assert_allclose(data['data_samples'].gt_img,\n torch.stack([gt_inp1, gt_inp2]))\n\n data = dict(inputs=[input1, input2], data_samples=data_samples)\n data = data_preprocessor(data, training=True)\n self.assertTrue((data['data_samples'].gt_img <= 1).all())\n\n # 7. Input is wrong type\n data = dict(inputs='wrong type')\n with self.assertRaises(ValueError):\n data = data_preprocessor(data)\n\n def test_destruct(self):\n \"\"\"We test the following cases in this unit test:\n\n 1. test un-padding\n 2. test un-norm + no un-padding\n 3. test no un-norm + no un-padding\n \"\"\"\n # 1. test un-padding\n data_preprocessor = DataPreprocessor()\n input1 = torch.randn(3, 3, 5)\n input2 = torch.randn(3, 5, 3)\n data = dict(inputs=[input1, input2])\n data = data_preprocessor(data)\n self.assertEqual(data['inputs'].shape, (2, 3, 5, 5))\n tar_pad_size = torch.FloatTensor([[0, 2, 0], [0, 0, 2]])\n padding_sizes = data['data_samples'].metainfo['padding_size']\n for padding_size, tar in zip(padding_sizes, tar_pad_size):\n assert_allclose(padding_size, tar)\n destruct_batch = data_preprocessor.destruct(data['inputs'],\n data['data_samples'])\n self.assertEqual(destruct_batch.shape, (2, 3, 3, 5))\n\n # 2. test no un-padding + un-norm\n data_preprocessor = DataPreprocessor()\n input1 = torch.randint(0, 255, (3, 5, 5))\n input2 = torch.randint(0, 255, (3, 5, 5))\n data = dict(inputs=[input1, input2])\n data = data_preprocessor(data)\n self.assertEqual(data['inputs'].shape, (2, 3, 5, 5))\n tar_pad_size = torch.FloatTensor([[0, 0, 0], [0, 0, 0]])\n padding_sizes = data['data_samples'].metainfo['padding_size']\n for padding_size, tar in zip(padding_sizes, tar_pad_size):\n assert_allclose(padding_size, tar)\n destruct_batch = data_preprocessor.destruct(data['inputs'],\n data['data_samples'])\n self.assertEqual(destruct_batch.shape, (2, 3, 5, 5))\n assert_allclose(destruct_batch,\n torch.stack([input1, input2], dim=0).float())\n\n # 3. test no un-norm\n data_preprocessor = DataPreprocessor(std=None, mean=None)\n input1 = torch.randint(0, 255, (1, 5, 5))\n input2 = torch.randint(0, 255, (1, 5, 5))\n inputs = torch.stack([input1, input2], dim=0)\n destruct_batch = data_preprocessor.destruct(inputs)\n self.assertEqual(destruct_batch.shape, (2, 1, 5, 5))\n assert_allclose(destruct_batch.float(), inputs.float())\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_data_preprocessors/test_mattor_preprocessor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nfrom mmengine.testing import assert_allclose\n\nfrom mmagic.models.data_preprocessors import MattorPreprocessor\nfrom mmagic.structures import DataSample\n\n\ndef test_mattor_preprocessor():\n\n processor = MattorPreprocessor()\n # prepare data\n inputs = torch.rand(1, 3, 20, 20)\n target = torch.rand(3, 20, 20)\n gt_fg = torch.rand(3, 20, 20)\n gt_bg = torch.rand(3, 20, 20)\n data_sample = DataSample(trimap=target, gt_fg=gt_fg, gt_bg=gt_bg)\n data = dict(inputs=inputs, data_samples=[data_sample])\n # process\n data = processor(data, True)\n batch_inputs, batch_data_samples = data['inputs'], data['data_samples']\n assert isinstance(batch_inputs, torch.Tensor)\n assert batch_inputs.shape == (1, 6, 20, 20)\n assert isinstance(batch_data_samples, DataSample)\n assert batch_data_samples.trimap.shape == (1, 3, 20, 20)\n\n # test proc_batch_trimap\n processor = MattorPreprocessor(proc_trimap='as_is')\n inputs = torch.rand(1, 3, 20, 20)\n target = torch.rand(3, 20, 20)\n data_sample = DataSample(trimap=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n data = processor(data, True)\n batch_inputs, batch_data_samples = data['inputs'], data['data_samples']\n assert isinstance(batch_inputs, torch.Tensor)\n assert batch_inputs.shape == (1, 6, 20, 20)\n assert isinstance(batch_data_samples, DataSample)\n assert batch_data_samples.trimap.shape == (1, 3, 20, 20)\n assert_allclose(batch_data_samples.trimap[0], target)\n\n # test error in proc_batch_trimap\n processor = MattorPreprocessor(proc_trimap='wrong_method')\n with pytest.raises(ValueError):\n processor(dict(inputs=inputs, data_samples=[data_sample]))\n\n # test training is False\n processor = MattorPreprocessor(proc_trimap='as_is')\n inputs = torch.rand(1, 3, 20, 20)\n target = torch.rand(3, 20, 20)\n gt_fg = torch.rand(3, 20, 20)\n gt_bg = torch.rand(3, 20, 20)\n data_sample = DataSample(trimap=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n data = processor(data, training=False)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_diffusion_schedulers/test_ddim_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler\n\n\ndef test_ddim():\n modelout = torch.rand((1, 8, 32, 32))\n sample = torch.rand((1, 4, 32, 32))\n ddim = EditDDIMScheduler(\n num_train_timesteps=1000, variance_type='learned_range')\n ddim.set_timesteps(10)\n result = ddim.step(modelout, 980, sample)\n assert result['prev_sample'].shape == (1, 4, 32, 32)\n\n noise = torch.rand((1, 4, 32, 32))\n result = ddim.add_noise(sample, noise, 10)\n assert result.shape == (1, 4, 32, 32)\n\n assert len(ddim) == 1000\n\n\ndef test_ddim_init():\n ddim = EditDDIMScheduler(\n num_train_timesteps=1000, beta_schedule='scaled_linear')\n\n ddim = EditDDIMScheduler(\n num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')\n\n assert isinstance(ddim, EditDDIMScheduler)\n\n with pytest.raises(Exception):\n EditDDIMScheduler(num_train_timesteps=1000, beta_schedule='fake')\n\n\ndef test_ddim_step():\n modelout = torch.rand((1, 8, 32, 32))\n sample = torch.rand((1, 4, 32, 32))\n ddim = EditDDIMScheduler(\n num_train_timesteps=1000, variance_type='learned_range')\n with pytest.raises(Exception):\n ddim.step(modelout, 980, sample)\n\n ddim.set_timesteps(10)\n result = ddim.step(\n modelout, 980, sample, eta=1, use_clipped_model_output=True)\n assert result['prev_sample'].shape == (1, 4, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_diffusion_schedulers/test_init.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport shutil\nfrom unittest import TestCase\n\nfrom mmagic.registry import DIFFUSION_SCHEDULERS\n\ntest_dir = osp.join(osp.dirname(__file__), '../../..', 'tests')\nconfig_path = osp.join(test_dir, 'configs', 'scheduler_cfg')\n\n\nclass TestWrapper(TestCase):\n\n def test_build(self):\n # 1. test init by args\n config = dict(\n type='EulerDiscreteScheduler',\n num_train_timesteps=2000,\n beta_schedule='scaled_linear')\n scheduler = DIFFUSION_SCHEDULERS.build(config)\n self.assertEqual(len(scheduler.timesteps), 2000)\n # self.assertEqual(scheduler.beta_schedule, 'scaled_linear')\n scheduler_str = repr(scheduler)\n self.assertIn(\n 'Wrapped Scheduler Class: '\n '', scheduler_str)\n self.assertIn('Wrapped Scheduler Name: EulerDiscreteScheduler',\n scheduler_str)\n self.assertNotIn('From Pretrained: ', scheduler_str)\n\n # 2. test save as diffuser\n scheduler.save_pretrained(config_path)\n\n # 3. test from_pretrained\n config = dict(\n type='EulerDiscreteScheduler', from_pretrained=config_path)\n scheduler = DIFFUSION_SCHEDULERS.build(config)\n scheduler_str = repr(scheduler)\n self.assertIn('From Pretrained: ', scheduler_str)\n\n # 4. test attribute error\n with self.assertRaises(AttributeError):\n scheduler.unsupported_attr('do not support')\n\n # tear down\n shutil.rmtree(config_path)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_animatediff.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest import TestCase\n\nimport pytest\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\n# from mmengine.utils import digit_version\n\n# from mmengine.utils.dl_utils import TORCH_VERSION\n\nregister_all_modules()\n\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\n\ndiffusion_scheduler = dict(\n type='DDIMScheduler',\n beta_end=0.012,\n beta_schedule='linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n prediction_type='epsilon',\n set_alpha_to_one=True,\n clip_sample=False,\n thresholding=False,\n steps_offset=1)\n\nmodel = dict(\n type='AnimateDiff',\n vae=dict(\n type='AutoencoderKL',\n from_pretrained=stable_diffusion_v15_url,\n subfolder='vae'),\n unet=dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1)),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_v15_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_v15_url,\n scheduler=diffusion_scheduler,\n test_scheduler=diffusion_scheduler,\n data_preprocessor=dict(type='DataPreprocessor'),\n dream_booth_lora_cfg=dict(type='ToonYou', steps=25, guidance_scale=7.5))\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower()\n or digit_version(TORCH_VERSION) <= digit_version('1.9.2'),\n reason='skip on windows due to limited RAM'\n 'and torch >= 1.10.0')\nclass TestAnimateDiff(TestCase):\n\n def setUp(self):\n animatediff = MODELS.build(model)\n assert not any([p.requires_grad for p in animatediff.vae.parameters()])\n assert not any(\n [p.requires_grad for p in animatediff.text_encoder.parameters()])\n assert not any(\n [p.requires_grad for p in animatediff.unet.parameters()])\n self.animatediff = animatediff\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\n def test_infer(self):\n videos = self.animatediff.infer(\n 'best quality, masterpiece, 1girl, cloudy sky, \\\n dandelion, contrapposto, alternate hairstyle',\n negative_prompt='',\n video_length=16,\n height=32,\n width=32)['samples']\n assert videos.shape == (1, 3, 16, 32, 32)\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_attention3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.animatediff.attention_3d import (CrossAttention,\n Transformer3DModel)\n\n\ndef test_crossattention():\n input = torch.rand((2, 64, 64))\n crossattention = CrossAttention(64)\n crossattention.set_attention_slice(2)\n output = crossattention.forward(input)\n assert output.shape == (2, 64, 64)\n with pytest.raises(Exception):\n crossattention.set_attention_slice(10)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_Transformer3DModel_init():\n with pytest.raises(Exception):\n Transformer3DModel(\n in_channels=32,\n num_vector_embeds=4,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False)\n\n with pytest.raises(Exception):\n Transformer3DModel()\n\n Transformer3DModel(\n in_channels=32,\n use_linear_projection=True,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False)\n\n\n# if __name__ == '__main__':\n# test_crossattention()\n# test_Transformer3DModel_init()\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_motion_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.animatediff.motion_module import (\n FeedForward, VanillaTemporalModule)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_VanillaTemporalModule():\n # This also test TemporalTransformerBlock\n input = torch.rand((1, 64, 16, 32, 32))\n text_feat = torch.rand([1, 77, 768])\n transformer = VanillaTemporalModule(in_channels=64)\n output = transformer.forward(input, 10, text_feat)\n assert output.shape == (1, 64, 16, 32, 32)\n\n\ndef test_FeedForward():\n input = torch.rand((2, 64, 64))\n feed_forward = FeedForward(64, 64, activation_fn='geglu')\n output = feed_forward.forward(input)\n assert output.shape == (2, 64, 64)\n\n\n# if __name__ == '__main__':\n# test_VanillaTemporalModule()\n# test_FeedForward()\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_res_blocks3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.animatediff.unet_block import (Downsample3D,\n ResnetBlock3D,\n Upsample3D)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_resnetblock3d():\n input = torch.rand((1, 64, 16, 16, 16))\n resblock = ResnetBlock3D(in_channels=64)\n output = resblock.forward(input, None)\n assert output.shape == (1, 64, 16, 16, 16)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_Downsample3D():\n input = torch.rand((1, 64, 16, 16, 16))\n downsample = Downsample3D(channels=64, use_conv=True, padding=1)\n output = downsample.forward(input)\n assert output.shape == (1, 64, 16, 8, 8)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_Upsample3D():\n input = torch.rand((1, 64, 16, 16, 16))\n upsample = Upsample3D(channels=64, use_conv_transpose=False, use_conv=True)\n\n output = upsample.forward(input)\n assert output.shape == (1, 64, 16, 32, 32)\n\n\n# if __name__ == '__main__':\n# test_Downsample3D()\n# test_Upsample3D()\n# test_resnetblock3d()\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_unet3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.utils import build_module\nfrom mmagic.registry import MODELS\n\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nunet_cfg = dict(\n type='UNet3DConditionMotionModel',\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False,\n use_motion_module=True,\n motion_module_resolutions=[1, 2, 4, 8],\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type='Vanilla',\n motion_module_kwargs=dict(\n num_attention_heads=8,\n num_transformer_block=1,\n attention_block_types=['Temporal_Self', 'Temporal_Self'],\n temporal_position_encoding=True,\n temporal_position_encoding_max_len=24,\n temporal_attention_dim_div=1),\n subfolder='unet')\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower()\n or digit_version(TORCH_VERSION) <= digit_version('1.9.2'),\n reason='skip on windows due to limited RAM'\n 'and torch >= 1.10.0')\ndef test_Unet3D():\n input = torch.rand((1, 4, 16, 8, 8))\n text_feat = torch.rand([1, 20, 768])\n unet = build_module(unet_cfg, MODELS)\n output = unet.forward(input, 10, text_feat)\n assert output['sample'].shape == (1, 4, 16, 8, 8)\n\n\nif __name__ == '__main__':\n test_Unet3D()\n" }, { "path": "tests/test_models/test_editors/test_animatediff/test_unet_blocks3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.animatediff.unet_block import (\n CrossAttnDownBlock3D, CrossAttnUpBlock3D, UNetMidBlock3DCrossAttn,\n get_down_block, get_up_block)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_UNetMidBlock3DCrossAttn():\n input = torch.rand((1, 64, 16, 8, 8))\n midblock = UNetMidBlock3DCrossAttn(\n 64,\n 64,\n cross_attention_dim=64,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False)\n output = midblock.forward(input)\n assert output.shape == (1, 64, 16, 8, 8)\n\n\ndef test_CrossAttnDownBlock3D():\n input = torch.rand((1, 64, 16, 8, 8))\n downblock = CrossAttnDownBlock3D(\n 64,\n 64,\n 64,\n cross_attention_dim=64,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False)\n output, _ = downblock.forward(input)\n assert output.shape == (1, 64, 16, 4, 4)\n\n\ndef test_CrossAttnUpBlock3D():\n input = torch.rand((1, 64, 16, 8, 8))\n upblock = CrossAttnUpBlock3D(\n 64,\n 64,\n 64,\n 64,\n cross_attention_dim=64,\n unet_use_cross_frame_attention=False,\n unet_use_temporal_attention=False)\n output = upblock.forward(input, [input])\n assert output.shape == (1, 64, 16, 16, 16)\n\n\ndef test_get_down_block():\n with pytest.raises(Exception):\n get_down_block('tem', 1, 1, 1, 1, True, 'silu', 1)\n\n\ndef test_get_up_block():\n with pytest.raises(Exception):\n get_up_block('tem', 1, 1, 1, 1, 1, True, 'silu', 1)\n\n\n# if __name__ == '__main__':\n# test_CrossAttnDownBlock3D()\n# test_CrossAttnUpBlock3D()\n# test_get_down_block()\n# test_get_up_block()\n" }, { "path": "tests/test_models/test_editors/test_aotgan/test_aot_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_gl_dec():\n input_x = torch.randn(1, 4, 256, 256)\n template_cfg = dict(type='AOTEncoderDecoder')\n\n cfg_ = template_cfg.copy()\n cfg_['decoder'] = dict(type='AOTDecoder')\n aot_encdec = MODELS.build(cfg_)\n output = aot_encdec(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_aotgan/test_aot_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_gl_enc():\n input_x = torch.randn(1, 4, 256, 256)\n template_cfg = dict(type='AOTEncoderDecoder')\n\n cfg_ = template_cfg.copy()\n cfg_['encoder'] = dict(type='AOTEncoder')\n aot_encdec = MODELS.build(cfg_)\n output = aot_encdec(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_aotgan/test_aot_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_gl_encdec():\n input_x = torch.randn(1, 4, 64, 64)\n template_cfg = dict(type='AOTEncoderDecoder')\n\n aot_encdec = MODELS.build(template_cfg)\n aot_encdec.init_weights()\n output = aot_encdec(input_x)\n assert output.shape == (1, 3, 64, 64)\n\n cfg_ = template_cfg.copy()\n cfg_['encoder'] = dict(type='AOTEncoder')\n aot_encdec = MODELS.build(cfg_)\n output = aot_encdec(input_x)\n assert output.shape == (1, 3, 64, 64)\n\n cfg_ = template_cfg.copy()\n cfg_['decoder'] = dict(type='AOTDecoder')\n aot_encdec = MODELS.build(cfg_)\n output = aot_encdec(input_x)\n assert output.shape == (1, 3, 64, 64)\n\n if torch.cuda.is_available():\n aot_encdec = MODELS.build(template_cfg)\n aot_encdec.init_weights()\n aot_encdec = aot_encdec.cuda()\n output = aot_encdec(input_x.cuda())\n assert output.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_aotgan/test_aot_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom os.path import dirname, join\n\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models import AOTEncoderDecoder\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\ndef test_aot_inpaintor():\n register_all_modules()\n\n config_file = join(dirname(__file__), '../../..', 'configs', 'aot_test.py')\n cfg = Config.fromfile(config_file)\n\n inpaintor = MODELS.build(cfg.model)\n\n # optimizer\n optim_g = torch.optim.Adam(\n inpaintor.generator.parameters(), lr=0.0001, betas=(0.0, 0.9))\n optim_d = torch.optim.Adam(\n inpaintor.disc.parameters(), lr=0.0001, betas=(0.0, 0.9))\n optims = dict(generator=OptimWrapper(optim_g), disc=OptimWrapper(optim_d))\n\n # test attributes\n assert inpaintor.__class__.__name__ == 'AOTInpaintor'\n assert isinstance(inpaintor.generator, AOTEncoderDecoder)\n assert inpaintor.with_l1_valid_loss\n assert inpaintor.with_composed_percep_loss\n assert inpaintor.with_out_percep_loss\n assert inpaintor.with_gan\n assert inpaintor.is_train\n assert inpaintor.train_cfg['disc_step'] == 1\n assert inpaintor.disc_step_count == 0\n\n if torch.cuda.is_available():\n inpaintor = inpaintor.cuda()\n\n gt_img = torch.randn(3, 64, 64)\n mask = torch.zeros((1, 64, 64))\n mask[..., 12:45, 14:42] = 1.\n masked_img = gt_img.unsqueeze(0) * (1. - mask) + mask\n mask_bbox = [25, 25, 27, 27]\n data_batch = {\n 'inputs':\n masked_img,\n 'data_samples':\n [DataSample(\n mask=mask,\n mask_bbox=mask_bbox,\n gt_img=gt_img,\n )]\n }\n\n # check train_step\n for i in range(5):\n log_vars = inpaintor.train_step(data_batch, optims)\n\n assert 'loss_l1_valid' in log_vars\n assert 'loss_out_percep' in log_vars\n assert 'disc_losses' in log_vars\n assert 'loss_g_fake' in log_vars\n\n # # check forward_test\n data = inpaintor.data_preprocessor(data_batch, True)\n data_inputs, data_sample = data['inputs'], data['data_samples']\n output = inpaintor.forward_test(data_inputs, data_sample)\n prediction = output\n assert 'fake_res' in prediction\n assert 'fake_img' in prediction\n assert 'pred_img' in prediction\n assert prediction.pred_img.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_aotgan/test_aot_neck.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import AOTBlockNeck\n\n\ndef test_aot_dilation_neck():\n neck = AOTBlockNeck(\n in_channels=32, dilation_rates=(1, 2, 4, 8), num_aotblock=4)\n x = torch.rand((2, 32, 64, 64))\n res = neck(x)\n assert res.shape == (2, 32, 64, 64)\n\n if torch.cuda.is_available():\n neck = AOTBlockNeck(\n in_channels=32, dilation_rates=(1, 2, 4, 8),\n num_aotblock=4).cuda()\n x = torch.rand((2, 32, 64, 64)).cuda()\n res = neck(x)\n assert res.shape == (2, 32, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_arcface/test_arcface_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.arcface.arcface_modules import get_blocks\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_get_blocks():\n blocks = get_blocks(num_layers=100)\n assert len(blocks) == 4\n\n blocks = get_blocks(num_layers=152)\n assert len(blocks) == 4\n with pytest.raises(ValueError):\n get_blocks(num_layers=1000)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_arcface/test_id_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models import IDLossModel\n\n\nclass TestArcFace:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n input_size=224,\n num_layers=50,\n mode='ir',\n drop_ratio=0.4,\n affine=True)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_arcface_cpu(self):\n # test loss model\n id_loss_model = IDLossModel()\n x1 = torch.randn((2, 3, 224, 224))\n x2 = torch.randn((2, 3, 224, 224))\n y, _ = id_loss_model(pred=x1, gt=x2)\n assert y >= 0\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_arcface_cuda(self):\n # test loss model\n id_loss_model = IDLossModel().cuda()\n x1 = torch.randn((2, 3, 224, 224)).cuda()\n x2 = torch.randn((2, 3, 224, 224)).cuda()\n y, _ = id_loss_model(pred=x1, gt=x2)\n assert y >= 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_arcface/test_model_irse.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.arcface.model_irse import Backbone\n\n\nclass TestIRSEModel:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n input_size=224,\n num_layers=50,\n mode='ir',\n drop_ratio=0.4,\n affine=True)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_arcface_cpu(self):\n model = Backbone(**self.default_cfg)\n x = torch.randn((2, 3, 224, 224))\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different input size\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_size=112))\n model = Backbone(**cfg)\n x = torch.randn((2, 3, 112, 112))\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different num_layers\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(num_layers=50))\n model = Backbone(**cfg)\n x = torch.randn((2, 3, 224, 224))\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different mode\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(mode='ir_se'))\n model = Backbone(**cfg)\n x = torch.randn((2, 3, 224, 224))\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different drop ratio\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(drop_ratio=0.8))\n model = Backbone(**cfg)\n x = torch.randn((2, 3, 224, 224))\n y = model(x)\n assert y.shape == (2, 512)\n\n # test affine=False\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(affine=False))\n model = Backbone(**cfg)\n x = torch.randn((2, 3, 224, 224))\n y = model(x)\n assert y.shape == (2, 512)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_arcface_cuda(self):\n model = Backbone(**self.default_cfg).cuda()\n x = torch.randn((2, 3, 224, 224)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different input size\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_size=112))\n model = Backbone(**cfg).cuda()\n x = torch.randn((2, 3, 112, 112)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different num_layers\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(num_layers=50))\n model = Backbone(**cfg).cuda()\n x = torch.randn((2, 3, 224, 224)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different mode\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(mode='ir_se'))\n model = Backbone(**cfg).cuda()\n x = torch.randn((2, 3, 224, 224)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n # test different drop ratio\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(drop_ratio=0.8))\n model = Backbone(**cfg).cuda()\n x = torch.randn((2, 3, 224, 224)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n # test affine=False\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(affine=False))\n model = Backbone(**cfg).cuda()\n x = torch.randn((2, 3, 224, 224)).cuda()\n y = model(x)\n assert y.shape == (2, 512)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_basicvsr/test_basicvsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import BasicVSR, BasicVSRNet, DataPreprocessor\nfrom mmagic.models.losses import CharbonnierLoss\nfrom mmagic.structures import DataSample\n\n\ndef test_basicvsr():\n\n model = BasicVSR(\n generator=dict(\n type='BasicVSRNet',\n mid_channels=8,\n num_blocks=1,\n spynet_pretrained=None),\n pixel_loss=dict(\n type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=1),\n ensemble=None,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, BasicVSR)\n assert isinstance(model.generator, BasicVSRNet)\n assert isinstance(model.pixel_loss, CharbonnierLoss)\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # prepare data\n inputs = torch.rand(5, 3, 16, 16)\n target = torch.rand(5, 3, 64, 64)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=[inputs], data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert model.generator.spynet.basic_module[0].basic_module[\n 0].conv.weight.requires_grad is False\n assert isinstance(log_vars, dict)\n\n log_vars = model.train_step(data, optim_wrapper)\n assert model.generator.spynet.basic_module[0].basic_module[\n 0].conv.weight.requires_grad is True\n assert isinstance(log_vars, dict)\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (5, 3, 64, 64)\n\n data['data_samples'][0].gt_img.data = torch.rand(3, 64, 64)\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 64, 64)\n\n model = BasicVSR(\n generator=dict(\n type='BasicVSRNet',\n mid_channels=8,\n num_blocks=1,\n spynet_pretrained=None),\n pixel_loss=dict(\n type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=1),\n ensemble=dict(type='SpatialTemporalEnsemble'),\n data_preprocessor=DataPreprocessor())\n\n with pytest.raises(NotImplementedError):\n model = BasicVSR(\n generator=dict(\n type='BasicVSRNet',\n mid_channels=8,\n num_blocks=1,\n spynet_pretrained=None),\n pixel_loss=dict(\n type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),\n train_cfg=dict(fix_iter=1),\n ensemble=dict(type=''),\n data_preprocessor=DataPreprocessor())\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_basicvsr/test_basicvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import BasicVSRNet\n\n\ndef test_basicvsr_net():\n \"\"\"Test BasicVSR.\"\"\"\n\n # cpu\n basicvsr = BasicVSRNet(\n mid_channels=8, num_blocks=1, spynet_pretrained=None)\n\n input_tensor = torch.rand(1, 5, 3, 64, 64)\n basicvsr(input_tensor)\n assert not basicvsr._raised_warning\n\n input_tensor = torch.rand(1, 5, 3, 16, 16)\n output = basicvsr(input_tensor)\n assert output.shape == (1, 5, 3, 64, 64)\n assert basicvsr._raised_warning\n\n # gpu\n if torch.cuda.is_available():\n basicvsr = BasicVSRNet(\n mid_channels=8, num_blocks=1, spynet_pretrained=None).cuda()\n input_tensor = torch.rand(1, 5, 3, 16, 16).cuda()\n output = basicvsr(input_tensor)\n assert output.shape == (1, 5, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_basicvsr_plusplus_net/test_basicvsr_plusplus_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import BasicVSRPlusPlusNet\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_basicvsr_plusplus_cpu():\n \"\"\"Test BasicVSR++.\"\"\"\n\n # cpu\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained=None,\n cpu_cache_length=100)\n input_tensor = torch.rand(1, 5, 3, 64, 64)\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n # with cpu_cache (no effect on cpu)\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained=None,\n cpu_cache_length=3)\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n with pytest.raises(AssertionError):\n # The height and width of inputs should be at least 64\n input_tensor = torch.rand(1, 5, 3, 61, 61)\n model(input_tensor)\n\n # output has the same size as input\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=False,\n spynet_pretrained=None,\n cpu_cache_length=100)\n input_tensor = torch.rand(1, 5, 3, 256, 256)\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_basicvsr_plusplus_cuda():\n # gpu\n if torch.cuda.is_available():\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained=None,\n cpu_cache_length=100).cuda()\n input_tensor = torch.rand(1, 5, 3, 64, 64).cuda()\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n # with cpu_cache\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=True,\n spynet_pretrained=None,\n cpu_cache_length=3).cuda()\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n with pytest.raises(AssertionError):\n # The height and width of inputs should be at least 64\n input_tensor = torch.rand(1, 5, 3, 61, 61).cuda()\n model(input_tensor)\n\n # output has the same size as input\n model = BasicVSRPlusPlusNet(\n mid_channels=64,\n num_blocks=7,\n is_low_res_input=False,\n spynet_pretrained=None,\n cpu_cache_length=100).cuda()\n input_tensor = torch.rand(1, 5, 3, 256, 256).cuda()\n output = model(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import BigGAN, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='BigGANGenerator',\n output_scale=32,\n noise_size=128,\n num_classes=10,\n base_channels=64,\n with_shared_embedding=False,\n sn_eps=1e-8,\n sn_style='torch',\n # init_type='N02',\n split_noise=False,\n auto_sync_bn=False,\n init_cfg=dict(type='N02'))\ndiscriminator = dict(\n type='BigGANDiscriminator',\n input_scale=32,\n num_classes=10,\n base_channels=64,\n sn_eps=1e-8,\n sn_style='torch',\n # init_type='N02',\n with_spectral_norm=True,\n init_cfg=dict(type='N02'))\n\n\nclass TestBigGAN(TestCase):\n\n def test_init(self):\n gan = BigGAN(\n num_classes=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator,\n generator_steps=1,\n discriminator_steps=4)\n gan.init_weights()\n\n self.assertIsInstance(gan, BigGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = BigGAN(\n noise_size=10,\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = BigGAN(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = BigGAN(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n # test init with different num_classes\n gan = BigGAN(\n num_classes=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator,\n generator_steps=1,\n discriminator_steps=4)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('mmgen')\n gan = BigGAN(\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n gan.init_weights()\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n label = torch.randint(0, 10, (3, 1))\n\n data_sample = DataSample(gt_img=img)\n data_sample.set_gt_label(label)\n\n data = dict(inputs=dict(), data_samples=[data_sample])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_deep_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.biggan import BigGANDeepDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestBigGANDeepDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n num_classes = 1000\n cls.default_config = dict(\n type='BigGANDeepDiscriminator',\n input_scale=128,\n num_classes=num_classes,\n base_channels=8)\n cls.x = torch.randn((2, 3, 128, 128))\n cls.label = torch.randint(0, num_classes, (2, ))\n\n def test_biggan_deep_discriminator(self):\n # test default settings\n d = MODELS.build(self.default_config)\n assert isinstance(d, BigGANDeepDiscriminator)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test different init types\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_type='N02'))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_type='xavier'))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0))\n d = MODELS.build(cfg)\n y = d(self.x, None)\n assert y.shape == (2, 1)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_deep_discriminator_cuda(self):\n # test default settings\n d = MODELS.build(self.default_config).cuda()\n assert isinstance(d, BigGANDeepDiscriminator)\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n # test different init types\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_type='N02'))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_type='xavier'))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), None)\n assert y.shape == (2, 1)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_deep_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom functools import partial\n\nimport pytest\nimport torch\n\n# yapf:disable\nfrom mmagic.models.editors.biggan import BigGANDeepGenerator\nfrom mmagic.registry import MODELS\n\n# yapf:enable\n\n\nclass TestBigGANDeepGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((3, 120))\n num_classes = 1000\n cls.label = torch.randint(0, num_classes, (3, ))\n cls.default_config = dict(\n type='BigGANDeepGenerator',\n output_scale=128,\n num_classes=num_classes,\n base_channels=4)\n\n def test_biggan_deep_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, BigGANDeepGenerator)\n res = g(self.noise, self.label)\n assert res.shape == (3, 3, 128, 128)\n\n # test 'return_noise'\n res = g(self.noise, self.label, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n res = g(None, None, num_batches=3, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n # test callable\n noise = torch.randn\n label = partial(torch.randint, 0, 1000)\n res = g(noise, label, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test different output scale\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=256))\n g = MODELS.build(cfg)\n noise = torch.randn((3, 120))\n res = g(noise, self.label)\n assert res.shape == (3, 3, 256, 256)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 256, 256)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=512))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 512, 512)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=64))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 64, 64)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=32))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 32, 32)\n\n # test with `concat_noise=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(concat_noise=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(\n dict(\n num_classes=0, with_shared_embedding=False,\n concat_noise=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test no shared embedding\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_shared_embedding=False, concat_noise=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n g = MODELS.build(cfg)\n res = g(self.noise, self.label)\n assert res.shape == (3, 3, 128, 128)\n\n # test init --> N02\n cfg = deepcopy(self.default_config)\n cfg.update(init_type='N02')\n g = MODELS.build(cfg)\n\n # test init --> xavier\n cfg = deepcopy(self.default_config)\n cfg.update(init_type='xavier')\n g = MODELS.build(cfg)\n\n # test init --> raise error\n cfg = deepcopy(self.default_config)\n cfg.update(init_type='dont know')\n with pytest.raises(NotImplementedError):\n g = MODELS.build(cfg)\n\n cfg = deepcopy(self.default_config)\n cfg.update(pretrained=1234)\n with pytest.raises(TypeError):\n g = MODELS.build(cfg)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_deep_generator_cuda(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, BigGANDeepGenerator)\n res = g(self.noise.cuda(), self.label.cuda())\n assert res.shape == (3, 3, 128, 128)\n\n # test 'return_noise'\n res = g(self.noise.cuda(), self.label.cuda(), return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n res = g(None, None, num_batches=3, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n # test callable\n noise = torch.randn\n label = partial(torch.randint, 0, 1000)\n res = g(noise, label, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test different output scale\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=256))\n g = MODELS.build(cfg).cuda()\n noise = torch.randn((3, 120))\n res = g(noise.cuda(), self.label.cuda())\n assert res.shape == (3, 3, 256, 256)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 256, 256)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=512))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 512, 512)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=64))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 64, 64)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=32))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 32, 32)\n\n # test with `concat_noise=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(concat_noise=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(\n dict(\n num_classes=0, with_shared_embedding=False,\n concat_noise=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test no shared embedding\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_shared_embedding=False, concat_noise=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.biggan import BigGANDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestBigGANDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n num_classes = 1000\n cls.default_config = dict(\n type='BigGANDiscriminator',\n input_scale=128,\n num_classes=num_classes,\n base_channels=8)\n cls.x = torch.randn((2, 3, 128, 128))\n cls.label = torch.randint(0, num_classes, (2, ))\n\n def test_biggan_discriminator(self):\n # test default settings\n d = MODELS.build(self.default_config)\n assert isinstance(d, BigGANDiscriminator)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test different init types\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='N02')))\n d = MODELS.build(cfg)\n d.init_weights()\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='xavier')))\n d = MODELS.build(cfg)\n d.init_weights()\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='ortho')))\n g = MODELS.build(cfg)\n g.init_weights()\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0))\n d = MODELS.build(cfg)\n y = d(self.x, None)\n assert y.shape == (2, 1)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n d = MODELS.build(cfg)\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_discriminator_cuda(self):\n # test default settings\n d = MODELS.build(self.default_config).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n # test different init types\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='N02')))\n d = MODELS.build(cfg).cuda()\n d.init_weights()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='xavier')))\n d = MODELS.build(cfg).cuda()\n d.init_weights()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='ortho')))\n g = MODELS.build(cfg)\n g.init_weights()\n y = d(self.x, self.label)\n assert y.shape == (2, 1)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), None)\n assert y.shape == (2, 1)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n d = MODELS.build(cfg).cuda()\n y = d(self.x.cuda(), self.label.cuda())\n assert y.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom functools import partial\n\nimport pytest\nimport torch\n\n# yapf:disable\nfrom mmagic.models.editors.biggan import BigGANGenerator\nfrom mmagic.registry import MODELS\n\n# yapf:enable\n\n\nclass TestBigGANGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((3, 120))\n num_classes = 1000\n cls.label = torch.randint(0, num_classes, (3, ))\n cls.default_config = dict(\n type='BigGANGenerator',\n output_scale=128,\n num_classes=num_classes,\n base_channels=4)\n\n def test_biggan_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, BigGANGenerator)\n res = g(self.noise, self.label)\n assert res.shape == (3, 3, 128, 128)\n\n # test 'return_noise'\n res = g(self.noise, self.label, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n res = g(None, None, num_batches=3, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n # test callable\n noise = torch.randn\n label = partial(torch.randint, 0, 1000)\n res = g(noise, label, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test different output scale\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=256))\n g = MODELS.build(cfg)\n noise = torch.randn((3, 119))\n res = g(noise, self.label)\n assert res.shape == (3, 3, 256, 256)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 256, 256)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=512))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 512, 512)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=64))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 64, 64)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=32))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 32, 32)\n\n # test with `split_noise=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(split_noise=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0, with_shared_embedding=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test no shared embedding\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_shared_embedding=False, split_noise=False))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test init --> ortho\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='ortho')))\n g = MODELS.build(cfg)\n g.init_weights()\n\n # test init --> N02\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='N02')))\n g = MODELS.build(cfg)\n g.init_weights()\n\n # test init --> xavier\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='xavier')))\n g = MODELS.build(cfg)\n g.init_weights()\n\n # test init --> raise error\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='dont know')))\n with pytest.raises(NotImplementedError):\n g = MODELS.build(cfg)\n g.init_weights()\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_generator_cuda(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, BigGANGenerator)\n res = g(self.noise.cuda(), self.label.cuda())\n assert res.shape == (3, 3, 128, 128)\n\n # test 'return_noise'\n res = g(self.noise.cuda(), self.label.cuda(), return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n res = g(None, None, num_batches=3, return_noise=True)\n assert res['fake_img'].shape == (3, 3, 128, 128)\n assert res['noise_batch'].shape == (3, 120)\n assert res['label'].shape == (3, )\n\n # test callable\n noise = torch.randn\n label = partial(torch.randint, 0, 1000)\n res = g(noise, label, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test different output scale\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=256))\n g = MODELS.build(cfg).cuda()\n noise = torch.randn((3, 119)).cuda()\n res = g(noise, self.label.cuda())\n assert res.shape == (3, 3, 256, 256)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 256, 256)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=512))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 512, 512)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=64))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 64, 64)\n\n cfg = deepcopy(self.default_config)\n cfg.update(dict(output_scale=32))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 32, 32)\n\n # test with `split_noise=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(split_noise=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test with `with_spectral_norm=False`\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_spectral_norm=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test different num_classes\n cfg = deepcopy(self.default_config)\n cfg.update(dict(num_classes=0, with_shared_embedding=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test no shared embedding\n cfg = deepcopy(self.default_config)\n cfg.update(dict(with_shared_embedding=False, split_noise=False))\n g = MODELS.build(cfg).cuda()\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test torch-sn\n cfg = deepcopy(self.default_config)\n cfg.update(dict(sn_style='torch'))\n g = MODELS.build(cfg)\n res = g(None, None, num_batches=3)\n assert res.shape == (3, 3, 128, 128)\n\n # test init --> ortho\n cfg = deepcopy(self.default_config)\n cfg.update(dict(init_cfg=dict(type='ortho')))\n g = MODELS.build(cfg)\n g.init_weights()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\n# yapf:disable\nfrom mmagic.models.editors.biggan import (BigGANConditionBN,\n BigGANDeepDiscResBlock,\n BigGANDeepGenResBlock,\n BigGANDiscResBlock,\n BigGANGenResBlock,\n SelfAttentionBlock)\nfrom mmagic.registry import MODELS\n\n# yapf:enable\n\n\nclass TestBigGANDeepGenResBlock:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n type='BigGANDeepGenResBlock',\n in_channels=32,\n out_channels=16,\n dim_after_concat=100,\n act_cfg=dict(type='ReLU'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n sn_eps=1e-6,\n bn_eps=1e-5,\n with_spectral_norm=True,\n input_is_label=False,\n auto_sync_bn=True,\n channel_ratio=4)\n cls.x = torch.randn(2, 32, 8, 8)\n cls.y = torch.randn(2, 100)\n cls.label = torch.randint(0, 100, (2, ))\n\n def test_biggan_deep_gen_res_block(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, BigGANDeepGenResBlock)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 16, 16)\n\n # test without upsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(upsample_cfg=None))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 8, 8)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg)\n out = module(self.x, self.label)\n assert out.shape == (2, 16, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 16, 16)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_deep_gen_res_block_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, BigGANDeepGenResBlock)\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n # test without upsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(upsample_cfg=None))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 8, 8)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n\nclass TestBigGANDeepDiscResBlock:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n type='BigGANDeepDiscResBlock',\n in_channels=32,\n out_channels=64,\n channel_ratio=4,\n act_cfg=dict(type='ReLU', inplace=False),\n sn_eps=1e-6,\n with_downsample=True,\n with_spectral_norm=True)\n cls.x = torch.randn(2, 32, 16, 16)\n\n def test_biggan_deep_disc_res_block(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, BigGANDeepDiscResBlock)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n # test with_downsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(with_downsample=False))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 16, 16)\n\n # test different channel_ratio\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(channel_ratio=8))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_deep_disc_res_block_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, BigGANDeepDiscResBlock)\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 8, 8)\n\n # test with_downsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(with_downsample=False))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 16, 16)\n\n # test different channel_ratio\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(channel_ratio=8))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 8, 8)\n\n\nclass TestBigGANConditionBN:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n type='BigGANConditionBN',\n num_features=64,\n linear_input_channels=80)\n cls.x = torch.randn(2, 64, 32, 32)\n cls.y = torch.randn(2, 80)\n cls.label = torch.randint(0, 80, (2, ))\n\n def test_biggan_condition_bn(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, BigGANConditionBN)\n out = module(self.x, self.y)\n assert out.shape == (2, 64, 32, 32)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg)\n out = module(self.x, self.label)\n assert out.shape == (2, 64, 32, 32)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 64, 32, 32)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 64, 32, 32)\n\n # test not-implemented sn-style\n with pytest.raises(NotImplementedError):\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='tero'))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 64, 32, 32)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_condition_bn_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, BigGANConditionBN)\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 64, 32, 32)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.label.cuda())\n assert out.shape == (2, 64, 32, 32)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 64, 32, 32)\n\n # test not-implemented sn-style\n with pytest.raises(NotImplementedError):\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='tero'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 64, 32, 32)\n\n\nclass TestSelfAttentionBlock:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(type='SelfAttentionBlock', in_channels=16)\n cls.x = torch.randn(2, 16, 8, 8)\n\n def test_self_attention_block(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, SelfAttentionBlock)\n out = module(self.x)\n assert out.shape == (2, 16, 8, 8)\n\n # test different in_channels\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(in_channels=10))\n module = MODELS.build(cfg)\n x = torch.randn(2, 10, 8, 8)\n out = module(x)\n assert out.shape == (2, 10, 8, 8)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 16, 8, 8)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_self_attention_block_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, SelfAttentionBlock)\n out = module(self.x.cuda())\n assert out.shape == (2, 16, 8, 8)\n\n # test different in_channels\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(in_channels=10))\n module = MODELS.build(cfg).cuda()\n x = torch.randn(2, 10, 8, 8).cuda()\n out = module(x)\n assert out.shape == (2, 10, 8, 8)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda())\n assert out.shape == (2, 16, 8, 8)\n\n\nclass TestBigGANGenResBlock:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n type='BigGANGenResBlock',\n in_channels=32,\n out_channels=16,\n dim_after_concat=100,\n act_cfg=dict(type='ReLU'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n sn_eps=1e-6,\n with_spectral_norm=True,\n input_is_label=False,\n auto_sync_bn=True)\n cls.x = torch.randn(2, 32, 8, 8)\n cls.y = torch.randn(2, 100)\n cls.label = torch.randint(0, 100, (2, ))\n\n def test_biggan_gen_res_block(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, BigGANGenResBlock)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 16, 16)\n\n # test without upsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(upsample_cfg=None))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 8, 8)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg)\n out = module(self.x, self.label)\n assert out.shape == (2, 16, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x, self.y)\n assert out.shape == (2, 16, 16, 16)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_gen_res_block_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, BigGANGenResBlock)\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n # test without upsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(upsample_cfg=None))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 8, 8)\n\n # test input_is_label\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(input_is_label=True))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda(), self.y.cuda())\n assert out.shape == (2, 16, 16, 16)\n\n\nclass TestBigGANDiscResBlock:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n type='BigGANDiscResBlock',\n in_channels=32,\n out_channels=64,\n act_cfg=dict(type='ReLU', inplace=False),\n sn_eps=1e-6,\n with_downsample=True,\n with_spectral_norm=True,\n is_head_block=False)\n cls.x = torch.randn(2, 32, 16, 16)\n\n def test_biggan_disc_res_block(self):\n # test default setting\n module = MODELS.build(self.default_cfg)\n assert isinstance(module, BigGANDiscResBlock)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n # test with_downsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(with_downsample=False))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg)\n out = module(self.x)\n assert out.shape == (2, 64, 8, 8)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_biggan_disc_res_block_cuda(self):\n # test default setting\n module = MODELS.build(self.default_cfg).cuda()\n assert isinstance(module, BigGANDiscResBlock)\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 8, 8)\n\n # test with_downsample\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(with_downsample=False))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 16, 16)\n\n # test torch-sn\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(sn_style='torch'))\n module = MODELS.build(cfg).cuda()\n out = module(self.x.cuda())\n assert out.shape == (2, 64, 8, 8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_biggan/test_biggan_snmodule.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors.biggan.biggan_snmodule import SpectralNorm\n\n\nclass MyBlock(nn.Module):\n\n def __init__(self, num_channels):\n super().__init__()\n self.weight = nn.Parameter(torch.randn(num_channels))\n\n def forward(self, x):\n return x + self.weight\n\n\nclass MySNBlock(MyBlock, SpectralNorm):\n\n def __init__(self, num_channels):\n MyBlock.__init__(self, num_channels)\n SpectralNorm.__init__(\n self, num_svs=2, num_iters=2, num_outputs=num_channels)\n\n def forward(self, x):\n return x + self.sn_weight()\n\n\n# test Spectral Norm with my own layer\ndef test_SpectralNorm():\n sn_block = MySNBlock(num_channels=4)\n x = torch.randn(1, 4)\n out = sn_block(x)\n assert out.shape == (1, 4)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_cain/test_cain.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import DataPreprocessor\nfrom mmagic.models.editors import CAIN, CAINNet\nfrom mmagic.models.losses import L1Loss\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_cain_net_cpu():\n\n model_cfg = dict(type='CAINNet')\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'CAINNet'\n\n # prepare data\n inputs0 = torch.rand(1, 2, 3, 5, 5)\n inputs = torch.rand(1, 2, 3, 256, 248)\n target = torch.rand(1, 3, 256, 248)\n\n # test on cpu\n output = model(inputs)\n output = model(inputs, padding_flag=True)\n model(inputs0, padding_flag=True)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n with pytest.raises(AssertionError):\n output = model(inputs[:, :1])\n\n model_cfg = dict(type='CAINNet', norm='in')\n model = MODELS.build(model_cfg)\n model(inputs)\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg)\n model(inputs)\n with pytest.raises(ValueError):\n model_cfg = dict(type='CAINNet', norm='lys')\n MODELS.build(model_cfg)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_cain_net_cuda():\n\n # prepare data\n inputs0 = torch.rand(1, 2, 3, 5, 5)\n target0 = torch.rand(1, 3, 5, 5)\n inputs = torch.rand(1, 2, 3, 256, 248)\n target = torch.rand(1, 3, 256, 248)\n\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg)\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n target = target.cuda()\n output = model(inputs)\n output = model(inputs, True)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n inputs0 = inputs0.cuda()\n target0 = target0.cuda()\n model(inputs0, padding_flag=True)\n\n model_cfg = dict(type='CAINNet', norm='in')\n model = MODELS.build(model_cfg).cuda()\n model(inputs)\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg).cuda()\n model(inputs)\n with pytest.raises(ValueError):\n model_cfg = dict(type='CAINNet', norm='lys')\n MODELS.build(model_cfg).cuda()\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_cain():\n\n # build model\n model = CAIN(\n generator=dict(type='CAINNet'),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=DataPreprocessor(pad_mode='reflect'))\n\n # test attributes\n assert isinstance(model, CAIN)\n assert isinstance(model.data_preprocessor, DataPreprocessor)\n assert isinstance(model.generator, CAINNet)\n assert isinstance(model.pixel_loss, L1Loss)\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # prepare data\n inputs = torch.rand(2, 3, 32, 32)\n target = torch.rand(3, 32, 32)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=[inputs], data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars['loss'], torch.Tensor)\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.data.shape == (3, 32, 32)\n\n # feat\n output = model(torch.rand(1, 2, 3, 32, 32), mode='tensor')\n assert output.shape == (1, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_cain/test_cain_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_cain_net_cpu():\n\n model_cfg = dict(type='CAINNet')\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'CAINNet'\n\n # prepare data\n inputs0 = torch.rand(1, 2, 3, 5, 5)\n inputs = torch.rand(1, 2, 3, 256, 248)\n target = torch.rand(1, 3, 256, 248)\n\n # test on cpu\n output = model(inputs)\n output = model(inputs, padding_flag=True)\n model(inputs0, padding_flag=True)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n with pytest.raises(AssertionError):\n output = model(inputs[:, :1])\n\n model_cfg = dict(type='CAINNet', norm='in')\n model = MODELS.build(model_cfg)\n model(inputs)\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg)\n model(inputs)\n with pytest.raises(ValueError):\n model_cfg = dict(type='CAINNet', norm='lys')\n MODELS.build(model_cfg)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_cain_net_cuda():\n\n # prepare data\n inputs0 = torch.rand(1, 2, 3, 5, 5)\n target0 = torch.rand(1, 3, 5, 5)\n inputs = torch.rand(1, 2, 3, 256, 248)\n target = torch.rand(1, 3, 256, 248)\n\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg)\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n target = target.cuda()\n output = model(inputs)\n output = model(inputs, True)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n inputs0 = inputs0.cuda()\n target0 = target0.cuda()\n model(inputs0, padding_flag=True)\n\n model_cfg = dict(type='CAINNet', norm='in')\n model = MODELS.build(model_cfg).cuda()\n model(inputs)\n model_cfg = dict(type='CAINNet', norm='bn')\n model = MODELS.build(model_cfg).cuda()\n model(inputs)\n with pytest.raises(ValueError):\n model_cfg = dict(type='CAINNet', norm='lys')\n MODELS.build(model_cfg).cuda()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_controlnet/test_controlnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport os.path as osp\nimport platform\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\ntest_dir = osp.join(osp.dirname(__file__), '../../../..', 'tests')\nconfig_path = osp.join(test_dir, 'configs', 'diffuser_wrapper_cfg')\nmodel_path = osp.join(test_dir, 'configs', 'tmp_weight')\nckpt_path = osp.join(test_dir, 'configs', 'ckpt')\n\nregister_all_modules()\n\nstable_diffusion_tiny_url = 'hf-internal-testing/tiny-stable-diffusion-pipe'\nconfig = dict(\n type='ControlStableDiffusion',\n vae=dict(type='AutoencoderKL', sample_size=64),\n unet=dict(\n sample_size=64,\n type='UNet2DConditionModel',\n down_block_types=('DownBlock2D', ),\n up_block_types=('UpBlock2D', ),\n block_out_channels=(32, ),\n cross_attention_dim=16,\n ),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_tiny_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_tiny_url,\n controlnet=dict(\n type='ControlNetModel',\n # from_pretrained=controlnet_canny_rul\n from_config=config_path # train from scratch\n ),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n data_preprocessor=dict(type='DataPreprocessor'),\n enable_xformers=False,\n init_cfg=dict(type='init_from_unet'))\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestControlStableDiffusion(TestCase):\n\n def setUp(self):\n # mock SiLU\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n from mmagic.models.editors.ddpm.denoising_unet import SiLU\n torch.nn.SiLU = SiLU\n control_sd = MODELS.build(config)\n assert not any([p.requires_grad for p in control_sd.vae.parameters()])\n assert not any(\n [p.requires_grad for p in control_sd.text_encoder.parameters()])\n assert not any([p.requires_grad for p in control_sd.unet.parameters()])\n self.control_sd = control_sd\n\n def test_init_weights(self):\n control_sd = self.control_sd\n # test init_from_unet\n control_sd.init_weights()\n\n # test init_convert_from_unet\n unet = dict(\n type='UNet2DConditionModel',\n down_block_types=('DownBlock2D', ),\n up_block_types=('UpBlock2D', ),\n block_out_channels=(32, ),\n cross_attention_dim=16)\n control_sd.init_cfg = dict(type='convert_from_unet', base_model=unet)\n control_sd.init_weights()\n\n def test_infer(self):\n control_sd = self.control_sd\n\n def mock_encode_prompt(prompt, do_classifier_free_guidance,\n num_images_per_prompt, *args, **kwargs):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n batch_size *= num_images_per_prompt\n if do_classifier_free_guidance:\n batch_size *= 2\n return torch.randn(batch_size, 5, 16) # 2 for cfg\n\n encode_prompt = control_sd._encode_prompt\n control_sd._encode_prompt = mock_encode_prompt\n\n # one prompt, one control, repeat 1 time\n self._test_infer(control_sd, 1, 1, 1, 1)\n\n # two prompt, two control, repeat 1 time\n # NOTE: skip this due to memory limit\n # self._test_infer(control_sd, 2, 2, 1, 2)\n\n # one prompt, one control, repeat 2 times\n # NOTE: skip this due to memory limit\n # self._test_infer(control_sd, 1, 1, 2, 2)\n\n # two prompt, two control, repeat 2 times\n # NOTE: skip this due to memory limit\n # self._test_infer(control_sd, 2, 2, 2, 4)\n\n control_sd._encode_prompt = encode_prompt\n\n def _test_infer(self, control_sd, num_prompt, num_control, num_repeat,\n tar_shape):\n prompt = ''\n control = torch.ones([1, 3, 64, 64])\n\n result = control_sd.infer(\n [prompt] * num_prompt,\n control=[control] * num_control,\n height=64,\n width=64,\n num_images_per_prompt=num_repeat,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (tar_shape, 3, 64, 64)\n\n def test_val_step(self):\n control_sd = self.control_sd\n data = dict(\n inputs=[\n dict(\n target=torch.ones([3, 64, 64]),\n source=torch.ones([3, 64, 64]))\n ],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(*args, **kwargs):\n return dict(samples=torch.randn(2, 3, 64, 64))\n\n encode_prompt = control_sd._encode_prompt\n control_sd._encode_prompt = mock_encode_prompt\n\n infer = control_sd.infer\n control_sd.infer = mock_infer\n\n # control_sd.text_encoder = mock_text_encoder()\n output = control_sd.val_step(data)\n assert len(output) == 1\n control_sd._encode_prompt = encode_prompt\n control_sd.infer = infer\n\n def test_test_step(self):\n control_sd = self.control_sd\n data = dict(\n inputs=[\n dict(\n target=torch.ones([3, 64, 64]),\n source=torch.ones([3, 64, 64]))\n ],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(*args, **kwargs):\n return dict(samples=torch.randn(2, 3, 64, 64))\n\n encode_prompt = control_sd._encode_prompt\n control_sd._encode_prompt = mock_encode_prompt\n\n infer = control_sd.infer\n control_sd.infer = mock_infer\n\n # control_sd.text_encoder = mock_text_encoder()\n output = control_sd.test_step(data)\n assert len(output) == 1\n control_sd._encode_prompt = encode_prompt\n control_sd.infer = infer\n\n def test_train_step(self):\n control_sd = self.control_sd\n data = dict(\n inputs=[\n dict(\n target=torch.ones([3, 64, 64]),\n source=torch.ones([3, 64, 64]))\n ],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n optimizer = MagicMock()\n update_params = MagicMock()\n optimizer.update_params = update_params\n optim_wrapper = {'controlnet': optimizer}\n\n class mock_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n\n def forward(self, *args, **kwargs):\n return [torch.randn(1, 5, 16)]\n\n control_sd.text_encoder = mock_text_encoder()\n\n control_sd.train_step(data, optim_wrapper)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_controlnet/test_controlnet_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport os.path as osp\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.controlnet.controlnet_utils import change_base_model\n\n\ndef make_state_dict(d):\n new_d = dict()\n for k, v in d.items():\n new_d[k] = torch.FloatTensor([v])\n return new_d\n\n\ndef check_state_dict(s1, s2):\n # s1, s2 = m1.state_dict(), m2.state_dict()\n assert s1.keys() == s2.keys()\n for k in s1.keys():\n assert (s1[k] == s2[k]).all()\n\n\ndef parameters(model):\n state_dict = model.state_dict()\n for v in state_dict.values():\n yield v\n\n\ndef test_change_base_model():\n control_state_dict = make_state_dict(dict(k1=1, k2=2, k3=3))\n target_control_state_dict = make_state_dict(dict(k1=1.5, k2=2.5, k3=3))\n\n base_state_dict = make_state_dict(dict(k1=2, k2=3))\n curr_state_dict = make_state_dict(dict(k1=2.5, k2=3.5))\n\n controlnet = MagicMock()\n basemodel = MagicMock()\n currmodel = MagicMock()\n controlnet.parameters = MagicMock(return_value=parameters(controlnet))\n basemodel.parameters = MagicMock(return_value=parameters(basemodel))\n currmodel.parameters = MagicMock(return_value=parameters(currmodel))\n controlnet.state_dict = MagicMock(return_value=control_state_dict)\n basemodel.state_dict = MagicMock(return_value=base_state_dict)\n currmodel.state_dict = MagicMock(return_value=curr_state_dict)\n\n change_base_model(controlnet, currmodel, basemodel)\n check_state_dict(controlnet.state_dict(), target_control_state_dict)\n\n # test save\n control_state_dict = make_state_dict(dict(k1=1, k2=2, k3=3))\n controlnet.state_dict = MagicMock(return_value=control_state_dict)\n save_path = osp.abspath(\n osp.join(__file__, '../../../../data/out', 'test.pth'))\n change_base_model(controlnet, currmodel, basemodel, save_path=save_path)\n assert os.path.isfile(save_path)\n control_state_dict_loaded = torch.load(save_path)\n check_state_dict(control_state_dict_loaded, target_control_state_dict)\n os.remove(save_path)\n\n # test error\n wrong_base_state_dict = make_state_dict(dict(k1=2, k2=[3, 5]))\n wrong_model = MagicMock()\n wrong_model.state_dict = MagicMock(return_value=wrong_base_state_dict)\n with pytest.raises(Exception):\n change_base_model(controlnet, currmodel, wrong_model)\n # change_base_model(controlnet, currmodel, wrong_base_state_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_cyclegan/test_cyclegan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport sys\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\n\nfrom mmagic.models import CycleGAN, DataPreprocessor\nfrom mmagic.models.archs import PatchDiscriminator\nfrom mmagic.models.editors.cyclegan import ResnetGenerator\nfrom mmagic.structures import DataSample\n\n\ndef obj_from_dict(info: dict, parent=None, default_args=None):\n \"\"\"Initialize an object from dict.\n\n The dict must contain the key \"type\", which indicates the object type, it\n can be either a string or type, such as \"list\" or ``list``. Remaining\n fields are treated as the arguments for constructing the object.\n\n Args:\n info (dict): Object types and arguments.\n parent (:class:`module`): Module which may containing expected object\n classes.\n default_args (dict, optional): Default arguments for initializing the\n object.\n\n Returns:\n any type: Object built from the dict.\n \"\"\"\n assert isinstance(info, dict) and 'type' in info\n assert isinstance(default_args, dict) or default_args is None\n args = info.copy()\n obj_type = args.pop('type')\n # if mmcv.is_str(obj_type):\n if isinstance(obj_type, str):\n if parent is not None:\n obj_type = getattr(parent, obj_type)\n else:\n obj_type = sys.modules[obj_type]\n elif not isinstance(obj_type, type):\n raise TypeError('type must be a str or valid type, but '\n f'got {type(obj_type)}')\n if default_args is not None:\n for name, value in default_args.items():\n args.setdefault(name, value)\n return obj_type(**args)\n\n\ndef test_cyclegan():\n\n model_cfg = dict(\n default_domain='photo',\n reachable_domains=['photo', 'mask'],\n related_domains=['photo', 'mask'],\n generator=dict(\n type='ResnetGenerator',\n in_channels=3,\n out_channels=3,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=3,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='IN'),\n init_cfg=dict(type='normal', gain=0.02)))\n\n train_settings = None\n\n # build synthesizer\n synthesizer = CycleGAN(**model_cfg, data_preprocessor=DataPreprocessor())\n\n # test attributes\n assert synthesizer.__class__.__name__ == 'CycleGAN'\n assert isinstance(synthesizer.generators['photo'], ResnetGenerator)\n assert isinstance(synthesizer.generators['mask'], ResnetGenerator)\n assert isinstance(synthesizer.discriminators['photo'], PatchDiscriminator)\n assert isinstance(synthesizer.discriminators['mask'], PatchDiscriminator)\n\n # prepare data\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n data_batch = {'img_mask': inputs, 'img_photo': targets}\n\n # prepare optimizer\n optim_cfg = dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(params=getattr(synthesizer, 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n\n # test forward_test\n with torch.no_grad():\n outputs = synthesizer(inputs, target_domain='photo', test_mode=True)\n assert torch.equal(outputs['source'], data_batch['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n with torch.no_grad():\n outputs = synthesizer(targets, target_domain='mask', test_mode=True)\n assert torch.equal(outputs['source'], data_batch['img_photo'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n # test forward_train\n with torch.no_grad():\n outputs = synthesizer(inputs, target_domain='photo', test_mode=True)\n assert torch.equal(outputs['source'], data_batch['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n with torch.no_grad():\n outputs = synthesizer(targets, target_domain='mask', test_mode=True)\n assert torch.equal(outputs['source'], data_batch['img_photo'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n # test train_step\n message_hub = MessageHub.get_instance('cyclegan-test')\n message_hub.update_info('iter', 0)\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n data_batch = dict(inputs={'img_mask': inputs, 'img_photo': targets})\n log_vars = synthesizer.train_step(data_batch, optimizer)\n assert isinstance(log_vars, dict)\n for v in [\n 'loss_gan_d_mask', 'loss_gan_d_photo', 'loss_gan_g_mask',\n 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]:\n assert isinstance(log_vars[v].item(), float)\n\n # test train_step and forward_test (gpu)\n if torch.cuda.is_available():\n synthesizer = synthesizer.cuda()\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n data_batch = {'img_mask': inputs, 'img_photo': targets}\n data_batch_cuda = copy.deepcopy(data_batch)\n data_batch_cuda['img_mask'] = inputs.cuda()\n data_batch_cuda['img_photo'] = targets.cuda()\n\n # forward_test\n with torch.no_grad():\n outputs = synthesizer(\n data_batch_cuda['img_mask'],\n target_domain='photo',\n test_mode=True)\n assert torch.equal(outputs['source'].cpu(),\n data_batch_cuda['img_mask'].cpu())\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n with torch.no_grad():\n outputs = synthesizer(\n data_batch_cuda['img_photo'],\n target_domain='mask',\n test_mode=True)\n assert torch.equal(outputs['source'].cpu(),\n data_batch_cuda['img_photo'].cpu())\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n # test forward_train\n with torch.no_grad():\n outputs = synthesizer(\n data_batch_cuda['img_mask'],\n target_domain='photo',\n test_mode=False)\n assert torch.equal(outputs['source'], data_batch_cuda['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n with torch.no_grad():\n outputs = synthesizer(\n data_batch_cuda['img_photo'],\n target_domain='mask',\n test_mode=False)\n assert torch.equal(outputs['source'], data_batch_cuda['img_photo'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 64, 64)\n\n # train_step\n inputs = torch.rand(1, 3, 64, 64).cuda()\n targets = torch.rand(1, 3, 64, 64).cuda()\n data_batch_cuda = dict(inputs={\n 'img_mask': inputs,\n 'img_photo': targets\n })\n\n log_vars = synthesizer.train_step(data_batch_cuda, optimizer)\n assert isinstance(log_vars, dict)\n for v in [\n 'loss_gan_d_mask', 'loss_gan_d_photo', 'loss_gan_g_mask',\n 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]:\n assert isinstance(log_vars[v].item(), float)\n\n # test disc_steps and disc_init_steps\n train_settings = dict(discriminator_steps=2, disc_init_steps=2)\n synthesizer = CycleGAN(\n **model_cfg, **train_settings, data_preprocessor=DataPreprocessor())\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(params=getattr(synthesizer, 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n data_batch = dict(inputs={'img_mask': inputs, 'img_photo': targets})\n # iter 0, 1\n for i in range(2):\n message_hub.update_info('iter', i)\n log_vars = synthesizer.train_step(data_batch, optimizer)\n assert isinstance(log_vars, dict)\n for v in [\n 'loss_gan_g_mask', 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]:\n assert log_vars.get(v) is None\n assert isinstance(log_vars['loss_gan_d_mask'].item(), float)\n assert isinstance(log_vars['loss_gan_d_photo'].item(), float)\n\n # iter 2, 3, 4, 5\n for i in range(2, 6):\n message_hub.update_info('iter', i)\n log_vars = synthesizer.train_step(data_batch, optimizer)\n print(log_vars.keys())\n assert isinstance(log_vars, dict)\n log_check_list = [\n 'loss_gan_d_mask', 'loss_gan_d_photo', 'loss_gan_g_mask',\n 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]\n if (i + 1) % 2 == 1:\n log_None_list = [\n 'loss_gan_g_mask', 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]\n for v in log_None_list:\n assert log_vars.get(v) is None\n log_check_list.remove(v)\n for v in log_check_list:\n assert isinstance(log_vars[v].item(), float)\n\n # test GAN image buffer size = 0\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n data_batch = dict(inputs={'img_mask': inputs, 'img_photo': targets})\n train_settings = dict(buffer_size=0)\n synthesizer = CycleGAN(\n **model_cfg, **train_settings, data_preprocessor=DataPreprocessor())\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(params=getattr(synthesizer, 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n log_vars = synthesizer.train_step(data_batch, optimizer)\n assert isinstance(log_vars, dict)\n for v in [\n 'loss_gan_d_mask', 'loss_gan_d_photo', 'loss_gan_g_mask',\n 'loss_gan_g_photo', 'cycle_loss', 'id_loss'\n ]:\n assert isinstance(log_vars[v].item(), float)\n\n # test get opposite domain\n assert synthesizer._get_opposite_domain('photo') == 'mask'\n\n # test val_step and test_step\n data = dict(\n inputs=dict(\n img_photo=torch.randn(1, 3, 64, 64),\n img_mask=torch.randn(1, 3, 64, 64)),\n data_samples=[DataSample(mode='test')])\n out = synthesizer.test_step(data)\n assert len(out) == 1\n assert out[0].fake_photo.data.shape == (3, 64, 64)\n assert out[0].fake_mask.data.shape == (3, 64, 64)\n out = synthesizer.val_step(data)\n assert len(out) == 1\n assert out[0].fake_photo.data.shape == (3, 64, 64)\n assert out[0].fake_mask.data.shape == (3, 64, 64)\n\n data = dict(\n inputs=dict(\n img_photo=torch.randn(1, 3, 64, 64),\n img_mask=torch.randn(1, 3, 64, 64)))\n out = synthesizer.test_step(data)\n assert len(out) == 1\n assert out[0].fake_photo.data.shape == (3, 64, 64)\n assert out[0].fake_mask.data.shape == (3, 64, 64)\n out = synthesizer.val_step(data)\n assert len(out) == 1\n assert out[0].fake_photo.data.shape == (3, 64, 64)\n assert out[0].fake_mask.data.shape == (3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_cyclegan/test_cyclegan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.cyclegan import ResnetGenerator\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\nclass TestResnetGenerator:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=3,\n base_channels=64,\n norm_cfg=dict(type='IN'),\n use_dropout=False,\n num_blocks=9,\n padding_mode='reflect',\n init_cfg=dict(type='normal', gain=0.02))\n\n def test_cyclegan_generator_cpu(self):\n # test with default cfg\n real_a = torch.randn((2, 3, 256, 256))\n gen = ResnetGenerator(**self.default_cfg)\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['num_blocks'] = 8\n gen = ResnetGenerator(**cfg)\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_cyclegan_generator_cuda(self):\n # test with default cfg\n real_a = torch.randn((2, 3, 256, 256)).cuda()\n gen = ResnetGenerator(**self.default_cfg).cuda()\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['num_blocks'] = 8\n gen = ResnetGenerator(**cfg).cuda()\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_cyclegan/test_cyclegan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\nfrom mmagic.models.editors.cyclegan.cyclegan_modules import (\n GANImageBuffer, ResidualBlockWithDropout)\n\n\ndef test_residual_block_with_dropout():\n block = ResidualBlockWithDropout(16, 'zeros')\n input = torch.rand((2, 16, 128, 128))\n output = block(input)\n assert output.detach().numpy().shape == (2, 16, 128, 128)\n\n block = ResidualBlockWithDropout(16, 'zeros', use_dropout=False)\n assert len(block.block) == 2\n\n\ndef test_gan_image_buffer():\n # test buffer size = 0\n buffer = GANImageBuffer(buffer_size=0)\n img_np = np.random.randn(1, 3, 256, 256)\n img_tensor = torch.from_numpy(img_np)\n img_tensor_return = buffer.query(img_tensor)\n assert torch.equal(img_tensor_return, img_tensor)\n\n # test buffer size > 0\n buffer = GANImageBuffer(buffer_size=1)\n img_np = np.random.randn(2, 3, 256, 256)\n img_tensor = torch.from_numpy(img_np)\n img_tensor_0 = torch.unsqueeze(img_tensor[0], 0)\n img_tensor_1 = torch.unsqueeze(img_tensor[1], 0)\n img_tensor_00 = torch.cat([img_tensor_0, img_tensor_0], 0)\n img_tensor_return = buffer.query(img_tensor)\n assert (torch.equal(img_tensor_return, img_tensor)\n and torch.equal(buffer.image_buffer[0], img_tensor_0)) or \\\n (torch.equal(img_tensor_return, img_tensor_00)\n and torch.equal(buffer.image_buffer[0], img_tensor_1))\n\n # test buffer size > 0, specify buffer chance\n buffer = GANImageBuffer(buffer_size=1, buffer_ratio=0.3)\n img_np = np.random.randn(2, 3, 256, 256)\n img_tensor = torch.from_numpy(img_np)\n img_tensor_0 = torch.unsqueeze(img_tensor[0], 0)\n img_tensor_1 = torch.unsqueeze(img_tensor[1], 0)\n img_tensor_00 = torch.cat([img_tensor_0, img_tensor_0], 0)\n img_tensor_return = buffer.query(img_tensor)\n assert (torch.equal(img_tensor_return, img_tensor)\n and torch.equal(buffer.image_buffer[0], img_tensor_0)) or \\\n (torch.equal(img_tensor_return, img_tensor_00)\n and torch.equal(buffer.image_buffer[0], img_tensor_1))\n\n # set buffer ratio as 1 and 0 to cover more lines\n buffer = GANImageBuffer(buffer_size=1, buffer_ratio=1)\n img_np = np.random.randn(2, 3, 256, 256)\n img_tensor = torch.from_numpy(img_np)\n img_tensor_0 = torch.unsqueeze(img_tensor[0], 0)\n img_tensor_1 = torch.unsqueeze(img_tensor[1], 0)\n img_tensor_00 = torch.cat([img_tensor_0, img_tensor_0], 0)\n img_tensor_return = buffer.query(img_tensor)\n assert (torch.equal(img_tensor_return, img_tensor)\n and torch.equal(buffer.image_buffer[0], img_tensor_0)) or \\\n (torch.equal(img_tensor_return, img_tensor_00)\n and torch.equal(buffer.image_buffer[0], img_tensor_1))\n\n buffer = GANImageBuffer(buffer_size=1, buffer_ratio=0)\n img_np = np.random.randn(2, 3, 256, 256)\n img_tensor = torch.from_numpy(img_np)\n img_tensor_0 = torch.unsqueeze(img_tensor[0], 0)\n img_tensor_1 = torch.unsqueeze(img_tensor[1], 0)\n img_tensor_00 = torch.cat([img_tensor_0, img_tensor_0], 0)\n img_tensor_return = buffer.query(img_tensor)\n assert (torch.equal(img_tensor_return, img_tensor)\n and torch.equal(buffer.image_buffer[0], img_tensor_0)) or \\\n (torch.equal(img_tensor_return, img_tensor_00)\n and torch.equal(buffer.image_buffer[0], img_tensor_1))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dcgan/test_dcgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import DCGAN, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='DCGANGenerator', noise_size=10, output_scale=16, base_channels=16)\ndiscriminator = dict(\n type='DCGANDiscriminator', input_scale=16, output_scale=4, out_channels=1)\n\n\nclass TestDCGAN(TestCase):\n\n def test_init(self):\n gan = DCGAN(\n noise_size=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator)\n\n self.assertIsInstance(gan, DCGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = DCGAN(\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = DCGAN(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = DCGAN(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('test-lsgan')\n gan = DCGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dcgan/test_dcgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.dcgan import DCGANDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestDCGANDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n cls.input_tensor = torch.randn((2, 3, 32, 32))\n cls.default_config = dict(\n type='DCGANDiscriminator',\n input_scale=32,\n output_scale=4,\n out_channels=5)\n\n def test_dcgan_discriminator(self):\n # test default setting with builder\n d = MODELS.build(self.default_config)\n pred = d(self.input_tensor)\n assert pred.shape == (2, 5)\n assert d.num_downsamples == 3\n assert len(d.downsamples) == 3\n assert not d.downsamples[0].with_norm\n assert not d.output_layer.with_norm\n assert not d.output_layer.with_activation\n assert isinstance(d.downsamples[1].activate, torch.nn.LeakyReLU)\n assert isinstance(d.downsamples[1].norm, torch.nn.BatchNorm2d)\n\n # sanity check for args with cpu model\n d = DCGANDiscriminator(input_scale=64, output_scale=8, out_channels=2)\n assert d.input_scale == 64 and d.output_scale == 8\n assert d.num_downsamples == 3\n assert d.out_channels == 2\n pred = d(torch.randn((1, 3, 64, 64)))\n assert pred.shape == (1, 50)\n\n with pytest.raises(TypeError):\n _ = DCGANDiscriminator(32, 4, 2, pretrained=dict())\n\n # check for cuda\n if not torch.cuda.is_available():\n return\n\n # test default setting with builder on GPU\n d = MODELS.build(self.default_config).cuda()\n pred = d(self.input_tensor.cuda())\n assert pred.shape == (2, 5)\n assert d.num_downsamples == 3\n assert len(d.downsamples) == 3\n assert not d.downsamples[0].with_norm\n assert not d.output_layer.with_norm\n assert not d.output_layer.with_activation\n assert isinstance(d.downsamples[1].activate, torch.nn.LeakyReLU)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dcgan/test_dcgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.dcgan import DCGANGenerator\nfrom mmagic.registry import MODELS\n\n\nclass TestDCGANGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((2, 100))\n cls.default_config = dict(\n type='DCGANGenerator', output_scale=16, base_channels=32)\n\n def test_dcgan_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, DCGANGenerator)\n assert g.num_upsamples == 2\n assert not g.output_layer.with_norm\n assert len(g.upsampling) == 1\n assert g.upsampling[0].with_norm\n assert g.noise2feat.with_norm\n assert isinstance(g.output_layer.activate, torch.nn.Tanh)\n # check forward function\n img = g(self.noise)\n assert img.shape == (2, 3, 16, 16)\n img = g(self.noise[:, :, None, None])\n assert img.shape == (2, 3, 16, 16)\n img = g(torch.randn, num_batches=2)\n assert img.shape == (2, 3, 16, 16)\n img = g(None, num_batches=2)\n assert img.shape == (2, 3, 16, 16)\n with pytest.raises(ValueError):\n _ = g(torch.randn((1, 100, 3)))\n with pytest.raises(AssertionError):\n _ = g(torch.randn)\n with pytest.raises(AssertionError):\n _ = g(None)\n with pytest.raises(AssertionError):\n _ = g(torch.randn(2, 10))\n results = g(self.noise, return_noise=True)\n assert results['noise_batch'].shape == (2, 100, 1, 1)\n\n # sanity check for args with cpu model\n g = DCGANGenerator(32, base_channels=64)\n img = g(self.noise)\n assert img.shape == (2, 3, 32, 32)\n assert g.base_channels == 64\n g = DCGANGenerator(16, out_channels=1, base_channels=32)\n img = g(self.noise)\n assert img.shape == (2, 1, 16, 16)\n g = DCGANGenerator(16, noise_size=10, base_channels=32)\n with pytest.raises(AssertionError):\n _ = g(self.noise)\n img = g(torch.randn(2, 10))\n assert img.shape == (2, 3, 16, 16)\n g = DCGANGenerator(\n 16, default_act_cfg=dict(type='LeakyReLU'), base_channels=32)\n assert isinstance(g.noise2feat.activate, torch.nn.LeakyReLU)\n assert isinstance(g.upsampling[0].activate, torch.nn.LeakyReLU)\n assert isinstance(g.output_layer.activate, torch.nn.Tanh)\n\n with pytest.raises(TypeError):\n _ = DCGANGenerator(\n 16, noise_size=10, base_channels=32, pretrained=dict())\n\n # check for cuda\n if not torch.cuda.is_available():\n return\n\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, DCGANGenerator)\n assert g.num_upsamples == 2\n assert not g.output_layer.with_norm\n assert len(g.upsampling) == 1\n assert g.upsampling[0].with_norm\n # check forward function\n img = g(self.noise)\n assert img.shape == (2, 3, 16, 16)\n img = g(self.noise[:, :, None, None])\n assert img.shape == (2, 3, 16, 16)\n img = g(torch.randn, num_batches=2)\n assert img.shape == (2, 3, 16, 16)\n img = g(None, num_batches=2)\n assert img.shape == (2, 3, 16, 16)\n with pytest.raises(ValueError):\n _ = g(torch.randn((1, 100, 3)))\n with pytest.raises(AssertionError):\n _ = g(torch.randn)\n with pytest.raises(AssertionError):\n _ = g(None)\n results = g(self.noise, return_noise=True)\n assert results['noise_batch'].shape == (2, 100, 1, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_attention.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.ddpm.attention import (ApproximateGELU,\n CrossAttention, FeedForward,\n Transformer2DModel)\n\n\ndef test_ApproximateGELU():\n input = torch.rand((16, 16))\n gelu = ApproximateGELU(16, 24)\n output = gelu.forward(input)\n assert output.shape == (16, 24)\n\n\ndef test_crossattention():\n input = torch.rand((2, 64, 64))\n crossattention = CrossAttention(64)\n crossattention._slice_size = 2\n output = crossattention.forward(input)\n assert output.shape == (2, 64, 64)\n\n\ndef test_Transformer2DModel_init():\n with pytest.raises(Exception):\n Transformer2DModel(in_channels=32, num_vector_embeds=4)\n\n with pytest.raises(Exception):\n Transformer2DModel()\n\n Transformer2DModel(in_channels=32, use_linear_projection=True)\n\n\ndef test_FeedForward():\n input = torch.rand((2, 64, 64))\n feed_forward = FeedForward(64, 64, activation_fn='geglu-approximate')\n output = feed_forward.forward(input)\n assert output.shape == (2, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_ddpm_scheduler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.diffusion_schedulers.ddpm_scheduler import EditDDPMScheduler\n\n\ndef test_ddpm():\n modelout = torch.rand((1, 8, 32, 32))\n sample = torch.rand((1, 4, 32, 32))\n ddpm = EditDDPMScheduler(\n num_train_timesteps=1000, variance_type='learned_range')\n result = ddpm.step(modelout, 980, sample)\n assert result['prev_sample'].shape == (1, 4, 32, 32)\n\n ddpm.set_timesteps(100)\n\n predicted_variance = torch.tensor(1.0)\n ddpm._get_variance(t=0, predicted_variance=predicted_variance)\n ddpm._get_variance(t=1, variance_type='fixed_large')\n ddpm._get_variance(t=1, variance_type='fixed_large_log')\n ddpm._get_variance(t=1, variance_type='learned')\n\n with pytest.raises(Exception):\n ddpm.training_loss(1, 2, 3)\n\n with pytest.raises(Exception):\n ddpm.sample_timestep()\n\n steps = len(ddpm)\n assert steps == 1000\n\n\ndef test_ddpm_init():\n EditDDPMScheduler(trained_betas=1)\n\n EditDDPMScheduler(beta_schedule='scaled_linear')\n\n EditDDPMScheduler(beta_schedule='squaredcos_cap_v2')\n\n with pytest.raises(Exception):\n EditDDPMScheduler(beta_schedule='tem')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_denoising_unet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,\n NormWithEmbedding)\n\n\ndef test_DenoisingUnet():\n input = torch.rand((1, 3, 32, 32))\n unet = DenoisingUnet(32)\n output = unet.forward(input, 10)\n assert output['sample'].shape == (1, 6, 32, 32)\n\n\ndef test_NormWithEmbedding():\n input = torch.rand((4, 32))\n emb = torch.rand((4, 32))\n ins = NormWithEmbedding(32, 32)\n output = ins.forward(input, emb)\n assert output.shape == (4, 32, 4, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_embeddings.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.ddpm.embeddings import TimestepEmbedding, Timesteps\n\n\ndef test_TimestepEmbedding():\n input = torch.rand((1, 64, 16))\n timestep_emb = TimestepEmbedding(\n in_channels=16, time_embed_dim=16, act_fn='mish')\n output = timestep_emb.forward(input)\n assert output.shape == (1, 64, 16)\n\n timestep_emb = TimestepEmbedding(\n in_channels=16, time_embed_dim=16, out_dim=96)\n timestep_emb.act = None\n output = timestep_emb.forward(input)\n assert output.shape == (1, 64, 96)\n\n\ndef test_Timesteps():\n input = torch.tensor([4])\n timesteps = Timesteps(num_channels=9)\n emb = timesteps.forward(input)\n assert emb.shape == (1, 9)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_res_blocks.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.ddpm.res_blocks import (Downsample2D, ResnetBlock2D,\n Upsample2D)\n\n\ndef test_resnetblock2d():\n input = torch.rand((1, 64, 16, 16))\n resblock = ResnetBlock2D(in_channels=64, up=True)\n output = resblock.forward(input, None)\n assert output.shape == (1, 64, 64, 64)\n\n resblock = ResnetBlock2D(in_channels=64, down=True)\n output = resblock.forward(input, None)\n assert output.shape == (1, 64, 8, 8)\n\n\ndef test_Downsample2D():\n input = torch.rand((1, 64, 16, 16))\n downsample = Downsample2D(channels=64, use_conv=True, padding=0)\n output = downsample.forward(input)\n assert output.shape == (1, 64, 8, 8)\n\n\ndef test_Upsample2D():\n input = torch.rand((1, 64, 16, 16))\n upsample = Upsample2D(channels=64, use_conv_transpose=True)\n output = upsample.forward(input)\n assert output.shape == (1, 64, 32, 32)\n\n upsample = Upsample2D(channels=64)\n output = upsample.forward(input, output_size=(32, 32))\n assert output.shape == (1, 64, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ddpm/test_unet_blocks.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.ddpm.unet_blocks import (CrossAttnDownBlock2D,\n CrossAttnUpBlock2D,\n UNetMidBlock2DCrossAttn,\n get_down_block,\n get_up_block)\n\n\ndef test_UNetMidBlock2DCrossAttn():\n input = torch.rand((1, 64, 64, 64))\n midblock = UNetMidBlock2DCrossAttn(64, 64, cross_attention_dim=64)\n midblock.set_attention_slice(1)\n output = midblock.forward(input)\n assert output.shape == (1, 64, 64, 64)\n\n with pytest.raises(Exception):\n midblock.set_attention_slice(8)\n\n\ndef test_CrossAttnDownBlock2D():\n input = torch.rand((1, 64, 64, 64))\n downblock = CrossAttnDownBlock2D(64, 64, 64, cross_attention_dim=64)\n downblock.set_attention_slice(1)\n output, _ = downblock.forward(input)\n assert output.shape == (1, 64, 32, 32)\n\n with pytest.raises(Exception):\n downblock.set_attention_slice(8)\n\n\ndef test_CrossAttnUpBlock2D():\n downblock = CrossAttnUpBlock2D(64, 64, 64, 64, cross_attention_dim=64)\n downblock.set_attention_slice(1)\n\n\ndef test_get_down_block():\n with pytest.raises(Exception):\n get_down_block('tem', 1, 1, 1, 1, True, 'silu', 1)\n\n\ndef get_get_up_block():\n with pytest.raises(Exception):\n get_up_block('tem', 1, 1, 1, 1, 1, True, 'silu', 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deblurganv2/test_deblurganv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import Adam\n\nfrom mmagic.models import DataPreprocessor, DeblurGanV2\nfrom mmagic.models.losses import PerceptualLoss\nfrom mmagic.models.losses.adv_loss import DiscLossWGANGP\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='DeblurGanV2Generator',\n backbone='FPNMobileNet',\n norm_layer='instance',\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128,\n)\ndiscriminator = dict(\n type='DeblurGanV2Discriminator',\n backbone='DoubleGan',\n norm_layer='instance',\n d_layers=3,\n)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestDeblurGanV2(TestCase):\n\n def test_init(self):\n gan = DeblurGanV2(\n generator=generator,\n discriminator=discriminator,\n pixel_loss=dict(\n type='PerceptualLoss',\n layer_weights={'14': 1},\n criterion='mse'),\n adv_lambda=0.001,\n warmup_num=3,\n disc_loss=dict(type='AdvLoss', loss_type='wgan-gp'),\n data_preprocessor=DataPreprocessor())\n\n self.assertIsInstance(gan, DeblurGanV2)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n self.assertIsInstance(gan.pixel_loss, PerceptualLoss)\n self.assertIsInstance(gan.disc_loss, DiscLossWGANGP)\n gen_cfg = deepcopy(generator)\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = DeblurGanV2(\n generator=gen,\n discriminator=disc,\n pixel_loss=dict(\n type='PerceptualLoss',\n layer_weights={'14': 1},\n criterion='mse'),\n adv_lambda=0.001,\n warmup_num=3,\n disc_loss=dict(type='AdvLoss', loss_type='wgan-gp'),\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = DeblurGanV2(\n generator=gen_cfg, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('test-lsgan')\n gan = DeblurGanV2(\n generator=generator,\n discriminator=discriminator,\n pixel_loss=dict(\n type='PerceptualLoss',\n layer_weights={'14': 1},\n criterion='mse'),\n adv_lambda=0.001,\n warmup_num=3,\n disc_loss=dict(type='AdvLoss', loss_type='wgan-gp'),\n data_preprocessor=DataPreprocessor())\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = Adam(\n gan.generator.parameters(), lr=0.0001, betas=(0.5, 0.999))\n disc_optim = Adam(\n list(gan.discriminator.patch_gan.parameters()) +\n list(gan.discriminator.full_gan.parameters()),\n lr=0.0001,\n betas=(0.5, 0.999))\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 256, 256)\n data = dict(inputs=[img], data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set(['loss_d', 'loss_g_content', 'loss_g_adv', 'loss_g']))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(), set(['loss_g_content', 'loss_g_adv',\n 'loss_g']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deblurganv2/test_deblurganv2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.deblurganv2 import DeblurGanV2Discriminator\nfrom mmagic.models.editors.deblurganv2.deblurganv2_discriminator import \\\n DoubleGan\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestDeblurGanv2Discriminator(object):\n\n @classmethod\n def setup_class(cls):\n cls.input_tensor = torch.randn((1, 3, 256, 256))\n cls.default_config = dict(\n type='DeblurGanV2Discriminator',\n backbone='DoubleGan',\n norm_layer='instance',\n d_layers=3)\n\n def test_deblurganv2_discriminator(self):\n # test default setting with builder\n d = MODELS.build(self.default_config)\n assert isinstance(d, DoubleGan)\n pred = d(self.input_tensor)\n assert isinstance(pred, list)\n assert len(pred) == 2\n assert d.full_gan\n assert d.patch_gan\n with pytest.raises(TypeError):\n _ = DeblurGanV2Discriminator()\n\n # sanity check for args with cpu model\n d = DeblurGanV2Discriminator(\n backbone='DoubleGan', norm_layer='instance', d_layers=3)\n pred = d(torch.randn((1, 3, 256, 256)))\n assert d.full_gan\n assert d.patch_gan\n assert isinstance(pred, list)\n assert len(pred) == 2\n\n # check for cuda\n if not torch.cuda.is_available():\n return\n\n # test default setting with builder on GPU\n d = MODELS.build(self.default_config).cuda()\n pred = d(self.input_tensor.cuda())\n assert d.full_gan\n assert d.patch_gan\n assert isinstance(pred, list)\n assert len(pred) == 2\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deblurganv2/test_deblurganv2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.deblurganv2 import DeblurGanV2Generator\nfrom mmagic.models.editors.deblurganv2.deblurganv2_generator import \\\n FPNMobileNet\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestDeblurGanv2Generator(object):\n\n @classmethod\n def setup_class(cls):\n cls.input_tensor = torch.randn((1, 3, 256, 256))\n cls.default_config = dict(\n type='DeblurGanV2Generator',\n backbone='FPNMobileNet',\n norm_layer='instance',\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128)\n\n def test_deblurganv2_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, FPNMobileNet)\n\n # check forward function\n img = g(self.input_tensor)\n assert img.shape == (1, 3, 256, 256)\n img = g(torch.randn(4, 3, 256, 256))\n assert img.shape == (4, 3, 256, 256)\n with pytest.raises(TypeError):\n _ = DeblurGanV2Generator()\n\n # sanity check for args with cpu model\n g = DeblurGanV2Generator(\n backbone='FPNMobileNet',\n norm_layer='instance',\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128)\n img = g(self.input_tensor)\n assert img.shape == (1, 3, 256, 256)\n\n # check for cuda\n if not torch.cuda.is_available():\n return\n\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, DeblurGanV2Generator)\n g = DeblurGanV2Generator(\n backbone='FPNMobileNet',\n norm_layer='instance',\n output_ch=3,\n num_filter=64,\n num_filter_fpn=128).cuda()\n img = g(self.input_tensor)\n assert img.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_contextual_attention.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import ContextualAttentionModule\n\n\ndef test_deepfill_contextual_attention_module():\n cmodule = ContextualAttentionModule()\n x = torch.rand((2, 128, 64, 64))\n mask = torch.zeros((2, 1, 64, 64))\n mask[..., 20:100, 23:90] = 1.\n res, offset = cmodule(x, x, mask)\n assert res.shape == (2, 128, 64, 64)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_contextual_attention_neck.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.models.editors import ContextualAttentionNeck\n\n\ndef test_deepfill_contextual_attention_neck():\n # TODO: add unittest for contextual attention module\n neck = ContextualAttentionNeck(in_channels=128)\n x = torch.rand((2, 128, 64, 64))\n mask = torch.zeros((2, 1, 64, 64))\n mask[..., 20:100, 23:90] = 1.\n\n res, offset = neck(x, mask)\n\n assert res.shape == (2, 128, 64, 64)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n if torch.cuda.is_available():\n neck.cuda()\n res, offset = neck(x.cuda(), mask.cuda())\n\n assert res.shape == (2, 128, 64, 64)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n neck = ContextualAttentionNeck(\n in_channels=128, conv_type='gated_conv').cuda()\n res, offset = neck(x.cuda(), mask.cuda())\n assert res.shape == (2, 128, 64, 64)\n assert offset.shape == (2, 32, 32, 32, 32)\n assert isinstance(neck.conv1, SimpleGatedConvModule)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_deepfill_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import DeepFillDecoder\n\n\ndef test_deepfill_dec():\n\n decoder = DeepFillDecoder(128, out_act_cfg=None)\n assert not decoder.with_out_activation\n\n decoder = DeepFillDecoder(128)\n x = torch.randn((2, 128, 64, 64))\n input_dict = dict(out=x)\n res = decoder(input_dict)\n\n assert res.shape == (2, 3, 256, 256)\n assert decoder.dec2.stride == (1, 1)\n assert decoder.dec2.out_channels == 128\n assert not decoder.dec7.with_activation\n assert res.min().item() >= -1. and res.max().item() <= 1\n if torch.cuda.is_available():\n decoder = DeepFillDecoder(128).cuda()\n x = torch.randn((2, 128, 64, 64)).cuda()\n input_dict = dict(out=x)\n res = decoder(input_dict)\n assert res.shape == (2, 3, 256, 256)\n assert decoder.dec2.stride == (1, 1)\n assert decoder.dec2.out_channels == 128\n assert not decoder.dec7.with_activation\n assert res.min().item() >= -1. and res.max().item() <= 1\n\n decoder = DeepFillDecoder(\n 128, conv_type='gated_conv', channel_factor=0.75).cuda()\n x = torch.randn((2, 128, 64, 64)).cuda()\n input_dict = dict(out=x)\n res = decoder(input_dict)\n assert res.shape == (2, 3, 256, 256)\n assert decoder.dec2.conv.stride == (1, 1)\n assert decoder.dec2.conv.out_channels == 96 * 2\n assert not decoder.dec7.with_feat_act\n assert res.min().item() >= -1. and res.max().item() <= 1\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_deepfill_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.archs import MultiLayerDiscriminator\nfrom mmagic.models.editors import DeepFillv1Discriminators\n\n\ndef test_deepfillv1_disc():\n model_config = dict(\n global_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=256,\n fc_in_channels=256 * 16 * 16,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),\n local_disc_cfg=dict(\n type='MultiLayerDiscriminator',\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 8 * 8,\n fc_out_channels=1,\n num_convs=4,\n norm_cfg=None,\n act_cfg=dict(type='ELU'),\n out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2)))\n disc = DeepFillv1Discriminators(**model_config)\n disc.init_weights()\n global_x = torch.rand((2, 3, 256, 256))\n local_x = torch.rand((2, 3, 128, 128))\n global_pred, local_pred = disc((global_x, local_x))\n assert global_pred.shape == (2, 1)\n assert local_pred.shape == (2, 1)\n assert isinstance(disc.global_disc, MultiLayerDiscriminator)\n assert isinstance(disc.local_disc, MultiLayerDiscriminator)\n\n with pytest.raises(TypeError):\n disc.init_weights(model_config)\n\n if torch.cuda.is_available():\n disc = DeepFillv1Discriminators(**model_config).cuda()\n disc.init_weights()\n global_x = torch.rand((2, 3, 256, 256)).cuda()\n local_x = torch.rand((2, 3, 128, 128)).cuda()\n global_pred, local_pred = disc((global_x, local_x))\n assert global_pred.shape == (2, 1)\n assert local_pred.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_deepfill_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.models.editors import DeepFillEncoder\n\n\ndef test_deepfill_enc():\n encoder = DeepFillEncoder()\n x = torch.randn((2, 5, 256, 256))\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.stride == (2, 2)\n assert encoder.enc2.out_channels == 64\n\n encoder = DeepFillEncoder(encoder_type='stage2_conv')\n x = torch.randn((2, 5, 256, 256))\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.out_channels == 32\n assert encoder.enc3.out_channels == 64\n assert encoder.enc4.out_channels == 64\n\n encoder = DeepFillEncoder(encoder_type='stage2_attention')\n x = torch.randn((2, 5, 256, 256))\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.out_channels == 32\n assert encoder.enc3.out_channels == 64\n assert encoder.enc4.out_channels == 128\n if torch.cuda.is_available():\n encoder = DeepFillEncoder().cuda()\n x = torch.randn((2, 5, 256, 256)).cuda()\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.stride == (2, 2)\n assert encoder.enc2.out_channels == 64\n\n encoder = DeepFillEncoder(encoder_type='stage2_conv').cuda()\n x = torch.randn((2, 5, 256, 256)).cuda()\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.out_channels == 32\n assert encoder.enc3.out_channels == 64\n assert encoder.enc4.out_channels == 64\n\n encoder = DeepFillEncoder(encoder_type='stage2_attention').cuda()\n x = torch.randn((2, 5, 256, 256)).cuda()\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 128, 64, 64)\n assert encoder.enc2.out_channels == 32\n assert encoder.enc3.out_channels == 64\n assert encoder.enc4.out_channels == 128\n\n encoder = DeepFillEncoder(\n conv_type='gated_conv', channel_factor=0.75).cuda()\n x = torch.randn((2, 5, 256, 256)).cuda()\n outputs = encoder(x)\n assert isinstance(outputs, dict)\n assert 'out' in outputs\n res = outputs['out']\n assert res.shape == (2, 96, 64, 64)\n assert isinstance(encoder.enc2, SimpleGatedConvModule)\n assert encoder.enc2.conv.stride == (2, 2)\n assert encoder.enc2.conv.out_channels == 48 * 2\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_deepfill_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import (ContextualAttentionNeck, DeepFillDecoder,\n DeepFillEncoder, DeepFillRefiner,\n GLDilationNeck)\n\n\ndef test_deepfill_refiner():\n refiner = DeepFillRefiner()\n\n x = torch.rand((2, 5, 256, 256))\n mask = x.new_ones((2, 1, 256, 256))\n mask[..., 30:100, 40:100] = 0.\n res, offset = refiner(x, mask)\n assert res.shape == (2, 3, 256, 256)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n # check model architecture\n assert isinstance(refiner.encoder_attention, DeepFillEncoder)\n assert isinstance(refiner.encoder_conv, DeepFillEncoder)\n assert isinstance(refiner.contextual_attention_neck,\n ContextualAttentionNeck)\n assert isinstance(refiner.decoder, DeepFillDecoder)\n assert isinstance(refiner.dilation_neck, GLDilationNeck)\n\n if torch.cuda.is_available():\n refiner = DeepFillRefiner().cuda()\n\n x = torch.rand((2, 5, 256, 256)).cuda()\n res, offset = refiner(x, mask.cuda())\n assert res.shape == (2, 3, 256, 256)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n # check model architecture\n assert isinstance(refiner.encoder_attention, DeepFillEncoder)\n assert isinstance(refiner.encoder_conv, DeepFillEncoder)\n assert isinstance(refiner.contextual_attention_neck,\n ContextualAttentionNeck)\n assert isinstance(refiner.decoder, DeepFillDecoder)\n assert isinstance(refiner.dilation_neck, GLDilationNeck)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv1/test_deepfillv1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom os import path as osp\n\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.models.editors import (DeepFillEncoderDecoder, DeepFillRefiner,\n GLEncoderDecoder)\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\ndef test_deepfill_encdec():\n encdec = DeepFillEncoderDecoder()\n assert isinstance(encdec.stage1, GLEncoderDecoder)\n assert isinstance(encdec.stage2, DeepFillRefiner)\n\n if torch.cuda.is_available():\n img = torch.rand((2, 3, 256, 256)).cuda()\n mask = img.new_zeros((2, 1, 256, 256))\n mask[..., 20:100, 30:120] = 1.\n input_x = torch.cat([img, torch.ones_like(mask), mask], dim=1)\n encdec.cuda()\n stage1_res, stage2_res = encdec(input_x)\n assert stage1_res.shape == (2, 3, 256, 256)\n assert stage2_res.shape == (2, 3, 256, 256)\n encdec = DeepFillEncoderDecoder(return_offset=True).cuda()\n stage1_res, stage2_res, offset = encdec(input_x)\n assert offset.shape == (2, 32, 32, 32, 32)\n\n\ndef test_deepfillv1_inpaintor():\n register_all_modules()\n\n config_file = osp.join(\n osp.dirname(__file__), '../../..', 'configs', 'deepfillv1_test.py')\n cfg = Config.fromfile(config_file)\n\n deepfillv1 = MODELS.build(cfg.model)\n\n assert deepfillv1.__class__.__name__ == 'DeepFillv1Inpaintor'\n\n if torch.cuda.is_available():\n deepfillv1.cuda()\n\n # check architecture\n assert deepfillv1.stage1_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_composed_percep', 'loss_tv')\n assert deepfillv1.stage2_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_gan')\n assert deepfillv1.with_l1_hole_loss\n assert deepfillv1.with_l1_valid_loss\n assert deepfillv1.with_composed_percep_loss\n assert not deepfillv1.with_out_percep_loss\n assert deepfillv1.with_gan\n\n # prepare data\n gt_img = torch.rand((3, 256, 256))\n mask = torch.zeros((1, 256, 256))\n mask[..., 50:180, 60:170] = 1.\n masked_img = gt_img.unsqueeze(0) * (1. - mask)\n mask_bbox = [100, 100, 110, 110]\n data_batch = {\n 'inputs':\n masked_img,\n 'data_samples': [\n DataSample(\n metainfo=dict(mask_bbox=mask_bbox),\n mask=mask,\n gt_img=gt_img,\n )\n ]\n }\n\n # prepare model and optimizer\n optim_g = torch.optim.Adam(deepfillv1.generator.parameters(), lr=0.0001)\n optim_d = torch.optim.Adam(deepfillv1.disc.parameters(), lr=0.0001)\n optims = dict(generator=OptimWrapper(optim_g), disc=OptimWrapper(optim_d))\n\n # check train_step with standard deepfillv1 model\n for i in range(5):\n log_vars = deepfillv1.train_step(data_batch, optims)\n\n if i % 2 == 0:\n assert 'real_loss_global' in log_vars\n assert 'fake_loss_global' in log_vars\n assert 'real_loss_local' in log_vars\n assert 'fake_loss_local' in log_vars\n assert 'loss' in log_vars\n else:\n assert 'real_loss_global' in log_vars\n assert 'fake_loss_global' in log_vars\n assert 'real_loss_local' in log_vars\n assert 'fake_loss_local' in log_vars\n assert 'stage1_loss_l1_hole' in log_vars\n assert 'stage1_loss_l1_valid' in log_vars\n assert 'stage2_loss_l1_hole' in log_vars\n assert 'stage2_loss_l1_valid' in log_vars\n assert 'stage2_loss_g_fake' in log_vars\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_deepfillv2/test_two_stage_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport platform\nfrom os.path import dirname, join\n\nimport pytest\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_two_stage_inpaintor():\n register_all_modules()\n\n config_file = join(\n dirname(__file__), '../../..', 'configs', 'two_stage_test.py')\n cfg = Config.fromfile(config_file)\n\n inpaintor = MODELS.build(cfg.model)\n\n assert inpaintor.__class__.__name__ == 'TwoStageInpaintor'\n\n if torch.cuda.is_available():\n inpaintor.cuda()\n\n # check architecture\n assert inpaintor.stage1_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_composed_percep', 'loss_tv')\n assert inpaintor.stage2_loss_type == ('loss_l1_hole', 'loss_l1_valid',\n 'loss_gan')\n assert inpaintor.with_l1_hole_loss\n assert inpaintor.with_l1_valid_loss\n assert inpaintor.with_composed_percep_loss\n assert not inpaintor.with_out_percep_loss\n assert inpaintor.with_gan\n\n # prepare data\n gt_img = torch.randn(3, 128, 128)\n mask = torch.zeros((1, 128, 128))\n mask[..., 12:45, 14:42] = 1.\n masked_img = gt_img.unsqueeze(0) * (1. - mask) + mask\n mask_bbox = [25, 25, 27, 27]\n data_batch = {\n 'inputs':\n masked_img,\n 'data_samples':\n [DataSample(\n mask=mask,\n mask_bbox=mask_bbox,\n gt_img=gt_img,\n )]\n }\n\n optim_g = torch.optim.Adam(inpaintor.generator.parameters(), lr=0.0001)\n optim_d = torch.optim.Adam(inpaintor.disc.parameters(), lr=0.0001)\n optims = dict(generator=OptimWrapper(optim_g), disc=OptimWrapper(optim_d))\n\n # check train_step with standard two_stage model\n for i in range(5):\n log_vars = inpaintor.train_step(data_batch, optims)\n\n if i % 2 == 0:\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_disc_shift' in log_vars\n assert 'loss' in log_vars\n else:\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_disc_shift' in log_vars\n assert 'loss' in log_vars\n assert 'stage1_loss_l1_hole' in log_vars\n assert 'stage1_loss_l1_valid' in log_vars\n assert 'stage2_loss_l1_hole' in log_vars\n assert 'stage2_loss_l1_valid' in log_vars\n\n # check for forward_test\n data = inpaintor.data_preprocessor(data_batch, True)\n data_inputs, data_sample = data['inputs'], data['data_samples']\n output = inpaintor.forward_test(data_inputs, data_sample)\n prediction = output.split()[0]\n assert 'fake_res' in prediction\n assert 'fake_img' in prediction\n assert 'pred_img' in prediction\n assert prediction.pred_img.shape == (3, 128, 128)\n\n # check for gp_loss\n cfg_copy = copy.deepcopy(cfg)\n cfg_copy.model.disc_input_with_mask = False\n cfg_copy.model.disc.in_channels = 3\n cfg_copy.model.loss_gp = dict(type='GradientPenaltyLoss', loss_weight=10.)\n inpaintor = MODELS.build(cfg_copy.model)\n assert inpaintor.with_gp_loss\n\n log_vars = inpaintor.train_step(data_batch, optims)\n assert 'real_loss' in log_vars\n assert 'stage1_loss_l1_hole' in log_vars\n assert 'stage1_loss_l1_valid' in log_vars\n assert 'stage2_loss_l1_hole' in log_vars\n assert 'stage2_loss_l1_valid' in log_vars\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dic/test_dic.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import patch\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import DIC, DataPreprocessor, DICNet, LightCNN\nfrom mmagic.models.losses import (GANLoss, L1Loss, LightCNNFeatureLoss,\n PerceptualVGG)\nfrom mmagic.structures import DataSample\n\n\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_dic(init_weights):\n\n model = DIC(\n generator=dict(\n type='DICNet', in_channels=3, out_channels=3, mid_channels=4),\n discriminator=dict(type='LightCNN', in_channels=3),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n align_loss=dict(type='MSELoss', loss_weight=0.1, reduction='mean'),\n feature_loss=dict(\n type='LightCNNFeatureLoss',\n pretrained=None,\n loss_weight=0.1,\n criterion='l1'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=0.005,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=dict(),\n test_cfg=dict(),\n data_preprocessor=DataPreprocessor(\n mean=[129.795, 108.12, 96.39],\n std=[255, 255, 255],\n ))\n\n assert isinstance(model, DIC)\n assert isinstance(model.generator, DICNet)\n assert isinstance(model.discriminator, LightCNN)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.feature_loss, LightCNNFeatureLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 16, 16)\n target = torch.rand(3, 128, 128)\n data_sample = DataSample(gt_img=target, gt_heatmap=torch.rand(68, 32, 32))\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_pixel_v0', 'loss_align_v0', 'loss_pixel_v1', 'loss_align_v1',\n 'loss_pixel_v2', 'loss_align_v2', 'loss_pixel_v3', 'loss_align_v3',\n 'loss_feature', 'loss_gan', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 128, 128)\n\n # feat\n output = model(torch.rand(1, 3, 16, 16), mode='tensor')\n assert output.shape == (1, 3, 128, 128)\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dic/test_dic_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors import (FeedbackBlock, FeedbackBlockCustom,\n FeedbackBlockHeatmapAttention)\nfrom mmagic.registry import MODELS\n\n\ndef test_feedback_block():\n x1 = torch.rand(2, 16, 32, 32)\n\n model = FeedbackBlock(16, 3, 8)\n x2 = model(x1)\n assert x2.shape == x1.shape\n x3 = model(x2)\n assert x3.shape == x2.shape\n\n\ndef test_feedback_block_custom():\n x1 = torch.rand(2, 3, 32, 32)\n\n model = FeedbackBlockCustom(3, 16, 3, 8)\n x2 = model(x1)\n assert x2.shape == (2, 16, 32, 32)\n\n\ndef test_feedback_block_heatmap_attention():\n x1 = torch.rand(2, 16, 32, 32)\n heatmap = torch.rand(2, 5, 32, 32)\n\n model = FeedbackBlockHeatmapAttention(16, 2, 8, 5, 2)\n x2 = model(x1, heatmap)\n assert x2.shape == x1.shape\n x3 = model(x2, heatmap)\n assert x3.shape == x2.shape\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_dic_net_cpu():\n\n model_cfg = dict(\n type='DICNet',\n in_channels=3,\n out_channels=3,\n mid_channels=48,\n num_blocks=6,\n hg_mid_channels=256,\n hg_num_keypoints=68,\n num_steps=4,\n upscale_factor=8,\n detach_attention=False)\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'DICNet'\n\n # prepare data\n inputs = torch.rand(1, 3, 16, 16)\n targets = torch.rand(1, 3, 128, 128)\n\n # prepare loss\n loss_function = nn.L1Loss()\n\n # prepare optimizer\n optimizer = torch.optim.Adam(model.parameters())\n\n # test on cpu\n output, _ = model(inputs)\n optimizer.zero_grad()\n loss = loss_function(output[-1], targets)\n loss.backward()\n optimizer.step()\n\n assert len(output) == 4\n assert torch.is_tensor(output[-1])\n assert output[-1].shape == targets.shape\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_dic_net_cuda():\n # prepare data\n inputs = torch.rand(1, 3, 16, 16)\n targets = torch.rand(1, 3, 128, 128)\n\n model_cfg = dict(\n type='DICNet',\n in_channels=3,\n out_channels=3,\n mid_channels=48,\n num_blocks=6,\n hg_mid_channels=256,\n hg_num_keypoints=68,\n num_steps=4,\n upscale_factor=8,\n detach_attention=False)\n\n # build model\n model = MODELS.build(model_cfg)\n\n # prepare loss\n loss_function = nn.L1Loss()\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n optimizer = torch.optim.Adam(model.parameters())\n inputs = inputs.cuda()\n targets = targets.cuda()\n output, _ = model(inputs)\n optimizer.zero_grad()\n loss = loss_function(output[-1], targets)\n loss.backward()\n optimizer.step()\n assert len(output) == 4\n assert torch.is_tensor(output[-1])\n assert output[-1].shape == targets.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dic/test_feedback_hour_glass.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.dic.feedback_hour_glass import (\n Hourglass, ResBlock, reduce_to_five_heatmaps)\nfrom mmagic.registry import MODELS\n\n\ndef test_res_block():\n\n res_block = ResBlock(16, 32)\n x = torch.rand(2, 16, 64, 64)\n y = res_block(x)\n assert y.shape == (2, 32, 64, 64)\n\n res_block = ResBlock(16, 16)\n x = torch.rand(2, 16, 64, 64)\n y = res_block(x)\n assert y.shape == (2, 16, 64, 64)\n\n\ndef test_hour_glass():\n hour_glass = Hourglass(2, 16)\n x = torch.rand(2, 16, 64, 64)\n y = hour_glass(x)\n assert y.shape == x.shape\n\n\ndef test_feedback_hour_glass():\n model_cfg = dict(\n type='FeedbackHourglass', mid_channels=16, num_keypoints=20)\n\n fhg = MODELS.build(model_cfg)\n assert fhg.__class__.__name__ == 'FeedbackHourglass'\n\n x = torch.rand(2, 3, 64, 64)\n heatmap, last_hidden = fhg.forward(x)\n assert heatmap.shape == (2, 20, 16, 16)\n assert last_hidden.shape == (2, 16, 16, 16)\n heatmap, last_hidden = fhg.forward(x, last_hidden)\n assert heatmap.shape == (2, 20, 16, 16)\n assert last_hidden.shape == (2, 16, 16, 16)\n\n\ndef test_reduce_to_five_heatmaps():\n heatmap = torch.rand((2, 5, 64, 64))\n new_heatmap = reduce_to_five_heatmaps(heatmap, False)\n assert new_heatmap.shape == (2, 5, 64, 64)\n new_heatmap = reduce_to_five_heatmaps(heatmap, True)\n assert new_heatmap.shape == (2, 5, 64, 64)\n\n heatmap = torch.rand((2, 68, 64, 64))\n new_heatmap = reduce_to_five_heatmaps(heatmap, False)\n assert new_heatmap.shape == (2, 5, 64, 64)\n new_heatmap = reduce_to_five_heatmaps(heatmap, True)\n assert new_heatmap.shape == (2, 5, 64, 64)\n\n heatmap = torch.rand((2, 194, 64, 64))\n new_heatmap = reduce_to_five_heatmaps(heatmap, False)\n assert new_heatmap.shape == (2, 5, 64, 64)\n new_heatmap = reduce_to_five_heatmaps(heatmap, True)\n assert new_heatmap.shape == (2, 5, 64, 64)\n\n with pytest.raises(NotImplementedError):\n heatmap = torch.rand((2, 12, 64, 64))\n reduce_to_five_heatmaps(heatmap, False)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dic/test_light_cnn.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import MaxFeature\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_max_feature():\n # cpu\n conv2d = MaxFeature(16, 16, filter_type='conv2d')\n x1 = torch.rand(3, 16, 16, 16)\n y1 = conv2d(x1)\n assert y1.shape == (3, 16, 16, 16)\n linear = MaxFeature(16, 16, filter_type='linear')\n x2 = torch.rand(3, 16)\n y2 = linear(x2)\n assert y2.shape == (3, 16)\n # gpu\n if torch.cuda.is_available():\n x1 = x1.cuda()\n x2 = x2.cuda()\n conv2d = conv2d.cuda()\n linear = linear.cuda()\n y1 = conv2d(x1)\n assert y1.shape == (3, 16, 16, 16)\n y2 = linear(x2)\n assert y2.shape == (3, 16)\n # filter_type should be conv2d or linear\n with pytest.raises(ValueError):\n MaxFeature(12, 12, filter_type='conv1d')\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_light_cnn():\n cfg = dict(type='LightCNN', in_channels=3)\n net = MODELS.build(cfg)\n # cpu\n inputs = torch.rand((2, 3, 128, 128))\n output = net(inputs)\n assert output.shape == (2, 1)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(inputs.cuda())\n assert output.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dim/test_dim.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport numpy as np\nimport pytest\nimport torch\nfrom mmengine.config import ConfigDict\n\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.models.editors import DIM\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef _demo_input_train(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n alpha = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n ori_merged = torch.from_numpy(\n np.random.random(color_shape).astype(np.float32))\n fg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n bg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n if cuda:\n merged = merged.cuda()\n trimap = trimap.cuda()\n alpha = alpha.cuda()\n ori_merged = ori_merged.cuda()\n fg = fg.cuda()\n bg = bg.cuda()\n\n inputs = torch.cat((merged, trimap), dim=1)\n data_samples = []\n for a, m, f, b in zip(alpha, ori_merged, fg, bg):\n ds = DataSample()\n\n ds.gt_alpha = a\n ds.gt_merged = m\n ds.gt_fg = f\n ds.gt_bg = b\n for k, v in meta.items():\n ds.set_field(name=k, value=v, field_type='metainfo', dtype=None)\n\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef _demo_input_test(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n test_trans (str): what test transformation is used in data pipeline.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n ori_shape = (img_shape[0], img_shape[1], 1)\n\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n inputs = torch.cat((merged, trimap), dim=1)\n\n results = {\n 'ori_alpha': np.random.random(ori_shape).astype(np.float32),\n 'ori_trimap': np.random.randint(256,\n size=ori_shape).astype(np.float32),\n 'ori_merged_shape': img_shape,\n }\n packinputs = PackInputs()\n\n data_samples = []\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n\n if cuda:\n inputs = inputs.cuda()\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef assert_pred_alpha(predictions, batch_size):\n assert isinstance(predictions, list)\n assert isinstance(predictions[0], DataSample)\n pred_alpha = predictions[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == batch_size\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_dim_config():\n\n data_preprocessor = dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='rescale_to_zero_one',\n )\n backbone = dict(\n type='SimpleEncoderDecoder',\n encoder=dict(type='VGG16', in_channels=4),\n decoder=dict(type='PlainDecoder'))\n refiner = dict(type='PlainRefiner')\n train_cfg = dict(train_backbone=True, train_refiner=True)\n test_cfg = dict(refine=True)\n loss_alpha = dict(type='L1Loss')\n\n # build mattor without refiner\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner=None,\n loss_alpha=loss_alpha,\n train_cfg=train_cfg,\n test_cfg=test_cfg.copy())\n assert not mattor.with_refiner\n assert not mattor.test_cfg.refine\n\n # only train the refiner, this will freeze the backbone\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner,\n loss_alpha=loss_alpha,\n train_cfg=dict(train_backbone=False, train_refiner=True),\n test_cfg=test_cfg.copy())\n assert not mattor.train_cfg.train_backbone\n assert mattor.train_cfg.train_refiner\n assert mattor.test_cfg.refine\n\n # only train the backbone while the refiner is used for inference but not\n # trained, this behavior is allowed currently but will cause a warning.\n mattor = DIM(\n data_preprocessor,\n backbone,\n refiner,\n loss_alpha=loss_alpha,\n train_cfg=dict(train_backbone=True, train_refiner=False),\n test_cfg=test_cfg.copy())\n assert mattor.train_cfg.train_backbone\n assert not mattor.train_cfg.train_refiner\n assert mattor.test_cfg.refine\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_dim():\n model_cfg = ConfigDict(\n type='DIM',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(type='VGG16', in_channels=4),\n decoder=dict(type='PlainDecoder')),\n refiner=dict(type='PlainRefiner'),\n loss_alpha=dict(type='CharbonnierLoss', loss_weight=0.5),\n loss_comp=dict(type='CharbonnierCompLoss', loss_weight=0.5),\n loss_refine=dict(type='CharbonnierLoss'),\n train_cfg=dict(train_backbone=True, train_refiner=True),\n test_cfg=dict(\n refine=False,\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ),\n )\n\n # 1. test dim model with refiner\n model_cfg.train_cfg.train_refiner = True\n model_cfg.test_cfg.refine = True\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64))\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train,\n ['loss_alpha', 'loss_comp', 'loss_refine'])\n\n # test model forward in train mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_train = _demo_input_train((64, 64), cuda=True)\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train,\n ['loss_alpha', 'loss_comp', 'loss_refine'])\n\n # test model forward in test mode\n with torch.no_grad():\n model = MODELS.build(model_cfg)\n inputs, data_samples = _demo_input_test((48, 48))\n output_test = model(inputs, data_samples, mode='predict')\n assert isinstance(output_test, list)\n assert isinstance(output_test[0], DataSample)\n pred_alpha = output_test[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == (48, 48)\n\n # test model forward in test mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = model(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # 2. test dim model without refiner\n model_cfg.refiner = None\n model_cfg.test_cfg.refine = True\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64))\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train, ['loss_alpha', 'loss_comp'])\n\n # test model forward in train mode with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_train = _demo_input_train((64, 64), cuda=True)\n output_train = model(*input_train, mode='loss')\n assert_dict_keys_equal(output_train, ['loss_alpha', 'loss_comp'])\n\n # test model forward in test mode\n with torch.no_grad():\n model = MODELS.build(model_cfg)\n input_test = _demo_input_test((48, 48))\n output_test = model(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # check test with gpu\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = model(*input_test, mode='predict')\n\n # test forward_raw\n model.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n model.forward(inputs)\n\n\ntest_dim()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_disco_diffusion/test_disco_diffusion.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport unittest\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import patch\n\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom torchvision.version import __version__ as TV_VERSION\n\nfrom mmagic.models import DenoisingUnet, DiscoDiffusion\nfrom mmagic.models.diffusion_schedulers import EditDDIMScheduler\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass clip_mock(nn.Module):\n\n def __init__(self, device='cuda'):\n super().__init__()\n self.register_buffer('tensor', torch.randn([1, 512]))\n\n def encode_image(self, inputs):\n return inputs.mean() * self.tensor.repeat(inputs.shape[0], 1).to(\n inputs.device)\n\n def encode_text(self, inputs):\n return self.tensor.repeat(inputs.shape[0], 1).to(inputs.device)\n\n def forward(self, x):\n return x\n\n\nclass clip_mock_wrapper(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.model = clip_mock()\n\n def forward(self, x):\n return x\n\n\nclass TestDiscoDiffusion(TestCase):\n\n def setUp(self):\n # unet\n self.unet32 = DenoisingUnet(\n image_size=32,\n in_channels=3,\n base_channels=8,\n resblocks_per_downsample=2,\n attention_res=(8, ),\n norm_cfg=dict(type='GN32', num_groups=8),\n dropout=0.0,\n num_classes=0,\n use_fp16=True,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=2,\n num_head_channels=8,\n use_new_attention_order=False),\n use_scale_shift_norm=True)\n # mock clip\n self.clip_models = [clip_mock_wrapper(), clip_mock_wrapper()]\n # diffusion_scheduler\n self.diffusion_scheduler = EditDDIMScheduler(\n variance_type='learned_range',\n beta_schedule='linear',\n clip_sample=False)\n\n def test_init(self):\n unet32 = deepcopy(self.unet32)\n diffusion_scheduler = deepcopy(self.diffusion_scheduler)\n clip_models = deepcopy(self.clip_models)\n self.disco_diffusion = DiscoDiffusion(\n unet=unet32,\n diffusion_scheduler=diffusion_scheduler,\n secondary_model=None,\n clip_models=clip_models,\n use_fp16=True)\n\n @unittest.skipIf(\n digit_version(TV_VERSION) <= digit_version('0.7.0'),\n reason='torchvision version limitation')\n @unittest.skipIf(not torch.cuda.is_available(), reason='requires cuda')\n def test_infer(self):\n unet32 = deepcopy(self.unet32)\n diffusion_scheduler = deepcopy(self.diffusion_scheduler)\n clip_models = deepcopy(self.clip_models)\n self.disco_diffusion = DiscoDiffusion(\n unet=unet32,\n diffusion_scheduler=diffusion_scheduler,\n secondary_model=None,\n clip_models=clip_models,\n use_fp16=True)\n self.disco_diffusion.cuda().eval()\n\n # test model structure\n text_prompts = {\n 0: ['clouds surround the mountains and palaces,sunshine,lake']\n }\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 32, 32)\n # test with different text prompts\n text_prompts = {\n 0: [\n 'a portrait of supergirl, by artgerm, rosstran, trending on artstation.' # noqa\n ]\n }\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 32, 32)\n\n # test with init_image\n init_image = 'tests/data/image/face/000001.png'\n text_prompts = {\n 0: [\n 'a portrait of supergirl, by artgerm, rosstran, trending on artstation.' # noqa\n ]\n }\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n init_image=init_image,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 32, 32)\n\n # test with different image resolution\n text_prompts = {\n 0: ['clouds surround the mountains and palaces,sunshine,lake']\n }\n image = self.disco_diffusion.infer(\n height=64,\n width=128,\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 64, 128)\n\n # clip guidance scale\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n clip_guidance_scale=8000,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 32, 32)\n\n # test with different loss settings\n tv_scale = 0.5\n sat_scale = 0.5\n range_scale = 100\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8,\n tv_scale=tv_scale,\n sat_scale=sat_scale,\n range_scale=range_scale)['samples']\n assert image.shape == (1, 3, 32, 32)\n\n # test with different cutter settings\n cut_overview = [12] * 100 + [4] * 900\n cut_innercut = [4] * 100 + [12] * 900\n cut_ic_pow = [1] * 200 + [0] * 800\n cut_icgray_p = [0.2] * 200 + [0] * 800\n cutn_batches = 2\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8,\n cut_overview=cut_overview,\n cut_innercut=cut_innercut,\n cut_ic_pow=cut_ic_pow,\n cut_icgray_p=cut_icgray_p,\n cutn_batches=cutn_batches)['samples']\n assert image.shape == (1, 3, 32, 32)\n\n # test with different unet\n unet64 = DenoisingUnet(\n image_size=64,\n in_channels=3,\n base_channels=8,\n resblocks_per_downsample=2,\n attention_res=(8, ),\n norm_cfg=dict(type='GN32', num_groups=8),\n dropout=0.0,\n num_classes=0,\n use_fp16=True,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=2,\n num_head_channels=8,\n use_new_attention_order=False),\n use_scale_shift_norm=True).cuda()\n unet64.convert_to_fp16()\n self.disco_diffusion.unet = unet64\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 64, 64)\n\n class affineMock(nn.Module):\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n\n def forward(self, x):\n return x\n\n mock_path = ('mmagic.models.editors.disco_diffusion.guider.'\n 'TORCHVISION_VERSION')\n affine_mock_path = ('mmagic.models.editors.disco_diffusion.guider.T.'\n 'RandomAffine')\n with patch(affine_mock_path, new=affineMock):\n with patch(mock_path, '0.8.1'):\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 64, 64)\n\n with patch(mock_path, '0.9.0'):\n image = self.disco_diffusion.infer(\n text_prompts=text_prompts,\n show_progress=True,\n num_inference_steps=2,\n eta=0.8)['samples']\n assert image.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_disco_diffusion/test_disco_diffusion_clip_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.archs import TokenizerWrapper\nfrom mmagic.models.editors import ClipWrapper\nfrom mmagic.models.editors.disco_diffusion.clip_wrapper import \\\n EmbeddingLayerWithFixes\n\n\nclass TestClipWrapper(TestCase):\n\n def test_clip_not_installed(self):\n with patch.dict('sys.modules', {'clip': None}):\n with self.assertRaises(ImportError):\n ClipWrapper('clip')\n\n def test_open_clip_not_installed(self):\n with patch.dict('sys.modules', {'open_clip': None}):\n with self.assertRaises(ImportError):\n ClipWrapper('open_clip')\n\n def test_transformers_not_installed(self):\n with patch.dict('sys.modules', {'transformers': None}):\n with self.assertRaises(ImportError):\n ClipWrapper('huggingface')\n\n @patch('clip.load')\n def test_clip_load(self, mock_clip_load):\n mock_model = MagicMock()\n mock_clip_load.return_value = (mock_model, None)\n\n model = ClipWrapper('clip', name='test_model')\n\n mock_clip_load.assert_called_once_with(name='test_model')\n self.assertEqual(model.model, mock_model)\n\n def test_open_clip_load(self):\n mock_model = MagicMock()\n create_model_mock = MagicMock()\n create_model_mock.return_value = mock_model\n\n open_clip_mock = MagicMock()\n open_clip_mock.create_model = create_model_mock\n\n with patch.dict('sys.modules', {'open_clip': open_clip_mock}):\n model = ClipWrapper('open_clip', model_name='RN50')\n create_model_mock.assert_called_once_with(model_name='RN50')\n self.assertEqual(model.model, mock_model)\n\n @patch('transformers.CLIPTextModel.from_pretrained')\n def test_huggingface_load(self, mock_from_pretrained):\n mock_model = MagicMock()\n mock_model.config = MagicMock()\n mock_from_pretrained.return_value = mock_model\n\n model = ClipWrapper(\n 'huggingface',\n pretrained_model_name_or_path='runwayml/stable-diffusion-v1-5',\n subfolder='text_encoder')\n mock_from_pretrained.assert_called_once_with(\n pretrained_model_name_or_path='runwayml/stable-diffusion-v1-5',\n subfolder='text_encoder')\n self.assertEqual(model.model, mock_model)\n self.assertEqual(model.model.config, mock_model.config)\n\n\ndef test_embedding_layer_with_fixes():\n embedding_layer = nn.Embedding(10, 15)\n print(embedding_layer.weight.shape)\n embedding_layer_wrapper = EmbeddingLayerWithFixes(embedding_layer)\n\n assert embedding_layer_wrapper.external_embeddings == []\n # test naive forward\n input_ids = torch.randint(0, 10, (3, 20)).type(torch.long)\n out_feat = embedding_layer_wrapper(input_ids)\n print(out_feat.shape)\n\n tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')\n # 'Goodbye' in kiswahili\n tokenizer.add_placeholder_token('kwaheri', num_vec_per_token=1)\n # 'how much' in kiswahili\n tokenizer.add_placeholder_token('ngapi', num_vec_per_token=4)\n\n # test add single embedding\n new_embedding = {\n 'name': 'kwaheri', # 'Goodbye' in kiswahili\n 'embedding': torch.ones(1, 15) * 4,\n 'start': tokenizer.get_token_info('kwaheri')['start'],\n 'end': tokenizer.get_token_info('kwaheri')['end']\n }\n embedding_layer_wrapper.add_embeddings(new_embedding)\n input_text = ['hello world, kwaheri!', 'hello world', 'kwaheri']\n input_ids = tokenizer(\n input_text, padding='max_length', truncation=True,\n return_tensors='pt')['input_ids']\n out_feat = embedding_layer_wrapper(input_ids)\n assert (out_feat[0, 4] == 4).all()\n assert (out_feat[2, 1] == 4).all()\n\n new_embedding = {\n 'name': 'ngapi', # 'how much' in kiswahili\n 'embedding': torch.ones(4, 15) * 2.3,\n 'start': tokenizer.get_token_info('ngapi')['start'],\n 'end': tokenizer.get_token_info('ngapi')['end']\n }\n embedding_layer_wrapper.add_embeddings(new_embedding)\n input_text = ['hello, ngapi!', 'hello world', 'kwaheri my friend, ngapi?']\n input_ids = tokenizer(\n input_text, padding='max_length', truncation=True,\n return_tensors='pt')['input_ids']\n out_feat = embedding_layer_wrapper(input_ids)\n assert (out_feat[0, 3:7] == 2.3).all()\n assert (out_feat[2, 5:9] == 2.3).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_dreambooth/test_dreambooth.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport platform\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\ntest_dir = osp.join(osp.dirname(__file__), '../../../..', 'tests')\nconfig_path = osp.join(test_dir, 'configs', 'diffuser_wrapper_cfg')\nmodel_path = osp.join(test_dir, 'configs', 'tmp_weight')\nckpt_path = osp.join(test_dir, 'configs', 'ckpt')\n\nregister_all_modules()\n\nstable_diffusion_tiny_url = 'diffusers/tiny-stable-diffusion-torch'\nval_prompts = ['a sks dog in basket']\n\nconfig = dict(\n type='DreamBooth',\n vae=dict(type='AutoencoderKL', sample_size=64),\n unet=dict(\n sample_size=64,\n type='UNet2DConditionModel',\n down_block_types=('DownBlock2D', ),\n up_block_types=('UpBlock2D', ),\n block_out_channels=(32, ),\n cross_attention_dim=16,\n ),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_tiny_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_tiny_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n dtype='fp32',\n data_preprocessor=dict(type='DataPreprocessor'),\n enable_xformers=False,\n val_prompts=val_prompts)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestControlStableDiffusion(TestCase):\n\n def setUp(self):\n # mock SiLU\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n from mmagic.models.editors.ddpm.denoising_unet import SiLU\n torch.nn.SiLU = SiLU\n dreambooth = MODELS.build(config)\n assert not any([p.requires_grad for p in dreambooth.vae.parameters()])\n assert not any(\n [p.requires_grad for p in dreambooth.text_encoder.parameters()])\n self.dreambooth = dreambooth\n\n def test_val_step(self):\n dreambooth = self.dreambooth\n data = dict(\n inputs=[torch.ones([3, 64, 64])],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(prompt, *args, **kwargs):\n length = len(prompt)\n return dict(samples=torch.randn(length, 3, 64, 64))\n\n encode_prompt = dreambooth._encode_prompt\n infer = dreambooth.infer\n dreambooth._encode_prompt = mock_encode_prompt\n dreambooth.infer = mock_infer\n\n output = dreambooth.val_step(data)\n assert len(output) == 1\n dreambooth._encode_prompt = encode_prompt\n dreambooth.infer = infer\n\n def test_test_step(self):\n dreambooth = self.dreambooth\n data = dict(\n inputs=[torch.ones([3, 64, 64])],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(prompt, *args, **kwargs):\n length = len(prompt)\n return dict(samples=torch.randn(length, 3, 64, 64))\n\n encode_prompt = dreambooth._encode_prompt\n infer = dreambooth.infer\n dreambooth._encode_prompt = mock_encode_prompt\n dreambooth.infer = mock_infer\n\n output = dreambooth.test_step(data)\n assert len(output) == 1\n dreambooth._encode_prompt = encode_prompt\n dreambooth.infer = infer\n\n def test_train_step(self):\n dreambooth = self.dreambooth\n data = dict(\n inputs=[torch.ones([3, 64, 64])],\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n\n optimizer = MagicMock()\n update_params = MagicMock()\n optimizer.update_params = update_params\n optim_wrapper = optimizer\n\n class mock_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n\n def forward(self, *args, **kwargs):\n return [torch.randn(1, 5, 16)]\n\n dreambooth.text_encoder = mock_text_encoder()\n\n dreambooth.train_step(data, optim_wrapper)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_duf/test_duf.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.duf import DynamicUpsamplingFilter\n\n\ndef test_dynamic_upsampling_filter():\n \"\"\"Test DynamicUpsamplingFilter.\"\"\"\n with pytest.raises(TypeError):\n # The type of filter_size must be tuple\n DynamicUpsamplingFilter(filter_size=3)\n with pytest.raises(ValueError):\n # The length of filter size must be 2\n DynamicUpsamplingFilter(filter_size=(3, 3, 3))\n\n duf = DynamicUpsamplingFilter(filter_size=(5, 5))\n x = torch.rand(1, 3, 4, 4)\n filters = torch.rand(1, 25, 16, 4, 4)\n output = duf(x, filters)\n assert output.shape == (1, 48, 4, 4)\n\n duf = DynamicUpsamplingFilter(filter_size=(3, 3))\n x = torch.rand(1, 3, 4, 4)\n filters = torch.rand(1, 9, 16, 4, 4)\n output = duf(x, filters)\n assert output.shape == (1, 48, 4, 4)\n\n # gpu (since it has dcn, only supports gpu testing)\n if torch.cuda.is_available():\n duf = DynamicUpsamplingFilter(filter_size=(3, 3)).cuda()\n x = torch.rand(1, 3, 4, 4).cuda()\n filters = torch.rand(1, 9, 16, 4, 4).cuda()\n output = duf(x, filters)\n assert output.shape == (1, 48, 4, 4)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_edsr/test_edsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import EDSRNet\n\n\ndef test_edsr_cpu():\n \"\"\"Test EDSRNet.\"\"\"\n\n # x2 model\n EDSRNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=2)\n # x3 model, initialization and forward (cpu)\n net = EDSRNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=3)\n img = torch.rand(1, 3, 12, 12)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 36, 36)\n # x4 modeland, initialization and forward (cpu)\n net = EDSRNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=4)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 48, 48)\n # gray x4 modeland, initialization and forward (cpu)\n net = EDSRNet(\n in_channels=1,\n out_channels=1,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=4,\n rgb_mean=[0],\n rgb_std=[1])\n gray = torch.rand((1, 1, 12, 12))\n output = net(gray)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 1, 48, 48)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_edsr_cuda():\n\n net = EDSRNet(\n in_channels=1,\n out_channels=1,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=4,\n rgb_mean=[0],\n rgb_std=[1])\n gray = torch.rand((1, 1, 12, 12))\n\n # x4 model forward (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(gray.cuda())\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 1, 48, 48)\n\n with pytest.raises(ValueError):\n # Currently supported upscale_factor is 2^n and 3\n EDSRNet(\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=5)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_edvr/test_edvr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import EDVR, EDVRNet\nfrom mmagic.models.losses import CharbonnierLoss\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_edvr():\n\n model = EDVR(\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_frames=5,\n deform_groups=1,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=True),\n pixel_loss=dict(\n type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict(tsa_iter=5000),\n data_preprocessor=dict(type='DataPreprocessor'))\n\n assert isinstance(model, EDVR)\n assert isinstance(model.generator, EDVRNet)\n assert isinstance(model.pixel_loss, CharbonnierLoss)\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # prepare data\n inputs = torch.rand(1, 5, 3, 20, 20)\n target = torch.rand(5, 3, 80, 80)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n log_vars = model.train_step(data, optim_wrapper)\n assert model.generator.conv_first.weight.requires_grad is False\n assert isinstance(log_vars, dict)\n\n model.step_counter = torch.tensor(5000)\n log_vars = model.train_step(data, optim_wrapper)\n assert model.generator.conv_first.weight.requires_grad is True\n assert isinstance(log_vars, dict)\n\n with pytest.raises(KeyError):\n model = EDVR(\n generator=dict(\n type='EDVRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_frames=5,\n deform_groups=1,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=True),\n pixel_loss=dict(\n type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),\n train_cfg=dict())\n model.train_step(data, optim_wrapper)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_edvr/test_edvr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.edvr.edvr_net import (EDVRNet, PCDAlignment,\n TSAFusion)\n\n\ndef test_pcd_alignment():\n \"\"\"Test PCDAlignment.\"\"\"\n\n # cpu\n pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2)\n input_list = []\n for i in range(3, 0, -1):\n input_list.append(torch.rand(1, 4, 2**i, 2**i))\n\n pcd_alignment = pcd_alignment\n input_list = [v for v in input_list]\n output = pcd_alignment(input_list, input_list)\n assert output.shape == (1, 4, 8, 8)\n\n with pytest.raises(AssertionError):\n pcd_alignment(input_list[0:2], input_list)\n\n # gpu\n if torch.cuda.is_available():\n pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2)\n input_list = []\n for i in range(3, 0, -1):\n input_list.append(torch.rand(1, 4, 2**i, 2**i))\n\n pcd_alignment = pcd_alignment.cuda()\n input_list = [v.cuda() for v in input_list]\n output = pcd_alignment(input_list, input_list)\n assert output.shape == (1, 4, 8, 8)\n\n with pytest.raises(AssertionError):\n pcd_alignment(input_list[0:2], input_list)\n\n\ndef test_tsa_fusion():\n \"\"\"Test TSAFusion.\"\"\"\n\n # cpu\n tsa_fusion = TSAFusion(mid_channels=4, num_frames=5, center_frame_idx=2)\n input_tensor = torch.rand(1, 5, 4, 8, 8)\n\n output = tsa_fusion(input_tensor)\n assert output.shape == (1, 4, 8, 8)\n\n # gpu\n if torch.cuda.is_available():\n tsa_fusion = tsa_fusion.cuda()\n input_tensor = input_tensor.cuda()\n output = tsa_fusion(input_tensor)\n assert output.shape == (1, 4, 8, 8)\n\n\ndef test_edvr_net():\n \"\"\"Test EDVRNet.\"\"\"\n\n # cpu\n\n # with tsa\n edvrnet = EDVRNet(\n 3,\n 3,\n mid_channels=8,\n num_frames=5,\n deform_groups=2,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=True)\n input_tensor = torch.rand(1, 5, 3, 8, 8)\n output = edvrnet(input_tensor)\n assert output.shape == (1, 3, 32, 32)\n\n # without tsa\n edvrnet = EDVRNet(\n 3,\n 3,\n mid_channels=8,\n num_frames=5,\n deform_groups=2,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=False)\n\n output = edvrnet(input_tensor)\n assert output.shape == (1, 3, 32, 32)\n\n with pytest.raises(AssertionError):\n # The height and width of inputs should be a multiple of 4\n input_tensor = torch.rand(1, 5, 3, 3, 3)\n edvrnet(input_tensor)\n\n # gpu\n if torch.cuda.is_available():\n # with tsa\n edvrnet = EDVRNet(\n 3,\n 3,\n mid_channels=8,\n num_frames=5,\n deform_groups=2,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=True).cuda()\n input_tensor = torch.rand(1, 5, 3, 8, 8).cuda()\n output = edvrnet(input_tensor)\n assert output.shape == (1, 3, 32, 32)\n\n # without tsa\n edvrnet = EDVRNet(\n 3,\n 3,\n mid_channels=8,\n num_frames=5,\n deform_groups=2,\n num_blocks_extraction=1,\n num_blocks_reconstruction=1,\n center_frame_idx=2,\n with_tsa=False).cuda()\n\n output = edvrnet(input_tensor)\n assert output.shape == (1, 3, 32, 32)\n\n with pytest.raises(AssertionError):\n # The height and width of inputs should be a multiple of 4\n input_tensor = torch.rand(1, 5, 3, 3, 3).cuda()\n edvrnet(input_tensor)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_camera.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import patch\n\nimport torch\nfrom mmengine.testing import assert_allclose\n\nfrom mmagic.models.editors.eg3d.camera import (BaseCamera, GaussianCamera,\n UniformCamera)\n\n\nclass TestBaseCamera(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n horizontal_mean=0,\n vertical_mean=3.141 / 2,\n horizontal_std=3.141 * 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n radius=1.3)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = BaseCamera(**cfg_)\n self.assertEqual(camera.sampling_statregy.upper(), 'UNIFORM')\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['sampling_strategy'] = 'gaussian'\n camera = BaseCamera(**cfg_)\n self.assertEqual(camera.sampling_statregy.upper(), 'GAUSSIAN')\n\n # test FOV and FOCAL is both passed\n cfg_ = deepcopy(self.default_cfg)\n cfg_['fov'] = 2333\n with self.assertRaises(AssertionError):\n BaseCamera(**cfg_)\n\n # test FOV and FOCAL is neither passed\n cfg_ = deepcopy(self.default_cfg)\n cfg_['focal'] = None\n camera = BaseCamera(**cfg_)\n self.assertIsNone(camera.focal)\n self.assertIsNone(camera.fov)\n\n def test_repr(self):\n\n cfg_ = deepcopy(self.default_cfg)\n camera = BaseCamera(**cfg_)\n repr_string = repr(camera)\n attribute_list = [\n 'horizontal_mean', 'vertical_mean', 'horizontal_std',\n 'vertical_std', 'focal', 'look_at', 'up', 'radius',\n 'sampling_statregy'\n ]\n for attr in attribute_list:\n self.assertIn(attr, repr_string)\n self.assertIn('BaseCamera', repr_string)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['focal'] = None\n camera = BaseCamera(**cfg_)\n repr_string = repr(camera)\n self.assertNotIn('focal', repr_string)\n self.assertNotIn('fov', repr_string)\n\n def test_sample_intrinsic(self):\n target = [1.025390625, 0.0, 0.5, 0.0, 1.025390625, 0.5, 0.0, 0.0, 1.0]\n target = torch.FloatTensor(target).reshape(3, 3)\n\n # test sample with default focal\n cfg_ = deepcopy(self.default_cfg)\n camera = BaseCamera(**cfg_)\n intrinsic = camera.sample_intrinsic(batch_size=3)\n assert_allclose(intrinsic, target[None, ...].repeat(3, 1, 1))\n\n # test sample with default FOV\n cfg_ = deepcopy(self.default_cfg)\n cfg_['focal'] = None\n cfg_['fov'] = 69.18818449595669\n camera = BaseCamera(**cfg_)\n intrinsic = camera.sample_intrinsic(batch_size=4)\n assert_allclose(intrinsic, target[None, ...].repeat(4, 1, 1))\n\n # test sample fov and focal is passed at the same time\n with self.assertRaises(AssertionError):\n intrinsic = camera.sample_intrinsic(fov=1, focal=2, batch_size=4)\n\n # test focal is passed\n intrinsic = camera.sample_intrinsic(focal=1, batch_size=4)\n self.assertTrue((intrinsic[:, 0, 0] == 1).all())\n self.assertTrue((intrinsic[:, 1, 1] == 1).all())\n\n # test fov is passed\n target_focal = float(1 / (math.tan(1 * math.pi / 360) * 1.414))\n intrinsic = camera.sample_intrinsic(fov=1, batch_size=4)\n self.assertTrue((intrinsic[:, 0, 0] == target_focal).all())\n self.assertTrue((intrinsic[:, 1, 1] == target_focal).all())\n\n # test focal and fov is not passed and not be initialized\n cfg_ = deepcopy(self.default_cfg)\n cfg_['focal'] = None\n camera = BaseCamera(**cfg_)\n with self.assertRaises(ValueError):\n camera.sample_intrinsic(batch_size=4)\n\n def test_sample_camera2world(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = BaseCamera(**cfg_)\n cam2world = camera.sample_camera2world()\n self.assertEqual(cam2world.shape, (1, 4, 4))\n\n mock_path = 'mmagic.models.editors.eg3d.camera.TORCH_VERSION'\n with patch(mock_path, '1.6.0'):\n print(torch.__version__)\n cfg_ = deepcopy(self.default_cfg)\n camera = BaseCamera(**cfg_)\n cam2world = camera.sample_camera2world()\n self.assertEqual(cam2world.shape, (1, 4, 4))\n\n def test_sample_in_range(self):\n cfg_ = deepcopy(self.default_cfg)\n cfg_['sampling_strategy'] = 'unknow'\n camera = BaseCamera(**cfg_)\n with self.assertRaises(ValueError):\n camera._sample_in_range(1, 1, 1)\n\n\nclass TestUniformCamera(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n horizontal_mean=0,\n vertical_mean=3.141 / 2,\n horizontal_std=3.141 * 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n radius=1.3)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = UniformCamera(**cfg_)\n self.assertEqual(camera.sampling_statregy.upper(), 'UNIFORM')\n\n def test_repr(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = UniformCamera(**cfg_)\n repr_string = repr(camera)\n self.assertIn('UniformCamera', repr_string)\n\n\nclass TestGaussianCamera(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n horizontal_mean=0,\n vertical_mean=3.141 / 2,\n horizontal_std=3.141 * 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n radius=1.3)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = GaussianCamera(**cfg_)\n self.assertEqual(camera.sampling_statregy.upper(), 'GAUSSIAN')\n\n def test_repr(self):\n cfg_ = deepcopy(self.default_cfg)\n camera = GaussianCamera(**cfg_)\n repr_string = repr(camera)\n self.assertIn('GaussianCamera', repr_string)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_dual_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.eg3d.dual_discriminator import DualDiscriminator\n\n\nclass TestEG3DDiscriminator(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(in_size=32, cond_size=25)\n\n def test_init(self):\n cfg = deepcopy(self.default_cfg)\n disc = DualDiscriminator(**cfg)\n self.assertEqual(disc.convs[0][0].conv.weight.shape[1], 6)\n self.assertTrue(disc.use_dual_disc)\n\n cfg = deepcopy(self.default_cfg)\n cfg['use_dual_disc'] = False\n cfg['img_channels'] = 2\n disc = DualDiscriminator(**cfg)\n self.assertEqual(disc.convs[0][0].conv.weight.shape[1], 2)\n self.assertFalse(disc.use_dual_disc)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_forward(self):\n cfg = deepcopy(self.default_cfg)\n disc = DualDiscriminator(**cfg)\n img, img_raw = torch.randn(2, 3, 32, 32), torch.randn(2, 3, 16, 16)\n cond = torch.randn(2, 25)\n out = disc(img, img_raw, cond)\n self.assertEqual(out.shape, (2, 1))\n # test raise error with img_raw is None\n with self.assertRaises(AssertionError):\n disc(img, None, cond)\n\n # test cond_noise is not None\n cfg = deepcopy(self.default_cfg)\n cfg['disc_c_noise'] = 0.2\n disc = DualDiscriminator(**cfg)\n img, img_raw = torch.randn(2, 3, 32, 32), torch.randn(2, 3, 16, 16)\n cond = torch.randn(2, 25)\n out = disc(img, img_raw, cond)\n self.assertEqual(out.shape, (2, 1))\n\n # test input_bgr2rgb\n cfg = deepcopy(self.default_cfg)\n cfg['input_bgr2rgb'] = True\n disc = DualDiscriminator(**cfg)\n img, img_raw = torch.randn(2, 3, 32, 32), torch.randn(2, 3, 16, 16)\n cond = torch.randn(2, 25)\n out = disc(img, img_raw, cond)\n self.assertEqual(out.shape, (2, 1))\n\n # test img_raw is None + use_dual_disc is False\n cfg = deepcopy(self.default_cfg)\n cfg['use_dual_disc'] = False\n disc = DualDiscriminator(**cfg)\n img = torch.randn(2, 3, 32, 32)\n cond = torch.randn(2, 25)\n out = disc(img, None, cond)\n self.assertEqual(out.shape, (2, 1))\n\n # test img_raw is None + use_dual_disc is False + input_bgr2rgb\n cfg = deepcopy(self.default_cfg)\n cfg['use_dual_disc'] = False\n cfg['input_bgr2rgb'] = True\n disc = DualDiscriminator(**cfg)\n img = torch.randn(2, 3, 32, 32)\n cond = torch.randn(2, 25)\n out = disc(img, None, cond)\n self.assertEqual(out.shape, (2, 1))\n\n # test cond is None\n cfg = deepcopy(self.default_cfg)\n cfg['cond_size'] = None\n disc = DualDiscriminator(**cfg)\n out = disc(img, img_raw, None)\n self.assertEqual(out.shape, (2, 1))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_eg3d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors.eg3d.eg3d import EG3D\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestEG3D(TestCase):\n\n def setUp(self):\n self.generator_cfg = dict(\n type='TriplaneGenerator',\n out_size=32,\n noise_size=8,\n style_channels=8,\n num_mlps=1,\n triplane_size=8,\n triplane_channels=4,\n sr_in_size=8,\n sr_in_channels=8,\n neural_rendering_resolution=5,\n cond_scale=1,\n renderer_cfg=dict(\n ray_start=0.1,\n ray_end=2.6,\n box_warp=1.6,\n depth_resolution=4,\n white_back=True,\n depth_resolution_importance=4,\n ),\n rgb2bgr=True)\n self.camera_cfg = dict(\n type='UniformCamera',\n horizontal_mean=3.141,\n horizontal_std=3.141,\n vertical_mean=3.141 / 2,\n vertical_std=3.141 / 2,\n focal=1.025390625,\n up=[0, 0, 1],\n radius=1.2)\n # self.discriminator_cfg = dict()\n self.default_cfg = dict(\n generator=self.generator_cfg,\n camera=self.camera_cfg,\n data_preprocessor=dict(type='DataPreprocessor'))\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n model = EG3D(**cfg_)\n self.assertEqual(model.noise_size, 8)\n self.assertEqual(model.num_classes, None)\n self.assertIsNotNone(model.camera)\n\n # test camera is None\n cfg_ = deepcopy(self.default_cfg)\n cfg_['camera'] = None\n model = EG3D(**cfg_)\n self.assertIsNone(model.camera)\n\n # test camers is module\n camera_mock = MagicMock(spec=nn.Module)\n cfg_['camera'] = camera_mock\n model = EG3D(**cfg_)\n self.assertEqual(model.camera, camera_mock)\n\n def test_label_fn(self):\n # test camera is None\n cfg_ = deepcopy(self.default_cfg)\n cfg_['camera'] = None\n model = EG3D(**cfg_)\n label = torch.randn(1, 25)\n self.assertTrue((label == model.label_fn(label)).all())\n\n with self.assertRaises(AssertionError):\n model.label_fn(None)\n\n def _check_datasample_output(self, outputs, out_size, n_points):\n target_keys = [\n 'fake_img', 'depth', 'lr_img', 'ray_origins', 'ray_directions'\n ]\n target_shape = [(3, out_size, out_size), (1, n_points, n_points),\n (3, n_points, n_points), (n_points**2, 3),\n (n_points**2, 3)]\n for output in outputs:\n for key, shape in zip(target_keys, target_shape):\n self.assertTrue(hasattr(output, key))\n self.assertEqual(getattr(output, key).shape, shape)\n\n def _check_dict_output(self, outputs, out_size, n_points, bz):\n target_keys = [\n 'fake_img', 'depth', 'lr_img', 'ray_origins', 'ray_directions'\n ]\n target_shape = [(bz, 3, out_size, out_size),\n (bz, 1, n_points, n_points),\n (bz, 3, n_points, n_points), (bz, n_points**2, 3),\n (bz, n_points**2, 3)]\n for output in outputs:\n for key, shape in zip(target_keys, target_shape):\n self.assertIn(key, output)\n self.assertEqual(output[key].shape, shape)\n\n def test_forward(self):\n cfg_ = deepcopy(self.default_cfg)\n model = EG3D(**cfg_)\n outputs = model(dict(num_batches=4))\n self.assertEqual(len(outputs), 4)\n\n # test label is passed\n data_samples = []\n for _ in range(4):\n data_sample = DataSample()\n data_sample.set_gt_label(torch.randn(25))\n data_samples.append(data_sample)\n data_samples = DataSample.stack(data_samples)\n outputs = model(dict(num_batches=4), data_samples)\n self.assertEqual(len(outputs), 4)\n self._check_datasample_output(outputs, 32, 5)\n\n # test noise is passed\n outputs = model(torch.randn(4, 8))\n self.assertEqual(len(outputs), 4)\n self._check_datasample_output(outputs, 32, 5)\n\n # test orig/ema\n cfg_ = deepcopy(self.default_cfg)\n cfg_['ema_config'] = dict(interval=1)\n model = EG3D(**cfg_)\n outputs = model(dict(num_batches=4, sample_model='ema/orig'))\n self.assertEqual(len(outputs), 4)\n self.assertTrue(all([hasattr(output, 'orig') for output in outputs]))\n self.assertTrue(all([hasattr(output, 'ema') for output in outputs]))\n self._check_datasample_output([output.orig for output in outputs], 32,\n 5)\n self._check_datasample_output([output.ema for output in outputs], 32,\n 5)\n\n def test_interpolation(self):\n cfg_ = deepcopy(self.default_cfg)\n model = EG3D(**cfg_)\n output_list = model.interpolation(num_images=3, num_batches=2)\n self.assertEqual(len(output_list), 3)\n self._check_dict_output(output_list, 32, 5, 2)\n\n output_list = model.interpolation(\n num_images=3, num_batches=2, mode='camera', show_pbar=False)\n self.assertEqual(len(output_list), 3)\n self._check_dict_output(output_list, 32, 5, 2)\n\n output_list = model.interpolation(\n num_images=3, num_batches=2, mode='conditioning', show_pbar=False)\n self.assertEqual(len(output_list), 3)\n self._check_dict_output(output_list, 32, 5, 2)\n\n # test ema\n cfg_ = deepcopy(self.default_cfg)\n cfg_['ema_config'] = dict(interval=1)\n model = EG3D(**cfg_)\n\n output_list = model.interpolation(\n num_images=3, num_batches=2, sample_model='ema')\n self.assertEqual(len(output_list), 3)\n self._check_dict_output(output_list, 32, 5, 2)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_eg3d_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import Mock, patch\n\nimport torch\n\nfrom mmagic.models.editors.eg3d.eg3d_generator import TriplaneGenerator\n\n\nclass TestEG3DGenerator(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n out_size=32,\n noise_size=8,\n style_channels=8,\n num_mlps=1,\n triplane_size=8,\n triplane_channels=4,\n sr_in_size=8,\n sr_in_channels=8,\n neural_rendering_resolution=5,\n cond_scale=1,\n renderer_cfg=dict(\n ray_start=0.1,\n ray_end=2.6,\n box_warp=1.6,\n depth_resolution=4,\n white_back=True,\n depth_resolution_importance=4,\n ),\n rgb2bgr=True)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n gen = TriplaneGenerator(**cfg_)\n\n # check decoder in/out channels\n decoder_in_chns = gen.renderer.decoder.net[0].weight.shape[1]\n decoder_out_chns = gen.renderer.decoder.net[-1].weight.shape[0]\n self.assertEqual(decoder_in_chns, 4)\n self.assertEqual(decoder_out_chns, 8 + 1) # (sr_in_channels + 1)\n\n # test sr_factor not in [2, 4, 8]\n cfg_ = deepcopy(self.default_cfg)\n cfg_['out_size'] = 64\n with self.assertRaises(AssertionError):\n TriplaneGenerator(**cfg_)\n\n cfg_['out_size'] = 10\n with self.assertRaises(AssertionError):\n TriplaneGenerator(**cfg_)\n\n def test_forward(self):\n cfg_ = deepcopy(self.default_cfg)\n gen = TriplaneGenerator(**cfg_)\n noise = torch.randn(2, 8)\n cond = torch.randn(2, 25)\n out = gen(noise, cond)\n self.assertEqual(out['fake_img'].shape, (2, 3, 32, 32))\n self.assertEqual(out['lr_img'].shape, (2, 3, 5, 5))\n self.assertEqual(out['depth'].shape, (2, 1, 5, 5))\n self.assertEqual(out['ray_directions'].shape, (2, 25, 3))\n self.assertEqual(out['ray_origins'].shape, (2, 25, 3))\n\n # test render_kwargs is work in forward\n render_kwargs = dict(a=1, b='b')\n render_mock = Mock(\n return_value=(torch.randn(2, 25, 8), torch.randn(2, 25, 1), None))\n patch_func = 'mmagic.models.editors.eg3d.renderer.EG3DRenderer.forward'\n with patch(patch_func, new=render_mock):\n gen = TriplaneGenerator(**cfg_)\n gen(noise, cond, render_kwargs=render_kwargs)\n _, called_kwargs = render_mock.call_args\n self.assertIn('render_kwargs', called_kwargs)\n self.assertDictEqual(called_kwargs['render_kwargs'], render_kwargs)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_eg3d_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\n\nfrom mmagic.models.editors.eg3d.eg3d_modules import (SuperResolutionModule,\n TriPlaneBackbone)\n\n\nclass TestTriPlaneBackbone(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n out_size=32,\n noise_size=10,\n out_channels=9,\n style_channels=16,\n num_mlps=2,\n cond_size=4,\n cond_scale=1,\n cond_mapping_channels=4,\n zero_cond_input=False)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n backbone = TriPlaneBackbone(**cfg_)\n self.assertEqual(backbone.cond_scale, 1)\n self.assertEqual(backbone.zero_cond_input, False)\n\n def test_mapping(self):\n cfg_ = deepcopy(self.default_cfg)\n backbone = TriPlaneBackbone(**cfg_)\n noise = torch.randn(2, 10)\n label = torch.randn(2, 4)\n style_code = backbone.mapping(noise, label)\n self.assertEqual(style_code.shape, (2, 16))\n\n # test truncation < 1\n style_code = backbone.mapping(noise, label, truncation=0.5)\n self.assertEqual(style_code.shape, (2, 16))\n style_code = backbone.mapping(noise, label, truncation=0.5)\n self.assertEqual(style_code.shape, (2, 16))\n\n # test zero_cond_input + label is None\n cfg_ = deepcopy(self.default_cfg)\n cfg_['zero_cond_input'] = True\n backbone = TriPlaneBackbone(**cfg_)\n style_code = backbone.mapping(noise)\n self.assertEqual(style_code.shape, (2, 16))\n\n # test zero_cond_input + label is not None\n style_code = backbone.mapping(noise, label)\n self.assertEqual(style_code.shape, (2, 16))\n\n # test cond_size < 0\n cfg_ = deepcopy(self.default_cfg)\n cfg_['cond_size'] = None\n backbone = TriPlaneBackbone(**cfg_)\n # noise = torch.randn(2, 10)\n style_code = backbone.mapping(noise)\n self.assertEqual(style_code.shape, (2, 16))\n\n def test_synthesis(self):\n cfg_ = deepcopy(self.default_cfg)\n backbone = TriPlaneBackbone(**cfg_)\n noise = torch.randn(2, 10)\n label = torch.randn(2, 4)\n style_code = backbone.mapping(noise, label)\n outputs_forward = backbone(noise, label)\n outputs_synthesis = backbone.synthesis(style_code)\n self.assertTrue((outputs_forward == outputs_synthesis).all())\n\n\nclass TestSuperResolutionModule(TestCase):\n\n def setUp(self):\n self.default_cfg = dict(\n in_channels=8,\n in_size=4,\n hidden_size=8,\n out_size=8,\n style_channels=5,\n sr_antialias=True)\n\n def test_init(self):\n cfg_ = deepcopy(self.default_cfg)\n sr_model = SuperResolutionModule(**cfg_)\n self.assertTrue(sr_model.block0.upsample)\n self.assertFalse(sr_model.block1.upsample)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['hidden_size'] = 4\n sr_model = SuperResolutionModule(**cfg_)\n self.assertFalse(sr_model.block0.upsample)\n self.assertTrue(sr_model.block1.upsample)\n\n def test_forward(self):\n cfg_ = deepcopy(self.default_cfg)\n sr_model = SuperResolutionModule(**cfg_)\n styles = torch.randn(2, 5)\n imgs = torch.randn(2, 3, 4, 4)\n features = torch.randn(2, 8, 4, 4)\n sr_imgs = sr_model(imgs, features, styles)\n self.assertEqual(sr_imgs.shape, (2, 3, 8, 8))\n\n # test style is a list\n styles = [torch.randn(2, 5)]\n imgs = torch.randn(2, 3, 4, 4)\n features = torch.randn(2, 8, 4, 4)\n sr_imgs = sr_model(imgs, features, styles)\n self.assertEqual(sr_imgs.shape, (2, 3, 8, 8))\n\n # test style's ndim is 3\n styles = torch.randn(3, 2, 5)\n imgs = torch.randn(2, 3, 4, 4)\n features = torch.randn(2, 8, 4, 4)\n sr_imgs = sr_model(imgs, features, styles)\n self.assertEqual(sr_imgs.shape, (2, 3, 8, 8))\n\n # test interpolation\n imgs = torch.randn(2, 3, 3, 3)\n features = torch.randn(2, 8, 3, 3)\n sr_imgs = sr_model(imgs, features, styles)\n self.assertEqual(sr_imgs.shape, (2, 3, 8, 8))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_eg3d_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.eg3d.eg3d_utils import (get_ray_limits_box,\n inverse_transform_sampling,\n linspace_batch)\n\n\ndef test_get_ray_limits_box_and_linspace_batch():\n rays_o = torch.rand(2, 4, 4, 3)\n rays_d = torch.rand(2, 4, 4, 3)\n rays_start, rays_end = get_ray_limits_box(rays_o, rays_d, 0.5)\n assert rays_start.shape == (2, 4, 4, 1)\n assert rays_end.shape == (2, 4, 4, 1)\n\n # linspace\n sampled_depth = linspace_batch(rays_start, rays_end, 5)\n assert sampled_depth.shape == (5, 2, 4, 4, 1)\n\n\ndef test_inverse_transform_sampling():\n bins = torch.randn(10, 11)\n weights = torch.randn(10, 9)\n n_importance = 5\n samples = inverse_transform_sampling(bins, weights, n_importance)\n assert samples.shape == (10, 5)\n\n samples_det_1 = inverse_transform_sampling(\n bins, weights, n_importance, deterministic=True)\n samples_det_2 = inverse_transform_sampling(\n bins, weights, n_importance, deterministic=True)\n assert samples_det_1.shape == (10, 5)\n assert samples_det_2.shape == (10, 5)\n assert (samples_det_1 == samples_det_2).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_ray_sampler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.eg3d.ray_sampler import sample_rays\n\n\ndef test_sample_rays():\n cond1 = [\n 0.5186675190925598, -0.8265361189842224, -0.21868225932121277,\n 0.2842874228954315, 0.8549759387969971, 0.5014145970344543,\n 0.13266240060329437, -0.17246174812316895, 2.3841852225814364e-07,\n -0.2557757496833801, 0.9667359590530396, -1.2567564249038696, -0.0,\n 0.0, -0.0, 1.0, 1.025390625, 0.0, 0.5, 0.0, 1.025390625, 0.5, 0.0, 0.0,\n 1.0\n ]\n cond2 = [\n 0.828181266784668, -0.29340365529060364, -0.47752493619918823,\n 0.6207822561264038, 0.5604602694511414, 0.43355682492256165,\n 0.7056292295455933, -0.9173181653022766, -2.9802322387695312e-08,\n -0.8520227670669556, 0.5235047340393066, -0.680556058883667, -0.0, 0.0,\n -0.0, 1.0, 1.025390625, 0.0, 0.5, 0.0, 1.025390625, 0.5, 0.0, 0.0, 1.0\n ]\n cond1, cond2 = torch.FloatTensor(cond1), torch.FloatTensor(cond2)\n cond = torch.stack((cond1, cond2))\n cam2world = cond[:, :16].view(-1, 4, 4)\n intrinsics = cond[:, 16:25].view(-1, 3, 3)\n resolution = 16\n ray_origins, ray_directions = sample_rays(cam2world, intrinsics,\n resolution)\n assert ray_origins.shape[:2] == (2, resolution**2)\n assert ray_directions.shape[:2] == (2, resolution**2)\n # check if camera origin in one batch is all same\n for origin in ray_origins:\n assert (origin == origin[0]).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_eg3d/test_renderer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\nfrom unittest.mock import Mock, patch\n\nimport torch\n\nfrom mmagic.models.editors.eg3d.renderer import EG3DDecoder, EG3DRenderer\n\n\nclass TestEG3DRenderer(TestCase):\n\n def setUp(self) -> None:\n self.n_tri, self.tri_feat, self.tri_res = 3, 16, 8\n self.nerf_res = 10\n self.decoder_out_channels = 5\n self.decoder_rgb_padding = 0.233\n\n self.decoder_cfg = dict(\n in_channels=self.tri_feat,\n hidden_channels=10,\n out_channels=self.decoder_out_channels,\n rgb_padding=self.decoder_rgb_padding)\n self.renderer_cfg = dict(\n ray_start=0.1,\n ray_end=2.6,\n box_warp=1.6,\n depth_resolution=5,\n white_back=True,\n depth_resolution_importance=10,\n decoder_cfg=deepcopy(self.decoder_cfg))\n\n def test_init(self):\n cfg_ = deepcopy(self.renderer_cfg)\n renderer = EG3DRenderer(**cfg_)\n self.assertIsInstance(renderer.decoder, EG3DDecoder)\n self.assertEqual(renderer.decoder.rgb_padding,\n self.decoder_rgb_padding)\n\n def test_forward(self):\n nerf_res = self.nerf_res\n n_tri, tri_feat, tri_res = self.n_tri, self.tri_feat, self.tri_res\n\n plane = torch.randn(4, n_tri, tri_feat, tri_res, tri_res)\n ray_origins = torch.randn(4, nerf_res * nerf_res, 3)\n ray_directions = torch.randn(4, nerf_res * nerf_res, 3)\n\n cfg_ = deepcopy(self.renderer_cfg)\n renderer = EG3DRenderer(**cfg_)\n rgb, depth, weights = renderer(plane, ray_origins, ray_directions)\n\n self.assertEqual(rgb.shape,\n (4, nerf_res * nerf_res, self.decoder_out_channels))\n self.assertEqual(depth.shape, (4, nerf_res * nerf_res, 1))\n self.assertEqual(weights.shape, (4, nerf_res * nerf_res, 1))\n\n # test white_back is True + density_noise > 0\n cfg_ = deepcopy(self.renderer_cfg)\n cfg_['white_back'] = False\n cfg_['density_noise'] = 2\n renderer = EG3DRenderer(**cfg_)\n rgb, depth, weights = renderer(plane, ray_origins, ray_directions)\n\n self.assertEqual(rgb.shape,\n (4, nerf_res * nerf_res, self.decoder_out_channels))\n self.assertEqual(depth.shape, (4, nerf_res * nerf_res, 1))\n self.assertEqual(weights.shape, (4, nerf_res * nerf_res, 1))\n\n # test ray_start and end is auto\n cfg_ = deepcopy(self.renderer_cfg)\n cfg_['ray_start'] = cfg_['ray_end'] = 'auto'\n renderer = EG3DRenderer(**cfg_)\n rgb, depth, weights = renderer(plane, ray_origins, ray_directions)\n\n self.assertEqual(rgb.shape,\n (4, nerf_res * nerf_res, self.decoder_out_channels))\n self.assertEqual(depth.shape, (4, nerf_res * nerf_res, 1))\n self.assertEqual(weights.shape, (4, nerf_res * nerf_res, 1))\n\n # test clamp mode is valid\n cfg_ = deepcopy(self.renderer_cfg)\n cfg_['clamp_mode'] = 'unsupport'\n renderer = EG3DRenderer(**cfg_)\n with self.assertRaises(AssertionError):\n renderer(plane, ray_origins, ray_directions)\n\n # test without hierarchical sampling + test render_kwargs\n render_kwargs = dict(depth_resolution_importance=0)\n mock_func = Mock(return_value=(rgb, depth, weights[..., None]))\n mock_path = ('mmagic.models.editors.eg3d.renderer.EG3DRenderer.'\n 'volume_rendering')\n with patch(mock_path, new=mock_func):\n renderer = EG3DRenderer(**cfg_)\n renderer(\n plane,\n ray_origins,\n ray_directions,\n render_kwargs=render_kwargs)\n mock_func.assert_called_once()\n\n # cover TORCH_VERSION < 1.8.0\n mock_path = 'mmagic.models.editors.eg3d.renderer.TORCH_VERSION'\n with patch(mock_path, '1.6.0'):\n cfg_ = deepcopy(self.renderer_cfg)\n renderer = EG3DRenderer(**cfg_)\n renderer(\n plane,\n ray_origins,\n ray_directions,\n render_kwargs=render_kwargs)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_esrgan/test_esrgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import patch\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import ESRGAN, DataPreprocessor, ModifiedVGG, RRDBNet\nfrom mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss, PerceptualVGG\nfrom mmagic.structures import DataSample\n\n\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_esrgan(init_weights):\n\n model = ESRGAN(\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_blocks=4,\n growth_channels=4,\n upscale_factor=4),\n discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=64),\n pixel_loss=dict(type='L1Loss', loss_weight=1e-2, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'34': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-3,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=None,\n test_cfg=None,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, ESRGAN)\n assert isinstance(model.generator, RRDBNet)\n assert isinstance(model.discriminator, ModifiedVGG)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.perceptual_loss, PerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 32, 32)\n target = torch.rand(3, 128, 128)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.data.shape == (3, 128, 128)\n\n # feat\n output = model(torch.rand(1, 3, 32, 32), mode='tensor')\n assert output.shape == (1, 3, 128, 128)\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_esrgan/test_rrdb_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport pytest\nimport torch\n\nfrom mmagic.models import RRDBNet\n\n\ndef test_rrdbnet_backbone():\n \"\"\"Test RRDBNet backbone.\"\"\"\n\n # model, initialization and forward (cpu)\n # x4 model\n net = RRDBNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n growth_channels=4,\n upscale_factor=4)\n input_shape = (1, 3, 12, 12)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 3, 48, 48)\n\n # x3 model\n with pytest.raises(ValueError):\n net = RRDBNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n growth_channels=4,\n upscale_factor=3)\n\n # x2 model\n net = RRDBNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n growth_channels=4,\n upscale_factor=2)\n input_shape = (1, 3, 12, 12)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 3, 24, 24)\n\n # model forward (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 3, 24, 24)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_fastcomposer/test_fastcomposer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport gc\nimport platform\nfrom unittest import TestCase\n\nimport numpy as np\nimport pytest\nimport torch\nfrom PIL import Image\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\ngc.collect()\ntorch.cuda.empty_cache()\nregister_all_modules()\nstable_diffusion_tiny_url = 'diffusers/tiny-stable-diffusion-torch'\nfinetuned_model_path = ''\n\nvision_config = dict(\n attention_dropout=0.0,\n dropout=0.0,\n hidden_act='quick_gelu',\n hidden_size=1024,\n image_size=64,\n initializer_factor=1.0,\n initializer_range=0.02,\n intermediate_size=64,\n layer_norm_eps=1e-05,\n model_type='clip_vision_model',\n num_attention_heads=16,\n num_channels=3,\n num_hidden_layers=24,\n patch_size=14,\n projection_dim=768,\n transformers_version='4.29.1')\n\nconfig = dict(\n type='FastComposer',\n vae=dict(type='AutoencoderKL', sample_size=64),\n unet=dict(\n sample_size=64,\n type='UNet2DConditionModel',\n down_block_types=('DownBlock2D', ),\n up_block_types=('UpBlock2D', ),\n block_out_channels=(32, ),\n cross_attention_dim=16,\n ),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_tiny_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_tiny_url,\n pretrained_cfg=dict(\n finetuned_model_path=finetuned_model_path,\n enable_xformers_memory_efficient_attention=None,\n pretrained_model_name_or_path=stable_diffusion_tiny_url,\n image_encoder=vision_config,\n revision=None,\n non_ema_revision=None,\n object_localization=None,\n object_localization_weight=0.01,\n localization_layers=5,\n mask_loss=None,\n mask_loss_prob=0.5,\n object_localization_threshold=1.0,\n object_localization_normalize=None,\n no_object_augmentation=True,\n object_resolution=64),\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n dtype='fp32',\n data_preprocessor=dict(type='DataPreprocessor'))\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\nclass TestFastComposer(TestCase):\n\n def setUp(self):\n self.fastcomposer = MODELS.build(config)\n\n def test_infer(self):\n fastcomposer = self.fastcomposer\n\n def mock_encode_prompt(prompt, do_classifier_free_guidance,\n num_images_per_prompt, *args, **kwargs):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n batch_size *= num_images_per_prompt\n if do_classifier_free_guidance:\n batch_size *= 2\n return torch.randn(batch_size, 5, 16) # 2 for cfg\n\n encode_prompt = fastcomposer._encode_prompt\n fastcomposer._encode_prompt = mock_encode_prompt\n\n prompt = 'A man img'\n negative_prompt = ''\n alpha_ = 0.75\n guidance_scale = 5\n num_steps = 1\n num_images = 1\n image = []\n seed = -1\n augmented_prompt_embeds = torch.randn(1, 5, 16)\n image.append(\n Image.fromarray(\n np.random.randint(0, 255, size=(64, 64, 3)).astype('uint8')))\n\n if len(image) == 0:\n raise Exception('You need to upload at least one image.')\n\n num_subject_in_text = (np.array(\n self.fastcomposer.special_tokenizer.encode(prompt)) ==\n self.fastcomposer.image_token_id).sum()\n if num_subject_in_text != len(image):\n raise Exception(\n \"Number of subjects in the text description doesn't match \"\n 'the number of reference images, #text subjects: '\n f'{num_subject_in_text} #reference image: {len(image)}', )\n\n if seed == -1:\n seed = np.random.randint(0, 1000000)\n\n generator = torch.manual_seed(seed)\n\n result = fastcomposer.infer(\n prompt,\n negative_prompt=negative_prompt,\n height=64,\n width=64,\n num_inference_steps=num_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images,\n generator=generator,\n alpha_=alpha_,\n reference_subject_images=image,\n augmented_prompt_embeds=augmented_prompt_embeds,\n output_type='tensor')\n fastcomposer._encode_prompt = encode_prompt\n assert result['samples'].shape == (num_images, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_fba/test_fba_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import FBADecoder\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef test_fba_decoder():\n\n with pytest.raises(AssertionError):\n # pool_scales must be list|tuple\n FBADecoder(pool_scales=1, in_channels=32, channels=16)\n inputs = dict()\n conv_out_1 = _demo_inputs((1, 11, 320, 320))\n conv_out_2 = _demo_inputs((1, 64, 160, 160))\n conv_out_3 = _demo_inputs((1, 256, 80, 80))\n conv_out_4 = _demo_inputs((1, 512, 40, 40))\n conv_out_5 = _demo_inputs((1, 1024, 40, 40))\n conv_out_6 = _demo_inputs((1, 2048, 40, 40))\n inputs['conv_out'] = [\n conv_out_1, conv_out_2, conv_out_3, conv_out_4, conv_out_5, conv_out_6\n ]\n inputs['merged'] = _demo_inputs((1, 3, 320, 320))\n inputs['two_channel_trimap'] = _demo_inputs((1, 2, 320, 320))\n model = FBADecoder(\n pool_scales=(1, 2, 3, 6),\n in_channels=2048,\n channels=256,\n norm_cfg=dict(type='GN', num_groups=32))\n\n alpha, F, B = model(inputs)\n assert_tensor_with_shape(alpha, torch.Size([1, 1, 320, 320]))\n assert_tensor_with_shape(F, torch.Size([1, 3, 320, 320]))\n assert_tensor_with_shape(B, torch.Size([1, 3, 320, 320]))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_fba/test_fba_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import FBAResnetDilated\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef test_fba_encoder():\n \"\"\"Test FBA encoder.\"\"\"\n\n with pytest.raises(KeyError):\n # ResNet depth should be in [18, 34, 50, 101, 152]\n FBAResnetDilated(\n 20,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n )\n\n with pytest.raises(AssertionError):\n # In ResNet: 1 <= num_stages <= 4\n FBAResnetDilated(\n 50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n num_stages=0)\n\n with pytest.raises(AssertionError):\n # In ResNet: 1 <= num_stages <= 4\n FBAResnetDilated(\n 50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n num_stages=5)\n\n with pytest.raises(AssertionError):\n # len(strides) == len(dilations) == num_stages\n FBAResnetDilated(\n 50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n strides=(1, ),\n dilations=(1, 1),\n num_stages=3)\n\n with pytest.raises(TypeError):\n # pretrained must be a string path\n model = FBAResnetDilated(\n 50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n )\n model.init_weights(pretrained=233)\n\n model = FBAResnetDilated(\n depth=50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n conv_cfg=dict(type='ConvWS'),\n norm_cfg=dict(type='GN', num_groups=32))\n\n model.init_weights()\n model.train()\n\n input = _demo_inputs((1, 14, 320, 320))\n\n output = model(input)\n\n assert 'conv_out' in output.keys()\n assert 'merged' in output.keys()\n assert 'two_channel_trimap' in output.keys()\n\n assert isinstance(output['conv_out'], list)\n assert len(output['conv_out']) == 6\n\n assert isinstance(output['merged'], torch.Tensor)\n assert_tensor_with_shape(output['merged'], torch.Size([1, 3, 320, 320]))\n\n assert isinstance(output['two_channel_trimap'], torch.Tensor)\n assert_tensor_with_shape(output['two_channel_trimap'],\n torch.Size([1, 2, 320, 320]))\n if torch.cuda.is_available():\n model = FBAResnetDilated(\n depth=50,\n in_channels=11,\n stem_channels=64,\n base_channels=64,\n conv_cfg=dict(type='ConvWS'),\n norm_cfg=dict(type='GN', num_groups=32))\n model.init_weights()\n model.train()\n model.cuda()\n\n input = _demo_inputs((1, 14, 320, 320)).cuda()\n output = model(input)\n\n assert 'conv_out' in output.keys()\n assert 'merged' in output.keys()\n assert 'two_channel_trimap' in output.keys()\n\n assert isinstance(output['conv_out'], list)\n assert len(output['conv_out']) == 6\n\n assert isinstance(output['merged'], torch.Tensor)\n assert_tensor_with_shape(output['merged'], torch.Size([1, 3, 320,\n 320]))\n\n assert isinstance(output['two_channel_trimap'], torch.Tensor)\n assert_tensor_with_shape(output['two_channel_trimap'],\n torch.Size([1, 2, 320, 320]))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_flavr/test_flavr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import FLAVR, FLAVRNet\n\n\ndef test_flavr():\n\n model = FLAVR(\n generator=dict(\n type='FLAVRNet',\n num_input_frames=4,\n num_output_frames=2,\n mid_channels_list=[64, 32, 16, 8],\n encoder_layers_list=[1, 1, 1, 1],\n bias=False,\n norm_cfg=None,\n join_type='concat',\n up_mode='transpose'),\n pixel_loss=dict(type='L1Loss'),\n required_frames=4)\n assert isinstance(model, FLAVR)\n assert isinstance(model.generator, FLAVRNet)\n assert model.pixel_loss.__class__.__name__ == 'L1Loss'\n\n input_tensors = torch.rand((1, 9, 3, 16, 16))\n input_tensors = model.split_frames(input_tensors)\n assert input_tensors.shape == (6, 4, 3, 16, 16), input_tensors.shape\n\n output_tensors = torch.rand((6, 1, 3, 16, 16))\n result = model.merge_frames(input_tensors, output_tensors)\n assert len(result) == 15\n assert result[0].shape == (16, 16, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_flavr/test_flavr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.flavr.flavr_net import UpConv3d\nfrom mmagic.registry import MODELS\n\n\ndef test_flavr_net():\n\n model_cfg = dict(\n type='FLAVRNet',\n num_input_frames=4,\n num_output_frames=2,\n mid_channels_list=[64, 32, 16, 8],\n encoder_layers_list=[1, 1, 1, 1],\n bias=False,\n norm_cfg=None,\n join_type='concat',\n up_mode='transpose')\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'FLAVRNet'\n\n # prepare data\n inputs = torch.rand(1, 4, 3, 256, 256)\n target = torch.rand(1, 2, 3, 256, 256)\n\n # test on cpu\n output = model(inputs)\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n\n out = model.decoder._join_tensors(inputs, inputs)\n assert out.shape == (1, 8, 3, 256, 256)\n\n conv3d = UpConv3d(4, 4, 1, 1, 1, up_mode='trilinear', batchnorm=True)\n out = conv3d(inputs)\n assert out.shape == (1, 4, 3, 512, 512)\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n target = target.cuda()\n output = model(inputs)\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == target.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_gca/test_gca.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport numpy as np\nimport torch\nfrom mmengine.config import ConfigDict\n\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef _demo_input_train(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n alpha = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n ori_merged = torch.from_numpy(\n np.random.random(color_shape).astype(np.float32))\n fg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n bg = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n if cuda:\n merged = merged.cuda()\n trimap = trimap.cuda()\n alpha = alpha.cuda()\n ori_merged = ori_merged.cuda()\n fg = fg.cuda()\n bg = bg.cuda()\n\n inputs = torch.cat((merged, trimap), dim=1)\n data_samples = []\n for a, m, f, b in zip(alpha, ori_merged, fg, bg):\n ds = DataSample()\n\n ds.gt_alpha = a\n ds.gt_merged = m\n ds.gt_fg = f\n ds.gt_bg = b\n for k, v in meta.items():\n ds.set_field(name=k, value=v, field_type='metainfo', dtype=None)\n\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef _demo_input_test(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n test_trans (str): what test transformation is used in data pipeline.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n ori_shape = (img_shape[0], img_shape[1], 1)\n\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n ori_alpha = np.random.random(ori_shape).astype(np.float32)\n ori_trimap = np.random.randint(256, size=ori_shape).astype(np.float32)\n\n inputs = torch.cat((merged, trimap), dim=1)\n if cuda:\n inputs = inputs.cuda()\n\n results = dict(\n ori_alpha=ori_alpha, ori_trimap=ori_trimap, ori_merged_shape=img_shape)\n\n data_samples = []\n packinputs = PackInputs()\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef assert_pred_alpha(predictions, batch_size):\n assert isinstance(predictions, list)\n assert isinstance(predictions[0], DataSample)\n pred_alpha = predictions[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == batch_size\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\ndef test_gca():\n model_cfg = ConfigDict(\n type='GCA',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='as_is',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='ResGCAEncoder',\n block='BasicBlock',\n layers=[3, 4, 4, 2],\n in_channels=6,\n with_spectral_norm=True,\n ),\n decoder=dict(\n type='ResGCADecoder',\n block='BasicBlockDec',\n layers=[2, 3, 3, 2],\n with_spectral_norm=True)),\n loss_alpha=dict(type='L1Loss'),\n test_cfg=dict(\n resize_method='pad',\n resize_mode='reflect',\n size_divisor=32,\n ))\n\n # test model forward in train mode\n model = MODELS.build(model_cfg)\n meta = {'format_trimap_to_onehot': True}\n inputs, data_samples = _demo_input_train((64, 64), batch_size=2, meta=meta)\n inputs6 = torch.cat((inputs, inputs[:, 3:, :, :], inputs[:, 3:, :, :]),\n dim=1)\n outputs = model(inputs6, data_samples, mode='loss')\n assert_dict_keys_equal(outputs, ['loss'])\n\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n inputs, data_samples = _demo_input_train((64, 64),\n batch_size=2,\n cuda=True,\n meta=meta)\n inputs6 = torch.cat((inputs, inputs[:, 3:, :, :], inputs[:, 3:, :, :]),\n dim=1)\n outputs = model(inputs6, data_samples, mode='loss')\n assert_dict_keys_equal(outputs, ['loss'])\n\n # test model forward in test mode\n with torch.no_grad():\n model_cfg.backbone.encoder.in_channels = 4\n model = MODELS.build(model_cfg)\n inputs = _demo_input_test((48, 48), meta=meta)\n outputs = model(*inputs, mode='predict')\n assert_pred_alpha(outputs, (48, 48))\n\n if torch.cuda.is_available():\n model = MODELS.build(model_cfg)\n model.cuda()\n inputs = _demo_input_test((48, 48), cuda=True, meta=meta)\n outputs = model(*inputs, mode='predict')\n assert_pred_alpha(outputs, (48, 48))\n\n # test forward_dummy\n model.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n model.forward(inputs)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_gca/test_gca_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.gca import GCAModule, ResGCAEncoder\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef test_res_gca_encoder():\n \"\"\"Test resnet encoder with shortcut and guided contextual attention.\"\"\"\n with pytest.raises(NotImplementedError):\n ResGCAEncoder('UnknownBlock', [3, 4, 4, 2], 3)\n\n target_shape = [(2, 32, 64, 64), (2, 32, 32, 32), (2, 64, 16, 16),\n (2, 128, 8, 8), (2, 256, 4, 4)]\n # target shape for model with late downsample\n target_late_ds = [(2, 32, 64, 64), (2, 64, 32, 32), (2, 64, 16, 16),\n (2, 128, 8, 8), (2, 256, 4, 4)]\n\n model = ResGCAEncoder('BasicBlock', [3, 4, 4, 2], 4)\n model.init_weights()\n model.train()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_shape[i])\n\n model = ResGCAEncoder('BasicBlock', [3, 4, 4, 2], 6)\n model.init_weights()\n model.train()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_shape[i])\n\n # test resnet shortcut encoder with late downsample\n model = ResGCAEncoder('BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_late_ds[i])\n\n if torch.cuda.is_available():\n # repeat above code again\n model = ResGCAEncoder('BasicBlock', [3, 4, 4, 2], 4)\n model.init_weights()\n model.train()\n model.cuda()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_shape[i])\n\n model = ResGCAEncoder('BasicBlock', [3, 4, 4, 2], 6)\n model.init_weights()\n model.train()\n model.cuda()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_shape[i])\n\n # test resnet shortcut encoder with late downsample\n model = ResGCAEncoder(\n 'BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n model.cuda()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['img_feat'], (2, 128, 8, 8))\n assert_tensor_with_shape(outputs['unknown'], (2, 1, 8, 8))\n for i in range(5):\n assert_tensor_with_shape(outputs[f'feat{i+1}'], target_late_ds[i])\n\n\ndef test_gca_module():\n img_feat = torch.rand(1, 128, 64, 64)\n alpha_feat = torch.rand(1, 128, 64, 64)\n unknown = None\n gca = GCAModule(128, 128, rate=1)\n output = gca(img_feat, alpha_feat, unknown)\n assert output.shape == (1, 128, 64, 64)\n\n img_feat = torch.rand(1, 128, 64, 64)\n alpha_feat = torch.rand(1, 128, 64, 64)\n unknown = torch.rand(1, 1, 64, 64)\n gca = GCAModule(128, 128, rate=2)\n output = gca(img_feat, alpha_feat, unknown)\n assert output.shape == (1, 128, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_gca/test_resgca_dec.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.gca import (ResGCADecoder, ResGCAEncoder, ResNetDec,\n ResNetEnc, ResShortcutDec,\n ResShortcutEnc)\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef test_resnet_decoder():\n \"\"\"Test resnet decoder.\"\"\"\n with pytest.raises(NotImplementedError):\n ResNetDec('UnknowBlock', [2, 3, 3, 2], 512)\n\n model = ResNetDec('BasicBlockDec', [2, 3, 3, 2], 512, kernel_size=5)\n model.init_weights()\n model.train()\n encoder = ResNetEnc('BasicBlock', [2, 4, 4, 2], 6)\n img = _demo_inputs((1, 6, 64, 64))\n feat = encoder(img)\n prediction = model(feat)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n model = ResNetDec(\n 'BasicBlockDec', [2, 3, 3, 2], 512, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n encoder = ResNetEnc('BasicBlock', [2, 4, 4, 2], 6)\n img = _demo_inputs((1, 6, 64, 64))\n feat = encoder(img)\n prediction = model(feat)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = ResNetDec('BasicBlockDec', [2, 3, 3, 2], 512, kernel_size=5)\n model.init_weights()\n model.train()\n model.cuda()\n encoder = ResNetEnc('BasicBlock', [2, 4, 4, 2], 6)\n encoder.cuda()\n img = _demo_inputs((1, 6, 64, 64)).cuda()\n feat = encoder(img)\n prediction = model(feat)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n model = ResNetDec(\n 'BasicBlockDec', [2, 3, 3, 2], 512, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n model.cuda()\n encoder = ResNetEnc('BasicBlock', [2, 4, 4, 2], 6)\n encoder.cuda()\n img = _demo_inputs((1, 6, 64, 64)).cuda()\n feat = encoder(img)\n prediction = model(feat)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n\ndef test_res_shortcut_decoder():\n \"\"\"Test resnet decoder with shortcut.\"\"\"\n with pytest.raises(NotImplementedError):\n ResShortcutDec('UnknowBlock', [2, 3, 3, 2], 512)\n\n model = ResShortcutDec('BasicBlockDec', [2, 3, 3, 2], 512)\n model.init_weights()\n model.train()\n\n encoder = ResShortcutEnc('BasicBlock', [2, 4, 4, 2], 6)\n img = _demo_inputs((1, 6, 64, 64))\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = ResShortcutDec('BasicBlockDec', [2, 3, 3, 2], 512)\n model.init_weights()\n model.train()\n model.cuda()\n encoder = ResShortcutEnc('BasicBlock', [2, 4, 4, 2], 6)\n encoder.cuda()\n img = _demo_inputs((1, 6, 64, 64)).cuda()\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n\ndef test_res_gca_decoder():\n \"\"\"Test resnet decoder with shortcut and guided contextual attention.\"\"\"\n with pytest.raises(NotImplementedError):\n ResGCADecoder('UnknowBlock', [2, 3, 3, 2], 512)\n\n model = ResGCADecoder('BasicBlockDec', [2, 3, 3, 2], 512)\n model.init_weights()\n model.train()\n\n encoder = ResGCAEncoder('BasicBlock', [2, 4, 4, 2], 6)\n img = _demo_inputs((2, 6, 32, 32))\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([2, 1, 32, 32]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = ResGCADecoder('BasicBlockDec', [2, 3, 3, 2], 512)\n model.init_weights()\n model.train()\n model.cuda()\n encoder = ResGCAEncoder('BasicBlock', [2, 4, 4, 2], 6)\n encoder.cuda()\n img = _demo_inputs((2, 6, 32, 32)).cuda()\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([2, 1, 32, 32]))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_gca/test_resgca_enc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.gca import ResNetEnc, ResShortcutEnc\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef test_resnet_encoder():\n \"\"\"Test resnet encoder.\"\"\"\n with pytest.raises(NotImplementedError):\n ResNetEnc('UnknownBlock', [3, 4, 4, 2], 3)\n\n with pytest.raises(TypeError):\n model = ResNetEnc('BasicBlock', [3, 4, 4, 2], 3)\n model.init_weights(list())\n\n model = ResNetEnc('BasicBlock', [3, 4, 4, 2], 4, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64))\n feat = model(img)\n assert_tensor_with_shape(feat, torch.Size([2, 512, 2, 2]))\n\n # test resnet encoder with late downsample\n model = ResNetEnc('BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64))\n feat = model(img)\n assert_tensor_with_shape(feat, torch.Size([2, 512, 2, 2]))\n\n if torch.cuda.is_available():\n # repeat above code again\n model = ResNetEnc(\n 'BasicBlock', [3, 4, 4, 2], 4, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n model.cuda()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64)).cuda()\n feat = model(img)\n assert_tensor_with_shape(feat, torch.Size([2, 512, 2, 2]))\n\n # test resnet encoder with late downsample\n model = ResNetEnc('BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n model.cuda()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64)).cuda()\n feat = model(img)\n assert_tensor_with_shape(feat, torch.Size([2, 512, 2, 2]))\n\n\ndef test_res_shortcut_encoder():\n \"\"\"Test resnet encoder with shortcut.\"\"\"\n with pytest.raises(NotImplementedError):\n ResShortcutEnc('UnknownBlock', [3, 4, 4, 2], 3)\n\n target_shape = [(2, 32, 64, 64), (2, 32, 32, 32), (2, 64, 16, 16),\n (2, 128, 8, 8), (2, 256, 4, 4)]\n # target shape for model with late downsample\n target_late_ds_shape = [(2, 32, 64, 64), (2, 64, 32, 32), (2, 64, 16, 16),\n (2, 128, 8, 8), (2, 256, 4, 4)]\n\n model = ResShortcutEnc(\n 'BasicBlock', [3, 4, 4, 2], 4, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_shape[4])\n\n model = ResShortcutEnc('BasicBlock', [3, 4, 4, 2], 6)\n model.init_weights()\n model.train()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_shape[4])\n\n # test resnet shortcut encoder with late downsample\n model = ResShortcutEnc('BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64))\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_late_ds_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_late_ds_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_late_ds_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_late_ds_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_late_ds_shape[4])\n\n if torch.cuda.is_available():\n # repeat above code again\n model = ResShortcutEnc(\n 'BasicBlock', [3, 4, 4, 2], 4, with_spectral_norm=True)\n assert hasattr(model.conv1.conv, 'weight_orig')\n model.init_weights()\n model.train()\n model.cuda()\n # trimap has 1 channels\n img = _demo_inputs((2, 4, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_shape[4])\n\n model = ResShortcutEnc('BasicBlock', [3, 4, 4, 2], 6)\n model.init_weights()\n model.train()\n model.cuda()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_shape[4])\n\n # test resnet shortcut encoder with late downsample\n model = ResShortcutEnc(\n 'BasicBlock', [3, 4, 4, 2], 6, late_downsample=True)\n model.init_weights()\n model.train()\n model.cuda()\n # both image and trimap has 3 channels\n img = _demo_inputs((2, 6, 64, 64)).cuda()\n outputs = model(img)\n assert_tensor_with_shape(outputs['out'], (2, 512, 2, 2))\n assert_tensor_with_shape(outputs['feat1'], target_late_ds_shape[0])\n assert_tensor_with_shape(outputs['feat2'], target_late_ds_shape[1])\n assert_tensor_with_shape(outputs['feat3'], target_late_ds_shape[2])\n assert_tensor_with_shape(outputs['feat4'], target_late_ds_shape[3])\n assert_tensor_with_shape(outputs['feat5'], target_late_ds_shape[4])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ggan/test_ggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import GGAN, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='DCGANGenerator', noise_size=10, output_scale=16, base_channels=16)\ndiscriminator = dict(\n type='DCGANDiscriminator', input_scale=16, output_scale=4, out_channels=1)\n\n\nclass TestGGAN(TestCase):\n\n def test_init(self):\n gan = GGAN(\n noise_size=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator)\n\n self.assertIsInstance(gan, GGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = GGAN(\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = GGAN(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = GGAN(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('test-ggan')\n gan = GGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_glean/test_glean_styleganv2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import SRGAN, DataPreprocessor, GLEANStyleGANv2\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Discriminator\nfrom mmagic.models.losses import (GANLoss, MSELoss, PerceptualLoss,\n PerceptualVGG)\nfrom mmagic.structures import DataSample\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_glean(init_weights):\n\n model = SRGAN(\n generator=dict(\n type='GLEANStyleGANv2', in_size=16, out_size=64, style_channels=4),\n discriminator=dict(type='StyleGANv2Discriminator', in_size=64),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'21': 1.0},\n vgg_type='vgg16',\n perceptual_weight=1e-2,\n style_weight=0,\n norm_img=True,\n criterion='mse',\n pretrained='torchvision://vgg16'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-2,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=None,\n test_cfg=None,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, SRGAN)\n assert isinstance(model.generator, GLEANStyleGANv2)\n assert isinstance(model.discriminator, StyleGAN2Discriminator)\n assert isinstance(model.pixel_loss, MSELoss)\n assert isinstance(model.perceptual_loss, PerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 16, 16)\n target = torch.rand(3, 64, 64)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_pix', 'loss_perceptual', 'loss_gan', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 64, 64)\n\n # feat\n output = model(torch.rand(1, 3, 16, 16), mode='tensor')\n assert output.shape == (1, 3, 64, 64)\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_glide/test_glide.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nimport unittest\nfrom unittest import TestCase\n\nimport torch\n\nfrom mmagic.models.diffusion_schedulers import EditDDIMScheduler\nfrom mmagic.utils import register_all_modules\nfrom projects.glide.models import Glide, SuperResText2ImUNet, Text2ImUNet\n\nregister_all_modules()\n\n\nclass TestGLIDE(TestCase):\n\n def setUp(self):\n # low resolution cfg\n unet_cfg = dict(\n image_size=64,\n base_channels=192,\n in_channels=3,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=0,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=1,\n num_head_channels=64,\n use_new_attention_order=False,\n encoder_channels=512),\n use_scale_shift_norm=True,\n text_ctx=128,\n xf_width=512,\n xf_layers=16,\n xf_heads=8,\n xf_final_ln=True,\n xf_padding=True,\n )\n diffusion_scheduler_cfg = dict(\n variance_type='learned_range', beta_schedule='squaredcos_cap_v2')\n\n # unet\n self.unet = Text2ImUNet(**unet_cfg)\n # diffusion_scheduler\n self.diffusion_scheduler = EditDDIMScheduler(**diffusion_scheduler_cfg)\n\n # high resolution cfg\n unet_up_cfg = dict(\n image_size=256,\n base_channels=192,\n in_channels=3,\n output_cfg=dict(var='FIXED'),\n resblocks_per_downsample=2,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=0,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=1,\n num_head_channels=64,\n use_new_attention_order=False,\n encoder_channels=512),\n use_scale_shift_norm=True,\n text_ctx=128,\n xf_width=512,\n xf_layers=16,\n xf_heads=8,\n xf_final_ln=True,\n xf_padding=True,\n )\n diffusion_scheduler_up_cfg = dict(\n variance_type='learned_range', beta_schedule='linear')\n\n # unet up\n self.unet_up = SuperResText2ImUNet(**unet_up_cfg)\n self.diffusion_scheduler_up = EditDDIMScheduler(\n **diffusion_scheduler_up_cfg)\n\n @unittest.skipIf(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\n def test_init(self):\n self.GLIDE = Glide(\n unet=self.unet,\n diffusion_scheduler=self.diffusion_scheduler,\n unet_up=self.unet_up,\n diffusion_scheduler_up=self.diffusion_scheduler_up)\n\n @unittest.skipIf(\n ('win' in platform.system().lower())\n or (not torch.cuda.is_available()),\n reason='skip on windows and cpu due to limited RAM.')\n def test_infer(self):\n # test infer resolution\n text_prompts = 'clouds surround the mountains and palaces,sunshine'\n image = self.GLIDE.infer(\n prompt=text_prompts, show_progress=True,\n num_inference_steps=2)['samples']\n assert image.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\n\ndef test_gl_decoder():\n register_all_modules()\n input_x = torch.randn(1, 256, 64, 64)\n template_cfg = dict(type='GLDecoder')\n\n gl_decoder = MODELS.build(template_cfg)\n output = gl_decoder(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n cfg_copy = template_cfg.copy()\n cfg_copy['out_act'] = 'sigmoid'\n gl_decoder = MODELS.build(cfg_copy)\n output = gl_decoder(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n with pytest.raises(ValueError):\n # conv_cfg must be a dict or None\n cfg_copy = template_cfg.copy()\n cfg_copy['out_act'] = 'relu'\n gl_decoder = MODELS.build(cfg_copy)\n output = gl_decoder(input_x)\n\n if torch.cuda.is_available():\n gl_decoder = MODELS.build(template_cfg)\n gl_decoder = gl_decoder.cuda()\n output = gl_decoder(input_x.cuda())\n assert output.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_dilation.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.archs import SimpleGatedConvModule\nfrom mmagic.models.editors.global_local import GLDilationNeck\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_gl_dilation_neck():\n x = torch.rand((2, 8, 64, 64))\n template_cfg = dict(type='GLDilationNeck', in_channels=8)\n\n neck = MODELS.build(template_cfg)\n res = neck(x)\n assert res.shape == (2, 8, 64, 64)\n\n if torch.cuda.is_available():\n neck = GLDilationNeck(in_channels=8).cuda()\n x = torch.rand((2, 8, 64, 64)).cuda()\n res = neck(x)\n assert res.shape == (2, 8, 64, 64)\n\n neck = GLDilationNeck(in_channels=8, conv_type='gated_conv').cuda()\n res = neck(x)\n assert isinstance(neck.dilation_convs[0], SimpleGatedConvModule)\n assert res.shape == (2, 8, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\ndef test_gl_discs():\n global_disc_cfg = dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=6,\n norm_cfg=dict(type='BN'))\n local_disc_cfg = dict(\n in_channels=3,\n max_channels=512,\n fc_in_channels=512 * 4 * 4,\n fc_out_channels=1024,\n num_convs=5,\n norm_cfg=dict(type='BN'))\n gl_disc_cfg = dict(\n type='GLDiscs',\n global_disc_cfg=global_disc_cfg,\n local_disc_cfg=local_disc_cfg)\n gl_discs = MODELS.build(gl_disc_cfg)\n gl_discs.init_weights()\n\n input_g = torch.randn(1, 3, 256, 256)\n input_l = torch.randn(1, 3, 128, 128)\n output = gl_discs((input_g, input_l))\n assert output.shape == (1, 1)\n\n with pytest.raises(TypeError):\n gl_discs.init_weights(pretrained=dict(igccc=777))\n\n if torch.cuda.is_available():\n gl_discs = gl_discs.cuda()\n input_g = torch.randn(1, 3, 256, 256).cuda()\n input_l = torch.randn(1, 3, 128, 128).cuda()\n output = gl_discs((input_g, input_l))\n assert output.shape == (1, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\n\ndef test_gl_encoder():\n register_all_modules()\n input_x = torch.randn(1, 4, 256, 256)\n template_cfg = dict(type='GLEncoder')\n\n gl_encoder = MODELS.build(template_cfg)\n output = gl_encoder(input_x)\n assert output.shape == (1, 256, 64, 64)\n\n if torch.cuda.is_available():\n gl_encoder = MODELS.build(template_cfg)\n gl_encoder = gl_encoder.cuda()\n output = gl_encoder(input_x.cuda())\n assert output.shape == (1, 256, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\n\ndef test_gl_encdec():\n register_all_modules()\n input_x = torch.randn(1, 4, 256, 256)\n template_cfg = dict(type='GLEncoderDecoder')\n\n gl_encdec = MODELS.build(template_cfg)\n gl_encdec.init_weights()\n output = gl_encdec(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n cfg_ = template_cfg.copy()\n cfg_['decoder'] = dict(type='GLDecoder', out_act='sigmoid')\n gl_encdec = MODELS.build(cfg_)\n output = gl_encdec(input_x)\n assert output.shape == (1, 3, 256, 256)\n\n with pytest.raises(ValueError):\n cfg_ = template_cfg.copy()\n cfg_['decoder'] = dict(type='GLDecoder', out_act='igccc')\n gl_encdec = MODELS.build(cfg_)\n\n with pytest.raises(TypeError):\n gl_encdec.init_weights(pretrained=dict(igccc=4396))\n\n if torch.cuda.is_available():\n gl_encdec = MODELS.build(template_cfg)\n gl_encdec.init_weights()\n gl_encdec = gl_encdec.cuda()\n output = gl_encdec(input_x.cuda())\n assert output.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_global_local/test_gl_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom os.path import dirname, join\n\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\ndef test_gl_inpaintor():\n register_all_modules()\n\n config_file = join(dirname(__file__), '../../..', 'configs', 'gl_test.py')\n cfg = Config.fromfile(config_file)\n\n gl = MODELS.build(cfg.model)\n\n assert gl.__class__.__name__ == 'GLInpaintor'\n\n if torch.cuda.is_available():\n gl.cuda()\n gt_img = torch.randn(3, 256, 256)\n mask = torch.zeros_like(gt_img)[0:1, ...]\n mask[..., 100:210, 100:210] = 1.\n masked_img = gt_img.unsqueeze(0) * (1. - mask)\n mask_bbox = [100, 100, 110, 110]\n data_batch = {\n 'inputs':\n masked_img,\n 'data_samples': [\n DataSample(\n metainfo=dict(mask_bbox=mask_bbox),\n mask=mask,\n gt_img=gt_img,\n )\n ]\n }\n\n optim_g = torch.optim.SGD(gl.generator.parameters(), lr=0.1)\n optim_d = torch.optim.SGD(gl.disc.parameters(), lr=0.1)\n optim_dict = dict(\n generator=OptimWrapper(optim_g), disc=OptimWrapper(optim_d))\n\n for i in range(5):\n log_vars = gl.train_step(data_batch, optim_dict)\n\n if i <= 2:\n assert 'loss_l1_hole' in log_vars\n assert 'fake_loss' not in log_vars\n assert 'real_loss' not in log_vars\n assert 'loss_g_fake' not in log_vars\n elif i == 3:\n assert 'loss_l1_hole' not in log_vars\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_g_fake' not in log_vars\n else:\n assert 'loss_l1_hole' in log_vars\n assert 'fake_loss' in log_vars\n assert 'real_loss' in log_vars\n assert 'loss_g_fake' in log_vars\n\n gl_dirty = MODELS.build(cfg.model_dirty)\n if torch.cuda.is_available():\n gl_dirty.cuda()\n res, loss = gl_dirty.generator_loss(gt_img, gt_img, gt_img, gt_img, mask,\n masked_img)\n assert len(loss) == 0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_guided_diffusion/test_adm.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport pytest\nimport torch\n\nfrom mmagic.models import AblatedDiffusionModel\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestAdm(TestCase):\n\n def setup_class(cls):\n # test init\n cls.model = AblatedDiffusionModel(\n data_preprocessor=dict(type='DataPreprocessor'),\n unet=dict(\n type='DenoisingUnet',\n image_size=64,\n in_channels=3,\n base_channels=192,\n resblocks_per_downsample=3,\n attention_res=(32, 16, 8),\n norm_cfg=dict(type='GN32', num_groups=32),\n dropout=0.1,\n num_classes=1000,\n use_fp16=False,\n resblock_updown=True,\n attention_cfg=dict(\n type='MultiHeadAttentionBlock',\n num_heads=4,\n num_head_channels=64,\n use_new_attention_order=True),\n use_scale_shift_norm=True),\n diffusion_scheduler=dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_schedule='squaredcos_cap_v2'),\n rgb2bgr=True,\n use_fp16=False)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_infer(self):\n # test no label infer\n self.model.cuda()\n samples = self.model.infer(\n init_image=None,\n batch_size=1,\n num_inference_steps=5,\n labels=None,\n classifier_scale=0.0,\n show_progress=False)['samples']\n assert samples.shape == (1, 3, 64, 64)\n # test classifier guidance\n samples = self.model.infer(\n init_image=None,\n batch_size=1,\n num_inference_steps=5,\n labels=333,\n classifier_scale=1.0,\n show_progress=False)['samples']\n assert samples.shape == (1, 3, 64, 64)\n # test with ddpm scheduler\n scheduler_kwargs = dict(\n type='EditDDPMScheduler',\n variance_type='learned_range',\n num_train_timesteps=5)\n # test no label infer\n samples = self.model.infer(\n scheduler_kwargs=scheduler_kwargs,\n init_image=None,\n batch_size=1,\n num_inference_steps=5,\n labels=None,\n classifier_scale=0.0,\n show_progress=False)['samples']\n assert samples.shape == (1, 3, 64, 64)\n # test classifier guidance\n samples = self.model.infer(\n scheduler_kwargs=scheduler_kwargs,\n init_image=None,\n batch_size=1,\n num_inference_steps=5,\n labels=333,\n classifier_scale=1.0,\n show_progress=False)['samples']\n assert samples.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_iconvsr/test_iconvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import IconVSRNet\n\n\ndef test_iconvsr():\n \"\"\"Test IconVSRNet.\"\"\"\n\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=None,\n edvr_pretrained=None)\n\n input_tensor = torch.rand(1, 5, 3, 64, 64)\n output = iconvsr(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n assert not iconvsr._raised_warning\n\n input_tensor = torch.rand(1, 5, 3, 16, 16)\n output = iconvsr(input_tensor)\n assert output.shape == (1, 5, 3, 64, 64)\n assert iconvsr._raised_warning\n\n # spynet_pretrained should be str or None\n with pytest.raises(TypeError):\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=123,\n edvr_pretrained=None)\n\n # edvr_pretrained should be str or None\n with pytest.raises(TypeError):\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=None,\n edvr_pretrained=123)\n\n if torch.cuda.is_available():\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=None,\n edvr_pretrained=None).cuda()\n\n # input_tensor = torch.rand(1, 5, 3, 64, 64).cuda()\n input_tensor = torch.rand(1, 5, 3, 16, 16).cuda()\n output = iconvsr(input_tensor)\n # assert output.shape == (1, 5, 3, 256, 256)\n assert output.shape == (1, 5, 3, 64, 64)\n\n # spynet_pretrained should be str or None\n with pytest.raises(TypeError):\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=123,\n edvr_pretrained=None).cuda()\n\n # edvr_pretrained should be str or None\n with pytest.raises(TypeError):\n iconvsr = IconVSRNet(\n mid_channels=64,\n num_blocks=1,\n keyframe_stride=5,\n padding=2,\n spynet_pretrained=None,\n edvr_pretrained=123).cuda()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_indexnet/test_indexnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\nfrom mmengine.config import ConfigDict\n\nfrom mmagic.datasets.transforms import PackInputs\nfrom mmagic.models.editors import IndexedUpsample\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\ndef _demo_input_train(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n ori_shape = (img_shape[0], img_shape[1], 1)\n\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n inputs = torch.cat((merged, trimap), dim=1)\n if cuda:\n inputs = inputs.cuda()\n\n results = dict(\n alpha=np.random.random(ori_shape).astype(np.float32),\n merged=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32),\n fg=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32),\n bg=np.random.random(\n (img_shape[0], img_shape[1], 3)).astype(np.float32),\n )\n\n data_samples = []\n packinputs = PackInputs()\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef _demo_input_test(img_shape, batch_size=1, cuda=False, meta={}):\n \"\"\"Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n test_trans (str): what test transformation is used in data pipeline.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n ori_shape = (img_shape[0], img_shape[1], 1)\n\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n trimap = torch.from_numpy(\n np.random.randint(255, size=gray_shape).astype(np.float32))\n inputs = torch.cat((merged, trimap), dim=1)\n if cuda:\n inputs = inputs.cuda()\n\n results = dict(\n ori_alpha=np.random.random(ori_shape).astype(np.float32),\n ori_trimap=np.random.randint(256, size=ori_shape).astype(np.float32),\n ori_merged_shape=img_shape)\n\n data_samples = []\n packinputs = PackInputs()\n for _ in range(batch_size):\n ds = packinputs(results)['data_samples']\n if cuda:\n ds = ds.cuda()\n data_samples.append(ds)\n\n data_samples = DataSample.stack(data_samples)\n return inputs, data_samples\n\n\ndef assert_pred_alpha(predictions, batch_size):\n assert isinstance(predictions, list)\n assert isinstance(predictions[0], DataSample)\n pred_alpha = predictions[0].output.pred_alpha.data\n assert isinstance(pred_alpha, torch.Tensor)\n assert pred_alpha.dtype == torch.uint8\n assert pred_alpha.shape[-2:] == batch_size\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\ndef test_indexnet():\n register_all_modules()\n model_cfg = ConfigDict(\n type='IndexNet',\n data_preprocessor=dict(\n type='MattorPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n proc_trimap='rescale_to_zero_one',\n ),\n backbone=dict(\n type='SimpleEncoderDecoder',\n encoder=dict(\n type='IndexNetEncoder',\n in_channels=4,\n freeze_bn=True,\n ),\n decoder=dict(type='IndexNetDecoder')),\n loss_alpha=dict(\n type='CharbonnierLoss', loss_weight=0.5, sample_wise=True),\n loss_comp=dict(\n type='CharbonnierCompLoss', loss_weight=1.5, sample_wise=True),\n test_cfg=dict(\n resize_method='interp',\n resize_mode='bicubic',\n size_divisor=32,\n ),\n )\n\n model_cfg.backbone.encoder.init_cfg = None\n\n # test indexnet inference\n with torch.no_grad():\n indexnet = MODELS.build(model_cfg)\n indexnet.eval()\n input_test = _demo_input_test((48, 48))\n output_test = indexnet(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # test inference with gpu\n if torch.cuda.is_available():\n indexnet = MODELS.build(model_cfg).cuda()\n indexnet.eval()\n input_test = _demo_input_test((48, 48), cuda=True)\n output_test = indexnet(*input_test, mode='predict')\n assert_pred_alpha(output_test, (48, 48))\n\n # test forward train though we do not guarantee the training for present\n model_cfg.loss_alpha = None\n model_cfg.loss_comp = dict(type='L1CompositionLoss')\n indexnet = MODELS.build(model_cfg)\n input_train = _demo_input_train((64, 64), batch_size=2)\n output_train = indexnet(*input_train, mode='loss')\n # assert output_train['num_samples'] == 2\n assert_dict_keys_equal(output_train, ['loss_comp'])\n\n if torch.cuda.is_available():\n model_cfg.loss_alpha = dict(type='L1Loss')\n model_cfg.loss_comp = None\n indexnet = MODELS.build(model_cfg).cuda()\n input_train = _demo_input_train((64, 64), batch_size=2, cuda=True)\n output_train = indexnet(*input_train, mode='loss')\n # assert output_train['num_samples'] == 2\n assert_dict_keys_equal(output_train, ['loss_alpha'])\n\n # test forward\n indexnet.cpu().eval()\n inputs = torch.ones((1, 4, 32, 32))\n indexnet.forward(inputs)\n\n\ndef test_indexed_upsample():\n \"\"\"Test indexed upsample module for indexnet decoder.\"\"\"\n indexed_upsample = IndexedUpsample(12, 12)\n\n # test indexed_upsample without dec_idx_feat (no upsample)\n x = torch.rand(2, 6, 32, 32)\n shortcut = torch.rand(2, 6, 32, 32)\n output = indexed_upsample(x, shortcut)\n assert_tensor_with_shape(output, (2, 12, 32, 32))\n\n # test indexed_upsample without dec_idx_feat (with upsample)\n x = torch.rand(2, 6, 32, 32)\n dec_idx_feat = torch.rand(2, 6, 64, 64)\n shortcut = torch.rand(2, 6, 64, 64)\n output = indexed_upsample(x, shortcut, dec_idx_feat)\n assert_tensor_with_shape(output, (2, 12, 64, 64))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_indexnet/test_indexnet_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import IndexNetDecoder, IndexNetEncoder\n\n\ndef test_indexnet_decoder():\n \"\"\"Test Indexnet decoder.\"\"\"\n # test indexnet decoder with default indexnet setting\n with pytest.raises(AssertionError):\n # shortcut must have four dimensions\n indexnet_decoder = IndexNetDecoder(\n 160, kernel_size=5, separable_conv=False)\n x = torch.rand(2, 256, 4, 4)\n shortcut = torch.rand(2, 128, 8, 8, 8)\n dec_idx_feat = torch.rand(2, 128, 8, 8, 8)\n outputs_enc = dict(\n out=x, shortcuts=[shortcut], dec_idx_feat_list=[dec_idx_feat])\n indexnet_decoder(outputs_enc)\n\n indexnet_decoder = IndexNetDecoder(\n 160, kernel_size=5, separable_conv=False)\n indexnet_decoder.init_weights()\n indexnet_encoder = IndexNetEncoder(4)\n x = torch.rand(2, 4, 32, 32)\n outputs_enc = indexnet_encoder(x)\n out = indexnet_decoder(outputs_enc)\n assert out.shape == (2, 1, 32, 32)\n\n # test indexnet decoder with other setting\n indexnet_decoder = IndexNetDecoder(160, kernel_size=3, separable_conv=True)\n indexnet_decoder.init_weights()\n out = indexnet_decoder(outputs_enc)\n assert out.shape == (2, 1, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_indexnet/test_indexnet_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\ntry:\n import Iterable\nexcept ImportError:\n from collections.abc import Iterable\nimport pytest\nimport torch\n\nfrom mmagic.models import (DepthwiseIndexBlock, HolisticIndexBlock,\n IndexNetEncoder)\n\n\ndef test_indexnet_encoder():\n \"\"\"Test Indexnet encoder.\"\"\"\n with pytest.raises(ValueError):\n # out_stride must be 16 or 32\n IndexNetEncoder(4, out_stride=8)\n with pytest.raises(NameError):\n # index_mode must be 'holistic', 'o2o' or 'm2o'\n IndexNetEncoder(4, index_mode='unknown_mode')\n\n # test indexnet encoder with default indexnet setting\n indexnet_encoder = IndexNetEncoder(\n 4,\n out_stride=32,\n width_mult=1,\n index_mode='m2o',\n aspp=True,\n use_nonlinear=True,\n use_context=True)\n indexnet_encoder.init_weights()\n x = torch.rand(2, 4, 32, 32)\n outputs = indexnet_encoder(x)\n assert outputs['out'].shape == (2, 160, 1, 1)\n assert len(outputs['shortcuts']) == 7\n target_shapes = [(2, 32, 32, 32), (2, 16, 16, 16), (2, 24, 16, 16),\n (2, 32, 8, 8), (2, 64, 4, 4), (2, 96, 2, 2),\n (2, 160, 2, 2)]\n for shortcut, target_shape in zip(outputs['shortcuts'], target_shapes):\n assert shortcut.shape == target_shape\n assert len(outputs['dec_idx_feat_list']) == 7\n target_shapes = [(2, 32, 32, 32), None, (2, 24, 16, 16), (2, 32, 8, 8),\n (2, 64, 4, 4), None, (2, 160, 2, 2)]\n for dec_idx_feat, target_shape in zip(outputs['dec_idx_feat_list'],\n target_shapes):\n if dec_idx_feat is not None:\n assert dec_idx_feat.shape == target_shape\n\n # test indexnet encoder with other config\n indexnet_encoder = IndexNetEncoder(\n 4,\n out_stride=16,\n width_mult=2,\n index_mode='o2o',\n aspp=False,\n use_nonlinear=False,\n use_context=False)\n indexnet_encoder.init_weights()\n x = torch.rand(2, 4, 32, 32)\n outputs = indexnet_encoder(x)\n assert outputs['out'].shape == (2, 160, 2, 2)\n assert len(outputs['shortcuts']) == 7\n target_shapes = [(2, 64, 32, 32), (2, 32, 16, 16), (2, 48, 16, 16),\n (2, 64, 8, 8), (2, 128, 4, 4), (2, 192, 2, 2),\n (2, 320, 2, 2)]\n for shortcut, target_shape in zip(outputs['shortcuts'], target_shapes):\n assert shortcut.shape == target_shape\n assert len(outputs['dec_idx_feat_list']) == 7\n target_shapes = [(2, 64, 32, 32), None, (2, 48, 16, 16), (2, 64, 8, 8),\n (2, 128, 4, 4), None, None]\n for dec_idx_feat, target_shape in zip(outputs['dec_idx_feat_list'],\n target_shapes):\n if dec_idx_feat is not None:\n assert dec_idx_feat.shape == target_shape\n\n # test indexnet encoder with holistic index block\n indexnet_encoder = IndexNetEncoder(\n 4,\n out_stride=16,\n width_mult=2,\n index_mode='holistic',\n aspp=False,\n freeze_bn=True,\n use_nonlinear=False,\n use_context=False)\n indexnet_encoder.init_weights()\n x = torch.rand(2, 4, 32, 32)\n outputs = indexnet_encoder(x)\n assert outputs['out'].shape == (2, 160, 2, 2)\n assert len(outputs['shortcuts']) == 7\n target_shapes = [(2, 64, 32, 32), (2, 32, 16, 16), (2, 48, 16, 16),\n (2, 64, 8, 8), (2, 128, 4, 4), (2, 192, 2, 2),\n (2, 320, 2, 2)]\n for shortcut, target_shape in zip(outputs['shortcuts'], target_shapes):\n assert shortcut.shape == target_shape\n assert len(outputs['dec_idx_feat_list']) == 7\n target_shapes = [(2, 1, 32, 32), None, (2, 1, 16, 16), (2, 1, 8, 8),\n (2, 1, 4, 4), None, None]\n for dec_idx_feat, target_shape in zip(outputs['dec_idx_feat_list'],\n target_shapes):\n if dec_idx_feat is not None:\n assert dec_idx_feat.shape == target_shape\n\n\ndef test_index_blocks():\n \"\"\"Test index blocks for indexnet encoder.\"\"\"\n # test holistic index block\n # test holistic index block without context and nonlinearty\n block = HolisticIndexBlock(128, use_context=False, use_nonlinear=False)\n assert not isinstance(block.index_block, Iterable)\n x = torch.rand(2, 128, 8, 8)\n enc_idx_feat, dec_idx_feat = block(x)\n assert enc_idx_feat.shape == (2, 1, 8, 8)\n assert dec_idx_feat.shape == (2, 1, 8, 8)\n\n # test holistic index block with context and nonlinearty\n block = HolisticIndexBlock(128, use_context=True, use_nonlinear=True)\n assert len(block.index_block) == 2 # nonlinear mode has two blocks\n x = torch.rand(2, 128, 8, 8)\n enc_idx_feat, dec_idx_feat = block(x)\n assert enc_idx_feat.shape == (2, 1, 8, 8)\n assert dec_idx_feat.shape == (2, 1, 8, 8)\n\n # test depthwise index block\n # test depthwise index block without context and nonlinearty in o2o mode\n block = DepthwiseIndexBlock(\n 128, use_context=False, mode='oso', use_nonlinear=False)\n assert not isinstance(block.index_blocks[0], Iterable)\n x = torch.rand(2, 128, 8, 8)\n enc_idx_feat, dec_idx_feat = block(x)\n assert enc_idx_feat.shape == (2, 128, 8, 8)\n assert dec_idx_feat.shape == (2, 128, 8, 8)\n\n # test depthwise index block with context and nonlinearty in m2o mode\n block = DepthwiseIndexBlock(\n 128, use_context=True, mode='m2o', use_nonlinear=True)\n assert len(block.index_blocks[0]) == 2 # nonlinear mode has two blocks\n x = torch.rand(2, 128, 8, 8)\n enc_idx_feat, dec_idx_feat = block(x)\n assert enc_idx_feat.shape == (2, 128, 8, 8)\n assert dec_idx_feat.shape == (2, 128, 8, 8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_inst_colorization/test_color_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch\n\nfrom mmagic.models.editors.inst_colorization import color_utils\n\n\nclass TestColorUtils:\n color_data_opt = dict(\n ab_thresh=0,\n p=1.0,\n sample_PS=[\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n ],\n ab_norm=110,\n ab_max=110.,\n ab_quant=10.,\n l_norm=100.,\n l_cent=50.,\n mask_cent=0.5)\n\n def test_xyz2lab(self):\n xyz = torch.rand(1, 3, 8, 8)\n lab = color_utils.xyz2lab(xyz)\n\n sc = torch.Tensor((0.95047, 1., 1.08883))[None, :, None, None]\n xyz_scale = xyz / sc\n mask = (xyz_scale > .008856).type(torch.FloatTensor)\n\n xyz_int = xyz_scale**(1 / 3.) * mask + (7.787 * xyz_scale +\n 16. / 116.) * (1 - mask)\n L = 116. * xyz_int[:, 1, :, :] - 16.\n a = 500. * (xyz_int[:, 0, :, :] - xyz_int[:, 1, :, :])\n b = 200. * (xyz_int[:, 1, :, :] - xyz_int[:, 2, :, :])\n\n assert lab.shape == (1, 3, 8, 8)\n assert lab.equal(\n torch.cat((L[:, None, :, :], a[:, None, :, :], b[:, None, :, :]),\n dim=1))\n\n def test_rgb2xyz(self):\n rgb = torch.rand(1, 3, 8, 8)\n xyz = color_utils.rgb2xyz(rgb)\n\n mask = (rgb > .04045).type(torch.FloatTensor)\n rgb = (((rgb + .055) / 1.055)**2.4) * mask + rgb / 12.92 * (1 - mask)\n\n x = .412453 * rgb[:, 0, :, :] + .357580 * rgb[:, 1, :, :] \\\n + .180423 * rgb[:, 2, :, :]\n y = .212671 * rgb[:, 0, :, :] + .715160 * rgb[:, 1, :, :] \\\n + .072169 * rgb[:, 2, :, :]\n z = .019334 * rgb[:, 0, :, :] + .119193 * rgb[:, 1, :, :] \\\n + .950227 * rgb[:, 2, :, :]\n\n assert xyz.shape == (1, 3, 8, 8)\n assert xyz.equal(\n torch.cat((x[:, None, :, :], y[:, None, :, :], z[:, None, :, :]),\n dim=1))\n\n def test_rgb2lab(self):\n rgb = torch.rand(1, 3, 8, 8)\n lab = color_utils.rgb2lab(rgb, self.color_data_opt)\n _lab = color_utils.xyz2lab(color_utils.rgb2xyz(rgb))\n\n l_rs = (_lab[:, [0], :, :] -\n self.color_data_opt['l_cent']) / self.color_data_opt['l_norm']\n ab_rs = _lab[:, 1:, :, :] / self.color_data_opt['ab_norm']\n\n assert lab.shape == (1, 3, 8, 8)\n assert lab.equal(torch.cat((l_rs, ab_rs), dim=1))\n\n def test_lab2rgb(self):\n lab = torch.rand(1, 3, 8, 8)\n rgb = color_utils.lab2rgb(lab, self.color_data_opt)\n\n L = lab[:, [0], :, :] * self.color_data_opt[\n 'l_norm'] + self.color_data_opt['l_cent']\n AB = lab[:, 1:, :, :] * self.color_data_opt['ab_norm']\n\n lab = torch.cat((L, AB), dim=1)\n\n assert rgb.shape == (1, 3, 8, 8)\n assert rgb.equal(color_utils.xyz2rgb(color_utils.lab2xyz(lab)))\n\n def test_lab2xyz(self):\n lab = torch.rand(1, 3, 8, 8)\n xyz = color_utils.lab2xyz(lab)\n y_int = (lab[:, 0, :, :] + 16.) / 116.\n x_int = (lab[:, 1, :, :] / 500.) + y_int\n z_int = y_int - (lab[:, 2, :, :] / 200.)\n z_int = torch.max(torch.Tensor((0, )), z_int)\n\n out = torch.cat(\n (x_int[:, None, :, :], y_int[:, None, :, :], z_int[:, None, :, :]),\n dim=1)\n mask = (out > .2068966).type(torch.FloatTensor)\n sc = torch.Tensor((0.95047, 1., 1.08883))[None, :, None, None]\n out = (out**3.) * mask + (out - 16. / 116.) / 7.787 * (1 - mask)\n target = sc * out\n assert xyz.shape == (1, 3, 8, 8)\n assert xyz.equal(target)\n\n def test_xyz2rgb(self):\n xyz = torch.rand(1, 3, 8, 8)\n\n rgb = color_utils.xyz2rgb(xyz)\n\n r = 3.24048134 * xyz[:, 0, :, :] - 1.53715152 * xyz[:, 1, :, :] \\\n - 0.49853633 * xyz[:, 2, :, :]\n g = -0.96925495 * xyz[:, 0, :, :] + 1.87599 * xyz[:, 1, :, :] \\\n + .04155593 * xyz[:, 2, :, :]\n b = .05564664 * xyz[:, 0, :, :] - .20404134 * xyz[:, 1, :, :] \\\n + 1.05731107 * xyz[:, 2, :, :]\n\n _rgb = torch.cat(\n (r[:, None, :, :], g[:, None, :, :], b[:, None, :, :]), dim=1)\n _rgb = torch.max(_rgb, torch.zeros_like(_rgb))\n\n mask = (_rgb > .0031308).type(torch.FloatTensor)\n\n assert rgb.shape == (1, 3, 8, 8) and mask.shape == (1, 3, 8, 8)\n assert rgb.equal((1.055 * (_rgb**(1. / 2.4)) - 0.055) * mask +\n 12.92 * _rgb * (1 - mask))\n\n def test_get_colorization_data(self):\n data_raw = torch.rand(1, 3, 8, 8)\n\n res = color_utils.get_colorization_data(data_raw, self.color_data_opt)\n\n assert isinstance(res, dict)\n assert 'A' in res.keys() and 'B' in res.keys() \\\n and 'hint_B' in res.keys() and 'mask_B' in res.keys()\n assert res['A'].shape == res['mask_B'].shape == (1, 1, 8, 8)\n assert res['hint_B'].shape == res['B'].shape == (1, 2, 8, 8)\n\n def test_encode_ab_ind(self):\n data_ab = torch.rand(1, 2, 8, 8)\n data_q = color_utils.encode_ab_ind(data_ab, self.color_data_opt)\n A = 2 * 110. / 10. + 1\n\n data_ab_rs = torch.round((data_ab * 110 + 110.) / 10.)\n\n assert data_q.shape == (1, 1, 8, 8)\n assert data_q.equal(data_ab_rs[:, [0], :, :] * A +\n data_ab_rs[:, [1], :, :])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_inst_colorization/test_colorization_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\ndef test_colorization_net():\n\n model_cfg = dict(\n type='ColorizationNet', input_nc=4, output_nc=2, norm_type='batch')\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'ColorizationNet'\n\n # prepare data\n input_A = torch.rand(1, 1, 256, 256)\n input_B = torch.rand(1, 2, 256, 256)\n mask_B = torch.rand(1, 1, 256, 256)\n\n target_shape = (1, 2, 256, 256)\n\n # test on cpu\n (out_class, out_reg, feature_map) = model(input_A, input_B, mask_B)\n assert isinstance(feature_map, dict)\n assert feature_map['conv1_2'].shape == (1, 64, 256, 256) \\\n and feature_map['out_reg'].shape == target_shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n input_A = input_A.cuda()\n input_B = input_B.cuda()\n mask_B = mask_B.cuda()\n (out_class, out_reg, feature_map) = \\\n model(input_A, input_B, mask_B)\n\n assert isinstance(feature_map, dict)\n for item in feature_map.keys():\n assert torch.is_tensor(feature_map[item])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_inst_colorization/test_fusion_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\ndef test_fusion_net():\n\n model_cfg = dict(\n type='FusionNet', input_nc=4, output_nc=2, norm_type='batch')\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'FusionNet'\n\n # prepare data\n input_A = torch.rand(1, 1, 256, 256)\n input_B = torch.rand(1, 2, 256, 256)\n mask_B = torch.rand(1, 1, 256, 256)\n\n instance_feature = dict(\n conv1_2=torch.rand(1, 64, 256, 256),\n conv2_2=torch.rand(1, 128, 128, 128),\n conv3_3=torch.rand(1, 256, 64, 64),\n conv4_3=torch.rand(1, 512, 32, 32),\n conv5_3=torch.rand(1, 512, 32, 32),\n conv6_3=torch.rand(1, 512, 32, 32),\n conv7_3=torch.rand(1, 512, 32, 32),\n conv8_up=torch.rand(1, 256, 64, 64),\n conv8_3=torch.rand(1, 256, 64, 64),\n conv9_up=torch.rand(1, 128, 128, 128),\n conv9_3=torch.rand(1, 128, 128, 128),\n conv10_up=torch.rand(1, 128, 256, 256),\n conv10_2=torch.rand(1, 128, 256, 256),\n )\n\n target_shape = (1, 2, 256, 256)\n\n box_info_box = [\n torch.tensor([[175, 29, 96, 54, 52, 106], [14, 191, 84, 61, 51, 111],\n [117, 64, 115, 46, 75, 95], [41, 165, 121, 47, 50, 88],\n [46, 136, 94, 45, 74, 117], [79, 124, 62, 115, 53, 79],\n [156, 64, 77, 138, 36, 41], [200, 48, 114, 131, 8, 11],\n [115, 78, 92, 81, 63, 83]]),\n torch.tensor([[87, 15, 48, 27, 26, 53], [7, 96, 42, 31, 25, 55],\n [58, 32, 57, 23, 38, 48], [20, 83, 60, 24, 25, 44],\n [23, 68, 47, 23, 37, 58], [39, 62, 31, 58, 27, 39],\n [78, 32, 38, 69, 18, 21], [100, 24, 57, 66, 4, 5],\n [57, 39, 46, 41, 32, 41]]),\n torch.tensor([[43, 8, 24, 14, 13, 26], [3, 48, 21, 16, 13, 27],\n [29, 16, 28, 12, 19, 24], [10, 42, 30, 12, 12, 22],\n [11, 34, 23, 12, 19, 29], [19, 31, 15, 29, 14, 20],\n [39, 16, 19, 35, 9, 10], [50, 12, 28, 33, 2, 3],\n [28, 20, 23, 21, 16, 20]]),\n torch.tensor([[21, 4, 12, 7, 7, 13], [1, 24, 10, 8, 7, 14],\n [14, 8, 14, 6, 10, 12], [5, 21, 15, 6, 6, 11],\n [5, 17, 11, 6, 10, 15], [9, 16, 7, 15, 7, 10],\n [19, 8, 9, 18, 5, 5], [25, 6, 14, 17, 1, 1],\n [14, 10, 11, 11, 8, 10]])\n ]\n\n # test on cpu\n out = model(input_A, input_B, mask_B, instance_feature, box_info_box)\n assert torch.is_tensor(out)\n assert out.shape == target_shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n input_A = input_A.cuda()\n input_B = input_B.cuda()\n mask_B = mask_B.cuda()\n for item in instance_feature.keys():\n instance_feature[item] = instance_feature[item].cuda()\n box_info_box = [i.cuda() for i in box_info_box]\n output = model(input_A, input_B, mask_B, instance_feature,\n box_info_box)\n assert torch.is_tensor(output)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_inst_colorization/test_inst_colorization.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nimport unittest\n\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\nclass TestInstColorization:\n\n def test_inst_colorization(self):\n if not torch.cuda.is_available():\n # RoI pooling only support in GPU\n return unittest.skip('test requires GPU and torch+cuda')\n\n register_all_modules()\n model_cfg = dict(\n type='InstColorization',\n data_preprocessor=dict(\n type='DataPreprocessor',\n mean=[127.5],\n std=[127.5],\n ),\n image_model=dict(\n type='ColorizationNet',\n input_nc=4,\n output_nc=2,\n norm_type='batch'),\n instance_model=dict(\n type='ColorizationNet',\n input_nc=4,\n output_nc=2,\n norm_type='batch'),\n fusion_model=dict(\n type='FusionNet', input_nc=4, output_nc=2, norm_type='batch'),\n color_data_opt=dict(\n ab_thresh=0,\n p=1.0,\n sample_PS=[\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n ],\n ab_norm=110,\n ab_max=110.,\n ab_quant=10.,\n l_norm=100.,\n l_cent=50.,\n mask_cent=0.5),\n which_direction='AtoB',\n loss=dict(type='HuberLoss', delta=.01))\n\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'InstColorization'\n\n # prepare data\n inputs = torch.rand(1, 3, 256, 256)\n target_shape = (1, 3, 256, 256)\n\n data_sample = DataSample(gt_img=inputs)\n metainfo = dict(\n cropped_img=torch.rand(9, 3, 256, 256),\n box_info=torch.tensor([[175, 29, 96, 54, 52, 106],\n [14, 191, 84, 61, 51, 111],\n [117, 64, 115, 46, 75, 95],\n [41, 165, 121, 47, 50, 88],\n [46, 136, 94, 45, 74, 117],\n [79, 124, 62, 115, 53, 79],\n [156, 64, 77, 138, 36, 41],\n [200, 48, 114, 131, 8, 11],\n [115, 78, 92, 81, 63, 83]]),\n box_info_2x=torch.tensor([[87, 15, 48, 27, 26, 53],\n [7, 96, 42, 31, 25, 55],\n [58, 32, 57, 23, 38, 48],\n [20, 83, 60, 24, 25, 44],\n [23, 68, 47, 23, 37, 58],\n [39, 62, 31, 58, 27, 39],\n [78, 32, 38, 69, 18, 21],\n [100, 24, 57, 66, 4, 5],\n [57, 39, 46, 41, 32, 41]]),\n box_info_4x=torch.tensor([[43, 8, 24, 14, 13, 26],\n [3, 48, 21, 16, 13, 27],\n [29, 16, 28, 12, 19, 24],\n [10, 42, 30, 12, 12, 22],\n [11, 34, 23, 12, 19, 29],\n [19, 31, 15, 29, 14, 20],\n [39, 16, 19, 35, 9, 10],\n [50, 12, 28, 33, 2, 3],\n [28, 20, 23, 21, 16, 20]]),\n box_info_8x=torch.tensor([[21, 4, 12, 7, 7, 13],\n [1, 24, 10, 8, 7, 14],\n [14, 8, 14, 6, 10, 12],\n [5, 21, 15, 6, 6, 11],\n [5, 17, 11, 6, 10, 15],\n [9, 16, 7, 15, 7, 10],\n [19, 8, 9, 18, 5, 5],\n [25, 6, 14, 17, 1, 1],\n [14, 10, 11, 11, 8, 10]]),\n empty_box=False)\n data_sample.set_metainfo(metainfo=metainfo)\n\n data = dict(\n inputs=inputs, data_samples=DataSample.stack([data_sample]))\n\n res = model(mode='tensor', **data)\n\n assert torch.is_tensor(res)\n assert res.shape == target_shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_inst_colorization/test_weight_layer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.inst_colorization.weight_layer import WeightLayer\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_weight_layer():\n\n weight_layer = WeightLayer(64)\n\n instance_feature_conv1_2 = torch.rand(1, 64, 256, 256)\n conv1_2 = torch.rand(1, 64, 256, 256)\n box_info = torch.tensor([[175, 29, 96, 54, 52, 106],\n [14, 191, 84, 61, 51, 111],\n [117, 64, 115, 46, 75, 95],\n [41, 165, 121, 47, 50, 88],\n [46, 136, 94, 45, 74, 117],\n [79, 124, 62, 115, 53, 79],\n [156, 64, 77, 138, 36, 41],\n [200, 48, 114, 131, 8, 11],\n [115, 78, 92, 81, 63, 83]])\n conv1_2 = weight_layer(instance_feature_conv1_2, conv1_2, box_info)\n\n assert conv1_2.shape == instance_feature_conv1_2.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_liif/test_liif.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom typing import List\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import LIIF, DataPreprocessor\nfrom mmagic.structures import DataSample\n\n\ndef test_liif():\n\n model = LIIF(\n generator=dict(\n type='LIIFEDSRNet',\n encoder=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_blocks=2),\n imnet=dict(\n type='MLPRefiner',\n in_dim=64,\n out_dim=3,\n hidden_list=[4, 4, 4, 4]),\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=64),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n data_preprocessor=DataPreprocessor(\n mean=[0.4488 * 255, 0.4371 * 255, 0.4040 * 255],\n std=[255., 255., 255.],\n # input_view=(-1, 1, 1),\n # output_view=(1, -1)\n ))\n\n # test attributes\n assert model.__class__.__name__ == 'LIIF'\n\n # prepare data\n inputs = torch.rand(1, 3, 8, 8)\n data_sample = DataSample(\n metainfo=dict(coord=torch.rand(256, 2), cell=torch.rand(256, 2)))\n data_sample.gt_img = torch.rand(256, 3)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars['loss'], torch.Tensor)\n save_loss = log_vars['loss']\n log_vars = model.train_step(data, optim_wrapper)\n log_vars = model.train_step(data, optim_wrapper)\n assert save_loss > log_vars['loss']\n\n # val\n predictions = model.val_step(data)\n # predictions = predictions.split()\n assert isinstance(predictions, List)\n assert len(predictions) == 1\n assert isinstance(predictions[0], DataSample)\n assert isinstance(predictions[0].output.pred_img.data, torch.Tensor)\n assert predictions[0].output.pred_img.shape == (3, 16, 16)\n\n # feat\n output = model(\n torch.rand(1, 3, 8, 8), DataSample.stack([data_sample]), mode='tensor')\n assert output.shape == (1, 256, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_liif/test_liif_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_liif_edsr_net():\n\n model_cfg = dict(\n type='LIIFEDSRNet',\n encoder=dict(\n type='EDSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16),\n imnet=dict(\n type='MLPRefiner',\n in_dim=64,\n out_dim=3,\n hidden_list=[256, 256, 256, 256]),\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=30000)\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'LIIFEDSRNet'\n\n # prepare data\n inputs = torch.rand(1, 3, 22, 11)\n targets = torch.rand(1, 128 * 64, 3)\n coord = torch.rand(1, 128 * 64, 2)\n cell = torch.rand(1, 128 * 64, 2)\n\n # test on cpu\n output = model(inputs, coord, cell)\n output = model(inputs, coord, cell, True)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n coord = coord.cuda()\n cell = cell.cuda()\n output = model(inputs, coord, cell)\n output = model(inputs, coord, cell, True)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef test_liif_rdn_net():\n\n model_cfg = dict(\n type='LIIFRDNNet',\n encoder=dict(\n type='RDNNet',\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=4,\n num_layers=8,\n channel_growth=64),\n imnet=dict(\n type='MLPRefiner',\n in_dim=64,\n out_dim=3,\n hidden_list=[256, 256, 256, 256]),\n local_ensemble=True,\n feat_unfold=True,\n cell_decode=True,\n eval_bsize=30000)\n\n # build model\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert model.__class__.__name__ == 'LIIFRDNNet'\n\n # prepare data\n inputs = torch.rand(1, 3, 22, 11)\n targets = torch.rand(1, 128 * 64, 3)\n coord = torch.rand(1, 128 * 64, 2)\n cell = torch.rand(1, 128 * 64, 2)\n\n # test on cpu\n output = model(inputs, coord, cell)\n output = model(inputs, coord, cell, True)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n coord = coord.cuda()\n cell = cell.cuda()\n output = model(inputs, coord, cell)\n output = model(inputs, coord, cell, True)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_liif/test_mlp_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import MLPRefiner\n\n\ndef test_MLPRefiner():\n model = MLPRefiner(8, 2, [6, 4])\n inputs = torch.randn(1, 8)\n outputs = model(inputs)\n assert outputs.shape == (1, 2)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_lsgan/test_lsgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import LSGAN, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='DCGANGenerator', noise_size=10, output_scale=16, base_channels=16)\ndiscriminator = dict(\n type='DCGANDiscriminator', input_scale=16, output_scale=4, out_channels=1)\n\n\nclass TestLSGAN(TestCase):\n\n def test_init(self):\n gan = LSGAN(\n noise_size=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator)\n\n self.assertIsInstance(gan, LSGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = LSGAN(\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = LSGAN(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = LSGAN(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('test-lsgan')\n gan = LSGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_lsgan/test_lsgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.lsgan import LSGANDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestLSGANDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n cls.x = torch.randn((2, 3, 128, 128))\n cls.default_config = dict(\n type='LSGANDiscriminator', in_channels=3, input_scale=128)\n\n def test_lsgan_discriminator(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config)\n assert isinstance(d, LSGANDiscriminator)\n score = d(self.x)\n assert score.shape == (2, 1)\n\n # test different input_scale\n config = dict(type='LSGANDiscriminator', in_channels=3, input_scale=64)\n d = MODELS.build(config)\n assert isinstance(d, LSGANDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x)\n assert score.shape == (2, 1)\n\n # test different config\n config = dict(\n type='LSGANDiscriminator',\n in_channels=3,\n input_scale=64,\n out_act_cfg=dict(type='Sigmoid'))\n d = MODELS.build(config)\n assert isinstance(d, LSGANDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x)\n assert score.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_lsgan_discriminator_cuda(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config).cuda()\n assert isinstance(d, LSGANDiscriminator)\n score = d(self.x.cuda())\n assert score.shape == (2, 1)\n\n # test different input_scale\n config = dict(type='LSGANDiscriminator', in_channels=3, input_scale=64)\n d = MODELS.build(config).cuda()\n assert isinstance(d, LSGANDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x.cuda())\n assert score.shape == (2, 1)\n\n # test different config\n config = dict(\n type='LSGANDiscriminator',\n in_channels=3,\n input_scale=64,\n out_act_cfg=dict(type='Sigmoid'))\n d = MODELS.build(config).cuda()\n assert isinstance(d, LSGANDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x.cuda())\n assert score.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_lsgan/test_lsgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.lsgan import LSGANGenerator\nfrom mmagic.registry import MODELS\n\n\nclass TestLSGANGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((3, 128))\n cls.default_config = dict(\n type='LSGANGenerator', noise_size=128, output_scale=128)\n\n def test_lsgan_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, LSGANGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n x = g(None, num_batches=3, return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n x = g(self.noise, return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n x = g(torch.randn, num_batches=3, return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n\n # test different output_scale\n config = dict(type='LSGANGenerator', noise_size=128, output_scale=64)\n g = MODELS.build(config)\n assert isinstance(g, LSGANGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 64, 64)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_lsgan_generator_cuda(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, LSGANGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n x = g(None, num_batches=3, return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n x = g(self.noise.cuda(), return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n x = g(torch.randn, num_batches=3, return_noise=True)\n assert x['noise_batch'].shape == (3, 128)\n\n # test different output_scale\n config = dict(type='LSGANGenerator', noise_size=128, output_scale=64)\n g = MODELS.build(config).cuda()\n assert isinstance(g, LSGANGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_mspie_stylegan2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\n\nfrom mmagic.models import DataPreprocessor, MSPIEStyleGAN2\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestMSPIEStyleGAN2(TestCase):\n\n @classmethod\n def setup_class(cls):\n cls.generator_cfg = dict(\n type='MSStyleGANv2Generator', out_size=32, style_channels=16)\n cls.disc_cfg = dict(type='MSStyleGAN2Discriminator', in_size=32)\n\n # reg params\n d_reg_interval = 16\n g_reg_interval = 4\n ema_half_life = 10. # G_smoothing_kimg\n\n cls.ema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\n cls.loss_config = dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2)\n\n @pytest.mark.skipif(\n ('win' in platform.system().lower() and 'cu' in torch.__version__)\n or not torch.cuda.is_available(),\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan2_cpu(self):\n accu_iter = 1\n message_hub = MessageHub.get_instance('test-mspie-s2')\n gan = MSPIEStyleGAN2(\n self.generator_cfg,\n self.disc_cfg,\n data_preprocessor=DataPreprocessor(),\n ema_config=self.ema_config,\n loss_config=self.loss_config,\n train_settings=dict(num_upblocks=3))\n\n optimizer_g = torch.optim.SGD(gan.generator.parameters(), lr=0.01)\n optimizer_d = torch.optim.SGD(gan.discriminator.parameters(), lr=0.01)\n\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(optimizer_g, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n optimizer_d, accumulative_counts=accu_iter))\n\n # prepare inputs\n img = torch.randn(3, 32, 32)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n message_hub.update_info('iter', 0)\n _ = gan.train_step(data, optim_wrapper_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_mspie_stylegan2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.mspie import MSStyleGAN2Discriminator\n\n\nclass TestMSStyleGANv2Disc:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(in_size=64, channel_multiplier=1)\n\n def test_msstylegan2_disc_cpu(self):\n d = MSStyleGAN2Discriminator(**self.default_cfg)\n img = torch.randn((2, 3, 64, 64))\n score = d(img)\n assert score.shape == (2, 1)\n\n d = MSStyleGAN2Discriminator(\n with_adaptive_pool=True, **self.default_cfg)\n img = torch.randn((2, 3, 64, 64))\n score = d(img)\n assert score.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_msstylegan2_disc_cuda(self):\n d = MSStyleGAN2Discriminator(**self.default_cfg).cuda()\n img = torch.randn((2, 3, 64, 64)).cuda()\n score = d(img)\n assert score.shape == (2, 1)\n\n d = MSStyleGAN2Discriminator(\n with_adaptive_pool=True, **self.default_cfg).cuda()\n img = torch.randn((2, 3, 64, 64)).cuda()\n score = d(img)\n assert score.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_mspie_stylegan2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors.mspie import MSStyleGANv2Generator\nfrom mmagic.registry import MODELS\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\n@MODELS.register_module()\nclass MockHeadPosEncoding(nn.Module):\n\n def __init__(self):\n super().__init__()\n\n\nclass TestMSStyleGAN2:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(out_size=32, style_channels=16)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_msstylegan2_cpu(self):\n\n # test normal forward\n cfg = deepcopy(self.default_cfg)\n g = MSStyleGANv2Generator(**cfg)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n # set mix_prob as 1.0 and 0 to force cover lines\n cfg = deepcopy(self.default_cfg)\n cfg['mix_prob'] = 1\n g = MSStyleGANv2Generator(**cfg)\n res = g(torch.randn, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n # test style-mixing with inject_inde is passed\n res = g(torch.randn, num_batches=2, inject_index=0)\n assert res.shape == (2, 3, 32, 32)\n\n cfg = deepcopy(self.default_cfg)\n cfg['mix_prob'] = 0\n g = MSStyleGANv2Generator(**cfg)\n res = g(torch.randn, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n cfg = deepcopy(self.default_cfg)\n cfg['mix_prob'] = 1\n g = MSStyleGANv2Generator(**cfg)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n cfg = deepcopy(self.default_cfg)\n cfg['mix_prob'] = 0\n g = MSStyleGANv2Generator(**cfg)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n # test truncation less than 1\n res = g(None, num_batches=2, truncation=0.5)\n assert res.shape == (2, 3, 32, 32)\n\n # test chosen scale\n res = g(None, num_batches=2, chosen_scale=2, randomize_noise=False)\n print(res.shape)\n\n # test injected_noise is not None\n injected_noise = g.make_injected_noise()\n res = g(None, num_batches=2, injected_noise=injected_noise)\n\n # test return noise is True\n res = g(None, num_batches=2, return_noise=True)\n assert isinstance(res, dict)\n\n # test chosen_scale is Tuple\n res = g(None, num_batches=2, chosen_scale=(0, 2))\n print(res.shape)\n\n def test_train(self):\n cfg = deepcopy(self.default_cfg)\n g = MSStyleGANv2Generator(**cfg)\n # test train -> train\n g.train()\n assert g.training\n assert g.default_style_mode == g._default_style_mode\n # test train -> eval\n g.eval()\n assert not g.training\n assert g.default_style_mode == g.eval_style_mode\n # test eval -> eval\n g.eval()\n assert not g.training\n assert g.default_style_mode == g.eval_style_mode\n # test train -> train\n g.train()\n assert g.training\n assert g.default_style_mode == g._default_style_mode\n\n # def test_make_injected_noise(self):\n # cfg = deepcopy(self.default_cfg)\n # g = MSStyleGANv2Generator(**cfg)\n # pass\n\n def test_mean_latent(self):\n cfg = deepcopy(self.default_cfg)\n g = MSStyleGANv2Generator(**cfg)\n mean_latent = g.get_mean_latent(num_samples=4, bs_per_repeat=2)\n assert mean_latent.shape == (1, 16)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_head_pos_encoding(self):\n cfg = deepcopy(self.default_cfg)\n g = MSStyleGANv2Generator(**cfg, head_pos_encoding=dict(type='CSG'))\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n g = MSStyleGANv2Generator(\n **cfg, head_pos_encoding=dict(type='CSG'), interp_head=True)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 32, 32)\n\n g = MSStyleGANv2Generator(\n **cfg, head_pos_encoding=dict(type='MockHeadPosEncoding'))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_mspie_stylegan2_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.mspie.mspie_stylegan2_modules import (\n ModulatedPEConv2d, ModulatedPEStyleConv)\n\n\nclass TestModulatedPEStyleConv(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.default_cfg = dict(\n in_channels=8, out_channels=8, kernel_size=3, style_channels=16)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_upsample(self):\n cfg = deepcopy(self.default_cfg)\n conv = ModulatedPEStyleConv(**cfg, upsample=True)\n x = torch.randn(1, 8, 32, 32)\n style = torch.randn(1, 16)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 64, 64))\n\n # test return noise\n noise = torch.randn(1, 8, 64, 64)\n out = conv(x, style, noise)\n self.assertEqual(out.shape, (1, 8, 64, 64))\n\n out, noise_return = conv(x, style, noise, return_noise=True)\n assert (noise_return == noise).all()\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_downsample(self):\n\n cfg = deepcopy(self.default_cfg)\n conv = ModulatedPEStyleConv(**cfg, downsample=True)\n x = torch.randn(1, 8, 32, 32)\n style = torch.randn(1, 16)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 16, 16))\n\n # test return noise\n noise = torch.randn(1, 8, 16, 16)\n out = conv(x, style, noise)\n self.assertEqual(out.shape, (1, 8, 16, 16))\n\n out, noise_return = conv(x, style, noise, return_noise=True)\n assert (noise_return == noise).all()\n\n\nclass TestModulatedPEConv2d(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.default_cfg = dict(\n in_channels=8, out_channels=8, kernel_size=3, style_channels=16)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_equalized_lr_cfg(self):\n cfg = deepcopy(self.default_cfg)\n conv = ModulatedPEConv2d(**cfg, equalized_lr_cfg=None)\n x = torch.randn(1, 8, 32, 32)\n style = torch.randn(1, 16)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 32, 32))\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_demodulate(self):\n cfg = deepcopy(self.default_cfg)\n conv = ModulatedPEConv2d(**cfg, demodulate=False)\n x = torch.randn(1, 8, 32, 32)\n style = torch.randn(1, 16)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 32, 32))\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_up_after_conv(self):\n x = torch.randn(1, 8, 32, 32)\n style = torch.randn(1, 16)\n cfg = deepcopy(self.default_cfg)\n\n conv = ModulatedPEConv2d(\n **cfg, upsample=True, deconv2conv=True, up_after_conv=True)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 64, 64))\n\n conv = ModulatedPEConv2d(\n **cfg, upsample=True, deconv2conv=True, up_after_conv=False)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 64, 64))\n\n conv = ModulatedPEConv2d(\n **cfg, upsample=True, deconv2conv=True, interp_pad=3)\n out = conv(x, style)\n self.assertEqual(out.shape, (1, 8, 67, 67))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_pe_singan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine import MessageHub\n\nfrom mmagic.engine import SinGANOptimWrapperConstructor\nfrom mmagic.models import PESinGAN\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestPESinGAN:\n\n @classmethod\n def setup_class(cls):\n cls.generator = dict(\n type='SinGANMSGeneratorPE',\n in_channels=3,\n out_channels=3,\n num_scales=3,\n interp_pad=True,\n noise_with_pad=True)\n\n cls.disc = dict(\n type='SinGANMultiScaleDiscriminator', in_channels=3,\n num_scales=3) # noqa\n\n cls.data_preprocessor = dict(\n type='DataPreprocessor', non_image_keys=['input_sample'])\n\n cls.noise_weight_init = 0.1\n cls.iters_per_scale = 2\n cls.disc_steps = 3\n cls.generator_steps = 3\n cls.fixed_noise_with_pad = True\n cls.lr_scheduler_args = dict(milestones=[1600], gamma=0.1)\n\n cls.data_batch = dict(\n inputs=dict(\n real_scale0=torch.randn(1, 3, 25, 25),\n real_scale1=torch.randn(1, 3, 30, 30),\n real_scale2=torch.randn(1, 3, 32, 32),\n ))\n cls.data_batch['inputs']['input_sample'] = torch.zeros_like(\n cls.data_batch['inputs']['real_scale0'])\n\n cls.optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))))\n\n def test_pesingan_cpu(self):\n message_hub = MessageHub.get_instance('test-pe-singan')\n message_hub.update_info('iter', 0)\n singan = PESinGAN(\n self.generator,\n self.disc,\n num_scales=3,\n data_preprocessor=self.data_preprocessor,\n noise_weight_init=self.noise_weight_init,\n iters_per_scale=self.iters_per_scale,\n lr_scheduler_args=self.lr_scheduler_args,\n fixed_noise_with_pad=self.fixed_noise_with_pad)\n\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n\n for i in range(6):\n singan.train_step(self.data_batch, optim_wrapper_dict)\n message_hub.update_info('iter', message_hub.get_info('iter') + 1)\n # img = singan.forward(dict(num_batches=1), None)\n\n # if i in [0, 1]:\n # assert singan.curr_stage == 0\n # assert img.shape[-2:] == (25, 25)\n # elif i in [2, 3]:\n # assert singan.curr_stage == 1\n # assert img.shape[-2:] == (30, 30)\n # elif i in [4, 5]:\n # assert singan.curr_stage == 2\n # assert img.shape[-2:] == (32, 32)\n\n singan = PESinGAN(\n dict(\n type='SinGANMSGeneratorPE',\n in_channels=3,\n out_channels=3,\n num_scales=3,\n interp_pad=True,\n noise_with_pad=False),\n self.disc,\n num_scales=3,\n data_preprocessor=self.data_preprocessor,\n noise_weight_init=0.1,\n iters_per_scale=2,\n discriminator_steps=1,\n generator_steps=1,\n lr_scheduler_args=dict(milestones=[1600], gamma=0.1),\n fixed_noise_with_pad=False)\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n\n message_hub.update_info('iter', 0)\n for i in range(6):\n singan.train_step(self.data_batch, optim_wrapper_dict)\n message_hub.update_info('iter', message_hub.get_info('iter') + 1)\n outputs = singan.forward(dict(num_batches=1), None)\n img = torch.stack([out.fake_img.data for out in outputs])\n if i in [0, 1]:\n assert singan.curr_stage == 0\n assert img.shape[-2:] == (25, 25)\n elif i in [2, 3]:\n assert singan.curr_stage == 1\n assert img.shape[-2:] == (30, 30)\n elif i in [4, 5]:\n assert singan.curr_stage == 2\n assert img.shape[-2:] == (32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_pe_singan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.mspie import SinGANMSGeneratorPE\n\n\nclass TestSinGANPEGen:\n\n @classmethod\n def setup_class(cls):\n cls.default_args = dict(\n in_channels=3,\n out_channels=3,\n kernel_size=3,\n num_layers=3,\n base_channels=32,\n num_scales=3,\n min_feat_channels=16)\n\n cls.fixed_noises = [\n torch.randn(1, 1, 8, 8),\n torch.randn(1, 3, 10, 10),\n torch.randn(1, 3, 12, 12),\n torch.randn(1, 3, 16, 16)\n ]\n cls.input_sample = torch.zeros((1, 3, 8, 8))\n cls.noise_weights = [1., 0.5, 0.5, 0.5]\n\n def test_singan_gen_pe(self):\n gen = SinGANMSGeneratorPE(**self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n output = gen(\n self.input_sample,\n self.fixed_noises,\n self.noise_weights,\n 'rand',\n 2,\n get_prev_res=True)\n\n assert output['prev_res_list'][0].shape == (1, 3, 8, 8)\n\n gen = SinGANMSGeneratorPE(\n padding_mode='reflect', **self.default_args) # noqa\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n with pytest.raises(NotImplementedError):\n _ = SinGANMSGeneratorPE(\n padding_mode='circular', **self.default_args)\n\n gen = SinGANMSGeneratorPE(\n padding=1, pad_at_head=False, **self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n gen = SinGANMSGeneratorPE(\n pad_at_head=True, interp_pad=True, **self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n gen = SinGANMSGeneratorPE(\n positional_encoding=dict(\n type='SPE2d', embedding_dim=4, padding_idx=0),\n allow_no_residual=True,\n first_stage_in_channels=8,\n **self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n gen = SinGANMSGeneratorPE(\n positional_encoding=dict(type='CSG2d'),\n allow_no_residual=True,\n first_stage_in_channels=2,\n **self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n gen = SinGANMSGeneratorPE(\n interp_pad=True, noise_with_pad=True, **self.default_args)\n res = gen(None, self.fixed_noises, self.noise_weights, 'rand', 2)\n assert res.shape == (1, 3, 6, 6)\n\n gen = SinGANMSGeneratorPE(\n interp_pad=True, noise_with_pad=False, **self.default_args)\n res = gen(None, self.fixed_noises, self.noise_weights, 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_mspie/test_positional_encoding.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.mspie import CatersianGrid as CSG\nfrom mmagic.models.editors.mspie import SinusoidalPositionalEmbedding as SPE\n\n\nclass TestSPE:\n\n @classmethod\n def setup_class(cls):\n cls.spe = SPE(4, 0, 32)\n\n def test_spe_cpu(self):\n # test spe 1d\n embed = self.spe(torch.randn((2, 10)))\n assert embed.shape == (2, 10, 4)\n\n # test spe 2d\n embed = self.spe(torch.randn((2, 3, 8, 8)))\n assert embed.shape == (2, 8, 8, 8)\n\n with pytest.raises(AssertionError):\n _ = self.spe(torch.randn(2, 3, 3))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_spe_gpu(self):\n spe = self.spe.cuda()\n # test spe 1d\n embed = spe(torch.randn((2, 10)).cuda())\n assert embed.shape == (2, 10, 4)\n assert embed.is_cuda\n\n # test spe 2d\n embed = spe(torch.randn((2, 3, 8, 8)).cuda())\n assert embed.shape == (2, 8, 8, 8)\n\n with pytest.raises(AssertionError):\n _ = spe(torch.randn(2, 3, 3))\n\n\nclass TestCSG:\n\n @classmethod\n def setup_class(cls):\n cls.csg = CSG()\n\n def test_csg_cpu(self):\n csg = self.csg(torch.randn((2, 3, 4, 4)))\n assert csg.shape == (2, 2, 4, 4)\n\n with pytest.raises(AssertionError):\n _ = self.csg(torch.randn((2, 3, 3)))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_csg_cuda(self):\n embed = self.csg(torch.randn((2, 4, 5, 5)).cuda())\n assert embed.shape == (2, 2, 5, 5) and embed.is_cuda\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_nafnet/test_naf_avgpool2d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.nafnet.naf_avgpool2d import NAFAvgPool2d\n\n\ndef test_avgpool2d():\n inputs = torch.ones((1, 3, 32, 32))\n targets = torch.Tensor([1., 1., 1.]).view(1, 3, 1, 1)\n tar_info = 'kernel_size=None, base_size=(48, 48),' \\\n + ' stride=None, fast_imp=False'\n base_size = (int(inputs.shape[-2] * 1.5), int(inputs.shape[-1] * 1.5))\n train_size = inputs.shape\n\n avg_pool_2d = NAFAvgPool2d(\n base_size=base_size, train_size=train_size, fast_imp=False)\n info = avg_pool_2d.extra_repr()\n outputs = avg_pool_2d(inputs)\n print(outputs)\n assert info == tar_info\n assert torch.all(torch.eq(targets, outputs))\n\n avg_pool_2d = NAFAvgPool2d(\n base_size=int(inputs.shape[-2] * 1.5),\n train_size=train_size,\n fast_imp=False)\n info = avg_pool_2d.extra_repr()\n tar_info = 'kernel_size=None, base_size=48,' \\\n + ' stride=None, fast_imp=False'\n outputs = avg_pool_2d(inputs)\n print(outputs)\n assert info == tar_info\n assert torch.all(torch.eq(targets, outputs))\n\n avg_pool_2d = NAFAvgPool2d(\n base_size=base_size, train_size=train_size, fast_imp=True)\n info = avg_pool_2d.extra_repr()\n tar_info = 'kernel_size=None, base_size=(48, 48),' \\\n + ' stride=None, fast_imp=True'\n outputs = avg_pool_2d(inputs)\n print(outputs)\n assert info == tar_info\n assert torch.all(torch.eq(targets, outputs))\n\n avg_pool_2d = NAFAvgPool2d(\n base_size=(16, 16), train_size=train_size, fast_imp=True)\n info = avg_pool_2d.extra_repr()\n tar_info = 'kernel_size=None, base_size=(16, 16),' \\\n + ' stride=None, fast_imp=True'\n outputs = avg_pool_2d(inputs)\n print(outputs)\n assert info == tar_info\n assert torch.all(torch.eq(targets, outputs))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_nafnet/test_naf_layernorm2d.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.nafnet.naf_layerNorm2d import LayerNorm2d\n\n\ndef test_layer_norm():\n inputs = torch.ones((1, 3, 64, 64))\n targets = torch.zeros((1, 3, 64, 64))\n\n layer_norm_2d = LayerNorm2d(inputs.shape[1])\n outputs = layer_norm_2d(inputs)\n assert outputs.shape == targets.shape\n assert torch.all(torch.eq(outputs, targets))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_nafnet/test_nafbaseline.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models import NAFBaseline, NAFBaselineLocal\n\n\ndef test_nafnet():\n\n model = NAFBaseline(\n img_channel=3,\n mid_channels=64,\n enc_blk_nums=[2, 2, 4, 8],\n middle_blk_num=12,\n dec_blk_nums=[2, 2, 2, 2],\n )\n\n # test attributes\n assert model.__class__.__name__ == 'NAFBaseline'\n\n # prepare data\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n\n # test on cpu\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef test_baseline_local():\n\n model = NAFBaselineLocal(\n img_channel=3,\n mid_channels=64,\n enc_blk_nums=[1, 1, 1, 28],\n middle_blk_num=1,\n dec_blk_nums=[1, 1, 1, 1],\n )\n\n # test attributes\n assert model.__class__.__name__ == 'NAFBaselineLocal'\n\n # prepare data\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n\n # test on cpu\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_nafnet/test_nafnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models import NAFNet, NAFNetLocal\n\n\ndef test_nafnet():\n\n model = NAFNet(\n img_channels=3,\n mid_channels=64,\n enc_blk_nums=[2, 2, 4, 8],\n middle_blk_num=12,\n dec_blk_nums=[2, 2, 2, 2],\n )\n\n # test attributes\n assert model.__class__.__name__ == 'NAFNet'\n\n # prepare data\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n\n # test on cpu\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef test_nafnet_local():\n\n model = NAFNetLocal(\n img_channels=3,\n mid_channels=64,\n enc_blk_nums=[1, 1, 1, 28],\n middle_blk_num=1,\n dec_blk_nums=[1, 1, 1, 1],\n )\n\n # test attributes\n assert model.__class__.__name__ == 'NAFNetLocal'\n\n # prepare data\n inputs = torch.rand(1, 3, 64, 64)\n targets = torch.rand(1, 3, 64, 64)\n\n # test on cpu\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n targets = targets.cuda()\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_mask_conv_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors import MaskConvModule\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_mask_conv_module():\n with pytest.raises(KeyError):\n # conv_cfg must be a dict or None\n conv_cfg = dict(type='conv')\n MaskConvModule(3, 8, 2, conv_cfg=conv_cfg)\n\n with pytest.raises(AssertionError):\n # norm_cfg must be a dict or None\n norm_cfg = ['norm']\n MaskConvModule(3, 8, 2, norm_cfg=norm_cfg)\n\n with pytest.raises(AssertionError):\n # order elements must be ('conv', 'norm', 'act')\n order = ['conv', 'norm', 'act']\n MaskConvModule(3, 8, 2, order=order)\n\n with pytest.raises(AssertionError):\n # order elements must be ('conv', 'norm', 'act')\n order = ('conv', 'norm')\n MaskConvModule(3, 8, 2, order=order)\n\n with pytest.raises(KeyError):\n # softmax is not supported\n act_cfg = dict(type='softmax')\n MaskConvModule(3, 8, 2, act_cfg=act_cfg)\n\n conv_cfg = dict(type='PConv', multi_channel=True)\n conv = MaskConvModule(3, 8, 2, conv_cfg=conv_cfg)\n x = torch.rand(1, 3, 256, 256)\n mask_in = torch.ones_like(x)\n mask_in[..., 20:130, 120:150] = 0.\n output, mask_update = conv(x, mask_in)\n assert output.shape == (1, 8, 255, 255)\n assert mask_update.shape == (1, 8, 255, 255)\n\n # add test for ['norm', 'conv', 'act']\n conv = MaskConvModule(\n 3, 8, 2, order=('norm', 'conv', 'act'), conv_cfg=conv_cfg)\n x = torch.rand(1, 3, 256, 256)\n output = conv(x, mask_in, return_mask=False)\n assert output.shape == (1, 8, 255, 255)\n\n conv = MaskConvModule(\n 3, 8, 3, padding=1, conv_cfg=conv_cfg, with_spectral_norm=True)\n assert hasattr(conv.conv, 'weight_orig')\n output = conv(x, return_mask=False)\n assert output.shape == (1, 8, 256, 256)\n\n conv = MaskConvModule(\n 3,\n 8,\n 3,\n padding=1,\n norm_cfg=dict(type='BN'),\n padding_mode='reflect',\n conv_cfg=conv_cfg)\n assert isinstance(conv.padding_layer, nn.ReflectionPad2d)\n output = conv(x, mask_in, return_mask=False)\n assert output.shape == (1, 8, 256, 256)\n\n conv = MaskConvModule(\n 3, 8, 3, padding=1, act_cfg=dict(type='LeakyReLU'), conv_cfg=conv_cfg)\n output = conv(x, mask_in, return_mask=False)\n assert output.shape == (1, 8, 256, 256)\n\n with pytest.raises(KeyError):\n conv = MaskConvModule(3, 8, 3, padding=1, padding_mode='igccc')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_partial_conv.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import PartialConv2d\n\n\ndef test_pconv2d():\n pconv2d = PartialConv2d(\n 3, 2, kernel_size=1, stride=1, multi_channel=True, eps=1e-8)\n\n x = torch.rand(1, 3, 6, 6)\n mask = torch.ones_like(x)\n mask[..., 2, 2] = 0.\n output, updated_mask = pconv2d(x, mask=mask)\n assert output.shape == (1, 2, 6, 6)\n assert updated_mask.shape == (1, 2, 6, 6)\n\n output = pconv2d(x, mask=None)\n assert output.shape == (1, 2, 6, 6)\n\n pconv2d = PartialConv2d(\n 3, 2, kernel_size=1, stride=1, multi_channel=True, eps=1e-8)\n output = pconv2d(x, mask=None)\n assert output.shape == (1, 2, 6, 6)\n\n pconv2d = PartialConv2d(\n 3, 2, kernel_size=1, stride=1, multi_channel=False, eps=1e-8)\n output = pconv2d(x, mask=None)\n assert output.shape == (1, 2, 6, 6)\n\n pconv2d = PartialConv2d(\n 3,\n 2,\n kernel_size=1,\n stride=1,\n bias=False,\n multi_channel=True,\n eps=1e-8)\n output = pconv2d(x, mask=mask, return_mask=False)\n assert output.shape == (1, 2, 6, 6)\n\n with pytest.raises(AssertionError):\n pconv2d(x, mask=torch.ones(1, 1, 6, 6))\n\n pconv2d = PartialConv2d(\n 3,\n 2,\n kernel_size=1,\n stride=1,\n bias=False,\n multi_channel=False,\n eps=1e-8)\n output = pconv2d(x, mask=None)\n assert output.shape == (1, 2, 6, 6)\n\n with pytest.raises(AssertionError):\n output = pconv2d(x, mask=mask[0])\n\n with pytest.raises(AssertionError):\n output = pconv2d(x, mask=torch.ones(1, 3, 6, 6))\n\n if torch.cuda.is_available():\n pconv2d = PartialConv2d(\n 3,\n 2,\n kernel_size=1,\n stride=1,\n bias=False,\n multi_channel=False,\n eps=1e-8).cuda().half()\n output = pconv2d(x.cuda().half(), mask=None)\n assert output.shape == (1, 2, 6, 6)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_pconv_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import PConvDecoder, PConvEncoder\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef test_pconv_dec():\n pconv_enc = PConvEncoder()\n img = torch.randn(2, 3, 128, 128)\n mask = torch.ones_like(img)\n output = pconv_enc(img, mask)\n\n pconv_dec = PConvDecoder()\n input = {\n 'hidden_feats': output['hidden_feats'],\n 'hidden_masks': output['hidden_masks']\n }\n h, h_mask = pconv_dec(input)\n assert h.detach().numpy().shape == (2, 3, 128, 128)\n assert h_mask.detach().numpy().shape == (2, 3, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_pconv_encoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm\n\nfrom mmagic.models.editors import PConvEncoder\n\n\ndef test_pconv_enc():\n pconv_enc = PConvEncoder(norm_eval=False)\n pconv_enc.train()\n for name, module in pconv_enc.named_modules():\n if isinstance(module, _BatchNorm):\n assert module.training\n\n pconv_enc = PConvEncoder(norm_eval=True)\n pconv_enc.train()\n for name, module in pconv_enc.named_modules():\n if isinstance(module, _BatchNorm):\n assert not module.training\n\n pconv_enc = PConvEncoder()\n img = torch.randn(2, 3, 128, 128)\n mask = torch.ones_like(img)\n output = pconv_enc(img, mask)\n assert isinstance(output['hidden_feats'], dict)\n assert isinstance(output['hidden_masks'], dict)\n assert output['out'].detach().numpy().shape == (2, 512, 1, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_pconv_encoder_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import PConvEncoderDecoder\n\n\ndef test_pconv_encdec():\n pconv_enc_cfg = dict(type='PConvEncoder')\n pconv_dec_cfg = dict(type='PConvDecoder')\n\n if torch.cuda.is_available():\n pconv_encdec = PConvEncoderDecoder(pconv_enc_cfg, pconv_dec_cfg)\n pconv_encdec.init_weights()\n pconv_encdec.cuda()\n x = torch.randn((1, 3, 256, 256)).cuda()\n mask = torch.ones_like(x)\n mask[..., 50:150, 100:250] = 1.\n res, updated_mask = pconv_encdec(x, mask)\n assert res.shape == (1, 3, 256, 256)\n assert mask.shape == (1, 3, 256, 256)\n\n with pytest.raises(TypeError):\n pconv_encdec.init_weights(pretrained=dict(igccc=8989))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pconv/test_pconv_inpaintor.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom os.path import dirname, join\n\nimport pytest\nimport torch\nfrom mmengine import Config\nfrom mmengine.optim import OptimWrapper\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_pconv_inpaintor():\n register_all_modules()\n\n config_file = join(\n dirname(__file__), '../../..', 'configs', 'pconv_test.py')\n cfg = Config.fromfile(config_file)\n\n inpaintor = MODELS.build(cfg.model)\n\n assert inpaintor.__class__.__name__ == 'PConvInpaintor'\n\n if torch.cuda.is_available():\n inpaintor = inpaintor.cuda()\n\n gt_img = torch.randn(3, 256, 256)\n mask = torch.zeros_like(gt_img)[0:1, ...]\n mask[..., 100:210, 100:210] = 1.\n masked_img = gt_img * (1. - mask)\n mask_bbox = [100, 100, 110, 110]\n data_batch = {\n 'inputs': [masked_img],\n 'data_samples':\n [DataSample(\n mask=mask,\n mask_bbox=mask_bbox,\n gt_img=gt_img,\n )]\n }\n\n optim_g = torch.optim.Adam(inpaintor.generator.parameters(), lr=0.0001)\n optim_g = OptimWrapper(optim_g)\n\n for i in range(5):\n log_vars = inpaintor.train_step(data_batch, optim_g)\n assert 'loss_l1_hole' in log_vars\n assert 'loss_l1_valid' in log_vars\n assert 'loss_tv' in log_vars\n\n # check for forward_test\n data = inpaintor.data_preprocessor(data_batch, True)\n data_inputs, data_sample = data['inputs'], data['data_samples']\n output = inpaintor.forward_test(data_inputs, data_sample)\n prediction = output.split()[0]\n assert 'fake_res' in prediction\n assert 'fake_img' in prediction\n assert 'pred_img' in prediction\n assert prediction.pred_img.shape == (3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pggan/test_pggan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport numpy as np\nimport pytest\nimport torch\nfrom mmengine import MessageHub\n\nfrom mmagic.engine import PGGANOptimWrapperConstructor\nfrom mmagic.models import ProgressiveGrowingGAN\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestPGGAN(TestCase):\n\n generator_cfg = dict(\n type='PGGANGenerator',\n noise_size=8,\n out_scale=16,\n base_channels=32,\n max_channels=32)\n\n discriminator_cfg = dict(\n type='PGGANDiscriminator', in_scale=16, label_size=0)\n\n data_preprocessor = dict(type='DataPreprocessor')\n\n nkimgs_per_scale = {'4': 0.004, '8': 0.008, '16': 0.016}\n\n lr_schedule = dict(generator={'8': 0.0015}, discriminator={'8': 0.0015})\n optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=lr_schedule)\n\n def test_pggan_cpu(self):\n message_hub = MessageHub.get_instance('test-pggan')\n message_hub.update_info('iter', 0)\n\n # test default config\n pggan = ProgressiveGrowingGAN(\n self.generator_cfg,\n self.discriminator_cfg,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n ema_config=dict(interval=1))\n\n constructor = PGGANOptimWrapperConstructor(self.optim_wrapper_cfg)\n optim_wrapper_dict = constructor(pggan)\n\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 16, 16)) for _ in range(3)\n ])\n\n for iter_num in range(6):\n pggan.train_step(data_batch, optim_wrapper_dict)\n # print(iter_num, pggan._next_scale_int)\n if iter_num in [0, 1]:\n assert pggan.curr_scale[0] == 4\n elif iter_num in [2, 3]:\n assert pggan.curr_scale[0] == 8\n elif iter_num in [4, 5]:\n assert pggan.curr_scale[0] == 16\n\n if iter_num == 2:\n assert np.isclose(pggan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 3:\n assert np.isclose(pggan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 5:\n assert np.isclose(pggan._actual_nkimgs[-1], 0.012, atol=1e-8)\n\n # test forward\n outputs = pggan.forward(dict(img=torch.randn(3, 3, 16, 16)))\n assert len(outputs) == 3\n assert all(['gt_img' in out for out in outputs])\n\n outputs = pggan.forward(dict(num_batches=2))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 16, 16) for out in outputs])\n\n outputs = pggan.forward(\n dict(\n num_batches=2,\n return_noise=True,\n transition_weight=0.2,\n sample_model='ema'))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 16, 16) for out in outputs])\n\n outputs = pggan.forward(dict(num_batches=2, sample_model='orig'))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 16, 16) for out in outputs])\n\n outputs = pggan.forward(dict(num_batches=2, sample_model='ema/orig'))\n assert len(outputs) == 2\n assert all([out.ema.fake_img.shape == (3, 16, 16) for out in outputs])\n assert all([out.orig.fake_img.shape == (3, 16, 16) for out in outputs])\n\n outputs = pggan.forward(dict(num_batches=2, curr_scale=8))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 8, 8) for out in outputs])\n\n outputs = pggan.forward(dict(noise=torch.randn(2, 8)))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 16, 16) for out in outputs])\n\n outputs = pggan.forward(torch.randn(2, 8))\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 16, 16) for out in outputs])\n\n # test train_step with error\n with pytest.raises(RuntimeError):\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 4, 32)) for _ in range(3)\n ])\n _ = pggan.train_step(data_batch, optim_wrapper_dict)\n\n # test train_step without ema\n pggan = ProgressiveGrowingGAN(\n self.generator_cfg,\n self.discriminator_cfg,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale)\n optim_wrapper_dict = constructor(pggan)\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 16, 16)) for _ in range(3)\n ])\n pggan.train_step(data_batch, optim_wrapper_dict)\n\n # test train_step with disc_step != 1\n pggan._disc_steps = 2\n pggan.train_step(data_batch, optim_wrapper_dict)\n\n # test default configs\n pggan = ProgressiveGrowingGAN(\n self.generator_cfg,\n self.discriminator_cfg,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n interp_real=dict(mode='bicubic'),\n ema_config=dict(interval=1))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pggan_cuda(self):\n pggan = ProgressiveGrowingGAN(\n self.generator_cfg,\n self.discriminator_cfg,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n ema_config=dict(interval=1)).cuda()\n\n constructor = PGGANOptimWrapperConstructor(self.optim_wrapper_cfg)\n optim_wrapper_dict = constructor(pggan)\n\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 16, 16)) for _ in range(3)\n ])\n\n for iter_num in range(6):\n pggan.train_step(data_batch, optim_wrapper_dict)\n if iter_num in [0, 1]:\n assert pggan.curr_scale[0] == 4\n elif iter_num in [2, 3]:\n assert pggan.curr_scale[0] == 8\n elif iter_num in [4, 5]:\n assert pggan.curr_scale[0] == 16\n\n if iter_num == 2:\n assert np.isclose(pggan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 3:\n assert np.isclose(pggan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 5:\n assert np.isclose(pggan._actual_nkimgs[-1], 0.012, atol=1e-8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pggan/test_pggan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.pggan import PGGANDiscriminator\n\n\nclass TestPGGANDiscriminator:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(in_scale=16, label_size=2)\n cls.default_inputx16 = torch.randn((2, 3, 16, 16))\n cls.default_inputx4 = torch.randn((2, 3, 4, 4))\n cls.default_inputx8 = torch.randn((2, 3, 8, 8))\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_pggan_discriminator(self):\n # test with default cfg\n disc = PGGANDiscriminator(**self.default_cfg)\n\n score, label = disc(self.default_inputx16, transition_weight=0.1)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score, label = disc(\n self.default_inputx8, transition_weight=0.1, curr_scale=8)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score, label = disc(\n self.default_inputx4, transition_weight=0.1, curr_scale=4)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n\n disc = PGGANDiscriminator(\n in_scale=16,\n mbstd_cfg=None,\n downsample_cfg=dict(type='nearest', scale_factor=0.5))\n\n score = disc(self.default_inputx16, transition_weight=0.1)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score = disc(self.default_inputx8, transition_weight=0.1, curr_scale=8)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score = disc(self.default_inputx4, transition_weight=0.1, curr_scale=4)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n assert not disc.with_mbstd\n\n with pytest.raises(NotImplementedError):\n _ = PGGANDiscriminator(\n in_scale=16, mbstd_cfg=None, downsample_cfg=dict(type='xx'))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pggan_discriminator_cuda(self):\n # test with default cfg\n disc = PGGANDiscriminator(**self.default_cfg).cuda()\n\n score, label = disc(\n self.default_inputx16.cuda(), transition_weight=0.1)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score, label = disc(\n self.default_inputx8.cuda(), transition_weight=0.1, curr_scale=8)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n score, label = disc(\n self.default_inputx4.cuda(), transition_weight=0.1, curr_scale=4)\n assert score.shape == (2, 1)\n assert label.shape == (2, 2)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pggan/test_pggan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.pggan import PGGANGenerator\n\n\nclass TestPGGANGenerator:\n\n @classmethod\n def setup_class(cls):\n cls.default_noise = torch.randn((2, 8))\n cls.default_cfg = dict(\n noise_size=8, out_scale=16, base_channels=32, max_channels=32)\n\n def test_pggan_generator(self):\n # test with default cfg\n gen = PGGANGenerator(**self.default_cfg)\n res = gen(None, num_batches=2, transition_weight=0.1)\n assert res.shape == (2, 3, 16, 16)\n\n res = gen(self.default_noise, transition_weight=0.2)\n assert res.shape == (2, 3, 16, 16)\n with pytest.raises(AssertionError):\n _ = gen(self.default_noise[:, :, None], transition_weight=0.2)\n\n with pytest.raises(AssertionError):\n _ = gen(torch.randn((2, 1)), transition_weight=0.2)\n\n res = gen(torch.randn, num_batches=2, transition_weight=0.2)\n assert res.shape == (2, 3, 16, 16)\n\n # test with input scale\n res = gen(None, num_batches=2, curr_scale=4)\n assert res.shape == (2, 3, 4, 4)\n res = gen(None, num_batches=2, curr_scale=8)\n assert res.shape == (2, 3, 8, 8)\n\n # test return noise\n res = gen(None, num_batches=2, curr_scale=8, return_noise=True)\n assert res['fake_img'].shape == (2, 3, 8, 8)\n assert res['label'] is None\n assert isinstance(res['noise_batch'], torch.Tensor)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['out_scale'] = 32\n gen = PGGANGenerator(**cfg)\n res = gen(None, num_batches=2, transition_weight=0.1)\n assert res.shape == (2, 3, 32, 32)\n\n cfg = deepcopy(self.default_cfg)\n cfg['out_scale'] = 4\n gen = PGGANGenerator(**cfg)\n res = gen(None, num_batches=2, transition_weight=0.1)\n assert res.shape == (2, 3, 4, 4)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pggan_generator_cuda(self):\n # test with default cfg\n gen = PGGANGenerator(**self.default_cfg).cuda()\n res = gen(None, num_batches=2, transition_weight=0.1)\n assert res.shape == (2, 3, 16, 16)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['out_scale'] = 32\n gen = PGGANGenerator(**cfg).cuda()\n res = gen(None, num_batches=2, transition_weight=0.1)\n assert res.shape == (2, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pggan/test_pggan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.editors.pggan import (EqualizedLR,\n EqualizedLRConvDownModule,\n EqualizedLRConvModule,\n EqualizedLRConvUpModule,\n EqualizedLRLinearModule,\n MiniBatchStddevLayer,\n PGGANNoiseTo2DFeat, PixelNorm,\n equalized_lr)\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestEqualizedLR:\n\n @classmethod\n def setup_class(cls):\n cls.default_conv_cfg = dict(\n in_channels=1,\n out_channels=1,\n kernel_size=3,\n stride=1,\n padding=1,\n norm_cfg=dict(type='BN'))\n cls.conv_input = torch.randn((2, 1, 5, 5))\n cls.linear_input = torch.randn((2, 2))\n\n def test_equalized_conv_module(self):\n conv = EqualizedLRConvModule(**self.default_conv_cfg)\n res = conv(self.conv_input)\n assert res.shape == (2, 1, 5, 5)\n has_equalized_lr = False\n for _, v in conv.conv._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert has_equalized_lr\n\n conv = EqualizedLRConvModule(\n equalized_lr_cfg=None, **self.default_conv_cfg)\n res = conv(self.conv_input)\n assert res.shape == (2, 1, 5, 5)\n has_equalized_lr = False\n for _, v in conv.conv._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert not has_equalized_lr\n\n conv = EqualizedLRConvModule(\n equalized_lr_cfg=dict(gain=1), **self.default_conv_cfg)\n res = conv(self.conv_input)\n assert res.shape == (2, 1, 5, 5)\n has_equalized_lr = False\n for _, v in conv.conv._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n assert v.gain == 1\n has_equalized_lr = True\n assert has_equalized_lr\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_equalized_conv_module_cuda(self):\n conv = EqualizedLRConvModule(**self.default_conv_cfg).cuda()\n res = conv(self.conv_input.cuda())\n assert res.shape == (2, 1, 5, 5)\n has_equalized_lr = False\n for _, v in conv.conv._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert has_equalized_lr\n\n def test_equalized_linear_module(self):\n linear = EqualizedLRLinearModule(2, 2)\n res = linear(self.linear_input)\n assert res.shape == (2, 2)\n has_equalized_lr = False\n for _, v in linear._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert has_equalized_lr\n\n linear = EqualizedLRLinearModule(2, 2, equalized_lr_cfg=None)\n res = linear(self.linear_input)\n assert res.shape == (2, 2)\n has_equalized_lr = False\n for _, v in linear._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert not has_equalized_lr\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_equalized_linear_module_cuda(self):\n linear = EqualizedLRLinearModule(2, 2).cuda()\n res = linear(self.linear_input.cuda())\n assert res.shape == (2, 2)\n has_equalized_lr = False\n for _, v in linear._forward_pre_hooks.items():\n if isinstance(v, EqualizedLR):\n has_equalized_lr = True\n assert has_equalized_lr\n\n def test_equalized_lr(self):\n with pytest.raises(RuntimeError):\n conv = nn.Conv2d(1, 1, 3, 1, 1)\n conv = equalized_lr(conv)\n conv = equalized_lr(conv)\n\n\nclass TestEqualizedLRConvUpModule:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=1,\n kernel_size=3,\n padding=1,\n stride=2,\n conv_cfg=dict(type='deconv'),\n upsample=dict(type='fused_nn'),\n norm_cfg=dict(type='PixelNorm'))\n cls.default_input = torch.randn((2, 3, 5, 5))\n\n def test_equalized_lr_convup_module(self, ):\n convup = EqualizedLRConvUpModule(**self.default_cfg)\n\n res = convup(self.default_input)\n assert res.shape == (2, 1, 10, 10)\n # test bp\n res = convup(torch.randn((2, 3, 5, 5), requires_grad=True))\n assert res.shape == (2, 1, 10, 10)\n res.mean().backward()\n\n # test nearest\n cfg_ = deepcopy(self.default_cfg)\n cfg_['upsample'] = dict(type='nearest', scale_factor=2)\n cfg_['kernel_size'] = 4\n convup = EqualizedLRConvUpModule(**cfg_)\n\n res = convup(self.default_input)\n assert res.shape == (2, 1, 20, 20)\n\n # test nearest\n cfg_ = deepcopy(self.default_cfg)\n cfg_['upsample'] = None\n cfg_['kernel_size'] = 4\n convup = EqualizedLRConvUpModule(**cfg_)\n\n res = convup(self.default_input)\n assert res.shape == (2, 1, 10, 10)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_equalized_lr_convup_module_cuda(self):\n convup = EqualizedLRConvUpModule(**self.default_cfg).cuda()\n\n res = convup(self.default_input.cuda())\n assert res.shape == (2, 1, 10, 10)\n # test bp\n res = convup(torch.randn((2, 3, 5, 5), requires_grad=True).cuda())\n assert res.shape == (2, 1, 10, 10)\n res.mean().backward()\n\n\nclass TestEqualizedLRConvDownModule:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=1,\n kernel_size=3,\n padding=1,\n stride=2,\n downsample=dict(type='fused_pool'))\n cls.default_input = torch.randn((2, 3, 8, 8))\n\n def test_equalized_lr_conv_down(self):\n convdown = EqualizedLRConvDownModule(**self.default_cfg)\n res = convdown(self.default_input)\n assert res.shape == (2, 1, 4, 4)\n # test bp\n res = convdown(torch.randn((2, 3, 8, 8), requires_grad=True))\n assert res.shape == (2, 1, 4, 4)\n res.mean().backward()\n\n # test avg pool\n cfg_ = deepcopy(self.default_cfg)\n cfg_['downsample'] = dict(type='avgpool', kernel_size=2, stride=2)\n convdown = EqualizedLRConvDownModule(**cfg_)\n res = convdown(self.default_input)\n assert res.shape == (2, 1, 2, 2)\n\n # test downsample is None\n cfg_ = deepcopy(self.default_cfg)\n cfg_['downsample'] = None\n convdown = EqualizedLRConvDownModule(**cfg_)\n res = convdown(self.default_input)\n assert res.shape == (2, 1, 4, 4)\n\n with pytest.raises(NotImplementedError):\n cfg_ = deepcopy(self.default_cfg)\n cfg_['downsample'] = dict(type='xxx', kernel_size=2, stride=2)\n _ = EqualizedLRConvDownModule(**cfg_)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_equalized_lr_conv_down_cuda(self):\n convdown = EqualizedLRConvDownModule(**self.default_cfg).cuda()\n res = convdown(self.default_input.cuda())\n assert res.shape == (2, 1, 4, 4)\n # test bp\n res = convdown(torch.randn((2, 3, 8, 8), requires_grad=True).cuda())\n assert res.shape == (2, 1, 4, 4)\n res.mean().backward()\n\n\nclass TestPixelNorm:\n\n @classmethod\n def setup_class(cls):\n cls.input_tensor = torch.randn((2, 3, 4, 4))\n\n def test_pixel_norm(self):\n pn = PixelNorm()\n res = pn(self.input_tensor)\n assert res.shape == (2, 3, 4, 4)\n\n # test zero case\n res = pn(self.input_tensor * 0)\n assert res.shape == (2, 3, 4, 4)\n assert (res == 0).all()\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pixel_norm_cuda(self):\n pn = PixelNorm().cuda()\n res = pn(self.input_tensor.cuda())\n assert res.shape == (2, 3, 4, 4)\n\n # test zero case\n res = pn(self.input_tensor.cuda() * 0)\n assert res.shape == (2, 3, 4, 4)\n assert (res == 0).all()\n\n\nclass TestMiniBatchStddevLayer:\n\n @classmethod\n def setup_class(cls):\n cls.default_input = torch.randn((2, 3, 4, 4))\n\n def test_minibatch_stddev_layer(self):\n ministd_layer = MiniBatchStddevLayer()\n res = ministd_layer(self.default_input)\n assert res.shape == (2, 4, 4, 4)\n\n with pytest.raises(AssertionError):\n _ = ministd_layer(torch.randn((5, 4, 3, 3)))\n\n ministd_layer = MiniBatchStddevLayer(group_size=3)\n res = ministd_layer(torch.randn((2, 6, 4, 4)))\n assert res.shape == (2, 7, 4, 4)\n\n # test bp\n ministd_layer = MiniBatchStddevLayer()\n res = ministd_layer(self.default_input.requires_grad_())\n assert res.shape == (2, 4, 4, 4)\n res.mean().backward()\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_minibatch_stddev_layer_cuda(self):\n ministd_layer = MiniBatchStddevLayer().cuda()\n res = ministd_layer(self.default_input.cuda())\n assert res.shape == (2, 4, 4, 4)\n\n ministd_layer = MiniBatchStddevLayer(group_size=3).cuda()\n res = ministd_layer(torch.randn((2, 6, 4, 4)).cuda())\n assert res.shape == (2, 7, 4, 4)\n\n # test bp\n ministd_layer = MiniBatchStddevLayer().cuda()\n res = ministd_layer(self.default_input.requires_grad_().cuda())\n assert res.shape == (2, 4, 4, 4)\n res.mean().backward()\n\n\nclass TestPGGANNoiseTo2DFeat:\n\n @classmethod\n def setup_class(cls):\n cls.default_input = torch.randn((2, 10))\n cls.default_cfg = dict(noise_size=10, out_channels=1)\n\n def test_pggan_noise2feat(self):\n module = PGGANNoiseTo2DFeat(**self.default_cfg)\n res = module(self.default_input)\n assert res.shape == (2, 1, 4, 4)\n assert isinstance(module.linear, EqualizedLRLinearModule)\n assert not module.linear.bias\n assert module.with_norm\n assert isinstance(module.norm, PixelNorm)\n assert isinstance(module.activation, nn.LeakyReLU)\n\n module = PGGANNoiseTo2DFeat(**self.default_cfg, act_cfg=None)\n res = module(self.default_input)\n assert res.shape == (2, 1, 4, 4)\n assert isinstance(module.linear, EqualizedLRLinearModule)\n assert not module.linear.bias\n assert module.with_norm\n assert not module.with_activation\n\n module = PGGANNoiseTo2DFeat(\n **self.default_cfg, norm_cfg=None, normalize_latent=False)\n res = module(self.default_input)\n assert res.shape == (2, 1, 4, 4)\n assert isinstance(module.linear, EqualizedLRLinearModule)\n assert not module.linear.bias\n assert not module.with_norm\n assert isinstance(module.activation, nn.LeakyReLU)\n\n with pytest.raises(AssertionError):\n _ = module(torch.randn((2, 1, 2)))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pggan_noise2feat_cuda(self):\n module = PGGANNoiseTo2DFeat(**self.default_cfg).cuda()\n res = module(self.default_input.cuda())\n assert res.shape == (2, 1, 4, 4)\n assert isinstance(module.linear, EqualizedLRLinearModule)\n assert not module.linear.bias\n assert module.with_norm\n assert isinstance(module.activation, nn.LeakyReLU)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pix2pix/test_pix2pix.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport platform\nimport sys\n\nimport pytest\nimport torch\nfrom mmengine.logging import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\n\nfrom mmagic.models import DataPreprocessor, Pix2Pix\nfrom mmagic.models.archs import PatchDiscriminator\nfrom mmagic.models.editors.pix2pix import UnetGenerator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\ndef obj_from_dict(info: dict, parent=None, default_args=None):\n \"\"\"Initialize an object from dict.\n\n The dict must contain the key \"type\", which indicates the object type, it\n can be either a string or type, such as \"list\" or ``list``. Remaining\n fields are treated as the arguments for constructing the object.\n\n Args:\n info (dict): Object types and arguments.\n parent (:class:`module`): Module which may containing expected object\n classes.\n default_args (dict, optional): Default arguments for initializing the\n object.\n\n Returns:\n any type: Object built from the dict.\n \"\"\"\n assert isinstance(info, dict) and 'type' in info\n assert isinstance(default_args, dict) or default_args is None\n args = info.copy()\n obj_type = args.pop('type')\n # if mmcv.is_str(obj_type):\n if isinstance(obj_type, str):\n if parent is not None:\n obj_type = getattr(parent, obj_type)\n else:\n obj_type = sys.modules[obj_type]\n elif not isinstance(obj_type, type):\n raise TypeError('type must be a str or valid type, but '\n f'got {type(obj_type)}')\n if default_args is not None:\n for name, value in default_args.items():\n args.setdefault(name, value)\n return obj_type(**args)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_pix2pix():\n # model settings\n model_cfg = dict(\n data_preprocessor=DataPreprocessor(),\n generator=dict(\n type='UnetGenerator',\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02)),\n default_domain='photo',\n reachable_domains=['photo'],\n related_domains=['photo', 'mask'])\n\n # build synthesizer\n synthesizer = Pix2Pix(**model_cfg)\n # test attributes\n assert synthesizer.__class__.__name__ == 'Pix2Pix'\n assert isinstance(synthesizer.generators['photo'], UnetGenerator)\n assert isinstance(synthesizer.discriminators['photo'], PatchDiscriminator)\n\n # prepare data\n img_mask = torch.rand(1, 3, 256, 256)\n img_photo = torch.rand(1, 3, 256, 256)\n data_batch = dict(inputs={'img_mask': img_mask, 'img_photo': img_photo})\n\n # prepare optimizer\n optim_cfg = dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(params=getattr(synthesizer, 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n\n # test forward_test\n domain = 'photo'\n with torch.no_grad():\n outputs = synthesizer(img_mask, target_domain=domain, test_mode=True)\n assert torch.equal(outputs['source'], data_batch['inputs']['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 256, 256)\n\n # test forward_train\n outputs = synthesizer(img_mask, target_domain=domain, test_mode=False)\n assert torch.equal(outputs['source'], data_batch['inputs']['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 256, 256)\n\n # test train_step\n message_hub = MessageHub.get_instance('pix2pix-test')\n message_hub.update_info('iter', 0)\n log_vars = synthesizer.train_step(data_batch, optimizer)\n print(log_vars.keys())\n assert isinstance(log_vars, dict)\n for v in ['loss_gan_d_fake', 'loss_gan_d_real', 'loss_gan_g']:\n assert isinstance(log_vars[v].item(), float)\n\n # test cuda\n if torch.cuda.is_available():\n synthesizer = synthesizer.cuda()\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n data_batch_cuda = copy.deepcopy(data_batch)\n data_batch_cuda['inputs']['img_mask'] = img_mask.cuda()\n data_batch_cuda['inputs']['img_photo'] = img_photo.cuda()\n\n # forward_test\n with torch.no_grad():\n outputs = synthesizer(\n data_batch_cuda['inputs']['img_mask'],\n target_domain=domain,\n test_mode=True)\n assert torch.equal(outputs['source'].cpu(),\n data_batch_cuda['inputs']['img_mask'].cpu())\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 256, 256)\n\n # test forward_train\n outputs = synthesizer(\n data_batch_cuda['inputs']['img_mask'],\n target_domain=domain,\n test_mode=False)\n assert torch.equal(outputs['source'],\n data_batch_cuda['inputs']['img_mask'])\n assert torch.is_tensor(outputs['target'])\n assert outputs['target'].size() == (1, 3, 256, 256)\n\n # train_step\n message_hub.update_info('iter', 0)\n log_vars = synthesizer.train_step(data_batch_cuda, optimizer)\n print(log_vars)\n assert isinstance(log_vars, dict)\n for v in ['loss_gan_d_fake', 'loss_gan_d_real', 'loss_gan_g']:\n assert isinstance(log_vars[v].item(), float)\n\n # test disc_steps and disc_init_steps\n data_batch['inputs']['img_mask'] = img_mask.cpu()\n data_batch['inputs']['img_photo'] = img_photo.cpu()\n synthesizer = Pix2Pix(\n **model_cfg, discriminator_steps=2, disc_init_steps=2).cpu()\n optimizer = OptimWrapperDict(\n generators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(params=getattr(synthesizer, 'generators').parameters()))),\n discriminators=OptimWrapper(\n obj_from_dict(\n optim_cfg, torch.optim,\n dict(\n params=getattr(synthesizer,\n 'discriminators').parameters()))))\n\n # iter 0, 1\n for i in range(2):\n message_hub.update_info('iter', i)\n log_vars = synthesizer.train_step(data_batch, optimizer)\n assert isinstance(log_vars, dict)\n assert log_vars.get('loss_gan_g') is None\n for v in ['loss_gan_d_fake', 'loss_gan_d_real']:\n assert isinstance(log_vars[v].item(), float)\n\n # iter 2, 3, 4, 5\n for i in range(2, 6):\n message_hub.update_info('iter', i)\n log_vars = synthesizer.train_step(data_batch, optimizer)\n assert isinstance(log_vars, dict)\n log_check_list = ['loss_gan_d_fake', 'loss_gan_d_real', 'loss_gan_g']\n if (i + 1) % 2 == 1:\n assert log_vars.get('loss_gan_g') is None\n log_check_list.remove('loss_gan_g')\n\n for v in log_check_list:\n assert isinstance(log_vars[v].item(), float)\n\n\ndef test_pix2pix_val_step():\n # model settings\n model_cfg = dict(\n data_preprocessor=DataPreprocessor(),\n generator=dict(\n type='UnetGenerator',\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02)),\n discriminator=dict(\n type='PatchDiscriminator',\n in_channels=6,\n base_channels=64,\n num_conv=3,\n norm_cfg=dict(type='BN'),\n init_cfg=dict(type='normal', gain=0.02)),\n default_domain='photo',\n reachable_domains=['photo'],\n related_domains=['photo', 'mask'])\n synthesizer = Pix2Pix(**model_cfg)\n img_mask = torch.rand(1, 3, 256, 256)\n img_photo = torch.rand(1, 3, 256, 256)\n data_batch = dict(inputs={'img_mask': img_mask, 'img_photo': img_photo})\n out = synthesizer.val_step(data_batch)\n assert isinstance(out, list)\n assert len(out) == 1\n # assert 'gt_photo' in out[0]\n # assert 'gt_mask' in out[0]\n assert 'fake_photo' in out[0]\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pix2pix/test_pix2pix_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.pix2pix import UnetGenerator\n\n\nclass TestUnetGenerator:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=3,\n num_down=8,\n base_channels=64,\n norm_cfg=dict(type='BN'),\n use_dropout=True,\n init_cfg=dict(type='normal', gain=0.02))\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_pix2pix_generator_cpu(self):\n # test with default cfg\n real_a = torch.randn((2, 3, 256, 256))\n gen = UnetGenerator(**self.default_cfg)\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['num_down'] = 7\n gen = UnetGenerator(**cfg)\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n with pytest.raises(TypeError):\n gen = UnetGenerator(**self.default_cfg)\n gen.init_weights(pretrained=10)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_pix2pix_generator_cuda(self):\n # test with default cfg\n real_a = torch.randn((2, 3, 256, 256)).cuda()\n gen = UnetGenerator(**self.default_cfg).cuda()\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n # test args system\n cfg = deepcopy(self.default_cfg)\n cfg['num_down'] = 7\n gen = UnetGenerator(**cfg).cuda()\n fake_b = gen(real_a)\n assert fake_b.shape == (2, 3, 256, 256)\n\n with pytest.raises(TypeError):\n gen = UnetGenerator(**self.default_cfg)\n gen.init_weights(pretrained=10)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_pix2pix/test_pix2pix_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.pix2pix.pix2pix_modules import \\\n UnetSkipConnectionBlock\n\n\ndef test_unet_skip_connection_block():\n block = UnetSkipConnectionBlock(16, 16, is_innermost=True)\n input = torch.rand((2, 16, 128, 128))\n output = block(input)\n assert output.detach().numpy().shape == (2, 32, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_plain/test_plain_decoder.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.archs import VGG16\nfrom mmagic.models.editors import PlainDecoder\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef _demo_inputs(input_shape=(1, 4, 64, 64)):\n \"\"\"Create a superset of inputs needed to run encoder.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 4, 64, 64).\n \"\"\"\n img = np.random.random(input_shape).astype(np.float32)\n img = torch.from_numpy(img)\n\n return img\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_plain_decoder():\n \"\"\"Test PlainDecoder.\"\"\"\n\n with torch.no_grad():\n model = PlainDecoder(512)\n model.init_weights()\n model.train()\n # create max_pooling index for training\n encoder = VGG16(4)\n img = _demo_inputs()\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = PlainDecoder(512)\n model.init_weights()\n model.train()\n model.cuda()\n encoder = VGG16(4)\n encoder.cuda()\n img = _demo_inputs().cuda()\n outputs = encoder(img)\n prediction = model(outputs)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_plain/test_plain_refiner.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import PlainRefiner\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_plain_refiner():\n with torch.no_grad():\n plain_refiner = PlainRefiner()\n plain_refiner.init_weights()\n input = torch.rand((2, 4, 128, 128))\n raw_alpha = torch.rand((2, 1, 128, 128))\n output = plain_refiner(input, raw_alpha)\n assert output.detach().numpy().shape == (2, 1, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_rdn/test_rdn_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models import RDNNet\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_rdn():\n\n scale = 4\n\n model = RDNNet(\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n channel_growth=32,\n num_blocks=16,\n upscale_factor=scale)\n\n # test attributes\n assert model.__class__.__name__ == 'RDNNet'\n\n # prepare data\n inputs = torch.rand(1, 3, 32, 16)\n targets = torch.rand(1, 3, 128, 64)\n\n # prepare loss\n loss_function = nn.L1Loss()\n\n # prepare optimizer\n optimizer = torch.optim.Adam(model.parameters())\n\n # test on cpu\n output = model(inputs)\n optimizer.zero_grad()\n loss = loss_function(output, targets)\n loss.backward()\n optimizer.step()\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n optimizer = torch.optim.Adam(model.parameters())\n inputs = inputs.cuda()\n targets = targets.cuda()\n output = model(inputs)\n optimizer.zero_grad()\n loss = loss_function(output, targets)\n loss.backward()\n optimizer.step()\n assert torch.is_tensor(output)\n assert output.shape == targets.shape\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_real_basicvsr/test_real_basicvsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors import (RealBasicVSR, RealBasicVSRNet,\n UNetDiscriminatorWithSpectralNorm)\nfrom mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss, PerceptualVGG\nfrom mmagic.structures import DataSample\n\n\n@patch.object(PerceptualVGG, 'init_weights')\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_real_basicvsr(init_weights):\n\n model = RealBasicVSR(\n generator=dict(\n type='RealBasicVSRNet',\n mid_channels=4,\n num_propagation_blocks=1,\n num_cleaning_blocks=1,\n dynamic_refine_thres=5, # change to 5 for test\n spynet_pretrained=None,\n is_fix_cleaning=False,\n is_sequential_cleaning=False),\n discriminator=dict(\n type='UNetDiscriminatorWithSpectralNorm',\n in_channels=3,\n mid_channels=4,\n skip_connection=True),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n cleaning_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-2,\n real_label_val=1.0,\n fake_label_val=0),\n is_use_sharpened_gt_in_pixel=True,\n is_use_sharpened_gt_in_percep=True,\n is_use_sharpened_gt_in_gan=False,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, RealBasicVSR)\n assert isinstance(model.generator, RealBasicVSRNet)\n assert isinstance(model.discriminator, UNetDiscriminatorWithSpectralNorm)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.cleaning_loss, L1Loss)\n assert isinstance(model.perceptual_loss, PerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(5, 3, 64, 64)\n target = torch.rand(5, 3, 256, 256)\n data_sample = DataSample(gt_img=target, gt_unsharp=target)\n data = dict(inputs=[inputs], data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_clean', 'loss_d_real',\n 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (5, 3, 256, 256)\n\n # feat\n output = model(torch.rand(1, 5, 3, 64, 64), mode='tensor')\n assert output.shape == (1, 5, 3, 256, 256)\n\n # train_unsharp\n model.is_use_sharpened_gt_in_pixel = True\n model.is_use_sharpened_gt_in_percep = True\n model.is_use_sharpened_gt_in_gan = False\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_clean', 'loss_d_real',\n 'loss_d_fake'\n ])\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_real_basicvsr/test_real_basicvsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import RealBasicVSRNet\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_real_basicvsr_net():\n \"\"\"Test RealBasicVSR.\"\"\"\n\n # cpu\n # is_fix_cleaning = False\n real_basicvsr = RealBasicVSRNet(is_fix_cleaning=False)\n\n # is_sequential_cleaning = False\n real_basicvsr = RealBasicVSRNet(\n is_fix_cleaning=True, is_sequential_cleaning=False)\n input_tensor = torch.rand(1, 5, 3, 64, 64)\n output = real_basicvsr(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n # is_sequential_cleaning = True, return_lq = True\n real_basicvsr = RealBasicVSRNet(\n is_fix_cleaning=True, is_sequential_cleaning=True)\n output, lq = real_basicvsr(input_tensor, return_lqs=True)\n assert output.shape == (1, 5, 3, 256, 256)\n assert lq.shape == (1, 5, 3, 64, 64)\n\n # gpu\n if torch.cuda.is_available():\n # is_fix_cleaning = False\n real_basicvsr = RealBasicVSRNet(is_fix_cleaning=False).cuda()\n\n # is_sequential_cleaning = False\n real_basicvsr = RealBasicVSRNet(\n is_fix_cleaning=True, is_sequential_cleaning=False).cuda()\n input_tensor = torch.rand(1, 5, 3, 64, 64).cuda()\n output = real_basicvsr(input_tensor)\n assert output.shape == (1, 5, 3, 256, 256)\n\n # is_sequential_cleaning = True, return_lq = True\n real_basicvsr = RealBasicVSRNet(\n is_fix_cleaning=True, is_sequential_cleaning=True).cuda()\n output, lq = real_basicvsr(input_tensor, return_lqs=True)\n assert output.shape == (1, 5, 3, 256, 256)\n assert lq.shape == (1, 5, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_real_esrgan/test_real_esrgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import patch\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import (DataPreprocessor, RealESRGAN, RRDBNet,\n UNetDiscriminatorWithSpectralNorm)\nfrom mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss, PerceptualVGG\nfrom mmagic.structures import DataSample\n\n\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_real_esrgan(init_weights):\n\n model = RealESRGAN(\n generator=dict(\n type='RRDBNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_blocks=4,\n growth_channels=4,\n upscale_factor=4),\n discriminator=dict(\n type='UNetDiscriminatorWithSpectralNorm',\n in_channels=3,\n mid_channels=4,\n skip_connection=True),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={\n '2': 0.1,\n '7': 0.1,\n '16': 1.0,\n '25': 1.0,\n '34': 1.0,\n },\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-1,\n real_label_val=1.0,\n fake_label_val=0),\n is_use_sharpened_gt_in_pixel=False,\n is_use_sharpened_gt_in_percep=False,\n is_use_sharpened_gt_in_gan=False,\n is_use_ema=False,\n train_cfg=None,\n test_cfg=None,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, RealESRGAN)\n assert isinstance(model.generator, RRDBNet)\n assert isinstance(model.discriminator, UNetDiscriminatorWithSpectralNorm)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.perceptual_loss, PerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 32, 32)\n target = torch.rand(3, 128, 128)\n data_sample = DataSample(gt_img=target, gt_unsharp=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 128, 128)\n\n # val_ema\n model.generator_ema = model.generator\n model.is_use_ema = True\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 128, 128)\n\n # feat\n output = model(torch.rand(1, 3, 32, 32), mode='tensor')\n assert output.shape == (1, 3, 128, 128)\n\n # train_unsharp\n model.is_use_sharpened_gt_in_pixel = True\n model.is_use_sharpened_gt_in_percep = True\n model.is_use_sharpened_gt_in_gan = False\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_d_real', 'loss_d_fake'\n ])\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_real_esrgan/test_unet_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models import UNetDiscriminatorWithSpectralNorm\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_unet_disc_with_spectral_norm():\n # cpu\n disc = UNetDiscriminatorWithSpectralNorm(in_channels=3)\n img = torch.randn(1, 3, 16, 16)\n output = disc(img)\n assert output.detach().numpy().shape == (1, 1, 16, 16)\n\n # cuda\n if torch.cuda.is_available():\n disc = disc.cuda()\n img = img.cuda()\n output = disc(img)\n assert output.detach().cpu().numpy().shape == (1, 1, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_restormer/test_restormer_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import Restormer\n\n\n@pytest.mark.skipif(\n torch.__version__ < '1.8.0',\n reason='skip on torch<1.8 due to unsupported PixelUnShuffle')\ndef test_restormer_cpu():\n \"\"\"Test Restormer.\"\"\"\n\n # Motion Deblurring or Image Deraining\n net = Restormer(\n inp_channels=3,\n out_channels=3,\n dim=24,\n num_blocks=[2, 2, 2, 4],\n num_refinement_blocks=1,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False)\n img = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n # Image Denoising Gray\n net = Restormer(\n inp_channels=1,\n out_channels=1,\n dim=24,\n num_blocks=[2, 2, 2, 4],\n num_refinement_blocks=1,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False)\n img = torch.rand(1, 1, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 1, 16, 16)\n\n # Image Denoising Color\n net = Restormer(\n inp_channels=3,\n out_channels=3,\n dim=24,\n num_blocks=[2, 2, 2, 4],\n num_refinement_blocks=1,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='BiasFree',\n dual_pixel_task=False)\n img = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n # Image Dual Defocus Deblurring\n net = Restormer(\n inp_channels=6,\n out_channels=3,\n dim=24,\n num_blocks=[2, 2, 2, 4],\n num_refinement_blocks=1,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=True,\n dual_keys=['imgL', 'imgR'])\n img = dict()\n img['imgL'] = torch.rand(1, 3, 16, 16)\n img['imgR'] = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n\n@pytest.mark.skipif(\n torch.__version__ < '1.8.0',\n reason='skip on torch<1.8 due to unsupported PixelUnShuffle')\ndef test_restormer_cuda():\n net = Restormer(\n inp_channels=3,\n out_channels=3,\n dim=24,\n num_blocks=[2, 2, 2, 4],\n num_refinement_blocks=1,\n heads=[1, 2, 4, 8],\n ffn_expansion_factor=2.66,\n bias=False,\n LayerNorm_type='WithBias',\n dual_pixel_task=False)\n img = torch.rand(1, 3, 16, 16)\n\n # Image Deblurring or Image Deraining (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_sagan/test_sagan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import SAGAN, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='SAGANGenerator',\n # noise_size=10,\n num_classes=10,\n output_scale=32,\n base_channels=256,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=2,\n with_spectral_norm=True)\ndiscriminator = dict(\n type='ProjDiscriminator',\n num_classes=10,\n input_scale=32,\n base_channels=128,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=1,\n with_spectral_norm=True)\n\n\nclass TestSAGAN(TestCase):\n\n def test_init(self):\n gan = SAGAN(\n noise_size=10,\n num_classes=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator,\n generator_steps=1,\n discriminator_steps=4)\n\n self.assertIsInstance(gan, SAGAN)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = SAGAN(\n noise_size=10,\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = SAGAN(\n noise_size=10,\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = SAGAN(\n noise_size=10, generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n # test init with different num_classes\n gan = SAGAN(\n num_classes=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator,\n generator_steps=1,\n discriminator_steps=4)\n\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('mmgen')\n gan = SAGAN(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n label = torch.randint(0, 10, (1, ))\n\n data_sample = DataSample(gt_img=img)\n data_sample.set_gt_label(label)\n\n data = dict(inputs=dict(), data_samples=[data_sample])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_sagan/test_sagan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.sagan import ProjDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestSNGANPROJDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n cls.x = torch.randn((2, 3, 32, 32))\n cls.label = torch.randint(0, 10, (2, ))\n cls.default_config = dict(\n type='ProjDiscriminator',\n input_scale=32,\n num_classes=10,\n input_channels=3)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_sngan_proj_discriminator(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different input_scale\n config = deepcopy(self.default_config)\n config['input_scale'] = 64\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x, self.label)\n assert score.shape == (2, 1)\n\n # test num_classes == 0 (w/o proj_y)\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different base_channels\n config = deepcopy(self.default_config)\n config['base_channels'] = 128\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different channels_cfg --> list\n config = deepcopy(self.default_config)\n config['channels_cfg'] = [1, 1, 1]\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different channels_cfg --> dict\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {32: [1, 1, 1], 64: [2, 4, 8, 16]}\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different channels_cfg --> error (key not find)\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {64: [2, 4, 8, 16]}\n with pytest.raises(KeyError):\n d = MODELS.build(config)\n\n # test different channels_cfg --> error (type not match)\n config = deepcopy(self.default_config)\n config['channels_cfg'] = '1234'\n with pytest.raises(ValueError):\n d = MODELS.build(config)\n\n # test different downsample_cfg --> list\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = [True, False, False]\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different downsample_cfg --> dict\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = {\n 32: [True, False, False],\n 64: [True, True, True, True]\n }\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different downsample_cfg --> error (key not find)\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = {64: [True, True, True, True]}\n with pytest.raises(KeyError):\n d = MODELS.build(config)\n\n # test different downsample_cfg --> error (type not match)\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = '1234'\n with pytest.raises(ValueError):\n d = MODELS.build(config)\n\n # test downsample_cfg and channels_cfg not match\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = [True, False, False]\n config['channels_cfg'] = [1, 1, 1, 1]\n with pytest.raises(ValueError):\n d = MODELS.build(config)\n\n # test different act_cfg\n config = deepcopy(self.default_config)\n config['act_cfg'] = dict(type='Sigmoid')\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different with_spectral_norm\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = False\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different init_cfg --> studio\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='studio')\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different init_cfg --> BigGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='biggan')\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different init_cfg --> sngan\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan-proj')\n d = MODELS.build(config)\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x, self.label)\n assert score.shape == (2, 1)\n\n # test different init_cfg --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n d = MODELS.build(config)\n\n # test pretrained --> raise error\n config = deepcopy(self.default_config)\n config['pretrained'] = 42\n with pytest.raises(TypeError):\n d = MODELS.build(config)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_sngan_proj_discriminator_cuda(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different input_scale\n config = deepcopy(self.default_config)\n config['input_scale'] = 64\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n x = torch.randn((2, 3, 64, 64)).cuda()\n score = d(x, self.label.cuda())\n assert score.shape == (2, 1)\n\n # test num_classes == 0 (w/o proj_y)\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different base_channels\n config = deepcopy(self.default_config)\n config['base_channels'] = 128\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different channels_cfg --> list\n config = deepcopy(self.default_config)\n config['channels_cfg'] = [1, 1, 1]\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different channels_cfg --> dict\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {32: [1, 1, 1], 64: [2, 4, 8, 16]}\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different downsample_cfg --> list\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = [True, False, False]\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different downsample_cfg --> dict\n config = deepcopy(self.default_config)\n config['downsample_cfg'] = {\n 32: [True, False, False],\n 64: [True, True, True, True]\n }\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different act_cfg\n config = deepcopy(self.default_config)\n config['act_cfg'] = dict(type='Sigmoid')\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different with_spectral_norm\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = False\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different init_cfg --> BigGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='biggan')\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n # test different init_cfg --> sngan\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan-proj')\n d = MODELS.build(config).cuda()\n assert isinstance(d, ProjDiscriminator)\n score = d(self.x.cuda(), self.label.cuda())\n assert score.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_sagan/test_sagan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.sagan import SNGANGenerator\nfrom mmagic.registry import MODELS\n\n\nclass TestSNGANPROJGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((2, 128))\n cls.label = torch.randint(0, 10, (2, ))\n cls.default_config = dict(\n type='SNGANGenerator',\n noise_size=128,\n output_scale=32,\n num_classes=10,\n base_channels=32)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_sngan_proj_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test return noise\n x = g(None, num_batches=2, return_noise=True)\n assert x['fake_img'].shape == (2, 3, 32, 32)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n x = g(self.noise, label=self.label, return_noise=True)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n x = g(torch.randn, num_batches=2, return_noise=True)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n # test different output_scale\n config = deepcopy(self.default_config)\n config['output_scale'] = 64\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 64, 64)\n\n # test num_classes == 0 and `use_cbn = True`\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n with pytest.raises(ValueError):\n g = MODELS.build(config)\n\n # test num_classes == 0 and `use_cbn = False`\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n config['use_cbn'] = False\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different base_channels\n config = deepcopy(self.default_config)\n config['base_channels'] = 64\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different channels_cfg --> list\n config = deepcopy(self.default_config)\n config['channels_cfg'] = [1, 1, 1]\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different channels_cfg --> dict\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {32: [1, 1, 1], 64: [16, 8, 4, 2]}\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different channels_cfg --> error (key not find)\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {64: [16, 8, 4, 2]}\n with pytest.raises(KeyError):\n g = MODELS.build(config)\n\n # test different channels_cfg --> error (type not match)\n config = deepcopy(self.default_config)\n config['channels_cfg'] = '1234'\n with pytest.raises(ValueError):\n g = MODELS.build(config)\n\n # test different act_cfg\n config = deepcopy(self.default_config)\n config['act_cfg'] = dict(type='Sigmoid')\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test with_spectral_norm\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test with_embedding_spectral_norm\n config = deepcopy(self.default_config)\n config['with_embedding_spectral_norm'] = True\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test norm_eps\n config = deepcopy(self.default_config)\n config['norm_eps'] = 1e-9\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test sn_eps\n config = deepcopy(self.default_config)\n config['sn_eps'] = 1e-12\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> Studio\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='studio')\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> BigGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='biggan')\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> SNGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> SAGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sagan')\n g = MODELS.build(config)\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n g = MODELS.build(config)\n\n # test pretrained --> raise error\n config = deepcopy(self.default_config)\n config['pretrained'] = 42\n with pytest.raises(TypeError):\n g = MODELS.build(config)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_sngan_proj_generator_cuda(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test return noise\n x = g(None, num_batches=2, return_noise=True)\n assert x['fake_img'].shape == (2, 3, 32, 32)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n x = g(self.noise.cuda(), label=self.label.cuda(), return_noise=True)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n x = g(torch.randn, num_batches=2, return_noise=True)\n assert x['noise_batch'].shape == (2, 128)\n assert x['label'].shape == (2, )\n\n # test different output_scale\n config = deepcopy(self.default_config)\n config['output_scale'] = 64\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 64, 64)\n\n # test different base_channels\n config = deepcopy(self.default_config)\n config['base_channels'] = 64\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different channels_cfg --> list\n config = deepcopy(self.default_config)\n config['channels_cfg'] = [1, 1, 1]\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different channels_cfg --> dict\n config = deepcopy(self.default_config)\n config['channels_cfg'] = {32: [1, 1, 1], 64: [16, 8, 4, 2]}\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different act_cfg\n config = deepcopy(self.default_config)\n config['act_cfg'] = dict(type='Sigmoid')\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test with_spectral_norm\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test with_embedding_spectral_norm\n config = deepcopy(self.default_config)\n config['with_embedding_spectral_norm'] = True\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test norm_eps\n config = deepcopy(self.default_config)\n config['norm_eps'] = 1e-9\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test sn_eps\n config = deepcopy(self.default_config)\n config['sn_eps'] = 1e-12\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2).cuda()\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> BigGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='biggan')\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n # test different init_cfg --> SNGAN\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n g = MODELS.build(config).cuda()\n assert isinstance(g, SNGANGenerator)\n x = g(None, num_batches=2)\n assert x.shape == (2, 3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_sagan/test_sagan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\nclass TestSNGANGenResBlock(object):\n\n @classmethod\n def setup_class(cls):\n cls.input = torch.randn(2, 16, 5, 5)\n cls.label = torch.randint(0, 10, (2, ))\n cls.default_config = dict(\n type='SNGANGenResBlock',\n num_classes=10,\n in_channels=16,\n out_channels=16,\n use_cbn=True,\n use_norm_affine=False,\n norm_cfg=dict(type='BN'),\n upsample_cfg=dict(type='nearest', scale_factor=2),\n upsample=True,\n init_cfg=dict(type='BigGAN'))\n\n def test_snganGenResBlock(self):\n\n # test default config\n block = MODELS.build(self.default_config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 10, 10)\n\n # test no upsample config and no learnable sc\n config = deepcopy(self.default_config)\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test learnable shortcut + w/o upsample\n config = deepcopy(self.default_config)\n config['out_channels'] = 32\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 32, 5, 5)\n\n # test init_cfg + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cfg == studio + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='studio')\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cfg == sagan + learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sagan')\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 10, 10)\n\n # test init_cfg == sagan + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sagan')\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cft --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n block = MODELS.build(config)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 10, 10)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_snganGenResBlock_cuda(self):\n\n # test default config\n block = MODELS.build(self.default_config).cuda()\n out = block(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 10, 10)\n\n # test no upsample config and no learnable sc\n config = deepcopy(self.default_config)\n config['upsample'] = False\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cfg == studio + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='studio')\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cfg == sagan + learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sagan')\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 10, 10)\n\n # test init_cfg == sagan + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sagan')\n config['upsample'] = False\n block = MODELS.build(config)\n out = block(self.input, self.label)\n assert out.shape == (2, 16, 5, 5)\n\n # test learnable shortcut + w/o upsample\n config = deepcopy(self.default_config)\n config['out_channels'] = 32\n config['upsample'] = False\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 32, 5, 5)\n\n # test init_cfg + w/o learnable shortcut\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n config['upsample'] = False\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 16, 10, 10)\n\n\nclass TestSNDiscResBlock(object):\n\n @classmethod\n def setup_class(cls):\n cls.input = torch.randn(2, 16, 10, 10)\n cls.default_config = dict(\n type='SNGANDiscResBlock',\n in_channels=16,\n out_channels=16,\n downsample=True,\n init_cfg=dict(type='BigGAN'))\n\n def test_snganDiscResBlock(self):\n # test default config\n block = MODELS.build(self.default_config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test w/o learnabel shortcut + w/o downsample\n config = deepcopy(self.default_config)\n config['downsample'] = False\n config['out_channels'] = 8\n block = MODELS.build(config)\n out = block(self.input)\n assert out.shape == (2, 8, 10, 10)\n\n # test init cfg + w or w/o downsample\n for init_method in [\n 'studio', 'biggan', 'sagan', 'sngan', 'sngan-proj', 'gan-proj'\n ]:\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type=init_method)\n config['out_channels'] = 8\n for downsample in [True, False]:\n config['downsample'] = downsample\n block = MODELS.build(config)\n out = block(self.input)\n if downsample:\n assert out.shape == (2, 8, 5, 5)\n else:\n assert out.shape == (2, 8, 10, 10)\n\n # test init_cft --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n block = MODELS.build(config)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_snganDiscResBlock_cuda(self):\n # test default config\n block = MODELS.build(self.default_config).cuda()\n out = block(self.input.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n # test w/o learnabel shortcut + w/o downsample\n config = deepcopy(self.default_config)\n config['downsample'] = False\n config['out_channels'] = 8\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda())\n assert out.shape == (2, 8, 10, 10)\n\n # test init cfg + w or w/o downsample\n for init_method in [\n 'studio', 'biggan', 'sagan', 'sngan', 'sngan-proj', 'gan-proj'\n ]:\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type=init_method)\n config['out_channels'] = 8\n for downsample in [True, False]:\n config['downsample'] = downsample\n block = MODELS.build(config)\n out = block(self.input)\n if downsample:\n assert out.shape == (2, 8, 5, 5)\n else:\n assert out.shape == (2, 8, 10, 10)\n\n\nclass TestSNDiscHeadResBlock(object):\n\n @classmethod\n def setup_class(cls):\n cls.input = torch.randn(2, 16, 10, 10)\n cls.default_config = dict(\n type='SNGANDiscHeadResBlock',\n in_channels=16,\n out_channels=16,\n init_cfg=dict(type='BigGAN'))\n\n def test_snganDiscHeadResBlock(self):\n # test default config\n block = MODELS.build(self.default_config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test init cfg + w or w/o downsample\n for init_method in [\n 'studio', 'biggan', 'sagan', 'sngan', 'sngan-proj', 'gan-proj'\n ]:\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type=init_method)\n block = MODELS.build(config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test init_cft --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n block = MODELS.build(config)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_snganDiscHeadResBlock_cuda(self):\n # test default config\n block = MODELS.build(self.default_config).cuda()\n out = block(self.input.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n # test init cfg + w or w/o downsample\n for init_method in [\n 'studio', 'biggan', 'sagan', 'sngan', 'sngan-proj', 'gan-proj'\n ]:\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type=init_method)\n block = MODELS.build(config)\n out = block(self.input)\n assert out.shape == (2, 16, 5, 5)\n\n # test conv_cfg\n config = deepcopy(self.default_config)\n config['conv_cfg'] = dict(\n kernel_size=1, stride=1, padding=0, act_cfg=None)\n block = MODELS.build(config).cuda()\n out = block(self.input.cuda())\n assert out.shape == (2, 16, 5, 5)\n\n\nclass TestSNConditionalNorm(object):\n\n @classmethod\n def setup_class(cls):\n cls.input = torch.randn((2, 4, 4, 4))\n cls.label = torch.randint(0, 10, (2, ))\n cls.default_config = dict(\n type='SNConditionNorm',\n in_channels=4,\n num_classes=10,\n use_cbn=True,\n cbn_norm_affine=False,\n init_cfg=dict(type='BigGAN'))\n\n def test_conditionalNorm(self):\n # test build from default config\n norm = MODELS.build(self.default_config)\n out = norm(self.input, self.label)\n assert out.shape == (2, 4, 4, 4)\n\n # test w/o use_cbn\n config = deepcopy(self.default_config)\n config['use_cbn'] = False\n norm = MODELS.build(config)\n out = norm(self.input)\n assert out.shape == (2, 4, 4, 4)\n\n # test num_class < 0 and cbn = False\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n config['use_cbn'] = False\n norm = MODELS.build(config)\n out = norm(self.input)\n assert out.shape == (2, 4, 4, 4)\n\n # test num_classes <= 0 and cbn = True\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n with pytest.raises(ValueError):\n norm = MODELS.build(config)\n\n # test IN\n config = deepcopy(self.default_config)\n config['norm_cfg'] = dict(type='IN')\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style == ajbrock\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'ajbrock'\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n for buffer in ['u0', 'sv0']:\n assert hasattr(norm.weight_embedding, buffer)\n assert hasattr(norm.bias_embedding, buffer)\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style == torch\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'torch'\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n for buffer in ['weight_u', 'weight_v', 'weight_orig']:\n assert hasattr(norm.weight_embedding, buffer)\n assert hasattr(norm.bias_embedding, buffer)\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style --> raise error\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'studio'\n with pytest.raises(NotImplementedError):\n norm = MODELS.build(config)\n\n # test SyncBN\n # config = deepcopy(self.default_config)\n # config['norm_cfg'] = dict(type='SyncBN')\n # norm = MODELS.build(config)\n # out = norm(self.input, self.label)\n # assert out.shape == (2, 4, 4, 4)\n\n # test unknown norm type\n config = deepcopy(self.default_config)\n config['norm_cfg'] = dict(type='GN')\n with pytest.raises(ValueError):\n norm = MODELS.build(config)\n\n # test init_cfg\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n assert out.shape == (2, 4, 4, 4)\n\n # test init_cft --> raise error\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='wgan-gp')\n with pytest.raises(NotImplementedError):\n norm = MODELS.build(config)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_conditionalNorm_cuda(self):\n # test build from default config\n norm = MODELS.build(self.default_config).cuda()\n out = norm(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 4, 4, 4)\n\n # test w/o use_cbn\n config = deepcopy(self.default_config)\n config['use_cbn'] = False\n norm = MODELS.build(config).cuda()\n out = norm(self.input.cuda())\n assert out.shape == (2, 4, 4, 4)\n\n # test num_class < 0 and cbn = False\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n config['use_cbn'] = False\n norm = MODELS.build(config).cuda()\n out = norm(self.input.cuda())\n assert out.shape == (2, 4, 4, 4)\n\n # test num_classes <= 0 and cbn = True\n config = deepcopy(self.default_config)\n config['num_classes'] = 0\n with pytest.raises(ValueError):\n norm = MODELS.build(config)\n\n # test IN\n config = deepcopy(self.default_config)\n config['norm_cfg'] = dict(type='IN')\n norm = MODELS.build(config).cuda()\n out = norm(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style == ajbrock\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'ajbrock'\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n for buffer in ['u0', 'sv0']:\n assert hasattr(norm.weight_embedding, buffer)\n assert hasattr(norm.bias_embedding, buffer)\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style == torch\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'torch'\n norm = MODELS.build(config)\n out = norm(self.input, self.label)\n for buffer in ['weight_u', 'weight_v', 'weight_orig']:\n assert hasattr(norm.weight_embedding, buffer)\n assert hasattr(norm.bias_embedding, buffer)\n assert out.shape == (2, 4, 4, 4)\n\n # test sn_style --> raise error\n config = deepcopy(self.default_config)\n config['with_spectral_norm'] = True\n config['sn_style'] = 'studio'\n with pytest.raises(NotImplementedError):\n norm = MODELS.build(config)\n\n # test SyncBN\n # config = deepcopy(self.default_config)\n # config['norm_cfg'] = dict(type='SyncBN')\n # norm = MODELS.build(config)\n # out = norm(self.input, self.label)\n # assert out.shape == (2, 4, 4, 4)\n\n # test unknown norm type\n config = deepcopy(self.default_config)\n config['norm_cfg'] = dict(type='GN')\n with pytest.raises(ValueError):\n norm = MODELS.build(config)\n\n # test init_cfg\n config = deepcopy(self.default_config)\n config['init_cfg'] = dict(type='sngan')\n norm = MODELS.build(config).cuda()\n out = norm(self.input.cuda(), self.label.cuda())\n assert out.shape == (2, 4, 4, 4)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_singan/test_singan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine import MessageHub\n\nfrom mmagic.engine import SinGANOptimWrapperConstructor\nfrom mmagic.models import SinGAN\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestSinGAN:\n\n @classmethod\n def setup_class(cls):\n cls.generator = dict(\n type='SinGANMultiScaleGenerator',\n in_channels=3,\n out_channels=3,\n num_scales=3)\n\n cls.disc = dict(\n type='SinGANMultiScaleDiscriminator', in_channels=3, num_scales=3)\n\n cls.data_preprocessor = dict(\n type='DataPreprocessor', non_image_keys=['input_sample'])\n cls.noise_weight_init = 0.1\n cls.curr_scale = -1\n cls.iters_per_scale = 2\n cls.lr_scheduler_args = dict(milestones=[1600], gamma=0.1)\n\n cls.data_batch = dict(\n inputs=dict(\n real_scale0=torch.randn(1, 3, 25, 25),\n real_scale1=torch.randn(1, 3, 30, 30),\n real_scale2=torch.randn(1, 3, 32, 32),\n ))\n cls.data_batch['inputs']['input_sample'] = torch.zeros_like(\n cls.data_batch['inputs']['real_scale0'])\n\n cls.optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.0005, betas=(0.5, 0.999))))\n\n def test_singan_cpu(self):\n message_hub = MessageHub.get_instance('singan-test')\n message_hub.update_info('iter', 0)\n\n singan = SinGAN(\n self.generator,\n self.disc,\n num_scales=3,\n data_preprocessor=self.data_preprocessor,\n noise_weight_init=self.noise_weight_init,\n iters_per_scale=self.iters_per_scale,\n lr_scheduler_args=self.lr_scheduler_args)\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n\n for i in range(6):\n singan.train_step(self.data_batch, optim_wrapper_dict)\n message_hub.update_info('iter', message_hub.get_info('iter') + 1)\n outputs = singan.forward(dict(num_batches=1), None)\n\n img = torch.stack([out.fake_img.data for out in outputs], dim=0)\n if i in [0, 1]:\n assert singan.curr_stage == 0\n assert img.shape[-2:] == (25, 25)\n elif i in [2, 3]:\n assert singan.curr_stage == 1\n assert img.shape[-2:] == (30, 30)\n elif i in [4, 5]:\n assert singan.curr_stage == 2\n assert img.shape[-2:] == (32, 32)\n\n outputs = singan.forward(\n dict(num_batches=1, get_prev_res=True), None)\n assert all([hasattr(out, 'prev_res_list') for out in outputs])\n\n # test forward singan with ema\n singan = SinGAN(\n self.generator,\n self.disc,\n num_scales=3,\n data_preprocessor=self.data_preprocessor,\n noise_weight_init=self.noise_weight_init,\n iters_per_scale=self.iters_per_scale,\n lr_scheduler_args=self.lr_scheduler_args,\n ema_confg=dict(type='ExponentialMovingAverage'))\n optim_wrapper_dict_builder = SinGANOptimWrapperConstructor(\n self.optim_wrapper_cfg)\n optim_wrapper_dict = optim_wrapper_dict_builder(singan)\n\n for i in range(6):\n singan.train_step(self.data_batch, optim_wrapper_dict)\n message_hub.update_info('iter', message_hub.get_info('iter') + 1)\n\n outputs = singan.forward(\n dict(num_batches=1, sample_model='ema/orig'), None)\n\n img = torch.stack([out.orig.fake_img.data for out in outputs],\n dim=0)\n img_ema = torch.stack([out.ema.fake_img.data for out in outputs],\n dim=0)\n if i in [0, 1]:\n assert singan.curr_stage == 0\n assert img.shape[-2:] == (25, 25)\n assert img_ema.shape[-2:] == (25, 25)\n elif i in [2, 3]:\n assert singan.curr_stage == 1\n assert img.shape[-2:] == (30, 30)\n assert img_ema.shape[-2:] == (30, 30)\n elif i in [4, 5]:\n assert singan.curr_stage == 2\n assert img.shape[-2:] == (32, 32)\n assert img_ema.shape[-2:] == (32, 32)\n\n outputs = singan.forward(\n dict(\n num_batches=1, sample_model='ema/orig', get_prev_res=True),\n None)\n\n assert all([hasattr(out.orig, 'prev_res_list') for out in outputs])\n assert all([hasattr(out.ema, 'prev_res_list') for out in outputs])\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_singan/test_singan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.singan import SinGANMultiScaleDiscriminator\n\n\nclass TestSinGANDisc:\n\n @classmethod\n def setup_class(cls):\n cls.default_args = dict(\n in_channels=3,\n kernel_size=3,\n padding=0,\n num_layers=3,\n base_channels=32,\n num_scales=3,\n min_feat_channels=16)\n\n def test_singan_disc(self):\n disc = SinGANMultiScaleDiscriminator(**self.default_args)\n img = torch.randn(1, 3, 24, 24)\n res = disc(img, 2)\n assert res.shape[0] == 1\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_singan/test_singan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.singan import SinGANMultiScaleGenerator\n\n\nclass TestSinGANGen:\n\n @classmethod\n def setup_class(cls):\n cls.default_args = dict(\n in_channels=3,\n out_channels=3,\n kernel_size=3,\n padding=0,\n num_layers=3,\n base_channels=32,\n num_scales=3,\n min_feat_channels=16)\n\n cls.fixed_noises = [\n torch.randn(1, 3, 8, 8),\n torch.randn(1, 3, 10, 10),\n torch.randn(1, 3, 12, 12),\n torch.randn(1, 3, 16, 16)\n ]\n cls.input_sample = torch.zeros_like(cls.fixed_noises[0])\n cls.noise_weights = [1., 0.5, 0.5, 0.5]\n\n def test_singan_gen(self):\n gen = SinGANMultiScaleGenerator(**self.default_args)\n res = gen(self.input_sample, self.fixed_noises, self.noise_weights,\n 'rand', 2)\n assert res.shape == (1, 3, 12, 12)\n\n output = gen(\n self.input_sample,\n self.fixed_noises,\n self.noise_weights,\n 'rand',\n 2,\n get_prev_res=True)\n\n assert output['prev_res_list'][0].shape == (1, 3, 8, 8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_singan/test_singan_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.singan.singan_modules import (DiscriminatorBlock,\n GeneratorBlock)\n\n\ndef test_GeneratorBlock():\n gen_block = GeneratorBlock(3, 6, 3, 1, 3, 4, 4)\n x = torch.randn(1, 3, 6, 6)\n prev = torch.randn(1, 6, 6, 6)\n out = gen_block(x, prev)\n assert out.shape == (1, 6, 6, 6)\n\n gen_block = GeneratorBlock(3, 6, 3, 1, 3, 4, 4, allow_no_residual=True)\n x = torch.randn(1, 3, 6, 6)\n prev = torch.randn(1, 3, 6, 6)\n out = gen_block(x, prev)\n assert out.shape == (1, 6, 6, 6)\n\n\ndef test_DiscriminatorBlock():\n disc_block = DiscriminatorBlock(3, 4, 1, 3, 1, 3)\n x = torch.randn(1, 3, 6, 6)\n out = disc_block(x)\n assert out.shape == (1, 1, 6, 6)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_srcnn/test_srcnn_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models import SRCNNNet\n\n\ndef test_srcnn():\n # model, initialization and forward (cpu)\n net = SRCNNNet(\n channels=(3, 4, 6, 3), kernel_sizes=(9, 1, 5), upscale_factor=4)\n img = torch.rand(1, 3, 4, 4)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n net = SRCNNNet(\n channels=(1, 4, 8, 1), kernel_sizes=(3, 3, 3), upscale_factor=2)\n img = torch.rand(1, 1, 4, 4)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 1, 8, 8)\n\n # model forward (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 1, 8, 8)\n\n with pytest.raises(AssertionError):\n # The length of channel tuple should be 4\n net = SRCNNNet(\n channels=(3, 4, 3), kernel_sizes=(9, 1, 5), upscale_factor=4)\n with pytest.raises(AssertionError):\n # The length of kernel tuple should be 3\n net = SRCNNNet(\n channels=(3, 4, 4, 3), kernel_sizes=(9, 1, 1, 5), upscale_factor=4)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_srgan/test_modified_vgg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import ModifiedVGG\n\n\ndef test_modifiedVGG():\n model = ModifiedVGG(3, 16)\n inputs = torch.randn(1, 3, 128, 128)\n outputs = model(inputs)\n assert outputs.shape == (1, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_srgan/test_sr_resnet.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models import ModifiedVGG, MSRResNet\n\n\ndef test_srresnet_backbone():\n \"\"\"Test SRResNet backbone.\"\"\"\n\n # x2 model\n MSRResNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=2)\n # x3 model, initialization and forward (cpu)\n net = MSRResNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=3)\n input_shape = (1, 3, 12, 12)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 3, 36, 36)\n # x4 modeland, initialization and forward (cpu)\n net = MSRResNet(\n in_channels=3,\n out_channels=3,\n mid_channels=8,\n num_blocks=2,\n upscale_factor=4)\n output = net(img)\n assert output.shape == (1, 3, 48, 48)\n\n # x4 model forward (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 3, 48, 48)\n\n with pytest.raises(ValueError):\n # Currently supported upscale_factor is [2, 3, 4]\n MSRResNet(\n in_channels=3,\n out_channels=3,\n mid_channels=64,\n num_blocks=16,\n upscale_factor=16)\n\n\ndef test_discriminator():\n \"\"\"Test discriminator backbone.\"\"\"\n\n # model, initialization and forward (cpu)\n net = ModifiedVGG(in_channels=3, mid_channels=64)\n input_shape = (1, 3, 128, 128)\n img = torch.rand(input_shape)\n output = net(img)\n assert output.shape == (1, 1)\n\n # model, initialization and forward (gpu)\n if torch.cuda.is_available():\n net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 1)\n\n with pytest.raises(AssertionError):\n # input size must be 128 * 128\n input_shape = (1, 3, 64, 64)\n img = torch.rand(input_shape)\n output = net(img)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_srgan/test_srgan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import patch\n\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import SRGAN, DataPreprocessor, ModifiedVGG, MSRResNet\nfrom mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss, PerceptualVGG\nfrom mmagic.structures import DataSample\n\n\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_srgan_resnet(init_weights):\n\n model = SRGAN(\n generator=dict(\n type='MSRResNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_blocks=4,\n upscale_factor=4),\n discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=8),\n pixel_loss=dict(type='L1Loss', loss_weight=1e-2, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'34': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1.0,\n style_weight=0,\n norm_img=False),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=5e-3,\n real_label_val=1.0,\n fake_label_val=0),\n train_cfg=None,\n test_cfg=None,\n data_preprocessor=DataPreprocessor())\n\n assert isinstance(model, SRGAN)\n assert isinstance(model.generator, MSRResNet)\n assert isinstance(model.discriminator, ModifiedVGG)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.perceptual_loss, PerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 32, 32)\n target = torch.rand(3, 128, 128)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_gan', 'loss_pix', 'loss_perceptual', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 128, 128)\n\n # feat\n output = model(torch.rand(1, 3, 32, 32), mode='tensor')\n assert output.shape == (1, 3, 128, 128)\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion/test_clip_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport importlib\nimport sys\n\nimport pytest\nimport torch\n\n\ndef test_clip_wrapper():\n from transformers import CLIPConfig\n\n from mmagic.models.editors.stable_diffusion.clip_wrapper import \\\n StableDiffusionSafetyChecker\n clipconfig = CLIPConfig()\n safety_checker = StableDiffusionSafetyChecker(clipconfig)\n\n clip_input = torch.rand((1, 3, 224, 224))\n images_input = torch.rand((1, 512, 512, 3))\n\n result = safety_checker.forward(clip_input, images_input)\n assert result[0].shape == (1, 512, 512, 3)\n\n\ndef test_load_clip_submodels():\n from mmagic.models.editors.stable_diffusion.clip_wrapper import \\\n load_clip_submodels\n init_cfg = dict(\n type='Pretrained',\n pretrained_model_path='tem',\n )\n\n submodels = []\n with pytest.raises(Exception):\n load_clip_submodels(init_cfg, submodels, True)\n\n\ndef test_load_clip_submodels_transformers_none():\n transformer_location = sys.modules['transformers']\n sys.modules['transformers'] = None\n importlib.reload(\n sys.modules['mmagic.models.editors.stable_diffusion.clip_wrapper'])\n from mmagic.models.editors.stable_diffusion.clip_wrapper import \\\n load_clip_submodels\n\n init_cfg = dict(\n type='Pretrained',\n pretrained_model_path='tem',\n )\n submodels = []\n with pytest.raises(ImportError):\n load_clip_submodels(init_cfg, submodels, True)\n\n sys.modules['transformers'] = transformer_location\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion/test_stable_diffusion.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom addict import Dict\nfrom mmengine import MODELS, Config\n\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nunet = dict(\n type='DenoisingUnet',\n image_size=128,\n base_channels=32,\n channels_cfg=[1, 2],\n unet_type='stable',\n act_cfg=dict(type='silu', inplace=False),\n cross_attention_dim=768,\n num_heads=2,\n in_channels=4,\n layers_per_block=1,\n down_block_types=['CrossAttnDownBlock2D', 'DownBlock2D'],\n up_block_types=['UpBlock2D', 'CrossAttnUpBlock2D'],\n output_cfg=dict(var='fixed'))\n\nvae = dict(\n type='EditAutoencoderKL',\n act_fn='silu',\n block_out_channels=[128],\n down_block_types=['DownEncoderBlock2D'],\n in_channels=3,\n latent_channels=4,\n layers_per_block=1,\n norm_num_groups=32,\n out_channels=3,\n sample_size=128,\n up_block_types=[\n 'UpDecoderBlock2D',\n ])\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\ninit_cfg = dict(type='Pretrained', pretrained_model_path=None)\n\n\nclass dummy_tokenizer(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.model_max_length = 0\n\n def __call__(self,\n prompt,\n padding='max_length',\n max_length=0,\n truncation=False,\n return_tensors='pt'):\n text_inputs = Dict()\n text_inputs['input_ids'] = torch.ones([1, 77])\n text_inputs['attention_mask'] = torch.ones([1, 77])\n return text_inputs\n\n\nclass dummy_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.config = None\n\n def __call__(self, x, attention_mask):\n result = torch.rand([1, 77, 768])\n return [result]\n\n\nmodel = dict(\n type='StableDiffusion',\n scheduler=diffusion_scheduler,\n unet=unet,\n vae=vae,\n init_cfg=init_cfg,\n text_encoder=dummy_text_encoder(),\n tokenizer=dummy_text_encoder())\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_stable_diffusion():\n StableDiffuser = MODELS.build(Config(model))\n StableDiffuser.tokenizer = dummy_tokenizer()\n StableDiffuser.text_encoder = dummy_text_encoder()\n\n with pytest.raises(Exception):\n StableDiffuser.infer(1, height=64, width=64)\n\n with pytest.raises(Exception):\n StableDiffuser.infer('temp', height=31, width=31)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (1, 3, 64, 64)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='image')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion/test_stable_diffusion_inpaint.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom addict import Dict\nfrom mmengine import MODELS, Config\n\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nunet = dict(\n type='DenoisingUnet',\n image_size=128,\n base_channels=32,\n channels_cfg=[1, 2],\n unet_type='stable',\n act_cfg=dict(type='silu', inplace=False),\n cross_attention_dim=768,\n num_heads=2,\n in_channels=9,\n out_channels=4,\n layers_per_block=1,\n down_block_types=['CrossAttnDownBlock2D', 'DownBlock2D'],\n up_block_types=['UpBlock2D', 'CrossAttnUpBlock2D'],\n output_cfg=dict(var='fixed'))\n\nvae = dict(\n type='EditAutoencoderKL',\n act_fn='silu',\n block_out_channels=[128],\n down_block_types=['DownEncoderBlock2D'],\n in_channels=3,\n latent_channels=4,\n layers_per_block=1,\n norm_num_groups=32,\n out_channels=3,\n sample_size=128,\n up_block_types=[\n 'UpDecoderBlock2D',\n ])\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\ninit_cfg = dict(type='Pretrained', pretrained_model_path=None)\n\n\nclass dummy_tokenizer(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.model_max_length = 0\n\n def __call__(self,\n prompt,\n padding='max_length',\n max_length=0,\n truncation=False,\n return_tensors='pt'):\n text_inputs = Dict()\n text_inputs['input_ids'] = torch.ones([1, 77])\n text_inputs['attention_mask'] = torch.ones([1, 77])\n return text_inputs\n\n\nclass dummy_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.config = None\n\n def __call__(self, x, attention_mask):\n result = torch.rand([1, 77, 768])\n return [result]\n\n\nmodel = dict(\n type='StableDiffusionInpaint',\n scheduler=diffusion_scheduler,\n unet=unet,\n vae=vae,\n init_cfg=init_cfg,\n text_encoder=dummy_text_encoder(),\n tokenizer=dummy_text_encoder())\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\ndef test_stable_diffusion():\n StableDiffuser = MODELS.build(Config(model))\n StableDiffuser.tokenizer = dummy_tokenizer()\n StableDiffuser.text_encoder = dummy_text_encoder()\n config = getattr(StableDiffuser.vae, 'config', None)\n if config is None:\n\n class DummyConfig:\n pass\n\n config = DummyConfig()\n setattr(config, 'scaling_factor', 1.2)\n setattr(StableDiffuser.vae, 'config', config)\n\n image = torch.clip(torch.randn((1, 3, 64, 64)), -1, 1)\n mask = torch.clip(torch.randn((1, 1, 64, 64)), 0, 1)\n\n with pytest.raises(Exception):\n StableDiffuser.infer('temp', image, mask, height=31, width=31)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n image=image,\n mask_image=mask,\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (1, 3, 64, 64)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n image=image,\n mask_image=mask,\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='image')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion/test_stable_diffusion_tomesd.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom addict import Dict\nfrom mmengine import MODELS, Config\n\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nunet = dict(\n type='DenoisingUnet',\n image_size=128,\n base_channels=32,\n channels_cfg=[1, 2],\n unet_type='stable',\n act_cfg=dict(type='silu', inplace=False),\n cross_attention_dim=768,\n num_heads=2,\n in_channels=4,\n layers_per_block=1,\n down_block_types=['CrossAttnDownBlock2D', 'DownBlock2D'],\n up_block_types=['UpBlock2D', 'CrossAttnUpBlock2D'],\n output_cfg=dict(var='fixed'))\n\nvae = dict(\n type='EditAutoencoderKL',\n act_fn='silu',\n block_out_channels=[128],\n down_block_types=['DownEncoderBlock2D'],\n in_channels=3,\n latent_channels=4,\n layers_per_block=1,\n norm_num_groups=32,\n out_channels=3,\n sample_size=128,\n up_block_types=[\n 'UpDecoderBlock2D',\n ])\n\ndiffusion_scheduler = dict(\n type='EditDDIMScheduler',\n variance_type='learned_range',\n beta_end=0.012,\n beta_schedule='scaled_linear',\n beta_start=0.00085,\n num_train_timesteps=1000,\n set_alpha_to_one=False,\n clip_sample=False)\n\ninit_cfg = dict(type='Pretrained', pretrained_model_path=None)\n\n\nclass dummy_tokenizer(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.model_max_length = 0\n\n def __call__(self,\n prompt,\n padding='max_length',\n max_length=0,\n truncation=False,\n return_tensors='pt'):\n text_inputs = Dict()\n text_inputs['input_ids'] = torch.ones([1, 77])\n text_inputs['attention_mask'] = torch.ones([1, 77])\n return text_inputs\n\n\nclass dummy_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.config = None\n\n def __call__(self, x, attention_mask):\n result = torch.rand([1, 77, 768])\n return [result]\n\n\nmodel = dict(\n type='StableDiffusion',\n scheduler=diffusion_scheduler,\n unet=unet,\n vae=vae,\n init_cfg=init_cfg,\n text_encoder=dummy_text_encoder(),\n tokenizer=dummy_text_encoder(),\n tomesd_cfg=dict(ratio=0.5))\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\n@pytest.mark.skipif(\n not hasattr(torch.Tensor, 'scatter_reduce')\n or torch.__version__ < '1.12.1',\n reason='required method')\ndef test_stable_diffusion():\n StableDiffuser = MODELS.build(Config(model))\n StableDiffuser.tokenizer = dummy_tokenizer()\n StableDiffuser.text_encoder = dummy_text_encoder()\n\n with pytest.raises(Exception):\n StableDiffuser.infer(1, height=64, width=64)\n\n with pytest.raises(Exception):\n StableDiffuser.infer('temp', height=31, width=31)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (1, 3, 64, 64)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='image')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion/test_vae.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.stable_diffusion.vae import (\n AttentionBlock, AutoencoderKL, DiagonalGaussianDistribution, Downsample2D,\n ResnetBlock2D, Upsample2D)\n\n\ndef test_vae():\n input = torch.rand((1, 3, 32, 32))\n vae = AutoencoderKL()\n output = vae.forward(input)\n assert output['sample'].shape == (1, 3, 32, 32)\n\n\ndef test_resnetblock2d():\n input = torch.rand((1, 64, 16, 16))\n resblock = ResnetBlock2D(in_channels=64, up=True)\n output = resblock.forward(input, None)\n assert output.shape == (1, 64, 64, 64)\n\n resblock = ResnetBlock2D(in_channels=64, down=True)\n output = resblock.forward(input, None)\n assert output.shape == (1, 64, 8, 8)\n\n\ndef test_DiagonalGaussianDistribution():\n param = torch.rand((1, 2, 16, 16))\n sample = torch.rand((1, 1, 16, 16))\n\n gauss_dist = DiagonalGaussianDistribution(param, deterministic=False)\n gauss_dist.sample()\n gauss_dist.kl()\n output = gauss_dist.nll(sample)\n assert output.shape == (1, )\n\n gauss_dist = DiagonalGaussianDistribution(param, deterministic=True)\n gauss_dist.sample()\n gauss_dist.kl()\n output = gauss_dist.nll(sample)\n assert output.shape == (1, )\n\n\ndef test_AttentionBlock():\n input = torch.rand((1, 64, 32, 32))\n attention = AttentionBlock(64, num_head_channels=8)\n output = attention.forward(input)\n assert output.shape == (1, 64, 32, 32)\n\n\ndef test_Downsample2D():\n input = torch.rand((1, 64, 16, 16))\n downsample = Downsample2D(channels=64, use_conv=True, padding=0)\n output = downsample.forward(input)\n assert output.shape == (1, 64, 8, 8)\n\n\ndef test_Upsample2D():\n input = torch.rand((1, 64, 16, 16))\n upsample = Upsample2D(channels=64, use_conv_transpose=True)\n output = upsample.forward(input)\n assert output.shape == (1, 64, 32, 32)\n\n upsample = Upsample2D(channels=64)\n output = upsample.forward(input, output_size=(32, 32))\n assert output.shape == (1, 64, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stable_diffusion_xl/test_stable_diffusion_xl.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nfrom mmengine import MODELS, Config\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import SGD\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\nstable_diffusion_xl_tiny_url = 'hf-internal-testing/tiny-stable-diffusion-xl-pipe' # noqa\nmodel = dict(\n type='StableDiffusionXL',\n unet=dict(\n type='UNet2DConditionModel',\n subfolder='unet',\n from_pretrained=stable_diffusion_xl_tiny_url),\n vae=dict(type='AutoencoderKL', sample_size=64),\n text_encoder_one=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_xl_tiny_url,\n subfolder='text_encoder'),\n tokenizer_one=stable_diffusion_xl_tiny_url,\n text_encoder_two=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_xl_tiny_url,\n subfolder='text_encoder_2'),\n tokenizer_two=stable_diffusion_xl_tiny_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_xl_tiny_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_xl_tiny_url,\n subfolder='scheduler'),\n val_prompts=['a dog', 'a dog'])\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\n@pytest.mark.skipif(\n torch.__version__ < '1.9.0',\n reason='skip on torch<1.9 due to unsupported torch.concat')\ndef test_stable_xl_diffusion():\n StableDiffuser = MODELS.build(Config(model))\n\n with pytest.raises(Exception):\n StableDiffuser.infer(1, height=64, width=64)\n\n with pytest.raises(Exception):\n StableDiffuser.infer('temp', height=31, width=31)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (1, 3, 64, 64)\n\n result = StableDiffuser.infer(\n 'an insect robot preparing a delicious meal',\n height=64,\n width=64,\n num_inference_steps=1,\n return_type='image')\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower(),\n reason='skip on windows due to limited RAM.')\n@pytest.mark.skipif(\n torch.__version__ < '1.9.0',\n reason='skip on torch<1.9 due to unsupported torch.concat')\ndef test_stable_diffusion_xl_step():\n StableDiffuser = MODELS.build(Config(model))\n\n # train step\n data = dict(\n inputs=torch.ones([1, 3, 64, 64]),\n time_ids=torch.zeros((1, 6)),\n data_samples=[\n DataSample(prompt='an insect robot preparing a delicious meal')\n ])\n optimizer = SGD(StableDiffuser.parameters(), lr=0.1)\n optim_wrapper = OptimWrapper(optimizer)\n log_vars = StableDiffuser.train_step(data, optim_wrapper)\n assert log_vars\n assert isinstance(log_vars['loss'], torch.Tensor)\n\n # val step\n data = dict(data_samples=[DataSample()])\n outputs = StableDiffuser.val_step(data)\n assert len(outputs) == 2\n assert isinstance(outputs[0].fake_img, torch.Tensor)\n assert outputs[0].fake_img.shape == (3, 32, 32)\n\n # test step\n outputs = StableDiffuser.test_step(data)\n assert len(outputs) == 2\n assert isinstance(outputs[0].fake_img, torch.Tensor)\n assert outputs[0].fake_img.shape == (3, 32, 32)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan1/test_stylegan1.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest import TestCase\n\nimport numpy as np\nimport pytest\nimport torch\nfrom mmengine import MessageHub\n\nfrom mmagic.engine import PGGANOptimWrapperConstructor\nfrom mmagic.models import StyleGAN1\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestStyleGAN1(TestCase):\n style_channels = 16\n generator_cfg = dict(\n type='StyleGAN1Generator', out_size=64, style_channels=16)\n discriminator_cfg = dict(type='StyleGAN1Discriminator', in_size=64)\n\n nkimgs_per_scale = {'16': 0.004, '32': 0.008, '64': 0.016}\n\n data_preprocessor = dict(type='DataPreprocessor')\n\n lr_schedule = dict(generator={'32': 0.0015}, discriminator={'32': 0.0015})\n optim_wrapper_cfg = dict(\n generator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n discriminator=dict(\n optimizer=dict(type='Adam', lr=0.001, betas=(0., 0.99))),\n lr_schedule=lr_schedule)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan_cpu(self):\n message_hub = MessageHub.get_instance('test-s1')\n message_hub.update_info('iter', 0)\n\n # test default config\n stylegan = StyleGAN1(\n self.generator_cfg,\n self.discriminator_cfg,\n style_channels=self.style_channels,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n ema_config=dict(interval=1))\n\n constructor = PGGANOptimWrapperConstructor(self.optim_wrapper_cfg)\n optim_wrapper_dict = constructor(stylegan)\n\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 64, 64)) for _ in range(3)\n ])\n\n for iter_num in range(6):\n stylegan.train_step(data_batch, optim_wrapper_dict)\n if iter_num in [0, 1]:\n assert stylegan.curr_scale[0] == 16\n elif iter_num in [2, 3]:\n assert stylegan.curr_scale[0] == 32\n elif iter_num in [4, 5]:\n assert stylegan.curr_scale[0] == 64\n\n if iter_num == 2:\n assert np.isclose(stylegan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 3:\n assert np.isclose(stylegan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 5:\n assert np.isclose(\n stylegan._actual_nkimgs[-1], 0.012, atol=1e-8)\n\n # test forward\n outputs = stylegan.forward(dict(num_batches=2))\n # assert outputs.shape == (2, 3, 64, 64)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 64, 64) for out in outputs])\n\n outputs = stylegan.forward(\n dict(\n num_batches=2,\n return_noise=True,\n transition_weight=0.2,\n sample_model='ema'))\n # assert outputs.shape == (2, 3, 64, 64)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 64, 64) for out in outputs])\n\n outputs = stylegan.forward(dict(num_batches=2, sample_model='orig'))\n # assert outputs.shape == (2, 3, 64, 64)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 64, 64) for out in outputs])\n\n outputs = stylegan.forward(\n dict(num_batches=2, sample_model='ema/orig'))\n # assert isinstance(outputs, dict)\n # assert list(outputs.keys()) == ['ema', 'orig']\n # assert all([o.shape == (2, 3, 64, 64) for o in outputs.values()])\n assert len(outputs) == 2\n assert all([out.ema.fake_img.shape == (3, 64, 64) for out in outputs])\n assert all([out.orig.fake_img.shape == (3, 64, 64) for out in outputs])\n\n outputs = stylegan.forward(dict(num_batches=2, curr_scale=8))\n # assert outputs.shape == (2, 3, 8, 8)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 8, 8) for out in outputs])\n\n outputs = stylegan.forward(dict(noise=torch.randn(2, 16)))\n # assert outputs.shape == (2, 3, 64, 64)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 64, 64) for out in outputs])\n\n outputs = stylegan.forward(torch.randn(2, 16))\n # assert outputs.shape == (2, 3, 64, 64)\n assert len(outputs) == 2\n assert all([out.fake_img.shape == (3, 64, 64) for out in outputs])\n\n # test train_step with error\n with pytest.raises(RuntimeError):\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 4, 32)) for _ in range(3)\n ])\n _ = stylegan.train_step(data_batch, optim_wrapper_dict)\n\n # test train_step without ema\n stylegan = StyleGAN1(\n self.generator_cfg,\n self.discriminator_cfg,\n style_channels=self.style_channels,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale)\n optim_wrapper_dict = constructor(stylegan)\n\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 64, 64)) for _ in range(3)\n ])\n stylegan.train_step(data_batch, optim_wrapper_dict)\n\n # test train_step with disc_step != 1\n stylegan._disc_steps = 2\n stylegan.train_step(data_batch, optim_wrapper_dict)\n\n # test default configs\n stylegan = StyleGAN1(\n self.generator_cfg,\n self.discriminator_cfg,\n style_channels=self.style_channels,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n interp_real=dict(mode='bicubic'),\n ema_config=dict(interval=1))\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_stylegan_cuda(self):\n stylegan = StyleGAN1(\n self.generator_cfg,\n self.discriminator_cfg,\n style_channels=self.style_channels,\n data_preprocessor=self.data_preprocessor,\n nkimgs_per_scale=self.nkimgs_per_scale,\n ema_config=dict(interval=1)).cuda()\n\n constructor = PGGANOptimWrapperConstructor(self.optim_wrapper_cfg)\n optim_wrapper_dict = constructor(stylegan)\n\n data_batch = dict(\n inputs=dict(),\n data_samples=[\n DataSample(gt_img=torch.randn(3, 64, 64)) for _ in range(3)\n ])\n\n for iter_num in range(6):\n stylegan.train_step(data_batch, optim_wrapper_dict)\n if iter_num in [0, 1]:\n assert stylegan.curr_scale[0] == 16\n elif iter_num in [2, 3]:\n assert stylegan.curr_scale[0] == 32\n elif iter_num in [4, 5]:\n assert stylegan.curr_scale[0] == 64\n\n if iter_num == 2:\n assert np.isclose(stylegan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 3:\n assert np.isclose(stylegan._actual_nkimgs[0], 0.006, atol=1e-8)\n elif iter_num == 5:\n assert np.isclose(\n stylegan._actual_nkimgs[-1], 0.012, atol=1e-8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan1/test_stylegan1_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan1 import StyleGAN1Discriminator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestStyleGANv1Disc:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(in_size=64)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_stylegan1_disc_cuda(self):\n d = StyleGAN1Discriminator(**self.default_cfg).cuda()\n img = torch.randn((2, 3, 64, 64)).cuda()\n score = d(img)\n assert score.shape == (2, 1)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan1_disc_cpu(self):\n d = StyleGAN1Discriminator(**self.default_cfg)\n img = torch.randn((2, 3, 64, 64))\n score = d(img)\n assert score.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan1/test_stylegan1_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan1 import StyleGAN1Generator\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestStyleGAN1Generator:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n out_size=256,\n style_channels=512,\n num_mlps=8,\n blur_kernel=[1, 2, 1],\n lr_mlp=0.01,\n default_style_mode='mix',\n eval_style_mode='single',\n mix_prob=0.9)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_g_cuda(self):\n # test default config\n g = StyleGAN1Generator(**self.default_cfg).cuda()\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n random_noise = g.make_injected_noise()\n res = g(\n None,\n num_batches=1,\n injected_noise=random_noise,\n randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n res = g(\n None, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n styles = [torch.randn((1, 512)).cuda() for _ in range(2)]\n res = g(\n styles, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n res = g(\n torch.randn,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n g.eval()\n assert g.default_style_mode == 'single'\n\n g.train()\n assert g.default_style_mode == 'mix'\n\n with pytest.raises(AssertionError):\n styles = [torch.randn((1, 6)).cuda() for _ in range(2)]\n _ = g(styles, injected_noise=None, randomize_noise=False)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['out_size'] = 128\n g = StyleGAN1Generator(**cfg_).cuda()\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test generate function\n truncation_latent = g.get_mean_latent()\n assert truncation_latent.shape == (1, 512)\n style_mixing_images = g.style_mixing(\n curr_scale=32,\n truncation_latent=truncation_latent,\n n_source=4,\n n_target=4)\n assert style_mixing_images.shape == (25, 3, 32, 32)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_g_cpu(self):\n # test default config\n g = StyleGAN1Generator(**self.default_cfg)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n random_noise = g.make_injected_noise()\n res = g(\n None,\n num_batches=1,\n injected_noise=random_noise,\n randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n res = g(\n None, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n styles = [torch.randn((1, 512)) for _ in range(2)]\n res = g(\n styles, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n res = g(\n torch.randn,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False)\n assert res.shape == (1, 3, 256, 256)\n\n g.eval()\n assert g.default_style_mode == 'single'\n\n g.train()\n assert g.default_style_mode == 'mix'\n\n with pytest.raises(AssertionError):\n styles = [torch.randn((1, 6)) for _ in range(2)]\n _ = g(styles, injected_noise=None, randomize_noise=False)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['out_size'] = 128\n g = StyleGAN1Generator(**cfg_)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 128, 128)\n\n # test generate function\n truncation_latent = g.get_mean_latent()\n assert truncation_latent.shape == (1, 512)\n style_mixing_images = g.style_mixing(\n curr_scale=32,\n truncation_latent=truncation_latent,\n n_source=4,\n n_target=4)\n assert style_mixing_images.shape == (25, 3, 32, 32)\n\n # set mix_prob as 1.0 and 0.0 to force cover lines\n cfg_ = deepcopy(self.default_cfg)\n cfg_['mix_prob'] = 1\n g = StyleGAN1Generator(**cfg_)\n res = g(torch.randn, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['mix_prob'] = 1\n g = StyleGAN1Generator(**cfg_)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['mix_prob'] = 0\n g = StyleGAN1Generator(**cfg_)\n res = g(torch.randn, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['mix_prob'] = 0\n g = StyleGAN1Generator(**cfg_)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan1/test_stylegan1_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan1.stylegan1_modules import (\n AdaptiveInstanceNorm, StyleConv)\n\n\nclass TestAdaptiveInstanceNorm:\n\n @classmethod\n def setup_class(cls):\n cls.in_channel = 512\n cls.style_dim = 512\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_adain_cuda(self):\n adain = AdaptiveInstanceNorm(self.in_channel, self.style_dim).cuda()\n x = torch.randn((2, 512, 8, 8)).cuda()\n style = torch.randn((2, 512)).cuda()\n res = adain(x, style)\n\n assert res.shape == (2, 512, 8, 8)\n\n\nclass TestStyleConv:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=512,\n out_channels=256,\n kernel_size=3,\n style_channels=512,\n padding=1,\n initial=False,\n blur_kernel=[1, 2, 1],\n upsample=True,\n fused=False)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_styleconv_cuda(self):\n conv = StyleConv(**self.default_cfg).cuda()\n input_x = torch.randn((2, 512, 32, 32)).cuda()\n input_style1 = torch.randn((2, 512)).cuda()\n input_style2 = torch.randn((2, 512)).cuda()\n\n res = conv(input_x, input_style1, input_style2)\n assert res.shape == (2, 256, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan1/test_stylegan_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock, patch\n\nimport torch\n\nfrom mmagic.models.editors.stylegan1 import get_mean_latent, style_mixing\n\nget_module_device_str = 'mmagic.models.editors.stylegan1.stylegan_utils.get_module_device' # noqa\n\n\n@patch(get_module_device_str, MagicMock(return_value='cpu'))\ndef test_get_mean_latent():\n generator = MagicMock()\n generator.style_mapping = MagicMock(return_value=torch.randn(1024, 16))\n\n mean_style = get_mean_latent(generator)\n assert mean_style.shape == (1, 16)\n\n\ndef mock_generator(code, *args, **kwargs):\n if isinstance(code, torch.Tensor):\n n_sample = code.shape[0]\n elif isinstance(code, list):\n # is list\n n_sample = code[0].shape[0]\n return torch.randn(n_sample, 3, 16, 16)\n\n\n@patch(get_module_device_str, MagicMock(return_value='cpu'))\ndef test_style_mixing():\n out = style_mixing(mock_generator, n_source=10, n_target=11)\n assert out.shape == ((1 + 10 + (1 + 10) * 11), 3, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan2/test_ada/test_augment.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.editors.stylegan2.ada.augment import AugmentPipe\n\n\nclass TestAuementPipe(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.default_cfg = dict(\n xflip=1,\n rotate90=1,\n xint=1,\n xint_max=1,\n scale=1,\n rotate=1,\n aniso=1,\n xfrac=1,\n scale_std=1,\n rotate_max=1,\n aniso_std=1,\n xfrac_std=1,\n brightness=1,\n contrast=1,\n lumaflip=1,\n hue=1,\n saturation=1,\n brightness_std=1,\n contrast_std=1,\n hue_max=1,\n saturation_std=1,\n imgfilter=1,\n imgfilter_bands=[1, 1, 1, 1],\n imgfilter_std=1,\n noise=1,\n cutout=1,\n noise_std=1,\n cutout_size=0.5,\n )\n\n @pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0')\n or 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason=('torch version lower than 1.7.0 does not have '\n '`torch.exp2` api, skip on windows due to uncompiled ops.'))\n def test_forward(self):\n augment_pipeline = AugmentPipe(**self.default_cfg)\n\n inp = torch.rand(2, 3, 64, 64)\n out = augment_pipeline(inp)\n assert out.shape == (2, 3, 64, 64)\n\n out = augment_pipeline(inp, debug_percentile=0.1)\n\n no_aug_cfg = {\n k: 0\n for k, v in self.default_cfg.items()\n if isinstance(v, (float, int))\n }\n augment_pipeline = AugmentPipe(**no_aug_cfg)\n\n inp = torch.rand(2, 3, 64, 64)\n out = augment_pipeline(inp)\n assert out.shape == (2, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan2/test_stylegan2.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\n\nfrom mmagic.models import DataPreprocessor, StyleGAN2\nfrom mmagic.structures import DataSample\n\n\nclass TestStyleGAN2(TestCase):\n\n @classmethod\n def setup_class(cls):\n cls.generator_cfg = dict(\n type='StyleGANv2Generator', out_size=32, style_channels=16)\n cls.disc_cfg = dict(type='StyleGAN2Discriminator', in_size=32)\n\n # reg params\n d_reg_interval = 16\n g_reg_interval = 4\n ema_half_life = 10. # G_smoothing_kimg\n\n cls.ema_config = dict(\n type='ExponentialMovingAverage',\n interval=1,\n momentum=1. - (0.5**(32. / (ema_half_life * 1000.))))\n\n cls.loss_config = dict(\n r1_loss_weight=10. / 2. * d_reg_interval,\n r1_interval=d_reg_interval,\n norm_mode='HWC',\n g_reg_interval=g_reg_interval,\n g_reg_weight=2. * g_reg_interval,\n pl_batch_shrink=2)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan2_cpu(self):\n accu_iter = 1\n message_hub = MessageHub.get_instance('test-s2')\n stylegan2 = StyleGAN2(\n self.generator_cfg,\n self.disc_cfg,\n data_preprocessor=DataPreprocessor(),\n ema_config=self.ema_config,\n loss_config=self.loss_config)\n\n optimizer_g = torch.optim.SGD(\n stylegan2.generator.parameters(), lr=0.01)\n optimizer_d = torch.optim.SGD(\n stylegan2.discriminator.parameters(), lr=0.01)\n\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(optimizer_g, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n optimizer_d, accumulative_counts=accu_iter))\n\n # prepare inputs\n img = torch.randn(3, 32, 32)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n message_hub.update_info('iter', 0)\n _ = stylegan2.train_step(data, optim_wrapper_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan2/test_stylegan2_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.editors.stylegan2 import (ADAAug, ADAStyleGAN2Discriminator,\n StyleGAN2Discriminator)\nfrom mmagic.models.utils import get_module_device\n\n\n@pytest.mark.skipif(\n ('win' in platform.system().lower() and 'cu' in torch.__version__)\n or not torch.cuda.is_available(),\n reason='skip on windows-cuda due to limited RAM.')\nclass TestStyleGANv2Disc:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(in_size=64, channel_multiplier=1)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan2_disc_cpu(self):\n d = StyleGAN2Discriminator(**self.default_cfg)\n img = torch.randn((2, 3, 64, 64))\n score = d(img)\n assert score.shape == (2, 1)\n\n cfg = deepcopy(self.default_cfg)\n cfg['cond_size'] = 5\n cfg['cond_mapping_channels'] = 16\n\n d = StyleGAN2Discriminator(**cfg)\n score = d(img, torch.randn(2, 5))\n assert score.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_stylegan2_disc_cuda(self):\n d = StyleGAN2Discriminator(**self.default_cfg).cuda()\n img = torch.randn((2, 3, 64, 64)).cuda()\n score = d(img)\n assert score.shape == (2, 1)\n\n cfg = deepcopy(self.default_cfg)\n cfg['cond_size'] = 5\n cfg['cond_mapping_channels'] = 16\n\n d = StyleGAN2Discriminator(**cfg).cuda()\n score = d(img, torch.randn(2, 5).cuda())\n assert score.shape == (2, 1)\n\n\ndef test_get_module_device_cpu():\n device = get_module_device(nn.Conv2d(3, 3, 3, 1, 1))\n assert device == torch.device('cpu')\n\n # The input module should contain parameters.\n with pytest.raises(ValueError):\n get_module_device(nn.Flatten())\n\n\n@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\ndef test_get_module_device_cuda():\n module = nn.Conv2d(3, 3, 3, 1, 1).cuda()\n device = get_module_device(module)\n assert device == next(module.parameters()).get_device()\n\n # The input module should contain parameters.\n with pytest.raises(ValueError):\n get_module_device(nn.Flatten().cuda())\n\n\nclass TestStyleGANv2AdaDisc:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_size=64, data_aug=dict(type='ADAAug'), channel_multiplier=1)\n\n @pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0'),\n reason='torch version lower than 1.7.0 does not have `torch.exp2` api')\n def test_styleganv2_ada(self):\n disc = ADAStyleGAN2Discriminator(**self.default_cfg)\n assert hasattr(disc, 'ada_aug')\n img = torch.randn(2, 3, 64, 64)\n score = disc(img)\n assert score.shape == (2, 1)\n\n cfg = deepcopy(self.default_cfg)\n cfg['data_aug'] = None\n disc = ADAStyleGAN2Discriminator(**cfg)\n assert not hasattr(disc, 'ada_aug')\n img = torch.randn(2, 3, 64, 64)\n score = disc(img)\n assert score.shape == (2, 1)\n\n\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0'),\n reason='torch version lower than 1.7.0 does not have `torch.exp2` api')\ndef test_ada_pipeline():\n ada = ADAAug()\n ada.update(0, 2)\n ada.update(1, 2)\n ada.update(2, 2)\n assert (ada.log_buffer == 0).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan2/test_stylegan2_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Generator\n\n\nclass TestStyleGAN2Generator:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n out_size=64, style_channels=16, num_mlps=4, channel_multiplier=1)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_stylegan2_g_cpu(self):\n # test default config\n g = StyleGAN2Generator(**self.default_cfg)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 64, 64)\n\n # test truncation_latent is None\n assert not hasattr(g, 'truncation_latent')\n res = g(None, num_batches=2, truncation=0.9)\n assert res.shape == (2, 3, 64, 64)\n assert hasattr(g, 'truncation_latent')\n assert g.truncation_latent.shape == (1, 16)\n res = g(None, num_batches=2, truncation=0.9)\n assert res.shape == (2, 3, 64, 64)\n\n truncation_mean = g.get_mean_latent()\n res = g(\n None,\n num_batches=2,\n randomize_noise=False,\n truncation=0.7,\n truncation_latent=truncation_mean)\n assert res.shape == (2, 3, 64, 64)\n\n res = g.style_mixing(2, 2, truncation_latent=truncation_mean)\n assert res.shape[2] == 64\n\n random_noise = g.make_injected_noise()\n res = g(\n None,\n num_batches=1,\n injected_noise=random_noise,\n randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n random_noise = g.make_injected_noise()\n res = g(\n None, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n styles = [torch.randn((1, 16)) for _ in range(2)]\n res = g(\n styles, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n res = g(\n torch.randn,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n g.eval()\n assert g.default_style_mode == 'single'\n\n g.train()\n assert g.default_style_mode == 'mix'\n\n with pytest.raises(AssertionError):\n styles = [torch.randn((1, 6)) for _ in range(2)]\n _ = g(styles, injected_noise=None, randomize_noise=False)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['out_size'] = 256\n g = StyleGAN2Generator(**cfg_)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n # set mix_prob as 1 and 0 to cover all lines\n g.mix_prob = 1\n res = g(None, num_batches=2)\n g.mix_prob = 0\n res = g(None, num_batches=2)\n\n # test cond channels is negative number\n cfg_ = deepcopy(self.default_cfg)\n cfg_['cond_size'] = -1\n g = StyleGAN2Generator(**cfg_)\n assert not hasattr(g, 'embed')\n\n # test cond channels > 0\n cfg_ = deepcopy(self.default_cfg)\n cfg_['cond_size'] = 10\n g = StyleGAN2Generator(**cfg_)\n assert hasattr(g, 'embed')\n assert hasattr(g, 'w_avg')\n # test raise error\n with pytest.raises(AssertionError):\n g(None, num_batches=2)\n res = g(None, num_batches=2, label=torch.randn(2, 10))\n assert res.shape == (2, 3, 64, 64)\n\n # test update_mean_latent_with_ema\n cfg_ = deepcopy(self.default_cfg)\n cfg_['update_mean_latent_with_ema'] = True\n g = StyleGAN2Generator(**cfg_)\n assert hasattr(g, 'w_avg')\n # test get_mean_latent\n mean_latent = g.get_mean_latent().clone() # copy for test\n assert mean_latent.shape == (16, )\n assert (mean_latent == 0).all()\n\n # test update w_avg with ema\n g.eval()\n res = g(\n None,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False,\n update_ws=True)\n mean_latent_test = g.get_mean_latent().clone() # copy for test\n # should not be update in test\n assert (mean_latent_test == mean_latent).all()\n\n # test return features\n g.eval()\n res = g(\n None,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False,\n return_noise=True,\n return_features=True,\n feat_idx=1)\n assert res['feats'].shape == (1, 512, 8, 8)\n assert res['latent'].shape == (1, 10, 16)\n\n # test return latent only\n g.eval()\n res = g(\n None,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False,\n return_latent_only=True)\n assert res.shape == (1, 10, 16)\n\n g.train()\n res = g(\n None,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False,\n update_ws=True)\n mean_latent_train = g.get_mean_latent().clone() # copy for test\n # should be update in train\n assert (mean_latent_train != mean_latent).any()\n\n res = g(\n None,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False,\n update_ws=False)\n mean_latent_no_update = g.get_mean_latent().clone() # copy for test\n assert (mean_latent_train == mean_latent_no_update).all()\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_g_cuda(self):\n # test default config\n g = StyleGAN2Generator(**self.default_cfg).cuda()\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 64, 64)\n\n random_noise = g.make_injected_noise()\n res = g(\n None,\n num_batches=1,\n injected_noise=random_noise,\n randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n random_noise = g.make_injected_noise()\n res = g(\n None, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n styles = [torch.randn((1, 16)).cuda() for _ in range(2)]\n res = g(\n styles, num_batches=1, injected_noise=None, randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n res = g(\n torch.randn,\n num_batches=1,\n injected_noise=None,\n randomize_noise=False)\n assert res.shape == (1, 3, 64, 64)\n\n g.eval()\n assert g.default_style_mode == 'single'\n\n g.train()\n assert g.default_style_mode == 'mix'\n\n with pytest.raises(AssertionError):\n styles = [torch.randn((1, 6)).cuda() for _ in range(2)]\n _ = g(styles, injected_noise=None, randomize_noise=False)\n\n cfg_ = deepcopy(self.default_cfg)\n cfg_['out_size'] = 256\n g = StyleGAN2Generator(**cfg_).cuda()\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 256, 256)\n\n # set mix_prob as 1 and 0 to cover all lines\n g.mix_prob = 1\n res = g(None, num_batches=2)\n g.mix_prob = 0\n res = g(None, num_batches=2)\n\n # test cond channels is negative number\n cfg_ = deepcopy(self.default_cfg)\n cfg_['cond_size'] = -1\n g = StyleGAN2Generator(**cfg_)\n assert not hasattr(g, 'embed')\n\n # test cond channels > 0\n cfg_ = deepcopy(self.default_cfg)\n cfg_['cond_size'] = 10\n g = StyleGAN2Generator(**cfg_)\n assert hasattr(g, 'embed')\n # test raise error\n with pytest.raises(AssertionError):\n g(None, num_batches=2)\n res = g(None, num_batches=2, label=torch.randn(2, 10))\n assert res.shape == (2, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan2/test_stylegan2_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan1 import get_mean_latent, style_mixing\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Generator\nfrom mmagic.models.editors.stylegan2.stylegan2_modules import (\n Blur, DownsampleUpFIRDn, ModulatedConv2d, ModulatedStyleConv,\n ModulatedToRGB)\nfrom mmagic.models.utils import get_module_device\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_get_module_device():\n config = dict(\n out_size=64, style_channels=16, num_mlps=4, channel_multiplier=1)\n g = StyleGAN2Generator(**config)\n res = g(None, num_batches=2)\n assert res.shape == (2, 3, 64, 64)\n\n truncation_mean = get_mean_latent(g, 4096)\n res = g(\n None,\n num_batches=2,\n randomize_noise=False,\n truncation=0.7,\n truncation_latent=truncation_mean)\n\n # res = g.style_mixing(2, 2, truncation_latent=truncation_mean)\n res = style_mixing(\n g,\n n_source=2,\n n_target=2,\n truncation_latent=truncation_mean,\n style_channels=g.style_channels)\n\n assert get_module_device(g) == torch.device('cpu')\n\n\nclass TestDownsampleUpFIRDn():\n\n def test_DownsampleUpFIRDn(self):\n downsample = DownsampleUpFIRDn((2, 2), 2)\n assert downsample.pad == (0, 0)\n\n inp = torch.randn(1, 3, 4, 4)\n out = downsample(inp)\n assert out.shape == (1, 3, 2, 2)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_DownsampleUpFIRDn_cuda(self):\n downsample = DownsampleUpFIRDn((2, 2), 2).cuda()\n assert downsample.pad == (0, 0)\n\n inp = torch.randn(1, 3, 4, 4).cuda()\n out = downsample(inp)\n assert out.shape == (1, 3, 2, 2)\n\n\nclass TestBlur:\n\n @classmethod\n def setup_class(cls):\n cls.kernel = [1, 3, 3, 1]\n cls.pad = (1, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_blur_cuda(self):\n blur = Blur(self.kernel, self.pad)\n x = torch.randn((2, 3, 8, 8))\n res = blur(x)\n\n assert res.shape == (2, 3, 7, 7)\n\n\nclass TestModStyleConv:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=1,\n kernel_size=3,\n style_channels=5,\n upsample=True)\n\n def test_mod_styleconv_cpu(self):\n conv = ModulatedStyleConv(**self.default_cfg)\n input_x = torch.randn((2, 3, 4, 4))\n input_style = torch.randn((2, 5))\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 8, 8)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n conv = ModulatedStyleConv(**_cfg)\n input_x = torch.randn((2, 3, 4, 4))\n input_style = torch.randn((2, 5))\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n # test add noise\n noise = torch.randn(2, 1, 4, 4)\n res = conv(input_x, input_style, noise)\n assert res.shape == (2, 1, 4, 4)\n\n # test add noise + return_noise\n res = conv(input_x, input_style, noise, return_noise=True)\n assert isinstance(res, tuple)\n assert res[0].shape == (2, 1, 4, 4)\n assert (res[1] == noise).all()\n\n # test add noise is False\n res = conv(input_x, input_style, noise, add_noise=False)\n assert res.shape == (2, 1, 4, 4)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_mod_styleconv_cuda(self):\n conv = ModulatedStyleConv(**self.default_cfg).cuda()\n input_x = torch.randn((2, 3, 4, 4)).cuda()\n input_style = torch.randn((2, 5)).cuda()\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 8, 8)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n conv = ModulatedStyleConv(**_cfg).cuda()\n input_x = torch.randn((2, 3, 4, 4)).cuda()\n input_style = torch.randn((2, 5)).cuda()\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n\nclass TestModulatedConv2d():\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(\n in_channels=3,\n out_channels=1,\n kernel_size=3,\n style_channels=5,\n upsample=True)\n\n def test_mod_conv_cpu(self):\n conv = ModulatedConv2d(**self.default_cfg)\n input_x = torch.randn((2, 3, 4, 4))\n input_style = torch.randn((2, 5))\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 8, 8)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n conv = ModulatedConv2d(**_cfg)\n input_x = torch.randn((2, 3, 4, 4))\n input_style = torch.randn((2, 5))\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n _cfg['downsample'] = True\n conv = ModulatedConv2d(**_cfg)\n input_x = torch.randn((2, 3, 8, 8))\n input_style = torch.randn((2, 5))\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n # test input gain\n res = conv(input_x, input_style, input_gain=torch.randn(2, 3))\n assert res.shape == (2, 1, 4, 4)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_mod_conv_cuda(self):\n conv = ModulatedConv2d(**self.default_cfg).cuda()\n input_x = torch.randn((2, 3, 4, 4)).cuda()\n input_style = torch.randn((2, 5)).cuda()\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 8, 8)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n conv = ModulatedConv2d(**_cfg).cuda()\n input_x = torch.randn((2, 3, 4, 4)).cuda()\n input_style = torch.randn((2, 5)).cuda()\n\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n _cfg = deepcopy(self.default_cfg)\n _cfg['upsample'] = False\n _cfg['downsample'] = True\n conv = ModulatedConv2d(**_cfg).cuda()\n input_x = torch.randn((2, 3, 8, 8)).cuda()\n input_style = torch.randn((2, 5)).cuda()\n res = conv(input_x, input_style)\n assert res.shape == (2, 1, 4, 4)\n\n # test input gain\n res = conv(input_x, input_style, input_gain=torch.randn(2, 3).cuda())\n assert res.shape == (2, 1, 4, 4)\n\n\nclass TestToRGB:\n\n @classmethod\n def setup_class(cls):\n cls.default_cfg = dict(in_channels=5, style_channels=5, out_channels=3)\n\n def test_torgb_cpu(self):\n model = ModulatedToRGB(**self.default_cfg)\n input_x = torch.randn((2, 5, 4, 4))\n style = torch.randn((2, 5))\n\n res = model(input_x, style)\n assert res.shape == (2, 3, 4, 4)\n\n input_x = torch.randn((2, 5, 8, 8))\n style = torch.randn((2, 5))\n skip = torch.randn(2, 3, 4, 4)\n res = model(input_x, style, skip)\n assert res.shape == (2, 3, 8, 8)\n\n # test skip is passed + upsample is False\n cfg = deepcopy(self.default_cfg)\n cfg['upsample'] = False\n cfg['out_channels'] = 7\n model = ModulatedToRGB(**cfg)\n input_x = torch.randn((2, 5, 4, 4))\n skip = torch.randn(2, 7, 4, 4)\n style = torch.randn((2, 5))\n res = model(input_x, style, skip)\n assert res.shape == (2, 7, 4, 4)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_torgb_cuda(self):\n model = ModulatedToRGB(**self.default_cfg).cuda()\n input_x = torch.randn((2, 5, 4, 4)).cuda()\n style = torch.randn((2, 5)).cuda()\n\n res = model(input_x, style)\n assert res.shape == (2, 3, 4, 4)\n\n input_x = torch.randn((2, 5, 8, 8)).cuda()\n style = torch.randn((2, 5)).cuda()\n skip = torch.randn(2, 3, 4, 4).cuda()\n res = model(input_x, style, skip)\n assert res.shape == (2, 3, 8, 8)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan3/test_stylegan3.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\n\nfrom mmagic.models import StyleGAN3\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\nregister_all_modules()\n\n\nclass TestStyleGAN3(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.default_cfg = dict(\n data_preprocessor=dict(type='DataPreprocessor'),\n generator=dict(\n type='StyleGANv3Generator',\n noise_size=6,\n style_channels=8,\n out_size=16,\n img_channels=3,\n synthesis_cfg=dict(\n type='SynthesisNetwork',\n channel_base=1024,\n channel_max=16,\n magnitude_ema_beta=0.999)),\n discriminator=dict(\n type='StyleGAN2Discriminator',\n in_size=16,\n channel_multiplier=1),\n ema_config=dict(\n type='RampUpEMA',\n interval=1,\n ema_kimg=10,\n ema_rampup=0.05,\n batch_size=32,\n eps=1e-08,\n start_iter=0),\n loss_config=dict(\n r1_loss_weight=16.0,\n r1_interval=16,\n norm_mode='HWC',\n g_reg_interval=4,\n g_reg_weight=8.0,\n pl_batch_shrink=2))\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_val_and_test_step(self):\n cfg = deepcopy(self.default_cfg)\n stylegan = StyleGAN3(**cfg)\n\n data = dict(inputs=dict(num_batches=2))\n outputs = stylegan.test_step(data)\n self.assertEqual(len(outputs), 2)\n self.assertEqual(outputs[0].fake_img.data.shape, (3, 16, 16))\n\n data = dict(inputs=dict(num_batches=2))\n outputs = stylegan.val_step(data)\n self.assertEqual(len(outputs), 2)\n self.assertEqual(outputs[0].fake_img.data.shape, (3, 16, 16))\n\n eq_cfg = dict(\n compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True)\n data = dict(inputs=dict(num_batches=2, eq_cfg=eq_cfg, mode='orig'))\n outputs = stylegan.test_step(data)\n outputs = stylegan.val_step(data)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\n def test_train_step(self):\n message_hub = MessageHub.get_instance('test-s3-train-step')\n cfg = deepcopy(self.default_cfg)\n stylegan = StyleGAN3(**cfg)\n optimizer_g = torch.optim.SGD(stylegan.generator.parameters(), lr=0.01)\n optimizer_d = torch.optim.SGD(\n stylegan.discriminator.parameters(), lr=0.01)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(optimizer_g, accumulative_counts=1),\n discriminator=OptimWrapper(optimizer_d, accumulative_counts=1))\n\n img = torch.randn(3, 16, 16)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n message_hub.update_info('iter', 0)\n _ = stylegan.train_step(data, optim_wrapper_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan3/test_stylegan3_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan3 import StyleGAN3Generator\n\n\nclass TestStyleGAN3Generator:\n\n @classmethod\n def setup_class(cls):\n synthesis_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 1024,\n 'channel_max': 16,\n 'magnitude_ema_beta': 0.999\n }\n cls.default_cfg = dict(\n noise_size=6,\n style_channels=8,\n out_size=16,\n img_channels=3,\n synthesis_cfg=synthesis_cfg)\n synthesis_r_cfg = {\n 'type': 'SynthesisNetwork',\n 'channel_base': 1024,\n 'channel_max': 16,\n 'magnitude_ema_beta': 0.999,\n 'conv_kernel': 1,\n 'use_radial_filters': True\n }\n cls.s3_r_cfg = dict(\n noise_size=6,\n style_channels=8,\n out_size=16,\n img_channels=3,\n synthesis_cfg=synthesis_r_cfg)\n\n @pytest.mark.skipif(\n ('win' in platform.system().lower() and 'cu' in torch.__version__)\n or not torch.cuda.is_available(),\n reason='skip on windows-cuda due to limited RAM.')\n def test_cpu(self):\n generator = StyleGAN3Generator(**self.default_cfg)\n z = torch.randn((2, 6))\n c = None\n y = generator(z, c)\n assert y.shape == (2, 3, 16, 16)\n\n y = generator(None, num_batches=2)\n assert y.shape == (2, 3, 16, 16)\n\n res = generator(torch.randn, num_batches=1)\n assert res.shape == (1, 3, 16, 16)\n\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(rgb2bgr=True))\n generator = StyleGAN3Generator(**cfg)\n y = generator(None, num_batches=2)\n assert y.shape == (2, 3, 16, 16)\n\n # test return latents\n result = generator(None, num_batches=2, return_latents=True)\n assert isinstance(result, dict)\n assert result['fake_img'].shape == (2, 3, 16, 16)\n assert result['noise_batch'].shape == (2, 6)\n assert result['latent'].shape == (2, 16, 8)\n\n # test input_is_latent\n result = generator(\n None, num_batches=2, input_is_latent=True, return_latents=True)\n assert isinstance(result, dict)\n assert result['fake_img'].shape == (2, 3, 16, 16)\n assert result['noise_batch'].shape == (2, 8)\n assert result['latent'].shape == (2, 16, 8)\n\n generator = StyleGAN3Generator(**self.s3_r_cfg)\n z = torch.randn((2, 6))\n c = None\n y = generator(z, c)\n assert y.shape == (2, 3, 16, 16)\n\n y = generator(None, num_batches=2)\n assert y.shape == (2, 3, 16, 16)\n\n res = generator(torch.randn, num_batches=1)\n assert res.shape == (1, 3, 16, 16)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_cuda(self):\n generator = StyleGAN3Generator(**self.default_cfg).cuda()\n z = torch.randn((2, 6)).cuda()\n c = None\n y = generator(z, c)\n assert y.shape == (2, 3, 16, 16)\n\n res = generator(torch.randn, num_batches=1)\n assert res.shape == (1, 3, 16, 16)\n\n cfg = deepcopy(self.default_cfg)\n cfg.update(dict(rgb2bgr=True))\n generator = StyleGAN3Generator(**cfg).cuda()\n y = generator(None, num_batches=2)\n assert y.shape == (2, 3, 16, 16)\n\n generator = StyleGAN3Generator(**self.s3_r_cfg).cuda()\n z = torch.randn((2, 6)).cuda()\n c = None\n y = generator(z, c)\n assert y.shape == (2, 3, 16, 16)\n\n res = generator(torch.randn, num_batches=1)\n assert res.shape == (1, 3, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan3/test_stylegan3_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.stylegan3.stylegan3_modules import MappingNetwork\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip on windows due to uncompiled ops.')\ndef test_MappingNetwork():\n mapping_network = MappingNetwork(16, 4, 5, cond_size=8)\n z = torch.randn(1, 16)\n c = torch.randn(1, 8)\n out = mapping_network(z, c)\n assert out.shape == (1, 5, 4)\n\n # test w/o conditional input\n mapping_network = MappingNetwork(16, 4, 5, cond_size=-1)\n out = mapping_network(z)\n assert out.shape == (1, 5, 4)\n\n # test w/o noise input\n mapping_network = MappingNetwork(0, 4, 5, cond_size=8)\n out = mapping_network(None, c)\n assert out.shape == (1, 5, 4)\n\n # test num_ws is None --> no broadcast\n mapping_network = MappingNetwork(16, 4, num_ws=None, w_avg_beta=None)\n assert not hasattr(mapping_network, 'w_avg')\n out = mapping_network(z)\n assert out.shape == (1, 4)\n\n # test truncation is passed\n with pytest.raises(AssertionError):\n mapping_network(z, truncation=0.9)\n\n mapping_network = MappingNetwork(16, 4, 5)\n out = mapping_network(z, truncation=0.9)\n assert out.shape == (1, 5, 4)\n\n out_trunc_work = mapping_network(z, truncation=0.9, num_truncation_layer=3)\n assert out_trunc_work.shape == (1, 5, 4)\n assert (out_trunc_work[3:] == out[3:]).all()\n\n # test z is None + noise_size > 0\n with pytest.raises(AssertionError):\n mapping_network(None)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_stylegan3/test_stylegan3_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nfrom mmagic.models.editors.stylegan3.stylegan3_utils import (\n apply_fractional_pseudo_rotation, apply_fractional_rotation,\n apply_fractional_translation, apply_integer_translation)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip due to uncompiled ops.')\ndef test_integer_transformation():\n x = torch.randn(1, 3, 16, 16)\n t = torch.randn(2)\n z, m = apply_integer_translation(x, t[0], t[1])\n print(z.shape)\n print(m.shape)\n\n # cover more lines\n t = torch.zeros(2)\n z, m = apply_integer_translation(x, t[0], t[1])\n\n t = torch.ones(2) * 2\n z, m = apply_integer_translation(x, t[0], t[1])\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip due to uncompiled ops.')\ndef test_fractional_translation():\n x = torch.randn(1, 3, 16, 16)\n t = torch.randn(2)\n z, m = apply_fractional_translation(x, t[0], t[1])\n print(z.shape)\n print(m.shape)\n\n # cover more lines\n t = torch.zeros(2)\n z, m = apply_fractional_translation(x, t[0], t[1])\n\n t = torch.ones(2) * 2\n z, m = apply_fractional_translation(x, t[0], t[1])\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='skip due to uncompiled ops.')\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) < digit_version('1.8.0'),\n reason='version limitation')\ndef test_fractional_rotation():\n angle = torch.randn([])\n x = torch.randn(1, 3, 16, 16)\n ref, ref_mask = apply_fractional_rotation(x, angle)\n print(ref.shape)\n print(ref_mask.shape)\n\n\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) < digit_version('1.8.0')\n or 'win' in platform.system().lower() or not torch.cuda.is_available(),\n reason='version limitation')\ndef test_fractional_pseduo_rotation():\n angle = torch.randn([])\n x = torch.randn(1, 3, 16, 16)\n ref, ref_mask = apply_fractional_pseudo_rotation(x, angle)\n print(ref.shape)\n print(ref_mask.shape)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_swinir/test_swinir_modules.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.swinir.swinir_modules import (PatchEmbed,\n PatchUnEmbed,\n Upsample,\n UpsampleOneStep)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_patchEmbed():\n\n net = PatchEmbed(\n img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)\n\n img = torch.randn(1, 3, 4, 4)\n output = net(img)\n assert output.shape == (1, 16, 3)\n\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 16, 3)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_patchUnEmbed():\n\n net = PatchUnEmbed(\n img_size=16, patch_size=4, in_chans=3, embed_dim=3, norm_layer=None)\n\n img = torch.randn(1, 64, 3)\n output = net(img, (8, 8))\n assert output.shape == (1, 3, 8, 8)\n\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda(), (8, 8))\n assert output.shape == (1, 3, 8, 8)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_upsample():\n\n net = Upsample(scale=2, num_feat=3)\n\n img = torch.randn(1, 3, 8, 8)\n output = net(img)\n assert output.shape == (1, 3, 16, 16)\n\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 3, 16, 16)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_upsampleOneStep():\n\n net = UpsampleOneStep(\n scale=2,\n num_feat=3,\n num_out_ch=4,\n )\n\n img = torch.randn(1, 3, 8, 8)\n output = net(img)\n assert output.shape == (1, 4, 16, 16)\n\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert output.shape == (1, 4, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_swinir/test_swinir_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import SwinIRNet\n\n\ndef test_swinir_cpu():\n \"\"\"Test SwinIRNet.\"\"\"\n\n # x2 model classical SR\n net = SwinIRNet(\n upscale=2,\n in_channels=3,\n img_size=48,\n window_size=8,\n img_range=1.0,\n depths=[6],\n embed_dim=60,\n num_heads=[6],\n mlp_ratio=2,\n upsampler='pixelshuffledirect',\n resi_connection='3conv')\n img = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 32, 32)\n\n net = SwinIRNet(\n upscale=1,\n in_channels=3,\n img_size=48,\n window_size=8,\n img_range=1.0,\n depths=[6],\n embed_dim=60,\n num_heads=[6],\n mlp_ratio=2,\n upsampler='',\n resi_connection='1conv')\n img = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 16, 16)\n\n # x3 model classical SR, initialization and forward (cpu)\n net = SwinIRNet(\n upscale=3,\n in_channels=3,\n img_size=16,\n window_size=8,\n img_range=1.0,\n depths=[2],\n embed_dim=8,\n num_heads=[2],\n mlp_ratio=2,\n upsampler='pixelshuffle',\n resi_connection='1conv')\n img = torch.rand(1, 3, 16, 16)\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 48, 48)\n\n # x4 model lightweight SR, initialization and forward (cpu)\n net = SwinIRNet(\n upscale=4,\n in_channels=3,\n img_size=16,\n window_size=8,\n img_range=1.0,\n depths=[2],\n embed_dim=8,\n num_heads=[2],\n mlp_ratio=2,\n ape=True,\n upsampler='nearest+conv',\n resi_connection='1conv')\n output = net(img)\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 64, 64)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_swinir_cuda():\n net = SwinIRNet(\n upscale=4,\n in_channels=3,\n img_size=16,\n window_size=8,\n img_range=1.0,\n depths=[2],\n embed_dim=8,\n num_heads=[2],\n mlp_ratio=2,\n upsampler='pixelshuffledirect',\n resi_connection='1conv')\n img = torch.rand(1, 3, 16, 16)\n\n # x4 model lightweight SR forward (gpu)\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda())\n assert isinstance(output, torch.Tensor)\n assert output.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_swinir/test_swinir_rstb.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.swinir.swinir_rstb import RSTB\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_rstb():\n\n net = RSTB(\n dim=6, input_resolution=(8, 8), depth=6, num_heads=6, window_size=8)\n\n img = torch.randn(1, 64, 6)\n output = net(img, (8, 8))\n assert output.shape == (1, 64, 6)\n\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(img.cuda(), (8, 8))\n assert output.shape == (1, 64, 6)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_swinir/test_swinir_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.swinir.swinir_utils import (drop_path, to_2tuple,\n window_partition,\n window_reverse)\n\n\ndef test_drop_path():\n x = torch.randn(1, 3, 8, 8)\n x = drop_path(x)\n assert x.shape == (1, 3, 8, 8)\n\n\ndef test_to_2tuple():\n x = 8\n x = to_2tuple(x)\n assert x == (8, 8)\n\n\ndef test_window():\n x = torch.randn(1, 8, 8, 3)\n x = window_partition(x, 4)\n assert x.shape == (4, 4, 4, 3)\n x = window_reverse(x, 4, 8, 8)\n assert x.shape == (1, 8, 8, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_tdan/test_tdan.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models.data_preprocessors import DataPreprocessor\nfrom mmagic.models.editors import TDAN, TDANNet\nfrom mmagic.models.losses import MSELoss\nfrom mmagic.structures import DataSample\n\n\ndef test_tdan():\n\n model = TDAN(\n generator=dict(type='TDANNet'),\n pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),\n lq_pixel_loss=dict(type='MSELoss', loss_weight=0.01, reduction='mean'),\n data_preprocessor=DataPreprocessor(mean=[0.5, 0.5, 0.5]))\n\n assert model.__class__.__name__ == 'TDAN'\n assert isinstance(model.generator, TDANNet)\n assert isinstance(model.pixel_loss, MSELoss)\n assert isinstance(model.data_preprocessor, DataPreprocessor)\n\n optimizer = Adam(model.generator.parameters(), lr=0.001)\n optim_wrapper = OptimWrapper(optimizer)\n\n # prepare data\n inputs = torch.rand(5, 3, 16, 16)\n target = torch.rand(3, 64, 64)\n data_sample = DataSample(gt_img=target)\n data = dict(inputs=[inputs], data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 64, 64)\n\n # feat\n output = model(torch.rand(1, 5, 3, 16, 16), mode='tensor')\n assert output.shape == (1, 3, 64, 64)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_tdan/test_tdan_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import TDANNet\n\n\ndef test_tdan_net():\n \"\"\"Test TDANNet.\"\"\"\n\n # gpu (DCN is available only on GPU)\n if torch.cuda.is_available():\n tdan = TDANNet().cuda()\n input_tensor = torch.rand(1, 5, 3, 64, 64).cuda()\n\n output = tdan(input_tensor)\n assert len(output) == 2 # (1) HR center + (2) aligned LRs\n assert output[0].shape == (1, 3, 256, 256) # HR center frame\n assert output[1].shape == (1, 5, 3, 64, 64) # aligned LRs\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_tof/test_tof_vfi_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import TOFlowVFINet\n\n\ndef test_tof_vfi_net():\n\n model = TOFlowVFINet()\n\n # test attributes\n assert model.__class__.__name__ == 'TOFlowVFINet'\n\n # prepare data\n inputs = torch.rand(1, 2, 3, 256, 256)\n\n # test on cpu\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == (1, 3, 256, 256)\n\n # test on gpu\n if torch.cuda.is_available():\n model = model.cuda()\n inputs = inputs.cuda()\n output = model(inputs)\n assert torch.is_tensor(output)\n assert output.shape == (1, 3, 256, 256)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_tof/test_tof_vsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors import TOFlowVSRNet\n\n\ndef test_toflow_vsr_net():\n\n imgs = torch.rand(2, 7, 3, 16, 16)\n\n model = TOFlowVSRNet(adapt_official_weights=False)\n out = model(imgs)\n assert isinstance(out, torch.Tensor)\n assert out.shape == (2, 3, 16, 16)\n\n model = TOFlowVSRNet(adapt_official_weights=True)\n out = model(imgs)\n assert isinstance(out, torch.Tensor)\n assert out.shape == (2, 3, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ttsr/test_lte.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\ndef test_lte():\n model_cfg = dict(\n type='LTE',\n requires_grad=False,\n pixel_range=1.,\n load_pretrained_vgg=False)\n\n lte = MODELS.build(model_cfg)\n assert lte.__class__.__name__ == 'LTE'\n\n x = torch.rand(2, 3, 64, 64)\n\n x_level3, x_level2, x_level1 = lte(x)\n assert x_level1.shape == (2, 64, 64, 64)\n assert x_level2.shape == (2, 128, 32, 32)\n assert x_level3.shape == (2, 256, 16, 16)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ttsr/test_search_transformer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.registry import MODELS\n\n\ndef test_search_transformer():\n model_cfg = dict(type='SearchTransformer')\n model = MODELS.build(model_cfg)\n\n lr_pad_level3 = torch.randn((2, 32, 32, 32))\n ref_pad_level3 = torch.randn((2, 32, 32, 32))\n ref_level3 = torch.randn((2, 32, 32, 32))\n ref_level2 = torch.randn((2, 16, 64, 64))\n ref_level1 = torch.randn((2, 8, 128, 128))\n\n s, textures = model(lr_pad_level3, ref_pad_level3,\n (ref_level3, ref_level2, ref_level1))\n t_level3, t_level2, t_level1 = textures\n\n assert s.shape == (2, 1, 32, 32)\n assert t_level3.shape == (2, 32, 32, 32)\n assert t_level2.shape == (2, 16, 64, 64)\n assert t_level1.shape == (2, 8, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ttsr/test_ttsr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nfrom mmengine.optim import OptimWrapper\nfrom torch.optim import Adam\n\nfrom mmagic.models import (LTE, TTSR, DataPreprocessor, SearchTransformer,\n TTSRDiscriminator, TTSRNet)\nfrom mmagic.models.losses import (GANLoss, L1Loss, PerceptualVGG,\n TransferalPerceptualLoss)\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_ttsr(init_weights):\n model_cfg = dict(\n type='TTSR',\n generator=dict(\n type='TTSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=4,\n num_blocks=(1, 1, 1, 1)),\n extractor=dict(type='LTE', load_pretrained_vgg=False),\n transformer=dict(type='SearchTransformer'),\n discriminator=dict(type='TTSRDiscriminator', in_size=128),\n pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),\n perceptual_loss=dict(\n type='PerceptualLoss',\n layer_weights={'29': 1.0},\n vgg_type='vgg19',\n perceptual_weight=1e-2,\n style_weight=0.001,\n criterion='mse'),\n transferal_perceptual_loss=dict(\n type='TransferalPerceptualLoss',\n loss_weight=1e-2,\n use_attention=False,\n criterion='mse'),\n gan_loss=dict(\n type='GANLoss',\n gan_type='vanilla',\n loss_weight=1e-3,\n real_label_val=1.0,\n fake_label_val=0),\n data_preprocessor=DataPreprocessor(\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n ))\n\n # build restorer\n model = MODELS.build(model_cfg)\n\n # test attributes\n assert isinstance(model, TTSR)\n assert isinstance(model.generator, TTSRNet)\n assert isinstance(model.discriminator, TTSRDiscriminator)\n assert isinstance(model.transformer, SearchTransformer)\n assert isinstance(model.extractor, LTE)\n assert isinstance(model.pixel_loss, L1Loss)\n assert isinstance(model.transferal_perceptual_loss,\n TransferalPerceptualLoss)\n assert isinstance(model.gan_loss, GANLoss)\n\n optimizer_g = Adam(\n model.generator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optimizer_d = Adam(\n model.discriminator.parameters(), lr=0.0001, betas=(0.9, 0.999))\n optim_wrapper = dict(\n generator=OptimWrapper(optimizer_g),\n extractor=OptimWrapper(optimizer_g),\n discriminator=OptimWrapper(optimizer_d))\n\n # prepare data\n inputs = torch.rand(1, 3, 32, 32)\n data_sample = DataSample(\n gt_img=torch.rand(3, 128, 128),\n ref_img=torch.rand(3, 128, 128),\n img_lq=torch.rand(3, 128, 128),\n ref_lq=torch.rand(3, 128, 128))\n data = dict(inputs=inputs, data_samples=[data_sample])\n\n # train\n log_vars = model.train_step(data, optim_wrapper)\n log_vars = model.train_step(data, optim_wrapper)\n assert isinstance(log_vars, dict)\n assert set(log_vars.keys()) == set([\n 'loss_pix', 'loss_perceptual', 'loss_style', 'loss_transferal',\n 'loss_gan', 'loss_d_real', 'loss_d_fake'\n ])\n\n # val\n output = model.val_step(data)\n assert output[0].output.pred_img.shape == (3, 128, 128)\n\n # feat\n stacked_data_sample = DataSample.stack([data_sample])\n output = model(\n torch.rand(1, 3, 32, 32), stacked_data_sample, mode='tensor')\n assert output.shape == (1, 3, 128, 128)\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ttsr/test_ttsr_disc.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models import TTSRDiscriminator\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_ttsr_dict():\n net = TTSRDiscriminator(in_channels=3, in_size=160)\n # cpu\n inputs = torch.rand((2, 3, 160, 160))\n output = net(inputs)\n assert output.shape == (2, 1)\n # gpu\n if torch.cuda.is_available():\n net = net.cuda()\n output = net(inputs.cuda())\n assert output.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_ttsr/test_ttsr_net.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.ttsr.ttsr_net import (CSFI2, CSFI3, SFE,\n MergeFeatures)\nfrom mmagic.registry import MODELS\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_sfe():\n inputs = torch.rand(2, 3, 48, 48)\n sfe = SFE(3, 64, 16, 1.)\n outputs = sfe(inputs)\n assert outputs.shape == (2, 64, 48, 48)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_csfi():\n inputs1 = torch.rand(2, 16, 24, 24)\n inputs2 = torch.rand(2, 16, 48, 48)\n inputs4 = torch.rand(2, 16, 96, 96)\n\n csfi2 = CSFI2(mid_channels=16)\n out1, out2 = csfi2(inputs1, inputs2)\n assert out1.shape == (2, 16, 24, 24)\n assert out2.shape == (2, 16, 48, 48)\n\n csfi3 = CSFI3(mid_channels=16)\n out1, out2, out4 = csfi3(inputs1, inputs2, inputs4)\n assert out1.shape == (2, 16, 24, 24)\n assert out2.shape == (2, 16, 48, 48)\n assert out4.shape == (2, 16, 96, 96)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_merge_features():\n inputs1 = torch.rand(2, 16, 24, 24)\n inputs2 = torch.rand(2, 16, 48, 48)\n inputs4 = torch.rand(2, 16, 96, 96)\n\n merge_features = MergeFeatures(mid_channels=16, out_channels=3)\n out = merge_features(inputs1, inputs2, inputs4)\n assert out.shape == (2, 3, 96, 96)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_ttsr_net():\n inputs = torch.rand(2, 3, 24, 24)\n soft_attention = torch.rand(2, 1, 24, 24)\n t_level3 = torch.rand(2, 64, 24, 24)\n t_level2 = torch.rand(2, 32, 48, 48)\n t_level1 = torch.rand(2, 16, 96, 96)\n\n ttsr_cfg = dict(\n type='TTSRNet',\n in_channels=3,\n out_channels=3,\n mid_channels=16,\n texture_channels=16)\n ttsr = MODELS.build(ttsr_cfg)\n outputs = ttsr(inputs, soft_attention, (t_level3, t_level2, t_level1))\n\n assert outputs.shape == (2, 3, 96, 96)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_vico/test_vico.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport platform\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport pytest\nimport torch\nimport torch.nn as nn\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\n\ntest_dir = osp.join(osp.dirname(__file__), '../../../..', 'tests')\nconfig_path = osp.join(test_dir, 'configs', 'diffuser_wrapper_cfg')\nmodel_path = osp.join(test_dir, 'configs', 'tmp_weight')\nckpt_path = osp.join(test_dir, 'configs', 'ckpt')\n\nregister_all_modules()\n\nstable_diffusion_tiny_url = 'diffusers/tiny-stable-diffusion-torch'\nval_prompts = ['a photo of S*']\nimage_cross_layers = [\n # down blocks (2x transformer block) * (3x down blocks) = 6\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n # mid block (1x transformer block) * (1x mid block)= 1\n 0,\n # up blocks (3x transformer block) * (3x up blocks) = 9\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n]\nreg_loss_weight: float = 5e-4\nplaceholder: str = 'S*'\ninitialize_token: str = 'dog'\nnum_vectors_per_token: int = 1\n\nconfig = dict(\n type='ViCo',\n vae=dict(type='AutoencoderKL', sample_size=64),\n unet=dict(\n sample_size=64,\n type='UNet2DConditionModel',\n down_block_types=('DownBlock2D', ),\n up_block_types=('UpBlock2D', ),\n block_out_channels=(32, ),\n cross_attention_dim=16,\n ),\n text_encoder=dict(\n type='ClipWrapper',\n clip_type='huggingface',\n pretrained_model_name_or_path=stable_diffusion_tiny_url,\n subfolder='text_encoder'),\n tokenizer=stable_diffusion_tiny_url,\n scheduler=dict(\n type='DDPMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n test_scheduler=dict(\n type='DDIMScheduler',\n from_pretrained=stable_diffusion_tiny_url,\n subfolder='scheduler'),\n dtype='fp32',\n data_preprocessor=dict(type='DataPreprocessor'),\n enable_xformers=False,\n image_cross_layers=image_cross_layers,\n reg_loss_weight=reg_loss_weight,\n placeholder=placeholder,\n initialize_token=initialize_token,\n num_vectors_per_token=num_vectors_per_token,\n val_prompts=val_prompts)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower()\n or digit_version(TORCH_VERSION) <= digit_version('1.8.1'),\n reason='skip on windows due to limited RAM'\n 'and get_submodule requires torch >= 1.9.0')\nclass TestViCo(TestCase):\n\n def setUp(self):\n # mock SiLU\n if digit_version(TORCH_VERSION) <= digit_version('1.6.0'):\n from mmagic.models.editors.ddpm.denoising_unet import SiLU\n torch.nn.SiLU = SiLU\n vico = MODELS.build(config)\n assert not any([p.requires_grad for p in vico.vae.parameters()])\n self.vico = vico\n\n def test_infer(self):\n vico = self.vico\n\n def mock_encode_prompt(prompt, do_classifier_free_guidance,\n num_images_per_prompt, *args, **kwargs):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n batch_size *= num_images_per_prompt\n if do_classifier_free_guidance:\n batch_size *= 2\n return torch.randn(batch_size, 5, 16) # 2 for cfg\n\n def mock_infer(prompt, *args, **kwargs):\n length = len(prompt)\n return dict(samples=torch.randn(length, 3, 64, 64))\n\n encode_prompt = vico._encode_prompt\n infer = vico.infer\n vico._encode_prompt = mock_encode_prompt\n vico.infer = mock_infer\n\n self._test_infer(vico, 1, 1)\n vico._encode_prompt = encode_prompt\n vico.infer = infer\n\n def _test_infer(self, vico, num_prompt, num_repeat):\n prompt = ''\n image_reference = torch.ones([1, 3, 64, 64])\n\n result = vico.infer(\n [prompt] * num_prompt,\n image_reference=image_reference,\n height=64,\n width=64,\n num_images_per_prompt=num_repeat,\n num_inference_steps=1,\n return_type='numpy')\n assert result['samples'].shape == (1, 3, 64, 64)\n\n def test_val_step(self):\n vico = self.vico\n data = dict(\n inputs=[\n dict(\n img=torch.ones([3, 64, 64]),\n img_ref=torch.ones([3, 64, 64]))\n ],\n data_samples=[DataSample(prompt='a photo of S*')])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(prompt, image_reference, *args, **kwargs):\n length = len(prompt)\n return dict(samples=torch.randn(length, 3, 64, 64))\n\n encode_prompt = vico._encode_prompt\n infer = vico.infer\n vico._encode_prompt = mock_encode_prompt\n vico.infer = mock_infer\n\n output = vico.val_step(data)\n assert len(output) == 1\n vico._encode_prompt = encode_prompt\n vico.infer = infer\n\n def test_test_step(self):\n vico = self.vico\n data = dict(\n inputs=[\n dict(\n img=torch.ones([3, 64, 64]),\n img_ref=torch.ones([3, 64, 64]))\n ],\n data_samples=[DataSample(prompt='a photo of S*')])\n\n def mock_encode_prompt(*args, **kwargs):\n return torch.randn(2, 5, 16) # 2 for cfg\n\n def mock_infer(prompt, image_reference, *args, **kwargs):\n length = len(prompt)\n return dict(samples=torch.randn(length, 3, 64, 64))\n\n encode_prompt = vico._encode_prompt\n infer = vico.infer\n vico._encode_prompt = mock_encode_prompt\n vico.infer = mock_infer\n\n output = vico.test_step(data)\n assert len(output) == 1\n vico._encode_prompt = encode_prompt\n vico.infer = infer\n\n def test_train_step(self):\n vico = self.vico\n data = dict(\n inputs=[\n dict(\n img=torch.ones([3, 64, 64]),\n img_ref=torch.ones([3, 64, 64]))\n ],\n data_samples=[DataSample(prompt='a photo of S*')])\n\n optimizer = MagicMock()\n update_params = MagicMock()\n optimizer.update_params = update_params\n optim_wrapper = optimizer\n\n class mock_text_encoder(nn.Module):\n\n def __init__(self):\n super().__init__()\n\n def forward(self, *args, **kwargs):\n return [torch.randn(1, 5, 16)]\n\n vico.text_encoder = mock_text_encoder()\n\n vico.train_step(data, optim_wrapper)\n" }, { "path": "tests/test_models/test_editors/test_vico/test_vico_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport platform\n\nimport pytest\nfrom diffusers.models.unet_2d_condition import UNet2DConditionModel\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.editors.vico.vico_utils import set_vico_modules\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower()\n or digit_version(TORCH_VERSION) <= digit_version('1.8.1'),\n reason='skip on windows due to limited RAM'\n 'and get_submodule requires torch >= 1.9.0')\ndef test_set_vico_modules():\n model = UNet2DConditionModel()\n image_cross_layers = [1] * 16\n set_vico_modules(model, image_cross_layers)\n\n # test set vico modules\n for name, layer in model.named_modules():\n if len(name.split('.')) > 1:\n module_name = name.split('.')[-2]\n if module_name == 'attentions':\n assert layer.__class__.__name__ == 'ViCoTransformer2D'\n assert hasattr(layer, 'image_cross_attention')\n" }, { "path": "tests/test_models/test_editors/test_wgan_gp/test_wgan_discriminator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.wgan_gp import WGANGPDiscriminator\nfrom mmagic.registry import MODELS\n\n\nclass TestWGANGPDiscriminator(object):\n\n @classmethod\n def setup_class(cls):\n cls.x = torch.randn((2, 3, 128, 128))\n cls.default_config = dict(\n type='WGANGPDiscriminator', in_channel=3, in_scale=128)\n cls.conv_ln_module_config = dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='LN2d'),\n order=('conv', 'norm', 'act'))\n cls.conv_gn_module_config = dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='GN'),\n order=('conv', 'norm', 'act'))\n\n def test_wgangp_discriminator(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config)\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x)\n assert score.shape == (2, 1)\n\n # test different in_scale\n config = dict(type='WGANGPDiscriminator', in_channel=3, in_scale=64)\n d = MODELS.build(config)\n assert isinstance(d, WGANGPDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x)\n assert score.shape == (2, 1)\n\n # test different conv config\n config = dict(\n type='WGANGPDiscriminator',\n in_channel=3,\n in_scale=128,\n conv_module_cfg=self.conv_ln_module_config)\n d = MODELS.build(config)\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x)\n assert score.shape == (2, 1)\n\n config = dict(\n type='WGANGPDiscriminator',\n in_channel=3,\n in_scale=128,\n conv_module_cfg=self.conv_gn_module_config)\n d = MODELS.build(config)\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x)\n assert score.shape == (2, 1)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_wgangp_discriminator_cuda(self):\n\n # test default setting with builder\n d = MODELS.build(self.default_config).cuda()\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x.cuda())\n assert score.shape == (2, 1)\n\n # test different in_scale\n config = dict(type='WGANGPDiscriminator', in_channel=3, in_scale=64)\n d = MODELS.build(config).cuda()\n assert isinstance(d, WGANGPDiscriminator)\n x = torch.randn((2, 3, 64, 64))\n score = d(x.cuda())\n assert score.shape == (2, 1)\n\n # test different conv config\n config = dict(\n type='WGANGPDiscriminator',\n in_channel=3,\n in_scale=128,\n conv_module_cfg=self.conv_ln_module_config)\n d = MODELS.build(config).cuda()\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x.cuda())\n assert score.shape == (2, 1)\n\n config = dict(\n type='WGANGPDiscriminator',\n in_channel=3,\n in_scale=128,\n conv_module_cfg=self.conv_gn_module_config)\n d = MODELS.build(config).cuda()\n assert isinstance(d, WGANGPDiscriminator)\n score = d(self.x.cuda())\n assert score.shape == (2, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_wgan_gp/test_wgan_generator.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.wgan_gp import WGANGPGenerator\nfrom mmagic.registry import MODELS\n\n\nclass TestWGANGPGenerator(object):\n\n @classmethod\n def setup_class(cls):\n cls.noise = torch.randn((2, 100))\n cls.default_config = dict(\n type='WGANGPGenerator', noise_size=128, out_scale=128)\n\n def test_wgangp_generator(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config)\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n\n # test different out_scale\n config = dict(type='WGANGPGenerator', noise_size=128, out_scale=64)\n g = MODELS.build(config)\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 64, 64)\n\n # test different conv config\n config = dict(\n type='WGANGPGenerator',\n noise_size=128,\n out_scale=128,\n conv_module_cfg=dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='BN'),\n order=('conv', 'norm', 'act')))\n g = MODELS.build(config)\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_wgangp_generator_cuda(self):\n\n # test default setting with builder\n g = MODELS.build(self.default_config).cuda()\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n\n # test different out_scale\n config = dict(type='WGANGPGenerator', noise_size=128, out_scale=64)\n g = MODELS.build(config).cuda()\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 64, 64)\n\n # test different conv config\n config = dict(\n type='WGANGPGenerator',\n noise_size=128,\n out_scale=128,\n conv_module_cfg=dict(\n conv_cfg=None,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=True,\n act_cfg=dict(type='LeakyReLU', negative_slope=0.2),\n norm_cfg=dict(type='BN'),\n order=('conv', 'norm', 'act')))\n g = MODELS.build(config).cuda()\n assert isinstance(g, WGANGPGenerator)\n x = g(None, num_batches=3)\n assert x.shape == (3, 3, 128, 128)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_wgan_gp/test_wgan_gp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom copy import deepcopy\nfrom unittest import TestCase\n\nimport pytest\nimport torch\nfrom mmengine import MessageHub\nfrom mmengine.optim import OptimWrapper, OptimWrapperDict\nfrom torch.optim import SGD\n\nfrom mmagic.models import WGANGP, DataPreprocessor\nfrom mmagic.registry import MODELS\nfrom mmagic.structures import DataSample\n\ngenerator = dict(\n type='DCGANGenerator', noise_size=10, output_scale=16, base_channels=16)\ndiscriminator = dict(\n type='DCGANDiscriminator', input_scale=16, output_scale=4, out_channels=1)\n\n\nclass TestWGANGP(TestCase):\n\n def test_init(self):\n gan = WGANGP(\n noise_size=10,\n data_preprocessor=DataPreprocessor(),\n generator=generator,\n discriminator=discriminator)\n\n self.assertIsInstance(gan, WGANGP)\n self.assertIsInstance(gan.data_preprocessor, DataPreprocessor)\n\n # test only generator have noise size\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n gan = WGANGP(\n generator=gen_cfg,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.noise_size, 10)\n\n # test init with nn.Module\n gen_cfg = deepcopy(generator)\n gen_cfg['noise_size'] = 10\n disc_cfg = deepcopy(discriminator)\n gen = MODELS.build(gen_cfg)\n disc = MODELS.build(disc_cfg)\n gan = WGANGP(\n generator=gen,\n discriminator=disc,\n data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.generator, gen)\n self.assertEqual(gan.discriminator, disc)\n\n # test init without discriminator\n gan = WGANGP(generator=gen, data_preprocessor=DataPreprocessor())\n self.assertEqual(gan.discriminator, None)\n\n @pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n def test_train_step(self):\n # prepare model\n accu_iter = 1\n n_disc = 1\n message_hub = MessageHub.get_instance('test-wgangp')\n gan = WGANGP(\n noise_size=10,\n generator=generator,\n discriminator=discriminator,\n data_preprocessor=DataPreprocessor(),\n discriminator_steps=n_disc)\n # prepare messageHub\n message_hub.update_info('iter', 0)\n # prepare optimizer\n gen_optim = SGD(gan.generator.parameters(), lr=0.1)\n disc_optim = SGD(gan.discriminator.parameters(), lr=0.1)\n optim_wrapper_dict = OptimWrapperDict(\n generator=OptimWrapper(gen_optim, accumulative_counts=accu_iter),\n discriminator=OptimWrapper(\n disc_optim, accumulative_counts=accu_iter))\n # prepare inputs\n img = torch.randn(3, 16, 16)\n data = dict(inputs=dict(), data_samples=[DataSample(gt_img=img)])\n\n # simulate train_loop here\n for idx in range(n_disc * accu_iter):\n message_hub.update_info('iter', idx)\n log = gan.train_step(data, optim_wrapper_dict)\n if (idx + 1) == n_disc * accu_iter:\n # should update at after (n_disc * accu_iter)\n self.assertEqual(\n set(log.keys()),\n set([\n 'loss', 'loss_disc_fake', 'loss_disc_real', 'loss_gen',\n 'loss_gp'\n ]))\n else:\n # should not update when discriminator's updating is unfinished\n self.assertEqual(\n log.keys(),\n set(['loss', 'loss', 'loss_disc_fake', 'loss_disc_real']))\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_editors/test_wgan_gp/test_wgan_gp_module.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.editors.wgan_gp.wgan_gp_module import (ConvLNModule,\n WGANDecisionHead,\n WGANNoiseTo2DFeat)\n\n\ndef test_ConvLNModule():\n # test norm_cfg is None\n conv = ConvLNModule(3, 6, 3, 1, 1)\n assert conv.norm is None\n\n\ndef test_WGANDecisionHead():\n head = WGANDecisionHead(3, 8, 4)\n x = torch.randn(1, 3, 4, 4)\n out = head(x)\n assert out.shape == (1, 4)\n\n\ndef test_WGANNoiseTo2DFeat():\n noise2feat = WGANNoiseTo2DFeat(16, 32)\n noise = torch.randn(1, 16)\n feat = noise2feat(noise)\n assert feat.shape == (1, 32, 4, 4)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_clip_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport clip\nimport pytest\nimport torch\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.losses import CLIPLoss\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0'),\n reason='version limitation')\ndef test_clip_loss():\n clip_loss = CLIPLoss(clip_model=dict(in_size=32, clip_type='RN50'))\n\n image = torch.randn(1, 3, 32, 32)\n text = 'Image for test'\n text_inputs = torch.cat([clip.tokenize(text)])\n loss = clip_loss(image, text_inputs)\n print(loss)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_composition_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models import (CharbonnierCompLoss, L1CompositionLoss,\n MSECompositionLoss)\n\n\ndef test_composition_losses():\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n L1CompositionLoss(reduction='InvalidValue')\n\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n MSECompositionLoss(reduction='InvalidValue')\n\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n CharbonnierCompLoss(reduction='InvalidValue')\n\n unknown_h, unknown_w = (32, 32)\n weight = torch.zeros(1, 1, 64, 64)\n weight[0, 0, :unknown_h, :unknown_w] = 1\n pred_alpha = weight.clone() * 0.5\n ori_merged = torch.ones(1, 3, 64, 64)\n fg = torch.zeros(1, 3, 64, 64)\n bg = torch.ones(1, 3, 64, 64) * 4\n\n l1_comp_loss = L1CompositionLoss(loss_weight=1.0, reduction='mean')\n loss = l1_comp_loss(pred_alpha, fg, bg, ori_merged)\n assert loss.shape == ()\n assert loss.item() == 2.5\n\n l1_comp_loss = L1CompositionLoss(loss_weight=0.5, reduction='none')\n loss = l1_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == (1, 3, 64, 64)\n assert (loss == torch.ones(1, 3, 64, 64) * weight * 0.5).all()\n\n l1_comp_loss = L1CompositionLoss(loss_weight=0.5, reduction='sum')\n loss = l1_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == ()\n assert loss.item() == 1536\n\n mse_comp_loss = MSECompositionLoss(loss_weight=1.0, reduction='mean')\n loss = mse_comp_loss(pred_alpha, fg, bg, ori_merged)\n assert loss.shape == ()\n assert loss.item() == 7.0\n\n mse_comp_loss = MSECompositionLoss(loss_weight=0.5, reduction='none')\n loss = mse_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == (1, 3, 64, 64)\n assert (loss == torch.ones(1, 3, 64, 64) * weight * 0.5).all()\n\n mse_comp_loss = MSECompositionLoss(loss_weight=0.5, reduction='sum')\n loss = mse_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == ()\n assert loss.item() == 1536\n\n cb_comp_loss = CharbonnierCompLoss(\n loss_weight=1.0, reduction='mean', eps=1e-12)\n loss = cb_comp_loss(pred_alpha, fg, bg, ori_merged)\n assert loss.shape == ()\n assert loss.item() == 2.5\n\n cb_comp_loss = CharbonnierCompLoss(\n loss_weight=0.5, reduction='none', eps=1e-6)\n loss = cb_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == (1, 3, 64, 64)\n npt.assert_almost_equal(\n loss.numpy(), torch.ones(1, 3, 64, 64) * weight * 0.5, decimal=6)\n\n cb_comp_loss = CharbonnierCompLoss(\n loss_weight=0.5, reduction='sum', eps=1e-6)\n loss = cb_comp_loss(pred_alpha, fg, bg, ori_merged, weight)\n assert loss.shape == ()\n assert math.isclose(loss.item(), 1536, rel_tol=1e-6)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_face_id_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.losses import FaceIdLoss\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_face_id_loss():\n face_id_loss = FaceIdLoss(loss_weight=2.5)\n assert face_id_loss.loss_weight == 2.5\n gt, pred = torch.randn(1, 3, 224, 224), torch.randn(1, 3, 224, 224)\n loss = face_id_loss(pred, gt)\n assert loss.shape == ()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_feature_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models.losses import LightCNNFeatureLoss\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_light_cnn_feature_loss():\n\n pretrained = 'https://download.openmmlab.com/mmediting/' + \\\n 'restorers/dic/light_cnn_feature.pth'\n pred = torch.rand((3, 3, 128, 128))\n gt = torch.rand((3, 3, 128, 128))\n\n feature_loss = LightCNNFeatureLoss(pretrained=pretrained)\n loss = feature_loss(pred, gt)\n assert loss.item() > 0\n\n feature_loss = LightCNNFeatureLoss(pretrained=pretrained, criterion='mse')\n loss = feature_loss(pred, gt)\n assert loss.item() > 0\n\n if torch.cuda.is_available():\n pred = pred.cuda()\n gt = gt.cuda()\n feature_loss = feature_loss.cuda()\n pred.requires_grad = True\n\n loss = feature_loss(pred, gt)\n assert loss.item() > 0\n\n optim = torch.optim.SGD(params=[pred], lr=10)\n optim.zero_grad()\n loss.backward()\n optim.step()\n\n loss_new = feature_loss(pred, gt)\n assert loss_new < loss\n\n feature_loss = LightCNNFeatureLoss(\n pretrained=pretrained, criterion='mse').cuda()\n loss = feature_loss(pred, gt)\n assert loss.item() > 0\n\n # test criterion value error\n with pytest.raises(ValueError):\n LightCNNFeatureLoss(pretrained=pretrained, criterion='l2')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_gan_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models import GANLoss, GaussianBlur\n\n\ndef test_gan_losses():\n \"\"\"Test gan losses.\"\"\"\n with pytest.raises(NotImplementedError):\n GANLoss(\n 'xixihaha',\n loss_weight=1.0,\n real_label_val=1.0,\n fake_label_val=0.0)\n\n input_1 = torch.ones(1, 1)\n input_2 = torch.ones(1, 3, 6, 6) * 2\n\n # vanilla\n gan_loss = GANLoss(\n 'vanilla', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_1, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), 0.6265233)\n loss = gan_loss(input_1, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 2.6265232)\n loss = gan_loss(input_1, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 0.3132616)\n loss = gan_loss(input_1, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 1.3132616)\n\n # lsgan\n gan_loss = GANLoss(\n 'lsgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), 2.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 8.0)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 1.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 4.0)\n\n # wgan\n gan_loss = GANLoss(\n 'wgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 4)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), -2.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 2.0)\n\n # hinge\n gan_loss = GANLoss(\n 'hinge', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 0.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 3.0)\n\n # smgan\n mask = torch.ones(1, 3, 6, 6)\n gan_loss = GANLoss(\n 'smgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False, mask=mask)\n npt.assert_almost_equal(loss.item(), 2.0)\n loss = gan_loss(input_2, False, is_disc=False, mask=mask)\n npt.assert_almost_equal(loss.item(), 8.0)\n loss = gan_loss(input_2, True, is_disc=True, mask=mask)\n npt.assert_almost_equal(loss.item(), 1.0)\n loss = gan_loss(input_2, False, is_disc=True, mask=mask)\n npt.assert_almost_equal(loss.item(), 3.786323, decimal=6)\n mask = torch.ones(1, 3, 6, 5)\n loss = gan_loss(input_2, True, is_disc=False, mask=mask)\n npt.assert_almost_equal(loss.item(), 2.0)\n\n if torch.cuda.is_available():\n input_2 = input_2.cuda()\n mask = torch.ones(1, 3, 6, 6).cuda()\n gan_loss = GANLoss(\n 'smgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False, mask=mask)\n npt.assert_almost_equal(loss.item(), 2.0)\n loss = gan_loss(input_2, False, is_disc=False, mask=mask)\n npt.assert_almost_equal(loss.item(), 8.0)\n loss = gan_loss(input_2, True, is_disc=True, mask=mask)\n npt.assert_almost_equal(loss.item(), 1.0)\n loss = gan_loss(input_2, False, is_disc=True, mask=mask)\n npt.assert_almost_equal(loss.item(), 3.786323, decimal=6)\n\n # test GaussianBlur for smgan\n with pytest.raises(TypeError):\n gausian_blur = GaussianBlur(kernel_size=71, sigma=2)\n gausian_blur(mask).detach().cpu()\n\n with pytest.raises(TypeError):\n gausian_blur = GaussianBlur(kernel_size=(70, 70))\n gausian_blur(mask).detach().cpu()\n\n with pytest.raises(TypeError):\n mask = numpy.ones((1, 3, 6, 6))\n gausian_blur = GaussianBlur()\n gausian_blur(mask).detach().cpu()\n\n with pytest.raises(ValueError):\n mask = torch.ones(1, 3)\n gausian_blur = GaussianBlur()\n gausian_blur(mask).detach().cpu()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_gradient_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models import DiscShiftLoss, GradientLoss, GradientPenaltyLoss\n\n\ndef test_gradient_loss():\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n GradientLoss(reduction='InvalidValue')\n\n unknown_h, unknown_w = (32, 32)\n weight = torch.zeros(1, 1, 64, 64)\n weight[0, 0, :unknown_h, :unknown_w] = 1\n pred = weight.clone()\n target = weight.clone() * 2\n\n gradient_loss = GradientLoss(loss_weight=1.0, reduction='mean')\n loss = gradient_loss(pred, target)\n assert loss.shape == ()\n npt.assert_almost_equal(loss.item(), 0.1860352)\n\n gradient_loss = GradientLoss(loss_weight=0.5, reduction='none')\n loss = gradient_loss(pred, target, weight)\n assert loss.shape == (1, 1, 64, 64)\n npt.assert_almost_equal(torch.sum(loss).item(), 252)\n\n gradient_loss = GradientLoss(loss_weight=0.5, reduction='sum')\n loss = gradient_loss(pred, target, weight)\n assert loss.shape == ()\n npt.assert_almost_equal(loss.item(), 252)\n\n\ndef test_gradient_penalty_losses():\n \"\"\"Test gradient penalty losses.\"\"\"\n input = torch.ones(1, 3, 6, 6) * 2\n\n gan_loss = GradientPenaltyLoss(loss_weight=10.0)\n loss = gan_loss(lambda x: x, input, input, mask=None)\n assert loss.item() > 0\n mask = torch.ones(1, 3, 6, 6)\n mask[:, :, 2:4, 2:4] = 0\n loss = gan_loss(lambda x: x, input, input, mask=mask)\n assert loss.item() > 0\n\n\ndef test_disc_shift_loss():\n loss_disc_shift = DiscShiftLoss()\n x = torch.Tensor([0.1])\n loss = loss_disc_shift(x)\n\n npt.assert_almost_equal(loss.item(), 0.001)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_comps/test_clip_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport clip\nimport pytest\nimport torch\nfrom mmengine.utils import digit_version\nfrom mmengine.utils.dl_utils import TORCH_VERSION\n\nfrom mmagic.models.losses import CLIPLossComps\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n@pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0'),\n reason='version limitation')\ndef test_clip_loss():\n clip_loss_comps = CLIPLossComps(\n clip_model=dict(in_size=32),\n data_info=dict(image='fake_imgs', text='descriptions'))\n\n image = torch.randn(1, 3, 32, 32)\n text = 'Image for test'\n text_inputs = torch.cat([clip.tokenize(text)])\n data_dict = dict(fake_imgs=image, descriptions=text_inputs)\n loss = clip_loss_comps(outputs_dict=data_dict)\n print(loss)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_comps/test_disc_auxiliary_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom functools import partial\n\nimport pytest\nimport torch\n\nfrom mmagic.models.editors.dcgan import DCGANDiscriminator\nfrom mmagic.models.editors.pggan import PGGANDiscriminator\nfrom mmagic.models.losses import (DiscShiftLossComps, GradientPenaltyLossComps,\n R1GradientPenaltyComps)\nfrom mmagic.models.losses.gan_loss import (gradient_penalty_loss,\n r1_gradient_penalty_loss)\n\n\nclass TestDiscShiftLoss(object):\n\n @classmethod\n def setup_class(cls):\n cls.input_tensor = torch.randn((2, 10))\n cls.default_cfg = dict(\n loss_weight=0.1, data_info=dict(pred='disc_pred'))\n cls.default_input_dict = dict(disc_pred=cls.input_tensor)\n\n def test_module_wrapper(self):\n # test with default config\n loss_module = DiscShiftLossComps(**self.default_cfg)\n loss = loss_module(self.default_input_dict)\n assert loss.ndim == 2\n\n with pytest.raises(NotImplementedError):\n _ = loss_module(self.default_input_dict, 1)\n\n with pytest.raises(AssertionError):\n _ = loss_module(1, outputs_dict=self.default_input_dict)\n input_ = dict(outputs_dict=self.default_input_dict)\n loss = loss_module(**input_)\n assert loss.ndim == 2\n\n with pytest.raises(AssertionError):\n _ = loss_module(self.input_tensor)\n\n # test without data_info\n loss_module = DiscShiftLossComps(data_info=None)\n loss = loss_module(self.input_tensor)\n assert loss.ndim == 2\n\n\nclass TestGradientPenalty:\n\n @classmethod\n def setup_class(cls):\n cls.input_img = torch.randn((2, 3, 8, 8))\n cls.disc = DCGANDiscriminator(\n input_scale=8, output_scale=4, out_channels=5)\n cls.pggan_disc = PGGANDiscriminator(\n in_scale=8, base_channels=32, max_channels=32)\n cls.data_info = dict(\n discriminator='disc', real_data='real_imgs', fake_data='fake_imgs')\n\n def test_gp_loss(self):\n loss = gradient_penalty_loss(self.disc, self.input_img, self.input_img)\n assert loss > 0\n\n loss = gradient_penalty_loss(\n self.disc, self.input_img, self.input_img, norm_mode='HWC')\n assert loss > 0\n\n with pytest.raises(NotImplementedError):\n _ = gradient_penalty_loss(\n self.disc, self.input_img, self.input_img, norm_mode='xxx')\n\n loss = gradient_penalty_loss(\n self.disc, self.input_img, self.input_img, norm_mode='HWC')\n assert loss > 0\n\n loss = gradient_penalty_loss(\n self.disc,\n self.input_img,\n self.input_img,\n norm_mode='HWC',\n mask=torch.ones_like(self.input_img))\n assert loss > 0\n\n data_dict = dict(\n real_imgs=self.input_img,\n fake_imgs=self.input_img,\n disc=partial(self.pggan_disc, transition_weight=0.5, curr_scale=8))\n gp_loss = GradientPenaltyLossComps(\n loss_weight=10, norm_mode='pixel', data_info=self.data_info)\n\n loss = gp_loss(data_dict)\n assert loss > 0\n loss = gp_loss(outputs_dict=data_dict)\n assert loss > 0\n with pytest.raises(NotImplementedError):\n _ = gp_loss(asdf=1.)\n\n with pytest.raises(AssertionError):\n _ = gp_loss(1.)\n\n with pytest.raises(AssertionError):\n _ = gp_loss(1., 2, outputs_dict=data_dict)\n\n\nclass TestR1GradientPenalty:\n\n @classmethod\n def setup_class(cls):\n cls.data_info = dict(discriminator='disc', real_data='real_imgs')\n cls.disc = DCGANDiscriminator(\n input_scale=8, output_scale=4, out_channels=5)\n cls.pggan_disc = PGGANDiscriminator(\n in_scale=8, base_channels=32, max_channels=32)\n cls.input_img = torch.randn((2, 3, 8, 8))\n\n def test_r1_regularizer(self):\n loss = r1_gradient_penalty_loss(self.disc, self.input_img)\n assert loss > 0\n\n loss = r1_gradient_penalty_loss(\n self.disc, self.input_img, norm_mode='HWC')\n assert loss > 0\n\n with pytest.raises(NotImplementedError):\n _ = r1_gradient_penalty_loss(\n self.disc, self.input_img, norm_mode='xxx')\n\n loss = r1_gradient_penalty_loss(\n self.disc, self.input_img, norm_mode='HWC')\n assert loss > 0\n\n loss = r1_gradient_penalty_loss(\n self.disc,\n self.input_img,\n norm_mode='HWC',\n mask=torch.ones_like(self.input_img))\n assert loss > 0\n\n data_dict = dict(\n real_imgs=self.input_img,\n disc=partial(self.pggan_disc, transition_weight=0.5, curr_scale=8))\n gp_loss = R1GradientPenaltyComps(\n loss_weight=10, norm_mode='pixel', data_info=self.data_info)\n\n loss = gp_loss(data_dict)\n assert loss > 0\n loss = gp_loss(outputs_dict=data_dict)\n assert loss > 0\n with pytest.raises(NotImplementedError):\n _ = gp_loss(asdf=1.)\n\n with pytest.raises(AssertionError):\n _ = gp_loss(1.)\n\n with pytest.raises(AssertionError):\n _ = gp_loss(1., 2, outputs_dict=data_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_comps/test_face_id_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\n\nfrom mmagic.models import IDLossModel\nfrom mmagic.models.losses import FaceIdLossComps\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\ndef test_face_id_loss_comps():\n face_id_loss_comps = FaceIdLossComps(\n loss_weight=2.5, data_info=dict(gt='real_imgs', pred='fake_imgs'))\n assert isinstance(face_id_loss_comps.net, IDLossModel)\n data_dict = dict(\n a=1,\n real_imgs=torch.randn(1, 3, 224, 224),\n fake_imgs=torch.randn(1, 3, 224, 224))\n loss = face_id_loss_comps(outputs_dict=data_dict)\n assert loss.shape == ()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_comps/test_gan_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models.losses import GANLossComps\n\n\ndef test_gan_losses():\n \"\"\"Test gan losses.\"\"\"\n with pytest.raises(NotImplementedError):\n GANLossComps(\n 'xixihaha',\n loss_weight=1.0,\n real_label_val=1.0,\n fake_label_val=0.0)\n\n input_1 = torch.ones(1, 1)\n input_2 = torch.ones(1, 3, 6, 6) * 2\n\n # vanilla\n gan_loss = GANLossComps(\n 'vanilla', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_1, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), 0.6265233)\n loss = gan_loss(input_1, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 2.6265232)\n loss = gan_loss(input_1, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 0.3132616)\n loss = gan_loss(input_1, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 1.3132616)\n\n # lsgan\n gan_loss = GANLossComps(\n 'lsgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), 2.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 8.0)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 1.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 4.0)\n\n # wgan\n gan_loss = GANLossComps(\n 'wgan', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), 4)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), -2.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 2.0)\n\n # wgan\n gan_loss = GANLossComps(\n 'wgan-logistic-ns',\n loss_weight=2.0,\n real_label_val=1.0,\n fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n assert loss.item() > 0\n loss = gan_loss(input_2, False, is_disc=False)\n assert loss.item() > 0\n\n # hinge\n gan_loss = GANLossComps(\n 'hinge', loss_weight=2.0, real_label_val=1.0, fake_label_val=0.0)\n loss = gan_loss(input_2, True, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, False, is_disc=False)\n npt.assert_almost_equal(loss.item(), -4.0)\n loss = gan_loss(input_2, True, is_disc=True)\n npt.assert_almost_equal(loss.item(), 0.0)\n loss = gan_loss(input_2, False, is_disc=True)\n npt.assert_almost_equal(loss.item(), 3.0)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_comps/test_gen_auxiliary_loss_comps.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\n\nimport pytest\nimport torch\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nfrom mmagic.models.editors.stylegan2 import StyleGAN2Generator\nfrom mmagic.models.losses import GeneratorPathRegularizerComps\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\nclass TestPathRegularizer:\n\n @classmethod\n def setup_class(cls):\n cls.data_info = dict(generator='generator', num_batches='num_batches')\n cls.gen = StyleGAN2Generator(32, 10, num_mlps=2)\n\n def test_path_regularizer_cpu(self):\n gen = self.gen\n\n output_dict = dict(generator=gen, num_batches=2)\n pl = GeneratorPathRegularizerComps(data_info=self.data_info)\n pl_loss = pl(output_dict)\n assert pl_loss > 0\n\n output_dict = dict(generator=gen, num_batches=2, iteration=3)\n pl = GeneratorPathRegularizerComps(\n data_info=self.data_info, interval=2)\n pl_loss = pl(outputs_dict=output_dict)\n assert pl_loss is None\n\n with pytest.raises(NotImplementedError):\n _ = pl(asdf=1.)\n\n with pytest.raises(AssertionError):\n _ = pl(1.)\n\n with pytest.raises(AssertionError):\n _ = pl(1., 2, outputs_dict=output_dict)\n\n @pytest.mark.skipif(\n digit_version(TORCH_VERSION) <= digit_version('1.6.0'),\n reason='version limitation')\n @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')\n def test_path_regularizer_cuda(self):\n gen = self.gen.cuda()\n\n output_dict = dict(generator=gen, num_batches=2)\n pl = GeneratorPathRegularizerComps(data_info=self.data_info).cuda()\n pl_loss = pl(output_dict)\n assert pl_loss > 0\n\n output_dict = dict(generator=gen, num_batches=2, iteration=3)\n pl = GeneratorPathRegularizerComps(\n data_info=self.data_info, interval=2).cuda()\n pl_loss = pl(outputs_dict=output_dict)\n assert pl_loss is None\n\n with pytest.raises(NotImplementedError):\n _ = pl(asdf=1.)\n\n with pytest.raises(AssertionError):\n _ = pl(1.)\n\n with pytest.raises(AssertionError):\n _ = pl(1., 2, outputs_dict=output_dict)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_loss_wrapper.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models import mask_reduce_loss, reduce_loss\n\n\ndef test_utils():\n loss = torch.rand(1, 3, 4, 4)\n weight = torch.zeros(1, 3, 4, 4)\n weight[:, :, :2, :2] = 1\n\n # test reduce_loss()\n reduced = reduce_loss(loss, 'none')\n assert reduced is loss\n\n reduced = reduce_loss(loss, 'mean')\n npt.assert_almost_equal(reduced.numpy(), loss.mean())\n\n reduced = reduce_loss(loss, 'sum')\n npt.assert_almost_equal(reduced.numpy(), loss.sum())\n\n # test mask_reduce_loss()\n reduced = mask_reduce_loss(loss, weight=None, reduction='none')\n assert reduced is loss\n\n reduced = mask_reduce_loss(loss, weight=weight, reduction='mean')\n target = (loss *\n weight).sum(dim=[1, 2, 3]) / weight.sum(dim=[1, 2, 3]).mean()\n npt.assert_almost_equal(reduced.numpy(), target)\n\n reduced = mask_reduce_loss(loss, weight=weight, reduction='sum')\n npt.assert_almost_equal(reduced.numpy(), (loss * weight).sum())\n\n weight_single_channel = weight[:, 0:1, ...]\n reduced = mask_reduce_loss(\n loss, weight=weight_single_channel, reduction='mean')\n target = (loss *\n weight).sum(dim=[1, 2, 3]) / weight.sum(dim=[1, 2, 3]).mean()\n npt.assert_almost_equal(reduced.numpy(), target)\n\n loss_b = torch.rand(2, 3, 4, 4)\n weight_b = torch.zeros(2, 1, 4, 4)\n weight_b[0, :, :3, :3] = 1\n weight_b[1, :, :2, :2] = 1\n reduced = mask_reduce_loss(loss_b, weight=weight_b, reduction='mean')\n target = (loss_b * weight_b).sum() / weight_b.sum() / 3.\n npt.assert_almost_equal(reduced.numpy(), target)\n\n with pytest.raises(AssertionError):\n weight_wrong = weight[0, 0, ...]\n reduced = mask_reduce_loss(loss, weight=weight_wrong, reduction='mean')\n\n with pytest.raises(AssertionError):\n weight_wrong = weight[:, 0:2, ...]\n reduced = mask_reduce_loss(loss, weight=weight_wrong, reduction='mean')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_perceptual_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\n\nfrom mmagic.models import (PerceptualLoss, PerceptualVGG,\n TransferalPerceptualLoss)\n\n\n@pytest.mark.skipif(\n 'win' in platform.system().lower() and 'cu' in torch.__version__,\n reason='skip on windows-cuda due to limited RAM.')\n@patch.object(PerceptualVGG, 'init_weights')\ndef test_perceptual_loss(init_weights):\n if torch.cuda.is_available():\n loss_percep = PerceptualLoss(layer_weights={'0': 1.}).cuda()\n x = torch.randn(1, 3, 16, 16).cuda()\n x.requires_grad = True\n gt = torch.randn(1, 3, 16, 16).cuda()\n percep, style = loss_percep(x, gt)\n\n assert percep.item() > 0\n assert style.item() > 0\n\n optim = torch.optim.SGD(params=[x], lr=10)\n optim.zero_grad()\n percep.backward()\n optim.step()\n\n percep_new, _ = loss_percep(x, gt)\n assert percep_new < percep\n\n loss_percep = PerceptualLoss(\n layer_weights={\n '0': 1.\n }, perceptual_weight=0.).cuda()\n x = torch.randn(1, 3, 16, 16).cuda()\n gt = torch.randn(1, 3, 16, 16).cuda()\n percep, style = loss_percep(x, gt)\n assert percep is None and style > 0\n\n loss_percep = PerceptualLoss(\n layer_weights={\n '0': 1.\n }, style_weight=0., criterion='mse').cuda()\n x = torch.randn(1, 3, 16, 16).cuda()\n gt = torch.randn(1, 3, 16, 16).cuda()\n percep, style = loss_percep(x, gt)\n assert style is None and percep > 0\n\n loss_percep = PerceptualLoss(\n layer_weights={\n '0': 1.\n }, layer_weights_style={\n '1': 1.\n }).cuda()\n x = torch.randn(1, 3, 16, 16).cuda()\n gt = torch.randn(1, 3, 16, 16).cuda()\n percep, style = loss_percep(x, gt)\n assert percep > 0 and style > 0\n\n # test whether vgg type is valid\n with pytest.raises(AssertionError):\n loss_percep = PerceptualLoss(layer_weights={'0': 1.}, vgg_type='igccc')\n # test whether criterion is valid\n with pytest.raises(NotImplementedError):\n loss_percep = PerceptualLoss(\n layer_weights={'0': 1.}, criterion='igccc')\n\n layer_name_list = ['2', '10', '30']\n vgg_model = PerceptualVGG(\n layer_name_list,\n use_input_norm=False,\n vgg_type='vgg16',\n pretrained='torchvision://vgg16')\n x = torch.rand((1, 3, 32, 32))\n output = vgg_model(x)\n assert isinstance(output, dict)\n assert len(output) == len(layer_name_list)\n assert set(output.keys()) == set(layer_name_list)\n\n # test whether the layer name is valid\n with pytest.raises(AssertionError):\n layer_name_list = ['2', '10', '30', '100']\n vgg_model = PerceptualVGG(\n layer_name_list,\n use_input_norm=False,\n vgg_type='vgg16',\n pretrained='torchvision://vgg16')\n\n # reset mock to clear some memory usage\n init_weights.reset_mock()\n\n\ndef test_t_perceptual_loss():\n\n maps = [\n torch.rand((2, 8, 8, 8), requires_grad=True),\n torch.rand((2, 4, 16, 16), requires_grad=True)\n ]\n textures = [torch.rand((2, 8, 8, 8)), torch.rand((2, 4, 16, 16))]\n soft = torch.rand((2, 1, 8, 8))\n\n loss_t_percep = TransferalPerceptualLoss()\n t_percep = loss_t_percep(maps, soft, textures)\n assert t_percep.item() > 0\n\n loss_t_percep = TransferalPerceptualLoss(\n use_attention=False, criterion='l1')\n t_percep = loss_t_percep(maps, soft, textures)\n assert t_percep.item() > 0\n\n if torch.cuda.is_available():\n maps = [\n torch.rand((2, 8, 8, 8)).cuda(),\n torch.rand((2, 4, 16, 16)).cuda()\n ]\n textures = [\n torch.rand((2, 8, 8, 8)).cuda(),\n torch.rand((2, 4, 16, 16)).cuda()\n ]\n soft = torch.rand((2, 1, 8, 8)).cuda()\n loss_t_percep = TransferalPerceptualLoss().cuda()\n maps[0].requires_grad = True\n maps[1].requires_grad = True\n\n t_percep = loss_t_percep(maps, soft, textures)\n assert t_percep.item() > 0\n\n optim = torch.optim.SGD(params=maps, lr=10)\n optim.zero_grad()\n t_percep.backward()\n optim.step()\n\n t_percep_new = loss_t_percep(maps, soft, textures)\n assert t_percep_new < t_percep\n\n loss_t_percep = TransferalPerceptualLoss(\n use_attention=False, criterion='l1').cuda()\n t_percep = loss_t_percep(maps, soft, textures)\n assert t_percep.item() > 0\n\n # test whether vgg type is valid\n with pytest.raises(ValueError):\n TransferalPerceptualLoss(criterion='l2')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_losses/test_pixelwise_loss.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport numpy.testing as npt\nimport pytest\nimport torch\n\nfrom mmagic.models import (CharbonnierLoss, L1Loss, MaskedTVLoss, MSELoss,\n PSNRLoss)\n\n\ndef test_pixelwise_losses():\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n L1Loss(reduction='InvalidValue')\n\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n MSELoss(reduction='InvalidValue')\n\n with pytest.raises(ValueError):\n # only 'none', 'mean' and 'sum' are supported\n CharbonnierLoss(reduction='InvalidValue')\n\n unknown_h, unknown_w = (32, 32)\n weight = torch.zeros(1, 1, 64, 64)\n weight[0, 0, :unknown_h, :unknown_w] = 1\n pred = weight.clone()\n target = weight.clone() * 2\n\n # test l1 loss\n l1_loss = L1Loss(loss_weight=1.0, reduction='mean')\n loss = l1_loss(pred, target)\n assert loss.shape == ()\n assert loss.item() == 0.25\n\n l1_loss = L1Loss(loss_weight=0.5, reduction='none')\n loss = l1_loss(pred, target, weight)\n assert loss.shape == (1, 1, 64, 64)\n assert (loss == torch.ones(1, 1, 64, 64) * weight * 0.5).all()\n\n l1_loss = L1Loss(loss_weight=0.5, reduction='sum')\n loss = l1_loss(pred, target, weight)\n assert loss.shape == ()\n assert loss.item() == 512\n\n # test mse loss\n mse_loss = MSELoss(loss_weight=1.0, reduction='mean')\n loss = mse_loss(pred, target)\n assert loss.shape == ()\n assert loss.item() == 0.25\n\n mse_loss = MSELoss(loss_weight=0.5, reduction='none')\n loss = mse_loss(pred, target, weight)\n assert loss.shape == (1, 1, 64, 64)\n assert (loss == torch.ones(1, 1, 64, 64) * weight * 0.5).all()\n\n mse_loss = MSELoss(loss_weight=0.5, reduction='sum')\n loss = mse_loss(pred, target, weight)\n assert loss.shape == ()\n assert loss.item() == 512\n\n # test charbonnier loss\n charbonnier_loss = CharbonnierLoss(\n loss_weight=1.0, reduction='mean', eps=1e-12)\n loss = charbonnier_loss(pred, target)\n assert loss.shape == ()\n assert math.isclose(loss.item(), 0.25, rel_tol=1e-5)\n\n charbonnier_loss = CharbonnierLoss(\n loss_weight=0.5, reduction='none', eps=1e-6)\n loss = charbonnier_loss(pred, target, weight)\n assert loss.shape == (1, 1, 64, 64)\n npt.assert_almost_equal(\n loss.numpy(), torch.ones(1, 1, 64, 64) * weight * 0.5, decimal=6)\n\n charbonnier_loss = CharbonnierLoss(\n loss_weight=0.5, reduction='sum', eps=1e-12)\n loss = charbonnier_loss(pred, target)\n assert loss.shape == ()\n assert math.isclose(loss.item(), 512, rel_tol=1e-5)\n\n # test samplewise option, use L1Loss as an example\n unknown_h, unknown_w = (32, 32)\n weight = torch.zeros(2, 1, 64, 64)\n weight[0, 0, :unknown_h, :unknown_w] = 1\n # weight[1, 0, :unknown_h // 2, :unknown_w // 2] = 1\n pred = weight.clone()\n target = weight.clone()\n # make mean l1_loss of sample 2 different from sample 1\n target[0, ...] *= 2\n l1_loss = L1Loss(loss_weight=1.0, reduction='mean', sample_wise=True)\n loss = l1_loss(pred, target, weight)\n assert loss.shape == ()\n assert loss.item() == 0.5\n\n masked_tv_loss = MaskedTVLoss(loss_weight=1.0)\n pred = torch.zeros((1, 1, 6, 6))\n mask = torch.zeros_like(pred)\n mask[..., 2:4, 2:4] = 1.\n pred[..., 3, :] = 1.\n loss = masked_tv_loss(pred, mask)\n assert loss.shape == ()\n npt.assert_almost_equal(loss.item(), 1.)\n\n # test PSNR Loss\n psnr_loss = PSNRLoss(loss_weight=1.0)\n pred = torch.ones(1, 1, 64, 64)\n target = torch.zeros(1, 1, 64, 64)\n loss = psnr_loss(pred, target)\n assert loss.shape == ()\n assert loss.item() == 0.0\n loss = psnr_loss(target, target)\n assert loss.item() == -80.0\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_utils/test_bbox_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.editors import PlainRefiner\nfrom mmagic.models.utils import extract_around_bbox, extract_bbox_patch\n\n\ndef test_extract_bbox_patch():\n img_np = np.random.randn(100, 100, 3)\n bbox = np.asarray([10, 10, 10, 10])\n img_patch = extract_bbox_patch(bbox, img_np, channel_first=False)\n assert np.array_equal(img_patch, img_np[10:20, 10:20, ...])\n\n img_np = np.random.randn(1, 3, 100, 100)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_patch = extract_bbox_patch(bbox, img_np)\n assert np.array_equal(img_patch, img_np[..., 10:20, 10:20])\n\n img_tensor = torch.from_numpy(img_np)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_patch = extract_bbox_patch(bbox, img_tensor)\n assert np.array_equal(img_patch.numpy(), img_np[..., 10:20, 10:20])\n\n with pytest.raises(AssertionError):\n img_np = np.random.randn(100, 100)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_patch = extract_bbox_patch(bbox, img_np)\n\n with pytest.raises(AssertionError):\n img_np = np.random.randn(2, 3, 100, 100)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_patch = extract_bbox_patch(bbox, img_np)\n\n with pytest.raises(AssertionError):\n img_np = np.random.randn(3, 100, 100)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_patch = extract_bbox_patch(bbox, img_np)\n\n\ndef test_extract_around_bbox():\n with pytest.raises(AssertionError):\n img_np = np.random.randn(100, 100, 3)\n bbox = np.asarray([10, 10, 10, 10])\n extract_around_bbox(img_np, bbox, (4, 4))\n\n with pytest.raises(TypeError):\n bbox = dict(test='fail')\n img_np = np.random.randn(100, 100, 3)\n extract_around_bbox(img_np, bbox, (15, 15))\n\n img_np = np.random.randn(100, 100, 3)\n bbox = np.asarray([10, 10, 10, 10])\n img_new, bbox_new = extract_around_bbox(\n img_np, bbox, (14, 14), channel_first=False)\n assert np.array_equal(img_np[8:22, 8:22, ...], img_new)\n assert np.array_equal(bbox_new, np.asarray([8, 8, 14, 14]))\n\n img_np = np.random.randn(1, 3, 100, 100)\n bbox = np.asarray([[10, 10, 10, 10]])\n img_tensor = torch.from_numpy(img_np)\n bbox_tensor = torch.from_numpy(bbox)\n img_new, bbox_new = extract_around_bbox(\n img_tensor, bbox_tensor, target_size=[14, 14])\n assert np.array_equal(img_np[..., 8:22, 8:22], img_new.numpy())\n assert np.array_equal(bbox_new.numpy(), np.asarray([[8, 8, 14, 14]]))\n\n\ndef assert_dict_keys_equal(dictionary, target_keys):\n \"\"\"Check if the keys of the dictionary is equal to the target key set.\"\"\"\n assert isinstance(dictionary, dict)\n assert set(dictionary.keys()) == set(target_keys)\n\n\ndef assert_tensor_with_shape(tensor, shape):\n \"\"\"\"Check if the shape of the tensor is equal to the target shape.\"\"\"\n assert isinstance(tensor, torch.Tensor)\n assert tensor.shape == shape\n\n\ndef test_plain_refiner():\n \"\"\"Test PlainRefiner.\"\"\"\n model = PlainRefiner()\n model.init_weights()\n model.train()\n merged, alpha, trimap, raw_alpha = _demo_inputs_pair()\n prediction = model(torch.cat([merged, raw_alpha.sigmoid()], 1), raw_alpha)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n # test forward with gpu\n if torch.cuda.is_available():\n model = PlainRefiner()\n model.init_weights()\n model.train()\n model.cuda()\n merged, alpha, trimap, raw_alpha = _demo_inputs_pair(cuda=True)\n prediction = model(\n torch.cat([merged, raw_alpha.sigmoid()], 1), raw_alpha)\n assert_tensor_with_shape(prediction, torch.Size([1, 1, 64, 64]))\n\n\ndef _demo_inputs_pair(img_shape=(64, 64), batch_size=1, cuda=False):\n \"\"\"Create a superset of inputs needed to run refiner.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n \"\"\"\n color_shape = (batch_size, 3, img_shape[0], img_shape[1])\n gray_shape = (batch_size, 1, img_shape[0], img_shape[1])\n merged = torch.from_numpy(np.random.random(color_shape).astype(np.float32))\n alpha = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n trimap = torch.from_numpy(np.random.random(gray_shape).astype(np.float32))\n raw_alpha = torch.from_numpy(\n np.random.random(gray_shape).astype(np.float32))\n if cuda:\n merged = merged.cuda()\n alpha = alpha.cuda()\n trimap = trimap.cuda()\n raw_alpha = raw_alpha.cuda()\n return merged, alpha, trimap, raw_alpha\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_utils/test_flow_warp.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport pytest\nimport torch\n\nfrom mmagic.models.utils import flow_warp\n\n\ndef tensor_shift(x, shift=(1, 1), fill_val=0):\n \"\"\"Shift tensor for testing flow_warp.\n\n Args:\n x (Tensor): the input tensor. The shape is (b, c, h, w].\n shift (tuple): shift pixel.\n fill_val (float): fill value.\n\n Returns:\n Tensor: the shifted tensor.\n \"\"\"\n\n _, _, h, w = x.size()\n shift_h, shift_w = shift\n new = torch.ones_like(x) * fill_val\n\n len_h = h - shift_h\n len_w = w - shift_w\n new[:, :, shift_h:shift_h + len_h,\n shift_w:shift_w + len_w] = x.narrow(2, 0, len_h).narrow(3, 0, len_w)\n return new\n\n\ndef test_flow_warp():\n x = torch.rand(1, 3, 10, 10)\n flow = torch.rand(1, 4, 4, 2)\n with pytest.raises(ValueError):\n # The spatial sizes of input and flow are not the same.\n flow_warp(x, flow)\n\n # cpu\n x = torch.rand(1, 3, 10, 10)\n flow = -torch.ones(1, 10, 10, 2)\n result = flow_warp(x, flow)\n assert result.size() == (1, 3, 10, 10)\n error = torch.sum(torch.abs(result - tensor_shift(x, (1, 1))))\n assert error < 1e-5\n # gpu\n if torch.cuda.is_available():\n x = torch.rand(1, 3, 10, 10).cuda()\n flow = -torch.ones(1, 10, 10, 2).cuda()\n result = flow_warp(x, flow)\n assert result.size() == (1, 3, 10, 10)\n error = torch.sum(torch.abs(result - tensor_shift(x, (1, 1))))\n assert error < 1e-5\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_utils/test_model_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom collections import defaultdict\n\nimport pytest\nimport torch\nimport torch.nn as nn\n\nfrom mmagic.models.utils import (build_module, generation_init_weights,\n get_module_device, get_valid_num_batches,\n set_requires_grad)\nfrom mmagic.registry import MODELS\n\n\ndef test_generation_init_weights():\n # Conv\n module = nn.Conv2d(3, 3, 1)\n module_tmp = copy.deepcopy(module)\n generation_init_weights(module, init_type='normal', init_gain=0.02)\n generation_init_weights(module, init_type='xavier', init_gain=0.02)\n generation_init_weights(module, init_type='kaiming')\n generation_init_weights(module, init_type='orthogonal', init_gain=0.02)\n with pytest.raises(NotImplementedError):\n generation_init_weights(module, init_type='abc')\n assert not torch.equal(module.weight.data, module_tmp.weight.data)\n\n # Linear\n module = nn.Linear(3, 1)\n module_tmp = copy.deepcopy(module)\n generation_init_weights(module, init_type='normal', init_gain=0.02)\n generation_init_weights(module, init_type='xavier', init_gain=0.02)\n generation_init_weights(module, init_type='kaiming')\n generation_init_weights(module, init_type='orthogonal', init_gain=0.02)\n with pytest.raises(NotImplementedError):\n generation_init_weights(module, init_type='abc')\n assert not torch.equal(module.weight.data, module_tmp.weight.data)\n\n # BatchNorm2d\n module = nn.BatchNorm2d(3)\n module_tmp = copy.deepcopy(module)\n generation_init_weights(module, init_type='normal', init_gain=0.02)\n assert not torch.equal(module.weight.data, module_tmp.weight.data)\n\n # test update init info\n module = nn.BatchNorm2d(3)\n module._params_init_info = defaultdict(dict)\n for name, param in module.named_parameters():\n module._params_init_info[param][\n 'init_info'] = f'The value is the same before and ' \\\n f'after calling `init_weights` ' \\\n f'of {module.__class__.__name__} '\n module._params_init_info[param]['tmp_mean_value'] = param.data.mean(\n ).cpu()\n module_tmp = copy.deepcopy(module)\n generation_init_weights(module, init_type='normal', init_gain=0.02)\n assert not torch.equal(module.weight.data, module_tmp.weight.data)\n\n\ndef test_set_requires_grad():\n model = torch.nn.Conv2d(1, 3, 1, 1)\n set_requires_grad(model, False)\n for param in model.parameters():\n assert not param.requires_grad\n\n\ndef test_get_module_device_cpu():\n device = get_module_device(nn.Conv2d(3, 3, 3, 1, 1))\n assert device == torch.device('cpu')\n\n # The input module should contain parameters.\n with pytest.raises(ValueError):\n get_module_device(nn.Flatten())\n\n\ndef test_get_valid_num_batches():\n\n # 1. batch inputs is Tensor\n assert get_valid_num_batches(torch.rand(5, 3, 4, 4)) == 5\n\n # 2. batch inputs is dict\n batch_inputs = dict(num_batches=1, img=torch.randn(1, 3, 5, 5))\n assert get_valid_num_batches(batch_inputs) == 1\n\n # 3. batch inputs is dict but no tensor in it\n batch_inputs = dict(aaa='aaa', bbb='bbb')\n assert get_valid_num_batches(batch_inputs) == 1\n\n # 4. test no batch input and no data samples\n assert get_valid_num_batches() == 1\n\n # 5. test batch is None but data sample is not None\n data_samples = [None, None]\n assert get_valid_num_batches(None, data_samples) == 2\n\n # 6. test batch input and data sample are neither not None\n batch_inputs = dict(num_batches=2, img=torch.randn(2, 3, 5, 5))\n data_samples = [None, None]\n assert get_valid_num_batches(batch_inputs, data_samples) == 2\n\n\ndef test_build_module():\n module = nn.Conv2d(3, 3, 3)\n assert build_module(module, MODELS) == module\n\n @MODELS.register_module()\n class MiniModule(nn.Module):\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n for k, v in kwargs.items():\n setattr(self, k, v)\n\n def forward(self, *args, **kwargs):\n return\n\n module_cfg = dict(type='MiniModule', attr1=1, attr2=2)\n module = build_module(module_cfg, MODELS)\n assert module.attr1 == 1\n assert module.attr2 == 2\n\n with pytest.raises(TypeError):\n build_module('MiniModule', MODELS)\n\n # remove the registered module\n MODELS._module_dict.pop('MiniModule')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_utils/test_sampling_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.models.utils.sampling_utils import label_sample_fn, noise_sample_fn\n\n\ndef test_noise_sample_fn():\n # test noise is a callable function\n noise_callable = torch.randn\n noise = noise_sample_fn(noise_callable, noise_size=(2, 3), device='cpu')\n assert noise.shape == (1, 2, 3)\n\n\ndef test_label_sample_fn():\n label = label_sample_fn(None, num_classes=-1)\n assert label is None\n label = label_sample_fn(None)\n assert label is None\n\n label_inp = torch.randint(0, 10, (1, ))\n assert (label_sample_fn(label_inp, num_classes=10) == label_inp).all()\n\n # np.ndarray input\n label_inp = np.array([3, 2, 1])\n tar_label = torch.LongTensor([3, 2, 1])\n assert (label_sample_fn(label_inp, num_classes=10,\n device='cpu') == tar_label).all()\n\n # list input\n label_inp = [0, 1, 1]\n tar_label = torch.LongTensor([0, 1, 1])\n assert (label_sample_fn(label_inp, num_classes=10) == tar_label).all()\n label_inp = [np.array([0]), np.array([1]), np.array([0])]\n tar_label = torch.LongTensor([0, 1, 0])\n assert (label_sample_fn(label_inp, num_classes=10) == tar_label).all()\n\n label_inp = [\n torch.LongTensor([0]),\n torch.LongTensor([1]),\n torch.LongTensor([0])\n ]\n tar_label = torch.LongTensor([0, 1, 0])\n assert (label_sample_fn(label_inp, num_classes=10) == tar_label).all()\n\n # list input --> raise error\n label_inp = ['1', '2']\n with pytest.raises(AssertionError):\n label_sample_fn(label_inp, num_classes=10)\n\n # callable input\n def label_function(num_batches):\n return torch.randint(0, 3, size=(num_batches, ))\n\n assert label_sample_fn(label_function, num_batches=3).shape == (3, )\n\n # test raise error\n with pytest.raises(AssertionError):\n label_sample_fn(torch.randn(3, 3), num_classes=10)\n\n with pytest.raises(AssertionError):\n label_sample_fn(torch.randint(0, 3, (2, 2)))\n\n with pytest.raises(AssertionError):\n label_sample_fn([0, 10, 2, 3], num_classes=5)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_models/test_utils/test_tensor_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmagic.models.utils.tensor_utils import get_unknown_tensor, normalize_vecs\n\n\ndef test_tensor_utils():\n input = torch.rand((2, 3, 128, 128))\n output = get_unknown_tensor(input)\n assert output.detach().numpy().shape == (2, 1, 128, 128)\n input = torch.rand((2, 1, 128, 128))\n output = get_unknown_tensor(input)\n assert output.detach().numpy().shape == (2, 1, 128, 128)\n\n\ndef test_normalize_vector():\n inp = torch.randn(4, 10)\n out = normalize_vecs(inp)\n\n def l2_norm(a):\n return a / torch.norm(a, dim=1, keepdim=True)\n\n l2_norm_out = l2_norm(inp)\n\n assert (out == l2_norm_out).all()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_structures/test_data_sample.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest import TestCase\n\nimport numpy as np\nimport torch\nfrom mmengine.structures import LabelData\nfrom mmengine.testing import assert_allclose\n\nfrom mmagic.structures import DataSample\nfrom mmagic.structures.data_sample import is_splitable_var\n\n\ndef test_is_stacked_var():\n assert is_splitable_var(DataSample())\n assert is_splitable_var(torch.randn(10, 10))\n assert is_splitable_var(np.ndarray((10, 10)))\n assert is_splitable_var([1, 2])\n assert is_splitable_var((1, 2))\n assert not is_splitable_var({'a': 1})\n assert not is_splitable_var('a')\n\n\nclass TestDataSample(TestCase):\n\n def test_init(self):\n meta_info = dict(\n target_size=[256, 256],\n scale_factor=np.array([1.5, 1.5]),\n img_shape=torch.rand(4))\n\n data_sample = DataSample(metainfo=meta_info)\n assert 'target_size' in data_sample\n assert data_sample.target_size == [256, 256]\n assert data_sample.get('target_size') == [256, 256]\n assert len(data_sample) == 1\n\n def _check_in_and_same(self, data_sample, field, value, is_meta=False):\n if is_meta:\n self.assertIn(field, data_sample.metainfo)\n else:\n self.assertIn(field, data_sample)\n\n if is_meta:\n val_in_data = data_sample.metainfo[field]\n else:\n val_in_data = getattr(data_sample, field)\n\n if isinstance(value, str):\n self.assertEqual(val_in_data, value)\n else:\n assert_allclose(val_in_data, value)\n\n def test_set_prefined_data(self):\n \"\"\"Test fields mapping in this unit test.\"\"\"\n # DATA\n gt, gt_label = torch.randn(3, 256, 256), torch.randint(0, 2, (1, ))\n fg, bg = torch.randn(3, 256, 256), torch.randn(3, 256, 256)\n alpha = torch.randn(3, 256, 256)\n ref, ref_lq = torch.randn(3, 256, 256), torch.randn(3, 256, 256)\n\n # METAINFO\n img_path, gt_path, merged_path = 'aaa', 'bbb', 'ccc'\n gt_channel_order, gt_color_type = 'rgb', 'color'\n prompt = 'prompt'\n latent = torch.randn(1, 16, 512)\n feats = torch.randn(64, 256, 256)\n\n data = dict(\n gt=gt,\n gt_label=gt_label,\n fg=fg,\n bg=bg,\n alpha=alpha,\n ref=ref,\n ref_lq=ref_lq,\n img_path=img_path,\n gt_path=gt_path,\n merged_path=merged_path,\n gt_channel_order=gt_channel_order,\n gt_color_type=gt_color_type,\n prompt=prompt,\n latent=latent,\n feats=feats)\n data_sample = DataSample()\n data_sample.set_predefined_data(data)\n\n self._check_in_and_same(data_sample, 'gt_img', gt)\n self._check_in_and_same(data_sample, 'gt_fg', fg)\n self._check_in_and_same(data_sample, 'gt_bg', bg)\n self._check_in_and_same(data_sample, 'gt_alpha', alpha)\n self._check_in_and_same(data_sample, 'ref_img', ref)\n self._check_in_and_same(data_sample, 'ref_lq', ref_lq)\n self._check_in_and_same(data_sample, 'img_path', img_path, True)\n self._check_in_and_same(data_sample, 'gt_path', gt_path, True)\n self._check_in_and_same(data_sample, 'merged_path', merged_path, True)\n self._check_in_and_same(data_sample, 'gt_channel_order',\n gt_channel_order, True)\n self._check_in_and_same(data_sample, 'gt_color_type', gt_color_type,\n True)\n self._check_in_and_same(data_sample, 'prompt', prompt, False)\n self._check_in_and_same(data_sample, 'latent', latent)\n self._check_in_and_same(data_sample, 'feats', feats)\n # check gt label\n data_sample.gt_label.data = gt_label\n\n def _test_set_label(self, key):\n data_sample = DataSample()\n method = getattr(data_sample, 'set_' + key)\n # Test number\n method(1)\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertIsInstance(label.label, torch.LongTensor)\n\n # Test tensor with single number\n method(torch.tensor(2))\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertIsInstance(label.label, torch.LongTensor)\n\n # Test array with single number\n method(np.array(3))\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertIsInstance(label.label, torch.LongTensor)\n\n # Test tensor\n method(torch.tensor([1, 2, 3]))\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertIsInstance(label.label, torch.Tensor)\n self.assertTrue((label.label == torch.tensor([1, 2, 3])).all())\n\n # Test array\n method(np.array([1, 2, 3]))\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertTrue((label.label == torch.tensor([1, 2, 3])).all())\n\n # Test Sequence\n method([1, 2, 3])\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertTrue((label.label == torch.tensor([1, 2, 3])).all())\n\n # Test Sequence with float number\n method([0.2, 0, 0.8])\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertTrue((label.label == torch.tensor([0.2, 0, 0.8])).all())\n\n # Test unavailable type\n with self.assertRaisesRegex(TypeError, \" is not\"):\n method('hi')\n\n # Test set num_classes\n data_sample = DataSample(metainfo={'num_classes': 10})\n method = getattr(data_sample, 'set_' + key)\n method(5)\n self.assertIn(key, data_sample)\n label = getattr(data_sample, key)\n self.assertIsInstance(label, LabelData)\n self.assertIn('num_classes', label)\n self.assertEqual(label.num_classes, 10)\n\n # Test unavailable label\n with self.assertRaisesRegex(ValueError, r'data .*[15].* should '):\n method(15)\n\n def test_set_gt_label(self):\n self._test_set_label('gt_label')\n\n def test_del_gt_label(self):\n data_sample = DataSample()\n self.assertNotIn('gt_label', data_sample)\n data_sample.set_gt_label(1)\n self.assertIn('gt_label', data_sample)\n del data_sample.gt_label\n self.assertNotIn('gt_label', data_sample)\n\n def test_stack_and_split(self):\n # test stack\n data_sample1 = DataSample()\n data_sample1.set_gt_label(1)\n data_sample1.set_tensor_data({'img': torch.randn(3, 4, 5)})\n data_sample1.set_data({'mode': 'a'})\n data_sample1.set_data({'prompt': 'I\\'m a prompt!'})\n data_sample1.set_metainfo({\n 'channel_order': 'rgb',\n 'color_flag': 'color'\n })\n data_sample2 = DataSample()\n data_sample2.set_gt_label(2)\n data_sample2.set_tensor_data({'img': torch.randn(3, 4, 5)})\n data_sample2.set_data({'mode': 'b'})\n data_sample2.set_data({'prompt': 'I\\'m an another prompt!'})\n data_sample2.set_metainfo({\n 'channel_order': 'rgb',\n 'color_flag': 'color'\n })\n\n data_sample_merged = DataSample.stack([data_sample1, data_sample2])\n assert (data_sample_merged.img == torch.stack(\n [data_sample1.img, data_sample2.img])).all()\n assert (data_sample_merged.gt_label.label == torch.LongTensor(\n [[1], [2]])).all()\n assert data_sample_merged.mode == ['a', 'b']\n assert data_sample_merged.metainfo == dict(\n channel_order=['rgb', 'rgb'], color_flag=['color', 'color'])\n assert len(data_sample_merged) == 2\n assert data_sample_merged.prompt == [\n 'I\\'m a prompt!', 'I\\'m an another prompt!'\n ]\n\n # test split\n data_sample_merged.sample_model = 'ema'\n data_sample_merged.fake_img = DataSample(img=torch.randn(2, 3, 4, 4))\n\n data_splited_1, data_splited_2 = data_sample_merged.split(True)\n assert (data_splited_1.gt_label.label == 1).all()\n assert (data_splited_2.gt_label.label == 2).all()\n assert (data_splited_1.img.shape == data_sample1.img.shape)\n assert (data_splited_2.img.shape == data_sample2.img.shape)\n assert (data_splited_1.img == data_sample1.img).all()\n assert (data_splited_2.img == data_sample2.img).all()\n assert (data_splited_1.metainfo == dict(\n channel_order='rgb', color_flag='color'))\n assert (data_splited_2.metainfo == dict(\n channel_order='rgb', color_flag='color'))\n assert data_splited_1.sample_model == 'ema'\n assert data_splited_2.sample_model == 'ema'\n assert data_splited_1.fake_img.img.shape == (3, 4, 4)\n assert data_splited_2.fake_img.img.shape == (3, 4, 4)\n assert data_splited_1.prompt == 'I\\'m a prompt!'\n assert data_splited_2.prompt == 'I\\'m an another prompt!'\n\n with self.assertRaises(TypeError):\n data_sample_merged.split()\n\n # test stack and split when batch size is 1\n data_sample = DataSample()\n data_sample.set_gt_label(3)\n data_sample.set_tensor_data({'img': torch.randn(3, 4, 5)})\n data_sample.set_data({'mode': 'c'})\n data_sample.set_data({'prompt': 'proooommmmpt'})\n data_sample.set_metainfo({\n 'channel_order': 'rgb',\n 'color_flag': 'color'\n })\n\n data_sample_merged = DataSample.stack([data_sample])\n assert (data_sample_merged.img == torch.stack([data_sample.img])).all()\n assert (data_sample_merged.gt_label.label == torch.LongTensor(\n [[3]])).all()\n assert data_sample_merged.mode == ['c']\n assert data_sample_merged.metainfo == dict(\n channel_order=['rgb'], color_flag=['color'])\n data_sample_merged.prompt == ['proooommmmpt']\n assert len(data_sample_merged) == 1\n\n # test split\n data_splited = data_sample_merged.split()\n assert len(data_splited) == 1\n data_splited = data_splited[0]\n assert (data_splited.gt_label.label == 3).all()\n assert (data_splited.img == data_sample.img).all()\n assert data_splited.prompt == 'proooommmmpt'\n assert (data_splited.metainfo == dict(\n channel_order='rgb', color_flag='color'))\n\n def test_len(self):\n empty_data = DataSample(sample_kwargs={'a': 'a'})\n assert len(empty_data) == 1\n\n empty_data = DataSample()\n assert len(empty_data) == 1\n\n empty_data = DataSample(\n img=torch.randn(3, 3), metainfo=dict(img_shape=[3, 3]))\n assert len(empty_data) == 1\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_cli.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport sys\n\nfrom mmagic.utils import modify_args\n\n\ndef test_modify_args():\n sys.argv = ['test.py', '--arg_1=1', '--arg-2=2']\n modify_args()\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_img_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport pytest\nimport torch\n\nfrom mmagic.utils import (all_to_tensor, can_convert_to_image, tensor2img,\n to_numpy)\n\n\ndef test_all_to_tensor():\n\n data = [np.random.rand(64, 64, 3), np.random.rand(64, 64, 3)]\n tensor = all_to_tensor(data)\n assert tensor.shape == torch.Size([2, 3, 64, 64])\n\n data = np.random.rand(64, 64, 3)\n tensor = all_to_tensor(data)\n assert tensor.shape == torch.Size([3, 64, 64])\n\n data = 1\n tensor = all_to_tensor(data)\n assert tensor == torch.tensor(1)\n\n\ndef test_can_convert_to_image():\n values = [\n np.random.rand(64, 64, 3),\n [np.random.rand(64, 61, 3),\n np.random.rand(64, 61, 3)], (64, 64), 'b'\n ]\n targets = [True, True, False, False]\n for val, tar in zip(values, targets):\n assert can_convert_to_image(val) == tar\n\n\ndef test_tensor2img():\n input = torch.rand(1, 3, 8, 8)\n result = tensor2img(input)\n assert result.shape == (8, 8, 3)\n\n input = torch.rand(1, 1, 8, 8)\n result = tensor2img(input)\n assert result.shape == (8, 8)\n\n input = torch.rand(4, 3, 8, 8)\n result = tensor2img(input)\n assert result.shape == (22, 22, 3)\n\n input = [torch.rand(1, 3, 8, 8), torch.rand(1, 3, 8, 8)]\n result = tensor2img(input)\n assert len(result) == len(input)\n for r in result:\n assert r.shape == (8, 8, 3)\n\n with pytest.raises(TypeError):\n tensor2img('wrong type')\n\n with pytest.raises(ValueError):\n tensor2img([torch.randn(2, 3, 4, 4, 4) for _ in range(2)])\n\n\ndef test_to_numpy():\n input = torch.rand(1, 3, 8, 8)\n output = to_numpy(input)\n assert isinstance(output, np.ndarray)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_io_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nfrom mmagic.utils.io_utils import download_from_url\n\n\ndef test_download_from_url():\n # test to download a small file\n dest_path = download_from_url(\n 'https://download.openmmlab.com/mmgen/dataset/singan/balloons.png',\n dest_path='./')\n print(dest_path)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_logger.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.utils import print_colored_log\n\n\ndef test_print_colored_log():\n print_colored_log('Test print_colored_log info.')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_sampler.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import MagicMock\n\nfrom torch.utils.data import DataLoader\n\nfrom mmagic.utils.sampler import ArgumentsSampler, ValDataSampler\n\n\ndef test_argument_sampler():\n sample_kwargs = dict(\n a=1,\n b=2,\n max_times=10,\n num_batches=2,\n forward_kwargs=dict(forward_mode='gen'))\n sampler = ArgumentsSampler(sample_kwargs=sample_kwargs, )\n\n assert sampler.max_times == 10\n for sample in sampler:\n assert 'inputs' in sample\n assert sample['inputs'] == dict(forward_mode='gen', num_batches=2)\n\n\nclass MockDataset():\n\n def __init__(self, length):\n self.length = length\n\n def __getitem__(self, idx):\n return idx\n\n def __len__(self):\n return self.length\n\n\nclass MockValLoop():\n\n def __init__(self):\n self.dataloaders = None\n\n\ndef test_val_data_sampler():\n runner = MagicMock()\n val_loop = MockValLoop()\n val_loop.dataloaders = [\n DataLoader(MockDataset(10), batch_size=4),\n DataLoader(MockDataset(5), batch_size=4)\n ]\n # val_loop.dataloader = None\n runner.val_loop = val_loop\n\n val_sampler = ValDataSampler(\n sample_kwargs=dict(max_times=10), runner=runner)\n assert len(val_sampler._dataloader.dataset) == 15\n tar_out = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 0, 1], [2, 3, 4]]\n for idx, out in enumerate(val_sampler):\n assert out == tar_out[idx]\n\n setattr(val_loop, 'dataloader', DataLoader(MockDataset(8), batch_size=4))\n val_sampler = ValDataSampler(\n sample_kwargs=dict(max_times=10), runner=runner)\n assert len(val_sampler._dataloader.dataset) == 8\n for idx, out in enumerate(val_sampler):\n assert out == tar_out[idx]\n\n # test iteration times\n val_sampler = ValDataSampler(\n sample_kwargs=dict(max_times=1), runner=runner)\n tar_out = [[0, 1, 2, 3]]\n for idx, out in enumerate(val_sampler):\n assert out == tar_out[idx]\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_setup_env.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom mmagic.utils import register_all_modules, try_import\n\n\ndef test_register_all_modules():\n register_all_modules()\n\n\ndef test_try_import():\n import numpy as np\n assert try_import('numpy') is np\n assert try_import('numpy111') is None\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_utils/test_trans_utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom mmagic.datasets.transforms import (CropAroundCenter, CropAroundFg,\n CropAroundUnknown, LoadImageFromFile)\nfrom mmagic.utils import (adjust_gamma, bbox2mask, brush_stroke_mask,\n get_irregular_mask, random_bbox)\n\ndtype_range = {\n np.bool_: (False, True),\n np.bool8: (False, True),\n np.float16: (-1, 1),\n np.float32: (-1, 1),\n np.float64: (-1, 1)\n}\n\n\nclass TestCrop:\n\n @classmethod\n def setup_class(cls):\n \"\"\"Check the dimension of gray scale images read by\n LoadImageFromFile.\"\"\"\n image_loader = LoadImageFromFile(key='img')\n path_alpha = Path(\n __file__\n ).parent.parent / 'data' / 'matting_dataset' / 'alpha' / 'GT05.jpg' # noqa: E501\n result = image_loader({'img_path': path_alpha})\n if result['img'].ndim == 3:\n cls.ext_dim = (1, )\n elif result['img'].ndim == 2:\n cls.ext_dim = ()\n else:\n raise ValueError('invalid ndim')\n\n @classmethod\n def check_ndim(cls, result_img_shape):\n if cls.ext_dim:\n return len(result_img_shape) == 3\n else:\n return not (len(result_img_shape.ndim) == 3\n and result_img_shape[-1] == 1)\n\n @classmethod\n def check_crop(cls, result_img_shape, result_bbox):\n crop_w = result_bbox[2] - result_bbox[0]\n \"\"\"Check if the result_bbox is in correspond to result_img_shape.\"\"\"\n crop_h = result_bbox[3] - result_bbox[1]\n crop_shape = (crop_h, crop_w)\n return result_img_shape[:2] == crop_shape\n\n @staticmethod\n def check_crop_around_semi(alpha):\n return ((alpha > 0) & (alpha < 255)).any()\n\n @staticmethod\n def check_keys_contain(result_keys, target_keys):\n \"\"\"Check if all elements in target_keys is in result_keys.\"\"\"\n return set(target_keys).issubset(set(result_keys))\n\n def test_crop_around_center(self):\n\n with pytest.raises(TypeError):\n CropAroundCenter(320.)\n with pytest.raises(AssertionError):\n CropAroundCenter((320, 320, 320))\n\n target_keys = ['fg', 'bg', 'alpha', 'trimap', 'crop_bbox']\n\n fg = np.random.rand(240, 320, 3)\n bg = np.random.rand(240, 320, 3)\n trimap = np.random.rand(240, 320, *self.ext_dim)\n alpha = np.random.rand(240, 320, *self.ext_dim)\n\n # make sure there would be semi-transparent area\n trimap[128, 128] = 128\n results = dict(fg=fg, bg=bg, trimap=trimap, alpha=alpha)\n crop_around_center = CropAroundCenter(\n crop_size=330) # this will trigger rescale\n crop_around_center_results = crop_around_center(results)\n assert self.check_keys_contain(crop_around_center_results.keys(),\n target_keys)\n assert self.check_ndim(crop_around_center_results['alpha'].shape)\n assert self.check_ndim(crop_around_center_results['trimap'].shape)\n assert self.check_crop(crop_around_center_results['alpha'].shape,\n crop_around_center_results['crop_bbox'])\n assert self.check_crop_around_semi(crop_around_center_results['alpha'])\n\n # make sure there would be semi-transparent area\n trimap[:, :] = 128\n results = dict(fg=fg, bg=bg, trimap=trimap, alpha=alpha)\n crop_around_center = CropAroundCenter(crop_size=200)\n crop_around_center_results = crop_around_center(results)\n assert self.check_keys_contain(crop_around_center_results.keys(),\n target_keys)\n assert self.check_ndim(crop_around_center_results['alpha'].shape)\n assert self.check_ndim(crop_around_center_results['trimap'].shape)\n assert self.check_crop(crop_around_center_results['alpha'].shape,\n crop_around_center_results['crop_bbox'])\n assert self.check_crop_around_semi(crop_around_center_results['alpha'])\n\n repr_str = crop_around_center.__class__.__name__ + (\n f'(crop_size={(200, 200)})')\n assert repr(crop_around_center) == repr_str\n\n def test_crop_around_fg(self):\n with pytest.raises(ValueError):\n # keys must contain 'seg'\n CropAroundFg(['fg', 'bg'])\n with pytest.raises(TypeError):\n # bd_ratio_range must be a tuple of 2 float\n CropAroundFg(['seg', 'merged'], bd_ratio_range=0.1)\n\n keys = ['bg', 'merged', 'seg']\n target_keys = ['bg', 'merged', 'seg', 'crop_bbox']\n\n bg = np.random.rand(60, 60, 3)\n merged = np.random.rand(60, 60, 3)\n seg = np.random.rand(60, 60)\n results = dict(bg=bg, merged=merged, seg=seg)\n\n crop_around_fg = CropAroundFg(keys)\n crop_around_fg_results = crop_around_fg(results)\n assert self.check_keys_contain(crop_around_fg_results.keys(),\n target_keys)\n assert self.check_crop(crop_around_fg_results['seg'].shape,\n crop_around_fg_results['crop_bbox'])\n\n crop_around_fg = CropAroundFg(keys, test_mode=True)\n crop_around_fg_results = crop_around_fg(results)\n result_img_shape = crop_around_fg_results['seg'].shape\n assert self.check_keys_contain(crop_around_fg_results.keys(),\n target_keys)\n assert self.check_crop(result_img_shape,\n crop_around_fg_results['crop_bbox'])\n # it should be a square in test mode\n assert result_img_shape[0] == result_img_shape[1]\n\n def test_crop_around_unknown(self):\n with pytest.raises(ValueError):\n # keys must contain 'alpha'\n CropAroundUnknown(['fg', 'bg'], [320])\n with pytest.raises(TypeError):\n # crop_size must be a list\n CropAroundUnknown(['alpha'], 320)\n with pytest.raises(TypeError):\n # crop_size must be a list of int\n CropAroundUnknown(['alpha'], [320.])\n with pytest.raises(ValueError):\n # unknown_source must be either 'alpha' or 'trimap'\n CropAroundUnknown(['alpha', 'fg'], [320], unknown_source='fg')\n with pytest.raises(ValueError):\n # if unknown_source is 'trimap', then keys must contain it\n CropAroundUnknown(['alpha', 'fg'], [320], unknown_source='trimap')\n\n keys = ['fg', 'bg', 'merged', 'alpha', 'trimap', 'ori_merged']\n target_keys = [\n 'fg', 'bg', 'merged', 'alpha', 'trimap', 'ori_merged', 'crop_bbox'\n ]\n\n # test cropping using trimap to decide unknown area\n fg = np.random.rand(240, 320, 3)\n bg = np.random.rand(240, 320, 3)\n merged = np.random.rand(240, 320, 3)\n ori_merged = merged.copy()\n alpha = np.zeros((240, 320, *self.ext_dim))\n # make sure there would be unknown area\n alpha[:16, -16:] = 128\n trimap = np.zeros_like(alpha)\n trimap[alpha > 0] = 128\n trimap[alpha == 255] = 255\n results = dict(\n fg=fg,\n bg=bg,\n merged=merged,\n ori_merged=ori_merged,\n alpha=alpha,\n trimap=trimap)\n crop_around_semi_trans = CropAroundUnknown(\n keys, crop_sizes=[320], unknown_source='trimap')\n crop_around_semi_trans_results = crop_around_semi_trans(results)\n assert self.check_keys_contain(crop_around_semi_trans_results.keys(),\n target_keys)\n assert self.check_ndim(crop_around_semi_trans_results['alpha'].shape)\n assert self.check_ndim(crop_around_semi_trans_results['trimap'].shape)\n assert self.check_crop(crop_around_semi_trans_results['alpha'].shape,\n crop_around_semi_trans_results['crop_bbox'])\n assert self.check_crop_around_semi(\n crop_around_semi_trans_results['alpha'])\n\n keys = ['fg', 'bg', 'merged', 'alpha', 'ori_merged']\n target_keys = [\n 'fg', 'bg', 'merged', 'alpha', 'ori_merged', 'crop_bbox'\n ]\n\n # test cropping using alpha to decide unknown area\n fg = np.random.rand(240, 320, 3)\n bg = np.random.rand(240, 320, 3)\n merged = np.random.rand(240, 320, 3)\n ori_merged = merged.copy()\n alpha = np.random.rand(240, 320, *self.ext_dim)\n # make sure there would be unknown area\n alpha[120:160, 120:160] = 128\n results = dict(\n fg=fg, bg=bg, merged=merged, ori_merged=ori_merged, alpha=alpha)\n crop_around_semi_trans = CropAroundUnknown(\n keys, crop_sizes=[160], unknown_source='alpha')\n crop_around_semi_trans_results = crop_around_semi_trans(results)\n assert self.check_keys_contain(crop_around_semi_trans_results.keys(),\n target_keys)\n assert self.check_ndim(crop_around_semi_trans_results['alpha'].shape)\n assert self.check_crop(crop_around_semi_trans_results['alpha'].shape,\n crop_around_semi_trans_results['crop_bbox'])\n assert self.check_crop_around_semi(\n crop_around_semi_trans_results['alpha'])\n\n # test cropping when there is no unknown area\n fg = np.random.rand(240, 320, 3)\n bg = np.random.rand(240, 320, 3)\n merged = np.random.rand(240, 320, 3)\n ori_merged = merged.copy()\n alpha = np.zeros((240, 320, *self.ext_dim))\n results = dict(\n fg=fg, bg=bg, merged=merged, ori_merged=ori_merged, alpha=alpha)\n crop_around_semi_trans = CropAroundUnknown(\n keys, crop_sizes=[240], unknown_source='alpha')\n crop_around_semi_trans_results = crop_around_semi_trans(results)\n assert self.check_keys_contain(crop_around_semi_trans_results.keys(),\n target_keys)\n assert self.check_ndim(crop_around_semi_trans_results['alpha'].shape)\n assert self.check_crop(crop_around_semi_trans_results['alpha'].shape,\n crop_around_semi_trans_results['crop_bbox'])\n\n repr_str = (\n crop_around_semi_trans.__class__.__name__ +\n f\"(keys={keys}, crop_sizes={[(240, 240)]}, unknown_source='alpha',\"\n \" interpolations=['bilinear', 'bilinear', 'bilinear', 'bilinear', \"\n \"'bilinear'])\")\n assert crop_around_semi_trans.__repr__() == repr_str\n\n\ndef test_adjust_gamma():\n \"\"\"Test Gamma Correction.\n\n Adpted from\n # https://github.com/scikit-image/scikit-image/blob/7e4840bd9439d1dfb6beaf549998452c99f97fdd/skimage/exposure/tests/test_exposure.py#L534 # noqa\n \"\"\"\n # Check that the shape is maintained.\n img = np.ones([1, 1])\n result = adjust_gamma(img, 1.5)\n assert img.shape == result.shape\n\n # Same image should be returned for gamma equal to one.\n image = np.random.uniform(0, 255, (8, 8))\n result = adjust_gamma(image, 1)\n np.testing.assert_array_equal(result, image)\n\n # White image should be returned for gamma equal to zero.\n image = np.random.uniform(0, 255, (8, 8))\n result = adjust_gamma(image, 0)\n dtype = image.dtype.type\n np.testing.assert_array_equal(result, dtype_range[dtype][1])\n\n # Verifying the output with expected results for gamma\n # correction with gamma equal to half.\n image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))\n expected = np.array([[0, 31, 45, 55, 63, 71, 78, 84],\n [90, 95, 100, 105, 110, 115, 119, 123],\n [127, 131, 135, 139, 142, 146, 149, 153],\n [156, 159, 162, 165, 168, 171, 174, 177],\n [180, 183, 186, 188, 191, 194, 196, 199],\n [201, 204, 206, 209, 211, 214, 216, 218],\n [221, 223, 225, 228, 230, 232, 234, 236],\n [238, 241, 243, 245, 247, 249, 251, 253]],\n dtype=np.uint8)\n\n result = adjust_gamma(image, 0.5)\n np.testing.assert_array_equal(result, expected)\n\n # Verifying the output with expected results for gamma\n # correction with gamma equal to two.\n image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))\n expected = np.array([[0, 0, 0, 0, 1, 1, 2, 3], [4, 5, 6, 7, 9, 10, 12, 14],\n [16, 18, 20, 22, 25, 27, 30, 33],\n [36, 39, 42, 45, 49, 52, 56, 60],\n [64, 68, 72, 76, 81, 85, 90, 95],\n [100, 105, 110, 116, 121, 127, 132, 138],\n [144, 150, 156, 163, 169, 176, 182, 189],\n [196, 203, 211, 218, 225, 233, 241, 249]],\n dtype=np.uint8)\n\n result = adjust_gamma(image, 2)\n np.testing.assert_array_equal(result, expected)\n\n # Test invalid image input\n image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))\n with pytest.raises(ValueError):\n adjust_gamma(image, -1)\n\n\ndef test_bbox_mask():\n # default config for random bbox mask\n cfg = dict(\n img_shape=(256, 256),\n max_bbox_shape=100,\n max_bbox_delta=10,\n min_margin=10)\n\n bbox = random_bbox(**cfg)\n mask_bbox = bbox2mask(cfg['img_shape'], bbox)\n assert mask_bbox.shape == (256, 256, 1)\n zero_area = np.sum((mask_bbox == 0).astype(np.uint8))\n ones_area = np.sum((mask_bbox == 1).astype(np.uint8))\n assert zero_area + ones_area == 256 * 256\n assert mask_bbox.dtype == np.uint8\n\n with pytest.raises(ValueError):\n cfg_ = cfg.copy()\n cfg_['max_bbox_shape'] = 300\n bbox = random_bbox(**cfg_)\n\n with pytest.raises(ValueError):\n cfg_ = cfg.copy()\n cfg_['max_bbox_delta'] = 300\n bbox = random_bbox(**cfg_)\n\n with pytest.raises(ValueError):\n cfg_ = cfg.copy()\n cfg_['max_bbox_shape'] = 254\n bbox = random_bbox(**cfg_)\n\n cfg_ = cfg.copy()\n cfg_['max_bbox_delta'] = 1\n bbox = random_bbox(**cfg_)\n mask_bbox = bbox2mask(cfg['img_shape'], bbox)\n assert mask_bbox.shape == (256, 256, 1)\n\n\ndef test_free_form_mask():\n img_shape = (256, 256, 3)\n for _ in range(10):\n mask = brush_stroke_mask(img_shape)\n assert mask.shape == (256, 256, 1)\n\n img_shape = (256, 256, 3)\n mask = brush_stroke_mask(img_shape, num_vertices=8)\n assert mask.shape == (256, 256, 1)\n zero_area = np.sum((mask == 0).astype(np.uint8))\n ones_area = np.sum((mask == 1).astype(np.uint8))\n assert zero_area + ones_area == 256 * 256\n assert mask.dtype == np.uint8\n\n img_shape = (256, 256, 3)\n mask = brush_stroke_mask(img_shape, brush_width=10)\n assert mask.shape == (256, 256, 1)\n\n with pytest.raises(TypeError):\n mask = brush_stroke_mask(img_shape, num_vertices=dict())\n\n with pytest.raises(TypeError):\n mask = brush_stroke_mask(img_shape, brush_width=dict())\n\n\ndef test_irregular_mask():\n img_shape = (256, 256)\n for _ in range(10):\n mask = get_irregular_mask(img_shape)\n assert mask.shape == (256, 256, 1)\n assert 0.15 < (np.sum(mask) / (img_shape[0] * img_shape[1])) < 0.50\n zero_area = np.sum((mask == 0).astype(np.uint8))\n ones_area = np.sum((mask == 1).astype(np.uint8))\n assert zero_area + ones_area == 256 * 256\n assert mask.dtype == np.uint8\n\n with pytest.raises(TypeError):\n mask = get_irregular_mask(img_shape, brush_width=dict())\n\n with pytest.raises(TypeError):\n mask = get_irregular_mask(img_shape, length_range=dict())\n\n with pytest.raises(TypeError):\n mask = get_irregular_mask(img_shape, num_vertices=dict())\n\n mask = get_irregular_mask(img_shape, brush_width=10)\n assert mask.shape == (256, 256, 1)\n\n mask = get_irregular_mask(img_shape, length_range=10)\n assert mask.shape == (256, 256, 1)\n\n mask = get_irregular_mask(img_shape, num_vertices=10)\n assert mask.shape == (256, 256, 1)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_visualization/test_concat_visualizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport mmcv\nimport numpy as np\nimport torch\n\nfrom mmagic.structures import DataSample\nfrom mmagic.visualization import ConcatImageVisualizer\n\n\ndef test_concatimagevisualizer():\n data_sample = DataSample(\n path_rgb='fake_dir/rgb.png',\n path_bgr='fake_dir/bgr.png',\n tensor3d=torch.ones(3, 32, 32) *\n torch.tensor([[[0.1]], [[0.2]], [[0.3]]]),\n array3d=np.ones(shape=(32, 32, 3)) * [0.4, 0.5, 0.6],\n tensor4d=torch.ones(2, 3, 32, 32) * torch.tensor(\n [[[[0.1]], [[0.2]], [[0.3]]], [[[0.4]], [[0.5]], [[0.6]]]]),\n )\n\n vis = ConcatImageVisualizer(\n fn_key='path_rgb',\n img_keys=['tensor3d', 'array3d', 'tensor4d'],\n vis_backends=[dict(type='LocalVisBackend')],\n save_dir='work_dirs')\n\n vis.add_datasample(data_sample=data_sample, step=1)\n\n vis = ConcatImageVisualizer(\n fn_key='path_bgr',\n img_keys=['tensor3d', 'array3d', 'tensor4d'],\n vis_backends=[dict(type='LocalVisBackend')],\n save_dir='work_dirs',\n bgr2rgb=True)\n\n vis.add_datasample(data_sample=data_sample, step=2)\n\n for fn in 'rgb_1.png', 'bgr_2.png':\n img = mmcv.imread(f'work_dirs/vis_data/vis_image/{fn}')\n assert img.shape == (64, 16 * 3 * 2, 3)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_visualization/test_vis_backend.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport shutil\nimport sys\nimport time\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock\n\nimport numpy as np\nimport torch\nfrom mmengine import Config, MessageHub\n\nfrom mmagic.visualization import (PaviVisBackend, TensorboardVisBackend,\n VisBackend, WandbVisBackend)\n\n\nclass TestVisBackend(TestCase):\n\n def test_vis_backend(self):\n message_hub = MessageHub.get_instance('test-visbackend')\n config = Config(dict(work_dir='./mmagic/test/vis_backend_test/'))\n message_hub.update_info('cfg', config.pretty_text)\n\n data_root = 'tmp_dir'\n sys.modules['petrel_client'] = MagicMock()\n vis_backend = VisBackend(save_dir='tmp_dir', ceph_path='s3://xxx')\n\n self.assertEqual(vis_backend.experiment, vis_backend)\n # test path mapping\n src_path = osp.abspath(\n './mmagic/test/vis_backend_test/test_vis_data/test.png')\n tar_path = 's3://xxx/vis_backend_test/test_vis_data/test.png'\n file_client = vis_backend._file_client\n mapped_path = file_client._map_path(src_path)\n formatted_path = file_client._format_path(mapped_path)\n self.assertEqual(formatted_path, tar_path)\n # test with `delete_local` is True\n vis_backend.add_config(Config(dict(name='test')))\n vis_backend.add_image(\n name='add_img', image=np.random.random((8, 8, 3)).astype(np.uint8))\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3)\n\n # test with `delete_local` is False\n vis_backend._delete_local_image = False\n vis_backend.add_config(Config(dict(name='test')))\n vis_backend.add_image(\n name='add_img', image=np.random.random((8, 8, 3)).astype(np.uint8))\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3)\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3,\n file_path='new_scalar.json')\n self.assertTrue(osp.exists(osp.join(data_root, 'config.py')))\n self.assertTrue(\n osp.exists(osp.join(data_root, 'vis_image', 'add_img_0.png')))\n self.assertTrue(osp.exists(osp.join(data_root, 'new_scalar.json')))\n self.assertTrue(osp.exists(osp.join(data_root, 'scalars.json')))\n\n # test with `ceph_path` is None\n vis_backend = VisBackend(save_dir='tmp_dir')\n vis_backend.add_config(Config(dict(name='test')))\n vis_backend.add_image(\n name='add_img', image=np.random.random((8, 8, 3)).astype(np.uint8))\n vis_backend.add_scalar(\n name='scalar_tensor', value=torch.FloatTensor([0.693]), step=3)\n vis_backend.add_scalar(name='scalar', value=0.693, step=3)\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3)\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3,\n file_path='new_scalar.json')\n\n # raise error\n with self.assertRaises(AssertionError):\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001), step=3, file_path='scalars.json')\n with self.assertRaises(AssertionError):\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001), step=3, file_path='new_scalars')\n\n shutil.rmtree('tmp_dir')\n\n\nclass TestTensorboardBackend(TestCase):\n\n def test_tensorboard(self):\n save_dir = 'tmp_dir'\n vis_backend = TensorboardVisBackend(save_dir)\n sys.modules['torch.utils.tensorboard'] = MagicMock()\n\n # add image\n vis_backend.add_image(\n name='add_img', image=np.random.random((8, 8, 3)).astype(np.uint8))\n vis_backend.add_image(\n name='add_img',\n image=np.random.random((10, 8, 8, 3)).astype(np.uint8))\n\n # add scalars\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3)\n\n\nclass TestPaviBackend(TestCase):\n\n def test_pavi(self):\n save_dir = 'tmp_dir'\n exp_name = 'unit test'\n vis_backend = PaviVisBackend(save_dir=save_dir, exp_name=exp_name)\n with self.assertRaises(ImportError):\n vis_backend._init_env()\n sys.modules['pavi'] = MagicMock()\n vis_backend._init_env()\n\n exp = vis_backend.experiment\n self.assertEqual(exp, vis_backend._pavi)\n\n # add image\n vis_backend.add_image(\n name='add_img', image=np.random.random((8, 8, 3)).astype(np.uint8))\n\n # add scalars\n vis_backend.add_scalars(\n scalar_dict=dict(lr=0.001, loss=torch.FloatTensor([0.693])),\n step=3)\n\n\nclass TestWandbBackend(TestCase):\n\n def test_wandb(self):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))\n\n wandb_mock = MagicMock()\n sys.modules['wandb'] = wandb_mock\n\n vis_backend = WandbVisBackend(\n save_dir=f'parent_dir/exp_name/{timestamp}/vis_data',\n init_kwargs=dict(project='test_backend'))\n # test save gif image\n rgb_np = np.random.rand(11, 4, 4, 3).astype(np.uint8)\n vis_backend.add_image('test_gif', rgb_np, n_skip=2)\n vis_backend.add_image('test_gif', rgb_np, n_skip=1)\n\n # test save rgb image\n rgb_np = np.random.rand(4, 4, 3).astype(np.uint8)\n vis_backend.add_image('test_rgb', rgb_np)\n\n # test wandb backend with name\n wandb_mock.reset_mock()\n vis_backend = WandbVisBackend(\n save_dir=f'parent_dir/exp_name/{timestamp}/vis_data',\n init_kwargs=dict(project='test_backend', name='test_wandb'))\n vis_backend._init_env()\n _, called_kwargs = wandb_mock.init.call_args\n self.assertEqual(called_kwargs['name'], f'test_wandb_{timestamp}')\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tests/test_visualization/test_visualizer.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os.path as osp\nimport shutil\nfrom unittest import TestCase\n\nimport torch\n\nfrom mmagic.structures import DataSample\nfrom mmagic.utils import register_all_modules\nfrom mmagic.visualization import Visualizer\n\nregister_all_modules()\n\n\nclass TestGenVisualer(TestCase):\n\n def test_add_datasample(self):\n visualizer = Visualizer(\n save_dir='tmp_dir', vis_backends=[dict(type='VisBackend')])\n\n img_root = osp.join('tmp_dir', 'vis_data', 'vis_image')\n # construct gen sample to vis\n gen_sample = [\n DataSample(\n fake_img=torch.randn(3, 16, 16),\n gt_img=torch.randn(1, 16, 16),\n ema=DataSample(fake_img=torch.randn(10, 3, 16, 16)),\n gif=torch.randn(10, 3, 16, 16),\n something=DataSample(\n new=DataSample(img=torch.randn(3, 16, 16))))\n for _ in range(3)\n ]\n visualizer.add_datasample(\n name='fake_img',\n gen_samples=gen_sample,\n target_mean=None,\n target_std=None,\n target_keys=['fake_img', 'gt_img'],\n step=0)\n osp.exists(osp.join(img_root, 'fake_img_0.png'))\n\n visualizer.add_datasample(\n name='target_is_none',\n gen_samples=gen_sample,\n target_mean=None,\n target_std=None,\n step=1)\n osp.exists(osp.join(img_root, 'target_is_none_1.png'))\n\n visualizer.add_datasample(\n name='target_is_none_gif',\n gen_samples=gen_sample,\n target_mean=None,\n target_std=None,\n vis_mode='gif',\n step=2)\n osp.exists(osp.join(img_root, 'target_is_none_gif_2.gif'))\n\n visualizer.add_datasample(\n name='something',\n gen_samples=gen_sample,\n n_row=3,\n target_keys='something.new',\n step=3)\n osp.exists(osp.join(img_root, 'something_3.png'))\n\n visualizer.add_datasample(\n name='ema_padding',\n gen_samples=gen_sample,\n n_row=2,\n color_order='rgb',\n target_keys='ema',\n vis_mode='gif',\n step=4)\n osp.exists(osp.join(img_root, 'emd_padding_4.gif'))\n\n visualizer.add_datasample(\n name='fake_img_padding',\n gen_samples=gen_sample,\n target_mean=None,\n target_std=None,\n target_keys=['fake_img', 'gt_img'],\n n_row=4,\n step=5)\n osp.exists(osp.join(img_root, 'fake_img_padding_5.png'))\n\n shutil.rmtree(img_root)\n\n\ndef teardown_module():\n import gc\n gc.collect()\n globals().clear()\n locals().clear()\n" }, { "path": "tools/analysis_tools/get_flops.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\n\nimport torch\nfrom mmengine import Config\nfrom mmengine.registry import init_default_scope\n\nfrom mmagic.registry import MODELS\n\ntry:\n from mmengine.analysis import get_model_complexity_info\nexcept ImportError:\n raise ImportError('Please upgrade mmengine >= 0.6.0')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Get a editor complexity',\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('config', help='train config file path')\n parser.add_argument(\n '--shape',\n type=int,\n nargs='+',\n default=[3, 250, 250],\n help='Input shape. Supported tasks:\\n'\n 'Image Super-Resolution: --shape 3 h w\\n'\n 'Video Super-Resolution: --shape t 3 h w\\n'\n 'Video Interpolation: --shape t 3 h w\\n'\n 'Image Restoration: --shape 3 h w\\n'\n 'Inpainting: --shape 4 h w\\n'\n 'Matting: --shape 4 h w\\n'\n 'Unconditional GANs: --shape noisy_size\\n'\n 'Image Translation: --shape 3 h w')\n parser.add_argument(\n '--activations',\n action='store_true',\n help='Whether to show the Activations')\n parser.add_argument(\n '--out-table',\n action='store_true',\n help='Whether to show the complexity table')\n parser.add_argument(\n '--out-arch',\n action='store_true',\n help='Whether to show the complexity arch')\n args = parser.parse_args()\n return args\n\n\ndef main():\n \"\"\"\n Examples:\n\n Image Super-Resolution:\n `python tools/analysis_tools/get_flops.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py --shape 3 250 250` # noqa\n\n Video Super-Resolution:\n `python tools/analysis_tools/get_flops.py configs/edvr/edvrm_8xb4-600k_reds.py --shape 5 3 256 256` # noqa\n\n Video Interpolation:\n `python tools/analysis_tools/get_flops.py configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py --shape 4 3 64 64` # noqa\n\n Image Restoration:\n `python tools/analysis_tools/get_flops.py configs/nafnet/nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py --shape 3 128 128` # noqa\n\n Inpainting:\n `python tools/analysis_tools/get_flops.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py --shape 4 64 64` # noqa\n\n Matting:\n `python tools/analysis_tools/get_flops.py configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py --shape 4 256 256` # noqa\n\n Unconditional GANs:\n `python tools/analysis_tools/get_flops.py configs/wgan-gp/wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py --shape 128` # noqa\n\n Image Translation:\n `python tools/analysis_tools/get_flops.py configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py --shape 3 250 250`\n \"\"\"\n\n args = parse_args()\n\n input_shape = tuple(args.shape)\n\n cfg = Config.fromfile(args.config)\n\n init_default_scope(cfg.get('default_scope', 'mmagic'))\n\n model = MODELS.build(cfg.model)\n inputs = torch.randn(1, *input_shape)\n if torch.cuda.is_available():\n model.cuda()\n inputs = inputs.cuda()\n model.eval()\n\n if hasattr(model, 'generator'):\n model = model.generator\n elif hasattr(model, 'backbone'):\n model = model.backbone\n if hasattr(model, 'translation'):\n model.forward = model.translation\n elif hasattr(model, 'infer'):\n model.forward = model.infer\n\n analysis_results = get_model_complexity_info(model, inputs=inputs)\n flops = analysis_results['flops_str']\n params = analysis_results['params_str']\n activations = analysis_results['activations_str']\n\n split_line = '=' * 30\n print(f'{split_line}\\nInput shape: {input_shape}\\n'\n f'Flops: {flops}\\nParams: {params}\\n{split_line}\\n')\n if args.activations:\n print(f'Activations: {activations}\\n{split_line}\\n')\n if args.out_table:\n print(analysis_results['out_table'], '\\n')\n if args.out_arch:\n print(analysis_results['out_arch'], '\\n')\n\n if len(input_shape) == 4:\n print('!!!If your network computes N frames in one forward pass, you '\n 'may want to divide the FLOPs by N to get the average FLOPs '\n 'for each frame.')\n print('!!!Please be cautious if you use the results in papers. '\n 'You may need to check if all ops are supported and verify that the '\n 'flops computation is correct.')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/analysis_tools/print_config.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\n\nfrom mmengine import Config, DictAction\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Print the whole config')\n parser.add_argument('config', help='config file path')\n parser.add_argument(\n '--cfg-options',\n nargs='+',\n action=DictAction,\n help='override some settings in the used config, the key-value pair '\n 'in xxx=yyy format will be merged into config file. If the value to '\n 'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n 'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n 'Note that the quotation marks are necessary and that no white space '\n 'is allowed.')\n args = parser.parse_args()\n\n return args\n\n\ndef main():\n args = parse_args()\n\n cfg = Config.fromfile(args.config)\n if args.cfg_options is not None:\n cfg.merge_from_dict(args.cfg_options)\n print(f'Config:\\n{cfg.pretty_text}')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/cpu_train.sh", "content": "#!/usr/bin/env bash\n\nCONFIG=$1\nGPUS=$2\nNNODES=${NNODES:-1}\nNODE_RANK=${NODE_RANK:-0}\nPORT=${PORT:-29500}\nMASTER_ADDR=${MASTER_ADDR:-\"127.0.0.1\"}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\npython $(dirname \"$0\")/train.py \\\n $CONFIG \\\n --launcher none ${@:3}\n" }, { "path": "tools/dataset_converters/bgm/preprocess_bgm_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nfrom itertools import cycle\n\nimport mmengine\n\n\ndef generate_json(data_root, seg_root, bg_root, all_data):\n \"\"\"Generate training json list for Background Matting video dataset.\n\n Args:\n data_root (str): Background Matting video data root.\n seg_root (str): Segmentation of Background Matting video data root.\n bg_root (str): Background video data root.\n all_data (bool): Whether use the last 80 frames of each video. If True,\n the last 80 frames will be added to training json. In the original\n Background Matting github repo, due to the use of motion cue, the\n last 80 frames is not used.\n \"\"\"\n # use fixed-camera data to train the Background Matting model\n video_root = osp.join(data_root, 'fixed-camera/train')\n if seg_root is None:\n seg_root = video_root\n if bg_root is None:\n bg_root = osp.join(data_root, 'background')\n\n video_dirs = [\n entry for entry in os.listdir(video_root)\n if osp.isdir(osp.join(video_root, entry))\n ]\n bg_dirs = [\n entry for entry in os.listdir(bg_root)\n if osp.isdir(osp.join(bg_root, entry))\n ]\n\n # create an iterator that loops over all the background video frames\n bg_frames = []\n for bg_dir in bg_dirs:\n bg_frames.extend([\n osp.join(bg_root, bg_dir, f)\n for f in sorted(mmengine.scandir(osp.join(bg_root, bg_dir)))\n ])\n bg_stream = cycle(bg_frames)\n\n data_infos = []\n\n for video_dir in video_dirs:\n video_full_path = osp.join(video_root, video_dir)\n seg_full_path = osp.join(seg_root, video_dir)\n num_frames = len(\n list(mmengine.scandir(video_full_path, suffix='_img.png')))\n # In the original Background Matting github repo, the\n # last 80 frames is not used.\n effective_frames = num_frames if all_data else num_frames - 80\n for i in range(1, effective_frames + 1):\n # Though it's not composited, to be consistent with adobe data,\n # we call the captured image `merged`.\n # Since background video may not be under the same directory as\n # the Background Matting video data, we use full path in Background\n # Matting dataset annotation file.\n merged = osp.join(video_full_path, f'{i:04d}_img.png')\n seg = osp.join(seg_full_path, f'{i:04d}_masksDL.png')\n bg = video_full_path + '.png'\n bg_sup = next(bg_stream)\n data_info = dict(\n merged_path=merged,\n seg_path=seg,\n bg_path=bg,\n bg_sup_path=bg_sup)\n data_infos.append(data_info)\n save_json_path = 'fixed_camera_train.json'\n mmengine.dump(data_infos, osp.join(data_root, save_json_path))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare Background Matting video dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('data_root', help='Background Matting video data root')\n parser.add_argument(\n '--seg-root',\n help='Segmentation of Background Matting video data root. If not '\n 'specified, it will be considered segmentation results are placed in '\n 'the video frames folder just as the original repo.')\n parser.add_argument(\n '--bg-root',\n help='Background video data root. If not specified, it will use the '\n 'three background videos in the Captured_Data folder.')\n parser.add_argument(\n '--all-data',\n action='store_true',\n help='Also use the last 80 frames of each video')\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n if not osp.exists(args.data_root):\n raise FileNotFoundError(f'{args.data_root} does not exist!')\n\n print('generating Background Matting dataset annotation file...')\n generate_json(args.data_root, args.seg_root, args.bg_root, args.all_data)\n print('annotation file generated...')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/dataset_converters/celeba-hq/README.md", "content": "# Preparing CelebA-HQ Dataset\n\n\n\n```bibtex\n@article{karras2017progressive,\n title={Progressive growing of gans for improved quality, stability, and variation},\n author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},\n journal={arXiv preprint arXiv:1710.10196},\n year={2017}\n}\n```\n\nFollow the instructions [here](https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training) to prepare the dataset.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── CelebA-HQ\n│ │ ├── train_256\n| | ├── test_256\n| | ├── train_celeba_img_list.txt\n| | ├── val_celeba_img_list.txt\n\n```\n" }, { "path": "tools/dataset_converters/celeba-hq/README_zh-CN.md", "content": "# 准备 CelebA-HQ 数据集\n\n\n\n```bibtex\n@article{karras2017progressive,\n title={Progressive growing of gans for improved quality, stability, and variation},\n author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},\n journal={arXiv preprint arXiv:1710.10196},\n year={2017}\n}\n```\n\n请按照[此处](https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training)准备数据集。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── CelebA-HQ\n│ │ ├── train_256\n| | ├── test_256\n| | ├── train_celeba_img_list.txt\n| | ├── val_celeba_img_list.txt\n\n```\n" }, { "path": "tools/dataset_converters/classic5/README.md", "content": "# Preparing Classic5 Dataset\n\n\n\n```bibtex\n@article{zhang2017beyond,\n title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},\n author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},\n journal={IEEE Transactions on Image Processing},\n year={2017},\n volume={26},\n number={7},\n pages={3142-3155},\n}\n```\n\nThe test datasets can be download from [here](https://github.com/cszn/DnCNN/tree/master/testsets).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── Classic5\n```\n" }, { "path": "tools/dataset_converters/classic5/README_zh-CN.md", "content": "# 准备 Classic5 数据集\n\n\n\n```bibtex\n@article{zhang2017beyond,\n title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},\n author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},\n journal={IEEE Transactions on Image Processing},\n year={2017},\n volume={26},\n number={7},\n pages={3142-3155},\n}\n```\n\n测试数据集可以从 [此处](https://github.com/cszn/DnCNN/tree/master/testsets) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── Classic5\n```\n" }, { "path": "tools/dataset_converters/comp1k/README.md", "content": "# Preparing Composition-1k Dataset\n\n## Introduction\n\n\n\n```bibtex\n@inproceedings{xu2017deep,\n title={Deep image matting},\n author={Xu, Ning and Price, Brian and Cohen, Scott and Huang, Thomas},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2970--2979},\n year={2017}\n}\n```\n\nThe Adobe Composition-1k dataset consists of foreground images and their corresponding alpha images.\nTo get the full dataset, one need to composite the foregrounds with selected backgrounds from the COCO dataset and the Pascal VOC dataset.\n\n## Obtain and Extract\n\nPlease follow the instructions of [paper authors](https://sites.google.com/view/deepimagematting) to obtain the Composition-1k (comp1k) dataset.\n\n## Composite the full dataset\n\nThe Adobe composition-1k dataset contains only `alpha` and `fg` (and `trimap` in test set).\nIt is needed to merge `fg` with COCO data (training) or VOC data (test) before training or evaluation.\nUse the following script to perform image composition and generate annotation files for training or testing:\n\n```shell\n# The script is run under the root folder of MMagic\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --composite\n```\n\nThe generated data is stored under `adobe_composition-1k/Training_set` and `adobe_composition-1k/Test_set` respectively.\nIf you only want to composite test data (since compositing training data is time-consuming), you can skip compositing the training set by removing the `--composite` option:\n\n```shell\n# skip compositing training set\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit\n```\n\nIf you only want to preprocess test data, i.e. for FBA, you can skip the train set by adding the `--skip-train` option:\n\n```shell\n# skip preprocessing training set\npython tools/data/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --skip-train\n```\n\n> Currently, `GCA` and `FBA` support online composition of training data. But you can modify the data pipeline of other models to perform online composition instead of loading composited images (we called it `merged` in our data pipeline).\n\n## Check Directory Structure for DIM\n\nThe result folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── adobe_composition-1k\n│ │ ├── Test_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── trimaps\n│ │ │ ├── merged (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── Training_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ ├── Other\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ ├── merged (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── test_list.json (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── training_list.json (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ ├── coco\n│ │ ├── train2014 (or train2017)\n│ ├── VOCdevkit\n│ │ ├── VOC2012\n```\n\n## Prepare the dataset for FBA\n\nFBA adopts dynamic dataset augmentation proposed in [Learning-base Sampling for Natural Image Matting](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning-Based_Sampling_for_Natural_Image_Matting_CVPR_2019_paper.pdf).\nIn addition, to reduce artifacts during augmentation, it uses the extended version of foreground as foreground.\nWe provide scripts to estimate foregrounds.\n\nPrepare the test set as follows:\n\n```shell\n# skip preprocessing training set, as it composites online during training\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --skip-train\n```\n\nExtend the foreground of training set as follows:\n\n```shell\npython tools/dataset_converters/matting/comp1k/extend_fg.py data/adobe_composition-1k\n```\n\n## Check Directory Structure for DIM\n\nThe final folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── adobe_composition-1k\n│ │ ├── Test_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── trimaps\n│ │ │ ├── merged (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── Training_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── fg_extended (generated by tools/data/matting/comp1k/extend_fg.py)\n│ │ │ ├── Other\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── fg_extended (generated by tools/data/matting/comp1k/extend_fg.py)\n│ │ ├── test_list.json (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── training_list_fba.json (generated by tools/data/matting/comp1k/extend_fg.py)\n│ ├── coco\n│ │ ├── train2014 (or train2017)\n│ ├── VOCdevkit\n│ │ ├── VOC2012\n```\n" }, { "path": "tools/dataset_converters/comp1k/README_zh-CN.md", "content": "# 准备 Composition-1k 数据集\n\n## 介绍\n\n\n\n```bibtex\n@inproceedings{xu2017deep,\n title={Deep image matting},\n author={Xu, Ning and Price, Brian and Cohen, Scott and Huang, Thomas},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2970--2979},\n year={2017}\n}\n```\n\nAdobe Composition-1k 数据集由前景图像及其相应的 alpha 图像组成。要获得完整的数据集，需要将前景与来自 COCO 数据集和 Pascal VOC 数据集的选定背景进行合成。\n\n## 获取和提取\n\n请按照 [论文作者](https://sites.google.com/view/deepimagematting) 的说明获取 Composition-1k (comp1k) 数据集。\n\n## 合成完整数据集\n\nAdobe composition-1k 数据集仅包含 `alpha` 和 `fg`（以及测试集中的 `trimap`）。在训练或评估之前，需要将 `fg` 与 COCO 数据（训练）或 VOC 数据（测试）合并。使用以下脚本执行图像合成并生成用于训练或测试的注释文件：\n\n```shell\n# 在 MMagic 的根文件夹下运行脚本\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --composite\n```\n\n生成的数据分别存储在 “adobe_composition-1k/Training_set” 和 “adobe_composition-1k/Test_set” 下。如果你只想合成测试数据（因为合成训练数据很耗时），你可以通过删除 `--composite` 选项来跳过合成训练集：\n\n```shell\n# 跳过合成训练集\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit\n```\n\n如果您只想预处理测试数据，即对于 FBA，您可以通过添加 `--skip-train` 选项来跳过训练集：\n\n```shell\n# 跳过预处理训练集\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --skip-train\n```\n\n> 目前，`GCA` 和 `FBA` 支持在线合成训练数据。但是你可以修改其他模型的数据管道来执行在线合成，而不是加载合成图像（我们在数据管道中称之为“合并”）。\n\n## 检查 DIM 的目录结构\n\n最终的文件夹结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── adobe_composition-1k\n│ │ ├── Test_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── trimaps\n│ │ │ ├── merged (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── Training_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ ├── Other\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ ├── merged (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── test_list.json (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── training_list.json (generated by tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py)\n│ ├── coco\n│ │ ├── train2014 (or train2017)\n│ ├── VOCdevkit\n│ │ ├── VOC2012\n```\n\n## 为 FBA 准备数据集\n\nFBA采用 [Learning-base Sampling for Natural Image Matting](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning-Based_Sampling_for_Natural_Image_Matting_CVPR_2019_paper.pdf) 中提出的动态数据集增强。此外，为了减少增强过程中的伪影，它使用前景的扩展版本作为前景。我们提供脚本来估计前景。\n\n准备测试集：\n\n```shell\n# 跳过预处理训练集，因为它在训练期间在线合成\npython tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --skip-train\n```\n\n扩展训练集的前景：\n\n```shell\npython tools/dataset_converters/matting/comp1k/extend_fg.py data/adobe_composition-1k\n```\n\n## 检查 DIM 的目录结构\n\n最终的文件夹结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── adobe_composition-1k\n│ │ ├── Test_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── trimaps\n│ │ │ ├── merged (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ │ ├── bg (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── Training_set\n│ │ │ ├── Adobe-licensed images\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── fg_extended (generated by tools/data/matting/comp1k/extend_fg.py)\n│ │ │ ├── Other\n│ │ │ │ ├── alpha\n│ │ │ │ ├── fg\n│ │ │ │ ├── fg_extended (generated by tools/data/matting/comp1k/extend_fg.py)\n│ │ ├── test_list.json (generated by tools/data/matting/comp1k/preprocess_comp1k_dataset.py)\n│ │ ├── training_list_fba.json (generated by tools/data/matting/comp1k/extend_fg.py)\n│ ├── coco\n│ │ ├── train2014 (or train2017)\n│ ├── VOCdevkit\n│ │ ├── VOC2012\n```\n" }, { "path": "tools/dataset_converters/comp1k/check_extended_fg.py", "content": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n# This script checks the alpha-foreground difference between\n# the extended fg and the original fg\n\nimport glob\nimport os\nimport os.path as osp\n\nimport cv2\nimport numpy as np\n\nfolder = 'data/adobe_composition-1k/Training_set/Adobe-licensed images'\nfolder = osp.join(folder.split('/'))\nimgs = [\n os.path.splitext(os.path.basename(x))[0]\n for x in glob.glob(osp.join(folder, 'fg', '*.jpg'))\n]\n\nprint('max,avg,img')\nfor name in imgs:\n alpha = cv2.imread(\n osp.join(folder, 'alpha', '*.jpg'), cv2.IMREAD_GRAYSCALE).astype(\n np.float32)[..., None] / 255\n fg = cv2.imread(osp.join(folder, 'fg', f'{name}.jpg')).astype(np.float32)\n xt = cv2.imread(osp.join(folder, 'fg_extended',\n f'{name}.jpg')).astype(np.float32)\n diff = np.abs((fg - xt) * alpha)\n print(f'{diff.max()},{diff.mean()},\"{name}\"', flush=True)\n" }, { "path": "tools/dataset_converters/comp1k/evaluate_comp1k.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os.path as osp\nimport re\n\nimport cv2\nimport mmcv\nimport mmengine\nimport numpy as np\n\nfrom mmagic.evaluation import gauss_gradient\nfrom mmagic.utils import modify_args\n\n\ndef sad(alpha, trimap, pred_alpha):\n if alpha.ndim != 2 or trimap.ndim != 2 or pred_alpha.ndim != 2:\n raise ValueError(\n 'input alpha, trimap and pred_alpha should has two dimensions, '\n f'alpha {alpha.shape}, please check their shape: '\n f'trimap {trimap.shape}, pred_alpha {pred_alpha.shape}')\n assert (pred_alpha[trimap == 0] == 0).all()\n assert (pred_alpha[trimap == 255] == 255).all()\n alpha = alpha.astype(np.float64) / 255\n pred_alpha = pred_alpha.astype(np.float64) / 255\n sad_result = np.abs(pred_alpha - alpha).sum() / 1000\n return sad_result\n\n\ndef mse(alpha, trimap, pred_alpha):\n if alpha.ndim != 2 or trimap.ndim != 2 or pred_alpha.ndim != 2:\n raise ValueError(\n 'input alpha, trimap and pred_alpha should has two dimensions, '\n f'alpha {alpha.shape}, please check their shape: '\n f'trimap {trimap.shape}, pred_alpha {pred_alpha.shape}')\n assert (pred_alpha[trimap == 0] == 0).all()\n assert (pred_alpha[trimap == 255] == 255).all()\n alpha = alpha.astype(np.float64) / 255\n pred_alpha = pred_alpha.astype(np.float64) / 255\n weight_sum = (trimap == 128).sum()\n if weight_sum != 0:\n mse_result = ((pred_alpha - alpha)**2).sum() / weight_sum\n else:\n mse_result = 0\n return mse_result\n\n\ndef gradient_error(alpha, trimap, pred_alpha, sigma=1.4):\n \"\"\"Gradient error for evaluating alpha matte prediction.\n\n Args:\n alpha (ndarray): Ground-truth alpha matte.\n trimap (ndarray): Input trimap with its value in {0, 128, 255}.\n pred_alpha (ndarray): Predicted alpha matte.\n sigma (float): Standard deviation of the gaussian kernel. Default: 1.4.\n \"\"\"\n if alpha.ndim != 2 or trimap.ndim != 2 or pred_alpha.ndim != 2:\n raise ValueError(\n 'input alpha, trimap and pred_alpha should has two dimensions, '\n f'alpha {alpha.shape}, please check their shape: '\n f'trimap {trimap.shape}, pred_alpha {pred_alpha.shape}')\n if not ((pred_alpha[trimap == 0] == 0).all() and\n (pred_alpha[trimap == 255] == 255).all()):\n raise ValueError(\n 'pred_alpha should be masked by trimap before evaluation')\n alpha = alpha.astype(np.float64)\n pred_alpha = pred_alpha.astype(np.float64)\n alpha_normed = np.zeros_like(alpha)\n pred_alpha_normed = np.zeros_like(pred_alpha)\n cv2.normalize(alpha, alpha_normed, 1., 0., cv2.NORM_MINMAX)\n cv2.normalize(pred_alpha, pred_alpha_normed, 1., 0., cv2.NORM_MINMAX)\n\n alpha_grad = gauss_gradient(alpha_normed, sigma).astype(np.float32)\n pred_alpha_grad = gauss_gradient(pred_alpha_normed,\n sigma).astype(np.float32)\n\n grad_loss = ((alpha_grad - pred_alpha_grad)**2 * (trimap == 128)).sum()\n # same as SAD, divide by 1000 to reduce the magnitude of the result\n return grad_loss / 1000\n\n\ndef connectivity(alpha, trimap, pred_alpha, step=0.1):\n \"\"\"Connectivity error for evaluating alpha matte prediction.\n\n Args:\n alpha (ndarray): Ground-truth alpha matte with shape (height, width).\n Value range of alpha is [0, 255].\n trimap (ndarray): Input trimap with shape (height, width). Elements\n in trimap are one of {0, 128, 255}.\n pred_alpha (ndarray): Predicted alpha matte with shape (height, width).\n Value range of pred_alpha is [0, 255].\n step (float): Step of threshold when computing intersection between\n `alpha` and `pred_alpha`.\n \"\"\"\n if alpha.ndim != 2 or trimap.ndim != 2 or pred_alpha.ndim != 2:\n raise ValueError(\n 'input alpha, trimap and pred_alpha should has two dimensions, '\n f'alpha {alpha.shape}, please check their shape: '\n f'trimap {trimap.shape}, pred_alpha {pred_alpha.shape}')\n if not ((pred_alpha[trimap == 0] == 0).all() and\n (pred_alpha[trimap == 255] == 255).all()):\n raise ValueError(\n 'pred_alpha should be masked by trimap before evaluation')\n alpha = alpha.astype(np.float32) / 255\n pred_alpha = pred_alpha.astype(np.float32) / 255\n\n thresh_steps = np.arange(0, 1 + step, step)\n round_down_map = -np.ones_like(alpha)\n for i in range(1, len(thresh_steps)):\n alpha_thresh = alpha >= thresh_steps[i]\n pred_alpha_thresh = pred_alpha >= thresh_steps[i]\n intersection = (alpha_thresh & pred_alpha_thresh).astype(np.uint8)\n\n # connected components\n _, output, stats, _ = cv2.connectedComponentsWithStats(\n intersection, connectivity=4)\n # start from 1 in dim 0 to exclude background\n size = stats[1:, -1]\n\n # largest connected component of the intersection\n omega = np.zeros_like(alpha)\n if len(size) != 0:\n max_id = np.argmax(size)\n # plus one to include background\n omega[output == max_id + 1] = 1\n\n mask = (round_down_map == -1) & (omega == 0)\n round_down_map[mask] = thresh_steps[i - 1]\n round_down_map[round_down_map == -1] = 1\n\n alpha_diff = alpha - round_down_map\n pred_alpha_diff = pred_alpha - round_down_map\n # only calculate difference larger than or equal to 0.15\n alpha_phi = 1 - alpha_diff * (alpha_diff >= 0.15)\n pred_alpha_phi = 1 - pred_alpha_diff * (pred_alpha_diff >= 0.15)\n\n connectivity_error = np.sum(\n np.abs(alpha_phi - pred_alpha_phi) * (trimap == 128))\n # same as SAD, divide by 1000 to reduce the magnitude of the result\n return connectivity_error / 1000\n\n\ndef evaluate_one(args):\n \"\"\"Function to evaluate one sample of data.\n\n Args:\n args (tuple): Information needed to evaluate one sample of data.\n\n Returns:\n dict: The evaluation results including sad, mse, gradient error and\n connectivity error.\n \"\"\"\n pred_alpha_path, alpha_path, trimap_path = args\n pred_alpha = mmcv.imread(pred_alpha_path, flag='grayscale')\n alpha = mmcv.imread(alpha_path, flag='grayscale')\n if trimap_path is None:\n trimap = np.ones_like(alpha)\n else:\n trimap = mmcv.imread(trimap_path, flag='grayscale')\n sad_result = sad(alpha, trimap, pred_alpha)\n mse_result = mse(alpha, trimap, pred_alpha)\n grad_result = gradient_error(alpha, trimap, pred_alpha)\n conn_result = connectivity(alpha, trimap, pred_alpha)\n return (sad_result, mse_result, grad_result, conn_result)\n\n\ndef evaluate(pred_root, gt_root, trimap_root, verbose, nproc):\n \"\"\"Evaluate test results of Adobe composition-1k dataset.\n\n There are 50 different ground truth foregrounds and alpha mattes pairs,\n each of the foreground will be composited with 20 different backgrounds,\n producing 1000 images for testing. In some repo, the ground truth alpha\n matte will be copied 20 times and named the same as the images. This\n function accept both original alpha matte folder (contains 50 ground\n truth alpha mattes) and copied alpha matte folder (contains 1000 ground\n truth alpha mattes) for `gt_root`.\n\n Example of copied name:\n ```\n alpha_matte1.png -> alpha_matte1_0.png\n alpha_matte1_1.png\n ...\n alpha_matte1_19.png\n alpha_matte1_20.png\n ```\n\n Args:\n pred_root (str): Path to the predicted alpha matte folder.\n gt_root (str): Path to the ground truth alpha matte folder.\n trimap_root (str): Path to the predicted alpha matte folder.\n verbose (bool): Whether print result for each predicted alpha matte.\n nproc (int): number of processes.\n \"\"\"\n\n images = sorted(mmengine.scandir(pred_root))\n gt_files_num = len(list(mmengine.scandir(gt_root)))\n # If ground truth alpha mattes are not copied (number of files is 50), we\n # use the below pattern to recover the name of the original alpha matte.\n if gt_files_num == 50:\n pattern = re.compile(r'(.+)_(?:\\d+)(.png)')\n pairs = []\n for img in images:\n pred_alpha_path = osp.join(pred_root, img)\n # if ground truth alpha matte are not copied, recover the original name\n if gt_files_num == 50:\n groups = pattern.match(img).groups()\n alpha_path = osp.join(gt_root, ''.join(groups))\n # if ground truth alpha matte are copied, the name should be the same\n else: # gt_files_num == 1000\n alpha_path = osp.join(gt_root, img)\n trimap_path = (\n osp.join(trimap_root, img) if trimap_root is not None else None)\n pairs.append((pred_alpha_path, alpha_path, trimap_path))\n\n results = mmengine.track_parallel_progress(evaluate_one, pairs, nproc)\n\n if verbose:\n # for sad_result, mse_result, grad_result, conn_result in results:\n for i, img in enumerate(images):\n sad_result, mse_result, grad_result, conn_result = results[i]\n print(f'{img} SAD: {sad_result:.6g} MattingMSE: {mse_result:.6g} '\n f'GRAD: {grad_result:.6g} CONN: {conn_result:.6g}')\n\n sad_mean, mse_mean, grad_mean, conn_mean = np.mean(results, axis=0)\n print(f'MEAN: SAD: {sad_mean:.6g} MattingMSE: {mse_mean:.6g} '\n f'GRAD: {grad_mean:.6g} CONN: {conn_mean:.6g}')\n\n\ndef parse_args():\n modify_args()\n parser = argparse.ArgumentParser(\n description='evaluate composition-1k prediction result')\n parser.add_argument(\n 'pred_root', help='Path to the predicted alpha matte folder')\n parser.add_argument(\n 'gt_root', help='Path to the ground truth alpha matte folder')\n parser.add_argument(\n '--trimap-root',\n help='Path to trimap folder. If not specified, '\n 'results are calculated on the full image.')\n parser.add_argument(\n '-v',\n '--verbose',\n action='store_true',\n help='Whether print result for each predicted alpha matte')\n parser.add_argument(\n '--nproc', type=int, default=4, help='number of processes')\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n\n if not osp.exists(args.pred_root):\n raise FileNotFoundError(f'pred_root {args.pred_root} not found')\n if not osp.exists(args.gt_root):\n raise FileNotFoundError(f'gt_root {args.gt_root} not found')\n\n evaluate(args.pred_root, args.gt_root, args.trimap_root, args.verbose,\n args.nproc)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/dataset_converters/comp1k/extend_fg.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nimport re\nimport subprocess\n\nimport mmengine\nimport numpy as np\nfrom PIL import Image\nfrom pymatting import estimate_foreground_ml, load_image\n\n\ndef fix_png_file(filename, folder):\n \"\"\"Fix png files in the target filename using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n filename (str): png file to run pngfix.\n \"\"\"\n subprocess.call(\n f'pngfix --quiet --strip=color --prefix=fixed_ \"{filename}\"',\n cwd=f'{folder}',\n shell=True)\n subprocess.call(\n f'mv \"fixed_{filename}\" \"{filename}\"', cwd=f'{folder}', shell=True)\n\n\ndef join_first_contain(directories, filename, data_root):\n \"\"\"Join the first directory that contains the file.\n\n Args:\n directories (list[str]): Directories to search for the file.\n filename (str): The target filename.\n data_root (str): Root of the data path.\n \"\"\"\n for directory in directories:\n cur_path = osp.join(directory, filename)\n if osp.exists(osp.join(data_root, cur_path)):\n return cur_path\n raise FileNotFoundError(f'Cannot find {filename} in dirs {directories}')\n\n\nclass ExtendFg:\n\n def __init__(self, data_root, fg_dirs, alpha_dirs) -> None:\n self.data_root = data_root\n self.fg_dirs = fg_dirs\n self.alpha_dirs = alpha_dirs\n\n def extend(self, fg_name):\n fg_name = fg_name.strip()\n alpha_path = join_first_contain(self.alpha_dirs, fg_name,\n self.data_root)\n fg_path = join_first_contain(self.fg_dirs, fg_name, self.data_root)\n alpha_path = osp.join(self.data_root, alpha_path)\n fg_path = osp.join(self.data_root, fg_path)\n extended_path = re.sub('/fg/', '/fg_extended/', fg_path)\n extended_path = extended_path.replace('jpg', 'png')\n if not osp.exists(alpha_path):\n raise FileNotFoundError(f'{alpha_path} does not exist!')\n if not osp.exists(fg_path):\n raise FileNotFoundError(f'{fg_path} does not exist!')\n\n image = load_image(fg_path, 'RGB')\n alpha = load_image(alpha_path, 'GRAY')\n F = estimate_foreground_ml(image, alpha, return_background=False)\n fg = Image.fromarray(np.uint8(F * 255))\n fg.save(extended_path)\n fix_png_file(osp.basename(extended_path), osp.dirname(extended_path))\n data_info = dict()\n data_info['alpha_path'] = alpha_path\n data_info['fg_path'] = extended_path\n return data_info\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare Adobe composition 1k dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('data_root', help='Adobe composition 1k dataset root')\n parser.add_argument(\n '--nproc', type=int, default=4, help='number of processor')\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n if not osp.exists(args.data_root):\n raise FileNotFoundError(f'{args.data_root} does not exist!')\n\n data_root = args.data_root\n\n print('preparing training data...')\n\n dir_prefix = 'Training_set'\n fname_prefix = 'training'\n fg_dirs = [\n 'Training_set/Adobe-licensed images/fg', 'Training_set/Other/fg'\n ]\n alpha_dirs = [\n 'Training_set/Adobe-licensed images/alpha', 'Training_set/Other/alpha'\n ]\n extended_dirs = [\n 'Training_set/Adobe-licensed images/fg_extended',\n 'Training_set/Other/fg_extended'\n ]\n for p in extended_dirs:\n p = osp.join(data_root, p)\n os.makedirs(p, exist_ok=True)\n\n fg_names = osp.join(dir_prefix, f'{fname_prefix}_fg_names.txt')\n save_json_path = f'{fname_prefix}_list_fba.json'\n fg_names = open(osp.join(data_root, fg_names)).readlines()\n fg_iter = iter(fg_names)\n\n extend_fg = ExtendFg(data_root, fg_dirs, alpha_dirs)\n data_infos = mmengine.track_parallel_progress(extend_fg.extend,\n list(fg_iter), args.nproc)\n mmengine.dump(data_infos, osp.join(data_root, save_json_path))\n\n print('train done')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/dataset_converters/comp1k/filter_comp1k_anno.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os.path as osp\n\nimport mmengine\n\n\ndef generate_json(comp1k_json_path, target_list_path, save_json_path):\n data_infos = mmengine.load(comp1k_json_path)\n targets = mmengine.list_from_file(target_list_path)\n new_data_infos = []\n for data_info in data_infos:\n for target in targets:\n if data_info['alpha_path'].endswith(target):\n new_data_infos.append(data_info)\n break\n\n mmengine.dump(new_data_infos, save_json_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Filter composition-1k annotation file')\n parser.add_argument(\n 'comp1k_json_path',\n help='Path to the composition-1k dataset annotation file')\n parser.add_argument(\n 'target_list_path',\n help='Path to the file name list that need to filter out')\n parser.add_argument(\n 'save_json_path', help='Path to save the result json file')\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n\n if not osp.exists(args.comp1k_json_path):\n raise FileNotFoundError(f'{args.comp1k_json_path} does not exist!')\n if not osp.exists(args.target_list_path):\n raise FileNotFoundError(f'{args.target_list_path} does not exist!')\n\n generate_json(args.comp1k_json_path, args.target_list_path,\n args.save_json_path)\n\n print('Done!')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/dataset_converters/comp1k/preprocess_comp1k_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport math\nimport os.path as osp\nimport subprocess\nfrom itertools import chain, repeat\n\nimport mmengine\nimport numpy as np\nfrom PIL import Image\n\nfrom mmagic.utils import modify_args\n\n\ndef fix_png_files(directory):\n \"\"\"Fix png files in the target directory using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n directory (str): Directory to run pngfix.\n \"\"\"\n subprocess.call(\n 'pngfix --quiet --strip=color --prefix=fixed_ *.png',\n cwd=f'{directory}',\n shell=True)\n subprocess.call(\n 'for fixed_f in fixed_*; do mv \"$fixed_f\" \"${fixed_f:6}\"; done',\n cwd=f'{directory}',\n shell=True)\n\n\ndef fix_png_file(filename, folder):\n \"\"\"Fix png files in the target filename using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n filename (str): png file to run pngfix.\n \"\"\"\n subprocess.call(\n f'pngfix --quiet --strip=color --prefix=fixed_ \"{filename}\"',\n cwd=f'{folder}',\n shell=True)\n subprocess.call(\n f'mv \"fixed_{filename}\" \"{filename}\"', cwd=f'{folder}', shell=True)\n\n\ndef join_first_contain(directories, filename, data_root):\n \"\"\"Join the first directory that contains the file.\n\n Args:\n directories (list[str]): Directories to search for the file.\n filename (str): The target filename.\n data_root (str): Root of the data path.\n \"\"\"\n for directory in directories:\n cur_path = osp.join(directory, filename)\n if osp.exists(osp.join(data_root, cur_path)):\n return cur_path\n raise FileNotFoundError(f'Cannot find {filename} in dirs {directories}')\n\n\ndef get_data_info(args):\n \"\"\"Function to process one piece of data.\n\n Args:\n args (tuple): Information needed to process one piece of data.\n\n Returns:\n dict: The processed data info.\n \"\"\"\n name_with_postfix, source_bg_path, repeat_info, constant = args\n alpha, fg, alpha_path, fg_path = repeat_info\n data_root, composite, mode = constant\n\n if mode == 'training':\n dir_prefix = 'Training_set'\n trimap_dir = None\n elif mode == 'test':\n dir_prefix = 'Test_set'\n trimap_dir = 'Test_set/Adobe-licensed images/trimaps'\n else:\n raise KeyError(f'Unknown mode {mode}.')\n bg_path = osp.join(dir_prefix, 'bg',\n name_with_postfix).replace('.jpg', '.png')\n merged_path = osp.join(dir_prefix, 'merged',\n name_with_postfix).replace('.jpg', '.png')\n\n if not osp.exists(source_bg_path):\n raise FileNotFoundError(f'{source_bg_path} does not exist!')\n try:\n bg = Image.open(source_bg_path).convert('RGB')\n except Exception as ex:\n data_info = {\n 'alpha_path': alpha_path,\n 'fg_path': fg_path,\n 'bg_path': bg_path\n }\n print('err in ', data_info, ex)\n return data_info\n bw, bh = bg.size\n w, h = fg.size\n\n # rescale and crop bg\n wratio = float(w) / bw\n hratio = float(h) / bh\n ratio = wratio if wratio > hratio else hratio\n if ratio > 1:\n bg = bg.resize((math.ceil(bw * ratio), math.ceil(bh * ratio)),\n Image.BICUBIC)\n bg = bg.crop((0, 0, w, h))\n\n # save cropped bg and merged\n mmengine.utils.mkdir_or_exist(osp.join(data_root, dir_prefix, 'bg'))\n bgfilename = osp.join(data_root, bg_path)\n bg.save(bgfilename, 'PNG')\n fix_png_file(osp.basename(bgfilename), osp.dirname(bgfilename))\n if composite:\n merged = (fg * alpha + bg * (1. - alpha)).astype(np.uint8)\n mmengine.utils.mkdir_or_exist(\n osp.join(data_root, dir_prefix, 'merged'))\n mergedfilename = osp.join(data_root, merged_path)\n Image.fromarray(merged).save(mergedfilename, 'PNG')\n fix_png_file(osp.basename(mergedfilename), osp.dirname(mergedfilename))\n\n data_info = dict()\n data_info['alpha_path'] = alpha_path\n data_info['fg_path'] = fg_path\n data_info['bg_path'] = bg_path\n data_info['merged_path'] = merged_path\n if trimap_dir is not None:\n trimap_path = osp.join(trimap_dir, name_with_postfix)\n trimap_full_path = osp.join(data_root, trimap_path)\n if not osp.exists(trimap_full_path):\n raise FileNotFoundError(f'{trimap_full_path} does not exist!')\n data_info['trimap_path'] = trimap_path\n return data_info\n\n\ndef generate_json(data_root, source_bg_dir, composite, nproc, mode):\n \"\"\"Generate training json list or test json list.\n\n It should be noted except for `source_bg_dir`, other strings are incomplete\n relative path. When using these strings to read from or write to disk, a\n data_root is added to form a complete relative path.\n\n Args:\n data_root (str): path to Adobe composition-1k directory.\n source_bg_dir (str): source background directory.\n composite (bool): whether composite fg with bg and write to file.\n nproc (int): number of processes.\n mode (str): training or test mode.\n \"\"\"\n\n if mode == 'training':\n dir_prefix = 'Training_set'\n fname_prefix = 'training'\n num_bg = 100 # each training fg is composited with 100 bg\n fg_dirs = [\n 'Training_set/Adobe-licensed images/fg', 'Training_set/Other/fg'\n ]\n alpha_dirs = [\n 'Training_set/Adobe-licensed images/alpha',\n 'Training_set/Other/alpha'\n ]\n elif mode == 'test':\n dir_prefix = 'Test_set'\n fname_prefix = 'test'\n num_bg = 20 # each test fg is composited with 20 bg\n fg_dirs = ['Test_set/Adobe-licensed images/fg']\n alpha_dirs = ['Test_set/Adobe-licensed images/alpha']\n else:\n raise KeyError(f'Unknown mode {mode}.')\n fg_names = osp.join(dir_prefix, f'{fname_prefix}_fg_names.txt')\n bg_names = osp.join(dir_prefix, f'{fname_prefix}_bg_names.txt')\n save_json_path = f'{fname_prefix}_list.json'\n\n fg_names = open(osp.join(data_root, fg_names)).readlines()\n bg_names = open(osp.join(data_root, bg_names)).readlines()\n assert len(fg_names) * num_bg == len(bg_names)\n\n repeat_infos = []\n name_with_postfix = []\n # repeat fg and alpha num_bg time\n for fg_name in fg_names:\n fg_name = fg_name.strip()\n alpha_path = join_first_contain(alpha_dirs, fg_name, data_root)\n fg_path = join_first_contain(fg_dirs, fg_name, data_root)\n alpha_full_path = osp.join(data_root, alpha_path)\n fg_full_path = osp.join(data_root, fg_path)\n if not osp.exists(alpha_full_path):\n raise FileNotFoundError(f'{alpha_full_path} does not exist!')\n if not osp.exists(fg_full_path):\n raise FileNotFoundError(f'{fg_full_path} does not exist!')\n # to be consistent with DIM's composition code, use PIL to read images\n fg = Image.open(fg_full_path).convert('RGB')\n alpha = (\n np.array(Image.open(alpha_full_path).convert('RGB')) /\n 255. if composite else None)\n repeat_infos.append((alpha, fg, alpha_path, fg_path))\n\n for bg_idx in range(num_bg):\n name_with_postfix.append(fg_name[:-4] + '_' + str(bg_idx) +\n fg_name[-4:])\n repeat_infos = chain.from_iterable(\n (repeat(repeat_info, num_bg) for repeat_info in repeat_infos))\n source_bg_paths = []\n for bg_name in bg_names:\n bg_name = bg_name.strip()\n # in coco_2017, image names do not begin with 'COCO_train2014_'\n if '2017' in source_bg_dir:\n bg_name = bg_name[15:] # get rid of 'COCO_train2014_'\n source_bg_paths.append(osp.join(source_bg_dir, bg_name))\n constants = repeat((data_root, composite, mode), len(bg_names))\n\n data_infos = mmengine.track_parallel_progress(\n get_data_info,\n list(zip(name_with_postfix, source_bg_paths, repeat_infos, constants)),\n nproc)\n\n mmengine.dump(data_infos, osp.join(data_root, save_json_path))\n\n\ndef parse_args():\n modify_args()\n parser = argparse.ArgumentParser(\n description='Prepare Adobe composition 1k dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('data_root', help='Adobe composition 1k dataset root')\n parser.add_argument('coco_root', help='COCO2014 or COCO2017 dataset root')\n parser.add_argument('voc_root', help='VOCdevkit directory root')\n parser.add_argument(\n '--composite',\n action='store_true',\n help='whether to composite training foreground and background offline')\n parser.add_argument(\n '--nproc', type=int, default=4, help='number of processes')\n parser.add_argument(\n '--skip-train',\n action='store_true',\n help='whether to skip the training data')\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n if not osp.exists(args.data_root):\n raise FileNotFoundError(f'{args.data_root} does not exist!')\n if not osp.exists(args.coco_root):\n raise FileNotFoundError(f'{args.coco_root} does not exist!')\n if not osp.exists(args.voc_root):\n raise FileNotFoundError(f'{args.voc_root} does not exist!')\n data_root = args.data_root\n\n if not args.skip_train:\n print('preparing training data...')\n if osp.exists(osp.join(args.coco_root, 'train2017')):\n train_source_bg_dir = osp.join(args.coco_root, 'train2017')\n elif osp.exists(osp.join(args.coco_root, 'train2014')):\n train_source_bg_dir = osp.join(args.coco_root, 'train2014')\n else:\n raise FileNotFoundError(\n f'Could not find train2014 or train2017 under {args.coco_root}'\n )\n generate_json(data_root, train_source_bg_dir, args.composite,\n args.nproc, 'training')\n print('train done')\n\n fg_dir = 'Test_set/Adobe-licensed images/fg'\n alpha_dir = 'Test_set/Adobe-licensed images/alpha'\n print('fixing png of test fg')\n fix_png_files(osp.join(data_root, fg_dir))\n print('fixing png of test alpha')\n fix_png_files(osp.join(data_root, alpha_dir))\n\n print('\\npreparing test data...')\n test_source_bg_dir = osp.join(args.voc_root, 'VOC2012/JPEGImages')\n generate_json(data_root, test_source_bg_dir, True, args.nproc, 'test')\n\n print('\\nDone!')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/dataset_converters/denoising/README.md", "content": "# Preparing Denoising Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe test datasets (Set12, BSD68, CBSD68, Kodak, McMaster, Urban100) can be download from [here](https://drive.google.com/file/d/1mwMLt-niNqcQpfN_ZduG9j4k6P_ZkOl0/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── denoising_gaussian_test\n|   |   ├── Set12\n|   |   ├── BSD68\n|   |   ├── CBSD68\n|   |   ├── Kodak\n|   |   ├── McMaster\n|   |   ├── Urban100\n```\n" }, { "path": "tools/dataset_converters/denoising/README_zh-CN.md", "content": "# 准备 Denoising 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n测试数据集(Set12, BSD68, CBSD68, Kodak, McMaster, Urban100)可以从 [此处](https://drive.google.com/file/d/1P_-RAvltEoEhfT-9GrWRdpEi6NSswTs8/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── denoising_gaussian_test\n|   |   ├── Set12\n|   |   ├── BSD68\n|   |   ├── CBSD68\n|   |   ├── Kodak\n|   |   ├── McMaster\n|   |   ├── Urban100\n```\n" }, { "path": "tools/dataset_converters/deraining/README.md", "content": "# Preparing Deraining Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe test datasets (Rain100H, Rain100L, Test100, Test1200, Test2800) can be download from [here](https://drive.google.com/file/d/1P_-RAvltEoEhfT-9GrWRdpEi6NSswTs8/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── Rain100H\n|   |   ├── input\n|   |   ├── target\n|   ├── Rain100L\n|   |   ├── input\n|   |   ├── target\n|   ├── Test100\n|   |   ├── input\n|   |   ├── target\n|   ├── Test1200\n|   |   ├── input\n|   |   ├── target\n|   ├── Test2800\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/deraining/README_zh-CN.md", "content": "# 准备 Deraining 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n测试数据集(Rain100H, Rain100L, Test100, Test1200, Test2800)可以从 [此处](https://drive.google.com/file/d/1P_-RAvltEoEhfT-9GrWRdpEi6NSswTs8/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── Rain100H\n|   |   ├── input\n|   |   ├── target\n|   ├── Rain100L\n|   |   ├── input\n|   |   ├── target\n|   ├── Test100\n|   |   ├── input\n|   |   ├── target\n|   ├── Test1200\n|   |   ├── input\n|   |   ├── target\n|   ├── Test2800\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/df2k_ost/README.md", "content": "# Preparing DF2K_OST Dataset\n\n\n\n```bibtex\n@inproceedings{wang2021real,\n title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},\n author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={1905--1914},\n year={2021}\n}\n```\n\n- The DIV2K dataset can be downloaded from [here](https://data.vision.ee.ethz.ch/cvl/DIV2K/) (We use the training set only).\n- The Flickr2K dataset can be downloaded [here](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (We use the training set only).\n- The OST dataset can be downloaded [here](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip) (We use the training set only).\n\nPlease first put all the images into the `GT` folder (naming does not need to be in order):\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── df2k_ost\n│ │ ├── GT\n│ │ │ ├── 0001.png\n│ │ │ ├── 0002.png\n│ │ │ ├── ...\n...\n```\n\n## Crop sub-images\n\nFor faster IO, we recommend to crop the images to sub-images. We provide such a script:\n\n```shell\npython tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-root ./data/df2k_ost\n```\n\nThe generated data is stored under `df2k_ost` and the data structure is as follows, where `_sub` indicates the sub-images.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── df2k_ost\n│ │ ├── GT\n│ │ ├── GT_sub\n│ │ ├── meta_info_df2k_ost.txt\n...\n```\n\n## Prepare annotation list\n\nIf you use the annotation mode for the dataset, you first need to prepare a specific `txt` file.\n\nEach line in the annotation file contains the image names and image shape (usually for the ground-truth images), separated by a white space.\n\nExample of an annotation file:\n\n```text\n0001_s001.png (480,480,3)\n0001_s002.png (480,480,3)\n```\n\nNote that `preprocess_df2k_ost_dataset.py` will generate default annotation files.\n\n## Prepare LMDB dataset for DF2K_OST\n\nIf you want to use LMDB datasets for faster IO speed, you can make LMDB files by:\n\n```shell\npython tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-root ./data/df2k_ost --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/df2k_ost/README_zh-CN.md", "content": "# 准备 DF2K_OST 数据集\n\n\n\n```bibtex\n@inproceedings{wang2021real,\n title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},\n author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={1905--1914},\n year={2021}\n}\n```\n\n- DIV2K 数据集可以在 [这里](https://data.vision.ee.ethz.ch/cvl/DIV2K/) 下载 (我们只使用训练集)。\n- Flickr2K 数据集可以在 [这里](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) 下载 (我们只使用训练集)。\n- OST 数据集可以在 [这里](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip) 下载 (我们只使用训练集)。\n\n请先将所有图片放入 `GT` 文件夹（命名不需要按顺序）：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── df2k_ost\n│ │ ├── GT\n│ │ │ ├── 0001.png\n│ │ │ ├── 0002.png\n│ │ │ ├── ...\n...\n```\n\n## 裁剪子图像\n\n为了更快的 IO，我们建议将图像裁剪为子图像。 我们提供了这样一个脚本：\n\n```shell\npython tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-root ./data/df2k_ost\n```\n\n生成的数据存放在 `df2k_ost` 下，数据结构如下，其中 `_sub` 表示子图像。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── df2k_ost\n│ │ ├── GT\n│ │ ├── GT_sub\n│ │ ├── meta_info_df2k_ost.txt\n...\n```\n\n## 准备标注列表文件\n\n如果您想使用`标注模式`来处理数据集，需要先准备一个 `txt` 格式的标注文件。\n\n标注文件中的每一行包含了图片名以及图片尺寸（这些通常是 ground-truth 图片），这两个字段用空格间隔开。\n\n标注文件示例:\n\n```text\n0001_s001.png (480,480,3)\n0001_s002.png (480,480,3)\n```\n\n请注意，`preprocess_df2k_ost_dataset.py` 脚本默认生成一份标注文件。\n\n## Prepare LMDB dataset for DF2K_OST\n\n如果你想使用 LMDB 数据集来获得更快的 IO 速度，你可以通过以下方式制作 LMDB 文件：\n\n```shell\npython tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-root ./data/df2k_ost --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nimport sys\nfrom multiprocessing import Pool\n\nimport cv2\nimport lmdb\nimport mmcv\nimport mmengine\nimport numpy as np\n\n\ndef generate_anno_file(args):\n \"\"\"Generate annotation file for DF2K_OST datasets from the ground-truth\n folder.\"\"\"\n\n print('Generate annotation files ...')\n txt_file = osp.join(args.data_root, args.anno_path)\n mmengine.utils.mkdir_or_exist(osp.dirname(txt_file))\n img_list = sorted(os.listdir(osp.join(args.data_root, 'GT_sub')))\n with open(txt_file, 'w') as f:\n for img in img_list:\n f.write(f'{img} ({args.crop_size}, {args.crop_size}, 3)\\n')\n\n\ndef main_extract_subimages(args):\n \"\"\"A multi-thread tool to crop large images to sub-images for faster IO.\n\n It is used for DF2K_OST dataset.\n\n opt (dict): Configuration dict. It contains:\n n_thread (int): Thread number.\n compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9.\n A higher value means a smaller size and longer compression time.\n Use 0 for faster CPU decompression. Default: 3, same in cv2.\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is lower\n than thresh_size will be dropped.\n \"\"\"\n\n opt = {}\n opt['n_thread'] = args.n_thread\n opt['compression_level'] = args.compression_level\n\n # HR images\n opt['input_folder'] = osp.join(args.data_root, 'GT')\n opt['save_folder'] = osp.join(args.data_root, 'GT_sub')\n opt['crop_size'] = args.crop_size\n opt['step'] = args.step\n opt['thresh_size'] = args.thresh_size\n extract_subimages(opt)\n\n\ndef extract_subimages(opt):\n \"\"\"Crop images to subimages.\n\n Args:\n opt (dict): Configuration dict. It contains:\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n n_thread (int): Thread number.\n \"\"\"\n input_folder = opt['input_folder']\n save_folder = opt['save_folder']\n if not osp.exists(save_folder):\n os.makedirs(save_folder)\n print(f'mkdir {save_folder} ...')\n else:\n print(f'Folder {save_folder} already exists. Exit.')\n sys.exit(1)\n\n img_list = list(mmengine.scandir(input_folder, suffix='png'))\n img_list = [osp.join(input_folder, v) for v in img_list]\n\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(opt['n_thread'])\n for path in img_list:\n pool.apply_async(\n worker, args=(path, opt), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef worker(path, opt):\n \"\"\"Worker for each process.\n\n Args:\n path (str): Image path.\n opt (dict): Configuration dict. It contains:\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is smaller\n than thresh_size will be dropped.\n save_folder (str): Path to save folder.\n compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n crop_size = opt['crop_size']\n step = opt['step']\n thresh_size = opt['thresh_size']\n img_name, extension = osp.splitext(osp.basename(path))\n img = mmcv.imread(path, flag='unchanged')\n\n if img.ndim == 2 or img.ndim == 3:\n h, w = img.shape[:2]\n else:\n raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')\n\n h_space = np.arange(0, h - crop_size + 1, step)\n if h - (h_space[-1] + crop_size) > thresh_size:\n h_space = np.append(h_space, h - crop_size)\n w_space = np.arange(0, w - crop_size + 1, step)\n if w - (w_space[-1] + crop_size) > thresh_size:\n w_space = np.append(w_space, w - crop_size)\n\n index = 0\n for x in h_space:\n for y in w_space:\n index += 1\n cropped_img = img[x:x + crop_size, y:y + crop_size, ...]\n cv2.imwrite(\n osp.join(opt['save_folder'],\n f'{img_name}_s{index:03d}{extension}'), cropped_img,\n [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])\n process_info = f'Processing {img_name} ...'\n return process_info\n\n\ndef make_lmdb_for_df2k_ost(data_root):\n \"\"\"Create lmdb files for DF2K_OST dataset.\n\n Args:\n data_root (str): Data root path.\n \"\"\"\n\n folder_paths = [\n osp.join(data_root, 'df2k_ost/GT_sub'),\n ]\n lmdb_paths = [\n osp.join(data_root, 'df2k_ost/GT_sub.lmdb'),\n ]\n\n for folder_path, lmdb_path in zip(folder_paths, lmdb_paths):\n img_path_list, keys = prepare_keys_df2k_ost(folder_path)\n make_lmdb(folder_path, lmdb_path, img_path_list, keys)\n\n\ndef prepare_keys_df2k_ost(folder_path):\n \"\"\"Prepare image path list and keys for DF2K_OST dataset.\n\n Args:\n folder_path (str): Folder path.\n\n Returns:\n list[str]: Image path list.\n list[str]: Key list.\n \"\"\"\n\n print('Reading image path list ...')\n img_path_list = sorted(\n list(mmengine.scandir(folder_path, suffix='png', recursive=False)))\n keys = [img_path.split('.png')[0] for img_path in sorted(img_path_list)]\n\n return img_path_list, keys\n\n\ndef make_lmdb(data_path,\n lmdb_path,\n img_path_list,\n keys,\n batch=5000,\n compress_level=1,\n multiprocessing_read=False,\n n_thread=40):\n \"\"\"Make lmdb.\n\n Contents of lmdb. The file structure is:\n example.lmdb\n ├── data.mdb\n ├── lock.mdb\n ├── meta_info.txt\n\n The data.mdb and lock.mdb are standard lmdb files and you can refer to\n https://lmdb.readthedocs.io/en/release/ for more details.\n\n The meta_info.txt is a specified txt file to record the meta information\n of our datasets. It will be automatically created when preparing\n datasets by our provided dataset tools.\n Each line in the txt file records 1)image name (with extension),\n 2)image shape, and 3)compression level, separated by a white space.\n\n For example, the meta information could be:\n `000_00000000.png (720,1280,3) 1`, which means:\n 1) image name (with extension): 000_00000000.png;\n 2) image shape: (720,1280,3);\n 3) compression level: 1\n\n We use the image name without extension as the lmdb key.\n\n If `multiprocessing_read` is True, it will read all the images to memory\n using multiprocessing. Thus, your server needs to have enough memory.\n\n Args:\n data_path (str): Data path for reading images.\n lmdb_path (str): Lmdb save path.\n img_path_list (str): Image path list.\n keys (str): Used for lmdb keys.\n batch (int): After processing batch images, lmdb commits.\n Default: 5000.\n compress_level (int): Compress level when encoding images. Default: 1.\n multiprocessing_read (bool): Whether use multiprocessing to read all\n the images to memory. Default: False.\n n_thread (int): For multiprocessing.\n \"\"\"\n assert len(img_path_list) == len(keys), (\n 'img_path_list and keys should have the same length, '\n f'but got {len(img_path_list)} and {len(keys)}')\n print(f'Create lmdb for {data_path}, save to {lmdb_path}...')\n print(f'Total images: {len(img_path_list)}')\n if not lmdb_path.endswith('.lmdb'):\n raise ValueError(\"lmdb_path must end with '.lmdb'.\")\n if osp.exists(lmdb_path):\n print(f'Folder {lmdb_path} already exists. Exit.')\n sys.exit(1)\n\n if multiprocessing_read:\n # read all the images to memory (multiprocessing)\n dataset = {} # use dict to keep the order for multiprocessing\n shapes = {}\n print(f'Read images with multiprocessing, #thread: {n_thread} ...')\n prog_bar = mmengine.ProgressBar(len(img_path_list))\n\n def callback(arg):\n \"\"\"get the image data and update prog_bar.\"\"\"\n key, dataset[key], shapes[key] = arg\n prog_bar.update()\n\n pool = Pool(n_thread)\n for path, key in zip(img_path_list, keys):\n pool.apply_async(\n read_img_worker,\n args=(osp.join(data_path, path), key, compress_level),\n callback=callback)\n pool.close()\n pool.join()\n print(f'Finish reading {len(img_path_list)} images.')\n\n # create lmdb environment\n # obtain data size for one image\n img = mmcv.imread(osp.join(data_path, img_path_list[0]), flag='unchanged')\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n data_size_per_img = img_byte.nbytes\n print('Data size per image is: ', data_size_per_img)\n data_size = data_size_per_img * len(img_path_list)\n env = lmdb.open(lmdb_path, map_size=data_size * 10)\n\n # write data to lmdb\n prog_bar = mmengine.ProgressBar(len(img_path_list))\n txn = env.begin(write=True)\n txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')\n for idx, (path, key) in enumerate(zip(img_path_list, keys)):\n prog_bar.update()\n key_byte = key.encode('ascii')\n if multiprocessing_read:\n img_byte = dataset[key]\n h, w, c = shapes[key]\n else:\n _, img_byte, img_shape = read_img_worker(\n osp.join(data_path, path), key, compress_level)\n h, w, c = img_shape\n\n txn.put(key_byte, img_byte)\n # write meta information\n txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\\n')\n if idx % batch == 0:\n txn.commit()\n txn = env.begin(write=True)\n txn.commit()\n env.close()\n txt_file.close()\n print('\\nFinish writing lmdb.')\n\n\ndef read_img_worker(path, key, compress_level):\n \"\"\"Read image worker.\n\n Args:\n path (str): Image path.\n key (str): Image key.\n compress_level (int): Compress level when encoding images.\n\n Returns:\n str: Image key.\n byte: Image byte.\n tuple[int]: Image shape.\n \"\"\"\n img = mmcv.imread(path, flag='unchanged')\n if img.ndim == 2:\n h, w = img.shape\n c = 1\n else:\n h, w, c = img.shape\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n return (key, img_byte, (h, w, c))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare DF2K_OST dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument(\n '--anno-path',\n nargs='?',\n default='meta_info_df2k_ost.txt',\n type=str,\n help='annotation file path')\n parser.add_argument(\n '--crop-size',\n type=int,\n nargs='?',\n default=480,\n help='cropped size for HR images')\n parser.add_argument(\n '--step',\n type=int,\n nargs='?',\n default=240,\n help='step size for HR images')\n parser.add_argument(\n '--thresh-size',\n type=int,\n nargs='?',\n default=0,\n help='threshold size for HR images')\n parser.add_argument(\n '--compression-level',\n type=int,\n nargs='?',\n default=3,\n help='compression level when save png images')\n parser.add_argument(\n '--n-thread',\n type=int,\n nargs='?',\n default=20,\n help='thread number when using multiprocessing')\n parser.add_argument(\n '--make-lmdb',\n action='store_true',\n help='whether to prepare lmdb files')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n # extract subimages\n main_extract_subimages(args)\n\n # generate annotation files\n generate_anno_file(args)\n\n # prepare lmdb files if necessary\n if args.make_lmdb:\n make_lmdb_for_df2k_ost(args.data_root)\n" }, { "path": "tools/dataset_converters/div2k/README.md", "content": "# Preparing DIV2K Dataset\n\n\n\n```bibtex\n@InProceedings{Agustsson_2017_CVPR_Workshops,\n author = {Agustsson, Eirikur and Timofte, Radu},\n title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},\n month = {July},\n year = {2017}\n}\n```\n\n- Training dataset: [DIV2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/).\n- Validation dataset: [Set5](https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u) and [Set14](https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u).\n\nNote that we merge the original val dataset (image names from 0801 to 0900) to the original train dataset (image names from 0001 to 0800). The folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ │ ├── 0001.png\n│ │ │ ├── 0002.png\n│ │ │ ├── ...\n│ │ │ ├── 0800.png\n│ │ │ ├── 0801.png\n│ │ │ ├── ...\n│ │ │ ├── 0900.png\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ ├── DIV2K_valid_HR\n│ │ ├── DIV2K_valid_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ ├── Set5\n│ │ ├── GTmod12\n│ │ ├── LRbicx2\n│ │ ├── LRbicx3\n│ │ ├── LRbicx4\n│ ├── Set14\n│ │ ├── GTmod12\n│ │ ├── LRbicx2\n│ │ ├── LRbicx3\n│ │ ├── LRbicx4\n```\n\n## Crop sub-images\n\nFor faster IO, we recommend to crop the DIV2K images to sub-images. We provide such a script:\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K\n```\n\nThe generated data is stored under `DIV2K` and the data structure is as follows, where `_sub` indicates the sub-images.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ ├── DIV2K_train_HR_sub\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ │ ├── X2_sub\n│ │ │ ├── X3_sub\n│ │ │ ├── X4_sub\n│ │ ├── DIV2K_valid_HR\n│ │ ├── ...\n│ │ ├── meta_info_DIV2K800sub_GT.txt\n│ │ ├── meta_info_DIV2K100sub_GT.txt\n...\n```\n\n## Prepare annotation list\n\nIf you use the annotation mode for the dataset, you first need to prepare a specific `txt` file.\n\nEach line in the annotation file contains the image names and image shape (usually for the ground-truth images), separated by a white space.\n\nExample of an annotation file:\n\n```text\n0001_s001.png (480,480,3)\n0001_s002.png (480,480,3)\n```\n\nNote that `preprocess_div2k_dataset` will generate default annotation files.\n\n## Prepare LMDB dataset for DIV2K\n\nIf you want to use LMDB datasets for faster IO speed, you can make LMDB files by:\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/div2k/README_zh-CN.md", "content": "# 准备 DIV2K 数据集\n\n\n\n```bibtex\n@InProceedings{Agustsson_2017_CVPR_Workshops,\n author = {Agustsson, Eirikur and Timofte, Radu},\n title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},\n month = {July},\n year = {2017}\n}\n```\n\n- 训练集: [DIV2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/).\n- 验证集: [Set5](https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u) 和 [Set14](https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u).\n\n请注意，我们将原始的验证集（文件名 0801 到 0900）合并进了原始的训练集（文件名 0001 到 0800）。文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ │ ├── 0001.png\n│ │ │ ├── 0002.png\n│ │ │ ├── ...\n│ │ │ ├── 0800.png\n│ │ │ ├── 0801.png\n│ │ │ ├── ...\n│ │ │ ├── 0900.png\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ ├── DIV2K_valid_HR\n│ │ ├── DIV2K_valid_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ ├── Set5\n│ │ ├── GTmod12\n│ │ ├── LRbicx2\n│ │ ├── LRbicx3\n│ │ ├── LRbicx4\n│ ├── Set14\n│ │ ├── GTmod12\n│ │ ├── LRbicx2\n│ │ ├── LRbicx3\n│ │ ├── LRbicx4\n```\n\n## 裁剪子图\n\n为了加快 IO，建议将 DIV2K 中的图片裁剪为一系列子图，为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K\n```\n\n生成的数据保存在 `DIV2K` 目录下，其文件结构如下所示，其中 `_sub` 表示子图:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── DIV2K\n│ │ ├── DIV2K_train_HR\n│ │ ├── DIV2K_train_HR_sub\n│ │ ├── DIV2K_train_LR_bicubic\n│ │ │ ├── X2\n│ │ │ ├── X3\n│ │ │ ├── X4\n│ │ │ ├── X2_sub\n│ │ │ ├── X3_sub\n│ │ │ ├── X4_sub\n│ │ ├── DIV2K_valid_HR\n│ │ ├── ...\n│ │ ├── meta_info_DIV2K800sub_GT.txt\n│ │ ├── meta_info_DIV2K100sub_GT.txt\n...\n```\n\n## 准备标注列表文件\n\n如果您想使用`标注模式`来处理数据集，需要先准备一个 `txt` 格式的标注文件。\n\n标注文件中的每一行包含了图片名以及图片尺寸（这些通常是 ground-truth 图片），这两个字段用空格间隔开。\n\n标注文件示例:\n\n```text\n0001_s001.png (480,480,3)\n0001_s002.png (480,480,3)\n```\n\n请注意，`preprocess_div2k_dataset` 脚本默认生成一份标注文件。\n\n## 准备 LMDB 格式的 DIV2K 数据集\n\n如果您想使用 `LMDB` 以获得更快的 IO 速度，可以通过以下脚本来构建 LMDB 文件\n\n```shell\npython tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/div2k/preprocess_div2k_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nimport re\nimport sys\nfrom multiprocessing import Pool\n\nimport cv2\nimport lmdb\nimport mmcv\nimport mmengine\nimport numpy as np\n\n\ndef generate_anno_file(args):\n \"\"\"Generate annotation file for DIV2K datasets from the ground-truth\n folder.\"\"\"\n\n print('Generate annotation files ...')\n train_file = osp.join(args.data_root, args.anno_train_path)\n test_file = osp.join(args.data_root, args.anno_test_path)\n mmengine.utils.mkdir_or_exist(osp.dirname(train_file))\n mmengine.utils.mkdir_or_exist(osp.dirname(test_file))\n img_list = sorted(\n os.listdir(osp.join(args.data_root, 'DIV2K_train_HR_sub')))\n with open(train_file, 'w') as f1, open(test_file, 'w') as f2:\n for img in img_list:\n if img[:4] < '0801':\n f1.write(f'{img} ({args.crop_size}, {args.crop_size}, 3)\\n')\n else:\n f2.write(f'{img} ({args.crop_size}, {args.crop_size}, 3)\\n')\n\n\ndef main_extract_subimages(args):\n \"\"\"A multi-thread tool to crop large images to sub-images for faster IO.\n\n It is used for DIV2K dataset.\n\n opt (dict): Configuration dict. It contains:\n n_thread (int): Thread number.\n compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9.\n A higher value means a smaller size and longer compression time.\n Use 0 for faster CPU decompression. Default: 3, same in cv2.\n\n scales (list[int]): The downsampling factors corresponding to the\n LR folders you want to process.\n Default: [2, 3, 4].\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is lower\n than thresh_size will be dropped.\n\n Usage:\n For each folder, run this script.\n By default, there are four folders to be processed for DIV2K dataset\n according to scale factor list ([2,3,4])\n DIV2K_train_HR\n DIV2K_train_LR_bicubic/X2\n DIV2K_train_LR_bicubic/X3\n DIV2K_train_LR_bicubic/X4\n After process, each sub_folder should have the same number of\n subimages. You can also specify scales by modifying the argument\n 'scales'. Remember to modify opt configurations according to your\n settings.\n \"\"\"\n\n opt = {}\n opt['n_thread'] = args.n_thread\n opt['compression_level'] = args.compression_level\n\n # HR images\n opt['input_folder'] = osp.join(args.data_root, 'DIV2K_train_HR')\n opt['save_folder'] = osp.join(args.data_root, 'DIV2K_train_HR_sub')\n opt['crop_size'] = args.crop_size\n opt['step'] = args.step\n opt['thresh_size'] = args.thresh_size\n extract_subimages(opt)\n\n for scale in args.scales:\n opt['input_folder'] = osp.join(args.data_root,\n f'DIV2K_train_LR_bicubic/X{scale}')\n opt['save_folder'] = osp.join(args.data_root,\n f'DIV2K_train_LR_bicubic/X{scale}_sub')\n opt['crop_size'] = args.crop_size // scale\n opt['step'] = args.step // scale\n opt['thresh_size'] = args.thresh_size // scale\n extract_subimages(opt)\n\n\ndef extract_subimages(opt):\n \"\"\"Crop images to subimages.\n\n Args:\n opt (dict): Configuration dict. It contains:\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n n_thread (int): Thread number.\n \"\"\"\n input_folder = opt['input_folder']\n save_folder = opt['save_folder']\n if not osp.exists(save_folder):\n os.makedirs(save_folder)\n print(f'mkdir {save_folder} ...')\n else:\n print(f'Folder {save_folder} already exists. Exit.')\n sys.exit(1)\n\n img_list = list(mmengine.scandir(input_folder))\n img_list = [osp.join(input_folder, v) for v in img_list]\n\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(opt['n_thread'])\n for path in img_list:\n pool.apply_async(\n worker, args=(path, opt), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef worker(path, opt):\n \"\"\"Worker for each process.\n\n Args:\n path (str): Image path.\n opt (dict): Configuration dict. It contains:\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is smaller\n than thresh_size will be dropped.\n save_folder (str): Path to save folder.\n compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n crop_size = opt['crop_size']\n step = opt['step']\n thresh_size = opt['thresh_size']\n img_name, extension = osp.splitext(osp.basename(path))\n\n # remove the x2, x3, x4 and x8 in the filename for DIV2K\n img_name = re.sub('x[2348]', '', img_name)\n\n img = mmcv.imread(path, flag='unchanged')\n\n if img.ndim == 2 or img.ndim == 3:\n h, w = img.shape[:2]\n else:\n raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')\n\n h_space = np.arange(0, h - crop_size + 1, step)\n if h - (h_space[-1] + crop_size) > thresh_size:\n h_space = np.append(h_space, h - crop_size)\n w_space = np.arange(0, w - crop_size + 1, step)\n if w - (w_space[-1] + crop_size) > thresh_size:\n w_space = np.append(w_space, w - crop_size)\n\n index = 0\n for x in h_space:\n for y in w_space:\n index += 1\n cropped_img = img[x:x + crop_size, y:y + crop_size, ...]\n cv2.imwrite(\n osp.join(opt['save_folder'],\n f'{img_name}_s{index:03d}{extension}'), cropped_img,\n [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])\n process_info = f'Processing {img_name} ...'\n return process_info\n\n\ndef make_lmdb_for_div2k(data_root):\n \"\"\"Create lmdb files for DIV2K dataset.\n\n Args:\n data_root (str): Data root path.\n\n Usage:\n Typically, there are four folders to be processed for DIV2K dataset.\n DIV2K_train_HR_sub\n DIV2K_train_LR_bicubic/X2_sub\n DIV2K_train_LR_bicubic/X3_sub\n DIV2K_train_LR_bicubic/X4_sub\n Remember to modify opt configurations according to your settings.\n \"\"\"\n\n folder_paths = [\n osp.join(data_root, 'DIV2K_train_HR_sub'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic/X2_sub'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic/X3_sub'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic/X4_sub')\n ]\n lmdb_paths = [\n osp.join(data_root, 'DIV2K_train_HR_sub.lmdb'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic_X2_sub.lmdb'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic_X3_sub.lmdb'),\n osp.join(data_root, 'DIV2K_train_LR_bicubic_X4_sub.lmdb')\n ]\n\n for folder_path, lmdb_path in zip(folder_paths, lmdb_paths):\n img_path_list, keys = prepare_keys_div2k(folder_path)\n make_lmdb(folder_path, lmdb_path, img_path_list, keys)\n\n\ndef prepare_keys_div2k(folder_path):\n \"\"\"Prepare image path list and keys for DIV2K dataset.\n\n Args:\n folder_path (str): Folder path.\n\n Returns:\n list[str]: Image path list.\n list[str]: Key list.\n \"\"\"\n print('Reading image path list ...')\n img_path_list = sorted(\n list(mmengine.scandir(folder_path, suffix='png', recursive=False)))\n keys = [img_path.split('.png')[0] for img_path in sorted(img_path_list)]\n\n return img_path_list, keys\n\n\ndef make_lmdb(data_path,\n lmdb_path,\n img_path_list,\n keys,\n batch=5000,\n compress_level=1,\n multiprocessing_read=False,\n n_thread=40):\n \"\"\"Make lmdb.\n\n Contents of lmdb. The file structure is:\n example.lmdb\n ├── data.mdb\n ├── lock.mdb\n ├── meta_info.txt\n\n The data.mdb and lock.mdb are standard lmdb files and you can refer to\n https://lmdb.readthedocs.io/en/release/ for more details.\n\n The meta_info.txt is a specified txt file to record the meta information\n of our datasets. It will be automatically created when preparing\n datasets by our provided dataset tools.\n Each line in the txt file records 1)image name (with extension),\n 2)image shape, and 3)compression level, separated by a white space.\n\n For example, the meta information could be:\n `000_00000000.png (720,1280,3) 1`, which means:\n 1) image name (with extension): 000_00000000.png;\n 2) image shape: (720,1280,3);\n 3) compression level: 1\n\n We use the image name without extension as the lmdb key.\n\n If `multiprocessing_read` is True, it will read all the images to memory\n using multiprocessing. Thus, your server needs to have enough memory.\n\n Args:\n data_path (str): Data path for reading images.\n lmdb_path (str): Lmdb save path.\n img_path_list (str): Image path list.\n keys (str): Used for lmdb keys.\n batch (int): After processing batch images, lmdb commits.\n Default: 5000.\n compress_level (int): Compress level when encoding images. Default: 1.\n multiprocessing_read (bool): Whether use multiprocessing to read all\n the images to memory. Default: False.\n n_thread (int): For multiprocessing.\n \"\"\"\n assert len(img_path_list) == len(keys), (\n 'img_path_list and keys should have the same length, '\n f'but got {len(img_path_list)} and {len(keys)}')\n print(f'Create lmdb for {data_path}, save to {lmdb_path}...')\n print(f'Total images: {len(img_path_list)}')\n if not lmdb_path.endswith('.lmdb'):\n raise ValueError(\"lmdb_path must end with '.lmdb'.\")\n if osp.exists(lmdb_path):\n print(f'Folder {lmdb_path} already exists. Exit.')\n sys.exit(1)\n\n if multiprocessing_read:\n # read all the images to memory (multiprocessing)\n dataset = {} # use dict to keep the order for multiprocessing\n shapes = {}\n print(f'Read images with multiprocessing, #thread: {n_thread} ...')\n prog_bar = mmengine.ProgressBar(len(img_path_list))\n\n def callback(arg):\n \"\"\"get the image data and update prog_bar.\"\"\"\n key, dataset[key], shapes[key] = arg\n prog_bar.update()\n\n pool = Pool(n_thread)\n for path, key in zip(img_path_list, keys):\n pool.apply_async(\n read_img_worker,\n args=(osp.join(data_path, path), key, compress_level),\n callback=callback)\n pool.close()\n pool.join()\n print(f'Finish reading {len(img_path_list)} images.')\n\n # create lmdb environment\n # obtain data size for one image\n img = mmcv.imread(osp.join(data_path, img_path_list[0]), flag='unchanged')\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n data_size_per_img = img_byte.nbytes\n print('Data size per image is: ', data_size_per_img)\n data_size = data_size_per_img * len(img_path_list)\n env = lmdb.open(lmdb_path, map_size=data_size * 10)\n\n # write data to lmdb\n prog_bar = mmengine.ProgressBar(len(img_path_list))\n txn = env.begin(write=True)\n txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')\n for idx, (path, key) in enumerate(zip(img_path_list, keys)):\n prog_bar.update()\n key_byte = key.encode('ascii')\n if multiprocessing_read:\n img_byte = dataset[key]\n h, w, c = shapes[key]\n else:\n _, img_byte, img_shape = read_img_worker(\n osp.join(data_path, path), key, compress_level)\n h, w, c = img_shape\n\n txn.put(key_byte, img_byte)\n # write meta information\n txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\\n')\n if idx % batch == 0:\n txn.commit()\n txn = env.begin(write=True)\n txn.commit()\n env.close()\n txt_file.close()\n print('\\nFinish writing lmdb.')\n\n\ndef read_img_worker(path, key, compress_level):\n \"\"\"Read image worker.\n\n Args:\n path (str): Image path.\n key (str): Image key.\n compress_level (int): Compress level when encoding images.\n\n Returns:\n str: Image key.\n byte: Image byte.\n tuple[int]: Image shape.\n \"\"\"\n img = mmcv.imread(path, flag='unchanged')\n if img.ndim == 2:\n h, w = img.shape\n c = 1\n else:\n h, w, c = img.shape\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n return (key, img_byte, (h, w, c))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare DIV2K dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument(\n '--anno-train-path',\n nargs='?',\n default='meta_info_DIV2K800sub_GT.txt',\n type=str,\n help='annotation file path of train dataset')\n parser.add_argument(\n '--anno-test-path',\n nargs='?',\n default='meta_info_DIV2K100sub_GT.txt',\n type=str,\n help='annotation file path of test dataset')\n parser.add_argument(\n '--scales',\n nargs='*',\n default=[2, 3, 4],\n type=int,\n help='scale factor list')\n parser.add_argument(\n '--crop-size',\n nargs='?',\n default=480,\n type=int,\n help='cropped size for HR images')\n parser.add_argument(\n '--step',\n nargs='?',\n default=240,\n type=int,\n help='step size for HR images')\n parser.add_argument(\n '--thresh-size',\n nargs='?',\n default=0,\n type=int,\n help='threshold size for HR images')\n parser.add_argument(\n '--compression-level',\n nargs='?',\n default=3,\n type=int,\n help='compression level when save png images')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n parser.add_argument(\n '--make-lmdb',\n action='store_true',\n help='whether to prepare lmdb files')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n # extract subimages\n main_extract_subimages(args)\n\n # generate annotation files\n generate_anno_file(args)\n\n # prepare lmdb files if necessary\n if args.make_lmdb:\n make_lmdb_for_div2k(args.data_root)\n" }, { "path": "tools/dataset_converters/dpdd/README.md", "content": "# Preparing DPDD Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe test datasets can be download from [here](https://drive.google.com/file/d/1dDWUQ_D93XGtcywoUcZE1HOXCV4EuLyw/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── DPDD\n|   |   ├── inputL\n|   |   ├── inputR\n|   |   ├── inputC\n|   |   ├── target\n|   |   ├── indoor_labels.npy\n|   |   ├── indoor_labels.txt\n|   |   ├── outdoor_labels.npy\n|   |   ├── outdoor_labels.txt\n```\n" }, { "path": "tools/dataset_converters/dpdd/README_zh-CN.md", "content": "# 准备 DPDD 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n测试数据集可以从 [此处](https://drive.google.com/file/d/1dDWUQ_D93XGtcywoUcZE1HOXCV4EuLyw/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── DPDD\n|   |   ├── inputL\n|   |   ├── inputR\n|   |   ├── inputC\n|   |   ├── target\n|   |   ├── indoor_labels.npy\n|   |   ├── indoor_labels.txt\n|   |   ├── outdoor_labels.npy\n|   |   ├── outdoor_labels.txt\n```\n" }, { "path": "tools/dataset_converters/glean/README.md", "content": "# Preparing GLEAN Dataset\n\n\n\n```bibtex\n@InProceedings{chan2021glean,\n author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},\n title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n\n## Preparing cat_train dataset\n\n1. Download [cat dataset](http://dl.yf.io/lsun/objects/cat.zip) from [LSUN homepage](https://www.yf.io/p/lsun)\n\n2. Download [cat_train/meta_info_LSUNcat_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/cat_train/meta_info_LSUNcat_GT.txt) from [GLEAN homepage](https://github.com/ckkelvinchan/GLEAN)\n\n3. Export and downsample images\n\nExport images from lmdb file and resize the input images to the designated size. We provide such a script:\n\n```shell\npython tools/dataset_converters/glean/preprocess_cat_train_dataset.py --lmdb-path .data/cat --meta-file-path ./data/cat_train/meta_info_LSUNcat_GT.txt --out-dir ./data/cat_train\n```\n\nThe generated data is stored under `cat_train` and the folder structure is as follows.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── cat_train\n│ │ ├── GT\n│ │ ├── BIx8_down\n│ │ ├── BIx16_down\n│ │ ├── meta_info_LSUNcat_GT.txt\n...\n```\n\n## Preparing cat_test dataset\n\n1. Download [CAT dataset](https://archive.org/download/CAT_DATASET/CAT_DATASET_02.zip) from [here](https://archive.org/details/CAT_DATASET).\n\n2. Download [cat_test/meta_info_Cat100_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/cat_test/meta_info_Cat100_GT.txt) from [GLEAN homepage](https://github.com/ckkelvinchan/GLEAN)\n\n3. Downsample images\n\nResize the input images to the designated size. We provide such a script:\n\n```shell\npython tools/dataset_converters/glean/preprocess_cat_test_dataset.py --data-path ./data/CAT_03 --meta-file-path ./data/cat_test/meta_info_Cat100_GT.txt --out-dir ./data/cat_test\n```\n\nThe generated data is stored under `cat_test` and the folder structure is as follows.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── cat_test\n│ │ ├── GT\n│ │ ├── BIx8_down\n│ │ ├── BIx16_down\n│ │ ├── meta_info_Cat100_GT.txt\n...\n```\n\n## Preparing FFHQ dataset\n\n1. Download [FFHQ dataset (images1024x1024)](https://drive.google.com/drive/folders/1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL) from it's [homepage](https://github.com/NVlabs/ffhq-dataset)\n\nThen you can refactor the folder structure looks like:\n\n```text\nffhq\n├── images\n|   ├── 00000.png\n|   ├── 00001.png\n|   ├── ...\n|   ├── 69999.png\n```\n\n2. Download [ffhq/meta_info_FFHQ_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/FFHQ/meta_info_FFHQ_GT.txt) from [GLEAN homepage](https://github.com/ckkelvinchan/GLEAN)\n\n3. Downsample images\n\nResize the input images to the designated size. We provide such a script:\n\n```shell\npython tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data-root ./data/ffhq/images\n```\n\nThe generated data is stored under `ffhq` and the folder structure is as follows.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n| ├── ffhq\n| | ├── images\n│ │ ├── BIx8_down\n| | ├── BIx16_down\n| | ├── meta_info_FFHQ_GT.txt\n...\n```\n\n## Preparing CelebA-HQ dataset\n\n1. Preparing datasets following it's [homepage](https://github.com/tkarras/progressive_growing_of_gans)\n\nThen you can refactor the folder structure looks like:\n\n```text\nCelebA-HQ\n├── GT\n|   ├── 00000.png\n|   ├── 00001.png\n|   ├── ...\n|   ├── 30000.png\n```\n\n2. Download [CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt) from [GLEAN homepage](https://github.com/ckkelvinchan/GLEAN)\n\n3. Downsample images\n\nResize the input images to the designated size. We provide such a script:\n\n```shell\npython tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data-root ./data/CelebA-HQ/GT\n```\n\nThe generated data is stored under `CelebA-HQ` and the folder structure is as follows.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configsdata\n├── data\n| ├── CelebA-HQ\n| | ├── GT\n│ │ ├── BIx8_down\n| | ├── BIx16_down\n| | ├── meta_info_CelebAHQ_val100_GT.txt\n...\n```\n\n## Preparing FFHQ_CelebAHQ dataset\n\nWe merge FFHQ(`ffhq/images`) and CelebA-HQ(`CelebA-HQ/GT`) to generate FFHQ_CelebAHQ dataset.\n\nThe folder structure should looks like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n| ├── FFHQ_CelebAHQ\n| | ├── GT\n...\n```\n" }, { "path": "tools/dataset_converters/glean/README_zh-CN.md", "content": "# 准备 GLEAN 数据集\n\n\n\n```bibtex\n@InProceedings{chan2021glean,\n author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},\n title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},\n booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},\n year = {2021}\n}\n```\n\n## 准备 cat_train 数据集\n\n1. 从[LSUN 主页](https://www.yf.io/p/lsun)下载[cat 数据集](http://dl.yf.io/lsun/objects/cat.zip)。\n\n2. 从[GLEAN 主页](https://github.com/ckkelvinchan/GLEAN)下载[cat_train/meta_info_LSUNcat_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/cat_train/meta_info_LSUNcat_GT.txt)。\n\n3. 导出图像并下采样\n\n从 lmdb 文件中导出图像，并下采样到所需尺寸。为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/glean/preprocess_cat_train_dataset.py --lmdb-path .data/cat --meta-file-path ./data/cat_train/meta_info_LSUNcat_GT.txt --out-dir ./data/cat_train\n```\n\n生成的数据存储在 `cat_train` 目录下，目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── cat_train\n│ │ ├── GT\n│ │ ├── BIx8_down\n│ │ ├── BIx16_down\n│ │ ├── meta_info_LSUNcat_GT.txt\n...\n```\n\n## 准备 cat_test 数据集\n\n1. 从数据集[主页](https://archive.org/details/CAT_DATASET)下载[CAT 数据集](https://archive.org/download/CAT_DATASET/CAT_DATASET_02.zip)。\n\n2. 从[GLEAN 主页](https://github.com/ckkelvinchan/GLEAN)下载[cat_test/meta_info_Cat100_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/cat_test/meta_info_Cat100_GT.txt)。\n\n3. 下采样\n\n将图像下采样到所需尺寸。为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/glean/preprocess_cat_test_dataset.py --data-path ./data/CAT_03 --meta-file-path ./data/cat_test/meta_info_Cat100_GT.txt --out-dir ./data/cat_test\n```\n\n生成的数据存储在 `cat_test` 目录下，目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── cat_test\n│ │ ├── GT\n│ │ ├── BIx8_down\n│ │ ├── BIx16_down\n│ │ ├── meta_info_Cat100_GT.txt\n...\n```\n\n## 准备 FFHQ 数据集\n\n1. 从数据集[主页](https://github.com/NVlabs/ffhq-dataset)下载[FFHQ 数据集 (images1024x1024)](https://drive.google.com/drive/folders/1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL)。\n\n将文件目录重构为如下所示：\n\n```text\nffhq\n├── images\n|   ├── 00000.png\n|   ├── 00001.png\n|   ├── ...\n|   ├── 69999.png\n```\n\n2. 从[GLEAN 主页](https://github.com/ckkelvinchan/GLEAN)下载[ffhq/meta_info_FFHQ_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/FFHQ/meta_info_FFHQ_GT.txt)。\n\n3. 下采样\n\n将图像下采样到所需尺寸。为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data-root ./data/ffhq/images\n```\n\n生成的数据存储在 `ffhq` 目录下，目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n| ├── ffhq\n| | ├── images\n│ │ ├── BIx8_down\n| | ├── BIx16_down\n| | ├── meta_info_FFHQ_GT.txt\n...\n```\n\n## 准备 CelebA-HQ 数据集\n\n1. 根据数据集[主页](https://github.com/tkarras/progressive_growing_of_gans)文档准备数据集。\n\n将文件目录重构为如下所示：\n\n```text\nCelebA-HQ\n├── GT\n|   ├── 00000.png\n|   ├── 00001.png\n|   ├── ...\n|   ├── 30000.png\n```\n\n2. 从[GLEAN 主页](https://github.com/ckkelvinchan/GLEAN)下载[CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt](https://github.com/ckkelvinchan/GLEAN/blob/main/data/CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt)。\n\n3. 下采样\n\n将图像下采样到所需尺寸。为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data-root ./data/CelebA-HQ/GT\n```\n\n生成的数据存储在 `CelebA-HQ` 目录下，目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configsdata\n├── data\n| ├── CelebA-HQ\n| | ├── GT\n│ │ ├── BIx8_down\n| | ├── BIx16_down\n| | ├── meta_info_CelebAHQ_val100_GT.txt\n...\n```\n\n## 准备 FFHQ_CelebAHQ 数据集\n\n将 FFHQ(`ffhq/images`) 和 CelebA-HQ(`CelebA-HQ/GT`) 合并，生成 FFHQ_CelebAHQ 数据集。\n\n文件目录重构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n| ├── FFHQ_CelebAHQ\n| | ├── GT\n...\n```\n" }, { "path": "tools/dataset_converters/glean/preprocess_cat_test_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nfrom multiprocessing import Pool\n\nimport mmengine\nimport numpy as np\nfrom skimage import img_as_float\nfrom skimage.io import imread, imsave\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\n\n\ndef imresize(img_path, output_path, scale=None, output_shape=None):\n \"\"\"Resize the image using MATLAB-like downsampling.\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n \"\"\"\n\n matlab_resize = MATLABLikeResize(\n keys=['data'], scale=scale, output_shape=output_shape)\n img = imread(img_path)\n img = img_as_float(img)\n data = {'data': img}\n output = matlab_resize(data)['data']\n output = np.clip(output, 0.0, 1.0) * 255\n output = np.around(output).astype(np.uint8)\n imsave(output_path, output)\n\n\ndef worker(img_name, args):\n \"\"\"Worker for each process.\n\n Args:\n img_name (str): Image filename.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n\n gt_dir = osp.join(args.out_dir, 'GT')\n bix8_dir = osp.join(args.out_dir, 'BIx8_down')\n bix16_dir = osp.join(args.out_dir, 'BIx16_down')\n\n new_img_name = img_name + '.png'\n\n imresize(\n osp.join(args.data_path, img_name + '.jpg'),\n osp.join(gt_dir, new_img_name),\n output_shape=(256, 256))\n imresize(\n osp.join(gt_dir, new_img_name),\n osp.join(bix8_dir, new_img_name),\n scale=1 / 8)\n imresize(\n osp.join(gt_dir, new_img_name),\n osp.join(bix16_dir, new_img_name),\n scale=1 / 16)\n process_info = f'Processing {new_img_name} ...'\n return process_info\n\n\ndef downsample_images(args):\n \"\"\"Downsample images for cat_test datasets from the ground-truth.\"\"\"\n\n meta_file = open(args.meta_file_path, 'r')\n img_list = [_.split('.')[0] for _ in meta_file.readlines()]\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(args.n_thread)\n for path in img_list:\n pool.apply_async(\n worker, args=(path, args), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare cat dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-path', help='dataset root')\n parser.add_argument('--meta-file-path', help='meta file path')\n parser.add_argument('--out-dir', help='output directory of dataset')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n if not osp.exists(args.out_dir):\n os.makedirs(args.out_dir)\n gt_dir = osp.join(args.out_dir, 'GT')\n bix8_dir = osp.join(args.out_dir, 'BIx8_down')\n bix16_dir = osp.join(args.out_dir, 'BIx16_down')\n if not osp.exists(gt_dir):\n os.makedirs(gt_dir)\n if not osp.exists(bix8_dir):\n os.makedirs(bix8_dir)\n if not osp.exists(bix16_dir):\n os.makedirs(bix16_dir)\n\n # downsample images\n downsample_images(args)\n" }, { "path": "tools/dataset_converters/glean/preprocess_cat_train_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nfrom multiprocessing import Pool\n\nimport lmdb\nimport mmengine\nimport numpy as np\nfrom PIL import Image\nfrom skimage import img_as_float\nfrom skimage.io import imread, imsave\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\n\n\ndef export_images(lmdb_path, meta_file_path, out_dir):\n \"\"\"Export images from lmdb file.\n\n Ref: https://github.com/fyu/lsun\n\n Args:\n lmdb_path (str): Lmdb file path.\n meta_file_path (str): Meta file path.\n out_dir (str): Output directory of dataset.\n \"\"\"\n\n print('Exporting', lmdb_path, 'to', out_dir)\n env = lmdb.open(\n lmdb_path, map_size=1099511627776, max_readers=100, readonly=True)\n meta_file = open(meta_file_path, 'r')\n img_list = [_.split('.')[0] for _ in meta_file.readlines()]\n txn = env.begin(write=False)\n cursor = txn.cursor()\n count = 0\n for key, val in cursor:\n if key.decode('ascii') in img_list:\n image_out_path = osp.join(out_dir, key.decode('ascii') + '.webp')\n with open(image_out_path, 'wb') as fp:\n fp.write(val)\n count += 1\n if count % 1000 == 0:\n print('Finished', count, 'images')\n if count > len(img_list):\n break\n print('\\nFinish exporting images.')\n\n\ndef imresize(img_path, output_path, scale=None, output_shape=None):\n \"\"\"Resize the image using MATLAB-like downsampling.\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n \"\"\"\n\n matlab_resize = MATLABLikeResize(\n keys=['data'], scale=scale, output_shape=output_shape)\n img = imread(img_path)\n img = img_as_float(img)\n data = {'data': img}\n output = matlab_resize(data)['data']\n output = np.clip(output, 0.0, 1.0) * 255\n output = np.around(output).astype(np.uint8)\n imsave(output_path, output)\n\n\ndef worker(img_name, out_dir):\n \"\"\"Worker for each process.\n\n Args:\n img_name (str): Image filename.\n out_dir (str): Output directory of dataset.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n\n _gt_dir = osp.join(out_dir, '_GT')\n gt_dir = osp.join(out_dir, 'GT')\n bix8_dir = osp.join(out_dir, 'BIx8_down')\n bix16_dir = osp.join(out_dir, 'BIx16_down')\n\n new_img_name = img_name + '.png'\n img = Image.open(osp.join(_gt_dir, img_name + '.webp'))\n img.load()\n img.save(osp.join(_gt_dir, new_img_name))\n\n imresize(\n osp.join(_gt_dir, new_img_name),\n osp.join(gt_dir, new_img_name),\n output_shape=(256, 256))\n imresize(\n osp.join(gt_dir, new_img_name),\n osp.join(bix8_dir, new_img_name),\n scale=1 / 8)\n imresize(\n osp.join(gt_dir, new_img_name),\n osp.join(bix16_dir, new_img_name),\n scale=1 / 16)\n\n process_info = f'Processing {new_img_name} ...'\n return process_info\n\n\ndef downsample_images(args):\n \"\"\"Downsample images for cat_train datasets from the ground-truth.\"\"\"\n\n meta_file = open(args.meta_file_path, 'r')\n img_list = [_.split('.')[0] for _ in meta_file.readlines()]\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(args.n_thread)\n for path in img_list:\n pool.apply_async(\n worker,\n args=(path, args.out_dir),\n callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare cat dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--lmdb-path', help='lmdb file path')\n parser.add_argument('--meta-file-path', help='meta file path')\n parser.add_argument('--out-dir', help='output directory of dataset')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n if not osp.exists(args.out_dir):\n os.makedirs(args.out_dir)\n _gt_dir = osp.join(args.out_dir, '_GT')\n gt_dir = osp.join(args.out_dir, 'GT')\n bix8_dir = osp.join(args.out_dir, 'BIx8_down')\n bix16_dir = osp.join(args.out_dir, 'BIx16_down')\n if not osp.exists(_gt_dir):\n os.makedirs(_gt_dir)\n if not osp.exists(gt_dir):\n os.makedirs(gt_dir)\n if not osp.exists(bix8_dir):\n os.makedirs(bix8_dir)\n if not osp.exists(bix16_dir):\n os.makedirs(bix16_dir)\n\n # export images\n export_images(args.lmdb_path, args.meta_file_path, _gt_dir)\n\n # downsample images\n downsample_images(args)\n" }, { "path": "tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nfrom multiprocessing import Pool\n\nimport mmengine\nimport numpy as np\nfrom skimage import img_as_float\nfrom skimage.io import imread, imsave\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\n\n\ndef imresize(img_path, output_path, scale=None, output_shape=None):\n \"\"\"Resize the image using MATLAB-like downsampling.\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n \"\"\"\n\n matlab_resize = MATLABLikeResize(\n keys=['data'], scale=scale, output_shape=output_shape)\n img = imread(img_path)\n img = img_as_float(img)\n data = {'data': img}\n output = matlab_resize(data)['data']\n output = np.clip(output, 0.0, 1.0) * 255\n output = np.around(output).astype(np.uint8)\n imsave(output_path, output)\n\n\ndef worker(img_name, args):\n \"\"\"Worker for each process.\n\n Args:\n img_name (str): Image filename.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n\n gt_dir = args.data_root\n bix8_dir = osp.join(gt_dir, '../BIx8_down')\n bix16_dir = osp.join(gt_dir, '../BIx16_down')\n imresize(\n osp.join(gt_dir, img_name), osp.join(bix8_dir, img_name), scale=1 / 8)\n imresize(\n osp.join(gt_dir, img_name),\n osp.join(bix16_dir, img_name),\n scale=1 / 16)\n process_info = f'Processing {img_name} ...'\n return process_info\n\n\ndef downsample_images(args):\n \"\"\"Downsample images from the ground-truth.\"\"\"\n\n img_list = sorted(os.listdir(args.data_root))\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(args.n_thread)\n for path in img_list:\n pool.apply_async(\n worker, args=(path, args), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare ffhq dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n bix8_dir = osp.join(args.data_root, '../BIx8_down')\n bix16_dir = osp.join(args.data_root, '../BIx16_down')\n if not osp.exists(bix16_dir):\n os.makedirs(bix16_dir)\n if not osp.exists(bix8_dir):\n os.makedirs(bix8_dir)\n\n # downsample images\n downsample_images(args)\n" }, { "path": "tools/dataset_converters/gopro/README.md", "content": "# Preparing GoPro Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe train datasets can be download from [here](https://drive.google.com/file/d/1zgALzrLCC_tcXKu_iHQTHukKUVT1aodI/). The test datasets can be download from [here](https://drive.google.com/file/d/1k6DTSHu4saUgrGTYkkZXTptILyG9RRll/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── GoPro\n|   |   ├── train\n|   |   | ├── blur\n|   |   | ├── sharp\n|   |   ├── test\n|   |   | ├── blur\n|   |   | ├── sharp\n```\n" }, { "path": "tools/dataset_converters/gopro/README_zh-CN.md", "content": "# 准备 GoPro 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n训练数据集可以从 [此处](https://drive.google.com/file/d/1zgALzrLCC_tcXKu_iHQTHukKUVT1aodI/) 下载。测试数据集可以从 [此处](https://drive.google.com/file/d/1k6DTSHu4saUgrGTYkkZXTptILyG9RRll/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── GoPro\n|   |   ├── train\n|   |   | ├── blur\n|   |   | ├── sharp\n|   |   ├── test\n|   |   | ├── blur\n|   |   | ├── sharp\n```\n" }, { "path": "tools/dataset_converters/hide/README.md", "content": "# Preparing HIDE Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe test datasets can be download from [here](https://drive.google.com/file/d/1XRomKYJF1H92g1EuD06pCQe4o6HlwB7A/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── HIDE\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/hide/README_zh-CN.md", "content": "# 准备 HIDE 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n测试数据集可以从 [此处](https://drive.google.com/file/d/1XRomKYJF1H92g1EuD06pCQe4o6HlwB7A/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── HIDE\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/live1/README.md", "content": "# Preparing LIVE1 Dataset\n\n\n\n```bibtex\n@article{zhang2017beyond,\n title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},\n author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},\n journal={IEEE Transactions on Image Processing},\n year={2017},\n volume={26},\n number={7},\n pages={3142-3155},\n}\n```\n\nThe test datasets can be download from [here](https://github.com/cszn/DnCNN/tree/master/testsets).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── LIVE1\n```\n" }, { "path": "tools/dataset_converters/live1/README_zh-CN.md", "content": "# 准备 LIVE1 数据集\n\n\n\n```bibtex\n@article{zhang2017beyond,\n title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},\n author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},\n journal={IEEE Transactions on Image Processing},\n year={2017},\n volume={26},\n number={7},\n pages={3142-3155},\n}\n```\n\n测试数据集可以从 [此处](https://github.com/cszn/DnCNN/tree/master/testsets) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── LIVE1\n```\n" }, { "path": "tools/dataset_converters/ntire21_decompression/README.md", "content": "# Preparing NTIRE21 decompression Dataset\n\n\n\n```bibtex\n@inproceedings{yang2021dataset,\n title={{NTIRE 2021} Challenge on Quality Enhancement of Compressed Video: Dataset and Study},\n author={Ren Yang and Radu Timofte},\n booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},\n year={2021}\n}\n\n```\n\nThe test datasets can be download from it's [Homepage](https://github.com/RenYang-home/NTIRE21_VEnh).\n\nPlease follows the tutorials of the [Homepage](https://github.com/RenYang-home/NTIRE21_VEnh) to generate datasets.\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── NTIRE21_decompression_track1\n|   |   ├── GT\n|   |   | ├── 001\n|   |   | | ├── 001.png\n|   |   | | ├── ...\n|   |   | ├── ...\n|   |   | ├── 010\n|   |   ├── LQ\n|   |   | ├── 001\n|   |   | | ├── 001.png\n|   |   | | ├── ...\n|   |   | ├── ...\n|   |   | ├── 010\n|   ├── NTIRE21_decompression_track2\n|   |   ├── GT\n|   |   ├── LQ\n|   ├── NTIRE21_decompression_track3\n|   |   ├── GT\n|   |   ├── LQ\n```\n" }, { "path": "tools/dataset_converters/ntire21_decompression/README_zh-CN.md", "content": "# 准备 NTIRE21 decompression 数据集\n\n\n\n```bibtex\n@inproceedings{yang2021dataset,\n title={{NTIRE 2021} Challenge on Quality Enhancement of Compressed Video: Dataset and Study},\n author={Ren Yang and Radu Timofte},\n booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},\n year={2021}\n}\n```\n\n测试数据集可以从其[主页](https://github.com/RenYang-home/NTIRE21_VEnh)下载。\n\n请按照[主页](https://github.com/RenYang-home/NTIRE21_VEnh)教程生成数据集。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── NTIRE21_decompression_track1\n|   |   ├── GT\n|   |   | ├── 001\n|   |   | | ├── 001.png\n|   |   | | ├── ...\n|   |   | ├── ...\n|   |   | ├── 010\n|   |   ├── LQ\n|   |   | ├── 001\n|   |   | | ├── 001.png\n|   |   | | ├── ...\n|   |   | ├── ...\n|   |   | ├── 010\n|   ├── NTIRE21_decompression_track2\n|   |   ├── GT\n|   |   ├── LQ\n|   ├── NTIRE21_decompression_track3\n|   |   ├── GT\n|   |   ├── LQ\n```\n" }, { "path": "tools/dataset_converters/paired-pix2pix/README.md", "content": "# Preparing Paired Dataset for Pix2pix\n\n\n\n```bibtex\n@inproceedings{isola2017image,\n title={Image-to-image translation with conditional adversarial networks},\n author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={1125--1134},\n year={2017}\n}\n```\n\nYou can download paired datasets from [here](http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/).\nThen, you need to unzip and move corresponding datasets to follow the folder structure shown above. The datasets have been well-prepared by the original authors.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── paired\n│ │ ├── facades\n│ │ ├── maps\n| | ├── edges2shoes\n| | | ├── train\n| | | ├── test\n```\n" }, { "path": "tools/dataset_converters/paired-pix2pix/README_zh-CN.md", "content": "# 为 Pix2pix 准备配对数据集\n\n\n\n```bibtex\n@inproceedings{isola2017image,\n title={Image-to-image translation with conditional adversarial networks},\n author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={1125--1134},\n year={2017}\n}\n```\n\n您可以从[此处](http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/)下载配对数据集。然后，您需要解压缩并移动相应的数据集以遵循如下所示的文件夹结构。数据集已经由原作者准备好了。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── paired\n│ │ ├── facades\n│ │ ├── maps\n| | ├── edges2shoes\n| | | ├── train\n| | | ├── test\n```\n" }, { "path": "tools/dataset_converters/paris-street-view/README.md", "content": "# Preparing Paris Street View Dataset\n\n\n\n```bibtex\n@inproceedings{pathak2016context,\n title={Context encoders: Feature learning by inpainting},\n author={Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2536--2544},\n year={2016}\n}\n```\n\nObtain the dataset [here](https://github.com/pathak22/context-encoder/issues/24).\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── paris_street_view\n│ │ ├── train\n| | ├── val\n\n```\n" }, { "path": "tools/dataset_converters/paris-street-view/README_zh-CN.md", "content": "# 准备 Paris Street View 数据集\n\n\n\n```bibtex\n@inproceedings{pathak2016context,\n title={Context encoders: Feature learning by inpainting},\n author={Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={2536--2544},\n year={2016}\n}\n```\n\n请从[此处](https://github.com/pathak22/context-encoder/issues/24)获取数据集。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── paris_street_view\n│ │ ├── train\n| | ├── val\n\n```\n" }, { "path": "tools/dataset_converters/places365/README.md", "content": "# Preparing Places365 Dataset\n\n\n\n```bibtex\n @article{zhou2017places,\n title={Places: A 10 million Image Database for Scene Recognition},\n author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n year={2017},\n publisher={IEEE}\n }\n\n```\n\nPrepare the data from [Places365](http://places2.csail.mit.edu/download.html).\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Places\n│ │ ├── data_large\n│ │ ├── val_large\n| | ├── meta\n| | | ├── places365_train_challenge.txt\n| | | ├── places365_val.txt\n\n```\n" }, { "path": "tools/dataset_converters/places365/README_zh-CN.md", "content": "# 准备 Places365 数据集\n\n\n\n```bibtex\n @article{zhou2017places,\n title={Places: A 10 million Image Database for Scene Recognition},\n author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n year={2017},\n publisher={IEEE}\n }\n\n```\n\n请从 [Places365](http://places2.csail.mit.edu/download.html) 下载并准备数据。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Places\n│ │ ├── data_large\n│ │ ├── val_large\n| | ├── meta\n| | | ├── places365_train_challenge.txt\n| | | ├── places365_val.txt\n\n```\n" }, { "path": "tools/dataset_converters/realblur/README.md", "content": "# Preparing RealBlur Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe test datasets RealBlurR can be download from [here](https://drive.google.com/file/d/1glgeWXCy7Y0qWDc0MXBTUlZYJf8984hS/).\nThe test datasets RealBlurJ can be download from [here](https://drive.google.com/file/d/1Rb1DhhXmX7IXfilQ-zL9aGjQfAAvQTrW/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── RealBlur_R\n|   |   ├── input\n|   |   ├── target\n|   ├── RealBlur_J\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/realblur/README_zh-CN.md", "content": "# 准备 RealBlur 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n测试数据集 RealBlurR 可以从 [此处](https://drive.google.com/file/d/1glgeWXCy7Y0qWDc0MXBTUlZYJf8984hS/) 下载。\n测试数据集 RealBlurJ 可以从 [此处](https://drive.google.com/file/d/1Rb1DhhXmX7IXfilQ-zL9aGjQfAAvQTrW/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── RealBlur_R\n|   |   ├── input\n|   |   ├── target\n|   ├── RealBlur_J\n|   |   ├── input\n|   |   ├── target\n```\n" }, { "path": "tools/dataset_converters/realsrset/README.md", "content": "# Preparing RealSRSet Dataset\n\n\n\n```bibtex\n@inproceedings{zhang2021designing,\n title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},\n author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},\n booktitle={IEEE International Conference on Computer Vision},\n pages={4791--4800},\n year={2021}\n}\n```\n\nThe datasets RealSRSet can be download from [here](https://github.com/cszn/BSRGAN/tree/main/testsets/RealSRSet).\n\nThe datasets RealSRSet+5images can be download from [here](https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/RealSRSet+5images.zip).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── realsrset\n|   ├── RealSRSet+5images\n```\n" }, { "path": "tools/dataset_converters/realsrset/README_zh-CN.md", "content": "# 准备 RealSRSet 数据集\n\n\n\n```bibtex\n@inproceedings{zhang2021designing,\n title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},\n author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},\n booktitle={IEEE International Conference on Computer Vision},\n pages={4791--4800},\n year={2021}\n}\n```\n\n数据集 RealSRSet 可以从 [此处](https://github.com/cszn/BSRGAN/tree/main/testsets/RealSRSet) 下载。\n\n数据集 RealSRSet+5images 可以从 [此处](https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/RealSRSet+5images.zip) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── realsrset\n|   ├── RealSRSet+5images\n```\n" }, { "path": "tools/dataset_converters/reds/README.md", "content": "# Preparing REDS Dataset\n\n\n\n```bibtex\n@InProceedings{Nah_2019_CVPR_Workshops_REDS,\n author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},\n title = {NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},\n month = {June},\n year = {2019}\n}\n```\n\n- Training dataset: [REDS dataset](https://seungjunnah.github.io/Datasets/reds.html).\n- Validation dataset: [REDS dataset](https://seungjunnah.github.io/Datasets/reds.html) and Vid4.\n\nNote that we merge train and val datasets in REDS for easy switching between REDS4 partition (used in EDVR) and the official validation partition.\nThe original val dataset (clip names from 000 to 029) are modified to avoid conflicts with training dataset (total 240 clips). Specifically, the clip names are changed to 240, 241, ... 269.\n\nYou can prepare the REDS dataset by running:\n\n```shell\npython tools/dataset_converters/reds/preprocess_reds_dataset.py --root-path ./data/REDS\n```\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── REDS\n│ │ ├── train_sharp\n│ │ │ ├── 000\n│ │ │ ├── 001\n│ │ │ ├── ...\n│ │ ├── train_sharp_bicubic\n│ │ │ ├── X4\n│ │ │ | ├── 000\n│ │ │ | ├── 001\n│ │ │ | ├── ...\n│ │ ├── meta_info_reds4_train.txt\n│ │ ├── meta_info_reds4_val.txt\n│ │ ├── meta_info_official_train.txt\n│ │ ├── meta_info_official_val.txt\n│ │ ├── meta_info_REDS_GT.txt\n│ ├── REDS4\n│ │ ├── GT\n│ │ ├── sharp_bicubic\n```\n\n## Prepare LMDB dataset for REDS\n\nIf you want to use LMDB datasets for faster IO speed, you can make LMDB files by:\n\n```shell\npython tools/dataset_converters/reds/preprocess_reds_dataset.py --root-path ./data/REDS --make-lmdb\n```\n\n## Crop to sub-images\n\nMMagic also supports cropping REDS images to sub-images for faster IO. We provide such a script:\n\n```shell\npython tools/dataset_converters/reds/crop_sub_images.py --data-root ./data/REDS -scales 4\n```\n\nThe generated data is stored under `REDS` and the data structure is as follows, where `_sub` indicates the sub-images.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── REDS\n│ │ ├── train_sharp\n│ │ │ ├── 000\n│ │ │ ├── 001\n│ │ │ ├── ...\n│ │ ├── train_sharp_sub\n│ │ │ ├── 000_s001\n│ │ │ ├── 000_s002\n│ │ │ ├── ...\n│ │ │ ├── 001_s001\n│ │ │ ├── ...\n│ │ ├── train_sharp_bicubic\n│ │ │ ├── X4\n│ │ │ │ ├── 000\n│ │ │ │ ├── 001\n│ │ │ │ ├── ...\n│ │ │ ├── X4_sub\n│ │ │ ├── 000_s001\n│ │ │ ├── 000_s002\n│ │ │ ├── ...\n│ │ │ ├── 001_s001\n│ │ │ ├── ...\n```\n\nNote that by default `preprocess_reds_dataset.py` does not make lmdb and annotation file for the cropped dataset. You may need to modify the scripts a little bit for such operations.\n" }, { "path": "tools/dataset_converters/reds/README_zh-CN.md", "content": "# 准备 REDS 数据集\n\n\n\n```bibtex\n@InProceedings{Nah_2019_CVPR_Workshops_REDS,\n author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},\n title = {NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study},\n booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},\n month = {June},\n year = {2019}\n}\n```\n\n- 训练集: [REDS 数据集](https://seungjunnah.github.io/Datasets/reds.html).\n- 验证集: [REDS 数据集](https://seungjunnah.github.io/Datasets/reds.html) 和 Vid4.\n\n请注意，我们合并了 REDS 的训练集和验证集，以便在 REDS4 划分（在 `EDVR` 中会使用到）和官方验证集划分之间切换。\n\n原始验证集的名称被修改了（clip 000 到 029），以避免与训练集发生冲突（总共 240 个 clip）。具体而言，验证集中的 clips 被改名为 240、241、... 269。\n\n可通过运行以下命令来准备 REDS 数据集:\n\n```shell\npython tools/dataset_converters/reds/preprocess_reds_dataset.py ./data/REDS\n```\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── REDS\n│ │ ├── train_sharp\n│ │ │ ├── 000\n│ │ │ ├── 001\n│ │ │ ├── ...\n│ │ ├── train_sharp_bicubic\n│ │ │ ├── X4\n│ │ │ | ├── 000\n│ │ │ | ├── 001\n│ │ │ | ├── ...\n│ │ ├── meta_info_reds4_train.txt\n│ │ ├── meta_info_reds4_val.txt\n│ │ ├── meta_info_official_train.txt\n│ │ ├── meta_info_official_val.txt\n│ │ ├── meta_info_REDS_GT.txt\n│ ├── REDS4\n│ │ ├── GT\n│ │ ├── sharp_bicubic\n```\n\n## 准备 LMDB 格式的 REDS 数据集\n\n如果您想使用 `LMDB` 以获得更快的 IO 速度，可以通过以下脚本来构建 LMDB 文件：\n\n```shell\npython tools/dataset_converters/reds/preprocess_reds_dataset.py --root-path ./data/REDS --make-lmdb\n```\n\n## 裁剪为子图\n\nMMagic 支持将 REDS 图像裁剪为子图像以加快 IO。我们提供了这样一个脚本：\n\n```shell\npython tools/dataset_converters/reds/crop_sub_images.py --data-root ./data/REDS -scales 4\n```\n\n生成的数据存储在 `REDS` 下，数据结构如下，其中`_sub`表示子图像。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── REDS\n│ │ ├── train_sharp\n│ │ │ ├── 000\n│ │ │ ├── 001\n│ │ │ ├── ...\n│ │ ├── train_sharp_sub\n│ │ │ ├── 000_s001\n│ │ │ ├── 000_s002\n│ │ │ ├── ...\n│ │ │ ├── 001_s001\n│ │ │ ├── ...\n│ │ ├── train_sharp_bicubic\n│ │ │ ├── X4\n│ │ │ │ ├── 000\n│ │ │ │ ├── 001\n│ │ │ │ ├── ...\n│ │ │ ├── X4_sub\n│ │ │ ├── 000_s001\n│ │ │ ├── 000_s002\n│ │ │ ├── ...\n│ │ │ ├── 001_s001\n│ │ │ ├── ...\n```\n\n请注意，默认情况下，`preprocess_reds_dataset.py` 不会为裁剪后的数据集制作 lmdb 和注释文件。您可能需要为此类操作稍微修改脚本。\n" }, { "path": "tools/dataset_converters/reds/crop_sub_images.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nimport re\nimport sys\nfrom multiprocessing import Pool\n\nimport cv2\nimport mmcv\nimport mmengine\nimport numpy as np\n\n\ndef worker(path, opt):\n \"\"\"Worker for each process.\n\n Args:\n path (str): Image path.\n opt (dict): Configuration dict. It contains:\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is smaller\n than thresh_size will be dropped.\n save_folder (str): Path to save folder.\n compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n crop_size = opt['crop_size']\n step = opt['step']\n thresh_size = opt['thresh_size']\n sequence, img_name = re.split(r'[\\\\/]', path)[-2:]\n img_name, extension = osp.splitext(osp.basename(path))\n\n img = mmcv.imread(path, flag='unchanged')\n\n if img.ndim == 2 or img.ndim == 3:\n h, w = img.shape[:2]\n else:\n raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')\n\n h_space = np.arange(0, h - crop_size + 1, step)\n if h - (h_space[-1] + crop_size) > thresh_size:\n h_space = np.append(h_space, h - crop_size)\n w_space = np.arange(0, w - crop_size + 1, step)\n if w - (w_space[-1] + crop_size) > thresh_size:\n w_space = np.append(w_space, w - crop_size)\n\n index = 0\n for x in h_space:\n for y in w_space:\n index += 1\n cropped_img = img[x:x + crop_size, y:y + crop_size, ...]\n sub_folder = osp.join(opt['save_folder'],\n f'{sequence}_s{index:03d}')\n mmengine.mkdir_or_exist(sub_folder)\n cv2.imwrite(\n osp.join(sub_folder, f'{img_name}{extension}'), cropped_img,\n [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])\n process_info = f'Processing {img_name} ...'\n return process_info\n\n\ndef extract_subimages(opt):\n \"\"\"Crop images to subimages.\n\n Args:\n opt (dict): Configuration dict. It contains:\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n n_thread (int): Thread number.\n \"\"\"\n input_folder = opt['input_folder']\n save_folder = opt['save_folder']\n if not osp.exists(save_folder):\n os.makedirs(save_folder)\n print(f'mkdir {save_folder} ...')\n else:\n print(f'Folder {save_folder} already exists. Exit.')\n sys.exit(1)\n\n img_list = list(mmengine.scandir(input_folder, recursive=True))\n\n img_list = [osp.join(input_folder, v) for v in img_list]\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(opt['n_thread'])\n for path in img_list:\n pool.apply_async(\n worker, args=(path, opt), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef main_extract_subimages(args):\n \"\"\"A multi-thread tool to crop large images to sub-images for faster IO.\n\n It is used for REDS dataset.\n\n opt (dict): Configuration dict. It contains:\n n_thread (int): Thread number.\n compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9.\n A higher value means a smaller size and longer compression time.\n Use 0 for faster CPU decompression. Default: 3, same in cv2.\n\n scales (list[int]): The downsampling factors corresponding to the\n LR folders you want to process.\n Default: [].\n input_folder (str): Path to the input folder.\n save_folder (str): Path to save folder.\n crop_size (int): Crop size.\n step (int): Step for overlapped sliding window.\n thresh_size (int): Threshold size. Patches whose size is lower\n than thresh_size will be dropped.\n\n Usage:\n For each folder, run this script.\n For example, if scales = [4], there are two folders to be processed:\n train_sharp\n train_sharp_bicubic/X4\n After process, each sub_folder should have the same number of\n subimages. You can also specify scales by modifying the argument\n 'scales'. Remember to modify opt configurations according to your\n settings.\n \"\"\"\n\n opt = {}\n opt['n_thread'] = args.n_thread\n opt['compression_level'] = args.compression_level\n\n # HR images\n opt['input_folder'] = osp.join(args.data_root, 'train_sharp')\n opt['save_folder'] = osp.join(args.data_root, 'train_sharp_sub')\n opt['crop_size'] = args.crop_size\n opt['step'] = args.step\n opt['thresh_size'] = args.thresh_size\n extract_subimages(opt)\n\n for scale in args.scales:\n opt['input_folder'] = osp.join(args.data_root,\n f'train_sharp_bicubic/X{scale}')\n opt['save_folder'] = osp.join(args.data_root,\n f'train_sharp_bicubic/X{scale}_sub')\n opt['crop_size'] = args.crop_size // scale\n opt['step'] = args.step // scale\n opt['thresh_size'] = args.thresh_size // scale\n extract_subimages(opt)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Preprocess REDS datasets',\n epilog='You can first download REDS datasets using the script from:'\n 'https://gist.github.com/SeungjunNah/b10d369b92840cb8dd2118dd4f41d643')\n parser.add_argument('--data-root', type=str, help='root path for REDS')\n parser.add_argument(\n '--scales', nargs='*', default=[], help='scale factor list')\n parser.add_argument(\n '--crop-size',\n type=int,\n nargs='?',\n default=480,\n help='cropped size for HR images')\n parser.add_argument(\n '--step',\n type=int,\n nargs='?',\n default=240,\n help='step size for HR images')\n parser.add_argument(\n '--thresh-size',\n type=int,\n nargs='?',\n default=0,\n help='threshold size for HR images')\n parser.add_argument(\n '--compression-level',\n type=int,\n nargs='?',\n default=3,\n help='compression level when save png images')\n parser.add_argument(\n '--n-thread',\n type=int,\n nargs='?',\n default=20,\n help='thread number when using multiprocessing')\n\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n # extract subimages\n args.scales = [int(v) for v in args.scales]\n main_extract_subimages(args)\n" }, { "path": "tools/dataset_converters/reds/preprocess_reds_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport glob\nimport os\nimport os.path as osp\nimport re\nimport shutil\nimport sys\n\nimport cv2\nimport lmdb\nimport mmcv\nimport mmengine\n\n\ndef make_lmdb(mode, data_path, lmdb_path, batch=5000, compress_level=1):\n \"\"\"Create lmdb for the REDS dataset.\n\n Contents of lmdb. The file structure is:\n example.lmdb\n ├── data.mdb\n ├── lock.mdb\n ├── meta_info.txt\n\n The data.mdb and lock.mdb are standard lmdb files and you can refer to\n https://lmdb.readthedocs.io/en/release/ for more details.\n\n The meta_info.txt is a specified txt file to record the meta information\n of our datasets. It will be automatically created when preparing\n datasets by our provided dataset tools.\n Each line in the txt file records 1)image name (with extension),\n 2)image shape, and 3)compression level, separated by a white space.\n\n For example, the meta information could be:\n `000_00000000.png (720,1280,3) 1`, which means:\n 1) image name (with extension): 000_00000000.png;\n 2) image shape: (720,1280,3);\n 3) compression level: 1\n\n We use the image name without extension as the lmdb key.\n\n Args:\n mode (str): REDS dataset mode. Choices: ['train_sharp', 'train_blur',\n 'train_blur_comp', 'train_sharp_bicubic', 'train_blur_bicubic'].\n They are used to identify different reds dataset for different\n tasks. Specifically:\n 'train_sharp': GT frames;\n 'train_blur': Blur frames for deblur task.\n 'train_blur_comp': Blur and compressed frames for deblur and\n compression task.\n 'train_sharp_bicubic': Bicubic downsampled sharp frames for SR\n task.\n 'train_blur_bicubic': Bicubic downsampled blur frames for SR task.\n data_path (str): Data path for reading images.\n lmdb_path (str): Lmdb save path.\n batch (int): After processing batch images, lmdb commits.\n Default: 5000.\n compress_level (int): Compress level when encoding images. Default: 1.\n \"\"\"\n\n print(f'Create lmdb for {data_path}, save to {lmdb_path}...')\n if mode in ['train_sharp', 'train_blur', 'train_blur_comp']:\n h_dst, w_dst = 720, 1280\n else:\n h_dst, w_dst = 180, 320\n\n if osp.exists(lmdb_path):\n print(f'Folder {lmdb_path} already exists. Exit.')\n sys.exit(1)\n\n print('Reading image path list ...')\n img_path_list = sorted(\n list(mmengine.scandir(data_path, suffix='png', recursive=True)))\n keys = []\n for img_path in img_path_list:\n parts = re.split(r'[\\\\/]', img_path)\n folder = parts[-2]\n img_name = parts[-1].split('.png')[0]\n keys.append(folder + '_' + img_name) # example: 000_00000000\n\n # create lmdb environment\n # obtain data size for one image\n img = mmcv.imread(osp.join(data_path, img_path_list[0]), flag='unchanged')\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n data_size_per_img = img_byte.nbytes\n print('Data size per image is: ', data_size_per_img)\n data_size = data_size_per_img * len(img_path_list)\n env = lmdb.open(lmdb_path, map_size=data_size * 10)\n\n # write data to lmdb\n pbar = mmengine.ProgressBar(len(img_path_list))\n txn = env.begin(write=True)\n txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')\n for idx, (path, key) in enumerate(zip(img_path_list, keys)):\n pbar.update()\n key_byte = key.encode('ascii')\n img = mmcv.imread(osp.join(data_path, path), flag='unchanged')\n h, w, c = img.shape\n _, img_byte = cv2.imencode(\n '.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n assert h == h_dst and w == w_dst and c == 3, (\n f'Wrong shape ({h, w}), should be ({h_dst, w_dst}).')\n txn.put(key_byte, img_byte)\n # write meta information\n txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\\n')\n if idx % batch == 0:\n txn.commit()\n txn = env.begin(write=True)\n txn.commit()\n env.close()\n txt_file.close()\n print('\\nFinish writing lmdb.')\n\n\ndef merge_train_val(train_path, val_path):\n \"\"\"Merge the train and val datasets of REDS.\n\n Because the EDVR uses a different validation partition, so we merge train\n and val datasets in REDS for easy switching between REDS4 partition (used\n in EDVR) and the official validation partition.\n\n The original val dataset (clip names from 000 to 029) are modified to avoid\n conflicts with training dataset (total 240 clips). Specifically, the clip\n names are changed to 240, 241, ... 269.\n\n Args:\n train_path (str): Training folder paths.\n val_path (str): Validation folder paths.\n \"\"\"\n\n print(f'Move {val_path} to {train_path}...')\n val_folders = glob.glob(osp.join(val_path, '*'))\n for folder in val_folders:\n index = int(re.split(r'[\\\\/]', folder)[-1])\n new_folder_idx = f'{index + 240:03d}'\n shutil.move(folder, osp.join(train_path, new_folder_idx))\n\n\ndef generate_anno_file(root_path, file_name='meta_info_REDS_GT.txt'):\n \"\"\"Generate anno file for REDS datasets from the ground-truth folder.\n\n Args:\n root_path (str): Root path for REDS datasets.\n \"\"\"\n\n print(f'Generate annotation files {file_name}...')\n txt_file = osp.join(root_path, file_name)\n mmengine.utils.mkdir_or_exist(osp.dirname(txt_file))\n with open(txt_file, 'w') as f:\n for i in range(270):\n for j in range(100):\n f.write(f'{i:03d}/{j:08d}.png (720, 1280, 3)\\n')\n\n\ndef split_anno_file(root_path, val_partition='REDS4'):\n \"\"\"Split anno file for REDS datasets from the ground-truth folder.\n\n Args:\n root_path (str): Root path for REDS datasets.\n val_partition (str): The way of split.\n Selected from `RED4` and `official`. Default: 'REDS4'\n \"\"\"\n\n print(f'Split annotation files in {val_partition} mode...')\n if val_partition == 'official':\n val_partition = [f'{v:03d}' for v in range(240, 270)]\n train_txt_file = osp.join(root_path, 'meta_info_official_train.txt')\n val_txt_file = osp.join(root_path, 'meta_info_official_val.txt')\n else:\n val_partition = ['000', '011', '015', '020']\n train_txt_file = osp.join(root_path, 'meta_info_reds4_train.txt')\n val_txt_file = osp.join(root_path, 'meta_info_reds4_val.txt')\n\n train_list = []\n val_list = []\n for i in range(270):\n for j in range(100):\n if f'{i:03d}' in val_partition:\n val_list.append(f'{i:03d}/{j:08d}.png (720, 1280, 3)')\n else:\n train_list.append(f'{i:03d}/{j:08d}.png (720, 1280, 3)')\n\n mmengine.utils.mkdir_or_exist(osp.dirname(train_txt_file))\n with open(train_txt_file, 'w') as f:\n f.write('\\n'.join(train_list))\n\n mmengine.utils.mkdir_or_exist(osp.dirname(val_txt_file))\n with open(val_txt_file, 'w') as f:\n f.write('\\n'.join(val_list))\n\n\ndef unzip(zip_path):\n \"\"\"Unzip zip files. It will scan all zip files in zip_path and return unzip\n folder names.\n\n Args:\n zip_path (str): Path for zip files.\n\n Returns:\n list: unzip folder names.\n \"\"\"\n zip_files = mmengine.scandir(zip_path, suffix='zip', recursive=False)\n import shutil\n import zipfile\n unzip_folders = []\n for zip_file in zip_files:\n zip_file = osp.join(zip_path, zip_file)\n unzip_folder = zip_file.replace('.zip', '').split('_part')[0]\n print(f'Unzip {zip_file} to {unzip_folder}')\n with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n zip_ref.extractall(unzip_folder)\n data_name = osp.basename(unzip_folder)\n data_type = data_name.split('_')[0]\n # if data path like `train_sharp/train/train_sharp/*`\n # begin reorganizing to `train_sharp/*`\n if osp.isdir(osp.join(unzip_folder, data_type, data_name)):\n data_folder = osp.join(unzip_folder, data_type, data_name)\n for i in os.listdir(data_folder):\n shutil.move(osp.join(data_folder, i), unzip_folder)\n shutil.rmtree(osp.join(unzip_folder, data_type))\n # end reorganizing\n unzip_folders.append(unzip_folder)\n return unzip_folders\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Preprocess REDS datasets',\n epilog='You can first download REDS datasets using the script from:'\n 'https://gist.github.com/SeungjunNah/b10d369b92840cb8dd2118dd4f41d643')\n parser.add_argument('--root-path', type=str, help='root path for REDS')\n parser.add_argument(\n '--make-lmdb', action='store_true', help='create lmdb files')\n\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n \"\"\"You can first download datasets using the scripts:\n https://gist.github.com/SeungjunNah/b10d369b92840cb8dd2118dd4f41d643.\n\n The folder structure should be like:\n REDS\n ├── train_sharp_part1.zip\n ├── train_sharp_part2.zip\n ├── train_sharp_part3.zip\n ├── train_sharp_bicubic.zip\n ├── val_sharp.zip\n ├── val_sharp_bicubic.zip\n The following are optional:\n REDS\n ├── train_blur_bicubic.zip\n ├── val_blur_bicubic.zip\n ├── train_blur_part1.zip\n ├── train_blur_part2.zip\n ├── train_blur_part3.zip\n ├── val_blur.zip\n ├── train_blur_comp_part1.zip\n ├── train_blur_comp_part2.zip\n ├── train_blur_comp_part3.zip\n ├── val_blur_comp.zip\n \"\"\"\n\n args = parse_args()\n root_path = args.root_path\n\n # unzip files and obtain available folder names\n folder_paths = unzip(root_path)\n folder_paths = set(folder_paths)\n\n train_folders = [\n osp.basename(v) for v in folder_paths if 'train' in osp.basename(v)\n ]\n\n for train_folder in train_folders:\n train_path = osp.join(root_path, train_folder)\n val_path = osp.join(root_path, train_folder.replace('train_', 'val_'))\n # folders with 'bicubic' will have subfolder X4\n if 'bicubic' in train_folder:\n train_path = osp.join(train_path, 'X4')\n val_path = osp.join(val_path, 'X4')\n # merge train and val datasets\n merge_train_val(train_path, val_path)\n\n # remove validation folders\n if 'bicubic' in train_folder:\n val_path = osp.dirname(val_path)\n print(f'Remove {val_path}')\n shutil.rmtree(val_path)\n\n # generate image list anno file\n generate_anno_file(root_path)\n split_anno_file(root_path, val_partition='RED4')\n split_anno_file(root_path, val_partition='official')\n\n # create lmdb file\n if args.make_lmdb:\n for train_folder in train_folders:\n lmdb_path = osp.join(root_path, train_folder + '.lmdb')\n data_path = osp.join(root_path, train_folder)\n if 'bicubic' in train_folder:\n data_path = osp.join(data_path, 'X4')\n make_lmdb(\n mode=train_folder, data_path=data_path, lmdb_path=lmdb_path)\n" }, { "path": "tools/dataset_converters/sidd/README.md", "content": "# Preparing SIDD Dataset\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\nThe train datasets can be download from [here](https://drive.google.com/file/d/1UHjWZzLPGweA9ZczmV8lFSRcIxqiOVJw/). The validation datasets can be download from [here](https://drive.google.com/file/d/11vfqV-lqousZTuAit1Qkqghiv_taY0KZ/).\n\nFor validation datasets, we need to export images from mat file. We provide such a script:\n\n```shell\npython tools/dataset_converters/sidd/preprocess_sidd_test_dataset.py --data-root ./data/SIDD/val --out-dir ./data/SIDD/val\n```\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── SIDD\n|   |   ├── train\n|   |   | ├── gt\n|   |   | ├── noisy\n|   |   ├── val\n|   |   | ├── gt\n|   |   | ├── noisy\n|   |   | ├── ValidationNoisyBlocksSrgb.mat\n|   |   | ├── ValidationGtBlocksSrgb.mat\n```\n" }, { "path": "tools/dataset_converters/sidd/README_zh-CN.md", "content": "# 准备 SIDD 数据集\n\n\n\n```bibtex\n@inproceedings{Zamir2021Restormer,\n title={Restormer: Efficient Transformer for High-Resolution Image Restoration},\n author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n训练数据集可以从 [此处](https://drive.google.com/file/d/1UHjWZzLPGweA9ZczmV8lFSRcIxqiOVJw/) 下载。验证数据集可以从 [此处](https://drive.google.com/file/d/11vfqV-lqousZTuAit1Qkqghiv_taY0KZ/) 下载。\n\n验证数据集需要从 mat 文件中导出，为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/sidd/preprocess_sidd_test_dataset.py --data-root ./data/SIDD/val --out-dir ./data/SIDD/val\n```\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── SIDD\n|   |   ├── train\n|   |   | ├── gt\n|   |   | ├── noisy\n|   |   ├── val\n|   |   | ├── gt\n|   |   | ├── noisy\n|   |   | ├── ValidationNoisyBlocksSrgb.mat\n|   |   | ├── ValidationGtBlocksSrgb.mat\n```\n" }, { "path": "tools/dataset_converters/sidd/preprocess_sidd_test_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\n\nimport cv2\nimport numpy as np\nimport scipy.io as sio\nimport torch\nfrom skimage import img_as_ubyte\nfrom tqdm import tqdm\n\n\ndef export_images(args):\n \"\"\"Export images from mat file.\"\"\"\n\n noisy_dir = osp.join(args.out_dir, 'noisy')\n os.makedirs(noisy_dir, exist_ok=True)\n gt_dir = osp.join(args.out_dir, 'gt')\n os.makedirs(gt_dir, exist_ok=True)\n\n noisy_matfile = osp.join(args.data_root, 'ValidationNoisyBlocksSrgb.mat')\n gt_matfile = osp.join(args.data_root, 'ValidationGtBlocksSrgb.mat')\n noisy_images = sio.loadmat(noisy_matfile)\n gt_images = sio.loadmat(gt_matfile)\n\n noisy_images = np.float32(\n np.array(noisy_images['ValidationNoisyBlocksSrgb']))\n noisy_images /= 255.\n gt_images = np.float32(np.array(gt_images['ValidationGtBlocksSrgb']))\n gt_images /= 255.\n\n # processing noisy images\n print('Exporting', noisy_matfile, 'to', noisy_dir)\n for i in tqdm(range(40)):\n for k in range(32):\n noisy_patch = torch.from_numpy(\n noisy_images[i, k, :, :, :]).unsqueeze(0).permute(0, 3, 1, 2)\n noisy_patch = torch.clamp(noisy_patch, 0,\n 1).detach().permute(0, 2, 3,\n 1).squeeze(0)\n save_path = osp.join(noisy_dir, f'val_{str(i*32+k)}_NOISY.png')\n cv2.imwrite(\n save_path,\n cv2.cvtColor(img_as_ubyte(noisy_patch), cv2.COLOR_RGB2BGR))\n\n # processing gt images\n print('Exporting', gt_matfile, 'to', gt_dir)\n for i in tqdm(range(40)):\n for k in range(32):\n gt_patch = torch.from_numpy(\n gt_images[i, k, :, :, :]).unsqueeze(0).permute(0, 3, 1, 2)\n gt_patch = torch.clamp(gt_patch, 0,\n 1).detach().permute(0, 2, 3, 1).squeeze(0)\n save_path = osp.join(gt_dir, f'val_{str(i*32+k)}_GT.png')\n cv2.imwrite(\n save_path,\n cv2.cvtColor(img_as_ubyte(gt_patch), cv2.COLOR_RGB2BGR))\n\n print('\\nFinish exporting images.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare SIDD test dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument('--out-dir', help='output directory of dataset')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n # export images\n export_images(args)\n" }, { "path": "tools/dataset_converters/spmcs/README.md", "content": "# Preparing SPMCS Dataset\n\n\n\n```bibtex\n@InProceedings{tao2017spmc,\n author={Xin Tao and Hongyun Gao and Renjie Liao and Jue Wang and Jiaya Jia},\n title = {Detail-Revealing Deep Video Super-Resolution},\n booktitle = {The IEEE International Conference on Computer Vision (ICCV)},\n month = {Oct},\n year = {2017}\n}\n```\n\nThe datasets can be download from [here](https://opendatalab.org.cn/SPMCS).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── SPMCS\n|   |   ├── GT\n|   |   ├── BIx4\n|   |   ├── BDx4\n|   |   ├── meta_info_SPMCS_GT.txt\n```\n" }, { "path": "tools/dataset_converters/spmcs/README_zh-CN.md", "content": "# 准备 SPMCS 数据集\n\n\n\n```bibtex\n@InProceedings{tao2017spmc,\n author={Xin Tao and Hongyun Gao and Renjie Liao and Jue Wang and Jiaya Jia},\n title = {Detail-Revealing Deep Video Super-Resolution},\n booktitle = {The IEEE International Conference on Computer Vision (ICCV)},\n month = {Oct},\n year = {2017}\n}\n```\n\n数据集可以从 [此处](https://opendatalab.org.cn/SPMCS) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── SPMCS\n|   |   ├── GT\n|   |   ├── BIx4\n|   |   ├── BDx4\n|   |   ├── meta_info_SPMCS_GT.txt\n```\n" }, { "path": "tools/dataset_converters/udm10/README.md", "content": "# Preparing UDM10 Dataset\n\n\n\n```bibtex\n@inproceedings{PFNL,\n title={Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations},\n author={Yi, Peng and Wang, Zhongyuan and Jiang, Kui and Jiang, Junjun and Ma, Jiayi},\n booktitle={IEEE International Conference on Computer Vision (ICCV)},\n pages={3106-3115},\n year={2019},\n}\n```\n\nThe datasets can be downloaded from [here](https://drive.google.com/file/d/1G4V4KZZhhfzUlqHiSBBuWyqLyIOvOs0W/).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── UDM10\n|   |   ├── GT\n|   |   ├── BIx4\n|   |   ├── BDx4\n```\n" }, { "path": "tools/dataset_converters/udm10/README_zh-CN.md", "content": "# 准备 UDM10 数据集\n\n\n\n```bibtex\n@inproceedings{PFNL,\n title={Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations},\n author={Yi, Peng and Wang, Zhongyuan and Jiang, Kui and Jiang, Junjun and Ma, Jiayi},\n booktitle={IEEE International Conference on Computer Vision (ICCV)},\n pages={3106-3115},\n year={2019},\n}\n```\n\n数据集可以从 [此处](https://drive.google.com/file/d/1G4V4KZZhhfzUlqHiSBBuWyqLyIOvOs0W/) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── UDM10\n|   |   ├── GT\n|   |   ├── BIx4\n|   |   ├── BDx4\n```\n" }, { "path": "tools/dataset_converters/unconditional_gans/README.md", "content": "# Unconditional GANs Datasets\n\n**Data preparation for unconditional model** is simple. What you need to do is downloading the images and put them into a directory. Next, you should set a symlink in the `data` directory. For standard unconditional gans with static architectures, like DCGAN and StyleGAN2, `UnconditionalImageDataset` is designed to train such unconditional models. Here is an example config for FFHQ dataset:\n\n```python\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Flip', keys=['img'], direction='horizontal'),\n dict(type='PackInputs', keys=['img'], meta_keys=['img_path'])\n]\n\n# `batch_size` and `data_root` need to be set.\ntrain_dataloader = dict(\n batch_size=4,\n num_workers=8,\n persistent_workers=True,\n sampler=dict(type='InfiniteSampler', shuffle=True),\n dataset=dict(\n type=dataset_type,\n data_root=None, # set by user\n pipeline=train_pipeline))\n```\n\nHere, we adopt `InfinitySampler` to avoid frequent dataloader reloading, which will accelerate the training procedure. As shown in the example, `pipeline` provides important data pipeline to process images, including loading from file system, resizing, cropping, transferring to `torch.Tensor` and packing to `DataSample`. All of supported data pipelines can be found in `mmagic/datasets/transforms`.\n\nFor unconditional GANs with dynamic architectures like PGGAN and StyleGANv1, `GrowScaleImgDataset` is recommended to use for training. Since such dynamic architectures need real images in different scales, directly adopting `UnconditionalImageDataset` will bring heavy I/O cost for loading multiple high-resolution images. Here is an example we use for training PGGAN in CelebA-HQ dataset:\n\n```python\ndataset_type = 'GrowScaleImgDataset'\n\npipeline = [\n dict(type='LoadImageFromFile', key='img'),\n dict(type='Flip', keys=['img'], direction='horizontal'),\n dict(type='PackInputs')\n]\n\n# `samples_per_gpu` and `imgs_root` need to be set.\ntrain_dataloader = dict(\n num_workers=4,\n batch_size=64,\n dataset=dict(\n type='GrowScaleImgDataset',\n data_roots={\n '1024': './data/ffhq/images',\n '256': './data/ffhq/ffhq_imgs/ffhq_256',\n '64': './data/ffhq/ffhq_imgs/ffhq_64'\n },\n gpu_samples_base=4,\n # note that this should be changed with total gpu number\n gpu_samples_per_scale={\n '4': 64,\n '8': 32,\n '16': 16,\n '32': 8,\n '64': 4,\n '128': 4,\n '256': 4,\n '512': 4,\n '1024': 4\n },\n len_per_stage=300000,\n pipeline=pipeline),\n sampler=dict(type='InfiniteSampler', shuffle=True))\n```\n\nIn this dataset, you should provide a dictionary of image paths to the `data_roots`. Thus, you should resize the images in the dataset in advance.\nFor the resizing methods in the data pre-processing, we adopt bilinear interpolation methods in all of the experiments studied in MMagic.\n\nNote that this dataset should be used with `PGGANFetchDataHook`. In this config file, this hook should be added in the customized hooks, as shown below.\n\n```python\ncustom_hooks = [\n dict(\n type='VisualizationHook',\n interval=5000,\n fixed_input=True,\n # vis ema and orig at the same time\n vis_kwargs_list=dict(\n type='Noise',\n name='fake_img',\n sample_model='ema/orig',\n target_keys=['ema', 'orig'])),\n dict(type='PGGANFetchDataHook')\n]\n```\n\nThis fetching data hook helps the dataloader update the status of dataset to change the data source and batch size during training.\n\nHere, we provide several download links of datasets frequently used in unconditional models: [LSUN](http://dl.yf.io/lsun/), [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), [CelebA-HQ](https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P), [FFHQ](https://drive.google.com/drive/folders/1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP).\n" }, { "path": "tools/dataset_converters/unpaired-cyclegan/README.md", "content": "# Preparing Unpaired Dataset for CycleGAN\n\n\n\n```bibtex\n@inproceedings{zhu2017unpaired,\n title={Unpaired image-to-image translation using cycle-consistent adversarial networks},\n author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},\n booktitle={Proceedings of the IEEE international conference on computer vision},\n pages={2223--2232},\n year={2017}\n}\n```\n\nYou can download unpaired datasets from [here](https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/).\nThen, you need to unzip and move corresponding datasets to follow the folder structure shown above. The datasets have been well-prepared by the original authors.\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── unpaired\n│ │ ├── facades\n| | ├── horse2zebra\n| | ├── summer2winter_yosemite\n| | | ├── trainA\n| | | ├── trainB\n| | | ├── testA\n| | | ├── testB\n```\n" }, { "path": "tools/dataset_converters/unpaired-cyclegan/README_zh-CN.md", "content": "# 为 CycleGAN 准备未配对数据集\n\n\n\n```bibtex\n@inproceedings{zhu2017unpaired,\n title={Unpaired image-to-image translation using cycle-consistent adversarial networks},\n author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},\n booktitle={Proceedings of the IEEE international conference on computer vision},\n pages={2223--2232},\n year={2017}\n}\n```\n\n您可以从[此处](https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/)下载未配对的数据集。然后，您需要解压缩并移动相应的数据集以遵循如上所示的文件夹结构。数据集已经由原作者准备好了。\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── unpaired\n│ │ ├── facades\n| | ├── horse2zebra\n| | ├── summer2winter_yosemite\n| | | ├── trainA\n| | | ├── trainB\n| | | ├── testA\n| | | ├── testB\n```\n" }, { "path": "tools/dataset_converters/vid4/README.md", "content": "# Preparing Vid4 Dataset\n\n\n\n```bibtex\n@article{xue2019video,\n title={On Bayesian adaptive video super resolution},\n author={Liu, Ce and Sun, Deqing},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n volume={36},\n number={2},\n pages={346--360},\n year={2013},\n publisher={IEEE}\n}\n```\n\nThe Vid4 dataset can be downloaded from [here](https://drive.google.com/file/d/1ZuvNNLgR85TV_whJoHM7uVb-XW1y70DW/view?usp=sharing). There are two degradations in the dataset.\n\n1. BIx4 contains images downsampled by bicubic interpolation\n2. BDx4 contains images blurred by Gaussian kernel with σ=1.6, followed by a subsampling every four pixels.\n\nNote that we should prepare a annotation file (such as meta_info_Vid4_GT.txt) for Vid4 dataset as follows.\n\n```text\ncalendar 41 (576,720,3)\ncity 34 (576,704,3)\nfoliage 49 (480,720,3)\nwalk 47 (480,720,3)\n```\n\nFor ToFlow, we should prepare directly upsampling dataset. We provide such a script:\n\n```shell\npython tools/dataset_converters/vid4/preprocess_vid4_dataset.py --data-root ./data/Vid4/BIx4\n```\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Vid4\n│ │ ├── GT\n│ │ │ ├── calendar\n│ │ │ ├── city\n│ │ │ ├── foliage\n│ │ │ ├── walk\n│ │ ├── BDx4\n│ │ ├── BIx4\n│ │ ├── BIx4up_direct\n│ │ ├── meta_info_Vid4_GT.txt\n```\n" }, { "path": "tools/dataset_converters/vid4/README_zh-CN.md", "content": "# 准备 Vid4 数据集\n\n\n\n```bibtex\n@article{xue2019video,\n title={On Bayesian adaptive video super resolution},\n author={Liu, Ce and Sun, Deqing},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n volume={36},\n number={2},\n pages={346--360},\n year={2013},\n publisher={IEEE}\n}\n```\n\n可以从 [此处](https://drive.google.com/file/d/1ZuvNNLgR85TV_whJoHM7uVb-XW1y70DW/view?usp=sharing) 下载 Vid4 数据集，其中包含了由两种下采样方法得到的图片：\n\n1. BIx4 包含了由双线性插值下采样得到的图片\n2. BDx4 包含了由 `σ=1.6` 的高斯核模糊，然后每4个像素进行一次采样得到的图片\n\n请注意，应为 Vid4 数据集准备一个如下所列的标注文件（例如 meta_info_Vid4_GT.txt）。\n\n```text\ncalendar 41 (576,720,3)\ncity 34 (576,704,3)\nfoliage 49 (480,720,3)\nwalk 47 (480,720,3)\n```\n\n对于 ToFlow，应准备直接上采样的数据集，为此，我们提供了一个脚本：\n\n```shell\npython tools/dataset_converters/vid4/preprocess_vid4_dataset.py --data-root ./data/Vid4/BIx4\n```\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── Vid4\n│ │ ├── GT\n│ │ │ ├── calendar\n│ │ │ ├── city\n│ │ │ ├── foliage\n│ │ │ ├── walk\n│ │ ├── BDx4\n│ │ ├── BIx4\n│ │ ├── BIx4up_direct\n│ │ ├── meta_info_Vid4_GT.txt\n```\n" }, { "path": "tools/dataset_converters/vid4/preprocess_vid4_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\nfrom multiprocessing import Pool\n\nimport mmengine\nimport numpy as np\nfrom skimage import img_as_float\nfrom skimage.io import imread, imsave\n\nfrom mmagic.datasets.transforms import MATLABLikeResize\n\n\ndef imresize(img_path, output_path, scale=None, output_shape=None):\n \"\"\"Resize the image using MATLAB-like downsampling.\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n \"\"\"\n\n matlab_resize = MATLABLikeResize(\n keys=['data'], scale=scale, output_shape=output_shape)\n img = imread(img_path)\n img = img_as_float(img)\n data = {'data': img}\n output = matlab_resize(data)['data']\n output = np.clip(output, 0.0, 1.0) * 255\n output = np.around(output).astype(np.uint8)\n imsave(output_path, output)\n\n\ndef worker(img_name, args):\n \"\"\"Worker for each process.\n\n Args:\n img_name (str): Image filename.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n\n up_dir = osp.join(args.data_root, '../BIx4up_direct')\n mmengine.utils.mkdir_or_exist(osp.dirname(osp.join(up_dir, img_name)))\n imresize(\n osp.join(args.data_root, img_name),\n osp.join(up_dir, img_name),\n scale=4)\n process_info = f'Processing {img_name} ...'\n return process_info\n\n\ndef upsample_images(args):\n \"\"\"Upsample images.\"\"\"\n\n img_list = []\n clip_list = sorted(os.listdir(args.data_root))\n for clip in clip_list:\n clip_root = osp.join(args.data_root, clip)\n img_list.extend(\n [osp.join(clip, i) for i in sorted(os.listdir(clip_root))])\n prog_bar = mmengine.ProgressBar(len(img_list))\n pool = Pool(args.n_thread)\n for path in img_list:\n pool.apply_async(\n worker, args=(path, args), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare cat dataset',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n up_dir = osp.join(args.data_root, '../BIx4up_direct')\n if not osp.exists(up_dir):\n os.makedirs(up_dir)\n\n # upsample images\n upsample_images(args)\n" }, { "path": "tools/dataset_converters/videolq/README.md", "content": "# Preparing VideoLQ Dataset\n\n\n\n```bibtex\n@inproceedings{chan2022investigating,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {Investigating Tradeoffs in Real-World Video Super-Resolution},\n booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},\n year = {2022}\n}\n```\n\nYou can download the dataset using [Dropbox](https://www.dropbox.com/sh/hc06f1livdhutbo/AAAMPy92EOqVjRN8waT0ie8ja?dl=0) / [Google Drive](https://drive.google.com/drive/folders/1-1iJRNdqdFZWOnoUU4xG1Z1QhwsGwMDy?usp=sharing) / [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/chan0899_e_ntu_edu_sg/ErSugvUBxoBMlvSAHhqT5BEB9-4ZaqxzJIcc9uvVa8JGHg?e=WpHJTc).\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── VideoLQ\n│ │ ├── 000\n│ │ │ ├── 00000000.png\n│ │ │ ├── 00000001.png\n│ │ │ ├── ...\n│ │ ├── 001\n│ │ ├── 002\n│ │ ├── ...\n```\n" }, { "path": "tools/dataset_converters/videolq/README_zh-CN.md", "content": "# 准备 VideoLQ 数据集\n\n\n\n```bibtex\n@inproceedings{chan2022investigating,\n author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},\n title = {Investigating Tradeoffs in Real-World Video Super-Resolution},\n booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},\n year = {2022}\n}\n```\n\n数据集可以从 [Dropbox](https://www.dropbox.com/sh/hc06f1livdhutbo/AAAMPy92EOqVjRN8waT0ie8ja?dl=0) / [Google Drive](https://drive.google.com/drive/folders/1-1iJRNdqdFZWOnoUU4xG1Z1QhwsGwMDy?usp=sharing) / [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/chan0899_e_ntu_edu_sg/ErSugvUBxoBMlvSAHhqT5BEB9-4ZaqxzJIcc9uvVa8JGHg?e=WpHJTc) 下载。\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n|   ├── VideoLQ\n│ │ ├── 000\n│ │ │ ├── 00000000.png\n│ │ │ ├── 00000001.png\n│ │ │ ├── ...\n│ │ ├── 001\n│ │ ├── 002\n│ │ ├── ...\n```\n" }, { "path": "tools/dataset_converters/vimeo90k/README.md", "content": "# Preparing Vimeo90K Dataset\n\n\n\n```bibtex\n@article{xue2019video,\n title={Video Enhancement with Task-Oriented Flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision (IJCV)},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n\nThe training and test datasets can be downloaded from [here](http://toflow.csail.mit.edu/).\n\nThen you can rename the directory `vimeo_septuplet/sequences` to `vimeo90k/GT`. The Vimeo90K dataset has a `clip/sequence/img` folder structure:\n\n```text\nvimeo90k\n├── GT\n│ ├── 00001\n│ │ ├── 0001\n│ │ │ ├── im1.png\n│ │ │ ├── im2.png\n│ │ │ ├── ...\n│ │ ├── 0002\n│ │ ├── 0003\n│ │ ├── ...\n│ ├── 00002\n│ ├── ...\n├── sep_trainlist.txt\n├── sep_testlist.txt\n```\n\nTo generate the downsampling images BIx4 and BDx4 and prepare the annotation file, you need to run the following command:\n\n```shell\npython tools/dataset_converters/vimeo90k/preprocess_vimeo90k_dataset.py --data-root ./data/vimeo90k\n```\n\nThe folder structure should look like:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── vimeo_triplet\n│ │ ├── GT\n│ │ │ ├── 00001\n│ │ │ │ ├── 0001\n│ │ │ │ │ ├── im1.png\n│ │ │ │ │ ├── im2.png\n│ │ │ │ │ ├── ...\n│ │ │ │ ├── 0002\n│ │ │ │ ├── 0003\n│ │ │ │ ├── ...\n│ │ │ ├── 00002\n│ │ │ ├── ...\n│ │ ├── BIx4\n│ │ ├── BDx4\n│ │ ├── meta_info_Vimeo90K_test_GT.txt\n│ │ ├── meta_info_Vimeo90K_train_GT.txt\n```\n\n## Prepare LMDB dataset for Vimeo90K\n\nIf you want to use LMDB datasets for faster IO speed, you can make LMDB files by:\n\n```shell\npython tools/dataset_converters/vimeo90k/preprocess_vimeo90k_dataset.py --data-root ./data/vimeo90k --train_list ./data/vimeo90k/sep_trainlist.txt --gt-path ./data/vimeo90k/GT --lq-path ./data/Vimeo90k/BIx4 --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/vimeo90k/README_zh-CN.md", "content": "# 准备 Vimeo90K 数据集\n\n\n\n```bibtex\n@article{xue2019video,\n title={Video Enhancement with Task-Oriented Flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision (IJCV)},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n\n训练集和测试集可以从 [此处](http://toflow.csail.mit.edu/) 下载。\n\n将数据集路径 `vimeo_septuplet/sequences` 重命名为 `vimeo90k/GT`。Vimeo90K 数据集包含了如下所示的 `clip/sequence/img` 目录结构：\n\n```text\nvimeo90k\n├── GT\n│ ├── 00001\n│ │ ├── 0001\n│ │ │ ├── im1.png\n│ │ │ ├── im2.png\n│ │ │ ├── ...\n│ │ ├── 0002\n│ │ ├── 0003\n│ │ ├── ...\n│ ├── 00002\n│ ├── ...\n├── sep_trainlist.txt\n├── sep_testlist.txt\n```\n\n为了生成下采样图像BIx4和BDx4，以及准备所需的标注文件，需要执行如下命令：\n\n```shell\npython tools/dataset_converters/vimeo90k/preprocess_vimeo90k_dataset.py --data-root ./data/vimeo90k\n```\n\n文件目录结构应如下所示：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── vimeo_triplet\n│ │ ├── GT\n│ │ │ ├── 00001\n│ │ │ │ ├── 0001\n│ │ │ │ │ ├── im1.png\n│ │ │ │ │ ├── im2.png\n│ │ │ │ │ ├── ...\n│ │ │ │ ├── 0002\n│ │ │ │ ├── 0003\n│ │ │ │ ├── ...\n│ │ │ ├── 00002\n│ │ │ ├── ...\n│ │ ├── BIx4\n│ │ ├── BDx4\n│ │ ├── meta_info_Vimeo90K_test_GT.txt\n│ │ ├── meta_info_Vimeo90K_train_GT.txt\n```\n\n## 准备 LMDB 格式的 Vimeo90K 数据集\n\n如果您想使用 `LMDB` 以获得更快的 IO 速度，可以通过以下脚本来构建 LMDB 文件\n\n```shell\npython tools/dataset_converters/vimeo90k/preprocess_vimeo90k_dataset.py --data-root ./data/vimeo90k --train_list ./data/vimeo90k/sep_trainlist.txt --gt-path ./data/vimeo90k/GT --lq-path ./data/Vimeo90k/BIx4 --make-lmdb\n```\n" }, { "path": "tools/dataset_converters/vimeo90k/preprocess_vimeo90k_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport math\nimport os\nimport os.path as osp\nimport sys\nfrom multiprocessing import Pool\n\nimport cv2\nimport lmdb\nimport mmcv\nimport mmengine\nimport numpy as np\nfrom skimage import img_as_float\nfrom skimage.io import imread, imsave\n\nfrom mmagic.datasets.transforms import MATLABLikeResize, blur_kernels\nfrom mmagic.utils import modify_args\n\n\ndef make_lmdb(mode,\n data_path,\n lmdb_path,\n train_list,\n batch=5000,\n compress_level=1):\n \"\"\"Create lmdb for the Vimeo90K dataset.\n\n Contents of lmdb. The file structure is:\n example.lmdb\n ├── data.mdb\n ├── lock.mdb\n ├── meta_info.txt\n\n The data.mdb and lock.mdb are standard lmdb files and you can refer to\n https://lmdb.readthedocs.io/en/release/ for more details.\n\n The meta_info.txt is a specified txt file to record the meta information\n of our datasets. It will be automatically created when preparing\n datasets by our provided dataset tools.\n Each line in the txt file records 1)image name (with extension),\n 2)image shape, and 3)compression level, separated by a white space.\n\n For example, the meta information could be:\n `000_00000000.png (720,1280,3) 1`, which means:\n 1) image name (with extension): 000_00000000.png;\n 2) image shape: (720,1280,3);\n 3) compression level: 1\n\n We use the image name without extension as the lmdb key.\n\n Args:\n mode (str): Dataset mode. 'gt' or 'lq'.\n data_path (str): Data path for reading images.\n lmdb_path (str): Lmdb save path.\n train_list (str): Train list path for Vimeo90K datasets.\n batch (int): After processing batch images, lmdb commits.\n Default: 5000.\n compress_level (int): Compress level when encoding images. Default: 1.\n \"\"\"\n\n print(f'Create lmdb for {data_path}, save to {lmdb_path}...')\n if mode == 'gt':\n h_dst, w_dst = 256, 448\n else:\n h_dst, w_dst = 64, 112\n\n if osp.exists(lmdb_path):\n print(f'Folder {lmdb_path} already exists. Exit.')\n sys.exit(1)\n\n print('Reading image path list ...')\n with open(train_list) as f:\n train_list = [line.strip() for line in f]\n\n all_img_list = []\n keys = []\n for line in train_list:\n folder, sub_folder = line.split('/')\n for j in range(1, 8):\n all_img_list.append(\n osp.join(data_path, folder, sub_folder, f'im{j}.png'))\n keys.append('{}_{}_{}'.format(folder, sub_folder, j))\n all_img_list = sorted(all_img_list)\n keys = sorted(keys)\n\n if mode == 'gt': # only read the 4th frame for the gt mode\n print('Only keep the 4th frame for gt mode.')\n all_img_list = [v for v in all_img_list if v.endswith('im4.png')]\n keys = [v for v in keys if v.endswith('_4')]\n\n # create lmdb environment\n # obtain data size for one image\n img = mmcv.imread(osp.join(data_path, all_img_list[0]), flag='unchanged')\n _, img_byte = cv2.imencode('.png', img,\n [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n data_size_per_img = img_byte.nbytes\n print('Data size per image is: ', data_size_per_img)\n data_size = data_size_per_img * len(all_img_list)\n env = lmdb.open(lmdb_path, map_size=data_size * 10)\n\n # write data to lmdb\n pbar = mmengine.ProgressBar(len(all_img_list))\n txn = env.begin(write=True)\n txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')\n for idx, (path, key) in enumerate(zip(all_img_list, keys)):\n pbar.update()\n key_byte = key.encode('ascii')\n img = mmcv.imread(osp.join(data_path, path), flag='unchanged')\n h, w, c = img.shape\n _, img_byte = cv2.imencode(\n '.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])\n assert h == h_dst and w == w_dst and c == 3, (\n f'Wrong shape ({h, w}), should be ({h_dst, w_dst}).')\n txn.put(key_byte, img_byte)\n # write meta information\n txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\\n')\n if idx % batch == 0:\n txn.commit()\n txn = env.begin(write=True)\n txn.commit()\n env.close()\n txt_file.close()\n print('\\nFinish writing lmdb.')\n\n\ndef generate_anno_file(clip_list, file_name='meta_info_Vimeo90K_GT.txt'):\n \"\"\"Generate anno file for Vimeo90K datasets from the official clip list.\n\n Args:\n clip_list (str): Clip list path for Vimeo90K datasets.\n file_name (str): Saved file name. Default: 'meta_info_Vimeo90K_GT.txt'.\n \"\"\"\n\n print(f'Generate annotation files {file_name}...')\n with open(clip_list) as f:\n lines = [line.rstrip() for line in f]\n txt_file = osp.join(osp.dirname(clip_list), file_name)\n with open(txt_file, 'w') as f:\n for line in lines:\n f.write(f'{line} 7 (256, 448, 3)\\n')\n\n\ndef imresize(img_path, output_path, scale=None, output_shape=None):\n \"\"\"Resize the image using MATLAB-like downsampling.\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n scale (float | None, optional): The scale factor of the resize\n operation. If None, it will be determined by output_shape.\n Default: None.\n output_shape (tuple(int) | None, optional): The size of the output\n image. If None, it will be determined by scale. Note that if\n scale is provided, output_shape will not be used.\n Default: None.\n \"\"\"\n\n matlab_resize = MATLABLikeResize(\n keys=['data'], scale=scale, output_shape=output_shape)\n img = imread(img_path)\n img = img_as_float(img)\n data = {'data': img}\n output = matlab_resize(data)['data']\n output = np.clip(output, 0.0, 1.0) * 255\n output = np.around(output).astype(np.uint8)\n imsave(output_path, output)\n\n\ndef mesh_grid(kernel_size):\n \"\"\"Generate the mesh grid, centering at zero.\n\n Args:\n kernel_size (int): The size of the kernel.\n\n Returns:\n xy_grid (np.ndarray): stacked xy coordinates with shape\n (kernel_size, kernel_size, 2).\n \"\"\"\n range_ = np.arange(-(kernel_size - 1.) / 2., (kernel_size - 1.) / 2. + 1.)\n x_grid, y_grid = np.meshgrid(range_, range_)\n xy_grid = np.hstack((x_grid.reshape((kernel_size * kernel_size, 1)),\n y_grid.reshape(kernel_size * kernel_size,\n 1))).reshape(kernel_size, kernel_size,\n 2)\n\n return xy_grid\n\n\ndef bd_downsample(img_path, output_path, sigma=1.6, scale=4):\n \"\"\"Downsampling using BD degradation(Gaussian blurring and downsampling).\n\n Args:\n img_path (str): Input image path.\n output_path (str): Output image path.\n sigma (float): The sigma of Gaussian blurring kernel. Default: 1.6.\n scale (int): The scale factor of the downsampling. Default: 4.\n \"\"\"\n\n # Gaussian blurring\n kernelsize = math.ceil(sigma * 3) * 2 + 2\n kernel = blur_kernels.bivariate_gaussian(\n kernelsize, sigma, grid=mesh_grid(kernelsize))\n img = cv2.imread(img_path)\n img = img_as_float(img)\n output = cv2.filter2D(\n img,\n -1,\n kernel,\n anchor=((kernelsize - 1) // 2, (kernelsize - 1) // 2),\n borderType=cv2.BORDER_REPLICATE)\n\n # downsampling\n output = output[int(scale / 2) - 1:-int(scale / 2) + 1:scale,\n int(scale / 2) - 1:-int(scale / 2) + 1:scale, :]\n\n output = np.clip(output, 0.0, 1.0) * 255\n output = output.astype(np.float32)\n output = np.floor(output + 0.5)\n cv2.imwrite(output_path, output)\n\n\ndef worker(clip_path, args):\n \"\"\"Worker for each process.\n\n Args:\n clip_name (str): Path of the clip.\n\n Returns:\n process_info (str): Process information displayed in progress bar.\n \"\"\"\n\n gt_dir = osp.join(args.data_root, 'GT', clip_path)\n bi_dir = osp.join(args.data_root, 'BIx4', clip_path)\n bd_dir = osp.join(args.data_root, 'BDx4', clip_path)\n mmengine.utils.mkdir_or_exist(bi_dir)\n mmengine.utils.mkdir_or_exist(bd_dir)\n\n img_list = sorted(os.listdir(gt_dir))\n for img in img_list:\n imresize(osp.join(gt_dir, img), osp.join(bi_dir, img), scale=1 / 4)\n bd_downsample(osp.join(gt_dir, img), osp.join(bd_dir, img))\n\n process_info = f'Processing {clip_path} ...'\n return process_info\n\n\ndef downsample_images(args):\n \"\"\"Downsample images.\"\"\"\n\n clip_list = []\n gt_dir = osp.join(args.data_root, 'GT')\n sequence_list = sorted(os.listdir(gt_dir))\n for sequence in sequence_list:\n sequence_root = osp.join(gt_dir, sequence)\n clip_list.extend(\n [osp.join(sequence, i) for i in sorted(os.listdir(sequence_root))])\n\n prog_bar = mmengine.ProgressBar(len(clip_list))\n pool = Pool(args.n_thread)\n for path in clip_list:\n pool.apply_async(\n worker, args=(path, args), callback=lambda arg: prog_bar.update())\n pool.close()\n pool.join()\n print('All processes done.')\n\n\ndef parse_args():\n modify_args()\n parser = argparse.ArgumentParser(\n description='Preprocess Vimeo90K datasets',\n epilog='You can download the Vimeo90K dataset '\n 'from：http://toflow.csail.mit.edu/')\n parser.add_argument('--data-root', help='dataset root')\n parser.add_argument(\n '--n-thread',\n nargs='?',\n default=8,\n type=int,\n help='thread number when using multiprocessing')\n parser.add_argument(\n '--train_list',\n default=None,\n help='official training list path for Vimeo90K')\n parser.add_argument('--gt-path', default=None, help='GT path for Vimeo90K')\n parser.add_argument('--lq-path', default=None, help='LQ path for Vimeo90K')\n parser.add_argument(\n '--make-lmdb', action='store_true', help='create lmdb files')\n\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n # generate BIx4 and BDx4\n downsample_images(args)\n\n # generate image list anno file\n generate_anno_file(\n osp.join(args.data_root, 'sep_trainlist.txt'),\n 'meta_info_Vimeo90K_train_GT.txt')\n generate_anno_file(\n osp.join(args.data_root, 'sep_testlist.txt'),\n 'meta_info_Vimeo90K_test_GT.txt')\n\n # create lmdb files\n if args.make_lmdb:\n if args.gt_path is None or args.lq_path is None:\n raise ValueError('gt_path and lq_path cannot be None when '\n 'when creating lmdb files.')\n # create lmdb for gt\n lmdb_path = osp.join(\n osp.dirname(args.gt_path), 'vimeo90k_train_GT.lmdb')\n make_lmdb(\n mode='gt',\n data_path=args.gt_path,\n lmdb_path=lmdb_path,\n train_list=args.train_list)\n # create lmdb for lq\n lmdb_path = osp.join(\n osp.dirname(args.lq_path), 'vimeo90k_train_LR7frames.lmdb')\n make_lmdb(\n mode='lq',\n data_path=args.lq_path,\n lmdb_path=lmdb_path,\n train_list=args.train_list)\n" }, { "path": "tools/dataset_converters/vimeo90k-triplet/README.md", "content": "# Preparing Vimeo90K-triplet Dataset\n\n\n\n```bibtex\n@article{xue2019video,\n title={Video Enhancement with Task-Oriented Flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision (IJCV)},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n\nThe training and test datasets can be download from [here](http://toflow.csail.mit.edu/).\n\nThe Vimeo90K-triplet dataset has a `clip/sequence/img` folder structure:\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── vimeo_triplet\n│ │ ├── tri_testlist.txt\n│ │ ├── tri_trainlist.txt\n│ │ ├── sequences\n│ │ │ ├── 00001\n│ │ │ │ ├── 0001\n│ │ │ │ │ ├── im1.png\n│ │ │ │ │ ├── im2.png\n│ │ │ │ │ └── im3.png\n│ │ │ │ ├── 0002\n│ │ │ │ ├── 0003\n│ │ │ │ ├── ...\n│ │ │ ├── 00002\n│ │ │ ├── ...\n```\n" }, { "path": "tools/dataset_converters/vimeo90k-triplet/README_zh-CN.md", "content": "# 准备 Vimeo90K-triplet 数据集\n\n\n\n```bibtex\n@article{xue2019video,\n title={Video Enhancement with Task-Oriented Flow},\n author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},\n journal={International Journal of Computer Vision (IJCV)},\n volume={127},\n number={8},\n pages={1106--1125},\n year={2019},\n publisher={Springer}\n}\n```\n\n训练集和测试集可以从 [此处](http://toflow.csail.mit.edu/) 下载。\n\nVimeo90K-triplet 数据集包含了如下所示的 `clip/sequence/img` 目录结构：\n\n```text\nmmagic\n├── mmagic\n├── tools\n├── configs\n├── data\n│ ├── vimeo_triplet\n│ │ ├── tri_testlist.txt\n│ │ ├── tri_trainlist.txt\n│ │ ├── sequences\n│ │ │ ├── 00001\n│ │ │ │ ├── 0001\n│ │ │ │ │ ├── im1.png\n│ │ │ │ │ ├── im2.png\n│ │ │ │ │ └── im3.png\n│ │ │ │ ├── 0002\n│ │ │ │ ├── 0003\n│ │ │ │ ├── ...\n│ │ │ ├── 00002\n│ │ │ ├── ...\n```\n" }, { "path": "tools/dist_test.sh", "content": "#!/usr/bin/env bash\n\nCONFIG=$1\nCHECKPOINT=$2\nGPUS=$3\nNNODES=${NNODES:-1}\nNODE_RANK=${NODE_RANK:-0}\nPORT=${PORT:-29500}\nMASTER_ADDR=${MASTER_ADDR:-\"127.0.0.1\"}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\npython -m torch.distributed.launch \\\n --nnodes=$NNODES \\\n --node_rank=$NODE_RANK \\\n --master_addr=$MASTER_ADDR \\\n --nproc_per_node=$GPUS \\\n --master_port=$PORT \\\n $(dirname \"$0\")/test.py \\\n $CONFIG \\\n $CHECKPOINT \\\n --launcher pytorch \\\n ${@:4}\n" }, { "path": "tools/dist_train.sh", "content": "#!/usr/bin/env bash\n\nCONFIG=$1\nGPUS=$2\nNNODES=${NNODES:-1}\nNODE_RANK=${NODE_RANK:-0}\nPORT=${PORT:-29500}\nMASTER_ADDR=${MASTER_ADDR:-\"127.0.0.1\"}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\npython -m torch.distributed.launch \\\n --nnodes=$NNODES \\\n --node_rank=$NODE_RANK \\\n --master_addr=$MASTER_ADDR \\\n --nproc_per_node=$GPUS \\\n --master_port=$PORT \\\n $(dirname \"$0\")/train.py \\\n $CONFIG \\\n --launcher pytorch ${@:3}\n" }, { "path": "tools/gui/README.md", "content": "# MMagic Viewer\n\n- [Introduction](#introduction)\n- [Major features](#major-features)\n- [Prerequisites](#prerequisites)\n- [Getting Started](#getting-started)\n- [Examples](#examples)\n- [Contributing](#contributing)\n\n## Introduction\n\n**MMagic Viewer** is a qualitative comparison tools to facilitate your research.\n\n## Major features\n\n- **Patch-based comparison**\n - Crop a patch on multiple images to compare\n - Batch comparison\n - Flexible settings on number of columns and size of images.\n - Save your comparison result\n- **Before/After slider comparison**\n - Support both videos and images comparison\n - Record and save comparison results as a video clip\n\n## Prerequisites\n\nMMagic Viewer works on Linux, Windows and macOS. It requires:\n\n- Python >= 3.6\n- PyQt5\n- opencv-python (headless version)\n\n## Getting Started\n\n**Step 0.**\nInstall PyQt5.\n\n```shell\npip install PyQt5\n```\n\n**Step 1.**\nInstall and check opencv-python version.\nIf your meet following errors:\n\n```\nQObject::moveToThread: Current thread is not the object's thread.\nAvailable platform plugins are: xcb... .\n```\n\nPlease install opencv-python-headless version.\n\n```shell\npip install opencv-python-headless\n```\n\n**Step 2.**\nInstall MMagic.\n\n```shell\ngit clone https://github.com/open-mmlab/mmagic.git\n```\n\n**Step 3.**\nRun\n\n```shell\npython tools/gui/gui.py\n```\n\n## Examples\n\n**1. Patch-based comparison: batch images**\n\nhttps://user-images.githubusercontent.com/49083766/199232588-7a07a3d9-725d-48be-89bf-1ffb45bd5d74.mp4\n\n**2. Patch-based comparison: single image**\n\nhttps://user-images.githubusercontent.com/49083766/199232606-f8539191-4bda-4b2c-975a-59020927abae.mp4\n\n**3. Before/After slider comparison: images**\n\nhttps://user-images.githubusercontent.com/49083766/199232615-2c56dcf1-0b42-41a5-884c-16a8f28a2647.mp4\n\n**4. Before/After slider comparison: input video frames**\n\nhttps://user-images.githubusercontent.com/49083766/199232617-e03a06dc-727b-43bb-8110-049d0fff28ba.mp4\n\n**5. Before/After slider comparison: input Mp4 video**\n\nhttps://user-images.githubusercontent.com/49083766/199232651-87d8064e-cbaf-4d30-b90b-94ee0af7d497.mp4\n\n**6. Before/After slider comparison: record**\n\nhttps://user-images.githubusercontent.com/49083766/199232634-eca70d28-8437-400a-8ab9-d2fe396b6ea9.mp4\n\n## Contributing\n\nWe appreciate all contributions to improve MMagic Viewer. You can create your issue to report bugs or request new features. Welcome to give us suggestions or contribute your codes.\n" }, { "path": "tools/gui/component.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nimport time\n\nimport cv2\nimport numpy as np\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom utils import layout2widget\n\n\nclass QLabelClick(QtWidgets.QLabel):\n clicked = QtCore.pyqtSignal(str)\n\n def __init__(self):\n super().__init__()\n\n def mousePressEvent(self, event):\n self.clicked.emit(self.text())\n\n\nclass QLabelSlider(QtWidgets.QLabel):\n\n def __init__(self, parent, scale, label_1, label_2, title):\n super().__init__()\n self.parent = parent\n self.hSlider = -1\n self.vSlider = -1\n self.oldSlider = -1\n self.scale = scale\n self.label_1 = label_1\n self.label_2 = label_2\n self.title = title\n self.images = self.parent.images\n self.auto_mode = 0\n\n def mousePressEvent(self, ev: QtGui.QMouseEvent) -> None:\n if ev.button() == QtCore.Qt.LeftButton:\n if self.hSlider > -1:\n self.oldSlider = self.hSlider\n elif self.vSlider > -1:\n self.oldSlider = self.vSlider\n return super().mousePressEvent(ev)\n\n def mouseReleaseEvent(self, ev: QtGui.QMouseEvent) -> None:\n if ev.button() == QtCore.Qt.LeftButton:\n self.oldSlider = -1\n self.update()\n return super().mouseReleaseEvent(ev)\n\n def mouseMoveEvent(self, ev: QtGui.QMouseEvent) -> None:\n if self.oldSlider > -1:\n if self.hSlider > -1:\n self.hSlider = ev.pos().x()\n elif self.vSlider > -1:\n self.vSlider = ev.pos().y()\n self.update()\n return super().mouseMoveEvent(ev)\n\n def paintEvent(self, ev: QtGui.QPaintEvent) -> None:\n qp = QtGui.QPainter()\n qp.begin(self)\n qp.drawImage(0, 0, QtGui.QImage(self.getImage()))\n pen = QtGui.QPen(QtCore.Qt.green, 3)\n qp.setPen(pen)\n qp.drawLine(self.hSlider, 0, self.hSlider, self.height())\n length = 9\n qp.drawText(self.hSlider - 10 - len(self.label_1) * length, 20,\n self.label_1)\n qp.drawText(self.hSlider + 10, 20, self.label_2)\n qp.drawText(10, self.height() - 10, self.title)\n qp.end()\n\n def set_scale(self, scale):\n self.hSlider = int(self.hSlider * scale / self.scale)\n self.scale = scale\n self.update()\n\n def setImage(self, images):\n self.images = images\n self.update()\n\n def set_auoMode(self, mode):\n if mode != 3 or self.auto_mode < 3:\n self.auto_mode = mode\n\n def auto_slider(self):\n try:\n if self.auto_mode == 1:\n self.hSlider += 1\n if self.hSlider > self.w:\n self.hSlider = 0\n elif self.auto_mode == 2:\n self.hSlider -= 1\n if self.hSlider < 0:\n self.hSlider = self.w\n elif self.auto_mode == 3:\n self.hSlider += 1\n if self.hSlider >= self.w:\n self.auto_mode = 4\n elif self.auto_mode == 4:\n self.hSlider -= 1\n if self.hSlider <= 0:\n self.auto_mode = 3\n self.update()\n\n except Exception:\n print(Exception)\n pass\n\n def getImage(self):\n img1, img2 = self.images\n if img1 is None or img2 is None:\n return\n h1, w1, c = img1.shape\n h2, w2, c = img2.shape\n if w2 > w1:\n img1 = cv2.resize(img1, (w2, h2))\n h, w = h2, w2\n else:\n img2 = cv2.resize(img2, (w1, h1))\n h, w = h1, w1\n self.h = int(h * self.scale)\n self.w = int(w * self.scale)\n self.setFixedHeight(self.h)\n self.setFixedWidth(self.w)\n if self.hSlider < 0:\n self.hSlider = int(self.w / 2.0)\n\n v = int(self.hSlider / self.scale)\n img11 = img1[:, 0:v].copy()\n img22 = img2[:, v:].copy()\n img = np.hstack((img11, img22))\n # img = cv2.line(img, (v, 0), (v, h2), (0, 222, 0), 4)\n rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img_dis = QtGui.QImage(rgb_img, w, h, w * c,\n QtGui.QImage.Format_RGB888)\n img = QtGui.QPixmap.fromImage(img_dis).scaled(self.width(),\n self.height())\n return img\n\n\nclass QLabelPaint(QtWidgets.QLabel):\n\n def __init__(self, parent, beginPoint=None, endPoint=None):\n super().__init__()\n self.beginPoint = beginPoint\n self.endPoint = endPoint\n self.parent = parent\n self.statusBar = self.parent.statusBar\n if self.beginPoint and self.endPoint:\n self.isShow = True\n else:\n self.isShow = False\n\n def mousePressEvent(self, ev: QtGui.QMouseEvent) -> None:\n if ev.button() == QtCore.Qt.LeftButton:\n self.endPoint = None\n self.beginPoint = ev.pos()\n self.isShow = False\n return super().mousePressEvent(ev)\n\n def mouseReleaseEvent(self, ev: QtGui.QMouseEvent) -> None:\n if ev.button() == QtCore.Qt.LeftButton:\n self.endPoint = ev.pos()\n self.update()\n self.isShow = True\n self.parent.set_rect(self.beginPoint, self.endPoint)\n return super().mouseReleaseEvent(ev)\n\n def mouseMoveEvent(self, ev: QtGui.QMouseEvent) -> None:\n self.endPoint = ev.pos()\n self.update()\n self.statusBar.showMessage(\n f'Start: {self.beginPoint.x()},{self.beginPoint.y()}; \\\n End: {self.endPoint.x()},{self.endPoint.y()}')\n return super().mouseMoveEvent(ev)\n\n def paintEvent(self, ev: QtGui.QPaintEvent) -> None:\n super().paintEvent(ev)\n if self.beginPoint and self.endPoint:\n qp = QtGui.QPainter()\n qp.begin(self)\n pen = QtGui.QPen(QtCore.Qt.red, 2)\n qp.setPen(pen)\n w = abs(self.beginPoint.x() - self.endPoint.x())\n h = abs(self.beginPoint.y() - self.endPoint.y())\n qp.drawRect(self.beginPoint.x(), self.beginPoint.y(), w, h)\n qp.end()\n if self.isShow and isinstance(self.parent, ConcatImageWidget):\n self.parent.show_images(self.beginPoint.x(),\n self.beginPoint.y(), w, h)\n self.isShow = False\n\n\nclass ConcatImageWidget(QtWidgets.QWidget):\n\n def __init__(self, parent, mode, col_num=4):\n super(ConcatImageWidget, self).__init__(parent)\n self.parent = parent\n self.statusBar = self.parent.statusBar\n self.hlayout = QtWidgets.QHBoxLayout()\n self.setLayout(self.hlayout)\n self.mode = mode\n self.scale = 1\n self.col_num = col_num\n self.file_path = None\n self.labels = None\n self.gt = None\n self.img_h = 0\n self.rect = None\n\n def show_images(self, x=0, y=0, w=0, h=0):\n self.rect = [x, y, w, h]\n vlayout = QtWidgets.QVBoxLayout()\n vlayout.setContentsMargins(0, 0, 0, 0)\n hlayout_img = QtWidgets.QHBoxLayout()\n hlayout_img.setContentsMargins(0, 0, 0, 0)\n hlayout_text = QtWidgets.QHBoxLayout()\n hlayout_text.setContentsMargins(0, 0, 0, 0)\n\n for i, (path, text) in enumerate(zip(self.file_path, self.labels)):\n img = QtGui.QPixmap(path).scaled(self.gt_w, self.gt_h)\n if self.mode == 0:\n img = img.copy(QtCore.QRect(x, y, w, h))\n if self.img_h > 0:\n img_w = int(float(self.img_h) / img.height() * img.width())\n img = img.scaled(img_w, self.img_h)\n\n label = QtWidgets.QLabel()\n label.setFixedWidth(img.width())\n label.setFixedHeight(img.height())\n label.setMargin(0)\n label.setPixmap(img)\n hlayout_img.addWidget(label)\n\n label_text = QtWidgets.QLabel()\n label_text.setMargin(0)\n label_text.setAlignment(QtCore.Qt.AlignCenter)\n label_text.setText(text)\n label_text.adjustSize()\n hlayout_text.addWidget(label_text)\n\n if (i + 1) % self.col_num == 0:\n vlayout.addWidget(layout2widget(hlayout_img))\n vlayout.addWidget(layout2widget(hlayout_text))\n hlayout_img = QtWidgets.QHBoxLayout()\n hlayout_img.setContentsMargins(0, 0, 0, 0)\n hlayout_text = QtWidgets.QHBoxLayout()\n hlayout_text.setContentsMargins(0, 0, 0, 0)\n\n if len(hlayout_img) > 0:\n for i in range(0, self.col_num - len(hlayout_img)):\n label = QtWidgets.QLabel()\n label.setMargin(0)\n label.setFixedWidth(img.width())\n label.setFixedHeight(img.height())\n hlayout_img.addWidget(label)\n label = QtWidgets.QLabel()\n label.setMargin(0)\n hlayout_text.addWidget(label)\n vlayout.addWidget(layout2widget(hlayout_img))\n vlayout.addWidget(layout2widget(hlayout_text))\n\n total_w = self.gt_w + (img.width() + 2) * self.col_num\n self.setFixedWidth(total_w)\n if self.hlayout.count() > 1:\n item = self.hlayout.itemAt(1)\n self.hlayout.removeItem(item)\n if item.widget():\n item.widget().deleteLater()\n self.hlayout.addWidget(layout2widget(vlayout))\n else:\n self.hlayout.addWidget(layout2widget(vlayout))\n\n def set_images(self, file_path, labels, gt=None, scale=1, rect=None):\n self.file_path = file_path\n self.labels = labels\n self.gt = gt\n self.scale = scale\n\n for i in reversed(range(self.hlayout.count())):\n self.hlayout.itemAt(i).widget().deleteLater()\n self.hlayout.setContentsMargins(0, 0, 0, 0)\n\n img = QtGui.QPixmap(self.gt)\n self.gt_w, self.gt_h = img.width() * scale, img.height() * scale\n img = img.scaled(self.gt_w, self.gt_h)\n row = (len(self.file_path) + self.col_num - 1) // self.col_num\n self.img_h = int(float(self.gt_h - (row - 1) * 29) / row)\n\n beginPoint = None\n endPoint = None\n if rect:\n beginPoint = QtCore.QPoint(rect[0], rect[1])\n endPoint = QtCore.QPoint(rect[2], rect[3])\n label = QLabelPaint(self, beginPoint, endPoint)\n label.setMargin(0)\n label.setAlignment(QtCore.Qt.AlignTop)\n label.setPixmap(img)\n self.hlayout.addWidget(label)\n if rect:\n self.show_images(rect[0], rect[1], rect[2] - rect[0],\n rect[3] - rect[1])\n else:\n self.show_images(0, 0, self.gt_w, self.gt_h)\n\n def set_rect(self, beginPoint, endPoint):\n self.parent.rect = [\n beginPoint.x(),\n beginPoint.y(),\n endPoint.x(),\n endPoint.y()\n ]\n self.parent.old_scale = self.scale\n\n\nclass VideoPlayer(QtCore.QThread):\n sigout = QtCore.pyqtSignal(np.ndarray)\n sigend = QtCore.pyqtSignal(bool)\n\n def __init__(self, parent):\n super(VideoPlayer, self).__init__(parent)\n self.parent = parent\n\n def set(self, path, fps=None):\n self.path = path\n self.video, self.fps, self.actual_frames = self.setVideo(path, fps)\n self.time = self.actual_frames / self.fps\n self.total_frames = self.actual_frames\n self.num = 0\n self.working = True\n self.isPause = False\n self.mutex = QtCore.QMutex()\n self.cond = QtCore.QWaitCondition()\n\n def setVideo(self, path, fps):\n if os.path.isfile(path):\n v = cv2.VideoCapture(path)\n total_frames = v.get(cv2.CAP_PROP_FRAME_COUNT)\n if fps is None:\n fps = v.get(cv2.CAP_PROP_FPS)\n else:\n files = sorted(os.listdir(path))\n v = ['/'.join([path, f]) for f in files]\n total_frames = len(v)\n if fps is None:\n fps = 25\n return v, fps, total_frames\n\n def pause(self):\n self.isPause = True\n\n def resume(self):\n self.isPause = False\n self.cond.wakeAll()\n\n def __del__(self):\n self.working = False\n self.wait()\n\n def run(self):\n while self.working:\n self.mutex.lock()\n if self.isPause:\n self.cond.wait(self.mutex)\n if isinstance(self.video, list):\n img = cv2.imread(self.video[self.num])\n self.num += 1\n self.sigout.emit(img)\n if self.num >= self.total_frames:\n self.sigend.emit(True)\n self.num = 0\n time.sleep(1 / self.fps)\n self.mutex.unlock()\n\n\nclass VideoSlider(QtCore.QThread):\n sigout = QtCore.pyqtSignal(list)\n sigend = QtCore.pyqtSignal(bool)\n\n def __init__(self, parent):\n super(VideoSlider, self).__init__(parent)\n self.parent = parent\n self.mutex = QtCore.QMutex()\n self.cond = QtCore.QWaitCondition()\n\n def set(self, path1, path2, fps1=None, fps2=None):\n self.path1 = path1\n self.path2 = path2\n self.v1, self.fps1, self.total_frames1 = self.setVideo(path1, fps1)\n self.v2, self.fps2, self.total_frames2 = self.setVideo(path2, fps2)\n if self.fps1 != self.fps2:\n return False\n self.fps = self.fps1\n self.num = 0\n self.working = True\n self.isPause = False\n return True\n\n def setVideo(self, path, fps):\n if os.path.isfile(path):\n v = cv2.VideoCapture(path)\n total_frames = v.get(cv2.CAP_PROP_FRAME_COUNT)\n fps = v.get(cv2.CAP_PROP_FPS)\n else:\n files = sorted(os.listdir(path))\n v = ['/'.join([path, f]) for f in files]\n total_frames = len(v)\n if fps is None:\n fps = 25\n return v, fps, total_frames\n\n def pause(self):\n self.isPause = True\n\n def resume(self):\n self.isPause = False\n self.cond.wakeAll()\n\n def __del__(self):\n self.working = False\n self.wait()\n\n def run(self):\n while self.working:\n self.mutex.lock()\n if self.isPause:\n self.cond.wait(self.mutex)\n if isinstance(self.v1, list):\n num = self.num if self.num < self.total_frames1 \\\n else self.total_frames1 - 1\n img1 = cv2.imread(self.v1[num])\n elif isinstance(self.v1, cv2.VideoCapture):\n r, img1 = self.v1.read()\n if not r:\n self.v1, self.fps1, self.total_frames1 = self.setVideo(\n self.path1, self.fps1)\n if isinstance(self.v2, list):\n num = self.num if self.num < self.total_frames2 \\\n else self.total_frames2 - 1\n img2 = cv2.imread(self.v2[num])\n elif isinstance(self.v2, cv2.VideoCapture):\n r, img2 = self.v2.read()\n if not r:\n self.v2, self.fps2, self.total_frames2 = self.setVideo(\n self.path2, self.fps2)\n self.num += 1\n self.sigout.emit([img1, img2])\n if self.num >= self.total_frames1 and \\\n self.num >= self.total_frames2:\n self.num = 0\n time.sleep(1 / self.fps)\n self.mutex.unlock()\n" }, { "path": "tools/gui/gui.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport sys\n\nfrom component import QLabelClick\nfrom page_sr import SRPage\nfrom PyQt5 import QtWidgets\n\n\nclass Homepage(QtWidgets.QWidget):\n\n def __init__(self, main_window):\n super().__init__()\n self.main_window = main_window\n layout = QtWidgets.QGridLayout()\n self.setLayout(layout)\n\n t1 = QLabelClick()\n t2 = QLabelClick()\n t3 = QLabelClick()\n t4 = QLabelClick()\n t1.setText('general')\n t2.setText('sr')\n t3.setText('inpainting')\n t4.setText('matting')\n layout.addWidget(t1, 0, 0)\n layout.addWidget(t2, 0, 1)\n layout.addWidget(t3, 1, 0)\n layout.addWidget(t4, 1, 1)\n\n t1.clicked.connect(self.main_window.change_window)\n t2.clicked.connect(self.main_window.change_window)\n t3.clicked.connect(self.main_window.change_window)\n t4.clicked.connect(self.main_window.change_window)\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n\n def __init__(self):\n super(MainWindow, self).__init__()\n\n self.setWindowTitle('MMagic Viewer')\n # # MenuBar\n # menubar_Aaa = self.menuBar().addMenu('Aaa')\n # menubar_Bbb = self.menuBar().addMenu('Bbb')\n # menubar_Ccc = self.menuBar().addMenu('Ccc')\n # menubar_Aaa.addAction('New')\n # save = QtWidgets.QAction('Save', self)\n # save.setShortcut('Ctrl+S')\n # menubar_Aaa.addAction(save)\n # menubar_Bbb.addAction('New')\n # menubar_Ccc.addAction('New')\n\n # # ToolBar\n # self.toolBar = QtWidgets.QToolBar('ToolBar')\n # open = QtWidgets.QAction(QtGui.QIcon(), 'Open', self)\n # save = QtWidgets.QAction(QtGui.QIcon(), 'Save', self)\n # self.toolBar.addAction(open)\n # self.toolBar.addAction(save)\n # self.addToolBar(QtCore.Qt.ToolBarArea.LeftToolBarArea, self.toolBar)\n\n # StatusBar\n self.statusBar = QtWidgets.QStatusBar()\n self.setStatusBar(self.statusBar)\n self.statusBar.showMessage('')\n\n self.homepage = Homepage(self)\n self.sr = SRPage(self)\n self.setCentralWidget(self.sr)\n\n def change_window(self, wname):\n if wname == 'sr':\n self.setCentralWidget(self.sr)\n elif wname == 'general':\n self.setCentralWidget(self.general)\n\n\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n myWin = MainWindow()\n myWin.showMaximized()\n sys.exit(app.exec_())\n" }, { "path": "tools/gui/page_general.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport cv2\nimport numpy as np\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom utils import layout2widget\n\n\nclass SliderTab(QtWidgets.QWidget):\n\n def __init__(self):\n super().__init__()\n self.images = [None, None]\n\n self.cb_1 = QtWidgets.QComboBox()\n self.cb_2 = QtWidgets.QComboBox()\n self.cb_1.currentIndexChanged.connect(self.change_image)\n self.cb_2.currentIndexChanged.connect(self.change_image)\n self.btn_add = QtWidgets.QPushButton()\n self.btn_add.setText('Add')\n self.btn_add.clicked.connect(self.add)\n left_grid = QtWidgets.QGridLayout()\n left_grid.addWidget(self.cb_1, 0, 0)\n left_grid.addWidget(self.cb_2, 1, 0)\n left_grid.addWidget(self.btn_add, 2, 0)\n\n self.imageArea = QtWidgets.QLabel()\n self.imageArea.setFrameShape(QtWidgets.QFrame.Box)\n self.imageArea.setLineWidth(2)\n self.imageArea.setAlignment(QtCore.Qt.AlignBottom)\n self.imageArea.setStyleSheet(\n 'border-width: 0px; border-style: solid; border-color: rgb(100, 100, 100);background-color: rgb(255, 255, 255)' # noqa\n )\n self.slider = QtWidgets.QSlider(QtCore.Qt.Horizontal)\n self.slider.setMaximum(800)\n self.slider.valueChanged.connect(self.show_image)\n right_grid = QtWidgets.QGridLayout()\n right_grid.addWidget(self.imageArea, 0, 0)\n right_grid.addWidget(self.slider, 1, 0)\n\n # Splitter\n hsplitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n hsplitter.addWidget(layout2widget(left_grid))\n hsplitter.addWidget(layout2widget(right_grid))\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(hsplitter)\n self.setLayout(hlayout)\n\n def add(self):\n path, _ = QtWidgets.QFileDialog.getOpenFileName(\n self, 'Select gt file', '', 'Images (*.jpg *.png *.mp4 *.avi)')\n if self.cb_1.count() > 0:\n if self.cb_1.findText(path) > -1:\n self.cb_1.removeItem(self.cb_1.findText(path))\n if self.cb_2.findText(path) > -1:\n self.cb_2.removeItem(self.cb_2.findText(path))\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_2.setCurrentIndex(self.cb_2.count() - 1)\n else:\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_1.setCurrentIndex(0)\n self.cb_2.setCurrentIndex(0)\n\n def change_image(self):\n self.images[0] = cv2.imread(self.cb_1.currentText())\n self.images[1] = cv2.imread(self.cb_2.currentText())\n if self.images[0] is None or self.images[1] is None:\n return\n self.show_image()\n\n def show_image(self):\n img1, img2 = self.images\n h2, w2, c2 = img2.shape\n img2 = cv2.resize(img2, (800, int(800 / w2 * h2)))\n h2, w2, c2 = img2.shape\n img1 = cv2.resize(img1, (w2, h2))\n v = self.slider.value()\n img11 = img1[:, 0:v].copy()\n img22 = img2[:, v:].copy()\n img = np.hstack((img11, img22))\n img = cv2.line(img, (v, 0), (v, h2), (0, 222, 0), 4)\n rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img_dis = QtGui.QImage(rgb_img, w2, h2, w2 * c2,\n QtGui.QImage.Format_RGB888)\n jpg = QtGui.QPixmap.fromImage(img_dis).scaled(\n self.imageArea.width(), int(self.imageArea.width() / w2 * h2))\n self.imageArea.setPixmap(jpg)\n\n\nclass GeneralPage(QtWidgets.QWidget):\n\n def __init__(self) -> None:\n super().__init__()\n\n self.tab_slider = SliderTab()\n self.tabs = QtWidgets.QTabWidget()\n self.tabs.addTab(self.tab_slider, 'before/after slider')\n\n layout = QtWidgets.QVBoxLayout()\n self.setLayout(layout)\n layout.addWidget(self.tabs)\n" }, { "path": "tools/gui/page_sr.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport datetime\nimport os\n\nimport cv2\nfrom component import ConcatImageWidget, QLabelSlider, VideoSlider\nfrom PyQt5 import QtCore, QtWidgets\nfrom utils import layout2widget\n\n\nclass PatchTab(QtWidgets.QWidget):\n\n def __init__(self, parent):\n super().__init__()\n\n self.parent = parent\n self.statusBar = self.parent.statusBar\n self.file_paths = []\n self.labels = []\n self.rect = None\n self.images = None\n self.isShow = False\n\n # Left Widget\n self.btn_add_file = QtWidgets.QPushButton()\n self.btn_add_file.setText('Add')\n self.btn_add_file.clicked.connect(self.add_file)\n self.btn_open_file = QtWidgets.QPushButton()\n self.btn_open_file.setText('Open new')\n self.btn_open_file.clicked.connect(self.open_file)\n\n self.input_label = QtWidgets.QLineEdit()\n self.input_file = QtWidgets.QLineEdit()\n\n self.tb_files = QtWidgets.QTableWidget()\n self.tb_files.setColumnCount(3)\n self.tb_files.setHorizontalHeaderLabels(\n ['File/Folder', 'Label', 'Other'])\n self.tb_files.horizontalHeader().setSectionResizeMode(\n QtWidgets.QHeaderView.Stretch)\n self.tb_files.setSelectionBehavior(\n QtWidgets.QAbstractItemView.SelectRows)\n self.tb_files.setSelectionMode(\n QtWidgets.QAbstractItemView.SingleSelection)\n self.tb_files.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n self.tb_files.customContextMenuRequested.connect(self.tableMenu)\n\n left_grid = QtWidgets.QGridLayout()\n left_grid.addWidget(self.tb_files, 0, 0, 5, 10)\n left_grid.addWidget(self.input_file, 6, 0, 1, 4)\n left_grid.addWidget(self.input_label, 6, 4, 1, 2)\n left_grid.addWidget(self.btn_open_file, 6, 6, 1, 2)\n left_grid.addWidget(self.btn_add_file, 6, 8, 1, 2)\n\n # Right Widget\n styleSheet = '''\n QGroupBox {\n background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,\n stop: 0 #E0E0E0, stop: 1 #FFFFFF);\n border: 1px solid #999999;\n border-radius: 5px;\n margin-top: 2ex; /*leave space at the top for the title */\n padding: 2ex 10ex;\n font-size: 20px;\n color: black;\n }\n QGroupBox::title {\n subcontrol-origin: margin;\n subcontrol-position: top left; /* position at the top center */\n padding: 0 3px;\n left: 30px;\n font-size: 8px;\n color: black;\n }\n ''' # noqa\n # select mode\n self.modeRect = QtWidgets.QGroupBox('Select Mode')\n self.modeRect.setFlat(True)\n # self.modeRect.setStyleSheet(styleSheet)\n btn_mode1 = QtWidgets.QRadioButton('Input whole image', self.modeRect)\n btn_mode2 = QtWidgets.QRadioButton('Input crop image', self.modeRect)\n self.btnGroup_mode = QtWidgets.QButtonGroup()\n self.btnGroup_mode.addButton(btn_mode1, 0)\n self.btnGroup_mode.addButton(btn_mode2, 1)\n self.btnGroup_mode.button(0).setChecked(True)\n self.btnGroup_mode.idToggled.connect(self.reset)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(btn_mode1)\n hlayout.addWidget(btn_mode2)\n self.modeRect.setLayout(hlayout)\n\n # select dirType\n self.dirTypeRect = QtWidgets.QGroupBox('Select File or Directory')\n self.dirTypeRect.setFlat(True)\n # self.dirTypeRect.setStyleSheet(styleSheet)\n btn_dirType1 = QtWidgets.QRadioButton('Directory', self.dirTypeRect)\n btn_dirType2 = QtWidgets.QRadioButton('File', self.dirTypeRect)\n self.btnGroup_dirType = QtWidgets.QButtonGroup()\n self.btnGroup_dirType.addButton(btn_dirType1, 0)\n self.btnGroup_dirType.addButton(btn_dirType2, 1)\n self.btnGroup_dirType.button(0).setChecked(True)\n self.btnGroup_dirType.idToggled.connect(self.reset)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(btn_dirType1)\n hlayout.addWidget(btn_dirType2)\n self.dirTypeRect.setLayout(hlayout)\n\n # select gt\n self.cb_gt = QtWidgets.QComboBox()\n self.cb_gt.currentTextChanged.connect(self.change_gt)\n\n # set column\n self.spin_cols = QtWidgets.QSpinBox()\n self.spin_cols.setMinimum(1)\n self.spin_cols.setMaximum(10)\n self.spin_cols.setValue(4)\n self.spin_cols.valueChanged.connect(self.set_column)\n\n # set scale\n self.scale = 100\n self.txt_scale = QtWidgets.QLabel('100 %')\n self.slider_scale = QtWidgets.QSlider(QtCore.Qt.Horizontal)\n self.slider_scale.setMinimum(1)\n self.slider_scale.setMaximum(200)\n self.slider_scale.setValue(100)\n self.slider_scale.valueChanged.connect(self.set_scale)\n\n # operation\n self.btn_run = QtWidgets.QPushButton()\n self.btn_run.setText('Run')\n self.btn_run.clicked.connect(self.run)\n self.btn_reset = QtWidgets.QPushButton()\n self.btn_reset.setText('Reset')\n self.btn_reset.clicked.connect(self.reset)\n self.btn_save = QtWidgets.QPushButton()\n self.btn_save.setText('Save')\n self.btn_save.clicked.connect(self.save)\n\n right_grid = QtWidgets.QGridLayout()\n right_grid.addWidget(self.modeRect, 0, 0, 1, 20)\n right_grid.addWidget(self.dirTypeRect, 1, 0, 1, 20)\n right_grid.addWidget(self.cb_gt, 2, 0, 1, 15)\n right_grid.addWidget(QtWidgets.QLabel('Reference'), 2, 16, 1, 3)\n right_grid.addWidget(self.spin_cols, 3, 0, 1, 15)\n right_grid.addWidget(QtWidgets.QLabel('Set columns'), 3, 16, 1, 3)\n right_grid.addWidget(self.txt_scale, 4, 0, 1, 1)\n right_grid.addWidget(self.slider_scale, 4, 1, 1, 14)\n right_grid.addWidget(QtWidgets.QLabel('Set scale'), 4, 16, 1, 3)\n right_grid.addWidget(self.btn_run, 5, 0, 1, 20)\n right_grid.addWidget(self.btn_reset, 6, 0, 1, 20)\n right_grid.addWidget(self.btn_save, 7, 0, 1, 20)\n\n # Bottom Widget\n self.image_scroll = QtWidgets.QScrollArea()\n self.image_scroll.installEventFilter(self)\n\n # Splitter\n hsplitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n vsplitter = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n hsplitter.addWidget(layout2widget(left_grid))\n hsplitter.addWidget(layout2widget(right_grid))\n hsplitter.setStretchFactor(0, 1)\n hsplitter.setStretchFactor(1, 2)\n vsplitter.addWidget(hsplitter)\n vsplitter.addWidget(self.image_scroll)\n vsplitter.setStretchFactor(0, 1)\n vsplitter.setStretchFactor(1, 5)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(vsplitter)\n self.setLayout(hlayout)\n\n def open_file(self):\n \"\"\"Open a file or directory from dialog.\"\"\"\n if self.btnGroup_dirType.checkedId() == 0:\n path = QtWidgets.QFileDialog.getExistingDirectory()\n if len(path) <= 0:\n return\n label = path.split('/')[-1]\n self.input_file.setText(path)\n self.input_label.setText(label)\n else:\n path, _ = QtWidgets.QFileDialog.getOpenFileName(\n self, 'Select file', '', 'Images (*.jpg *.png)')\n if len(path) <= 0:\n return\n label = path.split('/')[-1].split('.')[0]\n self.input_file.setText(path)\n self.input_label.setText(label)\n\n def add_file(self):\n \"\"\"Add opened file or directory to table.\"\"\"\n if os.path.exists(self.input_file.text()):\n row = self.tb_files.rowCount()\n self.tb_files.setRowCount(row + 1)\n self.tb_files.setItem(\n row, 0, QtWidgets.QTableWidgetItem(self.input_file.text()))\n self.tb_files.setItem(\n row, 1, QtWidgets.QTableWidgetItem(self.input_label.text()))\n # if self.cb_gt.count() == 0:\n # self.set_gt(self.input_file.text())\n else:\n QtWidgets.QMessageBox.about(self, 'Message',\n 'Please input available file/folder')\n\n def tableMenu(self, pos):\n \"\"\"Set mouse right button menu of table.\"\"\"\n menu = QtWidgets.QMenu()\n menu_up = menu.addAction('move up')\n menu_down = menu.addAction('move down')\n menu_delete = menu.addAction('delete')\n menu_gt = menu.addAction('set reference')\n menu_pos = self.tb_files.mapToGlobal(pos)\n row = None\n for i in self.tb_files.selectionModel().selection().indexes():\n row = i.row()\n if row is None or row >= self.tb_files.rowCount():\n return\n action = menu.exec(menu_pos)\n if action == menu_up:\n self.tb_swap(row, -1)\n elif action == menu_down:\n self.tb_swap(row, 1)\n elif action == menu_delete:\n self.tb_files.removeRow(row)\n elif action == menu_gt:\n for r in range(self.tb_files.rowCount()):\n self.tb_files.setItem(r, 2, QtWidgets.QTableWidgetItem(''))\n self.tb_files.setItem(row, 2,\n QtWidgets.QTableWidgetItem('Reference'))\n self.set_gt(self.tb_files.item(row, 0).text())\n if self.isShow:\n self.run()\n\n def tb_swap(self, row, move):\n \"\"\"Move items of table.\"\"\"\n if move == -1:\n if 0 == row:\n return\n target = row - 1\n elif move == 1:\n if self.tb_files.rowCount() - 1 == row:\n return\n target = row + 1\n for col in range(self.tb_files.columnCount()):\n tmp = self.tb_files.takeItem(row, col)\n self.tb_files.setItem(row, col,\n self.tb_files.takeItem(target, col))\n self.tb_files.setItem(target, col, tmp)\n\n def set_gt(self, path):\n \"\"\"Set GT ComboBox items.\"\"\"\n self.cb_gt.clear()\n if os.path.isfile(path):\n self.cb_gt.addItem(path)\n else:\n files = sorted(os.listdir(path))\n for f in files:\n self.cb_gt.addItem(path + '/' + f)\n\n def change_gt(self):\n if self.isShow:\n self.run()\n\n def set_scale(self):\n \"\"\"Set scale.\"\"\"\n scale = self.slider_scale.value()\n self.txt_scale.setText(f'{scale} %')\n self.scale = scale\n if self.isShow:\n rect = None\n if self.rect:\n rect = [\n int(r * scale / 100.0 / self.old_scale) for r in self.rect\n ]\n self.run(rect)\n\n def set_column(self):\n \"\"\"Set column.\"\"\"\n if self.isShow:\n self.run()\n\n def run(self, rect=None):\n \"\"\"Generate patch compare result.\"\"\"\n if self.cb_gt.currentText() == '':\n QtWidgets.QMessageBox.about(self, 'Message', 'Please set gt!')\n return\n\n rows = self.tb_files.rowCount()\n if rows <= 0:\n QtWidgets.QMessageBox.about(self, 'Message',\n 'Please add a file at least!')\n return\n\n files = []\n self.labels = []\n for r in range(rows):\n if self.tb_files.item(r, 2).text() != 'Reference':\n files.append(self.tb_files.item(r, 0).text())\n self.labels.append(self.tb_files.item(r, 1).text())\n\n file_name = os.path.basename(self.cb_gt.currentText())\n self.file_paths = []\n for i, file in enumerate(files):\n if self.btnGroup_dirType.checkedId() == 0:\n self.file_paths.append(file + '/' + file_name)\n else:\n self.file_paths.append(file)\n\n mode = self.btnGroup_mode.checkedId()\n self.images = ConcatImageWidget(self, mode, self.spin_cols.value())\n self.images.set_images(self.file_paths, self.labels,\n self.cb_gt.currentText(), self.scale / 100.0,\n rect)\n self.image_scroll.setWidget(self.images)\n self.isShow = True\n\n def reset(self):\n \"\"\"Init window.\"\"\"\n self.file_paths = []\n self.labels = []\n self.isShow = False\n\n self.input_label.clear()\n self.input_file.clear()\n self.tb_files.setRowCount(0)\n self.cb_gt.clear()\n self.spin_cols.setValue(4)\n self.slider_scale.setValue(100)\n self.images = ConcatImageWidget(self, 0, self.spin_cols.value())\n self.image_scroll.setWidget(self.images)\n\n def save(self):\n \"\"\"Save patch compare result.\"\"\"\n path, _ = QtWidgets.QFileDialog.getSaveFileName(self, 'save')\n if len(path) <= 0:\n return\n if self.images:\n self.images.grab().save(path)\n QtWidgets.QMessageBox.about(self, 'Message', 'Success!')\n else:\n QtWidgets.QMessageBox.about(self, 'Message', 'Nothing to save.')\n\n def wheelEvent(self, ev) -> None:\n key = QtWidgets.QApplication.keyboardModifiers()\n if key == QtCore.Qt.ControlModifier:\n scale = ev.angleDelta().y() / 120\n self.scale += scale\n if self.scale < 1:\n self.scale = 1\n self.txt_scale.setText(f'{self.scale} %')\n self.slider_scale.setValue(self.scale)\n if self.scale > 200 and self.isShow:\n rect = None\n if self.rect:\n rect = [\n int(r * self.scale / 100.0 / self.old_scale)\n for r in self.rect\n ]\n self.run(rect)\n return super().wheelEvent(ev)\n\n def keyPressEvent(self, ev) -> None:\n if ev.key() == QtCore.Qt.Key_Left:\n if self.cb_gt.currentIndex() > 0:\n self.cb_gt.setCurrentIndex(self.cb_gt.currentIndex() - 1)\n else:\n self.cb_gt.setCurrentIndex(self.cb_gt.count() - 1)\n elif ev.key() == QtCore.Qt.Key_Right:\n if self.cb_gt.currentIndex() < self.cb_gt.count() - 1:\n self.cb_gt.setCurrentIndex(self.cb_gt.currentIndex() + 1)\n else:\n self.cb_gt.setCurrentIndex(0)\n return super().keyPressEvent(ev)\n\n def eventFilter(self, object, event) -> bool:\n if object == self.image_scroll:\n if event.type() == QtCore.QEvent.KeyPress:\n self.keyPressEvent(event)\n return False\n return super().eventFilter(object, event)\n\n\nclass SliderTab(QtWidgets.QWidget):\n\n def __init__(self, parent):\n super().__init__()\n self.parent = parent\n self.images = [None, None]\n self.imageArea = None\n\n # Type setting\n self.typeRect = QtWidgets.QGroupBox('Type')\n self.typeRect.setFlat(True)\n btn_type1 = QtWidgets.QRadioButton('Video', self.typeRect)\n btn_type2 = QtWidgets.QRadioButton('Image', self.typeRect)\n self.btnGroup_type = QtWidgets.QButtonGroup()\n self.btnGroup_type.addButton(btn_type1, 0)\n self.btnGroup_type.addButton(btn_type2, 1)\n self.btnGroup_type.button(0).setChecked(True)\n self.btnGroup_type.idToggled.connect(self.reset)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(btn_type1)\n hlayout.addWidget(btn_type2)\n self.typeRect.setLayout(hlayout)\n\n # Mode setting\n self.modeRect = QtWidgets.QGroupBox('Mode')\n self.modeRect.setFlat(True)\n btn_mode1 = QtWidgets.QRadioButton('Match', self.modeRect)\n btn_mode2 = QtWidgets.QRadioButton('Single', self.modeRect)\n self.btnGroup_mode = QtWidgets.QButtonGroup()\n self.btnGroup_mode.addButton(btn_mode1, 0)\n self.btnGroup_mode.addButton(btn_mode2, 1)\n self.btnGroup_mode.button(0).setChecked(True)\n self.btnGroup_mode.idToggled.connect(self.reset)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(btn_mode1)\n hlayout.addWidget(btn_mode2)\n self.modeRect.setLayout(hlayout)\n\n # Settings\n self.cb_1 = QtWidgets.QComboBox()\n self.cb_2 = QtWidgets.QComboBox()\n self.cb_1.currentIndexChanged.connect(self.change_image_1)\n self.cb_2.currentIndexChanged.connect(self.change_image_2)\n self.input_label_1 = QtWidgets.QLineEdit()\n self.input_label_2 = QtWidgets.QLineEdit()\n self.input_title = QtWidgets.QLineEdit()\n self.input_label_1.textChanged.connect(self.set_label)\n self.input_label_2.textChanged.connect(self.set_label)\n self.input_title.textChanged.connect(self.set_label)\n\n # Set scale\n self.scale = 100\n self.txt_scale = QtWidgets.QLabel('100 %')\n self.slider_scale = QtWidgets.QSlider(QtCore.Qt.Horizontal)\n self.slider_scale.setMinimum(1)\n self.slider_scale.setMaximum(200)\n self.slider_scale.setValue(100)\n self.slider_scale.valueChanged.connect(self.set_scale)\n\n # Auto slider setting\n self.autoRect = QtWidgets.QGroupBox('Auto Slider')\n self.autoRect.setFlat(True)\n btn_autoMode1 = QtWidgets.QRadioButton('Right', self.autoRect)\n btn_autoMode2 = QtWidgets.QRadioButton('Left', self.autoRect)\n btn_autoMode3 = QtWidgets.QRadioButton('Alternate', self.autoRect)\n self.btnGroup_auto = QtWidgets.QButtonGroup()\n self.btnGroup_auto.addButton(btn_autoMode1, 0)\n self.btnGroup_auto.addButton(btn_autoMode2, 1)\n self.btnGroup_auto.addButton(btn_autoMode3, 2)\n self.btnGroup_auto.button(2).setChecked(True)\n self.btnGroup_auto.idToggled.connect(self.set_autoSlider)\n self.slider_auto = QtWidgets.QSlider(QtCore.Qt.Horizontal)\n self.slider_auto.setMinimum(0)\n self.slider_auto.setMaximum(200)\n self.slider_auto.setValue(0)\n self.slider_auto.valueChanged.connect(self.set_autoSlider)\n glayout = QtWidgets.QGridLayout()\n glayout.addWidget(QtWidgets.QLabel('Direction:'), 0, 0, 1, 1)\n glayout.addWidget(btn_autoMode3, 0, 2, 1, 1)\n glayout.addWidget(btn_autoMode1, 0, 3, 1, 1)\n glayout.addWidget(btn_autoMode2, 0, 4, 1, 1)\n glayout.addWidget(QtWidgets.QLabel('Speed:'), 1, 0, 1, 1)\n glayout.addWidget(self.slider_auto, 1, 1, 1, 4)\n self.autoRect.setLayout(glayout)\n\n # Add file\n self.btn_add_1 = QtWidgets.QPushButton()\n self.btn_add_2 = QtWidgets.QPushButton()\n self.btn_add_1.setText('Add a video')\n self.btn_add_2.setText('Add frames')\n self.btn_add_1.clicked.connect(self.add_1)\n self.btn_add_2.clicked.connect(self.add_2)\n\n # Buttons\n self.btn_pause = QtWidgets.QPushButton()\n self.btn_pause.setText('Pause (Space)')\n self.btn_pause.clicked.connect(self.pause)\n self.btn_pause.setEnabled(False)\n\n self.btn_reset = QtWidgets.QPushButton()\n self.btn_reset.setText('Reset')\n self.btn_reset.clicked.connect(self.reset)\n\n self.btn_save = QtWidgets.QPushButton()\n self.btn_save.setText('Save')\n self.btn_save.clicked.connect(self.save)\n\n self.btn_record = QtWidgets.QPushButton()\n self.btn_record.setText('Record (Enter)')\n self.btn_record.clicked.connect(self.record)\n\n self.txt_prompt = QtWidgets.QLabel()\n self.txt_prompt.setStyleSheet('color: red; font-size: 28px')\n\n left_grid = QtWidgets.QGridLayout()\n left_grid.addWidget(self.typeRect, 0, 0, 1, 10)\n left_grid.addWidget(self.modeRect, 1, 0, 1, 10)\n left_grid.addWidget(QtWidgets.QLabel('Left'), 2, 0, 1, 1)\n left_grid.addWidget(self.cb_1, 2, 1, 1, 9)\n left_grid.addWidget(QtWidgets.QLabel('Right'), 3, 0, 1, 1)\n left_grid.addWidget(self.cb_2, 3, 1, 1, 9)\n left_grid.addWidget(QtWidgets.QLabel('Set label 1'), 4, 0, 1, 1)\n left_grid.addWidget(self.input_label_1, 4, 1, 1, 9)\n left_grid.addWidget(QtWidgets.QLabel('Set label 2'), 5, 0, 1, 1)\n left_grid.addWidget(self.input_label_2, 5, 1, 1, 9)\n left_grid.addWidget(QtWidgets.QLabel('Set title'), 6, 0, 1, 1)\n left_grid.addWidget(self.input_title, 6, 1, 1, 9)\n left_grid.addWidget(QtWidgets.QLabel('Set scale'), 7, 0, 1, 1)\n left_grid.addWidget(self.slider_scale, 7, 1, 1, 8)\n left_grid.addWidget(self.txt_scale, 7, 9, 1, 1)\n left_grid.addWidget(self.autoRect, 8, 0, 1, 10)\n left_grid.addWidget(self.btn_add_1, 9, 0, 1, 5)\n left_grid.addWidget(self.btn_add_2, 9, 5, 1, 5)\n left_grid.addWidget(self.btn_pause, 10, 0, 1, 10)\n left_grid.addWidget(self.btn_reset, 11, 0, 1, 10)\n left_grid.addWidget(self.btn_save, 12, 0, 1, 10)\n left_grid.addWidget(self.btn_record, 13, 0, 1, 10)\n left_grid.addWidget(QtWidgets.QLabel(), 14, 0, 10, 10)\n left_grid.addWidget(self.txt_prompt, 25, 0, 20, 10)\n\n # Image area\n self.image_scroll = QtWidgets.QScrollArea()\n self.image_scroll.setAlignment(QtCore.Qt.AlignCenter)\n self.image_scroll.installEventFilter(self)\n right_grid = QtWidgets.QGridLayout()\n right_grid.addWidget(self.image_scroll, 0, 0)\n\n # Splitter\n hsplitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n hsplitter.addWidget(layout2widget(left_grid))\n hsplitter.addWidget(layout2widget(right_grid))\n hsplitter.setStretchFactor(0, 1)\n hsplitter.setStretchFactor(1, 5)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(hsplitter)\n self.setLayout(hlayout)\n\n # Timer\n self.timer_slider = QtCore.QTimer(self)\n self.timer_slider.timeout.connect(self.auto_slider)\n self.timer_record = QtCore.QTimer(self)\n self.timer_record.timeout.connect(self.recording)\n\n # Player\n self.player = VideoSlider(self)\n self.player.sigout.connect(self.setImg)\n # self.player1_end = False\n # self.player2_end = False\n # self.player1 = VideoPlayer(self)\n # self.player2 = VideoPlayer(self)\n # self.player1.sigout.connect(self.setImg1)\n # self.player2.sigout.connect(self.setImg2)\n # self.player1.sigend.connect(self.set_player1)\n # self.player2.sigend.connect(self.set_player2)\n self.record_num = 0\n self.show_image()\n\n def set_autoSlider(self):\n if self.imageArea:\n self.timer_slider.stop()\n if self.slider_auto.value() > 0:\n self.imageArea.set_auoMode(self.btnGroup_auto.checkedId() + 1)\n self.timer_slider.start(1000 / (self.slider_auto.value()))\n else:\n self.imageArea.set_auoMode(0)\n\n def auto_slider(self):\n self.imageArea.auto_slider()\n\n def add_image(self, cb, btn):\n if self.btnGroup_mode.checkedId() == 0:\n path = QtWidgets.QFileDialog.getExistingDirectory(self)\n if len(path) <= 0:\n return\n cb.clear()\n files = sorted(os.listdir(path))\n for f in files:\n cb.addItem(path + '/' + f)\n else:\n if btn == 'add_1':\n path, _ = QtWidgets.QFileDialog.getOpenFileName(\n self, 'Select gt file', '', 'Images (*.jpg *.png)')\n if len(path) <= 0:\n return\n files = [path]\n elif btn == 'add_2':\n paths = QtWidgets.QFileDialog.getExistingDirectory(self)\n if len(paths) <= 0:\n return\n files = sorted(os.listdir(paths))\n files = [paths + '/' + f for f in files]\n\n for path in files:\n if self.cb_1.count() > 0:\n if self.cb_1.findText(path) > -1:\n self.cb_1.removeItem(self.cb_1.findText(path))\n if self.cb_2.findText(path) > -1:\n self.cb_2.removeItem(self.cb_2.findText(path))\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_2.setCurrentIndex(self.cb_2.count() - 1)\n else:\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_1.setCurrentIndex(0)\n self.cb_2.setCurrentIndex(0)\n\n def add_1(self):\n if self.btnGroup_type.checkedId() == 1:\n self.add_image(self.cb_1, 'add_1')\n else:\n path, _ = QtWidgets.QFileDialog.getOpenFileName(\n self, 'Select gt file', '', 'Images (*.mp4 *.avi)')\n if len(path) <= 0:\n return\n if self.cb_1.count() > 0:\n if self.cb_1.findText(path) > -1:\n self.cb_1.removeItem(self.cb_1.findText(path))\n if self.cb_2.findText(path) > -1:\n self.cb_2.removeItem(self.cb_2.findText(path))\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_2.setCurrentIndex(self.cb_2.count() - 1)\n else:\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_1.setCurrentIndex(0)\n self.cb_2.setCurrentIndex(0)\n\n def add_2(self):\n if self.btnGroup_type.checkedId() == 1:\n self.add_image(self.cb_2, 'add_2')\n else:\n path = QtWidgets.QFileDialog.getExistingDirectory(self)\n if len(path) <= 0:\n return\n if self.cb_1.count() > 0:\n if self.cb_1.findText(path) > -1:\n self.cb_1.removeItem(self.cb_1.findText(path))\n if self.cb_2.findText(path) > -1:\n self.cb_2.removeItem(self.cb_2.findText(path))\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_2.setCurrentIndex(self.cb_2.count() - 1)\n else:\n self.cb_1.addItem(path)\n self.cb_2.addItem(path)\n self.cb_1.setCurrentIndex(0)\n self.cb_2.setCurrentIndex(0)\n\n def set_label(self):\n if self.imageArea is not None:\n self.imageArea.label_1 = self.input_label_1.text()\n self.imageArea.label_2 = self.input_label_2.text()\n self.imageArea.title = self.input_title.text()\n self.imageArea.update()\n\n def set_scale(self):\n \"\"\"Set scale.\"\"\"\n self.scale = self.slider_scale.value()\n self.txt_scale.setText(f'{self.scale} %')\n if self.imageArea is not None:\n self.imageArea.set_scale(self.scale / 100.0)\n\n def change_image_1(self):\n if self.btnGroup_type.checkedId() == 0:\n self.change_video()\n else:\n if self.btnGroup_mode.checkedId() == 0:\n self.cb_2.setCurrentIndex(self.cb_1.currentIndex())\n self.images[0] = cv2.imread(self.cb_1.currentText())\n self.images[1] = cv2.imread(self.cb_2.currentText())\n self.show_image()\n\n def change_image_2(self):\n if self.btnGroup_type.checkedId() == 0:\n self.change_video()\n else:\n if self.btnGroup_mode.checkedId() == 0:\n self.cb_1.setCurrentIndex(self.cb_2.currentIndex())\n self.images[0] = cv2.imread(self.cb_1.currentText())\n self.images[1] = cv2.imread(self.cb_2.currentText())\n self.show_image()\n\n def change_video(self):\n if self.cb_1.currentText() != '' and self.cb_2.currentText() != '':\n self.player.set(self.cb_1.currentText(), self.cb_2.currentText())\n self.player.start()\n self.btn_pause.setEnabled(True)\n\n # def setImg1(self, img):\n # self.images[0] = img\n # self.imageArea.setImage(self.images)\n # def setImg2(self, img):\n # self.images[1] = img\n # self.imageArea.setImage(self.images)\n def setImg(self, images):\n self.images = images\n self.imageArea.setImage(self.images)\n\n def show_image(self):\n self.imageArea = QLabelSlider(self,\n self.slider_scale.value() / 100.0 + 1e-7,\n self.input_label_1.text(),\n self.input_label_2.text(),\n self.input_title.text())\n self.imageArea.setFrameShape(QtWidgets.QFrame.Box)\n self.imageArea.setLineWidth(2)\n self.imageArea.setAlignment(QtCore.Qt.AlignBottom)\n self.imageArea.setStyleSheet(\n 'border-width: 0px; border-style: solid; border-color: rgb(100, 100, 100);background-color: rgb(255, 255, 255)' # noqa\n )\n self.image_scroll.setWidget(self.imageArea)\n\n def pause(self):\n if self.btn_pause.text() == 'Pause (Space)':\n self.player.pause()\n self.timer_slider.stop()\n self.btn_pause.setText('Play (Space)')\n else:\n self.player.resume()\n self.set_autoSlider()\n self.btn_pause.setText('Pause (Space)')\n\n def reset(self):\n self.images = [None, None]\n self.cb_1.clear()\n self.cb_2.clear()\n self.show_image()\n if self.btnGroup_type.checkedId() == 1:\n if self.btnGroup_mode.checkedId() == 0:\n self.btn_add_1.setText('Set image 1')\n self.btn_add_2.setText('Set image 2')\n else:\n self.btn_add_1.setText('Add file')\n self.btn_add_2.setText('Add directory')\n else:\n self.btn_add_1.setText('Add a video')\n self.btn_add_2.setText('Add frames')\n\n self.btn_pause.setText('Pause (Space)')\n self.btn_pause.setEnabled(False)\n self.btn_record.setText('Record (Enter)')\n\n self.timer_slider.stop()\n self.timer_record.stop()\n\n def save(self):\n \"\"\"Save slider compare result.\"\"\"\n path, _ = QtWidgets.QFileDialog.getSaveFileName(self, 'save')\n if len(path) <= 0:\n return\n if self.imageArea:\n self.imageArea.grab().save(path)\n QtWidgets.QMessageBox.about(self, 'Message', 'Success!')\n else:\n QtWidgets.QMessageBox.about(self, 'Message', 'Nothing to save.')\n\n def record(self):\n if self.btn_record.text() == 'Record (Enter)':\n self.record_num = 0\n self.timer_record.start(1000 / 25)\n self.txt_prompt.setText('Recording...')\n self.btn_record.setText('End (Enter)')\n elif self.btn_record.text() == 'End (Enter)':\n paths = sorted(os.listdir('.tmp/'))\n paths = ['.tmp/' + p for p in paths]\n if len(paths) <= 0:\n return\n img = cv2.imread(paths[0])\n h, w, _ = img.shape\n cur = datetime.datetime.now()\n fname = f'{cur.year}{cur.month}{cur.day}{cur.hour}{cur.minute}{cur.second}.mp4' # noqa\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n self.recorder = cv2.VideoWriter(fname, fourcc, 25, (w, h))\n self.txt_prompt.setText('Saving...')\n for i in range(self.record_num):\n img = cv2.imread(paths[i])\n img = cv2.resize(img, (w, h))\n self.recorder.write(img)\n os.remove(paths[i])\n self.recorder.release()\n self.timer_record.stop()\n self.txt_prompt.setText('')\n self.btn_record.setText('Record (Enter)')\n QtWidgets.QMessageBox.about(self, 'Message',\n f'Save {fname} success!')\n\n def recording(self):\n if not os.path.isdir('.tmp/'):\n os.makedirs('.tmp/')\n fname = '.tmp/' + str(self.record_num).zfill(8) + '.png'\n self.record_num += 1\n self.imageArea.grab().save(fname)\n\n def wheelEvent(self, ev) -> None:\n key = QtWidgets.QApplication.keyboardModifiers()\n if key == QtCore.Qt.ControlModifier:\n scale = ev.angleDelta().y() / 120\n self.scale += scale\n if self.scale < 1:\n self.scale = 1\n self.txt_scale.setText(f'{self.scale} %')\n if self.scale > 200 and self.imageArea is not None:\n self.imageArea.set_scale(self.scale / 100.0)\n else:\n self.slider_scale.setValue(self.scale)\n return super().wheelEvent(ev)\n\n def keyPressEvent(self, ev) -> None:\n if ev.key() == QtCore.Qt.Key_Left:\n if self.cb_1.currentIndex() > 0:\n self.cb_1.setCurrentIndex(self.cb_1.currentIndex() - 1)\n else:\n self.cb_1.setCurrentIndex(self.cb_1.count() - 1)\n elif ev.key() == QtCore.Qt.Key_Right:\n if self.cb_1.currentIndex() < self.cb_1.count() - 1:\n self.cb_1.setCurrentIndex(self.cb_1.currentIndex() + 1)\n else:\n self.cb_1.setCurrentIndex(0)\n elif ev.key(\n ) == QtCore.Qt.Key_Enter or ev.key() + 1 == QtCore.Qt.Key_Enter:\n self.record()\n elif ev.key() == QtCore.Qt.Key_Space:\n self.pause()\n\n def eventFilter(self, object, event) -> bool:\n if object == self.image_scroll or object == self:\n if event.type() == QtCore.QEvent.KeyPress:\n self.keyPressEvent(event)\n return True\n return super().eventFilter(object, event)\n\n\nclass SRPage(QtWidgets.QWidget):\n\n def __init__(self, parent) -> None:\n super().__init__()\n\n self.parent = parent\n self.statusBar = self.parent.statusBar\n\n self.tab_patch = PatchTab(self)\n self.tab_slider = SliderTab(self)\n self.tabs = QtWidgets.QTabWidget()\n self.tabs.addTab(self.tab_patch, 'patch compare')\n self.tabs.addTab(self.tab_slider, 'before/after slider')\n\n layout = QtWidgets.QVBoxLayout()\n self.setLayout(layout)\n layout.addWidget(self.tabs)\n # self.tabs.currentChanged.connect(self.tabsCurrentChanged)\n" }, { "path": "tools/gui/utils.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport cv2\nimport numpy as np\nfrom PyQt5 import QtGui, QtWidgets\n\n\ndef layout2widget(layout):\n wg = QtWidgets.QWidget()\n wg.setLayout(layout)\n return wg\n\n\ndef qimage2array(img):\n w = img.width()\n h = img.height()\n img = img.convertToFormat(QtGui.QImage.Format.Format_RGBA8888)\n img = img.bits()\n img.setsize(w * h * 4)\n img = np.frombuffer(img, np.uint8)\n img = np.reshape(img, (h, w, 4))\n img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)\n return img\n" }, { "path": "tools/model_converters/publish_model.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport subprocess\n\nimport torch\nfrom packaging import version\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Process a checkpoint to be published')\n parser.add_argument('in_file', help='input checkpoint filename')\n parser.add_argument('out_file', help='output checkpoint filename')\n args = parser.parse_args()\n return args\n\n\ndef process_checkpoint(in_file, out_file):\n checkpoint = torch.load(in_file, map_location='cpu')\n # remove optimizer for smaller file size\n if 'optimizer' in checkpoint:\n del checkpoint['optimizer']\n # if it is necessary to remove some sensitive data in checkpoint['meta'],\n # add the code here.\n if version.parse(torch.__version__) >= version.parse('1.6'):\n torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)\n else:\n torch.save(checkpoint, out_file)\n sha = subprocess.check_output(['sha256sum', out_file]).decode()\n final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'\n subprocess.Popen(['mv', out_file, final_file])\n\n\ndef main():\n args = parse_args()\n process_checkpoint(args.in_file, args.out_file)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/slurm_test.sh", "content": "#!/usr/bin/env bash\n\nset -x\n\nPARTITION=$1\nJOB_NAME=$2\nCONFIG=$3\nCHECKPOINT=$4\nGPUS=${GPUS:-8}\nGPUS_PER_NODE=${GPUS_PER_NODE:-8}\nCPUS_PER_TASK=${CPUS_PER_TASK:-5}\nPY_ARGS=${@:5}\nSRUN_ARGS=${SRUN_ARGS:-\"\"}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\nsrun -p ${PARTITION} \\\n --job-name=${JOB_NAME} \\\n --gres=gpu:${GPUS_PER_NODE} \\\n --ntasks=${GPUS} \\\n --ntasks-per-node=${GPUS_PER_NODE} \\\n --cpus-per-task=${CPUS_PER_TASK} \\\n --kill-on-bad-exit=1 \\\n ${SRUN_ARGS} \\\n python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher=\"slurm\" ${PY_ARGS}\n" }, { "path": "tools/slurm_train.sh", "content": "#!/usr/bin/env bash\nexport MASTER_PORT=$((12000 + $RANDOM % 20000))\n\nset -x\n\nPARTITION=$1\nJOB_NAME=$2\nCONFIG=$3\nWORK_DIR=$4\nGPUS=${GPUS:-8}\nGPUS_PER_NODE=${GPUS_PER_NODE:-8}\nCPUS_PER_TASK=${CPUS_PER_TASK:-5}\nPY_ARGS=${@:5}\nSRUN_ARGS=${SRUN_ARGS:-\"\"}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\nsrun -p ${PARTITION} \\\n --job-name=${JOB_NAME} \\\n --gres=gpu:${GPUS_PER_NODE} \\\n --ntasks=${GPUS} \\\n --ntasks-per-node=${GPUS_PER_NODE} \\\n --cpus-per-task=${CPUS_PER_TASK} \\\n --kill-on-bad-exit=1 \\\n ${SRUN_ARGS} \\\n python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher=\"slurm\" ${PY_ARGS}\n" }, { "path": "tools/test.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os\nimport os.path as osp\n\nimport mmengine\nfrom mmengine.config import Config, DictAction\nfrom mmengine.hooks import Hook\nfrom mmengine.runner import Runner\n\nfrom mmagic.utils import print_colored_log\n\n\n# TODO: support fuse_conv_bn, visualization, and format_only\ndef parse_args():\n parser = argparse.ArgumentParser(description='Test (and eval) a model')\n parser.add_argument('config', help='test config file path')\n parser.add_argument('checkpoint', help='checkpoint file')\n parser.add_argument('--out', help='the file to save metric results.')\n parser.add_argument(\n '--work-dir',\n help='the directory to save the file containing evaluation metrics')\n parser.add_argument(\n '--cfg-options',\n nargs='+',\n action=DictAction,\n help='override some settings in the used config, the key-value pair '\n 'in xxx=yyy format will be merged into config file. If the value to '\n 'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n 'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n 'Note that the quotation marks are necessary and that no white space '\n 'is allowed.')\n parser.add_argument(\n '--launcher',\n choices=['none', 'pytorch', 'slurm', 'mpi'],\n default='none',\n help='job launcher')\n # When using PyTorch version >= 2.0.0, the `torch.distributed.launch`\n # will pass the `--local-rank` parameter to `tools/train.py` instead\n # of `--local_rank`.\n parser.add_argument('--local_rank', '--local-rank', type=int, default=0)\n args = parser.parse_args()\n if 'LOCAL_RANK' not in os.environ:\n os.environ['LOCAL_RANK'] = str(args.local_rank)\n return args\n\n\ndef main():\n args = parse_args()\n\n # load config\n cfg = Config.fromfile(args.config)\n cfg.launcher = args.launcher\n if args.cfg_options is not None:\n cfg.merge_from_dict(args.cfg_options)\n\n # work_dir is determined in this priority: CLI > segment in file > filename\n if args.work_dir is not None:\n # update configs according to CLI args if args.work_dir is not None\n cfg.work_dir = args.work_dir\n elif cfg.get('work_dir', None) is None:\n # use config filename as default work_dir if cfg.work_dir is None\n cfg.work_dir = osp.join('./work_dirs',\n osp.splitext(osp.basename(args.config))[0])\n\n cfg.load_from = args.checkpoint\n\n # build the runner from config\n runner = Runner.from_cfg(cfg)\n\n print_colored_log(f'Working directory: {cfg.work_dir}')\n print_colored_log(f'Log directory: {runner._log_dir}')\n\n if args.out:\n\n class SaveMetricHook(Hook):\n\n def after_test_epoch(self, _, metrics=None):\n if metrics is not None:\n mmengine.dump(metrics, args.out)\n\n runner.register_hook(SaveMetricHook(), 'LOWEST')\n\n # start testing\n runner.test()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "tools/train.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport logging\nimport os\nimport os.path as osp\n\nfrom mmengine.config import Config, DictAction\nfrom mmengine.runner import Runner\n\nfrom mmagic.utils import print_colored_log\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Train a model')\n parser.add_argument('config', help='train config file path')\n parser.add_argument('--work-dir', help='the dir to save logs and models')\n parser.add_argument(\n '--resume', action='store_true', help='Whether to resume checkpoint.')\n parser.add_argument(\n '--amp',\n action='store_true',\n default=False,\n help='enable automatic-mixed-precision training')\n parser.add_argument(\n '--auto-scale-lr',\n action='store_true',\n help='enable automatically scaling LR.')\n parser.add_argument(\n '--cfg-options',\n nargs='+',\n action=DictAction,\n help='override some settings in the used config, the key-value pair '\n 'in xxx=yyy format will be merged into config file. If the value to '\n 'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n 'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n 'Note that the quotation marks are necessary and that no white space '\n 'is allowed.')\n parser.add_argument(\n '--launcher',\n choices=['none', 'pytorch', 'slurm', 'mpi'],\n default='none',\n help='job launcher')\n # When using PyTorch version >= 2.0.0, the `torch.distributed.launch`\n # will pass the `--local-rank` parameter to `tools/train.py` instead\n # of `--local_rank`.\n parser.add_argument('--local_rank', '--local-rank', type=int, default=0)\n args = parser.parse_args()\n if 'LOCAL_RANK' not in os.environ:\n os.environ['LOCAL_RANK'] = str(args.local_rank)\n\n return args\n\n\ndef main():\n args = parse_args()\n\n # load config\n cfg = Config.fromfile(args.config)\n cfg.launcher = args.launcher\n if args.cfg_options is not None:\n cfg.merge_from_dict(args.cfg_options)\n\n # work_dir is determined in this priority: CLI > segment in file > filename\n if args.work_dir: # none or empty str\n # update configs according to CLI args if args.work_dir is not None\n cfg.work_dir = args.work_dir\n elif cfg.get('work_dir', None) is None:\n # use config filename as default work_dir if cfg.work_dir is None\n cfg.work_dir = osp.join('./work_dirs',\n osp.splitext(osp.basename(args.config))[0])\n\n # enable automatic-mixed-precision training\n if args.amp is True:\n if ('constructor' not in cfg.optim_wrapper) or \\\n cfg.optim_wrapper['constructor'] == 'DefaultOptimWrapperConstructor': # noqa\n optim_wrapper = cfg.optim_wrapper.type\n if optim_wrapper == 'AmpOptimWrapper':\n print_colored_log(\n 'AMP training is already enabled in your config.',\n logger='current',\n level=logging.WARNING)\n else:\n assert optim_wrapper == 'OptimWrapper', (\n '`--amp` is only supported when the optimizer wrapper '\n f'`type is OptimWrapper` but got {optim_wrapper}.')\n cfg.optim_wrapper.type = 'AmpOptimWrapper'\n cfg.optim_wrapper.loss_scale = 'dynamic'\n else:\n for key, val in cfg.optim_wrapper.items():\n if isinstance(val, dict) and 'type' in val:\n assert val.type == 'OptimWrapper', (\n '`--amp` is only supported when the optimizer wrapper '\n f'`type is OptimWrapper` but got {val.type}.')\n cfg.optim_wrapper[key].type = 'AmpOptimWrapper'\n cfg.optim_wrapper[key].loss_scale = 'dynamic'\n\n if args.resume:\n cfg.resume = True\n\n # build the runner from config\n runner = Runner.from_cfg(cfg)\n\n print_colored_log(f'Working directory: {cfg.work_dir}')\n print_colored_log(f'Log directory: {runner._log_dir}')\n\n # start training\n runner.train()\n\n print_colored_log(f'Log saved under {runner._log_dir}')\n print_colored_log(f'Checkpoint saved under {cfg.work_dir}')\n\n\nif __name__ == '__main__':\n main()\n" } ]